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Background and Objectives: Previous studies have reported a possible prodrome in multiple sclerosis (MS) defined by nonspecific symptoms including mood disorder or genitourinary symptoms and increased health care use detected several years before diagnosis. This study aimed to evaluate agnostically the associations between diseases and symptoms diagnosed in primary care and the risk of MS relative to controls and 2 other autoimmune inflammatory diseases with similar population characteristics, namely lupus and Crohn disease (CD)., Methods: A case-control study was conducted using electronic health records from the Health Improvement Network database in the United Kingdom and France. We agnostically assessed the associations between 113 diseases and symptoms in the 5 years before and after diagnosis in patients with subsequent diagnosis of MS. Individuals with a diagnosis of MS were compared with individuals without MS and individuals with 2 other autoimmune diseases, CD and lupus., Results: The study population consisted of patients with MS (n = 20,174), patients without MS (n = 54,790), patients with CD (n = 30,477), and patients with lupus (n = 7,337). Twelve ICD-10 codes were significantly positively associated with the risk of MS compared with controls without MS. After considering ICD-10 codes suggestive of neurologic symptoms as the first diagnosis of MS, 5 ICD-10 codes remained significantly associated with MS: depression (UK: odds ratio 1.22, 95% CI 1.11-1.34), sexual dysfunction (1.47, 1.11-1.95), constipation (1.5, 1.27-1.78), cystitis (1.21, 1.05-1.39), and urinary tract infections of unspecified site (1.38, 1.18-1.61). However, none of these conditions was selectively associated with MS in comparisons with both lupus and CD. All 5 ICD-10 codes identified were still associated with MS during the 5 years after diagnosis., Discussion: We identified 5 health conditions associated with subsequent MS diagnosis, which may be considered not only prodromal but also early-stage symptoms. However, these health conditions overlap with prodrome of 2 other autoimmune diseases; hence, they lack specificity to MS.

Background: Studies showed the impact of sex and onset site (spinal or bulbar) on disease onset and survival in ALS. However, they mainly result from cross-sectional or survival analysis, and the interaction of sex and onset site on the different proxies of disease trajectory has not been fully investigated., Methods: We selected all patients with repeated observations in the PRO-ACT database. We divided them into four groups depending on their sex and onset site. We estimated a multivariate disease progression model, named ALS Course Map, to investigate the combined temporal changes of the four sub-scores of the revised ALS functional rating scale (ALSFRSr), the forced vital capacity (FVC), and the body mass index (BMI). We then compared the progression rate, the estimated age at onset, and the relative progression of the outcomes across each group., Results: We included 1438 patients from the PRO-ACT database. They were 51% men with spinal onset, 12% men with bulbar onset, 26% women with spinal onset, and 11% women with bulbar onset. We showed a significant influence of both sex and onset site on the ALSFRSr progression. The BMI decreased 8.9 months earlier (95% CI [3.9, 13.8]) in women than men, after correction for the onset site. Among patients with bulbar onset, FVC was impaired 2.6 months earlier (95% CI [0.6, 4.6]) in women., Conclusion: Using a multivariable disease modelling approach, we showed that sex and onset site are important drivers of the progression of motor function, BMI, and FVC decline., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Background: Mandarin-speaking children with autism spectrum disorder (ASD) have difficulties producing aspect markers. The difficulties were explained in terms of pragmatic deficits since these children demonstrated strength in the comprehension of aspect markers using the Intermodal Preferential Looking (IPL) task., Aims: To verify whether this dissociation between production and comprehension could be replicated using another technique to the IPL, and if all children with ASD show difficulties in the production of aspect markers., Methods & Procedures: A total of 34 children with ASD without cognitive delay, half with language impairment (ALI; mean age = 61.25 months old) and half with normal language (ALN, mean age = 61.52 months old), as well as 17 age-matched typically developing (TD) children (mean age = 61.38 months old) participated in a sentence-picture-matching task and a priming picture-description task to investigate their comprehension and production of Mandarin aspect markers zai-, -le and -zhe., Outcomes & Results: In the comprehension task, children in the ALN group performed similarly to their TD peers, but those in the ALI group were less accurate on zai- and -le than TD children; children in all groups received higher accuracy when zai- was combined with Activity rather than Accomplishment verbs, and those in the ALI group was also more accurate when -le occurred with Achievement verbs, in contrast to Activity verbs. In the production task, children in the ALI group produced fewer targets and more irrelevant sentences with zai- than their TD peers, and they tended to produce bare verbs for -le and -zhe than TD children; children in all groups tended to combine zai- with Activity verbs, and those in the ALN group also tended to combine -le with Achievement verbs., Conclusions & Implications: The comprehension and production of Mandarin aspect markers by children with ASD are linked to general language abilities, and interactions between lexical and grammatical aspect. Patterns of performance are similar to those of TD peers only for the subgroup with spared global language, while pragmatic deficits are pervasive throughout the spectrum. Therefore, training on formal language, with a specific emphasis on aspectual rather than pragmatic abilities, may be more effective at enhancing the production of aspect markers., What This Paper Adds: What is already known on this subject Mandarin-speaking children with ASD have difficulties producing aspect markers but demonstrate strengths in aspectual comprehension via the IPL task. Therefore, it has been proposed that their 'specific' difficulties in aspectual production should be ascribed to their pragmatic deficits. However, pragmatic deficits are highly pervasive in children with ASD while only a subgroup of children with ASD who are impaired in language development (children with ALI) show difficulties in producing tense/aspect morphology. Pursuing this reasoning, pragmatic deficits might not be the critical factor impacting the performance of children with ASD in aspectual production. What this study adds Children with ASD were divided into one group with ALI and the other with normal language (ALN). Results of a sentence-picture-matching and a priming picture-description task illustrated that both groups preserved the comprehension of Mandarin aspect markers zai-, -le and -zhe. However, children with ALI performed worse than age-matched TD children, while children with ALN demonstrated similar performance to TD children in aspectual production. These findings, coupled with the fact that pragmatic challenges affect individuals throughout the spectrum, suggest that general language abilities rather than pragmatics better explain the performance of children with ASD on aspectual production. What are the potential or actual clinical implications of this work? Since general language abilities rather than pragmatic deficits of children with ASD determine their performance on the production of aspect markers, direct training on the use of aspect markers or more global language therapy could benefit children with ASD in the production of aspect markers., (© 2023 The Authors. International Journal of Language & Communication Disorders published by John Wiley & Sons Ltd on behalf of Royal College of Speech and Language Therapists.)

Disease modeling is an essential tool to describe disease progression and its heterogeneity across patients. Usual approaches use continuous data such as biomarkers to assess progression. Nevertheless, categorical or ordinal data such as item responses in questionnaires also provide insightful information about disease progression. In this work, we propose a disease progression model for ordinal and categorical data. We built it on the principles of disease course mapping, a technique that uniquely describes the variability in both the dynamics of progression and disease heterogeneity from multivariate longitudinal data. This extension can also be seen as an attempt to bridge the gap between longitudinal multivariate models and the field of item response theory. Application to the Parkinson's progression markers initiative cohort illustrates the benefits of our approach: a fine-grained description of disease progression at the item level, as compared to the aggregated total score, together with improved predictions of the patient's future visits. The analysis of the heterogeneity across individual trajectories highlights known disease trends such as tremor dominant or postural instability and gait difficulties subtypes of Parkinson's disease., (© 2023 John Wiley & Sons Ltd.)

Objective: The purpose of this study was to simultaneously contrast prediagnostic clinical characteristics of individuals with a final diagnosis of dementia with Lewy Bodies (DLB), Parkinson's disease (PD), and Alzheimer's disease (AD) compared with controls without neurodegenerative disorders., Methods: Using the longitudinal THIN database in the United Kingdom, we tested the association of each neurodegenerative disorder with a selected list of symptoms and broad families of treatments, and compared the associations between disorders to detect disease-specific effects. We replicated the main findings in the UK Biobank., Results: We used data of 28,222 patients with PD, 20,214 with AD, 4,682 with DLB, and 20,214 healthy controls. All neurodegenerative disorders were significantly associated with the presence of multiple clinical characteristics before their diagnosis, including sleep disorders, falls, psychiatric symptoms, and autonomic dysfunctions. When comparing patients with DLB with patients with PD and patients with AD patients, falls, psychiatric symptoms, and autonomic dysfunction were all more strongly associated with DLB in the 5 years preceding the first neurodegenerative diagnosis. The use of statins was lower in patients who developed PD and higher in patients who developed DLB compared to patients with AD. In patients with PD, the use of statins was associated with the development of dementia in the 5 years following PD diagnosis., Interpretation: Prediagnostic presentations of falls, psychiatric symptoms, and autonomic dysfunctions were more strongly associated with DLB than PD and AD. This study also suggests that although several associations with medications are similar in neurodegenerative disorders, statin usage is negatively associated with PD but positively with DLB and AD as well as development of dementia in PD. ANN NEUROL 2023;94:259-270., (© 2023 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Alzheimer's Disease (AD) is a heterogeneous disease that disproportionately affects women and people with the APOE- ε 4 susceptibility gene. We aim to describe the not-well-understood influence of both risk factors on the dynamics of brain atrophy in AD and healthy aging. Regional cortical thinning and brain atrophy were modeled over time using non-linear mixed-effect models and the FreeSurfer software with t1-MRI scans from the Alzheimer's Disease Neuroimaging Initiative ( N = 1,502 subjects, 6,728 images in total). Covariance analysis was used to disentangle the effect of sex and APOE genotype on the regional onset age and pace of atrophy, while correcting for educational level. A map of the regions mostly affected by neurodegeneration is provided. Results were confirmed on gray matter density data from the SPM software. Women experience faster atrophic rates in the temporal, frontal, parietal lobes and limbic system and earlier onset in the amygdalas, but slightly later onset in the postcentral and cingulate gyri as well as all regions of the basal ganglia and thalamus. APOE- ε 4 genotypes leads to earlier and faster atrophy in the temporal, frontal, parietal lobes, and limbic system in AD patients, but not in healthy patients. Higher education was found to slightly delay atrophy in healthy patients, but not for AD patients. A cohort of amyloid positive patients with MCI showed a similar impact of sex as in the healthy cohort, while APOE- ε 4 showed similar associations as in the AD cohort. Female sex is as strong a risk factor for AD as APOE-ε4 genotype regarding neurodegeneration. Women experience a sharper atrophy in the later stages of the disease, although not a significantly earlier onset. These findings may have important implications for the development of targeted intervention., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Sauty and Durrleman.)

Preschool children with neurotypical development (ND) trained on sentential complements ("X thinks/says that") improve their Theory of Mind (ToM) performance. Can complementation training also enhance ToM in children with Autism Spectrum Disorder (ASD)? Thirty-three children with ASD (Mage = 8;11) and 20 younger ND peers (Mage = 4;3) were trained on sentential complements (4-6 weeks, 2-3 times per week, via the DIRE i-Pad App). Pre-training and post-training comparisons show that (1) training boosted both complementation and ToM performance across groups; (2) improvements remained 4-6 weeks after training ended; (3) participants with milder ASD symptoms made most gains. Training on sentential complements thus seems beneficial for addressing ToM difficulties in children with ASD, especially those with milder symptoms., (© 2022. The Author(s).)

This article reviews the current knowledge state on pragmatic and structural language abilities in autism and their potential relation to extralinguistic abilities and autistic traits. The focus is on questions regarding autism language profiles with varying degrees of (selective) impairment and with respect to potential comorbidity of autism and language impairment: Is language impairment in autism the co-occurrence of two distinct conditions (comorbidity), a consequence of autism itself (no comorbidity), or one possible combination from a series of neurodevelopmental properties (dimensional approach)? As for language profiles in autism, three main groups are identified, namely, (i) verbal autistic individuals without structural language impairment, (ii) verbal autistic individuals with structural language impairment, and (iii) minimally verbal autistic individuals. However, this tripartite distinction hides enormous linguistic heterogeneity. Regarding the nature of language impairment in autism, there is currently no model of how language difficulties may interact with autism characteristics and with various extralinguistic cognitive abilities. Building such a model requires carefully designed explorations that address specific aspects of language and extralinguistic cognition. This should lead to a fundamental increase in our understanding of language impairment in autism, thereby paving the way for a substantial contribution to the question of how to best characterize neurodevelopmental disorders., (© 2023. The Author(s).)

The anticipation of progression of Alzheimer's disease (AD) is crucial for evaluations of secondary prevention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural progression of AD, notably because several functions decline at different ages and different rates in different patients. We evaluate here AD Course Map, a statistical model predicting the progression of neuropsychological assessments and imaging biomarkers for a patient from current medical and radiological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of more than 4,600 patients from four continents. We measured the accuracy of the method for selecting participants displaying a progression of clinical endpoints during a hypothetical trial. We show that enriching the population with the predicted progressors decreases the required sample size by 38% to 50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease and is therefore highly suitable for use in prevention trials., (© 2023. The Author(s).)

Introduction: Numerous studies have demonstrated the benefits of creativity from bilingualism. Divergent thinking and convergent thinking are considered the two most important components of creativity. Various (although not all) studies have concluded that bilingual children outperform monolingual children in divergent thinking, however, no study on children or adolescents so far has explored the relation between bilingualism and convergent thinking, or the brain structural basis of interaction between bilingualism and creativity. This study aimed to explore the impact of bilingualism on both convergent and divergent thinking in children and adolescents based on neuropsychological assessments, and the possible structural basis of the effect of bilingualism on creativity by a whole-brain analysis of regional gray matter volume (rGMV) and cortical thickness in children and adolescents., Methods: 92 healthy children and adolescents of age 4-18 were recruited from public or private schools in the French-speaking side of Switzerland. Demographic data of the participants were collected, including gender, age, pedagogy, usage of language, and parents' socioeconomic status. Most of the participants underwent the neuropsychological assessments of divergent thinking, convergent thinking, and fluid intelligence. Structural image data of 75 participants were analyzed. Both voxel-based morphometry (VBM) and surface-based morphometry (SBM) were processed, to perform the analyses of rGMV and cortical thickness respectively., Results: The outcomes indicated that convergent thinking, but not divergent thinking benefits from bilingualism in children and adolescents. However, this bilingual advantage appears to weaken across development. Unexpectedly, no significant correlation between morphometry and bilingualism was found. Neither divergent thinking scores nor convergent thinking scores showed any significant correlation with rGMV. However, the whole brain SBM showed that the cortical thickness in the right supplementary motor area (SMA) was negatively correlated with convergent thinking scores, which suggested that the children and adolescents with higher convergent thinking abilities may have thinner, more mature, and more activated cortex in the right SMA., Discussion: Bilingualism and cortical thinness in the right SMA might facilitate convergent thinking independently, by enhancing this selective ability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Zheng, Denervaud and Durrleman.)

This study assesses the impact of a working memory training program on the syntactic complexity of the spontaneous speech of French-speaking children with Developmental Language Disorder (DLD). Thirty-nine 6- to 12-year-old children with DLD were allocated to a WM training (DLD MM , N  = 20) or an active control group (DLD SQULA , N  = 19). The computerized training sessions took place three times a week, yielding 12 training hours per participant. Syntactic complexity was assessed in storytelling, measuring mean length of utterances, use of embedded clauses and rate of errors in complex utterances. The performance of participants with DLD was first compared to previous spontaneous data of 40 typically-developing (TD) children of the same age. Then, intragroup (pre- vs. post-test) and intergroup (DLD MM vs. DLD SQULA ) comparisons were made to assess the impact of the working memory training on the language measures. Global results confirmed syntactic impairment in children with DLD, as opposed to TD children, with large differences for the use of embedded clauses. Findings also suggested gains in the mastery of embedded clauses in children who participated in the WM training, whereas no gains were observed in the DLD control group. These findings confirm deficits in complex syntax in children with DLD, in particular in embedded clauses, and may encourage the clinical use of language sample analysis, which provides an ecological account of children's language performance. While our results should be replicated on a larger scale, they also suggest positive transfer effects of working memory training on the capacity of participants with DLD to produce embedded clauses, in line with previous studies showing a positive effect of WM training on tasks of expressive syntax. It thus seems that working memory training can yield benefits for language, which leaves open the door to new therapeutic approaches for children with DLD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Delage, Stanford, Baratti and Durrleman.)

Aim: This study explores how bilingual parents of autistic children made language decisions for their families, how the event of the SARS-CoV-2 pandemic and subsequent lockdown impacted the communication environment of their households, and whether these experiences affected their language habits., Method: Semi-structured interviews were conducted with five bilingual parents of autistic children who lived through lockdown in France. Data were analysed using interpretative phenomenological analysis. Demographic and background information was collected using an adapted version of the Questionnaire for Parents of Bilingual Children., Results: Participants reported conflicting advice given by a range of practitioners. Parents expressed differing beliefs about the impact of language choices on their children. Parents described active engagement with their children's home-learning as generally positive. Parents identified an increase in children's exposure to their first language during the lockdown. Parents reported an increase in children's overall communication abilities., Conclusion: Parents believed that their children's positive communication development during lockdown was related to increased exposure to their first language(s), and direct involvement in their children's learning programs., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2022.)

Variability in neurodegenerative disease progression poses great challenges for the evaluation of potential treatments. Identifying the persons who will experience significant progression in the short term is key for the implementation of trials with smaller sample sizes. We apply here disease course mapping to forecast biomarker progression for individual carriers of the pathological CAG repeat expansions responsible for Huntington disease. We used data from two longitudinal studies (TRACK-HD and TRACK-ON) to synchronize temporal progression of 15 clinical and imaging biomarkers from 290 participants with Huntington disease. We used then the resulting HD COURSE MAP to forecast clinical endpoints from the baseline data of 11,510 participants from ENROLL-HD, an external validation cohort. We used such forecasts to select participants at risk for progression and compute the power of trials for such an enriched population. HD COURSE MAP forecasts biomarkers 5 years after the baseline measures with a maximum mean absolute error of 10 points for the total motor score and 2.15 for the total functional capacity. This allowed reducing sample sizes in trial up to 50% including participants with a higher risk for progression ensuring a more homogeneous group of participants., (© 2022. The Author(s).)

In addition to deficits in pragmatics, children with autism spectrum disorders (ASD) have weaknesses in complex syntax and working memory (WM). These two deficits may be closely related. Previous work investigated the effects of WM training in developmental language disorders and showed significant improvement in both WM and syntax. The current study tests the impact of 12 h of WM training across 8 weeks in 30 children with ASD, aged 5 to 11. Results showed direct improvements on untrained WM tasks, as well as transfer effects to syntax and processing speed. Stronger WM led to better syntactic abilities. While they must be replicated, these exciting results provide impetus for further studies of WM interventions., (© 2021. The Author(s).)

Studies comparing outcomes of ICU patients admitted for either COVID-19 or seasonal influenza are limited. Our objective was to describe baseline clinical profiles, care procedures, and mortality outcomes by infection status (influenza vs COVID-19) of patients who received invasive mechanical ventilation in the ICU., Design: Retrospective observational study., Setting: Data were extracted from the Assistance Publique-Hopitaux de Paris database from September 1, 2016, to April 20, 2021. It includes data from the 39 university hospitals., Patients: A total of 752 influenza adult patients and 3,465 COVID-19 adult patients received invasive mechanical ventilation in one of the ICUs of the Paris area university hospitals, France., Intervention: The characteristics and outcome by infection status were compared. Factors associated with mortality were assessed using Cox proportional hazard models after controlling for potential confounders, including infection status., Measurements and Main Results: The median age at admission to the ICU was 67 (interquartile range [IQR], 57-77) and 63 yr (IQR, 54-71 yr) for influenza and COVID-19 patients, respectively. At ICU admission, COVID-19 patients were more frequently obese, more frequently had diabetes mellitus or high blood pressure, and were less likely to have chronic heart failure, chronic respiratory disease, chronic kidney failure, or active cancer than influenza patients. The overall survival at 90 days was 57% for COVID-19 patients and 66% for influenza patients ( p < 0.001). In a multivariable Cox model, higher age, organ transplant, severe acute respiratory syndrome coronavirus 2 infection, and chronic kidney failure were associated with shorter survival, whereas obesity and high blood pressure were associated with longer survival after invasive ventilation., Conclusions: COVID-19 and influenza patients requiring mechanical ventilation in the ICU differed by many characteristics. COVID-19 patients showed lower survival independently of potential confounders., (Copyright © 2022 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Children with developmental language disorder (DLD) reportedly struggle with the comprehension of aspect. However, since aspect and tense are closely entangled in the languages spoken by the children with DLD in previous studies, it is unclear whether the difficulty stems from aspect, tense, or both. Mandarin Chinese, a language without morphological manifestations of tense, is ideal to investigate whether the comprehension of aspect is specifically affected in children with DLD, yet to date work on this is scarce and presents methodological limitations. In this study, we examined whether preschool Mandarin-speaking children with DLD have difficulty in comprehending perfective aspect (represented with the aspect marker - le ) compared to imperfective aspect (represented with the aspect markers zai- and - zhe ), whether performance can be explained in terms of the pre- vs. post-verbal realization of the aspect markers, and the potential role played by lexical aspect in the comprehension of grammatical aspect. Fourteen preschool children with DLD (mean age: 61.11 months old) and 14 TD children (mean age: 63.4 months old) matched for age and nonverbal intelligence participated in a sentence-picture matching task. Global results showed that, similar to their TD peers, children with DLD performed better on imperfective aspect than perfective aspect. Concerning specific aspect markers, while children with DLD indeed performed similarly to TD children on imperfective -zhe , they obtained significantly lower accuracy than TD children on perfective -le and imperfective zai- . However, considering verb types combined with these aspect markers, results revealed that children with DLD scored significantly higher on the prototypical combination(s) (e.g., zai-  + Activity verbs) than on the non-prototypical combination(s) (e.g., zai-  + Accomplishment verbs). The performance pattern suggests that the comprehension of aspect markers by children with DLD is particularly affected by lexical aspect. As this also affects younger TD children, children with DLD are arguably at an earlier stage of aspectual development than their age and nonverbal intelligence matched TD peers. Therefore, the aspectual development of children with DLD appears to be delayed rather than deviant. Given this, language programs addressing difficulties in DLD may need to incorporate training on the use of aspect markers, especially targeting their combination with non-prototypical verbs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen and Durrleman.)

Background: The identification of modifiable risk factors for Alzheimer's disease is paramount for early prevention and the targeting of new interventions. We aimed to assess the associations between health conditions diagnosed in primary care and the risk of incident Alzheimer's disease over time, up to 15 years before a first Alzheimer's disease diagnosis., Methods: In this agnostic study of French and British health records, data from 20 214 patients with Alzheimer's disease in the UK and 19 458 patients with Alzheimer's disease in France were extracted from The Health Improvement Network database. We considered data recorded from Jan 1, 1996, to March 31, 2020 in the UK and from Jan 4, 1998, to Feb 20, 2019, in France. For each Alzheimer's disease case, a control was randomly assigned after matching for sex and age at last visit. We agnostically tested the associations between 123 different diagnoses of the International Classification of Diseases, 10th revision, extracted from health records, and Alzheimer's disease, by running a conditional logistic regression to account for matching of cases and controls. We focused on three time periods before diagnosis of Alzheimer's disease, to separate risk factors from early symptoms and comorbidities., Findings: Unadjusted odds ratios (ORs) and 95% CIs for the association between Alzheimer's disease and various health conditions were estimated, and p values were corrected for multiple comparisons. In both the British and French studies, ten health conditions were significantly positively associated with increased Alzheimer's disease risk, in a window of exposure from 2-10 years before Alzheimer's disease diagnosis, comprising major depressive disorder (UK OR 1·34, 95% CI 1·23-1·46; France OR 1·73, 1·57-1·91), anxiety (UK OR 1·36, 1·25-1·47; France OR 1·50, 1·36-1·65), reaction to severe stress and adjustment disorders (UK OR 1·40, 1·24-1·59; France OR 1·83, 1·55-2·15), hearing loss (UK OR 1·19, 1·11-1·28; France OR 1·51, 1·21-1·89), constipation (UK OR 1·31, 1·22-1·41; France OR 1·59, 1·44-1·75), spondylosis (UK OR 1·26, 1·14-1·39; France OR 1·62, 1·44-1·81), abnormal weight loss (UK OR 1·47, 1·33-1·63; France OR 1·88, 1·56-2·26), malaise and fatigue (UK OR 1·23, 1·14-1·32; France OR 1·59, 1·46-1·73), memory loss (UK OR 7·63, 6·65-8·76; France OR 4·41, 3·07-6·34), and syncope and collapse (UK OR 1·23, 1·10-1·37; France OR 1·57, 1·26-1·96). Depression was the first comorbid condition associated with Alzheimer's disease, appearing at least 9 years before the first clinical diagnosis, followed by anxiety, constipation, and abnormal weight loss., Interpretation: These results from two independent primary care databases provide new evidence on the temporality of risk factors and early signs of Alzheimer's disease that are observable at the general practitioner level. These results could guide the implementation of new primary and secondary prevention policies., Funding: Agence Nationale de la Recherche., Competing Interests: Declaration of interests SE reports personal fees from Biogen, Eisai, Roche, and GE Healthcare, for presentations or participation on advisory boards. FM, LG, and BE are full time employees of Cegedim. All other authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: We aimed to estimate the seroprevalence of SARS-CoV-2 infection in France and to identify the populations most exposed during the first epidemic wave., Methods: Random selection of individuals aged 15 years or over, from the national tax register (96% coverage). Socio-economic data, migration history, and living conditions were collected via self-computer-assisted-web or computer-assisted-telephone interviews. Home self-sampling was performed for a random subsample, to detect IgG antibodies against spike protein (Euroimmun), and neutralizing antibodies with in-house assays, in dried blood spots (DBS)., Results: The questionnaire was completed by 134,391 participants from May 2nd to June 2st, 2020, including 17,441 eligible for DBS 12,114 of whom were tested. ELISA-S seroprevalence was 4.5% [95% CI 3.9-5.0] overall, reaching up to 10% in the two most affected areas. High-density residences, larger household size, having reported a suspected COVID-19 case in the household, working in healthcare, being of intermediate age and non-daily tobacco smoking were independently associated with seropositivity, whereas living with children or adolescents did not remain associated after adjustment for household size. Adjustment for both residential density and household size accounted for much of the higher seroprevalence in immigrants born outside Europe, twice that in French natives in univariate analysis., Conclusion: The EPICOV cohort is one of the largest national representative population-based seroprevalence surveys for COVID-19. It shows the major role of contextual living conditions in the initial spread of COVID-19 in France, during which the availability of masks and virological tests was limited., (© 2022. The Author(s).)

Children who are deaf or hard of hearing (DHH) show delays in Theory of Mind (ToM) development. Complement sentences such as "Eliane says that Santa Clause exists" influence ToM performance. Can a training program targeting sentential complements enhance ToM? Twenty-one French-speaking DHH children (Mage = 8 years 11 months) with delays in ToM and sentential complements completed a first series of tests (T0). Children were tested again to control for maturation effects (T1), after which they were included in a 6- to 8-week training program targeting complements with verbs of communication. Post-training tests (T2) assessed if the training yielded improvements on complements (direct effect) and ToM (transfer effect). While no gains were noted in the absence of training (at T1), results indicate post-training (T2) improvements in complements and ToM tasks, suggesting that the acquisition of sentential complements provides a tool to represent subjective truths and boosts ToM reasoning in DDH children., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: We aim to understand how patients with Alzheimer's disease (AD) are treated by identifying in a longitudinal fashion the late-life changes in patients' medical history that precede and follow AD diagnosis., Methods: We use prescription history of 34,782 patients followed between 1996 and 2019 by French general practitioners. We compare patients with an AD diagnosis, patients with mild cognitive impairment (MCI), and patients free of mental disorders. We use a generalized mixed-effects model to study the longitudinal changes in the prescription of eight drug types for a period 15 years before diagnosis and 10 years after., Results: In the decades preceding diagnosis, we find that future AD patients are treated significantly more than MCI patients with most psychotropic drugs and that most studied drugs are increasingly prescribed with age. At the time of diagnosis, all psychotropic drugs except benzodiazepines show a significant increase in prescription, while other drugs are significantly less prescribed. In the 10 years after diagnosis, nearly all categories of drugs are less and less prescribed including antidementia drugs., Discussion: Pre-diagnosis differences between future AD patients and MCI patients may indicate that subtle cognitive changes are recognized and treated as psychiatric symptoms. The disclosure of AD diagnosis drastically changes patients' care, priority being given to the management of psychiatric symptoms. The decrease of all prescriptions in the late stages may reflect treatment discontinuation and simplification of therapeutic procedures. This study therefore provides new insights into the medical practices for management of AD., Competing Interests: Stéphane Epelbaum has received honoraria as a speaker or consultant for ELI‐LILLY, GE Healthcare, Astellas pharma, ROCHE, and BIOGEN. Béranger Lekens and Laurène Gantzer are employees of Cegedim. The other authors have nothing to report., (© 2021 The Authors. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring published by Wiley Periodicals, LLC on behalf of Alzheimer's Association.)

Gaussian graphical models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM inference methods: the nodewise approach and the penalised likelihood maximisation. We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one. As a result, we propose a composite procedure that explores a family of graphs with a nodewise numerical scheme and selects a candidate among them with an overall likelihood criterion. We demonstrate that, when the number of observations is small, this selection method yields graphs closer to the truth and corresponding to distributions with better KL divergence with regards to the real distribution than the other two. Finally, we show the interest of our algorithm on two concrete cases: first on brain imaging data, then on biological nephrology data. In both cases our results are more in line with current knowledge in each field.

Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18 F-florbetapir and 18 F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers., Competing Interests: Disclosure statement S. Gaubert, M. Houot, F. Raimondo, M. Ansart, M-C. Corsi, L. Naccache, J.D. Sitt, B. Dubois, F. De Vico Fallani and S. Durrleman report no relevant disclosures. S. Epelbaum has received honoraria as a speaker or consultant for Eli Lilly, Biogen, Astellas Pharma, Roche and GE Healthcare. M-O. Habert has received fees as a consultant from Blue Earth company., (Copyright © 2021 Elsevier Inc. All rights reserved.)

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Routier, Burgos, Díaz, Bacci, Bottani, El-Rifai, Fontanella, Gori, Guillon, Guyot, Hassanaly, Jacquemont, Lu, Marcoux, Moreau, Samper-González, Teichmann, Thibeau-Sutre, Vaillant, Wen, Wild, Habert, Durrleman and Colliot.)

This study examined whether bilingualism boosts Theory of Mind as measured by a non-verbal false belief (FB) task in children with autism spectrum disorder (ASD), and how this potential boost may stem from improvements in a variety of other domains, namely executive functions (EFs), language, metalinguistic awareness skills, as well as autism severity. One hundred and three children with ASD (7- to 15-year-olds) (43 bilingual and 60 age- and IQ-matched monolingual children) were tested on a nonverbal task of attentional switching, working memory and updating task, and an online, low-verbal first-order FB task. Results showed a clear FB benefit for bilingual children with ASD as compared with their monolingual peers. There were also boosts in EF, however, there is no evidence that these EF boosts drove the FB advantage. Enhanced FB was not explained either by language, metalinguistic skills, or lower autism severity. While the results do not conclusively settle the debate on what triggers the ToM advantage in bilingual children with ASD, the empirical picture of the current study suggests that the ToM component of FB understanding in bilingual children with ASD is enhanced by the bilingual experience per se. LAY SUMMARY: The current study aimed to determine if and how bilingualism may improve the ability to understand others' beliefs in children with autism spectrum disorder (ASD). We assessed their belief reasoning alongside a series of other skills hypothesized to be beneficial for such reasoning, namely understanding, producing, and thinking about language, recalling and switching between information, and the severity of their autistic symptoms. The overall findings highlight advantages for bilingual children with ASD over their monolingual peers for grasping beliefs, thus suggesting that pursuing bilingualism may be beneficial for cognition in ASD. Other boosts were also associated with bilingualism, such as recalling and switching between information, but these boosts were not directly related to belief understanding, highlighting the beneficial role of bilingualism per se., (© 2021 International Society for Autism Research and Wiley Periodicals LLC.)

Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability within groups of networks, i.e., the structure of connection similarities and differences across a set of networks. We propose a statistical framework to model these variations based on manifold-valued latent factors. Each network adjacency matrix is decomposed as a weighted sum of matrix patterns with rank one. Each pattern is described as a random perturbation of a dictionary element. As a hierarchical statistical model, it enables the analysis of heterogeneous populations of adjacency matrices using mixtures. Our framework can also be used to infer the weight of missing edges. We estimate the parameters of the model using an Expectation-Maximization-based algorithm. Experimenting on synthetic data, we show that the algorithm is able to accurately estimate the latent structure in both low and high dimensions. We apply our model on a large data set of functional brain connectivity matrices from the UK Biobank. Our results suggest that the proposed model accurately describes the complex variability in the data set with a small number of degrees of freedom.

Alzheimer's disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer's disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient's cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer's disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.

Purpose: In clinical practice, positron emission tomography (PET) images are mostly analyzed visually, but the sensitivity and specificity of this approach greatly depend on the observer's experience. Quantitative analysis of PET images would alleviate this problem by helping define an objective limit between normal and pathological findings. We present an anomaly detection framework for the individual analysis of PET images. Approach: We created subject-specific abnormality maps that summarize the pathology's topographical distribution in the brain by comparing the subject's PET image to a model of healthy PET appearance that is specific to the subject under investigation. This model was generated from demographically and morphologically matched PET scans from a control dataset. Results: We generated abnormality maps for healthy controls, patients at different stages of Alzheimer's disease and with different frontotemporal dementia syndromes. We showed that no anomalies were detected for the healthy controls and that the anomalies detected from the patients with dementia coincided with the regions where abnormal uptake was expected. We also validated the proposed framework using the abnormality maps as inputs of a classifier and obtained higher classification accuracies than when using the PET images themselves as inputs. Conclusions: The proposed method was able to automatically locate and characterize the areas characteristic of dementia from PET images. The abnormality maps are expected to (i) help clinicians in their diagnosis by highlighting, in a data-driven fashion, the pathological areas, and (ii) improve the interpretability of subsequent analyses, such as computer-aided diagnosis or spatiotemporal modeling., (© 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).)

Objective: 7-Tesla MRI of the hippocampus enhances the visualization of its internal substructures. Among these substructures, the cornu Ammonis and subiculum form a contiguous folded ribbon of gray matter. Here, we propose a method to analyze local thickness measurements of this ribbon., Methods: We introduce an original approach based upon the estimation of a diffeomorphic vector field that traverses the ribbon. The method is designed to handle specificities of the hippocampus and corresponding 7-Tesla acquisitions: highly convoluted surface, non-closed ribbon, incompletely defined inner/outer boundaries, anisotropic acquisitions. We furthermore propose to conduct group comparisons using a population template built from the central surfaces of individual subjects., Results: We first assessed the robustness of our approach to anisotropy, as well as to inter-rater variability, on a post-mortem scan and on in vivo acquisitions respectively. We then conducted a group study on a dataset of in vivo MRI from temporal lobe epilepsy (TLE) patients and healthy controls. The method detected local thinning patterns in patients, predominantly ipsilaterally to the seizure focus, which is consistent with medical knowledge., Conclusion: This new technique allows measuring the thickness of the hippocampus from 7-Tesla MRI. It shows good robustness with respect to anisotropy and inter-rater variability and has the potential to detect local atrophy in patients., Significance: As 7-Tesla MRI is increasingly available, this new method may become a useful tool to study local alterations of the hippocampus in brain disorders. It is made freely available to the community (code: https://github.com/aramis-lab/hiplay7-thickness, postmortem segmentation: https://doi.org/10.5281/zenodo.3533264).

Endocasts (i.e., replicas of the inner surface of the bony braincase) constitute a critical proxy for qualifying and quantifying variations in brain shape and organization in extinct taxa. In the absence of brain tissues preserved in the fossil record, endocasts provide the only direct evidence of brain evolution. However, debates on whether or not information inferred from the study of endocasts reflects brain shape and organization have polarized discussions in paleoneurology since the earliest descriptions of cerebral imprints in fossil hominin crania. By means of imaging techniques (i.e., MRIs and CT scans) and 3D modelling methods (i.e., surface-based comparisons), we collected consistent morphological (i.e., shape) and structural (i.e., sulci) information on the variation patterns between the brain and the endocast based on a sample of extant human individuals (N = 5) from the 3D clinical image database of the Steve Biko Academic Hospital in Pretoria (South Africa) and the Hôpitaux Universitaires Pitié Salpêtrière in Paris (France). Surfaces of the brain and endocast of the same individual were segmented from the 3D MRIs and CT images, respectively. Sulcal imprints were automatically detected. We performed a deformation-based shape analysis to compare both the shape and the sulcal pattern of the brain and the endocast. We demonstrated that there is close correspondence in terms of morphology and organization between the brain and the corresponding endocast with the exception of the superior region. By comparatively quantifying the shape and organization of the brain and endocast, this work represents an important reference for paleoneurological studies., (© 2020 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society.)

Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previous work (Samper-González et al. 2018), we propose an open-source framework for the reproducible evaluation of AD classification from T1-weighted (T1w) MRI and PET data. In the present paper, we first extend this framework to diffusion MRI data. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 5% up to 40% relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package ( www.clinica.run ) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML .

We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we reported the used data set, the feature types, the algorithm type, performance and potential methodological issues. The impact of these characteristics on the performance was evaluated using a multivariate mixed effect linear regressions. We found that using cognitive, fluorodeoxyglucose-positron emission tomography or potentially electroencephalography and magnetoencephalography variables significantly improved predictive performance compared to not including them, whereas including other modalities, in particular T1 magnetic resonance imaging, did not show a significant effect. The good performance of cognitive assessments questions the wide use of imaging for predicting the progression to AD and advocates for exploring further fine domain-specific cognitive assessments. We also identified several methodological issues, including the absence of a test set, or its use for feature selection or parameter tuning in nearly a fourth of the papers. Other issues, found in 15% of the studies, cast doubts on the relevance of the method to clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. These issues highlight the importance of adhering to good practices for the use of machine learning as a decision support system for the clinical practice., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: OC reports having received speaker fees from Roche (2015), Lundbeck (2012) and Guerbet (2010) and that his laboratory has received grants (paid to the institution) from EISAI (2007-2011), Air Liquide Medical Systems (2011-2016), Qynapse (2017-present) and myBrainTechnologies (2016-present). His spouse is an employee at myBrainTechnologies (2015-). SE has received honoraria as a speaker or consultant for ELI-LILLY, GE Healthcare, Astellas pharma, ROCHE and BIOGEN., (Copyright © 2020 Elsevier B.V. All rights reserved.)

We ranked third in the Predictive Analytics Competition (PAC) 2019 challenge by achieving a mean absolute error (MAE) of 3.33 years in predicting age from T1-weighted MRI brain images. Our approach combined seven algorithms that allow generating predictions when the number of features exceeds the number of observations, in particular, two versions of best linear unbiased predictor (BLUP), support vector machine (SVM), two shallow convolutional neural networks (CNNs), and the famous ResNet and Inception V1. Ensemble learning was derived from estimating weights via linear regression in a hold-out subset of the training sample. We further evaluated and identified factors that could influence prediction accuracy: choice of algorithm, ensemble learning, and features used as input/MRI image processing. Our prediction error was correlated with age, and absolute error was greater for older participants, suggesting to increase the training sample for this subgroup. Our results may be used to guide researchers to build age predictors on healthy individuals, which can be used in research and in the clinics as non-specific predictors of disease status., Competing Interests: OC reports having received consulting fees from AskBio (2020), fees for writing a lay audience short paper from Expression Santé (2019), and speaker fees for a lay audience presentation from Palais de la découverte (2017) and reports that his laboratory has received grants (paid to the institution) from Air Liquide Medical Systems (2011–2016) and Qynapse (2017–present). The members from his laboratory have co-supervised a Ph.D. thesis with myBrainTechnologies (2016-present). OC's spouse is an employee of myBrainTechnologies (2015–present). OC and SD have submitted a patent to the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, OC, SD, a method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2016). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2020 Couvy-Duchesne, Faouzi, Martin, Thibeau–Sutre, Wild, Ansart, Durrleman, Dormont, Burgos and Colliot.)

Multiple sclerosis (MS) is a demyelinating and inflammatory disease of the central nervous system (CNS). The demyelination process can be repaired by the generation of a new sheath of myelin around the axon, a process termed remyelination. In MS patients, the demyelination-remyelination cycles are highly dynamic. Over the years, magnetic resonance imaging (MRI) has been increasingly used in the diagnosis of MS and it is currently the most useful paraclinical tool to assess this diagnosis. However, conventional MRI pulse sequences are not specific for pathological mechanisms such as demyelination and remyelination. Recently, positron emission tomography (PET) with radiotracer [ 11 C]PIB has become a promising tool to measure in-vivo myelin content changes which is essential to push forward our understanding of mechanisms involved in the pathology of MS, and to monitor individual patients in the context of clinical trials focused on repair therapies. However, PET imaging is invasive due to the injection of a radioactive tracer. Moreover, it is an expensive imaging test and not offered in the majority of medical centers in the world. In this work, by using multisequence MRI, we thus propose a method to predict the parametric map of [ 11 C]PIB PET, from which we derived the myelin content changes in a longitudinal analysis of patients with MS. The method is based on the proposed conditional flexible self-attention GAN (CF-SAGAN) which is specifically adjusted for high-dimensional medical images and able to capture the relationships between the spatially separated lesional regions during the image synthesis process. Jointly applying the sketch-refinement process and the proposed attention regularization that focuses on the MS lesions, our approach is shown to outperform the state-of-the-art methods qualitatively and quantitatively. Specifically, our method demonstrated a superior performance for the prediction of myelin content at voxel-wise level. More important, our method for the prediction of myelin content changes in patients with MS shows similar clinical correlations to the PET-derived gold standard indicating the potential for clinical management of patients with MS., (Copyright © 2020. Published by Elsevier Inc.)

Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studies due to variations in components such as participant selection, image preprocessing or validation procedure. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible and because implementation details are lacking. Lastly, some of these papers may report a biased performance due to inadequate or unclear validation or model selection procedures. In the present work, we aim to address these limitations through three main contributions. First, we performed a systematic literature review. We identified four main types of approaches: i) 2D slice-level, ii) 3D patch-level, iii) ROI-based and iv) 3D subject-level CNN. Moreover, we found that more than half of the surveyed papers may have suffered from data leakage and thus reported biased performance. Our second contribution is the extension of our open-source framework for classification of AD using CNN and T1-weighted MRI. The framework comprises previously developed tools to automatically convert ADNI, AIBL and OASIS data into the BIDS standard, and a modular set of image preprocessing procedures, classification architectures and evaluation procedures dedicated to deep learning. Finally, we used this framework to rigorously compare different CNN architectures. The data was split into training/validation/test sets at the very beginning and only the training/validation sets were used for model selection. To avoid any overfitting, the test sets were left untouched until the end of the peer-review process. Overall, the different 3D approaches (3D-subject, 3D-ROI, 3D-patch) achieved similar performances while that of the 2D slice approach was lower. Of note, the different CNN approaches did not perform better than a SVM with voxel-based features. The different approaches generalized well to similar populations but not to datasets with different inclusion criteria or demographical characteristics. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://github.com/aramis-lab/AD-DL., Competing Interests: Declaration of Competing Interest OC reports having received speaker fees from Roche (2015), Lundbeck (2012) and Guerbet (2010), having received consulting fees from AskBio (2020), having received fees for writing a lay audience short paper from Expression Santé (2019), having received speaker fees for a lay audience presentation from Palais de la Découverte (2017) and that his laboratory has received grants (paid to the institution) from EISAI (2007-2011), Air Liquide Medical Systems (2011-2016), Qynapse (2017-present) and my Brain Technologies (2016-present). His spouse is an employee at my Brain Technologies (2015-)., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Purpose Children with developmental language disorder (DLD) experience difficulties with an important Theory of Mind milestone, namely, false belief (FB) reasoning. Their FB success relates to mastery of a linguistic structure that is also challenging for them, namely, sentential complements (e.g., Claire says/thinks [that Santa Claus exists] ). Training typically developing (TD) children on complements has been shown to boost complements and, in turn, enhance FB, but such training has never been explored with children with DLD, which is the aim of the current study. Method Fifty French-speaking children followed a novel training program: 30 with DLD ( M age = 7;3) and 20 TD ( M age = 4;3). They engaged in iPad applications targeting complementation with verbs of communication (e.g., say, shout, answer ) during eight to 12 sessions lasting 30 min. Training commenced within 1-2 weeks of pretests and ceased 1-2 weeks before immediate posttests. After immediate posttests, the majority of children were available to be tested with follow-up tests after 4-6 weeks of no training. Results Findings revealed that both TD and DLD groups benefited from the training to significantly improve their complementation and FB scores. The gains achieved during immediate posttests were moreover maintained 6-8 weeks after training ceased, as revealed by preserved levels of performance during follow-up posttests. Conclusion This research thus suggests new avenues for therapeutic interventions for children with DLD, namely, the incorporation of a program directly training complements, which holds the promise of a double benefit, both for these structures and for Theory of Mind.

The purpose of this study was to explore whether children with autism display selectivity in social learning. We investigated the processing of word mappings provided by speakers who differed on previously demonstrated accuracy and on potential degree of reliability in three groups of children (children with autism spectrum disorder, children with developmental language disorder, and typically developing children) aged 4-9 years. In Task 1, one speaker consistently misnamed familiar objects and the second speaker consistently gave correct names. In Task 2, both speakers provided correct information but differed on how they could achieve this accuracy. We analyzed how the speakers' profiles influenced children's decisions to rely on them in order to learn novel words. We also examined how children attended to the speakers' testimony by tracking their eye movements and comparing children' gaze distribution across speakers' faces and objects of their choice. Results show that children rely on associative trait attribution heuristics to selectively learn from accurate speakers. In Task 1, children in all groups preferred the novel object selected by accurate speakers and directly avoided information provided by previously inaccurate speakers, as revealed by the eye-tracking data. In Task 2, where more sophisticated reasoning about speakers' reliability was required, only children in the typically developing group performed above chance. Nonverbal intelligence score emerged as a predictor of children's preference for more reliable informational sources. In addition, children with autism exhibited reduced attention to speakers' faces compared with children in the comparison groups., (Copyright © 2019 Elsevier Inc. All rights reserved.)

We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs. We validate our method on three different cohorts, and consider five different classification algorithms for the pre-screening phase. We show that the best results are obtained using solely cognitive, genetic and socio-demographic features, as the slight increased performance when using MRI or longitudinal data is balanced by the cost increase they induce. We show that the proposed method generalizes well when tested on an independent cohort, and that the characteristics of the selected set of individuals are identical to the characteristics of a population selected in a standard way. The proposed approach shows how Machine Learning can be used effectively in practice to optimize recruitment costs in clinical trials.

Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the white matter of the brain and in the spinal cord. It is thus essential to measure the tissue myelin content to understand the physiopathology of MS, track progression and assess treatment efficacy. Positron emission tomography (PET) with [ 11 C]PIB is a reliable method to measure myelin content in vivo. However, the availability of PET in clinical centers is limited. Moreover, it is expensive to acquire and invasive due to the injection of a radioactive tracer. By contrast, MR imaging is non-invasive, less expensive and widely available, but conventional MRI sequences cannot provide a direct and reliable measure of myelin. In this work, we therefore propose, to the best of our knowledge for the first time, a method to predict the PET-derived myelin content map from multimodal MRI. To that purpose, we introduce a new approach called Sketcher-Refiner generative adversarial networks (GANs) with specifically designed adversarial loss functions. The first network (Sketcher) generates global anatomical and physiological information. The second network (Refiner) refines and generates the tissue myelin content. A visual attention saliency map is also proposed to interpret the attention of neural networks. Our approach is shown to outperform the state-of-the-art methods in terms of image quality and myelin content prediction. Particularly, our prediction results show similar results to the PET-derived gold standard at both global and voxel-wise levels indicating the potential for clinical management of patients with MS., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Training on complements in English, German, and Mandarin has been reported to trigger improvements on both complements and Theory of Mind (ToM), with typically developing (TD) pre-schoolers on the verge of developing these skills (Hale and Tager-Flusberg, 2003; Lohmann and Tomasello, 2003; Shuliang et al., 2014). In the current study, we build on the idea that increasing mastery of complementation holds the promise of enhancing ToM, and seek (i) to replicate the positive effects observed in previous work for this effect in French-speaking TD children, and (ii) to pilot extending this to clinical children, more specifically those with Autism Spectrum Disorder (ASD) and Developmental Language Disorder (DLD), through exploring whether improvement in the latter, clinical groups follows that of the TD group. Sixty children with ToM difficulties, 16 with ASD (aged 5;6-11;8), 20 with DLD (aged 4;8-9;0) and 24 typically developing children aged (2;9-5;3 years), participated in a 4-week training program. Half received training targeting sentential complements and half received a control training targeting lexical skills. Complementation training, but not lexical training, led to a significant direct increase in complements, and also had the indirect effect of significantly boosting belief reasoning. TD and clinical groups followed the same patterns of performance. These results confirm previous findings in other languages for TD, and further suggest promising new directions for therapeutic programs addressing ToM delays in populations of different aetiologies, namely the incorporation of a motivating training on complementation., (Copyright © 2019 Durrleman, Burnel, De Villiers, Thommen, Yan and Delage.)

Purpose Our work investigates the production of 3rd-person accusative clitic pronouns in French-speaking typically developing (TD) children and children with developmental language disorder (DLD) following a novel working memory (WM) training program (12 hrs of effective training) that specifically targets the components of WM that have been shown to be impaired in children with DLD and to be directly related to the mastery of clitics (Delage & Frauenfelder, submitted for publication; Durrleman & Delage, 2016). Method Sixteen TD children aged 5-12 years and 26 age-matched children with DLD completed our 8-week WM training program. Furthermore, an age-matched control group of 16 TD children and 17 children with DLD followed a scholastic training regime matched for intensity and frequency. Syntax and WM were assessed prior to and following the WM/scholastic training. Results Significant posttraining WM gains were found in TD children and children with DLD who took part in the WM training, and the production rate of 3rd-person accusative clitics significantly increased in children with DLD following the WM training. No significant WM or syntax gains were observed in the control group. Conclusion These findings are noteworthy as Melby-Lervåg and Hulme's (2013) meta-analysis concluded that existing WM training programs show short-lived generalized effects to other comparable measures of WM, but that there is no evidence that such training generalizes to less directly related tasks. That our study led to gains in skills that were not trained (i.e., syntax) suggests that a WM training regime that is firmly grounded in theory and that targets the specific mechanisms shown to underpin the acquisition of syntax may indeed provide effective remediation for children with DLD.

Objective: To assess the added value of neurite orientation dispersion and density imaging (NODDI) compared with conventional diffusion tensor imaging (DTI) and anatomical MRI to detect changes in presymptomatic carriers of chromosome 9 open reading frame 72 ( C9orf72 ) mutation., Methods: The PREV-DEMALS (Predict to Prevent Frontotemporal Lobar Degeneration and Amyotrophic Lateral Sclerosis) study is a prospective, multicentre, observational study of first-degree relatives of individuals carrying the C9orf72 mutation. Sixty-seven participants (38 presymptomatic C9orf72 mutation carriers (C9+) and 29 non-carriers (C9-)) were included in the present cross-sectional study. Each participant underwent one single-shell, multishell diffusion MRI and three-dimensional T1-weighted MRI. Volumetric measures, DTI and NODDI metrics were calculated within regions of interest. Differences in white matter integrity, grey matter volume and free water fraction between C9+ and C9- individuals were assessed using linear mixed-effects models., Results: Compared with C9- , C9+ demonstrated white matter abnormalities in 10 tracts with neurite density index and only 5 tracts with DTI metrics. Effect size was significantly higher for the neurite density index than for DTI metrics in two tracts. No tract had a significantly higher effect size for DTI than for NODDI. For grey matter cortical analysis, free water fraction was increased in 13 regions in C9+ , whereas 11 regions displayed volumetric atrophy., Conclusions: NODDI provides higher sensitivity and greater tissue specificity compared with conventional DTI for identifying white matter abnormalities in the presymptomatic C9orf72 carriers. Our results encourage the use of neurite density as a biomarker of the preclinical phase., Trial Registration Number: NCT02590276., Competing Interests: Competing interests: Competing financial interests unrelated to the present article: OC and SD are funded by European Union’s Horizon 2020 research and innovation programme under grant agreement no 666992 (EuroPOND) and no 720270 (HBP SGA1). SD is funded by the European Research Council (ERC) under grant agreement no 678304. PC received consultant fees from Boehringer Ingelheim and funding for his institution from Cytokinetics., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Multiple sclerosis (MS) is a white matter (WM) disease characterized by the formation of WM lesions, which can be visualized by magnetic resonance imaging (MRI). The fluid-attenuated inversion recovery (FLAIR) MRI pulse sequence is used clinically and in research for the detection of WM lesions. However, in clinical settings, some MRI pulse sequences could be missed because of various constraints. The use of the three-dimensional fully convolutional neural networks is proposed to predict FLAIR pulse sequences from other MRI pulse sequences. In addition, the contribution of each input pulse sequence is evaluated with a pulse sequence-specific saliency map. This approach is tested on a real MS image dataset and evaluated by comparing this approach with other methods and by assessing the lesion contrast in the synthetic FLAIR pulse sequence. Both the qualitative and quantitative results show that this method is competitive for FLAIR synthesis.

We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run.

A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer's disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation procedures. The performance of the different approaches is also difficult to compare objectively. In particular, it is often difficult to assess which part of the method (e.g. preprocessing, feature extraction or classification algorithms) provides a real improvement, if any. In the present paper, we propose a framework for reproducible and objective classification experiments in AD using three publicly available datasets (ADNI, AIBL and OASIS). The framework comprises: i) automatic conversion of the three datasets into a standard format (BIDS); ii) a modular set of preprocessing pipelines, feature extraction and classification methods, together with an evaluation framework, that provide a baseline for benchmarking the different components. We demonstrate the use of the framework for a large-scale evaluation on 1960 participants using T1 MRI and FDG PET data. In this evaluation, we assess the influence of different modalities, preprocessing, feature types (regional or voxel-based features), classifiers, training set sizes and datasets. Performances were in line with the state-of-the-art. FDG PET outperformed T1 MRI for all classification tasks. No difference in performance was found for the use of different atlases, image smoothing, partial volume correction of FDG PET images, or feature type. Linear SVM and L2-logistic regression resulted in similar performance and both outperformed random forests. The classification performance increased along with the number of subjects used for training. Classifiers trained on ADNI generalized well to AIBL and OASIS. All the code of the framework and the experiments is publicly available: general-purpose tools have been integrated into the Clinica software (www.clinica.run) and the paper-specific code is available at: https://gitlab.icm-institute.org/aramislab/AD-ML., (Copyright © 2018 Elsevier Inc. All rights reserved.)

The brain is composed of several neural circuits which may be seen as anatomical complexes composed of grey matter structures interconnected by white matter tracts. Grey and white matter components may be modeled as 3-D surfaces and curves, respectively. Neurodevelopmental disorders involve morphological and organizational alterations which cannot be jointly captured by usual shape analysis techniques based on single diffeomorphisms. We propose a new deformation scheme, called double diffeomorphism, which is a combination of two diffeomorphisms. The first one captures changes in structural connectivity, whereas the second one recovers the global morphological variations of both grey and white matter structures. This deformation model is integrated into a Bayesian framework for atlas construction. We evaluate it on a data-set of 3-D structures representing the neural circuits of patients with Gilles de la Tourette syndrome (GTS). We show that this approach makes it possible to localise, quantify, and easily visualise the pathological anomalies altering the morphology and organization of the neural circuits. Furthermore, results also indicate that the proposed deformation model better discriminates between controls and GTS patients than a single diffeomorphism.

Previous studies have suggested sentential complementation is the crucial ingredient of language that relates to false-belief (FB) reasoning, while the role of relative clauses (RCs) is less clear. Nevertheless, under the hypothesis that clausal embedding has a meta-representational effect, arguably implied in FB, one expects a link between FB and not only complementation but also relativization. Seventeen children with ASD (6 to 16 years, mean age 9;2) were assessed for RCs and FB. Comprehension of RCs significantly predicted FB performance, while none of the controlled factors played a predictive role (comprehension of simple sentences, vocabulary, morpho-syntax and working memory). Findings suggest that clausal embedding, common to both sentential complements and RCs, serves as a bootstrap for FB reasoning., (Copyright © 2018 Elsevier Inc. All rights reserved.)