Kathleen M. Gates

Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples

At its best, connectivity mapping can offer researchers great insight into how spatially disparate regions of the human brain coordinate activity during brain processing. A recent investigation conducted by Smith and colleagues (2011) on methods for estimating connectivity maps suggested that those which attempt to ascertain the direction of influence among ROIs rarely provide reliable results. Another problem gaining increasing attention is heterogeneity in connectivity maps. Most group-level methods require that the data come from homogeneous samples, and misleading findings may arise from current methods if the connectivity maps for individuals vary across the sample (which is likely the case). The utility of maps resulting from effective connectivity on the individual or group levels is thus diminished because they do not accurately inform researchers. The present paper introduces a novel estimation technique for fMRI researchers, Group Iterative Multiple Model Estimation (GIMME), which demonstrates that using information across individuals assists in the recovery of the existence of connections among ROIs used by Smith and colleagues (2011) and the direction of the influence. Using heterogeneous in-house data, we demonstrate that GIMME offers a unique improvement over current approaches by arriving at reliable group and individual structures even when the data are highly heterogeneous across individuals comprising the group. An added benefit of GIMME is that it obtains reliable connectivity map estimates equally well using the data from resting state, block, or event-related designs. GIMME provides researchers with a powerful, flexible tool for identifying directed connectivity maps at the group and individual levels.

Automatic search for fMRI connectivity mapping: An alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM

Modeling the relationships among brain regions of interest (ROIs) carries unique potential to explicate how the brain orchestrates information processing. However, hurdles arise when using functional MRI data. Variation in ROI activity contains sequential dependencies and shared influences on synchronized activation. Consequently, both lagged and contemporaneous relationships must be considered for unbiased statistical parameter estimation. Identifying these relationships using a data-driven approach could guide theory-building regarding integrated processing. The present paper demonstrates how the unified SEM attends to both lagged and contemporaneous influences on ROI activity. Additionally, this paper offers an approach akin to Granger causality testing, Lagrange multiplier testing, for statistically identifying directional influence among ROIs and employs this approach using an automatic search procedure to arrive at the optimal model. Rationale for this equivalence is offered by explicating the formal relationships among path modeling, vector autoregression, and unified SEM. When applied to simulated data, biases in estimates which do not consider both lagged and contemporaneous paths become apparent. Finally, the use of unified SEM with the automatic search procedure is applied to an empirical data example.

Extended unified SEM approach for modeling event-related fMRI data

There has been increasing emphasis in fMRI research on the examination of how regions covary in a distributed neural network. Event-related data designs present a unique challenge to modeling how couplings among regions change in the presence of experimental manipulations. The present paper presents the extended unified SEM (euSEM), a novel approach for acquiring effective connectivity maps with event-related data. The euSEM adds to the unified SEM, which models both lagged and contemporaneous effects, by estimating the direct effects that experimental manipulations have on blood-oxygen-level dependent activity as well as the modulating effects the manipulations have on couplings among regions. Monte Carlos simulations included in this paper offer support for the models ability to recover covariance patterns used to estimate data. Next, we apply the model to empirical data to demonstrate feasibility. Finally, the results of the empirical data are compared to those found using dynamic causal modeling. The euSEM provides a flexible approach for modeling event-related data as it may be employed in an exploratory, partially exploratory, or entirely confirmatory manner.

Neural changes underlying successful second language word learning: An fMRI study

Abstract A great deal of research has examined behavioral performance changes associated with second language learning. But what changes are taking place in the brain as learning progresses? How can we identify differences in brain changes that reflect successes of learning? To answer these questions, we conducted a functional magnetic resonance imaging (fMRI) study to examine the neural activities associated with second language word learning. Participants were 39 native English speakers who had no prior knowledge of Chinese or other tonal language, and were trained to learn a novel tonal vocabulary in a six-week training session. Functional MRI scans as well as behavioral performances were obtained from these learners at two different times (pre- and post-training). We performed region of interest (ROI) and connectivity analyses to identify effective connectivity changes associated with success in second language word learning. We compared a learner group with a control group, and also examined the differences between successful learners and less successful learners within the learner group across the two time points. Our results indicated that (1) after training, learners and non-learners rely on different patterns of brain networks to process tonal and lexical information of target L2 words; (2) within the learner group, successful learners compared to less successful learners showed significant differences in language-related regions; and (3) successful learners compared to less successful learners showed a more coherent and integrated multi-path brain network. These results suggest that second language experience shapes neural changes in short-term training, and that analyses of these neural changes also reflect individual differences in learning success.

Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks

Clinical investigations of many neuropsychiatric disorders rely on the assumption that diagnostic categories and typical control samples each have within-group homogeneity. However, research using human neuroimaging has revealed that much heterogeneity exists across individuals in both clinical and control samples. This reality necessitates that researchers identify and organize the potentially varied patterns of brain physiology. We introduce an analytical approach for arriving at subgroups of individuals based entirely on their brain physiology. The method begins with Group Iterative Multiple Model Estimation (GIMME) to assess individual directed functional connectivity maps. GIMME is one of the only methods to date that can recover both the direction and presence of directed functional connectivity maps in heterogeneous data, making it an ideal place to start since it addresses the problem of heterogeneity. Individuals are then grouped based on similarities in their connectivity patterns using a modularity approach for community detection. Monte Carlo simulations demonstrate that using GIMME in combination with the modularity algorithm works exceptionally well - on average over 97% of simulated individuals are placed in the accurate subgroup with no prior information on functional architecture or group identity. Having demonstrated reliability, we examine resting-state data of fronto-parietal regions drawn from a sample (N = 80) of typically developing and attention-deficit/hyperactivity disorder (ADHD) -diagnosed children. Here, we find 5 subgroups. Two subgroups were predominantly comprised of ADHD, suggesting that more than one biological marker exists that can be used to identify children with ADHD based from their brain physiology. Empirical evidence presented here supports notions that heterogeneity exists in brain physiology within ADHD and control samples. This type of information gained from the approach presented here can assist in better characterizing patients in terms of outcomes, optimal treatment strategies, potential gene-environment interactions, and the use of biological phenomenon to assist with mental health.

Inferring functional connectivity in MRI using Bayesian network structure learning with a modified PC algorithm

Resting state functional connectivity MRI (rs-fcMRI) is a popular technique used to gauge the functional relatedness between regions in the brain for typical and special populations. Most of the work to date determines this relationship by using Pearsons correlation on BOLD fMRI timeseries. However, it has been recognized that there are at least two key limitations to this method. First, it is not possible to resolve the direct and indirect connections/influences. Second, the direction of information flow between the regions cannot be differentiated. In the current paper, we follow-up on recent work by Smith et al. (2011), and apply PC algorithm to both simulated data and empirical data to determine whether these two factors can be discerned with group average, as opposed to single subject, functional connectivity data. When applied on simulated individual subjects, the algorithm performs well determining indirect and direct connection but fails in determining directionality. However, when applied at group level, PC algorithm gives strong results for both indirect and direct connections and the direction of information flow. Applying the algorithm on empirical data, using a diffusion-weighted imaging (DWI) structural connectivity matrix as the baseline, the PC algorithm outperformed the direct correlations. We conclude that, under certain conditions, the PC algorithm leads to an improved estimate of brain network structure compared to the traditional connectivity analysis based on correlations.

Networks involved in olfaction and their dynamics using independent component analysis and unified structural equation modeling

The study of human olfaction is complicated by the myriad of processing demands in conscious perceptual and emotional experiences of odors. Combining functional magnetic resonance imaging with convergent multivariate network analyses, we examined the spatiotemporal behavior of olfactory‐generated blood‐oxygenated‐level‐dependent signal in healthy adults. The experimental functional magnetic resonance imaging (fMRI) paradigm was found to offset the limitations of olfactory habituation effects and permitted the identification of five functional networks. Analysis delineated separable neuronal circuits that were spatially centered in the primary olfactory cortex, striatum, dorsolateral prefrontal cortex, rostral prefrontal cortex/anterior cingulate, and parietal‐occipital junction. We hypothesize that these functional networks subserve primary perceptual, affective/motivational, and higher order olfactory‐related cognitive processes. Results provided direct evidence for the existence of parallel networks with top‐down modulation for olfactory processing and clearly distinguished brain activations that were sniffing‐related versus odor‐related. A comprehensive neurocognitive model for olfaction is presented that may be applied to broader translational studies of olfactory function, aging, and neurological disease. Hum Brain Mapp 35:2055–2072, 2014.

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.

Greater BOLD activity but more efficient connectivity is associated with better cognitive performance within a sample of nicotine-deprived smokers.

The first few days of an attempt to quit smoking are marked by impairments in cognitive domains, such as working memory and attention. These cognitive impairments have been linked to increased risk for relapse. Little is known about individual differences in the cognitive impairments that accompany deprivation or the neural processing reflected in those differences. In order to address this knowledge gap, we collected functional magnetic resonance imaging (fMRI) data from 118 nicotine‐deprived smokers while they performed a verbal n‐back task. We predicted better performance would be associated with more efficient patterns of brain activation and effective connectivity. Results indicated that performance was positively related to load‐related activation in the left dorsolateral prefrontal cortex and the left lateral premotor cortex. Additionally, effective connectivity patterns differed as a function of performance, with more accurate participants having simpler, more parsimonious network models than did worse participants. Cognitive efficiency is typically thought of as less neural activation for equal or superior behavioral performance. Taken together, findings suggest cognitive efficiency should not be viewed solely in terms of amount of activation but that both the magnitude of activation within and degree of covariation between task‐critical structures must be considered. This research highlights the benefit of combining traditional fMRI analysis with newer methods for modeling brain connectivity. These results suggest a possible role for indices of network functioning in assessing relapse risk in quitting smokers as well as offer potentially useful targets for novel intervention strategies.

The first day is always the hardest: Functional connectivity during cue exposure and the ability to resist smoking in the initial hours of a quit attempt

&NA; Quitting smoking is the single best change in behavior that smokers can make to improve their health and extend their lives. Although most smokers express a strong desire to stop using cigarettes, the vast majority of quit attempts end in relapse. Relapse is particularly likely when smokers encounter cigarette cues. A striking number of relapses occur very quickly, with many occurring within as little as 24 h. Characterizing what distinguishes successful quit attempts from unsuccessful ones, particularly just after cessation is initiated, is a research priority. We addressed this significant issue by examining the association between functional connectivity during cigarette cue exposure and smoking behavior during the first 24 h of a quit attempt. Functional MRI was used to measure brain activity during cue exposure in nicotine‐deprived daily smokers during the first day of a quit attempt. Participants were then given the opportunity to smoke. Using data collected in two parent studies, we identified a subset of participants who chose to smoke and a matched subset who declined (n = 38). Smokers who were able to resist smoking displayed significant functional connectivity between the left anterior insula and the dorsolateral prefrontal cortex, whereas there was no such connectivity for those who chose to smoke. Notably, there were no differences in mean levels of activation in brain regions of interest, underscoring the importance of assessing interregional connectivity when investigating the links between cue‐related neural responses and overt behavior. To our knowledge, this is the first study to link patterns of functional connectivity and actual cigarette use during the pivotal first hours of attempt to change smoking behavior. HighlightsLapse in first day of quit attempt is predicted by cue‐related brain connectivity.Only those resisting smoking had connectivity between insula and dorsolateral PFC.Extend prior findings by highlighting insulas role during early behavior change.