Biology subjects

Soltanian-Zadeh, H.

Publications and source records attributed to Soltanian-Zadeh, H..

12 recordsLinked to original sources

bioRxiv2026

Local recurrence accounts for extended processing during occluded-object recognition

Recognizing objects from incomplete visual input often requires processing beyond the initial feedforward sweep, but the relative contributions of local recurrence and long-range top-down feedback remain unclear. We combined source-localized magnetoencephalography (MEG), time-resolved decoding, backward masking, representational Granger causality, and computational modeling to examine these mechanisms during occluded-object recognition. We characterized neural dynamics in early visual cortex (V1-3), the lateral occipital complex (LOC), and inferotemporal-parahippocampal cortex (IT-PHC). Occlusion delayed the emergence of category information and elicited a late component that was selectively disrupted by backward masking, consistent with dependence on continued processing. These temporal changes occurred without detectable changes in the relative timing of regional responses or directed interareal interactions, including no measurable occlusion-related increase in feedback among the regions examined. To assess candidate computational mechanisms, we compared three nested model variants sharing a trained backbone: feedforward, local recurrent, and local recurrent with added long-range top-down feedback. Local recurrence improved recognition under occlusion and increased model-brain correspondence during the late, mask-sensitive interval, particularly in V1-3. Adding the implemented top-down pathway provided no consistent further benefit. Together, these findings favor prolonged local recurrent processing as an account of the additional computation supporting occluded-object recognition under the conditions tested.

neuroscience↗

bioRxiv2026

Language-aligned models and structured scene descriptions reveal sensitivity to compositional scene structure in the high-level visual cortex

Natural scenes are defined not only by their contained objects, but also by the structured relations among those objects. Using 7T fMRI data from the Natural Scenes Dataset, we tested whether the high-level visual cortex is sensitive to this compositional scene structure. We related narrative scene descriptions to cortical responses in an encoding framework, contrasting intact narrative descriptions with lexical control descriptions that preserved word content while disrupting compositional structure via word randomization. Intact narrative descriptions better predicted cortical responses in high-level visual cortex, indicating sensitivity to structured scene information beyond lexical content alone. This advantage was particularly pronounced for scenes with richer compositional structure. Further, a language-aligned vision model outperformed a self-supervised vision-only model, and this advantage showed a similar high-level cortical distribution. These findings suggest that the high-level visual cortex represents structured scene information beyond simple entity co-occurrence and language supervision may help induce similar representations in artificial vision models.

neuroscience↗

bioRxiv2026

Coherent scene context accelerates and reshapes neural object representations

Coherent scenes facilitate object recognition, but the representational basis of this facilitation and its temporal evolution in the brain remain unclear. We tested this question using EEG and multivariate pattern analysis while 15 participants categorized objects from five semantic categories after a 500-ms preview of either an intact rendered scene or a phase-scrambled version of the same background. Reliable object decoding emerged earlier in intact scenes than scrambled scenes (142 {+/-} 5 vs. 162 {+/-} 10 ms), with higher decoding for intact scenes from 124 to 268 ms after object onset. Cross-condition decoding object information that generalized across scene formats, whereas subtracting cross-condition from within-condition decoding identified an earlier and stronger context-dependent component when scene structure was coherent. Cross-temporal representational similarity analysis (RSA) further showed that representational structure established during late scene preview generalized to early object processing only for intact scenes, linking contextual facilitation to anticipatory scene-derived representations. Finally, model-to-brain RSA showed that a language-aligned model explained neural representational geometry in intact scenes better than vision-only models, an advantage attenuated by scene scrambling. These findings indicate that coherent scene context shapes object coding by accelerating object-selective processing and contributing context-dependent representational structure beyond a context-invariant object code.

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bioRxiv2026

Structural Connectivity Selectively Constrains Intrinsic BOLD Timescales through Graph-Smooth Neural Activity

Structural connectivity defines the network architecture supporting large-scale brain dynamics, yet how this network constrains the temporal statistics of signals defined on it remains poorly understood. Prior work has reported associations between intrinsic timescales of resting-state fMRI and structural connectivity strength, but it is unclear which signal components primarily drive this relationship. Here, we adopt a graph signal processing framework to analyze intrinsic temporal properties of networked brain signals. Regional Blood Oxygenation Level Dependent (BOLD) activity is modeled as a graph signal supported on the structural connectome and decomposed via graph spectral filtering into low-frequency (structure-coupled) and high-frequency (structure-decoupled) components. Using diffusion MRI-derived structural connectivity and resting-state fMRI from 100 unrelated participants of the Human Connectome Project, intrinsic timescales are quantified using relatively low-frequency power and related to node-wise structural connectivity strength while controlling for regional volume. We show that intrinsic timescales derived from structure-coupled signals exhibit robust positive associations with structural connectivity strength at both group and inter-individual levels, whereas structure-decoupled signals display substantially weaker coupling. Notably, slow structure-decoupled dynamics are preferentially expressed in higher-order association cortex. Graph-spectral null models further demonstrate that these effects critically depend on the empirical organization of the structural network. Together, these results establish a graph-spectral interpretation of structure-timescale coupling, showing that network topology selectively constrains the temporal statistics of graph-smooth neural activity. Author SummaryA fundamental question in network neuroscience is how the brains structural connectivity shapes the temporal dynamics of functional activity. Previous studies have shown that brain regions with stronger anatomical connectivity tend to exhibit slower intrinsic activity fluctuations, but the functional signal components responsible for this relationship remain unclear. Here, we combine graph signal processing with analyses of intrinsic BOLD timescales to separate resting-state activity into structure-coupled and structure-decoupled components. Using multimodal neuroimaging data from the Human Connectome Project, we show that the association between structural connectivity strength and intrinsic timescales is primarily driven by structure-coupled, graph-smooth activity. In contrast, structure-decoupled dynamics exhibit substantially weaker dependence on anatomical connectivity, although transmodal association cortex retains selective structural influences. These findings provide new insight into how anatomical networks shape temporal processing in the human brain and suggest that intrinsic timescales emerge through distinct modes of interaction between structural constraints and functional dynamics.

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bioRxiv2025

Does Human-Like Contextual Object Recognition Emerge from Language Supervision and Language-Guided Inference?

Human vision is an active, context-sensitive process that interprets objects in relation to their surroundings. While behavioral research has long shown that scene context facilitates object recognition, the underlying computational mechanisms--and the extent to which artificial vision models replicate this ability--remain unclear. Here, we addressed this gap by combining human behavioral experiments with computational modeling to investigate how structured scene context influences object recognition. Using a novel 3D simulation framework, we embedded target objects into indoor scenes, and manipulated contextual coherence between objects and scenes by using either intact scenes or their phase-scrambled versions. Humans showed a robust object recognition advantage in coherent scenes, particularly under challenging conditions such as occlusion, crowding, or non-canonical viewpoints. Conventional vision models--including convolutional neural networks (CNNs) and vision transformers (ViTs)--failed to replicate this effect. In contrast, vision-language models (VLMs), particularly those using ViT architectures and trained with language supervision (e.g., CLIP), approached human-like accuracy. This shows that semantically rich and category-structured representations are required for modelling context sensitivity. Notably, context sensitive behavior was closest to humans in VLMs when using language-guided inference at test time. This suggests that how a model accesses its representations during inference is relevant for enabling context-sensitive behavior. Together, this work offers steps towards a computational account of contextual facilitation of objects by scenes, and highlights zero-shot inference as an interesting alignment metric when benchmarking artificial and biological vision.

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bioRxiv2025

Associations between epilepsy-related polygenic risk and brain morphology in childhood

AO_SCPLOWBSTRACTC_SCPLOWTemporal lobe epilepsy with hippocampal sclerosis (TLE-HS) is associated with a complex genetic architecture, but the translation from genetic risk factors to brain vulnerability remains unclear. Here, we examined associations between epilepsy-related polygenic risk scores for HS (PRS-HS) and brain structure in a large sample of neurotypical children, and correlated these signatures with case-control findings in in multicentric cohorts of patients with TLE-HS. Imaging-genetic analyses revealed PRS-related cortical thinning in temporo-parietal and fronto-central regions, strongly anchored to distinct functional and structural network epicentres. Compared to disease-related effects derived from epilepsy case-control cohorts, structural correlates of PRS-HS mirrored atrophy and epicentre patterns in patients with TLE-HS. By identifying a potential pathway between genetic vulnerability and disease mechanisms, our findings provide new insights into the genetic underpinnings of structural alterations in TLE-HS and highlight potential imaging-genetic biomarkers for early risk stratification and personalized interventions.

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bioRxiv2021

Structural network alterations in focal and generalized epilepsy follow axes of epilepsy risk gene expression: An ENIGMA study

Epilepsy is associated with genetic risk factors and cortico-subcortical network alterations, but associations between neurobiological mechanisms and macroscale connectomics remain unclear. This multisite ENIGMA-Epilepsy study examined whole-brain structural covariance networks in patients with epilepsy and related findings to postmortem co-expression patterns of epilepsy risk genes. Brain network analysis included 578 adults with temporal lobe epilepsy (TLE), 288 adults with idiopathic generalized epilepsy (IGE), and 1,328 healthy controls from 18 centres worldwide. Graph theoretical analysis of structural covariance networks revealed increased clustering and path length in orbitofrontal and temporal regions in TLE, suggesting a shift towards network regularization. Conversely, people with IGE showed decreased clustering and path length in fronto-temporo-parietal cortices, indicating a random network configuration. Syndrome-specific topological alterations reflected expression patterns of risk genes for hippocampal sclerosis in TLE and for generalized epilepsy in IGE. These imaging-genetic signatures could guide diagnosis, and ultimately, tailor therapeutic approaches to specific epilepsy syndromes.

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bioRxiv2020

Impaired dynamic profile of brain recurring functional connectivity states in Alzheimers disease

Up to now, disrupted functional organization of the brain in Alzheimers disease (AD) has mostly been examined by assessing "static" functional connectivity (FC). However, recent studies have provided clear evidence that brain FC exhibits intrinsic spatiotemporal "dynamic" organization also being significantly affected in AD. However, inconsistency in former studies is a motivation for further investigation of impaired dynamic profile of brain FC networks in Alzheimers disease._In this work, we identified recurring FC states during rest (eyes-open) in 24 AD patients and 37 healthy controls (HC) by combining sliding window-based method (examining dynamic FC) and k-means clustering algorithm (identifying recurring FC states over time). To define the optimum number of recurring FC states in k-means algorithm, we employed non-supervised validity criteria such as silhouette value and Dunn index over bootstrap samples. Then, we examined differences in dynamic properties of recurring FC states including probability of occurrence, lifetime and switching profile between groups. Afterward, association between impaired dynamic profile of recurring FC states and cognitive performance was assessed in AD patients. Our findings revealed three recurring FC states and among them, FC state with the most significant differences in probability of occurrence (corrected p-value < 0.0001) included brain regions involved in default mode, frontoparietal and salience networks. This recurring FC state occurred significantly less and lasted shorter in patients with AD than in the HC. Further, the probability of occurrence in this recurring FC state significantly correlated positively with cognitive decline in patients with AD. Our findings suggest more decreased in cognitive performance in patients with AD associated with more reduced ability of AD patients to access clinically relevant impaired brain FC networks. Impaired dynamic profile of recurring FC states in AD provide greater understanding of ADs pathophysiology.

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bioRxiv2020

Network-based atrophy modelling in the common epilepsies: a worldwide ENIGMA study

Epilepsy is increasingly conceptualized as a network disorder. In this cross-sectional mega-analysis, we integrated neuroimaging and connectome analysis to identify network associations with atrophy patterns in 1,021 adults with epilepsy compared to 1,564 healthy controls from 19 international sites. In temporal lobe epilepsy, areas of atrophy co-localized with highly interconnected cortical hub regions, whereas idiopathic generalized epilepsy showed preferential subcortical hub involvement. These morphological abnormalities were anchored to the connectivity profiles of distinct disease epicenters, pointing to temporo-limbic cortices in temporal lobe epilepsy and fronto-central cortices in idiopathic generalized epilepsy. Indices of progressive atrophy further revealed a strong influence of connectome architecture on disease progression in temporal lobe, but not idiopathic generalized, epilepsy. Our findings were reproduced across individual sites and single patients, and were robust across different analytical methods. Through worldwide collaboration in ENIGMA-Epilepsy, we provided novel insights into the macroscale features that shape the pathophysiology of common epilepsies.

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bioRxiv2019

Resting-state fMRI Signals of Intelligent People Wander in a Larger Space

Natural intelligence is one of the vastly explored research areas in cognitive science. Its evolution and manifestation through behavioral patterns in animal kingdom have been extensively investigated. Since early days of cognitive sciences, there have been considerable efforts to simulate intelligent behaviors through high-level cognitive models. In the framework of the computational theory of mind, production systems are top-down models which simulate intelligent behaviors by invoking their behavioral manifestations. These models describe an intelligent behavior as structured mental programming which decomposes a complex task into simpler independent parts, each one represented by a cognitive enclosure where attention is sequentially devoted, and finally the information obtained from all cognitive enclosures is integrated to accomplish the task. In this article, we investigate the relations between these models of intelligence and resting-state fMRI signals. Based on these models, we hypothesize that the capacity of distinct mental representations is the core feature of intelligent behaviors. Therefore, we reason that resting-state fMRI signals of intelligent individuals wander in a larger space and can be divided to more well-separated independent components. This may be interpreted as the functional equivalence of one of the most celebrated structural correlates of intelligence, its positive association with the total brain volume. In the general framework of topological data analysis, using a well-established non-linear dimensionality reduction method, we show that indeed resting-state fMRI signals of intelligent individuals occupy a larger space and can be divided to more well-separated components with less connections in the reduced two-dimensional space. To our knowledge, this is the first attempt to relate the functional space of resting-state fMRI signals with the behavioral signatures of the human intelligence.

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bioRxiv2017

Learning Complex Representations from Spatial Phase Statistics of Natural Scenes

Natural scenes contain higher-order statistical structures that can be encoded in their spatial phase information. Nevertheless, little progress has been made in modeling phase information of images, and understanding efficient representation of the image phases in the brain. In order to capture spatial phase structure under the efficient coding hypothesis, here we introduce a generative model of natural scenes by assuming independent source signals in a complex domain and non-uniform phase priors for the complex signals. Parameters of the proposed model are then estimated under the maximum-likelihood principle. This approach extends existing methods of independent component analysis for complex-valued signals to the one that utilizes phase information. Using simulated data, we demonstrate that the proposed model outperforms conventional models with a uniform phase prior in blind source separation of complex-valued signals. We then apply the proposed model to natural scenes in the Fourier domain. Real and imaginary parts of the learned complex features exhibit a pair of Gabor-like filters in quadratic phase structure with a similar shape. The proposed model significantly improved the goodness-of-the-fit from the model with a uniform phase prior, indicating that the structured spatial phases are important for removing redundancy in natural scenes. These results predict the presence of phase sensitive complex cells in the visual cortex.

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bioRxiv2017

A Change-point Analysis Method for Single-trial Study of Simultaneous EEG-fMRI of Auditory/Visual Oddball Task

Simultaneous EEG-fMRI has been vastly used to investigate functional networks of brain combining high spatial resolution (millimeter) of fMRI with high temporal resolution (millisecond) of EEG. However, to extract the most relevant information from the acquired data, it is necessary to develop analysis methods with less ad hoc assumptions. To this end, brain rhythms are often used which are the specific frequency-bands of EEG signal and assumed to represent diverse sub-second cognitive processes at different parts of the cortex. Furthermore, single-trial analysis of EEG is believed to show more realistic picture of ongoing and event-related activities of brain. Here, we present a nonparametric multiple change-point detection and estimation method for the single-trial analysis of simultaneous EEG-fMRI experiment recorded during auditory and visual oddball tasks. In a simple attention task like oddball, the frontal cortex of brain is responsible to distinguish and respond appropriately to target versus standard events. By using EEG signal at the frontal cortex, we show that the -band activity changes according to \"inhibition timing\" hypothesis and the {beta}-band activity is in line with \"maintaining the status quo\" hypothesis. Furthermore, using these activities to build regressors in the GLM analysis of fMRI, we localize active brain regions with high spatial and temporal resolutions and elaborate further on the coordination of attentional networks across brain.

neuroscience↗