A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Farina, Francesca R; Emek‐Savaş, Derya Durusu; Rueda‐Delgado, Laura M.; Boyle, Rory; Kiiski, Hanni; Yener, Görsev; Whelan, Robert

doi:10.1016/j.neuroimage.2020.116795

Cited by 70 publications

(63 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is in line with previous reports of frontal Theta and Delta increase during cognitively demanding tasks [36]–[38]. This difference was not present in the senior population, supporting the notion that Theta and Delta may be indicative to cognitive decline and serve as a predictor of deterioration status, consistent with previous findings [10], [11]. Finally, A0 showed separation between high and low cognitive load levels within the cognitive task, further supporting the ability of A0 to differentiate between cognitive states.…”

Section: Discussionsupporting

confidence: 93%

“…The biomarkers used here were calculated on a different dataset to avoid the risks associated with classification studies such as overfitting [32]. This is unlike other studies that use classifiers trained and tested via cross validation on the same dataset [10], [20], [21]. Specifically, the pre-extracted biomarkers used here, ST4 and VC9, were previously considered in a study performed on young healthy subjects.…”

Section: Discussionmentioning

confidence: 99%

“…Electroencephalography (EEG) offer such noninvasive and relatively inexpensive screening tool for cognitive assessment [9]. EEG studies investigating cognitive decline highlight the role of theta power as a possible indicator for early detection of cognitive decline [10]–[12]. Specifically, it was found that frontal theta activity differs substantially in cognitively impaired subjects performing cognitive tasks compared to healthy seniors.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, it was found that frontal theta activity differs substantially in cognitively impaired subjects performing cognitive tasks compared to healthy seniors. Studies suggest that novel diagnostic classification based on EEG signals could be even more useful than frontal theta for differentiating between clinical stages [10]. The development of machine learning (ML) alongside high-level signal processing, has largely contributed to the extraction of useful information from the raw EEG signal [13].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Detection of cognitive decline using a single-channel EEG with an interactive assessment tool

Molcho

Maimon

Pressburger

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: Cognitive decline remains highly underdiagnosed in the community despite extensive efforts to find novel biomarkers to detect it. Finding objective screening tools for cognitive decline may improve early diagnosis of Alzheimer′s disease (AD) in the community. EEG biomarkers based on machine learning (ML) may offer a noninvasive low-coast approach for identifying cognitive decline with clinically useful accuracy. However, most of the studies use multi-electrode systems which are not vastly accessible. This study aims to evaluate the ability to extract cognitive decline biomarkers using a wearable single-channel EEG system with a short interactive cognitive assessment tool. Methods: Seniors in different clinical stages of cognitive decline (healthy to mild dementia, n=60) and young healthy participants (n=22) performed a cognitive assessment which included auditory detection and resting state tasks, while being recorded with a single-channel EEG (Aurora by Neurosteer®). Seniors′ MMSE scores were obtained by clinicians and used in allocating the groups (Healthy: MMSE>28; MCI-R: 28>MMSE>24; and MD: MMSE<24). Data analysis included standard frequency bands as well as three novel biomarkers, A0, ST4 and VC9, previously extracted from a different dataset to minimize overfitting risks. Results: Correlation between MMSE scores and reaction times was significant, validating the cognitive assessment tool. Individual MMSE scores correlated significantly with two of the EEG biomarkers: A0 and ST4. Furthermore, A0 and ST4 showed significant separation between groups of seniors with high vs. low MMSE scores, as well as the healthy young group. ST4 separated between the healthy groups (young and seniors) and the low MMSE (MD) group. Conversely, A0 differentiated between the healthy young group and all three groups of seniors. In the healthy young group, activity of Theta band and VC9 biomarker significantly increased with higher cognitive load, with both separating between the high cognitive load task and resting state. Furthermore, VC9 showed a finer separation between high and low cognitive load levels within the cognitive task. This was not shown in the senior groups, suggesting VC9 may be indicative to cognitive decline in the senior population. Conclusions: These results introduce novel biomarkers which potentially detect cognitive decline, obtained by a wearable single-channel EEG with a short interactive cognitive assessment. Such objective screening tools can be used on a large scale to detect cognitive decline and potentially allow early diagnosis of AD in every clinic.

show abstract

Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detection of cognitive decline using a single-channel EEG with an interactive assessment tool

Molcho

Maimon

Pressburger

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…For over 80 years, EEG has been widely used in research of diseases of the nervous system, but the clinical significance of a number of phenomena detected by it continues to be the subject of debate due to the complexity of the EEG signal. For example, here are some papers that describe how EEG diagnostics can be used in cognitive impairment, in case Alzheimer's disease and other [1,2]. With the improvement of computer diagnostics software, along with the generally accepted mathematical methods of processing biosignals, the use of multidimensional statistical methods that allow obtaining sorted information from the total number of data sets is promising.…”

Section: Introductionmentioning

confidence: 99%

Application of multidimensional scaling method for analysis of EEG features in patients with dyscirculatory encephalopathy

et al. 2020

View full text Add to dashboard Cite

The object of our work is to test the possibility of using the method of multidimensional scaling to identify the features of EEG in order to classify subjects in patients with dyscirculatory encephalopathy and healthy people by establishing differences in the graphs of the dynamics of the location of vectors in the multidimensional coordinate space. For healthy subjects, in almost all tests, there is a coincidence or close location of the spatial coordinates of the multidimensional scaling. For patients with dyscirculatory encephalopathy, there is no marked coincidence of coordinates; however, in some cases it is possible to group the data into local areas according to individual tests. The use of the method of multidimensional scaling in the diagnosis of dyscirculatory encephalopathy allows the analysis of the patient's condition, makes it possible to identify the degree of the disease

show abstract

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole‐brain modeling

Coronel‐Oliveros,

Gómez,

Ranasinghe

et al. 2024

Alzheimer's & Dementia

View full text Add to dashboard Cite

INTRODUCTIONAlzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost‐effective technique for studying dementia globally, but lacks mechanistic models and produces non‐replicable results.METHODSWe developed a generative whole‐brain model that combines EEG source‐level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls).RESULTSMetaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings.DISCUSSIONThe results provide a novel agenda for developing mechanistic model‐inspired characterization and therapies in clinical, translational, and computational neuroscience settings.

show abstract

A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment

Cited by 70 publications

References 60 publications

Detection of cognitive decline using a single-channel EEG with an interactive assessment tool

Detection of cognitive decline using a single-channel EEG with an interactive assessment tool

Application of multidimensional scaling method for analysis of EEG features in patients with dyscirculatory encephalopathy

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole‐brain modeling

Contact Info

Product

Resources

About