Brain network connectivity      EEG
207755808
Connectivity strength of the EEG functional network in schizophrenia and bipolar disorder
The application of graph theory measures in the study of functional brain networks allows for the description of their general properties and their alterations in mental illness. Among these measures, connectivity strength (CS) estimates the degree of functional connectivity of the whole network. Previous studies in schizophrenia patients have reported higher baseline CS values and modulation deficits in EEG spectral properties during cognitive activity. The specificity of these alterations and their relationships with pharmacological treatments remain unknown. Therefore, in the present study, we assessed functional CS on EEG based brain networks in 79 schizophrenia and 29 bipolar patients in addition to 63 healthy controls. The subjects performed a P300 task during the EEG recordings from which the pre stimulus and the task related modulation CS values were computed in the global and theta bands. These values were compared between the groups and between the patients who had and had not received different treatments. The global band pre stimulus CS was significantly higher in the schizophrenia group compared with the bipolar and control groups. Theta band CS modulation was decreased in schizophrenia and bipolar patients. Treatment with antipsychotics, lithium, benzodiazepines, and anticonvulsants did not significantly alter these CS values. The first episode and chronic schizophrenia patients did not show significant differences in CS values. Higher global band pre stimulus CS values were associated with worse general cognition in schizophrenia patients. These data support increased connectivity in the whole brain network that is specific to schizophrenia and suggest a general hyper synchronized basal state that might hamper cognition in this syndrome.
Progress in Neuro Psychopharmacology and Biological Psychiatry
Benjamin Cea-Canas Javier Gomez-Pilar Pablo Nunez Eva Rodriguez-Vazquez Nieves de Uribe Alvaro Diez Adela Perez-Escudero Vicente Molina
220079069
Resting state connectivity and network parameter analysis in alcohol dependent males. A simultaneous EEG MEG study
There is supporting evidence of alcohol negative effects on the brain: neuroimaging and psychophysiological studies finding anatomical and functional connectivity (FC) changes associated with the dependence process. Thus, the aim of this work was to evaluate brain FC and network characteristics of alcohol dependent individuals in resting state. For this study, we included males diagnosed with alcohol dependence (N 25) and a group of healthy individuals (N 23) Simultaneous EEG MEG (electroencephalographic and magnetoencephalographic) activity was recorded in 5 min of eyes closed resting state. EEG MEG activity was preprocessed and FC was computed through the leakage corrected version of phase locking value (ciPLV) Additionally, local (degree, efficiency, clustering) and global (efficiency, characteristic path length) network parameters were computed. Connectivity analysis showed an increase in phase lagged synchronization, mainly between frontal and frontotemporal regions, in high beta band, and a decrease in interhemispheric gamma, for alcohol dependent individuals. Network analysis revealed intergroup differences at the local level for high beta, indicating higher degree, clustering, and efficiency, mostly at frontal nodes, together with a decrease in these measures at more posterior sites for patients' group. The hyper synchronization in beta, next to the hypo synchronization in gamma, could indicate an alteration in communication between hemispheres, but also a possible functional compensation mechanism in neural circuits. This could be also supported by network characteristic data, where local alterations in communication are observed.
Journal of neuroscience research
Ana Sion Ricardo Bruna Fernandez Andres Martinez Maldonado Isabel Dominguez Centeno Angel Torrado-Carvajal Gabriel Rubio Ernesto Pereda Rosa Jurado-Barba
220773667
Major depressive disorder diagnosis based on effective connectivity in EEG signals: a convolutional neural network and long short term memory approach
Deep learning techniques have recently made considerable advances in the field of artificial intelligence. These methodologies can assist psychologists in early diagnosis of mental disorders and preventing severe trauma. Major Depression Disorder (MDD) is a common and serious medical condition whose exact manifestations are not fully understood. So, early discovery of MDD patients helps to cure or limit the adverse effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as MDD due to having high temporal resolution information, and being a noninvasive, inexpensive and portable method. This paper has proposed an EEG based deep learning framework that automatically discriminates MDD patients from healthy controls. First, the relationships among EEG channels in the form of effective brain connectivity analysis are extracted by Generalized Partial Directed Coherence (GPDC) and Direct directed transfer function (dDTF) methods. A novel combination of sixteen connectivity methods (GPDC and dDTF in eight frequency bands) was used to construct an image for each individual. Finally, the constructed images of EEG signals are applied to the five different deep learning architectures. The first and second algorithms were based on one and two dimensional convolutional neural network (1DCNN 2DCNN) The third method is based on long short term memory (LSTM) model, while the fourth and fifth algorithms utilized a combination of CNN with LSTM model namely, 1DCNN LSTM and 2DCNN LSTM. The proposed deep learning architectures automatically learn patterns in the constructed image of the EEG signals. The efficiency of the proposed algorithms is evaluated on resting state EEG data obtained from 30 healthy subjects and 34 MDD patients. The experiments show that the 1DCNN LSTM applied on constructed image of effective connectivity achieves best results with accuracy of 99.24% due to specific architecture which captures the presence of spatial and temporal relations in the brain connectivity. The proposed method as a diagnostic tool is able to help clinicians for diagnosing the MDD patients for early diagnosis and treatment.
Cognitive Neurodynamics
Abdolkarim Saeedi Maryam Saeedi Arash Maghsoudi Ahmad Shalbaf
73425227
EEG Based Emotion Recognition by Combining Functional Connectivity Network and Local Activations
Objective: Spectral power analysis plays a predominant role in electroencephalogram based emotional recognition. It can reflect activity differences among multiple brain regions. In addition to activation difference, different emotions also involve different large scale network during related information processing. In this paper, both information propagation patterns and activation difference in the brain were fused to improve the performance of emotional recognition. Methods: We constructed emotion related brain networks with phase locking value and adopted a multiple feature fusion approach to combine the compensative activation and connection information for emotion recognition. Results: Recognition results on three public emotional databases demonstrated that the combined features are superior to either single feature based on power distribution or network character. Furthermore, the conducted feature fusion analysis revealed the common characters between activation and connection patterns involved in the positive, neutral, and negative emotions for information processing. Significance: The proposed feasible combination of both information propagation patterns and activation difference in the brain is meaningful for developing the effective human computer interaction systems by adapting to human emotions in the real world applications.
IEEE Transactions on Biomedical Engineering
Peiyang Li Huan Liu Yajing Si Cunbo Li Fali Li Xuyang Zhu Xiaoye Huang Yin Zeng Dezhong Yao Yangsong Zhang Peng Xu
196543150
Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity
The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.
Front. Neurol.
Pieter van Mierlo Yvonne Holler Niels K Focke Serge Vulliemoz
209314283
Decoding of single trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions.
Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. To address these questions, we applied several computational approaches to the EEG, including graph mining, machine learning (i.e. support vector machine) and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions. We infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RT, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e. became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization. Our results showed that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC=0.9) Our results corroborate previous studies by supporting the engagement of similar temporal (STG) parietal, motor, and prefrontal regions in CP using an entirely data driven approach.
Journal of neural engineering
Rakib Al-Fahad Mohammed Yeasin Gavin M Bidelman
203118659
Percept related EEG classification using machine learning approach and features of functional brain connectivity.
Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature extraction and selection techniques reducing the dimensionality of input data. Dimensionality reduction is usually implemented via the mathematical approaches (e.g. principal component analysis, linear discriminant analysis, etc. regardless of the origin of analyzed data. We hypothesize that since EEG features are determined by certain neurophysiological processes, they should have distinctive characteristics in spatiotemporal domain. If so, it is possible to specify the set of EEG principal features based on the prior knowledge about underlying neurophysiological processes. To test this hypothesis, we consider the classification of EEG trials related to the perception of ambiguous visual stimuli. We observe that EEG features, underlying the different ambiguous stimuli interpretations, are defined by the network properties of neuronal activity. Having analyzed functional neural interactions, we specify the brain area in which neural network architecture exhibits differences for different classes of EEG trials. We optimize the feedforward multilayer perceptron and develop a strategy for the training set selection to maximize the classification accuracy, being 85% when all channels are used. The revealed localization of the percept related features allows about 95% accuracy, when the number of channels is reduced up to 90% Obtained results can be used for classification of EEG trials associated with more complex cognitive tasks. Taking into account that cognitive activity is subserved by a distributed functional cortical network, its topological properties have to be considered when selecting optimal features for EEG trial classification.
Chaos
Alexander E Hramov Vladimir A Maksimenko Alexey A Koronovskii Anastasiya Runnova Maxim D Zhuravlev Alexander N Pisarchik Jurgen Kurths
106211478
EEG characteristic analysis of coach bus drivers based on brain connectivity as revealed via a graph theoretical network
This study describes the detection of driving fatigue using the characteristics of brain networks in a real driving environment. First, the th, b and 36 44 Hz rhythm from the EEG signals of drivers were extracted using wavelet packet decomposition (WPD) The correlation between EEG channels was calculated using a Pearson correlation coefficient and subsequently, the brain networks were built. Furthermore, the clustering coefficient (C) and global efficiency (G) of the complex brain networks were calculated to analyze the functional differences in the brains of drivers over time. Combined with the relative power spectrum ratio (b/th) of EEG signals and the mean value from questionnaires, the correlation of data characteristics between brain networks and subjective and objective data was analyzed. The results show that changes in the fatigue state of drivers can be effectively detected by calculating the data characteristics of brain networks in a real driving environment.
Fuwang Wang Xiaolei Zhang Rongrong Fu Guangbin Sun
159043007
Classification of EEG based Effective Brain Connectivity in Schizophrenia using Deep Neural Networks
Disrupted functional connectivity patterns have been increasingly used as features in pattern recognition algorithms to discriminate neuropsychiatric patients from healthy subjects. Deep neural networks (DNNs) were employed to fMRI functional network classification only very recently and its application to EEG based connectome is largely unexplored. We propose a DNN with deep belief network (DBN) architecture for automated classification of schizophrenia (SZ) based on EEG effective connectivity. We used vector autoregression based directed connectivity (DC) graph theoretical complex network (CN) measures and combination of both as input features. On a large resting state EEG dataset, we found a significant decrease in synchronization of neural oscillations measured by partial directed coherence, and a reduced network integration in terms of weighted degrees and transitivity in SZ compared to healthy controls. The proposed DNN DBN significantly outperforms three other traditional classifiers, due to its inherent capability as feature extractor to learn hierarchical representations from the aberrant brain network structure. Combined DC CN features gives further improvement over the raw DC and CN features alone, achieving remarkable classification accuracy of 95% for the theta and beta bands.
2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)
Chun-Ren Phang Chee-Ming Ting S Balqis Samdin Hernando C Ombao
199542975
Variation of functional brain connectivity in epileptic seizures: an EEG analysis with cross frequency phase synchronization
Frequency coupling in nervous system is believed to be associated with normal and impaired brain functions. However, most of the existing experiments have been concentrated on the coupling strength within frequency bands, while the coupling strength between different bands is ignored. In this work, we apply phase synchronization index (PSI) to investigate the cross frequency coupling (CFC) of Electroencephalogram (EEG) signals. The PSI matrixes for the multi channel EEG signals are calculated from interictal to ictal period in each sliding time window. The results show that CFC changes obviously once seizure occurs between the different bands, and such alteration is earlier than the appearance of clinical symptoms in seizure. Considering the similar role of the within frequency coupling (WFC) we further reconstruct multi layered brain networks, including CFC networks and WFC networks. The graph metrics are applied to investigate the variation of network structure of the epileptic brain. Significant decreases/increases of the local/global efficiency are found in d b, d a, th a and d th bands from the CFC network, while WFC network shows a significant decline in the local efficiency in th and a bands. These findings suggest that CFC may provide a new perspective to observe the alteration of brain structure when seizure occurs, and the investigation of functional connectivity across the full frequency spectrum can give us a deeper understanding of epileptic brains.
Cognitive Neurodynamics
Haitao Yu Lin Zhu Lihui Cai Jiang Wang Chen Liu Nan Shi Jing Michelle Liu