Modeling Categorical Variables by Mutual Information Decomposition.

This paper proposed the use of mutual information (MI) decomposition as a novel approach to identifying indispensable variables and their interactions for contingency table analysis. The MI analysis identified subsets of associative variables based on multinomial distributions and validated parsimonious log-linear and logistic models. The proposed approach was assessed using two real-world datasets dealing with ischemic stroke (with 6 risk factors) and banking credit (with 21 discrete attributes in a sparse table). This paper also provided an empirical comparison of MI analysis versus two state-of-the-art methods in terms of variable and model selections. The proposed MI analysis scheme can be used in the construction of parsimonious log-linear and logistic models with a concise interpretation of discrete multivariate data.

Brain Reactions to Opening and Closing the Eyes: Salivary Cortisol and Functional Connectivity.

This study empirically assessed the strength and duration of short-term effects induced by brain reactions to closing/opening the eyes on a few well-known resting-state networks. We also examined the association between these reactions and subjects' cortisol levels. A total of 55 young adults underwent 8-min resting-state fMRI (rs-fMRI) scans under 4-min eyes-closed and 4-min eyes-open conditions. Saliva samples were collected from 25 of the 55 subjects before and after the fMRI sessions and assayed for cortisol levels. Our empirical results indicate that when the subjects were relaxed with their eyes closed, the effect of opening the eyes on conventional resting-state networks (e.g., default-mode, frontal-parietal, and saliency networks) lasted for roughly 60-s, during which we observed a short-term increase in activity in rs-fMRI time courses. Moreover, brain reactions to opening the eyes had a pronounced effect on time courses in the temporo-parietal lobes and limbic structures, both of which presented a prolonged decrease in activity. After controlling for demographic factors, we observed a significantly positive correlation between pre-scan cortisol levels and connectivity in the limbic structures under both conditions. Under the eyes-closed condition, the temporo-parietal lobes presented significant connectivity to limbic structures and a significantly positive correlation with pre-scan cortisol levels. Future research on rs-fMRI could consider the eyes-closed condition when probing resting-state connectivity and its neuroendocrine correlates, such as cortisol levels. It also appears that abrupt instructions to open the eyes while the subject is resting quietly with eyes closed could be used to probe brain reactivity to aversive stimuli in the ventral hippocampus and other limbic structures.

Brain dynamics and connectivity networks under natural auditory stimulation.

The analysis of functional magnetic resonance imaging (fMRI) data is challenging when subjects are under exposure to natural sensory stimulation. In this study, a two-stage approach was developed to enable the identification of connectivity networks involved in the processing of information in the brain under natural sensory stimulation. In the first stage, the degree of concordance between the results of inter-subject and intra-subject correlation analyses is assessed statistically. The microstructurally (i.e., cytoarchitectonically) defined brain areas are designated either as concordant in which the results of both correlation analyses are in agreement, or as discordant in which one analysis method shows a higher proportion of supra-threshold voxels than does the other. In the second stage, connectivity networks are identified using the time courses of supra-threshold voxels in brain areas contingent upon the classifications derived in the first stage. In an empirical study, fMRI data were collected from 40 young adults (19 males, average age 22.76 ±â€¯3.25), who underwent auditory stimulation involving sound clips of human voices and animal vocalizations under two operational conditions (i.e., eyes-closed and eyes-open). The operational conditions were designed to assess confounding effects due to auditory instructions or visual perception. The proposed two-stage analysis demonstrated that stress modulation (affective) and language networks in the limbic and cortical structures were respectively engaged during sound stimulation, and presented considerable variability among subjects. The network involved in regulating visuomotor control was sensitive to the eyes-open instruction, and presented only small variations among subjects. A high degree of concordance was observed between the two analyses in the primary auditory cortex which was highly sensitive to the pitch of sound clips. Our results have indicated that brain areas can be identified as concordant or discordant based on the two correlation analyses. This may further facilitate the search for connectivity networks involved in the processing of information under natural sensory stimulation.

Listening to real-world sounds: fMRI data for analyzing connectivity networks.

There is a growing interest in functional magnetic resonance imaging (fMRI) studies on connectivity networks in the brain when subjects are under exposure to natural sensory stimulation. Because of a complicated coupling between spontaneous and evoked brain activity under real-world stimulation, there is no critical mapping between the experimental inputs and corresponding brain responses. The dataset contains auditory fMRI scans and T1-weighted anatomical scans acquired under eyes-closed and eyes-open conditions. Within each scanning condition, the subject was presented 12 different sound clips, including human voices followed by animal vocalizations. The dataset is meant to be used to assess brain dynamics and connectivity networks under natural sound stimulation; it also allows for empirical investigation of changes in fMRI responses between eyes-closed and eyes-open conditions, between animal vocalizations and human voices, as well as between the 12 different sound clips during auditory stimulation. The dataset is a supplement to the research findings in the paper "Brain dynamics and connectivity networks under natural auditory stimulation" published in NeuroImage.

Group factor analysis for Alzheimer's disease.

For any neuroimaging study in an institute, brain images are normally acquired from healthy controls and patients using a single track of protocol. Traditionally, the factor analysis procedure analyzes image data for healthy controls and patients either together or separately. The former unifies the factor pattern across subjects and the latter deals with measurement errors individually. This paper proposes a group factor analysis model for neuroimaging applications by assigning separate factor patterns to control and patient groups. The clinical diagnosis information is used for categorizing subjects into groups in the analysis procedure. The proposed method allows different groups of subjects to share a common covariance matrix of measurement errors. The empirical results show that the proposed method provides more reasonable factor scores and patterns and is more suitable for medical research based on image data as compared with the conventional factor analysis model.

Beyond p-values: averaged and reproducible evidence in fMRI experiments.

In functional magnetic resonance imaging studies, there might exist activation regions routinely involved in experimental sessions, but modest in response magnitude. These regions may not be easily detectable by the conventional p-value approach using a rigid threshold. With particular reference to the reproducibility analysis method proposed in Liou and colleagues, this study presents some within- and between-subject brain-activation patterns that are replicable between experimental modalities, and robust to the method used for generating the patterns. There is a neurophysiological basis behind these reproducible patterns, and the conventional p-value approach using averaged data across subjects might not suggest the complete patterns. For example, recent studies based on the group-averaged data showed a task-induced deactivation in the precuneus and posterior cingulate, but our reproducibility analysis suggests both increased and decreased responses in the two regions. The increased responses localize in these regions with differentially distributed patterns for individual subjects and for different experimental tasks. In this study, we discuss the neurophysiological basis of the reproducible patterns and propose some applications of our research findings to scientific and clinical studies.

MR image segmentation using a power transformation approach.

This study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue intensity distributions. One advantage of the method is that it is intuitively appealing and computationally simple. To avoid performance degradation caused by intensity inhomogeneity, different methods for correcting bias fields were applied prior to image segmentation, and their correction effects on the segmentation results were examined in the empirical study. The partitions of brain tissues (i.e., gray and white matter) resulting from the method were validated and evaluated against manual segmentation results based on 38 real T1-weighted image volumes from the internet brain segmentation repository, and 18 simulated image volumes from BrainWeb. The Jaccard and Dice similarity indexes were computed to evaluate the performance of the proposed approach relative to the expert segmentations. Empirical results suggested that the proposed segmentation method yielded higher similarity measures for both gray matter and white matter as compared with those based on the traditional segmentation using the Gaussian mixtures approach.

A method for generating reproducible evidence in fMRI studies.

Insights into cognitive neuroscience from neuroimaging techniques are now required to go beyond the localisation of well-known cognitive functions. Fundamental to this is the notion of reproducibility of experimental outcomes. This paper addresses the central issue that functional magnetic resonance imaging (fMRI) experiments will produce more desirable information if researchers begin to search for reproducible evidence rather than only p value significance. The study proposes a methodology for investigating reproducible evidence without conducting separate fMRI experiments. The reproducible evidence is gathered from the separate runs within the study. The associated empirical Bayes and ROC extensions of the linear model provide parameter estimates to determine reproducibility. Empirical applications of the methodology suggest that reproducible evidence is robust to small sample sizes and sensitive to both the magnitude and persistency of brain activation. It is demonstrated that research findings in fMRI studies would be more compelling with supporting reproducible evidence in addition to standard hypothesis testing evidence.

Mapping single-trial EEG records on the cortical surface through a spatiotemporal modality.

Event-related potentials (ERPs) induced by visual perception and cognitive tasks have been extensively studied in neuropsychological experiments. ERP activities time-locked to stimulus presentation and task performance are often observed separately at individual scalp channels based on averaged time series across epochs and experimental subjects. An analysis using averaged EEG dynamics could discount information regarding interdependency between ongoing EEG and salient ERP features. Advanced tools such as independent component analysis (ICA) have been developed for decomposing collections of single-trial EEG records into separate features. Those features (or independent components) can then be mapped onto the cortical surface using source localization algorithms to visualize brain activation maps and to study between-subject consistency. In this study, we propose a statistical framework for estimating the time course of spatiotemporally independent EEG components simultaneously with their cortical distributions. Within this framework, we implemented Bayesian spatiotemporal analysis for imaging the sources of EEG features on the cortical surface. The framework allows researchers to include prior knowledge regarding spatial locations as well as spatiotemporal independence of different EEG sources. The use of the Electromagnetic Spatiotemporal ICA (EMSICA) method is illustrated by mapping event-related EEG dynamics induced by events in a visual two-back continuous performance task. The proposed method successfully identified several interesting components with plausible corresponding cortical activation topographies, including processes contributing to the late positive complex (LPC) located in central parietal, frontal midline, and anterior cingulate cortex, to atypical mu rhythms associated with the precentral gyrus, and to the central posterior alpha activity in the precuneus.

Functional MR images and scientific inference: reproducibility maps.

Historically, reproducibility has been the sine qua non of experimental findings that are considered to be scientifically useful. Typically, findings from functional magnetic resonance imaging (fMRI) studies are assessed with statistical parametric maps (SPMs) using a p value threshold. However, a smaller p value does not imply that the observed result will be reproducible. In this study, we suggest interpreting SPMs in conjunction with reproducibility evidence. Reproducibility is defined as the extent to which the active status of a voxel remains the same across replicates conducted under the same conditions. We propose a methodology for assessing reproducibility in functional MR images without conducting separate experiments. Our procedures include the empirical Bayes method for estimating effects due to experimental stimuli, the threshold optimization procedure for assigning voxels to the active status, and the construction of reproducibility maps. In an empirical example, we implemented the proposed methodology to construct reproducibility maps based on data from the study by Ishai et al. (2000). The original experiments involved 12 human subjects and investigated brain regions most responsive to visual presentation of 3 categories of objects: faces, houses, and chairs. The brain regions identified included occipital, temporal, and fusiform gyri. Using our reproducibility analysis, we found that subjects in one of the experiments exercised at least 2 mechanisms in responding to visual objects when performing alternately matching and passive tasks. One gave activation maps closer to those reported in Ishai et al., and the other had related regions in the precuneus and posterior cingulate. The patterns of activated regions are reproducible for at least 4 out of 6 subjects involved in the experiment. Empirical application of the proposed methodology suggests that human brains exhibit different strategies to accomplish experimental tasks when responding to stimuli. It is important to correlate activations to subjects' behavior such as reaction time and response accuracy. Also, the latency between the stimulus presentation and the peak of the hemodynamic response function varies considerably among individual subjects according to types of stimuli and experimental tasks. These variations per se also deserve scientific inquiries. We conclude by discussing research directions relevant to reproducibility evidence in fMRI.