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Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation.

ABSTRACT: The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly sampled individuals to parse the magnitude and anatomical distribution of network variability across subjects, sessions, and tasks. Critically, we find that functional networks are dominated by common organizational principles and stable individual features, with substantially more modest contributions from task-state and day-to-day variability. Sources of variation were differentially distributed across the brain and differentially linked to intrinsic and task-evoked sources. We conclude that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.

SUBMITTER: Gratton C

PROVIDER: S-EPMC5912345 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation.

Gratton Caterina C Laumann Timothy O TO Nielsen Ashley N AN Greene Deanna J DJ Gordon Evan M EM Gilmore Adrian W AW Nelson Steven M SM Coalson Rebecca S RS Snyder Abraham Z AZ Schlaggar Bradley L BL Dosenbach Nico U F NUF Petersen Steven E SE

Neuron 20180401 2

The organization of human brain networks can be measured by capturing correlated brain activity with fMRI. There is considerable interest in understanding how brain networks vary across individuals or neuropsychiatric populations or are altered during the performance of specific behaviors. However, the plausibility and validity of such measurements is dependent on the extent to which functional networks are stable over time or are state dependent. We analyzed data from nine high-quality, highly ...[more]

PMID: 29673485

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Project description:The aim of the paper is to determine the effects of the cognitive reserve on brain tumour patients' cognitive functions and, specifically, if cognitive reserve helps patients cope with the negative effects of brain tumours on their cognitive functions. We retrospectively studied a large sample of around 700 patients, diagnosed with a brain tumour. Each received an MRI brain examination and performed a battery of tests measuring their cognitive abilities before they underwent neurosurgery. To account for the complexity of cognitive reserve, we construct our cognitive reserve proxy by combining three predictors of patients' cognitive performance, namely, patients' education, occupation, and the environment where they live. Our statistical analysis controls for the type, side, site, and size of the lesion, for fluid intelligence quotient, and for age and gender, in order to tease out the effect of cognitive reserve on each of these tests. Clinical neurological variables have the expected effects on cognitive functions. We find a robust positive effect of cognitive reserve on patients' cognitive performance. Moreover, we find that cognitive reserve modulates the effects of the volume of the lesion: the additional negative impact of an increase in the tumour size on patients' performance is less severe for patients with higher cognitive reserve. We also find substantial differences in these effects depending on the cerebral hemisphere where the lesion occurred and on the cognitive function considered. For several of these functions, the positive effect of cognitive reserve is stronger for patients with lesions in the left hemisphere than for patients whose lesions are in the right hemisphere. The development of prevention strategies and personalized rehabilitation interventions will benefit from our contribution to understanding the role of cognitive reserve, in addition to that of neurological variables, as one of the factors determining the patients' individual differences in cognitive performance caused by brain tumours.

Project description:Brain age can be estimated using different Magnetic Resonance Imaging (MRI) modalities including diffusion MRI. Recent studies demonstrated that white matter (WM) tracts that share the same function might experience similar alterations. Therefore, in this work, we sought to investigate such issue focusing on five WM bundles holding that feature that is Association, Brainstem, Commissural, Limbic and Projection fibers, respectively. For each tract group, we estimated brain age for 15,335 healthy participants from United Kingdom Biobank relying on diffusion MRI data derived endophenotypes, Bayesian ridge regression modeling and 10 fold-cross validation. Furthermore, we estimated brain age for an Ensemble model that gathers all the considered WM bundles. Association analysis was subsequently performed between the estimated brain age delta as resulting from the six models, that is for each tract group as well as for the Ensemble model, and 38 daily life style measures, 14 cardiac risk factors and cardiovascular magnetic resonance imaging features and genetic variants. The Ensemble model that used all tracts from all fiber groups (FG) performed better than other models to estimate brain age. Limbic tracts based model reached the highest accuracy with a Mean Absolute Error (MAE) of 5.08, followed by the Commissural ([Formula: see text]), Association ([Formula: see text]), and Projection ([Formula: see text]) ones. The Brainstem tracts based model was the less accurate achieving a MAE of 5.86. Accordingly, our study suggests that the Limbic tracts experience less brain aging or allows for more accurate estimates compared to other tract groups. Moreover, the results suggest that Limbic tract leads to the largest number of significant associations with daily lifestyle factors than the other tract groups. Lastly, two SNPs were significantly (p value [Formula: see text]) associated with brain age delta in the Projection fibers. Those SNPs are mapped to HIST1H1A and SLC17A3 genes.

Dataset Information

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation.

Publications

Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation.

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