Article

Linear Mixed-Effects Modeling Approach to FMRI Group Analysis.

Scientific and Statistical Computing Core, NIMH/NIH/HHS, USA. Electronic address: .
NeuroImage (Impact Factor: 6.13). 01/2013; DOI: 10.1016/j.neuroimage.2013.01.047
Source: PubMed

ABSTRACT Conventional group analysis is usually performed with Student-type t-test, regression, or standard AN(C)OVA in which the variance-covariance matrix is presumed to have a simple structure. Some correction approaches are adopted when assumptions about the covariance structure is violated. However, as experiments are designed with different degrees of sophistication, these traditional methods can become cumbersome, or even be unable to handle the situation at hand. For example, most current FMRI software packages have difficulty analyzing the following scenarios at group level: (1) taking within-subject variability into account when there are effect estimates from multiple runs or sessions; (2) continuous explanatory variables (covariates) modeling in the presence of a within-subject (repeated measures) factor, multiple subject-grouping (between-subjects) factors, or the mixture of both; (3) subject-specific adjustments in covariate modeling; (4) group analysis with estimation of hemodynamic response (HDR) function by multiple basis functions; (5) various cases of missing data in longitudinal studies; and (6) group studies involving family members or twins. Here we present a linear mixed-effects modeling (LME) methodology that extends the conventional group analysis approach to analyze many complicated cases, including the six prototypes delineated above, whose analysis would be otherwise either difficult or unfeasible under traditional frameworks such as AN(C)OVA and general linear model (GLM). In addition, the strength of the LME framework lies in its flexibility to model and estimate the variance-covariance structures for both random effects and residuals. The intraclass correlation (ICC) values can be easily obtained with an LME model with crossed random effects, even at the presence of confounding fixed effects. The simulations of one prototypical scenario indicate that the LME modeling keeps a balance between the control for false positives and the sensitivity for activation detection. The importance of hypothesis formulation is also illustrated in the simulations. Comparisons with alternative group analysis approaches and the limitations of LME are discussed in details.

0 Bookmarks
 · 
127 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Resting-state functional magnetic resonance image (rs-fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting-state networks (RSNs) using a novel voxel-wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large-scale RSNs during normal aging from early adulthood to late middle-age. In some networks, such as the default-mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto-parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex-related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.
    Human Brain Mapping 12/2014; DOI:10.1002/hbm.22720 · 6.92 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Individuals with anorexia nervosa (AN) override the drive to eat, forgoing immediate rewards in favor of longer-term goals. We examined delay discounting and its neural correlates in AN before and after treatment to test a potential mechanism of illness persistence. Inpatients with AN (n = 59) and healthy control subjects (HC, n = 39) performed a delay discounting task at two time points. A subset (n = 30 AN, n = 22 HC) participated in functional magnetic resonance imaging scanning during the task. The task consisted of a range of monetary choices with variable delay times, yielding individual discount rates-the rate by which money loses value over time. Before treatment, the AN group showed a preference for delayed over earlier rewards (i.e., less steep discount rates) compared with HC; after weight restoration, AN did not differ from HC. Underweight AN showed slower response times for earlier versus delayed choices; this reversed with treatment. Underweight AN showed abnormal neural activity in striatum and dorsal anterior cingulate; normalization of behavior was associated with increased activation in reward regions (striatum and dorsal anterior cingulate) and decision-making regions (dorsolateral prefrontal cortex and parietal cortex). The undernourished state of AN may amplify the tendency to forgo immediate rewards in favor of longer-term goals. The results suggest that behavior that looks phenotypically like excessive self-control does not correspond with enhanced prefrontal recruitment. Rather, the results point to alterations in cingulostriatal circuitry that offer new insights on the potential role of abnormalities in decision-making neural systems in the perpetuation of AN. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.
    Biological Psychiatry 01/2015; DOI:10.1016/j.biopsych.2014.12.016 · 9.47 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective Research conducted with adults suggests that successful weight losers demonstrate greater activation in brain regions associated with executive control in response to viewing high-energy foods. No previous studies have examined these associations in adolescents. Functional neuroimaging was used to assess brain response to food images among groups of overweight (OW), normal-weight (NW), and successful weight-losing (SWL) adolescents.Methods Eleven SWL, 12 NW, and 11 OW participants underwent functional magnetic resonance imaging while viewing images of high- and low-energy foods.ResultsWhen viewing high-energy food images, SWLs demonstrated greater activation in the dorsolateral prefrontal cortex (DLPFC) compared with OW and NW controls. Compared with NW and SWL groups, OW individuals demonstrated greater activation in the ventral striatum and anterior cingulate in response to food images.Conclusions Adolescent SWLs demonstrated greater neural activation in the DLPFC compared with OW/NW controls when viewing high-energy food stimuli, which may indicate enhanced executive control. OW individuals' brain responses to food stimuli may indicate greater reward incentive processes than either SWL or NW groups.
    Obesity 02/2015; DOI:10.1002/oby.21004 · 4.39 Impact Factor