Walter Hugo Lopez Pinaya
Cited by
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Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications
S Vieira, WHL Pinaya, A Mechelli
Neuroscience & Biobehavioral Reviews 74, 58-75, 2017
Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia
WHL Pinaya, A Gadelha, OM Doyle, C Noto, A Zugman, Q Cordeiro, ...
Scientific reports 6, 38897, 2016
Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large‐scale multi‐sample study
WHL Pinaya, A Mechelli, JR Sato
Human brain mapping 40 (3), 944-954, 2019
Using machine learning and structural neuroimaging to detect first episode psychosis: reconsidering the evidence
S Vieira, Q Gong, WHL Pinaya, C Scarpazza, S Tognin, B Crespo-Facorro, ...
Schizophrenia bulletin 46 (1), 17-26, 2020
Structural and functional papez circuit integrity in amyotrophic lateral sclerosis
APA Bueno, WHL Pinaya, LM Moura, M Bertoux, R Radakovic, ...
Brain Imaging and Behavior 12 (6), 1622-1630, 2018
Towards an EEG-based biomarker for Alzheimer's disease: Improving amplitude modulation analysis features
FJ Fraga, TH Falk, LR Trambaiolli, EF Oliveira, WHL Pinaya, PAM Kanda, ...
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
Detecting schizophrenia at the level of the individual: relative diagnostic value of whole-brain images, connectome-wide functional connectivity and graph-based metrics
D Lei, WHL Pinaya, T van Amelsvoort, M Marcelis, G Donohoe, ...
Psychological medicine 50 (11), 1852-1861, 2020
Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach
AM de Moura, WHL Pinaya, A Gadelha, A Zugman, C Noto, Q Cordeiro, ...
Psychiatry Research: Neuroimaging 275, 14-20, 2018
Regional dynamics of the resting brain in amyotrophic lateral sclerosis using fractional amplitude of low-frequency fluctuations and regional homogeneity analyses
APA Bueno, WHL Pinaya, K Rebello, LC de Souza, M Hornberger, ...
Brain Connectivity 9 (4), 356-364, 2019
Default mode network maturation and environmental adversities during childhood
K Rebello, LM Moura, WHL Pinaya, LA Rohde, JR Sato
Chronic Stress 2, 2470547018808295, 2018
WHL Pinaya, S Vieira, R Garcia-Dias, A Mechelli
Machine Learning, 193-208, 2020
Translating research findings into clinical practice: a systematic and critical review of neuroimaging-based clinical tools for brain disorders
C Scarpazza, M Ha, L Baecker, R Garcia-Dias, WHL Pinaya, S Vieira, ...
Translational Psychiatry 10 (1), 1-16, 2020
Integrating machining learning and multimodal neuroimaging to detect schizophrenia at the level of the individual
D Lei, WHL Pinaya, J Young, T van Amelsvoort, M Marcelis, G Donohoe, ...
Human Brain Mapping 41 (5), 1119-1135, 2020
Brazilian montane rainforest expansion induced by Heinrich Stadial 1 event
JLD Pinaya, FW Cruz, GCT Ceccantini, PLP Corrêa, N Pitman, F Vemado, ...
Scientific reports 9 (1), 1-14, 2019
Neuroharmony: A new tool for harmonizing volumetric MRI data from unseen scanners
R Garcia-Dias, C Scarpazza, L Baecker, S Vieira, WHL Pinaya, A Corvin, ...
NeuroImage 220, 2020
A step-by-step tutorial on how to build a machine learning model
S Vieira, R Garcia-Dias, WHL Pinaya
Machine Learning, 343-370, 2020
Deep neural networks
S Vieira, WHL Pinaya, R Garcia-Dias, A Mechelli
Machine Learning, 157-172, 2020
Clustering analysis
R Garcia-Dias, S Vieira, WHL Pinaya, A Mechelli
Machine Learning, 227-247, 2020
Analysis of an Automated Machine Learning Approach in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
J Dafflon, WHL Pinaya, F Turkheimer, JH Cole, R Leech, MA Harris, ...
arXiv preprint arXiv:1910.03349, 2019
Comparing methods for determining motor-hand lateralization based on fTCD signals
WHL Pinaya, FJ Fraga, SS Haratz, PJA Dean, AB Conforto, ...
Journal of medical systems 39 (2), 4, 2015
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Articles 1–20