Biology subjects

Katebi, M. E.

Publications and source records attributed to Katebi, M. E..

3 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↗

bioRxiv2025

Hemispheric laterality of the putamen predicts pseudoneglect

Healthy individuals tend to exhibit a subtle leftward attentional bias, a phenomenon termed pseudoneglect. While this bias is thought to reflect a right-hemisphere dominance when allocating spatial attention, the contribution of subcortical structures remains poorly understood. Although lesion and neuroimaging studies in clinical populations have implicated asymmetries in the basal ganglia and thalamus to dysfunction in spatial attention, it remains unclear whether naturally occurring subcortical asymmetries in healthy individuals predict behavioural biases in spatial attention. In this study, we investigated whether volumetric asymmetries in subcortical structures are associated with biases in spatial attention in a non-clinical population. This was achieved by using the landmark task to assess spatial biases, alongside acquiring T1-weighted MRI data from 44 healthy participants. The subcortical regions were segmented using the FIRST algorithm to estimate the volumetric asymmetry of specific regions. We found that variability in pseudoneglect was predicted by the degree of leftward lateralisation of putamen volume, indicating that the putamen contributes to the magnitude of spatial attention biases. This also suggests that the left putamen may support right-hemisphere neocortical dominance through the indirect pathway. These findings bridge anatomical and behavioural measures, highlighting the functional relevance of subcortical asymmetry in shaping attentional processes in the healthy brain. Our findings pave the way for early diagnosis of neurological disorders associated with subcortical atrophies including Parkinsons Disease and dementia. Significance StatementHemispheric dominance in cortical networks shape attentional biases, but the role of subcortical structures remains poorly understood. Here we show that natural variability in the lateralisation of the putamen predicts individual differences in pseudoneglect--a subtle leftward bias in spatial attention observed in healthy adults. Using MRI and psychophysics, we demonstrate that greater leftward putamen asymmetry is associated with stronger leftward bias, independent of eye dominance and microsaccadic behaviour, and moderated by handedness. These findings reveal a structural subcortical basis for attentional asymmetries and suggest that simple behavioural measures could serve as non-invasive markers of early subcortical changes relevant to disorders such as Parkinsons disease, Alzheimers disease, and ADHD.

neuroscience↗

bioRxiv2024

STEMorph: Morphed Emotional Face Stimuli

Emotion recognition through facial expressions is crucial for interpreting social cues. However, it is often influenced by biases, i.e., systematic recognition advantages for particular emotions. Nevertheless, these biases are inconsistently reported across studies, likely due to methodological variations which underline the necessity for a standardized approach. Traditional face morphing methods can create unnatural-looking stimuli, and may confound the interpretation of emotions. To address this issue, we here introduce STEMorph, a validated stimulus set based on the NimStim set. We employed neutral-anchored morphing and neural-network-generated masks to reduce morphing artifacts and preserve the coherence of the depicted expressions. We validated our stimulus set by having participants rate each face on a 9-point scale ranging from angry to happy, assessing the perceived emotional intensity. STEMorphs validity was confirmed through linear mixed-effects modelling, showing a strong association between subjective ratings and intended morph level while accounting for other effects. Moreover, aligning with previous research highlighting gender as a key factor in emotion recognition, STEMorph also showed variation across participant-gender dimension. STEMorphs reliability was confirmed through a two-week follow-up rating session with a subgroup of the same participants. By introducing a controlled and empirically evaluated stimulus set of morphed emotional faces, STEMorph provides a useful resource for future investigations of facial emotion recognition.

neuroscience↗