scholarly journals MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

2021 ◽  
Author(s):  
Luke Tait ◽  
Jiaxiang Zhang
Keyword(s):  
Functional Connectivity ◽  
Cortical Activation ◽  
Multivariate Pattern Analysis ◽  
Dynamic Activity ◽  
Neural Mass ◽  
Connectivity Patterns ◽  
Brain States ◽  
Source Level ◽  
Microstate Analysis ◽  
Multivariate Pattern

Abstract EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.

scholarly journals MEG cortical microstates: spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses

2021 ◽  
Author(s):  
Luke Tait ◽  
Jiaxiang Zhang
Keyword(s):  
Functional Connectivity ◽  
Cortical Activation ◽  
Multivariate Pattern Analysis ◽  
Dynamic Activity ◽  
Auditory Responses ◽  
Neural Mass ◽  
Brain States ◽  
Source Level ◽  
Microstate Analysis ◽  
Multivariate Pattern

EEG microstate analysis is a useful approach for studying brain states - nicknamed `atoms of thought' - and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight into the cortical mechanisms underpinning these states. In this study, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level MEG microstates were associated with distinct functional connectivity patterns. Using a passive auditory paradigm, we further demonstrated that the occurrence probability of MEG microstates were altered by evoked auditory responses, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of MEG source-level microstates as a data-driven method for investigating brain dynamic activity and connectivity at the millisecond scale.

scholarly journals Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women

2013 ◽  
Vol 20 (4) ◽  
pp. 391-401 ◽  
Author(s):  
S.M. Hadi Hosseini ◽  
Shelli R. Kesler
Keyword(s):  
Breast Cancer ◽  
Functional Connectivity ◽  
Breast Cancer Survivors ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Survival Rates ◽  
Connectivity Pattern ◽  
Multivariate Pattern Analysis ◽  
The Right ◽  
Multivariate Pattern

AbstractAdvances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C−) BC females and healthy female controls (HC). Twenty-seven C+, 29 C−, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C− groups (71%, p = .012). However, the accuracy of discrimination between C− and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C− groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C−. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1–11)

scholarly journals Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis

2016 ◽  
Vol 12 ◽  
pp. 348-358 ◽  
Author(s):  
Sheng Zhang ◽  
Sien Hu ◽  
Rajita Sinha ◽  
Marc N. Potenza ◽  
Robert T. Malison ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Pattern Analysis ◽  
Cocaine Dependence ◽  
Multivariate Pattern Analysis ◽  
Multivariate Pattern

scholarly journals Functional connectivity differences in early infancy precede autism symptoms: a multivariate pattern analysis

2019 ◽  
Author(s):  
Abigail Dickinson ◽  
Manjari Daniel ◽  
Andrew Marin ◽  
Bilwaj Goanker ◽  
Mirella Dapretto ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Abilities ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Autism Symptoms ◽  
Multivariate Pattern

AbstractFunctional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Mapping pre-symptomatic functional disruptions in ASD could identify infants based on neural risk, providing a crucial opportunity to mediate outcomes before behavioral symptoms emerge.Here we quantify functional connectivity using scalable EEG measures of oscillatory phase coherence (6-12Hz). Infants at high and low familial risk for ASD (N=65) underwent an EEG recording at 3 months of age and were assessed for ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule-Toddler Module. Multivariate pattern analysis was used to examine early functional patterns that are associated with later ASD symptoms.Support vector regression (SVR) algorithms accurately predicted observed ASD symptoms at 18 months from EEG data at 3 months (r=0.76, p=0.02). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity predicted higher ASD symptoms. The SVR model did not predict non-verbal cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain alterations to ASD.These data suggest that frontal and temporo-parietal dysconnectivity play important roles in the early pathophysiology of ASD. Early functional differences in ASD can be captured using EEG during infancy and may inform much-needed advancements in the early detection of ASD.

scholarly journals Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis

Brain ◽  
2012 ◽  
Vol 135 (5) ◽  
pp. 1498-1507 ◽  
Author(s):  
Ling-Li Zeng ◽  
Hui Shen ◽  
Li Liu ◽  
Lubin Wang ◽  
Baojuan Li ◽  
...  
Keyword(s):  
Major Depression ◽  
Functional Connectivity ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Multivariate Pattern ◽  
Brain Functional Connectivity

scholarly journals Mapping brain activation and information during category-specific visual working memory

2012 ◽  
Vol 107 (2) ◽  
pp. 628-639 ◽  
Author(s):  
David E. J. Linden ◽  
Nikolaas N. Oosterhof ◽  
Christoph Klein ◽  
Paul E. Downing
Keyword(s):  
Working Memory ◽  
Cortical Activation ◽  
Multivariate Pattern Analysis ◽  
Frontoparietal Network ◽  
Visual Areas ◽  
Occipitotemporal Cortex ◽  
Initial Processing ◽  
Multivariate Pattern ◽  
The Brain ◽  
Term Maintenance

How is working memory for different visual categories supported in the brain? Do the same principles of cortical specialization that govern the initial processing and encoding of visual stimuli also apply to their short-term maintenance? We investigated these questions with a delayed discrimination paradigm for faces, bodies, flowers, and scenes and applied both univariate and multivariate analyses to functional magnetic resonance imaging (fMRI) data. Activity during encoding followed the well-known specialization in posterior areas. During the delay interval, activity shifted to frontal and parietal regions but was not specialized for category. Conversely, activity in visual areas returned to baseline during that interval but showed some evidence of category specialization on multivariate pattern analysis (MVPA). We conclude that principles of cortical activation differ between encoding and maintenance of visual material. Whereas perceptual processes rely on specialized regions in occipitotemporal cortex, maintenance involves the activation of a frontoparietal network that seems to require little specialization at the category level. We also confirm previous findings that MVPA can extract information from fMRI signals in the absence of suprathreshold activation and that such signals from visual areas can reflect the material stored in memory.

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