A multimodal marker of conversion to AD

Being able to predict which MCI patients will convert to AD would be valuable for selecting individuals that could benefit from disease-modifying therapeutical trials before they suffer

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irreversible neuronal damage. Indeed, appropriate lifestyle, cognitive rehabilitation, currently available drugs as well as future treatments should be most effective if initiated before the clinical symptoms of AD fully develop (Rossini, Di Iorio et al. 2016). In our third study, we found that baseline imaging measures (RS FC in the whole brain and sMRI), as well as a combination of these measures with neuropsychological and sociodemographic indices, could accurately predict MCI conversion to AD.

4.3.1 The predictive value of imaging indices

Our results highlight the value of sMRI and RS fMRI in the prediction of AD prognosis. sMRI data was conveniently reduced to one discriminative principal component via SVD, with sMRI clearly driving the prediction performance of the classifier when it was combined with other modalities. Yet, SVD was not as successful to capture RS connectivity patterns that discriminated well between MCIc and MCInc in its first principal component. In contrast, when using MKL, RS fMRI weighted much more than sMRI when it was combined with other modalities.

Nonetheless, both feature extraction techniques agreed on the higher value of imaging compared to sociodemographic (age and education, but not sex) and neuropsychological modalities, in the prediction of conversion to AD.

sMRI has repeatedly shown its utility in classifying MCIc vs MCInc. As previously mentioned, atrophy parallels cognitive decline along the continuum from normal aging to MCI and then to AD (Fox, Scahill et al. 1999, Jack, Lowe et al. 2009). Importantly, this modality is available in many clinical and research centers, and is often part of the clinical diagnosis of AD. Moreover, the acquisition of a high resolution anatomical image is quite fast.

Even though RS fMRI has just started being used in predicting conversion to AD (Rahim, Thirion et al. 2015), it holds great potential. Indeed, as previously discussed, DMN connectivity is closely associated with disease severity, both in MCI (Zhang, Wang et al. 2010, Gili, Cercignani et al. 2011, Binnewijzend, Schoonheim et al. 2012, Brier, Thomas et al. 2012, Chhatwal, Schultz et al. 2013) and AD patients (Zhang, Wang et al. 2010, Brier, Thomas et al.

2012), and the degree of DMN FC decrease was correlated with cognitive impairment (Binnewijzend, Schoonheim et al. 2012, Dunn, Duffy et al. 2014, Zhan, Ma et al. 2016).

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4.3.2 Limitations

Because our cohort of single domain aMCI was very small (11 converters and 11 nonconverters), we decided to add aMCI patients that were also impaired in other cognitive domains (multi-domain aMCI). Multi(multi-domain aMCI patients represent the intermediary stage between single domain aMCI and AD. However, similarly to single domain aMCI, other aetiologies and prognoses are possible.

The main limitation in our analyses was the sex bias in our cohort. Indeed, we had twice as many females than males among our MCIc, and twice more males than females in MCInc. This bias could have possibly influenced classification results, especially sMRI indices, as males typically have larger heads than females. To verify this possibility, we tested whether we could classify males versus females using the same data. We found that sMRI could accurate predict the sex of an individual subject ; in contrast, data from other modalities could not reliably separate the two groups. Nonetheless, because sMRI data was driving our classification performance in multimodal classification (when using SVD for feature extraction), we recognized the fact that this confound was serious enough to undermine the importance of our findings, and therefore preferred to wait until our groups of converters and nonconvertes become more balanced (future analyses). We did try several options for regressing out the effect of sex (unshown data), but our attempts were not successful, as they mainly « damaged » our data rather than « cleaned » it.

Another possibility to deal with this bias was to exclude some individuals and reduce our cohort to obtain a balanced set ; however, this would have resulted in very small groups (i.e., 10 MCIc and 12 MCInc), and results obtained with such a small N are at risk of overfitting (i.e., the classifier learns parameters that are specific to the cohort, and is not generalizable to unseen individuals).

4.3.3 Which modalities should we use in the future?

In order to assess the usefulness of each modality in predicting conversion to AD, a better understanding of the disease progression is essential. In addition to the temporal order in which biomarkers become abnormal (Jack, Knopman et al. 2010, Jack, Knopman et al. 2013), it will be useful to clarify the way each biomarker brings additional value in ascertaining the diagnosis of AD.

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The central role of amyloid in the AD pathological cascade has been challenged in the past few years, as it has been shown that a substantial proportion of cognitively normal ECs harbored neurodegeneration without presenting amyloid burden (Wirth, Villeneuve et al. 2013). Recently, Besson and colleagues screened cognitively normal ECs using sMRI, FDG-PET and amyloid PET, and reported that individuals detected as positive using one biomarker were mostly different from those detected as positive when using another biomarker (Besson, La Joie et al. 2015).

Indeed, subjects tended to have either neurodegeneration or Aβ deposition, but not both. These findings suggest that sMRI and FDG-PET reflect at least partly distinct pathological processes and are complementary rather than redundant. Therefore, amyloid and neurodegeneration may interact but not necessarily appear in a systematic sequence (Small and Duff 2008, Fjell and Walhovd 2012, Chetelat 2013). AD could therefore be subtended by several partly independent pathological processes (Chetelat 2013), causing various sequences of pathological events and resulting in neuronal injury. It is possible, for example, that amyloid and tau (and possibly other) pathologies occur partly independently, influenced by both independent and common risk factors.

The additive presence of each biomarker will likely cause an incremental increase of the risk that these features represent AD pathophysiological processes, as well as the risk for the individual to progress to AD.

4.3.4 Other future perspectives

An interesting approach in predicting MCI conversion to AD is the use of semi-supervised learning methods, i.e., making use of both labeled and unlabeled data. This is especially relevant for subjects that have not been followed long enough to ascertain a reliable diagnosis. It has been shown that even a small number of unlabeled samples improved the classification performance (Moradi, Tohka et al. 2014).

Furthermore, another valuable information for the patient and his relatives would be the time to conversion to dementia. Cox proportional hazards models are typically used for this purpose.

Modalities that are commonly used include MTL volume, glucose metabolism, CSF markers and neuropsychological measures (Bouwman, Schoonenboom et al. 2007, Devanand, Pradhaban et al.

2007, Desikan, Cabral et al. 2009, Landau, Harvey et al. 2010, Ewers, Walsh et al. 2012).

Notably, no studies to date have used fMRI measures to predict time to conversion to AD in MCI patients.

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