Auditory Activation in Sleeping Infants Exhibits Distinct Spatial Modes of Co-Activation
Poster No:
1066
Submission Type:
Abstract Submission
Authors:
David Kennedy1, Jean Frazier1, Sohye Kim1
Institutions:
1University of Massachusetts Chan Medical School, Worcester, MA
First Author:
Co-Author(s):
Introduction:
Infant neuroimaging, including functional MRI, is becoming more common. fMRI being performed on sleeping infants adds an extra complexity to assessing the data quality and results analysis for any specific run, as sleep stage, amongst other factors, may interact with the neural responsiveness to the specific functional paradigm. While traditional quality factors, such as movement and SNR remain important, in task-based fMRI establishing that the functional stimuli are functioning as designed needs also to be assessed. Furthermore, spatial modes of activation pattern may vary over the course of the runs in a session.
Methods:
To this end, we have developed an approach that assesses regional activation patterns across the complete Harvard-Oxford (HO) cortical atlas. We have applied this to a social language response paradigm being applied to sleeping 6-7 month-old infants. Specifically, in every HO cortical parcellation unit, for every run we generated a summary of the response (characterized by mean and standard deviation percent signal change, mean and standard deviation of the Z statistic, the maximum Z statistic, and the percent of the region activated at a Z-threshold of 3.0). These features are generated for each of the contrasts in our paradigm (sound, voice, noise, familiar voice (mom), and unfamiliar voice). This generates a feature set of 1440 values per run (48 regions * 6 features * 5 contrasts). This feature set has been used in other work to train to a classifier to assist with quantitative QA assessment. Here we use this complete cortical feature set to examine the spatial distribution of activation pattern in runs that pass the QA procedure. Specifically, we examined the cross-correlation structure of the feature set, performed data reduction by selecting a subset of the features (regional percent activation) and then a principle component analysis to establish modes of covariance within the activation patterns observed.
Results:
Of our 68 runs, 33 runs passed our visual quality assessment, demonstrating substantive activation to the auditory aspects of our paradigm in the run-level statistical analysis. Figure1, panel a) shows the complete cross-correlation matrix for the runs passing the QA, documenting substantial regional and inter-regional correlation within this data. Panel b) shows the results of the PCA on the complete matrix, documenting the preponderance of the regional percent activation features on the component weightings. Finally, panel c) chows the PCA results for just the regional percent activation features. We selected to retain 10 principal components, which collectively accounted for ~90% of the overall variance. We projected the features onto the principal components and determined the maximum component for each run. We observed that 21%, 18% and 15% (collectively 54%) of the runs were associated with these components. Figure 2 documents the key features (loadings greater than plus or minus 0.2) and loading values for these components. Subject with multiple passing runs approximately 50% of the time loaded on the same component for multiple runs.
·Figure 1
·Figure 2
Conclusions:
In our social language response paradigm in sleeping infants we have demonstrated using principal component analysis that the observed activations patterns across the entire cortex can demonstrate discrete modes of spatial co-activation. These modes may be related to phenomena such as sleep stage, and be a dynamic marker of physiological state. Or they may reflect subject-specific variation in cognitive approach. An approach like this may help us to more systematically explore the sensitivity of our functional activation patterns in order to examine the temporal stability and possible state changes that occur over the course of the experiment.
Emotion, Motivation and Social Neuroscience:
Social Cognition
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Methods Development 2
Keywords:
FUNCTIONAL MRI
Hearing
Social Interactions
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-19, 2004
M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012