Neurophysiological processes reflecting the effects of the immediate past during the dynamic management of actions

In recent years, there has been many efforts to establish a comprehensive theoretical framework explaining the working mechanisms involved in perception-action integration. This framework stresses the importance of the immediate past on mechanisms supporting perception-action integration. The present study investigates the neurophysiological principles of dynamic perception-action bindings, particularly considering the influence of the immediate history on action control mechanisms. For this purpose, we conducted an established stimulus-response binding paradigm during EEG recording. The SR-task measures stimulus-response binding in terms of accuracy and reaction time differences depending on the degree of feature overlap between conditions. Alpha, beta and theta band activity in distinct time domains as well as associated brain regions were investigated applying time-frequency analyses, a beamforming approach as well as correlation analyses. We demonstrate, for the first time, interdependencies of neuronal processes relying on the immediate past. The reconfiguration of an action seems to overwrite immediately preceding processes. The analyses revealed modulations of theta (TBA), alpha (ABA) and beta band activity (BBA) in connection with fronto-temporal structures supporting the theoretical assumptions of the considered conceptual framework. The close interplay of attentional modulation by gating irrelevant information (ABA) and binding and retrieval processes (TBA) is reflected by the correlation of ABA in all pre-probe-intervals with post-probe TBA. Likewise, the role of BBA in maintaining the event file until retrieval is corroborated by BBA preceding the TBA-associated retrieval of perception-action codes. Following action execution, TBA shifted towards visual association cortices probably reflecting preparation for upcoming information, while ABA and BBA continue to reflect processes of attentional control and information selection for goal-directed behavior. The present work provides the first empirical support for concepts about the neurophysiological mechanisms of dynamic management of perception and action.

The temporal dynamics of how the brain structures natural scenes

Individuals organize the evolving stream of events in their environment by partitioning it into discrete units. Event segmentation theory (EST) provides a cognitive explanation for the process of this partitioning. Critically, the underlying time-resolved neural mechanisms are not understood, and thus a central conceptual aspect of how humans implement this central ability is missing. To gain better insight into the fundamental temporal dynamics of event segmentation, EEG oscillatory activity was measured while participants watched a narrative video and partitioned the movie into meaningful segments. Using EEG beamforming methods, we show that theta, alpha, and beta band activity in frontal, parietal, and occipital areas, as well as their interactions, reflect critical elements of the event segmentation process established by EST. In sum, we see a mechanistic temporal chain of processes that provides the neurophysiological basis for how the brain partitions and structures continuously evolving scenes and points to an integrated system that organizes the various subprocesses of event segmentation. This study thus integrates neurophysiology and cognitive theory to better understand how the human brain operates in rather variable and unpredictable situations. Therefore, it represents an important step toward studying neurophysiological dynamics in ecologically valid and naturalistic settings and, in doing so, addresses a critical gap in knowledge regarding the temporal dynamics of how the brain structures natural scenes.

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Frequency-dependent and time-variant alterations of neural activity in post-stroke depression: A resting-state fMRI study

BACKGROUND

Post-stroke depression (PSD) is one of the most frequent psychiatric disorders after stroke. However, the underlying brain mechanism of PSD remains unclarified. Using the amplitude of low-frequency fluctuation (ALFF) approach, we aimed to investigate the abnormalities of neural activity in PSD patients, and further explored the frequency and time properties of ALFF changes in PSD.

METHODS

Resting-state fMRI data and clinical data were collected from 39 PSD patients (PSD), 82 S patients without depression (Stroke), and 74 age- and sex-matched healthy controls (HC). ALFF across three frequency bands (ALFF-Classic: 0.01-0.08 Hz; ALFF-Slow4: 0.027-0.073 Hz; ALFF-Slow5: 0.01-0.027 Hz) and dynamic ALFF (dALFF) were computed and compared among three groups. Ridge regression analyses and spearman's correlation analyses were further applied to explore the relationship between PSD-specific alterations and depression severity in PSD.

RESULTS

We found that PSD-specific alterations of ALFF were frequency-dependent and time-variant. Specially, compared to both Stroke and HC groups, PSD exhibited increased ALFF in the contralesional dorsolateral prefrontal cortex (DLPFC) and insula in all three frequency bands. Increased ALFF in ipsilesional DLPFC were observed in both slow-4 and classic frequency bands which were positively correlated with depression scales in PSD, while increased ALFF in the bilateral hippocampus and contralesional rolandic operculum were only found in slow-5 frequency band. These PSD-specific alterations in different frequency bands could predict depression severity. Moreover, decreased dALFF in contralesional superior temporal gyrus were observed in PSD group.

LIMITATIONS

Longitudinal studies are required to explore the alterations of ALFF in PSD as the disease progress.

CONCLUSIONS

The frequency-dependent and time-variant properties of ALFF could reflect the PSD-specific alterations in complementary ways, which may assist to elucidate underlying neural mechanisms and be helpful for early diagnosis and interventions for the disease.

Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain

Aging is the inevitable biological process that results in a progressive structural and functional decline associated with alterations in the resting/task-related brain activity, morphology, plasticity, and functionality. In the present study, we analyzed the effects of physiological aging on the human brain through entropy measures of electroencephalographic (EEG) signals. One hundred sixty-one participants were recruited and divided according to their age into young (n = 72) and elderly (n = 89) groups. Approximate entropy (ApEn) values were calculated in each participant for each EEG recording channel and both for the total EEG spectrum and for each of the main EEG frequency rhythms: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-11 Hz), alpha 2 (11-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-45 Hz), to identify eventual statistical differences between young and elderly. To demonstrate that the ApEn represents the age-related brain changes, the computed ApEn values were used as features in an age-related classification of subjects (young vs elderly), through linear, quadratic, and cubic support vector machine (SVM). Topographic maps of the statistical results showed statistically significant difference between the ApEn values of the two groups found in the total spectrum and in delta, theta, beta 2, and gamma. The classifiers (linear, quadratic, and cubic SVMs) revealed high levels of accuracy (respectively 93.20 ± 0.37, 93.16 ± 0.30, 90.62 ± 0.62) and area under the curve (respectively 0.95, 0.94, 0.93). ApEn seems to be a powerful, very sensitive-specific measure for the study of cognitive decline and global cortical alteration/degeneration in the elderly EEG activity.

Thalamocortical coherence and causality in different sleep stages using deep brain stimulation recordings

Previous research has shown an interplay between the thalamus and cerebral cortex during NREM sleep in humans, however the directionality of the thalamocortical synchronization is as yet unknown. In this study thalamocortical connectivity during different NREM sleep stages using sleep scalp electroencephalograms and local field potentials from the left and right anterior thalamus was measured in three epilepsy patients implanted with deep brain stimulation electrodes. Connectivity was assessed as debiased weighted phase lag index and granger causality between the thalamus and cortex for the NREM sleep stages N1, N2 and N3. Results showed connectivity was most prominently directed from cortex to thalamus. Moreover, connectivity varied in strength between the different sleep stages, but barely in direction or frequency. These results imply relatively stable thalamocortical connectivity during NREM sleep directed from the cortex to the thalamus.

EEG electrode selection for person identification thru a genetic-algorithm method

New biometric identification techniques are continually being developed to meet various applications. Electroencephalography (EEG) signals may provide a reasonable option for this type of identification due its unique features that overcome the lacks of other common methods. Currently, however, the processing load for such signals requires considerable time and labor. New methods and algorithms have attempted to reduce EEG processing time, including a reduction of the number of electrodes and segmenting the EEG data into its typical frequency bands. This work complements other efforts by proposing a genetic algorithm to reduce the number of necessary electrodes for measurements by EEG devices. Using a public EEG dataset of 109 subjects who underwent relaxation with eye-open and eye-closed stimuli, we aimed to determine the minimum set of electrodes required for optimum identification accuracy in each EEG sub-band of both stimuli. The results were encouraging and it was possible to accurately identify a subject using about 10 out of 64 electrodes. Moreover, higher frequency bands required a fewer number of electrodes for identification compared with lower frequency bands.

Frequency-specific alterations in the amplitude and synchronization of resting-state spontaneous low-frequency oscillations in benign childhood epilepsy with centrotemporal spikes

OBJECTIVES

Spontaneous low-frequency oscillations in different frequency bands have diverse physiological meanings. The amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) in different frequency bands in Benign Childhood Epilepsy with Centrotemporal Spikes (BECTS) are unknown and worth exploring.

METHOD

Resting-state functional magnetic resonance imaging data were collected in 51 drug-naïve BECTS patients and 76 healthy controls. The ALFF was calculated for the typical (0.01 - 0.08 Hz), slow-5 (0.01-0.027 Hz), slow-4 (0.027-0.073 Hz), and slow-3 (0.073-0.198 Hz) frequency bands. The bilateral precuneus/posterior cingulate cortex (PCU/PCC) showed a common alteration of ALFF in different frequency bands, and was selected as the seed for calculating FC per voxel.

RESULTS

In the typical band, BECTS patients showed increased ALFF in the left rolandic operculum and the right pre/postcentral gyrus, and decreased ALFF in the bilateral PCU/PCC, some of which were shared by the slow-5, slow-4, and slow-3 bands. Decreased ALFF in the left angular gyrus was also found in the slow-3 band. Only the bilateral PCU/PCC showed a frequency-dependent correlation with the total seizure frequency and full-scale intelligence quotient. Regions having degenerated FC with the bilateral PCU/PCC in BECTS patients were mainly in the left prefrontal cortex and bilateral anterior cingulate cortex for the typical and slow-5 bands, and in the bilateral temporal limbic system and striatum for the slow-4 and slow-3 bands.

CONCLUSION

Alteration of the ALFF and FC differed with distinct frequency bands. Therefore, employing different frequency bands would provide more meaningful findings for BECTS patients.

The oscillatory boundary conditions of different frequency bands in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease that is common in the elderly population. The most important pathological change in PD is the degeneration and death of dopaminergic neurons in the substantia nigra of the midbrain, which results in a decrease in the dopamine (DA) content of the striatum. The exact cause of this pathological change is still unknown. Numerous studies have shown that the evolution of PD is associated with abnormal oscillatory activities in the basal ganglia, with different oscillation frequency ranges, such as the typical beta band (13-30 Hz), the alpha band (8-12 Hz), the theta band (4-7 Hz) and the delta band (1-3 Hz). Although some studies have implied that abnormal interactions between the subthalamic nucleus (STN) and globus pallidus (GP) neurons may be a key factor required to induce these oscillations, the relative mechanism is still unclear. The effects of other nerve nuclei in the basal ganglia, such as the striatum, on these oscillations are still unknown. The thalamus and cortex both have close input and output relationships with the basal ganglia, and many previous studies have indicated that they may also exert effects on Parkinson's disease oscillation, but the mechanisms involved are unclear. In this paper, we built a corticothalamic-basal ganglia (CTBG) mean firing-rate model to explore the onset mechanisms of these different oscillation phenomena. We found that, in addition to the STN-GP network, Parkinson's disease oscillations may also be induced by changing the coupling strength and delays in other pathways. Different frequency bands appear in the oscillating region, and various boundary conditions are depicted in parameter diagrams. The onset mechanism is well explained both by the model and by the numerical simulation results. Therefore, this model provides a unifying framework for studying the mechanism of Parkinson's disease oscillations, and we hope that the results obtained in this work can inspire future experimental studies.

Altered brain functional connectivity induced by physical exercise may improve neuropsychological functions in patients with benign epilepsy

OBJECTIVE

The objective of this study was to elucidate alteration in functional connectivity (FC) in patients with benign epilepsy with centrotemporal spikes (BECTS) as induced by physical exercise therapy and their correlation to the neuropsychological (NP) functions.

METHODS

We analyzed 115 artifact- and spike-free 2-second epochs extracted from resting state EEG recordings before and after 5weeks of physical exercise in eight patients with BECTS. The exact Low Resolution Electromagnetic Tomography (eLORETA) was used for source reconstruction. We evaluated the cortical current source density (CSD) power across five different frequency bands (delta, theta, alpha, beta, and gamma). Altered FC between 34 regions of interests (ROIs) was then examined using lagged phase synchronization (LPS) method. We further investigated the correlation between the altered FC measures and the changes in NP test scores.

RESULTS

We observed changes in CSD power following the exercise for all frequency bands and statistically significant increases in the right temporal region for the alpha band. There were a number of altered FC between the cortical ROIs in all frequency bands of interest. Furthermore, significant correlations were observed between FC measures and NP test scores at theta and alpha bands.

CONCLUSION

The increased localization power at alpha band may be an indication of the positive impact of exercise in patients with BECTS. Frequency band-specific alterations in FC among cortical regions were associated with the modulation of cognitive and NP functions. The significant correlation between FC and NP tests suggests that physical exercise may mitigate the severity of BECTS, thereby enhancing NP function.

Ultrasound frequency analysis for identification of aggregates and cement paste in concrete

The aim of the present study was to identify specific frequencies related to aggregates and cement paste during concrete hydration, by performing a Fourier analysis of the ultrasonic response of concrete specimens to different excitation frequencies. This identification will reduce the high influence of aggregates in the ultrasound signal analysis, enabling a better assessment of changes occurring in the cement paste. Thirty-five cylindrical specimens with a diameter of 100mm and a length of 200mm were cast with a water to cement ratio=0.60. Thirty specimens were destructively tested at 1, 3, 5, 7, 14 and 28days for their compressive strength. The remaining five specimens were non-destructively tested at 1, 3, 5, 7, 14, 28 and 56days using longitudinal and transversal ultrasonic wave transducers with frequencies from 50kHz to 500kHz. Analysis of the evolution in frequency observed in the specimens identified variations related to progressive hydration of the cement paste, in contrast with the invariant behavior of the inert aggregates. Results show that it is possible to distinguish the behavior of cement paste and aggregates in the frequency domain. As a consequence, it should be possible in future research to evaluate more efficiently different phenomena that affect only the cement paste.

A preliminary study of movement intensity during a Go/No-Go task and its association with ADHD outcomes and symptom severity

OBJECTIVE

At present, there are no well-validated biomarkers for attention-deficit/hyperactivity disorder (ADHD). The present study used an infrared motion tracking system to monitor and record the movement intensity of children and to determine its diagnostic precision for ADHD and its possible associations with ratings of ADHD symptom severity.

METHODS

A Microsoft motion sensing camera recorded the movement of children during a modified Go/No-Go Task. Movement intensity measures extracted from these data included a composite measure of total movement intensity (TMI measure) and a movement intensity distribution (MID measure) measure across 15 frequency bands (FB measures). In phase 1 of the study, 30 children diagnosed with ADHD or at subthreshold for ADHD and 30 matched healthy controls were compared to determine if measures of movement intensity successfully distinguished children with ADHD from healthy control children. In phase 2, associations between measures of movement intensity and clinician-rated ADHD symptom severity (Clinical Global Impression Scale [CGI] and the ADHD-Rating Scale IV [ADHD-RS]) were examined in a subset of children with ADHD (n = 14) from the phase I sample.

RESULTS

Both measures of movement intensity were able to distinguish children with ADHD from healthy controls. However, only the measures linked to the 15 pre-determined 1 Hz frequency bands were significantly correlated with both the CGI scores and ADHD-RS total scores.

CONCLUSIONS

Preliminary findings suggest that measures of movement intensity, particularly measures linked to the 10-11 and 12-13 Hz frequency bands, have the potential to become valid biomarkers for ADHD.

Learning to modulate the partial powers of a single sEMG power spectrum through a novel human-computer interface

New human-computer interfaces that use bioelectrical signals as input are allowing study of the flexibility of the human neuromuscular system. We have developed a myoelectric human-computer interface which enables users to navigate a cursor to targets through manipulations of partial powers within a single surface electromyography (sEMG) signal. Users obtain two-dimensional control through simultaneous adjustments of powers in two frequency bands within the sEMG spectrum, creating power profiles corresponding to cursor positions. It is unlikely that these types of bioelectrical manipulations are required during routine muscle contractions. Here, we formally establish the neuromuscular ability to voluntarily modulate single-site sEMG power profiles in a group of naïve subjects under restricted and controlled conditions using a wrist muscle. All subjects used the same pre-selected frequency bands for control and underwent the same training, allowing a description of the average learning progress throughout eight sessions. We show that subjects steadily increased target hit rates from 48% to 71% and exhibited greater control of the cursor's trajectories following practice. Our results point towards an adaptable neuromuscular skill, which may allow humans to utilize single muscle sites as limited general-purpose signal generators. Ultimately, the goal is to translate this neuromuscular ability to practical interfaces for the disabled by using a spared muscle to control external machines.

Evolution of cerebral cortico-cortical communication during visuomotor adaptation to a cognitive-motor executive challenge

Cortical dynamics were examined during a cognitive-motor adaptation task that required inhibition of a familiar motor plan. EEG coherence between the motor planning (Fz) and left hemispheric region was progressively reduced over trials (low-beta, high-beta, gamma bands) along with faster, straighter reaching movements during both planning and execution. The major reduction in coherence (delta, low/high-theta, low/high-alpha bands) between Fz and the left prefrontal region during both movement planning and execution suggests gradual disengagement of frontal executive following its initial role in the suppression of established visuomotor maps. Also, change in the directionality of phase lags (delta, high-alpha, high-beta, gamma bands) reflects a progressive shift from feedback to feedforward motor control. The reduction of cortico-cortical communication, particularly in the frontal region, and the strategic feedback/feedforward mode shift translated as higher quality motor performance. This study extends our understanding of the role of frontal executive beyond purely cognitive tasks to cognitive-motor tasks.

Neurofeedback for ADHD: a review of current evidence

Considerable scientific effort has been directed at developing effective treatments for attention-deficit/hyperactivity disorder (ADHD). Among alternative treatment approaches, neurofeedback has gained some promising empirical support in recent years from controlled studies as a treatment of core ADHD symptoms. However, a recent stringent meta-analysis of 8 randomized controlled trials published in 2013 found that the effects were stronger for unblinded measures and 3 recent subsequently published well-controlled trials found no effects for the most blinded ADHD outcome. Firmer conclusions must await upcoming evidence from larger controlled studies and future meta-analyses contrasting different forms of neurofeedback and different outcome measures.