Biology subjects

Yazdanbakhsh, A.

Publications and source records attributed to Yazdanbakhsh, A..

5 recordsLinked to original sources

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↗