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

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

Alpha-band hypersynchronization in progressive mild cognitive impairment. A magnetoencephalography study

People with mild cognitive impairment (MCI) show a high risk to develop Alzheimer?s disease (AD; Petersen et al., 2001). Nonetheless, there is a lack of studies about how functional connectivity patterns may distinguish between progressive (pMCI) and stable (sMCI) MCI patients. To examine whether there were differences in functional connectivity between groups, MEG eyes-closed recordings from 30 sMCI and 19 pMCI subjects were compared. The average conversion time of pMCI was 1 year, so they were considered as fast converters. To this end, functional connectivity in different frequency bands was assessed with phase locking value in source space. Then the significant differences between both groups were correlated with neuropsychological scores and entorhinal, parahippocampal, and hippocampal volumes. Both groups did not differ in age, gender, or educational level. pMCI patients obtained lower scores in episodic and semantic memory and also in executive functioning. At the structural level, there were no differences in hippocampal volume, although some were found in left entorhinal volume between both groups. Additionally, pMCI patients exhibit a higher synchronization in the alpha band between the right anterior cingulate and temporo-occipital regions than sMCI subjects. This hypersynchronization was inversely correlated with cognitive performance, both hippocampal volumes, and left entorhinal volume. The increase in phase synchro- nization between the right anterior cingulate and temporo-occipital areas may be predictive of conversion from MCI to AD

BDNF Val66Met Polymorphism and Gamma Band Disruption in Resting State Brain Functional Connectivity: A Magnetoencephalography Study in Cognitively Intact Older Females

The pathophysiological processes undermining brain functioning decades before the onset of the clinical symptoms associated with dementia are still not well understood. Several heritability studies have reported that the Brain Derived Neurotrophic Factor (BDNF) Val66Met genetic polymorphism could contribute to the acceleration of cognitive decline in aging. This mutation may affect brain functional connectivity (FC), especially in those who are carriers of the BDNF Met allele. The aim of this work was to explore the influence of the BDNF Val66Met polymorphism in whole brain eyes-closed, resting-state magnetoencephalography (MEG) FC in a sample of 36 cognitively intact (CI) older females. All of them were ε3ε3 homozygotes for the apolipoprotein E (APOE) gene and were divided into two subgroups according to the presence of the Met allele: Val/Met group (n = 16) and Val/Val group (n = 20). They did not differ in age, years of education, Mini-Mental State Examination scores, or normalized hippocampal volumes. Our results showed reduced antero-posterior gamma band FC within the Val/Met genetic risk group, which may be caused by a GABAergic network impairment. Despite the lack of cognitive decline, these results might suggest a selective brain network vulnerability due to the carriage of the BDNF Met allele, which is linked to a potential progression to dementia. This neurophysiological signature, as tracked with MEG FC, indicates that age-related brain functioning changes could be mediated by the influence of particular genetic risk factors