On a Quantitative Approach to Clinical Neuroscience in Psychiatry: Lessons from the Kuramoto Model

Modeling intentionality in the human brain

This paper is focusing on a rather neglected issue that concerns both aspects of philosophy and neurobiology in relation to the concept of intentionality. Intentionality is concerned with the 'directedness' or 'aboutness' of mental phenomena towards an object. Despite the fact that in philosophy both concepts of aboutness and directedness are conceptually identical with intentionality, a careful neuroscientific approach can demonstrate that these two phenomena represent two distinct conceptual and neurobiological aspects of intentionality with complementary functions. We described the interaction between a series of intentionality and pathogenetic psychobiological factors, the corresponding brain topography, and the resulting clinical manifestation and psychopathology. A permanent failure of intentionality dominates in psychosis, which includes an inappropriateness of the intentional object or connection, from the outset, or even from the prodromal phase of the disorder. Affective disorders may result from imprecise interoceptive prediction error signals, due to a confused identification of the intentional object. In suicidal patients there is an emotional intentionality failure, characterized by an absence of intentional object or a loss of conscious access to normal intentional objects. We may model an 'intentional system' as a higher order system, with a monitoring and regulatory role attributed to the brain and behavior. Also, we may consider mental disorders as the result of a radical disruption of intentionality, due to an inappropriateness or lack of the intentional object or due to an inappropriate connection in some points of the suggested brain pathways of intentionality.

Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics

Significant research has investigated synchronisation in brain networks, but the bulk of this work has explored the contribution of brain networks at the macroscale. Here we explore the effects of changing network topology on functional dynamics in spatially constrained random networks representing mesoscale neocortex. We use the Kuramoto model to simulate network dynamics and explore synchronisation and critical dynamics of the system as a function of topology in randomly generated networks with a distance-related wiring probability and no preferential attachment term. We show networks which predominantly make short-distance connections smooth out the critical coupling point and show much greater metastability, resulting in a wider range of coupling strengths demonstrating critical dynamics and metastability. We show the emergence of cluster synchronisation in these geometrically-constrained networks with functional organisation occurring along structural connections that minimise the participation coefficient of the cluster. We show that these cohorts of internally synchronised nodes also behave en masse as weakly coupled nodes and show intra-cluster desynchronisation and resynchronisation events related to inter-cluster interaction. While cluster synchronisation appears crucial to healthy brain function, it may also be pathological if it leads to unbreakable local synchronisation which may happen at extreme topologies, with implications for epilepsy research, wider brain function and other domains such as social networks.

Generative Models of Brain Dynamics

This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.

Understanding brain states across spacetime informed by whole-brain modelling

In order to survive in a complex environment, the human brain relies on the ability to flexibly adapt ongoing behaviour according to intrinsic and extrinsic signals. This capability has been linked to specific whole-brain activity patterns whose relative stability (order) allows for consistent functioning, supported by sufficient intrinsic instability needed for optimal adaptability. The emergent, spontaneous balance between order and disorder in brain activity over spacetime underpins distinct brain states. For example, depression is characterized by excessively rigid, highly ordered states, while psychedelics can bring about more disordered, sometimes overly flexible states. Recent developments in systems, computational and theoretical neuroscience have started to make inroads into the characterization of such complex dynamics over space and time. Here, we review recent insights drawn from neuroimaging and whole-brain modelling motivating using mechanistic principles from dynamical system theory to study and characterize brain states. We show how different healthy and altered brain states are associated to characteristic spacetime dynamics which in turn may offer insights that in time can inspire new treatments for rebalancing brain states in disease. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.