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3. RESULTS

3.1.2.2 Behavior PLS results

3.1.2.2.2 Memory scores as behavior

Accuracy (%)

RRN 98.33 ± 4.07 87.50 ± 10.02 4.15 <0.001 0.86

RRO 89.72 ± 8.88 65.56 ± 11.34 7.75 <0.001 0.96

RUN 98.33 ± 4.07 88.33 ± 14.07 3.24 0.001 0.77

RUO 94.44 ± 9.54 63.06 ± 16.55 7.58 <0.001 0.96

Total 95.21 ± 4.84 76.11 ± 10.07 7.89 <0.001 0.97

Reaction time (s)

RRN 1.73 ± 0.24 2.42 ± 0.36 -7.12 <0.001 0.95

RRO 2.02 ± 0.35 2.56 ± 0.27 -5.60 <0.001 0.88

RUN 1.66 ± 0.24 2.34 ± 0.38 -6.81 <0.001 0.95

RUO 1.99 ± 0.31 2.69 ± 0.45 -5.91 <0.001 0.92

Total 1.85 ± 0.24 2.50 ± 0.31 -7.60 <0.001 0.96

Statistically significant group differences are indicated in bold.

3.1.2.2 Behavior PLS results

3.1.2.2.1 Task accuracy as behavior

We found no significant LVs in any of the conditions.

3.1.2.2.2 Memory scores as behavior

We found 4 significant LVs in the encoding conditions : LV1 and LV2 for the ER condition (p=0.008 and p=0.040 respectively), and LV1 (p=0.025) and LV3 (p=0.046) for the EU condition.

In the recognition conditions, we found LV1 (p=0.016) and LV2 (p=0.017) to be significant in the RRN condition, LV1 (p=0.000) for RRO, and LV1 (p=0.023) for the RUN condition.

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Figure 12. LV1 for the ER condition, and LV1 for the EU condition.

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Figure 13. LV1 for the RRN condition, LV1 for the RRO condition, and LV1 for the RUN condition.

The brain maps for encoding and recognition conditions are shown in Figures 12 and 13 respectively, while correlations with each memory score are presented in Table 9.

Table 9. Correlations between meanbetas and memory tests for each group and condition.

Condition LV Memory test Correlation

EC aMCI

ER LV1 Grober-Buschke (immediate cued recall) -0.38 0.24

Grober-Buschke (delayed cued recall) -0.77 0.23

Doors and People A -0.11 0.05

Doors and People B -0.24 0.21

ER LV2 Grober-Buschke (immediate cued recall) -0.37 -0.36

Grober-Buschke (delayed cued recall) 0.31 -0.30

Doors and People A 0.18 0.50

Doors and People B -0.31 0.52

EU LV1 Grober-Buschke (immediate cued recall) 0.13 0.07

Grober-Buschke (delayed cued recall) 0.48 0.79

Doors and People A -0.15 -0.22

Doors and People B 0.01 -0.14

EU LV2 Grober-Buschke (immediate cued recall) -0.16 0.30

Grober-Buschke (delayed cued recall) 0.38 -0.38

Doors and People A -0.03 -0.24

Doors and People B -0.22 -0.13

RRN LV1 Grober-Buschke (immediate cued recall) -0.56 0.23

Grober-Buschke (delayed cued recall) -0.33 0.44

Doors and People A 0.48 -0.05

Doors and People B -0.25 0.00

RRN LV2 Grober-Buschke (immediate cued recall) 0.10 0.18

Grober-Buschke (delayed cued recall) -0.17 0.60

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Doors and People A -0.05 -0.07

Doors and People B 0.16 -0.34

RRO LV1 Grober-Buschke (immediate cued recall) -0.60 0.27

Grober-Buschke (delayed cued recall) -0.44 0.37

Doors and People A 0.31 0.21

Doors and People B -0.34 0.39

RUN LV1 Grober-Buschke (immediate cued recall) 0.26 -0.24

Grober-Buschke (delayed cued recall) 0.07 0.60

Doors and People A -0.10 -0.02

Doors and People B 0.20 -0.13

Statistically significant correlations are indicated in bold.

3.1.2.3 Classification performance 3.1.2.3.1 Task accuracy as behavior

The classification performance using brain scores of the first, second or both LVs as features is presented in Table 10. Although we found no significant relationship between task activity and accuracy, brain scores of a few LVs yielded a significant classification accuracy. While this may seem odd, it could be be explained by the possibility that our PLS models find some trends (not statistically significant brain-behavior correlations) between meanbetas and behavior scores, that do show group differences.

Brain scores of the first LV for the RRN condition distinguished EC from MCI patients with 73%

accuracy, while brain scores of LV1 and LV2 for the EU and RRN conditions yielded 73% and 70% accuracy respectively.

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Table 10. Prediction accuracies using brain scores of LV1, LV2 or both LVs as features.

Condition Features Accuracy Specificity Sensitivity AUC

ER LV1 58% 55% 60% 0.53

LV2 50% 35% 65% 0.51

LV1+2 58% 45% 70% 0.60

EU LV1 60% 45% 75% 0.66

LV2 55% 45% 65% 0.56

LV1+2 73% 65% 80% 0.72

RRN LV1 73% 65% 80% 0.77

LV2 58% 45% 70% 0.55

LV1+2 70% 50% 90% 0.68

RRO LV1 60% 65% 55% 0.63

LV2 58% 50% 60% 0.66

LV1+2 63% 70% 55% 0.71

RUN LV1 35% 35% 35% 0.28

LV2 53% 35% 70% 0.42

LV1+2 38% 15% 60% 0.34

RUO LV1 53% 40% 65% 0.65

LV2 45% 65% 25% 0.49

LV1+2 55% 45% 65% 0.68

Statistically significant predictions are indicated in bold.

3.1.2.3.2 Memory scores as behavior

The classification performance using brain scores of all 8 LVs as features is presented in Table 11. Brain scores of all LVs for conditions EU (74% accuracy) and RRN (67% accuracy) could significantly separate EC from MCI patients. Moreover, when using brain scores of all LVs for all encoding and all recognition conditions also yielded significant accuracies (67% and 69%

respectively).

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Table 11. Prediction accuracies using brain scores of all LVs as features.

Condition Accuracy Specificity Sensitivity AUC

ER 48% 38% 57% 0.43

EU 74% 52% 95% 0.67

RRN 67% 52% 81% 0.76

RRO 45% 29% 62% 0.42

RUN 55% 57% 52% 0.50

RUO 62% 57% 67% 0.65

Encoding 67% 43% 90% 0.64

Recognition 69% 62% 76% 0.72

All conditions 64% 52% 76% 0.74

Statistically significant predictions are indicated in bold.

Prediction accuracies obtained using brain scores of LV1-8 can be found in Table 12. The highest accuracies obtained was 74%.

Table 12. Prediction accuracies using brain scores of LV1-8.

Condition Latent

Variable Accuracy Specificity Sensitivity AUC

ER LV1 57% 52% 62% 0.48

LV1-2 48% 33% 62% 0.43

LV1-3 43% 38% 48% 0.46

LV1-4 52% 43% 62% 0.52

LV1-5 45% 29% 62% 0.50

LV1-6 40% 29% 52% 0.44

LV1-7 45% 38% 52% 0.41

LV1-8 48% 38% 57% 0.43

EU LV1 57% 62% 52% 0.63

LV1-2 64% 62% 67% 0.65

LV1-3 62% 57% 67% 0.70

LV1-4 64% 57% 71% 0.65

LV1-5 60% 52% 67% 0.62

LV1-6 69% 52% 86% 0.68

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Encoding LV1 48% 52% 43% 0.54

LV1-2 64% 52% 76% 0.59

LV1-3 64% 52% 76% 0.66

LV1-4 62% 52% 71% 0.63

LV1-5 64% 48% 81% 0.66

LV1-6 64% 48% 81% 0.65

LV1-7 67% 48% 86% 0.64

LV1-8 67% 43% 90% 0.64

Recognition LV1 64% 62% 67% 0.71

LV1-2 57% 48% 67% 0.70

LV1-3 62% 52% 71% 0.66

LV1-4 60% 57% 62% 0.66

LV1-5 57% 52% 62% 0.66

LV1-6 60% 57% 62% 0.71

LV1-7 67% 62% 71% 0.70

LV1-8 69% 62% 76% 0.72

All conditions LV1 74% 67% 81% 0.69

LV1-2 74% 71% 76% 0.75

LV1-3 69% 62% 76% 0.72

LV1-4 71% 62% 81% 0.70

LV1-5 69% 67% 71% 0.69

LV1-6 64% 52% 76% 0.73

LV1-7 67% 57% 76% 0.72

LV1-8 64% 52% 76% 0.74

Statistically significant predictions are indicated in bold.

3.2 Classifying EC vs MCI using RS fMRI

3.2.1 Sociodemographic and neuropsychological profile

The sociodemogaphic and neuropsychological profile of EC and aMCI is presented in Table 13 below. Patients scored significantly lower than controls in all global cognition measures, in verbal and visuospatial memory, in the Trail Making Test part B, and in semantic verbal fluency.

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Table 13. Sociodemographic and neuropsychological data.

EC aMCI

mean ± SD mean ± SD p-value

Sociodemographic

Females / Males 15 / 7 12 / 10 0.86 0.353

Age 71.36 ± 6.67 71.41 ± 7.85 -0.02 0.984

Education 14.14 ± 2.92 13.68 ± 3.40 0.48 0.637

Scanner 1 / Scanner 2 4 / 18 3 / 19 0.17 0.680

Handedness (left/right) 1 / 21 1 / 21 0.00

Global cognition

CDR total score 0 0.4 ± 0.2 -5.19 <0.001

MMSE 28.5 ± 1.5 25.9 ± 2.2 4.48 <0.001

DRS 1.11 ± 0.79 -0.19 ± 0.94 4.96 <0.001

Memory

Grober-Buschke (delayed cued recall) 0.39 ± 1.01 -2.84 ± 1.75 7.36 <0.001

Doors and People A 0.45 ± 0.77 -1.19 ± 1.24 5.22 <0.001

Doors and People B 0.49 ± 0.78 -0.62 ± 1.10 3.76 <0.001

Digit span forward 0.78 ± 1.03 0.28 ± 1.28 1.44 0.158

Digit span backward 0.56 ± 1.26 0.16 ± 1.12 -0.78 0.436

Attention/Executive functioning

Trail Making A 0.36 ± 0.66 0.06 ± 1.01 1.19 0.236

Trail Making B 0.45 ± 0.50 -0.79 ± 1.87 2.29 0.022

Stroop Test (Interference) 0.77 ± 0.62 0.81 ± 0.87 -0.15 0.878

Language

Boston Naming 0.13 ± 0.48 -0.21 ± 1.01 0.92 0.358

Verbal fluency (semantic) 0.16 ± 0.86 -0.71 ± 0.97 3.13 0.003 Verbal fluency (phonemic) 0.09 ± 0.80 -0.02 ± 1.19 0.34 0.739

Anxiety/Depression

HAD-Anxiety 5.23 ± 2.37 5.95 ± 2.94 -0.90 0.371

HAD-Depression 2.36 ± 1.29 3.32 ± 2.25 -1.72 0.092

Statistically significant group differences are indicated in bold.

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3.2.2 Classification performance using whole-brain FC

The classification performance using the 4 different classifiers and whole-brain FC in each of the 3 atlases as features is shown in Figure 14. FC between AAL regions did not yield above chance accuracies with any of the classifiers (accuracies between 48-61%). With the Shirer atlas however, prediction accuracies were between 80-89%, while with the Hammers atlas, we obtained accuracies that were intermediary between the two other atlases (accuracies between 64-82%).

Figure 14. Classification performance of 4 different classifiers using whole-brain RS connectivity of 3 different atlases as features. The red lign represents the threshold of p<0.01 accuracy significance.

3.2.3 Classification performance using within network/lobe FC

Prediction accuracies obtained using within-network FC using the Shirer atlas are presented in Figure 15. FC within the ventral (accuracies between 73-89%) and dorsal (59-73%) DMN, language network (64-89%), left (68-89%) and right (75-84%) executive control network, posterior salience network (66-75%), and visuospatial network (68-82%) could significantly classify EC vs aMCI patients, with a good consistency across different classifiers. We also observed that the highest accuracies were obtained using RF and SVM classifiers.

Classification performance using within-lobe FC in the AAL atlas is shown in Figure 16. Only the FC in the limbic lobe (accuracies between 52-80%) yielded a significant accuracy with one classifier.

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Figure 15. Classification performance of 4 different classifiers using within-network RS connectivity as features (Shirer atlas). The red lign represents the threshold of p<0.01 accuracy significance.

Figure 16. Classification performance of 4 different classifiers using within-lobe RS connectivity as features (AAL atlas). The red lign represents the threshold of p<0.01 accuracy significance.

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Classification performance using within-lobe RS connectivity in the Hammers atlas as features is shown in Figure 17 below. We do not display classification performance between ventricular regions, as we were interested in FC of grey matter regions only.

FC within the central (accuracies between 66-77%), occipital (59-82%), and parietal lobe (64-77%) consistently (with at least 2 classifiers) yielded significant accuracies across different classifiers.

Figure 17. Classification performance of 4 different classifiers using within-lobe RS connectivity as features (Hammers atlas). The red lign represents the threshold of p<0.01 accuracy significance.

3.3 Classifying MCIc vs MCInc using single and joint modalities 3.3.1 Sociodemographic and neuropsychological profile

The sociodemographic and neuropsychological profile of MCIc vs MCInc is presented in Table 14. Notably, there was no significant difference in any neuropsychological score between the two groups. There was a trend for worse performance in MCIc compared to MCInc in the Doors and People test parts A and B. Surprisingly, there was also a trend for lower performance in MCInc compared to MCIc in the Stroop interference and in the Boston Naming test. The absence of significant group differences in neuropsychological scores

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Table 14. Sociodemographic and neuropsychological data

MCInc MCIc

mean ± SD mean ± SD t/z/chi statistic p-value

Sociodemographic

Females / Males 6 / 12 10 / 5 3.64 0.056

Age 72.3 ± 7.8 72.4 ± 5.1 0.03 0.978

Education 13.3 ± 3.1 13.7 ± 3.4 0.40 0.691

Scanner 1 / Scanner 2 2 / 16 2 / 13 0.04 0.846

Handedness (left/right) 2 / 16 2 / 13 0.04 0.846

Global cognition

CDR (total score) 0.26 ± 0.26 0.40 ± 0.21 1.14 0.254

MMSE 26.11 ± 2.27 25.80 ± 2.01 -0.41 0.683

DRS -0.17 ± 1.13 -0.67 ± 1.20 -1.25 0.221

Memory

Grober-Buschke (immediate cued recall) -0.95 ± 1.23 -0.78 ± 1.28 0.37 0.711 Grober-Buschke (delayed cued recall) -1.95 ± 1.17 -2.10 ± 0.34 -0.14 0.888

Doors and People A -0.79 ± 1.30 -1.54 ± 0.91 -1.77 0.077

Doors and People B -0.43 ± 1.21 -1.24 ± 1.11 -1.98 0.057

Digit span (forward) -0.36 ± 0.90 0.28 ± 1.63 1.44 0.159

Digit span (backward) -0.16 ± 1.19 -0.19 ± 1.39 -0.07 0.948

Attention/Executive functioning

Trail Making A -0.34 ± 1.14 -0.34 ± 1.17 0.00 0.997

Trail Making B -1.26 ± 1.93 -1.31 ± 2.27 0.13 0.899

Stroop (interference) -0.03 ± 1.10 0.70 ± 0.99 1.99 0.056

Language

Boston Naming -0.95 ± 1.12 -0.29 ± 0.78 1.94 0.061

Verbal fluency (semantic) -0.85 ± 0.98 -0.53 ± 1.10 0.90 0.377

Verbal fluency (phonemic) -0.63 ± 1.03 -0.09 ± 1.23 1.37 0.181

Anxiety/Depression

HAD-Anxiety 5.61 ± 2.89 6.07 ± 3.53 0.41 0.687

HAD-Depression 3.22 ± 2.37 3.93 ± 2.63 0.82 0.420

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3.3.2 Classification performance using single modalities

Accuracies using single modalities, either in their original dimensions or reduced to the first principal component via SVD, as features are presented in Figure 18. Only the imaging modalities reached significance : sMRI when reduced via SVD (76% with RF), RS fMRI with the AAL (67% in its original dimensions with RF, and 67% reduced via SVD and classified with SVM) and the Shirer atlas (70-73% in its original dimensions with the both classifiers, and 70%

reduced via SVD and classified with SVM).

Figure 18. Classification performance of the RF and SVM classifiers using single modalities, either in their original dimensions (« High dim ») or reduced to the first principal component via SVD ( « SVD-reduced »). The red lign represents the threshold of p<0.01 accuracy significance.

3.3.3 Classification using joint modalities

Classification performance using combined modalities, either after dimensionality reduction via SVD (with the SVM and RF classifier), or with MKL (with the SVM classifier), is shown in Figure 19. Using concatenation of modalities after SVD, all combinations involving sMRI were

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found to be significant ; however, this performance was attaigned only with RF (not SVM).

Using MKL, combinations involving RS fMRI (Shirer atlas) were found to be significant.

Figure 19. Classification performance of combined modalities, either after dimensionality reduction via SVD (with the SVM and RF classifier), or with MKL (with the SVM classifier). The red lign represents the threshold of p<0.01 accuracy significance.

3.3.3.1 Weight of each modality in classification performance

The weight of each modality in the classification performance of all 4 modalities is presented in Figure 20. When concatening the first PC of each modality, sMRI yielded the highest weight in

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the classification performance, followed by RS fMRI features. Neuropsychological and sociodemographic features weighted twice less than RS features and three times less than sMRI features. In contrast, when using MKL to combine the four modalities, RS features weighted much more than all other modalities, followed by sMRI which weighted almost three times less.

Neuropsychological data weighted eight times less than RS features and three times less than sMRI features, while sociodemographic features had a null weight. Importantly, in both cases, imaging data contributed more to the classification performance than neuropsychological or sociodemographic data.

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Weightof each modality in the classification performance of the four combined modalities, using SVD RF (left) and MKL (right).

3.4 Fusing task and RS fMRI

3.4.1 Sociodemographic and neuropsychological profile

The sociodemographic and neuropsychological profile of the two groups is shown in Table 15 below. Similarly to earlier findings, MCI patients scored significantly lower on all measures of global cognition, as well as verbal and visuospatial memory. Moreover, they were also impaired in the digit span (forward) and verbal categorical fluency.

We found a significant main effect of the group, and a significant main effect of distractor familiarity on both accuracy and reaction time. Patients scored lower (F(3160)=266.65, p<0.000001) and responded slower (F(3160)=494.85, p<0.000001) than controls. Accuracy was

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lower (F(3160)=179.56, p<0.00001) and reaction time higher (F(3160)=98.97, p<0.00001) for trials with an « old » distractor.

Moreover, we found a significant interaction group x familiarity on accuracy (F(3160)=60.18, p<0.00001, Figure 21), and a significant triple interaction group x familiarity x relatedness (F(3160)=4.03, p=0.045) on accuracy. The effect of distractor was much stronger in patients, in whom task accuracy dropped significantly more than in controls. The triple interaction was explained by EC being less accurate for the RRO condition compared to RUO.

New

Old 6 0

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M C I

Figure 21. Histogram showing task accuracy depending on the distractor familiarity, in EC and in MCI patients.

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Table 15. Sociodemographic and neuropsychological data.

EC MCI

mean ± SD mean ± SD t/z/chi statistic p-value

Sociodemographic

Females / Males 15 / 7 12 / 10 0.86 0.353

Age 69.6 ± 5.4 71.9 ± 7.4 -1.19 0.240

Education 13.9 ± 2.6 13.2 ± 3.4 0.74 0.461

Scanner 1 / Scanner 2 7 / 15 4 / 18 1.09 0.296

Handedness (left/right) 1 / 21 2 / 20 0.36 0.550

Global cognition

CDR (total score) 0 ± 0 0.38 ± 0.22 -5.15 <0.001

MMSE 28.77 ± 1.45 25.73 ± 2.21 5.41 <0.001

DRS 1.23 ± 0.73 -0.41 ± 1.14 5.67 <0.001

Memory

Grober-Buschke (immediate cued recall) 0.35 ± 0.89 -0.87 ± 1.30 3.63 <0.001 Grober-Buschke (delayed cued recall) 0.37 ± 0.96 -2.11 ± 0.87 8.98 <0.001

Doors and People A 0.44 ± 0.89 -1.39 ± 1.17 5.86 <0.001

Doors and People B 0.53 ± 0.96 -1.03 ± 1.11 4.97 <0.001

Digit span (forward) 0.82 ± 1.02 -0.11 ± 1.22 2.73 0.009

Digit span (backward) 0.69 ± 1.25 -0.17 ± 1.20 1.82 0.069

Attention/Executive functioning

Trail Making A 0.19 ± 0.74 0.11 ± 0.89 0.33 0.742

Trail Making B 0.49 ± 0.59 -0.62 ± 1.88 1.85 0.064

Stroop (interference) 0.76 ± 0.68 0.54 ± 1.19 0.77 0.445

Language

Boston Naming -0.01 ± 0.55 -0.65 ± 1.04 1.86 0.063

Verbal fluency (semantic) 0.51 ± 0.94 -0.74 ± 1.23 3.60 <0.001

Verbal fluency (phonemic) 0.21 ± 0.71 -0.13 ± 1.14 1.17 0.250

Anxiety/Depression

HAD-Anxiety 5.32 ± 2.44 6.14 ± 3.14 -0.97 0.340

HAD-Depression 2.18 ± 1.50 3.64 ± 2.66 -1.66 0.097

Statistically significant group differences are indicated in bold.

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3.4.2 PLS

We found 10 significant LVs (out of 23 LVs in total), which altogether explained 73% of the total covariance between task-based activity and RS connectivity. The significance of each LV can be found in Table 16 below.

Table 16. P-values corresponding to each LV after permutation testing.

LV p-value

LV1 0.00000

LV2 0.00300

LV3 0.00060

LV4 0.00300

LV5 0.00060

LV6 0.00040

LV7 0.00360

LV8 0.00300

LV9 0.01240

LV10 0.02180

LV11 0.05799

LV12 0.21636

LV13 0.71406

LV14 0.79044

LV15 0.98400

LV16 0.99600

LV17 0.99980

LV18 0.99980

LV19 0.99980

LV20 0.99980

LV21 0.99980

LV22 0.99980

LV23 0.99980

Below, we describe LV1 and LV2 as they distinguish our groups, and LV7, LV8 and LV9 as they are correlated to memory measures.

The first LV (Figure 22) is represented by a positive pattern of task activity in the right inferior frontal gyrus (IFG), right insula, left rolandic operculum, bilateral middle and superior occipital gyri, left angular gyrus, right supplementary motor area (SMA), bilateral hippocampi, and left

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middle temporal gyrus (MTG). There is also a negative pattern of activity in bilateral postcentral gyri, left precentral gyrus, left supramarginal gyrus, and cerebellum. Notably, many of these regions are part of DMN. On the RS connectome are involved several DMN regions such as anterior, middle and posterior cingulate cortex (ACC-MCC-PCC), left MTG and right superior temporal gyrus (STG). Some regions from the frontoparietal network, such as right middle/superior frontal gyrus, and bilateral insula, are also represented. Finally, other regions, such as left inferior frontal/orbitofrontal gyrus, bilateral angular gyri and bilateral supramarginal/inferior parietal gyri, are commonly associated with both networks. All connections were positive. Importantly, the sign of the patterns displayed cannot be interpreted as increased/decreased activity/connectivity, but only in relation to the opposite modality. Notably, both rest and task scores for LV1 were significantly different between MCI converters and nonconverters (t=-2.74, p=0.006 and z=2.34, p=0.019 respectively).

The second LV (Figure 23) implicated positive task activity in the bilateral insula and IFG, right middle frontal and orbitofrontal gyri, bilateral ACC and MCC, bilateral precuneus and superior parietal lobules. In addition, a pattern of negative activity was present in the left hippocampus, MTG, IFG, and bilateral cerebellum. These regions are commonly associated with the salience network and the DMN. On the rest connectome were involved left and right STG, right insula and ACC, left IFG and ACC, left MTG and left IFG, right supramarginal gyrus and MCC, and left middle occipital gyrus and right MTG. Again, this network is reminiscent of the salience and DMN networks. Moreover, both rest and task scores for LV2 were significantly different between EC and MCI patients (t=2.78, p=0.008 and z=2.34, p=0.020 respectively). Rest scores were also significantly different between MCI converters and nonconverters (t=-2.45, p=0.024).

The seventh LV (Figure 24) is characterized by a positive pattern of activity in the right MTG, right fusiform gyrus and parahippocampal gyrus, right insula and ACC, bilateral supedior medial frontal gyri and angular gyri. Moreover, a negative pattern of activity in the right hippocampus and parahippocampal gyrus, right MTG, left precuneus and cuneus, left IFG and insula, and left superior temporal pole. On the RS connectome, many DMN regions such as ACC-MCC-PCC, right SPG and IPG, right supramarginal and angular gyrus, left ITG and MTG. Task and rest scores were not different between groups.

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The eighth LV (Figure 25) is represented by positive activity in the left cerebellum, bilateral superior occipital gyri and cuneus, left angular and supramarginal gyri, ACC, and right insula.

There was also a pattern of negative activity in the right IFG and left middle frontal gyrus, and right middle occipital gyrus. On the rest connectome, the MCC and ACC, right IFG and left MFG, retrosplenial cortex, bilateral supramarginal gyri and middle occipital gyri were involved.

Task and rest scores did not differ between groups.

The ninth LV (Figure 26) involved positive activity in medial frontal and parietal regions, but also the left IFG and MTG, the right parahippocampal gyrus, the right rolandic operculum, and left cerebellum. A pattern of negative activity was also present in the right orbitofrontal gyrus, left MTG, bilateral calcarine gyri and right postcentral gyrus. On the rest connectome were involved the right thalamus, bilateral insula, left MTG, left cerebellum, right supramarginal and angular gyri, among others. Task and rest scores did not differ between groups.

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Figure 22. Representation of LV1 in the task activity space and the RS FC space.

R angular R STG R IFG R MFG

R insula

R calcarine

R RSC PCC

R thalamus MCC

L caudate

L MTG

L ITG

L angular L SPG

R supramarginal L IFG

L insula ACC

L supramarginal

90

Figure 23. Representation of LV2 in the task activity space and the RS FC space.

L MFG

L mid occipital

R MTG

R angular

L STG R STG

R insula R SFG

L IFG ACC

L MTG

L PHG R supramarginal

R IFG

L precentral

R SPG

R mid occipital L cerebellum

R MCC

R MFG

91

Figure 24. Representation of LV7 in the task activity space and the RS FC space.

92

Figure 25. Representation of LV8 in the task activity space and the RS FC space.

R IFG

R mid/sup occipital R MFG

L mid/sup occipital

R angular R PHG

R hippocampus

L cerebellum L post insula

R caudate L thalamus

L hippocampus

L cerebellum L ITG

R MFG L MFG

L IFG

R MCC

L supramarginal

93

Figure 26. Representation of LV9 in the task activity space and the RS FC space.

R MFG

R mid/sup occipital R SFG/MFG

L MFG/SFG L IFG

L ACC

L mid/sup occipital

R caudate

L ITG

L cerebellum

PCC

R supramarginal

R RSC

precuneus

R STG L post insula

L MTG

IPS

R precentral

R MTG

94

Figure 27. Representation of LVs 3, 4, 5, 6, and 10 in both the task activity and RS FC space.

95

3.4.3 Canonical correlation analysis

When testing for associations between LV scores and memory measures across all subjects, we found no significant correlations. However, we found significant associations within each group.

For CCA within EC, we found significant correlations between task/rest scores for LV7 and LV9, and memory (respectively r=0.85, p=0.048 and r=0.86, p=0.011). The weights associated with each LV/behavioral score are shown on Figure 28. For LV7, the correlation was driven by both task and rest scores (with opposite signs), and mostly by delayed recall performance. In contrast, for LV9, the correlation was much more driven by task scores, and by immediate recall, digit span backward and task accuracy for conditions with a new distractor.

In MCI, a significant correlation was found between task and rest scores for LV8 and memory performance (r=0.83, p=0.032). This correlation was driven by task scores exclusively, and delayed recall. Notably, an outlier can be seen on the scatterplot corresponding to LV8 on Figure 28. We tried removing it and re-computing the CCA, which resulted in a non significant correlation (r=0.75, p=0.229).

96

Figure 28. Canonical correlations between LV scores and memory measures for the first component of LV7 (in EC), LV8 (in MCI) and LV9 (in EC). The scatterplots showing the correlations between behavioral and LV saliences is presented on the left, while the weights corresponding to each memory measure and each LV score are shown in the center and right, respectively.

97

4. DISCUSSION

In this project, we have sought to investigate the development of potential biomarkers based on task (first study) and RS fMRI (second study) in the early diagnosis of prodromal AD. In task fMRI data, we found that activation patterns in several regions and memory-related networks were able to separate EC from MCI patients, while in RS fMRI data, connectivity in the whole brain, but also within RSNs, could reliable distinguish controls from patients. We also studied the way alterations in both states covary within the same individuals across the AD clinical spectrum (fourth study), and found a strong correspondence between task-related activity and RS connectivity that persisted across healthy elderly individuals and MCI patients. Finally, we were interested in predicting the prognosis of MCI patients using a multimodal marker based on

In this project, we have sought to investigate the development of potential biomarkers based on task (first study) and RS fMRI (second study) in the early diagnosis of prodromal AD. In task fMRI data, we found that activation patterns in several regions and memory-related networks were able to separate EC from MCI patients, while in RS fMRI data, connectivity in the whole brain, but also within RSNs, could reliable distinguish controls from patients. We also studied the way alterations in both states covary within the same individuals across the AD clinical spectrum (fourth study), and found a strong correspondence between task-related activity and RS connectivity that persisted across healthy elderly individuals and MCI patients. Finally, we were interested in predicting the prognosis of MCI patients using a multimodal marker based on