Event-related brain potentials reveal enhancing and compensatory mechanisms during dual neurocognitive and cycling tasks.

BACKGROUND: Various neurocognitive tests have shown that cycling enhances cognitive performance compared to resting. Event-related potentials (ERPs) elicited by an oddball or flanker task have clarified the impact of dual-task cycling on perception and attention. In this study, we investigate the effect of cycling on cognitive recruitment during tasks that involve not only stimulus identification but also semantic processing and memory retention. METHODS: We recruited 24 healthy young adults (12 males, 12 females; mean age = 22.71, SD = 1.97 years) to perform three neurocognitive tasks (namely color-word matching, arithmetic calculation, and spatial working memory) at rest and while cycling, employing a within-subject design with rest/cycling counterbalancing. RESULTS: The reaction time on the spatial working memory task was faster while cycling than at rest at a level approaching statistical significance. The commission error percentage on the color-word matching task was significantly lower at rest than while cycling. Dual-task cycling while responding to neurocognitive tests elicited the following results: (a) a greater ERP P1 amplitude, delayed P3a latency, less negative N4, and less positivity in the late slow wave (LSW) during color-word matching; (b) a greater P1 amplitude during memory encoding and smaller posterior negativity during memory retention on the spatial working memory task; and (c) a smaller P3 amplitude, followed by a more negative N4 and less LSW positivity during arithmetic calculation. CONCLUSION: The encoding of color-word and spatial information while cycling may have resulted in compensatory visual processing and attention allocation to cope with the additional cycling task load. The dual-task cycling and cognitive performance reduced the demands of semantic processing for color-word matching and the cognitive load associated with temporarily suspending spatial information. While dual-tasking may have required enhanced semantic processing to initiate mental arithmetic, a compensatory decrement was noted during arithmetic calculation. These significant neurocognitive findings demonstrate the effect of cycling on semantic-demand and memory retention-demand tasks.

Resistance-induced brain activity changes during cycle ergometer exercises.

BACKGROUND: EEGs are frequently employed to measure cerebral activations during physical exercise or in response to specific physical tasks. However, few studies have attempted to understand how exercise-state brain activity is modulated by exercise intensity. METHODS: Ten healthy subjects were recruited for sustained cycle ergometer exercises at low and high resistance, performed on two separate days a week apart. Exercise-state EEG spectral power and phase-locking values (PLV) are analyzed to assess brain activity modulated by exercise intensity. RESULTS: The high-resistance exercise produced significant changes in beta-band PLV from early to late pedal stages for electrode pairs F3-Cz, P3-Pz, and P3-P4, and in alpha-band PLV for P3-P4, as well as the significant change rate in alpha-band power for electrodes C3 and P3. On the contrary, the evidence for changes in brain activity during the low-resistance exercise was not found. CONCLUSION: These results show that the cortical activation and cortico-cortical coupling are enhanced to take on more workload, maintaining high-resistance pedaling at the required speed, during the late stage of the exercise period.

Complex analysis of neuronal spike trains of deep brain nuclei in patients with Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been used to alleviate symptoms of Parkinson's disease. During image-guided stereotactic surgery, signals from microelectrode recordings are used to distinguish the STN from adjacent areas, particularly from the substantia nigra pars reticulata (SNr). Neuronal firing patterns based on interspike intervals (ISI) are commonly used. In the present study, arrival time-based measures, including Lempel-Ziv complexity and deviation-from-Poisson index were employed. Our results revealed significant differences in the arrival time-based measures among non-motor STN, motor STN and SNr and better discrimination than the ISI-based measures. The larger deviations from the Poisson process in the SNr implied less complex dynamics of neuronal discharges. If spike classification was not used, the arrival time-based measures still produced statistical differences among STN subdivisions and SNr, but the ISI-based measures only showed significant differences between motor and non-motor STN. Arrival time-based measures are less affected by spike misclassifications, and may be used as an adjunct for the identification of the STN during microelectrode targeting.

Detection of neuronal spikes using an adaptive threshold based on the max-min spread sorting method.

Neuronal spike information can be used to correlate neuronal activity to various stimuli, to find target neural areas for deep brain stimulation, and to decode intended motor command for brain-machine interface. Typically, spike detection is performed based on the adaptive thresholds determined by running root-mean-square (RMS) value of the signal. Yet conventional detection methods are susceptible to threshold fluctuations caused by neuronal spike intensity. In the present study we propose a novel adaptive threshold based on the max-min spread sorting method. On the basis of microelectrode recording signals and simulated signals with Gaussian noises and colored noises, the novel method had the smallest threshold variations, and similar or better spike detection performance than either the RMS-based method or other improved methods. Moreover, the detection method described in this paper uses the reduced features of raw signal to determine the threshold, thereby giving a simple data manipulation that is beneficial for reducing the computational load when dealing with very large amounts of data (as multi-electrode recordings).

Classification of neuronal spikes over the reconstructed phase space.

Spike information is beneficial to correlate neuronal activity to various stimuli or determine target neural area for deep brain stimulation. Data clustering based on neuronal spike features provides a way to separate spikes generated from different neurons. Nevertheless, some spikes are aligned incorrectly due to spike deformation or noise interference, thereby reducing the accuracy of spike classification. In the present study, we proposed unsupervised spike classification over the reconstructed phase spaces of neuronal spikes in which the derived phase space portraits are less affected by alignment deviations. Principal component analysis was used to extract major principal components of the portrait features and k-means clustering was used to distribute neuronal spikes into various clusters. Finally, similar clusters were iteratively merged based upon inter-cluster portrait differences.

Correlates of the shift in heart rate variability with postures and walking by time-frequency analysis.

Heart rate (HR) variability derived from electrocardiogram (ECG) can be used to assess the function of the autonomic nervous system. HR exhibits various characteristics during different physical activities attributed to the altered autonomic mediation, where it is also beneficial to reveal the autonomic shift in response to physical-activity change. In this paper, the physical-activity-related HR behaviors were delineated using a portable ECG and body acceleration recorder based on a personal digital assistant and the smoothed pseudo Wigner-Ville distribution. The results based upon eighteen subjects performing four sequential 5-min physical activities (supine, sitting, standing and spontaneous walking) showed that the high-frequency heartbeat fluctuations during supine and sitting were significantly larger than during standing, and that the ratio of low- to high-frequency fluctuation during standing was significantly higher than during supine and sitting. This could be linked with the parasympathetic predominance during supine and sitting, and a shift to sympathetic dominance while standing. During spontaneous walking, the high-frequency fluctuation was significant lower than during supine. The low- to high-frequency ratio decreased significantly from standing to spontaneous walking, which may imply an increased vagal predominance (autonomic effect) or an increased respiratory activity (mechanical effect).

Nonlinear characteristics of heart rate variability during unsupervised and steady physical activities.

The heart rate (HR) exhibits various behavior patterns in different postures and during physical activities, whereas a conventional long-term analysis of HR variability has the confounding effect whether the subject was physically active or immobilized. A specially designed ambulatory recorder that simultaneously measures the electrocardiogram and body accelerations was used to study the short-term (< or =11 beats, alpha1) fractal correlation property and the approximate entropy (ApEn) of RR interval data during sleep, sitting and standing (passive standing or mild walking) levels and immediately after rising in the morning in 15 healthy subjects. The alpha1 exponent that increased from sleep to sitting to standing implies an increased correlation of HR dynamics, which is concomitant with an increased ratio of low-frequency power to high-frequency power (LF/HF) that is usually linked with an increased sympathetic activity. A lower ApEn value during standing and after rising implies a reduced complexity of HR dynamics. Compared to the HR measures during the standing level, the LF/HF ratio showed a quick autonomic shift and alpha1 showed a rapid recruitment of fractal HR behavior after rising, whereas the ApEn value had a slower recovery of HR complexity. In conclusion, both linear and nonlinear HR behaviors during different unsupervised physical activities can be better interpreted with the aid of the recorded movement data.

Heart rate variability characterization in daily physical activities using wavelet analysis and multilayer fuzzy activity clustering.

A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed to classify the physical activity based on body accelerations. Discrete wavelet transform was incorporated to retrieve time-varying characteristics of heart rate variability under different physical activities. Nine healthy subjects were included to investigate activity-related heart rate variability during 24 h. The results showed that the heartbeat fluctuations in high frequencies were the greatest during lying and the smallest during standing. Moreover, very-low-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying).