Searching the Resource Information Network

Our searching services are busy right now. Please try again later

  • Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes

Increased functional connectivity between default mode network and visual network potentially correlates with duration of residual dizziness in patients with benign paroxysmal positional vertigo.

Zhengwei Chen | Yaxian Cai | Lijie Xiao | Xiu-E Wei | Yueji Liu | Cunxin Lin | Dan Liu | Haiyan Liu | Liangqun Rong
Frontiers in neurology | 2024

To assess changes in static and dynamic functional network connectivity (sFNC and dFNC) and explore their correlations with clinical features in benign paroxysmal positional vertigo (BPPV) patients with residual dizziness (RD) after successful canalith repositioning maneuvers (CRM) using resting-state fMRI.

Pubmed ID: 38500812

Research resources used in this publication

None found

Antibodies used in this publication

None found

Associated grants

None

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


MATLAB (tool)

RRID:SCR_001622

Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.

View all literature mentions

GRETNA (tool)

RRID:SCR_009487

A graph theoretical network analysis toolbox which allows researchers to perform comprehensive analysis on the topology of brain connectome by integrating the most of network measures studied in current neuroscience field.

View all literature mentions

MOCA (tool)

RRID:SCR_010638

The Museum of Comparative Anthropogeny (MOCA) is a collection of comparative information regarding humans and our closest evolutionary cousins (chimpanzees, bonobos, gorillas and orangutans i.e, great apes), with an emphasis on uniquely human features. MOCA is organized by Domains, each grouping Topics by areas of interest and scientific discipline. Each topic entry will eventually cover existing information about a particular difference (alleged or documented) between humans and non-human hominids. Comparisons of these non-human hominids with humans are difficult, as so little is known about their phenotypic features (phenomes), in contrast to humans. Ethical, fiscal and practical issues also limit collection of further information about great apes. MOCA attempts to collect existing information about human-specific differences from great apes, currently scattered in the literature. Having such information in one location could lead to new insights and multi-disciplinary interactions, and to ethically-sound studies to explain differences, and uniquely human specializations. MOCA is not targeted at experts in specific disciplines, but rather aims to communicate basic information to a broad audience of scientists from many backgrounds, and to the interested lay public. MOCA includes not only aspects wherein there are known or apparent differences between humans and great apes, but additionally, topics for which popular wisdom about claimed or assumed differences is not entirely correct. It is for all these reasons that MOCA is called a Museum, and not an Encyclopedia or Database.

View all literature mentions

GIFT (tool)

RRID:SCR_024416

Software MATLAB toolbox which implements multiple algorithms for independent component analysis and blind source separation of group and single subject functional magnetic resonance imaging data.

View all literature mentions

Group ICA of fMRI Toolbox (tool)

RRID:SCR_001953

A MATLAB toolbox which implements multiple algorithms for independent component analysis and blind source separation of group (and single subject) functional magnetic resonance imaging data. GIFT works on MATLAB 6.5 and higher. Many ICA algorithms were generously contributed by Dr. Andrzej Cichocki.

View all literature mentions