The US National Institutes of Mental Health Research Domain Criteria (RDoC) seek to stimulate research into biologically validated neuropsychological dimensions across mental illness symptoms and diagnoses. The RDoC framework comprises 39 functional constructs designed to be revised and refined, with the overall goal of improving diagnostic validity and treatments. This study aimed to reach a consensus among experts in the addiction field on the ‘primary’ RDoC constructs most relevant to substance and behavioural addictions.Forty‐four addiction experts were recruited from Australia, Asia, Europe and the Americas. The Delphi technique was used to determine a consensus as to the degree of importance of each construct in understanding the essential dimensions underpinning addictive behaviours. Expert opinions were canvassed online over three rounds (97% completion rate), with each consecutive round offering feedback for experts to review their opinions.Seven constructs were endorsed by ≥ 80% of experts as ‘primary’ to the understanding of addictive behaviour: five from the Positive Valence System ; one from the Cognitive Control System (response selection/inhibition); and one expert‐initiated construct (compulsivity). These constructs were rated to be related differentially to stages of the addiction cycle, with some linked more closely to addiction onset and others more to chronicity. Experts agreed that these neuropsychological dimensions apply across a range of addictions.The study offers a novel and neuropsychologically informed theoretical framework, as well as a cogent step forward to test transdiagnostic concepts in addiction research, with direct implications for assessment, diagnosis, staging of disorder, and treatment. The aetiopathogeny of addiction remains poorly understood, as we lack assessment models to identify vulnerability to addiction. Only 10–20% of patients with substance and behavioural addictions receive treatment Important developments in neuroscience have begun to reshape how addictions are understood Key dysfunctions commonly shared across addictions include aberrant reward‐processing  and increased stress sensitivity . These constructs may underlie reduced sensitivity to the negative consequences of addiction‐related actions (e.g. drug misuse or excessive betting) and have been associated with the development and later relapse of addictive behaviours Considering the above, superficially disparate  disorders, such as substance‐use and gambling disorders, may be underpinned by overlapping neuropsychological processes and neural circuits. Such disorders may respond to similar interventions that target these common underlying mechanisms, such as naltrexone (an opioid‐receptor antagonist), which is effective in treating alcohol use disorder and gambling disorders putatively by targeting overlapping dysfunctional neurobiological systems Nevertheless, with the exception of some developments such as cognitive‐bias modification To help address these shortcomings, the US National Institute of Mental Health (NIMH) developed the Research Domain Criteria (RDoC) initiative as a tool to encourage researchers to ‘…develop, for research purposes, new ways of classifying mental disorders’ beyond traditional nosologies, which were based on describing and counting overt signs and symptoms and arbitrary clinical thresholds and boundaries that encompass diverse and overlapping biological mechanisms 1https://www.nimh.nih.gov/news/science‐news/2018/nimh‐releases‐updates‐to‐its‐rdoc‐framework.shtml).Reflecting the evolving and dynamic nature of RDoC, changes were made recently made to the Positive Valence domain in late June 2018  The RDoC framework offers a neuroscientifically grounded approach to bridge clinical practice with neuroscience. It is operationalized via the RDoC matrix, which is designed to promote ongoing testing and refinement.A common approach in mental health research to developing and refining research criteria, guidelines, reporting standards and protocols is the Delphi method n = 8), Asia (n = 1), Europe (n = 18), North America (n = 9) and South America (n = 1). The study was approved by the local (Monash University) Human Research and Ethics Committee (CF15/3407–2 015 001 454).Recruited through purposive sampling, expert selection was based on being known to the research group , having relevant clinical and/or research experience or being internationally renowned experts in substance and/or behavioural addictions. A minimum of 5 years of professional experience and more than 50 scientific articles authored in peer‐reviewed journals were additional requirements. Using the procedure outlined by Okoli & Pawlowski Experts were required to participate in an online forum and rate the relevance of all 39 constructs of the RDoC to the concept of addiction see Fig. . AlthougA five‐point Likert scale ranging from 1 (unimportant) to 5  was used, with a non‐neutral midpoint of 3 (moderately important). Omitting a neutral mid‐point forces experts to deliberate and form an opinion, and where experts did not have the knowledge, a ‘don't know/unsure’ option was available as an addendum The criterion for concluding the Delphi was not solely contingent upon reaching consensus, but also on the stability of responses SPSS (version 22)  was used for all quantitative analyses. For the small percentage of missing data (2.7%), pairwise deletion was applied Experts’ comments underwent several stages of thematic analysis by the core committee , in order to process the data systematically, first by identifying categories and then by identifying common themes As suggested by Jorm Of the original 44 consenters, 37 experts completed round 1 of the Delphi questionnaires. Retention was very high, with 36 (97.3%) round 1 completers also completing both second‐ and third‐round surveys.n = 25) being male. They represented a range of professions and academic disciplines (some multiple) including scientist/neuroscientists , psychiatrists , psychologists/clinical psychologists/neuropsychologists , other medical doctors  and pharmacologists . Their professional settings were primarily universities , hospitals  and out‐patient clinics , and the most commonly held academic titles were Professor , Associate Professor  and Research Fellow/Assistant Professor , with 89% (n = 33) of experts holding a PhD. Experts represented many areas of addiction , which supports our transdiagnostic approach.Experts who completed round 1 were aged 32–67 years , with 67.5%  reward valuation; 2) expectancy/reward prediction error; 3) action selection/preference‐based decision‐making; (4) reward learning; (5) habit; (6) response selection/inhibition; and (7) compulsivity  but was introduced by experts. Considerable evidence exists supporting compulsivity as a core feature of addiction .The high degree of consensus among experts across seven core constructs supports the proposition that there exists a group of common neuropsychological functions  predisposing or maintaining addictive behaviours in individuals. These substrates primarily belong to the Positive Valence System in the RDoC matrix, which is noteworthy, as most neuropsychological assessment tools do not probe these functions thoroughly, focusing more upon cognitive skills . Much of the Positive Valence System research relies on neuroimaging methods and animal studies From a research perspective, our findings stimulate the development of new addiction models seeking to integrate these constructs in a unifying framework that accounts for disorder staging. By contrast, a widely used model for describing addictive behaviours—the ‘dual systems’ approach, referring to an imbalance between reward valuation and the cognitive control systems https://allofus.nih.gov). Such large‐scale data collection efforts will help greatly in clarifying constructs broadly relevant to addictive behaviours and the mechanisms and processes relevant to various stages of addiction. Looking ahead, we need to develop an assessment battery that is time‐efficient, ecologically valid, psychometrically sound, sensitive to the seven primary domains identified herein, incorporates performance‐ and questionnaire‐based measures and is well tolerated.Future research should delineate how these seven factors are independent or inter‐related. From a clinical perspective, a first step towards knowledge implementation is developing an assessment tool that measures these constructs validly and reliably. Along these lines, Kwako and colleagues proposed an assessment battery to target three primary domains  Our findings raise the important issue of how the primary constructs (i) contribute to vulnerability to, or maintenance of, addictive behaviour; and (ii) predate addiction and emerge as a consequence of repeated drug use in vulnerable individuals. In relation to the former, aspects of the Positive Valence System, and the associated attribution of incentive salience to reward‐related stimuli, are considered important. For instance, at the vulnerability stage, reward valuation and linked anticipation may be a prominent factor in determining an individual's responsiveness to addiction‐related cues. At later stages of the addiction cycle, an allostatic‐incentive salience role of substance‐ or addiction‐related cues is likely to be present, and therefore reward valuation remains relevant to both vulnerability to relapse and chronicity. In relation to vulnerability to relapse (or chronicity), all seven primary constructs were considered relevant drivers see Figs  and 5, bPre‐clinical data suggest that substance use may switch from being impulsive to compulsive over time, reflecting a shift from dopaminergic dysregulation of ventral to dorsal striatum function and related cortical, pallidal and thalamic circuitry Evidently, the relevance of many neuropsychological constructs are not constrained by traditional diagnostic boundaries, forming  shared dysfunctions at the core of many substance and behavioural addictions. Established approaches to the clinical assessment and management of individuals with addictive disorders have not benefited fully from these emerging insights, with neuroscientists typically more aligned to the laboratory than the clinic. The essential neuropsychological dimensions currently identified provide a framework to guide clinicians and researchers through a consensual, collaborative agenda. Consistent with the RDoC framework, this agenda involves examining the diagnostic and prognostic value of dimensional measures of the constructs identified here, and the design of targeted, transdiagnostic treatment approaches to address these vulnerabilities and dysfunctions. The identification of neuropsychological targets may facilitate alternative interventions to succeed where others have failed. For instance, in the case of habits and compulsions (i.e. constructs related to chronicity), activities that re‐engage the cognitive control/goal‐directed systems  may be effective in treating addictive behaviours In relation to staging of illness, this neuropsychological approach underscores the frequent finding that many relevant phenomena vary continuously within and between addictions and mental disorders more broadly and in the population at large. These neuropsychological dimensions (may/arguably) become pathological at the extremes of an otherwise normal distribution Experts were only included if they were fluent English speakers. A handful of experts disagreed fundamentally with the use of the RDoC, arguing that they are too biological and reductionist, making the translation to phenomenological and other applications difficult , guiding clinical decisions to the core issues that drive addictions and measuring the success and failure of treatment . It is envisioned that the findings will guide and fast‐track the development of a new generation of neuropsychological assessment tools, and improve the monitoring and effectiveness of both established and future novel interventions.The theoretical framework established in the current study provides a platform to test predictions that: (1) the majority of individuals with substance and behavioural addictions have specific dysfunctions in the primary constructs identified by our International Expert Consortium; (2) these dysfunctions cut across diagnostic boundaries ; and (3) these indices can be linked differentially to vulnerability and chronicity (i.e. stage of disorder). This framework may enable grouping of more homogeneous disorder subtypes, better linking of behavioural questionnaire phenotypes to neural, cellular and genetic dimensions


Seasonal influenza vaccination (SIV) uptake (SIVU) rates in France are below target. We (i) describe trends in French SIVU over 10 consecutive seasons among different target groups and (ii) examine the effects of the 2009 influenza A(H1N1) pandemic and the publication of new SIV recommendations in 2011 and 2013.Our study was based on records of vaccines delivered in community pharmacies for a permanent, representative sample of 805,000 beneficiaries of the French National Health Insurance Fund. For the first objective, we analysed SIVU rate trends among ≥ 65 year olds as well as among  < 65 year olds with each of the following conditions: diabetes, respiratory, cardiovascular, neuromuscular, or chronic liver disease. For the second goal, we computed segmented log-binomial regression analyses.After the 2009 pandemic, except for the target group with liver diseases, where the difference was not statistically significant, SIVU fell significantly in all groups during the 2010/11 season, remaining relatively stable until 2015/16 in groups not targeted by new recommendations. Crude SIVU rates in 2015/16 were 48%  for ≥ 65 year olds and between 16%  and 29%  for  < 65 year olds depending on their condition. SIVU increased modestly after new recommendations were published, but only in patients newly eligible for a free vaccine voucher.Our results suggest: (i) a prolonged confidence crisis in SIV, initially impelled by the 2009 pandemic vaccination campaign; (ii) that new recommendations are ineffective without additional measures. Interventional research in this field is a priority. In most high-income countries, seasonal influenza vaccination (SIV) is recommended as a direct protection for elderly individuals  and for people aged ≥ 6 months at clinical risk ,2. VacciIn 2009, the European Council recommended that national plans be developed and implemented to reach an SIV uptake (SIVU) rate of at least 75% in each of the at-risk groups by the 2014/15 season , an objeIn France, most influenza vaccinations are performed by private practitioners, but all the insurance funds that make up the National Health Insurance Fund (NHIF) send a voucher to members of the target groups identified as such (based on long-term illness (LTI) status) and ensured by them. The voucher enables these individuals to obtain the vaccine free of charge at the pharmacy, without a doctor's prescription. They must then make an appointment with either a doctor or a nurse for the vaccine administration. People in the target groups not identified in the NHIF databases (i.e. persons in a risk group but who do not have LTI status) can obtain a voucher from their doctor but this makes their pathway to vaccination more complex as it requires first a doctor's appointment to get a voucher for free vaccine, then going to the pharmacy for delivery, and then a second appointment for the actual vaccine injection.Despite programmes to provide vaccination access, few countries have reached the objective of 75% uptake of the at-risk groups; Northern Ireland being an exception . On the A further question is to what extent the publication of new influenza vaccination recommendations might affect SIVU rates. In France, new recommendations were introduced after the 2009 influenza A(H1N1) pandemic – in March 2011 and then in April 2013 (see the methods section) – to expand the list of chronic underlying diseases considered to pose a risk for influenza. The NHIF had already targeted some of these and had been sending annual vouchers to patients with such diseases for several years. In this group we can study the effect of the publication of the recommendations, separately from the effect of the voucher. For others (such as for persons with chronic liver diseases), the NHIF did not begin sending free vouchers until the new recommendations were published.Our study sought to: (i) describe trends in SIVU over 10 consecutive seasons in France among different target groups (objective 1); and (ii) examine how they were affected by the 2009 influenza A(H1N1) pandemic (objective 2a) and the publication of new SIV recommendations (objective 2b).The study consisted of repeated cross-sectional assessments of SIVU in a dynamic cohort: the Permanent Sample of Beneficiaries . It was set up in 2005, by a national random sampling of one of every 97 persons affiliated with one of the three major national health insurance funds in France  . The samData are updated monthly from the NHIF administrative databases and include age, sex, area of residence, insurance fund, reimbursement claims for laboratory tests performed or drugs purchased in the community  codes) and status for LTI . On requFor this study, we extracted data for salaried workers and their covered family members  only (ca 86% of the French population), because people benefiting from other insurance funds were not included in the EGB until 2011.Access to the EGB is regulated by law and authorised by the National Committee for Informatics and Privacy , and data are anonymous. We obtained authorisation to access the EGB database from the French NHIF.n and coverage by the NHIF that same year; alive at the start of each campaign ; age ≥ 65 years or with at least one of the five main chronic conditions specified in the 2015 French recommendations for SIV [The study population comprised most individuals eligible for SIV in at least one seasonal campaign between 2006/07 and 2015/16. Inclusion criteria were residence in mainland France at year  for SIV : diabete for SIV  based onFor both objectives, we stratified the analyses for two age categories  because SIVU rates are much lower among those < 65 years old, and the factors associated with uptake may differ according to these categories .n/n + 1, we estimated SIVU rates as the ratio of individuals with a recorded SIV delivery  pandemic vaccines) between 1 September of year n and 31 March of year n + 1 to the number of individuals in the corresponding group identified in the database in year n. Each SIV delivered in a community pharmacy is recorded in the NHIF database, including those delivered with a free voucher.For each season To describe the trends of SIVU rates, we standardised directly by age and sex, using the 2007 mainland France national census as the reference, to take into account population ageing and the varying age and sex distributions across target groups.To compare trends of SIVU rates before and after specific events – the 2009 influenza A(H1N1) pandemic (objective 2a), or publication of new recommendations (objective 2b) in March 2011  and in April 2013 (liver diseases) – we computed segmented log-binomial Generalised Estimating Equation (GEE) regression models, using the binary variable ’SIVU (yes/no)’ as the outcome. A log-binomial approach allows us to model the probability of SIVU directly and to take within-subject correlations  into account. Segmented regression analysis is an effective and robust method for assessing statistically the longitudinal effects of events on an outcome measure, instantaneously and/or in the long term ,16. DepeAmong groups unaffected by the new SIV recommendations , we used the following model:p) = β0 + Sex + tAge + β1 × tT + β2 ×  Pandemic + β3 × End_pandemic  + β4 × t,Time_after_pandemicLog ; tT is a continuous discrete variable indicating the season number at time t, varying from 1 (2006/07 season) to 10 (2015/16 season); Pandemic is a dummy variable coded 0 before and 1 as of the start of the 2009 influenza A(H1N1) pandemic (first changepoint); End_pandemic is a similar dummy variable coding for the end of the pandemic (second change point); tTime_after_pandemic is a continuous variable counting the number of seasons at time t after the end of the pandemic, coded 0 before 2010/11 and increased by 1 at each subsequent new season.where Log-binomial models make it possible to estimate the relative risks of SIVU by exponentiating the regression coefficients. For this model, we estimated We applied similar models including additional change points for the groups targeted by new recommendations in 2011 or 2013. To assess the choice of GEE working correlation structure, we calculated the quasi-likelihood under the independence model criterion (QIC) and selected the model with the lowest QIC. The QIC statistic is available for GEE models and is similar to the Akaike information criterion (AIC).All analyses were based on two-sided p-values, with p ≤ 0.05 indicating statistical significance, and were conducted with SAS, version 9.4.Over the study period, 121,343 beneficiaries contributed to the analyses among the ≥ 65 year age group (including people who were < 65 years old in 2006 and became older during the follow-up); 103,542 people contributed to the analyses in the < 65 year old group. Characteristics of each group for the first and the last season of the study period are described in In the seasons before the 2009 influenza A(H1N1) pandemic, segmented regressions  showed aImmediately after the pandemic started, SIVU rates increased significantly among people ≥ 65 years old (+ 5%) and among those aged < 65 years with diabetes (+ 6%), respiratory diseases (+ 9%) and cardiac insufficiency/valvular heart diseases (+ 9%). During the season after the pandemic, rates decreased steeply in all groups  and remained relatively stable until the last season  but not in the other three groups , for which new recommendations would be issued in 2011 and 2013. The end of the 2009 pandemic was followed by a significant decrease among people aged < 65 years with coronary heart diseases (-13%), strokes (-16%), and neuromuscular diseases (-20%).After the new SIV recommendations were published in March 2011 and April 2013, SIVU rates increased significantly only among the people aged < 65 years with coronary heart diseases (+ 40%) and those with chronic liver diseases (+ 42%)  that is In the 2015/16 season, the crude SIVU rate was 48% in the ≥ 65 year-old group, while in the groups younger than 65 years, it ranged from 16% to 29%, according to condition. After a significant increase in SIVU during the 2009/10 pandemic season in some target groups, a steep decrease occurred in most groups during the next season followed by relative stabilisation in groups unaffected by new recommendations (a slight erosion in people aged ≥ 65 years). A significant increase in SIVU in the season immediately after the publication of new recommendations occurred only in patients with coronary heart diseases or liver diseases.Vaccinations that took place during occupational medicine visits, vaccination centres or nursing homes, which buy the vaccines for their residents are not reported to the health-related administrative databases of the French NHIF; this resulted in underestimating the coverage rate. However, these limitations are unlikely to affect the trend analysis substantially as the vast majority of vaccinations in France, for all vaccines and all ages, are administered through the private sector, and there is no reason to believe that the small proportion of vaccination delivered outside the private sector has varied over the study period. A strength of our study is that vaccine delivery data are more reliable than self-reported vaccination behaviour in surveys . Indeed,Our results show SIVU rates very far from the target in all at-risk groups, especially in those younger than 65 years. We share this disappointing observation with many other countries, but our estimates for metropolitan France are particularly low, despite the availability of free vaccine vouchers . As explThis study confirmed a modest SIV peak during the 2009 A(H1N1) pandemic in five of the eight at-risk groups considered in this study (from + 5% to + 9%). During the pandemic, officials encouraged the population to be vaccinated against both seasonal and pandemic influenza.The clear inversion of the trend during the 2010/11 season in most of the at-risk groups and the relative stability of the SIVU rate over the next few seasons for most of them may be explained in part by the controversies that took place in France over the mass vaccination campaign in 2009 against pandemic influenza. They concerned especially its large size, its organisation, the initial non-involvement of general practitioners, the allegations of conflicts of interest of WHO expert advisors , the appThe absolute value of the SIVU rate increased, albeit modestly, during the vaccination season after the publication of the new recommendations among patients with liver and coronary heart diseases. It probably expresses the effect of the voucher (which these two groups received for the first time) rather than any effect associated with the actual publication of the recommendation. This conclusion is based on the finding that the SIVU rate did not change after this publication among people with neuromuscular diseases or a past stroke, who had been receiving this NHIF voucher for several years. These results suggest that the publication of recommendations for SIV alone is not sufficient to modify vaccination behaviour, probably because general practitioners and specialists require time to absorb and apply these recommendations into their practices. New recommendations must be accompanied by complementary measures. The automatic delivery of a free voucher for the SIV is one such measure and appears to have a favourable effect. Our results, however, show a ceiling to this effect and suggest that it is not enough.Beyond the essential need for research to develop better influenza vaccines, the strategy in France to convince the public of the effectiveness and safety of the currently available SIV should be thoroughly reviewed. Interventional research in this field remains a priority. Indeed, according to Dubé et al., strong evidence to recommend any specific intervention for vaccine hesitancy/refusal is lacking in the literature, and few interventions have directly been targeted at vaccine-hesitant individuals . Convinc


Patient limitations guide selection of heart failure therapies, for which indications often specify New York Heart Association Class.To determine the extent of patient‐reported limitations during daily activities and compare to New York Heart Association class assigned by providers during the same visit, and to left ventricular ejection fraction (LVEF) group.While waiting for their appointment, 948 patients on return visits to an ambulatory HF clinic completed a written questionnaire assessing specific activity limitations, which were compared to physician‐assigned NYHA class during the same visit. Patient‐reported limitation to perform daily activity ranged from 25% for bathing to 61% for yardwork or housework and 71% for jogging or hurrying. Most patients who did not report limitations to perform daily life activities were correctly classified as NYHA I by the physicians (76%), but 12% of the 376 patients classified as NYHA I reported limitations to showering or bathing and 73% reported limitations while doing yardwork or house work. Limitation to walking was reported by 172 patients (50%) classified as class II. Limitations to walking one block were most common in patients with LVEF ≥40% compared to patients with LVEF <40%, and least commonly, in HF with better EF (improved from 31 ± 13 to 52 ± 7).Activity limitations are commonly reported by ambulatory HF patients, but underestimated by physicians. It is not clear how this should guide therapy validated for NYHA class but focused activity questions may merit wider use to track limitations and improvement in ambulatory HF. The major aim of the present study was to determine the extent of patient‐reported limitations during daily activities and to compare with the physician assignment of NYHA class on the same day. A further aim was to compare the frequency of perceived limitations across the HF different left ventricular ejection fraction (LVEF) types.22.1The present study is an observational cross‐sectional analysis of patients seen in the ambulatory HF clinic at Brigham and Women's Hospital. As part of a quality improvement initiative to improve communication about quality of life, patients were asked to complete a questionnaire, while waiting for routine clinical visit. The questionnaire was handed to the clinic physician beneath the standard registration/vital sign form as the visit began. The clinic visit and the associated clinical note were completed directly by attending physicians, who had finished formal training in heart failure. There were no specific instructions regarding how to review or incorporate the information in the clinical assessment or documentation.Patients who did not provide information about limitations to perform daily life activities, those with unknown LVEF and those whose physicians did not assign a NYHA class in the chart were excluded from the present analysis.2.2The two‐sided, single page, self‐administered questionnaire included questions about limitations to perform daily life activities, quality of life, symptoms, and patients' perception about clinical stability. To assess self‐perceived limitation to perform daily life activities, the questionnaire included the following question: “Please indicate how much you are limited by your heart failure (shortness of breath or fatigue) in your ability to do the following over the last 4 weeks?”. The activities listed included showering/bathing; walking one city block on level ground; yardwork, housework, or carrying groceries; climbing a flight of stairs (10 steps) without stopping; and hurrying or jogging (such as to catch a bus).2.3Patient demographics and clinical characteristics, including HF etiology and medical comorbidities, were assessed from clinical charts. NYHA functional class and current medications on the day of the clinic visit were updated in the electronic medical record by the attending physician, as part of routine assessment and included in our database.LVEF was recorded from the most recent available echocardiogram within 18 months from the clinical visit. HF with reduced ejection fraction (HFrEF) was defined as LVEF <40%. HF with preserved ejection fraction (HFpEF) was identified by the clinical history of HF with left ventricle ejection fraction (LVEF) ≥40% without any previous echocardiogram with LVEF <40%. HF with better EF (HFbEF) was defined when prior echocardiographic EF had been <40% with subsequent improvement of at least 10% of LVEF in the present echocardiogram.The Institutional Review Board of Brigham and Women's Hospital approved this retrospective observational study. No consents were obtained for the questionnaires, which were administered to patients as part of a clinical care and quality improvement initiative.2.4Initially, normality of distribution was tested, validating the use of parametric statistics. Continuous variables are presented as mean ± SD and categorical variables are presented as frequencies and percentages. Multiple‐group comparisons were performed using analysis of variance or chi‐square tests as appropriate. Post‐hoc correction for multiple comparisons between groups was performed using the Bonferroni method.To compare patient‐perceived daily life activity limitations across LVEF‐HF types , we performed multivariate logistic regression adjusted by age, body mass index, and comorbidities.P values <.05 were considered to indicate statistical significance. Statistical analysis was performed using Stata version 14 .All statistical tests were two‐sided, and 3A total of 1020 patients with HF completed a questionnaire on the day of their routine office visit. Patients were excluded for lack of recent measurement of LVEF (n = 33) or incomplete information about limitations to perform daily life activities (n = 39). The following analysis included the remaining 948 patients. The majority of patients were classified by physicians as NYHA I ; 343 were NYHA II (36%), 216 were NYHA III (23%), and 12 were NYHA IV (1%) .4.125 reported a high dependence on assistance for bathing (28%) in a HF cohort, but their patients were much older (mean age 80 years) than our population with mean age of 57 years.The penetrance of guideline‐recommended therapies was high in the present cohort, although the timing of the study preceded the introduction of newer therapies that may further improve functional classification.4.2NYHA classification has been widely used in clinical trials as both an enrollment criterion and as an outcome measurement. It has consistently provided reliable discrimination for risk of hospitalizations and death.Discrepancies in classifying patients as NYHA II or III has been previously described, not only for differing perceptions between physicians regarding the same patient,It is not known how the accelerating time demands of ambulatory follow‐up clinics have affected the disparity between patient and physician perception of limitation. After the in‐depth evaluation as a new patient, returning patients may have decreased their activity gradually to match their disease progression, failing to appreciate the cumulative degree of limitation. However, there is likely also an unacknowledged patient‐provider collusion to focus on positive information. The patients may be reluctant to “disappoint” their physician but also resistant to negative news, new prescriptions, or new proscriptions. On their side of the collusion, physicians may consciously or unconsciously communicate their pleasure at favorable information and their time conflict when encountering new issues that warrant additional attention during a busy clinic where other patients are waiting.4.3In our study, patients with HFpEF and HFrEF perceived similar limitations to perform daily life activities after adjusting for age, gender, and comorbidities. Depending on the details of the populations studied, QOL impairment has been shown to be similar or slightly different between HFrEF and HFpEF groups.More recently, HFbetterEF has been described, beginning with low LVEF and improving to LVEF >0.40 or 0.50, depending on the study.4.4The present study analyzed patient self‐perceived limitations to daily life activity. Factors of resilience, spiritualism, duration of disease, and previous experience and expectations of disease progressionPatients filled the questionnaires while waiting for their office visits. Attending physicians had access to the answered questionnaires, which were expected to influence their decisions when classifying NYHA in patient notes. There was no report about how frequently physicians had read or used the information provided by the questionnaires or whether this information influenced their clinical assessment. However, considering that the activity information was provided in real time to the physicians on the same day, the discrepancies between their assessments and the patient descriptions are even more notable.Results of the present study arise from a HF referral center, where all the physicians providing assessment and care are HF specialists with access to heart transplants and implants of ventricular assist devices when needed. These results may not be generalizable to other care settings, particularly to general medicine clinics where multiple different diseases are assessed simultaneously.5The discrepancy between patient perception of activity limitation and the physician estimate of NYHA class suggests that routine outpatient evaluation should incorporate either patient reporting via a separate interface or physician elicitation of specific activity performance as suggested by Albert et alIt is less clear whether reclassification of functional level according to specific activities should refine the selection of current therapies according to the NYHA class designated in the trials that provided the evidence for approval, reimbursement, and guideline recommendations. New trials designed to demonstrate improvement in functional capacity may need to isolate the questions about specific activities from the integrated summary of questionnaires such as the Kansas City Cardiomyopathy QuestionnaireThe authors declare no potential conflict of interests.


New views were discussed on the specification of glutamatergic pyramidal and γ‐aminobutyric acid (GABA)ergic interneurons. With the advancement of various in vivo imaging methods, the histopathological observations can be now linked to in vivo normal conditions and to various diseases. Our review gives a general evaluation of the exciting new developments in these areas. The human cortex has a much enlarged association cortex with greater interconnectivity of cortical areas with each other and with an expanded thalamus. The human cortex has relative enlargement of the upper layers, enhanced diversity and function of inhibitory interneurons and a highly expanded transient subplate layer during development. Here we highlight recent studies that address how these differences emerge during development focusing on diverse facets of our evolution.The cerebral cortex constitutes more than half the volume of the human brain and is presumed to be responsible for the neuronal computations underlying complex phenomena, such as perception, thought, language, attention, episodic memory and voluntary movement. Rodent models are extremely valuable for the investigation of brain development, but cannot provide insight into aspects that are unique or highly derived in humans. Many human psychiatric and neurological conditions have developmental origins but cannot be studied adequately in animal models. The human cerebral cortex has some unique genetic, molecular, cellular and anatomical features, which need to be further explored. The Anatomical Society devoted its summer meeting to the topic of Human Brain Development in June 2018 to tackle these important issues. The meeting was organized by Gavin Clowry (Newcastle University) and Zoltán Molnár (University of Oxford), and held at St John's College, Oxford. The participants provided a broad overview of the structure of the human brain in the context of scaling relationships across the brains of mammals, conserved principles and recent changes in the human lineage. Speakers considered how neuronal progenitors diversified in human to generate an increasing variety of cortical neurons. The formation of the earliest cortical circuits of the earliest generated neurons in the subplate was discussed together with their involvement in neurodevelopmental pathologies. Gene expression networks and susceptibility genes associated to neurodevelopmental diseases were discussed and compared with the networks that can be identified in organoids developed from induced pluripotent stem cells that recapitulate some aspects of  There has undoubtedly been a great leap forward in the evolution of cognitive processing in the human brain as compared with the brains of other mammalian species, but where does this evolutionary boost come from? What, if any, are the fundamental differences in the neural circuitry of the human cerebral cortex and associated structures that make us different from the mouse, our currently favored model organism, or even other primates? Are we merely blessed with a relatively larger cortex giving us greater processing power, or are there specific enhancements in the way our neural circuits are organized? As gene expression changes during development have the greatest capacity to drive macro‐evolutionary changes Carroll, , it is pIn primates, higher order associative areas have greatly increased in size and complexity  infection Characterization of radial glial cells (RGCs) at cellular, molecular and physiological levels is revealing considerable species‐specific differences Rakic, . AlthougIntermediate progenitor cells (IPCs) are a type of transit‐amplifying cell that produce small numbers of glutamatergic neurons, with average clone size ~2 neurons in mice . The cerebral cortex shows considerable differences in the composition and proportions of these elements in both radial and tangential domains. The variations in cell composition and laminar proportions reflect the different computational functions these cortical areas have to perform  produce all neural progenitor cells, with the duration and timing of progenitor cell division contributing both to the size of the resultant neocortex and the identities of its constituent cells  and tangentially migrate into the cortex and, to a large extent, human fetal brain appears to share fundamental mechanisms for cortical interneuron development ; which is also consistent with in situ hybridization and quantitative polymerase chain reaction (qPCR) studies reporting elevated expression of various ‘GABAergic’ genes anteriorly, including CALB2  and evidence of vulnerability in hypoxia/ischemia (Sheikh et al. The subplate is the most voluminous, transient compartment in the fetal cerebral wall composed of post‐migratory and migratory neurons, glia and growing axonal plexi, which are embedded in abundant extracellular matrix (Kostović & Rakic, There are several questions to be answered that will require new experimental approaches. The first is the problem of production of an extraordinary amount of extracellular matrix. It was shown that laminar shifts of extracellular matrix closely follow all developmental phases of subplate (Kostović et al. in vitro electrophysiological studies have suggested that functioning synaptic contacts are rare (Moore et al. It is important to note that in humans the subplate exists from 8 to 42 weeks of gestation, and passes through many phases of structural‐functional organization. In general, the neurogenesis, structural and molecular differentiation, gene expression and pathway content of the subplate are better known than its functional activity. Early spontaneous activity seen in the subplate in animal models has been attributed to non‐synaptic intercellular junctions (Kanold & Luhmann, Human brain development is characterized by large‐scale changes in cellular composition and cytoarchitecture, during which various neurodevelopmental processes such as neurogenesis, synaptogenesis and myelination progress with distinct trajectories and over a prolonged period of time (Silbereis et al. To capture the transcriptional signatures of the developmental processes, early microarray‐based bulk tissue studies have profiled gene expression with relatively extensive sampling across brain regions and/or timepoints (Johnson et al. Because bulk genomic analyses aggregate data across the diverse range of cell types found in the brain, it is essential to analyze gene expression within individual cell populations. Thus, the ability to determine gene expression pattern of a small number of cells or single cells is of great importance for resolving a variety of problems in the context of human brain development. Recent advances in single‐cell genomics offer the ability to characterize cell‐type diversity in a massively parallel and hopefully unbiased fashion. An increasing number of cell types and subtypes in the developing and adult human cerebral cortex have been characterized in a systematic manner at an unprecedented pace using single‐cell or nucleus RNA‐seq (Florio et al. Recent advances in genetics and genomics have identified hundreds of genetic coding and non‐coding variants that increase the risk for major neuropsychiatric disorders, including autism spectrum disorder (ASD; Šestan & State, Cellular resolution expression profiling can differentiate between expression changes within cell types and changes in the frequencies of distinct cell types. As a step in this direction, expression levels of small populations of cells in individual cortical layers were profiled in rhesus monkey brain at 10 ages using laser‐capture micro‐dissection (Bakken et al. Of course, transcriptomic data do not always equate directly to protein expression (Mertins et al. in situ hybridization studies. For instance, Fietz et al. (Furthermore, in neurons in particular, proteins may be localized to cellular compartments far removed from the cell body, for instance on axon terminals or dendrites, and such information is not provided by single‐cell RNA‐seq or z et al. , using az et al.  confirmein situ molecular profiling of cells in their circuit context. For example, osmFISH revealed the spatial distributions of 31 cell types in adult mouse cortex (Codeluppi et al. A host of new molecular tools will provide additional insights into the molecular programs that contribute to human cortical development. High‐resolution maps of chromosome interactions can reveal the functional significance of disease‐associated mutations in non‐coding regions of the genome. For example, Hi‐C profiling of progenitor and post‐mitotic cell populations from fetal human cortex identified genes that are likely regulated by genetic risk variants for schizophrenia (Won et al. Currently there is a vast gap in our knowledge regarding early human brain development, as well as neuro‐psychiatric disorders and their etiology. Studying human brain development used to rely on descriptive histological studies of post mortem specimens, and our ability to ask mechanistic questions was rather limited (Clowry et al. in vitro with fetal human brain slices (Onorati et al. in vivo by injecting ZIKV intraperitoneally into pregnant mice (Wu et al. However, the utility of organoids for translational science has been proved following the recent outbreak of microcephaly related to ZIKV infection experienced throughout the world, but particularly in Brazil (Barbeito‐Andrés et al. Modern tools of neuroimaging are now routinely used to reduce the incidence and severity of neurological impairment caused by problems around the time of birth. Bench‐to‐bedside imaging research strategy is applied to investigate the mechanisms of perinatal brain injury in order to find effective therapies, and have developed the first successful treatment for birth asphyxia – hypothermic neural rescue therapy (Azzopardi et al. Early life events such as premature birth and low birth weight due to poor fetal environment are important modulators of brain development with neurological and neuropsychiatric consequences through childhood and into adult life. Seven–12% of all births worldwide occur prematurely, 1% of all infants are born very premature and 35% of them develop neurodevelopmental impairments. Evaluating longitudinal cohorts of preterm infants that are followed‐up both with newly developed neuroimaging tools combined with motor, cognitive and behavioral evaluations have in recent years provided new knowledge on how early brain development is affected by these early life events. These studies have allowed us to identify altered global brain tissue growth rates in preterm infants (Gui et al. It is now well established that the environment and the neurosensory stimuli have an impact on sensorimotor, cognitive and psychological development, particularly during the neonatal period, and fMRI and EEG are valid methods to study early functional competence of the developing brain (Adam‐Darque et al. The elucidation of the mechanistic underpinnings of a given neuropsychiatric disorder requires detailed knowledge of the ultimate phenotypic outcome of that disorder, the normal developmental processes that are subverted as the disease progresses, and the environmental, genetic or epigenetic insults that contribute to establish the disorder. In the last decade, we have made substantial progress identifying the brain regions and cell types affected by many neuropsychiatric disorders; begun to uncover the cellular diversity and processes that underlie cell division, differentiation and maturation in different regions of the brain; and identified numerous common and rare genetic variants, including single nucleotide polymorphisms, copy number variants and de novo mutations associated with disorders including Alzheimer's disease, schizophrenia, bipolar disorder and ASD. These advances have been greatly facilitated by new technologies, including improved and more cost‐effective sequencing, and the development of new model systems, such as the development of 2D and 3D organoid cultures. In particular, although post mortem tissue will likely remain essential for many analyses, organoid cultures allow the observation and manipulation of many aspects of human brain development with nearly unlimited temporal resolution and fewer ethical and practical concerns. Similarly, the ability to conduct longitudinal neuroimaging studies of both the neurotypical and diseased human brain will offer increased resolution concerning the timing, phenotype and variability of disease onset and progression. As a consequence, our ability to understand neurological and psychiatric disorders and potentially generate therapeutic remedies is likely to continue to accelerate.


Anthropogenic activities in urban ecosystems induce a myriad of environmental changes compared with adjacent rural areas. These environmental changes can be seen as series of abiotic and biotic selection filters affecting the distribution of plant species. What are the attributes of plant species that compose urban communities, compared with rural communities, as related to their ecological affinities , and reproductive traits ? Using a floristic dataset from a citizen science project recording plant species growing spontaneously in the streets, we analyzed the distribution of species according to their ecological requirements and reproductive traits along an urbanization gradient in the Parisian region. We developed an original floral and pollinator typology composed of five floral and four pollinator morphotypes. The proportion of impervious areas, used as a proxy of urbanization, was measured at different spatial scales, to reveal at which spatial scales urbanization is selecting plant traits. We found significant differences in plant communities along the urbanization gradient. As expected with the warmer and drier conditions of urban areas, species with higher affinities to higher temperature, light and nutrient soil content, and lower atmospheric moisture were over‐represented in urban plant communities. Interestingly, all of the significant changes in plant abiotical affinities were the most pronounced at the largest scale of analysis , probably because the specific urban conditions are more pronounced when they occur on a large surface. The proportion of autogamous, self‐compatible, and nonentomophilous species was significantly higher in urban plant communities, strongly suggesting a lower abundance or efficiency of the pollinating fauna in urban environments. Last, among insect‐pollinated species, those with relatively long and narrow tubular corollas were disadvantaged in urban areas, possibly resulting from a reduction in pollinator abundance particularly affecting specialized plant–pollinator interactions. We focused on the Île‐de‐France region (around Paris) to benefit both from a high sampling density in a full urbanization gradient and from previously published plant–pollinator studies in the region. We used the proportion of impervious areas around sampling sites as a measure of urbanization because it is obviously a major factor differentiating cities from other land uses, and because many studies have already used this measure , we produced two alternative hypotheses to explain the distribution of plants species according to floral morphology. The first hypothesis (H1) states that because pollinator taxa with a preference for nontubular flowers are rare in urban areas, the proportion of plant species with nontubular flowers should decrease in urban areas compared with rural areas. The alternative hypothesis (H2) states that because global pollinator abundance is low in urban areas, and because open flowers can be pollinated by a greater number of pollinator species , ecological strategies . Additional online identification keys and online assistance by botanists from the Tela Botanica network were also provided. The volunteers also recorded the habitat type of each plant in the street and the date of observation. For each species, a confidence degree of identification had to be specified, and pictures could be uploaded for subsequent data validation by botanists involved in the program. The program was launched in 2008 and has been running continuously since then.The present study was based on data from a French citizen science project called “Sauvages de ma rue” aiming at collecting floristic data in France following a standardized protocol. This scientific and pedagogic project was initiated by the Centre d'Ecologie et des Sciences de la Conservation (CESCO) of the Museum National d'Histoire Naturelle (MNHN) and was promoted by the association Tela Botanica. In this program, volunteers were invited to identify and list all the spontaneous plant species growing in a street of their choice located by the GPS coordinates of the starting point of the inventory . An identification guide and a mobile application were available to help the volunteers to identify the plants  it was one of the most densely sampled area, (b) it included seminatural habitats distant from urban influences as well as highly urbanized landscapes, and (c) studies on the distribution of floral visitors . For every 1,161 sampling sites, we calculated the proportion of impervious surfaces in different radius of 50, 100, 500, and 1,000 meters using a vector layer with Quantum GIS version 2.14.13  represented a full urbanization gradient, with samples (inventoried streets) presenting 0.5%–100% of impervious surfaces in a buffer radius of 500 m.We used 1/5,000 GIS maps reporting land cover over the Île‐de‐France region published in 2012 by the Institut d'Aménagement et d'Urbanisme. We used seven categories of land cover: (a) forest; (b) seminatural area; (c) agricultural land; (d) water; (e) artificial open space; (f) quarries, dumps, and worksites; and (g) impervious surfaces  algorithm with R . The final dataset included a total of 366 plant species.Based on the identifications made by the volunteers, we established a list of plant species occurring in the study region. Species names were checked, and synonyms were replaced by the currently accepted name (http://philippe.julve.pagesperso-orange.fr/catminat.htm), using the package TR8  within a flower are either easily accessible to pollinators (P) or hidden within the corolla (p). Based on three categories of corolla tube length and two categories of corolla tube width, we considered that the nectar  can be easily accessible (N), moderately accessible (n+), or hardly accessible (n) to pollinators. The combination of pollen and nectar accessibility allowed us to define five floral morphotypes “pn,” “pn+,” “Pn+,” “Pn,” and “PN” Figure . CategorIn addition, the presence or absence of a nectar spur and the presence or absence of tubular flowers  were scored for every plant species of the dataset. A plant species was considered to have tubular flowers when the corollas presented a tube longer than 4 mm, either with fused petals  or with free but contiguous petals forming a tube from the pollinator point of view .A list of the modalities of each trait and ecological affinity index  is presented in Table 2.4To identify traits over‐ or under‐represented in highly urbanized areas and potentially involved in plant species adaptation, mixed models were used on the 21 traits listed in Table Three categories of statistical tests were applied depending on the trait variable modalities. Multinomial logistic regressions (MLR) were used with the package nnet  defined by Anselin , allowing to identify the scale of analysis best predicting a plant trait according to the percentage of impervious area , we assessed the significance of the relationship between the Grime's CSR ecological strategy and all qualitative reproductive traits  by performing a chi‐squared independence test on the contingency table recording the total number of plant species for each combination of the modalities of both variables.We also tested the effect of the Grime's CSR ecological strategy, the level of autogamy, and all the other qualitative reproductive traits on the variable “end of flowering.” For this purpose, in addition to the proportion of impervious areas in a buffer of 1,000 m, either the Grime's ecological strategy or each of the qualitative reproductive traits was included as fixed factors in different GLMMs with the “end of flowering” as the response. For clarity and synthesis purpose, hereafter we focused our attention on variables that produced significant results.33.1The floral dataset used in this study included a total of 366 species belonging to 234 genera and 76 families . Observations were distributed in 167 spatial clusters of sampling sites, with 16 clusters presenting a mean proportion of impervious areas in a radius of 500 m comprised between “,” 44 clusters between “,” and 107 clusters between “.” The high number of clusters presenting a high percentage of impervious areas is due to the fact that the program “Sauvages de ma rue” is targeted on plant species growing in streets. On average, 25.6 species were observed per cluster , the increasing percentage of impervious areas was significantly associated with a reduced frequency of morphotypes pn and Pn, with nectar hardly accessible to pollinators Figure . A highep‐value = .06 with buffer 500 m). On the opposite, increasing percentage of impervious areas was significantly associated with high levels of self‐compatibility, autogamy, and affinity for high temperatures, for nutrient‐rich environments, and for unshaded environments. No significant results were obtained for the remaining plant ecological affinity nor for the occurrence of tubular corolla and nectar spur.The increasing percentage of impervious areas was significantly associated with a reduced level of plant vegetative reproduction and a reduced affinity for humid atmosphere, especially at large scales Table . There wp‐value < .001) and the effect of impervious areas became nonsignificant . Similarly, when the variable accounting for the level of autogamy was included in the GLMM model together with the proportion of impervious areas  to explain the flowering end, a significant effect of the level of autogamy was detected  and the effect of impervious areas became nonsignificant . Pairwise comparisons in both GLMM models revealed that the R‐strategists  had a significantly longer and later flowering period than the S‐strategists and C‐strategists, and that the “autogamous or facultative autogamous” species had a significantly longer and later flowering period than the species with “intermediate mating systems” and “allogamous or facultative allogamous” species. Furthermore, the chi‐squared independence test used to assess the relationship between the Grime's CSR ecological strategy and the level of self‐compatibility revealed a significant correlation between both traits . The R‐strategists were significantly more frequently composed of “autogamous or facultative autogamous” species, C‐strategists were significantly more frequently composed of “allogamous or facultative allogamous,” and S‐strategists were significantly more frequently composed of species with “intermediate mating” systems.Plants having a flowering period ending later and a longer flowering period (no significant differences for the beginning of flowering) were favoured in sites surrounded by a higher proportion of impervious areas  found here is consistent with the recent synthesis of Williams et al. . A highe4.2Concerning the distribution of Grime's ecological strategies along the urbanization gradient, we found a dominance in R‐strategists in urban areas, and a dominance of C‐strategists in periurban or suburban areas, as previously observed  have the potential to efficiently detect ecological trends and could help identify the most problematic environmental disturbances or urban planning for biodiversity persistence. This information could also help implement sustainable plant species introductions in urban green spaces , adapted to the abiotic and biotic conditions imposed by the urban environment, and favoring wild pollinators in cities.None declared.This study constitutes a part of the doctoral thesis of JD under the supervision of BC and SN. All authors contributed to the writing of the manuscript and gave approval for publication. Click here for additional data file. Click here for additional data file.


Contemporary imaging techniques have increased the potential for establishing how brain regions interact during spoken language. Some imaging methods report bilateral changes in brain activity during speech, whereas another approach finds that the relationship between individual variability in speech measures and individual variability in brain activity more closely resembles clinical observations. This approach has repeatedly demonstrated that speaking rate for phonological and lexical items can be predicted by an inverse relationship between cerebral blood flow in the left inferior frontal region and the right caudate nucleus. To determine whether morphology contributes to this relationship, we examined ipsilateral and contralateral white matter connections between these structures using diffusion tensor imaging, and we further assessed possible relationships between morphology and selected acoustic measures of participants' vocal productions. The ipsilateral connections between the inferior frontal regions and the caudate nuclei had higher average fractional anisotropy and mean diffusivity values than the contralateral connections. Neither contralateral connection between inferior frontal and caudate regions showed a significant advantage on any of the average morphology measures. However, individual differences in white matter morphology were significantly correlated with individual differences in vocal amplitude and frequency stability in the left frontal–right caudate connection. This cortical–striatal connection may be “tuned” for a role in the coordination of cortical and subcortical activity during speech. The structure–function relationship in this cortical-subcortical pathway supports the previous observation of a predictive pattern of cerebral blood flow during speech and may reflect a mechanism that ensures left-hemisphere control of the vocal expression of propositional language. Well before functional imaging, 19th-century observations on the effects of focal brain damage reported by Paul Broca postulated a speech production area in the inferior frontal region of the left cerebral hemisphere. The consistent localization of propositional speech and language  that accompanies disorders such as Parkinson's disease and the cerebellar ataxias added pieces to the puzzle, revealing the importance of the basal ganglia and cerebellum for normal speech. Neurological disorders sketch a network for speech production, whereas functional imaging offers the potential for creating a more detailed blueprint.As part of the developing blueprint for the speaking brain, we previously identified a simple, reliable pattern of blood flow changes with positron emission tomography (PET) that is predictive of speech rates during functional imaging. In normal speakers  by using diffusion tensor imaging data and structural images. These people were recruited from the Nathan Kline Institute (NKI)-Rockland Sample imaging project  consisted of 137 volumes, 128 acquisitions with diffusion-sensitizing gradient directions with diffusion weighting of b = 1500 s/mm2 and 9 acquisitions without diffusion weighting . The following acquisition parameters were used in DWI: TR = 2400 ms, TE = 85 ms, flip angle = 90°, measurements = 1, FOV = 212 mm, base resolution = 106, voxel size = 2.0 × 2.0 × 2.0 mm3, and slice thickness = 2 mm.All magnetic resonance images (MRI) were acquired by using a Siemens 3T scanner as part of a large-scale study of normal subjects  and targets  for each individual . SubjectAfter preprocessing the structural and diffusion images, diffusion parameters were estimated by using Bayesian estimation of diffusion parameters obtained by using sampling techniques BEDPOSTX  and target  ROIs and carried out by using FSL's routine “probtrackx” for all combinations of seeds and targets. This routine was run with the following parameters: number of iterations per voxel was 5000, maximum number of steps for a streamline was 2000 with the step size 0.5 mm, curvature threshold was 0.2, and the modified Euler's streamlining option was used. Two output files from this routine were used. The “fdt_paths.nii” file is a 3D image containing the output connectivity distribution between the seed and target ROIs. The text file “waytotal” contains one number per seed mask corresponding to the total number of tracts generated from each seed ROI that have successfully reached the target ROI. “Probtrackx” was run for one seed and one target at a time, so the “waytotal” file contains one number and “fdt_paths.nii” file is a 3D connectivity distribution image for a specific set of seed and target.Post-tractography calculations of connectivity and diffusion measures were performed by using the 3D image file for the tracts. As individual structural images were used to define the seed and target ROIs, the sizes of these ROIs varied for different participants and resulting path files could have been dependent on ROI size. To compare the results across participants, we divided the “fdt_paths.nii” data by the corresponding waytotal values and employed a threshold so that the normalized 3D images of the fdt_paths represented the upper 90% of the data. This provided a binary mask based on the tract probability. The volume for the normalized path calculated in this way was determined. The binary mask in structural space was registered to FA and MD 3D images, respectively. The mean FA and mean MD values were then calculated for the normalized tracts. FA values <0.2 were excluded.pa/, /ta/, and /ka/) and a syllable sequence (/pa-ta-ka/) as quickly as possible on a single breath. Syllable repetitions were digitally recorded for subsequent analyses. Recordings were made by using a Marantz Professional digital recorder (PMD660) and a Shure unidirectional head-worn dynamic microphone (SM10A). Simultaneous backup recordings were made with a separate PMD660 digital recorder and a boom-mounted AKG D5 microphone. All recordings were made in .wav format at a 48k sampling rate. The grooved pegboard  and pitch stability (jitter) in the vocalic portion of each syllable. These served as the performance measures of vocal production. A non-speech comparison task requiring fine motor control assessed dexterity for each hand . Probability values (p) <0.025 (two-tailed) in the correlation analyses were considered significant.Measures of white matter characteristics were analyzed by using mixed-design repeated-measures analyses of variance with a full-factorial model, and paired F = 12.17; p = 0.002]. The ipsilateral left inferior frontal–left caudate connection had higher FA than the contralateral left inferior frontal–right caudate connection . Similarly, the ipsilateral right inferior frontal–right caudate connection had higher FA than the contralateral right inferior frontal–left caudate connection . The MD demonstrated comparable results  = 14.27; p = 0.001]. The ipsilateral left inferior frontal–left caudate connection had higher MD than the contralateral left inferior frontal–right caudate connection . The ipsilateral right inferior frontal–right caudate connection had higher MD than the contralateral right inferior frontal–left caudate connection . In general, the ipsilateral connections had higher FA and MD values than the contralateral connections, but the answer to the first question posed in this study is that none of the group average white matter structural characteristics distinguished the left inferior frontal–right caudate connection from the other pathways.Analysis of normalized white matter volumes revealed large between-subject variability and no significant main effects of laterality for seeds or targets, no interaction between seeds and targets, and no pairwise differences involving laterality or ipsilateral–contralateral comparisons . There w results . There wPotential relationships between the acoustic characteristics of the speech samples from each participant, as measured at the vocalic portions of syllables, and their normalized white matter volumes, their FA values, and their MD measures for each of the four cortical–striatal tracts were examined by using Spearman's correlations. The results of this analysis addressing individual differences in speech and brain morphology are presented in The group average tractography data alone did not distinguish the left inferior frontal–right caudate connection from the other pathways. However, the analyses examining the relationship between individual differences in acoustic stability and individual differences in white matter morphology did identify a structure–function correlation in this cortical–subcortical pathway. Subjects with higher levels of vocal frequency and amplitude perturbations were more likely to have larger normalized white matter volumes, greater tissue integrity, and, to a lesser extent, greater tissue density in the white matter connecting the left inferior frontal region and the right caudate, areas repeatedly shown to be associated with speech rate using PET measures of blood flow. The positive relationships between acoustic instability and white matter integrity are interesting. Although it might seem that stronger white matter connections might yield more stable vocal production, alternatively, it can also be speculated that the stronger white matter connections could reflect structural adaptations in individuals for whom vocal stability requires greater control. Although its functional significance remains to be clarified, the white matter connection between the left inferior frontal region and the head of the right caudate nucleus appears to be tuned to intrinsic features of a planned vocal utterance. Such possible tuning may play a significant role in cortical–striatal interactions that are necessary for proper execution of a vocal expression.Tuning is prevalent in sensory systems where neuronal activity is related to specific stimulus features (Patel and Iversen, Lesions of either the left or right caudate can result in disordered speech, and this abnormality may be more common after right-sided lesions (Caplan et al., The clinical observations regarding the right caudate and speech, together with the present demonstration of a tuned white matter connection with the left hemisphere speech area, suggest a novel concept in cortical–striatal interactions: Ipsilateral and contralateral cortical input to the caudate may have different functional consequences with respect to motor control. For fluent speech, which requires a degree of asymmetrical hemispheric control, left hemisphere input to the right caudate may result in inhibition or disconnection of cortical areas in the right hemisphere from the speech control process. Although possible differences in the roles of ipsilateral and contralateral cortical–striatal connections have not been addressed in models of the basal ganglia, complementary functions for these connections could play a role in maintaining unilateral control of a complex production system such as that involved in speaking.One characterization of the basal ganglia's role in motor function proposes that these structures provide both a focused selection of a desired motor action and inhibition of competing motor programs Mink, , a generAlthough this formulation of cortical–subcortical control of speech is consistent with the production of phonological and lexical units in propositional speech, normal conversation speech likely represents a more complicated situation. Studies have shown that as much as 25% of normal conversations consist of formulaic expressions, a language mode that differs from propositional speech (Van Lancker and Rallon, Recognition of the plasticity of white matter in response to experience has facilitated new ways of thinking about functional systems in the brain. White matter plasticity has been demonstrated in an increasingly diverse range of motor skills from piano playing (Bengtsson et al., Complex functions, such as speech, reflect the output of integrated systems of specialized functional and structural anatomy. As with the original functional imaging data on speech rate, the relevant structural characteristics of a cortical–striatal network for motor speech control were not identified based on gross left/right or ipsilateral/contralateral differences, but on relationships between individual differences in both performance and morphological characteristics. Understanding the properties of this simple cortical–striatal network during speech may provide a clearer understanding of the speaking brain as well as new insights into the complex functions of the basal ganglia.


Adaptive behavior relies on the ability of the brain to form predictions and monitor action outcomes. In the human brain, the same system is thought to monitor action outcomes regardless of whether the information originates from internal  and external  sensory channels. Neural signatures of processing motor errors and action outcomes communicated by external feedback have been studied extensively; however, the existence of such a general action‐monitoring system has not been tested directly. Here, we use concurrent EEG‐MEG measurements and a probabilistic learning task to demonstrate that event‐related responses measured by electroencephalography and magnetoencephalography display spatiotemporal patterns that allow an effective transfer of a multivariate statistical model discriminating the outcomes across the following conditions: (a) erroneous versus correct motor output, (b) negative versus positive feedback, (c) high‐ versus low‐surprise negative feedback, and (d) erroneous versus correct brain–computer‐interface output. We further show that these patterns originate from highly‐overlapping neural sources in the medial frontal and the medial parietal cortices. We conclude that information about action outcomes arriving from internal or external sensory channels converges to the same neural system in the human brain, that matches this information to the internal predictions. ElectroA prominent theory by Holroyd and Coles  suggestsHere, to probe the functional similarity of ERN and FRN, we used the recently introduced across‐condition generalization technique . Using a BCI ensured active involvement of the participants (as compared to the passive viewing tasks used previously) and, at the same time, minimized the possibility of the spurious generalization with motor trials due to the presence of highly‐similar movement‐related activity  naïve to BCIs participated in the experiment. Aalto University Committee on Research Ethics approved the study. All subjects read the description of the experiment and signed the informed consent form before the experiment. The data of one subject were removed from the analysis involving the 2.2We used a probabilistic learning task 2 = 0.0625. The probability model for SVM was created using cross validation as implemented in the Scikit‐Learn package  ‐based BCI paradigm to control the task. On each trial, two target stimuli  appeared on the screen. Each stimulus was masked by a white noise pattern modulated at 12 or 15 Hz. The noise mask was sampled from a pre‐generated set at this frequency and the opacity of the noise image was scaled with a sinusoid of the same frequency so that it varied from 10% to 90%. Figure . The tim Vapnik, . We defiTo choose the target, the subjects were instructed to focus and maintain visual attention on it until the system indicated the response. When the system reached a decision about the subject's choice, a green arrow pointing to the selected target appeared in the middle of the screen for 500 ms indicating a choice. If the classifier failed to arrive to a decision in 5 s, the trial ended with the message “Too late” appearing on the screen.On making the selection, the subject received feedback whether he or she had earned  or had not earned  the score in this trial. The feedback was displayed 1,000 ms after the target selection. At the end of each trial, the subject was asked to report whether the system had indicated his or her choice correctly by lifting the left or right index finger. The association of the left/right finger and the correct “incorrect” indication was randomized across trials to ensure that no systematic preparatory activity contaminated the data following the correct or incorrect BCI performance. The subjects were instructed to indicate whether a system indicated their choice correctly (BCI‐correct) or incorrectly (BCI‐error) as accurately as possible; no time limit was set for reporting on BCI performance.We motivated the subjects to earn a high score in the learning task by telling them that the subject attaining the highest score will be awarded a prize. We also encouraged them to report the BCI performance as accurately as possible by saying that an incorrectly labeled trial may worsen the BCI performance and by mentioning that every trial counted as “incorrect” will be discarded and will thus prolong the experiment. Therefore, dishonest reporting was against the subject's interest.2.4The BCI calibration consisted of two blocks (4 min per block) comprising 16 trials each. The design of the calibration trials was similar to those in the actual experiment. On each trial, the target stimulus (star shape) and a blank rectangle appeared on the screen. Participants were instructed to sustain visual attention on the target stimulus for 5 s. The data from the first calibration block were used to initialize the spatial filters and the classifier. The performance of the system was then tested online during the second calibration block.Similarly to the rest of the experiment, subjects received positive feedback whenever the target stimulus was chosen and negative feedback if the non‐target, that is, the blank rectangle, was chosen.2.5The BCI task consisted of 360 trials and was split into five blocks consisting of 72 trials each. On each trial, a random pair from the set of four stimuli appeared on the screen. As the stimuli, we used letters from the Tigrinya alphabet, which was unfamiliar to all of our subjects. The probabilities of generating positive feedback were 0.8, 0.66, 0.33, and 0.2.positive versus negative feedback, high‐ versus low‐surprise feedback, and erroneous versus correct classification of subjects’ intentions by the BCI were taken from the BCI task.Trials containing evoked responses related to processing To ensure a sufficient number of BCI errors for the analysis, we introduced a randomly‐appearing error to the BCI control affecting 15% of the trials. These artificial BCI errors produced a distinct trigger code such that these trials could be separated from all other trials. BCI‐error trials were then defined as those with this trigger code followed by a subjective report of incorrect BCI performance. More details on the BCI control accuracy and trial counts are presented in Supporting Information positive vs. negative feedback, high‐ vs. low‐ surprise feedback), we retained only those trials where the subject's intention was correctly decoded by the BCI. For detailed definition of positive versus negative feedback, high‐ versus low‐ surprise feedback, see subsections For the analysis of the feedback‐related responses . As the stimuli and their respective values were already familiar to the subjects (see Section motor) error trials.The 2.7MEG data were acquired using a 306‐channel Elekta Neuromag System  comprising 102 magnetometers and 204 planar gradiometers. Data were sampled at 1,000 Hz after filtering to 0.1–330 Hz. EEG was recorded concurrently using a 64‐electrode Waveguard™  MEG‐compatible cap with the reference electrode at the AFz position. Prior to analysis, the EEG signals were re‐referenced to their average value.To control for eye‐movement‐related artifacts, a pair of electrooculographic (EOG) electrodes placed below the left eye and on the frontal processes of the left zygomatic bone were applied. Head movements were monitored continuously during the recordings using 5 head‐position‐indicator (HPI) coils.Prior to the MEG recording, anatomical landmarks (nasion and left and right preauricular points), HPI coils and EEG electrode positions, and 100 (+/−5) additional scalp‐surface points were digitized using the Isotrak 3D digitizer . To ensure roughly equal distances between the scalp and the frontal and occipital sensors, a special cushion was used whenever necessary.The stimuli were shown on a semi‐transparent back‐projection screen by a projector located outside the shielded room. The distance between a participant's eyes and the screen was 1.25 m.2.8For the MEG signal, external magnetic interference was suppressed and head movements compensated for using the signal‐space separation (SSS) method implemented in the MaxFilter software   and two least valuable stimuli (33% vs. 20%) until very late in the experiment. Thus, in such trials the expectation to get positive feedback was relatively low as compared to high‐surprise negative feedback trials .To assess the contribution of the high versus low surprise negative feedback should capture the activity specific to the expectancy of an outcome, but not its valence. This analysis was performed only on negative feedback trials due to the low number of trials where positive feedback resulted from picking the low‐value stimuli. Trial counts for this analysis are summarized in Supporting Information The behavioral results suggested that by the fourth experimental block  were probed using two‐tailed two‐sample permutation tests at the FCz electrode followed by maximum FWER correction for multiple comparisons. As no a priori hypothesis was available regarding the MEG sensor most sensitive to the magnetic counterpart of the ERN/FRN, no statistical analysis was performed on the evoked MEG data. For visualization purposes in Figure Evoked response analysis was performed to verify the presence of the evoked activity both in the EEG and MEG data. Evoked responses were baseline‐corrected by subtracting the mean amplitude value of the pre‐stimulus interval from the signal. In the EEG data, group‐level effects for all four conditions , we trained a separate logistic regression classifier at each time point  to discriminate between the respective outcomes and tested each of these classifiers on all other time points within this condition. This analysis was performed on the combined EEG–MEG sensor data for each subject separately. The resulting matrix contained within‐condition classification scores at each time‐point pair. These scores were obtained by computing the mean area under receiver‐operating characteristic curve (ROC AUC) via threefold cross validation. On each fold, a classifier was trained on 66% of the trials and tested on the held‐out 33%. Features from both the training and test sets were normalized by subtracting the mean and dividing by the standard deviation of the training set prior to the classification. The regularization parameter C for l2‐penalized logistic regression classifiers was set to 0.1 and no feature selection was performed.To identify time windows contributing to the discrimination within each condition  based on the single‐trial combined EEG–MEG sensor data for each subject separately. Then, the performance of each of these classifiers was tested at each time instance of the data measured at another condition to predict a different pair of class labels . The resulting matrix contained cross‐condition generalization scores at each time‐point pair for each subject separately. These scores were computed as the mean predictive ROC AUC across the six folds. On each fold, 66% of the trials from condition A were used to train the classifier. This classifier was then tested on all trials of condition B. The data from each condition were used as the training and test set alternatively . Similarly, to traditional cross‐validation, this sub‐sampling approach introduced a conservative bias onto the grand‐average ROC AUC estimates, reducing the variance of the classification score estimates and constraining the null‐distribution of ROC AUC estimates around the theoretical 50% chance level. Given the possible variability in evoked responses due to learning, this approach also allowed us to focus our analysis on the activity that was present in all trials.To test for similarity between outcome‐specific evoked responses across C for l2‐penalized logistic regression classifiers was set to 0.1 and no feature selection was performed.Features from both the training and test sets were normalized by subtracting the mean and dividing by the standard deviation of the training set prior to the classification. The regularization parameter 2.15t‐test where we permuted the signs of de‐meaned AUC scores.The group‐level statistical significance of the within‐ and cross‐condition classification scores was assessed using one‐sample permutation test, followed by cluster correction for multiple comparisons . The resulting cortical meshes were down‐sampled to 8,196 vertices to form the source spaces. A single‐compartment boundary element model describing the shape of the cranium was used as the volume‐conductor model. Noise‐covariance matrices were estimated from the 200‐ms pre‐stimulus intervals. Signal‐to‐noise ratio was defined as the ratio between traces of the respective covariance matrices.Source analysis was performed using only MEG data. Neural sources contributing to the observed differences in the evoked responses were estimated by computing dynamic statistical parametric mapping (dSPM) maps  around the maxima of the individual generalization maps. This source estimation was done to identify sources contributing specifically to the observed generalization effects.To derive the activation patterns that contributed to the successful transfer of the classifiers across the conditions, we identified for each condition and for each subject the time point where this transfer resulted in the highest generalization score. We then extracted the classifier weight vectors of that and the two adjacent time points  and multiplied them by the signal covariance matrix computed for this condition  of the BCI task, 11 out of 14 subjects chose the more valuable stimulus in more than 75% of the trials, indicating learning and therefore likely development of expectations with regard to their choices.SD) of trials. After the measurements, they were asked to rank the stimuli according to their value on a visual analog scale. The mean correlation of the reported and true order was 0.80.In the motor task, the subjects chose the higher‐valued alternative in 76.6 ± 7.6% . Supporting Information 3.3In the EEG data, the average evoked responses at the FCz electrode differed significantly between the outcomes in all conditions Figure , replica3.4motor selection), a separate classifier was trained on single trial EEG–MEG data at each time point (0–500 ms with respect to the triggering event) and tested on all other time points within the same condition. Group‐level permutation tests performed on individual within‐condition classification maps identified clusters of time points where the ROC AUC scores were significantly higher than the theoretical 50% chance level within all studied conditions  in one condition  and applied it to each time point of another condition . Group‐level permutation tests performed on the individual cross‐condition generalization maps indicated multiple statistically significant clusters of ROC AUC scores when comparing motor versus feedback:expectancy and motor versus bci conditions  versus feedback:expectancy (300–400 ms) and motor (20–120 ms) versus bci (330–430 ms) conditions, we observed major contributions from bilateral dACC (BA 32), posterior cingulate cortex  and right inferior temporal cortex including anterior insula  involving learning from probabilistic feedback, followed by a separate motor task. We then tested the similarity of single‐trial event‐related responses to negative outcomes in EEG–MEG data.The goal of this study was to probe whether the same neural process underlies the evaluation of action outcomes when operating a BCI, receiving feedback to a choice, or performing a motor task without feedback. To this end, we designed an experiment comprising a BCI‐controlled task . Moreover, the fact that the latencies  and the PCC , known to be involved in outcome processing  that cannot be assured in a passive viewing paradigm. Thus, BCI errors reflect the loss of control rather than reward‐prediction error associated with classical FRN. This distinction was supported by the fact that no generalization between feedback valence and BCI trials was observed. However, the across‐condition generalization largely depends on the structure of noise covariance as well as that of signal. Thus, by the absence of across‐condition classification performance alone, we cannot exclude the possibility that the same neural sources are active and class‐discriminative.Introducing a BCI in our study ensured that the cross‐condition generalization between ‐related error responses and ERN share at least some of the underlying neural generators.We observed that a failure to control the BCI triggers essentially the same neural processes as committing errors in the motor task. Neural signals following the incorrect operation of a BCI system have been reported earlier, mainly in the context of using such responses as an additional control or learning signal for optimizing BCI performance  ERN and FRN were estimated in separate tasks; (b) the subjects had learned to prefer the more valuable stimuli in the BCI task before proceeding to the motor task Supplementary Table V. Time‐resolved across‐condition generalization (EEG and MEG)Supplementary Table VI. Individual maximum scores in across‐condition generalization and the corresponding time pointsSupplementary Figure VII. Time‐resolved within‐ and across‐condition generalization computed separately for EEG (top) and MEG (bottom) data. Colored areas represent binary masks indicating clusters of time‐points where the generalization within  and across  the conditions indicated on the horizontal and vertical axes was statistically significant.Supplementary Figure VIII. Time‐resolved within‐ and across‐condition generalization computed for combined EEG–MEG data. Colored areas represent ROC AUC scores estimating the generalization within  and across  the conditions indicated on the horizontal and vertical axes.Supplementary Figure IX. Estimation of neural sources informing the classifiers that generalize across motor vs. bci conditions. Black markers indicate time points where maximum generalization scores were observed in each subject within time‐windows indicated by shaded areas. Visualization threshold for source estimates is set to 90% of the peak activation value.Supplementary Figure X. Estimation of neural sources informing the classifiers that generalize across feedback:expectancy vs. bci conditions. Black markers indicate time points where maximum generalization scores were observed in each subject within time‐windows indicated by shaded areas. In the bottom panel time‐window was identified based on results of generalization of the both studied condition to the motor condition. Visualization threshold for source estimates is set to 90% of the peak activation value.Click here for additional data file.