Jaeseung Jeong

EEG dynamics in patients with Alzheimer's disease

Alzheimers disease (AD) is the most common neurodegenerative disorder characterized by cognitive and intellectual deficits and behavior disturbance. The electroencephalogram (EEG) has been used as a tool for diagnosing AD for several decades. The hallmark of EEG abnormalities in AD patients is a shift of the power spectrum to lower frequencies and a decrease in coherence of fast rhythms. These abnormalities are thought to be associated with functional disconnections among cortical areas resulting from death of cortical neurons, axonal pathology, cholinergic deficits, etc. This article reviews main findings of EEG abnormalities in AD patients obtained from conventional spectral analysis and nonlinear dynamical methods. In particular, nonlinear alterations in the EEG of AD patients, i.e. a decreased complexity of EEG patterns and reduced information transmission among cortical areas, and their clinical implications are discussed. For future studies, improvement of the accuracy of differential diagnosis and early detection of AD based on multimodal approaches, longitudinal studies on nonlinear dynamics of the EEG, drug effects on the EEG dynamics, and linear and nonlinear functional connectivity among cortical regions in AD are proposed to be investigated. EEG abnormalities of AD patients are characterized by slowed mean frequency, less complex activity, and reduced coherences among cortical regions. These abnormalities suggest that the EEG has utility as a valuable tool for differential and early diagnosis of AD.

Mutual information analysis of the EEG in patients with Alzheimer's disease.

OBJECTIVE Mutual information provides a measure of both the linear and nonlinear statistical dependencies between two time series. Cross-mutual information (CMI) is used to quantify the information transmitted from one time series to another, while auto mutual information (AMI) in a time series estimates how much on average the value of the time series can be predicted from values of the time series at preceding points. The aim of this study is to assess information transmission between different cortical areas in Alzheimers disease (AD) patients by estimating the average CMI between EEG electrodes. METHODS We recorded the EEG from 16 scale electrodes in 15 AD patients and 15 age-matched normal controls, and estimated the local, distant, and interhemispheric CMIs of the EEG in both groups. The rate of decrease (with increasing delay) of the AMI of the EEG was also measured to evaluate the complexity of the EEG in AD patients. RESULTS The local CMI in AD subjects was lower than that in normal controls, especially over frontal and antero-temporal regions. A prominent decrease in information transmission between distant electrodes in the right hemisphere and between corresponding interhemispheric electrodes was detected in the AD patients. In addition, the AMIs throughout the cerebrums of the AD patients decreased significantly more slowly with delay than did the AMIs of normal controls. CONCLUSIONS These results are consistent with previous findings that suggest the association of EEG abnormalities in AD patients with functional impairment of information transmission in long cortico-cortical connections.

NON-LINEAR DYNAMICAL ANALYSIS OF THE EEG IN ALZHEIMER'S DISEASE WITH OPTIMAL EMBEDDING DIMENSION

We used non-linear analysis to investigate the dynamical properties underlying the EEG in patients with Alzheimers disease. We calculated the correlation dimension D2 and the first positive Lyapunov exponent L1. We employed a new method, which was proposed by Kennel et al., to calculate the non-linear invariant measures. That method determined the proper minimum embedding dimension by looking at the behavior of nearest neighbors under a change in the embedding dimension d from d to d + 1. We demonstrated that for limited noisy data, our algorithm was strikingly faster and more accurate than previous ones. Also, we found that, in almost all channels, patients with Alzheimers disease had significantly lower D2 and L1 values than those for age-approximated healthy controls. These results suggest that brains afflicted by Alzheimers disease show behaviors which are less chaotic than those of normal healthy brains. In this paper, we show that non-linear analysis can provide a fruitful tool for detecting relative changes, which cannot be detected by conventional linear analysis, in the complexity of brain dynamics. We propose that non-linear dynamical analyses of the EEGs from patients with Alzheimers disease will be a diagnostic modality in the appropriate clinical setting.

Nonlinear dynamic analysis of the EEG in patients with Alzheimer's disease and vascular dementia.

Summary To assess nonlinear EEG activity in patients with Alzheimer’s disease (AD) and vascular dementia (VaD), the authors estimated the correlation dimension (D2) and the first positive Lyapunov exponent (L1) of the EEGs in both patients and age-matched healthy control subjects. EEGs were recorded in 15 electrodes from 12 AD patients, 12 VaD patients, and 14 healthy subjects. The AD patients had significantly lower D2 values than the normal control subjects, (P < H > 0.05), except at the F7 and the O1 electrodes, and the VaD patients, except at the C3 and the C4 electrodes. The VaD patients had relatively increased values of D2 and L1 compared with the AD patients, and rather higher values of D2 than the normal control subjects at the F7, F4, F8, Fp2, O1, and O2 electrodes. The L1 values of the EEGs were also lower for the AD patients than for the normal control subjects, except in the O1 and the O2 channels, and for the VaD patients at all electrodes. The L1 values were higher for the VaD patients than for the normal control subjects (F3, F4, F8, O1, and O2). In addition, the authors detected that the VaD patients had an uneven distribution of D2 values over the regions than the AD patients and the normal control subjects, although the statistics do not confirm this. By contrast, AD patients had uniformly lower D2 values in most regions, indicating that AD patients have less complex temporal characteristics of the EEG in entire regions. These nonlinear analyses of the EEG may be helpful in understanding the nonlinear EEG activity in AD and VaD.

Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI

The brain-computer interface (BCI) technique is a novel control interface to translate human intentions into appropriate motion commands for robotic systems. The aim of this study is to apply an asynchronous direct-control system for humanoid robot navigation using an electroencephalograph (EEG), based active BCI. The experimental procedures consist of offline training, online feedback testing, and real-time control sessions. The amplitude features from EEGs are extracted using power spectral analysis, while informative feature components are selected based on the Fisher ratio. The two classifiers are hierarchically structured to identify human intentions and trained to build an asynchronous BCI system. For the performance test, five healthy subjects controlled a humanoid robot navigation to reach a target goal in an indoor maze by using their EEGs based on real-time images obtained from a camera on the head of the robot. The experimental results showed that the subjects successfully controlled the humanoid robot in the indoor maze and reached the goal by using the proposed asynchronous EEG-based active BCI system.

GIT1 is associated with ADHD in humans and ADHD-like behaviors in mice

Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder that affects ∼5% of school-aged children; however, the mechanisms underlying ADHD remain largely unclear. Here we report a previously unidentified association between G protein–coupled receptor kinase–interacting protein-1 (GIT1) and ADHD in humans. An intronic single-nucleotide polymorphism in GIT1, the minor allele of which causes reduced GIT1 expression, shows a strong association with ADHD susceptibility in humans. Git1-deficient mice show ADHD-like phenotypes, with traits including hyperactivity, enhanced electroencephalogram theta rhythms and impaired learning and memory. Hyperactivity in Git1−/− mice is reversed by amphetamine and methylphenidate, psychostimulants commonly used to treat ADHD. In addition, amphetamine normalizes enhanced theta rhythms and impaired memory. GIT1 deficiency in mice leads to decreases in ras-related C3 botulinum toxin substrate-1 (RAC1) signaling and inhibitory presynaptic input; furthermore, it shifts the neuronal excitation-inhibition balance in postsynaptic neurons toward excitation. Our study identifies a previously unknown involvement of GIT1 in human ADHD and shows that GIT1 deficiency in mice causes psychostimulant-responsive ADHD-like phenotypes.

Nonlinear analysis of the EEG of schizophrenics with optimal embedding dimension.

We estimated the correlation dimensions of EEGs in patients with schizophrenia to investigate the dynamical properties underlying the EEG. We employed a new method, proposed by Kennel et al. (Kennel MB, Brown R, Abarbanel HDI. Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys Rev A 1992;45:3403-11), to calculate the correlation dimension D2. That method determined the proper minimum embedding dimension by looking at the behaviour of nearest neighbours under a change in the embedding dimension d from d to d + 1. We demonstrated that for limited noisy data, our algorithm was strikingly faster and more accurate than previous ones. We estimated the D2 of EEGs from 16 channels in patients with schizophrenia according to DSM-IV whereas previous studies, which estimated chaoticity of EEG in schizophrenia, recorded EEG only in a limited number of channels. Schizophrenic patients had a lower correlation dimension in the left inferior frontal and anterior temporal regions compared with controls. Our finding of decreased left frontal and temporal chaotic activity in schizophrenics is in line with the findings of a hypofrontality and hypotemporality reported in previous clinical studies such as EEG, blood flow, brain MRI and positron emission tomography studies in schizophrenia. This result suggests that chaos analysis may be a useful tool in analysing EEG data to explore the brain mechanism of schizophrenia.

Alterations in cerebral perfusion in posttraumatic stress disorder patients without re-exposure to accident-related stimuli

UNLABELLED Functional neuroimaging studies have shown abnormalities of limbic regions in patients with posttraumatic stress disorder (PTSD) during symptom provocation and cognitive activation. OBJECTIVE The aim of this study was to determine whether PTSD patients without re-exposure to accident-related stimuli would exhibit alterations in cerebral perfusion compared with age-matched normal subjects. METHODS Brain perfusion SPECT was measured in medication-free 23 PTSD patients and 64 age-matched healthy subjects under resting conditions and analyzed using statistical parametric mapping to compare between the patient and control groups. RESULTS We found that PTSD patients exhibited increased cerebral blood perfusion in limbic regions and decreased perfusion in the superior frontal gyrus and parietal and temporal regions in comparison with those of the normal controls. CONCLUSIONS This result indicates that PTSD patients have alterations in cerebral perfusion of limbic regions and the frontal and temporal cortex without re-exposure to accident-related stimuli. SIGNIFICANCE This finding supports the hypothesis of the involvement of limbic regions, which might be associated with the regulation of emotion and memory, in the pathophysiology of PTSD.

Slowing and Loss of Complexity in Alzheimer's EEG: Two Sides of the Same Coin?

Medical studies have shown that EEG of Alzheimers disease (AD) patients is “slower” (i.e., contains more low-frequency power) and is less complex compared to age-matched healthy subjects. The relation between those two phenomena has not yet been studied, and they are often silently assumed to be independent. In this paper, it is shown that both phenomena are strongly related. Strong correlation between slowing and loss of complexity is observed in two independent EEG datasets: (1) EEG of predementia patients (a.k.a. Mild Cognitive Impairment; MCI) and control subjects; (2) EEG of mild AD patients and control subjects. The two data sets are from different patients, different hospitals and obtained through different recording systems. The paper also investigates the potential of EEG slowing and loss of EEG complexity as indicators of AD onset. In particular, relative power and complexity measures are used as features to classify the MCI and MiAD patients versus age-matched control subjects. When combined with two synchrony measures (Granger causality and stochastic event synchrony), classification rates of 83% (MCI) and 98% (MiAD) are obtained. By including the compression ratios as features, slightly better classification rates are obtained than with relative power and synchrony measures alone.

An estimation of the first positive Lyapunov exponent of the EEG in patients with schizophrenia.

We studied the complexity of the electroencephalogram (EEG) in schizophrenic patients by estimating the first Lyapunov exponent (L1), which might serve as an indicator of the specific brain function in schizophrenia. We recorded the EEG from 25 schizophrenic patients (12 male, 13 female; age=25.1+/-7.0 years) fulfilling DSM-IV criteria and 15 healthy controls (9 male, 6 female; age=27. 8+/-4.2 years) at 16 electrodes, different from previous studies which recorded the EEGs at limited electrodes. We employed a method with an optimal embedding dimension to calculate the L1s. For limited noisy data, this algorithm was strikingly faster and more accurate than previous ones. Our results showed that the schizophrenic patients had lower values of the L1 at the left inferior frontal and anterior temporal regions compared with normal controls. These results for L1 in non-linear analysis have some differences from those for power ratios in linear analysis. These suggest that the non-linear analysis of the EEGs such as the estimation of the L1 might be a useful tool in analyzing EEG data to explore the neurodynamics of the brains of schizophrenic patients.