Brain imaging of neurovascular dysfunction in Alzheimer’s disease
Axel Montagne1, Daniel A. Nation2, Judy Pa3, Melanie D. Sweeney1, Arthur W. Toga3, and Berislav V. Zlokovic1
1Zilkha Neurogenetic Institute and Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
2Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA
3Department of Neurology, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA 90089, USA
Abstract
Neurovascular dysfunction, including blood–brain barrier (BBB) breakdown and cerebral blood
flow (CBF) dysregulation and reduction, are increasingly recognized to contribute to Alzheimer’s
disease (AD). The spatial and temporal relationships between different pathophysiological events
during preclinical stages of AD, including cerebrovascular dysfunction and pathology, amyloid
and tau pathology, and brain structural and functional changes remain, however, still unclear.
Recent advances in neuroimaging techniques, i.e., magnetic resonance imaging (MRI) and
positron emission tomography (PET), offer new possibilities to understand how the human brain
works in health and disease. This includes methods to detect subtle regional changes in the
cerebrovascular system integrity. Here, we focus on the neurovascular imaging techniques to
evaluate regional BBB permeability (dynamic contrast-enhanced MRI), regional CBF changes
(arterial spin labeling- and functional-MRI), vascular pathology (structural MRI), and cerebral
metabolism (PET) in the living human brain, and examine how they can inform about
neurovascular dysfunction and vascular pathophysiology in dementia and AD. Altogether, these
neuroimaging approaches will continue to elucidate the spatio-temporal progression of vascular
and neurodegenerative processes in dementia and AD and how they relate to each other.
Keywords
Alzheimer’s disease; Neurovascular dysfunction; Blood–brain barrier; Cerebral blood flow; Magnetic resonance imaging
Introduction
Vascular contributions to dementia and Alzheimer’s disease (AD) are increasingly
recognized [115, 198]. It is estimated that up to 45 % of all dementias worldwide is wholly
or partly due to age-related small vessel disease of the brain [188]. AD is characterized by
✉Berislav V. Zlokovic, [emailprotected].
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the presence of neurovascular dysfunction, inflammation, tau neurofibrillary tangles,
amyloid-β (Aβ) plaques, neuronal loss and cognitive decline [198]. How these different
pathologies relate to each other and contribute to cognitive decline, particularly during early
stages of preclinical AD, and their exact role in the disease pathogenesis, remains, however,
still debatable. Neuroimaging methods are currently used to aid in AD diagnosis by
identifying structural and functional brain changes via magnetic resonance imaging (MRI)
or positron emission tomography (PET), which was virtually impossible to assess in the
living human brain until recently [115, 124]. Alongside to contributing to AD diagnostic
confirmation, imaging can also inform differential diagnosis by identifying alternative
and/or related pathologies, for instance cerebrovascular disorder and non-AD
neurodegenerative diseases contributing to a complex spectrum of dementias.
Determining early AD pathophysiological changes has immediate prognostic importance
during preclinical AD and mild cognitive impairment (MCI) stages. Only a fraction of MCI
patients progress to clinical AD over 5–10 years, and a recent meta-analysis concluded that
the majority of MCI patients will not progress to dementia even after 10 years follow-up
[114]. Interestingly, over one-third of patients diagnosed with MCI at baseline may
eventually return to normal cognition [66]. Therefore, during preclinical and MCI stages of
AD it would be enormously beneficial to predict when and who will progress to AD, and
have reliable and predictive neuroimaging biomarkers supporting conversion from
preclinical and MCI stages to AD, and/or back from the MCI stage to a normal cognitive
stage.
Recent technological advances in neuroimaging, including novel image acquisition
sequences and pre- and post-processing analyses, now enable sensitive regional and
quantitative measures of cerebrovascular functions and brain’s structural and functional
connectivity to be studied. For example, multiparametric neuroimaging approaches (i.e.,
dynamic-, functional-, and structural-MRI, and molecular PET imaging) are being used
recently to evaluate neurovascular dysfunction in early dementia and AD including MRI
methods to measure regional blood–brain barrier (BBB) permeability and cerebral blood
flow (CBF) dysregulation, cerebral blood volume (CBV), cerebral metabolic rate of oxygen
consumption (CMRO2), microbleeding events, white matter connectivity, white matter
lesions (WML), and regional brain atrophy, as well as PET imaging of BBB glucose
transport and amyloid cerebrovascular pathology. Neuroimaging can offer enormous benefit
to AD research and practice, particularly when applied longitudinally to elucidate regional
and temporal developments of different pathologies, enable early detection of pathological
and functional changes, inform novel treatment targets, and evaluate therapeutic efficacy.
Here, we review neurovascular imaging approaches and examine how they can inform us
about neurovascular dysfunction and vascular pathophysiology in AD and how they can
potentially contribute to identify individuals at risk for dementia and AD.
Neurovascular dysfunction in AD
The etiology of late-onset sporadic AD is currently unknown, but mounting evidence
suggests that cerebrovascular dysfunction contributes to AD etiology and progression [115,
198].
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The neurovascular unit
The neurovascular unit (NVU) is comprised of vascular cells (endothelial cells, pericytes,
vascular smooth muscle cells), glia (astrocytes, microglia, oligodendrocytes), and neurons
that collectively underlie all functional responses of the central nervous system (CNS) [198].
Brain microvessels form a physical BBB that normally prevents macromolecules such as
neuroactive peptides and proteins from entering the brain [199] unless they have specific
carriers and/or receptors in brain endothelium [109, 197], which contrasts with the highly
permeable vasculature in peripheral circulation [111]. Moreover, an intact BBB importantly
regulates Aβ clearance from brain via receptor-mediated transport mechanisms in
microvascular endothelium that control efflux of brain-derived Aβ from brain to circulation
and influx and/or re-entry of circulating Aβ into the brain [109]. Brain endothelial cells are
also able to regulate their own local coagulation environment within the brain
microcirculation that is critical for normal capillary blood flow [186]. In AD, however,
cerebrovascular dysfunction at the NVU including BBB breakdown, disrupted CBF,
hypoperfusion, oligemia, and impaired vascular brain-to-blood clearance and blood-to-brain
transport of Aβ contributes to disease pathophysiology [115, 198, 200].
Two-hit vascular hypothesis of AD
According to the two-hit vascular hypothesis, damage to brain microcirculation (hit one) in
the aging brain, which may result from the effects of genetic risk factors, environmental
factors, lifestyle or vascular risk factors such as hypertension, diabetes and/or
hyperlipidemia, initiates a cascade of pathogenic events. This includes changes in BBB
function such as BBB breakdown and increased permeability, and changes in brain perfusion
including CBF dysregulation and reduction. BBB breakdown allows entry into the brain of
blood-derived neurotoxic molecules (e.g., immunoglobulins, albumin, fibrinogen, and
thrombin) and cells (e.g., erythrocytes, leukocytes, among others) that cause vascular
pathology in brain parenchyma and can directly damage neurons [199]. Altogether, the
vascular disruption contributes to the second hit (hit two), where increased Aβ accumulation
in brain parenchyma and impaired clearance exerts neurotoxic effects on the brain leading to
neurodegeneration and dementia [46]. Increasing number of studies recognize a synergistic
relationship between BBB dysfunction and Aβ accumulation and neurofibrillary tangles,
which initially form in the medial temporal lobe and hippocampus during the early stages of
the disease before progressing to the neocortex [124]. Specifically, inadequate cerebral
perfusion can promote Aβ accumulation and neurofibrillary tangles, and the neurotoxic
effects of Aβ in turn impair vascular function, such as endothelial function and
neurovascular coupling, and induce reduced CBF, as recently reviewed [133]. A recent
review concluded that Aβ accumulation only partially explains BBB impairment in AD [56],
thus supporting the vascular-mediated neurodegeneration hypothesis.
Detecting neurovascular dysfunction in humans
To date, the majority of evidence of microvascular pathology in AD comes from histological
and biofluid analysis. For example, numerous post-mortem histological studies report
accumulation of blood-derived proteins (e.g., immunoglobulins, albumin, fibrinogen, and
thrombin) in the hippocampus and cortex and/or changes in tight junction endothelial
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protein expression at the BBB in human AD brains [76, 81]. Furthermore, a commonly used
in vivo measure of BBB breakdown is the albumin ratio (AR), namely the cerebrospinal
fluid (CSF) to serum ratio of albumin concentration [22, 75]. Albumin is a relatively large
molecule (molecular weight, 67 kDa) that is abundant in blood and cannot cross an intact
BBB, thus increased AR is interpreted as an indicator of BBB breakdown. AR is increased
in AD patients particularly in those with vascular risk factors [22], as well as in individuals
with MCI [115] and cognitively normal apolipoprotein E-ε4 (APOE4) carriers [75], which is
the major genetic risk factor for sporadic AD. Additional biofluid measures of
cerebrovascular dysfunction, including an early increase in CSF pericyte soluble platelet-
derived growth factor receptor-β (sPDGFRβ) marker and brain endothelial cells markers,
further support a role of vascular changes during early cognitive decline stages, as recently
reported in a comprehensive review of NVU cell-specific CSF biomarkers in MCI and AD
individuals [166].
Recent advances in neuroimaging approaches are now enabling quantitative, regional
detection of cerebrovascular dysfunction in the living human brain. For example, Montagne
et al. provide the first in vivo evidence of BBB disruption in the aging hippocampus which
worsens in MCI, using a high-resolution MRI method to simultaneously map the blood-to-
brain transfer constant of gadolinium (Ktrans) regionally and quantitatively with a resolution
sufficient to image discrete subregions of the hippocampus [115]. Interestingly, they also
found that BBB breakdown correlates with increases in CSF levels of the BBB-associated
pericyte injury marker sPDGFRβ [115]. Other neuroimaging studies demonstrated CBF
dysregulation in mild to moderate AD patients [1] and in older adults at risk for AD prior to
detectable Aβ accumulation or brain atrophy [142]. These recent findings support the
presence of early vascular changes such as BBB breakdown and abnormal CBF responses in
the aging brain that collectively may contribute to accelerated disease progression during
preclinical and early stages of AD.
Dynamic and functional neuroimaging of BBB integrity and blood flow
Imaging methodology of BBB permeability
The most widely used technique to investigate the BBB integrity in vivo is dynamic
contrast-enhanced (DCE)-MRI with paramagnetic gadolinium-based contrast agents
(GBCAs), small molecules (molecular weight, ~1 kDa) that cross disrupted BBB [121]. The
transfer rate of GBCAs from the intravascular into the extravascular space of the brain can
be measured to determine BBB permeability. Although GBCAs are considered relatively
safe compared to iodine-based agents, there are rare reports of nephrogenic systemic [171],
and more recently brain retention associated with the linear forms of GBCAs, compared to
macrocyclic GBCAs which are at low risk for brain deposition [117].
Several post-processing approaches of DCE dynamic images exist to visualize contrast agent
distribution over time. The most straightforward method is to determine the relative signal
enhancement in a region-of-interest (ROI), as was used in numerous MRI studies [77, 163,
184, 185]. A stronger signal enhancement of the ROI in the brain extravascular space
indicates a higher concentration of contrast agent, which is interpreted as a leakage across
the BBB. However, most applications of DCE in the brain have focused on imaging a
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relatively high degree of BBB breakdown (such as observed in malignant tumors, multiple
sclerosis or stroke), rather than subtle changes in BBB permeability, as found for example
during normal aging and MCI stages [115].
The BBB leakage can be determined quantitatively to compute the so-called blood-to-brain
transfer constant, Ktrans. There are several mathematical models available to compute Ktrans
that differ in complexity and assumptions under which they can be applied [158]. For
example, a two-compartment models such as the Tofts model [174] assumes that each ROI
contains a vascular compartment and an extravascular extracellular space compartment and
calculates both the influx and backflux of the tracer, whereas other models such as the Patlak
model determines only linear influx of tracer into the brain during the unidirectional entry
phase assuming its minimal backflux from brain back into the circulation [129]. Importantly,
the Ktrans values enable detection of a subtly damaged BBB as for example during normal
aging and preclinical AD stages and in MCI [115] that is at least one to two orders of
magnitude lower than the BBB breakdown found in brain tumors, during relapsing multiple
sclerosis attacks, or post-ischemic BBB changes after stroke [103, 156]. Interestingly,
Montagne and colleagues showed that the Patlak linear regression model can estimate low
vascular permeability (i.e., Ktrans = 0.1–0.6 × 10−3 min−1) as shown using an optimized
DCE-MRI protocol with improved pre- and post-processing analysis [14, 115].
Most pharmacokinetic models require the arterial input function (AIF) to be known. Hence,
determination of AIF represents a key issue in the reliable estimation of pharmacokinetic
parameters. There are several strategies for AIF selection and the optimal method varies
according to pathology, study aims and clinical requirements [28]. In most applications,
direct measurement of the AIF is generally considered preferable to population-based AIFs
averaged from superior sagittal sinus [104]. Limitations of a population approach are: not
always obtainable, susceptible to partial volume and in-flow artifacts, variability in which
vessel is sampled, and variability in vessel measurement with approaches ranging from
manual ROI selection to methods for automatic vessel detection [33]. Earlier this year,
Montagne et al. measured the AIF in each individual from the common carotid artery instead
of using an average value from the superior sagittal venous sinus to determine tracer
concentration in blood [115]. They have highlighted the fact that individual AIF
measurements are important particularly if the studied population diverges by age as
changes in blood volume and flow may affect AIF and the final Ktrans measurements.
The calculation of contrast agent concentration from signal enhancement requires reliable
estimation of intrinsic tissue parameters such as the pre-contrast longitudinal relaxation time
T1. There are several methods of estimating baseline T1, with variable flip-angle and
multiple time repetition being the most common. The effect of uncertainty in T1 estimation
on the calculation of contrast agent concentrations has been investigated by Schabel and
Parker [144]. They demonstrate that T1 produces a significant concentration bias, which
shows the importance of accounting for T1 when assessing BBB disruption in different
tissue types [9].
There is also disagreement on whether to describe the capillary bed in terms of blood
concentration or plasma concentration by correcting for the hematocrit (Hct). In the latter
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case, Hct should ideally be determined for every subject, but a standard value such as Hct =
0.45 is often assumed [158]. In theory, all model equations and resulting parameter values
can easily be converted between conventions. Other sources of uncertainty rarely considered
in DCE-MRI studies may arise due to a lack of available data. For example, relativity values
specific to a contrast agent, field strength and tissue pathology are rarely known, with the
consequence that uniform relativity across tissues and compartments is generally assumed.
Barnes and coworkers have developed a software (Rocketship) suite for dynamic MRI
datasets (in particular DCE-MRI) analysis [15]. This software allows for DCE-MRI data
analysis using several pharmacokinetic models used currently in the literature as well as
data-driven analysis methods. There is a possibility to correct for hematocrit and relativity
values specific to the contrast agent used, among many other parameters, which is important
for high accuracy of BBB Ktrans measurements.
Finally, DCE-MRI methodology can still be improved to increase the sensitivity to subtle
BBB permeability. Progress can be made by increasing signal-to-noise ratio (SNR) or by
increasing spatial resolution. A higher SNR would help to improve the sensitivity to low
concentrations of gadolinium due to limited BBB leakage, and a better spatial resolution
would help to detect small “hot spots” of leakage. Moving to higher field strengths such as 7
T may help in this regard [178]. Also, a multi-compartment model [129, 174], combined
with proper contrast agent concentration calculations [14, 115, 144] and whole-brain voxel-
wise analysis methods, should increase sensitivity for low and localized leakage.
Use of BBB imaging in AD
Vascular dysfunction, including BBB breakdown, is increasingly recognized as contributory
to AD pathophysiology. In vivo BBB imaging in humans can inform regional brain changes
in BBB integrity in the context of physiological circulation and homeostasis, which is
advantageous to other common methods to evaluate BBB integrity including analysis of
tight junctions and plasma-derived proteins in biofluids or histological markers in post-
mortem tissue.
Several early studies using DCE-MRI in the context of AD yielded semi-quantitative BBB
analysis interpretations that differ from the current understanding of DCE-MRI. For
instance, Wang et al. in 2006 used a DCE-MRI sequence and observed that the signal
enhancement curve was higher after contrast agent administration in the hippocampus of
MCI compared to control subjects [185]. This was interpreted as lower local blood volume
and longer signal retention, indicative of vascular changes in the hippocampus rather than an
indication of BBB permeability [185]. Additionally, DCE-MRI analysis in 15 subjects with
probable AD and their healthy spouses revealed aberrant temporal patterns of gadolinium-
based signal enhancement, interpreted as altered blood–brain–CSF compartment kinetics
[163]. Considering the results and timeframe of the study (30 min), the authors concluded
that contrast agent accumulation in brain was more conceivable to occur via blood–brain-
CSF pathway, rather than blood–CSF–brain pathway [163]. Several groups used DCE-MRI
protocols to study BBB integrity in the areas of white matter hyperintensities (WMH) and
reported different findings. For instance, BBB permeability in WMH areas was not changed
in subjects with dementia [184], but was found to be altered in Binswanger’s disease, a rare,
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more diffuse and subcortical form of vascular dementia [77]. These contradictory results
may have arisen from methodological differences.
In a more quantitative manner, two recent studies investigated BBB dysfunction in subjects
with vascular cognitive impairment [167, 168]. Both studies used a post-contrast scanning
time of 24.5 min and the Patlak model to calculate BBB permeability (Ki) maps. The
resulting Ki maps were investigated by means of distribution histograms. They demonstrated
an increased contrast agent leakage localized in the center of some WMH lesions, though
not in every WMH, nor in its periphery, and were inconclusive whether BBB breakdown
contributes to WMH lesions or not, suggesting more work needs to be done in this area.
Recent study using a novel high-resolution DCE-MRI technique to simultaneously map the
Ktrans constant regionally and quantitatively, and with a resolution sufficient to image
discrete regions of the hippocampus [115], suggested that Ktrans increases linearly with age
only in the hippocampus and its CA1 and dentate gyrus regions, which worsens in
individuals with MCI as indicated by ~60 % increase in Ktrans values compared to
cognitively intact age-matched controls (Fig. 1). These increases in BBB permeability were
not associated with reduced hippocampal volume, suggesting that they may precede the
development of tissue damage and hippocampal atrophy [115]. These observations provide
the first direct evidence for an age-dependent and regionally selective disruption of the BBB
in the hippocampus of cognitively normal individuals, which is exacerbated in MCI.
The above-described studies are heterogeneous in regard to image acquisition and analysis
methods, thus care must be taken when comparing results from different studies. For
example, tumor studies use higher temporal resolution and shorter imaging duration on
average compared with studies of less permeable tissue such as normal appearing brain and
dementia [14]. Additionally, motion artifacts can interfere with post-processing DCE
datasets even though new motion correction tools have emerged. Due to the wide range of
analysis techniques used and their strong dependence on underlying assumptions and
acquisition parameters, a lack of inter-study comparability represents a major problem. The
importance of appropriate model selection has been demonstrated both theoretically [14,
157] and experimentally in low-permeability brain tissues [42, 103, 115].
Imaging of CBF dysregulation
A full discussion of arterial spin labeling (ASL)-MRI methodology is beyond the scope of
this review and has been summarized elsewhere [48]. Briefly, in vivo assessment of CBF via
ASL-MRI may be promising as a potential tool for early detection and characterization of
AD progression. CBF refers to the rate of delivery of arterial blood to the capillary bed in
brain tissue and is typically quantified in milliliters of blood per 100 g of tissue per minute
[106]. ASL is a non-invasive and reliable MRI technique [128] that magnetically labels
arterial water in the brain and uses it as an endogenous tracer to measure CBF. Because
ASL-MRI provides a quantitative measure of CBF in the capillary bed, rather than a relative
measure such as the venous blood oxygen level-dependent (BOLD)-functional MRI (fMRI)
signal (see later subsection “Blood oxygen level-dependent contribution in functional
imaging”), it has the potential to more accurately estimate the magnitude and location of
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neuronal function [106], which is an advantage over peripheral measures of vascular
pathology.
Use of CBF imaging in AD
Several groups have now reported findings using ASL-MRI in different regions of AD
brains. For instance, studies frequently report hypoperfusion to precuneus, posterior
cingulate, inferior/superior parietal, and lateral prefrontal cortices [5, 6, 43, 82, 94, 195],
though additional areas of reduced perfusion have been described in the temporal lobe
including the parahippocampal gyrus and hippocampus [101, 191]. Importantly, as grey
matter loss could account for the reduced perfusion signal, several of these studies applied
an atrophy correction [4, 43, 94], which did not appear to significantly alter the findings of
regional hypoperfusion. In general, these regions displayed greater group level CBF
reductions than loss of grey matter tissue, consistent with the notion that the functional
change reflected by this measure exceeds volume loss. Interestingly, the above findings
correlate with regional reduction of glucose uptake by the brain, as reported in several
studies of AD using fluorodeoxyglucose (FDG)-PET imaging [90, 102] (see later subsection
“Molecular imaging of AD pathology”). Chen and colleagues directly compared ASL-MRI
with FDG-PET acquisition in the same subjects at the same time and found a high
correlation between hypoperfusion and impaired glucose uptake by the brain, which is not
surprising as glucose uptake is flow dependent [34].
Subjects with MCI are often conceptualized as representing individuals who are
transitioning from being cognitively normal to developing symptoms of early AD [131], and
some subjects remain in this prodromal stage for several years—up to 7.5 years according to
Roe and colleagues [140]—and some revert back to cognitively normal. A limited, but
growing, number of studies have applied ASL-MRI to this population [3, 32, 43, 94, 192].
Studies in MCI subjects generally report decreased CBF to precuneus, posterior cingulate,
and parietal regions [39, 120], and a further decrease in CBF to hippocampus, caudate, and
thalamus in AD compared to no cognitive impairment (NCI) or MCI (Fig. 2a). For example,
thalamic CBF decreased 20 % from MCI to AD stages and correlated with subjects’
Dementia Rating Scale (Fig. 2b, c). Interestingly, caudate CBF values also correlated
negatively with WML volume, more so in MCI and AD compared to NCI stages (Fig. 2d).
Despite an approximately 40 % global decrease in CBF in AD subjects compared to age-
matched cognitively normal adults [10], the medial temporal lobe has been reported to be
relatively hyperperfused in at-risk controls (i.e., APOE4 carriers) and subjects at early stages
of AD [4, 43, 61]. For example, in the study by Alsop and colleagues, despite the presence
of hypoperfused brain regions, hippocampal and parahippocampal regions were associated
with increased CBF in AD subjects relative to age-matched controls [4]. These inconsistent
findings remain across studies and are difficult to resolve to date. An appealing reason for
this discrepancy has been proposed by Østergaard et al., who suggested that brain capillary
dysfunction underlies the development of a neuronal energy crisis which triggers AD [125,
126]. They propose that increased capillary transit time heterogeneity for erythrocytes
passing through capillaries decreases the oxygen that can be extracted by the tissue so that,
as capillary transit times become more heterogeneous, a higher blood flow is required to
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maintain tissue oxygen supply. Obstruction of flow in some capillary branches may
therefore trigger an initial compensatory increase in blood flow in order to preserve tissue
oxygen extraction and neuronal function. Later on, hypoperfusion (which reflects
neurovascular adjustments in an attempt to maintain oxygen availability in the tissue), seen
in the progression from normal cognitive aging to mild dementia and AD, is therefore
consistent with early disturbances in capillary flow patterns and fits well into established
models of AD neuropathology [18]. However, this model needs further testing and validation
in human studies.
Despite the clear advantage of ASL-MRI to provide quantitative CBF measurement, several
methodological issues currently limit its widespread use. For example, multi-center studies
lack ASL-MRI standardization and many of the existing pulse sequences have limitations
(e.g., sensitivity to transit time effects, limited brain coverage, low spatial resolution, less
sensitivity to white matter CBF) which may account for some of the apparent conflicting
data reported in AD, at-risk AD, and MCI stages. The variability in methodology and
processing applied across studies has hindered the ability to define standard CBF reference
values. Altogether, while ASL-MRI holds promise, it has not been clearly demonstrated to
be ready for routine use in clinical trials and clinical practice, remaining a research tool
overall. Larger studies in MCI and AD with more direct comparison to existing molecular
and neurodegenerative biomarkers will be necessary to determine the clinical value of this
approach.
Blood oxygen level-dependent contribution in functional imaging
The BOLD contrast in fMRI has rapidly emerged as a powerful non-invasive technique for
studying brain function in humans. The BOLD-fMRI signal is produced by field
inhomogeneities induced by deoxyhemoglobin (dHb), an endogenous and natural contrast
agent. Specifically, the BOLD-fMRI signal reflects the loss of oxygen from hemoglobin,
causing its iron to become paramagnetic, which influences the magnetic field experienced
by proton spins within surrounding water molecules [123, 130]. Therefore, changes in the
local dHb concentration in the brain lead to modifications in MRI signal intensity [123,
172]. During neuronal activity, an increase of oxygen consumption is instantly followed by a
local increase in CBF and CBV, resulting in a net decrease of the amount of dHb, which
ultimately alters the MRI signal level [64, 107, 110, 135]. Additionally, it is important to
consider the dynamic pattern of the local dHb concentration. Although the dilation of
arteries and arterioles can be significant (up to 25 % of baseline) [173], their small volume
and high oxygenation means that arteries themselves contribute relatively little to the
BOLD-fMRI signal [80]. Veins, however, display large increases in dHb concentration that
contribute significantly to the BOLD-fMRI signal. Nevertheless, these increases can be
expected to be delayed with respect to the active capillary bed and may even be shifted as a
result of the venous drainage system [177]. Despite widespread use for over 20 years, the
nature of the BOLD-fMRI signal is a matter of debate [12, 67, 79, 107, 152].
Most fMRI studies treat the BOLD response as an indirect qualitative measure of neuronal
activity and interpret BOLD signal differences as changes in neuronal activity. However, the
BOLD signal reflects local changes in dHb content, which in turns exhibits an intricate
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dependence on changes in CBF, CBV, and the cerebral metabolic rate of oxygen
consumption (CMRO2) [27]. CMRO2 is assumed to be most tightly associated with neuronal
activity, echoing the notion that neurons necessarily expend energy to accomplish their work
[83]. The positive BOLD response observed in most fMRI experiments reflects the fact that
CBF increases relatively more than CMRO2, so that local capillary and venous blood are
more oxygenated during increased brain activity. Overall, the actual amplitude of the BOLD
response is a subtle balance between the relative increases in CBF and CMRO2 [24].
Elements that disturb the connection between CBF and CMRO2, such as aging and
degeneration in AD, may therefore alter the BOLD response even when neuronal activity is
unchanged. For example, there is growing evidence that variations in the cerebrovascular
system due to age and disease can significantly change the BOLD signal and complicate its
interpretation. Age-related factors include altered cerebrovascular ultrastructure, reduced
blood vessel elasticity, increased atherosclerosis, reduced resting state CBF, decreased
resting CMRO2, and reduced vascular reactivity to chemical modulators [47]. In fMRI
studies examining the effects of aging, researchers have found a significant age-dependent
decrease in the BOLD signal amplitude [26, 170], possibly correlating with age-related
decrease in the resistance of the cerebrovascular system [47, 179].
Use of functional MRI in AD
During the past decade, both task-based and resting state BOLD-fMRI studies have proved
to be a very useful tool in investigating brain functions and hemodynamic responses in
neurodegenerative disorders, especially AD.
In the mid-1990s, activation of the hippocampus and parahippocampal regions during
successful memory encoding has been shown in healthy young subjects during various
episodic memory tasks using a task-based fMRI paradigm [23, 164, 183]. A few years later,
the pioneering task-based fMRI studies on AD also focused on exploring alterations in
hippocampal activation [97, 108, 141, 153, 159]. To date, there are several task-dependent
fMRI studies which have consistently reported changes in hippocampal activation in AD
compared to healthy elderly controls, consistent with impaired memory function [52, 68,
108, 127, 138, 141, 153, 159]. Interestingly, several studies have reported increased
prefrontal cortical activity in AD subjects [69, 155, 159], suggesting that other networks
may increase activity as an attempted compensatory mechanism during hippocampal failure.
Although many of these studies found a decreased hippocampal activity in both MCI and
AD subjects, some studies revealed hyperactivity in the hippocampus in MCI individuals [4,
20, 52], which has been interpreted as a compensatory phenomenon, or a harbinger of
upcoming hippocampal failure. Cross-sectional studies suggest that this hyperactivity may
be present only at early stages of MCI, followed by a loss of activation in late stages of MCI,
similar to the pattern seen in individuals with AD [31]. Longitudinal studies furthermore
suggest that the presence of hyperactivity at baseline is a predictor of rapid cognitive decline
[21, 51, 113], and loss of hippocampal function on serial fMRI [122].
Interestingly, studies of older cognitively normal individuals or middle-aged adults have
demonstrated BOLD responses differ by APOE genotype [20, 60], including APOE4 carriers, as well as positive family history of AD [95]. Once more, many task-based fMRI
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studies reported a unique impaired functional connectivity/neuronal activity within the
hippocampus and to neighboring temporal regions in AD cases. However, modifications in
CBF and CBV, as well as changes in CMRO2 level should be accounted for and further
discussed since they are known to directly affect the BOLD-fMRI signal and thus may
contribute to the observed results. Consistent with this idea, some groups investigated task-
dependent BOLD signal changes in preclinical AD and reported that it might reflect altered
sensitivity to activation due to changes in baseline blood flow [7, 41].
In addition to task-based fMRI studies, recent functional imaging studies have demonstrated
AD-related alterations in the “default mode” network (DMN), during a resting state. The
resting state of the human brain was originally identified by its increased activity at wakeful
rest, including high resting glucose metabolism and blood flow [74, 136, 151]. An important
advantage of resting state fMRI imaging in AD is the ability to scan subjects who are too
impaired to actively participate in a task-based scanning paradigm or in whom the
interpretation of task-based fMRI responses would be confounded by differences in task
performance. The resting state fMRI findings in MCI and AD have been fairly consistent
across studies showing abnormalities in the DMN, including the hippocampus, precuneus,
medial prefrontal cortex, and lateral parietal cortices. These regions have been reported to be
active at rest [71, 136, 169], but during the performance of demanding cognitive tasks, DMN
activity decreased dramatically in AD individuals [44, 72, 150, 187].
Overall, a variety of recent reports have explored conditions under which the BOLD-fMRI
response is altered or abnormal in AD. While most of these studies interpreted that changed
BOLD responses were a reflection of altered underlying neuronal activity, it is becoming
increasingly recognized that in some situations, neurovascular coupling itself—including
hemodynamic changes of altered blood flow—could be affected. Neurovascular coupling
refers to the association of cellular activity and CBF, and alongside the BOLD-fMRI signal,
neurovascular coupling is also likely to undergo changes during healthy aging and during
AD-related pathological processes. Some of the changes that may occur even in healthy
elderly subjects include, for example, increased atherosclerosis [47]. In AD, the presence of
Aβ in the cerebral vasculature, together with altered neurotransmitter activity, impairs
synaptic, neuronal and glial function [85], may thus lead to an attenuated BOLD response.
Furthermore, Dumas and colleagues found changes in the hemodynamic response curve in
individuals with cerebral amyloid angiopathy (CAA) which supports that BOLD signal
changes are affected by vascular dysregulations present in AD [54]. Additionally, the
alterations in BOLD activity reported in AD also appear to be quite regionally specific and
dependent on the nature of the cognitive task, thus making it likely that the changes
observed in fMRI studies may represent local pathophysiological alterations in
neurovascular coupling.
BOLD-fMRI response is variable across subjects. The reproducibility of BOLD signal
changes within young healthy individuals during memory encoding tasks across separate
days is reported to be reasonable [78, 162]. Some studies have shown moderate-to-excellent
test–retest reproducibility of fMRI activation in older and cognitively impaired participants
[11, 40, 73, 134, 196]. Longitudinal functional imaging studies are needed to track the
evolution of alterations in the fMRI activation pattern over the course of normal aging and
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cognitive decline in clinical AD. In addition, fMRI studies should importantly take into
account the contribution of structural atrophy observed in dementias. A combination of
structural MRI, functional MRI and other imaging techniques such as PET amyloid-imaging
may serve as a valuable in vivo method to elucidate AD pathophysiological progression.
One promising area to palliate discrepancies regarding the meaning of the BOLD-fMRI
signal is the use of the ASL-MRI technique in combination with BOLD imaging (ASL/
BOLD). With the increasing application of fMRI techniques to study AD, there is a growing
need for quantitative measures that can more accurately reflect neuronal activity, as well as
local hemodynamic changes separately. Therefore, ASL/BOLD imaging would have the
potential to distinguish between the BOLD signal and the relative CBF response, and
thereby provide a more complete accounting of the functional changes occurring in specified
brain regions during the performance of cognitive or other tasks. These combined measures
appear promising to characterize the impact of aging, preclinical/clinical AD, genetic risk
factors, vascular risk factors, among others, on the CBF response to cognitive functions such
as episodic memory. In short, quantitative fMRI with combined ASL/BOLD may address
questions that cannot be fully answered with BOLD measures alone [24, 49, 146, 190].
Neuroimaging of vascular pathology and cerebral metabolism
Microbleeding events
Cerebral microbleeds (CMBs) are lesion-based MRI markers visualized as small
hypointense regions on T2*- and susceptibility-weighted imaging (SWI-MRI) scans [70].
These punctate lesions are thought to represent hemosiderin-laden macrophages
accumulated in perivascular spaces as a result of microhemorrhage [182]. Studies have
revealed two patterns of CMB distribution, including subcortical and posterior lobar.
Subcortical lesions are thought to be secondary to hypertensive injury, whereas posterior
lobar CMBs may be due to CAA [70]. CAA is present in 80–100 % of AD brains, but may
also occur in the absence of AD [182]. The posterior lobar involvement of CMBs in CAA is
consistent with the general pattern of posterior versus anterior findings from atrophy and
WML studies in AD. This evidence supports that evaluating CMBs, in combination with
other types of cerebrovascular dysfunction, has the potential to inform the
pathophysiological course of AD progression. A full discussion of CMBs in CAA and AD is
beyond the scope of this review and is summarized indepth elsewhere [182].
Connectivity and white matter lesions
Diffusion tensor imaging (DTI) is a promising structural MRI modality that has been useful
in the study of white matter pathology in AD [2, 29, 139]. This method indexes water
diffusion as an indirect measure of microstructural integrity, as discussed in a comprehensive
technical review [16]. Specifically, the restriction of water diffusion is anisotropic in brain
areas containing large white matter tracts since water is much more likely to diffuse along
these tracts than across them. Thus, by tracking the diffusion of water molecules, DTI
methods can provide “maps” of white matter tracts in the brain. To the extent that white
matter tracts exhibit degenerative changes, the surrounding water molecules can diffuse
more freely, making water diffusion considered isotropic. These changes can be quantified
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using DTI measures, also called DTI metrics, such as fractional anisotropy (FA) (as depicted
in Fig. 3) which indexes the ratio of axial versus radial diffusivity along white matter tracts.
Another measure is mean diffusivity (MD), which indexes the overall average of axial and
radial diffusivity.
These diffusion maps are statistically analyzed using robust methods, such as tract-based
spatial statistics, to evaluate white matter integrity associated with AD. For example, white
matter disruptions are observed in MCI compared to control subjects including regions such
as the inferior frontal and parahippocampal white matter (Fig. 3). Additionally, DTI studies
in AD report widespread and confluent abnormalities in the parietal, temporal, and
prefrontal white matter, but posterior white matter changes are most prominent, which are
best captured by examining absolute diffusivities [2]. Specifically, the long association fibers
and interhemispheric fibers are primarily implicated, including the posterior corpus
callosum, cingulum bundle at the level of the posterior cingulate, superior longitudinal
fasciculus, fronto-occipital fasciculus, the posterior thalamic radiation, and the superior
temporo-parietal white matter [2]. These data are consistent with the posterior to anterior
progression model also observed in volumetric grey and white matter studies in AD (see
later subsection “Regional brain atrophy”), as are results from a recent study reporting
predominantly posterior white matter changes in mild stages and more advanced stages
exhibiting both posterior and anterior changes [29].
Less research has been conducted on DTI metrics from grey matter, but the few studies that
have been conducted suggest that grey matter microstructural changes may be among the
earliest detectible brain changes in AD. In a recent study of individuals with autosomal
dominant AD, asymptomatic mutation carriers exhibited increased grey matter MD within
the hippocampus, caudate and thalamus before any other structural brain changes were
observable [143]. Similarly, other studies of late onset or sporadic AD have also found
increased MD within the hippocampus of mildly demented subjects [35, 193]. Although the
reason for increased MD within grey matter regions is currently unclear, further study of
regional DTI metrics within grey matter may yield new insights into microstructural changes
during the earliest stages of AD.
In addition to DTI-metrics and associated white matter tracts, studies utilizing T2/fluid-
attenuated inversion recovery (FLAIR)-weighted scans have identified circumscribed areas
of hyperintense signal that increase with normal aging [145], but are also associated with
AD-related cognitive decline [176]. These WMLs are thought to represent changes in tissue
water content secondary to multiple pathological processes, including ischemia, gliosis, and
demyelination [145]. These underlying pathological changes may be secondary to cerebral
small vessel disease and/or AD-related neurodegeneration. Recent research has focused on
differentiating qualitative aspects of WML pathology. These include attempts to parcellate
lesions based on the involvement of various neuroanatomical regions or even specific white
matter tracts [145]. As with cortical atrophy studies reviewed below (see later subsection
“Regional brain atrophy”), more posterior WMLs involving the parietal lobe may be of
particular importance in AD, as they correlate more strongly with cognitive impairment and
may interact with tau pathology to accelerate clinical progression [176].
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Regional brain atrophy
Gross cerebral atrophy is one of the cardinal features of AD neuropathology originally
described by Alois Alzheimer in 1906, and structural MRI methodologies therefore became
a major focus in studying differences between AD and normal brain aging. Early cerebral
atrophy in AD is thought to be due primarily to synaptic loss and subsequently to neuronal
loss [148]. The vast majority of early structural neuroimaging studies utilized manual tracing
methodologies to quantify the extent of atrophy, but these time-intensive methods limited the
study sample size and the number of brain areas that could be assessed. Voxel-based
morphometry is an alternative method that does not require manual tracing of a priori ROIs,
but may be relatively insensitive to more subtle changes in cortical thickness [50]. The
advent of freely available software systems providing semi-automated segmentation and
parcellation to assess regional volume and thickness (e.g., FreeSurfer software) has
improved reproducibility, allowed for larger studies, and provided for a broader survey of
brain-wide patterns of cerebral atrophy [58]. Studies comparing older adults with AD to
those without dementia report a partly overlapping yet distinct pattern of cerebral cortical
atrophy. Specifically, findings indicate the most salient regional atrophy in AD occurs within
more posterior regions, including parietal regions such as the precuneus, temporo-parietal
cortex and medial-temporal lobes [50, 148] as well as their underlying white matter
structures [37].
It is widely recognized that various white matter abnormalities are associated with AD,
including Wallerian degeneration, oligodendrocyte loss, and demyelination. Structural
neuroimaging studies in MCI and AD reveal that white matter abnormalities are particularly
found in the corpus callosum and paraventicular regions. For instance, although prefrontal
white matter volume loss occurs as part of normal aging [137], quantification of cerebral
white matter atrophy in T1-weighted anatomical images indicate that AD is associated with
prominent atrophy of temporal white matter and posterior corpus callosum [105]. In addition
to evaluating white matter abnormalities, the majority of volumetric MRI studies in AD have
focused on grey matter pathology.
As with normal aging, AD is largely known to be associated with substantial hippocampal
atrophy [62, 86]. A recent more sophisticated approach delves beyond overall hippocampal
volume to quantify atrophy within specific hippocampal subfields in AD versus normal
aging, as reviewed in [62]. Cumulative evidence from structural MRI studies focusing on
hippocampal subfields have confirmed that CA1 atrophy is associated with cognitive decline
[8], MCI [63], the presence of the APOE4 allele [98], and progression to AD [8]. However,
some studies also indicate additional involvement of other hippocampal subregions [119,
194]. The inconsistent findings of hippocampal subfield morphology in structural MRI
analyses are likely due to the great variety of manual tracing methods and/or magnetic field
strength-dependent SNR and spatial/temporal resolutions. T1/2-weighted images acquired at
3 and 7 T field strength have different spatial resolution (Fig. 4a) which directly impacts the
regional specificity that can be detected, for instance hippocampal analysis in images
acquired at 3 Tesla (T) and 7 T can differentially quantify 4 and 7–8 subregions, respectively
(Fig. 4b).
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Early cognitive decline and AD genetic risk factors are associated with atrophy of
hippocampal subfields [8, 62, 63, 98, 119, 119, 194], which is consistent with our recent
study in MCI reporting increased BBB Ktrans permeability predominantly in CA1 and
dentate gyrus hippocampal subfields [115]. These data highlight the benefit in adopting a
multiparametric approach, for instance combining structural and dynamic MRI sequences, to
detect different cerebrovascular measures in parallel. Structural and dynamic modalities
could also be used in conjunction with molecular neuroimaging that, through recent
advances, now enable in vivo visualization of the magnitude and regional brain distribution
of AD-related pathology. Such multiparametric approaches would shed additional light on
currently unclear spatial and temporal events in AD pathophysiology, such as the extended
prodromal period of AD.
Molecular imaging of AD pathology
Molecular imaging is used to visualize and quantify chemical processes at the cellular and
molecular level. The most common form of molecular imaging used to study AD is PET
imaging. PET detects pairs of gamma rays emitted from radioactive tracers and is used to
produce brain images of tracer binding. PET imaging biomarkers of metabolism and AD
neuropathology have substantially enhanced our understanding of AD-specific neural
changes in vivo [38, 99]. With the discovery of Aβ-sensitive radioligands over a decade ago,
the field experienced a dramatic increase in understanding the neurobiological cascade of
events in AD involving pathological burden, neurovascular dysfunction, neurodegeneration,
and cognitive decline [86, 99, 116].
The 18F-FDG-PET tracer is a glucose analog that measures glucose transport across the
BBB and entry into the brain. Reductions in cerebral metabolic rate of glucose (CMRglc)
have been reported in the AD literature for over three decades, consistently demonstrating
that individuals with AD have decreased transport of glucose across the BBB and therefore
decreased metabolism in AD-affected regions, such as posterior parietal and temporal
cortices [13, 55]. Metabolic patterns of FDG-PET uptake have also been shown to
discriminate between individuals with normal cognition, MCI, and clinical AD [100, 102].
Measurements derived from FDG-PET do not simply reflect neuronal activity but are
impacted by principles of neurovascular coupling involving vascular, astroglial, and
neuronal cells, and dynamic CBF [96, 149]. For instance, vasodilation is believed to be a
response to neural activity but is impacted by the control of astrocytes on cerebral
microcirculation [84, 132]. In vivo and post-mortem studies find that AD and vascular
pathology are often comorbid in individuals diagnosed with clinical AD, yet the interactions
between amyloid, tau, and vascular pathology in vivo are only beginning to be examined.
Amyloid PET tracers like 18F-AV45 and 11C-PiB (Pittsburgh compound B) have gained
traction in multi-site efforts [189] and across large cohorts at single sites [118], and
contribute to a growing body of literature of cross-sectional and longitudinal effects of Aβ on the brain [89]. However, the role of Aβ in the pathogenesis of AD is still under debate,
and ongoing questions about how Aβ affects brain function and whether amyloid PET is a
clinically useful biomarker remain. Many studies have reported associations of Aβ and brain
function, brain structure, and cognition. Individuals with significant Aβ have functional
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alterations in cortical hubs [25] and connectivity within networks measured with fMRI
[161]. Moreover, individuals who are considered amyloid-positive have lower brain volumes
and lower cortical thickness in AD-specific regions than amyloid-negative individuals [17].
However, the rate of Aβ accumulation over time may be independent of hippocampal
neurodegeneration [87]. There is a debate in the literature whether Aβ accumulation
correlates with impaired cognitive function [36], but emerging longitudinal studies suggest
that individuals with significant amyloid accumulation are at greater risk for future cognitive
decline [180, 181] and have poorer outcomes over time [160, 161]. The relationship between
amyloid PET binding and neurovascular integrity in AD are still under investigation, but it is
widely viewed that amyloid PET binding occurs not only in neuronal plaques, but vascular
plaques as in the case of CAA [30, 92].
Over the last few years, landmark developments of sensitive radioligands that bind to tau,
the other hallmark feature of AD, have been reported [88, 124]. These tracers bind to
hyperphosphorylated neurofibrillary tangles, the insoluble form of tau, and are not sensitive
to tau oligomers. Hyperphosphorylated tau impairs binding to microtubules, thereby
reducing neuronal stability, leading to synaptic dysfunction and ultimately cell death [45].
Evidence from animal models suggests a provocative relationship between the accumulation
of amyloid and tau for disrupting brain function and cognition in AD. That is, amyloid may
accumulate and disrupt neural activity in critical hub regions in the brain [53] while tau may
propagate through functional networks in a prion-like manner [65]. The spatial distribution
of the pathologies seem distinct, with amyloid depositing first in inferior frontal cortex and
spreading to association regions while tau deposition may follow Braak and Braak staging
with first sites in limbic regions and then to lateral cortical regions [93]. Data are emerging
to show a tight coupling between regional tau PET binding and Braak and Braak staging
(reviewed in [45]) and has been validated by post-mortem autoradiography studies [112].
Tau PET is an evolving science that over time will provide answers about the longitudinal
sensitivity of diagnostic and prognostic outcomes for AD and other tauopathies. Large-scale
efforts that collect multiple markers for metabolism, amyloid, tau, and neurovascular
integrity are needed to enhance our understanding of the interaction between events in AD
pathophysiology.
Clinical utility
The search for therapies that can modify the course of AD—e.g., to slow, delay, or prevent it
—is undoubtedly the most important challenge. That pursuit has led to an exploration for
neuroimaging markers to serve as diagnostic tools and/or outcome measures for drug
discovery and clinical trials; the clinical utility of each imaging modality will be judged on
these fronts. For instance, neuroimaging modalities are already being incorporated into
clinical trial design to improve diagnosis (with newly developed dynamic MRI modalities
such as DCE- and ASL-MRI) and/or to evaluate the therapeutic efficacy in reducing AD
pathophysiology such as vascular (with DCE-MRI) and CBF (with ASL-MRI) dysfunction,
atrophy (with structural MRI), disrupted metabolism (with FDG-PET and fMRI), and
fibrillary amyloid (with PiB-PET). There is a pronounced clinical need to define dynamic,
functional, structural, and molecular phenotypes of AD progression to support AD-specific
diagnostic criteria as well as treatment efforts.
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Recently improved DCE methodologies yield sensitive regional measures of BBB integrity.
DCE images are acquired via a 15-min sequence following GBCA intravenous injection, and
are currently used to clinically assess stroke and multiple sclerosis. Thus far, the diagnostic
use of DCE has not been applied to AD or other types of dementia; however, recently
improved DCE sensitivity has now shown increased hippocampal BBB permeability in CA1
and dentate gyrus subfields of MCI subjects [115]. DCE should therefore be incorporated
into clinical practice to evaluate subtle BBB damage that can potentially identify subjects
with early AD and related types of dementia. Additionally, recent comparison studies
indicate equivalent diagnostic performance between ASL-MRI and PET-MRI methods, but
ASL-MRI is advantageous due to its non-invasive, cost-effective, and easily repeatable
nature [34]. Hence, ASL-MRI offers promise to clinically detect CBF changes; however, the
specificity of ASL-measured CBF to distinguish between AD and other vascular pathologies
has not been firmly established, as reduced CBF is also reported in vascular dementia [147]
and a post-stroke non-demented group [57], it may mimic changes found in AD. This
suggests that regional cerebrovascular measures (i.e., BBB permeability and CBF) should be
clinically evaluated in combination with other imaging modalities, biofluid analysis, and
clinical information (e.g., neuropsychological performance; vascular/genetic risk factors).
Both task-related and resting state fMRI techniques have the potential to detect early brain
dysfunction related to AD; however, thus far the use of fMRI in aging, mild dementia and
AD populations has been largely restricted to research studies and clinical trials. Despite the
wide use of BOLD-fMRI in research, the BOLD signal remains a matter of debate since it is
a direct measure of hemodynamic changes and indirectly reflects neuronal activity. Two
current limitations hinder clinical use of fMRI in AD: (1) difficulties related to image
acquisition itself, mainly head motion and poor performance due to neurologic deficits; and
(2) difficulties related to interpretation in different aging and dementia populations. Resting
state fMRI may therefore be more feasible in severely impaired patients.
The clinical application of structural imaging techniques has remained elusive since brain
atrophy has a large degree of overlap in both normal and AD-related brain aging [59]. These
overlapping distributions become more prominent with advancing age, which thereby
attenuates the diagnostic value of brain atrophy scales as patients enter the ninth decade of
life [165]. Also, CMBs and WMLs (hyper/hypo-intense signals) and volumetric techniques
provide the least information regarding underlying mechanisms or pathology (e.g., gliosis,
demyelination, axonal loss). Although it may be possible to detect subtle hippocampal
volume changes during early MCI and preclinical phases of AD [19, 154, 175], gross
cortical and hippocampal atrophy may not emerge until relatively late in the AD process
after observable cognitive decline has occurred [91]. Although DTI metrics offer quantitative
indexes of microstructural change, as with other structural imaging modalities, a given
change in DTI metrics may reflect a number of different microstructural changes, thus
limiting the clinical utility of these scales.
Clinically, many types of AD-related dementias (i.e., fronto-temporal, vascular, mixed, and
Lewy-body dementias) in addition to normal aging exhibit both vascular dysfunction and
amyloid accumulation. Though amyloid changes occur during preclinical stages of AD,
measures of amyloid (PET tracer PiB) and tau (PET tracers targeting tau) do not appear to
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be sensitive enough to predict the onset of cognitive impairment and AD clinical diagnosis
[140]. Interestingly, many PET-amyloid positive cases will not go on to develop AD or AD-
related dementia, suggesting that a concomitant event (perhaps vascular) may be required to
initiate cognitive impairment and the neurodegenerative cascade.
Successful treatments of AD and other neurodegenerative diseases lie in early diagnosis and
early intervention, as opposed to disease reversal. As our understanding of AD advances, the
need for more accurate diagnosis will likely drive neuroimaging towards more ligand- and
functional-based technology such that molecular abnormalities and early functional changes
can be detected. Multimodal imaging, namely combining information across scanning
modalities, is an exciting development that would greatly enhance the clinical utility of each
imaging modality and thus potentially improve the sensitivity and/or specificity of
neuroimaging to detect various regional subtle cerebrovascular changes related to AD
development/progression.
Conclusions
In summary, this review discusses AD-related cerebrovascular dysfunctions measured via
different neuroimaging modalities, specifically: BBB integrity (DCE-MRI), CBF
dysregulation (ASL-MRI; BOLD fMRI), CBV and CMRO2 (BOLD fMRI), microbleeding
events (T2*- and SWI-MRI), white matter connectivity and WML (DTI-MRI), regional
brain atrophy (T1- and T2/FLAIR-weighted imaging), CMRglc (FDG-PET), and amyloid
deposition (PiB-PET). These neuroimaging approaches are contributing notably to the basic
understanding of AD pathophysiology, and have potential diagnostic utility for AD via their
ability to detect early neurovascular dysfunction. In particular, new dynamic MRI
approaches including DCE- and ASL-type modalities and novel ways of acquiring and of
analyzing imaging datasets have freshly contributed strong evidence of vasoreactivity
changes and BBB disruption in preclinical AD [39, 115, 120].
The use of newly improved dynamic, functional, structural, and molecular imaging
biomarkers conducted simultaneously with molecular biomarker detection in biofluids (i.e.,
blood and CSF) is necessary to better understand the pathophysiological processes
associated with defined stages of AD development. Future longitudinal neuroimaging (i.e.,
especially DCE- and ASL-MRI) and CSF/blood biomarker studies in human subjects with
NCI and/or MCI that also incorporate risk factors for AD (i.e., genetic, vascular,
environmental, and lifestyle) should continue to interrogate the role of neurovascular
mechanisms in the pathophysiology of dementia due to AD and other causes [116].
Clarifying the precise mechanisms through which vascular insults influence AD
development would have enormous benefit in the pursuits to identify novel biological targets
for drug development and to aid in patient-directed treatment efforts.
Additional research is necessary to elucidate the temporal sequence of AD pathophysiology
(i.e., increased BBB permeability, vasoreactivity disruptions, structural changes and atrophy,
functional alterations in brain networks, and molecular changes) and determine the
mechanism via which these events and AD risk factors are causally linked to AD etiology. In
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conclusion, it is our hope that this issue will enrich the quest to conquer AD using
multimodal dynamic, functional, structural, and molecular neuroimaging approaches.
Acknowledgments
We thank the National Institutes of Health (NIH), the Zilkha Senior Scholar program, and the Alzheimer’s Association for support. Dr. Zlokovic’s research is supported by the NIH through grants R37NS34467, R37AG23084, and R01AG039452. Dr. Pa’s research is supported by the NIH through grant R01AG046928 and the Alzheimer’s Association grant NIRP12259277. Dr. Toga’s research is supported by the NIH through grant P41EB015922. We apologize to those authors whose original work we were not able to cite due to the limited length of this review.
Abbreviations
AD Alzheimer’s disease
AIF Arterial input function
APOE Apolipoprotein E
AR Albumin ratio
ASL Arterial spin labeling
Aβ Amyloid beta
BBB Blood–brain barrier
BOLD Blood oxygen level-dependent
CA1 Cornu ammonis 1
CAA Cerebral amyloid angiopathy
CBF Cerebral blood flow
CBV Cerebral blood volume
CMB Cerebral microbleed
CMRglc Cerebral metabolic rate of glucose
CMRO2 Cerebral metabolic rate of oxygen consumption
CSF Cerebrospinal fluid
DCE Dynamic contrast-enhanced
dHb Deoxyhemoglobin
DMN Default mode network
DRS Dementia rating scale
DTI Diffusion tensor imaging
FA Fractional anisotropy
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FDG Fluorodeoxyglucose
FLAIR Fluid-attenuated inversion recovery
fMRI Functional magnetic resonance imaging
GBCA Gadolinium-based contrast agent
Hct Hematocrit
kDa KiloDalton
MCI Mild cognitive impairment
MD Mean diffusivity
MRI Magnetic resonance imaging
NCI No cognitive impairment
NVU Neurovascular unit
PET Positron emission tomography
PiB Pittsburgh compound B
ROI Region-of-interest
SNR Signal-to-noise ratio
sPDGFRβ Soluble platelet-derived growth factor receptor-β
SWI Susceptibility weighted imaging
T Tesla
WM White matter
WMH White matter hyperintensity
WML White matter lesion
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Fig. 1. BBB breakdown in the hippocampus during normal aging and aging associated with AD
using high-resolution DCE-MRI. Representative BBB Ktrans maps within the left
hippocampus in young (23–47 years) and older (55–91 years) individuals with no cognitive
impairment (NCI), as well as in older MCI and AD patients (Modified from [115], images
courtesy of Axel Montagne)
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Fig. 2. Decreases in regional CBF with dementia. a Coronal slices display reduced hippocampal,
caudate, and thalamic CBF in AD vs. MCI (right) and AD vs. no cognitive impairment
(NCI) (left) groups on voxel-level comparison. b The thalamic CBF decreases by 20 % in
AD compared to MCI individuals. c Thalamic CBF is associated with global cognitive
impairment on the Dementia Rating Scale (DRS) in AD (red) and MCI (green). d Caudate
CBF reduction is associated with increased white matter lesions (WMLs) severity across the
NCI-MCI-AD spectrum. All ps < 0.05 after correcting for voxel-level multiple comparisons.
NCI, n = 46; MCI, n = 23; AD, n = 12 (Modified from [39, 120]; images courtesy of Daniel
Nation)
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Fig. 3. White matter disruptions in individuals with MCI. Patients with MCI have lower fractional
anisotropy, a measure of white matter (WM) integrity, when compared to cognitively normal
older adults (n = 37) using tract-based spatial statistics, p < 0.05, threshold-free cluster
enhancement-corrected for multiple comparisons. The regions highlighted in red, including
the inferior frontal WM and parahippocampal WM (red arrows), may be early sites of
damage in individuals at-risk for AD (L left; R right) (images courtesy of Judy Pa)
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Fig. 4. Hippocampal subregion segmentation: variations in image acquisition and segmentation
protocols. a The figure shows the left hippocampus of a 36-year-old healthy control acquired
with different scanners (3 and 7 T) and sequences [T1-weighted (T1w) and T2w imaging].
Images were segmented by multiple groups (using their own segmentation protocol/atlas)
participating in the Hippocampal Subfields Group. Images correspond to the head of the
hippocampus. b Segmentation examples indicating which substructures were segmented in
each protocol (Modified from [62])
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