Biology subjects

Yeterian, E.

Publications and source records attributed to Yeterian, E..

6 recordsLinked to original sources

bioRxiv2026

Neuroanatomy of substantia nigra and ventral tegmental area dopaminergic, and dorsal raphe serotonergic circuits in the human brain using T1-weighted and diffusion magnetic resonance imaging: A morphometric pilot study with estimate of reliability

IntroductionWe present here a methodology for morphometric analysis of the substantia nigra (SN), the ventral tegmental area (VTA), the dorsal raphe nucleus (DRN) and their respective structural brain circuits. MethodsOur analyses were based on multimodal T1-weighted MRI and diffusion MRI (dMRI) segmentation and tractography in 12 human subjects drawn from the Human Connectome Project (HCP) repository. ResultsWe were able to demonstrate strong connections of the SN, VTA and DRN with several brain regions, in particular the dorsolateral prefrontal cortex (DLPFC) and the cerebellum. More specifically, we created comprehensive visualizations of the SN and VTA dopaminergic as well as the DRN serotonergic structural circuits in the human brain, which, although preliminary, demonstrate the potential of multimodal neuroimaging to investigate these circuits quantitatively in clinical conditions. Finally, we created a pilot dataset for the most frequently observed structural connections, specifically those that were present more than 92% of the time among all subjects. Discussion This pilot morphometric report examines the structural circuits of the SN, VTA and DRN, which are critically involved in several biobehaviors and clinical conditions such as addiction, stress, Parkinsons disease (PD), schizophrenia, obsessive-compulsive disorder, post-traumatic stress disorder, attention deficit hyperactivity disorder, mood disorders, COVID-19 and long COVID. Importantly, the strong structural connectivity of the DLPFC and cerebellum with the SN, VTA and DRN is expected to be a potential target of noninvasive neuromodulation treatments in neuropsychiatry. Our findings demonstrate the potential of current clinical multimodal neuroimaging to delineate the dopaminergic (DA) and serotonergic (5-HT) circuits in the human brain in clinical conditions.

neuroscience↗

bioRxiv2025

The superficial white matter in language processing: Broca's area connections are bilaterally associated with individual performance in children and adults

While deeper white matter connections, such as the arcuate fasciculus and frontal aslant tract, are well known for their role in language and show leftward asymmetries in adults, the contribution of the short-range cortico-cortical superficial white matter (SWM) connections remains less understood. In this preregistered study, we examined white matter connections of Brocas area and its right hemisphere homolog in early adolescents and young adults using two large, publicly available datasets: the Adolescent Brain Cognitive Development Study and the Human Connectome Project Young Adult Study, totaling over 10,000 participants. We anatomically curated the ODonnell Research Group (ORG) tractography atlas to identify SWM fiber clusters intersecting Brocas area (pars opercularis and pars triangularis), confirmed through expert visual inspection. We investigated the microstructure, structural connectivity, and lateralization of Brocas area SWM and its relationship with language performance (Picture Vocabulary and Oral Reading Recognition assessments), in comparison to the deeper white matter connections of the frontal aslant tract and arcuate fasciculus. The arcuate fasciculus demonstrated the strongest and most consistent leftward lateralization of both microstructure (fractional anisotropy, FA) and structural connectivity (number of streamlines, NoS), with structure-function associations that were bilateral in adolescents and left-dominant in adults. Interestingly, despite weaker lateralization than the arcuate fasciculus, both the SWM and the frontal aslant tract demonstrated comparable associations with language performance. The frontal aslant tract showed greater leftward lateralization (FA and NoS) with age and was bilaterally associated with language performance, particularly in adolescents. Compared to these deeper tracts, Brocas area SWM demonstrated left-lateralized FA and right-lateralized NoS in both adolescents and adults, with stronger FA lateralization in adults. Bilateral SWM FA and NoS were significantly associated with language performance in both hemispheres and age groups. Overall, these results suggest that Brocas area SWM may support language in a more bilaterally distributed manner and highlight the importance of considering SWM connections in studies of language development and neurosurgical planning.

neuroscience↗

bioRxiv2025

Neuroanatomy of catecholaminergic circuits in the brainstem and hypothalamus using T1-weighted and diffusion magnetic resonance imaging in humans: implications for brain-immune interactions, cardiovascular disease, neuropsychiatric disorders, stress response, and COVID-19

Neuroimaging allows the study of brain structures that previously were undetectable due to their small size and location. Herein we focused on the core catecholaminergic circuitries in the human brain, involving the coerulean noradrenergic (or norepi-nephrine, NE) and dopaminergic (DA) systems. Using T1-weighted MRI morphometry and dMRI tractography, this study was carried out in one post-mortem human ultra-high-resolution dataset of the brainstem and diencephalon and in healthy human datasets from the Human Connectome Project repository. We investigated 26 connections of brainstem origin (13 in the left side and 13 in the right side) associated with the NE and DA circuitries. We delineated the coerulean NE and DA core central catechol-aminergic circuitries of the brainstem and hypothalamus in the post-mortem dataset, including all targeted fiber connections. Importantly, this was also achieved in the HCP datasets. These results emphasize the importance of multispectral neuroimaging in the study of chemical neuroanatomical circuitries and its application in clinical conditions such as cardiovascular disease, major depression, schizophrenia, and other disorders associated with chronic stress and brain-immune interactions such as COVID-19.

neuroscience↗

bioRxiv2025

Towards an informed choice of diffusion MRI image contrasts for cerebellar segmentation

The fine-grained segmentation of cerebellar structures is an essential step towards supplying increasingly accurate anatomically informed analyses, including, for example, white matter diffusion magnetic resonance imaging (MRI) tractography. Cerebellar tissue segmentation is typically performed on structural magnetic resonance imaging data, such as T1-weighted data, while connectivity between segmented regions is mapped using diffusion MRI tractography data. Small deviations in structural to diffusion MRI data co-registration may negatively impact connectivity analyses. Reliable segmentation of brain tissue performed directly on diffusion MRI data helps to circumvent such inaccuracies. Diffusion MRI enables the computation of many image contrasts, including a variety of tissue microstructure maps. While multiple methods have been proposed for the segmentation of cerebellar structures using diffusion MRI, little attention has been paid to the systematic evaluation of the performance of different available input image contrasts for the segmentation task. In this work, we evaluate and compare the segmentation performance of diffusion MRI-derived contrasts on the cerebellar segmentation task. Specifically, we include spherical mean (diffusion-weighted image average) and b0 (non-diffusion-weighted image average) contrasts, local signal parameterization contrasts (diffusion tensor and kurtosis fit maps), and the structural T1-weighted MRI contrast that is most commonly employed for the task. We train a popular deep-learning architecture using a publicly available dataset (HCP-YA), leveraging cerebellar region labels from the atlas-based SUIT cerebellar segmentation pipeline. By training and testing using many diffusion-MRI-derived image inputs, we find that the spherical mean image computed from b=1000 s/mm2 shell data provides stable performance across different metrics and significantly outperforms the tissue microstructure contrasts that are traditionally used in machine learning segmentation methods for diffusion MRI. Key pointsO_LIWe provide evidence about the performance of different dMRI contrasts for cerebellar structure segmentation using a deep learning neural network. C_LIO_LIThe diffusion MRI spherical mean provides improved and stable cerebellar structure segmentation performance. C_LIO_LIThe spherical mean is easy to compute and can be used for cerebellar structure segmentation on retrospective clinical diffusion MRI data. C_LI

neuroscience↗

bioRxiv2019

3D Exploration of the Brainstem in 50-micron Resolution MRI

The brainstem, a structure of vital importance in the mammals, is currently becoming a principal focus in cognitive, affective and clinical neuroscience. Midbrain, pontine and medullar structures are the epicenter of conduit, cranial nerve and such integrative functions as consciousness, emotional processing, pain and motivation. In this study, we parcellated the nuclear masses and the principal fiber pathways that were visible in a high resolution T2-weighted MRI dataset of 50-micron isotropic voxels of a postmortem human brainstem. Based on this analysis, we generated a detailed map of the human brainstem. To assess the validity of our maps, we compared our observations with histological maps of traditional human brainstem atlases. Moreover, we reconstructed the motor, sensory and integrative neural systems of the brainstem and rendered them in 3D representations. We anticipate the utilization of these maps by the neuroimaging community at large for applications in basic neuroscience as well as in neurology, psychiatry and neurosurgery, due to their versatile computational nature in 2D and 3D representations in a publicly available capacity.

neuroscience↗

bioRxiv2019

MRI-based Parcellation and Morphometry of the Individual Rhesus Monkey Brain: a translational system referencing a standardized ontology

The rhesus macaque is the closest animal model to the human, and investigations into the brain of the rhesus monkey has shed light on the function and organization of the primate brain at a scale and resolution not yet possible in studies of the human brain A cornerstone of the linkage between non-human primate and human studies of the brain is magnetic resonance imaging, which allows for an association to be made between the detailed structural and physiological analysis of the non-human primate and that of the human brain. To further this end, we present a novel parcellation system for the rhesus monkey brain, referred to as the monkey Harvard Oxford Atlas (mHOA) which is based on the human Harvard-Oxford Atlas (HOA) and grounded in an ontological and taxonomic framework. Consistent anatomical features were used to delimit and parcellate brain regions in the macaque, which were then categorized according to functional systems. This system of parcellation will be expanded with advances in technology and like the HOA, will provide a framework upon which the results from other experimental studies (e.g., functional magnetic resonance imaging (fMRI), physiology, connection, graph theory) can be interpreted.

neuroscience↗