The Use of Quantitative EEG for Differentiating Frontotemporal Dementia From Late-Onset Bipolar Disorder

Abstract

The behavioral variant frontotemporal dementia (bvFTD) usually emerges with behavioral changes similar to changes in late-life bipolar disorder (BD) especially in the early stages. According to the literature, a substantial number of bvFTD cases have been misdiagnosed as BD. Since the literature lacks studies comparing differential diagnosis ability of electrophysiological and neuroimaging findings in BD and bvFTD, we aimed to show their classification power using an artificial neural network and genetic algorithm based approach. Eighteen patients with the diagnosis of bvFTD and 20 patients with the diagnosis of late-life BD are included in the study. All patients' clinical magnetic resonance imaging (MRI) scan and electroencephalography recordings were assessed by a double-blind method to make diagnosis from MRI data. Classification of bvFTD and BD from total 38 participants was performed using feature selection and a neural network based on general algorithm. The artificial neural network method classified BD from bvFTD with 76% overall accuracy only by using on EEG power values. The radiological diagnosis classified BD from bvFTD with 79% overall accuracy. When the radiological diagnosis was added to the EEG analysis, the total classification performance raised to 87% overall accuracy. These results suggest that EEG and MRI combination has more powerful classification ability as compared with EEG and MRI alone. The findings may support the utility of neurophysiological and structural neuroimaging assessments for discriminating the 2 pathologies.

Behavioral variant frontotemporal dementia in patients with previous severe mental illness: a systematic and critical review

“…On LILACS, none of the nine retrieved met the requirements. Therefore, 42 articles were selected, of which 10 were cohort studies 15,16,17,18,19,20,21,22,23,24 , three were case-control studies 25,26,27 , 12 were case repo rts 28,29,30,31,32,33,34,35,36,37,38,39 and 17 other studies were cross-sectional surveys 6,7,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54 .…”

“…Making a diagnosis of dementia in a patient with lifelong SMI is challenging. Cognitive and functional impairment constitute plausible outcomes from the premorbid mental illness itself and impairment in cognitive performance does not exclude primary psychiatric diagnoses in a dementia workup 42 . Indeed, long-term cognitive and functional outcomes from severe psychiatric illness-BD, SZA or SCZ-are still a source of an intense debate.…”

Global research on artificial intelligence-enhanced human electroencephalogram analysis

“…For instance, Garn et al [113] developed and evaluated a classifier based on quantitative EEG features for differential diagnosis between patients with diseases such as Alzheimer's and Parkinson's. Metin et al [114] applied quantitative EEGs to differentiate frontotemporal dementia from late-onset bipolar disorders through the use of an ANN and a genetic algorithm-based method. Additionally, EEGs offer promising alternatives to the development of effective and affordable biomarkers for mental disorders because of the low cost, portability, and robustness [115].…”

Bipolar and Schizophrenia Disorders Diagnosis Using Artificial Neural Network

“…Some related works also show classification functions to analyze the ANN training [31] [32]. In this work, we performed ten trainings for each number of neurons in hidden layer.…”