Electroencephalography as a Non-Invasive Biomarker of Alzheimer’s Disease: A Forgotten Candidate to Substitute CSF Molecules?

Monllor, Paloma; Cervera‐Ferri, Ana; Lloret, Maria-Angeles; Esteve, Daniel; López, Begoña; Leon, Jose-Luis; Lloret, Ana

doi:10.3390/ijms221910889

Cited by 17 publications

(27 citation statements)

References 149 publications

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“…Numerous lines of evidence have validated the possibility of using EEG to distinguish MCI and AD patients from healthy cohorts with diverse sensitivity and specificity [11]. Overall, previous EEG studies involving both MCI and AD patients reported relatively consistent neural alterations compared to healthy cohort, including decreased alpha and beta rhythms activity and increased delta and theta oscillations, which are probably the most promising neural biomarkers for early detection of AD, due to their good correlations with patients' cognitive function [11][12][13][14][15]. In addition, reduced complexity and coherence in EEG recordings, as well as decreased ratios of theta/gamma and high alpha/low alpha, were also reported as potential biomarkers for the diagnosis of AD [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 97%

“…In particular, previous studies were largely conducted based on limited sample sizes. During the last three decades, as pointed out in a recent study, more than 95% of the studies that focused on EEG-based classification of MCI or AD were conducted with fewer than 100 participants, making relevant findings unconvincing [15]. Moreover, while AD is the most common form of dementia, symptoms of preclinical and early AD mostly overlap with other types of dementia such as frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), and vascular cognitive impairment (VCI) [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology

Jiao

Zhou

et al. 2023

Alz Res Therapy

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Background Electroencephalogram (EEG) has emerged as a non-invasive tool to detect the aberrant neuronal activity related to different stages of Alzheimer’s disease (AD). However, the effectiveness of EEG in the precise diagnosis and assessment of AD and its preclinical stage, amnestic mild cognitive impairment (MCI), has yet to be fully elucidated. In this study, we aimed to identify key EEG biomarkers that are effective in distinguishing patients at the early stage of AD and monitoring the progression of AD. Methods A total of 890 participants, including 189 patients with MCI, 330 patients with AD, 125 patients with other dementias (frontotemporal dementia, dementia with Lewy bodies, and vascular cognitive impairment), and 246 healthy controls (HC) were enrolled. Biomarkers were extracted from resting-state EEG recordings for a three-level classification of HC, MCI, and AD. The optimal EEG biomarkers were then identified based on the classification performance. Random forest regression was used to train a series of models by combining participants’ EEG biomarkers, demographic information (i.e., sex, age), CSF biomarkers, and APOE phenotype for assessing the disease progression and individual’s cognitive function. Results The identified EEG biomarkers achieved over 70% accuracy in the three-level classification of HC, MCI, and AD. Among all six groups, the most prominent effects of AD-linked neurodegeneration on EEG metrics were localized at parieto-occipital regions. In the cross-validation predictive analyses, the optimal EEG features were more effective than the CSF + APOE biomarkers in predicting the age of onset and disease course, whereas the combination of EEG + CSF + APOE measures achieved the best performance for all targets of prediction. Conclusions Our study indicates that EEG can be used as a useful screening tool for the diagnosis and disease progression evaluation of MCI and AD.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology

Jiao

Zhou

et al. 2023

Alz Res Therapy

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“…Electroencephalography is a method to record the electrical activities of cortical pyramidal neurons in the brain ( Monllor et al, 2021 ). When there is a large number of neurons fired synchronously, sufficient postsynaptic potentials can be produced and thus detectable from the scalp of the brain.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, EEG spectral measures, synchronization patterns, and ERP latencies have served as potential indicators to screen individuals with mild cognitive impairment (MCI) and AD dementia ( Garcés et al, 2013 ; Paitel et al, 2021 ; Sedghizadeh et al, 2022 ). EEG spectral analysis captures the fluctuations of the electrical brain signals in the form of frequency with respect to their amplitude ( Monllor et al, 2021 ). In EEG spectral measures, the frequency components (e.g., peak frequency) reflect the speed of neural oscillation and power density features (e.g., spectrum triangular index) reveal the cortical neural synchronization which are often seen in neurodegenerative patients ( Garcés et al, 2013 ; Paitel et al, 2021 ; Sedghizadeh et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Prefrontal EEG slowing, synchronization, and ERP peak latency in association with predementia stages of Alzheimer’s disease

Choi

Ku²,

Doan³

et al. 2023

Front. Aging Neurosci.

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BackgroundEarly screening of elderly individuals who are at risk of dementia allows timely medical interventions to prevent disease progression. The portable and low-cost electroencephalography (EEG) technique has the potential to serve it.ObjectiveWe examined prefrontal EEG and event-related potential (ERP) variables in association with the predementia stages of Alzheimer’s disease (AD).MethodsOne hundred elderly individuals were recruited from the GARD cohort. The participants were classified into four groups according to their amyloid beta deposition (A+ or A−) and neurodegeneration status (N+ or N−): cognitively normal (CN; A−N−, n = 27), asymptomatic AD (aAD; A + N−, n = 15), mild cognitive impairment (MCI) with AD pathology (pAD; A+N+, n = 16), and MCI with non-AD pathology (MCI(−); A−N+, n = 42). Prefrontal resting-state eyes-closed EEG measurements were recorded for five minutes and auditory ERP measurements were recorded for 8 min. Three variables of median frequency (MDF), spectrum triangular index (STI), and positive-peak latency (PPL) were employed to reflect EEG slowing, temporal synchrony, and ERP latency, respectively.ResultsDecreasing prefrontal MDF and increasing PPL were observed in the MCI with AD pathology. Interestingly, after controlling for age, sex, and education, we found a significant negative association between MDF and the aAD and pAD stages with an odds ratio (OR) of 0.58. Similarly, PPL exhibited a significant positive association with these AD stages with an OR of 2.36. Additionally, compared with the MCI(-) group, significant negative associations were demonstrated by the aAD group with STI and those in the pAD group with MDF with ORs of 0.30 and 0.42, respectively.ConclusionSlow intrinsic EEG oscillation is associated with MCI due to AD, and a delayed ERP peak latency is likely associated with general cognitive impairment. MCI individuals without AD pathology exhibited better cortical temporal synchronization and faster EEG oscillations than those with aAD or pAD.SignificanceThe EEG/ERP variables obtained from prefrontal EEG techniques are associated with early cognitive impairment due to AD and non-AD pathology. This result suggests that prefrontal EEG/ERP metrics may serve as useful indicators to screen elderly individuals’ early stages on the AD continuum as well as overall cognitive impairment.

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“…The use of high-density EEG (e.g., >60 channels) now provides a greater degree of spatial resolution ( 8 ). In addition to its clinical use in epilepsy, sleep disorders, and dementia with Lewy bodies ( 8 , 9 ), EEG has been used to investigate cortical activity and functional connectivity in stroke ( 10 , 11 ), consciousness disorders ( 12 ), Alzheimer's disease ( 13 ), Parkinson's disease ( 14 ), schizophrenia ( 15 ), and mood disorders ( 16 ).…”

Section: Introductionmentioning

confidence: 99%

Electroencephalogram (EEG) With or Without Transcranial Magnetic Stimulation (TMS) as Biomarkers for Post-stroke Recovery: A Narrative Review

et al. 2022

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Stroke is one of the leading causes of death and disability. Despite the high prevalence of stroke, characterizing the acute neural recovery patterns that follow stroke and predicting long-term recovery remains challenging. Objective methods to quantify and characterize neural injury are still lacking. Since neuroimaging methods have a poor temporal resolution, EEG has been used as a method for characterizing post-stroke recovery mechanisms for various deficits including motor, language, and cognition as well as predicting treatment response to experimental therapies. In addition, transcranial magnetic stimulation (TMS), a form of non-invasive brain stimulation, has been used in conjunction with EEG (TMS-EEG) to evaluate neurophysiology for a variety of indications. TMS-EEG has significant potential for exploring brain connectivity using focal TMS-evoked potentials and oscillations, which may allow for the system-specific delineation of recovery patterns after stroke. In this review, we summarize the use of EEG alone or in combination with TMS in post-stroke motor, language, cognition, and functional/global recovery. Overall, stroke leads to a reduction in higher frequency activity (≥8 Hz) and intra-hemispheric connectivity in the lesioned hemisphere, which creates an activity imbalance between non-lesioned and lesioned hemispheres. Compensatory activity in the non-lesioned hemisphere leads mostly to unfavorable outcomes and further aggravated interhemispheric imbalance. Balanced interhemispheric activity with increased intrahemispheric coherence in the lesioned networks correlates with improved post-stroke recovery. TMS-EEG studies reveal the clinical importance of cortical reactivity and functional connectivity within the sensorimotor cortex for motor recovery after stroke. Although post-stroke motor studies support the prognostic value of TMS-EEG, more studies are needed to determine its utility as a biomarker for recovery across domains including language, cognition, and hemispatial neglect. As a complement to MRI-based technologies, EEG-based technologies are accessible and valuable non-invasive clinical tools in stroke neurology.

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Electroencephalography as a Non-Invasive Biomarker of Alzheimer’s Disease: A Forgotten Candidate to Substitute CSF Molecules?

Cited by 17 publications

References 149 publications

Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology

Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology

Prefrontal EEG slowing, synchronization, and ERP peak latency in association with predementia stages of Alzheimer’s disease

Electroencephalogram (EEG) With or Without Transcranial Magnetic Stimulation (TMS) as Biomarkers for Post-stroke Recovery: A Narrative Review

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