Automated voxel classification used with atlas-guided diffuse optical tomography for assessment of functional brain networks in young and older adults

Abstract. Atlas-guided diffuse optical tomography (atlas-DOT) is a computational means to image changes in cortical hemodynamic signals during human brain activities. Graph theory analysis (GTA) is a network analysis tool commonly used in functional neuroimaging to study brain networks. Atlas-DOT has not been analyzed with GTA to derive large-scale brain connectivity/networks based on near-infrared spectroscopy (NIRS) measurements. We introduced an automated voxel classification (AVC) method that facilitated the use of GTA with atlas-DOT images by grouping unequal-sized finite element voxels into anatomically meaningful regions of interest within the human brain. The overall approach included volume segmentation, AVC, and cross-correlation. To demonstrate the usefulness of AVC, we applied reproducibility analysis to resting-state functional connectivity measurements conducted from 15 young adults in a two-week period. We also quantified and compared changes in several brain network metrics between young and older adults, which were in agreement with those reported by a previous positron emission tomography study. Overall, this study demonstrated that AVC is a useful means for facilitating integration or combination of atlas-DOT with GTA and thus for quantifying NIRS-based, voxel-wise resting-state functional brain networks.

[1]  Hanli Liu,et al.  Depth-compensated diffuse optical tomography enhanced by general linear model analysis and an anatomical atlas of human head , 2014, NeuroImage.

[2]  Cornelis J Stam,et al.  Graph theoretical analysis of complex networks in the brain , 2007, Nonlinear biomedical physics.

[3]  Yong He,et al.  Resting-State Functional Brain Connectivity : Lessons from Functional Near-Infrared Spectroscopy , 2022 .

[4]  Jonathan D. Power,et al.  The Development of Human Functional Brain Networks , 2010, Neuron.

[5]  Abraham Z. Snyder,et al.  Statistical analysis of high density diffuse optical tomography , 2014, NeuroImage.

[6]  Abraham Z. Snyder,et al.  A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping , 2012, NeuroImage.

[7]  G. Strangman,et al.  Depth Sensitivity and Source-Detector Separations for Near Infrared Spectroscopy Based on the Colin27 Brain Template , 2013, PLoS ONE.

[8]  Hamid Dehghani,et al.  Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction. , 2009, Communications in numerical methods in engineering.

[9]  Theodore J. Huppert,et al.  Whole brain functional connectivity using phase locking measures of resting state magnetoencephalography , 2014, Front. Neurosci..

[10]  Abraham Z. Snyder,et al.  Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: In vivo validation against fMRI , 2014, NeuroImage.

[11]  Jianfei Liu,et al.  A new point containment test algorithm based on preprocessing and determining triangles , 2010, Comput. Aided Des..

[12]  S. Rombouts,et al.  Reduced resting-state brain activity in the "default network" in normal aging. , 2008, Cerebral cortex.

[13]  Yong He,et al.  Test-Retest Reliability of Graph Metrics in Functional Brain Networks: A Resting-State fNIRS Study , 2013, PloS one.

[14]  Adam T. Eggebrecht,et al.  Mapping cortical responses to speech using high-density diffuse optical tomography , 2015, NeuroImage.

[15]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[16]  Hamid Dehghani,et al.  Image Quality Analysis of High-Density Diffuse Optical Tomography Incorporating a Subject-Specific Head Model , 2012, Front. Neuroenerg..

[17]  R. Peters,et al.  Ageing and the brain , 2006, Postgraduate Medical Journal.

[18]  Deborah Yurgelun-Todd,et al.  Emotional and cognitive changes during adolescence , 2007, Current Opinion in Neurobiology.

[19]  Keith A. Johnson,et al.  Disruption of Functional Connectivity in Clinically Normal Older Adults Harboring Amyloid Burden , 2009, The Journal of Neuroscience.

[20]  David A. Boas,et al.  The utility of near-infrared spectroscopy in the regression of low-frequency physiological noise from functional magnetic resonance imaging data , 2012, NeuroImage.

[21]  E Zarahn,et al.  Empirical analyses of BOLD fMRI statistics. II. Spatially smoothed data collected under null-hypothesis and experimental conditions. , 1997, NeuroImage.

[22]  S. Arridge,et al.  Optical tomography: forward and inverse problems , 2009, 0907.2586.

[23]  Olaf Sporns,et al.  Complex network measures of brain connectivity: Uses and interpretations , 2010, NeuroImage.

[24]  Archana K. Singh,et al.  Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI , 2005, NeuroImage.

[25]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[26]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[27]  Zhenghui Hu,et al.  Changes in Topological Organization of Functional PET Brain Network with Normal Aging , 2014, PloS one.

[28]  Hanli Liu,et al.  Development of a compensation algorithm for accurate depth localization in diffuse optical tomography. , 2010, Optics letters.

[29]  Andreas Heinz,et al.  Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures , 2012, NeuroImage.

[30]  Anthony Randal McIntosh,et al.  Age-related Changes in Brain Activity across the Adult Lifespan , 2006, Journal of Cognitive Neuroscience.

[31]  A. Yodh,et al.  Diffuse optics for tissue monitoring and tomography , 2010, Reports on progress in physics. Physical Society.

[32]  Joseph P Culver,et al.  Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex. , 2014, Biomedical optics express.

[33]  Alan C. Evans,et al.  Age- and Gender-Related Differences in the Cortical Anatomical Network , 2009, The Journal of Neuroscience.

[34]  David A Boas,et al.  Simulation study of magnetic resonance imaging-guided cortically constrained diffuse optical tomography of human brain function. , 2005, Applied optics.

[35]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[36]  Marco Zorzi,et al.  A new method based on ICBM152 head surface for probe placement in multichannel fNIRS , 2011, NeuroImage.

[37]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

[38]  Ippeita Dan,et al.  Spatial registration for functional near-infrared spectroscopy: From channel position on the scalp to cortical location in individual and group analyses , 2014, NeuroImage.

[39]  C. Keysers,et al.  An introduction to anatomical ROI-based fMRI classification analysis , 2009, Brain Research.

[40]  Danielle Smith Bassett,et al.  Small-World Brain Networks , 2006, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[41]  Olaf Sporns,et al.  Disturbed resting state EEG synchronization in bipolar disorder: A graph-theoretic analysis☆ , 2013, NeuroImage: Clinical.

[42]  Mahlega S. Hassanpour,et al.  Mapping distributed brain function and networks with diffuse optical tomography , 2014, Nature Photonics.

[43]  Hanli Liu,et al.  Neuroimage: Clinical Prefrontal Responses to Digit Span Memory Phases in Patients with Post-traumatic Stress Disorder (ptsd): a Functional near Infrared Spectroscopy Study , 2022 .

[44]  Alex Martin,et al.  A wavelet-based method for measuring the oscillatory dynamics of resting-state functional connectivity in MEG , 2011, NeuroImage.

[45]  Hanli Liu,et al.  Atlas‐guided volumetric diffuse optical tomography enhanced by generalized linear model analysis to image risk decision‐making responses in young adults , 2014, Human brain mapping.

[46]  E. Bullmore,et al.  A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs , 2006, The Journal of Neuroscience.

[47]  James Allen Fill,et al.  The Moore-Penrose Generalized Inverse for Sums of Matrices , 1999, SIAM J. Matrix Anal. Appl..

[48]  Andrew J Berger,et al.  Short-channel functional near-infrared spectroscopy regressions improve when source-detector separation is reduced , 2014, Neurophotonics.

[49]  Kimberly S. Mapes,et al.  Test-retest assessment of cortical activation induced by repetitive transcranial magnetic stimulation with brain atlas-guided optical topography , 2012, Journal of biomedical optics.

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

[51]  Han Zhang,et al.  Test–retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy , 2011, NeuroImage.

[52]  Yong He,et al.  Probabilistic Diffusion Tractography and Graph Theory Analysis Reveal Abnormal White Matter Structural Connectivity Networks in Drug-Naive Boys with Attention Deficit/Hyperactivity Disorder , 2013, The Journal of Neuroscience.

[53]  W. Eric L. Grimson,et al.  Anatomical atlas-guided diffuse optical tomography of brain activation , 2009, NeuroImage.

[54]  Mathieu Bourguignon,et al.  A geometric correction scheme for spatial leakage effects in MEG/EEG seed‐based functional connectivity mapping , 2015, Human brain mapping.

[55]  Edward T. Bullmore,et al.  Efficiency and Cost of Economical Brain Functional Networks , 2007, PLoS Comput. Biol..

[56]  Yong He,et al.  Revealing Topological Organization of Human Brain Functional Networks with Resting-State Functional near Infrared Spectroscopy , 2012, PloS one.

[57]  Yong He,et al.  Graph theoretical modeling of brain connectivity. , 2010, Current opinion in neurology.

[58]  R. Poldrack.  Region of interest analysis for fMRI. , 2007, Social cognitive and affective neuroscience.

[59]  David A. Boas,et al.  Validating atlas-guided DOT: A comparison of diffuse optical tomography informed by atlas and subject-specific anatomies , 2012, NeuroImage.

[60]  Edward T. Bullmore,et al.  Age-related changes in modular organization of human brain functional networks , 2009, NeuroImage.