Eduardo Castro Witting

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Neuroimaging Researcher
Thomas J. Watson Research Center, Yorktown Heights, NY USA
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2020

Resting-state connectivity stratifies premanifest Huntington's disease by longitudinal cognitive decline rate
Polosecki, Pablo; Castro, Eduardo; Rish, Irina; Pustina, Dorian; Warner, John H; Wood, Andrew; Sampaio, Cristina; Cecchi, Guillermo A.
Scientific Reports, 2020

2018

Baseline multimodal information predicts future motor impairment in premanifest Huntington's disease
Castro, Eduardo and Polosecki, Pablo and Rish, Irina and Pustina, Dorian and Warner, John H and Wood, Andrew and Sampaio, Cristina and Cecchi, Guillermo A
NeuroImage: Clinical, Elsevier, 2018
Abstract

2017

Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia
Gupta, Cota Navin and Castro, Eduardo and Rachkonda, Srinivas and van Erp, Theo G. M. and Potkin, Steven and Ford, Judith M. and Mathalon Daniel and Lee, Hyo Jong and Mueller, Bryon A. and Greve, Douglas N. and Andreassen, Ole and Agartz, Ingrid and Mayer, Andrew R. And Stephen, Julia and Jung, Rex E. and Bustillo, Juan and Calhoun, Vince D. and Turner, Jessica A.
Frontiers in Psychiatry8, 2017
Abstract medial frontal gyrus, middle frontal gyrus, superior frontal gyrus, inferior frontal gyrus, superior temporal gyrus, independent component analysis, biomarker, schizophrenia, developmental psychology, psychiatry, psychology

Clinical and cognitive symptoms domain based subtyping in schizophrenia has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We therefore present a novel data driven framework using biclustered independent component analysis (B-ICA) to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with schizophrenia (Sz). The developed methodology consists of the following steps: source based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus /middle frontal gyrus /medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with schizophrenia regressed of age, gender and site voxelwise. We observed two subtypes (i.e. two different subsets of subjects) each heavily weighted on these two components respectively. These subsets of subjects were characterized by significant differences in PANSS positive clinical symptoms (p=0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r=0.25; p=0.07). The reconstructed subtype specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with schizophrenia could possibly indicate the need for personalized treatment and targeted drug development.

2016

Deep Independence Network Analysis of Structural Brain Imaging: Application to Schizophrenia
E. Castro, R. D. Hjelm, S. M. Plis, L. Dinh, J. A. Turner, V. D. Calhoun
IEEE Transactions on Medical Imaging 35(7), 1729-1740, 2016

2015

Deep independence network analysis of structural brain imaging: A simulation study
Castro, Eduardo and Hjelm, Devon and Plis, Sergey and Dinh, Laurent and Turner, Jessica and Calhoun, Vince
2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1--6

A group ICA based framework for evaluating resting fMRI markers when disease categories are unclear: application to schizophrenia, bipolar, and schizoaffective disorders
Du, Yuhui and Pearlson, Godfrey D and Liu, Jingyu and Sui, Jing and Yu, Qingbao and He, Hao and Castro, Eduardo and Calhoun, Vince D
NeuroImage122, 272--280, Elsevier, 2015

2014

Accurate classification of schizophrenia patients based on novel resting-state fMRI features
Arbabshirani, Mohammad R and Castro, Eduardo and Calhoun, Vince D
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6691--6694

Combination of FMRI-SMRI-EEG data improves discrimination of schizophrenia patients by ensemble feature selection
Sui, Jing and Castro, Eduardo and He, Hao and Bridwell, David and Du, Yuhui and Pearlson, Godfrey D and Jiang, Tianzi and Calhoun, Vince D
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3889--3892

Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging
Castro, Eduardo and Gupta, Cota Navin and Martinez-Ramon, Manel and Calhoun, Vince D and Arbabshirani, Mohammad R and Turner, Jessica
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1513--1516

A multiple kernel learning approach to perform classification of groups from complex-valued fMRI data analysis: Application to schizophrenia
Castro, Eduardo and Gomez-Verdejo, Vanessa and Martinez-Ramon, Manel and Kiehl, Kent A and Calhoun, Vince D
NeuroImage87, 1--17, Elsevier, 2014

The tenth annual MLSP competition: schizophrenia classification challenge
Silva, Rogers F and Castro, Eduardo and Gupta, Cota Navin and Cetin, Mustafa and Arbabshirani, Mohammad and Potluru, Vamsi K and Plis, Sergey M and Calhoun, Vince D
2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1--6

2013

Three-way (N-way) fusion of brain imaging data based on mCCA+ jICA and its application to discriminating schizophrenia
Sui, Jing and He, Hao and Pearlson, Godfrey D and Adali, T{"u}lay and Kiehl, Kent A and Yu, Qingbao and Clark, Vince P and Castro, Eduardo and White, Tonya and Mueller, Bryon A and others
Neuroimage66, 119--132, Elsevier, 2013

2011

Characterization of groups using composite kernels and multi-source fMRI analysis data: application to schizophrenia
Castro, Eduardo and Martinez-Ramon, Manel and Pearlson, Godfrey and Sui, Jing and Calhoun, Vince D
Neuroimage 58(2), 526--536, Elsevier, 2011

Projects and Groups

Neuroimaging

Technical Areas

Computational Biology

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