Phase Locking Value revisited: teaching new tricks to an old dog

Despite the increase in calculation power in the last decades, the estimation of brain connectivity is still a tedious task. The high computational cost of the algorithms escalates with the square of the number of signals evaluated, usually in the range of thousands. In this work we propose a re-formulation of a widely used algorithm that allows the estimation of whole brain connectivity in much smaller times. We start from the original implementation of Phase Locking Value (PLV) and re-formulated it in a highly computational efficient way. Besides, this formulation stresses its strong similarity with coherence, which we used to introduce two new metrics insensitive to zero lag synchronization, the imaginary part of PLV (iPLV) and its corrected counterpart (ciPLV). The new implementation of PLV avoids some highly CPU-expensive operations, and achieved a 100-fold speedup over the original algorithm. The new derived metrics were highly robust in the presence of volume conduction. ciPLV, in particular, proved capable of ignoring zero-lag connectivity, while correctly estimating nonzero-lag connectivity. Our implementation of PLV makes it possible to calculate whole-brain connectivity in much shorter times. The results of the simulations using ciPLV suggest that this metric is ideal to measure synchronization in the presence of volume conduction or source leakage effects

Tactile expectancy modulates occipital alpha oscillations in early blindness

Alpha oscillatory activity is thought to contribute to visual expectancy through the engagement of task-relevant occipital regions. In early blindness, occipital alpha oscillations are systematically reduced, suggesting that occipital alpha depends on visual experience. However, it remains possible that alpha activity could serve expectancy in non-visual modalities in blind people, especially considering that previous research has shown the recruitment of the occipital cortex for non-visual processing. To test this idea, we used electroencephalography to examine whether alpha oscillations reflected a differential recruitment of task-relevant regions between expected and unexpected conditions in two haptic tasks (texture and shape discrimination). As expected, sensor-level analyses showed that alpha suppression in parieto-occipital sites was significantly reduced in early blind individuals compared with sighted participants. The source reconstruction analysis revealed that group differences originated in the middle occipital cortex. In that region, expected trials evoked higher alpha desynchronization than unexpected trials in the early blind group only. Our results support the role of alpha rhythms in the recruitment of occipital areas in early blind participants, and for the first time we show that although posterior alpha activity is reduced in blindness, it remains sensitive to expectancy factors. Our findings therefore suggest that occipital alpha activity is involved in tactile expectancy in blind individuals, serving a similar function to visual anticipation in sighted populations but switched to the tactile modality. Altogether, our results indicate that expectancy-dependent modulation of alpha oscillatory activity does not depend on visual experience. Significance statement: Are posterior alpha oscillations and their role in expectancy and anticipation dependent on visual experience? Our results show that tactile expectancy can modulate posterior alpha activity in blind (but not sighted) individuals through the engagement of occipital regions, suggesting that in early blindness, alpha oscillations maintain their proposed role in visual anticipation but subserve tactile processing. Our findings bring a new understanding of the role that alpha oscillatory activity plays in blindness, contrasting with the view that alpha activity is task unspecific in blind populations

How to build a functional connectomic biomarker for mild cognitive impairment from source reconstructed MEG resting-state activity: the combination of ROI representation and connectivity estimator matters

Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal and cingulo-opercular network. Our analysis supports the notion of analysing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings

ASMR amplifies low frequency and reduces high frequency oscillations

Autonomous sensory meridian response (ASMR) describes an atypical multisensory experience of calming, tingling sensations in response to a specific subset of social audiovisual triggers. To date, the electrophysiological (EEG) correlates of ASMR remain largely unexplored. Here we sought to provide source-level signatures of oscillatory changes induced by this phenomenon and investigate potential decay effects—oscillatory changes in the absence of self-reported ASMR. We recorded brain activity using EEG as participants watched ASMR-inducing videos and self-reported changes in their state: no change (Baseline); enhanced relaxation (Relaxed); and ASMR sensations (ASMR). Statistical tests in the sensor-space were used to inform contrasts in the source-space, executed with beamformer reconstruction. ASMR modulated oscillatory power by decreasing high gamma (52–80 Hz) relative to Relaxed and by increasing alpha (8–13 Hz) and decreasing delta (1–4 Hz) relative to Baseline. At the source level, ASMR increased power in the low-mid frequency ranges (8–18 Hz) and decreased power in high frequency (21–80 Hz). ASMR decay effects reduced gamma (30–80 Hz) and in the source-space reduced high-beta/gamma power (21–80 Hz). The temporal profile of ASMR modulations in high-frequency power later shifts to lower frequencies (1–8 Hz), except for an enhanced alpha, which persists for up to 45 min post self-reported ASMR. Crucially, these results provide the first evidence that the cortical sources of ASMR tingling sensations may arise from decreases in higher frequency oscillations and that ASMR may induce a sustained relaxation state.</p

An Individual Data-Driven Virtual Resection Model Based on Epileptic Network Dynamics in Children with Intractable Epilepsy: A Magnetoencephalography Interictal Activity Application

Epilepsy surgery continues to be a recommended treatment for intractable (medication-resistant) epilepsy; however, 30-70% of epilepsy surgery patients can continue to have seizures. Surgical failures are often associated with incomplete resection or inaccurate localization of the epileptogenic zone. This retrospective study aims to improve surgical outcome through in silico testing of surgical hypotheses through a personalized computational neurosurgery model created from individualized patient\u27s magnetoencephalography recording and MRI. The framework assesses the extent of the epileptic network and evaluates underlying spike dynamics, resulting in identification of one single brain volume as a candidate for resection. Dynamic-locked networks were utilized for virtual cortical resection. This in silico protocol was tested in a cohort of 24 paediatric patients with focal drug-resistant epilepsy who underwent epilepsy surgery. Of 24 patients who were included in the analysis, 79% (19 of 24) of the models agreed with the patient\u27s clinical surgery outcome and 21% (5 of 24) were considered as model failures (accuracy 0.79, sensitivity 0.77, specificity 0.82). Patients with unsuccessful surgery outcome typically showed a model cluster outside of the resected cavity, while those with successful surgery showed the cluster model within the cavity. Two of the model failures showed the cluster in the vicinity of the resected tissue and either a functional disconnection or lack of precision of the magnetoencephalography-MRI overlapping could explain the results. Two other cases were seizure free for 1 year but developed late recurrence. This is the first study that provides in silico personalized protocol for epilepsy surgery planning using magnetoencephalography spike network analysis. This model could provide complementary information to the traditional pre-surgical assessment methods and increase the proportion of patients achieving seizure-free outcome from surgery

Study of resting state cortico-cortical synchronization aimed to accurately discriminate Parkinson and essential tremor patients: A MEG source-space connectivity study

Motor tremor-related syndromes like essential tremor (ET) and Parkinson's disease (PD) have a common symptomatology in early stages: the presence of tremor. Even when both diseases have a different aetiology and, thus, different prognosis and treatment, the symptoms in early stages are quite similar. This usually leads to misdiagnosis, with the associated risks and limitations. A PD patient with an ET treatment will continue developing the disease, loosing an important window of action. On the other hand, an ET patient with a PD treatment will suffer strong side effects. A correct diagnosis is in both cases mandatory for the well-being of the patients. In this experiment we tried to find a biomarker based in magneto-physiological data that allows clinicians a faster and easier diagnosis of ET and PD patients, saving time and money to both patients and hospitals

Episodic memory dysfunction and hypersynchrony in brain functional networks in cognitively intact subjects and MCI: A study of 379 individuals

Delayed recall (DR) impairment is one of the most significant predictive factors in defining the progression to Alzheimer’s disease (AD). Changes in brain functional connectivity (FC) could accompany this decline in the DR performance even in a resting state condition from the preclinical stages to the diagnosis of AD itself, so the characterization of the relationship between the two phenomena has attracted increasing interest. Another aspect to contemplate is the potential moderator role of the APOE genotype in this association, considering the evidence about their implication for the disease. 379 subjects (118 mild cognitive impairment (MCI) and 261 cognitively intact (CI) individuals) underwent an extensive evaluation, including MEG recording. Applying cluster-based permutation test, we identified a cluster of differences in FC and studied which connections drove such an effect in DR. The moderation effect of APOE genotype between FC results and delayed recall was evaluated too. Higher FC in beta band in the right occipital region is associated with lower DR scores in both groups. A significant anteroposterior link emerged in the seed-based analysis with higher values in MCI. Moreover, APOE genotype appeared as a moderator between beta FC and DR performance only in the CI group. An increased beta FC in the anteroposterior brain region appears to be associated with lower memory performance in MCI. This finding could help discriminate the pattern of the progression of healthy aging to MCI and the relation between resting state and memory performance

Cognitive reserve is associated with the functional organization of brain networks in healthy aging: a MEG study

The proportion of elderly people in the population has increased rapidly in the last century and consequently "healthy aging" is expected to become a critical area of research in neuroscience. Evidence reveals how healthy aging depends on three main behavioral factors: social lifestyle, cognitive activity and physical activity. In this study, we focused on the role of cognitive activity, concentrating specifically on educational and occupational attainment factors, which were considered two of the main pillars of cognitive reserve. 21 subjects with similar rates of social lifestyle, physical and cognitive activity were selected from a sample of 55 healthy adults. These subjects were divided into two groups according to their level of cognitive reserve; one group comprised subjects with high cognitive reserve (9 members) and the other contained those with low cognitive reserve (12 members). To evaluate the cortical brain connectivity network, all participants were recorded by Magnetoencephalography (MEG) while they performed a memory task (modified version of the Sternberg¿s Task). We then applied two algorithms (Phase Locking Value & Phase-Lag Index) to study the dynamics of functional connectivity. In response to the same task, the subjects with lower cognitive reserve presented higher functional connectivity than those with higher cognitive reserve. These results may indicate that participants with low cognitive reserve needed a greater 'effort' than those with high cognitive reserve to achieve the same level of cognitive performance. Therefore, we conclude that cognitive reserve contributes to the modulation of the functional connectivity patterns of the aging brain

Study of resting state cortico-cortical synchronization aimed to accurately discriminate Parkinson and essential tremor patients: A MEG signal-space connectivity study

Motor tremor-related syndromes like essential tremor (ET) and Parkinson?s disease (PD) have a common symptomatology in early stages: the presence of tremor. Even when both diseases have a different aetiology and, thus, different prognosis and treatment, the symptoms in early stages are quite similar. This usually leads to misdiagnosis, with the associated risks and limitations. A PD patient with an ET treatment will continue developing the disease, loosing an important window of action. On the other hand, an ET patient with a PD treatment will suffer strong side effects. A correct diagnosis is in both cases mandatory for the well-being of the patients. In this experiment we tried to find a biomarker based in magneto-physiological data that allows clinicians a faster and easier diagnosis of ET and PD patients, saving time and money to both patients and hospitals

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

Alzheimer's disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz–Mancini–Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI