Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research on Individual Differences

Published: July 24, 2024

Linking neurobiology to relatively stable individual differences in cognition, emotion, motivation, and behavior can require large sample sizes yield replicable results. Given the nature of between-person research, at least hundreds are likely be necessary most neuroimaging studies differences, regardless whether they investigating whole brain or more focal hypotheses. However, appropriate size depends on expected effect size. Therefore, we propose four strategies increase which may help enable detection effects samples rather than thousands: (1) theoretical matching between tasks behavioral constructs interest; (2) increasing reliability both neural psychological measurement; (3) individualization measures for each participant; (4) using multivariate approaches with cross-validation instead univariate approaches. We discuss challenges associated these methods highlight improvements that will field move toward a robust accessible neuroscience differences.

Language: Английский

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Evidence for a competitive relationship between executive functions and statistical learning

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Jan. 19, 2023

The ability of the brain to extract patterns from environment and predict future events, known as statistical learning, has been proposed interact in a competitive manner with prefrontal lobe related networks their characteristic cognitive or executive functions. However, it remains unclear whether these functions also show relationship implicit learning across individuals at level latent function components. In order address this currently unknown aspect, we investigated, two independent experiments (N Study1 = 186, N Study2 157), between measured by Alternating Serial Reaction Time task, functions, multiple neuropsychological tests. both studies, modest, but consistent negative correlation most measures was observed. Factor analysis further revealed that factor representing verbal fluency complex working memory seemed drive correlations. Thus, an antagonism might specifically be mediated updating component or/and long-term access.

Language: Английский

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Active reinforcement learning versus action bias and hysteresis: control with a mixture of experts and nonexperts

PLoS Computational Biology, Journal Year: 2024, Volume and Issue: 20(3), P. e1011950 - e1011950

Published: March 29, 2024

Active reinforcement learning enables dynamic prediction and control, where one should not only maximize rewards but also minimize costs such as of inference, decisions, actions, time. For an embodied agent a human, decisions are shaped by physical aspects actions. Beyond the effects reward outcomes on processes, to what extent can modeling behavior in reinforcement-learning task be complicated other sources variance sequential action choices? What bias (for actions per se) hysteresis determined history chosen previously? The present study addressed these questions with incremental assembly models for choice data from hierarchical structure additional complexity learning. With systematic comparison falsification computational models, human choices were tested signatures parallel modules representing enhanced form generalized hysteresis. We found evidence substantial differences across participants—even comparable magnitude individual Individuals who did learn well revealed greatest biases, those accurately significantly biased. direction varied among individuals repetition or, more commonly, alternation biases persisting multiple previous Considering that button presses trivial motor demands, idiosyncratic forces biasing sequences robust enough suggest ubiquity tasks requiring various In light how function heuristic efficient control adapts uncertainty or low motivation minimizing cost effort, phenomena broaden consilient theory mixture experts encompass expert nonexpert controllers behavior.

Language: Английский

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Tunnel Runner: a Proof-of-principle for the Feasibility and Benefits of Facilitating Players' Sense of Control in Cognitive Assessment Games

Published: May 11, 2024

Cognitive assessment games attempt to improve cognitive assessment's experience and data quality by implementing game-like features, e.g., points narratives. However, maintain the repetitiveness restricted control common in traditional tasks, which thwart players' sense of impair their motivation experience. Leading only modest improvements over tasks.

Language: Английский

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Two distinct stimulus-locked EEG signatures reliably encode domain-general confidence during decision formation

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: April 22, 2023

Abstract Decision confidence, an internal estimate of how accurate our choices are, is essential for metacognitive self-evaluation and guides behaviour. However, it can be suboptimal hence understanding the underlying neurocomputational mechanisms crucial. To do so, to establish extent which both behavioural neurophysiological measures metacognition are reliable over time shared across cognitive domains. The evidence regarding domain-generality has been mixed, while test-retest reliability most widely used not reported. Here, in human participants sexes, we examined electroencephalographic (EEG) two tasks that engage distinct domains – visual perception semantic memory. all was additionally tested experimental sessions. results revealed a dissociation between bias efficiency, whereby only showed strong whilst efficiency (measured by M-ratio) neither nor domain-general. Hence, overall confidence calibration (i.e., bias) stable trait-like characteristic underpinned domain-general may rely on more domain-specific computations. Additionally, found stimulus-locked EEG signatures related trial-by-trial fluctuations ratings during decision formation. A late event-related potential reliably linked domains, evoked spectral power predicted knowledge domain. Establishing neural predictors represents important step advancing self-evaluation. Significance Statement Understanding addressing deficits Open questions exist metacognition. We show time, whereas adopted measure shows poor reliability. metacognition, tailored specific needed. further signatures: potentials alpha/beta power. While former predicts latter confidence. These findings provide crucial insights into computations

Language: Английский

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Individual Differences in Computational Psychiatry: A Review of Current Challenges

Published: Dec. 11, 2022

Bringing precision to the understanding and treatment of mental disorders requires instruments for studying clinically relevant individual differences. One promising approach is development computational assays: integrating models with cognitive tasks infer latent patient-specific disease processes in brain computations. While recent years have seen many methodological advancements modelling cross-sectional patient studies, much less attention has been paid basic psychometric properties (reliability construct validity) measures provided by assays. In this review, we assess extent issue examining emerging empirical evidence. To contextualize this, also provide a more general perspective on key developments that are needed translating assays clinical practice. Emerging evidence suggests most show poor-to-moderate reliability often little improvement over simple behavioral measures. Furthermore, used test accounts lack convergent validity, which compromises their interpretability. Taken together, these issues pose risk invalidating previous findings undermining ongoing research efforts using study (and even group) We suggest single-task designs, currently dominate landscape, partly blame problems therefore not suitable solving them. Instead, validity need be studied systematically longitudinal designs batteries tasks. Finally, enable applications, it will necessary establish predictive make efficient burdensome.

Language: Английский

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Modeling uncertainty in individual predictions of cognitive functioning for untreated glioma patients using Bayesian regression

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 8, 2024

Introduction Cognitive impairments of patients with a glioma are increasingly considered when making treatment decisions considering personalized onco-functional balance. Predicting cognitive functioning before surgery can serve as steppingstone for the clinical goal predicting after surgery. However, in previous study, machine-learning models could not reliably predict using comprehensive set variables. The current study aims to improve predictions while uncertainty individual explicit. Method Pre-operative was predicted 340 across eight tests. This done six multivariate Bayesian regression following approach Four included interactions with- or multilevel structure over histopathological diagnosis. Point-wise were compared coefficient determination (R 2 ) and best-performing model interpreted. Results outperformed benefitted from shrinkage priors. R ranged between 0.3% 21.5% median tests 7.2%. Estimated errors prediction high. allowed parameters differ diagnoses pulling them toward population mean. Conclusion providing estimates predictions. Despite this, pre-operative variables remained Consequently, clinicians should infer these Different best treated distinct yet related. Highlights models. Predictions uncertain despite improvements. Importance serves is important two reasons. First, it demonstrates that popular Second, explicitly shows based on readily available uncertain. Last, may benefit multifaceted view treating different

Language: Английский

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Predicting cognitive function three months after surgery in patients with a glioma

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 8, 2024

Introduction Patients with a glioma often suffer from cognitive impairments both before and after anti-tumor treatment. Ideally, clinicians can rely on predictions of post-operative functioning for individual patients based information obtainable surgery. Such would facilitate selecting the optimal treatment considering patients’ onco-functional balance. Method Cognitive three months surgery was predicted 317 across eight tests. Nine multivariate Bayesian regression models were used following machine-learning approach while employing pre-operative neuropsychological test scores comprehensive set clinical predictors Model performances compared using Expected Log Pointwise Predictive Density (ELPD), pointwise assessed Coefficient Determination (R²) Mean Absolute Error. Models against only best-performing model interpreted. Moreover, an example prediction including uncertainty use provided. Results The obtained median R² 34.20%. Individual predictions, however, uncertain. Pre-operative most influential predictor. performed similarly to those (ΔELPD 14.4±10.0, ΔR² −0.53%.). Conclusion Post-operative cannot yet reliably be included predictors. relied strongly functioning. Consequently, should not infer it stresses need collect larger cross-center multimodal datasets obtain more certain patients. Importance study able that is balance could improve patient counseling. First, our shows predictors, being important Second, results demonstrate how resulting models, their estimates, may ultimately in practice. Third, show importance collecting additional stress datasets. Key points - predictor Additional are needed

Language: Английский

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Process models of verbal memory in cancer survivors: Bayesian process modeling approach to variation in test scores

Journal of Clinical and Experimental Neuropsychology, Journal Year: 2023, Volume and Issue: 45(7), P. 705 - 714

Published: Dec. 7, 2023

Verbal memory is a complex and fundamental aspect of human cognition. However, traditional sum-score analyses verbal learning tests oversimplify underlying processes. We propose using process models to subdivide into multiple processes, which helps in localizing the most affected processes impaired memory. Additionally, model can be used address score variability. This study aims investigate effects cancer its treatment on memory, as well provide demonstration how uncertainty neuropsychological test scores.

Language: Английский

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Complementary benefits of multivariate and hierarchical models for identifying individual differences in cognitive control

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: April 28, 2024

Understanding individual differences in cognitive control is a central goal psychology and neuroscience. Reliably measuring these differences, however, has proven extremely challenging, at least when using standard measures neuroscience such as response times or task-based fMRI activity. While prior work pinpointed the source of issue - vast amount cross-trial variability within solutions remain elusive. Here, we propose one potential way forward: an analytic framework that combines hierarchical Bayesian modeling with multivariate decoding trial-level data. Using this longitudinal data from Dual Mechanisms Cognitive Control project, estimated individuals' neural responses associated color-word Stroop task, then assessed reliability across time interval several months. We show many prefrontal parietal brain regions, test-retest was near maximal, only models were able to reveal state affairs. Further, compared traditional univariate contrasts, enabled individual-level correlations be significantly greater precision. specifically link improvements precision optimized suppression decoding. Together, findings not indicate control-related individuate people highly stable manner time, but also suggest integrating provides powerful approach for investigating control, can effectively address high-variability measures.

Language: Английский

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