Computational modeling of psychiatric illnesses via well-defined neurophysiological and neurocognitive biomarkers

Siekmeier, Peter J.

doi:10.1016/j.neubiorev.2015.09.014

Cited by 9 publications

(5 citation statements)

References 178 publications

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“…Many have argued that a shift toward endophenotypic measures would provide clearer mappings between these measures and the underlying genetic alterations, ultimately facilitating this drug development. As Siekmeier points out (Siekmeier, 2015), detailed computational models of endophenotypic measures can provide a crucial tool in such an effort. However, often computational modeling efforts seem to neglect the multifactorial nature of these system level measures and only investigate one specific mechanisms that might produce abnormal results, without exploring the many other ways which could possibly produce the same abnormality (Pavão et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…This effort has mainly focused on the identification and validation of biomarkers for psychiatric disorders using in vivo and in vitro studies. As Siekmeier (2015) argues, computational modeling approaches are ideally suited to complement these efforts in order to construct biomarker based models of psychiatric disorders for two reasons: (1) Models can allow for an identification and a mechanistic understanding of illness mechanisms. Not only is “ in silico ” testing of such models easier and cheaper than human or animal studies, they also offer the great advantage of making all available variables and assumptions explicit and accessible.…”

Section: Introductionmentioning

confidence: 99%

“…We focus on abnormal gamma rhythms in the auditory system, since very convincing evidence for abnormalities in this frequency band has accumulated over the last decades (see Siekmeier, 2015), It has been proposed that oscillations in and between circuits underlie efficient communication of ensembles and routing of information in the brain (“Communication through coherence,” Fries, 2005), with gamma frequency oscillations playing an important role (Bastos et al, 2015). Although this concept has been critiqued by some authors (e.g., Ray and Maunsell, 2010; Ray et al, 2013; Buzsáki and Schomburg, 2015), there is consensus that neural oscillations at least constitute a signature of the underlying computations performed in the circuit.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multifactorial Modeling of Impairment of Evoked Gamma Range Oscillations in Schizophrenia

Metzner

Schweikard²,

Zurowski

2016

Front. Comput. Neurosci.

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Despite a significant increase in efforts to identify biomarkers and endophenotypic measures of psychiatric illnesses, only a very limited amount of computational models of these markers and measures has been implemented so far. Moreover, existing computational models dealing with biomarkers typically only examine one possible mechanism in isolation, disregarding the possibility that other combinations of model parameters might produce the same network behavior (what has been termed “multifactoriality”). In this study we describe a step toward a computational instantiation of an endophenotypic finding for schizophrenia, namely the impairment of evoked auditory gamma and beta oscillations in schizophrenia. We explore the multifactorial nature of this impairment using an established model of primary auditory cortex, by performing an extensive search of the parameter space. We find that single network parameters contain only little information about whether the network will show impaired gamma entrainment and that different regions in the parameter space yield similar network level oscillation abnormalities. These regions in the parameter space, however, show strong differences in the underlying network dynamics. To sum up, we present a first step toward an in silico instantiation of an important biomarker of schizophrenia, which has great potential for the identification and study of disease mechanisms and for understanding of existing treatments and development of novel ones.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multifactorial Modeling of Impairment of Evoked Gamma Range Oscillations in Schizophrenia

Metzner

Schweikard²,

Zurowski

2016

Front. Comput. Neurosci.

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“…In particular, the heterogeneity of macroscopic brain signals, such as EEG, in both spatial and temporal domains makes it difficult to link cellular phenotypes to clinical observations (108). This presents a challenge for psychotic disorders, where the symptoms and phenotypes are complex and the cellular and network mechanisms underlying them are largely unknown (109).…”

Section: The Computational Psychiatry Approachmentioning

confidence: 99%

Biophysical Psychiatry—How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders

et al. 2019

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The brain is the most complex of human organs, and the pathophysiology underlying abnormal brain function in psychiatric disorders is largely unknown. Despite the rapid development of diagnostic tools and treatments in most areas of medicine, our understanding of mental disorders and their treatment has made limited progress during the last decades. While recent advances in genetics and neuroscience have a large potential, the complexity and multidimensionality of the brain processes hinder the discovery of disease mechanisms that would link genetic findings to clinical symptoms and behavior. This applies also to schizophrenia, for which genome-wide association studies have identified a large number of genetic risk loci, spanning hundreds of genes with diverse functionalities. Importantly, the multitude of the associated variants and their prevalence in the healthy population limit the potential of a reductionist functional genetics approach as a stand-alone solution to discover the disease pathology. In this review, we outline the key concepts of a “biophysical psychiatry,” an approach that employs large-scale mechanistic, biophysics-founded computational modelling to increase transdisciplinary understanding of the pathophysiology and strive toward robust predictions. We discuss recent scientific advances that allow a synthesis of previously disparate fields of psychiatry, neurophysiology, functional genomics, and computational modelling to tackle open questions regarding the pathophysiology of heritable mental disorders. We argue that the complexity of the increasing amount of genetic data exceeds the capabilities of classical experimental assays and requires computational approaches. Biophysical psychiatry, based on modelling diseased brain networks using existing and future knowledge of basic genetic, biochemical, and functional properties on a single neuron to a microcircuit level, may allow a leap forward in deriving interpretable biomarkers and move the field toward novel treatment options.

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“…Computational Psychiatry has been described as a bridge from neuroscience to clinical applications (Huys et al, 2016). With the availability of high-capacity computing platforms and information generated by basic neuroscience research, including the promising use of patient-derived cell models, this human biologically based computational framework is a powerful research tool (Siekmeier, 2015). Neuromarkers can be identified and validated more easily using in silico modeling, and novel treatment targets determined, relevant to the prevention, treatment and recovery from relevant to prevent, treat, and recover from psychiatric disorders can be determined.…”

Section: Resultsmentioning

confidence: 99%

Time to Abolish the Forced Swim Test in Rats for Depression Research?

Carvalho

Herrmann

Marques

et al. 2021

J. Appl. Anim. Ethics Res.

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The forced swim test (FST) is a controversial rodent test that has been used for decades, mainly in depression studies. The severity of the procedure makes it ethically questionable and its validity has also been questioned. In this paper we contribute new data to this debate. We identified original research papers related to Major Depressive Disorder (MDD), using rats as models. We compared the citations received by studies that used the FST and by studies that did not, within subsequent human medical papers. The results show that the number of citations received by both groups was very low, but in the papers describing the FST data the median citation number was zero. Citation analysis indicates that the FST is not contributing significantly to the understanding or cure of MDD. We briefly review other approaches that overcome the ethical limitations of the FST, and which might also surpass its efficacy.

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Computational modeling of psychiatric illnesses via well-defined neurophysiological and neurocognitive biomarkers

Cited by 9 publications

References 178 publications

Multifactorial Modeling of Impairment of Evoked Gamma Range Oscillations in Schizophrenia

Multifactorial Modeling of Impairment of Evoked Gamma Range Oscillations in Schizophrenia

Biophysical Psychiatry—How Computational Neuroscience Can Help to Understand the Complex Mechanisms of Mental Disorders

Time to Abolish the Forced Swim Test in Rats for Depression Research?

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