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Ng HYH, Wu CW, Huang FY, Huang CM, Hsu CF, Chao YP, Jung TP, Chuang CH. Enhanced electroencephalography effective connectivity in frontal low-gamma band correlates of emotional regulation after mindfulness training. J Neurosci Res 2023; 101:901-915. [PMID: 36717762 DOI: 10.1002/jnr.25168] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 12/08/2022] [Accepted: 01/06/2023] [Indexed: 02/01/2023]
Abstract
Practicing mindfulness, focusing attention on the internal and external experiences occurring in the present moment with open and nonjudgement stance, can lead to the development of emotional regulation skills. Yet, the effective connectivity of brain regions during mindfulness has been largely unexplored. Studies have shown that mindfulness practice promotes functional connectivity in practitioners, potentially due to improved emotional regulation abilities and increased connectivity in the lateral prefrontal areas. To examine the changes in effective connectivity due to mindfulness training, we analyzed electroencephalogram (EEG) signals taken before and after mindfulness training, focusing on training-related effective connectivity changes in the frontal area. The mindfulness training group participated in an 8-week mindfulness-based stress reduction (MBSR) program. The control group did not take part. Regardless of the specific mindfulness practice used, low-gamma band effective connectivity increased globally after the mindfulness training. High-beta band effective connectivity increased globally only during Breathing. Moreover, relatively higher outgoing effective connectivity strength was seen during Resting and Breathing and Body-scan. By analyzing the changes in outgoing and incoming connectivity edges, both F7 and F8 exhibited strong parietal connectivity during Resting and Breathing. Multiple regression analysis revealed that the changes in effective connectivity of the right lateral prefrontal area predicted mindfulness and emotional regulation abilities. These results partially support the theory that the lateral prefrontal areas have top-down modulatory control, as these areas have high outflow effective connectivity, implying that mindfulness training cultivates better emotional regulation.
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Affiliation(s)
- Hei-Yin Hydra Ng
- Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan.,Department of Educational Psychology and Counseling, College of Education, National Tsing Hua University, Hsinchu, Taiwan
| | - Changwei W Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan.,Brain and Consciousness Research Center, Taipei Medical University-Shuang Ho Hospital, New Taipei, Taiwan
| | - Feng-Ying Huang
- Department of Education, National Taipei University of Education, Taipei, Taiwan
| | - Chih-Mao Huang
- Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.,Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chia-Fen Hsu
- Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan, Taiwan.,Department of Child Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yi-Ping Chao
- Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.,Department of Otolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tzyy-Ping Jung
- Institute for Neural Computation and Institute of Engineering in Medicine, University of California, San Diego, California, La Jolla, USA
| | - Chun-Hsiang Chuang
- Research Center for Education and Mind Sciences, College of Education, National Tsing Hua University, Hsinchu, Taiwan.,Institute of Information Systems and Applications, College of Electrical Engineering and Computer Science, National Tsing Hua University, Hsinchu, Taiwan
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Vázquez MA, Maghsoudi A, Mariño IP. An Interpretable Machine Learning Method for the Detection of Schizophrenia Using EEG Signals. Front Syst Neurosci 2021; 15:652662. [PMID: 34122021 PMCID: PMC8194273 DOI: 10.3389/fnsys.2021.652662] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/30/2021] [Indexed: 01/01/2023] Open
Abstract
In this work we propose a machine learning (ML) method to aid in the diagnosis of schizophrenia using electroencephalograms (EEGs) as input data. The computational algorithm not only yields a proposal of diagnostic but, even more importantly, it provides additional information that admits clinical interpretation. It is based on an ML model called random forest that operates on connectivity metrics extracted from the EEG signals. Specifically, we use measures of generalized partial directed coherence (GPDC) and direct directed transfer function (dDTF) to construct the input features to the ML model. The latter allows the identification of the most performance-wise relevant features which, in turn, provide some insights about EEG signals and frequency bands that are associated with schizophrenia. Our preliminary results on real data show that signals associated with the occipital region seem to play a significant role in the diagnosis of the disease. Moreover, although every frequency band might yield useful information for the diagnosis, the beta and theta (frequency) bands provide features that are ultimately more relevant for the ML classifier that we have implemented.
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Affiliation(s)
- Manuel A Vázquez
- Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Leganés, Spain
| | - Arash Maghsoudi
- Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Inés P Mariño
- Department of Biology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Móstoles, Spain.,Research Laboratory Systemic Medicine of Healthy Ageing, Institute of Biology and Medicine, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Institute for Women's Health, University College London, London, United Kingdom
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Alipour A, Mozhdehfarahbakhsh A, Nouri S, Petramfar P, Tahamtan M, Kamali AM, Rao KS, Nami M. Studies on the Bottom-Up and Top-Down Neural Information Flow Alterations in Neurodegeneration. J Alzheimers Dis 2020; 78:169-183. [PMID: 32955463 DOI: 10.3233/jad-200590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND A proper explanation for perceptual symptoms in neurodegenerative disorders including Alzheimer's disease and Parkinson's disease (PD) is still lacking. OBJECTIVE This study aimed at investigating the imbalance between 'bottom-up' and 'top-down' information flow (IF) and processing in PD in relation with visual hallucination symptoms. METHODS Here, we looked at bottom-up and top-down IF markers using resting state electroencephalographic (EEG) data from PD patients analyzed through three different IF measures (direct Directed Transfer Function (dDTF), full frequency Directed Transfer Function (ff-DTF), and renormalized Partial Directed Coherence (rPDC). RESULTS We observed an increased gamma band IF and a reduced beta band IF in PD patients compared to healthy controls. Additionally, we noticed a reduced theta band IF in PD patients using dDTF as a measure of IF. By source localizing the EEG activity of the PD patients and healthy controls, we looked at the alterations of IF in the prefrontal cortex of PD patients as well. CONCLUSION In line with previous studies, our results suggest that the delicate balance between bottom-up and top-down IF is disrupted in Parkinson's disease potentially contributing to the cognitive symptoms of PD patients.
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Affiliation(s)
- Abolfazl Alipour
- Neuroscience Laboratory, NSL (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran.,Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.,Program in Neuroscience, Indiana University, Bloomington, IN, USA
| | - Azadeh Mozhdehfarahbakhsh
- Neuroscience Laboratory, NSL (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Saba Nouri
- Neuroscience Laboratory, NSL (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Peyman Petramfar
- Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mahshid Tahamtan
- DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
| | - Ali-Mohammad Kamali
- DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran
| | - K S Rao
- Centre for Neuroscience, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama
| | - Mohammad Nami
- Neuroscience Laboratory, NSL (Brain, Cognition and Behavior), Department of Neuroscience, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran.,DANA Brain Health Institute, Iranian Neuroscience Society-Fars Chapter, Shiraz, Iran.,Centre for Neuroscience, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama City, Panama.,Institute for Cognitive Science Studies-ICSS, Brain and Cognition Clinic, Tehran, Iran
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