Dynamic recruitment of resting state sub-networks

Resting state networks (RSNs) are of fundamental importance in human systems neuroscience with evidence suggesting that they are integral to healthy brain function and perturbed in pathology. Despite rapid progress in this area, the temporal dynamics governing the functional connectivities that underlie RSN structure remain poorly understood. Here, we present a framework to help further our understanding of RSN dynamics. We describe a methodology which exploits the direct nature and high temporal resolution of magnetoencephalography (MEG). This technique, which builds on previous work, extends from solving fundamental confounds in MEG (source leakage) to multivariate modelling of transient connectivity. The resulting processing pipeline facilitates direct (electrophysiological) measurement of dynamic functional networks. Our results show that, when functional connectivity is assessed in small time windows, the canonical sensorimotor network can be decomposed into a number of transiently synchronising sub-networks, recruitment of which depends on current mental state. These rapidly changing sub-networks are spatially focal with, for example, bilateral primary sensory and motor areas resolved into two separate sub-networks. The likely interpretation is that the larger canonical sensorimotor network most often seen in neuroimaging studies reflects only a temporal aggregate of these transient sub-networks. Our approach opens new frontiers to study RSN dynamics, showing that MEG is capable of revealing the spatial, temporal and spectral signature of the human connectome in health and disease

A multi-layer network approach to MEG connectivity analysis

Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiological signal makes gaining a complete picture challenging. Specifically, connectivity can be calculated as statistical interdependencies between neural oscillations within a large range of different frequency bands. Further, connectivity can be computed between frequency bands. This pan-spectral network hierarchy likely helps to mediate simultaneous formation of multiple brain networks, which support ongoing task demand. However, to date it has been largely overlooked, with many electrophysiological functional connectivity studies treating individual frequency bands in isolation. Here, we combine oscillatory envelope based functional connectivity metrics with a multi-layer network framework in order to derive a more complete picture of connectivity within and between frequencies. We test this methodology using MEG data recorded during a visuomotor task, highlighting simultaneous and transient formation of motor networks in the beta band, visual networks in the gamma band and a beta to gamma interaction. Having tested our method, we use it to demonstrate differences in occipital alpha band connectivity in patients with schizophrenia compared to healthy controls. We further show that these connectivity differences are predictive of the severity of persistent symptoms of the disease, highlighting their clinical relevance. Our findings demonstrate the unique potential of MEG to characterise neural network formation and dissolution. Further, we add weight to the argument that dysconnectivity is a core feature of the neuropathology underlying schizophrenia

Measuring temporal, spectral and spatial changes in electrophysiological brain network connectivity

The topic of functional connectivity in neuroimaging is expanding rapidly and many studies now focus on coupling between spatially separate brain regions. These studies show that a relatively small number of large scale networks exist within the brain, and that healthy function of these networks is disrupted in many clinical populations. To date, the vast majority of studies probing connectivity employ techniques that compute time averaged correlation over several minutes, and between specific pre-defined brain locations. However, increasing evidence suggests that functional connectivity is non-stationary in time. Further, electrophysiological measurements show that connectivity is dependent on the frequency band of neural oscillations. It is also conceivable that networks exhibit a degree of spatial inhomogeneity, i.e. the large scale networks that we observe may result from the time average of multiple transiently synchronised sub-networks, each with their own spatial signature. This means that the next generation of neuroimaging tools to compute functional connectivity must account for spatial inhomogeneity, spectral non-uniformity and temporal non-stationarity. Here, we present a means to achieve this via application of windowed canonical correlation analysis (CCA) to source space projected MEG data. We describe the generation of time–frequency connectivity plots, showing the temporal and spectral distribution of coupling between brain regions. Moreover, CCA over voxels provides a means to assess spatial non-uniformity within short time–frequency windows. The feasibility of this technique is demonstrated in simulation and in a resting state MEG experiment where we elucidate multiple distinct spatio-temporal-spectral modes of covariation between the left and right sensorimotor areas

Dynamics of large-scale electrophysiological networks: a technical review

For several years it has been argued that neural synchronisation is crucial for cognition. The idea that synchronised temporal patterns between different neural groups carries information above and beyond the isolated activity of these groups has inspired a shift in focus in the field of functional neuroimaging. Specifically, investigation into the activation elicited within certain regions by some stimulus or task has, in part, given way to analysis of patterns of co-activation or functional connectivity between distal regions. Recently, the functional connectivity community has been looking beyond the assumptions of stationarity that earlier work was based on, and has introduced methods to incorporate temporal dynamics into the analysis of connectivity. In particular, non-invasive electrophysiological data (magnetoencephalography / electroencephalography (MEG/EEG)), which provides direct measurement of whole-brain activity and rich temporal information, offers an exceptional window into such (potentially fast) brain dynamics. In this review, we discuss challenges, solutions, and a collection of analysis tools that have been developed in recent years to facilitate the investigation of dynamic functional connectivity using these imaging modalities. Further, we discuss the applications of these approaches in the study of cognition and neuropsychiatric disorders. Finally, we review some existing developments that, by using realistic computational models, pursue a deeper understanding of the underlying causes of non-stationary connectivity

Whole-body MRI for the investigation of joint involvement in inflammatory arthritis.

Acknowledgements: The authors wish to thank the NIHR Biomedical Research Centre in Leeds for providing the equipment and expertise required for this study. We also wish to thank Versus Arthritis (formerly Arthritis Research UK) for funding Dr. Freeston’s Clinician Scientist fellowship, of which this study was a core component. Additionally, we would like to thank Dr. Richard Hodgson for assisting with the radiology scoring and radiographers Rob Evans, Dr. Carole Burnett and Brian Chaka for their assistance in acquiring the data. This is independent research funded by Versus Arthritis (previously Arthritis Research UK) and carried out at the National Institute for Health and Care Research (NIHR) Leeds Biomedical Research Centre (BRC). The views expressed are those of the author(s) and not necessarily those of the Versus Arthritis, the NIHR or the Department of Health and Social Care.Funder: Versus Arthritis; doi: http://dx.doi.org/10.13039/501100012041OBJECTIVES: This study aimed to develop a novel whole-body MRI protocol capable of assessing inflammatory arthritis at an early stage in multiple joints in one examination. MATERIALS AND METHODS: Forty-six patients with inflammatory joint symptoms and 9 healthy volunteers underwent whole-body MR imaging on a 3.0 T MRI scanner in this prospective study. Image quality and pathology in each joint, bursae, entheses and tendons were scored by two of three radiologists and compared to clinical joint scores. Participants were divided into three groups based on diagnosis at 1-year follow-up (healthy volunteers, rheumatoid arthritis and all other types of arthritis). Radiology scores were compared between the three groups using a Kruskal-Wallis test. The clinical utility of radiology scoring was compared to clinical scoring using ROC analysis. RESULTS: A protocol capable of whole-body MR imaging of the joints with an image acquisition time under 20 min was developed with excellent image quality. Synovitis scores were significantly higher in patients who were diagnosed with rheumatoid arthritis at 12 months (p < 0.05). Radiology scoring of bursitis showed statistically significant differences between each of the three groups-healthy control, rheumatoid arthritis and non-rheumatoid arthritis (p < 0.05). There was no statistically significant difference in ROC analysis between MRI and clinical scores. CONCLUSION: This study has developed a whole-body MRI joint imaging protocol that is clinically feasible and shows good differentiation of joint pathology between healthy controls, patients with rheumatoid arthritis and patients with other forms of arthritis

Relationship between Mitochondrial Quality Control Markers, Lower Extremity Tissue Composition, and Physical Performance in Physically Inactive Older Adults

Altered mitochondrial quality and function in muscle may be involved in age-related physical function decline. The role played by the autophagy–lysosome system, a major component of mitochondrial quality control (MQC), is incompletely understood. This study was undertaken to obtain initial indications on the relationship between autophagy, mitophagy, and lysosomal markers in muscle and measures of physical performance and lower extremity tissue composition in young and older adults. Twenty-three participants were enrolled, nine young (mean age: 24.3 ± 4.3 years) and 14 older adults (mean age: 77.9 ± 6.3 years). Lower extremity tissue composition was quantified volumetrically by magnetic resonance imaging and a tissue composition index was calculated as the ratio between muscle and intermuscular adipose tissue volume. Physical performance in older participants was assessed via the Short Physical Performance Battery (SPPB). Protein levels of the autophagy marker p62, the mitophagy mediator BCL2/adenovirus E1B 19 kDa protein-interacting protein 3 (BNIP3), the lysosomal markers transcription factor EB, vacuolar-type ATPase, and lysosomal-associated membrane protein 1 were measured by Western immunoblotting in vastus lateralis muscle biopsies. Older adults had smaller muscle volume and lower tissue composition index than young participants. The protein content of p62 and BNIP3 was higher in older adults. A negative correlation was detected between p62 and BNIP3 and the tissue composition index. p62 and BNIP3 were also related to the performance on the 5-time sit-to-stand test of the SPPB. Our results suggest that an altered expression of markers of the autophagy/mitophagy–lysosomal system is related to deterioration of lower extremity tissue composition and muscle dysfunction. Additional studies are needed to clarify the role of defective MQC in human muscle aging and identify novel biological targets for drug development

Inter- and Intra-Subject Variability of Neuromagnetic Resting State Networks.

Functional connectivity studies conducted at the group level using magnetoencephalography (MEG) suggest that resting state networks (RSNs) emerge from the large-scale envelope correlation structure within spontaneous oscillatory brain activity. However, little is known about the consistency of MEG RSNs at the individual level. This paper investigates the inter- and intra-subject variability of three MEG RSNs (sensorimotor, auditory and visual) using seed-based source space envelope correlation analysis applied to 5 min of resting state MEG data acquired from a 306-channel whole-scalp neuromagnetometer (Elekta Oy, Helsinki, Finland) and source projected with minimum norm estimation. The main finding is that these three MEG RSNs exhibit substantial variability at the single-subject level across and within individuals, which depends on the RSN type, but can be reduced after averaging over subjects or sessions. Over- and under-estimations of true RSNs variability are respectively obtained using template seeds, which are potentially mislocated due to inter-subject variations, and a seed optimization method minimizing variability. In particular, bounds on the minimal number of subjects or sessions required to obtain highly consistent between- or within-subject averages of MEG RSNs are derived. Furthermore, MEG RSN topography positively correlates with their mean connectivity at the inter-subject level. These results indicate that MEG RSNs associated with primary cortices can be robustly extracted from seed-based envelope correlation and adequate averaging. MEG thus appears to be a valid technique to compare RSNs across subjects or conditions, at least when using the current methods.JOURNAL ARTICLESCOPUS: ar.jinfo:eu-repo/semantics/publishe