Functional connectivity density mapping: comparing multiband and conventional EPI protocols

Functional connectivity density mapping (FCDM) is a newly developed data-driven technique that quantifies the number of local and global functional connections for each voxel in the brain. In this study, we evaluated reproducibility, sensitivity, and specificity of both local functional connectivity density (lFCD) and global functional connectivity density (gFCD). We compared these metrics using the human connectome project (HCP) compatible high-resolution (2 mm isotropic, TR = 0.8 s) multiband (MB), and more typical, lower resolution (3.5 mm isotropic, TR = 2.0 s) single-band (SB) resting state functional MRI (rs-fMRI) acquisitions. Furthermore, in order to be more clinically feasible, only rs-fMRI scans that lasted seven minutes were tested. Subjects were scanned twice within a two-week span. We found sensitivity and specificity increased and reproducibility either increased or did not change for the MB compared to the SB acquisitions. The MB scans also showed improved gray matter/white matter contrast compared to the SB scans. The lFCD and gFCD patterns were similar across MB and SB scans and confined predominantly to gray matter. We also observed a strong spatial correlation of FCD between MB and SB scans indicating the two acquisitions provide similar information. These findings indicate high-resolution MB acquisitions improve the quality of FCD data, and seven minute rs-fMRI scan can provide robust FCD measurements.

[1]  Daniele Marinazzo,et al.  Functional Connectivity Density and Balance in Young Patients with Traumatic Axonal Injury , 2014, Brain Connect..

[2]  N. Volkow,et al.  Alcohol Affects Brain Functional Connectivity and its Coupling with Behavior: Greater Effects in Male Heavy Drinkers , 2016, Molecular Psychiatry.

[3]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[4]  Tianzi Jiang,et al.  Abnormal functional connectivity density in Parkinson's disease , 2015, Behavioural Brain Research.

[5]  Dardo Tomasi,et al.  Gender differences in brain functional connectivity density , 2012, Human brain mapping.

[6]  Stephen M. Smith,et al.  Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.

[7]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[8]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[9]  Xenophon Papademetris,et al.  Spatial resolution, signal-to-noise ratio, and smoothing in multi-subject functional MRI studies , 2006, NeuroImage.

[10]  N. Volkow,et al.  Aging and Functional Brain Networks , 2011, Molecular Psychiatry.

[11]  N. Volkow,et al.  Abnormal Functional Connectivity in Children with Attention-Deficit/Hyperactivity Disorder , 2012, Biological Psychiatry.

[12]  Yingli Lu,et al.  Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.

[13]  J. Polimeni,et al.  Blipped‐controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g‐factor penalty , 2012, Magnetic resonance in medicine.

[14]  N. Volkow,et al.  Functional connectivity density and the aging brain , 2012, Molecular Psychiatry.

[15]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[16]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[17]  L. K. Hansen,et al.  Independent component analysis of functional MRI: what is signal and what is noise? , 2003, Current Opinion in Neurobiology.

[18]  N. D. Volkow,et al.  Temporal Changes in Local Functional Connectivity Density Reflect the Temporal Variability of the Amplitude of Low Frequency Fluctuations in Gray Matter , 2016, PloS one.

[19]  P. Zhao,et al.  Resting-state functional connectivity density mapping of etiology confirmed unilateral pulsatile tinnitus patients: Altered functional hubs in the early stage of disease , 2015, Neuroscience.

[20]  Yufeng Zang,et al.  Functional brain hubs and their test–retest reliability: A multiband resting-state functional MRI study , 2013, NeuroImage.

[21]  P. Hluštík,et al.  Effects of spatial smoothing on fMRI group inferences. , 2008, Magnetic resonance imaging.

[22]  Chunshui Yu,et al.  Functional connectivity density alterations in schizophrenia , 2014, Front. Behav. Neurosci..

[23]  T. Adali,et al.  Ieee Workshop on Machine Learning for Signal Processing Semi-blind Ica of Fmri: a Method for Utilizing Hypothesis-derived Time Courses in a Spatial Ica Analysis , 2022 .

[24]  Nora D. Volkow,et al.  Temporal Evolution of Brain Functional Connectivity Metrics: Could 7 Min of Rest be Enough? , 2016, Cerebral cortex.

[25]  N. Volkow,et al.  Functional connectivity density mapping , 2010, Proceedings of the National Academy of Sciences.

[26]  J. Maldjian,et al.  Effect of resting-state functional MR imaging duration on stability of graph theory metrics of brain network connectivity. , 2011, Radiology.

[27]  Ninon Burgos,et al.  New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .

[28]  Steen Moeller,et al.  Evaluation of slice accelerations using multiband echo planar imaging at 3T , 2013, NeuroImage.

[29]  N. Volkow,et al.  Association between functional connectivity hubs and brain networks. , 2011, Cerebral cortex.

[30]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[31]  Y. Zang,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI , 2007, Brain and Development.

[32]  Tianzi Jiang,et al.  Functional Connectivity Hubs Could Serve as a Potential Biomarker in Alzheimer's Disease: A Reproducible Study. , 2015, Current Alzheimer research.

[33]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[34]  Nora D. Volkow,et al.  Functional connectivity hubs in the human brain , 2011, NeuroImage.

[35]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[36]  Chaozhe Zhu,et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF , 2008, Journal of Neuroscience Methods.

[37]  Peter A. Bandettini,et al.  Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI , 2006, NeuroImage.

[38]  Stefan Skare,et al.  How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging , 2003, NeuroImage.

[39]  Dardo Tomasi,et al.  High-Resolution Functional Connectivity Density: Hub Locations, Sensitivity, Specificity, Reproducibility, and Reliability. , 2016, Cerebral cortex.