The early diagnosis of AD using RS fMRI

In the second study, we classified EC vs MCI using RS FC in either the whole-brain or within networks/lobes, comparing the impact of the atlas choice in the classification performance. We found that prediction accuracies obtained with a functional atlas (Shirer) were higher than those obtained with both anatomical atlases. Moreover, the probabilistic anatomical atlas based on 38 subjects (Hammers) yielded higher accuracies compared to the anatomical atlas based on a single subject (AAL).

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4.2.1 The choice of brain parcellation

Fundamentally, the choice of the brain parcellation method depends on how one chooses to represent the underlying organization of the brain. There are different methods for partitioning the brain into a set of regions, as these regions can be defined as anatomic parcels, random subdivisions of these parcels, or even as voxels. Moreover, functionally-driven parcellation can also be employed, via clustering techniques that define regions with some degree of signal homogeneity (e.g., similarity in connectivity pattern) (Thirion, Varoquaux et al. 2014, Eickhoff, Thirion et al. 2015). As brain areas may dramatically vary in shape or size, it is crucial to delineate brains areas that have distinct properties. One can rely on anatomical landmarks, as it has been shown that RS FC is constrained by anatomical architecture to a certain degree (Honey, Sporns et al. 2009). However, there is considerable variation across macro-anatomical landmarks that limit the extent to which a given parcellation can be applied across subjects (Wig, Schlaggar et al. 2011). It is also possible to parcellate the brain into regions that have functionally relevant borders, such as using cytoarchitecture, which reflects the cellular organization of cortical areas (Amunts, Schleicher et al. 2007). Cytoarchitectonic probabilistic maps for multiple brain regions are available nowadays, but they cover only about 70% of the cerebral cortex at the moment.

We tested three different atlases, relying upon both anatomical and functional parcellation of the brain. Importantly, it has been demonstrated that the way to define regions could dramatically affect network properties (Zalesky, Fornito et al. 2010, Wig, Schlaggar et al. 2011). Our results are in line with this conclusion, as we showed that the classification performance was strongly impacted by the choice of the atlas. Unlike many studies that used FC measures derived from the AAL atlas, we found that the single-subject parcellation did not yield any predictive accuracy above chance. Only FC within the limbic lobe was discriminative enough, but only with one classifier. In contrast, the Hammers atlas is also based on anatomical landmarks, but it is a probabilistic atlas built on 38 subjects. Using FC measures derived from this atlas, we were able to classify EC versus MCI patients using both whole-brain and within-lobe features. Finally, the Shirer atlas is based on a parcellation of RSNs extracted using ICA on RS fMRI data in 15 young subjects. We found that whole-brain FC measures were able to significantly discriminate between our groups, a consistent finding across all four classifiers. Moreover, FC within several networks was highly discriminative, such in the ventral (mean accuracy 82%) and dorsal (68%) DMN, left

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(80%) and right (79%) executive control network, visuospatial (78%), language (77%), and posterior salience (72%) network. FC within several of these networks, such as the DMN, lateralized frontoparietal/executive control and salience network, was found to be altered in MCI patients (Sorg, Riedl et al. 2007, Liang, Wang et al. 2011, Liang, Wang et al. 2012, Xie, Bai et al.

2012, Zhan, Ma et al. 2016). Of the three atlases, the Shirer atlas is perhaps the most relevant for assessing FC within networks. On the downside, there is spatial overlap between regions from different networks; moreover, this atlas does not provide a full coverage of the brain, unlike the AAL and Hammers atlases. Naturally, in the two anatomical atlases, regions can be assembled into networks, based on the literature, and FC measures can be extracted from these networks.

Yet, anatomical atlases are not fine-grained enough to do that well.

4.2.2 Limitations

One issue with the atlases that we used is that they are all based on young and healthy subjects. In the context of classifying EC versus MCI or AD patients, it could perhaps be more relevant to use an atlas that is based on a cohort with similar characteristics (sociodemographic traits, disease).

In addition to the effect of different atlases, different preprocessing procedures can have a great impact on the classification performance. We explored several alternative preprocessing procedures (unshown data): bandpass filtering versus wavelet transform, regression vs. no regression of WM and CSF signal, FSL vs. SPM co-registration, smoothing vs. no smoothing, and global regression vs. no global regression. All these experimentations led to low classification rates with all atlases, which led us to conclude that our results lacked reliability as they were possibly dependent on our preprocessing procedure.

4.2.3 A network disruption

FC both within and between networks becomes progressively abnormal in AD (e.g., (Brier, Thomas et al. 2012)). Surprisingly, few studies have used FC within networks to classify EC from MCI patients. Jiang and collaborators used measures derived from FC within and between 10 RSNs (described by (Smith, Fox et al. 2009)), as well as network characteristics, and obtained 92% accuracy (90% specificity, 94% sensitivity) in separating 50 EC from 100 MCI patients using 30 of these features (Jiang, Zhang et al. 2014). Hu and collaborators identified subgraphs

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(or subnetworks) based on DTI-derived structural connectomes, and then measured RS FC of these subnetworks (Hu, Zhu et al. 2013); the most discriminative subnetworks were then chosen as features for classifying MCI from EC. Interestingly, discriminative subnetworks contained mostly temporal and frontal regions, and were dominated by decreased FC, but some also showed increased FC. They obtained 90% sensitivity and 94% specificity in classifying 10 MCI and 18 EC. Finally, Fei and colleagues measured the discriminative ability of frequent subnetworks extracted from each of the two groups (EC and MCI patients), and then classified the two groups using the most discriminative subnetworks using a graph kernel method (Fei, Jie et al. 2014).

Notably, the most discriminative subnetworks did not involve DMN regions, but instead were comprised of vision- and auditory-related brain regions. They reached 97% accuracy (AUC 0.96) in classifying 12 MCI and 25 EC.

The two latter studies used graph theory, either to build “subnetworks” or as a feature extraction method. Graph theoretical metrics (e.g., small-worldness, centrality, efficiency) have often been used to demonstrate differences in the topology of brain networks between EC and MCI patients (Wee, Yap et al. 2012, Wee, Yap et al. 2012, Wee, Yap et al. 2014). Importantly, graph theory allows to model the dynamics of the entire brain simultaneously. In AD, specific networks alterations have been described, such as loss of “small-world” networks, a global reduction of networks’ characteristic path length, and increased randomization (Supekar, Menon et al. 2008, Sanz-Arigita, Schoonheim et al. 2010, Ciftci 2011, Wang, Li et al. 2013, Brier, Thomas et al.

2014, Brier, Thomas et al. 2014). Moreover, changes in the global FC affecting specifically long-distance connectivity have been reported, as well as a loss of global information integration (Sanz-Arigita, Schoonheim et al. 2010). These findings show evidence of AD disrupting network organization at a whole brain level, not just in specific systems. Interestingly, it was shown that network hubs, such as the PCC, were particularly disrupted (Buckner, Sepulcre et al. 2009), which suggests that these hubs could be particularly important in AD pathogenesis.

Interestingly, graph theory allows to test specific hypotheses about the propagation of neuropathology via networks, or “network-based degeneration” (Greicius and Kimmel 2012).

This hypothesis states that different neurodegenerative diseases target specific neuronal networks, and then spread along connectional lines into neighboring networks, in particular those that are highly connected to the primary target network (Seeley, Crawford et al. 2009, Zhou, Gennatas et

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al. 2012). This was first proposed almost three decades ago, when Saper and collaborators noted a stereotypic pattern of neuronal involvement in system degenerations, and suggested that neurogenerative diseases caused the selective loss of specific populations of neurons, which are often functionally related and often share a common metabolic abnormality (Saper, Wainer et al.

1987). This hypothesis offers an explanation to the way brain regions that are largely distinct from the sites of pathological accumulation also become impaired. Indeed, the topography of FC deficits cannot be fully explained by the topography of amyloid and tau pathology in any AD stage.

Brier and colleagues have refined this hypothesis for AD specifically (Brier, Thomas et al. 2014).

They proposed that dysfunction begins in the DMN during the preclinical stage of the disease.

But DMN regions do not disconnect simultaneously; rather, they deteriorate progressively, as pathology accumulates over time. These regions then remain in a semi-functional state of increasingly impaired function, which enables the diseased region to interact with the still-intact parts of the network. The diseased region can then propagate dysfunction through its interactions with healthy regions via processes similar to Hebbian dynamics.

Jones and colleagues recently showed that the FC of posterior DMN was altered prior to the appearance of amyloid plaques (Jones, Knopman et al. 2016). Moreover, FC between the posterior and ventral DMN increased as cognition declined. Therefore, they proposed that dysfunction originates in the PCC, then propagates to other hubs through hyper-connectivity, further straining the synaptic and cellular machinery. These findings are thus in favor of a prion-like, transneuronal propagation model to explain the network-based vulnerability that has been observed in several studies (Seeley, Crawford et al. 2009, Raj, Kuceyeski et al. 2012, Zhou, Gennatas et al. 2012). However, these findings (Jones, Knopman et al. 2016) were obtained in a cross-sectional study; therefore, the way disease spreads ought to be tested in a longitudinal analysis to validate these conclusions at the intra-individual level.

4.2.4 The potential of RS fMRI in the early diagnosis of AD

RS fMRI has great potential to serve as a tool for the early diagnosis of AD. It is shorter to administer than a task and easier to standardize across centers. It was recently demonstrated that RS FC reproducibility was high (0.81 intra-class correlation coefficient) across 13 clinical MRI

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scanners (Jovicich, Minati et al. 2016). Moreover, the DMN was consistently extracted across all sites and analysis methods. It also offers the possibility to simultaneously evaluate the integrity of several functional networks in a single session. Finally, it has also shown its potential in the differential diagnosis of AD, as it can differentiate AD from dementia with Lewy bodies (Galvin, Price et al. 2011), behavioral variant frontotemporal dementia (Zhou, Greicius et al. 2010), subcortical vascular and mixed dementia (Kim, Cha et al. 2015).

Crucially, RS fMRI allows to detect AD-related changes very early in the course of the disease, prior to the onset of clinical symptoms. Indeed, FC alterations have been demonstrated in cognitively normal individuals with a family history of AD (Fleisher, Sherzai et al. 2009) and APOE ε4 carriers (Filippini, MacIntosh et al. 2009, Fleisher, Sherzai et al. 2009, Matura, Prvulovic et al. 2014). These changes can already be detected at a young age (20-35 years old) in the latter group (Filippini, MacIntosh et al. 2009). Surprisingly, all three studies showed increased FC within the DMN. Furthermore, increased FC within the DMN was also shown in patients with subjective memory complaints (Hafkemeijer, Altmann-Schneider et al. 2013), a clinical state characterized by an increased risk of developing MCI and AD (Mitchell, Beaumont et al. 2014). In contrast, significantly reduced FC within the DMN has been shown in cognitively normal amyloid positive individuals (Hedden, Van Dijk et al. 2009). Furthermore, subtle decreases in the DMN FC were detected in asymptomatic individuals with autosomal dominant AD (Chhatwal, Schultz et al. 2013).

Not only is RS fMRI sensitive to early changes in the preclinical stage of AD, it can also be used to monitor disease progression. In particular, DMN connectivity decrease has been correlated with increasing AD severity, from the MCI stage to mild and severe AD (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). Moreover, the degree of DMN FC reduction correlated with worse general cognition (Binnewijzend, Schoonheim et al. 2012, Zhan, Ma et al. 2016), memory retrieval performance (Binnewijzend, Schoonheim et al. 2012, Dunn, Duffy et al. 2014) as well as working memory, executive functioning and attention scores (Binnewijzend, Schoonheim et al. 2012). Furthermore, reduced salience connectivity was correlated with episodic memory deficits (Liang, Wang et al. 2012, Xie, Bai et al. 2012), and decreased connectivity between the

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DMN and salience network was also correlated with severity of cognitive impairment (Zhan, Ma et al. 2016).

In addition to its potential role in diagnosis and disease progression monitoring, RS fMRI can also be used as a treatment-monitoring tool. Indeed, RS measures can test the ability of potential therapeutic agents in normalizing RS FC, and serve as a quantitative index for outcome prediction in drug development studies (Cavedo, Lista et al. 2014). For instance, AD patients treated with donepezil showed increased FC within the HIP (Goveas, Xie et al. 2011), and within the DMN (Sole-Padulles, Bartres-Faz et al. 2013), compared to untreated patients. Memantine, another drug used to treat AD patients, that reduces excess activity of the glutamate by blocking NMDA receptors, also induced an increase of the DMN FC in AD patients (Lorenzi, Beltramello et al. 2011).

Therefore, RS fMRI indices, in particular involving the DMN FC, could play a multifaceted role in the primary, secondary, and tertiary prevention of AD.

4.2.5 The links between RS FC and biomarkers

Alterations in RS fMRI have been associated with changes in AD biomarkers. As Buckner initially pointed out (Buckner, Snyder et al. 2005), there is a strong spatial overlap between functional alterations detected in the DMN, decreased glucose metabolism, atrophy and amyloid deposition in AD.

As is to be expected, a large number of MCI patients (35-67%) show high amyloid retention on imaging (Pike, Savage et al. 2007, Rowe, Ng et al. 2007, Jack, Lowe et al. 2008). Perhaps more surprisingly, there is also a considerable amount of cognitively normal elderly individuals (10-55%) that shows significant amyloid retention without presenting notable clinical symptoms (Mintun, Larossa et al. 2006, Pike, Savage et al. 2007, Rowe, Ng et al. 2007, Aizenstein, Nebes et al. 2008, Jack, Lowe et al. 2008, Hedden, Van Dijk et al. 2009, Jack, Wiste et al. 2013). These figures are in accordance with findings from autopsy studies reporting evidence of amyloid plaques and NFTs in individuals who were cognitively normal prior to death (Bennett, Schneider et al. 2006). Interestingly, increased amyloid retention in nondemented elderly individuals with high amyloid burden was shown to correlate with reduced FC within the DMN (Hedden, Van

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Dijk et al. 2009, Sheline, Raichle et al. 2010). Furthermore, decreased DMN FC was shown in both asymptomatic and symptomatic carriers of the autosomal genetic form of AD (Chhatwal, Schultz et al. 2013, Thomas, Brier et al. 2014). In addition to the DMN, other RSNs (dorsal attentional, executive control, sensorimotor) also showed decreased FC near the estimated age of symptom onset in asymptomatic mutation carriers, and correlated with increasing clinical severity (Thomas, Brier et al. 2014); moreover, internetwork FC also decreased with respect to disease severity. Interestingly, in children (9-17 years old) with autosomal dominant AD, increased RS FC within the DMN was shown, compared to age-matched noncarriers (Quiroz, Schultz et al. 2015). Therefore, there is evidence for these functional alterations being an expression of amyloid beta neuronal toxicity.

Yet, some studies have shown that RS FC changes have been detected before amyloid deposition, which could reveal earlier abnormalities involving synaptic function (Sheline and Raichle 2013, Jones, Knopman et al. 2016). Alternatively, RS disruption could also be consequent to tau pathology. Li and colleagues found that the CSF amyloid/tau ratio correlated with RS FC in the precuneus (Li, Li et al. 2013). Wang and colleagues further reported that CSF amyloid and tau independently contributed to RS FC disruption between the PCC and MTL in cognitively normal elderly individuals (Wang, Brier et al. 2013). Furthermore, there are also regions showing FC disruption that are not strongly affected by amyloid or tau, as well as there are regions that are strongly affected by amyloid and tau, but in which FC disruptions are not prominent (Brier, Thomas et al. 2014).

In addition to amyloid, RS FC seems to be related to brain metabolism (Drzezga, Becker et al.

2011). Indeed, reduced glucose metabolism shows the greatest spatial overlap with RS dysfunction in the DMN regions (medial and lateral parietal, and temporal regions). It has been suggested that the two pathological mechanisms specifically target hub regions (Drzezga, Becker et al. 2011). The two dysfunctions could represent early functional consequences of molecular AD pathology (Drzezga, Becker et al. 2011).