The Validity of Brief Phenotyping in Population Biobanks for Psychiatric Genome-Wide Association Studies on the Biobank Scale

Coleman, Jonathan R. I.

doi:10.1159/000516837

Cited by 10 publications

(6 citation statements)

References 36 publications

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“…Yet in contrast to the brief self-reports utilized for phenotyping many health traits in clinical settings (e.g., depressive symptoms), or self-reports of history of speech/language/reading problems, a linguistic trait such as prosodic perception needs to be tested more objectively, disentangled from participants' meta-awareness about these abilities. This is in line with calls for brief, reliable, and scalable tools to enable large-scale GWAS of complex cognitive traits (Coleman, 2021).…”

Section: Introductionsupporting

confidence: 71%

Test of Prosody via Syllable Emphasis (“TOPsy”): Psychometric Validation of a Brief Scalable Test of Lexical Stress Perception

et al. 2022

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Prosody perception is fundamental to spoken language communication as it supports comprehension, pragmatics, morphosyntactic parsing of speech streams, and phonological awareness. A particular aspect of prosody: perceptual sensitivity to speech rhythm patterns in words (i.e., lexical stress sensitivity), is also a robust predictor of reading skills, though it has received much less attention than phonological awareness in the literature. Given the importance of prosody and reading in educational outcomes, reliable and valid tools are needed to conduct large-scale health and genetic investigations of individual differences in prosody, as groundwork for investigating the biological underpinnings of the relationship between prosody and reading. Motivated by this need, we present the Test of Prosody via Syllable Emphasis (“TOPsy”) and highlight its merits as a phenotyping tool to measure lexical stress sensitivity in as little as 10 min, in scalable internet-based cohorts. In this 28-item speech rhythm perception test [modeled after the stress identification test from Wade-Woolley (2016)], participants listen to multi-syllabic spoken words and are asked to identify lexical stress patterns. Psychometric analyses in a large internet-based sample shows excellent reliability, and predictive validity for self-reported difficulties with speech-language, reading, and musical beat synchronization. Further, items loaded onto two distinct factors corresponding to initially stressed vs. non-initially stressed words. These results are consistent with previous reports that speech rhythm perception abilities correlate with musical rhythm sensitivity and speech-language/reading skills, and are implicated in reading disorders (e.g., dyslexia). We conclude that TOPsy can serve as a useful tool for studying prosodic perception at large scales in a variety of different settings, and importantly can act as a validated brief phenotype for future investigations of the genetic architecture of prosodic perception, and its relationship to educational outcomes.

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Section: Introductionsupporting

confidence: 71%

Test of Prosody via Syllable Emphasis (“TOPsy”): Psychometric Validation of a Brief Scalable Test of Lexical Stress Perception

et al. 2022

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“…The MHQ‐factor in this study was derived from online MHQ items collected by the UKB and they were not clinical diagnoses. While it might not be adequate to represent the general dimension of psychiatric disorders, it can be interpreted as a measurement of general mental health that is potentially more informative than binary case–control distinctions (Coleman, 2021 ). We acknowledge that p‐factor derived from factor modelling is less well validated as in other measures, for example, the g‐factor, and results should be considered accordingly (Cervin et al, 2020 ; van Bork et al, 2017 ; Watts et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Yeung

Stolicyn

Buchanan

et al. 2022

Human Brain Mapping

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There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size.

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“…The MHQ-factor in this study was derived from online MHQ items collected by the UKB and they were not clinical diagnoses. While it might not be adequate to represent the general dimension of psychiatric disorders, it can be interpreted as a measurement of general mental health that is potentially more informative than binary case control distinctions (Coleman, 2021). We acknowledge that p -factor derived from factor modelling is less well validated as in other measures, for example the g -factor, and results should be considered accordingly (Cervin et al, 2020; van Bork et al, 2017; Watts et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Yeung

Stolicyn

Buchanan

et al. 2022

Preprint

View full text Add to dashboard Cite

There is increasing expectation that advanced, computationally expensive machine learning techniques, when applied to large population-wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different machine learning algorithms from deep learning model (BrainNetCNN) to classical machine learning methods. We modelled N = 8, 183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided highest prediction accuracies in all prediction tasks. Deep learning did not improve on prediction accuracies from simpler linear models. Further, high correlations between model coefficients from deep learning and linear models suggested similar decision mechanisms were used. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes.

show abstract

The Validity of Brief Phenotyping in Population Biobanks for Psychiatric Genome-Wide Association Studies on the Biobank Scale

Cited by 10 publications

References 36 publications

Test of Prosody via Syllable Emphasis (“TOPsy”): Psychometric Validation of a Brief Scalable Test of Lexical Stress Perception

Test of Prosody via Syllable Emphasis (“TOPsy”): Psychometric Validation of a Brief Scalable Test of Lexical Stress Perception

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

Predicting sex, age, general cognition and mental health with machine learning on brain structural connectomes

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