PSIICOS projection optimality for EEG and MEG based functional coupling detection.

Functional connectivity is crucial for cognitive processes in the healthy brain and serves as a marker for a range of neuropathological conditions. Non-invasive exploration of functional coupling using temporally resolved techniques such as MEG allows for a unique opportunity of exploring this fundamental brain mechanism. The indirect nature of MEG measurements complicates the estimation of functional coupling due to the volume conduction and spatial leakage effects. In the previous work (Ossadtchi et al., 2018), we introduced PSIICOS, a method that for the first time allowed us to suppress the volume conduction effect and yet retain information about functional networks whose nodes are coupled with close to zero or zero mutual phase lag. In this paper, we demonstrate analytically that the PSIICOS projection is optimal in achieving a controllable trade-off between suppressing mutual spatial leakage and retaining information about zero- or close to zero-phase coupled networks. We also derive an alternative solution using the regularization-based inverse of the mutual spatial leakage matrix and show its equivalence to the original PSIICOS. We then discuss how PSIICOS solution to the functional connectivity estimation problem can be incorporated into the conventional source estimation framework. Instead of sources, the unknowns are the elementary dyadic networks and their activation time series are formalized by the corresponding source-space cross-spectral coefficients. This view on connectivity estimation as a regression problem opens up new opportunities for formulating a set of principled estimators based on the rich intuition accumulated in the neuroimaging community.

Globally elevated excitation-inhibition ratio in children with autism spectrum disorder and below-average intelligence.

BACKGROUND: Altered neuronal excitation-inhibition (E-I) balance is strongly implicated in ASD. However, it is not known whether the direction and degree of changes in the E-I ratio in individuals with ASD correlates with intellectual disability often associated with this developmental disorder. The spectral slope of the aperiodic 1/f activity reflects the E-I balance at the scale of large neuronal populations and may uncover its putative alternations in individuals with ASD with and without intellectual disability. METHODS: Herein, we used magnetoencephalography (MEG) to test whether the 1/f slope would differentiate ASD children with average and below-average (< 85) IQ. MEG was recorded at rest with eyes open/closed in 49 boys with ASD aged 6-15 years with IQ ranging from 54 to 128, and in 49 age-matched typically developing (TD) boys. The cortical source activity was estimated using the beamformer approach and individual brain models. We then extracted the 1/f slope by fitting a linear function to the log-log-scale power spectra in the high-frequency range. RESULTS: The global 1/f slope averaged over all cortical sources demonstrated high rank-order stability between the two conditions. Consistent with previous research, it was steeper in the eyes-closed than in the eyes-open condition and flattened with age. Regardless of condition, children with ASD and below-average IQ had flatter slopes than either TD or ASD children with average or above-average IQ. These group differences could not be explained by differences in signal-to-noise ratio or periodic (alpha and beta) activity. LIMITATIONS: Further research is needed to find out whether the observed changes in E-I ratios are characteristic of children with below-average IQ of other diagnostic groups. CONCLUSIONS: The atypically flattened spectral slope of aperiodic activity in children with ASD and below-average IQ suggests a shift of the global E-I balance toward hyper-excitation. The spectral slope can provide an accessible noninvasive biomarker of the E-I ratio for making objective judgments about treatment effectiveness in people with ASD and comorbid intellectual disability.

NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines.

Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html, and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.

Visbrain: A Multi-Purpose GPU-Accelerated Open-Source Suite for Multimodal Brain Data Visualization.

We present Visbrain, a Python open-source package that offers a comprehensive visualization suite for neuroimaging and electrophysiological brain data. Visbrain consists of two levels of abstraction: (1) objects which represent highly configurable neuro-oriented visual primitives (3D brain, sources connectivity, etc.) and (2) graphical user interfaces for higher level interactions. The object level offers flexible and modular tools to produce and automate the production of figures using an approach similar to that of Matplotlib with subplots. The second level visually connects these objects by controlling properties and interactions through graphical interfaces. The current release of Visbrain (version 0.4.2) contains 14 different objects and three responsive graphical user interfaces, built with PyQt: Signal, for the inspection of time-series and spectral properties, Brain for any type of visualization involving a 3D brain and Sleep for polysomnographic data visualization and sleep analysis. Each module has been developed in tight collaboration with end-users, i.e., primarily neuroscientists and domain experts, who bring their experience to make Visbrain as transparent as possible to the recording modalities (e.g., intracranial EEG, scalp-EEG, MEG, anatomical and functional MRI). Visbrain is developed on top of VisPy, a Python package providing high-performance 2D and 3D visualization by leveraging the computational power of the graphics card. Visbrain is available on Github and comes with a documentation, examples, and datasets (http://visbrain.org).

Power of Feedback-Induced Beta Oscillations Reflect Omission of Rewards: Evidence From an EEG Gambling Study.

The functional role of high beta oscillations (20-35 Hz) during feedback processing has been suggested to reflect unexpected gains. Using a novel gambling task that separates gains and losses across blocks and directly compares reception of monetary rewards to a 'no-reward/punishment' condition with equal probability we aimed to further investigate the role of beta oscillations. When contrasting different feedback conditions across rewards, we found that a late low beta component (12-20 Hz) had increased in power during the omission of rewards relative to the reception of rewards, while no differences were observed during the loss domain. These findings may indicate that late low beta oscillations in the context of feedback processing may respond to omission of gains relative to other potential outcomes. We speculate that late low beta oscillations may operate as a learning mechanism that signals the brain to make future adequate decisions. Overall, our study provides new insights for the role of late low beta oscillations in reward processing.