Multi-dimensional connectivity: a conceptual and mathematical review.

The estimation of functional connectivity between regions of the brain, for example based on statistical dependencies between the time series of activity in each region, has become increasingly important in neuroimaging. Typically, multiple time series (e.g. from each voxel in fMRI data) are first reduced to a single time series that summarises the activity in a region of interest, e.g. by averaging across voxels or by taking the first principal component; an approach we call one-dimensional connectivity. However, this summary approach ignores potential multi-dimensional connectivity between two regions, and a number of recent methods have been proposed to capture such complex dependencies. Here we review the most common multi-dimensional connectivity methods, from an intuitive perspective, from a formal (mathematical) point of view, and through a number of simulated and real (fMRI and MEG) data examples that illustrate the strengths and weaknesses of each method. The paper is accompanied with both functions and scripts, which implement each method and reproduce all the examples

Disclosing large-scale directed functional connections in MEG with the multivariate phase slope index.

Abstract The phase slope index (PSI) is a method to disclose the direction of frequency-specific neural interactions from magnetoencephalographic (MEG) time series. A fundamental property of PSI is that of vanishing for linear mixing of independent neural sources. This property allows PSI to cope with the artificial instantaneous connectivity among MEG sensors or brain sources induced by the field spread. Nevertheless, PSI is limited by being a bivariate estimator of directionality as opposite to the multidimensional nature of brain activity as revealed by MEG. The purpose of this work is to provide a multivariate generalization of PSI. We termed this measure as the multivariate phase slope index (MPSI). In order to test the ability of MPSI in estimating the directionality, and to compare the MPSI results to those obtained by bivariate PSI approaches based on maximizing imaginary part of coherency and on canonical correlation analysis, we used extensive simulations. We proved that MPSI achieves the highest performance and that in a large number of simulated cases, the bivariate methods, as opposed to MPSI, do not detect a statistically significant directionality. Finally, we applied MPSI to assess seed-based directed functional connectivity in the alpha band from resting state MEG data of 61 subjects from the Human Connectome Project. The obtained results highlight a directed functional coupling in the alpha band between the primary visual cortex and several key regions of well-known resting state networks, e.g. dorsal attention network and fronto-parietal network

Mindfulness meditation styles differently modulate source-level MEG microstate dynamics and complexity

BackgroundThe investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates.MethodsTen right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed.ResultsOur results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM.DiscussionImportantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics

Analysing linear multivariate pattern transformations in neuroimaging data.

Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated ridge regression approach. We used three functional connectivity metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings explain a significant amount of response variance in the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. The mappings are also characterised by different levels of pattern deformations, thus indicating that the transformations differentially amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.This work was funded by a British Academy Postdoctoral Fellowship (PS140117) to MM, by the Medical Research Council UK (SUAG/058 G101400) to OH, and conducted under the framework of the Departments of Excellence 2018–2022 initiative of the Italian Ministry of Education, University and Research for the Department of Neuroscience, Imaging and Clinical Sciences (DNISC) of the University of Chieti-Pescara

Alexithymia and Psychological Distress in Patients With Fibromyalgia and Rheumatic Disease

BACKGROUND: Fibromyalgia syndrome (FMS) is a chronic rheumatologic disease characterized by widespread musculoskeletal pain and other psychopathological symptoms which have a negative impact on patients' quality of life. FMS is frequently associated with alexithymia, a multidimensional construct characterized by difficulty in identifying feelings (DIF) and verbally communicating them difficulty describing feelings (DDF) and an externally oriented cognitive thinking style (EOT). The aim of the present study was to investigate the relationship between alexithymia, anxious and depressive symptoms and pain perception, in patients with FMS and other rheumatic diseases (RD). METHODS: The sample consisted of 127 participants (M = 25, F = 102; mean age: 51.97; SD: 11.14), of which 48 with FMS, 41 with RD and 38 healthy control group (HC). All groups underwent to a test battery investigating anxiety and depressive symptoms (HADS), pain (VAS; QUID-S/-A) and alexithymia (TAS-20). RESULTS: A high prevalence of alexithymia (TAS ≥ 61) was found in FMS (47.9%) and RD (41.5%) patients, compared to the HC group (2.6%). FMS patients showed significant higher scores than HC on DIF, DDF, EOT, anxiety and depression. The clinical sample, FMS and RD groups combined (n = 89), alexithymic patients (AL, n = 40) exhibited higher scores in pain and psychological distress compared to non-alexithymic patients (N-AL, n = 34). Regression analysis found no relationship between alexithymia and pain in AL, meanwhile pain intensity was predicted by anxiety in N-AL. CONCLUSION: While increasing clinical symptoms (pain intensity and experience, alexithymia, anxiety, and depression) in patients with fibromyalgia or rheumatic diseases, correlations were found on the one side, between alexithymia and psychological distress, on the other side, between pain experience and intensity. Meanwhile, when symptoms of psychological distress and alexithymia were subthreshold, correlations with pain experience and intensity became stronger

COVID-19 atypical Parsonage-Turner syndrome: a case report

Background Neurological manifestations of Sars-CoV-2 infection have been described since March 2020 and include both central and peripheral nervous system manifestations. Neurological symptoms, such as headache or persistent loss of smell and taste, have also been documented in COVID-19 long-haulers. Moreover, long lasting fatigue, mild cognitive impairment and sleep disorders appear to be frequent long term neurological manifestations after hospitalization due to COVID-19. Less is known in relation to peripheral nerve injury related to Sars-CoV-2 infection. Case presentation We report the case of a 47-year-old female presenting with a unilateral chest pain radiating to the left arm lasting for more than two months after recovery from Sars-CoV-2 infection. After referral to our post-acute outpatient service for COVID-19 long haulers, she was diagnosed with a unilateral, atypical, pure sensory brachial plexus neuritis potentially related to COVID-19, which occurred during the acute phase of a mild Sars-CoV-2 infection and persisted for months after resolution of the infection. Conclusions We presented a case of atypical Parsonage-Turner syndrome potentially triggered by Sars-CoV-2 infection, with symptoms and repercussion lasting after viral clearance. A direct involvement of the virus remains uncertain, and the physiopathology is unclear. The treatment of COVID-19 and its long-term consequences represents a relatively new challenge for clinicians and health care providers. A multidisciplinary approach to following-up COVID-19 survivors is strongly advised