Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study.

Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45-91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain's anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging.

Influence of ROI selection on resting state functional connectivity: an individualized approach for resting state fMRI analysis.

The differences in how our brain is connected are often thought to reflect the differences in our individual personalities and cognitive abilities. Individual differences in brain connectivity has long been recognized in the neuroscience community however it has yet to manifest itself in the methodology of resting state analysis. This is evident as previous studies use the same region of interest (ROIs) for all subjects. In this paper we demonstrate that the use of ROIs which are standardized across individuals leads to inaccurate calculations of functional connectivity. We also show that this problem can be addressed by taking an individualized approach by using subject-specific ROIs. Finally we show that ROI selection can affect the way we interpret our data by showing different changes in functional connectivity with aging.

Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis.

IMPORTANCE: Amyloid-ß positron emission tomography (PET) imaging allows in vivo detection of fibrillar plaques, a core neuropathological feature of Alzheimer disease (AD). Its diagnostic utility is still unclear because amyloid plaques also occur in patients with non-AD dementia. OBJECTIVE: To use individual participant data meta-analysis to estimate the prevalence of amyloid positivity on PET in a wide variety of dementia syndromes. DATA SOURCES: The MEDLINE and Web of Science databases were searched from January 2004 to April 2015 for amyloid PET studies. STUDY SELECTION: Case reports and studies on neurological or psychiatric diseases other than dementia were excluded. Corresponding authors of eligible cohorts were invited to provide individual participant data. DATA EXTRACTION AND SYNTHESIS: Data were provided for 1359 participants with clinically diagnosed AD and 538 participants with non-AD dementia. The reference groups were 1849 healthy control participants (based on amyloid PET) and an independent sample of 1369 AD participants (based on autopsy). MAIN OUTCOMES AND MEASURES: Estimated prevalence of positive amyloid PET scans according to diagnosis, age, and apolipoprotein E (APOE) ε4 status, using the generalized estimating equations method. RESULTS: The likelihood of amyloid positivity was associated with age and APOE ε4 status. In AD dementia, the prevalence of amyloid positivity decreased from age 50 to 90 years in APOE ε4 noncarriers (86% [95% CI, 73%-94%] at 50 years to 68% [95% CI, 57%-77%] at 90 years; n = 377) and to a lesser degree in APOE ε4 carriers (97% [95% CI, 92%-99%] at 50 years to 90% [95% CI, 83%-94%] at 90 years; n = 593; P < .01). Similar associations of age and APOE ε4 with amyloid positivity were observed in participants with AD dementia at autopsy. In most non-AD dementias, amyloid positivity increased with both age (from 60 to 80 years) and APOE ε4 carriership (dementia with Lewy bodies: carriers [n = 16], 63% [95% CI, 48%-80%] at 60 years to 83% [95% CI, 67%-92%] at 80 years; noncarriers [n = 18], 29% [95% CI, 15%-50%] at 60 years to 54% [95% CI, 30%-77%] at 80 years; frontotemporal dementia: carriers [n = 48], 19% [95% CI, 12%-28%] at 60 years to 43% [95% CI, 35%-50%] at 80 years; noncarriers [n = 160], 5% [95% CI, 3%-8%] at 60 years to 14% [95% CI, 11%-18%] at 80 years; vascular dementia: carriers [n = 30], 25% [95% CI, 9%-52%] at 60 years to 64% [95% CI, 49%-77%] at 80 years; noncarriers [n = 77], 7% [95% CI, 3%-18%] at 60 years to 29% [95% CI, 17%-43%] at 80 years. CONCLUSIONS AND RELEVANCE: Among participants with dementia, the prevalence of amyloid positivity was associated with clinical diagnosis, age, and APOE genotype. These findings indicate the potential clinical utility of amyloid imaging for differential diagnosis in early-onset dementia and to support the clinical diagnosis of participants with AD dementia and noncarrier APOE ε4 status who are older than 70 years.

Blood viscosity in subcortical vascular mild cognitive impairment with versus without cerebral amyloid burden.

BACKGROUND: Subcortical vascular dementia (SVaD) is a common form of dementia, attributed to ischemic small-vessel disease. Blood viscosity (BV) may contribute to the pathophysiology of SVaD. However, SVaD patients with coexisting amyloid deposition may not show differences in BV because their small-vessel disease may result from amyloid angiopathy independently of BV. We, therefore, hypothesized that BV might show different changes compared with control subjects in subcortical vascular mild cognitive impairment (svMCI) that refers to the prodromal stage of SVaD according to cerebral amyloid burden detected by the [(11)C] Pittsburgh compound B (PiB) PET (positron emission tomography), and apolipoprotein 4 (ApoE4) genotype (a known risk factor for vascular and parenchymal amyloid). METHODS: Our subjects consisted of 33 healthy normal controls (NC), 28 patients with PiB(-) svMCI, and 12 with PiB(+) svMCI. They underwent scanning capillary tube viscometer measuring BV during systolic and diastolic phases. RESULTS: Compared with the NC group, the PiB(-) svMCI group showed increased diastolic blood viscosity (DBV) but no difference in systolic blood viscosity (SBV). By contrast, there was no significant difference in SBV and DBV between the NC and PiB(+) svMCI groups. Within the PiB(+) svMCI group, ApoE4(-) subgroup showed increased DBV compared with the ApoE4(+) subgroup. CONCLUSIONS: Increased DBV is an important contributor to the development of "pure" svMCI (ie, without cerebral amyloid deposition). The relationship between BV and PiB(+) svMCI differed according to ApoE genotype, suggesting that the pathogenesis of PiB(+) svMCI might also be heterogeneous.