The development of advanced neuroimaging techniques and their deployment in large cohorts has enabled an assessment of functional and structural brain network architecture at an unprecedented level of detail. Across many temporal and spatial scales, network neuroscience has emerged as a central focus of intellectual efforts, seeking meaningful descriptions of brain networks and explanatory sets of network features that underlie circuit function in health and dysfunction in disease. However, the tools of network science commonly deployed provide insight into brain function at a fundamentally descriptive level, often failing to identify (patho-)physiological mechanisms that link system-level phenomena to the multiple hierarchies of brain function. Here we describe recently developed techniques stemming from advances in complex systems and network science that have the potential to overcome this limitation, thereby contributing mechanistic insights into neuroanatomy, functional dynamics, and pathology. Finally, we build on the Research Domain Criteria framework, highlighting the notion that mental illnesses can be conceptualized as dysfunctions of neural circuitry present across conventional diagnostic boundaries, to sketch how network-based methods can be combined with pharmacological, intermediate phenotype, genetic, and magnetic stimulation studies to probe mechanisms of psychopathology.
From Maps to Multi-dimensional Network Mechanisms of Mental Disorders
Urs Braun, Axel Schaefer, Richard F. Betzel, Heike Tost, Andreas Meyer-Lindenberg, Danielle S. Bassett
Volume 97, Issue 1, 3 January 2018, Pages 14-31
Spreading broadly refers to the notion of an entity propagating throughout a networked system via its interacting components. Evidence of its ubiquity and severity can be seen in a range of phenomena, from disease epidemics to financial systemic risk. In order to understand the dynamics of these critical phenomena, computational models map the probability of propagation as a function of direct exposure, typically in the form of pairwise interactions between components. By doing so, the important role of indirect exposure remains unexplored. In response, we develop a simple model that accounts for the effect of both direct and indirect exposure, which we deploy in the novel context of failure propagation within a real-world engineering project. We show that indirect exposure has a significant effect in key aspects, including the: (a) final spreading event size, (b) propagation rate, and (c) spreading event structure. In addition, we demonstrate the existence of hidden influentials in large-scale spreading events, and evaluate the role of direct and indirect exposure in their emergence. Given the evidence of the importance of indirect exposure, our findings offer new insight on particular aspects that need to be included when modelling network dynamics in general, and spreading processes specifically.
Modelling indirect interactions during failure spreading in a project activity network
• Sustainability challenges require both specialized and integrative approaches.
• Domination of specialism and reductionism calls for emphasis on comprehensiveness.
• The GHH framework can be used as a tool to add comprehensiveness in education.
• The framework consists of three dimensions: generalism, holism, and holarchism.
• The dialectical approach combines comprehensive and differentiative approaches.
Despite the increasing effectiveness of breast cancer treatments over the last 50 years, tumors often become resistent to the drugs used. While drug combinations could be part of the solution to this problem, their development is very challenging. In this blog post Jorge Zanudo explains how it is possible to combine physical and mathemathical models with clinical and biological data to determine which drug combinations would be most effective in breast cancer therapy.
Intelligence and consciousness have fascinated humanity for a long time and we have long sought to replicate this in machines. In this work we show some design principles for a compassionate and conscious artificial intelligence. We present a computational framework for engineering intelligence, empathy and consciousness in machines. We hope that this framework will allow us to better understand consciousness and design machines that are conscious and empathetic. Our hope is that this will also shift the discussion from a fear of artificial intelligence towards designing machines that embed our cherished values in them. Consciousness, intelligence and empathy would be worthy design goals that can be engineered in machines.
Banerjee S. (2018) A framework for designing compassionate and ethical artificial intelligence and artificial consciousness. PeerJ Preprints 6:e3502v2 https://doi.org/10.7287/peerj.preprints.3502v2