In order to successfully obtain a faculty position, postdoctoral fellows or ‘postdocs’, must submit an application which requires considerable time and effort to produce. These job applications are often reviewed by mentors and colleagues, but rarely are postdocs offered the opportunity to solicit feedback multiple times from reviewers with the same breadth of expertise often found on an academic search committee. To address this gap, this manuscript describes an international peer reviewing program for small groups of postdocs with a broad range of expertise to reciprocally and iteratively provide feedback to each other on their application materials. Over 145 postdocs have participated, often multiple times, over three years. A survey of participants in this program revealed that nearly all participants would recommend participation in such a program to other faculty applicants. Furthermore, this program was more likely to attract participants who struggled to find mentoring and support elsewhere, either because they changed fields or because of their identity as a woman or member of an underrepresented population in STEM. Participation in programs like this one could provide early career academics like postdocs with a diverse and supportive community of peer mentors during the difficult search for a faculty position. Such psychosocial support and encouragement has been shown to prevent attrition of individuals from these populations and programs like this one target the largest ‘leak’ in the pipeline, that of postdoc to faculty. Implementation of similar peer reviewing programs by universities or professional scientific societies could provide a valuable mechanism of support and increased chances of success for early-career academics in their search for independence.
Dynamic Causal Modeling (DCM) is a Bayesian framework for inferring on hidden (latent) neuronal states, based on measurements of brain activity. Since its introduction in 2003 for functional magnetic resonance imaging data, DCM has been extended to electrophysiological data, and several variants have been developed. Their biophysically motivated formulations make these models promising candidates for providing a mechanistic understanding of human brain dynamics, both in health and disease. However, due to their complexity and reliance on concepts from several fields, fully understanding the mathematical and conceptual basis behind certain variants of DCM can be challenging. At the same time, a solid theoretical knowledge of the models is crucial to avoid pitfalls in the application of these models and interpretation of their results. In this paper, we focus on one of the most advanced formulations of DCM, i.e. conductance-based DCM for cross-spectral densities, whose components are described across multiple technical papers. The aim of the present article is to provide an accessible exposition of the mathematical background, together with an illustration of the model's behavior. To this end, we include step-by-step derivations of the model equations, point to important aspects in the software implementation of those models, and use simulations to provide an intuitive understanding of the type of responses that can be generated and the role that specific parameters play in the model. Furthermore, all code utilized for our simulations is made publicly available alongside the manuscript to allow readers an easy hands-on experience with conductance-based DCM.
This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.
Despite the success of modern neuroimaging techniques in furthering our understanding of cognitive and pathophysiological processes, translation of these advances into clinically relevant tools has been virtually absent until now. Neuromodeling represents a powerful framework for overcoming this translational deadlock, and the development of computational models to solve clinical problems has become a major scientific goal over the last decade, as reflected by the emergence of clinically oriented neuromodeling fields like Computational Psychiatry, Computational Neurology, and Computational Psychosomatics. Generative models of brain physiology and connectivity in the human brain play a key role in this endeavor, striving for computational assays that can be applied to neuroimaging data from individual patients for differential diagnosis and treatment prediction. In this review, we focus on dynamic causal modeling (DCM) and its use for Computational Psychiatry. DCM is a widely used generative modeling framework for functional magnetic resonance imaging (fMRI) and magneto-/electroencephalography (M/EEG) data. This article reviews the basic concepts of DCM, revisits examples where it has proven valuable for addressing clinically relevant questions, and critically discusses methodological challenges and recent methodological advances. We conclude this review with a more general discussion of the promises and pitfalls of generative models in Computational Psychiatry and highlight the path that lies ahead of us.
Dynamic causal modeling (DCM) is a generic Bayesian framework for inferring hidden neuronal states from measurements of brain activity. It provides posterior estimates of neurobiologically interpretable quantities such as the effective strength of synaptic connections among neuronal populations and their context-dependent modulation. DCM is increasingly used in the analysis of a wide range of neuroimaging and electrophysiological data. Given the relative complexity of DCM, compared to conventional analysis techniques, a good knowledge of its theoretical foundations is needed to avoid pitfalls in its application and interpretation of results. By providing good practice recommendations for DCM, in the form of ten simple rules, we hope that this article serves as a helpful tutorial for the growing community of DCM users.
The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.
G protein-coupled receptors (GPCRs) are key regulators of information transmission between cells and organs. Despite this, we have only limited understanding of the behavior of GPCRs in the apo state and the conformational changes upon agonist binding that lead to G protein recruitment and activation. We expressed and purified unmodified apo and peptide-bound calcitonin gene-related peptide (CGRP) receptors to determine their cryo-EM structures and complemented these with analysis of protein conformational dynamics using hydrogen-deuterium exchange mass spectrometry (HDX-MS) and 3D variance analysis of the cryo-EM data. Together with our previously published structure of the active, Gs-bound, CGRP receptor complex, our work provides important insight into mechanisms of class B1 GPCR activation.
We present Memory FORESHADOW: Memory FOREnSics of HArDware cryptOcurrency Wallets. To the best of our knowledge, this is the primary account of cryptocurrency hardware wallet client memory forensics. Our exploratory analysis revealed forensically relevant data in memory including transaction history, extended public keys, passphrases, and unique device identifiers. Data extracted with FORESHADOW can be used to associate a hardware wallet with a computer and allow an observer to deanonymize all past and future transactions due to hierarchical deterministic wallet address derivation. Additionally, our novel visualization framework enabled us to measure both the persistence and integrity of artifacts produced by the Ledger and Trezor hardware wallet clients. The framework can be generalized for use in future memory forensics work.
The advent of stablecoins offers new and innovative ways to improve financial inclusion, reduce transaction costs, and increase the efficiency of the global financial system. The following paper explores the assets and process necessary for creating a central bank digital currency (CBDC) on the Celo platform, as well as the potential impact on the financial system. Perhaps most importantly, the paper also introduces the idea that current technological advancements allow for a better understanding of the velocity of money, and may afford central banks the ability to influence money velocity, thus potentially creating a new transmission channel for monetary policy.
Two of the biggest barriers to the large-scale adoption of cryptocurrencies as a means of payment are ease-of-use and purchasing-power volatility. We introduce Celo, a protocol that addresses these issues with an address-based encryption scheme and a stable-value token. We show how these attributes together can be used to foster a monetary ecology that includes global reference currencies, local and regional stable-value currencies, and a social dividend. Our first application is a social-payments system centered around mobile phones.