Caregiving concerns and clinical characteristics across neurodegenerative and cerebrovascular disorders in the Ontario neurodegenerative disease research initiative

Objectives: Caregiving burdens are a substantial concern in the clinical care of persons with neurodegenerative disorders. In the Ontario Neurodegenerative Disease Research Initiative, we used the Zarit\u27s Burden Interview (ZBI) to examine: (1) the types of burdens captured by the ZBI in a cross-disorder sample of neurodegenerative conditions (2) whether there are categorical or disorder-specific effects on caregiving burdens, and (3) which demographic, clinical, and cognitive measures are related to burden(s) in neurodegenerative disorders?. Methods/Design: N = 504 participants and their study partners (e.g., family, friends) across: Alzheimer\u27s disease/mild cognitive impairment (AD/MCI; n = 120), Parkinson\u27s disease (PD; n = 136), amyotrophic lateral sclerosis (ALS; n = 38), frontotemporal dementia (FTD; n = 53), and cerebrovascular disease (CVD; n = 157). Study partners provided information about themselves, and information about the clinical participants (e.g., activities of daily living (ADL)). We used Correspondence Analysis to identify types of caregiving concerns in the ZBI. We then identified relationships between those concerns and demographic and clinical measures, and a cognitive battery. Results: We found three components in the ZBI. The first was “overall burden” and was (1) strongly related to increased neuropsychiatric symptoms (NPI severity r = 0.586, NPI distress r = 0.587) and decreased independence in ADL (instrumental ADLs r = −0.566, basic ADLs r = −0.43), (2) moderately related to cognition (MoCA r = −0.268), and (3) showed little-to-no differences between disorders. The second and third components together showed four types of caregiving concerns: current care of the person with the neurodegenerative disease, future care of the person with the neurodegenerative disease, personal concerns of study partners, and social concerns of study partners. Conclusions: Our results suggest that the experience of caregiving in neurodegenerative and cerebrovascular diseases is individualized and is not defined by diagnostic categories. Our findings highlight the importance of targeting ADL and neuropsychiatric symptoms with caregiver-personalized solutions

Targeted copy number variant identification across the neurodegenerative disease spectrum

Background: Although genetic factors are known to contribute to neurodegenerative disease susceptibility, there remains a large amount of heritability unaccounted for across the diagnoses. Copy number variants (CNVs) contribute to these phenotypes, but their presence and influence on disease state remains relatively understudied. Methods: Here, we applied a depth of coverage approach to detect CNVs in 80 genes previously associated with neurodegenerative disease within participants of the Ontario Neurodegenerative Disease Research Initiative (n = 519). Results: In total, we identified and validated four CNVs in the cohort, including: (1) a heterozygous deletion of exon 5 in OPTN in an Alzheimer\u27s disease participant; (2) a duplication of exons 1–5 in PARK7 in an amyotrophic lateral sclerosis participant; (3) a duplication of \u3e3 Mb, which encompassed ABCC6, in a cerebrovascular disease (CVD) participant; and (4) a duplication of exons 7–11 in SAMHD1 in a mild cognitive impairment participant. We also identified 43 additional CNVs that may be candidates for future replication studies. Conclusion: The identification of the CNVs suggests a portion of the apparent missing heritability of the phenotypes may be due to these structural variants, and their assessment is imperative for a thorough understanding of the genetic spectrum of neurodegeneration

The use of random forests to classify amyloid brain PET

Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.Purpose: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification. Methods: The data set included 57 baseline 18F-florbetapir (Amyvid; Lilly, Indianapolis, IN) brain PET scans in participants with severe white matter disease, presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a multicenter prospective observational trial. Scans were processed using the MINC toolkit to generate SUV ratios, normalized to cerebellar gray matter, and clinically read by 2 nuclear medicine physicians with interpretation based on consensus (35 negative, 22 positive). SUV ratio data and clinical reads were used for super- vised training of an RF classifier programmed in MATLAB. Results: A 10,000-tree RF, each tree using 15 randomly selected cases and 20 randomly selected features (SUV ratio per region of interest), with 37 cases for training and 20 cases for testing, had sensitivity = 86% (95% confidence in- terval [CI], 42%–100%), specificity = 92% (CI, 64%–100%), and classification accuracy = 90% (CI, 68%–99%). The most common features at the root node (key regions for stratification) were (1) left posterior cingulate (1039 trees), (2) left middle frontal gyrus (1038 trees), (3) left precuneus (857 trees), (4) right an- terior cingulate gyrus (655 trees), and (5) right posterior cingulate (588 trees). Conclusions: Random forests can classify brain PET as positive or negative for amyloid deposition and suggest key clinically relevant, regional features for classification.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals

The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score

Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.Purpose: The aim of this study was to evaluate random forests (RFs) to identify ROIs on 18F-florbetapir and 18F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score. Materials and Methods: Fifty-seven subjects with significant white matter disease presenting with either transient ischemic attack/lacunar stroke or mild cognitive impairment from early Alzheimer disease, enrolled in a mul- ticenter prospective observational trial, had MoCA and 18F-florbetapir PET; 55 had 18F-FDG PET. Scans were processed using the MINC toolkit to gen- erate SUV ratios, normalized to cerebellar gray matter (18F-florbetapir PET), or pons (18F-FDG PET). SUV ratio data and MoCA score were used for su- pervised training of RFs programmed in MATLAB. Results: 18F-Florbetapir PETs were randomly divided into 40 training and 17 testing scans; 100 RFs of 1000 trees, constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: right posterior cingulate gyrus, right anterior cingulate gyrus, left precuneus, left posterior cingulate gyrus, and right precuneus. Amyloid in- creased with decreasing MoCA score. 18F-FDG PETs were randomly di- vided into 40 training and 15 testing scans; 100 RFs of 1000 trees, each tree constructed from a random subset of 16 training scans and 20 ROIs, identified ROIs associated with MoCA score: left fusiform gyrus, left precuneus, left posterior cingulate gyrus, right precuneus, and left middle orbitofrontal gyrus. 18F-FDG decreased with decreasing MoCA score. Conclusions: Random forests help pinpoint clinically relevant ROIs associ- ated with MoCA score; amyloid increased and 18F-FDG decreased with de- creasing MoCA score, most significantly in the posterior cingulate gyrus.CIHR MITNEC C6 || Linda C Campbell Foundation || Lilly-Avid Radiopharmaceuticals