A neuroinflammatory disorder, multiple sclerosis (MS), causes damage to structural connectivity's integrity. The restorative processes inherent in the nervous system can, to some measure, repair the damage caused. Nevertheless, quantifying remodeling in MS remains hampered by the scarcity of suitable biomarkers. Our investigation centers on graph theory metrics (particularly modularity) as a potential biomarker, linking these metrics with cognitive function and remodeling in MS. Sixty relapsing-remitting multiple sclerosis patients and 26 healthy controls were recruited. The process involved cognitive and disability evaluations, in addition to structural and diffusion MRI. Our analysis of modularity and global efficiency relied on connectivity matrices derived from tractography. The relationship between graph metrics, T2 lesion burden, cognitive function, and disability was assessed using general linear models, which accounted for age, sex, and disease duration, as appropriate. Analysis revealed that MS patients exhibited higher modularity and lower global efficiency than the control group. Modularity demonstrated an inverse correlation with cognitive performance and a direct correlation with T2 lesion load among participants with MS. Minimal associated pathological lesions Lesions in MS are associated with a rise in modularity due to the disruption of intermodular connections, without any improvements or preservation of cognitive functions.
A study exploring the correlation between brain structural connectivity and schizotypy utilized data from two cohorts of healthy participants, each recruited from separate neuroimaging centers. The first cohort comprised 140 individuals, while the second cohort included 115 participants. Participants, having completed the Schizotypal Personality Questionnaire (SPQ), had their schizotypy scores calculated. Utilizing diffusion-MRI data, participants' structural brain networks were produced via the procedure of tractography. The network edges' weights were established through the inverse radial diffusivity value. The relationship between schizotypy scores and graph-theoretical metrics from the default mode, sensorimotor, visual, and auditory subnetworks was assessed through correlation analysis. In our assessment, this constitutes the first occasion for examining graph theoretical measurements of structural brain networks alongside the manifestation of schizotypy. Significant positive correlation was determined between the schizotypy score and the average node degree, along with the average clustering coefficient, specifically within the sensorimotor and default mode subnetworks. The right postcentral gyrus, left paracentral lobule, right superior frontal gyrus, left parahippocampal gyrus, and bilateral precuneus were the nodes underlying these correlations; these nodes demonstrate compromised functional connectivity in schizophrenia. Implications for both schizophrenic and schizotypic conditions are thoroughly discussed.
Information processing timescales in the brain's functional layout are generally presented in a posterior-anterior gradient, aligning with the specialized roles of different areas. Sensory areas in the back handle information faster than the more complex associative areas situated at the front, which are dedicated to information integration. Despite the significance of local information processing, cognitive functions necessitate coordinated activity across diverse brain regions. Functional connectivity at the edge level (between two regions), as measured by magnetoencephalography, exhibits a back-to-front gradient of timescales, aligning with the observed regional gradient. Nonlocal interactions, surprisingly, produce a reverse front-to-back gradient in our observations. Hence, the intervals of time are dynamic and can change from a backward-forward pattern to a forward-backward sequence.
Data-driven modeling of various complex phenomena is heavily reliant on the crucial component of representation learning. Learning a representation that is contextually informative is particularly beneficial for fMRI data analysis, given the complex and dynamic dependencies in such datasets. This study introduces a framework, employing transformer models, for deriving an embedding of fMRI data, while considering its spatiotemporal contextual factors. This approach ingests the multivariate BOLD time series of brain regions and their functional connectivity network concurrently, generating meaningful features for use in downstream tasks like classification, feature extraction, and statistical analysis. The proposed spatiotemporal framework uses the attention mechanism and graph convolution neural network in tandem to incorporate contextual information about the time series data's dynamic and connection properties into the representation. Through its application to two resting-state fMRI datasets, we illuminate the framework's strengths and offer a detailed discussion on its advantages in comparison to other widely used architectures.
Brain network analyses, a burgeoning field in recent years, are poised to significantly advance our understanding of typical and atypical brain operation. In these analyses, network science approaches have proved instrumental in illuminating how the brain is structurally and functionally organized. Although the need exists, there has been a lag in the development of statistical techniques that can connect this organizational structure to phenotypic characteristics. Through our preceding work, we developed a pioneering analytic system to assess the correlation between brain network architecture and phenotypic variations, controlling for potentially confounding influences. New Rural Cooperative Medical Scheme More pointedly, this innovative regression framework mapped distances (or similarities) between brain network features from a single task onto the impact of absolute differences in continuous covariates, and the indicators of divergence for categorical variables. We expand the scope of our previous work to encompass multiple tasks and sessions, facilitating the analysis of multiple brain networks per individual. Using diverse similarity metrics, our framework examines the spatial relationships between connection matrices and employs various methods for parameter estimation and inference, specifically including the conventional F-test, the F-test with the incorporation of scan-level effects (SLE), and our unique mixed model for multitask (and multisession) brain network regression, 3M BANTOR. A novel approach is employed to simulate symmetric positive-definite (SPD) connection matrices, enabling the evaluation of metrics on the Riemannian manifold. Using simulations, we evaluate every strategy for estimating and inferring, placing them in direct comparison with the extant multivariate distance matrix regression (MDMR) methods. Illustrating our framework's utility, we then examine the relationship between fluid intelligence and brain network distances, particularly within the Human Connectome Project (HCP) dataset.
Within the context of graph theory, the structural connectome has successfully been leveraged to highlight changes in brain networks observed in patients with traumatic brain injury (TBI). The substantial heterogeneity of neuropathological presentations among TBI patients is a well-documented phenomenon, which results in comparisons between patient groups and control groups being confounded by the considerable variability present within each patient group. Recently, innovative profiling techniques for individual patients have been designed to highlight the variations between patient groups. Our personalized connectomics approach investigates structural brain alterations in five chronic patients with moderate-to-severe TBI, who have had both anatomical and diffusion MRI scans performed. We generated personalized profiles of lesion characteristics and network metrics—including personalized GraphMe plots and node/edge-based brain network modifications—and assessed brain damage at the individual level by comparing them to healthy controls (N=12), both qualitatively and quantitatively. A notable diversity in brain network alterations was found between patients, according to our study. This method, validated against stratified and normative healthy controls, allows clinicians to craft personalized rehabilitation programs based on a patient's unique lesion load and connectome, in line with principles of neuroscience-guided integrative rehabilitation for TBI.
Neural systems are molded by numerous restrictions that prioritize the balance between the need for regional communication and the expense of creating and preserving their physical infrastructure. To reduce the spatial and metabolic consequences on the organism, shortening the lengths of neural projections has been proposed. Although local connections abound within connectomes of various species, long-range connections are nonetheless widespread; consequently, instead of modifying existing pathways to shorten them, an alternative theory suggests that the brain minimizes total wiring length by strategically positioning its different components, a strategy known as component placement optimization. Previous studies of non-human primates have disproven this theory by identifying an inefficient spatial organization of brain regions, demonstrating that a computer-simulated realignment of these regions reduces the total neural path length. The optimization of component placement is, for the first time in humans, being evaluated through experimentation. Selleck ML323 Our results from the Human Connectome Project (280 participants, 22-30 years, 138 female) showcase a non-optimal component placement across all subjects, hinting at the existence of constraints—namely, a reduction in processing steps between regions—that are juxtaposed against elevated spatial and metabolic burdens. Furthermore, by replicating neural communication between brain regions, we suggest this suboptimal component configuration supports cognitive improvements.
Following awakening, there is a brief period of impaired mental sharpness and physical proficiency, termed sleep inertia. The neural mechanisms underlying this phenomenon are yet to be fully elucidated. Insights into the neural processes occurring during sleep inertia might shed light on how we awaken.