UH Biocomputation Group - Computational psychiatryhttp://biocomputation.herts.ac.uk/2017-03-29T15:22:49+01:00Automated validation and comparison of neurophysiological biomarkers of psychiatric disorders2017-03-29T15:22:49+01:002017-03-29T15:22:49+01:00Christoph Metznertag:biocomputation.herts.ac.uk,2017-03-29:/2017/03/29/automated-validation-and-comparison-of-neurophysiological-biomarkers-of-psychiatric-disorders.html<p class="first last">Christoph Metzner's journal club session on the automatic validation and comparison of neurophysiological biomarkers of psychiatric disorders.</p>
<p>Research on psychiatric disorders has gradually shifted its focus from complex clinical phenotypes towards the identification of biomarkers and endophenotypic measures. Computational approaches have gained significantly more attention over the last years, and this has led to the emergence of 'Computational Psychiatry' as an independent discipline. Computational modelling of biomarkers promises to more readily shed light on the mechanisms underlying disorders and to facilitate the discovery of novel medications [1].</p>
<p>However, in order to develop a computational model, scientists need to have an in-depth understanding of the current, relevant experimental data, the current state of computational modeling and the state-of-the-art of statistical testing. Based on this knowledge, they have to choose the appropriate criteria with which the model predictions and experimental observations will be compared [2]. In a field where both the number of experimental and computational studies grows rapidly, as is the case for psychiatry, this becomes more and more impracticable. Omar et al. therefore proposed a framework for automated validation of scientific models, SciUnit [3].</p>
<p>Here, we propose to adopt this framework for the computational psychiatry community and to collaboratively build common repositories of experimental observations, computational models, test suites and tools. As a case in point, we have implemented test suites for auditory steady-state response deficits in schizophrenic patients, which are based on observations from several experimental studies [4,5,6], and we demonstrate how existing computational models [6,7] can be validated against these observations and compared against each other. We have included sets of observations from three experimental studies, which concur on most findings but also disagree on some. This allows us to demonstrate the usefulness of our approach in highlighting and clarifying existing, potentially conflicting, experimental data. We have included computational models that not only comprise biophysically detailed as well as abstract models, but that also differ in implementation (native Python vs. Genesis vs NeuroML2), in order to demonstrate the flexibility of the approach. Furthermore, this additionally allows us to showcase the ability of the framework to compare models against each other based on a set of experimental observations. Furthermore, our approach enables us to assess the variability of the produced model output, and therefore the robustness of the findings, by generating a distribution of model instances where certain parameters, such as the precise timing of noise inputs (however, not the strength and type of noise) or the precise connectivity (however, not the underlying distribution of connections) vary, which then are used to produce a distribution of model outputs. This can inform on the robustness of the findings and can also be compared against the variability of experimental observations.</p>
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<ol class="arabic simple">
<li>Siekmeier, P.: Computational modeling of psychiatric illnesses via well-defined neurophysiological and neurocognitive biomarkers. Neuroscience & Biobehavioral Reviews 57: 365-380, 2015.</li>
<li>Gerkin, R.C. and Omar, C.: NeuroUnit: Validation Tests for Neuroscience Models. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00013</li>
<li>Omar, C., Aldrich, J., and Gerkin, R.C.: Collaborative infrastructure for test-driven scientific model validation. In CompanionProceedings of the 36th International Conference on Software Engineering, ACM, 2014.</li>
<li>Kwon J.S., O’Donnell B.F., Wallenstein G.V., Greene R.W., Hirayasu Y., Nestor P.G., Hasselmo M.E., Potts G.F., Shenton M.E., and McCarley R.W..: Gamma frequency–range abnormalities to auditory stimulation in schizophrenia. Archives of General Psychiatry, 56(11):1001–1005, 1999.</li>
<li>Krishnan, G.P., Hetrick, W.P., Brenner, C.A., Shekhar, A., Steffen, A.N., and O’Donnell, B.F.: Steady state and induced auditory gamma deficits in schizophrenia. Neuroimage, 47(4):1711–1719, 2009.</li>
<li>Vierling-Claassen, D., Siekmeier, P., Stufflebeam, S., and Kopell, N.: Modeling GABA alterations in schizophrenia: a link between impaired inhibition and altered gamma and beta range auditory entrainment. Journal of Neurophysiology, 99(5):2656–2671, 2008.</li>
<li>Metzner, C., Schweikard, A. and Zurowski, B.: Multi-factorial modeling of impairment of evoked gamma range oscillations in schizophrenia. Frontiers in Computational Neuroscience, 10, 2016.</li>
</ol>
<p><strong>Date:</strong> 31/03/2017 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>
Computational psychiatry: bridging the gap between genes and symptoms2015-09-21T14:10:30+01:002015-09-21T14:10:30+01:00Christoph Metznertag:biocomputation.herts.ac.uk,2015-09-21:/2015/09/21/computational-psychiatry-bridging-the-gap-between-genes-and-symptoms.html<p class="first last">Christoph Metzner's journal club session discussing the field of computational psychiatry and his work on a model of auditory click entrainment deficits in shizophrenic patients. The model incorporates experimentally identified cellular and circuit abnormalities in patients and explores how their interaction might give rise to experimentally observed deficits.</p>
<p>Over the last years, the traditional diagnostic classifications used in psychiatry have been questioned and a breakdown into simpler categories like endophenotypes or cognitive domains has been proposed. This is mainly due to the fact that the gap between symptom-based classifications on the one hand and genes and molecules on the other hand is huge and a clear mapping inbetween not in sight. Underlying these new proposals is the hope that the simpler categories will map nicely to alterations at the genetic/molecular level. However, this hope might be overly optimistic. Not only are disorders such as schizophrenia highly polygenic (more than 100 risk genes have been identified), the proposed network-level endophenotypes can potentially be produced by a myriad of different configurations on the cellular level (multifactoriality).</p>
<p>In order to overcome these limitations, the use of biophysically detailed computational models in psychiatry has been proposed, which enable the implementation of genetic alterations and the exploration of their multifactorial interplay.</p>
<p>In this talk I present some of my efforts to contribute to this new computational psychiatry effort. I will describe a model of auditory click entrainment deficits in schizophrenic patients which incorporates experimentally identified cellular and circuit abnormalities in patients and explores how their interaction might give rise to experimentally observed deficits. Furthermore, I will point out the limitations of the presented approach, especially the crucial influence of the 'illness metric' (i.e. which deficits are incorporated in the analysis and which are not) on the results. I will then generally discuss how to overcome these limitations and briefly present the next steps in the above project (focusing on a new collaboration with the labs of <a class="reference external" href="http://arken.umb.no/~gautei/index_english.html">Gaute Einevoll</a> and <a class="reference external" href="http://www.med.uio.no/klinmed/english/people/aca/olean/">Ole Andreassen</a> from Oslo and Os, respectively).</p>
<p><strong>Date:</strong> 25/09/2015 <br />
<strong>Time:</strong> 16:00 <br />
<strong>Location</strong>: LB252</p>