Biology subjects

Rajimehr, R.

Publications and source records attributed to Rajimehr, R..

6 recordsLinked to original sources

bioRxiv2026

Decomposition of task-specific responses in the multiple demand network

Neuroimaging evidence suggests that a distributed network of brain areas, known as the multiple demand (MD) network, is consistently recruited across a wide range of cognitive tasks. The MD network includes specific areas in prefrontal and parietal cortices. However, the fine-grained organization of this network is poorly understood. Here we aim to comprehensively characterize the functional subdivisions within the MD network using a naturalistic fMRI paradigm. 20 subjects were instructed to perform 14 different tasks on a set of movie stimuli. These tasks were designed to target a wide range of cognitive domains including visual, spatial, categorical, emotional, auditory, linguistic, social, and semantic processes. fMRI data were also collected while subjects passively watched the movies. The MD network was first delineated by localizing cortical areas that were significantly more active in all 14 tasks compared to the passive-viewing condition. The principal component analysis was then applied on task-specific responses of cortical points within the MD network. The first component was correlated with activities for all tasks, and its spatial map revealed the core, highly multimodal subregions of the MD network. The other components showed preferences for a subset of tasks. In particular, the second component revealed a sharp distinction between MD regions that were preferentially active in visual/spatial versus linguistic/semantic tasks. A graph analysis on the entire cortex also showed a large-scale distinction between visual/spatial and linguistic/semantic areas, with MD regions linking the two communities of cortical areas. Overall, our results provide new insights into how the MD network and its fine-grained architecture contribute to the human intelligent behavior.

neuroscience↗

bioRxiv2026

Selective reorganization of face-processing lateralization in participants with right-lateralized language network

A well-known feature of cortical organization is the lateralization of the language network (LN) to the left hemisphere (LH). Although atypical LN lateralization to the right hemisphere (RH) is well documented, its consequences for other lateralized functions remain poorly understood. Face processing and other category-selective visual areas, which are typically RH-biased, provide key test cases for examining whether atypical LN lateralization is associated with reorganization in other networks. Using functional magnetic resonance imaging (fMRI) data from the Human Connectome Project (HCP), we identified participants with right-hemisphere language dominance (RHLD) and examined whether category-selective visual areas exhibit reorganized hemispheric biases in these participants relative to those with typical left-hemisphere language dominance (LHLD). The results demonstrated selective reorganization within the face-selective areas: some regions showed a leftward shift in hemispheric bias in RHLD participants, whereas others preserved the canonical RH preference. This pattern indicates that cortical reorganization associated with atypical LN lateralization is region-specific rather than global, consistent with a flexible neural architecture in which hemispheric specialization can be selectively reconfigured. These results clarify how atypical LN lateralization impacts the hemispheric organization of face-selective areas and provide evidence for altered network-level specialization in the cerebral cortex.

neuroscience↗

bioRxiv2025

A network of face patches in human prefrontal cortex for social processing of faces

The human cerebral cortex contains localized regions for processing faces. These regions or patches, which are classically found in the occipito-temporal cortex, encode visual properties of faces. Using naturalistic movie-watching fMRI data from 176 human subjects and multivariate functional connectivity analysis, here we comprehensively characterize a novel network of four frontal face patches (FFPs) arranged dorsoventrally in the lateral prefrontal cortex. FFPs are strongly coupled with a face-selective region in the middle superior temporal sulcus, appear to be primarily involved in processing high-level social aspects of faces during movie-watching, and show partial correlations of activity with distinct cognitive networks. Activations in FFPs are correlated with the performance of subjects in a social cognition task. We further identify two groups of subjects who showed a remarkable difference in the topographical organization of FFPs. The discovery of FFPs provides new insights into the understanding of social processing in the brain.

neuroscience↗

bioRxiv2022

Functional impairment of the Lateral Geniculate Nucleus in Multiple Sclerosis

Visual impairment is one of the early symptoms of Multiple sclerosis (MS) disease. The objective of this study is evaluating function of Lateral geniculate nucleus, which bridges visual information from retina to other higher order visual processing areas. We collected BOLD fMRI data from 19 MS and 19 control subjects by employing selective visual stimulation tasks to provoke the whole LGN, Magnocellular, Parvocellular, and Koniocellular pathways as part of LGN multilayer structure. Through statistical analysis, we observed a significant reduction (p<0.05) of the average BOLD signal from the whole LGN structure in MS group. Further investigations showed a significant reduction of BOLD signal (p<0.05) in response to Magno and Parvo stimuli compared to healthy controls that suggested a selective functional impairment emerging in primary visual pathways in MS. In summary, we showed functional abnormalities in LGN structure and its M and P subdivisions based on functional MRI.

bioengineering↗

bioRxiv2022

Functional architecture of cerebral cortex during naturalistic movie-watching

Characterizing the functional organization of cerebral cortex is a fundamental step in understanding how different kinds of information are processed in the brain. Neuroimaging studies over the past decades have uncovered the function of many cortical areas in response to selected stimulus categories. However, it is still unclear how cortical areas are organized during naturalistic visual and auditory stimulation. Here we used high-resolution functional MRI data from 176 human subjects to map the macro-architecture of the entire cerebral cortex based on responses to a 60-minute audiovisual movie stimulus. A data-driven clustering approach revealed a map of 24 functional areas/networks, each related to a specific aspect of sensory or cognitive processing. The map included three distinct executive control (domain-general) networks which showed a strong push-pull interaction with domain-specific regions in visual, auditory, and language cortex. The cortical parcellation scheme presented here provides a comprehensive and unified map of functionally defined areas, which could replace a large set of functional localizer maps.

neuroscience↗

bioRxiv2017

An open resource for nonhuman primate imaging

Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience.\n\nUnfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMate Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 13 independent data collections aggregated across 11 sites (total = 98 macaque monkeys). We also outline the unique pitfalls and challenges that should be considered in the analysis of the non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.

neuroscience↗