MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning

Wang, Liye; Wee, Chong Yaw; Suk, Heung-Il; Tang, Xiaoying; Shen, Dinggang

doi:10.1371/journal.pone.0117295

Cited by 34 publications

(30 citation statements)

References 38 publications

(46 reference statements)

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“…A pairing factor was included for each subject. BDI and scanner hardware maintenance (<2 months before maintenance of a RF transmitter coil or RF amplifier) were included as covariates (Mcdaniel, 2005;Wang, Wee, Suk, Tang, & Shen, 2015;Webb, Weber, Mundy, & Killgore, 2014). All reported p-values were family-wise-error corrected on cluster level at p < .05, with an uncorrected voxel-level threshold of p < .001.…”

Section: Discussionmentioning

confidence: 99%

Age‐dependent brain volume and neuropsychological changes after chemotherapy in breast cancer patients

Blommaert

Schroyen

Vandenbulcke

et al. 2019

Human Brain Mapping

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This study investigated volumetric brain changes and cognitive performance in premenopausal and postmenopausal patients treated for early‐stage breast cancer. Participants underwent elaborate neurocognitive assessments (neuropsychological testing, cognitive failure questionnaire, and high‐resolution T1‐weighted structural MRI) before and after chemotherapy. Volumetric brain changes were estimated, using longitudinal deformation‐based morphometry, and correlated with cognitive changes. In total, 180 women participated in this study, of whom 72 patients with breast cancer had received adjuvant chemotherapy (C+), 49 patients did not receive chemotherapy (C−), and 59 healthy controls (HC). The population was categorized into two age groups: A young group who were premenopausal and younger than 52 years at baseline (n = 55C+/32C−/41HC), and an older group who were postmenopausal and older than 60 years (n = 17C+/17C−/18HC). Cognitive impairment occurred after chemotherapy in both young and older patients, although older patients showed more decline in processing speed (Trail making test b). White matter volume expansion was observed after chemotherapy, only significantly present in the younger subgroup of patients. In patients not treated with chemotherapy, diffuse gray and white matter volume reduction was observed. Less white matter expansion concurred with more cognitive decline (r > .349, p < .05). In conclusion, we found age‐dependent cognitive decline and white matter volume changes in patients with breast cancer after chemotherapy, which could possibly be linked to neuroinflammatory processes. White matter expansion after chemotherapy, more pronounced in premenopausal patients, correlated with less cognitive decline. This suggests such expansion to be age‐dependent, possibly caused by a protective response in the younger brain to chemotherapy‐induced neurotoxicity.

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Section: Discussionmentioning

confidence: 99%

Age‐dependent brain volume and neuropsychological changes after chemotherapy in breast cancer patients

Blommaert

Schroyen

Vandenbulcke

et al. 2019

Human Brain Mapping

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“…Finn et al (2015) also established the relevance of the connectivity profiles to behavior by demonstrating, in a leave-one-subject-out cross-validation analysis, that functional connectivity profiles can be used to predict the fundamental cognitive trait of fluid intelligence across subjects. It shows that the characteristic connectivity patterns are distributed throughout the brain, but the fron- Wang et al (2015) employed multi-and single-kernel Support Vector Regression (SVR) to predict FSIQ from gray and white matter regional volumetry in typical children and adolescents. The authors report an optimal R2 of 0.516, remarkably higher than that of the total brain volume.…”

Section: Predicting Intelligence From Neuroimaging Data the State-ofmentioning

confidence: 99%

Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data

Dizaji

Vieira

Khodaei

et al. 2021

BCN

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Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman’s general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of sophisticated brain imaging devices using Diffusion-Weighted Imaging (DWI) and functional Magnetic Resonance Imaging (fMRI) has allowed researchers to test hypotheses about neural correlates of intelligence in humans.This review summarizes recent findings on the associations of human intelligence with neuroimaging data. To this end, first, we review the literature that has related brain morphometry to intelligence. Next, we elaborate on the applications of DWI and resting-state fMRI on the investigation of intelligence. Then, we provide a survey of literature that has used multimodal DWI-fMRI to shed light on intelligence. Finally, we discuss the state-of-the-art of individualized prediction of intelligence from neuroimaging data and point out future strategies. Future studies hold promising outcomes for machine learning-based predictive frameworks using neuroimaging features to estimate human intelligence.

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“…Regression based on nonlinear models was reported in 5 (17%) studies. These include polynomial Kernel SVR (Wang et al 2015), corre-lation kernel ridge regression (KRR) (He et al 2020;Li et al 2019), dice overlap KRR (Kong et al 2019) and deep learning, based on convolutional neural networks (CNNs), graph neural networks and fully connected deep networks (He et al 2020) or recurrent neural networks (RNNs) (Fan 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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MRI-Based Intelligence Quotient (IQ) Estimation with Sparse Learning

Cited by 34 publications

References 38 publications

Age‐dependent brain volume and neuropsychological changes after chemotherapy in breast cancer patients

Age‐dependent brain volume and neuropsychological changes after chemotherapy in breast cancer patients

Linking Brain Biology to Intellectual Endowment: A Review on the Associations of Human Intelligence With Neuroimaging Data

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

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