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h5 指数:134
h5 中位数:185

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2018-2022 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

#4 Neurology

标题/作者	引用次数	年份
The dynamic functional connectome: State-of-the-art and perspectives MG Preti, TAW Bolton, D Van De Ville NeuroImage 160, 41-54	825	2017
MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation JD Tournier, R Smith, D Raffelt, R Tabbara, T Dhollander, M Pietsch, ... NeuroImage 202, 116137	821	2019
Towards a consensus regarding global signal regression for resting state functional connectivity MRI K Murphy, MD Fox NeuroImage 154, 169-173	648	2017
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls MR Arbabshirani, S Plis, J Sui, VD Calhoun NeuroImage 145, 137-165	621	2017
Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity R Ciric, DH Wolf, JD Power, DR Roalf, GL Baum, K Ruparel, ... NeuroImage 154, 174-187	620	2017
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank F Alfaro-Almagro, M Jenkinson, NK Bangerter, JLR Andersson, L Griffanti, ... NeuroImage 166, 400-424	605	2018
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images H Chen, Q Dou, L Yu, J Qin, PA Heng NeuroImage 170, 446-455	571	2018
Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker JH Cole, RPK Poudel, D Tsagkrasoulis, MWA Caan, C Steves, ... NeuroImage 163, 115-124	466	2017
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI L Parkes, B Fulcher, M Yücel, A Fornito NeuroImage 171, 415-436	446	2018
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	446	2017
Quicksilver: Fast predictive image registration–A deep learning approach X Yang, R Kwitt, M Styner, M Niethammer NeuroImage 158, 378-396	437	2017
Harmonization of multi-site diffusion tensor imaging data JP Fortin, D Parker, B Tunç, T Watanabe, MA Elliott, K Ruparel, DR Roalf, ... NeuroImage 161, 149-170	428	2017
Harmonization of cortical thickness measurements across scanners and sites JP Fortin, N Cullen, YI Sheline, WD Taylor, I Aselcioglu, PA Cook, ... NeuroImage 167, 104-120	410	2018
EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review CM Michel, T Koenig NeuroImage 180, 577-593	409	2018
A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages S Rathore, M Habes, MA Iftikhar, A Shacklett, C Davatzikos NeuroImage 155, 530-548	407	2017
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example A Abraham, MP Milham, A Di Martino, RC Craddock, D Samaras, ... NeuroImage 147, 736-745	401	2017
ICLabel: An automated electroencephalographic independent component classifier, dataset, and website L Pion-Tonachini, K Kreutz-Delgado, S Makeig NeuroImage 198, 181-197	400	2019
Cross-validation failure: Small sample sizes lead to large error bars G Varoquaux NeuroImage 180, 68-77	394	2018
Methods for cleaning the BOLD fMRI signal C Caballero-Gaudes, RC Reynolds NeuroImage 154, 128-149	389	2017
Multi-scale brain networks RF Betzel, DS Bassett NeuroImage 160, 73-83	378	2017

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