Evolving Evidence for the Value of Neuroimaging Methods and Biological Markers in Subjects Categorized with Subjective Cognitive Decline

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1 Journal of Alzheimer s Disease 48 (2015) S171 S191 DOI /JAD IOS Press Review S171 Evolving Evidence for the Value of Neuroimaging Methods and Biological Markers in Subjects Categorized with Subjective Cognitive Decline Simone Lista a,b,, Jose L. Molinuevo c,d, Enrica Cavedo b,e,f, Lorena Rami c,d, Philippe Amouyel g,h,i,j, Stefan J. Teipel k, Francesco Garaci l,m, Nicola Toschi m,n,o, Marie-Odile Habert p,q, Kaj Blennow r,s, Henrik Zetterberg r,t, Sid E. O Bryant u, Leigh Johnson u, Samantha Galluzzi f, Arun L.W. Bokde v,w, Karl Broich x, Karl Herholz y, Hovagim Bakardjian z, Bruno Dubois b, Frank Jessen aa,bb, Maria C. Carrillo cc, Paul S. Aisen dd and Harald Hampel a,b a AXA Research Fund & UPMC Chair, Paris, France b Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Institut de la Mémoire et de la Maladie d Alzheimer (IM2A) & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Département de Neurologie, Hôpital de la Pitié-Salpêtrière, Paris, France c Alzheimer s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic, Barcelona, Spain d Institut d Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain e CATI Multicenter Neuroimaging Platform, France f Laboratory of Epidemiology, Neuroimaging and Telemedicine, IRCCS Istituto Centro San Giovanni di Dio-Fatebenefratelli, Brescia, Italy g Inserm, U1157, Lille, France h Université de Lille, Lille, France i Institut Pasteur de Lille, Lille, France j Centre Hospitalier Régional Universitaire de Lille, Lille, France k Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany & German Center for Neurodegenerative Diseases (DZNE) Rostock/Greifswald, Rostock, Germany l Department of Diagnostic Imaging, Molecular Imaging, Interventional Radiology and Radiotherapy, University Hospital of Tor Vergata, Rome, Italy m Department of Biomedicine and Prevention University of Rome Tor Vergata, Rome, Italy n Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, USA o Harvard Medical School, Boston, MA, USA Correspondence to: Simone Lista, PhD, AXA Research Fund & UPMC Chair, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Institut de la Mémoire et de la Maladie d Alzheimer (IM2A), Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Pavillon François Lhermitte, Hôpital de la Pitié-Salpêtrière, 47 Boulevard de l Hôpital, Paris, CEDEX 13, France. Tel.: ; Fax: ; slista@libero.it. ISSN /15/$ IOS Press and the authors. All rights reserved

2 S172 S. Lista et al. / Biomarkers in Subjective Cognitive Decline p Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Laboratoire d Imagerie Biomédicale, Paris, France q AP-HP, Pitié-Salpêtrière Hospital, Nuclear Medicine Department, Paris, France r Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden s The Torsten Söderberg Professorship in Medicine at the Royal Swedish Academy of Sciences t Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK u Institute for Aging and Alzheimer s Disease Research & Department of Internal Medicine, University of North Texas Health Science Center, Fort Worth, TX, USA v Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland w Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland x President, Federal Institute of Drugs and Medical Devices (BfArM), Bonn, Germany y Institute of Brain, Behaviours and Mental Health, University of Manchester, Manchester, UK z IM2A - Institute of Memory and Alzheimer s Disease, IHU-A-ICM - Paris Institute of Translational Neurosciences, Pitié-Salpêtrière University Hospital, Paris, France aa Department of Psychiatry, University of Cologne, Cologne, Germany bb German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany cc Medical & Scientific Relations, Alzheimer s Association, Chicago, IL, USA dd Department of Neurosciences, University of California, San Diego, San Diego, CA, USA Accepted: 8 June 2015 Abstract. There is evolving evidence that individuals categorized with subjective cognitive decline (SCD) are potentially at higher risk for developing objective and progressive cognitive impairment compared to cognitively healthy individuals without apparent subjective complaints. Interestingly, SCD, during advancing preclinical Alzheimer s disease (AD), may denote very early, subtle cognitive decline that cannot be identified using established standardized tests of cognitive performance. The substantial heterogeneity of existing SCD-related research data has led the Subjective Cognitive Decline Initiative (SCD-I) to accomplish an international consensus on the definition of a conceptual research framework on SCD in preclinical AD. In the area of biological markers, the cerebrospinal fluid signature of AD has been reported to be more prevalent in subjects with SCD compared to healthy controls; moreover, there is a pronounced atrophy, as demonstrated by magnetic resonance imaging, and an increased hypometabolism, as revealed by positron emission tomography, in characteristic brain regions affected by AD. In addition, SCD individuals carrying an apolipoprotein 4 allele are more likely to display AD-phenotypic alterations. The urgent requirement to detect and diagnose AD as early as possible has led to the critical examination of the diagnostic power of biological markers, neurophysiology, and neuroimaging methods for AD-related risk and clinical progression in individuals defined with SCD. Observational studies on the predictive value of SCD for developing AD may potentially be of practical value, and an evidence-based, validated, qualified, and fully operationalized concept may inform clinical diagnostic practice and guide earlier designs in future therapy trials. Keywords: Alzheimer s disease, biological markers, blood-based biomarkers, cerebrospinal fluid biomarkers, clinical trials, functional MRI markers, molecular imaging markers, preclinical Alzheimer s disease, structural MRI markers, subjective cognitive decline INTRODUCTION: CONCEPTUAL FRAMEWORK OF SUBJECTIVE COGNITIVE DECLINE Research on the diagnostic construct of subjective cognitive decline (SCD) as a potential very early clinical sign of Alzheimer s disease (AD) is diverse. Different research groups have used different definitions of SCD and methods of assessment. This limits the comparability of findings and restricts general conclusions beyond individual studies. This fact has led to the recent international consensus process on the definition of a conceptual research framework on SCD in preclinical AD [1]. The framework provides a general and broad definition of SCD in preclinical AD. It also proposes

3 S. Lista et al. / Biomarkers in Subjective Cognitive Decline S173 a list of characteristics of studies that should always be reported in publications. Finally, it suggests a set of criteria referred to as SCD plus which increases, according to current knowledge, the likelihood that an individual with SCD will be at the preclinical state of AD. RISK OF PRODROMAL AD, MILD COGNITIVE IMPAIRMENT, AND DEMENTIA IN OLDER PEOPLE WITH SCD Recently, subjects characterized with SCD have been proposed as potentially being at increased risk for developing objective future cognitive impairment. Such a risk has been further characterized as a function of well-defined, core, feasible AD biomarkers [2 6], which have been useful to define subgroups of SCD individuals with clear increased risk. Overall, SCD subjects present greater rates of incident mild cognitive impairment (MCI) or dementia due to AD [1, 7 9] compared to normal cognitive subjects without perceived subjective decline. A recent meta-analysis [10] based on 28 studies with SCD individuals has concluded that the annual conversion rate of SCD to dementia is 2.33% (95% CI: 1.93% 2.78%), with a relative risk (RR) of 2.07 (95% CI: ) compared with those without subjective decline. The annual conversion rate of developing MCI, defined in the meta-analysis of 11 studies, is 6.67% (95% CI: %). Long-term studies, with a follow up period over 4 years, have shown that 14.1% (95% CI: %) of SCD individuals develop dementia and 26.6% (95% CI: ) go on to develop MCI. In summary, older people with SCD but preserved cognition are twice as likely to develop dementia as individuals without SCD, and approximately 2.3% and 6.6% of older people with SCD will progress to dementia and MCI per year [10]. Interestingly, recent studies have described that the cerebrospinal fluid (CSF) profile of AD is more common in subjects with SCD (31 out of 60, 52%) [11] than in healthy controls (28 of out 89, 31%) and they have a greater atrophy on magnetic resonance imaging (MRI) and greater hypometabolism in characteristic brain areas [12, 13]. CSF amyloid progressive SCD subjects present significant worse cognition in episodic memory tests and decline faster during follow up [14]. Furthermore, in a recent analysis, over 50% of SCD subjects have been reported to progress either to prodromal AD or dementia due to AD during a five-year follow up period. Moreover, a relationship between a subjective memory decline degree and cortical Pittsburgh compound B (PiB) binding has resulted to be significant in cognitively normal older individuals [15]. Furthermore, some studies including pathological analysis have found that SCD subjects present higher levels of AD pathology (elevated neuritic amyloid plaques in the neocortex and medial temporal lobe) even when cognitive impairment does not occur [16] and SCD subjects with an apolipoprotein (APOE) 4 allele are more likely to show AD-type changes [16], also doubling the odds of cognitive impairment in 6.1 years on average [16]. HOW THE CONCEPT OF SCD IS INTEGRATED IN THE INTERNATIONAL WORKING GROUP CRITERIA AND IN THE NATIONAL INSTITUTE ON AGING-ALZHEIMER S ASSOCIATION CRITERIA The concept of preclinical states has been introduced by the International Working Group (IWG) on AD in 2010, proposing the asymptomatic at-risk stage of AD (AR-AD, AD pathology evidenced by biomarkers and no symptoms) [17]. Additionally, a second group has introduced a very similar concept through the US National Institute on Aging-Alzheimer s Association (NIA-AA) research criteria in which the preclinical stage of AD has been proposed (no impairment in cognition on standard assessments and biomarker evidence for AD) [18]. By definition, individuals are cognitively normal with either a positive biomarker of AD pathology ( asymptomatic at-risk state ) or a proven AD autosomal dominant mutation ( presymptomatic state ) [17]. Individuals belonging to this category may complain about their memory/cognitive functions despite a normal performance in cognitive testing. Therefore, SCD has been proposed as a potential indicator of a preclinical state of AD [1]. Indeed, it has been demonstrated that presence of SCD may be associated with subsequent cognitive decline and progression to dementia [19] and may be related to brain amyloid deposition on amyloid positron emission tomography (PET) [15, 20]. Recent data raise the question as to whether SCD could be a way to identify people at risk for dementia, specifically AD, and may be used to recognize asymptomatic at-risk individuals. Importantly, it should be noted that the existence of cognitive complaints does not guarantee or imply a progression to clinical AD: only 16% of the cases of the

4 S174 S. Lista et al. / Biomarkers in Subjective Cognitive Decline Amsterdam Dementia Cohort in a clinical setting have progressed to a clinical diagnosis of AD. SCD may be non-specific and result from other causes frequently observed in the aging population, including attention difficulties, depressed mood, sleep disorders, and drug side-effects. Given the substantial heterogeneity in underlying etiology, SCD can be considered as a syndromic condition similar to the concept of MCI, but, in this context, at the preclinical or asymptomatic stages of the disease. The precise added value of SCD and its integration within the AR-AD concept remain undetermined and should be investigated. For SCD to be considered a possible risk factor for dementia or AR-AD, individuals with SCD need to be studied so that their cognitive trajectory is better elucidated with an understanding of the possible etiologies underlying these early changes. Additionally, underlying biological changes as assessed by biomarkers should be examined in conjunction with SCD, to determine what changes are associated with given etiologies. What we know today is that the predictive value of a positive AD biomarker increases when SCD is associated with other variables such as APOE 4 allele, hippocampal atrophy on volumetric MRI, and amyloid accumulation in PET imaging as well as CSF [13 15, 21 25]. These AD associated biomarkers indicate that SCD could represent a very early risk for dementia/ad; however, more information about cognitive, CSF, and imaging markers ought to give us more insight into the future trajectory of persons who present with SCD, allowing for the potential to use SCD as an enrichment tool for dementia/ad studies as well as the ability to identify a population eligible for early interventions when they become available. Much progress has been made to date, and the framework for SCD in preclinical AD provided by Jessen and colleagues has led to great strides in creating common terminology for the assessment of SCD on an international level [1]. The authors hope that this framework will serve as the basis for joint research efforts, synergies across studies, and elaboration of knowledge about SCD. The application of SCD as an enrichment tool for dementia (specifically AD) clinical trials holds significant promise. Beyond the research realm, the future of treatment for dementia/ad is counting on the research enterprise to be able to identify the earliest presence of change, cognitive or biomarker, with sufficient confidence for clinicians to characterize an individual with MCI due to AD [26] and to combine this diagnosis with a disease-modifying treatment to delay or stop the progression of symptoms. Much work remains ahead, but what is clear is that international consensus on terminology, diagnostic criteria, and application of criteria in clinical studies is the way forward to eradicate such a devastating disease that has global implications on family, communities, healthcare, and government. DEVELOPMENT OF THE SUBJECTIVE COGNITIVE DECLINE INITIATIVE (SCD-I): ESTABLISHING A COMMON SCD RESEARCH CONCEPT Due to the diversity in the field of SCD research, an international working group was established in fall The working group is referred to as the Subjective Cognitive Decline Initiative (SCD-I). It comprises clinical and epidemiological researchers who work in the field of SCD as a potential predictor or indicator of very early AD. The group has consented the recently published subjective cognitive research framework on SCD in preclinical AD [1]. In particular, the following comprehensive criteria for the definition of pre-mci SCD have been proposed [1]: I) Self-experienced persistent decline in cognitive capacity in comparison with a previously normal status and unrelated to an acute event; II) Normal age-, gender-, and educationadjusted performance on standardized cognitive tests, which are used to classify MCI or prodromal AD. Points I and II must be present. Exclusion criteria include: MCI, prodromal AD, or dementia; symptoms that may be explained by a psychiatric or neurologic disease apart from AD medical disorder, medication, or substance use. The overall aim of the group is to achieve harmonization and standardization across studies and to stimulate collaborations between groups to establish a more solid knowledge on the role of SCD as a very first symptom of AD. Current projects include analysis of SCD assessment and outcomes across various samples and consensus papers on standardization of studies on this topic. The group has also provided research symposia as the Alzheimer Association International Conference (AAIC) and has been accepted as a Professional Interest Area (PIA) of the International Society to Advance Alzheimer s Research and Treatment (ISTAART) in Members of the group have also received funding of the Joint Programming on Neurodegenerative Disorders (JPND) on a transnational harmonization initiative (Euro-SCD). The group is open to everybody has interest in this field.

5 S. Lista et al. / Biomarkers in Subjective Cognitive Decline S175 DEVELOPMENT OF BIOLOGICAL MARKERS AND NEUROIMAGING METHODS ACROSS EVOLVING DIAGNOSTIC CONCEPTS FOR NEURODEGENERATIVE DISEASES: EXISTING EVIDENCE In recent times, the term preclinical AD has been introduced by the IWG and the NIA-AA groups of investigators to denote individuals who have biomarkers positively supporting ongoing AD pathology, but do not fulfill the operationalized clinical criteria for MCI or dementia [17, 18]. As the field positions itself for upcoming secondary prevention trials, the detection of sensitive indicators, i.e., markers, of preclinical AD is eagerly needed. Notably, it has been recommended that SCD, at the stage of preclinical AD, may point to very early cognitive decline that cannot be observed using standardized objective tests of cognitive performance [27]. Neuropathological alterations related to AD are supposed to precede clinical dementia by many years [28, 29]. Neurochemical and neuroimaging markers of pathological alterations are thought to be of help to detect subjects at early stages of AD when they present slight or no cognitive impairment. The urgent requirement to diagnose AD as early as possible has encouraged scientists to critically appraise the diagnostic accuracy and predictive power of neurochemical and neuroimaging markers for clinical progression in individuals with SCD as well as in long-term follow-up periods. The detection and diagnosis of AD at very early stages is expected to become of great relevance with the introduction of potential disease-modifying treatments. Neurogenetic markers SCD, considered as a potential marker of a cognitive decline and of an increased risk of conversion to a probable AD, should share at least partly the same genetic pattern than AD. In the Nurse Health studies, years old women with the APOE 4 allele performed worse at baseline and declined more rapidly [30] before any evidence of neurodegenerative disease appears. This observation suggests that the strongest genetic risk factor for sporadic AD, APOE 4, is also a predisposing factor for SCD. Since 2009, 20 other AD susceptibility loci have been identified in several genome wide association studies [31], offering a list of candidate genes and loci to test for SCD susceptibility. SCD has also been associated with the presence of other AD biomarkers such as smaller hippocampal volumes [32]. Thus, the genetic susceptibility loci associated with morphological variations of neuroimaging examination could constitute also an excellent source of candidate genes for SCD [33]. One of the main questions when SCD is identified at an early stage, is the estimation of the probability to convert, years later, to the dementia syndrome and to specific neurodegenerative diseases. The characterization of the genetic background of this person including the 20 confirmed loci, the 16 putative others associated with AD [31], the loci associated with AD related disorders and some other neurobiological marker susceptibility genes could help to better identify these potential converters. As for any diseases with such a long natural history, various interactions between environmental factors and the genetic background may explain the heterogeneity of the rate of progression of SCD. Today evidence is accumulating suggesting improvements in the cognitive performances of various consecutive aging birth cohorts [34]. One of the most common explanations for this phenomenon is the improvement of the trends of some protective risk factors in the general population, as higher education levels or better vascular risk factor controls [35]. In that condition, the characterization of a neurogenetic score of risk could help to identify among the population of SCD individuals, the one for whom an improved prevention of these environmental risk factors could be engaged more aggressively to potentially delay the time to conversion. Neurochemical markers: Evidence and perspectives of CSF and blood-based biomarkers in SCD CSF biomarkers Multiple studies have investigated the diagnostic accuracy and predictive power of combined CSF tests for total tau (T-tau, a marker of axonal degeneration), hyperphosphorylated tau (P-tau, a marker of AD-type tau phosphorylation), and the 42 amino acidlong form of the amyloid- (A ) peptide (A 1-42, a marker of amyloid plaque pathology) during different stages of AD [36]. These studies collectively point to sensitivities and specificities of 85 95% in cross-sectional AD-control studies, as well as in longitudinal studies of patients fulfilling MCI criteria [36]. Longitudinal studies have correlated baseline levels of the core biomarkers in cognitively normal individuals

6 S176 S. Lista et al. / Biomarkers in Subjective Cognitive Decline with decrease in cognitive function or development of MCI or AD. CSF levels of A 1-42 alone [37] or in combination with T-tau [38] or P-tau [39] have been associated with future development of subjective and/or objective cognitive decline in individuals cognitively normal at the time of CSF sampling. In another study, healthy older adults have been examined at baseline and after a four-year follow-up [40]. It has been found that reduced CSF levels of A 1-42 from baseline to follow-up are associated with worse performance on cognitive tests compared with individuals presenting stable normal levels. One study including healthy controls from the Alzheimer Disease Neuroimaging Initiative (ADNI) has shown that individuals with low CSF A 1-42 levels display significantly higher rates of brain atrophy over one year than individuals with higher A 1-42 levels [41]. Increased T-tau:A 1-42 ratio [42] or low levels of CSF A 1-42 [43] have been found to predict conversion to MCI in cognitively normal individuals and AD in non-demented elderly individuals, respectively. One study on individuals with SCD has found that CSF A 1-42 alone is superior to T-Tau, P-tau, or a combination of all three biomarkers in predicting progression to MCI or AD [44]. Similar results have been obtained in two other studies, in which CSF A 1-42 exhibits the strongest association with cognitive performance in cognitively normal individuals or individuals with SCD, whereas CSF T-tau predominantly shows such associations in MCI and dementia stages of AD [45, 46]. The longest follow-up study so far extends over 10 years in a population of MCI subjects [47]. Approximately 90% of the MCI subjects with pathologic CSF biomarker levels at baseline have developed AD within 10 years. CSF A 1-42 levels result to be fully decreased at least 5 to 10 years before conversion to AD dementia, again supporting CSF A 1-42 as a very early marker, whereas T-tau and P-tau appear to be markers changing in closer proximity to the dementia stage of the disease. Similar results have been found in another MCI study with long clinical follow-up [48]. An alternative interpretation of the results is that low CSF A 1-42 is present early in disease development, but does not predict rate of cognitive decline, as high T-tau and P-tau do. A longitudinal study on cognitively normal elderly has also suggested an increase of the predictive power when adding CSF T-tau/P-tau as compared with positive either PET or CSF amyloid biomarkers [49]. There is as yet no prospective long-term study that has investigated whether CSF biomarkers can predict development of AD in healthy elderly individuals or individuals with SCD over time periods longer than 10 years. Such studies will be vital to determine whether most individuals without objectively verifiable cognitive impairment but with low CSF A 1-42 (alone or in combination with tau) indeed will develop AD dementia within 1-2 decades. Blood-based markers There has been a surge in the search for blood-based biomarkers that have diagnostic, prognostic, and therapeutic utility in AD, in recent years [50 55]. In fact, recent work has initiated the process of establishing guidelines for this work [56]. While research has begun to explore biomarkers related to SCD, few studies have focused on blood-based approaches. Blood-based methods have the advantage of being more cost- and time-effective than advanced computer-based MR or PET neuroimaging, CSF, or clinical methods and can be utilized as the first-step in the multi-step detection process [57, 58]. It is noteworthy that several studies examining biomarkers related to SCD include individuals with cognitive impairment (MCI and/or early-to-moderate dementia) [59, 60] and the lack of consensus in diagnostic criteria makes interpretations/generalizations across studies difficult [1]. Kearney-Schwartz and colleagues [59] have scrutinized cross-sectional data from 198 elderly hypertensive patients with SCD, some of which presenting with MCI and have found that, among men, increased white matter hyperintensities are associated with increased levels of von Willebrand factor (a marker of endothelial dysfunction), which has previously been included in a blood-based biomarker profile of AD [50]. When inspecting data from 150 participants of the Australian Imaging, Biomarker and Lifestyle (AIBL) Flagship Study of Ageing classified as SCD, Verdile and colleagues [61] have found luteinizing hormone to be positively correlated with plasma A 1-40,A 1-42, and PET A levels (according to the standardized uptake value ratio) and follicle-stimulating hormone levels to be positively correlated with those of plasma A In addition, standardized uptake value ratio has been observed to be significantly increased with APOE 4 copy number and increasing luteinizing hormone. On the other hand, gene polymorphisms related to vascular alterations (blood pressure, lipid and homocysteine metabolism, thrombosis, and inflammation) are not significantly related to cognitive functioning among hypertensive individuals with SCD [62]. For this review, pilot analyses have been run on a sample of 560 community-dwelling adults/elders (age 50 and above). Individuals classified as SCD (n = 259) per recently suggested guidelines [1] have significantly

7 S. Lista et al. / Biomarkers in Subjective Cognitive Decline S177 lower serum levels of tumor necrosis factor alpha (p < 0.001) and interleukin 7 (p < 0.05), prominent markers in an AD algorithm proposed by O Bryant and colleagues [51] with alterations in the opposite direction (unpublished data). In summary, blood-based biomarkers that have been previously associated with AD have also been found to be linked to SCD and require additional investigation. It is likely that, as with AD, a multi-marker approach will be superior to any individual marker. The identification of blood-based biomarkers of SCD will be of great benefit for early sensitive and precise detection as well as evolving therapeutic trials and clinical strategies. Neuroimaging markers: State-of-art of neuroimaging studies of SCD individuals Perspectives for MRI and DTI as imaging biomarkers of SCD Brain atrophy detected by structural MRI likely reflects the loss of different tissue components, such as neurons, synapses, and dendrites, but also of glial cells and vessels. Hippocampus volume in AD as determined using MRI or stereotactical measurement postmortem is highly correlated with histopathological estimates of total neuron numbers upon autopsy with an R 2 of up to 0.8 [63, 64]. If hippocampus volume is determined antemortem using MRI, associations with neuron numbers are lower, but still reach an R 2 of about 0.5 [65]. However, cell loss is not necessarily the only cause of the volumetric changes seen with MRI. Thinning of the frontotemporal neocortex observed in normal aging, for instance, has been found associated with alterations in cell morphology and dendritic architecture, rather than neuronal loss [66]. While medial temporal lobe atrophy on MRI is not specific for AD pathology [67, 68], the differential pattern of brain-wide atrophy can accurately separate pathologically-confirmed AD patients from healthy controls (sensitivity: 97%, specificity: 94%) [69] as well as from dementias with other underlying pathologies, including frontotemporal lobar degeneration and Lewy body disease (sensitivity: 91%, specificity: 84%) [70]. Accuracy for predicting conversion from MCI into AD dementia ranges between 70% to 80% over 1 to 3 years of follow up, varying with region and method of volumetry, such as manual volumetry of hippocampus [71, 72] or automated analysis of a pattern of multiple regions [73 76], and setting, such as monocenter or multicenter design [77]. In respect to the application in SCD, the first studies performed in SCD individuals have revealed a pattern of atrophy reflecting the spatial and temporal sequence of neurodegeneration typical of AD, affecting initially the entorhinal cortex and the hippocampus [12, 32, 78, 79]. Reduced hippocampus volume in individuals with persistent, but not with remitting SCD in a sample of more than 200 individuals, with limited effect size, has been found [78]. Jessen and colleagues have found an 18% reduction of the entorhinal cortex volume and a 6% reduction of the hippocampal volume in SCD individuals compared to healthy controls [80]. Moreover, the degree of hippocampal atrophy results to be exacerbated in those SCD individuals carrying the APOE 4 allele compared with non-carriers [81]. The brain atrophy observed in SCD seems not to be uniquely restricted to the hippocampus and the entorhinal cortex, but it also involves the entire temporal lobe structures, including the amygdala [82], and white matter tracts such as the corpus callosum [83]. The clinical profile of SCD has been found to be associated to subsequent changes in the rate of hippocampal volume change as measured longitudinally [84]. Measures of cortical thickness have shown no significant difference between SCD and non-scd cognitively healthy controls in the absence of neurological disease with a relatively low numbers of participants (n = 45), underscoring the limited effect size of volumetric changes in SCD cases [85]. A more recent study using a voxel-based morphometry analysis technique has confirmed previous evidences on the medial temporal lobe atrophy, also showing a significant atrophy in cortical areas such as the anterior cingulate cortex, the medial prefontal cortex, the cuneus, the precuneus, and the precentral gyrus [86]. A study by Peter and colleagues, applying the support vector machine algorithm, an unbiased automated methods learning from training data to identify a disease-specific pattern of gray matter changes, has reported a greater similarity to a typical AD gray matter pattern in SCD individuals than in control subjects without SCD [87]. Diffusion tensor imaging (DTI) is a more recently developed structural MRI technique; it detects alterations of diffusion properties that are related to microstructural alterations of cerebral grey and white matter [88]. Alterations in diffusion tensor (DT) invariants, such as mean diffusivity, fractional anisotropy, and radial diffusivity, have been validated as sensitive indicators of disease in both AD and MCI, with abnormalities presenting in parietal and temporal regions (which included the hippocampus) suggesting unspecific bundle degeneration [89, 90]. Abnormal mean

8 S178 S. Lista et al. / Biomarkers in Subjective Cognitive Decline diffusivity and fractional anisotrophy values have also been reported in frontal regions, the posterior cingulum, the corpus callosum, the superior longitudinal fasciculus, and the uncinate fasciculus [91 93]. Also, DT indices of white matter integrity have been seen to correlate with disease severity, suggesting that DTI may be used as a method providing bioindicators of disease progression [94, 95]. Additionally, the inclusion of a third tensor invariant (tensor mode [96]) has allowed to unveil selective white matter bundle degeneration in a cluster of crossing fibers, suggesting a relative preservation of motor-related projection fibers crossing the association fibers of the superior longitudinal fasciculus in the early-stage of MCI [97]. Determining the most robust acquisition parameters and processing strategies for DTI for a multicenter setting is still an active area of research, and initial clinical and physical phantom data, i.e., scans obtained from a volunteer as well as a physical object with defined diffusion properties, suggest that the variability of DTI-based diffusion metrics across a range of MRI scanners is at least 50% higher than that of volumetric measures [98]. For prediction of conversion from MCI into AD dementia, DTI has provided an accuracy of about 77% 95% at 2 to 3 years follow up [99 101] in monocenter studies; prediction accuracy for multicenter studies still needs to be studied. Following the assumption that microstructural alterations precede macroscopic volume loss in preclinical stages of AD [102], DTI, which is considered a measure of microstructural changes of fiber tract integrity, should be more sensitive toward the earliest changes in progressive SCD cases compared to MRI volumetry. In a study on 64 SCD cases and non-scd healthy age-matched controls, DTI based scalar markers of diffusion properties have been significantly associated with rates of cognitive decline and hippocampus atrophy at clinical follow-up, with odds ratios up to 3 [101], indicating a moderate effect size. Also, in the same longitudinal study using regression models, DT invariants have been seen to be more sensitive than CSF biomarkers in predicting cognitive decline and medial temporal atrophy in subjective cognitive impairment and MCI subjects [101]. These data suggest that both MRI (which can identify cross-sectional as well as longitudinal atrophy through follow-up imaging) and DTI (which is uniquely sensitive to white matter microstructure) detect early structural changes in those SCD subjects that have persistent SCD and progress to cognitive decline, supporting the potential role of SCD as a risk marker for progression to MCI and AD dementia. The effect size of these structural changes, however, is not sufficient for accurate prediction of disease course in SCD cases, for example for single case risk assessment. Multicenter longitudinal studies with large enough sample sizes and appropriate statistical models to take censoring of observations into account (an important issue with SCD where the annual rate of conversion to MCI or dementia is low) have yet to be conducted to finally conclude on the role of volumetric MRI and DTI for risk enrichment in SCD. Advanced methodologies for detecting subtle alterations While in principle diffusion-weighted imaging allows unprecedented probing of microstructural tissue characteristics, the relatively few reports about the use of DTI in separating and characterizing borderline conditions such as subjective cognitive impairment point toward the need for technological and analytical advances in order to sharpen the demarcation between a number of heterogeneous conditions associated with an increased risk of dementia. Lately, an important contribution in highlighting previously undetectable white matter alterations has come from advanced non-gaussian diffusion neuroimaging techniques such as diffusion spectrum imaging [103], q-ball imaging [104], the composite hindered, restricted model of diffusion [ ] (CHARMED), and Diffusional Kurtosis Imaging (DKI) [108]. In particular, DKI, which quantifies the amount of deviation from Gaussianity of the average diffusion probability profile along a given direction, does not require long acquisition times, can be easily included in clinical protocols [109], has shown the ability to assess microstructural tissue properties not detectable using DTI [110, 111], and has already proven useful in studying MCI and AD [112, 113] as well as aging [114, 115]. All diffusion weighted imaging protocols suffer from the relatively low signal-to-noise ratio afforded by fast EPI techniques, and the increase in signal-tonoise ratio commonly afforded by increasing the static magnetic field (e.g., by moving to ultra-high field imaging at, for example, 7T) may be counteracted by the signal loss caused by the rapid shortening of transverse (T2) relaxation times with increasing field strength. While ultra-high field diffusion weighted imaging therefore poses significant challenges, improved distortion correction techniques [116] coupled with unipolar monopolar acquisition schemes which allow a significant (about 30%) shortening of echo times, and the additional use of multislice excitation strategies [117] may allow diffusion-weighted imaging to reap

9 S. Lista et al. / Biomarkers in Subjective Cognitive Decline S179 Fig. 1. Machine learning techniques can be employed to a) fuse multimodal imaging data and b) classify unseen single subjects on the basis of this data or a subset thereof. In the training phase, the most informative features and patterns are selected with the aim of allocating single subjects to multiple predefined groups (e.g., diseased and controls). The trained artificial intelligence algorithm will then use the nonlinear model that has been parameterized in the training phase to compute class probability estimates (e.g., the likelihood of belonging to one of the predefined groups) for new patients, along with accuracy (sensitivity and specificity estimates). DKI, Diffusional Kurtosis Imaging; fmri, functional magnetic resonance imaging; CSF, cerebrospinal fluid. the benefits of ultra-high field and advance towards sub-millimeter imaging. Accordingly, ex vivo studies have defined white matter lesions in aging and AD at 11.4T [118], and 7T imaging has been helpful in discriminating Parkinson s disease [119] and amyotrophic lateral sclerosis [120]. While multimodal neuroimaging is expected to aid in better discrimination of AD and related disorders [ ] and non-invasive brain stimulation techniques [125, 126] are currently being employed to discriminate among patients with memory impairments those in which cholinergic degeneration is already present [127], the translation of advanced imaging protocols into diagnostic practice demands the implementation of analysis strategies that are designed to classify single patients rather than perform group comparisons. In this context, machine learning (ML) and/or artificial intelligence (AI) algorithms can integrate any type of data (genetic, neuropsychological, imaging) into a single predictor, which is able to allocate an unseen patient to a predefined group(e.g., diseased or control, converter or non-converter) (see Fig. 1). Such techniques have recently shown promise in neuroimaging data analysis as well as the(commonly problematic)

10 S180 S. Lista et al. / Biomarkers in Subjective Cognitive Decline Fig. 2. Increased resting state connectivity between left and right hippocampus in memory decliners compared to non-decliners (indicated in green), where hippocampus segmentation is indicated in red. Figure from [141]. Reprinted with permission from PNAS. integration of multicenter data [128], and are experiencing a breakthrough in cognitive neuroimaging [129]. ML techniques have been used to predict AD [130, 131] and MCI [132, 133], decode continuous variables on the basis of neuroimaging [134], classify dementia [135] and predict MCI to AD dementia conversion [136]. It is hoped that ongoing development of large-scale public databases such as ADNI ( loni.usc.edu) will eventually allow comprehensive training of AI/ML classifiers, which could then assist the expert neuroradiologist and neurologist in formulating a computer-assisted diagnosis and possibly a prediction of future disease evolution based on automated pattern recognition strategies. Functional MRI markers (resting state and activation) Functional MRI studies examining cognitive paradigms have not found differences in task performance between SCD and control groups but there was generally increased activation in the SCD group compared to control group, for example during a n-back working memory task [137], during performance of an associative memory and working memory tasks [138], during a divided attention task [139], and a verbal memory task [140]. In regards to resting state fmri, two studies have found increased connectivity within the default mode network and visual network in the SCD group compared to the control group [86, 141]. In addition, Salami and colleagues [141] have found that the increased connectivity between left and right hippocampus was associated with reduced activation during a memory encoding task as well as greater decline in memory performance over a 20 year period (see Fig. 2). Molecular imaging markers Molecular imaging using PET (see Fig. 3) provides two types of biomarkers: markers of neurodegeneration and pathophysiological markers. Decreased 2-[18F] fluoro-2-deoxy-d-glucose (FDG) uptake with a lateral parieto-temporal and posterior cingulate, precuneus pattern, reflects the synaptic dysfunction accompanying neurodegeneration in AD [142]. PET ligands binding to amyloid plaques such as PiB and fluorinated alternatives can detect A accumulation throughout the cortex of AD patients [ ]. Multiple cross-sectional and longitudinal studies suggest that AD-like hypometabolism occurs at presymptomatic stages. Altered metabolism has been observed in presymptomatic individuals carrying mutations responsible for early-onset familial AD [147], and in cognitively unimpaired carriers of the APOE 4 allele [148, 149]. Glucose utilization decrease is well correlated with disease progression in MCI and AD [150], and has been associated with subsequent cognitive decline in healthy individuals with or without SCD [22, 151]. Conversely, A deposition as measured by PiB-PET remains high but relatively stable in AD, despite further decreases in cortical FDG uptake and

11 S. Lista et al. / Biomarkers in Subjective Cognitive Decline S181 Fig. 3. FDG and amyloid PET imaging in a 70 years-old male, with a memory complaint, but cognitively unimpaired. A) FDG-PET scan showing a hypometabolism in the left parieto-temporal junction and left temporal associative cortices (white arrows). B) 18 F-Florbetapir PET scan showing a diffuse cortical tracer retention, with loss of contrast between white and grey matter. cognitive function [152]. The fact that a high amyloid retention has been observed in MCI related to AD suggests that A burden often reaches a plateau early in the course of the disease [153, 154], while the course of accumulation may vary considerably among individuals [155]. Amyloid-PET positive scans have also been observed in 20 to 40% of elderly cognitively normal subjects, with a pattern of tracer retention more prominent in anterior cingulate, orbito-frontal, posterior cingulate cortex, and precuneus [156, 157]. A burden is associated in cognitively normal people with age and APOE 4 allele dose [158, 159]. The relation of A deposition to concomitant cognitive performance is more controversial, with many studies showing no relationship and others showing a significant, although rarely strong, effect [24]. Similarly, SCD in cognitively unimpaired individuals may [13], or may not [160] be associated with higher A burden. In contrast, longitudinal studies have reported consistently that the presence of A deposition is associated with cognitive decline in normal older people [ ]. Evidence from the ADNI suggests that amyloid changes precede hypometabolism or hippocampal atrophy [165, 166], supporting the hypothesis that amyloidosis is a key early event in the physiopathology of AD. However, recent findings show that neuronal injury biomarkers are independent of A deposition in normal elderly [20, 167, 168]. Impairment of glucose metabolism predicts progression of cognitive deficits even in the absence of amyloid deposition [169]. The future use of selective tau PET ligands might help to elucidate the underlying pathology in individuals that present with AD-like neurodegeneration in the absence of A deposition [170]. Neurophysiological markers SCD is a strong predictor of AD in elderly subjects with no baseline cognitive deficits [171]. At the same time, the significant diagnostic power of electroencephalography (EEG) and magnetoencephalography (MEG) measures to identify various stages of AD has been repeatedly demonstrated [ ]. As reflected by the bulk of the EEG/MEG literature, brain changes in cognitive decline are more successfully revealed with the progression of the disease; nevertheless, very few studies have sought specifically to identify the subpopulation of normal elderly subjective complainers who will display cognitive decline within several years. Pritchep and colleagues have presented evidence with 44 subjects that baseline neurophysiological features such as an increase in left lateral theta-band power, and a decrease in coherence between the right central and posterior regions can distinguish between future subjective cognitive decliners and non-decliners [175]. Furthermore, a set of 9 frequency-based EEG variables has allowed them to reach a predictive accuracy of 90% at baseline. Comparisons between EEG measures in SCD and AD subjects indicate that

12 S182 S. Lista et al. / Biomarkers in Subjective Cognitive Decline the neurophysiological processes reflected by these conditions are not merely consecutive points on a continuum but suggest a more complex biphasic evolution of the disease. While AD subjects have shown a decrease of the oscillatory alpha and beta coupled with an increase in the theta and delta frequency band power, subjective complainers have actually presented increases in the alpha and beta, as well as the theta band power compared to controls [176]. Such activity enhancement has also been demonstrated for evoked potentials [177] and cortical synchronization measures in AD, indicating the presence of transient compensatory mechanisms in the initial stages of the disease. Even though currently no standard neurodynamics biomarkers are routinely involved in SCD evaluation criteria, EEG/MEG-derived features show substantial promise to deliver valuable diagnostic and predictive information. PERSPECTIVES OF BIOMARKERS AND NEUROIMAGING METHODS IN CLINICAL TRIALS An important trend in clinical trials aiming to develop disease-slowing agents for AD is to test interventions at the earliest feasible stage of the disease [178]. The first study has now been launched at the stage of preclinical AD [179], i.e., in individuals who are clinically normal but who have elevated brain amyloid as demonstrated by amyloid PET scanning or measurement of A 1-42 in the CSF. In such trials, SCD may play two critical roles: (I) identification of individuals for amyloid screening, and (II) assessment of treatment effects. Subjective cognitive symptoms will likely be important in the screening process for any very early stage trial defined by amyloid biomarkers. In contrast to later stage trials, which can draw from memory clinics, early trials, particularly involving clinically normal people, will recruit subjects from the community. Subjects will tend to self-select for screening on the basis of subjective concerns, in addition to those based on family history. Further, such concerns may improve subject compliance and retention. A leading candidate primary outcome measure for trials conducted at the asymptomatic stage of AD is a cognitive composite sensitive to amyloid-related synaptic dysfunction in the absence of objective evidence of cognitive deficit [180]. Regulatory agencies may accept such an endpoint for accelerated approval; however, they are likely to require additional evidence of a clinically-meaningful treatment effect for full approval. An interview-based assessment of subjective concerns of individuals and/or study partners may fill this critical role. There is preliminary evidence that longitudinal change in scores on such instruments may indeed be correlated to decline on cognitive composite measures in clinically normal individuals, raising hope that clinical relevance might be established in a preclinical AD therapeutic trial. REGULATORY PERSPECTIVE ON BIOMARKERS AND NEUROIMAGING IN SCD Consensus is shared by regulators that AD is a continuum and that new diagnostic criteria did change from a clinico-pathological entity to a clinico-biological entity. These new diagnostic criteria start to allow identification of subjects/patients at very early stages of AD with use of distinct biomarkers and simultaneous assumption that the earlier an intervention is started the more efficacious it might be. This creates new issues for regulatory guidance and possible acceptance of definition of study populations, study designs and endpoints for drug development in these early stages of AD, however, there are still many uncertainties on the ideal study population for drug development. Should it start in subjects/patients with prodromal/mci due to AD, who show cognitive impairment and positive biomarkers? Could these subjects/patients be studied together with mild AD dementia patients? Should it start in subjects/patients with preclinical AD, which show no evidence of any clinically overt impairment yet, but positive biomarkers? Several biomarkers [2 6, ] have been accepted by European regulators in qualification procedures for enrichment of study populations at risk for AD, but they have not been validated and qualified as surrogate endpoints as the relationship between changes in biomarker values and changes in clinical signs and symptoms under a certain treatment conditions has not yet been established. So, for the time being new assessment tools for very early and subtle MCI and subtle functional impairment are still needed to define these early disease stages. With the concept of SCD, it might be possible to better define a study population with preclinical AD; however, what would be acceptable clinical endpoints: time to objective cognitive impairments, to functional impairments? Therefore, the concept should be further studied prospectively and then be discussed in a qualification procedure for possible regulatory acceptance.