Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more “interesting” sources than with time-domain ICA, and to more meaningfully sort the obtained components. The method is especially useful for finding sources of rhythmic activity. Furthermore, we propose to use a complex mixing matrix to model sources which are spatially extended and have different phases in different EEG/MEG channels. Simulations with artificial data and experiments on resting-state MEG demonstrate the utility of the method.     

Development, validation, and comparison of ICA-based gradient artifact reduction algorithms for simultaneous EEG-spiral in/out and echo-planar fMRI recordings

EEG data acquired in an MRI scanner are heavily contaminated by gradient artifacts that can significantly compromise signal quality. We developed two new methods based on independent component analysis (ICA) for reducing gradient artifacts from spiral in–out and echo-planar pulse sequences at 3 T, and compared our algorithms with four other commonly used methods: average artifact subtraction (Allen, P., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage 12, 230–239.), principal component analysis (Niazy, R., Beckmann, C., Iannetti, G., Brady, J., Smith, S., 2005. Removal of FMRI environment artifacts from EEG data using optimal basis sets. NeuroImage 28, 720–737.), Taylor series ( Wan, X., Iwata, K., Riera, J., Kitamura, M., Kawashima, R., 2006. Artifact reduction for simultaneous EEG/fMRI recording: adaptive FIR reduction of imaging artifacts. Clin. Neurophysiol. 117, 681–692.) and a conventional temporal ICA algorithm. Models of gradient artifacts were derived from simulations as well as a water phantom and performance of each method was evaluated on datasets constructed using visual event-related potentials (ERPs) as well as resting EEG. Our new methods recovered ERPs and resting EEG below the beta band (< 12.5 Hz) with high signal-to-noise ratio (SNR > 4). Our algorithms outperformed all of these methods on resting EEG in the theta and alpha bands (SNR > 4); however, for all methods, signal recovery was modest (SNR  1) in the beta band and poor (SNR < 0.3) in the gamma band and above. We found that the conventional ICA algorithm performed poorly with uniformly low SNR (< 0.1). Taken together, our new ICA-based methods offer a more robust technique for gradient artifact reduction when scanning at 3 T using spiral in–out and echo-planar pulse sequences. We provide new insights into the strengths and weaknesses of each method using a unified subspace framework.     

Temporal dynamics of alpha and beta rhythms in human SI and SII after galvanic median nerve stimulation. A MEG study

In this MEG study, we investigated cortical alpha/sigma and beta ERD/ERS induced by median nerve stimulation to extend previous evidence on different resonant and oscillatory behavior of SI and SII (NeuroImage 13 [2001] 662). Here, we tested whether simple somatosensory stimulation could induce a distinctive sequence of alpha/sigma and beta ERD/ERS over SII compared to SI. We found that for both alpha/sigma (around 10 Hz) and beta (around 20 Hz) rhythms, the latencies of ERD and ERS were larger in bilateral SII than in contralateral SI. In addition, the peak amplitude of alpha/sigma and beta ERS was smaller in bilateral SII than in contralateral SI. These results indicate a delayed and prolonged activation of SII responses, reflecting a protracted information elaboration possibly related to SII higher order role in the processing of somatosensory information. This temporal dynamics of alpha/sigma and beta rhythms may be related to a sequential activation scheme of SI and SII during the somatosensory information processes. Future studies should evaluate in SII the possible different functional significance of alpha/sigma with respect to beta rhythms during somatosensory processing.     

Group independent component analysis of language fMRI from word generation tasks

Language fMRI has been used to study brain regions involved in language processing and has been applied to pre-surgical language mapping. However, in order to provide clinicians with optimal information, the sensitivity and specificity of language fMRI needs to be improved. Type II error of failing to reach statistical significance when the language activations are genuinely present may be particularly relevant to pre-surgical planning, by falsely indicating low surgical risk in areas where no activations are shown. Furthermore, since the execution of language paradigms involves cognitive processes other than language function per se, the conventional general linear model (GLM) method may identify non-language-specific activations. In this study, we assessed an exploratory approach, independent component analysis (ICA), as a potential complementary method to the inferential GLM method in language mapping applications. We specifically investigated whether this approach might reduce type II error as well as generate more language-specific maps. Fourteen right-handed healthy subjects were studied with fMRI during two word generation tasks. A similarity analysis across tasks was proposed to select components of interest. Union analysis was performed on the language-specific components to increase sensitivity, and conjunction analysis was performed to identify language areas more likely to be essential. Compared with GLM, ICA identified more activated voxels in the putative language areas, and signals from other sources were isolated into different components. Encouraging results from one brain tumor patient are also presented. ICA may be used as a complementary tool to GLM in improving pre-surgical language mapping.     

Neural correlates of social cognition in naturalistic settings: A model-free analysis approach

Neuroimaging studies have consistently identified a network of brain regions subserving inferences of other humans' mental states. This network consists of the superior temporal sulcus, temporoparietal junction, medial prefrontal cortex, temporal poles, and precuneus. Little is known, however, about the neural substrate underlying Theory of Mind processes in close to real-life conditions. To investigate those processes in more naturalistic settings, we used an fMRI adaption of the video-based Movie for the Assessment of Social Cognition (MASC; Dziobek et al., 2006), which considers separate analysis of implicit mental state reasoning during rapidly changing perceptual cues as demanded in naturalistic settings and explicit mental state reasoning. We analyzed fMRI data by means of both a standard general linear model (GLM) approach and a tensor probabilistic independent component analysis (T-PICA), which is a novel model-free approach that allows decomposition of activation into independent spatio-temporally coherent functional networks. The model-based GLM approach revealed the typical explicit mental state reasoning network. Complementary to the GLM approach, the model-free T-PICA approach showed that those regions are also recruited during implicit mental state reasoning and that they are represented in three independent, functionally connected networks. The first component, mediating face processing and recognition, comprises the occipito-parietotemporal cortices, while the second component, involved in language comprehension, comprises the temporal lobes, lateral prefrontal cortex, and precuneus. The dorsomedial prefrontal cortex and the precuneus comprise the third component, which is likely responsible for self-referential mental activity.These results show that the mental state reasoning network can be decomposed into circumscribed functional networks mediating differential aspects of Theory of Mind.     

Model-free characterization of brain functional networks for motor sequence learning using fMRI

Neuroimaging experiments have identified several brain regions that appear to play roles in motor learning. Here we apply a novel multivariate analytical approach to explore the dynamic interactions of brain activation regions as spatio-temporally coherent functional networks. We acquired BOLD fMRI signal during explicit motor sequence learning task to characterize the adaptive functional changes in the early phase of motor learning. Subjects practiced a 10-digit, visually cued, fixed motor sequence during 15 consecutive 30 s practice blocks interleaved with similarly cued random sequence blocks. Tensor Independent Component Analysis (TICA) decomposed the data into statistically independent spatio-temporal processes. Two components were identified that represented task-related activations. The first component showed decreasing activity of a fronto-parieto-cerebellar network during task conditions. The other exclusively related to sequence learning blocks showed activation in a network including the posterior parietal and premotor cortices. Variation in expression of this component across individual subjects correlated with differences in behavior. Relative deactivations also were found in patterns similar to those described previously as "resting state" networks. Some of these deactivation components also showed task- and time-related modulations and were related to the behavioral improvement. The spatio-temporal coherence within these networks suggests that their elements are functionally integrated. Their anatomical plausibility and correlation with behavioral measures also suggest that this approach allows characterization of the interactions of functional networks relevant to the task. Particular value for multi-variant, model-free methods such as TICA lies in the potential for generating hypotheses regarding functional anatomical networks underlying specific behaviors.     

利用独立成分分析技术和静息fMRI数据对脑功能区进行定位

目的 验证可否利用独立成分分析(ICA)技术和静息fMRI数据对脑功能区进行定位.方法 利用ICA方法,通过研究静息状态的脑功能联结来获取功能区的定位.静息数据的采集采用短TR,在低通滤波(截止频率0.08 Hz)后可以去除生理噪声的主要影响.在数据分析中,对ICA结果进行了可复制性分析,只保留可复制性较高的成分,之后将ICA结果与传统的"种子像素"方法获得的结果进行定量的一致性分析.结果 ICA能够在不设定"种子像素"的情况下从静息fMRI数据中分解出运动系统和初级视觉系统的功能联结图,并在所有被试上都与"种子像素"方法有较高一致性.ICA在同一数据中可以同时分解出上述两个系统的功能联结图. 结论 ICA克服了"种子像素"方法的主观性,稳定、准确地从静息fMRI数据中分解出了脑功能联结图.本研究支持初级功能系统内的联系要明显强于系统间的联系的假设,显示ICA方法具有良好的临床应用潜力.     

Resting-state networks in the macaque at 7 T

Assessment of brain connectivity has revealed that the structure and dynamics of large-scale network organization are altered in multiple disease states suggesting their use as diagnostic or prognostic indicators. Further investigation into the underlying mechanisms, organization, and alteration of large-scale brain networks requires a homologous animal model that would allow neurophysiological recordings and experimental manipulations. The current study presents a comprehensive assessment of macaque resting-state networks based on evaluation of intrinsic low-frequency fluctuations of the blood oxygen-level-dependent signal using group independent component analysis. Networks were found underlying multiple levels of sensory, motor, and cognitive processing. The results demonstrate that macaques share remarkable homologous network organization with humans, thereby providing strong support for their use as an animal model in the study of normal and abnormal brain connectivity as well as aiding the interpretation of electrophysiological recordings within the context of large-scale brain networks.     

The role of GABAergic modulation in motor function related neuronal network activity

At rest, the primary motor cortex (M1) exhibits spontaneous neuronal network oscillations in the beta (15-30 Hz) frequency range, mediated by inhibitory interneuron drive via GABA-A receptors. However, questions remain regarding the neuropharmacological basis of movement related oscillatory phenomena, such as movement related beta desynchronisation (MRBD), post-movement beta rebound (PMBR) and movement related gamma synchronisation (MRGS). To address this, we used magnetoencephalography (MEG) to study the movement related oscillatory changes in M1 cortex of eight healthy participants, following administration of the GABA-A modulator diazepam. Results demonstrate that, contrary to initial hypotheses, neither MRGS nor PMBR appear to be GABA-A dependent, whilst the MRBD is facilitated by increased GABAergic drive. These data demonstrate that while movement-related beta changes appear to be dependent upon spontaneous beta oscillations, they occur independently of one other. Crucially, MRBD is a GABA-A mediated process, offering a possible mechanism by which motor function may be modulated. However, in contrast, the transient increase in synchronous power observed in PMBR and MRGS appears to be generated by a non-GABA-A receptor mediated process; the elucidation of which may offer important insights into motor processes.     

Human cortical EEG rhythms during long-term episodic memory task. A high-resolution EEG study of the HERA model

Many recent neuroimaging studies of episodic memory have indicated an asymmetry in prefrontal involvement, with the left prefrontal cortex more involved than the right in encoding, the right more than the left in retrieval (hemispheric encoding and retrieval asymmetry, or HERA model). In this electroencephalographic (EEG) high-resolution study, we studied brain rhythmicity during a visual episodic memory (recognition) task. The theta (4-6 Hz), alpha (6-12 Hz) and gamma (28-48 Hz) oscillations were investigated during a visuospatial long-term episodic memory task including an encoding (ENC) and retrieval (RET) phases. During the ENC phase, 25 figures representing interiors of buildings ("indoor") were randomly intermingled with 25 figures representing landscapes ("landscapes"). Subject's response was given at left ("indoor") or right ("landscapes") mouse button. During the RET phase (1 h later), 25 figures representing previously presented "indoor" pictures ("tests") were randomly intermingled with 25 figures representing novel "indoor" ("distractors"). Again, a mouse response was required. Theta and alpha EEG results showed no change of frontal rhythmicity. In contrast, the HERA prediction of asymmetry was fitted only by EEG gamma responses, but only in the posterior parietal areas. The ENC phase was associated with gamma EEG oscillations over left parietal cortex. Afterward, the RET phase was associated with gamma EEG oscillations predominantly over right parietal cortex. The predicted HERA asymmetry was thus observed in an unexpected location. This discrepancy may be due to the differential sensitivity of neuroimaging methods to selected components of cognitive processing. The strict relation between gamma response and perception suggests that retrieval processes of long-term memory deeply impinged upon sensory representation of the stored material.     

A unified framework for group independent component analysis for multi-subject fMRI data

Independent component analysis (ICA) is becoming increasingly popular for analyzing functional magnetic resonance imaging (fMRI) data. While ICA has been successfully applied to single-subject analysis, the extension of ICA to group inferences is not straightforward and remains an active topic of research. Current group ICA models, such as the GIFT [Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J., 2001. A method for making group inferences from functional MRI data using independent component analysis. Hum. Brain Mapp. 14, 140–151.] and tensor PICA [Beckmann, C.F., Smith, S.M., 2005. Tensorial extensions of independent component analysis for multisubject FMRI analysis. Neuroimage 25, 294–311.], make different assumptions about the underlying structure of the group spatio-temporal processes and are thus estimated using algorithms tailored for the assumed structure, potentially leading to diverging results. To our knowledge, there are currently no methods for assessing the validity of different model structures in real fMRI data and selecting the most appropriate one among various choices. In this paper, we propose a unified framework for estimating and comparing group ICA models with varying spatio-temporal structures. We consider a class of group ICA models that can accommodate different group structures and include existing models, such as the GIFT and tensor PICA, as special cases. We propose a maximum likelihood (ML) approach with a modified Expectation–Maximization (EM) algorithm for the estimation of the proposed class of models. Likelihood ratio tests (LRT) are presented to compare between different group ICA models. The LRT can be used to perform model comparison and selection, to assess the goodness-of-fit of a model in a particular data set, and to test group differences in the fMRI signal time courses between subject subgroups. Simulation studies are conducted to evaluate the performance of the proposed method under varying structures of group spatio-temporal processes. We illustrate our group ICA method using data from an fMRI study that investigates changes in neural processing associated with the regular practice of Zen meditation.     

Testing of a measurement model for baccalaureate nursing students' self-evaluation of core competencies

Title. Testing of a measurement model for baccalaureate nursing students’ self‐evaluation of core competencies. Aim.  This paper is a report of a study to test the psychometric properties of the Self‐Evaluated Core Competencies Scale for baccalaureate nursing students. Background.  Baccalaureate nursing students receive basic nursing education and continue to build competency in practice settings after graduation. Nursing students today face great challenges. Society demands analytic, critical, reflective and transformative attitudes from graduates. It also demands that institutions of higher education take the responsibility to encourage students, through academic work, to acquire knowledge and skills that meet the needs of the modern workplace, which favours highly skilled and qualified workers. Methods.  A survey of 802 senior nursing students in their last semester at college or university was conducted in Taiwan in 2007 using the Self‐Evaluated Core Competencies Scale. Half of the participants were randomly assigned either to principal components analysis with varimax rotation or confirmatory factor analysis. Results.  Principal components analysis revealed two components of core competencies that were named as humanity/responsibility and cognitive/performance. The initial model of confirmatory factor analysis was then converged to an acceptable solution but did not show a good fit; however, the final model of confirmatory factor analysis was converged to an acceptable solution with acceptable fit. The final model has two components, namely humanity/responsibility and cognitive/performance. Both components have four indicators. In addition, six indicators have their correlated measurement errors. Conclusion.  Self‐Evaluated Core Competencies Scale could be used to assess the core competencies of undergraduate nursing students. In addition, it should be used as a teaching guide to increase students’ competencies to ensure quality patient care in hospitals.     

Models for predicting turnover of residential aged care nurses: A structural equation modelling analysis of secondary data

Background

Nurse turnover in the residential aged care industry is a pressing issue. Researchers have shown ongoing interest in exploring how the factors that are amendable to change in aged care policy, regulation and funding and in organizational procedures (e.g. job demands, coping resources and psychological health of nurses) impact on turnover. However, the findings are mixed.

Objective

This study tested two theoretical models of turnover to examine the structural relationships among job demands, coping resources, psychological health and turnover of residential aged care nurses. Although many previous studies operationalized turnover as intention to leave, the present study investigated actual turnover by following up with the same individuals over time, and thus provided more accurate predictive models of turnover behaviour.

Design and methods

The sample, 239 Australian residential aged care nurses, came from the Nurses and Midwives e-cohort Study. Job demands, coping resources, and psychological health were measured using standardized instruments. Structural equation modelling was used to test the measurement and structural models.

Results

Controlling for a number of workforce and individual characteristics, coping resources (measured by job control, supervisor support, and co-worker support) were negatively and directly associated with turnover. Additionally, the findings supported the Job Demand-Control-Support model in that higher coping resources and lower job demands (indicated by psychological demands, physical demands, and effort) were related to better psychological health (measured by vitality, social functioning, role emotional, and mental health), and higher job demands were related to lower coping resources.

Conclusions

Findings suggest that aged care policy makers and service providers might consider increasing coping resources available to nurses and minimizing job demands of care work to reduce turnover and improve nurses’ psychological health. Moreover, findings from this Australian study may provide valuable practical and policy implications for other developed countries.