White Matter Connectivity Research Articles

The Effect of TNF-alpha rs1800629 Polymorphism on White Matter Structures and Memory Function in Patients With Schizophrenia: A Pilot Study.

This study investigated the effect of TNF-α rs1800629 polymorphism on white matter integrity and memory function in patients with schizophrenia. Fifty-five participants with schizophrenia were enrolled in this study. They were genotyped for TNF-α rs1800629 polymorphism and underwent diffusion tensor imaging. Memory function was assessed using the Rey-Kim memory test. Participants with schizophrenia were grouped into GG homozygotes and A-allele carriers. Compared to GG homozygotes, A-allele carriers had significantly lower scores for immediate and delayed recall and recognition of verbal memory and showed significantly lower fractional anisotropy in extensive brain regions. Lower total scores in immediate and delayed recall of verbal memory, immediate recall of visual memory, and figure copy of visual memory were significantly correlated with decreased mean fractional anisotropy in the white matter tracts of the corresponding brain regions. Our findings suggest that the A-allele, which is associated with higher levels of TNF-α expression, correlates with lower connectivity of the fronto-temporal white matter compared to that in GG homozygotes. Impaired fronto-temporal connectivity may be associated with genetic vulnerability to schizophrenia, leading to verbal and visual memory deficits in patients with schizophrenia.

Reply: The Wernicke conundrum is misinterpreted.

In their Letter to the Editor (doi:10.1093/brain/awac482), Dr Mesulam and colleagues provided their opinion on our recent paper in Brain entitled ‘The Wernicke conundrum revisited: evidence from connectome-based lesion-symptom mapping’. We are thankful for the opportunity here to address these points. Dr Mesulam and colleagues did not object to the empirical data and analyses that we reported. However, they expressed objections to the logic of our approach, incorrectly claimed that the motivation for our manuscript was to ‘to discredit’ their work, and accuse us of battling a ‘straw man of our own erection’. We strongly disagree with these characterizations. Mesulam et al.’s work radically and incisively reconsiders the basic neuroanatomy of language. For almost 200 years, it has been canon that the posterior temporal lobe supports core aspects of language comprehension, based in large part on post-stroke Wernicke’s aphasia. Mesulam et al.1 in 2015 presented data from primary progressive aphasia (PPA) that appear to flatly contradict this received wisdom. The double disconnection hypothesis, the main explanation offered in that paper to explain the apparent discrepancy between PPA and stroke-based aphasia, is succinctly articulated and testable with current neuroimaging methods incorporating both lesion information and degree of white matter connectivity. Therefore, we considered it highly relevant and important to the field to directly address and test this new theory of post-stroke comprehension deficits in aphasia. Naturally, expanding existing knowledge by rigorously testing new hypotheses is a central tenet of scientific progress.

White matter connectivity networks predict early dementia conversion in Parkinson’s disease

AbstractBackgroundSeveral clinical and neuroimaging biomarkers have been proposed to identify individuals at high risk for dementia conversion in Parkinson’s disease (PD). This study aimed to explore whether white matter (WM) connectivity disruption can predict the dementia conversion in patients with newly diagnosed PD with mild cognitive impairment (PD‐MCI).MethodNeuroimaging analyses of WM structural connectivity were performed in 75 patients with drug‐naïve PD‐MCI who underwent serial cognitive assessments during the follow‐up period (>5 years). The patients were classified into either the PD with dementia (PDD) high‐risk group (PDD‐H, n = 38) or low‐risk group (PDD‐L, n = 37), depending on whether they converted to dementia within 5 years of PD diagnosis. We conducted degree‐based statistics (DBS) analyses based on a graph‐theoretical concept to identify the subnetworks whose WM connectivity was disrupted in the PDD‐H group compared with the PDD‐L group. We also performed partial correlation analyses to investigate whether the network connectivity strength was correlated with either the cognitive composite scores or the estimated risk score for PDD conversion within 5 years.ResultThe PDD‐H group showed poorer cognitive performance on frontal/executive, visual memory/visuospatial, and attention/working memory/language function than the PDD‐L group at baseline assessment, even though there was no difference in parkinsonian motor deficits severity, disease duration, and years of education. The PDD‐H group exhibited more severely disrupted WM connectivity in both frontal and posterior cortical regions with eight hub nodes in the DBS analysis. The strength of structural connectivity within the identified subnetworks was correlated with the composite scores of the frontal/executive function domain (γ = 0.393, p = 0.010) and the risk score of PDD conversion within 5 years (γ = ‐0.480, p = 0.001).ConclusionThe present study demonstrated that disrupted WM connectivity in frontal and posterior cortical regions, which was correlated with frontal/executive dysfunction, could be used as a marker for early dementia conversion in patients with PD‐MCI.

Limbic Predominant Age‐related TDP‐43 Encephalopathy Neuropathological Change (LATE‐NC) is Associated with Lower Diffusion Anisotropy in Medial Temporal Lobe White Matter

AbstractBackgroundLimbic predominant age‐related TDP‐43 encephalopathy neuropathological change (LATE‐NC) is now recognized as a common neuropathological disorder of the aging brain and is associated with accelerated cognitive decline. However, the MRI signature of LATE‐NC has not been fully determined. In this work, we hypothesized that abnormalities in white matter (WM) structural integrity caused by LATE‐NC may be detected by means of diffusion tensor imaging (DTI).MethodThis study included 148 individuals participating in Rush Memory and Aging Project, and Religious Orders Study (Fig.1). Ex‐vivo DTI images were acquired using 3T scanners, followed by detailed histopathologic examination by a board‐certified neuropathologist (Fig.2). As a post‐processing step, fractional anisotropy (FA) maps generated from DTI images were aligned to an ex‐vivo FA template and projected onto the corresponding WM skeleton.Voxel‐wise analysis was performed on the WM skeleton to investigate the association of FA with LATE‐NC, controlling for Alzheimer’s disease, Lewy bodies, cerebral amyloid angiopathy, cerebral infarcts, atherosclerosis, arteriolosclerosis, age at death, sex, years of education, postmortem interval to fixation and imaging, total white matter hyperintensity volume, and scanner. Statistical significance was set at p<0.05. Using the voxels that showed significance in this analysis, (1) probable WM connections passing through them were extracted, (2) FA values were compared between LATE‐NC stages 1, 2, 3 and stage 0 (after adjusting for covariates).ResultVoxel‐wise analysis revealed lower FA for greater LATE‐NC burden in medial temporal lobe WM (Fig.3). The connections traversing this WM region included fibers connecting amygdala, temporal pole, hippocampus, entorhinal cortex, fusiform, insula, and putamen (Fig.4). No voxel showed positive association between FA and LATE‐NC. Comparison of adjusted FA values in medial temporal lobe WM across LATE‐NC stages revealed significant FA anomalies between stages 0 and 3 (ΔFA=‐0.06, FDR‐corrected p<10‐6) (Fig.5).ConclusionThe present study in autopsied brains of community‐based older adults showed lower diffusion anisotropy with greater LATE‐NC burden involving WM connections between regions consistent with the known distribution of LATE‐NC. Overall, this pattern may potentially contribute towards the development of an in‐vivo tool for the prediction of this devastating, recently recognized disease entity.

Discriminative patterns of white matter changes in Alzheimer's

Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome. Then, the CNN classification decision is visually analyzed using gradient-based localization techniques to identify the discriminative changes in white matter connectivity in Alzheimer's. The cortical regions involved in the identified discriminative structural connectivity changes in AD are highly covered in the temporal/subcortical regions. A specific pattern is identified in the discriminative changes in structural connectivity of AD, where the white matter changes are revealed within the temporal/subcortical regions and from the temporal/subcortical regions to the frontal and parietal regions in both left and right hemispheres. The proposed approach has the potential to comprehensively analyze the discriminative structural connectivity differences in AD, change the way of detecting biomarkers, and help clinicians better understand the structural changes in AD and provide them with more confidence in automated diagnostic systems.

Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome.

Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.

White matter brain structure predicts language performance and learning success.

Individual differences in the ability to process language have long been discussed. Much of the neural basis of these, however, is yet unknown. Here we investigated the relationship between long-range white matter connectivity of the brain, as revealed by diffusion tractography, and the ability to process syntactically complex sentences in the participants' native language as well as the improvement thereof by multiday training. We identified specific network motifs by singular value decomposition that indeed related white matter structural connectivity to individual language processing performance. First, for two such motifs, one in the left and one in the right hemisphere, their individual prevalence significantly predicted the individual language performance, suggesting an anatomical predisposition for the individual ability to process syntactically complex sentences. Both motifs comprise a number of cortical regions, but seem to be dominated by areas known for the involvement in working memory rather than the classical language network itself. Second, we identified another left hemispheric network motif, whose change of prevalence over the training period significantly correlated with the individual change in performance, thus reflecting training induced white matter plasticity. This motif comprises diverse cortical areas including regions known for their involvement in language processing, working memory and motor functions. The present findings suggest that individual differences in language processing and learning can be explained, in part, by individual differences in the brain's white matter structure. Brain structure may be a crucial factor to be considered when discussing variations in human cognitive performance, more generally.

Structural brain connectivity predicts early acute pain after mild traumatic brain injury.

Mild traumatic brain injury (mTBI), is a leading cause of disability worldwide, with acute pain manifesting as one of its most debilitating symptoms. Understanding acute postinjury pain is important because it is a strong predictor of long-term outcomes. In this study, we imaged the brains of 157 patients with mTBI, following a motorized vehicle collision. We extracted white matter structural connectivity networks and used a machine learning approach to predict acute pain. Stronger white matter tracts within the sensorimotor, thalamiccortical, and default-mode systems predicted 20% of the variance in pain severity within 72 hours of the injury. This result generalized in 2 independent groups: 39 mTBI patients and 13 mTBI patients without whiplash symptoms. White matter measures collected at 6 months after the collision still predicted mTBI pain at that timepoint (n = 36). These white matter connections were associated with 2 nociceptive psychophysical outcomes tested at a remote body site-namely, conditioned pain modulation and magnitude of suprathreshold pain-and with pain sensitivity questionnaire scores. Our findings demonstrate a stable white matter network, the properties of which determine an important amount of pain experienced after acute injury, pinpointing a circuitry engaged in the transformation and amplification of nociceptive inputs to pain perception.

White matter connectivity in uncinate fasciculus accounts for visual attention span in developmental dyslexia

The present study aimed to investigate the role of connectivity disruptions in two fiber pathways, the uncinate fasciculus (UF) and the frontal aslant tract (FAT), in developmental dyslexia and determine the relationship between the connectivity of these pathways and behavioral performance in children with dyslexia. A total of 26 French children with dyslexia and 31 age-matched control children were included. Spherical deconvolution tractography was used to reconstruct the two fiber pathways. Hindrance-modulated oriented anisotropy (HMOA) was used to measure the connectivity of each fiber pathway in both hemispheres. Only boys with dyslexia showed reduced HMOA in the UF compared to control boys. Furthermore, HMOA of the UF correlated with individual differences in the visual attention span in participants with dyslexia. All significant results found in HMOA of the UF were verified in fractional anisotropy (FA) of the UF using standard diffusion imaging model. This study suggests a differential sex effect on the connectivity disruption in the UF in developmental dyslexia. It also indicates that the UF may play an essential role in the visual attention span deficit in developmental dyslexia.

Altered controllability of white matter networks and related brain function changes in first-episode drug-naive schizophrenia.

Understanding how structural connectivity alterations affect aberrant dynamic function using network control theory will provide new mechanistic insights into the pathophysiology of schizophrenia. The study included 140 drug-naive schizophrenia patients and 119 healthy controls (HCs). The average controllability (AC) quantifying capacity of brain regions/networks to shift the system into easy-to-reach states was calculated based on white matter connectivity and was compared between patients and HCs as well as functional network topological and dynamic properties. The correlation analysis between AC and duration of untreated psychosis (DUP) were conducted to characterize the controllability progression pattern without treatment effects. Relative to HCs, patients exhibited reduced AC in multiple nodes, mainly distributed in default mode network (DMN), visual network (VN), and subcortical regions, and increased AC in somatomotor network. These networks also had impaired functional topology and increased temporal variability in dynamic functional connectivity analysis. Longer DUP was related to greater reductions of AC in VN and DMN. The current study highlighted potential structural substrates underlying altered functional dynamics in schizophrenia, providing a novel understanding of the relationship of anatomic and functional network alterations.

Neural network bases of thematic semantic processing in language production.

Semantic processing is a central component of language and cognition. The anterior temporal lobe is postulated to be a key hub for semantic processing, but the posterior temporoparietal cortex is also involved in thematic associations during language. It is possible that these regions act in concert and depend on an anteroposterior network linking the temporal pole with posterior structures to support thematic semantic processing during language production. We employed connectome-based lesion-symptom mapping to examine the causal relationship between lesioned white matter pathways and thematic processing language deficits among individuals with post-stroke aphasia. Seventy-nine adults with chronic aphasia completed the Philadelphia Naming Test, and semantic errors were coded as either thematic or taxonomic to control for taxonomic errors. Controlling for nonverbal conceptual-semantic knowledge as measured by the Pyramids and Palm Trees Test, lesion size, and the taxonomic error rate, thematic error rate was associated with loss of white matter connections from the temporal pole traversing in peri-Sylvian regions to the posterior cingulate and the insula. These findings support the existence of a distributed network underlying thematic relationship processing in language as opposed to discrete cortical areas.

Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy

The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using multi-modal MRI data that are clinically acquired from children with drug-resistant epilepsy. A cortical parcellation was applied to localize the SOZ in cortical nodes of the epileptogenic hemisphere. At each node, the laminar surface analysis was followed to sample 1) the relative intensity of gray matter and white matter in multi-modal MRI and 2) the neighboring white matter connectivity using diffusion tractography edge strengths. A cross-validation was employed to train and test all layers of a multi-scale residual neural network (msResNet) that can classify SOZ node in an end-to-end fashion. A prediction probability of a given node belonging to the SOZ class was proposed as a non-invasive MRI marker of seizure onset likelihood. In an independent validation cohort, the proposed MRI marker provided a very large effect size of Cohen's d = 1.21 between SOZ and non-SOZ, and classified SOZ with a balanced accuracy of 0.75 in lesional and 0.67 in non-lesional MRI groups. The subsequent multi-variate logistic regression found the incorporation of the proposed MRI marker into interictal intracranial EEG (iEEG) markers further improves the differentiation between the epileptogenic focus (defined as SOZ resected during surgery) and non-epileptogenic sites (i.e., non-SOZ sites preserved during surgery) up to 15 % in non-lesional MRI group, suggesting that the proposed MRI marker could improve the localization of epileptogenic foci for successful pediatric epilepsy surgery.

Volumetric development of hippocampal subfields and hippocampal white matter connectivity: Relationship with episodic memory.

The hippocampus is a complex structure composed of distinct subfields. It has been central to understanding neural foundations of episodic memory. In the current cross-sectional study, using a large sample of 830, 3- to 21-year-olds from a unique, publicly available dataset we examined the following questions: (1) Is there elevated grey matter volume of the hippocampus and subfields in late compared to early development? (2) How does hippocampal volume compare with the rest of the cerebral cortex at different developmental stages? and (3) What is the relation between hippocampal volume and connectivity with episodic memory performance? We found hippocampal subfield volumes exhibited a nonlinear relation with age and showed a lag in volumetric change with age when compared to adjacent cortical regions (e.g., entorhinal cortex). We also observed a significant reduction in cortical volume across older cohorts, while hippocampal volume showed the opposite pattern. In addition to age-related differences in gray matter volume, dentate gyrus/cornu ammonis 3 volume was significantly related to episodic memory. We did not, however, find any associations with episodic memory performance and connectivity through the uncinate fasciculus, fornix, or cingulum. The results are discussed in the context of current research and theories of hippocampal development and its relation to episodic memory.

Longitudinal association of executive function and structural network controllability in the aging brain

Executive function encompasses effortful cognitive processes that are particularly susceptible to aging. Functional brain networks supporting executive function—such as the frontoparietal control network and the multiple demand system—have been extensively investigated. However, it remains unclear how structural networks facilitate and constrain the dynamics of functional networks to contribute to aging-related executive function declines. We examined whether changes in structural network modal controllability—a network’s ability to facilitate effortful brain state transitions that support cognitive functions—are associated with changes in executive function cross-sectionally and longitudinally. Diffusion-weighted imaging and neuropsychological testing were conducted at two time points (Time 1: ages 56 to 66, N = 172; Time 2: ages 61 to 70, N = 267) in community-dwelling men from the Vietnam Era Twin Study of Aging. An executive function factor score was computed from six neuropsychological tasks. Structural networks constructed from white matter connectivity were used to estimate modal controllability in control network and multiple demand system. We showed that higher modal controllability in control network and multiple demand system was associated with better executive function at Time 2, after controlling for age, young adult general cognitive ability, and physical health status. Moreover, changes in executive function over a period of 5 to 6 years (Time 1-Time 2, N = 105) were associated with changes in modal controllability of the multiple demand system and weakly in the control network over the same time period. These findings suggest that changes in the ability of structural brain networks in facilitating effortful brain state transitions may be a key neural mechanism underlying aging-related executive function declines and cognitive aging.

Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion.

Diffusion tensor imaging (DTI) has been used as an outcome measure in clinical trials for several psychiatric disorders but has rarely been explored in autism clinical trials. This is despite a large body of research suggesting altered white matter structure in autistic individuals. The current study is a secondary analysis of changes in white matter connectivity from a double-blind placebo-control trial of a single intravenous cord blood infusion in 2-7-year-old autistic children (1). Both clinical assessments and DTI were collected at baseline and 6 months after infusion. This study used two measures of white matter connectivity: change in node-to-node connectivity as measured through DTI streamlines and a novel measure of feedback network connectivity, Ollivier-Ricci curvature (ORC). ORC is a network measure which considers both local and global connectivity to assess the robustness of any given pathway. Using both the streamline and ORC analyses, we found reorganization of white matter pathways in predominantly frontal and temporal brain networks in autistic children who received umbilical cord blood treatment versus those who received a placebo. By looking at changes in network robustness, this study examined not only the direct, physical changes in connectivity, but changes with respect to the whole brain network. Together, these results suggest the use of DTI and ORC should be further explored as a potential biomarker in future autism clinical trials. These results, however, should not be interpreted as evidence for the efficacy of cord blood for improving clinical outcomes in autism. This paper presents a secondary analysis using data from a clinical trial that was prospectively registered with ClinicalTrials.gov(NCT02847182).

The Prediction of Brain Activity from Connectivity: Advances and Applications.

The human brain is composed of multiple, discrete, functionally specialized regions that are interconnected to form large-scale distributed networks. Using advanced brain-imaging methods and machine-learning analytical approaches, recent studies have demonstrated that regional brain activity during the performance of various cognitive tasks can be accurately predicted from patterns of task-independent brain connectivity. In this review article, we first present evidence for the predictability of brain activity from structural connectivity (i.e., white matter connections) and functional connectivity (i.e., temporally synchronized task-free activations). We then discuss the implications of such predictions to clinical populations, such as patients diagnosed with psychiatric disorders or neurologic diseases, and to the study of brain-behavior associations. We conclude that connectivity may serve as an infrastructure that dictates brain activity, and we pinpoint several open questions and directions for future research.

Prenatal exposure to maternal social disadvantage and psychosocial stress and neonatal white matter connectivity at birth

Early life adversity (social disadvantage and psychosocial stressors) is associated with altered microstructure in fronto-limbic pathways important for socioemotional development. Understanding when these associations begin to emerge may inform the timing and design of preventative interventions. In this longitudinal study, 399 mothers were oversampled for low income and completed social background measures during pregnancy. Measures were analyzed with structural equation analysis resulting in two latent factors: social disadvantage (education, insurance status, income-to-needs ratio [INR], neighborhood deprivation, and nutrition) and psychosocial stress (depression, stress, life events, and racial discrimination). At birth, 289 healthy term-born neonates underwent a diffusion MRI (dMRI) scan. Mean diffusivity (MD) and fractional anisotropy (FA) were measured for the dorsal and inferior cingulum bundle (CB), uncinate, and fornix using probabilistic tractography in FSL. Social disadvantage and psychosocial stress were fitted to dMRI parameters using regression models adjusted for infant postmenstrual age at scan and sex. Social disadvantage, but not psychosocial stress, was independently associated with lower MD in the bilateral inferior CB and left uncinate, right fornix, and lower MD and higher FA in the right dorsal CB. Results persisted after accounting for maternal medical morbidities and prenatal drug exposure. In moderation analysis, psychosocial stress was associated with lower MD in the left inferior CB among the lower-to-higher socioeconomic status (SES) (INR ≥ 200%) group, but not the extremely low SES (INR < 200%) group. Increasing access to social welfare programs that reduce the burden of social disadvantage and related psychosocial stressors may be an important target to protect fetal brain development in fronto-limbic pathways.

Morphological similarity of amygdala-ventral prefrontal pathways represents trait anxiety in younger and older adults

Stronger amygdala-ventral prefrontal white matter connectivity has been associated with lower trait anxiety, possibly reflecting an increased capacity for efficient communication between the two regions. However, there are also reports arguing against this brain-anxiety association. To address these inconsistencies in the literature, we tested the possibility that idiosyncratic tract morphology may account for meaningful individual differences in trait anxiety, even among those with comparable microstructural integrity. Here, we adopted intersubject representational similarity analysis, an analytic framework that captures multivariate patterns of similarity, to analyze the morphological similarity of amygdala-ventral prefrontal pathways. Data drawn from the Leipzig Study for Mind-Body-Emotion Interactions dataset showed that younger adults (20 to 35 y of age) with low trait anxiety, in contrast to trait-anxious individuals, had consistently similar morphological configurations in their left amygdala-ventral prefrontal pathways. Additional tests on an independent sample of older adults (60 to 75 y of age) validated this finding. Our study reveals a generalizable pattern of brain-anxiety association that is embedded within the shared geometries between fiber tract morphology and trait anxiety data.

Structural connectome quantifies tumour invasion and predicts survival in glioblastoma patients.

Glioblastoma is characterized by diffuse infiltration into the surrounding tissue along white matter tracts. Identifying the invisible tumour invasion beyond focal lesion promises more effective treatment, which remains a significant challenge. It is increasingly accepted that glioblastoma could widely affect brain structure and function, and further lead to reorganization of neural connectivity. Quantifying neural connectivity in glioblastoma may provide a valuable tool for identifying tumour invasion. Here we propose an approach to systematically identify tumour invasion by quantifying the structural connectome in glioblastoma patients. We first recruit two independent prospective glioblastoma cohorts: the discovery cohort with 117 patients and validation cohort with 42 patients. Next, we use diffusion MRI of healthy subjects to construct tractography templates indicating white matter connection pathways between brain regions. Next, we construct fractional anisotropy skeletons from diffusion MRI using an improved voxel projection approach based on the tract-based spatial statistics, where the strengths of white matter connection and brain regions are estimated. To quantify the disrupted connectome, we calculate the deviation of the connectome strengths of patients from that of the age-matched healthy controls. We then categorize the disruption into regional disruptions on the basis of the relative location of connectome to focal lesions. We also characterize the topological properties of the patient connectome based on the graph theory. Finally, we investigate the clinical, cognitive and prognostic significance of connectome metrics using Pearson correlation test, mediation test and survival models. Our results show that the connectome disruptions in glioblastoma patients are widespread in the normal-appearing brain beyond focal lesions, associated with lower preoperative performance (P < 0.001), impaired cognitive function (P < 0.001) and worse survival (overall survival: hazard ratio = 1.46, P = 0.049; progression-free survival: hazard ratio = 1.49, P = 0.019). Additionally, these distant disruptions mediate the effect on topological alterations of the connectome (mediation effect: clustering coefficient -0.017, P < 0.001, characteristic path length 0.17, P = 0.008). Further, the preserved connectome in the normal-appearing brain demonstrates evidence of connectivity reorganization, where the increased neural connectivity is associated with better overall survival (log-rank P = 0.005). In conclusion, our connectome approach could reveal and quantify the glioblastoma invasion distant from the focal lesion and invisible on the conventional MRI. The structural disruptions in the normal-appearing brain were associated with the topological alteration of the brain and could indicate treatment target. Our approach promises to aid more accurate patient stratification and more precise treatment planning.

White matter connectivity in brain networks supporting social and affective processing predicts real-world social network characteristics

Human behavior is embedded in social networks. Certain characteristics of the positions that people occupy within these networks appear to be stable within individuals. Such traits likely stem in part from individual differences in how people tend to think and behave, which may be driven by individual differences in the neuroanatomy supporting socio-affective processing. To investigate this possibility, we reconstructed the full social networks of three graduate student cohorts (N = 275; N = 279; N = 285), a subset of whom (N = 112) underwent diffusion magnetic resonance imaging. Although no single tract in isolation appears to be necessary or sufficient to predict social network characteristics, distributed patterns of white matter microstructural integrity in brain networks supporting social and affective processing predict eigenvector centrality (how well-connected someone is to well-connected others) and brokerage (how much one connects otherwise unconnected others). Thus, where individuals sit in their real-world social networks is reflected in their structural brain networks. More broadly, these results suggest that the application of data-driven methods to neuroimaging data can be a promising approach to investigate how brains shape and are shaped by individuals’ positions in their real-world social networks.