Cortical connectivity during word association search

Free Associations as A Measure of Stable Implicit Attitudes

Two studies explored the psychometric properties of free association methods for the assessment of attitudes. Even though the stability of the actual associations was rather low, psychometric properties of the valence estimates of the free associations were highly satisfactory. Valence estimates of associations were provided by independent judges who rated the valence of the associations that were generated by participants. Valence estimates of the associations showed satisfactory internal consistencies and retest reliabilities over three weeks. Additionally, valence estimates of the associations were significantly and independently related to both explicit self–reported attitudes and implicit attitudes that were assessed with an Ossi–Wessi Implicit Association Test. Free association methods represent a useful complement to the family of implicit measures and are especially suitable for the assessment of non–relative attitudes towards single attitude objects. Copyright © 2012 John Wiley & Sons, Ltd.

Brain-computer interface analysis of a dynamic visuo-motor task

BACKGROUND

The area of brain-computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's "thoughts" to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain-computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data.

OBJECTIVE

In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain-computer interface.

MATERIALS AND METHODS

For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filtering, double phase demodulation and double principal component analyses (PCA), each with a separate set of parameters. For the movement-prediction model a fuzzy inference system was used.

RESULTS

The results have shown that the EEG signals measured during the dVM tasks carry enough information about the subjects' wrist movements for them to be successfully decoded using the presented methodology. Reasonably high values of the correlation coefficients suggest that the validation of the proposed approach is satisfactory. Moreover, since the causality of the rhythm filtering and the PCA transformation has been achieved, we have shown that these methods can also be used in a real-time, brain-computer interface. The study revealed that using non-causal, optimized methods yields better prediction results in comparison with the causal, non-optimized methodology; however, taking into account that the causality of these methods allows real-time processing, the minor decrease in prediction quality is acceptable.

CONCLUSION

The study suggests that the methodology that was proposed in our previous studies is also valid for identifying the EEG-coded content during dVM tasks, albeit with various modifications, which allow better prediction results and real-time data processing. The results have shown that wrist movements can be predicted in simulated or real time; however, the results of the non-causal, optimized methodology (simulated) are slightly better. Nevertheless, the study has revealed that these methods should be suitable for use in the development of a non-invasive, brain-computer interface.

Two semantic systems in the brain for rapid and slow differentiation of abstract and concrete words

Most studies of semantic processing address changes in the late (300-800 msec) components of evoked potentials. However, recent years have seen the appearance of data showing that humans can perceive the sense of stimuli presented to them in significantly shorter periods of time. We report here studies of the mechanism of semantic analysis of written abstract and concrete words in four series of experiments: 1) reading of words on a monitor screen; 2) simple classification of all presented words into the categories "abstract" and "concrete;" 3) complex, i.e., selective classification of words written only in a specified color with a prompt as to which color would be used for the word; 4) complex classification of words of only a specified color without a prior prompt. Early (40-100 msec) differences in evoked brain potentials were seen on comparison of responses to abstract and concrete words, predominantly in the frontal areas in the case of simple reading of words and in the more dorsal areas in the case of tasks with simple classification. All cases of explicit classification of words were characterized by differences in late (450-700 msec) components in the left frontal zone. The results indicate the existence of two semantic systems: a rapid, implicit system associated with activation of the right frontal area, and a slow, explicit system of word classification which is predominantly associated with activity in the left frontal area. The relationship between the two systems is to a certain extent reciprocal: the rapid system can by inhibited by introduction of a word classification task.

Rapid and slow brain systems of abstract and concrete words differentiation

Previous studies have repeatedly found that late (300-800 ms) components of event-related potentials (ERP) reflected semantic analysis, i.e. the differentiation between abstract and concrete words. However, the human brain may detect the meaning of the words much earlier. This study investigated the brain mechanisms of the processing of abstract and concrete written words in four experimental conditions: i) Simple Reading, during which volunteers were required to silently read words; ii) Simple Classification, during which volunteers were required to classify the presented word into the abstract and concrete categories; and iii) Cued and iv) Uncued Selective Classification conditions, during which subjects had to classify only the words typed in a particular colour. 19-channel EEG was recorded during the experiment from 13 subjects. The ERP to abstract and concrete words differed not only at the late but also at early (40-100 ms) latencies in the Simple Reading and Classification conditions, as well as for the words that should not be explicitly classified in the Cued Selective Classification condition. This means that semantic analysis can occur in a manner which is both very rapid and implicit. Moreover, increasing task demands can even suppress this rapid semantic analysis. The functional microstate analysis revealed a topographical difference in response to abstract and concrete words, which indicated that at least partly distinct brain networks are involved in the processing of words during both early (implicit differentiation) and late (explicit classification) latencies.

Lagged covariance structure models for studying functional connectivity in the brain

Most cognitive processes are supported by large networks of brain regions. To describe the operation of these networks, it is critical to understand how individual areas are functionally connected. Here, we establish a statistical framework for studying effective and functional brain connectivity, using data obtained with a relatively new neuroimaging method, the event-related optical signal (EROS). The novelty of our approach is the use of timing information (in the form of lagged cross-correlations) in interpreting the connections between areas. Interpretation of lagged cross-correlations exploits the combination of spatial and temporal resolution provided by EROS. In this paper, we apply dynamic factor analysis as a method for testing various structural models on the lagged covariance matrices derived from the EROS data. We first illustrate the approach by testing a simple path model of neural activity propagation from area V1 to V3 in a visual stimulation task. We then build more complex structural equation models with latent variables, describing both within-hemisphere integrity, and interactions between the two hemispheres, to interpret data from a second task involving inter-hemispheric competition. The results demonstrate how the integrity of anatomical connections between the two hemispheres explains different patterns of cross-hemispheric interactions. This approach allows for fitting brain imaging data to complex models that capture dynamic cognitive processes as they rapidly evolve over time.

Evoked phase synchronization between adjacent high-density electrodes in human scalp EEG: Duration and time course related to behavior

OBJECTIVE

Data from a previous event-related potential (ERP) study in visual-perceptual grouping [Nikolaev AR, van Leeuwen C. Flexibility in spatial and non-spatial feature grouping: an event-related potentials study. Brain Res Cogn Brain Res 2004;22:13-25] were re-analyzed to identify event-related dynamics of phase-synchronization.

METHODS

In 20 Hz activity, uniform spreading of phase synchronization in closely spaced (approximately 2 cm) scalp electrodes appears and disappears spontaneously. The lengths of synchronized activity intervals and how they vary as a function of stimulus presentation were compared between task and control conditions.

RESULTS

Synchronization reached a maximum in the task condition about 180 ms post-stimulus onset, coinciding with the peak N180 ERP marking the deployment of task-specific attention. Synchronized intervals were longer in the task than in the control condition. Long (above 80 ms) intervals occurred at a stable rate before and just after stimulus onset, but steeply decreased 200-400 ms afterwards.

CONCLUSIONS

Perceptual tasks lead to longer synchronized intervals in early visual areas. Attention deployment resets the ongoing synchronization. Event-related activity, besides low-frequency ERP, consists of high-frequency short and long synchronized intervals corresponding to evoked bursts and ongoing oscillations, respectively.

SIGNIFICANCE

High-density scalp recorded EEG revealed synchronization dynamics in a local, early visual area of cortex that can be interpreted as modulation of spontaneous ongoing task-related processes by attention.

Cortical connectivity in high frequency beta-rhythm in schizophrenics with positive and negative symptoms

During the last decade the role of high frequency EEG activity in the 'binding phenomenon' was discovered. It was supposed that this phenomenon provided the integration between different brain structures underlying higher nervous functions and possibly even consciousness [Proc. Natl. Acad. Sci. 90 (1993) 2078; Annu. Rev. Neurosci. 18 (1995) 555; J. Neurosci. V 16 (1996) 4240; Am. Physiol. Soc. (1998) 1567; Induced Rhythms in the Brain (1992) 425; NeuroReport 8 (1997) 531; Proc. Natl. Acad. Sci. USA 94 (1997) 12198]. Schizophrenia is considered as a disorder of the integration between different brain regions [Review of Psychiatry 18 (1999a) 29; Conceptual Advances in Russian Neuroscience: Complex Brain Functions (1999) 151; Brain Res. Rev. 31 (2000) 301], and in the present work we have studied cortical connectivity, focusing on those connections which are maintained by high frequency EEG-rhythm (20-40 Hz). The results showed a high degree of biopotential synchronisation between definite cortical areas during cognitive processes in normal subjects and have evidenced significant functional connectivity disturbances in schizophrenia in this EEG frequency domain.