Biology subjects

Kakaei, E.

Publications and source records attributed to Kakaei, E..

4 recordsLinked to original sources

bioRxiv2024

Distinct decision processes for 3D and motion stimuli in both humans and monkeys revealed by computational modelling

Decision-making models distil principles of information processing that underlie a range of cognitive functions. For the cases of motion detection in random-dot kinematograms and decisions about the rotation of 3D structure-from-motion cylinders, contributing neural processes have been localized to specific circuits in extrastriate area V5/MT on the basis of both causal and correlative evidence, suggesting a common decision path. Here, we arranged for humans and rhesus monkeys to make perceptual choices about these two stimulus types, indicating their decision with either hand or eye movements. In both species, the parameter distributions of a hierarchical Drift Diffusion Model (DDM) revealed systematic differences for stimulus type but not the different modes of response. States in a hidden Markov model also indicated distinct decision strategies, again for the two stimulus types but not for response mode. Although physiological evidence points to area V5/MT as a vehicle for relevant perceptual signals for both stimulus types, computational modelling of the present results reveal distinct decision processes, therefore predicting that different neural processes underlie judgements about motion and 3D-depth, consistently for humans and monkeys.

neuroscience↗

bioRxiv2023

Incidental learning of predictive temporal context within cortical representations of visual shape.

Incidental learning of spatio-temporal regularities and consistencies - also termed statistical learning - may be important for discovering the causal principles gov-erning the world. We studied statistical learning of temporal structure at two temporal scales: the presentation of rotating visual objects (3 s) and predictive temporal context (30 s) in a sequence of such objects. Observers viewed fifteen initially unfamiliar synthetic objects recurring many times each, intermixed with other similar objects that appeared only once, while whole-brain BOLD activity was recorded. Analyzing multivariate BOLD activity, we located view-independent responses to synthetic objects throughout the ventral occipitotemporal cortex, revealing a view-invariant representation of object shape. Predictive temporal context modified representations of object shape at early and middle levels of the visual processing hierarchy. Approximately 20% of the sites representing shape also exhibited a significant representation of temporal context. A few higher-level sites represented temporal context, without also representing object shape. Additionally, objects marking transitions between communities were distinctly represented at many sites. We conclude that, at the sensitivity afforded by fMRI, the cortical represen-tations of invariant visual shape and of predictive temporal context are largely coextensive. Our results suggest that incidental learning of causal principles and structures operating at different timescales ("structural learning") may involve vi-sually responsive areas in the ventral occipitotemporal cortex.

neuroscience↗

bioRxiv2023

Gradual change of cortical representations with growing visual expertise for synthetic shapes.

Visual expertise for particular categories of objects (e.g., birds, mushrooms, minerals, and so on) is known to enhance cortical responses in parts of the ventral occipitotemporal cortex. How is such additional expertise integrated into the prior cortical representation of life-long cumulative visual experience? To address this question, we record multivariate BOLD responses to synthetic visual objects and track changes in pairwise distance as initially unfamiliar objects gradually become familiar ( representational similarity analysis, RSA). We find view-independent responses to synthetic shapes (i.e., an invariant representation) in large parts of the ventral occipitotemporal cortex, including the primary visual cortex. Surprisingly, the quality of representation is already high even when shapes are still unfamiliar. As shapes become familiar with repeated viewing, this quality remains high, but representational geometry shifts with more experience. For visually similar shapes that are not repeated and therefore remain unfamiliar, the representation degrades and becomes progressively marginalized. We conclude that, at least for highly dissimilar synthetic shapes, functional imaging responses can reveal detailed representational geometry at the level of exemplars, as well as gradual changes in this geometry due to experience and learning.

neuroscience↗

bioRxiv2017

BRAPH: A Graph Theory Software for the Analysis of Brain Connectivity

The brain is a large-scale complex network whose workings rely on the interaction between its various regions. In the past few years, the organization of the human brain network has been studied extensively using concepts from graph theory, where the brain is represented as a set of nodes connected by edges. This representation of the brain as a connectome can be used to assess important measures that reflect its topological architecture. We have developed a freeware MatLab-based software (BRAPH - BRain Analysis using graPH theory) for connectivity analysis of brain networks derived from structural magnetic resonance imaging (MRI), functional MRI (fMRI), positron emission tomography (PET) and electroencephalogram (EEG) data. BRAPH allows building connectivity matrices, calculating global and local network measures, performing non-parametric permutations for group comparisons, assessing the modules in the network, and comparing the results to random networks. By contrast to other toolboxes, it allows performing longitudinal comparisons of the same patients across different points in time. Furthermore, even though a user-friendly interface is provided, the architecture of the program is modular (object-oriented) so that it can be easily expanded and customized. To demonstrate the abilities of BRAPH, we performed structural and functional graph theory analyses in two separate studies. In the first study, using MRI data, we assessed the differences in global and nodal network topology in healthy controls, patients with amnestic mild cognitive impairment, and patients with Alzheimers disease. In the second study, using resting-state fMRI data, we compared healthy controls and Parkinsons patients with mild cognitive impairment.

neuroscience↗