EPA guidance on physical activity as a treatment for severe mental illness: a meta-review of the evidence and Position Statement from the European Psychiatric Association (EPA), supported by the International Organization of Physical Therapists in Mental Health (IOPTMH)

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 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

Synaptosomal Proteome of the Orbitofrontal Cortex from Schizophrenia Patients Using Quantitative Label-Free and iTRAQ-Based Shotgun Proteomics

Schizophrenia is a chronic and incurable neuropsychiatric disorder that affects about one percent of the world population. The proteomic characterization of the synaptosome fraction of the orbitofrontal cortex is useful for providing valuable information about the molecular mechanisms of synaptic functions in these patients. Quantitative analyses of synaptic proteins were made with eight paranoid schizophrenia patients and a pool of eight healthy controls free of mental diseases. Label-free and iTRAQ labeling identified a total of 2018 protein groups. Statistical analyses revealed 12 and 55 significantly dysregulated proteins by iTRAQ and label-free, respectively. Quantitative proteome analyses showed an imbalance in the calcium signaling pathway and proteins such as reticulon-1 and cytochrome <i>c</i>, related to endoplasmic reticulum stress and programmed cell death. Also, it was found that there is a significant increase in limbic-system-associated membrane protein and α-calcium/calmodulin-dependent protein kinase II, associated with the regulation of human behavior. Our data contribute to a better understanding about apoptosis as a possible pathophysiological mechanism of this disease as well as neural systems supporting social behavior in schizophrenia. This study also is a joint effort of the Chr 15 C-HPP team and the Human Brain Proteome Project of B/D-HPP. All MS proteomics data are deposited in the ProteomeXchange Repository under PXD006798

DataSheet1_Kalpra: A kernel approach for longitudinal pathway regression analysis integrating network information with an application to the longitudinal PsyCourse Study.docx

A popular approach to reduce the high dimensionality resulting from genome-wide association studies is to analyze a whole pathway in a single test for association with a phenotype. Kernel machine regression (KMR) is a highly flexible pathway analysis approach. Initially, KMR was developed to analyze a simple phenotype with just one measurement per individual. Recently, however, the investigation into the influence of genomic factors in the development of disease-related phenotypes across time (trajectories) has gained in importance. Thus, novel statistical approaches for KMR analyzing longitudinal data, i.e. several measurements at specific time points per individual are required. For longitudinal pathway analysis, we extend KMR to long-KMR using the estimation equivalence of KMR and linear mixed models. We include additional random effects to correct for the dependence structure. Moreover, within long-KMR we created a topology-based pathway analysis by combining this approach with a kernel including network information of the pathway. Most importantly, long-KMR not only allows for the investigation of the main genetic effect adjusting for time dependencies within an individual, but it also allows to test for the association of the pathway with the longitudinal course of the phenotype in the form of testing the genetic time-interaction effect. The approach is implemented as an R package, kalpra. Our simulation study demonstrates that the power of long-KMR exceeded that of another KMR method previously developed to analyze longitudinal data, while maintaining (slightly conservatively) the type I error. The network kernel improved the performance of long-KMR compared to the linear kernel. Considering different pathway densities, the power of the network kernel decreased with increasing pathway density. We applied long-KMR to cognitive data on executive function (Trail Making Test, part B) from the PsyCourse Study and 17 candidate pathways selected from Reactome. We identified seven nominally significant pathways.</p

Additional file 1: Figure S1. of Loss of Munc18-1 long splice variant in GABAergic terminals is associated with cognitive decline and increased risk of dementia in a community sample

Immunohistochemical characterization of Munc18-1 splice variants in human dentate gyrus reveals similar cellular and subcellular distributions of M18L and M18S than those in rat brain. Confocal images show a preferential localization of M18L to inhibitory presynaptic terminals, as its immunofluorescence fully overlaps with that of VGAT, but not VGLUT1. M18S shows ubiquitous distribution. Figure S2. M18L, but not M18S, immunodensity is reduced in the DLPFC of MAP participants with clinical dementia, compared to those with no or mild cognitive impairment, as well as in those presenting high burden of Alzheimer’s disease pathology, using either NIA/Reagan or Braak scales. (PDF 986 kb

Additional file 1: Figure S1. of Decreased cortical FADD protein is associated with clinical dementia and cognitive decline in an elderly community sample

Colocalization of FADD and HLA-DR positive (activated) microglia in the DLPFC of neuropathology-free NCI (n = 3) MAP participants. Single-channel (in greys) or merged confocal images correspond to double co-immunolabeled sections with antibodies against FADD (H181, Santa-Cruz, 1:50; magenta) and HLA-DR (clone CR3/43, Dako, 1:100; green). In merged image, colors were arbitrarily assigned to maximize overlap visualization. Overlap panel is an ImageJ-generated bitmap highlighting those pixels where significant colocalization over an unbiased threshold of intensities between the indicated channels was detected in pairwise colocalization analyses. Unlike its neuronal localization pattern, FADD seems absent from the microglial nuclei, and mayor colocalization between these markers appears in activated microglial processes (see yellow arrows). Possibly, FADD microglial inclusions might derive from post-apoptotic neurons. Scale bar: 20 μm. (PDF 292 kb

Data_Sheet_1_Patterns of risk—Using machine learning and structural neuroimaging to identify pedophilic offenders.DOCX

BackgroundChild sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA.AimTo use machine learning and MRI data to identify PO individuals.MethodsFrom a single-center male cohort of 14 PO individuals and 15 matched healthy control (HC) individuals, we acquired diffusion tensor imaging data (anisotropy, diffusivity, and fiber tracking) in literature-based regions of interest (prefrontal cortex, anterior cingulate cortex, amygdala, and corpus callosum). We trained a linear support vector machine to discriminate between PO and HC individuals using these WM microstructure data. Post hoc, we investigated the PO model decision scores with respect to sociodemographic (age, education, and IQ) and forensic characteristics (psychopathy, sexual deviance, and future risk of sexual violence) in the PO subpopulation. We assessed model specificity in an external cohort of 53 HC individuals.ResultsThe classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P5000 = 0.018) and an out-of-sample specificity to correctly identify HC individuals of 94.3%. The predictive brain pattern contained bilateral fractional anisotropy in the anterior cingulate cortex, diffusivity in the left amygdala, and structural prefrontal cortex-amygdala connectivity in both hemispheres. This brain pattern was associated with the number of previous child victims, the current stance on sexuality, and the professionally assessed risk of future sexual violent reoffending.ConclusionAberrant white matter microstructure in the prefronto-temporo-limbic circuit could be a potential neurobiological correlate for PO individuals at high-risk of reoffending with CSA. Although preliminary and exploratory at this point, our findings highlight the general potential of MRI-based biomarkers and particularly WM microstructure patterns for future CSA risk assessment and preventive efforts.</p

Novel mutations identified in <i>SLITRK1</i>.

<p>(<b>a</b>) Allelic frequency of novel <i>SLITRK1</i> mutations identified in obsessive-compulsive disorder (OCD) cases. (<b>b</b>) The <i>SLITRK1</i> L63L mutation has been demonstrated by sequencing in one of 762 OC spectrum alleles and in zero of 712 control alleles. (<b>c</b>) The <i>SLITRK1</i> N400I mutation has been demonstrated via sequencing and genotyping in one of 646 OC spectrum alleles and in zero of 2070 control alleles, respectively; <i>^t</i>includes genotyping of 1358 alleles. (<b>d</b>) The <i>SLITRK1</i> T418S mutation has been demonstrated by sequencing in three of 762 OC spectrum alleles and in one of 410 control alleles. (<b>d</b>) Conservation map of <i>SLITRK1</i> region where the three novel mutations were identified (Green – novel variants, gray – evolutionarily conserved regions). (<b>e</b>) A schematic of the <i>SLITRK1</i> protein with the detected variants identified (red – novel variants, gray – previously published variants in Tourette syndrome⁴, black – published variants in trichotillomania⁵. Dotted outline depicts leucine rich repeat (LRR) region 9. Diagram of <i>SLITRK1</i> is available at <a href="http://smart.embl-heidelberg.de" target="_blank">http://smart.embl-heidelberg.de</a>. LRR typ – LRR typical subfamily, LRR CT – LRR C-terminal domain, LRR N-terminal domain; dark blue bar – transmembrane domain.</p

<i>SLITRK1</i> variant N400I fails to induce neurite outgrowth.

<p>(<b>a</b>) LS: Representative images of primary rat E18 hippocampal neurons nucleofected with wildtype <i>SLITRK1</i> or the <i>SLITRK1</i>–N400I variant. RS: Images are also traced to facilitate visualization of thin neurites. Scale bar = 50 µm (<b>b</b>) LS: Representative images of primary mouse E17 cortical neurons nucleofected with wildtype <i>SLITRK1</i> or the <i>SLITRK1</i>–N400I variant. RS: Representative E17 cortical neuron trace. Scale bar = 50 µm (<b>c</b>) The summed total neurite length per hippocampal neuron at 7 <i>div</i> is shown. Each bar on the bar graph represents pooled data of at least 50 neurons per experiment (<i>n</i> = 3). Images are uniformly overexposed to improve neurite visibility. (<b>d</b>) The summed total neurite length per cortical neuron at 3 <i>div</i> is shown. Each bar on the bar graph represents pooled data of at least 14–22 neurons per experiment (<i>n</i> = 2). Statistical significance was assessed using a student's t-test as described under <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0070376#s4" target="_blank">Materials and Methods</a>. Error bars are 95% confidence intervals. * <i>p</i><0.05, ** <i>p</i><0.01, *** <i>p</i><0.001.</p

Pedigree diagrams of families with <i>SLITRK1</i> variants.

<p>Pedigrees for individuals in whom <i>SLITRK1</i> variants were identified. Each obsessive-compulsive (OC) spectrum proband is labeled with his/her identifier and is designated by a black arrowhead. Individuals affected with an OC spectrum disorder are represented by shaded symbols, with red shading indicating obsessive-compulsive disorder (OCD) and blue shading indicating Tourette syndrome (TS). Psychiatric conditions outside of the OC spectrum are represented by a magenta circle in the center of the symbol. Male family members are represented with squares, females with circles, persons with unspecified gender are diamonds with the number of individuals indicated directly below. All psychiatric pathology is listed under each affected individual. OCD – Obsessive-Compulsive Disorder, TS – Tourette syndrome, BDD – Body Dysmorphic Disorder, GAD – Generalized Anxiety Disorder, MDD – Major Depressive Disorder, N.O.S. – not otherwise specified, PD – Panic Disorder, PTSD – Post-Traumatic Stress Disorder, w/ - with, y/o – years old, ? – psychiatric history is unavailable for the individual.</p

Empirical and theoretical distributions of the total score in the Consortium on Lithium Genetics sample.

<p>Frequentist, <b>A</b>, and Bayesian minimum message length, <b>B</b>, mixture modeling identify three subpopulations of non responders (grey), partial responders (red), and full responders (blue) in total scores of 1,308 bipolar disorder patients characterized for response to lithium maintenance treatment.</p