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 Task-Based Functional MRI Across Tasks Markedly Boosts Prediction and Reliability of Brain-Cognition Relationship

2021 ◽  
Author(s):  
Alina Tetereva ◽  
Jean Li ◽  
Jeremiah Deng ◽  
Argyris Stringaris ◽  
Narun Pat
Keyword(s):  
Individual Differences ◽  
Functional Mri ◽  
Cognitive Abilities ◽  
Brain Mri ◽  
Integration Theory ◽  
Sources Of Information ◽  
Resting State Functional Connectivity ◽  
Retest Reliability ◽  
Human Connectome Project ◽  
Test Retest Reliability

Capturing individual differences in cognitive abilities is central to human neuroscience. Yet our ability to estimate cognitive abilities via brain MRI is still poor in both prediction and reliability. Our study tested if this inability was partly due to the over-reliance on 1) non-task MRI modalities and 2) single modalities. We directly compared predictive models comprising of different sets of MRI modalities (e.g., task vs. non-task). Using the Human Connectome Project (n=873 humans, 473 females, after exclusions), we integrated task-based functional MRI (tfMRI) across seven tasks along with other non-task MRI modalities (structural MRI, resting-state functional connectivity) via a machine-learning, stacking approach. The model integrating all modalities provided unprecedented prediction (r=.581) and excellent test-retest reliability (ICC>.75) in capturing general cognitive abilities. Importantly, comparing to the model integrating among non-task modalities (r=.367), integrating tfMRI across tasks led to significantly higher prediction (r=.544) while still providing excellent test-retest reliability (ICC>.75). The model integrating tfMRI across tasks was driven by areas in the frontoparietal network and by tasks that are cognition-related (working-memory, relational processing, and language). This result is consistent with the parieto-frontal integration theory of intelligence. Accordingly, our results sharply contradict the recently popular notion that tfMRI is not appropriate for capturing individual differences in cognition. Instead, our study suggests that tfMRI, when used appropriately (i.e., by drawing information across the whole brain and across tasks and by integrating with other modalities), provides predictive and reliable sources of information for individual differences in cognitive abilities, more so than non-task modalities.

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 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 Effect sizes and test-retest reliability of the fMRI-based Neurologic Pain Signature

2021 ◽  
Author(s):  
Xiaochun Han ◽  
Yoni K. Ashar ◽  
Philip Kragel ◽  
Bogdan Petre ◽  
Victoria Schelkun ◽  
...  
Keyword(s):  
Individual Differences ◽  
Effect Size ◽  
Mental States ◽  
Effect Sizes ◽  
Medium Effect ◽  
Retest Reliability ◽  
Medium Effect Size ◽  
Pain Reports ◽  
Nociceptive Input ◽  
Test Retest Reliability

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49, average r = 0.20). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was comparable in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicates that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports, but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.

scholarly journals Learning from the Reliability Paradox: How Theoretically Informed Generative Models Can Advance the Social, Behavioral, and Brain Sciences

2020 ◽  
Author(s):  
Nathaniel Haines ◽  
Peter D. Kvam ◽  
Louis H. Irving ◽  
Colin Smith ◽  
Theodore P. Beauchaine ◽  
...  
Keyword(s):  
Individual Differences ◽  
Theory Development ◽  
Generative Models ◽  
Parameter Estimates ◽  
Group Level ◽  
Retest Reliability ◽  
Individual Level ◽  
Generative Modeling ◽  
Behavioral Tasks ◽  
Test Retest Reliability

Behavioral tasks (e.g., Stroop task) that produce replicable group-level effects (e.g., Stroop effect) often fail to reliably capture individual differences between participants (e.g., low test-retest reliability). This “reliability paradox” has led many researchers to conclude that most behavioral tasks cannot be used to develop and advance theories of individual differences. However, these conclusions are derived from statistical models that provide only superficial summary descriptions of behavioral data, thereby ignoring theoretically-relevant data-generating mechanisms that underly individual-level behavior. More generally, such descriptive methods lack the flexibility to test and develop increasingly complex theories of individual differences. To resolve this theory-description gap, we present generative modeling approaches, which involve using background knowledge to specify how behavior is generated at the individual level, and in turn how the distributions of individual-level mechanisms are characterized at the group level—all in a single joint model. Generative modeling shifts our focus away from estimating descriptive statistical “effects” toward estimating psychologically meaningful parameters, while simultaneously accounting for measurement error that would otherwise attenuate individual difference correlations. Using simulations and empirical data from the Implicit Association Test and Stroop, Flanker, Posner Cueing, and Delay Discounting tasks, we demonstrate how generative models yield (1) higher test-retest reliability estimates, and (2) more theoretically informative parameter estimates relative to traditional statistical approaches. Our results reclaim optimism regarding the utility of behavioral paradigms for testing and advancing theories of individual differences, and emphasize the importance of formally specifying and checking model assumptions to reduce theory-description gaps and facilitate principled theory development.

scholarly journals The Relevance of Insomnia in the Diagnosis of Perinatal Depression: Validation of the Italian Version of the Insomnia Symptom Questionnaire

2021 ◽  
Vol 18 (23) ◽  
pp. 12507
Author(s):  
Lavinia De Chiara ◽  
Cristina Mazza ◽  
Eleonora Ricci ◽  
Alexia Emilia Koukopoulos ◽  
Georgios D. Kotzalidis ◽  
...  
Keyword(s):  
Convergent Validity ◽  
Predictive Power ◽  
Discriminant Validity ◽  
Late Pregnancy ◽  
Italian Version ◽  
Retest Reliability ◽  
Dsm 5 ◽  
Insomnia Symptom ◽  
Symptom Questionnaire ◽  
Test Retest Reliability

Background. Sleep disorders are common in perinatal women and may underlie or trigger anxiety and depression. We aimed to translate and validate and evaluate the psychometric properties of the Italian version of the Insomnia Symptom Questionnaire (ISQ), in a sample of women during late pregnancy and 6-months postpartum according to the DSM-5 criteria. Methods. The ISQ was administered to 292 women prenatally along with other measures of sleep quality, depression, and anxiety, to examine its construct and convergent validity. Women were readministered the ISQ six months postdelivery to assess test–retest reliability. Women were divided into DSM-5 No-Insomnia (N = 253) and Insomnia (N = 39) groups. Results. The insomnia group had received more psychopharmacotherapy, had more psychiatric family history, increased rates of medically assisted reproduction, of past perinatal psychiatric disorders, and scored higher on almost all TEMPS-A dimensions, on the EPDS, HCL-32, PSQI, and on ISQ prenatally and postnatally. ISQ scores correlated with all scales, indicating adequate convergent and discriminant validity; furthermore, it showed antenatal–postnatal test–retest reliability, 97.5% diagnostic accuracy, 79.5% sensitivity, 94.9% specificity, 70.5% positive predictive power, and 92.8% negative predictive power. Conclusions. The ISQ is useful, valid, and reliable for assessing perinatal insomnia in Italian women. The Italian version showed equivalent properties to the original version.

scholarly journals The Heritability of Cortical Folding: Evidence from the Human Connectome Project

2020 ◽  
Vol 31 (1) ◽  
pp. 702-715
Author(s):  
J Eric Schmitt ◽  
Armin Raznahan ◽  
Siyuan Liu ◽  
Michael C Neale
Keyword(s):  
Individual Differences ◽  
Mean Curvature ◽  
Genetic Factors ◽  
Cortical Folding ◽  
Quantitative Genetic ◽  
Large N ◽  
Human Connectome Project ◽  
Surface Metrics ◽  
Permutation Analysis ◽  
Sulcal Depth

Abstract The mechanisms underlying cortical folding are incompletely understood. Prior studies have suggested that individual differences in sulcal depth are genetically mediated, with deeper and ontologically older sulci more heritable than others. In this study, we examine FreeSurfer-derived estimates of average convexity and mean curvature as proxy measures of cortical folding patterns using a large (N = 1096) genetically informative young adult subsample of the Human Connectome Project. Both measures were significantly heritable near major sulci and primary fissures, where approximately half of individual differences could be attributed to genetic factors. Genetic influences near higher order gyri and sulci were substantially lower and largely nonsignificant. Spatial permutation analysis found that heritability patterns were significantly anticorrelated to maps of evolutionary and neurodevelopmental expansion. We also found strong phenotypic correlations between average convexity, curvature, and several common surface metrics (cortical thickness, surface area, and cortical myelination). However, quantitative genetic models suggest that correlations between these metrics are largely driven by nongenetic factors. These findings not only further our understanding of the neurobiology of gyrification, but have pragmatic implications for the interpretation of heritability maps based on automated surface-based measurements.

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 Stable individual differences in strategies within, but not between, visual search tasks

2020 ◽  
pp. 174702182092919 ◽  
Author(s):  
Alasdair DF Clarke ◽  
Jessica L Irons ◽  
Warren James ◽  
Andrew B Leber ◽  
Amelia R Hunt
Keyword(s):  
Individual Differences ◽  
Visual Search ◽  
Search Task ◽  
The Other ◽  
Retest Reliability ◽  
Search Tasks ◽  
And Performance ◽  
Context Specific ◽  
Test Retest Reliability ◽  
Over Time

A striking range of individual differences has recently been reported in three different visual search tasks. These differences in performance can be attributed to strategy, that is, the efficiency with which participants control their search to complete the task quickly and accurately. Here, we ask whether an individual’s strategy and performance in one search task is correlated with how they perform in the other two. We tested 64 observers and found that even though the test–retest reliability of the tasks was high, an observer’s performance and strategy in one task was not predictive of their behaviour in the other two. These results suggest search strategies are stable over time, but context-specific. To understand visual search, we therefore need to account not only for differences between individuals but also how individuals interact with the search task and context.

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