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2. MATERIAL AND METHODS

2.1 General methods

2.1.1 Participants

Amnestic MCI (aMCI) patients and elderly controls (EC) participated in this study. Recruitment started approximately 3,5 years prior to the beginning of this project, and was ongoing for the whole duration of the project. Therefore, a slightly different cohort was used for each research question.

Most aMCI patients were recruited from the Neurology Department of the Geneva University Hospital and the Memory Clinic of Geneva. All ECs, as well as a few aMCI patients, were recruited from the community through public advertisement. The study was approved by the ethics committee of the Geneva University Hospitals, and written informed consent was obtained from all subjects. aMCI patients fulfilled Petersen’s criteria (Petersen 2004), consisting of subjective memory complaint, objective memory impairment (informed by a score of 1.5 standard deviation (SD) below age- and education-adjusted norms on at least two memory subtests), intact general cognitive function, no impairment in activities of daily living, and no dementia. The final diagnosis of aMCI required the agreement of an experienced neurologist specializing in dementia and a research assistant trained in neuropsychological testing. ECs were to have a normal neurological and neuropsychological examination, and no significant cognitive complaint. Exclusion criteria were presence of neurological, cardiovascular or psychiatric disease, history of stroke, head trauma or substance abuse, structural anomalies detected on MRI, or significant anxiety or depression.

2.1.2 Experimental pipeline

At baseline, subjects were first administered a neuropsychologication evaluation to assess if they met inclusion criteria ; then they went through MRI scanning, which included both functional and

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structural image acquisition. A follow-up consisting of a yearly neuropsychological evaluation was then conducted (see Figure 6 below).

Figure 6. Timeline of subjects’ participation in the study. At baseline, after an initial neuropsychological evaluation, participants undergo structural and functional MRI (task and rest paradigms). Then, they are seen each year to complete a follow-up neuropsychological assessment until conversion to AD.

2.1.3 Neuropsychological assessment

All subjects were administered a battery of neuropsychological tests validated in French. Briefly, general cognitive functioning was assessed using the Mini-Mental Status Examination (MMSE) (Folstein, Folstein et al. 1975), the Dementia Rating Scale (DRS)(Mattis 1988) and the Clinical Dementia Rating (CDR)(Morris 1993). Language abilities were tested with the Boston Naming Test (Kaplan, Goodglass et al. 1976), as well as with verbal fluency tests (semantic and phonemic)(Cardebat, Doyon et al. 1990). Attention and executive functions were assessed using the Trail Making Test part A and B (Reitan 1958), and the Stroop Test interference score (Stroop 1935). Immediate and delayed cued recall were tested with the Grober-Buschke Memory Impairment Screen (Buschke, Kuslansky et al. 1999). As two different versions of this test were used (either the 16 or 48 items versions), we selected the immediate and delayed cued recall score of each version to compare scores across subjects. Visuospatial memory was assessed with the Doors and People Test part A and B (Baddeley, Emslie et al. 1994), and working memory was tested with the Digit Span test (forward and backward) from the Wechsler Memory Scale

Baseline +1 year +2 years +3 years

Neuropsy Structural MRI Functional MRI

(task & rest) Neuropsy Neuropsy Neuropsy Neuropsy

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(Wechsler 1997). Finally, the Hospital Anxiety and Depression (HAD)(Zigmond and Snaith 1983) scale was administered to assess depression and anxiety. Normative scores that accounted for age and education level were available for all tests, except the MMSE, CDR and HAD.

2.1.4 Data acquisition

Two identical scanners, both 3T Siemens Trio Tim using a 12-channel head coil, were used to acquire the imaging data. The majority of participants were scanned in the second scanner.

Identical imaging protocols were used with both scanners. All subjects were scanned during a single session that started with the task fMRI, followed by a resting-state paradigm as well as an anatomical scan. Visual stimuli were presented on a projection screen inside the scanner using E-prime (E-E-prime 1.0, Psychology Software Tools Inc, Pittsburgh). Responses were recorded with a bimanual response button box (HH-2×4-C, Current Designs Inc., USA). During rest, subjects were presented a white rotating cross on a grey background for 8:21 minutes. They were instructed to keep their eyes open and watch the cross rotate, while thinking of nothing in particular.

2.1.5 MRI acquisition parameters

Whole brain functional images acquired during the task were collected using a susceptibility weighted EPI sequence (TR/TE = 1810/30 ms; flip angle = 90 degrees; PAT factor = 2; FOV = 192 mm; matrix size = 64 x 64 pixels). Thirty-two transversal slices were acquired sequentially with a 4 mm thickness and an interslice gap of 1 mm, yielding a voxel size of 3 x 3 x 5 mm.

Functional images acquired during rest were collected using an EPI sequence (TR/TE=1100/27ms; flip angle = 90 degrees; PAT factor = 2; FOV = 240mm; matrix size = 64 x 64 pixels). We acquired 21 transversal slices sequentially with a 4.5 mm thickness and an interslice gap of 1.125 mm, yielding a voxel size of 3.8 x 3.8 x 5.6 mm. High-resolution whole brain anatomical scans were acquired with a T1-weighted, 3D sequence (MPRAGE; TR/TI/TE = 1900/900/2.32 ms; flip angle = 9 degrees; voxel dimensions = 0.9 mm isotropic; 256 x 256 x 192 voxels).

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2.1.6 Experimental paradigm

We used a validated associative memory task that employs picture pairs (van der Meulen, Lederrey et al. 2012). Two phases, encoding and recognition, were alternatively conducted in 3 successive blocks (within each run), each comprising 8 picture pairs (24 pairs per run)(see Figure 7). The pictures were color drawings of common objects and animals. This sequence was repeated for 3 runs. In the encoding phase, the pairs of pictures presented were either semantically related (e.g., two musical instruments) or unrelated. Subjects were instructed to memorize these pairs and press a button to acknowledge their encoding of each pair. In the recognition phase, 3 pictures were presented: one cue picture at the top, together with two

“choice” pictures at the bottom. Subjects were asked to indicate (using the corresponding button key) which of those two bottom pictures was presented with the top picture. Of these two, one was the target (the picture previously associated with the cue picture during the encoding phase), while the other was a distractor. Critically, the distractor could be a new picture, not previously seen, or an old picture, taken from another pair from the same block. This manipulation allowed for the assessment of two different types of memory recognition, namely recognition based on visual familiarity (trials with a new distractor) and recognition based on associative recollection (trials with an old distractor). Encoding conditions featured either semantically related or unrelated picture pairs, while in recognition trials, pairs could have a semantic link or not, and be presented with a new or an old distractor, resulting in 6 experimental conditions.

Each pair (or triad at recognition) was presented for 4 seconds at encoding and recognition. The inter-stimulus interval was a random jitter of 2-7 seconds. The pairs and triads were presented in the same order during the two phases in order to keep the delay between the two as similar as possible for each trial. For semantically related pairs, the distractor was also semantically related to the cue picture. In all recognition trials, there was an equal number of left- and right-side-positioned targets, which were counterbalanced across trials. Of the 72 pairs shown in total, 36 were semantically related and 36 semantically unrelated; likewise, 36 pairs were shown with a new distractor and 36 with an old distractor. These conditions were counterbalanced within and across runs. Instructions were presented for 4 seconds before each encoding phase, and for 8 seconds before each recognition phase. All subjects underwent a training session on the task outside the scanner (using a different set of stimuli).

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Figure 7. Associative memory task. In the encoding phase, 8 pairs of pictures were shown for 4 seconds.

Then, in the recognition phase, subjects had to select between the two pictures at the bottom which one had been memorized with the cue picture at the top. Adapted from (van der Meulen, Lederrey et al. 2012), by permission of Wolters Kluwer Health, Inc.

2.1.7 MRI analysis 2.1.7.1 sMRI preprocessing

Structural MRI data were preprocessed and analyzed using the VBM8 toolbox (http://dbm.neuro.uni-jena.de/vbm/) implemented in SPM8 (http://www.fil.ion.ucl.ac.uk/spm/).

Anatomical images were first segmented into grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) using the ‘new segmentation’ algorithm (Ashburner and Friston 2005).

GM images were warped to the MNI space, then spatially normalized, modulated and smoothed with a 4mm full-width-at-half-maximum (FWHM) Gaussian filter.

2.1.7.2 Task fMRI preprocessing and modeling

Task-related functional images were preprocessed using SPM8 or 12 (depending on the experiment; http://www.fil.ion.ucl.ac.uk/spm). Slice-time correction was used to resample all

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slices to the acquisition time of the middle slice, after which functional scans were realigned, co-registered with the anatomical scan, spatially normalized to the MNI EPI template image, resampled to 3mm isotropic voxels, and smoothed using an 8mm FWHM Gaussian kernel.

Only images matching successful trials (when participants gave a correct answer) were analyzed.

The first level (within subject) general linear model (GLM) included regressors for each experimental condition convolved with the canonical hemodynamic response function (HRF), plus 6 or 24 movement parameters (6 head motion parameters of the current volume and the preceding volume, plus the 12 corresponding squared items, also known as Friston 24-parameter model)(Friston, Williams et al. 1996). A high-pass filter (with a cut-off period of 128s) and a first-order autoregressive function to account for temporal autocorrelation were included in the fitting procedure. Individual fitted coefficient maps or “beta maps” were extracted for each run and condition. The 6 task conditions were “encoding related” (ER, referring to the encoding of semantically related picture pairs), “encoding unrelated” (EU), “recognition related new” (RRN, referring to recognition of trials in which the pairs were semantically related, and the distractor was a new picture), “recognition related old” (RRO, in which the pairs were semantically related, and the distractor had been previously seen but paired with a different picture), “recognition unrelated new” (RUN), and “recognition unrelated old” (RRO). Beta maps were then transformed into a single “mean beta” for each subject and condition by averaging over the 3 runs, in order to exploit inter-run consistency. Beta maps averaging all encoding and all recognition conditions over the 3 runs were also computed.

2.1.7.3 RS fMRI preprocessing and modeling

For RS data, functional images were first realigned, then the mean image of the functional data was co-registered with the anatomical scan. A customized version of the IBASPM toolbox (Alemán-Gomez, Melie-García et al. 2006) was used to build an individual structural brain atlas, based on 3 different atlases (for comparison purposes): the AAL structural atlas (Tzourio-Mazoyer, Landeau et al. 2002), the Hammers probabilistic structural atlas (Hammers, Allom et al. 2003, Gousias, Rueckert et al. 2008), and the Shirer ICA-derived functional atlas (Shirer, Ryali et al. 2012). We selected 88 regions in the AAL atlas (whole atlas without cerebellum and pallidum), all 83 regions from the Hammers atlas, and all 90 regions from the Shirer atlas. The atlas was then mapped back onto the native resolution of the functional data, time series were

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linearly detrended, and region-averaged time series were extracted. The first 10 time series were discarded to ensure magnetization equilibrium, and movement parameters (either 6 or 24 depending on the experiment) were regressed out. These time series were Winsorized to the 95th percentile to increase robustness to outliers (e.g., spikes). Time courses were then filtered into frequency subbands using a wavelet transform (cubic orthogonal B-spline wavelets). Four frequency subbands were extracted, respectively containing frequencies between 0.23-0.45 Hz, 0.11-0.23 Hz, 0.06-0.11 Hz, 0.03-0.06 Hz. We were mainly interested in the two last subbands, as resting-state BOLD fluctuations are typically measured in low-frequency subbands (below 0.1 Hz).

After computing pairwise Pearson correlations between all regions in the atlas, a correlation matrix (number of regions x number of regions) was obtained for each subject. This functional connectivity matrix was used as the adjacency matrix of a connectivity graph, where each atlas ROI corresponded to a vertex and the strength of functional connectivity (the correlation coefficient) between two regions was encoded in the edge weight. We used direct graph embedding, in which the upper triangular part of the adjacency matrix is lexicographically organized in a vector representation.

2.1.8 Behavioral analyses

Between-groups differences in neuropsychological tests (z-scores when available, otherwise raw scores) and behavioral performance (accuracy and reaction time) on the fMRI task were assessed using either two-sample t-tests or Mann-Whitney U tests, depending on the normality of the distribution (evaluated with the Jarque-Bera test).

2.1.9 Classifiers

We used two different classifiers, a linear (support vector machine) and a nonlinear one (random forest), so that we could assess the consistency of our predictive accuracies. For both classifiers, we report the balanced accuracy, specificity (true negative rate, i.e., percentage of controls correctly classifier), sensitivity (true positive rate, i.e., percentage of patients correctly classified), and area under the receiver operating characteristic (ROC) curve (AUC) for each prediction. The ROC curve reflects the true positive rate (i.e., sensitivity) against the false positive rate (i.e., the

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percentage of misclassified controls). In order to evaluate the performance and generalization ability of our classifier, we either chose a leave-one-subject-out or a leave-one-subject-per-group-out cross-validation approach to separate between training and testing data. We relied on Wilson binomial interval to assess the significance of the obtained prediction accuracies.

2.1.9.1 Support vector machine

The support vector machine (SVM) classifier is a supervised learning method performing linear binary classification. Training an SVM is a minimization problem where the largest-margin hyperplane allowing for an optimal separation of the training examples is identified. The separating hyperplane is defined by w • x + b = 0, where the weight vector w is a linear combination of the support vectors, and b is the intercept. The support vectors are training instances that are the closest to the decision boundary (as they are the hardest to classify), and they largely determine the hyperplane. We used the SVM implementation of the LIBSVM package (Chang and Lin 2011)(http://www.csie.ntu.edu.tw/~cjlin/libsvm).

2.1.9.2 Random forest

Random forest (RF) is an nonlinear classification method that uses decision trees. It recursively selects features and computes the result of applying different cutpoints to them, and tries to minimize the entropy of the class labels in the partition. The classifier yields a discriminative weight wi for each feature, which represents the relative ability of this feature to discriminate between two classes. However, it is important to note that these weights only make sense as part of a multivariate pattern. The RF algorithm used is implemented in Matlab (https://code.google.com/p/randomforest-matlab/); it features 501 trees, with the number of features per tree being the square root of the number of features, and a minimum of 3 cases per tree leaf.

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