Pradeep Reddy Raamana
Pradeep Reddy Raamana
Postdoctoral fellow, University of Toronto
Verified email at - Homepage
TitleCited byYear
Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
G Varoquaux, PR Raamana, DA Engemann, A Hoyos-Idrobo, Y Schwartz, ...
NeuroImage 145, 166-179, 2017
BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
KJ Gorgolewski, F Alfaro-Almagro, T Auer, P Bellec, M Capotă, ...
PLoS computational biology 13 (3), e1005209, 2017
Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach
Y Cui, W Wen, DM Lipnicki, MF Beg, JS Jin, S Luo, W Zhu, NA Kochan, ...
Neuroimage 59 (2), 1209-1217, 2012
Three-class differential diagnosis among Alzheimer disease, frontotemporal dementia, and controls
PR Raamana, H Rosen, B Miller, MW Weiner, L Wang, MF Beg
Frontiers in neurology 5, 71, 2014
Thickness network features for prognostic applications in dementia
PR Raamana, MW Weiner, L Wang, MF Beg, ...
Neurobiology of aging 36, S91-S102, 2015
Comparison of four shape features for detecting hippocampal shape changes in early Alzheimer's
MF Beg, PR Raamana, S Barbieri, L Wang
Statistical methods in medical research 22 (4), 439-462, 2013
Human action recognition in table-top scenarios: an HMM-based analysis to optimize the performance
PR Raamana, D Grest, V Krueger
International Conference on Computer Analysis of Images and Patterns, 101-108, 2007
Novel ThickNet features for the discrimination of amnestic MCI subtypes
PR Raamana, W Wen, NA Kochan, H Brodaty, PS Sachdev, L Wang, ...
NeuroImage: Clinical 6, 284-295, 2014
The Sub-Classification of Amnestic Mild Cognitive Impairment Using MRI-Based Cortical Thickness Measures
MF Raamana, Pradeep Reddy and Wen, Wei and Kochan, Nicole A and Brodaty ...
Frontiers in Neurology 5 (76), 2014
``Bids apps: Improving ease of use, accessibility and reproducibility of neuroimaging data analysis methods,''bioRxiv, 2016
KJ Gorgolewski, F Alfaro-Almagro, T Auer, P Bellec, M Capota, ...
doi 10 (079145), 079145, 0
Cleaning up the fMRI time series: Mitigating noise with advanced acquisition and correction strategies
M Bright, K Murphy
Neuroimage 154, 1-3, 2017
Optimizing fMRI Preprocessing Pipelines for Block-Design Tasks as a Function of Age
NW Churchill, PR Raamana, R Spring, SC Strother
Neuroimage, 2017
Thickness network (thicknet) features for the detection of prodromal ad
PR Raamana, L Wang, MF Beg, ...
International Workshop on Machine Learning in Medical Imaging, 114-122, 2013
Differential diagnosis among Alzheimer's disease, frontotemporal disease and healthy aging: Comparative study using subcortical features
P Raamana, L Wang, H Rosen, B Miller, M Weiner, MF Beg
Alzheimer's & Dementia: The Journal of the Alzheimer's Association 8 (4†…, 2012
Human Action Recognition in Table-top Scenarios: An HMM-based Analysis to Optimize the Performace
PK Reddy, D Grest, V Krueger
Computer Analysis of Images and Patterns, Vienna, Austria, 2007
Impact of spatial scale and edge weight on predictive power of cortical thickness networks. bioRxiv
PR Raamana, SC Strother
Cold Spring Harbor Labs Journals. doi 10, 170381, 2017
Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience
PR Raamana, SC Strother
Journal of Open Source Software 2 (19), 380, 2017
Impact of spatial scale and edge weight on predictive power of cortical thickness networks
PR Raamana, SC Strother
bioRxiv, 170381, 2017
Novel histogram-weighted cortical thickness networks and a multi-scale analysis of predictive power in Alzheimer’s disease
PR Raamana, S Strother
Pattern Recognition in Neuroimaging, 2016
Thickness Network (ThickNet) Features for Prognostic Applications in Dementia
P Raamana, MW Weiner, L Wang, MF Beg
Neurobiology of Aging,(To Appear), 2014
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