Pradeep Reddy Raamana
Pradeep Reddy Raamana
Assistant Professor, University of Pittsburgh
Verified email at - Homepage
Cited by
Cited by
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
Optimizing fMRI Preprocessing Pipelines for Block-Design Tasks as a Function of Age
NW Churchill, PR Raamana, R Spring, SC Strother
Neuroimage, 2017
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
Cleaning up the fMRI time series: Mitigating noise with advanced acquisition and correction strategies
M Bright, K Murphy
NeuroImage 154, 1-3, 2017
Predictive power of single-subject morphometric networks is insensitive to spatial scale and edge weight
PR Raamana, SC Strother, Alzheimer’s Disease Neuroimaging Initiative
bioRxiv, 170381, 2020
neuropredict: easy machine learning and standardized predictive analysis of biomarkers
PR Raamana
zenodo, 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
graynet: single-subject morphometric networks for neuroscience connectivity applications
PR Raamana, SC Strother
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
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 4 (8), P163-P164, 2012
Python class defining a machine learning dataset ensuring key-based correspondence and maintaining integrity
PR Raamana, SC Strother
Journal of Open Source Software 2 (17), 382, 2017
mrivis: Medical image visualization library for neuroscience in python
PR Raamana, SC Strother
Journal of Open Source Software 3 (30), 897, 2018
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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