Generalized time-frequency coherency for assessing neural interactions in electrophysiological recordings

Time-frequency coherence has been widely used to quantify statistical dependencies in bivariate data and has proven to be vital for the study of neural interactions in electrophysiological recordings. Conventional methods establish time-frequency coherence by smoothing the cross and power spectra using identical smoothing procedures. Smoothing entails a trade-off between time-frequency resolution and statistical consistency and is critical for detecting instantaneous coherence in single-trial data. Here, we propose a generalized method to estimate time-frequency coherency by using different smoothing procedures for the cross spectra versus power spectra. This novel method has an improved trade-off between time resolution and statistical consistency compared to conventional methods, as verified by two simulated data sets. The methods are then applied to single-trial surface encephalography recorded from human subjects for comparative purposes. Our approach extracted robust alpha- and gamma-band synchronization over the visual cortex that was not detected by conventional methods, demonstrating the efficacy of this method

Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

The central nervous system needs to coordinate multiple muscles during postural control. Functional coordination is established through the neural circuitry that interconnects different muscles. Here we used multivariate information decomposition of multichannel EMG acquired from 14 healthy participants during postural tasks to investigate the neural interactions between muscles. A set of information measures were estimated from an instantaneous linear regression model and a time-lagged VAR model fitted to the EMG envelopes of 36 muscles. We used network analysis to quantify the structure of functional interactions between muscles and compared them across experimental conditions. Conditional mutual information and transfer entropy revealed sparse networks dominated by local connections between muscles. We observed significant changes in muscle networks across postural tasks localized to the muscles involved in performing those tasks. Information decomposition revealed distinct patterns in task-related changes: unimanual and bimanual pointing were associated with reduced transfer to the pectoralis major muscles, but an increase in total information compared to no pointing, while postural instability resulted in increased information, information transfer and information storage in the abductor longus muscles compared to normal stability. These findings show robust patterns of directed interactions between muscles that are task-dependent and can be assessed from surface EMG recorded during static postural tasks. We discuss directed muscle networks in terms of the neural circuitry involved in generating muscle activity and suggest that task-related effects may reflect gain modulations of spinal reflex pathways

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 (rho = 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 (rho = 0.25) than for badges (rho = 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.Comment: 20 pages, 5 figure

Using Bluetooth Low Energy in smartphones to map social networks

Social networks have an important role in an individual's health, with the propagation of health-related features through a network, and correlations between network structures and symptomatology. Using Bluetooth-enabled smartphones to measure social connectivity is an alternative to traditional paper-based data collection; however studies employing this technology have been restricted to limited sets of homogenous handsets. We investigated the feasibility of using the Bluetooth Low Energy (BLE) protocol, present on users' own smartphones, to measure social connectivity. A custom application was designed for Android and iOS handsets. The app was configured to simultaneously broadcast via BLE and perform periodic discovery scans for other nearby devices. The app was installed on two Android handsets and two iOS handsets, and each combination of devices was tested in the foreground, background and locked states. Connectivity was successfully measured in all test cases, except between two iOS devices when both were in a locked state with their screens off. As smartphones are in a locked state for the majority of a day, this severely limits the ability to measure social connectivity on users' own smartphones. It is not currently feasible to use Bluetooth Low Energy to map social networks, due to the inability of iOS devices to detect another iOS device when both are in a locked state. While the technology was successfully implemented on Android devices, this represents a smaller market share of partially or fully compatible devices.Comment: 6 pages, 1 tabl

Estimating the Quality of Electroconvulsive Therapy Induced Seizures Using Decision Tree and Fuzzy Inference System Classifiers

Electroconvulsive therapy (ECT) is an effective and widely used treatment for major depressive disorder, in which a brief electric current is passed through the brain to trigger a brief seizure. This study aims to identify seizure quality rating by utilizing a set of seizure parameters. We used 750 ECT EEG recordings in this experiment. Four seizure related parameters, (time of slowing, regularity, stereotypy and post-ictal suppression) are used as inputs to two classifiers, decision tree and fuzzy inference system (FIS), to predict seizure quality ratings. The two classifiers produced encouraging results with error rate of 0.31 and 0.25 for FIS and decision tree, respectively. The classification results show that the four seizure parameters provide relevant information about the rating of seizure quality. Automatic scoring of seizure quality may be beneficial to clinicians working in this field

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

Critical fluctuations in cortical models near instability

Australian Research Council, the National Health and Medical Research Council, the Brain Network Recovery Group Grant JSMF22002082, and Netherlands Organization for Scientific Research (NWO #451–10-030

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, maintenenace 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 P3 amplitudes relative to controls. 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, as well as compensatory hyperactivity in frontal 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 workig memory performance to controls.</p

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

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