Measurements of brain activity complexity for varying mental loads

Coordination dynamics of large-scale neural circuitry underlying rhythmic sensorimotor behavior

In coordination dynamics, rate is a nonspecific control parameter that alters the stability of behavioral patterns and leads to spontaneous pattern switching. We used fMRI in conjunction with measures of effective connectivity to investigate the neural basis of behavioral dynamics by examining two coordination patterns known to be differentially stable (synchronization and syncopation) across a range of rates (0.75 to 1.75 Hz). Activity in primary auditory and motor cortices increased linearly with rate, independent of coordination pattern. On the contrary, activity in a premotor-cerebellar circuit varied directly with the stability of the collective variable (relative phase) that specifies coordinated behavioral patterns. Connectivity between premotor and motor cortices was also modulated by the stability of the behavioral pattern indicative of greater reliance on sensorimotor integration as action becomes more variable. By establishing a critical connection between behavioral and large scale brain dynamics, these findings reveal a basic principle for the neural organization underlying coordinated action.

Strengthening of laterality of verbal and visuospatial functions during childhood and adolescence

Cognitive functions in the child's brain develop in the context of complex adaptive processes, determined by genetic and environmental factors. Little is known about the cerebral representation of cognitive functions during development. In particular, knowledge about the development of right hemispheric (RH) functions is scarce. Considering the dynamics of brain development, localization and lateralization of cognitive functions must be expected to change with age. Twenty healthy subjects (8.6-20.5 years) were examined with fMRI and neuropsychological tests. All participants completed two fMRI tasks known to activate left hemispheric (LH) regions (language tasks) and two tasks known to involve predominantly RH areas (visual search tasks). A laterality index (LI) was computed to determine the asymmetry of activation. Group analysis revealed unilateral activation of the LH language circuitry during language tasks while visual search tasks induced a more widespread RH activation pattern in frontal, superior temporal, and occipital areas. Laterality of language increased between the ages of 8-20 in frontal (r = 0.392, P = 0.049) and temporal (r = 0.387, P = 0.051) areas. The asymmetry of visual search functions increased in frontal (r = -0.525, P = 0.009) and parietal (r = -0.439, P = 0.027) regions. A positive correlation was found between Verbal-IQ and the LI during a language task (r = 0.585, P = 0.028), while visuospatial skills correlated with LIs of visual search (r = -0.621, P = 0.018). To summarize, cognitive development is accompanied by changes in the functional representation of neuronal circuitries, with a strengthening of lateralization not only for LH but also for RH functions. Our data show that age and performance, independently, account for the increases of laterality with age.

Reconstruction of a system's dynamics from short trajectories

Long data sets are one of the prime requirements of time series analysis techniques to unravel the dynamics of an underlying system. However, acquiring long data sets is often not possible. In this paper, we address the question of whether it is still possible to understand the complete dynamics of a system if only short (but many) time series are observed. The key idea is to generate a single long time series from these short segments using the concept of recurrences in phase space. This long time series is constructed so as to exhibit a dynamics similar to that of a long time series obtained from the corresponding underlying system.

Tapping with intentional drift

When tapping a desired frequency, subjects tend to drift away from this target frequency. This compromises the estimate of the correlation between inter-tap intervals (ITIs) as predicted by the two-level model of Wing and Kristofferson which consists of an internal timer ('clock') and motor delays. Whereas previous studies on the timing of rhythmic tapping attempted to eliminate drift, we compared the production of three constant frequencies (1.5, 2.0, and 2.5 Hz) to the production of tapping sequences with a linearly decreasing inter-tap interval (ITI) (corresponding to an increase in tapping frequency from 1.5 to 2.5 Hz). For all conditions a synchronization-continuation paradigm was used. Tapping forces and electromyograms of the index-finger flexor and extensor were recorded and ITIs were derived yielding interval variability and model parameters, i.e., clock and motor variances. Electromyographic recordings served to study the influence of tapping frequency on the peripheral part of the tap event. The condition with an increasing frequency was more difficult to perform, as evidenced by an increase in deviation from the intended ITIs. In general, tapping frequency affected force level, inter-tap variability, model parameters, and muscle co-activation. Parameters for the condition with a decreasing ITI were comparable to those found in the constant frequency conditions. That is, although tapping with an intentional drift is different from constant tapping and more difficult to perform, the timing properties of both forms of tapping are remarkably similar and described well by the Wing and Kristofferson model.

Task demand modulation of steady-state functional connectivity to primary motor cortex

Correlations in blood oxygen level-dependent (BOLD) MRI signals from separate areas within the human brain have been used as a measure of functional connectivity. Steady-state measures of interregional correlations are particularly useful because they do not depend on the specific design of a task nor on subtracting conditions in a blocked design task. However, the conditions under which such correlations are measured may influence these indices of functional connectivity. The aim of this study was to investigate the influence of task demand on interregional correlations within the motor system. Specifically, tapping rates in audibly paced finger-tapping tasks were controlled and varied between runs in order to observe their effects on interregional correlations to contralateral primary motor cortex (PM). Regions of interest included the supplementary motor area, ipsilateral cerebellum, ipsilateral auditory cortex, and a control region. It was found that tapping rate was a significant factor in determining the mean correlation of some regions to PM, and that correlations measured during tapping in general increased relative to resting state. Furthermore, analysis of the percent of voxels in each region significantly correlated to PM suggested that changes in the mean correlation of that region to PM could be accounted for by changes in the fraction of significantly correlated voxels within a region. This provides insight into the manner in which steady-state correlations are modified in response to different task demands and further evidence that low-frequency fluctuations in BOLD signals reflect functional connectivity.

Dimensional complexity of neuromagnetic activity reduced during finger movement of greater difficulty

OBJECTIVE

We investigated the variation in dimensionality (D2) of neuromagnetic activity over the primary sensorimotor cortex (SM1) in healthy adults performing motor tasks of different difficulty.

METHODS

Magnetoencephalography (MEG) was used to record neuromagnetic activity during self-paced, brisk unimanual finger extension at a rate of 1 and 2 Hz using the index finger of the dominant and non-dominant hands in 16 healthy subjects. Motor task difficulty was rated by the relative difference in time measurement between 1 and 2 Hz finger movements of both hands. The relative difference in dimensionality of SM1 activity was calculated by subtracting the D2 value in 2 Hz movement from that in 1 Hz one within subjects.

RESULTS

Simple regression analyses show a significantly negative relationship between the relative dimensional complexity and the relative motor task difficulty in the contralateral SM1 for the left- (p<0.05), but not the right- (p=0.447) hand movement.

CONCLUSIONS

The present data suggest that a motor task of greater difficulty may engender a reduction of simultaneously active quasi-independent neuronal generators in the contralateral SM1 underpinned by stronger neuronal connectivity of a relatively low dimensionality.

SIGNIFICANCE

The decrease in dimensional complexity of MEG activity associated with a motor task of greater difficulty gives new insights to motor control strategy.

Tissue specificity of nonlinear dynamics in baseline fMRI

In this work, recent advances in the field of nonlinear dynamics (NLD) were applied to fMRI data to examine the spatio-temporal properties of BOLD resting state fluctuations. Five human subjects were imaged during resting state (visual fixation) at 3T using single-shot gradient echo planar imaging (EPI). Respiration and cardiac signals were concurrently recorded for retrospectively removing fluctuations due to these physiologic activities. Patterns of singularity in the complex plane (PSC) and Lempel-Ziv complexity (LZ) were used to study the deterministic nonlinearity in resting state fMRI data. The results show that there is greater nonlinearity (higher PSC) and determinism (lower LZ) in gray matter compared to white matter and CSF. In addition, the removal of respiratory and cardiac pulsations decreases the nonlinearity and determinism but does not alter the relative difference between gray matter and white matter. Therefore, our results demonstrate that determinism and nonlinearity in the fMRI data are tissue-specific, suggesting that they reflect native physiologic and metabolic fluctuations and are not a result of physiologic artifacts due to respiration and cardiac pulsation.

Nonlinear dynamical analysis of EEG and MEG: review of an emerging field

Many complex and interesting phenomena in nature are due to nonlinear phenomena. The theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a stage, where it becomes possible to study self-organization and pattern formation in the complex neuronal networks of the brain. One approach to nonlinear time series analysis consists of reconstructing, from time series of EEG or MEG, an attractor of the underlying dynamical system, and characterizing it in terms of its dimension (an estimate of the degrees of freedom of the system), or its Lyapunov exponents and entropy (reflecting unpredictability of the dynamics due to the sensitive dependence on initial conditions). More recently developed nonlinear measures characterize other features of local brain dynamics (forecasting, time asymmetry, determinism) or the nonlinear synchronization between recordings from different brain regions. Nonlinear time series has been applied to EEG and MEG of healthy subjects during no-task resting states, perceptual processing, performance of cognitive tasks and different sleep stages. Many pathologic states have been examined as well, ranging from toxic states, seizures, and psychiatric disorders to Alzheimer's, Parkinson's and Cre1utzfeldt-Jakob's disease. Interpretation of these results in terms of 'functional sources' and 'functional networks' allows the identification of three basic patterns of brain dynamics: (i) normal, ongoing dynamics during a no-task, resting state in healthy subjects; this state is characterized by a high dimensional complexity and a relatively low and fluctuating level of synchronization of the neuronal networks; (ii) hypersynchronous, highly nonlinear dynamics of epileptic seizures; (iii) dynamics of degenerative encephalopathies with an abnormally low level of between area synchronization. Only intermediate levels of rapidly fluctuating synchronization, possibly due to critical dynamics near a phase transition, are associated with normal information processing, whereas both hyper-as well as hyposynchronous states result in impaired information processing and disturbed consciousness.

Chaos and schizophrenia: does the method fit the madness?

Over the past 30 years, investigators have used nonlinear and so-called chaos theory-based techniques to examine a wide range of phenomena ranging from electroencephalogram and cardiac rate and rhythm analyses to stock market and weather predictions. Psychiatric neuroscientists are now beginning to apply nonlinear methods to mental disorders such as schizophrenia. These applications are relevant from the level of complex genetic architecture and calcium channel dynamics to the symptomatic, behavioral, and functional outcome of schizophrenia. The key point of this surge of interest is distinguishing complex, nonlinear but lawfully mediated systems from truly random systems. The application of these methods to studies in schizophrenia has yielded findings that are consistent with the general hypothesis that an altered sequential or temporal architecture is a key feature of this disorder. Specifically, we propose that the temporal architecture of schizophrenia is characterized by bursts of complex, nonlinear phenomena alternating with truly random events. Analyzing these patterns of molecular (e.g., calcium channel activity) to molar (e.g., symptom level) phenomena via nonlinear systems methods can provide new approaches to understanding complex temporal and sequential shifts in neural substrate activity, pathophysiology, and the course and treatment and outcome of schizophrenia.