Intrinsic Synchronization Analysis of Brain Activity in Obsessive–compulsive Disorders

2020 ◽  
Vol 30 (09) ◽  
pp. 2050046
Author(s):  
Pinar Ozel ◽  
Ali Karaca ◽  
Ali Olamat ◽  
Aydin Akan ◽  
Mehmet Akif Ozcoban ◽  
...  
Keyword(s):  
Brain Activity ◽  
Detailed Knowledge ◽  
Intrinsic Mode Functions ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Multivariate Empirical Mode Decomposition ◽  
Synchronization Method ◽  
Synchronization Likelihood ◽  
Mode Decomposition ◽  
Processing Techniques

Obsessive–compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements; intrinsic phase-locked value, intrinsic coherence, intrinsic synchronization likelihood, and intrinsic visibility graph similarity that quantifies the synchronization level and complexity in electroencephalography (EEG) signals. This intrinsic synchronization is achieved by utilizing Multivariate Empirical Mode Decomposition (MEMD), a data-driven method that resolves nonlinear and nonstationary data into their intrinsic mode functions. Our intrinsic technique in this study demonstrates that MEMD-based synchronization analysis gives us much more detailed knowledge rather than utilizing the synchronization method alone. Furthermore, the nonlinear synchronization method presents more consistent results considering OCD heterogeneity. Statistical evaluation using sample [Formula: see text]-test and [Formula: see text]-test has shown the significance of such new methodology.

scholarly journals Detection of Startle-Type Epileptic Seizures using Machine Learning Technique

2018 ◽  
Vol 05 (02) ◽  
pp. 092-098
Author(s):  
Pushpa Balakrishnan ◽  
S. Hemalatha ◽  
Dinesh Nayak Shroff Keshav
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Absolute Deviation ◽  
Mode Decomposition ◽  
Life Threatening

Abstract Background Epilepsy is a common neurological disorder characterized by seizures and can lead to life-threatening consequences. The electroencephalogram (EEG) is a diagnostic test used to analyze brain activity in various neurological conditions including epilepsy and interpreted by the clinician for appropriate diagnosis. However, the process of EEG analysis for diagnosis can be automated using machine learning algorithms (MLAs) to aid the clinician. The objective of the study was to test different algorithms that could be used for the detection of seizures. Materials and Methods Video EEG (vEEG) was collected from subjects diagnosed to have episodes of seizures. The epilepsy dataset thus obtained was subjected to empirical mode decomposition (EMD) and the signal was decomposed into intrinsic mode functions (IMFs). The first five levels of decomposition were considered for analysis as per the established protocol. Statistical features such as interquartile range (IQR), entropy, and mean absolute deviation (MAD) were extracted from these IMFs. Results In this study, different MLAs such as nearest neighbor (NN), naïve Bayes (NB), and support vector machines (SVMs) were used to distinguish between normal (interictal) and abnormal (ictal) states. The demonstrated accuracy rates were 97.32% for NN, 99.02% for NB, and 93.75% for SVM. Conclusion Based on this accuracy and sensitivity, it may be posited that the NB classifier provides significantly better results for the detection of abnormal signals indicating that MLA can detect the seizure with better accuracy.

scholarly journals A Systematic Review of Behavioral, Physiological, and Neurobiological Cognitive Regulation Alterations in Obsessive-Compulsive Disorder

2020 ◽  
Vol 10 (11) ◽  
pp. 797
Author(s):  
Sónia Ferreira ◽  
José Miguel Pêgo ◽  
Pedro Morgado
Keyword(s):  
Systematic Review ◽  
Obsessive Compulsive Disorder ◽  
Brain Activity ◽  
Intrusive Thoughts ◽  
Network Activity ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Cognitive Regulation ◽  
Frontoparietal Network ◽  
Pubmed Database

Obsessive-compulsive disorder (OCD) is characterized by cognitive regulation deficits. However, the current literature has focused on executive functioning and emotional response impairments in this disorder. Herein, we conducted a systematic review of studies assessing the behavioral, physiological, and neurobiological alterations in cognitive regulation in obsessive-compulsive patients using the PubMed database. Most of the studies included explored behavioral (distress, arousal, and frequency of intrusive thoughts) and neurobiological measures (brain activity and functional connectivity) using affective cognitive regulation paradigms. Our results pointed to the advantageous use of reappraisal and acceptance strategies in contrast to suppression to reduce distress and frequency of intrusive thoughts. Moreover, we observed alterations in frontoparietal network activity during cognitive regulation. Our conclusions are limited by the inclusion of underpowered studies with treated patients. Nonetheless, our findings support the OCD impairments in cognitive regulation of emotion and might help to improve current guidelines for cognitive therapy.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

scholarly journals Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

2020 ◽  
Author(s):  
Eduardo Arrufat-Pié ◽  
Mario Estévez-Báez ◽  
José Mario Estévez-Carreras ◽  
Calixto Machado Curbelo ◽  
Gerry Leisman ◽  
...  
Keyword(s):  
Spectral Analysis ◽  
Empirical Mode Decomposition ◽  
A Priori ◽  
Quantitative Eeg ◽  
Frequency Resolution ◽  
Intrinsic Mode Functions ◽  
Multivariate Empirical Mode Decomposition ◽  
Healthy Humans ◽  
Mode Decomposition ◽  
Mixing Problem

AbstractConsidering the properties of the empirical mode decomposition to extract from a signal its natural oscillatory components known as intrinsic mode functions (IMFs), the spectral analysis of these IMFs could provide a novel alternative for the quantitative EEG analysis without a priori establish more or less arbitrary band limits. This approach has begun to be used in the last years for studies of EEG records of patients included in database repositories or including a low number of individuals or of limited EEG leads, but a detailed study in healthy humans has not yet been reported. Therefore, in this study the aims were to explore and describe the main spectral indices of the IMFs of the EEG in healthy humans using a method based on the FFT and another on the Hilbert-Huang transform (HHT). The EEG of 34 healthy volunteers was recorded and decomposed using a recently developed multivariate empirical mode decomposition algorithm. Extracted IMFs were submitted to spectral analysis with, and the results were compared with an ANOVA test. The first six decomposed IMFs from the EEG showed frequency values in the range of the classical bands of the EEG (1.5 to 56 Hz). Both methods showed in general similar results for mean weighted frequencies and estimations of power spectral density, although the HHT is recommended because of its better frequency resolution. It was shown the presence of the mode-mixing problem producing a slight overlapping of spectral frequencies mainly between the IMF3 and IMF4 modes.

Three Dimensional PET/MRI Images in OCD and Schizophrenia

CNS Spectrums ◽  
1997 ◽  
Vol 2 (4) ◽  
pp. 26-31 ◽  
Author(s):  
Monte S. Buchsbaum ◽  
Jacqueline Spiegel-Cohen ◽  
Tsechung Wei
Keyword(s):  
Frontal Lobe ◽  
Brain Activity ◽  
New Technology ◽  
Three Dimensional ◽  
Normal Activity ◽  
Functional Brain Imaging ◽  
Obsessive Compulsive ◽  
Opposite Pattern ◽  
Compulsive Disorder ◽  
Specific Disorders

AbstractFunctional brain-imaging studies have suggested an opposite pattern of brain activity in obsessive-compulsive disorder (OCD) and schizophrenia. Patients with OCD have higher than normal activity in the frontal lobe and caudate nucleus while patients with schizophrenia have lower than normal activity in these areas. Changes in the nature of the connections between the executive and impulse control regions of the frontal lobe and the basal ganglia might be involved in both illnesses. These findings are statistical in nature and involve structures of complex three-dimensional shapes. New technology for studying the function of these structures may be useful in exploring the relation of each structure to symptoms of specific disorders. This technology may also enable identification of anatomical and functional causes of individual differences in medication response.

Neurofeedback for Peak Performance Training

2016 ◽  
Vol 38 (2) ◽  
pp. 116-123 ◽  
Author(s):  
Nicole C. Pacheco
Keyword(s):  
Brain Activity ◽  
Peak Performance ◽  
Obsessive Compulsive ◽  
Brain Wave ◽  
Compulsive Disorder ◽  
Performance Training ◽  
Hyperactivity Disorder ◽  
Clinical Disorders ◽  
Improved Performance ◽  
Electroencephalographic Activity

Neurofeedback has been found to be effective in the treatment of a number of clinical disorders, such as attention-deficit/hyperactivity disorder (ADHD/ADD) (Lubar, 2003), obsessive-compulsive disorder (Hammond, 2003), seizures (Sterman, 2000), and substance abuse (Burkett, Cummins, Dickson, & Skolnick, 2005; Saxby & Peniston, 1995). The benefits of neurofeedback have also been found useful in peak performance training. These benefits include improving attention/concentration, imagery, arousal level, and decreasing worry and rumination (Williams, 2006). The combination of cognitive, emotional, and psychophysiological benefits from neurofeedback results in improved performance. Due to individual differences in brain activity, as well as the large diversity of skills required in different sports, neurofeedback for performance training is not a “one size fits all” approach (Wilson, Thompson, Thompson, & Peper, 2011). In order to obtain optimal results, neurofeedback for peak performance training begins with appropriate assessment and evaluation of an individual's brain wave (electroencephalographic) activity. Individualized training plans are based upon the assessment findings and the specific needs of the targeted sport or activity (Wilson et al., 2011). This article will discuss the benefits and applications of neurofeedback for peak performance training and the importance of assessment to create effective training programs.

EEG Signal Processing Based on Multivariate Empirical Mode Decomposition and Common Spatial Pattern Hybrid Algorithm

2019 ◽  
Vol 33 (09) ◽  
pp. 1959030 ◽  
Author(s):  
Linyan Wu ◽  
Tao Wang ◽  
Qi Wang ◽  
Qing Zhu ◽  
Jinhuan Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Nearest Neighbor ◽  
Classification Model ◽  
Eeg Signal ◽  
Intrinsic Mode Functions ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition

The high accuracy of electroencephalogram (EEG) signal classification is the premise for the wide application of brain computer interface (BCI). In this paper, a hybrid method consisting of multivariate empirical mode decomposition (MEMD) and common space pattern (CSP) is proposed to recognize left-hand and right-hand hypothetical motion from EEG signals. Experiments were carried out using the BCI competition II imagery database. EEG signals were decomposed into multiple intrinsic mode functions (IMFs) by MEMD. The IMF functions with high correlation were processed by CSP, and AR coefficients and entropy values were extracted as features. After genetic algorithm optimization, classification is carried out. Our research results show that the K nearest neighbor (KNN) as an optimal classification model produces 85.36% accuracy. We also compare the proposed algorithm with the existing algorithms. The experimental results show that the performance of the proposed algorithm is comparable to or better than that of many existing algorithms.

scholarly journals Altered Brain Activity during Reward Anticipation in Pathological Gambling and Obsessive-Compulsive Disorder

PLoS ONE ◽  
2012 ◽  
Vol 7 (9) ◽  
pp. e45938 ◽  
Author(s):  
Jung-Seok Choi ◽  
Young-Chul Shin ◽  
Wi Hoon Jung ◽  
Joon Hwan Jang ◽  
Do-Hyung Kang ◽  
...  
Keyword(s):  
Pathological Gambling ◽  
Obsessive Compulsive Disorder ◽  
Brain Activity ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Reward Anticipation

scholarly journals Increased error-related brain activity in youth with obsessive-compulsive disorder and other anxiety disorders

2013 ◽  
Vol 541 ◽  
pp. 214-218 ◽  
Author(s):  
Melisa Carrasco ◽  
Christina Hong ◽  
Jenna K. Nienhuis ◽  
Shannon M. Harbin ◽  
Kate D. Fitzgerald ◽  
...  
Keyword(s):  
Anxiety Disorders ◽  
Obsessive Compulsive Disorder ◽  
Brain Activity ◽  
Obsessive Compulsive ◽  
Compulsive Disorder

scholarly journals Comparing effectiveness between a mobile application program and traditional cognitive behavior therapy in the treatment of obsessive-compulsive disorder (Preprint)

2020 ◽  
Author(s):  
Hyunchan Hwang ◽  
Sujin Bae ◽  
Jisun Hong ◽  
Doug Hyun Han
Keyword(s):  
Obsessive Compulsive Disorder ◽  
Behavior Therapy ◽  
Cognitive Behavior Therapy ◽  
Clinical Symptoms ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Cognitive Behavior ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Significant Difference

BACKGROUND This study proposes a digital program for the treatment of mental illness that could increase motivation and learning outcomes for patients. Several studies have already applied this method by using an exposure and response prevention inspired serious game to treat patients with obsessive-compulsive disorder (OCD). OBJECTIVE We hypothesized that a mobile cognitive behavior therapy (CBT) program would be effective in treating OCD as much as traditional offline CBT. In addition, the treatment efficacy in response to mobile CBT for OCD might be associated with increased brain activity within the cortico-striato-thalamo-cortical (CSTC) tract. METHODS The digital CBT treatment program for OCD, OCfree, consists of 6 education sessions, 10 quests, and 7 casual games. The information of 27 patients with OCD (15 offline CBT and 12 OCFree CBT) were gathered. During the 6-week intervention period, changes in the clinical symptoms and brain function activity were analyzed. RESULTS There was no significant difference in the change in OCD symptoms and depressive symptoms between the two groups. However, the OCfree group showed greater improvement in anxiety symptoms compared to the offline CBT group. Both offline CBT and OCfree CBT increased the functional connectivity within the CSTC tract in all patients with OCD. However, CBT using OCfree showed greater changes in brain connectivity within the thalamus and insula, compared to offline CBT. CONCLUSIONS : The OC free, an OCD treatment App program, was effective in the treatment of drug-naïve patients with OCD. The treatment effects of OCfree are associated with increased brain connectivity within the CSTC tract. Multisensory stimulation by education, quest, and games in OCfree increases the activity within the thalamus and insula in patients with OCD. CLINICALTRIAL

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