Biology subjects

Abbass, M.

Publications and source records attributed to Abbass, M..

5 recordsLinked to original sources

bioRxiv2026

AFIDs-Validator: An Open-Access AI-Guided Platform for Learning Anatomical Landmark Placement

Accurate localization of anatomical landmarks is a foundational skill in anatomy and imaging that is often taught informally through expert mentorship, requiring access to data and desktop software. There is no openly accessible, interactive resource that teaches neuroanatomy with quantitative feedback. We present the AFIDs-Validator (validator.afids.io), an open-access, browser-based platform that pairs guided instruction with quantitative assessment. The platform combines (1) a learning mode in which a language-model neuroanatomy tutor operates inside an MRI viewer, giving anatomy-first instruction that responds to the learner's current image slice, orientation, and cursor position; and (2) a validation engine that accepts a learner's landmark file and returns per-landmark Euclidean error against expert-annotated references spanning 21 brain templates. To make the feedback interpretable, we analyzed 15,000 landmark annotations across 132 human subjects and found that landmark difficulty varies fourfold (median error ranged from 0.37 mm at the anterior commissure to 1.50 mm at the temporal horns) with heavy-tailed distributions at every landmark. These distributions are compiled into per-landmark reliability priors, so learners are scored against the empirical spread of trained raters rather than an arbitrary threshold, and difficult landmarks are not mistaken for poor performance. The AFIDs-Validator requires no installation, licensed software, or local data, and all code, reference data, and tutor design are openly released.

scientific communication and education↗

bioRxiv2024

Spike frequency adaptation in primate lateral prefrontal cortex neurons results from interplay between intrinsic properties and circuit dynamics

Recordings of cortical neurons isolated from brain slices and dissociated from their networks, display intrinsic spike frequency adaptation (I-SFA) to a constant current input. Interestingly, extracellular recordings in behaving subjects also show extrinsic-SFA (E-SFA) in response to sustained visual stimulation. Because neurons are isolated from brain networks in slice recordings, it is challenging to infer how I-SFA contributes to E-SFA in interconnected brains during behavior. To investigate this, we recorded responses of macaque lateral prefrontal cortex neurons in vivo during a visually guided saccade task and in acute brain slices in vitro. Broad spiking (BS) putative pyramidal cells and narrow spiking (NS) putative inhibitory interneurons exhibited E-SFA in vivo. In acute brain slices, both cell types displayed I-SFA though their magnitudes differed. To investigate how in vitro I-SFA contributes to in vivo E-SFA, we developed a data-driven hybrid circuit model in which local NS neurons are driven by BS input. We observed that model NS cell responses show longer SFA than observed in vivo. Introducing inhibition of NS cells to the model circuit removed this discrepancy. Our results indicate that both I-SFA and inhibitory circuit dynamics contribute to E-SFA in LPFC neurons. They highlight the contribution of single neuron and network dependent computations to neural activity underlying behavior.

neuroscience↗

bioRxiv2024

Neural Ensembles in the Lateral Prefrontal Cortex Temporally Multiplex Task Features During Virtual Navigation

Neuronal populations can expand their information encoding capacity using mixed selective neurons. This is particularly prominent in association areas such as the Lateral Prefrontal Cortex (LPFC) that integrate information from multiple sensory systems. During naturalistic conditions where subjects have agency, it is unclear how LPFC neuronal ensembles process space and time varying information. Here we show that during a virtual navigation task that requires associative memory and decision making, individual neurons and neuronal ensembles in the LPFC time-multiplex their selectivity for different task features to fluidly encode the temporal contingencies of the task and the animals choice. Neurons in ventral regions showed more selectivity for non-spatial features, while dorsal regions showed selectivity for space and eye movements. These results demonstrate that during naturalistic tasks with fluid scenery and spatiotemporal dynamics, LPFC neurons and neuronal ensembles time-multiplex the components of their selectivity, expanding the spatiotemporal capacity of their neural codes.

neuroscience↗

bioRxiv2022

Magnetic resonance imaging datasets with anatomical fiducials for quality control and registration

Tools available for reproducible, quantitative assessment of brain correspondence have been limited. We previously validated the anatomical fiducial (AFID) placement protocol for point-based assessment of image registration with millimetric (mm) accuracy. In this data descriptor, we release curated AFID placements for some of the most commonly used structural magnetic resonance imaging templates and datasets. The release of our accurate placements allows for rapid quality control of image registration, teaching neuroanatomy, and clinical applications such as disease diagnosis and surgical targeting. We release placements on individual subjects from four datasets (n = 132 subjects for a total of 15,232 fiducials) and more than 10 brain templates (4,288 fiducials), compiling over 300 human rater hours of annotation. We also validate human rater accuracy of released placements to be within 1-2 mm (using a total of 50,336 Euclidean distances), consistent with prior studies. Our data is compliant with the Brain Imaging Data Structure (BIDS) allowing for facile incorporation into modern neuroimaging analysis pipelines. Data is accessible on GitHub (https://github.com/afids/afids-data).

neuroscience↗

bioRxiv2020

Application of the Anatomical Fiducials Framework to a Clinical Dataset of Patients with Parkinsons Disease

Establishing spatial correspondence between subject and template images is necessary in neuroimaging research and clinical applications such as brain mapping and stereotactic neurosurgery. Our anatomical fiducials (AFIDs) framework has recently been validated to serve as a quantitative measure of image registration based on salient anatomical features. In this study, we sought to apply the AFIDs protocol to the clinic, focusing on structural magnetic resonance images obtained from patients with Parkinsons Disease (PD). We confirmed AFIDs could be placed to millimetric accuracy in the PD dataset with results comparable to those in normal control subjects. We evaluated subject-to-template registration using this framework by aligning the clinical scans to standard template space using a robust open preprocessing workflow. We found that registration errors measured using AFIDs were higher than previously reported, suggesting the need for optimization of image processing pipelines for clinical grade datasets. Finally, we examined the utility of using point-to-point distances between AFIDs as a morphometric biomarker of PD, finding evidence of reduced distances between AFIDs that circumscribe regions known to be affected in PD including the substantia nigra. Overall, we provide evidence that AFIDs can be successfully applied in a clinical setting and utilized to provide localized and quantitative measures of registration error. AFIDs provide clinicians and researchers with a common, open framework for quality control and validation of spatial correspondence and the location of anatomical structures, facilitating aggregation of imaging datasets and comparisons between various neurological conditions.

neuroscience↗