BACKGROUND:Multi-compartment diffusion models and multi-shell acquisitions are increasingly used to overcome limitations of conventional diffusion tensor imaging, but require longer scans and more complex processing. Meanwhile, diffusion MRI classification studies often rely on a narrow set of tensor-derived metrics, especially fractional anisotropy. We investigated whether broader use of tensor-derived features, including shape descriptors, could improve classification without increasing acquisition complexity. METHODS:Multi-shell diffusion MRI (b = 0, 1000 and 2000 s/mm2) was acquired in 220 participants, including 84 healthy controls and 136 patients with relapsing-remitting multiple sclerosis. Thirteen tensor-derived metrics and three neurite orientation dispersion and density imaging (NODDI) metrics were extracted from 98 automatically parcellated brain regions. Classification was performed using L2-regularized logistic regression within a repeated stratified cross-validation framework. RESULTS:A four-metric set comprising fractional anisotropy, mean diffusivity, spherical and linear anisotropy reached an AUC of 0.967, above fractional anisotropy alone (0.922) and comparable to the full 13-metric representation. The point-estimate gain was concentrated in linear anisotropy; spherical anisotropy did not increase performance. NODDI showed no advantage at matched dimensionality; single-shell yielded similar point estimates, with no significant difference detected. Removing lesion voxels reduced performance only in white matter, where classification remained well above chance. Free-water correction and adjustment for age, sex and intracranial volume did not improve classification. CONCLUSIONS:In this ROI-based diffusion MRI classification, broader use of conventional tensor-derived information showed similar within-cohort discrimination to more complex representations. Linear anisotropy, computed from eigenvalues already available, adds complementary information without requiring additional diffusion contrasts. These findings support exploiting conventional tensor-derived features more comprehensively before adopting more complex diffusion MRI frameworks.
BACKGROUND:Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy alleviates tremor in tremor-dominant Parkinson's disease (TD-PD). However, its effects on the functional network topology remain unclear. METHODS:Twenty-one TD-PD patients underwent unilateral MRgFUS thalamotomy and clinical and MRI assessments. For the overall cohort, graph theory analysis was employed to examine differences in small-world properties and modular structure between baseline and 6 months after MRgFUS. Changes in topological properties were assessed using paired t-tests or Wilcoxon tests, and their associations with clinical improvement. Additionally, subgroup analyses were performed on responder (n = 10) and relapser (n = 9) data. RESULTS:Tremor scores decreased significantly after MRgFUS contralateral to the lesioned thalamus. Significant increases were observed in small-worldness and normalized clustering coefficient after MRgFUS. Inter-modular connectivity showed trend-level enhancement between cortical-basal ganglia and thalamic modules, and between cerebellar and temporal-hippocampal modules. In responder, significant increases were observed in small-worldness, normalized clustering coefficient, and global efficiency, whereas characteristic path length showed a significant decrease. Changes in global efficiency and characteristic path length were significantly correlated with clinical improvement. CONCLUSION:MRgFUS thalamotomy for TD-PD induced partial reorganization of small-world properties, indicating a shift toward a more integrated network topology, especially in responder. Changes correlated with tremor reduction and improved functional disability, highlighting that effective treatment impacts on global network integration.
This study investigated whether childhood white matter organization in those with and without ADHD predicted the emergence of internalizing problems in adolescence. Further, we aimed to determine whether (i) this longitudinal effect was mediated by motor skill level, and/or (ii) white matter organization in children with ADHD mediated the expected relationship between motor skill level and internalizing problems in adolescence. Participants were 40 children with ADHD and 36 typically developing (TD) controls aged between 8 and 13 years, who were followed-up during adolescence (12-18 years). All underwent diffusion MRI at Time 1, and assessment of motor competence using the Movement ABC-2 (MABC-2). At Time 2, internalizing problems (i.e., 'depression', 'anxiety', and 'somatization') were measured using the parent- and self-report Behavior Assessment System for Children (BASC). Following pre-processing of diffusion MRI scans, fixel based analysis was conducted and fibre cross-section (FC) extracted. TractSeg was then used to delineate the superior longitudinal fasciculus (SLF), inferior longitudinal fasciculus (ILF) and the uncinate fasciculus (UC). Results showed that childhood FC within the UF was a significant predictor of parent and child-rated depression scores in adolescence. Further, childhood FC of the bilaterial ILF was a significant predictor of parent report depression scores in adolescence. These effects were stable for those with and without ADHD, and no group differences in FC were observed in those white matter regions found to be associated with adolescent depressive symptoms. We failed to find evidence that motor ability mediated the relationship between white matter organization of the UF of ILF and depressive symptoms. When childhood motor ability was considered as a predictor of adolescent depressive scores (an effect that was significant for parent reported effects), this effect was mediated by childhood FC of the UF. This work suggests that low motor skill in children with and without ADHD may provide a developmental marker for emotional and behavioral dysregulation in adolescence, with this effect partly explained by childhood white matter organization within fronto-limbic circuitry.
Lesion-symptom mapping is widely used to identify causal relationships between brain structures and behaviour, and has played a central role in neuropsychologically informed network models of cognition. However, even recent approaches remain constrained by a one-to-one mapping framework, which oversimplifies the complex relationships between network-level damage and cognitive deficits. In addition, the non-orthogonality of cortical and white matter damage makes it difficult to disentangle their distinct contributions. Here, we used graph-based multilayer network analysis to address these limitations and evaluate clinical relevance. Using neuroanatomical and longitudinal neuropsychological data from 252 patients who underwent awake neurosurgery for low-grade glioma, we constructed interactive, three-layer networks for each hemisphere. Layer 1 comprised neuropsychological tasks (NT), layer 2 structural disconnections (SD), and layer 3 cortical damage (CD). Nodes represented tasks, white matter tracts, and cortical parcels, respectively, whereas within-layer edges captured correlations in performance or co-occurring damage patterns. Multilayer community detection identified domain- and hemisphere-specific brain-behaviour motifs linking executive, language, and spatial functions to distinct combinations of cortical and white matter disruption, a pattern confirmed by two spatial embedding approaches. Centrality analyses revealed a continuum of mapping relationships, ranging from one-to-one to one-to-many associations, indicating that tasks such as verbal fluency are better explained by multiple disconnection mechanisms. Additional analyses uncovered many-to-one and many-to-many relationships and highlighted tracts and cortical regions with domain-general relevance. Together, these findings support a neurobiologically grounded, network-oriented account of how structural brain damage gives rise to cognitive deficits, with implications for clinical care.
Objectives Parkinson's disease (PD) involves large-scale network disruptions, yet functional connectivity (FC) alterations specific to distinct primary motor cortex (M1) subregions and their subcortical targets remain poorly characterized. We aimed to investigate subregion-specific resting-state FC alterations in PD and their clinical associations. Methods Resting-state fMRI data from 37 PD patients and 37 matched healthy controls were analyzed. Seed-to-voxel and expanded ROI-to-ROI analyses (incorporating subcortical nuclei) mapped FC patterns of two M1 subregions: inter-effector and effector-specific. A System Segregation index quantitatively assessed large-scale network boundaries. Results PD patients exhibited local intra-M1 hyperconnectivity and subsystem-specific cortico-subcortical hyperconnectivity, particularly involving the putamen and thalamus for the effector-specific region. Seed-to-voxel analyses revealed divergent long-range dysconnectivity: the inter-effector region showed hypoconnectivity with sensorimotor and limbic areas, whereas the effector-specific region exhibited sensorimotor hypoconnectivity and a pathological loss of anti-correlation with cerebellar and frontoparietal networks. Quantitative evaluation confirmed a significant collapse in the System Segregation index, validating network boundary breakdown. Clinically, inter-effector hypoconnectivity to the right Rolandic operculum correlated with freezing of gait severity, while aberrant effector-specific-cerebellar positive FC correlated with tremor severity. Conclusions This study delineates a multilayered network pathology in PD, involving local hyperconnectivity, cortico-subcortical desegregation, and quantitatively verified impairment of large-scale network segregation. These subregion-specific alterations, particularly maladaptive cortico-cerebellar and cortico-striatal coupling, reframe PD as a disorder of network dedifferentiation, highlighting M1 subcircuits as targets for symptom-specific neuromodulation.
