Biology subjects

Mirzaeian, S.

Publications and source records attributed to Mirzaeian, S..

3 recordsLinked to original sources

bioRxiv2025

Toward Granular Brain Intrinsic Connectivity Networks and Insights into Schizophrenia

Spatial group independent component analysis (sgr-ICA) has become a crucial method to understand brain function in functional magnetic resonance imaging (fMRI) research, especially in resting-state fMRI (rsfMRI) studies. Early studies identified large-scale brain networks using sgr-ICA with lower order (e.g., 20 - 45 components); however, more recent studies have employed higher model orders (e.g., 200 components) to reveal more refined intrinsic connectivity networks (ICNs), offering a more detailed representation of functional brain architecture. This increased granularity has encouraged researchers to explore even higher model orders to better capture the brains function. Although previous studies explored higher model orders, small datasets often limited them. In this study, we addressed this gap by assessing an sgr-ICA model with 500 components using a large rsfMRI dataset of over 100,000 subjects. This extensive data set allowed us to provide a robust estimation of fine-grained ICNs. We further assessed diagnostic effects and cognitive performance using whole-brain functional network connectivity (FNC) of 502 individuals with schizophrenia and 640 typical controls from these ICNs. We also compared the results with ICNs obtained using a lower-order, multi-spatial-scale template. Results demonstrate that our approach yields a large set of reliable and fine-grained ICNs, enhancing characterization of schizophrenia related dysconnectivity patterns. Specifically, we observed a relatively large number of ICNs within the cerebellar and paralimbic area. We detected significant hypoconnectivity between the cerebellar and subcortical domains, including the basal ganglia and thalamic regions. We also found hyperconnectivity between the cerebellar domain and the visual, sensorimotor, and higher cognitive domains, as well as between the sensorimotor and subcortical domains. Our finding revealed that granular ICNs can detect significant FNC differences between cohorts which are missed in larger scale ICNs. This work highlights the capability of higher model order ICA to capture distinct, fine-grained ICNs, enriching our understanding of FNC and serving as a valuable addition to current multiscale ICN templates. The ICNs derived from this study may serve as valuable references for future research, with the potential to improve the clinical utility of rsfMRI and advance the study of psychiatric disorders.

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↗

bioRxiv2024

A Telescopic Independent Component Analysis on Functional Magnetic Resonance Imaging Data Set

Brain function can be modeled as the dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed "telescopic independent component analysis (TICA)," designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive ICA strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of DMN, VS, and RFPN. In addition, TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources. Author summaryOur study introduces "telescopic independent component analysis (TICA)", a novel approach using a recursive ICA strategy for exploring brain function across multiple spatial scales using fMRI data. TICA constructs spatial hierarchies and identifies functional sources such as default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). By applying TICA, we uncovered hierarchical structures within these networks and revealed differences in the DMN between healthy individuals and those with schizophrenia. This approach offers a promising new tool for studying function dynamics comprehensively.

neuroscience↗