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Sandra Iglesias, Sara Tomiello, Maya Schneebeli, Klaas E Stephan
Psychiatry faces fundamental challenges: based on a syndrome-based nosology, it presently lacks clinical tests to infer on disease processes that cause symptoms of individual patients and must resort to trial-and-error treatment strategies. These challenges have fueled the recent emergence of a novel field-computational psychiatry-that strives for mathematical models of disease processes at physiological and computational (information processing) levels. This review is motivated by one particular goal of computational psychiatry: the development of 'computational assays' that can be applied to behavioral or neuroimaging data from individual patients and support differential diagnosis and guiding patient-specific treatment...
September 21, 2016: Wiley Interdisciplinary Reviews. Cognitive Science
Karl Friston, Harriet R Brown, Jakob Siemerkus, Klaas E Stephan
Twenty years have passed since the dysconnection hypothesis was first proposed (Friston and Frith, 1995; Weinberger, 1993). In that time, neuroscience has witnessed tremendous advances: we now live in a world of non-invasive neuroanatomy, computational neuroimaging and the Bayesian brain. The genomics era has come and gone. Connectomics and large-scale neuroinformatics initiatives are emerging everywhere. So where is the dysconnection hypothesis now? This article considers how the notion of schizophrenia as a dysconnection syndrome has developed - and how it has been enriched by recent advances in clinical neuroscience...
October 2016: Schizophrenia Research
Helene Haker, Maya Schneebeli, Klaas Enno Stephan
Diagnosis and individualized treatment of autism spectrum disorder (ASD) represent major problems for contemporary psychiatry. Tackling these problems requires guidance by a pathophysiological theory. In this paper, we consider recent theories that re-conceptualize ASD from a "Bayesian brain" perspective, which posit that the core abnormality of ASD resides in perceptual aberrations due to a disbalance in the precision of prediction errors (sensory noise) relative to the precision of predictions (prior beliefs)...
2016: Frontiers in Psychiatry
Klaas E Stephan, Andreea O Diaconescu, Sandra Iglesias
No abstract text is available yet for this article.
July 2016: Brain: a Journal of Neurology
Katharina Schmack, Veith Weilnhammer, Jakob Heinzle, Klaas E Stephan, Philipp Sterzer
Visual perception is strongly shaped by expectations, but it is poorly understood how such perceptual expectations are learned in our dynamic sensory environment. Here, we applied a Bayesian framework to investigate whether perceptual expectations are continuously updated from different aspects of ongoing experience. In two experiments, human observers performed an associative learning task in which rapidly changing expectations about the appearance of ambiguous stimuli were induced. We found that perception of ambiguous stimuli was biased by both learned associations and previous perceptual outcomes...
2016: Frontiers in Human Neuroscience
Gijs W D Landman, Nanne Kleefstra, Klaas H Groenier, Stephan J L Bakker, Geert H Groeneveld, Henk J G Bilo, Kornelis J J van Hateren
BACKGROUND: C-reactive protein (CRP), procalcitonin (PCT) and pro-adrenomedullin (MR-proADM) are inflammation markers associated with long-term mortality risk. We compared the associations and predictive capacities of CRP, PCT and MR-proADM with cardiovascular and all-cause mortality in patients with type 2 diabetes. METHODS: This study included primary care treated patients with type 2 diabetes participating in the ZODIAC cohort study. A total of 1005 out of 1688 patients (60%) had complete baseline variables...
July 2016: Atherosclerosis
Sudhir Raman, Lorenz Deserno, Florian Schlagenhauf, Klaas Enno Stephan
BACKGROUND: Generative models of neuroimaging data, such as dynamic causal models (DCMs), are commonly used for inferring effective connectivity from individual subject data. Recently introduced "generative embedding" approaches have used DCM-based connectivity parameters for supervised classification of individual patients or to find unknown subgroups in heterogeneous groups using unsupervised clustering methods. NEW METHOD: We present a novel framework which combines DCMs with finite mixture models into a single hierarchical model...
August 30, 2016: Journal of Neuroscience Methods
Slavé Petrovski, Sébastien Küry, Candace T Myers, Kwame Anyane-Yeboa, Benjamin Cogné, Martin Bialer, Fan Xia, Parisa Hemati, James Riviello, Michele Mehaffey, Thomas Besnard, Emily Becraft, Alexandrea Wadley, Anya Revah Politi, Sophie Colombo, Xiaolin Zhu, Zhong Ren, Ian Andrews, Tracy Dudding-Byth, Amy L Schneider, Geoffrey Wallace, Aaron B I Rosen, Susan Schelley, Gregory M Enns, Pierre Corre, Joline Dalton, Sandra Mercier, Xénia Latypova, Sébastien Schmitt, Edwin Guzman, Christine Moore, Louise Bier, Erin L Heinzen, Peter Karachunski, Natasha Shur, Theresa Grebe, Alice Basinger, Joanne M Nguyen, Stéphane Bézieau, Klaas Wierenga, Jonathan A Bernstein, Ingrid E Scheffer, Jill A Rosenfeld, Heather C Mefford, Bertrand Isidor, David B Goldstein
Whole-exome sequencing of 13 individuals with developmental delay commonly accompanied by abnormal muscle tone and seizures identified de novo missense mutations enriched within a sub-region of GNB1, a gene encoding the guanine nucleotide-binding protein subunit beta-1, Gβ. These 13 individuals were identified among a base of 5,855 individuals recruited for various undiagnosed genetic disorders. The probability of observing 13 or more de novo mutations by chance among 5,855 individuals is very low (p = 7...
May 5, 2016: American Journal of Human Genetics
Patrick Freund, Karl Friston, Alan J Thompson, Klaas E Stephan, John Ashburner, Dominik R Bach, Zoltan Nagy, Gunther Helms, Bogdan Draganski, Siawoosh Mohammadi, Martin E Schwab, Armin Curt, Nikolaus Weiskopf
No abstract text is available yet for this article.
June 2016: Brain: a Journal of Neurology
Steven H Hendriks, Peter R van Dijk, Kornelis J J van Hateren, Joost L van Pelt, Klaas H Groenier, Henk J G Bilo, Stephan J L Bakker, Gijs W D Landman, Nanne Kleefstra
BACKGROUND: We aimed to investigate whether high-sensitive cardiac troponin T (hs-cTnT) is associated with all-cause and cardiovascular mortality in stable type 2 diabetes (T2D) outpatients treated in primary care. METHODS: Cardiac troponin T was measured with a high-sensitive assay at baseline in patients with T2D participating in the observational ZODIAC study. Cox proportional hazards models were used to investigate the relationship between hs-cTnT and mortality with adjustment for selected confounders...
April 2016: American Heart Journal
Shouliang Qi, Stephan Meesters, Klaas Nicolay, Bart M Ter Haar Romeny, Pauly Ossenblok
Structural brain networks constructed based on diffusion-weighted MRI (dMRI) have provided a systems perspective to explore the organization of the human brain. Some redundant and nonexistent fibers, however, are inevitably generated in whole brain tractography. We propose to add one critical step while constructing the networks to remove these fibers using the linear fascicle evaluation (LiFE) method, and study the differences between the networks with and without LiFE optimization. For a cohort of nine healthy adults and for 9 out of the 35 subjects from Human Connectome Project, the T 1-weighted images and dMRI data are analyzed...
2016: Frontiers in Computational Neuroscience
Klaas E Stephan, Dominik R Bach, Paul C Fletcher, Jonathan Flint, Michael J Frank, Karl J Friston, Andreas Heinz, Quentin J M Huys, Michael J Owen, Elisabeth B Binder, Peter Dayan, Eve C Johnstone, Andreas Meyer-Lindenberg, P Read Montague, Ulrich Schnyder, Xiao-Jing Wang, Michael Breakspear
Contemporary psychiatry faces major challenges. Its syndrome-based disease classification is not based on mechanisms and does not guide treatment, which largely depends on trial and error. The development of therapies is hindered by ignorance of potential beneficiary patient subgroups. Neuroscientific and genetics research have yet to affect disease definitions or contribute to clinical decision making. In this challenging setting, what should psychiatric research focus on? In two companion papers, we present a list of problems nominated by clinicians and researchers from different disciplines as candidates for future scientific investigation of mental disorders...
January 2016: Lancet Psychiatry
Klaas E Stephan, Elisabeth B Binder, Michael Breakspear, Peter Dayan, Eve C Johnstone, Andreas Meyer-Lindenberg, Ulrich Schnyder, Xiao-Jing Wang, Dominik R Bach, Paul C Fletcher, Jonathan Flint, Michael J Frank, Andreas Heinz, Quentin J M Huys, P Read Montague, Michael J Owen, Karl J Friston
This is the second of two companion papers proposing priority problems for research on mental disorders. Whereas the first paper focuses on questions of nosology and diagnosis, this Personal View concerns pathogenesis and aetiology of psychiatric diseases. We hope that this (non-exhaustive and subjective) list of problems, nominated by scientists and clinicians from different fields and institutions, provides guidance and perspectives for choosing future directions in psychiatric science.
January 2016: Lancet Psychiatry
Karl J Friston, Vladimir Litvak, Ashwini Oswal, Adeel Razi, Klaas E Stephan, Bernadette C M van Wijk, Gabriel Ziegler, Peter Zeidman
This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds)...
March 2016: NeuroImage
Jakob Heinzle, Peter J Koopmans, Hanneke E M den Ouden, Sudhir Raman, Klaas Enno Stephan
High-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at the sub-millimeter scale has become feasible with recent advances in MR technology. In principle, this would enable the study of layered cortical circuits, one of the fundaments of cortical computation. However, the spatial layout of cortical blood supply may become an important confound at such high resolution. In particular, venous blood draining back to the cortical surface perpendicularly to the layered structure is expected to influence the measured responses in different layers...
January 15, 2016: NeuroImage
Stefan Frässle, Frieder Michel Paulus, Sören Krach, Stefan Robert Schweinberger, Klaas Enno Stephan, Andreas Jansen
Perceiving human faces constitutes a fundamental ability of the human mind, integrating a wealth of information essential for social interactions in everyday life. Neuroimaging studies have unveiled a distributed neural network consisting of multiple brain regions in both hemispheres. Whereas the individual regions in the face perception network and the right-hemispheric dominance for face processing have been subject to intensive research, the functional integration among these regions and hemispheres has received considerably less attention...
January 1, 2016: NeuroImage
Eduardo A Aponte, Sudhir Raman, Biswa Sengupta, Will D Penny, Klaas E Stephan, Jakob Heinzle
BACKGROUND: Dynamic causal modeling (DCM) for fMRI is an established method for Bayesian system identification and inference on effective brain connectivity. DCM relies on a biophysical model that links hidden neuronal activity to measurable BOLD signals. Currently, biophysical simulations from DCM constitute a serious computational hindrance. Here, we present Massively Parallel Dynamic Causal Modeling (mpdcm), a toolbox designed to address this bottleneck. NEW METHOD: mpdcm delegates the generation of simulations from DCM's biophysical model to graphical processing units (GPUs)...
January 15, 2016: Journal of Neuroscience Methods
Karl J Friston, Klaas Enno Stephan, Read Montague, Raymond J Dolan
In this Review, we discuss advances in computational neuroscience that relate to psychiatry. We review computational psychiatry in terms of the ambitions of investigators, emerging domains of application, and future work. Our focus is on theoretical formulations of brain function that put subjective beliefs and behaviour within formal (computational) frameworks-frameworks that can be grounded in neurophysiology down to the level of synaptic mechanisms. Understanding the principles that underlie the brain's functional architecture might be essential for an informed phenotyping of psychopathology in terms of its pathophysiological underpinnings...
July 2014: Lancet Psychiatry
Meltem Sevgi, Lionel Rigoux, Anne B Kühn, Jan Mauer, Leonhard Schilbach, Martin E Hess, Theo O J Gruendler, Markus Ullsperger, Klaas Enno Stephan, Jens C Brüning, Marc Tittgemeyer
Variations in the fat mass and obesity-associated (FTO) gene are linked to obesity. However, the underlying neurobiological mechanisms by which these genetic variants influence obesity, behavior, and brain are unknown. Given that Fto regulates D2/3R signaling in mice, we tested in humans whether variants in FTO would interact with a variant in the ANKK1 gene, which alters D2R signaling and is also associated with obesity. In a behavioral and fMRI study, we demonstrate that gene variants of FTO affect dopamine (D2)-dependent midbrain brain responses to reward learning and behavioral responses associated with learning from negative outcome in humans...
September 9, 2015: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Klaas E Stephan, Sandra Iglesias, Jakob Heinzle, Andreea O Diaconescu
Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches-biophysical network models, generative models, and model-based fMRI analyses of neuromodulation-strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data...
August 19, 2015: Neuron
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