White matter bundle segmentation using diffusion magnetic resonance imaging fiber tractography enables detailed evaluation of individual white matter tracts three-dimensionally, and plays a crucial role in studying human brain anatomy, function, development, and diseases. Manual extraction of streamlines utilizing a combination of the inclusion and exclusion of regions of interest can be considered the current gold standard for extracting white matter bundles from whole-brain tractograms. However, this is a time-consuming and operator-dependent process with limited reproducibility. Several automated approaches using different strategies to reconstruct the white matter tracts have been proposed to address the issues of time, labor, and reproducibility. In this review, we discuss few of the most well-validated approaches that automate white matter bundle segmentation with an end-to-end pipeline, including TRActs Constrained by UnderLying Anatomy (TRACULA), Automated Fiber Quantification, and TractSeg.
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http://dx.doi.org/10.1007/s12565-023-00715-9 | DOI Listing |
J Prev Alzheimers Dis
February 2025
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, PR China. Electronic address:
Background: Cognitive decline and the progression to Alzheimer's disease (AD) are traditionally associated with amyloid-beta (Aβ) and tau pathologies. This study aims to evaluate the relationships between microstructural white matter injury, cognitive decline and AD core biomarkers.
Methods: We conducted a longitudinal study of 566 participants using peak width of skeletonized mean diffusivity (PSMD) to quantify microstructural white matter injury.
J Prev Alzheimers Dis
February 2025
School of Nursing, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. Electronic address:
Background: The associations of early-onset coronary heart disease (CHD) and genetic susceptibility with incident dementia and brain white matter hyperintensity (WMH) remain unclear. Elucidation of this problem could promote understanding of the neurocognitive impact of early-onset CHD and provide suggestions for the prevention of dementia.
Objectives: This study aimed to investigate whether observed and genetically predicted early-onset CHD were related to subsequent dementia and WMH volume.
J Prev Alzheimers Dis
February 2025
Dementia Research Centre (Singapore), Lee Kong Chian School of Medicine - Nanyang Technological University, Singapore. Electronic address:
Background: Cardiovascular risk factors (CRFs) like hypertension, high cholesterol, and diabetes mellitus are increasingly linked to cognitive decline and dementia, especially in cerebral small vessel disease (cSVD). White matter hyperintensities (WMH) are closely associated with cognitive impairment, but the mechanisms behind their development remain unclear. Blood-brain barrier (BBB) dysfunction may be a key factor, particularly in cSVD.
View Article and Find Full Text PDFAm J Pathol
January 2025
Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Alzheimer's disease (AD) is the most common type of dementia and one of the leading causes of death in elderly patients. The number of patients with AD in the United States is projected to double by 2060. Thus, understanding modifiable risk factors for AD is an urgent public health priority.
View Article and Find Full Text PDFJ Equine Vet Sci
January 2025
School of Animal Sciences, Virginia Polytechnic Institute and State University, 175 West Campus Dr., Blacksburg, VA, USA, 24061. Electronic address:
Our objectives were to use a quantitative literature review to explore dietary and feed factors influencing apparent total-tract digestibility of dry matter (DMD), crude protein (CPD), neutral detergent fiber (NDFD), ether extract (EED), non-structural carbohydrates (NSCD), non-fiber carbohydrates (NFCD), and residual organic matter (rOMD) in equine diets, and to assess their contributions to digestible energy (DE) supplies. Data from 54 studies were modeled using linear mixed-effect regressions, with publication as a random effect to account for study variability. For each nutrient, five models were derived with explanatory variables including: dry matter intake (DMI; % BW/day) and DM (% as-fed), and dietary components (CP, organic matter, EE, NDF, acid detergent fiber, NSC, starch, and NFC as % of DM), and feed types (forage, non-forage fiber, legumes, cereal, and oil proportions).
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