Virtually all adults with Down syndrome (DS) develop Alzheimer’s disease (AD)‐associated neuropathology by the age of 40, with risk for dementia increasing from the early 50s. White matter (WM) pathology has been reported in sporadic AD, including early demyelination, microglial activation, loss of oligodendrocytes and reactive astrocytes but has not been extensively studied in the at‐risk DS population.
Fifty‐six adults with DS (35 cognitively stable adults, 11 with mild cognitive impairment, 10 with dementia) underwent diffusion‐weighted magnetic resonance imaging (MRI), amyloid imaging, and had assessments of cognition and functional abilities using tasks appropriate for persons with intellectual disability.
Early changes in late‐myelinating and relative sparing of early‐myelinating pathways, consistent with the retrogenesis model proposed for sporadic AD, were associated with AD‐related cognitive deficits and with regional amyloid deposition.
Our findings suggest that quantification of WM changes in DS could provide a promising and clinically relevant biomarker for AD clinical onset and progression.
he Alzheimer’s Biomarkers Consortium – Down Syndrome (ABC-DS), a multi-institution research team, co-led by members from the University of California, Irvine, has been awarded an unprecedented five-year, $109 million grant by the National Institutes of Health (NIH), to expand research on the biomarkers of Alzheimer’s disease in adults with Down syndrome.
INTRODUCTION: Down syndrome (DS) is associated with elevated risk for Alzheimer’s disease (AD) due to beta amyloid (Aβ) lifelong accumulation. We hypothesized that the spatial distribution of brain Aβ predicts future dementia conversion in individuals with DS.
METHODS: We acquired 18F-Florbetapir PET scans from 19 nondemented individuals with DS at baseline and monitored them for four years, with five individuals transitioning to dementia. Machine learning classification using an independent test set determined features on 18F-Florbetapir standardized uptake value ratio (SUVR) maps that predicted transition.
RESULTS: In addition to “AD signature” regions including the inferior parietal cortex, temporal lobes, and the cingulum, we found that Aβ cortical binding in the prefrontal and superior frontal cortices distinguished subjects who transitioned to dementia. Classification did well in predicting transitioners.
DISCUSSION: Our study suggests that specific regional profiles of brain amyloid in older adults with DS may predict cognitive decline and are informative in evaluating the risk for dementia.
Repetitive head impacts represent a risk factor for neurological impairment in team-sport athletes. In the absence of symptoms, a physiological basis for acute injury has not been elucidated. A basic brain function that is disrupted after mild traumatic brain injury is the regulation of homeostasis, instantiated by activity across a specific set of brain regions that comprise a central autonomic network. We sought to relate head-to-ball impact exposure to changes in functional connectivity in a core set of central autonomic regions and then to determine the relation between changes in brain and changes in behavior, specifically cognitive control. Thirteen collegiate men’s soccer players and eleven control athletes (golf, cross-country) underwent resting-state fMRI and behavioral testing before and after the season, and a core group of cortical, subcortical, and brainstem regions was selected to represent the central autonomic network. Head-to-ball impacts were recorded for each soccer player. Cognitive control was assessed using a Dot Probe Expectancy task. We observed that head-to-ball impact exposure was associated with diffuse increases in functional connectivity across a core CAN subnetwork. Increased functional connectivity between the left insula and left medial orbitofrontal cortex was associated with diminished proactive cognitive control after the season in those sustaining the greatest number of head-to-ball impacts. These findings encourage measures of autonomic physiology to monitor brain health in contact and collision sport athletes.
Adults with Down syndrome (DS) develop Alzheimer’s disease (AD) pathology by their fifth decade. Compared with the general population, traditional vascular risks in adults with DS are rare, allowing examination of cerebrovascular disease in this population and insight into its role in AD without the confound of vascular risk factors. We examined in vivo MRI‐based biomarkers of cerebrovascular pathology in adults with DS, and determined their cross‐sectional relationship with age, beta‐amyloid pathology, and mild cognitive impairment or clinical AD diagnostic status.
The findings highlight the prevalence of cerebrovascular disease in adults with DS and add to a growing body of evidence that implicates cerebrovascular disease as a core feature of AD and not simply a comorbidity.
Full paper: https://alz-journals.onlinelibrary.wiley.com/doi/full/10.1002/dad2.12013
Down syndrome (DS) is associated with a higher risk of dementia. We hypothesize that amyloid beta (Aβ) in specific brain regions differentiates mild cognitive impairment in DS (MCI‐DS) and test these hypotheses using cross‐sectional and longitudinal data.
18F‐AV‐45 (florbetapir) positron emission tomography (PET) data were collected to analyze amyloid burden in 58 participants clinically classified as cognitively stable (CS) or MCI‐DS and 12 longitudinal CS participants.
The study confirmed our hypotheses of increased amyloid in inferior parietal, lateral occipital, and superior frontal regions as the main effects differentiating MCI‐DS from the CS groups. The largest annualized amyloid increases in longitudinal CS data were in the rostral middle frontal, superior frontal, superior/middle temporal, and posterior cingulate cortices.
This study helps us to understand amyloid in the MCI‐DS transitional state between cognitively stable aging and frank dementia in DS. The spatial distribution of Aβ may be a reliable indicator of MCI‐DS in DS.
Watch my ReproNim webinar on what you can do with the Neuroimaging Data Model (NIDM) and tools to annotate your datasets to make them more FAIR.
ReproNim Webinar, April 4, 2020. Dr. David Keator.