Increasing robustness of pairwise methods for effective connectivity in Magnetic Resonance Imaging by using fractional moment series of BOLD signal distributions

Estimating causal interactions in the brain from functional magnetic resonance imaging (fMRI) data remains a challenging task. Multiple studies have demonstrated that all current approaches to determine direction of connectivity perform poorly even when applied to synthetic fMRI datasets. Recent advances in this field include methods for pairwise inference, which involve creating a sparse connectome in the first step, and then using a classifier in order to determine the directionality of connection between of every pair of nodes in the second step. In this work, we introduce an advance to the second step of this procedure, by building a classifier based on fractional moments of the BOLD distribution combined into cumulants. The classifier is trained on datasets generated under the Dynamic Causal Modeling (DCM) generative model. The directionality is inferred based upon statistical dependencies between the two node time series, e.g. assigning a causal link from time series of low variance to time series of high variance. Our approach outperforms or performs as well as other methods for effective connectivity when applied to the benchmark datasets. Crucially, it is also more resilient to confounding effects such as differential noise level across different areas of the connectome.Comment: 41 pages, 12 figure

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Effective Self-Management for Early Career Researchers in the Natural and Life Sciences

Early career researchers (ECRs) are faced with a range of competing pressures in academia, making self-management key to building a successful career. The Organization for Human Brain Mapping undertook a group effort to gather helpful advice for ECRs in self-management. Keywords: ECRs; career development; early career researchers; mentoring; networking; self-managemen

Neuronal Causes and Behavioural Effects: a Review on Logical, Methodological, and Technical Issues With Respect to Causal Explanations of Behaviour in Neuroscience

Elucidating causal, neurobiological underpinnings of behaviour is an ultimate goal of every neuroscientific study. However, due to the complexity of the brain as well as the complexity of the human environment, finding a~causal architecture that underlies behaviour remains a~formidable challenge. In this manuscript, we review the logical and conceptual issues with respect to causal research in neuroscience.First, we review the state of the art interventional and computational approaches to infer causal brain-behaviour relationships. We provide an~overview of potential issues, flaws, and confounds in these studies. We conclude that studies on the causal structure underlying behaviour should be performed by accumulating evidence coming from several lines of experimental and modelling studies. Lastly, we also propose computational models including artificial neuronal networks and simulated animats as a~potential breakthrough to causal brain-behaviour investigations

Circuit to construct mapping: a mathematical tool for assisting the diagnosis and treatment in Major Depressive Disorder

Major Depressive Disorder (MDD) is a serious condition with a lifetime prevalence exceeding 16% worldwide. MDD is a heterogeneous disorder that involves multiple behavioral symptoms on the one hand, and multiple neuronal circuits on the other hand. In this review, we integrate the literature on cognitive and physiological biomarkers of MDD with the insights derived from mathematical models of brain networks, especially models that can be used for fMRI datasets. We refer to the recent NIH RDoC Initiative, in which a concept of ‘constructs’ as functional units of mental disorders is introduced. Constructs are biomarkers present at multiple levels of brain functioning - cognition, genetics, brain anatomy and neurophysiology.In this review, we propose a new approach which we called Circuit to Construct Mapping (CCM), which aims to characterize causal relations between the underlying network dynamics (as the cause) and the constructs referring to the clinical symptoms of MDD (as the effect). CCM involves extracting diagnostic categories from behavioral data, linking circuits that are causal to these categories with use of clinical neuroimaging data, and modeling the dynamics of the emerging circuits with attractor dynamics in order to provide new, neuroimaging-related biomarkers for MDD.The CCM approach optimizes the clinical diagnosis and patient stratification. It also addresses the recent demand for linking circuits to behavior, and provides a new insight into clinical treatment by investigating the dynamics of neuronal circuits underneath cognitive dimensions of MDD. CCM can serve as a new regime towards personalized medicine, assisting the diagnosis and treatment of MDD

Introducing Massively Open Online Papers (MOOPs)

An enormous wealth of digital tools now exists for collaborating on scholarly research projects. In particular, it is now possible to collaboratively author research articles in an openly participatory and dynamic format. Here we describe and provide recommendations for a more open process of digital collaboration, and discuss the potential issues and pitfalls that come with managing large and diverse authoring communities. We summarize our personal experiences in a form of ‘ten simple recommendations’. Typically, these collaborative, online projects lead to the production of what we here introduce as Massively Open Online Papers (MOOPs). We consider a MOOP to be distinct from a ‘traditional’ collaborative article in that it is defined by an openly participatory process, not bound within the constraints of a predefined contributors list. This is a method of organised creativity designed for the efficient generation and capture of ideas in order to produce new knowledge. Given the diversity of potential authors and projects that can be brought into this process, we do not expect that these tips will address every possible project. Rather, these tips are based on our own experiences and will be useful when different groups and communities can uptake different elements into their own workflows. We believe that creating inclusive, interdisciplinary, and dynamic environments is ultimately good for science, providing a way to exchange knowledge and ideas as a community. We hope that these Recommendations will prove useful for others who might wish to explore this space

Introducing Massively Open Online Papers (MOOPs)

An enormous wealth of digital tools now exists for collaborating on scholarly research projects. In particular, it is now possible to collaboratively author research articles in an openly participatory and dynamic format. Here we describe and provide recommendations for a more open process of digital collaboration, and discuss the potential issues and pitfalls that come with managing large and diverse authoring communities. We summarize our personal experiences in a form of ‘ten simple recommendations’. Typically, these collaborative, online projects lead to the production of what we here introduce as Massively Open Online Papers (MOOPs). We consider a MOOP to be distinct from a ‘traditional’ collaborative article in that it is defined by an openly participatory process, not bound within the constraints of a predefined contributors list. This is a method of organised creativity designed for the efficient generation and capture of ideas in order to produce new knowledge. Given the diversity of potential authors and projects that can be brought into this process, we do not expect that these tips will address every possible project. Rather, these tips are based on our own experiences and will be useful when different groups and communities can uptake different elements into their own workflows. We believe that creating inclusive, interdisciplinary, and dynamic environments is ultimately good for science, providing a way to exchange knowledge and ideas as a community. We hope that these Recommendations will prove useful for others who might wish to explore this space