Arthur C. Tsai

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.

EEG-correlates of trait anxiety in the stop-signal paradigm

The relationship between trait anxiety and event-related EEG oscillatory reactions in the stop-signal paradigm was studied in 15 non-clinical subjects with average age of 26 years (13 men). In the paradigm, subjects responded to target stimuli by pressing one of the two choice buttons. In 30 out of 130 trials, target presentation was followed by a stop-signal, indicating that subjects had to refrain from a prepared motor response. The subjects level of anxiety was assessed using the State Trait Anxiety Inventory. Wide-band desynchronization (8-25 Hz) was found before button-press. It was sustained after the subjects pressed the button at 7-14 Hz frequency range. Also, synchronization at 15-25 Hz band occurred in 400-1400 ms after the button-press. Synchronization at lower frequencies (1-7 Hz) was also found during 0-700 ms after the stop-signal onset. Also, desynchronization at 8-20 Hz was found in 300-800 ms after stop-signal onset. The group with higher anxiety showed desynchronization at 10-13 Hz in 0-800 ms after the button-press, whereas the group with lower anxiety showed synchronization at the same frequency range. In 0-600 ms after stop-signal onset, desynchronization at 8-13 Hz was observed in the group with higher anxiety, whereas the group with lower anxiety demonstrated synchronization or weak desynchronization. Our findings support the Eysenck et al. [M.W. Eysenck, N. Derakshan, R. Santos, M.G. Calvo, Anxiety and cognitive performance: attentional control theory, Emotion 7(2) (2007) 336-356] theory that subjects with higher anxiety have more attentional control over reaction and increased use of processing resources as compared with lower anxiety subjects.

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.

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.

Cortical surface alignment in multi-subject spatiotemporal independent EEG source imaging.

Brain responses to stimulus presentations may vary widely across subjects in both time course and spatial origins. Multi-subject EEG source imaging studies that apply Independent Component Analysis (ICA) to data concatenated across subjects have overlooked the fact that projections to the scalp sensors from functionally equivalent cortical sources vary from subject to subject. This study demonstrates an approach to spatiotemporal independent component decomposition and alignment that spatially co-registers the MR-derived cortical topographies of individual subjects to a well-defined, shared spherical topology (Fischl et al., 1999). Its efficacy for identifying functionally equivalent EEG sources in multi-subject analysis is demonstrated by analyzing EEG and behavioral data from a stop-signal paradigm using two source-imaging approaches, both based on individual subject independent source decompositions. The first, two-stage approach uses temporal infomax ICA to separate each subjects data into temporally independent components (ICs), then estimates the source density distribution of each IC process from its scalp map and clusters similar sources across subjects (Makeig et al., 2002). The second approach, Electromagnetic Spatiotemporal Independent Component Analysis (EMSICA), combines ICA decomposition and source current density estimation of the artifact-rejected data into a single spatiotemporal ICA decomposition for each subject (Tsai et al., 2006), concurrently identifying both the spatial source distribution of each cortical source and its event-related dynamics. Applied to the stop-signal task data, both approaches gave IC clusters that separately accounted for EEG processes expected in stop-signal tasks, including pre/postcentral mu rhythms, anterior-cingulate theta rhythm, and right-inferior frontal responses, the EMSICA clusters exhibiting more tightly correlated source areas and time-frequency features.

Face recognition in Asperger syndrome: A study on EEG spectral power changes

EEG reactions in emotional face recognition were studied in five participants with Asperger syndrome (AS) and seven control subjects. Control subjects showed a spectral power increase following the stimulus onset in two time-frequency intervals-(1) 150-300ms in the 1-16Hz frequency range and (2) 300-650ms in the 1-8Hz range. Also, alpha/beta desynchronization occurred 400-1000ms after the stimulus onset with maximal amplitude in the posterior region. Theta synchronization (4-8Hz) was weaker in the AS group than in the control group, but beta2 desynchronization was stronger in the AS group. The results were interpreted in terms of automatic and voluntary control of perception.

Conscious and non-conscious representations of emotional faces in Asperger's syndrome

Several neuroimaging studies have suggested that the low spatial frequency content in an emotional face mainly activates the amygdala, pulvinar, and superior colliculus especially with fearful faces(1-3). These regions constitute the limbic structure in non-conscious perception of emotions and modulate cortical activity either directly or indirectly(2). In contrast, the conscious representation of emotions is more pronounced in the anterior cingulate, prefrontal cortex, and somatosensory cortex for directing voluntary attention to details in faces(3,4). Aspergers syndrome (AS)(5,6) represents an atypical mental disturbance that affects sensory, affective and communicative abilities, without interfering with normal linguistic skills and intellectual ability. Several studies have found that functional deficits in the neural circuitry important for facial emotion recognition can partly explain social communication failure in patients with AS(7-9). In order to clarify the interplay between conscious and non-conscious representations of emotional faces in AS, an EEG experimental protocol is designed with two tasks involving emotionality evaluation of either photograph or line-drawing faces. A pilot study is introduced for selecting face stimuli that minimize the differences in reaction times and scores assigned to facial emotions between the pretested patients with AS and IQ/gender-matched healthy controls. Information from the pretested patients was used to develop the scoring system used for the emotionality evaluation. Research into facial emotions and visual stimuli with different spatial frequency contents has reached discrepant findings depending on the demographic characteristics of participants and task demands(2). The experimental protocol is intended to clarify deficits in patients with AS in processing emotional faces when compared with healthy controls by controlling for factors unrelated to recognition of facial emotions, such as task difficulty, IQ and gender.

Different Brain Wave Patterns and Cortical Control Abilities in Relation to Different Creative Potentials.

Contemporary understanding of brain functions provides a way to probe into the mystery of creativity. However, the prior evidence regarding the relationship between creativity and brain wave patterns reveals inconsistent conclusions. One possible reason might be that the means of selecting creative individuals in the past has varied in each study. By distinguishing creative potential as open-ended versus closed-ended based on theoretical views, this study examined different brain wave patterns and cortical control abilities in relation to different creative potentials by using electroencephalogram (EEG) biofeedback equipment. The results demonstrated that participants’ performance on the open-ended creative problem was positively related to EEG alpha frequencies, whereas performance on the closed-ended creative problem was related to larger variability in EEG dynamics between alpha and beta waves when performing either open-ended or closed-ended creativity tasks. Further, better control in changing states of brain wave activities according to the EEG biofeedback signals could predict closed-ended creativity performance. Open-ended creativity was related only to the enhancement of alpha signals. These results help clarify previous inconsistent findings, reveal different natures of distinct creativities, and further suggest ways to improve different aspects of creativity with modified biofeedback procedures.

On hyperbolic transformations to normality

In biological and social sciences, it is essential to consider data transformations to normality for detecting structural effects and for better data representation and interpretation. An array of transformations to normality has been derived for data exhibiting skewed, leptokurtic and unimodal shapes, but is less amenable to data exhibiting platykurtic shapes, such as a nearly bimodal distribution. This study proposes and constructs a new family of hyperbolic power transformations for improving normality of raw data with varying degrees of skewness and kurtosis. An advantage this new family has is its effectiveness in transforming platykurtic or bimodal data distributions to normal. A simulation study and a real data example on mathematics achievement test scores are used to illustrate the wide-ranging applications of the proposed family of transformations. As a cautionary note, usefulness and limitations of the proposed method will be discussed for stabilizing the variance of DNA microarray data and for symmetrizing the data distribution towards normality. The empirical applications also illustrate an example of conservative t- and ANOVA F-tests when the assumption of normality is violated.

Advanced Electroencephalogram Processing: Automatic Clustering of EEG Components

The study of electroencephalography (EEG) data can involve independent component analysis and further clustering of the components according to relation of the components to certain processes in a brain or to external sources of electricity such as muscular motion impulses, electrical fields inducted by power mains, electrostatic discharges, etc. At present, known methods for clustering of components are costly because require additional measurements with magnetic-resonance imaging (MRI), for example, or have accuracy restrictions if only EEG data is analyzed. A new method and algorithm for automatic clustering of physiologically similar but statistically independent EEG components is described in this paper. Developed clustering algorithm has been compared with algorithms implemented in the EEGLab toolbox. The paper contains results of algorithms testing on real EEG data obtained under two experimental tasks: voluntary movement control under conditions of stop-signal paradigm and syntactical error recognition in written sentences. The experimental evaluation demonstrated more than 90% correspondence between the results of automatic clustering and clustering made by an expert physiologist.