New Publication: Joint-label fusion brain atlases for dementia research in Down syndrome

Research suggests a link between Alzheimer’s Disease in Down Syndrome (DS) and the overproduction of amyloid plaques. Using Positron Emission Tomography (PET) we can assess the in-vivo regional amyloid load using several available ligands. To measure amyloid distributions in specific brain regions, a brain atlas is used. A popular method of creating a brain atlas is to segment a participant’s structural Magnetic Resonance Imaging (MRI) scan. Acquiring an MRI is often challenging in intellectually-imparied populations because of contraindications or data exclusion due to significant motion artifacts or incomplete sequences related to general discomfort. When an MRI cannot be acquired, it is typically replaced with a standardized brain atlas derived from neurotypical populations (i.e. healthy individuals without DS) which may be inappropriate for use in DS. In this project, we create a series of disease and diagnosis-specific (cognitively stable (CS-DS), mild cognitive impairment (MCI-DS), and dementia (DEM-DS)) probabilistic group atlases of participants with DS and evaluate their accuracy of quantifying regional amyloid load compared to the individually-based MRI segmentations. Further, we compare the diagnostic-specific atlases with a probabilistic atlas constructed from similar-aged cognitively-stable neurotypical participants. We hypothesized that regional PET signals will best match the individually-based MRI segmentations by using DS group atlases that aligns with a participant’s disorder and disease status (e.g. DS and MCI-DS). Our results vary by brain region but generally show that using a disorder-specific atlas in DS better matches the individually-based MRI segmentations than using an atlas constructed from cognitively-stable neurotypical participants. We found no additional benefit of using diagnose-specific atlases matching disease status. All atlases are made publicly available for the research community.

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New Publication: Linear Regression Tool for NIDM Documents

Congratulations to Ashmita Kumar on her first scientific publication!

Abstract

The Neuroimaging Data Model (NIDM) is a series of specifications for describing all aspects of the neuroimaging data lifecycle from raw data to analyses and provenance. NIDM uses community-driven terminologies along with unambiguous data dictionaries within a Resource Description Framework (RDF) document to describe data and metadata for integration and query. Data from different studies, using locally defined variable names, can be retrieved by linking them to higher-order concepts from established ontologies and terminologies. Through these capabilities, NIDM documents are expected to improve reproducibility and facilitate data discovery and reuse. PyNIDM is a Python toolbox supporting the creation, manipulation, and querying of NIDM documents. Using the query tools available in PyNIDM, users are able interrogate datasets to find studies that have collected variables measuring similar phenotypic properties. This, in turn, facilitates the transformation and combination of data across multiple studies.
Full Paper: https://f1000research.com/articles/11-228/v1

New Publication: Alzheimer‐related altered white matter microstructural integrity in Down syndrome: A model for sporadic AD?

Introduction

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.

Methods

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.

Results

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.

Discussion

Our findings suggest that quantification of WM changes in DS could provide a promising and clinically relevant biomarker for AD clinical onset and progression.

https://alz-journals.onlinelibrary.wiley.com/doi/10.1002/dad2.12040

NIH Awards Over $100 Million to Examine Biomarkers of Alzheimer’s Disease in Adults with Down Syndrome

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.

https://som.uci.edu/news_releases/ABC-DS-grant-awarded.asp?fbclid=IwAR3fuTF3dXAfjKezO3CaNtcEBbd31wIOPEdXRRKLYtbh8lJDwRPDeCHX7_o

New Publication: Brain amyloid and the transition to dementia in 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.

https://doi.org/10.1002/dad2.12126

New Publication: One season of head-to-ball impact exposure alters functional connectivity in a central autonomic network

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.

https://doi.org/10.1016/j.neuroimage.2020.117306

New Publication: Alzheimer’s‐related cerebrovascular disease in Down syndrome

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.

http://dx.doi.org/10.1002/ana.25905