K V Giudici

P De

Souto Barreto

Sophie Guyonnet

Christelle Cantet

H Zetterberg

C Boschat

J Hudry

Sandrine Andrieu

J A J Schmitt

Bruno Vellas

Julien Delrieu
email: juliendelrieudelrieu.j@chu-toulouse.fr

Vitaliy Ovod

Yan Li

James Bollinger

Randall Bateman

Mapt / Dsa

Effect of a 1-Year Nutritional Blend Supplementation on Plasma p-tau181 and GFAP Levels among Community-Dwelling Older Adults: A Secondary Analysis of the Nolan Trial

Keywords: NCT01 513252 Clinical trial, Alzheimer's disease, Amyloid blood biomarker, Prevention

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Background

The MAPT (Multidomain Alzheimer Prevention Trial) study has tested cognitive effect of omega 3 polyunsaturated fatty acid supplementation (omega 3) and multidomain intervention (MI) in non-demented subjects with memory complaint [START_REF] Vellas | MAPT study: a multidomain approach for preventing Alzheimer's disease: design and baseline data[END_REF]. In the total population of the MAPT study, MI and omega 3 had no significant effect on cognitive decline over 3 years [START_REF] Andrieu | Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebocontrolled trial[END_REF]. Nevertheless, the FINGER (Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability) and MAPT studies showed concordant effects in subgroups of at-risk subjects. In FINGER, the cognitive beneficial effect of the MI was greater than that of the control intervention in APOE ε4 carriers but not in non-carriers [START_REF] Solomon | Effect of the apolipoprotein E genotype on cognitive change during a multidomain lifestyle intervention: a subgroup analysis of a randomized clinical trial[END_REF]. In the ancillary amyloid MAPT study (MAPT-AV45), the MI effect was positive only in non-demented subjects with positive amyloid positron emission tomography (PET) [START_REF] Delrieu | Multidomain intervention and/or omega-3 in nondemented elderly subjects according to amyloid status[END_REF]. These findings could suggest cognitive effect of a MI in early stage on the continuum of Alzheimer disease (AD). However, MAPT-AV45 and FINGER studies had several methodological limitations: (1) the long-term impact of MI was not evaluated after interruption of the interventional program to test durability, (2) the sub-group size of MAPT-AV45 was relatively low, and (3) APOE ε4 status used in FINGER to define at-risk subjects for cognitive decline cannot be considered as a diagnosis biomarker of AD. To date, amyloid level assessed by PET and cerebrospinal fluid (CSF) measures of Aβ isoforms are the most widely used amyloid biomarkers. Screening by amyloid PET is difficult to generalize in real-world settings given its cost and limited access. Blood-based biomarkers are less invasive and cost-effective options for identification of at-risk subjects eligible for these non-pharmacological interventions [START_REF] Blennow | Biomarkers for Alzheimer's disease: current status and prospects for the future[END_REF]. Recent improvements in technologies used to assess amyloid blood levels have shown promising results [START_REF] Ovod | Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis[END_REF]. Several groups have showed that the blood Aβ42/40 ratio provides a sensitive and reliable measure of amyloid status, well correlated to CSF Aβ42/40, that can predict future brain amyloidosis (i.e., conversion to positive amyloid PET) [START_REF] Park | Chemically treated plasma Aβ is a potential blood-based biomarker for screening cerebral amyloid deposition[END_REF][START_REF] Vergallo | Plasma amyloid β 40/42 ratio predicts cerebral amyloidosis in cognitively normal individuals at risk for Alzheimer's disease[END_REF][START_REF] Schindler | High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis[END_REF]. These promising results suggest that plasma Aβ42/40 ratio could be used to detect amyloid plaques in individuals before cognitive symptoms onset. However, these markers still need to be validated in interventional studies for the selection of potential participants. In prevention trials, a blood Aβ42/40 test could be used as screening tool to identify at-risk subjects for AD and to facilitate pharmacological and non-pharmacological program discovery [START_REF] Verberk | Plasma amyloid as prescreener for the earliest Alzheimer pathological changes[END_REF][START_REF] De Rojas | Correlations between plasma and PET betaamyloid levels in individuals with subjective cognitive decline: the Fundació ACE Healthy Brain Initiative (FACEHBI)[END_REF][START_REF] Bateman | Plasma biomarkers of AD emerging as essential tools for drug development: an EU/US CTAD Task Force report[END_REF].

In a subgroup of the MAPT study, amyloid blood assays have been performed from the MAPT biobank to determinate amyloid status of the participants. These data are an opportunity to validate encouraging findings from MAPT-AV45 and to assess the possibility of such preventive trials based on blood biomarkers in the future. Moreover, two additional years of clinical observation were performed after completion of the MAPT interventional program to track durability of the intervention once discontinued. Therefore, we evaluated the longterm cognitive effect over a 36-month treatment period and at 60 months, 24 months after discontinuation of non-pharmacological intervention in the subgroup characterized by blood biomarkers.

Methods

Study design and participants

All subjects included in the present analysis were participants, from the MAPT and MAPT-PLUS studies, that were tested for amyloid blood biomarkers (Fig. 1). MAPT was a multicenter (13 memory centres in France and Monaco), randomized, placebo-controlled, 3-year trial whose objective was to assess effect of MI and omega 3 on cognitive performance. MAPT-PLUS was a 2-year observational and optional extension of MAPT after completion of the interventional program [START_REF] Vellas | MAPT study: a multidomain approach for preventing Alzheimer's disease: design and baseline data[END_REF]. The objective of MAPT-PLUS was to evaluate the long-term cognitive effect of MAPT preventive strategies. This additional follow-up was systematically offered to MAPT participants during the end-of-study visit.

Based on MAPT inclusion criteria, subjects included in the present analysis were non-demented, 70 years old and over, and fulfilled one of the following three criteria: spontaneous memory complaint, limitation in one instrumental activity of daily living, or slow gait speed [START_REF] Andrieu | Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebocontrolled trial[END_REF].

Randomization and masking

In MAPT, participants were randomly assigned (1:1:1:1) to the MI plus omega 3, MI plus placebo, omega 3 alone, or placebo alone group. A computer-generated randomization procedure was used with block sizes of eight and stratification by center. A clinical research assistant used a centralized interactive voice response system to identify which group to allocate the participant and which lot number to administer [START_REF] Andrieu | Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebocontrolled trial[END_REF]. All participants and research staff including neuropsychologists were blinded to omega 3 or placebo assignment and to amyloid blood status.

Procedures

Participants took two capsules of either the placebo or omega 3 daily. The active supplement used was V0137, an oil mixture containing natural fish oil with a minimum of 65% docosahexaenoic acid (400 mg) and a maximum of 15% eicosapentaenoic acid (no more than 112.5 mg). As described previously, MI program consisted of 12 2-h group sessions focusing on three domains (cognitive stimulation, demonstrations of physical activity, and nutritional advice) and a preventive consultation for the management of cardiovascular risks at baseline, 12 and 24 months [START_REF] Andrieu | Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebocontrolled trial[END_REF]. This interventional program lasted 3 years, and 2-year observational follow-up was added in MAPT-PLUS.

Clinical visits in MAPT and MAPT-PLUS were scheduled every 6 or 12 months to assess physical and functional conditions and adherence [START_REF] Vellas | MAPT study: a multidomain approach for preventing Alzheimer's disease: design and baseline data[END_REF]. Cognitive assessment included the Free and Cued Selective Reminding Test (FCSRT) [START_REF] Grober | Screening for dementia by memory testing[END_REF], the Controlled Oral Word Association Test and Category Naming Test (CNT) [START_REF] Cardebat | Formal and semantic lexical evocation in normal subjects. Performance and dynamics of production as a function of sex, age and educational level[END_REF], the Digit Symbol Substitution Subtest of the Wechsler Adult Intelligence Scale-Revised [START_REF] Wechsler | Wechsler adult intelligence scale-revised[END_REF], the Trail-Making Tests [START_REF] Reitan | Validity of the Trail Making Test as an indicator of brain damage[END_REF], the Mini-mental State Examination [START_REF] Folstein | Mini-mental state". A practical method for grading the cognitive state of patients for the clinician[END_REF], and the Clinical Dementia Rating scale (CDR) [START_REF] Hughes | A new clinical scale for the staging of dementia[END_REF]. Physical evaluation included the Short Physical Performance Battery (SPPB) [START_REF] Guralnik | Lower extremity function and subsequent disability: consistency across studies, predictive models, and value of gait speed alone compared with the short physical performance battery[END_REF] and Fried frailty criteria [START_REF] Fried | Frailty in older adults: evidence for a phenotype[END_REF]. Autonomy in daily living activities was evaluated by the Alzheimer's disease Cooperative Study-Activities of Daily Living Prevention Instrument (ADCS-ADL) [START_REF] Galasko | ADCS Prevention Instrument Project: assessment of instrumental activities of daily living for community-dwelling elderly individuals in dementia prevention clinical trials[END_REF]. One blood sample of 15 ml (10 ml in an EDTA vacutainer and a pair of × 2.5 ml in PAXgene RNA tubes) was collected yearly for the biobank. These samples were transferred directly at ambient temperature to the Cellular Biology and Cytology Laboratory at each site. The two PAXgene RNA tubes were frozen at -20° directly. The EDTA tube was centrifuged then aliquoted; the serum and the pellet were collected in two 5-ml dry tubes, then frozen at -20°. A biobank scientific committee has identified amyloid blood biomarkers as a research priority.

Plasma Aβ42/Aβ40 immunoprecipitation/mass spectrometry assay methods

Plasma samples of 0.46 ml were assessed to test plasma Aβ 42 and Aβ 40 levels by immunoprecipitation mass spectrometry as previously described [START_REF] Schindler | High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis[END_REF][START_REF] He | Cross-sectional and longitudinal associations between plasma neurodegenerative biomarkers and physical performance among community-dwelling older adults[END_REF]. Aβ levels were analyzed and calculated by integrated peak area ratios to known concentrations of the internal standards using the Skyline software package [START_REF] Pino | The Skyline ecosystem: informatics for quantitative mass spectrometry proteomics[END_REF].

Aβ42/Aβ40 cutoff (≤ 0.0107) has been defined, by Randall Bateman laboratory at Washington University School of Medicine in Saint-Louis, to discriminate as accurately as possible negative and positive amyloid participants in comparison to PET [START_REF] Raffin | Associations between physical activity, blood-based biomarkers of neurodegeneration, and cognition in healthy older adults: the MAPT study[END_REF]. Indeed, many subjects included in the present analysis (n = 233) were participants from MAPT-AV45 with amyloid PET (Fig. 1). In the MAPT-AV45 study, the positivity threshold for amyloid PET was set at SUVr > 1.17 [START_REF] Delrieu | Multidomain intervention and/or omega-3 in nondemented elderly subjects according to amyloid status[END_REF].

Adherence

For omega 3 supplementation and placebo, subjects were considered as adherent if they returned less than 25% of the prescribed capsules. For MI program, participants were considered as adherent if they attended at least 75% of the group sessions (if applicable) [START_REF] Andrieu | Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebocontrolled trial[END_REF].

Primary outcome and objectives

The primary outcome measure was the change in cognitive composite score after a 1, 3, and 5-year followup. We used a composite of four measures, close to the PACC (Preclinical Alzheimer Cognitive Composite), well established to show sensitivity to decline in early stages of AD [START_REF] Donohue | The preclinical Alzheimer cognitive composite: measuring amyloidrelated decline[END_REF]. The MAPT cognitive composite score has been already described previously [START_REF] Andrieu | Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebocontrolled trial[END_REF][START_REF] Delrieu | Multidomain intervention and/or omega-3 in nondemented elderly subjects according to amyloid status[END_REF][START_REF] Chhetri | Trajectory of the MAPT-PACC-preclinical Alzheimer cognitive composite in the placebo group of a randomized control trial: results from the MAPT study: lessons for further trials[END_REF]. This cognitive composite score was calculated by combining FCSRT, CNT, Digit Symbol Substitution Subtest, and MMSE orientation scores.

The main objectives were to assess according to amyloid blood status: (1) the cognitive effect of MAPT interventions at 12 and 36 months and (2) the long-term impact at 60 months after 2-year interruption of these interventions.

Statistical analysis

Analysis was completed in the intention-to-treat (ITT, n = 483) and per-protocol (n = 457) populations. The ITT population consisted of all randomly assigned participants who completed a cognitive composite score at baseline and a minimum of one post-baseline visit.

Per-protocol population excluded all major protocol violations at baseline and during follow-up [START_REF] Andrieu | Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebocontrolled trial[END_REF]. Efficacy in subgroups according to amyloid blood status was assessed by post-hoc analysis.

We used the same statistical method as for the work carried out to determine the cognitive effect of MAPT interventions according to PET amyloid status [START_REF] Delrieu | Multidomain intervention and/or omega-3 in nondemented elderly subjects according to amyloid status[END_REF]. Linear mixed-model repeated-measures analyses were used including baseline, 6, 12, 24, 36, 48, and 60-month follow-up data to assess between-group differences in the change in cognitive composite score from baseline to 12, 36, and 60 months. Time was used as a continuous variable, with a cubic trajectory, because the terms time 2 and time 3 were significant. For each linear mixed model, we included subject-specific random effects to consider the intra-subject correlation: a random intercept to consider the heterogeneity of the composite score at baseline and a random slope to consider the heterogeneity of the slopes between subjects. In the unadjusted linear mixed models, we included these fixed effects: intervention group by their amyloid blood status (8 categories), time, and interaction between group and time [START_REF] Delrieu | Multidomain intervention and/or omega-3 in nondemented elderly subjects according to amyloid status[END_REF]. Then, to test the difference of the effect of the intervention between the negative and positive amyloid blood groups, we used the estimates of the interaction term parameters with the ESTIMATE command from the SAS MIXED procedure.

All the models were completed with and without adjustments for gender, age, educational level, CDR global score, and APOE ε4 genotype. All p values were presented before and after adjustment for multiple comparisons (using the Hochberg procedure) and the statistical significance was set at a P value < 0.05. All confidence intervals were two-sided with a 95% confidence level. All statistical analyses were achieved using SAS software version 9.4 (SAS Institute Inc, Cary, NC).

Standard protocol approvals, registrations, and patient consents

The MAPT protocol is listed in a public-access clinical trial database (www. clini caltr ials. gov, no. NCT01513252). Written informed consent was given by all participants. A new informed consent form was signed by participants who volunteer for MAPT-PLUS during the end-of-study visit.

Data availability

The datasets generated and/or analyzed during this study are not publicly available. However, clinical data can be shared upon request following completion of the MAPT/DSA Data Access Application form (for further information contact the Data Sharing Alzheimer group: Info.u1027-dsa@inserm.fr).

Results

Enrollment and rates of study completion

Among the 1680 participants in MAPT and 1503 in its biobank, 483 amyloid blood assays were performed for this analysis at 12 (448 subjects) and 24 months (35 subjects). These subjects (n = 483) were selected from MAPT biobank based on their participation in the MAPT-AV45 study and an available blood sample as close as possible to the baseline visit. Subjects were enrolled in the MAPT biobank from October 2009. The mean time interval between blood collection and baseline visit is 12.99 ± 3.15 months. From 483 subjects included in this analysis, 323 subjects had observational data at 48 months and 299 at 60 months in MAPT-PLUS. The flow chart of participants in this analysis is shown in Fig. 2.

The ITT population included 161 positive and 322 negative amyloid subjects based on plasma Aβ42/Aβ40 ratio. In the ITT subgroup with positive amyloid blood status, 128 (79.5%) and 84 (52.2%) subjects completed respectively 36-and 60-month visits. In ITT subgroup with negative blood amyloid status, 273 (84.8%) and 215 (66.8%) subjects completed 36-and 60-month visits.

Baseline characteristics

Subjects who had amyloid blood assays (n = 483) were significantly older (on average 75.78 ± 4.55 vs. 75.15 ± 4.36 years, p = 0.0099), more frequently male (40.79 vs. 33.03%, p = 0.0026), APOE ε4 carriers (27.63 vs. 20.65%, p = 0.0.0047) and compliant to 3-year intervention (68.26 vs. 60.67%, p = 0.0045), had more frequently a CDR global score at 0.5 (47.00 vs. 40.08%, p = 0.0094), lower cognitive and functional performances respectively in composite cognitive (-0.10 ± 0.69 vs. 0.01 ± 0.67, p = 0.0017) and ADCS-ADL scores (39.13 ± 5.08 vs. 39.91 ± 4.66, p = 0.0035), than MAPT subjects not included in this analysis (n = 1196).

Baseline characteristics (clinical and blood-based biomarkers) of the ITT population are shown in Table 1. In the positive amyloid ITT population, the four groups are different in total SPPB (p = 0.0117) but not in the cognitive composite score (p = 0.4467, Table 1). In negative amyloid subjects, the four groups are different in plasma Aβ42/40 ratio (p = 0.0322) and DHA (p = 0.0310) but not in cognitive composite score (p = 0.6723, Table 1).

Cognitive impact of MAPT interventions at 12-, 36-, and 60-month visits

The main results are presented in Fig. 3 and Tables 2,3, and 4.

Positive amyloid group

In the positive amyloid ITT population (n = 161), we observed a positive effect of combined interventions (MI plus omega 3) on the change in composite cognitive score in 12 (raw p = 0.0350, 0.01917, 95% CI = [0.0136 to 0.3699]) and 36 months (raw p = 0.0357, 0.2818, 95% CI = [0.0190 to 0.5446]). After correction of multiple comparisons and adjustments, these differences were not significant (adjusted p = 0.1144 and 0.0690). In the per-protocol population (n = 154), showed a significant cognitive effect at 12 (adjusted p = 0.0313, 0.2424, 95% CI = [0.0571 to 0.4276]) and 36 months (adjusted p = 0.0195, 0.3747, 95% CI = [0.1055 to 0.6439]) in favor of MI plus omega 3 group that persisted after adjustments and correction of multiple comparisons (Table 2). To assess if the interventional effect was durable after 2-year interruption of the interventional program, we tested at 60 months. In both ITT and per-protocol populations, we did not observe a remaining effect at 60 months between the three interventional (MI plus omega 3, omega 3 alone, MI alone) and placebo groups (Table 3).

Negative amyloid group

In the ITT and per-protocol populations (respectively n = 322 and n = 303), no cognitive difference was observed on cognitive composite score change at 12, 36, and 60 months for any of the three interventional groups in comparison to placebo group.

Comparison of cognitive impact between negative and positive amyloid subjects

In the ITT population, we showed a non-significant trend in the impact of the MI plus omega 3 on the cognitive composite score at 12 and 36 months for the positive amyloid group in comparison to the negative amyloid group (respectively adjusted p = 0.1282/0.0584, 0.1693/0.3067, 95% CI = [-0.0490 to 0.3875]/[-0.0110 to 0.6244]). This difference was significant in the per-protocol population at 36-month visit (adjusted p = 0.0269, 0.3695, 95% CI = [0.0424 to 0.6967]). There was no difference for the three interventional groups on cognitive composite score between the positive and negative amyloid groups at 60-month visit (Table 4).

Discussion

This work suggests a significant benefit of combined interventions at 1 and 3 years only in the amyloid positive group. These effects were significant both in magnitude and statistically in the per protocol population. These findings indicate that future prevention trials could target amyloid positive non-demented individuals for interventions utilizing multi-domains. We have demonstrated the utility of a blood-based biomarker to determine amyloid status of individuals likely to respond to intervention. This could enable future prevention trials to have more rapid screening and to enroll many more positive amyloid participants. The blood-based biomarker also enables prevention trials in regions without access to amyloid PET or CSF analyses. We failed to reach significantly different cognitive effect of a prevention program in non-demented subjects according to amyloid blood status at 5 years, after 2 years off treatment, demonstrating that the intervention effect is not durable after 2-year discontinuation.

Previously, in MAPT-AV45, we showed a cognitive impact of MI at 36 months in subjects with a positive amyloid PET and an association between MI and amyloid burden (lower in participants receiving MI) [START_REF] Delrieu | Multidomain intervention and/or omega-3 in nondemented elderly subjects according to amyloid status[END_REF][START_REF] Hooper | Cortical β-amyloid in older adults is associated with multidomain interventions with and without omega 3 polyunsaturated fatty acid supplementation[END_REF]. Our findings confirm the potential cognitive benefit of non-pharmacological prevention strategies as MI in subjects with early AD. One of the main goals of prevention and precision medicine in AD is to deliver diagnosis and prevention "tailored" to the biological characteristics of cognitive unimpaired individuals [START_REF] Molinuevo | Current state of Alzheimer's fluid biomarkers[END_REF]. Amyloid PET is proposed to be part of precision medicine [START_REF] Hampel | A Precision Medicine Initiative for Alzheimer's disease: the road ahead to biomarker-guided integrative disease modeling[END_REF] but bloodbased biomarkers are potentially more cost-efficient and accessible tools in real-world settings and thus could be promising screening exams in a prevention and precision strategy.

Strengths

The strengths of our ancillary study were the long duration of interventional and observational periods. The implementation of an observational period after completion of interventional program allowed to assess long-term cognitive effect and its potential durability. In our knowledge, this work is the first analysis-to date-that assessed cognitive effect of a non-pharmacological intervention considering amyloid status defined by blood-based biomarkers.

Limitations

Our study has several limitations. First, the sample size is limited given that 483 subjects were divided into 8 groups. Second, amyloid blood biomarkers were not performed at the baseline visit, but in 12 (n = 448) and 24 months (n = 35). As in MAPT-AV45 study, we hypothesized that amyloid status does not change during follow-up and the risk of amyloid status misclassification is relatively low marginal in the present analysis [START_REF] Delrieu | Multidomain intervention and/or omega-3 in nondemented elderly subjects according to amyloid status[END_REF]. Third, the sensitivity and specificity of the plasma amyloid cutoff (≤ 0.0107) were 43.3% and 79.4% respectively with an area under curve (AUC) of 0.634 in comparison to amyloid PET. This AUC is relatively poor and potentially related to the time interval between blood test and amyloid PET scan. Kappa coefficient was 0.2365 (95% CI = 0.1126-0.3605) between amyloid blood ratio and amyloid PET. Most blood biomarkers were performed at 1-year visit while PET scans were performed all along the MAPT follow-up. Also, it is known that amyloid blood tests become positive about 5 years before amyloid PET scans [START_REF] Schindler | High-precision plasma β-amyloid 42/40 predicts current and future brain amyloidosis[END_REF], and this could account for some discrepancy. Another limitation in using blood biomarkers is that the difference in amyloid ratio between positive and negative groups is relatively small (10-15%) potentially due to dilution of Aß from central nervous system to peripheral compartment. Thus, inter-assay variability and accuracy of the measurement may significantly contribute to decrease in AUC. Participants were not blinded to MI. It is possible some of difference between the MI plus omega 3 and placebo was attributable to the fact that participants knew whether or not the MI was given [START_REF] Delrieu | Multidomain intervention and/or omega-3 in nondemented elderly subjects according to amyloid status[END_REF][START_REF] Hooper | Cortical β-amyloid in older adults is associated with multidomain interventions with and without omega 3 polyunsaturated fatty acid supplementation[END_REF]. It is also noted that the analysis of subjects according to amyloid blood status was not pre-specified in the statistical analysis plan and was only exploratory.

Conclusions

Considering the mentioned limitations, these results show a consistent pattern in favor of a MI effect in positive amyloid subjects. A new model of services in dementia prevention may need to be developed and Blood biomarkers could offer opportunities to screen non-demented subject in future prevention programs and also detect brain amyloidosis in subjects with memory complaint in primary care [START_REF] Molinuevo | Current state of Alzheimer's fluid biomarkers[END_REF]. Other blood tests could be evaluated to select subjects eligible for prevention programs. Subjects with a positive ptau blood test have also the potential to respond to prevention programs such as MI. These promising results need to be confirmed in others prevention studies prior their use in prevention trials and general practice [START_REF] Richard | Prevention of dementia by intensive vascular care (PreDIVA): a cluster-randomized trial in progress[END_REF][START_REF] Rosenberg | Multidomain interventions to prevent cognitive impairment, Alzheimer's disease, and dementia: from FINGER to World-Wide FINGERS[END_REF]

Fig. 1

 1 Fig. 1 Place of the amyloid plasma analysis in relation to MAPT and ancillary studies. MAPT multidomain Alzheimer's preventive trial

Fig. 2

 2 Fig. 2 Trial profile for the amyloid blood MAPT study. MAPT multidomain Alzheimer's preventive trial, MI multidomain intervention

  Male gender, N (%) 40 (42.55)

Fig. 3

 3 Fig. 3 Mean change from baseline in composite cognitive score over 60 months (intention-to-treat population, n = 483). MI multidomain intervention, Amyloid+ positive amyloid status, Amyloid -negative amyloid status

Table 1

 1 Baseline characteristics of the intention-to-treat ancillary amyloid blood MAPT study population (n ADCS-ADL Alzheimer's Disease Cooperative Study-activities of daily living, CDR Clinical Dementia Rating score, CNT Naming Test, COWAT Controlled Oral Word Association Test, DHA docosahexaenoic acid, FCSRT Free and Cued Selective Reminding Test, GDS Geriatric Depression Scale, MMSE Mini-Mental State Examination, SPPB Short Physical Performance Battery, TMT Trail Making Test

	= 483)

Table 2

 2 Estimated mean difference in 1-and 3-year change from baseline on composite Z score for the three intervention groups compared to the placebo group

Groups Estimated mean change from baseline (95% CI) Estimated mean between-group difference in change from baseline (95% CI) Unadjusted Adjusted a vs. placebo Raw P Hochberg P vs. placebo Raw P

	Hochberg P		0.1144	0.9338	0.4306	-		0.9306	0.9306	0.9306	-			0.0313	0.9383	0.4428	-		0.9902	0.9902	0.9902	-			0.0690	0.3353	0.3353	-		0.9684	0.9684	0.9684	-
			0.0381	0.9338	0.2153	-		0.7578	0.8445	0.9306	-			0.0104	0.9383	0.2214	-		0.7298	0.9902	0.7947	-			0.0230	0.3353	0.2808	-		0.9684	0.8443	0.8512	-
			0.1891 (0.0104; 0.3679)	0.0074 (-0.1676; 0.1824)	0.1113 (-0.0649; 0.2874)	-		0.0199 (-0.1066; 0.1463)	0.0135 (-0.1584; 0.1879)	0.0058 (-0.1244; 0.1360)	-			0.2424 (0.0571; 0.4276)	0.0070 (-0.1705; 0.1845)	0.1132 (-0.0684; 0.2949)	-		0.0229 (-0.1073; 0.1532)	-0.0009 (-0.1375; 0.1358)	0.0178 (-0.1165; 0.1522)	-			0.3030 (0.0420; 0.5640)	0.1257 (-0.1305; 0.3820)	0.1399 (-0.1148; 0.3946)	-		-0.0037 (-0.1861; 0.1787)	0.0194 (-0.1748; 0.2136)	0.0180 (-0.1710; 0.2071)	-
			0.1049	0.7669	0.2519	-		0.9950	0.9950	0.9950	-			0.0302	0.7437	0.2540	-		0.8971	0.8971	0.8971	-			0.1071	0.3461	0.3461	-		0.9352	0.9352	0.9352	-
			0.0350	0.7669	0.1260	-		0.7050	0.9950	0.8494	-			0.0101	0.7437	0.1270	-		0.6611	0.8971	0.7735	-			0.0357	0.3461	0.1907	-		0.9352	0.7775	0.9206	-
			0.1917 (0.0136; 0.3699)	0.0259 (-0.1458; 0.1976)	0.1361 (-0.0383; 0.3106)	-		0.0241 (-0.1007; 0.1488)	0.0004 (-0.1325; 0.1334)	0.0124 (-0.1162; 0.1411)	-			0.2435 (0.0582; 0.4287)	0.0291 (-0.1455; 0.2037)	0.1402 (-0.0400; 0.3203)	-		0.0288 (-0.1002; 0.1578)	-0.0089 (-0.1443; 0.1265)	0.0195 (-0.1136; 0.1526)	-			0.2818 (0.0190; 0.5446)	0.1219 (-0.1322; 0.3760)	0.1698 (-0.0849; 0.4244)	-		-0.0075 (-0.1890; 0.1740)	0.0278 (-0.1657; 0.2213)	0.0096 (-0.1791; 0.1982)	-
	1-year ITT MAPT analysis (n = 483)	Positive plasma Aβ42/40	Multidomain plus omega 3 0.1392 (0.0018; 0.2765)	Omega 3 alone -0.0267 (-0.1555; 0.1022)	Multidomain plus placebo 0.0835 (-0.0490; 0.2160)	Placebo -0.0526 (-0.1661; 0.0609)	Negative plasma Aβ42/40	Multidomain plus omega 3 -0.0059 (-0.0884; 0.0765)	Omega 3 alone -0.0296 (-0.1240; 0.0648)	Multidomain plus placebo -0.0176 (-0.1058; 0.0707)	Placebo -0.0300 (-0.1236; 0.0636)	1-year per-protocol MAPT analysis (n = 457)	Positive plasma Aβ42/40	Multidomain plus omega 3 0.1880 (0.0440; 0.3319)	Omega 3 alone -0.0264 (-0.1564; 0.1036)	Multidomain plus placebo 0.0846 (-0.0527; 0.2220)	Placebo -0.0555 (-0.1721; 0.0611)	Negative plasma Aβ42/40	Multidomain plus omega 3 -0.0022 (-0.0894; 0.0850)	Omega 3 alone -0.0399 (-0.1363; 0.0565)	Multidomain plus placebo -0.0115 (-0.1046; 0.0816)	Placebo -0.0310 (0.1261; 0.0641)	3-year ITT MAPT analysis (n = 483)	Positive plasma Aβ42/40	Multidomain plus omega 3 -0.0931 (-0.2959; 0.1098)	Omega 3 alone -0.2530 (-0.4443; -0.0616)	Multidomain plus placebo -0.2051 (-0.3972; -0.0130)	Placebo -0.3749 (-0.5420; -0.2077)	Negative plasma Aβ42/40	Multidomain plus omega 3 -0.1527 (-0.2729; -0.0325)	Omega 3 alone -0.1173 (-0.2550; 0.0203)	Multidomain plus placebo -0.1356 (-0.2663; -0.0049)	Placebo -0.1452 (-0.2812; -0.0091)

Table 2 (continued) Groups Estimated mean change from baseline (95% CI) Estimated mean between-group difference in change from baseline (95% CI) Unadjusted Adjusted a vs. placebo Raw P Hochberg P vs. placebo Raw P

 2

	Hochberg P

a Analysis adjusted for age, sex, level of education, APOE ε4 genotype, and clinical dementia rating global score

Table 3

 3 Estimated mean difference in 5-year change from baseline on composite Z score for the three intervention groups compared to the placebo group ITT Intention-to-treat, MAPT Multidomain Alzheimer Prevention Trial a Analysis adjusted for age, sex, level of education, APOE ε4 genotype, and clinical dementia rating global score

	Groups	Estimated mean change
		from baseline (95% CI)

Estimated mean between-group difference in change from baseline (95% CI)

			Unadjusted			Adjusted a
			vs. placebo	Raw P	Hochberg P vs. placebo	Raw P Hochberg P
	5-year ITT MAPT-PLUS analysis (n = 483)
	Positive plasma Aβ42/40
	Multidomain plus omega 3	-0.2023 (-0.4824; 0.0778)	0.2575 (-0.1004; 0.6154)	0.1579	0.4737	0.2501 (-0.1071; 0.6073) 0.1691 0.5074
	Omega 3 alone	-0.4255 (-0.6705; -0.1804)	0.0343 (-0.2969; 0.3656)	0.8385	0.8385	-0.0288 (-0.3665; 0.3089) 0.8666 0.8666
	Multidomain plus placebo	-0.5209 (-0.7967; -0.2451)	-0.0611 (-0.4157; 0.2935) 0.7348	0.8385	-0.1582 (-0.5153; 0.1988) 0.3838 0.7676
	Placebo	-0.4598 (-0.6827; -0.2369)	-	-	-	-	-	-
	Negative plasma Aβ42/40
	Multidomain plus omega 3	-0.2870 (-0.4388; -0.1351)	-0.0358 (-0.2674; 0.1959) 0.7614	0.7614	-0.0237 (-0.2569; 0.2095) 0.8415 0.8415
	Omega 3 alone	-0.1524 (-0.3287; 0.0239)	0.0988 (-0.1495; 0.3471)	0.4339	0.7614	0.1008 (-0.1489; 0.3506) 0.4271 0.8415
	Multidomain plus placebo	-0.3384 (-0.5079; -0.1690)	-0.0872 (-0.3307; 0.1563) 0.4812	0.7614	-0.1107 (-0.3563; 0.1348) 0.3753 0.8415
	Placebo	-0.2512 (-0.4261; -0.0763)	-	-	-	-	-	-
	5-year per-protocol MAPT-PLUS analysis (n = 457)
	Positive plasma Aβ42/40
	Multidomain plus omega 3	-0.1315 (-0.4197; 0.1567)	0.3233 (-0.0453; 0.6919)	0.0853	0.2560	0.3202 (-0.0475; 0.6880) 0.0876 0.2628
	Omega 3 alone	-0.4254 (-0.6737; -0.1771)	0.0293 (-0.3090; 0.3677)	0.8645	0.8645	-0.0423 (-0.3874; 0.3028) 0.8095 0.8095
	Multidomain plus placebo	-0.5349 (-0.8177; -0.2522)	-0.0802 (-0.4445; 0.2842) 0.6652	0.8645	-0.1807 (-0.5482; 0.1868) 0.3340 0.6679
	Placebo	-0.4548 (-0.6846; -0.2250)	-	-	-	-	-	-
	Negative plasma Aβ42/40
	Multidomain plus omega 3	-0.2920 (-0.4524; -0.1315)	-0.0378 (-0.2773; 0.2016) 0.7559	0.7559	-0.0277 (-0.2686; 0.2132) 0.8209 0.8209
	Omega 3 alone	-0.1613 (-0.3418; 0.0192)	0.0929 (-0.1605; 0.3462)	0.4710	0.7559	0.0825 (-0.1719; 0.3370) 0.5234 0.8209
	Multidomain plus placebo	-0.3388 (-0.5183; -0.1593)	-0.0847 (-0.3373; 0.1679) 0.5098	0.7559	-0.1017 (-0.3570; 0.1536) 0.4334 0.8209
	Placebo	-0.2541 (-0.4319; -0.0764)	-	-	-	-	-	-

Table 4

 4 Estimated mean difference between positive and negative participants in 1-, 3-, and 5-year change from baseline on composite Z score for each intervention group compared to the control group

ITT Intention-to-treat, MAPT Multidomain Alzheimer Prevention Trial a Analysis adjusted for age, sex, level of education, APOE ε4 genotype, and clinical dementia rating global

score Groups Estimated difference between positive and negative subjects for each intervention group (95% CI)

	to update the health offer with more efficient access to
	blood AD biomarkers and prevention program as MI.
				P value
		Unadjusted	Adjusted a	Unadjusted	Adjusted a
	1-year ITT MAPT analysis (n = 483)
	Multidomain plus omega 3	0.1677 (-0.0498; 0.3852)	0.1693 (-0.0490; 0.3875)	0.1305	0.1282
	Omega 3 alone	0.0255 (-0.1916; 0.2427)	-0.0060 (-0.2277; 0.2156)	0.8176	0.9573
	Multidomain plus placebo	0.1237 (-0.0931; 0.3404)	0.1055 (-0.1145; 0.3255)	0.2629	0.3467
	1-year per-protocol MAPT analysis (n = 457)
	Multidomain plus omega 3	0.2147 (-0.0110; 0.4404)	0.2194 (-0.0061; 0.4450)	0.0623	0.0565
	Omega 3 alone	0.0380 (-0.1830; 0.2590)	0.0079 (-0.2170; 0.2327)	0.7356	0.9453
	Multidomain plus placebo	0.1207 (-0.1033; 0.3446)	0.0954 (-0.1311; 0.3219)	0.2904	0.4084
	3-year ITT MAPT analysis (n = 483)
	Multidomain plus omega 3	0.2893 (-0.0301; 0.6087)	0.3067 (-0.0110; 0.6244)	0.0757	0.0584
	Omega 3 alone	0.0941 (-0.2253; 0.4135)	0.1063 (-0.2169; 0.4295)	0.5628	0.5181
	Multidomain plus placebo	0.1602 (-0.1567; 0.4771)	0.1218 (-0.1969; 0.4406)	0.3209	0.4528
	3-year per-protocol MAPT analysis (n = 457)
	Multidomain plus omega 3	0.3442 (0.0137; 0.6747)	0.3695 (0.0424; 0.6967)	0.0413	0.0269
	Omega 3 alone	0.1038 (-0.2201; 0.4277)	0.1186 (-0.2078; 0.4450)	0.5291	0.4754
	Multidomain plus placebo	0.1452 (-0.1810; 0.4714)	0.1008 (-0.2261; 0.4277)	0.3819	0.5447
	5-year ITT MAPT-PLUS analysis (n = 483)
	Multidomain plus omega 3	0.2932 (-0.1331; 0.7196)	0.2738 (-0.1520; 0.6997)	0.1768	0.2065
	Omega 3 alone	-0.0645 (-0.4785; 0.3495)	-0.1297 (-0.5534; 0.2940)	0.7592	0.5471
	Multidomain plus placebo	0.0261 (-0.4040; 0.4563)	-0.0475 (-0.4828; 0.3878)	0.9049	0.8300
	5-year per-protocol MAPT-PLUS analysis (n = 457)
	Multidomain plus omega 3	0.3611 (-0.0784; 0.8006)	0.3479 (-0.0903; 0.7861)	0.1069	0.1191
	Omega 3 alone	-0.0635 (-0.4862; 0.3591)	-0.1248 (-0.5571; 0.3075)	0.7674	0.5701
	Multidomain plus placebo	0.0045 (-0.4389; 0.4478)	-0.0790 (-0.5277; 0.3697)	0.9841	0.7292

  . Using blood biomarkers as a tool for cognitive interventions may be valuable and this work may help open that door for future trials. Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design; Analysis or interpretation of data. BV: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design; Analysis or interpretation of data. SG: Major role in the acquisition of data; Analysis or interpretation of data. CC: Study concept or design; Analysis or interpretation of data, statistical analyses. Statistical Analyses: Christelle Cantet, MS (Toulouse University) performed the statistical analyses of this manuscript. VO: Major role in the acquisition of data; Analysis or interpretation of data. YL: Major role in the acquisition of data; Analysis or interpretation of data. JB: Major role in the acquisition of data; Analysis or interpretation of data. RB: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Analysis or interpretation of data. SA: Drafting/revision of the manuscript for content, including medical writing for content; Major role in the acquisition of data; Study concept or design; Analysis or interpretation of data. Funding The MAPT study was supported by grants from the Gérontopôle of Toulouse, the French Ministry of Health (PHRC 2008, 2009), the Pierre Fabre Research Institute (manufacturer of the polyunsaturated fatty acid supplement), Exonhit Therapeutics, and Avid Radiopharmaceuticals. The blood amyloid-beta measures were supported by departmental funds (RJB) and the NIH National Institute on Aging grants R56AG061900 and RF1AG061900 (R.J. Bateman, PI), The promotion of this study was supported by the University Hospital Center of Toulouse. The data sharing activity was supported by the Association Monegasque pour la Recherche sur la maladie d' Alzheimer (AMPA) and the INSERM-University of Toulouse III UMR 1027 Research Unit. We are indebted to the investigators from the University Hospital of Toulouse, Hôpital de Tarbes, Hôpital de Foix, Hôpital de Castres, the University Hospital of Limoges, the University Hospital of Bordeaux, Hôpital de Lavaur, the University Hospital of Montpellier, Hôpital de Montauban, and the University Hospital of Nice for their participation in this study. The funders had no role in study design; data collection, analysis, or interpretation; or writing of the article. at baseline visit. Participants in MAPT-PLUS gave separate written consent for clinical follow-up.

	Authors' contributions JD: Consent for publication Not applicable.

Acknowledgements

MAPT Study Group

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available. However, clinical and blood biomarkers data can be shared by request via « Application for Access to the MAPT Database» (for further information contact of the Data Sharing Alzheimer group: Info.u1027-dsa@ inserm.fr).

Abbreviations

AD

• fast, convenient online submission

• thorough peer review by experienced researchers in your field

• rapid publication on acceptance

• support for research data, including large and complex data types

• gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year

•

At BMC, research is always in progress.

Learn more biomedcentral.com/submissions

Ready to submit your research Ready to submit your research ? Choose BMC and benefit from: ? Choose BMC and benefit from:

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Irene Lorenzini

Léo Varnet

Christian Lorenzi

Laurianne Cabrera

DEVELOPMENT OF AUDITORY SENSITIVITY TO AMPLITUDE MODULATION CUES: SENSORY AND COGNITIVE DETERMINANTS AND RELATIONSHIP WITH SPEECH INTELLIGIBILITY

Keywords: Auditory development, Speech perception development, Amplitude Modulation detection, Speech-innoise perception development

Speech sounds convey relatively slow Amplitude Modulation cues whose processing plays a crucial role for speech comprehension. However, the development of AM processing and its interaction with speech intelligibility remains unclear. Previous studies suggested that AM processing development relates to changes in the central filtering of AM cues or in 'processing efficiency' (i.e., a reduction in internal noise and/or improvements in the optimality of decision making). Here, we explored the contribution of (i) the ability to combine AM cues over time (temporal integration), (ii) response consistency for AM detection, on children's in-noise consonant discrimination. Temporal integration developed until 11 years. Response consistency in AM detection also increased with age. Temporal integration at higher AM rates and AM detection consistency were statistically related to identification thresholds in noise for a subset of the tested consonants. Children vocabulary was not a better predictor of speech intelligibility compared to the measures of AM processing. Overall, the development of AM processing and its interaction with speech intelligibility may result from changes in (central) processing efficiency for AM.

INTRODUCTION

The human auditory system extracts and represents the audio-frequency components of complex sounds and their changes over time [START_REF] Moore | An introduction to the psychology of hearing[END_REF]. Slow and fast changes in amplitudeamplitude modulation (AM) or "temporal envelope" cuesconvey information about syllabic and phonetic information, as well as voice pitch and supra-segmental information [START_REF] Rosen | Temporal information in speech: acoustic, auditory and linguistic aspects[END_REF]. Both slow and fast AM cues are commonly assumed to be extracted by broadly tuned modulation filters located at central (i.e., post-cochlear) stages of the human auditory system [START_REF] Dau | Modeling auditory processing of amplitude modulation. I. Detection and masking with narrow-band carriers[END_REF]. The specific tuning characteristics of these AM filters (i.e., their bandwidth) explain masking or interference effects demonstrated repeatedly in the AM domain [START_REF] Dau | Modeling auditory processing of amplitude modulation. I. Detection and masking with narrow-band carriers[END_REF]. The ability to detect and combine these slow and fast AM cues over time (also called 'temporal integration') by human listeners is currently understood as resulting from the limitations introduced by internal variabilityalso called 'internal noise'and the operation of (late) decision-making mechanisms based on template matching [START_REF] Dau | Modeling auditory processing of amplitude modulation. II. Spectral and temporal integration[END_REF]. The relative importance of slow and fast AM for speech perception in adult listeners has been addressed in a wealth of studies. Overall, it has been shown that adults rely on the slowest AM cues (< 16 Hz) to identify speech sounds in 2 quiet. However, they do require the faster AM cues (> 16 Hz) to correctly perceive speech in complex backgrounds such as noise of competing voices [START_REF] Eaves | Benefit of temporal fine structure to speech perception in noise measured with controlled temporal envelopes[END_REF]. More recently, speech perception in noise has been shown to be highly dependent on the ability to detect and use the AM cues of speech that interfere with the AM cues of a masking sound [START_REF] Stone | Notionally steady background noise acts primarily as a modulation masker of speech[END_REF]. Indeed, [START_REF] Stone | Notionally steady background noise acts primarily as a modulation masker of speech[END_REF] showed that 'notionally' steady-state noise maskers with high levels of intrinsic random envelope fluctuations lead to a greater reduction in the intelligibility of speech compared to noise maskers with lower inherent fluctuations. In the same vein, speech intelligibility in noise for cochlear-implant adult users is significantly correlated with the basic ability to detect AM [START_REF] Won | Acoustic temporal modulation detection and speech perception in cochlear implant listeners[END_REF].

Interestingly, these two abilitiesauditory temporal processing and speech perception in noiseboth take a long time to reach adult-levels. A pioneering study compared children's sensitivity to AM at several rates (the 'temporal modulation transfer function' or TMTF) between 4 and 10 years of age using a broadband noise carrier [START_REF] Hall | Development of temporal resolution in children as measured by the temporal modulation transfer function[END_REF]. This study indicated that AM detection thresholds (i.e., AM sensitivity) improve with age and reach adult-like levels around 9 years. Moreover, the ability to track AM (measured using nonspeech sounds) constrains children's capacity in perceiving speech in noise [START_REF] Stuart | Development of auditory temporal resolution in school-age children revealed by word recognition in continuous and interrupted noise[END_REF]. Thus, poor speech perception in noise in childhood may relate to poor auditory temporal processing. In a series of experimental and computational studies, we explored the development of AM processing from 5 to 11 years of age and investigated the development of sensory vs cognitive determinants in sensitivity to AM.

In a first study [START_REF] Cabrera | Development of temporal auditory processing in childhood: Changes in efficiency rather than temporalmodulation selectivity[END_REF], we evaluated AM sensitivity at slow AM rates (4, 8, and 32 Hz) and masking effects produced by the intrinsic random AM fluctuations conveyed by a narrowband noise carrier on auditory detection of a sinusoidal AM target. This was achieved to assess whether children have broader modulation filters than adults. In a second experiment [START_REF] Cabrera | Temporal integration for amplitude modulation in childhood: Interaction between internal noise and memory[END_REF], we evaluated temporal integration in the AM domain, that is the improvement of AM detection thresholds with increasing number of AM cycles, to assess whether children have less efficient central mechanisms involved in decision making than adults. In a third experiment [START_REF] Lorenzini | Reduced processing efficiency impacts auditory detection of amplitude modulation in children: evidence from an experimental and modelling study[END_REF], we measured response consistency in AM detection over trial-to-trial when listeners were tested at detection thresholds and when sinusoidal AM targets were presented with a narrow-band noise carrier. This final study was achieved to assess whether children have higher internal noise in the AM domain than adults. Overall, the results indicated that children of 5-6 years show the worst AM detection thresholds in all conditions and that children from 5-to-11 years show worse thresholds than young adults. Nevertheless, all age groups showed: 1) similar effects of AM rate and carrier, that is, better thresholds with increasing modulation rates and when presented with a deterministic tone carrier compared to narrow-band noise carriers, 2) similar temporal integration effects, that is, better AM detection thresholds with increasing number of AM cycles. Importantly, age-related changes were also observed on AM detection consistency. To better understand these developmental trends in AM detection, we developed a series of computational models of human auditory processing based on the modulationfilterbank and template-matching concepts [START_REF] Dau | Modeling auditory processing of amplitude modulation. I. Detection and masking with narrow-band carriers[END_REF], [START_REF] Dau | Modeling auditory processing of amplitude modulation. II. Spectral and temporal integration[END_REF], [START_REF] Wallaert | Sensorineural hearing loss enhances auditory sensitivity and temporal integration for amplitude modulation[END_REF], [START_REF] Attia | Double-pass consistency for amplitude-and frequency-modulation detection in normal-hearing listeners[END_REF], to simulate the AM detection thresholds of children and adults in each condition. Two sets of model parameters were manipulated to distinguish the role of sensory factors (i.e., selectivity of modulation filters) and processing efficiency: 1) the effects of internal-noise sources modelled as additional gaussian noises at the output of modulation filters, and 2) sub-optimal decision strategies modelled as template matching operating on the noisy output of modulation filters. In these three studies, modelling revealed that age-related changes in AM detection were better simulated by changes in processing efficiency (maturation of internal noise and template matching strategy) rather than changes in sensory processing (i.e. maturation of modulation filtering).

In those studies, we also measured consonant-identification thresholds in a steady-state speech-shaped noise and performance for receptive vocabulary for all children. Consonant-identification thresholds improved in parallel with AM detection thresholds. Moreover, preliminary analyses showed that AM detection thresholds for 8-Hz target modulations, but not receptive vocabulary skills, were significantly predictive of consonant-identification thresholds in noise for place of articulation contrasts of fricative consonants. The goal of the present paper was to explore thoroughly the relationship between the different measures of AM processing and consonant-in-noise identification in children. In a first experiment, we investigated the relationship between temporal integration capacities and fricative consonant identification in noise. In a second experiment, we considered the relationship between AM detection consistency and identification thresholds of three phonetic features (voicing, place and manner of articulation) in noise. We hypothesized that measures of auditory AM processing related to processing efficiency, may be more strongly associated with speech-in-noise abilities compared to a measure of general linguistic development.

Participants

English-speaking children from 5 to 11 years were recruited in this experiment (see also [START_REF] Cabrera | Development of temporal auditory processing in childhood: Changes in efficiency rather than temporalmodulation selectivity[END_REF], [START_REF] Cabrera | Temporal integration for amplitude modulation in childhood: Interaction between internal noise and memory[END_REF]). Consent forms were obtained from parents as approved by the university ethics committee (Project ID: 8107/001). All children included in these experiments had normal hearing (absolute thresholds for pure tones between 0.25 and 8 kHz < 20 dB HL) and typical cognitive development (reported by teachers and the block subtest from the Wechsler Intelligence Scale for Children evaluating non-verbal reasoning [START_REF] Wechsler | Wechsler preschool and primary scale of intelligence-fourth edition[END_REF]). Twenty-one 5-6-year-old children (10 females; mean age = 5.7 years, SD = 0.4), 27 7-8-year-olds (13 females; mean age = 7.8 years, SD = 0.5) and 24 10-11-year-olds (12 females; mean age = 10.7, SD = 0.4) were included in the data analyses.

AM tasks

Stimuli

In order to assess temporal integration for AM detection, four experimental conditions were designed to manipulate AM rate (4 vs 32 Hz) and number of modulation cycles (2 vs 8 cycles) see [START_REF] Cabrera | Temporal integration for amplitude modulation in childhood: Interaction between internal noise and memory[END_REF]. These manipulations involved changes in sound duration such that the target sounds modulated at 32 Hz were 62.5-and 250-ms long in the 2 and 8-cycle conditions, respectively, and the target sounds modulated at 4 Hz were 500-and 2000-ms long in the 2 and 8-cycle conditions, respectively. The carriers were sinusoidal tones centred at 1027 Hz, generated with a random starting phase. All stimuli were generated at a sampling frequency of 44.1 kHz. In all conditions, the stimuli included 50-ms raisedcosine onset/offset ramps. Standard sounds were not modulated in amplitude, and target sounds were modulated at depths m ranging from m=100% to m=1% in 20 steps of 2 dB. The starting phase of the modulation was randomized on each trial.

Procedure

In each of the four AM conditions, AM detection thresholds were measured using a three-interval, three-alternative forced-choice (3I-3AFC) adaptive procedure. Stimuli were presented using headphones (Sennheiser HD 25-SP II) at 65 dB SPL. Responses were collected using a touch-screen tablet. On each trial, three animal characters appeared on the screen and produced a sound one after the other. The interstimulus interval was 500 ms and inter-trial intervals 600 ms.

Children were asked to select the odd-one out, corresponding to the modulated target. Visual and corrective feedback was presented on each trial. A first one-down, one-up rule was used until the first reversal followed by a 2-down 1-up adaptive procedure [START_REF] Levitt | Transformed up-down methods in psychoacoustics[END_REF]. The first trial was modulated at m = 100%. The first step size was 6 dB, reduced to 4 dB after the first reversal, and to 2 dB after the second reversal. The run stopped after the 8th reversal or after 32 trials. One estimate was collected for each participant in each condition and a second estimate was collected if fewer than 5 reversals were obtained, or if the track did not converge. The threshold in dB was the geometric mean of the last four reversals. Estimates of temporal integration were calculated as the difference between threshold obtained in the 8-cycle and 2cycle condition at each modulation rate. The outcome variable analyzed was the average of these two scores for each participant standardized by age.

Speech-in-noise tasks

Stimuli

Three native Southern British English speakers were recorded: one male (F0 = 112 Hz) and two females (F0 = 153 and 160 Hz). /aCa/ syllables were selected, where C = /f/, /v/, /ʃ/, /ʒ/, /s/, /z/. A steady speech-shaped noise masker was generated with long-term spectrum similar to the spectrum of the syllables uttered by the female voices. Two phonetic conditions were presented to participants: one presenting a minimal change in voicing /f/-/v/, /s/-/z/, /ʃ/-/ʒ/ ('Voicing' condition), and one presenting a minimal change in place of articulation /f/-/s/, /v/-/z/, /s/-/ʃ/, /z/-/ʒ/ ('Place of articulation' condition) see [START_REF] Cabrera | Development of temporal auditory processing in childhood: Changes in efficiency rather than temporalmodulation selectivity[END_REF]. Both conditions entailed fricative phonemes only.

Procedure

A forced-choice XAB task was used to present the syllables. It was implemented on the same tablet and used the same headphones calibrated at 65 dB SPL as above. On each trial, a first character on the top of the screen produced a sound X in quiet (always uttered by the male speaker), then, two characters on the bottom of the screen produced the sounds A and B, played in noise (each uttered by one of the two female speakers). The inter-stimulus and inter-trial intervals were the same as in the previous task and feedback was also provided. Children were asked to select the character at the bottom who pronounced the same sound as the one on the top. The noise level was varied on a trial-to-trial basis following an adaptive 2-down 1-up procedure. The starting signal-to-noise ratio (SNR) was +20 dB. The first step size was 5 dB, reduced to 2 dB after two reversals. The run stopped after the 6th reversal was reached or after 32 trials.

4

The consonant identification threshold in noise was calculated as the geometric mean of the last four reversals and was then standardized by age.

Receptive Vocabulary Assessment

Spoken language receptive vocabulary was assessed using the British Picture Vocabulary Scale (BPVS third edition [START_REF] Dunn | The British picture vocabulary scale[END_REF]). Children were presented with four pictures on each trial and required to select the one that best illustrated the meaning of a word uttered by the experimenter. The raw scores were normalized by the age of the child.

Results

Temporal integration for AM and speech-in-noise identification thresholds

As detailed in [START_REF] Cabrera | Temporal integration for amplitude modulation in childhood: Interaction between internal noise and memory[END_REF] [START_REF] Cabrera | Temporal integration for amplitude modulation in childhood: Interaction between internal noise and memory[END_REF], statistical analyses showed no significant effect of Rate [F(1,98) = 1.03, p = .312], a significant effect of Age [F(3,98) = 5.60, p = .001, η2 = 0.15] and no interaction between these factors on temporal-integration scores. The 5-6-year-olds showed higher integration scores (that is, better ability to combine AM cues) compared to older children due to their worst thresholds in the 2-cycle conditions. For the two conditions of speech-in-noise, a significant main effect of Age and Condition on identification thresholds was observed, but no interaction between the two factors (see [START_REF] Cabrera | Development of temporal auditory processing in childhood: Changes in efficiency rather than temporalmodulation selectivity[END_REF] for details). Higher (worse) thresholds for voicing contrasts as compared to place of articulation was observed overall, and the group of 5-6 years showed the worst thresholds.

Relationship between temporal integration for AM and speech-in-noise identification thresholds

Using backward regression analyses, we assessed whether temporal-integration scores, as averaged on the two AM rates (4 and 32 Hz), and receptive vocabulary scores could predict speech-in-noise identification thresholds in each phonetic condition. None of the regression models predicted a significant part of the variance of the speech-in-noise thresholds (see Tab. 1). We then ran regression models including temporalintegration scores at 4 and 32 Hz separately and receptive vocabulary scores. This model showed a significant contribution of the integration scores obtained in the 32-Hz condition in predicting identification thresholds for place of articulation [R 2 = 9.4%, adj. Table 1. Summary of the backward regression models from Experiment I. β refers to the standardized regression coefficient. Bold indicates significance at α<0.05.

EXPERIMENT II

Methods

Participants

French-speaking children from 6 to 9 years were recruited in this second experiment, see [START_REF] Lorenzini | Reduced processing efficiency impacts auditory detection of amplitude modulation in children: evidence from an experimental and modelling study[END_REF]. Consent forms were obtained from parents as approved by the university ethics committee (Numéro CER -Paris Descartes : 2018-41). All children included had normal hearing (absolute thresholds between 0.25 and 8 kHz < 20 dB HL) and typical cognitive development. Twenty-eight 6-to-7-year-olds (18 females; mean age = 6.6 years, SD = 0.3), 29 7-to-8-year-olds (17 females; mean age = 7.6 years, SD = 0.3), and 29 8-to-9-yearolds (17 females; mean age = 8.5 years, SD = 0.4) were included in the final sample.

Stimuli

Narrow-band noise carriers centered at 500 Hz were generated with a 4-Hz bandwidth. A total of 500 different noise carriers were generated by adding together five equalamplitude sine tones with frequencies 498, 499, 500, 501 and 502 Hz. All sounds were 500-ms long, including a 14-ms raised-cosine onset/offset ramps and with a fixed starting phase. Sounds with "low" values of envelope SD were selected (ranging between 0.059 and 0.069 arbitrary units). AM spectra of these carriers show greater modulation energy below about 4 Hz (see [START_REF] Cabrera | Development of temporal auditory processing in childhood: Changes in efficiency rather than temporalmodulation selectivity[END_REF]) and AM masking effects were shown previously in children using such carriers for AM detection of an 8-Hz modulation target.

Procedure

AM detection thresholds were first measured using a 2I-2AFC implemented on a touch-screen tablet. A transformed and weighted 1-up-1-down adaptive procedure was implemented [START_REF] Garcıa-Pérez | Forced-choice staircases with fixed step sizes: asymptotic and small-sample properties[END_REF] targeting 76 % response correct corresponding to a d' = 1. Then, a constant-stimuli procedure was implemented using a 2I-2AFC paradigm where the stimuli were repeated in two passes. Each noise carrier was only presented once in each pass. Between the two passes, the trials were exactly the same, that is, the same (physical) modulated and unmodulated carriers were presented in the same order within a trial. The order of trial presentation was the same in each pass. Each participant completed two AMdetection passes of 200 trials each, as divided into 10 blocks of 20 trials played at individual threshold. Once the 10 blocks of pass 1 were completed, the exact same blocks were repeated in pass 2.

The outcome measures in this task were: Percent Correct (PC) of AM detection in each pass (calculated on the 200 trials) and across the two passes (average PC), which signals accuracy in the perceptual decision, and Percent of Agreement (PA) between the response given for the same stimuli in each pass. PA signals response consistency in the double-pass, a proxy of internal noise for AM detection, and was calculated on a trial-to-trial basis between pass 1 and 2.

Speech-in-noise tasks

Stimuli

Three native French speakers were recorded: one male (F0 = 110 Hz) and two females (F0 = 195 and 205 Hz). /aCa/ syllables were selected such as C = /f/, /v/, /ʃ/, /ʒ/, /s/, /z/ (fricatives), /b/, /p/, /d/, /t/, /g/, /k/ (stops). A steady speechshaped noise masker was generated based on the female tokens. Six phonetic conditions were presented with a minimal change in: 1) voicing, for either fricative or stop consonants (conditions 'Voicing fricatives' and 'Voicing stop'; 2) place of articulation for either fricative or stop consonants (conditions 'Place fricatives' and 'Place stop'); 5) manner of articulation for either voiceless or voiced consonants (conditions 'Manner voiced' or 'Manner voiceless').

Procedure

The exact same XAB procedure as in Exp. I was used to present the French consonants in the six phonetic conditions. An identification threshold in noise was obtained in each condition, calculated as the geometric mean of the last four reversals.

Receptive Vocabulary Assessment

Receptive vocabulary was measured using the standardized French version of Picture Vocabulary Scale tests (Échelle de Vocabulaire en Images Peabody).

Results

Precision and consistency in AM detection

Percentage Correct (PC) and Percentage of Agreement (PA) for AM detection were analyzed in linear mixed-effects models including the fixed effects of Age, Pass (1, 2) and Block (from 1 to 10) and participants as the random factor. This analyses showed a significant Age effect on both PC and PA [for PC and PA respectively: F(1,86) = 4.466; p = 0.037; η2 = 0.049; F(1,86) = 4.780; p = 0.031; η 2 = 0.052]

Speech-in-noise identification thresholds

A linear mixed-effect model was run to assess the fixed effects of Age and Phonetic Condition ('Voicing fricative', 'Voicing stop', 'Place fricative', 'Place stop', 'Place fricative', 'Manner Voiced' and 'Manner Voiceless') on identification thresholds. The fixed effect of Phonetic Condition proved significant [F(5, 415.79) = 63.950 ; p < .001; η 2 = 0.435]. Post-hoc Tukey comparisons highlighted a pattern of differences in performance in the various conditions signaling growing difficulty going from the condition Place fricative, yielding the lowest (best) thresholds to the condition Place stop, yielding the highest (worse) thresholds. Precisely, thresholds in the condition Place Fricative were significantly lower (i.e., better) than in the conditions Manner (Voiced and Voiceless) and Voicing (Fricative and Stop), yielding similar thresholds. Thresholds obtained in condition Place Stop, in turn, were significantly higher (i.e., worse) than all other conditions. Note that an analogous pattern was identified in a group of 15 French adults performing the same experiment. Fig. 1 reports identification thresholds in the six conditions. The main effect of Age was marginally significant [F(1, 82.15) = 3.911; p = .051; η 2 = 0.045] and no significant interaction was detected [F(5, 415.79) = 0.517; p = 0.763; η 2 = 0.006].

Relationship between precision and consistency in AM detection and speech-in-noise

We assessed whether precision and consistency in AM detection (PC and PA, respectively) and receptive vocabulary scores were predictive of speech-in-noise identification thresholds. We tested this hypothesis using backward regression analyses in independent models for PC and PA as the two variables were highly correlated (Pearson's r = .851; p < 0.001).

DISCUSSION

With these experiments, we sought to study the relationship between AM processing skills and speech-innoise perception during childhood. Precisely, we respectively targeted the links between speech-in-noise identification thresholds and, in a first study, temporal integration (the improvement of AM detection thresholds with increasing number of AM cycles) while, in a second study, precision and consistency in AM detection (i.e., percentage correct and percentage of agreement in a double-pass procedure). Our first experiment did not show strong links between general temporal integration capacities for AM detection and fricative consonants in-noise identification thresholds during childhood. In this group of English-speaking children aged from 5 to 11 years of age, only the integration scores in the 32-Hz condition representing how much children' AM detection thresholds improve from 2 vs 8 cycles, were predictive of the identification thresholds in noise obtained in a condition contrasting fricative consonants on the place contrast (adj R 2 = 5%). Temporal integration for AM has been interpreted as reflecting the properties of the correlation operation of the decisional mechanism based on template matching [START_REF] Dau | Modeling auditory processing of amplitude modulation. II. Spectral and temporal integration[END_REF][START_REF] Cabrera | Temporal integration for amplitude modulation in childhood: Interaction between internal noise and memory[END_REF][START_REF] Wechsler | Wechsler preschool and primary scale of intelligence-fourth edition[END_REF]. Further work is warranted to explain why a relationship was specifically observed for relatively fast (32 Hz) AM cues and place of articulation identification of fricative consonants.

In our second experiment, we then found that identification thresholds in the same phonetic condition (Place Fricative) were best predicted by a combination of children's vocabulary scores and their precision in an 8Hz-AM detection, PC scores (adj R 2 = 7%). Interestingly, this condition contrasting fricative consonants on place of articulation was the easiest condition for both English (Exp. I) and French (Exp. II) children. Our results showed that the identification thresholds in this specific condition are somewhat related to AM detection abilities. Fricative consonants and place of articulation contrasts are known to be the more difficult to process by children with sensorineural hearing loss using cochlear implants. It therefore seems very important to measure the identification thresholds of children using cochlear implants for this phonetic contrast and to assess whether their thresholds can be predicted by their processing of AM information, that is conveyed by their implants. Finally, we also observed a strong and very specific link between PA (measuring consistency in AM detection at 8 Hz) and consonant identification thresholds in noise for minimal phonetic contrasts of voicing on stop consonants. Voicing cues for stops are conveyed by rather slow AM variations < 16 Hz and in light of our previous modeling studies, PA can be considered as a proxy of internal noise for AM processing [START_REF] Attia | Double-pass consistency for amplitude-and frequency-modulation detection in normal-hearing listeners[END_REF]. Thus, the present results suggest that, to some extent, internal noise (and, thus, processing efficiency for AM) plays a role in the development of speech-in-noise identification skills, supporting at least the ability to track minimal differences in voicing while listening in noisy conditions. This result can be interpreted as the sign of a developmental relationship between the progressive reduction of neural variability along the auditory pathwaysuch as the stochastic nature of neuronal firing, the internal state of the listeners' organism, or random fluctuations in attentionimpacting perceptual decisions and more efficient perception of the linguistic signal. Further work combining electrophysiological measures (to measure efficiency in neural temporal processing during development) and psychophysical tasks is required to test this hypothesis.

The present experiments revealed some links between AM processing and consonant-identification in noise for children with normal hearing. Vocabulary levels, overall, were not strongly predictive of identification thresholds in noise.

3 2

 3

  R 2 = 5%, F(3,61) = 2.111, p = 0.108, see Tab. 1 for more details].

Figure 1 .

 1 Figure 1. Average Speech-in-Noise identification thresholds (in dB SNR) in the six conditions of Experiment II. As thresholds were comparable in the conditions Manner Voiced and Manner Voiceless on one hand, and in the conditions Voicing Stop and Voicing Fricative on the other hand, thresholds were averaged for "Manner" and for "Voicing" (note, however, that, as a marginal effect was observed in the model targeting PA and Vocabulary as predictors of the Voicing condition, for this model only, separate analyses were then run for Voicing Fricative and Voicing Stop). A first series of models assessed the effects of PC and vocabulary scores. Only one model significantly explained the variance in the Place Fricative condition [R 2 = 9.3%, adj R 2 = 7%, F(2,77) = 3.966, p = .23] where Vocabulary scores significantly predicted the thresholds while PC scores were marginally predictive (see Tab. 2). A second series of models then assessed PA and vocabulary scores as predictors and only revealed a marginal effect of PA in the Voicing condition. Subsequent analyses were run on the conditions Voicing Stop and Voicing Fricative separately. Thresholds obtained in the Voicing Stop condition were best predicted by a model including PA scores [R 2 = 13%, adj. R 2 = 11.9%, F(1,76) = 11.534, p = 0.001, see Tab. 2].

Fig. 2

 2 Fig.2shows that better PA scores were related to lower (better) identification thresholds in the Voicing Stop condition.

Figure 2 .

 2 Figure 2. Identification thresholds in the Voicing Stop condition (in dB SNR) plotted as a function of Percentage of Agreement for AM detection obtained in the double-pass procedure, Experiment II.

Table 2 .

 2 Summary of the backward regression models from Experiment II. β refers to the standardized regression coefficient. Bold indicates significant at α<0.05.

	Outcome variable	Predictors	β	t	p	Status
	Place	•Vocabulary	-0.177	-0.156	.876	excluded
	Stop	•PC	0.049	0.428	.669	excluded
	Place	•Vocabulary	-0.244	-2.244	.028	included
	Fricative	•PC	-0.181	-1.670	.099	excluded
	Manner	•Vocabulary •PC	-0.205 0.092	-1.864 0.840	.066 .403	excluded excluded
	Voicing	•Vocabulary •PC	-0.101 -0.125	-0.881 -1.091	.381 .279	excluded excluded
	Place	•Vocabulary	-0.047	-0.412	.682	excluded
	Stop	•PA	0.089	0.768	.434	excluded
	Place	Vocabulary	-0.188	-1.682	.096	excluded
	Fricative	•PA	-0.092	-0.828	.410	excluded
	Manner	•Vocabulary •PA	-0.211 -0.030	-1.905 -0.273	.060 .785	excluded excluded
	Voicing	•Vocabulary •PA	-0.047 -0.213	-0.423 -1.903	.674 .061	excluded excluded
	Voicing	•Vocabulary	-0.358	-3.326	.793	excluded
	Stop	•PA	-0.028	-0.264	.001	included
	Voicing	•Vocabulary	-0.074	-0.662	.510	excluded
	Fric	•PA	-0.051	-0.453	.652	excluded

ACKNOWLEDGMENTS

This work was supported by the ANR-17-CE28-0008 (Agence National de la Recherche) to Laurianne Cabrera and by the MSCA-INCODE (Grant Agreement n. 895484) to Irene Lorenzini. Leo Varnet and Christian Lorenzi were supported by the FrontCog ANR-17-EURE-0017 grant. The authors wish to thank Emmanuel Ponsot for valuable insights on the analysis and modelling of the data; all the children that took part into the project and all the teachers that helped realizing the testing sessions; Paula Perrineau and Alicia Lefebvre for help with testing; Sarah Attia and Andrew King for help with setting up the experimental design.