Non-linear dynamical analysis of the EEG in Alzheimer's disease with optimal embedding dimension

Multi-Threshold Recurrence Rate Plot: A Novel Methodology for EEG Analysis in Alzheimer's Disease and Frontotemporal Dementia

This study introduces Multi-Threshold Recurrence Rate Plots (MTRRP), a novel methodology for analyzing dynamic patterns in complex systems, such as those influenced by neurodegenerative diseases in brain activity. MTRRP characterizes how recurrence rates evolve with increasing recurrence thresholds. A key innovation of our approach, Recurrence Complexity, captures structural complexity by integrating local randomness and global structural features through the product of Recurrence Rate Gradient and Recurrence Hurst, both derived from MTRRP. We applied this technique to resting-state EEG data from patients diagnosed with Alzheimer's Disease (AD), Frontotemporal Dementia (FTD), and age-matched healthy controls. The results revealed significantly higher recurrence complexity in the occipital areas of AD and FTD patients, particularly pronounced in the Alpha and Beta frequency bands. Furthermore, EEG features derived from MTRRP were evaluated using a Support Vector Machine with leave-one-out cross-validation, achieving a classification accuracy of 87.7%. These findings not only underscore the utility of MTRRP in detecting distinct neurophysiological patterns associated with neurodegenerative diseases but also highlight its broader applicability in time series analysis, providing a substantial tool for advancing medical diagnostics and research.

Two-channel EEG based diagnosis of panic disorder and major depressive disorder using machine learning and non-linear dynamical methods

The current study aimed to investigate the possibility of rapid and accurate diagnoses of Panic disorder (PD) and Major depressive disorder (MDD) using machine learning. The support vector machine method was applied to 2-channel EEG signals from the frontal lobes (Fp1 and Fp2) of 149 participants to classify PD and MDD patients from healthy individuals using non-linear measures as features. We found significantly lower correlation dimension and Lempel-Ziv complexity in PD patients and MDD patients in the left hemisphere compared to healthy subjects at rest. Most importantly, we obtained a 90% accuracy in classifying MDD patients vs. healthy individuals, a 68% accuracy in classifying PD patients vs. controls, and a 59% classification accuracy between PD and MDD patients. In addition to demonstrating classification performance in a simplified setting, the observed differences in EEG complexity between subject groups suggest altered cortical processing present in the frontal lobes of PD patients that can be captured through non-linear measures. Overall, this study suggests that machine learning and non-linear measures using only 2-channel frontal EEGs are useful for aiding the rapid diagnosis of panic disorder and major depressive disorder.

Assessment and Comparison of Nonlinear Measures in Resting-State Magnetoencephalograms in Alzheimer's Disease and Mild Cognitive Impairment

BACKGROUND

Nonlinear dynamical measures, such as fractal dimension (FD), entropy, and Lempel-Ziv complexity (LZC), have been extensively investigated individually for detecting information content in magnetoencephalograms (MEGs) from patients with Alzheimer's disease (AD).

OBJECTIVE

To compare systematically the performance of twenty conventional and recently introduced nonlinear dynamical measures in studying AD versus mild cognitive impairment (MCI) and healthy control (HC) subjects using MEG.

METHODS

We compared twenty nonlinear measures to distinguish MEG recordings from 36 AD (mean age = 74.06±6.95 years), 18 MCI (mean age = 74.89±5.57 years), and 26 HC subjects (mean age = 71.77±6.38 years) in different brain regions and also evaluated the effect of the length of MEG epochs on their performance. We also studied the correlation between these measures and cognitive performance based on the Mini-Mental State Examination (MMSE).

RESULTS

The results obtained by LZC, zero-crossing rate (ZCR), FD, and dispersion entropy (DispEn) measures showed significant differences among the three groups. There was no significant difference between HC and MCI. The highest Hedge's g effect sizes for HC versus AD and MCI versus AD were respectively obtained by Higuchi's FD (HFD) and fuzzy DispEn (FuzDispEn) in the whole brain and was most prominent in left lateral. The results obtained by HFD and FuzDispEn had a significant correlation with the MMSE scores. DispEn-based techniques, LZC, and ZCR, compared with HFD, were less sensitive to epoch length in distinguishing HC form AD.

CONCLUSIONS

FuzDispEn was the most consistent technique to distinguish MEG dynamical patterns in AD compared with HC and MCI.

Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations

There has been an increasing trend towards the use of complexity analysis in quantifying neural activity measured by electroencephalography (EEG) signals. On top of revealing complex neuronal processes of the brain that may not be possible with linear approaches, EEG complexity measures have also demonstrated their potential as biomarkers of psychopathology such as depression and schizophrenia. Unfortunately, the opacity of algorithms and descriptions originating from mathematical concepts have made it difficult to understand what complexity is and how to draw consistent conclusions when applied within psychology and neuropsychiatry research. In this review, we provide an overview and entry-level explanation of existing EEG complexity measures, which can be broadly categorized as measures of predictability and regularity. We then synthesize complexity findings across different areas of psychological science, namely, in consciousness research, mood and anxiety disorders, schizophrenia, neurodevelopmental and neurodegenerative disorders, as well as changes across the lifespan, while addressing some theoretical and methodological issues underlying the discrepancies in the data. Finally, we present important considerations when choosing and interpreting these metrics.

Computational Modeling of Therapy with the NMDA Antagonist in Neurodegenerative Disease: Information Theory in the Mechanism of Action of Memantine

(1) Background: in patients with neurodegenerative diseases, noncompetitive N-methyl-D-aspartate (NMDA) receptor antagonists provide neuroprotective advantages. We performed memantine therapy and proved mathematical and computer modeling of neurodegenerative disease in this study. (2) Methods: a computer simulation environment of the N-methyl-D-aspartate receptor incorporating biological mechanisms of channel activation by high extracellular glutamic acid concentration. In comparison to controls, pathological models were essentially treated with doses of memantine 3−30 µM. (3) Results: the mean values and 95% CI for Shannon entropy in Alzheimer’s disease (AD) and memantine treatment models were 1.760 (95% CI, 1.704−1.818) vs. 2.385 (95% CI, 2.280−2.490). The Shannon entropy was significantly higher in the memantine treatment model relative to AD model (p = 0.0162). The mean values and 95% CI for the positive Lyapunov exponent in AD and memantine treatment models were 0.125 (95% CI, NE−NE) vs. 0.058 (95% CI, 0.044−0.073). The positive Lyapunov exponent was significantly higher in the AD model relative to the memantine treatment model (p = 0.0091). The mean values and 95% CI for transfer entropy in AD and memantine treatment models were 0.081 (95% CI, 0.048−0.114) vs. 0.040 (95% CI, 0.019−0.062). The transfer entropy was significantly higher in the AD model relative to the memantine treatment model (p = 0.0146). A correlation analysis showed positive and statistically significant correlations of the memantine concentrations and the positive Lyapunov exponent (correlation coefficient R = 0.87, p = 0.0023) and transfer entropy (TE) (correlation coefficient R = 0.99, p < 0.000001). (4) Conclusions: information theory results of simulation studies show that the NMDA antagonist, memantine, causes neuroprotective benefits in patients with AD. Our simulation study opens up remarkable new scenarios in which a medical product, drug, or device, can be developed and tested for efficacy based on parameters of information theory.

Fully automated discrimination of Alzheimer's disease using resting-state electroencephalography signals

Background

The Alzheimer's disease (AD) population increases worldwide, placing a heavy burden on the economy and society. Presently, there is no cure for AD. Developing a convenient method of screening for AD and mild cognitive impairment (MCI) could enable early intervention, thus slowing down the progress of the disease and enabling better overall disease management.

Methods

In the current study, resting-state electroencephalography (EEG) data were acquired from 113 normal cognition (NC) subjects, 116 amnestic MCI patients, and 72 probable AD patients. After preprocessing by an automatic algorithm, features including spectral power, complexity, and functional connectivity were extracted, and machine-learning classifiers were built to differentiate among the 3 groups. The classification performance was evaluated from multiple perspectives, including accuracy, specificity, sensitivity, area under the curve (AUC) with 95% confidence intervals, and compared to the empirical chance level by permutation tests.

Results

The analysis of variance results (P<0.05 with false discovery rate correction) confirmed the tendency to slow brain activity, reduced complexity, and connectivity with AD progress. By combining the features, the ability of the machine-learning classifiers, especially the ensemble trees, to differentiate among the 3 groups, was significantly better than that of the empirical chance level of the permutation test. The AUC of the classifier with the best performance was 80.08% for AD vs. NC, 70.82% for AD vs. MCI, and 63.95% for MCI vs. NC.

Conclusions

The current study presented a fully automatic procedure that could significantly distinguish NC, MCI, and AD subjects via resting-state EEG signals. The study was based on a large data set with evidence-based medical diagnosis and provided further evidence that resting-state EEG data could assist in the discrimination of AD patients.

A Comparative Study of Functional Connectivity Measures for Brain Network Analysis in the Context of AD Detection with EEG

This work addresses brain network analysis considering different clinical severity stages of cognitive dysfunction, based on resting-state electroencephalography (EEG). We use a cohort acquired in real-life clinical conditions, which contains EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, and Alzheimer’s disease (AD) patients. We propose to exploit an epoch-based entropy measure to quantify the connectivity links in the networks. This entropy measure relies on a refined statistical modeling of EEG signals with Hidden Markov Models, which allow a better estimation of the spatiotemporal characteristics of EEG signals. We also propose to conduct a comparative study by considering three other measures largely used in the literature: phase lag index, coherence, and mutual information. We calculated such measures at different frequency bands and computed different local graph parameters considering different proportional threshold values for a binary network analysis. After applying a feature selection procedure to determine the most relevant features for classification performance with a linear Support Vector Machine algorithm, our study demonstrates the effectiveness of the statistical entropy measure for analyzing the brain network in patients with different stages of cognitive dysfunction.

Diagnosis of Alzheimer's Disease by Time-Dependent Power Spectrum Descriptors and Convolutional Neural Network Using EEG Signal

Using strategies that obtain biomarkers where early symptoms coincide, the early detection of Alzheimer's disease and its complications is essential. Electroencephalogram is a technology that allows thousands of neurons with equal spatial orientation of the duration of cerebral cortex electrical activity to be registered by postsynaptic potential. Therefore, in this paper, the time-dependent power spectrum descriptors are used to diagnose the electroencephalogram signal function from three groups: mild cognitive impairment, Alzheimer's disease, and healthy control test samples. The final feature used in three modes of traditional classification methods is recorded: k-nearest neighbors, support vector machine, linear discriminant analysis approaches, and documented results. Finally, for Alzheimer's disease patient classification, the convolutional neural network architecture is presented. The results are indicated using output assessment. For the convolutional neural network approach, the accurate meaning of accuracy is 82.3%. 85% of mild cognitive impairment cases are accurately detected in-depth, but 89.1% of the Alzheimer's disease and 75% of the healthy population are correctly diagnosed. The presented convolutional neural network outperforms other approaches because performance and the k-nearest neighbors' approach is the next target. The linear discriminant analysis and support vector machine were at the low area under the curve values.

Measures of resting state EEG rhythms for clinical trials in Alzheimer's disease: Recommendations of an expert panel

The Electrophysiology Professional Interest Area (EPIA) and Global Brain Consortium endorsed recommendations on candidate electroencephalography (EEG) measures for Alzheimer's disease (AD) clinical trials. The Panel reviewed the field literature. As most consistent findings, AD patients with mild cognitive impairment and dementia showed abnormalities in peak frequency, power, and "interrelatedness" at posterior alpha (8-12 Hz) and widespread delta (< 4 Hz) and theta (4-8 Hz) rhythms in relation to disease progression and interventions. The following consensus statements were subscribed: (1) Standardization of instructions to patients, resting state EEG (rsEEG) recording methods, and selection of artifact-free rsEEG periods are needed; (2) power density and "interrelatedness" rsEEG measures (e.g., directed transfer function, phase lag index, linear lagged connectivity, etc.) at delta, theta, and alpha frequency bands may be use for stratification of AD patients and monitoring of disease progression and intervention; and (3) international multisectoral initiatives are mandatory for regulatory purposes.

Relationship between the Presence of the ApoE ε4 Allele and EEG Complexity along the Alzheimer's Disease Continuum

Alzheimer's disease (AD) is the most prevalent cause of dementia, being considered a major health problem, especially in developed countries. Late-onset AD is the most common form of the disease, with symptoms appearing after 65 years old. Genetic determinants of AD risk are vastly unknown, though, ε 4 allele of the ApoE gene has been reported as the strongest genetic risk factor for AD. The objective of this study was to analyze the relationship between brain complexity and the presence of ApoE ε 4 alleles along the AD continuum. For this purpose, resting-state electroencephalography (EEG) activity was analyzed by computing Lempel-Ziv complexity (LZC) from 46 healthy control subjects, 49 mild cognitive impairment subjects, 45 mild AD patients, 44 moderate AD patients and 33 severe AD patients, subdivided by ApoE status. Subjects with one or more ApoE ε 4 alleles were included in the carriers subgroups, whereas the ApoE ε 4 non-carriers subgroups were formed by subjects without any ε 4 allele. Our results showed that AD continuum is characterized by a progressive complexity loss. No differences were observed between AD ApoE ε 4 carriers and non-carriers. However, brain activity from healthy subjects with ApoE ε 4 allele (carriers subgroup) is more complex than from non-carriers, mainly in left temporal, frontal and posterior regions (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These results suggest that the presence of ApoE ε 4 allele could modify the EEG complexity patterns in different brain regions, as the temporal lobes. These alterations might be related to anatomical changes associated to neurodegeneration, increasing the risk of suffering dementia due to AD before its clinical onset. This interesting finding might help to advance in the development of new tools for early AD diagnosis.

Potential New Approaches for Diagnosis of Alzheimer's Disease and Related Dementias

Dementia is an umbrella term-caused by a large number of specific diagnoses, including several neurodegenerative disorders. Alzheimer's disease (AD) is now the most common cause of dementia in advanced countries, while dementia due to neurosyphilis was the leading cause a century ago. Many challenges remain for diagnosing dementia definitively. Some of these include variability of early symptoms and overlap with similar disorders, as well as the possibility of combined, or mixed, etiologies in some cases. Newer technologies, including the incorporation of PET neuroimaging and other biomarkers (genomics and proteomics), are being incorporated into revised diagnostic criteria. However, the application of novel diagnostic methods at clinical sites is plagued by many caveats including availability and access. This review surveys new diagnostic methods as well as remaining challenges-for clinical care and clinical research.

Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review

Alzheimer's disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000-2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.

EEG Complexity Maps to Characterise Brain Dynamics during Upper Limb Motor Imagery

The Electroencephalogram (EEG) can be considered as the output of a nonlinear system whose dynamics is significantly affected by motor tasks. Nevertheless, computational approaches derived from the complex system theory has not been fully exploited for characterising motor imagery tasks. To this extent, in this study we investigated EEG complexity changes throughout the following categories of imaginary motor tasks of the upper limb: transitive (actions involving an object), intransitive (meaningful gestures that do not include the use of objects), and tool-mediated (actions using an object to interact with another one). EEG irregularity was quantified following the definition of Fuzzy Entropy, which has been demonstrated to be a reliable quantifier of system complexity with low dependence on data length. Experimental results from paired statistical analyses revealed minor topographical changes between EEG complexity associated with transitive and tool-mediated tasks, whereas major significant differences were shown between the intransitive actions vs. the others. Our results suggest that EEG complexity level during motor imagery tasks of the upper limb are strongly biased by the presence of an object.

Effects of Inducing Gamma Oscillations in Hippocampal Subregions DG, CA3, and CA1 on the Potential Alleviation of Alzheimer's Disease-Related Pathology: Computer Modeling and Simulations

The aim of this study was to evaluate the possibility of the gamma oscillation function (40–130 Hz) to reduce Alzheimer’s disease related pathology in a computer model of the hippocampal network dentate gyrus, CA3, and CA1 (DG-CA3-CA1) regions. Methods: Computer simulations were made for a pathological model in which Alzheimer’s disease was simulated by synaptic degradation in the hippocampus. Pathology modeling was based on sequentially turning off the connections with entorhinal cortex layer 2 (EC2) and the dentate gyrus on CA3 pyramidal neurons. Gamma induction modeling consisted of simulating the oscillation provided by the septo-hippocampal pathway with band frequencies from 40–130 Hz. Pathological models with and without gamma induction were compared with a control. Results: In the hippocampal regions of DG, CA3, and CA1, and jointly DG-CA3-CA1 and CA3-CA1, gamma induction resulted in a statistically significant improvement in terms of increased numbers of spikes, spikes per burst, and burst duration as compared with the model simulating Alzheimer’s disease (AD). The positive maximal Lyapunov exponent was negative in both the control model and the one with gamma induction as opposed to the pathological model where it was positive within the DG-CA3-CA1 region. Gamma induction resulted in decreased transfer entropy in accordance with the information flow in DG → CA3 and CA3 → CA1. Conclusions: The results of simulation studies show that inducing gamma oscillations in the hippocampus may reduce Alzheimer’s disease related pathology. Pathologically higher transfer entropy values after gamma induction returned to values comparable to the control model.

Computer Model of Synapse Loss During an Alzheimer's Disease-Like Pathology in Hippocampal Subregions DG, CA3 and CA1-The Way to Chaos and Information Transfer

The aim of the study was to compare the computer model of synaptic breakdown in an Alzheimer’s disease-like pathology in the dentate gyrus (DG), CA3 and CA1 regions of the hippocampus with a control model using neuronal parameters and methods describing the complexity of the system, such as the correlative dimension, Shannon entropy and positive maximal Lyapunov exponent. The model of synaptic breakdown (from 13% to 50%) in the hippocampus modeling the dynamics of an Alzheimer’s disease-like pathology was simulated. Modeling consisted in turning off one after the other EC2 connections and connections from the dentate gyrus on the CA3 pyramidal neurons. The pathological model of synaptic disintegration was compared to a control. The larger synaptic breakdown was associated with a statistically significant decrease in the number of spikes (R = −0.79, P < 0.001), spikes per burst (R = −0.76, P < 0.001) and burst duration (R = −0.83, P < 0.001) and an increase in the inter-burst interval (R = 0.85, P < 0.001) in DG-CA3-CA1. The positive maximal Lyapunov exponent in the control model was negative, but in the pathological model had a positive value of DG-CA3-CA1. A statistically significant decrease of Shannon entropy with the direction of information flow DG->CA3->CA1 (R = −0.79, P < 0.001) in the pathological model and a statistically significant increase with greater synaptic breakdown (R = 0.24, P < 0.05) of the CA3-CA1 region was obtained. The reduction of entropy transfer for DG->CA3 at the level of synaptic breakdown of 35% was 35%, compared with the control. Entropy transfer for CA3->CA1 at the level of synaptic breakdown of 35% increased to 95% relative to the control. The synaptic breakdown model in an Alzheimer’s disease-like pathology in DG-CA3-CA1 exhibits chaotic features as opposed to the control. Synaptic breakdown in which an increase of Shannon entropy is observed indicates an irreversible process of Alzheimer’s disease. The increase in synapse loss resulted in decreased information flow and entropy transfer in DG->CA3, and at the same time a strong increase in CA3->CA1.

EEG Window Length Evaluation for the Detection of Alzheimer's Disease over Different Brain Regions

Alzheimer's Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy ranging from 88.79% to 96.78% for whole-brain classification. Also, the classification accuracy was higher at the posterior and central regions than at the frontal area and the right side of temporal lobe for all classification problems.

Altered dynamic electroencephalography connectome phase-space features of emotion regulation in social anxiety

Emotion regulation deficits are commonly observed in social anxiety disorder (SAD). We used manifold-learning to learn the phase-space connectome manifold of EEG brain dynamics in twenty SAD participants and twenty healthy controls. The purpose of the present study was to utilize manifold-learning to understand EEG brain dynamics associated with emotion regulation processes. Our emotion regulation task (ERT) contains three conditions: Neutral, Maintain and Reappraise. For all conditions and subjects, EEG connectivity data was converted into series of temporally-consecutive connectomes and aggregated to yield this phase-space manifold. As manifold geodesic distances encode intrinsic geometry, we visualized this space using its geodesic-informed minimum spanning tree and compared neurophysiological dynamics across conditions and groups using the corresponding trajectory length. Results showed that SAD participants had significantly longer trajectory lengths during Neutral and Maintain. Further, trajectory lengths during Reappraise were significantly associated with the habitual use of reappraisal strategies, while Maintain trajectory lengths were significantly associated with the negative affective state during Maintain. In sum, an unsupervised connectome manifold-learning approach can reveal emotion regulation associated phase-space features of brain dynamics.

Systematic Review on Resting-State EEG for Alzheimer's Disease Diagnosis and Progression Assessment

Alzheimer's disease (AD) is a neurodegenerative disorder that accounts for nearly 70% of the more than 46 million dementia cases estimated worldwide. Although there is no cure for AD, early diagnosis and an accurate characterization of the disease progression can improve the quality of life of AD patients and their caregivers. Currently, AD diagnosis is carried out using standardized mental status examinations, which are commonly assisted by expensive neuroimaging scans and invasive laboratory tests, thus rendering the diagnosis time consuming and costly. Notwithstanding, over the last decade, electroencephalography (EEG) has emerged as a noninvasive alternative technique for the study of AD, competing with more expensive neuroimaging tools, such as MRI and PET. This paper reports on the results of a systematic review on the utilization of resting-state EEG signals for AD diagnosis and progression assessment. Recent journal articles obtained from four major bibliographic databases were analyzed. A total of 112 journal articles published from January 2010 to February 2018 were meticulously reviewed, and relevant aspects of these papers were compared across articles to provide a general overview of the research on this noninvasive AD diagnosis technique. Finally, recommendations for future studies with resting-state EEG were presented to improve and facilitate the knowledge transfer among research groups.

A Pilot Study Investigating a Novel Non-Linear Measure of Eyes Open versus Eyes Closed EEG Synchronization in People with Alzheimer’s Disease and Healthy Controls

Background: The incidence of Alzheimer disease (AD) is increasing with the ageing population. The development of low cost non-invasive diagnostic aids for AD is a research priority. This pilot study investigated whether an approach based on a novel dynamic quantitative parametric EEG method could detect abnormalities in people with AD. Methods: 20 patients with probable AD, 20 matched healthy controls (HC) and 4 patients with probable fronto temporal dementia (FTD) were included. All had detailed neuropsychology along with structural, resting state fMRI and EEG. EEG data were analyzed using the Error Reduction Ratio-causality (ERR-causality) test that can capture both linear and nonlinear interactions between different EEG recording areas. The 95% confidence intervals of EEG levels of bi-centroparietal synchronization were estimated for eyes open (EO) and eyes closed (EC) states. Results: In the EC state, AD patients and HC had very similar levels of bi-centro parietal synchronization; but in the EO resting state, patients with AD had significantly higher levels of synchronization (AD = 0.44; interquartile range (IQR) 0.41 vs. HC = 0.15; IQR 0.17, p < 0.0001). The EO/EC synchronization ratio, a measure of the dynamic changes between the two states, also showed significant differences between these two groups (AD ratio 0.78 versus HC ratio 0.37 p < 0.0001). EO synchronization was also significantly different between AD and FTD (FTD = 0.075; IQR 0.03, p < 0.0001). However, the EO/EC ratio was not informative in the FTD group due to very low levels of synchronization in both states (EO and EC). Conclusion: In this pilot work, resting state quantitative EEG shows significant differences between healthy controls and patients with AD. This approach has the potential to develop into a useful non-invasive and economical diagnostic aid in AD.

Analysis of long range dependence in the EEG signals of Alzheimer patients

Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

Diagnosis of Alzheimer's disease with Electroencephalography in a differential framework

This study addresses the problem of Alzheimer’s disease (AD) diagnosis with Electroencephalography (EEG). The use of EEG as a tool for AD diagnosis has been widely studied by comparing EEG signals of AD patients only to those of healthy subjects. By contrast, we perform automated EEG diagnosis in a differential diagnosis context using a new database, acquired in clinical conditions, which contains EEG data of 169 patients: subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients, possible Alzheimer’s disease (AD) patients, and patients with other pathologies. We show that two EEG features, namely epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) are sufficient for efficient discrimination between these groups. We studied the performance of our methodology for the automatic discrimination of possible AD patients from SCI patients and from patients with MCI or other pathologies. A classification accuracy of 91.6% (specificity = 100%, sensitivity = 87.8%) was obtained when discriminating SCI patients from possible AD patients and 81.8% to 88.8% accuracy was obtained for the 3-class classification of SCI, possible AD and other patients.

Analysis of long range dependence in the EEG signals of Alzheimer patients

Alzheimer's disease (AD), a cognitive disability is analysed using a long range dependence parameter, hurst exponent (HE), calculated based on the time domain analysis of the measured electrical activity of brain. The electroencephalogram (EEG) signals of controls and mild cognitive impairment (MCI)-AD patients are evaluated under normal resting and mental arithmetic conditions. Simultaneous low pass filtering and total variation denoising algorithm is employed for preprocessing. Larger values of HE observed in the right hemisphere of the brain for AD patients indicated a decrease in irregularity of the EEG signal under cognitive task conditions. Correlations between HE and the neuropsychological indices are analysed using bivariate correlation analysis. The observed reduction in the values of Auto mutual information and cross mutual information in the local antero-frontal and distant regions in the brain hemisphere indicates the loss of information transmission in MCI-AD patients.

Fuzzy Entropy Analysis of the Electroencephalogram in Patients with Alzheimer's Disease: Is the Method Superior to Sample Entropy?

Alzheimer’s disease (AD) is the most prevalent form of dementia in the world, which is characterised by the loss of neurones and the build-up of plaques in the brain, causing progressive symptoms of memory loss and confusion. Although definite diagnosis is only possible by necropsy, differential diagnosis with other types of dementia is still needed. An electroencephalogram (EEG) is a cheap, portable, non-invasive method to record brain signals. Previous studies with non-linear signal processing methods have shown changes in the EEG due to AD, which is characterised reduced complexity and increased regularity. EEGs from 11 AD patients and 11 age-matched control subjects were analysed with Fuzzy Entropy (FuzzyEn), a non-linear method that was introduced as an improvement over the frequently used Approximate Entropy (ApEn) and Sample Entropy (SampEn) algorithms. AD patients had significantly lower FuzzyEn values than control subjects (p < 0.01) at electrodes T6, P3, P4, O1, and O2. Furthermore, when diagnostic accuracy was calculated using Receiver Operating Characteristic (ROC) curves, FuzzyEn outperformed both ApEn and SampEn, reaching a maximum accuracy of 86.36%. These results suggest that FuzzyEn could increase the insight into brain dysfunction in AD, providing potentially useful diagnostic information. However, results depend heavily on the input parameters that are used to compute FuzzyEn.

Multiple characteristics analysis of Alzheimer's electroencephalogram by power spectral density and Lempel-Ziv complexity

To investigate the electroencephalograph (EEG) background activity in patients with Alzheimer's disease (AD), power spectrum density (PSD) and Lempel-Ziv (LZ) complexity analysis are proposed to extract multiple effective features of EEG signals from AD patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared with the control group, the relative PSD of AD group is significantly higher in the theta frequency band while lower in the alpha frequency bands. In order to explore the nonlinear information, Lempel-Ziv complexity (LZC) and multi-scale LZC is further applied to all electrodes for the four frequency bands. Analysis results demonstrate that the group difference is significant in the alpha frequency band by LZC and multi-scale LZC analysis. However, the group difference of multi-scale LZC is much more remarkable, manifesting as more channels undergo notable changes, particularly in electrodes O1 and O2 in the occipital area. Moreover, the multi-scale LZC value provided a better classification between the two groups with an accuracy of 85.7 %. In addition, we combine both features of the relative PSD and multi-scale LZC to discriminate AD patients from the normal controls by applying a support vector machine model in the alpha 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, reaching 91.4 %. The obtained results show that analysis of PSD and multi-scale LZC can be taken as a potential comprehensive measure to distinguish AD patients from the normal controls, which may benefit our understanding of the disease.

Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson's disease patients

Recent studies show right hemisphere has a unique contribution to emotion processing. The present study investigated EEG using non-linear measures during emotional processing in PD patients with respect to motor symptom asymmetry (i.e., most affected body side). We recorded 14-channel wireless EEGs from 20 PD patients and 10 healthy age-matched controls (HC) by eliciting emotions such as happiness, sadness, fear, anger, surprise and disgust. PD patients were divided into two groups, based on most affected body side and unilateral motor symptom severity: left side-affected (LPD, n = 10) or right side-affected PD patients (RPD, n = 10). Nonlinear analysis of these emotional EEGs were performed by using approximate entropy, correlation dimension, detrended fluctuation analysis, fractal dimension, higher order spectra, hurst exponent (HE), largest Lyapunov exponent and sample entropy. The extracted features were ranked using analysis of variance based on F value. The ranked features were then fed into classifiers namely fuzzy K-nearest neighbor and support vector machine to obtain optimal performance using minimum number of features. From the experimental results, we found that (a) classification performance across all frequency bands performed well in recognizing emotional states of LPD, RPD, and HC; (b) the emotion-specific features were mainly related to higher frequency bands; and (c) predominantly LPD patients (inferred right-hemisphere pathology) were more impaired in emotion processing compared to RPD, as showed by a poorer classification performance. The results suggest that asymmetric neuronal degeneration in PD patients may contribute to the impairment of emotional communication.

Epoch-based Entropy for Early Screening of Alzheimer’s Disease

In this paper, we introduce a novel entropy measure, termed epoch-based entropy. This measure quantifies disorder of EEG signals both at the time level and spatial level, using local density estimation by a Hidden Markov Model on inter-channel stationary epochs. The investigation is led on a multi-centric EEG database recorded from patients at an early stage of Alzheimer’s disease (AD) and age-matched healthy subjects. We investigate the classification performances of this method, its robustness to noise, and its sensitivity to sampling frequency and to variations of hyperparameters. The measure is compared to two alternative complexity measures, Shannon’s entropy and correlation dimension. The classification accuracies for the discrimination of AD patients from healthy subjects were estimated using a linear classifier designed on a development dataset, and subsequently tested on an independent test set. Epoch-based entropy reached a classification accuracy of 83% on the test dataset (specificity = 83.3%, sensitivity = 82.3%), outperforming the two other complexity measures. Furthermore, it was shown to be more stable to hyperparameter variations, and less sensitive to noise and sampling frequency disturbances than the other two complexity measures.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

The Electrophysiological Phenomenon of Alzheimer's Disease: A Psychopathology Theory

The current understanding of Alzheimer's disease (AD) is based on the Aβ and tau pathology and the resulting neuropathological changes, which are associated with manifested clinical symptoms. However, electrophysiological brain changes may provide a more expansive understanding of AD. Hence, the objective of this systematic review is to propose a theory about the electrophysiological phenomenon of Alzheimer's disease (EPAD). The review of literature resulted from an extensive search of PubMed and MEDLINE databases. One-hundred articles were purposively selected. They provided an understanding of the concepts establishing the theory of EPAD (neuropathological changes, neurochemical changes, metabolic changes, and electrophysiological brain changes). Changes in the electrophysiology of the brain are foundational to the association or interaction of the concepts. Building on Berger's Psychophysical Model, it is evident that electrophysiological brain changes occur and affect cortical areas to generate or manifest symptoms from onset and across the stages of AD, which may be prior to pathological changes. Therefore, the interaction of the concepts demonstrates how the psychopathology results from affected electrophysiology of the brain. The theory of the EPAD provides a theoretical foundation for appropriate measurements of AD without dependence on neuropathological changes. Future research is warranted to further test this theory. Ultimately, this theory contributes to existing knowledge because it shows how electrophysiological changes are useful in understanding the risk and progression of AD across the stages.

Nonlinear complexity analysis of brain FMRI signals in schizophrenia

We investigated the differences in brain fMRI signal complexity in patients with schizophrenia while performing the Cyberball social exclusion task, using measures of Sample entropy and Hurst exponent (H). 13 patients meeting diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM IV) criteria for schizophrenia and 16 healthy controls underwent fMRI scanning at 1.5 T. The fMRI data of both groups of participants were pre-processed, the entropy characterized and the Hurst exponent extracted. Whole brain entropy and H maps of the groups were generated and analysed. The results after adjusting for age and sex differences together show that patients with schizophrenia exhibited higher complexity than healthy controls, at mean whole brain and regional levels. Also, both Sample entropy and Hurst exponent agree that patients with schizophrenia have more complex fMRI signals than healthy controls. These results suggest that schizophrenia is associated with more complex signal patterns when compared to healthy controls, supporting the increase in complexity hypothesis, where system complexity increases with age or disease, and also consistent with the notion that schizophrenia is characterised by a dysregulation of the nonlinear dynamics of underlying neuronal systems.

Decreased cortical complexity in methamphetamine abusers

This study aimed to investigate if methamphetamine (MA) abusers exhibit alterations in complexity of the electroencephalogram (EEG) and to determine if these possible alterations are associated with their abuse patterns. EEGs were recorded from 48 former MA-dependent males and 20 age- and sex-matched healthy subjects. Approximate Entropy (ApEn), an information-theoretical measure of irregularity, of the EEGs was estimated to quantify the degree of cortical complexity. The ApEn values in MA abusers were significantly lower than those of healthy subjects in most of the cortical regions, indicating decreased cortical complexity of MA abusers, which may be associated with impairment in specialization and integration of cortical activities owing to MA abuse. Moreover, ApEn values exhibited significant correlations with the clinical factors including abuse patterns, symptoms of psychoses, and their concurrent drinking and smoking habits. These findings provide insights into abnormal information processing in MA abusers and suggest that ApEn of EEG recordings may be used as a potential supplementary tool for quantitative diagnosis of MA abuse. This is the first investigation to assess the "severity-dependent dynamical complexity" of EEG patterns in former MA abusers and their associations with the subjects' abuse patterns and other clinical measures.

RECURRENCE-BASED ESTIMATION OF TIME-DISTORTION FUNCTIONS FOR ERP WAVEFORM RECONSTRUCTION

We introduce an approach to compensate for temporal distortions of repeated measurements in event-related potential research. The algorithm uses a combination of methods from nonlinear time-series analysis and is based on the construction of pairwise registration functions from cross-recurrence plots of the phase-space representations of ERP signals. The globally optimal multiple-alignment path is approximated by hierarchical cluster analysis, i.e. by iteratively combining pairs of trials according to similarity. By the inclusion of context information in form of externally acquired time markers (e.g. reaction time) into a regularization scheme, the extracted warping functions can be guided near paths that are implied by the experimental procedure. All parameters occurring in the algorithm can be optimized based on the properties of the data and there is a broad regime of parameter configurations where the algorithm produces good results. Simulations on artificial data and the analysis of ERPs from a psychophysical study demonstrate the robustness and applicability of the algorithm.

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.

Alteration of brain functional network at rest and in response to YMCA physical stress test in concussed athletes: RsFMRI study

There is still controversy in the literature whether a single episode of mild traumatic brain injury (mTBI) results in short- and/or long-term functional and structural deficits in the concussed brain. With the inability of traditional brain imaging techniques to properly assess the severity of brain damage induced by a concussive blow, there is hope that more advanced applications such as resting state functional magnetic resonance imaging (rsFMRI) will be more specific in accurately diagnosing mTBI. In this rsFMRI study, we examined 17 subjects 10±2 days post-sports-related mTBI and 17 age-matched normal volunteers (NVs) to investigate the possibility that the integrity of the resting state brain network is disrupted following a single concussive blow. We hypothesized that advanced brain imaging techniques may reveal subtle alterations of functional brain connections in asymptomatic mTBI subjects. There are several findings of interest. All mTBI subjects were asymptomatic based upon clinical evaluation and neuropsychological (NP) assessments prior to the MRI session. The mTBI subjects revealed a disrupted functional network both at rest and in response to the YMCA physical stress test. Specifically, interhemispheric connectivity was significantly reduced in the primary visual cortex, hippocampal and dorsolateral prefrontal cortex networks (p<0.05). The YMCA physical stress induced nonspecific and similar changes in brain network connectivity patterns in both the mTBI and NV groups. These major findings are discussed in relation to underlying mechanisms, clinical assessment of mTBI, and current debate regarding functional brain connectivity in a clinical population. Overall, our major findings clearly indicate that functional brain alterations in the acute phase of injury are overlooked when conventional clinical and neuropsychological examinations are used.

Entropy and Complexity Analyses in Alzheimer's Disease: An MEG Study

Alzheimer’s disease (AD) is one of the most frequent disorders among elderly population and it is considered the main cause of dementia in western countries. This irreversible brain disorder is characterized by neural loss and the appearance of neurofibrillary tangles and senile plaques. The aim of the present study was the analysis of the magnetoencephalogram (MEG) background activity from AD patients and elderly control subjects. MEG recordings from 36 AD patients and 26 controls were analyzed by means of six entropy and complexity measures: Shannon spectral entropy (SSE), approximate entropy (ApEn), sample entropy (SampEn), Higuchi’s fractal dimension (HFD), Maragos and Sun’s fractal dimension (MSFD), and Lempel-Ziv complexity (LZC). SSE is an irregularity estimator in terms of the flatness of the spectrum, whereas ApEn and SampEn are embbeding entropies that quantify the signal regularity. The complexity measures HFD and MSFD were applied to MEG signals to estimate their fractal dimension. Finally, LZC measures the number of different substrings and the rate of their recurrence along the original time series. Our results show that MEG recordings are less complex and more regular in AD patients than in control subjects. Significant differences between both groups were found in several brain regions using all these methods, with the exception of MSFD (p-value < 0.05, Welch’s t-test with Bonferroni’s correction). Using receiver operating characteristic curves with a leave-one-out cross-validation procedure, the highest accuracy was achieved with SSE: 77.42%. We conclude that entropy and complexity analyses from MEG background activity could be useful to help in AD diagnosis.

Complexity analysis of spontaneous brain activity in Alzheimer disease and mild cognitive impairment: an MEG study

Nonlinear analyses have shown that Alzheimer disease (AD) patients' brain activity is characterized by a reduced complexity and connectivity. The aim of this study is to define complexity patterns of mild cognitive impairment (MCI) patients. Whole-head magnetoencephalography recordings were obtained from 18 diagnosed AD patients, 18 MCI patients, and 18 healthy controls during resting conditions. Lempel-Ziv complexity (LZC) values were calculated. MCI patients exhibited intermediary LZC scores between AD patients and controls. A combination of age and posterior LZC scores allowed ADs-MCIs discrimination with 94.4% sensitivity and specificity, whereas no LZC score allowed MCIs---controls discrimination. AD patients and controls showed a parallel tendency to diminished LZC scores as a function of age, but MCI patients did not exhibit such "normal" tendency. Accordingly, anterior LZC scores allowed MCIs-controls discrimination for subjects below 75 years. MCIs exhibited a qualitatively distinct relationship between aging and complexity reduction, with scores higher than controls in older individuals. This fact might be considered a new example of compensatory mechanism in MCI before fully established dementia.

Study of the MEG background activity in Alzheimer's disease patients with scaling analysis methods

Alzheimer's disease (AD) is one of the most prominent neurodegenerative disorders. The aim of this research work is to study the magnetoencephalogram (MEG) background activity in AD patients using two scaling analysis methods: detrended fluctuation analysis (DFA) and backward detrended moving average (BDMA). Both measures have been designed to quantify correlations in noisy and non-stationary signals. Five minutes of recording were acquired with a 148-channel whole-head magnetometer in 15 patients with probable AD and 15 control subjects. Both DFA and BDMA exhibited two scaling regions with different slopes. Significant differences between both groups were found in the second region of DFA and in the first region of BDMA (p < 0.01, Student's t-test). Using receiver operating characteristic curves, accuracies of 83.33% with DFA and of 80% with BDMA were reached. Our findings show the usefulness of these scaling analysis methods to increase our insight into AD.

Alteration of cortical functional connectivity as a result of traumatic brain injury revealed by graph theory, ICA, and sLORETA analyses of EEG signals

In this paper, a novel approach to examine the cortical functional connectivity using multichannel electroencephalographic (EEG) signals is proposed. First we utilized independent component analysis (ICA) to transform multichannel EEG recordings into independent processes and then applied source reconstruction algorithm [i.e., standardize low resolution brain electromagnetic (sLORETA)] to identify the cortical regions of interest (ROIs). Second, we performed a graph theory analysis of the bipartite network composite of ROIs and independent processes to assess the connectivity between ROIs. We applied this proposed algorithm and compared the functional connectivity network properties under resting state condition using 29 student-athletes prior to and shortly after sport-related mild traumatic brain injury (MTBI). The major findings of interest are the following. There was 1) alterations in vertex degree at frontal and occipital regions in subjects suffering from MTBI, ( p < 0.05); 2) a significant decrease in the long-distance connectivity and significant increase in the short-distance connectivity as a result of MTBI, ( p < 0.05); 3) a departure from small-world network configuration in MTBI subjects. These major findings are discussed in relation to current debates regarding the brain functional connectivity within and between local and distal regions both in normal controls in pathological subjects.

Nonlinear features of surface EEG showing systematic brain signal adaptations with muscle force and fatigue

Nonlinear dynamics has been introduced to the analysis of biological data and increasingly recognized to be functionally relevant. The purpose of this study was to examine chaotic properties of human scalp EEG signals associated with voluntary motor tasks using the largest Lyapunov exponent (L1). 64-channel scalp EEG data were recorded from eight healthy subjects in two tasks: (1) intermittent handgrip contractions at 20, 40, 60, and 80% of maximal voluntary contraction (MVC) with 20 trials at each level. No significant fatigue were induced; (2) intermittent handgrip MVCs (100 trials) that resulted in significant fatigue. The L1 values of all EEG channels were calculated in each trial first then averaged across the 20 trials at each force level (Task 1) or over each of the 5-trial blocks (Task 2) before the group means were obtained. A multivariate statistical model was used to examine the effect of force and fatigue on L1. L1 values were greater with higher force (Task 1), and decreased significantly with fatigue (Task 2). The L1 of the EEG signals changes systematically and correlates significantly with muscle force and fatigue. The results suggest that nonlinear chaotic index L1 may serve as a quantitative measure for motor control-related cortical signal adaptations.

Evidence-based evaluation of diagnostic accuracy of resting EEG in dementia and mild cognitive impairment

Cognitive impairment is the most frequent chronic condition in the elderly, and dementia is the most disabling form of cognitive impairment in elderly. In the absence of specific and reliable markers of etiologically different dementia syndromes and their preclinical stages, diagnosis in living patients is probabilistic and based on standardized clinical diagnostic criteria. There is still not enough information on the validity of the EEG method in dementia work-up, and an updated evidence-based consensus on appropriateness of this method in the initial evaluation of patients with suspected cognitive disorder and dementia is missing. Using an evidence-based technique we searched for articles on diagnostic accuracy of spontaneous EEG in dementia disorders published from 1980 until June 2008. Inclusion criteria were: original article published in English with 10 or more subjects per diagnostic group, diagnosed according to the established consensus clinical diagnostic criteria used as a "gold standard." In addition, it should have been possible to calculate from the reported results indexes of diagnostic test accuracy: sensitivity, specificity, likelihood ratios and diagnostic odds ratios. Forty-six articles were retrieved that satisfied eligibility criteria. Thirty-four (74%) studies employed case-control design where study population was recruited from consecutive patients at specialist clinic settings, 12 (26%) were prospective in terms of reported clinical followup of study population. Four (9%) studies used population-based samples and 5 (11%) studies stated in methods the recruitment procedures for patients and healthy subjects. Number of patients included in diagnostic groups and healthy subjects varied in included studies between 10 and 180 and 10 and 171, respectively. Figures on sensitivity and specificity across the studies varied widely. Positive likelihood ratio in studies reporting classification accuracies between Alzheimer's disease and controls ranged between 2.3 and 38.5, and diagnostic odds ratios consequently showed large variations between 7 and 219. In conclusion, despite the wealth of published research and reported high indexes of diagnostic accuracy of EEG, and qEEG in particular, in individual studies, evidence of diagnostic utility of resting EEG in dementia and mild cognitive impairment (MCI) is still not sufficient to establish this method for the initial evaluation of subjects with cognitive impairment in the routine clinical practice. Joint effort of preferably multicenter studies using uniform standards should develop optimized methods, investigate added diagnostic value of EEG in clinically established dementia diagnosis and predictive utility of EEG in MCI and questionable dementia.

Complexity analysis of spontaneous brain activity in attention-deficit/hyperactivity disorder: diagnostic implications

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is defined as the most common neurobehavioral disorder of childhood, but an objective diagnostic test is not available yet to date. Neurophychological, neuroimaging, and neurophysiological research offer ample evidence of brain and behavioral dysfunctions in ADHD, but these findings have not been useful as a diagnostic test.

METHODS

Whole-head magnetoencephalographic recordings were obtained from 14 diagnosed ADHD patients and 14 healthy children during resting conditions. Lempel-Ziv complexity (LZC) values were obtained for each channel and child and averaged in five sensor groups: anterior, central, left lateral, right lateral, and posterior.

RESULTS

Lempel-Ziv complexity scores were significantly higher in control subjects, with the maximum value in anterior region. Combining age and anterior complexity values allowed the correct classification of ADHD patients and control subjects with a 93% sensitivity and 79% specificity. Control subjects showed an age-related monotonic increase of LZC scores in all sensor groups, while children with ADHD exhibited a nonsignificant tendency toward decreased LZC scores. The age-related divergence resulted in a 100% specificity in children older than 9 years.

CONCLUSIONS

Results support the role of a frontal hypoactivity in the diagnosis of ADHD. Moreover, the age-related divergence of complexity scores between ADHD patients and control subjects might reflect distinctive developmental trajectories. This interpretation of our results is in agreement with recent investigations reporting a delay of cortical maturation in the prefrontal cortex.