Functional Effective Connectivity Research Articles

Bilingual Language Control in the Brain: Evidence from Structural and Effective Functional Brain Connectivity.

Experience in bilingual language control is often accompanied by changes in the structure and function of the brain. Brain structural changes are also often closely related to changes in functions. Previous studies, however, have not directly explored the relationship between structural connectivity and effective functional connectivity of the brain during bilingual language control, and whether the two types of connectivity are associated with behavioral performance of language control. Using behavioral performance, functional, and diffusion imaging techniques, we found that: (1) during language control, the left dorsal lateral prefrontal cortex (dlPFC), left caudate nucleus (CN), inferior parietal lobe, precuneus, and dorsal anterior cingulate cortex (dACC)/pre-SMA were significantly activated. (2) In the language control model with left dlPFC, dACC/pre-SMA, and left CN as ROIs (selected based on activation results and language control models from previous studies), stimuli first enter dACC/pre-SMA and then to left CN. At the left CN, a bidirectional effective connectivity is formed with left dlPFC. (3) There is a nonlinear relationship between effective connectivity during language control and the structural connectivity of the second language learners' brains. Specifically, the fiber density between dACC/pre-SMA and left dlPFC has a positive influence on the bidirectional effective connectivity between left dlPFC and left CN. Findings of the present study contribute evidence toward functional effective connectivity during bilingual language control; toward structural connectivity in the brains of second language learners; as well as toward nonlinear relationships between functional effective connectivity, structural connectivity, and behavioral performance in relation to bilingual language control.

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations

Brain activity flow models estimate the movement of task-evoked activity over brain connections to help explain network-generated task functionality. Activity flow models have been shown to accurately generate task-evoked brain activations across a wide variety of brain regions and task conditions. However, these models have had limited explanatory power, given known issues with causal interpretations of the standard functional connectivity measures used to parameterize activity flow models. We show here that functional/effective connectivity (FC) measures grounded in causal principles facilitate mechanistic interpretation of activity flow models. We progress from simple to complex FC measures, with each adding algorithmic details reflecting causal principles. This reflects many neuroscientists’ preference for reduced FC measure complexity (to minimize assumptions, minimize compute time, and fully comprehend and easily communicate methodological details), which potentially trades off with causal validity. We start with Pearson correlation (the current field standard) to remain maximally relevant to the field, estimating causal validity across a range of FC measures using simulations and empirical fMRI data. Finally, we apply causal-FC-based activity flow modeling to a dorsolateral prefrontal cortex region (DLPFC), demonstrating distributed causal network mechanisms contributing to its strong activation during a working memory task. Notably, this fully distributed model is able to account for DLPFC working memory effects traditionally thought to rely primarily on within-region (i.e., not distributed) recurrent processes. Together, these results reveal the promise of parameterizing activity flow models using causal FC methods to identify network mechanisms underlying cognitive computations in the human brain.

Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings

Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions and pathologies. Inferring neuronal connectivity from electrophysiological recordings presents open challenges and unsolved problems. In this work, we present a cross-correlation based method for reliably estimating not only excitatory but also inhibitory links, by analyzing multi-unit spike activity from large-scale neuronal networks. The method is validated by means of realistic simulations of large-scale neuronal populations. New results related to functional connectivity estimation and network topology identification obtained by experimental electrophysiological recordings from high-density and large-scale (i.e., 4096 electrodes) microtransducer arrays coupled to in vitro neural populations are presented. Specifically, we show that: (i) functional inhibitory connections are accurately identified in in vitro cortical networks, providing that a reasonable firing rate and recording length are achieved; (ii) small-world topology, with scale-free and rich-club features are reliably obtained, on condition that a minimum number of active recording sites are available. The method and procedure can be directly extended and applied to in vivo multi-units brain activity recordings.

TOOLCONNECT: A powerful toolbox for functional connectivity analysis of in vitro neural networks

Event Abstract Back to Event TOOLCONNECT: A powerful toolbox for functional connectivity analysis of in vitro neural networks Vito P. Pastore1*, Aleksandar Godjoski1, Sergio Martinoia1 and Paolo Massobrio1 1 University of Genova, Department of Informatics, Bioengineering, Robotics, System Engineering , Italy Motivation One of the goal of contemporary neuroscience is to study the interplay between topology, structure, functional-effective connectivity and neuronal dynamics at different level of complexity and on different experimental models (from simple in-vitro networks to whole brain areas). Functional connectivity is defined as the strength of the influence one network member has on another during ongoing behavior [1]. The analysis of multiple neural spike train data recorded from experimental models has gained tremendous relevance recently with the widespread application of Micro-Electrode Arrays (MEAs) [2]. At present, to the best of our knowledge, there is no available dedicated software that puts together a set of different functional connectivity analysis methods. Thus, we developed a user-friendly toolbox [3] in order to provide the researchers community a powerful tool to perform functional connectivity analysis on in-vitro neuronal networks coupled to standard and high-density MEAs, while guaranteeing computational efficiency and high accuracy. Material and Methods We implemented 'ToolConnect' toolbox as a standalone windows Graphical User Interface (GUI) application, using C# and Microsoft Visual Studio with .NET framework 4.5 development environment. The software is designed to be intuitive and straightforward to use. It is based on several windows forms and a friendly and modular GUI through which provides the user with powerful tools to manipulate and analyze data. ToolConnect offers functional connectivity analysis based on two correlation (cross-correlation and partial correlation) and two information theory based methods (transfer entropy and joint entropy). Cross correlation measures the frequency at which one cell fires as a function of time relative to the firing of a spike in another cell [4]. Partial correlation allows to distinguish between direct and indirect connections by removing the portion of the relationship between two spike trains that can be attributed to linear relationships with recorded spike trains from other neurons [5, 6]. Transfer Entropy is an information theoretic measure able to estimate causal relationships between time series taking into account their past activity [7]. Joint Entropy analyzes the cross Inter-Spike-Intervals (cISI): if two neurons are strongly connected the cISI histogram will show a peak and Joint Entropy will be close to zero, otherwise the cISI histogram will be almost flat, and the Joint Entropy will be high. The graphical section offers dedicated interfaces and allows the user to: i) plot the correlograms between each couple of the set of analyzed electrodes (for cross- and partial correlation); ii) manage, thresh and plot the Connectivity Matrix (CM) and the connectivity graph; iii) compute some powerful metrics that allow to extract the main topological features (degree, cluster coefficient, path length). One of the major features of the toolbox is its independence from the acquisition system (e.g. Multi Channel Systems, Qwane Biosciences, 3Brain) and from the MEA layout (number of microelectrodes and spatial organization). Results We designed and developed ToolConnect taking care to satisfy the user-friendliness requirement. According to this, our software has a GUI, which permits also to inexperienced users to perform functional connectivity analysis, to graphically represent the results, hiding the algorithms implementation specifics and software’s code design. Figure 1 shows a screenshot of ToolConnect’s GUI. Figure 2 shows the connectivity graphs obtained from the analysis of cortical networks coupled to the MEA60 and the MEA2100 acquisition systems of Multi Channel Systems (www.multichannelsystems.com; MCS, Reutlingen, Germany) and the BioCam acquisition system of 3Brain Systems. To assess the performances of tool connect we performed functional-connectivity analysis based on the cross-correlation method on hippocampal neuronal networks coupled to the MEA 60 from Multi-Channel Systems (MCS), during spontaneous and stimulus-evoked activity[3]. Discussion To the best of our knowledge, ToolConnect is the first functional connectivity toolbox dedicated to the analysis of multiple spike trains recorded from in-vitro neural networks coupled to MEAs. It provides the user with a complete set of computational and graphical tools of intuitive and straightforward usage through a dedicated and modular GUI. ToolConnect is implemented taking care of the optimization of the resources usage (requested RAM) and the reduction of the computational time. We were able to obtain acceptable performances with computational time lower than 2 minutes (for 10 minutes of recording sampled at 10 kHz). These performances make ToolConnect compatible with high-density recording systems (e.g., the 4096 electrodes of the 3brain system). In this way, it will be possible to perform functional connectivity analysis on neural networks with dimensions of thousands of neurons preserving an acceptable spatial and temporal resolution, hence allowing to obtain realistic and complete information on the dynamics and the topology of such systems.

What is the nature of poststroke language recovery and reorganization?

This review focuses on three main topics related to the nature of poststroke language recovery and reorganization. The first topic pertains to the nature of anatomical and physiological substrates in the infarcted hemisphere in poststroke aphasia, including the nature of the hemodynamic response in patients with poststroke aphasia, the nature of the peri-infarct tissue, and the neuronal plasticity potential in the infarcted hemisphere. The second section of the paper reviews the current neuroimaging evidence for language recovery in the acute, subacute, and chronic stages of recovery. The third and final section examines changes in connectivity as a function of recovery in poststroke aphasia, specifically in terms of changes in white matter connectivity, changes in functional effective connectivity, and changes in resting state connectivity after stroke. While much progress has been made in our understanding of language recovery, more work needs to be done. Future studies will need to examine whether reorganization of language in poststroke aphasia corresponds to a tighter, more coherent, and efficient network of residual and new regions in the brain. Answering these questions will go a long way towards being able to predict which patients are likely to recover and may benefit from future rehabilitation.