3. RESULTS
3.3 Classifying MCIc vs MCInc using single and joint modalities…
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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Neuropsy Sociodemog sMRI rs-fMRI (AAL)
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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.
Figure 20
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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
8 0 1 0 0
D i s t r a c t o r
Accuracy (%) E C
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
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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
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Figure 24. Representation of LV7 in the task activity space and the RS FC space.
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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
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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
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Figure 27. Representation of LVs 3, 4, 5, 6, and 10 in both the task activity and RS FC space.
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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).
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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.
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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 clinical and imaging measures, both structural and functional (third study). We found that imaging markers (sMRI and RS fMRI) were able to accurately predict conversion to AD, while sociodemographic and clinical data were less informative.
4.1 The early diagnosis of AD using task-based fMRI
In the first study, we classified EC vs MCI patients, first using task-based fMRI activity extracted from atlas-based ROIs, then using memory-related task activation patterns. The associative memory task that we used induced significant group differences in both accuracy and reaction time. We found several ROIs in which task-related activity could reliably discriminate between controls and patients. Then, by extracting task activation patterns that maximally covaried with memory measures, we found that brain scores (i.e., the extent to which a subject expresses the covariance pattern) could accurately separate our groups.
4.1.1 Using an associative memory task in the early diagnosis of AD 4.1.1.1 From a behavior perspective
A previous study of our group validated the use of an associative memory task for highlighting memory deficits in MCI patients (van der Meulen, Lederrey et al. 2012), particularly in recollection-based processes, but also in familiarity-based recognition, to a lesser extent. We corroborate the behavioral results in a partially overlapping cohort of subjects, and report very high AUCs (between 0.71-0.96) in discriminating between EC and MCI patients for both accuracy and reaction time in all recognition trials. These data are in accordance with studies
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reporting that MCI patients are impaired in both recollection and familiarity (Wolk, Signoff et al.
2008, Algarabel, Escudero et al. 2009, Ally, Gold et al. 2009, Algarabel, Fuentes et al. 2012, Wolk, Mancuso et al. 2013), although recollection-based processes are usually found to be more affected. In fact, recollection-based recognition is consistently found to be impaired in aging, independently of the methods used (Prull, Dawes et al. 2006). Familiarity, on the other hand, is altered in some tests, but not in others, in healthy elderly individuals (Prull, Dawes et al. 2006, Wolk, Mancuso et al. 2013). These findings led Algarabel and colleagues to suggest that familiarity impairment could represent a more specific marker of AD than recollection, as it could potentially better separate normal aging from prodromal AD (Algarabel, Escudero et al.
2009). Another argument for familiarity being a good early marker for AD is the fact that it relies upon the integrity of the perirhinal cortex, which is the first brain region affected by tau pathology (Braak and Braak 1991). Yet, we found that accuracy in conditions relying upon recollection yielded higher AUCs (AUC between 0.94-0.96) than conditions relying upon familiarity (AUC between 0.71-0.86); however, reaction times in familiarity conditions (AUC between 0.90-0.95) were a little more discriminative than in recollection conditions (AUC 0.87-0.92). Therefore, we conclude that both processes add informative value in the between-group discriminability.
Our associative memory task also allowed us to manipulate the semantic relationship between the pictures within a pair in order to test whether it facilitated the recognition of the target picture. In a previous study of our group, elderly controls were less accurate in trials with semantically related compared to unrelated pairs, suggesting that unrelated pairs required a deeper and more elaborate encoding than related pairs (van der Meulen, Lederrey et al. 2012). We tested the effect of this variable (in the fourth study), but did not find any main effect of semantic relatedness on either accuracy or reaction time.
4.1.1.2 From an imaging perspective 4.1.1.2.1 ROI-based classification
We found that task-related imaging patterns allowed to distinguish MCI from EC, but to a lesser degree than behavioral measures. When using activity within ROIs as features for our classifiers, we noted that the experimental conditions that generated the highest imaging-based predictive accuracies were not the trials that relied upon recollection-based recognition. Indeed, the two
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conditions relying upon familiarity (RRN and RUN) induced significantly more discriminative activity in several regions, and that with two different classifiers. One possible explanation for this finding is that differences in activation patterns may be easier to detect when using a higher number of trials. As we analyzed only images corresponding to correct trials, conditions that relied upon familiarity generated more correct responses and therefore more images.
Notably, the highest accuracy (86% with SVM, survived to FDR correction) was found using activity of the right PCC during encoding of semantically related pairs. The accuracy was also significant (76% with SVM, survived to FDR correction) when using activity of all encoding trials, both semantically related and unrelated. This finding could represent the impaired deactivation of medial parietal regions that is typically reported in MCI patients during encoding (Lustig, Snyder et al. 2003, Celone, Calhoun et al. 2006, Petrella, Wang et al. 2007).
Furthermore, activity in the left PHG during encoding of related pairs yielded a significant classification accuracy (71% using SVM, did not survive FDR correction). This result could reveal either hypo- or hyper-activation in MCI patients in this MTL region, as both aberrant patterns have been demonstrated in MCI patients (Small, Perera et al. 1999, Machulda, Ward et al. 2003, Dickerson, Salat et al. 2004, Dickerson, Salat et al. 2005, Celone, Calhoun et al. 2006, Johnson, Schmitz et al. 2006, Hamalainen, Pihlajamaki et al. 2007, Petrella, Wang et al. 2007, Hanseeuw, Dricot et al. 2011, Putcha, Brickhouse et al. 2011, van der Meulen, Lederrey et al.
2012), depending on the stage patients are in (early or late MCI). These findings add further evidence to previous reports of pathological processes taking place in these two regions.
Importantly, the dysfunctional patterns of fMRI activity in these regions had never been used for individual classification before.
A few regions that belong to the networks described in familiarity (anterior MTL, lateral temporal and PFC) and recollection-based (posterior MTL, thalamus, medial and lateral parietal) recognition could reliably separate normal aging from MCI. Among familiarity-related regions, activity in the left middle and superior temporal gyrus yielded respectively 71% and 76%
accuracy in the RRN condition (with RF) ; and activity in the left superior temporal pole yielded 71% accuracy in the RUN condition (also with RF). Among recollection-related regions, activity in the right angular gyrus yielded 74% accuracy (with RF) during recognition of related pairs with an old distractor (RRO). These findings complement our behavioral results, and suggest that
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the neural correlates of both familiarity and recollection-based processes are altered in MCI patients. Finally, discriminative activity patterns were also found in the PFC, particularly during encoding (both conditions) and in one recognition condition (RUO). This finding could represent compensatory processes, as has been previously reported in AD (Grady, McIntosh et al. 2003, Pariente, Cole et al. 2005) and MCI patients, as well as in low-performing EC (Miller, Celone et al. 2008). However, this is hypothetical, as we have not inspected the directionality of discriminative activation patterns.
We were surprised to find that only one MTL region (left PHG, which did not survive FDR correction) could reliably classify our groups. One possibility is that because our analyses were limited to a mask comprising grey matter voxels that were common across all subjects, even if one patient presented severe enough atrophy in the MTL, task fMRI activity in this area could not be used for classification. However, by doing this, we made sure that the discriminative regions that we found were not driven by structural alterations.
To our surprise, the majority of discriminative regions in recognition conditions were located in the occipital lobe (calcarine sulcus, inferior and middle occipital gyrus, lingual and fusiform gyrus). One possibility is that patients rely excessively on their visual pathway to recognize the pairs of images, perhaps by relying on visual imagery (e.g., colors of picture pairs), suggesting poorer strategies for memorizing associations between images. Alternatively, patients may turn to the distinctive features of visual information to enhance their recognition of items. However, these hypotheses imply that patients hyperactivate occipital regions during recognition, which is again only speculative as we have not examined the directionality of activity in these discriminative patterns.
4.1.1.2.2 PLS-based classification
Concerning the PLS analyses, we had high expectations regarding the use of PLS in extracting memory-related networks that could be used to discriminate between our groups. To our surprise, we did not find any significant relationship between task-related fMRI activity and task accuracy.
Concerning the PLS analyses, we had high expectations regarding the use of PLS in extracting memory-related networks that could be used to discriminate between our groups. To our surprise, we did not find any significant relationship between task-related fMRI activity and task accuracy.