scholarly journals Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability

2021 ◽  
Vol 17 (3) ◽  
pp. e1008347 ◽  
Author(s):  
Javier Rasero ◽  
Amy Isabella Sentis ◽  
Fang-Cheng Yeh ◽  
Timothy Verstynen
Keyword(s):  
Individual Differences ◽  
Cognitive Function ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Predictive Power ◽  
Fluid Intelligence ◽  
Prediction Models ◽  
Principal Component ◽  
Individual Variability ◽  
Human Connectome Project

Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.

scholarly journals Individual Cortical Entropy Profile: Test–Retest Reliability, Predictive Power for Cognitive Ability, and Neuroanatomical Foundation

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Mianxin Liu ◽  
Xinyang Liu ◽  
Andrea Hildebrandt ◽  
Changsong Zhou
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Predictive Power ◽  
Structural Connectivity ◽  
Cortical Folding ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Out Of Sample ◽  
Test Retest Reliability

Abstract The entropy profiles of cortical activity have become novel perspectives to investigate individual differences in behavior. However, previous studies have neglected foundational aspects of individual entropy profiles, that is, the test–retest reliability, the predictive power for cognitive ability in out-of-sample data, and the underlying neuroanatomical basis. We explored these issues in a large young healthy adult dataset (Human Connectome Project, N = 998). We showed the whole cortical entropy profile from resting-state functional magnetic resonance imaging is a robust personalized measure, while subsystem profiles exhibited heterogeneous reliabilities. The limbic network exhibited lowest reliability. We tested the out-of-sample predictive power for general and specific cognitive abilities based on reliable cortical entropy profiles. The default mode and visual networks are most crucial when predicting general cognitive ability. We investigated the anatomical features underlying cross-region and cross-individual variations in cortical entropy profiles. Cortical thickness and structural connectivity explained spatial variations in the group-averaged entropy profile. Cortical folding and myelination in the attention and frontoparietal networks determined predominantly individual cortical entropy profile. This study lays foundations for brain-entropy-based studies on individual differences to understand cognitive ability and related pathologies. These findings broaden our understanding of the associations between neural structures, functional dynamics, and cognitive ability.

scholarly journals Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability

2020 ◽  
Author(s):  
Javier Rasero ◽  
Amy Isabella Sentis ◽  
Fang-Cheng Yeh ◽  
Timothy Verstynen
Keyword(s):  
Individual Differences ◽  
Cognitive Function ◽  
Cognitive Abilities ◽  
Prediction Accuracy ◽  
Predictive Power ◽  
Learning Approach ◽  
Cognitive Domains ◽  
Out Of Sample ◽  
Multi Level ◽  
Brain Measurements

AbstractVariation in cognitive ability arises from subtle differences in underlying neural architectural properties. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N=1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to 4% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.Author summaryCognition is a complex and interconnected process whose underlying mechanisms are still unclear. In order to unravel this question, studies usually look at one neuroimaging modality (e.g. functional MRI) and associate the observed brain properties with individual differences in cognitive performance. However, this approach is limiting because it fails to incorporate other sources of brain information and does not generalize well to new data. Here we tackled both problems by using out-of-sample testing and a multi-level learning approach that can efficiently integrate across simultaneous brain measurements. We tested this scenario by evaluating individual differences across several cognitive domains, using five measures that represent morphological, functional and structural aspects of the brain network architecture. We predicted individual cognitive differences using each brain property group separately and then stacked these predictions, forming a new matrix with as many columns as separate brain measurements, that was then fit using a regularized regression model that isolated unique information among modalities and substantially helped enhance prediction accuracy across most of the cognitive domains. This holistic approach provides a framework for capturing non-redundant variability across different imaging modalities, opening a window to easily incorporate more sources of brain information to further understand cognitive function.

scholarly journals Cognitive function, disease burden and the structural connectome in systemic lupus erythematosus

Lupus ◽  
2018 ◽  
Vol 27 (8) ◽  
pp. 1329-1337 ◽  
Author(s):  
S J Wiseman ◽  
M E Bastin ◽  
E N Amft ◽  
J F F Belch ◽  
S H Ralston ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Cognitive Function ◽  
Lupus Erythematosus ◽  
Cognitive Abilities ◽  
Structural Connectivity ◽  
Principal Component ◽  
Clustering Coefficient ◽  
Screening Tests ◽  
Systemic Lupus ◽  
Network Metrics

Objective To investigate brain structural connectivity in relation to cognitive abilities and systemic damage in systemic lupus erythematosus (SLE). Methods Structural and diffusion MRI data were acquired from 47 patients with SLE. Brains were segmented into 85 cortical and subcortical regions and combined with whole brain tractography to generate structural connectomes using graph theory. Global cognitive abilities were assessed using a composite variable g, derived from the first principal component of three common clinical screening tests of neurological function. SLE damage ( LD) was measured using a composite of a validated SLE damage score and disease duration. Relationships between network connectivity metrics, cognitive ability and systemic damage were investigated. Hub nodes were identified. Multiple linear regression, adjusting for covariates, was employed to model the outcomes g and LD as a function of network metrics. Results The network measures of density (standardised ß = 0.266, p = 0.025) and strength (standardised ß = 0.317, p = 0.022) were independently related to cognitive abilities. Strength (standardised ß = –0.330, p = 0.048), mean shortest path length (standardised ß = 0.401, p = 0.020), global efficiency (standardised ß = –0.355, p = 0.041) and clustering coefficient (standardised ß = –0.378, p = 0.030) were independently related to systemic damage. Network metrics were not related to current disease activity. Conclusion Better cognitive abilities and more SLE damage are related to brain topological network properties in this sample of SLE patients, even those without neuropsychiatric involvement and after correcting for important covariates. These data show that connectomics might be useful for understanding and monitoring cognitive function and white matter damage in SLE.

scholarly journals Individual learning phenotypes drive collective behavior

2020 ◽  
Vol 117 (30) ◽  
pp. 17949-17956 ◽  
Author(s):  
Chelsea N. Cook ◽  
Natalie J. Lemanski ◽  
Thiago Mosqueiro ◽  
Cahit Ozturk ◽  
Jürgen Gadau ◽  
...  
Keyword(s):  
Individual Differences ◽  
Social Interactions ◽  
Cognitive Ability ◽  
Honey Bee ◽  
Collective Behavior ◽  
Cognitive Abilities ◽  
Learning Behavior ◽  
Collective Foraging ◽  
Familiar Stimuli ◽  
Bee Colonies

Individual differences in learning can influence how animals respond to and communicate about their environment, which may nonlinearly shape how a social group accomplishes a collective task. There are few empirical examples of how differences in collective dynamics emerge from variation among individuals in cognition. Here, we use a naturally variable and heritable learning behavior called latent inhibition (LI) to show that interactions among individuals that differ in this cognitive ability drive collective foraging behavior in honey bee colonies. We artificially selected two distinct phenotypes: high-LI bees that ignore previously familiar stimuli in favor of novel ones and low-LI bees that learn familiar and novel stimuli equally well. We then provided colonies differentially composed of different ratios of these phenotypes with a choice between familiar and novel feeders. Colonies of predominantly high-LI individuals preferred to visit familiar food locations, while low-LI colonies visited novel and familiar food locations equally. Interestingly, in colonies of mixed learning phenotypes, the low-LI individuals showed a preference to visiting familiar feeders, which contrasts with their behavior when in a uniform low-LI group. We show that the shift in feeder preference of low-LI bees is driven by foragers of the high-LI phenotype dancing more intensely and attracting more followers. Our results reveal that cognitive abilities of individuals and their social interactions, which we argue relate to differences in attention, drive emergent collective outcomes.

scholarly journals Education and Cognitive Functioning Across the Life Span

2020 ◽  
Vol 21 (1) ◽  
pp. 6-41 ◽  
Author(s):  
Martin Lövdén ◽  
Laura Fratiglioni ◽  
M. Maria Glymour ◽  
Ulman Lindenberger ◽  
Elliot M. Tucker-Drob
Keyword(s):  
Cognitive Function ◽  
Educational Attainment ◽  
Cognitive Decline ◽  
Life Span ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Cognitive Aging ◽  
Early Adulthood ◽  
Late Life ◽  
Positive Effects

Cognitive abilities are important predictors of educational and occupational performance, socioeconomic attainment, health, and longevity. Declines in cognitive abilities are linked to impairments in older adults’ everyday functions, but people differ from one another in their rates of cognitive decline over the course of adulthood and old age. Hence, identifying factors that protect against compromised late-life cognition is of great societal interest. The number of years of formal education completed by individuals is positively correlated with their cognitive function throughout adulthood and predicts lower risk of dementia late in life. These observations have led to the propositions that prolonging education might (a) affect cognitive ability and (b) attenuate aging-associated declines in cognition. We evaluate these propositions by reviewing the literature on educational attainment and cognitive aging, including recent analyses of data harmonized across multiple longitudinal cohort studies and related meta-analyses. In line with the first proposition, the evidence indicates that educational attainment has positive effects on cognitive function. We also find evidence that cognitive abilities are associated with selection into longer durations of education and that there are common factors (e.g., parental socioeconomic resources) that affect both educational attainment and cognitive development. There is likely reciprocal interplay among these factors, and among cognitive abilities, during development. Education–cognitive ability associations are apparent across the entire adult life span and across the full range of education levels, including (to some degree) tertiary education. However, contrary to the second proposition, we find that associations between education and aging-associated cognitive declines are negligible and that a threshold model of dementia can account for the association between educational attainment and late-life dementia risk. We conclude that educational attainment exerts its influences on late-life cognitive function primarily by contributing to individual differences in cognitive skills that emerge in early adulthood but persist into older age. We also note that the widespread absence of educational influences on rates of cognitive decline puts constraints on theoretical notions of cognitive aging, such as the concepts of cognitive reserve and brain maintenance. Improving the conditions that shape development during the first decades of life carries great potential for improving cognitive ability in early adulthood and for reducing public-health burdens related to cognitive aging and dementia.

scholarly journals Making Cognitive Latent Variables Manifest: Distinct Neural Networks for Fluid Reasoning and Processing Speed

2015 ◽  
Vol 27 (6) ◽  
pp. 1249-1258 ◽  
Author(s):  
Christian Habeck ◽  
Jason Steffener ◽  
Daniel Barulli ◽  
Yunglin Gazes ◽  
Qolamreza Razlighi ◽  
...  
Keyword(s):  
Individual Differences ◽  
Cognitive Ability ◽  
Cognitive Abilities ◽  
Latent Variables ◽  
Latent Variable ◽  
Cognitive Tasks ◽  
Neural Activation ◽  
Strongly Correlated ◽  
Activation Patterns ◽  
Fluid Reasoning

Cognitive psychologists posit several specific cognitive abilities that are measured with sets of cognitive tasks. Tasks that purportedly tap a specific underlying cognitive ability are strongly correlated with one another, whereas performances on tasks that tap different cognitive abilities are less strongly correlated. For these reasons, latent variables are often considered optimal for describing individual differences in cognitive abilities. Although latent variables cannot be directly observed, all cognitive tasks representing a specific latent ability should have a common neural underpinning. Here, we show that cognitive tasks representing one ability (i.e., either perceptual speed or fluid reasoning) had a neural activation pattern distinct from that of tasks in the other ability. One hundred six participants between the ages of 20 and 77 years were imaged in an fMRI scanner while performing six cognitive tasks, three representing each cognitive ability. Consistent with prior research, behavioral performance on these six tasks clustered into the two abilities based on their patterns of individual differences and tasks postulated to represent one ability showed higher similarity across individuals than tasks postulated to represent a different ability. This finding was extended in the current report to the spatial resemblance of the task-related activation patterns: The topographic similarity of the mean activation maps for tasks postulated to reflect the same reference ability was higher than for tasks postulated to reflect a different reference ability. Furthermore, for any task pairing, behavioral and topographic similarities of underlying activation patterns are strongly linked. These findings suggest that differences in the strengths of correlations between various cognitive tasks may be because of the degree of overlap in the neural structures that are active when the tasks are being performed. Thus, the latent variable postulated to account for correlations at a behavioral level may reflect topographic similarities in the neural activation across different brain regions.

scholarly journals Cognitive Ability does not and cannot Correlate with Susceptibility to Anchoring Effects

2019 ◽  
Author(s):  
Lukas Röseler ◽  
Astrid Schütz ◽  
Ulrike Starker
Keyword(s):  
Cognitive Ability ◽  
Internal Consistency ◽  
Big Five ◽  
Cognitive Abilities ◽  
Fluid Intelligence ◽  
Meta Analysis ◽  
Online Experiment ◽  
Reliable Measure ◽  
Anchoring Effects

On a scale from 1 to 10, would you consider anchoring effects to be more or less important than a rating of 9? The seminal studies on anchoring effects are still surrounded by many mysteries regarding the question of how these effects come about. One of the mysteries is the influence of a person’s cognitive ability. Some studies found that cognitive abilities moderated participants’ susceptibility to anchoring, but others did not. We aimed to resolve these inconsistencies by making a distinction between informative and uninformative (e.g., random) anchors. In a preregistered online experiment, we tested the hypothesis that anchoring effects are weaker for people with higher cognitive abilities but that this relation only occurs if the anchors are perceived as random, and it vanishes if they are presented as informative. We found no support for the hypothesis. Results from a meta-analysis of 15 effects across our study and four other studies revealed no overall effect of cognitive ability on the susceptibility to anchoring, g = 0.003, 95% CI [-0.031, 0.037], Ntotal = 1165. Moreover, we observed that 10 items across three typical anchoring tasks had only very low internal consistency (α = .11). Our analysis of additional published and unpublished data confirmed that a person’s susceptibility to anchoring cannot be measured reliably. This explains why previous results on possible moderators of anchoring (e.g., studies on the Big Five or on fluid intelligence) have been highly inconsistent. We suggest that research on anchoring moderators needs to take a step back and develop a reliable measure for the susceptibility to anchoring.

scholarly journals Theory of Mind Measurements and Mechanisms: An Investigation of Construct Validity and Cognitive Processes in Theory of Mind Tasks

2021 ◽  
Author(s):  
◽  
Ester Navarro Garcia
Keyword(s):  
Individual Differences ◽  
Theory Of Mind ◽  
Construct Validity ◽  
Cognitive Abilities ◽  
Fluid Intelligence ◽  
Metalinguistic Awareness ◽  
Latent Factors ◽  
Confirmatory Factor Analyses ◽  
Crystallized Intelligence ◽  
Important Challenge

Understanding the perspectives of others is a critical skill. Theory of mind (ToM) is an essential ability for social competence and communication, and it is necessary for understanding behaviors that differ from our own (Premack and Woodruff, 1978). Although all individuals possess a ToM to varying degrees, bilinguals are especially adept to perspective-taking. Research has reported that bilinguals outperform monolinguals in ToM tasks (e.g., Goetz, 2003; Rubio-Fernandez & Glucksberg, 2012). However, the mechanisms underlying this effect are unclear. Studying individual differences in ToM performance between bilinguals and monolinguals can help explain these mechanisms. Yet this promising area of research faces an important challenge: the lack of psychometric research on ToM measurement. Recent research suggests that tests that measure the ToM construct might not be as reliable as previously thought (Warnell & Redcay, 2019). This hinders the interpretation of experimental and correlational findings and puts into question the validity of the ToM construct. This dissertation addresses these two questions empirically to improve our understanding of what constitutes ToM. Study 1 examines the structure of ToM, crystallized intelligence (Gc), and fluid intelligence (Gf) to understand (a) whether ToM constitutes a construct separate from other cognitive abilities and (b) to explore whether tasks of ToM present adequate construct validity. For this, three confirmatory factor analyses (CFAs) were conducted. The results demonstrated that a model with three latent factors (ToM, Gf and Gc) did not adequately fit the data and was not significantly different from a model with only two latent factors (ToM-Gf and Gc). In addition, an exploratory factor analysis (EFA) showed that two of the ToM tasks loaded onto a Gf factor whereas one of the tasks loaded onto a third factor by itself. Finally, an exploratory network analysis (NMA) was conducted to observe relationships among the tasks. The results showed that the ToM tasks were no more related to each other than to some tasks of Gf and Gc, and that ToM tasks did not form a consistent cluster. Overall, the results of Study 1 suggest that ToM tasks are likely not measuring a monolithic ToM construct. Study 2 examines individual differences in metalinguistic awareness, executive function, and bilingualism as predictors of ToM. The results showed that all variables significantly predicted ToM, but bilingualism was not a significant moderator of ToM. Overall, the findings suggest that in this sample there was no difference in the processes used to predict ToM based on being bilingual or monolingual. Implications for measurement and individual differences in ToM are discussed.

scholarly journals Novel Age-Dependent Cortico-Subcortical Morphologic Interactions Predict Fluid Intelligence: A Multi-Cohort Geometric Deep Learning Study

2021 ◽  
Author(s):  
Yunan Wu ◽  
Pierre Besson ◽  
Emanuel Azcona ◽  
Sarah Bandt ◽  
Todd Parrish ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Fluid Intelligence ◽  
State Of The Art ◽  
Brain Morphology ◽  
Subcortical Structures ◽  
Human Connectome Project ◽  
Strong Hypothesis ◽  
Age Dependent ◽  
Relationship Of ◽  
The Relationship

Abstract The relationship of human brain structure to cognitive function is complex, and how this relationship differs between childhood and adulthood is poorly understood. One strong hypothesis suggests the cognitive function of Fluid Intelligence (Gf) is dependent on prefrontal cortex and parietal cortex. In this work, we developed a novel graph convolutional neural networks (gCNNs) for the analysis of localized anatomic shape and prediction of Gf. Morphologic information of the cortical ribbons and subcortical structures was extracted from T1-weighted MRIs within two independent cohorts, the Adolescent Brain Cognitive Development Study (ABCD; age: 9.93 ± 0.62 years) of children and the Human Connectome Project (HCP; age: 28.81 ± 3.70 years). Prediction combining cortical and subcortical surfaces together yielded the highest accuracy of Gf for both ABCD (R = 0.314) and HCP datasets (R = 0.454), outperforming the state-of-the-art prediction of Gf from any other brain measures in the literature. Across both datasets, the morphology of the amygdala, hippocampus, and nucleus accumbens, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a significant reframing of the relationship between brain morphology and Gf to include systems involved with reward/aversion processing, judgment and decision-making, motivation, and emotion.

scholarly journals Is baseline pupil size related to cognitive ability? Yes (under proper lighting conditions).

2020 ◽  
Author(s):  
Jason S. Tsukahara ◽  
Randall W Engle
Keyword(s):  
Working Memory ◽  
Individual Differences ◽  
Cognitive Ability ◽  
Pupil Size ◽  
Large Scale ◽  
Fluid Intelligence ◽  
Functional Organization ◽  
Working Memory Capacity ◽  
Memory Capacity ◽  
Lighting Conditions

We found that individual differences in baseline pupil size correlated with fluid intelligence and working memory capacity. Larger pupil size was associated with higher cognitive ability. However, other researchers have not been able to replicate our 2016 finding – though they only measured working memory capacity and not fluid intelligence. In a reanalysis of Tsukahara et al. (2016) we show that reduced variability on baseline pupil size will result in a higher probability of obtaining smaller and non-significant correlations with working memory capacity. In two large-scale studies, we demonstrated that reduced variability in baseline pupil size values was due to the monitor being too bright. Additionally, fluid intelligence and working memory capacity did correlate with baseline pupil size except in the brightest lighting conditions. Overall, our findings demonstrated that the baseline pupil size – working memory capacity relationship was not as strong or robust as that with fluid intelligence. Our findings have strong methodological implications for researchers investigating individual differences in task-free or task-evoked pupil size. We conclude that fluid intelligence does correlate with baseline pupil size and that this is related to the functional organization of the resting-state brain through the locus coeruleus-norepinephrine system.

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