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Jin MX, Qin PP, Xia AWL, Kan RLD, Zhang BBB, Tang AHP, Li ASM, Lin TTZ, Giron CG, Pei JJ, Kranz GS. Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies. Neurosci Biobehav Rev 2024; 162:105695. [PMID: 38710424 DOI: 10.1016/j.neubiorev.2024.105695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 04/10/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024]
Abstract
Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure. We systematically reviewed and meta-analyzed studies that leveraged neurophysiological and neuroimaging markers to predict rTMS response in MDD. Five databases were searched from inception to May 25, 2023. The primary meta-analytic outcome was predictive accuracy pooled from classification models. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. Searching yielded 36 studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operating characteristic curve of 0.87 (95% CI = 0.83-0.92), with 86.8% sensitivity (95% CI = 80.6-91.2%) and 81.9% specificity (95% CI = 76.1-86.4%). Frontal theta cordance measured by electroencephalography is closest to proof of concept. Predicting rTMS response using neurophysiological and neuroimaging markers is promising for clinical decision-making. However, replications by different research groups are needed to establish rigorous markers.
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Affiliation(s)
- Min Xia Jin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Penny Ping Qin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Adam Wei Li Xia
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Rebecca Lai Di Kan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Bella Bing Bing Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Alvin Hong Pui Tang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Ami Sin Man Li
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Tim Tian Ze Lin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Cristian G Giron
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Jun Jie Pei
- Department of Rehabilitation Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, China
| | - Georg S Kranz
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Austria.
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Cai G, Xu J, Ding Q, Lin T, Chen H, Wu M, Li W, Chen G, Xu G, Lan Y. Electroencephalography oscillations can predict the cortical response following theta burst stimulation. Brain Res Bull 2024; 208:110902. [PMID: 38367675 DOI: 10.1016/j.brainresbull.2024.110902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/28/2024] [Accepted: 02/14/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND Continuous theta burst stimulation and intermittent theta burst stimulation are clinically popular models of repetitive transcranial magnetic stimulation. However, they are limited by high variability between individuals in cortical excitability changes following stimulation. Although electroencephalography oscillations have been reported to modulate the cortical response to transcranial magnetic stimulation, their association remains unclear. This study aims to explore whether machine learning models based on EEG oscillation features can predict the cortical response to transcranial magnetic stimulation. METHOD Twenty-three young, healthy adults attended two randomly assigned sessions for continuous and intermittent theta burst stimulation. In each session, ten minutes of resting-state electroencephalography were recorded before delivering brain stimulation. Participants were classified as responders or non-responders based on changes in resting motor thresholds. Support vector machines and multi-layer perceptrons were used to establish predictive models of individual responses to transcranial magnetic stimulation. RESULT Among the evaluated algorithms, support vector machines achieved the best performance in discriminating responders from non-responders for intermittent theta burst stimulation (accuracy: 91.30%) and continuous theta burst stimulation (accuracy: 95.66%). The global clustering coefficient and global characteristic path length in the beta band had the greatest impact on model output. CONCLUSION These findings suggest that EEG features can serve as markers of cortical response to transcranial magnetic stimulation. They offer insights into the association between neural oscillations and variability in individuals' responses to transcranial magnetic stimulation, aiding in the optimization of individualized protocols.
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Affiliation(s)
- Guiyuan Cai
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Jiayue Xu
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Qian Ding
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China; Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 519041 China
| | - Tuo Lin
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Hongying Chen
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Manfeng Wu
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Wanqi Li
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China
| | - Gengbin Chen
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China; Postgraduate Research Institute, Guangzhou Sport University, Guangzhou, 510500 China
| | - Guangqing Xu
- Department of Rehabilitation Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 519041 China.
| | - Yue Lan
- Department of Rehabilitation Medicine, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, 510013 China; Guangzhou Key Laboratory of Aging Frailty and Neurorehabilitation, Guangzhou 510013, China.
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Asgarinejad M, Saviz M, Sadjadi SM, Saliminia S, Kakaei A, Esmaeili P, Hammoud A, Ebrahimzadeh E, Soltanian-Zadeh H. Repetitive transcranial magnetic stimulation (rTMS) as a tool for cognitive enhancement in healthy adults: a review study. Med Biol Eng Comput 2024; 62:653-673. [PMID: 38044385 DOI: 10.1007/s11517-023-02968-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/06/2023] [Indexed: 12/05/2023]
Abstract
As human beings, we have always sought to expand on our abilities, including our cognitive and motor skills. One of the still-underrated tools employed to this end is repetitive transcranial magnetic stimulation (rTMS). Until recently, rTMS was almost exclusively used in studies with rehabilitation purposes. Only a small strand of literature has focused on the application of rTMS on healthy people with the aim of enhancing cognitive abilities such as decision-making, working memory, attention, source memory, cognitive control, learning, computational speed, risk-taking, and impulsive behaviors. It, therefore, seems that the findings in this particular field are the indirect results of rehabilitation research. In this review paper, we have set to investigate such studies and evaluate the rTMS effectuality in terms of how it improves the cognitive skills in healthy subjects. Furthermore, since the most common brain site used for rTMS protocols is the dorsolateral prefrontal cortex (DLPFC), we have added theta burst stimulation (TBS) wave patterns that are similar to brain patterns to increase the effectiveness of this method. The results of this study can help people who have high-risk jobs including firefighters, surgeons, and military officers with their job performance.
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Affiliation(s)
| | - Marzieh Saviz
- Faculty of Psychology and Education, University of Tehran, Tehran, Iran.
| | - Seyyed Mostafa Sadjadi
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Sarah Saliminia
- Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Amineh Kakaei
- Department of Clinical Psychology, School of Behavioral Sciences and Mental Health, Iran University of Medical Sciences, Tehran, Iran
| | - Peyman Esmaeili
- Department of Health, Safety and Environment, Shahid Beheshti Medical University, Tehran, Iran
| | - Ahmad Hammoud
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, Moscow, Russia
| | - Elias Ebrahimzadeh
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Hamid Soltanian-Zadeh
- CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Ebrahimzadeh E, Dehghani A, Asgarinejad M, Soltanian-Zadeh H. Non-linear processing and reinforcement learning to predict rTMS treatment response in depression. Psychiatry Res Neuroimaging 2024; 337:111764. [PMID: 38043370 DOI: 10.1016/j.pscychresns.2023.111764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 11/05/2023] [Accepted: 11/09/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR). METHODS Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field. RESULTS We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM. CONCLUSIONS The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.
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Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Amin Dehghani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | | | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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Hall PA, Burhan AM, MacKillop JC, Duarte D. Next-generation cognitive assessment: Combining functional brain imaging, system perturbations and novel equipment interfaces. Brain Res Bull 2023; 204:110797. [PMID: 37875208 DOI: 10.1016/j.brainresbull.2023.110797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/14/2023] [Accepted: 10/19/2023] [Indexed: 10/26/2023]
Abstract
Conventional cognitive assessment is widely used in clinical and research settings, in educational institutions, and in the corporate world for personnel selection. Such approaches involve having a client, a patient, or a research participant complete a series of standardized cognitive tasks in order to challenge specific and global cognitive abilities, and then quantify performance for the desired end purpose. The latter may include a diagnostic confirmation of a disease, description of a state or ability, or matching cognitive characteristics to a particular occupational role requirement. Metrics derived from cognitive assessments are putatively informative about important features of the brain and its function. For this reason, the research sector also makes use of cognitive assessments, most frequently as a stimulus for cognitive activity from which to extract functional neuroimaging data. Such "task-related activations" form the core of the most widely used neuroimaging technologies, such as fMRI. Much of what we know about the brain has been drawn from the interleaving of cognitive assessments of various types with functional brain imaging technologies. Despite innovation in neuroimaging (i.e., quantifying the neural response), relatively little innovation has occurred on task presentation and volitional response measurement; yet these together comprise the core of cognitive performance. Moreover, even when cognitive assessment is interleaved with functional neuroimaging, this is most often undertaken in the research domain, rather than the primary applications of cognitive assessment in diagnosis and monitoring, education and personnel selection. There are new ways in which brain imaging-and even more importantly, brain modulation-technologies can be combined with automation and artificial intelligence to deliver next-generation cognitive assessment methods. In this review paper, we describe some prototypes for how this can be done and identify important areas for progress (technological and otherwise) to enable it to happen. We will argue that the future of cognitive assessment will include semi- and fully-automated assessments involving neuroimaging, standardized perturbations via neuromodulation technologies, and artificial intelligence. Furthermore, the fact that cognitive assessments take place in a social/interpersonal context-normally between the patient and clinician-makes the human-machine interface consequential, and this will also be discussed.
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Affiliation(s)
- Peter A Hall
- School of Public Health Sciences, Faculty of Health, University of Waterloo, Waterloo, Ontario, Canada; Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, Ontario, Canada.
| | - Amer M Burhan
- Ontario Shores Centre for Mental Health Sciences, Whitby, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - James C MacKillop
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Dante Duarte
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada; Seniors Mental Health Program, St. Joseph's Healthcare, Hamilton, Ontario, Canada
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Yousefi MR, Dehghani A, Taghaavifar H. Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks. Front Hum Neurosci 2023; 17:1174104. [PMID: 37881690 PMCID: PMC10597690 DOI: 10.3389/fnhum.2023.1174104] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/25/2023] [Indexed: 10/27/2023] Open
Abstract
Introduction Emotions play a critical role in human communication, exerting a significant influence on brain function and behavior. One effective method of observing and analyzing these emotions is through electroencephalography (EEG) signals. Although numerous studies have been dedicated to emotion recognition (ER) using EEG signals, achieving improved accuracy in recognition remains a challenging task. To address this challenge, this paper presents a deep-learning approach for ER using EEG signals. Background ER is a dynamic field of research with diverse practical applications in healthcare, human-computer interaction, and affective computing. In ER studies, EEG signals are frequently employed as they offer a non-invasive and cost-effective means of measuring brain activity. Nevertheless, accurately identifying emotions from EEG signals poses a significant challenge due to the intricate and non-linear nature of these signals. Methods The present study proposes a novel approach for ER that encompasses multiple stages, including feature extraction, feature selection (FS) employing clustering, and classification using Dual-LSTM. To conduct the experiments, the DEAP dataset was employed, wherein a clustering technique was applied to Hurst's view and statistical features during the FS phase. Ultimately, Dual-LSTM was employed for accurate ER. Results The proposed method achieved a remarkable accuracy of 97.5% in accurately classifying emotions across four categories: arousal, valence, liking/disliking, dominance, and familiarity. This high level of accuracy serves as strong evidence for the effectiveness of the deep-learning approach to emotion recognition (ER) utilizing EEG signals. Conclusion The deep-learning approach proposed in this paper has shown promising results in emotion recognition using EEG signals. This method can be useful in various applications, such as developing more effective therapies for individuals with mood disorders or improving human-computer interaction by allowing machines to respond more intelligently to users' emotional states. However, further research is needed to validate the proposed method on larger datasets and to investigate its applicability to real-world scenarios.
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Affiliation(s)
- Mohammad Reza Yousefi
- Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran
- Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
| | - Amin Dehghani
- Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Hamid Taghaavifar
- Digital Processing and Machine Vision Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran
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