Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study

Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this paper we (i) describe nine challenges that occur when applying causal discovery to fMRI data, (ii) discuss the space of decisions that need to be made, (iii) review how a recent case study made those decisions, (iv) and identify existing gaps that could potentially be solved by the development of new methods. Overall, causal discovery is a promising approach for analyzing fMRI data, and multiple successful applications have indicated that it is superior to traditional fMRI functional connectivity methods, but current causal discovery methods for fMRI leave room for improvement

Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees

Learning graphical conditional independence structures is an important machine learning problem and a cornerstone of causal discovery. However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables -- for instance, recovering brain networks from fMRI data. We introduce the best order score search (BOSS) and grow-shrink trees (GSTs) for learning directed acyclic graphs (DAGs) in this paradigm. BOSS greedily searches over permutations of variables, using GSTs to construct and score DAGs from permutations. GSTs efficiently cache scores to eliminate redundant calculations. BOSS achieves state-of-the-art performance in accuracy and execution time, comparing favorably to a variety of combinatorial and gradient-based learning algorithms under a broad range of conditions. To demonstrate its practicality, we apply BOSS to two sets of resting-state fMRI data: simulated data with pseudo-empirical noise distributions derived from randomized empirical fMRI cortical signals and clinical data from 3T fMRI scans processed into cortical parcels. BOSS is available for use within the TETRAD project which includes Python and R wrappers

Cognitive Control Errors in Nonhuman Primates Resembling Those in Schizophrenia Reflect Opposing Effects of NMDA Receptor Blockade on Causal Interactions Between Cells and Circuits in Prefrontal and Parietal Cortices

Background: The causal biology underlying schizophrenia is not well understood, but it is likely to involve a malfunction in how neurons adjust synaptic connections in response to patterns of activity in networks. We examined statistical dependencies between neural signals at the cell, local circuit, and distributed network levels in prefrontal and parietal cortices of monkeys performing a variant of the AX continuous performance task paradigm. We then quantified changes in the pattern of neural interactions across levels of scale following NMDA receptor (NMDAR) blockade and related these changes to a pattern of cognitive control errors closely matching the performance of patients with schizophrenia. Methods: We recorded the spiking activity of 1762 neurons along with local field potentials at multiple electrode sites in prefrontal and parietal cortices concurrently, and we generated binary time series indicating the presence or absence of spikes in single neurons or local field potential power above or below a threshold. We then applied causal discovery analysis to the time series to detect statistical dependencies between the signals (causal interactions) and compared the pattern of these interactions before and after NMDAR blockade. Results: Global blockade of NMDAR produced distinctive and frequently opposite changes in neural interactions at the cell, local circuit, and network levels in prefrontal and parietal cortices. Cognitive control errors were associated with decreased interactions at the cell level and with opposite changes at the network level in prefrontal and parietal cortices. Conclusions: NMDAR synaptic deficits change causal interactions between neural signals at different levels of scale that correlate with schizophrenia-like deficits in cognitive control

Just how versatile are domains?

<p>Abstract</p> <p>Background</p> <p>Creating new protein domain arrangements is a frequent mechanism of evolutionary innovation. While some domains always form the same combinations, others form many different arrangements. This ability, which is often referred to as versatility or promiscuity of domains, its a random evolutionary model in which a domain's promiscuity is based on its relative frequency of domains.</p> <p>Results</p> <p>We show that there is a clear relationship across genomes between the promiscuity of a given domain and its frequency. However, the strength of this relationship differs for different domains. We thus redefine domain promiscuity by defining a new index, <it>DV I </it>("domain versatility index"), which eliminates the effect of domain frequency. We explore links between a domain's versatility, when unlinked from abundance, and its biological properties.</p> <p>Conclusion</p> <p>Our results indicate that domains occurring as single domain proteins and domains appearing frequently at protein termini have a higher <it>DV I</it>. This is consistent with previous observations that the evolution of domain re-arrangements is primarily driven by fusion of pre-existing arrangements and single domains as well as loss of domains at protein termini. Furthermore, we studied the link between domain age, defined as the first appearance of a domain in the species tree, and the <it>DV I</it>. Contrary to previous studies based on domain promiscuity, it seems as if the <it>DV I </it>is age independent. Finally, we find that contrary to previously reported findings, versatility is lower in Eukaryotes. In summary, our measure of domain versatility indicates that a random attachment process is sufficient to explain the observed distribution of domain arrangements and that several views on domain promiscuity need to be revised.</p

Dynamics and Adaptive Benefits of Protein Domain Emergence and Arrangements during Plant Genome Evolution

Plant genomes are generally very large, mostly paleopolyploid, and have numerous gene duplicates and complex genomic features such as repeats and transposable elements. Many of these features have been hypothesized to enable plants, which cannot easily escape environmental challenges, to rapidly adapt. Another mechanism, which has recently been well described as a major facilitator of rapid adaptation in bacteria, animals, and fungi but not yet for plants, is modular rearrangement of protein-coding genes. Due to the high precision of profile-based methods, rearrangements can be well captured at the protein level by characterizing the emergence, loss, and rearrangements of protein domains, their structural, functional, and evolutionary building blocks. Here, we study the dynamics of domain rearrangements and explore their adaptive benefit in 27 plant and 3 algal genomes. We use a phylogenomic approach by which we can explain the formation of 88% of all arrangements by single-step events, such as fusion, fission, and terminal loss of domains. We find many domains are lost along every lineage, but at least 500 domains are novel, that is, they are unique to green plants and emerged more or less recently. These novel domains duplicate and rearrange more readily within their genomes than ancient domains and are overproportionally involved in stress response and developmental innovations. Novel domains more often affect regulatory proteins and show a higher degree of structural disorder than ancient domains. Whereas a relatively large and well-conserved core set of single-domain proteins exists, long multi-domain arrangements tend to be species-specific. We find that duplicated genes are more often involved in rearrangements. Although fission events typically impact metabolic proteins, fusion events often create new signaling proteins essential for environmental sensing. Taken together, the high volatility of single domains and complex arrangements in plant genomes demonstrate the importance of modularity for environmental adaptability of plants

Tracking Time-varying Graphical Structure

Structure learning algorithms for graphical models have focused almost exclusively on stable environments in which the underlying generative process does not change; that is, they assume that the generating model is globally stationary. In real-world environments, however, such changes often occur without warning or signal. Real-world data often come from generating models that are only locally stationary. In this paper, we present LoSST, a novel, heuristic structure learning algorithm that tracks changes in graphical model structure or parameters in a dynamic, real-time manner. We show by simulation that the algorithm performs comparably to batch-mode learning when the generating graphical structure is globally stationary, and significantly better when it is only locally stationary.</p