Predicting intelligence from brain gray matter volume

Hilger, Kirsten; Winter, Nils R.; Leenings, Ramona; Sassenhagen, Jona; Hahn, Tim; Basten, Ulrike; Fiebach, Christian J.

doi:10.1007/s00429-020-02113-7

Cited by 39 publications

(47 citation statements)

References 79 publications

(129 reference statements)

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“…The brain regions implicated in better cognitive performance in the chimpanzees in the current study included regions within the middle and inferior frontal gyri, supramarginal, and angulargyrus, and the cerebellum. In humans, similar brain regions have been reported to be associated with general intelligence or "g" (Haier et al, 2004(Haier et al, , 2010Hilger et al, 2020;Hogan et al, 2011), suggesting some homology in brain regions implicated in general cognitive functions between humans and chimpanzees. Similarly, the findings reported here are consistent with a previous study in a subset of these chimpanzees that showed that increasing cognitive performance as assessed by the PCTB was associated with larger brain volumes and increased cortical thickness (Hopkins, Li, et al, 2019).…”

Section: Discussionmentioning

confidence: 86%

Age‐ and cognition‐related differences in the gray matter volume of the chimpanzee brain (Pan troglodytes): A voxel‐based morphometry and conjunction analysis

Mulholland

Sherwood

Schapiro

et al. 2021

American J Primatol

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Several primate species have been shown to exhibit age-related changes in cognition, brain, and behavior. However, severe neurodegenerative illnesses, such as Alzheimer's disease (AD), were once thought to be uniquely human. Recently, some chimpanzees naturally were documented to develop both neurofibrillary tangles and amyloid plaques, the main characteristics of AD pathology. In addition, like humans and other primates, chimpanzees show similar declines in cognition and motor function with age. Here, we used voxel-based morphometry to examine the relationships among gray matter volume, age, and cognition using magnetic resonance imaging scans previously acquired from chimpanzees (N = 216). We first determined the relationship between age and gray matter volume, identifying the regions that declined with age. With a subset of our sample (N = 103), we also determined differences in gray matter volume between older chimpanzees with higher cognition scores than expected for their age, and older chimpanzees with lower than expected scores. Finally, we ran a conjunction analysis to determine any overlap in brain regions between these two analyses. We found that as chimpanzees age, they lose gray matter in regions associated with cognition. In addition, cognitively healthy older chimpanzees (those performing better for their age) have greater gray matter volume in many brain regions compared with chimpanzees who underperform for their age. Finally, the conjunction analysis revealed that regions of age-related decline overlap with the regions that differ between cognitively healthy chimpanzees and those who underperform. This study provides further evidence that chimpanzees are an important model for research on the neurobiology of aging.Future studies should investigate the effects of cognitive stimulation on both cognitive performance and brain structure in aging nonhuman primates.

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Section: Discussionmentioning

confidence: 86%

Age‐ and cognition‐related differences in the gray matter volume of the chimpanzee brain (Pan troglodytes): A voxel‐based morphometry and conjunction analysis

Mulholland

Sherwood

Schapiro

et al. 2021

American J Primatol

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“…Inter-voxel predictors appear in 2 (7%) studies (Powell et al 2017;Zhang et al 2019), in the form of local dMRI structural connectivity. Intra-voxel predictors appear in 5 (17%) studies (Hilger et al 2020;Jiang et al 2020b;Kong et al 2019;Li et al 2018;Wu et al 2020) From the 30 studies encompassed in this review, 9 did not directly mention the tests used (Dadi et al 2019;Dryburgh et al 2020;He et al 2020;Kashyap et al 2019;Kong et al 2019;Li et al 2019;Wang et al 2015;Wu et al 2020;Yoo et al 2019). Supplementary materials and citations were consulted to identify tests used in all but one study (Dryburgh et al 2020).…”

Section: Resultsmentioning

confidence: 99%

“…There was, however, little information about the measurement's validity for the populations under study. 3 studies cited references deemed adequate (Ferguson et al 2017;Wei et al 2020;Yang et al 2019), whereas partial references were cited in 2 studies (Hilger et al 2020;Lin et al 2020).…”

Section: Resultsmentioning

confidence: 99%

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

Vieira¹,

Pamplona

Fachinello

et al. 2021

Preprint

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Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSFC) is the most studied predictor, and the Human Connectome Project is the most employed dataset. A meta-analysis revealed a significant difference between the performance obtained in the prediction of general and fluid intelligence from fMRI data, confirming that the quality of measurement moderates this association. The expected performance of studies predicting general intelligence from fMRI was estimated to be r = 0.42 (CI95% = [0.35, 0.50]) while for studies predicting fluid intelligence obtained from a single test, expected performance was estimated as r = 0.15 (CI95% = [0.13, 0.17]). We further enumerate some virtues and pitfalls we identified in the methods for the assessment of intelligence and machine learning. The lack of treatment of confounder variables, including kinship, and small sample sizes were two common occurrences in the literature which increased risk of bias. Reporting quality was fair across studies, although reporting of results and discussion could be vastly improved. We conclude that the current literature on the prediction of intelligence from neuroimaging data is reaching maturity. Performance has been reliably demonstrated, although extending findings to new populations is imperative. Current results could be used by future works to foment new theories on the biological basis of intelligence differences.

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“…Our results of significantly decreased total volume of GMV and WMV in ID compared to HC are in accordance with previous findings ( 14 , 20 , 28 – 30 ). The higher GMV and WMV in HC are mainly related to a larger number of neuron cells and fibers for better computational and communicational ability ( 31 ). Although most mature processes of the brain are completed before age 2 years like shape, volume, and fold, and the brain size reaches about 90% of adult size by 5 years of age, it keeps plasticity across the lifespan ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

Aberrant Structural and Functional Developmental Trajectories in Children With Intellectual Disability

Tan

Jiang

et al. 2021

Front. Psychiatry

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Intellectual disability (ID) is associated with aberrant structural and functional development of the brain, yet how the dynamical developmental changes of the structure and function of ID from childhood to around puberty remains unknown. To explore the abnormal developmental trajectories of structure and function, 40 children with ID aged 6–13 years and 30 sex-, age-, and educational level-matched healthy controls (HC) with age range from 6 to 13 were recruited. The automatic voxel-based morphometry (VBM) and resting-state functional connectivity (FC) analyses were adopted to delineate the structural and functional differences. Significantly decreased total gray matter volume (GMV) and white matter volume (WMV) in children with ID were found, and the developmental trajectories of GMV and WMV in children with ID showed an opposite direction as compared with HC. The voxel-wise VMB analysis further revealed significantly increased GMV in the dorsal medial prefrontal cortex (dmPFC), bilateral orbital part of the inferior frontal gyrus (orb_IFG.L, orb_IFG.R), right cuneus (cuneus.R), and bilateral middle frontal gyrus (MFG.L, MFG.R) in children with ID. The following seed-based whole-brain functional connectivity analyses of the brain areas with changed GMV found decreased FCs between the cuneus.R and left intraparietal sulcus (IPS.L) and between the MFG.R and anterior cingulate cortex (ACC) in children with ID. Moreover, negative correlations between GMV values in the dmPFC, orb_IFG.L, cuneus.R, and intelligence quotient (IQ) scores and positive correlations between the FCs of the cuneus.R with IPS.L and MFG.R with ACC and IQ scores were found in children with ID and HC. Our findings provide evidence for the abnormal structural and functional development in children with ID and highlight the important role of frontoparietal network in the typical development. The abnormal development of GMV and functional couplings found in this study may be the neuropathological bases of children with ID.

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Predicting intelligence from brain gray matter volume

Cited by 39 publications

References 79 publications

Age‐ and cognition‐related differences in the gray matter volume of the chimpanzee brain (Pan troglodytes): A voxel‐based morphometry and conjunction analysis

Age‐ and cognition‐related differences in the gray matter volume of the chimpanzee brain (Pan troglodytes): A voxel‐based morphometry and conjunction analysis

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

Aberrant Structural and Functional Developmental Trajectories in Children With Intellectual Disability

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