ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data

The modalities of communication are the sum of the expression dimension (linguistics) and the expressivity dimension (prosody), both being equally important in language communication. The expressivity dimension which comes first in the act of speech, is the basis on which phonemes, syllables, words, grammar and morphosyntax, i.e., the expression dimension of speech is superimposed. We will review evidence (1) revealing the importance of prosody in language acquisition and (2) showing that prosody triggers the involvement of specific brain areas dedicated to sentences and word-list processing. To support the first point, we will not only rely on experimental psychology studies conducted in newborns and young children but also on neuroimaging studies that have helped to validate these behavioral experiments. Then, neuroimaging data on adults will allow for concluding that the expressivity dimension of speech modulates both the right hemisphere prosodic areas and the left hemisphere network in charge of the expression dimension

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Neuroimaging of Individual Differences: A Latent Variable Modeling Perspective

10.31234/osf.io/jryfm ◽

2018 ◽

Author(s):

Shelly Renee Cooper ◽

Joshua James Jackson ◽

Deanna Barch ◽

Todd Samuel Braver

Keyword(s):

Individual Differences ◽

Individual Difference ◽

Latent Variable ◽

Structural Equation ◽

Behavioral Measure ◽

Equation Modeling ◽

Latent Variable Modeling ◽

Psychometric Theory ◽

Human Connectome Project ◽

Neuroimaging Data

Neuroimaging data is being increasingly utilized to address questions of individual difference. When examined with task-related fMRI (t-fMRI), individual differences are typically investigated via correlations between the BOLD activation signal at every voxel and a particular behavioral measure. This can be problematic because: 1) correlational designs require evaluation of t-fMRI psychometric properties, yet these are not well understood; and 2) bivariate correlations are severely limited in modeling the complexities of brain-behavior relationships. Analytic tools from psychometric theory such as latent variable modeling (e.g., structural equation modeling) can help simultaneously address both concerns. This review explores the advantages gained from integrating psychometric theory and methods with cognitive neuroscience for the assessment and interpretation of individual differences. The first section provides background on classic and modern psychometric theories and analytics. The second section details current approaches to t-fMRI individual difference analyses and their psychometric limitations. The last section uses data from the Human Connectome Project to provide illustrative examples of how t-fMRI individual differences research can benefit by utilizing latent variable models.

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Faculty Opinions recommendation of Models of functional neuroimaging data.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717955925.793460846 ◽

2012 ◽

Author(s):

Stasa Tadic

Keyword(s):

Functional Neuroimaging ◽

Neuroimaging Data

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Faculty Opinions recommendation of A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.725312077.793504220 ◽

2015 ◽

Author(s):

Gerard H Koppelman

Keyword(s):

Drug Response ◽

Disease Heterogeneity ◽

Disease Module

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SPECIES DISTRIBUTION MODELING OF WESTERN INTERIOR AMMONOIDS DURING OCEAN ANOXIC EVENT 2

10.1130/abs/2020am-355164 ◽

2020 ◽

Author(s):

Margaret M. Yacobucci ◽

Keyword(s):

Species Distribution ◽

Species Distribution Modeling ◽

Distribution Modeling ◽

Anoxic Event

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Positive Mood States and Gambling Disorder

The Oxford Handbook of Positive Emotion and Psychopathology ◽

10.1093/oxfordhb/9780190653200.013.24 ◽

2019 ◽

pp. 366-379

Author(s):

Sarah W. Yip ◽

Zu Wei Zhai ◽

Iris M. Balodis ◽

Marc N. Potenza

Keyword(s):

Impulse Control ◽

Gambling Disorder ◽

Adult Population ◽

Approach Motivation ◽

Gambling Problems ◽

Research Domain Criteria ◽

Monetary Rewards ◽

Neuroimaging Data ◽

Positive Valence ◽

Research Domain

Gambling problems are experienced by about 1% of the adult population, with higher estimates reported in adolescents. Both positive and negative motivations for gambling exist and may contribute to gambling problems. Positive valence disturbances involving how people process rewards, including monetary rewards relevant to gambling, have been reported in gambling disorder and have been associated with the disorder and clinically relevant measures relating to impaired impulse control. Positive valence systems as they relate to gambling disorder and clinically relevant features thereof are considered in this chapter. Findings from neuroimaging data related to the positive valence system constructs of approach motivation, initial and sustained/longer term responsiveness to reward, habit and reward learning are reviewed. Possible interactions between positive valence systems and other Research Domain Criteria (RDoC) systems are also discussed within the context of gambling disorder, as is how the application of an RDoC framework can be used to further understanding of gambling disorder.

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