Multimodal data revealed different neurobiological correlates of intelligence between males and females

Rongtao Jiang, Vince D. Calhoun, Yue Cui, Shile Qi, Chuanjun Zhuo, Jin Li, Rex Jung, Jian Yang, Yuhui Du, Tianzi Jiang, Jing Sui

Research output: Contribution to journalArticle

Abstract

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

Original languageEnglish (US)
JournalBrain Imaging and Behavior
DOIs
StatePublished - Jan 1 2019
Externally publishedYes

Fingerprint

Intelligence
Connectome
Parietal Lobe
Neuroimaging
Verbal Learning
Putamen
Cognition
Brain

Keywords

  • Connectome-based predictive modeling
  • Gender difference
  • Individualized prediction
  • Intelligence quotient
  • Multimodal

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Cognitive Neuroscience
  • Clinical Neurology
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health
  • Behavioral Neuroscience

Cite this

Multimodal data revealed different neurobiological correlates of intelligence between males and females. / Jiang, Rongtao; Calhoun, Vince D.; Cui, Yue; Qi, Shile; Zhuo, Chuanjun; Li, Jin; Jung, Rex; Yang, Jian; Du, Yuhui; Jiang, Tianzi; Sui, Jing.

In: Brain Imaging and Behavior, 01.01.2019.

Research output: Contribution to journalArticle

Jiang, Rongtao ; Calhoun, Vince D. ; Cui, Yue ; Qi, Shile ; Zhuo, Chuanjun ; Li, Jin ; Jung, Rex ; Yang, Jian ; Du, Yuhui ; Jiang, Tianzi ; Sui, Jing. / Multimodal data revealed different neurobiological correlates of intelligence between males and females. In: Brain Imaging and Behavior. 2019.
@article{ae72c8b2c294450583f52d6d48d83cab,
title = "Multimodal data revealed different neurobiological correlates of intelligence between males and females",
abstract = "Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.",
keywords = "Connectome-based predictive modeling, Gender difference, Individualized prediction, Intelligence quotient, Multimodal",
author = "Rongtao Jiang and Calhoun, {Vince D.} and Yue Cui and Shile Qi and Chuanjun Zhuo and Jin Li and Rex Jung and Jian Yang and Yuhui Du and Tianzi Jiang and Jing Sui",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/s11682-019-00146-z",
language = "English (US)",
journal = "Brain Imaging and Behavior",
issn = "1931-7557",
publisher = "Springer New York",

}

TY - JOUR

T1 - Multimodal data revealed different neurobiological correlates of intelligence between males and females

AU - Jiang, Rongtao

AU - Calhoun, Vince D.

AU - Cui, Yue

AU - Qi, Shile

AU - Zhuo, Chuanjun

AU - Li, Jin

AU - Jung, Rex

AU - Yang, Jian

AU - Du, Yuhui

AU - Jiang, Tianzi

AU - Sui, Jing

PY - 2019/1/1

Y1 - 2019/1/1

N2 - Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

AB - Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

KW - Connectome-based predictive modeling

KW - Gender difference

KW - Individualized prediction

KW - Intelligence quotient

KW - Multimodal

UR - http://www.scopus.com/inward/record.url?scp=85068866211&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068866211&partnerID=8YFLogxK

U2 - 10.1007/s11682-019-00146-z

DO - 10.1007/s11682-019-00146-z

M3 - Article

C2 - 31278651

AN - SCOPUS:85068866211

JO - Brain Imaging and Behavior

JF - Brain Imaging and Behavior

SN - 1931-7557

ER -