Digital Phenotypes of Mobile Keyboard Backspace Rates and Their Associations With Symptoms of Mood Disorder: Algorithm Development and Validation (Preprint) DOI
Qimin Liu, Emma Ning, Mindy K. Ross

et al.

Published: July 26, 2023

BACKGROUND Passive sensing through smartphone keyboard data can be used to identify and monitor symptoms of mood disorders with low participant burden. Behavioral phenotyping based on mobile keystroke aid in clinical decision-making provide insights into the individual disorders. OBJECTIVE This study aims derive digital phenotypes backspace use among 128 community adults across 2948 observations using a Bayesian mixture model. METHODS Eligible participants completed virtual screening visit where all eligible were instructed download custom-built BiAffect (University Illinois). The unobtrusively captures dynamics. All consenting this exclusively for up 4 weeks real life, participants’ compliance was checked at 2 follow-up visits week 4. As part research protocol, every underwent evaluations by psychiatrist during each visit. RESULTS We found that derived associated not only diagnoses severity depression mania but also specific symptoms. Using linear mixed-effects model random intercepts accounting nested structure from daily data, rates continuous scale did differ between healthy control groups (<i>P</i>=.11). 3-class had mean 0.112, 0.180, 0.268, respectively, SD 0.048. In total, 3 classes, estimated comprise 37.5% (n=47), 54.4% (n=72), 8.1% (n=9) sample. grouped individuals Low, Medium, High rate groups. Individuals unipolar disorder predominantly Medium group (n=54), some Low (n=27) few (n=6). group, compared significantly higher ratings (<i>b</i>=2.32, <i>P</i>=.008). (<i>P</i>=.88) or without (<i>P</i>=.27) adjustment medication diagnoses. both nonzero (<i>b</i>=1.91, <i>P</i>=.02) (<i>b</i>=1.46, <i>P</i>&lt;.001). showed odds elevated (<i>P</i>=.03), motor activity (<i>P</i>=.04), irritability (<i>P</i>&lt;.05). CONCLUSIONS demonstrates promise typing kinematics practice. Monitoring single kinematic feature, is, rates, passive imposes burden participants. Based real-life our feature useful researchers practitioners distinguish those CLINICALTRIAL

Language: Английский

Individual differences in computational psychiatry: A review of current challenges DOI Creative Commons
Povilas Karvelis, Martin P. Paulus, Andreea O. Diaconescu

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 148, P. 105137 - 105137

Published: March 20, 2023

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. We find that suffer from poor properties, which poses a risk invalidating previous findings undermining ongoing research efforts using assays study (and even group) provide recommendations how address these problems and, crucially, embed them within broader perspective on key developments are needed translating clinical practice.

Language: Английский

Citations

49
From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research DOI
Nathaniel Haines, Holly Sullivan‐Toole, Thomas M. Olino

et al.

Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Journal Year: 2023, Volume and Issue: 8(8), P. 822 - 831

Published: Jan. 11, 2023

Language: Английский

Citations

23
On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time DOI Creative Commons
Christina Thunberg, Thea Wiker, Carsten Bundt

et al.

Cortex, Journal Year: 2024, Volume and Issue: 175, P. 81 - 105

Published: Feb. 27, 2024

Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet control, impulsivity, and self-regulation. The stop signal task argued to be purest inhibition we have, it thus central much work investigating role in areas like development psychopathology. Most this quantifies behavior by calculating reaction time as measure individual latency. Individual difference studies aiming investigate why how latencies differ between people do under assumption that indexes stable, dispositional trait. However, empirical support for lacking, common measures control tend show low test-retest reliability appear unstable over time. reasons could methodological, where stability driven measurement noise, substantive, larger influence state-like situational factors. To this, characterized split-half range behavioral electrophysiological derived from task. Across three independent studies, different modalities, systematic review literature, found pattern temporal higher manifest non-inhibitory processing. This not explained noise internal consistency. Consequently, response appears have mostly determinants, there little validity conceptualizing reflecting stable traits.

Language: Английский

Citations

12
A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States DOI Creative Commons
Artūras Kaklauskas, Ajith Abraham, Ieva Ubartė

et al.

Sensors, Journal Year: 2022, Volume and Issue: 22(20), P. 7824 - 7824

Published: Oct. 14, 2022

Affective, emotional, and physiological states (AFFECT) detection recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim to review publications on how techniques that use brain biometric sensors can be used for AFFECT recognition, consolidate the findings, provide rationale current methods, compare effectiveness of existing quantify likely they are address issues/challenges in field. In efforts achieve key goals Society 5.0, Industry human-centered design better, affective, progressively becoming an important matter offers tremendous growth knowledge progress these other related fields. this research, sensors, applications was performed, based Plutchik’s wheel emotions. Due immense variety sensing systems, study aimed analysis available define AFFECT, classify them type area their efficiency real implementations. Based statistical multiple criteria 169 nations, our outcomes introduce connection between nation’s success, its number Web Science articles published, frequency citation recognition. principal conclusions present contributes big picture field under explore forthcoming trends.

Language: Английский

Citations

30
Machine learning applied to functional magnetic resonance imaging in anxiety disorders DOI
Sahar Rezaei, Esmaeil Gharepapagh, Fatemeh Rashidi

et al.

Journal of Affective Disorders, Journal Year: 2023, Volume and Issue: 342, P. 54 - 62

Published: Sept. 6, 2023

Language: Английский

Citations

19
An ‘embedded brain’ approach to understanding antisocial behaviour DOI Creative Commons
Essi Viding, Eamon McCrory, Arielle Baskin–Sommers

et al.

Trends in Cognitive Sciences, Journal Year: 2023, Volume and Issue: 28(2), P. 159 - 171

Published: Sept. 15, 2023

Antisocial behaviour (ASB) incurs substantial costs to the individual and society. Cognitive neuroscience has potential shed light on developmental risk for ASB, but it cannot achieve this in an 'essentialist' framework that focuses brain cognition isolated from environment. Here, we present case studying social transactional iterative unfolding of cognitive development a relational context. This approach, which call study 'embedded brain', is needed fully understand how ASB arises during development. Concentrated efforts are required develop unify methods approach reap benefits improved prevention intervention ASB.

Language: Английский

Citations

14
Challenges and Solutions to the Measurement of Neurocognitive Mechanisms in Developmental Settings DOI Creative Commons
Patrizia Pezzoli, Sam Parsons, Rogier Kievit

et al.

Biological Psychiatry Cognitive Neuroscience and Neuroimaging, Journal Year: 2023, Volume and Issue: 8(8), P. 815 - 821

Published: March 30, 2023

Identifying early neurocognitive mechanisms that confer risk for mental health problems is one important avenue as we seek to develop successful interventions. Currently, however, have limited understanding of the involved in shaping trajectories from childhood through young adulthood, and this constrains our ability effective clinical In particular, there an urgent need more sensitive, reliable, scalable measures individual differences use developmental settings. review, outline methodological shortcomings explain why widely used task-based neurocognition currently tell us little about risk. We discuss specific challenges arise when studying settings, share suggestions overcoming them. also propose a novel experimental approach—which refer "cognitive microscopy"—that involves adaptive design optimization, temporally sensitive task administration, multilevel modeling. This approach addresses some outlined above provides stability, variability, change within multivariate framework.

Language: Английский

Citations

8
Clarifying the reliability paradox: poor test-retest reliability attenuates group differences DOI Open Access
Povilas Karvelis, Andreea O. Diaconescu

Published: May 4, 2024

Cognitive sciences are grappling with the reliability paradox: measures that robustly produce within-group effects tend to have low test-retest reliability, rendering them unsuitable for studying individual differences. Despite growing awareness of this paradox, its full extent remains underappreciated. Specifically, most research focuses exclusively on how affects correlational analyses differences, while largely ignoring group Moreover, by conflating within- and between-group effects, some studies erroneously suggest poor does not pose problems This brief report aims clarify misunderstanding through simple data simulations. To make argument more intuitive, we consider two illustrative cases: comparing patients versus controls groups formed a median split. We demonstrate attenuates observed differences just as much it Given dichotomizing/grouping continuous - which is implicit in many leads loss statistical power, proves be even problematic While here focused cognitive psychiatry, our findings quite general could inform other areas research, including education, sex, gender, age, race, ethnicity, etc.

Language: Английский

Citations

2
Beyond Increasing Sample Sizes: Optimizing Effect Sizes in Neuroimaging Research on Individual Differences DOI
Colin G. DeYoung, Kirsten Hilger, Jamie L. Hanson

et al.

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

Citations

2
Using machine learning to determine a functional classifier of reward responsiveness and its association with adolescent psychiatric symptomatology DOI Creative Commons
James Blair, Johannah Bashford‐Largo, Ahria Dominguez

et al.

Psychological Medicine, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 10

Published: Nov. 18, 2024

Machine learning (ML) has developed classifiers differentiating patient groups despite concerns regarding diagnostic reliability. An alternative strategy, used here, is to develop a functional classifier (hyperplane) (e.g. distinguishing the neural responses received reward

Language: Английский

Citations

2