Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

A genetic investigation of sex bias in the prevalence of attention-deficit/hyperactivity disorder

Background Attention-deficit/hyperactivity disorder (ADHD) shows substantial heritability and is 2-7 times more common in males than females. We examined two putative genetic mechanisms underlying this sex bias: sex-specific heterogeneity and higher burden of risk in female cases. Methods We analyzed genome-wide autosomal common variants from the Psychiatric Genomics Consortium and iPSYCH Project (20,183 cases, 35,191 controls) and Swedish populationregister data (N=77,905 cases, N=1,874,637 population controls). Results Genetic correlation analyses using two methods suggested near complete sharing of common variant effects across sexes, with rg estimates close to 1. Analyses of population data, however, indicated that females with ADHD may be at especially high risk of certain comorbid developmental conditions (i.e. autism spectrum disorder and congenital malformations), potentially indicating some clinical and etiological heterogeneity. Polygenic risk score (PRS) analysis did not support a higher burden of ADHD common risk variants in female cases (OR=1.02 [0.98-1.06], p=0.28). In contrast, epidemiological sibling analyses revealed that the siblings of females with ADHD are at higher familial risk of ADHD than siblings of affected males (OR=1.14, [95% CI: 1.11-1.18], p=1.5E-15). Conclusions Overall, this study supports a greater familial burden of risk in females with ADHD and some clinical and etiological heterogeneity, based on epidemiological analyses. However, molecular genetic analyses suggest that autosomal common variants largely do not explain the sex bias in ADHD prevalence

Improving the acceptability to enhance the efficiency of stroke rehabilitation procedures based on brain-computer interfaces: General public results

International audienceStroke leaves around 40% of surviving patients dependent in their activities of daily living, notably due to severe motor disabilities [Inserm, 2019]. Brain-Computer Interfaces (BCIs) have been shown to be efficient for improving motor recovery after stroke [Cervera et al., 2018], but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified, they are insufficient: fully optimised BCIs are pointless if patients and clinicians do not want to use them [Blain-Moraes et al., 2012].We hypothesise that improving BCI acceptability and acceptance, by better informing stakeholders about BCI functioning and by personalising the BCI-based rehabilitation procedures to each patient, respectively, will favour engagement in the rehabilitation process and result in an increased efficiency.Our first objective was to identify the factors influencing the intention to use (IU) BCIs [Davis, 1989]. Based on the literature, we constructed a model of BCI acceptability and adapted it in questionnaires addressed to the general population (n=753) and post-stroke patients (n=33). Videos were included, one about the general functioning of BCIs, the second about their relevance for rehabilitation.We used random forest algorithms to explain IU based on our model's factors. After the first video, IU was mainly explained by subjective and personal factors, i.e., perceived usefulness (PU), perceived ease of use (PEOU) and BCI playfulness for the general population, and PU, autonomy and engagement in the rehabilitation for the patients. After the second video, the explanatory factors became more scientific/rational, with PU, cost-benefits ratio and scientific relevance for the general population, and PU, scientific relevance and ease of learning for patients.The shift of main explanatory factors (before/after second video) from subjective representations to scientific arguments highlights the impact of providing patients with clear information regarding BCIs

Improving the acceptability to enhance the efficiency of stroke rehabilitation procedures based on brain-computer interfaces: General public results

International audienceStroke leaves around 40% of surviving patients dependent in their activities of daily living, notably due to severe motor disabilities [Inserm, 2019]. Brain-Computer Interfaces (BCIs) have been shown to be efficient for improving motor recovery after stroke [Cervera et al., 2018], but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified, they are insufficient: fully optimised BCIs are pointless if patients and clinicians do not want to use them [Blain-Moraes et al., 2012].We hypothesise that improving BCI acceptability and acceptance, by better informing stakeholders about BCI functioning and by personalising the BCI-based rehabilitation procedures to each patient, respectively, will favour engagement in the rehabilitation process and result in an increased efficiency.Our first objective was to identify the factors influencing the intention to use (IU) BCIs [Davis, 1989]. Based on the literature, we constructed a model of BCI acceptability and adapted it in questionnaires addressed to the general population (n=753) and post-stroke patients (n=33). Videos were included, one about the general functioning of BCIs, the second about their relevance for rehabilitation.We used random forest algorithms to explain IU based on our model's factors. After the first video, IU was mainly explained by subjective and personal factors, i.e., perceived usefulness (PU), perceived ease of use (PEOU) and BCI playfulness for the general population, and PU, autonomy and engagement in the rehabilitation for the patients. After the second video, the explanatory factors became more scientific/rational, with PU, cost-benefits ratio and scientific relevance for the general population, and PU, scientific relevance and ease of learning for patients.The shift of main explanatory factors (before/after second video) from subjective representations to scientific arguments highlights the impact of providing patients with clear information regarding BCIs

Modelling the Acceptance of BCI-based Stroke Rehabilitation Procedures: Heading for Efficiently Personalised Therapies

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Identifying profiles of patients to personalise BCI-based procedures for motor rehabilitation after stroke

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140).Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient.Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) “scientific relevance” & “ease of learning”; (c2) “benefits/risks ratio”, “ease of learning”, “visual aesthetic” & “result demonstrability”; (c3) “scientific relevance” & “benefits/risks ratio”;(c4) none.Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one

Acceptability of BCI-based procedures for motor rehabilitation after stroke: A questionnaire study among patients

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140). Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient. Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) "scientific relevance" & "ease of learning"; (c2) "benefits/risks ratio", "ease of learning", "visual aesthetic" & "result demonstrability"; (c3) "scientific relevance" & "benefits/risks ratio";(c4) none. Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one. Sources [1] Inserm, 2019 [2] Cervera, María A., et al. "Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis.

Etudier l’acceptabilité des interfaces cerveau-ordinateur en rééducation motrice post-AVC pour proposer des protocoles personnalisés en fonction du profil du patient

International audienceMalgré leur intérêt, les interfaces cerveau-ordinateur (ICO) ne sont pas utilisées en soins courants dans le domaine de la rééducation post-accident vasculaire cérébral. Nous faisons l’hypothèse que l’amélioration de l’acceptabilité des ICO, obtenue par une personnalisation des protocoles, permettra aux patients d’être moins anxieux et plus engagés, et ainsi d’optimiser l’efficacité en termes de récupération motrice, mais aussi l’utilisabilité.Nous avons conçu un questionnaire basé sur notre modèle théorique d’acceptabilité des ICO, auquel 140 sujets post-AVC ont répondu. Une identification des profils de sujets avec une analyse en composante principale (ACP) puis une clusterisation (méthode Elbow et K-mean clustering) a été réalisée. Un arbre de classification a été construit pour classer les nouveaux sujets dans leur cluster. Il a été validé par « leave-one-out cross validation » (LOOV). Pour chaque cluster, nous avons effectué des régressions et des corrélations pour identifier les facteurs à personnaliser.L’ACP a permis de passer de 27 à 15 facteurs, à partir desquels nous avons obtenu 5 clusters (Fig.1A, 1B). La performance de classification en LOOV était de 65,00 % (niveau de hasard pour α=5% : 30,75%) (Fig. 1C). Les facteurs d’importance majeurs sont (N = nombre de clusters les intégrant) : pertinence scientifique (4), facilité d’apprentissage (3), balance bénéfices/risques (2), esthétique et démonstrabilité (1). Ces résultats sont intégrés dans un logiciel « plug&play » utilisable en soins courants.Un essai contrôlé randomisé multicentrique permettra d’évaluer et d’optimiser les protocoles personnalisés proposés afin que les ICO soient plus utilisables/acceptables pour les patients et les soignants

Etudier l’acceptabilité des interfaces cerveau-ordinateur en rééducation motrice post-AVC pour proposer des protocoles personnalisés en fonction du profil du patient

International audienceMalgré leur intérêt, les interfaces cerveau-ordinateur (ICO) ne sont pas utilisées en soins courants dans le domaine de la rééducation post-accident vasculaire cérébral. Nous faisons l’hypothèse que l’amélioration de l’acceptabilité des ICO, obtenue par une personnalisation des protocoles, permettra aux patients d’être moins anxieux et plus engagés, et ainsi d’optimiser l’efficacité en termes de récupération motrice, mais aussi l’utilisabilité.Nous avons conçu un questionnaire basé sur notre modèle théorique d’acceptabilité des ICO, auquel 140 sujets post-AVC ont répondu. Une identification des profils de sujets avec une analyse en composante principale (ACP) puis une clusterisation (méthode Elbow et K-mean clustering) a été réalisée. Un arbre de classification a été construit pour classer les nouveaux sujets dans leur cluster. Il a été validé par « leave-one-out cross validation » (LOOV). Pour chaque cluster, nous avons effectué des régressions et des corrélations pour identifier les facteurs à personnaliser.L’ACP a permis de passer de 27 à 15 facteurs, à partir desquels nous avons obtenu 5 clusters (Fig.1A, 1B). La performance de classification en LOOV était de 65,00 % (niveau de hasard pour α=5% : 30,75%) (Fig. 1C). Les facteurs d’importance majeurs sont (N = nombre de clusters les intégrant) : pertinence scientifique (4), facilité d’apprentissage (3), balance bénéfices/risques (2), esthétique et démonstrabilité (1). Ces résultats sont intégrés dans un logiciel « plug&play » utilisable en soins courants.Un essai contrôlé randomisé multicentrique permettra d’évaluer et d’optimiser les protocoles personnalisés proposés afin que les ICO soient plus utilisables/acceptables pour les patients et les soignants

Identifying profiles of patients to personalise BCI-based procedures for motor rehabilitation after stroke

International audienceIntroduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140).Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient.Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) “scientific relevance” & “ease of learning”; (c2) “benefits/risks ratio”, “ease of learning”, “visual aesthetic” & “result demonstrability”; (c3) “scientific relevance” & “benefits/risks ratio”;(c4) none.Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one