Seyyed M. H. Haddad1, Christopher J. M. Scott2, Stephen R. Arnott2, Miracle Ozzoude2, Stephen C. Strother2, Sandra E. Black2, Michel J. Borrie1, Elizabeth Finger1, Maria C. Tartaglia2, Donna Kwan3, Derek Beaton2, Sean Symons4, Andrea Soddu1, Manuel Montero-Odasso1, and Robert Bartha1
1Western University, London, ON, Canada, 2University of Toronto, Toronto, ON, Canada, 3Ontario Brain Institute, Toronto, ON, Canada, 4Sunnybrook Health Sciences Centre, Toronto, ON, Canada
Synopsis
Our group recently introduced
a neuronal activity (NA) metric based on a texture feature of the low-frequency
fluctuations within the resting-state BOLD signal. This NA measure was associated with decreased
glucose metabolism in mild Alzheimer’s disease (AD) measured by FDG-PET. To improve
the sensitivity of this NA metric we introduce two variations achieved by
rigorously regulating (using cross-correlation and cross-covariance) the amplitude
of the BOLD signal oscillations derived from the constituent neuronal components.
These novel metrics were evaluated in people with mild cognitive impairment and
AD (N=14) demonstrating lower neuronal activity compared to healthy elderly
(N=14).
Introduction
Blood-oxygenation level
dependent (BOLD) functional MRI (fMRI) is sensitive to local magnetic field
variations caused by fluctuations in the concentration of the paramagnetic deoxygenated
hemoglobin within an active brain region.1,2 Recently, resting-state fMRI (rs-fMRI) was introduced to examine the
functional connectivity of the brain at rest and has been applied in diverse pathophysiological
conditions.3 While rs-fMRI is relatively simple in terms of pulse sequence and image
acquisition, it has provided quantitative biomarkers for different neurodegenerative
conditions.4–8 Furthermore, diverse neuronal activity (NA) measures were developed based
on various resting-state BOLD signal features.9–12 Our group recently described a
novel NA metric based on a first-order texture feature of the resting-state
BOLD signal, specifically incorporating the standard deviation (SD) of
low-frequency fluctuations.13 It
was demonstrated that this measure of NA was associated with decreased glucose
metabolism in mild Alzheimer disease (AD) measured by FDG-PET.13 The purpose of this
study was to improve the sensitivity of this NA metric by introducing two more
sophisticated metrics achieved by rigorously regulating the amplitude of the
BOLD signal oscillations derived from the constituent neuronal components. We
hypothesized that the novel NA metrics would be lower in people with mild
cognitive impairment (MCI) and AD compared to healthy elderly.
Theory and Methods
Two
novel NA metrics were directly derived from the rs-fMRI signal. To this end, the
rs-fMRI signal was pre-processed using FSL FEAT.13,14 Then the rs-fMRI signal was decomposed into independent
components (ICs) using IC analysis (ICA).15 A data-driven neuronality test was applied to
the ICs using a support vector machine (SVM) classifier to identify ICs with
neuronal origins.10 The voxel-wise rs-fMRI signal and neuronal ICs were
used to define NA measures as described in Figure 1. In the previously
introduced metric13 a simple Hadamard product was used to measure
the amplitude of each neuronal IC in the BOLD signal of each voxel, and the NA
metric was proportional to the SD of this Hadamard product. Here, we introduce
two more sophisticated and rigorous NA metrics by regulating the amplitude of
the neuronal ICs in the voxel-wise rs-fMRI signals. Both new metrics were hypothesized
to be proportional to the SD of the neuronal ICs. For the first metric, a
cross-correlation function was used to accurately quantify similarity between
the neuronal IC time-courses and the rs-fMRI signal. For the second metric, the
cross-covariance was utilized to calculate similarity while eliminating the
variability of the fMRI signal means from different brain voxels. This latter approach may increase
measurement precision as it reflects exclusively fluctuations in the BOLD
signal. These two new metrics were compared to the original,13 to determine which produced greater differences
in NA between a group of healthy controls (N=14) from the Gait and Brain Study16 (aged 58-85, 71% female) and a group of 14
subjects with MCI and AD from the Ontario Neurodegenerative Disease Research
Initiative (ONDRI) (aged 57-86, 50% female). The rs-fMRI data were acquired
on 3T Siemens scanners using single-shot echo planer imaging: voxel dimensions
3.5×3.5×3.5 mm3, flip angle=70˚, TE=30 ms, TR~2400 ms, and matrix
size 64×64×41.Results
All
metrics showed higher average NA in gray matter (GM) compared to white matter
(p<0.05, two-tailed, repeated measures ttest). Using the Hadamard product
and cross-correlation based metrics the average NA in GM was lower (p<0.05,
one-tailed ttest) in AD/MCI compared to healthy elderly. Example NA maps in a
healthy subject and an AD/MCI subject (Figure 2) calculated using the
cross-covariance based NA metric demonstrate large differences across the brain.
Normalized group average NA maps are provided for the healthy elderly (Figure
3) and for AD/MCI (Figure 4) showing lower activity in the AD/MCI subjects for
all metrics. The percentage difference maps between the MCI/AD and healthy
elderly groups (Figure 5) also demonstrate regional variability. The average
percentage differences between these groups across the brain metrics was 34% based
on the Hadamard product,13 35% based on the cross-correlation, and 29%
based on cross-covariance.Discussion
The
pattern of NA observed using the novel metrics defined in this study show
greater activity in cortical GM compared to white matter as observed in our
initial study, and as commonly observed with fluorodeoxyglucose positron
emission tomography. Although showing smaller average differences between
groups, the metric based on cross-covariance, may better highlight changes specific
to the cortex.Conclusion
All
three NA metrics derived from rs-fMRI showed ~30% lower average activity in the
AD/MCI group compared to healthy elderly subjects. This novel approach to
processing rs-fMRI data may have applications in the evaluation of
neurodegenerative disease progression.Acknowledgements
This work was supported by the Ontario Brain Institute through the
Ontario Neurodegenerative Disease Research Initiative (ONDRI). Dr. Haddad is
supported by an ONDRI postdoctoral scholar fellowship. The Centre for
Functional and Metabolic Mapping is supported by Brain Canada and the Canada
First Research Excellence Fund (CFREF).
The authors would like to thank all ONDRI participants including the
ONDRI investigators and the ONDRI governing committees: executive committee;
steering committee; publication committee; recruiting clinicians; assessment
platforms leaders; and the ONDRI project management team. For a full list of
the ONDRI investigators, please visit: www.ONDRI.ca/people.
The ONDRI project is funded by the Ontario Brain
Institute through the Government of Ontario with matching funds provided by
participating hospital and research institute foundations, including the
Baycrest Foundation, Bruyère Research Institute, Centre for Addiction and
Mental Health Foundation, London Health Sciences Foundation, McMaster
University Faculty of Health Sciences, Ottawa Brain and Mind Research
Institute, Queen’s University Faculty of Health Sciences, Providence Care
(Kingston), Sunnybrook Health Sciences Foundation, the Thunder Bay Regional
Health Sciences Centre, the University of Ottawa Faculty of Medicine, and the
Windsor/Essex County ALS Association. The Temerty Family Foundation provided
the major infrastructure matching funds.References
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