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

Abstract

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.

The Effect of Alterations of Schizophrenia-Associated Genes on Gamma Band Oscillations

“…GWAS studies, such as Ripke et al [83], have identified numerous variants associated with psychiatric disorders, however, we know very little about their functional effects. As we have argued before [63,62], the modelling framework presented in this study is ideally suited to build hypotheses about their effects and to make experimentally testable predictions. To be more specific, the biophysically detailed model used here can provide very specific associations between genetic variants and phenotypes, while explicitly revealing the cellular properties through which the two are mechanistically linked.…”

“…[99]). Importantly, recent advances in computational modelling allow for the integration of knowledge about genetic contributions to ion channels and excitability and can be used to predict changes to macroscopic electroencephalography (EEG) or magnetoencephalography (MEG) signals ( [35,107,60], for a review of this emerging subfield of computational psychiatry see [62]). For example, simulations of a detailed model of tufted layer 5 pyramidal cells have recently been used to predict the effect of SCZ-associated variants of ion channel-encoding genes on neural activity in the delta frequency band [63].…”

Biophysical modeling of the neural origin of EEG and MEG signals

“…EEG and, later, MEG signals have been an important part of neuroscience for a long time, but still very little is known about the neural origin of the signals [Cohen, 2017]. A better understanding of these signals could lead to important discoveries about how the brain works [Lopes da Silva, 2013;Uhlirova et al, 2016;Pesaran et al, 2018;Ilmoniemi and Sarvas, 2019], and provide new insights into mental disorders [Mäki-Marttunen et al, 2019a;Sahin et al, 2019]. This work lays some of the foundation for obtaining a better understanding of EEG/MEG recordings, by allowing easy calculation of the signals from arbitrary neural activity.…”

“…given that studies of healthy human brains necessarily are limited to non-invasive technologies [Lopes da Silva, 2013;Uhlirova et al, 2016;Cohen, 2017]. However, given all the valuable insights that could be gained from an increased understanding of non-invasive measurements of neural activity in humans, an important challenge in modern neuroscience is to build on the mechanistic insights from animal studies and use them for understanding non-invasive signals in humans [Lopes da Silva, 2013;Uhlirova et al, 2016;Cohen, 2017;Einevoll et al, 2019;Mäki-Marttunen et al, 2019a]. The approach for calculating EEG/MEG signals in this paper should therefore ideally be used in combination with animal studies simultaneously measuring multisite laminar LFP (and MUA) signals within cortex, as well as EEG/MEG signals (see for example Bruyns-Haylett et al [2017]) [Cohen, 2017].…”

“…In other words, we know very little about exactly which types of neural activity that cause even the most well-studied characteristics of the EEG signal, such as different types of oscillations (e.g., alpha, beta, and gamma waves) and stereotyped EEG shapes in response to sensory stimuli (event-related potentials, ERPs) [Cohen, 2017]. Importantly, these different EEG characteristics are affected in predictable ways by various brain conditions, such as sleep and attention [Klimesch et al, 1998;Palva and Palva, 2011;Siegel et al, 2012], and by brain disorders including epilepsy and schizophrenia [Niedermeyer, 2003;Light and Näätänen, 2013;Freestone et al, 2015;Mäki-Marttunen et al, 2019a]. This means that a better insight into how different types of brain activity is reflected in cortical current dipoles could help us not only in making better inverse models for source localization, but also in providing a better understanding of the mechanisms of human cortical activity and possibly curing brain diseases [Uhlirova et al, 2016;Cohen, 2017;Mäki-Marttunen et al, 2019a].…”