Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study

Making Brains run Faster: are they Becoming Smarter?

A brief overview of structural and functional brain characteristics related to g is presented in the light of major neurobiological theories of intelligence: Neural Efficiency, P-FIT and Multiple-Demand system. These theories provide a framework to discuss the main objective of the paper: what is the relationship between individual alpha frequency (IAF) and g? Three studies were conducted in order to investigate this relationship: two correlational studies and a third study in which we experimentally induced changes in IAF by means of transcranial alternating current stimulation (tACS). (1) In a large scale study (n = 417), no significant correlations between IAF and IQ were observed. However, in males IAF positively correlated with mental rotation and shape manipulation and with an attentional focus on detail. (2) The second study showed sex-specific correlations between IAF (obtained during task performance) and scope of attention in males and between IAF and reaction time in females. (3) In the third study, individuals’ IAF was increased with tACS. The induced changes in IAF had a disrupting effect on male performance on Raven’s matrices, whereas a mild positive effect was observed for females. Neuro-electric activity after verum tACS showed increased desynchronization in the upper alpha band and dissociation between fronto-parietal and right temporal brain areas during performance on Raven’s matrices. The results are discussed in the light of gender differences in brain structure and activity.

Regularized Linear Discriminant Analysis of EEG Features in Dementia Patients

The present study explores if EEG spectral parameters can discriminate between healthy elderly controls (HC), Alzheimer's disease (AD) and vascular dementia (VaD) using. We considered EEG data recorded during normal clinical routine with 114 healthy controls (HC), 114 AD, and 114 VaD patients. The spectral features extracted from the EEG were the absolute delta power, decay from lower to higher frequencies, amplitude, center and dispersion of the alpha power and baseline power of the entire frequency spectrum. For discrimination, we submitted these EEG features to regularized linear discriminant analysis algorithm with a 10-fold cross-validation. To check the consistency of the results obtained by our classifiers, we applied bootstrap statistics. Four binary classifiers were used to discriminate HC from AD, HC from VaD, AD from VaD, and HC from dementia patients (AD or VaD). For each model, we measured the discrimination performance using the area under curve (AUC) and the accuracy of the cross-validation (cv-ACC). We applied this procedure using two different sets of predictors. The first set considered all the features extracted from the 22 channels. For the second set of features, we automatically rejected features poorly correlated with their labels. Fairly good results were obtained when discriminating HC from dementia patients with AD or VaD (AUC = 0.84). We also obtained AUC = 0.74 for discrimination of AD from HC, AUC = 0.77 for discrimination of VaD from HC, and finally AUC = 0.61 for discrimination of AD from VaD. Our models were able to separate HC from dementia patients, and also and to discriminate AD from VaD above chance. Our results suggest that these features may be relevant for the clinical assessment of patients with dementia.

Using Magnetoencephalography to Study Patterns of Brain Magnetic Activity in Alzheimer’s Disease

The use of magnetoencephalography to study neurophysiologic abnormalities associated with Alzheimer’s disease is reviewed. The most consistent observation is that Alzheimer’s disease patients exhibit an increase in focal slow-wave activity that covaried with cognitive performance. It is still unclear whether generation of focal slow-wave activity precedes or is a consequence of Alzheimer’s disease-related neuropathology. Also reviewed is the use of magnetoencephalography to identify early functional changes preceding the diagnosis of dementia. Magnetoencephalography detected neurophysiologic abnormalities associated with cognitive deficits before the diagnosis of mild cognitive impairment. This is supported by evidence presented suggesting that some patients with subjective cognitive complaints, without evidence of dementia, show an increase in focal slow-wave generators. Further research is needed to determine whether the outstanding spatial and temporal resolution of the magnetoencephalography technique could complement other neuroimaging techniques in identifying neurophysiologic abnormalities preceding the diagnosis of Alzheimer’s disease and mild cognitive impairment.

Aging Affects Neural Synchronization to Speech-Related Acoustic Modulations

As people age, speech perception problems become highly prevalent, especially in noisy situations. In addition to peripheral hearing and cognition, temporal processing plays a key role in speech perception. Temporal processing of speech features is mediated by synchronized activity of neural oscillations in the central auditory system. Previous studies indicate that both the degree and hemispheric lateralization of synchronized neural activity relate to speech perception performance. Based on these results, we hypothesize that impaired speech perception in older persons may, in part, originate from deviances in neural synchronization. In this study, auditory steady-state responses that reflect synchronized activity of theta, beta, low and high gamma oscillations (i.e., 4, 20, 40, and 80 Hz ASSR, respectively) were recorded in young, middle-aged, and older persons. As all participants had normal audiometric thresholds and were screened for (mild) cognitive impairment, differences in synchronized neural activity across the three age groups were likely to be attributed to age. Our data yield novel findings regarding theta and high gamma oscillations in the aging auditory system. At an older age, synchronized activity of theta oscillations is increased, whereas high gamma synchronization is decreased. In contrast to young persons who exhibit a right hemispheric dominance for processing of high gamma range modulations, older adults show a symmetrical processing pattern. These age-related changes in neural synchronization may very well underlie the speech perception problems in aging persons.

Clinical and biomarker profiling of prodromal Alzheimer's disease in workpackage 5 of the Innovative Medicines Initiative PharmaCog project: a 'European ADNI study'

BACKGROUND

In the field of Alzheimer's disease (AD), the validation of biomarkers for early AD diagnosis and for use as a surrogate outcome in AD clinical trials is of considerable research interest.

OBJECTIVE

To characterize the clinical profile and genetic, neuroimaging and neurophysiological biomarkers of prodromal AD in amnestic mild cognitive impairment (aMCI) patients enrolled in the IMI WP5 PharmaCog (also referred to as the European ADNI study).

METHODS

A total of 147 aMCI patients were enrolled in 13 European memory clinics. Patients underwent clinical and neuropsychological evaluation, magnetic resonance imaging (MRI), electroencephalography (EEG) and lumbar puncture to assess the levels of amyloid β peptide 1-42 (Aβ42), tau and p-tau, and blood samples were collected. Genetic (APOE), neuroimaging (3T morphometry and diffusion MRI) and EEG (with resting-state and auditory oddball event-related potential (AO-ERP) paradigm) biomarkers were evaluated.

RESULTS

Prodromal AD was found in 55 aMCI patients defined by low Aβ42 in the cerebrospinal fluid (Aβ positive). Compared to the aMCI group with high Aβ42 levels (Aβ negative), Aβ positive patients showed poorer visual (P = 0.001), spatial recognition (P < 0.0005) and working (P = 0.024) memory, as well as a higher frequency of APOE4 (P < 0.0005), lower hippocampal volume (P = 0.04), reduced thickness of the parietal cortex (P < 0.009) and structural connectivity of the corpus callosum (P < 0.05), higher amplitude of delta rhythms at rest (P = 0.03) and lower amplitude of posterior cingulate sources of AO-ERP (P = 0.03).

CONCLUSION

These results suggest that, in aMCI patients, prodromal AD is characterized by a distinctive cognitive profile and genetic, neuroimaging and neurophysiological biomarkers. Longitudinal assessment will help to identify the role of these biomarkers in AD progression.

Corticothalamic network dysfunction and behavioral deficits in a mouse model of Alzheimer's disease

Alzheimer's disease is associated with cognitive decline and seizures. Growing evidence indicates that seizures contribute to cognitive deficits early in disease, but how they develop and impact cognition are unclear. To investigate potential mechanisms, we studied a mouse model that overexpresses mutant human amyloid precursor protein with high levels of amyloid beta (Aβ). These mice develop generalized epileptiform activity, including nonconvulsive seizures, consistent with alterations in corticothalamic network activity. Amyloid precursor protein mice exhibited reduced activity marker expression in the reticular thalamic nucleus, a key inhibitory regulatory nucleus, and increased activity marker expression in downstream thalamic relay targets that project to cortex and limbic structures. Slice recordings revealed impaired cortical inputs to the reticular thalamic nucleus that may contribute to corticothalamic dysfunction. These results are consistent with our findings of impaired sleep maintenance in amyloid precursor protein mice. Finally, the severity of sleep impairments predicted the severity of deficits in Morris water maze, suggesting corticothalamic dysfunction may relate to hippocampal dysfunction, and may be a pathophysiological mechanism underlying multiple behavioral and cognitive alterations in Alzheimer's disease.

Delta Activity at Sleep Onset and Cognitive Performance in Community-Dwelling Older Adults

STUDY OBJECTIVES

Frontal intermittent rhythmic delta activity (FIRDA) has long been considered to be an abnormal variant in the electroencephalogram (EEG) among older adults. Prior work also indicates a predominance of slow wave EEG activity among patients with dementia. However, instability of state control occurring with aging generally and among many neurodegenerative diseases raises the possibility that FIRDA might represent the intrusion of sleep related elements of the EEG into the waking state. We examined delta activity at sleep onset (DASO) in community-dwelling, older adults without dementia, and examined whether this activity is related to poorer cognitive performance.

METHODS

153 community-dwelling, older adults without dementia underwent overnight polysomnography and measures of global cognition, delayed verbal memory, information processing speed, attention, inhibition, verbal naming, and visuospatial ability. Delta activity during sleep/wake transitions (scored either as Waking or N1) was analyzed visually.

RESULTS

Participants were 83 women and 70 men, mean age 71.3 ± 0.6 y. DASO was present in 30 participants (19.6%). Age, years of education, sex, and body mass index did not differ between DASO (+) and (-) groups. Multiple regression analyses indicated faster reading of the Stroop color words in DASO (+) subjects (P = 0.007). None of the other cognitive domains differed between the two groups.

CONCLUSIONS

DASO was relatively common in our sample of community-dwelling, older adults without dementia. DASO was not associated with poorer performance on any cognitive domain. Instead, individuals with DASO demonstrated better performance on a simple reading task. Although these findings suggest that an abnormal EEG activity may represent normal variation, our work underscores the importance of distinguishing DASO from FIRDA when examining sleep in older adults.

COMMENTARY

A commentary on this article appears in this issue on page 725.

Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG Rhythms

Previous studies have shown abnormal power and functional connectivity of resting state electroencephalographic (EEG) rhythms in groups of Alzheimer's disease (AD) compared to healthy elderly (Nold) subjects. Here we tested the best classification rate of 120 AD patients and 100 matched Nold subjects using EEG markers based on cortical sources of power and functional connectivity of these rhythms. EEG data were recorded during resting state eyes-closed condition. Exact low-resolution brain electromagnetic tomography (eLORETA) estimated the power and functional connectivity of cortical sources in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz) were the frequency bands of interest. The classification rates of interest were those with an area under the receiver operating characteristic curve (AUROC) higher than 0.7 as a threshold for a moderate classification rate (i.e., 70%). Results showed that the following EEG markers overcame this threshold: (i) central, parietal, occipital, temporal, and limbic delta/alpha 1 current density; (ii) central, parietal, occipital temporal, and limbic delta/alpha 2 current density; (iii) frontal theta/alpha 1 current density; (iv) occipital delta/alpha 1 inter-hemispherical connectivity; (v) occipital-temporal theta/alpha 1 right and left intra-hemispherical connectivity; and (vi) parietal-limbic alpha 1 right intra-hemispherical connectivity. Occipital delta/alpha 1 current density showed the best classification rate (sensitivity of 73.3%, specificity of 78%, accuracy of 75.5%, and AUROC of 82%). These results suggest that EEG source markers can classify Nold and AD individuals with a moderate classification rate higher than 80%.

Discrimination of mild cognitive impairment and Alzheimer's disease using transfer entropy measures of scalp EEG

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.

Neuroplastic effects of combined computerized physical and cognitive training in elderly individuals at risk for dementia: an eLORETA controlled study on resting states

The present study investigates whether a combined cognitive and physical training may induce changes in the cortical activity as measured via electroencephalogram (EEG) and whether this change may index a deceleration of pathological processes of brain aging. Seventy seniors meeting the clinical criteria of mild cognitive impairment (MCI) were equally divided into 5 groups: 3 experimental groups engaged in eight-week cognitive and/or physical training and 2 control groups: active and passive. A 5-minute long resting state EEG was measured before and after the intervention. Cortical EEG sources were modelled by exact low resolution brain electromagnetic tomography (eLORETA). Cognitive function was assessed before and after intervention using a battery of neuropsychological tests including the minimental state examination (MMSE). A significant training effect was identified only after the combined training scheme: a decrease in the post- compared to pre-training activity of precuneus/posterior cingulate cortex in delta, theta, and beta bands. This effect was correlated to improvements in cognitive capacity as evaluated by MMSE scores. Our results indicate that combined physical and cognitive training shows indices of a positive neuroplastic effect in MCI patients and that EEG may serve as a potential index of gains versus cognitive declines and neurodegeneration. This trial is registered with ClinicalTrials.gov Identifier NCT02313935.

EEG Spectral Features Discriminate between Alzheimer's and Vascular Dementia

Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n = 77) and VaD (n = 77) and healthy elderly normal controls (NC) (n = 77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.

Frequent and discriminative subnetwork mining for mild cognitive impairment classification

Recent studies on brain networks have suggested that many brain diseases, such as Alzheimer's disease and mild cognitive impairment (MCI), are related to a large-scale brain network, rather than individual brain regions. However, it is challenging to find such a network from the whole brain network due to the complexity of brain networks. In this article, the authors propose a novel method to mine the discriminative subnetworks for classifying MCI patients from healthy controls (HC). Specifically, the authors first extract a set of frequent subnetworks from each of the two groups (i.e., MCI and HC), respectively. Then, measure the discriminative ability of those frequent subnetworks using the graph kernel-based classification method and select the most discriminative subnetworks for subsequent classification. The results on the functional connectivity networks of 12 MCI and 25 HC show that this method can obtain competitive results compared with state-of-the-art methods on MCI classification.

Review of evoked and event-related delta responses in the human brain

In the last decade, the brain's oscillatory responses have invaded the literature. The studies on delta (0.5-3.5Hz) oscillatory responses in humans upon application of cognitive paradigms showed that delta oscillations are related to cognitive processes, mainly in decision making and attentional processes. The present manuscript comprehensively reviews the studies on delta oscillatory responses upon cognitive stimulation in healthy subjects and in different pathologies, namely Alzheimer's disease, Mild Cognitive Impairment (MCI), bipolar disorder, schizophrenia and alcoholism. Further delta oscillatory response upon presentation of faces, facial expressions, and affective pictures are reviewed. The relationship between pre-stimulus delta activity and post-stimulus evoked and event-related responses and/or oscillations is discussed. Cross-frequency couplings of delta oscillations with higher frequency windows are also included in the review. The conclusion of this review includes several important remarks, including that delta oscillatory responses are involved in cognitive and emotional processes. A decrease of delta oscillatory responses could be a general electrophysiological marker for cognitive dysfunction (Alzheimer's disease, MCI, bipolar disorder, schizophrenia and alcoholism). The pre-stimulus activity (phase or amplitude changes in delta activity) has an effect on post-stimulus EEG responses.

Frontal delta event-related oscillations relate to frontal volume in mild cognitive impairment and healthy controls

Amnesic mild cognitive impairment (MCI) represents a risk of developing Alzheimer's disease (AD), but not all MCI subjects progress to dementia of AD type. Magnetic resonance imaging (MRI) of cortical and hippocampal atrophy supports early diagnosis of AD in MCI subjects, while frontal event-related oscillations (EROs) at delta frequencies (<4Hz) are appealing markers for this purpose, as they are both cost-effective and largely available. The present study tested the hypothesis that these EROs reflect cortical frontal neurodegeneration in the continuum between normal and amnesic MCI subjects. EROs and volumetric MRI data were recorded in 28 amnesic MCI and in 28 healthy elderly controls (HCs). EROs were collected during a standard visual oddball paradigm including frequent (66.6%) and rare (33.3%; targets to be mentally counted) stimuli. Peak-to-peak amplitude of delta target EROs (<4Hz) was measured. Volume of frontal cortex was estimated from MRIs. Frontal volume was lower in MCI compared to the HC group. Furthermore, widespread delta target EROs were lower in amplitude in the former than in the latter group. Finally, there was a positive correlation between frontal volume and frontal delta target EROs in MCI and HC subjects as a whole group. These results suggest that frontal delta EROs reflect frontal neurodegeneration in the continuum between normal and amnesic MCI subjects.

Occipital sources of resting-state alpha rhythms are related to local gray matter density in subjects with amnesic mild cognitive impairment and Alzheimer's disease

Occipital sources of resting-state electroencephalographic (EEG) alpha rhythms are abnormal, at the group level, in patients with amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here, we evaluated the hypothesis that amplitude of these occipital sources is related to neurodegeneration in occipital lobe as measured by magnetic resonance imaging. Resting-state eyes-closed EEG rhythms were recorded in 45 healthy elderly (Nold), 100 MCI, and 90 AD subjects. Neurodegeneration of occipital lobe was indexed by weighted averages of gray matter density, estimated from structural MRIs. EEG rhythms of interest were alpha 1 (8-10.5 Hz) and alpha 2 (10.5-13 Hz). EEG cortical sources were estimated by low-resolution brain electromagnetic tomography. Results showed a positive correlation between occipital gray matter density and amplitude of occipital alpha 1 sources in Nold, MCI, and AD subjects as a whole group (r = 0.3, p = 0.000004, N = 235). Furthermore, there was a positive correlation between the amplitude of occipital alpha 1 sources and cognitive status as revealed by Mini Mental State Examination score across all subjects (r = 0.38, p = 0.000001, N = 235). Finally, amplitude of occipital alpha 1 sources allowed a moderate classification of individual Nold and AD subjects (sensitivity: 87.8%; specificity: 66.7%; area under the receiver operating characteristic curve: 0.81). These results suggest that the amplitude of occipital sources of resting-state alpha rhythms is related to AD neurodegeneration in occipital lobe along pathologic aging.

A novel method of early diagnosis of Alzheimer's disease based on EEG signals

Studies have reported that electroencephalogram signals in Alzheimer's disease patients usually have less synchronization than those of healthy subjects. Changes in electroencephalogram signals start at early stage but, clinically, these changes are not easily detected. To detect this perturbation, three neural synchrony measurement techniques: phase synchrony, magnitude squared coherence, and cross correlation are applied to three different databases of mild Alzheimer's disease patients and healthy subjects. We have compared the right and left temporal lobes of the brain with the rest of the brain areas (frontal, central, and occipital) as temporal regions are relatively the first ones to be affected by Alzheimer's disease. Moreover, electroencephalogram signals are further classified into five different frequency bands (delta, theta, alpha beta, and gamma) because each frequency band has its own physiological significance in terms of signal evaluation. A new approach using principal component analysis before applying neural synchrony measurement techniques has been presented and compared with Average technique. The simulation results indicated that applying principal component analysis before synchrony measurement techniques shows significantly better results as compared to the lateral one. At the end, all the aforementioned techniques are assessed by a statistical test (Mann-Whitney U test) to compare the results.

The application of electro- and magneto-encephalography in tinnitus research - methods and interpretations

In recent years, there has been a significant increase in the use of electroencephalography (EEG) and magnetoencephalography (MEG) to investigate changes in oscillatory brain activity associated with tinnitus with many conflicting results. Current view of the underlying mechanism of tinnitus is that it results from changes in brain activity in various structures of the brain as a consequence of sensory deprivation. This in turn gives rise to increased spontaneous activity and/or synchrony in the auditory centers but also involves modulation from non-auditory processes from structures of the limbic and paralimbic system. Some of the neural changes associated with tinnitus may be assessed non-invasively in human beings with MEG and EEG (M/EEG) in ways, which are superior to animal studies and other non-invasive imaging techniques. However, both MEG and EEG have their limitations and research results can be misinterpreted without appropriate consideration of these limitations. In this article, I intend to provide a brief review of these techniques, describe what the recorded signals reflect in terms of the underlying neural activity, and their strengths and limitations. I also discuss some pertinent methodological issues involved in tinnitus-related studies and conclude with suggestions to minimize possible discrepancies between results. The overall message is that while MEG and EEG are extremely useful techniques, the interpretation of results from tinnitus studies requires much caution given the individual variability in oscillatory activity and the limits of these techniques.

Alpha-band hypersynchronization in progressive mild cognitive impairment: a magnetoencephalography study

People with mild cognitive impairment (MCI) show a high risk to develop Alzheimer's disease (AD; Petersen et al., 2001). Nonetheless, there is a lack of studies about how functional connectivity patterns may distinguish between progressive (pMCI) and stable (sMCI) MCI patients. To examine whether there were differences in functional connectivity between groups, MEG eyes-closed recordings from 30 sMCI and 19 pMCI subjects were compared. The average conversion time of pMCI was 1 year, so they were considered as fast converters. To this end, functional connectivity in different frequency bands was assessed with phase locking value in source space. Then the significant differences between both groups were correlated with neuropsychological scores and entorhinal, parahippocampal, and hippocampal volumes. Both groups did not differ in age, gender, or educational level. pMCI patients obtained lower scores in episodic and semantic memory and also in executive functioning. At the structural level, there were no differences in hippocampal volume, although some were found in left entorhinal volume between both groups. Additionally, pMCI patients exhibit a higher synchronization in the alpha band between the right anterior cingulate and temporo-occipital regions than sMCI subjects. This hypersynchronization was inversely correlated with cognitive performance, both hippocampal volumes, and left entorhinal volume. The increase in phase synchronization between the right anterior cingulate and temporo-occipital areas may be predictive of conversion from MCI to AD.

Transient epileptic amnesia mistaken for mild cognitive impairment? A high-density EEG study

Mild cognitive impairment (MCI) converts to Alzheimer's disease within a few years of diagnosis in up to 80% of patients. The identification among such a population of a rare form of epilepsy (transient epileptic amnesia [TEA]), characterized by mixed anterograde and retrograde amnesia with apparent preservation of other cognitive functions, excessively rapid decay of newly acquired memories, and loss of memories for salient personal events of the remote past, strongly affects prognosis and medical treatment. Our aim was to define the clinical utility of routine high-density electroencephalography (EEG) in patients with MCI for the detection of epilepsy, especially TEA. Using high-density EEG (256 channels), we were able to single out 3 cases of TEA previously misdiagnosed as MCI in this cohort of 76 consecutive patients with MCI diagnosed at our center. Antiepileptic treatment effectively stopped the acute episodes of memory loss. To our knowledge, this is the first report of an incidence of 4% of TEA recorded in such a cohort.

Association between EEG abnormalities and CSF biomarkers in a memory clinic cohort

BACKGROUND

The aim of the study was to describe distinct electroencephalogram (EEG) phenotypes defined after routine visual EEG analysis in a large memory clinic cohort and to investigate their relationship to cerebrospinal fluid (CSF) biomarkers.

METHODS

Patients with Alzheimer's disease (n = 131), mild cognitive impairment (n = 285), subjective cognitive impairment (n = 310), and mixed dementia (n = 29) were assessed clinically with neuroimaging, EEG and CSF investigations. EEG phenotypes were based on frequency of background activity (BA) and presence and degree of episodic abnormalities (EA).

RESULTS

BA and EA differed significantly (p < 0.001) between diagnostic groups. A lower CSF amyloid β42/phospho-tau ratio and higher total tau were associated with slower BA (p < 0.01) and a higher degree of EA (p < 0.04).

CONCLUSIONS

Slowing of BA in combination with EA seems to be related to biological markers of neurodegeneration.

MEG spectral analysis in subtypes of mild cognitive impairment

Mild cognitive impairment (MCI) has been described as an intermediate stage between normal aging and dementia. Previous studies characterized the alterations of brain oscillatory activity at this stage, but little is known about the differences between single and multidomain amnestic MCI patients. In order to study the patterns of oscillatory magnetic activity in amnestic MCI subtypes, a total of 105 subjects underwent an eyes-closed resting-state magnetoencephalographic recording: 36 healthy controls, 33 amnestic single domain MCIs (a-sd-MCI), and 36 amnestic multidomain MCIs (a-md-MCI). Relative power values were calculated and compared among groups. Subsequently, relative power values were correlated with neuropsychological tests scores and hippocampal volumes. Both MCI groups showed an increase in relative power in lower frequency bands (delta and theta frequency ranges) and a decrease in power values in higher frequency bands (alpha and beta frequency ranges), as compared with the control group. More importantly, clear differences emerged from the comparison between the two amnestic MCI subtypes. The a-md-MCI group showed a significant power increase within delta and theta ranges and reduced relative power within alpha and beta ranges. Such pattern correlated with the neuropsychological performance, indicating that the a-md-MCI subtype is associated not only with a "slowing" of the spectrum but also with a poorer cognitive status. These results suggest that a-md-MCI patients are characterized by a brain activity profile that is closer to that observed in Alzheimer disease. Therefore, it might be hypothesized that the likelihood of conversion to dementia would be higher within this subtype.

Right ear advantage for vocal communication in frogs results from both structural asymmetry and attention modulation

Right-ear/left-hemisphere advantage (REA) in processing species-specific vocalizations has been demonstrated in mammals including humans. Two models for REA are typically proposed, a structural model and an attentional model. These hypotheses were tested in an anuran species, the Emei music frog (Babina daunchina) in which females strongly prefer male calls produced from inside mud-retuse burrows (high sexual attractiveness or HSA calls) to those produced in open fields (low sexual attractiveness or LSA calls). Isochronic playbacks were used to control for attention to stimuli presented to either the left or right sides of female subjects while electroencephalogram (EEG) signals were recorded from the left and right midbrain and telencephalon. The results show that relative EEG power in the delta band declined while those of the alpha and beta bands increased with time in the left but not the right midbrain. Since the anuran midbrain receives auditory information derived primarily from the contralateral auditory nerve, these results support the idea that REA occurs in frogs because communication sounds are processed preferentially in the left midbrain. Furthermore, though differences in the dynamic changes of the delta, alpha and beta bands in the left midbrain between acoustic stimuli were not statistically significant, these changes were stronger during the playback of HSA calls toward which females tend to allocate greater attentional resources. These results imply that REA in frogs results from the combined effects of structural asymmetry and attention modulation.

Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference

The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.

Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease

Amnestic mild cognitive impairment (aMCI) often is an early stage of Alzheimer's disease (AD). MCI is characterized by cognitive decline departing from normal cognitive aging but that does not significantly interfere with daily activities. This study explores the potential of scalp EEG for early detection of alterations from cognitively normal status of older adults signifying MCI and AD. Resting 32-channel EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls (NC), 16 early MCI, and 17 early stage AD-are examined. Regional spectral and complexity features are computed and used in a support vector machine model to discriminate between groups. Analyses based on three-way classifications demonstrate overall discrimination accuracies of 83.3%, 85.4%, and 79.2% for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. These results demonstrate the great promise for scalp EEG spectral and complexity features as noninvasive biomarkers for detection of MCI and early AD.

Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes

OBJECTIVE

Diabetes is a risk factor for dementia and mild cognitive impairment. The aim of this study was to investigate whether some features of resting-state EEG (rsEEG) could be applied as a biomarker to distinguish the subjects with amnestic mild cognitive impairment (aMCI) from normal cognitive function in type 2 diabetes.

MATERIALS AND METHODS

In this study, 28 patients with type 2 diabetes (16 aMCI patients and 12 controls) were investigated. Recording of the rsEEG series and neuropsychological assessments were performed. The rsEEG signal was first decomposed into delta, theta, alpha, beta, gamma frequency bands. The relative power of each given band/sum of power and the coherence of waves from different brain areas were calculated. The extracted features from rsEEG and neuropsychological assessments were analyzed as well.

RESULTS

The main findings of this study were that: (1) compared with the control group, the ratios of power in theta band [P(theta)] vs. power in alpha band [P(alpha)] [P(theta)/P(alpha)] in the frontal region and left temporal region were significantly higher for aMCI, and (2) for aMCI, the alpha coherences in posterior, fronto-right temporal, fronto-posterior, right temporo-posterior were decreased; the theta coherences in left central-right central (LC-RC) and left posterior-right posterior (LP-RP) regions were also decreased; but the delta coherences in left temporal-right temporal (LT-RT) region were increased.

CONCLUSION

The proposed indexes from rsEEG recordings could be employed to track cognitive function of diabetic patients and also to help in the diagnosis of those who develop aMCI.

Relative power and coherence of EEG series are related to amnestic mild cognitive impairment in diabetes

Objective: Diabetes is a risk factor for dementia and mild cognitive impairment. The aim of this study was to investigate whether some features of resting-state EEG (rsEEG) could be applied as a biomarker to distinguish the subjects with amnestic mild cognitive impairment (aMCI) from normal cognitive function in type 2 diabetes.

Materials and Methods: In this study, 28 patients with type 2 diabetes (16 aMCI patients and 12 controls) were investigated. Recording of the rsEEG series and neuropsychological assessments were performed. The rsEEG signal was first decomposed into delta, theta, alpha, beta, gamma frequency bands. The relative power of each given band/sum of power and the coherence of waves from different brain areas were calculated. The extracted features from rsEEG and neuropsychological assessments were analyzed as well.

Results: The main findings of this study were that: (1) compared with the control group, the ratios of power in theta band [P(theta)] vs. power in alpha band [P(alpha)] [P(theta)/P(alpha)] in the frontal region and left temporal region were significantly higher for aMCI, and (2) for aMCI, the alpha coherences in posterior, fronto-right temporal, fronto-posterior, right temporo-posterior were decreased; the theta coherences in left central-right central (LC-RC) and left posterior-right posterior (LP-RP) regions were also decreased; but the delta coherences in left temporal-right temporal (LT-RT) region were increased.

Conclusion: The proposed indexes from rsEEG recordings could be employed to track cognitive function of diabetic patients and also to help in the diagnosis of those who develop aMCI.

Spectral Analysis of EEG in Familial Alzheimer's Disease with E280A Presenilin-1 Mutation Gene

To evaluate the hypothesis that quantitative EEG (qEEG) analysis is susceptible to detect early functional changes in familial Alzheimer's disease (AD) preclinical stages. Three groups of subjects were selected from five extended families with hereditary AD: a Probable AD group (18 subjects), an asymptomatic carrier (ACr) group (21 subjects), with the mutation but without any clinical symptoms of dementia, and a normal group of 18 healthy subjects. In order to reveal significant differences in the spectral parameter, the Mahalanobis distance (D (2)) was calculated between groups. To evaluate the diagnostic efficiency of this statistic D (2), the ROC models were used. The ROC curve was summarized by accuracy index and standard deviation. The D (2) using the parameters of the energy in the fast frequency bands shows accurate discrimination between normal and ACr groups (area ROC = 0.89) and between AD probable and ACr groups (area ROC = 0.91). This is more significant in temporal regions. Theses parameters could be affected before the onset of the disease, even when cognitive disturbance is not clinically evident. Spectral EEG parameter could be firstly used to evaluate subjects with E280A Presenilin-1 mutation without impairment in cognitive function.

Stochastic coupled oscillator model of EEG for Alzheimer's disease

Coupled nonlinear oscillator models of EEG signals during resting eyes-closed and eyes-open conditions are presented based on Duffing-van der Pol oscillator dynamics. The frequency and information entropy contents of the output of the nonlinear model and the actual EEG signal is matched through an optimization algorithm. The framework is used to model and compare EEG signals recorded from Alzheimer's disease (AD) patients and age-matched healthy controls (CTL) subjects. The results show that 1) the generated model signal can capture the frequency and information entropy contents of the EEG signal with very similar power spectral distribution and non-periodic time history; 2) the EEG and the generated signal from the eyes-closed model are α band dominant for CTL subjects and θ band dominant for AD patients; and 3) statistically distinct models represent the EEG signals from AD patients and CTL subject during resting eyes-closed condition.

Brain-wide slowing of spontaneous alpha rhythms in mild cognitive impairment

The neurophysiological changes associated with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) include an increase in low frequency activity, as measured with electroencephalography or magnetoencephalography (MEG). A relevant property of spectral measures is the alpha peak, which corresponds to the dominant alpha rhythm. Here we studied the spatial distribution of MEG resting state alpha peak frequency and amplitude values in a sample of 27 MCI patients and 24 age-matched healthy controls. Power spectra were reconstructed in source space with linearly constrained minimum variance beamformer. Then, 88 Regions of Interest (ROIs) were defined and an alpha peak per ROI and subject was identified. Statistical analyses were performed at every ROI, accounting for age, sex and educational level. Peak frequency was significantly decreased (p < 0.05) in MCIs in many posterior ROIs. The average peak frequency over all ROIs was 9.68 ± 0.71 Hz for controls and 9.05 ± 0.90 Hz for MCIs and the average normalized amplitude was (2.57 ± 0.59)·10⁻² for controls and (2.70 ± 0.49)·10⁻² for MCIs. Age and gender were also found to play a role in the alpha peak, since its frequency was higher in females than in males in posterior ROIs and correlated negatively with age in frontal ROIs. Furthermore, we examined the dependence of peak parameters with hippocampal volume, which is a commonly used marker of early structural AD-related damage. Peak frequency was positively correlated with hippocampal volume in many posterior ROIs. Overall, these findings indicate a pathological alpha slowing in MCI.

Age-dependent effect of Alzheimer's risk variant of CLU on EEG alpha rhythm in non-demented adults

Polymorphism in the genomic region harboring the CLU gene (rs11136000) has been associated with the risk for Alzheimer’s disease (AD). CLU C allele is assumed to confer risk for AD and the allele T may have a protective effect. We investigated the influence of the AD-associated CLU genotype on a common neurophysiological trait of brain activity (resting-state alpha-rhythm activity) in non-demented adults and elucidated whether this influence is modified over the course of aging. We examined quantitative electroencephalography (EEG) in a cohort of non-demented individuals (age range 20–80) divided into young (age range 20–50) and old (age range 51–80) cohorts and stratified by CLU polymorphism. To rule out the effect of the apolipoprotein E (ApoE) genotype on EEG characteristics, only subjects without the ApoE ε4 allele were included in the study. The homozygous presence of the AD risk variant CLU CC in non-demented subjects was associated with an increase of alpha3 absolute power. Moreover, the influence of CLU genotype on alpha3 was found to be higher in the subjects older than 50 years of age. The study also showed age-dependent alterations of alpha topographic distribution that occur independently of the CLU genotype. The increase of upper alpha power has been associated with hippocampal atrophy in patients with mild cognitive impairment (Moretti etal., 2012a). In our study, the CLU CC-dependent increase in upper alpha rhythm, particularly enhanced in elderly non-demented individuals, may imply that the genotype is related to preclinical dysregulation of hippocampal neurophysiology in aging and that this factor may contribute to the pathogenesis of AD.

A point process approach to identifying and tracking transitions in neural spiking dynamics in the subthalamic nucleus of Parkinson's patients

Understanding the role of rhythmic dynamics in normal and diseased brain function is an important area of research in neural electrophysiology. Identifying and tracking changes in rhythms associated with spike trains present an additional challenge, because standard approaches for continuous-valued neural recordings--such as local field potential, magnetoencephalography, and electroencephalography data--require assumptions that do not typically hold for point process data. Additionally, subtle changes in the history dependent structure of a spike train have been shown to lead to robust changes in rhythmic firing patterns. Here, we propose a point process modeling framework to characterize the rhythmic spiking dynamics in spike trains, test for statistically significant changes to those dynamics, and track the temporal evolution of such changes. We first construct a two-state point process model incorporating spiking history and develop a likelihood ratio test to detect changes in the firing structure. We then apply adaptive state-space filters and smoothers to track these changes through time. We illustrate our approach with a simulation study as well as with experimental data recorded in the subthalamic nucleus of Parkinson's patients performing an arm movement task. Our analyses show that during the arm movement task, neurons underwent a complex pattern of modulation of spiking intensity characterized initially by a release of inhibitory control at 20-40 ms after a spike, followed by a decrease in excitatory influence at 40-60 ms after a spike.

Hypercapnia affects the functional coupling of resting state electroencephalographic rhythms and cerebral haemodynamics in healthy elderly subjects and in patients with amnestic mild cognitive impairment

OBJECTIVE

Cerebral vasomotor reactivity (VMR) and coherence of resting state electroencephalographic (EEG) rhythms are impaired in Alzheimer's disease (AD) patients. Here we tested the hypothesis that these two variables could be related.

METHODS

We investigated VMR and coherence of resting state EEG rhythms in nine normal elderly (Nold) and in 10 amnesic mild cognitive impairment (MCI) subjects. Resting state eyes-closed EEG data were recorded at baseline pre-CO₂ (ambient air, 2 min), during 7% CO₂/air mixture inhalation (hypercapnia, 90 s) and post-CO₂ (ambient air, 2 min) conditions. Simultaneous frontal bilateral near-infrared spectroscopy (NIRS) was performed to assess VMR by cortical oxy- and deoxy-haemoglobin concentration changes. EEG coherence across all electrodes was computed at delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz) and gamma (30-40 Hz) bands.

RESULTS

In Nold subjects, 'total coherence' of EEG across all frequency bands and electrode pairs decreased during hypercapnia, with full recovery during post-CO₂. Total coherence resulted lower in pre-CO₂ and post-CO₂ and presented poor reactivity during CO₂ inhalation in MCI patients compared with Nold subjects. Hypercapnia increased oxy-haemoglobin and decreased deoxy-haemoglobin concentrations in both groups. Furthermore, the extent of changes in these variables during CO₂ challenge was correlated with the EEG coherence, as a reflection of neurovascular coupling.

CONCLUSIONS

Hypercapnia induced normal frontal VMR that was detected by NIRS in both Nold and amnesic MCI groups, while it produced a reactivity of global functional coupling of resting state EEG rhythms only in the Nold group.

SIGNIFICANCE

In amnesic MCI patients, global EEG functional coupling is basically low in amplitude and does not react to hypercapnia.

Integrative EEG biomarkers predict progression to Alzheimer's disease at the MCI stage

Alzheimer's disease (AD) is a devastating disorder of increasing prevalence in modern society. Mild cognitive impairment (MCI) is considered a transitional stage between normal aging and AD; however, not all subjects with MCI progress to AD. Prediction of conversion to AD at an early stage would enable an earlier, and potentially more effective, treatment of AD. Electroencephalography (EEG) biomarkers would provide a non-invasive and relatively cheap screening tool to predict conversion to AD; however, traditional EEG biomarkers have not been considered accurate enough to be useful in clinical practice. Here, we aim to combine the information from multiple EEG biomarkers into a diagnostic classification index in order to improve the accuracy of predicting conversion from MCI to AD within a 2-year period. We followed 86 patients initially diagnosed with MCI for 2 years during which 25 patients converted to AD. We show that multiple EEG biomarkers mainly related to activity in the beta-frequency range (13–30 Hz) can predict conversion from MCI to AD. Importantly, by integrating six EEG biomarkers into a diagnostic index using logistic regression the prediction improved compared with the classification using the individual biomarkers, with a sensitivity of 88% and specificity of 82%, compared with a sensitivity of 64% and specificity of 62% of the best individual biomarker in this index. In order to identify this diagnostic index we developed a data mining approach implemented in the Neurophysiological Biomarker Toolbox (http://www.nbtwiki.net/). We suggest that this approach can be used to identify optimal combinations of biomarkers (integrative biomarkers) also in other modalities. Potentially, these integrative biomarkers could be more sensitive to disease progression and response to therapeutic intervention.

Characterizing Alzheimer's disease severity via resting-awake EEG amplitude modulation analysis

Changes in electroencephalography (EEG) amplitude modulations have recently been linked with early-stage Alzheimer's disease (AD). Existing tools available to perform such analysis (e.g., detrended fluctuation analysis), however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD) showed significant differences in amplitude modulations between the three groups. Most notably, i) delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD), ii) delta modulation of the theta band appeared with an increase in severity, and iii) delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer's disease, but also to monitor its progression.

Cortical sources of resting state electroencephalographic alpha rhythms deteriorate across time in subjects with amnesic mild cognitive impairment

Cortical sources of resting state electroencephalographic (EEG) rhythms are abnormal in subjects with mild cognitive impairment (MCI). Here, we tested the hypothesis that these sources in amnesic MCI subjects further deteriorate over 1 year. To this aim, the resting state eyes-closed EEG data were recorded in 54 MCI subjects at baseline (Mini Mental State Examination I = 26.9; standard error [SE], 0.2) and at approximately 1-year follow-up (13.8 months; SE, 0.5; Mini Mental State Examination II = 25.8; SE, 0.2). As a control, EEG recordings were also performed in 45 normal elderly and in 50 mild Alzheimer's disease subjects. EEG rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz). Cortical EEG sources were estimated using low-resolution brain electromagnetic tomography. Compared with the normal elderly and mild Alzheimer's disease subjects, the MCI subjects were characterized by an intermediate power of posterior alpha1 sources. In the MCI subjects, the follow-up EEG recordings showed a decreased power of posterior alpha1 and alpha2 sources. These results suggest that the resting state EEG alpha sources were sensitive-at least at the group level-to the cognitive decline occurring in the amnesic MCI group over 1 year, and might represent cost-effective, noninvasive and widely available markers to follow amnesic MCI populations in large clinical trials.

Is sleep essential for neural plasticity in humans, and how does it affect motor and cognitive recovery?

There is a general consensus that sleep is strictly linked to memory, learning, and, in general, to the mechanisms of neural plasticity, and that this link may directly affect recovery processes. In fact, a coherent pattern of empirical findings points to beneficial effect of sleep on learning and plastic processes, and changes in synaptic plasticity during wakefulness induce coherent modifications in EEG slow wave cortical topography during subsequent sleep. However, the specific nature of the relation between sleep and synaptic plasticity is not clear yet. We reported findings in line with two models conflicting with respect to the underlying mechanisms, that is, the "synaptic homeostasis hypothesis" and the "consolidation" hypothesis, and some recent results that may reconcile them. Independently from the specific mechanisms involved, sleep loss is associated with detrimental effects on plastic processes at a molecular and electrophysiological level. Finally, we reviewed growing evidence supporting the notion that plasticity-dependent recovery could be improved managing sleep quality, while monitoring EEG during sleep may help to explain how specific rehabilitative paradigms work. We conclude that a better understanding of the sleep-plasticity link could be crucial from a rehabilitative point of view.

The relationship between the diagnosis method of neuronal dysfunction (DIMENSION) and brain pathology in the early stages of Alzheimer's disease

OBJECTIVES

To examine whether the diagnosis method of neuronal dysfunction (DIMENSION), a new electroencephalogram (EEG) analysis method, reflected pathological changes in the early stages of Alzheimer's disease (AD), we conducted a comparative study of cerebrospinal fluid markers and single-photon emission computed tomography.

METHODS

Subjects cincluded 32 patients in the early stages of AD with a Mini-Mental State Examination score ≥24 (14 men, 18 women; mean age, 77.3 ± 9.2 years). Cerebrospinal fluid samples were collected from AD patients, and cerebrospinal fluid levels of phosphorylated tau protein (p-tau) 181 and amyloid β (Aβ) 42 were measured with sandwich ELISA. EEG recordings were performed for 5 min with the subjects awake in a resting state with their eyes closed. Then, the mean value of the EEG alpha dipolarity (Dα) and the standard deviation of the EEG alpha dipolarity (Dσ) were calculated with DIMENSION. Single-photon emission computed tomography analyses were also performed for comparison with DIMENSION measures.

RESULTS

Patients with parietal hypoperfusion had significantly increasing p-tau181, decreasing Dα, and increasing Dσ. In addition, there was a negative correlation between Dα and p-tau181, p-tau181/Aβ42, and a positive correlation between Dσ and p-tau181/Aβ42.

CONCLUSION

Dα and Dσ were related to cerebral hypoperfusion and p-tau181/Aβ42. DIMENSION was able to detect changes in the early-stage Alzheimer's brain, suggesting that it is possibility as a useful examination for early-stage AD with a difficult discrimination in clinical conditions. Moreover, EEG measurement is a quick and easy diagnostic test and is useful for repeated examinations.

Alzheimer's disease biomarkers: correspondence between human studies and animal models

Alzheimer's disease (AD) represents an escalating global threat as life expectancy and disease prevalence continue to increase. There is a considerable need for earlier diagnoses to improve clinical outcomes. Fluid biomarkers measured from cerebrospinal fluid (CSF) and blood, or imaging biomarkers have considerable potential to assist in the diagnosis and management of AD. An additional important utility of biomarkers is in novel therapeutic development and clinical trials to assess efficacy and side effects of therapeutic interventions. Because many biomarkers are initially examined in animal models, the extent to which markers translate from animals to humans is an important issue. The current review highlights many existing and pipeline biomarker approaches, focusing on the degree of correspondence between AD patients and animal models. The review also highlights the need for greater translational correspondence between human and animal biomarkers.

Resting state cortical EEG rhythms in Alzheimer's disease: toward EEG markers for clinical applications: a review

The human brain contains an intricate network of about 100 billion neurons. Aging of the brain is characterized by a combination of synaptic pruning, loss of cortico-cortical connections, and neuronal apoptosis that provoke an age-dependent decline of cognitive functions. Neural/synaptic redundancy and plastic remodeling of brain networking, also secondary to mental and physical training, promote maintenance of brain activity and cognitive status in healthy elderly subjects for everyday life. However, age is the main risk factor for neurodegenerative disorders such as Alzheimer's disease (AD) that impact on cognition. Growing evidence supports the idea that AD targets specific and functionally connected neuronal networks and that oscillatory electromagnetic brain activity might be a hallmark of the disease. In this line, digital electroencephalography (EEG) allows noninvasive analysis of cortical neuronal synchronization, as revealed by resting state brain rhythms. This review provides an overview of the studies on resting state eyes-closed EEG rhythms recorded in amnesic mild cognitive impairment (MCI) and AD subjects. Several studies support the idea that spectral markers of these EEG rhythms, such as power density, spectral coherence, and other quantitative features, differ among normal elderly, MCI, and AD subjects, at least at group level. Regarding the classification of these subjects at individual level, the most previous studies showed a moderate accuracy (70-80%) in the classification of EEG markers relative to normal and AD subjects. In conclusion, resting state EEG makers are promising for large-scale, low-cost, fully noninvasive screening of elderly subjects at risk of AD.