Stroke leads to widespread brain connectivity changes, impacting areas both close and remote to the lesion. Post-stroke recovery dynamics are not fully understood. Investigating structural network reorganization over time can thus provide valuable information on adaptive and maladaptive neural plasticity changes on a subject-specific level. Four first-time ischemic stroke patients (3 M, aged 50-69 years) with upper-extremity motor impairment were examined using ultra-high field 7T MRI structural imaging protocols. For each patient, we performed longitudinal lesion quantification and white matter connectivity analysis at three critical timepoints associated with post-stroke recovery: within 1 week, at ~ 1 month, and at ~ 3 months. Using the structural MRI images, we generated patient-specific weighted structural connectivity matrices at each timepoint. We utilized the Schaefer-Yeo and Automated Anatomical Labeling atlases to derive both anatomical regions and resting-state networks based on pre-defined parcel assignments. We examined lesion evolution and white matter connectivity changes as disconnections, re-emerging connections, and existing connections with an increase in estimated connectivity strength over time. We further conducted exploratory edge-level analyses to examine the connectivity strength changes for each patient. Across all patients and timepoints, an increase in estimated connectivity strength of pre-existing connections dominated structural reorganization. Temporally, the four patients revealed distinct neural reorganization patterns. Patient 1 exhibited robust structural changes in the late ~1 to ~ 3 months stage, whereas Patient 2 in the early < 1 week to ~ 1 month stage. Patient 3 had continuous network growth, while Patient 4 demonstrated stable network reorganization. In our sample, the somatomotor and attention networks underwent the most dynamic reorganization. Somatomotor and salience/ventral attention regions exhibited increased connectivity strength, and in cortical stroke cases, dorsal attention regions demonstrated decreased connectivity strength. In this longitudinal case series, post-stroke neural network reorganization appears to be driven by an increase in estimated connectivity strength of surviving white matter connections, suggesting compensatory neuroplasticity. Adaptive changes were most evident in the somatomotor and salience/ventral attention networks within this sample, while the dorsal attention network suggested a more limited contribution to adaptive network changes. Individual differences in the timing and pattern or reorganization highlight the potential need for further research into personalized treatment approaches to promote adaptive recovery.

Cocaine use disorder (CUD) is highly prevalent and characterized by widespread gray matter atrophy across the cerebral cortex. Yet, it remains unclear whether and how connectome-based circuits and biological features shape these structural abnormalities. We mapped cortical atrophy patterns in CUD (discovery cohort: N = 53; replication cohort: N = 74; controls: N = 364) onto the brain's structural connectome, functional connectivity, transcriptomic similarity and receptor similarity architecture. Using a multimodal and multiscale connectivity-based framework, we identified CUD epicenters and evaluated their spatial correspondence with therapeutic brain stimulation targets and individual variations in clinical symptoms. We found that CUD-related regional atrophy is constrained by the white matter (WM) structural connectome. Along these WM pathways, regions that share similar haemodynamic activity and molecular features are more likely to exhibit convergent atrophy profiles. By integrating the structural connectome with multiple connectivity blueprints, we subsequently identified CUD epicenters and revealed that the prefrontal and visual cortices serve as core systems. Furthermore, we linked these epicenters to cortical transcriptomic patterns and receptor architectures, identifying synaptic and neural homeostasis-related gene enrichment and the strongest spatial correspondence with serotonergic (5-HT1B/5-HT4) and dopaminergic (D2) receptors. Finally, we demonstrated that the spatial distribution of these epicenters correlates with cocaine craving-response maps derived from repeated transcranial magnetic stimulation and can track individual variations in clinical behavioural representations, suggesting their potential as targets for therapeutic intervention. Altogether, our findings establish a structurally constrained framework for the spread of pathology underlying cortical atrophy in CUD, where initial perturbations propagate via structural connectome pathways to vulnerable regions shaped by neural activity and molecular landscapes.

White matter (WM) connections support efficient interregional communication and form the structural basis of human fluid intelligence. However, the shared genetic architecture between WM structural connectome and fluid intelligence remains largely unknown. In this study, we analyzed diffusion-weighted MRI data from 26,655 UK Biobank participants to construct individual WM connectome and performed genome-wide association analyses on global and regional network topology. We identified 41 single nucleotide polymorphisms (SNPs) significantly associated with global efficiency and 45 SNPs linked to nodal efficiency. Genetic correlations with fluid intelligence were observed for 128 brain regions, with 44 and 3 regions sharing SNPs within chromosomes 6q21 and 3p21.1, respectively. Mendelian randomization revealed causal effects from WM connectome to fluid intelligence, particularly in the orbital and superior frontal gyrus. Finally, integrating polygenic scores with network efficiency improved the prediction of individual fluid intelligence. These findings highlight the genetic basis linking WM connectome topology and fluid intelligence, providing new insights into the neurogenetic underpinnings of fluid intelligence.

Personality traits capture stable patterns of behavior and thought, and neurobiological correlates were identified in structural and functional brain networks. Here, we investigate whether the coupling between structural and functional brain networks (SC-FC coupling), during resting state and seven tasks of varying trait-relevance, is associated with individual differences in the Big Five personality traits. We used diffusion-weighted and functional magnetic resonance imaging from 764 participants of the Human Connectome Project and modelled individual differences in SC-FC coupling with similarity and communication measures. These measures approximate functional interactions unfolding on top of the structural connectome and were set in relation to individual variations in personality traits. Small but significant associations in the main analysis were only observed during trait-relevant tasks: for agreeableness during social cognition, and conscientiousness could be predicted from task-general coupling patterns. We conclude that optimizing trait-relevance of tasks during neuroscientific measurement presents a promising means to increase effect sizes in studies on brain-behavior associations.

Longitudinal alterations in morphometric inverse divergence networks among diabetes patients with progressive cognitive decline.

Adaptive behavior depends on the brain's capacity to vary its activity across multiple spatial and temporal scales. Yet, how distinct facets of this variability evolve from childhood to older adulthood remains poorly understood, limiting mechanistic models of neurocognitive aging. Here, we characterize lifespan neural variability using an integrated empirical-computational approach. We analyzed high-density EEG cohort data spanning 111 healthy individuals aged 9-75 years, recorded at rest and during a passive and an attended auditory oddball stimulation task. We extracted scale-dependent measures of EEG fluctuation amplitude and entropy, together with millisecond-resolved phase-synchrony networks in the 2-20 Hz range. Multi-condition partial least squares decomposition analysis revealed two independent lifespan trajectories. First, slow-frequency power, variance,and complexity at longer timescales declined monotonically with age, indicating a progressive dampening of low-frequency fluctuations and large-scale coherence. Second, the temporal organization of phase-synchrony reconfigurations followed an inverted U-shaped trend: young adults exhibited the slowest yet most diverse switching-characterized by low mean but high variance and low kurtosis of jump lengths at 2-6 Hz,and the opposite pattern at 8-20 Hz-whereas children and older adults showed faster, more stereotyped dynamics. To mechanistically account for these patterns, we fitted a ten-node phase-oscillator model constrained by the human structural connectome. Only an intermediate, metastable coupling regime qualitativelyreproduced the empirical finding of maximally heterogeneous synchrony dynamics observed in young adults, whereas deviations toward weaker or stronger coupling mimicked the children's and older adults' profiles. Our results demonstrate that development and aging entail changes in the switching dynamics of EEG phase synchronization by differentially sculpting stationary and transient aspects of neural variability. This establishes time-resolved phase-synchrony metrics as sensitive, mechanistically grounded markers of neurocognitive status across the lifespan.

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on neuroimaging data are increasingly used as tools for predicting behavioural phenotypes, enhancing precision medicine and improving generalizability compared with traditional MRI studies. However, the high dimensionality of brain connectivity data makes model interpretation challenging. Prevailing practices rely on selecting features and, implicitly, interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Despite its widespread use, how univariate feature selection balances the trade-off between simplification for optimizing modelling and oversimplification that misrepresents true neurobiology remains understudied. Here, using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that edges discarded by feature selection can achieve significant prediction accuracies while yielding different neurobiological interpretations. These results are observed across cognitive, developmental and psychiatric phenotypes, extend to both functional connectivity (functional MRI) and structural (diffusion tensor imaging) connectomes, and remain evident in external validation. They suggest that focusing on only the top features may simplify the neurobiological bases of brain-behaviour associations. Such interpretations present only the tip of the iceberg when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results reinforce that subtle brain-wide signals should not be ignored.

Cognitive flexibility supports efficient switching between mental sets and contributes to the preservation of general cognition in aging. It relies on the integration between brain functional dynamics and structural architecture. However, how this structure-function integration changes with age and contributes to cognitive flexibility decline in older adults remains unclear. In this study, we investigated longitudinal aging-related changes in multimodal structure-function integration, quantified as functional signal alignment (i.e., coupling) versus liberality (i.e., decoupling) relative to individual structural connectomes, which represent distinct spectral components, and tested their longitudinal associations with cognitive flexibility. Resting-state fMRI signals were decomposed based on diffusion MRI-derived structural networks using a graph signal processing framework. We focused on subnetworks within three core large-scale cognitive systems: the executive control network (ECN), default mode network (DMN), and salience network (SN). Across two independent datasets, the task-positive SN-A subnetwork, which includes core SN regions such as the anterior insula and dorsal anterior cingulate cortex, exhibited decreased coupling and increased decoupling with aging. Importantly, these changes were associated with a greater decline in cognitive flexibility (measured by the Trail Making Test and Color Trails Test) over time. In contrast, task-negative DMN-A (centered in the medial prefrontal and posterior cingulate cortex) showed aging-related changes in the opposite direction, with increased coupling and decreased decoupling over time. Together, these findings reveal network-specific trajectories of intrinsic structure-function integration in normal aging and indicate that preserved structure-function integration within the SN may be particularly important for maintaining cognitive flexibility in older adults.

Background Multiple sclerosis (MS) is associated with widespread network disruption, but whether specific white-matter structural connectivity (WMSC) phenotypes contribute causally to MS susceptibility remains unclear. Methods We performed bidirectional two-sample Mendelian randomization (MR) using genome-wide association study (GWAS) summary statistics for 206 tractography-derived WMSC phenotypes (UK Biobank; N = 26,333) and MS susceptibility (IMSGC; N = 115,803 of European ancestry). Primary inference used inverse-variance weighted (IVW) MR under a multiplicative random-effects model, complemented by MR-Egger, weighted median, weighted mode, and simple mode. Results In forward MR, 12 WMSC phenotypes remained associated with MS susceptibility after direction-concordance filtering and robustness assessment. These signals were not randomly distributed across the structural connectome: risk-increasing effects were concentrated in salience/control-related cortico-subcortical and sensorimotor couplings, particularly connections involving the amygdala, putamen, and contralateral somatomotor network, whereas inverse associations were more prominent in default-mode/limbic and cross-network connections, including limbic–accumbens, limbic–caudate, default-mode–hippocampus, and visual–default-mode links. Sensitivity analyses did not indicate directional pleiotropy, outlier-driven distortion, or single-variant dependence among the retained traits. No reverse causal effect of MS liability on the prioritized WMSC phenotypes was supported. Conclusions These bidirectional MR results support a circuit-selective model in which genetically influenced variation in specific cortico–subcortical WMSC phenotypes is associated with MS susceptibility, with risk-increasing effects concentrated in salience/control–basal ganglia–sensorimotor circuits and inverse associations enriched in default-mode/limbic and cross-network couplings. The absence of robust reverse effects is more consistent with predisposing connectivity architectures than with MS liability causally altering WMSC.

The brain's functional activity is shaped by the complex architecture of its fibers. Yet, the lack of a direct one-to-one mapping between functional and structural connections makes this relationship elusive. To date, most studies on structure-function coupling (SFC) have conceptualized function in terms of resting-state functional Magnetic Resonance Imaging (fMRI) connectivity. Here, we extend this framework to neurophysiological data by examining how magnetoencephalography (MEG) activity relates to the structural connectome, leveraging its rich spectral content and direct sensitivity to neuronal population dynamics. We show that the decoupling of MEG activity from structure is strongly associated with the expression levels of synaptic plasticity markers, pointing to a link between flexible functional reconfiguration and the molecular mechanisms of plasticity. Moreover, regions with greater decoupling exhibit higher neurotransmitter receptor diversity, underscoring neuromodulatory heterogeneity as a substrate for functional flexibility. This association is especially pronounced for slow-acting metabotropic receptors, whose diffuse and prolonged signaling may facilitate functional reorganization atop the structural connectome.

Large-scale population analyses of structural connectome organization remain challenging because of cross-subject alignment, pathway interpretability and computational burden. No widely adopted standard exists for systematic evaluation across processing methods. We developed connectome-based spatial statistics (CBSS), a scalable framework for anatomically aligned and functionally informed quantification of white-matter microstructure that yields atlas-defined pathways organized into 13 functional networks. Using data from 56,510 UK Biobank participants together with five independent lifespan cohorts, we evaluated the streamline-, voxel- and network-level measures in the aspects of reliability, heritability, structure-function coupling, cognitive and behavioral prediction, brain aging patterns and lifespan trajectories across cohorts. The systematic evaluation workflow compares population-level white-matter representations across methods, spatial scales, tasks and datasets. The results support CBSS as a common connectome reference for large-scale, cross-cohort diffusion MRI studies.

The nuclei of the brainstem fundamentally modulate neuronal activity throughout the central nervous system. Yet connectome reconstructions typically do not include the brainstem because it is notoriously difficult to image. As a result, the influence of the brainstem on structure-function coupling in the brain is unknown. Here we use high-resolution 7 Tesla magnetic resonance imaging (MRI) to reconstruct structural and functional brain connectomes encompassing cortex and 58 brainstem nuclei spanning the midbrain, pons, and medulla. We identify structural connectional profiles of individual brainstem nuclei to the cortex and find that they align with a spectrum of functions, spanning sensory and motor processing to higher-order cognition. Structural and functional connectivity in brainstem-augmented connectomes are positively correlated, and pairs of regions with direct anatomical projections display greater functional connectivity than pairs without. Structure-function coupling is heterogeneous across brainstem nuclei, with greatest coupling in both modulatory and relay nuclei. Collectively, this work presents an initial step towards understanding how the brainstem shapes structure-function relationships in the brain.

Brain oscillations organise neural communication, yet why specific frequencies couple to specific spatial modes remains analytically unresolved. The walk-sum algebra of the structural connectome determines a frequency-dependent transfer function, the resolvent, whose spatial structure follows entirely from topology. With zero free parameters, the bare resolvent predicts a parcellation-invariant crossover near 12.6 Hz, an eigenmodel correlation of ρ = 0.965, and five testable spatial predictions. These are confirmed in source-reconstructed MEG from 912 subjects across three datasets and intracranial EEG from 90 epilepsy patients, ruling out volume conduction. A two-parameter dressed resolvent improves prediction; a neural mass negative control (ρ ≈ 0.006) confirms the resolvent describes channels, not dynamics. Propofol anaesthesia collapses alpha channels; in schizophrenia, weakened local dynamics expose the structural scaffold-topological transparency. This framework provides the first analytical derivation of frequency-band communication architecture from connectome topology.

Background: Glioblastoma is an extremely aggressive brain tumor that diffusely infiltrates white matter and alters large-scale brain connectivity. Most prognostic models focus on localized tumor features and clinical variables, overlooking broader effects on the brain's structural connectome. This study addressed this limitation by integrating graph-theoretical analysis of preoperative diffusion tensor imaging (DTI)-derived structural connectomes with machine learning (ML) to improve prediction of overall survival (OS) in newly diagnosed glioblastoma. Methods: Preoperative DTI data from 871 glioblastoma patients from the UPenn-GBM and UCSF-PDGM cohorts were processed to construct whole-brain structural connectomes weighted by tract count and quantitative anisotropy (QA). Global and nodal graph-theoretical network metrics were extracted and combined with demographic and clinical information. Ten ML models were trained and validated on 784 patients (90% of the cohort). The three best-performing algorithms were tested on a held-out cohort of 87 patients (10%). Results: Random forest, adaptive boosting, and KStar showed the strongest validation performance. In held-out internal testing, random forest models using degree and QA-weighted strength achieved accuracies of 0.862 and 0.874, with AUROCs of 0.929 and 0.909, for predicting OS beyond one year. Strength and clustering coefficient were key predictors, with over two-thirds of significant nodes localized in the temporal lobe, particularly the parahippocampal, and superior, middle, and inferior temporal gyri. Conclusions: Graph-theoretical quantification of structural brain network disruption combined with ML allows accurate prediction of OS in glioblastoma. These results support a network-based conceptualization of the disease and indicate that connectome-derived metrics may complement established prognostic frameworks.

The relationship between the structural connectome and functional activity in the brain is highly complex, and understanding of the connection between the two is limited. Previous work has shown a marginal reliance of functional brain activity on underlying structural connections, indicating significant flexibility of neural communication. Here, we introduce a new method to quantify structure-function coupling and compare it with a standard coupling technique by evaluating the structure-function relationship across numerous fMRI task paradigms. Through this comparison, we investigate how structure-function relationships change during different cognitive demands and we evaluate how they relate to behavior. The new method introduced here, structural reliance, exhibits different structure-function correspondence patterns throughout the brain, and it generally outperforms the standard coupling measure in coupling-based behavioral measure predictions.

General cognitive function ('g') reflects a broad capacity for flexible information processing, yet how it is supported by brain-wide structural connectivity remains unclear. We mapped this relationship in 38,824 individuals (26-84 years) across three cohorts, showing that g is supported by a widely distributed white matter network whose macroscopic wiring capacity, microstructural organisation, and age sensitivity contribute in distinct ways. Across streamline count (SC), fractional anisotropy (FA), and mean diffusivity (MD) weightings, meta-analytic associations with g were widespread at global, nodal and edge levels, spanning all cerebral lobes and key subcortical structures and dependent on both inter- and intra-hemispheric connectivity, particularly ipsilateral long-range inter-lobar connections. White matter node-g associations spatially mirrored independent cortical morphometry-g associations, indicating convergence of grey and white matter contributions to cognitive performance. Effect sizes increased with age: MD associations became more negative and FA more positive, particularly in frontal regions. Edge-level findings replicated across cohorts and predicted g in a hold-out sample. Together, these findings indicate that g reflects a distributed structural communication backbone whose integrity becomes increasingly relevant across adulthood.

Cigarette smoking poses a pervasive health risk increasingly recognized for its impact on the nervous system, yet its effects on brain structure and organization remain incompletely understood. This study examined how smoking addiction influences white matter microstructure and the architecture of structural brain networks in adults. We used diffusion tensor imaging to compare fractional anisotropy between participants who smoke (n = 122) and non-smoking control participants (n = 55) to detect white matter abnormalities. Whole-brain structural connectomes were constructed, with connections categorized by fiber length. Network features quantified included degree centrality and hemispheric asymmetry. Adaptive Best-Subset Selection method was applied to identify white matter connections that may serve as biomarkers for distinguishing individuals who smoke from those who do not. Participants who smoke showed widespread white matter alterations in pathways related to reward valuation, habit formation, and craving, along with abnormalities in both short- and medium-range connections. Reduced rightward asymmetry in the cuneus indicated lateralized structural changes possibly linked to cue processing in nicotine dependence. Discriminative connections involved orbitofrontal, sensorimotor, and visual regions, underscoring how smoking reshapes brain networks supporting reward valuation, motor control, and cue reactivity. Nicotine dependence is associated with extensive alterations in white matter microstructure and network organization, including reduced hemispheric asymmetry. These findings suggest that smoking affects structural connectivity in circuits involved in emotional regulation and reward-related memory.

Abstract Brain tumors are a leading cause of disability and death among children. Brain tumor treatments are often required for cure, but are damaging to brain tissue – particularly white matter, and is related to cognitive impairments for pediatric brain tumor survivors (PBTS). White matter tracts are critical for neural communication and supports cognition, including information processing speed, which underlies higher-order cognitive processes and is mediated by the brain’s default mode (DMN) and executive control (ECN) networks. It is unknown how white matter damage results in compromised information processing speed in PBTS. Thus, we explore the impact of DMN and ECN structural and functional connectivity on cognition during resting state, a visual-motor task, and through computational modeling. Forty-one healthy children and fifty-two PBTS were scanned at The Hospital for Sick Children. Tractography was conducted to examine DMN and ECN structural connectivity. MEG and measures of neural communication was conducted to examine DMN and ECN functional connectivity. Computational models were built using experimentally acquired structural connectomes to simulate functional connectivity. Partial least-squares path modeling was conducted to describe the relationship among white matter organization, neural communication, and information processing speed. Compared to healthy children, PBTS showed: slower information processing speed, DMN and ECN white matter compromise, and resting state DMN and ECN neural communication compromise that generalized to simulated and task-based neural synchrony compromise. Overall, DMN and ECN structural connectivity significantly influenced network neural communication and information processing speed, and white matter compromise had an indirect adverse impact on reaction time via perturbed neural synchrony. Our findings show an important connection between DMN and ECN connectivity that is essential for information processing speed. Further establishing alterations in DMN and ECN structural and functional connectivity as novel biomarkers of cognitive impairments could facilitate early intervention and monitoring of these deficits following brain tumor treatment. Citation Format: Noor Al Dahhan. Mechanisms of Cognitive Impairment in Children Treated for Brain Tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Brain Cancer; 2026 Mar 23-25; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(6_Suppl):Abstract nr PR006.

Integrating structural and functional connectomes remains challenging because their relationship is non-linear and organized over nested modular hierarchies. We propose a hierarchical multiscale structure-function coupling framework for connectome integration that jointly learns individualized modular organization and hierarchical coupling across structural connectivity (SC) and functional connectivity (FC). The framework includes: (i) Prototype-based Modular Pooling (PMPool), which learns modality-specific multiscale communities by selecting prototypical ROIs and optimizing a differentiable modularity-inspired objective; (ii) an Attention-based Hierarchical Coupling Module (AHCM) that models both within-hierarchy and cross-hierarchy SC-FC interactions to produce enriched hierarchical coupling representations; and (iii) a Coupling-guided Clustering loss (CgC-Loss) that regularizes SC and FC community assignments with coupling signals, allowing cross-modal interactions to shape community alignment across hierarchies. We evaluate the model's performance across four cohorts for predicting brain age, cognitive score, and disease classification. Our model consistently outperforms baselines and other state-of-the-art approaches across three tasks. Ablation and sensitivity analyses verify the contributions of key components. Finally, the visualizations of learned coupling reveal interpretable differences, suggesting that the framework captures biologically meaningful structure-function relationships.

Balance control is fundamental to the quality of life among older adults, yet its neural underpinnings remain only partially understood. Despite advances in neuroimaging techniques, the neural correlates of balance are often examined at a regional level and typically restricted to either the functional or structural connectome alone. In this study, we employed connectome-based predictive modelling (CPM) for a large-scale discovery of brain connections predictive of individual balance abilities using both structural and functional connectomes in a cohort of 54 older adults. The test-retest reliability and specificity of the constructed models was evaluated using repeated-measurement data and strength performance data. Our results show that both structural and functional connectomes can successfully predict balance performance on an unstable device measured using mean sway area. A comprehensive system, encompassing motor-subcortical connections, medial-frontal and fronto-parietal networks emerged from both connectome types as consistent predictors of balance. Notably, connections with visual networks uniquely contributed to prediction in the structural but not in the functional connectomes. Structural connectomes also showed better prediction performance and test-retest reliability compared to functional connectomes. The specificity of constructed models was validated using strength performance data. In summary, our study shows that structural and functional connectomes are strong predictors of motor control abilities in challenging conditions in the elderly highlighting their interdependency and complementary roles in balance control.