Biology subjects

Yazdanbakhsh, A.

Publications and source records attributed to Yazdanbakhsh, A..

11 recordsLinked to original sources

bioRxiv2026

Biophysical modeling of excitation/inhibition imbalance in Alzheimer's disease

INTRODUCTION: Alzheimer's disease (AD) involves early sensory and perceptual changes that can precede memory decline. We developed a two-layer rate-based neural model of early and higher visual areas with feedforward, feedback, and recurrent connections to examine how excitatory/inhibitory (E/I) balance shapes visual motion perception in AD. METHODS: Using Motion-Induced Position Shift and Altered Motion Repulsion stimuli, we factorially manipulated excitatory (E) and inhibitory (I) subfield power and connectivity to examine their effects on illusion representation across visual layers. RESULTS: Increased E, decreased I, and increased E/I reduced illusion representation, consistent with reduced illusion susceptibility in AD, whereas the converse increased illusion representation, reflecting neurotypical processing. Higher-level layer 2 representations were more resistant to E/I perturbations than lower-level layer 1. Illusion representation was less affected by E/I increases in intra-areal (recurrent) connectivity than in inter-areal (feedforward/feedback) connectivity. DISCUSSION: This mechanistic framework links E/I balance and connectivity to altered visual motion illusion representations in AD and suggests that E/I and connectivity measures may provide probes of early AD neurobiology. The framework may also generalize beyond vision.

neuroscience↗

bioRxiv2026

The rotating tilted lines illusion for the evaluation of cognitive abnormalities

BackgroundThe population receptive field (pRF) in vision reflects the functional receptive field arising from millions of overlapping single receptive fields across visual areas and eccentricities. pRFs are typically estimated with fMRI to gain insight into visual processing. Alternative methods of pRF estimation, such as using optical illusions, have been explored only sparingly. In this study, we explore the rotating tilted lines illusion (RTLI), in which a circle formed by tilted lines appears to rotate as it expands or contracts in the visual field (e.g., from moving the head back and forth). New MethodWe propose a novel set of computer-generated animations of the RTLI that measure the visual and temporal characteristics of the illusory rotation, enabling quantitative estimation of the spatial extent and temporal dynamics of the pRF. ResultsWe derived pRF size estimates consistent with those estimated from fMRI and electrophysiological methods. We then projected changes in RTLI percept trends according to abnormalities in visual processing in autism spectrum disorder (ASD), schizophrenia (SZ), aging, and Alzheimers disease (AD). Comparison with existing methodsCompared to fMRI and electrophysiology, RTLI-based pRF estimation is accessible, low-cost, and feasible at home or during inpatient visits without specialized equipment. ConclusionsWe show that our novel method can approximate pRFs, which in turn can be potentially applied for early detection, probing the progress, and treatment screening in AD, SZ and ASD.

neuroscience↗

bioRxiv2026

Biophysical Modeling of Thalamocortical Circuit Dynamics: Species-Specific Insights into Neural Synchrony, Sleep Spindles, and Mechanisms of Neuropsychiatric Disorders

Thalamocortical circuits play a fundamental role in cognitive functions, and neural synchronization, with disruptions implicated in disorders. Here, we investigated the neural dynamics of thalamocortical connectivity using computational modeling of rodent and primate thalamocortical loops. We incorporated distinct projections and varying network configurations and examined their impact on circuit synchrony, spiking patterns, and sleep spindle generation. Circuits included distinct core and matrix thalamocortical projections, with core pathways providing focal, driving input to middle cortical layers, while matrix pathways mediate widespread, modulatory signaling across superficial layers, and the presence of thalamic interneurons, which are scarce in rodents but comprise up to a third of the thalamic neurons in primates. In our simulations, these distinctions produced clear species-and loop architecture-dependent effects: rodent circuits were markedly more sensitive to parameter changes in core and matrix thalamocortical connectivity strength, while primate circuits maintained relatively stable spatiotemporal patterns across parameter variations, exhibiting greater stability and synchrony. Sleep spindle analysis likewise revealed species differences. Overall, across all thalamocortical configurations, rodent simulations produced spindles with greater spatiotemporal variability, showing irregular event structure and timing. In contrast, primate spindles were more uniform and coherent, with clearer and more consistent organization across neurons and time. These findings provide insights into species-specific differences in thalamocortical dynamics and have implications for modeling sensory and cognitive disruptions in disorders such as autism and schizophrenia. By incorporating distinct configurations, and interspecies differences, our model contributes to understanding how thalamocortical dysregulation may differentially impact spindle generation, network synchrony, and information processing across species.

neuroscience↗

bioRxiv2025

Explainable 3D CNNs link regional and network level disruption in early Parkinson's MRIs to symptom progression

Parkinsons Disease (PD) is a progressive neurodegenerative disorder affecting approximately 1% of the population over 65. Clinical diagnosis typically depends on tracking gradually developing motor symptoms as the disease progresses, underscoring the need for early detection methods to aid intervention while symptoms are still minor. Inexpensive and widely available imaging modalities such as T1-weighted MRI (T1w MRI) have potential for early PD diagnosis but lack established systematic biomarkers of PD pathology. In this study, a 3D convolutional neural network (3D CNN) was trained on 100 predominately early-state PD and 100 control T1w MRIs from Parkinsons Progression Markers Initiative (PPMI), achieving a classification accuracy of 84.5%. Misclassified subjects were majority unmedicated and particularly early PD (< 3 years since first symptoms). To interrogate the biological basis behind the models decisions, novel explainability methods were applied to generate regional saliency maps from both PD and control classifications. Regional saliency across subjects correlated best with cognitive and motor scores in nigrostriatal and other subcortical regions, as well as in temporal and insular cortices, indicating changes in these areas were best connected with symptom progression. The model was also sensitive to changes in the left frontal cortex across many subjects, which exhibited the greatest raw saliency magnitude. Pairwise saliency correlation was most pronounced between areas within the same functional network, suggesting the CNN was sensitive to network level changes in structural MRI. These findings demonstrate the potential of explainable 3D CNNs to identify network and regional biomarkers of early PD from T1w MRI.

neuroscience↗

bioRxiv2025

Structural changes in autism reflect atypical brain network organization and phenotypical heterogeneity: a hybrid deep network approach

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by heterogeneity in social and emotional responses, language, and behavior. Assessments such as the Social Responsiveness Scale (SRS) can quantify this variability but understanding underlying mechanisms and identifying distinct and shared atypical organization and function of brain networks remains a challenge. Convolutional Neural Networks (CNNs) have been used to analyze imaging data. However, the relationship between structural brain changes observed in structural MRI (sMRI), the affected brain functional networks inferred from these structural changes, and their connection to ASD phenotypes and scores still requires systematic investigation. In this study, we used 3D CNNs to conduct a comprehensive analysis of macrostructural changes in ASD. We found consistently dominant involvement of (a) the left hemisphere, (b) the frontal and temporal lobe, and (c) the default mode network and dorsal/ventral attention networks in ASD. Our systematic AI approach utilized the phenotypic heterogeneity and spectral nature of ASD to uncover significant structural changes across brain regions and functional networks, correlating the structural, functional, and phenotypical heterogeneity of individuals with ASD. This enabled us to identify known and novel global and local brain region and network changes in ASD in relation to phenotypes and clinical scores that can guide diagnostic subtyping.

neuroscience↗

bioRxiv2024

Artificial intelligence networks combining histopathology and machine learning can extract axon pathology in autism spectrum disorder

Axon features that underlie the structural and functional organization of cortical pathways have distinct patterns in the brains of neurotypical controls (CTR) compared to individuals with Autism Spectrum Disorder (ASD). However, detailed axon study demands labor-intensive surveys and time-consuming analysis of microscopic sections from post-mortem human brain tissue, making it challenging to systematically examine large regions of the brain. To address these challenges, we developed an approach that uses machine learning to automatically classify microscopic sections from ASD and CTR brains, while also considering different white matter regions: superficial white matter (SWM), which contains a majority of axons that connect nearby cortical areas, and deep white matter (DWM), which is comprised exclusively by axons that participate in long-range pathways. The result was a deep neural network that can successfully classify the white matter below the anterior cingulate cortex (ACC) of ASD and CTR groups with 98% accuracy, while also distinguishing between DWM and SWM pathway composition with high average accuracy, up to 80%. Multidimensional scaling analysis and sensitivity maps further underscored the reliability of ASD vs CTR classification, based on the consistency of axon pathology, while highlighting the important role of white matter location that constrains pathway dysfunction, based on several shared anatomical markers. Large datasets that can be used to expand training, validation, and testing of this network have the potential to automate high-resolution microscopic analysis of post-mortem brain tissue, so that it can be used to systematically study white matter across brain regions in health and disease. One Sentence StatementHistopathology-trained AI can identify ASD network disruptions and guide development of diagnostics and targeted therapeutics.

neuroscience↗

bioRxiv2023

A neural modeling approach to study mechanisms underlying the heterogeneity of visual spatial frequency sensitivity in schizophrenia

Patients with schizophrenia exhibit abnormalities in spatial frequency sensitivity, and it is believed that these abnormalities indicate more widespread dysfunction and dysregulation of bottom-up processing. The early visual system, including the first-order Lateral Geniculate Nucleus of the thalamus (LGN) and the primary visual cortex (V1), are key contributors to spatial frequency sensitivity. Medicated and unmedicated patients with schizophrenia exhibit contrasting changes in spatial frequency sensitivity, thus making it a useful probe for examining potential effects of the disorder and antipsychotic medications in neural processing. We constructed a parameterized, rate-based neural model of on-center/off-surround neurons in the early visual system to investigate the impacts of changes to the excitatory and inhibitory receptive field subfields. By incorporating changes in both the excitatory and inhibitory subfields that are associated with pathophysiological findings in schizophrenia, the model successfully replicated perceptual data from behavioral/functional studies involving medicated and unmedicated patients. Among several plausible mechanisms, our results highlight the dampening of excitation and/or increase in the spread and strength of the inhibitory subfield in medicated patients and the contrasting decreased spread and strength of inhibition in unmedicated patients. Given that the model was successful at replicating results from perceptual data under a variety of conditions, these elements of the receptive field may be useful markers for the imbalances seen in patients with schizophrenia.

neuroscience↗

bioRxiv2023

A neural model of modified excitation/inhibition and feedback levels in schizophrenia

The strength of certain visual illusions is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms. To investigate whether and how these factors influence the perceived illusions in individuals with schizophrenia, we propose a two-layer network that can model visual receptive fields (RFs), their inhibitory and excitatory subfields, and the top-down feedback. Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups.

neuroscience↗

bioRxiv2023

Deep Learning and Transfer Learning for Brain Tumor Detection and Classification

Convolutional neural networks (CNNs) are powerful tools that can be trained on image classification tasks and share many structural and functional similarities with biological visual systems and mechanisms of learning. In addition to serving as a model of biological systems, CNNs possess the convenient feature of transfer learning where a network trained on one task may be repurposed for training on another, potentially unrelated, task. In this retrospective study of public domain MRI data, we investigate the ability of neural network models to be trained on brain cancer imaging data while introducing a unique camouflage animal detection transfer learning step as a means of enhancing the networks tumor detection ability. Training on glioma and normal brain MRI data, post-contrast T1-weighted and T2-weighted, we demonstrate the potential success of this training strategy for improving neural network classification accuracy. Qualitative metrics such as feature space and DeepDreamImage analysis of the internal states of trained models were also employed, which show improved generalization ability by the models following camouflage animal transfer learning. Image sensitivity functions further this investigation by allowing us to visualize the most salient image regions from a networks perspective while learning. Such methods demonstrate that the networks not only look at the tumor itself when deciding, but also at the impact on the surrounding tissue in terms of compressions and midline shifts. These results suggest an approach to brain tumor MRIs that is comparatively similar to that of trained radiologists while also exhibiting a high sensitivity to subtle structural changes resulting from the presence of a tumor. These findings present an opportunity for further research and potential use in a clinical setting.

bioinformatics↗

bioRxiv2022

Perception of Rigidity in Three- and Four-Dimensional Spaces

Our brain employs mechanisms to adapt to changing visual conditions. In addition to natural changes in our physiology and those in the environment, our brain is also capable of adapting to "unnatural" changes, such as inverted visual-inputs generated by inverting prisms. In this study, we examined the brains capability to adapt to hyperspaces. We generated four spatial-dimensional stimuli in virtual reality and tested the ability to distinguish between rigid and non-rigid motion. We found that observers are able to differentiate rigid and non-rigid motion of tesseracts (4D) with a performance comparable to that obtained using cubes (3D). Moreover, observers performance improved when they were provided with more immersive 3D experience but remained robust against increasing shape variations. At this juncture, we characterize our findings as "[Formula] perception" since, while we show the ability to extract and use 4D information, we do not have yet evidence of a complete phenomenal 4D experience.

neuroscience↗

bioRxiv2022

Sleep spindles in primates: modelling the effects of distinct laminar thalamocortical connectivity in core, matrix, and reticular thalamic circuits

Sleep spindles are associated with the beginning of deep sleep and memory consolidation and are disrupted in schizophrenia and autism. In primates, distinct core and matrix thalamocortical (TC) circuits regulate sleep-spindle activity, through communications that are filtered by the inhibitory thalamic reticular nucleus (TRN) however, little is known about typical TC network interactions and the mechanisms that are disrupted in brain disorders. We developed a primate-specific, circuit-based TC computational model with distinct core and matrix loops that can simulate sleep spindles. We implemented novel multilevel cortical and thalamic mixing, and included local thalamic inhibitory interneurons, and direct layer 5 projections of variable density to TRN and thalamus to investigate the functional consequences of different ratios of core and matrix node connectivity contribution to spindle dynamics. Our simulations showed that spindle power in primates can be modulated based on the level of cortical feedback, thalamic inhibition, and engagement of model core vs. matrix, with the latter having a greater role in spindle dynamics. The study of the distinct spatial and temporal dynamics of core-, matrix-, and mix-generated sleep spindles establishes a framework to study disruption of TC circuit balance underlying deficits in sleep and attentional gating seen in autism and schizophrenia.

neuroscience↗