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SummaryAutomatic segmentation was performed on T1-MPRAGE structural MRI data acquired at 3T and 7T from 37 and 69 distinct healthy controls, respectively. Additionally, segmentation was performed on imaging acquired from 215 major depressive disorder (MDD) patients at 3T and 40 MDD patients at 7T. Of 259 segmentation-derived imaging features evaluated, 120 showed significant 3T vs. 7T differences among controls, and 153 among patients. 7T imaging metrics showed consistently lower cortical thickness and cortical gray/white matter ratios. Subcortical and cortical volumes measured at 7T were more mixed, with 7T images showing greater frontal lobe volume, but lower cortical volumes elsewhere.

Importance: Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Identifying neuroimaging-based biomarkers might aid in defining the disease-related dimensions that characterize MDD and predict treatment response. Objective: To investigate the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressant or placebo. Design: Big data consortium (COORDINATE-MDD) sharing raw MRI data in first episode and recurrent MDD, deep clinical phenotyping, and state-of-the art machine learning analysis, involving harmonization of multi-center MRI data and the application of semi-supervised machine learning clustering, HYDRA, to regional brain volumes. Setting: International, multi-center, community-based MDD and healthy controls. Participants: International sample (N=1384), consisting of medication-free, first episode and recurrent MDD individuals (N=685) in a current depressive episode of moderate to severe intensity, that is not treatment resistant depression, and healthy controls (N=699). Prospective longitudinal treatment response data were available in a subset of MDD individuals (N=359 MDD). Treatments were SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Treatment duration was 6-8 weeks, and symptom severity was measured by clinician-rated scales. Main outcomes: First episode and recurrent MDD is optimally characterized by two neuroanatomical dimensions, which show distinct treatment effects to placebo and SSRI antidepressant medications. Results: Dimension 1 is characterized by preserved gray and white matter (N=290 MDD), whereas Dimension 2 is characterized by widespread subtle reductions in gray and white matter (N=395 MDD) relative to healthy controls. There are no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, but there is a significant dimension by treatment response interaction effect. Dimension 1 shows a significant decrease in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%), whereas Dimension 2 shows a comparable improvement to either SSRI (46.9%) or placebo (42.2%) (β=-18.3, 95% CI (-0.34 to -0.2), p=0.03). Conclusions and Relevance: Neuroimaging-based markers may aid in characterizing the MDD dimensions that predict treatment response. In an iterative process, we can characterize the disease-based dimensions that comprise MDD.

Neuroscience has seen a dramatic increase in the types of recording modalities and complexity of neural time-series data collected from them. The brain is a highly recurrent system producing rich, complex dynamics that result in different behaviors. Correctly distinguishing such nonlinear neural time series in real-time, especially those with non-obvious links to behavior, could be useful for a wide variety of applications. These include detecting anomalous clinical events such as seizures in epilepsy, and identifying optimal control spaces for brain machine interfaces. It remains challenging to correctly distinguish nonlinear time-series patterns because of the high intrinsic dimensionality of such data, making accurate inference of state changes (for intervention or control) difficult. Simple distance metrics, which can be computed quickly do not yield accurate classifications. On the other end of the spectrum of classification methods, ensembles of classifiers or deep supervised tools offer higher accuracy but are slow, data-intensive, and computationally expensive. We introduce a reservoir-based tool, state tracker (TRAKR), which offers the high accuracy of ensembles or deep supervised methods while preserving the computational benefits of simple distance metrics. After one-shot training, TRAKR can accurately, and in real time, detect deviations in test patterns. By forcing the weighted dynamics of the reservoir to fit a desired pattern directly, we avoid many rounds of expensive optimization. Then, keeping the output weights frozen, we use the error signal generated by the reservoir in response to a particular test pattern as a classification boundary. We show that, using this approach, TRAKR accurately detects changes in synthetic time series. We then compare our tool to several others, showing that it achieves highest classification performance on a benchmark dataset, sequential MNIST, even when corrupted by noise. Additionally, we apply TRAKR to electrocorticography (ECoG) data from the macaque orbitofrontal cortex (OFC), a higher-order brain region involved in encoding the value of expected outcomes. We show that TRAKR can classify different behaviorally relevant epochs in the neural time series more accurately and efficiently than conventional approaches. Therefore, TRAKR can be used as a fast and accurate tool to distinguish patterns in complex nonlinear time-series data, such as neural recordings.

This study represents a post-implementation qualitative inquiry for a maturing flowsheet design that aims to replace multiple disparate devices used for data entry. The flowsheet has already experienced multiple iterative development cycles based on formal feedback from formative and summative usability studies. This next phase focused on a semi-structured qualitative interview to provide new feedback that will be used to further refine the product. Results of the 9-item interview were both actionable and provocative, revealing multiple avenues of improvement and a new usability map that can inform future studies and design plans.

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), established in 2005, is an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards. INCF has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

The aim of this study was to measure the test–retest reliability of 11C-N,N-dimethyl-2-(2′-amino-4′-hydroxymethylphenylthio)benzylamine (11C-HOMADAM) imaging of serotonin transporter (SERT) density in healthy control subjects. Methods: Two female and 2 male volunteers participated in the study, with each undergoing three 90-min 11C-HOMADAM PET scans. Time–activity curves were derived from SERT-rich structures and fit to 2 models: a simplified reference tissue model and a multilinear graphical model. Binding potential, the ratio of specifically bound uptake to nondisplaceable uptake at equilibrium, was calculated from the model parameter estimates. Ninety-five percent confidence intervals and the intraclass correlation coefficient (ICC) were calculated and adjusted for repeated measures. Results: The ICC values ranged from −0.13 in the dorsal raphe to 0.88 in the caudate nucleus. The highest average ICC values were in the striatum, but other regions were sensitive to measurement outliers. Conclusion: Good-to-excellent test–retest reliability was observed for SERT binding in the striatum. The dorsal raphe ICC value was sensitive to a measurement outlier. 11C-HOMADAM binding potential calculated from the simplified reference tissue model and the multilinear graphical model were robust and in good agreement.

BACKGROUND: Hispanics, particularly those with limited English proficiency, are underrepresented in psychiatric clinical research studies. We developed a bilingual and bicultural research clinic dedicated to the recruitment and treatment of Spanish-speaking subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study, a large clinical trial of treatment-naive subjects with major depressive disorder (MDD). METHODS: Demographic and clinical data derived from screening evaluations of the first 1,174 subjects presenting for participation were compared between the Spanish-speaking site (N = 275) and the primary English-speaking site (N = 899). Reasons for ineligibility (N = 888) for the PReDICT study were tallied for each site. RESULTS: Compared to English speakers, Spanish speakers had a lower level of education and were more likely to be female, uninsured, and have uncontrolled medical conditions. Clinically, Spanish speakers demonstrated greater depression severity, with higher mean symptom severity scores, and a greater number of previous suicide attempts. Among the subjects who were not randomized into the PReDICT study, Spanish-speaking subjects were more likely to have an uncontrolled medical condition or refuse participation, whereas English-speaking subjects were more likely to have bipolar disorder or a non-MDD depressive disorder. CONCLUSION: Recruitment of Hispanic subjects with MDD is feasible and may enhance efforts at signal detection, given the higher severity of depression among Spanish-speaking participants presenting for clinical trials. Specific approaches for the recruitment and retention of Spanish-speaking participants are required. Language: en

Hispanics, particularly those with limited English proficiency, are underrepresented in psychiatric clinical research studies. We developed a bilingual and bicultural research clinic dedicated to the recruitment and treatment of Spanish-speaking subjects in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) study, a large clinical trial of treatment-naïve subjects with major depressive disorder (MDD).Demographic and clinical data derived from screening evaluations of the first 1,174 subjects presenting for participation were compared between the Spanish-speaking site (N = 275) and the primary English-speaking site (N = 899). Reasons for ineligibility (N = 888) for the PReDICT study were tallied for each site.Compared to English speakers, Spanish speakers had a lower level of education and were more likely to be female, uninsured, and have uncontrolled medical conditions. Clinically, Spanish speakers demonstrated greater depression severity, with higher mean symptom severity scores, and a greater number of previous suicide attempts. Among the subjects who were not randomized into the PReDICT study, Spanish-speaking subjects were more likely to have an uncontrolled medical condition or refuse participation, whereas English-speaking subjects were more likely to have bipolar disorder or a non-MDD depressive disorder.Recruitment of Hispanic subjects with MDD is feasible and may enhance efforts at signal detection, given the higher severity of depression among Spanish-speaking participants presenting for clinical trials. Specific approaches for the recruitment and retention of Spanish-speaking participants are required.

Insecure attachment has been linked to depression and to outcome in psychotherapy. The neural mechanisms subserving the relationship between attachment security and depression are not well understood. We have developed a method to examine attachment-related brain activity in depression. Twenty-eight women, half depressed, viewed images of their mother, a female friend, and female strangers during fMRI scanning. The effects of depression and insecure attachment were determined with whole-brain multiple linear regression of blood-oxygen-level-dependent response against subjects’ Beck Depression Inventory and Adult Attachment Interview (AAI) coherence of mind scores. Interaction effects were analyzed with ANOVA. Activity associated with depression and with insecure attachment was found in the cortico-striato-thalamic circuits of affect regulation. For early attachment (mother–friend contrast), depression scores correlated with activation of cortical and sub-cortical components of these circuits, while attachment insecurity correlated with sub-cortical activity in the same circuitry. Depression and attachment insecurity correlated with both cortical and sub-cortical activities for mother–stranger, and areas of overlap and of enhancing interactions between depression and insecure attachment were found. For late attachment (friend–stranger contrast), only cortical effects were found. Depression and attachment insecurity may be subserved by similar but distinct components of affect regulating circuits. Their interactions may explain the greater difficulty of treating depression in insecurely attached patients and suggest a contributing role for insecure attachment in depression. Further, differential sub-cortical vs cortical encoding of early vs late attachment suggests a top-down model of late attachment, potentially relevant to psychotherapeutic outcome.

Chittaranjan Andrade (India), Andreas Conca (Austria), Delcir da Costa (Brazil), Gerhard Eschweiler (Germany), Max Fink (USA), Paul Fitzgerald (Australia), Loes Gabriels (Belgium), Christian Geretsegger (Austria), Benjamin Greenberg (USA), Paul Holtzheimer (USA), Mindaugas Jasulaitis (Lithuania), Andy Krystal (USA), Yechiel Levkovitz (Israel), Daniel Lijtenstein (Uruguay), Sarah H. Lisanby (USA), Philip Mitchell (Australia), Nobutaka Motohashi (Japan), Angela Naderi-Heiden (Austria), Jose Otegui (Uruguay), Harold Sackeim (USA), E. Tsukarzi (Russia), Ioannis Zervas (Greece).

Psychiatric disorders such as depression, anxiety and schizophrenia are leading causes of disability worldwide, and have a huge societal impact. However, despite the clear need for better therapies, and major advances in the understanding of the molecular basis of these disorders in recent years, efforts to discover and develop new drugs for neuropsychiatric disorders, particularly those that might revolutionize disease treatment, have been relatively unsuccessful. A multidisciplinary approach will be crucial in addressing this problem, and in the first Advances in Neuroscience for Medical Innovation symposium, experts in multiple areas of neuroscience considered key questions in the field, in particular those related to the importance of neuronal plasticity. The discussions were used as a basis to propose steps that can be taken to improve the effectiveness of drug discovery for psychiatric disorders.

We report a case of classic Wegener's granulomatosis. Direct immunofluorescent study showed finely granular deposits of IgG and IgM in some of the alveolar walls, and of IgM in the maxillary sinus arteries. Electron-microscopic study of the lung and maxillary sinus showed intravascular fibrin, but failed to demonstrate electron-dense deposits in the blood vessel walls. Our patient also had circulating cryoglobulins, consisting of IgG, IgM, Clq, and C3, and evidence of circulating immune complexes as demonstrated by the Clq-binding test. These findings suggest that circulating immune complexes may play an important role in the pathogenesis of the respiratory lesions of Wegener's granulomatosis.