Gary Hasey

Evolved mechanisms in depression: the role and interaction of attachment and social rank in depression

Evolved mechanisms underpinning attachment and social rank behavior may be the basis for some forms of major depression, especially those associated with chronic stress. We note the heterogeneity of depression, but suggest that some of its core symptoms, such as behavioral withdrawal, low self-esteem and anhedonia, may have evolved in order to regulate behavior and mood and convey sensitivity to threats and safety. Focusing on the evolved mental mechanisms for attachment and social rank helps to make sense of (1) depressions common early vulnerability factors (e.g., attachment disruptions, neglect and abuse), (2) the triggering events (e.g., loss of close relationships, being defeated and/or trapped in low socially rewarding or hostile environments), and (3) the psychological preoccupations of depressed people (e.g., sense of unlovableness, self as inferior and a failure). This focus offers clues as to how these two systems interact and on how to intervene.

Comorbidity of obsessive compulsive disorder in bipolar disorder

The comorbidity of OCD and bipolar disorder has not been systematically examined. Therefore, we determined the frequency of patients meeting DSM-III criteria for OCD syndrome in a sample of 149 inpatients with DSM-III major affective disorder who had received a clinically reviewed structured diagnostic interview. The frequency of OCD syndrome was not significantly different between subjects with major depression (35.2%, n = 105) and bipolar disorder (35.1%, n = 37). This suggests that OCD is equally common in bipolar as in unipolar patients.

Transcranial magnetic stimulation in the treatment of mood disorder: a review and comparison with electroconvulsive therapy.

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.

Gabapentin as an adjunctive treatment in bipolar disorder.

OBJECTIVE To evaluate the efficacy of gabapentin as an adjunctive treatment for bipolar disorder in both depressed and manic phases. METHOD Thirty seven patients with bipolar type I or II with or without a rapid cycling course were openly treated with gabapentin added to current treatment for up to six months. Mood symptoms were rated weekly for 12 weeks then monthly for 3 months utilizing the HamD and YMS. RESULTS Participants experienced a significant reduction in both depressive and manic symptoms. CONCLUSIONS These findings are consistent with others in establishing the efficacy of gabapentin in both phases of bipolar disorder. LIMITATIONS Small sample size and the use of an open uncontrolled design limit interpretation of results.

The cholinergic-adrenergic hypothesis of depression reexamined using clonidine, metoprolol, and physostigmine in an animal model.

The role of central nervous system (CNS) cholinergic and noradrenergic mechanisms in the pathogenesis of depression and hypothalamic-pituitary-adrenal (HPA) axis hyperactivity is examined using the Behavioral Despair rat model of depression. Immobility (IM), the analog of depression in this model, and plasma corticosterone (C) were increased by physostigmine (PHYSO). Neostigmine (NEO), which does not cross the blood-brain barrier, produced the same peripheral cholinomimetic effects and motor inhibition as PHYSO, but did not change IM. PHYSOs effects on C and IM were blocked by metoprolol pretreatment and partially blocked by clonidine pretreatment. PHYSO increased acetylcholine in the striatum. In this animal model of depression, cholinergic and noradrenergic mechanisms are interactively involved in the regulation of behavioral depression and the HPA axis.

Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression

We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes. A leave-2-out (L2O) cross-validation procedure is used to evaluate the prediction performance. This pilot study involves 27 subjects who received either left high-frequency (HF) active rTMS therapy or simultaneous left HF and right low-frequency active rTMS therapy. Our results indicate that it is possible to predict rTMS treatment efficacy of either treatment modality with a specificity of 83% and a sensitivity of 78%, for a combined accuracy of 80%.

Using pre-treatment EEG data to predict response to SSRI treatment for MDD

The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we propose a machine learning (ML) methodology to predict the response to a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD), using pre-treatment electroencephalograph (EEG) measurements. The proposed feature selection technique is a modification of the method of Peng et al [10] that is based on a Kullback-Leibler (KL) distance measure. The classifier was realized as a kernelized partial least squares regression procedure, whose output is the predicted response. A low-dimensional kernelized principal component representation of the feature space was used for the purposes of visualization and clustering analysis. The overall method was evaluated using an 11-fold nested cross-validation procedure for which over 85% average prediction performance is obtained. The results indicate that ML methods hold considerable promise in predicting the efficacy of SSRI antidepressant therapy for major depression.

Diagnosis of psychiatric disorders using EEG data and employing a statistical decision model

An automated diagnosis procedure based on a statistical machine learning methodology using electroencephalograph (EEG) data is proposed for diagnosis of psychiatric illness. First, a large collection of candidate features, mostly consisting of various statistical quantities, are calculated from the subjects EEG. This large set of candidate features is then reduced into a much smaller set of most relevant features using a feature selection procedure. The selected features are then used to evaluate the class likelihoods, through the use of a mixture of factor analysis (MFA) statistical model [7]. In a training set of 207 subjects, including 64 subjects with major depressive disorder (MDD), 40 subjects with chronic schizophrenia, 12 subjects with bipolar depression and 91 normal or healthy subjects, the average correct diagnosis rate attained using the proposed method is over 85%, as determined by various cross-validation experiments. The promise is that, with further development, the proposed methodology could serve as a valuable adjunctive tool for the medical practitioner.

Repetitive transcranial magnetic stimulation for treatment of medication-resistant depression in older adults: a case series.

The antidepressant effects of repetitive transcranial magnetic stimulation (rTMS) are well documented, but studies to date have produced heterogeneous results in late-life depression. Objective: To address this matter, we evaluated the efficacy of both high- and low-frequency rTMS delivered to the prefrontal cortex of older adults with treatment-resistant major depression. Methods: Forty-nine older adults (69 ± 6.7 years) with treatment-refractory major depressive disorders underwent a series of rTMS treatments as an adjuvant to pharmacotherapy. Patients received high-frequency rTMS delivered to the left dorsolateral prefrontal cortex, low-frequency stimulation to the right dorsolateral prefrontal cortex, or a combination thereof, at 80-110% of the motor threshold. Results: There was a modest, but statistically significant, mean reduction (24.7%) in Hamilton Depression Rating Scale (HDRS) scores from baseline to the end of treatment. Nine patients were classified as responders (50% HDRS reduction), and 4 patients reached remission status (final HDRS score <8). Similar improvements in HDRS scores were observed for high- and low-frequency rTMS. Treatment was generally well tolerated, and no serious adverse effects were reported. Conclusions: The findings support the contention that in older adults with treatment-refractory depression, rTMS can be an effective treatment alternative for some patients.

Effects of electroconvulsive therapy on cognitive functioning in patients with depression: protocol for a systematic review and meta-analysis.

Introduction Depression is the leading cause of disability worldwide, affecting approximately 350 million people. Evidence indicates that only 60–70% of persons with major depressive disorder who tolerate antidepressants respond to first-line drug treatment; the remainder become treatment resistant. Electroconvulsive therapy (ECT) is considered an effective therapy in persons with treatment-resistant depression. The use of ECT is controversial due to concerns about temporary cognitive impairment in the acute post-treatment period. We will conduct a meta-analysis to examine the effects of ECT on cognition in persons with depression. Methods This systematic review and meta-analysis has been registered with PROSPERO (registration number: CRD42014009100). We developed our methods following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. We are searching MEDLINE, PsychINFO, EMBASE, CINAHL and Cochrane from the date of database inception to the end of October 2014. We are also searching the reference lists of published reviews and evidence reports for additional citations. Comparative studies (randomised controlled trials, cohort and case–control) published in English will be included in the meta-analysis. Three clinical neuropsychologists will group the cognitive tests in each included article into a set of mutually exclusive cognitive subdomains. The risk of bias of randomised controlled trials will be assessed using the Jadad scale. We will supplement the Jadad scale with additional questions based on the Cochrane risk of bias tool. The risk of bias of cohort and case–control studies will be assessed using the Newcastle-Ottawa Scale. We will employ the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) to assess the strength of evidence. Statistical analysis Separate meta-analyses will be conducted for each ECT treatment modality and cognitive subdomain using Comprehensive Meta-Analysis V.2.0.