TYPE
Methods
04 May 2023
10.3389/fnins.2023.1076824
PUBLISHED
DOI
OPEN ACCESS
EDITED BY
Richard Craig Reynolds,
Clinical Center (NIH), United States
REVIEWED BY
Jennifer Evans,
National Institutes of Health (NIH),
United States
Martha J. Holmes,
University of Cape Town, South Africa
Quality control in resting-state
fMRI: the benefits of visual
inspection
Rebecca J. Lepping 1,2*, Hung-Wen Yeh 3,4, Brent C. McPherson 5,
Morgan G. Brucks 2,6, Mohammad Sabati 2,7, Rainer T. Karcher 2,
William M. Brooks 1,2, Joshua D. Habiger 8, Vlad B. Papa 2 and
Laura E. Martin 2,6
*CORRESPONDENCE
Rebecca J. Lepping
rlepping@kumc.edu
RECEIVED 22
October 2022
April 2023
PUBLISHED 04 May 2023
ACCEPTED 07
CITATION
Lepping RJ, Yeh H-W, McPherson BC,
Brucks MG, Sabati M, Karcher RT, Brooks WM,
Habiger JD, Papa VB and Martin LE (2023)
Quality control in resting-state fMRI: the
benefits of visual inspection.
Front. Neurosci. 17:1076824.
doi: 10.3389/fnins.2023.1076824
COPYRIGHT
© 2023 Lepping, Yeh, McPherson, Brucks,
Sabati, Karcher, Brooks, Habiger, Papa and
Martin. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in this
journal is cited, in accordance with accepted
academic practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
1
Department of Neurology, University of Kansas Medical Center, Kansas City, KS, United States,
Hoglund Biomedical Imaging Center, University of Kansas Medical Center, Kansas City, KS, United
States, 3 Division of Health Services and Outcomes Research, Department of Pediatrics, Children’s Mercy
Research Institute, Kansas City, MO, United States, 4 Department of Pediatrics, School of Medicine,
University of Missouri-Kansas City, Kansas City, MO, United States, 5 Department of Neurology and
Neurosurgery, McGill University, Montreal, QC, Canada, 6 Department of Population Health, University
of Kansas Medical Center, Kansas City, KS, United States, 7 Bioengineering Program, School of
Engineering, University of Kansas, Lawrence, KS, United States, 8 Department of Statistics, Oklahoma
State University, Stillwater, OK, United States
2
Background: A variety of quality control (QC) approaches are employed in
resting-state functional magnetic resonance imaging (rs-fMRI) to determine data
quality and ultimately inclusion or exclusion of a fMRI data set in group analysis.
Reliability of rs-fMRI data can be improved by censoring or “scrubbing” volumes
affected by motion. While censoring preserves the integrity of participant-level
data, including excessively censored data sets in group analyses may add noise.
Quantitative motion-related metrics are frequently reported in the literature;
however, qualitative visual inspection can sometimes catch errors or other issues
that may be missed by quantitative metrics alone. In this paper, we describe our
methods for performing QC of rs-fMRI data using software-generated quantitative
and qualitative output and trained visual inspection.
Results: The data provided for this QC paper had relatively low motion-censoring,
thus quantitative QC resulted in no exclusions. Qualitative checks of the data
resulted in limited exclusions due to potential incidental findings and failed preprocessing scripts.
Conclusion: Visual inspection in addition to the review of quantitative QC metrics
is an important component to ensure high quality and accuracy in rs-fMRI data
analysis.
KEYWORDS
artifacts, functional magnetic resonance imaging (fMRI), resting state—fMRI,
reproducibility of results, quality control
Introduction
Quality control (QC) in functional magnetic resonance imaging (fMRI) data is a critical
step in ensuring accurate interpretation of results and reliable and replicable findings. Data may
be corrupted at acquisition due to hardware or software malfunctions, artifacts from metallic
objects, spurious physiological signals (heart rate, respiration, etc.) or participant motion.
Further, incidental findings of atypical anatomic formations, lesions, or other injury may
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be grounds for data exclusion if those findings are related to inclusion
and exclusion criteria for the study, or if they cause errors in certain
processing steps. There is a clear need for consensus on QC approaches
for fMRI data, and for a revisiting of reporting standards to improve
cross-study interpretation and replicability (Esteban et al., 2017).
There are emerging approaches to crowd-source the QC of imaging
data sets using a combination of expert curation and a gamified
interface for identifying scans. In this paper, we describe our fMRI QC
methods from data acquisition through individual preprocessing. Our
methods rely on standard tools available through the analysis software
we use and also include visual inspection by trained reviewers at
multiple stages of the process. While the field recognizes the value of
quantitative metrics and automated processes for evaluating data
quality, we believe there is added value in qualitative assessment that
cannot be captured by quantitative measures of displacement,
censoring, or signal intensity or homogeneity. We apply these QC
strategies to a publicly available data set and report out standardized
outcomes identified in the Frontiers Research Topic, Demonstrating
Quality Control (QC) Procedures in fMRI.
Across MRI imaging protocols, fMRI data are particularly
sensitive to participant head motion. Strategies exist to minimize
participant head motion at data acquisition, such as the use of foam
padding around the head, a strap across the forehead, bite bars, or
real-time feedback to the participant and prospective motion
corrections (Thulborn, 1999; Lazar, 2008; Vanderwal et al., 2015).
However, these often require specialized settings, sequences, or
equipment and are not sufficient to eliminate all movement and some
data will be lost to motion corruption.
One of the most observable effects of head motion on fMRI data
is the increase or decrease in signal in the affected volumes. In the case
of blood oxygen level dependent (BOLD) imaging, data are acquired
in slices through the volume of the brain over the course of a few
seconds. The slice to be imaged is excited with a radio-frequency (RF)
pulse, and the echo is read out a few milliseconds later. If the excited
slice has moved in space, the echo will not be accurately read, leading
to reduced signal in that slice. Additionally, the next slice to
be acquired may have been excited by the preceding pulse and may
have residual signal. A second RF pulse in that slice would lead to
increases in signal readout. For these reasons, the volumes
surrounding a motion spike are often also unreliable, and these effects
may last for several seconds (Power et al., 2014). Compounding this
issue is that all voxels within a slice or volume are not likely to
be impacted the same way, as motion is rarely limited to translation
along a single axis. Because of this, the relationship between signal
within a given voxel and motion parameters is not linear (Power et al.,
2015). Motion can decrease the fMRI signal temporal stability by
causing signal alterations across volumes which eventually increase
false outcomes (Satterthwaite et al., 2013). Moreover, motion can
potentially modulate connectivity-related measurements because it
produces global signal changes resulting in spurious results (Rogers
et al., 2007).
Certain populations may be especially prone to movement during
fMRI scanning. Children, older people, people with back pain, or
people with high impulsivity may not be able to hold still for an entire
functional scan, which can last for several minutes (Fox and Greicius,
2010; Couvy-Duchesne et al., 2014; Kong et al., 2014; CouvyDuchesne et al., 2016; Pardoe et al., 2016). Therefore, the development
of new approaches and the optimization of current strategies to reduce
Frontiers in Neuroscience
motion-related artifacts in fMRI data sets are critical for imaging
studies of these populations. Because resting state correlation relies on
low frequency modulation within the signal, longer scans are
recommended (up to ~10 min in some cases) (Birn et al., 2013),
potentially exacerbating the problem of participant movement.
Participants may tolerate several shorter scans with breaks in between
– collecting multiple resting state scans and concatenating across them
improves the signal-to-noise ratio (Chen et al., 2010); however, no
strategy completely eliminates the impacts of participant head motion
(Power et al., 2014, 2015).
The statistical approach of including motion parameters as
nuisance regressors in the analysis reduces the impact of motion and
has been widely adopted as a standard processing step (Johnstone
et al., 2006). It has been shown that removing, or censoring, only the
volumes most affected by motion prior to statistical analysis improves
reliability (Power et al., 2012; Carp, 2013; Power et al., 2013).
Additional ‘scrubbing’ or removing physiological noise signals is also
helpful for removing spurious correlations due to head motion (Siegel
et al., 2014). However, censoring alone still has problems. One is how
to choose the optimal censoring threshold, which may depend on the
level of motion in a data set (Power et al., 2014). Once a threshold has
been chosen, another concern is that correlation estimates from
participants with reduced data sets after censoring may be noisy or
have extreme values that may influence group statistics or reduce
power. To address this, many studies also exclude entire participants
or scans that exceed pre-specified censoring limits (Power et al., 2015).
Excluding participants with greater than 10% censored is often used
as a threshold, and less conservative censoring thresholds of 15–25%
have been used with pediatric populations (Siegel et al., 2014). An
entirely different approach from censoring is to use independent
components analysis (ICA) to identify the signal associated with head
motion (Griffanti et al., 2014; Siegel et al., 2014; Patriat et al., 2015,
2016; Pruim et al., 2015). Since reliability is dependent on the length
of usable data, some researchers exclude participants with usable
resting state scan data less than ~5 min after censoring (Van Dijk et al.,
2012; Andellini et al., 2015).
Censoring or scrubbing solutions allow for removing motion
corrupted data will preserving some data and avoiding excluding
entire participant data sets. If motion corruption causes data to not
be missing at random, excluding more data in one group than another
can cause bias in estimation and result in loss in power or invalid
testing procedures (Little and Rubin, 2002). Moreover, excluding
acquired data introduces a waste of resources and excessive costs for
research services and personnel time. Given the challenge of recruiting
well-characterized participants from clinical populations, the
commitment of participants, and the cost of data collection and
analytic staff, there are financial and social burdens to unnecessarily
excluding data.
While motion artifacts have been well-documented to lead to
both type I and type II errors in downstream analyses, other issues can
and do arise during functional data acquisition and analysis. These
include incidental findings of anatomic variability in the images which
could indicate a medical concern or a benign anatomic difference that
is of little medical concern. These findings, however, could be reason
for participant exclusion, for example there is an incidental finding
that indicates a previous stroke and stroke is an exclusion criterion for
the study. Also, these anatomic variabilities could lead to issues with
misalignment or normalization into template space, therefore, visual
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inspection of the results is warranted. In addition to anatomic
variabilities and incidental findings, script failures are another source
of error in rs-fMRI data analysis. Analysis of rs-fMRI data is
performed as a series of steps, with each step taking the output from
the previous step, performing another process, and then generating a
new output image. Errors are possible at each step, and it is critical to
determine that scripts are performing correctly so that the input–
output–input chain does not result in errors in the final output data
set. These errors are sometimes difficult to find if one only examines
quantitative QC metrics, but can be easy to assess visually, for example
if the entire functional series of images have been flipped upside down
during processing but are centered with the anatomic image, global
metrics of homogeneity will not differ between a correctly aligned and
incorrectly aligned image. If such processing errors are allowed into
group analysis, the spatial location of anatomy will not match across
all participants.
In this paper, we describe our processes for rs-fMRI QC, including
review of quantitative and qualitative software-generated metrics and
visual inspection at each processing step to ensure the most accurate
data are carried forward in the analysis process. Further, we advocate
for including as much data as possible to minimize bias and honor the
participant time and research resources provided.
Volumes where more than 5% of the brain voxels were considered
outliers and were removed from the analysis. In addition, volumes
with motion greater than 0.2 mm within a volume were censored and
removed from the analysis. Nuisance variables included motion
parameters (3 translation, 3 rotation), average ventricle signal, and
average white matter signal. Ventricle signals were estimated by
combining an MNI ventricle mask with the participant’s cerebral
spinal fluid mask derived from the anatomic images. Using multiple
regression, a residual time series was calculated for each voxel. The
residual time series was then smoothed with a 4 mm FWHM Gaussian
kernel, resampled to a 2.5 × 2.5 × 2.5 mm grid, and transformed to
MNI space.
Quality control process
Data quality was determined using a combination of quantitative
metrics and qualitative assessment (Figure 1). Quantitative metrics
included verification of final voxel resolution and outputs from AFNI’s
APQC of average motion per TR, max motion displacement, and
censor fraction. Quantitative metrics were recorded in our REDCap
QC checklist (see supplement) for ease of summary and comparison
across participants.
Qualitatively, data were viewed by trained staff who made
inclusion/exclusion decisions. Training of staff included walking
through each step of our REDCap QC checklist (see supplement) and
implementing a double data check system where new staff and trained
staff both check and verify the same datasets. Staff were considered
trained after inclusion/exclusion decisions were consistent with those
made by trained/established staff. This method is a step-by-step
approach to reviewing data and documenting the results of each of
these steps utilizing a standardized REDCap form. This approach is
easy to train new raters – we have successfully trained people across
all levels of education, from high school students to those with PhDs
– and the double-data entry step facilitates inter-rater reliability
assessment. Data entry into REDCap also allows summary data to
be easily compiled across participants, and if the checklist is used
across multiple studies, data can be easily compared across projects.
The inclusion of image examples of poor quality data within our
REDCap checklist should improve the replicability and inter-rater
reliability as well.
Raw DICOM files were converted to NIFTI format prior to being
shared publicly, however, when starting from raw DICOM files, our
QC process begins with a verification of data completeness comparing
file count, file size, and image acquisition parameters against study
protocols. We downloaded the NIFTI files and scrolled through the
brain slice by slice within AFNI in order to assess each modality for
any acquisition issues, distortion of images, or incidental findings. @
SSwarper outputs were visually inspected for good alignment (clear
match between the skull-stripped brain and the MNI base template
space) and skull-stripping (little to no clipped/missing brain data)
prior to being processed with the individual data set afni_proc.py
script. We then followed AFNI’s standard processing guidelines to
check the processed data using the afni.proc.py quality control output.
REDCap QC included checking the APQC and recording of following:
excessive motion, warping, and distortion of the original data,
alignment issues between the epi to anatomy and anatomy to the MNI
template, inspection of the statistics volumes for excessive noise within
Materials and methods
We performed an analysis of previously published and publicly
available human participants’ data provided as part of the
Demonstrating QC Procedures in fMRI Research Topic (Biswal et al.,
2010; Di Martino et al., 2014; Markiewicz et al., 2021). Briefly, resting
state fMRI (rs-fMRI) data were pulled from publicly available datasets
(ABIDE, ABIDE-II, functional Connectome Project, Open Neuro)
across seven imaging sites, with approximately 20 participants from
each site. Imaging parameters are summarized in Table 1. For this
Research Topic, the Project leaders renamed the data with new
participant IDs and organized them into BIDS common directory
format. Each participant had one anatomical image and one or two
rs-fMRI sequences. Imaging parameters for the rs-fMRI sequences are
reported in Table 1. No information on participant demographics or
other characteristics was provided. For the remainder of the paper,
we will refer to this data set as the “QC data set.” All procedures
involving human participants were performed in accordance with the
ethical standards of the Declaration of Helsinki, and the study was
approved by the Institutional Review Board where the data were
collected. Informed consent was obtained from all participants.
Data processing
MRI data preprocessing and statistical analyses took place in
Analysis of Functional Neuroimages (AFNI v22.1.10) (Cox, 1996) and
implemented using afni_proc.py (Example 11b). Anatomical data
were skull stripped and normalized to standard Montreal Neurological
Institute (MNI) space using non-linear warping with AFNI command
@SSwarper and these parameters were applied to the functional data
for spatial normalization. The first two volumes of the functional scans
were removed, and data were despiked. Volumes were slice time
corrected and co-registered to the minimum outlier within the run.
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TABLE 1 Resting state fMRI imaging parameters from the seven imaging sites.
Site
1
Scanner
Phillips
Field
strength
Orientation
In-plane resolution
Spacing
between Slices
Repetition time
(TR)
Echo time
(TE)
Number
of Slices
Number
of
volumes
Parallel
reduction
(Yes/No)
3T
Axial
2.67 mm × 2.67 mm
3.0 mm
2,500 ms
30 ms
47
156
Yes
3T
Axial
3.0 mm × 3.0 mm
3.84 mm
2,000 ms
28 ms
38
150
Yes
3T
Axial
2.56 mm × 2.56 mm
3.1 mm
2,500 ms
30 ms
45
162
Yes
Achieva
2
Phillips
Achieva
3
Phillips
Achieva DS
04
4
Unknown
3T
Unknown
2.67 mm × 2.67 mm
3.0 mm
2,500 ms
Unknown
47
123
Unknown
5
Phillips
3T
Axial
1.88 mm × 1.88 mm/3.0 mm × 3.0 mm/3.0 mm ×
4.0 mm/4.0 mm/4.0 mm
2,000 ms/2000 ms/2,000 ms
34 ms/30 ms/
Varied
144/144/144
Unknown
25 ms
34–39
27 ms
32
varied 130–
Unknown
Achieva OR
3.0 mm
Siemens
TrioTim OR
Siemens
Prisma_fit
6
Siemens
3T
Unknown
4.0 mm × 4.0 mm
4.0 mm
2,500 ms
MAGNETOM
724
Trio
7
Siemens Verio
3T
Unknown
3.0 mm × 3.0 mm
3.51 mm
2,500 ms
30 ms
39
198
Unknown
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FIGURE 1
Data checking steps include qualitative and quantitative evaluation of the imaging data to determine inclusion in group level analysis.
and outside of the brain, excessive motion, low degrees of freedom,
warnings, and a brief summary of the @ss_review_basic. Motion and
warnings regarding the severity of the overall censor fraction were
recorded at three thresholds based on AFNI warning levels (excluding
Frontiers in Neuroscience
severe censoring >50%, excluding medium censoring >20%, excluding
mild censoring >10%).
In addition to the steps described above, which follow
standard processing guidelines from AFNI, if excessive motion
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Results
was present (>20% censoring), we further checked the epi using
@epi_review to visually inspect each run. If major alignment or
warping issues were present, we used the @ss_review_driver to
visualize the data and troubleshoot challenges in the
pre-processing steps. This additional visual inspection process
may help identify when a script failed and provide visualization
of slices that may not be shown in the APQC file. Data were
considered usable if there were no incidental findings, if the
functional images were clear with little to no warping or blurring,
and if the functional images were well aligned with both the
anatomic images and the template. Data were excluded if the
preprocessing scripts did not successfully complete after
three attempts.
Of the 129 available data sets, six data sets were excluded due to (A)
Script did not complete successfully (n = 2), (B) Distortion in the
functional image (n = 1), or (C) Incidental findings (n = 3; Table 2). No
data sets were excluded due to motion, leaving 123 data sets to be included
for subsequent analysis (Figure 2). The QC data set contained relatively
low levels of motion in terms of quantitative metrics: total censor fraction
(Mean = 11%, SD = 17%) and max displacement (Mean = 1.25 mm,
SD = 0.77 mm). Despite a relatively low censor fraction and max
displacement, 30.9% of the data sets had mild censoring or greater
(>10%), 14.6% had medium censoring or greater (>20%), and 6.5% of the
data sets had severe censoring greater than 50% (Table 3).
TABLE 2 Excluded resting state data sets.
ID
Exclude
QC criteria failed (rationale)
315
X
C (incidental finding, black hole in epi file)
405
X
C (incidental finding, black hole in epi file)
409
X
B (distortion in the epi file)
518
X
A (brain was flipped, script failed 3+ times)
519
X
A (brain was flipped, script failed 3+ times)
716
X
Notes/Examples
During the volume registration step the functional data flipped and problem could not
be resolved
During the volume registration step the functional data flipped and problem could not
be resolved
C (incidental finding, atrophy and lesions in epi
file)
FIGURE 2
Resting state fMRI data processing and QC workflow.
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TABLE 3 Resting state data sets exceeding quantitative QC criteria for motion by severity level.
ID
Mild censoring (>10%)
Medium censoring (>20%)
101
X
X
102
X
104
X
105
X
106
X
X
107
X
X
109
X
111
X
112
X
114
X
208
X
214
X
307
X
X
309
X
X
314
X
X
316
X
X
X
402
X
408
X
422
X
502
X
504
X
506
X
507
X
X
X
508
X
509
X
511
X
X
512
X
X
601
X
620
X
X
701
X
X
703
X
X
705
X
X
706
X
X
X
708
X
X
X
710
X
712
X
X
X
713
X
X
714
X
X
X
715
X
X
Totals
39
21
Discussion
X
X
X
8
of QC approaches can be used to determine data quality and
ultimately inclusion or exclusion of a fMRI data set in group
analysis, and there are no standards for reporting qualitative
approaches. Image artifacts, incidental anatomic findings, and
alignment failures that may cause mislocalization of functional data
The QC approach described above avoids the use of thresholds
for excluding participants and favors inclusion of as many data sets
as possible and emphasizes qualitative approaches to QC. A variety
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Severe censoring (>50%)
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in anatomic space are our primary reasons for excluding data. These
features may be missed if only quantitative metrics are used to
evaluate data quality. Global metrics such as homogeneity and
censoring are unlikely to vary if an image is flipped upside down or
if there is an area of localized hypointensity on the BOLD images
indicating a potential incidental finding.
Regarding motion, rather than removing entire data sets from
group analysis based on excessive censoring as is commonly done,
we advocate for relying on within participant censoring and
scrubbing methods to clean motion-related artifacts. There is a
non-inclusion aspect as well as real dollar cost when excluding data.
Often funded by grants, research money is spent recruiting
participants, acquiring data, and paying staff to analyze those data.
In addition, participants have volunteered their time into studies.
Hence, we as researchers have a social and financial obligation to
use the data we have collected to the fullest extent and to get the
greatest power out of them that we can. This dataset had relatively
little motion; however, nearly 15% would have been excluded had
we used a threshold approach at medium (>20%) censoring.
We have successfully used this inclusive approach in several studies
where motion was a greater concern, including studies in a pediatric
population (Lepping et al., 2015, 2019).
Some aspects of motion are more challenging to compensate.
Minimizing participant motion at data acquisition is ideal; however,
this is not realistic in all situations. Several publications offer
methods for prospective motion correction for echo-planar imaging
(EPI) (Muraskin et al., 2013; Herbst et al., 2015; Maziero et al.,
2020). This is achieved by using an in-scanner camera for head
tracking to measure head motion in real time and prospectively
adjusting the acquisition positioning accordingly. Other useful
methods have been developed for fMRI to adjust acquisition
positioning during scanning by measuring and correcting for head
motion in real time and prospectively for EPI sequences and with
further improvement when combined with retrospective motion
correction methods (Lee et al., 1998; Thesen et al., 2000; Beall and
Lowe, 2014; Lanka and Deshpande, 2019). While not perfect, some
of these prospective methods have been successfully used in
resting-state functional connectivity analyses (Lanka and
Deshpande, 2019), however, these methods are not available for all
researchers, and additional sequence and statistical considerations
are still needed.
Many of the imaging analysis software packages have added QC
tools that have made it easier to assess data quality and report standard
quality metrics across packages. AFNI’s APQC html output solidified
many of the quality assessment steps we were doing already, including
many of the qualitative visual inspection steps. Additional
functionality, if provided in the software packages, would further
improve the QC process. First, the APQC html file does not currently
support saving the data checking within the file itself. Because of this,
we have used a separate tool, our REDCap checklist to house the
assessments. Second, we have incorporated examples of poor quality
data within our REDCap checklist. If that were included in the
software output, raters could easily see what the data should not look
like, and training for qualitative assessment would be more consistent.
Next, other tools within AFNI create QC output files that indicate
whether alignment or other downstream steps are likely to fail. Adding
that to the APQC process would be useful. Finally, we use the REDCap
checklist and project database to export summary QC data for an
Frontiers in Neuroscience
entire project. It would be helpful to have a group summary QC
output directly from the analysis software.
Conclusion
While quantitative QC metrics including motion are important data
to consider when assessing fMRI data quality, some data quality issues
may be missed if only quantitative assessments are conducted. Our use
of visual inspection throughout the data analysis process ensures that
anatomic incidental findings, image artifacts, and processing errors are
removed prior to group analysis. Our REDCap checklist can be used to
facilitate training of staff and reporting image quality.
Data availability statement
Publicly available datasets were analyzed in this study. This data
can be found at: https://osf.io/qaesm/wiki/home/. Analysis scripts
used in this manuscript can be found here: https://github.com/
rlepping/kumc-hbic/tree/rsfMRI-qc-paper.
Ethics statement
All procedures involving human participants were performed in
accordance with the ethical standards of the Declaration of Helsinki,
and the study was approved by the Institutional Review Board where
the data were collected. Informed consent was obtained from
all participants.
Author contributions
RL, H-WY, BM, WB, JH, and LM conceived and designed the
approach. RL, H-WY, BM, JH, RK, VP, and LM contributed to the
analysis. All authors contributed to the interpretation of the data,
drafting and revising of the manuscript, and provide approval for
the manuscript.
Funding
This work was funded in part by the Hoglund Biomedical
Imaging Center—which is supported by a generous gift from
Forrest and Sally Hoglund—and funding from the National
Institutes of Health: S10 RR29577 to the Hoglund Biomedical
Imaging Center, UL1 TR000001 to the Frontiers: Heartland
Institute for Clinical and Translational Research (CTSA), and P30
AG035982 to the University of Kansas Alzheimer’s Disease Research
Center (KU ADRC).
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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Publisher’s note
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reviewers. Any product that may be evaluated in this article, or claim
that may be made by its manufacturer, is not guaranteed or endorsed
by the publisher.
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
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