Advancing functional connectivity research from association to causation

Cognition and behavior emerge from brain network interactions, such that investigating causal interactions should be central to the study of brain function. Approaches that characterize statistical associations among neural time series-functional connectivity (FC) methods-are likely a good starting point for estimating brain network interactions. Yet only a subset of FC methods ('effective connectivity') is explicitly designed to infer causal interactions from statistical associations. Here we incorporate best practices from diverse areas of FC research to illustrate how FC methods can be refined to improve inferences about neural mechanisms, with properties of causal neural interactions as a common ontology to facilitate cumulative progress across FC approaches. We further demonstrate how the most common FC measures (correlation and coherence) reduce the set of likely causal models, facilitating causal inferences despite major limitations. Alternative FC measures are suggested to immediately start improving causal inferences beyond these common FC measures

Risk Assessment Algorithms Based On Recursive Neural Networks

The assessment of highly-risky situations at road intersections have been recently revealed as an important research topic within the context of the automotive industry. In this paper we shall introduce a novel approach to compute risk functions by using a combination of a highly non-linear processing model in conjunction with a powerful information encoding procedure. Specifically, the elements of information either static or dynamic that appear in a road intersection scene are encoded by using directed positional acyclic labeled graphs. The risk assessment problem is then reformulated in terms of an inductive learning task carried out by a recursive neural network. Recursive neural networks are connectionist models capable of solving supervised and non-supervised learning problems represented by directed ordered acyclic graphs. The potential of this novel approach is demonstrated through well predefined scenarios. The major difference of our approach compared to others is expressed by the fact of learning the structure of the risk. Furthermore, the combination of a rich information encoding procedure with a generalized model of dynamical recurrent networks permit us, as we shall demonstrate, a sophisticated processing of information that we believe as being a first step for building future advanced intersection safety system

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

Capacity and tendency: A neuroscientific framework for the study of emotion regulation.

It is widely accepted that the ability to effectively regulate one's emotions is a cornerstone of physical and mental health. As such, it should come as no surprise that the number of neuroimaging studies focused on emotion regulation and associated processes has increased exponentially in the past decade. To date, neuroimaging research on this topic has examined two distinct but complementary features of emotion regulation - the capacity to effectively utilize a strategy to regulate emotion and to a lesser extent, the tendency to choose to regulate. However, theoretical accounts of emotion regulation have only recently begun to distinguish capacity from tendency. In the present review, we provide a novel framework for conceptualizing these two intertwined, yet distinct, facets of emotion regulation. First we characterize brain regions that support emotion generation and are thus targeted by emotion regulation. Next, we synthesize findings from the dozens of neuroimaging studies that have examined emotion regulation capacity, focusing in particular on the most commonly studied emotion regulation strategy - reappraisal. Finally, we discuss emerging neuroimaging research examining state and trait regulatory tendencies. We conclude by integrating findings from neuroimaging research on emotion regulation capacity and tendency and suggest ways that this integrated model can inform basic and translational neuroscientific research on emotion regulation

Abnormal connectivity between the default mode and the visual system underlies the manifestation of visual hallucinations in Parkinson’s disease:A task-based fMRI study

Background: The neural substrates of visual hallucinations remain an enigma, due primarily to the difficulties associated with directly interrogating the brain during hallucinatory episodes. Aims: To delineate the functional patterns of brain network activity and connectivity underlying visual hallucinations in Parkinson’s disease. Methods: In this study, we combined functional magnetic resonance imaging (MRI) with a behavioral task capable of eliciting visual misperceptions, a confirmed surrogate for visual hallucinations, in 35 patients with idiopathic Parkinson’s disease. We then applied an independent component analysis to extract time series information for large-scale neuronal networks that have been previously implicated in the pathophysiology of visual hallucinations. These data were subjected to a task-based functional connectivity analysis, thus providing the first objective description of the neural activity and connectivity during visual hallucinations in patients with Parkinson’s disease. Results: Correct performance of the task was associated with increased activity in primary visual regions; however, during visual misperceptions, this same visual network became actively coupled with the default mode network (DMN). Further, the frequency of misperception errors on the task was positively correlated with the strength of connectivity between these two systems, as well as with decreased activity in the dorsal attention network (DAN), and with impaired connectivity between the DAN and the DMNs, and ventral attention networks. Finally, each of the network abnormalities identified in our analysis were significantly correlated with two independent clinical measures of hallucination severity. Conclusions: Together, these results provide evidence that visual hallucinations are due to increased engagement of the DMN with the primary visual system, and emphasize the role of dysfunctional engagement of attentional networks in the pathophysiology of hallucinations

Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework

In this paper, we argue that the future of Artificial Intelligence research resides in two keywords: integration and embodiment. We support this claim by analyzing the recent advances of the field. Regarding integration, we note that the most impactful recent contributions have been made possible through the integration of recent Machine Learning methods (based in particular on Deep Learning and Recurrent Neural Networks) with more traditional ones (e.g. Monte-Carlo tree search, goal babbling exploration or addressable memory systems). Regarding embodiment, we note that the traditional benchmark tasks (e.g. visual classification or board games) are becoming obsolete as state-of-the-art learning algorithms approach or even surpass human performance in most of them, having recently encouraged the development of first-person 3D game platforms embedding realistic physics. Building upon this analysis, we first propose an embodied cognitive architecture integrating heterogenous sub-fields of Artificial Intelligence into a unified framework. We demonstrate the utility of our approach by showing how major contributions of the field can be expressed within the proposed framework. We then claim that benchmarking environments need to reproduce ecologically-valid conditions for bootstrapping the acquisition of increasingly complex cognitive skills through the concept of a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017 conference (Lisbon, Portugal

Hippocampal BDNF regulates a shift from flexible, goal-directed to habit memory system function following cocaine abstinence.

The transition from recreational drug use to addiction involves pathological learning processes that support a persistent shift from flexible, goal-directed to habit behavioral control. Here, we examined the molecular mechanisms supporting altered function in hippocampal (HPC) and dorsolateral striatum (DLS) memory systems following abstinence from repeated cocaine. After 3 weeks of cocaine abstinence (experimenter- or self-administered), we tested new behavioral learning in male rats using a dual-solution maze task, which provides an unbiased approach to assess HPC- versus DLS-dependent learning strategies. Dorsal hippocampus (dHPC) and DLS brain tissues were collected after memory testing to identify transcriptional adaptations associated with cocaine-induced shifts in behavioral learning. Our results demonstrate that following prolonged cocaine abstinence rats show a bias toward the use of an inflexible, habit memory system (DLS) in lieu of a more flexible, easily updated memory system involving the HPC. This memory system bias was associated with upregulation and downregulation of brain-derived neurotrophic factor (BDNF) gene expression and transcriptionally permissive histone acetylation (acetylated histone H3, AcH3) in the DLS and dHPC, respectively. Using viral-mediated gene transfer, we overexpressed BDNF in the dHPC during cocaine abstinence and new maze learning. This manipulation restored HPC-dependent behavioral control. These findings provide a system-level understanding of altered plasticity and behavioral learning following cocaine abstinence and inform mechanisms mediating the organization of learning and memory more broadly