Biology subjects

Abi-Dargham, A.

Publications and source records attributed to Abi-Dargham, A..

3 recordsLinked to original sources

bioRxiv2024

Functional Localization of the Human Auditory and Visual Thalamus Using a Thalamic Localizer Functional Magnetic Resonance Imaging Task

Functional magnetic resonance imaging (fMRI) of the auditory and visual sensory systems of the human brain is an active area of investigation in the study of human health and disease. The medial geniculate nucleus (MGN) and lateral geniculate nucleus (LGN) are key thalamic nuclei involved in the processing and relay of auditory and visual information, respectively, and are the subject of blood-oxygen-level-dependent (BOLD) fMRI studies of neural activation and functional connectivity in human participants. However, localization of BOLD fMRI signal originating from neural activity in MGN and LGN remains a technical challenge, due in part to the poor definition of boundaries of these thalamic nuclei in standard T1-weighted and T2-weighted magnetic resonance imaging sequences. Here, we report the development and evaluation of an auditory and visual sensory thalamic localizer (TL) fMRI task that produces participant-specific functionally-defined regions of interest (fROIs) of both MGN and LGN, using 3 Tesla multiband fMRI and a clustered-sparse temporal acquisition sequence, in less than 16 minutes of scan time. We demonstrate the use of MGN and LGN fROIs obtained from the TL fMRI task in standard resting-state functional connectivity (RSFC) fMRI analyses in the same participants. In RSFC analyses, we validated the specificity of MGN and LGN fROIs for signals obtained from primary auditory and visual cortex, respectively, and benchmark their performance against alternative atlas- and segmentation-based localization methods. The TL fMRI task and analysis code (written in Presentation and MATLAB, respectively) have been made freely available to the wider research community.

neuroscience↗

bioRxiv2020

Multi-Task Learning based 3-Dimensional Striatal Segmentation of MRI - a Multi-modal Objective Assessment

The human striatum is a collection of subcortical nuclei that serves as a critical node in an information processing network. Recent studies have established a clear topographical and functional organization of projections to and from striatal regions that involves detailed and complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor-intensive and time-consuming, yet automated methods have also been a challenging computational problem. Recently, deep learning has emerged as a powerful tool for various tasks, including semantic segmentation. Multi-Task Learning (MTL) is a machine learning technique that allows latent representations between related tasks to be shared during model prediction of multiple tasks with better accuracy. We utilized MTL to segment subregions of the striatum consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST). Dice similarity coefficients (DSC) demonstrate excellent spatial agreement between manual and MTL-generated segmentations ([≥] 0.72 across all striatal subregions). Further quantitative task-based analysis was also conducted. Binding potential values, BPND, of [11C]raclopride PET, and ROI time-series and whole-brain connectivity using fMRI images were compared between results generated from manual segmentations and MTL-generated segmentations. BPND values from MTL-generated segmentations were shown to correlate well with manual segmentations with R2 [≥] 0.91 in all caudate and putamen subregions, and R2=0.69 in VST. Mean Pearson correlation coefficients of the fMRI data between MTL-generated and manual segmentations were also high in time-series ([≥]0.86) and whole-brain connectivity ([≥]0.89) across all subregions. We conclude that the MTL approach is a fast, robust and reliable method for 3D striatal subregion segmentation with results comparable to manually segmented ROIs.

bioengineering↗

bioRxiv2019

Reproducibility Assessment of Neuromelanin-Sensitive Magnetic Resonance Imaging Protocols for Region-of-Interest and Voxelwise Analyses

Neuromelanin-sensitive MRI (NM-MRI) provides a noninvasive measure of the content of neuromelanin (NM), a product of dopamine metabolism that accumulates with age in dopamine neurons of the substantia nigra (SN). NM-MRI has been validated as a measure of both dopamine neuron loss, with applications in neurodegenerative disease, and dopamine function, with applications in psychiatric disease. Furthermore, a voxelwise-analysis approach has been validated to resolve substructures, such as the ventral tegmental area (VTA), within midbrain dopaminergic nuclei thought to have distinct anatomical targets and functional roles. NM-MRI is thus a promising tool that could have diverse research and clinical applications to noninvasively interrogate in vivo the dopamine system in neuropsychiatric illness. Although a test-retest reliability study by Langley et al. using the standard NM-MRI protocol recently reported high reliability, a systematic and comprehensive investigation of the performance of the method for various acquisition parameters and preprocessing methods has not been conducted. In particular, most previous studies used relatively thick MRI slices ([~]3 mm), compared to the typical in-plane resolution ([~]0.5 mm) and to the height of the SN ([~]15 mm), to overcome technical limitations such as specific absorption rate and signal-to-noise ratio, at the cost of partial-volume effects. Here, we evaluated the effect of various acquisition and preprocessing parameters on the strength and test-retest reliability of the NM-MRI signal to determine optimized protocols for both region-of-interest (including whole SN/VTA-complex and atlas-defined dopaminergic nuclei) and voxelwise measures. Namely, we determined a combination of parameters that optimizes the strength and reliability of the NM-MRI signal, including acquisition time, slice-thickness, spatial-normalization software, and degree of spatial smoothing. Using a newly developed, detailed acquisition protocol, across two scans separated by 13 days on average, we obtained intra-class correlation values indicating excellent reliability and high contrast-to-noise, which could be achieved with a different set of parameters depending on the measures of interest and experimental constraints such as acquisition time. Based on this, we provide detailed guidelines covering acquisition through analysis and recommendations for performing NM-MRI experiments with high quality and reproducibility. This work provides a foundation for the optimization and standardization of NM-MRI, a promising MRI approach with growing applications throughout clinical and basic neuroscience.\n\nHighlightsO_LIA detailed NM-MRI volume placement protocol is described.\nC_LIO_LIGuidelines covering acquisition through analysis for NM-MRI are given.\nC_LIO_LIA test-retest study in 10 healthy subjects shows high reproducibility for region-of-interest (ROI) and voxelwise analyses.\nC_LIO_LI[~]3 minutes of NM-MRI data is needed for high-quality ROI-analysis.\nC_LIO_LI[~]6 minutes of NM-MRI data is needed for high-quality voxelwise-analysis.\nC_LI

neuroscience↗