Contextuality in Three Types of Quantum-Mechanical Systems

We present a formal theory of contextuality for a set of random variables grouped into different subsets (contexts) corresponding to different, mutually incompatible conditions. Within each context the random variables are jointly distributed, but across different contexts they are stochastically unrelated. The theory of contextuality is based on the analysis of the extent to which some of these random variables can be viewed as preserving their identity across different contexts when one considers all possible joint distributions imposed on the entire set of the random variables. We illustrate the theory on three systems of traditional interest in quantum physics (and also in non-physical, e.g., behavioral studies). These are systems of the Klyachko-Can-Binicioglu-Shumovsky-type, Einstein-Podolsky-Rosen-Bell-type, and Suppes-Zanotti-Leggett-Garg-type. Listed in this order, each of them is formally a special case of the previous one. For each of them we derive necessary and sufficient conditions for contextuality while allowing for experimental errors and contextual biases or signaling. Based on the same principles that underly these derivations we also propose a measure for the degree of contextuality and compute it for the three systems in question.Comment: Foundations of Physics 7, 762-78

Study of cortical rhythmic activity and connectivity with magnetoencephalography

Intracranial recordings in animals and neuroimaging studies on humans have indicated that oscillatory activity and its modulations may play a fundamental role in large-scale neural information processing. Furthermore, rhythmic interactions between cortical areas have been detected across a variety of tasks with electroencephalography (EEG) and magnetoencephalography (MEG). This kind of coupling has been proposed to be a key mechanism through which information is integrated across segregated areas. So far, rhythmic interactions have been analyzed primarily at the EEG/MEG sensor level, without explicit knowledge of cortical areas involved. In this thesis work we developed new methods that can be used to image oscillatory activity and coherence at the cortical level with MEG. Dynamic Imaging of Coherent Sources (DICS) enables localization of interacting areas both using external reference signals and directly from the MEG data. When the interacting areas have been determined it is possible to use additional measures beyond coherence to further quantify interactions within the networks. DICS was originally designed for study of continuous data; its further development into event-related DICS (erDICS) adds the possibility to image modulations of rhythmic activity that are locked to stimulus or movement timing. Furthermore, permutation testing incorporated into erDICS allows the evaluation of the statistical significance of the results. Analysis of simulated and real data showed that DICS and erDICS yield accurate localization and quantification of oscillatory activity and coherence. Comparison of DICS to other methods of localizing oscillatory activity revealed that it is equally accurate and that it can better separate the activity originating from two nearby areas. We applied DICS to two datasets, recorded from groups of subjects while they performed slow finger movements and when they were reading continuously. In both cases, we were able to systematically identify interacting cortico-cortical networks and, using phase coupling and causality measures, to quantify the manner in which the nodes within these networks influenced each other. Furthermore, we compared the identified reading network to results reported in neurophysiological and hemodynamic activation studies. In addition to areas typically detected in activation studies of reading the network included areas that are normally found in language production rather than perception tasks, indicating more extensive networking of neural systems than usually observed in activation studies

Dynamic Reconfiguration of the Language Network Preceding Onset of Speech in Picture Naming

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Time-resolved classification of dog brain signals reveals early processing of faces, species and emotion

Dogs process faces and emotional expressions much like humans, but the time windows important for face processing in dogs are largely unknown. By combining our non-invasive electroencephalography (EEG) protocol on dogs with machine-learning algorithms, we show category-specific dog brain responses to pictures of human and dog facial expressions, objects, and phase-scrambled faces. We trained a support vector machine classifier with spatiotemporal EEG data to discriminate between responses to pairs of images. The classification accuracy was highest for humans or dogs vs. scrambled images, with most informative time intervals of 100-140 ms and 240-280 ms. We also detected a response sensitive to threatening dog faces at 30-40 ms; generally, responses differentiating emotional expressions were found at 130-170 ms, and differentiation of faces from objects occurred at 120-130 ms. The cortical sources underlying the highest-amplitude EEG signals were localized to the dog visual cortex.Peer reviewe

Dog Experts' Brains Distinguish Socially Relevant Body Postures Similarly in Dogs and Humans

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The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming.

Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation

The relationship between electrophysiological and hemodynamic measures of neural activity varies across picture naming tasks: A multimodal magnetoencephalography-functional magnetic resonance imaging study

Different neuroimaging methods can yield different views of taskdependent neural engagement. Studies examining the relationship between electromagnetic and hemodynamic measures have revealed correlated patterns across brain regions but the role of the applied stimulation or experimental tasks in these correlation patterns is still poorly understood. Here, we evaluated the across-tasks variability of MEG-fMRI relationship using data recorded during three distinct naming tasks (naming objects and actions from action images, and objects from object images), from the same set of participants. Our results demonstrate that the MEG-fMRI correlation pattern varies according to the performed task, and that this variability shows distinct spectral profiles across brain regions. Notably, analysis of the MEG data alone did not reveal modulations across the examined tasks in the timefrequency windows emerging from the MEG-fMRI correlation analysis. Our results suggest that the electromagnetic-hemodynamic correlation could serve as a more sensitive proxy for task-dependent neural engagement in cognitive tasks than isolated within-modality measures.Peer reviewe

Reactivity of dogs' brain oscillations to visual stimuli measured with non-invasive electroencephalography

Studying cognition of domestic dogs has gone through a renaissance within the last decades. However, although the behavioral studies of dogs are beginning to be common in the field of animal cognition, the neural events underlying cognition remain unknown. Here, we employed a non-invasive electroencephalography, with adhesive electrodes attached to the top of the skin, to measure brain activity of from 8 domestic dogs (Canis familiaris) while they stayed still to observe photos of dog and human faces. Spontaneous oscillatory activity of the dogs, peaking in the sensors over the parieto-occipital cortex, was suppressed statistically significantly during visual task compared with resting activity at the frequency of 15–30 Hz. Moreover, a stimulus-induced low-frequency (~2–6 Hz) suppression locked to the stimulus onset was evident at the frontal sensors, possibly reflecting a motor rhythm guiding the exploratory eye movements. The results suggest task-related reactivity of the macroscopic oscillatory activity in the dog brain. To our knowledge, the study is the first to reveal non-invasively measured reactivity of brain electrophysiological oscillations in healthy dogs, and it has been based purely on positive operant conditional training, without the need for movement restriction or medication.Peer reviewe

Cardiorespiratory Fitness Estimation Based on Heart Rate and Body Acceleration in Adults With Cardiovascular Risk Factors : Validation Study

Publisher Copyright: © Antti-Pekka E Rissanen, Mirva Rottensteiner, Urho M Kujala, Jari L O Kurkela, Jan Wikgren, Jari A Laukkanen. Originally published in JMIR Cardio (https://cardio.jmir.org), 25.10.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on https://cardio.jmir.org, as well as this copyright and license information must be included.Background: Cardiorespiratory fitness (CRF) is an independent risk factor for cardiovascular morbidity and mortality. Adding CRF to conventional risk factors (eg, smoking, hypertension, impaired glucose metabolism, and dyslipidemia) improves the prediction of an individual's risk for adverse health outcomes such as those related to cardiovascular disease. Consequently, it is recommended to determine CRF as part of individualized risk prediction. However, CRF is not determined routinely in everyday clinical practice. Wearable technologies provide a potential strategy to estimate CRF on a daily basis, and such technologies, which provide CRF estimates based on heart rate and body acceleration, have been developed. However, the validity of such technologies in estimating individual CRF in clinically relevant populations is poorly known. Objective: The objective of this study is to evaluate the validity of a wearable technology, which provides estimated CRF based on heart rate and body acceleration, in working-aged adults with cardiovascular risk factors. Methods: In total, 74 adults (age range 35-64 years; n=56, 76% were women; mean BMI 28.7, SD 4.6 kg/m2) with frequent cardiovascular risk factors (eg, n=64, 86% hypertension; n=18, 24% prediabetes; n=14, 19% type 2 diabetes; and n=51, 69% metabolic syndrome) performed a 30-minute self-paced walk on an indoor track and a cardiopulmonary exercise test on a treadmill. CRF, quantified as peak O2 uptake, was both estimated (self-paced walk: a wearable single-lead electrocardiogram device worn to record continuous beat-to-beat R-R intervals and triaxial body acceleration) and measured (cardiopulmonary exercise test: ventilatory gas analysis). The accuracy of the estimated CRF was evaluated against that of the measured CRF. Results: Measured CRF averaged 30.6 (SD 6.3; range 20.1-49.6) mL/kg/min. In all participants (74/74, 100%), mean difference between estimated and measured CRF was −0.1 mL/kg/min (P = .90), mean absolute error was 3.1 mL/kg/min (95% CI 2.6-3.7), mean absolute percentage error was 10.4% (95% CI 8.5-12.5), and intraclass correlation coefficient was 0.88 (95% CI 0.80-0.92). Similar accuracy was observed in various subgroups (sexes, age, BMI categories, hypertension, prediabetes, and metabolic syndrome). However, mean absolute error was 4.2 mL/kg/min (95% CI 2.6-6.1) and mean absolute percentage error was 16.5% (95% CI 8.6-24.4) in the subgroup of patients with type 2 diabetes (14/74, 19%). Conclusions: The error of the CRF estimate, provided by the wearable technology, was likely below or at least very close to the clinically significant level of 3.5 mL/kg/min in working-aged adults with cardiovascular risk factors, but not in the relatively small subgroup of patients with type 2 diabetes. From a large-scale clinical perspective, the findings suggest that wearable technologies have the potential to estimate individual CRF with acceptable accuracy in clinically relevant populations.Peer reviewe

Cortical beta burst dynamics are altered in Parkinson's disease but normalized by deep brain stimulation

Exaggerated subthalamic beta oscillatory activity and increased beta range cortico-subthalamic synchrony have crystallized as the electrophysiological hallmarks of Parkinson's disease. Beta oscillatory activity is not tonic but occurs in 'bursts' of transient amplitude increases. In Parkinson's disease, the characteristics of these bursts are altered especially in the basal ganglia. However, beta oscillatory dynamics at the cortical level and how they compare with healthy brain activity is less well studied. We used magnetoencephalography (MEG) to study sensorimotor cortical beta bursting and its modulation by subthalamic deep brain stimulation in Parkinson's disease patients and age-matched healthy controls. We show that the changes in beta bursting amplitude and duration typical of Parkinson's disease can also be observed in the sensorimotor cortex, and that they are modulated by chronic subthalamic deep brain stimulation, which, in turn, is reflected in improved motor function at the behavioural level. In addition to the changes in individual beta bursts, their timing relative to each other was altered in patients compared to controls: bursts were more clustered in untreated Parkinson's disease, occurring in 'bursts of bursts', and re-burst probability was higher for longer compared to shorter bursts. During active deep brain stimulation, the beta bursting in patients resembled healthy controls' data. In summary, both individual bursts' characteristics and burst patterning are affected in Parkinson's disease, and subthalamic deep brain stimulation normalizes some of these changes to resemble healthy controls' beta bursting activity, suggesting a non-invasive biomarker for patient and treatment follow-up.Peer reviewe