Atypical paroxysmal slow cortical activity in healthy adults: Relationship to age and cognitive performance.

Paroxysmal patterns of slow cortical activity have been detected in EEG recordings from individuals with age-related neuropathology and have been shown to be correlated with cognitive dysfunction and blood-brain barrier disruption in these participants. The prevalence of these events in healthy participants, however, has not been studied. In this work, we inspect MEG recordings from 623 healthy participants from the Cam-CAN dataset for the presence of paroxysmal slow wave events (PSWEs). PSWEs were detected in approximately 20% of healthy participants in the dataset, and participants with PSWEs tended to be older and have lower cognitive performance than those without PSWEs. In addition, event features changed linearly with age and cognitive performance, resulting in longer and slower events in older adults, and more widespread events in those with low cognitive performance. These findings provide the first evidence of PSWEs in a subset of purportedly healthy adults. Going forward, these events may have utility as a diagnostic biomarker for atypical brain activity in older adults.

Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset.

Human neuromagnetic activity is characterised by a complex combination of transient bursts with varying spatial and temporal characteristics. The characteristics of these transient bursts change during task performance and normal ageing in ways that can inform about underlying cortical sources. Many methods have been proposed to detect transient bursts, with the most successful ones being those that employ multi-channel, data-driven approaches to minimize bias in the detection procedure. There has been little research, however, into the application of these data-driven methods to large datasets for group-level analyses. In the current work, we apply a data-driven convolutional dictionary learning (CDL) approach to detect neuromagnetic transient bursts in a large group of healthy participants from the Cam-CAN dataset. CDL was used to extract repeating spatiotemporal motifs in 538 participants between the ages of 18-88 during a sensorimotor task. Motifs were then clustered across participants based on similarity, and relevant task-related clusters were analysed for age-related trends in their spatiotemporal characteristics. Seven task-related motifs resembling known transient burst types were identified through this analysis, including beta, mu, and alpha type bursts. All burst types showed positive trends in their activation levels with age that could be explained by increasing burst rate with age. This work validated the data-driven CDL approach for transient burst detection on a large dataset and identified robust information about the complex characteristics of human brain signals and how they change with age.

Age-related trends in the cortical sources of transient beta bursts during a sensorimotor task and rest.

Interpreting neurophysiology recordings as a series of transient bursts with varying temporal and spectral characteristics provides meaningful insight into mechanisms underlying neural networks. Previous research has revealed age-related changes in the time-frequency dynamics of sensorimotor beta bursts, but to date, there has been little focus on the spatial localization of these beta bursts or how the localization patterns change with normal healthy ageing. The objective of the current study is to implement existing source localization algorithms for use in the detection of the cortical sources of transient beta bursts, and to uncover age-related trends in the resulting source localization patterns. Two well-established source localization algorithms (minimum-norm estimation and beamformer) were applied to localize beta bursts detected over the sensorimotor cortices in a cohort of 561 healthy participants between the ages of 18 and 88 (CamCAN open access dataset). Age-related trends were then investigated by applying regression analysis between participant age and average source power within several cortical regions of interest. This analysis revealed that beta bursts localized primarily to the sensorimotor cortex ipsilateral to the side of the sensor used for their detection. Region of interest analysis revealed that there were age-related changes in the beta burst localization pattern, with most substantial changes evidenced in frontal brain regions. In addition, regression analysis revealed a tendency of age-related trends to peak around 60 years of age suggesting that 60 is a potential critical age in this population. These results show for the first time that source localization techniques can be implemented for the identification of the sources of transient beta bursts. The exploration of these sources provides us with insight into the anatomical generators of transient beta activity and how they change across the lifespan.

Age-related trends in neuromagnetic transient beta burst characteristics during a sensorimotor task and rest in the Cam-CAN open-access dataset.

Non-invasive neurophysiological recordings, such as those measured by magnetoencelography (MEG), provide insight into the behaviour of neural networks and how these networks change with factors such as task performance, disease state, and age. Recently, there has been a trend in describing neurophysiological recordings as a series of transient bursts of neural activity rather than averaged sustained oscillations as burst characteristics may be more directly correlated with the neurological generators of brain activity. In this work, we investigate how beta burst characteristics change with age in a large open access dataset. The objectives are (1) to detect and characterize transient beta bursts over the ipsilateral and contralateral primary sensorimotor cortices during a unilateral motor task performance and during wakeful resting, and (2) to identify age-related changes in beta burst characteristics, in the context of earlier reports of age-related changes in beta suppression and the post-movement beta rebound. MEG data, acquired at the Cambridge Centre for Ageing and Neuroscience, of roughly 600 participants with a nearly uniform distribution of ages between 18 and 88 years old was used for analysis. We found that burst rate is the predominant factor related to age-related changes in the amplitude of the induced beta rhythm responses associated with a button press task. Furthermore, we present a cross-validation of burst parameters detected at the sensor- (peak sensor and sensor ROI) and source-level (beamformer spatial filter). This work is as an important step in characterizing transient bursts in neuromagnetic signals in the temporal domain, towards a better understanding of the healthy aging human brain.

Variability and bias between magnetoencephalography systems in localization of the primary visual cortex.

OBJECTIVES: When using MEG for pre-surgical mapping it is critically important that reliable estimates of functional locations, such as the primary visual cortex (V1) can be provided. Several different models of MEG systems exist, each with varying software and hardware configurations, and it is not currently known how the system type contributes to variability in V1 localization. PATIENTS AND METHODS: In this study, participants underwent MEG sessions using two different systems (Vector View and CTF) during which they were presented with a repeating grating stimulus to the lower-left visual quadrant to generate a visual evoked field (VEF). The location, amplitude and latency of the VEF source was compared between systems for each participant. RESULTS: No significant differences were found in latency and amplitude between systems, however, a significant bias in the latero-medial position of the localization was present. The median inter-system Euclidian distance between V1 localization across participants was 10.5 mm. CONCLUSIONS: Overall, our results indicate that mapping of V1 can be reliably reproduced within approximately one centimetre by different MEG systems. SIGNIFICANCE: This result provides knowledge of the useful limits on the reliability of localization which can be taken into consideration in clinical practice.

Efficacy of low-cost wireless neurofeedback to modulate brain activity during motor imagery.

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.

Variability and bias between magnetoencephalography systems in non-invasive localization of the primary somatosensory cortex.

OBJECTIVES: Magnetoencephalography (MEG) provides functional neuroimaging data for pre-surgical planning in patients with epilepsy or brain tumour. For mapping the primary somatosensory cortex (S1), MEG data are acquired while a patient undergoes median nerve stimulation (MNS) to localize components of the somatosensory evoked field (SEF). In clinical settings, only one MEG imaging session is usually possible due to limited resources. As such, it is important to have an a priori estimate of the expected variability in localization. Variability in S1 localization between mapping sessions using the same MEG system has been previously measured as 8 mm. There are different types of MEG systems available with varied hardware and software, and it is not known how using a different MEG system will impact on S1 localization. PATIENTS AND METHODS: In our study, healthy participants underwent the MNS procedure with two different MEG systems (Vector View and CTF). We compared the location, amplitude and latency of SEF components between data from each system to quantify variability and bias between MEG systems. RESULTS: We found 8-11 mm variability in S1 localization between the two MEG systems, and no evidence for a systematic bias in location, amplitude or latency between the two systems. CONCLUSION: These findings suggest that S1 localization is not biased by the type of MEG system used, and that differences between the two systems are not a major contributor to variability in localization.