Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores.
Rongtao Jiang,Vince D. Calhoun,Lingzhong Fan,Nianming Zuo,Rex E. Jung,Shile Qi,Dongdong Lin,Jin Li,Chuanjun Zhuo,Ming Song,Zening Fu,Tianzi Jiang,Jing Sui +12 more
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TL;DR: It is demonstrated that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions—particularly prefrontal–parietal and basal ganglia—whereas the network pattern differs between genders.
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Abstract: Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions-particularly prefrontal-parietal and basal ganglia-whereas the network pattern differs between genders.
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Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises.
TL;DR: An overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade is provided and connectome-based predictive modeling, which has grown in popularity in recent years is highlighted.
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Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships
Rongtao Jiang,Nianming Zuo,Judith M. Ford,Shile Qi,Dongmei Zhi,Chuanjun Zhuo,Yong Xu,Zening Fu,Juan R. Bustillo,Jessica A. Turner,Vince D. Calhoun,Jing Sui +11 more
TL;DR: This replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits.
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Sex and gender in neurodevelopmental conditions
Sven Bölte,Janina Neufeld,Peter B. Marschik,Zachary J. Williams,Louise Gallagher,Meng-Chuan Lai +5 more
TL;DR: In this article , the effects of sex and gender on neurodevelopmental conditions are discussed, including autism, attention-deficit/hyperactivity disorder (ADHD), and schizophrenia.
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Intelligence and creativity share a common cognitive and neural basis
Emily Frith,Daniel Elbich,Alexander P. Christensen,Monica D. Rosenberg,Qunlin Chen,Michael J. Kane,Paul J. Silvia,Paul Seli,Roger E. Beaty +8 more
TL;DR: It is found that functional brain networks that predict intelligence facets overlap to varying degrees with a network that predicts creative ability, particularly within the prefrontal cortex of the executive control network.
Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
Ye Tian,Andrew Zalesky +1 more
TL;DR: The authors investigated feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults and found that feature weight reliability is generally poor for all predictive models (ICC < 0.3), and significantly poorer than predictive models for overt biological attributes such as sex.
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