The function of the claustrum and its role in neurological disorders remains a subject of interest in the field of neurology. Given the claustrum’s susceptibility to seizure-induced damage, there is speculation that it could serve as a node in a dysfunctional epileptic network. This perspective article aims to address a pivotal question: Does the claustrum play a role in epilepsy? Building upon existing literature, we propose the following hypotheses for the involvement of the claustrum in epilepsy: (1) Bilateral T2/FLAIR magnetic resonance imaging (MRI) hyperintensity of the claustrum after status epilepticus represents a radiological phenomenon that signifies inflammation-related epileptogenesis; (2) The ventral claustrum is synonymous with a brain area known as ‘area tempestas,’ an established epileptogenic center; (3) The ventral subsector of the claustrum facilitates seizure generalization/propagation through its connections with limbic and motor-related brain structures; (4) Disruption of claustrum connections during seizures might contribute to the loss of consciousness observed in impaired awareness seizures; (5) Targeting the claustrum therapeutically could be advantageous in seizures that arise from limbic foci. Together, evidence from both clinical case reports and animal studies identify a significant role for the ventral claustrum in the generation, propagation, and intractable nature of seizures in a subset of epilepsy syndromes.

Background Brain metastases are the most common intracranial tumors in adults and are associated with significant morbidity and mortality. Whole-brain radiotherapy (WBRT) is used frequently in patients for palliation, but can result in neurocognitive deficits. While dose-dependent injury to individual areas such as the hippocampus has been demonstrated, global structural shape changes after WBRT remain to be studied. Methods We studied healthy controls and patients with brain metastases and examined MRI brain anatomic surface data before and after WBRT. We implemented a validated graph convolutional neural network model to estimate patient's "brain age". We further developed a mixed-effects linear model to compare the estimated age of the whole brain and substructures before and after WBRT. Results 4220 subjects were analyzed (4148 healthy controls and 72 patients). The median radiation dose was 30 Gy (range 25-37.5 Gy). The whole brain and substructures underwent structural change resembling rapid aging in radiated patients compared to healthy controls; the whole brain "aged" 9.32 times faster, the cortex 8.05 times faster, the subcortical structures 12.57 times faster, and the hippocampus 10.14 times faster. In a subset analysis, the hippocampus "aged" 8.88 times faster in patients after conventional WBRT versus after hippocampal avoidance (HA)-WBRT. Conclusions Our findings suggest that WBRT causes the brain and its substructures to undergo structural changes at a pace up to 13x of the normal aging pace, where hippocampal avoidance offers focal structural protection. Correlating these structural imaging changes with neurocognitive outcomes following WBRT or HA-WBRT would benefit from future analysis.

Background Brain age has historically been investigated primarily at the whole brain level. The ability to deconstruct the brain into its composite parts and explore brain age at the sub-structure level offers unique advantages. These include the exploration of dynamic and interconnected relationships between different brain structures in healthy and pathologic aging. To achieve this, individual brain structures can be rendered as surface representations on which morphologic analysis is carried out. Combining the advantages of deep learning with the strengths of surface analysis, we investigate the aging process at the individual structure level with the hypothesis being that pathologic aging does not uniformly affect the aging process of individual structures. Methods MRI data, age at scan time and diagnosis of dementia were collected from seven publicly available data repositories. The data from 17,440 unique subjects were collected, representing a total of 26,276 T1-weighted MRI accounting for longitudinal acquisitions. Surfaces were extracted for the cortex and seven subcortical structures. Deep learning networks were trained to estimate a subject’s age either using several structures together or a single structure. We conducted a cross-sectional analysis to assess the difference between the predicted and actual ages for all structures between healthy subjects, individuals with mild cognitive impairment (MCI) or Alzheimer’s disease dementia (ADD). We then performed a longitudinal analysis to assess the difference in the aging pace for each structure between stable healthy controls and healthy controls converting to either MCI or ADD. Findings Using an independent cohort of healthy subjects, age was well estimated for all structures. Cross-sectional analysis identified significantly larger predicted age for all structures in patients with either MCI and ADD compared to healthy subjects. Longitudinal analysis revealed varying degrees of involvement of individual subcortical structures for both age difference across groups and aging pace across time. These findings were most notable in the whole brain, cortex, hippocampus and amygdala. Conclusion Although similar patterns of abnormal aging were found related to MCI and ADD, the involvement of individual subcortical structures varied greatly and was consistently more pronounced in ADD patients compared to MCI patients.

The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.

Functional MRI offers unique insights for the characterization and presurgical evaluation of people with epilepsy (PWE). In this paper, we develop a graph-based variational auto-encoder (gVAEs) to 1) learn the patterns of resting state functional MRI (rsfMRI) within the brain’s subcortical structures in healthy subjects and 2) reconstruct it in PWE to identify findings unique to patients with epilepsy. The gVAE was enriched with Sequential Long Short Term Memory (LSTM) and perceptual loss to learn temporal rsfMRI features and smooth the reconstructed signals. Using a cross-validation approach on healthy controls, our best model yielded an average spatial correlation of 0.791 and an average temporal correlation of 0.793. When applied to PWE, the average and spatial correlation decreased to 0.752 and 0.750 respectively. Our findings pave the path to the development of a whole brain data-driven tool that may be valuable for the characterization of abnormalities within the epileptic brain. This may advance our understanding as to how these abnormalities are related to the location of seizure onset and can inform the care of patients with epilepsy. The code is available at: GitHub

We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (.6 mm)3 in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T1 -weighted brain template (.5 mm)3 using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and 12 thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T1 values derived from the 3D-MP2RAGE volumes, after correction for residual B1+ biases. Three metrics (the Dice and the volumetric similarity coefficients and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration, and their impact on the measurements of the quantitative T1 values was highlighted. This represents a fully automated segmentation process permitting the extraction of unbiased normative T1 values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.

The primary risk factor for cervical cancer is infection with high-risk genotypes of human papillomavirus (HPV). This study compared HPV DNA detection between cervical swabs (CX) and self-administered vaginal swabs (SV). Phase I participants were 199 women chosen from a study comparing the detection of Chlamydia trachomatis from various anogenital sites. Phase II participants were 135 women from either the Colposcopy or HIV Outpatient Clinic. HPV DNA testing was performed using polymerase chain reaction and Roche reverse line blot hybridization. In Phase I samples, more CX samples amplified and more HPV genotypes (P < 0.05) were detected in CX. Genotype 52 were seen more in the cervix, whereas genotype 82 (MM4) was detected solely in the vagina. The presence of high-risk HPV genotypes in the cervix was a predictor of an abnormal Papanicolaou (Pap) smear. In Phase II samples, CX samples amplified more, but similar rates of HPV genotypes were seen in SV and CX samples. Higher concordance rates of high-risk genotypes were seen in Phase II compared to Phase I samples. Phase II demonstrated the feasibility of utilizing SV sampling to reflect cervical status. If validated, a self-vaginal swab method to detect cervical HPV DNA status could be utilized to triage women with indeterminate Pap smears and be a useful method to collect epidemiological data from large populations.

The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about this relationship, and better understand the etiology of a variety of pathologies involving diverse degrees of cortical folding abnormalities. Hypothesis-driven surface-based approaches have been shown to be particularly efficient in their ability to accurately describe unique features of the folded sheet topology of the cortical ribbon. However, the utility of these approaches has been blunted by their reliance on manually defined features aiming to capture the relevant geometric properties of cortical folding. In this paper, we propose an entirely novel, data-driven deep-learning based method to analyze the brain's shape that eliminates this reliance on manual feature definition. This method builds on the emerging field of geometric deep-learning and uses traditional convolutional neural network architecture uniquely adapted to the surface representation of the cortical ribbon. This method is a complete departure from prior brain MRI CNN investigations, all of which have relied on three dimensional MRI data and interpreted features of the MRI signal for prediction. MRI data from 6410 healthy subjects obtained from 11 publicly available data repositories were used for analysis. Ages ranged from 6 to 89 years. Both inner and outer cortical surfaces were extracted using Freesurfer and then registered into MNI space. For purposes of method development, both a classification and regression challenge were introduced for network learning including sex and age prediction, respectively. Two independent graph convolutional neural networks (gCNNs) were trained, the first of which to predict subject's self-identified sex, the second of which to predict subject's age. Class Activation Maps (CAM) and Regression Activation Maps (RAM) were constructed respectively to map the topographic distribution of the most influential brain regions involved in the decision process for each gCNN. Using this approach, the gCNN was able to predict a subject's sex with an average accuracy of 87.99 % and achieved a Person's coefficient of correlation of 0.93 with an average absolute error 4.58 years when predicting a subject's age. We believe this shape-based convolutional classifier offers a novel, data-driven approach to define biomedically relevant features from the brain at both the population and single subject levels and therefore lays a critical foundation for future precision medicine applications.

Apprécier la réalité histo-architecturale in vivo des noyaux gris centraux notamment des thalami est un véritable challenge en imagerie. La définition précise de cette anatomie est cependant un prérequis nécessaire pour mesurer avec précision des altérations structurales parenchymateuses ou réaliser le ciblage stéréotaxique en neurochirurgie [1]. Des images tridimensionnelles MP2RAGE (Magnetization Prepared with two Rapid Acquisition Gradient Echoes sequence) [2] ont été acquises en 0,6 mm isotropique à 7 T chez 60 volontaires sains. Une méthode de normalisation « group-wize » [3] a permis de créer un template T1 haute résolution (0,5 mm3) à partir de 30 des 60 sujets (Fig. 1). Deux neuroradiologues (R1 et R2) ont réalisé une segmentation de 24 régions de substance gris profonde et de douze noyaux thalamiques dans chaque hémisphère, selon l’atlas de Morel [4]. Un atlas moyenné (7TAMIbrainDGN) à partir des deux segmentations a été validé par un troisième neuroradiologue (Fig. 2). Les structures correspondantes ont également été extraites à partir de deux autres atlas numériques (e-Morel et CIT168). Les valeurs quantitatives moyennes absolues des temps de relaxation T1 ont été ensuite extraites après correction du B1+. Un score de Dice a été calculé pour mesurer la concordance inter-observateur et la variabilité inter-atlas. Les scores de Dice entre les deux neuroradiologues, entre le R1 et 7TAMIbrainDGN et entre R2 et 7TAMIbrainDGN étaient respectivement de 0,74 ± 0,13, 0,87 ± 0,07, 0,86 ± 0,08. Les Dice entre 7TAMIbrainDGN et l’atlas e-Morel étaient de 0,53 ± 0,16 et de 0,80 ± 0,07 entre 7TAMIbrainDGN et CIT168. Les temps de relaxation T1 variaient de 1322 ms à 2003 ms. Cette étude a permis de créer un atlas des noyaux gris centraux et des thalamis à 7 T et de valider un processus de segmentation automatisé avec extraction des temps de relaxation T1.

Several patterns of atrophy have been identified and strongly related to Alzheimer’s disease (AD) pathology and its progression. Morphological changes in brain shape have been identified up to ten years before clinical diagnoses of AD, making its early detection more relevant. We propose novel geometric deep learning frameworks for the analysis of brain shape in the context of neurodegeneration caused by AD. Our deep neural networks learn low-dimensional shape descriptors of multiple neuroanatomical structures, instead of handcrafted features for each structure. A discriminative network using spiral convolution on 3D meshes is constructed for the in-vivo binary classification of AD from healthy controls (HCs) using a fast and efficient “spiral” convolution operator on 3D triangular mesh surfaces of human brain subcortical structures extracted from T1-weighted magnetic resonance imaging (MRI). Our network architecture consists of modular learning blocks using residual connections to improve overall classifier performance. In this work: (1) a discriminative network is used to analyze the efficacy of disease classification using input data from multiple brain structures and compared to using a single hemisphere or a single structure. It also outperforms prior work using spectral graph convolution on the same the same tasks, as well as alternative methods that operate on intermediate point cloud representations of 3D shapes. (2) Additionally, visual interpretations for regions on the surface of brain structures that are associated to true positive AD predictions are generated and fall in accordance with the current reports on the structural localization of pathological changes associated to AD. (3) A conditional generative network is also implemented to analyze the effects of phenotypic priors given to the model (i.e. AD diagnosis) in generating subcortical structures. The generated surface meshes by our model indicate learned morphological differences in the presence of AD that agrees with the current literature on patterns of atrophy associated to the disease. In particular, our inference results demonstrate an overall reduction in subcortical mesh volume and surface area in the presence of AD, especially in the hippocampus. The low-dimensional shape descriptors obtained by our generative model are also evaluated in our discriminative baseline comparisons versus our discriminative network and the alternative shape-based approaches. ### Competing Interest Statement The authors have declared no competing interest.

We propose a mesh-based technique to aid in the classification of Alzheimer's disease dementia (ADD) using mesh representations of the cortex and subcortical structures. Deep learning methods for classification tasks that utilize structural neuroimaging often require extensive learning parameters to optimize. Frequently, these approaches for automated medical diagnosis also lack visual interpretability for areas in the brain involved in making a diagnosis. This work: (a) analyzes brain shape using surface information of the cortex and subcortical structures, (b) proposes a residual learning framework for state-of-the-art graph convolutional networks which offer a significant reduction in learnable parameters, and (c) offers visual interpretability of the network via class-specific gradient information that localizes important regions of interest in our inputs. With our proposed method leveraging the use of cortical and subcortical surface information, we outperform other machine learning methods with a 96.35% testing accuracy for the ADD vs. healthy control problem. We confirm the validity of our model by observing its performance in a 25-trial Monte Carlo cross-validation. The generated visualization maps in our study show correspondences with current knowledge regarding the structural localization of pathological changes in the brain associated to dementia of the Alzheimer's type.

BACKGROUND AND PURPOSE:Transient global amnesia (TGA) is a sudden onset of anterograde and retrograde amnesia. We aimed to assess differences in terms of cortical thickness and structural brain connectome between patients with TGA (at acute and delayed postrecovery stages) and matched controls. MATERIALS AND METHODS:We report on 18 consecutive patients with TGA who underwent 3T MR imaging, including DTI and MPRAGE sequences, at the acute (mean delay postonset: 44 hours) and delayed post-recovery (mean delay: 35 days) stages. Structural connectome was assessed in patients with TGA and in 18 age- and sex-matched controls by using probabilistic fiber- tracking and segmentation of 164 cortical/subcortical structures ("nodes"). Connectivity graphs were computed and global network metrics were calculated. Network-based statistical analysis (NBS) was applied to compare patients with TGA at each stage with controls. We also compared cortical thickness between patients with TGA and healthy controls. RESULTS:Global network metrics were not altered in patients with TGA. NBS-analysis showed structural connectome alterations in patients with TGA compared with controls, in core regions involving the limbic network, with 113 nodes and 114 connections (33 left intrahemispheric, 31 right intrahemispheric, and 50 interhemispheric connections) showing significantly decreased structural connectivity (P < .05 NBS corrected, t-values ranging from 3.03 to 8.73). Lower cortical thickness compared with controls was associated with these structural alterations in patients with TGA, involving the orbitofrontal, cingulate, and inferior temporal cortices. All the abnormalities were visible at both acute and delayed postrecovery stages. CONCLUSIONS:Our preliminary study suggests there are structural abnormalities of the limbic network in patients with TGA compared with controls, including decreased structural connectivity and cortical thickness.

Objectives: Evidence from animal studies suggests that aerobic exercise may promote neuroplasticity and could, therefore, provide therapeutic benefits for neurological diseases such as multiple sclerosis (MS). However, the effects of exercise in human CNS disorders on the topology of brain networks, which might serve as an outcome at the interface between biology and clinical performance, remain poorly understood. Methods: We investigated functional and structural networks in patients with relapsing-remitting MS in a clinical trial of standardized aerobic exercise. Fifty-seven patients were randomly assigned to moderate-intensity exercise for 3 months or a non-exercise control group. We reconstructed functional networks based on resting-state functional magnetic resonance imaging (MRI) and used probabilistic tractography on diffusion-weighted imaging data for structural networks. Results: At baseline, compared to 30 healthy controls, patients exhibited decreased structural connectivity that was most pronounced in hub regions of the brain. Vice versa, functional connectivity was increased in hubs. After 3 months, we observed hub independent increased functional connectivity in the exercise group while the control group presented a loss of functional hub connectivity. On a structural level, the control group remained unchanged, while the exercise group had also increased connectivity. Increased clustering of hubs indicates a better structural integration and internal connectivity at the top of the network hierarchy. Conclusion: Increased functional connectivity of hubs contrasts a loss of structural connectivity in relapsing-remitting MS. Under an exercise condition, a further hub independent increase of functional connectivity seems to translate in higher structural connectivity of the whole brain.

AbstractBackgroundMemory complaints are widespread among the elderly and aging is a major risk factor for Alzheimer’s disease (AD), leading to the impression that gradual loss of memory ability is a nearly universal consequence of getting old. Our longitudinal studies of SuperAgers, 80+ year‐olds with episodic memory performance that remains in the range that is at least normal for 50‐60 year‐olds suggests an alternative aging trajectory is possible. This session will highlight some of the emerging biologic features of the SuperAgers.MethodParticipants include SuperAgers and cognitively average 80+ year‐old cognitively average normal controls. Data from detailed neuropsychological assessments, quantitative neuroimaging measurements (MR and amyloid PET), genetic features and neuropathologic findings will be reported.ResultInitial evidence suggest SuperAgers tend to show mismatch between chronologic and biologic age, including maintenance of cortical but not necessarily hippocampal volume, a tendency to resist significant amyloid PET retention, an abundance of anterior cingulate Von Economo neurons, and some with resistance to cortical Alzheimer’s pathology.ConclusionThese studies contribute to our understanding mechanisms of resilience and resistance in cognitive aging and may help isolate factors that are potentially important for promoting successful cognitive aging and avoiding age‐related brain diseases such as AD.

Structural and functional connectivity abnormalities have been reported previously in multiple sclerosis. However, little is known about how each modality evolution relates to the other. Recent studies in other neurological disorders have suggested that structural-functional coupling may be more sensitive in detecting brain alterations than any single modality. Accordingly, this study aimed to investigate the longitudinal evolution of structural-functional coupling, both at the global and modular levels, in the first year following clinically isolated syndrome. We hypothesized that during the course of multiple sclerosis, patients exhibit a decoupling between functional and structural connectivity due to the disruptive nature of the disease. Forty-one consecutive patients with clinically isolated syndrome were prospectively enrolled in this study, along with 19 age-, sex- and educational level-matched healthy control subjects. These participants were followed for 1 year and underwent resting-state functional MRI and diffusion tensor imaging at each time point, along with an extensive neuropsychological assessment. Graph theory analysis revealed structural reorganization at baseline that appeared as an increase in the clustering coefficient in patients compared to controls (P<0.05), as well as modular-specific alterations. After 1 year of follow-up, both structural and functional reorganization was depicted with abnormal modular-specific connectivity and an increase of the functional betweenness centrality in patients compared to controls (P<0.01). More importantly, structural-functional decoupling was observed in the salience, visual and somatomotor networks. These alterations were present along with preserved cognitive performance at this stage. These results depict structural damage preceding functional reorganization at a global and modular level during the first year following clinically isolated syndrome along with normal cognitive performance, suggesting a compensation mechanism at this stage of the disease. Principally, structural-functional decoupling observed for the first time in multiple sclerosis suggests that functional reorganization occurs along indirect anatomical pathways.

The relationship between the epilepsy network, intrinsic brain networks and hypersynchrony in epilepsy remains incompletely understood. To converge upon a synthesized understanding of these features, we studied two elements of functional connectivity in epilepsy: correlation and time lag structure using resting state fMRI data from both SEEG-defined epileptic brain regions and whole-brain fMRI analysis. Functional connectivity (FC) was analyzed in 15 patients with epilepsy and 36 controls. Correlation strength and time lag were selected to investigate the magnitude of and temporal interdependency across brain regions. Zone-based analysis was carried out investigating directed correlation strength and time lag between both SEEG-defined nodes of the epilepsy network and between the epileptogenic zone and all other brain regions. Findings were compared between patients and controls and against a functional atlas. FC analysis on the nodal and whole brain levels identifies consistent patterns of altered correlation strength and altered time lag architecture in epilepsy patients compared to controls. These patterns include 1) broadly distributed increased strength of correlation between the seizure onset node and the remainder of the brain, 2) decreased time lag within the seizure onset node, and 3) globally increased time lag throughout all regions of the brain not involved in seizure onset or propagation. Comparing the topographic distribution of findings against a functional atlas, all resting state networks were involved to a variable degree. These local and whole brain findings presented here lead us to propose the network steal hypothesis as a possible mechanistic explanation for the non-seizure clinical manifestations of epilepsy.

Background: There is a lack of longitudinal studies exploring the topological organization of functional brain networks at the early stages of multiple sclerosis (MS). Objective: This study aims to assess potential brain functional reorganization at rest in patients with CIS (PwCIS) after 1 year of evolution and to characterize the dynamics of functional brain networks at the early stage of the disease. Methods: We prospectively included 41 PwCIS and 19 matched healthy controls (HCs). They were scanned at baseline and after 1 year. Using graph theory, topological metrics were calculated for each region. Hub disruption index was computed for each metric. Results: Hub disruption indexes of degree and betweenness centrality were negative at baseline in patients (p < 0.05), suggesting brain reorganization. After 1 year, hub disruption indexes for degree and betweenness centrality were still negative (p < 0.00001), but such reorganization appeared more pronounced than at baseline. Different brain regions were driving these alterations. No global efficiency differences were observed between PwCIS and HCs either at baseline or at 1 year. Conclusion: Dynamic changes in functional brain networks appear at the early stages of MS and are associated with the maintenance of normal global efficiency in the brain, suggesting a compensatory effect.