EEG signals contain detectable biomarkers that indicate the early signs of dementia.
the verdict
SUPPORTED
the evidence backs this
refutedsupported
the weight of evidence
5 sources for · 0 against
Multiple peer-reviewed studies and expert panel reports demonstrate that electroencephalography (EEG) measures, including spectral analysis and functional connectivity patterns, can identify early-stage signs and track prodromal phases of neurodegenerative dementias.
Alzheimer's disease (AD) is the most common neurodegenerative disease among the elderly with a progressive decline in cognitive function significantly affecting quality of life. Both the prevalence and emotional and financial burdens of AD on patients, their families, and society are predicted to grow significantly in the near future, due to a prolongation of the lifespan. Several lines of evidence suggest that modifications of risk-enhancing life styles and initiation of pharmacological and non-pharmacological treatments in the early stage of disease, although not able to modify its course, helps to maintain personal autonomy in daily activities and significantly reduces the total costs of disease management. Moreover, many clinical trials with potentially disease-modifying drugs are devoted to prodromal stages of AD. Thus, the identification of markers of conversion from prodromal form to clinically AD may be crucial for developing strategies of early interventions. The current available markers, including volumetric magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebral spinal fluid (CSF) analysis are expensive, poorly available in community health facilities, and relatively invasive. Taking into account its low cost, widespread availability and non-invasiveness, electroencephalography (EEG) would represent a candidate for tracking the prodromal phases of cognitive decline in routine clinical settings eventually in combination with other markers. In this scenario, the present paper provides an overview of epidemiology, genetic risk factors, neuropsychological, fluid and neuroimaging biomarkers in AD and describes the potential role of EEG in AD investigation, trying in particular to point out whether advanced analysis of EEG rhythms exploring brain function has sufficient specificity/sensitivity/accuracy for the early diagnosis of AD.
Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts - University of Edinburgh Research Explorer Skip to main navigation Skip to search Skip to main content Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts Paolo M. Rossini , Riccardo Di Iorio , Francesco Vecchio , M Anfossi , Claudio Babiloni , M Bozzali , A.C. Bruni , S.F. Cappa , Javier Escudero , F.J. Fraga , P. Giannakopoulos , Bahar Guntekin , Giancarlo Logroscino , C Marra , F Miraglia , F Panza , F Tecchio , Alvaro Pascual-Leone , B.
Original language English Pages (from-to) 1287-1310 Journal Clinical Neurophysiology Volume 131 Issue number 6 Early online date 12 Mar 2020 DOIs https://doi.org/10.1016/j.clinph.2020.03.003 Publication status Published - Jun 2020 Access to Document 10.1016/j.clinph.2020.03.003 Early Diagnosis of Alzheimers Disease Accepted author manuscript, 1.12 MB https://www.sciencedirect.com/science/article/pii/S1388245720300870 Fingerprint Dive into the research topics of 'Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts'. Together they form a unique fingerprint.
J., Giannakopoulos, P., Guntekin, B., Logroscino, G., Marra, C., Miraglia, F., Panza, F., Tecchio, F., Pascual-Leone, A., & Dubois, B. (2020). Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts . Clinical Neurophysiology , 131 (6), 1287-1310. https://doi.org/10.1016/j.clinph.2020.03.003 Rossini, Paolo M. ; Di Iorio, Riccardo ; Vecchio, Francesco et al. / Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts . In: Clinical Neurophysiology . 2020 ; Vol. 131, No. 6. pp. 1287-1310.
@article{9a1e034990044bb194b42da4c1f33a71, title = "Early diagnosis of Alzheimer{\textquoteright}s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts", abstract = "Alzheimer{\textquoteright}s disease (AD) is the most common neurodegenerative disease among the elderly with a progressive decline in cognitive function significantly affecting quality of life. Both the prevalence and emotional and financial burdens of AD on patients, their families, and society are predicted to grow significantly in the near future, due to a prolongation of the lifespan.
Dubois", year = "2020", month = jun, doi = "10.1016/j.clinph.2020.03.003", language = "English", volume = "131", pages = "1287--1310", journal = "Clinical Neurophysiology", issn = "1388-2457", publisher = "Elsevier", number = "6", } Rossini, PM, Di Iorio, R, Vecchio, F, Anfossi, M, Babiloni, C, Bozzali, M, Bruni, AC, Cappa, SF , Escudero, J , Fraga, FJ, Giannakopoulos, P, Guntekin, B, Logroscino, G, Marra, C, Miraglia, F, Panza, F, Tecchio, F, Pascual-Leone, A & Dubois, B 2020, ' Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts ', Clinical Neurophysiology , vol. 131, no. 6, pp. 1287-1310.
https://doi.org/10.1016/j.clinph.2020.03.003 Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts. / Rossini, Paolo M.; Di Iorio, Riccardo; Vecchio, Francesco et al. In: Clinical Neurophysiology , Vol. 131, No. 6, 06.2020, p. 1287-1310. Research output : Contribution to journal › Review article › peer-review TY - JOUR T1 - Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of
Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. An I.F.C.N.-sponsored panel of Experts . Clinical Neurophysiology . 2020 Jun;131(6):1287-1310. Epub 2020 Mar 12. doi: 10.1016/j.clinph.2020.03.003
AbstractIntroductionDementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline—including Alzheimer's disease (AD) dementia—does not develop in few days; disease mechanisms act progressively for several years before clinical evidence.MethodsA preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to—or, more accurately, is already in a prodromal form of—AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods.ResultsGraph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers.DiscussionOn clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.
379 blackwellopen Alzheimer's & Dementia Alzheimers Dement PMC10083993 10083993 10083993 35388959 10.1002/alz.12645 Early dementia diagnosis, MCI‐to‐dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis Rossini Paolo Maria 1 ✉ Miraglia Francesca 1 2 Vecchio Fabrizio 1 2 1 Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy 2 Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy * Correspondence , Paolo Maria Rossini, Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy.
Recent evidence supports the hypothesis that an early diagnosis and prognosis of AD during prodromal stage in amnestic MCI (aMCI) subjects might be remarkably facilitated by a proper combination of multimodal instrumental assays collecting biological (genomics, metallomics, proteomics in cerebrospinal fluid [CSF] or blood samples), structural neuroimaging (i.e., structural magnetic resonance imaging [MRI]), functional neuroimaging (positron emission tomography [PET]), and neurophysiological (i.e., electroencephalography [EEG]) biomarkers.
An SVM algorithm trained on a training dataset can generate feature weights corresponding to the relative contribution of an individual feature to successful differentiation of two groups. Following this, the classifier can be applied to a separate testing dataset to assess the accuracy of the classifier in differentiating two groups of subjects/patients. We present in detail the approach for EEG analysis, but the same is replicable for any biomarker or biomarker combination (Figure 1 ). FIGURE 1 Different steps of a machine learning study of electroencephalogram (EEG) data toward the early prediction of mild cognitive impairment subjects’ conversion to dementia.
SVM, support vector machine As previously stated, synaptic transmission and functional brain connectivity (later in the disease course also structural) are the initial targets within a brain under neurodegenerative attack as in dementias. It is widely accepted that neurophysiological techniques, namely the EEG analysis, provide remarkable information on both parameters (synaptic transmission and functional connectivity). On this basic assumption, it is argued that EEG signal analysis for connectivity can probe quite nicely the synaptopathy characterizing early dementia stages.
When graph theory analysis of EEG signals is combined with APOE genotyping an extremely high level of accuracy in identification of AD from cognitively unimpaired elderly as well as MCI‐prodromal‐to‐AD cases can be reached. 17 Within the ‘‘quest’’ for the “best performing biomarker” one of the main ‘‘competitors’’ are neuroimaging methods. Seminal guidelines by Dubois et al. 33 have emphasized functional
Indeed, modern software for EEG signal analysis can quantify on an individual basis slowing of brain EEG rhythms in AD and MCI subjects, and measure abnormalities of synchronization mechanisms of rhythmic oscillations (EEG/MEG frequency bands) of cortical pyramidal neuronal assemblies, which are at the basis of defective information transfer across brain regions 17 , 29 , 44 , 45 , 46 being the synaptic dysfunction leading to degradation of information transfer an early process in the neurodegenerative cascade.
Within this theoretical framework, methods assessing mechanisms of synchronization and de‐synchronization of rhythmic neuronal discharges, as measured via EEG scalp recordings, have been used to assess early connectivity dysfunction. 45 , 47 Several studies have showed signs of MCI and dementia in EEG signals (such as in spectral analysis of EEG rhythms and connectivity parameters) and they were used as features for classification in the machine learning environment.
CONFLICTS OF INTEREST The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ACKNOWLEDGMENTS This work was partially supported by H2020‐SC1‐BHC‐2018‐2020 Grant (964220 — AI‐Mind) and by Italian Ministry of Health for Institutional Research (Ricerca corrente). Furthermore, the authors are grateful to the Merck Sharp & Dohme, MSD for the sponsorship. Rossini PM, Miraglia F, Vecchio F. Early dementia diagnosis, MCI‐to‐dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis.
The clinical distinction of frontotemporal dementia (FTD) and Alzheimer's disease (AD) may be difficult. In this narrative review we summarize and discuss the most relevant electroencephalography (EEG) studies which have been applied to demented patients with the aim of distinguishing the various types of cognitive impairment. EEG studies revealed that patients at an early stage of FTD or AD displayed different patterns in the cortical localization of oscillatory activity across different frequency bands and in functional connectivity. Both classical EEG spectral analysis and EEG topography analysis are able to differentiate the different dementias at group level. The combination of standardized low-resolution brain electromagnetic tomography (sLORETA) and power parameters seems to improve the sensitivity, but spectral and connectivity biomarkers able to differentiate single patients have not yet been identified. The promising EEG findings should be replicated in larger studies, but could represent an additional useful, noninvasive, and reproducible diagnostic tool for clinical practice.
EEG studies revealed that patients at an early stage of FTD or AD displayed different patterns in the cortical localization of oscillatory activity across different frequency bands and in functional connectivity. Both classical EEG spectral analysis and EEG topography analysis are able to differentiate the different dementias at group level. The combination of standardized low-resolution brain electromagnetic tomography (sLORETA) and power parameters seems to improve the sensitivity, but spectral and connectivity biomarkers able to differentiate single patients have not yet been identified.
In contrast, the electroencephalogram (EEG) represents a noninvasive technique that is cheap, highly available, and sensitive to changes in the functional state of the human brain. We aimed in this narrative review to summarize and discuss the most relevant studies dealing with EEG techniques in order to distinguish FTD from AD, other dementias, and healthy subjects. 2.
EEG Analysis The EEG represents an old and inexpensive method that has been employed for many years in dementia research. EEG has been examined in demented patients in order to differentiate individuals with various types and severity of cognitive impairment from healthy subjects. While visual EEG analysis still prevails in routine clinical practice, the differential diagnosis of the various subtypes of dementia relies on quantitative EEG (qEEG), where extensive technical knowledge is needed in the field of digital signal processing.
One of the most frequently used research methods is the spectral analysis, and therefore sometimes the term qEEG is used to indicate quantitative spectral analysis. However, qEEG offers a wider spectrum of possible applications. By means of computational algorithms, such as fast Fourier transform (FFT) or autoregressive (AR) models [ 5 – 10 ], the characteristics of the EEG can be documented in an objective and quantitative way. Moreover, the EEG analysis is not restricted
Therefore, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings. 4. Discussion Most studies on the differentiation among FTD, AD, DLB, or PDD, were done using SPECT, PET, or MRI. However, besides their expensiveness, these imaging techniques are not sufficient to provide information on the pathophysiological mechanisms of dementia, in particular in the early stages.
aimed to evaluate whether also at an early stage of FTD EEG differences can be found in comparison to mild AD and HC, using a combined spectral and sLORETA approach. In AD patients compared to HC, EEG spectral analysis showed a significant occipital power increase within the δ band but a significant parietooccipital α 1 and temporal α 2 power decrease and widespread β 1 and β 2 power decrease [ 25 ]. Notably, these findings are consistent with those of many previous quantitative EEG studies [ 20 , 21 , 32 – 34 ].
Slowing of the EEG frequency spectrum, which has long been known to be a hallmark in dementia, was confirmed to represent one of the two most significant features for differential diagnosis. This is well in line with the results of previous studies [ 48 ]. Interestingly, qEEG features correlated with the severity of disease measured by MMSE scores. EEG variability in DLB may be associated with the fluctuating cognition seen in these patients. This might have clinical implications for the diagnosis of DLB.
TMS-EEG has been demonstrated to be a suitable, reliable, and affordable tool for detecting changes in cortical excitability, connectivity, and functional synchronization of EEG activity both in normal aging [ 52 , 53 ] and in AD [ 54 ]. The analysis of TMS-evoked oscillations could possibly allow detecting subtle and area-specific alterations of natural oscillatory activities [ 55 ] with good sensitivity and specificity for different types of dementia. Some systems do already allow for a quantitative spectral analysis, but further processing of the signal is highly warranted for clinical decision making.
Modern qEEG scoring systems such as, for instance the dementia index SIGLA ( http://www.mentiscura.com ), give an exact answer to the question whether a patient suffers from DLB or whether he will develop AD. We anticipate that such systems will enter the clinical arena within the next 10 years and ease the use of qEEG for clinicians tremendously. In conclusion, application of EEG techniques in neurodegenerative diseases has provided important pathophysiological insights, leading to the development of pathogenic and diagnostic biomarkers that could be used in the clinical setting and therapeutic trials. Conflicts of Interest The authors declare no conflict of interest. References
Accounts for approximately 75% of sCJD cases. Characterized by rapidly progressive dementia, myoclonus, and typical EEG findings. Neuropathology: Synaptic-type
Creutzfeldt–Jakob disease (CJD) is an incurable, always fatal, neurodegenerative disease belonging to the transmissible spongiform encephalopathy (TSE) group, also known as prion diseases. Early symptoms include memory problems, behavioral changes, poor coordination, visual disturbances and auditory disturbances. Later symptoms include dementia, involuntary movements, blindness, deafness, weaknes
Testing for CJD has historically been problematic, due to the nonspecific nature of early symptoms and difficulty in safely obtaining brain tissue for confirmation. The diagnosis may initially be suspected in a person with rapidly progressing dementia, particularly when it is also found with the characteristic medical signs and symptoms such as involuntary muscle jerking, difficulty with coordination/balance and walking, and visual disturbances. Further testing can support the diagnosis and may include:
Focal or diffuse diffusion-restriction involving the cerebral cortex or basal ganglia. The most characteristic and striking cortical abnormality has been called "cortical ribboning" or "cortical ribbon sign" due to hyperintensities resembling ribbons appearing in the cortex on MRI. The involvement of the thalamus can be found in sCJD, is even stronger and constant in vCJD.
Varying degree of symmetric T2 hyperintense signal changes in the basal ganglia (i.e., caudate and putamen), and to a lesser extent globus pallidus and occipital cortex.
Brain FDG PET-CT tends to be markedly abnormal, and is increasingly used in the investigation of dementias.
Everything we examined (5)
This check searched the claim as stated. It did not run a separate search for evidence against it.