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Journal of Alzheimer’s Disease 42 (2014) S495–S502 DOI 10.3233/JAD-141419 IOS Press

Review

Characterizing Early Alzheimer’s Disease and Disease Progression Using Hippocampal Volume and Arterial Spin Labeling Perfusion MRI Ze Wanga,b,∗ a Center for Cognition and Brain Disorders, Hangzhou Normal University, Affiliated Hospital of Hangzhou Normal

University, Hangzhou, Zhejiang, China b Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania Treatment Research Center, Philadelphia, PA, USA Accepted 12 August 2014

Abstract. Searching for biomarkers sensitive to early Alzheimer’s disease (AD) and its progression has been a research priority for two decades. MRI has been increasingly used for this endeavor because of its capability of detecting both structural and functional brain patterns without injecting external contrast agent or radioactive tracers. Recent work has shown sensitivity of hippocampal volume and regional cerebral blood flow for differentiating prodromal AD from normal controls as well as AD. This review provides a summary for the existing literature describing the applications of either or both modalities in early AD studies as well as disease progression assessment. The various findings in the reviewed studies lead to a conclusion of assessing hippocampal volume and arterial spin labeling cerebral blood flow as potential markers for disease detection, and progression monitoring though longitudinal studies are still lacking to fully examine their sensitivity and specificity. Keywords: Arterial spin labeling, cerebral blood flow, diagnosis, grey matter volume, hippocampus, imaging

INTRODUCTION Alzheimer’s disease (AD) is a progressive neurodegenerative disease, whose prevalence increases with age and has become a huge global health problem because of increased life expectance [1–5]. While AD is currently not curable, early interventions through symptomatic or disease-modifying approaches have shown effects on slowing down AD progression [6]. Finding biomarkers that are better linked to early consequences of AD pathology, and sensitive to disease progression is then crucial to applying early interven∗ Correspondence to: Ze Wang, Hospital of Hangzhou Normal University, Building 7, MRI Room, 126 Wenzhou RD, Hangzhou, Zhejiang 310015, China. Tel./Fax: +86 571 88285650; E-mail: [email protected].

tions, searching and evaluating effective treatments, or even to reducing the costly diagnosis process. Early AD is widely characterized by the definition of mild cognitive impairment (MCI), though several other similar terminologies have been proposed as well, such as isolated memory impairment, minimal AD, predementia AD, prodromal AD, and early AD, among others [7, 8]. AD is defined by both neuropathology and clinical symptoms. The neuropathology of AD consists of two hallmark elements: the depositions of plaques (amyloid-␤) and tangles (tau protein) [9]. It is likely that these two biochemical conditions lead to the destruction or death of nerve cells and subsequently cause the clinical symptoms, suggesting that both amyloid-␤ and tau can be assessed as early AD markers. A recent PET imaging study showed that the

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concentration changes of both amyloid-␤ and tau were found to be a decade (>15 years) before AD symptom onset [10]. However, measuring amyloid-␤ and tau through cerebrospinal fluid is not easily used in clinical diagnosis, and both of them may not be sensitive to disease progression. Nevertheless, this is a research topic still under rapid investigation and extensive literature review has been done in other review articles included in the same supplemental issue as the current review. Here, we focused on the neuroimaging assessment of the AD brain regarding macroscopic brain tissue atrophy and functional decay. Clinically, AD is characterized by both structural and functional degeneration. Potential AD biomarkers may then be identified by measuring brain atrophy or functional brain changes. While several other techniques can be used to assess brain structure and or function, MRI has been an indispensable tool for AD studies because of its non-invasiveness and its capability for providing both structural and functional brain information. It is also widely distributed and relatively cheap as compared to PET. Using high resolution structural MRI, brain atrophy can be assessed by manually drawing a region-of-interest (ROI) in the target regions or automatically measuring regional brain volume after registering each individual brain into a common brain space [11]. Brain function can be assessed with the blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) or arterial spin labeling (ASL) perfusion MRI [12, 13]. While BOLD fMRI has relatively higher signal-to-noise-ratio (SNR) than ASL and is more widely used for measuring dynamic brain activations or functional connectivity [14, 15], ASL MRI provides a quantitative measure of cerebral blood flow (CBF), which is tightly coupled with brain function, so it can be used to measure brain states at different time and thereafter be assessed as a potential physiological biomarker for AD. Over the past decades, both structural MRI and ASL perfusion MRI have been increasingly used in AD studies. Tissue atrophy has been revealed in AD or MCI in various brain regions, more often in the medial temporal lobe [16–19]. Using ASL, many groups [20–22] have demonstrated hypoperfusion patterns in AD in medial temporal lobe as well as parietal cortex [20, 23–32]. While atrophy and perfusion alteration patterns in AD showed some overlap, most of them were spatially unrelated, suggesting a different role of each imaging modality for predicting disease severity or progression. The purpose of this review was to summarize the various MRI studies on brain volume or CBF alterations in early disease and their variation patterns

during disease transition. A brief summary to the key references reviewed below can be found in the Supplementary Material. While atrophy has been found in various places, we focused on hippocampal atrophy because the hippocampus is a critical brain structure for memory function and memory impairment is a hallmark of early AD [33]. For ASL CBF, we focused on studies including MCI or both MCI and AD. HIPPOCAMPAL VOLUME (HCV) HCV calculation HCV is usually calculated from the hippocampus region-of-interest (ROI) drawn by hand or created by automatic algorithms. A relative HCV in relation to the total brain volume is generally used for cross-sectional studies to avoid the across subject brain size difference. For longitudinal studies, relative HCV should be calculated with regard to HCV at baseline. Cross-sectional studies Most studies showed that HCV follow the trend: normal elderly controls (NC)>MCI>AD. Convit et al. [34] found a smaller HCV in MCI compared to NC, which was further reduced in mild AD. HCV yielded an MCI/NC classification accuracy of 74%; when combined with fusiform volume, HCV differentiated MCI from dementia type subjects with an accuracy of 80%. In addition they found that HCV was related to delayed recall memory performance. Visser et al. [35] found that baseline parahippocampal volume was smaller in AD converters (MCI who developed AD after 3 years follow-up) than nonconverters; the medial temporal lobe atrophy did not show better performance than memory impairment for predicting dementia, but combining them improved prediction accuracy. de Toledo-Morrell et al. [36] showed that HCV was smaller in mild AD than incipient AD and NC but it was entorhinal cortex volume (ECV) rather than HCV that could separate mild AD or incipient AD from NC and separate AD converters from nondemented patients. At the same time, Xu et al. [37] found that HCV and ECV were approximately equivalent for discriminating MCI or AD from NC. Du et al. [38] shortly claimed that ECV was better than HCV for differentiating MCI from AD though they found that ECV and HCV were correlated in MCI and AD and combining them improved classification between AD and NC. Wolf et al. also showed a trend of decreased HCV when disease severity increased [39]. More recently, Devanand et al. [40]

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demonstrated that HCV gradually decreased from the largest in NC, to an intermediate value in MCI nonconverters, and then to the smallest in MCI converters (to AD). The above studies were based on manual brain segmentations, which are time consuming and may be subject to inter-rater variability (up to 5% as reported in [41]). But similar results were found in studies based on automatic or semi-automatic segmentation approaches. Using voxel-based morphometry [11], Chetelat et al. [42] found lower baseline HCV in converters (to AD) than non-converters. Using an automated segmentation method, Morra et al. showed a similar trend of HCV reduction from NC, to MCI, and AD in an across-sectional comparison [43]. Baseline HCV predicting disease progression studies HCV at baseline has been shown to be predictive of disease conversion or progression. Jack et al. [44] found that HCV measured at baseline (within 4 months of the initial clinical assessment for MCI) was associated with conversion from MCI to AD; adding other variables did not substantially improve prediction accuracy. Visser et al. [45] showed a significant association of HCV to cognitive decline at a 1.9 year follow-up. They found an increased prediction power for cognitive decline and for Alzheimer-type dementia by combing the medial temporal lobe measures (including HCV) with age and delayed recall score. Convit et al. [46] demonstrated high sensitivity of brain volume in the medial occipito-temporal and the combined middle and inferior temporal gyri for predicting cognitive decline after 3.2 years from the initial evaluations. Adding the structural measures with age and other measures only moderately improved the group classification accuracy. In [40], Devanand et al. found that baseline HCV is predictive of time of conversion from MCI to AD and that combing HCV and ECV, age, and cognitive values contributed to higher prediction accuracy for disease conversion at a follow-up of 5 years. Eckerstrom et al. [47] reported an association of small baseline left HCV with conversion of MCI into dementia at a 2 year follow-up. These studies were based on manual brain segmentations and only reported association between hippocampal volume and conversion between MCI to AD. Longitudinal hippocampal volume changes The prediction efficacy of HCV decline for disease progression is inconsistent among the few longitudinal

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studies. Using a modified voxel-based morphometry, Chetelat et al. [42] showed greater longitudinal grey matter loss in converters relative to non-converters in the hippocampus and other regions. Eckerstr¨om et al. [47] reported higher HCV loss in converters than nonconverting MCI. In a 10-year follow-up study, den Heijer et al. [28] used both manual and automatic segmentation methods to extract HCV and found that a one standard deviation faster decline of HCV was associated with a higher risk to develop dementia; HCV decline predicted onset of clinical dementia. The same research group recently reported that the hippocampal shape provided additional predictive value for dementia conversion [48], which is consistent with the hippocampal shape-based AD conversion prediction study by Costafreda et al. [49]. Using multiple follow-ups, Leung et al. found a small acceleration rate of HCV decline in MCI (0.22%/year2 ), which was mainly driven by the converters [50], which along had an acceleration rate of 0.5%/year2 . Hippocampus subfields While the majority of AD hippocampal volume studies focused on the entire volume, researchers started to examine subfields of the hippocampus. Apostolova et al. showed that smaller CA1 volumes were associated with a higher risk of converting from MCI to AD [51]. Devanand et al. found that AD converters showed larger atrophy in the CA1 region and subiculum [52]. Using a semi-automatic segmentation tool and T2weighted MRI, Pluta et al. [53] showed a smaller HCV in subfields including CA1 and CA4 in amnestic MCI (aMCI). ASL MRI ASL methodology ASL MRI is an MRI-based technique for measuring CBF by magnetically labeling arterial blood water as an endogenous diffusive tracer [12, 13]. Because CBF relates brain metabolism [54], ASL MRI can and has been used in both basic and clinical neuroscience to measure regional changes in brain activity [55]. Depending on how the blood water is labeled, ASL techniques can be roughly divided into pulsed ASL (PASL) and continuous ASL (CASL) (please see the ASL white paper [56] for technical details). PASL uses a short radio frequency (RF) pulse to invert the blood water spins in a very short time; while CASL uses a long low amplitude RF pulse or a series of short

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low amplitude RF pulses (as in the pseudo-continuous CASL (pCASL) scheme [57]) to tag the arterial spins over a long duration. PASL provides lower SNR than CASL as well as increased physiological noise [58], but it is easier to implement and many ASL studies (including ADNI II, http://www.adni-info.org/) are based on PASL. Perfusion signal in ASL is determined by pair-wise comparison between images acquired with spin labeling and those acquired without labeling (control image – label image or C-L) [59]. By normalizing the C-L signal difference to the MRI signal acquired at rest and during full relaxation stage without ASL preparations, ASL techniques are able to quantify CBF in well-characterized physiological units of ml/100 g/min based on measured or assumed values for a few key parameters and an appropriate model [56]. CBF measurements with ASL perfusion MRI have been shown to agree with results from 15O-PET [60, 61], suggesting ASL MRI as a potential alternative to PET imaging. Because ASL does not require exogenous contrast agents or radioactive tracers, it is safer, more economical, more acceptable to human subjects, and more convenient than other CBF measuring approaches such as PET. Since it can be easily inserted into any clinical scan and can be repeated as often as required in the same scan session without cumulative effects, it is also more practical and less-costly for longitudinal research [55]. ASL MRI in AD and early AD ASL MRI has been demonstrated to be effective for differentiating AD from NC by many groups [20–22, 27, 32, 62, 63] and the revealed ASL hypoperfusion patterns in AD were similar to those detected using PET and SPECT [23]. Its potential for early AD detection [24, 26, 27, 30, 32, 62–65] is paralleled by FDG PET work in which the hypometabolism detected by FDG PET appears to precede atrophy revealed by structural imaging [66–69] or even precede disease onset [31, 70] and is potentially more accurate in the prediction of dementia from MCI [66, 67]. ASL identified CBF alteration patterns in AD have been recently summarized by a review paper [23]. In this review, we focused on AD ASL studies that included MCI. Cross-sectional ASL studies in early AD Johnson et al. [64] presented the first ASL study in early AD. Using PASL, they found significant hypoperfusion in AD patients (n = 20) (lower than NC, n = 23) in bilateral inferior parietal cortex, bilateral posterior

cingulate cortex (PCC), bilateral superior and middle frontal gyri, while MCI (n = 18) showed a trend of hypoperfusion in right inferior parietal cortex only with less significance. No medial temporal lobe changes were reported due to the insufficient coverage of the ASL technique used. In another PASL-based study, Xu et al. [65] found hypoperfusion in the right precuneus and cuneus in aMCI patients (n = 12) as compared to NC (n = 14). CBF of these regions was correlated with the Mini-Mental State Examination score, an index of cognitive impairment, and the Rey Auditory Verbal Learning Test score, a measure for verbal learning and memory. Reduced perfusion was also shown in PCC in aMCI during performing a sceneencoding memory task. In [24], Dai et al. used CASL to measure CBF in patients with AD (n = 37) and MCI (n = 29), and NC (n = 38). They found hypoperfusion in MCI and AD patients in PCC and medial precuneus. Interestingly, MCI also showed increased CBF in left hippocampus, right amygdala, and rostral head of the right caudate and ventral putamen and globus pallidus. The coexistence of hypo- and hyper-perfusion in MCI was postulated to reflect a possible neural compensation mechanism in early AD stage. AD patients had increased CBF in right anterior cingulate as compared to NC but not to MCI. As compared to NC and MCI, AD patients showed reduced CBF in left inferior parietal, left lateral frontal, left superior temporal, and left orbitofrontal gyri. No associations between CBF and clinical measures were reported. Using the same PASL technique as in [64], Chao et al. [30] examined CBF difference between two types of MCIs and NC. Using a priori ROIs, they found hypoperfusion in PCC in all MCI patients. Patients with executive dysfunctions (n = 12) showed hypoperfusion in bilateral middle frontal cortex and left precuneus as compared to NC (n = 12). As compared to aMCI (n = 12), the executive dysfunction MCI showed lower CBF in left middle frontal cortex, left posterior cingulate, and left precuneus. CBF in PCC was found to be correlated with memory performance and CBF in the other four ROIs especially on the left side was correlated with executive function performance. In [32], Wang et al. analyzed the Alzheimer’s Disease Neuroimaging Initiative (ADNI) II PASL data and demonstrated the sensitivity of ASL MRI to prodromal and early AD in a multi-site context. Measures of CBF in a predetermined set of regions (meta-ROI) [27, 66, 71], and HCV were compared between NC (n = 47), patients with early and late MCI [EMCI (n = 32), LMCI (n = 35)], and AD (n = 15). Meta-ROI CBF was associated with group status measured by Clinical Dementia Rating

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Follow-up on CBF variations Chao et al. reported the first follow-up AD ASL study in [29], where they found that MCI who later converted into AD had lower baseline perfusion in precuneus, middle cingulum, inferior parietal and middle frontal cortices than the non-converters. When combined with hippocampal volume, ASL CBF at baseline showed better prediction power for disease progression. In summary, hypoperfusion has been consistently shown in early AD in precuneus and bilateral parietal cortex though hyperperfusion was also found in other brain regions such as hippocampus and basal ganglia area [24]. CBF in the hypoperfused places was correlated with disease severity. COMBING HCV AND ASL CBF FOR EARLY AD DETECTION Several groups have compared or combined HCV and ASL CBF for early AD detection and disease progression. Wang et al. [32] showed that the meta-ROI (containing smaller ROIs from precuneus, bilateral parietal cortex, and temporal cortex) CBF appears to better track the disease stages than HCV, but meta-ROI CBF and HCV appeared to provide independent prediction of disease status. In [73], Bron et al. found that ASL CBF has similar diagnostic power to grey matter volume for differentiating MCI or AD from NC but combing both modalities did not significantly improve the prediction accuracy. In [74], Mak et al. showed an AD predicting accuracy of 89.3%, 82.1%, 75.0%, and 71.4% by using ASL CBF in right and left HCV and middle and PCC. By combing CBF of the middle cingulate gyrus and normalized HCV, they achieved the best AD prediction accuracy as measured by the area-under-curve of Receiver Operating Characteristic curves.

DISCUSSION AND CONCLUSION Both HCV and ASL CBF showed promise for early AD detection and disease progression with considerable variations, but both measures can be affected by many factors including disease heterogeneity, diagnosis errors, age, and education level, which may explain the different effects (such as different classification or prediction sensitivity) across different studies and should be carefully controlled during patient characterization and data analysis. Test-retest data acquired within a short time interval, e.g., within a month, are necessary to assess the stability of both HCV and ASL CBF for early AD detection. Methodological errors are a third concern for using both measures as potential AD markers. HCV can suffer from variations of identifying the hippocampus boundaries. A combination of manual and semi-automatic segmentation algorithms can be a solution. The ROI-based HCV analysis cannot assess regional effects. Fortunately, high resolution MRI and advanced sub-field HCV analysis started showing promise for better examining disease related sub-regional or voxel-level damages. ASL CBF is known to have a low SNR. With the various advances of ASL acquisition techniques made in the past decade [56], we expect to see a better sensitivity of using ASL MRI for early AD detection and disease progression monitoring. But careful postprocessing strategies including motion correction [59, 75], temporal nuisance correction [75, 76], and outlier cleaning [32, 59] should still be adopted especially for studying patients with more severe disease. For example, Wang et al. [32] found that the number of bad ASL images during a fixed scan time tracked the disease severity as shown in Fig. 1. ROI-based analysis may provide better SNR for ASL CBF but it loses capability 15

number of bad CB images

scale sum of boxes: it gradually decreased from normal status to EMCI, LMCI, and AD. As compared to NC, LMCI and AD showed significant hypoperfusion. HCV was associated with group status too, but only AD patients had significantly smaller volumes than NC. Using the 3D background suppressed pCASL sequence, Binnewijzend et al. [72] examined CBF difference in 71 AD patients, 35 MCIs, and 73 NC. Their findings were consistent with previous ASL studies. MCI showed hypoperfusion in precuneus and parietal and occipital lobes as compared to NC. CBF in PCC was correlated with MMSE score.

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0 NC

EM

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Fig. 1. Number of outlier CBF images identified from ADNI II PASL data increases with disease severity. NC, elderly healthy normal control; EM, early MCI, LM, later MCI.

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for finding the regional effects. A fourth burning issue is that both HCV and ASL CBF may not be more sensitive than clinical measurements such as age and cognitive variables like memory performance for differentiating patients from NC or for predicting time to conversion [35, 40]. Combining them with clinical measures may provide a better AD prediction tool as evidenced by the minimal [44], modest [46], to considerable improvement [45] gained after combining HCV and the clinical measures. Presumably, HCV should decrease while AD pathology progresses. However, CBF can both decrease and increase in the same brain, a phenomenon that might relate to a mechanism to compensate the partial functional decay during the early disease stage. In the literature reviewed above, AD or MCI-related hypoperfusion still remains the most consistent patterns across different studies. The spatially more extended hypoperfusion patterns in MCI and AD repeated by the many studies suggest that functional change might be more prominent than structural decay in MCI/AD brain and can then be easier to detect even using the relatively lower SNR ASL technique. While the cross-sectional findings seem promising of using HCV and ASL CBF or a combination of both for early AD and AD disease progression study, both HCV and CBF should be evaluated over time in order to be assessed as AD markers. Longitudinal studies along with larger sample sizes than the published ones will be essential and should provide more information for assessing both HCV and ASL CBF as potential markers for AD and its progression.

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ACKNOWLEDGMENTS This work was supported by Hangzhou Qianjiang Endowed Professor Program and Youth 1000 Talent Program of China. The author’s disclosure is available online (http: //www.j-alz.com/disclosures/view.php?id=2500).

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SUPPLEMENTARY MATERIAL The supplementary material is available in the electronic version of this article: http://dx.doi.org/10.3233 /JAD-141419.

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