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BACKGROUND: Many subjects with major depression (MDD) exhibit subthreshold mania symptoms (MDD+). This study investigated, for the first time, using emotional inhibition tasks, whether the neural organization of MDD+ subjects is more similar to bipolar depression (BDD) or to MDD subjects without subthreshold bipolar symptoms (MDD−). METHOD: This study included 118 medication-free young adults (15 – 30 yrs.): 20 BDD, 28 MDD+, 41 MDD− and 29 HC subjects. Participants underwent fMRI during emotional and non-emotional Go/No-go tasks during which they responded for Go stimuli and inhibited response for happy, fear, and non-emotional (gender) faces No-go stimuli. Univariate linear mixed-effects (LME) analysis for group effects and multivariate Gaussian Process Classifier (GPC) analyses were conducted. RESULTS: MDD− group compared to both the BDD and MDD+ groups, exhibited significantly lower activation in parietal, temporal and frontal regions (cluster-wise corrected p

Patients who have not responded to two antidepressant trials in their current depressive episode are usually considered to have treatment-resistant depression (TRD). A detailed search on PubMed was conducted for reported response and remission rates for non-ketamine and ketamine treatments for TRD. From a variety of pharmacological and brain stimulation open-label studies, the treatments were found to have similar response and remission rates of 30% to 70% and 20% to 50%, respectively. For randomized placebo-controlled trials, reported response and remission rates were 15% to 60% and 10% to 40%, respectively. Ketamine and electroconvulsive therapy both have response and remission rates at the higher end of these ranges, and their significant advantage is their fast action in an acute setting. Direct comparative efficacy studies with large sample sizes will be needed to establish any margin of increased benefit of one treatment modality compared to the other. [ Psychiatr Ann . 2020;50(2):62–67.]

Differentiation between Bipolar Disorder Depression (BDD) and Unipolar Major Depressive Disorder (MDD) is critical to clinical practice. This study investigated machine learning classification of BDD and MDD using graph properties of Diffusion-weighted Imaging (DWI)-based structural connectome.This study included a large number of medication-free (N =229) subjects: 60 BDD, 95 MDD, and 74 Healthy Control (HC) subjects. DWI probabilistic tractography was performed to create Fractional Anisotropy (FA) and Total Streamline (TS)-based structural connectivity matrices. Global and nodal graph properties were computed from these matrices and tested for group differences. Next, using identified graph properties, machine learning classification (MLC) between BDD, MDD, MDD with risk factors for developing BD (MDD+), and MDD without risk factors for developing BD (MDD-) was conducted.Communicability Efficiency of the left superior frontal gyrus (SFG) was significantly higher in BDD vs. MDD. In particular, Communicability Efficiency using TS-based connectivity in the left SFG as well as FA-based connectivity in the right middle anterior cingulate area was higher in the BDD vs. MDD- group. There were no significant differences in graph properties between BDD and MDD+. Direct comparison between MDD+ and MDD- showed differences in Eigenvector Centrality (TS-based connectivity) of the left middle frontal sulcus. Acceptable Area Under Curve (AUC) for classification were seen between the BDD and MDD- groups, and between the MDD+ and MDD- groups, using the differing graph properties.Graph properties of DWI-based connectivity can discriminate between BDD and MDD subjects without risk factors for BD.

Gender identity development is complex and involves several key processes. Transgender people experience incongruence between their biological and identified gender. This incongruence can cause significant impairment in overall functioning and lead to gender dysphoria (GD). The pathophysiology of GD is complex and is poorly understood. A PubMed search based on predetermined eligibility criteria was conducted to review neuropsychiatric articles focused on neurological, biological and neuroimaging aspects of gender development, transgender identity and GD. The information obtained from the literature was then used to formulize a GD model. Distinct gray matter volume and brain activation and connectivity differences were found in individuals with GD compared to controls, suggesting a neurobiological basis of GD; which leads to the concept of brain gender. Individuals with GD encounter a recurrent conflict between their brain gender and the societal feedback; which causes recurrent and ongoing cognitive dissonance, finally leading to GD and functional connectivity and activation changes in the transgender brain. GD has neurobiological basis, but it is closely associated with the individuals' interaction with the external world, their self-perception and the feedback received in return. We propose a novel model where the development of GD includes cognitive dissonance, involving anterior cingulate cortex and ventral striatum as the key brain structures. This model can be used to generate testable hypotheses using behavioral and neuroimaging techniques to understand the neuropsychobiology of GD.

Unique ethical issues arise in the provision of gender-affirming care to transgender and gender diverse people. One of the distinctive trends in transgender health care has been the development of interdisciplinary specialty teams with expertise in gender-affirming care. Clinical ethicists can play an important role on these teams in helping gender variant patients and gender-affirming providers navigate complex ethical issues, creating opportunities for enhancing patient experience, and easing provider moral uncertainty. Many opportunities exist for clinical ethicists to lend their skills to this area of clinical care. It is important for interdisciplinary transgender health care teams and other health care professionals providing transgender-specific care to understand the ethical issues involved in such care, the ways in which ethics expertise can be a resource, and the benefits and drawbacks of integrating a clinical ethicist into their team.

This study, for the first time, compared illness and antidepressant response characteristics of young subjects with major depression (MDD) at low (LRMDD) or high-risk (HRMDD) for developing bipolar disorder with characteristics of young bipolar (BPD) subjects and healthy controls (HC).One hundred and six young (15-30 yr), medication-free subjects MDD subjects (HRMDD, N = 51; LRMDD, N = 55) were compared with 32 BPD (Type I: 14; Type II: 18) as well as 49 HC subjects. Baseline illness characteristics and frequency of comorbid conditions were examined using Analysis of Variance and Cochran-Armitage trend test. Additionally, in MDD subjects, the effect of open-label antidepressant treatment for up to 24 months with periodic assessments was compared between HRMDD and LRMDD groups for treatment response, remission and (hypo)mania switch while controlling for attrition.Significant gradation from LRMDD to HRMDD to BPD groups was found for increasing occurrence of alcohol dependence (p = 0.006), comorbid PTSD (p = 0.006), borderline personality traits (p = 0.001), and occurrence of melancholic features (p 0.005). Antidepressant treatment response was similar between the two groups except that for the 12-month period HRMDD showed a trend for a lower response. Switch to (hypo)mania was infrequent in both groups though the HRMDD showed a higher occurrence of spikes in (hypo)mania symptoms (25% increase in YMRS scores)(p = 0.04).Findings of the study indicate that a substantial proportion of young MDD subjects share BPD illness characteristics. These HRMDD subjects, if treated with antidepressants, need to be monitored for development of BPD.NCT01811147.

Major Depressive Disorder (MDD) is frequently associated with risk factors for the development of Bipolar Disorder (BD). Using graph theory, we investigated brain network properties associated with BD risk factors in young MDD subjects.Resting-state fMRI was acquired from a large cohort (N= 104) of medication-free currently depressed participants (25 BD depression (BDD), 79 MDD). Lifetime mania symptom count (LMSC), current Young Mania Rating Scale (YMRS) score, and family history of mood disorders (FHMD) were examined as BD risk factors. Functional connectivity matrices from 280 regions of interests (ROIs) were first entered into the Network Based Statistic (NBS) toolbox to identify connections that varied with each risk factor. Next, within the correlated network for each risk factor, global and nodal graph properties for the top five linked nodes were calculated. Last, using identified graph properties, machine learning classification (MLC) between BDD, MDD with BD risk factors (MDD+), and without BD risk factors (MDD-) was conducted.LMSC positively correlated with left lateral orbitofrontal cortex (LOFC) Communication Efficiency and with left middle temporal Eigenvector Centrality. Current YMRS score positively correlated with right amygdala Communication Efficiency and Closeness Centrality. FHMD positively correlated with right insula Eigenvector Centrality. Acceptable MLC accuracy was seen between BDD and MDD- using middle temporal Eigenvector Centrality, whereas moderate accuracy was seen between MDD+ and MDD- using OFC Communication Efficiency.Although participants were medication-free, they were not medication-naïve.Functional connectome graph properties may serve as BD vulnerability biomarkers in young individuals with MDD.

BackgroundA significant number of subjects with major depression (MDD) exhibit subthreshold mania symptoms (MDD+). This study investigated, for the first time, using emotional inhibition tasks, whether the neurobiology of MDD+ subjects is more akin to bipolar disorder depression (BDD) or to MDD subjects without any subthreshold bipolar symptoms (MDD−).MethodThis study included 118 medication-free young adult subjects (16 – 30 yrs.): 20 BDD, 28 MDD+, 41 MDD−, and 29 HC subjects. Participants underwent fMRI during emotional and non-emotional Go/No-go tasks during which they responded for Go stimuli and inhibited response for happy, fear, emotional (happy + fear) and non-emotional (gender) faces No-go stimuli. Linear mixed effects (LME) analysis for group effects and Gaussian Process Classifier (GPC) analyses was conducted.ResultsMDD− group compared to both the BDD and MDD+ groups, exhibited significantly lower activation in parietal, temporal and frontal regions (cluster-wise corrected p ConclusionUsing an fMRI emotional Go-Nogo task, MDD− subjects can be discriminated from BDD subjects and MDD+.

We performed a cross-sectional analysis of the prevalence of psychiatric diagnoses among transgender patients in clinical care using an all-payer electronic health record database. Of 10,270 transgender patients identified, 58% (n=5940) had at least one psychiatric diagnosis compared with 13.6% (n=7,311,780) in the control patient population (p

Purpose There is an immediate need for more sustainable, effective therapies for treatment-resistant depression in patients who do not respond to traditional psychopharmacology. The aim of this study was to determine the efficacy and safety of intravenous ketamine infusions on the elderly population by using a case series of 6 geriatric patients with treatment-resistant depression. Methods Eligible patients aged 65 to 82 were given a subanesthetic ketamine hydrochloride dose of 0.5 mg/kg delivered intravenously over 40 minutes twice weekly for an acute series. If patients reported a 50% decrease in depression symptoms after the acute series of 2 to 4 infusions, they would be moved to a maintenance series of infusions, which would occur every 2 to 6 weeks on an individual basis. Results Of the 6 patients given ketamine, 1 failed to respond to the acute treatment phase, 4 responded to the acute infusion phase but failed to sustain a response after a range of 8 to 22 maintenance infusions, and 1 responded to the infusions but relapsed into alcohol use; therefore, treatment was discontinued. Conclusions The relative safety of intravenous ketamine in the elderly was demonstrated by the mild, transient adverse effects seen by this patient group. The geriatric population is unable to maintain an antidepressant response to intravenous ketamine over time, signifying that ketamine has low efficacy for the elderly.

Research on resting functional brain networks in bipolar disorder (BP) has been unable to differentiate between disturbances related to mania or depression, which is necessary to understand the mechanisms leading to each state. Past research has also been unable to elucidate the impact of BP-related network disturbances on the organizational properties of the brain (eg, communication efficiency). Thus, the present work sought to isolate network disturbances related to BP, fractionate these into components associated with manic and depressive symptoms, and characterize the impact of disturbances on network function. Graph theory was used to analyze resting functional magnetic resonance imaging data from 60 medication-free patients meeting the criteria for BP and either a current hypomanic (n=30) or depressed (n=30) episode and 30 closely age/sex-matched healthy controls. Correction for multiple comparisons was carried out. Compared with controls, BP patients evidenced hyperconnectivity in a network involving right amygdala. Fractionation revealed that (hypo)manic symptoms were associated with hyperconnectivity in an overlapping network and disruptions in the brain's ‘small-world' network organization. Depressive symptoms predicted hyperconnectivity in a network involving orbitofrontal cortex along with a less resilient global network organization. Findings provide deeper insight into the differential pathophysiological processes associated with hypomania and depression, along with the particular impact these differential processes have on network function.

Major depressive disorder (MDD) is the most common mental illness and the leading cause of disability worldwide. Electroconvulsive therapy (ECT) is the most effective treatment for MDD and the gold-standard therapy for treatment-resistant depression (TRD), yet it remains underutilized due to factors such as limited availability, stigma, and concerns about cognitive side effects. Ketamine has emerged as the first rapid-acting antidepressant and shows robust short-term efficacy in clinical trials, but there are concerns about its long-term safety and efficacy. While response rates are similar between ECT and ketamine in clinical trials, these treatments have never been compared head-to-head in a sufficiently large, well-powered randomized study. Here we describe the study protocol for ELEctroconvulsive therapy (ECT) vs. Ketamine in patients with Treatment-resistant Depression (ELEKT-D), a non-inferiority, comparative effectiveness trial. Patients with TRD seeking clinical treatment are randomized (1:1) to receive ECT (thrice weekly) or intravenous ketamine (twice weekly) for 3–5 weeks. The primary outcome is the proportion of responders in each group at the end of study visit, as measured by a patient-reported outcome measure (Quick Inventory of Depressive Symptomatology-Self Report). The study is powered such that the non-inferiority margin allows for ketamine to retain 90% of the ECT treatment effect, with a projected sample size of 400 patients (200 per group). Secondary outcomes include remission rates, depression severity, cognitive functioning, quality of life, adverse events, and tolerability. The results of the ELEKT-D study will have important implications for patient choice, clinical practice, and health insurance policies.

Background This study, for the first time, investigated lithium monotherapy associated effects on amygdala- ventromedial prefrontal cortex (vMPFC) resting-state functional connectivity and correlation with clinical improvement in bipolar disorder (BP) Methods Thirty-six medication-free subjects − 24 BP (12 hypomanic BPM) and 12 depressed (BPD)) and 12 closely matched healthy controls (HC), were included. BP subjects were treated with lithium and scanned at baseline, after 2 weeks and 8 weeks. HC were scanned at same time points but were not treated. The effect of lithium was studied for the BP group as a whole using two way (group, time) ANOVA while regressing out effects of state. Next, correlation between changes in amygdala-vMPFC resting-state connectivity and clinical global impression (CGI) of severity and improvement scale scores for overall BP illness was calculated. An exploratory analysis was also conducted for the BPD and BPM subgroups separately. Results Group by time interaction revealed that lithium monotherapy in patients was associated with increase in amygdala-medial OFC connectivity after 8 weeks of treatment (p = 0.05 (cluster-wise corrected)) compared to repeat testing in healthy controls. Increased amygdala-vMPFC connectivity correlated with clinical improvement at week 2 and week 8 as measured with the CGI-I scale. Limitations The results pertain to open-label treatment and do not account for non-treatment related improvement effects. Only functional connectivity was measured which does not give information regarding one regions effect on the other. Conclusions Lithium monotherapy in BP is associated with modulation of amygdala-vMPFC connectivity which correlates with state-independent global clinical improvement.

This study investigated extended release quetiapine (quetiapine XR) associated changes in functional MRI (fMRI) measures of task-induced amygdalar activation and resting state connectivity in anxious unipolar major depressive disorder (AMDD).Anxious unipolar major depressive disorder patients (n = 15) (17-item Hamilton Depression Rating Scale (HAM-D)18 and Hamilton Anxiety Scale (HAM-A)18) and closely matched healthy control (HC) subjects were compared at baseline for task induced amygdala activation and resting state connectivity on fMRI. Subsequently, AMDD patients were treated for 8 weeks with open-label quetiapine XR. Weekly HAM-D and HAM-A ratings were obtained, and the fMRI scan was repeated at weeks 2 and 8. Changes in fMRI measures were calculated using repeated-measures analysis of variance and correlation with decrease in HAM-D and HAM-A scores was examined.At baseline, AMDD compared with HC exhibited increased task-induced left amygdalar activation (P = 0.05 clusterwise corrected) and decreased resting state amygdala-cortical and amygdala-pons connectivity (P0.05 clusterwise corrected). Quetiapine XR treatment was associated with significant decrease in HAM-D (df = 1,28; female [F] = 39; P = 0.001) and HAM-A scores (df = 1,28; F = 55; P = 0.001). The AMDD group showed increased amygdala-cortical connectivity (P0.05 [clusterwise corrected]) at week 2, which was maintained at week 8. At week 8, additional areas showed increased connectivity including insula and putamen. At 8 weeks, decrease in HAM-D scores correlated with increase in amygdala-mid cingulate and amygdala-cuneus connectivity (P = 0.05 [clusterwise corrected]). Decrease in HAM-A scores correlated with increase in amygdala-cuneus and parietal cortex connectivity (P = 0.05 [clusterwise corrected]).Small sample-size, open-label single-arm design, HC only tested at baseline, focused only on amygdala.Quetiapine XR effects in the treatment of AMDD are associated with modulation of amygdala connectivity.

Bipolar disorder (BP) is characterized by periods of depression (BPD) and (hypo)mania (BPM), but the underlying state-related brain circuit abnormalities are not fully understood. Striatal functional activation and connectivity abnormalities have been noted in BP, but consistent findings have not been reported. To further elucidate striatal abnormalities in different BP states, this study investigated differences in resting-state functional connectivity of six striatal subregions in BPD, BPM, and healthy control (HC) subjects. Ninety medication-free subjects (30 BPD, 30 BPM, and 30 HC), closely matched for age and gender, were scanned using 3T functional magnetic resonance imaging (fMRI) acquired at resting state. Correlations of low-frequency blood oxygen level dependent signal fluctuations for six previously described striatal subregions were used to obtain connectivity maps of each subregion. Using a factorial design, main effects for differences between groups were obtained and post hoc pairwise group comparisons performed. BPD showed increased connectivity of the dorsal caudal putamen with somatosensory areas such as the insula and temporal gyrus. BPM group showed unique increased connectivity between left dorsal caudate and midbrain regions, as well as increased connectivity between ventral striatum inferior and thalamus. In addition, both BPD and BPM exhibited widespread functional connectivity abnormalities between striatal subregions and frontal cortices, limbic regions, and midbrain structures. In summary, BPD exhibited connectivity abnormalities of associative and somatosensory subregions of the putamen, while BPM exhibited connectivity abnormalities of associative and limbic caudate. Most other striatal subregion connectivity abnormalities were common to both groups and may be trait related.

Vagus nerve stimulation (VNS) appears to be a safe and reasonably effective treatment modality and represents a new addition to the toolbox of the clinician in treating patients with severe, recurrent unipolar, and bipolar affective disorders. VNS is a bottom-up approach (brain stem to cortex) in treating depression. It modulates the neural circuitry of depression by stimulating vagal afferent fibers in the neck, which carry impulses to the brain stem and target structures involved in mood regulation, including the noradrenergic locus ceruleus and the serotonergic dorsal raphe nucleus.

Major depressive disorder is a serious medical illness which is responsible for considerable morbidity and disability. Despite decades of research, the neural basis for depression is still incompletely understood. In this review, evidence from neuroimaging, neuropsychiatric and brain stimulations studies are explored to answer the question regarding the localization of depression in the brain. Neuroimaging studies indicate that although many regions of the brain have been repeatedly implicated in the pathophysiology of depression, not many consistent findings have been found until present. In recent times, the focus of neuroimaging has shifted from regional brain abnormalities to circuit level connectivity abnormalities. However, connectivity models are inherently more complicated, and the validity of these models remains to be tested. Neuropsychiatric studies of illnesses such as Parkinson's disease and stroke provide promising clues regarding areas involved in depression, but again consistent findings are rare. Similarly, stimulation of a variety of brain regions and circuits has been reported as being effective in depression. Therefore, the current knowledge indicates that the pathophysiology of depression may be distributed across many brain regions and circuits. In future studies, this distributed nature of depression needs to be further investigated, primary and secondary areas affected need to be identified, and new paradigms to explain complex mental functions need to be explored.

Measurement of brain change due to neurodegenerative disease and treatment is one of the fundamental tasks of neuroimaging. Deformation-based morphometry (DBM) has been long recognized as an effective and sensitive tool for estimating the change in the volume of brain regions over time. This paper demonstrates that a straightforward application of DBM to estimate the change in the volume of the hippocampus can result in substantial bias, i.e., an overestimation of the rate of change in hippocampal volume. In ADNI data, this bias is manifested as a non-zero intercept of the regression line fitted to the 6 and 12 month rates of hippocampal atrophy. The bias is further confirmed by applying DBM to repeat scans of subjects acquired on the same day. This bias appears to be the result of asymmetry in the interpolation of baseline and followup images during longitudinal image registration. Correcting this asymmetry leads to bias-free atrophy estimation.

Electroconvulsive therapy (ECT) is recommended by the American Psychiatric Association Task Force on ECT as a safe and effective treatment of depression throughout pregnancy. We report here administration of ECT in the third trimester of pregnancy in a 33-year-old patient with severe bipolar depression. The patient had a good antidepressant response to ECT. She experienced, however, delayed onset premature uterine contractions at home after her sixth session of ECT (10 hours post-ECT administration). After receiving tocolytics, the patient's contractions did not progress to premature labor. In consultation with the obstetrics team, it was decided to terminate the ECT course earlier than planned. The patient is delivered of a healthy female newborn infant spontaneously at 37 weeks' gestational age. Four months after delivery, the baby's development is progressing normally. This case illustrates that premature contractions in association with ECT during the third trimester of pregnancy may be delayed in onset. Patients and treatment team need to be aware of this possibility, particularly when ECT is conducted on an outpatient basis.

Automatic segmentation using multi-atlas label fusion has been widely applied in medical image analysis. To simplify the label fusion problem, most methods implicitly make a strong assumption that the segmentation errors produced by different atlases are uncorrelated. We show that violating this assumption significantly reduces the efficiency of multi-atlas segmentation. To address this problem, we propose a regression-based approach for label fusion. Our experiments on segmenting the hippocampus in magnetic resonance images (MRI) show significant improvement over previous label fusion techniques.

Multi-atlas based segmentation has been applied widely in medical image analysis. For label fusion, previous studies show that image similarity-based local weighting techniques produce the most accurate results. However, these methods ignore the correlations between results produced by different atlases. Furthermore, they rely on pre-selected weighting models and ad hoc methods to choose model parameters. We propose a novel label fusion method to address these limitations. Our formulation directly aims at reducing the expectation of the combined error and can be efficiently solved in a closed form. In our hippocampus segmentation experiment, our method significantly outperforms similarity-based local weighting. Using 20 atlases, we produce results with 0.898 +/- 0.019 Dice overlap to manual labelings for controls.

We develop a new algorithm to segment the hippocampus from MR images. Our method uses a new classifier ensemble algorithm to correct segmentation errors produced by a multi-atlas based segmentation method. Our classifier ensemble algorithm searches for the maximum likelihood solution via gradient ascent optimization. Compared to the additive regression based algorithm, LogitBoost, our algorithm avoids the numerical instability problem. Experiments on a hippocampus segmentation problem using the ADNI data show that our algorithm consistently converges faster and generalizes better than AdaBoost.

We propose a simple but generally applicable approach to improving the accuracy of automatic image segmentation algorithms relative to manual segmentations. The approach is based on the hypothesis that a large fraction of the errors produced by automatic segmentation are systematic, i.e., occur consistently from subject to subject, and serves as a wrapper method around a given host segmentation method. The wrapper method attempts to learn the intensity, spatial and contextual patterns associated with systematic segmentation errors produced by the host method on training data for which manual segmentations are available. The method then attempts to correct such errors in segmentations produced by the host method on new images. One practical use of the proposed wrapper method is to adapt existing segmentation tools, without explicit modification, to imaging data and segmentation protocols that are different from those on which the tools were trained and tuned. An open-source implementation of the proposed wrapper method is provided, and can be applied to a wide range of image segmentation problems. The wrapper method is evaluated with four host brain MRI segmentation methods: hippocampus segmentation using FreeSurfer ( Fischl et al., 2002 ); hippocampus segmentation using multi-atlas label fusion ( Artaechevarria et al., 2009 ); brain extraction using BET ( Smith, 2002 ); and brain tissue segmentation using FAST ( Zhang et al., 2001 ). The wrapper method generates 72%, 14%, 29% and 21% fewer erroneously segmented voxels than the respective host segmentation methods. In the hippocampus segmentation experiment with multi-atlas label fusion as the host method, the average Dice overlap between reference segmentations and segmentations produced by the wrapper method is 0.908 for normal controls and 0.893 for patients with mild cognitive impairment. Average Dice overlaps of 0.964, 0.905 and 0.951 are obtained for brain extraction, white matter segmentation and gray matter segmentation, respectively.

We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.

We develop a semi-automatic technique for segmentation of hippocampal subfields in T2-weighted in vivo brain MRI. The technique takes the binary segmentation of the whole hippocampus as input, and automatically labels the subfields inside the hippocampus segmentation. Shape priors for the hippocampal subfields are generated from shape-based normalization of whole hippocampi via the continuous medial representation method. To combine the shape priors with appearance features, we use a machine learning based method. The key novelty is that we treat the mistakes made by the shape priors as bias, which can be detected and corrected via learning. The main advantage of this formulation is that it significantly simplifies the learning problem by taking full advantage of current segmentations and focusing on only improving their drawbacks. Experiments show that the bias removal approach achieves significant improvement in all subfields. Our bias removal idea is general, and can be applied to improve other segmentation methods as well.

To the Editor: We agree with several excellent points raised by Niciu and colleagues: 1) the case report that we presented was a description of possible induction of mania in a patient receiving ketamine therapy. Because it was a case report, several issues could not be controlled for, including toxic or withdrawal effects of medications that the patient was receiving/using; 2) the data presented from controlled studies of effects of ketamine in depression is likely to be much more reliable; 3) an increase in manic symptoms is likely in bipolar (particularly bipolar I) patients, if antidepressants are not given under the cover of mood stabilizer medication. However, despite there being insufficient evidence from 98 patients studied with ketamine by the National Institutes of Health group, there are several indications that ketamine, like other antidepressants, could potentially induce mania. At least one bipolar II patient in the ketamine study developed mania despite being on lithium, and two patients in the ketamineriluzole study developed transitory mania-like symptoms. If ketamine is classified as an antidepressant, then there is no reason to think that it would be less likely than a traditional