Very low birth weight (VLBW: ≤ 1500 g) individuals have an increased risk of white matter alterations and neurodevelopmental problems, including fine and gross motor problems. In this hospital-based follow-up study, the main aim was to examine white matter microstructure and its relationship to fine and gross motor function in 31 VLBW young adults without cerebral palsy compared with 31 term-born controls, at mean age 22.6 ± 0.7 years. The participants were examined with tests of fine and gross motor function (Trail Making Test-5: TMT-5, Grooved Pegboard, Triangle from Movement Assessment Battery for Children-2: MABC-2 and High-level Mobility Assessment Tool: HiMAT) and diffusion tensor imaging (DTI). Probabilistic tractography of motor pathways of the corticospinal tract (CST) and corpus callosum (CC) was performed. Fractional anisotropy (FA) was calculated in non-crossing (capsula interna in CST, body of CC) and crossing (centrum semiovale) fibre regions along the tracts and examined for group differences. Associations between motor test scores and FA in the CST and CC were investigated with linear regression. Tract-based spatial statistics (TBSS) was used to examine group differences in DTI metrics in all major white matter tracts. The VLBW group had lower scores on all motor tests compared with controls, however, only statistically significant for TMT-5. Based on tractography, FA in the VLBW group was lower in non-crossing fibre regions and higher in crossing fibre regions of the CST compared with controls. Within the VLBW group, poorer fine motor function was associated with higher FA in crossing fibre regions of the CST, and poorer bimanual coordination was additionally associated with lower FA in crossing fibre regions of the CC. Poorer gross motor function was associated with lower FA in crossing fibre regions of the CST and CC. There were no associations between motor function and FA in non-crossing fibre regions of the CST and CC within the VLBW group. In the TBSS analysis, the VLBW group had lower FA and higher mean diffusivity compared with controls in all major white matter tracts. The findings in this study may indicate that the associations between motor function and FA are caused by other tracts crossing the CST and CC, and/or by alterations in the periventricular white matter in the centrum semiovale. Some of the associations were in the opposite direction than hypothesized, thus higher FA does not always indicate better function. Furthermore, widespread white matter alterations in VLBW individuals persist into young adulthood.
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http://dx.doi.org/10.1016/j.nicl.2017.10.006 | 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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