A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

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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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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IC-P-144: BLAZER: A VERSATILE AND EFFICIENT WORKFLOW FOR ANALYZING PET/MR NEUROIMAGING DATA IN ALZHEIMER'S DISEASE

Alzheimer s & Dementia ◽

10.1016/j.jalz.2019.06.4258 ◽

2019 ◽

Vol 15 ◽

pp. P117-P118

Author(s):

Fabio Raman ◽

Sameera Grandhi ◽

Charles F. Murchison ◽

Richard E. Kennedy ◽

Susan M. Landau ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Neuroimaging Data

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Fear-induced brain activations distinguish anxious and trauma-exposed brains

Translational Psychiatry ◽

10.1038/s41398-020-01193-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhenfu Wen ◽

Marie-France Marin ◽

Jennifer Urbano Blackford ◽

Zhe Sage Chen ◽

Mohammed R. Milad

Keyword(s):

Neural Network ◽

Fear Conditioning ◽

Psychiatric Diagnosis ◽

Data Analytics ◽

Conditioned Fear ◽

Brain Regions ◽

Brain Network ◽

Neuroimaging Data ◽

Task Irrelevant ◽

Translational Models

AbstractTranslational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.

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Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning

Neuroinformatics ◽

10.1007/s12021-021-09523-w ◽

2021 ◽

Author(s):

Wen Zhang ◽

B. Blair Braden ◽

Gustavo Miranda ◽

Kai Shu ◽

Suhang Wang ◽

...

Keyword(s):

Representation Learning ◽

Network Representation ◽

Neuroimaging Data

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Deep Learning with Neuroimaging and Genomics in Alzheimer’s Disease

International Journal of Molecular Sciences ◽

10.3390/ijms22157911 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7911

Author(s):

Eugene Lin ◽

Chieh-Hsin Lin ◽

Hsien-Yuan Lane

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Deep Learning ◽

Future Research ◽

Learning Approaches ◽

Learning Models ◽

Learning Techniques ◽

Neuroimaging Data ◽

Similarities And Differences ◽

Normal Controls

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

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