Biology subjects

Arafat, B.

Publications and source records attributed to Arafat, B..

5 recordsLinked to original sources

bioRxiv2026

Individualized parcellation reveals functional boundaries in human prefrontal cortex

Human prefrontal cortex (PFC) supports diverse higher-order cognitive functions during task execution. Increasing evidence suggests that these functions are organized along large-scale gradients, such as a rostro-caudal axis supporting progressively abstract-to-concrete information processing. At the same time, regional specialization within PFC, including focal patches selective for specific stimulus categories, suggests the presence of discrete functional boundaries. Whether PFC organization is best characterized by continuous gradients or discrete subdivisions remains unresolved. Here we show that task-evoked functional organization in PFC exhibits substantial inter-individual variability, limiting the usefulness of conventional group atlases in testing for functional boundaries. To address this challenge, we estimated individualized functional parcellations by combining a group atlas with task-evoked fMRI data spanning diverse cognitive tasks. The group atlas revealed large-scale functional gradients across PFC, whereas individualized parcellations additionally uncovered sharp functional boundaries obscured by group averaging. Moreover, functional organization in PFC was substantially more fine-grained than in other association cortices, consistent with its integrative role in cognitive control. Together, these findings suggest that PFC organization reflects an individualized mosaic of fine-grained functional subdivisions embedded within broader large-scale gradients, with important implications for defining core constructs such as the multiple-demand system.

neuroscience↗

bioRxiv2026

SUITPy: A Python-based toolbox for the analysis of cerebellar functional and anatomical imaging data across the human lifespan

The human cerebellum plays a central role in motor, emotional, and cognitive functions, and is implicated in many brain disorders. To improve the analysis of functional and anatomical imaging from the cerebellum, we introduce SUITPy, an improved and fully revised Python implementation of the widely used SUIT toolbox. For this new version, we developed a U-Net based model to automatically isolate the cerebellum from adjacent cortical tissue, which achieves higher fidelity than existing algorithms. The isolation works robustly without manual corrections for imaging data across the lifespan. We show that isolation and subsequent normalization to a cerebellum-only template lead to a more precise alignment of cerebellar structures across participants compared to normalization using a whole-brain template. We also show the utility of the cerebellar mask to prevent contamination of cerebellar functional data from surrounding cortical structures. The toolbox also provides functionality for visualizing cerebellar data on a flatmap, along with a range of anatomical and functional cerebellar atlases, thereby offering an essential tool that enables accurate cerebellar analysis across the lifespan.

neuroscience↗

bioRxiv2026

Multi-Task Batteries for Precision Functional Mapping

Functional brain mapping is an important tool to understand the organization of the human brain, both at the group level, but also to an increasing degree at the level of the individual. There are currently two main approaches to do so. Resting-state fMRI relies on inter-regional correlations of random fluctuations of the signal. In contrast, task-based localizers typically use a single-contrast between a task of interest and a matched control task to identify the location of a functional region in an individual brain. In this paper, we propose and evaluate a third approach: the use of multi-task batteries for both localization of a single functional region and parcellation of multiple functional regions. We show that multi-task localizers produce more consistent estimation of a single functional region across subjects than the single-contrast approach using the same amount of fMRI data. Furthermore, we demonstrate that the multi-task approach is sensitive to true inter-individual differences in region size, and does not suffer the same influence of signal-to-noise ratio that biases the single-contrast localizer. We then address the question of how to select tasks for the battery, and present a data-driven strategy that optimizes the characterization of a brain structure of interest. We show that such batteries outperform randomly selected batteries both for building individual parcellations as well as individual connectivity models. Finally, we demonstrate that an interspersed design - where all tasks are presented in each imaging run - yields more reliable results than splitting the tasks across different runs. We present an open source toolbox for the implementation of multi-task batteries, along with a library containing group-averaged activity patterns that can be used to optimize battery selection for different brain structures of interest.

neuroscience↗

bioRxiv2026

Multi-task fMRI outperforms resting-state fMRI for revealing task-invariant organization of the human brain

Resting-state functional Magnetic Resonance Imaging (fMRI) is widely used to infer the intrinsic functional organization of the brain, yet it remains unclear how well this approach can predict the structure of brain activity observed across a diverse set of mental states. Here we compare resting-state to task-based fMRI using diverse task batteries within the same individuals. We find that multi-task fMRI data consistently outperform resting-state estimates in predicting functional organization during novel tasks. This advantage persists across preprocessing strategies, brain regions, and independent datasets. While task activation estimates do show task-dependency when using only few tasks, increasing task diversity reduced task-specific bias, with convergence achieved using modest task sets. These improvements translate into superior individual parcellations and connectivity models. Together, our results dissociate reliability from validity in neuroimaging and challenge the prevailing assumption that rest provides a privileged window into intrinsic brain organization. Instead, functional architecture appears most faithfully revealed when the brain is actively driven through diverse task states.

neuroscience↗

bioRxiv2026

Neural representations of speech production in neocortical and cerebellar regions

Speech production requires the coordinated control of the supralaryngeal articulators (e.g., the lips, tongue, and soft palate) together with laryngeal control of phonation. While cortical sensorimotor regions are known to encode phonetic features, how the cerebellum represents speech and how cerebellar representations integrate within cortico-cerebellar circuits remain poorly understood. Using 7T functional MRI and representational similarity analysis, we examined neural representations of speech production during overt syllable production varying in place of articulation and voice onset time. We found reliable, syllable-specific activity patterns across both cortical and cerebellar speech regions. Ventral primary sensorimotor cortex distinguished syllables by place of articulation, whereas dorsal sensorimotor cortex showed sensitivity to voice onset time, consistent with a functional dissociation between articulatory and phonatory control. We also found secondary speech areas that encoded phonetic features. Specifically, the parietal operculum showed sensitivity to both place of articulation and voice onset time, positioning it as an integrative node within the speech motor network. In the cerebellum we found superior and inferior speech areas, which did not differ in their representational geometry. Surprisingly, the cerebellar syllable representations were most similar to those found in the parietal operculum rather than in primary motor cortex, with both regions encoding both phonetic features. These findings indicate that cerebellar speech representations are not solely determined by a single primary sensorimotor area but instead align more closely with the representations in the parietal operculum, suggesting that cerebellar speech areas contribute to the coordination of phonation and articulation.

neuroscience↗