EEG Dominant Frequency Peak Differentiates Between Alzheimer’s Disease and Frontotemporal Lobar Degeneration

Goossens, Joery; Laton, Jorne; Gielen, Jeroen; Struyfs, Hanne; Mossevelde, Sara Van; Bossche, Tobi Van den; Goeman, Johan; Deyn, Peter Paul De; Sieben, Anne; Martin, Jean‐Jacques; Broeckhoven, Christine Van; Zee, Julie van der; Engelborghs, Sebastiaan; Nagels, Guy

doi:10.3233/jad-160188

Cited by 18 publications

(30 citation statements)

References 25 publications

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“…https://doi.org/10.1101/19009316 doi: medRxiv preprint adding the EEG parameters, which are slightly behind on their own. We observed the same effect of increased accuracy in the definite group in our previous study focussing on EEG (Goossens et al, 2017 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.…”

Section: Classificationsupporting

confidence: 85%

“…To reduce redundancy, balance the number of neurochemical and EEG features and deal with the fact that some channels did not always exhibit peaks, we summarised the peaks found over all channels into four regions. To do this, we fine-tuned the algorithm from our previous study (Goossens et al, 2017) to detect the DFP with the highest amplitude over the channels within a specific region, as defined in table 2. This procedure was also helpful in avoiding missing data, as only one channel per region needed to show a clearly distinguishable peak.…”

Section: Dominant Frequency Peak Extractionmentioning

confidence: 99%

“…Electroencephalography (EEG) is an easily accessible, non-invasive, inexpensive technique, and is already being investigated as an adjunctive investigation in dementia (Ferreira et al, 2016). We have previously shown that EEG maxpeak frequency is an easy and useful measure with an added value in the differentiation between AD and FTLD, reaching a diagnostic accuracy of 78.4% (Goossens et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Improved Alzheimer’s disease versus frontotemporal lobar degeneration differential diagnosis combining EEG and neurochemical biomarkers

Laton

Goossens

Wiels

et al. 2019

Preprint

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Introduction: Distinguishing between two of the most common causes of dementia, Alzheimer disease (AD) and frontotemporal lobar degeneration (FTLD), on clinical diagnostic criteria alone has poor diagnostic accuracy. Promising tools to increase the diagnostic accuracy of AD are the use of cerebrospinal fluid (CSF) biomarkers and electroencephalography (EEG), which is being investigated as a diagnostic tool for neurodegenerative brain disorders. In this study, we investigated the utility of EEG-based biomarkers in comparison and in addition to established neurochemical biomarkers in the AD versus FTLD differential diagnosis. Methods: Our study cohort comprised 37 AD and 32 FTLD patients, of which 19 AD and 11 FTLD had definite diagnoses. All these participants had CSF biomarker analyses resulting in four neurochemical (NCM) biomarkers (Aβ1-42, T-tau, P-tau181 and Nf-L) and underwent 19-channel resting-state EEG. From the EEG spectra, dominant frequency peaks (DFP) were extracted in four regions resulting in four dominant frequencies (in left-temporal, frontal, right-temporal and parieto-occipital regions). This yielded a total of eight features (4 NCM + 4 EEG), which we used to train and test a classifier and assess the diagnostic value of the markers separately (using only the NCM or EEG subset) and combined. Results: The classification accuracies were much higher when training and testing on the definite subgroup than on the whole group. Furthermore, we found that the NCM feature subset allowed a better accuracy than the EEG feature subset, both when training and testing on the whole group (NCM 82% vs EEG 72%), as on the definite group only (92% vs 86%). Using both feature subsets together increased the accuracy to 86% in the whole group and 94% in the definite subgroups. Another interesting finding was the presence of two spectral peaks in a considerable number of patients in both groups. Conclusion: Combining EEG with neurochemical biomarkers resulted in differential diagnostic accuracies of 86% in clinically diagnosed AD and FTD patients and of 94% in patients having a definite diagnosis. Furthermore, we found evidence that the slowing down of the dominant EEG rhythm might be a gradual appearance of a slow rhythm and fading out of the normal ground rhythm, rather than a gradual slowing down of the ground rhythm. Finally, we have discovered two differences in the occurrence of the dominant EEG frequency: people lacking a clear dominant peak almost all had definite AD, while people with two peaks more often had FTLD. These unexpected but interesting findings need to be explored further.

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Section: Classificationsupporting

confidence: 85%

Section: Dominant Frequency Peak Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Alzheimer’s disease versus frontotemporal lobar degeneration differential diagnosis combining EEG and neurochemical biomarkers

Laton

Goossens

Wiels

et al. 2019

Preprint

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“…A recent meta-analysis revealed that there is currently insufficient evidence to recommend the use of SPECT scans in routine practice [28]. EEG may be helpful [29][30][31] but it is not usually employed in a dementia clinic.…”

Section: The Variability and Inaccuracy Of The Diagnostic Criteriamentioning

confidence: 99%

Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement

Brzezicki

Kobetić

Neumann³

et al. 2019

Advances in Medical Sciences

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PURPOSE Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life. The rate of misdiagnosis of FTD is very high, with patients often waiting for years without a firm diagnosis. This study investigates the current state of the misdiagnosis of FTD using a novel artificial intelligencebased algorithm. PATIENTS & METHODS An artificial intelligence algorithm has been developed to retrospectively analyse the patient journeys of 47 individuals diagnosed with FTD (age range 52-80). The algorithm analysed the efficiency of patient pathways by utilizing a reward signal of-1 to +1 to assess the symptoms, imaging techniques, and clinical judgement in both behavioural and language variants of the disease. RESULTS On average, every patient was subjected to 4.93 investigations, of which 67.4% were radiological scans. From first presentation it took on average 939 days for a firm diagnosis. The mean time between appointments was 204 days, and the average patient had their diagnosis altered 7.37 times during their journey. The algorithm proposed improvements by evaluating the interventions that resulted in a decreased reward signal to both the individual and the population as a whole. CONCLUSIONS The study proves that the algorithm can efficiently guide clinical practice and improve the accuracy of the diagnosis of FTD whilst making the process of auditing faster and more economically viable.

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“…Furthermore, Van der Meer et al found a decrease in upper-alpha power (10–12 Hz) and an increase in lower-alpha power (8–10 Hz), which may relate to the slowing of the alpha-peak in Alzheimer's dementia (Goossens et al, 2017). Functional connectivity studies have observed an increase in functional connectivity in the beta band [assessed by the phase lag index (Tewarie et al, 2013), and synchronization likelihood (Schoonheim et al, 2013)] and the functional connectivity in the beta band correlated with an overall cognitive score (Tewarie et al, 2013).…”

Section: Neurophysiologymentioning

confidence: 99%

Targeting Cognitive Impairment in Multiple Sclerosis—The Road toward an Imaging-based Biomarker

Schependom

Nagels

2017

Front. Neurosci.

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Multiple Sclerosis (MS) is a neuro-degenerative and -inflammatory disease leading to physical and cognitive impairment, pathological fatigue and depression, and affecting patients' quality of life and employment status. The combination of inflammation, demyelination, and neurodegeneration leads to the emergence of MS lesions, reduced white and gray matter brain volumes, a reduced conduction velocity and microstructural changes in the so-called Normal Appearing White Matter (NAWM). Currently, there are very limited options to treat cognitive impairment and its origin is only poorly understood. Therefore, several studies have attempted to relate clinical scores with features calculated either using T1- and/or FLAIR weighted MR images or using neurophysiology. The aim of those studies is not only to provide an improved understanding of the processes that underlie the different symptoms, but also to develop a biomarker—sensitive to therapy induced change—that could be used to speed up therapeutic developments (e.g., cognitive training/drug discovery/…). Here, we provide an overview of studies that have established relationships between either neuro-anatomical or neurophysiological measures and cognitive outcome scores. We discuss different avenues that may help to improve the prediction of cognitive impairment, and how well we can expect them to predict cognitive scores.

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EEG Dominant Frequency Peak Differentiates Between Alzheimer’s Disease and Frontotemporal Lobar Degeneration

Cited by 18 publications

References 25 publications

Improved Alzheimer’s disease versus frontotemporal lobar degeneration differential diagnosis combining EEG and neurochemical biomarkers

Improved Alzheimer’s disease versus frontotemporal lobar degeneration differential diagnosis combining EEG and neurochemical biomarkers

Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement

Targeting Cognitive Impairment in Multiple Sclerosis—The Road toward an Imaging-based Biomarker

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