Biology subjects

Biswal, B. B.

Publications and source records attributed to Biswal, B. B..

At least 19 recordsLinked to original sources

bioRxiv2026

Dimensional Arousal and Categorical Gaze Instability: Uncoupling the Baseline Oculomotor Phenotypes of Autism and ADHD

BackgroundAutism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) share substantial clinical and physiological overlap. While naturalistic and sensory-driven paradigms increasingly capture evoked neurophysiological responses, the intrinsic baseline physiology of these conditions remains poorly defined. We characterized resting-state pupillary volatility and oculomotor stability across the ASD-ADHD spectrum using dimensional and categorical (DSM-5) frameworks. MethodsWe analyzed resting-state eye-tracking data from a large pediatric cohort (N = 2,315) from the Healthy Brain Network, extracting Pupil Relative Volatility (Coefficient of Variation [CV]) and Bivariate Contour Ellipse Area (BCEA) to index pupillary and spatial gaze stability. Data were evaluated via continuous regressions against Social Responsiveness Scale (SRS) and SWAN inventories, then 2x2 factorial ANCOVAs based on clinical diagnoses, with sensitivity analyses for extreme values, hardware heterogeneity, and psychostimulant medication. ResultsDimensional models revealed no significant association between either metric and continuous ASD or ADHD trait severity, apart from a modest sub-additive SRS x SWAN interaction on pupillary CV; an apparent pooled BCEA-trait association proved attributable to hardware differences and was absent within hardware-consistent subgroups. Categorical models showed robust diagnostic differentiation for both metrics: isolated ASD was associated with elevated CV and impaired BCEA, with ADHD additionally impairing BCEA. Comorbid ASD+ADHD produced a sub-additive CV interaction robust across outlier-resistant, hardware-stratified, and medication-adjusted analyses; an analogous BCEA interaction was directionally consistent but not significant under standard estimation. ConclusionsCategorical diagnostic status was consistently associated with baseline pupillary and oculomotor physiology across the ASD-ADHD spectrum in this cohort, with a shared sub-additive comorbidity effect most robustly expressed in pupillary volatility; continuous trait severity showed little independent association with either metric. These findings, obtained using a rigorously validated, hardware-heterogeneous multi-site sample, establish a foundation for future naturalistic and sensory-evoked investigations of the shared physiological architecture underlying ASD-ADHD comorbidity.

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bioRxiv2026

Context-Specific Decoupling and Competing Phenotypes: Transdiagnostic Eye-Tracking Biomarkers of ASD and ADHD During Naturalistic Viewing in a Large Pediatric Cohort

BackgroundAutism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) exhibit high clinical overlap, but categorical diagnostic boundaries obscure their shared, dynamic physiological vulnerabilities during real-world sensory processing. MethodsWe analyzed multimodal eye-tracking synchrony in a large transdiagnostic pediatric cohort (N = 2,026) during naturalistic viewing of four distinct media paradigms. A novel 2D complex correlation framework captured gaze inter-subject correlation (ISC) magnitude and spatiotemporal phase divergence, while 1D pupil ISC measured autonomic arousal synchrony. Linear models evaluated dimensional (RDoC) and categorical (2x2 ANCOVA) diagnostic frameworks alongside rigorous medication and severity controls. ResultsDimensional models revealed a domain-general vulnerability: autistic traits independently predicted widespread reductions across gaze synchrony in all media contexts, and pupillary synchrony in narrative-driven contexts, whereas continuous ADHD traits showed minimal independent effects. In contrast, severe spatiotemporal misalignment (phase divergence) did not scale dimensionally but emerged strictly at clinical boundaries, reflecting highly idiosyncratic spatial locking in isolated ASD. Furthermore, categorical models demonstrated a robust, non-additive interaction: the clinical co-occurrence of ADHD paradoxically buffered against this severe spatiotemporal decoupling. Crucially, this protective phenotype was localized strictly to character-driven social narratives and remained highly significant after rigorously adjusting for daily stimulant medication, outlier instability, and baseline autism trait severity. ConclusionsThese findings validate model-free physiological synchrony as a candidate transdiagnostic biomarker. Rather than compounding impairment, comorbid ASD and ADHD reflect competing, non-additive neurocognitive strategies that yield distinct, context-dependent visual phenotypes.

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bioRxiv2026

Unique Amygdala Signatures and Shared Prefrontal Deficits in Autism: Mapping Social Heterogeneity via Naturalistic functional Magnetic Resonance Imaging

BackgroundNaturalistic fMRI provides an ecologically valid window into social brain function, yet binary diagnostic labels may obscure neural signatures linked to the continuous spectrum of social deficits. We investigated whether social brain alterations in autism spectrum disorder (ASD) follow a categorical, dimensional, or "dual-track" architecture. MethodsWe analyzed fMRI data from 428 youth (262 ASD, 166 typically developing; ages 5-22) watching two films: The Present and Despicable Me. Using Principal Component Analysis (PCA) to quantify primary (PC1) and secondary (PC2) synchronization, we employed variance partitioning to disentangle the contributions of categorical diagnosis from continuous symptom severity (Social Responsiveness Scale-2, SRS-2). ResultsDuring The Present, reduced synchronization was widespread. In social-motivational hubs (medial prefrontal cortex, caudate), reductions were largely explained by variance shared between diagnosis and SRS-2 scores. In contrast, the left amygdala exhibited a unique dimensional association with SRS-2 scores independent of categorical diagnosis. Secondary response patterns (PC2), reflecting complex temporal integration, revealed further unique dimensional effects in the cuneus. Notably, these signatures were stimulus-dependent, manifesting during the emotionally complex narrative of The Present but not during the slapstick-oriented Despicable Me. ConclusionsWhile core social-motivational hubs reflect overlapping diagnostic and dimensional deficits, the amygdala and secondary visual patterns provide distinct, dimension-specific signatures of social impairment. This variance partitioning approach supports a Research Domain Criteria (RDoC) framework, highlighting the necessity of integrating dimensional assessments and narrative complexity to characterize the neural architecture of autism.

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bioRxiv2026

fMRI-Based Prediction of Eye Gaze During Naturalistic Movie Viewing Reveals Eye-Movement-Related Brain Activity

BackgroundEye gaze provides crucial insights into perceptual and cognitive processes during naturalistic movie viewing, yet concurrent eye tracking is often unavailable in functional MRI (fMRI) research. While deep learning models can estimate gaze directly from fMRI eyeball signals, their out-of-the-box generalizability across heterogeneous datasets requires empirical evaluation. MethodsWe applied a specific pre-trained model from the DeepMReye framework in a zero-shot setting (without dataset-specific fine-tuning) to estimate gaze during movie watching across three independent fMRI datasets. Model accuracy was evaluated against camera-based eye-tracking data and via inter-subject correlations. Furthermore, we derived eye-movement-related time series from the predicted gaze signals to map their associated brain activation. ResultsAt the individual level, predicted gaze showed modest correspondence with measured ground-truth data (r {approx} 0.24-0.37), yielding brain activation maps largely restricted to the visual cortex. In contrast, group-averaged gaze predictions exhibited substantially higher reliability (r {approx} 0.73-0.84). First-level General Linear Models (GLMs) derived from group-averaged predictions successfully revealed widespread activation across established oculomotor control regions, including the frontal and parietal eye fields. Exploratory analyses of age-related effects on gaze prediction and brain activity yielded inconsistent results across datasets. ConclusionsUnder a zero-shot implementation, the pre-trained model exhibits limitations for individual-level inference, likely reflecting the absence of dataset-specific training. However, group-averaged fMRI-based gaze estimates successfully capture shared viewing behaviors and robustly support the investigation of eye-movement-related brain activity. These findings inform the appropriate use of fMRI-based gaze decoding for naturalistic neuroimaging datasets lacking ground-truth eye-tracking logs.

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bioRxiv2025

Timing Is Everything: Temporal Dynamics of BOLD Responses to Naturalistic Features in Movie Watching

Naturalistic stimuli, such as movies and narratives, are increasingly used in cognitive neuroscience to map cognitive and affective processes onto brain activity measured with functional MRI (fMRI). Features extracted from movies span multiple levels, from computational visual and auditory inputs to physiological signals and subjective ratings. However, the temporal alignment between these features and the blood-oxygen-level-dependent (BOLD) response varies considerably, and the commonly used canonical hemodynamic response function (HRF) with temporal derivatives may not adequately capture these delays. In this study, we analyzed three movie-watching datasets using cross-correlation and finite impulse response (FIR) deconvolution to map the unconstrained temporal dynamics of visual, auditory, pupillary, and Theory of Mind (ToM) features across the brain. Our results demonstrate that while the canonical HRF effectively captures basic sensory features, it introduces systematic misalignments for inherently delayed signals. Because physiological markers (pupil size) and subject reports (ToM) intrinsically lag the underlying neural events, standard HRF convolution overcompensates for their biological latency, introducing a redundant phase mismatch or "double-delay." Furthermore, our unconstrained FIR models revealed distinct inter-regional temporal hierarchies across the cortex. Recognizing the inherent collinearity of real-world stimuli, these estimated profiles capture the bundled, multi-dimensional dynamics of naturalistic processing rather than perfectly isolated feature effects. Ultimately, these findings highlight the necessity of flexible, reliability-tested temporal modeling to accurately map the complex processing timescales engaged during naturalistic viewing.

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bioRxiv2025

Disrupted Regional Dynamics of Structure-Function Connectivity Coupling in Euthymic Bipolar Disorder

BackgroundBipolar disorder (BD) is characterized by persistent disturbances in emotional regulation and cognitive function, even during euthymia, yet its neural mechanisms remain unclear. Given evidence of structural and dynamic functional connectivity (dFC) abnormalities in BD, investigating dynamic structure-function connectivity coupling (SC-FC coupling) may provide insights into the pathophysiology of the disorder. MethodsDiffusion tensor imaging and resting-state fMRI data were collected from 61 euthymic BD-I patients and 54 healthy controls (HC). Structural connectivity was derived using probabilistic tractography, and dFC was estimated with a sliding-window approach. SC-FC coupling was quantified as the extent to which structural features predicted dFC using multivariate linear regression. Abnormal SC-FC coupling in BD was assessed at both the network and regional levels, relative to HC. Over time, SC-FC coupling was categorized into high- and low-coupling states, and dynamic state analyses of dwell time, occurrence rate, and transition probabilities were performed in the affected brain regions. ResultsNetwork-level analyses indicated that BD exhibits lower SC-FC coupling in subcortical regions compared with HC. Regional analyses revealed significantly reduced dynamic SC-FC coupling in two thalamic subregions (medial posterior and rostral temporal) and in the right primary somatosensory cortex in BD compared to HC. Dynamic analyses further demonstrated thalamic instability: BD showed shorter dwell times in high-coupling states, increased occurrence of low-coupling states, and more frequent high-to-low coupling transitions than HC. These abnormalities suggest that BD is characterized by disrupted structure-function integration and unstable thalamocortical dynamics. ConclusionOur findings reveal regional abnormalities in SC-FC coupling dynamics in BD, indicating persistent dysfunction of structure-function integration during euthymia. Dynamic coupling measures may provide preliminary insights into potential biomarkers for disease monitoring.

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bioRxiv2024

Comparing Intra- and Inter-individual Correlational Brain Connectivity from Functional and Structural Neuroimaging Data

Inferring brain connectivity from inter-individual correlations has been applied across various neuroimaging modalities, including positron emission tomography (PET) and MRI. The variability underlying these inter-individual correlations is generally attributed to factors such as genetics, life experiences, and long-term influences like aging. This study leveraged two unique longitudinal datasets to examine intra-individual correlations of structural and functional brain measures across an extended time span. By focusing on intra-individual correlations, we aimed to minimize individual differences and investigate how aging and state-like effects contribute to brain connectivity patterns. Additionally, we compared intra-individual correlations with inter-individual correlations to better understand their relationship. In the first dataset, which included repeated scans from a single individual over 15 years, we found that intra-individual correlations in both regional homogeneity (ReHo) during resting-state and gray matter volumes (GMV) from structural MRI closely resembled resting-state functional connectivity. However, ReHo correlations were primarily driven by state-like variability, whereas GMV correlations were mainly influenced by aging. The second dataset, comprising multiple participants with longitudinal Fludeoxyglucose (18F) FDG-PET and MRI scans, replicated these findings. Both intra- and inter-individual correlations were strongly associated with resting-state functional connectivity, with functional measures (i.e., ReHo and FDG-PET) exhibiting greater similarity to resting-state connectivity than structural measures. This study demonstrated that controlling for various factors can enhance the interpretability of brain correlation structures. While inter- and intra-individual correlation patterns showed similarities, accounting for additional variables may improve our understanding of inter-individual connectivity measures.

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bioRxiv2024

Intelligence and Cognitive Flexibility Selectively Modulate Working Memory Functioning

Working memory is a key component of intelligence, which can be underpinned by task connectome, while cognitive flexibility plays a role in adaptive and executive function that supports working memory processes. However, it remains unknown how intelligence and cognitive flexibility modulate working memory functioning, in terms of behavioural performance and task-related brain hierarchy. Current study investigated the working memory task-related behaviour and brain connectomic gradient in healthy adults together with their intelligence and cognitive flexibility measures. Results showed cognitive flexibility and intelligence were not significantly correlated with each other but with working memory function. Principal component and partial least square analysis revealed that nearly half of variance was explained by their overlap component, which linked with task gradient values of frontoparietal and ventral attention network representing executive, control and cognitive functioning. Individuals with higher intelligence and cognitive flexibility showed less distance between frontoparietal/ventral attention network and unimodal regions in gradient axis during working memory performance. Further analyses demonstrated that task gradient of frontoparietal and ventral attention network was significantly associated with working memory function, and selectively mediated by cognitive flexibility and intelligence, which may suggest partly convergent neural mechanisms for higher order functions that are involved in working memory functioning. In conclusion, current study provides new insight into the relationship among task connectomic gradient, working memory, intelligence, and cognitive flexibility, which helps understand the multi-component model of working memory and underlying task gradient principle.

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bioRxiv2024

Effects of Tasks on Functional Brain Connectivity Derived from Inter-Individual Correlations: Insights from Regional Homogeneity of Functional MRI Data

Research on brain functional connectivity often relies on intra-individual moment-to-moment correlations of functional activity, typically using functional MRI (fMRI). Inter-individual correlations are also employed on data from fMRI and positron emission tomography (PET). Many studies have not specified tasks during scanning, keeping participants in an implicit "resting" condition. This lack of task specificity raises questions about how different tasks impact inter-individual correlation estimates. In our analysis of fMRI data from 100 unrelated participants, scanned during seven tasks and in a resting state, we calculated Regional Homogeneity (ReHo) for each task as a regional measure of brain functions. We found that changes in ReHo due to tasks were relatively small compared with its variations across brain regions. Cross-region variations of ReHo were highly correlated among tasks. Similarly, whole-brain inter-individual correlation patterns were remarkably consistent across the tasks, showing correlations greater than 0.78. Changes in inter-individual correlations between tasks were primarily driven by connectivity in the visual, somatomotor, default mode network, and the interactions between them. This subtle yet statistically significant differences in functional connectivity may be linked to specific brain regions associated with the studied tasks. Future studies should consider task design when exploring inter-individual connectivity in specific brain systems. Impact StatementInter-individual correlation is increasingly used to estimate brain connectivity, complementing intra-individual correlations in fMRI, particularly for measures like cerebral blood flow obtained via fMRI and PET. However, how task performance affects inter-individual correlations is largely unknown. This study used regional homogeneity as a summary measure of brain functions from task-based fMRI data across eight tasks. The inter-individual correlations were highly similar across tasks, indicating the underlying brain network structure can be inferred under various conditions. Subtle but statistically significant differences in connectivity estimates suggest the functional significance of this approach.

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bioRxiv2024

Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI

Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brains functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signals spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.

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bioRxiv2024

Mapping Serotonergic Dynamics using Drug-Modulated Molecular Connectivity

Understanding the complex workings of the brain is one of the most significant challenges in neuroscience, providing insights into normal brain function, neurological diseases, and the effects of potential therapeutics. A major challenge in this field lies in the limitations of traditional brain imaging techniques, which often capture only fragments of the complex puzzle of brain function. Our research employs a novel approach termed "molecular connectivity" (MC), which combines the strengths of various imaging methods to provide a comprehensive view of how specific molecules, such as the serotonin transporter, interact across different brain regions and influence brain function. This innovative technique bridges the gap between functional magnetic resonance imaging (fMRI), known for its ability to monitor brain activity by tracking blood flow, and positron emission tomography (PET), which visualizes specific molecular changes. By integrating these methods, we can better understand how drugs influence brain function. Our study focuses on the application of dynamic [11C]DASB PET scans to map the distribution of serotonin transporters, key players in regulating mood and emotions and examines how these transporters are altered following exposure to methylenedioxymethamphetamine (MDMA), which is commonly known as ecstasy. Through a detailed comparison of MCs with traditional measures of brain connectivity, we reveal significant patterns that closely align with physiological changes. Our results revealed clear changes in molecular connectivity after a single dose of MDMA, establishing a direct link between the effects of drugs on serotonin transporter occupancy and changes in the functional brain network. This work offers a novel methodology for the in-depth study of brain function at the molecular level and opens new pathways for understanding how drugs modulate brain activity.

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bioRxiv2023

The Significance and Limited Influence of Cerebrovascular Reactivity on Age and Sex Effects in Task- and Resting-State Brain Activity

Functional MRI (fMRI) measures the blood-oxygen-level dependent (BOLD) signals, which provide an indirect measure of neural activity mediated by neurovascular responses. Cerebrovascular reactivity affects both task-induced and resting-state BOLD activity and may confound inter-individual effects observed in BOLD-based measures, such as those related to aging and biological sex. To investigate this, we examined a large open-access fMRI dataset containing a breath-holding task, checkerboard task, and resting-state scans. We used the breath-holding task to measure cerebrovascular reactivity, used the checkerboard task to obtain task-based activations, and from the resting-state data, we quantified the resting-state amplitude of low-frequency fluctuations (ALFF), and resting-state regional homogeneity (ReHo). We hypothesized that cerebrovascular reactivity would be correlated with BOLD measures and that accounting for these correlations would result in better estimates of age and sex effects. Our analysis showed that cerebrovascular reactivity was correlated with checkerboard task activations in the visual cortex and with ALFF and ReHo in widespread fronto-parietal regions, as well as regions with large vessels. We also found significant age and sex effects in cerebrovascular reactivity, some of which overlapped with those observed in ALFF and ReHo scores. Finally, we demonstrated that correcting for the effects of cerebrovascular reactivity had very limited influence on the estimates of age and sex. Our results highlight the limitations of accounting for cerebrovascular reactivity with the current breath-holding task.

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bioRxiv2023

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: Effects of age, sex, and behavioral associations

Spatially remote brain regions exhibit dynamic functional interactions across various task conditions. While time-varying functional connectivity during movie watching shows sensitivity to movie content, stationary functional connectivity remains relatively stable across videos. These findings suggest that dynamic and stationary functional interactions may represent different aspects of brain function. However, the relationship between individual differences in time-varying and stationary connectivity and behavioral phenotypes remains elusive. To address this gap, we analyzed an open-access functional MRI dataset comprising participants aged 5 to 22 years, who watched two cartoon movie clips. We calculated regional brain activity, time-varying connectivity, and stationary connectivity, examining associations with age, sex, and behavioral assessments. Model comparison revealed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models exhibited quadratic log age or quadratic age effects, indicative of inverted-U shaped developmental patterns. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity demonstrated the ability to predict full-scale intelligence quotient. These findings suggest that individual differences in time-varying and stationary connectivity may capture distinct aspects of behavioral phenotypes.

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bioRxiv2022

Individual coactivation patterns improve the subject identification and their behavior association

As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, has been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group-level, which might neglect meaningful individual differences. Here, we estimate individual CAP states at both subject- and scan-level based on a densely-sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and intersubject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best discrimination ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12~15 mins/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 mins). Overall, this work has constructed reliable subject-specific CAP states with meaningful individual differences and provides a starting point for the subsequent applications of individual brain dynamics.

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bioRxiv2021

Dynamic and stable brain connectivity during movie watching as revealed by functional MRI

Spatially remote brain regions show synchronized activity as typically revealed by correlated functional MRI (fMRI) signals. An emerging line of research has focused on the temporal fluctuations of connectivity; however, its relationships with stationary connectivity have not been clearly illustrated. We examined dynamic and stationary connectivity when the participants watched four different movie clips. We calculated point-by-point multiplication between two regional time series to estimate the time-resolved dynamic connectivity, and estimated the inter-individual consistency of the dynamic connectivity time series. Widespread consistent dynamic connectivity was observed for each movie clip, which also showed differences between the clips. For example, a cartoon movie clip, Wall-E, showed more consistent of dynamic connectivity with the posterior cingulate cortex and supramarginal gyrus, while a court drama clip, A Few Good Men, showed more consistent of dynamic connectivity with the auditory cortex and temporoparietal junction, which might suggest the involvement of specific brain processing for different movie contents. In contrast, the stationary connectivity as measured by the correlations between regional time series was highly similar among the movie clips, and showed fewer statistically significant differences. The patterns of consistent dynamic connectivity could be used to classify different movie clips with higher accuracy than the stationary connectivity and regional activity. These results support the functional significance of dynamic connectivity in reflecting functional brain changes, which could provide more functionally relevant information than stationary connectivity.

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bioRxiv2021

Structural-functional connectivity mapping of the insular cortex system: A combined data-driven and meta-analytic topic mapping approach

In this study, we examined structural and functional profiles of the insular cortex and mapped associations with well-described functional networks (FNs) throughout the brain using diffusion tensor imaging (DTI) and resting-state functional connectivity (RSFC) data. We used a data-driven method to independently estimate the structural-functional connectivity of the insular cortex. Data were obtained from the Human Connectome Project comprising 108 adult participants. Overall, we observed moderate to high associations between the structural and functional mapping scores of three different insular subregions: the posterior insula (associated with the sensorimotor network: RSFC, DTI = 50% and 72%, respectively), dorsal anterior insula (associated with ventral attention: RSFC, DTI = 83% and 83%, respectively), and ventral anterior insula (associated with the frontoparietal: RSFC, DTI = 42% and 89%, respectively). Further analyses utilized meta-analytic decoding maps to demonstrate specific cognitive and affective as well as gene expression profiles of the three subregions reflecting the core properties of the insular cortex. In summary, given the central role of the insular in the human brain, our results revealing correspondence between DTI and RSFC mappings provide a complementary approach and insight for clinical researchers to identify dysfunctional brain organization in various neurological disorders associated with insular pathology.

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bioRxiv2021

Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: an fMRI study

The resting-state human brain is a dynamic system that shows frequency-specific characteristics. Coactivation pattern (CAP) analysis has been recently used to identify recurring brain states sharing similar coactivation configurations. However, whether and how CAPs differ across different sub-frequency bands are unknown. In the current study, in addition to the typical low-frequency range (0.01 - 0.08 Hz), the spatial and temporal characteristics of CAPs in four sub-frequency bands, slow-5 (0.01 - 0.027 Hz), slow-4 (0.027 - 0.073 Hz), slow-3 (0.073 - 0.198 Hz), and slow-2 (0.198 - 0.25 Hz), were studied. Six CAP states were obtained for each band., The CAPs from the typical frequency range were spatially largely overlapped with those in slow-5, slow-4 and slow-3 but not with those in slow-2. With the increase of frequency, the CAP state became more unstable and resulted in an overall shorter persistence. The spatial and temporal characteristics of slow-4 and slow-5 were further compared, because they constitute most power of the resting-state fMRI signals. In general, slow-4 showed stronger coactivations or co-deactivations in subcortical regions, while slow-5 showed stronger coactivations or co-deactivations in large-scale cortical networks such as the dorsal attention network. Lastly, frequency-dependent dynamic alterations were also observed in schizophrenia patients. Combining the information obtained from both slow-5 and slow-4 increased the classification accuracy of schizophrenia patients than only using the typical range. In conclusion, our results revealed that the spatial and temporal characteristics of CAP state varied at different frequency bands, which could be helpful for identifying brain alterations in schizophrenia.

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bioRxiv2021

Striatal and prefrontal D2R and SERT distributions contrastingly correlate with default-mode connectivity

The molecular substrate of resting-state functional connectivity (rs-FC) remains poorly understood. We aimed to elucidate interactions of dopamine D2 receptor (D2R) and serotonin transporter (SERT) availabilities in main dopaminergic and serotonergic projection areas with the default-mode network (DMN) and two other resting-state networks (RSNs), the salience (SN) and sensorimotor networks (SMN). We performed simultaneous PET/fMRI scans in rats using [11C]raclopride and [11C]DASB to image D2R and SERT distributions, showing for the first time direct relationships between rs-FC and molecular properties of the rodent brain. We found negative associations between CPu D2R availability and all RSNs investigated. Strikingly, medial prefrontal SERT correlated both positively with anterior DMN rs-FC and negatively with rs-FC between the other networks, underlining serotonins intricate role in this region. By further elucidating the link between molecular brain properties and its network-level function, our data support future diagnostic and therapeutic strategies. TeaserSimultaneous PET/fMRI indicates direct associations between monoaminergic neurotransmission and brain functional networks.

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