Prediction of Treatment Outcome in Patients with Obsessive-Compulsive Disorder with Low-Resolution Brain Electromagnetic Tomography: A Prospective EEG Study

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.

Deep Transcranial Magnetic Stimulation Effects on the Electrophysiological Parameters in Obsessive-Compulsive Disorder

Backgrounds. Deep Transcranial Magnetic Stimulation (dTMS) is a non-invasive treatment cleared by FDA as a safe and efficient intervention for the treatment of depression and obsessive-compulsive disorder (OCD). Objectives. In this retrospective single-center study, the effects of dTMS on the electrophysiological parameters and the clinical outcomes of patients with OCD were tested. Methods. Thirty sessions of dTMS were administered to 29 OCD patients (15 female and 14 male). Quantitative electroencephalography (QEEG) recordings and Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) were measured at baseline and endpoint. Paired sample t-test was used to measure the change in Y-BOCS scores and QEEG activity after dTMS practice. Results. All 29 patients responded to the dTMS intervention by indicating at least 35% reduction in Y-BOCS scores. QEEG recordings revealed a significant decrease in theta, alpha and the beta rhythms. The decrease in the severity of OCD symptoms correlated with the decrease in beta activity at left central region. Conclusions. Historically, excess fast oscillations in OCD are correlated with the unresponsiveness to selective serotonin reuptake inhibitor (SSRI) treatment. We hypothesize that the decrease in the power of beta bands by deep TMS is related to the mechanism of the therapeutic response.

The Role of Neurophysiological Biomarkers in Obsessive-Compulsive Disorder

BACKGROUND

Obsessive-compulsive disorder is a highly debilitating psychiatric disorder with a high rate of treatment resistance. Biomarkers for obsessive-compulsive disorder may assist clinicians by predicting response to treatments and prognosis.

OBJECTIVE

To review the literature with regards to two of the more easily ascertainable and relatively inexpensive physiological biomarkers, i.e. heart rate variability and electroencephalography.

METHODS

Narrative review of the literature.

RESULTS

Decreased heart rate variability has been associated with increased symptom severity of obsessive-compulsive disorder. Findings from electroencephalography have also predicted response to pharmacotherapy and it is likely that biomarkers for OCD will have their greatest utility in predicting response to different pharmacological agents. However, the number of studies is small and results are inconsistent.

CONCLUSIONS

More research is required to determine whether heart rate variability and electrophysiological studies have a clinical role as biomarkers for obsessive-compulsive disorder.

A Diagnostic Strategy via Multiresolution Synchrosqueezing Transform on Obsessive Compulsive Disorder

This research presents a new method for detecting obsessive-compulsive disorder (OCD) based on time-frequency analysis of multi-channel electroencephalogram (EEG) signals using the multi-variate synchrosqueezing transform (MSST). With the evolution of multi-channel sensor implementations, the employment of multi-channel techniques for the extraction of features arising from multi-channel dependency and mono-channel characteristics has become common. MSST has recently been proposed as a method for modeling the combined oscillatory mechanisms of multi-channel signals. It makes use of the concepts of instantaneous frequency (IF) and bandwidth. Electrophysiological data, like other nonstationary signals, necessitates both joint time-frequency analysis and independent time and frequency domain studies. The usefulness and effectiveness of a multi-variate, wavelet-based synchrosqueezing algorithm paired with a band extraction method are tested using electroencephalography data obtained from OCD patients and control groups in this research. The proposed methodology yields substantial results when analyzing differences between patient and control groups.

Electroencephalographic Correlates and Predictors of Treatment Outcome in OCD: A Brief Narrative Review

Electroencephalography (EEG) measures the brain's electrical activity with high temporal resolution. In comparison to neuroimaging modalities such as MRI or PET, EEG is relatively cheap, non-invasive, portable, and simple to administer, making it an attractive tool for clinical deployment. Despite this, studies utilizing EEG to investigate obsessive-compulsive disorder (OCD) are relatively sparse. This contrasts with a robust literature using other brain imaging methodologies. The present review examines studies that have used EEG to examine predictors and correlates of response in OCD and draws tentative conclusions that may guide much needed future work. Key findings include a limited literature base; few studies have attempted to predict clinical change from EEG signals, and they are confounded by the effects of both pharmacotherapy and psychotherapy. The most robust literature, consisting of several studies, has examined event-related potentials, including the P300, which several studies have reported to be abnormal at baseline in OCD and to normalize with treatment; but even here the literature is quite heterogeneous, and more work is needed. With more robust research, we suggest that the relatively low cost and convenience of EEG, especially in comparison to fMRI and PET, make it well-suited to the development of feasible personalized treatment algorithms.

Use of EEG for Predicting Treatment Response to Transcranial Magnetic Stimulation in Obsessive Compulsive Disorder

Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be helpful before deciding about treatment strategy in OCD. The limitations of our study are moderate sample size and limited number of nonresponsive patients and that treatment response was defined by clinicians and not by using a formal symptom measurement scale. Future studies with larger samples and prospective design would show the role of qEEG in predicting TMS response better.

Prediction of treatment resistance in obsessive compulsive disorder patients based on EEG complexity as a biomarker

OBJECTIVE

This study aimed to identify an Electroencephalography (EEG) complexity biomarker that could predict treatment resistance in Obsessive compulsive disorder (OCD) patients. Additionally, the statistical differences between EEG complexity values in treatment-resistant and treatment-responsive patients were determined. Moreover, the existence of correlations between EEG complexity and Yale-Brown Obsessive Compulsive Scale (YBOCS) score were evaluated.

METHODS

EEG data for 29 treatment-resistant and 28 treatment-responsive OCD patients were retrospectively evaluated. Approximate entropy (ApEn) method was used to extract the EEG complexity from both whole EEG data and filtered EEG data, according to 4 common frequency bands, namely delta, theta, alpha, and beta. The random forests method was used to classify ApEn complexity.

RESULTS

ApEn complexity extracted from beta band EEG segments discriminated treatment-responsive and treatment-resistant OCD patients with an accuracy of 89.66% (sensitivity: 89.44%; specificity: 90.64%). Beta band EEG complexity was lower in the treatment-resistant patients and the severity of OCD, as measured by YBOCS score, was inversely correlated with complexity values.

CONCLUSIONS

The results indicate that, EEG complexity could be considered a biomarker for predicting treatment response in OCD patients.

SIGNIFICANCE

The prediction of treatment response in OCD patients might help clinicians devise and administer individualized treatment plans.

Tourette syndrome research highlights from 2018

This is the fifth yearly article in the Tourette Syndrome Research Highlights series, summarizing research from 2018 relevant to Tourette syndrome and other tic disorders. The authors briefly summarize reports they consider most important or interesting. The  highlights from 2019 article is being drafted on the Authorea online authoring platform, and readers are encouraged to add references or give feedback on our selections using the comments feature on that page. After the calendar year ends, the article is submitted as the annual update for the  Tics collection on F1000Research.

Time estimation and beta segregation: An EEG study and graph theoretical approach

Elucidation of the neural correlates of time perception constitutes an important research topic in cognitive neuroscience. The focus to date has been on durations in the millisecond to seconds range, but here we used electroencephalography (EEG) to examine brain functional connectivity during much longer durations (i.e., 15 min). For this purpose, we conducted an initial exploratory experiment followed by a confirmatory experiment. Our results showed that those participants who overestimated time exhibited lower activity of beta (18–30 Hz) at several electrode sites. Furthermore, graph theoretical analysis indicated significant differences in the beta range (15–30 Hz) between those that overestimated and underestimated time. Participants who underestimated time showed higher clustering coefficient compared to those that overestimated time. We discuss our results in terms of two aspects. FFT results, as a linear approach, are discussed within localized/dedicated models (i.e., scalar timing model). Second, non-localized properties of psychological interval timing (as emphasized by intrinsic models) are addressed and discussed based on results derived from graph theory. Results suggested that although beta amplitude in central regions (related to activity of BG-thalamocortical pathway as a dedicated module) is important in relation to timing mechanisms, the properties of functional activity of brain networks; such as the segregation of beta network, are also crucial for time perception. These results may suggest subjective time may be created by vector units instead of scalar ticks.

EEG correlates of induced anxiety in obsessive-compulsive patients: comparison of autobiographical and general anxiety scenarios

BACKGROUND

The underlying symptomatology of obsessive-compulsive disorder (OCD) can be viewed as an impairment in both cognitive and behavioral inhibition, regarding difficult inhibition of obsessions and behavioral compulsions. Converging results from neuroimaging and electroencephalographic (EEG) studies have identified changes in activities throughout the medial frontal and orbital cortex and subcortical structures supporting the cortico-striato-thalamo-cortical circuit model of OCD. This study aimed to elucidate the electrophysiological changes induced by autobiographical and general anxiety scenarios in patients with OCD.

METHODS

Resting-state eyes-closed EEG data were recorded in 19 OCD patients and 15 healthy controls. Cortical EEG sources were estimated by standardized low-resolution electromagnetic tomography (sLORETA). The changes in the emotional state were induced by two different scenarios: the autobiographical script related to patient's OCD symptoms and the script triggering general anxiety.

RESULTS

During the resting state, we proved increased delta activity in the frontal, limbic and temporal lobe and the sub-lobar area in OCD patients. In a comparison of neural activities during general anxiety in OCD patients and the control group, we proved an increase in delta (parietal, temporal, occipital, frontal and limbic lobes, and sub-lobal area), theta (temporal, parietal and occipital lobes) and alpha-1 activities (parietal lobe). Finally, we explored the neural activity of OCD patients during exposure to the autobiographic scenario. We proved an increase in beta-3 activity (left frontal lobe).

CONCLUSION

Our study proved differences in neural activation in OCD patients and healthy controls during imagination of general anxiety. Exposure to the autobiographic OCD scenario leads to activation of left frontal brain areas. The results show the possibility of using specific scenarios in OCD therapy.