Dyconnmap: Dynamic connectome mapping—A neuroimaging python module-Reference-Cited by-同舟云学术

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Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

Published:2021-07-11 Issue:15 Volume:42 Page:4909-4939
ISSN:1065-9471
Container-title:Human Brain Mapping
language:en
Short-container-title:Hum Brain Mapp

Author:

Marimpis Avraam D.¹²³^ORCID,Dimitriadis Stavros I.²⁴⁵⁶⁷^ORCID,Goebel Rainer¹³

Affiliation:

1. Cognitive Neuroscience Department, Faculty of Psychology and Neuroscience Maastricht University Maastricht The Netherlands

2. Neuroinformatics Group, Cardiff University Brain Research Imaging Center (CUBRIC) School of Psychology, Cardiff University Cardiff United Kingdom

3. Brain Innovation B.V Maastricht The Netherlands

4. Institute of Psychological Medicine and Clinical Neurosciences Cardiff University School of Medicine Cardiff United Kingdom

5. Cardiff University Brain Research Imaging Center (CUBRIC) School of Psychology, Cardiff University Cardiff United Kingdom

6. School of Psychology Cardiff University Cardiff United Kingdom

7. Neuroscience and Mental Health Research Institute Cardiff University Cardiff United Kingdom

Publisher

Wiley

Subject

Clinical Neurology,Neurology,Radiology Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Anatomy

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/hbm.25589

Reference176 articles.

1. Machine learning for neuroimaging with scikit-learn

2. Abrol A. Chaze C. Damaraju E. &Calhoun V.D(2016).The chronnectome: evaluating replicability of dynamic connectivity patterns in 7500 resting fMRI datasets.In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society(EMBC) 5571–5574. IEEE. doi:https://doi.org/10.1109/EMBC.2016.7591989

3. Replicability of time-varying connectivity patterns in large resting state fMRI samples

4. EEG Signatures of Dynamic Functional Network Connectivity States

5. Tracking Whole-Brain Connectivity Dynamics in the Resting State

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