Dynamics of brain-muscle networks reveal effects of age and somatosensory function on gait

Walking is a complex motor activity that requires coordinated interactions between the sensory and motor systems. We used mobile EEG and EMG to investigate the brain-muscle networks involved in gait control during overground walking in young people, older people, and individuals with Parkinson's disease. Dynamic interactions between the sensorimotor cortices and eight leg muscles within a gait cycle were assessed using multivariate analysis. We identified three distinct brain-muscle networks during a gait cycle. These networks include a bilateral network, a left-lateralized network activated during the left swing phase, and a right-lateralized network active during the right swing. The trajectories of these networks are contracted in older adults, indicating a reduction in neuromuscular connectivity with age. Individuals with the impaired tactile sensitivity of the foot showed a selective enhancement of the bilateral network, possibly reflecting a compensation strategy to maintain gait stability. These findings provide a parsimonious description of interindividual differences in neuromuscular connectivity during gait.

Transcranial Direct Current Stimulation Modulates Working Memory Maintenance Processes in Healthy Individuals

The effects of transcranial direct current stimulation (tDCS) at the pFC are often investigated using cognitive paradigms, particularly working memory tasks. However, the neural basis for the neuromodulatory cognitive effects of tDCS, including which subprocesses are affected by stimulation, is not completely understood. We investigated the effects of tDCS on working memory task-related spectral activity during and after tDCS to gain better insights into the neurophysiological changes associated with stimulation. We reanalyzed data from 100 healthy participants grouped by allocation to receive either sham (0 mA, 0.016 mA, and 0.034 mA) or active (1 mA or 2 mA) stimulation during a 3-back task. EEG data were used to analyze event-related spectral power in frequency bands associated with working memory performance. Frontal theta event-related synchronization (ERS) was significantly reduced post-tDCS in the active group. Participants receiving active tDCS had slower RTs following tDCS compared with sham, suggesting interference with practice effects associated with task repetition. Theta ERS was not significantly correlated with RTs or accuracy. tDCS reduced frontal theta ERS poststimulation, suggesting a selective disruption to working memory cognitive control and maintenance processes. These findings suggest that tDCS selectively affects specific subprocesses during working memory, which may explain heterogenous behavioral effects.

The Effects of Conscious Movement Processing on the Neuromuscular Control of Posture

Maintaining balance is thought to primarily occur sub-consciously. Occasionally, however, individuals will direct conscious attention towards balance, e.g., in response to a threat to balance. Such conscious movement processing (CMP) increases the reliance on attentional resources and may disrupt balance performance. However, the underlying changes in neuromuscular control remain poorly understood. We investigated the effects of CMP (manipulated using verbal instructions) on neural control of posture in twenty-five adults (11 females, mean age = 23.9, range = 18-33). Participants performed 90-s, bipedal stance balance trials in high- and low-CMP conditions, during both stable (solid surface) and unstable (foam) task conditions. Postural sway amplitude, frequency and complexity were used to assess postural control. Surface EMG was recorded bilaterally from lower leg muscles (Soleus, Tibialis Anterior, Gastrocnemius Medialis, Peroneus Longus) and intermuscular coherence (IMC) was assessed for 12 muscle pairs across four frequency bands. We observed significantly increased sway amplitude, and decreased sway frequency and complexity in the high- compared to the low-CMP conditions. All sway variables increased in the unstable compared to the stable conditions. We observed reduced beta band IMC between several muscle pairs during high- compared to low-CMP, but these findings did not remain significant after controlling for multiple comparisons. Finally, IMC significantly increased in the unstable conditions for most muscle combinations and frequency bands. In all, results tentatively suggest that CMP-induced changes in sway outcomes may be facilitated by reduced beta-band IMC, but these findings need to be replicated before they can be interpreted more conclusively.

Coordination variability reduced for soccer players compared to non-athletes during the stance phase of gait

BACKGROUND

Soccer is a unilateral sports activity that may alter the spatiotemporal characteristics of gait. This may alter motor control of gait in the dominant leg in soccer players and lead to a sport-specific gait pattern, which has not yet been considered. We aimed to determine whether soccer players exhibit differences in the lower extremity coupling variability during gait compared to healthy non-athletes.

METHODS

Hip, knee, and ankle joint angles from fourteen soccer players and sixteen controls were acquired during treadmill walking. Hip-knee coupling, knee-ankle coupling and coupling angle variability (CAV) of the right leg in the sagittal plane were assessed using a vector coding technique.

RESULTS

Soccer players showed reduced hip-knee CAV during the mid-stance and terminal-stance phases of gait compared to the control group (P<inf>adj</inf> =0.04 and P<inf>adj</inf> <0.001, respectively). In addition, soccer players less often used an ankle coordination pattern, in which only the ankle joint but not the knee joint rotates (P<inf>adj</inf> =0.01).

CONCLUSIONS

In summary, soccer players show altered gait dynamics during normal walking, possibly due to intense soccer training. These changes provide evidence of adaptive strategies of the motor control system to sports activities that can be used for gait rehabilitation. Clinicians should note that some sport, such as soccer, may result in sport-specific gait patterns However, further works are needed to confirm this finding.

Smartphone Sensor Data for Identifying and Monitoring Symptoms of Mood Disorders: A Longitudinal Observational Study

BACKGROUND

Mood disorders are burdensome illnesses that often go undetected and untreated. Sensor technologies within smartphones may provide an opportunity for identifying the early changes in circadian rhythm and social support/connectedness that signify the onset of a depressive or manic episode.

OBJECTIVE

Using smartphone sensor data, this study investigated the relationship between circadian rhythm, which was determined by GPS data, and symptoms of mental health among a clinical sample of adults diagnosed with major depressive disorder or bipolar disorder.

METHODS

A total of 121 participants were recruited from a clinical setting to take part in a 10-week observational study. Self-report questionnaires for mental health outcomes, social support, social connectedness, and quality of life were assessed at 6 time points throughout the study period. Participants consented to passively sharing their smartphone GPS data for the duration of the study. Circadian rhythm (ie, regularity of location changes in a 24-hour rhythm) was extracted from GPS mobility patterns at baseline.

RESULTS

Although we found no association between circadian rhythm and mental health functioning at baseline, there was a positive association between circadian rhythm and the size of participants' social support networks at baseline (r=0.22; P=.03; R²=0.049). In participants with bipolar disorder, circadian rhythm was associated with a change in anxiety from baseline; a higher circadian rhythm was associated with an increase in anxiety and a lower circadian rhythm was associated with a decrease in anxiety at time point 5.

CONCLUSIONS

Circadian rhythm, which was extracted from smartphone GPS data, was associated with social support and predicted changes in anxiety in a clinical sample of adults with mood disorders. Larger studies are required for further validations. However, smartphone sensing may have the potential to monitor early symptoms of mood disorders.

Behavioural and neurophysiological differences in working memory function of depressed patients and healthy controls

OBJECTIVE

Major depressive disorder (MDD) is associated with deficits in working memory. Several cognitive subprocesses interact to produce working memory, including attention, encoding, maintenance and manipulation. We sought to clarify the contribution of functional deficits in these subprocesses in MDD by varying cognitive load during a working memory task.

METHODS

41 depressed participants and 41 age and gender-matched healthy controls performed the n-back working memory task at three levels of difficulty (0-, 1-, and 2-back) in a pregistered study. We assessed response times, accuracy, and event-related electroencephalography (EEG), including P2 and P3 amplitudes, and frontal theta power (4-8 Hz).

RESULTS

MDD participants had prolonged response times and more positive frontal P3 amplitudes (i.e., Fz) relative to controls, mainly in the most difficult 2-back condition. Working memory accuracy, P2 amplitudes and frontal theta event-related synchronisation did not differ between groups at any level of task difficulty.

CONCLUSIONS

Depression is associated with generalized psychomotor slowing of working memory processes, and may involve compensatory hyperactivity in frontal and parietal regions.

SIGNIFICANCE

These findings provide insights into MDD working memory deficits, indicating that depressed individuals dedicate greater levels of cortical processing and cognitive resources to achieve comparable working memory performance to controls.

The relationship between linguistic expression in blog content and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study

Data generated within social media platforms may present a new way to identify individuals who are experiencing mental illness. This study aimed to investigate the associations between linguistic features in individuals' blog data and their symptoms of depression, generalised anxiety, and suicidal ideation. Individuals who blogged were invited to participate in a longitudinal study in which they completed fortnightly symptom scales for depression and anxiety (PHQ-9, GAD-7) for a period of 36 weeks. Blog data published in the same period was also collected, and linguistic features were analysed using the LIWC tool. Bivariate and multivariate analyses were performed to investigate the correlations between the linguistic features and symptoms between subjects. Multivariate regression models were used to predict longitudinal changes in symptoms within subjects. A total of 153 participants consented to the study. The final sample consisted of the 38 participants who completed the required number of symptom scales and generated blog data during the study period. Between-subject analysis revealed that the linguistic features "tentativeness" and "non-fluencies" were significantly correlated with symptoms of depression and anxiety, but not suicidal thoughts. Within-subject analysis showed no robust correlations between linguistic features and changes in symptoms. The findings may provide evidence of a relationship between some linguistic features in social media data and mental health; however, the study was limited by missing data and other important considerations. The findings also suggest that linguistic features observed at the group level may not generalise to, or be useful for, detecting individual symptom change over time.

Muscle Synergies and Coherence Networks Reflect Different Modes of Coordination During Walking

When walking speed is increased, the frequency ratio between the arm and leg swing switches spontaneously from 2:1 to 1:1. We examined whether these switches are accompanied by changes in functional connectivity between multiple muscles. Subjects walked on a treadmill with their arms swinging along their body while kinematics and surface electromyography (EMG) of 26 bilateral muscles across the body were recorded. Walking speed was varied from very slow to normal. We decomposed EMG envelopes and intermuscular coherence spectra using non-negative matrix factorization (NMF), and the resulting modes were combined into multiplex networks and analyzed for their community structure. We found five relevant muscle synergies that significantly differed in activation patterns between 1:1 and 2:1 arm-leg coordination and the transition period between them. The corresponding multiplex network contained a single module indicating pronounced muscle co-activation patterns across the whole body during a gait cycle. NMF of the coherence spectra distinguished three EMG frequency bands: 4-8, 8-22, and 22-60 Hz. The community structure of the multiplex network revealed four modules, which clustered functional and anatomical linked muscles across modes of coordination. Intermuscular coherence at 4-22 Hz between upper and lower body and within the legs was particularly pronounced for 1:1 arm-leg coordination and was diminished when switching between modes of coordination. These findings suggest that the stability of arm-leg coordination is associated with modulations in long-distant neuromuscular connectivity.

Assessing neurophysiological changes associated with combined transcranial direct current stimulation and cognitive-emotional training for treatment-resistant depression

Transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation, is a promising treatment for depression. Recent research suggests that tDCS efficacy can be augmented using concurrent cognitive-emotional training (CET). However, the neurophysiological changes associated with this combined intervention remain to be elucidated. We therefore examined the effects of tDCS combined with CET using electroencephalography (EEG). A total of 20 participants with treatment-resistant depression took part in this open-label study and received 18 sessions over 6 weeks of tDCS and concurrent CET. Resting-state and task-related EEG during a 3-back working memory task were acquired at baseline and immediately following the treatment course. Results showed an improvement in mood and working memory accuracy, but not response time, following the intervention. We did not find significant effects of the intervention on resting-state power spectral density (frontal theta and alpha asymmetry), time-frequency power (alpha event-related desynchronisation and theta event-related synchronisation) or event-related potentials (P2 and P3 components). We therefore identified little evidence of neurophysiological changes associated with treatment using tDCS and concurrent CET, despite significant improvements in mood and near-transfer effects of cognitive training to working memory accuracy. Further research incorporating a sham-controlled group may be necessary to identify the neurophysiological effects of the intervention.

Smartphone app to investigate the relationship between social connectivity and mental health

Interpersonal relationships are necessary for successful daily functioning and wellbeing. Numerous studies have demonstrated the importance of social connectivity for mental health, both through direct peer-to-peer influence and by the location of individuals within their social network. Passive monitoring using smartphones provides an advanced tool to map social networks based on the proximity between individuals. This study investigates the feasibility of using a smartphone app to measure and assess the relationship between social network metrics and mental health. The app collected Bluetooth and mental health data in 63 participants. Social networks of proximity were estimated from Bluetooth data and 95% of the edges were scanned at least every 30 minutes. The majority of participants found this method of data collection acceptable and reported that they would be likely to participate in future studies using this app. These findings demonstrate the feasibility of using a smartphone app that participants can install on their own phone to investigate the relationship between social connectivity and mental health.

Functional connectivity analysis of multiplex muscle network across frequencies

Physiological networks reveal information about the interaction between subsystems of the human body. Here we investigated the interaction between the central nervous system and the musculoskeletal system by mapping functional muscle networks. Muscle networks were extracted using coherence analysis of muscle activity assessed using surface electromyography (EMG). Surface EMG was acquired from 36 muscles distributed throughout the body while participants were standing upright and performing a bimanual pointing task. Non-negative matrix factorization revealed functional connectivity in four frequency bands. The spatial arrangement differed considerably across frequencies supporting a multiplex network organisation. Graph-theory analysis of layer-specific network revealed a consistent fat-tail distribution of the edges weights, distinct efficiency values, and core-periphery properties. These frequency bands may be spectral fingerprints of different neural pathways that innervate the spinal motor neurons to control the musculoskeletal system.

Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions

Background

Mobile phone sensor technology has great potential in providing behavioral markers of mental health. However, this promise has not yet been brought to fruition.

Objective

The objective of our study was to examine challenges involved in developing an app to extract behavioral markers of mental health from passive sensor data.

Methods

Both technical challenges and acceptability of passive data collection for mental health research were assessed based on literature review and results obtained from a feasibility study. Socialise, a mobile phone app developed at the Black Dog Institute, was used to collect sensor data (Bluetooth, location, and battery status) and investigate views and experiences of a group of people with lived experience of mental health challenges (N=32).

Results

On average, sensor data were obtained for 55% (Android) and 45% (iOS) of scheduled scans. Battery life was reduced from 21.3 hours to 18.8 hours when scanning every 5 minutes with a reduction of 2.5 hours or 12%. Despite this relatively small reduction, most participants reported that the app had a noticeable effect on their battery life. In addition to battery life, the purpose of data collection, trust in the organization that collects data, and perceived impact on privacy were identified as main factors for acceptability.

Conclusions

Based on the findings of the feasibility study and literature review, we recommend a commitment to open science and transparent reporting and stronger partnerships and communication with users. Sensing technology has the potential to greatly enhance the delivery and impact of mental health care. Realizing this requires all aspects of mobile phone sensor technology to be rigorously assessed.

Network structure of the human musculoskeletal system shapes neural interactions on multiple time scales

Human motor control requires the coordination of muscle activity under the anatomical constraints imposed by the musculoskeletal system. Interactions within the central nervous system are fundamental to motor coordination, but the principles governing functional integration remain poorly understood. We used network analysis to investigate the relationship between anatomical and functional connectivity among 36 muscles. Anatomical networks were defined by the physical connections between muscles, and functional networks were based on intermuscular coherence assessed during postural tasks. We found a modular structure of functional networks that was strongly shaped by the anatomical constraints of the musculoskeletal system. Changes in postural tasks were associated with a frequency-dependent reconfiguration of the coupling between functional modules. These findings reveal distinct patterns of functional interactions between muscles involved in flexibly organizing muscle activity during postural control. Our network approach to the motor system offers a unique window into the neural circuitry driving the musculoskeletal system.

Dynamics of corticospinal motor control during overground and treadmill walking in humans

Increasing evidence suggests cortical involvement in the control of human gait. However, the nature of corticospinal interactions remains poorly understood. We performed time-frequency analysis of electrophysiological activity acquired during treadmill and overground walking in 22 healthy, young adults. Participants walked at their preferred speed (4.2, SD 0.4 km/h), which was matched across both gait conditions. Event-related power, corticomuscular coherence (CMC), and intertrial coherence (ITC) were assessed for EEG from bilateral sensorimotor cortices and EMG from the bilateral tibialis anterior (TA) muscles. Cortical power, CMC, and ITC at theta, alpha, beta, and gamma frequencies (4-45 Hz) increased during the double support phase of the gait cycle for both overground and treadmill walking. High beta (21-30 Hz) CMC and ITC of EMG was significantly increased during overground compared with treadmill walking, as well as EEG power in theta band (4-7 Hz). The phase spectra revealed positive time lags at alpha, beta, and gamma frequencies, indicating that the EEG response preceded the EMG response. The parallel increases in power, CMC, and ITC during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. The evoked responses are not consistent with the idea of synchronization of ongoing corticospinal oscillations but instead suggest coordinated cortical and spinal inputs during the double support phase. Frequency-band dependent differences in power, CMC, and ITC between overground and treadmill walking suggest differing neural control for the two gait modalities, emphasizing the task-dependent nature of neural processes during human walking. NEW & NOTEWORTHY We investigated cortical and spinal activity during overground and treadmill walking in healthy adults. Parallel increases in power, corticomuscular coherence, and intertrial coherence during double support suggest evoked responses at spinal and cortical populations rather than a modulation of ongoing corticospinal oscillatory interactions. These findings identify neurophysiological mechanisms that are important for understanding cortical control of human gait in health and disease.

Validation of a smartphone app to map social networks of proximity

Social network analysis is a prominent approach to investigate interpersonal relationships. Most studies use self-report data to quantify the connections between participants and construct social networks. In recent years smartphones have been used as an alternative to map networks by assessing the proximity between participants based on Bluetooth and GPS data. While most studies have handed out specially programmed smartphones to study participants, we developed an application for iOS and Android to collect Bluetooth data from participants’ own smartphones. In this study, we compared the networks estimated with the smartphone app to those obtained from sociometric badges and self-report data. Participants (n = 21) installed the app on their phone and wore a sociometric badge during office hours. Proximity data was collected for 4 weeks. A contingency table revealed a significant association between proximity data (ϕ = 0.17, p<0.0001), but the marginal odds were higher for the app (8.6%) than for the badges (1.3%), indicating that dyads were more often detected by the app. We then compared the networks that were estimated using the proximity and self-report data. All three networks were significantly correlated, although the correlation with self-reported data was lower for the app (ρ = 0.25) than for badges (ρ = 0.67). The scanning rates of the app varied considerably between devices and was lower on iOS than on Android. The association between the app and the badges increased when the network was estimated between participants whose app recorded more regularly. These findings suggest that the accuracy of proximity networks can be further improved by reducing missing data and restricting the interpersonal distance at which interactions are detected.

Combined effect of prefrontal transcranial direct current stimulation and a working memory task on heart rate variability

Prefrontal cortex activity has been associated with changes to heart rate variability (HRV) via mediation of the cortico-subcortical pathways that regulate the parasympathetic and sympathetic branches of the autonomic nervous system. Changes in HRV due to altered prefrontal cortex functioning can be predicted using the neurovisceral integration model, which suggests that prefrontal hyperactivity increases parasympathetic tone and decreases contributions from the sympathetic nervous system. Working memory (WM) tasks and transcranial direct current stimulation (tDCS) have been used independently to modulate brain activity demonstrating changes to HRV in agreement with the model. We investigated the combined effects of prefrontal tDCS and a WM task on HRV. Bifrontal tDCS was administered for 15 minutes at 2mA to 20 participants in a sham controlled, single-blind study using parallel groups. A WM task was completed by participants at three time points; pre-, during-, and post-tDCS, with resting state data collected at similar times. Frequency-domain HRV was computed for high frequency (HF; 0.15-0.4Hz) and low frequency (LF; 0.04-0.15Hz) power reflecting parasympathetic and sympathetic branch activity, respectively. Response time on the WM task, but not accuracy, improved from baseline to during-tDCS and post-tDCS with sham, but not active, stimulation. HF-HRV was significantly increased in the active tDCS group compared to sham, lasting beyond cessation of stimulation. Additionally, HF-HRV showed a task-related reduction in power during performance on the WM task. Changes in LF-HRV were moderately inversely correlated (r > 0.4) with changes in WM accuracy during and following tDCS compared to baseline levels. Stimulation of the prefrontal cortex resulted in changes to the parasympathetic branch of the nervous system in agreement with a linearly additive interpretation of effects. Sympathetic activity was not directly altered by tDCS, but was correlated with changes in WM performance. This suggests that the parasympathetic and sympathetic branches respond differentially due to similar, but distinct neural pathways. Given the ease of HRV data collection, studies of prefrontal tDCS would benefit from collection of this data as it provides unique insight into tDCS effects resulting from propagation through brain networks.

Predicting brain stimulation treatment outcomes of depressed patients through the classification of EEG oscillations

Major depressive disorder (MDD) is a mental disorder that is characterized by negative thoughts, mood and behavior. Transcranial direct current stimulation (tDCS) has recently emerged as a promising brain-stimulation treatment for MDD. A standard tDCS treatment involves numerous sessions that run over a few weeks, however, not all participants respond to this type of treatment. This study aims to predict which patients improve in mood and cognition in response to tDCS treatment by analyzing electroencephalography (EEG) of MDD patients that was collected at the start of tDCS treatment. This is achieved through classifying power spectral density (PSD) of resting-state EEG using support vector machine (SVM), linear discriminate analysis (LDA) and extreme learning machine (ELM). Participants were labelled as improved/not improved based on the change in mood and cognitive scores. The obtained classification results of all channel pair combinations are used to identify the most relevant brain regions and channels for this classification task. We found the frontal channels to be particularly informative for the prediction of the clinical outcome of the tDCS treatment. Subject independent results reveal that our proposed method enables the correct identification of the treatment outcome for seven of the ten participants for mood improvement and nine of ten participants for cognitive improvement. This represents an encouraging sign that EEG-based classification may help to tailor the selection of patients for treatment with tDCS brain stimulation.

Predicting tDCS treatment outcomes of patients with major depressive disorder using automated EEG classification

BACKGROUND

Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing.

METHODS

We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz) and gamma (30-100Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach.

RESULTS

Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004).

LIMITATIONS

Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept.

CONCLUSIONS

These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach.

Functional connectivity in the neuromuscular system underlying bimanual coordination

Neural synchrony has been suggested as a mechanism for integrating distributed sensorimotor systems involved in coordinated movement. To test the role of corticomuscular and intermuscular coherence in bimanual coordination, we experimentally manipulated the degree of coordination between hand muscles by varying the sensitivity of the visual feedback to differences in bilateral force. In 16 healthy participants, cortical activity was measured using EEG and muscle activity of the flexor pollicis brevis of both hands using high-density electromyography (HDsEMG). Using the uncontrolled manifold framework, coordination between bilateral forces was quantified by the synergy index R_V in the time and frequency domain. Functional connectivity was assessed using corticomuscular coherence between muscle activity and cortical source activity and intermuscular coherence between bilateral EMG activity. The synergy index increased in the high coordination condition. R_V was higher in the high coordination condition in frequencies between 0 and 0.5 Hz; for the 0.5- to 2-Hz frequency band, this pattern was inverted. Corticomuscular coherence in the beta band (16-30 Hz) was maximal in the contralateral motor cortex and was reduced in the high coordination condition. In contrast, intermuscular coherence was observed at 5-12 Hz and increased with bimanual coordination. Within-subject comparisons revealed a negative correlation between R_V and corticomuscular coherence and a positive correlation between R_V and intermuscular coherence. Our findings suggest two distinct neural pathways: 1) corticomuscular coherence reflects direct corticospinal projections involved in controlling individual muscles; and 2) intermuscular coherence reflects diverging pathways involved in the coordination of multiple muscles.

Change in Mean Frequency of Resting-State Electroencephalography after Transcranial Direct Current Stimulation

Transcranial direct current stimulation (tDCS) is proposed as a tool to investigate cognitive functioning in healthy people and as a treatment for various neuropathological disorders. However, the underlying cortical mechanisms remain poorly understood. We aim to investigate whether resting-state electroencephalography (EEG) can be used to monitor the effects of tDCS on cortical activity. To this end we tested whether the spectral content of ongoing EEG activity is significantly different after a single session of active tDCS compared to sham stimulation. Twenty participants were tested in a sham-controlled, randomized, crossover design. Resting-state EEG was acquired before, during and after active tDCS to the left dorsolateral prefrontal cortex (15 min of 2 mA tDCS) and sham stimulation. Electrodes with a diameter of 3.14 cm² were used for EEG and tDCS. Partial least squares (PLS) analysis was used to examine differences in power spectral density (PSD) and the EEG mean frequency to quantify the slowing of EEG activity after stimulation. PLS revealed a significant increase in spectral power at frequencies below 15 Hz and a decrease at frequencies above 15 Hz after active tDCS (P = 0.001). The EEG mean frequency was significantly reduced after both active tDCS (P < 0.0005) and sham tDCS (P = 0.001), though the decrease in mean frequency was smaller after sham tDCS than after active tDCS (P = 0.073). Anodal tDCS of the left DLPFC using a high current density bi-frontal electrode montage resulted in general slowing of resting-state EEG. The similar findings observed following sham stimulation question whether the standard sham protocol is an appropriate control condition for tDCS.

Muscle networks: Connectivity analysis of EMG activity during postural control

Understanding the mechanisms that reduce the many degrees of freedom in the musculoskeletal system remains an outstanding challenge. Muscle synergies reduce the dimensionality and hence simplify the control problem. How this is achieved is not yet known. Here we use network theory to assess the coordination between multiple muscles and to elucidate the neural implementation of muscle synergies. We performed connectivity analysis of surface EMG from ten leg muscles to extract the muscle networks while human participants were standing upright in four different conditions. We observed widespread connectivity between muscles at multiple distinct frequency bands. The network topology differed significantly between frequencies and between conditions. These findings demonstrate how muscle networks can be used to investigate the neural circuitry of motor coordination. The presence of disparate muscle networks across frequencies suggests that the neuromuscular system is organized into a multiplex network allowing for parallel and hierarchical control structures.

Mapping dynamic social networks in real life using participants' own smartphones

Interpersonal relationships are vital for our daily functioning and wellbeing. Social networks may form the primary means by which environmental influences determine individual traits. Several studies have shown the influence of social networks on decision-making, behaviors and wellbeing. Smartphones have great potential for measuring social networks in a real world setting. Here we tested the feasibility of using people's own smartphones as a data collection platform for face-to-face interactions. We developed an application for iOS and Android to collect Bluetooth data and acquired one week of data from 14 participants in our organization. The Bluetooth scanning statistics were used to quantify the time-resolved connection strength between participants and define the weights of a dynamic social network. We used network metrics to quantify changes in network topology over time and non-negative matrix factorization to identify cliques or subgroups that reoccurred during the week. The scanning rate varied considerably between smartphones running Android and iOS and egocentric networks metrics were correlated with the scanning rate. The time courses of two identified subgroups matched with two meetings that took place that week. These findings demonstrate the feasibility of using participants' own smartphones to map social network, whilst identifying current limitations of using generic smartphones. The bias introduced by variations in scanning rate and missing data is an important limitation that needs to be addressed in future studies.

Intrinsic coupling modes in source-reconstructed electroencephalography

Intrinsic coupling of neuronal assemblies constitutes a key feature of ongoing brain activity, yielding the rich spatiotemporal patterns observed in neuroimaging data and putatively supporting cognitive processes. Intrinsic coupling has been investigated in electrophysiological recordings using two types of functional connectivity measures: amplitude and phase coupling. These two coupling modes differ in their likely causes and functions, and have been proposed to provide complementary insights into intrinsic neuronal interactions. Here, we investigate the relationship between amplitude and phase coupling in source-reconstructed electroencephalography (EEG). Volume conduction is a key obstacle for connectivity analysis in EEG-we therefore also test the envelope correlation of orthogonalized signals and the phase lag index. Functional connectivity between six seed source regions (bilateral visual, sensorimotor, and auditory cortices) and all other cortical voxels was computed. For all four measures, coupling between homologous sensory areas in both hemispheres was significantly higher than with other voxels at the same physical distance. The frequency of significant coupling differed between sensory areas: 10 Hz for visual, 30 Hz for auditory, and 40 Hz for sensorimotor cortices. By contrasting envelope correlations and phase locking values, we observed two distinct clusters of voxels showing a different relationship between amplitude and phase coupling. Large clusters contiguous to the seed regions showed an identity (1:1) relationship between amplitude and phase coupling, whereas a cluster located around the contralateral homologous regions showed higher phase than amplitude coupling. These results show a relationship between intrinsic coupling modes that is distinct from the effect of volume conduction.

We feel: mapping emotion on Twitter

Research data on predisposition to mental health problems, and the fluctuations and regulation of emotions, thoughts, and behaviors are traditionally collected through surveys, which cannot provide a real-time insight into the emotional state of individuals or communities. Large datasets such as World Health Organization (WHO) statistics are collected less than once per year, whereas social network platforms, such as Twitter, offer the opportunity for real-time analysis of expressed mood. Such patterns are valuable to the mental health research community, to help understand the periods and locations of greatest demand and unmet need. We describe the "We Feel" system for analyzing global and regional variations in emotional expression, and report the results of validation against known patterns of variation in mood. 2.73 ×10(9) emotional tweets were collected over a 12-week period, and automatically annotated for emotion, geographic location, and gender. Principal component analysis (PCA) of the data illustrated a dominant in-phase pattern across all emotions, modulated by antiphase patterns for "positive" and "negative" emotions. The first three principal components accounted for over 90% of the variation in the data. PCA was also used to remove the dominant diurnal and weekly variations allowing identification of significant events within the data, with z-scores showing expression of emotions over 80 standard deviations from the mean. We also correlate emotional expression with WHO data at a national level and although no correlations were observed for the burden of depression, the burden of anxiety and suicide rates appeared to correlate with expression of particular emotions.

The use of technology in Suicide Prevention

Suicide is one of the leading causes of death globally, and is notably a significant cause of death amongst young people. A suicide outcome is a complex combination of personal, social, and health factors, and therefore suicide prevention is a challenge, requiring a systems approach incorporating public health strategies, screening at-risk individuals, targeted interventions, and follow-up for suicide survivors and those bereaved by suicide. Engineering practice has been implicated in the hindrance of the adoption of suicide prevention strategies, such as installing safety barriers at the Golden Gate Bridge, however technological developments offer new opportunities in suicide prevention, and the potential to reduce the number of deaths by suicide. We present an overview of current technological developments which are facilitating research in the field of suicide prevention, including multiple modes of screening such as network analysis of mobile-phone collected connectivity data, automatic detection of suicidality from social media content, and crisis detection from acoustic variability in speech patterns. The current field of mhealth apps for suicide prevention is assessed, and an innovative app for an Indigenous population is presented. From this overview, future challenges - technical and ethical - are discussed.

The reorganization of corticomuscular coherence during a transition between sensorimotor states

Recent research suggests that neural oscillations in different frequency bands support distinct and sometimes parallel processing streams in neural circuits. Studies of the neural dynamics of human motor control have primarily focused on oscillations in the beta band (15-30 Hz). During sustained muscle contractions, corticomuscular coherence is mainly present in the beta band, while coherence in the alpha (8-12 Hz) and gamma (30-80 Hz) bands has not been consistently found. Here we test the hypothesis that the frequency of corticomuscular coherence changes during transitions between sensorimotor states. Corticomuscular coherence was investigated in twelve participants making rapid transitions in force output between two targets. Corticomuscular coherence was present in the beta band during sustained contractions but vanished before movement onset, being replaced by transient synchronization in the alpha and gamma bands during dynamic force output. Analysis of the phase spectra suggested a time delay from muscle to cortex for alpha-band coherence, by contrast to a time delay from cortex to muscle for gamma-band coherence, indicating afferent and efferent corticospinal interactions respectively. Moreover, alpha and gamma-band coherence revealed distinct spatial topologies, suggesting different generative mechanisms. Coherence in the alpha and gamma bands was almost exclusively confined to trials showing a movement overshoot, suggesting a functional role related to error correction. We interpret the dual-band synchronization in the alpha and gamma bands as parallel streams of corticospinal processing involved in parsing prediction errors and generating new motor predictions.

High-density surface electromyography improves the identification of oscillatory synaptic inputs to motoneurons

Many studies have addressed corticomuscular coherence (CMC), but broad applications are limited by low coherence values and the variability across subjects and recordings. Here, we investigated how the use of high-density surface electromyography (HDsEMG) can improve the detection of CMC. Sixteen healthy subjects performed isometric contractions at six low-force levels using a pinch-grip, while HDsEMG of the adductor pollicis transversus and flexor and abductor pollicis brevis and whole-head magnetoencephalography were recorded. Different configurations were constructed from the HDsEMG grid, such as a bipolar and Laplacian montage, as well as a montage based on principal component analysis (PCA). CMC was estimated for each configuration, and the strength of coherence was compared across configurations. As expected, performance of the precision-grip task resulted in significant CMC in the β-frequency band (16-26 Hz). Compared with a bipolar EMG montage, all multichannel configurations obtained from the HDsEMG grid revealed a significant increase in CMC. The configuration, based on PCA, showed the largest (37%) increase. HDsEMG did not reduce the between-subject variability; rather, many configurations showed an increased coefficient of variation. Increased CMC presumably reflects the ability of HDsEMG to counteract inherent EMG signal factors-such as amplitude cancellation-which impact the detection of oscillatory inputs. In contrast, the between-subject variability of CMC most likely has a cortical origin.

Effects of mnemonic load on cortical activity during visual working memory: linking ongoing brain activity with evoked responses

The mechanisms generating task-locked changes in cortical potentials remain poorly understood, despite a wealth of research. It has recently been proposed that ongoing brain oscillations are not symmetric, so that task-related amplitude modulations generate a baseline shift that does not average out, leading to slow event-related potentials. We test this hypothesis using multivariate methods to formally assess the co-variation between task-related evoked potentials and spectral changes in scalp EEG during a visual working memory task, which is known to elicit both evoked and sustained cortical activities across broadly distributed cortical regions. 64-channel EEG data were acquired from eight healthy human subjects who completed a visuo-spatial associative working memory task as memory load was parametrically increased from easy to hard. As anticipated, evoked activity showed a complex but robust spatio-temporal waveform maximally expressed bilaterally in the parieto-occipital and anterior midline regions, showing robust effects of memory load that were specific to the stage of the working memory trial. Similarly, memory load was associated with robust spectral changes in the theta and alpha range, throughout encoding in posterior regions and through maintenance and retrieval in anterior regions, consistent with the additional resources required for decision making in prefrontal cortex. Analysis of the relationship between event-related changes in slow potentials and cortical rhythms, using partial least squares, is indeed consistent with the notion that the former make a causal contribution to the latter.

Critical fluctuations in cortical models near instability

Computational studies often proceed from the premise that cortical dynamics operate in a linearly stable domain, where fluctuations dissipate quickly and show only short memory. Studies of human electroencephalography (EEG), however, have shown significant autocorrelation at time lags on the scale of minutes, indicating the need to consider regimes where non-linearities influence the dynamics. Statistical properties such as increased autocorrelation length, increased variance, power law scaling, and bistable switching have been suggested as generic indicators of the approach to bifurcation in non-linear dynamical systems. We study temporal fluctuations in a widely-employed computational model (the Jansen–Rit model) of cortical activity, examining the statistical signatures that accompany bifurcations. Approaching supercritical Hopf bifurcations through tuning of the background excitatory input, we find a dramatic increase in the autocorrelation length that depends sensitively on the direction in phase space of the input fluctuations and hence on which neuronal subpopulation is stochastically perturbed. Similar dependence on the input direction is found in the distribution of fluctuation size and duration, which show power law scaling that extends over four orders of magnitude at the Hopf bifurcation. We conjecture that the alignment in phase space between the input noise vector and the center manifold of the Hopf bifurcation is directly linked to these changes. These results are consistent with the possibility of statistical indicators of linear instability being detectable in real EEG time series. However, even in a simple cortical model, we find that these indicators may not necessarily be visible even when bifurcations are present because their expression can depend sensitively on the neuronal pathway of incoming fluctuations.

Modulation of human muscle spindle discharge by arterial pulsations--functional effects and consequences

Arterial pulsations are known to modulate muscle spindle firing; however, the physiological significance of such synchronised modulation has not been investigated. Unitary recordings were made from 75 human muscle spindle afferents innervating the pretibial muscles. The modulation of muscle spindle discharge by arterial pulsations was evaluated by R-wave triggered averaging and power spectral analysis. We describe various effects arterial pulsations may have on muscle spindle afferent discharge. Afferents could be "driven" by arterial pulsations, e.g., showing no other spontaneous activity than spikes generated with cardiac rhythmicity. Among afferents showing ongoing discharge that was not primarily related to cardiac rhythmicity we illustrate several mechanisms by which individual spikes may become phase-locked. However, in the majority of afferents the discharge rate was modulated by the pulse wave without spikes being phase locked. Then we assessed whether these influences changed in two physiological conditions in which a sustained increase in muscle sympathetic nerve activity was observed without activation of fusimotor neurones: a maximal inspiratory breath-hold, which causes a fall in systolic pressure, and acute muscle pain, which causes an increase in systolic pressure. The majority of primary muscle spindle afferents displayed pulse-wave modulation, but neither apnoea nor pain had any significant effect on the strength of this modulation, suggesting that the physiological noise injected by the arterial pulsations is robust and relatively insensitive to fluctuations in blood pressure. Within the afferent population there was a similar number of muscle spindles that were inhibited and that were excited by the arterial pulse wave, indicating that after signal integration at the population level, arterial pulsations of opposite polarity would cancel each other out. We speculate that with close-to-threshold stimuli the arterial pulsations may serve as an endogenous noise source that may synchronise the sporadic discharge within the afferent population and thus facilitate the detection of weak stimuli.

Neural mechanisms of intermuscular coherence: implications for the rectification of surface electromyography

Oscillatory activity plays a crucial role in corticospinal control of muscle synergies and is widely investigated using corticospinal and intermuscular synchronization. However, the neurophysiological mechanisms that translate these rhythmic patterns into surface electromyography (EMG) are not well understood. This is underscored by the ongoing debate on the rectification of surface EMG before spectral analysis. Whereas empirical studies commonly rectify surface EMG, computational approaches have argued against it. In the present study, we employ a computational model to investigate the role of the motor unit action potential (MAUP) on the translation of oscillatory activity. That is, diverse MUAP shapes may distort the transfer of common input into surface EMG. We test this in a computational model consisting of two motor unit pools receiving common input and compare it to empirical results of intermuscular coherence between bilateral leg muscles. The shape of the MUAP was parametrically varied, and power and coherence spectra were investigated with and without rectification. The model shows that the effect of EMG rectification depends on the uniformity of MUAP shapes. When output spikes of different motor units are convolved with identical MUAPs, oscillatory input is evident in both rectified and nonrectified EMG. In contrast, a heterogeneous MAUP distribution distorts common input and oscillatory components are only manifest as periodic amplitude modulations, i.e., in rectified EMG. The experimental data showed that intermuscular coherence was mainly discernable in rectified EMG, hence providing empirical support for a heterogeneous distribution of MUAPs. These findings implicate that the shape of MUAPs is an essential parameter to reconcile experimental and computational approaches.

Multi-frequency phase locking in human somatosensory cortex

Cortical population responses to sensory input arise from the interaction between external stimuli and the intrinsic dynamics of the densely interconnected neuronal population. Although there is a large body of knowledge regarding single neuron responses to periodic stimuli, responses at the scale of cortical populations are incompletely understood. The characteristics of large-scale neuronal activity during periodic stimulation speak directly to the mechanisms underlying collective neuronal activity. Their accurate elucidation is hence a vital prelude to constructing and evaluating large-scale computational and biophysical models of the brain. Electroencephalographic data was recorded from eight human subjects while periodic vibrotactile stimuli were applied to the fingertip. Time-frequency decomposition was performed on the multi-channel data in order to investigate relative changes in the power and phase distributions at stimulus-related frequencies. We observed phase locked oscillatory activity at multiple stimulus-specific frequencies, in particular at ratios of 1:1, 2:1 and 2:3 to the stimulus frequency. These phase locked components were found to be modulated differently across the range of stimulus frequencies, with oscillatory responses most robustly sustained around 30 Hz. In contrast, no robust frequency-locked responses were apparent in the power changes. These results demonstrate n:m phase synchronization between cortical oscillations in the somatosensory system and an external periodic signal. We argue that neuronal populations evidence a collective nonlinear response to periodic sensory input. The existence of n:m phase synchronization demonstrates the contribution of intrinsic cortical dynamics to stimulus encoding and provides a novel phenomenological criteria for the validation of large-scale models of the brain.

Bilateral motor unit synchronization of leg muscles during a simple dynamic balance task

To handle the rich repertoire of behavioural goals, the CNS has to control the many degrees of freedom of the musculoskeletal system in a flexible manner. This problem can be drastically simplified if muscle synergies serve as the to-be-controlled building blocks of motor performance, instead of the individual degrees of freedom. Muscle synergies have been identified as coherent activation patterns of a group of muscles in space or time, but the neural mechanisms underlying their formation remain largely unknown. Here we evaluated the hypothesis that synergies are reflected in common input to different contributing muscles, and investigated modulations in motor unit (MU) synchronization of homologous muscles during a rhythmic balance task. If common input is related to muscle synergies, the resultant MU synchronization should not be static but task dependent and, in the present context, vary in time. Coherence between surface electromyographic signals of bilateral leg muscles revealed MU synchronization in two distinct frequency bands. MU synchronization was not constant but modulated within a movement cycle, and its time course resembled the activation patterns of the muscles. These results are congruent with a linkage between MU synchronization and muscle synergies, and suggest that MU synchronization provides an expedient method for studying synergy-related neural mechanisms.

Fatigue-related changes in motor-unit synchronization of quadriceps muscles within and across legs

Two experiments were conducted to examine effects of muscle fatigue on motor-unit synchronization of quadriceps muscles (rectus femoris, vastus medialis, vastus lateralis) within and between legs. We expected muscle fatigue to result in an increased common drive to different motor units of synergists within a leg and, hence, to increased synchronization, i.e., an increased coherence between corresponding surface EMGs. We further expected fatigue-related motor overflow to cause motor-unit synchronization of homologous muscles of both legs, although to a lesser extent than for synergists within a leg. In the first experiment, different levels of fatigue were induced by varying posture (knee angle), whereas in the second experiment fatigue was induced in a fixed posture by instructing participants to produce different force levels. EMG coherence was found in two distinct frequency bands (6-11 and 13-18 Hz) and was higher within a leg than between legs. The fatigue-related increase of 6-11 Hz inter-limb synchronization resembled the increased motor overflow during unimanual contractions and thus hinted at an increase in bilateral coupling. Synchronization at 13-18 Hz was clearly different and appeared to be related to posture.

Multivariate time–frequency analysis of electromagnetic brain activity during bimanual motor learning

Although the relationship between brain activity and motor performance is reasonably well established, the manner in which this relationship changes with motor learning remains incompletely understood. This paper presents a study of cortical modulations of event-related beta activity when participants learned to perform a complex bimanual motor task: 151 channel MEG data were acquired from nine healthy adults whilst learning a bimanual 3:5 polyrhythm. Sources of MEG activity were determined by means of synthetic aperture magnetometry that yielded locations and time courses of beta activities. The relationship between changes in performance and corresponding changes in event-related power were assessed using partial least squares. Behavioral data revealed that participants successfully learned to perform the 3:5 polyrhythm and that performance improvement was mainly achieved through the proper timing of the finger producing the slow rhythm. We found event-related modulation of beta power in the contralateral motor cortex that was inversely related to force output. The degree of beta modulation increased during the experiment - although the force level remained constant - and was positively correlated with motor performance, in particular for the motor cortex contralateral to the slow hand. These electrophysiological findings support the view that activity in motor cortex co-varies closely with behavioral changes over the course of learning.

Effects of sleep deprivation on neural functioning: an integrative review

Sleep deprivation has a broad variety of effects on human performance and neural functioning that manifest themselves at different levels of description. On a macroscopic level, sleep deprivation mainly affects executive functions, especially in novel tasks. Macroscopic and mesoscopic effects of sleep deprivation on brain activity include reduced cortical responsiveness to incoming stimuli, reflecting reduced attention. On a microscopic level, sleep deprivation is associated with increased levels of adenosine, a neuromodulator that has a general inhibitory effect on neural activity. The inhibition of cholinergic nuclei appears particularly relevant, as the associated decrease in cortical acetylcholine seems to cause effects of sleep deprivation on macroscopic brain activity. In general, however, the relationships between the neural effects of sleep deprivation across observation scales are poorly understood and uncovering these relationships should be a primary target in future research.

Bilateral motor unit synchronization is functionally organized

To elucidate the neural interactions underlying bimanual coordination, we investigated in 11 participants the bilateral coupling of homologous muscles in an isometric force production task involving fatiguing elbow flexion and extension. We focused on changes in motor unit (MU) synchronization as evident in EMG recordings of relevant muscles. In contrast to a related study on leg muscles, the arm muscles did not exhibit MU synchronization around 16 Hz, consistent with our hypothesis that 16 Hz MU synchronization is linked to balance maintenance. As expected, bilateral MU synchronization was apparent between 8 and 12 Hz and increased with fatigue and more strongly so for extensor than for flexor muscles. MU synchronization in that frequency band is interpreted in terms of common bilateral input and substantiates the idea that common input is functionally organized. Since these findings are consistent with the literature on mirror movements, they suggest that both phenomena may be related.

Amplitude and phase dynamics associated with acoustically paced finger tapping

To gain insight into the brain activity associated with the performance of an acoustically paced synchronization task, we analyzed the amplitude and phase dynamics inherent in magnetoencephalographic (MEG) signals across frequency bands in order to discriminate between evoked and induced responses. MEG signals were averaged with respect to motor and auditory events (tap and tone onsets). Principal component analysis was used to compare amplitude and phase changes during listening and during paced and unpaced tapping, allowing a separation of brain activity related to motor and auditory processes, respectively. Motor performance was accompanied by phasic amplitude changes and increased phase locking in the beta band. Auditory processing of acoustic stimuli resulted in a simultaneous increase of amplitude and phase locking in the theta and alpha band. The temporal overlap of auditory-related amplitude changes and phase locking indicated an evoked response, in accordance with previous studies on auditory perception. The temporal difference of movement-related amplitude and phase dynamics in the beta band, on the other hand, suggested a change in ongoing brain activity, i.e., an induced response supporting previous results on motor-related brain dynamics in the beta band.

Effects of sleep deprivation on event-related fields and alpha activity during rhythmic force production

The influence of sleep deprivation (SD) on event-related fields and the distribution of power over the scalp of MEG imaged brain activity was studied during acoustically paced rhythmic force production. At the behavioral level, SD resulted in a reduction of the lag (negative asynchrony) between produced forces and acoustic stimuli at higher movement tempos. Principal component analysis of the accompanying MEG activity showed that auditory- and motor-evoked fields were attenuated after SD and revealed an anterior shift of power towards more frontal channels. These results were interpreted in terms of a change of central processing of afferent sensory input due to SD.

MEG-compatible force sensor

By use of an insulating material we constructed a strain gauge based sensor to measure isometric forces in parallel with magneto-encephalographic recordings (i.e. without interference). The sensor can be used in different geometries to measure force production in different dimensions. Furthermore, it can easily be adapted or modified for specific experimental applications. Finally, on-line processing of the recorded forces, e.g., for the purpose of feedback, can be realized using standard MEG equipment.