A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 143

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3098
Function: getPubMedXML

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: Attempt to read property "Count" on bool

Filename: helpers/my_audit_helper.php

Line Number: 3100

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3100
Function: _error_handler

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy. | LitMetric

Graph theory application with functional connectivity to distinguish left from right temporal lobe epilepsy.

Epilepsy Res

Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences(TUMS), Tehran, Iran; Research Center for Molecular and Cellular Imaging, Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran. Electronic address:

Published: November 2020

AI Article Synopsis

  • The study investigated how graph theory and functional connectivity can differentiate between left and right temporal lobe epilepsy (TLE) using resting-state functional MRI.
  • It analyzed functional connectivity changes in several brain networks and found distinct patterns in left TLE patients compared to right TLE and control subjects.
  • The research concluded that graph theory could serve as a reliable tool for identifying seizure laterality, with an impressive accuracy of 94.3% when using local nodal degree attributes from multiple brain networks.

Article Abstract

Objective: To investigate the application of graph theory with functional connectivity to distinguish left from right temporal lobe epilepsy (TLE).

Methods: Alterations in functional connectivity within several brain networks - default mode (DMN), attention (AN), limbic (LN), sensorimotor (SMN) and visual (VN) - were examined using resting-state functional MRI (rs-fMRI). The study accrued 21 left and 14 right TLE as well as 17 nonepileptic control subjects. The local nodal degree, a feature of graph theory, was calculated foreach of the brain networks. Multivariate logistic regression analysis was performed to determine the accuracy of identifying seizure laterality based on significant differences in local nodal degree in the selected networks.

Results: Left and right TLE patients showed dissimilar patterns of alteration in functional connectivity when compared to control subjects. Compared with right TLE, patients with left TLE exhibited greater nodal degree' (i.e. hyperconnectivity) with right superomedial frontal gyrus (in DMN), inferior frontal gyrus pars triangularis (in AN), right caudate and left superior temporal gyrus (in LN) and left paracentral lobule (in SMN), while showing lesser nodal degree (i.e. hypoconnectivity) with left temporal pole (in DMN), right insula (in LN), left supplementary motor area (in SMN), and left fusiform gyrus (in VN). The LN showed the highest accuracy of 82.9% among all considered networks in determining laterality of the TLE. By combinations of local degree attributes in the DMN, AN, LN, and VN, logistic regression analysis demonstrated an accuracy of 94.3% by comparison.

Conclusion: Our study demonstrates the utility of graph theory application to brain network analysis as a potential biomarker to assist in the determination of TLE laterality and improve the confidence in presurgical decision-making in cases of TLE.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.eplepsyres.2020.106449DOI Listing

Publication Analysis

Top Keywords

graph theory
16
functional connectivity
16
left temporal
12
left tle
12
nodal degree
12
left
10
theory application
8
connectivity distinguish
8
distinguish left
8
temporal lobe
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!

A PHP Error was encountered

Severity: Notice

Message: fwrite(): Write of 34 bytes failed with errno=28 No space left on device

Filename: drivers/Session_files_driver.php

Line Number: 272

Backtrace:

A PHP Error was encountered

Severity: Warning

Message: session_write_close(): Failed to write session data using user defined save handler. (session.save_path: /var/lib/php/sessions)

Filename: Unknown

Line Number: 0

Backtrace: