Executive function-related functional connectomes predict intellectual abilities

Individualized prediction of multi-domain intelligence quotient in bipolar disorder patients using resting-state functional connectivity

BACKGROUND

Although accumulating studies have explored the neural underpinnings of intelligence quotient (IQ) in patients with bipolar disorder (BD), these studies utilized a classification/comparison scheme that emphasized differences between BD and healthy controls at a group level. The present study aimed to infer BD patients' IQ scores at the individual level using a prediction model.

METHODS

We applied a cross-validated Connectome-based Predictive Modeling (CPM) framework using resting-state fMRI functional connectivity (FCs) to predict BD patients' IQ scores, including verbal IQ (VIQ), performance IQ (PIQ), and full-scale IQ (FSIQ). For each IQ domain, we selected the FCs that contributed to the predictions and described their distribution across eight widely-recognized functional networks. Moreover, we further explored the overlapping patterns of the contributed FCs for different IQ domains.

RESULTS

The CPM achieved statistically significant prediction performance for three IQ domains in BD patients. Regarding the contributed FCs, we observed a widespread distribution of internetwork FCs across somatomotor, visual, dorsal attention, and ventral attention networks, demonstrating their correspondence with aberrant FCs correlated to cognition deficits in BD patients. A convergent pattern in terms of contributed FCs for different IQ domains was observed, as evidenced by the shared-FCs with a leftward hemispheric dominance.

CONCLUSIONS

The present study preliminarily explored the feasibility of inferring individual IQ scores in BD patients using the FCs-based CPM framework. It is a step toward the development of applicable techniques for quantitative and objective cognitive assessment in BD patients and contributes novel insights into understanding the complex neural mechanisms underlying different IQ domains.

Regional brain structure mediates the association between sleep quality and intellectual abilities: the moderating role of socioeconomic status

This study aims to reveal the association between sleep quality and crystallized intelligence (Gc), fluid intelligence (Gf), and the underlying brain structural basis. Using the data from the Human Connectome Project (N = 1087), we performed mediation analysis to explore whether regional brain structure related to sleep quality mediate the association between sleep quality and intellectual abilities, and further examined whether socioeconomic status (i.e., income and education level) moderate the mediation effect. Results showed that poorer sleep quality was associated with lower Gc rather than Gf, and worse sleep quality was associated with smaller volume and surface area in temporal lobe, including inferior temporal gyrus and middle temporal gyrus. Notably, temporal lobe structures mediated the association between sleep quality and Gc rather than Gf. Furthermore, socioeconomic status (i.e., income and education level) moderated the mediating effect, showing low socioeconomic status has a more significant mediating effect with stronger association between sleep quality and Gc as well as stronger association between temporal lobe structure and Gc in low socioeconomic status group. These findings suggest that individuals with higher socioeconomic status are less susceptible to the effect of sleep quality on Gc.

Unveiling the core functional networks of cognition: An ontology-guided machine learning approach

Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.

Network and state specificity in connectivity-based predictions of individual behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Unveiling the Core Functional Networks of Cognition: An Ontology-Guided Machine Learning Approach

Deciphering the functional architecture that underpins diverse cognitive functions is fundamental quest in neuroscience. In this study, we employed an innovative machine learning framework that integrated cognitive ontology with functional connectivity analysis to identify brain networks essential for cognition. We identified a core assembly of functional connectomes, primarily located within the association cortex, which showed superior predictive performance compared to two conventional methods widely employed in previous research across various cognitive domains. Our approach achieved a mean prediction accuracy of 0.13 across 16 cognitive tasks, including working memory, reading comprehension, and sustained attention, outperforming the traditional methods' accuracy of 0.08. In contrast, our method showed limited predictive power for sensory, motor, and emotional functions, with a mean prediction accuracy of 0.03 across 9 relevant tasks, slightly lower than the traditional methods' accuracy of 0.04. These cognitive connectomes were further characterized by distinctive patterns of resting-state functional connectivity, structural connectivity via white matter tracts, and gene expression, highlighting their neurogenetic underpinnings. Our findings reveal a domain-general functional network fingerprint that pivotal to cognition, offering a novel computational approach to explore the neural foundations of cognitive abilities.

Network and State Specificity in Connectivity-Based Predictions of Individual Behavior

Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning. This may apply in particular if predictions are based on features derived from circumscribed, a priori defined functional networks, which improves interpretability. Furthermore, some evidence suggests that task-based FC data may yield more successful predictions of behavior than resting-state FC data. Here, we comprehensively examined to what extent the correspondence of functional network priors and task states with behavioral target domains influences the predictability of individual performance in cognitive, social, and affective tasks. To this end, we used data from the Human Connectome Project for large-scale out-of-sample predictions of individual abilities in working memory (WM), theory-of-mind cognition (SOCIAL), and emotion processing (EMO) from FC of corresponding and non-corresponding states (WM/SOCIAL/EMO/resting-state) and networks (WM/SOCIAL/EMO/whole-brain connectome). Using root mean squared error and coefficient of determination to evaluate model fit revealed that predictive performance was rather poor overall. Predictions from whole-brain FC were slightly better than those from FC in task-specific networks, and a slight benefit of predictions based on FC from task versus resting state was observed for performance in the WM domain. Beyond that, we did not find any significant effects of a correspondence of network, task state, and performance domains. Together, these results suggest that multivariate FC patterns during both task and resting states contain rather little information on individual performance levels, calling for a reconsideration of how the brain mediates individual differences in mental abilities.

Predicting executive functioning from brain networks: modality specificity and age effects

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from the gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether the differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate-to-weak brain-behavior associations (R2 < 0.07, r < 0.28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

Predicting Executive Functioning from Brain Networks: Modality Specificity and Age Effects

Healthy aging is associated with structural and functional network changes in the brain, which have been linked to deterioration in executive functioning (EF), while their neural implementation at the individual level remains unclear. As the biomarker potential of individual resting-state functional connectivity (RSFC) patterns has been questioned, we investigated to what degree individual EF abilities can be predicted from gray-matter volume (GMV), regional homogeneity, fractional amplitude of low-frequency fluctuations (fALFF), and RSFC within EF-related, perceptuo-motor, and whole-brain networks in young and old adults. We examined whether differences in out-of-sample prediction accuracy were modality-specific and depended on age or task-demand levels. Both uni- and multivariate analysis frameworks revealed overall low prediction accuracies and moderate to weak brain-behavior associations (R2 < .07, r < .28), further challenging the idea of finding meaningful markers for individual EF performance with the metrics used. Regional GMV, well linked to overall atrophy, carried the strongest information about individual EF differences in older adults, whereas fALFF, measuring functional variability, did so for younger adults. Our study calls for future research analyzing more global properties of the brain, different task-states and applying adaptive behavioral testing to result in sensitive predictors for young and older adults, respectively.

Multivariate resting-state functional connectomes predict and characterize obesity phenotypes

The univariate obesity-brain associations have been extensively explored, while little is known about the multivariate associations between obesity and resting-state functional connectivity. We therefore utilized machine learning and resting-state functional connectivity to develop and validate predictive models of 4 obesity phenotypes (i.e. body fat percentage, body mass index, waist circumference, and waist-height ratio) in 3 large neuroimaging datasets (n = 2,992). Preliminary evidence suggested that the resting-state functional connectomes effectively predicted obesity/weight status defined by each obesity phenotype with good generalizability to longitudinal and independent datasets. However, the differences between resting-state functional connectivity patterns characterizing different obesity phenotypes indicated that the obesity-brain associations varied according to the type of measure of obesity. The shared structure among resting-state functional connectivity patterns revealed reproducible neuroimaging biomarkers of obesity, primarily comprising the connectomes within the visual cortex and between the visual cortex and inferior parietal lobule, visual cortex and orbital gyrus, and amygdala and orbital gyrus, which further suggested that the dysfunctions in the perception, attention and value encoding of visual information (e.g. visual food cues) and abnormalities in the reward circuit may act as crucial neurobiological bases of obesity. The recruitment of multiple obesity phenotypes is indispensable in future studies seeking reproducible obesity-brain associations.

Predicting executive functioning from functional brain connectivity: network specificity and age effects

Healthy aging is associated with altered executive functioning (EF). Earlier studies found age-related differences in EF performance to be partially accounted for by changes in resting-state functional connectivity (RSFC) within brain networks associated with EF. However, it remains unclear which role RSFC in EF-associated networks plays as a marker for individual differences in EF performance. Here, we investigated to what degree individual abilities across 3 different EF tasks can be predicted from RSFC within EF-related, perceptuo-motor, whole-brain, and random networks separately in young and old adults. Specifically, we were interested if (i) young and old adults differ in predictability depending on network or EF demand level (high vs. low), (ii) an EF-related network outperforms EF-unspecific networks when predicting EF abilities, and (iii) this pattern changes with demand level. Both our uni- and multivariate analysis frameworks analyzing interactions between age × demand level × networks revealed overall low prediction accuracies and a general lack of specificity regarding neurobiological networks for predicting EF abilities. This questions the idea of finding markers for individual EF performance in RSFC patterns and calls for future research replicating the current approach in different task states, brain modalities, different, larger samples, and with more comprehensive behavioral measures.

Determining the neurocognitive profile of children with tuberous sclerosis complex within the Western Cape region of South Africa

BACKGROUND

Tuberous sclerosis complex (TSC) is a multisystem genetic disorder associated with a wide spectrum of cognitive impairments that can often result in impaired academic, social and adaptive functioning. However, studies investigating TSC have found it difficult to determine whether TSC is associated with a distinct cognitive phenotype and more specifically which aspects of functioning are impaired. Furthermore, children with TSC living in low-income and middle-income countries, like South Africa, experience additional burdens due to low socio-economic status, high mortality rates and poor access to health care and education. Hence, the clinical population of South Africa may vary considerably from those populations from high-income countries discussed in the literature.

METHODS

A comprehensive neuropsychological battery composed of internationally recognised measures examining attention, working memory, language comprehension, learning and memory, areas of executive function and general intellectual functioning was administered to 17 children clinically diagnosed with TSC.

RESULTS

The exploration of descriptive data indicated generalised cognitive difficulties in most cognitive domains, aside from memory. With only two participants performing in the average to above-average ranges, the rest of the sample showed poor verbal comprehension, perceptual reasoning, working memory, processing speed, disinhibition, and problems with spatial planning, problem solving, frustration tolerance, set shifting and maintaining a set of rules. Furthermore, correlational findings indicated several associations between socio-demographic and cognitive variables.

CONCLUSIONS

Importantly, this is the first study to comprehensively examine multiple domains of neurocognitive functioning in a low-resource setting sample of children with TSC. Current study findings showed that children with TSC have generalised impairments across several cognitive domains, rather than domain-specific impairments. Therefore, although examining individual aspects of cognition, such as those found in previous literature, is important, this approach is limiting. With a comprehensive assessment, including understanding the associations between domains, appropriate and directed support can be provided to ensure all aspects of development are addressed and considered.

Application of a Bayesian Network Learning Model to Predict Longitudinal Trajectories of Executive Function Difficulties in Elementary School Students

Executive function is the mental ability to modulate behavior or thinking to accomplish a task. This is developmentally important for children's academic achievements and ability to adjust to school. We classified executive function difficulties (EFDs) in longitudinal trajectories in Korean children from 7 to 10 years old. We found predictors of EFDs using latent class growth analysis and Bayesian network learning methods with Panel Study data. Three types of latent class models of executive function difficulties were identified: low, intermediate, and high EFDs. The modeling performance of the high EFD group was excellent (AUC = .91), and the predictors were the child's gender, temperamental emotionality, happiness, DSM (Diagnostic and Statistical Manual of Mental Disorders) anxiety problems, and the mother's depression as well as coparenting conflict recognized by the mother. The results show that using latent class growth analysis and Bayesian network learning are helpful in classifying the longitudinal EFD patterns in elementary school students. Furthermore, school-age EFD is affected by emotional problems in parents and children that continue from early life. These findings can support children's development and prevent risk by preclassifying children who may experience persistent EFD and tracing causes.

Development and plasticity of executive functions: A value-based account

Executive functions are core to multiple aspects of daily cognitive, social and affective functioning. An extensive body of work has charted developmental trajectories and neural substrates of executive functions through the lifespan. Robust associations between executive functions early in life, and later, wellbeing and success has led to considerable efforts to improve executive functions through bespoke interventions. Here, we discuss recent findings on the role of cost-benefit computations in how executive functions are deployed in development. We propose leveraging these insights to design more effective interventions for improving executive functions.