Structural Disconnection Research Articles

Connectivity Alterations in Vascular Parkinsonism: A Structural Covariance Study

This study aimed to investigate the structural covariance between the striatum and large-scale brain regions in patients with vascular parkinsonism (VP) compared to Parkinson’s disease (PD) and control subjects, and then explore the relationship between brain connectivity and the clinical features of our patients. Forty subjects (13 VP, 15 PD, and 12 age-and-sex-matched healthy controls) were enrolled in this study. They each underwent a careful clinical and neuropsychological evaluation, DAT-SPECT scintigraphy and 3T MRI scan. While there were no differences between PD and VP in the disease duration and severity, nor in terms of the DAT-SPECT evaluations, VP patients had a reduction in structural covariance between the bilateral corpus striatum (both putamen and caudate) and several brain regions, including the insula, thalamus, hippocampus, anterior cingulate cortex and orbito-frontal cortex compared to PD and controls. VP patients also showed lower scores on several neuropsychological tests. Interestingly, in the VP group, structural connectivity alterations were significantly related to cognitive evaluations exploring executive functions, memory, anxiety and depression. This compelling evidence suggests that structural disconnection in the basal ganglia circuits spreading in critical cortical regions may be involved in the pathophysiology of cognitive impairment in VP.

Evaluating the rare cases of cortical vertigo using disconnectome mapping

In rare cases, cortical infarcts lead to vertigo. We evaluated structural and functional disconnection in patients with acute vertigo due to unilateral ischemic cortical infarcts compared to infarcts without vertigo in a similar location with a focus on the connectivity of the vestibular cortex, i.e., the parieto-opercular (retro-)insular cortex (PIVC). Using lesion maps from the ten published case reports, we computed lesion–functional connectivity networks in a set of healthy individuals from the human connectome project. The probability of lesion disconnection was evaluated by white matter disconnectome mapping. In all ten cases with rotational vertigo, disconnections of interhemispheric connections via the corpus callosum were present but were spared in lesions of the PIVC without vertigo. Further, the arcuate fascicle was affected in 90% of the lesions that led to vertigo and spared in lesions that did not lead to vertigo. The lesion–functional connectivity network included vestibulo-cerebellar hubs, the vestibular nuclei, the PIVC, the retro-insular and posterior insular cortex, the multisensory vestibular ventral intraparietal area, motion-sensitive areas (temporal area MT+ and cingulate visual sulcus) as well as hubs for ocular motor control (lateral intraparietal area, cingulate and frontal eye fields). However, this was not sufficient to differentiate between lesions with and without vertigo. Disruption of interhemispheric connections of both PIVC via the corpus callosum and intra-hemispheric disconnection via the arcuate fascicle might be the distinguishing factor between vestibular cortical network lesions that manifest with vertigo compared to those without vertigo.

Mapping lesion, structural disconnection, and functional disconnection to symptoms in semantic aphasia

Patients with semantic aphasia have impaired control of semantic retrieval, often accompanied by executive dysfunction following left hemisphere stroke. Many but not all of these patients have damage to the left inferior frontal gyrus, important for semantic and cognitive control. Yet semantic and cognitive control networks are highly distributed, including posterior as well as anterior components. Accordingly, semantic aphasia might not only reflect local damage but also white matter structural and functional disconnection. Here, we characterise the lesions and predicted patterns of structural and functional disconnection in individuals with semantic aphasia and relate these effects to semantic and executive impairment. Impaired semantic cognition was associated with infarction in distributed left-hemisphere regions, including in the left anterior inferior frontal and posterior temporal cortex. Lesions were associated with executive dysfunction within a set of adjacent but distinct left frontoparietal clusters. Performance on executive tasks was also associated with interhemispheric structural disconnection across the corpus callosum. In contrast, poor semantic cognition was associated with small left-lateralized structurally disconnected clusters, including in the left posterior temporal cortex. Little insight was gained from functional disconnection symptom mapping. These results demonstrate that while left-lateralized semantic and executive control regions are often damaged together in stroke aphasia, these deficits are associated with distinct patterns of structural disconnection, consistent with the bilateral nature of executive control and the left-lateralized yet distributed semantic control network.

Structural disconnection and functional reorganization in Fabry disease: a multimodal MRI study.

Central nervous system involvement in Fabry disease, a rare systemic X-linked lysosomal storage disorder, is characterized by the presence of heterogeneous but consistent functional and microstructural changes. Nevertheless, knowledge about the degree and extension of macro-scale brain connectivity modifications is to date missing. In this work, we performed connectomic analyses of diffusion and resting-state functional MRI to investigate changes of both structural and functional brain organization in Fabry disease, as well as to explore the relationship between the two and their clinical correlates. In this retrospective cross-sectional study, 46 patients with Fabry disease (28F, 42.2 ± 13.2years) and 49 healthy controls (21F, 42.3 ± 16.3years) were included. All subjects underwent an MRI examination including anatomical, diffusion and resting-state functional sequences. Images were processed to obtain quantitative structural and functional connectomes, where the connections between regions of interest were weighted by the total intra-axonal signal contribution of the corresponding bundle and by the correlation between blood-oxygen level–dependent time series, respectively. We explored between-group differences in terms of both global network properties, expressed with graph measures and specific connected subnetworks, identified using a network-based statistics approach. As exploratory analyses, we also investigated the possible association between cognitive performance and structural and functional connectome modifications at both global and subnetwork level in a subgroup of patients (n = 11). Compared with healthy controls, patients with Fabry disease showed a significantly reduced global efficiency (P = 0.005) and mean strength (P < 0.001) in structural connectomes, together with an increased modularity (P = 0.005) in functional networks. As for the network-based statistics analysis, a subnetwork with decreased structural connectivity in patients with Fabry disease compared with healthy controls emerged, with eight nodes mainly located at the level of frontal or deep grey-matter areas. When probing the relation between altered global network metrics and neuropsychological tests, correlations emerged between the structural and functional disruption with results at verbal and working memory tests, respectively. Furthermore, structural disruption at subnetwork level was associated with worse executive functioning, with a significant moderation effect of functional changes suggesting a compensation mechanism. Taken together, these results further expand the current knowledge about brain involvement in Fabry disease, showing widespread structural disconnection and functional reorganization, primarily sustained by loss in axonal integrity and correlating with cognitive performance.

Connectomic assessment of injury burden and longitudinal structural network alterations in moderate-to-severe traumatic brain injury.

Traumatic brain injury (TBI) is a major public health problem. Caused by external mechanical forces, a major characteristic of TBI is the shearing of axons across the white matter, which causes structural connectivity disruptions between brain regions. This diffuse injury leads to cognitive deficits, frequently requiring rehabilitation. Heterogeneity is another characteristic of TBI as severity and cognitive sequelae of the disease have a wide variation across patients, posing a big challenge for treatment. Thus, measures assessing network‐wide structural connectivity disruptions in TBI are necessary to quantify injury burden of individuals, which would help in achieving personalized treatment, patient monitoring, and rehabilitation planning. Despite TBI being a disconnectivity syndrome, connectomic assessment of structural disconnectivity has been relatively limited. In this study, we propose a novel connectomic measure that we call network normality score (NNS) to capture the integrity of structural connectivity in TBI patients by leveraging two major characteristics of the disease: diffuseness of axonal injury and heterogeneity of the disease. Over a longitudinal cohort of moderate‐to‐severe TBI patients, we demonstrate that structural network topology of patients is more heterogeneous and significantly different than that of healthy controls at 3 months postinjury, where dissimilarity further increases up to 12 months. We also show that NNS captures injury burden as quantified by posttraumatic amnesia and that alterations in the structural brain network is not related to cognitive recovery. Finally, we compare NNS to major graph theory measures used in TBI literature and demonstrate the superiority of NNS in characterizing the disease.

Classification of multiple sclerosis patients based on structural disconnection: A robust feature selection approach.

Background and PurposeAlthough structural disconnection represents the hallmark of multiple sclerosis (MS) pathophysiology, classification attempts based on structural connectivity have achieved low accuracy levels. Here, we set out to fill this gap, exploring the performance of supervised classifiers on features derived from microstructure informed tractography and selected applying a novel robust approach.MethodsUsing microstructure informed tractography with diffusion MRI data, we created quantitative connectomes of 55 MS patients and 24 healthy controls. We then used a robust approach—based on two classical methods of feature selection— to select relevant features from three network representations (whole connectivity matrices, node strength, and local efficiency). Classification accuracy of the selected features was tested with five different classifiers, while their meaningfulness was tested via correlation with clinical scales. As a comparison, the same classifiers were run on features selected with the standard procedure in network analysis (thresholding).ResultsOur procedure identified 11 features for the whole net, five for local efficiency, and seven for node strength. For all classifiers, the accuracy was in the range 64.5%‐91.1%, with features extracted from the whole net reaching the maximum, and overcoming results obtained with the standard procedure in all cases. Correlations with clinical scales were identified across functional domains, from motor and cognitive abilities to fatigue and depression.ConclusionApplying a robust feature selection procedure to quantitative structural connectomes, we were able to classify MS patients with excellent accuracy, while providing information on the white matter connections and gray matter regions more affected by MS pathology.

Traditional Thoughts and Modern Development of the Historical Urban Landscape in China: Lessons Learned from the Example of Pingyao Historical City

In the past 40 years, China has undergone a rapid urbanization process which has led to a significant contradiction between the desire to develop modern urban spaces and the need to protect historic urban sites. Urban construction has brought not only the destruction of the physical space of the historic urban sites, but also the fragmentation of the natural landscape and its structural disconnection to the historic urban landscape. Ancient Chinese planners had their own thoughts regarding urban construction and the specific patterns of the urban landscape. The urban landscape of Chinese historic cities focuses predominantly on the structural relationship to its neighborhood. This paper aims to explore the value and character of the historic landscape of the ancient city, finding the key causes of its decline in the process of urbanization. The World Cultural Heritage city Pingyao is taken as a case study. Firstly, an analysis of its historical spatial structure and urban planning ideas of the ancient city of Pingyao using ancient maps and historical documents is presented. Then, a quantitative analysis of the urban space expansion in Pingyao city from 1989 to 2016 is conducted and its land use structure further analyzed. Additionally, four editions of the urban master plan in Pingyao have comprehensively shown that modern urban construction and planning tend to focus more on urban economic functions and social needs. In contrast, ancient urban planning relied on the spatial connection between urban space and its natural environment to construct a higher urban cultural connotation. As a result, the different development mode between modern urbanization and construction of ancient cities could be the key reason for the decline of spatial structure and landscape fragmentation of historical cities in China. With respect to the experience and thoughts of Pingyao’s ancient urban planning and construction, technical ideas and suggestions are put forward as reference in future spatial planning for Pingyao’s urban development and cultural protection. Our findings have been incorporated into the relevant sections of the spatial planning of Pingyao.

Inferring the dynamical effects of stroke lesions through whole-brain modeling

Understanding the effect of focal lesions (stroke) on brain structure-function traditionally relies on behavioral analyses and correlation with neuroimaging data. Here we use structural disconnection maps from individual lesions to derive a causal mechanistic generative whole-brain model able to explain both functional connectivity alterations and behavioral deficits induced by stroke. As compared to other models that use only the local lesion information, the similarity to the empirical fMRI connectivity increases when the widespread structural disconnection information is considered. The presented model classifies behavioral impairment severity with higher accuracy than other types of information (e.g.: functional connectivity). We assessed topological measures that characterize the functional effects of damage. With the obtained results, we were able to understand how network dynamics change emerge, in a nontrivial way, after a stroke injury of the underlying complex brain system. This type of modeling, including structural disconnection information, helps to deepen our understanding of the underlying mechanisms of stroke lesions.

Voxelwise structural disconnection mapping: Methodological validation and recommendations

Voxelwise disconnection mapping is a novel approach to disclose lesion-symptom relationships for symptoms caused by white matter disconnection. It uses MRI-based fiber tracking in healthy subjects seeded from patient’s focal brain lesions. Resulting individual disconnection maps can then be statistically associated with symptoms. Despite increasing use in the recent years, the validity of this approach remains to be investigated. In this study, we validated both, our own implementation and the implementation provided within BCBtoolkit. For technical validation, we used simulated symptoms based on overlap of 70 real stroke lesions with tracts from a white matter atlas. For clinical validation, paresis scores and lesions from 316 patients with stroke were used. We found that voxelwise disconnection mapping is technically valid and outperforms the standard voxel-based lesion-symptom mapping approach for symptoms caused by white matter disconnection. Supporting its clinical validity and utility, we were able to reproduce the known association between corticospinal tract damage and contralateral hemiparesis. In addition, we demonstrate that the validity can be substantially diminished by relatively minor methodological changes. Based on these results, we derive methodological recommendations for the future use of voxelwise disconnection mapping. Our study highlights the importance of validating novel methodological approaches in the rapidly evolving field of neuroimaging.

Mapping delusions of space onto a structural disconnectome that decouples familiarity and place networks

Interpretation of space is an important determinant of human behaviour. Delusions of space, or reduplicative paramnesias, are a particularly disturbing form of spatial disorientation characterized by the patients' strong belief of place reduplication, transformation or mislocation. Their occurrence following focal brain damage provides a unique opportunity to unveil the structural-functional basis of space misinterpretations.First, we identified reports of lesion-associated reduplicative paramnesias with brain images available through a systematic review of the literature (n = 24). Each lesion was matched with 4 stroke controls and the sample was randomly split in an exploratory (n = 60) and in a validation (n = 60) dataset. Second, we used 178 7T tractographies to compute structural disconnectome maps and analysed lesion topography and disconnection patterns. Delusions of space were significantly associated with structural disconnection of right ventrolateral prefrontal and right temporal regions, and this finding was replicated in the validation sample. Third, we performed a functional meta-analysis of syndrome-related terms. We demonstrated that the structural disconnectomes of delusions of space were spatially correlated with the functional meta-analytic maps of familiarity and place, and replicated the previous evidence that the lesion topography maps are spatially correlated with belief-related functional networks. No association was found with control terms.These results reveal that structural disconnection putatively mediates functional changes associated with reduplicative paramnesias and provide a possible neural basis for the content specificity for places that characterizes these delusional beliefs.

Structural disconnectivity from paramagnetic rim lesions is related to disability in multiple sclerosis.

BackgroundIn people with multiple sclerosis (pwMS), lesions with a hyperintense rim (rim+) on Quantitative Susceptibility Mapping (QSM) have been shown to have greater myelin damage compared to rim‐ lesions, but their association with disability has not yet been investigated. Furthermore, how QSM rim+ and rim‐ lesions differentially impact disability through their disruptions to structural connectivity has not been explored. We test the hypothesis that structural disconnectivity due to rim+ lesions is more predictive of disability compared to structural disconnectivity due to rim‐ lesions.MethodsNinety‐six pwMS were included in our study. Individuals with Expanded Disability Status Scale (EDSS) <2 were considered to have lower disability (n = 59). For each gray matter region, a Change in Connectivity (ChaCo) score, that is, the percent of connecting streamlines also passing through a rim‐ or rim+ lesion, was computed. Adaptive Boosting was used to classify the pwMS into lower versus greater disability groups based on ChaCo scores from rim+ and rim‐ lesions. Classification performance was assessed using the area under ROC curve (AUC).ResultsThe model based on ChaCo from rim+ lesions outperformed the model based on ChaCo from rim‐ lesions (AUC = 0.67 vs 0.63, p‐value < .05). The left thalamus and left cerebellum were the most important regions in classifying pwMS into disability categories.Conclusionrim+ lesions may be more influential on disability through their disruptions to the structural connectome than rim‐ lesions. This study provides a deeper understanding of how rim+ lesion location/size and resulting disruption to the structural connectome can contribute to MS‐related disability.

Divergent Connectivity Changes in Gray Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease.

Alzheimer’s disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD.

TONR: An exploration for a novel way combining neural network with topology optimization

The rapid development of deep learning has opened a new door to the exploration of topology optimization methods. The combination of deep learning and topology optimization has become one of the hottest research fields at the moment. Different from most existing work, this paper conducts an in-depth study on the method of directly using neural networks (NN) to carry out topology optimization. Inspired by the idea from the field of “Inverting Representation of Image” and “Physics-Informed Neural Network”, a topology optimization via neural reparameterization framework (TONR) that can solve various topology optimization problems is formed. The core idea of TONR is Reparameterization, which means the update of the design variables (pseudo-density) in the conventional topology optimization method is transformed into the update of the NN’s parameters. The sensitivity analysis in the conventional topology optimization method is realized by automatic differentiation technology. With the update of NN’s parameters, the density field is optimized. Some strategies for dealing with design constraints, determining NN’s initial parameters, and accelerating training are proposed in the paper. In addition, the solution of the multi-constrained topology optimization problem is also embedded in the TONR framework. Numerical examples show that TONR can stably obtain optimized structures for different optimization problems, including the stress-constrained problem, structural natural frequency optimization problems, compliant mechanism design problems, heat conduction system design problems, and the optimization problem of hyperelastic structures. Compared with the existing methods that combine deep learning with topology optimization, TONR does not need to construct a dataset in advance and does not suffer from structural disconnection. The structures obtained by TONR can be comparable to the conventional methods.

Anatomy of phonemic and semantic fluency: A lesion and disconnectome study in 1231 stroke patients

Disturbances of semantic and phonemic fluency are common after brain damage, as a manifestation of language, executive, or memory dysfunction. Lesion-symptom mapping (LSM) studies can provide fundamental insights in shared and distinct anatomical correlates of these cognitive functions and help to understand which patients suffer from these deficits. We performed a multivariate support vector regression-based lesion-symptom mapping and structural disconnection study on semantic and phonemic fluency in 1231 patients with acute ischemic stroke. With the largest-ever LSM study on verbal fluency we achieved almost complete brain lesion coverage. Lower performance on both fluency types was related to left hemispheric frontotemporal and parietal cortical regions, and subcortical regions centering on the left thalamus. Distinct correlates for phonemic fluency were the anterior divisions of middle and inferior frontal gyri. Distinct correlates for semantic fluency were the posterior regions of the middle and inferior temporal gyri, parahippocampal and fusiform gyri and triangular part of the inferior frontal gyrus. The disconnectome-based analyses additionally revealed phonemic fluency was associated with a more extensive frontoparietal white matter network, whereas semantic fluency was associated with disconnection of the fornix, mesiotemporal white matter, splenium of the corpus callosum. These results provide the most detailed outline of the anatomical correlates of phonemic and semantic fluency to date, stress the crucial role of subcortical regions and reveal a novel dissociation in the left temporal lobe.

Unravelling the Neural Basis of Spatial Delusions After Stroke.

Knowing explicitly where we are is an interpretation of our spatial representations. Reduplicative paramnesia is a disrupting syndrome in which patients present a firm belief of spatial mislocation. Here, we studied the largest sample of patients with delusional misidentifications of space (ie, reduplicative paramnesia) after stroke to shed light on their neurobiology. In a prospective, cumulative, case-control study, we screened 400 patients with acute right-hemispheric stroke. We included 64 cases and 233 controls. First, lesions were delimited and normalized. Then, we computed structural and functional disconnection maps using methods of lesion-track and network-mapping. The maps were compared, controlling for confounders. Second, we built a multivariate logistic model, including clinical, behavioral, and neuroimaging data. Finally, we performed a nested cross-validation of the model with a support-vector machine analysis. The most frequent misidentification subtype was confabulatory mislocation (56%), followed by place reduplication (19%), and chimeric assimilation (13%). Our results indicate that structural disconnection is the strongest predictor of the syndrome and included 2 distinct streams, connecting right fronto-thalamic and right occipitotemporal structures. In the multivariate model, the independent predictors of reduplicative paramnesia were the structural disconnection map, lesion sparing of right dorsal fronto-parietal regions, age, and anosognosia. Good discrimination accuracy was demonstrated (area under the curve=0.80 [0.75-0.85]). Our results localize the anatomic circuits that may have a role in the abnormal spatial-emotional binding and in the defective updating of spatial representations underlying reduplicative paramnesia. This novel data may contribute to better understand the pathophysiology of delusional syndromes after stroke. ANN NEUROL 2021;89:1181-1194.

Traumatic brain injury in forensic psychiatry

IntroductionAssessment of neuropsychiatric sequelae of traumatic brain injury (TBI) brings about challenges in the forensic setting, comprising analysis of neurobiological variables, preinjury variables (personality/psychiatric disturbances), postinjury psychosocial, allowing the expert witness to provide clear and appropriate explanations, so the court can decide with justice, particularly in civil law cases.ObjectivesDiscuss the main clinical and neuroimagiologic aspects to consider in civil litigation of TBI cases.MethodsComprehensive literature review.ResultsAlthough accurate predictions are difficult, some generalizations can be made. Recovery from hypoxic and diffuse axonal injury (DAI) takes longer and is less complete than focal contusions. Posttraumatic amnesia is the main predictor of long-term cognitive outcome. In moderate/severe TBI (m/sTBI) occurs chronic lesion expansion (axonal degeneration) and brain atrophy. DAI topography determinates the cognitive disfunction pattern yet underestimated in conventional neuroimaging. Diffusion-Tension-Imaging (DTI) may be valuable to outcome predictions in m/sTBI: structural disconnection within the Default Mode and the Salience Networks are linked to attention and executive impairments; hippocampus and fornix damage correlates with memory/learning impairments. Conversely, DTI findings can be misleading in mild TBI (mTBI), and case-by-case analysis seldomly prove its scientific validity.ConclusionsTo elaborate formulations within reasonable medical certainty, outcome predictions should not be made until at least six months following the TBI, considering that most mTBI symptoms resolve in few months, and up to 1-½ years, when m/sTBI neuropathologic changes stabilize. The neurobiological underpinnings are fundamental for causality formulations, however atypical outcomes in mTBI are frequently predicated upon non–brain-injury psychiatric conditions and psychosocial factors.DisclosureNo significant relationships.

Two-stage convolutional encoder-decoder network to improve the performance and reliability of deep learning models for topology optimization

A vital necessity when employing state-of-the-art deep neural networks (DNNs) for topology optimization is to predict near-optimal structures while satisfying pre-defined optimization constraints and objective function. Existing studies, on the other hand, suffer from the structural disconnections which result in unexpected errors in the objective and constraints. In this study, a two-stage network model is proposed using convolutional encoder-decoder networks that incorporate a new way of loss functions to reduce the number of structural disconnection cases as well as to reduce pixel-wise error to enhance the predictive performance of DNNs for topology optimization without any iteration. In the first stage, a single DNN model architecture is proposed and used in two parallel networks using two different loss functions for each called the mean square error (MSE) and mean absolute error (MAE). Once the priori information is generated from the first stage, it is instantly fed into the second stage, which acts as a rectifier network over the priori predictions. Finally, the second stage is trained using the binary cross-entropy (BCE) loss to provide the final predictions. The proposed two-stage network with the proposed loss functions is implemented for both two-dimensional (2D) and three-dimensional (3D) topology optimization datasets to observe its generalization ability. The validation results showed that the proposed two-stage framework could improve network prediction ability compared to a single network while significantly reducing compliance and volume fraction errors.

Validating atlas-based lesion disconnectomics in multiple sclerosis: A retrospective multi-centric study

The translational potential of MR-based connectivity modelling is limited by the need for advanced diffusion imaging, which is not part of clinical protocols for many diseases. In addition, where diffusion data is available, brain connectivity analyses rely on tractography algorithms which imply two major limitations. First, tracking algorithms are known to be sensitive to the presence of white matter lesions and therefore leading to interpretation pitfalls and poor inter-subject comparability in clinical applications such as multiple sclerosis. Second, tractography quality is highly dependent on the acquisition parameters of diffusion sequences, leading to a trade-off between acquisition time and tractography precision.Here, we propose an atlas-based approach to study the interplay between structural disconnectivity and lesions without requiring individual diffusion imaging. In a multi-centric setting involving three distinct multiple sclerosis datasets (containing both 1.5 T and 3 T data), we compare our atlas-based structural disconnectome computation pipeline to disconnectomes extracted from individual tractography and explore its clinical utility for reducing the gap between radiological findings and clinical symptoms in multiple sclerosis. Results using topological graph properties showed that overall, our atlas-based disconnectomes were suitable approximations of individual disconnectomes from diffusion imaging. Small-worldness was found to decrease for larger total lesion volumes thereby suggesting a loss of efficiency in brain connectivity of MS patients. Finally, the global efficiency of the created brain graph, combined with total lesion volume, allowed to stratify patients into subgroups with different clinical scores in all three cohorts.

Structural brain disconnectivity mapping of post-stroke fatigue

Stroke patients commonly suffer from post stroke fatigue (PSF). Despite a general consensus that brain perturbations constitute a precipitating event in the multifactorial etiology of PSF, the specific predictive value of conventional lesion characteristics such as size and localization remains unclear. The current study represents a novel approach to assess the neural correlates of PSF in chronic stroke patients. While previous research has focused primarily on lesion location or size, with mixed or inconclusive results, we targeted the extended structural network implicated by the lesion, and evaluated the added explanatory value of a structural disconnectivity approach with regards to the brain correlates of PSF. To this end, we estimated individual structural brain disconnectome maps in 84 S survivors in the chronic phase (≥3 months post stroke) using information about lesion location and normative white matter pathways obtained from 170 healthy individuals. PSF was measured by the Fatigue Severity Scale (FSS). Voxel wise analyses using non-parametric permutation-based inference were conducted on disconnectome maps to estimate regional effects of disconnectivity. Associations between PSF and global disconnectivity and clinical lesion characteristics were tested by linear models, and we estimated Bayes factor to quantify the evidence for the null and alternative hypotheses, respectively. The results revealed no significant associations between PSF and disconnectome measures or lesion characteristics, with moderate evidence in favor of the null hypothesis. These results suggest that symptoms of post-stroke fatigue among chronic stroke patients are not simply explained by lesion characteristics or the extent and distribution of structural brain disconnectome, and are discussed in light of methodological considerations.

Structural Disconnection of the Tool Use Network after Left Hemisphere Stroke Predicts Limb Apraxia Severity.

Producing a tool use gesture is a complex process drawing upon the integration of stored knowledge of tools and their associated actions with sensory–motor mechanisms supporting the planning and control of hand and arm actions. Understanding how sensory–motor systems in parietal cortex interface with semantic representations of actions and objects in the temporal lobe remains a critical issue and is hypothesized to be a key determinant of the severity of limb apraxia, a deficit in producing skilled action after left hemisphere stroke. We used voxel-based and connectome-based lesion-symptom mapping with data from 57 left hemisphere stroke participants to assess the lesion sites and structural disconnection patterns associated with poor tool use gesturing. We found that structural disconnection among the left inferior parietal lobule, lateral and ventral temporal cortices, and middle and superior frontal gyri predicted the severity of tool use gesturing performance. Control analyses demonstrated that reductions in right-hand grip strength were associated with motor system disconnection, largely bypassing regions supporting tool use gesturing. Our findings provide evidence that limb apraxia may arise, in part, from a disconnection between conceptual representations in the temporal lobe and mechanisms enabling skilled action production in the inferior parietal lobule.