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Structural Gray Matter Differences between First-Episode Schizophrenics and Normal Controls Using Voxel-Based Morphometry
Abstract
The aim of this study was to compare the gray matter segments from T1 structural MR images of the brain in first-episode schizophrenic subjects (n = 34) and normal control subjects (n = 36) using automated voxel-based morphometry (VBM). This study is novel in that few studies have examined subjects in their first episode of schizophrenia. The subjects were recruited for the Edinburgh High Risk project and regional brain volumes were previously measured using a semi-automated volumetric region of interest (ROI) method of analysis. The primary interest was to compare the results from the compatible parts of the ROI study and the primary VBM approach. Our secondary interest was to compare the results of a study-specific template that was constructed from the control group to those using the generic T1 template (152 Montreal Neurological Institute brains) supplied with SPM99 (statistical parametric mapping). The images were processed and statistically analyzed using the SPM99 program. VBM analysis identified significant decreases in gray matter in the schizophrenics relative to the normal control group at the corrected voxel level (P < 0.05) in the right anterior cingulate, right medial frontal lobe, left middle temporal gyrus, left postcentral gyrus, and the left limbic lobe. There were no increases in gray matter in the schizophrenics relative to the control group. The construction of a customized template appeared to improve the detection of structural abnormalities. The analyses were subsequently restricted to voxels within the amygdala-hippocampal complex using the SPM small-volume correction. This identified gray matter decreases in the schizophrenics, at the corrected voxel level (P < 0.05), in the left and right uncus and parahippocampal gyri and the right amygdala. These results are compatible with and extend the relevant findings of the previous volumetric ROI analysis, when allowing for the differences between the methods and interpretation of their results.
... Based on structural magnetic resonance imaging (MRI), significant whole brain volume deficits have been identified in first-episode schizophrenia (FES) (2)(3)(4)(5)(6), especially those in the frontal lobe, striatum, and limbic system. Two key components of the limbic system are the amygdala and the hippocampus, and the volumes of those two structures have been found to be affected significantly by the neuropathology of FES (4)(5)(6)(7)(8)(9)(10)(11)(12)(13). ...
... However, findings are controversial and some neuroimaging studies reported conflicting results. For example, some studies reported volumetric reductions of the bilateral amygdalas in FES (3,8,9,13) whereas some other studies reported no significant amygdalar volume changes when comparing FES patients to matched healthy control (HC) participants (6,12). This type of conflicting findings occur to the hippocampus as well, the structure which has been investigated more extensively than the amygdala in FES literature; some MRI studies observed no significant FES related hippocampal volume abnormalities (8,10,12,13) even though a majority of existing studies have identified hippocampal volume atrophies in FES patients (5)(6)(7)11). ...
... In our study, we found significant amygdalar and hippocampal atrophies in FES, either globally or locally. Those observed abnormalities in FES generally agree well with previous findings (3,5,9), and confirm the presence of such structural abnormalities at the onset of schizophrenia which manifest in chronic patients as well (36,37). These findings to some degree confirm the presence of amygdalar and hippocampal atrophies at an early phase in the pathology of schizophrenia. ...
In this study, we investigated and quantified the amygdalar and hippocampal morphometry abnormalities exerted by first-episode schizophrenia using a total of 92 patients and 106 healthy control participants. Magnetic resonance imaging (MRI) based automated segmentation was conducted to obtain the amygdalar and hippocampal segmentations. Disease-versus-control volume differences of the bilateral amygdalas and hippocampi were quantified. In addition, deformation-based statistical shape analysis was employed to quantify the region-specific shape abnormalities of each structure of interest. To better identify the key relevant areas in the pathology of first-episode schizophrenia, each structure was divided into four subregions; CA1, CA2, CA3 combined with dentate gyrus for the hippocampus in each hemisphere and basolateral, basomedial, centromedial, and lateral nucleus for the amygdala in each hemisphere. We observed significant global volume reduction and localized shape atrophy in each of the four structures of interest. The amygdalar shape abnormalities mainly occurred at the basolateral and centromedial subregions, whereas the hippocampal shape abnormalities mainly concentrated on the CA1 and CA2 subregions. For the same structure, the one on the right hemisphere was affected more by the disease pathology than that on the left hemisphere. To conclude, we have successfully quantified the global and local morphometric abnormalities of the bilateral amygdalas and hippocampi using a sophisticated statistical analysis pipeline and high-field subregion segmentations, with MRI data of a considerable sample size. This study is one of the very first of such kind in first-episode schizophrenia analyses.
... Gray matter (GM) abnormalities are an established finding in patients with schizophrenia. Decreases in regional gray matter volumes (GMVs) and cortical thickness (CTh) are consistently reported [1][2][3] and are acknowledged to be present from even first-episode psychosis (FEP) [4,5]. While the degree of GM reductions is greater in the chronic stage [3,6], the rate of change is thought to be greatest during the early stages [2,6], with frontotemporal regions being the most affected [2]. ...
... Our study aims to fill this gap by focusing on GM ROIs implicated in early-stage psychosis. More specifically, given the sensitivity of texture measures to microstructural abnormalities [15,22,31] and the nature of the established [4,5] yet spatially uneven GM changes [1,2,6,30] during these early stages, we posit that conducting a granular investigation of individual GM ROIs using TA could provide additional useful insights into the dynamic GM changes that occur during these crucial stages. This more detailed, ROI-focused methodology stands in contrast to existing practices [24][25][26][27][28][29] and may enable a more refined understanding of the subtle, dynamic GM changes that unfold during the early stages of psychosis, as compared to relying solely on measures such as volume, CTh, or unsegmented whole-brain images. ...
Although gray matter (GM) abnormalities are present from the early stages of psychosis, subtle/miniscule changes may not be detected by conventional volumetry. Texture analysis (TA), which permits quantification of the complex interrelationship between contrasts at the individual voxel level, may capture subtle GM changes with more sensitivity than does volume or cortical thickness (CTh). We performed three-dimensional TA in nine GM regions of interest (ROIs) using T1 magnetic resonance images from 101 patients with first-episode psychosis (FEP), 85 patients at clinical high risk (CHR) for psychosis, and 147 controls. Via principal component analysis, three features of gray-level cooccurrence matrix – informational measure of correlation 1 (IMC1), autocorrelation (AC), and inverse difference (ID) – were selected to analyze cortical texture in the ROIs that showed a significant change in volume or CTh in the study groups. Significant reductions in GM volume and CTh of various frontotemporal regions were found in the FEP compared with the controls. Increased frontal AC was found in the FEP group compared to the controls after adjusting for volume and CTh changes. While volume and CTh were preserved in the CHR group, a stagewise nonlinear increase in frontal IMC1 was found, which exceeded both the controls and FEP group. Increased frontal IMC1 was also associated with a lesser severity of attenuated positive symptoms in the CHR group, while neither volume nor CTh was. The results of the current study suggest that frontal IMC1 may reflect subtle, dynamic GM changes and the symptomatology of the CHR stage with greater sensitivity, even in the absence of gross GM abnormalities. Some structural mechanisms that may contribute to texture changes (e.g., macrostructural cortical lamina, neuropil/myelination, cortical reorganization) and their possible implications are explored and discussed. Texture may be a useful tool to investigate subtle and dynamic GM abnormalities, especially during the CHR period.
... Meanwhile, other authors used meta-analyses to summarize GMV alterations in BD and also informed the regions located in frontal-temporal cortices [18]. These common brain structural alterations were supported by the findings of other researchers [19,20]. Other similarly altered GMVs in patients with SZ and BD, such as cingulate and insula, were also documented [19][20][21][22]. ...
... These common brain structural alterations were supported by the findings of other researchers [19,20]. Other similarly altered GMVs in patients with SZ and BD, such as cingulate and insula, were also documented [19][20][21][22]. ...
Background:
Cognitive impairments are documented in schizophrenia (SZ) and bipolar disorder (BD) and may be related to gray matter volumes (GMVs). Thus, this study is aimed at exploring whether the association between cognitive impairments and GMV alterations is similar in patients with SZ and BD and understanding the underlying neurobiological mechanisms.
Methods:
A total of 137 adult subjects (46 with SZ, 35 with BD, and 56 age-, sex-, and education-matched healthy controls (HC)) completed the MATRICS Consensus Cognitive Battery (MCCB) and structural magnetic resonance imaging scanning. We performed group comparisons of the cognitive impairments, the GMV alterations, and the association between them.
Results:
Compared with HC, the patients with SZ and BD showed shared deficits in 4 cognitive domains (i.e., processing speed, working memory, problem solving, and social cognition) and the composite. SZ and BD had commonly decreased GMVs, mainly in the insula, superior temporal pole, amygdala, anterior cingulate, and frontal cortices (superior, middle, opercular inferior, and orbital frontal gyrus). No correlation between MCCB scores and GMVs was detected in SZ. However, for BD, working memory was relevant to the right hemisphere (i.e., right insula, amygdala, superior temporal pole, and medial and dorsolateral superior frontal gyrus). Limitations. The major limitations were that not all patients were the first-episode status and no medication.
Conclusions:
The association was mainly limited to the BD group. Thus, the underlying pathophysiology of the cognitive deficits, in terms of GMV alterations, may be diverse between two disorders.
... The parahippocampal gyrus The scatter plots between significant cluster in cerebellum and task accuracy (r = 0.46, p = 0.01), as assessed by 2 back task plays an important role in memory encoding and retrieval. A reduced GMD of the parahippocampal gyrus has also been found in other first-episode schizophrenia studies [23][24][25][26]. Another two morphological indexes, cortical thickness and gyrification have also be shown to be disturbed in the parahippocampal gyrus of firstepisode schizophrenia [27] and chronically hallucinating schizophrenic patients [23]. ...
... A reduced GMD of the parahippocampal gyrus has also been found in other first-episode schizophrenia studies [23][24][25][26]. Another two morphological indexes, cortical thickness and gyrification have also be shown to be disturbed in the parahippocampal gyrus of firstepisode schizophrenia [27] and chronically hallucinating schizophrenic patients [23]. Decreased GMD of left cerebellar posterior lobe was found in first-episode schizophrenia patients [28].Compared with previous studies, our study showed consistent results in firstepisode schizophrenia patients. ...
Background
Cognitive deficits are a core feature of early schizophrenia. However, the pathological foundations underlying cognitive deficits are still unknown. The present study examined the association between gray matter density and cognitive deficits in first-episode schizophrenia.
Method
Structural magnetic resonance imaging of the brain was performed in 34 first-episode schizophrenia patients and 21 healthy controls. Patients were divided into two subgroups according to working memory task performance. The three groups were well matched for age, gender, and education, and the two patient groups were also further matched for diagnosis, duration of illness, and antipsychotic treatment. Voxel-based morphometric analysis was performed to estimate changes in gray matter density in first-episode schizophrenia patients with cognitive deficits. The relationships between gray matter density and clinical outcomes were explored.
Results
Patients with cognitive deficits were found to have reduced gray matter density in the vermis and tonsil of cerebellum compared with patients without cognitive deficits and healthy controls, decreased gray matter density in left supplementary motor area, bilateral precentral gyrus compared with patients without cognitive deficits. Classifier results showed GMD in cerebellar vermis tonsil cluster could differentiate SZ-CD from controls, left supplementary motor area cluster could differentiate SZ-CD from SZ-NCD. Gray matter density values of the cerebellar vermis cluster in patients groups were positively correlated with cognitive severity.
Conclusions
Decreased gray matter density in the vermis and tonsil of cerebellum may underlie early psychosis and serve as a candidate biomarker for schizophrenia with cognitive deficits.
... Manning et al. (2012) documented increased number of proliferative cells in the granule cell layer of the DG in phospholipase C-b1 knockout mice aged 2 months, which show several schizophrenia-like endophenotypes. To our knowledge, the present study is the first to demonstrate increased cell proliferation in the ACC in an animal model of neonatal hypoxia, a brain area that appears to be critically involved in the pathophysiology of schizophrenia (Job et al. 2002;Fornito et al. 2008;Witthaus et al. 2009;Lui et al. 2009). The ACC has been shown to be involved in other environmental risk factors of schizophrenia such as urban upbringing (Lederbogen et al. 2011). ...
... Our data of reduced striatum (CPU) volume at PD 13 is consistent with findings of volumetric deficits in multiple brain regions, including the striatum (caudate nuclei, nucleus accumbens, putamen) in never-medicated, firstepisode schizophrenic patients (Corson et al. 1999;Lieberman et al. 2001;Job et al. 2002;Chua et al. 2007;Glenthoj et al. 2007;Kaspárek et al. 2007;Fornito et al. 2008;Witthaus et al. 2009;Lui et al. 2009;Watson et al. 2012;Asami et al. 2012), when many confounders such as age of onset, duration of illness and medication are reduced or absent. However, except the volume of CPU we did not measure volumes or neuronal cell numbers in other brain regions of hypoxia-treated rats and hypoxia may cause apoptosis-induced cell loss. ...
As a consequence of obstetric complications, neonatal hypoxia has been discussed as an environmental factor in the pathophysiology of schizophrenia. However, the biological consequences of hypoxia are unclear. The neurodevelopmental hypothesis of schizophrenia suggests that the onset of abnormal brain development and neuropathology occurs perinatally, whereas symptoms of the disease appear in early adulthood. In our animal model of chronic neonatal hypoxia, we have detected behavioral alterations resembling those known from schizophrenia. Disturbances in cell proliferation possibly contribute to the pathophysiology of this disease. In the present study, we used postnatal rats to investigate cell proliferation in several brain areas following neonatal hypoxia. Rats were repeatedly exposed to hypoxia (89 % N2, 11 % O2) from postnatal day (PD) 4–8. We then evaluated cell proliferation on PD 13 and 39, respectively. These investigations were performed in the anterior cingulate cortex (ACC), caudate-putamen (CPU), dentate gyrus, and subventricular zone. Rats exposed to hypoxia exhibited increased cell proliferation in the ACC at PD 13, normalizing at PD 39. In other brain regions, no alterations have been detected. Additionally, hypoxia-treated rats showed decreased CPU volume at PD 13. The results of the present study on the one hand support the assumption of chronic hypoxia influencing transient cell proliferation in the ACC, and on the other hand reveal normalization during ageing.
... Dysfunction of temporolimbic structures is thought to critically contribute to the emergence of key clinical features of schizophrenia, including both positive and negative symptoms [60]. While none of the large multi-site cross-sectional VBM investigations of schizophrenia to date assessed first-episode schizophrenia patients separately, a number of single-site MRI studies have evaluated relatively large first-episode schizophrenia samples [15,[61][62][63][64] . In the largest singlesite cross-sectional VBM study of schizophrenia to date, Meisenzahl et al. [15] found GM volume reductions in several brain regions in first-episode schizophrenia patients relative to healthy controls, affecting most significantly the perisylvian areas bilaterally, as well as the left insula, superior temporal gyrus, amygdala and hippocampus. ...
... Despite the absence of significant findings involving notably the superior temporal gyrus in our study (relative reduction in the left superior temporal gyrus is the most significant finding in more than 50% of VBM studies of schizophrenia according to a meta-analysis [14]), the results of Meisenzahl et al. [15] are still partially in agreement with ours especially regarding the insular and temporo-limbic GM volume abnormalities in first-episode schizophrenia patients. In accord with our findings, other VBM studies that evaluated relatively large first-episode schizophrenia samples have also detected GM volume reductions in circumscribed regions rather than in a widespread fashion throughout the brain [15,[61][62][63][64]. ...
Background: Structural brain abnormalities in schizophrenia have been repeatedly demonstrated in magnetic resonance imaging (MRI) studies, but it remains unclear whether these are static or progressive in nature. While longitudinal MRI studies have been traditionally used to assess the issue of progression of brain abnormalities in schizophrenia, information from cross-sectional neuroimaging studies directly comparing first-episode and chronic schizophrenia patients to healthy controls may also be useful to further clarify this issue. With the recent interest in multisite mega-analyses combining structural MRI data from multiple centers aiming at increased statistical power, the present multisite voxel-based morphometry (VBM) study was carried out to examine patterns of brain structural changes according to the different stages of illness and to ascertain which (if any) of such structural abnormalities would be specifically correlated to potential clinical moderators, including cumulative exposure to antipsychotics, age of onset, illness duration and overall illness severity.
Methods: We gathered a large sample of schizophrenia patients (161, being 99 chronic and 62 first-episode) and controls (151) from four previous morphometric MRI studies (1.5 T) carried out in the same geographical region of Brazil. Image processing and analyses were conducted using Statistical Parametric Mapping (SPM8) software with the diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) algorithm. Group effects on regional gray matter (GM) volumes were investigated through whole-brain voxel-wise comparisons using General Linear Model Analysis of Co-variance (ANCOVA), always including total GM volume, scan protocol, age and gender as nuisance variables. Finally, correlation analyses were performed between the aforementioned clinical moderators and regional and global brain volumes.
Results: First-episode schizophrenia subjects displayed subtle volumetric deficits relative to controls in a circumscribed brain regional network identified only in small volume-corrected (SVC) analyses (p < 0.05, FWE-corrected), including the insula, temporolimbic structures and striatum. Chronic schizophrenia patients, on the other hand, demonstrated an extensive pattern of regional GM volume decreases relative to controls, involving bilateral superior, inferior and orbital frontal cortices, right middle frontal cortex, bilateral anterior cingulate cortices, bilateral insulae and right superior and middle temporal cortices (p < 0.05, FWE-corrected over the whole brain). GM volumes in several of those brain regions were directly correlated with age of disease onset on SVC analyses for conjoined (first-episode and chronic) schizophrenia groups. There were also widespread foci of significant negative correlation between duration of illness and relative GM volumes, but such findings remained significant only for the right dorsolateral prefrontal cortex after accounting for the influence of age of disease onset. Finally, significant negative correlations were detected between life-time cumulative exposure to antipsychotics and total GM and white matter volumes in schizophrenia patients, but no significant relationship was found between indices of antipsychotic usage and relative GM volume in any specific brain region.
Conclusion: The above data indicate that brain changes associated with the diagnosis of schizophrenia are more widespread in chronic schizophrenia compared to first-episode patients. Our findings also suggest that relative GM volume deficits may be greater in (presumably more severe) cases with earlier age of onset, as well as varying as a function of illness duration in specific frontal brain regions. Finally, our results highlight the potentially complex effects of the continued use of antipsychotic drugs on structural brain abnormalities in schizophrenia, as we found that cumulative doses of antipsychotics affected brain volumes globally rather than selectively on frontal-temporal regions.
... Post-mortem studies in patients with schizophrenia provided evidence for neuroanatomical abnormalities, in particular the cingulate gyrus [7,13,72]. Moreover, in vivo neuroimaging studies comparing patients with schizophrenia to healthy controls have shown evidence of decreased gray matter volume in the posterior cingulate gyrus [36,68], the anterior cingulate gyrus [31,38,66,67], and across the entire cingulate gyrus [51,53,80]. Beckmann and colleagues [6] performed a connectivity based parcellation of the cingulate cortex and related the different sub regions to specific functions based on peak activations of 171 functional magnetic resonance studies. ...
Schizophrenia has been associated with structural brain abnormalities and cognitive deficits that partly change during the course of illness. In the present study, cortical thickness in five subregions of the cingulate gyrus was assessed in 44 patients with schizophrenia-spectrum disorder and 47 control persons and related to illness duration and memory capacities. In the patients group, cortical thickness was increased in the posterior part of the cingulate gyrus and related to illness duration whereas cortical thickness was decreased in anterior parts unrelated to illness duration. In contrast, cortical thickness was related to episodic and working memory performance only in the anterior but not posterior parts of the cingulate gyrus. Our finding of a posterior cingulate increase may point to either increased parietal communication that is accompanied by augmented neural plasticity or to effects of altered neurodegenerative processes in schizophrenia.
... SZs have been shown to have significantly lower FA values than HCs in the ATR [82,[84][85][86], ACR [87], CST [88], and SLF regions [89]. In addition, the GM volume in the POG [90], PCG [91], STG [92], uncus [93], ITG [94,95], MOG [96], and FG [97] has been shown to be significantly decreased in SZs compared to in HCs. ...
Brain signals can be measured using multiple imaging modalities, such as magnetic resonance imaging (MRI)-based techniques. Different modalities convey distinct yet complementary information; thus, their joint analyses can provide valuable insight into how the brain functions in both healthy and diseased conditions. Data-driven approaches have proven most useful for multimodal fusion as they minimize assumptions imposed on the data, and there are a number of methods that have been developed to uncover relationships across modalities. However, none of these methods, to the best of our knowledge, can discover “one-to-many associations”, meaning one component from one modality is linked with more than one component from another modality. However, such “one-to-many associations” are likely to exist, since the same brain region can be involved in multiple neurological processes. Additionally, most existing data fusion methods require the signal subspace order to be identical for all modalities—a severe restriction for real-world data of different modalities. Here, we propose a new fusion technique—the consecutive independence and correlation transform (C-ICT) model—which successively performs independent component analysis and independent vector analysis and is uniquely flexible in terms of the number of datasets, signal subspace order, and the opportunity to find “one-to-many associations”. We apply C-ICT to fuse diffusion MRI, structural MRI, and functional MRI datasets collected from healthy controls (HCs) and patients with schizophrenia (SZs). We identify six interpretable triplets of components, each of which consists of three associated components from the three modalities. Besides, components from these triplets that show significant group differences between the HCs and SZs are identified, which could be seen as putative biomarkers in schizophrenia.
... Schizophrenia patients with AVH have attenuated activation in regions implicated in the routine monitoring of inner speech (e.g., temporal cortex; McGuire et al., 1995;Shergill et al., 2000b). The structural abnormalities of the temporal cortex (e.g., reduced GMV) are also frequently reported in schizophrenia patients with AVH (Rajarethinam et al., 2000;Job et al., 2002;Onitsuka et al., 2004). One possible explanation for larger GMV in the right inferior temporal gyrus post-treatment is that rTMS directly stimulating over the left TPJ area causes brain structural changes on adjacent regions such as the inferior temporal gyrus because of intrinsic connections within these regions (Scheinost et al., 2012). ...
Background
Low-frequency transcranial magnetic stimulation (rTMS) over the left temporoparietal cortex reduces the auditory verbal hallucination (AVH) in schizophrenia. However, the underlying neural basis of the rTMS treatment effect for schizophrenia remains not well understood. This study investigates the rTMS induced brain functional and structural alternations and their associations with clinical as well as neurocognitive profiles in schizophrenia patients with AVH.
Methods
Thirty schizophrenia patients with AVH and thirty-three matched healthy controls were enrolled. The patients were administered by 15 days of 1 Hz rTMS delivering to the left temporoparietal junction (TPJ) area. Clinical symptoms and neurocognitive measurements were assessed at pre- and post-rTMS treatment. The functional (amplitude of low-frequency fluctuation, ALFF) and structural (gray matter volume, GMV) alternations were compared, and they were then used to related to the clinical and neurocognitive measurements after rTMS treatment.
Results
The results showed that the positive symptoms, including AVH, were relieved, and certain neurocognitive measurements, including visual learning (VisLearn) and verbal learning (VerbLearn), were improved after the rTMS treatment in the patient group. Furthermore, the rTMS treatment induced brain functional and structural alternations in patients, such as enhanced ALFF in the left superior frontal gyrus and larger GMV in the right inferior temporal cortex. The baseline ALFF and GMV values in certain brain areas (e.g., the inferior parietal lobule and superior temporal gyrus) could be associated with the clinical symptoms (e.g., positive symptoms) and neurocognitive performances (e.g., VerbLearn and VisLearn) after rTMS treatment in patients.
Conclusion
The low-frequency rTMS over the left TPJ area is an efficacious treatment for schizophrenia patients with AVH and could selectively modulate the neural basis underlying psychiatric symptoms and neurocognitive domains in schizophrenia.
... Reduced WM efficiencies of PoCG and PHG regions were reported by previous studies on schizophrenia (Sun et al. 2015;Wang et al. 2012), reflecting disrupted WM integration. In addition, a structural research (Job et al. 2002) showed a decrease in the gray matter volume of the PoCG and PHG regions in schizophrenia, which is consistent with our findings. Notably, these regions are located in the limbic lobe. ...
Hemispheric lateralization is a prominent feature of the human brain and is grounded into intra- and inter-hemispheric white matter (WM) connections. However, disruptions in hemispheric lateralization involving both intra- and inter-hemispheric WM connections in schizophrenia is still unclear. Hence, a quantitative measure of the hemispheric lateralization of intra- and inter-hemispheric WM connections could provide new insights into schizophrenia. This work performed diffusion tensor imaging on 50 patients and 58 matched healthy controls. Using graph theory, the global and nodal efficiencies were computed for both intra- and inter-hemispheric networks. We found that patients with schizophrenia showed significantly decrease in both global and nodal efficiency of hemispheric networks relative to healthy controls. Specially, deficits in intra-hemispheric integration and inter-hemispheric communication were revealed in frontal and temporal regions for schizophrenia. We also found disrupted hemispheric asymmetries in brain regions associated with emotion, memory, and visual processes for schizophrenia. Moreover, abnormal hemispheric asymmetry of nodal efficiency was significantly correlated with the symptom of the patients. Our finding indicated that the hemispheric WM lateralization of intra- and inter-hemispheric connections could serve as a potential imaging biomarker for schizophrenia.
... Another explanation is the sampling method: previous studies recruited patients within days or weeks after treatment of catatonia symptoms, assessing brain morphology as close as possible to the episode. Importantly, whole brain group differences between psychosis patients and healthy control subjects map onto established findings of VBM analyses and schizophrenia, with significant decreased grey matter volume in patients in the anterior cingulate, insular, temporal, and medial frontal cortices (Baiano et al., 2007;Honea et al., 2005;Job et al., 2002). ...
There is growing interest in understanding the behavioral and neural mechanisms of catatonia. Here, we examine cognition and brain structure in schizophrenia spectrum disorder (SSD) patients with a history of catatonia.
A total of 172 subjects were selected from a data repository; these included SSD patients with (n = 43) and without (n = 43) a history of catatonia and healthy control subjects (n = 86). Cognitive functioning was assessed using the Screen for Cognitive Impairment in Psychiatry (SCIP) and brain structure was assessed using voxel-based morphometry (VBM) in the CAT12 toolbox.
SSD patients with a history of catatonia showed worse performance on tests of verbal fluency and processing speed compared to SSD patients without such a history, even after controlling for current antipsychotic and benzodiazepine use. No differences were found between patients with and without a history of catatonia in terms of brain structure. Both patient groups combined showed significantly smaller grey matter volumes compared to healthy control subjects in brain regions consistent with prior studies, including the anterior cingulate, insular, temporal, and medial frontal cortices.
The results highlight a cognitive-motor impairment in SSD patients with a history of catatonia. Challenges and limitations of examining brain structure in patients with a history of catatonia are discussed.
... They found significant reductions in regional grey matter in the medial temporal lobe. Job et al. [17] reported grey matter deficiency in schizophrenia patients compared to healthy controls using voxel-based morphometry (VBM) analysis of grey matter volume determined from MRI of 34 first episode schizophrenia patients and 36 controls. Grey matter volumes of subcortical brain regions were studied by Khodaei et al. [18]. ...
Schizophrenia, a serious psychological disorder, causes auditory and visual hallucinations, and delusions in a person. Several studies have shown that schizophrenia imposes structural changes in human brain. Relative changes in the grey matter volume of the schizophrenia patients in comparison to healthy controls have been well explored. However, identification of relevant brain regions that exhibit grey matter atrophy and also aid in the classification of schizophrenic patients and healthy controls has not been extensively investigated. In this study, a novel application of the non-dominated sorting genetic algorithm has been developed to select a set of relevant features (voxels) that show grey matter changes in the brain regions attributable to schizophrenia. This study uses MRI data of 32 healthy controls and 28 schizophrenia patients. The results show notable shrink in the gray matter volume in the brain of the schizophrenia patients, mostly in inferior frontal gyrus, superior temporal gyrus, middle occipital gyrus, and insula. The proposed approach yields a mean classification accuracy close to 90% with a feature set having around 70 voxels. This study may open a means of investigation of underlying neurobiology of schizophrenic brain for effective clinical intervention.
... In accordance with our hypothesis, two ICs significantly differentiated SSD patients from HC in both modalities (GMV and INA/fALFF; Figure 1), suggesting that SSD-specific pathology may lie in the coaltered brain structure and function of temporoparietal and frontocerebellar as well as cortical sensorimotor and frontoparietal networks (Keshavan, Tandon, Boutros, & Nasrallah, 2008). The identified joint group-discriminative ICs corroborate previous sMRI and fMRI studies that postulated either cortico-cerebellar-thalamo-cortical circuit (CCTCC) dysfunction (Haijma et al., 2013;Horga et al., 2011;Job et al., 2002) or frontotemporal dysconnectivity hypothesis in the pathogenesis of SSD (Friston & Frith, 1995;Pettersson-Yeo, Allen, Benetti, McGuire, & Mechelli, 2011 (Iwashiro et al., 2012;Kikinis et al., 2010;Koo et al., 2008;Shimizu et al., 2007;Tang et al., 2012;Torii et al., 2012;Zhou et al., 2005). Furthermore, the present study is in line with the recent mCCA + jICA study by Lottman and colleagues (Lottman et al., 2018), once again highlighting the relationship between GMV alterations and aberrant INA in SSD. ...
Neurological soft signs (NSS) comprise a broad range of subtle neurological deficits and are considered to represent external markers of sensorimotor dysfunction frequently found in mental disorders of presumed neurodevelopmental origin. Although NSS frequently occur in schizophrenia spectrum disorders (SSD), specific patterns of co‐altered brain structure and function underlying NSS in SSD have not been investigated so far. It is unclear whether gray matter volume (GMV) alterations or aberrant brain activity or a combination of both, are associated with NSS in SSD. Here, 37 right‐handed SSD patients and 37 matched healthy controls underwent motor assessment and magnetic resonance imaging (MRI) at 3 T. NSS were examined on the Heidelberg NSS scale. We used a multivariate data fusion technique for multimodal MRI data—multiset canonical correlation and joint independent component analysis (mCCA + jICA)—to investigate co‐altered patterns of GMV and intrinsic neural fluctuations (INF) in SSD patients exhibiting NSS. The mCCA + jICA model indicated two joint group‐discriminating components (temporoparietal/cortical sensorimotor and frontocerebellar/frontoparietal networks) and one modality‐specific group‐discriminating component (p < .05, FDR corrected). NSS motor score was associated with joint frontocerebellar/frontoparietal networks in SSD patients. This study highlights complex neural pathomechanisms underlying NSS in SSD suggesting aberrant structure and function, predominantly in cortical and cerebellar systems that critically subserve sensorimotor dynamics and psychomotor organization.
... 52 Previous studies have found that schizophre nia patients have reduced white matter integrity of 2 major ascending tracts to the primary somatosensory cortex, 53 and reduced grey matter volume of the postcentral gyrus. 54 These findings are supported by decreased functional activation of the postcentral gyrus in schizophrenia. 55,56 Decreased nodal effi ciency in the postcentral gyrus may be a distinct feature of schizophrenia, separating it from bipolar disorder and MDD. ...
Background:
White matter network alterations have increasingly been implicated in major depressive disorder, bipolar disorder and schizophrenia. The aim of this study was to identify shared and distinct white matter network alterations among the 3 disorders.
Methods:
We used analysis of covariance, with age and gender as covariates, to investigate white matter network alterations in 123 patients with schizophrenia, 123 with bipolar disorder, 124 with major depressive disorder and 209 healthy controls.
Results:
We found significant group differences in global network efficiency (F = 3.386, p = 0.018), nodal efficiency (F = 8.015, p < 0.001 corrected for false discovery rate [FDR]) and nodal degree (F = 5.971, pFDR < 0.001) in the left middle occipital gyrus, as well as nodal efficiency (F = 6.930, pFDR < 0.001) and nodal degree (F = 5.884, pFDR < 0.001) in the left postcentral gyrus. We found no significant alterations in patients with major depressive disorder. Post hoc analyses revealed that compared with healthy controls, patients in the schizophrenia and bipolar disorder groups showed decreased global network efficiency, nodal efficiency and nodal degree in the left middle occipital gyrus. Furthermore, patients in the schizophrenia group showed decreased nodal efficiency and nodal degree in the left postcentral gyrus compared with healthy controls.
Limitations:
Our findings could have been confounded in part by treatment differences.
Conclusion:
Our findings implicate graded white matter network alterations across the 3 disorders, enhancing our understanding of shared and distinct pathophysiological mechanisms across diagnoses and providing vital insights into neuroimaging-based methods for diagnosis and research.
... These studies could reveal intermodal plasticity in various disorders, such as schizophrenia (e.g. Job et al., 2002;Zhou et al., 2003;Kubicki et al., 2002), narcolepsy (Overeem et al., 2003), Alzheimer (e.g. Karas et al., 2003;Busatto et al., 2003;Burton et al., 2002), dyslexia (Temple & Gabrieli, 2003a;Brambati et al., 2003;Brown et al., 2001), depression (e.g. ...
Basierend auf einem Literaturüberblick über die physiologische Basis des Hörens und der auditorischen Informationsverarbeitung (AI), sowie über Plastizität im Gehirn und Prinzipien der funktionellen Magnetresonanztomographie (fMRT) wurden in dieser Arbeit fünf fMRT und eine Verhaltensstudie durchgeführt. Ziel lag in der Untersuchung von Wahrnehmung, Plastizität und Gedächtnis im auditorischen Bereich des Gehirns, sowie der Untersuchung des Einflusses von Performanz, Geschlecht, musikalischer Erfahrung und Schlaf auf AI. Basierend auf speziellen Kriterien wurde ein experimentelles Paradigma zur Untersuchung des Tonhöhengedächtnisses (TG) entwickelt und getestet. Für die fMRT Studien wurde eine "sparse temporal sampling"- Methode entwickelt, die die vollständige Trennung von auditorischer Stimulation und Geräusch des MRT ermöglicht und zusätzlich den funktionellen Zeitverlauf über 7sek. aufzeigt. Zusammenfassend konnte diese Arbeit wichtige Ergebnisse sowohl hinsichtlich des Zeitverlaufs der funktionellen Anatomie der TG, als auch trainingsinduzierter Aktivierungsveränderungen und nicht zuletzt den Einfluss von musikalischer Erfahrung, Geschlecht und Schlaf auf AI aufzeigen. Diese Studien zeigen erstmals, dass der Gyrus supramarginalis und das Zerebellum eine zentrale Rolle bei der kortikalen Verarbeitung der TG spielen. Darüber hinaus unterstreichen die hier präsentierten Studien die Notwendigkeit, Verhaltensdaten in die Analyse funktioneller Daten zu integrieren. Zusätzlich konnten die hier präsentierten Daten in ein von Petersen et al. (1998) entwickeltes theoretisches Modell integriert werden, welches den Einfluss von Training auf die funktionelle Anatomie zu erklären versucht. Based on a review of the literature regarding the physiological basis of hearing and auditory processing, brain plasticity and the principles of functional magnet resonance imaging (fMRI), five fMRI studies and one behavioral study were designed. The general aim of these studies were the assessment of perception, plasticity and memory in the auditory system as well as the influence of performance, gender, musicianship and sleep on auditory processing and learning. Each study employed the same pitch memory task, which was designed based on several criteria and tested in a behavioral pilot study. For the fMRI studies, a sparse temporal sampling technique was developed that completely separated auditory stimulation and scanner background noise and furthermore made it possible to assess the time course of the pitch memory task during a time period of 7 seconds. Overall, important findings regarding the time course and functional anatomy of pitch perception and pitch memory could be revealed. In addition, training-induced changes following a short-term training period as well as the influence of musicianship, gender and sleep on pitch processing and auditory learning were shown. The most important findings with regard to the neural correlates of pitch memory were the roles of the supramarginal gyrus and the cerebellum in this pitch task. The here-described studies further demonstrate the need to include performance scores in analyses of functional imaging data. Furthermore, the presented data was integrated into a theoretical framework proposed by Peteresen et a. (1998) which seeks to explain effects of practice on the functional anatomy of a given task.
... VBA complements region of interest (ROI) volumetry by providing a comprehensive assessment of anatomical differences throughout the brain, while not being limited by a-priori regional hypotheses. VBA typically performs mass-univariate statistical tests on either tissue composition or deformation fields, aiming to reveal regional anatomical or shape differences [5,60,3,33,25,51,78,90,26,138,13,59,77,107,2]. However, voxel-wise methods often suffer from low statistical power and more importantly, ignore multivariate relationships in the data. ...
Modern neuroimaging techniques allow us to investigate the brain in vivo and in high resolution, providing us with high dimensional information regarding the structure and the function of the brain in health and disease. Statistical analysis techniques transform this rich imaging information into accessible and interpretable knowledge that can be used for investigative as well as diagnostic and prognostic purposes. ^ A prevalent area of research in neuroimaging is group comparison, i.e., the comparison of the imaging data of two groups (e.g. patients vs. healthy controls or people who respond to treatment vs. people who don't) to identify discriminative imaging patterns that characterize different conditions. In recent years, the neuroimaging community has adopted techniques from mathematics, statistics, and machine learning to introduce novel methodologies targeting the improvement of our understanding of various neuropsychiatric and neurodegenerative disorders. ^ However, existing statistical methods are limited by their reliance on ad-hoc assumptions regarding the homogeneity of disease effect, spatial properties of the underlying signal and the covariate structure of data, which imposes certain constraints about the sampling of datasets. • First, the overarching assumption behind most analytical tools, which are commonly used in neuroimaging studies, is that there is a single disease effect that differentiates the patients from controls. In reality, however, the disease effect may be heterogeneously expressed across the patient population. As a consequence, when searching for a single imaging pattern that characterizes the difference between healthy controls and patients, we may only get a partial or incomplete picture of the disease effect. • Second, and importantly, most analyses assume a uniform shape and size of disease effect. As a consequence, a common step in most neuroimaging analyses it to apply uniform smoothing of the data to aggregate regional information to each voxel to improve the signal to noise ratio. However, the shape and size of the disease patterns may not be uniformly represented across the brain. • Lastly, in practical scenarios, imaging datasets commonly include variations due to multiple covariates, which often have effects that overlap with the searched disease effects. To minimize the covariate effects, studies are carefully designed by appropriately matching the populations under observation. The difficulty of this task is further exacerbated by the advent of big data analyses that often entail the aggregation of large datasets collected across many clinical sites. ^ The goal of this thesis is to address each of the aforementioned assumptions and limitations by introducing robust mathematical formulations, which are founded on multivariate machine learning techniques that integrate discriminative and generative approaches. ^ Specifically, 1. First, we introduce an algorithm termed HYDRA which stands for heterogeneity through discriminative analysis . This method parses the heterogeneity in neuroimaging studies by simultaneously performing clustering and classification by use of piecewise linear decision boundaries. 2. Second, we propose to perform regionally linear multivariate discriminative statistical mapping (MIDAS ) toward finding the optimal level of variable smoothing across the brain anatomy and tease out group differences in neuroimaging datasets. This method makes use of overlapping regional discriminative filters to approximate a matched filter that best delineates the underlying disease effect. 3. Lastly, we develop a method termed generative discriminative machines (GDM) toward reducing the effect of confounds in biased samples. The proposed method solves for a discriminative model that can also optimally generate the data when taking into account the covariate structure. ^ We extensively validated the performance of the developed frameworks in the presence of diverse types of simulated scenarios. Furthermore, we applied our methods on a large number of clinical datasets that included structural and functional neuroimaging data as well as genetic data. Specifically, HYDRA was used for identifying distinct subtypes of Alzheimer's Disease. MIDAS was applied for identifying the optimally discriminative patterns that differentiated between truth-telling and lying functional tasks. GDM was applied on a multi-site prediction setting with severely confounded samples. Our promising results demonstrate the potential of our methods to advance neuroimaging analysis beyond the set of assumptions that limit its capacity and improve statistical power.
... Group analyses are ubiquitous in neuroimaging, which are commonly applied to study the differences between populations. Typical applications include, but are not limited to, analyses for describing disease effects by comparing patients and controls [44,79,167], studies for characterizing aging effects by comparing old and young subjects [17,59], as well as efforts to characterize brain development by comparing subjects of different ages [57,143]. Statistical group analyses are carried out throughout studies using diverse types of images, including functional MRI [148,162], structural MRI [29,50,67], and diffusion tensor imaging [60,144]. ...
Automated quantitative neuroimaging analysis methods have been crucial in elucidating normal and pathological brain structure and function, and in building in vivo markers of disease and its progression. Commonly used methods can identify and precisely quantify subtle and spatially complex imaging patterns of brain change associated with brain diseases. However, the overarching premise of these methods is that the disease group is a homogeneous entity resulting from a single, unifying pathophysiological process that has a single imaging signature. This assumption ignores ample evidence for the heterogeneous nature of neurodegenerative diseases and neuropsychiatric disorders, resulting in incomplete or misleading descriptions. Accurate characterization of heterogeneity is important for deepening our understanding of neurobiological processes, thus leading to improved disease diagnosis and prognosis.^ In this thesis, we leveraged machine learning techniques to develop novel tools that can analyze the heterogeneity in both cross-sectional and longitudinal neuroimaging studies. Specifically, we developed a semi-supervised clustering method for characterizing heterogeneity in cross-sectional group comparison studies, where normal and patient populations are modeled as high-dimensional point distributions, and heterogeneous disease effects are captured by estimating multiple transformations that align the two distributions, while accounting for the effect of nuisance covariates. Moreover, toward dissecting the heterogeneity in longitudinal cohorts, we proposed a method which simultaneously fits multiple population longitudinal multivariate trajectories and clusters subjects into subgroups. Longitudinal trajectories are modeled using spatiotemporally regularized cubic splines, while clustering is performed by assigning subjects to the subgroup whose population trajectory best fits their data.^ The proposed tools were extensively validated using synthetic data. Importantly, they were applied to study the heterogeneity in large clinical neuroimaging cohorts. We identified four disease subtypes with distinct imaging signatures using data from Alzheimer’s Disease Neuroimaging Initiative, and revealed two subgroups with different longitudinal patterns using data from Baltimore Longitudinal Study on Aging. Critically, we were able to further characterize the subgroups in each of the studies by performing statistical analyses evaluating subgroup differences with additional information such as neurocognitive data. Our results demonstrate the strength of the developed methods, and may pave the road for a broader understanding of the complexity of brain aging and Alzheimer’s disease.
... Correlations between groups when comparing GM IC7 mixing coefficients with RBANS language scores (z 5 2.13, p 5 .033; Figure 6a), RBANS attention scores (z 5 2.44, p 5 .015; Figure 6b), and RBANS total scores (z 5 2.18, p 5 .029; Figure 6c) were significant. It is previous studies in schizophrenia patients Job et al., 2002;Zhou et al., 2007). Additionally, the abnormalities in GM volume found in the postcentral gyrus, insula, thalamus, and putamen are corroborated by results from previously published meta-analyses of GM anomalies in schizophrenia Glahn et al., 2008;Haijma et al., 2013). ...
Acquisition of multimodal brain imaging data for the same subject has become more common leading to a growing interest in determining the intermodal relationships between imaging modalities to further elucidate the pathophysiology of schizophrenia. Multimodal data have previously been individually analyzed and subsequently integrated; however, these analysis techniques lack the ability to examine true modality inter-relationships. The utilization of a multiset canonical correlation and joint independent component analysis (mCCA + jICA) model for data fusion allows shared or distinct abnormalities between modalities to be examined. In this study, first-episode schizophrenia patients (nSZ =19) and matched controls (nHC =21) completed a resting-state functional magnetic resonance imaging (fMRI) scan at 7 T. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and amplitude of low frequency fluctuation (ALFF) maps were used as features in a mCCA + jICA model. Results of the mCCA + jICA model indicated three joint group-discriminating components (GM-CSF, WM-ALFF, GM-ALFF) and two modality-unique group-discriminating components (GM, WM). The joint component findings are highlighted by GM basal ganglia, somatosensory, parietal lobe, and thalamus abnormalities associated with ventricular CSF volume; WM occipital and frontal lobe abnormalities associated with temporal lobe function; and GM frontal, temporal, parietal, and occipital lobe abnormalities associated with caudate function. These results support and extend major findings throughout the literature using independent single modality analyses. The multimodal fusion of 7 T data in this study provides a more comprehensive illustration of the relationships between underlying neuronal abnormalities associated with schizophrenia than examination of imaging data independently.
... Neuroanatomical studies and meta-analyses in patients with schizophrenia and patients with first-episode psychosis (FEP) have consistently reported evidence of brain structural abnormalities, as measured by structural MRI (sMRI). [1][2][3][4][5][6][7][8][9][10][11] Structural alterations commonly found in these studies are volumetric alterations in the anterior cingulate, frontal and temporal regions; hippocampus; amygdala; thalamus; and insula. 2,8,[12][13][14][15] Decreases, increases and negative findings were found, with most studies reporting reduced total and regional grey matter volumes. ...
Background:
There is only limited agreement with respect to location, directionality and functional implications of brain structural alterations observed in patients with schizophrenia. Additionally, their link to occurrence of psychotic symptoms remains unclear. A viable way of addressing these questions is to examine populations in an at-risk mental state (ARMS) before the transition to psychosis.
Methods:
We tested for structural brain alterations in individuals in an ARMS compared with healthy controls and patients with first-episode psychosis (FEP) using voxel-based morphometry and measures of cortical thickness. Furthermore, we evaluated if these alterations were modified by age and whether they were linked to the observed clinical symptoms.
Results:
Our sample included 59 individuals with ARMS, 26 healthy controls and 59 patients with FEP. We found increased grey matter volume and cortical thickness in individuals with ARMS and a similar pattern of structural alterations in patients with FEP. We further found stronger age-related reductions in grey matter volume and cortical thickness in both patients with FEP and individuals with ARMS, linking these alterations to observed clinical symptoms.
Limitations:
The ARMS group comprised subgroups with heterogeneous levels of psychosis risk and medication status. Furthermore, the cross-sectional nature of our study and the reduced number of older patients limit conclusions with respect to observed interactions with age.
Conclusion:
Our findings on consistent structural alterations in individuals with ARMS and patients with FEP and their link to clinical symptoms have major implications for understanding their time of occurrence and relevance to psychotic symptoms. Interactions with age found for these alterations may explain the heterogeneity of findings reported in the literature.
... A decrease in gyrification throughout the entire brain has also been reported when comparing non-syndromic patients with schizophrenia to healthy participants (Sallet et al., 2003). Moreover, non-syndromic subjects at risk for developing schizophrenia also present morphological alterations, specifically changes in cortical volume in the temporal (Job et al., 2006(Job et al., , 2005 and prefrontal regions (Job et al., 2005(Job et al., , 2002 when compared to healthy controls. Most of the mentioned findings, however, did not investigate differences between subgroups of patients with distinct symptomatic profiles, but instead conducted post-hoc correlation analyses that may have only detected linear correlations between symptom scores and morphological measures. ...
Approximately 30% of individuals with 22q11.2 Deletion Syndrome (22q11DS) develop schizophrenia during adolescence/early adulthood, making this syndrome a model for the disorder. Furthermore, negative symptoms exist in up to 80% of patients diagnosed with 22q11DS. The present study aims to uncover morphological brain alterations associated with negative symptoms in a cohort of patients with 22q11DS who are at-risk for developing schizophrenia. A total of 71 patients with 22q11DS aged 12 to 35 (54% females) with no past or present diagnosis of a schizophrenia were included in the study. Psychotic symptom scores were used to divide patients into subgroups by means of a cluster analysis. Three major subgroups were evident: patients with low negative and positive symptoms; patients with high negative symptoms and low positive symptoms; and patients with high negative and positive symptoms. Cortical volume, thickness and gyrification were compared between subgroups using FreeSurfer software. Results showed that patients with high negative symptoms, compared to those with low negative symptoms, have decreased gyrification in the medial occipito-temporal (MOT) and lateral temporo-parietal (LTP) cortices of the left hemisphere, and in the medial temporal (MT)/posterior cingulate (PCC) cortices of the right hemisphere. These findings suggest that high negative symptoms are associated with gyrification reductions predominantly in medial occipital and temporal regions, which are areas implicated in social cognition and early visual processing. Furthermore, as cortical folding develops in utero and during the first years of life, reduced gyrification may represent an early biomarker predicting the development of negative symptoms.
... The correlation between baseline whole-brain GM volume and baseline HADS 'A' scores was conducted using multiple regression in SPM 8, in which age, sex, education (in years), and the severity of depressive symptoms were included as nuisance covariates. We first explored the whole brain to identify regions associated with anxiety, followed by a regional analysis only focusing on few a priori hypothesised brain regions using small volume correction [18]. A binary mask was created using the Automated Anatomical Labelling toolbox (ALL) [19] to include a priori hypothesised brain regions (i.e., bilateral amygdala, insula, ACC and precuneus) before the analysis. ...
BACKGROUND
Anxiety is prevalent in patients with Parkinson's disease (PD) and may affect patients' quality of life. Yet, little is known about the neural basis of anxiety in PD, and none have used a longitudinal design.
METHODS
73 patients with mild PD were recruited and followed up for 18 months. A whole-brain analysis was first used to identify brain regions associated with anxiety symptoms, followed by a regional analysis focusing on a priori hypothesised regions at baseline. A multivariate generalized estimating equations analysis was then conducted to determine the longitudinal association between grey matter (GM) volumetric changes of these significant regions and changes of anxiety symptoms.
RESULTS
At baseline, anxiety symptom severity was associated with decreased GM volumes in the bilateral precuneus and anterior cingulate cortex (ACC). Over 18 months, increased severity of anxiety symptoms was associated with decreased GM volume in the left precuneus and ACC, independent of age, gender, education, depressive symptom severity or use of psychiatric medication.
CONCLUSIONS
These results mainly implicate the precuneus and ACC in the pathogenesis of anxiety in PD. We speculate that these structural changes could reflect the disrupted default mode network due to PD pathology, contributing to spontaneous anxiety-related self-focused thoughts.
... We did not observe predicted volume reductions of hippocampal complex. Although concentration VBM studies have reported reductions of medial temporal lobe structures (Job et al 2002;Kubicki et al 2002;Suzuki et al 2002;Wright et al 1999;but see Wilke et al 2001), a volumetric VBM study (Ananth et al 2002) did not. Differences in patient populations, variations in preprocessing steps for VBM analysis, the use of concen- tration versus volume indices, as well as the choice of global covariates might account for these inconsistencies. ...
... VBA complements region of interest (ROI) volumetry by providing a comprehensive assessment of anatomical differences throughout the brain, while not being limited by a priori regional hypotheses. VBA typically performs mass-univariate statistical tests on either tissue composition or deformation fields, aiming to reveal regional anatomical or shape differences (Ashburner et al., 1998;Goldszal et al., 1998;Ashburner and Friston, 2000;Davatzikos et al., 2001;Chung et al., 2001;Fox et al., 2001;Job et al., 2002;Kubicki et al., 2002;Chung et al., 2003;Studholme et al., 2004;Bernasconi et al., 2004;Giuliani et al., 2005;Job et al., 2005;Meda et al., 2008;Ashburner, 2009). However, voxel-wise methods often suffer from low statistical power and more importantly, ignore multivariate relationships in the data. ...
There is ample evidence for the heterogeneous nature of diseases. For example, Alzheimer’s Disease, Schizophrenia and Autism Spectrum Disorder are typical disease examples that are characterized by high clinical heterogeneity, and likely by heterogeneity in the underlying brain phenotypes. Parsing this heterogeneity as captured by neuroimaging studies is important both for better understanding of disease mechanisms, and for building subtype-specific classifiers. However, few existing methodologies tackle this problem in a principled machine learning framework. In this work, we developed a novel non-linear learning algorithm for integrated binary classification and subpopulation clustering. Non-linearity is introduced through the use of multiple linear hyperplanes that form a convex polytope that separates healthy controls from pathologic samples. Disease heterogeneity is disentangled by implicitly clustering pathologic samples through their association to single linear sub-classifiers. We show results of the proposed approach from an imaging study of Alzheimer’s Disease, which highlight the potential of the proposed approach to map disease heterogeneity in neuroimaging studies.
Schizophrenia is a chronic brain disorder, and neuroimaging abnormalities have been reported in different stages of the illness for decades. However, when and how these brain abnormalities occur and evolve remains undetermined. We hypothesized structural and functional brain abnormalities progress throughout the illness course at different rates in schizophrenia. A total of 115 patients with schizophrenia were recruited and stratified into three groups of different illness periods: 5-year group (illness duration: ≤5 years), 15-year group (illness duration: 12–18 years), and 25-year group (illness duration: ≥25 years); 230 healthy controls were matched by age and sex to the three groups, respectively. All participants underwent resting-state MRI scanning. Each group of patients with schizophrenia was compared with the corresponding controls in terms of voxel-based morphometry (VBM), fractional anisotropy (FA), global functional connectivity density (gFCD), and sample entropy (SampEn) abnormalities. In the 5-year group we observed only SampEn abnormalities in the putamen. In the 15-year group, we observed VBM abnormalities in the insula and cingulate gyrus and gFCD abnormalities in the temporal cortex. In the 25-year group, we observed FA abnormalities in nearly all white matter tracts, and additional VBM and gFCD abnormalities in the frontal cortex and cerebellum. By using two structural and two functional MRI analysis methods, we demonstrated that individual functional abnormalities occur in limited brain areas initially, functional connectivity and gray matter density abnormalities ensue later in wider brain areas, and structural connectivity abnormalities involving almost all white matter tracts emerge in the third decade of the course in schizophrenia.
Recent studies show that multi-modal data fusion techniques combine information from diverse sources for comprehensive diagnosis and prognosis of complex brain disorders, often resulting in improved accuracy compared to single-modality approaches. However, many existing data fusion methods extract features from homogeneous networks, ignoring heterogeneous structural information among multiple modalities. To this end, we propose a Hypergraph-based Multi-modal data Fusion algorithm, namely HMF. Specifically, we first generate a hypergraph similarity matrix to represent the high-order relationships among subjects, and then enforce the regularization term based upon both the inter- and intra-modality relationships of the subjects. Finally, we apply HMF to integrate imaging and genetics datasets. Validation of the proposed method is performed on both synthetic data and real samples from schizophrenia study. Results show that our algorithm outperforms several competing methods, and reveals significant interactions among risk genes, environmental factors and abnormal brain regions.
Schizophrenia (SCZ) is a serious and complex mental disorder with high heritability. Polygenic risk score (PRS) is a useful tool calculating the accumulating effects of multiple common genetic variants of schizophrenia. The amplitude of low-frequency fluctuation (ALFF) is an efficient index to reflect spontaneous, intrinsic neuronal activity. Aberrant ALFF of brain regions were reported in schizophrenia frequently, but the relationship between PRS and ALFF has not been studied. In the present study, we compared PRS and ALFF in 101 schizophrenia patients and 106 age-matched healthy controls to test their associations with schizophrenia. Then, the correlation of PRS with ALFF was measured to reveal the effect of polygenic risk on brain activity in schizophrenia. We found that schizophrenia patients showed significant differences in PRS and ALFF compared with controls. Twenty-six brain regions showed significant difference of ALFF between schizophrenia cases and controls, of which left inferior frontal gyrus, triangular part (IFGtriang.L) showed increased activity in schizophrenia. PRS-SCZ was positively correlated with ALFF in IFGtriang.L in 57 non-chronic patients. Genes involved in synaptic organization and transmission, especially in glutamatergic synapse, were highly enriched in PRS-SCZ genes, suggesting the dysfunction of synapses in schizophrenia. These results help to understand the molecular mechanism underlying schizophrenia and related brain dysfunction.
This book was originally published in 2004 and concerns developmental neurobiology. In the decade preceding publication, developmental neurobiology made important strides towards elucidating the pathophysiology of psychiatric disorders. Nowhere has this link between basic science and clinical insights become clearer than in the field of schizophrenia research. Each contributor to this volume provides a fresh overview of the relevant research, including directions for further investigation. The book begins with a section on advances in developmental neurobiology. This is followed by sections on etiological and pathophysiological developments, and models that integrate this knowledge. The final section addresses the clinical insights that emerge from the developmental models. This book will be valuable to researchers in psychiatry and neurobiology, students in psychology, and all mental health practitioners.
This book was originally published in 2004 and concerns developmental neurobiology. In the decade preceding publication, developmental neurobiology made important strides towards elucidating the pathophysiology of psychiatric disorders. Nowhere has this link between basic science and clinical insights become clearer than in the field of schizophrenia research. Each contributor to this volume provides a fresh overview of the relevant research, including directions for further investigation. The book begins with a section on advances in developmental neurobiology. This is followed by sections on etiological and pathophysiological developments, and models that integrate this knowledge. The final section addresses the clinical insights that emerge from the developmental models. This book will be valuable to researchers in psychiatry and neurobiology, students in psychology, and all mental health practitioners.
This book was originally published in 2004 and concerns developmental neurobiology. In the decade preceding publication, developmental neurobiology made important strides towards elucidating the pathophysiology of psychiatric disorders. Nowhere has this link between basic science and clinical insights become clearer than in the field of schizophrenia research. Each contributor to this volume provides a fresh overview of the relevant research, including directions for further investigation. The book begins with a section on advances in developmental neurobiology. This is followed by sections on etiological and pathophysiological developments, and models that integrate this knowledge. The final section addresses the clinical insights that emerge from the developmental models. This book will be valuable to researchers in psychiatry and neurobiology, students in psychology, and all mental health practitioners.
This book was originally published in 2004 and concerns developmental neurobiology. In the decade preceding publication, developmental neurobiology made important strides towards elucidating the pathophysiology of psychiatric disorders. Nowhere has this link between basic science and clinical insights become clearer than in the field of schizophrenia research. Each contributor to this volume provides a fresh overview of the relevant research, including directions for further investigation. The book begins with a section on advances in developmental neurobiology. This is followed by sections on etiological and pathophysiological developments, and models that integrate this knowledge. The final section addresses the clinical insights that emerge from the developmental models. This book will be valuable to researchers in psychiatry and neurobiology, students in psychology, and all mental health practitioners.
This book was originally published in 2004 and concerns developmental neurobiology. In the decade preceding publication, developmental neurobiology made important strides towards elucidating the pathophysiology of psychiatric disorders. Nowhere has this link between basic science and clinical insights become clearer than in the field of schizophrenia research. Each contributor to this volume provides a fresh overview of the relevant research, including directions for further investigation. The book begins with a section on advances in developmental neurobiology. This is followed by sections on etiological and pathophysiological developments, and models that integrate this knowledge. The final section addresses the clinical insights that emerge from the developmental models. This book will be valuable to researchers in psychiatry and neurobiology, students in psychology, and all mental health practitioners.
This book was originally published in 2004 and concerns developmental neurobiology. In the decade preceding publication, developmental neurobiology made important strides towards elucidating the pathophysiology of psychiatric disorders. Nowhere has this link between basic science and clinical insights become clearer than in the field of schizophrenia research. Each contributor to this volume provides a fresh overview of the relevant research, including directions for further investigation. The book begins with a section on advances in developmental neurobiology. This is followed by sections on etiological and pathophysiological developments, and models that integrate this knowledge. The final section addresses the clinical insights that emerge from the developmental models. This book will be valuable to researchers in psychiatry and neurobiology, students in psychology, and all mental health practitioners.
This book was originally published in 2004 and concerns developmental neurobiology. In the decade preceding publication, developmental neurobiology made important strides towards elucidating the pathophysiology of psychiatric disorders. Nowhere has this link between basic science and clinical insights become clearer than in the field of schizophrenia research. Each contributor to this volume provides a fresh overview of the relevant research, including directions for further investigation. The book begins with a section on advances in developmental neurobiology. This is followed by sections on etiological and pathophysiological developments, and models that integrate this knowledge. The final section addresses the clinical insights that emerge from the developmental models. This book will be valuable to researchers in psychiatry and neurobiology, students in psychology, and all mental health practitioners.
This book was originally published in 2004 and concerns developmental neurobiology. In the decade preceding publication, developmental neurobiology made important strides towards elucidating the pathophysiology of psychiatric disorders. Nowhere has this link between basic science and clinical insights become clearer than in the field of schizophrenia research. Each contributor to this volume provides a fresh overview of the relevant research, including directions for further investigation. The book begins with a section on advances in developmental neurobiology. This is followed by sections on etiological and pathophysiological developments, and models that integrate this knowledge. The final section addresses the clinical insights that emerge from the developmental models. This book will be valuable to researchers in psychiatry and neurobiology, students in psychology, and all mental health practitioners.
The development of new therapies for psychiatric disorders is of the utmost importance given the enormous toll these disorders pose to society nowadays. This should be based on the identification of neural substrates and mechanisms that underlie disease etiopathophysiology. Adult neural stem cells (NSCs) have been emerging as a promising platform to counteract brain damage. In this perspective article, we put forth a detailed view of how NSCs operate in the adult brain and influence brain homeostasis, having profound implications at both behavioural and functional levels. We appraise evidence suggesting that adult NSCs play important roles in regulating several forms of brain plasticity, particularly emotional and cognitive flexibility, and that NSC dynamics are altered upon brain pathology. Further, we discuss the potential therapeutic value of utilizing adult endogenous NSCs as vessels for regeneration, highlighting their importance as targets for the treatment of multiple mental illnesses, such as affective disorders, schizophrenia and addiction. Finally, we speculate on strategies to surpass current challenges in neuropsychiatric disease modelling and brain repair.
We studied the evoked changes in brain rhythmic activity during reading semantic (words) and meaningless verbal information (pseudowords) in patients with paranoid schizophrenia (n=40) and in control group of healthy subjects (n=64). Patients with paranoid schizophrenia showed the decrease in the event-related synchronization (ERS) of alpha and theta rhythms compared to controls when reading semantic verbal information. Reduced event-related synchronization of the alpha rhythm in time window 105 - 145 ms may be associated with the severity of hallucinations (P3 scale) by PANSS. In contrast to the control group, the patients had no differences in the gamma range when reading words and pseudowords. This fact may indicate that patients with paranoid schizophrenia assign significance to those stimuli that are not normally considered as significant (pseudowords).
Objective
Cannabis consumption during adolescence has been reported as a risk factor for psychotic-like experiences (PLEs) and schizophrenia. However, brain developmental processes associated with cannabis-related PLEs are still poorly described.
Method
A total of 706 adolescents from the general population who were recruited by the IMAGEN consortium had structural magnetic resonance imaging scans at both 14 and 19 years of age. We used deformation-based morphometry to map voxelwise brain changes between the two time points, using the pairwise algorithm in SPM12b. We used an a priori region-of-interest approach focusing on the hippocampus/parahippocampus to perform voxelwise linear regressions. Lifetime cannabis consumption was assessed using the European School Survey Project on Alcohol and other Drugs (ESPAD), and PLEs were assessed with the Comprehensive Assessment Psychotic-like experiences (CAPE) tool. We first tested whether hippocampus/parahippocampus development was associated with PLEs. Then we formulated and tested an a priori simple mediation model in which uncus development mediates the association between lifetime cannabis consumption and PLEs.
Results
We found that PLEs were associated with reduced expansion within a specific region of the right hippocampus/parahippocampus formation, the uncus (p = .002 at the cluster level, p = .018 at the peak level). The partial simple mediation model revealed a significant total effect from lifetime cannabis consumption to PLEs (b = 0.069, 95% CI = 0.04−0.1, p =2 × 10−16), as well as a small yet significant, indirect effect of right uncus development (0.004; 95% CI = 0.0004−0.01, p = .026).
Conclusion
We show here that the uncus development is involved in the cerebral basis of PLEs in a population-based sample of healthy adolescents.
Frequently implicated in psychotic spectrum disorders, the amygdala serves as an important hub for elucidating the convergent and divergent neural substrates in schizophrenia and bipolar disorder, the two most studied groups of psychotic spectrum conditions. A systematic search of electronic databases through December 2017 was conducted to identify neuroimaging studies of the amygdala in schizophrenia and bipolar disorder, focusing on structural MRI, diffusion tensor imaging (DTI), and resting-state functional connectivity studies, with an emphasis on cross-diagnostic studies. Ninety-four independent studies were selected for the present review (49 structural MRI, 27 DTI, and 18 resting-state functional MRI studies). Also selected, and analyzed in a separate meta-analysis, were 33 volumetric studies with the amygdala as the region-of-interest. Reduced left, right, and total amygdala volumes were found in schizophrenia, relative to both healthy controls and bipolar subjects, even when restricted to cohorts in the early stages of illness. No volume abnormalities were observed in bipolar subjects relative to healthy controls. Shape morphometry studies showed either amygdala deformity or no differences in schizophrenia, and no abnormalities in bipolar disorder. In contrast to the volumetric findings, DTI studies of the uncinate fasciculus tract (connecting the amygdala with the medial- and orbitofrontal cortices) largely showed reduced fractional anisotropy (a marker of white matter microstructure abnormality) in both schizophrenia and bipolar patients, with no cross-diagnostic differences. While decreased amygdalar-orbitofrontal functional connectivity was generally observed in schizophrenia, varying patterns of amygdalar-orbitofrontal connectivity in bipolar disorder were found. Future studies can consider adopting longitudinal approaches with multimodal imaging and more extensive clinical subtyping to probe amygdalar subregional changes and their relationship to the sequelae of psychotic disorders.
Schizophrenia (SZ) is a complex disease. Single nucleotide polymorphism (SNP), brain activity measured by functional magnetic resonance imaging (fMRI) and DNA methylation are all important biomarkers that can be used for the study of SZ. To our knowledge, there has been little effort to combine these three datasets together. In this study, we propose a group sparse joint nonnegative matrix factorization (GSJNMF) model to integrate SNP, fMRI and DNA methylation for the identification of multi-dimensional modules associated with SZ, which can be used to study regulatory mechanisms underlying SZ at multiple levels. The proposed GSJNMF model projects multiple types of data onto a common feature space, in which heterogeneous variables with large coefficients on the same projected bases are used to find multi-dimensional module. We also incorporate group structural information available from each dataset. The genomic factors in such modules have significant correlations or functional associations with some brain activities. We have applied the method to the analysis of data collected from the Mind Clinical Imaging Consortium (MCIC) for the study of SZ and identified significant biomarkers. These biomarkers were further used to identify genes and corresponding brain regions, which were confirmed to be significantly associated with SZ.
Background:
This study investigated characteristic large-scale brain changes in schizophrenia. Numerous imaging studies have demonstrated brain changes in schizophrenia, particularly aberrant intrinsic functional connectivity (iFC) of ongoing brain activity, measured by resting-state functional magnetic resonance imaging, and aberrant gray matter volume (GMV) of distributed brain regions, measured by structural magnetic resonance imaging. It is unclear, however, which iFC changes are specific to schizophrenia compared with those of other disorders and whether such specific iFC changes converge with GMV changes. To address this question of specific substantial dysconnectivity in schizophrenia, we performed a transdiagnostic multimodal meta-analysis of resting-state functional and structural magnetic resonance imaging studies in schizophrenia and other psychiatric disorders.
Methods:
Multiple databases were searched up to June 2017 for whole-brain seed-based iFC studies and voxel-based morphometry studies in schizophrenia, major depressive disorder, bipolar disorder, addiction, and anxiety. Coordinate-based meta-analyses were performed to detect 1) schizophrenia-specific hyperconnectivity or hypoconnectivity of intrinsic brain networks (compared with hyperconnectivity or hypoconnectivity of each other disorder both separately and combined across comparisons) and 2) the overlap between dysconnectivity and GMV changes (via multimodal conjunction analysis).
Results:
For iFC meta-analysis, 173 publications comprising 4962 patients and 4575 control subjects were included, and for GMV meta-analysis, 127 publications comprising 6311 patients and 6745 control subjects were included. Disorder-specific iFC dysconnectivity in schizophrenia (consistent across comparisons with other disorders) was found for limbic, frontoparietal executive, default mode, and salience networks. Disorder-specific dysconnectivity and GMV reductions converged in insula, lateral postcentral cortex, striatum, and thalamus.
Conclusions:
Results demonstrated specific substantial dysconnectivity in schizophrenia in insula, lateral postcentral cortex, striatum, and thalamus. Data suggest that these regions are characteristic targets of schizophrenia.
Schizophrenia (SZ) is a mental disorder that involves cerebral and cerebellar abnormalities. The cerebellum plays an indispensable role in the pathophysiology of SZ. However, individual studies pertaining to the structural and resting-state functional cerebellar abnormalities in patients with SZ have been inconsistent. To make a relatively robust conclusion with little interference, such as different disease episode times and antipsychotic treatment, we conducted this meta-analysis as a first attempt to comprehensively analyze and combine studies of voxel-based morphometry (VBM), amplitude of low-frequency fluctuation (ALFF), and functional connectivity strength (FCS) in first-episode and drug-naive SZ patients, employing the Seed-based d Mapping (SDM) method. Thirteen VBM studies, eight ALFF studies, and three FCS studies involving 783 patients and 704 matched healthy controls were included. Our results showed the presence of structural and functional abnormalities within the cerebellar regions, including most superior/anterior cerebellum (lobule III-V or VI) and posterior/inferior cerebellum (lobule VIII) related to motor function, and posterior cerebellum (lobule VIIa, Crus I, and II) associated with cognition and emotion, and such anomalies might be related to illness duration and clinical symptom severity.
Recently, brain imaging techniques have been developed by numerous efforts directed at multimodal data fusion, which seeks to combine high-temporal resolution information, as can be provided by electromagnetic-based techniques (M-EEG), with high-spatial resolution, as has been traditionally achieved by the use of hemodynamic-based neuroimaging methods such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) [1]. In particular, the EEG/fMRI fusion has been well known for more than a decade owing to the higher levels of information on brain activity that can be obtained by the simultaneous combination of these complementary modalities, and the significantly increasing capacities for carrying out these studies [2,3].
Schizophrenia is a neuropsychiatric disorder characterized by broad cognitive and functional impairments, including deficits in executive attention, working memory, and social cognition, as well as the presence of delusional thinking and hallucinatory experiences. The insula is a target of structural alteration in schizophrenia, suffering losses in gray matter, white matter, and cortical surface area that correspond to abnormalities in its functional connectivity. Given the wide diversity of neurocognitive functions subserved by the insula, insular dysfunction may explain several symptoms of schizophrenia. We review here the morphometric and functional alterations of the insula over the course of schizophrenia and their relation to resulting neurological deficits.
Objective:
To demonstrate a correlation between anatomic regional changes in Spinocerebellar Ataxia type 6 (SCA6) patients and measures of cognitive performance on neuropsychological tests.
Methods:
Neurocognitive testing was conducted on 24 SCA6 and 28 control subjects. For each cognitive test, SCA6 patients were compared against the controls using Student's t-test. For the cerebellar patients, using voxel based morphometry, correlations between cerebellar gray matter volume at each voxel and performance on the neuropsychological exams were calculated using the Pearson correlation coefficient implemented in SPM8.
Results:
Compared to controls, SCA6 patients exhibited significantly impaired performance on the following cognitive tests: Rey-Auditory Verbal Learning Test Trial V, Controlled Oral Word Association phonemic test and semantic-verb test, Rey-Osterrieth Complex Figure copy test as well as immediate and delayed visuo-spatial memory recall test, Trail Making Test (TMT) Part A and Part B, Stroop Color Task completion time, Stroop Color-Word Task score, and Grooved Pegboard Test (GPT) Dominant and Non-Dominant Hand time. Correlations of gray matter density with cognitive test performance were determined for all SCA6 subjects. Using a p-value threshold of 0.001 and family-wise small volume error correction, significant correlations were found for GPT Non-Dominant, GPT Dominant, TMT Part A, and TMT Part B.
Conclusion:
Different regional patterns of cerebellar involvement were found for the motoric GPT task and the executive version of the TMT. The results for the GPT strongly indicated that the integrity of medial superior hemispheric regions was associated with motor task performance, whereas executive cognitive function was localized in distinctly different inferior regions. This is the first VBM study to differentiate cognitive and motor contributions of the cerebellum.
Over 100 structural MRI (sMRI) studies of schizophrenia have demonstrated that the structure of the brain in vivo is different from that of healthy controls. When the brain abnormalities arise and how they relate to the clinical symptoms are less certain. Such questions are not only of interest for theoretical reasons, but are also relevant to clinicians who may seek to detect schizophrenia at an earlier stage or perhaps to prevent it altogether. A better understanding of regional brain anomalies and their relationship to clinical symptoms may also inform us of the presence of subsyndromes (endophenotypes) and the boundaries of schizophrenia with other illnesses such as affective disorder.
Migraine with aura (MA) is characterized by cortical dysfunction. Frequent aura attacks may alter cerebral cortical structure in patients, or structural grey matter abnormalities may predispose MA patients to aura attacks. In the present study we aimed to investigate cerebral grey matter structure in a large group of MA patients with and without sensory aura (i.e. gradually developing, transient unilateral sensory disturbances). We included 60 patients suffering from migraine with typical visual aura and 60 individually age and sex-matched controls. Twenty-nine of the patients additionally experienced sensory aura regularly. We analysed high-resolution structural MR images using two complimentary approaches and compared patients with and without sensory aura. Patients were also compared to controls. We found no differences of grey matter density or cortical thickness between patients with and without sensory aura and no differences for the cortical visual areas between patients and controls. The somatosensory cortex was thinner in patients (1.92mm vs. 1.96mm, P=0.043) and the anterior cingulate cortex of patients had a decreased grey matter density (P=0.039) compared to controls. These differences were not correlated to the clinical characteristics. Our results suggest that sensory migraine aura is not associated with altered grey matter structure and that patients with visual aura have normal cortical structure of areas involved in visual processing. The observed decreased grey matter volume of the cingulate gyrus in patients compared to controls have previously been reported in migraine with and without aura, but also in a wide range of other neurologic and psychiatric disorders. Most likely, this finding generally reflects bias between patients and healthy controls.
The authors' goal was to determine whether patients with schizophrenia differ from comparison subjects in regional brain volumes and whether these differences are similar in male and female subjects.
They conducted a systematic search for structural magnetic resonance imaging (MRI) studies of patients with schizophrenia that reported volume measurements of selected cortical, subcortical, and ventricular regions in relation to comparison groups. They carried out a meta-analysis of the volumes of these regions in the patients with schizophrenia and the comparison subjects using a random effects model; they also used random effects regression analysis to examine the influence of gender on effect sizes.
Fifty-eight studies were identified as suitable for analysis; these studies included 1,588 independent patients with schizophrenia. Assuming a volume of 100% in the comparison group, they found that the mean cerebral volume of the subjects with schizophrenia was smaller (98%), but the mean total ventricular volume of the subjects with schizophrenia was greater (126%). Relative to the cerebral volume differences, the regional volumes of the subjects with schizophrenia were 94% in the left and right amygdala, 94% in the left and 95% in the right hippocampus/amygdala, and 93% in the left and 95% in the right parahippocampus. Relative to the global ventricular system differences, the largest differences in ventricular subdivisions were in the right and left body of the lateral ventricle, where the volumes of schizophrenic subjects were 116% and 116%, respectively. For most regions, effect size was not significantly related to gender.
Regional structural differences in patients with schizophrenia include bilaterally reduced volume of medial temporal lobe structures. There is a need for greater integration of results from structural MRI studies to avoid redundant research activity.
The Edinburgh High-Risk Study is designed to explore the underlying pathogenesis of schizophrenia.
To establish the sample characteristics of the first 100 subjects in this study of young adults at risk of schizophrenia for genetic reasons, and to compare them with appropriate controls.
Details of the recruitment of the first 100 high-risk subjects aged 16-25 years into a prospective Scotland-wide study are given. Subjects and 30 age- and gender-matched normal controls were interviewed using the PSE, SADS-L and SIS and an unstructured psychiatric interview.
Some significant differences emerged between the high-risk group and the control group, namely in previous psychiatric history (31 v. 6.3%), forensic contacts (19 v. 3.1%) and delinquent behaviour (20 v. 3.1%). There were also differences in some parameters from the SIS: childhood social isolation, interpersonal sensitivity, social isolation, suicidal ideation, restricted affect, oddness and disordered speech.
These differences may represent increased risk of developing schizophrenia although their true significance will not be revealed until the cohort has been followed through the at-risk years.
Hypofrontality or reduced activity in the prefrontal cortex, measured as reduced frontal perfusion or glucose uptake, has gained the status of an established finding in the medical literature on schizophrenia. Many relevant studies, however, have potential sources of bias, such as small subject numbers, or unreliable performance of activation tasks by the patients during the scanning procedure. Seventy patients with non-affective and non-organic psychoses were recruited--most qualifying for DSM III-R schizophrenia or schizophreniform psychosis (n = 60)--together with 20 healthy volunteers. They underwent single photon emission computed tomography with 99mTc-exametazime, carried out at rest. Tracer uptake was normalised to the occipital cortex. Group differences in tracer uptake were predicted in anterior regions of interest (prefrontal cortex and mesial frontal/cingulate cortex). Actively psychotic (including schizophrenic) patients not taking any drugs showed increased uptake in the prefrontal cortex. Reduced tracer uptake occurred in the mesial frontal cortex of schizophrenic patients, particularly if they were taking drugs. Relatively increased prefrontal tracer uptake associated with relatively decreased mesial frontal uptake characterised the patients in comparison with the controls. Generalised hypofrontality is, therefore, not a feature of schizophrenic patients at rest whether taking drugs or not.
Numerous in vivo brain imaging studies suggest that cerebral structure is abnormal in schizophrenia, but implicate different regions to varying extents.
We identified published MRI studies in schizophrenia with searches of the computerised literature and key journals. Reports giving the volumes of cortical structures in people with schizophrenia and controls were included. The percentage differences in volumes were calculated and the median taken as a summary measure for each brain region.
Forty relevant studies were identified. The median percentage volume differences revealed overall reductions in the whole brain (3%), temporal lobe (6% left, 9.5% right), and the amygdala/ hippocampal complex (6.5%, 5.5%); and increases in the lateral ventricles (44%, 36%), that were greatest in the body and occipital horns. Segmentation studies suggest that grey matter is reduced but that white matter volumes may actually be increased. In men, substantial reductions were also evident in the amygdala and hippocampus, as well as the largest reductions of all in the parahippocampus (14%, 9%). Few studies gave figures for women alone.
Several brain structures in schizophrenia are affected to a greater extent than expected from overall reductions in brain volume. Further studies are required in affected women, and to try to identify clinical and aetiological associations of these findings.
The aetiology of treatment-resistant major depression is little understood; its apparent intractability may reflect brain abnormality.
Magnetic resonance images of the brains of 20 subjects with major depression lasting for two years or more were compared with 20 healthy control subjects and 20 other subjects who had completely recovered from depression. Subjects were individually matched for age, gender, years of education and premorbid IQ. Grey matter was segmented from the images, and compared between groups on a voxel-by-voxel basis.
Subjects with chronic depression showed reduced grey matter density in the left temporal cortex including the hippocampus. There was also a trend for reduction in the right hippocampus. Left hippocampal grey matter density was correlated with measures of verbal memory, supporting the functional significance of the observed magnetic resonance imaging changes.
Our results potentially challenge the accepted view of depression as a functional and fully reversible illness, implying instead that more permanent brain changes may be associated with chronicity. Confirmatory longitudinal and prospective studies are required to determine whether these differences pre-date the onset of depression or are the result of the chronic illness process or its treatment.
Fundamental to the concept of idiopathic or primary headache, including migraine, tension-type headache and cluster headache, is the currently accepted view that these conditions are due to abnormal brain function with completely normal brain structure. Cluster headache is one such idiopathic headache with many similarities to migraine, including normal brain structure on magnetic resonance imaging and abnormal function in the hypothalamic grey matter by positron emission tomography. Given the consistency of the positron emission tomography findings with the clinical presentation, we sought to assess whether the brains of such patients were structurally normal. We used voxel-based morphometry, an objective and automated method of analyzing changes in brain structure, to study the structure of the brains of patients with cluster headache. We found a co-localization of structural changes and changes in local brain activity with positron emission tomography in the same area of the brain in the same patients. The results indicate that the current view of the neurobiology of cluster headache requires complete revision and that this periodic headache is associated with a hitherto unrecognized brain abnormality in the hypothalamic region. We believe that voxel-based morphometry has the potential to change in the most fundamental way our concept of primary headache disorders, requiring a radical reappraisal of the tenet of structural normality.
Structural MRIs of the brains of humans with extensive navigation experience, licensed London taxi drivers, were analyzed and compared with those of control subjects who did not drive taxis. The posterior hippocampi of taxi drivers were significantly larger relative to those of control subjects. A more anterior hippocampal region was larger in control subjects than in taxi drivers. Hippocampal volume correlated with the amount of time spent as a taxi driver (positively in the posterior and negatively in the anterior hippocampus). These data are in accordance with the idea that the posterior hippocampus stores a spatial representation of the environment and can expand regionally to accommodate elaboration of this representation in people with a high dependence on navigational skills. It seems that there is a capacity for local plastic change in the structure of the healthy adult human brain in response to environmental demands.
Few magnetic resonance imaging studies of schizophrenia have investigated brain tissue volumes and their relation to clinical symptoms in patients with an early age at illness onset. The twofold purpose of the study was to investigate both gray and white matter volumes in schizophrenic men with an early age at illness onset, and to determine whether clinical features correlated with tissue volume changes, using an automated voxel-by-voxel image analysis procedure. Twenty male patients with DSM-IV diagnoses of schizophrenia, and an early age at onset (m+/-SD=19+/-2) were compared with 20 age-matched health men. Magnetic resonance (1.5-T) scans were obtained with an Inversion-Recovery prepared fast gradient echo sequence enhancing gray and white matter contrast. Statistical Parametric Mapping was used for image segmentation and comparison. Patients had significant gray matter reductions in medial frontal gyri, left insula, left parahippocampus, and left fusiform gyrus; bilateral white matter reductions in frontal lobes, and increased total cerebrospinal fluid volume were also observed. Negative symptom scores were negatively related to white matter volumes in cingulate regions, and in the right internal capsule. These findings emphasize a pattern of left-hemisphere gray matter abnormalities, and suggest that fronto-paralimbic connectivity may be altered in men with early onset schizophrenia.
At what levels of brain organization might pathological change in schizophrenia be anatomically expressed: global, regional or supraregional? We hypothesised that brain structure reflects a set of supra-regional anatomical systems with common developmental influences. We conducted an exploratory analysis to identify supraregional brain systems and to investigate whether abnormal brain architecture in schizophrenia is manifested within one or more of these systems. Magnetic resonance (MR) images were acquired from 27 patients with schizophrenia and 37 control subjects. After segmentation and registration of each individual MRI dataset in the standard space of Talairach and Tournoux, grey matter and ventricular-cerebrospinal fluid (CSF) maps were automatically parcellated into 104 regions. We used principal components analysis of the multiple regional grey matter and ventricular-CSF measurements, on all 64 subjects, to extract the five main normative supra-regional systems. The first two of these components represented global variation in grey matter and ventricular-CSF regional measures. We interpreted the other three components as representing supra-regional systems comprising: a frontal‐parietal system, a frontal‐temporal system and a frontal‐basal ganglia system. Schizophrenic group mean scores on the first component (global grey matter‐ventricular contrast) and fourth component (frontal‐temporal system) were significantly reduced compared to controls. These results suggest that pathological change in schizophrenia may be expressed at two mutually independent levels of anatomical organization: global change in a grey matter/ventricular system and supra-regional change in a frontal‐temporal system.
This paper concerns the spatial and intensity transformations that map one image onto another. We present a general technique that facilitates nonlinear spatial (stereotactic) normalization and image realignment. This technique minimizes the sum of squares between two images following nonlinear spatial deformations and transformations of the voxel (intensity) values. The spatial and intensity transformations are obtained simultaneously, and explicitly, using a least squares solution and a series of linearising devices. The approach is completely noninteractive (automatic), nonlinear, and noniterative. It can be applied in any number of dimensions.
Various applications are considered, including the realignment of functional magnetic resonance imaging (MRI) time-series, the linear (affine) and nonlinear spatial normalization of positron emission tomography (PET) and structural MRI images, the coregistration of PET to structural MRI, and, implicitly, the conjoining of PET and MRI to obtain high resolution functional images.
Studies of brain changes in schizophrenia have suggested that the disorder is associated with reductions in both global and regional grey matter. In this study, we used structural neuroimaging to differentiate between these two types of change and to examine regional grey matter throughout the whole brain. Grey matter from magnetic resonance images was segmented and transformed into stereotactic space, and patients with schizophrenia and controls were compared with respect to regional grey matter (after compensating for global grey matter differences). In two preliminary analyses to test our methodology, we demonstrated that: (1) in the transformed grey matter maps, voxel values at the location of the caudate nuclei were correlated with region-of-interest measurements of caudate area in native image space, and (2) the technique detected regional grey matter changes resulting from artificial lesions created in the native images. We then used a factorial design to examine data from two studies, comprising a total of 42 schizophrenics and 52 controls. Analysis of the main effect of schizophrenia on regional grey matter revealed significant reductions in (a) the right temporal pole, insula and amygdala, (b) the left temporal pole, insula and dorsolateral prefrontal cortex.
Statistical parametric maps are spatially extended statistical processes that are used to test hypotheses about regionally specific effects in neuroimaging data. The most established sorts of statistical parametric maps (e.g., Friston et al. [1991]: J Cereb Blood Flow Metab 11:690–699; Worsley et al. [1992]: J Cereb Blood Flow Metab 12:900–918) are based on linear models, for example ANCOVA, correlation coefficients and t tests. In the sense that these examples are all special cases of the general linear model it should be possible to implement them (and many others) within a unified framework. We present here a general approach that accomodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors). This approach brings together two well established bodies of theory (the general linear model and the theory of Gaussian fields) to provide a complete and simple framework for the analysis of imaging data.
The importance of this framework is twofold: (i) Conceptual and mathematical simplicity, in that the same small number of operational equations is used irrespective of the complexity of the experiment or nature of the statistical model and (ii) the generality of the framework provides for great latitude in experimental design and analysis.
The Edinburgh High Risk Project is a longitudinal study of brain structure (and function) in subjects at high risk of developing schizophrenia in the next 5–10 years for genetic reasons. In this article we describe the methods of volumetric analysis of structural magnetic resonance images used in the study. We also consider potential sources of error in these methods: the validity of our image analysis techniques; inter- and intra-rater reliability; possible positional variation; and thresholding criteria used in separating brain from cerebro-spinal fluid (CSF). Investigation with a phantom test object (of similar imaging characteristics to the brain) provided evidence for the validity of our image acquisition and analysis techniques. Both inter- and intra-rater reliability were found to be good in whole brain measures but less so for smaller regions. There were no statistically significant differences in positioning across the three study groups (patients with schizophrenia, high risk subjects and normal volunteers). A new technique for thresholding MRI scans longitudinally is described (the ‘rescale’ method) and compared with our established method (thresholding by eye). Few differences between the two techniques were seen at 3- and 6-month follow-up. These findings demonstrate the validity and reliability of the structural MRI analysis techniques used in the Edinburgh High Risk Project, and highlight methodological issues of general concern in cross-sectional and longitudinal studies of brain structure in healthy control subjects and neuropsychiatric populations.
Thisarticle has been written in response to Dr. Fred L. Bookstein's article entitled ‘“Voxel-Based Morphometry” Should Not Be Used with Imperfectly Registered Images’ in this issue of NeuroImage. We will address three main issues: (i) Dr. Bookstein appears to have misunderstood the objective of voxel-based morphometry (VBM) and the nature of the continuum we referred to. (ii) We agree with him when he states that findings from VBM can pertain to systematic registration errors during spatial normalization. (iii) His argument about voxelwise tests on smooth data holds in the absence of error variance, but is of no consequence when using actual data. We first review the tenets of VBM, paying particular attention to the relationship between VBM and tensor-based morphometry. The last two sections of this response deal with the specific concerns raised by Dr. Bookstein.
At its simplest, voxel-based morphometry (VBM) involves a voxel-wise comparison of the local concentration of gray matter between two groups of subjects. The procedure is relatively straightforward and involves spatially normalizing high-resolution images from all the subjects in the study into the same stereotactic space. This is followed by segmenting the gray matter from the spatially normalized images and smoothing the gray-matter segments. Voxel-wise parametric statistical tests which compare the smoothed gray-matter images from the two groups are performed. Corrections for multiple comparisons are made using the theory of Gaussian random fields. This paper describes the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with nonuniformity artifact. We provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.
Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly with age, with a significantly steeper decline in males. Local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci. Areas exhibiting little or no age effect (relative preservation) were noted in the amygdala, hippocampi, and entorhinal cortex. Global white matter did not decline with age, but local areas of relative accelerated loss and preservation were seen. There was no interaction of age with sex for regionally specific effects. These results corroborate previous reports and indicate that VBM is a useful technique for studying structural brain correlates of ageing through life in humans.
Voxel-based morphometry (VBM) is a powerful tool for analyzing changes in gray or white matter density of the brain. By using an automated segmentation procedure and standardized parametric statistics it avoids biases inherent in operator-dependent morphological operations (J. Ashburner and K. J. Friston, 2000, NeuroImage 11, 805–821). Since its introduction in 1995, VBM has been used to examine anatomical changes in a variety of diseases associated with neurologic and psychiatric dysfunction. Given the power of this technique for discerning subtle anatomical changes, we wanted to assess its performance on brains with gross structural abnormalities. Such results could have implications regarding the difficulties to be faced when examining other types of distorted brains (e.g., brains with changes due to degenerative disease). This report describes the use of VBM for examining individual and group changes in gray matter concentration in five patients who had recovered from herpes simplex encephalitis (HSE) compared with age- and sex-matched controls. Because HSE tends to affect a specific set of brain regions we thought that this would (1) provide an opportunity to assess the anatomical face validity of VBM, (2) allow us to assess the problems of this technique when used on distorted brains, and (3) provide an in vivo demonstration of the gray matter changes due to HSE. We found that, despite problems in normalizing and segmenting these severely distorted brains, VBM was able to identify correctly a number of the regional gray matter abnormalities in HSE. The results, while consistent with the well-known histopathology of the disease, also demonstrate potential difficulties with this method.
PET images of cerebral blood flow (CBF) in an activation study are usually smoothed to a resolution much poorer than the intrinsic resolution of the PET camera. This is done to reduce noise and to overcome problems caused by neuroanatomic variability among different subjects undertaking the same experimental task. In many studies the choice of this smoothing is arbitrarily fixed at about 20 mm FWHM, and the resulting statistical field or parametric map is searched for local maxima. Poline and Mazoyer [(1994): J Cereb Blood Flow Metab 14:690-699; (1994): IEEE Trans Med Imaging 13(4):702-710] have proposed a 4-D search over smoothing kernel widths as well as the usual three spatial dimensions. If the peaks are well separated then this makes it possible to estimate the size of regions of activation as well as their location. One of the main problems identified by Poline and Mazoyer is how to assess the significance of scale space peaks. In this paper we provide a solution for the case of pooled-variance Z-statistic images (Gaussian fields). Our main result is a unified P value for the 4-D local maxima that is accurate for searches over regions of any shape or size. Our results apply equally well to any Gaussian statistical field, such as those resulting from fMRI.
Objective: Imaging studies of schizophrenia have repeatedly demonstrated global abnormalities of cerebral and ventricular volumes. However, pathological changes at more local levels of brain organization have not yet been so clearly characterized because of the few brain regions of interest heretofore included in morphometric analyses as well as heterogeneity of patient samples.
Method: Dual echo magnetic resonance imaging (MRI) data were acquired at 1.5 T from 27 right-handed patients who met DSM-IV criteria for schizophrenia with enduring negative symptoms and from 27 healthy comparison subjects. Between-group differences in gray and white matter volume were estimated at each intracerebral voxel after registration of the images in standard space. The relationship between clinical symptom scores and brain structure was also examined within the patient group. Spatial statistics and permutation tests were used for inference.
Results: Significant deficits of gray matter volume in the patient group were found at three main locations: 1) the left superior temporal gyrus and insular cortex, 2) the left medial temporal lobe (including the parahippocampal gyrus and hippocampus), and 3) the anterior cingulate and medial frontal gyri. The volume of these three regions combined was 14% lower in the patients relative to the comparison subjects. White matter deficits were found in similar locations in the left temporal lobe and extended into the left frontal lobe. The patient group showed a relative excess of gray matter volume in the basal ganglia. Within the patient group, basal ganglia gray matter volume was positively correlated with positive symptom scores.
Conclusions: Anatomical abnormalities in these schizophrenic patients with marked negative symptoms were most evident in left hemispheric neocortical and limbic regions and related white matter tracts. These data are compatible with models that depict schizophrenia as a supraregional disorder of multiple, distributed brain regions and the axonal connections between them.
Symptoms in schizophrenia cluster into syndromes, each of which may be associated with a particular pattern of cerebral blood flow. We sought to investigate whether these syndromes are also related to neuroanatomical changes.
A semi-automated method was used to examine structural magnetic resonance images in 12 patients with schizophrenia. The relationship between the relative regional grey matter volume and ratings of the syndromes of psychomotor poverty, disorganisation and reality distortion was investigated.
There was a significant negative correlation between psychomotor poverty score and the relative volume of the left ventro-medial prefrontal grey matter, and a significant positive correlation between disorganisation and the relative volumes of the hippocampus, and the parahippocampal/fusiform gyrus bilaterally.
The correlation between psychomotor poverty and left prefrontal grey matter volume resembles that previously seen with prefrontal blood flow in the same patient, suggesting that this functional abnormality is related to an underlying anatomical change.
We describe a novel technique for characterizing regional cerebral gray and white matter differences in structural magnetic resonance images by the application of methods derived from functional imaging. The technique involves automatic scalp-editing of images followed by segmentation, smoothing, and spatial normalization to a symmetrical template brain in stereotactic Talairach space. The basic idea is (i) to convert structural magnetic resonance image data into spatially normalized images of gray (or white) matter density, effected by segmenting the images and smoothing, and then (ii) to use Statistical Parametric Mapping to make inferences about the relationship between gray (or white) matter density and symptoms (or other pathophysiological measures) in a regionally specific fashion. Because the whole brain sum of gray (or white) matter indices is treated as a confound, the analysis reduces to a characterization of relative gray (or white) matter density on a voxel by voxel basis. We suggest that this is a powerful approach to voxel-based statistical anatomy. Using the technique, we constructed maps of the regional cerebral gray and white matter density correlates of syndrome scores (distinct psychotic symptoms) in a group of 15 schizophrenic patients. There was a negative correlation between the score for the reality distortion syndrome and regional gray matter density in the left superior temporal lobe (P = 0.01) and regional white matter density in the corpus callosum (P < 0.001). These abnormalities may be associated with functional changes predisposing to auditory hallucinations and delusions. This method permits the detection of structural differences within the entire brain (as opposed to selected regions of interest) and may be of value in the investigation of structural gray and white matter abnormalities in a variety of brain diseases.
Studies of brain changes in schizophrenia have suggested that the disorder is associated with reductions in both global and regional grey matter. In this study, we used structural neuroimaging to differentiate between these two types of change and to examine regional grey matter throughout the whole brain. Grey matter from magnetic resonance images was segmented and transformed into stereotactic space, and patients with schizophrenia and controls were compared with respect to regional grey matter (after compensating for global grey matter differences). In two preliminary analyses to test our methodology, we demonstrated that: (1) in the transformed grey matter maps, voxel values at the location of the caudate nuclei were correlated with region-of-interest measurements of caudate area in native image space, and (2) the technique detected regional grey matter changes resulting from artificial lesions created in the native images. We then used a factorial design to examine data from two studies, comprising a total of 42 schizophrenics and 52 controls. Analysis of the main effect of schizophrenia on regional grey matter revealed significant reductions in (a) the right temporal pole, insula and amygdala, (b) the left temporal pole, insula and dorsolateral prefrontal cortex.
Functional neuroimaging provides a novel means of exploring neurophysiological function in schizophrenia. However, most of the studies that have been carried out report their findings in terms of regionally localized abnormalities. In this paper we propose an alternative method of data analysis that emphasizes global integration rather than isolated regional changes in response to psychological tasks. In doing so, we suggest that brain abnormalities in schizophrenia are best characterized as a disturbance in the integration of activity across a number of brain regions. Using a hypothesis-led analysis, we show that the condition is associated with a disruption of the normal anterior cingulate modulation of prefronto-temporal integration. This analytical technique, we suggest, provides a conceptually powerful approach to the imaging of abnormal brain function in psychopathological conditions.
Despite a hundred years' research, the neuropathology of schizophrenia remains obscure. However, neither can the null hypothesis be sustained--that it is a 'functional' psychosis, a disorder with no structural basis. A number of abnormalities have been identified and confirmed by meta-analysis, including ventricular enlargement and decreased cerebral (cortical and hippocampal) volume. These are characteristic of schizophrenia as a whole, rather than being restricted to a subtype, and are present in first-episode, unmedicated patients. There is considerable evidence for preferential involvement of the temporal lobe and moderate evidence for an alteration in normal cerebral asymmetries. There are several candidates for the histological and molecular correlates of the macroscopic features. The probable proximal explanation for decreased cortical volume is reduced neuropil and neuronal size, rather than a loss of neurons. These morphometric changes are in turn suggestive of alterations in synaptic, dendritic and axonal organization, a view supported by immunocytochemical and ultrastructural findings. Pathology in subcortical structures is not well established, apart from dorsal thalamic nuclei, which are smaller and contain fewer neurons. Other cytoarchitectural features of schizophrenia which are often discussed, notably entorhinal cortex heterotopias and hippocampal neuronal disarray, remain to be confirmed. The phenotype of the affected neuronal and synaptic populations is uncertain. A case can be made for impairment of hippocampal and corticocortical excitatory pathways, but in general the relationship between neurochemical findings (which centre upon dopamine, 5-hydroxytryptamine, glutamate and GABA systems) and the neuropathology of schizophrenia is unclear. Gliosis is not an intrinsic feature; its absence supports, but does not prove, the prevailing hypothesis that schizophrenia is a disorder of prenatal neurodevelopment. The cognitive impairment which frequently accompanies schizophrenia is not due to Alzheimer's disease or any other recognized neurodegenerative disorder. Its basis is unknown. Functional imaging data indicate that the pathophysiology of schizophrenia reflects aberrant activity in, and integration of, the components of distributed circuits involving the prefrontal cortex, hippocampus and certain subcortical structures. It is hypothesized that the neuropathological features represent the anatomical substrate of these functional abnormalities in neural connectivity. Investigation of this proposal is a goal of current neuropathological studies, which must also seek (i) to establish which of the recent histological findings are robust and cardinal, and (ii) to define the relationship of the pathological phenotype with the clinical syndrome, its neurochemistry and its pathogenesis.
Volumetric studies have consistently shown reductions in cerebral gray matter volume between childhood and adolescence, with the most dramatic changes occurring in the more dorsal cortices of the frontal and parietal lobes. The purpose of this study was to examine the spatial location of these changes employing methods typical of functional imaging studies. T1-weighted structural MRI data (1.2 mm) were analyzed for nine normally developing children and nine normal adolescents. Validity and reliability of the tissue segmentation protocol were assessed as part of several preprocessing analyses prior to statistical parametric mapping (SPM). Using SPM96, a simple contrast of average gray matter differences between the two age groups revealed 57 significant clusters (SPM[Z] height threshold, P<0.001, extent threshold 50, uncorrected). The pattern and distribution of differences were consistent with earlier findings from the volumetric assessment of the same subjects. Specifically, more differences were observed in dorsal frontal and parietal regions with relatively few differences observed in cortices of the temporal and occipital lobes. Permutation tests were conducted to assess the overall significance of the gray matter differences and validity of the parametric maps. Twenty SPMs were created with subjects randomly assigned to groups. None of the random SPMs approached the number of significant clusters observed in the age difference SPM (mean number of significant clusters = 5.8). The age effects observed appear to result from regions that consistently segment as gray matter in the younger group and consistently segment as white matter in the older group. The utility of these methods for localizing relatively subtle structural changes that occur between childhood and adolescence has not previously been examined.
This paper describes a new method for detecting structural brain differences based on the analysis of deformation fields. Deformations are obtained by an intensity-based nonlinear registration routine that transforms one brain onto another one. We present a general multivariate statistical approach to analyze deformation fields in different subjects. This method was applied to the brains of 85 schizophrenic patients and 75 healthy volunteers to examine whether low frequency deformations are sufficiently sensitive to detect regional deviations in the brains of both groups. We observed significant changes caused by volume reduction in brains of schizophrenics bilaterally in the thalamus and in the superior temporal gyrus. On the left side, the superior frontal gyrus and precentral gyrus are found to be changed, while on the right side, the middle frontal gyrus was altered. In addition, there were significant changes in the occipital lobe (left lingual gyrus) and in the left cerebellum. Volume enlargement in brains of schizophrenics was observed in the right putamen and in the adjacent white matter of the thalamic region. Our data suggest a disturbance in the nodes of a prefrontal-thalamic-cerebellar circuitry. This provides further support for the model of "cognitive dysmetria," which postulates a disruption in these nodes. We have demonstrated the application of deformation-based morphometry by detecting structural changes in the whole brain. This technique is fully automatic, thus allowing for the inclusion of large samples, with no user bias or a priori-defined regions of interest.
At what levels of brain organization might pathological change in schizophrenia be anatomically expressed: global, regional or supraregional? We hypothesised that brain structure reflects a set of supra-regional anatomical systems with common developmental influences. We conducted an exploratory analysis to identify supraregional brain systems and to investigate whether abnormal brain architecture in schizophrenia is manifested within one or more of these systems. Magnetic resonance (MR) images were acquired from 27 patients with schizophrenia and 37 control subjects. After segmentation and registration of each individual MRI dataset in the standard space of Talairach and Tournoux, grey matter and ventricular-cerebrospinal fluid (CSF) maps were automatically parcellated into 104 regions. We used principal components analysis of the multiple regional grey matter and ventricular-CSF measurements, on all 64 subjects, to extract the five main normative supra-regional systems. The first two of these components represented global variation in grey matter and ventricular-CSF regional measures. We interpreted the other three components as representing supra-regional systems comprising: a frontal-parietal system, a frontal-temporal system and a frontal-basal ganglia system. Schizophrenic group mean scores on the first component (global grey matter-ventricular contrast) and fourth component (frontal-temporal system) were significantly reduced compared to controls. These results suggest that pathological change in schizophrenia may be expressed at two mutually independent levels of anatomical organization: global change in a grey matter/ventricular system and supra-regional change in a frontal-temporal system.
Quantitative evaluation of MRI in patients with epilepsy can give more information than qualitative assessment. Previously developed volume-of-interest-based methods identified subtle widespread structural changes in the neocortex beyond the visualized lesions in patients with malformations of cortical development (MCD) and hippocampal sclerosis (HS) and also in MRI-negative patients with juvenile myoclonic epilepsy (JME). This study evaluates a voxel-based automated analysis of structural MRI in epilepsy. After fully automated segmentation of cerebral gray matter from structural T1-weighted, high-resolution MRI scans, we applied the automated and objective technique of statistical parametric mapping (SPM) to the analysis of gray matter of 35 control subjects, 10 patients with partial seizures and MCD, 10 patients with left temporal lobe epilepsy (TLE) and HS, 10 patients with left TLE and normal MR quantitation of the hippocampus, and 20 patients with JME. At a corrected threshold of P < 0.05, significant abnormalities were found in 3/35 controls; in all 10 patients with MCD, 6 of whom had additional lesions beyond the margins of the visualized abnormalities; in 2/10 TLE patients with HS; in 2/10 MRI-negative TLE; and in 4/20 JME patients. Group comparisons between control subjects and HS patients identified the affected left temporal lobe with an increase in gray matter in the posterior temporal lobe, but did not identify hippocampal atrophy. The group of MRI-negative TLE patients showed no abnormalities compared with control subjects. Group comparison between control subjects and JME patients identified a mesial frontal increase in gray matter. The SPM-based voxel-by-voxel comparison of gray matter distribution identified MCD and abnormalities beyond the visualized lesion in individual MCD patients. The method did not reliably identify HS in individual patients or identify abnormalities in individual MRI-negative patients with TLE or JME in a proportion larger than the chance findings in the control group. Using group comparisons, structural abnormalities in the neocortical gray matter of patients with TLE and HS were lateralized to the affected temporal lobe. In patients with JME as a group, an increase in gray matter was localized to the mesial frontal area, corroborating earlier quantitative MRI findings.
This study reports findings of the Edinburgh High Risk Study four years after it began. This study is designed to explore the pathogenesis of schizophrenia by examining a large sample of young adults aged 16-25 years who are at enhanced risk of developing schizophrenia by having two close relatives with the disorder, and comparing them with matched controls. This paper presents comparisons of the high risk subjects, well controls and subjects with first-episode schizophrenia in terms of demographic, childhood, psychopathological, educational and employment, forensic and social work variables. High risk subjects have more psychological difficulties, poorer educational and employment attainment, and more social work contact than controls. The enhanced social work involvement related to the presence of a schizophrenic parent (especially a mother) but the other difficulties could not be attributed to that situation. Neurotic, partially held psychotic and fully held psychotic symptoms all occurred in both subjects and controls, but all were significantly more common in high risk subjects. Clinical schizophrenia has so far developed in 10 high risk subjects and in no controls. Possible confounding effects of drug or alcohol misuse were considered but were found unlikely to be important.
Voxel-based morphometry has recently been used successfully to detect gray matter volume reductions in schizophrenic patients. The aim of the present study was to confirm the findings on gray-matter changes and to complement these by applying the methodology to CSF-differences. Also, we wanted to determine whether a correlation exists between a clinically defined parameter of disease severity and brain morphology in schizophrenic patients. We investigated 48 schizophrenic patients and compared them with 48 strictly age- and sex-matched controls. High-resolution whole-brain MR-images were segmented and analyzed using SPM99. In a further analysis, the covariate effect of the global assessment of functioning-score (GAF) was calculated. Main findings were (i) left-dominant frontal, temporal, and insular GM-reductions and (ii) GM-increases in schizophrenic patients in the right basal ganglia and bilaterally in the superior cerebellum; (iii) CSF-space increases in patients complementary to some GM-reductions; (iv) a correlation between the GAF-score and local GM-volume in the left inferior frontal and inferior parietal lobe of schizophrenic patients. This study confirms and extends some earlier findings on GM-reduction and detected distinct GM-increases in schizophrenic patients. These changes were corroborated by complementary CSF-increases. Most importantly, a correlation could be established between two particular gray matter-regions and the overall disease severity, with more severely ill patients displaying a local GM-deficit. These findings may be of potentially large importance for both the future interpretation and design of neuroimaging studies in schizophrenia and the further elucidation of possible pathophysiological processes occurring in this disease.
Structural magnetic resonance imaging (MRI) of the brain in patients with schizophrenia has consistently demonstrated several abnormalities. These are thought to be neurodevelopmental in origin, as they have also been described in first episode cases, although there may be a progressive component. It is not known at which point in development these abnormalities are evident, nor to what extent they are genetically or environmentally mediated.
One hundred forty-seven high-risk subjects (with at least two affected first or second degree relatives), 34 patients in their first episode, and 36 healthy control subjects received an MRI scan covering the whole brain. After inhomogeneity correction, regions of interest were traced by three group-blind raters with good inter-rater reliability. Regional brain volumes were related to measures of genetic liability to schizophrenia and to psychotic symptoms elicited at structured psychiatric interviews.
High-risk subjects had statistically significantly reduced mean volumes of the left and right amygdalo-hippocampus and thalamus, as compared to healthy control subjects. They also had bilaterally larger amygdalo-hippocampi and bilaterally smaller lenticular nuclei than the schizophrenics. High-risk subjects with symptoms had smaller brains than those without. The volumes of the prefrontal lobes and the thalamus were the only consistent associates of genetic liability.
Subjects at high risk of developing schizophrenia have abnormalities of brain structure similar to but not identical to those found in schizophrenia. Our results suggest that some structural abnormalities are genetic trait or vulnerability markers, others are environmentally mediated, and that the development of symptoms is associated with a third overlapping group of structural changes. Particular risk factors for schizophrenia may interact at discrete time points of neurodevelopment with different effects on specific brain regions and may represent relatively distinct disease processes.
Up till now, the study of regional gray matter atrophy in Alzheimer's disease (AD) has been assessed with regions of interest, but this method is time-consuming, observer dependent, and poorly reproducible (especially in terms of cortical regions boundaries) and in addition is not suited to provide a comprehensive assessment of the brain. In this study, we have mapped gray matter density by means of voxel-based morphometry on T1-weighted MRI volume sets in 19 patients with mild AD and 16 healthy subjects of similar age and gender ratio and report highly significant clusters of gray matter loss with almost symmetrical distribution, affecting mainly and in decreasing order of significance the medial temporal structures, the posterior cingulate gyrus and adjacent precuneus, and the temporoparietal association and perisylvian neocortex, with only little atrophy in the frontal lobe. The findings are discussed in light of previous studies of gray matter atrophy in AD based either on postmortem or neuroimaging data and in relation to PET studies of resting glucose consumption. The limitations of the method are also discussed in some detail, especially with respect to the segmentation and spatial normalization procedures as they apply to pathological brains. Some potential applications of voxel-based morphometry in the study of AD are also mentioned.
John Ashburner and Karl Friston (2000) introduced a standardized method of "voxel-based morphometry" (VBM) for comparisons of local concentrations of gray matter between two groups of subjects. Segmented images of gray matter from grossly normalized high-resolution images are smoothed and their group differences analyzed by the now-conventional voxelwise Worsley approach to Gaussian random fields of differences. This comment concerns an unfortunate interaction between the algorithm's spatial normalization and voxelwise comparison steps, whereby several obvious quantitative confounds are injected at the core of the inference engine the authors put forward. Specifically, the statistics of the resulting voxelwise comparisons are uninformative about group differences wherever the spatial normalization algorithm has failed to register on any robustly appearing image gradient. The method of Ashburner and Friston is defensible only far from all image gradients.
The view that schizophrenia is a brain disease particularly involving decrements in gray matter is supported by findings from many imaging studies. However, it is unknown whether the (progressive) loss of tissue affects the brain globally or whether tissue loss is more prominent in some areas than in others.
Magnetic resonance whole brain images were acquired from 159 patients with schizophrenia or a schizophreniform disorder and 158 healthy subjects across a 55-year age span. Gray matter density maps were made and analyzed using voxel-based morphometry.
Compared with healthy subjects, decreases in gray matter density were found in the left amygdala; left hippocampus; right supramarginal gyrus; thalamus; (orbito) frontal, (superior) temporal, occipitotemporal, precuneate, posterior cingulate, and insular cortices bilaterally in patients with schizophrenia or schizophreniform disorder. Compared with healthy subjects, increases in gray matter density were exclusively found in the right caudate and globus pallidus in patients with schizophrenia or schizophreniform disorder. A group-by-age interaction for density was found in the left amygdala, owing to a negative regression slope of gray matter density on age in the left amygdala in patients compared with healthy subjects.
Gray matter density is decreased in distinct focal areas in the brains of patients with schizophrenia or schizophreniform disorder. The decreased density in the left amygdala is more pronounced in older patients with schizophrenia.
Requiring only minimal assumptions for validity, nonparametric permutation testing provides a flexible and intuitive methodology for the statistical analysis of data from functional neuroimaging experiments, at some computational expense. Introduced into the functional neuroimaging literature by Holmes et al. ([1996]: J Cereb Blood Flow Metab 16:7-22), the permutation approach readily accounts for the multiple comparisons problem implicit in the standard voxel-by-voxel hypothesis testing framework. When the appropriate assumptions hold, the nonparametric permutation approach gives results similar to those obtained from a comparable Statistical Parametric Mapping approach using a general linear model with multiple comparisons corrections derived from random field theory. For analyses with low degrees of freedom, such as single subject PET/SPECT experiments or multi-subject PET/SPECT or fMRI designs assessed for population effects, the nonparametric approach employing a locally pooled (smoothed) variance estimate can outperform the comparable Statistical Parametric Mapping approach. Thus, these nonparametric techniques can be used to verify the validity of less computationally expensive parametric approaches. Although the theory and relative advantages of permutation approaches have been discussed by various authors, there has been no accessible explication of the method, and no freely distributed software implementing it. Consequently, there have been few practical applications of the technique. This article, and the accompanying MATLAB software, attempts to address these issues. The standard nonparametric randomization and permutation testing ideas are developed at an accessible level, using practical examples from functional neuroimaging, and the extensions for multiple comparisons described. Three worked examples from PET and fMRI are presented, with discussion, and comparisons with standard parametric approaches made where appropriate. Practical considerations are given throughout, and relevant statistical concepts are expounded in appendices.
The study of high-risk groups and the development of schizophrenia.
To investigate further schizotypy, measured by the Structured Interview for Schizotypy (SIS), and to examine relationships between schizotypal components, psychotic symptoms on the Present State Examination (PSE) and subsequent schizophrenia.
The SIS and PSE were administered on entry. Schizophrenia onsets were recorded during follow-up.
The SIS yielded four principal components labelled social withdrawal, psychotic symptoms, socio-emotional dysfunction and odd behaviour. On entry, these differentiated between controls, subjects at risk for schizophrenia with and without symptoms and patients with schizophrenia. Seven of 78 subjects at risk developed schizophrenia within 39 months. This was best predicted by combining the four SIS components.
Schizotypy is heterogeneous and may become psychosis, particularly if several of its components are present. As psychosis develops, odd behaviour gives way to psychotic symptoms and social function deteriorates.
Hallucinations typically are associated with severe psychiatric illness but also are reported by individuals with no psychiatric history.
To examine the prevalence of hallucinations in White and ethnic minority samples using data from the Fourth National Survey of Ethnic Minorities.
Interviews of 5196 ethnic minority and 2867 White respondents were carried out. The respondents were screened for mental health problems and the Psychosis Screening Questionnaire asked about hallucinations. Those who screened positive underwent a validation interview using the Present State Examination.
Four per cent of the White sample endorsed a hallucination question. Hallucinations were 2.5-fold higher in the Caribbean sample and half as common in the South Asian sample. Of those who reported hallucinatory experiences, only 25% met the criteria for psychosis.
The results provide an estimate of the annual prevalence of hallucinations in the general population. The variation across ethnic groups suggests cultural differences in these experiences. Hallucinations are not invariably associated with psychosis.
Voxel-based-morphometry (VBM) is a whole-brain, unbiased technique for characterizing regional cerebral volume and tissue concentration differences in structural magnetic resonance images. We describe an optimized method of VBM to examine the effects of age on grey and white matter and CSF in 465 normal adults. Global grey matter volume decreased linearly with age, with a significantly steeper decline in males. Local areas of accelerated loss were observed bilaterally in the insula, superior parietal gyri, central sulci, and cingulate sulci. Areas exhibiting little or no age effect (relative preservation) were noted in the amygdala, hippocampi, and entorhinal cortex. Global white matter did not decline with age, but local areas of relative accelerated loss and preservation were seen. There was no interaction of age with sex for regionally specific effects. These results corroborate previous reports and indicate that VBM is a useful technique for studying structural brain correlates of ageing through life in humans.
Diagnostic and Statistical Manual of Mental Disorders American Psychiatric Association, Washington, DC. Ashburner J., Functional Imaging Laboratory Voxel-based morphometry— The methods
- American Psychiatric Association
- K J Friston
American Psychiatric Association. 1994. Diagnostic and Statistical Manual of Mental Disorders, 4th Edition. American Psychiatric Association, Washington, DC. Ashburner J., Functional Imaging Laboratory. Wellcome Depart-ment of Cognitive Neurology, London, UK. Ashburner, J., and Friston, K. J. 2000. Voxel-based morphometry— The methods. NeuroImage 11: 805– 821.
The MNI brain and the Talairach atlas. MRC Cog-nition and Brain Sciences Unit
- M Brett
Brett, M. 1999. The MNI brain and the Talairach atlas. MRC Cog-nition and Brain Sciences Unit. http://www.mrc-cbu.cam.ac.uk/ Imaging/.