Biology subjects

Hong, S.-J.

Publications and source records attributed to Hong, S.-J..

At least 19 recordsLinked to original sources

bioRxiv2026

EigenBrain maps cortical molecular architecture from functional, geometric, and structural eigenmodes

Neurotransmitter receptor and transporter distributions shape neural communication across the cortex and underpin mechanistic interpretations of brain function. Magnetic resonance imaging (MRI)-derived eigenmodes, low-dimensional spatial patterns that capture the principal axes of cortical organization, are increasingly used to reconstruct brain maps and offer a potential route to approximating molecular topographies in large cohorts where direct measurements are unavailable. Yet whether the type of MRI data used to derive these eigenmodes determines which receptor and transporter distributions can be approximated, and which molecular systems are accessible through this approach at all, remains unknown. Using a spatial reconstruction framework applied to 23 molecular systems spanning neurotransmitter, metabolic, and pathological markers, we show that eight receptor and transporter maps, spanning serotonin, dopamine, histamine, cholinergic, and opioid markers, can be selectively approximated from functional eigenmodes but not structural or geometric ones. This selectivity also reveals that functional brain organization encodes a specific subset of neuromodulatory topographies that structural and geometric organization does not. To illustrate translational potential in clinical cohorts where molecular imaging is unavailable, we applied individual-level eigenmode decompositions of MRI data projected onto the eight validated molecular systems and detected cross-sectional differences in autism and longitudinal changes in schizophrenia. Together, these findings reveal a difference in how geometric, structural, and functional brain organization relate to molecular architecture, offering a method for approximating validated molecular profiles in population neuroimaging cohorts that lack direct molecular imaging.

neuroscience↗

bioRxiv2026

Differential subcortical-cortical circuit reorganization in autism and ADHD

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) often co-occur and share symptoms as well as genetic risk factors. While subcortical-cortical networks have been implicated in both conditions, particularly involving thalamic and striatal circuits, patterns of shared and diverging disruptions remain unclear. Here we integrated resting-state functional connectivity (FC) and regression dynamic causal modeling (rDCM) across a transdiagnostic multi-site cohort. We used statistical group comparisons and individualized normative deviation mapping to map network deviations in ASD and ADHD relative to neurotypical controls and against one another. Multivariate associative techniques examined associations to clinical and cognitive phenotypes. Our transdiagnostic analyses revealed significant differentiation of ASD and ADHD across subcortical-cortical circuits. While ASD was characterized by widespread thalamocortical hyperconnectivity and bidirectional elevated coupling across thalamic subdivisions, ADHD exhibited a focal pattern of striatocortical disruption, anchored predominantly within the caudate. Multivariate analyses identified shared and disorder-specific brain-behavior associations. Across both conditions, aberrant striatocortical signaling was associated with reduced overall intelligence measures. Clinically, ASD-related thalamocortical connectivity disruptions mapped onto generalized cognitive impairment, whereas ADHD-related subcortical alterations tracked hyperactive and inattentive symptom dimensions. Our findings differentiate ASD and ADHD through distinct patterns of functional and directional subcortical-cortical dysregulations.

neuroscience↗

bioRxiv2026

FUNCTIONAL FRAGMENTATION AND STRUCTURAL DRIVERS OF THALAMO-CORTICAL CIRCUITS IN TEMPORAL LOBE EPILEPSY

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSObjectiveC_ST_ABSIn temporal lobe epilepsy (TLE), the thalamus acts as a nexus in a pathophysiological network that implicates mesiotemporal, subcortical, and neocortical regions. Studying a large multimodal and multicentre dataset, we profiled thalamic, hippocampal, and neocortical functional connectivity (FC), assessed structural mediators, and examined clinical associations. MethodsWe studied resting-state FC alongside structural and diffusion MRI data in 250 unilateral TLE patients and 259 healthy controls, with measures aggregated across four independent datasets. Data were processed using open-access neuroinformatics workflows and analyzed at a subregional level to maximize anatomical precision. Statistical analysis and mediation models assessed between-group FC changes, structural contributors, and clinical correlations. ResultsCompared to controls, TLE patients presented with reduced thalamo-cortical FC, which was most marked in mesiotemporal, fronto-central, and occipital regions. Thalamo-hippocampal FC was also reduced, with effects seen in all CA subfields. In the thalamus, FC reductions peaked in the ventral posterior nucleus when considering neocortical target regions and in the mediodorsal nucleus when considering hippocampal target regions. While ipsilateral hippocampal volume and diffusion changes mediated thalamo-hippocampal FC, thalamo-cortical FC appeared decoupled from structural alterations. Findings were consistent in left and right TLE patients, in patients with short and long disease duration, and across imaging sites, suggesting that thalamo-cortical FC imbalances are a consistent signature of TLE. Conversely, thalamo-hippocampal FC was elevated in patients with focal-to-bilateral-tonic-clonic seizures and FC alterations were more marked in the subgroup of operated patients that became seizure-free after surgery. ConclusionOur multi-site findings demonstrate marked thalamic circuit fragmentation in TLE. Ipsilateral findings robustly showed subdivision-specific effects, which point to both mesiotemporal co-lateralization as well as broader system-level involvement. Mediation analyses furthermore confirmed a key role of hippocampal pathology in disrupted thalamo-hippocampal connectivity in TLE, while broader thalamo-cortical fragmentation becomes increasingly independent of mesiotemporal compromise. Critically, thalamic FC represents a network substrate for seizure generalization and can serve as a prognostic indicator for surgical outcome. These results underscore the contribution of the thalamus as a hub in macroscale dysfunction in TLE.

neuroscience↗

bioRxiv2025

Biomarker Discovery via Integrative Multi-omics for Children exposed to Humidifier Disinfectant

RationaleExposure to humidifier disinfectants has been linked to an array of pulmonary disorders and diminished lung functionality particularly reduced Forced Vital Capacity (FVC). ObjectivesThis investigation sought to identify diagnostic biomarkers for early detection of children at elevated risk of developing chronic respiratory conditions following such exposure. MethodsOur research employed a comprehensive multi-omics strategy analyzing 70 pediatric patients alongside 10 controls, seamlessly integrating clinical assessments with transcriptomics, methylomics, proteomics, and metabolomics data. The analytical framework utilized a sophisticated combination of Non-negative Matrix Factorization (NMF), Multi-Omics Factor Analysis (MOFA), and advanced machine learning algorithms. Measurements and Main ResultsNMF clustering uncovered distinctive protein expression patterns associated with integrin-mediated signaling pathways and immune response mechanisms. Complementarily, MOFA identified latent factors correlating with lung function metrics, highlighting critical molecular pathways involved in integrin cell surface interactions and lipid metabolism regulation. Machine learning-based analysis facilitated the development of a multi-marker panel-comprising IGHV2-70, LysoPC (16:0), and hexadecyl ferulate-which achieved 81.46% accuracy in identifying pulmonary dysfunction cohort. ConclusionsThese findings suggest that alterations in integrin-related signaling networks and dysregulation of lipid metabolism play pivotal roles in mediating the long-term pulmonary consequences of humidifier disinfectant exposure. The proposed multi-marker panel offers significant potential for enhanced risk stratification and timely therapeutic intervention. At a Glance CommentaryO_ST_ABSScientific Knowledge on the SubjectC_ST_ABSExtensive epidemiological evidence has established the causal relationship between humidifier disinfectant exposure and pulmonary dysfunction; however, clinically validated biomarkers for predicting chronic lung disease progression remain limited. Pediatric populations demonstrate unique pathophysiological mechanisms distinct from adults, highlighting the critical necessity for biomarker identification grounded in comprehensive molecular understanding. Despite advances in omics technologies, recent investigations have encountered significant obstacles in achieving deeper mechanistic insights, predominantly attributable to methodological constraints in harmonizing clinical phenotypes with high-dimensional molecular datasets. What This Study Adds to the FieldThis investigation elucidates the fundamental contributions of integrin-mediated signaling cascades and lipid metabolic networks to persistent pulmonary dysfunction following humidifier disinfectant exposure. Our analyses revealed coordinated regulation of integrin signaling pathways and immune response networks through NMF clustering, indicating dynamic temporal evolution of inflammatory responses during chronic disease progression, with temporally distinct molecular signatures identified across discrete observation intervals. Multi-omics factor analysis (MOFA) corroborated integrin pathway dysregulation while additionally uncovering systematic suppression of lipid metabolic processes. Furthermore, machine learning algorithms enabled development of a robust three-component biomarker panel--encompassing IGHV2-70, LysoPC (16:0), and hexadecyl ferulate--demonstrating 81.46% classification accuracy for pulmonary dysfunction phenotypes. Collectively, these findings substantially advance mechanistic understanding of chronic lung injury in vulnerable pediatric cohorts and identify clinically relevant biomarkers with translational potential for risk stratification and therapeutic targeting in clinical practice.

systems biology↗

bioRxiv2025

In vivo cartography of state-dependent signal flow hierarchy in the human cerebral cortex

Understanding the principle of information flow across distributed brain networks is of paramount importance in neuroscience. Here, we introduce a novel neuroimaging framework, leveraging integrated effective connectivity (iEC) and unconstrained signal flow mapping for data-driven discovery of the human cerebral functional hierarchy. Simulation and empirical validation demonstrated the high fidelity of iEC in recovering connectome directionality and its potential relationship with histologically defined feedforward and feedback pathways. Notably, the iEC-derived hierarchy revealed a monotonically increasing level along the axis where the sensorimotor, association, and paralimbic areas are sequentially ordered - a pattern supported by the Structural Model of laminar connectivity. This hierarchy was further demonstrated to flexibly reorganize across brain states: flattening during an externally oriented condition, evidenced by a reduced slope in the hierarchy, and steepening during an internally focused condition, reflecting heightened engagement of interoceptive regions. Our study highlights the unique role of macroscale directed functional connectivity in uncovering a biologically interpretable state-dependent signal flow hierarchy.

neuroscience↗

bioRxiv2025

Derepression of a single microRNA target causes female infertility in mice

The miR-200a and miR-200b families control mouse ovulation and are essential for female fertility. The ZEB1 transcription factor is a conserved target of both families and has been implicated as a key player in female fertility at multiple levels. Using gene-edited mice that express a miR-200a/b-resistant form of Zeb1, we found that derepression of Zeb1 in the female pituitary caused decreased production of luteinizing hormone and anovulatory infertility. These phenotypes were accompanied by widespread changes in pituitary gene expression characterized by decreased levels of ZEB1 targets, which include the miR-200a/b miRNAs, as expected from the miR-200a/b-ZEB1 double-negative feedback loop. Also observed were increased levels of mesenchymal genes, neuronal genes, and miR-200a/b targets. These results show that a double-negative feedback loop centered on the miRNA regulation of a single transcription factor can significantly influence the expression of thousands of genes and have dramatic phenotypic consequences.

molecular biology↗

bioRxiv2025

Sub-second Fluctuation between Top-Down and Bottom-Up Modes Distinguishes Diverse Human Brain States

Information continuously flows between regions of the human brain, exhibiting distinct patterns that dynamically shift across states of consciousness, cognitive modes, and neuropsychiatric conditions. In this study, we introduce Relative Phase Analysis (RPA), a method that leverages phase-lead/lag relationships to reveal the real-time dynamics of dominant directional patterns and their rapid transitions. We demonstrate that the human brain switches on a sub-second timescale between a top-down mode-where anterior regions drive posterior activity-and a bottom-up mode, characterized by reverse directionality. These dynamics are most pronounced during full consciousness and gradually become less distinct as awareness diminishes. Furthermore, we find from simultaneous EEG-fMRI recordings that the top-down mode is expressed when higher-order cognitive networks are more active while the bottom-up mode is expressed when sensory systems are more active. Moreover, comparisons of an attention deficit hyperactivity disorder (ADHD) inattentive cohort with typically developing individuals reveal distinct imbalances in these transition dynamics, highlighting the potential of RPA as a diagnostic biomarker. Complementing our empirical findings, a coupled-oscillator model of the structural brain network recapitulates these emergent patterns, suggesting that such directional modes and transitions may arise naturally from inter-regional neural interactions. Altogether, this study provides a framework for understanding whole-brain dynamics in real-time and identifies sub-second fluctuations in top-down versus bottom-up directionality as a fundamental mechanism underlying human information processing.

neuroscience↗

bioRxiv2025

Developmental Variations in Recurrent Spatiotemporal Brain Propagations from Childhood to Adulthood

The brain undergoes profound structural and functional transformations from childhood to adolescence. Convergent evidence suggests that neurodevelopment proceeds in a hierarchical manner, characterized by heterogeneous maturation patterns across brain regions and networks. However, the maturation of the intrinsic spatiotemporal propagations of brain activity remains largely unexplored. This study aims to bridge this gap by delineating spatiotemporal propagations from childhood to early adulthood. By leveraging a recently developed approach that captures time-lag dynamic propagations, we characterized intrinsic dynamic propagations along three axes: sensory-association (S-A), task-positive to default networks (TP-D), and somatomotor-visual (SM-V) networks, which progress towards adult-like brain dynamics from childhood to early adulthood. Importantly, we demonstrated that as participants mature, there is a prolonged occurrence of the S-A and TP-D propagation states, indicating that they spend more time in these states. Conversely, the prevalence of SM-V propagation states declines during development. Notably, top-down propagations along the S-A axis exhibited an age-dependent increase in occurrence, serving as a superior predictor of cognitive scores compared to bottom-up S-A propagation. These findings were replicated across two independent cohorts (N = 677 in total), emphasizing the robustness and generalizability of these findings. Our results provide new insights into the emergence of adult-like functional dynamics during youth and their role in supporting cognition.

neuroscience↗

bioRxiv2024

Three Subtypes Of Autism Spectrum Disorder With Transcriptomic Signatures Derived From Morphometric Similarity Networks

Autism spectrum disorder (ASD) is a prevalent and highly heterogeneous neurodevelopmental disorder. Previous studies have attempted to identify ASD subgroups by analyzing isolated cortical structural features. However, these studies have not considered the relationship between multiple structural features, which provide information on the structural organization of the brain. Morphometric similarity network (MSN), a structural brain network contributed by multiple anatomical features (gray matter volume, mean cortical thickness, surface area, mean curvature, Gaussian curvature, curvature index, and fold index), strongly relates to cytoarchitectonic and genomic measures of histological similarity between cortical areas. We applied K-means clustering on MSN from 236 individuals with ASD and identified three subtypes. Subtype-1 showed relatively similar MSN values with typically developmental individuals (TD). Subtype-2 showed higher morphometric similarities in the lateral frontal and temporal cortical regions and lower in anterior prefrontal and occipital regions compared to TD. These patterns were the opposite in subtype-3. Behaviorally, subtype-3 had more severe verbal and social deficits compared to subtype-2. The weaker resting-state functional connectivity (rs-FC) between the language and salience networks was observed between subtype-2 and TD. Subtype-3 had stronger rs-FC between salience and default mode networks (DMN), between frontoparietal and visual networks, and between language and dorsal attention networks, while weaker rs-FC within DMN, within sensorimotor, and within salience networks. In addition, genes with expression patterns associated with regional MS changes in ASD subtypes were functionally enriched in neuron-specific biological processes related to nervous system development, synaptic signaling and chromatin organization. These genes were particularly enriched in GABAergic neurons, glutamatergic neurons, astrocytes and microglia. Taken together, our findings suggest the existence of different neuroanatomical subtypes based on multiple anatomical features in ASD with distinct transcriptomic signatures and functional connectome patterns.

neuroscience↗

bioRxiv2024

Pharmaco-resistant temporal lobe epilepsy gradually perturbs the cortex-wide excitation-inhibition balance

AO_SCPLOWBSTRACTC_SCPLOWExcitation-inhibition (E/I) imbalance is theorized as a key mechanism in the pathophysiology of epilepsy, with a mounting body of previous research focusing on elucidating its cellular manifestations. However, there are limited studies into E/I imbalance at macroscale and its microcircuit-level mechanisms and clinical associations. In our current work, we computed the Hurst exponent--a previously validated index of the E/I ratio--from resting-state fMRI time series, and simulated microcircuit parameters using biophysical computational models. We found a broad reduction in the Hurst exponent in pharmaco-resistant temporal lobe epilepsy (TLE), indicative of a shift towards more excitable network dynamics. Connectome decoders pointed to temporolimbic and frontocentral areas as plausible network epicenters of E/I imbalance. Computational simulations further revealed that enhancing cortical excitability in patients likely reflected atypical increases in recurrent connection strength of local neuronal ensembles. Moreover, mixed cross-sectional and longitudinal analyses revealed heightened E/I elevation in patients with longer disease duration, more frequent electroclinical seizures and inter-ictal epileptic spikes, and worse cognitive functioning. Replicated in an independent dataset, our work provides compelling in-vivo evidence of a macroscale shift in E/I balance in TLE patients that undergoes progressive changes and underpins cognitive impairments, potentially informing treatment strategies targeting E/I mechanisms.

neuroscience↗

bioRxiv2024

A shifting role of thalamocortical connectivity in the emergence of large-scale functional brain organization across early lifespan development

While cortical patterning has been a perennial research topic in neuroscience, the mechanism for its consequence, namely functional specialization at the macro scale, remains an open question in the human brain. Here, we focused on age-dependent changes of resting-state thalamocortical connectivity to investigate its role in the emergence of large-scale functional networks across infancy, childhood and young adulthood. We found that the thalamocortical connectivity during infancy reflects an early differentiation of sensorimotor networks and genetically-influenced axonal projection. This initial role of the thalamus, however, seems to change during childhood, by establishing connectivity with the salience network and decoupling externally- and internally-oriented functional processes. Developmental simulation and perturbation analyses corroborated these findings, demonstrating the highest contribution of thalamic connectivity, especially in the later age of youth, in the formation of key characteristics of the mature brain, such as functional gradient and cortical hierarchy. Our study highlights a developmentally shifting role of the thalamus in orchestrating complex brain organization and its potential implications for developmental conditions characterized by compromised internal and external processing.

neuroscience↗

bioRxiv2024

Contracted Functional Connectivity Profiles in Autism

ObjectiveAutism spectrum disorder (ASD) is a pervasive neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long-relative to short-range functional connectivity profiles. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes. MethodsWe studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean{+/-}SD age=20.8{+/-}8.1 years) and 108 neurotypical controls (NT, 19.2{+/-}7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types. ResultsCompared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores. ConclusionsOur study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

neuroscience↗

bioRxiv2024

Temporal Dynamics of Brain Mediation in Predictive Cue-induced Pain Modulation

Pain is not a mere reflection of noxious input. Rather, it is constructed through the dynamic integration of prior predictions with incoming sensory input. However, the temporal dynamics of the behavioral and neural processes underpinning this integration remain elusive. Here, we identified a series of brain mediators that integrated cue-induced expectations with noxious inputs into ongoing pain predictions using a semicircular scale designed to capture rating trajectories. Temporal mediation analysis revealed that during the early-to-mid stages of integration, the frontoparietal and dorsal attention network regions, such as the lateral prefrontal, premotor, and parietal cortex, mediated the cue effects. Conversely, during the mid-to-late stages of integration, the somatomotor network regions mediated the effects of stimulus intensity, suggesting that the integration occurs along the cortical hierarchy from transmodal to unimodal brain systems. Our findings advance the understanding of how the brain integrates prior and sensory information into pain experience over time.

neuroscience↗

bioRxiv2023

Connectome-wide structure-function coupling models implicate polysynaptic alterations in autism

AO_SCPLOWBSTRACTC_SCPLOWAutism spectrum disorder (ASD) is one of the most common neurodevelopmental diagnoses. Although incompletely understood, structural and functional network alterations are increasingly recognized to be at the core of the condition. We utilized multimodal imaging and connectivity modeling to study structure-function coupling in ASD, and probed mono- and polysynaptic mechanisms on structurally-governed network function. We examined multimodal magnetic resonance imaging data in 47 ASD and 37 neurotypical controls from the Autism Brain Imaging Data Exchange (ABIDE) II initiative. We predicted intrinsic functional connectivity from structural connectivity data in each participant using a Riemannian optimization procedure that varies the times that simulated signals can unfold along tractography-derived personalized connectomes. In both ASD and neurotypical controls, we observed improved structure-function prediction at longer diffusion time scales, indicating better modeling of brain function when polysynaptic mechanisms are accounted for. Prediction improvements were marked in transmodal association systems, such as the default mode network, in both controls and ASD. Improvements were, however, lower in ASD in a polysynaptic regime at higher simulated diffusion times. Regional differences followed a sensory-to-transmodal cortical hierarchy, with an increased gap between groups in transmodal compared to sensory/motor systems. Multivariate associative techniques revealed that structure-function differences reflected inter-individual differences in autistic symptoms and verbal as well as non-verbal intelligence. Our network modeling approach sheds light on atypical structure-function coupling in autism, and suggests that polysynaptic network mechanisms are implicated in the condition and can help explain its wide range of associated symptoms.

neuroscience↗

bioRxiv2023

Diverging asymmetry of intrinsic functional organization in autism

Autism is a neurodevelopmental condition involving atypical sensory-perceptual functions together with language and socio-cognitive deficits. Previous work has reported subtle alterations in the asymmetry of brain structure and reduced laterality of functional activation in individuals with autism relative to non-autistic individuals (NAI). However, whether functional asymmetries show altered intrinsic systematic organization in autism remains unclear. Here, we computed inter- and intra-hemispheric asymmetry of intrinsic functional gradients capturing connectome organization along three axes, stretching between sensory-default, somatomotor-visual, and default-multiple demand networks, to study system-level hemispheric imbalances in autism. We observed decreased leftward functional asymmetry of language network organization in individuals with autism, relative to NAI. Whereas language network asymmetry varied across age groups in NAI, this was not the case in autism, suggesting atypical functional laterality in autism may result from altered developmental trajectories. Finally, we observed that intra-but not inter-hemispheric features were predictive of the severity of autistic traits. In sum, our findings illustrate how regional and patterned functional lateralization is altered in autism at the system level. Such differences may be rooted in altered developmental trajectories of functional organization asymmetry in autism.

neuroscience↗

bioRxiv2023

Whole-brain structural connectome asymmetry in autism

AO_SCPLOWBSTRACTC_SCPLOWAutism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated. We studied whole-brain structural connectivity-based brain asymmetry in 47 individuals with autism and 37 healthy controls using diffusion magnetic resonance imaging obtained from the Autism Brain Imaging Data Exchange initiative. By leveraging dimensionality reduction techniques, we constructed low-dimensional representations of structural connectivity and calculated their asymmetry index. We compared the asymmetry index between individuals with autism and neurotypical controls and found atypical structural connectome asymmetry in the sensory, default-mode, and limbic networks and the caudate in autism. Network communication provided topological underpinnings by demonstrating that the temporal and dorsolateral prefrontal regions showed reduced global network communication efficiency and decreased send-receive network navigation in the caudate region in individuals with autism. Finally, supervised machine learning revealed that structural connectome asymmetry is associated with communication-related autistic symptoms and nonverbal intelligence. Our findings provide insights into macroscale structural connectome alterations in autism and their topological underpinnings.

neuroscience↗

bioRxiv2022

Subgroups of eating behavior traits independent of obesity defined using functional connectivity and feature representation learning

AO_SCPLOWBSTRACTC_SCPLOWEating behavior is highly heterogeneous across individuals, and thus, it cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors. This study was conducted on 424 healthy adults. We generated low-dimensional representations of functional connectivity defined using the resting-state functional magnetic resonance imaging, and calculated latent features using the feature representation capabilities of an autoencoder by nonlinearly compressing the functional connectivity information. The clustering approaches applied to latent features identified three distinct subgroups. The subgroups exhibited different disinhibition and hunger traits; however, their body mass indices were comparable. The model interpretation technique of integrated gradients revealed that these distinctions were associated with the functional reorganization in higher-order associations and limbic networks and reward-related subcortical structures. The cognitive decoding analysis revealed that these systems are associated with reward- and emotion-related systems. We replicated our findings using an independent dataset, thereby suggesting generalizability. Our findings provide insights into the macroscopic brain organization of eating behavior-related subgroups independent of obesity.

neuroscience↗

bioRxiv2022

BrainStat: A toolbox for brain-wide statistics and neuroscientific contextualization

AO_SCPLOWBSTRACTC_SCPLOWAnalysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.

neuroscience↗