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 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.

The Functional Language Network

2017 ◽  
Author(s):  
Angela D. Friederici ◽  
Noam Chomsky
Keyword(s):  
Functional Connectivity ◽  
Information Transfer ◽  
Inferior Frontal Gyrus ◽  
Brain Regions ◽  
Oscillatory Activity ◽  
Neural Basis ◽  
Functional Neural Network ◽  
Ventral Pathway ◽  
Open Issue ◽  
Language Network

How information content is encoded and decoded in the sending and receiving brain areas is still an open issue. A possible though speculative view is that encoding and decoding requires similarity at the neuronal level in the encoding and decoding regions. This chapter discusses the functional neural network of language. It first describes the language network at the neurotransmitter level and then discusses the available data at the level of functional connectivity and oscillatory activity. Section 1 looks at the neural basis of information transfer, namely at the neurotransmitters which are crucially involved in the transmission of information from one neuron to the next. Section 2 uses functional connectivity analyses to provide information about how different brain regions work together. They allow us to make statements about which regions work together, and moreover, about the direction of the information flow between these. Section 3 models the language circuit as a a dynamic temporo-frontal network with initial input-driven information processed bottom-up from the auditory cortex to the frontal cortex along the ventral pathway, with semantic information reaching the anterior inferior frontal gyrus, and syntactic information reaching the posterior inferior frontal gyrus.

Task-fMRI Group and Functional Connectivity Analysis of the Brain During Faradarmani Consciousness Field Connection

2021 ◽  
Author(s):  
Mohammad Ali Taheri ◽  
Sara Torabi ◽  
Noushin Nabavi ◽  
Fatemeh Modarresi-Asem ◽  
Majid Abbasi Sisara ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Frontal Lobe ◽  
Critical Role ◽  
Group Analysis ◽  
Brain Regions ◽  
Connectivity Analysis ◽  
Functional Connectivity Analysis ◽  
Cognitive Activities ◽  
The Brain

Task fMRI has played a critical role in recognizing the specific functions of the different regions of human brain during various cognitive activities. This study aimed to investigate group analysis and functional connectivity in the Faradarmangars brain during the Faradarmani CF (FCF) connection. Using task functional MRI (task-fMRI), we attempted the identification of different activated and deactivated brain regions during the Consciousness Filed connection. Clusters that showed significant differences in peak intensity between task and rest group were selected as seeds for seed-voxel analysis. Connectivity of group differences in functional connectivity analysis was determined following each activation and deactivation network. In this study, we report the fMRI-based representation of the FCF connection at the human brain level. The group analysis of FCF connection task revealed activation of frontal lobe (BA6/BA10/BA11). Moreover, seed based functional connectivity analysis showed decreased connectivity within activated clusters and posterior Cingulate Gyrus (BA31). Moreover, we observed an increased connectivity within deactivated clusters and frontal lobe (BA11/BA47) during the FCF connection. Activation clusters as well as the increased and decreased connectivity between different regions of the brain during the FCF connection, firstly, validates the significant effect of the FCF and secondly, indicates a distinctive pattern of connection with this non-material and non-energetic field, in the brain.

scholarly journals Propofol-induced Changes in α-β Sensorimotor Cortical Connectivity

2018 ◽  
Vol 128 (2) ◽  
pp. 305-316 ◽  
Author(s):  
Mahsa Malekmohammadi ◽  
Nicholas AuYong ◽  
Collin M. Price ◽  
Evangelia Tsolaki ◽  
Andrew E. Hudson ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Parkinson Disease ◽  
Essential Tremor ◽  
Spectral Power ◽  
Peak Frequency ◽  
Brain Regions ◽  
Loss Of Consciousness ◽  
Sensorimotor Network ◽  
Before And After ◽  
Cortical Regions

Abstract Background Anesthetics are believed to alter functional connectivity across brain regions. However, network-level analyses of anesthesia, particularly in humans, are sparse. The authors hypothesized that propofol-induced loss of consciousness results in functional disconnection of human sensorimotor cortices underlying the loss of volitional motor responses. Methods The authors recorded local field potentials from sensorimotor cortices in patients with Parkinson disease (N = 12) and essential tremor (N = 7) undergoing deep brain stimulation surgery, before and after propofol-induced loss of consciousness. Local spectral power and interregional connectivity (coherence and imaginary coherence) were evaluated separately across conditions for the two populations. Results Propofol anesthesia caused power increases for frequencies between 2 and 100 Hz across the sensorimotor cortices and a shift of the dominant spectral peak in α and β frequencies toward lower frequencies (median ± SD peak frequency: 24.5 ± 2.6 Hz to 12.8 ± 2.3 Hz in Parkinson disease; 13.8 ± 2.1 Hz to 12.1 ± 1.0 Hz in essential tremor). Despite local increases in power, sensorimotor cortical coherence was suppressed with propofol in both cohorts, specifically in β frequencies (18 to 29 Hz) for Parkinson disease and α and β (10 to 48 Hz) in essential tremor. Conclusions The decrease in functional connectivity between sensory and motor cortices, despite an increase in local spectral power, suggests that propofol causes a functional disconnection of cortices with increases in autonomous activity within cortical regions. This pattern occurs across diseases evaluated, suggesting that these may be generalizable effects of propofol in patients with movement disorders and beyond. Sensorimotor network disruption may underlie anesthetic-induced loss of volitional control.

scholarly journals The natural frequencies of the resting human brain: an MEG-based atlas

2021 ◽  
Author(s):  
Almudena Capilla ◽  
Lydia Arana ◽  
Marta Garcia-Huescar ◽  
Maria Melcon ◽  
Joachim Gross ◽  
...  
Keyword(s):  
Human Brain ◽  
Natural Frequencies ◽  
Spectral Power ◽  
Brain Regions ◽  
Oscillatory Activity ◽  
Mu Rhythm ◽  
Neural Communication ◽  
Data Driven Approach ◽  
Spectral Profiles ◽  
High Beta

Brain oscillations are considered to play a pivotal role in neural communication. However, detailed information regarding the typical oscillatory patterns of individual brain regions is surprisingly scarce. In this study we applied a multivariate data-driven approach to create an atlas of the natural frequencies of the resting human brain on a voxel-by-voxel basis. We analysed resting-state magnetoencephalography (MEG) data from 128 healthy adult volunteers obtained from the Open MEG Archive (OMEGA). Spectral power was computed in source space in 500 ms steps for 82 frequency bins logarithmically spaced from 1.7 to 99.5 Hz. We then applied k-means clustering to detect characteristic spectral profiles and to eventually identify the natural frequency of each voxel. Our results revealed a region-specific organisation of intrinsic oscillatory activity, following both a medial-to-lateral and a posterior-to-anterior gradient of increasing frequency. In particular, medial fronto-temporal regions were characterised by slow rhythms (delta/theta). Posterior regions presented natural frequencies in the alpha band, although with differentiated generators in the precuneus and in sensory-specific cortices (i.e., visual and auditory). Somatomotor regions were distinguished by the mu rhythm, while the lateral prefrontal cortex was characterised by oscillations in the high beta range (>20 Hz). Importantly, the brain map of natural frequencies was highly replicable in two independent subsamples of individuals. To the best of our knowledge, this is the most comprehensive atlas of ongoing oscillatory activity performed to date. Furthermore, the identification of natural frequencies is a fundamental step towards a better understanding of the functional architecture of the human brain.

The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRI

2016 ◽  
Vol 28 (10) ◽  
pp. 2250-2290 ◽  
Author(s):  
Nathan W. Churchill ◽  
Kristoffer Madsen ◽  
Morten Mørup
Keyword(s):  
Functional Connectivity ◽  
Gaussian Mixture ◽  
Brain Regions ◽  
Model Parameters ◽  
Group Level ◽  
Integration Model ◽  
Connectivity Structure ◽  
Functional Relationships ◽  
Subject Specific ◽  
The Brain

The brain consists of specialized cortical regions that exchange information between each other, reflecting a combination of segregated (local) and integrated (distributed) processes that define brain function. Functional magnetic resonance imaging (fMRI) is widely used to characterize these functional relationships, although it is an ongoing challenge to develop robust, interpretable models for high-dimensional fMRI data. Gaussian mixture models (GMMs) are a powerful tool for parcellating the brain, based on the similarity of voxel time series. However, conventional GMMs have limited parametric flexibility: they only estimate segregated structure and do not model interregional functional connectivity, nor do they account for network variability across voxels or between subjects. To address these issues, this letter develops the functional segregation and integration model (FSIM). This extension of the GMM framework simultaneously estimates spatial clustering and the most consistent group functional connectivity structure. It also explicitly models network variability, based on voxel- and subject-specific network scaling profiles. We compared the FSIM to standard GMM in a predictive cross-validation framework and examined the importance of different model parameters, using both simulated and experimental resting-state data. The reliability of parcellations is not significantly altered by flexibility of the FSIM, whereas voxel- and subject-specific network scaling profiles significantly improve the ability to predict functional connectivity in independent test data. Moreover, the FSIM provides a set of interpretable parameters to characterize both consistent and variable aspects functional connectivity structure. As an example of its utility, we use subject-specific network profiles to identify brain regions where network expression predicts subject age in the experimental data. Thus, the FSIM is effective at summarizing functional connectivity structure in group-level fMRI, with applications in modeling the relationships between network variability and behavioral/demographic variables.

scholarly journals Disrupted stepwise functional brain organization in overweight individuals

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Hyebin Lee ◽  
Junmo Kwon ◽  
Jong-eun Lee ◽  
Bo-yong Park ◽  
Hyunjin Park
Keyword(s):  
Functional Connectivity ◽  
Eating Behaviors ◽  
Brain Regions ◽  
Healthy Weight ◽  
Connectivity Analysis ◽  
Brain Organization ◽  
Attention Networks ◽  
Functional Hierarchy ◽  
Functional Brain Organization ◽  
The Brain

AbstractFunctional hierarchy establishes core axes of the brain, and overweight individuals show alterations in the networks anchored on these axes, particularly in those involved in sensory and cognitive control systems. However, quantitative assessments of hierarchical brain organization in overweight individuals are lacking. Capitalizing stepwise functional connectivity analysis, we assess altered functional connectivity in overweight individuals relative to healthy weight controls along the brain hierarchy. Seeding from the brain regions associated with obesity phenotypes, we conduct stepwise connectivity analysis at different step distances and compare functional degrees between the groups. We find strong functional connectivity in the somatomotor and prefrontal cortices in both groups, and both converge to transmodal systems, including frontoparietal and default-mode networks, as the number of steps increased. Conversely, compared with the healthy weight group, overweight individuals show a marked decrease in functional degree in somatosensory and attention networks across the steps, whereas visual and limbic networks show an increasing trend. Associating functional degree with eating behaviors, we observe negative associations between functional degrees in sensory networks and hunger and disinhibition-related behaviors. Our findings suggest that overweight individuals show disrupted functional network organization along the hierarchical axis of the brain and these results provide insights for behavioral associations.

Sign in / Sign up

Export Citation Format

Share Document