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Kubicki, M.

Publications and source records attributed to Kubicki, M..

6 recordsLinked to original sources

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↗

Exploring the limits of ComBat method for multi-site diffusion MRI harmonization

The findings from diffusion-weighted magnetic resonance imaging (dMRI) studies often show inconsistent and sometimes contradictory results due to small sample sizes as well as differences in acquisition parameters and pre-/post-processing methods. To address these challenges, collaborative multi-site initiatives have provided an opportunity to collect larger and more diverse groups of subjects, including those with neuropsychiatric disorders, leading to increased power and findings that may be more representative at the group and individual level. With the availability of these datasets openly, the ability of joint analysis of multi-site dMRI data has become more important than ever. However, intrinsic- or acquisition-related variability in scanner models, acquisition protocols, and reconstruction settings hinder pooling multi-site dMRI directly. One powerful and fast statistical harmonization method called ComBat (https://github.com/Jfortin1/ComBatHarmonization) was developed to mitigate the "batch effect" in gene expression microarray data and was adapted for multi-site dMRI harmonization to reduce scanner/site effect. Our goal is to evaluate this commonly used harmonization approach using a large diffusion MRI dataset involving 542 individuals from 5 sites. We investigated two important aspects of using ComBat for harmonization of fractional anisotropy (FA) across sites: First, we assessed how well ComBat preserves the inter-subject biological variability (measured by the effect sizes of between-group FA differences) after harmonization. Second, we evaluated the effect of minor differences in pre-processing on ComBats performance. While the majority of effect sizes are mostly preserved in some sites after harmonization, they are not well-preserved at other sites where non-linear scanner contributions exist. Further, even minor differences in pre-processing can yield unwanted effects during ComBat harmonization. Thus, our findings suggest paying careful attention to the data being harmonized as well as using the same processing pipeline while using ComBat for data harmonization.

neuroscience↗

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↗

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↗

Individual variations of the human corticospinal tract and its hand-related motor fibers using diffusion MRI tractography

The corticospinal tract (CST) is one of the most well-studied tracts in human neuroanatomy. Its clinical significance can be demonstrated in many notable traumatic conditions and diseases such as stroke, spinal cord injury (SCI) or amyotrophic lateral sclerosis (ALS). With the advent of diffusion MRI and tractography the computational representation of the human CST in a 3D model became available. However, the representation of the entire CST and, specifically, the hand motor area has remained elusive. In this paper we proposed a novel method, using manually-drawn ROIs based on robustly identifiable neuroanatomic structures to delineate the entire CST and isolate its hand motor representation as well as to estimate their variability and generate a database of their volume, length and biophysical parameters. Using 37 healthy human subjects we performed a qualitative and quantitative analysis of the CST and the hand-related motor fiber tracts (HMFTs). Finally, we have created variability heatmaps from 37 subjects for both the aforementioned tracts, which could be utilized as reference for clinicians to explore neuropathology in both trauma and disease states.

neuroscience↗

Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters

A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we present a dMRI harmonization method that, when applied to multi-site data, is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions in the dMRI data (typical for multi-site clinical research scans). We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8 to 22 years) and Brigham and Womens Hospital (BWH) with 70 healthy subjects (ages 14 to 54 years). In particular, we show that gender differences and maturation in different age groups are preserved after harmonization, as measured using effect sizes (small, medium and large), irrespective of the test sample size. Further, because we use matched control subjects from different scanners to estimate scanner-specific effects, we tested how many subjects are needed from each site to achieve best harmonization results. Our results indicate that at-least 16 to 18 well-matched healthy controls from each site are needed to reliably capture scanner related differences. The proposed method can thus be used for retrospective harmonization of raw dMRI data across sites despite differences in acquisition parameters, while preserving inter-subject anatomical variability.

neuroscience↗