Lagged and instantaneous dynamical influences related to brain structural connectivity

Contemporary neuroimaging methods can shed light on the basis of human neural and cognitive specializations, with important implications for neuroscience and medicine. Different MRI acquisitions provide different brain networks at the macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural connectivity (SC) coincident with the bundles of parallel fibers between brain areas, functional MRI (fMRI) accounts for the variations in the blood-oxygenation-level-dependent T2* signal, providing functional connectivity (FC).Understanding the precise relation between FC and SC, that is, between brain dynamics and structure, is still a challenge for neuroscience. To investigate this problem, we acquired data at rest and built the corresponding SC (with matrix elements corresponding to the fiber number between brain areas) to be compared with FC connectivity matrices obtained by 3 different methods: directed dependencies by an exploratory version of structural equation modeling (eSEM), linear correlations (C) and partial correlations (PC). We also considered the possibility of using lagged correlations in time series; so, we compared a lagged version of eSEM and Granger causality (GC). Our results were two-fold: firstly, eSEM performance in correlating with SC was comparable to those obtained from C and PC, but eSEM (not C nor PC) provides information about directionality of the functional interactions. Second, interactions on a time scale much smaller than the sampling time, captured by instantaneous connectivity methods, are much more related to SC than slow directed influences captured by the lagged analysis. Indeed the performance in correlating with SC was much worse for GC and for the lagged version of eSEM. We expect these results to supply further insights to the interplay between SC and functional patterns, an important issue in the study of brain physiology and function.Comment: Accepted and published in Frontiers in Psychology in its current form. 27 pages, 1 table, 5 figures, 2 suppl. figure

Meta-Analysis of the Structural Equation Models' Parameters for the Estimation of Brain Connectivity with fMRI.

Structural Equation Models (SEM) is among of the most extensively applied statistical techniques in the study of human behavior in the fields of Neuroscience and Cognitive Neuroscience. This paper reviews the application of SEM to estimate functional and effective connectivity models in work published since 2001. The articles analyzed were compiled from Journal Citation Reports, PsycInfo, Pubmed, and Scopus, after searching with the following keywords: fMRI, SEMs, and Connectivity. Results: A 100 papers were found, of which 25 were rejected due to a lack of sufficient data on basic aspects of the construction of SEM. The other 75 were included and contained a total of 160 models to analyze, since most papers included more than one model. The analysis of the explained variance (R2) of each model yields an effect of the type of design used, the type of population studied, the type of study, the existence of recursive effects in the model, and the number of paths defined in the model. Along with these comments, a series of recommendations are included for the use of SEM to estimate of functional and effective connectivity models

State space modeling of time-varying contemporaneous and lagged relations in connectivity maps

Most connectivity mapping techniques for neuroimaging data assume stationarity (i.e., network parameters are constant across time), but this assumption does not always hold true. The authors provide a description of a new approach for simultaneously detecting time-varying (or dynamic) contemporaneous and lagged relations in brain connectivity maps. Specifically, they use a novel raw data likelihood estimation technique (involving a second-order extended Kalman filter/smoother embedded in a nonlinear optimizer) to determine the variances of the random walks associated with state space model parameters and their autoregressive components. The authors illustrate their approach with simulated and blood oxygen level-dependent functional magnetic resonance imaging data from 30 daily cigarette smokers performing a verbal working memory task, focusing on seven regions of interest (ROIs). Twelve participants had dynamic directed functional connectivity maps: Eleven had one or more time-varying contemporaneous ROI state loadings, and one had a time-varying autoregressive parameter. Compared to smokers without dynamic maps, smokers with dynamic maps performed the task with greater accuracy. Thus, accurate detection of dynamic brain processes is meaningfully related to behavior in a clinical sample

A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

Functional magnetic resonance imaging (fMRI) technique allows us to capture activities occurring in a human brain via signals from blood flow, known as BOLD (blood oxygen level-dependent) signals. Exploring a relationship among brain regions inside human brains from fMRI data is an active and challenging research topic. Relationships or associations between brain regions are commonly referred to as brain connectivity or brain network. This connectivity can be divided into two groups, the functional connectivity which describes the statistical information among brain regions and the effective connectivity which specifies how one region interacts with others by a causal model. This survey paper provides a review on learning brain connectivities via fMRI data, mathematical definitions or dependence measures of such connectivities. These well-known measures include correlation, partial correlation, conditional independence, dynamical causal modeling, Granger causality, and structural equation modeling, which all can be translated in terms of mathematical conditions of model parameters. We also discusses about relevant estimation techniques that have been widely used in the problems of fMRI modeling. Understanding a rigorous definition on relationships in human brain allows us to interpret or compare the results in the context of learning brain network more clearly

A Linear Structural Equation Model for Covert Verb Generation Based on Independent Component Analysis of fMRI Data from Children and Adolescents

Human language is a complex and protean cognitive ability. Young children, following well defined developmental patterns learn language rapidly and effortlessly producing full sentences by the age of 3 years. However, the language circuitry continues to undergo significant neuroplastic changes extending well into teenage years. Evidence suggests that the developing brain adheres to two rudimentary principles of functional organization: functional integration and functional specialization. At a neurobiological level, this distinction can be identified with progressive specialization or focalization reflecting consolidation and synaptic reinforcement of a network (Lenneberg, 1967; Muller et al., 1998; Berl et al., 2006). In this paper, we used group independent component analysis and linear structural equation modeling (McIntosh and Gonzalez-Lima, 1994; Karunanayaka et al., 2007) to tease out the developmental trajectories of the language circuitry based on fMRI data from 336 children ages 5–18 years performing a blocked, covert verb generation task. The results are analyzed and presented in the framework of theoretical models for neurocognitive brain development. This study highlights the advantages of combining both modular and connectionist approaches to cognitive functions; from a methodological perspective, it demonstrates the feasibility of combining data-driven and hypothesis driven techniques to investigate the developmental shifts in the semantic network

Changes in Alcohol-Related Brain Networks Across the First Year of College: A Prospective Pilot Study Using fMRI Effective Connectivity Mapping

The upsurge in alcohol use that often occurs during the first year of college has been convincingly linked to a number of negative psychosocial consequences and may negatively affect brain development. In this longitudinal functional magnetic resonance imaging (fMRI) pilot study, we examined changes in neural responses to alcohol cues across the first year of college in a normative sample of late adolescents. Participants (N=11) were scanned three times across their first year of college (summer, first semester, second semester), while completing a go/no-go task in which images of alcoholic and non-alcoholic beverages were the response cues. A state-of-the-art effective connectivity mapping technique was used to capture spatiotemporal relations among brain regions of interest (ROIs) at the level of the group and the individual. Effective connections among ROIs implicated in cognitive control were greatest at the second assessment (when negative consequences of alcohol use increased), and effective connections among ROIs implicated in emotion processing were lower (and response times were slower) when participants were instructed to respond to alcohol cues compared to non-alcohol cues. These preliminary findings demonstrate the value of a prospective effective connectivity approach for understanding adolescent changes in alcohol-related neural processes.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/123049/1/Changes in Alcohol-Related Brain Networks Across the First Year of College_A Prospective Pilot Study Using fMRI Effective Connectivity Mapping.pd

Mapping temporal dynamics in social interactions with unified structural equation modeling: A description and demonstration revealing time-dependent sex differences in play behavior

Developmental science is rich with observations of social interactions, but few available methodological and statistical approaches take full advantage of the information provided by these data. The authors propose implementation of the unified structural equation model (uSEM), a network analysis technique, for observational data coded repeatedly across time; uSEM captures the temporal dynamics underlying changes in behavior at the individual level by revealing the ways in which a single person influences – concurrently and in the future – other people. To demonstrate the utility of uSEM, the authors applied it to ratings of positive affect and vigor of activity during children’s unstructured laboratory play with unfamiliar, same-sex peers. Results revealed the time-dependent nature of sex differences in play behavior. For girls more than boys, positive affect was dependent upon peers’ prior positive affect. For boys more than girls, vigor of activity was dependent upon peers’ current vigor of activity.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/123050/1/Mapping temporal dynamics in social interactions with unified structural equation modeling_ A description and demonstration revealing time-dependent sex differences in play behavior.pd

Characterizing the role of the pre‐SMA in the control of speed/accuracy trade‐off with directed functional connectivity mapping and multiple solution reduction

Several plausible theories of the neural implementation of speed/accuracy trade‐off (SAT), the phenomenon in which individuals may alternately emphasize speed or accuracy during the performance of cognitive tasks, have been proposed, and multiple lines of evidence point to the involvement of the pre‐supplemental motor area (pre‐SMA). However, as the nature and directionality of the pre‐SMA’s functional connections to other regions involved in cognitive control and task processing are not known, its precise role in the top‐down control of SAT remains unclear. Although recent advances in cross‐sectional path modeling provide a promising way of characterizing these connections, such models are limited by their tendency to produce multiple equivalent solutions. In a sample of healthy adults (N = 18), the current study uses the novel approach of Group Iterative Multiple Model Estimation for Multiple Solutions (GIMME‐MS) to assess directed functional connections between the pre‐SMA, other regions previously linked to control of SAT, and regions putatively involved in evidence accumulation for the decision task. Results reveal a primary role of the pre‐SMA for modulating activity in regions involved in the decision process but suggest that this region receives top‐down input from the DLPFC. Findings also demonstrate the utility of GIMME‐MS and solution‐reduction methods for obtaining valid directional inferences from connectivity path models.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149347/1/hbm24493.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149347/2/hbm24493_am.pd

Testing the structure and process of personality using ambulatory assessment data : an overview of within-person and person-specific techniques

In the present article, we discuss the potential of ambulatory assessment for an idiographic study of the structure and process of personality. To this end, we first review important methodological issues related to the design and implementation of an ambulatory assessment study in the personality domain, including methods of ambulatory assessment, frequency of measurement and duration of the study, ambulatory assessment scales and questionnaires, participant selection, training and motivation, and ambulatory assessment hard- and software. Next, we provide a detailed outline of available analytical approaches that can be used to analyze the intensive longitudinal data generated by an ambulatory assessment study. By doing this, we hope to familiarize personality scholars with these methods and to provide guidance for their use in the field of personality psychology and beyond