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Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases

Abstract Social feedback can selectively enhance learning in diverse domains. Relevant neurocognitive mechanisms have been studied mainly in healthy persons, yielding correlational findings. Neurodegenerative lesion models, coupled with multimodal brain measures, can complement standard approaches by revealing direct multidimensional correlates of the phenomenon. To this end, we assessed socially reinforced and non-socially reinforced learning in 40 healthy participants as well as persons with behavioural variant frontotemporal dementia (n = 21), Parkinson’s disease (n = 31) and Alzheimer’s disease (n = 20). These conditions are typified by predominant deficits in social cognition, feedback-based learning and associative learning, respectively, although all three domains may be partly compromised in the other conditions. We combined a validated behavioural task with ongoing EEG signatures of implicit learning (medial frontal negativity) and offline MRI measures (voxel-based morphometry). In healthy participants, learning was facilitated by social feedback relative to non-social feedback. In comparison with controls, this effect was specifically impaired in behavioural variant frontotemporal dementia and Parkinson’s disease, while unspecific learning deficits (across social and non-social conditions) were observed in Alzheimer’s disease. EEG results showed increased medial frontal negativity in healthy controls during social feedback and learning. Such a modulation was selectively disrupted in behavioural variant frontotemporal dementia. Neuroanatomical results revealed extended temporo-parietal and fronto-limbic correlates of socially reinforced learning, with specific temporo-parietal associations in behavioural variant frontotemporal dementia and predominantly fronto-limbic regions in Alzheimer’s disease. In contrast, non-socially reinforced learning was consistently linked to medial temporal/hippocampal regions. No associations with cortical volume were found in Parkinson’s disease. Results are consistent with core social deficits in behavioural variant frontotemporal dementia, subtle disruptions in ongoing feedback-mechanisms and social processes in Parkinson’s disease and generalized learning alterations in Alzheimer’s disease. This multimodal approach highlights the impact of different neurodegenerative profiles on learning and social feedback. Our findings inform a promising theoretical and clinical agenda in the fields of social learning, socially reinforced learning and neurodegeneration.


Kruskal-Wallis test
To estimate the power of our final sample size for our main analyses, we performed an estimation analysis through Monte Carlo Simulation in Study Size 3.0 software (CreoStat HB, Frölunda, Sweden). Given our statistical design (two-sided Kruskal-Wallis test for two different variables comparing four groups), we considered the following parameters: Sample size = 112, sample size k = 30, alpha level of P = 0.05, n of A-factor levels = 4, n of simulations = 1000, Uniform distribution. This analysis showed that the final sample size of our study reaches a power of 0.99 (higher than the median of powers analyzed in previous studies in the area for detecting large effect sizes). 1

Wilcoxon-Mann-Whitney test
First, to estimate the appropriate sample size for our main analyses, we performed an a priori estimation analysis in G*Power 3.1. 2 Given our statistical design (Wilcoxon-Mann-Whitney test for different variables comparing two groups), we considered the following parameters: effect size Cohen's d = 0.9, 1 alpha level of P = 0.05, and power = 0.8 (higher than the median of powers analyzed in previous studies in the area for detecting large effect sizes). 1 This analysis showed that a sample size of 16 and 32 for the groups (considering the relation between samples similar to those used in this work, n1/n2 = 2) is adequate to detect the estimated effects. Finally, a post-hoc power estimation analysis indicated that the final sample size of our study (with the smallest group featuring 20 participants) reaches a power of 0.88.

Supplementary Material Functional assessment of neurodegenerative samples
Functional decline in participants with neurodegenerative conditions was assessed through three scales. (1) Functional Activities Questionnaire (FAQ): is a 10-item rating scale that measures instrumental activities of daily living (such as preparing meals and personal finance). 3 A score above 9 suggest a possible impaired function and possible cognitive impairment. (2) Frontotemporal dementia Rating Scale (FRS): is a 30-item scale that evaluates severity in people with dementia. 4,5 Scores from 1.92 to -2.58 indicates a moderate/severe disease stage, and from -2.58 to -6.66, very severe/profound disease stage.
(3) Clinical Dementia Rating Scale (CDR): is an 8-item dementia rating scale that assess cognitive and functional decline. Scores: 0 = Healthy, 0.5 = questionable dementia, 1 = mild dementia, 2 = moderate dementia, 3 = severe dementia. 6 Middle Temporal L  -1,191E-15 1  -1,831  1,774  23,375 0,001  Parahippocampal R  5,1138E-16 1  -1,305  1,213  23,221 0,001  Inferior Parietal L  6,8632E-16 1  -1, To identify the whole-brain atrophy pattern of the participants with neurodegeneration, an ANCOVA was performed between cortical volumes of each group and HCs obtained through surface-based morphometry (SBM) on FreeSurfer software suite (v 6.0, https://surfer.nmr.mgh.harvard.edu/). Full details on preprocessing and implemented methods can be found elsewhere. 8,9 The plain-text output of the FreeSurfer's pipeline was post processed on Python (Version 3.7.4, Python Software Foundation). To avoid potential biases due to differences among the participants' head size, 10 volume measures of each area were normalized as a percentage of the estimated total intracranial volume (eTIV, provided in FreeSurfer's results). Finally, a site normalization was performed to avoid MRIsetup-dependent bias in the measurements. For each center, volume of both HCs and participants with neurodegenerative conditions was z-scored based on the mean and SD of the corresponding center's HCs. 11 Sex was included as covariate of no interest and significance was set to P < 0.05 with false discovery rate (FDR) correction. Results are presented using Desikan-Killiany cortical atlas. 12 No grey matter volume atrophy was found in PD. AD: Alzheimer's disease, bvFTD: behavioral variant of fronto-temporal dementia, L: Left, R: Right. To assess feedback valence comprehension among groups, participants were explicitly asked about the valence (positive, negative) of each feedback type [Social (smiling face, angry face); Non-social (green circle, red circle)] at the end of the experiment. A generalized linear mixed-effects model was fitted with accuracy as dependent variable; valence type, feedback condition and group as fixed effects; and subject as effect with random intercept. Type II Wald Chi-square tests were run for fixed effects in the model. No significant differences were found in accuracy between groups (χ2 = 5.46, P = 0.14), feedback condition (χ2 = 0.05, P = 0.81) or valence (χ2 = 1.85, P = 0.17), neither significant effects were found in the interaction between group x condition x valence (χ2 = 0.28, P = 0.96).

Supplementary Material Task design details
The paradigm design was based on a validated task. 13 In addition, stimuli were controlled by the distribution between trials, and frequency of digits across blocks, categories, and feedback conditions. Final design is reported in Supplementary

Supplementary Material Group comparisons controlling for sex
A significant difference in sex was found between bvFTD and HCs ( Table 2). Consequently, additional group comparisons controlling for sex were tested using the aovperm function from the permuco R package, which conducts analysis of covariance (ANCOVA) through permutation testing. 14 Permutation tests were used for group comparisons as they do not make assumptions about the distribution of the data, are more robust to nonnormality than are parametric tests, and allows for covariation. Between-group comparisons were first computed for each condition using the learning index (Spearman correlation's Rho slope of cycles and accuracy score calculated for each feedback condition) as the dependent variable, group as the independent variable, and sex as a covariate. Each test was run using 10,000 permutations and a significance level of P < 0.05. Significant tests were followed by post hoc tests using the permutation analysis of covariance (ANCOVA) for each pairwise group comparison (bvFTD-HCs, PD-HCs, AD-HCs). 15 Multiple comparisons in post hoc testing were corrected for using the Benjamini and Hochberg procedure to reduce false discovery rate using the p.adjust R function.
Main results were replicated when controlling for sex. In this sense, significant differences were found in the learning index between all groups in SRL (F(3,106) = 5.61, P = 0.001) and NSRL (F(3,107) = 3.34, P = 0.02) feedback conditions. Moreover, post hoc tests between each neurodegenerative sample and HCs also revealed the same pattern as main results: participants with bvFTD performed significantly worse than HCs in the SRL condition (F(1,58)

Supplementary Material Group comparisons controlling for valence recognition
To rule out potential effects of impaired facial emotion recognition in bvFTD, 16,17 we performed additional group comparisons controlling for feedback valence recognition and sex (see Supplementary Material 3.5) using the aovperm function from the permuco R package, which conducts analysis of covariance (ANCOVA) through permutation testing. 18 Permutation tests were used for group comparisons as they avoid assumptions about data distribution, prove more robust to nonnormality than parametric tests, and allow for covariation. Between-group comparisons were first computed for each condition using the learning index as the dependent variable, group as the independent variable, and feedback valence recognition and sex as covariates. Each test was run using 10,000 permutations with a significance level of P < 0.05. Significant effects were further inspected via post hoc tests using permutation ANCOVAs for each pairwise group comparison (bvFTD-HCs, PD-HCs, AD-HCs). 19 Post hoc tests were corrected for multiple comparisons through the Benjamini and Hochberg procedure to reduce false discovery rate using R's p.adjust function.
Main results were replicated when controlling for feedback valence recognition. Significant differences were found in the learning index between all groups regarding SRL (F(3,106) = 5.57, P = 0.001) and NSRL (F(3,106) = 2.87, P = 0.04) feedback conditions. Moreover, post hoc tests between each neurodegenerative sample and HCs revealed the same pattern: participants with bvFTD performed significantly worse than HCs in the SRL condition (F(1,57)

Supplementary Material Crawford's test in neurodegenerative individuals
We also identified the individuals with impaired scores per neurodegenerative group compared to HCs, and evaluated whether there was a significant dissociation between SRL and NSRL conditions in those individuals, using the modified t tests from the Dissocs_ES.exe program. 20 To this end, we first compared the learning indexes of every single neurodegenerative case to the learning index's mean of the HCs sample, for SRL and NSRL conditions, using the modified one-tailed Crawford's t test. [21][22][23] This methodology allows the assessment of significance by comparing multiple individuals' test scores with small samples' norms, is more robust for non-normal distributions, presents low values of type I error, and has already been reported in previous similar studies. 24,25 Only values with P < 0.05 were considered statistically significant in all comparisons. Effect sizes obtained using the same methods were reported as point estimates, as recommended. 20 Results are presented in Supplementary Table 3

.7.1.
Cases with impaired scores were subjected to single-case analyses to describe whether those individuals had a significant dissociation between SRL and NSRL conditions. To this end, we applied the Revised Standardized Difference Test (RSDT), which allowed us to test whether the standardized difference between a patient's SRL and NSRL learning indexes was (two-tailed) statistically significant. The RSDT test controls the Type I error rate regardless of the control sample size and the magnitude of the correlation between tasks conditions. 26 Results indicated that 75% bvFTD, 60% PD and 50% AD individuals with impaired learning indexes, supporting the criteria for dissociation between SRL and NSRL standardized scores (Supplementary Table 3.7.2). In sum, these findings suggest distinguished deficit patterns in each neurodegenerative group, with selectively enhanced social impairments in the bvFTD sample. Mean learning index score, t, P and Zcc of individuals with impaired scores per neurodegenerative group is reported. * indicates significant differences from the HCs learning index (SRL mean: 0.46; SD: 0.35 -NSRL mean: 0.21; SD: 0.45). AD: Alzheimer's disease; bvFTD: behavioral variant frontotemporal dementia; NSRL: non-socially reinforced learning; PD: Parkinson's disease; SRL: socially reinforced learning; Zcc: effect size for difference between neurodegenerative group and healthy controls.  Moreover, we conducted separate analyses to assess learning effects independently in SRL and in NSRL conditions. A significant effect of learning (increased accuracy in the last cycles) was found in both SRL [F (5,195 (SD). The asterisk (*) indicates significant differences with an alpha level of P < 0.05 and Tukey post-hoc comparisons.  Results are presented as mean (SD). The asterisk (*) indicates significant differences with an alpha level of P < 0.05. The dagger ( †) indicates variables with significant differences (P < 0.05) between neurodegenerative groups, precluding comparisons between them in our target measures.

Supplementary Discussion Visuoperceptual complexity influence of feedback cues
Learning effects between conditions may be influenced by features differences of feedback (e.g., visuoperceptual complexity). However, several reasons suggest that the social nature of the SRL stimulus and not stimulus complexity may better explain our results. First, our paradigm is based on an already validated task with robust effects 13 , as most studies in the area that use comparable social vs non-social stimuli with different designs. 28- 30 We used one face identity per social feedback condition to reduce stimulus variability and to control for a possible cognitive load influence. 31 Second, if learning effects would be affected by the visuoperceptual complexity of the stimuli, results would have shown the opposite tendency, with increased learning associated with simpler (then NSRL color cues) feedback stimuli. In addition, learning results were replicated when controlling for valence recognition (see Supplementary Material 2.7). Third, the MFN modulations are mostly modulated by learning [32][33][34] and social-feedback 35 (even when social and non-social stimuli are explicitly matched in complexity), 36 while the N170 component is primarily modulated by stimulus complexity (i.e., faces vs. objects). 37 As all groups (except bvFTD) showed MFN and not N170 waveforms, this process seems to reflect MFN learning effects and not stimulus complexity. Only the bvFTD group presented evidence of abolished MFN learning effects and a concomitant posterior cluster modulation resembling the N170 38 of stimulus type discrimination. Finally, our neuroimaging findings also support a social vs. non-social rather than a simple vs. complex stimuli processing interpretation, with SRL learning indexes being associated to grey matter volume of critical hubs for socio-contextual learning (i.e., temporoparietal junction) 39 in all groups results. Altogether, these reasons and findings suggest that results are better interpreted in terms of social stimuli influences.