Neuroimaging Research into Disorders of Consciousness: Moral Imperative or Ethical and Legal Failure?

This article explores the ethical and legal implications of enrolling individuals with disorders of consciousness (DOC) in neuroimaging research studies. Many scientists have strongly emphasized the need for additional neuroimaging research into DOC, characterizing the conduct of such studies as morally imperative. On the other hand, institutional review boards charged with approving research protocols, scientific journals deciding whether to publish study results, and federal agencies that disburse grant money have limited the conduct, publication, and funding of consciousness investigations based on ethical and legal concerns. Following a detailed examination of the risks and benefits of neuroimaging research involving individuals with DOC, the author urges IRBs, scientific journals, and funding agencies to no longer stall the conduct, publication, and funding of neuroimaging research into DOC if certain criteria designed to protect the health and safety of individuals with DOC are satisfied

Clinical application of brain imaging for the diagnosis of mood disorders: the current state of play

In response to queries about whether brain imaging technology has reached the point where it is useful for making a clinical diagnosis and for helping to guide treatment selection, the American Psychiatric Association (APA) has recently written a position paper on the Clinical Application of Brain Imaging in Psychiatry. The following perspective piece is based on our contribution to this APA position paper, which specifically emphasized the application of neuroimaging in mood disorders. We present an introductory overview of the challenges faced by researchers in developing valid and reliable biomarkers for psychiatric disorders, followed by a synopsis of the extant neuroimaging findings in mood disorders, and an evidence-based review of the current research on brain imaging biomarkers in adult mood disorders. Although there are a number of promising results, by the standards proposed below, we argue that there are currently no brain imaging biomarkers that are clinically useful for establishing diagnosis or predicting treatment outcome in mood disorders

Robust and Efficient Semi-supervised Learning for Ising Model

In biomedical studies, it is often desirable to characterize the interactive mode of multiple disease outcomes beyond their marginal risk. Ising model is one of the most popular choices serving for this purpose. Nevertheless, learning efficiency of Ising models can be impeded by the scarcity of accurate disease labels, which is a prominent problem in contemporary studies driven by electronic health records (EHR). Semi-supervised learning (SSL) leverages the large unlabeled sample with auxiliary EHR features to assist the learning with labeled data only and is a potential solution to this issue. In this paper, we develop a novel SSL method for efficient inference of Ising model. Our method first models the outcomes against the auxiliary features, then uses it to project the score function of the supervised estimator onto the EHR features, and incorporates the unlabeled sample to augment the supervised estimator for variance reduction without introducing bias. For the key step of conditional modeling, we propose strategies that can effectively leverage the auxiliary EHR information while maintaining moderate model complexity. In addition, we introduce approaches including intrinsic efficient updates and ensemble, to overcome the potential misspecification of the conditional model that may cause efficiency loss. Our method is justified by asymptotic theory and shown to outperform existing SSL methods through simulation studies. We also illustrate its utility in a real example about several key phenotypes related to frequent ICU admission on MIMIC-III data set

Discharge against medical advice: Ethico-legal implications from an African perspective

Background. Discharge against medical advice (DAMA) is a problematic issue for physicians worldwide, which can disrupt the physicianpatientrelationship, have adverse medical outcomes and increase healthcare costs. This review aims to highlight the ethical and legal aspects of the issue from the perspective of developing countries in Africa, and make suggestions for resolving them.Methods. A comprehensive literature review of articles relating to DAMA was performed using databases such as PubMed, Medline and Google Scholar. The search criteria used were ‘discharge against medical advice AND ethics*’, ‘discharge against medical advice AND Africa’, ‘leaving against medical advice’, ‘discharge against medical advice AND legal issues’ and ‘self-discharge’. Relevant articles published from 1980 till 31 December 2011 were included.Results. The conflict between the professional values (beneficence) of the physician and the autonomy (self-determination) of the patient isthe most prominent ethical dilemma in cases of DAMA. The issue of DAMA is more complicated in developing countries, especially because of communal models of decision making. One important ethical dilemma is the rationing of hospital admissions, especially for chronic conditions with poor prognosis. We have suggested a communal model for dealing with the issues. The main legal issue found in this review is the possibility of medical doctors being sued for medical malpractice.Conclusion. DAMA is associated with numerous ethical and legal issues of which physicians need to take cognizance

Mechanisms of Cognitive Impairment in Cerebral Small Vessel Disease: Multimodal MRI Results from the St George's Cognition and Neuroimaging in Stroke (SCANS) Study.

Cerebral small vessel disease (SVD) is a common cause of vascular cognitive impairment. A number of disease features can be assessed on MRI including lacunar infarcts, T2 lesion volume, brain atrophy, and cerebral microbleeds. In addition, diffusion tensor imaging (DTI) is sensitive to disruption of white matter ultrastructure, and recently it has been suggested that additional information on the pattern of damage may be obtained from axial diffusivity, a proposed marker of axonal damage, and radial diffusivity, an indicator of demyelination. We determined the contribution of these whole brain MRI markers to cognitive impairment in SVD. Consecutive patients with lacunar stroke and confluent leukoaraiosis were recruited into the ongoing SCANS study of cognitive impairment in SVD (n = 115), and underwent neuropsychological assessment and multimodal MRI. SVD subjects displayed poor performance on tests of executive function and processing speed. In the SVD group brain volume was lower, white matter hyperintensity volume higher and all diffusion characteristics differed significantly from control subjects (n = 50). On multi-predictor analysis independent predictors of executive function in SVD were lacunar infarct count and diffusivity of normal appearing white matter on DTI. Independent predictors of processing speed were lacunar infarct count and brain atrophy. Radial diffusivity was a stronger DTI predictor than axial diffusivity, suggesting ischaemic demyelination, seen neuropathologically in SVD, may be an important predictor of cognitive impairment in SVD. Our study provides information on the mechanism of cognitive impairment in SVD

Quantitative identification of functional connectivity disturbances in neuropsychiatric lupus based on resting-state fMRI: a robust machine learning approach

Neuropsychiatric systemic lupus erythematosus (NPSLE) is an autoimmune entity comprised of heterogenous syndromes affecting both the peripheral and central nervous system. Research on the pathophysiological substrate of NPSLE manifestations, including functional neuroimaging studies, is extremely limited. The present study examined person-specific patterns of whole-brain functional connectivity in NPSLE patients (n = 44) and age-matched healthy control participants (n = 39). Static functional connectivity graphs were calculated comprised of connection strengths between 90 brain regions. These connections were subsequently filtered through rigorous surrogate analysis, a technique borrowed from physics, novel to neuroimaging. Next, global as well as nodal network metrics were estimated for each individual functional brain network and were input to a robust machine learning algorithm consisting of a random forest feature selection and nested cross-validation strategy. The proposed pipeline is data-driven in its entirety, and several tests were performed in order to ensure model robustness. The best-fitting model utilizing nodal graph metrics for 11 brain regions was associated with 73.5% accuracy (74.5% sensitivity and 73% specificity) in discriminating NPSLE from healthy individuals with adequate statistical power. Closer inspection of graph metric values suggested an increased role within the functional brain network in NSPLE (indicated by higher nodal degree, local efficiency, betweenness centrality, or eigenvalue efficiency) as compared to healthy controls for seven brain regions and a reduced role for four areas. These findings corroborate earlier work regarding hemodynamic disturbances in these brain regions in NPSLE. The validity of the results is further supported by significant associations of certain selected graph metrics with accumulated organ damage incurred by lupus, with visuomotor performance and mental flexibility scores obtained independently from NPSLE patients. View Full-Text Keywords: neuropsychiatric systemic lupus erythematosus; rs-fMRI; graph theory; functional connectivity; surrogate data; machine learning; visuomotor ability; mental flexibilit

Non-invasive brain stimulation for gambling disorder: a systematic review

Background:Gambling disorder (GD) is the most common behavioral addiction and shares pathophysiological and clinical features with substance use disorders (SUDs). Effective therapeutic interventions for GD are lacking. Non-invasive brain stimulation (NIBS) may represent a promising treatment option for GD. Objective:This systematic review aimed to provide a comprehensive and structured overview of studies applying NIBS techniques to GD and problem gambling. Methods:A literature search using Pubmed, Web of Science, and Science Direct was conducted from databases inception to December 19, 2019, for studies assessing the effects of repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (t-DCS) on subjects with GD or problem gambling. Studies using NIBS techniques on healthy subjects and those without therapeutic goals but only aiming to assess basic neurophysiology measures were excluded. Results:A total of 269 articles were title and abstract screened, 13 full texts were assessed, and 11 were included, of which six were controlled and five were uncontrolled. Most studies showed a reduction of gambling behavior, craving for gambling, and gambling-related symptoms. NIBS effects on psychiatric symptoms were less consistent. A decrease of the behavioral activation related to gambling was also reported. Some studies reported modulation of behavioral measures (i.e., impulsivity, cognitive and attentional control, decision making, cognitive flexibility). Studies were not consistent in terms of NIBS protocol, site of stimulation, clinical and surrogate outcome measures, and duration of treatment and follow-up. Sample size was small in most studies. Conclusions:The clinical and methodological heterogeneity of the included studies prevented us from drawing any firm conclusion on the efficacy of NIBS interventions for GD. Further methodologically sound, robust, and well-powered studies are needed

Predictors of switching antipsychotic medications in the treatment of schizophrenia

<p>Abstract</p> <p>Background</p> <p>To identify patient characteristics and early changes in patients' clinical status that best predict subsequent switching of antipsychotic agents in the long-term treatment of schizophrenia.</p> <p>Methods</p> <p>This post-hoc analysis used data from a one-year randomized, open-label, multisite study of antipsychotics in the treatment of schizophrenia. The study protocol permitted switching of antipsychotics when clinically warranted after the first eight weeks. Baseline patient characteristics were assessed using standard psychiatric measures and reviews of medical records. The prediction model included baseline sociodemographics, comorbid psychiatric and non-psychiatric conditions, body weight, clinical and functional variables, as well as change scores on standard efficacy and tolerability measures during the first two weeks of treatment. Cox proportional hazards modeling was used to identify the best predictors of switching from the initially assigned antipsychotic medication.</p> <p>Results</p> <p>About one-third of patients (29.5%, 191/648) switched antipsychotics before the end of the one-year study. There were six variables identified as the best predictors of switching: lack of antipsychotic use in the prior year, pre-existing depression, female gender, lack of substance use disorder, worsening of akathisia (as measured by the Barnes Akathisia Scale), and worsening of symptoms of depression/anxiety (subscale score on the Positive and Negative Syndrome Scale) during the first two weeks of antipsychotic therapy.</p> <p>Conclusions</p> <p>Switching antipsychotics appears to be prevalent in the naturalistic treatment of schizophrenia and can be predicted by a small and distinct set of variables. Interestingly, worsening of anxiety and depressive symptoms and of akathisia following two weeks of treatment were among the more robust predictors of subsequent switching of antipsychotics.</p