A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction

Wu, Yunan; Besson, Pierre; Azcona, Emanuel A.; Bandt, S. Kathleen; Parrish, Todd B.; Breiter, Hans C.; Katsaggelos, Aggelos K.

doi:10.1038/s41598-022-22313-x

Cited by 4 publications

(2 citation statements)

References 69 publications

(73 reference statements)

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“…For subjects of the HCP Young Adult study, total, fluid and crystallized intelligence values were measured as cognition composite scores. The overall low predictability of intelligence by whole brain structural connectome features confirmed the statement of Wu and colleagues’ work [ 38 ] that their prediction combining cortical and subcortical surfaces together yielded the highest accuracy of fluid intelligence for both ABCD (N = 8070, r = 0.314) and HCP datasets (N = 1097, r = 0.454), outperforming the state-of-the-art prediction of fluid intelligence from any other brain measures in the literature. Wu and colleagues developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of fluid intelligence.…”

Section: Discussionsupporting

confidence: 82%

Predictability of intelligence and age from structural connectomes

Kopetzky,

Li,

Kaiser

et al. 2024

PLoS ONE

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In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

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Section: Discussionsupporting

confidence: 82%

Predictability of intelligence and age from structural connectomes

Kopetzky,

Li,

Kaiser

et al. 2024

PLoS ONE

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“…Finally, we mention the use of geometric deep learning techniques in neuroimaging and the study of the brain connectome (Gurbuz and Rekik, 2020 ; Huang et al, 2021 ; Williams et al, 2021 ), as well as the study on (i) the relationship of human brain structure to cognitive function (Wu et al, 2022 ), (ii) the topographic heterogeneity of cortical organisation as a necessary step toward precision modelling of neuropsychiatric disorders (Williams et al, 2021 ), (iii) brain aging (Besson et al, 2022 ).…”

Section: Geometric Deep Learningmentioning

confidence: 99%

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Lecca,

Lecca

2023

Front. Artif. Intell.

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Graphs are used as a model of complex relationships among data in biological science since the advent of systems biology in the early 2000. In particular, graph data analysis and graph data mining play an important role in biology interaction networks, where recent techniques of artificial intelligence, usually employed in other type of networks (e.g., social, citations, and trademark networks) aim to implement various data mining tasks including classification, clustering, recommendation, anomaly detection, and link prediction. The commitment and efforts of artificial intelligence research in network biology are motivated by the fact that machine learning techniques are often prohibitively computational demanding, low parallelizable, and ultimately inapplicable, since biological network of realistic size is a large system, which is characterised by a high density of interactions and often with a non-linear dynamics and a non-Euclidean latent geometry. Currently, graph embedding emerges as the new learning paradigm that shifts the tasks of building complex models for classification, clustering, and link prediction to learning an informative representation of the graph data in a vector space so that many graph mining and learning tasks can be more easily performed by employing efficient non-iterative traditional models (e.g., a linear support vector machine for the classification task). The great potential of graph embedding is the main reason of the flourishing of studies in this area and, in particular, the artificial intelligence learning techniques. In this mini review, we give a comprehensive summary of the main graph embedding algorithms in light of the recent burgeoning interest in geometric deep learning.

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Predictive models demonstrate age‐dependent association of subcortical volumes and cognitive measures

Weerasekera

Ion-Mărgineanu

Green

et al. 2022

Human Brain Mapping

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Whether brain matter volume is correlated with cognitive functioning and higher intelligence is controversial. We explored this relationship by analysis of data collected on 193 healthy young and older adults through the "Leipzig Study for Mind-Body-Emotion Interactions" (LEMON) study. Our analysis involved four cognitive measures: fluid intelligence, crystallized intelligence, cognitive flexibility, and working memory. Brain subregion volumes were determined by magnetic resonance imaging.We normalized each subregion volume to the estimated total intracranial volume and conducted training simulations to compare the predictive power of normalized volumes of large regions of the brain (i.e., gray matter, cortical white matter, and cerebrospinal fluid), normalized subcortical volumes, and combined normalized volumes of large brain regions and normalized subcortical volumes. Statistical tests showed significant differences in the performance accuracy and feature importance of the subregion volumes in predicting cognitive skills for young and older adults. Random forest feature selection analysis showed that cortical white matter was the key feature in predicting fluid intelligence in both young and older adults. In young adults, crystallized intelligence was best predicted by caudate nucleus, thalamus, pallidum, and nucleus accumbens volumes, whereas putamen, amygdala, nucleus accumbens, and hippocampus volumes were selected for older adults. Cognitive flexibility was best predicted by the caudate, nucleus accumbens, and hippocampus in young adults and caudate and amygdala in older adults. Finally, working memory was best predicted by the putamen, pallidum, and nucleus accumbens in the younger group, whereas amygdala and hippocampus volumes were predictive in the older group. Thus, machine learning predictive models demonstrated an age-dependent association between subcortical volumes and cognitive measures. These approaches may be useful in predicting the likelihood of age-related cognitive decline and in testing of approaches for targeted improvement of cognitive functioning in older adults.Akila Weerasekera and Adrian Ion-M argineanus contributed equally to this study.

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A multicohort geometric deep learning study of age dependent cortical and subcortical morphologic interactions for fluid intelligence prediction

Cited by 4 publications

References 69 publications

Predictability of intelligence and age from structural connectomes

Predictability of intelligence and age from structural connectomes

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

Predictive models demonstrate age‐dependent association of subcortical volumes and cognitive measures

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