Biology subjects

Calhoun, V.

Publications and source records attributed to Calhoun, V..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Investigating White Matter Functional Network Connectivity Across the Alzheimers Disease Spectrum Using Resting-State fMRI

White matter (WM) has traditionally been considered structurally important but functionally inert in fMRI research. However, growing evidence indicates that WM exhibits meaningful BOLD fluctuations and participates in functional connectivity. Here, we investigate alterations in WM functional network connectivity (FNC) across the Alzheimers disease (AD) spectrum using resting-state fMRI data from the Alzheimers Disease Neuroimaging Initiative (ADNI; 415 cognitively normal (CN), 283 mild cognitive impairment (MCI), 91 AD). We applied a guided independent component analysis (ICA) approach based on a combined multiscale template including 202 intrinsic connectivity networks (ICNs; 97 WM, 105 gray matter (GM)) to estimate subject-specific timecourses and compute static FNC (sFNC). Group differences in WM-WM, GM-GM, and WM-GM connectivity (AD-CN, AD-MCI, MCI-CN) were assessed using two-sample t-tests with covariates for age, sex, and motion, with false discovery rate correction. Results showed robust alterations in WM-WM and WM-GM connectivity in AD, particularly involving WM subcortical, frontal, sensorimotor, and occipitotemporal networks. Several WM-GM interactions with cerebellar and hippocampal GM networks were also disrupted, including reduced GM-cerebellar:WM-frontal coupling and increased GM-hippocampal:WM- frontal connectivity. Notably, MCI already showed WM-GM dysconnectivity relative to CN, suggesting that functional disruption of WM circuits emerges prior to overt dementia. These findings provide converging evidence that WM functional connectivity is both measurable and selectively altered across the AD continuum. Our findings support WM sFNC as a complementary candidate biomarker to GM-based measures for staging and monitoring AD. This is, to our knowledge, the first large-scale ADNI study to jointly model WM and GM intrinsic connectivity networks and quantify WM-GM dysconnectivity across CN, MCI, and AD.

neuroscience↗

bioRxiv2026

Microstructural Disruption of the Forceps Minor in Schizophrenia: A Potential Clinical Imaging Biomarker Using Translational DTI

Schizophrenia is one of the most complex brain disorders, arising from multidimensional pathophysiological processes that span genetic vulnerability, neurotransmitter dysregulation, structural brain damage, and large-scale brain network dysfunction. Large-scale neuroimaging studies have consistently demonstrated the critical role of frontal brain regions in schizophrenia. Despite substantial progress, the precise localization of structural damage within these regions and the neurobiological mechanisms linking such alterations to disease pathology remain poorly understood. In this study, we included a total of 115 subjects from two sites of the B-SNIP dataset, comprising 60 healthy controls and 55 individuals with schizophrenia. We employed diffusion tensor imaging (DTI) to precisely characterize specific structural alterations in the frontal brain regions associated with schizophrenia. Our findings reveal significant microstructural abnormalities in the forceps minor, a major commissural white-matter tract that serves as a critical interhemispheric bridge between the bilateral frontal lobes. Network-level mapping further demonstrates that the forceps minor is closely integrated with large-scale brain networks, particularly the default mode network, and maintains strong structural connectivity with orbitofrontal regions-both of which are known to exhibit dysfunction in schizophrenia. Moreover, converging evidence suggests that the forceps minor plays an important role in the regulation of social behavior, a core domain of impairment in schizophrenia. Collectively, these findings identify the forceps minor as a promising structural imaging biomarker for schizophrenia and provide novel insights into the microstructural mechanisms underlying the disorder.

neuroscience↗

bioRxiv2026

Structural co-modulation: An individualized measure of inter-component interactions in source-based morphometry

Source-based morphometry (SBM) is a powerful multivariate method for identifying covarying structural brain networks. However, standard SBM provides only a single loading value per component for each subject, which limits the characterization of relationships between these components. We propose a novel technical co-modulation approach to derive an individualized, network-like measure of structural brain organization. This method transforms the subject-specific SBM loading vector into a symmetric co-modulation matrix by computing the vectors outer product. Each element of this matrix quantifies the pairwise interaction between structural components, creating a subject-specific fingerprint. Similar to functional connectivity that maps the temporal synchronization between networks, this matrix maps their joint structural prominence, reflecting how strongly two networks co-occur within an individual. To demonstrate the utility of this method, we applied it to structural MRI data from 210 patients with schizophrenia (SZ) and 195 healthy controls (HC) from the fBIRN psychosis dataset using functional networks as priors for SBM. We observed widespread reductions in structural co-modulation in the SZ group, particularly within and between visual, default-mode, and cognitive control networks. Furthermore, co-modulation patterns were significantly correlated with cognitive performance and clinical symptom severity in patients. Structural co-modulation provides a robust framework for quantifying individualized relationships between structural brain features, overcoming key limitations of standard SBM and offering a new avenue for integrating structural and functional brain analyses.

bioengineering↗

bioRxiv2026

Neuromark Fusion: A Replicable Multimodal Template for Structure-Function Fusion of Brain MRI

Multimodal data fusion is a powerful technique for extracting shared and complementary information about the brain that is captured across neuroimaging modalities. Independent component analysis (ICA)-based approaches are among the most widely utilized methods for multimodal fusion, as they are data-driven, robust to noise, and capable of identifying complex, hidden linkages of varying strengths across high-dimensional datasets. However, the data-driven nature of ICA fusion approaches can make comparisons across analyses difficult without a normative framework in place. In this work, we utilize resting state functional MRI (rsfMRI) and structural MRI (sMRI) scans from >15,000 subjects to generate a normative model of multimodal structure-function linkages that can be used as a template to guide ICA fusions of new datasets. When applying this template in two datasets, resultant components exhibit high correspondence to the template even in small sample sizes, and subject-level loadings from template-derived ICs show significant associations to age.

neuroscience↗

bioRxiv2026

Dynamic Co-Modulation (DyCoM): A Unified Operator Framework for Time-Varying Interactions

Dynamic connectivity is central to understanding time-varying interactions between brain regions. Despite decades of methodological development, approaches to measuring dynamic connectivity remain fragmented, leading to inconsistent findings, limited comparability across studies, and difficulty attributing observed effects to computational choices. Here, we introduce dynamic co-modulation (DyCoM), a compact operator-level framework that expresses dynamic connectivity estimators as compositions of a small set of fundamental signal processing operations. Using simulations and resting-state fMRI data, we show that DyCoM disentangles previously conflated findings by revealing that lower-order sensory and higher-order executive control neurobiological signatures, state-transition sensitivity, and medication-linked clinical associations arise from distinct operator choices within a single unified framework. Together, these results establish DyCoM as a unifying foundation for dynamic interaction analysis, revealing how differences in estimator design give rise to divergent biological interpretations and offering a principled and extensible framework for coherence, interpretability, and estimator development.

bioengineering↗

bioRxiv2026

Fidelity of Spatiotemporal Patterns of Brain Activity Across Sampling Rate, Scan Duration, and Frequency Content

Intrinsic brain activity is characterized by large-scale spatiotemporal patterns that underpin functional connectivity and cognition. Quasi-periodic patterns (QPPs) and complex principal component analysis (cPCA) have emerged as reproducible methods for capturing spatiotem-poral network interactions in resting-state functional magnetic resonance imaging (rs-fMRI). However, these methods remain sensitive to methodological factors such as scan duration, repetition time (TR), and frequency band selection. This study systematically evaluates how these parameters influence the stability and reliability of QPP- and cPCA-derived functional connectivity patterns across multiple datasets. Using five independent rs-fMRI datasets, we evaluate the impact of scan length on pattern reliability, explore the effects of TR on spatiotemporal patterns, and compare the sensitivity of different frequency bands (Slow-5, Slow-4, infraslow) in capturing network dynamics. Our findings reveal that while both QPPs and cPCA detect intrinsic network activity, their reliability varies with acquisition parameters. QPPs exhibit greater stability in shorter scans, making them suitable for individual-level analyses, whereas cPCA provides a broader representation of phase-coherent fluctuations but shows greater between-subject variability and benefits more from longer, group-level acquisi-tions. Additionally, frequency band selection significantly influences the temporal structure of extracted patterns: in our analyses, Slow-5 (0.01-0.027 Hz) tended to emphasize more recurrent, synchronized network configurations, whereas Slow-4 (0.027-0.073 Hz) more often revealed transitions between connectivity states. These results provide critical insights into optimizing methodological choices for dynamic functional connectivity analysis, enhancing the interpretability of spatiotemporal patterns in both basic and clinical neuroimaging research.

neuroscience↗

bioRxiv2025

Spatiotemporal Network Dynamics Reveal Alzheimer's Disease Progression

Alzheimers disease (AD) is characterized by progressive disruptions in large-scale brain networks that precede cognitive decline, yet conventional functional connectivity analyses often fail to detect disruptions in coordination among large-scale brain networks that may be critical for early detection. This study leverages quasi periodic patterns (QPPs) and complex principal component analysis (cPCA) to characterize spatiotemporal network alterations across longitudinally stable (normal cognitive, mild cognitive impairment, dementia of Alzheimers type) and transitioning (normal cognitive to mild cognitive impairment, mild cognitive impairment to dementia of Alzheimers type) cohorts from the Alzheimers Disease Neuroimaging Initiative using resting state fMRI. QPPs were used to derive recurrent spatiotemporal templates and network integrity measures at the intrinsic connectivity network level, while cPCA decomposed Hilbert transformed time series into complex valued patterns that capture amplitude and phase relationships. Nonparametric group comparisons revealed a structured trajectory in which limbic, subcortical, and higher cognition networks, including triple network components, are affected early, followed by progressive disruption in visual, cerebellar, sensorimotor, and additional triple network systems. Transitioning cohorts showed many of these alterations before formal diagnostic conversion, indicating that spatiotemporal signatures carry preclinical information. QPP based metrics were particularly sensitive to limbic and subcortical degradation, whereas cPCA emphasized changes in higher order, visual, and cerebellar patterns, revealing complementary aspects of the same underlying pathology. These findings extend prior QPP only work and highlight the utility of combining QPP and cPCA based measures as a dynamic, network-level biomarker framework for AD progression. with potential applications in early detection, characterizing disease trajectories, and treatment monitoring.

neuroscience↗

bioRxiv2025

The Role of Sleep Duration and Age in Neural Reactivation and Episodic Memory Performance Across the Adult Lifespan

Sleep is essential for episodic memory consolidation, involving the reactivation of neural patterns during encoding and retrieval. This study examined how sleep duration influences encoding-retrieval similarity (ERS) and memory performance across the adult lifespan. Fifty-five adults (18-79 years) participated in a seven-day protocol, combining actigraphy-based sleep monitoring with EEG and memory assessments during a paired associate learning task. Significant correlations between sleep duration and ERS were observed, with clusters showing positive and negative associations across brain regions. In match context trials, ERS in the left frontal region correlated with sleep duration, and age moderated these effects. In mismatch context trials, positive correlations were identified in the left posterior, left frontal, and right posterior regions. These results highlight the role of sleep in modulating neural reactivation patterns during memory retrieval and emphasize the influence of age. The study advances our understanding of how sleep variability impacts memory consolidation and provides a basis for exploring targeted sleep interventions. Clinical RelevanceThis study highlights the impact of habitual sleep variability on neural reactivation patterns and memory retrieval across the adult lifespan. The findings emphasize the moderating role of age and suggest that improving sleep duration may enhance episodic memory performance. These insights could guide clinicians in developing personalized sleep interventions to support cognitive health and mitigate age-related memory decline.

neuroscience↗

bioRxiv2025

DECODING BRAIN DYNAMICS VIA CROSS-NETWORK NON-LINEAR TRAJECTORIES

Infant brain networks mature rapidly and nonlinearly, yet cross-network comparisons of developmental trajectories remain rare. We introduce cross-network non-linear trajectory characterization (CNTC), which fits cubic polynomials to age-related change in three spatial measures--network-averaged spatial similarity (NASS), network strength, and network size--derived from resting-state fMRI. Using 137 scans from 74 neurotypical infants spanning 0-6 months of age, we estimated networks via group ICA, modeled age trajectories within each network, and then contrasted coefficients across networks. Across seven canonical systems, trajectories showed robust age effects. Cross-network differences were dominated by linear terms, with selective quadratic effects and few cubic effects. Visual and cerebellar systems exhibited the steepest NASS slopes and pronounced increases in strength and size, whereas motor/attention changes were more gradual. CNTC provides a compact, coefficient-level summary of inter-network maturation and a basis for benchmarking atypical development in future work.

neuroscience↗

bioRxiv2025

Mapping the Developmental Trajectory of Functional Brain Networks in Early Infancy: Insights into Typical Maturation

Early infancy is a crucial period for brain development, during which fundamental functional and structural frameworks are established. Understanding the maturation of large-scale brain networks during this stage is essential for characterizing normative neurodevelopment and identifying potential deviations linked to neurodevelopmental disorders. In this study, we investigated developmental changes in the spatial organization of functional brain networks in infants using a longitudinal resting-state fMRI dataset comprising 137 scans from 74 low-likelihood developing infants aged 0-6 months. We applied independent component analysis to extract large-scale brain networks and utilized advanced spatial metrics, including network-averaged spatial similarity (NASS) to assess alignment with group-level patterns, network strength to quantify neural engagement based on voxel intensities, and network size to examine spatial distribution. Our findings reveal significant age-related increases in NASS across multiple networks, indicating greater consistency in functional organization over time. Additionally, most networks demonstrated increased network strength, reflecting heightened neural involvement, while network size exhibited distinct developmental trajectories, with some networks expanding and others remaining stable. These results highlight the dynamic evolution of functional brain architecture during early infancy, providing critical insights into neurodevelopmental processes. Clinical RelevanceThis study provides critical insights into early brain network development, which is essential for identifying biomarkers of neurodevelopmental disorders such as autism and schizophrenia. By mapping typical maturation patterns using advanced spatial metrics, our findings offer a foundation for early detection of atypical development. Deviations in network organization and strength could serve as early indicators, supporting neuroimaging-based screening and intervention strategies to optimize neurodevelopmental outcomes.

neuroscience↗

bioRxiv2025

SPATIAL DISSIMILARITY TRAJECTORIES REVEAL RAPID FUNCTIONAL NETWORK REFINEMENT DURING EARLY INFANCY

The first six months of life mark a critical window for human brain development, characterized by rapid synaptic formation, axonal growth, and network reorganization. During this period, large-scale functional networks progressively mature. Although prior infant fMRI studies have characterized temporal functional connectivity, the spatial organization and refinement of brain networks during early infancy remain underexplored. In this study, we analyzed longitudinal resting-state fMRI from 74 typically developing infants (0-6 months; 137 scans) using group independent component analysis (ICA) with group-information-guided ICA (GIG-ICA) back-reconstruction. Spatial dissimilarity between each infants spatial map and the group-level map was quantified using cosine dissimilarity and Euclidean distance. Generalized additive models were applied to model age-related trends. Both cosine and Euclidean dissimilarity metrics showed significant decreases with age, indicating a progressive convergence of individual network topographies toward the group-level architecture. These findings reveal increasing spatial coherence and refinement of large-scale brain networks during early infancy, suggesting that spatial maturation complements the well-established functional integration observed in temporal connectivity studies.

neuroscience↗

bioRxiv2025

Functional Inertia Index of Memory-Retaining Brain Dynamics: A Measure of Large-Scale Brain Adaptability

The brain does not process the present in isolation. Its responses are shaped by the accumulating weight of prior states, yet existing frameworks treat large-scale brain dynamics as sequences of transient configurations, leaving the constraining influence of history formally unspecified. Here we show that the brain possesses an intrinsic, representation-agnostic organizing constraint, the degree to which accumulated prior states resist ongoing reorganization, which we term functional inertia. Using an inertial state-space model replicated across two independent observational frameworks, we demonstrate that functional inertia operates coherently across three levels of brain organization: it structures activity into dynamical regimes, links these regimes to clinical and cognitive expression through a system-level inertial magnitude, and distributes across circuits in patterns that reconcile the longstanding coexistence of associative rigidity and sensory volatility in schizophrenia. Critically, removing the cumulative integration collapses this multilevel organization. Strikingly, the system-level inertial magnitude carries opposite cognitive signatures across diagnostic contexts, predicting better performance in healthy individuals but greater symptom severity in schizophrenia, reflecting context-dependent expression of a single constraint. These findings position functional inertia as a unifying, multilevel constraint on brain reorganization, recasting stability and volatility not as opposing properties but as context-dependent expressions of a single constraint.

neuroscience↗

bioRxiv2025

Multimodal Fusion Analysis of Florbetapir PET and Multiscale Functional Network Connectivity in Alzheimer's Disease

Accumulation of amyloid-beta plaques and disruption of intrinsic brain networks are two important characteristics of Alzheimers disease (AD), yet the relationship between amyloid accumulation and network dysfunction remains unclear. In this study, we integrated [18F]Florbetapir PET and resting-state fMRI (rsfMRI) derived Functional Network Connectivity (FNC) from 552 temporally matched longitudinal PET-rsfMRI sessions across 395 participants spanning Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and AD stages. With a model order of 11, joint Independent Component Analysis (jICA) was applied to the fused PET-FNC data, identifying 11 stable components, of which 9 PET-derived components corresponded to previously characterized brain regions or networks. The multimodal analysis revealed disease progression markers, including (1) a pattern of reduced subject loadings across clinical stages (CN > MCI > AD) in white matter and cerebellar regions, reflecting structural degeneration; (2) increased amyloid accumulation in affected individuals in grey matter regions, particularly in frontal, sensorimotor, extended hippocampal, and default mode network (DMN) regions, accompanied by functional connectivity alterations that reflected both compensatory and disruptive network dynamics. We identified PET-derived components that captured distinct stages of disease progression, with the DMN component emerging as a late-stage biomarker and a white matter component showing early-stage changes with limited progression thereafter. Additionally, several components showed significant variation in loadings between APOE{varepsilon} 4 carriers and non-carriers, linking the multimodal signatures to a well-established genetic risk factor for AD.

neuroscience↗

bioRxiv2025

Neural flexibility in metabolic demand dynamics reveals sex-specific differences and supports cognition in late childhood

Dynamic coordination of metabolic demand across brain networks supports emerging cognitive abilities and may drive overall cognitive development, yet how these dynamics vary by sex and relate to cognition in late childhood remains unclear. Using resting-state fMRI from 2,000 healthy 9-to 11-year-olds in the ABCD study, we applied time-resolved dynamic time warping to quantify amplitude mismatches, a proxy of relative energy demand across brain intrinsic networks. Clustering revealed three recurring states: convergent (globally balanced), divergent (imbalanced), and mixed (intermediate). Females spent engaged more with the flexible mixed state, whereas males lingered longer in convergent and divergent states. Across the cohort, better performance on cognitive flexibility, processing speed, and long-term memory tasks correlated with greater overall time in the mixed state and with higher transition rates, but with shorter dwell in any single state. These findings indicate that neural flexibility, rather than prolonged stability, supports cognition during late childhood and that sex differences in dynamic energy coordination emerge well before adolescence.

neuroscience↗

bioRxiv2025

PhaseICA: Complex-Valued Decomposition of Spatially Independent Brain Waves

Understanding complex dynamics from spatiotemporal signals requires robust tools capable of decoding reoccurring patterns. Traditional ICA methods often overlook the spatially non-stationarity nature of brain activity across both the frequency and spatial domains. We propose a novel data-driven approach, named PhaseICA, designed to extract reoccurring spatiotemporal patterns, referred to as brain waves. Unlike conventional ICA methods that focus solely on amplitude, PhaseICA incorporates phase information directly into the component estimation, preserving the nonstationary property that real-valued ICA methods typically discard. The method performs spatial independence optimization in the complex domain by minimizing a complex entropy bound over the eigenvectors of Hilbert-transformed signals. The proposed method captures spatial propagation across brain regions with interpretable and compact representations, offering a promising foundation for decoding brain dynamic systems and revealing the temporal relationship of regions.

neuroscience↗

bioRxiv2025

Automated Annotation and Validation of Human Respiratory Virus Sequences using VADR

Accurate annotation of viral genomes is essential for reliable downstream analysis and public data sharing. While NCBIs Viral Annotation DefineR (VADR) pipeline provides standardized annotation and quality control, it only supports six viral groups to date. Here, we developed and validated 12 new reference sequence-based VADR models targeting key human respiratory viruses: measles virus, mumps virus, rubella virus, human metapneumovirus, human parainfluenza virus types 1-4, and seasonal coronaviruses (229E, NL63, OC43, HKU1). Model construction was guided by a comprehensive analysis of intra-species genomic and phylogenetic diversity, enabling the development of genotype-specific models associated with reference genomes that defined expected genome structure and annotation. Models were trained on 5,327 publicly available complete viral genomes and tested on 372 viral genomes not yet submitted to GenBank. VADR passed 96.3% of publicly available viral genomes and 98.1% of viral genomes not in the training set, correctly identifying overlapping ORFs, mature peptides, and transcriptional slippage as well as genome misassemblies. VADR detected novel viral biology including the first reported HCoV-OC43 NS2 knockout in a human infection and novel G and SH coding sequence lengths in human metapneumovirus. These VADR models are publicly available and are used by NCBI curators as part of the GenBank submission pipeline, supporting high-quality, scalable viral genome annotation for research and public health.

bioinformatics↗

bioRxiv2025

Dynamic Inter-Modality Source Coupling Reveals Sex Differences in Children based on Brain Structural-Functional Network Connectivity: A Multimodal MRI Study of the ABCD Dataset

BackgroundSex differences in brain development are well-documented, yet the dynamic coupling between structure and function remains underexplored. We introduce dynamic inter-modality source coupling (dIMSC), extending our previous work to link structural MRI source-based morphometry (SBM) with dynamic functional network connectivity (dFNC). MethodsWe used data from the Adolescent Brain Cognitive Development (ABCD) study (ages 9-11) and combined SBM-derived gray matter sources with sliding-window dFNC. dIMSC was computed as the time-resolved cross-correlation between these modalities to quantify structure-function coupling strength. We evaluated sex differences in these profiles and their interaction with cognitive performance. ResultsSignificant sex-specific patterns emerged: males exhibited stronger positive coupling in sensorimotor regions (postcentral gyrus), while females showed stronger coupling in higher-order associative regions (inferior parietal lobule). These configurations were functionally distinct: higher positive coupling occupancy predicted better crystallized cognition (vocabulary) in females, whereas it predicted better fluid cognition (working memory) in males. ConclusionTogether, these findings suggest that males and females utilize distinct structural-functional configurations to support cognitive processing, males relying on a sensorimotor-anchored organization and females on an associative-anchored one. The dIMSC method advances our earlier work by enabling time-resolved analysis of brain coupling, providing a powerful framework for investigating sex-specific neurodevelopmental mechanisms.

neuroscience↗

bioRxiv2025

Structural connectivity-informed dynamic estimation (STRiDE): Multimodal connectivity constrained ICA

Understanding the dynamic intrinsic interactions between brain regions has been advanced by functional magnetic resonance imaging (fMRI), particularly through the connectivity analysis used to characterize reoccurring patterns in the brain known as brain states. However, previous studies have primarily focused on unimodal models, which can hinder optimal dynamic state estimation, especially in cognitive disorders, cases where there may be both structure and function disruption. To better estimate intrinsic brain interactions, it is important to account for the factors shaping this estimation not only in terms of time resolved variability of the connectivity but also regarding the underlying physical pathways between brain regions. However current approaches mostly use deterministic weighting of structural connectivity. To address this, we propose a flexible multimodal connectivity constrained independent component analysis (ICA) model, termed structural connectivity-informed dynamic state estimation (STRiDE), that enhances stability and sensitivity by leveraging white matter structural connectivity and dynamic functional connectivity information. Using this model, we decompose brain interactions into independent, reoccurring multimodal patterns or structural-functional states, guided by maximally independent structural connectivity priors derived from the group level data. We first evaluate our proposed model using a simulation pipeline, showing the approach works as design and improves sensitivity to group differences and enhances robustness to noise. Next, we applied the proposed multimodal model to real dataset including a cohort of subjects with schizophrenia (SZ) and healthy controls (HC). Results demonstrated its potential to enhance group-differences in both connectivity domain and temporal dynamics parameters. Specifically results highlighted disruption within and between sensory and trans-modal domains, through a SZ vs HC comparison. Symptoms severity and cognitive scores statistical analysis specifies their significant association with default mode domain, offering insights into the disrupted functional and neural mechanisms underlying schizophrenia. In addition, temporal interplay of the estimated STRiDEs reveals that visual-related STRiDE is significantly impacted in SZ, regarding the speed of processing score, underscoring the link between visual system and speed of processing. In sum, the STRiDE approach provides a flexible way to link structural and functional connectivity at the network level and represents a general approach for studying multimodal dynamic patterns and leveraging these to study the typical and disordered brain.

bioengineering↗