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. 2024 Jul-Sep;24(3):100498.
doi: 10.1016/j.ijchp.2024.100498. Epub 2024 Sep 7.

Imaging body-mind crosstalk in young adults

Qian Yu  1   2 , Zhaowei Kong  1 , Liye Zou  2   3 , Fabian Herold  2   4 , Sebastian Ludyga  5 , Zhihao Zhang  2 , Meijun Hou  2 , Arthur F Kramer  6   7   8 , Kirk I Erickson  9   10 , Marco Taubert  11 , Charles H Hillman  6   10 , Sean P Mullen  8   12 , Markus Gerber  5 , Notger G Müller  4 , Keita Kamijo  13 , Toru Ishihara  14 , Robert Schinke  15 , Boris Cheval  16   17 , Terry McMorris  18 , Ka Kit Wong  1 , Qingde Shi  19 , Jinlei Nie  19
Affiliations

Imaging body-mind crosstalk in young adults

Qian Yu et al. Int J Clin Health Psychol. 2024 Jul-Sep.

Abstract

Objective: There is evidence that complex relationships exist between motor functions, brain structure, and cognitive functions, particularly in the aging population. However, whether such relationships observed in older adults could extend to other age groups (e.g., younger adults) remains to be elucidated. Thus, the current study addressed this gap in the literature by investigating potential associations between motor functions, brain structure, and cognitive functions in a large cohort of young adults.

Methods: In the current study, data from 910 participants (22-35 yr) were retrieved from the Human Connectome Project. Interactions between motor functions (i.e., cardiorespiratory fitness, gait speed, hand dexterity, and handgrip strength), brain structure (i.e., cortical thickness, surface area, and subcortical volumes), and cognitive functions were examined using linear mixed-effects models and mediation analyses. The performance of different machine-learning classifiers to discriminate young adults at three different levels (related to each motor function) was compared.

Results: Cardiorespiratory fitness and hand dexterity were positively associated with fluid and crystallized intelligence in young adults, whereas gait speed and handgrip strength were correlated with specific measures of fluid intelligence (e.g., inhibitory control, flexibility, sustained attention, and spatial orientation; false discovery rate [FDR] corrected, p < 0.05). The relationships between cardiorespiratory fitness and domains of cognitive function were mediated by surface area and cortical volume in regions involved in the default mode, sensorimotor, and limbic networks (FDR corrected, p < 0.05). Associations between handgrip strength and fluid intelligence were mediated by surface area and volume in regions involved in the salience and limbic networks (FDR corrected, p < 0.05). Four machine-learning classifiers with feature importance ranking were built to discriminate young adults with different levels of cardiorespiratory fitness (random forest), gait speed, hand dexterity (support vector machine with the radial kernel), and handgrip strength (artificial neural network).

Conclusions: In summary, similar to observations in older adults, the current study provides empirical evidence (i) that motor functions in young adults are positively related to specific measures of cognitive functions, and (ii) that such relationships are at least partially mediated by distinct brain structures. Furthermore, our analyses suggest that machine-learning classifier has a promising potential to be used as a classification tool and decision support for identifying populations with below-average motor and cognitive functions.

Keywords: Brain structure; Fluid and crystallized intelligence; Machine learning; Motor function; Young adults.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig 1
Fig. 1
Correlation heatmap of motor functions, brain morphometrics, and cognition. Note. Fig. 1a, the correlation between 4 motor functions and 20 cognitive measures; Fig. 1b, the correlation between 4 motor functions and 150 brain structure measures; Fig. 1c, the correlation between 20 cognitive measures and 150 brain structure measures. The mat color bar represented the correlation coefficient, with the pink and the purple indicating a positive and negative correlation, respectively. Abbreviations, more details in Appendix B & C.).
Fig 2
Fig. 2
Visualization of brain structure measures associated with cardiorespiratory fitness in young adults. Notes. Figure a1 shows the associated cerebral thickness of the left insula; Figure b1–8 shows the associated surface areas of 8 brain regions, including the left entorhinal, left posterior cingulate, left precentral, right inferior parietal, right inferior temporal, right middle temporal, right precentral, and right superior frontal gyri; Figure c1–2 shows the associated subcortical volumes of the left and right hippocampus. We also illustrate the calculation of brain structure measures: cortical thickness, the distance from the white surface vertex to the closest point on the pial surface; surface area, the sum of all mesh surfaces composed of vertex [intersection of the triangle] and edge [connection between vertices]; subcortical volume, an approximate estimate of the volume obtained by multiplying the area by thickness at each vertex. The regions associated with endurance are marked in bright green, while the other regions are shown in different colors corresponding to various brain lobes: yellow for the frontal lobe, red for the parietal lobe, pink for the temporal lobe, orange for the occipital lobe, brown for the limbic lobe, blue for the cerebellum, and purple for the cerebral nuclei.).
Fig 3
Fig. 3
Visualization of brain structure measures associated with handgrip strength in young adults. Notes. Figure a1–4 shows the associated cerebral thickness of 4 brain regions, including the left medial orbitofrontal, left and right rostral anterior cingulate, and right insula gyri; Figure b1–26 shows the associated surface areas of 26 brain regions, including the left and right cuneus, left and right entorhinal, left and right fusiform, left and right inferior parietal, left lateral occipital, left and right pars triangularis, left and right peri calcarine, left and right posterior cingulate, left precuneus, left and right rostral middle frontal, left and right superior temporal, left and right supramarginal, right pars orbitalis, right precentral, right superior frontal, and right insula gyri; Figure c1–6 shows the associated subcortical volumes of 6 brain regions including the left and right putamen, amygdala, and accumbens areas. The regions associated with grip strength are marked in bright green, while the other regions are shown in different colors corresponding to various brain lobes: yellow for the frontal lobe, red for the parietal lobe, pink for the temporal lobe, orange for the occipital lobe, brown for the limbic lobe, blue for the cerebellum, and purple for the cerebral nuclei.).
Fig 4
Fig. 4
Machine-learning classifiers for motor functions. Note 1. Figure a-d show the recommended classifier, model evaluation, and feature importance of brain structure measures for each motor function. Figure a, random forest model for cardiorespiratory fitness; Figure b, support vector machine with radial kernel for gait speed; Figure c, support vector machine with radial kernel for hand dexterity; Figure d, artificial neural network model for handgrip strength.) Note 2. for machine learning classifiers. Random forest classifier, a meta-estimator that fits multiple decision tree classifiers on subsamples from the dataset and uses averaging; Support vector machine with radial kernel classifier, a classifier that does nonlinear transformations according to the features and maps data from original space to a higher-dimensional space; Artificial neural network classifier, a classifier works based on artificial neural structure and is composed of three layers including input layer, hidden layer, and output layer.) Note 3. for model evaluation. Accuracy, the number of correct classifications divided by all types of classifications; Sensitivity, the true positive rate; Specificity, the proportion of correct identification of actual negatives; Positive predictive value, Negative predictive value, Precision, the proportion of correct positive classification; Recall, the proportion of correct positives; F1, the harmonic mean of precision and recall; Prevalence, the number of positives divided by the number of positives and negatives; Detection rate, the proportion of correct detections; Detection prevalence, the number of positive detections divided by the number of positive and negative detections; Balance accuracy, the arithmetic mean of sensitivity and specificity; Area under the receiver operating characteristic curve, the entire two-dimensional area underneath the receiver operating characteristic curve, which probably measures the performance across all classification thresholds. It is worth noting that the metrics of model evaluation [except accuracy] represent the average performance of three groups.) Note 4 for feature importance. The feature importance represents the contribution of each brain structure measure in the classification, with a higher value indicating a more significant contribution.) Abbreviations. PPV, positive predictive value; NPV, negative predictive value; DR, detection rate; DP, detection prevalence; BA, balanced accuracy; ROC-AUC, the area under the receiver operating characteristic curve.).
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