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fMRI maps (group analysis). Statistical maps (axial slices oriented in radiological convention, i.e. where the left side of the images corresponds to the right side of the brain; MNI coordinates, z = -15, -5, 15, 23, 55) of fMRI BOLD signal following a sentence-decision task (see text for details): group analysis of 20 healthy subjects (SPM8; statistical threshold set at p < 0.05, with family-wise error correction; cluster > 2 contiguous voxels). Cortical areas of activations are visible within the primary/secondary visual cortex, middle/superior temporal gyrus, inferior frontal gyrus and supplementary motor area of the left and right hemispheres, as well as within the temporal pole, inferior parietal lobule, premotor cortex and middle frontal gyrus of the left hemisphere.
Source publication
Despite a better understanding of brain language organization into large-scale cortical networks, the underlying white matter (WM) connectivity is still not mastered. Here we combined diffusion tensor imaging (DTI) fiber tracking (FT) and language functional magnetic resonance imaging (fMRI) in twenty healthy subjects to gain new insights into the...
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Diffusion MRI tractography allows in-vivo characterization of white matter architecture, including the localization and description of brain fibre bundles. However, some primary bundles are still only partially reconstructed, or not reconstructed at all. The acoustic radiation (AR) represents a primary sensory pathway that has been largely omitted...
Citations
... We propose that the left IFOF connects gray matter regions in the temporal lobes supporting semantic processing with the IFG, allowing for information in perceptual and semantic processing regions to be transferred to the IFG for semantic maintenance. There is also evidence that, in some people, the IFOF includes terminations in the precuneus region, which includes the AG [61]. Thus, another explanation for the IFOF's relation to semantic WM could be that it connects two semantic WM regions, the IFG and the AG, as part of a larger network supporting semantic WM. ...
... In patients with the bulbar-onset of the disease, significant atrophy is also observed in the superior longitudinal fasciculus tract (FDR corrected; left: p = 1.72 × 10 −3 , t-statistic= 4.88; right: p = 5.34 × 10 −3 , t-statistic= 4.40). This tract is known to play role in speech [140] and language functions [95]. Decrease in fractional anisotropy of the superior longitudinal fasciculus tract in bulbar-onset ALS patients has been reported in previous studies [18,127]. ...
Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that predominantly targets the motor system. Spread of pathology is thought to be driven by both local vulnerability and network architecture. Namely, molecular and cellular features may confer vulnerability to specific neuronal populations, while synaptic contacts may also increase exposure to pathology in connected neuronal populations. However, these principles are typically studied in isolation and it remains unknown how local vulnerability and network spreading interact to shape cortical atrophy. Here we investigate how network structure and local biological features jointly shape the spatial patterning of atrophy in ALS. We analyze the Canadian ALS Neuroimaging Consortium (CALSNIC) dataset and estimate cortical atrophy using deformation-based morphometry (DBM). We find that structural connectivity closely aligns with the course of atrophy. Atrophy is also more likely to occur in regions that share similar transcriptomic, neurotransmitter receptor and metabolic profiles. We identify disease epicenters in motor cortex. Epicenter probability maps show transcriptomic enrichment for biological pathways involved in mitochondrial function as well as support cells, including endothelial cells and pericytes. Finally, individual differences in epicenter location correspond to individual differences in clinical and cognitive symptoms, and differentiate patient subtypes.
... Language mapping reduces postoperative functional impairment and enables safe maximal resection [1]. Presurgical language mapping with functional magnetic resonance imaging (fMRI) and diffusion tensor imagingderived tractography (DTI-t) enables a comprehensive understanding of the language network system [2] and localization of the language area [3,4]. Language mapping in fMRI is based on blood oxygen level dependence (BOLD) effects caused by increased local brain activity [5]. ...
Objective:
Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging-derived tractography (DTI-t) contribute to the localization of language areas, but their accuracy remains controversial. This study aimed to investigate the diagnostic performance of preoperative fMRI and DTI-t obtained with a simultaneous multi-slice technique using intraoperative direct cortical stimulation (DCS) or corticocortical evoked potential (CCEP) as reference standards.
Materials and methods:
This prospective study included 26 patients (23-74 years; male:female, 13:13) with tumors in the vicinity of Broca's area who underwent preoperative fMRI and DTI-t. A site-by-site comparison between preoperative (fMRI and DTI-t) and intraoperative language mapping (DCS or CCEP) was performed for 226 cortical sites to calculate the sensitivity and specificity of fMRI and DTI-t for mapping Broca's areas. For sites with positive signals on fMRI or DTI-t, the true-positive rate (TPR) was calculated based on the concordance and discordance between fMRI and DTI-t.
Results:
Among 226 cortical sites, DCS was performed in 100 sites and CCEP was performed in 166 sites. The specificities of fMRI and DTI-t ranged from 72.4% (63/87) to 96.8% (122/126), respectively. The sensitivities of fMRI (except for verb generation) and DTI-t were 69.2% (9/13) to 92.3% (12/13) with DCS as the reference standard, and 40.0% (16/40) or lower with CCEP as the reference standard. For sites with preoperative fMRI or DTI-t positivity (n = 82), the TPR was high when fMRI and DTI-t were concordant (81.2% and 100% using DCS and CCEP, respectively, as the reference standards) and low when fMRI and DTI-t were discordant (≤ 24.2%).
Conclusion:
fMRI and DTI-t are sensitive and specific for mapping Broca's area compared with DCS and specific but insensitive compared with CCEP. A site with a positive signal on both fMRI and DTI-t represents a high probability of being an essential language area.
... Therefore, incorporating MEG/EEG and fMRI would be much more valuable to achieve the goal of the second release for the HCP processing pipelines (Ulbert et al., 2004), which is the precise determination of spectral properties for neural processes or spectral network topographies and topologies with cortical support (Neal, 1998;Nagasaka, Shimoda and Fujii, 2011;Nunez, Nunez and Srinivasan, 2019;Nolte et al., 2020). Electrophysiological Source Imaging (ESI) reflects brain activity with millisecond temporal accuracy and could help avoid temporal aliasing in determining neural causal relations (Avants, Tustison and others, 2014;Vassal et al., 2016). EEG/MEG ESI is affected differently due to their low resolution and blurring effect of head volume conduction-leakage (Das et al., 2012;Sherif et al., 2014;Helbling et al., 2015). ...
... Cortically mapped structural connectivity that is extracted from the dMRI tractography, in combination with information fMRI or MEG connectivity or functional cortical parcellations extracted from fMRI, Figure 13 General tab in the visual interface of the BC-VARETA Toolbox provides an unprecedented level of detail in mapping cortical connectomes that are relevant for the study of brain function (Wang, Yoldemir and Abugharbieh, 2015;Vassal et al., 2016). ...
Extracting cortical features, which are the most relevant at characterizing structure and function for normal or abnormal brain conditions, would greatly benefit from multimodal neuroimage processing following the surface-based style. This style recognizes the natural definition space for such features due to the layered (surface-based) Cortex structural and functional organization. It may therefore be more sensitive and specific than the former volume-based style. The Human Connectome Project (HCP) multimodal pipelines render high-quality surface-based processing for some of the most consistently acquired neuroimaging modalities, with the quality too reliant on their precise acquisition requirements. Relevant international brain initiatives are espoused to develop an HCP-compatible neuroinformatic facility for the quality-ensured processing of international neuroimaging datasets, which may not follow the specific HCP acquisition requirements, also coined as legacy datasets. We appointed some initiatives to introduce multimodal pipelines in two HCP-compatible processing branches. a) Structural: forward- modeling with geometry (sources and head) and Lead Fields defined for legacy MEG, or EEG, in the HCP individual cortical space (Cifti) obtained from legacy MRI. Our pipeline (Ciftify-MEEG) leverages a more diverse neuroinformatic repository than the HCP structural or MEG pipelines. Ciftify-MEEG produces substantial processing illustrated here with EEG examples, incorporating alternative processing paths and corrections based on a quality control loop and upon qualitative and quantitative indicators. b) Functional: identifying spectral topographies and connectomes for the cortical oscillatory activity observed in the MEG or EEG frequency bands. We leverage a novel repository of Bayesian sparse inverse methods that target identifying the topographies and connectomes with actual statistical guarantees, the brain connectivity Variable Resolution Electromagnetic Tomographic Analysis (BC-VARETA). Our pipeline design for BC-VARETA, which we denominate Ciftify-bcVARETA, is integrated into the Ciftify- MEEG outputs and with Bayesian sparse priors structured upon Cifti space information. Ciftify-bcVARETA identification is less biased to forward models and more biased to the observations than the HCP pipeline illustrated here with topographies obtained for MEG and EEG legacy databases.
... Therefore, incorporating MEG/EEG and fMRI would be much more valuable to achieve the goal of the second release for the HCP processing pipelines (Ulbert et al., 2004), which is the precise determination of spectral properties for neural processes or spectral network topographies and topologies with cortical support (Neal, 1998;Nagasaka, Shimoda and Fujii, 2011;Nunez, Nunez and Srinivasan, 2019;Nolte et al., 2020). Electrophysiological Source Imaging (ESI) reflects brain activity with millisecond temporal accuracy and could help avoid temporal aliasing in determining neural causal relations (Avants, Tustison and others, 2014;Vassal et al., 2016). EEG/MEG ESI is affected differently due to their low resolution and blurring effect of head volume conduction-leakage (Das et al., 2012;Sherif et al., 2014;Helbling et al., 2015). ...
... Cortically mapped structural connectivity that is extracted from the dMRI tractography, in combination with information fMRI or MEG connectivity or functional cortical parcellations extracted from fMRI, Figure 13 General tab in the visual interface of the BC-VARETA Toolbox provides an unprecedented level of detail in mapping cortical connectomes that are relevant for the study of brain function (Wang, Yoldemir and Abugharbieh, 2015;Vassal et al., 2016). ...
Extracting cortical features, which are the most relevant at characterizing structure and function for normal or abnormal brain conditions, would greatly benefit from multimodal neuroimage processing following the surface-based style. This style recognizes the natural definition space for such features due to the layered (surface-based) Cortex structural and functional organization. It may therefore be more sensitive and specific than the former volume-based style. The Human Connectome Project (HCP) multimodal pipelines render high-quality surface-based processing for some of the most consistently acquired neuroimaging modalities, with the quality too reliant on their precise acquisition requirements. Relevant international brain initiatives are espoused to develop an HCP-compatible neuroinformatic facility for the quality-ensured processing of international neuroimaging datasets, which may not follow the specific HCP acquisition requirements, also coined as legacy datasets. We appointed some initiatives to introduce multimodal pipelines in two HCP-compatible processing branches. a) Structural: forward- modeling with geometry (sources and head) and Lead Fields defined for legacy MEG, or EEG, in the HCP individual cortical space (Cifti) obtained from legacy MRI. Our pipeline (Ciftify-MEEG) leverages a more diverse neuroinformatic repository than the HCP structural or MEG pipelines. Ciftify-MEEG produces substantial processing illustrated here with EEG examples, incorporating alternative processing paths and corrections based on a quality control loop and upon qualitative and quantitative indicators. b) Functional: identifying spectral topographies and connectomes for the cortical oscillatory activity observed in the MEG or EEG frequency bands. We leverage a novel repository of Bayesian sparse inverse methods that target identifying the topographies and connectomes with actual statistical guarantees, the brain connectivity Variable Resolution Electromagnetic Tomographic Analysis (BC-VARETA). Our pipeline design for BC-VARETA, which we denominate Ciftify-bcVARETA, is integrated into the Ciftify- MEEG outputs and with Bayesian sparse priors structured upon Cifti space information. Ciftify-bcVARETA identification is less biased to forward models and more biased to the observations than the HCP pipeline illustrated here with topographies obtained for MEG and EEG legacy databases.
... For the sensorimotor, default mode, and language networks, higher clustering could represent a dominance of connectivity of the left hemisphere, as our population was mainly right-handed. A left-lateralized clustering of these networks is supported by similar findings in functional MRI studies and other diffusion studies across the lifespan (Agcaoglu et al., 2015;Banks et al., 2018;Vassal et al., 2016). Whether this increase in clustering is caused by activity-dependent neuroplasticity or itself drives dominance is yet to be determined. ...
Developmental lateralization of brain function is imperative for behavioral specialization, yet few studies have investigated differences between hemispheres in structural connectivity patterns, especially over the course of development. The present study compares the lateralization of structural connectivity patterns, or topology, across children, adolescents, and young adults. We applied a graph theory approach to quantify key topological metrics in each hemisphere including efficiency of information transfer between regions (global efficiency), clustering of connections between regions (clustering coefficient [CC]), presence of hub-nodes (betweenness centrality [BC]), and connectivity between nodes of high and low complexity (hierarchical complexity [HC]) and investigated changes in these metrics during development. Further, we investigated BC and CC in seven functionally defined networks. Our cross-sectional study consisted of 211 participants between the ages of 6 and 21 years with 93% being right-handed and 51% female. Global efficiency, HC, and CC demonstrated a leftward lateralization, compared to a rightward lateralization of BC. The sensorimotor, default mode, salience, and language networks showed a leftward asymmetry of CC. BC was only lateralized in the salience (right lateralized) and dorsal attention (left lateralized) networks. Only a small number of metrics were associated with age, suggesting that topological organization may stay relatively constant throughout school-age development, despite known underlying changes in white matter properties. Unlike many other imaging biomarkers of brain development, our study suggests topological lateralization is consistent across age, highlighting potential nonlinear mechanisms underlying developmental specialization.
... AF runs contiguously with SLF II and may help the prefrontal cortex to modulate audiospatial information. Subsequent multimodal studies 19,20 have further contributed to our understanding of the functional roles of the SLF subcomponents in the human brain. However, there is a lack of studies investigating specific relationships between the structural aberrations of SLF subcomponents and their implications on the symptomatology of SZ. ...
Although the potential role of superior longitudinal fasciculus (SLF) in intellectual deficits and treatment response (TR) in patients with schizophrenia (SZ) has been previously described, little is known about the white-matter (WM) integrity of SLF subcomponents (SLF I, II, III, and arcuate fasciculus) and their particular relationships with the clinical presentations of the illness. This study examined the associations between fractional anisotropy (FA) of SLF subcomponents and intelligence level and 6-month treatment response (TR) of negative symptoms (NS) in patients with SZ. At baseline, 101 patients with SZ and 101 healthy controls (HCs) underwent structural magnetic resonance imaging. Voxel-wise group comparison analysis showed significant SLF FA reductions in patients with SZ compared with HCs. Voxel-wise correlation analyses revealed significant positive correlations of FAs of right SLF II with Korean–Wechsler Adult Intelligence Scale at baseline and the percentage reduction of negative syndrome subscale of the Positive and Negative Syndrome Scales at 6 months. These findings suggest that aberrance in WM microstructure in SLF II may be associated with intellectual deficits in patients with SZ and TR of NS, which may support the potential role of SLF II as a novel neuroimaging biomarker for clinical outcomes of the illness.
... Structural properties included volumetric values like cortical thickness and cortical surface area (Rasero et al, 2021), microstructure like gray and white matter directionality (e.g., fractional anisotropy) (Chavan et al, 2015), white matter connectivity like number of tract streamlines connecting region pairs (Sokolov et al, 2018b), and symmetry scores assigned to reflect how symmetric these properties are across the two brain hemispheres ( Josse et al, 2009). Functional properties have included properties of evoked activity like strength, count of activated voxels and laterality of activation (Zuo et al, 2016), and strength of FC as reflected by the correlation in signal intensities across remote regions (Rasero et al, 2021). Finally, inferential statistics have been used to assess if organizational characteristics of structural and functional networks significantly differ ( Jung et al, 2018). ...
Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Many studies have combined structural and functional neuroimaging techniques to uncover the complex relationship between them. Here, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data, and structural and functional neuroimaging data. Five percent of screened studies met all inclusion criteria. Next, 50% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 31% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterisation of the relationship between brain structure, function and cognition; comparative, predictive, fusion and complementary. We discuss the insights provided each approach about the relationship between brain structure and function and how it impacts cognitive performance. In addition, we discuss how authors can select approaches to suit their research questions.
... We are honorable to work on the reconstruction of the AF and previously reported a feasible method combining 1.5-T intraoperative MRI with AF neuronavigation to maximize resection and minimize language deficits when removing gliomas adjacent to AF (22), and we further found that the cutoff distance from a glioma to nearby AF was 3.2 mm for preventing aphasia, as seen on postoperative DTI-T (23). In fact, a number of studies demonstrated a correlation between DTI and fMRI, indicating that DTI could define language lateralization (12,49,50). Previous studies showed the concordances between DTT and Wada test ranged from 80% to 95.8% (18)(19)(20)(21). ...
... Thus, it seems to be controversial to consider the laterality of FA of AF as a robust predictor of language lateralization in our patients with low-grade gliomas. However, higher FA values of the AF were actually found in the dominant hemisphere of the healthy volunteers (49,50). Ellmore et al. also showed the same result in patients with epilepsy (19). ...
Language lateralization is unique to humans, so clarifying dominant side is helpful for removing gliomas involving language areas. This study investigated the arcuate fasciculus (AF) reconstructed by diffusion tensor imaging–based tractography (DTT) in predicting language lateralization in patients with low-grade gliomas. Wada test was performed to determine the language Dominant Hemisphere (DH) and the Contralateral Hemisphere. DTI data [1.5-T magnetic resonance imaging (MRI)] was used to reconstruct AF by two independent operators using a DTT method. Fiber number, volume, and fractional anisotropy (FA) of bilateral reconstructed AF were measured. Lateralization indexes (LIs), including Number Index (NI), Volume Index (VI), and FA Index (FI), were accordingly calculated by mean values. A total of 21 patients with WHO Grade II gliomas in the left hemisphere were included. Every patient received a successful Wada test and reconstruction of bilateral AF. DTT metrics of reconstructed AF, such as fiber number, volume, and FA, showed significantly asymmetric between hemispheres. All the LI (NI, VI, and FI) values were statistically higher in the DH determined by the Wada test. No discrepancy was found between the prediction using the cutoff values of DTT metrics and the results of WADA test. The Kappa values were 0.829, 0.696, and 0.611, indicating NI and VI as more reliable predictor than FI although FI itself may also be feasible. Compared with the Wada test, we consider that DTT of AF is a non-invasive, simple, relatively accurate, and feasible method in predicting language lateralization in patients with low-grade gliomas.
... The disorganization of tissue architecture, i.e., of the fascicular organization of fiber bundles, seems less severe in BAT of GBM. Indeed, in GBM, MV and BAT FA values were lower than in metastasis, and BAT mean-FA values were about 0.20, which is a normal threshold value usually considered for fiber tracking of fascicles in white matter [37][38][39]. Normal FA values range from 0.4 to 0.8 [40,41], and depend on the white matter architecture, i.e., the 3D organization of fascicles and the conservation of axonal membranes [42], and at a lesser extent on an MRI machine [43]. ...
(1) Background: Glioblastoma multiforme (GBM) shows complex mechanisms of spreading of the tumor cells, up to remote areas, and little is still known of these mechanisms, thus we focused on MRI abnormalities observable in the tumor and the brain adjacent to the lesion, up to the contralateral hemisphere, with a special interest on tensor diffusion imaging informing on white matter architecture; (2) Material and Methods: volumes, macroscopic volume (MV), brain-adjacent-tumor (BAT) volume and abnormal color-coded DTI volume (aCCV), and region-of-interest samples (probe volumes, ipsi, and contra lateral to the lesion), with their MRI characteristics, apparent diffusion coefficient (ADC), fractional anisotropy (FA) values, and number of fibers (DTI fiber tracking) were analyzed in patients suffering GBM (n = 15) and metastasis (n = 9), and healthy subjects (n = 15), using ad hoc statistical methods (type I error = 5%) (3) Results: GBM volumes were larger than metastasis volumes, aCCV being larger in GBM and BAT ADC was higher in metastasis, ADC decreased centripetally in metastasis, FA increased centripetally either in GBM or metastasis, MV and BAT FA values were higher in GBM, ipsi FA values of GBM ROIs were higher than those of metastasis, and the GBM ipsi number of fibers was higher than the GBM contra number of fibers; (4) Conclusions: The MV, BAT and especially the aCCV, as well as their related water diffusion characteristics, could be useful biomarkers in oncology and functional oncology.
