General theory of topological explanations and explanatory asymmetry

The expanding horizons of network neuroscience: From description to prediction and control

The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.

Integrating Philosophy of Understanding With the Cognitive Sciences

We provide two programmatic frameworks for integrating philosophical research on understanding with complementary work in computer science, psychology, and neuroscience. First, philosophical theories of understanding have consequences about how agents should reason if they are to understand that can then be evaluated empirically by their concordance with findings in scientific studies of reasoning. Second, these studies use a multitude of explanations, and a philosophical theory of understanding is well suited to integrating these explanations in illuminating ways.

Using network models in person-centered care in psychiatry: How perspectivism could help to draw boundaries

In this paper, we explore the conceptual problems that arise when using network analysis in person-centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that perspectival reasoning can make more explicit what questions personalized network models can address in PCC, given their boundaries.

Descriptive understanding and prediction in COVID-19 modelling

COVID-19 has substantially affected our lives during 2020. Since its beginning, several epidemiological models have been developed to investigate the specific dynamics of the disease. Early COVID-19 epidemiological models were purely statistical, based on a curve-fitting approach, and did not include causal knowledge about the disease. Yet, these models had predictive capacity; thus they were used to ground important political decisions, in virtue of the understanding of the dynamics of the pandemic that they offered. This raises a philosophical question about how purely statistical models can yield understanding, and if so, what the relationship between prediction and understanding in these models is. Drawing on the model that was developed by the Institute of Health Metrics and Evaluation, we argue that early epidemiological models yielded a modality of understanding that we call descriptive understanding, which contrasts with the so-called explanatory understanding which is assumed to be the main form of scientific understanding. We spell out the exact details of how descriptive understanding works, and efficiently yields understanding of the phenomena. Finally, we vindicate the necessity of studying other modalities of understanding that go beyond the conventionally assumed explanatory understanding.

Causal and non-causal explanations in code biology

In the philosophy of science, we can consider debates about the nature of non-causal explanations in general (e.g. Reutlinger, Saatsi 2018; Lange 2017) and then especially those in the life sciences (e.g. Huneman, 2018; Kostić 2020). These debates are accompanied by the development of a new mechanism that is becoming the major response to the nature of scientific explanation in the life sciences (e.g. Craver, Darden 2013; Craver 2006); and also by the development of a design explanation (e.g. Eck, Mennes 2016) that represents a modern variant of a functional explanation. In this paper, we will methodically: 1. evaluate the plurality of explanatory strategies in contemporary science (chapter 2). 2. describe the mechanical philosophy and mechanistic explanation (Glennan 2016; Craver, Darden 2013, etc.) (chapter 3). 3. explicate the role of mechanisms in code biology (Barbieri 2015, 2002, etc.) and its relation to the new mechanism (chapter 4). 4. fulfill the main goal of the paper - to apply mechanistic explanations in code biology (Barbieri 2019, etc.) and to apply their suitability for this scientific domain (chapter 5).

The Network Theory of Psychiatric Disorders: A Critical Assessment of the Inclusion of Environmental Factors

Borsboom and colleagues have recently proposed a "network theory" of psychiatric disorders that conceptualizes psychiatric disorders as relatively stable networks of causally interacting symptoms. They have also claimed that the network theory should include non-symptom variables such as environmental factors. How are environmental factors incorporated in the network theory, and what kind of explanations of psychiatric disorders can such an "extended" network theory provide? The aim of this article is to critically examine what explanatory strategies the network theory that includes both symptoms and environmental factors can accommodate. We first analyze how proponents of the network theory conceptualize the relations between symptoms and between symptoms and environmental factors. Their claims suggest that the network theory could provide insight into the causal mechanisms underlying psychiatric disorders. We assess these claims in light of network analysis, Woodward's interventionist theory, and mechanistic explanation, and show that they can only be satisfied with additional assumptions and requirements. Then, we examine their claim that network characteristics may explain the dynamics of psychiatric disorders by means of a topological explanatory strategy. We argue that the network theory could accommodate topological explanations of symptom networks, but we also point out that this poses some difficulties. Finally, we suggest that a multilayer network account of psychiatric disorders might allow for the integration of symptoms and non-symptom factors related to psychiatric disorders and could accommodate both causal/mechanistic and topological explanations.

Are topological explanations really free of mechanisms?

Topological explanations in biology have been largely assumed to be free of mechanisms. However, by examining two classic topological explanations in the philosophical literature, this article has identified mechanisms in the corrected and complete formulations of both explanations. This constitutes the major work of this article. The minor work of this article is to address a follow-up question: given that these two topological explanations contain mechanisms, would this significantly blur the widely assumed boundary between topological and mechanistic explanations? My answer to this question is negative and the argument I have developed is that although these two topological explanations contain mechanisms, these mechanisms are explanatorily irrelevant to the target properties, which is in stark contrast to the situation in mechanistic explanations.

Shifting levels of ecological network's analysis reveals different system properties

Network analyses applied to models of complex systems generally contain at least three levels of analyses. Whole-network metrics summarize general organizational features (properties or relationships) of the entire network, while node-level metrics summarize similar organization features but consider individual nodes. The network- and node-level metrics build upon the primary pairwise relationships in the model. As with many analyses, sometimes there are interesting differences at one level that disappear in the summary at another level of analysis. We illustrate this phenomenon with ecosystem network models, where nodes are trophic compartments and pairwise relationships are flows of organic carbon, such as when a predator eats a prey. For this demonstration, we analysed a time-series of 16 models of a lake planktonic food web that describes carbon exchanges within an autumn cyanobacteria bloom and compared the ecological conclusions drawn from the three levels of analysis based on inter-time-step comparisons. A general pattern in our analyses was that the closer the levels are in hierarchy (node versus network, or flow versus node level), the more they tend to align in their conclusions. Our analyses suggest that selecting the appropriate level of analysis, and above all regularly using multiple levels, may be a critical analytical decision. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Unifying the essential concepts of biological networks: biological insights and philosophical foundations

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organizational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels and network hierarchies. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Hierarchy and levels: analysing networks to study mechanisms in molecular biology

Network representations are flat while mechanisms are organized into a hierarchy of levels, suggesting that the two are fundamentally opposed. I challenge this opposition by focusing on two aspects of the ways in which large-scale networks constructed from high-throughput data are analysed in systems biology: identifying clusters of nodes that operate as modules or mechanisms and using bio-ontologies such as gene ontology (GO) to annotate nodes with information about where entities appear in cells and the biological functions in which they participate. Of particular importance, GO organizes biological knowledge about cell components and functions hierarchically. I illustrate how this supports mechanistic interpretation of networks with two examples of network studies, one using epistatic interactions among genes to identify mechanisms and their parts and the other using deep learning to predict phenotypes. As illustrated in these examples, when network research draws upon hierarchical information such as provided by GO, the results not only can be interpreted mechanistically but provide new mechanistic knowledge. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.