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Connectome-based models predict attentional control in aging adults.
Neuroimage 2019; 186:1-13N

Abstract

There are well-characterized age-related differences in behavioral and neural responses to tasks of attentional control. However, there is also increasing recognition of individual variability in the process of neurocognitive aging. Using connectome-based predictive modeling, a method for predicting individual-level behaviors from whole-brain functional connectivity, a sustained attention connectome-based prediction model (saCPM) has been derived in young adults. The saCPM consists of two large-scale functional networks: a high-attention network whose strength predicts better attention and a low-attention network whose strength predicts worse attention. Here we examined the generalizability of the saCPM for predicting inhibitory control in an aging sample. Forty-two healthy young adults (n = 21, ages 18-30) and older adults (n = 21, ages 60-80) performed a modified Stroop task, on which older adults exhibited poorer performance, indexed by higher reaction time cost between incongruent and congruent trials. The saCPM generalized to predict reaction time cost across age groups, but did not account for age-related differences in performance. Exploratory analyses were conducted to characterize the effects of age on functional connectivity and behavior. We identified subnetworks of the saCPM that exhibited age-related differences in strength. The strength of two low-attention subnetworks, consisting of frontoparietal, medial frontal, default mode, and motor nodes that were more strongly connected in older adults, mediated the effect of age group on performance. These results support the saCPM's ability to capture attention-related patterns reflected in each individual's functional connectivity signature across both task context and age. However, older and younger adults exhibit functional connectivity differences within components of the saCPM networks, and it is these connections that better account for age-related deficits in attentional control.

Authors+Show Affiliations

Department of Psychology, The Ohio State University, USA.Department of Psychology, The Ohio State University, USA.Department of Psychology, Yale University, USA.Department of Psychology, The Ohio State University, USA. Electronic address: prakash.30@osu.edu.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

30394324

Citation

Fountain-Zaragoza, Stephanie, et al. "Connectome-based Models Predict Attentional Control in Aging Adults." NeuroImage, vol. 186, 2019, pp. 1-13.
Fountain-Zaragoza S, Samimy S, Rosenberg MD, et al. Connectome-based models predict attentional control in aging adults. Neuroimage. 2019;186:1-13.
Fountain-Zaragoza, S., Samimy, S., Rosenberg, M. D., & Prakash, R. S. (2019). Connectome-based models predict attentional control in aging adults. NeuroImage, 186, pp. 1-13. doi:10.1016/j.neuroimage.2018.10.074.
Fountain-Zaragoza S, et al. Connectome-based Models Predict Attentional Control in Aging Adults. Neuroimage. 2019 02 1;186:1-13. PubMed PMID: 30394324.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Connectome-based models predict attentional control in aging adults. AU - Fountain-Zaragoza,Stephanie, AU - Samimy,Shaadee, AU - Rosenberg,Monica D, AU - Prakash,Ruchika Shaurya, Y1 - 2018/10/28/ PY - 2018/09/17/received PY - 2018/10/24/revised PY - 2018/10/26/accepted PY - 2018/11/6/pubmed PY - 2019/6/30/medline PY - 2018/11/6/entrez KW - Aging KW - Attentional control KW - Connectome modeling KW - Functional connectivity KW - fMRI SP - 1 EP - 13 JF - NeuroImage JO - Neuroimage VL - 186 N2 - There are well-characterized age-related differences in behavioral and neural responses to tasks of attentional control. However, there is also increasing recognition of individual variability in the process of neurocognitive aging. Using connectome-based predictive modeling, a method for predicting individual-level behaviors from whole-brain functional connectivity, a sustained attention connectome-based prediction model (saCPM) has been derived in young adults. The saCPM consists of two large-scale functional networks: a high-attention network whose strength predicts better attention and a low-attention network whose strength predicts worse attention. Here we examined the generalizability of the saCPM for predicting inhibitory control in an aging sample. Forty-two healthy young adults (n = 21, ages 18-30) and older adults (n = 21, ages 60-80) performed a modified Stroop task, on which older adults exhibited poorer performance, indexed by higher reaction time cost between incongruent and congruent trials. The saCPM generalized to predict reaction time cost across age groups, but did not account for age-related differences in performance. Exploratory analyses were conducted to characterize the effects of age on functional connectivity and behavior. We identified subnetworks of the saCPM that exhibited age-related differences in strength. The strength of two low-attention subnetworks, consisting of frontoparietal, medial frontal, default mode, and motor nodes that were more strongly connected in older adults, mediated the effect of age group on performance. These results support the saCPM's ability to capture attention-related patterns reflected in each individual's functional connectivity signature across both task context and age. However, older and younger adults exhibit functional connectivity differences within components of the saCPM networks, and it is these connections that better account for age-related deficits in attentional control. SN - 1095-9572 UR - https://www.unboundmedicine.com/medline/citation/30394324/Connectome_based_models_predict_attentional_control_in_aging_adults_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1053-8119(18)32055-X DB - PRIME DP - Unbound Medicine ER -