Biology subjects

Bajracharya, P.

Publications and source records attributed to Bajracharya, P..

4 recordsLinked to original sources

bioRxiv2025

LINKING MULTI-SCALE BRAIN CONNECTIVITY WITH VIGILANCE, WORKING MEMORY, AND BEHAVIOR IN ADOLESCENTS

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in "the fronto-thalamo-cerebellar (FTC) circuitry" and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD).

neuroscience↗

bioRxiv2025

Identifying Neurobiological Psychosis Biotypes Using Multi-Scale Functional Network Connectivity and its Latent Independent Subspace

This study aims to identify Psychosis Imaging Neurosubtypes (PINs)-- homogeneous subgroups of individuals with psychosis characterized by distinct neurobiology derived from imaging features. Specifically, we utilized resting-state fMRI data from 2103 B-SNIP 1&2 participants (1127 with psychosis, 350 relatives, 626 controls) to compute subject-specific multiscale functional network connectivity (msFNC). We then derived a low-dimensional neurobiological subspace, termed Latent Network Connectivity (LNC), which captured system-wide interconnected multiscale information across three components (cognitive-related, typical, psychosis-related). Projections of psychosis participants msFNC onto this subspace revealed three PINs through unsupervised learning, each with distinct cognitive, clinical, and connectivity profiles, spanning all DSM diagnoses (Schizophrenia, Bipolar, Schizoaffective). PIN-1, the most cognitively impaired, showed Cerebellar-Subcortical and Visual-Sensorimotor hypoconnectivity, alongside Visual-Subcortical hyperconnectivity. Most cognitively preserved PIN-2 showed Visual-Subcortical, Subcortical-Sensorimotor, and Subcortical-Higher Cognition hypoconnectivity. PIN-3 exhibited intermediate cognitive function, showing Cerebellar-Subcortical hypoconnectivity alongside Cerebellar-Sensorimotor and Subcortical-Sensorimotor hyperconnectivity. Notably, 55% of relatives aligned with the same neurosubtype as their affected family members--a significantly higher rate than random chance (p-valueRelatives-to-PIN-1 < 0.001, p-valueRelatives-to-PIN-2 < 0.05, p-valueRelatives-to-PIN-3 < 0.001) compared to a non-significant 37% DSM-based classification, supporting a biological basis of these neurosubtypes. Cognitive performance reliably aligns with distinct brain connectivity patterns, which are also evident in relatives, supporting their construct validity. Our PINs differed from original B-SNIP Biotypes, which were determined from electrophysiological, cognitive, and oculomotor data. These findings underscore the limitations of DSM-based classifications in capturing the biological complexity of psychotic disorders and highlight the potential of imaging-based neurosubtypes to enhance our understanding of the psychosis spectrum.

neuroscience↗

bioRxiv2025

Deciphering Multiway Multiscale Brain Network Connectivity:Insights from Birth to 6 Months

Converging evidence suggests that understanding the human brain requires more than just examining pairwise functional brain interactions. The human brain is a complex, nonlinear system, and focusing solely on linear pairwise functional connectivity often overlooks important nonlinear and higher-order relationships. Infancy is a critical period marked by significant brain development that could contribute to future learning, health, and life success. Exploring higher-order functional relationships in the brain can provide insight into brain function and development. To the best of our knowledge, there is no existing research on multiway, multiscale brain network interactions in infants. In this study, we comprehensively investigate the interactions among brain intrinsic connectivity networks (ICNs), including both pairwise (pair-FNC) and triple relationships (tri-FNC). We focused on an infant dataset collected between birth and six months, a critical period for brain maturation. Our results revealed significant hierarchical, multiway, multiscale brain functional network interactions in the infant brain. These findings suggest that tri-FNC provide additional insights beyond what pairwise interactions reveal during early brain development. The tri-FNC predominantly involve the default mode, sensorimotor, visual, limbic, language, salience, and central executive domains. Notably, these triplet networks align with the classical triple network model of the human brain, which includes the default mode network, the salience network, and the central executive network. This suggests that the brain network system might already be initially established during the first six months of infancy. Interestingly, tri-FNC in the default mode and salience domains showed significantly stronger nonlinear interactions with age compared to pair-FNC. We also found that pair-FNC were less effective at detecting these networks. The present study suggests that exploring tri-FNC can offer additional insights beyond pair-FNC by capturing higher-order nonlinear interactions, potentially yielding more reliable biomarkers to characterize developmental trajectories.

neuroscience↗

bioRxiv2024

Born Connected: Do Infants Already Have Adult-Like Multi-Scale Connectivity Networks?

The human brain undergoes remarkable development during the first six postnatal months, a period of dramatic structural and functional change critical for understanding neurodevelopmental trajectories. Previous infant resting-state functional MRI (rsfMRI) studies identified intrinsic connectivity networks (ICNs) but reported widely varying network numbers and organization, limiting cross-study comparisons. A recent analysis of over 100,000 subjects spanning from adolescents to adults generated a 105-network multi-scale template that greatly enhanced replicability, but the presence of these canonical ICNs in infants has not been investigated. We analysed resting-state scans from infants aged 0-6 months using two complementary approaches: burst independent component analysis (burstICA), a fully data-driven, model-order-agnostic method, and the reference-informed NeuroMark framework. burstICA successfully recovered the full set of canonical ICNs directly from infant data, and simulation-based validation confirmed that the recovered networks were biologically specific rather than methodological biases. The NeuroMark framework enhanced spatial correspondence with the reference template and maintained distinct, reproducible network profiles across scans, demonstrating high reliability for cross-study and cross-age comparisons. Networks critical for higher-order cognition, such as the default mode and salience networks, displayed adult-like topography comparable to that of primary sensory networks even within the first 6 months after birth. Subcortical networks also exhibited precise spatial organization, underscoring early maturation of deep brain structures. Functional network connectivity analyses revealed close similarity to adult profiles and clear antagonistic patterns between sensory systems and higher-order networks, indicating that foundational aspects of mature brain organization are already emerging in early infancy. Together, these findings establish a methodological foundation for early network mapping in infancy and lay critical groundwork for longitudinal and translational studies of brain development.

neuroscience↗