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

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 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 Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

2022 ◽  
Author(s):  
Matthew J. Hoptman ◽  
Umit Tural ◽  
Kelvin O. Lim ◽  
Daniel C. Javitt ◽  
Lauren E. Oberlin
Keyword(s):  
Diffusion Tensor Imaging ◽  
Functional Connectivity ◽  
Resting State ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Resting State Functional Connectivity ◽  
Syndrome Scale ◽  
Healthy Control ◽  
Negative Syndrome

Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects and acquired resting state fMRI and diffusion tensor imaging. The Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) was used to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal components analysis. Nine of 28 possible region pairs showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS values correlated with several aspects of the Positive and Negative Syndrome Scale, positively with those involving inwardly directed psychopathology, and negatively with those involving external psychopathology. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.

scholarly journals Relationships between Diffusion Tensor Imaging and Resting State Functional Connectivity in Patients with Schizophrenia and Healthy Controls: A Preliminary Study

2021 ◽  
Author(s):  
Matthew J. Hoptman ◽  
Umit Tural ◽  
Kelvin O. Lim ◽  
Daniel C. Javitt ◽  
Lauren E. Oberlin
Keyword(s):  
Diffusion Tensor Imaging ◽  
Functional Connectivity ◽  
Resting State ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Resting State Fmri ◽  
Resting State Functional Connectivity ◽  
Syndrome Scale ◽  
Healthy Control ◽  
Negative Syndrome

Schizophrenia is widely seen as a disorder of dysconnectivity. Neuroimaging studies have examined both structural and functional connectivity in the disorder, but these modalities have rarely been integrated directly. We scanned 29 patients with schizophrenia and 25 healthy control subjects and acquired resting state fMRI and diffusion tensor imaging. The Functional and Tractographic Connectivity Analysis Toolbox (FATCAT) was used to estimate functional and structural connectivity of the default mode network. Correlations between modalities were investigated, and multimodal connectivity scores (MCS) were created using principal components analysis. Nine of 28 possible region pairs showed consistent (>80%) tracts across participants. Correlations between modalities were found among those with schizophrenia for the prefrontal cortex, posterior cingulate, and lateral temporal lobes with frontal and parietal regions, consistent with frontotemporoparietal network involvement in the disorder. In patients, MCS values correlated with several aspects of the Positive and Negative Syndrome Scale, positively with those involving inwardly directed psychopathology, and negatively with those involving external psychopathology. In this preliminary sample, we found FATCAT to be a useful toolbox to directly integrate and examine connectivity between imaging modalities. A consideration of conjoint structural and functional connectivity can provide important information about the network mechanisms of schizophrenia.

scholarly journals Episodic memory in aspects of brain information transfer by resting-state network topology

2021 ◽  
Author(s):  
Tianyi Yan ◽  
Gongshu Wang ◽  
Li Wang ◽  
Tiantian Liu ◽  
Ting Li ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Episodic Memory ◽  
Resting State ◽  
Information Transfer ◽  
Memory Performance ◽  
Structural Connectivity ◽  
Transfer Mechanism ◽  
Resting State Functional Connectivity ◽  
Cognitive Information ◽  
Encoding And Retrieval

Studies suggest that resting-state functional connectivity conveys cognitive information; also, activity flow mediates cognitive information transfer. However, the exact mechanism of interregional interactions underlying episodic memory remains unclear. We performed a combined analysis of task-evoked activity and resting-state functional connectivity by activity flow mapping to estimate the information transfer mechanism of episodic memory. We found that the cognitive control and attentional networks were the most recruited structures in information transfers during both encoding and retrieval processes; these networks were correlated with task-evoked activation. Differences in information transfer intensity between encoding and retrieval mainly existed in the visual, somatomotor and hippocampal systems. Furthermore, information transfer showed high predictive power for episodic memory ability and mediated relationships between task-evoked activation and memory performance. Additional analysis indicated that structural connectivity had a transportive role in information transfer. Finally, our study presented the information transfer mechanism of episodic memory from multiple neural perspectives.

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