Biology subjects

Avidan, G.

Publications and source records attributed to Avidan, G..

5 recordsLinked to original sources

bioRxiv2026

Time-averaged and Time-varying Structure of the Gastric Network Revealed Through fMRI-Electrogastrogram Synchronization

The gastric network, comprised of brain regions whose activity synchronizes with the stomach's slow-wave rhythm, offers a unique window into the brain-body interaction involved in interoceptive processing. While previous work has established the existence of this network, its intrinsic organization and temporal unfolding remain poorly understood. Here, we reanalyzed resting-state fMRI-electrogastrogram data from 43 healthy adults of both sexes to characterize the time-averaged architecture and time-varying reconfiguration of the gastric network. We identified regions exhibiting phase-locked synchronization with the stomach slow electrical rhythm (0.05 Hz) and characterized cortical parcels comprising this network. Time-averaged graph-theoretical analysis revealed a fixed unimodal organization of functional communities, with primary visual, default mode network (DMN) and dorsal attention regions emerging as the principal time-averaged hubs. Next, we applied edge-centric functional connectivity (eFC) to capture the network state during transient high-amplitude "bursts". Time-varying community detection revealed communities whose compositions formed integrative combinations of DMN, visual, attentional and control elements. Edge-derived hubs shifted away from primary visual dominancy in the time-averaged analysis, and were instead directed by DMN regions, suggesting that moments of heightened connectivity in the network are coordinated by multisensory integration rather than passive sensory processing. These findings demonstrate that the gastric network is not merely a time-averaged, sensory-bound system, but rather a flexible and dynamically reconfiguring interoceptive network whose organization is selectively coordinated by transient cofluctuation events. This work provides a comprehensive network analysis of gastric-brain coupling and reveals a temporally structured mode of interoceptive integration that may support adaptive physiological and cognitive regulation.

neuroscience↗

bioRxiv2022

Fine-scale dynamics of functional connectivity in the face processing network during movie watching

Face are naturally dynamic, multimodal and embedded in rich social context. However, mapping the face processing network in the human brain and its relation to behavior is typically done during rest or using isolated, static face images. The use of such contrived stimuli might result in overlooking widespread cortical interactions obtained in response to naturalistic context and the temporal dynamics of these interactions. Here we examined large-scale cortical connectivity patterns measured in response to a dynamic movie in a sample of typical adults (n=517), to determine how inter-subject functional connectivity (ISFC) relates to face recognition scores. We found a positive correlation with recognition scores in edges connecting the occipital visual and anterior temporal regions and a negative correlation in edges connecting attentional dorsal, frontal default, and occipital visual regions. These ISFC patterns resembled previous findings comparing individuals with congenital prosopagnosia to normal controls and the viewing of inverted compared to upright faces. To further examine these connectivity patterns, we developed a novel method that allows analysis of inter-subject stimulus-evoked node/edge responses at a single TR resolution. Using this method, we demonstrated that co-fluctuations in face-selective edges observed here and in previous work are related to local activity in core face-selective regions. Finally, correlating this temporal decomposition of the observed ISFC patterns to the movie content revealed that they peak during boundaries between movie segments rather than during the presence of faces in the movie. Our novel approach demonstrates how visual processing of faces is linked to fine-scale dynamics in attentional, memory, and perceptual neural circuitry.

neuroscience↗

bioRxiv2021

Mapping structure to function and behavior with individual-level connectome embedding

The connectome, a comprehensive map of the brains anatomical connections, is often summarized as a matrix comprising all dyadic connections among pairs of brain regions. This representation cannot capture higher-order relations within the brain graph. Connectome embedding (CE) addresses this limitation by creating compact vectorized representations of brain nodes capturing their context in the global network topology. Here, nodes "context" is defined as random walks on the brain graph and as such, represents a generative model of diffusive communication around nodes. Applied to group-averaged structural connectivity, CE was previously shown to capture relations between inter-hemispheric homologous brain regions and uncover putative missing edges from the network reconstruction. Here we extend this framework to explore individual differences with a novel embedding alignment approach. We test this approach in two lifespan datasets (NKI: n=542; Cam-CAN: n=601) that include diffusion-weighted imaging, resting-state fMRI, demographics and behavioral measures. We demonstrate that modeling functional connectivity with CE substantially improves structural to functional connectivity mapping both at the group and subject level. Furthermore, age-related differences in this structure-function mapping are preserved and enhanced. Importantly, CE captures individual differences by out-of-sample prediction of age and intelligence. The resulting predictive accuracy was higher compared to using structural connectivity and functional connectivity. We attribute these findings to the capacity of the CE to incorporate aspects of both anatomy (the structural graph) and function (diffusive communication). Our novel approach allows mapping individual differences in the connectome through structure to function and behavior.

neuroscience↗

bioRxiv2019

From a deep learning model back to the brain - inferring morphological markers and their relation to aging

We present a Deep Learning framework for the prediction of chronological age from structural MRI scans. Previous findings associate an overestimation of brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contributions to the prediction in a single image, resulting in explanation maps that were found noisy and unreliable. To address this problem, we developed an inference framework for combining these maps across subjects, thus creating a population-based rather than a subject-specific map. We applied this method to a CNN ensemble trained on predicting subjects age from raw T1 brain images of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r=0.98. Using the inference method, we revealed that cavities containing CSF, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel-based morphometry age studies and highlighted brain regions whose volumetric variability contributed the most to the prediction.\n\nHighlightsO_LICNNs ensemble is shown to estimate \"brain age\" from sMRI with an MAE of [~]3.1 years\nC_LIO_LIA novel framework enables to highlight brain regions contributing to the prediction\nC_LIO_LIThis framework results in explanation maps showing consistency with the literature\nC_LIO_LIAs sample size increases, these maps show higher inter-sample replicability\nC_LIO_LICSF cavities reflecting general atrophy were found as a prominent aging biomarker\nC_LI

neuroscience↗

bioRxiv2017

Altered topology of neural circuits in congenital prosopagnosia

Using a novel fMRI-based inter-subject functional correlation (ISFC) approach, which isolates stimulus-locked inter-regional correlation patterns, we compared the cortical topology of the neural circuit for face processing in participants with congenital prosopagnosia (CP) and matched controls. Whereas the anterior temporal lobe served as the major network hub for face processing in controls, this was not the case for the CPs. Instead, this group evinced hyper-connectivity in posterior regions of the visual cortex, mostly associated with the lateral occipital and the inferior temporal cortices. Moreover, the extent to which the network organization was atypical differed as a function of the severity of the face recognition deficit. These results offer new insights into the perturbed cortical topology in CP, which may serve as the underlying neural basis of the behavioral deficits typical of this disorder. The approach adopted here has the potential to uncover altered topologies in other neurodevelopmental disorders, as well.\n\nSignificance StatementCongenital prosopagnosia (CP; face blindness), a developmental deficit in face recognition, is thought to affect up to 3% of the population. Understanding its neural basis is challenging as there is no obvious deficit on conventional structural or functional MRI scans. Using an innovative, fMRI-based inter-subject correlation approach geared towards tracking inter-regional stimulus-locked brain activation, the present study uncovers marked topological differences in a distributed brain network of higher-order visual regions in CP relative to controls. Alteration in topology also differs as a function of the severity of the deficit. These findings shed new light on the neural perturbations underlying CP, and the analytic approach we have adopted may have utility in elucidating the neural basis of other neurodevelopmental disorders such as dyslexia or amusia.

neuroscience↗