Test–Retest Reproducibility of the Intrinsic Default Mode Network: Influence of Functional Magnetic Resonance Imaging Slice-Order Acquisition and Head-Motion Correction Methods

2017 ◽  
Vol 7 (2) ◽  
pp. 69-83 ◽  
Author(s):  
Rocco Marchitelli ◽  
Olivier Collignon ◽  
Jorge Jovicich
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Default Mode Network ◽  
Motion Correction ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Default Mode

scholarly journals Role of the default mode network in cognitive transitions

2018 ◽  
Author(s):  
Verity Smith ◽  
Daniel J Mitchell ◽  
John Duncan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Default Mode Network ◽  
Task Switch ◽  
Thought Processes ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Default Mode

ABSTRACTA frequently repeated finding is that the default mode network (DMN) shows activation decreases during externally-focused tasks. This finding has led to an emphasis in DMN research on internally-focused self-relevant thought processes. A recent study, in contrast, implicates the DMN in substantial externally-focused task switches. Using functional magnetic resonance imaging, we scanned 24 participants performing a task switch experiment. Whilst replicating previous DMN task switch effects, we also found large DMN increases for brief rests as well as task restarts after rest. Our findings are difficult to explain using theories strictly linked to internal or self-directed cognition. In line with principal results from the literature, we suggest that the DMN encodes scene, episode or context, by integrating spatial, self-referential and temporal information. Context representations are strong at rest, but re-reference to context also occurs at major cognitive transitions.

scholarly journals Informative Biomarkers for Autism Spectrum Disorder Diagnosis in Functional Magnetic Resonance Imaging Data on the Default Mode Network

2021 ◽  
Vol 11 (13) ◽  
pp. 6216
Author(s):  
Aikaterini S. Karampasi ◽  
Antonis D. Savva ◽  
Vasileios Ch. Korfiatis ◽  
Ioannis Kakkos ◽  
George K. Matsopoulos
Keyword(s):  
Magnetic Resonance Imaging ◽  
Autism Spectrum Disorder ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Default Mode Network ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Dynamic Connectivity

Effective detection of autism spectrum disorder (ASD) is a complicated procedure, due to the hundreds of parameters suggested to be implicated in its etiology. As such, machine learning methods have been consistently applied to facilitate diagnosis, although the scarcity of potent autism-related biomarkers is a bottleneck. More importantly, the variability of the imported attributes among different sites (e.g., acquisition parameters) and different individuals (e.g., demographics, movement, etc.) pose additional challenges, eluding adequate generalization and universal modeling. The present study focuses on a data-driven approach for the identification of efficacious biomarkers for the classification between typically developed (TD) and ASD individuals utilizing functional magnetic resonance imaging (fMRI) data on the default mode network (DMN) and non-physiological parameters. From the fMRI data, static and dynamic connectivity were calculated and fed to a feature selection and classification framework along with the demographic, acquisition and motion information to obtain the most prominent features in regard to autism discrimination. The acquired results provided high classification accuracy of 76.63%, while revealing static and dynamic connectivity as the most prominent indicators. Subsequent analysis illustrated the bilateral parahippocampal gyrus, right precuneus, midline frontal, and paracingulate as the most significant brain regions, in addition to an overall connectivity increment.

Altered Functional Connectivity within Default Mode Network in Patients with Transient Ischemic Attack: A Resting-State Functional Magnetic Resonance Imaging Study

2019 ◽  
Vol 48 (1-2) ◽  
pp. 61-69 ◽  
Author(s):  
Tingting Zhu ◽  
Lingyu Li ◽  
Yulin Song ◽  
Yu Han ◽  
Chengshu Zhou ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Functional Magnetic Resonance Imaging ◽  
Transient Ischemic Attack ◽  
Default Mode Network ◽  
Resting State ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Ischemic Attack

Default mode network (DMN) is an important functional brain network that supports aspects of cognition. Stroke has been reported to be associated with functional connectivity (FC) impairments within DMN. However, whether FC within DMN changes in transient ischemic attack (TIA), an important risk factor for stroke, remains unclear. Forty-eight TIA patients and 41 age- and sex-matched healthy controls (HCs) were recruited in this study. Using resting-state functional magnetic resonance imaging seed-based FC methods, we examined FC alterations within DMN in TIA patients, tested its associations with clinical information, and further explored the ability of FC abnormalities to predict follow-up ischemic attacks. We found significantly decreased FC of left middle temporal gyrus/angular gyrus both with medial prefrontal cortex (mPFC) and posterior cingulate cortex/precuneus (PCC/Pcu) and significantly decreased FC among each pair of mPFC, left PCC, and right Pcu in patients with TIA as compared with HCs. Moreover, the connectivity between mPFC and left PCC could predict future ischemic attacks of the patients. Collectively, these findings may provide insights into further understanding of the underlying pathological mechanism in TIA, and aberrant FC between the hubs within DMN may provide a reference for the imaging diagnosis and early intervention of TIA.

scholarly journals Improved motion correction of submillimetre 7T fMRI time series with boundary-based registration (BBR)

2019 ◽  
Author(s):  
Pei Huang ◽  
Johan D. Carlin ◽  
Richard N. Henson ◽  
Marta M. Correia
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Functional Data ◽  
Structural Data ◽  
Target Volume ◽  
Head Motion ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Difference

AbstractUltra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

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