Biology subjects

Abdelmotaleb, M.

Publications and source records attributed to Abdelmotaleb, M..

5 recordsLinked to original sources

bioRxiv2026

Temporo-Occipital and Medial Temporal Networks Underlying Object-Location Learning

Object-location memory (OLM), a fundamental component of spatial memory, is essential for everyday functioning yet declines with aging and neurodegenerative disorders. Functional neuroimaging studies have consistently implicated medial temporal lobe (MTL) structures, including the hippocampus and parahippocampal gyrus, as well as medial and lateral temporo-occipital regions, in object-location associative learning. However, the neural mechanisms and functional network interactions supporting OLM acquisition remain largely unknown. To address this gap, we examined twenty healthy adults (18-45 years) performing an object-location learning task during functional MRI. As a first main finding, task-related functional connectivity analyses revealed enhanced coupling between MTL structures, ventral visual regions, and temporo-occipital cortices during OLM learning. Secondly, stronger connectivity within this network was associated with higher learning accuracy, highlighting the behavioral relevance of coordinated cortical-MTL interactions. Thirdly, these connectivity patterns were not static but evolved across learning stages: interregional coupling was strongest during early learning and progressively attenuated as performance stabilized, suggesting sharpening and increased efficiency of network interactions with learning. Together, our results provide mechanistic insight into OLM acquisition at the functional network level and offer an evidence-based framework for identifying target networks to enhance spatial memory through non-invasive brain stimulation.

neuroscience↗

bioRxiv2026

Normative Deviations Reveal Task-Evoked and Clinical Network Reorganization

Understanding how cognitive demands and pathology reshape large-scale functional connectivity (FC) requires methods that are both multivariate and region-specific. Here we introduce One-class SVM-based Connectome Anomaly Recognition (OSCAR), a normative modelling framework that detects condition-related deviations in the multivariate connectivity profile of a brain region. OSCAR learns the distribution of region-to-whole-brain connectivity patterns given a reference state sample (e.g. resting-state data; RS) using a one-class support vector machine (OCSVM). The trained models are then applied to FC profiles from a target condition (e.g., task or patient group). The outlier proportions are used to quantify the difference between the reference and target condition. We validated OSCAR on three diverse tasks and a patient cohort with early psychosis. OSCAR consistently identified condition-sensitive regions in networks known to support conflict processing, object-location memory, lexical learning, and early psychosis, respectively, including thalamic and basal ganglia regions. Moreover, it detected additional well-established task- or disease-relevant parcels not captured by the comparison method permutation-based multivariate analysis of variance (perMANOVA). Regions flagged by OSCAR were at least as close, and often closer, to independent task-activation findings than those identified by perMANOVA. These results demonstrate that OSCAR provides an interpretable, region-centred normative modelling approach that is sensitive to subtle multivariate FC deviations, and offers a practical tool for mapping condition-specific reconfigurations of functional brain networks with high external validity, in both experimental and clinical settings.

neuroscience↗

bioRxiv2026

Multimodal Imaging-Based Targeting Approach for Network-Level Brain Stimulation

BackgroundNeural network effects of transcranial direct current stimulation (tDCS) are poorly understood. Here, we introduce an empirically informed, multimodal functional magnetic resonance imaging (fMRI) based framework suited for guiding stimulation target selection and hypothesis-based data analysis in focal tDCS-fMRI studies. MethodsWe illustrate our approach using data of 37 healthy individuals (19 females; mean {+/-} SD age = 25.8 {+/-} 5.9) recruited from a multicenter tDCS-fMRI study. Participants completed resting-state (RS- and task-fMRI (object-location memory, OLM, or associative picture-pseudoword learning, APPL, experiments) with placebo tDCS. Seed-based RS data analysis identified the functional networks originating from project-specific target regions for focal tDCS (right occipito-temporal cortex, rOTC; left ventral IFG, lvIFG) and also established their test-retest reliability using intraclass correlation coefficients (ICC). Dice coefficients analyzed the overlap between the seeded RS- and task-evoked networks. This aimed to identify task-active regions potentially affected by downstream neural network effects from the target regions. ResultsSeed-based analyses identified two highly reliable ventral visual-limbic (rOTC) and language-related networks (lvIFG), with >72-77% of voxels showing good-to-excellent TRR (ICC [≥] 0.75). Only a subset of voxels identified by the RS analyses overlapped with activity elicited by the experimental paradigms (ranging from 7.5-55%), with larger correspondence for the OLM task (Dice OLM: 0.249-0.349; APPL 0.065-0.106). Therefore, the degree of potential tDCS network effects varied substantially depending on the target region, the extent of its functional network and task-specific activity patterns. Degree of correspondence was further mediated by the selected contrasts-of-interest in the task-based analyses, with more conservative control conditions resulting in reduced overlap. ConclusionWe established a principled, multimodal fMRI framework bridging a critical gap in neuromodulation research. By integrating reliable intrinsic connectivity maps with task-evoked activity patterns, we provide a method to prospectively identify network-level targets for focal brain stimulation and to generate hypotheses for data analyses in tDCS-fMRI studies. This approach paves the way for investigating modulation of specific functional networks, shifting the rationale from stimulating an isolated brain region to strategically targeting key nodes within a predefined functional pathway.

neuroscience↗

bioRxiv2025

Deep learning to overcome human error and bias in electrode position extraction in tDCS-fMRI studies

Combining transcranial direct current stimulation (tDCS) with fMRI enables investigation of stimulation effects, while structural MRI verifies electrode positioning, critical for focal montages where minor misplacements reduce target current dose. Currently, no fully automated methods exist for precise electrode extraction from MRI. We developed an Attention U-Net-based deep learning approach to automate electrode detection in focal tDCS-fMRI studies, using structural pointwise-encoding time reduction with radial acquisition (PETRA) MRI scans from a multicenter trial (N = 392 images; 1,568 electrodes, https://www.memoslap.de/en/home/). Performance was compared to manual and semi-automated methods for a 3x1 montage (three cathodes around a central anode). The network achieved robust segmentation (Dice Score = 0.76, Hausdorff distance = 36.76 mm), identifying all electrodes in 95% of cases (323/340). These metrics compared network-segmented electrodes to highly accurate, manually segmented electrodes, which are also called "ground truth". Linear mixed-effects models on 52 "ground truth" images showed deep learning outperformed manual and semi-automated methods, aligning best with ground truth. Fully/semi-automated methods comparison of 290 images showed highest agreement (ICC = 0.990, bias = 1.3 mm), while manual extraction exhibited larger biases (-5.77 to -7.06 mm) and systematic errors. The automated approach overcomes manual limitations by improving precision, eliminating human variability and bias, and enabling scalability for large studies. It sets a new standard for electrode verification in focal tDCS, particularly for studies requiring precise localization. The model is open-source, with future refinements discussed

neuroscience↗

bioRxiv2025

Functionally Relevant and Reliable Brain Stimulation Targets for Enhancement of Novel Word-Learning

Linking word-forms and their meanings is central to language learning. Transcranial direct current stimulation (tDCS), has shown potential to enhance this process, but with variable effects. This study aimed to (1) identify reliable and functionally relevant tDCS target brain regions to enhance novel-word learning and (2) assess test-retest reliability (TRR) of behavioral and imaging outcomes. Twenty healthy individuals completed two functional magnetic resonance imaging (fMRI) sessions using parallel task versions. Participants learned picture-pseudoword associations across six learning blocks. Behavioral learning was analyzed using linear-mixed-models. Whole-brain and region-of-interest (ROI) analyses examined learning-related activity changes and their behavioral relevance. TRR was assessed using intraclass correlation coefficients (ICCs). Participants successfully acquired the novel-word forms, indexed by increased accuracy and faster latency across stages. Behavioral outcomes showed good-to-excellent TRR. The task elicited robust language-learning related activity and activity changes across stages were correlated with learning success. Task-related activity was variable, but voxels within significant clusters ([~]81%) and most ROIs showed moderate-to-excellent consistency. Power analyses confirmed a sufficient sample size for detecting the reported ICCs. Current modeling suggested that focal-tDCS can induce neurophysiologically relevant electrical field strength in the identified target regions. Hence, we identified accessible, reliable and functionally relevant cortical targets for enhancing novel-word learning. TRR results support the usefulness of the paradigm for future concurrent tDCS-fMRI research. Our study also outlines a general path towards optimization of brain stimulation studies by implementing an empirically informed approach for selecting reliable and relevant target regions and implementation of reliable experimental and imaging paradigms.

neuroscience↗