Ontological Dimensions of Cognitive-Neural Mappings

A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria Framework

Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific or excessively broad relative to the underlying brain circuitry it seeks to elucidate. To address these concerns of the RDoC framework, our study employed a latent variable approach, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. External validation was performed with 36 peak coordinate activation maps from Neurosynth, using terms of RDoC constructs as seeds for topic meta-analysis. Our results indicate that a bifactor model with a task-general domain and splitting the cognitive systems domain into sub-domains better fits the current corpus of tfMRI data than the current RDoC framework. Our data-driven validation supports revising the RDoC framework to accurately reflect underlying brain circuitry.

Neuroimaging-ITM: A Text Mining Pipeline Combining Deep Adversarial Learning with Interaction Based Topic Modeling for Enabling the FAIR Neuroimaging Study

Sharing various neuroimaging digital resources have received widespread attention in FAIR (Findable, Accessible, Interoperable and Reusable) neuroscience. In order to support a comprehensive understanding of brain cognition, neuroimaging provenance should be constructed to characterize both research processes and results, and integrates various digital resources for quick replication and open cooperation. This brings new challenges to neuroimaging text mining, including fragmented information, lack of labelled corpora, and vague topics. This paper proposes a text mining pipeline for enabling the FAIR neuroimaging study. In order to avoid fragmented information, the Brain Informatics provenance model is redesigned based on NIDM (Neuroimaging Data Model) and FAIR facets. It can systematically capture the provenance requests from the FAIR neuroimaging study and then transform them into a group of text mining tasks. A neuroimaging text mining pipeline combining deep adversarial learning with interaction based topic modeling, called neuroimaging interaction topic model (Neuroimaging-ITM), is proposed to automatically extract neuroimaging provenance and identify research topics in the few-shot scenario. Finally, a group of experiments is completed by using real data from the journal PloS One. The experimental results show that Neuroimaging-ITM can systematically and accurately extract provenance information and obtain high-quality research topics from the full text of neuroimaging articles. Most of the mean F1 values of provenance extraction exceed 0.9. The topic coherence and KL (Kullback-Leibler) divergence reach 9.95 and 0.96 respectively. The results are obviously better than baseline methods.

AT-NeuroEAE: A Joint Extraction Model of Events With Attributes for Research Sharing-Oriented Neuroimaging Provenance Construction

Provenances are a research focus of neuroimaging resources sharing. An amount of work has been done to construct high-quality neuroimaging provenances in a standardized and convenient way. However, besides existing processed-based provenance extraction methods, open research sharing in computational neuroscience still needs one way to extract provenance information from rapidly growing published resources. This paper proposes a literature mining-based approach for research sharing-oriented neuroimaging provenance construction. A group of neuroimaging event-containing attributes are defined to model the whole process of neuroimaging researches, and a joint extraction model based on deep adversarial learning, called AT-NeuroEAE, is proposed to realize the event extraction in a few-shot learning scenario. Finally, a group of experiments were performed on the real data set from the journal PLOS ONE. Experimental results show that the proposed method provides a practical approach to quickly collect research information for neuroimaging provenance construction oriented to open research sharing.

Automated meta-analysis of the event-related potential (ERP) literature

Event-related potentials (ERPs) are a common approach for investigating the neural basis of cognition and disease. There exists a vast and growing literature of ERP-related articles, the scale of which motivates the need for efficient and systematic meta-analytic approaches for characterizing this research. Here we present an automated text-mining approach as a form of meta-analysis to examine the relationships between ERP terms, cognitive domains and clinical disorders. We curated dictionaries of terms, collected articles of interest, and measured co-occurrence probabilities in published articles between ERP components and cognitive and disorder terms. Collectively, this literature dataset allows for creating data-driven profiles for each ERP, examining key associations of each component, and comparing the similarity across components, ultimately allowing for characterizing patterns and associations between topics and components. Additionally, by examining large literature collections, novel analyses can be done, such as examining how ERPs of different latencies relate to different cognitive associations. This openly available dataset and project can be used both as a pedagogical tool, and as a method of inquiry into the previously hidden structure of the existing literature. This project also motivates the need for consistency in naming, and for developing a clear ontology of electrophysiological components.

A data-driven framework for mapping domains of human neurobiology

Functional neuroimaging has been a mainstay of human neuroscience for the past 25 years. Interpretation of fMRI data has often occurred within knowledge frameworks crafted by experts, which have the potential to amplify biases that limit the replicability of findings. Here, we employ a computational approach to derive a data-driven framework for neurobiological domains that synthesizes the texts and data of nearly 20,000 human neuroimaging articles. Across multiple levels of domain specificity, the structure-function links within domains better replicate in held-out articles than those mapped from dominant frameworks in neuroscience and psychiatry. We further show that the data-driven framework partitions the literature into modular subfields, for which domains serve as generalizable prototypes of structure-function patterns in single articles. The approach to computational ontology we present here is the most comprehensive characterization of human brain circuits quantifiable with fMRI and may be extended to synthesize other scientific literatures.

Measuring Behavior and Social Cognition in FTLD

Because changes to socioemotional cognition and behavior are an early and central symptom in many of the FTLD syndromes, an objective and standardized approach to patient identification and staging relies on availability of validated socioemotional measures. Such tests should reflect functioning in key selectively vulnerable brain networks central to socioemotional behavior, specifically the intrinsically connected networks underpinning salience (SN) and semantic appraisal (SAN). There have been many challenges to the development of appropriate tests for patients with the FTLD syndromes, including the difficulty of creating standardized evaluations for the highly idiosyncratic deficits caused by salience-driven attention impairments, the trade-off between behaviorally or psychophysiologically precise measures versus the need for easily administered measures that can scale to broader clinical contexts, and the complexities of measuring socioemotional behavior across linguistically and culturally diverse samples. A subset of available socioemotional tests are reviewed with respect to evidence for their ability to reflect structural and functional changes to the FTLD-specific SN and SAN networks, and their differential diagnostic utility in the neurodegenerative disease syndromes is discussed.

Combining local and global evolutionary trajectories of brain-behaviour relationships through game theory

The study of the evolution of brain-behaviour relationships concerns understanding the causes and repercussions of cross- and within-species variability. Understanding such variability is a main objective of evolutionary and cognitive neuroscience, and it may help explaining the appearance of psychopathological phenotypes. Although brain evolution is related to the progressive action of selection and adaptation through multiple paths (e.g. mosaic vs. concerted evolution, metabolic vs. structural and functional constraints), a coherent, integrative framework is needed to combine evolutionary paths and neuroscientific evidence. Here, we review the literature on evolutionary pressures focusing on structural-functional changes and developmental constraints. Taking advantage of recent progress in neuroimaging and cognitive neuroscience, we propose a twofold hypothetical model of brain evolution. Within this model, global and local trajectories imply rearrangements of neural subunits and subsystems and of behavioural repertoires of a species, respectively. We incorporate these two processes in a game in which the global trajectory shapes the structural-functional neural substrates (i.e. players), while the local trajectory shapes the behavioural repertoires (i.e. stochastic payoffs).