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Schwock F, Bloch J, Khateeb K, Zhou J, Atlas L, Yazdan-Shahmorad A. Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582177. [PMID: 38464147 PMCID: PMC10925120 DOI: 10.1101/2024.02.26.582177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.
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Affiliation(s)
- Felix Schwock
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
- Primate Research Center, Seattle, WA, USA
| | - Julien Bloch
- Department of Bioengineering, University of Washington, Seattle, WA, USA. Washington National
- Primate Research Center, Seattle, WA, USA
| | - Karam Khateeb
- Department of Bioengineering, University of Washington, Seattle, WA, USA. Washington National
- Primate Research Center, Seattle, WA, USA
| | - Jasmine Zhou
- Department of Bioengineering, University of Washington, Seattle, WA, USA. Washington National
- Primate Research Center, Seattle, WA, USA
| | - Les Atlas
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Azadeh Yazdan-Shahmorad
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
- Department of Bioengineering, University of Washington, Seattle, WA, USA. Washington National
- Primate Research Center, Seattle, WA, USA
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Pan R, Ye S, Zhong Y, Chen Q, Cai Y. Transcranial alternating current stimulation for the treatment of major depressive disorder: from basic mechanisms toward clinical applications. Front Hum Neurosci 2023; 17:1197393. [PMID: 37731669 PMCID: PMC10507344 DOI: 10.3389/fnhum.2023.1197393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 08/22/2023] [Indexed: 09/22/2023] Open
Abstract
Non-pharmacological treatment is essential for patients with major depressive disorder (MDD) that is medication resistant or who are unable to take medications. Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation method that manipulates neural oscillations. In recent years, tACS has attracted substantial attention for its potential as an MDD treatment. This review summarizes the latest advances in tACS treatment for MDD and outlines future directions for promoting its clinical application. We first introduce the neurophysiological mechanism of tACS and its novel developments. In particular, two well-validated tACS techniques have high application potential: high-definition tACS targeting local brain oscillations and bifocal tACS modulating interarea functional connectivity. Accordingly, we summarize the underlying mechanisms of tACS modulation for MDD. We sort out the local oscillation abnormalities within the reward network and the interarea oscillatory synchronizations among multiple MDD-related networks in MDD patients, which provide potential modulation targets of tACS interventions. Furthermore, we review the latest clinical studies on tACS treatment for MDD, which were based on different modulation mechanisms and reported alleviations in MDD symptoms. Finally, we discuss the main challenges of current tACS treatments for MDD and outline future directions to improve intervention target selection, tACS implementation, and clinical validations.
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Affiliation(s)
- Ruibo Pan
- Department of Psychiatry, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shengfeng Ye
- Department of Psychology and Behavioral Science, Zhejiang University, Hangzhou, China
| | - Yun Zhong
- Department of Psychology and Behavioral Science, Zhejiang University, Hangzhou, China
| | - Qiaozhen Chen
- Department of Psychiatry, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Ying Cai
- Department of Psychology and Behavioral Science, Zhejiang University, Hangzhou, China
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Čukić MB, Savić D, Bachmann M. Editorial: Innovations in depression diagnosis and treatment outcome monitoring. Front Digit Health 2023; 5:1224999. [PMID: 37363271 PMCID: PMC10289282 DOI: 10.3389/fdgth.2023.1224999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023] Open
Affiliation(s)
- Milena B. Čukić
- Empa Swiss Federal Labs for Materials Science and Technology, Biomimetic Membranes and Textiles, St. Gallen, Switzerland
| | - Danka Savić
- Vinča Institute of Nuclear Science, University of Belgrade, Belgrade, Serbia
| | - Maie Bachmann
- Biosignal Processing Laboratory, Tallinn University of Technology, Tallinn, Estonia
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Aydın S. Investigation of global brain dynamics depending on emotion regulation strategies indicated by graph theoretical brain network measures at system level. Cogn Neurodyn 2023; 17:331-344. [PMID: 37007189 PMCID: PMC10050309 DOI: 10.1007/s11571-022-09843-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/03/2022] [Accepted: 07/01/2022] [Indexed: 11/26/2022] Open
Abstract
In the present study, new findings reveal the close association between graph theoretic global brain connectivity measures and cognitive abilities the ability to manage and regulate negative emotions in healthy adults. Functional brain connectivity measures have been estimated from both eyes-opened and eyes-closed resting-state EEG recordings in four groups including individuals who use opposite Emotion Regulation Strategies (ERS) as follow: While 20 individuals who frequently use two opposing strategies, such as rumination and cognitive distraction, are included in 1st group, 20 individuals who don't use these cognitive strategies are included in 2nd group. In 3rd and 4th groups, there are matched individuals who use both Expressive Suppression and Cognitive Reappraisal strategies together frequently and never use them, respectively. EEG measurements and psychometric scores of individuals were both downloaded from a public dataset LEMON. Since it is not sensitive to volume conduction, Directed Transfer Function has been applied to 62-channel recordings to obtain cortical connectivity estimations across the whole cortex. Regarding well defined threshold, connectivity estimations have been transformed into binary numbers for implementation of Brain Connectivity Toolbox. The groups are compared to each other through both statistical logistic regression models and deep learning models driven by frequency band specific network measures referring segregation, integration and modularity of the brain. Overall results show that high classification accuracies of 96.05% (1st vs 2nd) and 89.66% (3rd vs 4th) are obtained in analyzing full-band ( 0.5 - 45 H z ) EEG. In conclusion, negative strategies may upset the balance between segregation and integration. In particular, graphical results show that frequent use of rumination induces the decrease in assortativity referring network resilience. The psychometric scores are found to be highly correlated with brain network measures of global efficiency, local efficiency, clustering coefficient, transitivity and assortativity in even resting-state.
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Affiliation(s)
- Serap Aydın
- Medical Faculty, Biophysics Department, Hacettepe University, Ankara, Turkey
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Song Y, Wang K, Wei Y, Zhu Y, Wen J, Luo Y. Graph Theory Analysis of the Cortical Functional Network During Sleep in Patients With Depression. Front Physiol 2022; 13:858739. [PMID: 35721531 PMCID: PMC9199990 DOI: 10.3389/fphys.2022.858739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
Depression, a common mental illness that seriously affects the psychological health of patients, is also thought to be associated with abnormal brain functional connectivity. This study aimed to explore the differences in the sleep-state functional network topology in depressed patients. A total of 25 healthy participants and 26 depressed patients underwent overnight 16-channel electroencephalography (EEG) examination. The cortical networks were constructed by using functional connectivity metrics of participants based on the weighted phase lag index (WPLI) between the EEG signals. The results indicated that depressed patients exhibited higher global efficiency and node strength than healthy participants. Furthermore, the depressed group indicated right-lateralization in the δ band. The top 30% of connectivity in both groups were shown in undirected connectivity graphs, revealing the distinct link patterns between the depressed and control groups. Links between the hemispheres were noted in the patient group, while the links in the control group were only observed within each hemisphere, and there were many long-range links inside the hemisphere. The altered sleep-state functional network topology in depressed patients may provide clues for a better understanding of the depression pathology. Overall, functional network topology may become a powerful tool for the diagnosis of depression.
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Affiliation(s)
- Yingjie Song
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Kejie Wang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yu Wei
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yongpeng Zhu
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Jinfeng Wen
- Department of Psychology, Guangdong, 999 Brain Hospital, Guangzhou, China
| | - Yuxi Luo
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Sensing Technology and Biomedical Instruments, Sun Yat-sen University, Guangzhou, China
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Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Olejarczyk E, Valiulis V, Dapsys K, Gerulskis G, Germanavicius A. Effect of repetitive transcranial magnetic stimulation on fronto-posterior and hemispheric asymmetry in depression. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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