Literature DB >> 12566600

Quantitative EEG abnormalities and cognitive dysfunctions in frontotemporal dementia and Alzheimer's disease.

M Lindau1, V Jelic, S-E Johansson, C Andersen, L-O Wahlund, O Almkvist.   

Abstract

OBJECTIVE: To investigate the relationship between quantitative EEG (qEEG) measurements in frontotemporal dementia (FTD), Alzheimer's disease (AD) and healthy controls and to study to what extent qEEG in FTD and AD or neuropsychological test results of FTD and AD patients or a combination of both contribute to classification accuracy.
METHOD: The FTD sample consisted of 19 patients, the AD sample of 16 patients, and the control group of 19 subjects. Groups were matched on the group level with respect to demographic variables. For qEEG the global field power was calculated for six frequency bands: delta (1.0-3.5 Hz), theta (4.0-7.5 Hz), alpha (8.0-11.0 Hz), beta1 (12.0-15.5 Hz), beta2 (16.0-19.5 Hz), beta3 (20.0-23.5 Hz), and spectral ratio as the ratio of the sum of fast frequency bands alpha + beta1 + beta2 + beta3 and slow frequency bands delta + theta.
RESULTS: In comparison to controls FTD patients were marked by an absence of an increase in slow qEEG activities and a decrease in fast activities, whereas AD patients were marked by an increase in slow activities and a smaller decrease in fast activities. According to the Mann-Whitney U test the cognitive functions of attention, visuospatial thinking and episodic memory were significantly better in FTD than in AD. Using logistic regression analysis the best predictors of FTD and AD were in a model using the delta and theta activities, and high levels of visuospatial ability and episodic memory. Classification accuracy of the model was 93.3%.
CONCLUSION: FTD patients reveal a different pattern of qEEG changes than AD patients. This result demonstrates the importance of qEEG for FTD diagnosis. Cognition is selectively better in FTD than in AD. A combination of qEEG and neuropsychology is recommended for differential diagnoses of FTD and AD. Copyright 2003 S. Karger AG, Basel

Entities:  

Mesh:

Year:  2003        PMID: 12566600     DOI: 10.1159/000067973

Source DB:  PubMed          Journal:  Dement Geriatr Cogn Disord        ISSN: 1420-8008            Impact factor:   2.959


  19 in total

1.  Fractal dimension values of cerebral and cerebellar activity in rats loaded with aluminium.

Authors:  Goran Kekovic; Milka Culic; Ljiljana Martac; Gordana Stojadinovic; Ivan Capo; Dusan Lalosevic; Slobodan Sekulic
Journal:  Med Biol Eng Comput       Date:  2010-04-28       Impact factor: 2.602

2.  MMSE scores decline at a greater rate in frontotemporal degeneration than in AD.

Authors:  Tiffany W Chow; Linda S Hynan; Anne M Lipton
Journal:  Dement Geriatr Cogn Disord       Date:  2006-08-07       Impact factor: 2.959

Review 3.  [Frontotemporal dementias].

Authors:  K Witt; G Deuschl; T Bartsch
Journal:  Nervenarzt       Date:  2013-01       Impact factor: 1.214

Review 4.  Neurophysiological markers of network dysfunction in neurodegenerative diseases.

Authors:  Roisin McMackin; Peter Bede; Niall Pender; Orla Hardiman; Bahman Nasseroleslami
Journal:  Neuroimage Clin       Date:  2019-02-02       Impact factor: 4.881

Review 5.  Neuronal Network Oscillations in Neurodegenerative Diseases.

Authors:  Volker Nimmrich; Andreas Draguhn; Nikolai Axmacher
Journal:  Neuromolecular Med       Date:  2015-04-29       Impact factor: 3.843

6.  Comparative quantitative study of 'signature' pathological lesions in the hippocampus and adjacent gyri of 12 neurodegenerative disorders.

Authors:  Richard A Armstrong; Nigel J Cairns
Journal:  J Neural Transm (Vienna)       Date:  2015-05-01       Impact factor: 3.575

Review 7.  Medical management of frontotemporal dementias: the importance of the caregiver in symptom assessment and guidance of treatment strategies.

Authors:  Gregory A Jicha
Journal:  J Mol Neurosci       Date:  2011-06-07       Impact factor: 3.444

8.  Comparative multiresolution wavelet analysis of ERP spectral bands using an ensemble of classifiers approach for early diagnosis of Alzheimer's disease.

Authors:  Robi Polikar; Apostolos Topalis; Deborah Green; John Kounios; Christopher M Clark
Journal:  Comput Biol Med       Date:  2006-09-20       Impact factor: 4.589

9.  Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing.

Authors:  Martin Dottori; Lucas Sedeño; Miguel Martorell Caro; Florencia Alifano; Eugenia Hesse; Ezequiel Mikulan; Adolfo M García; Amparo Ruiz-Tagle; Patricia Lillo; Andrea Slachevsky; Cecilia Serrano; Daniel Fraiman; Agustin Ibanez
Journal:  Sci Rep       Date:  2017-06-19       Impact factor: 4.379

10.  Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory.

Authors:  Willem de Haan; Yolande A L Pijnenburg; Rob L M Strijers; Yolande van der Made; Wiesje M van der Flier; Philip Scheltens; Cornelis J Stam
Journal:  BMC Neurosci       Date:  2009-08-21       Impact factor: 3.288

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.