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

Vieira, Bruno Hebling; Pamplona, Gustavo Santo Pedro; Fachinello, Karim; Silva, Alice Kamensek; Foss, Maria Paula; Salmon, Carlos Ernesto Garrido

doi:10.1016/j.intell.2022.101654

Cited by 31 publications

(39 citation statements)

References 101 publications

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“…This is supported by our prediction results (Figure 2 and Supplementary Figure S1), which show that regardless of the rsfMRI features used and the sample size, the processing speed measure was usually predicted better than the visual and numerical memory scores. A recent review of human fluid intelligence prediction using neuroimaging data has reported an average Pearson correlation of 0.15 with a CI 95% of [0.13, 0.17] across the fMRI literature [9]. This is confirmed by our fluid intelligence prediction results with a maximum correlation score of 0.23 using combined LCOR and individual characteristics and at very high sample sizes (20,000 -see Figure 2).…”

Section: Discussionsupporting

confidence: 85%

“…Functional brain connectivity (FC) is an important aspect of rsfMRI defined as the statistical dependence between cortical and subcortical areas during periods of rest or low cognitive demand [4]. A common application of rsfMRI is for the prediction of cognitive performance [5]–[9], and clinical phenotypes [5], [10]–[13] [14]–[20]. It is usually accomplished by extracting various features from rsfMRI, such as widely used FC measures, and using them for predictive modeling.…”

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confidence: 99%

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Is resting state fMRI better than individual characteristics at predicting cognition?

Omidvarnia

Sasse

Larabi

et al. 2023

Preprint

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Changes in spontaneous brain activity at rest provide rich information about behavior and cognition. The mathematical properties of resting-state functional magnetic resonance imaging (rsfMRI) are a depiction of brain function and are frequently used to predict cognitive phenotypes. Individual characteristics such as age, gender, and total intracranial volume (TIV) play an important role in predictive modeling of rsfMRI (for example, as "confounders" in many cases). It is unclear, however, to what extent rsfMRI carries independent information from the individual characteristics that is able to predict cognitive phenotypes. Here, we used kernel ridge regression modeling to thoroughly examine the predictability of four cognitive phenotypes in 20,000 healthy UK Biobank subjects. We extracted common rsfMRI features of functional brain connectivity (FC) and temporal complexity (TC). We assessed the ability of these features to predict outcomes in the presence and absence of age, gender, and TIV. Additionally, we assessed the predictiveness of age, gender, and TIV only. We find TC and FC features to perform comparably with regard to predicting cognitive phenotypes. As compared to rsfMRI features, individual characteristics provide systematically better predictions with smaller sample sizes and, to some extent, in larger cohorts. It is also consistent across different levels of inherent temporal noise in rsfMRI. Our results suggest that when the objective is to perform cognitive predictions as opposed to understanding the relationship between brain and behavior, individual characteristics outperform rsfMRI features.

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Section: Discussionsupporting

confidence: 85%

mentioning

confidence: 99%

Is resting state fMRI better than individual characteristics at predicting cognition?

Omidvarnia

Sasse

Larabi

et al. 2023

Preprint

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“…We picked two behavioral measures, full-scale intelligence quotient (FSIQ) from the Wechsler Intelligence Scale for Children -Fifth Edition (Wechsler, 2014) and the Social Communication Questionnaire (SCQ) (Rutter et al, 2003). FSIQ measures general cognitive ability, which is widely used in studies of brain-behavior relationships (Vieira et al, 2022). 64 and 60 participants with the video clip 'The Present' and with the clip 'Despicable Me' had FSIQ scores available, respectively.…”

Section: Behavioral Measuresmentioning

confidence: 99%

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: age, sex, and behavioral associations

Uddin

et al. 2023

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Spatially remote brain regions show dynamic functional interactions during various task conditions. Time-varying functional connectivity measured during movie watching was sensitive to movie content, while stationary functional connectivity remains stable across videos. Therefore, it has been suggested that dynamic and stationary functional interactions may reflect different aspects of brain function. However, how individual differences in time-varying and stationary connectivity are associated with behavioral phenotypes is still unclear. We analyzed an open-access functional MRI dataset collected from participants (5 to 22 years old) as they watched two cartoon movie clips. Regional brain activity, time-varying and stationary functional connectivity were calculated, and associations with age, sex, and behavioral assessments were examined. Using a model comparison method, we showed that time-varying connectivity was more sensitive to age and sex effects compared with stationary connectivity. The preferred age models were quadratic log age or quadratic age effects, corresponding to inverted-U shaped developmental curves. In addition, females showed higher consistency in regional brain activity and time-varying connectivity than males. However, in terms of behavioral predictions, only stationary connectivity could predict full-scale intelligence quotient. The results suggest that individual differences in time-varying and stationary connectivity may reflect different aspects of behavioral phenotypes.

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“…Third and finally, can we further improve our ability to capture the decline in cognitionfluid by using, not only Brain Age and chronological age, but also another biomarker, Brain Cognition? Analogous to Brain Age, Brain Cognition is defined as a predicted value from machine-learning models that predict cognitionfluid based on a person's brain data (Dubois et al, 2018;Pat, Wang, Anney, et al, 2022;Rasero et al, 2021;Sripada et al, 2020;Tetereva et al, 2022; for review, see Vieira et al, 2022). Age-related cognitive decline is not only related to the changes in age, but also to the changes in cognition.…”

Section: Introductionmentioning

confidence: 99%

The (Limited?) Utility of Brain Age as a Biomarker for Capturing Fluid Cognition in Older Individuals

Tetereva¹,

Pornpattananangkul²

2023

Preprint

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For decades, neuroscientists have been on a quest to search for a biomarker that can help capture age-related cognitive decline. One well-known candidate is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI data. Here we aim to formally evaluate the utility of Brain Age as a biomarker for capturing cognitive decline. Using 504 aging participants (36-100 years old) from the Human Connectome Project in Aging, we created 26 age-prediction models for Brain Age based on different combinations of MRI modalities. We first tested how much Brain Age from these age-prediction models added to what we had already known from a chronological age in capturing cognitive decline. Based on the commonality analyses, we found a large degree of overlap between Brain Age and chronological age, so much so that, at best, Brain Age could uniquely add only around 1.6% in explaining the variation in cognitive decline. Next, the age-prediction models that performed better at predicting chronological age did not necessarily create better Brain Age for capturing cognitive decline beyond chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining cognitive decline. Lastly, unlike Brain Age, Brain Cognition, or a predicted value based on machine-learning models built to predict cognitive abilities from brain MRI data, provided much higher unique effects. Brain Cognition added over 11% to explain the variation in cognitive decline beyond chronological age, leading to around a 1/3-time improvement of the total variation explained. Accordingly, while demonstrating the limited utility of Brain Age, we provided a solution to improve our ability to use brain MRI data as a biomarker for cognitive decline.

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

Cited by 31 publications

References 101 publications

Is resting state fMRI better than individual characteristics at predicting cognition?

Is resting state fMRI better than individual characteristics at predicting cognition?

Individual differences in time-varying and stationary brain connectivity during movie watching from childhood to early adulthood: age, sex, and behavioral associations

The (Limited?) Utility of Brain Age as a Biomarker for Capturing Fluid Cognition in Older Individuals

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