Brain-wide resting-state connectivity regulation by the hippocampus and medial prefrontal cortex is associated with fluid intelligence

Li, Rui; Wu, Xia; Wen, Xiaotong; Han, Bo

doi:10.1007/s00429-020-02077-8

Cited by 13 publications

(11 citation statements)

References 80 publications

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“…Our current findings add to this prior work, particularly in the context of improved Gf prediction resulting from combination of subcortical regions, including the hippocampus, with cortical regions. This work reflects but does not replicate previous reports of an association between Gf and rs-fMRI connectivity between the right hippocampus and medial prefrontal cortex 34 . The current work further indicates how important it is to consider hippocampus morphometry in the context of the morphometry of other subcortical regions, particularly those with minimal association to Gf in the literature, that also have been linked to other aspects of cognitive science, such as reward processing in judgement and decision-making as well as emotion regulation (e.g., NAc and amygdala) 11,12,[35][36][37][38] .…”

Section: Discussioncontrasting

confidence: 99%

Novel Age-Dependent Cortico-Subcortical Morphologic Interactions Predict Fluid Intelligence: A Multi-Cohort Geometric Deep Learning Study

Wu¹,

Besson²,

Azcona³

et al. 2021

Preprint

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The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.

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Section: Discussioncontrasting

confidence: 99%

Novel Age-Dependent Cortico-Subcortical Morphologic Interactions Predict Fluid Intelligence: A Multi-Cohort Geometric Deep Learning Study

Wu¹,

Besson²,

Azcona³

et al. 2021

Preprint

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“…However, it is challenging to delineate information flow as this could be disrupted for many connections and give rise to different alterations in connectivity. Nonetheless we did expect to observe associations for control networks in relation to cognitive abilities as this has been reported by others (Cole, Yarkoni, Repovs, Anticevic, & Braver, 2012;Li & Tian, 2014;R. Li, Zhang, Wu, Wen, & Han, 2020).…”

Section: Discussionsupporting

confidence: 65%

Functional connectivity directionality between large-scale resting-state networks in children and adolescence from the Healthy Brain Network sample

Lund

Alnæs

Rokicki

et al. 2020

Preprint

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Objective: Mental disorders often emerge during adolescence, and age-related differences in connection strengths of brain networks (static connectivity) have been identified. However, little is known about the directionality of information flow (directed connectivity) in this period of brain development. Methods: We employed dynamic graphical models (DGM) to estimate directed functional connectivity from resting state functional magnetic resonance imaging data on 979 participants aged 6 to 17 years from the healthy brain network (HBN) sample. We tested for effects of age, sex, cognitive abilities and psychopathology on directionality. Results: We show robust bi-directionality in information flow between visual-medial and visual-lateral nodes of the network, in line with prior studies in adult samples. Furthermore, we found that age in this developmental sample was associated with directionality of information flow in sensorimotor and executive control networks, yet we did not find associations with cognitive abilities or psychopathology. Discussion: Our results revealed that directionality in information flow of large-scale brain networks is sensitive to age during adolescence, warranting further studies that may explore trajectories of development in more fine-grained network parcellations and in different populations.

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“…Others reported hippocampal volume predicting Gf only in musically trained people (58), and the volumes of hippocampal subfields being more relevant for Gf than working memory (59), even though working memory has been linked to Gf (11). The current findings support the prior work, particularly in the context that increased Gf prediction resulted when subcortical regions such as the hippocampus were combined with cortical regions; this work resembles but does not replicate others who have reported that rs-fMRI connectivity between the right hippocampus and medial prefrontal cortex was associated with Gf (60). The current work further indicates how important it is to consider hippocampus morphometry in the context of the morphometry of other subcortical regions, particularly those with minimal association to Gf in the literature, that also have been linked to other cognitive science literatures, such as reward processing in judgement and decisionmaking and emotion regulation (e.g., nucleus accumbens and amygdala).…”

Section: Cortical and Subcortical Regions Involved In The Prediction Of Gfsupporting

confidence: 63%

Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study

Besson

Azcona

et al. 2020

Preprint

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Brain structure is tightly coupled with brain functions, but it remains unclear how cognition is related to brain morphology, and what is consistent across neurodevelopment. In this work, we developed graph convolutional neural networks (gCNNs) to predict Fluid Intelligence (Gf) from shapes of cortical ribbons and subcortical structures. T1-weighted MRIs from two independent cohorts, the Human Connectome Project (HCP; age: 28.81 ± 3.70) and the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62) were independently analyzed. Cortical and subcortical surfaces were extracted and modeled as surface meshes. Three gCNNs were trained and evaluated using six-fold nested cross-validation. Overall, combining cortical and subcortical surfaces yielded the best predictions on both HCP (R=0.454) and ABCD datasets (R=0.314), and outperformed the current literature. Across both datasets, the morphometry of the amygdala and hippocampus, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a novel reframing of the morphometry underlying Gf.

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Brain-wide resting-state connectivity regulation by the hippocampus and medial prefrontal cortex is associated with fluid intelligence

Cited by 13 publications

References 80 publications

Novel Age-Dependent Cortico-Subcortical Morphologic Interactions Predict Fluid Intelligence: A Multi-Cohort Geometric Deep Learning Study

Novel Age-Dependent Cortico-Subcortical Morphologic Interactions Predict Fluid Intelligence: A Multi-Cohort Geometric Deep Learning Study

Functional connectivity directionality between large-scale resting-state networks in children and adolescence from the Healthy Brain Network sample

Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study

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