Lindsey Power

Background: Thought disorder in schizophrenia reflects disrupted internal model maintenance in predictive processing. Beta oscillations have been proposed to index top-down predictive signalling, but prior resting-state studies have focused on spectral power, overlooking transient burst dynamics that enable the stabilization and reinstatement of internal representations, especially in the default mode network (DMN). Methods: Resting-state MEG was acquired from 25 patients (7 females) with schizophrenia and 25 matched controls (8 females) between May 2024 and October 2025. Beta bursts (15-30 Hz) were extracted from source-localized cortical time series, and burst rate, duration, power, and inter-regional synchrony were quantified. Associations with computationally-derived semantic and syntactic speech features were assessed using principal-component regression within patients. Results: Conventional beta power did not differ between groups after correction. In contrast, beta-burst dynamics showed significant group-by-anterior-posterior-position interactions for burst rate (p = 0.018), power (p = 0.0014) and duration (p < 0.001). Within the DMN, patients showed shorter inferior parietal burst duration (t(46.0) = -2.74, FDR-p = 0.0343) and increased burst synchrony within left prefrontal cortex (t(31.0) = 2.81, FDR-p = 0.0343) and between prefrontal cortices (t(29.8) = 2.73, FDR-p = 0.0423). Within patients, burst components predicted speech organization (R-squared = 0.47, F(3,19) = 5.66, p = 0.006), driven by the inferior parietal burst-duration component (β = -0.55, p = 0.0036), but did not predict overall clinical severity. Limitations: The modest patient sample, short resting-state recording, cross-sectional design, and lack of laminar specificity limit causal and mechanistic inference; burst-speech associations require replication. Conclusion: Schizophrenia was associated with dissociable anterior and posterior beta-burst abnormalities that tracked objective speech markers of thought disorder. These findings suggest impaired hierarchical coordination of predictive beta signalling as a candidate mechanism linking intrinsic network dysfunction to disorganized thought.

Neural bursts—brief episodes of heightened oscillatory activity—are increasingly studied as fundamental building blocks of brain function, with relevance to perception, cognition, and disease. As such, detecting and characterizing these bursts in electrophysiological recordings has driven rapid methodological innovation in neuroscience research. However, the growing number of analysis techniques can be overwhelming, making it difficult for researchers to select the most appropriate method for their specific goals. In this review, we provide a structured and practical guide for neuroscientists to measure and interpret neural burst data. We offer an overview of current detection methods, accompanied by a suite of tutorials, including code notebooks and data, to enable concrete implementation and critical evaluation. We then conclude with actionable recommendations to help researchers select the best burst detection strategy in diverse research contexts. This guide is intended for newcomers to the field as well as more experienced neuroscientists seeking to expand their methodological toolkit.

Background: Predictive processing abnormalities offer a unifying account of perceptual and expressive disturbances in psychosis, yet classical predictive coding frameworks remain difficult to translate due to limited neurophysiological grounding. Emerging evidence positions beta-band oscillations and their transient burst dynamics as a biologically plausible mechanism for implementing top-down predictions that stabilize internal models. Study Design: This narrative review synthesizes evidence from electrophysiology, laminar physiology, computational modelling, language research, and clinical neuroimaging to evaluate beta oscillations as a mechanistic target for predictive processing deficits in psychosis. We integrate data from modified predictive routing frameworks and dendritic computation models to clarify how beta rhythms prepare cortical pathways for predicted inputs. Study Results: Across sensory, motor, cognitive, and language domains, schizophrenia features impaired generation, timing, and contextual deployment of beta activity. These include attenuated post-movement beta rebound, reduced or mistimed beta bursts during working memory and inhibition, abnormal beta-gamma interactions during perception, and weakened beta-mediated contextual guidance during language comprehension. Laminar and computational findings indicate that beta bursts arise from the integration of apical (contextual) and basal (sensory) dendritic inputs in layer 5 pyramidal neurons, providing a mechanistic substrate for top-down predictions. Beta disruptions, therefore, offer a parsimonious account of disorganization, psychomotor slowing, and failures of contextual maintenance. Early neuromodulation, pharmacologic, and neurofeedback studies suggest that beta dynamics are modifiable. Conclusions: Beta oscillations provide a tractable and mechanistically grounded target for predictive processing deficits in psychosis. Standardizing burst metrics and developing individualized, closed-loop approaches will be critical for advancing beta-based interventions.

Pre-surgical localization of language function in the brain is critical for patients with medically intractable epilepsy. MEG has emerged as a valuable clinical tool for localizing language areas in clinical populations, however, it is limited for widespread application due to the low availability of the system. Recent advances in optically pumped magnetometer (OPM) systems account for some of the limitations of traditional MEG and have been shown to have a similar signal-to-noise ratio. However, the novelty of these systems means that they have only been tested for limited sensory and motor applications. In this work, we aim to validate a novel on-head OPM MEG procedure for lateralizing language processes. OPM recordings, using a soft cap with flexible sensor placement, were collected from 19 healthy, right-handed controls during an auditory word recognition task. The resulting evoked fields were assessed for hemispheric laterality of the response. Principal component analysis (PCA) of the grand average language response indicated that the first two principal components were lateralized to the left hemisphere. The PCA also revealed that all participants had evoked topographies that closely resembled the average left-lateralized response. Left-lateralized responses were consistent with what is expected for a group of healthy right-handed individuals. These findings demonstrate that language-related evoked fields can be elucidated from on-head OPM MEG recordings in a group of healthy adult participants. In the future, on-head OPM MEG and the associated lateralization methods should be validated in patient populations as they may have utility in the pre-surgical mapping of language functions in patients with epilepsy.

Objectives : Motor imagery can be used as an adjunct to traditional stroke rehabilitation therapies for individuals who have hand and arm impairment resulting from their stroke. The provision of neurofeedback during motor imagery allows individuals to receive real time information regarding their motor imagery-related brain activity. However, the equipment required to administer this feedback is expensive and largely inaccessible to many of the individuals who could benefit from it. Available EEG-based technology provides an accessible, low-cost, wireless alternative to traditional neurofeedback methods, with the tradeoff of lower gain and channel count resulting in reduced signal quality. This study investigated the efficacy of this wireless technology for the provision of motor imagery-related neurofeedback. Approach : Twenty-eight healthy individuals participated in a 2-group, double-blinded study which involved imagining performing a unimanual button pressing task while receiving neurofeedback that is either a direct transform of their motor imagery-related brain activity (i.e., real) or is related to someone else’s brain activity (i.e., sham). The change in amplitude of 15–30 Hz (beta) rhythmic brain activity elicited during the task blocks was calculated and analyzed across sessions and groups. Main Results: We found that individuals who received real neurofeedback showed a statistically significant positive trajectory in modulating the amplitude of the beta rhythm across sessions, while those who received sham feedback showed a negative trajectory. Our results did not indicate a trend of increased lateralization across sessions, as has been shown in previous studies. Significance: Our main findings replicated previous results with research-grade equipment indicating that there is potential for introducing this wireless technology for the provision of neurofeedback. Given the marginal longitudinal effect of neurofeedback in our study, further study is required to address the limitations associated with this technology before our protocol can be implemented in a clinical setting.