scholarly journals Predicting MEG resting-state functional connectivity using microstructural information

2020 ◽  
Author(s):  
Eirini Messaritaki ◽  
Sonya Foley ◽  
Simona Schiavi ◽  
Lorenzo Magazzini ◽  
Bethany Routley ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Shortest Path ◽  
Resting State ◽  
Path Length ◽  
Structural Connectivity ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Brain Microstructure ◽  
Structural Metrics

AbstractUnderstanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries at four frequency bands: delta (1 − 4 Hz), theta (3 − 8 Hz), alpha (8 − 13 Hz) and beta (13 − 30 Hz). Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant.The microstructurally-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

scholarly journals Predicting MEG resting-state functional connectivity from microstructural information

2021 ◽  
pp. 1-42
Author(s):  
Eirini Messaritaki ◽  
Sonya Foley ◽  
Simona Schiavi ◽  
Lorenzo Magazzini ◽  
Bethany Routley ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Shortest Path ◽  
Resting State ◽  
Path Length ◽  
Structural Connectivity ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Brain Microstructure ◽  
Functional Mechanisms

Understanding how human brain microstructure influences functional connectivity is an important endeavor. In this work, magnetic resonance imaging data from ninety healthy participants were used to calculate structural connectivity matrices using the streamline count, fractional anisotropy, radial diffusivity and a myelin measure (derived from multi-component relaxometry) to assign connection strength. Unweighted binarized structural connectivity matrices were also constructed. Magnetoencephalography resting-state data from those participants were used to calculate functional connectivity matrices, via correlations of the Hilbert envelopes of beamformer timeseries in the delta, theta, alpha and beta frequency bands. Non-negative matrix factorization was performed to identify the components of the functional connectivity. Shortest-path-length and search-information analyses of the structural connectomes were used to predict functional connectivity patterns for each participant. The microstructure-informed algorithms predicted the components of the functional connectivity more accurately than they predicted the total functional connectivity. This provides a methodology to understand functional mechanisms better. The shortest-path-length algorithm exhibited the highest prediction accuracy. Of the weights of the structural connectivity matrices, the streamline count and the myelin measure gave the most accurate predictions, while the fractional anisotropy performed poorly. Overall, different structural metrics paint very different pictures of the structural connectome and its relationship to functional connectivity.

scholarly journals 3046 Reduced structural and functional connectivity in infants with prenatal opioid exposure

2019 ◽  
Vol 3 (s1) ◽  
pp. 52-52
Author(s):  
Stephanie Merhar ◽  
Adebayo Braimah ◽  
Traci Beiersdorfer ◽  
Brenda Poindexter ◽  
Nehal Parikh
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Behavioral Problems ◽  
Resting State ◽  
Maternal Education ◽  
Structural Connectivity ◽  
Resting State Functional Connectivity ◽  
Small Pilot Study ◽  
Sensorimotor Network

OBJECTIVES/SPECIFIC AIMS:. This study aims to understand the effects of prenatal opioid exposure on structural and functional connectivity in the neonatal brain. Our central hypothesis is that infants with prenatal opioid exposure will have decreased structural and functional connectivity as compared to non-exposed controls. Our overarching goal is to improve neurodevelopmental and behavioral outcomes in infants with prenatal opioid exposure. METHODS/STUDY POPULATION:. Infants with prenatal opioid exposure were recruited from 2 birth hospitals in our area. Control infants were recruited from the larger community. Infants underwent MRI between 4-6 weeks of age in the Cincinnati Children’s Hospital Imaging Research Center. MRI sequences included 3D structural T1 and T2-weighted imaging, resting state functional connectivity MRI, and multi-shell DTI (36 directions at b=800 and 68 directions at b=2000). Tract-based spatial statistics (TBSS) was used to identify differences in fractional anisotropy (a measure of white matter integrity) between groups. Group independent component analysis was used to identify differences in resting-state networks between groups RESULTS/ANTICIPATED RESULTS:. There were 5 subjects enrolled in the study with evaluable imaging, 3 infants with prenatal opioid exposure and 2 unexposed controls. Structural MRI was normal in all cases. Infants with prenatal opioid exposure had reduced structural connectivity as measured by fractional anisotropy (FA) in the genu and splenium of the corpus callosum as compared with controls. The orange/red color represents areas in which the FA of the opioid-exposed group was lower than controls and green represents the white matter skeleton common to both groups. Infants with prenatal opioid exposure also had significantly reduced within-network functional connectivity strength (z-transformed partial correlation coefficient 0.358 vs 0.199, p = 0.03) in the sensorimotor network as compared with controls. DISCUSSION/SIGNIFICANCE OF IMPACT:. In this small pilot study, both structural and functional connectivity were reduced in opioid-exposed infants compared with controls. This data suggests that differences in structural and functional connectivity may underlie the later developmental and behavioral problems seen in opioid-exposed children. These findings must be validated in a larger population with correction for confounding factors such as maternal education

scholarly journals Predicting human resting-state functional connectivity from structural connectivity

2009 ◽  
Vol 106 (6) ◽  
pp. 2035-2040 ◽  
Author(s):  
C. J. Honey ◽  
O. Sporns ◽  
L. Cammoun ◽  
X. Gigandet ◽  
J. P. Thiran ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Resting State Functional Connectivity

Altered Resting-State Functional and White Matter Tract Connectivity in Stroke Patients With Dysphagia

2013 ◽  
Vol 28 (3) ◽  
pp. 260-272 ◽  
Author(s):  
Shasha Li ◽  
Zhenxing Ma ◽  
Shipeng Tu ◽  
Muke Zhou ◽  
Sihan Chen ◽  
...  
Keyword(s):  
White Matter ◽  
Functional Connectivity ◽  
Fractional Anisotropy ◽  
Resting State ◽  
Structural Connectivity ◽  
White Matter Tract ◽  
Stroke Patients ◽  
Hemispheric Stroke ◽  
Swallowing Dysfunction ◽  
The Mean

Background. Swallowing dysfunction is intractable after acute stroke. Our understanding of the alterations in neural networks of patients with neurogenic dysphagia is still developing. Objective. The aim was to investigate cerebral cortical functional connectivity and subcortical structural connectivity related to swallowing in unilateral hemispheric stroke patients with dysphagia. Methods. We combined a resting-state functional connectivity with a white matter tract connectivity approach, recording 12 hemispheric stroke patients with dysphagia, 12 hemispheric stroke patients without dysphagia, and 12 healthy controls. Comparisons of the patterns in swallowing-related functional connectivity maps between patient groups and control subjects included ( a) seed-based functional connectivity maps calculated from the primary motor cortex (M1) and the supplementary motor area (SMA) to the entire brain, ( b) a swallowing-related functional connectivity network calculated among 20 specific regions of interest (ROIs), and ( c) structural connectivity described by the mean fractional anisotropy of fibers bound through the SMA and M1. Results. Stroke patients with dysphagia exhibited dysfunctional connectivity mainly in the sensorimotor-insula-putamen circuits based on seed-based analysis of the left and right M1 and SMA and decreased connectivity in the bilateral swallowing-related ROIs functional connectivity network. Additionally, white matter tract connectivity analysis revealed that the mean fractional anisotropy of the white matter tract was significantly reduced, especially in the left-to-right SMA and in the corticospinal tract. Conclusions. Our results indicate that dysphagia secondary to stroke is associated with disruptive functional and structural integrity in the large-scale brain networks involved in motor control, thus providing new insights into the neural remodeling associated with this disorder.

scholarly journals Sex-dependent and sex-independent brain resting-state functional connectivity in children with autism spectrum disorder

2016 ◽  
Author(s):  
Xin Di ◽  
Bharat B Biswal
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Resting State Functional Connectivity ◽  
Main Effects

Background: Males are more likely to suffer from autism spectrum disorder (ASD) than females. As to whether females with ASD have similar brain alterations remain an open question. The current study aimed to examine sex-dependent as well as sex-independent alterations in resting-state functional connectivity in individuals with ASD compared with typically developing (TD) individuals. Method: Resting-state functional MRI data were acquired from the Autism Brain Imaging Data Exchange (ABIDE). Subjects between 6 to 20 years of age were included for analysis. After matching the intelligence quotient between groups for each dataset, and removing subjects due to excessive head motion, the resulting effective sample contained 28 females with ASD, 49 TD females, 129 males with ASD, and 141 TD males, with a two (diagnosis) by two (sex) design. Functional connectivity among 153 regions of interest (ROIs) comprising the whole brain was computed. Two by two analysis of variance was used to identify connectivity that showed diagnosis by sex interaction or main effects of diagnosis. Results: The main effects of diagnosis were found mainly between visual cortex and other brain regions, indicating sex-independent connectivity alterations. We also observed two connections whose connectivity showed diagnosis by sex interaction between the precuneus and medial cerebellum as well as the precunes and dorsal frontal cortex. While males with ASD showed higher connectivity in these connections compared with TD males, females with ASD had lower connectivity than their counterparts. Conclusions: Both sex-dependent and sex-independent functional connectivity alterations are present in ASD.

scholarly journals Extended Invariant Information Clustering Is Effective for Leave-One-Site-Out Cross-Validation in Resting State Functional Connectivity Modeling

2021 ◽  
Vol 15 ◽  
Author(s):  
Naoki Okamoto ◽  
Hiroyuki Akama
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Cross Validation ◽  
Data Augmentation ◽  
Rapid Change ◽  
Autism Spectrum ◽  
Batch Size ◽  
Imaging Data ◽  
Resting State Functional Connectivity

Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by transfer learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy >0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility.

scholarly journals Extended Invariant Information Clustering is Effective for Leave-One- Site-Out Cross-Validation in Resting State Functional Connectivity Modelling

2021 ◽  
Author(s):  
Naoki Okamoto ◽  
Hiroyuki Akama
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Data Exchange ◽  
Cross Validation ◽  
Data Augmentation ◽  
Rapid Change ◽  
Autism Spectrum ◽  
Batch Size ◽  
Imaging Data ◽  
Resting State Functional Connectivity

Herein, we propose a new deep neural network model based on invariant information clustering (IIC), proposed by Ji et al., to improve the modeling performance of the leave-one-site-out cross-validation (LOSO-CV) for a multi-source dataset. Our Extended IIC (EIIC) is a type of contrastive learning; however, unlike the original IIC, it is characterized by supervised learning with labeled data pairs, but without the need for a data augmentation technique. Each site in LOSO-CV is left out in turn from the remaining sites used for training and receives a value for modeling evaluation. We applied the EIIC to the resting state functional connectivity magnetic resonance imaging dataset of the Autism Brain Imaging Data Exchange. The challenging nature of brain analysis for autism spectrum disorder (ASD) can be attributed to the variability of subjects, particularly the rapid change in the neural system of children as the target ASD age group. However, EIIC demonstrated higher LOSO-CV classification accuracy for the majority of scanning locations than previously used methods. Particularly, with the adjustment of a mini-batch size, EIIC outperformed other classifiers with an accuracy >0.8 for the sites with highest mean age of the subjects. Considering its effectiveness, our proposed method might be promising for harmonization in other domains, owing to its simplicity and intrinsic flexibility. We are currently submitting this manuscript to Frontiers in Neuroinformatics.

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