Classification of Parkinson's disease with dementia using phase locking factor of event-related oscillations to visual and auditory stimuli.

Objective.In the last decades, machine learning approaches have been widely used to distinguish Parkinson's disease (PD) and many other neuropsychiatric diseases. They also speed up the clinicians and facilitate decision-making for several conditions with similar clinical symptoms. The current study attempts to detect PD with dementia (PDD) by event-related oscillations (EROs) during cognitive processing in two modalities, i.e. auditory and visual.Approach.The study was conducted to discriminate PDD from healthy controls (HC) using event-related phase-locking factors in slow frequency ranges (delta and theta) during visual and auditory cognitive tasks. Seventeen PDD and nineteen HC were included in the study, and linear discriminant analysis was used as a classifier. During classification analysis, multiple settings were implemented by using different sets of channels (overall, fronto-central and temporo-parieto-occipital (TPO) region), frequency bands (delta-theta combined, delta, theta, and low theta), and time of interests (0.1-0.7 s, 0.1-0.5 s and 0.1-0.3 s for delta, delta-theta combined; 0.1-0.4 s for theta and low theta) for spatial-spectral-temporal searchlight procedure.Main results.The classification performance results of the current study revealed that if visual stimuli are applied to PDD, the delta and theta phase-locking factor over fronto-central region have a remarkable contribution to detecting the disease, whereas if auditory stimuli are applied, the phase-locking factor in low theta over TPO and in a wider range of frequency (1-7 Hz) over the fronto-central region classify HC and PDD with better performances.Significance.These findings show that the delta and theta phase-locking factor of EROs during visual and auditory stimuli has valuable contributions to detecting PDD.

Does transcranial direct current stimulation enhance cognitive performance in Parkinson's disease mild cognitive impairment? An event-related potentials and neuropsychological assessment study.

BACKGROUND: Parkinson's disease-mild cognitive impairment (PD-MCI) is garnering attention as a key interventional period for cognitive impairment. Currently, there are no approved treatments for PD-MCI and encouraging results of transcranial direct current stimulation (tDCS) combined with other interventions have been proposed, though the efficacy and neural mechanisms of tDCS alone have not been studied in PD-MCI yet. OBJECTIVES: The present double-blind, randomized, sham-controlled study assessed the effects of tDCS over the dorsolateral prefrontal cortex on cognitive functions via neuropsychological and electrophysiological evaluations in individuals with PD-MCI for the first time. METHOD: Twenty-six individuals with PD-MCI were administered 10 sessions of active (n = 13) or sham (n = 13) prefrontal tDCS twice a day, for 5 days. Changes were tested through a comprehensive neuropsychological battery and event-related potential recordings, which were performed before, immediately, and 1 month after the administrations. RESULTS: Neuropsychological assessment showed an improvement in delayed recall and executive functions in the active group. N1 amplitudes in response to targets in the oddball test-likely indexing attention and discriminability and NoGo N2 amplitudes in the continuous performance test-likely indexing cognitive control and conflict monitoring increased in the active group. Active stimulation elicited higher benefits 1 month after the administrations. CONCLUSION: The present findings substantiate the efficacy of tDCS on cognitive control and episodic memory, along with the neural underpinnings of cognitive control, highlighting its potential for therapeutic utility in PD-MCI. TRIAL REGISTRATION: NCT 04,171,804. Date of registration: 21/11/2019.

Evoked and induced EEG oscillations to visual targets reveal a differential pattern of change along the spectrum of cognitive decline in Alzheimer's Disease.

In recent years, quantitative variables derived from the electroencephalogram (EEG) attract an increasing interest for the evaluation of neurodegenerative diseases, as EEG registers the neuro-electric activity with a high temporal resolution and provides a cost-effective and easily accessible, non-invasive method. Event-related oscillations (EROs) as oscillatory responses in the EEG to specific events further provide the possibility to track the cognitive decline in a task-specific manner. Current study in search for potential ERO biomarkers to distinguish different stages of cognitive decline along the Alzheimer's Disease (AD) continuum re-analyzed a combined set of data collected and analyzed in previous studies by Basar and coworkers. Target responses of a visual oddball experiment recorded from 33 AD patients, 46 Mild Cognitive Impairment (MCI) patients and 48 age, gender, and education matched normal elderly controls were analyzed for both evoked (phase-locked) and total (phase-locked + non-phase-locked) ERO powers in delta, theta, alpha, beta and gamma bands by applying continuous wavelet transform (WT) on averaged and single trial data, respectively. The cluster-based nonparametric permutation test implemented in the FieldTrip toolbox revealed significant differences among the three groups. While the total delta and theta responses already significantly declined in the MCI stage with further spatial expansion of the decline in AD, the evoked delta response reached a statistically significant reduction level in the AD stage. We obtained no significant difference among groups for alpha, beta and gamma frequency bands. These results suggest that total delta and theta EROs to oddball targets may be useful for early detection of the disease in MCI stage, while the evoked delta response allows detecting the conversion to AD.

Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker.

OBJECTIVE: This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated. METHODS: EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity. RESULTS: ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values. CONCLUSIONS: The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients. SIGNIFICANCE: The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans.

Predictive Value of Slow and Fast EEG Oscillations for Methylphenidate Response in ADHD.

Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder and is characterized by symptoms of inattention and/or hyperactivity and impulsivity. In the current study, we obtained quantitative EEG (QEEG) recordings of 51 children aged between 6 and 12 years before the initiation of methylphenidate treatment. The relationship between changes in the scores of ADHD symptoms and initial QEEG features (power/power ratios values) were assessed. In addition, the children were classified as responder and nonresponder according to the ratio of their response to the medication (>25% improvement after medication). Logistic regression analyses were performed to analyze the accuracy of QEEG features for predicting responders. The findings indicate that patients with increased delta power at F8, theta power at Fz, F4, C3, Cz, T5, and gamma power at T6 and decreased beta powers at F8 and P3 showed more improvement in ADHD hyperactivity symptoms. In addition, increased delta/beta power ratio at F8 and theta/beta power ratio at F8, F3, Fz, F4, C3, Cz, P3, and T5 showed negative correlations with Conners' score difference of hyperactivity as well. This means, those with greater theta/beta and delta/beta powers showed more improvement in hyperactivity following medication. Theta power at Cz and T5 and theta/beta power ratios at C3, Cz, and T5 have significantly classified responders and nonresponders according to the logistic binary regression analysis. The results show that slow and fast oscillations may have predictive value for treatment response in ADHD. Future studies should seek for more sensitive biomarkers.

Multimodal Neuroimaging: Basic Concepts and Classification of Neuropsychiatric Diseases.

Neuroimaging techniques are widely used in neuroscience to visualize neural activity, to improve our understanding of brain mechanisms, and to identify biomarkers-especially for psychiatric diseases; however, each neuroimaging technique has several limitations. These limitations led to the development of multimodal neuroimaging (MN), which combines data obtained from multiple neuroimaging techniques, such as electroencephalography, functional magnetic resonance imaging, and yields more detailed information about brain dynamics. There are several types of MN, including visual inspection, data integration, and data fusion. This literature review aimed to provide a brief summary and basic information about MN techniques (data fusion approaches in particular) and classification approaches. Data fusion approaches are generally categorized as asymmetric and symmetric. The present review focused exclusively on studies based on symmetric data fusion methods (data-driven methods), such as independent component analysis and principal component analysis. Machine learning techniques have recently been introduced for use in identifying diseases and biomarkers of disease. The machine learning technique most widely used by neuroscientists is classification-especially support vector machine classification. Several studies differentiated patients with psychiatric diseases and healthy controls with using combined datasets. The common conclusion among these studies is that the prediction of diseases increases when combining data via MN techniques; however, there remain a few challenges associated with MN, such as sample size. Perhaps in the future N-way fusion can be used to combine multiple neuroimaging techniques or nonimaging predictors (eg, cognitive ability) to overcome the limitations of MN.

High Frequencies in QEEG Are Related to the Level of Insight in Patients With Schizophrenia.

Lack of insight is a neurocognitive problem commonly encountered in patients with psychotic disorders that negatively affects treatment compliance and prognosis. Measurement of insight is based on self-report scales, which are limited due to subjectivity. This study aimed to determine the correlation between resting state beta and gamma power in 23 patients with schizophrenia and insight. It was observed that as beta and gamma power measured via qualitative electroencephalography (qEEG) increased the level of insight decreased. Negative correlation was found in F3, C3, Cz for gamma activity and in F3 and C3 for beta activity. This finding indicates that resting state qEEG could be used to evaluate the level of insight in patients with schizophrenia.

Lithium excessively enhances event related beta oscillations in patients with bipolar disorder.

BACKGROUND: Previous resting-state electroencephalography studies have consistently shown that lithium enhances delta and theta oscillations in default mode networks. Cognitive task based networks differ from resting-state networks and this is the first study to investigate effects of lithium on evoked and event-related beta oscillatory responses of patients with bipolar disorder. METHODS: The study included 16 euthymic patients with bipolar disorder on lithium monotherapy, 22 euthymic medication-free patients with bipolar disorder and 21 healthy participants. The maximum peak-to-peak amplitudes were measured for each subject's averaged beta responses (14-28 Hz) in the 0-300 ms time window. Auditory simple and oddball paradigm were presented to obtain evoked and event-related beta oscillatory responses. RESULTS: There were significant differences in beta oscillatory responses between groups (p=0.010). Repeated measures ANOVA revealed location (p=0.007), laterality X group (p=0.043) and stimulus X location (p=0.013) type effects. Serum lithium levels were correlated with beta responses. LIMITATIONS: The lithium group had higher number of previous episodes, suggesting that patients of the lithium were more severe cases than patients of the medication-free group. DISCUSSION: Lithium stimulates neuroplastic cascades and beta oscillations become prominent during neuroplastic changes. Excessively enhanced beta oscillatory responses in the lithium-treated patients may be indicative of excessive activation of the neuron groups of the certain cognitive networks and dysfunctional GABAergic modulation during cognitive activity.