INSTANCIAS PÚBLICAS DE DEFENSORÍA CIUDADANA EN CADA PAÍS DE LA CAN.

Neurodegenerative diseases can coexist and their clinically-presented phenotypes (patient history; symptoms; MMSE score) may overlap (Clark et al., 2008). Biomarkers should ideally be specific (to aide differential diagnosis); cheap; non-invasive; reproducible, unbiased and objective (to lend confidence); and predictive of future progression (to help patients and families plan). Brookmeyer et al. (2007) estimated that a short (one-year) delay to symptom onset would reduce worldwide cases in 2050 by 11.8 million. Therefore, they should also be sensitive to early disease onset and the effects of potential treatment (Hampel et al., 2008; 2010), which also reduces the costs of clinical trials, by improving power and requiring fewer subjects. The Alzheimer’s Disease Neuroimaging Initiative (ADNI), an international, longitudinal study, was formed in 2004 to assess and develop such biomarkers (Weiner et al., 2013). Promising candidates include cerebrospinal fluid (CSF) sampling, positron emission tomography (PET), and structural MRI.

Jack et al. (2013) proposed a sigmoidal model (Fig 2.13), now widely reproduced, in which biomarkers progress simultaneously, but are most dynamic, and reach maximum abnormality, in an ordered sequence.

Figure 2.13 Theoretical progression of AD biomarker abnormality with time.

Each biomarker surfaces above the detection threshold, and reaches a peak rate of change, at different times prior to dementia, but all are dynamic simultaneously. Some “cognitive reserve” differentiates low- and high-risk patients with the same biomarker abnormality (Vemuri et al., 2011), and there is a long “preclinical phase” prior to diagnosis. Atrophy, detected with MRI, coincides with the onset of cognitive problems (*). Two sampling time-points, and illustrate that MRI may be more useful than CSF at the MCI stage. Adapted from Jack et al. (2013).

CSF biomarkers include reduced levels, reduced ⁄ ratio, and elevated phosphorylated tau. These follow directly from neurochemical brain changes. Additionally, elevated total tau concentration reflects neurodegeneration (Hampel et al., 2008; 2010). CSF markers are sensitive, track the severity of disease, and in combination are specific to AD. However, they omit spatial information, and sampling requires an invasive, often painful lumbar puncture (Vemuri et al., 2009).

For PET to map deposits or monitor brain metabolism, radiotracers are injected minutes before a scan. 11_{C-labelled Pittsburgh Compound-B (PiB) binds selectively to} . PiB uptake very closely correlates with CSF decrease (Vemuri et al., 2009) and has detected high plaque burdens in MCI patients, but also in cognitively normal elderly

people: it is only loosely predictive of decline (Hampel et al., 2010; Jack et al., 2013). 18_F fluorodeoxyglucose (FDG) may be injected to measure resting state glucose metabolism: its uptake, like neuronal activity, is locally reduced in neurodegeneration-afflicted regions. FDG-PET is highly predictive of conversion from MCI to AD (Hampel et al., 2008; 2010). The short half-lives25 _{of the isotopes} 11_{C and} 18_{F; their radioactivity; and} PET’s high cost and limited spatial resolution may, however, limit its usefulness, especially in small animal studies (Clark et al., 2008; Götz & Ittner, 2008).

In Fig 2.13, prodromal amyloid markers (CSF ; PiB-PET) are the first to surface above the current “detection threshold”. Buchhave et al. (2012) found that CSF levels reached maximum abnormality in MCI patients up to 10 years prior to conversion to AD. It is increasingly recognised that underlying pathophysiological processes, as measured by these techniques, begin a decade or more before cognitive decline and subsequent diagnosis. This asymptomatic “preclinical phase”, during which subjects can be cognitively normal (Ewers et al., 2011; Sperling et al., 2011), motivated the extension of ADNI to even earlier biomarkers for detecting and distinguishing newly-defined early MCI and subjective (worries of) memory impairment (SMI) stages, which carry an elevated risk of eventually developing AD (Jessen et al., 2014; Beckett et al., 2015). There is histological evidence from autopsy of young brains (under 30 years) that pathologic, phosphorylated tau appears before amyloid deposits (Spillantini & Goedert, 2013). CSF total tau levels are directly related to age and tangles also form in healthy ageing. They may cause the slow atrophy which accompanies all healthy ageing, and underlie associated mild memory impairment (Fjell et al., 2014). Hence, in Fig 2.13, tau abnormality is postulated to precede amyloid deposits, but is only detectable at autopsy.

MRI biomarkers

Non-invasively, and relatively cheaply, structural brain MRI provides high tissue contrast and resolution, enabling the sensitive and localised detection of atrophy, which can both differentiate dementias and objectively stage AD in vivo (Frisoni et al., 2010; Vemuri et

al., 2010). Atrophy follows NFT pathology in the signature Braak stages, and its rate correlates with cognitive decline (Fox et al., 1999). Vemuri et al. (2009) found that MRI performed better than CSF biomarkers at separating MCI and AD groups, and at predicting MMSE score. These advantages have propelled MRI to the early stages of clinical diagnosis. Other MR techniques, including diffusion tensor imaging (DTI, to measure white matter integrity) and functional MRI (fMRI, to assess the decline in connectivity), have been used as biomarkers of AD (Reitz & Mayeux, 2014), and may be acquired in the same patient visit, but these are currently not clinically established (Frisoni et al., 2010), and this thesis is exclusively concerned with structural images.

Figure 2.14 Human T1W MRI showing structural changes with MCI and AD.

Equivalent coronal T1W slices from cognitively normal (CN), amnestic MCI (aMCI) and

AD brains, with ventricular enlargement (V), hippocampal volume loss (H), and atrophy of the entorhinal cortex (E) and neocortex (NC). Adapted from Vemuri et al. (2010).

The first MR atrophy measures involved visual assessment, or manual volumetry via delineation of vulnerable regions, such as the hippocampus (Fig 2.14). The advance of clinical scanner field strengths from 1.5T to 3 and 7T has improved resolution and contrast, and hence the feasibility of these techniques. However, visual inspection is subjective and manual measurements are extremely time-consuming, and variable. To better-elucidate and quantify structural brain changes, advanced image processing techniques – including VBM and TBM – have emerged over the past two decades, exploiting increases in computer processing power, which have enabled high-dimensional

image registration and accurate automated anatomical parcellation. These techniques are widely available for human studies, thanks to free software packages (§2.2.7) and atlas databases (§2.2.1).

For example, in a longitudinal study, Chételat et al. (2005) showed VBM could distinguish MCI patients who converted to AD from those who did not. Whitwell et al. (2008) used VBM in a cross-sectional study of 82 human subjects with probable AD and different Braak stages, and showed regional GM loss proportional to the degree of NFT pathology (Fig 2.15). Hua et al. (2013) used an unbiased implementation of TBM and detected constant atrophy rates in AD over 6—24 months, with most changes localised to the cortical and hippocampal GM, as well as temporal lobe WM.

Figure 2.15 VBM-derived GM loss at different Braak stages.

In a cross-sectional study, human brain MRI scans from patients with late Braak stages were compared with controls from earlier stages (Braak 0—II). VBM detected the characteristic pattern of progressive atrophy. 3D surface map (top) and four coronal slices for each set of stages. N=20 controls; N=23 III-IV; N=32 V; N=27 VI. FDR-corrected,

= 0.005. From Whitwell et al. (2008), with permission.

The onset of NFT-related neurodegeneration likely precedes MCI by several years (Teipel et al., 2013). Fig 2.13 illustrates that MRI-measured atrophy is highly dynamic during MCI and conversion to AD (between − , the MRI biomarker’s gradient is one of the largest). Meanwhile, CSF approaches its maximum abnormality, and the degree

of change between time-points is smaller. A multi-time-point study of atrophy rates, to stage disease, distinguish high risk from low risk patients, or to monitor drug effectiveness, may thus use more closely-spaced samples and require lower sensitivity or fewer subjects than CSF (Frisoni et al., 2010). While CSF may be abnormal and sensitive earlier, without symptoms or a policy of population screening, it is unlikely to be measured so early in a patient’s life. Screening is expensive, may induce needless worry, and currently there are few treatment options, even with a positive diagnosis (§2.3.3). The side-effects of existing treatments also mean that confidence in diagnosis is vital.

Recent efforts have therefore focused on using imaging to detect subtle changes prior to the onset of symptoms (lowering Fig 2.13’s “detection threshold”), by improving robustness (such as by using symmetric registration, with brain masks, for TBM: Hua et al., 2013), and focusing on volumes of the entorhinal cortex, hippocampus, ventricles and whole brain (Frisoni et al., 2010; Vemuri et al., 2010; Teipel et al., 2013). Using VBM, Tondelli et al. (2012) detected structural differences between the brains of healthy controls and people who would go on to develop AD symptoms up to 10 years before they did so.