Task-induced brain state manipulation improves prediction of individual traits.
Task-induced brain state manipulation improves prediction of individual traits.
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DOI：
10.1038/s41467-018-04920-3
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发表时间：
2018-07-18
影响因子：
16.6
通讯作者：
Constable RT
中科院分区：
文献类型：
--
作者：
Greene AS;Gao S;Scheinost D;Constable RT
最近的工作已经开始将大脑功能组织的个体差异与人类行为和认知联系起来，但揭示这种关系的最佳大脑状态仍然是一个悬而未决的问题。在两个大的，独立的数据集，我们在这里表明，认知任务放大功能连接模式的特征相关的个体差异，这样的预测模型建立任务功能磁共振成像数据优于模型建立从静息状态功能磁共振成像数据。此外，某些任务始终比其他任务更好地预测流体智力，并且产生最佳性能模型的任务因性别而异。通过考虑任务引起的大脑状态和性别，表现最好的模型解释了流体智力分数超过20%的方差，而基于休息的模型解释的方差小于6%。这表明，识别和诱导特定群体的右脑状态可以更好地揭示大脑-行为关系，从而激发从基于休息的功能连接分析到基于任务的功能连接分析的范式转变。从大脑活动中解码或预测认知特征是一个令人兴奋的前景。在这里，作者表明，基于任务的功能连接比基于休息的连接更好地预测智力相关的措施，这表明认知任务放大了特质相关电路的个体差异。
Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses. Decoding or predicting cognitive traits from brain activity is an exciting prospect. Here, the authors show that task-based functional connectivity better predicts intelligence-related measures than rest-based connectivity, suggesting that cognitive tasks amplify individual differences in trait-relevant circuitry.
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影响因子：
82.9
作者：
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
影响因子：
25
作者：
Cohen JD;Daw N;Engelhardt B;Hasson U;Li K;Niv Y;Norman KA;Pillow J;Ramadge PJ;Turk-Browne NB;Willke TL
通讯作者：
Willke TL
影响因子：
3.1
作者：
Barbey, Aron K.;Colom, Roberto;Grafman, Jordan
通讯作者：
Grafman, Jordan
影响因子：
5.7
作者：
Finn ES;Scheinost D;Finn DM;Shen X;Papademetris X;Constable RT
通讯作者：
Constable RT
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y