Exploring the correlation and causation between alpha oscillations and one-second time perception through EEG and tACS.

Alpha oscillations have been implicated in time perception, yet a consensus on their precise role remains elusive. This study directly investigates this relationship by examining the impact of alpha oscillations on time perception. Resting-state EEG recordings were used to extract peak alpha frequency (PAF) and peak alpha power (PAP) characteristics. Participants then performed a time generalization task under transcranial alternating current stimulation (tACS) at frequencies of PAF-2, PAF, and PAF+2, as well as a sham condition. Results revealed a significant correlation between PAP and accuracy, and between PAF and precision of one-second time perception in the sham condition. This suggests that alpha oscillations may influence one-second time perception by modulating their frequency and power. Interestingly, these correlations weakened with real tACS stimulations, particularly at higher frequencies. A second analysis aimed to establish a causal relationship between alpha peak modulation by tACS and time perception using repeated measures ANOVAs, but no significant effect was observed. Results were interpreted according to the state-dependent networks and internal clock model.

Functional brain network features specify DBS outcome for patients with treatment resistant depression.

Deep brain stimulation (DBS) has shown therapeutic benefits for treatment resistant depression (TRD). Stimulation of the subcallosal cingulate gyrus (SCG) aims to alter dysregulation between subcortical and cortex. However, the 50% response rates for SCG-DBS indicates that selection of appropriate patients is challenging. Since stimulation influences large-scale network function, we hypothesized that network features can be used as biomarkers to inform outcome. In this pilot project, we used resting-state EEG recorded longitudinally from 10 TRD patients with SCG-DBS (11 at baseline). EEGs were recorded before DBS-surgery, 1-3 months, and 6 months post surgery. We used graph theoretical analysis to calculate clustering coefficient, global efficiency, eigenvector centrality, energy, and entropy of source-localized EEG networks to determine their topological/dynamical features. Patients were classified as responders based on achieving a 50% or greater reduction in Hamilton Depression (HAM-D) scores from baseline to 12 months post surgery. In the delta band, false discovery rate analysis revealed that global brain network features (segregation, integration, synchronization, and complexity) were significantly lower and centrality of subgenual anterior cingulate cortex (ACC) was higher in responders than in non-responders. Accordingly, longitudinal analysis showed SCG-DBS increased global network features and decreased centrality of subgenual ACC. Similarly, a clustering method separated two groups by network features and significant correlations were identified longitudinally between network changes and depression symptoms. Despite recent speculation that certain subtypes of TRD are more likely to respond to DBS, in the SCG it seems that underlying brain network features are associated with ability to respond to DBS. SCG-DBS increased segregation, integration, and synchronizability of brain networks, suggesting that information processing became faster and more efficient, in those patients in whom it was lower at baseline. Centrality results suggest these changes may occur via altered connectivity in specific brain regions especially ACC. We highlight potential mechanisms of therapeutic effect for SCG-DBS.

The first genome-wide association study of internet addiction; Revealed substantial shared risk factors with neurodevelopmental psychiatric disorders.

BACKGROUND: Internet addiction disorder (IAD) is listed as a disorder requiring further studies in the diagnostic and statistical manual of mental disorders (DSM-V). Psychological studies showed significant co-morbidity of IAD with depression, alcohol abuse, and anxiety disorder. Etiology and genetic bases of IAD are unclear. AIMS: Present study aimed to investigate the genetic, psychological, and cognitive bases of a tendency to internet addiction. METHODS AND PROCEDURES: DNA was extracted from blood samples of IADs (N = 16,520) and 18,000 matched non-psychiatric subjects. Genotyping for the subjects was performed using SNP Array. Psychological, neuropsychological, and neurological characteristics were conducted. OUTCOMES AND RESULTS: Seventy-two SNPs in 24 genes have been detected significantly associated with IAD. Most of these SNPs were risk factors for psychiatric disorders. Most similarity detected with autism spectrum disorder, bipolar disorder and schizophrenia. Higher anxiety, stress, and neuroticism and deficits in working memory, attention, planning, and processing speed were detected in IADs. CONCLUSIONS: This study is the first genome-wide association study of IAD that showed strong shared genetic bases with neurodevelopmental disabilities and psychiatric disorders. IMPLICATIONS: Genetic risk factors in IADs may cause several cognitive and neurodevelopmental brain function abnormalities, which lead to excessive Internet usage. It may suggest that IAD could be a marker for vulnerability to developmental psychiatric disorders.

Transcranial photobiomodulation changes topology, synchronizability, and complexity of resting state brain networks.

Objective. Transcranial photobiomodulation (tPBM) is a recently proposed non-invasive brain stimulation approach with various effects on the nervous system from the cells to the whole brain networks. Specially in the neural network level, tPBM can alter the topology and synchronizability of functional brain networks. However, the functional properties of the neural networks after tPBM are still poorly clarified.Approach. Here, we employed electroencephalography and different methods (conventional and spectral) in the graph theory analysis to track the significant effects of tPBM on the resting state brain networks. The non-parametric statistical analysis showed that just one short-term tPBM session over right medial frontal pole can significantly change both topological (i.e. clustering coefficient, global efficiency, local efficiency, eigenvector centrality) and dynamical (i.e. energy, largest eigenvalue, and entropy) features of resting state brain networks.Main results. The topological results revealed that tPBM can reduce local processing, centrality, and laterality. Furthermore, the increased centrality of central electrode was observed.Significance. These results suggested that tPBM can alter topology of resting state brain network to facilitate the neural information processing. On the other hand, the dynamical results showed that tPBM reduced stability of synchronizability and increased complexity in the resting state brain networks. These effects can be considered in association with the increased complexity of connectivity patterns among brain regions and the enhanced information propagation in the resting state brain networks. Overall, both topological and dynamical features of brain networks suggest that although tPBM decreases local processing (especially in the right hemisphere) and disrupts synchronizability of network, but it can increase the level of information transferring and processing in the brain network.

Resting-State Brain Network Deficits in Multiple Sclerosis Participants: Evidence from Electroencephalography and Graph Theoretical Analysis.

Background: Multiple sclerosis (MS) is a chronic inflammatory disease leading to demyelination and axonal loss in the central nervous system that causes focal lesions of gray and white matter. However, the functional impairments of brain networks in this disease are still unspecified and need to be clearer. Materials and Methods: In the present study, we investigate the resting-state brain network impairments for MS participants in comparison to a normal group using electroencephalography (EEG) and graph theoretical analysis with a source localization method. Thirty-four age- and gender-matched participants from each MS group and normal group participated in this study. We recorded 5 min of EEG in the resting-state eyes open condition for each participant. One min (15 equal 4-sec artifact-free segments) of the EEG signals were selected for each participant, and the Low-Resolution Electromagnetic Tomography software was employed to calculate the functional connectivity among whole cortical regions in six frequency bands (delta, theta, alpha, beta1, beta2, and beta3). Graph theoretical analysis was used to calculate the clustering coefficient (CL), betweenness centrality (BC), shortest path length (SPL), and small-world propensity (SWP) for weighted connectivity matrices. Nonparametric permutation tests were utilized to compare these measures between groups. Results: Significant differences between the MS group and the normal group in the average of BC and SWP were found in the alpha band. The significant differences in the BC were spread over all lobes. Conclusion: These results suggest that the resting-state brain network for the MS group is disrupted in local and global scales, and EEG has the capability of revealing these impairments.

Synchrony and Complexity in State-Related EEG Networks: An Application of Spectral Graph Theory.

The brain may be considered as a synchronized dynamic network with several coherent dynamical units. However, concerns remain whether synchronizability is a stable state in the brain networks. If so, which index can best reveal the synchronizability in brain networks? To answer these questions, we tested the application of the spectral graph theory and the Shannon entropy as alternative approaches in neuroimaging. We specifically tested the alpha rhythm in the resting-state eye closed (rsEC) and the resting-state eye open (rsEO) conditions, a well-studied classical example of synchrony in neuroimaging EEG. Since the synchronizability of alpha rhythm is more stable during the rsEC than the rsEO, we hypothesized that our suggested spectral graph theory indices (as reliable measures to interpret the synchronizability of brain signals) should exhibit higher values in the rsEC than the rsEO condition. We performed two separate analyses of two different datasets (as elementary and confirmatory studies). Based on the results of both studies and in agreement with our hypothesis, the spectral graph indices revealed higher stability of synchronizability in the rsEC condition. The k-mean analysis indicated that the spectral graph indices can distinguish the rsEC and rsEO conditions by considering the synchronizability of brain networks. We also computed correlations among the spectral indices, the Shannon entropy, and the topological indices of brain networks, as well as random networks. Correlation analysis indicated that although the spectral and the topological properties of random networks are completely independent, these features are significantly correlated with each other in brain networks. Furthermore, we found that complexity in the investigated brain networks is inversely related to the stability of synchronizability. In conclusion, we revealed that the spectral graph theory approach can be reliably applied to study the stability of synchronizability of state-related brain networks.

A spatial profile difference in electrical distribution of resting-state EEG in ADHD children using sLORETA.

Purpose:In this article, we propose current source density (CSD) as a marker for diagnosis of Attention Deficit and Hyperactivity Disorder (ADHD) children for the first time.Materials and methods: A source localization method (sLORETA) was used to find the source of abnormality in the CSD in electrical distribution of different frequency bands in resting state EEG for the ADHD children in comparison to the normal children using statistical nonparametric mapping (SnPM) test. Resting-state EEG in eye-open (EO) condition was recorded from 13 ADHD and 15 age-matched normal children (aged between 6 and 13).Results: Significant differences were found in the CSD of three frequency bands: delta, theta, and alpha in the parietal lobe, between ADHD and normal groups.Conclusions: Higher CSD in the parietal lobe for ADHD children was found which suggests that an abnormality exists in the parietal lobe of children with ADHD which can be related to the attention shifting problem in these children.

Functional brain segregation changes during demanding mathematical task.

Aim of the study: The neural basis of demanding mathematical problem solving is currently indeterminate and unclear. Mathematical problem solving engages higher order cognition and a complex associative activity of functional neural networks occurs during demanding problem solving. Method: Twenty right handed subjects (mean age: 24.6 years; SD = 3.97 years; 50% female) participated in this study. An arithmetic logic puzzle was used as a demanding mathematical task. EEGs were recorded in the eye open rest and eye open task conditions. To clarify functional connectivity of brain networks, clustering coefficient, transitivity, global efficiency, degree and entropy were investigated in two conditions. Results: During problem solving, disrupted brain connectivity and decreased brain segregation were observed in the alpha band. However, in the beta band, increased connectivity, transitivity and clustering associated with higher modularity were observed. Theta exhibited unaltered brain network function. Conclusion: In the demanding problem solving task, decreased local alpha coupling may suggest that default mode network activity is interrupted. Since there is no significant difference within the theta network, the central executive network may not be as strongly involved. Increased segregation of functional brain network (without increasing of integration level) can be discussed in relation of demanding aspects of mathematical problem. We suggest a complex network may involve in the real situation of demanding problem solving.

Heat transfer, entropy and time perception: Toward finding a possible relation between subjective and objective time.

The interaction between subjective and objective time is an ambiguous issue in physics and psychology. Here, I try to describe these two timing systems within a common framework. To this aim, I will use thermodynamic entropy, which is a parameter that can create the arrow of time in physical systems (i.e. universe and brain). In the universe, which can be thought of as a closed system, heat transfer (dQ) is always positive and it leads to an increase in entropy (dS > 0). The positive dS leads to the generation of the irreversible arrow of time. Given that dS is constant, the time units have a similar direction and magnitude. In contrast to the universe, the brain is an open thermodynamic system which transfers heat to its surroundings. In this system, dQ and dS can become negative. This causes a reversible timing system and time units can be laid along different arrows and have different magnitudes. Theoretically, this mismatch can cause different timing in the brain and universe.

Time estimation and beta segregation: An EEG study and graph theoretical approach.

Elucidation of the neural correlates of time perception constitutes an important research topic in cognitive neuroscience. The focus to date has been on durations in the millisecond to seconds range, but here we used electroencephalography (EEG) to examine brain functional connectivity during much longer durations (i.e., 15 min). For this purpose, we conducted an initial exploratory experiment followed by a confirmatory experiment. Our results showed that those participants who overestimated time exhibited lower activity of beta (18-30 Hz) at several electrode sites. Furthermore, graph theoretical analysis indicated significant differences in the beta range (15-30 Hz) between those that overestimated and underestimated time. Participants who underestimated time showed higher clustering coefficient compared to those that overestimated time. We discuss our results in terms of two aspects. FFT results, as a linear approach, are discussed within localized/dedicated models (i.e., scalar timing model). Second, non-localized properties of psychological interval timing (as emphasized by intrinsic models) are addressed and discussed based on results derived from graph theory. Results suggested that although beta amplitude in central regions (related to activity of BG-thalamocortical pathway as a dedicated module) is important in relation to timing mechanisms, the properties of functional activity of brain networks; such as the segregation of beta network, are also crucial for time perception. These results may suggest subjective time may be created by vector units instead of scalar ticks.

Transcranial direct current stimulation based on quantitative electroencephalogram combining positive psychotherapy for major depression.

Frontal cortex activity is reduced in the left hemisphere during depression. Transcranial direct current stimulation is a noninvasive neuromodulation technique that can increase frontal cortex activity. Therapy based on transcranial Direct Current Stimulation and positive psychology therapy was applied for improving patients' quality of life. The present study compared three conditions of subjects with clinical depression; (a) transcranial Direct Current Stimulation therapy, (b) positive psychotherapy, and(c) combined treatment. Hamilton Depression Rating Scale, Adult State Hope Scale and Optimism/Pessimism Scale was used at baseline, 2-week, 4-week, and 3-month follow-up. Combined condition participants showed greater reduction in depressed mood, improved hope and optimism after 4-weeks as well as during 3-month follow-up than the other conditions. Results are discussed in terms of additive or synergistic relation between transcranial direct current stimulation and positive psychology treatment.

Gene expression study of mitochondrial complex I in schizophrenia and paranoid personality disorder.

OBJECTIVES: The aetiology and molecular mechanisms of schizophrenia (SCZ) and paranoid personality disorder (PPD) are not yet clarified. The present study aimed to assess the role of mitochondrial complex I and cell bioenergetic pathways in the aetiology and characteristics of SCZ and PPD. METHODS: mRNA levels of all genomic and mitochondrial genes which encode mitochondrial complex I subunits (44 genes) were assessed in blood in 634 SCZ, 340 PPD patients and 528 non-psychiatric subjects using quantitative real-time PCR, and associated comprehensive psychiatric, neurological and biochemical assessments. RESULTS: Significant expression changes of 18 genes in SCZ patients and 11 genes in PPD patients were detected in mitochondrial complex I. Most of these genes were novel candidate genes for SCZ and PPD. Several correlations between mRNA levels and severity of symptoms, drug response, deficits in attention, working memory, executive functions and brain activities were found. CONCLUSIONS: Deregulations of both core and supernumerary subunits of complex I are involved in the aetiology of SCZ and PPD. These deregulations have effects on brain activity as well as disorder characteristics.

Functional Brain Connectivity Differences Between Different ADHD Presentations: Impaired Functional Segregation in ADHD-Combined Presentation but not in ADHD-Inattentive Presentation.

INTRODUCTION: Contrary to Diagnostic and Statistical Manual of Mental Disorders (DSM-5), fifth edition, some studies indicate that ADHD-inattentive presentation (ADHD-I) is a distinct diagnostic disorder and not an ADHD presentation. METHODS: In this study, 12 ADHD-combined presentation (ADHD-C), 10 ADHD-I, and 13 controls were enrolled and their resting state EEG recorded. Following this, a graph theoretical analysis was performed and functional integration and segregation of brain network was calculated. RESULTS: The results show that clustering coefficient of theta band was significantly different among three groups and significant differences were observed in theta global efficiency between controls and ADHD-C. Regarding the alpha band, a lower clustering coefficient was observed in control subjects. In the beta band, clustering coefficient was significantly different between the control and children with ADHD-C and also between ADHD-I and ADHD-C. The clustering coefficient, in the subjects with ADHD-C, demonstrated a rapid decline and was significantly lower than the subjects with ADHD-I and control. CONCLUSION: Decreased clustering, in high thresholds, may be associated with hyperactivity while increased segregation in low thresholds with inattentiveness. A different functional network occurs in the ADHD-C brain that is consistent with several studies that have reported ADHD-I as a distinct disorder.