Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data

Abstract

Task-based fMRI activation mapping has been widely used in clinical neuroscience in order to assess different functional activity patterns in conditions such as prenatal alcohol exposure (PAE) affected brains and healthy controls. In this paper, we propose a novel, alternative approach of group-wise sparse representation of the fMRI data of multiple groups of subjects (healthy control, exposed non-dysmorphic PAE and exposed dysmorphic PAE) and assess the systematic functional activity differences among these three populations. Specifically, a common time series signal dictionary is learned from the aggregated fMRI signals of all three groups of subjects, and then the weight coefficient matrices (named statistical coefficient map (SCM)) associated with each common dictionary were statistically assessed for each group separately. Through inter-group comparisons based on the correspondence established by the common dictionary, our experimental results have demonstrated that the group-wise sparse coding strategy and the SCM can effectively reveal a collection of brain networks/regions that were affected by different levels of severity of PAE.

Radiological studies of fetal alcohol spectrum disorders in humans and animal models: An updated comprehensive review

“…It should be noted that the performance of nonsyndromal PAE children was comparable to the controls, implying that poor performance increases with increased severity of the syndrome. The size of activated networks was also observed to be decreased with increasing severity of PAE [119]. In a number-related task requiring the judgement of number proximity, children with FASD either showed less activation in the intraparietal sulcus, one of the regions in the frontoparietal network involved in number processing [120], or used a different network to solve the problems [118].…”

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder

“…Thus, faithful reconstruction and quantitative modeling of those concurrent neural networks from noisy fMRI data has been of a major neuroscientific research topic for years (Bullmore and Sporns, 2009; Dosenbach et al, 2006; Duncan, 2010; Fedorenko et al, 2013; Fox et al, 2005; Huettel, Scott A., Allen W. Song, 2004; Pessoa et al, 2012). Popular brain network reconstruction techniques based on fMRI data include general linear model (GLM) (Friston et al, 1994; Worsley, 1997) for task-based fMRI (tfMRI), independent component analysis (ICA) (Beckmann et al, 2005; Calhoun et al, 2004) for resting state fMRI (rsfMRI), and dictionary learning/sparse representation (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2016, 2015) for both tfMRI and rsfMRI, all of which can effectively reconstruct concurrent network maps from whole brain fMRI data. For instance, by using the dictionary learning and sparse coding algorithms (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Mairal et al, 2010; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2015), several hundred of concurrent functional brain networks, characterized by both spatial maps and associated temporal time series, can be effectively decomposed from either tfMRI or rsfMRI data of an individual brain.…”

“…Popular brain network reconstruction techniques based on fMRI data include general linear model (GLM) (Friston et al, 1994; Worsley, 1997) for task-based fMRI (tfMRI), independent component analysis (ICA) (Beckmann et al, 2005; Calhoun et al, 2004) for resting state fMRI (rsfMRI), and dictionary learning/sparse representation (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2016, 2015) for both tfMRI and rsfMRI, all of which can effectively reconstruct concurrent network maps from whole brain fMRI data. For instance, by using the dictionary learning and sparse coding algorithms (Ge et al, 2016; Jiang et al, 2015; Li et al, 2016; Lv et al, 2015a, 2015b, 2015c, 2015d; Mairal et al, 2010; Xintao Hu et al, 2015; Zhang et al, 2016; Zhao et al, 2015), several hundred of concurrent functional brain networks, characterized by both spatial maps and associated temporal time series, can be effectively decomposed from either tfMRI or rsfMRI data of an individual brain. Pooling and integrating the spatial maps of those functional networks from many brains such as those of Human Connectome Project (HCP) subjects can significantly advance our understanding of the regularity and variability of brain functions across individuals and populations (Lv et al, 2015a, 2015b; Zhao et al, 2016)(Zhao et al, 2016)(Zhao et al, 2016)(Zhao et al, 2016)(Zhao et al, 2016)(Zhao et al, 2016).…”

Review of Current Neuroimaging Studies of the Effects of Prenatal Drug Exposure: Brain Structure and Function

“…General linear modeling has been used most widely because it is effective, simple, and robust [140]. However, typical approaches to the statistical analysis of fMRI data are limited in that they are not able to detect activation in heterogeneous brain regions that have the potential to play diverse roles in multiple types of task performance [141]. A recent study successfully demonstrated the advantages of group-wise sparse representation of fMRI data and statistical coefficient mapping to evaluate the effect of prenatal alcohol exposure on functional activity.…”

“…A recent study successfully demonstrated the advantages of group-wise sparse representation of fMRI data and statistical coefficient mapping to evaluate the effect of prenatal alcohol exposure on functional activity. The advantages reported for this method included increased adaptability, more systematic in detecting diverse brain networks, and better able to identify commonalities and differences across subjects and groups [141].…”