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A posteriori model validation for the temporal order of directed functional connectivity maps.

Beltz AM, Molenaar PC - Front Neurosci (2015)

Bottom Line: Directed functional connectivity was determined by the unified structural equation model family of approaches in order to map contemporaneous and first order lagged connections among brain regions at the group- and individual-levels while incorporating external input, then white noise tests were run.Findings revealed that the validation procedure successfully detected unmodeled sequential dependencies among residuals and recovered higher order (greater than one) simulated connections, and that the procedure can accommodate task-related input.Results have broad methodological relevance (e.g., temporal validation is necessary after directed functional connectivity analyses because the presence of unmodeled higher order sequential dependencies may bias parameter estimates) and substantive implications (e.g., higher order lags may be common in resting state data).

View Article: PubMed Central - PubMed

Affiliation: Department of Human Development and Family Studies, The Pennsylvania State University University Park, PA, USA.

ABSTRACT
A posteriori model validation for the temporal order of neural directed functional connectivity maps is rare. This is striking because models that require sequential independence among residuals are regularly implemented. The aim of the current study was (a) to apply to directed functional connectivity maps of functional magnetic resonance imaging data an a posteriori model validation procedure (i.e., white noise tests of one-step-ahead prediction errors combined with decision criteria for revising the maps based upon Lagrange Multiplier tests), and (b) to demonstrate how the procedure applies to single-subject simulated, single-subject task-related, and multi-subject resting state data. Directed functional connectivity was determined by the unified structural equation model family of approaches in order to map contemporaneous and first order lagged connections among brain regions at the group- and individual-levels while incorporating external input, then white noise tests were run. Findings revealed that the validation procedure successfully detected unmodeled sequential dependencies among residuals and recovered higher order (greater than one) simulated connections, and that the procedure can accommodate task-related input. Findings also revealed that lags greater than one were present in resting state data: With a group-level network that contained only contemporaneous and first order connections, 44% of subjects required second order, individual-level connections in order to obtain maps with white noise residuals. Results have broad methodological relevance (e.g., temporal validation is necessary after directed functional connectivity analyses because the presence of unmodeled higher order sequential dependencies may bias parameter estimates) and substantive implications (e.g., higher order lags may be common in resting state data).

No MeSH data available.


Directed functional connectivity map from euSEM analysis of single-subject empirical data with external input (i.e., a vector indicating the experimental condition of a verbal working memory task). Dashed lines reflect first order connections, solid lines reflect contemporaneous connections, arrows reflect ROI connections, circular endpoints reflect direct effects of the task, diamond endpoints reflect modulating effects of the task, and values show connection strengths (i.e., beta-weights; all are significant at p = 0.05). The map fit the data well and had white noise residuals; see fit statistics in the text. ACC, anterior cingulate cortex; R/L DLPFC, right/left dorsolateral prefrontal cortex; R/L LPM, right/left lateral premotor cortex; R/L IPL, right/left inferior parietal lobule.
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Figure 3: Directed functional connectivity map from euSEM analysis of single-subject empirical data with external input (i.e., a vector indicating the experimental condition of a verbal working memory task). Dashed lines reflect first order connections, solid lines reflect contemporaneous connections, arrows reflect ROI connections, circular endpoints reflect direct effects of the task, diamond endpoints reflect modulating effects of the task, and values show connection strengths (i.e., beta-weights; all are significant at p = 0.05). The map fit the data well and had white noise residuals; see fit statistics in the text. ACC, anterior cingulate cortex; R/L DLPFC, right/left dorsolateral prefrontal cortex; R/L LPM, right/left lateral premotor cortex; R/L IPL, right/left inferior parietal lobule.

Mentions: The resulting map is shown in Figure 3, and it depicts several interesting results. First, there were more contemporaneous than lagged connections in the network, and only two autoregressive components (for the right and left IPL). Second, the 3-back task impacted network connectivity. The presence (vs. absence) of the task was associated with the presence of a lagged connection from the left IPL to the left LPM, lagged decreases in ACC activity, and lagged increases but contemporaneous decreases in right DLPFC activity, perhaps reflecting a feedback mechanism. Third, the ACC and right IPL appear to be hubs of the network, as they had the highest degree of all ROIs (i.e., the most incoming and outgoing connections with other ROIs).


A posteriori model validation for the temporal order of directed functional connectivity maps.

Beltz AM, Molenaar PC - Front Neurosci (2015)

Directed functional connectivity map from euSEM analysis of single-subject empirical data with external input (i.e., a vector indicating the experimental condition of a verbal working memory task). Dashed lines reflect first order connections, solid lines reflect contemporaneous connections, arrows reflect ROI connections, circular endpoints reflect direct effects of the task, diamond endpoints reflect modulating effects of the task, and values show connection strengths (i.e., beta-weights; all are significant at p = 0.05). The map fit the data well and had white noise residuals; see fit statistics in the text. ACC, anterior cingulate cortex; R/L DLPFC, right/left dorsolateral prefrontal cortex; R/L LPM, right/left lateral premotor cortex; R/L IPL, right/left inferior parietal lobule.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4551081&req=5

Figure 3: Directed functional connectivity map from euSEM analysis of single-subject empirical data with external input (i.e., a vector indicating the experimental condition of a verbal working memory task). Dashed lines reflect first order connections, solid lines reflect contemporaneous connections, arrows reflect ROI connections, circular endpoints reflect direct effects of the task, diamond endpoints reflect modulating effects of the task, and values show connection strengths (i.e., beta-weights; all are significant at p = 0.05). The map fit the data well and had white noise residuals; see fit statistics in the text. ACC, anterior cingulate cortex; R/L DLPFC, right/left dorsolateral prefrontal cortex; R/L LPM, right/left lateral premotor cortex; R/L IPL, right/left inferior parietal lobule.
Mentions: The resulting map is shown in Figure 3, and it depicts several interesting results. First, there were more contemporaneous than lagged connections in the network, and only two autoregressive components (for the right and left IPL). Second, the 3-back task impacted network connectivity. The presence (vs. absence) of the task was associated with the presence of a lagged connection from the left IPL to the left LPM, lagged decreases in ACC activity, and lagged increases but contemporaneous decreases in right DLPFC activity, perhaps reflecting a feedback mechanism. Third, the ACC and right IPL appear to be hubs of the network, as they had the highest degree of all ROIs (i.e., the most incoming and outgoing connections with other ROIs).

Bottom Line: Directed functional connectivity was determined by the unified structural equation model family of approaches in order to map contemporaneous and first order lagged connections among brain regions at the group- and individual-levels while incorporating external input, then white noise tests were run.Findings revealed that the validation procedure successfully detected unmodeled sequential dependencies among residuals and recovered higher order (greater than one) simulated connections, and that the procedure can accommodate task-related input.Results have broad methodological relevance (e.g., temporal validation is necessary after directed functional connectivity analyses because the presence of unmodeled higher order sequential dependencies may bias parameter estimates) and substantive implications (e.g., higher order lags may be common in resting state data).

View Article: PubMed Central - PubMed

Affiliation: Department of Human Development and Family Studies, The Pennsylvania State University University Park, PA, USA.

ABSTRACT
A posteriori model validation for the temporal order of neural directed functional connectivity maps is rare. This is striking because models that require sequential independence among residuals are regularly implemented. The aim of the current study was (a) to apply to directed functional connectivity maps of functional magnetic resonance imaging data an a posteriori model validation procedure (i.e., white noise tests of one-step-ahead prediction errors combined with decision criteria for revising the maps based upon Lagrange Multiplier tests), and (b) to demonstrate how the procedure applies to single-subject simulated, single-subject task-related, and multi-subject resting state data. Directed functional connectivity was determined by the unified structural equation model family of approaches in order to map contemporaneous and first order lagged connections among brain regions at the group- and individual-levels while incorporating external input, then white noise tests were run. Findings revealed that the validation procedure successfully detected unmodeled sequential dependencies among residuals and recovered higher order (greater than one) simulated connections, and that the procedure can accommodate task-related input. Findings also revealed that lags greater than one were present in resting state data: With a group-level network that contained only contemporaneous and first order connections, 44% of subjects required second order, individual-level connections in order to obtain maps with white noise residuals. Results have broad methodological relevance (e.g., temporal validation is necessary after directed functional connectivity analyses because the presence of unmodeled higher order sequential dependencies may bias parameter estimates) and substantive implications (e.g., higher order lags may be common in resting state data).

No MeSH data available.