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• W2896638598 abstract "Repetitive mild traumatic brain injury (mTBI) can produce cognitive and behavioral deficits overlapping with those of Alzheimer's disease (AD), and can lead to Chronic Traumatic Encephalopathy (CTE), which shares several clinical, structural, and pathology attributes with AD. There is a need for in-vivo methods that can detect mTBI effects, predict downstream cognitive decline and CTE, and distinguish between CTE and AD. Using MRI data from the Cleveland Clinic Professional Fighters Brain Health Study (PFBHS) and ADNI, we applied machine learning techniques to identify the effects of mTBI on brain volume and cortical thickness, and to investigate differentiation of mTBI effects from AD-related MCI and dementia. The T1 MRI scans of 15 PFBHS healthy volunteers, 18 cognitively unimpaired boxers, 27 impaired boxers, 9 mixed martial arts fighters (MMA), 29 amyloid-negative (A-) cognitively normal subjects, 59 amyloid-positive (A+) early MCI, 71 A+ late MCI, and 40 A+ AD subjects from ADNI (Table 1) were processed using Freesurfer 6.0, producing volumetric and cortical thickness values. Images were spatially transformed to a template, producing modulated gray segments. Regional volumes were corrected for intracranial volume; all values were age-adjusted. Using discrete values as inputs, and separately, modulated gray segments, canonical variates analysis was applied within an iterative resampling software framework to identify volumetric patterns differentiating conditions, optimized for reproducibility and prediction, and tested using Leave-One-Out and independent subjects. A pattern of atrophy in thalamus, striatum, hippocampus, and inferior temporal gyrus was increasingly expressed in unimpaired and impaired boxers relative to NLs (Figures 1, 2). Expression increased with fight exposure and with age (Figure 3), and boxing vs. MMA. Impaired fighters were differentiated from AD-related MCI and dementia by atrophy in regions including midbrain, thalamus, striatum, and inferior temporal cortex in fighters relative to inferior parietal, fusiform, and entorhinal regions in AD-related subjects, with shared ventricular expansion and hippocampal reduction (Figure 4). Discrete value and voxel-based results were consistent. Voxel based comparison of healthy volunteers separated into young (18 to 36) and older (38 to 71) age groups, compared to unimpaired and impaired retired boxers. Higher CV1 scores reflect greater expression of the pattern of atrophy shown at left. Young and older HV do not differ, evidencing that the pattern is not reflective of aging. Error bars = SEM. Leave-One-Out canonical variate (CV) scores by group for a classifier using discrete volumetric values as inputs. Subjects in upper thirties and older are included in this comparison. Regions of atrophy that are increasingly expressed in boxers with more fights and with impairment are consistent with those in Figure 1. Comparison of canonical variate scores for classifier reflecting an atrophy pattern expressed most greatly by older, impaired boxers with high fight exposure. Low exposure - less than 30 fights; high exposure ranged from 30 to over 60 cumulative fights. Subjects are further grouped by age decade. a) Canonical variate scores reflecting expression of pattern that discriminates subjects with impairment in boxers vs. that in amyloid positive early MCI, late MCI, and AD independent test subjects. Age matched subsets extracted impaired fighters with the oldest ages for comparison to the youngest available ADNI subjects among those analyzed (see Table 1); b) subset of CV pattern showing regions where boxer atrophy exceeds that of amyloid positive (A+) MCI and AD patients; c) subset of CV pattern showing regions where atrophy in A+ subjects exceeds that in boxers. Machine learning with volumetric MRI provides quantitative biomarkers that characterize the presymptomatic and symptomatic effects of repetitive head trauma related to exposure, time, and impairment, and differentiate effects of mTBI versus AD-related pathology . Leave-One-Out canonical variate (CV) scores by group for a classifier that compared normal subjects form the PFBHS data set with those from ADNI, vs. AD, to ensure that inter-study effects did not outweigh other group discrimination. PFBHS younger subjects = ages 18 to 36; PFBHS older subjects = ages 40 to 75, ADNI NL Am- = ages 56 to 68." @default.
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• W2896638598 date "2018-07-01" @default.
• W2896638598 modified "2023-10-16" @default.
• W2896638598 title "P1‐380: MRI CLASSIFIERS CHARACTERIZE MILD TRAUMATIC BRAIN INJURY IN SYMPTOMATIC AND PRESYMPTOMATIC STAGES AND DIFFERENTIATE FROM ALZHEIMER'S DISEASE–RELATED IMPAIRMENT" @default.
• W2896638598 doi "https://doi.org/10.1016/j.jalz.2018.06.388" @default.
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