Cortical source multivariate EEG synchronization analysis on amnestic mild cognitive impairment in type 2 diabetes

Changes in Electrical Activities of the Brain Associated with Cognitive Functions in Type 2 Diabetes Mellitus: A Systematic Review

Introduction: Electroencephalogram (EEG) has the potentials to decipher the neural underpinnings of cognitive processes in clinical and healthy populations. Objective: The current systematic review is intended to examine the functional brain changes underlying cognitive dysfunctions in T2DM patients. Methods: The review was conducted on studies published in the PubMed, WebofScience, Cochrane, PsycInfo database till June 2021. The keywords used were electroencephalogram, T2DM, cognitive impairment/dysfunction. We considered studies using resting-state EEG and ERP. The preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines were followed to compile the studies. Results: The search yielded a total of 2384 studies. Finally, 16 independent studies were included. There was a pattern of a shift in EEG power observed from higher to lower frequencies in T2DM patients, though to a lesser degree than Alzheimer's disease patients. P300 latency was increased in T2DM patients mainly over frontal, parietal, and posterior regions. P300 and N100 amplitudes were decreased in T2DM patients than in healthy controls. Conclusion: The results indicate that T2DM has consequences for cognitive functions, and it finds a place in the continuum of healthy cognition to dementia.

Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes

Mild cognitive impairment (MCI) is one of the important comorbidities of type 2 diabetes mellitus (T2DM). It is critical to find appropriate methods for early diagnosis and objective assessment of mild cognitive impairment patients with type 2 diabetes (T2DM-MCI). Our study aimed to investigate potential early alterations in phase lag index (PLI) and determine whether it can distinguish between T2DM-MCI and normal controls with T2DM (T2DM-NC). EEG was recorded in 30 T2DM-MCI patients and 30 T2DM-NC patients. The phase lag index was computed and used in a logistic regression model to discriminate between groups. The correlation between the phase lag index and Montreal Cognitive Assessment (MoCA) score was assessed. The α-band phase lag index was significantly decreased in the T2DM-MCI group compared with the T2DM-NC group and showed a moderate degree of classification accuracy. The MoCA score was positively correlated with the α-band phase lag index (r = 0.4812, moderate association, p = 0.015). This work shows that the functional connectivity analysis of EEG may offer an effective way to track the cortical dysfunction linked to the cognitive deterioration of T2DM patients, and the α-band phase lag index may have a role in guiding the diagnosis of T2DM-MCI.

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.

EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer's disease and type 2 diabetes

Rhythmic neural activity has been proposed to play a fundamental role in cognition. Both healthy and pathological aging are characterized by frequency-specific changes in oscillatory activity. However, the cognitive relevance of these changes across the spectrum from normal to pathological aging remains unknown. We examined electroencephalography (EEG) correlates of cognitive function in healthy aging and 2 of the most prominent and debilitating age-related disorders: type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD). Relative to healthy controls (HC), patients with AD were impaired on nearly every cognitive measure, whereas patients with T2DM performed worse mainly on learning and memory tests. A continuum of alterations in resting-state EEG was associated with pathological aging, generally characterized by reduced alpha (α) and beta (β) power (AD < T2DM < HC) and increased delta (δ) and theta (θ) power (AD > T2DM > HC), with some variations across different brain regions. There were also reductions in the frequency and power density of the posterior dominant rhythm in AD. The ratio of (α + β)/(δ + θ) was specifically associated with cognitive function in a domain- and diagnosis-specific manner. The results thus captured both similarities and differences in the pathophysiology of cerebral oscillations in T2DM and AD. Overall, pathological brain aging is marked by a shift in oscillatory power from higher to lower frequencies, which can be captured by a single cognitively relevant measure of the ratio of (α + β) over (δ + θ) power.

Analgesic Effects of Compression at Trigger Points Are Associated With Reduction of Frontal Polar Cortical Activity as Well as Functional Connectivity Between the Frontal Polar Area and Insula in Patients With Chronic Low Back Pain: A Randomized Trial

Background

Compression of myofascial trigger points (MTrPs) in muscles is reported to reduce chronic musculoskeletal pain. Although the prefrontal cortex (PFC) is implicated in development of chronic pain, the mechanisms of how MTrP compression at low back regions affects PFC activity remain under debate. In this study, we investigated effects of MTrP compression on brain hemodynamics and EEG oscillation in subjects with chronic low back pain.

Methods

The study was a prospective, randomized, parallel-group trial and an observer and subject-blinded clinical trial. Thirty-two subjects with chronic low back pain were divided into two groups: subjects with compression at MTrPs (n = 16) or those with non-MTrPs (n = 16). Compression at MTrP or non-MTrP for 30 s was applied five times, and hemodynamic activity (near-infrared spectroscopy; NIRS) and EEGs were simultaneously recorded during the experiment.

Results

The results indicated that compression at MTrPs significantly (1) reduced subjective pain (P < 0.05) and increased the pressure pain threshold (P < 0.05), (2) decreased the NIRS hemodynamic activity in the frontal polar area (pPFC) (P < 0.05), and (3) increased the current source density (CSD) of EEG theta oscillation in the anterior part of the PFC (P < 0.05). CSD of EEG theta oscillation was negatively correlated with NIRS hemodynamic activity in the pPFC (P < 0.05). Furthermore, functional connectivity in theta bands between the medial pPFC and insula cortex was significantly decreased in the MTrP group (P < 0.05). The functional connectivity between those regions was positively correlated with subjective low back pain (P < 0.05).

Discussion

The results suggest that MTrP compression at the lumbar muscle modulates pPFC activity and functional connectivity between the pPFC and insula, which may relieve chronic musculoskeletal pain.

Trial registration

This trial was registered at University Hospital Medical Information Network Clinical Trials Registry (UMIN000033913) on 27 August 2018, at https://upload.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000038660.

Is resting state frontal alpha connectivity asymmetry a useful index to assess depressive symptoms? A preliminary investigation in a sample of university students

BACKGROUND

Frontal alpha asymmetry (FAA) has been widely investigated in depressive disorders (DDs) with contradictory and not conclusive results. The main aim of the current study was to explore the association between a new neurophysiological index, the so-called frontal alpha connectivity asymmetry index (FACA-I), and depressive symptoms.

METHODS

One hundred and thirteen participants (45 men and 68 women, mean age: 22.83 ± 2.26 years) were enrolled. Electroencephalographic (EEG) recordings were performed during 5 min of resting state (RS). FACA-I was computed by subtracting connectivity at left frontal regions from right frontal regions and dividing by their sum. RS FAA were also computed and compared to the FACA-I in all analyses.

RESULTS

After controlling for the presence of potential confounding variables (i.e., sex, age and anxiety symptoms), only FACA-I scores between medial prefrontal cortex and subgenual anterior cingulate cortex were negatively associated with both somatic and cognitive/affective depressive symptoms and were lower in individuals with significant level of depressive symptoms.

LIMITATIONS

We focused on a sample of university students with no formal diagnosis of depression and we did not assess FAA and FACA-I during cognitive and/or emotional tasks, which make our interpretation specific to the RS condition.

CONCLUSIONS

Taken together our data suggest that alpha connectivity asymmetry between medial prefrontal cortex and subgenual anterior cingulate cortex may be a useful neurophysiological index in the assessment of depressive symptoms.

Global Synchronization Index as an Indicator for Tracking Cognitive Function Changes in a Traumatic Brain Injury Patient: A Case Report

Traumatic brain injury is a main cause of long-term neurological disability, and many patients suffer from cognitive impairment for a lengthy period. Cognitive impairment is a fatal malady to that limits active rehabilitation, and functional recovery in patients with traumatic brain injury. In severe cases, it is impossible to assess cognitive function precisely, and severe cognitive impairment makes it difficult to establish a rehabilitation plan, as well as evaluate the course of rehabilitation. Evaluation of cognitive function is essential for establishing a rehabilitation plan, as well as evaluating the course of rehabilitation. We report a case of the analysis of electroencephalography with global synchronization index and low-resolution brain electromagnetic tomography applied, for evaluation of cognitive function that was difficult with conventional tests, due to severe cognitive impairment in a 77-year-old male patient that experienced traumatic brain injury.

Brain changes in diabetes mellitus patients with gastrointestinal symptoms

Diabetes mellitus is a common disease and its prevalence is increasing worldwide. In various studies up to 30%-70% of patients present dysfunction and complications related to the gut. To date several clinical studies have demonstrated that autonomic nervous system neuropathy and generalized neuropathy of the central nervous system (CNS) may play a major role. This systematic review provides an overview of the neurodegenerative changes that occur as a consequence of diabetes with a focus on the CNS changes and gastrointestinal (GI) dysfunction. Animal models where diabetes was induced experimentally support that the disease induces changes in CNS. Recent investigations with electroencephalography and functional brain imaging in patients with diabetes confirm these structural and functional brain changes. Encephalographic studies demonstrated that altered insular processing of sensory stimuli seems to be a key player in symptom generation. In fact one study indicated that the more GI symptoms the patients experienced, the deeper the insular electrical source was located. The electroencephalography was often used in combination with quantitative sensory testing mainly showing hyposensitivity to stimulation of GI organs. Imaging studies on patients with diabetes and GI symptoms mainly showed microstructural changes, especially in brain areas involved in visceral sensory processing. As the electrophysiological and imaging changes were associated with GI and autonomic symptoms they may represent a future therapeutic target for treating diabetics either pharmacologically or with neuromodulation.