White Matter Hyperintensities, Grey Matter Atrophy, and Cognitive Decline in
Neurodegenerative Diseases
Mahsa Dadar
1,2
(PhD) mahsa.dadar@mail.mcgill.ca
Ana Laura Manera
1,2
(MD) ana.manera@mail.mcgill.ca
D. Louis Collins
1,2
(PhD) louis.collins@mcgill.ca
1. NeuroImaging and Surgical Tools Laboratory, Montreal Neurological Institute, McGill University, Montreal,
Quebec, Canada.
2. McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec,
Canada.
Corresponding Author Information:
Mahsa Dadar, Montreal Neurological Institute, 3801 University Street, Room WB320, Montréal, QC, H3A 2B4
Email: mahsa.dadar@mcgill.ca
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Abstract:
Introduction: White matter hyperintensities (WMHs) as seen on T2w and FLAIR scans represent
small-vessel disease related changes in the brain. WMHs are associated with cognitive decline in
the normal aging population in general and more specifically in patients with neurodegenerative
diseases. In this study, we assessed the different spatial patterns and relationships between WMHs
and grey matter (GM) atrophy in normal aging, individuals with mild cognitive impairment (MCI),
Alzheimer’s dementia (AD), fronto-temporal dementia (FTD), and de novo Parkinson’s disease
(PD).
Methods: Imaging and clinical data were obtained from 3 large multi-center databases: The
Alzheimer's Disease Neuroimaging Initiative (ADNI), the frontotemporal lobar degeneration
neuroimaging initiative (NIFD), and the Parkinson’s Progression Markers Initiative (PPMI).
WMHs and GM atrophy maps were measured in normal controls (N= 571), MCI (N= 577), AD
(N= 222), FTD (N= 144), and PD (N= 363). WMHs were segmented using T1w and T2w/PD or
FLAIR images and mapped onto 45 white matter tracts using the Yeh WM atlas. GM volume was
estimated from the Jacobian determinant of the nonlinear deformation field required to map the
subject’s MRI to a standard template. The CerebrA atlas was used to obtain volume estimates in
84 GM regions. Mixed effects models were used to compare WMH in different WM tracts and
volume of multiple GM structures between patients and controls, assess the relationship between
regional WMHs and GM loss for each disease, and investigate their impact on cognition.
Results: MCI, AD, and FTD patients had significantly higher WMH loads than the matched
controls. There was no significant difference in WMHs between PD and controls. For each cohort,
significant interactions between WMH load and GM atrophy were found for several regions and
tracts, reflecting additional contribution of WMH burden to GM atrophy. While these associations
were more relevant for insular and parieto-occipital regions in MCI and AD cohorts, WMH burden
in FTD subjects had greater impact on frontal and basal ganglia atrophy. Finally, we found
additional contribution of WMH burden to cognitive deficits in AD and FTD subjects compared
with matched controls, whereas their impact on cognitive performance in MCI and PD were not
significantly different from controls.
Conclusions: WMHs occur more extensively in MCI, AD, and FTD patients than age-matched
normal controls. WMH burden on WM tracts also correlates with regional GM atrophy in regions
anatomically and functionally related to those tracts, suggesting a potential involvement of WMHs
in the neurodegenerative process.
Keywords: White matter hyperintensities, small-vessel disease, neurodegenerative disease,
Alzheimer’s disease, fronto-temporal dementia, Parkinson’s disease, mild cognitive impairment
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Introduction
White matter hyperintensites (WMHs), defined as nonspecific hyperintense regions in the white
matter tissue of the brain on T2-weighted or FLuid-Attenuated Inversion Recovery (FLAIR)
magnetic resonance images (MRIs) are common findings in the aging population in general
(Hachinski et al., 1987). These age-related WMHs are considered to be the most common MRI
signs of cerebral small vessel disease and are generally due to chronic hypoperfusion and
alterations in the blood brain barrier (McAleese et al., 2016). Other pathological correlates of
WMHs include demyelination, axonal and neuronal loss, higher levels of microglial activation, as
well as arteriosclerosis due to hypoxia, inflammation, degeneration, and amyloid angiopathy
(Abraham et al., 2016; Gouw et al., 2010).
WMHs are known to have a higher incidence in neurodegenerative diseases such as Alzheimer’s
disease (AD) (Capizzano et al., 2004; Dadar et al., 2017a; Dubois et al., 2014; Tosto et al., 2014),
dementia with Lewy bodies (DLB) (Barber et al., 1999), Parkinson’s disease (PD) (Mak et al.,
2015; Piccini et al., 1995), fronto-temporal dementia (FTD) (Varma et al., 2002), as well as
individuals with mild cognitive impairment (MCI) (DeCarli et al., 2001; Lopez et al., 2003; Dadar
et al., 2017a). Patients with WMHs generally present with significantly more severe cognitive
deficits and suffer greater future cognitive decline compared with individuals with the same level
of neurodegeneration related pathologies without WMHs (Au et al., 2006; Carmichael et al., 2010;
Prins and Scheltens, 2015; Dadar et al., 2020b, 2019, 2020a, 2018b, 2020b).
Few studies have investigated the relationship between the longitudinal changes in WMHs in
different white matter tracts, neurodegenerative changes, and cognitive decline. In a relatively
small sample, Burton et al. studied the impact of WMHs in late-life dementia in DLB, PD and AD
(Burton et al., 2006). They found significantly greater total load of WMHs in AD, but not PD or
DLB. They did not find a significant association between the rate of change in WMH load and
cognitive performance (Burton et al., 2006). In a community-based cohort of 519 older adults,
Rizvi et al. found that increased WMH load in association and projection tracts were related to
worse memory function (Rizvi et al., 2020). However, they did not investigate the relationships
with measures of grey matter atrophy. In another aging sample of 2367 adults (age range 20-90
years), Habes et al. reported that WMHs in most tracts were related to age-related atrophy patterns,
as measured by Spatial Pattern of Alteration for Recognition of Brain Aging index (Habes et al.,
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2018). However, they did not investigate regional grey matter atrophy patterns or the relationships
with cognitive performance.
In this study, we used a previously validated automated WMH segmentation technique (Dadar et
al., 2017c, 2017b) to quantify the WMHs in 3 large multi-center cohorts of neurodegenerative
diseases, with a total of 1730 subjects and 5774 timepoints, and investigated the differences
between spatial distribution of regional WMHs in AD, PD, FTD, MCI, and cognitively normal
individuals. In addition, we investigated the relationship between WM tracts containing WMH
lesions and regional grey matter atrophy and cognitive performance.
Methods
Participants
Data used in this study includes subjects from Alzheimer's Disease Neuroimaging Initiative
(ADNI) database, the Parkinson's Progression Markers Initiative (PPMI), and the frontotemporal
lobar degeneration neuroimaging initiative (NIFD) that had either FLAIR or T2-weighted MR
images.
ADNI
The ADNI (adni.loni.usc.edu) was launched in 2003 as a public-private partnership led by
Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test
whether serial MRI, positron emission tomography, other biological markers, and clinical and
neuropsychological assessment can be combined to measure the progression of MCI and early AD.
ADNI was carried out with the goal of recruiting 800 adults aged from 55 to 90 years and consists
of approximately 200 cognitively normal patients, 400 patients with MCI, and 200 patients with
AD (http://adni.loni.usc.edu/wp-content/uploads/2010/09/ADNI_GeneralProceduresManual.pdf).
ADNIGO is a later study that followed ADNI participants who were in cognitively normal or early
MCI stages (http://adni.loni.usc.edu/wp-
content/uploads/2008/07/ADNI_GO_Procedures_Manual_06102011.pdf). The ADNI2 study
followed patients in the same categories, recruiting 550 new patients (http://adni.loni.usc.edu/wp-
content/uploads/2008/07/adni2-procedures-manual.pdf). The longitudinal MRI data used in this
study included T1w, T2w/proton density–weighted acquisitions from ADNI1 patients and T1w
and FLAIR acquisitions from ADNI2/GO patients. The scanner information and image acquisition
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parameters have been previously described (Dadar et al., 2017a). The ADNI1, ADNI2 and
ADNIgo studies acquired data from subjects on a yearly basis.
PPMI
The PPMI (http://www.ppmi-info.org) is a longitudinal multi-site clinical study of approximately
600 de novo PD patients and 200 age-matched healthy controls followed over the course of five
years (Marek et al., 2011). The study was approved by the institutional review board of all
participating sites and written informed consent was obtained from all participants before inclusion
in the study.
NIFD
The frontotemporal lobar degeneration neuroimaging initiative (FTLDNI) is founded through the
National Institute of Aging and started in 2010. The primary goals of FTLDNI are to identify
neuroimaging modalities and methods of analysis for tracking frontotemporal lobar degeneration
(FTLD) and to assess the value of imaging versus other biomarkers in diagnostic roles. The
Principal Investigator of FTLDNI is Dr. Howard Rosen, MD at the University of California, San
Francisco. The data is the result of collaborative efforts at three sites in North America. For up-to-
date information on participation and protocol, please
visit: http://memory.ucsf.edu/research/studies/nifd. The FTLDNI contains 120 cognitively normal
controls and 120 patients with FTD followed yearly for three years.
MRI Measurements
WMHs
All T1-weighted, T2-weighted, proton density (PD), and FLAIR MRI scans were preprocessed in
3 steps using our standardized pipeline: denoising (Manjón et al., 2010), intensity non-uniformity
correction (Sled et al., 1998), and intensity normalization into the range 0–100. For each subject,
the T2-weighted, PD, and FLAIR scans were then co-registered to the T1-weighted scan of the
same visit using a 6-parameter rigid registration and a mutual information objective function
(Collins et al., 1994; Dadar et al., 2018a). Using a previously validated fully automated WMH
segmentation method and a library of manual segmentations based on 53 patients from ADNI1
and 46 patients from ADNI2/GO, the WMHs were automatically segmented for all longitudinal
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was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made
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