Effect of Electroconvulsive Therapy on Striatal Morphometry in Major Depressive Disorder

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force

OBJECTIVES

The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD.

METHOD

A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD.

RESULTS

The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding.

CONCLUSION

Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

[Neuronal effects and biomarkers of antidepressant treatments : Current review from the perspective of neuroimaging]

Depression is one of the most frequent and disabling mental disorders worldwide and is accompanied by a severe impairment in the quality of life. There are numerous imaging studies showing differences in the volume of gray and white brain matter and function between patients suffering from depression and healthy controls. Neuroimaging studies show that pharmacotherapy and electroconvulsive therapy are accompanied by an increase of hippocampal gray matter volume while as a result of psychotherapy activity changes in the anterior cingulate cortex (ACC) have repeatedly been reported. By the identification of neuroanatomical markers, baseline volumes of the ACC have also been shown to be associated with therapy response to all treatments. The identification of such neuronal biomarkers in combination with machine learning techniques provide a promising step towards a neurobiologically based application for the prediction of treatment response.

Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal

BACKGROUND

Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we proposed a method based on machine learning to classify responders (R) and non- responders (NR) to rTMS treatment for major depression disorder (MDD) patients.

METHODS

19 electrodes resting state EEG was recorded from 46 MDD patients before treatment. Then patients underwent 7 weeks of rTMS, and 23 of them responded to treatment. Features extracted from EEG include Lempel-Ziv complexity (LZC), Katz fractal dimension (KFD), correlation dimension (CD), the power spectral density, features based on bispectrum, frontal and prefrontal cordance and combination of them. The most relevant features were selected by the minimal-redundancy-maximal-relevance (mRMR) feature selection algorithm. For classifying two groups of R and NR, k-nearest neighbors (KNN) were applied. The performance of the proposed method was evaluated by leave-1-out cross-validation. For further study, the capability of features in differentiating R and NR was investigated by a statistical test.

RESULTS

Effective EEG features for prediction of rTMS treatment response were found. EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands and CD were the most discriminative features. Power of beta classified R and NR with the high performance of 91.3% accuracy, 91.3% specificity, and 91.3% sensitivity.

LIMITATIONS

Lack of large sample size restricted our method for using in clinical applications.

CONCLUSION

This considerable high accuracy indicates that our proposed method with power and some of the nonlinear and bispectral features can lead to promising results in predicting treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording.

Subcortical shape and neuropsychological function among U.S. service members with mild traumatic brain injury

In a recent manuscript, our group demonstrated shape differences in the thalamus, nucleus accumbens, and amygdala in a cohort of U.S. Service Members with mild traumatic brain injury (mTBI). Given the significant role these structures play in cognitive function, this study directly examined the relationship between shape metrics and neuropsychological performance. The imaging and neuropsychological data from 135 post-deployed United States Service Members from two groups (mTBI and orthopedic injured) were examined. Two shape features modeling local deformations in thickness (RD) and surface area (JD) were defined vertex-wise on parametric mesh-representations of 7 bilateral subcortical gray matter structures. Linear regression was used to model associations between subcortical morphometry and neuropsychological performance as a function of either TBI status or, among TBI patients, subjective reporting of initial concussion severity (CS). Results demonstrated several significant group-by-cognition relationships with shape metrics across multiple cognitive domains including processing speed, memory, and executive function. Higher processing speed was robustly associated with more dilation of caudate surface area among patients with mTBI who reported more than one CS variables (loss of consciousness (LOC), alteration of consciousness (AOC), and/or post-traumatic amnesia (PTA)). These significant patterns indicate the importance of subcortical structures in cognitive performance and support a growing functional neuroanatomical literature in TBI and other neurologic disorders. However, prospective research will be required before exact directional evolution and progression of shape can be understood and utilized in predicting or tracking cognitive outcomes in this patient population.

Neuroimaging Biomarkers at Baseline Predict Electroconvulsive Therapy Overall Clinical Response in Depression: A Systematic Review

OBJECTIVE

Major depressive disorder is a frequent and disabling disease and can be treated with antidepressant drugs. When faced with severe or resistant major depressive disorder, however, psychiatrists may resort to electroconvulsive therapy (ECT). Although very effective, the response falls short of 100%. A recent meta-analysis established clinical and biological predictive factors of the response to ECT. We decided to explore neuroimaging biomarkers that could be predictors of the ECT response.

METHODS

We performed a systematic literature review up to January 1, 2018, using a Boolean combination of MeSH terms. We included 19 studies matching our inclusion criteria.

RESULTS

Lower hippocampal, increased amygdala, and subgenual cingulate gyrus volumes were predictive for a better ECT response. Functional magnetic resonance imaging also found that the connectivity between the dorsolateral prefrontal cortex and posterior default-mode network is predictive of increased efficacy. Conversely, deep white matter hyperintensities in basal ganglia and Virchow-Robin spaces, medial temporal atrophy, ratio of left superior frontal to left rostral middle frontal cortical thickness, cingulate isthmus thickness asymmetry, and a wide range of gray and white matter anomalies were predictive for a poorer response.

CONCLUSIONS

Our review addresses the positive or negative predictive value of neuroimaging biomarkers for the ECT response, indispensable in a personalized medicine dynamic. These data could reduce the risk of nonresponders or resistance with earlier effective management. It might also help researchers elucidate the complex pathophysiology of depressive disorders and the functioning of ECT.

Mapping abnormal subcortical neurodevelopment in a cohort of Thai children with HIV

Alterations in subcortical brain structures have been reported in adults with HIV and, to a lesser extent, pediatric cohorts. The extent of longitudinal structural abnormalities in children with perinatal HIV infection (PaHIV) remains unclear. We modeled subcortical morphometry from whole brain structural magnetic resonance imaging (1.5 T) scans of 43 Thai children with PaHIV (baseline age = 11.09±2.36 years) and 50 HIV- children (11.26±2.80 years) using volumetric and surface-based shape analyses. The PaHIV sample were randomized to initiate combination antiretroviral treatment (cART) when CD4 counts were 15-24% (immediate: n = 22) or when CD4 < 15% (deferred: n = 21). Follow-up scans were acquired approximately 52 weeks after baseline. Volumetric and shape descriptors capturing local thickness and surface area dilation were defined for the bilateral accumbens, amygdala, putamen, pallidum, thalamus, caudate, and hippocampus. Regression models adjusting for clinical and demographic variables examined between and within group differences in morphometry associated with HIV. We assessed whether baseline CD4 count and cART status or timing associated with brain maturation within the PaHIV group. All models were adjusted for multiple comparisons using the false discovery rate. A pallidal subregion was significantly thinner in children with PaHIV. Regional thickness, surface area, and volume of the pallidum was associated with CD4 count in children with PaHIV. Longitudinal morphometry was not associated with HIV or cART status or timing, however, the trajectory of the left pallidum volume was positively associated with baseline CD4 count. Our findings corroborate reports in adult cohorts demonstrating a high predilection for HIV-mediated abnormalities in the basal ganglia, but suggest the effect of stable PaHIV infection on morphological aspects of brain development may be subtle.

Social anhedonia in major depressive disorder: a symptom-specific neuroimaging approach

While research concerning brain structural biomarkers of major depressive disorder (MDD) is continuously progressing, our state of knowledge regarding biomarkers of specific clinical profiles of MDD is still limited. The aim of the present study was to investigate brain structural correlates of social anhedonia as a cardinal symptom of MDD. In a cross-sectional study, we investigated n = 166 patients with MDD and n = 166 matched healthy controls (HC) using structural magnetic resonance imaging (MRI). Social anhedonia was assessed using the Chapman Scales for Social Anhedonia (SAS). An anhedonia x group ANCOVA was performed in a region of interest approach of the dorsal and ventral striatum (bilateral caudate nucleus, putamen, nucleus accumbens respectively) as well as on whole-brain level. The analyses revealed a significant main effect for social anhedonia: higher SAS-scores were associated with reduced gray matter volume in the bilateral caudate nucleus in both the MDD-group (p_FWE = 0.002) and the HC-group (p_FWE = 0.032). The whole-brain analysis confirmed this association (left: p_FWE = 0.036, right: p_FWE = 0.047). There was no significant main effect of group and no significant anhedonia x group interaction effect. This is the first study providing evidence for volumetric aberrations in the reward system related to social anhedonia independently of diagnosis, depression severity, medication status, and former course of disease. These results support the hypothesis that social anhedonia has a brain biomarker serving as a possible endophenotype of depression and possibly providing an alternative approach for a more precise and effective treatment.

Mechanisms of Antidepressant Response to Electroconvulsive Therapy Studied With Perfusion Magnetic Resonance Imaging

BACKGROUND

Converging evidence suggests that electroconvulsive therapy (ECT) induces neuroplasticity in patients with severe depression, though how this relates to antidepressant response is less clear. Arterial spin-labeled functional magnetic resonance imaging tracks absolute changes in cerebral blood flow (CBF) linked with brain function and offers a potentially powerful tool when observing neurofunctional plasticity with functional magnetic resonance imaging.

METHODS

Using arterial spin-labeled functional magnetic resonance imaging, we measured global and regional CBF associated with clinically prescribed ECT and therapeutic response in patients (n = 57, 30 female) before ECT, after two treatments, after completing an ECT treatment "index" (∼4 weeks), and after long-term follow-up (6 months). Age- and sex-matched control subjects were also scanned twice (n = 36, 19 female), ∼4 weeks apart.

RESULTS

Patients with lower baseline global CBF were more likely to respond to ECT. Regional CBF increased in the right anterior hippocampus in all patients irrespective of clinical outcome, both after 2 treatments and after ECT index. However, hippocampal CBF increases postindex were more pronounced in nonresponders. ECT responders exhibited CBF increases in the dorsomedial thalamus and motor cortex near the vertex ECT electrode, as well as decreased CBF within lateral frontoparietal regions.

CONCLUSIONS

ECT induces functional neuroplasticity in the hippocampus, which could represent functional precursors of ECT-induced increases in hippocampal volume reported previously. However, excessive functional neuroplasticity within the hippocampus may not be conducive to positive clinical outcome. Instead, our results suggest that although hippocampal plasticity may contribute to antidepressant response in ECT, balanced plasticity in regions relevant to seizure physiology including thalamocortical networks may also play a critical role.

Orexin prevents depressive-like behavior by promoting stress resilience

Hypothalamic neuropeptide orexin has been implicated in the pathophysiology of psychiatric disorders and accumulating clinical evidence indicates a potential link between orexin and depression. However, the exact role of orexin in depression, particularly the underlying neural substrates and mechanisms, remains unknown. In this study, we reveal a direct projection from the hypothalamic orexinergic neurons to the ventral pallidum (VP), a structure that receives an increasing attention for its critical position in rewarding processing, stress responses, and depression. We find that orexin directly excites GABAergic VP neurons and prevents depressive-like behaviors in rats. Two orexin receptors, OX1R and OX2R, and their downstream Na+-Ca2+ exchanger and L-type Ca2+ channel co-mediate the effect of orexin. Furthermore, pharmacological blockade or genetic knockdown of orexin receptors in VP increases depressive-like behaviors in forced swim test and sucrose preference test. Intriguingly, blockage of orexinergic inputs in VP has no impact on social proximity in social interaction test between novel partners, but remarkably strengthens social avoidance under an acute psychosocial stress triggered by social rank. Notably, a significantly increased orexin level in VP is accompanied by an increase in serum corticosterone in animals exposed to acute stresses, including forced swimming, food/water deprivation and social rank stress, rather than non-stress situations. These results suggest that endogenous orexinergic modulation on VP is especially critical for protecting against depressive reactions to stressful events. The findings define an indispensable role for the central orexinergic system in preventing depression by promoting stress resilience.

Brain Volumetric Correlates of Right Unilateral Versus Bitemporal Electroconvulsive Therapy for Treatment-Resistant Depression

OBJECTIVE

The selection of a bitemporal (BT) or right unilateral (RUL) electrode placement affects the efficacy and side effects of ECT. Previous studies have not entirely described the neurobiological underpinnings of such differential effects. Recent neuroimaging research on gray matter volumes is contributing to our understanding of the mechanism of action of ECT and could clarify the differential mechanisms of BT and RUL ECT.

METHODS

To assess the whole-brain gray matter volumetric changes observed after treating patients with treatment-resistant depression with BT or RUL ECT, the authors used MRI to assess 24 study subjects with treatment-resistant depression (bifrontotemporal ECT, N=12; RUL ECT, N=12) at two time points (before the first ECT session and after ECT completion).

RESULTS

Study subjects receiving BT ECT showed gray matter volume increases in the bilateral limbic system, but subjects treated with RUL ECT showed gray matter volume increases limited to the right hemisphere. The authors observed significant differences between the two groups in midtemporal and subcortical limbic structures in the left hemisphere.

CONCLUSIONS

These findings highlight that ECT-induced gray matter volume increases may be specifically observed in the stimulated hemispheres. The authors suggest that electrode placement may relevantly contribute to the development of personalized ECT protocols.

A Critical Review and Synthesis of Clinical and Neurocognitive Effects of Noninvasive Neuromodulation Antidepressant Therapies

There is a plethora of current and emerging antidepressant therapies in the psychiatric armamentarium for the treatment of major depressive disorder. Noninvasive neuromodulation therapies are one such therapeutic category; they typically involve the transcranial application of electrical or magnetic stimulation to modulate cortical and subcortical brain activity. Although electroconvulsive therapy (ECT) has been used since the 1930s, with the prevalence of major depressive disorder and treatment-resistant depression (TRD), the past three decades have seen a proliferation of noninvasive neuromodulation antidepressant therapeutic development. The purpose of this critical review was to synthesize information regarding the clinical effects, neurocognitive effects, and possible mechanisms of action of noninvasive neuromodulation therapies, including ECT, transcranial magnetic stimulation, magnetic seizure therapy, and transcranial direct current stimulation. Considerable research has provided substantial information regarding their antidepressant and neurocognitive effects, but their mechanisms of action remain unknown. Although the four therapies vary in how they modulate neurocircuitry and their resultant antidepressant and neurocognitive effects, they are nonetheless useful for patients with acute and chronic major depressive disorder and TRD. Continued research is warranted to inform dosimetry, algorithm for administration, and integration among the noninvasive neuromodulation therapies and with other antidepressant strategies to continue to maximize their safety and antidepressant benefit.

Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review

BACKGROUND

No previous study has comprehensively reviewed the application of machine learning algorithms in mood disorders populations. Herein, we qualitatively and quantitatively evaluate previous studies of machine learning-devised models that predict therapeutic outcomes in mood disorders populations.

METHODS

We searched Ovid MEDLINE/PubMed from inception to February 8, 2018 for relevant studies that included adults with bipolar or unipolar depression; assessed therapeutic outcomes with a pharmacological, neuromodulatory, or manual-based psychotherapeutic intervention for depression; applied a machine learning algorithm; and reported predictors of therapeutic response. A random-effects meta-analysis of proportions and meta-regression analyses were conducted.

RESULTS

We identified 639 records: 75 full-text publications were assessed for eligibility; 26 studies (n=17,499) and 20 studies (n=6325) were included in qualitative and quantitative review, respectively. Classification algorithms were able to predict therapeutic outcomes with an overall accuracy of 0.82 (95% confidence interval [CI] of [0.77, 0.87]). Pooled estimates of classification accuracy were significantly greater (p < 0.01) in models informed by multiple data types (e.g., composite of phenomenological patient features and neuroimaging or peripheral gene expression data; pooled proportion [95% CI] = 0.93[0.86, 0.97]) when compared to models with lower-dimension data types (pooledproportion=0.68[0.62,0.74]to0.85[0.81,0.88]).

LIMITATIONS

Most studies were retrospective; differences in machine learning algorithms and their implementation (e.g., cross-validation, hyperparameter tuning); cannot infer importance of individual variables fed into learning algorithm.

CONCLUSIONS

Machine learning algorithms provide a powerful conceptual and analytic framework capable of integrating multiple data types and sources. An integrative approach may more effectively model neurobiological components as functional modules of pathophysiology embedded within the complex, social dynamics that influence the phenomenology of mental disorders.

Variations in Hippocampal White Matter Diffusivity Differentiate Response to Electroconvulsive Therapy in Major Depression

BACKGROUND

Electroconvulsive therapy (ECT) is an effective treatment for severe depression and is shown to increase hippocampal volume and modulate hippocampal functional connectivity. Whether variations in hippocampal structural connectivity occur with ECT and relate to clinical response is unknown.

METHODS

Patients with major depression (n = 36, 20 women, age 41.49 ± 13.57 years) underwent diffusion magnetic resonance imaging at baseline and after ECT. Control subjects (n = 32, 17 women, age 39.34 ± 12.27 years) underwent scanning twice. Functionally defined seeds in the left and right anterior hippocampus and probabilistic tractography were used to extract tract volume and diffusion metrics (fractional anisotropy and axial, radial, and mean diffusivity). Statistical analyses determined effects of ECT and time-by-response group interactions (>50% change in symptoms before and after ECT defined response). Differences between baseline measures across diagnostic groups and in association with treatment outcome were also examined.

RESULTS

Significant effects of ECT (all p < .01) and time-by-response group interactions (all p < .04) were observed for axial, radial, and mean diffusivity for right, but not left, hippocampal pathways. Follow-up analyses showed that ECT-related changes occurred in responders only (all p < .01) as well as in relation to change in mood examined continuously (all p < .004). Baseline measures did not relate to symptom change or differ between patients and control subjects. All measures remained stable across time in control subjects. No significant effects were observed for fractional anisotropy and volume.

CONCLUSIONS

Structural connectivity of hippocampal neural circuits changed with ECT and distinguished treatment responders. The findings suggested neurotrophic, glial, or inflammatory response mechanisms affecting axonal integrity.

Cortisol trajectory, melancholia, and response to electroconvulsive therapy

While biomarkers have been used to define pathophysiological types and to optimize treatment in many areas of medicine, in psychiatry such biomarkers remain elusive. Based on previously described abnormalities of hypothalamic-pituitary-adrenal (HPA) axis function in severe forms of depression, we hypothesized that the temporal trajectory of basal cortisol levels would vary among individuals with depression due to heterogeneity in pathophysiology, and that cortisol trajectories that reflect elevated or increasing HPA activity would predict better response to electroconvulsive therapy (ECT). To test that hypothesis, we sampled scalp hair from 39 subjects with treatment-resistant depression just before ECT. Cortisol trajectory over the 12 weeks preceding ECT was reconstructed from cortisol concentrations in sequential hair segments. Cortisol trajectories varied widely between individuals, and exploratory analyses of clinical features revealed associations with melancholia and global severity. ECT non-responders showed a decreasing trajectory (mean change -25%, 95%-CI = [-1%,-43%]) during the 8 weeks preceding ECT (group-by-time interaction, p = 0.004). The association between cortisol trajectory and subsequent ECT response was independent of clinical features. A classification algorithm showed that cortisol trajectory predicted ECT response with 80% accuracy, suggesting that this biomarker might be developed into a clinically useful test for ECT-responsive depression. In conclusion, cortisol trajectory mapped onto symptoms of melancholia and independently predicted response to ECT in this severely depressed sample. These findings deserve to be replicated in a larger sample. Cortisol trajectory holds promise as a reliable, noninvasive, inexpensive biomarker for psychiatric disorders.

SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets

Owing to the rapid and robust clinical effects, electroconvulsive therapy (ECT) represents an optimal model to develop and test treatment predictors for major depressive disorders (MDDs), whereas imaging markers can be informative in identifying MDD patients who will respond to a specific antidepressant treatment or not. Here we aim to predict post-ECT depressive rating changes and remission status using pre-ECT gray matter (GM) in 38 MDD patients and validate in two independent data sets. Six GM regions including the right hippocampus/parahippocampus, right orbitofrontal gyrus, right inferior temporal gyrus (ITG), left postcentral gyrus/precuneus, left supplementary motor area, and left lingual gyrus were identified as predictors of ECT response, achieving accuracy of 89, 90 and 86% for remission prediction in three independent, age-matched data sets, respectively. For MDD patients, GM density increases only in the left supplementary motor cortex and left postcentral gyrus/precuneus after ECT. These results suggest that treatment-predictive and treatment-responsive regions may be anatomically different but functionally related in the context of ECT response. To the best of our knowledge, this is the first attempt to quantitatively identify and validate the ECT treatment biomarkers using multi-site GM data. We address a major clinical challenge and provide potential opportunities for more effective and timely interventions for electroconvulsive treatment.

Fronto-Temporal Connectivity Predicts ECT Outcome in Major Depression

BACKGROUND

Electroconvulsive therapy (ECT) is arguably the most effective available treatment for severe depression. Recent studies have used MRI data to predict clinical outcome to ECT and other antidepressant therapies. One challenge facing such studies is selecting from among the many available metrics, which characterize complementary and sometimes non-overlapping aspects of brain function and connectomics. Here, we assessed the ability of aggregated, functional MRI metrics of basal brain activity and connectivity to predict antidepressant response to ECT using machine learning.

METHODS

A radial support vector machine was trained using arterial spin labeling (ASL) and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) metrics from n = 46 (26 female, mean age 42) depressed patients prior to ECT (majority right-unilateral stimulation). Image preprocessing was applied using standard procedures, and metrics included cerebral blood flow in ASL, and regional homogeneity, fractional amplitude of low-frequency modulations, and graph theory metrics (strength, local efficiency, and clustering) in BOLD data. A 5-repeated 5-fold cross-validation procedure with nested feature-selection validated model performance. Linear regressions were applied post hoc to aid interpretation of discriminative features.

RESULTS

The range of balanced accuracy in models performing statistically above chance was 58-68%. Here, prediction of non-responders was slightly higher than for responders (maximum performance 74 and 64%, respectively). Several features were consistently selected across cross-validation folds, mostly within frontal and temporal regions. Among these were connectivity strength among: a fronto-parietal network [including left dorsolateral prefrontal cortex (DLPFC)], motor and temporal networks (near ECT electrodes), and/or subgenual anterior cingulate cortex (sgACC).

CONCLUSION

Our data indicate that pattern classification of multimodal fMRI metrics can successfully predict ECT outcome, particularly for individuals who will not respond to treatment. Notably, connectivity with networks highly relevant to ECT and depression were consistently selected as important predictive features. These included the left DLPFC and the sgACC, which are both targets of other neurostimulation therapies for depression, as well as connectivity between motor and right temporal cortices near electrode sites. Future studies that probe additional functional and structural MRI metrics and other patient characteristics may further improve the predictive power of these and similar models.

Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a multisite study

Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches.

The antidepressant efficacy of subanesthetic-dose ketamine does not correlate with baseline subcortical volumes in a replication sample with major depressive disorder

BACKGROUND

This study sought to reproduce, in a larger sample, previous findings of a correlation between smaller raw 3-Tesla (3T) hippocampal volumes and improved antidepressant efficacy of ketamine in individuals with major depressive disorder (MDD). A secondary analysis stratified subjects according to functional BDNF rs6265 (val66met) genotype.

METHODS

Unmedicated subjects with treatment-resistant MDD ( n=55) underwent baseline structural 3T MRI. Data processing was conducted with FSL/FIRST and Freesurfer software. The amygdala, hippocampus, and thalamus were selected a priori for analysis. All subjects received a single 0.5mg/kg × 40-minute ketamine infusion. Pearson correlations were performed with subcortical volumes and percent change in MADRS score (from baseline to 230 minutes, 1 day, and 1 week post-infusion).

RESULTS

Raw and corrected subcortical volumes did not correlate with antidepressant response at any timepoint. In val/val subjects ( n=23), corrected left and right thalamic volume positively correlated with antidepressant response to ketamine at 230 minutes post-infusion but did not reach statistical significance. In met carriers ( n=14), corrected left and right thalamic volume negatively correlated with antidepressant response to ketamine.

CONCLUSION

Baseline subcortical volumes implicated in MDD did not correlate with ketamine's antidepressant efficacy. Baseline thalamic volume and BDNF genotype may be a combinatorial rapid antidepressant response biomarker.

The impact of machine learning techniques in the study of bipolar disorder: A systematic review

Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.

DATA-DRIVEN CLUSTER SELECTION FOR SUBCORTICAL SHAPE AND CORTICAL THICKNESS PREDICTS RECOVERY FROM DEPRESSIVE SYMPTOMS

Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed patients. However, prediction of which candidates are most likely to improve after ECT remains challenging. Using structural MRI data from 42 ECT patients scanned prior to ECT treatment, we developed a random forest classifier based on data-driven shape cluster selection and cortical thickness features to predict remission. Right hemisphere hippocampal shape and right inferior temporal cortical thickness was most predictive of remission, with the predicted probability of recovery decreasing when these regions were thicker prior to treatment. Remission was predicted with an average 73% balanced accuracy. Classification of MRI data may help identify treatment-responsive patients and aid in clinical decision-making. Our results show promise for the development of personalized treatment strategies.

Cortico-Striatal-Thalamic Loop Circuits of the Orbitofrontal Cortex: Promising Therapeutic Targets in Psychiatric Illness

Corticostriatal circuits through the orbitofrontal cortex (OFC) play key roles in complex human behaviors such as evaluation, affect regulation and reward-based decision-making. Importantly, the medial and lateral OFC (mOFC and lOFC) circuits have functionally and anatomically distinct connectivity profiles which differentially contribute to the various aspects of goal-directed behavior. OFC corticostriatal circuits have been consistently implicated across a wide range of psychiatric disorders, including major depressive disorder (MDD), obsessive compulsive disorder (OCD), and substance use disorders (SUDs). Furthermore, psychiatric disorders related to OFC corticostriatal dysfunction can be addressed via conventional and novel neurostimulatory techniques, including deep brain stimulation (DBS), electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), and transcranial direct current stimulation (tDCS). Such techniques elicit changes in OFC corticostriatal activity, resulting in changes in clinical symptomatology. Here we review the available literature regarding how disturbances in mOFC and lOFC corticostriatal functioning may lead to psychiatric symptomatology in the aforementioned disorders, and how psychiatric treatments may exert their therapeutic effect by rectifying abnormal OFC corticostriatal activity. First, we review the role of OFC corticostriatal circuits in reward-guided learning, decision-making, affect regulation and reappraisal. Second, we discuss the role of OFC corticostriatal circuit dysfunction across a wide range of psychiatric disorders. Third, we review available evidence that the therapeutic mechanisms of various neuromodulation techniques may directly involve rectifying abnormal activity in mOFC and lOFC corticostriatal circuits. Finally, we examine the potential of future applications of therapeutic brain stimulation targeted at OFC circuitry; specifically, the role of OFC brain stimulation in the growing field of individually-tailored therapies and personalized medicine in psychiatry.