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Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state.
Neuroimage 2017; 148:364-372N

Abstract

Age-related differences in cognitive agility vary greatly between individuals and cognitive functions. This heterogeneity is partly mirrored in individual differences in brain network connectivity as revealed using resting-state functional magnetic resonance imaging (fMRI), suggesting potential imaging biomarkers for age-related cognitive decline. However, although convenient in its simplicity, the resting state is essentially an unconstrained paradigm with minimal experimental control. Here, based on the conception that the magnitude and characteristics of age-related differences in brain connectivity is dependent on cognitive context and effort, we tested the hypothesis that experimentally increasing cognitive load boosts the sensitivity to age and changes the discriminative network configurations. To this end, we obtained fMRI data from younger (n=25, mean age 24.16±5.11) and older (n=22, mean age 65.09±7.53) healthy adults during rest and two load levels of continuous multiple object tracking (MOT). Brain network nodes and their time-series were estimated using independent component analysis (ICA) and dual regression, and the edges in the brain networks were defined as the regularized partial temporal correlations between each of the node pairs at the individual level. Using machine learning based on a cross-validated regularized linear discriminant analysis (rLDA) we attempted to classify groups and cognitive load from the full set of edge-wise functional connectivity indices. While group classification using resting-state data was highly above chance (approx. 70% accuracy), functional connectivity (FC) obtained during MOT strongly increased classification performance, with 82% accuracy for the young and 95% accuracy for the old group at the highest load level. Further, machine learning revealed stronger differentiation between rest and task in young compared to older individuals, supporting the notion of network dedifferentiation in cognitive aging. Task-modulation in edgewise FC was primarily observed between attention- and sensorimotor networks; with decreased negative correlations between attention- and default mode networks in older adults. These results demonstrate that the magnitude and configuration of age-related differences in brain functional connectivity are partly dependent on cognitive context and load, which emphasizes the importance of assessing brain connectivity differences across a range of cognitive contexts beyond the resting-state.

Authors+Show Affiliations

Sunnaas Rehabilitation Hospital HT, Nesodden, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway.NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway.NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway.NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway.Sunnaas Rehabilitation Hospital HT, Nesodden, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway.Sunnaas Rehabilitation Hospital HT, Nesodden, Norway; NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway.Sunnaas Rehabilitation Hospital HT, Nesodden, Norway.NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway; Department of Psychology, University of Oslo, Norway. Electronic address: l.t.westlye@psykologi.uio.no.

Pub Type(s)

Comparative Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

28111190

Citation

Dørum, Erlend S., et al. "Increased Sensitivity to Age-related Differences in Brain Functional Connectivity During Continuous Multiple Object Tracking Compared to Resting-state." NeuroImage, vol. 148, 2017, pp. 364-372.
Dørum ES, Kaufmann T, Alnæs D, et al. Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state. Neuroimage. 2017;148:364-372.
Dørum, E. S., Kaufmann, T., Alnæs, D., Andreassen, O. A., Richard, G., Kolskår, K. K., ... Westlye, L. T. (2017). Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state. NeuroImage, 148, pp. 364-372. doi:10.1016/j.neuroimage.2017.01.048.
Dørum ES, et al. Increased Sensitivity to Age-related Differences in Brain Functional Connectivity During Continuous Multiple Object Tracking Compared to Resting-state. Neuroimage. 2017 03 1;148:364-372. PubMed PMID: 28111190.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Increased sensitivity to age-related differences in brain functional connectivity during continuous multiple object tracking compared to resting-state. AU - Dørum,Erlend S, AU - Kaufmann,Tobias, AU - Alnæs,Dag, AU - Andreassen,Ole A, AU - Richard,Geneviève, AU - Kolskår,Knut K, AU - Nordvik,Jan Egil, AU - Westlye,Lars T, Y1 - 2017/01/20/ PY - 2016/10/26/received PY - 2016/12/16/revised PY - 2017/01/18/accepted PY - 2017/1/24/pubmed PY - 2018/3/1/medline PY - 2017/1/24/entrez KW - Aging KW - Brain network connectivity KW - Machine learning KW - SDSA KW - fMRI SP - 364 EP - 372 JF - NeuroImage JO - Neuroimage VL - 148 N2 - Age-related differences in cognitive agility vary greatly between individuals and cognitive functions. This heterogeneity is partly mirrored in individual differences in brain network connectivity as revealed using resting-state functional magnetic resonance imaging (fMRI), suggesting potential imaging biomarkers for age-related cognitive decline. However, although convenient in its simplicity, the resting state is essentially an unconstrained paradigm with minimal experimental control. Here, based on the conception that the magnitude and characteristics of age-related differences in brain connectivity is dependent on cognitive context and effort, we tested the hypothesis that experimentally increasing cognitive load boosts the sensitivity to age and changes the discriminative network configurations. To this end, we obtained fMRI data from younger (n=25, mean age 24.16±5.11) and older (n=22, mean age 65.09±7.53) healthy adults during rest and two load levels of continuous multiple object tracking (MOT). Brain network nodes and their time-series were estimated using independent component analysis (ICA) and dual regression, and the edges in the brain networks were defined as the regularized partial temporal correlations between each of the node pairs at the individual level. Using machine learning based on a cross-validated regularized linear discriminant analysis (rLDA) we attempted to classify groups and cognitive load from the full set of edge-wise functional connectivity indices. While group classification using resting-state data was highly above chance (approx. 70% accuracy), functional connectivity (FC) obtained during MOT strongly increased classification performance, with 82% accuracy for the young and 95% accuracy for the old group at the highest load level. Further, machine learning revealed stronger differentiation between rest and task in young compared to older individuals, supporting the notion of network dedifferentiation in cognitive aging. Task-modulation in edgewise FC was primarily observed between attention- and sensorimotor networks; with decreased negative correlations between attention- and default mode networks in older adults. These results demonstrate that the magnitude and configuration of age-related differences in brain functional connectivity are partly dependent on cognitive context and load, which emphasizes the importance of assessing brain connectivity differences across a range of cognitive contexts beyond the resting-state. SN - 1095-9572 UR - https://www.unboundmedicine.com/medline/citation/28111190/Increased_sensitivity_to_age_related_differences_in_brain_functional_connectivity_during_continuous_multiple_object_tracking_compared_to_resting_state_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1053-8119(17)30057-5 DB - PRIME DP - Unbound Medicine ER -