DeepNuParc: A novel deep clustering framework for fine-scale parcellation of brain nuclei using diffusion MRI tractography.

Brain nuclei are clusters of anatomically distinct neurons that serve as important hubs for processing and relaying information in various neural circuits. Fine-scale parcellation of the brain nuclei is vital for a comprehensive understanding of their anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the nuclei of interest for studying their subdivisions.

Supplementary motor area microstructure defines the extent of gait impairment in Parkinson's disease.

Gait disorders and freezing of gait are challenging symptoms in Parkinson's disease. Cortical gait centers as the supplementary motor area appear to be relevant in gait control. We hypothesize that diffusion-MRI microstructural markers in this area are associated with quantitative gait performance in participants with Parkinson's Disease.

Fiber microstructure quantile (FMQ) regression: A novel statistical approach for analyzing white matter bundles from periphery to core.

The structural connections of the brain's white matter are critical for brain function. Diffusion MRI tractography enables the in-vivo reconstruction of white matter fiber bundles and the study of their relationship to covariates of interest, such as neurobehavioral or clinical factors. In this work, we introduce Fiber Microstructure Quantile (FMQ) Regression, a new statistical approach for studying the association between white matter fiber bundles and scalar factors (e.

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 MRI 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.

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 Broca's 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 O'Donnell Research Group (ORG) tractography atlas to identify SWM fiber clusters intersecting Broca's area (pars opercularis and pars triangularis), confirmed through expert visual inspection.

Multishot Dual Polarity GRAPPA: Robust Nyquist Ghost Correction for multishot EPI.

Purpose: This work aims to develop a robust Nyquist ghost correction method for multishot echo-planar imaging (EPI). The method helps correct challenging Nyquist ghosts, particularly on scanners with high-performance gradients or ultra-high fields.

Methods: A method for multishot EPI ghost correction, called multishot dual-polarity GRAPPA (msDPG), is developed by extending the DPG concept to multishot readouts.

Association of Superficial White Matter Microstructure With Cortical Pathology Deposition Across Early Stages of the AD Continuum.

Background And Objectives: β-amyloid (Aβ) and tau, 2 prominent pathologies of Alzheimer disease (AD), originate in cortical regions and primarily affect, and even spread along, the white matter tracts directly connected to these cortical regions. Superficial white matter (SWM), containing short-range association connections beneath the cortex, has been affected in mild cognitive impairment and AD, with gaps in understanding the disease's early stages. We perform a detailed investigation of individual SWM connections with cortical pathology deposition and cognition in the early stages of the AD continuum.

White matter correlates of autonomic cardiac dysfunction in anorexia nervosa.

Anorexia nervosa (AN) is linked to changes in autonomic function, but the specific neuroanatomical substrates of these changes are not well understood. In this study, we used diffusion-weighted imaging to examine white matter structure in the ventromedial prefrontal cortex-dorsal vagal complex (vmPFC-DVC) pathway, which is essential for autonomic regulation. Compared to healthy controls, individuals with AN showed significantly higher fractional anisotropy and axial diffusivity, as well as more streamlines connecting the vmPFC and DVC-indicating altered structural organization.

Vendor-agnostic 3D multiparametric relaxometry improves cross-platform reproducibility.

Purpose: To address the unmet need for a cross-platform, multiparametric relaxometry technique to facilitate data harmonization across different sites.

Methods: A simultaneous T and T mapping technique, 3D quantification using an interleaved Look-Locker acquisition sequence with a T preparation pulse (3D-QALAS), was implemented using the open-source vendor-agnostic Pulseq platform. The technique was tested on four 3 T scanners from two vendors across two sites, evaluating cross-scanner, cross-software version, cross-site, and cross-vendor variability.

Cross-site harmonization of diffusion MRI data without matched training subjects.

Purpose: Diffusion MRI (dMRI) data typically suffer of significant cross-site variability, which prevents naively performing pooled analyses. To attenuate cross-site variability, harmonization methods such as the rotational invariant spherical harmonics (RISH) have been introduced to harmonize the dMRI data at the signal level. A common requirement of the RISH method is the availability of healthy individuals who are matched at the group level, which may not always be readily available, particularly retrospectively.

TractCloud-FOV: Deep Learning-Based Robust Tractography Parcellation in Diffusion MRI With Incomplete Field of View.

Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions are partially imaged, leading to partial, or truncated fiber tracts. To address this challenge, we introduce TractCloud-FOV, a deep learning framework that robustly parcellates tractography under conditions of incomplete FOV.

The MR neuroimaging protocol for the Accelerating Medicines Partnership® Schizophrenia Program.

Neuroimaging with MRI has been a frequent component of studies of individuals at clinical high risk (CHR) for developing psychosis, with goals of understanding potential brain regions and systems impacted in the CHR state and identifying prognostic or predictive biomarkers that can enhance our ability to forecast clinical outcomes. To date, most studies involving MRI in CHR are likely not sufficiently powered to generate robust and generalizable neuroimaging results. Here, we describe the prospective, advanced, and modern neuroimaging protocol that was implemented in a complex multi-site, multi-vendor environment, as part of the large-scale Accelerating Medicines Partnership® Schizophrenia Program (AMP® SCZ), including the rationale for various choices.

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.

The Shape of the Brain's Connections Is Predictive of Cognitive Performance: An Explainable Machine Learning Study.

The shape of the brain's white matter connections is relatively unexplored in diffusion magnetic resonance imaging (dMRI) tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance.

A deep learning approach to multi-fiber parameter estimation and uncertainty quantification in diffusion MRI.

Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as variable dimensionalities (reflecting the unknown number of distinct white matter fiber populations in a voxel), low signal-to-noise ratios, and non-linear forward models. These challenges have led many existing methods to use biologically implausible simplified models to stabilize estimation, for instance, assuming shared microstructure across all fiber populations within a voxel.

DDCSR: A Novel End-to-End Deep Learning Framework for Cortical Surface Reconstruction from Diffusion MRI.

Diffusion MRI (dMRI) plays a crucial role in studying brain white matter connectivity. Cortical surface reconstruction (CSR), including the inner whiter matter (WM) and outer pial surfaces, is one of the key tasks in dMRI analyses such as fiber tractography and multimodal MRI analysis. Existing CSR methods rely on anatomical T1-weighted data and map them into the dMRI space through inter-modality registration.

A Deep Learning Approach to Multi-Fiber Parameter Estimation and Uncertainty Quantification in Diffusion MRI.

Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysical models is a challenging inverse problem, due to factors such as variable dimensionalities (reflecting the unknown number of distinct white matter fiber populations in a voxel), low signal-to-noise ratios, and non-linear forward models. These challenges have led many existing methods to use biologically implausible simplified models to stabilize estimation, for instance, assuming shared microstructure across all fiber populations within a voxel.

The shape of the brain's connections is predictive of cognitive performance: an explainable machine learning study.

The shape of the brain's white matter connections is relatively unexplored in diffusion MRI tractography analysis. While it is known that tract shape varies in populations and across the human lifespan, it is unknown if the variability in dMRI tractography-derived shape may relate to the brain's functional variability across individuals. This work explores the potential of leveraging tractography fiber cluster shape measures to predict subject-specific cognitive performance.

Consistent cerebellar pathway-cognition associations across pre-adolescents & young adults: a diffusion MRI study of 9000+ participants.

The cerebellum, long implicated in movement, is now recognized as a contributor to higher-order cognition. The cerebellar pathways provide key structural links between the cerebellum and cerebral regions integral to language, memory, and executive function. Here, we present a large-scale, cross-sectional diffusion MRI (dMRI) analysis investigating the relationships between cerebellar pathway microstructure and cognitive performance in over 9,000 participants spanning pre-adolescence (n>8,000 from the ABCD dataset) and young adulthood (n>900 from the HCP-YA dataset).

Characterization of cognitive decline in long-duration type 1 diabetes by cognitive, neuroimaging, and pathological examinations.

BACKGROUNDWe aimed to characterize factors associated with the under-studied complication of cognitive decline in aging people with long-duration type 1 diabetes (T1D).METHODSJoslin "Medalists" (n = 222; T1D ≥ 50 years) underwent cognitive testing. Medalists (n = 52) and age-matched nondiabetic controls (n = 20) underwent neuro- and retinal imaging.

Assessing the cortical microstructure in contralesional sensorimotor areas after stroke.

Cortical thickness analyses have provided valuable insights into changes in cortical brain structure after stroke and their association with recovery. Across studies though, relationships between cortical structure and function show inconsistent results. Recent developments in diffusion-weighted imaging of the cortex have paved the way to uncover hidden aspects of stroke-related alterations in cortical microstructure, going beyond cortical thickness as a surrogate for cortical macrostructure.

TractGraphFormer: Anatomically informed hybrid graph CNN-transformer network for interpretable sex and age prediction from diffusion MRI tractography.

The relationship between brain connections and non-imaging phenotypes is increasingly studied using deep neural networks. However, the local and global properties of the brain's white matter networks are often overlooked in convolutional network design. We introduce TractGraphFormer, a hybrid Graph CNN-Transformer deep learning framework tailored for diffusion MRI tractography.

Combined T1-weighted MRI and diffusion MRI tractography of paraventricular, locus coeruleus, and dorsal vagal complex connectivity in brainstem-hypothalamic nuclei.

Background: Current multimodal neuroimaging plays a critical role in studying clinical conditions such as cardiovascular disease, major depression, and other disorders related to chronic stress. These conditions involve the brainstem-hypothalamic network, specifically the locus coeruleus (LC), dorsal vagal complex (DVC), and paraventricular nucleus (PVN) of the hypothalamus, collectively referred to as the "DVC-LC-PVN circuitry." This circuitry is strongly associated with the norepinephrine (NE) and epinephrine (E) neurotransmitter systems, which are implicated in the regulation of key autonomic functions, such as cardiovascular and respiratory control, stress response, and cognitive and emotional behaviors.

DDEvENet: Evidence-based ensemble learning for uncertainty-aware brain parcellation using diffusion MRI.

In this study, we developed an Evidential Ensemble Neural Network based on Deep learning and Diffusion MRI, namely DDEvENet, for anatomical brain parcellation. The key innovation of DDEvENet is the design of an evidential deep learning framework to quantify predictive uncertainty at each voxel during a single inference. To do so, we design an evidence-based ensemble learning framework for uncertainty-aware parcellation to leverage the multiple dMRI parameters derived from diffusion MRI.

MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI.

White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols.