Article

Nonlinear spatial normalization using basis functions

February 1999
Human Brain Mapping 7(4):254-66

February 1999
7(4):254-66

DOI:10.1002/(SICI)1097-0193(1999)7:43.3.CO;2-7

Source
PubMed

Authors:

John Ashburner

University College London

Karl J Friston

University College London

We describe a comprehensive framework for performing rapid and automatic nonlabel-based nonlinear spatial normalizations. The approach adopted minimizes the residual squared difference between an image and a template of the same modality. In order to reduce the number of parameters to be fitted, the nonlinear warps are described by a linear combination of low spatial frequency basis functions. The objective is to determine the optimum coefficients for each of the bases by minimizing the sum of squared differences between the image and template, while simultaneously maximizing the smoothness of the transformation using a maximum a posteriori (MAP) approach. Most MAP approaches assume that the variance associated with each voxel is already known and that there is no covariance between neighboring voxels. The approach described here attempts to estimate this variance from the data, and also corrects for the correlations between neighboring voxels. This makes the same approach suitable for the spatial normalization of both high-quality magnetic resonance images, and low-resolution noisy positron emission tomography images. A fast algorithm has been developed that utilizes Taylor's theorem and the separable nature of the basis functions, meaning that most of the nonlinear spatial variability between images can be automatically corrected within a few minutes.

Aplicaci\'on de redes neuronales convolucionales profundas al diagn\'ostico asistido de la enfermedad de Alzheimer

Preprint

Oct 2022

Ángel de la Vega Jiménez

Currently, the diagnosis of Alzheimer's disease is a complex and error-prone process. Improving this diagnosis could allow earlier detection of the disease and improve the quality of life of patients and their families. For this work, we will use 249 brain images from two modalities: PET and MRI, taken from the ADNI database, and labelled into three classes according to the degree of development of Alzheimer's disease. We propose the development of a convolutional neural network to perform the classification of these images, during which, we will study the appropriate depth of the networks for this problem, the importance of pre-processing medical images, the use of transfer learning and data augmentation techniques as tools to reduce the effects of the problem of having too little data, and the simultaneous use of multiple medical imaging modalities. We also propose the application of an evaluation method that guarantees a good degree of repeatability of the results even when using a small dataset. Following this evaluation method, our best final model, which makes use of transfer learning with COVID-19 data, achieves an accuracy d 68\%. In addition, in an independent test set, this same model achieves 70\% accuracy, a promising result given the small size of our dataset. We further conclude that augmenting the depth of the networks helps with this problem, that image pre-processing is a fundamental process to address this type of medical problem, and that the use of data augmentation and the use of pre-trained networks with images of other diseases can provide significant improvements.

Automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging of rat brain

Article

Full-text available

Jul 2022

Aims To construct an automatic method for individual parcellation of manganese-enhanced magnetic resonance imaging (MEMRI) of rat brain with high accuracy, which could preserve the inherent voxel intensity and Regions of interest (ROI) morphological characteristics simultaneously. Methods and results The transformation relationship from standardized space to individual space was obtained by firstly normalizing individual image to the Paxinos space and then inversely transformed. On the other hand, all the regions defined in the atlas image were separated and resaved as binary mask images. Then, transforming the mask images into individual space via the inverse transformations and reslicing using the 4th B-spline interpolation algorithm. The boundary of these transformed regions was further refined by image erosion and expansion operator, and finally combined together to generate the individual parcellations. Moreover, two groups of MEMRI images were used for evaluation. We found that the individual parcellations were satisfied, and the inherent image intensity was preserved. The statistical significance of case-control comparisons was further optimized. Conclusions We have constructed a new automatic method for individual parcellation of rat brain MEMRI images, which could preserve the inherent voxel intensity and further be beneficial in case-control statistical analyses. This method could also be extended to other imaging modalities, even other experiments species. It would facilitate the accuracy and significance of ROI-based imaging analyses.

Automated semi-quantitative amyloid PET analysis technique without MR images for Alzheimer’s disease

Article

Full-text available

Jul 2022

Objective Although beta-amyloid (Aβ) positron emission tomography (PET) images are interpreted visually as positive or negative, approximately 10% are judged as equivocal in Alzheimer’s disease. Therefore, we aimed to develop an automated semi-quantitative analysis technique using ¹⁸ F-flutemetamol PET images without anatomical images. Methods Overall, 136 cases of patients administered ¹⁸ F-flutemetamol were enrolled. Of 136 cases, five PET images each with the highest and lowest values of standardized uptake value ratio (SUVr) of cerebral cortex-to-pons were used to create positive and negative templates. Using these templates, PET images of the remaining 126 cases were standardized, and SUVr images were produced with the pons as a reference region. The mean of SUVr values in the volume of interest delineated on the cerebral cortex was compared to those in the CortexID Suite (GE Healthcare). Furthermore, centiloid (CL) values were calculated for the 126 cases using data from the Centiloid Project ( http://www.gaain.org/centiloid-project ) and both templates. ¹⁸ F-flutemetamol-PET was interpreted visually as positive/negative based on Aβ deposition in the cortex. However, the criterion "equivocal" was added for cases with focal or mild Aβ accumulation that were difficult to categorize. Optimal cutoff values of SUVr and CL maximizing sensitivity and specificity for Aβ detection were determined by receiver operating characteristic (ROC) analysis using the visual evaluation as a standard of truth. Results SUVr calculated by our method and CortexID were highly correlated ( R ² = 0.9657). The 126 PET images comprised 84 negative and 42 positive cases of Aβ deposition by visual evaluation, of which 11 and 10 were classified as equivocal, respectively. ROC analyses determined the optimal cutoff values, sensitivity, and specificity for SUVr as 0.544, 89.3%, and 92.9%, respectively, and for CL as 12.400, 94.0%, and 92.9%, respectively. Both semi-quantitative analyses showed that 12 and 9 of the 21 equivocal cases were negative and positive, respectively, under the optimal cutoff values. Conclusions This semi-quantitative analysis technique using ¹⁸ F-flutemetamol-PET calculated SUVr and CL automatically without anatomical images. Moreover, it objectively and homogeneously interpreted positive or negative Aβ burden in the brain as a supplemental tool for the visual reading of equivocal cases in routine clinical practice.

Digital Analysis of Smart Registration Methods for Magnetic Resonance Images in Public Healthcare

Article

Full-text available

Jun 2022

Brain development and atrophy accompany people's life. Brain development diseases, such as autism and Alzheimer's disease, affect a large part of the population. Analyzing brain development is very important in public healthcare, and image registration is essential in medical brain image analysis. Many previous studies investigate registration accuracy by the “ground truth” dataset, marker-based similarity calculation, and expert check to find the best registration algorithms. But the evaluation of image registration technology only at the accuracy level is not comprehensive. Here, we compare the performance of three publicly available registration techniques in brain magnetic resonance imaging (MRI) analysis based on some key features widely used in previous MRI studies for classification and detection tasks. According to the analysis results, SPM12 has a stable speed and success rate, and it always works as a guiding tool for newcomers to medical image analysis. It can preserve maximum contrast information, which will facilitate studies such as tumor diagnosis. FSL is a mature and widely applicable toolkit for users, with a relatively stable success rate and good performance. It has complete functions and its function-based integrated toolbox can meet the requirements of different researchers. AFNI is a flexible and complex tool that is more suitable for professional researchers. It retains most details in medical image analysis, which makes it useful in fine-grained analysis such as volume estimation. Our study provides a new idea for comparing registration tools, where tool selection strategy mainly depends on the research task in which the selected tool can leverage its unique advantages.

Visual looming is a primitive for human emotion

Article

Full-text available

May 2024

The neural computations for looming detection are strikingly similar across species. In mammals, information about approaching threats is conveyed from the retina to the midbrain superior colliculus, where approach variables are computed to enable defensive behavior. Although neuroscientific theories posit that midbrain representations contribute to emotion through connectivity with distributed brain systems, it remains unknown whether a computational system for looming detection can predict both defensive behavior and phenomenal experience in humans. Here, we show that a shallow convolutional neural network based on the Drosophila visual system predicts defensive blinking to looming objects in infants and superior colliculus responses to optical expansion in adults. Further, the neural network’s responses to naturalistic video clips predict self-reported emotion largely by way of subjective arousal. These findings illustrate how a simple neural network architecture optimized for a species-general task relevant for survival explains motor and experiential components of human emotion.

Clinical Performance Evaluation of an Artificial Intelligence-Powered Amyloid Brain PET Quantification Method

Article

Full-text available

May 2024

This study assesses the clinical performance of BTXBrain-Amyloid, an artificial intelligence-powered software for quantifying amyloid uptake in brain PET images. 150 amyloid brain PET images were visually assessed by experts and categorized as negative and positive. Standardized uptake value ratio (SUVR) was calculated with cerebellum grey matter as the reference region, and receiver operating characteristic (ROC) and precision-recall (PR) analysis for BTXBrain-Amyloid were conducted. For comparison, same image processing and analysis was performed using Statistical Parametric Mapping (SPM) program. In addition, to evaluate the spatial normalization (SN) performance, mutual information (MI) between MRI template and spatially normalized PET images was calculated and SPM group analysis was conducted. Both BTXBrain and SPM methods discriminated between negative and positive groups. However, BTXBrain exhibited lower SUVR standard deviation (0.06 and 0.21 for negative and positive, respectively) than SPM method (0.11 and 0.25). In ROC analysis, BTXBrain had an AUC of 0.979, compared to 0.959 for SPM, while PR curves showed an AUC of 0.983 for BTXBrain and 0.949 for SPM. At the optimal cut-off, the sensitivity and specificity were 0.983 and 0.921 for BTXBrain and 0.917 and 0.921 for SPM12, respectively. MI evaluation also favored BTXBrain (0.848 vs. 0.823), indicating improved SN. In SPM group analysis, BTXBrain exhibited higher sensitivity in detecting basal ganglia differences between negative and positive groups. BTXBrain-Amyloid outperformed SPM in clinical performance evaluation, also demonstrating superior SN and improved detection of deep brain differences. These results suggest the potential of BTXBrain-Amyloid as a valuable tool for clinical amyloid PET image evaluation.

Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images

Article

Full-text available

Apr 2024

Purpose Bayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging. Methods Images were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [¹⁸F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid β deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR). Results The overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing β values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal β value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91–0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods. Conclusion Gibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [¹⁸F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [¹⁸F]flutemetamol amyloid PET imaging.

MRI atlas of the pituitary gland in young female adults

Article

Full-text available

Mar 2024
BRAIN STRUCT FUNCT

The probabilistic topography and inter-individual variability of the pituitary gland (PG) remain undetermined. The absence of a standardized reference atlas hinders research on PG volumetrics. In this study, we aimed at creating maximum probability maps for the anterior and posterior PG in young female adults. We manually delineated the anterior and posterior parts of the pituitary glands in 26 healthy subjects using high-resolution MRI T1 images. A three-step procedure and a cost function-masking approach were employed to optimize spatial normalization for the PG. We generated probabilistic atlases and maximum probability maps, which were subsequently coregistered back to the subjects’ space and compared to manual delineations. Manual measurements led to a total pituitary volume of 705 ± 88 mm³, with the anterior and posterior volumes measuring 614 ± 82 mm³ and 91 ± 20 mm³, respectively. The mean relative volume difference between manual and atlas-based estimations was 1.3%. The global pituitary atlas exhibited an 80% (± 9%) overlap for the DICE index and 67% (± 11%) for the Jaccard index. Similarly, these values were 77% (± 13%) and 64% (± 14%) for the anterior pituitary atlas and 62% (± 21%) and 47% (± 17%) for the posterior PG atlas, respectively. We observed a substantial concordance and a significant correlation between the volume estimations of the manual and atlas-based methods for the global pituitary and anterior volumes. The maximum probability maps of the anterior and posterior PG lay the groundwork for automatic atlas-based segmentation methods and the standardized analysis of large PG datasets.

Assessment of a one-week ketogenic diet on brain glycolytic metabolism and on the status epilepticus stage of a lithium-pilocarpine rat model

Article

Full-text available

Mar 2024

The ketogenic diet (KD) has been shown to be effective in refractory epilepsy after long-term administration. However, its interference with short-term brain metabolism and its involvement in the early process leading to epilepsy remain poorly understood. This study aimed to assess the effect of a short-term ketogenic diet on cerebral glucose metabolic changes, before and after status epilepticus (SE) in rats, by using [¹⁸F]-FDG PET. Thirty-nine rats were subjected to a one-week KD (KD-rats, n = 24) or to a standard diet (SD-rats, n = 15) before the induction of a status epilepticus (SE) by lithium-pilocarpine administrations. Brain [¹⁸F]-FDG PET scans were performed before and 4 h after this induction. Morphological MRIs were acquired and used to spatially normalize the PET images which were then analyzed voxel-wisely using a statistical parametric-based method. Twenty-six rats were analyzed (KD-rats, n = 15; SD-rats, n = 11). The 7 days of the KD were associated with significant increases in the plasma β-hydroxybutyrate level, but with an unchanged glycemia. The PET images, recorded after the KD and before SE induction, showed an increased metabolism within sites involved in the appetitive behaviors: hypothalamic areas and periaqueductal gray, whereas no area of decreased metabolism was observed. At the 4th hour following the SE induction, large metabolism increases were observed in the KD- and SD-rats in areas known to be involved in the epileptogenesis process late—i.e., the hippocampus, parahippocampic, thalamic and hypothalamic areas, the periaqueductal gray, and the limbic structures (and in the motor cortex for the KD-rats only). However, no statistically significant difference was observed when comparing SD and KD groups at the 4th hour following the SE induction. A one-week ketogenic diet does not prevent the status epilepticus (SE) and associated metabolic brain abnormalities in the lithium-pilocarpine rat model. Further explorations are needed to determine whether a significant prevention could be achieved by more prolonged ketogenic diets and by testing this diet in less severe experimental models, and moreover, to analyze the diet effects on the later and chronic stages leading to epileptogenesis.

The Swedish National Facility for Magnetoencephalography Parkinson’s disease dataset

Article

Full-text available

Jan 2024

Parkinson’s disease (PD) is characterised by a loss of dopamine and dopaminergic cells. The consequences hereof are widespread network disturbances in brain function. It is an ongoing topic of investigation how the disease-related changes in brain function manifest in PD relate to clinical symptoms. We present The Swedish National Facility for Magnetoencephalography Parkinson’s Disease Dataset (NatMEG-PD) as an Open Science contribution to identify the functional neural signatures of Parkinson’s disease and contribute to diagnosis and treatment. The dataset contains whole-head magnetoencephalographic (MEG) recordings from 66 well-characterised PD patients on their regular dose of dopamine replacement therapy and 68 age- and sex-matched healthy controls. NatMEG-PD contains three-minute eyes-closed resting-state MEG, MEG during an active movement task, and MEG during passive movements. The data includes anonymised MRI for source analysis and clinical scores. MEG data is rich in nature and can be used to explore numerous functional features. By sharing these data, we hope other researchers will contribute to advancing our understanding of the relationship between brain activity and disease state or symptoms.

How brain structure–function decoupling supports individual cognition and its molecular mechanism

Article

Full-text available

Jan 2024

Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region‐specific and hierarchical across the neocortex. However, the relationship between hierarchical structure–function decoupling and the manifestation of individual behavior and cognition, along with the significance of the functional systems involved, and the specific molecular mechanism underlying structure–function decoupling remain incompletely characterized. Here, we used the structural‐decoupling index (SDI) to quantify the dependency of functional signals on the structural connectome using a significantly larger cohort of healthy subjects. Canonical correlation analysis (CCA) was utilized to assess the general multivariate correlation pattern between region‐specific SDIs across the whole brain and multiple cognitive traits. Then, we predicted five composite cognitive scores resulting from multivariate analysis using SDIs in primary networks, association networks, and all networks, respectively. Finally, we explored the molecular mechanism related to SDI by investigating its genetic factors and relationship with neurotransmitter receptors/transporters. We demonstrated that structure–function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. We revealed better performance in cognition prediction is achieved by using high‐level hierarchical SDIs, with varying significance of different brain regions in predicting cognitive processes. We found that the SDIs were associated with the gene expression level of several receptor‐related terms, and we also found the spatial distributions of four receptors/transporters significantly correlated with SDIs, namely D2, NET, MOR, and mGluR5, which play an important role in the flexibility of neuronal function. Collectively, our findings corroborate the association between hierarchical macroscale structure–function decoupling and individual cognition and provide implications for comprehending the molecular mechanism of structure–function decoupling. Practitioner Points Structure–function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. High‐level hierarchical structure–function decoupling contributes much more than low‐level decoupling to individual cognition. Structure–function decoupling could be regulated by genes associated with pivotal receptors that are crucial for neuronal function flexibility.

How Can I Integrate iEEG Recordings with Patients’ Brain Anatomy?

Chapter

Full-text available

Aug 2023

Preparing intracranial electroencephalography (iEEG) datasets for analysis presents a unique set of methodological challenges that are absent in non-invasive investigative techniques. Because iEEG is primarily used in epilepsy patients with varying brain pathologies, the main challenges pertain to variability in electrode coverage and therefore the regions of the brain from which electrophysiological recordings can be obtained. In this chapter, we outline how to efficiently integrate the raw anatomical images and electrophysiological recordings during preprocessing, allowing iEEG datasets to be analyzed in an anatomically precise and consistent way.

Compromised Cerebello-Thalamic White Matter Integrity in Medication-Free Patients With Obsessive-Compulsive Disorder

Article

Full-text available

Jun 2023

Objective: The cerebello-thalamic tract is the only efferent white matter (WM) bundle of the cerebellum that connects the cerebellum to the thalamus and has recently attracted much attention in obsessive-compulsive disorder (OCD) with its integral role in higher order cognitive functions commonly impaired in OCD patients. Previous neuroimaging studies have shown that the cerebello-thalamic circuit is functionally impaired in OCD patients. However, the WM integrity of the cerebello-thalamic tract in OCD, which may underly functional abnormalities of the cerebello-thalamic circuit, is not yet sufficiently understood. Therefore, the current study aimed to elucidate whether compromised cerebello-thalamic WM integrity is observed in medication-free OCD patients. Methods: In this study, diffusion tensor imaging was acquired from 106 medication-free OCD patients and 105 matched healthy controls (HCs). Probabilistic tractography was then used to reconstruct the cerebello-thalamic tract with accurate anatomical features. Three diffusion indices (fractional anisotropy, FA; mean diffusivity, MD; radial diffusivity, RD) were measured from the reconstructed bilateral cerebello-thalamic tract and then compared between groups. Results: We found that patients with OCD showed significantly increased MD and RD in the right cerebello-thalamic tract compared to HCs, and there was no difference in FA between groups. Conclusion: Our findings may indicate the underlying structural abnormalities of the dysfunctional cerebello-thalamic circuit in OCD patients. Therefore, our findings are expected to provide novel insights into the pathophysiology of OCD on the cerebello-thalamic WM architecture, extending our knowledge from the existing functional neurobiological model of OCD.

A detailed dosimetric comparative study of IMRT and VMAT in normal brain tissues for nasopharyngeal carcinoma patients treated with radiotherapy

Article

Full-text available

May 2023

Background Radiotherapy (RT) is the primary treatment for nasopharyngeal carcinoma (NPC). However, it can cause implicit RT-induced injury by irradiating normal brain tissue. To date, there have been no detailed reports on the radiated exact location in the brain, the corresponding radiation dose, and their relationship. Methods We analyzed 803 Chinese NPC patients treated with RT and used a CT brain template in a Montreal Neurological Institute (MNI) space to compare the group differences in RT dose distribution for different RT technologies (IMRT or VMAT). Results Brain regions that received high doses (>50 Gy) of radiation were mainly located in parts of the temporal and limbic lobes, where radioactive damage often occurs. Brain regions that accepted higher doses with IMRT were mainly located near the anterior region of the nasopharyngeal tumor, while brain regions that accepted higher doses with VMAT were mainly located near the posterior region of the tumor. No significant difference was detected between IMRT and VMAT for T1 stage patients. For T2 stage patients, differences were widely distributed, with VMAT showing a significant dose advantage in protecting the normal brain tissue. For T3 stage patients, VMAT showed an advantage in the superior temporal gyrus and limbic lobe, while IMRT showed an advantage in the posterior cerebellum. For T4 stage patients, VMAT showed a disadvantage in protecting the normal brain tissue. These results indicate that IMRT and VMAT have their own advantages in sparing different organs at risk (OARs) in the brain for different T stages of NPC patients treated with RT. Conclusion Our approach for analyzing dosimetric characteristics in a standard MNI space for Chinese NPC patients provides greater convenience in toxicity and dosimetry analysis with superior localization accuracy. Using this method, we found interesting differences from previous reports: VMAT showed a disadvantage in protecting the normal brain tissue for T4 stage NPC patients.

Deficits in naming pictures of objects are associated with glioma infiltration of the inferior longitudinal fasciculus: A study with diffusion MRI tractography, volumetric MRI, and neuropsychology

Article

Full-text available

May 2023
HUM BRAIN MAPP

It has been suggested that the inferior longitudinal fasciculus (ILF) may play an important role in several aspects of language processing such as visual object recognition, visual memory, lexical retrieval, reading, and specifically, in naming visual stimuli. In particular, the ILF appears to convey visual information from the occipital lobe to the anterior temporal lobe (ATL). However, direct evidence proving the essential role of the ILF in language and semantics remains limited and controversial. The first aim of this study was to prove that patients with a brain glioma damaging the left ILF would be selectively impaired in picture naming of objects; the second aim was to prove that patients with glioma infiltrating the ATL would not be impaired due to functional reorganization of the lexical retrieval network elicited by the tumor. We evaluated 48 right-handed patients with neuropsychological testing and magnetic resonance imaging (MRI) before and after surgery for resection of a glioma infiltrating aspects of the left temporal, occipital, and/or parietal lobes; diffusion tensor imaging (DTI) was acquired preoperatively in all patients. Damage to the ILF, inferior frontal occipital fasciculus (IFOF), uncinate fasciculus (UF), arcuate fasciculus (AF), and associated cortical regions was assessed by means of preoperative tractography and pre-/pos-toperative MRI volumetry. The association of fascicles damage with patients' performance in picture naming and three additional cognitive tasks, namely, verbal fluency (two verbal non-visual tasks) and the Trail Making Test (a visual attentional task), was evaluated. Nine patients were impaired in the naming test before surgery. ILF damage was demonstrated with tractography in six (67%) of these patients. The odds of having an ILF damage was 6.35 (95% CI: 1.27-34.92) times higher among patients with naming deficit than among those without it. The ILF was the only fascicle to be significantly associated with naming deficit when all the fascicles were considered together, achieving an adjusted odds ratio of 15.73 (95% CI: 2.30-178.16, p = .010). Tumor infiltration of temporal and occipital cortices did not contribute to increase the odd of having a naming deficit. ILF damage was found to be selectively associated with picture naming deficit and not with lexical retrieval assessed by means of verbal fluency. Early after surgery, 29 patients were impaired in naming objects. The association of naming deficit with percentage of ILF resection (assessed by 3D-MRI) was confirmed (beta = -56.78 ± 20.34, p = .008) through a robust multiple linear regression model; no significant association was found with damage of IFOF, UF or AF. Crucially, postoperative neuropsychological evaluation showed that naming scores of patients with tumor infiltration of the anterior temporal cortex were not significantly associated with the percentage of ILF damage (rho = .180, p > .999), while such association was significant in patients without ATL infiltration (rho = -.556, p = .004). The ILF is selectively involved in picture naming of objects; however, the naming deficits are less severe in patients with glioma infiltration of the ATL probably due to release of an alternative route that may involve the posterior segment of the AF. The left ILF, connecting the extrastriatal visual cortex to the anterior region of the temporal lobe, is crucial for lexical retrieval on visual stimulus, such as in picture naming. However, when the ATL is also damaged, an alternative route is released and the performance improves.

Influence of the electrical rhythm of the stomach on the functional organization of the resting brain

Thesis

Oct 2019

Ignacio Rebollo

This thesis endeavors to determine in humans the impact of visceral inputs on brain activity, a field that has received surprisingly little attention despite the heavy connections between the brain and viscera. More precisely, during my PhD I studied the influence of the gastric rhythm on spontaneous brain dynamics measured by functional magnetic resonance imaging. In the first part of my PhD, I found an extended network of cortical regions synchronized to the slow electric rhythm continuously produced by the stomach. This gastric network is characterized by a precise temporal sequence of activations within each cycle of the stomach, and is composed of regions with convergent functional properties involved in mapping bodily space through touch, action or vision, as well as mapping external space in bodily coordinates. In the second part of My PhD, I extended these findings by using a larger sample and further characterized the anatomy, effect sizes and inter-individual variability of the gastric network. The gastric network is mostly confined to motor, somatosensory, insular, visual and auditory and, to a lesser extent, in the piriform cortex, indicating that all sensory-motor cortices corresponding to both exteroceptive and interoceptive modalities are coupled to the gastric rhythm during rest. These results indicate that two major functions of the brain which are usually studied separately - i.e. interaction with the external environment and interaction with the viscera -, are in fact probably tightly intertwined, and that gastric monitoring and sensory-motor processes are likely to interact.

Theta-gamma phase-amplitude coupling in auditory cortex is modulated by language proficiency

Article

Full-text available

Feb 2023
HUM BRAIN MAPP

The coordination between the theta phase (3-7 Hz) and gamma power (25-35 Hz) oscillations (namely theta-gamma phase-amplitude coupling, PAC) in the auditory cortex has been proposed as an essential neural mechanism involved in speech processing. However, it has not been established how this mechanism is related to the efficiency with which a listener processes speech. Speech processing in a non-native language offers a useful opportunity to evaluate if theta-gamma PAC is modulated by the challenges imposed by the reception of speech input in a non-native language. The present study investigates how auditory theta-gamma PAC (recorded with magnetoencephalography) is modulated in both native and non-native speech reception. Participants were Spanish native (L1) speakers studying Basque (L2) at three different levels: beginner (Grade 1), intermediate (Grade 2), and advanced (Grade 3). We found that during L2 speech processing (i) theta-gamma PAC was more highly coordinated for intelligible compared to unintelligible speech; (ii) this coupling was modulated by proficiency in Basque being lower for beginners, higher for intermediate, and highest for advanced speakers (no difference observed in Spanish); (iii) gamma power did not differ between languages and groups. These findings highlight how the coordinated theta-gamma oscillatory activity is tightly related to speech comprehension: the stronger this coordination is, the more the comprehension system will proficiently parse the incoming speech input.

Automatic Detection and Segmentation of Postoperative Cerebellar Damage Based on Normalization

Article

Full-text available

Jan 2023

Background Surgical resection is the gold standard in the treatment of pediatric posterior fossa tumors. However, surgical damage is often unavoidable and its association with postoperative complications is not well understood. A reliable localization and measure of cerebellar damage is fundamental to study the relationship between the damaged cerebellar regions and postoperative neurological outcomes. Existing cerebellum normalization methods are likely to fail on postoperative scans, therefore current approaches to measure postoperative damage rely on manual labelling. In this work, we develop a robust algorithm to automatically detect and measure cerebellum damage in postoperative 3D T1 magnetic resonance imaging (MRI). Methods In our approach, normal brain tissues are first segmented using a Bayesian algorithm customized for postoperative scans. Next, the cerebellum is isolated by nonlinear registration of a whole-brain template to the native space. The isolated cerebellum is then normalized into the spatially unbiased atlas (SUIT) space using anatomical information derived from the previous step. Finally, the damage is detected in the atlas space by comparing the normalized cerebellum and the SUIT template. Results We evaluated our damage detection tool on postoperative scans of 153 patients with medulloblastoma based on inspection by human experts. We also designed a simulation to evaluate performance without human intervention and with an explicitly controlled and defined ground truth. Our results show that the approach performs adequately under various realistic conditions. Conclusions We develop an accurate, robust, and fully automatic localization and measurement of cerebellar damage in the atlas space using postoperative MRI.

Fast and Accurate Amyloid Brain PET Quantification Without MRI Using Deep Neural Networks

Article

Full-text available

Nov 2022

This paper proposes a novel method for the automatic quantification of amyloid positron emission tomography (PET) using the deep learning (DL)-based spatial normalization (SN) of PET images, which does not require magnetic resonance imaging (MRI) or computed tomography images of the same patient. The accuracy of the method was evaluated for three different amyloid PET radiotracers compared to MRI-parcellation-based PET quantification using FreeSurfer. Methods: A deep neural network model used for the SN of amyloid PET images was trained using 994 multicenter amyloid PET images (367 18F-Flutemetamol and 627 18F-Florbetaben) and the corresponding 3D MRIs of patients with Alzheimer's disease or mild cognitive impairment, and cognitively normal subjects. For comparison, PET SN was also conducted using the SPM12 program (SPM-based SN). The accuracy of DL- and SPM-based SN and standardized uptake value ratio (SUVR) quantification relative to the FreeSurfer-based estimation in individual brain spaces was evaluated using 148 other amyloid PET images (64 18F-Flutemetamol and 84 18F-Florbetaben). Additional external validation was performed using an unseen independent external dataset (30 18F-Flutemetamol, 67 18F-Florbetaben, and 39 18F-Florbetapir). Results: Quantification results using the proposed DL-based method showed stronger correlations with the FreeSurfer estimates than SPM-based SN using MRI did. For example, the slope, y-intercept and R2 values between SPM and FreeSurfer for the global cortex were 0.869, 0.113, and 0.946, respectively. In contrast, the slope, y-intercept, and R2 values between the proposed DL-based method and FreeSurfer were 1.019, -0.016, and 0.986, respectively. The external validation study also demonstrated better performance of the proposed method without MR images than that of SPM with MRI. In most brain regions, the proposed method outperformed the SPM SN in terms of linear regression parameters and intraclass correlation coefficients. Conclusion: We evaluated a novel DL-based SN method, which allows quantitative analysis of amyloid brain PET images without structural MRI. The quantification results using the proposed method showed a strong correlation with MRI-parcellation-based quantification using FreeSurfer for all clinical amyloid radiotracers. Therefore, the proposed method will be useful for investigating Alzheimer's disease and related brain disorders using amyloid PET scans.

Preparing fMRI Data for Statistical Analysis

Preprint

Full-text available

Oct 2022

Alfonso Nieto-Castanon

This chapter describes several procedures used to prepare fMRI data for statistical analyses. It includes the description of common preprocessing steps, such as spatial realignment, coregistration, and spatial normalization, aimed at the spatial alignment of all fMRI data within- and between- subjects, as well as several denoising procedures aimed at minimizing the impact of common noise sources, including physiological and residual subject motion effects, on the BOLD signal time series. The chapter ends with a description of quality control procedures recommended for detecting potential problems in the fMRI data and evaluating its suitability for subsequent statistical analyses.

neuromaps: structural and functional interpretation of brain maps

Article

Full-text available

Oct 2022
Br J Pharmacol

Imaging technologies are increasingly used to generate high-resolution reference maps of brain structure and function. Comparing experimentally generated maps to these reference maps facilitates cross-disciplinary scientific discovery. Although recent data sharing initiatives increase the accessibility of brain maps, data are often shared in disparate coordinate systems, precluding systematic and accurate comparisons. Here we introduce neuromaps, a toolbox for accessing, transforming and analyzing structural and functional brain annotations. We implement functionalities for generating high-quality transformations between four standard coordinate systems. The toolbox includes curated reference maps and biological ontologies of the human brain, such as molecular, microstructural, electrophysiological, developmental and functional ontologies. Robust quantitative assessment of map-to-map similarity is enabled via a suite of spatial autocorrelation-preserving null models. neuromaps combines open-access data with transparent functionality for standardizing and comparing brain maps, providing a systematic workflow for comprehensive structural and functional annotation enrichment analysis of the human brain.

A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data

Article

Mar 2023

Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data being solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal, and temporal lobes.

Characterization of brain mGluR5 binding in a pilot study of late-life major depressive disorder using positron emission tomography and [ 11 C]ABP688

Article

Full-text available

Dec 2015

The metabotropic glutamate receptor subtype 5 (mGluR5) has been implicated in the pathophysiology of mood and anxiety disorders and is a potential treatment target in major depressive disorder (MDD). This study compared brain mGluR5 binding in elderly patients suffering from MDD with that in elderly healthy volunteers using positron emission tomography (PET) and [11C]ABP688. Twenty elderly (mean age: 63.0±6.3) subjects with MDD and twenty-two healthy volunteers in the same age range (mean age: 66.4±7.3) were examined with PET after a single bolus injection of [11C]ABP688, with many receiving arterial sampling. PET images were analyzed on a region of interest and a voxel level to compare mGluR5 binding in the brain between the two groups. Differences in [11C]ABP688 binding between patients with early- and late-onset depression were also assessed. In contrast to a previously published report in a younger cohort, no significant difference in [11C]ABP688 binding was observed between elderly subjects with MDD and healthy volunteers. [11C]ABP688 binding was also similar between subgroups with early- or late-onset depression. We believe this is the first study to examine mGluR5 expression in depression in the elderly. Although future work is required, results suggest potential differences in the pathophysiology of elderly depression versus depression earlier in life.

Classification of 18F-Flutemetamol scans in cognitively normal older adults using machine learning trained with neuropathology as ground truth

Article

Full-text available

Sep 2022
EUR J NUCL MED MOL I

Purpose End-of-life studies have validated the binary visual reads of ¹⁸ F-labeled amyloid PET tracers as an accurate tool for the presence or absence of increased neuritic amyloid plaque density. In this study, the performance of a support vector machine (SVM)-based classifier will be tested against pathological ground truths and its performance determined in cognitively healthy older adults. Methods We applied SVM with a linear kernel to an ¹⁸ F-Flutemetamol end-of-life dataset to determine the regions with the highest feature weights in a data-driven manner and to compare between two different pathological ground truths: based on neuritic amyloid plaque density or on amyloid phases, respectively. We also trained and tested classifiers based on the 10% voxels with the highest amplitudes of feature weights for each of the two neuropathological ground truths. Next, we tested the classifiers’ diagnostic performance in the asymptomatic Alzheimer’s disease (AD) phase, a phase of interest for future drug development, in an independent dataset of cognitively intact older adults, the Flemish Prevent AD Cohort-KU Leuven (F-PACK). A regression analysis was conducted between the Centiloid (CL) value in a composite volume of interest (VOI), as index for amyloid load, and the distance to the hyperplane for each of the two classifiers, based on the two pathological ground truths. A receiver operating characteristic analysis was also performed to determine the CL threshold that optimally discriminates between neuritic amyloid plaque positivity versus negativity, or amyloid phase positivity versus negativity, within F-PACK. Results The classifiers yielded adequate specificity and sensitivity within the end-of-life dataset (neuritic amyloid plaque density classifier: specificity of 90.2% and sensitivity of 83.7%; amyloid phase classifier: specificity of 98.4% and sensitivity of 84.0%). The regions with the highest feature weights corresponded to precuneus, caudate, anteromedial prefrontal, and also posterior inferior temporal and inferior parietal cortex. In the cognitively normal cohort, the correlation coefficient between CL and distance to the hyperplane was −0.66 for the classifier trained with neuritic amyloid plaque density, and −0.88 for the classifier trained with amyloid phases. This difference was significant. The optimal CL cut-off for discriminating positive versus negative scans was CL = 48–51 for the different classifiers (area under the curve ( AUC ) = 99.9%), except for the classifier trained with amyloid phases and based on the 10% voxels with highest feature weights. There the cut-off was CL = 26 ( AUC = 99.5%), which closely matched the CL threshold for discriminating phases 0–2 from 3–5 based on the end-of-life dataset and the neuropathological ground truth. Discussion Among a set of neuropathologically validated classifiers trained with end-of-life cases, transfer to a cognitively normal population works best for a classifier trained with amyloid phases and using only voxels with the highest amplitudes of feature weights.

Unbalanced fronto-pallidal neurocircuit underlying set shifting in obsessive-compulsive disorder

Article

Apr 2022
BRAIN

Maladaptive habitual behaviours of obsessive-compulsive disorder are characterized by cognitive inflexibility, which hypothetically arises from dysfunctions of a certain cortico-basal ganglia-thalamo-cortical circuit including the ventrolateral prefrontal region. Inside this neurocircuit, an imbalance between distinct striatal projections to basal ganglia output nuclei, either directly or indirectly via the external globus pallidus, is suggested to be relevant for impaired arbitration between facilitation and inhibition of cortically initiated activity. However, current evidence of individually altered cortico-striatal or thalamo-cortical connectivities is insufficient to understand how cortical dysconnections are linked to the imbalanced basal ganglia system in patients. In this study, we aimed to identify aberrant ventrolateral prefronto-basal ganglia-thalamic subnetworks representing direct-indirect imbalance and its association with cognitive inflexibility in patients. To increase network detection sensitivity, we constructed a cortico-basal ganglia-thalamo-cortical network model incorporating striatal, pallidal and thalamic subregions defined by unsupervised clustering in 105 medication-free patients with obsessive-compulsive disorder (age = 25.05 ± 6.55 years, male/female = 70/35) and 99 healthy controls (age = 23.93 ± 5.80 years, male/female = 64/35). By using the network-based statistic method, we analysed group differences in subnetworks formed by suprathreshold dysconnectivities. Using linear regression models, we tested subnetwork dysconnectivity effects on symptom severity and set-shifting performance assessed by well-validated clinical and cognitive tests. Compared with the healthy controls, patients were slower to track the Part B sequence of the Trail Making Test when the effects of psychomotor and visuospatial functions were adjusted (t = 3.89, P < 0.001) and made more extradimensional shift errors (t = 4.09, P < 0.001). In addition to reduced fronto-striatal and striato-external pallidal connectivities and hypoconnected striato-thalamic subnetwork [P = 0.001, family-wise error rate (FWER) corrected], patients had hyperconnected fronto-external pallidal (P = 0.012, FWER corrected) and intra-thalamic (P = 0.015, FWER corrected) subnetworks compared with the healthy controls. Among the patients, the fronto-pallidal subnetwork alteration, especially ventrolateral prefronto-external globus pallidal hyperconnectivity, was associated with relatively fewer extradimensional shifting errors (β = −0.30, P = 0.001). Our findings suggest that the hyperconnected fronto-external pallidal subnetwork may have an opposite effect to the imbalance caused by the reduced indirect pathway (fronto-striato-external pallidal) connectivities in patients. This ventrolateral prefrontal hyperconnectivity may help the external globus pallidus disinhibit basal ganglia output nuclei, which results in behavioural inhibition, so as to compensate for the impaired set shifting. We suggest the ventrolateral prefrontal and external globus pallidus as neuromodulatory targets for inflexible habitual behaviours in obsessive-compulsive disorder.

An efficient hybrid method for 3D to 2D medical image registration

Article

Full-text available

Apr 2022

Purpose The purpose of this paper is to present a method for registration of 3D computed tomography to 2D single-plane fluoroscopy knee images to provide 3D motion information for knee joints. This 3D kinematic information has unique utility for examining joint kinematics in conditions such as ligament injury, osteoarthritis and after joint replacement. Methods We proposed a non-invasive rigid body image registration method which is based on two different multimodal similarity measures. This hybrid registration method helps to achieve a trade-off among different challenges including, time complexity and accuracy. Results We performed a number of experiments to evaluate the performance of the proposed method. The experimental results show that the proposed method is as accurate as one of the most recent registration methods while it is several times faster than that method. Conclusion The proposed method is a non-invasive, fast and accurate registration method, which can provide 3D information for knee joint kinematic measurements. This information can be very helpful in improving the accuracy of diagnosis and providing targeted treatment.

How can I integrate iEEG recordings with patients’ brain anatomy?

Preprint

Full-text available

Apr 2022

Perceiving social injustice during arrests of Black and White civilians by White police officers: An fMRI investigation

Article

Mar 2022
NEUROIMAGE

From social media to courts of law, recordings of interracial police officer-civilian interactions are now widespread and publicly available. People may be motivated to preferentially understand the dynamics of these interactions when they perceive injustice towards those whose communities experience disproportionate policing relative to others (e.g., non-White racial/ethnic groups). To explore these questions, two studies were conducted (study 1 neuroimaging n = 69 and study 2 behavioral n = 58). The fMRI study examined White participants’ neural activity when viewing real-world videos with varying degrees of aggression or conflict of White officers arresting a Black or White civilian. Activity in brain regions supporting social cognition was greater when viewing Black (vs. White) civilians involved in more aggressive police encounters. Additionally, although an independent sample of perceivers rated videos featuring Black and White civilians as similar in overall levels of aggression when civilian race was obscured, participants in the fMRI study (where race was not obscured) rated officers as more aggressive and their use of force as less legitimate when the civilian was Black. In study 2, participants who had not viewed the videos also reported that they believe police are generally more unjustly aggressive towards Black compared with White civilians. These findings inform our understanding of how perceptions of conflict with the potential for injustice shape social cognitive engagement when viewing arrests of Black and White individuals by White police officers.

Multidimensional inhibitory signatures of sentential negation in behavioral variant frontotemporal dementia

Article

Full-text available

Mar 2022
CEREB CORTEX

Background: Processing of linguistic negation has been associated to inhibitory brain mechanisms. However, no study has tapped this link via multimodal measures in patients with core inhibitory alterations, a critical approach to reveal direct neural correlates and potential disease markers. Methods: Here we examined oscillatory, neuroanatomical, and functional connectivity signatures of a recently reported Go/No-go negation task in healthy controls and behavioral variant frontotemporal dementia (bvFTD) patients, typified by primary and generalized inhibitory disruptions. To test for specificity, we also recruited persons with Alzheimer's disease (AD), a disease involving frequent but nonprimary inhibitory deficits. Results: In controls, negative sentences in the No-go condition distinctly involved frontocentral delta (2-3 Hz) suppression, a canonical inhibitory marker. In bvFTD patients, this modulation was selectively abolished and significantly correlated with the volume and functional connectivity of regions supporting inhibition (e.g. precentral gyrus, caudate nucleus, and cerebellum). Such canonical delta suppression was preserved in the AD group and associated with widespread anatomo-functional patterns across non-inhibitory regions. Discussion: These findings suggest that negation hinges on the integrity and interaction of spatiotemporal inhibitory mechanisms. Moreover, our results reveal potential neurocognitive markers of bvFTD, opening a new agenda at the crossing of cognitive neuroscience and behavioral neurology.

Analyzing the Progression of Alzheimer's Disease in Human Brain Networks

Conference Paper

Mar 2024

Distinct neurochemical influences on fMRI response polarity in the striatum

Article

Full-text available

Mar 2024

The striatum, known as the input nucleus of the basal ganglia, is extensively studied for its diverse behavioral roles. However, the relationship between its neuronal and vascular activity, vital for interpreting functional magnetic resonance imaging (fMRI) signals, has not received comprehensive examination within the striatum. Here, we demonstrate that optogenetic stimulation of dorsal striatal neurons or their afferents from various cortical and subcortical regions induces negative striatal fMRI responses in rats, manifesting as vasoconstriction. These responses occur even with heightened striatal neuronal activity, confirmed by electrophysiology and fiber-photometry. In parallel, midbrain dopaminergic neuron optogenetic modulation, coupled with electrochemical measurements, establishes a link between striatal vasodilation and dopamine release. Intriguingly, in vivo intra-striatal pharmacological manipulations during optogenetic stimulation highlight a critical role of opioidergic signaling in generating striatal vasoconstriction. This observation is substantiated by detecting striatal vasoconstriction in brain slices after synthetic opioid application. In humans, manipulations aimed at increasing striatal neuronal activity likewise elicit negative striatal fMRI responses. Our results emphasize the necessity of considering vasoactive neurotransmission alongside neuronal activity when interpreting fMRI signal.

Improving acute stroke assessment in non-enhanced computed tomography: automated tool for early ischemic lesion volume detection

Article

Jan 2024
NEUROL SCI

ASPECTs is a widely used marker to identify early stroke signs on non-enhanced computed tomography (NECT), yet it presents interindividual variability and it may be hard to use for non-experts. We introduce an algorithm capable of automatically estimating the NECT volumetric extension of early acute ischemic changes in the 3D space. We compared the power of this marker with ASPECTs evaluated by experienced practitioner in predicting the clinical outcome. We analyzed and processed neuroimaging data of 153 patients admitted with acute ischemic stroke. All patients underwent a NECT at admission and on follow-up. The developed algorithm identifies the early ischemic hypodense region based on an automatic comparison of the gray level in the images of the two hemispheres, assumed to be an approximate mirror image of each other in healthy patients. In the two standard axial slices used to estimate the ASPECTs, the regions identified by the algorithm overlap significantly with those identified by experienced practitioners. However, in many patients, the regions identified automatically extend significantly to other slices. In these cases, the volume marker provides supplementary and independent information. Indeed, the clinical outcome of patients with volume marker = 0 can be distinguished with higher statistical confidence than the outcome of patients with ASPECTs = 10. The volumetric extension and the location of acute ischemic region in the 3D-space, automatically identified by our algorithm, provide data that are mostly in agreement with the ASPECTs value estimated by expert practitioners, and in some cases complementary and independent.

Distinct changes in brain metabolism in patients with dementia and hearing loss

Article

Full-text available

Jan 2024

Introduction Previous studies have reported that hearing loss (HL) is associated with dementia, although the mechanistic underpinnings remain elusive. This study aimed to evaluate the changes in brain metabolism in patients with HL and different types of dementia. Methods Patients with cognitive impairment (CI) and HL treated at the university‐based memory clinic from May 2016 to October 2021 were included. In total, 108 patients with CI and HL prospectively underwent audiometry, neuropsychological test, magnetic resonance imaging, and ¹⁸F‐fluorodeoxyglucose positron emission tomography. Twenty‐seven individuals without cognitive impairment and hearing loss were enrolled as a control group. Multivariable regression was performed to evaluate brain regions correlated with each pathology type after adjusting for confounding factors. Results Multivariable regression analyses revealed that Alzheimer's disease‐related CI (ADCI) was associated with hypometabolic changes in the right superior temporal gyrus (STG), right middle temporal gyrus (MTG), and bilateral medial temporal lobe. Lewy body disease‐related CI (LBDCI) and vascular CI were associated with hypermetabolic and hypometabolic changes in the ascending auditory pathway, respectively. In the pure ADCI group, the degree of HL was positively associated with abnormal increase of brain metabolism in the right MTG, whereas it was negatively associated with decreased brain metabolism in the right STG in the pure LBDCI group. Conclusion Each dementia type is associated with distinct changes in brain metabolism in patients with HL.

The involvement of brain regions associated with lower KPS and shorter survival time predicts a poor prognosis in glioma

Article

Full-text available

Dec 2023

Background Isocitrate dehydrogenase-wildtype glioblastoma (IDH-wildtype GBM) and IDH-mutant astrocytoma have distinct biological behaviors and clinical outcomes. The location of brain tumors is closely associated not only with clinical symptoms and prognosis but also with key molecular alterations such as IDH. Therefore, we hypothesize that the key brain regions influencing the prognosis of glioblastoma and astrocytoma are likely to differ. This study aims to (1) identify specific regions that are associated with the Karnofsky Performance Scale (KPS) or overall survival (OS) in IDH-wildtype GBM and IDH-mutant astrocytoma and (2) test whether the involvement of these regions could act as a prognostic indicator. Methods A total of 111 patients with IDH-wildtype GBM and 78 patients with IDH-mutant astrocytoma from the Cancer Imaging Archive database were included in the study. Voxel-based lesion-symptom mapping (VLSM) was used to identify key brain areas for lower KPS and shorter OS. Next, we analyzed the structural and cognitive dysfunction associated with these regions. The survival analysis was carried out using Kaplan–Meier survival curves. Another 72 GBM patients and 48 astrocytoma patients from Harbin Medical University Cancer Hospital were used as a validation cohort. Results Tumors located in the insular cortex, parahippocampal gyrus, and middle and superior temporal gyrus of the left hemisphere tended to lead to lower KPS and shorter OS in IDH-wildtype GBM. The regions that were significantly correlated with lower KPS in IDH-mutant astrocytoma included the subcallosal cortex and cingulate gyrus. These regions were associated with diverse structural and cognitive impairments. The involvement of these regions was an independent predictor for shorter survival in both GBM and astrocytoma. Conclusion This study identified the specific regions that were significantly associated with OS or KPS in glioma. The results may help neurosurgeons evaluate patient survival before surgery and understand the pathogenic mechanisms of glioma in depth.

Visual looming is a primitive for human emotion

Preprint

Full-text available

Aug 2023

Looming objects afford threat of collision across the animal kingdom. Defensive responses to looming and neural computations for looming detection are strikingly conserved across species. In mammals, information about rapidly approaching threats is conveyed from the retina to the midbrain superior colliculus, where variables that indicate the position and velocity of approach are computed to enable defensive behavior. Neuroscientific theories posit that emotional feelings are based on representations in the midbrain, which are further elaborated in cortical systems. However, how these computations relate to phenomenal experience in humans is largely unknown. Here, we show that a shallow convolutional neural network based on the Drosophila visual system predicts human behavioral and brain responses across development. In laboratory experiments using controlled visual stimuli, we find that this model explains defensive behaviors to looming objects in infants and superior colliculus responses to optical expansion in adults. Further, examining responses to a broader array of naturalistic video clips, we observe that representations of looming from this model predict self-reported emotion largely on the basis of subjective arousal. Our results illustrate how human emotions may be supported by species-general systems for survival in unpredictable environments.

The neurocognitive impact of loneliness and social networks on social adaptation

Article

Full-text available

Jul 2023

Social adaptation arises from the interaction between the individual and the social environment. However, little empirical evidence exists regarding the relationship between social contact and social adaptation. We propose that loneliness and social networks are key factors explaining social adaptation. Sixty-four healthy subjects with no history of psychiatric conditions participated in this study. All participants completed self-report questionnaires about loneliness, social network, and social adaptation. On a separate day, subjects underwent a resting state fMRI recording session. A hierarchical regression model on self-report data revealed that loneliness and social network were negatively and positively associated with social adaptation. Functional connectivity (FC) analysis showed that loneliness was associated with decreased FC between the fronto-amygdalar and fronto-parietal regions. In contrast, the social network was positively associated with FC between the fronto-temporo-parietal network. Finally, an integrative path model examined the combined effects of behavioral and brain predictors of social adaptation. The model revealed that social networks mediated the effects of loneliness on social adaptation. Further, loneliness-related abnormal brain FC (previously shown to be associated with difficulties in cognitive control, emotion regulation, and sociocognitive processes) emerged as the strongest predictor of poor social adaptation. Findings offer insights into the brain indicators of social adaptation and highlight the role of social networks as a buffer against the maladaptive effects of loneliness. These findings can inform interventions aimed at minimizing loneliness and promoting social adaptation and are especially relevant due to the high prevalence of loneliness around the globe. These findings also serve the study of social adaptation since they provide potential neurocognitive factors that could influence social adaptation.

Carving Out the Path to Computational Biomarkers for Mental Disorders

Chapter

May 2023

For the last 20 years, there has been impressive progress in cognitive neuroscience and neuroimaging. Unfortunately, the translation from basic research to real world clinical applications in the domain of mental disorders has been limited. A major contribution of functional MRI would be to provide computational biomarkers for precision medicine and treatment optimization. Before this is possible, a number of open conceptual and methodological challenges need to be addressed, and some of them will be outlined in this chapter. For illustration purposes, we focus on the amygdala and its relevance in anxiety disorders. We cover a few central aspects on how certain pitfalls can preclude reliable empirical work on the amygdala and, thus, robust computational biomarkers. Overcoming them requires multidisciplinary collaboration on the level of psychiatric and neuroscientific theories on the one hand, and on fMRI acquisition, signal processing, and analysis on the other. While we are optimistic that neuroimaging will provide a substantial contribution for the discovery of computational biomarkers in psychiatry, psychopathologies and their neuropathophysiology are complex so we cannot propose an easy solution. Instead, we focus on some challenges that emerge in the overlap of theory and application.Key wordsEmotionsAnxietyMental disordersAmygdalaFunctional MRI

Understanding Tacit Knowledge in Surgery and Developing an Efficient Skill Transfer Support System外科手術における暗黙知の理解と効率的な技能伝達支援システムの開発

Article

May 2023

PET Imaging for Alzheimer's Neuroanatomical Disease Diagnosis

Conference Paper

Jan 2023

Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation

Article

Full-text available

Jan 2023

MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies—which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation—can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.

Linking Impulsivity to Activity Levels in Pre-Supplementary Motor Area during Sequential Gambling

Article

Full-text available

Jan 2023

Impulsivity refers to the tendency to act prematurely or without forethought, and excessive impulsivity is a key problem in many neuropsychiatric disorders. Since the pre-supplementary motor area (preSMA) has been implicated in inhibitory control, this region may also contribute to impulsivity. Here, we examined whether functional recruitment of preSMA may contribute to risky choice behavior (state impulsivity) during sequential gambling and its relation to self-reported trait impulsivity. To this end, we performed task-based functional MRI (fMRI) after low-frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) of the preSMA. We expected low-frequency rTMS to modulate task-related engagement of the preSMA and, hereby, tune the tendency to make risky choices. Twenty-four healthy volunteers (12 females, 19-52 years) received real or sham rTMS on separate days in counterbalanced order. Thereafter, participants performed a sequential gambling task with concurrently increasing stakes and risk during whole-brain fMRI. In the sham-rTMS session, self-reported trait impulsivity scaled positively with state impulsivity (riskier choice behavior) during gambling. The higher the trait - impulsivity, the lower was the task-related increase in preSMA activity with increasingly risky choices. Following real-rTMS, low-impulsivity participants increased their preference for risky choices, while the opposite was true for high-impulsivity participants resulting in an overall decoupling of trait impulsivity and state impulsivity during gambling. This rTMS-induced behavioral shift was mirrored in the rTMS-induced change in preSMA activation. These results provide converging evidence for a causal link between the level of task-related preSMA activity and the propensity for impulsive risk-taking behavior in the context of sequential gambling. SIGNIFICANCE STATEMENT: Impulsivity is a personal trait characterized by a tendency to act prematurely or without forethought, and excessive impulsivity is a key problem in many neuropsychiatric disorders. Here we provide evidence that the pre-supplementary motor area, preSMA, is causally involved in implementing general impulsive tendencies (trait impulsivity) into actual behavior (state impulsivity). Participants’ self-reported impulsivity levels (trait impulsivity) were reflected in their choice behavior (state impulsivity) when involved in a sequential gambling task. This relationship was uncoupled after perturbing the preSMA with repetitive transcranial stimulation (rTMS). This effect was contingent on trait impulsivity and was echoed in rTMS-induced changes in preSMA activity. PreSMA is key in translating trait impulsivity into behavior, possibly by integrating prefrontal goals with cortico-striatal motor control.

Large-Scale Thalamocortical Triple Network Dysconnectivities in Patients With First-Episode Psychosis and Individuals at Risk for Psychosis

Article

Dec 2022
SCHIZOPHRENIA BULL

Background and Hypothesis Aberrant thalamocortical connectivity and large-scale network interactions among the default mode network (DMN), salience network (SN), and executive control network (ECN) (ie, triple networks) have been regarded as critical in schizophrenia pathophysiology. Despite the importance of network properties and the role of the thalamus as an integrative hub, large-scale thalamocortical triple network functional connectivities (FCs) in different stages of the psychotic disorder have not yet been reported. Study Design Thirty-nine first-episode psychosis (FEP) patients, 75 individuals at clinical high risk (CHR) for psychosis, 46 unaffected relatives (URs) of schizophrenia patients with high genetic loading, and 110 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Modular community detection was used to identify cortical and thalamic resting-state networks, and thalamocortical network interactions were compared across the groups. Study Results Thalamic triple networks included higher-order thalamic nuclei. Thalamic SN–cortical ECN FC was greater in the FEP group than in the CHR, UR, and HC groups. Thalamic DMN–cortical DMN and thalamic SN–cortical DMN FCs were greater in FEP and CHR participants. Thalamic ECN–cortical DMN and thalamic ECN–cortical SN FCs were greater in FEP patients and URs. Conclusions These results highlight critical modulatory functions of thalamic triple networks and the shared and distinct patterns of thalamocortical triple network dysconnectivities across different stages of psychotic disorders. The current study findings suggest that large-scale thalamocortical triple network dysconnectivities may be used as an integrative biomarker for extending our understanding of the psychosis pathophysiology and for targeting network-based neuromodulation therapeutics.

Neurodevelopmental oscillatory basis of speech processing in noise

Article

Full-text available

Nov 2022

Humans’ extraordinary ability to understand speech in noise relies on multiple processes that develop with age. Using magnetoencephalography (MEG), we characterize the underlying neuromaturational basis by quantifying how cortical oscillations in 144 participants (aged 5 to 27 years) track phrasal and syllabic structures in connected speech mixed with different types of noise. While the extraction of prosodic cues from clear speech was stable during development, its maintenance in a multi-talker background matured rapidly up to age 9 and was associated with speech comprehension. Furthermore, while the extraction of subtler information provided by syllables matured at age 9, its maintenance in noisy backgrounds progressively matured until adulthood. Altogether, these results highlight distinct behaviorally relevant maturational trajectories for the neuronal signatures of speech perception. In accordance with grain-size proposals, neuromaturational milestones are reached increasingly late for linguistic units of decreasing size, with further delays incurred by noise.

MR Template-Based Individual Brain PET Volumes-of-Interest Generation Neither Using MR nor Using Spatial Normalization

Article

Oct 2022

Unlabelled: For more anatomically precise quantitation of mouse brain PET, spatial normalization (SN) of PET onto MR template and subsequent template volumes-of-interest (VOIs)-based analysis are commonly used. Although this leads to dependency on the corresponding MR and the process of SN, routine preclinical/clinical PET images cannot always afford corresponding MR and relevant VOIs. To resolve this issue, we propose a deep learning (DL)-based individual-brain-specific VOIs (i.e., cortex, hippocampus, striatum, thalamus, and cerebellum) directly generated from PET images using the inverse-spatial-normalization (iSN)-based VOI labels and deep convolutional neural network model (deep CNN). Our technique was applied to mutated amyloid precursor protein and presenilin-1 mouse model of Alzheimer's disease. Eighteen mice underwent T2-weighted MRI and 18F FDG PET scans before and after the administration of human immunoglobin or antibody-based treatments. To train the CNN, PET images were used as inputs and MR iSN-based target VOIs as labels. Our devised methods achieved decent performance in terms of not only VOI agreements (i.e., Dice similarity coefficient) but also the correlation of mean counts and SUVR, and CNN-based VOIs was highly accordant with ground-truth (the corresponding MR and MR template-based VOIs). Moreover, the performance metrics were comparable to that of VOI generated by MR-based deep CNN. In conclusion, we established a novel quantitative analysis method both MR-less and SN-less fashion to generate individual brain space VOIs using MR template-based VOIs for PET image quantification. Supplementary information: The online version contains supplementary material available at 10.1007/s13139-022-00772-4.

Functional connectivity of the cortico-subcortical sensorimotor loop is modulated by the severity of nigrostriatal dopaminergic denervation in Parkinson’s Disease

Article

Full-text available

Sep 2022

To assess if the severity of nigrostriatal innervation loss affects the functional connectivity (FC) of the sensorimotor cortico-striato-thalamic-cortical loop (CSTCL) in Parkinson’s Disease (PD), Resting-State functional MRI and 18F-DOPA PET data, simultaneously acquired on a hybrid PET/MRI scanner, were retrospectively analyzed in 39 PD and 16 essential tremor patients. Correlations between posterior Putamen DOPA Uptake (pPDU) and the FC of the main CSTCL hubs were assessed separately in the two groups, analyzing the differences between the two groups by a group-by-pPDU interaction analysis of the resulting clusters’ FC. Unlike in essential tremor, in PD patients pPDU correlated inversely with the FC of the thalamus with the sensorimotor cortices, and of the postcentral gyrus with the dorsal cerebellum, and directly with the FC of pre- and post-central gyri with both the superior and middle temporal gyri and the paracentral lobule, and of the caudate with the superior parietal cortex. The interaction analysis confirmed the significance of the difference between the two groups in these correlations. In PD patients, the post-central cortex FC, in the clusters correlating directly with pPDU, negatively correlated with both UPDRS motor examination score and Hoehn and Yahr stage, independent of the pPDU, suggesting that these FC changes contribute to motor impairment. In PD, nigrostriatal innervation loss correlates with a decrease in the FC within the sensorimotor network and between the sensorimotor network and the superior temporal cortices, possibly contributing to motor impairment, and with a strengthening of the thalamo-cortical FC, that may represent ineffective compensatory phenomena.

A variational Bayesian method for similarity learning in non-rigid image registration

Conference Paper

Jun 2022

Construction of brain area risk map for decision making using surgical navigation and motor evoked potential monitoring information

Article

Sep 2022

PurposeSurgical devices or systems typically operate in a stand-alone manner, making it difficult to perform integration analysis of both intraoperative anatomical and functional information. To address this issue, the intraoperative information integration system OPeLiNK® was developed. The objective of this study is to generate information for decision making using surgical navigation and intraoperative monitoring information accumulated in the OPeLiNK® database and to analyze its utility.Methods We accumulated intraoperative information from 27 brain tumor patients who underwent resection surgery. First, the risk rank for postoperative paralysis was set according to the attenuation rate and amplitude width of the motor evoked potential (MEP). Then, the MEP and navigation log data were combined and plotted on an intraoperative magnetic resonance image of the individual brain. Finally, statistical parametric mapping (SPM) transformation was performed to generate a standard brain risk map of postoperative paralysis. Additionally, we determined the anatomical high-risk areas using atlases and analyzed the relationship with each set risk rank.ResultsThe average distance between the navigation log corresponding to each MEP risk rank and the anatomical high-risk area differed significantly between the with postoperatively paralyzed and without postoperatively paralyzed groups, except for “safe.” Furthermore, no excessive deformation was observed resulting from SPM conversion to create the standard brain risk map. There were cases in which no postoperative paralysis occurred even when MEP decreased intraoperatively, and vice versa.Conclusion The time synchronization reliability of the study data is very high. Therefore, our created risk map can be reported as being functional at indicating the risk areas. Our results suggest that the statistical risks of postoperative complications can be presented for each area where brain surgery is to be performed. In the future, it will be possible to provide surgical navigation with intraoperative support that reflects the risk maps created.

Der altersabhängige Kurvenverlauf der Gehirnentwicklung mit besonderer Widmung der Zahlenverarbeitung.

Thesis

Jan 2021

Mina Raahimi

Free-water imaging of the cholinergic basal forebrain and pedunculopontine nucleus in Parkinson's disease

Article

Full-text available

Apr 2022
BRAIN

Background: Free-water imaging can predict and monitor dopamine system degeneration in people with Parkinson's disease. It can also enhance the sensitivity of traditional diffusion tensor imaging (DTI) metrics for indexing neurodegeneration. However, these tools are yet to be applied to investigate cholinergic system degeneration in Parkinson's (which involves both the pedunculopontine nucleus (PPN) and cholinergic basal forebrain (cBF)). Methods: Free-water imaging, free-water-corrected DTI, and volumetry were used to extract structural metrics from the cBF and PPN in 99 people with Parkinson's and 46 age-matched controls. Cognitive ability was tracked over 4.5-years. Results: Pearson's partial correlations revealed that free-water-corrected DTI metrics in the PPN were associated with performance on cognitive tasks that required participants to make rapid choices (behavioural flexibility). Volumetric, free-water content and DTI metrics in the cBF were elevated in a sub-group of people with Parkinson's with evidence of cognitive impairment, and linear mixed modelling revealed that these metrics were differently associated with current and future changes to cognition. Conclusions: Free water and free-water-corrected DTI can index cholinergic degeneration that could enable stratification of patients in clinical trials of cholinergic interventions for cognitive decline. In addition, degeneration of the PPN impairs behavioural flexibility in Parkinson's, which may explain this region's role in increased risk of falls.

Quantitative analysis of regional distribution of tau pathology with 11C-PBB3-PET in a clinical setting

Article

Full-text available

Apr 2022
PLOS ONE

Purpose The recent developments of tau-positron emission tomography (tau-PET) enable in vivo assessment of neuropathological tau aggregates. Among the tau-specific tracers, the application of ¹¹ C-pyridinyl-butadienyl-benzothiazole 3 ( ¹¹ C-PBB3) in PET shows high sensitivity to Alzheimer disease (AD)-related tau deposition. The current study investigates the regional tau load in patients within the AD continuum, biomarker-negative individuals (BN) and patients with suspected non-AD pathophysiology (SNAP) using ¹¹ C-PBB3-PET. Materials and methods A total of 23 memory clinic outpatients with recent decline of episodic memory were examined using ¹¹ C-PBB3-PET. Pittsburg compound B ( ¹¹ C-PIB) PET was available for 17, ¹⁸ F-flurodeoxyglucose ( ¹⁸ F-FDG) PET for 16, and cerebrospinal fluid (CSF) protein levels for 11 patients. CSF biomarkers were considered abnormal based on Aβ 42 (< 600 ng/L) and t-tau (> 450 ng/L). The PET biomarkers were classified as positive or negative using statistical parametric mapping (SPM) analysis and visual assessment. Using the amyloid/tau/neurodegeneration (A/T/N) scheme, patients were grouped as within the AD continuum, SNAP, and BN based on amyloid and neurodegeneration status. The ¹¹ C-PBB3 load detected by PET was compared among the groups using both atlas-based and voxel-wise analyses. Results Seven patients were identified as within the AD continuum, 10 SNAP and 6 BN. In voxel-wise analysis, significantly higher ¹¹ C-PBB3 binding was observed in the AD continuum group compared to the BN patients in the cingulate gyrus, tempo-parieto-occipital junction and frontal lobe. Compared to the SNAP group, patients within the AD continuum had a considerably increased ¹¹ C-PBB3 uptake in the posterior cingulate cortex. There was no significant difference between SNAP and BN groups. The atlas-based analysis supported the outcome of the voxel-wise quantification analysis. Conclusion Our results suggest that ¹¹ C-PBB3-PET can effectively analyze regional tau load and has the potential to differentiate patients in the AD continuum group from the BN and SNAP group.

An Automated 3D non-linear image deformation procedure for Determination of Gross Morphometric Variability in Human Brain

Article

Full-text available

Jan 1994

We describe an automated method to register MRI volumetric datasets to a digital human brain model. The technique employs 3D non-linear warping based on the estimation of local deformation fields using cross-correlation of invariant intensity features derived from image data. Results of the non-linear registration on a simple phantom, a complex brain phantom and real MRI data are presented. Anatomical variability is expressed with respect to the Talairach-like standardized brain-based coordinate system of the model. We show that the automated non-linear registration reduces the inter-subject variability of homologous points in standardized space by 15% over linear registration methods. A 3D variability map is shown.

Structural Image Restoration Through Deformable Templates

Article

Full-text available

Jun 1991

Prior knowledge on the space of possible images is given in the form of a function or template in some domain. The set of all possible true images is assumed to be formed by a composition of that function with continuous mappings of the domain into itself. A prior Gaussian distribution is given on the set of continuous mappings. The observed image is assumed to be a degradation of the true image with additive noise. Given the observed image, a posterior distribution is then obtained and has the form of a nonlinear perturbation of the Gaussian measure on the space of mappings. We present simulations of the posterior distribution that lead to structural reconstructions of the true image in the sense that it enables us to determine landmarks and other characteristic features of the image, as well as to spot pathologies in it. Moreover, we show that the reconstruction algorithm is relatively robust when the images are degraded by noise that is not necessarily additive.

Mathematical Textbook Of Deformable Neuroanatomies

Article

Full-text available

Jan 1994

Mathematical techniques are presented for the transformation of digital anatomical textbooks from the ideal to the individual, allowing for the representation of the variabilities manifest in normal human anatomies. The ideal textbook is constructed on a fixed coordinate system to contain all of the information currently available about the physical properties of neuroanatomies. This information is obtained via sensor probes such as magnetic resonance, as well as computed axial and emission tomography, along with symbolic information such as white- and gray-matter tracts, nuclei, etc. Human variability associated with individuals is accommodated by defining probabilistic transformations on the textbook coordinate system, the transformations forming mathematical translation groups of high dimension. The ideal is applied to the individual patient by finding the transformation which is consistent with physical properties of deformable elastic solids and which brings the coordinate system of the textbook to that of the patient. Registration, segmentation, and fusion all result automatically because the textbook carries symbolic values as well as multisensor features.

(3D) Brain mapping using a deformable neuroanatomy

Article

Full-text available

Apr 1994

This paper presents two different mathematical methods that can be used separately or in conjunction to accommodate shape variabilities between normal human neuroanatomies. Both methods use a digitized textbook to represent the complex structure of a typical normal neuroanatomy. Probabilistic transformations on the textbook coordinate system are defined to accommodate shape differences between the textbook and images of other normal neuroanatomies. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second. Results presented in this paper demonstrate how a single deformable textbook can be used to accommodate normal shape variability.

Deformable template using large deformation kinematics

Article

Full-text available

Feb 1996

A general automatic approach is presented for accommodating local shape variation when mapping a two-dimensional (2-D) or three-dimensional (3-D) template image into alignment with a topologically similar target image. Local shape variability is accommodated by applying a vector-field transformation to the underlying material coordinate system of the template while constraining the transformation to be smooth (globally positive definite Jacobian). Smoothness is guaranteed without specifically penalizing large-magnitude deformations of small subvolumes by constraining the transformation on the basis of a Stokesian limit of the fluid-dynamical Navier-Stokes equations. This differs fundamentally from quadratic penalty methods, such as those based on linearized elasticity or thin-plate splines, in that stress restraining the motion relaxes over time allowing large-magnitude deformations. Kinematic nonlinearities are inherently necessary to maintain continuity of structures during large-magnitude deformations, and are included in all results. After initial global registration, final mappings are obtained by numerically solving a set of nonlinear partial differential equations associated with the constrained optimization problem. Automatic regridding is performed by propagating templates as the nonlinear transformations evaluated on a finite lattice become singular. Application of the method to intersubject registration of neuroanatomical structures illustrates the ability to account for local anatomical variability.

3D Statistical Neuroanatomical Models from 305 MRI Volumes

Conference Paper

Full-text available

Jan 1993

Recently, there has been a rapid growth in the use of 3D multi-modal correlative imaging for studies of the human brain. Regional cerebral blood flow (CBF) changes indicate brain areas involved in stimulus processing. These focal changes are often too small (<10%) to be discerned from a single subject and the experiment is repeated in a series of individuals. To investigate the extent of residual variability the authors have collected over 300 MRI volumetric datasets from normal individuals and transformed these datasets into stereotaxic space using a 3D linear re-sampling algorithm. The authors then generated a series of statistical measures which express this population nonlinear variability in the form of parametric volumes, e.g. mean intensity, intensity variance. A model for anatomical variability, expressed as the width of a Gaussian blurring kernel applied to an ideal single subject, was developed and tested against the observed data

Advances in elastic matching theory and its implementation

Conference Paper

Jan 1997
Lect Notes Comput Sci

Computational anatomy via the deformable modeling or elastic matching paradigm is gaining increased prominence in medical imaging research. Our work in atlas-based localization of neuroanatomy has progressed toward statistical approaches that subsume the original elastic matching while retaining its practical flavor. In view of the complex geometries involved and the sparsity of image features in the localization problem, elastic matching is reformulated using variational principles to facilitate its numerical solution by the finite element method. The variational formulation in addition exposes the means by which Gibbs modeling and, thus, Bayesian analysis can be applied to the problem. In this paper, we review these developments and demonstrate the methods on MRI data, including the computation of interval estimates.

Landmark methods for forms without landmarks: morphometrics of group differences in outline shape

Article

Apr 1997
MED IMAGE ANAL

Fred Bookstein

Morphometrics, a new branch of statistics, combines tools from geometry, computer graphics and biometrics in techniques for the multivariate analysis of biological shape variation. Although medical image analysts typically prefer to represent scenes by way of curving outlines or surfaces, the most recent developments in this associated statistical methodology have emphasized the domain of landmark data: size and shape of configurations of discrete, named points in two or three dimensions. This paper introduces a combination of Procrustes analysis and thin-plate splines, the two most powerful tools of landmark-based morphometrics, for multivariate analysis of curving outlines in samples of biomedical images. The thin-plate spline is used to assign point-to-point correspondences, called semi-landmarks, between curves of similar but variable shape, while the standard algorithm for Procrustes shape averages and shape coordinates is altered to accord with the ways in which semi-landmarks formally differ from more traditional landmark loci. Subsequent multivariate statistics and visualization proceed mainly as in the landmark-based methods. The combination provides a range of complementary filters, from high pass to low pass, for effects on outline shape in grouped studies. The low-pass version is based on the spectrum of the spline, the high pass, on a familiar special case of Procrustes analysis. This hybrid method is demonstrated in a comparison of the shape of the corpus callosum from mid-sagittal sections of MRI of 25 human brains, 12 normal and 13 with schizophrenia.

Principal warps: Thin-plate splines and the decomposition of deformations

Article

Jan 1992

Fred L. Bookstein

The decomposition of deformations by principal warps is demonstrated. The method is extended to deal with curving edges between landmarks. This formulation is related to other applications of splines current in computer vision. How they might aid in the extraction of features for analysis, comparison, and diagnosis of biological and medical images is indicated.

Spatial Registration and Normalization of Images

Article

Jul 1995
HUM BRAIN MAPP

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.

A Probabilistic Atlas of the Human Brain: Theory and Rationale for its development

Article

Jan 1985

An MRI-based Probablistic Atlas of Neuroanatomy

Article

Jan 1994

Three-dimensional MRI imaging techniques offer new possibilities for qualitative and quantitative studies of gross neuroanatomy, functional neuroanatomy and for neurosurgical planning. The digital nature of the data allows for the reconstruction of realistic three-dimensional models of an individual brain which can be sliced at arbitrary orientations for optimal visual inspection of often complex neuroanatomy and pathology. This is particularly relevant in the assessment of potential neuroanatomical correlates of temporal lobe epilepsy. Re-formatting of contiguous thinly sliced (1–2 mm thick) volumetric MRI data along planes parallel and perpendicular to the temporal plane allow finer visual discrimination and greater standardisation in qualitative procedures than previously possible. Perhaps more exciting are the applications of quantitative analysis where, for instance, accurate measurements of hippocampus and/or amygdala volumes provide important indicators of unilateral mesial temporal sclerosis which compare favourably with EEG and more invasive methods of lateralising the epileptogenic focus (Jack et al., 1990; Cascino et al., 1991; Watson et al, 1992; Cendes et al., 1993 a,b). For instance, by combining volumetric measurements of both hippocampus and amygdala, Cendes et al., (Chapter 9) quote correct lateralisation of focus in 93 of 100 temporal lobe epilepsy cases. The study of epilepsy arising from cortical abnormalities has been limited in the past by the difficulties of visualising the cortical surface from a set of conventional two-dimensional MRI slices. New acquisition techniques with gradient echo as opposed to spin echo techniques allow for an improved signal-to-noise ratio in thin slices in times compatible with clinical examinations. Whole brain coverage with thin slice data is now possible, such that partial volume effects are minimised with consequent improvements in fine detail. Numerous authors have reported dramatic improvements in the assessment of cortical dysplasia and grey matter heterotopias, particularly for more subtle abnormalities (Palmini et al., 1991a,b,c; Barkovich and Kjos, 1992a,b,c). The impact of this improved raw data when combined with new techniques for generating three-dimensional surface renderings in reasonably interactive circumstances is yet to be fully realised but initial experience is promising. At present, most studies have relied upon visual inspection to identify abnormalities in gyration on three-dimensional surface-rendered MRI. Such methods are quite acceptable for gross pathologies but, in a manner similar to mesial temporal volumetrics, the identification of more subtle distortions may require quantitative analysis of left/right differences and comparison of individual gyral surface area or gyral/sulcal locations with previously established population norms. Cook et al., (Chapter 47) have approached the problem by application of fractal analysis to two-dimensional MRI images from normal and frontal lobe epilepsy (FLE) patients. The grey-white matter interface was extracted by image processing procedures as a continuous contour and the fractal dimension, an index of contour complexity, derived. Results indicate that 10 of 16 FLE patients had a fractal index more than 3 standard deviations (3SD) below normal. In its present form, the method provides a non-specific indicator of cortical abnormality, yielding an overall index of complexity rather than identifying specific abnormalities, and is implemented in two dimensions rather than three dimensions. Nevertheless, it illustrates the potential of quantitative analysis for detecting aberrant cortical morphology. For a more directed analysis of cortical folding, a model of normal neuroanatomical variability, expressed in three-dimensional coordinates, is necessary. Keyserlingk and co-workers have developed methods for digitising sulcal patterns from post-mortem brains and constructed a map, with cuboid elements of 4 mm or 8 mm edge length, of major sulcal anatomy from 30 such brains (Keyserlingk et al., 1983, 1985, 1988; Niemann et al, 1988). The advent of high resolution MRI scanning offers finer spatial and contrast resolution in normal brain in vivo. At the Montreal Neurological Institute, MRI and PET imaging techniques are combined with three-dimensional graphics and computational analysis in the study of functional neuroanatomy of cognitive and sensorimotor processing. As part of this “brain mapping” programme, we have collected a database of over 300 MRI volumes from young normal subjects and are presently engaged in a series of projects whose long-term goal is the construction of a probabilistic description of normal neuroanatomy derived from high-resolution (1 mm thick slices) MRI data. In this chapter we briefly describe the current brain mapping environment at our institute and the current development of the MRI atlas project in both volumetric and surface domains.

Automated Multi-Modality Image Registration Based on Information Theory

Article

Jan 1995
Lect Notes Comput Sci

A new approach to the problem of multi-modality medical image registration is proposed, using a basic concept from information theory, Mutual Information or relative e n tropy, as a new matching criterion. The method presented in this paper applies Mutual Information to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of Mutual Information is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the mutual information criterion is validated for rigid body registration of CT, MR and PET images by comparison with the stereotactic registration solution, while ro-bustness is evaluated with respect to implementation issues, such a s i n terpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction or other pre-processing steps, which makes this method very well suited for clinical applications.

Topological properties of smooth anatomic maps

Article

Jan 1995

Automatic 3D segmentation of neuro-anatomical structures from MRI

Article

Jan 1995

Spatial normalization origins: Objectives, applications, and alternatives

Article

Jan 1995
HUM BRAIN MAPP

Peter T. Fox

Spatial Normalization of Brain Images with Focal Lesions Using Cost Function Masking

Article

Sep 2001

In studies of patients with focal brain lesions, it is often useful to coregister an image of the patient's brain to that of another subject or a standard template. We refer to this process as spatial normalization. Spatial normalization can improve the presentation and analysis of lesion location in neuropsychological studies; it can also allow other data, for example from functional imaging, to be compared to data from other patients or normal controls. In functional imaging, the standard procedure for spatial normalization is to use an automated algorithm, which minimizes a measure of difference between image and template, based on image intensity values. These algorithms usually optimize both linear (translations, rotations, zooms, and shears) and nonlinear transforms. In the presence of a focal lesion, automated algorithms attempt to reduce image mismatch between template and image at the site of the lesion. This can lead to significant inappropriate image distortion, especially when nonlinear transforms are used. One solution is to use cost-function masking—masking the areas used in the calculation of image difference—to exclude the area of the lesion, so that the lesion does not bias the transformations. We introduce and evaluate this technique using normalizations of a selection of brains with focal lesions and normal brains with simulated lesions. Our results suggest that cost-function masking is superior to the standard approach to this problem, which is affine-only normalization; we propose that cost-function masking should be used routinely for normalizations of brains with focal lesions.

Analysis of Fmri Time-Series Revisited

Article

Apr 1995

This paper presents a general approach to the analysis of functional MRI time-series from one or more subjects. The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing. This extension uses the effective degrees of freedom associated with the error term. The effective degrees of freedom are a simple function of the number of scans and the temporal autocorrelation function. A specific form for the latter can be assumed if the data are smoothed, in time, to accentuate hemodynamic responses with a neural basis. This assumption leads to an expedient implementation of a flexible statistical framework. The importance of this small extension is that, in contradistinction to our previous approach, any parametric statistical analysis can be implemented. We demonstrate this point using a multiple regression analysis that tests for effects of interest (activations due to word generation), while taking explicit account of some obvious confounds.

Visualization and Mapping of Anatomic Abnormalities Using a Probabilistic Brain Atlas Based on Random Fluid Transformations.

Conference Paper

Jan 1996
Lect Notes Comput Sci

This paper describes the design, implementation and preliminary results of a technique for creating a comprehensive probabilistic atlas of the human brain based on high-dimensional fluid transformations. The goal of the atlas is to detect and quantify subtle and distributed patterns of deviation from normal anatomy, in a 3D brain image from any given subject. The algorithm analyzes a reference population of normal scans, and automatically generates color-coded probability maps of the anatomy of new subjects. Given a 3D brain image of a new subject, the algorithm calculates a set of high-dimensional volumetric maps (typically with 38422563≈0.1 billion degrees of freedom) fluidly deforming this scan into structural correspondence with other scans, selected one by one from an anatomic image database. The family of volumetric warps so constructed encodes statistical properties and directional biases of local anatomical variation throughout the architecture of the brain. A probability space of random transformations, based on the theory of anisotropic Gaussian random fields, is then developed to reflect the observed variability in stereotaxic space of the points whose correspondences are found by the warping algorithm. A complete system of 3842256 probability density functions is computed, yielding confidence limits in stereotaxic space for the location of every point represented in the 3D image lattice of the new subject's brain. Color-coded probability maps are generated, densely-defined throughout the anatomy of the new subject. These indicate locally the probability of each anatomic point being as unusually situated, given the distributions of corresponding points in the scans of normal subjects. 3D MRI and high-resolution cryosection volumes are analyzed, from subjects with metastatic tumors and Alzheimer's disease. Applications of the random fluid-based probabilistic atlas include the transfer of multi-subject 3D functional, vascular and histologie maps onto a single anatomic template, the mapping of 3D atlases onto the scans of new subjects, and the rapid detection, quantification and mapping of local shape changes in 3D medical images in disease, and during normal or abnormal growth and development.

Fast Fluid Registration of Medical Images.

Conference Paper

Jan 1996
Lect Notes Comput Sci

This paper offers a new fast algorithm for non-rigid Viscous Fluid Registration of medical images that is at least an order of magnitude faster than the previous method by Christensen et al. [4]. The core algorithm in the fluid registration method is based on a linear elastic deformation of the velocity field of the fluid. Using the linearity of this deformation we derive a convolution filter which we use in a scalespace framework. We also demonstrate that the ’demon’-based registration method of Thirion [13] can be seen as an approximation to the fluid registration method and point to possible problems.

Deformation for Image Guided Interventions Using a Three Component Tissue Model.

Conference Paper

Jun 1997
Lect Notes Comput Sci

In image guided neurosurgery it is necessary to align preoperative image data with the patient. The rigid body approximation is usually applied, but is often not valid due to tissue deformation. Most non-rigid registration algorithms, such as those used for atlas matching, provide a smooth deformation, which does not model the characteristics of different tissues accurately since, for example, bone will appear to deform. We suggest that a physically based model of tissue could provide a powerful tool for tracking tissue movement. Since the algorithm must ultimately run in real time, we have developed a simplified model of tissue deformation based on a three component system. Regions are labelled as either rigid, deformable or fluid. A novel strategy to avoid folding in the transformation is described. Our model was applied to MRI and CT data from a neurosurgery patient with epilepsy. The test data is limited and the current implementation is in 2D, but initial results are promising.

Quadratic Variation of Deformations.

Conference Paper

Jun 1997
Lect Notes Comput Sci

Fred Bookstein

Hitherto no constitutive formalism of deformations provides a parameterization for the visually obvious features of their transformation grids. This paper notes a property of the thin-plate spline that one may exploit to this end. The bending energy that is minimized by the spline, usually expressed in matrix form, is also the double integral of the output of a nonlinear differential operator, the quadratic variation (sum of squared second partial derivatives of displacement), over the whole picture plane. Displaying this integrand as a scalar field over the medical image or template may prove a helpful guide to the interesting regions of a deformation, and the peaks of this field localize and orient a promising set of features for simplistically parameterized deformations that approximate the original.

Co-Planar Stereotaxic Atlas of The Human Brain

Book

Jan 1988

Deformable Shape Models For Anatomy

Article

Jan 1994

Gary E Christensen

Thesis (D. Sc.)--Washington University, 1994. Dept. of Electrical Engineering. Vita. Includes bibliographical references.

A review of image-warping methods

Article

Apr 1998

Image warping is a transformation which maps all positions in one image plane to positions in a second plane. It arises in many image analysis problems, whether in order to remove optical distortions introduced by a camera or a particular viewing perspective, to register an image with a map or template, or to align two or more images. The choice of warp is a compromise between a smooth distortion and one which achieves a good match. Smoothness can be ensured by assuming a parametric form for the warp or by constraining it using differential equations. Matching can be specified by points to be brought into alignment, by local measures of correlation between images, or by the coincidence of edges. Parametric and non-parametric approaches to warping, and matching criteria, are reviewed.

Rapid Automated Algorithm for Aligning and Reslicing PET Images

Article

Jul 1992

A computer algorithm for the three-dimensional (3D) alignment of PET images is described. To align two images, the algorithm calculates the ratio of one image to the other on a voxel-by-voxel basis and then iteratively moves the images relative to one another to minimize the variance of this ratio across voxels. Since the method relies on anatomic information in the images rather than on external fiducial markers, it can be applied retrospectively. Validation studies using a 3D brain phantom show that the algorithm aligns images acquired at a wide variety of positions with maximum positional errors that are usually less than the width of a voxel (1.745 mm). Simulated cortical activation sites do not interfere with alignment. Global errors in quantitation from realignment are less than 2%. Regional errors due to partial volume effects are largest when the gantry is rotated by large angles or when the bed is translated axially by one-half the interplane distance. To minimize such partial volume effects, the algorithm can be used prospectively, during acquisition, to reposition the scanner gantry and bed to match an earlier study. Computation requires 3-6 min on a Sun SPARCstation 2.

Accurate Three-Dimensional Registration of CT, PET, and/or MR Images of the Brain

Article

Jan 1989

A surface matching technique has been developed to register multiple imaging scans of the brain in three dimensions, with accuracy on the order of the image pixel sizes. Anatomic information visualized in X-ray CT and magnetic resonance images may be integrated with each other and with functional information from positron emission tomography. Anatomical structures and other volumes of interest may be mapped from one scan to another, and corresponding sections through multiple scans may be directly compared. This capability provides a novel quantitative method to address the fundamental problem of relating structure to function in the brain. Applications include basic and clinical problems in the neurosciences and delivery and assessment of brain tumor therapy.

Estimating Smoothness in Statistical Parametric Maps

Article

Sep 1995

The smoothness parameter that characterises the spatial dependence of pixel values in functional brain images is usually estimated empirically from the data. Since this parameter is essential for the assessment of significant changes in brain activity, it is important to know (a) the variance of its estimator and (b) how this variability affects the results of the ensuing statistical analysis. In this article, we derive an approximate expression for the variance of the smoothness estimator and investigate the effects of this variability on assessing the significance of cerebral activation in statistical parametric maps using a verbal fluency PET activation experiment. Our results suggest that, for p values around 0.05, the variability in the p value (due to smoothness estimation) is approximately 20%. The effect of the assessment of the spatial dependency of the data is far from being negligible, and this suggests a more comprehensive methodology for functional imaging than the one used so far. This work provides a simple tool for taking into account this effect.

Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space

Article

Mar 1994

In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. The registration method is based on multi-scale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talariach stereotaxic space. Once the data set is re-sampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the re-sampled volume. the use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space. With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume. We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.

Spatial Normalization of 3D Brain Images Using Deformable Models

Article

Jul 1996

Christos Davatzikos

The spatial normalization and registration of tomographic images from different subjects is a major problem in several medical imaging areas, including functional image analysis, morphometrics, and computer-aided neurosurgery. The focus of this article is the development of a computerized methodology for the spatial normalization of 3D images. We propose a technique that is based on geometric deformable models. In particular, we first describe a deformable surface algorithm that finds a mathematical representation of the outer cortical surface. Based on this representation, a procedure for obtaining a map between corresponding regions of the outer cortex in two different images is established. This map is subsequently used to derive a 3D elastic warping transformation, which brings two images into register. The performance of our algorithm is demonstrated on several datasets. In particular, we first test our deformable surface algorithm on MR images. We then register MR images to atlas images. In our third experiment, we apply a procedure for matching distinct cortical features identified through the curvature map of the outer cortex. Finally, we apply our technique to images from elderly individuals with substantial ventricular enlargement, and we show a good registration in the ventricular area and the surrounding brain structures. We present a highly automated methodology for spatial normalization of images, using deformable models. Applications of our methodology include stereotactic normalization of functional and structural images, morphological analysis of the brain, and computer-aided neurosurgery.

Analysis of FMRI time series revisited—Again

Article

Oct 1995

Friston et al. (1995, NeuroImage 2:45-53) presented a method for detecting activations in fMRI time-series based on the general linear model and a heuristic analysis of the effective degrees of freedom. In this communication we present corrected results that replace those of the previous paper and solve the same problem without recourse to heuristic arguments. Specifically we introduce a proper and unbiased estimator for the error terms and provide a more generally correct expression for the effective degrees of freedom. The previous estimates of error variance were biased and, in some instances, could have led to a 10-20% overestimate of Z values. Although the previous results are almost correct for the random regressors chosen for validation, the present theoretical results are exact for any covariate or waveform. We comment on some aspects of experimental design and data analysis, in the light of the theoretical framework discussed here.

A Probabilistic Atlas of the Human Brain: Theory and Rationale for Its Development:: The International Consortium for Brain Mapping (ICBM)

Article

Jul 1995

Multimodal Image Coregistration and Partitioning—A Unified Framework

Article

Nov 1997

This paper presents a method for the coregistration and partitioning (i.e., tissue segmentation) of brain images that have been acquired in different modalities. The basic idea is that instead of matching two images directly, one performs intermediate within-modality registrations to two template images that are already in register. One can use a least-squares minimization to determine the affine transformations that map between the templates and the images. By incorporating suitable constraints, a rigid body transformation which directly maps between the images can be extracted from these more general affine transformations. A further refinement capitalizes on the implicit normalization of both images into a standard space. This facilitates segmentation or partitioning of both original images into homologous tissue classifications. Once partitioned, the partitions can be jointly matched, further increasing the accuracy of the coregistration. In short, these techniques reduce the between-modality problem to a series of simpler within-modality problems. These methods are relatively robust, address a number of problems in image transformations, and require no manual intervention.

Incorporating Prior Knowledge into Image Registration

Article

Dec 1997

The first step in the spatial normalization of brain images is usually to determine the affine transformation that best maps the image to a template image in a standard space. We have developed a rapid and automatic method for performing this registration, which uses a Bayesian scheme to incorporate prior knowledge of the variability in the shape and size of heads. We compared affine registrations with and without incorporating the prior knowledge. We found that the affine transformations derived using the Bayesian scheme are much more robust and that the rate of convergence is greater.

Automated Image Registration: II. Intersubject Validation of Linear and Nonlinear Models

Article

Jul 1998

Our goal was to validate linear and nonlinear intersubject image registration using an automated method (AIR 3.0) based on voxel intensity. PET and MRI data from 22 normal subjects were registered to corresponding averaged PET or MRI brain atlases using several specific linear and nonlinear spatial transformation models with an automated algorithm. Validation was based on anatomically defined landmarks. Automated registration produced results that were superior to a manual nine parameter variant of the Talairach registration method. Increasing the degrees of freedom in the spatial transformation model improved the accuracy of automated intersubject registration. Linear or nonlinear automated intersubject registration based on voxel intensities is computationally practical and produces more accurate alignment of homologous landmarks than manual nine parameter Talairach registration. Nonlinear models provide better registration than linear models but are slower.

Automated Image Registration: I. General Methods and Intrasubject, Intramodality Validation

Article

Jan 1998

We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities. Different cost functions, different minimization methods, and various sampling, smoothing, and editing strategies were compared. Internal consistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phantom for PET data. All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accuracy of registration of structural MRI images was in the 75 to 150 microns range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy. The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.

Bookstein F. L. — Landmark methods for forms without landmarks: localizing group differences in outline shape. Medical Image Analysis

Article

May 1997
MED IMAGE ANAL

Fred Bookstein

Accurate alignment of functional EPI data to anatomical MRI physics based distortion model

Article

Dec 2000

Mapping of functional magnetic resonance imaging (fMRI) to conventional anatomical MRI is a valuable step in the interpretation of fMRI activations. One of the main limits on the accuracy of this alignment arises from differences in the geometric distortion induced by magnetic field inhomogeneity. This paper describes an approach to the registration of echo planar image (EPI) data to conventional anatomical images which takes into account this difference in geometric distortion. We make use of an additional spin echo EPI image and use the known signal conservation in spin echo distortion to derive a specialized multimodality nonrigid registration algorithm. We also examine a plausible modification using log-intensity evaluation of the criterion to provide increased sensitivity in areas of low EPI signal. A phantom-based imaging experiment is used to evaluate the behavior of the different criteria, comparing nonrigid displacement estimates to those provided by a imagnetic field mapping acquisition. The algorithm is then applied to a range of nine brain imaging studies illustrating global and local improvement in the anatomical alignment and localization of fMRI activations.

Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain

Article

Feb 2002
NEURON

We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.

HAMMER: Hierarchical attribute matching mechanism for elastic registration

Article

Dec 2002

A new approach is presented for elastic registration of medical images, and is applied to magnetic resonance images of the brain. Experimental results demonstrate very high accuracy in superposition of images from different subjects. There are two major novelties in the proposed algorithm. First, it uses an attribute vector, i.e., a set of geometric moment invariants (GMIs) that are defined on each voxel in an image and are calculated from the tissue maps, to reflect the underlying anatomy at different scales. The attribute vector, if rich enough, can distinguish between different parts of an image, which helps establish anatomical correspondences in the deformation procedure; it also helps reduce local minima, by reducing ambiguity in potential matches. This is a fundamental deviation of our method, referred to as the hierarchical attribute matching mechanism for elastic registration (HAMMER), from other volumetric deformation methods, which are typically based on maximizing image similarity. Second, in order to avoid being trapped by local minima, i.e., suboptimal poor matches, HAMMER uses a successive approximation of the energy function being optimized by lower dimensional smooth energy functions, which are constructed to have significantly fewer local minima. This is achieved by hierarchically selecting the driving features that have distinct attribute vectors, thus, drastically reducing ambiguity in finding correspondence. A number of experiments demonstrate that the proposed algorithm results in accurate superposition of image data from individuals with significant anatomical differences.

Optimization of Mutual Information for Multiresolution Image Registration

Article

Feb 2000

We propose a new method for the intermodal registration of images using a criterion known as mutual information. Our main contribution is an optimizer that we specifically designed for this criterion. We show that this new optimizer is well adapted to a multiresolution approach because it typically converges in fewer criterion evaluations than other optimizers. We have built a multiresolution image pyramid, along with an interpolation process, an optimizer, and the criterion itself, around the unifying concept of spline-processing. This ensures coherence in the way we model data and yields good performance. We have tested our approach in a variety of experimental conditions and report excellent results. We claim an accuracy of about a hundredth of a pixel under ideal conditions. We are also robust since the accuracy is still about a tenth of a pixel under very noisy conditions. In addition, a blind evaluation of our results compares very favorably to the work of several other researchers.

Bookstein, F.L.: Principal Warps: Thin-Plate Splines and the Decomposition of Deformations. IEEE Trans. Pattern Anal. Mach. Intell. 11, 567-585

Article

Jul 1989

Fred Bookstein

Multiresolution voxel similarity measures for MR-PET coregistration Information Processing in Medical Imaging

Jan 1995
287-298

C Studholme
Dlg Hill
Dj Hawkes
Y Bizais
C Barillot
Di Paola

Studholme C, Hill DLG, Hawkes DJ. 1995. Multiresolution voxel similarity measures for MR-PET coregistration. In: Bizais Y, Barillot C, Di Paola R. Information Processing in Medical Imaging. Dordrecht: Kluwer Academic Publishers. p 287–298.

Nonlinear spatial normalization using basis functions

Abstract

No full-text available

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