1
|
Xia Z, Yang PY, Chen SL, Zhou HY, Yan C. Uncovering the power of neurofeedback: a meta-analysis of its effectiveness in treating major depressive disorders. Cereb Cortex 2024; 34:bhae252. [PMID: 38889442 DOI: 10.1093/cercor/bhae252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 05/25/2024] [Accepted: 06/04/2024] [Indexed: 06/20/2024] Open
Abstract
Neurofeedback, a non-invasive intervention, has been increasingly used as a potential treatment for major depressive disorders. However, the effectiveness of neurofeedback in alleviating depressive symptoms remains uncertain. To address this gap, we conducted a comprehensive meta-analysis to evaluate the efficacy of neurofeedback as a treatment for major depressive disorders. We conducted a comprehensive meta-analysis of 22 studies investigating the effects of neurofeedback interventions on depression symptoms, neurophysiological outcomes, and neuropsychological function. Our analysis included the calculation of Hedges' g effect sizes and explored various moderators like intervention settings, study designs, and demographics. Our findings revealed that neurofeedback intervention had a significant impact on depression symptoms (Hedges' g = -0.600) and neurophysiological outcomes (Hedges' g = -0.726). We also observed a moderate effect size for neurofeedback intervention on neuropsychological function (Hedges' g = -0.418). As expected, we observed that longer intervention length was associated with better outcomes for depressive symptoms (β = -4.36, P < 0.001) and neuropsychological function (β = -2.89, P = 0.003). Surprisingly, we found that shorter neurofeedback sessions were associated with improvements in neurophysiological outcomes (β = 3.34, P < 0.001). Our meta-analysis provides compelling evidence that neurofeedback holds promising potential as a non-pharmacological intervention option for effectively improving depressive symptoms, neurophysiological outcomes, and neuropsychological function in individuals with major depressive disorders.
Collapse
Affiliation(s)
- Zheng Xia
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China
- Shanghai Changning Mental Health Center, 299 Xiehe Road, Shanghai 200335, China
| | - Peng-Yuan Yang
- Department of Methodology and Statistics, Faculty of Behavioral and Social Sciences, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, The Netherlands
| | - Si-Lu Chen
- Shanghai Changning Mental Health Center, 299 Xiehe Road, Shanghai 200335, China
| | - Han-Yu Zhou
- Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China
| | - Chao Yan
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China
- Shanghai Changning Mental Health Center, 299 Xiehe Road, Shanghai 200335, China
- Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, 1688 Lianhua Road, Hefei 230601, China
| |
Collapse
|
2
|
Li CT, Chen CS, Cheng CM, Chen CP, Chen JP, Chen MH, Bai YM, Tsai SJ. Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation. J Affect Disord 2023; 343:86-95. [PMID: 37579885 DOI: 10.1016/j.jad.2023.08.059] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND 10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals. The second objective was to study an optimal ML model for predicting responses to rTMS and iTBS. METHODS Two rTMS and iTBS datasets (n = 163) were used: one randomized controlled trial dataset (RCTD; n = 96) and one outpatient dataset (OPD; n = 67). Frontal theta and non-linear EEG features that reflect trend, stability, and complexity were extracted. Pretreatment frontal EEG and ML algorithms, including classical support vector machine(SVM), random forest(RF), XGBoost, and CatBoost, were analyzed. Responses were defined as ≥50 % depression improvement after treatment. Response rates between those with and without pretreatment prediction in another independent outpatient cohort (n = 208) were compared. RESULTS Prediction accuracy using combined EEG features by SVM was better than frontal theta by logistic regression. The accuracy for OPD patients significantly dropped using the RCTD-trained SVM model. Modern ML models, especially RF (rTMS = 83.3 %, iTBS = 88.9 %, p-value(ACC > NIR) < 0.05 for iTBS), performed significantly above chance and had higher accuracy than SVM using both selected features (p < 0.05, FDR corrected for multiple comparisons) or all EEG features. Response rates among those receiving prediction before treatment were significantly higher than those without prediction (p = 0.035). CONCLUSION The first study combining linear and non-linear EEG features could accurately predict responses to left PFC iTBS. The bootstraps-based ML model (i.e., RF) had the best predictive accuracy for rTMS and iTBS.
Collapse
Affiliation(s)
- Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Cognitive Neuroscience, National Central University, Jhongli, Taiwan.
| | - Chi-Sheng Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan
| | - Chih-Ming Cheng
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chung-Ping Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan
| | - Jen-Ping Chen
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| |
Collapse
|
3
|
Tsai YC, Li CT, Juan CH. A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment. Front Psychiatry 2023; 14:1073984. [PMID: 37260762 PMCID: PMC10228658 DOI: 10.3389/fpsyt.2023.1073984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/11/2023] [Indexed: 06/02/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.
Collapse
Affiliation(s)
- Yi-Chun Tsai
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
| | - Cheng-Ta Li
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan City, Taiwan
- Department of Psychology, Kaohsiung Medical University, Kaohsiung, Taiwan
| |
Collapse
|
4
|
Rakús T, Hubčíková K, Bruncvik L, Petrášová Z, Brunovsky M. Retrospective analysis of quantitative electroencephalography changes in a dissimulating patient after dying by suicide: A single case report. Front Psychiatry 2023; 14:1002215. [PMID: 37009100 PMCID: PMC10050719 DOI: 10.3389/fpsyt.2023.1002215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Accepted: 02/15/2023] [Indexed: 03/17/2023] Open
Abstract
We present the case of a 49-year-old man who was diagnosed with depressive disorder, with the first episode having a strong reactive factor. He was involuntarily admitted to a psychiatric hospital after a failed attempt at taking his own life, where he responded to psychotherapy and antidepressant therapy, as evidenced by a >60% reduction in his MADRS total score. He was discharged after 10 days of treatment, denied having suicidal ideations, and was motivated to follow the recommended outpatient care. The risk for suicide during hospitalization was also assessed using suicide risk assessment tools and psychological assessments, including projective tests. The patient underwent a follow-up examination with an outpatient psychiatrist on the 7th day after discharge, during which the suicide risk assessment tool was administered. The results indicated no acute suicide risk or worsening of depressive symptoms. On the 10th day after discharge, the patient took his own life by jumping out of the window of his flat. We believe that the patient had dissimulated his symptoms and possessed suicidal ideations, which were not detected despite repeated examinations specifically designed to assess suicidality and depression symptoms. We retrospectively analyzed his quantitative electroencephalography (QEEG) records to evaluate the change in prefrontal theta cordance as a potentially promising biomarker of suicidality, given the inconclusive results of studies published to date. An increase in prefrontal theta cordance value was found after the first week of antidepressant therapy and psychotherapy in contrast to the expected decrease due to the fading of depressive symptoms. As demonstrated by the provided case study, we hypothesized that prefrontal theta cordance may be an EEG indicator of a higher risk of non-responsive depression and suicidality despite therapeutic improvement.
Collapse
Affiliation(s)
- Tomáš Rakús
- Department of Neuropsychiatry, Philippe Pinel Psychiatric Hospital, Slovak Medical University in Bratislava, Pezinok, Slovakia
- Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
- *Correspondence: Tomáš Rakús
| | - Katarína Hubčíková
- Department of Neuropsychiatry, Philippe Pinel Psychiatric Hospital, Slovak Medical University in Bratislava, Pezinok, Slovakia
- Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Lucia Bruncvik
- Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
- Landesklinikum Hainburg, Hainburg an der Donau, Austria
| | - Zuzana Petrášová
- Department of Neuropsychiatry, Philippe Pinel Psychiatric Hospital, Slovak Medical University in Bratislava, Pezinok, Slovakia
- Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
| | - Martin Brunovsky
- Third Faculty of Medicine, Charles University in Prague, Prague, Czechia
- Department of Neurophysiology and EEG, National Institute of Mental Health, Klecany, Czechia
| |
Collapse
|
5
|
Sajno E, Bartolotta S, Tuena C, Cipresso P, Pedroli E, Riva G. Machine learning in biosignals processing for mental health: A narrative review. Front Psychol 2023; 13:1066317. [PMID: 36710855 PMCID: PMC9880193 DOI: 10.3389/fpsyg.2022.1066317] [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: 10/10/2022] [Accepted: 12/16/2022] [Indexed: 01/15/2023] Open
Abstract
Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.
Collapse
Affiliation(s)
- Elena Sajno
- Humane Technology Lab, Università Cattolica del Sacro Cuore, Milan, Italy,Department of Computer Science, University of Pisa, Pisa, Italy,*Correspondence: Elena Sajno, ✉
| | - Sabrina Bartolotta
- ExperienceLab, Università Cattolica del Sacro Cuore, Milan, Italy,Department of Psychology, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Cosimo Tuena
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| | - Pietro Cipresso
- Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy,Department of Psychology, University of Turin, Turin, Italy
| | - Elisa Pedroli
- Department of Psychology, eCampus University, Novedrate, Italy
| | - Giuseppe Riva
- Humane Technology Lab, Università Cattolica del Sacro Cuore, Milan, Italy,Applied Technology for Neuro-Psychology Lab, IRCCS Istituto Auxologico Italiano, Milan, Italy
| |
Collapse
|
6
|
Zhou P, Wu Q, Zhan L, Guo Z, Wang C, Wang S, Yang Q, Lin J, Zhang F, Liu L, Lin D, Fu W, Wu X. Alpha peak activity in resting-state EEG is associated with depressive score. Front Neurosci 2023; 17:1057908. [PMID: 36960170 PMCID: PMC10027937 DOI: 10.3389/fnins.2023.1057908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 02/17/2023] [Indexed: 03/09/2023] Open
Abstract
Introduction Depression is a serious psychiatric disorder characterized by prolonged sadness, loss of interest or pleasure. The dominant alpha peak activity in resting-state EEG is suggested to be an intrinsic neural marker for diagnosis of mental disorders. Methods To investigate an association between alpha peak activity and depression severity, the present study recorded resting-state EEG (EGI 128 channels, off-line average reference, source reconstruction by a distributed inverse method with the sLORETA normalization, parcellation of 68 Desikan-Killiany regions) from 155 patients with depression (42 males, mean age 35 years) and acquired patients' scores of Self-Rating Depression Scales. We measured both the alpha peak amplitude that is more related to synchronous neural discharging and the alpha peak frequency that is more associated with brain metabolism. Results The results showed that over widely distributed brain regions, individual patients' alpha peak amplitudes were negatively correlated with their depressive scores, and individual patients' alpha peak frequencies were positively correlated with their depressive scores. Discussion These results reveal that alpha peak amplitude and frequency are associated with self-rating depressive score in different manners, and the finding suggests the potential of alpha peak activity in resting-state EEG acting as an important neural factor in evaluation of depression severity in supplement to diagnosis.
Collapse
Affiliation(s)
- Peng Zhou
- Bao’an Traditional Chinese Medicine Hospital, Shenzhen, China
- Sanming Project of Medicine in Shenzhen, Fuwenbin’s Acupuncture and Moxibustion Team of Guangdong Provincial Hospital of Chinese Medicine, Shenzhen, China
| | - Qian Wu
- Department of Acupuncture and Moxibustion, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Liying Zhan
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Zhihan Guo
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Chaolun Wang
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Shanze Wang
- Department of Acupuncture and Moxibustion, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Qing Yang
- Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jiating Lin
- Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Fangyuan Zhang
- Clinical Medical College of Acupuncture Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Lu Liu
- Clinical Medical College of Acupuncture Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Dehui Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Wenbin Fu
- Sanming Project of Medicine in Shenzhen, Fuwenbin’s Acupuncture and Moxibustion Team of Guangdong Provincial Hospital of Chinese Medicine, Shenzhen, China
- Department of Acupuncture and Moxibustion, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- Innovative Research Team of Acupuncture for Depression and Related Disorders, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
- *Correspondence: Wenbin Fu,
| | - Xiang Wu
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
- Xiang Wu,
| |
Collapse
|
7
|
Liu C, Xie Y, Hao Y, Zhang W, Yang L, Bu J, Wei Z, Wu H, Pescetelli N, Zhang X. Using multisession tDCS stimulation as an early intervention on memory bias processing in subthreshold depression. Psychophysiology 2022; 60:e14148. [PMID: 35819779 DOI: 10.1111/psyp.14148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 05/24/2022] [Accepted: 06/13/2022] [Indexed: 12/01/2022]
Abstract
Transcranial direct current stimulation (tDCS) as an intervention tool has gained promising results in major depression disorder. However, studies related to subthreshold depression's (SD) cognitive deficits and neuromodulation approaches for the treatment of SD are still rare. We adopted Beck's cognitive model of depression and tested the tDCS stimulation effects on attentional and memory deficits on SD. First, this was a single-blinded, randomized, sham-controlled clinical trial to determine a 13-day tDCS modulation effect on 49 SD (27: Stimulation; 22: Sham) and 17 healthy controls. Second, the intervention effects of the consecutive and single-session tDCS were compared. Furthermore, the attentional and memory biases were explored in SD. Anodal tDCS was administrated over left dorsolateral prefrontal cortex for 13 consecutive days. Attentional and memory bias were assessed through a modified Sternberg task and a dot-probe task on the 1st, 2nd, and 15th day while their EEG was being recorded. After the 13-day tDCS stimulation (not after single-session stimulation), we found reduced memory bias (Stimulation vs. Sham, p = .02, r2 = .09) and decreased mid-frontal alpha power (p < .01, r2 = .13). In contrast, tDCS did not affect any attentional related behavioral or neural indexes (all ps > .15). Finally, reduced depressive symptoms (e.g., BDI score) were found for both groups. The criteria of SD varied across studies; the efficacy of this protocol should be tested in elderly patients. Our study suggests memory bias of SD can be modulated by the multisession tDCS and alpha power could serve as a neural index for intervention.
Collapse
Affiliation(s)
- Chialun Liu
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, China
| | - Yunlu Xie
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, China
| | - Yaru Hao
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, China
| | - Wei Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, China
| | - Lizhuang Yang
- Anhui Province Key Laboratory of Medical Physics and Technology, Institute of health and medical technique, Hefei Institute of Physical Science, Chinese Academy of Science, Hefei, China
| | - Junjie Bu
- School of Biomedical Engineering, Research and Engineering Center of Biomedical Materials, Anhui Medical University, Hefei, China
| | - Zhengde Wei
- Hefei National Laboratory for Physical Sciences at the Microscale and School of Life Sciences, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, China
| | - Haiyan Wu
- Centre for Cognitive and Brain Sciences (CCBS), University of Macau (UM), Macau, China
| | - Niccolo' Pescetelli
- Hybrid Collective Intelligence Group, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
| | - Xiaochu Zhang
- Department of Psychology, School of Humanities & Social Science, University of Science & Technology of China, Hefei, China.,Department of Radiology, the First Affiliated Hospital of USTC, Hefei National Research Center for Physical Sciences at the Microscale and School of Life Science, Division of Life Science and Medicine, University of Science & Technology of China, Hefei, China.,Application Technology Center of Physical Therapy to Brain Disorders, Institute of Advanced Technology, University of Science & Technology of China, Hefei, China.,Biomedical Sciences and Health Laboratory of Anhui Province, University of Science & Technology of China, Hefei, China
| |
Collapse
|
8
|
Galkin S, Ivanova S, Bokhan N. Current methods for predicting therapeutic response in patients with depressive disorders. Zh Nevrol Psikhiatr Im S S Korsakova 2022; 122:15-21. [DOI: 10.17116/jnevro202212202115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
9
|
Iznak AF, Iznak EV. [EEG predictors of therapeutic response in psychiatry]. Zh Nevrol Psikhiatr Im S S Korsakova 2021; 121:145-151. [PMID: 34037368 DOI: 10.17116/jnevro2021121041145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The literature review provides data on one of the types of biomarkers - EEG predictors of the therapeutic response of patients with different types of mental pathology. It has been shown that the quantitative parameters of the electroencephalogram (EEG) recorded before the start of the treatment course reflect not only the current functional state of the patient's brain, but also its adaptive resources in terms of the possibility and magnitude of response to therapy. The identified EEG predictors of the therapeutic response in patients with depression, schizophrenia and some other mental disorders have a sufficiently high prognostic ability, sensitivity and specificity in determining responders and non-responders, make it possible to carry out a quantitative prediction of the patient's condition after a course of treatment, and also to assist the clinician in choosing medications for optimal therapy.
Collapse
Affiliation(s)
- A F Iznak
- Mental Health Research Centre, Moscow, Russia
| | - E V Iznak
- Mental Health Research Centre, Moscow, Russia
| |
Collapse
|
10
|
Abstract
Two major trends have been dominant in health care in recent years. First, there is a growing consensus that standardization of health care procedures and methods can result in improved effectiveness and safety of treatments. Second, there is increased interest in "personalized medicine," which refers to the tailoring of treatments to individual patients. Here I discuss how these trends apply to the field of quantitative EEG (qEEG), where de-artifacted resting state EEGs of individuals are compared with a normative database in order to assess clinically meaningful deviations, which can be used for diagnostic procedures, to guide personalized treatment protocols, and to assess treatment effectiveness. Standardized and automated de-artifacting procedures are increasingly being used in scientific research and in clinical practice. The advantages of these procedures over manual de-artifacting will be discussed. The results of a systematic comparison between 2 commonly used qEEG databases show that these databases produce very comparable results, illustrating not only the validity and reliability of both databases but also the opportunity to move forward to a standardized use of qEEG in clinical practice. Finally, the standardization of qEEG interpretation as both a diagnostic and treatment selection tool provides an example of how qEEG can merge both personalized medicine and standardization in the treatment of psychological disorders.
Collapse
Affiliation(s)
- André W Keizer
- Neurofeedback Instituut Nederland, Eindhoven, the Netherlands.,qEEG-Pro. Eindhoven, the Netherlands
| |
Collapse
|
11
|
Bruun CF, Arnbjerg CJ, Kessing LV. Electroencephalographic Parameters Differentiating Melancholic Depression, Non-melancholic Depression, and Healthy Controls. A Systematic Review. Front Psychiatry 2021; 12:648713. [PMID: 34489747 PMCID: PMC8417250 DOI: 10.3389/fpsyt.2021.648713] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 07/27/2021] [Indexed: 01/03/2023] Open
Abstract
Introduction: The objective of this systematic review was to investigate whether electroencephalographic parameters can serve as a tool to distinguish between melancholic depression, non-melancholic depression, and healthy controls in adults. Methods: A systematic review comprising an extensive literature search conducted in PubMed, Embase, Google Scholar, and PsycINFO in August 2020 with monthly updates until November 1st, 2020. In addition, we performed a citation search and scanned reference lists. Clinical trials that performed an EEG-based examination on an adult patient group diagnosed with melancholic unipolar depression and compared with a control group of non-melancholic unipolar depression and/or healthy controls were eligible. Risk of bias was assessed by the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) checklist. Results: A total of 24 studies, all case-control design, met the inclusion criteria and could be divided into three subgroups: Resting state studies (n = 5), sleep EEG studies (n = 10), and event-related potentials (ERP) studies (n = 9). Within each subgroup, studies were characterized by marked variability on almost all levels, preventing pooling of data, and many studies were subject to weighty methodological problems. However, the main part of the studies identified one or several EEG parameters that differentiated the groups. Conclusions: Multiple EEG modalities showed an ability to distinguish melancholic patients from non-melancholic patients and/or healthy controls. The considerable heterogeneity across studies and the frequent methodological difficulties at the individual study level were the main limitations to this work. Also, the underlying premise of shifting diagnostic paradigms may have resulted in an inhomogeneous patient population. Systematic Review Registration: Registered in the PROSPERO registry on August 8th, 2020, registration number CRD42020197472.
Collapse
Affiliation(s)
- Caroline Fussing Bruun
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen, Denmark
| | - Caroline Juhl Arnbjerg
- Department of Public Health, Center for Global Health, Aarhus University, Aarhus, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
12
|
Chen ST, Ku LC, Chen SJ, Shen TW. The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder. Brain Sci 2020; 10:brainsci10110828. [PMID: 33171848 PMCID: PMC7695214 DOI: 10.3390/brainsci10110828] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 10/30/2020] [Accepted: 11/05/2020] [Indexed: 01/30/2023] Open
Abstract
Evaluating brain function through biosignals remains challenging. Quantitative electroencephalography (qEEG) outcomes have emerged as a potential intermediate biomarker for diagnostic clarification in psychological disorders. The Test of Variables of Attention (TOVA) was combined with qEEG to evaluate biomarkers such as absolute power, relative power, cordance, and approximate entropy from covariance matrix images to predict major depressive disorder (MDD). EEG data from 18 healthy control and 18 MDD patients were monitored during the resting state and TOVA. TOVA was found to provide aspects for the evaluation of MDD beyond resting electroencephalography. The results showed that the prefrontal qEEG theta cordance of the control and MDD groups were significantly different. For comparison, the changes in qEEG approximate entropy (ApEn) patterns observed during TOVA provided features to distinguish between participants with or without MDD. Moreover, ApEn scores during TOVA were a strong predictor of MDD, and the ApEn scores correlated with the Beck Depression Inventory (BDI) scores. Between-group differences in ApEn were more significant for the testing state than for the resting state. Our results provide further understanding for MDD treatment selection and response prediction during TOVA.
Collapse
Affiliation(s)
- Shao-Tsu Chen
- Department of Psychiatry, Hualien Tzu Chi Hospital, Buddhist Tzu-Chi Medical Foundation, Hualien 970, Taiwan;
- Department of Psychiatry, Tzu Chi University, Hualien 970, Taiwan
| | - Li-Chi Ku
- Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan;
| | - Shaw-Ji Chen
- Department of Psychiatry, Taitung MacKay Memorial Hospital, Taitung County 950, Taiwan;
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
| | - Tsu-Wang Shen
- Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
- Master’s Program Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung 40724, Taiwan
- Correspondence: ; Tel.: +886-4-24517250 (ext. 3937)
| |
Collapse
|
13
|
Zhu J, Wang Z, Gong T, Zeng S, Li X, Hu B, Li J, Sun S, Zhang L. An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data. IEEE Trans Nanobioscience 2020; 19:527-537. [PMID: 32340958 DOI: 10.1109/tnb.2020.2990690] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
At present, depression has become a main health burden in the world. However, there are many problems with the diagnosis of depression, such as low patient cooperation, subjective bias and low accuracy. Therefore, reliable and objective evaluation method is needed to achieve effective depression detection. Electroencephalogram (EEG) and eye movements (EMs) data have been widely used for depression detection due to their advantages of easy recording and non-invasion. This research proposes a content based ensemble method (CBEM) to promote the depression detection accuracy, both static and dynamic CBEM were discussed. In the proposed model, EEG or EMs dataset was divided into subsets by the context of the experiments, and then a majority vote strategy was used to determine the subjects' label. The validation of the method is testified on two datasets which included free viewing eye tracking and resting-state EEG, and these two datasets have 36,34 subjects respectively. For these two datasets, CBEM achieves accuracies of 82.5% and 92.65% respectively. The results show that CBEM outperforms traditional classification methods. Our findings provide an effective solution for promoting the accuracy of depression identification, and provide an effective method for identificationof depression, which in the future could be used for the auxiliary diagnosis of depression.
Collapse
|
14
|
Rolle CE, Fonzo GA, Wu W, Toll R, Jha MK, Cooper C, Chin-Fatt C, Pizzagalli DA, Trombello JM, Deckersbach T, Fava M, Weissman MM, Trivedi MH, Etkin A. Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial. JAMA Psychiatry 2020; 77:397-408. [PMID: 31895437 PMCID: PMC6990859 DOI: 10.1001/jamapsychiatry.2019.3867] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
IMPORTANCE Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice. OBJECTIVE To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment. DESIGN, SETTING, AND PARTICIPANTS In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019. INTERVENTIONS Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks. MAIN OUTCOMES AND MEASURES Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit. RESULTS Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points. CONCLUSIONS AND RELEVANCE These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01407094.
Collapse
Affiliation(s)
- Camarin E. Rolle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California
| | - Gregory A. Fonzo
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California,Department of Psychiatry, Dell Medical School, The University of Texas at Austin
| | - Wei Wu
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Russ Toll
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California
| | - Manish K. Jha
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Cherise Chin-Fatt
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | | | - Joseph M. Trombello
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Thilo Deckersbach
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Myrna M. Weissman
- New York State Psychiatric Institute, Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California,now at Alto Neuroscience Inc, Los Altos, California
| |
Collapse
|
15
|
Vlcek P, Bares M, Novak T, Brunovsky M. Baseline Difference in Quantitative Electroencephalography Variables Between Responders and Non-Responders to Low-Frequency Repetitive Transcranial Magnetic Stimulation in Depression. Front Psychiatry 2020; 11:83. [PMID: 32174854 PMCID: PMC7057228 DOI: 10.3389/fpsyt.2020.00083] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 02/03/2020] [Indexed: 12/13/2022] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depressive disorder, with outcomes approaching 45-55% response and 30-40% remission. Eligible predictors of treatment outcome, however, are still lacking. Few studies have investigated quantitative electroencephalography (QEEG) parameters as predictors of rTMS treatment outcome and none of them have addressed the source localization techniques to predict the response to low-frequency rTMS (LF rTMS). We investigated electrophysiological differences based on scalp EEG data and inverse solution method, exact low-resolution brain electromagnetic tomography (eLORETA), between responders and non-responders to LF rTMS in resting brain activity recorded prior to the treatment. Twenty-five unmedicated depressive patients (mean age of 45.7 years, 20 females) received a 4-week treatment of LF rTMS (1 Hz; 20 sessions per 600 pulses; 100% of the motor threshold) over the right dorsolateral prefrontal cortex. Comparisons between responders (≥50% reduction in Montgomery-Åsberg Depression Rating Scale score) and non-responders were made at baseline for measures of eLORETA current density, spectral absolute power, and inter-hemispheric and intra-hemispheric EEG asymmetry. Responders were found to have lower current source densities in the alpha-2 and beta-1 frequency bands bilaterally (with predominance on the left side) in the inferior, medial, and middle frontal gyrus, precentral gyrus, cingulate gyrus, anterior cingulate, and insula. The most pronounced difference was found in the left middle frontal gyrus for alpha-2 and beta-1 bands (p < 0.05). Using a spectral absolute power analysis, we found a negative correlation between the absolute power in beta and theta frequency bands on the left frontal electrode F7 and the change in depressive symptomatology. None of the selected asymmetries significantly differentiated responders from non-responders in any frequency band. Pre-treatment reduction of alpha-2 and beta-1 sources, but not QEEG asymmetry, was found in patients with major depressive disorder who responded to LF rTMS treatment. Prospective trials with larger groups of subjects are needed to further validate these findings.
Collapse
Affiliation(s)
- Premysl Vlcek
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Martin Bares
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Tomas Novak
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| |
Collapse
|
16
|
It is time to investigate integrative approaches to enhance treatment outcomes for depression? Med Hypotheses 2019; 126:82-94. [DOI: 10.1016/j.mehy.2019.03.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 03/11/2019] [Accepted: 03/21/2019] [Indexed: 12/14/2022]
|