The effect of physical fatigue on oscillatory dynamics of the sensorimotor cortex

Aim: While physical fatigue is known to arise in part from supraspinal mechanisms within the brain exactly how brain activity is modulated during fatigue is not well understood. Therefore, this study examined how typical neural oscillatory responses to voluntary muscle contractions were affected by fatigue. Methods: Eleven healthy adults (age 27±4 years) completed two experimental sessions in a randomised crossover design. Both sessions first assessed baseline maximal voluntary isometric wrist-flexion force (MVFb). Participants then performed an identical series of fourteen test contractions (2 × 100%MVFb, 10 × 40%MVFb, 2 × 100%MVFb) both before and after one of two interventions: forty 12-s contractions at 55%MVFb (fatigue intervention) or 5%MVFb (control intervention). Magnetoencephalography (MEG) was used to characterise both the movement-related mu and beta decrease (MRMD and MRBD) and the post-movement beta rebound (PMBR) within the contralateral sensorimotor cortex during the 40%MVFb test contractions, while the 100%MVFb test contractions were used to monitor physical fatigue. Results: The fatigue intervention induced a substantial physical fatigue that endured throughout the post-intervention measurements (28.9-29.5% decrease in MVF, P<0.001). Fatigue had a significant effect on both PMBR (ANOVA, session × time-point interaction: P=0.018) and MRBD (P=0.021): the magnitude of PMBR increased following the fatigue but not the control interventions, whereas MRBD was decreased post-control but not post-fatigue. Mu oscillations were unchanged throughout both sessions. Conclusion: Physical fatigue resulted in an increased PMBR, and offset attenuations in MRBD associated with task habituation

Seasonal analysis of submicron aerosol in Old Delhi using high-resolution aerosol mass spectrometry: chemical characterisation, source apportionment and new marker identification

We present the first real-time composition of submicron particulate matter (PM1) in Old Delhi using high-resolution aerosol mass spectrometry (HR-AMS). Old Delhi is one of the most polluted locations in the world, and PM1 concentrations reached ∼ 750 µg m−3 during the most polluted period, the post-monsoon period, where PM1 increased by 188 % over the pre-monsoon period. Sulfate contributes the largest inorganic PM1 mass fraction during the pre-monsoon (24 %) and monsoon (24 %) periods, with nitrate contributing most during the post-monsoon period (8 %). The organics dominate the mass fraction (54 %–68 %) throughout the three periods, and, using positive matrix factorisation (PMF) to perform source apportionment analysis of organic mass, two burning-related factors were found to contribute the most (35 %) to the post-monsoon increase. The first PMF factor, semi-volatility biomass burning organic aerosol (SVBBOA), shows a high correlation with Earth observation fire counts in surrounding states, which links its origin to crop residue burning. The second is a solid fuel OA (SFOA) factor with links to local open burning due to its high composition of polyaromatic hydrocarbons (PAHs) and novel AMS-measured marker species for polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Two traffic factors were resolved: one hydrocarbon-like OA (HOA) factor and another nitrogen-rich HOA (NHOA) factor. The N compounds within NHOA were mainly nitrile species which have not previously been identified within AMS measurements. Their PAH composition suggests that NHOA is linked to diesel and HOA to compressed natural gas and petrol. These factors combined make the largest relative contribution to primary PM1 mass during the pre-monsoon and monsoon periods while contributing the second highest in the post-monsoon period. A cooking OA (COA) factor shows strong links to the secondary factor, semi-volatility oxygenated OA (SVOOA). Correlations with co-located volatile organic compound (VOC) measurements and AMS-measured organic nitrogen oxides (OrgNO) suggest SVOOA is formed from aged COA. It is also found that a significant increase in chloride concentrations (522 %) from pre-monsoon to post-monsoon correlates well with SVBBOA and SFOA, suggesting that crop residue burning and open waste burning are responsible. A reduction in traffic emissions would effectively reduce concentrations across most of the year. In order to reduce the post-monsoon peak, sources such as funeral pyres, solid waste burning and crop residue burning should be considered when developing new air quality policy

Modulation of post-movement beta rebound by contraction force and rate of force development

Movement induced modulation of the beta rhythm is one of the most robust neural oscillatory phenomena in the brain. In the preparation and execution phases of movement, a loss in beta amplitude is observed (movement related beta decrease (MRBD)). This is followed by a rebound above baseline on movement cessation (post movement beta rebound (PMBR)). These effects have been measured widely, and recentwork suggests that they may have significant importance. Specifically, they have potential to form the basis of biomarkers for disease, and have been used in neuroscience applications ranging from brain computer interfaces to markers of neural plasticity. However, despite the robust nature of both MRBD and PMBR, the phenomena themselves are poorly understood. In this study, we characterise MRBD and PMBR during a carefully controlled isometric wrist flexion paradigm, isolating two fundamental movement parameters;force output, and the rate of force development (RFD). Our results show that neither altered force output nor RFD has a significant effect on MRBD. In contrast, PMBR was altered by both parameters. Higher force output results in greater PMBR amplitude, and greater RFD results in a PMBR which is higher in amplitude and shorter in duration. These findings demonstrate that careful control of movement parameters cansystematically change PMBR. Further, for temporally protracted movements, the PMBR can be over 7 s in duration. This means accurate control of movement and judicious selection of paradigm parameters are critical in future clinical and basic neuroscientific studies of sensorimotor beta oscillations

Regional Brain Correlates of Beta Bursts in Health and Psychosis: A Concurrent Electroencephalography and Functional Magnetic Resonance Imaging Study

Background: There is emerging evidence for abnormal beta oscillations in psychosis. Beta-oscillations are likely to play a key role in the coordination of sensorimotor information, crucial to healthy mental function. Growing evidence suggests that beta oscillations typically manifest as transient “beta-bursts” that increase in probability following a motor response, observable as Post-Movement Beta Rebound (PMBR). Evidence indicates that PMBR is attenuated in psychosis, with greater attenuation associated with greater symptom severity and impairment. Delineating the functional role of beta-bursts may therefore be key to understanding the mechanisms underlying persistent psychotic illness.Methods: We used concurrent EEG and fMRI to identify BOLD correlates of beta-bursts during the N-back working memory task and intervening rest periods in healthy participants (N = 30) and patients with psychosis (N = 48). Results: During both task-blocks and intervening rest periods, beta-bursts phasically activated regions implicated in task-relevant content, while suppressing currently tonically active regions. Patients showed attenuated PMBR that was associated with persisting Disorganisation symptoms, as well as impairments in cognition and role function. Patients also showed greater task-related reductions in overall beta-burst rate, and greater, more extensive, beta-burst-related BOLD activation.Conclusions: Our evidence supports a model in which beta-bursts reactivate latently maintained sensorimotor information and are dysregulated and inefficient in psychosis. We propose that abnormalities in the mechanisms by which beta-bursts coordinate reactivation of contextually appropriate content can manifest as Disorganisation, working memory deficits and inaccurate forward models, and may underlie a “core deficit” associated with persisting symptoms and impairment

Spatiotemporal neural characterization of prediction error valence and surprise during reward learning in humans

Reward learning depends on accurate reward associations with potential choices. These associations can be attained with reinforcement learning mechanisms using a reward prediction error (RPE) signal (the difference between actual and expected rewards) for updating future reward expectations. Despite an extensive body of literature on the influence of RPE on learning, little has been done to investigate the potentially separate contributions of RPE valence (positive or negative) and surprise (absolute degree of deviation from expectations). Here, we coupled single-trial electroencephalography with simultaneously acquired fMRI, during a probabilistic reversal-learning task, to offer evidence of temporally overlapping but largely distinct spatial representations of RPE valence and surprise. Electrophysiological variability in RPE valence correlated with activity in regions of the human reward network promoting approach or avoidance learning. Electrophysiological variability in RPE surprise correlated primarily with activity in regions of the human attentional network controlling the speed of learning. Crucially, despite the largely separate spatial extend of these representations our EEG-informed fMRI approach uniquely revealed a linear superposition of the two RPE components in a smaller network encompassing visuo mnemonic and reward areas. Activity in this network was further predictive of stimulus value updating indicating a comparable contribution of both signals to reward learning

Two spatiotemporally distinct value systems shape reward-based learning in the human brain

Avoiding repeated mistakes and learning to reinforce rewarding decisions is critical for human survival and adaptive actions. Yet, the neural underpinnings of the value systems that encode different decision-outcomes remain elusive. Here coupling single-trial electroencephalography with simultaneously acquired functional magnetic resonance imaging, we uncover the spatiotemporal dynamics of two separate but interacting value systems encoding decision-outcomes. Consistent with a role in regulating alertness and switching behaviours, an early system is activated only by negative outcomes and engages arousal-related and motor-preparatory brain structures. Consistent with a role in reward-based learning, a later system differentially suppresses or activates regions of the human reward network in response to negative and positive outcomes, respectively. Following negative outcomes, the early system interacts and downregulates the late system, through a thalamic interaction with the ventral striatum. Critically, the strength of this coupling predicts participants’ switching behaviour and avoidance learning, directly implicating the thalamostriatal pathway in reward-based learning

Mapping Interictal activity in epilepsy using a hidden Markov model: A magnetoencephalography study

Abstract: Epilepsy is a highly heterogeneous neurological disorder with variable etiology, manifestation, and response to treatment. It is imperative that new models of epileptiform brain activity account for this variability, to identify individual needs and allow clinicians to curate personalized care. Here, we use a hidden Markov model (HMM) to create a unique statistical model of interictal brain activity for 10 pediatric patients. We use magnetoencephalography (MEG) data acquired as part of standard clinical care for patients at the Children's Hospital of Philadelphia. These data are routinely analyzed using excess kurtosis mapping (EKM); however, as cases become more complex (extreme multifocal and/or polymorphic activity), they become harder to interpret with EKM. We assessed the performance of the HMM against EKM for three patient groups, with increasingly complicated presentation. The difference in localization of epileptogenic foci for the two methods was 7 ± 2 mm (mean ± SD over all 10 patients); and 94% ± 13% of EKM temporal markers were matched by an HMM state visit. The HMM localizes epileptogenic areas (in agreement with EKM) and provides additional information about the relationship between those areas. A key advantage over current methods is that the HMM is a data‐driven model, so the output is tuned to each individual. Finally, the model output is intuitive, allowing a user (clinician) to review the result and manually select the HMM epileptiform state, offering multiple advantages over previous methods and allowing for broader implementation of MEG epileptiform analysis in surgical decision‐making for patients with intractable epilepsy

Predicting haemodynamic networks using electrophysiology: The role of non-linear and cross-frequency interactions

Understanding the electrophysiological basis of resting state networks (RSNs) in the human brain is a critical step towards elucidating how inter-areal connectivity supports healthy brain function. In recent years, the relationship between RSNs (typically measured using haemodynamic signals) and electrophysiology has been explored using functional Magnetic Resonance Imaging (fMRI) and magnetoencephalography (MEG). Significant progress has been made, with similar spatial structure observable in both modalities. However, there is a pressing need to understand this relationship beyond simple visual similarity of RSN patterns. Here, we introduce a mathematical model to predict fMRI-based RSNs using MEG. Our unique model, based upon a multivariate Taylor series, incorporates both phase and amplitude based MEG connectivity metrics, as well as linear and non-linear interactions within and between neural oscillations measured in multiple frequency bands. We show that including non-linear interactions, multiple frequency bands and cross-frequency terms significantly improves fMRI network prediction. This shows that fMRI connectivity is not only the result of direct electrophysiological connections, but is also driven by the overlap of connectivity profiles between separate regions. Our results indicate that a complete understanding of the electrophysiological basis of RSNs goes beyond simple frequency-specific analysis, and further exploration of non-linear and cross-frequency interactions will shed new light on distributed network connectivity, and its perturbation in pathology