scholarly journals Analysis of Functional Connectivity and Oscillatory Power Using DICS: From Raw MEG Data to Group-Level Statistics in Python

2018 ◽  
Vol 12 ◽  
Author(s):  
Marijn van Vliet ◽  
Mia Liljeström ◽  
Susanna Aro ◽  
Riitta Salmelin ◽  
Jan Kujala
Keyword(s):  
Functional Connectivity ◽  
Group Level ◽  
Level Statistics ◽  
Oscillatory Power

scholarly journals Analysis of functional connectivity and oscillatory power using DICS: from raw MEG data to group-level statistics in Python

2018 ◽  
Author(s):  
Marijn van Vliet ◽  
Mia Liljeström ◽  
Susanna Aro ◽  
Riitta Salmelin ◽  
Jan Kujala
Keyword(s):  
Functional Connectivity ◽  
Spectral Power ◽  
Brain Regions ◽  
Dynamic Imaging ◽  
Oscillatory Activity ◽  
Complete Analysis ◽  
Cortical Mapping ◽  
Group Level ◽  
Connectivity Analysis ◽  
Level Statistics

AbstractCommunication between brain regions is thought to be facilitated by the synchronization of oscillatory activity. Hence, large-scale functional networks within the brain may be estimated by measuring synchronicity between regions. Neurophysiological recordings, such as magnetoencephalography (MEG) and electroencephalography (EEG), provide a direct measure of oscillatory neural activity with millisecond temporal resolution. In this paper, we describe a full data analysis pipeline for functional connectivity analysis based on dynamic imaging of coherent sources (DICS) of MEG data. DICS is a beamforming technique in the frequency-domain that enables the study of the cortical sources of oscillatory activity and synchronization between brain regions. All the analysis steps, starting from the raw MEG data up to publication-ready group-level statistics and visualization, are discussed in depth, including methodological considerations, rules of thumb and tradeoffs. We start by computing cross-spectral density (CSD) matrices using a wavelet approach in several frequency bands (alpha, theta, beta, gamma). We then provide a way to create comparable source spaces across subjects and discuss the cortical mapping of spectral power. For connectivity analysis, we present a canonical computation of coherence that facilitates a stable estimation of all-to-all connectivity. Finally, we use group-level statistics to limit the network to cortical regions for which significant differences between experimental conditions are detected and produce vertex-and parcel-level visualizations of the different brain networks. Code examples using the MNE-Python package are provided at each step, guiding the reader through a complete analysis of the freely available openfMRI ds000117 “familiar vs. unfamiliar vs. scrambled faces” dataset. The goal is to educate both novice and experienced data analysts with the “tricks of the trade” necessary to successfully perform this type of analysis on their own data.

scholarly journals Neural Connectivity Subtypes Predict Discrete Attentional-Bias Profiles Among Heterogeneous Anxiety Patients

2020 ◽  
Vol 8 (3) ◽  
pp. 491-505 ◽  
Author(s):  
Rebecca B. Price ◽  
Adriene M. Beltz ◽  
Mary L. Woody ◽  
Logan Cummings ◽  
Danielle Gilchrist ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Selective Attention ◽  
Attentional Bias ◽  
Sensory Processing ◽  
Brain Regions ◽  
Data Driven ◽  
Neural Connectivity ◽  
Group Level ◽  
Behavioral Measures ◽  
Substantial Heterogeneity

On average, anxious patients show altered attention to threat—including early vigilance toward threat and later avoidance of threat—accompanied by altered functional connectivity across brain regions. However, substantial heterogeneity within clinical, neural, and attentional features of anxiety is overlooked in typical group-level comparisons. We used a well-validated method for data-driven parsing of neural connectivity to reveal connectivity-based subgroups among 60 adults with transdiagnostic anxiety. Subgroups were externally compared on attentional patterns derived from independent behavioral measures. Two subgroups emerged. Subgroup A (68% of patients) showed stronger executive network influences on sensory processing regions and a paradigmatic “vigilance–avoidance” pattern on external behavioral measures. Subgroup B was defined by a larger number of limbic influences on sensory regions and exhibited a more atypical and inconsistent attentional profile. Neural connectivity-based categorization revealed an atypical, limbic-driven pattern of connectivity in a subset of anxious patients that generalized to atypical patterns of selective attention.

scholarly journals Using a machine learning approach to complement group level statistics in experimental psychology: A case study to reveal different levels of inhibition in a modified Flanker Task

2018 ◽  
Author(s):  
Tanja Krumpe ◽  
Christian Scharinger ◽  
Wolfgang Rosenstiel ◽  
Peter Gerjets ◽  
Martin Spueler
Keyword(s):  
Machine Learning ◽  
Executive Function ◽  
Inhibitory Control ◽  
Flanker Task ◽  
Mental States ◽  
Support Vector ◽  
Group Level ◽  
Single Trial ◽  
Level Statistics ◽  
Different Levels

In this paper, we demonstrate how machine learning (ML) can be used to beneficially complement the traditional analysis of behavioral and physiological data to provide new insights into the structure of mental states, in this case, executive functions (EFs) with a focus on inhibitory control. We used a modified Flanker task with the aim to distinguish three levels of inhibitory control: no inhibition, readiness for inhibition and the actual execution of inhibitory control. A simultaneously presented n-back task was used to additionally induce demands on a second executive function. This design enabled us to investigate how the overlap of resources influences the distinction between three levels of inhibitory control. A support vector machine (SVM) based classification approach has been used on EEG data to predict the level of inhibitory control on single-subject and single-trial level. The SVM classification is a subject-specific and single-trial based approach which will be compared to standard group-level statistical approaches to reveal that both methodologies access different properties of the data. We show that considering both methods can give new insights into mental states which cannot be discovered when only using group-level statistics alone. Machine learning results indicate that three different levels of inhibitory control can be distinguished, while the group-average analysis does not give rise to this assumption. In addition, we highlight one other important benefit of the ML approach. We are able to define specific properties of the executive function inhibition by investigating the neural activation patterns that were used during the classification process.

scholarly journals Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

2021 ◽  
Author(s):  
Etienne Combrisson ◽  
Michele Allegra ◽  
Ruggero Basanisi ◽  
Robin A. A. Ince ◽  
Bruno L Giordano ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Scientific Practice ◽  
Local Level ◽  
Random Effect ◽  
Brain Networks ◽  
Brain Regions ◽  
Group Level ◽  
Starting Point ◽  
Neurophysiological Data ◽  
Statistical Approaches

The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines and principles for neuroscientists known as Good Scientific Practice for conducting more reliable research. Still, every study remains almost unique in its combination of analytical and statistical approaches. While it is understandable considering the diversity of designs and brain data recording, it also represents a striking point against reproducibility. Here, we propose a non-parametric permutation-based statistical framework, primarily designed for neurophysiological data, in order to perform group-level inferences on non-negative measures of information encompassing metrics from information-theory, machine-learning or measures of distances. The framework supports both fixed- and random-effect models to adapt to inter-individuals and inter-sessions variability. Using numerical simulations, we compared the accuity in ground-truth retrieving of both group models, such as test- and cluster-wise corrections for multiple comparisons. We then reproduced and extended existing results using both spatially uniform MEG and non-uniform intracranial neurophysiological data. We showed how the framework can be used to extract stereotypical task- and behavior-related effects across the population covering scales from the local level of brain regions, inter-areal functional connectivity to measures summarizing network properties. We also present a open-source Python toolbox called Frites that includes all of the methods used here, from functional connectivity estimations to the extraction of cognitive brain networks. Taken together, we believe that this framework deserves careful attention as its robustness and flexibility could be the starting point toward the uniformization of statistical approaches.

scholarly journals Group-level cortical functional connectivity patterns using fNIRS: assessing the effect of bilingualism in young infants

2021 ◽  
Vol 8 (02) ◽  
Author(s):  
Borja Blanco ◽  
Monika Molnar ◽  
Manuel Carreiras ◽  
Liam H. Collins-Jones ◽  
Ernesto Vidal ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Group Level ◽  
Young Infants ◽  
Connectivity Patterns

scholarly journals Nonergodicity in Load and Recovery: Group Results Do Not Generalize to Individuals

2021 ◽  
pp. 1-9
Author(s):  
Niklas D. Neumann ◽  
Nico W. Van Yperen ◽  
Jur J. Brauers ◽  
Wouter Frencken ◽  
Michel S. Brink ◽  
...  
Keyword(s):  
Perceived Exertion ◽  
Rating Of Perceived Exertion ◽  
Group Level ◽  
Total Quality ◽  
Individual Level ◽  
Quality Of Recovery ◽  
Level Statistics ◽  
The Individual ◽  
Level Analysis

Purpose: The study of load and recovery gained significant interest in the last decades, given its important value in decreasing the likelihood of injuries and improving performance. So far, findings are typically reported on the group level, whereas practitioners are most often interested in applications at the individual level. Hence, the aim of the present research is to examine to what extent group-level statistics can be generalized to individual athletes, which is referred to as the “ergodicity issue.” Nonergodicity may have serious consequences for the way we should analyze, and work with, load and recovery measures in the sports field. Methods: The authors collected load, that is, rating of perceived exertion × training duration, and total quality of recovery data among youth male players of a professional football club. This data were collected daily across 2 seasons and analyzed on both the group and the individual level. Results: Group- and individual-level analysis resulted in different statistical outcomes, particularly with regard to load. Specifically, SDs within individuals were up to 7.63 times larger than SDs between individuals. In addition, at either level, the authors observed different correlations between load and recovery. Conclusions: The results suggest that the process of load and recovery in athletes is nonergodic, which has important implications for the sports field. Recommendations for training programs of individual athletes may be suboptimal, or even erroneous, when guided by group-level outcomes. The utilization of individual-level analysis is key to ensure the optimal balance of individual load and recovery.

scholarly journals Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns

2015 ◽  
Vol 112 (28) ◽  
pp. 8762-8767 ◽  
Author(s):  
Javier Gonzalez-Castillo ◽  
Colin W. Hoy ◽  
Daniel A. Handwerker ◽  
Meghan E. Robinson ◽  
Laura C. Buchanan ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processing ◽  
Mental States ◽  
Robust Tracking ◽  
Group Level ◽  
Whole Brain ◽  
Connectivity Patterns ◽  
Single Subjects ◽  
Short Time ◽  
Brain Functional Connectivity

Functional connectivity (FC) patterns in functional MRI exhibit dynamic behavior on the scale of seconds, with rich spatiotemporal structure and limited sets of whole-brain, quasi-stable FC configurations (FC states) recurring across time and subjects. Based on previous evidence linking various aspects of cognition to group-level, minute-to-minute FC changes in localized connections, we hypothesized that whole-brain FC states may reflect the global, orchestrated dynamics of cognitive processing on the scale of seconds. To test this hypothesis, subjects were continuously scanned as they engaged in and transitioned between mental states dictated by tasks. FC states computed within windows as short as 22.5 s permitted robust tracking of cognition in single subjects with near perfect accuracy. Accuracy dropped markedly for subjects with the lowest task performance. Spatially restricting FC information decreased accuracy at short time scales, emphasizing the distributed nature of whole-brain FC dynamics, beyond univariate magnitude changes, as valuable markers of cognition.

scholarly journals Reproducible functional connectivity endophenotype confers high risk of ASD diagnosis in a subset of individuals

2020 ◽  
Author(s):  
Sebastian GW. Urchs ◽  
Hien Duy Nguyen ◽  
Clara Moreau ◽  
Christian Dansereau ◽  
Angela Tam ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Predictive Models ◽  
Brain Connectivity ◽  
Fold Increase ◽  
Autism Spectrum ◽  
Individual Risk ◽  
Independent Data ◽  
Group Level ◽  
Risk Increase ◽  
Increase In Risk

AbstractFunctional connectivity (FC) analyses of individuals with autism spectrum disorder (ASD) have established robust alterations of brain connectivity at the group level. Yet, the translation of these imaging findings into robust markers of individual risk is hampered by the extensive heterogeneity among ASD individuals. Here, we report an FC endophenotype that confers a greater than 7-fold risk increase of ASD diagnosis, yet is still identified in an estimated 1 in 200 individuals in the general population. By focusing on a subset of individuals with ASD and highly predictive FC alterations, we achieved a greater than 3-fold increase in risk over previous predictive models. The identified FC risk endophenotype was characterized by underconnectivity of transmodal brain networks and generalized to independent data. Our results demonstrate the ability of a highly targeted prediction model to meaningfully decompose part of the heterogeneity of the autism spectrum. The identified FC signature may help better delineate the multitude of etiological pathways and behavioural symptoms that challenge our understanding of the autism spectrum.

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