Multiple sclerosis (MS), a neuroinflammatory disorder, leads to the impairment of structural connectivity. Natural nervous system remodeling, to a degree, has the capacity to restore the damage incurred. However, the inadequacy of available biomarkers poses a significant impediment to evaluating remodeling in MS. We aim to assess graph theory metrics, particularly modularity, as a biomarker for MS-related cognitive and remodeling processes. Sixty relapsing-remitting multiple sclerosis patients and 26 healthy controls were selected for our research. Assessments of cognitive function and disability, alongside structural and diffusion MRI, were undertaken. From tractography-derived connectivity matrices, we assessed modularity and global efficiency. General linear models, accounting for age, gender, and disease duration where appropriate, were employed to analyze the relationship of graph metrics to T2 lesion burden, cognitive capacity, and functional limitations. Subjects with multiple sclerosis (MS) exhibited higher modularity and lower global efficiency than control participants. The MS group's modularity levels inversely predicted cognitive performance but were positively associated with the total T2 lesion load. Medicare Advantage MS lesion-induced disruption of intermodular connections is implicated in the observed increase in modularity, coupled with no signs of cognitive function improvement or preservation.
Two independent cohorts of healthy participants, each from different neuroimaging centers, were studied to understand the link between brain structural connectivity and schizotypy. These groups consisted of 140 and 115 individuals, respectively. Participants' schizotypy scores were derived from their completion of the Schizotypal Personality Questionnaire (SPQ). Structural brain networks for participants were generated via tractography, employing diffusion-MRI data. Weights of the networks' edges were calibrated using the reciprocal of radial diffusivity. Graph theoretical metrics from the default mode, sensorimotor, visual, and auditory subnetworks were calculated, and the correlation of these metrics with schizotypy scores was quantified. In our assessment, this constitutes the first occasion for examining graph theoretical measurements of structural brain networks alongside the manifestation of schizotypy. The schizotypy score exhibited a positive correlation, statistically, with the mean node degree and mean clustering coefficient found in both 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 relating to schizophrenia and schizotypy are addressed.
The brain's functional organization typically exhibits a posterior-to-anterior gradient of temporal scales, showcasing regional specialization where sensory areas (rear) process information at a faster rate than associative areas (front), responsible for integrating information. In spite of local information processing being vital, cognitive procedures demand the coordinated function between various regions of the brain. Magnetoencephalography recordings reveal a back-to-front timescale gradient in functional connectivity at the edge level between regions, mirroring the regional timescale gradient. Against expectations, a reverse front-to-back gradient emerges when nonlocal interactions are substantial. Hence, the timeframes are adaptable, altering between backward-forward and forward-backward arrangements.
Various intricate phenomena are effectively modeled using data, with representation learning being a cornerstone. An analysis of fMRI data can significantly benefit from a contextually informative representation due to the intricate and dynamic dependencies within these datasets. This study introduces a framework, employing transformer models, for deriving an embedding of fMRI data, while considering its spatiotemporal contextual factors. Inputting the multivariate BOLD time series of brain regions and their functional connectivity network, this method produces a set of meaningful features usable for various downstream tasks, such as classification, feature extraction, and statistical analysis. By combining attention mechanisms with graph convolutional neural networks, the proposed spatiotemporal framework incorporates contextual information regarding the dynamics and connectivity of time series data into the representation. This framework's utility is demonstrated through its application to two resting-state fMRI datasets, further detailed through a comparative analysis of its advantages relative to other commonly adopted architectural approaches.
Recent years have seen an explosion of research in brain network analysis, offering valuable insights into both typical and atypical brain functions. In these analyses, network science approaches have proved instrumental in illuminating how the brain is structurally and functionally organized. In contrast, the advancement of statistical means for correlating this organizational structure with phenotypic traits has lagged considerably. In our past work, a fresh analytical framework was developed for assessing the association between brain network architecture and phenotypic discrepancies, with adjustments made to control for potentially confounding variables. In vivo bioreactor Specifically, this innovative regression framework correlated distances (or similarities) between brain network features from a single task with functions of absolute differences in continuous covariates, and markers of difference for categorical variables. By examining multiple tasks and multiple sessions, we extend previous work to model and assess multiple brain networks in a single person. We examine various similarity metrics to gauge the distances between connection matrices, and we adapt several established methods for estimation and inference within our framework, including the standard F-test, the F-test incorporating scan-level effects (SLE), and our novel mixed-effects model for multi-task (and multi-session) brain network regression (3M BANTOR). For the purpose of simulating symmetric positive-definite (SPD) connection matrices, a novel strategy has been implemented, which permits testing of metrics on the Riemannian manifold. We utilize simulation studies to assess all approaches to estimation and inference, benchmarking them against existing multivariate distance matrix regression (MDMR) techniques. We subsequently demonstrate the practical application of our framework by examining the connection between fluid intelligence and brain network distances within the Human Connectome Project (HCP) dataset.
A graph-theoretic examination of the structural connectome has proven effective in defining modifications to brain networks in individuals experiencing 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, profiling methods for single patients have been created to identify the variances that exist between individual patients. Analyzing structural brain modifications within five chronic TBI patients (moderate to severe), this personalized connectomics approach leverages data from anatomical and diffusion MRI scans. To assess individual-level brain damage, we generated and compared profiles of lesion characteristics and network metrics (including customized GraphMe plots, and nodal and edge-based brain network modifications) against a healthy control group (N=12), analyzing the damage both qualitatively and quantitatively. Brain network changes presented high individual differences, according to our findings, showcasing significant variability between patients. This approach, capable of validating and comparing results to stratified normative healthy control cohorts, enables clinicians to develop tailored neuroscience-integrated rehabilitation programs for TBI patients, informed by individual lesion load and connectome analyses.
Neural systems' form is dictated by multiple constraints, navigating the trade-off between the necessity for communication across distinct regions and the resources devoted to creating and sustaining their physical connections. To reduce the spatial and metabolic consequences on the organism, shortening the lengths of neural projections has been proposed. Although numerous short-range connections exist within the connectomes of diverse species, long-range connections are also prevalent; consequently, an alternative theory, instead of proposing pathway restructuring for length reduction, suggests that the brain minimizes total wiring length by strategically positioning its different components, termed component placement optimization. Research using non-human primates has debunked this concept by finding an inappropriate arrangement of brain regions, showing that a simulated repositioning of these areas results in a reduction in overall wiring length. Using human subjects for the first time, we are assessing the optimal placement strategy for components. Selleck THZ1 Our Human Connectome Project sample (280 participants, aged 22-30 years, 138 female) reveals a non-optimal placement of components for all subjects, suggesting the presence of constraints—such as a reduction in the processing steps between regions—which are counterbalanced by the increased spatial and metabolic costs. Furthermore, by replicating neural communication between brain regions, we suggest this suboptimal component configuration supports cognitive improvements.
A brief period of reduced alertness and impaired performance is commonly encountered immediately after awakening, and this is referred to as sleep inertia. The intricacies of the neural mechanisms involved in this phenomenon are still veiled in obscurity. Understanding the neural processes involved in sleep inertia might yield important insights into the dynamics of the awakening transition.