PREDICTIVE MODELING

Much of my recent work has applied and developed methods to predict phenotypic traits from functional brain imaging data. This work began during my 4th year of medical school, during a six-month post-doc at Oxford University with Eugene Duff, Catherine Harmer, and Michael Browning. Here, I used a support vector machine learning algorithm to predict antidepressant response in task-based fMRI.

 

More recently, I have worked with Todd Constable to apply connectome-based predictive modeling to predict phenotypic traits. I am currently collecting computational phenotypes in multiple patient populations with the hope of predicting these phenotypes from connectome data. 

RELEVANT PUBLICATIONS:

Barron DS, Salehi M, Browning M, Harmer CJ, Constable RT, Duff E (2018). Exploring the prediction of emotional valence and pharmacologic effect across fMRI studies of antidepressants. NeuroImage: Clinical 20, 407-414.

 

Scheinost D, Noble S, Horien C, Greene AS, Lake E, Salehi M, Gao S, Shen X, O’Connor D, Barron DS, Yip SW, Rosenberg MD, Constable RT (2019). Ten Simple Rules for Predictive Modeling of Individual Differences in Neuroimaging. Neuroimage, 193, 35-45.

 

Salehi M, Karbasi A, Barron DS, Scheinost D, Constable RT (2020). State-specific individualized functional networks form a predictive signature of brain state. Neuroimage, 206: 116233.

Makary MM, Polosecki P, Cecchi GA, DeAraujo IE, Barron DS, Constable TR, Whang PG, Thomas DA, Mowafi H, Small DM, Geha P (2020). Loss of nucleus accumbens low-frequency fluctuations is a signature of chronic pain. Proceedings of the National Academy of Sciences, 117: 18, 10015-10023

 

Barron DS, Gao S, Dadashkarimi J, Green AS, Span MN, Noble S, Lake E, Krystal J, Constable RT, Scheinost D. Task-Based Functional Connectomes Predict Phenotypic Traits Across Psychiatric Diagnosis. Posted on bioRxiv. In review, Cerebral Cortex (10/2020)

 

Barron D, Heisig S, Norel R, Agurto C, Quagan B, Powers A, et al. (2020): Preliminary Phenotypic Feature Capture During Clinical Interaction. Biol Psychiat 87: S212–S213.

LARGE-SCALE META-ANALYTICAL MODELING OF BRAIN STRUCTURE & FUNCTION

As a graduate student working with Peter Fox, I applied and helped develop coordinate-based meta-analytic methods, primarily in reference to the BrainMap database. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, I helped expand the BrainMap database to include voxel-based morphometry (VBM) studies.

 

I have used BrainMap data to model changes in brain structure and/or function in multiple diseases including temporal lobe epilepsy (more below in Biomarker Development), stuttering, Parkinsonism, and eating disorders. I have further used BrainMap to model the functional divisions of brain regions such as the thalamic pulvinar and hippocampus.

RELEVANT PUBLICATIONS:

Barron DS, Fox PM, Laird AR, Robinson JL, Fox PT. Thalamic Medial Dorsal Nucleus Atrophy in Medial Temporal Lobe Epilepsy: a VBM meta-analysis. NeuroImage: (2013) Clinical; 2: 25–32.

 

Barron DS, Fox PT. (2014) BrainMap Database as a Resource for Computational Modeling. Brain Mapping: An Encyclopedic Reference. Elsevier Press.

 

Budde KS, Barron DS, Fox PT. (2014) Stuttering, Induced Fluency, and Natural Fluency: A Hierarchical Series of Activation Likelihood Estimation Meta-Analyses. Brain & Language; 139: 99-107.

 

Barron DS, Clos M, Eickhoff SE, Fox PT. (2015) Human Pulvinar Functional Organization and Connectivity. Human Brain Mapping. Published Online 28 Mar 2015. DOI: 10.1002/hbm.22781

 

Robinson, J. L., Barron, D. S., Kirby, L. A. J., Bottenhorn, K. L., Hill, A. C., Murphy, J. E., et al. (2015). Neurofunctional topography of the human hippocampus. Human Brain Mapping, 36(12), 5018–5037.

Yu F, Barron DS, Bundhit T, Fox PT. (2015) Patterns of Grey Matter Atrophy in Atypical Parkinsonism Syndromes: A Meta-Analysis. Brain & Behavior.  Published Online 1.March.2015, DOI: 10.1002/brb3.329

 

Yu R, Barron DS, Tantiwongkosi B, Fox M, Fox PT (2018). Characterisation of meta-analytical functional connectivity in progressive supranuclear palsy. Clinical radiology 73 (4), 415. e1-415. e7

 

Vanasse T, Fox M, Barron DS, Robertson M, Eickhoff S, Lancaster J, Fox P. (2018)  BrainMap VBM: An Environment for Structural Meta-analysis. Human Brain Mapping 39 (8), 3308-3325. DOI: 10.102/ hbm.24078.

 

Bangshan L, Liu J, Ju Y, Wang M, Liu , Zhang Y, Li L, Potenza MN, Barron DS. Altered brain function in anorexia nervosa and bulimia nervosa: A hierarchical series of task-based fMRI meta-analyses. In revision from AJP, Biological Psychiatry; In review Neuroscience & Biobehavioral Reviews. Posted on bioRxiv.

BIOMARKER

DEVELOPMENT

My graduate school work developed a brain-based biomarker to lateralize seizure onset zone in temporal lobe epilepsy patients. We first developed a refined, meta-analytic model of brain damage in temporal lobe epilepsy patients that focused on the thalamus and hippocampus (paper above).

 

We then applied this model in independent structural (T1 and diffusion weighted MRI) and resting-state fMRI datasets. My efforts in predictive modeling (above) are born of my desire to develop quantative biomarkers for brain disease.

RELEVANT PUBLICATIONS:

Barron DS, Lancaster JL, Tandon N, Fox PT. (2014) Thalamic Structural Connectivity in Medial Temporal Lobe Epilepsy. Epilepsia; 55:6, e50-e55. doi: 10.1111/epi.12637.

 

Barron DS, Fox PT, Pardoe H, Lancaster J, Price LR, Blackmon K, Berry K, Cavazos JE, Devinsky O, Kuzniekcy R, Thesen T. (2014) Thalamic Functional Connectivity Predicts Seizure Laterality in Individual TLE Patients: application of a biomarker development strategy. Neuroimage: Clinical.  Published Online Aug 7, 2014: 10.1016/j.nicl.2014.08.002

 

Barron DS, Krystal J. Why Does Psychiatry Lack Biomarkers? In preparation.

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