Objective Treatment resistance complicates the management of schizophrenia. Research and clinical translation is limited by inconsistent definitions. To address this we evaluated current approaches and then developed consensus criteria and guidelines. Method A systematic review of randomized antipsychotic clinical trials in treatment resistant schizophrenia was performed. Definitions of treatment resistance were extracted. Subsequently, consensus operationalized criteria were developed by a working group of researchers and clinicians through i) a multi-phase, mixed methods approach; ii) identifying key criteria via an online survey; and iii) meetings to achieve consensus. Results 42 studies met inclusion criteria. Of these, 21 (50%) studies did not provide operationalized criteria, whilst in others, criteria varied considerably, particularly regarding symptom severity, prior treatment duration and antipsychotic dose thresholds. Important for the inability to compare results, only two (5%) studies utilized the same criteria. The consensus group identified minimum and optimal criteria, employing the following principles: 1) current symptoms of a minimum duration and severity determined by a standardized rating scale; 2) ≥moderate functional impairment; 3) prior treatment consisting of ≥2 different antipsychotic trials, each for a minimum duration and dose; 4) adherence systematically assessed and meeting minimum criteria; 5) ideally at least one prospective treatment trial; 6) criteria that clearly separated responsive from treatment resistant patients. Conclusions There is considerable variation in current approaches to defining treatment resistance in schizophrenia. We present consensus guidelines that operationalize criteria for determining and reporting treatment resistance, adequate treatment and treatment response in schizophrenia, providing a benchmark for research and clinical translation.

Highlights d Three groups of highly genetically-related disorders among 8 psychiatric disorders d Identified 109 pleiotropic loci affecting more than one disorder d Pleiotropic genes show heightened expression beginning in 2 nd prenatal trimester d Pleiotropic genes play prominent roles in neurodevelopmental processes Authors Cross-Disorder Group of the Psychiatric Genomics Consortium

No abstract

Machine learning approaches for clinical psychology and psychiatry explicitly focus on learning statistical functions from multidimensional data sets to make generalizable predictions about individuals. The goal of this review is to provide an accessible understanding of why this approach is important for future practice given its potential to augment decisions associated with the diagnosis, prognosis, and treatment of people suffering from mental illness using clinical and biological data. To this end, the limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies. A selective literature review is then presented aiming to reinforce the usefulness of machine learning methods and provide evidence of their potential. In the context of promising initial results, the current limitations of machine learning approaches are addressed, and considerations for future clinical translation are outlined.

These results indicate that in both healthy subjects and patients with schizophrenia hippocampal volume is plastic in response to aerobic exercise.

These updated guidelines are based on a first edition of the World Federation of Societies of Biological Psychiatry Guidelines for Biological Treatment of Schizophrenia published in 2005. For this 2012 revision, all available publications pertaining to the biological treatment of schizophrenia were reviewed systematically to allow for an evidence-based update. These guidelines provide evidence-based practice recommendations that are clinically and scientifically meaningful and these guidelines are intended to be used by all physicians diagnosing and treating people suffering from schizophrenia. Based on the first version of these guidelines, a systematic review of the MEDLINE/PUBMED database and the Cochrane Library, in addition to data extraction from national treatment guidelines, has been performed for this update. The identified literature was evaluated with respect to the strength of evidence for its efficacy and then categorised into six levels of evidence (A-F; Bandelow et al. 2008b, World J Biol Psychiatry 9:242). This first part of the updated guidelines covers the general descriptions of antipsychotics and their side effects, the biological treatment of acute schizophrenia and the management of treatment-resistant schizophrenia.

Structural brain abnormalities are central to schizophrenia (SZ), but it remains unknown whether they are linked to dysmaturational processes crossing diagnostic boundaries, aggravating across disease stages, and driving the neurodiagnostic signature of the illness. Therefore, we investigated whether patients with SZ (N = 141), major depression (MD; N = 104), borderline personality disorder (BPD; N = 57), and individuals in at-risk mental states for psychosis (ARMS; N = 89) deviated from the trajectory of normal brain maturation. This deviation was measured as difference between chronological and the neuroanatomical age (brain age gap estimation [BrainAGE]). Neuroanatomical age was determined by a machine learning system trained to individually estimate age from the structural magnetic resonance imagings of 800 healthy controls. Group-level analyses showed that BrainAGE was highest in SZ (+5.5 y) group, followed by MD (+4.0), BPD (+3.1), and the ARMS (+1.7) groups. Earlier disease onset in MD and BPD groups correlated with more pronounced BrainAGE, reaching effect sizes of the SZ group. Second, BrainAGE increased across at-risk, recent onset, and recurrent states of SZ. Finally, BrainAGE predicted both patient status as well as negative and disorganized symptoms. These findings suggest that an individually quantifiable "accelerated aging" effect may particularly impact on the neuroanatomical signature of SZ but may extend also to other mental disorders.

Physical activity (PA) may be therapeutic for people with severe mental illness (SMI) who generally have low PA and experience numerous life style-related medical complications. We conducted a meta-review of PA interventions and their impact on health outcomes for people with SMI, including schizophrenia-spectrum disorders, major depressive disorder (MDD) and bipolar disorder. We searched major electronic databases until January 2018 for systematic reviews with/without meta-analysis that investigated PA for any SMI. We rated the quality of studies with the AMSTAR tool, grading the quality of evidence, and identifying gaps, future research needs and clinical practice recommendations. For MDD, consistent evidence indicated that PA can improve depressive symptoms versus control conditions, with effects comparable to those of antidepressants and psychotherapy. PA can also improve cardiorespiratory fitness and quality of life in people with MDD, although the impact on physical health outcomes was limited. There were no differences in adverse events versus control conditions. For MDD, larger effect sizes were seen when PA was delivered at moderate-vigorous intensity and supervised by an exercise specialist. For schizophrenia-spectrum disorders, evidence indicates that aerobic PA can reduce psychiatric symptoms, improves cognition and various subdomains, cardiorespiratory fitness, whilst evidence for the impact on anthropometric measures was inconsistent. There was a paucity of studies investigating PA in bipolar disorder, precluding any definitive recommendations. No cost effectiveness analyses in any SMI condition were identified. We make multiple recommendations to fill existing research gaps and increase the use of PA in routine clinical care aimed at improving psychiatric and medical outcomes.