Radiological-pathological correlation in Alzheimer’s ...

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Radiological-pathological correlation in Alzheimer’s disease: systematic

review of antemortem MRI findings

Caroline Dallaire-Théroux1,2, Brandy L. Callahan1,2,3, Olivier Potvin1,2, Stephan Saikali2,4, Simon

Duchesne1,2

1Centre de recherche de l’Institut universitaire en santé mentale de Québec, Quebec City,

Quebec, Canada

2Faculty of Medicine, Université Laval, Quebec City, Quebec, Canada

3Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada

4Department of pathology, Centre Hospitalier Universitaire de Quebec, Quebec, Canada

Corresponding author:

Caroline Dallaire-Théroux Centre recherche Institut universitaire en santé mentale de Québec 2601 de la Canardière / F-4435 Québec, QC, Canada G1J 2G3 Tel: (418) 663-5000 x6714 Fax: (418) 663-5971 Email: [email protected] Running head: Alzheimer’s MRI-pathological correlation

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Abstract

Background: The standard method of ascertaining Alzheimer’s disease (AD) remains post-

mortem assessment of amyloid plaques and neurofibrillary degeneration. Vascular pathology,

Lewy bodies, TDP-43, and hippocampal sclerosis are frequent comorbidities. There is therefore

a need for biomarkers that can assess these aetiologies and provide a diagnosis in vivo.

Objective: We conducted a systematic review of published radiological-pathological correlation

studies to determine the relationship between antemortem magnetic resonance imaging (MRI)

and neuropathological findings in AD.

Methods: We explored PubMed in June-July 2015 using “Alzheimer’s disease” and

combinations of radiological and pathological terms. After exclusion following screening and

full-text assessment of the 552 extracted manuscripts, three others were added from their

reference list. In fine, we report results based on 27 articles.

Results: Independently of normal age-related brain atrophy, AD pathology is associated with

whole-brain and hippocampal atrophy and ventricular expansion as observed on T1-weighted

images. Moreover, cerebral amyloid angiopathy and cortical microinfarcts are also related to

brain volume loss in AD. Hippocampal sclerosis and TDP-43 are respectively associated with

hippocampal and medial temporal lobe atrophy. Brain volume loss correlates more strongly with

tangles than with any other pathological finding. White matter hyperintensities observed on

proton density, T2-weighted and FLAIR images are strongly related to vascular pathologies, but

are also associated with other histological changes such as gliosis or demyelination.

Discussion: Cerebral atrophy and white matter changes in the living brain reflect underlying

neuropathology and may be detectable using antemortem MRI. In vivo MRI may therefore be an

avenue for AD pathological staging.

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Keywords: Neuroimaging • Neuropathology • MRI • Antemortem Diagnosis • Alzheimer

Disease • Dementia

Abbreviations

AD = Alzheimer!s disease; Aβ = beta-amyloid; CAA = cerebral amyloid angiopathy; CA1 = cornu ammonis area I; CA4 = cornu

ammonis area IV; CSF = cerebrospinal fluid; DLB = dementia with Lewy bodies; HS = hippocampal sclerosis; MCI = mild

cognitive impairment; MRI = magnetic resonance imaging; NFT = neurofibrillary tangle; NP = neuritic plaque; SP = senile

plaque; WMH = white matter hyperintensities.

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INTRODUCTION

Alzheimer’s disease (AD), the most prevalent cause of dementia among the elderly, has

significant economic and societal consequences for these patients, their caregivers and the whole

community [1]. A definitive diagnosis of AD can only be established following postmortem

evaluation; an accepted standard follows Montine’s “ABC staging” [2], which requires the

pathological assessment of diffuse amyloid beta (Aβ) plaques, neurofibrillary tangles (NFTs),

and neuritic plaque (NP) burden [3]. Beyond AD-specific pathology, however, evidence from

autopsy studies indicates frequent comorbid conditions in subjects with a clinical diagnosis of

AD [3,4]. These include cerebrovascular disease [4], cerebral amyloid angiopathy![4, 5], region-

specific findings (e.g. hippocampal sclerosis [6]), as well as pathologies associated with other

dementias (e.g. Lewy bodies, TDP-43 inclusions) [7].

Due to this heterogeneity in disease presentation, the clinical diagnosis of AD can be

problematic for clinicians, especially in its early phase, with unsatisfactory concordance with

postmortem pathology. Additional tools and methodologies are therefore needed to improve this

concordance. Non-invasive imaging techniques such as anatomical magnetic resonance imaging

(MRI) have the potential to address this issue by measuring aetiologies in vivo. While the MRI-

clinical relationship in AD has been well-researched [8-11], in this report we provide an

overview of the less abundant literature on MRI-pathological correlation, with the ambition of

highlighting the relevance of clinical MRI as a potential biomarker of pathological processes in

the continuum of AD.

Operational definitions

For the sake of clarity, we elected to provide operational definitions of the pathological

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features investigated in this review.

Diffuse plaques are typical extracellular Aβ deposits evenly dispersed in the brains.

Found in patients with AD [12], they are also very commonly observed in those of cognitively

intact elderly people [13]. Diffuse plaques usually present as amorphous irregular configurations

with no evidence of neuritic reaction [14]. Neuritic plaques are focal collections, within the

cortical gray matter, of dystrophic neurites surrounding a central extracellular amyloid core,

composed of several abnormal proteins, of which the Aβ peptide is the major component [15].

The neuritic corona of NPs is an area of neuronal accumulation of fibrillar hyperphosphorylated

tau [16]. The terminology “senile plaque (SP)” is an older and more general term that only

refers to focal extracellular Aβ deposit. Both diffuse plaques and NPs are encompassed in the

concept of senile plaque [14, 17].

Neurofibrillary tangles are dense intraneuronal aggregates of characteristic paired

helical filaments, predominantly composed of hyperphosphorylated microtubule-associated tau

protein. Neuropil threads in the adjacent cerebral parenchyma normally accompany these lesions.

Paired helical filaments are also found in the outer portions of NPs, within the dystrophic

neurites [15]. NFTs are commonly found in AD, as well as in other neurodegenerative diseases

(tauopathies) [12], and form an essential part of its pathological diagnosis.

Cerebrovascular disease is commonly found in association with AD pathology [18, 19].

It may manifest in the brain as arteriosclerosis, atherosclerosis, arteriolosclerosis, infarcts, or

bleeds. Arteriosclerosis consists of the thickening, hardening and loss of elasticity of arterial

walls. It is a general term which refers to both medium and large arteries and encompasses both

atherosclerosis and arteriolosclerosis [15]. Atherosclerosis refers to intimal lesions of arteries,

characterized by the formation of fibrofatty (or atheromatous) plaques leading to a progressive

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luminal occlusion, a limited blood flow and eventual ischemic lesions [15, 20]. Arteriolosclerosis

is the thickening of small vessels and arteriole walls in association with narrowing of the

vascular lumen, which may cause ischemic injury. Arteriolosclerosis is mostly seen in

association with diabetes and hypertension [15, 21]. Lacunar infarcts are caused by the occlusion

of penetrating vessels that supply deep brain structures, and large-vessel infarcts are caused by

the occlusion of major blood vessels on the surface of the brain [22]. These two types of infarcts

can be radiologically detected; microinfarcts, on the other hand, are generally invisible lesions

seen only during microscopic examination [23]. Lastly, cerebral microbleeds are small foci of

chronic brain hemorrhages caused by small vessel structural abnormalities [24]. Although the

articles selected for this review did not specifically select for this pathological feature, it is

important to note that microbleeds have been suspected to contribute to cognitive impairment.

Some interesting articles addressing this topic can be found in the literature [25-27].

Cerebral amyloid angiopathy (CAA) is a neuropathological lesion characteristic of AD,

in which Aβ and other amyloidogenic peptide deposits form in the walls of small- and medium-

caliber meningeal and cortical blood vessels, typically in the interstitium between smooth muscle

cells of the media [15]. These vascular lesions can lead to hematomas, microbleeds and

microinfarcts, contributing to cognitive impairment [12]. CAA is most often found in parieto-

occipital brain regions, and least often found in the hippocampus and subcortical white matter

[28].

Hippocampal sclerosis (HS) refers to important hippocampal neuronal loss and glial

proliferation, with a higher susceptibility in the cornu ammonis 1 (CA1), the cornu ammonis 4

(CA4) and the subiculum subdivisions of the hippocampus [29, 30]. This condition can be

unilateral or bilateral, complete or focal, pure (e.g. related to temporal lobe epilepsy [31]) or in

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association with a dementia like AD, and typically causes severe memory loss [6]. We include

HS in this review because it is often found in association with cerebrovascular disease [29, 32],

and is believed to result from ischemia in the brain [32] (although this has been disputed; see

[33]). Primarily age-related, HS occurs with high frequency in the oldest-old [33] and may

contribute to misdiagnosis of AD by mimicking its clinical presentation [32].

Lewy bodies are dense eosinophilic cytoplasmic inclusions, mainly containing α-

synuclein, but also parkin and ubiquitin proteins, with a lighter halo. They are found in neurons

of individuals with neurodegenerative conditions like Parkinson’s disease and dementia with

Lewy bodies (DLB) [15]. Lewy bodies are relatively common pathological lesions in patients

with AD as well [34, 35].

TAR DNA-binding protein of 43 kDa (TDP-43) is an intracellular phosphorylated and

proteolytically cleaved protein that typically accumulates in the brain of patients with the

ubiquitine-positive subtype of frontotemporal lobar degeneration and in those with amyotrophic

lateral sclerosis [36, 37]. However, studies have also revealed a high prevalence of these

inclusions in other neurodegenerative diseases, with up to 57% of brains otherwise

pathologically categorized as AD [38]. The amygdala appears to be the first and most common

structure presenting with pathological accumulation of TDP-43 in AD [36, 38-40].

MATERIALS AND METHODS

Eligibility criteria

Studies were included in this review if they collected MRI and postmortem pathological

data in individuals with clinically- or pathologically-diagnosed AD. Since cognitively-silent

lesions are often found in elderly brains [41], cognitive performance was not an inclusion

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criterion. This is relevant given that the purpose of this research was to report on the correlation

between AD-associated neuropathological features and MRI findings alongside the

neuropathological continuum. Studies had to meet the following criteria:

(a) be an original study (i.e. narrative or systematic reviews or meta-analyses were not included);

(b) use clinical (1.0T-3.0T) MRI scanning, in order to maintain consistency with current practice

standards; and

(c) be an in vivo MRI study, as MRI scanning ex vivo may be affected by formalin fixation,

brain shrinkage and normal changes following death, regardless of the time between death and

scanning [42]. Further, postmortem MRI is less sensitive than in vivo MRI for detecting smaller

white matter hyperintensities (WMH) [25, 43] and perivascular atrophic demyelination [44].

Search strategy and study selection

We explored the PubMed database from June to July 2015 using “Alzheimer’s disease”

combined with the following terms: “postmortem pathology in vivo MRI correlation”, “autopsy

MRI”, “neuropathology MRI correlation”, “postmortem histopathology MRI association”,

“quantitative MRI pathology correlation”, “volumetric MRI histopathology correlation”,

“antemortem MRI autopsy”, “structural neuroimaging postmortem pathology”, “MRI cerebral

amyloid angiopathy postmortem pathology”, “MRI vascular postmortem pathology”, “MRI

Lewy bodies postmortem pathology”, “MRI hippocampal sclerosis postmortem pathology” and

“MRI TDP-43 pathology” as keywords and MeSH headings. No limit was applied to the date of

publication, MRI being a relatively recent technology and the discoveries already made still

applicable to date. The first author (C.D.-T.) screened the articles and selected studies for

inclusion first by examining the title and abstract of each article, then the full text version. The

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remaining articles were then reviewed by other authors (B.L.C. and S.D.) to confirm their

relevance. This present systematic review was done following Preferred Reporting Items for

Systematic Reviews and Meta-Analyses (PRISMA) guidelines [45] (Figure 1).

Data extraction

We extracted necessary data for description of the collected articles. Study design,

population characteristics (source, number of subjects, selection criteria, age and gender

distribution), imaging characteristics (modality, resolution(s) and sequence(s)), time between

MRI and death, types of dementia, radiologic and histopathologic variables examined and main

study findings were noted in Table 1. We further assessed the articles alongside three main

categories: AD-related findings, cerebrovascular disease-related findings, and incidental

findings.

We extracted relevant significant (p < .05) coefficients from studies included in the

review (Table 2) and performed a general analysis based on significance of correlations between

neuropathological and MRI findings (Figure 2). The interpretation of p-values was based on

recommendations from B. Rosner [46].

RESULTS

Studies description

Our PubMed search yielded 552 articles, from which we excluded 481 at initial screening

and 47 at full-text assessment. The relevance of the remaining 24 articles was confirmed by the

second evaluation. Three articles were added by hand after searching the reference lists of the 24

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saved articles. Our review is based on a total of 27 studies published between 1995 and 2015

(Table 1 and Figure 1).

Twenty-four were longitudinal studies [7, 47-50] or used subjects from longitudinal

cohort studies but analyzed the data cross-sectionally [6, 38, 40, 51-66]; the remaining three

articles were descriptive cross-sectional studies [67-69]. All studies were published in English;

most of them were conducted in the United States [6, 7, 38, 40, 47-55, 57-60, 62, 63, 65-68], one

in Canada and the United States [61], two in the United Kingdom [56, 64] and one in the

Netherlands [69]. Subjects in all studies were between 43-108 years old from enrollment to

death, and all included both men and women. MRI field strengths of 1.0T to 3.0T were used in

every study, except for five in which field strength was not explicitly reported [40, 48, 52, 55,

60]. One study also compared the sensitivity to neuropathological changes between 7.0T and

3.0T MRI [69]. In total, T1-weighted images were evaluated in 21 studies [7, 38, 40, 47, 48, 50,

51, 53-58, 60, 61, 63, 64, 66-69], T2-weighted images in five studies [50, 59, 62, 67, 69], proton

density images in three studies [50, 59, 67], FLAIR images in four studies [48, 63, 65, 69], and

type of images was unspecified in three of the reviewed articles [6, 49, 52]. Time interval

between last MRI and death varied from 0 to 10.5 years, but information was not available in

two studies [38, 40].

Neuropathological findings

Figures 2 and 3 give a brief overview of results from studies included in the present

review.

AD-related findings

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Diffuse and neuritic plaques

Radiological-pathological evidence suggests that NPs are associated with brain volumes.

Of the 27 studies reviewed, 10 specifically investigated correlations between brain volumes and

diffuse or neuritic plaques. Eight of these studies reported specifically on NPs [48, 49, 52, 54, 56,

58, 62, 64], and two reported on SPs more generally (i.e., made no distinction between neuritic

and diffuse plaques) [47, 68]. The majority of these studies agree that total brain volume [62],

medial temporal lobe volume [56], hippocampal volume [52, 58], ventricular size [62] or

ventricular expansion over time [49] are significantly associated with NPs in postmortem brain

tissue. NFT and senile plaque burden have also been associated with these measures [47, 68],

and some authors have reported the rate of ventricular volume enlargement as their strongest

correlate [47]. A moderate correlation (ρ=.52) has also been reported between CERAD [70] NP

count and the adjusted STructural Abnormality iNDex (aSTAND) score, which evaluates the

degree and pattern of cerebral atrophy adjusted for age and gender [54]. One study [68] found a

non-significant correlation between left and right HVs and hippocampal senile plaque density,

but a moderate correlation between total cerebral volume and hippocampal senile plaque density

(ρ=-.68). Only one study [48] suggested a lack of association between total Aβ burden (i.e.,

compact and diffuse Aβ) and rates of whole-brain atrophy and ventricular expansion in patients

with low, intermediate or high probability of AD according to the National Institute on Aging

and Reagan Institute Working Group on Diagnostic Criteria for the Neuropathological

Assessment of Alzheimer’s disease [71].

Neurofibrillary tangles and Braak stage

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Sixteen studies selected for this review focused on NFTs as a pathological variable of

interest [47-52, 54-62, 68] and nearly all reported moderate to strong positive associations

between NFT pathology and brain atrophy. In fact, several authors have suggested that MRI

brain volumes correlate more strongly with NFTs than with NPs [48, 56, 62] or SPs [47].

Selected transversal studies addressing NFT pathology found significant correlations between

whole-brain atrophy on MRI and NFT staging (according to the Braak and Braak scale [72]) [48,

55, 62], as well as between Braak staging and ventricular size [48, 62]. Moreover, a moderate

correlation (ρ=.62) was found between Braak NFT staging and aSTAND scores [54].

Evidence suggests that Braak stage is also significantly associated with hippocampal

atrophy [51, 52, 54, 55, 58, 68], although some authors note that these correlations are weaker

than those involving whole-brain atrophy or aSTAND scores [54]. Importantly, total Braak NFT

score, and not percent area of tangles, appeared to be the only significant predictor of MRI-

measured medial temporal lobe atrophy [56]. These associations may, to some extent, be

influenced by regional distribution of pathology. For example, Burton and colleagues [56]

reported that total (left and right) medial temporal atrophy rating was correlated (ρ=.49) with

percent area of NFTs in the hippocampus. Further, MRI-detected hippocampal atrophy has been

specifically linked to the accumulation of NFTs in the CA1 subfield of the hippocampus [68].

Medial temporal atrophy rating based on a 5-point scale (from absent to severe; based on the

height of hippocampus and the width of cerebrospinal fluid (CSF)-filled spaces) shows high

sensitivity (91%) for autopsy-confirmed AD [56].

According to Whitwell and colleagues [55], the severity of gray matter loss may be more

closely related to quantitative NFT load than to Braak NFT stage. Their study was conducted

using voxel-based morphometry with group-wise comparisons between each Braak stage (III to

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VI) and the control group (Braak stages 0 to II). Because no regions of grey matter atrophy were

found at Braak stages III or IV when compared to the control group, group analyses were also

performed between subjects with low tau burden (<10%) and high tau burden (≥10%) in subjects

with Braak stages 0 to IV. Those with high tau burden showed greater gray matter loss in medial

and lateral temporal lobes than those with low tau burden. In addition, results from this study

showed a graded pattern of increasing brain atrophy across Braak stages V and VI. In subjects

with Braak stage V, atrophy was found in temporal, parietal and frontal lobes only. In subjects

with Braak stage VI, basal forebrain, posterior cingulate, precuneus, anterior cingulate, insula,

subcortical nuclei, and occipital lobe including the primary visual cortex were also involved. In

other words, the presence of NFTs in the brain tissue is associated with patterns of gray matter

atrophy, with NFT burden playing a more important role in determining the pattern of gray

matter loss at Braak stages III and IV, and Braak stage itself (i.e. regional distribution of NFTs)

having a greater correlation with patterns of brain atrophy at Braak stages V and VI.

Longitudinal data also support associations between repeated structural MRI measures

and AD pathology: ventricular expansion [47-49] and rates of cerebral atrophy [47, 48], but not

hippocampal atrophy [47, 49], have been significantly associated with the amount of NFTs at

autopsy. In fact, the rate of total ventricular CSF volume increase (an indicator of ventricular

expansion) was identified by one study as the best radiological biomarker of AD pathology

(R2=.422 for cortical NFTs and R2=.397 for cortical SPs), when compared to the rate of cerebral

atrophy (R2=.419 for cortical NFTs, and R2=.22 for cortical SPs) [47]. Postmortem pathological

diagnosis of AD has also been associated with whole-brain atrophy rate as measured using

repeated MRI during life [7].

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In addition to the substantial evidence linking NFTs to cerebral atrophy, one study

reported that more extensive NFT pathology, as rated by Braak staging, was associated with

MRI-evidence of increased longitudinal WMH accumulation [50], while another study reported

an inverse correlation when assessed cross-sectionally [65]. The relationship between Braak

stage and WMH remains to be confirmed by other studies, as no other work included in this

literature review assessed similar associations with WMH.

Cerebrovascular disease – related findings

Arterio-, arteriolo- and atherosclerosis, infarcts and bleeds

Based on results identified in mixed cohorts of both demented and non-demented

participants (see Table 1 for more details), WMH on proton density, T2-weighted and FLAIR

MRI seem to be a good in vivo marker of vascular pathology. In cross-sectional studies, WMH

have been associated with subcortical infarcts [53], cerebral hemorrhages [65], arteriosclerosis

[65, 67], demyelination [53, 65, 67], breakdown of the ventricular lining [65], reduced

periventricular vessels [65], and gliosis [67] in autopsied brains. Microinfarcts, particularly in the

deep nuclei, may also be related to WMH burden on antemortem MRI [62, 65] (but see [63] for

other results). The only longitudinal study found that, of all vascular lesions, arteriolosclerosis

was the most important correlate of accumulating WMH over time [50].

Imaging evidence of vascular disease (e.g., infarcts, hemorrhage, stroke) is also a strong

indicator of underlying vascular pathology at autopsy. Raman and colleagues [63] have shown

that the presence and number of cortical and subcortical infarcts detected on antemortem MRI

are associated with the presence and number of microinfarcts seen on postmortem pathological

examination. One study aiming to detect cortical microinfarcts using in vivo MRI, followed by

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postmortem pathological validation, found that higher-powered 7.0T MRI is more sensitive than

3.0T MRI, with an ability to detect cortical microinfarcts of ≥1 mm as hyperintensities on

FLAIR and T2-weighted scans [69]. It has been reported that detectable lacunes on MRI (well-

circumscribed foci >2 mm in diameter and hyperintense when compared to CSF on proton

density MRI) are highly reflective of subcortical vascular pathology, representing small-vessel

infarcts and arteriosclerosis [53].

Brain volumetrics have been less extensively studied as correlates of vascular pathology.

Only four of the selected articles cross-sectionally investigated radio-pathological associations

between cerebrovascular disease and total brain volume [57], cortical gray matter volume [53],

ventricular volume [57], and HV [52, 57, 62]. Jagust’s group reported that lower cortical gray

matter volume was associated with gross subcortical infarcts, but not cortical infarcts [53]. Kaur

and colleagues [62] also reported that lower hippocampal volume was associated with higher

number of noncavitated and subcortical microinfarcts, while WMH were associated with more

deep nuclei microinfarcts. In contrast, the two other groups found no significant association

between whole-brain, ventricular and hippocampal volumes and ischemic, hemorrhagic or

vascular pathology [52, 57].

Similarly, only two studies longitudinally assessed vascular correlates of ventricular

expansion [49] and rates of whole-brain and regional atrophy [63]. Gross infarcts at autopsy have

been associated with ventricular volume trajectory, with more ventricular enlargement over time

observed in individuals with this type of vascular lesion [49]. Moreover, Raman and colleagues

[63] found that whole-brain atrophy rate was the only MRI variable that differentiated subjects

with and without microinfarcts after controlling for AD-related pathology, compared to imaging

measures of hippocampal atrophy or WMH accumulation.

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Cerebral amyloid angiopathy

Despite its vascular involvement, CAA was not significantly associated with WMH in

demented or non-demented individuals in any of the studies reviewed [53, 59, 65]. However,

CAA has been correlated with increased hippocampal atrophy over time with a -0.0008%

volume change as proportion of intracranial volume (p=.009) and also showed a trend for

association with total brain volume atrophy over time with a -0.13% volume change as

proportion of intracranial volume (p=.030; the significance threshold was adjusted for multiple

comparisons to p < .017) [49].

Incidental findings

Lewy bodies

Results from many cross-sectional studies examining both cognitively intact and

cognitively impaired subjects have shown no significant associations between Lewy bodies and

occipital gray matter [61], medial temporal lobe [56] or hippocampal [51, 61, 64] volumes.

Overall, within the selected publications, no MRI-detected changes have been related to this

neuropathological feature, except for a positive correlation between percent area of Lewy bodies

in the hippocampus and total hippocampal volume [64].

Hippocampal sclerosis

Hippocampal atrophy is the most prominent radiological correlate of HS. Several cross-

sectional studies have reported that the presence of HS in AD and non-AD brains is strongly

inversely associated with HV [52, 53, 62] with greater pathologic severity related to greater

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atrophy on MRI [51]. Moreover, Zarow and colleagues [52] found a moderate negative

correlation (ρ=-.63) between number of neurons in CA1 subfield of the hippocampus and HS.

The same authors also reported a strong association between MRI HV and number of neurons in

CA1 (ρ=.72) as well as volume of CA1 (ρ=.54). Jagust and colleagues [53] have suggested that

hippocampal volume is independently related to both typical AD lesions and HS, as hippocampal

atrophy attributed to HS seems to be more severe in subjects with low levels of AD pathology.

Interestingly, hippocampal atrophy on anatomical MRI was associated with neuronal loss (i.e.,

reduction in number of neurons) and not shrinkage (i.e., change in neuron size), regardless of

etiology [52]. MRI appearance has also been linked to HS in the context of epilepsy [30].

TDP-43

Given the growing literature reporting a high prevalence of TDP-43 in AD brains [36, 40,

73-76], radiological features associated with this pathological lesion have been examined in

many transversal studies captured in the present review [6, 38, 40, 61, 66]. When comparing AD

subjects with and without abnormal TDP-43 immunoreactivity using voxel-based morphometry,

those with TDP-43 pathology showed greater volume loss in the medial temporal lobes [38, 66],

with greatest loss in the hippocampus [66]. Consistent with these findings, additional group

comparisons using MRI segmentation techniques showed similar results: TDP-43 pathology was

associated with lower amygdala [38], hippocampal [38, 40, 61] and entorhinal cortex [38, 40]

volumes. These atrophy sites have been correlated to both increased TDP-43 burden [38] and

higher TDP-43 stage (i.e., regional distribution) [40]. In one study [6], while HS was found in

61% of AD cases, it was found in as many as 92% of HS cases. This finding suggests a possible

association between TDP-43 and HS pathology. However, no differences in hippocampal

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volume were found between AD TDP-positive subjects with and without HS [6], and

hippocampal volumes remain significantly associated with the presence of TDP-43 even after

accounting for HS [38].

DISCUSSION

Sensitivity of MRI for AD identification

Overall, the results from this systematic review suggest that in vivo clinical-resolution

MRI is sensitive to most neuropathological lesions observed in AD subjects. It has been shown

that AD-related anatomical changes (i.e., SPs and NFTs) are associated with volumetric

measurements of several brain structures on MRI. Whole-brain, medial temporal lobe and

hippocampal atrophy, as well as ventricular enlargement, can predict underlying Aβ and tau

pathologies accumulating in the brain tissue with high sensitivity, with ventricular enlargement

clearly identified by one study as the best radiological biomarker for AD pathology [47].

Interestingly, the association between brain volumes and plaques or tangles may be limited to

individuals with cognitive impairment, and absent in those who are cognitively intact [47, 51].

This might be explained by an existing threshold after which AD neuropathology burden leads to

brain volume loss, similar to the onset of cognitive impairment occurring after many years of

accumulation of these lesions [77]. Unfortunately, two of the studies investigating the

relationship between structural atrophy and amyloid neuropathology [47, 51] did not distinguish

between neuritic and diffuse amyloid plaques, and potential differences between them may

warrant further investigation.

Importantly, neuronal and synaptic losses have primarily been associated in extant

literature with the accumulation of NFTs, rather than with plaque formation [13]. Only one study

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[62] examined radio-pathologic relationships separately for tau (NFTs) and amyloid (NPs), and

found that although whole-brain volume was associated with both pathological variables, only

NFTs remained a significant correlate when both were entered into linear regression models.

This suggests that the associations between structural atrophy and amyloid plaques reported in

the present review may have been confounded to some extent by the degree of tau pathology,

and that MRI may be more sensitive for detecting tau than amyloid pathology.

The association between vascular pathology and cerebral atrophy is less clear-cut.

Vascular lesions (e.g., arteriosclerotic patterns and infarcts) can generally be easily detected as

WMH on antemortem MRI, except for microinfarcts, which are macroscopically invisible by

definition. Lacunar infarcts may manifest differently, but are still radiologically detectable as

lacunes in the white matter [78].

Underlying HS can also be identified on brain MRI, the most reliable indicator being

significant hippocampal volume loss, particularly in the CA1 and CA2 subfields of the

hippocampus [52]. Studies included in this review did not show any MRI sensitivity for

comorbid Lewy body pathology in individuals with AD, although other authors have identified a

specific pattern of relatively focused atrophy, separate from that exemplified in AD subjects, in

the midbrain and hypothalamus with relative sparing of the hippocampus and temporoparietal

cortices, in individuals with a clinical diagnosis of DLB [79]. There is also increasing evidence

showing that concomitant TDP-43 can be visualized on MRI as medial temporal lobe atrophy,

independently of HS. Overall, MRI has a good sensitivity for AD pathology and most other

frequent comorbidities.

Specificity of MRI for AD identification

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AD-related pathology

Our results suggest moderate specificity of MRI-defined changes in AD subjects. First,

cerebral atrophy, decidedly the best-known radiological feature of AD, is also common in other

types of neurodegenerative diseases. Increased rates of whole-brain atrophy and ventricular

expansion, two usually associated features, are found in AD as well as other dementias such as

pathologically confirmed cases of mixed AD/DLB, fronto-temporal degeneration, corticobasal

degeneration and progressive supranuclear palsy, when compared to normal aging subjects [7]

and therefore cannot, on their own, serve as reliable or specific biomarkers for AD.

However, Whitwell and colleagues [7] have suggested that longitudinal rates of cerebral

atrophy differ according to the underlying neuropathological process, which may improve

specificity. In their study, the lowest rates were observed in pure DLB pathology, while the

highest rates were observed in corticobasal degeneration, followed by fronto-temporal dementia.

Nonetheless, AD, mixed AD/DLB and progressive supranuclear palsy showed similar

intermediary rates of atrophy, and conventional MRI volumetry could not help differentiate those

three. Relative to whole–brain atrophy, medial temporal lobe atrophy rating based on a 5-point

scale has been shown to be a more accurate diagnostic marker for differentiating AD from DLB

and vascular dementia, with good discriminatory power and 94% specificity [56]. Furthermore,

the capacity of MRI-derived hippocampal volume to differentiate between AD and vascular

dementia has been touted, as neuronal loss in CA1 is more consistently found in AD subjects

than in those with vascular dementia [52]. Additional studies using atrophy maps specific to each

neurodegenerative dementia [80] or more developed techniques such as image appearance [81]

or grading [82], especially focused on medial temporal areas, are needed to confirm these

findings, since other authors have suggested that cumulative ischemia without amyloid pathology

! 21!

(i.e. pure subcortical vascular dementia) might lead to atrophy and shape changes in the CA1 and

subiculum regions of the hippocampus [83].

Structural MRI may also be used to differentiate pathologically-defined subtypes of AD.

For example, the greatest cortical atrophy is seen in hippocampal-sparing AD, followed by

typical AD and then limbic-predominant AD [60]. Conversely, the greatest medial temporal lobe

atrophy is observed with limbic-predominant AD, followed by typical AD, and then

hippocampal-sparing AD [60]. However, the best discriminator of the three subtypes of AD is

the ratio of hippocampal to cortical volumes [60].

One group of researchers found that the estimated number of neurons in both CA1 and

CA2 regions of the hippocampus is decreased in patients with pure AD relative to those with

ischemic vascular dementia or normal controls [52]. This group also reported an evident

correlation between CA1 neuron counts and MRI-derived hippocampal volume. The severity of

CA1 neuron loss (i.e., hippocampal atrophy) might even help to distinguish between different

stages of AD: the authors reported 48% reduction in CA1 neurons in subjects with isocortical

stage AD, and 8% reduction in subjects with limbic stage AD. Unfortunately, hippocampal

atrophy is not specific to AD neurodegeneration. In fact, subjects with a pathologically

confirmed diagnosis of AD, HS, fronto-temporal dementia or NFT-only degeneration usually

have considerable hippocampal atrophy compared to those with normal age-related changes [51].

Concomitant cerebrovascular disease

Regarding radiologically-detectable white matter changes, many studies showed that

there is no relationship between WMH and AD pathology or CAA [42, 43, 53]. As suggested by

myriad radiological-pathological correlation studies, WMH best reflect vascular changes in the

! 22!

brain [42-44, 50, 53, 62]. In fact, WMH are related to age and vascular risk factors, and are

strongly reflective of underlying cerebrovascular disease and complete and incomplete infarction

[53].

Takao and colleagues [42] evaluated the pathologic significance of different types of

WMH on T2-weighted MR images of non-demented subjects. They found that small patchy

WMH (dot-like or confluent lesions not attached to the lateral ventricles) corresponded to

demyelination, gliosis and arteriosclerosis. Neither infarction nor amyloid deposits were found to

be associated with these types of lesions. ‘Caps’ (WMH around the anterior or posterior poles of

the lateral ventricles) represented demyelination, dilated perivascular spaces and arteriosclerosis.

No infarction was noted. ‘Rims’ (thin WMH along the body of the lateral ventricles) accounted

for subependymal gliosis and partial disruption of ependymal lining. Lastly, most histologically-

discovered silent white matter lesions on MRI were found to reflect partial demyelination and

arteriosclerosis. Only two studies reported a positive [59] and a negative [65] relationship

between cross-sectional MRI-detected WMH and AD-related deposits (NPs and NFTs) as

assessed by CERAD score, Braak stage and a composite AD pathology score [59], but these

conflicting findings have yet to be clarified by other research groups. Increased accumulation of

WMH over time was also correlated to NFT pathology, when comparing low (0, I or II) vs. high

(V or VI) Braak stage [50]. The authors from these papers hypothesized that this association may

be explained by the fact that subjects were much older at death than those in most other cohorts!

[50, 59].

The impact of aging on atrophy

! 23!

The moderate specificity of antemortem MRI may at least help differentiate normal aging

from pathological processes, although age at the time of scan seems to influence the rates of

brain volume change. The rates of both brain volume loss and ventricular expansion decrease as

the age at scan increases, which means that greater atrophic effects of AD pathology are seen in

younger individuals [7, 48]. This measure may therefore be particularly useful in detecting cases

of early-onset AD.

Interestingly, cerebral atrophy over time differs in subjects with and without cognitive

impairment even after accounting for the severity of SPs [47], NPs [49] and NFT [47, 49],

suggesting that there may be underlying protective factors or other mechanisms that can lead to

increased brain reserve and resistance to atrophy. Similarly, one study reported that larger total

brain and hippocampal volumes were associated with preserved cognitive function during life

despite a high number of AD-related lesions [57].

Limitations

Several sources of potential bias in this review must be acknowledged. The major

limitation of this research is the publication bias, which may have resulted in incomplete or

inexact conclusions. Since this review is based on published literature, studies that have not been

published because of negative results (no association found or opposite to expectations) are not

included in the analysis. A second factor is related to the populations under study, the vast

majority (23 out of 27 studies) being from the United States. Geographic, ethnic or cultural

differences, such as lifestyle, risk factor exposure and genetics, may influence findings, and thus

the generalizability of these results is limited. Another important limitation is that several studies

have small samples, which reduces the statistical power of observations. Moreover, studies

! 24!

mainly looked at only one type of structural measure in T1-weighted scans (i.e., volume), and

focused in only one region apart from the whole brain and ventricles (i.e., medial temporal lobe,

with specific attention to hippocampal volume). More studies on other cortical and subcortical

regions of the brain and using other techniques such as additional morphological measures

(surfaces, thicknesses) or diffusion are needed in order to draw adequate conclusions.

CONCLUSIONS

In summary, the reviewed studies have demonstrated the capacity of clinical-resolution

MRI (1.0T-3.0T) to detect most pathological alterations due to AD before death with high

sensitivity and moderate specificity. MR-defined cerebral atrophy in the pre-mortem brain

appear to be the most strongly reflective of underlying AD neuropathology and other incidental

neurodegenerative findings such as HS and TDP-43, while WMH correlate better with

concomitant cerebrovascular disease as seen at autopsy. New protocols and especially post-

acquisition image processing techniques should focus on improving the specificity of MRI to

detect pathological lesions related to AD. Should this be achieved, then in vivo pathological

staging of AD will be possible noninvasively, raising the hope of identifying affected patients

well before they develop full-blown dementia.

ACKNOWLEDGMENTS

Contributors: C.D.-T. conceptualized the study, analyzed the data, interpreted the results and

wrote the manuscript. B.L.C. reviewed the data collection process, interpreted the results and

revised the manuscript for important intellectual content. O.P. and S.S. provided manuscript

! 25!

revision for important intellectual content. S.D. conceptualized the study, supervised the study

process, and revised the manuscript for important intellectual content.

Funding: We gratefully acknowledge financial support from the Alzheimer’s Society of Canada

(#13-32). B.L.C. receives financial support from the Alzheimer’s Association (AACF-16-

443540). S.D. is a Research Scholar from the Fonds de recherche du Québec – Santé (#30801).

Conflict of Interest Disclosures: S.D. is officer and shareholder of True Positive Medical

Devices inc.

! 26!

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CW, Oh SJ, Kim JS, Choe YS, Lee KH, Kim ST, Hwang JW, Jeong JH, Na DL (2015)

Hippocampal volume and shape in pure subcortical vascular dementia. Neurobiol Aging

36, 485-491.

!

TABLES

Table 1 Radiological-pathological correlation studies of aging and AD using in vivo MRI

Study Subjects Source MRI resolution

Images Interval from last MRI to death

Radiologic variable(s) examined

Histopathologic variable(s) examined

Main findings

Brilliant 1995 [67]

94 probable AD 45 possible AD Diagnoses were clinical *Only 11 participants underwent autopsy *3/11 cases also had a neuropathological diagnosis of Parkinson’s disease

Center for Alzheimer Disease and Related Disorders at Southern Illinois University School of Medicine

1.5T PD T1 T2 *PD and T2 scans were used for WMH

2.5 ± 1.3 years Brain volume (cortical & ventricular), and WMH

Vascular pathology (arterio- and atherosclerosis, infarcts, microinfarcts), CAA, demyelination, gliosis. AD pathology (NFTs, SPs) and Lewy bodies were examined only for pathological confirmation of clinical diagnoses.

Grade 4 WMH (large confluent subcortical areas) (n=2): Axon and myelin loss and gliosis in areas of WMH. Arteriolar hyalinosis without sclerosis. CAA in one case.

Grade 3 WMH (thick, irregular periventricular rim) (n=2): Demyelination, gliosis, arteriolar hyalinosis without sclerosis, Large and micro infarcts and CAA in 1 case.

Grade 2 WMH (thin rim surrounding the lateral ventricles) (n=4): Mild demyelination. CAA, enlarged ventricles and microinfarcts with gliosis in 1 case. Severe atherosclerosis in 1 case.

Grade 1 WMH (small caps at the frontal and occipital horns) (n=2): No significant cerebrovascular disease.

Note. This study did not distinguish neuritic from diffuse plaques. Results are reported separately for each of the 11 autopsied cases in the original article but summarized here for brevity.

Jack 2002 [51]

23 AD 3 HS 3 DLB 2 FTD 1 NFTD 4 AD+DLB

Mayo AD Research Center/AD Patient Registry

1.5T T1 0.1 to 7.1 years

HV Cerebrovascular pathology, NFTs, and HS, as well as pathological evidence of

Lower HV = More hippocampal NFTs in persons with AD.

Lower HV is also found with AD, HS, FTD or NFTD pathologies (but not normal aging). Thus, HV atrophy is not specific to

! 39!

2 AD+HS 1 AD+infarct 2 FTD+HS 1 HS+DLB 25 controls Diagnoses were neuropathological

different dementias (e.g., DLB, FTD, etc.). SPs were examined only for pathological confirmation of AD.

AD.

Note. This study did not distinguish neuritic from diffuse plaques.

Silbert 2003 [47]

24 probable AD 15 nondemented controls Diagnoses were clinical

Oregon Aging and AD Center

1.5T T1 1.2 to 82.8 months

Brain volume (whole-brain, ventricular & individual regions)

AD pathology (NFTs, SPs), vascular disease (infarcts, hemorrhage, and atherosclerotic large vessel narrowing)

Cross-sectionally:

Lower whole-brain and higher ventricular volumes = More cortical NFTs, only if cognitive impairment was present.

Higher ventricular volume = More SPs, only if cognitive impairment was present.

Lower HV = More hippocampal NFTs.

Rates over time:

Faster total ventricular expansion = more cortical NFTs and SPs (best predictor).

Faster rate of cerebral atrophy = more NFTs (less sensitive biomarker).

Rate of HV atrophy = not associated with severity/progression of NFTs.


Csernansky 2004 [68]

10 AD 3 controls Diagnoses were neuropathological

AD Research Center at Washington University School of Medicine

1.5T T1 2.2 years (range 0.75-4 years)

HV NFTs and senile plaques (total, cored, and NPs together)

Lower HV = More hippocampal NFTs in the CA1 subfield of the hippocampus.

HV = Stronger correlation with hippocampal NFT density than plaque density.

Total cerebral volume = Stronger correlation with hippocampal plaque

! 40!

density than NFT density.


Zarow 2005 [52]

9 AD 6 VaD 2 mixed dem. 4 MCI no AD 7 controls Diagnoses were neuropathological

Ischemic Vascular Dementia (IVD) Program Project and Honolulu Asia Aging Study (HAAS)

Not specified, but other publications describe 1.5T imaging in the IVD [12, 20] and in the HAAS [Yun & White, Ethn Dis 2010]

N/A 2.6 ± 1.8 years HV

NFTs, NPs, infarcts (cystic, lacunar, microinfarcts), Lewy bodies, CAA, HS

Lower HV = More NFTs, more NPs, and more HS.

Whitwell 2007 [7]

12 AD 9 DLB 13 AD+DLB 12 FTD 5 CBD 5 PSP 25 controls Diagnoses were neuropathological

Mayo Clinic AD Research Center/AD Patient Registry

1.5T T1 N/A (baseline scan-death = 1 to 9 years) *rates of atrophy

Brain volume (whole-brain)

Pathological evidence of different dementias (e.g., NPs, NFTs, Lewy bodies, ubiquitin, tau, etc.) used to make diagnosis

Whole-brain atrophy rate = associated with all pathological diagnoses except DLB.

Highest whole-brain atrophy rates = CBD pathology (cortical neuronal loss, gliosis, astrocytic plaques, corticobasal bodies, neuropil threads located in regions meeting diagnostic criteria for CBD).

Increased rates of whole-brain atrophy and ventricular expansion are also found in subjects with pathological evidence of AD, DLB, FTD and PSP, compared to controls.

Rates of cerebral atrophy and ventricular expansion cannot differentiate AD from mixed AD/DLB pathology.

Note. This study was longitudinal (i.e., studied rates of change).

Jagust 46 AD Ischemic 1.5T T1 0.2 to 8.7 Brain AD pathology Lower cortical gray matter volumes =

! 41!

2008 [53]

14 VaD 9 AD/VaD 12 cognitive impairment without significant pathology 12 controls Diagnoses were neuropathological

Vascular Dementia (IVD) Program Project

years volume (whole-brain & HV), as well as MRI evidence of CVD (WMH, lacunes)

(NFTs, NPs), HS, infarcts (cortical, subcortical, cystic, lacunar, microinfarcts), white matter infarcts, demyelination

More AD pathology (composite NFT + NP score), arteriosclerosis and subcortical infarcts.

Lower HV = More AD pathology (composite NFT + NP score) and HS.

More WMH = white matter infarcts and demyelination. No association with AD pathology or CAA.

Josephs 2008(a) [48]

45 subjects with pathological diagnoses of low, intermediate or high probability of AD (32 with clinical dementia, 13 cognitively normal) Pathologies other than AD were excluded (e.g., cortical infarcts, HS)


Not specified, but other publications describe 1.5T imaging in the Mayo AD Research Center/AD Patient Registry [2,8,14,26]

T1 FLAIR

0 to 3 years Brain volume (whole-brain & ventricular)

Primary outcome measure was amyloid-beta burden score (both NPs and total SPs considered); NFTs were only considered for diagnostic purposes

No association between NP or total SP amyloid-beta burden score and brain or ventricular volumes.

No association between cerebral atrophy or ventricular expansion and the degree of vascular pathology (assessed by WMH and lacunar infarcts visible on MRI).

Post-hoc findings:

Faster rates of whole-brain atrophy = More NFTs.

Faster ventricular expansion = More NFTs.


Josephs 2008(b) [66]

84 AD (29 with TDP-43 and 55 without TDP-43) 84 controls Diagnoses were neuropathological


1.5T T1 3.8 ± 2.6 years (AD with TDP-43) 3.5 ± 2.3 years (AD without TDP-43)

Patterns of gray matter loss *Voxel-based morphometry study.

Main variable of interest was TDP-43 in the medial temporal lobe; NFTs and NPs were examined primarily for pathological confirmation

AD with and without TDP-43 compared to controls = similar pattern of gray matter loss (i.e., medial temporal lobes and temporoparietal neocortex).

AD with TDP-43 compared to AD without TDP-43 = greater volume loss in medial temporal lobe, particularly the hippocampus.

No differences between HS and no HS in

! 42!

*Only AD participants underwent autopsy

of AD. Lewy bodies and HS also recorded.

AD subjects with TDP-43.

Vemuri 2008 [54]

101 AD Diagnoses were neuropathological


1.5T T1 0.1 to 10.5 years (within 48 months for patients with Braak stages II-VI)

STAND scores (AD-type atrophy)

NFTs, NPs Higher STAND scores (i.e., more AD-typical atrophy) = More NFT and more NPs.

NFTs correlate better with STAND scores than with HV.

Whitwell 2008 [55]

83 AD 20 controls Diagnoses were neuropathological



T1 Within 4 years Patterns of gray matter loss *Voxel-based morphometry study.

NFTs, argyrophilic grains, Lewy bodies, tau burden

Braak stages III-IV = no regions of gray matter loss compared to Braak stages 0-II; patterns of gray matter loss associated with tau burden

Braak stages V-VI = graded pattern of increasing atrophy associated with Braak stage

Braak stage V = atrophy in temporal, parietal and frontal lobes only

Braak stage VI = basal forebrain, posterior cingulate, precuneus, anterior cingulate, insula, subcortical nuclei, and occipital lobe including the primary visual cortex also involved

High tau burden (≥10%) = greater gray matter loss in medial and lateral temporal lobes

Burton 2009 [56]

11 AD 23 DLB 12 VCI Diagnoses were neuropathological

Newcastle Brain Tissue Resource

1.0T 1.5T

T1 1.62 ± 1.09 years (AD) 1.53 ± 1.07 years (DLB) 2.04 ± 1.51 years (VCI)

Brain volume (MTL)

NFTs, NPs, Lewy bodies, micro and macroscopic vascular disease

Lower MTL volume = more NFTs, more hippocampal NPs, and more hippocampal NFTs.

No association between MTL volume and Lewy bodies.

Age and NFTs are the only predictors of MTL atrophy.

MTL atrophy = Highly accurate diagnostic

! 43!

marker for AD (91% sensitivity, 94% specificity) compared with DLB and VCI.

Erten-Lyons 2009 [57]

36 subjects with high levels of AD pathology at autopsy (24 with clinical dementia, 12 cognitively intact)

National Institute on Aging (NIA)-OHSU Layton Aging and AD Center

1.5T T1

3.63 ± 2.9 years (AD) 0.86 ± 0.52 years (controls)

Brain volume (whole-brain & individual regions)

Ischemic, hemorrhagic, or vascular pathology, CAA, large vessel strokes, lacunes, Lewy bodies, HS, arteriosclerosis. NFTs and NPs were assessed to recruit subjects with high levels of AD pathology.

Both demented and cognitively intact subjects showed high levels of AD pathology, but different amounts of whole-brain and HV atrophy. This suggests that atrophy is not caused solely by NFTs and NPs.

No significant association between the presence of vasculopathy and whole-brain or regional volumes.

Note. Authors did not examine atrophy in subjects with low pathology burden.

Burton 2012 [64]

8 AD 23 DLB Diagnoses were neuropathological

Newcastle Brain Tissue Resource

1.0T 1.5T

T1 1.5 ± 1.0 years Brain volume (amygdala, hippocampus & entorhinal cortex)

AD pathology (NFTs, NPs, dystrophic neuritis), Lewy bodies

No significant correlation between amygdala, hippocampus and entorhinal cortex and the burden of NFTs, NPs and Lewy bodies, excepted a positive correlation between HV and percent area of Lewy bodies in the hippocampus.

HV can differentiate between AD and DLB (lower HV and amygdala volume in AD).

Kantarci 2012 [58]

21 DLB (only 3 went to autopsy) 21 AD (only 2 went to autopsy) 42 cognitively normal (none went to autopsy)

Mayo Clinic AD Research Center/Mayo Clinic Study of Aging

3.0T T1 10 to 18 months

Brain volume (whole-brain & individual regions) Also PiB and PET imaging, but not

NFTs, NPs Overall, smaller HV = More AD pathology.

Case 1: Significant HV atrophy = Braak Stage IV & frequent NPs.

Case 2: Moderate HV atrophy = Braak Stage II, sparse NPs.

Case 3: Mild HV atrophy = Braak Stage III, sparse NPs.

! 44!

Diagnoses were clinical

considered for the present review

Case 4: Mild HV atrophy = Braak Stage II, moderate NPs.

Case 5: Mild HV atrophy = Braak Stage III, sparse NPs.

Moghekar 2012 [59]

50 elderly (Neur – at baseline) *26/50 developed dementia.

Baltimore Longitudinal Study of Aging (BLSA) Autopsy Program

1.5T PD T2

Within 25 months

WMH NFTs, NPs, infarcts, CAA, atherosclerosis

More WMH = more AD pathology (composite NFT + NP score; “similar results” when examined independently).

Infarcts, CAA and atherosclerosis were no longer associated with WMH after adjusting for other variables.

Whitwell 2012 [60]

177 AD 177 controls Diagnoses were clinical

Mayo Clinic AD Research Center/AD Patient Registry


T1 4 to 9 years Brain volume (whole-brain & individual regions)

NFTs (NPs, Lewy bodies, arteriosclerosis, atherosclerosis, micro/large infarcts and lacunar infarcts were noted but not considered variables of interest)

Lower MTL volume = more hippocampal NFTs.

Lower whole-brain volume = more NFTs in mid-frontal, superior temporal and inferior parietal regions.

The best discrimination between pathological subtypes of AD (typical, limbic-predominant, hippocampal-sparing) is made by the ratio of HV to cortical volumes.

Note. Authors only included subjects with Braak stage IV, V or VI.

Zarow 2012 [6]

51 AD* 6 DLB 28 VaD 2 FTD 1 MS 1 PSP 1 pure CAA 7 HS* 12 AD+VaD 2 AD+DLB 1 DLB+VaD 18 non-

Ischemic Vascular Dementia (IVD) Program Project

1.5T N/A 3.0 ± 2.1 years HV Main variable of interest was HS, but also recorded NFTs, NPs, micro and large infarcts, lacunar infarcts, Lewy bodies, CAA and TDP-43

Lower HV = HS.

HV are smallest for bilateral HS.

HV atrophy in bilateral HS (52%) exceeds that observed in AD (30%).

Not specific to AD (found also in VaD).

Note. Only subjects with AD and HS were examined on MRI.

! 45!

demented controls Diagnoses were neuropathological *Only subjects with AD and HS were examined on MRI.

Erten-Lyons 2013(a) [49]

71 elderly (Neur – at baseline) *44/71 developed cognitive impairment (20 AD, 5 VaD, 16 MCI, and 3 mixed dementia).

Oregon Brain Aging Study

1.5T N/A 15.36 ± 9.96 months

Brain volume (whole-brain, ventricular & HV)

NFTs, NPs, CAA, lacunar infarcts, large-vessel strokes, gross and microinfarcts, Lewy bodies, HS

Whole-brain atrophy = no pathological correlates.

Ventricular expansion = infarcts, NFTs and NPs.

HV atrophy = CAA.

Ventricular expansion, compared with total brain and HV atrophy, have the strongest association with neuropathological measures of AD and vascular disease.


Erten-Lyons 2013(b) [50]

66 elderly (Neur – at baseline) *42/66 developed cognitive impairment (22 AD, 3 VaD, 16 MCI, 1 mixed dementia).

Oregon Brain Aging Study

1.5T PD T1 T2 *PD and T2 were used for WMH

15.3 ± 9.8 months

WMH NFTs, NPs, CAA, Lewy bodies, lacunar infarcts, large-vessel strokes, microinfarcts, myelin pallor, arteriolosclerosis, atherosclerosis

More WMH = more arteriolosclerosis, myelin pallor and NFTs.

Note. This study was longitudinal (i.e., studied accumulation of WMH over time).

Toledo 22 MCI or Alzheimer 1.5T T1 N/A Brain AD pathology Lower left HV = increased MTL TDP-

! 46!

2013 [61]

AD* Diagnoses were clinical *18/22 cases had another neuropathological diagnosis in addition to AD

’s Disease Neuro-imaging Initiative

(baseline-death = 80.9 to 289.0 weeks)

volume (occipital & HV)

(NFTs, NPs, diffuse plaques) as well as pathological evidence of other dementias (Lewy bodies) or MTL pathology (TDP-43 proteinopathy, argyrophilic grain disease, HS)

43.

No difference in HV or occipital gray matter volume between mixed AD/DLB, compared to AD alone.

van Veluw 2013 [69]

22 elderly (Neur -)

Community Sample, Utrecht (The Netherlands)

3.0T (7.0T)

T1 T2 FLAIR

Not applicable *in vivo MRI study with histopathologic validation of similar appearing lesions on ex vivo MRI

Microinfarcts

Microinfarcts Low correlation between microinfarcts visible on 3.0T MRI and microinfarcts present at autopsy.

Microinfarcts 1mm³ can be detected in vivo with clinical resolution 7.0T MRI as hyperintensities on FLAIR and T2-weighted scans. 3.0T MRI has significantly decreased sensitivity for detecting microinfarcts in vivo.

Josephs 2014(a) [40]

342 AD (195 with TDP-43 and 147 without TDP-43) Diagnoses were neuropathological *Only 248 AD participants had antemortem


Not specified, but other publications describe 1.5T imaging in the Mayo AD Research Center/AD Patient Registry

T1 SPGR

N/A Brain volume (amygdala, hippocampus, entorhinal cortex, occipitotemporal cortex, lateral temporal lobe & lateral

Main variable of interest was TDP-43; but also recorded NFTs, NPs, Lewy bodies and HS

Lower hippocampal and entorhinal cortex volumes = higher TDP-43 stage (based on regional distribution).

! 47!

MRI [2,8,14,26]

frontal lobe)

Josephs 2014(b) [38]

342 AD (195 with TDP-43 and 147 without TDP-43) 46 controls Diagnoses were neuropathological *Only 248 AD participants had antemortem MRI


1.5T (for 220 subjects; not specified for the others)

T1 SPGR

N/A Brain volume (medial temporal regions & association cortices) & patterns of gray matter loss (voxel-based morphometry)

Main variable of interest was TDP-43; but also recorded NFTs, NPs, Lewy bodies, infarcts and HS

AD with TDP-43 compared to AD without TDP-43 = lower hippocampus, amygdala and entorhinal cortex volumes after accounting for other variables & greater medial temporal loss on voxel-based morphometry.

Lower hippocampal volume remains significantly associated with the presence of TDP-43 even after accounting for HS.

Higher TDP-43 burden = increased volume loss.

Higher TDP-43 distribution = increased volume loss.

Kaur 2014 [62]

59 elderly (11 with clinical dementia, 48 nondemented)

Framingham Heart Study

1.0T T2 2.67 ± 1.76 years (controls) 4.82 ± 2.24 years (dementia)

Brain volume (whole-brain & individual regions) & WMH

NFTs, NPs, HS, gross infarcts, microinfarcts, lacunes, atherosclerosis, arteriosclerosis, white matter disease

Lower whole-brain volume = More NFTs and more NPs.

Lower HV = More cortical NFTs, more noncavitated & subcortical microinfarcts, and more HS.

More ventricular expansion = More NFTs and more NPs.

More WMH = More deep nuclei microinfarcts.

MRI volumes correlate better with NFTs than SPs.

Raman 2014 [63]

66 elderly (22 with microinfarcts at autopsy, 44 without microinfarcts)

Mayo Clinic AD Research Center/AD Patient Registry/ Mayo Clinic

1.5T 3.0T

T1 FLAIR

0.11 to 3.29 years

Brain volume (whole-brain & HV), WMH and infarcts

Main variable of interest was microinfarcts; but also recorded NFTs, NPs, and CAA

Whole-brain atrophy rate of change = Greater in subjects with microinfarcts.

HV atrophy = No association with microinfarcts.

WMH = No association with microinfarcts.

The presence and number of cortical and

! 48!

Study of Aging

subcortical infarcts detected on MRI are related to microinfarcts at autopsy.

Shim 2015 [65]

26 AD 17 advanced risk for CVD 14 controls Diagnoses were clinical *Only brains from 14 participants were used to study neuropathological correlates of WMH

Washington University School of Medicine Memory and Aging Project

1.5T FLAIR N/A (last clinical evaluation-autopsy = 6.12 ± 3.26 years)

WMH (total, periventricular & subcortical), ventricular volume

NFTs, NPs, vascular pathology (atherosclerosis, arteriolosclerosis, CAA, infarcts, microinfarcts, lacunes, hemorrhages, subcortical arteriosclerotic leukoencephalopathy)

Higher total and periventricular WMH volume = lower Braak NFT stage, more arteriosclerosis, more severe breakdown of the ventricular lining and less periventricular vessels

Higher subcortical WMH volume = less SPs, more demyelination, more microinfarcts and more cerebral hemorrhages

Higher ventricular volume = less deep white matter demyelination.

AD Alzheimer’s disease; CA1 cornu ammonis area 1; CBD corticobasal degeneration; CJD Creutzfeldt-Jakob disease; CSF cerebrospinal fluid; CVD cerebrovascular disease; DLB dementia with Lewy bodies; FTD fronto-temporal degeneration; HS hippocampal sclerosis; HV hippocampal volume; MCI mild cognitive impairment; MRI magnetic resonance imaging; MTL medial temporal lobe; Neur - no neurological disease; NFT neurofibrillary tangle; NFTD neurofibrillary tangle-only degeneration; NP neuritic plaque; PD proton density; PSP progressive supranuclear palsy; SP senile plaque; VaD vascular dementia; VCI vascular cognitive impairment; WMH white matter hyperintensities.

! 49!

Table 2 Relevant coefficients from studies included in the review

MRI Biomarker

Num

ber

of su

bjec

ts

Diff

use

plaq

ues

Neu

ritic

pla

ques

NFT

Bra

ak st

age

Art

erio

scle

rosi

s

Art

erio

losc

lero

sis

Mic

roin

farc

ts

Cer

ebra

l he

mor

rhag

es

Cer

ebra

l Am

yloi

d A

ngio

path

y

Hip

poca

mpa

l Sc

lero

sis

TD

P-43

Los

s of v

entr

icul

ar

linin

g

Dee

p W

M

dem

yelin

atio

n

P-va

lue

Rem

arks

Ref

eren

ce

Whole-Brain Volume

59

12

45 59

39

39

71

-0.03(β)

-0.68(ρ)

0.419 (R2)

0.192 (R2)

-0.54(ρ)

-0.15(β)

-0.13(β)

0.01

0.01

<0.001

0.016

<0.001

<0.01

0.03

SPs density in the hippocampus

Whole-brain boundary shift integral, adjusted for age at second scan and

tome from second scan to death

Rate of CNS volume decrease and

cortical NFTs

Final CNS volume and cortical NFTs

Age x CAA and total brain volume change as proportion of intracranial volume (%) – mixed-effect model

[62]

[68] [48]

[62]

[47]

[47]

[49]

aSTAND-Score 101 101

0.52(ρ) 0.62(ρ)

<0.0001 <0.0001

CERAD NP counts [54] [54]

Ventricular Volume

59

39

39

71

59

39

39

45 14

0.03(β)

0.397 (R2)

0.254 (R2)

0.05(β)

0.422 (R2)

0.371 (R2)

0.22(β)

0.39(ρ)

-0.509 (ρ)

0.001 <0.001

<0.01

0.001

0.001

<0.001

<0.0001

0.022

0.032

Logarithm of temporal horn volume

Rate of ventricular CSF volume increase and cortical SPs

Final ventricular CSF volume and

cortical SPs

Age x CERAD SP burden moderate of frequent and ventricular volume

change as proportion of intracranial volume (%) – mixed-effect model

Logarithm of temporal horn volume

Rate of ventricular CSF volume

increase and cortical NFTs

Final ventricular CSF volume and cortical NFTs

Ventricular boundary shift integral, adjusted for age at second scan and

tome from second scan to death

Ventricular Index

[62]

[47]

[47] [49]

[62]

[47] [47] [48]

[65]

Medial Temporal Lobe

46

0.365(ρ)

0.014

Medial temporal lobe atrophy rating and percent area of Aβ plaques in the

hippocampus

[56]

! 50!

Volume 46

46

195

0.486(ρ)

0.504(ρ)

-0.024 -0.012 -0.005

(β)

<0.001

0.001

0.02 <0.001

0.02

Medial temporal lobe atrophy rating

Medial temporal lobe atrophy rating

and percent area of NFT in the hippocampus

Hippocampus, entorhinal cortex and amygdala volumes (respectively) as a percentage of total intracranial volume

and quantitative TDP-43 burden

[56] [56]

[38]

Hippocampal Volume

28

67

28

12

12

101

12

12

59

59

71

93

59

28

-0.75(ρ) -0.39(ρ)

-0.73(ρ) -0.80(ρ)

-0.76(ρ)

-0.4(ρ)

-0.62(ρ) -0.55(ρ)

0.64(β)

0.57(β)

-0.0008

(β)

-0.02(β)

-1.23(β) -0.63(ρ)

0.0001

<0.01

0.0002

0.002

0.004 0.0001

0.03

0.06

0.015

0.036

0.009

0.009

<0.001

0.003

Association with number of neurons in CA1, which is associated with

hippocampal atrophy

Bilateral hippocampal W score Association with number of neurons in

CA1, which is associated with hippocampal atrophy

Left hippocampus

Right hippocampus

Hippocampal W-score

Left hippocampus volume, NFT

pathology in CA1

Right hippocampus volume, NFT pathology in CA1

Noncavitated microinfarcts

Subcortical microinfarcts

Age x CAA and hippocampal volume change as proportion of intracranial volume (%) – mixed-effect model

Association with number of neurons in

CA1, which is associated with hippocampal atrophy

[52]

[51] [52]

[68]

[68] [54] [68]

[68]

[62] [62] [49]

[53]

[62] [52]

Total WMH Volume

66

66

59

57

57

14

0.05(β)

-0.268

(ρ)

0.213(ρ)

0.05(β)

0.53(β)

0.559(ρ)

0.008

<0.0001

0.016

0.023

0.058

0.019

Braak (5 or 6 vs 0,1, or 2) x time – mixed-effect model

Arteriolosclerosis (high vs low) x time

– mixed-effect model

Logarithm of WMH volume and deep nuclei microinfarcts

[50]

[50]

[62] [65]

[65]

[65] Periventricular 57 -0.276 0.02 [65]

! 51!

WMH Volume

57

14

(ρ)

0.260(ρ)

0.559(ρ)

0.027

0.019

[65]

[65]

Deep (subcortical)

WMH Volume

57

57

57

14

-0.234 (ρ)

0.259(ρ)

0.344(ρ)

0.571(ρ)

0.041

0.027

0.004

0.016

Cortical microinfarcts

[65]

[65]

[65]

[65]

Aβ amyloid beta; CAA cerebral amyloid angiopathy; CA1 cornu ammonis area 1; CNS central nervous system; CSF cerebrospinal fluid; HS hippocampal sclerosis; HV hippocampal volume; MRI magnetic resonance imaging; MTL medial temporal lobe; NFT neurofibrillary tangle; NP neuritic plaques; SP senile plaque; TDP-43 TAR DNA-binding protein of 43 kDa; WM white matter; WMH white matter hyperintensities.

! 52!

FIGURES

Figure 1 PRISMA flow diagram through the different phases of the systematic review

! 53!

Figure 2 Significance of radiological-pathological correlations in Alzheimer’s disease using in vivo MRI (n=27 studies).

CAA cerebral amyloid angiopathy; HS hippocampal sclerosis; HV hippocampal volume; MTL medial temporal lobe atrophy; NFT neurofibrillary tangle; NP neuritic plaque; TDP-43 TAR DNA-binding protein of 43 kDa; WM white matter; WMH white matter hyperintensities.

! 54!

Figure 3 Neuropathology and assessment of Alzheimer’s disease.

Aβ amyloid beta; CAA cerebral amyloid angiopathy; HS hippocampal sclerosis; NFT neurofibrillary tangle; TDP-43 TAR DNA-binding protein of 43 kDa; WMH white matter hyperintensities.

AD-related anatomic changes

Vascular lesions

Aβ plaques NFTs

Arterio/arteriolo/atherosclerosis Infarcts Bleedings

CAA

Medial temporal lobe

atrophy

WMH

Neuropathologicallesions

Lacunes

Ventricular expansion

Whole-brain atrophy

In vivo MRI-detected changes Clinicaldiagnosis

Cognitive impairment Death

Autopsy

HS

TDP-43

Radiological-pathological correlation in Alzheimer’s ...

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