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Article ; Online: EEG Biomarkers to Predict Response to Sertraline and Placebo Treatment in Major Depressive Disorder.

Oakley, Thomas / Coskuner, Jonathan / Cadwallader, Andrew / Ravan, Maryam / Hasey, Gary

IEEE transactions on bio-medical engineering

2023 Volume 70, Issue 3, Page(s) 909–919

Abstract: Objective: Major depressive disorder (MDD) is a persistent psychiatric condition, and the leading cause of disability, affecting up to 5% of the population worldwide. Antidepressant medications (ADMs) are often the first-line treatment for MDD, but it ... ...

Abstract	Objective: Major depressive disorder (MDD) is a persistent psychiatric condition, and the leading cause of disability, affecting up to 5% of the population worldwide. Antidepressant medications (ADMs) are often the first-line treatment for MDD, but it may take the clinician months of "trial and error" to find an effective ADM for a particular patient. Therefore, identification of predictive biomarkers that can be used to accurately determine the effectiveness of a specific treatment for an individual patient is extremely valuable. Method: Using resting EEG data, we develop a machine learning algorithm (MLA) that searches for connectivity patterns within an individual's EEG signal that are predictive of the probability of responding to the antidepressant Sertraline or Placebo. The MLA has two steps: 1) Directed phase lag index (DPLI), a measure of phase synchronization between brain regions, that is not sensitive to volume conduction is applied to resting-state EEG data, 2) the resulting DPLI matrix is searched for a pattern set of features that can be used to successfully predict the response to Sertraline or Placebo. Results: Our MLA predicted response to Sertraline (N = 105) or Placebo (N = 119) with more than 80% accuracy. Conclusion: Our findings suggest that feature patterns selected from a DPLI matrix may be predictive of response to Sertraline and to Placebo. Significance: The proposed MLA may provide an inexpensive, non-invasive, and readily available tool that clinicians may use to enhance treatment effectiveness, accelerate time to recovery, reduce personal suffering, and decrease treatment costs.
MeSH term(s)	Humans ; Antidepressive Agents/pharmacology ; Antidepressive Agents/therapeutic use ; Biomarkers ; Depressive Disorder, Major/drug therapy ; Depressive Disorder, Major/epidemiology ; Depressive Disorder, Major/psychology ; Electroencephalography/methods ; Sertraline/therapeutic use
Chemical Substances	Antidepressive Agents ; Biomarkers ; Sertraline (QUC7NX6WMB)
Language	English
Publishing date	2023-02-17
Publishing country	United States
Document type	Clinical Trial ; Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2022.3204861
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Article ; Online: A Machine Learning Algorithm to Discriminating Between Bipolar and Major Depressive Disorders Based on Resting EEG Data.

Margarette Sanchez, M / Borden, L / Alam, N / Noroozi, A / Ravan, M / Flor-Henry, P / Hasey, G

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2022 Volume 2022, Page(s) 2635–2638

Abstract: Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge due to the lack of known biomarkers. Conventional methods of diagnosis rest exclusively on symptomatic presentation, and personal and family history. ...

Abstract	Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge due to the lack of known biomarkers. Conventional methods of diagnosis rest exclusively on symptomatic presentation, and personal and family history. As a result, BD-depressed episode (BD-DE) is often misdiagnosed as MDD, and inappropriate therapy is given. Electroencephalography (EEG) has been widely studied as a potential source of biomarkers to differentiate these disorders. Previous attempts using machine learning (ML) methods have delivered insufficient sensitivity and specificity for clinical use, likely as a consequence of the small training set size, and inadequate ML methodology. We hope to overcome these limitations by employing a training dataset of resting-state EEG from 71 MDD and 71 BD patients. We introduce a robust 3 steps ML technique: 1) a multi-step preprocessing method is used to improve the quality of the EEG signal 2) symbolic transfer entropy (STE), which is an effective connectivity measure, is applied to the resultant EEG signals 3) the ML algorithm uses the extracted STE features to distinguish MDD from BD patients. Clinical Relevance--- The accuracy of our algorithm, derived from a large sample of patients, suggests that this method may hold significant promise as a clinical tool. The proposed method delivered total accuracy, sensitivity, and specificity of 84.9%, 83.4%, and 87.1%, respectively.
MeSH term(s)	Algorithms ; Bipolar Disorder/diagnosis ; Depressive Disorder, Major/diagnosis ; Electroencephalography ; Humans ; Machine Learning
Language	English
Publishing date	2022-09-09
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC48229.2022.9871453
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Article ; Online: Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data.

Ravan, M / Noroozi, A / Sanchez, M Margarette / Borden, L / Alam, N / Flor-Henry, P / Hasey, G

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology

2022 Volume 146, Page(s) 30–39

Abstract: Objective: Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge as effective treatment is quite different for each condition. In this study electroencephalography (EEG) was explored as an objective ... ...

Abstract	Objective: Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge as effective treatment is quite different for each condition. In this study electroencephalography (EEG) was explored as an objective biomarker for distinguishing MDD from BD using an efficient machine learning algorithm (MLA) trained by a relatively large and balanced dataset. Methods: A 3 step MLA was applied: (1) a multi-step preprocessing method was used to improve the quality of the EEG signal, (2) symbolic transfer entropy (STE), an effective connectivity measure, was applied to the resultant EEG and (3) the MLA used the extracted STE features to distinguish MDD (N = 71) from BD (N = 71) subjects. Results: 14 connectivity features were selected by the proposed algorithm. Most of the selected features were related to the frontal, parietal, and temporal lobe electrodes. The major involved regions were the Broca region in the frontal lobe and the somatosensory association cortex in the parietal lobe. These regions are near electrodes FC5 and CPz and are involved in processing language and sensory information, respectively. The resulting classifier delivered an evaluation accuracy of 88.5% and a test accuracy of 89.3%, using 80% of the data for training and evaluation and the remaining 20% for testing, respectively. Conclusions: The high evaluation and test accuracies of our algorithm, derived from a large balanced training sample suggests that this method may hold significant promise as a clinical tool. Significance: The proposed MLA may provide an inexpensive and readily available tool that clinicians may use to enhance diagnostic accuracy and shorten time to effective treatment.
MeSH term(s)	Humans ; Depressive Disorder, Major/diagnosis ; Depressive Disorder, Major/therapy ; Bipolar Disorder/diagnosis ; Machine Learning ; Frontal Lobe ; Electroencephalography/methods
Language	English
Publishing date	2022-12-07
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1463630-x
ISSN	1872-8952 ; 0921-884X ; 1388-2457
ISSN (online)	1872-8952
ISSN	0921-884X ; 1388-2457
DOI	10.1016/j.clinph.2022.11.014
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Article ; Online: Wavelet-Based Muscle Artefact Noise Reduction for Short Latency rTMS Evoked Potentials.

Chrapka, Philip / de Bruin, Hubert / Hasey, Gary / Reilly, Jim

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society

2019 Volume 27, Issue 7, Page(s) 1449–1457

Abstract: This paper presents a new method of reducing the noise in the EEG response signal recorded during repetitive transcranial magnetic stimulation (rTMS). This noise is principally composed of the residual stimulus artefact and millivolt amplitude compound ... ...

Abstract	This paper presents a new method of reducing the noise in the EEG response signal recorded during repetitive transcranial magnetic stimulation (rTMS). This noise is principally composed of the residual stimulus artefact and millivolt amplitude compound muscle action potentials (CMAP) recorded from the scalp muscles and precludes analysis of the cortical evoked potentials, especially during the first 20-ms post stimulus. The proposed method uses the wavelet transform with a fourth-order Daubechies mother wavelet and a novel coefficient reduction algorithm based on cortical amplitude thresholds. Four other mother wavelets as well as digital filtering have been tested, and the Coiflets 2 and 3 also found to be effective with Coiflet 3 results marginally better than Daubechies 4. The approach has been tested using data recorded from 16 normal subjects during a study of cortical sensitivity to rTMS at different cortical locations using stimulation amplitudes, frequencies, and sites typically used in clinical practice to treat major depressive disorder.
MeSH term(s)	Action Potentials/physiology ; Adult ; Algorithms ; Artifacts ; Computer Simulation ; Depressive Disorder, Major/therapy ; Electroencephalography ; Electromyography ; Female ; Healthy Volunteers ; Humans ; Male ; Middle Aged ; Muscle, Skeletal/physiology ; Scalp/physiology ; Transcranial Magnetic Stimulation/methods ; Wavelet Analysis ; Young Adult
Language	English
Publishing date	2019-04-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	1166307-8
ISSN	1558-0210 ; 1063-6528 ; 1534-4320
ISSN (online)	1558-0210
ISSN	1063-6528 ; 1534-4320
DOI	10.1109/TNSRE.2019.2908951
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Article: New Fungicides and Application Strategies Based on Inoculum and Precipitation for Managing Stone Fruit Rust on Peach in California.

Soto-Estrada, A / Förster, H / Hasey, J / Adaskaveg, J E

Plant disease

2019 Volume 87, Issue 9, Page(s) 1094–1101

Abstract: ... sulfur, an agricultural oil, a substituted aromatic (e.g., chlorothalonil), as well as benzimidazole (e.g ... benomyl, thiophanate-methyl), sterol biosynthesis inhibiting (e.g., myclobutanil, propiconazole ... tebuconazole), and strobilurin (e.g., azoxystrobin) fungicides. The timing of treatments was a significant ...

Abstract	In greenhouse and field studies, rust on cling peach caused by Tranzschelia discolor was significantly reduced on leaves and fruit compared with that on controls by foliar applications of wettable sulfur, an agricultural oil, a substituted aromatic (e.g., chlorothalonil), as well as benzimidazole (e.g., benomyl, thiophanate-methyl), sterol biosynthesis inhibiting (e.g., myclobutanil, propiconazole, tebuconazole), and strobilurin (e.g., azoxystrobin) fungicides. The timing of treatments was a significant factor in reducing disease in most trials. Protective programs using wettable sulfur, tebuconazole, or azoxystrobin applied after stem lesion detection (ASLD) and before forecasted rains were highly effective. In the most effective programs for managing the disease, however, these fungicides were applied after stem lesion detection and occurrence of rainfall. Under highly conducive field environments for disease, single applications of azoxystrobin or tebuconazole at ASLD were significantly more efficacious than sulfur. Dormant treatments of liquid lime sulfur, chlorothalonil, or thiophanate-methyl/agricultural oil, however, were ineffective in reducing the disease during the subsequent spring and summer seasons. This is the first management program for rust on cling peach that utilizes inoculum and precipitation events to optimize timing of fungicide applications.
Language	English
Publishing date	2019-02-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	754182-x
ISSN	0191-2917
ISSN	0191-2917
DOI	10.1094/PDIS.2003.87.9.1094
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Article ; Online: Diagnostic deep learning algorithms that use resting EEG to distinguish major depressive disorder, bipolar disorder, and schizophrenia from each other and from healthy volunteers.

Ravan, Maryam / Noroozi, Amin / Sanchez, Mary Margarette / Borden, Lee / Alam, Nafia / Flor-Henry, Pierre / Colic, Sinisa / Khodayari-Rostamabad, Ahmad / Minuzzi, Luciano / Hasey, Gary

Journal of affective disorders

2023 Volume 346, Page(s) 285–298

Abstract: Background: Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing ... ...

Abstract	Background: Mood disorders and schizophrenia affect millions worldwide. Currently, diagnosis is primarily determined by reported symptomatology. As symptoms may overlap, misdiagnosis is common, potentially leading to ineffective or destabilizing treatment. Diagnostic biomarkers could significantly improve clinical care by reducing dependence on symptomatic presentation. Methods: We used deep learning analysis (DLA) of resting electroencephalograph (EEG) to differentiate healthy control (HC) subjects (N = 239), from those with major depressive disorder (MDD) (N = 105), MDD-atypical (MDD-A) (N = 27), MDD-psychotic (MDD-P) (N = 35), bipolar disorder-depressed episode (BD-DE) (N = 71), BD-manic episode (BD-ME) (N = 49), and schizophrenia (SCZ) (N = 122) and also differentiate subjects with mental disorders on a pair-wise basis. DSM-III-R diagnoses were determined and supplemented by computerized Quick Diagnostic Interview Schedule. After EEG preprocessing, robust exact low-resolution electromagnetic tomography (ReLORETA) computed EEG sources for 82 brain regions. 20 % of all subjects were then set aside for independent testing. Feature selection methods were then used for the remaining subjects to identify brain source regions that are discriminating between diagnostic categories. Results: Pair-wise classification accuracies between 90 % and 100 % were obtained using independent test subjects whose data were not used for training purposes. The most frequently selected features across various pairs are in the postcentral, supramarginal, and fusiform gyri, the hypothalamus, and the left cuneus. Brain sites discriminating SCZ from HC were mainly in the left hemisphere while those separating BD-ME from HC were on the right. Limitations: The use of superseded DSM-III-R diagnostic system and relatively small sample size in some disorder categories that may increase the risk of overestimation. Conclusions: DLA of EEG could be trained to autonomously classify psychiatric disorders with over 90 % accuracy compared to an expert clinical team using standardized operational methods.
MeSH term(s)	Humans ; Depressive Disorder, Major/diagnosis ; Depressive Disorder, Major/psychology ; Bipolar Disorder/diagnosis ; Schizophrenia/diagnosis ; Deep Learning ; Healthy Volunteers ; Electroencephalography
Language	English
Publishing date	2023-11-12
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	135449-8
ISSN	1573-2517 ; 0165-0327
ISSN (online)	1573-2517
ISSN	0165-0327
DOI	10.1016/j.jad.2023.11.017
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Article ; Online: Competencies for Repetitive Transcranial Magnetic Stimulation in Postgraduate Medical Education: Expert Consensus Using a Modified Delphi Process.

Lai, Ka Sing Paris / Waxman, Robyn / Blumberger, Daniel M / Giacobbe, Peter / Hasey, Gary / McMurray, Lisa / Milev, Roumen / Palaniyappan, Lena / Ramasubbu, Rajamannar / Rybak, Yuri E / Sacevich, Tegan / Vila-Rodriguez, Fidel / Burhan, Amer M

Canadian journal of psychiatry. Revue canadienne de psychiatrie

2023 Volume 68, Issue 12, Page(s) 916–924

Abstract: Background: Repetitive transcranial magnetic stimulation (rTMS) is recommended in Canadian guidelines as a first-line treatment for major depressive disorder. With the shift towards competency-based medical education, it remains unclear how to determine ...

Abstract	Background: Repetitive transcranial magnetic stimulation (rTMS) is recommended in Canadian guidelines as a first-line treatment for major depressive disorder. With the shift towards competency-based medical education, it remains unclear how to determine when a resident is considered competent in applying knowledge of rTMS to patient care. Given inconsistencies between postgraduate training programmes with regards to training requirements, defining competencies will improve the standard of care in rTMS delivery. Objective: The goal of this study was to develop competencies for rTMS that can be implemented into a competency-based training curriculum in postgraduate training programmes. Methods: A working group drafted competencies for postgraduate psychiatry trainees. Fourteen rTMS experts from across Canada were invited to participate in the modified Delphi process. Results: Ten experts participated in all three rounds of the modified Delphi process. A total of 20 items reached a consensus. There was improvement in the Cronbach's alpha over the rounds of modified Delphi process (Cronbach's alpha increased from 0.554 to 0.824) suggesting improvement in internal consistency. The intraclass correlation coefficient (ICC) increased from 0.543 to 0.805 suggesting improved interrater agreement. Conclusions: This modified Delphi process resulted in expert consensus on competencies to be acquired during postgraduate medical education programmes where a learner is training to become competent as a consultant and/or practitioner in rTMS treatment. This is a field that still requires development, and it is expected that as more evidence emerges the competencies will be further refined. These results will help the development of other curricula in interventional psychiatry.
MeSH term(s)	Humans ; Consensus ; Transcranial Magnetic Stimulation ; Depressive Disorder, Major ; Canada ; Clinical Competence ; Education, Medical ; Curriculum
Language	English
Publishing date	2023-03-23
Publishing country	United States
Document type	Journal Article
ZDB-ID	304227-3
ISSN	1497-0015 ; 0008-4824 ; 0706-7437
ISSN (online)	1497-0015
ISSN	0008-4824 ; 0706-7437
DOI	10.1177/07067437231164571
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Article: Transcranial magnetic stimulation in the treatment of mood disorder: a review and comparison with electroconvulsive therapy.

Hasey, G

Canadian journal of psychiatry. Revue canadienne de psychiatrie

2001 Volume 46, Issue 8, Page(s) 720–727

Abstract: Objective: To review repetitive transcranial magnetic stimulation (rTMS) as a mode of therapy for depression.: Method: The following aspects of rTMS were reviewed and compared with electroconvulsive therapy (ECT): history, basic principles, technical ...

Abstract	Objective: To review repetitive transcranial magnetic stimulation (rTMS) as a mode of therapy for depression. Method: The following aspects of rTMS were reviewed and compared with electroconvulsive therapy (ECT): history, basic principles, technical considerations, possible mode of action, safety, adverse effects, and effects on mood in both healthy individuals and those suffering from bipolar disorder (BD) or depression. Results: rTMS may selectively increase or decrease neuronal activity over discrete brain regions. As a result of this focused intervention with TMS, the potential for unwanted side effects is substantially reduced, compared with ECT. In open trials, rTMS and ECT are reported to be equally efficacious for patients having depression without psychosis, but the therapeutic benefits reported in double-blind sham-rTMS controlled trials are more modest. Conclusion: The antidepressant and antimanic effects of rTMS depend on technical considerations such as stimulus frequency, intensity, and magnetic coil placement, which may not yet be optimized. Biological heterogeneity among the patients treated with rTMS may also contribute to differing efficacy across clinical trials. rTMS may possess tremendous potential as a treatment for mood disorder, but this has not yet been realized. rTMS must still be regarded as an experimental intervention requiring further refinement.
MeSH term(s)	Aphasia/etiology ; Brain/physiopathology ; Cognition Disorders/etiology ; Depressive Disorder/physiopathology ; Depressive Disorder/psychology ; Depressive Disorder/therapy ; Electroconvulsive Therapy/adverse effects ; Electroconvulsive Therapy/methods ; Humans ; Seizures/therapy ; Transcranial Magnetic Stimulation/therapeutic use
Language	English
Publishing date	2001-10
Publishing country	United States
Document type	Comparative Study ; Journal Article ; Review
ZDB-ID	304227-3
ISSN	1497-0015 ; 0706-7437 ; 0008-4824
ISSN (online)	1497-0015
ISSN	0706-7437 ; 0008-4824
DOI	10.1177/070674370104600804
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Article ; Online: Minimum variance brain source localization for short data sequences.

Ravan, Maryam / Reilly, James P / Hasey, Gary

IEEE transactions on bio-medical engineering

2014 Volume 61, Issue 2, Page(s) 535–546

Abstract: In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization methods that rely on estimating second-order statistics often fail when the number of samples of the recorded data sequences is small in comparison to the ... ...

Abstract	In the electroencephalogram (EEG) or magnetoencephalogram (MEG) context, brain source localization methods that rely on estimating second-order statistics often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. Due to the correlated background EEG/MEG signal, an adaptive approach to localization is desirable. Previous work has addressed these issues by reducing the adaptive degrees of freedom (DoFs). This reduction results in decreased resolution and accuracy of the estimated source configuration. This paper develops and tests a new multistage adaptive processing technique based on the minimum variance beamformer for brain source localization that has been previously used in the radar statistical signal processing context. This processing, referred to as the fast fully adaptive (FFA) approach, can significantly reduce the required sample support, while still preserving all available DoFs. To demonstrate the performance of the FFA approach in the limited data scenario, simulation and experimental results are compared with two previous beamforming approaches; i.e., the fully adaptive minimum variance beamforming method and the beamspace beamforming method. Both simulation and experimental results demonstrate that the FFA method can localize all types of brain activity more accurately than the other approaches with limited data.
MeSH term(s)	Algorithms ; Brain/physiology ; Brain Mapping/methods ; Computer Simulation ; Databases, Factual ; Electroencephalography/methods ; Evoked Potentials ; Humans ; Magnetoencephalography/methods ; Signal Processing, Computer-Assisted
Language	English
Publishing date	2014-02
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	160429-6
ISSN	1558-2531 ; 0018-9294
ISSN (online)	1558-2531
ISSN	0018-9294
DOI	10.1109/TBME.2013.2283514
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Article: Transcranial magnetic stimulation: using a law of physics to treat psychopathology.

Hasey, G M

Journal of psychiatry & neuroscience : JPN

1999 Volume 24, Issue 2, Page(s) 97–101

MeSH term(s)	Electromagnetic Fields/adverse effects ; Humans ; Mental Disorders/therapy
Language	English
Publishing date	1999-03
Publishing country	Canada
Document type	Journal Article ; Review
ZDB-ID	1077443-9
ISSN	1180-4882
ISSN	1180-4882
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