Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions.
Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions.
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DOI：
10.1038/s41537-021-00165-0
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发表时间：
2021-08-19
影响因子：
5.4
通讯作者：
Subramaniam K
中科院分区：
文献类型：
--
作者：
Kambeitz-Ilankovic L;Vinogradov S;Wenzel J;Fisher M;Haas SS;Betz L;Penzel N;Nagarajan S;Koutsouleris N;Subramaniam K
Cognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.
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影响因子：
4.6
作者：
Herman, Alexander B.;Brown, Ethan G.;Nagarajan, Srikantan S.
通讯作者：
Nagarajan, Srikantan S.
影响因子：
6.6
作者：
Green, MF;Kern, RS;Mintz, J
通讯作者：
Mintz, J
影响因子：
4.7
作者：
Kambeitz-Ilankovic L;Wenzel J;Haas SS;Ruef A;Antonucci LA;Sanfelici R;Paolini M;Koutsouleris N;Biagianti B
通讯作者：
Biagianti B
影响因子：
6.6
作者：
Dale, Corby L.;Brown, Ethan G.;Vinogradov, Sophia
通讯作者：
Vinogradov, Sophia
影响因子：
4.2
作者：
Collin, Guusje;Nieto-Castanon, Alfonso;Whitfield-Gabrieli, Susan
通讯作者：
Whitfield-Gabrieli, Susan