EEG coherence in Alzheimer's dementia

Impaired theta-gamma coupling in APP-deficient mice

Amyloid precursor protein (APP) is critically involved in the pathophysiology of Alzheimer's disease, but its physiological functions remain elusive. Importantly, APP knockout (APP-KO) mice exhibit cognitive deficits, suggesting that APP plays a role at the neuronal network level. To investigate this possibility, we recorded local field potentials (LFPs) from the posterior parietal cortex, dorsal hippocampus and lateral prefrontal cortex of freely moving APP-KO mice. Spectral analyses showed that network oscillations within the theta- and gamma-frequency bands were not different between APP-KO and wild-type mice. Surprisingly, however, while gamma amplitude coupled to theta phase in all recorded regions of wild-type animals, in APP-KO mice theta-gamma coupling was strongly diminished in recordings from the parietal cortex and hippocampus, but not in LFPs recorded from the prefrontal cortex. Thus, lack of APP reduces oscillatory coupling in LFP recordings from specific brain regions, despite not affecting the amplitude of the oscillations. Together, our findings reveal reduced cross-frequency coupling as a functional marker of APP deficiency at the network level.

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%.

Using static and dynamic canonical correlation coefficients as quantitative EEG markers for Alzheimer's disease severity

We analyzed the relation between Alzheimer's disease (AD) severity as measured by Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (qEEG) markers that were derived from canonical correlation analysis. This allowed an investigation of EEG synchrony between groups of EEG channels. In this study, we applied the data from 79 participants in the multi-centric cohort study PRODEM-Austria with probable AD. Following a homogeneous protocol, the EEG was recorded both in resting state and during a cognitive task. A quadratic regression model was used to describe the relation between MMSE and the qEEG synchrony markers. This relation was most significant in the δ and θ frequency bands in resting state, and between left-hemispheric central, temporal and parietal channel groups during the cognitive task. Here, the MMSE explained up to 40% of the qEEG marker's variation. QEEG markers showed an ambiguous trend, i.e. an increase of EEG synchrony in the initial stage of AD (MMSE>20) and a decrease in later stages. This effect could be caused by compensatory brain mechanisms. We conclude that the proposed qEEG markers are closely related to AD severity. Despite the ambiguous trend and the resulting diagnostic ambiguity, the qEEG markers could provide aid in the diagnostics of early-stage AD.

Declining functional connectivity and changing hub locations in Alzheimer's disease: an EEG study

BACKGROUND

EEG studies have shown that patients with Alzheimer's disease (AD) have weaker functional connectivity than controls, especially in higher frequency bands. Furthermore, active regions seem more prone to AD pathology. How functional connectivity is affected in AD subgroups of disease severity and how network hubs (highly connected brain areas) change is not known. We compared AD patients with different disease severity and controls in terms of functional connections, hub strength and hub location.

METHODS

We studied routine 21-channel resting-state electroencephalography (EEG) of 318 AD patients (divided into tertiles based on disease severity: mild, moderate and severe AD) and 133 age-matched controls. Functional connectivity between EEG channels was estimated with the Phase Lag Index (PLI). From the PLI-based connectivity matrix, the minimum spanning tree (MST) was derived. For each node (EEG channel) in the MST, the betweenness centrality (BC) was computed, a measure to quantify the relative importance of a node within the network. Then we derived color-coded head plots based on BC values and calculated the center of mass (the exact middle had x and y values of 0). A shifting of the hub locations was defined as a shift of the center of mass on the y-axis across groups. Multivariate general linear models with PLI or BC values as dependent variables and the groups as continuous variables were used in the five conventional frequency bands.

RESULTS

We found that functional connectivity decreases with increasing disease severity in the alpha band. All, except for posterior, regions showed increasing BC values with increasing disease severity. The center of mass shifted from posterior to more anterior regions with increasing disease severity in the higher frequency bands, indicating a loss of relative functional importance of the posterior brain regions.

CONCLUSIONS

In conclusion, we observed decreasing functional connectivity in the posterior regions, together with a shifted hub location from posterior to central regions with increasing AD severity. Relative hub strength decreases in posterior regions while other regions show a relative rise with increasing AD severity, which is in accordance with the activity-dependent degeneration theory. Our results indicate that hubs are disproportionally affected in AD.

Neurophysiological biomarkers for Lewy body dementias

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Biomarkers are needed to improve Lewy body dementia (LBD) diagnosis and measure treatment response.

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There is substantial heterogeneity in neurophysiology biomarker methodologies limiting comparison.

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However, there is tentative evidence to suggest neurophysiological approaches may show promise as potential biomarkers of LBD.

Objective

Lewy body dementias (LBD) include both dementia with Lewy bodies (DLB) and Parkinson’s disease with dementia (PDD), and the differentiation of LBD from other neurodegenerative dementias can be difficult. Currently, there are few biomarkers which might assist early diagnosis, map onto LBD symptom severity, and provide metrics of treatment response. Traditionally, biomarkers in LBD have focussed on neuroimaging modalities; however, as biomarkers need to be simple, inexpensive and non-invasive, neurophysiological approaches might also be useful as LBD biomarkers.

Methods

In this review, we searched PubMED and PsycINFO databases in a semi-systematic manner in order to identify potential neurophysiological biomarkers in the LBDs.

Results

We identified 1491 studies; of these, 37 studies specifically examined neurophysiological biomarkers in LBD patients. We found that there was substantial heterogeneity with respect to methodologies and patient cohorts.

Conclusion

Generally, many of the findings have yet to be replicated, although preliminary findings reinforce the potential utility of approaches such as quantitative electroencephalography and motor cortical stimulation paradigms.

Significance

Various neurophysiological techniques have the potential to be useful biomarkers in the LBDs. We recommend that future studies focus on maximising the diagnostic specificity and sensitivity of the most promising neurophysiological biomarkers.

An improved I-FAST system for the diagnosis of Alzheimer's disease from unprocessed electroencephalograms by using robust invariant features

OBJECTIVE

This paper proposes a new, complex algorithm for the blind classification of the original electroencephalogram (EEG) tracing of each subject, without any preliminary pre-processing. The medical need in this field is to reach an early differential diagnosis between subjects affected by mild cognitive impairment (MCI), early Alzheimer's disease (AD) and the healthy elderly (CTR) using only the recording and the analysis of few minutes of their EEG.

METHODS AND MATERIAL

This study analyzed the EEGs of 272 subjects, recorded at Rome's Neurology Unit of the Policlinico Campus Bio-Medico. The EEG recordings were performed using 19 electrodes, in a 0.3-70Hz bandpass, positioned according to the International 10-20 System. Many powerful learning machines and algorithms have been proposed during the last 20 years to effectively resolve this complex problem, resulting in different and interesting outcomes. Among these algorithms, a new artificial adaptive system, named implicit function as squashing time (I-FAST), is able to diagnose, with high accuracy, a few minutes of the subject's EEG track; whether it manifests an AD, MCI or CTR condition. An updating of this system, carried out by adding a new algorithm, named multi scale ranked organizing maps (MS-ROM), to the I-FAST system, is presented, in order to classify with greater accuracy the unprocessed EEG's of AD, MCI and control subjects.

RESULTS

The proposed system has been measured on three independent pattern recognition tasks from unprocessed EEG tracks of a sample of AD subjects, MCI subjects and CTR: (a) AD compared with CTR; (b) AD compared with MCI; (c) CTR compared with MCI. While the values of accuracy of the previous system in distinguishing between AD and MCI were around 92%, the new proposed system reaches values between 94% and 98%. Similarly, the overall accuracy with best artificial neural networks (ANNs) is 98.25% for the distinguishing between CTR and MCI.

CONCLUSIONS

This new version of I-FAST makes different steps forward: (a) avoidance of pre-processing phase and filtering procedure of EEG data, being the algorithm able to directly process an unprocessed EEG; (b) noise elimination, through the use of a training variant with input selection and testing system, based on naïve Bayes classifier; (c) a more robust classification phase, showing the stability of results on nine well known learning machine algorithms; (d) extraction of spatial invariants of an EEG signal using, in addition to the unsupervised ANN, the principal component analysis and the multi scale entropy, together with the MS-ROM; a more accurate performance in this specific task.

Coherence of brain electrical activity: a quality of life indicator in Alzheimer’s disease?Coerência da atividade elétrica cerebral: indicador da qualidade de vida na doença de Alzheimer?

Objective To investigate the relationships between quality of life (QOL) and clinical and electroencephalogram (EEG) aspects in patients with Alzheimer’s disease (AD). Method Twenty-eight patients with mild or moderate AD, 31 with Parkinson’s disease (PD), and 27 normal controls (NC) were submitted to: CERAD neuropsychological battery, Hamilton Depression and Anxiety Rating Scales, Functional Activities Questionnaire, QOL scale for patients with AD, and quantitative EEG measures. Results AD and PD patients had similar QOL (31.0 ± 5.8; 31.7 ± 4.8, respectively), worse than that of NC (37.5 ± 6.3). AD patients had lower global interhemispheric theta coherence (0.49 ± 0.04; 0.52 ± 0.05; 0.52 ± 0.05; respectively) than PD and NC. Multiple linear regression for QOL of AD patients revealed that global interhemispheric theta coherence, and Hamilton depression scores were significant factors (coefficients; 58.2 and -0.27, respectively; R2, 0.377). Conclusion Interhemispheric coherence correlates with QOL regardless of cognitive and functional variables and seems to be a neurophysiological indicator of QOL in AD patients.

Principal Dynamic Mode Analysis of EEG Data for Assisting the Diagnosis of Alzheimer's Disease

We examine whether modeling of the causal dynamic relationships between frontal and occipital electroencephalogram (EEG) time-series recordings reveal reliable differentiating characteristics of Alzheimer’s patients versus control subjects in a manner that may assist clinical diagnosis of Alzheimer’s disease (AD). The proposed modeling approach utilizes the concept of principal dynamic modes (PDMs) and their associated nonlinear functions (ANF) and hypothesizes that the ANFs of some PDMs for the AD patients will be distinct from their counterparts in control subjects. To this purpose, global PDMs are extracted from 1-min EEG signals of 17 AD patients and 24 control subjects at rest using Volterra models estimated via Laguerre expansions, whereby the O1 or O2 recording is viewed as the input signal and the F3 or F4 recording as the output signal. Subsequent singular value decomposition of the estimated Volterra kernels yields the global PDMs that represent an efficient basis of functions for the representation of the EEG dynamics in all subjects. The respective ANFs are computed for each subject and characterize the specific dynamics of each subject. For comparison, signal features traditionally used in the analysis of EEG signals in AD are computed as benchmark. The results indicate that the ANFs of two specific PDMs, corresponding to the delta–theta and alpha bands, can delineate the two groups well.

Traumatic brain injury detection using electrophysiological methods

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

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.

Power spectral density and coherence analysis of Alzheimer's EEG

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. The power spectral density (PSD) which represents the power distribution of EEG series in the frequency domain is used to evaluate the abnormalities of AD brain. Spectrum analysis based on autoregressive Burg method shows that the relative PSD of AD group is increased in the theta frequency band while significantly reduced in the alpha2 frequency bands, particularly in parietal, temporal, and occipital areas. Furthermore, the coherence of two EEG series among different electrodes is analyzed in the alpha2 frequency band. It is demonstrated that the pair-wise coherence between different brain areas in AD group are remarkably decreased. Interestingly, this decrease of pair-wise electrodes is much more significant in inter-hemispheric areas than that in intra-hemispheric areas. Moreover, the linear cortico-cortical functional connectivity can be extracted based on coherence matrix, from which it is shown that the functional connections are obviously decreased, the same variation trend as relative PSD. In addition, we combine both features of the relative PSD and the normalized degree of functional network to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha2 frequency band. It is indicated that the two groups can be clearly classified by the combined feature. Importantly, the accuracy of the classification is higher than that of any one feature. The obtained results show that analysis of PSD and coherence-based functional network can be taken as a potential comprehensive measure to distinguish AD patients from the normal, which may benefit our understanding of the disease.

Decreased coherence and functional connectivity of electroencephalograph in Alzheimer's disease

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. Coherence is introduced to measure the pair-wise normalized linear synchrony and functional correlations between two EEG signals in different frequency domains, and graph analysis is further used to investigate the influence of AD on the functional connectivity of human brain. Data analysis results show that, compared with the control group, the pair-wise coherence of AD group is significantly decreased, especially for the theta and alpha frequency bands in the frontal and parieto-occipital regions. Furthermore, functional connectivity among different brain regions is reconstructed based on EEG, which exhibit obvious small-world properties. Graph analysis demonstrates that the local functional connections between regions for AD decrease. In addition, it is found that small-world properties of AD networks are largely weakened, by calculating its average path lengths, clustering coefficients, global efficiency, local efficiency, and small-worldness. The obtained results show that both pair-wise coherence and functional network can be taken as effective measures to distinguish AD patients from the normal, which may benefit our understanding of the disease.

Patients with mild cognitive impairment have an abnormal upper-alpha event-related desynchronization/synchronization (ERD/ERS) during a task of temporal attention

There are several evidences indicating that an impairment in attention-executive functions is present in prodromal Alzheimer's disease and predict future global cognitive decline. In particular, the issue of temporal orienting of attention in patients with mild cognitive impairment (MCI) due to Alzheimer's disease has been overlooked. The present research aimed to explore whether subtle deficits of cortical activation are present in these patients early in the course of the disease. We studied the upper-alpha event-related synchronization/desynchronization phenomenon during a paradigm of temporal orientation of attention. MCI patients (n = 27) and healthy elderly controls (n = 15) performed a task in which periodically omitted tones had to be predicted and their virtual onset time had to be marked by pressing a button. Single-trial responses were measured, respectively, before and after the motor response. Then, upper-alpha responses were compared to upper-alpha power during eyes-closed resting state. The time course of the task was characterized by two different behavioral conditions: (1) a pre-event epoch, in which the subject awaited the virtual onset of the omitted tone, (2) a post-event epoch (after button pressing), in which the subject was in a post-motor response condition. The principal findings are: (1) during the waiting epoch, only healthy elderly had an upper-alpha ERD at the level of both temporal and posterior brain regions; (2) during the post-motor epoch, the aMCI patients had a weaker upper-alpha ERS on prefrontal regions; (3) only healthy elderly showed a laterality effect: (a) during the waiting epoch, the upper-alpha ERD was greater at the level of the right posterior-temporal lead; during the post-motor epoch, the upper alpha ERS was greater on the left prefrontal lead. The relevance of these findings is that the weaker upper-alpha response observed in aMCI patients is evident even if the accuracy of the behavioral performance (i.e., button pressing) is still spared. This abnormal upper-alpha response might represent an early biomarker of the attention-executive network impairment in MCI due to Alzheimer's disease.

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.

Classifying different emotional states by means of EEG-based functional connectivity patterns

This study aimed to classify different emotional states by means of EEG-based functional connectivity patterns. Forty young participants viewed film clips that evoked the following emotional states: neutral, positive, or negative. Three connectivity indices, including correlation, coherence, and phase synchronization, were used to estimate brain functional connectivity in EEG signals. Following each film clip, participants were asked to report on their subjective affect. The results indicated that the EEG-based functional connectivity change was significantly different among emotional states. Furthermore, the connectivity pattern was detected by pattern classification analysis using Quadratic Discriminant Analysis. The results indicated that the classification rate was better than chance. We conclude that estimating EEG-based functional connectivity provides a useful tool for studying the relationship between brain activity and emotional states.

The effects of automated artifact removal algorithms on electroencephalography-based Alzheimer's disease diagnosis

Over the last decade, electroencephalography (EEG) has emerged as a reliable tool for the diagnosis of cortical disorders such as Alzheimer's disease (AD). EEG signals, however, are susceptible to several artifacts, such as ocular, muscular, movement, and environmental. To overcome this limitation, existing diagnostic systems commonly depend on experienced clinicians to manually select artifact-free epochs from the collected multi-channel EEG data. Manual selection, however, is a tedious and time-consuming process, rendering the diagnostic system “semi-automated.” Notwithstanding, a number of EEG artifact removal algorithms have been proposed in the literature. The (dis)advantages of using such algorithms in automated AD diagnostic systems, however, have not been documented; this paper aims to fill this gap. Here, we investigate the effects of three state-of-the-art automated artifact removal (AAR) algorithms (both alone and in combination with each other) on AD diagnostic systems based on four different classes of EEG features, namely, spectral, amplitude modulation rate of change, coherence, and phase. The three AAR algorithms tested are statistical artifact rejection (SAR), blind source separation based on second order blind identification and canonical correlation analysis (BSS-SOBI-CCA), and wavelet enhanced independent component analysis (wICA). Experimental results based on 20-channel resting-awake EEG data collected from 59 participants (20 patients with mild AD, 15 with moderate-to-severe AD, and 24 age-matched healthy controls) showed the wICA algorithm alone outperforming other enhancement algorithm combinations across three tasks: diagnosis (control vs. mild vs. moderate), early detection (control vs. mild), and disease progression (mild vs. moderate), thus opening the doors for fully-automated systems that can assist clinicians with early detection of AD, as well as disease severity progression assessment.

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.

Effect of oxcarbazepine on background EEG activity and cognition in epilepsy

BACKGROUND AND PURPOSE

Cognitive dysfunction related to antiepileptic drugs (AEDs) is an important issue in the management of patients with epilepsy. The aim of the present study was to evaluate relative long-term effects of oxcarbazepine (OXC) on cognition in drug-naive patients with epilepsy.

METHODS

Fifteen drug-naïve epilepsy patients were enrolled. Electroencephalogram (EEG) recordings and neuropsychological (NP) tests were performed before and after OXC monotherapy. The relative power of the discrete frequency bandwas obtained. In addition, interhemispheric and intrahemispheric spectral coherence was also calculated.

RESULTS

NP tests showed significant improvement in visuo-spatial, memory and executive function after OXC treatment. However, neither spectral power nor coherence changed significantly with OXC treatment.

CONCLUSIONS

Our study supports the notion that OXC has no significant cognitive side effect in patients with epilepsy.

Comparison of quantitative EEG between patients with Alzheimer's disease and those with Parkinson's disease dementia

OBJECTIVE

Dementia frequently occurs in Parkinson's disease (PD) but its pathophysiological basis is little known. Comparative EEG studies of Alzheimer's disease (AD) and Parkinson's disease dementia (PDD) are still rare, but could provide knowledge on the different pathophysiological mechanisms involved. The objective of the present study was to comparatively evaluate the absolute power and coherence on the EEG for patients with AD and PDD.

METHODS

This study assessed 38 adults with AD, 12 with PDD, 31 with Parkinson's disease without dementia, and 37 controls (CG) by a neurological evaluation, CERAD neuropsychological battery, executive functions tests and qEEG, calculating global absolute powers for the delta, theta, alpha and beta bands and inter- and intra-hemispheric coherences.

RESULTS

The delta and theta powers were highest in PDD and lowest in CG (p<0.05). The beta frontal-occipital inter-hemispheric coherence was highest in PDD (p<0.05). Whereas, alpha and beta frontal inter-hemispheric coherence was highest in PDD and lowest in AD (p<0.05).

CONCLUSION

These results suggest that qEEG power and coherence measures are different in AD and PDD.

SIGNIFICANCE

These qEEG differences must be related to the distinct mechanisms of cortical neural connections in AD and PDD.

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.

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.

Resting-state network disruption and APOE genotype in Alzheimer's disease: a lagged functional connectivity study

Background

The apolipoprotein E epsilon 4 (APOE-4) is associated with a genetic vulnerability to Alzheimer's disease (AD) and with AD-related abnormalities in cortical rhythms. However, it is unclear whether APOE-4 is linked to a specific pattern of intrinsic functional disintegration of the brain after the development of the disease or during its different stages. This study aimed at identifying spatial patterns and effects of APOE genotype on resting-state oscillations and functional connectivity in patients with AD, using a physiological connectivity index called “lagged phase synchronization”.

Methodology/Principal Findings

Resting EEG was recorded during awake, eyes-closed state in 125 patients with AD and 60 elderly controls. Source current density and functional connectivity were determined using eLORETA. Patients with AD exhibited reduced parieto-occipital alpha oscillations compared with controls, and those carrying the APOE-4 allele had reduced alpha activity in the left inferior parietal and temporo-occipital cortex relative to noncarriers. There was a decreased alpha2 connectivity pattern in AD, involving the left temporal and bilateral parietal cortex. Several brain regions exhibited increased lagged phase synchronization in low frequencies, specifically in the theta band, across and within hemispheres, where temporal lobe connections were particularly compromised. Areas with abnormal theta connectivity correlated with cognitive scores. In patients with early AD, we found an APOE-4-related decrease in interhemispheric alpha connectivity in frontal and parieto-temporal regions.

Conclusions/Significance

In addition to regional cortical dysfunction, as indicated by abnormal alpha oscillations, there are patterns of functional network disruption affecting theta and alpha bands in AD that associate with the level of cognitive disturbance or with the APOE genotype. These functional patterns of nonlinear connectivity may potentially represent neurophysiological or phenotypic markers of AD, and aid in early detection of the disorder.

A short review of alpha activity in cognitive processes and in cognitive impairment

AIM OF THE REPORT: In the companion report (Başar, this volume), the physiological fundaments of alpha activity in integrative brain function are described. The present report is a review of the significant role of alpha activity in memory and cognitive processes in healthy subjects, and in cognitive impairment. The role of neurotransmitters is also described, briefly, in this context. TOWARDS AN UNDERSTANDING OF BRAIN ALPHA: Despite numerous experimental studies, it is indicated that the presented results are only appropriate to establish an ensemble of reasonings and suggestions for analyzing "alphas" in the whole brain. In turn, in the near future, these reasonings and suggestions may serve (or are indispensable to serve) as fundaments of more general and tenable hypotheses on the genesis and function of "alphas".

Brain excitability and connectivity of neuronal assemblies in Alzheimer's disease: from animal models to human findings

The human brain contains about 100 billion neurons forming an intricate network of innumerable connections, which continuously adapt and rewire themselves following inputs from external and internal environments as well as the physiological synaptic, dendritic and axonal sculpture during brain maturation and throughout the life span. Growing evidence supports the idea that Alzheimer's disease (AD) targets selected and functionally connected neuronal networks and, specifically, their synaptic terminals, affecting brain connectivity well before producing neuronal loss and compartmental atrophy. The understanding of the molecular mechanisms underlying the dismantling of neuronal circuits and the implementation of 'clinically oriented' methods to map-out the dynamic interactions amongst neuronal assemblies will enhance early/pre-symptomatic diagnosis and monitoring of disease progression. More important, this will open the avenues to innovative treatments, bridging the gap between molecular mechanisms and the variety of symptoms forming disease phenotype. In the present review a set of evidence supports the idea that altered brain connectivity, exhausted neural plasticity and aberrant neuronal activity are facets of the same coin linked to age-related neurodegenerative dementia of Alzheimer type. Investigating their respective roles in AD pathophysiology will help in translating findings from basic research to clinical applications.

Cortical sources of EEG rhythms in congestive heart failure and Alzheimer's disease

INTRODUCTION

The brain needs continuous oxygen supply even in resting-state. Hypoxia enhances resting-state electroencephalographic (EEG) rhythms in the delta range, and reduces those in the alpha range, with a pattern similar to that observed in Alzheimer's disease (AD). Here we tested whether resting-state cortical EEG rhythms in patients with congestive heart failure (CHF), as a model of acute hypoxia, present frequency similarities with AD patients, comparable by cognitive status revealed by the mini mental state examination (MMSE).

METHODS

Eyes-closed EEG data were recorded in 10 CHF patients, 20 AD patients, and 20 healthy elderly subjects (Nold) as controls. LORETA software estimated cortical EEG generators.

RESULTS

Compared to Nold, both AD and CHF groups presented higher delta (2-4Hz) and lower alpha (8-13Hz) temporal sources. The highest delta and lowest alpha sources were observed in CHF subjects. In these subjects, the global amplitude of delta sources correlated with brain natriuretic peptide (BNP) level in the blood, as a marker of disease severity.

CONCLUSIONS

Resting-state delta and alpha rhythms suggest analogies between the effects of acute hypoxia and AD neurodegeneration on the cortical neurons' synchronization.

SIGNIFICANCE

Acute ischemic hypoxia could affect the mechanisms of cortical neural synchronization generating resting state EEG rhythms, inducing the "slowing" of EEG rhythms typically observed in AD patients.

Multiway array decomposition analysis of EEGs in Alzheimer's disease

Methods for the extraction of features from physiological datasets are growing needs as clinical investigations of Alzheimer's disease (AD) in large and heterogeneous population increase. General tools allowing diagnostic regardless of recording sites, such as different hospitals, are essential and if combined to inexpensive non-invasive methods could critically improve mass screening of subjects with AD. In this study, we applied two state of the art multiway array decomposition (MAD) methods to extract unique features from electroencephalograms (EEGs) of AD patients obtained from multiple sites. In comparison to MAD, spectral-spatial average filter (SSFs) of control and AD subjects were used as well as a common blind source separation method, algorithm for multiple unknown signal extraction (AMUSE), and singular value decomposition (SVD) coupled to tensor unfolding. We trained a feed-forward multilayer perceptron (MLP) to validate and optimize AD classification from two independent databases. Using a third EEG dataset, we demonstrated that features extracted from MAD outperformed features obtained from SSFs AMUSE in terms of root mean squared error (RMSE) and reaching up to 100% of accuracy in test condition. We propose that MAD maybe a useful tool to extract features for AD diagnosis offering great generalization across multi-site databases and opening doors to the discovery of new characterization of the disease.

Alzheimer's disease: relationship between cognitive aspects and power and coherence EEG measures

OBJECTIVE: To evaluate the relationship between specific cognitive aspects and quantitative EEG measures, in patients with mild or moderate Alzheimer's disease (AD). METHOD: Thirty-eight AD patients and 31 controls were assessed by CERAD neuropsychological battery (Consortium to Establish a Registry for AD) and the electroencephalogram (EEG). The absolute power and coherences EEG measures were calculated at rest. The correlations between the cognitive variables and the EEG were evaluated. RESULTS: In the AD group there were significant correlations between different coherence EEG measures and Mini-Mental State Examination, verbal fluency, modified Boston naming, word list memory with repetition, word list recall and recognition, and constructional praxis (p<0.01). These correlations were all negative for the delta and theta bands and positive for alpha and beta. There were no correlations between cognitive aspects and absolute EEG power. CONCLUSION: The coherence EEG measures reflect different forms in the relationship between regions related to various cognitive dysfunctions.

Stability of clinical condition in mild cognitive impairment is related to cortical sources of alpha rhythms: an electroencephalographic study

Previous evidence has shown that resting eyes-closed cortical alpha rhythms are higher in amplitude in mild cognitive impairment (MCI) than Alzheimer's disease (AD) subjects (Babiloni et al. [2006a]: Human Brain Mapp 27:162-172; [2006b]: Clin Neurophysiol 117:252-268; [2006c]: Neuroimage 29:948-964; [2006d]: Ann Neurol 59:323-334; [2006e]: Clin Neurophysiol 117:1113-1129; [2006f]: Neuroimage 31:1650-1665). This study tested the hypothesis that, in amnesic MCI subjects, high amplitude of baseline cortical alpha rhythms is related to long-term stability of global cognition on clinical follow-up. Resting electroencephalographic (EEG) data were recorded in 100 amnesic MCI subjects during eyes-closed condition. 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 by low-resolution brain electromagnetic tomography (LORETA). Global cognition was indexed by mini mental state evaluation (MMSE) score at the time of EEG recordings (baseline) and about after 1 year. Based on the MMSE percentage difference between baseline and 1-year follow-up (MMSEvar), the MCI subjects were retrospectively divided into three arbitrary groups: DECREASED (MMSEvar ≤ -4%; N = 43), STABLE (MMSEvar ≈ 0; N = 27), and INCREASED (MMSEvar ≥ +4%; N = 30). Subjects' age, education, individual alpha frequency, gender, and MMSE scores were used as covariates for statistical analysis. Baseline posterior cortical sources of alpha 1 rhythms were higher in amplitude in the STABLE than in the DECREASED and INCREASED groups. These results suggest that preserved resting cortical neural synchronization at alpha frequency is related to a long-term (1 year) stable cognitive function in MCI subjects. Future studies should use serial MMSE measurements to confirm and refine the present results.

EEG theta and alpha reactivity on opening the eyes in the diagnosis of Alzheimer's disease

The objective of this study was to evaluate the contribution of EEG theta and alpha reactivity on opening the eyes, in the diagnosis of slight and moderate Alzheimer's disease (AD). Thirty four patients with AD and a control group of 30 individuals were studied, all being assessed using a neurological evaluation, CERAD neuropsychological battery (consortium to establish a registry for Alzheimer's disease), incorporating the Mini Mental State Examination (MMSE), Clinical Dementia Rating (CDR) and a qEEG analysis of the absolute band power at rest, with the eyes both open and closed. The theta and alpha reactivity indices were calculated on opening the eyes, defined from the relationship between the absolute powers in the respective bands in the periods with the eyes open and with them closed, the quotient of the relationship between the alpha and theta indices, the alpha/theta ratio, was also calculated. Multiple regression models were used to determine the accuracy in discriminating between the AD and control groups. A regression model using only cognitive data provided an accuracy of 92.2%, whereas a regression model combining cognitive data and qEEG measurements provided an accuracy of 95.3% in the classification between AD and the controls. The variable for the qEEG was the left hemisphere alpha/theta index, since the other parameters were shown to be inferior with respect to the clinical data in the regression analysis. The integrated study of the theta and alpha reactivity indices on opening the eyes and the alpha/theta index, was shown to be a useful approach in qEEG in the evaluation of AD and should be evaluated with larger samples and with other data analysis methods, with the aim of increasing the accuracy.

Electroencephalographic rhythms in Alzheimer's disease

Physiological brain aging is characterized by synapses loss and neurodegeneration that slowly lead to an age-related decline of cognition. Neural/synaptic redundancy and plastic remodelling of brain networking, also due to mental and physical training, promotes maintenance of brain activity in healthy elderly subjects for everyday life and good social behaviour and intellectual capabilities. However, age is the major risk factor for most common neurodegenerative disorders that impact on cognition, like Alzheimer's disease (AD). Brain electromagnetic activity is a feature of neuronal network function in various brain regions. Modern neurophysiological techniques, such as electroencephalography (EEG) and event-related potentials (ERPs), are useful tools in the investigation of brain cognitive function in normal and pathological aging with an excellent time resolution. These techniques can index normal and abnormal brain aging analysis of corticocortical connectivity and neuronal synchronization of rhythmic oscillations at various frequencies. The present review suggests that discrimination between physiological and pathological brain aging clearly emerges at the group level, with suggested applications also at the level of single individual. The possibility of combining the use of EEG together with biological/neuropsychological markers and structural/functional imaging is promising for a low-cost, non-invasive, and widely available assessment of groups of individuals at-risk.

Direction of Information Flow in Alzheimer's Disease and MCI Patients

Is directionality of electroencephalographic (EEG) synchronization abnormal in amnesic mild cognitive impairment (MCI) and Alzheimer's disease (AD)? And, do cerebrovascular and AD lesions represent additive factors in the development of MCI as a putative preclinical stage of AD? Here we reported two studies that tested these hypotheses. EEG data were recorded in normal elderly (Nold), amnesic MCI, and mild AD subjects at rest condition (closed eyes). Direction of information flow within EEG electrode pairs was performed by directed transfer function (DTF) at δ (2-4 Hz), θ (4-8 Hz), α1 (8-10 Hz), α2 (10-12 Hz), β1 (13-20 Hz), β2 (20-30 Hz), and γ (30-40 Hz). Parieto-to-frontal direction was stronger in Nold than in MCI and/or AD subjects for α and β rhythms. In contrast, the directional flow within interhemispheric EEG functional coupling did not discriminate among the groups. More interestingly, this coupling was higher at θ, α1, α2, and β1 in MCI with higher than in MCI with lower vascular load. These results suggest that directionality of parieto-to-frontal EEG synchronization is abnormal not only in AD but also in amnesic MCI, supporting the additive model according to which MCI state would result from the combination of cerebrovascular and neurodegenerative lesions.

Mapping the Alzheimer's brain with connectomics

Alzheimer's disease (AD) is the most common form of dementia. As an incurable, progressive, and neurodegenerative disease, it causes cognitive and memory deficits. However, the biological mechanisms underlying the disease are not thoroughly understood. In recent years, non-invasive neuroimaging and neurophysiological techniques [e.g., structural magnetic resonance imaging (MRI), diffusion MRI, functional MRI, and EEG/MEG] and graph theory based network analysis have provided a new perspective on structural and functional connectivity patterns of the human brain (i.e., the human connectome) in health and disease. Using these powerful approaches, several recent studies of patients with AD exhibited abnormal topological organization in both global and regional properties of neuronal networks, indicating that AD not only affects specific brain regions, but also alters the structural and functional associations between distinct brain regions. Specifically, disruptive organization in the whole-brain networks in AD is involved in the loss of small-world characters and the re-organization of hub distributions. These aberrant neuronal connectivity patterns were associated with cognitive deficits in patients with AD, even with genetic factors in healthy aging. These studies provide empirical evidence to support the existence of an aberrant connectome of AD. In this review we will summarize recent advances discovered in large-scale brain network studies of AD, mainly focusing on graph theoretical analysis of brain connectivity abnormalities. These studies provide novel insights into the pathophysiological mechanisms of AD and could be helpful in developing imaging biomarkers for disease diagnosis and monitoring.

Quantitative electroencephalography power and coherence measurements in the diagnosis of mild and moderate Alzheimer's disease

OBJECTIVE: To evaluate the contribution of quantitative electroencephalographic (qEEG) analyses in the diagnosis of Alzheimer's disease (AD). METHOD: Thirty-five patients from the Neurology Outpatients Clinic of PUC-Campinas, diagnosed with AD according to the NINCDS/ADRDA were evaluated, and compared with a control group consisting of 30 individuals with no cognitive deficit. The procedures consisted of clinical-neurological, cognitive and behavioral analyses and the qEEG (absolute power and coherence). RESULTS: The AD group presented greater absolute power values in the delta and theta bands, greater theta/alpha indices and less frontal alpha and beta coherence. Logistic multiple regression models were constructed and those only showing variations in the qEEG (frontal alpha coherence and left frontal absolute theta power) showed an accuracy classification (72.3%) below that obtained in the mini-mental state examination (93%). CONCLUSION: The study of coherence and power in the qEEG showed a relatively limited accuracy with respect to its application in routine clinical practice.

Inter-hemispheric functional coupling of eyes-closed resting EEG rhythms in adolescents with Down syndrome

OBJECTIVE

We tested the hypothesis that inter-hemispheric directional functional coupling of eyes-closed resting EEG rhythms is abnormal in adolescents with Down syndrome (DS).

METHODS

Eyes-closed resting EEG data were recorded in 38 DS adolescents (18.7 years +/-0.67 SE, IQ=49+/-1.9 SE) and in 17 matched normal control subjects (NYoung=19.1 years +/-0.39 SE). The EEG data were recorded from 8 electrodes (Fp1, Fp2, C3, C4, T3, T4, O1, O2) referenced to vertex. EEG rhythms of interest were 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), and beta 2 (20-30 Hz). Power of EEG rhythms was evaluated by FFT for control purposes, whereas inter-hemispheric directional EEG functional coupling was computed by directed transfer function (DTF).

RESULTS

As expected, alpha, beta, and gamma power was widely higher in NYoung than DS subjects, whereas the opposite was true for delta power. As a novelty, DTF (directionality) values globally prevailed from right to left occipital areas in NYoung subjects and in the opposite direction in DS patients. A control experiment showed that this DTF difference could not be observed in the comparison between DS adults with mild cognitive impairment and normal age-matched adults.

CONCLUSIONS

These results indicate a peculiar abnormal directional inter-hemispheric interplay in visual occipital areas of DS adolescents.

SIGNIFICANCE

Direction of inter-hemispheric EEG functional coupling unveils a new abnormal brain network feature in DS adolescents.

Neuronal networks in Alzheimer's disease

Alzheimer's disease (AD) is a progressive, neurodegenerative disease that can be clinically characterized by impaired memory and many other cognitive functions. Previous studies have demonstrated that the impairment is accompanied by not only regional brain abnormalities but also changes in neuronal connectivity between anatomically distinct brain regions. Specifically, using neurophysiological and neuroimaging techniques as well as advanced graph theory-based computational approaches, several recent studies have suggested that AD patients have disruptive neuronal integrity in large-scale structural and functional brain systems underlying high-level cognition, as demonstrated by a loss of small-world network characteristics. Small world is an attractive model for the description of complex brain networks because it can support both segregated and integrated information processing. The altered small-world organization thus reflects aberrant neuronal connectivity in the AD brain that is most likely to explain cognitive deficits caused by this disease. In this review, we will summarize recent advances in the brain network research on AD, focusing mainly on the large-scale structural and functional descriptions. The literature reviewed here suggests that AD patients are associated with integrative abnormalities in the distributed neuronal networks, which could provide new insights into the disease mechanism in AD and help us to uncover an imaging-based biomarker for the diagnosis and monitoring of the disease.