Nova Publishers
My Account Nova Publishers Shopping Cart
HomeBooksSeriesJournalsReference CollectionseBooksInformationSalesImprintsFor Authors
            
  Top » Catalog » Journals » Functional Neurology, Rehabilitation, and Ergonomics » Volume 3 Issue 2-3 Articles » My Account  |  Cart Contents  |  Checkout   
Quick Find
  
Use keywords to find the product you are looking for.
Advanced Search
What's New? more
Advances in Materials Science Research. Volume 33
$225.00
Shopping Cart more
0 items
Information
Shipping & Returns
Privacy Notice
Conditions of Use
Contact Us
Bestsellers
01.The Potential Impact of Various Physiological Mechanisms on Outcomes in TBI, mTBI, Concussion and PPCS (pp. 215-256)
02.A Novel ERP Pattern Analysis Method for Revealing Invariant Reference Brain Network Models (pp. 295-317)
Notifications more
NotificationsNotify me of updates to A Novel ERP Pattern Analysis Method for Revealing Invariant Reference Brain Network Models (pp. 295-317)
Tell A Friend
 
Tell someone you know about this product.
A Novel ERP Pattern Analysis Method for Revealing Invariant Reference Brain Network Models (pp. 295-317) $45.00
Authors:  Amit Reches, Dan Kerem, Noga Gal, Ilan Laufer, Revital Shani-Hershkovitch, Dalia Dickman, and Amir B. Geva
Abstract:
Background: Objective and reliable neuro-electrophysiological methods for the longitudinal monitoring of a patientís cognitive state are scarce. Since baseline neuro-electrophysiological measurements are generally not performed as standard practice, this type of longitudinal monitoring requires an invariant normal reference to which the individual brain activity may be scored, with sufficiently high within-subject repeatability.

Methods: Group-common functionally connected Reference Brain Network Models (RBNMs) were extracted with the Brain Network Activation (BNA) Technology from multi-channel ERPs of 120 young healthy subjects (Reference Group) who underwent the three-stimulus Auditory Oddball Task. The test-retest repeatability of an age-matched group of 116 subjects (Database Group), whose individual brain activity on the oddball task was scored on the RBNMs, was determined and the Standard Error of Measurement (SEM) computed as a measure of Minimal Important Clinical Difference. The general applicability was cross-validated on 36 healthy patients of a wider and marginally overlapping age-range (Validation Group).

Results: Intraclass correlation values of scores on repeated tests in the Database Group ranged between 0.58-0.81. SEM values ranged from 14.1-17.9, on a score scale of 0-100. Scoring, repeatability and SEM applicability were all successfully validated on subjects from the Validation Group, yielding values that were similar to or higher than those of the Database Group.

Conclusions: The BNA generated brain network models are largely invariant and repeatable and as such may be useful for diagnosis and follow-up of disease progression and treatment management. To further test the algorithmís utility, additional research should be conducted on diseased populations. 


Available Options:
Version:
Special Focus Titles
01.Peter Singerís Ethics: A Critical Appraisal
02.Sexism: Past, Present and Future Perspectives
03.Body and Politics: Elite Disability Sport in China
04.Childhood and Adolescence: Tribute to Emanuel Chigier, 1928-2017
05.Renal Replacement Therapy: Controversies and Future Trends
06.Food-Drug Interactions: Pharmacokinetics, Prevention and Potential Side Effects
07.Terrorism and Violence in Islamic History and Theological Responses to the Arguments of Terrorists
08.International Event Management: Bridging the Gap between Theory and Practice
09.The Sino-Indian Border War and the Foreign Policies of China and India (1950-1965)
10.Tsunamis: Detection, Risk Assessment and Crisis Management
11.Sediment Watch: Monitoring, Ecological Risk Assessment and Environmental Management
12.Self-Regulated Learners: Strategies, Performance, and Individual Differences

Nova Science Publishers
© Copyright 2004 - 2018

A Novel ERP Pattern Analysis Method for Revealing Invariant Reference Brain Network Models (pp. 295-317)