Dataset Information

Contribution of normal aging to brain atrophy in MS.

ABSTRACT: OBJECTIVE:To identify the top brain regions affected by MS-specific atrophy (i.e., atrophy in excess of normal aging) and to test whether normal aging and MS-specific atrophy increase or decrease in these regions with age. METHODS:Six hundred fifty subjects (2,790 MRI time points) were analyzed: 520 subjects with relapse-onset MS from a 5-year prospective cohort with annual standardized 1-mm 3D T1-weighted images (3DT1s; 2,483 MRIs) and 130 healthy controls with longitudinal 3DT1s (307 MRIs). Rates of change in all FreeSurfer regions (v5.3) and Structural Image Evaluation Using Normalization of Atrophy (SIENA) were estimated with mixed-effects models. All FreeSurfer regions were ranked by the MS-specific atrophy slope/standard error ratio (?MS × time/SE?MS × time). In the top regions, age was added as an effect modifier to test whether MS-specific atrophy varied by age. RESULTS:The top-ranked regions were all gray matter structures. For SIENA, normal aging increased from 0.01%/y at age 30 years to -0.31%/y at age 60 years (-0.11% ± 0.032%/decade, p < 0.01), whereas MS-specific atrophy decreased from -0.38%/y at age 30 years to -0.12%/y at age 60 years (0.09% ± 0.035%/decade, p = 0.01). Similarly, in the thalamus, normal aging increased from -0.15%/y at age 30 years to -0.62%/y at age 60 years (-0.16% ± 0.079%/decade, p < 0.05), and MS-specific atrophy decreased from -0.59%/y at age 30 years to -0.05%/y at age 60 years (0.18% ± 0.08%/decade, p < 0.05). In the putamen and caudate, normal aging and MS-specific atrophy did not vary by age. CONCLUSIONS:For SIENA and thalamic atrophy, the contribution of normal aging increases with age, but does not change in the putamen and caudate. This may have substantial implications to understand the biology of brain atrophy in MS.

SUBMITTER: Azevedo CJ

PROVIDER: S-EPMC6807662 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Contribution of normal aging to brain atrophy in MS.

Azevedo Christina J CJ Cen Steven Y SY Jaberzadeh Amir A Zheng Ling L Hauser Stephen L SL Pelletier Daniel D

Neurology(R) neuroimmunology & neuroinflammation 20190925 6

<h4>Objective</h4>To identify the top brain regions affected by MS-specific atrophy (i.e., atrophy in excess of normal aging) and to test whether normal aging and MS-specific atrophy increase or decrease in these regions with age.<h4>Methods</h4>Six hundred fifty subjects (2,790 MRI time points) were analyzed: 520 subjects with relapse-onset MS from a 5-year prospective cohort with annual standardized 1-mm 3D T1-weighted images (3DT1s; 2,483 MRIs) and 130 healthy controls with longitudinal 3DT1s ...[more]

PMID: 32330116

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Project description:Background: Neuroscience lacks a reliable method of screening the early stages of dementia. Objective: To improve the diagnostics of age-related cognitive functions by developing insight into the proportionality of age-related changes in cognitive subdomains. Materials and Methods: We composed a battery of psychophysiological tests and collected an open-access psychophysiological outcomes of brain atrophy (POBA) dataset by testing individuals without dementia. To extend the utility of machine learning (ML) classification in cognitive studies, we proposed estimates of the disproportional changes in cognitive functions: an index of simple reaction time to decision-making time (ISD), ISD with the accuracy performance (ISDA), and an index of performance in simple and complex visual-motor reaction with account for accuracy (ISCA). Studying the distribution of the values of the indices over age allowed us to verify whether diverse cognitive functions decline equally throughout life or there is a divergence in age-related cognitive changes. Results: Unsupervised ML clustering shows that the optimal number of homogeneous age groups is four. The sample is segregated into the following age-groups: Adolescents ∈ [0, 20), Young adults ∈ [20, 40), Midlife adults ∈ [40, 60) and Older adults ≥60 year of age. For ISD, ISDA, and ISCA values, only the median of the Adolescents group is different from that of the other three age-groups sharing a similar distribution pattern (p > 0.01). After neurodevelopment and maturation, the indices preserve almost constant values with a slight trend toward functional decline. The reaction to a moving object (RMO) test results (RMO_mean) follow another tendency. The Midlife adults group's median significantly differs from the remaining three age subsamples (p < 0.01). No general trend in age-related changes of this dependent variable is observed. For all the data (ISD, ISDA, ISCA, and RMO_mean), Levene's test reveals no significant changes of the variances in age-groups (p > 0.05). Homoscedasticity also supports our assumption about a linear dependency between the observed features and age. Conclusion: In healthy brain aging, there are proportional age-related changes in the time estimates of information processing speed and inhibitory control in task switching. Future studies should test patients with dementia to determine whether the changes of the aforementioned indicators follow different patterns.

Dataset Information

Contribution of normal aging to brain atrophy in MS.

Publications

Contribution of normal aging to brain atrophy in MS.

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