Whole-brain Structural Connectomes Research Articles

Whole-brain structural connectome asymmetry in autism

Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in individuals with autism using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. Comparing the asymmetry index between individuals with autism and neurotypical controls, we found atypical structural connectome asymmetry in the sensory and default-mode regions, particularly showing weaker asymmetry towards the right hemisphere in autism. Network communication provided topological underpinnings by demonstrating that the inferior temporal cortex and limbic and frontoparietal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the inferior temporal and lateral visual cortices in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry could be used as a measure for predicting communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.

Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning

Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction techniques have the potential to uncover new biomarkers, the analysis of whole-brain structural connectome abnormalities in a low-dimensional latent space is underinvestigated. In this study, we utilized autoencoder-based feature representation learning for diffusion magnetic resonance imaging-based structural connectivity in 80 individuals with autism and 61 neurotypical controls that passed strict quality controls. We generated low-dimensional latent features using the autoencoder model for each group and adopted an integrated gradient approach to assess the contribution of the input data for predicting latent features during the encoding process. Subsequently, we compared the integrated gradient values between individuals with autism and neurotypical controls and observed differences within the transmodal regions and between the sensory and limbic systems. Finally, we identified significant associations between integrated gradient values and communication abilities in individuals with autism. Our findings provide insights into the whole-brain structural connectome in autism and may help identify potential biomarkers for autistic connectopathy.

Differences in structural connectome organization across sleep quality

Sleep quality, which measures satisfaction with overall sleep, is an important factor affecting an individual's health. Although previous neuroimaging studies based on magnetic resonance imaging (MRI) have observed altered regional morphology and functional activation related to poor sleep quality, the impact of sleep quality on whole-brain structural connectome organization is relatively under-investigated. To address this gap, we utilized dimensionality reduction techniques to estimate low-dimensional eigenvectors of structural connectivity derived from diffusion MRI tractography, and assessed their associations with measures of sleep quality. We found significant effects in the unimodal association and limbic regions. Additionally, the meta-analytic cognitive decoding analysis revealed associations with negative terms, including nociceptive and pain. Our findings suggest a link between alterations in the whole-brain structural organization and sleep quality, providing insights into the relationship between sleep quality and brain structure.

A Robust Modular Automated Neuroimaging Pipeline for Model Inputs to TheVirtualBrain.

TheVirtualBrain, an open-source platform for large-scale network modeling, can be personalized to an individual using a wide range of neuroimaging modalities. With the growing number and scale of neuroimaging data sharing initiatives of both healthy and clinical populations comes an opportunity to create large and heterogeneous sets of dynamic network models to better understand individual differences in network dynamics and their impact on brain health. Here we present TheVirtualBrain-UK Biobank pipeline, a robust, automated and open-source brain image processing solution to address the expanding scope of TheVirtualBrain project. Our pipeline generates connectome-based modeling inputs compatible for use with TheVirtualBrain. We leverage the existing multimodal MRI processing pipeline from the UK Biobank made for use with a variety of brain imaging modalities. We add various features and changes to the original UK Biobank implementation specifically for informing large-scale network models, including user-defined parcellations for the construction of matching whole-brain functional and structural connectomes. Changes also include detailed reports for quality control of all modalities, a streamlined installation process, modular software packaging, updated software versions, and support for various publicly available datasets. The pipeline has been tested on various datasets from both healthy and clinical populations and is robust to the morphological changes observed in aging and dementia. In this paper, we describe these and other pipeline additions and modifications in detail, as well as how this pipeline fits into the TheVirtualBrain ecosystem.

Structural brain network topological alterations in stuttering adults.

Persistent developmental stuttering is a speech disorder that primarily affects normal speech fluency but encompasses a complex set of symptoms ranging from reduced sensorimotor integration to socioemotional challenges. Here, we investigated the whole-brain structural connectome and its topological alterations in adults who stutter. Diffusion-weighted imaging data of 33 subjects (13 adults who stutter and 20 fluent speakers) were obtained along with a stuttering severity evaluation. The structural brain network properties were analysed using network-based statistics and graph theoretical measures particularly focussing on community structure, network hubs and controllability. Bayesian power estimation was used to assess the reliability of the structural connectivity differences by examining the effect size. The analysis revealed reliable and wide-spread decreases in connectivity for adults who stutter in regions associated with sensorimotor, cognitive, emotional and memory-related functions. The community detection algorithms revealed different subnetworks for fluent speakers and adults who stutter, indicating considerable network adaptation in adults who stutter. Average and modal controllability differed between groups in a subnetwork encompassing frontal brain regions and parts of the basal ganglia. The results revealed extensive structural network alterations and substantial adaptation in neural architecture in adults who stutter well beyond the sensorimotor network. These findings highlight the impact of the neurodevelopmental effects of persistent stuttering on neural organization and the importance of examining the full structural connectome and the network alterations that underscore the behavioural phenotype.

The structural connectome and motor recovery after stroke: predicting natural recovery.

Stroke patients vary considerably in terms of outcomes: some patients present ‘natural’ recovery proportional to their initial impairment (fitters), while others do not (non-fitters). Thus, a key challenge in stroke rehabilitation is to identify individual recovery potential to make personalized decisions for neuro-rehabilitation, obviating the ‘one-size-fits-all’ approach. This goal requires (i) the prediction of individual courses of recovery in the acute stage; and (ii) an understanding of underlying neuronal network mechanisms. ‘Natural’ recovery is especially variable in severely impaired patients, underscoring the special clinical importance of prediction for this subgroup. Fractional anisotropy connectomes based on individual tractography of 92 patients were analysed 2 weeks after stroke (TA) and their changes to 3 months after stroke (TC − TA). Motor impairment was assessed using the Fugl-Meyer Upper Extremity (FMUE) scale. Support vector machine classifiers were trained to separate patients with natural recovery from patients without natural recovery based on their whole-brain structural connectomes and to define their respective underlying network patterns, focusing on severely impaired patients (FMUE < 20). Prediction accuracies were cross-validated internally, in one independent dataset and generalized in two independent datasets. The initial connectome 2 weeks after stroke was capable of segregating fitters from non-fitters, most importantly among severely impaired patients (TA: accuracy = 0.92, precision = 0.93). Secondary analyses studying recovery-relevant network characteristics based on the selected features revealed (i) relevant differences between networks contributing to recovery at 2 weeks and network changes over time (TC − TA); and (ii) network properties specific to severely impaired patients. Important features included the parietofrontal motor network including the intraparietal sulcus, premotor and primary motor cortices and beyond them also attentional, somatosensory or multimodal areas (e.g. the insula), strongly underscoring the importance of whole-brain connectome analyses for better predicting and understanding recovery from stroke. Computational approaches based on structural connectomes allowed the individual prediction of natural recovery 2 weeks after stroke onset, especially in the difficult to predict group of severely impaired patients, and identified the relevant underlying neuronal networks. This information will permit patients to be stratified into different recovery groups in clinical settings and will pave the way towards personalized precision neurorehabilitative treatment.

Two types of phonological reading impairment in stroke aphasia.

Alexia is common in the context of aphasia. It is widely agreed that damage to phonological and semantic systems not specific to reading causes co-morbid alexia and aphasia. Studies of alexia to date have only examined phonology and semantics as singular processes or axes of impairment, typically in the context of stereotyped alexia syndromes. However, phonology, in particular, is known to rely on subprocesses, including sensory-phonological processing, motor-phonological processing, and sensory-motor integration. Moreover, many people with stroke aphasia demonstrate mild or mixed patterns of reading impairment that do not fit neatly with one syndrome. This cross-sectional study tested whether the hallmark symptom of phonological reading impairment, the lexicality effect, emerges from damage to a specific subprocess of phonology in stroke patients not selected for alexia syndromes. Participants were 30 subjects with left-hemispheric stroke and 37 age- and education-matched controls. A logistic mixed-effects model tested whether post-stroke impairments in sensory phonology, motor phonology, or sensory-motor integration modulated the effect of item lexicality on patient accuracy in reading aloud. Support vector regression voxel-based lesion-symptom mapping localized brain regions necessary for reading and non-orthographic phonological processing. Additionally, a novel support vector regression structural connectome-symptom mapping method identified the contribution of both lesioned and spared but disconnected, brain regions to reading accuracy and non-orthographic phonological processing. Specifically, we derived whole-brain structural connectomes using constrained spherical deconvolution-based probabilistic tractography and identified lesioned connections based on comparisons between patients and controls. Logistic mixed-effects regression revealed that only greater motor-phonological impairment related to lower accuracy reading aloud pseudowords versus words. Impaired sensory-motor integration was related to lower overall accuracy in reading aloud. No relationship was identified between sensory-phonological impairment and reading accuracy. Voxel-based and structural connectome lesion-symptom mapping revealed that lesioned and disconnected left ventral precentral gyrus related to both greater motor-phonological impairment and lower sublexical reading accuracy. In contrast, lesioned and disconnected left temporoparietal cortex is related to both impaired sensory-motor integration and reduced overall reading accuracy. These results clarify that at least two dissociable phonological processes contribute to the pattern of reading impairment in aphasia. First, impaired sensory-motor integration, caused by lesions disrupting the left temporoparietal cortex and its structural connections, non-selectively reduces accuracy in reading aloud. Second, impaired motor-phonological processing, caused at least partially by lesions disrupting left ventral premotor cortex and structural connections, selectively reduces sublexical reading accuracy. These results motivate a revised cognitive model of reading aloud that incorporates a sensory-motor phonological circuit.

Connectivity within regions characterizes epilepsy duration and treatment outcome.

Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole‐brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes). However, such global parcellation‐based connectomes may be unsuitable for detecting more localized changes in patients. Here, we use a high‐resolution network (~50,000‐nodes overall) to identify changes at the local level (within brain regions) and test its relation with duration and surgical outcome. Patients with TLE (n = 33) and age‐, sex‐matched healthy subjects (n = 36) underwent high‐resolution (~50,000 nodes) structural network construction based on deterministic tracking of diffusion tensor imaging. Nodes were allocated to 68 cortical regions according to the Desikan–Killany atlas. The connectivity within regions was then used to predict surgical outcome. MRI processing, network reconstruction, and visualization of network changes were integrated into the NICARA (https://nicara.eu). Lower clustering coefficient and higher edge density were found for local connectivity within regions in patients, but were absent for the global network between regions (68 cortical regions). Local connectivity changes, in terms of the number of changed regions and the magnitude of changes, increased with disease duration. Local connectivity yielded a better surgical outcome prediction (Mean value: 95.39% accuracy, 92.76% sensitivity, and 100% specificity) than global connectivity. Connectivity within regions, compared to structural connectivity between brain regions, can be a more efficient biomarker for epilepsy assessment and surgery outcome prediction of medically intractable TLE.

Navigating the link between processing speed and network communication in the human brain.

Processing speed on cognitive tasks relies upon efficient communication between widespread regions of the brain. Recently, novel methods of quantifying network communication like 'navigation efficiency' have emerged, which aim to be more biologically plausible compared to traditional shortest path length-based measures. However, it is still unclear whether there is a direct link between these communication measures and processing speed. We tested this relationship in forty-five healthy adults (27 females), where processing speed was defined as decision-making time and measured using drift rate from the hierarchical drift diffusion model. Communication measures were calculated from a graph theoretical analysis of the whole-brain structural connectome and of a task-relevant fronto-parietal structural subnetwork, using the large-scale Desikan-Killiany atlas. We found that faster processing speed on trials that require greater cognitive control are correlated with higher navigation efficiency (of both the whole-brain and the task-relevant subnetwork). In contrast, faster processing speed on trials that require more automatic processing are correlated with shorter path length within the task-relevant subnetwork. Our findings reveal that differences in the way communication is modelled between shortest path length and navigation may be sensitive to processing of automatic and controlled responses, respectively. Further, our findings suggest that there is a relationship between the speed of cognitive processing and the structural constraints of the human brain network.

The relationship between dorsal stream connections to the caudate and verbal fluency in Parkinson disease.

Performance in verbal fluency tasks are widely used as a marker of cognitive impairment in Parkinson disease. However, the anatomical substrate of its impairment remains undetermined. Based on the dual-stream language model, we hypothesized cortical input to the subcortical circuitry would be a crucial determinant of fluency. We performed a retrospective study using individual whole-brain structural connectomes derived from 135 individuals with PD and assessed the relationship between white matter integrity and verbal fluency tasks. Controlling for multiple factors, including dysarthria, we observed higher integrity of dorsal stream-caudate connectivity was associated with better letter fluency. This preliminary study indicates the possible dissociation between dorsal and ventral stream connectivity and letter fluency in PD. In addition, it suggests a non-motor role of the frontostriatal fibers in letter fluency.

A Large-Scale High-Density Weighted Structural Connectome of the Macaque Brain Acquired by Predicting Missing Links.

As a substrate for function, large-scale brain structural networks are crucial for fundamental and systems-level understanding of primate brains. However, it is challenging to acquire a complete primate whole-brain structural connectome using track tracing techniques. Here, we acquired a weighted brain structural network across 91 cortical regions of a whole macaque brain hemisphere with a connectivity density of 59% by predicting missing links from the CoCoMac-based binary network with a low density of 26.3%. The prediction model combines three factors, including spatial proximity, topological similarity, and cytoarchitectural similarity—to predict missing links and assign connection weights. The model was tested on a recently obtained high connectivity density yet partial-coverage experimental weighted network connecting 91 sources to 29 target regions; the model showed a prediction sensitivity of 74.1% in the predicted network. This predicted macaque hemisphere-wide weighted network has module segregation closely matching functional domains. Interestingly, the areas that act as integrators linking the segregated modules are mainly distributed in the frontoparietal network and correspond to the regions with large wiring costs in the predicted weighted network. This predicted weighted network provides a high-density structural dataset for further exploration of relationships between structure, function, and metabolism in the primate brain.

Altered white matter structural networks in drug-naïve patients with obsessive-compulsive disorder.

White matter (WM) alteration is considered to be a vital neurological mechanism of obsessive-compulsive disorder (OCD). However, little is known regarding the changes in topological organization of WM structural network in OCD. We acquired diffusion tensor imaging (DTI) datasets from 28 drug-naïve OCD patients and 28 well-matched healthy controls (HC). A deterministic fiber tracking approach was used to construct the whole-brain structural connectome. Group differences in global and nodal topological properties as well as rich-club organizations were compared by using graph theory analysis. The relationship between the altered network metrics and the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) was calculated. Compared with controls, OCD patients exhibited a significantly decreased small-worldness (σ), normalized clustering coefficient (γ) and shortest path length (Lp), as well as an increased global efficiency (Eglob). The nodal efficiency (Enodal) was found to be reduced in the left middle frontal gyrus, and increased in the right parahippocampal gyrus and bilateral putamen in OCD patients. Besides, OCD patients showed increased rich-club, feeder and local connection strength, and the connection strength of the rich-club was positively correlated with the total Y-BOCS score. Our findings emphasized a central role for the complicatedly changed topological architecture of brain structural networks in the pathological mechanism underlying OCD.

Disrupted brain structural connectivity in Pediatric Bipolar Disorder with psychosis

Bipolar disorder (BD) has been linked to disrupted structural and functional connectivity between prefrontal networks and limbic brain regions. Studies of patients with pediatric bipolar disorder (PBD) can help elucidate the developmental origins of altered structural connectivity underlying BD and provide novel insights into the aetiology of BD. Here we compare the network properties of whole-brain structural connectomes of euthymic PBD patients with psychosis, a variant of PBD, and matched healthy controls. Our results show widespread changes in the structural connectivity of PBD patients with psychosis in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, hippocampus and basal ganglia. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular fingerprint and inter-modular communication, compared to healthy participants. The relationship between network features and neurocognitive and psychotic scores was also assessed, revealing trends of association between patients’ IQ and affective psychotic symptoms with the local efficiency of the orbitofrontal cortex. Our findings reveal that PBD with psychosis is associated with significant widespread changes in structural network topology, thus strengthening the hypothesis of a reduced capacity for integrative processing of information across brain regions. Localised network changes involve core regions for emotional processing and regulation, as well as memory and executive function, some of which show trends of association with neurocognitive faculties and symptoms. Together, our findings provide the first comprehensive characterisation of the alterations in local and global structural brain connectivity and network topology, which may contribute to the deficits in cognition and emotion processing and regulation found in PBD.

Altered brain structural topological properties in Parkinson's disease with levodopa-induced dyskinesias

ObjectivesIn this study, the alterations of structural topological properties in Parkinson's disease (PD) patients with levodopa-induced dyskinesias (LIDs) were explored using white matter structural network connectome derived from diffusion tensor imaging (DTI). Methods21 dyskinetic PD patients, 21 non-dyskinetic PD patients and 25 healthy controls were studied in global and nodal topological properties of structural networks after controlling age, gender and education. Afterwards, post hoc analyses were performed to explore further differences. Finally, multiple linear regression analysis was employed to test the associations between significant different properties and the severity of dyskinesias in PD. ResultsDyskinetic PD patients exhibited significant increased global efficiency, local efficiency, clustering coefficient, but decreased shortest path length compared with the non-dyskinetic. Additionally, increased nodal efficiency in bilateral inferior frontal gyrus (IFG), right putamen, right thalamus, and decreased nodal shortest path length in bilateral IFG and right thalamus, were discovered in dyskinetic PD in comparison with non-dyskinetic PD. Notably, a negative correlation between the Abnormal Involuntary Movement Scale (AIMS) scores and shortest path length of whole-brain network was found in PD with LIDs. ConclusionsOur results indicated excessively optimized topological organization of whole-brain structural connectome in PD patients with LIDs. These findings also illustrated that excessively strengthened basal ganglia-thalamocortical nodal structural connections played an important role in the presence of LIDs.

Development of frontoparietal connectivity predicts longitudinal symptom changes in young people with autism spectrum disorder

Structural neuroimaging studies suggest altered brain maturation in autism spectrum disorder (ASD) compared with typically developing controls (TDC). However, the prognostic value of whole-brain structural connectivity analysis in ASD has not been established. Diffusion magnetic imaging data were acquired in 27 high-functioning young ASD participants (2 females) and 29 age-matched TDC (12 females; age 8–18 years) at baseline and again following 3–7 years. Whole-brain structural connectomes were reconstructed from these data and analyzed using a longitudinal statistical model. We identified distinct patterns of widespread brain connections that exhibited either significant increases or decreases in connectivity over time (p < 0.001). There was a significant interaction between diagnosis and time in brain development (p < 0.001). This was expressed by a decrease in structural connectivity within the frontoparietal network—and its broader connectivity—in ASD during adolescence and early adulthood. Conversely, these connections increased with time in TDC. Crucially, stronger baseline connectivity in this subnetwork predicted a lower symptom load at follow-up (p = 0.048), independent of the expression of symptoms at baseline. Our findings suggest a clinically meaningful relationship between the atypical development of frontoparietal structural connections and the dynamics of the autism phenotype through early adulthood. These results highlight a potential marker of future outcome.

Automated Parcellation of the Cortex Using Structural Connectome Harmonics.

Meaningful division of the human cortex into distinct regions is a longstanding goal in neuroscience. Many of the most widely cited parcellations utilize anatomical priors or depend on functional magnetic resonance imaging (MRI) data while there exists a relative dearth of parcellations that use only structural data based on diffusion MRI. In light of this, and the fact that structural connectivity represents the underlying substrates of functional connectivity, we employ a novel high-resolution, vertex-level graph model of the whole-brain structural connectome and show that the harmonic modes of this graph can be used to achieve parcellations that qualitatively agree with the widely accepted atlases in the literature. Further, we detail a multi-layer formulation of the structural connectome graph and demonstrate that hierarchical clustering of its harmonic modes yields subject-specific parcellations at varying resolutions with ensured and tunable group-level correspondence.

Remapping the cognitive and neural profiles of children who struggle at school.

Our understanding of learning difficulties largely comes from children with specific diagnoses or individuals selected from community/clinical samples according to strict inclusion criteria. Applying strict exclusionary criteria overemphasizes within group homogeneity and between group differences, and fails to capture comorbidity. Here, we identify cognitive profiles in a large heterogeneous sample of struggling learners, using unsupervised machine learning in the form of an artificial neural network. Children were referred to the Centre for Attention Learning and Memory (CALM) by health and education professionals, irrespective of diagnosis or comorbidity, for problems in attention, memory, language, or poor school progress (n = 530). Children completed a battery of cognitive and learning assessments, underwent a structural MRI scan, and their parents completed behavior questionnaires. Within the network we could identify four groups of children: (a) children with broad cognitive difficulties, and severe reading, spelling and maths problems; (b) children with age-typical cognitive abilities and learning profiles; (c) children with working memory problems; and (d) children with phonological difficulties. Despite their contrasting cognitive profiles, the learning profiles for the latter two groups did not differ: both were around 1 SD below age-expected levels on all learning measures. Importantly a child’s cognitive profile was not predicted by diagnosis or referral reason. We also constructed whole-brain structural connectomes for children from these four groupings (n = 184), alongside an additional group of typically developing children (n = 36), and identified distinct patterns of brain organization for each group. This study represents a novel move toward identifying data-driven neurocognitive dimensions underlying learning-related difficulties in a representative sample of poor learners.

Vitamin D deficiency is associated with reduced hippocampal volume and disrupted structural connectivity in patients with mild cognitive impairment.

Vitamin D deficiency may exacerbate adverse neurocognitive outcomes in the progression of diseases such as Parkinson's, Alzheimer's, and other dementias. Mild cognitive impairment (MCI) is prodromal for these neurocognitive disorders and neuroimaging studies suggest that, in the elderly, this cognitive impairment is associated with a reduction in hippocampal volume and white matter structural integrity. To test whether vitamin D is associated with neuroanatomical correlates of MCI, we analyzed an existing structural and diffusion MRI dataset of elderly patients with MCI. Based on serum 25-OHD levels, patients were categorized into serum 25-OHD deficient (<12 ng/mL, n = 27) or not-deficient (>12 ng/mL, n = 29). Freesurfer 6.0 was used to parcellate the whole brain into 164 structures and segment the hippocampal subfields. Whole-brain structural connectomes were generated using probabilistic tractography with MRtrix. The network-based statistic (NBS) was used to identify subnetworks of connections that significantly differed between the groups. We found a significant reduction in total hippocampal volume in the serum 25-OHD deficient group especially in the CA1, molecular layer, dentate gyrus, and fimbria. We observed a connection deficit in 13 regions with the right hippocampus at the center of the disrupted network. Our results demonstrate that low vitamin D is associated with reduced volumes of hippocampal subfields and connection deficits in elderly people with MCI, which may exacerbate neurocognitive outcomes. Longitudinal studies are now required to determine if vitamin D can serve as a biomarker for Alzheimer's disease and if intervention can prevent the progression from MCI to major cognitive disorders.

Disrupted rich club organization and structural brain connectome in unmedicated bipolar disorder.

Bipolar disorder (BD) has been associated with altered brain structural and functional connectivity. However, little is known regarding alterations of the structural brain connectome in BD. The present study aimed to use diffusion-tensor imaging (DTI) and graph theory approaches to investigate the rich club organization and white matter structural connectome in BD. Forty-two patients with unmedicated BD depression and 59 age-, sex- and handedness-matched healthy control participants underwent DTI. The whole-brain structural connectome was constructed by a deterministic fiber tracking approach. Graph theory analysis was used to examine the group-specific global and nodal topological properties, and rich club organizations, and then nonparametric permutation tests were used for group comparisons of network parameters. Compared with healthy control participants, the patients with BD showed abnormal global properties, including increased characteristic path length, and decreased global efficiency and local efficiency. Locally, the patients with BD showed abnormal nodal parameters (nodal strength, nodal efficiency, and nodal betweenness) predominantly in the parietal, orbitofrontal, occipital, and cerebellar regions. Moreover, the patients with BD showed decreased rich club and feeder connectivity density. Our results may reflect the disrupted white matter topological organization in the whole-brain, and abnormal regional connectivity supporting cognitive and affective functioning in depressed BD, which, in part, be due to impaired rich club connectivity.

Intra-subject reliability of the high-resolution whole-brain structural connectome

Recent advances in diffusion weighted image acquisition and processing allow for the construction of anatomically highly precise structural connectomes. In this study, we introduce a method to compute high-resolution whole-brain structural connectome. Our method relies on cortical and subcortical triangulated surface models, and on a large number of fiber tracts generated using a probabilistic tractography algorithm. Each surface triangle is a node of the structural connectivity graph while edges are fiber tract densities across pairs of nodes. Surface-based registration and downsampling to a common surface space are introduced for group analysis whereas connectome surface smoothing aimed at improving whole-brain network estimate reliability.Based on 10 datasets acquired from a single healthy subject, we evaluated the effects of repeated probabilistic tractography, surface smoothing, surface registration and downsampling to the common surface space. We show that, provided enough fiber tracts and surface smoothing, good to excellent intra-acquisition reliability could be achieved. Surface registration and downsampling efficiently established triangle-to-triangle correspondence across acquisitions and high inter-acquisition reliability was obtained. Computational time and disk/memory usages were monitored throughout the steps.Although further testing on large cohort of subjects is required, our method presents the potential to accurately model whole-brain structural connectivity at high-resolution.