Negation recognition in clinical natural language processing using a combination of the NegEx algorithm and a convolutional neural network.

BACKGROUND: Important clinical information of patients is present in unstructured free-text fields of Electronic Health Records (EHRs). While this information can be extracted using clinical Natural Language Processing (cNLP), the recognition of negation modifiers represents an important challenge. A wide range of cNLP applications have been developed to detect the negation of medical entities in clinical free-text, however, effective solutions for languages other than English are scarce. This study aimed at developing a solution for negation recognition in Spanish EHRs based on a combination of a customized rule-based NegEx layer and a convolutional neural network (CNN). METHODS: Based on our previous experience in real world evidence (RWE) studies using information embedded in EHRs, negation recognition was simplified into a binary problem ('affirmative' vs. 'non-affirmative' class). For the NegEx layer, negation rules were obtained from a publicly available Spanish corpus and enriched with custom ones, whereby the CNN binary classifier was trained on EHRs annotated for clinical named entities (cNEs) and negation markers by medical doctors. RESULTS: The proposed negation recognition pipeline obtained precision, recall, and F1-score of 0.93, 0.94, and 0.94 for the 'affirmative' class, and 0.86, 0.84, and 0.85 for the 'non-affirmative' class, respectively. To validate the generalization capabilities of our methodology, we applied the negation recognition pipeline on EHRs (6,710 cNEs) from a different data source distribution than the training corpus and obtained consistent performance metrics for the 'affirmative' and 'non-affirmative' class (0.95, 0.97, and 0.96; and 0.90, 0.83, and 0.86 for precision, recall, and F1-score, respectively). Lastly, we evaluated the pipeline against two publicly available Spanish negation corpora, the IULA and NUBes, obtaining state-of-the-art metrics (1.00, 0.99, and 0.99; and 1.00, 0.93, and 0.96 for precision, recall, and F1-score, respectively). CONCLUSION: Negation recognition is a source of low precision in the retrieval of cNEs from EHRs' free-text. Combining a customized rule-based NegEx layer with a CNN binary classifier outperformed many other current approaches. RWE studies highly benefit from the correct recognition of negation as it reduces false positive detections of cNE which otherwise would undoubtedly reduce the credibility of cNLP systems.

Development of a predictive model of venous thromboembolism recurrence in anticoagulated cancer patients using machine learning.

INTRODUCTION: Patients with cancer and venous thromboembolism (VTE) show a high risk of VTE recurrence during anticoagulant treatment. This study aimed to develop a predictive model to assess the risk of VTE recurrence within 6 months at the moment of primary VTE diagnosis in these patients. MATERIALS AND METHODS: Using the EHRead® technology, based on Natural Language Processing (NLP) and machine learning (ML), the unstructured data in electronic health records from 9 Spanish hospitals between 2014 and 2018 were extracted. Both clinically- and ML-driven feature selection were performed to identify predictors for VTE recurrence. Logistic regression (LR), decision tree (DT), and random forest (RF) algorithms were used to train different prediction models, which were subsequently validated in a hold-out data set. RESULTS: A total of 16,407 anticoagulated cancer patients with diagnosis of VTE were identified (54.4 % male and median age 70). Deep vein thrombosis, pulmonary embolism and metastases were observed in 67.2 %, 26.6 %, and 47.7 % of the patients, respectively. During the study follow-up, 11.4 % of the patients developed a recurrent VTE, being more frequent in patients with lung cancer. Feature selection and model training based on ML identified primary pulmonary embolism, deep vein thrombosis, metastasis, adenocarcinoma, hemoglobin and serum creatinine levels, platelet and leukocyte count, family history of VTE, and patients' age as predictors of VTE recurrence within 6 months of VTE diagnosis. The LR model had an AUC-ROC (95 % CI) of 0.66 (0.61, 0.70), the DT of 0.69 (0.65, 0.72) and the RF of 0.68 (0.63, 0.72). CONCLUSIONS: This is the first ML-based predictive model designed to predict 6-months VTE recurrence in patients with cancer. These results hold great potential to assist clinicians to identify the high-risk patients and improve their clinical management.

Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence.

Amyotrophic lateral sclerosis (ALS) is a fatal, neurodegenerative motor neuron disease. Although an early diagnosis is crucial to provide adequate care and improve survival, patients with ALS experience a significant diagnostic delay. This study aimed to use real-world data to describe the clinical profile and timing between symptom onset, diagnosis, and relevant outcomes in ALS. Retrospective and multicenter study in 5 representative hospitals and Primary Care services in the SESCAM Healthcare Network (Castilla-La Mancha, Spain). Using Natural Language Processing (NLP), the clinical information in electronic health records of all patients with ALS was extracted between January 2014 and December 2018. From a source population of all individuals attended in the participating hospitals, 250 ALS patients were identified (61.6% male, mean age 64.7 years). Of these, 64% had spinal and 36% bulbar ALS. For most defining symptoms, including dyspnea, dysarthria, dysphagia and fasciculations, the overall diagnostic delay from symptom onset was 11 (6-18) months. Prior to diagnosis, only 38.8% of patients had visited the neurologist. In a median post-diagnosis follow-up of 25 months, 52% underwent gastrostomy, 64% non-invasive ventilation, 16.4% tracheostomy, and 87.6% riluzole treatment; these were more commonly reported (all Ps < 0.05) and showed greater probability of occurrence (all Ps < 0.03) in bulbar ALS. Our results highlight the diagnostic delay in ALS and revealed differences in the clinical characteristics and occurrence of major disease-specific events across ALS subtypes. NLP holds great promise for its application in the wider context of rare neurological diseases.

Coupling between the prelimbic cortex, nucleus reuniens, and hippocampus during NREM sleep remains stable under cognitive and homeostatic demands.

The interplay between the medial prefrontal cortex and hippocampus during non-rapid eye movement (NREM) sleep contributes to the consolidation of contextual memories. To assess the role of the thalamic nucleus reuniens (Nre) in this interaction, we investigated the coupling of neuro-oscillatory activities among prelimbic cortex, Nre, and hippocampus across sleep states and their role in the consolidation of contextual memories using multi-site electrophysiological recordings and optogenetic manipulations. We showed that ripples are time-locked to the Up state of cortical slow waves, the transition from UP to DOWN state in thalamic slow waves, the troughs of cortical spindles, and the peaks of thalamic spindles during spontaneous sleep, rebound sleep and sleep following a fear conditioning task. In addition, spiking activity in Nre increased before hippocampal ripples, and the phase-locking of hippocampal ripples and thalamic spindles during NREM sleep was stronger after acquisition of a fear memory. We showed that optogenetic inhibition of Nre neurons reduced phase-locking of ripples to cortical slow waves in the ventral hippocampus whilst their activation altered the preferred phase of ripples to slow waves in ventral and dorsal hippocampi. However, none of these optogenetic manipulations of Nre during sleep after acquisition of fear conditioning did alter sleep-dependent memory consolidation. Collectively, these results showed that Nre is central in modulating hippocampus and cortical rhythms during NREM sleep.

How can artificial intelligence optimize value-based contracting?

Efforts in the pharmaceutical market have been aimed at ensuring that the benefits obtained from the introduction of new therapies justify the associated costs. In recent years, drug payment models in healthcare have undergone a dramatic shift from focusing on volume (i.e., size of the target clinical population) to focusing on value (i.e., drug performance in real-world settings). In this context, value-based contracts (VBCs) were designed to align the payment of a drug to its clinical performance outside clinical trials by evaluating the effectiveness using real-word evidence (RWE). Despite their widespread implementation, different factors jeopardize the application of VBCs to most marketed drugs in a near future, including the need for easily measurable and relevant outcomes associated with clinical improvements, and access to a large patient population to assess said outcomes. Here, we argue that the extraction and analysis of massive amounts of RWE captured in patients' electronic health records (EHRs) will circumvent these issues and optimize negotiations in VBCs. Particularly, the use of Natural Language Processing (NLP) has proven successful in the analysis of structured and unstructured clinical information in EHRs in multicenter research studies. Thus, the application of NLP to analyze patient-centered information in EHRs in the context of innovative contracting can be utterly beneficial as it enables the real-time evaluation of treatment response and financial impact in real-world settings.

Sleep as a window on the sensorimotor foundations of the developing hippocampus.

The hippocampal formation plays established roles in learning, memory, and related cognitive functions. Recent findings also suggest that the hippocampus integrates sensory feedback from self-generated movements to modulate ongoing motor responses in a changing environment. Such findings support the view of Bland and Oddie (Behavioural Brain Research, 2001, 127, 119-136) that the hippocampus is a site of sensorimotor integration. In further support of this view, we review neurophysiological evidence in developing rats that hippocampal function is built on a sensorimotor foundation and that this foundation is especially evident early in development. Moreover, at those ages when the hippocampus is first establishing functional connectivity with distant sensory and motor structures, that connectivity is preferentially expressed during periods of active (or REM) sleep. These findings reinforce the notion that sleep, as the predominant state of early infancy, provides a critical context for sensorimotor development, including development of the hippocampus and its associated network.

Assessing the Performance of Clinical Natural Language Processing Systems: Development of an Evaluation Methodology.

BACKGROUND: Clinical natural language processing (cNLP) systems are of crucial importance due to their increasing capability in extracting clinically important information from free text contained in electronic health records (EHRs). The conversion of a nonstructured representation of a patient's clinical history into a structured format enables medical doctors to generate clinical knowledge at a level that was not possible before. Finally, the interpretation of the insights gained provided by cNLP systems has a great potential in driving decisions about clinical practice. However, carrying out robust evaluations of those cNLP systems is a complex task that is hindered by a lack of standard guidance on how to systematically approach them. OBJECTIVE: Our objective was to offer natural language processing (NLP) experts a methodology for the evaluation of cNLP systems to assist them in carrying out this task. By following the proposed phases, the robustness and representativeness of the performance metrics of their own cNLP systems can be assured. METHODS: The proposed evaluation methodology comprised five phases: (1) the definition of the target population, (2) the statistical document collection, (3) the design of the annotation guidelines and annotation project, (4) the external annotations, and (5) the cNLP system performance evaluation. We presented the application of all phases to evaluate the performance of a cNLP system called "EHRead Technology" (developed by Savana, an international medical company), applied in a study on patients with asthma. As part of the evaluation methodology, we introduced the Sample Size Calculator for Evaluations (SLiCE), a software tool that calculates the number of documents needed to achieve a statistically useful and resourceful gold standard. RESULTS: The application of the proposed evaluation methodology on a real use-case study of patients with asthma revealed the benefit of the different phases for cNLP system evaluations. By using SLiCE to adjust the number of documents needed, a meaningful and resourceful gold standard was created. In the presented use-case, using as little as 519 EHRs, it was possible to evaluate the performance of the cNLP system and obtain performance metrics for the primary variable within the expected CIs. CONCLUSIONS: We showed that our evaluation methodology can offer guidance to NLP experts on how to approach the evaluation of their cNLP systems. By following the five phases, NLP experts can assure the robustness of their evaluation and avoid unnecessary investment of human and financial resources. Besides the theoretical guidance, we offer SLiCE as an easy-to-use, open-source Python library.

The impact of COVID-19 on patients with asthma.

BACKGROUND: An association between the severity of coronavirus disease 2019 (COVID-19) and the presence of certain chronic conditions has been suggested. However, unlike influenza and other viruses, the disease burden of COVID-19 in patients with asthma has been less evident. OBJECTIVE: To understand the impact of COVID-19 in patients with asthma. METHODS: Using big-data analytics and artificial intelligence through the SAVANA Manager clinical platform, we analysed clinical data from patients with asthma from January 1 to May 10, 2020. RESULTS: Out of 71 182 patients with asthma, 1006 (1.41%) suffered from COVID-19. Compared to asthmatic individuals without COVID-19, patients with asthma and COVID-19 were significantly older (55 versus 42 years), predominantly female (66% versus 59%), smoked more frequently and had higher prevalence of hypertension, dyslipidaemias, diabetes and obesity. Allergy-related factors such as rhinitis and eczema were less common in asthmatic patients with COVID-19 (p<0.001). In addition, higher prevalence of these comorbidities was observed in patients with COVID-19 who required hospital admission. The use of inhaled corticosteroids (ICS) was lower in patients who required hospitalisation due to COVID-19, as compared to non-hospitalised patients (48.3% versus 61.5%; OR 0.58, 95% CI 0.44-0.77). Although patients treated with biologics (n=865; 1.21%) showed increased severity and more comorbidities at the ear, nose and throat level, COVID-19-related hospitalisations in these patients were relatively low (0.23%). CONCLUSION: Patients with asthma and COVID-19 were older and at increased risk due to comorbidity-related factors. ICS and biologics are generally safe and may be associated with a protective effect against severe COVID-19 infection.

Towards a symbiotic relationship between big data, artificial intelligence, and hospital pharmacy.

The digitalization of health and medicine and the growing availability of electronic health records (EHRs) has encouraged healthcare professionals and clinical researchers to adopt cutting-edge methodologies in the realms of artificial intelligence (AI) and big data analytics to exploit existing large medical databases. In Hospital and Health System pharmacies, the application of natural language processing (NLP) and machine learning to access and analyze the unstructured, free-text information captured in millions of EHRs (e.g., medication safety, patients' medication history, adverse drug reactions, interactions, medication errors, therapeutic outcomes, and pharmacokinetic consultations) may become an essential tool to improve patient care and perform real-time evaluations of the efficacy, safety, and comparative effectiveness of available drugs. This approach has an enormous potential to support share-risk agreements and guide decision-making in pharmacy and therapeutics (P&T) Committees.

Characteristics and Prognosis of COVID-19 in Patients with COPD.

Patients with Chronic Obstructive Pulmonary Disease (COPD) have a higher prevalence of coronary ischemia and other factors that put them at risk for COVID-19-related complications. We aimed to explore the impact of COVID-19 in a large population-based sample of patients with COPD in Castilla-La Mancha, Spain. We analyzed clinical data in electronic health records from 1 January to 10 May 2020 by using Natural Language Processing through the SAVANA Manager® clinical platform. Out of 31,633 COPD patients, 793 had a diagnosis of COVID-19. The proportion of patients with COVID-19 in the COPD population (2.51%; 95% CI 2.33-2.68) was significantly higher than in the general population aged >40 years (1.16%; 95% CI 1.14-1.18); p < 0.001. Compared with COPD-free individuals, COPD patients with COVID-19 showed significantly poorer disease prognosis, as evaluated by hospitalizations (31.1% vs. 39.8%: OR 1.57; 95% CI 1.14-1.18) and mortality (3.4% vs. 9.3%: OR 2.93; 95% CI 2.27-3.79). Patients with COPD and COVID-19 were significantly older (75 vs. 66 years), predominantly male (83% vs. 17%), smoked more frequently, and had more comorbidities than their non-COPD counterparts. Pneumonia was the most common diagnosis among COPD patients hospitalized due to COVID-19 (59%); 19% of patients showed pulmonary infiltrates suggestive of pneumonia and heart failure. Mortality in COPD patients with COVID-19 was associated with older age and prevalence of heart failure (p < 0.05). COPD patients with COVID-19 showed higher rates of hospitalization and mortality, mainly associated with pneumonia. This clinical profile is different from exacerbations caused by other respiratory viruses in the winter season.

Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 in hospital workers.

BACKGROUND: Hospital workers have been the most frequently and severely affected professional group during the COVID-19 pandemic, and have a big impact on transmission. In this context, innovative tools are required to measure the symptoms compatible with COVID-19, the spread of infection, and testing capabilities within hospitals in real time. OBJECTIVE: We aimed to develop and test an effective and user-friendly tool to identify and track symptoms compatible with COVID-19 in hospital workers. METHODS: We developed and pilot tested Hospital Epidemics Tracker (HEpiTracker), a newly designed app to track the spread of COVID-19 among hospital workers. Hospital staff in 9 hospital centers across 5 Spanish regions (Andalusia, Balearics, Catalonia, Galicia, and Madrid) were invited to download the app on their phones and to register their daily body temperature, COVID-19-compatible symptoms, and general health score, as well as any polymerase chain reaction and serological test results. RESULTS: A total of 477 hospital staff participated in the study between April 8 and June 2, 2020. Of note, both health-related (n=329) and non-health-related (n=148) professionals participated in the study; over two-thirds of participants (68.8%) were health workers (43.4% physicians and 25.4% nurses), while the proportion of non-health-related workers by center ranged from 40% to 85%. Most participants were female (n=323, 67.5%), with a mean age of 45.4 years (SD 10.6). Regarding smoking habits, 13.0% and 34.2% of participants were current or former smokers, respectively. The daily reporting of symptoms was highly variable across participating hospitals; although we observed a decline in adherence after an initial participation peak in some hospitals, other sites were characterized by low participation rates throughout the study period. CONCLUSIONS: HEpiTracker is an already available tool to monitor COVID-19 and other infectious diseases in hospital workers. This tool has already been tested in real conditions. HEpiTracker is available in Spanish, Portuguese, and English. It has the potential to become a customized asset to be used in future COVID-19 pandemic waves and other environments. TRIAL REGISTRATION: ClinicalTrials.gov NCT04326400; https://clinicaltrials.gov/ct2/show/NCT04326400.

Active Sleep Promotes Coherent Oscillatory Activity in the Cortico-Hippocampal System of Infant Rats.

Active sleep (AS) provides a unique developmental context for synchronizing neural activity within and between cortical and subcortical structures. In week-old rats, sensory feedback from myoclonic twitches, the phasic motor activity that characterizes AS, promotes coherent theta oscillations (4-8 Hz) in the hippocampus and red nucleus, a midbrain motor structure. Sensory feedback from twitches also triggers rhythmic activity in sensorimotor cortex in the form of spindle bursts, which are brief oscillatory events composed of rhythmic components in the theta, alpha/beta (8-20 Hz), and beta2 (20-30 Hz) bands. Here we ask whether one or more of these spindle-burst components are communicated from sensorimotor cortex to hippocampus. By recording simultaneously from whisker barrel cortex and dorsal hippocampus in 8-day-old rats, we show that AS, but not other behavioral states, promotes cortico-hippocampal coherence specifically in the beta2 band. By cutting the infraorbital nerve to prevent the conveyance of sensory feedback from whisker twitches, cortical-hippocampal beta2 coherence during AS was substantially reduced. These results demonstrate the necessity of sensory input, particularly during AS, for coordinating rhythmic activity between these two developing forebrain structures.

Active Sleep Promotes Functional Connectivity in Developing Sensorimotor Networks.

A ubiquitous feature of active (REM) sleep in mammals and birds is its relative abundance in early development. In rat pups across the first two postnatal weeks, active sleep promotes the expression of synchronized oscillatory activity within and between cortical and subcortical sensorimotor structures. Sensory feedback from self-generated myoclonic twitches - which are produced exclusively during active sleep - also triggers neural oscillations in those structures. We have proposed that one of the functions of active sleep in early infancy is to provide a context for synchronizing developing structures. Specifically, neural oscillations contribute to a variety of neurodevelopmental processes, including synapse formation, neuronal differentiation and migration, apoptosis, and the refinement of topographic maps. In addition, synchronized oscillations promote functional connectivity between distant brain areas. Consequently, any condition or manipulation that restricts active sleep can, in turn, deprive the infant animal of substantial sensory experience, resulting in atypical developmental trajectories.

Theta Oscillations during Active Sleep Synchronize the Developing Rubro-Hippocampal Sensorimotor Network.

Neuronal oscillations comprise a fundamental mechanism by which distant neural structures establish and express functional connectivity. Long-range functional connectivity between the hippocampus and other forebrain structures is enabled by theta oscillations. Here, we show for the first time that the infant rat red nucleus (RN)-a brainstem sensorimotor structure-exhibits theta (4-7 Hz) oscillations restricted primarily to periods of active (REM) sleep. At postnatal day 8 (P8), theta is expressed as brief bursts immediately following myoclonic twitches; by P12, theta oscillations are expressed continuously across bouts of active sleep. Simultaneous recordings from the hippocampus and RN at P12 show that theta oscillations in both structures are coherent, co-modulated, and mutually interactive during active sleep. Critically, at P12, inactivation of the medial septum eliminates theta in both structures. The developmental emergence of theta-dependent functional coupling between the hippocampus and RN parallels that between the hippocampus and prefrontal cortex. Accordingly, disruptions in the early expression of theta could underlie the cognitive and sensorimotor deficits associated with neurodevelopmental disorders such as autism and schizophrenia.

Spontaneous activity and functional connectivity in the developing cerebellorubral system.

The development of the cerebellar system depends in part on the emergence of functional connectivity in its input and output pathways. Characterization of spontaneous activity within these pathways provides insight into their functional status in early development. In the present study we recorded extracellular activity from the interpositus nucleus (IP) and its primary downstream target, the red nucleus (RN), in unanesthetized rats at postnatal days 8 (P8) and P12, a period of dramatic change in cerebellar circuitry. The two structures exhibited state-dependent activity patterns and age-related changes in rhythmicity and overall firing rate. Importantly, sensory feedback (i.e., reafference) from myoclonic twitches (spontaneous, self-generated movements that are produced exclusively during active sleep) drove neural activity in the IP and RN at both ages. Additionally, anatomic tracing confirmed the presence of cerebellorubral connections as early as P8. Finally, inactivation of the IP and adjacent nuclei using the GABAA receptor agonist muscimol caused a substantial decrease in neural activity in the contralateral RN at both ages, as well as the disappearance of rhythmicity; twitch-related activity in the RN, however, was preserved after IP inactivation, indicating that twitch-related reafference activates the two structures in parallel. Overall, the present findings point to the contributions of sleep-related spontaneous activity to the development of cerebellar networks.

Sensorimotor processing in the newborn rat red nucleus during active sleep.

Sensory feedback from sleep-related myoclonic twitches is thought to drive activity-dependent development in spinal cord and brain. However, little is known about the neural pathways involved in the generation of twitches early in development. The red nucleus (RN), source of the rubrospinal tract, has been implicated in the production of phasic motor activity during active sleep in adults. Here we hypothesized that the RN is also a major source of motor output for twitching in early infancy, a period when twitching is an especially abundant motor behavior. We recorded extracellular neural activity in the RN during sleep and wakefulness in 1-week-old unanesthetized rats. Neurons in the RN fired phasically before twitching and wake movements of the contralateral forelimb. A subpopulation of neurons in the RN exhibited a significant peak of activity after forelimb movement onset, suggesting reafferent sensory processing. Consistent with this observation, manual stimulation of the forelimb evoked RN responses. Unilateral inactivation of the RN using a mixture comprising GABAA, GABAB, and glycine receptor agonists caused an immediate and temporary increase in motor activity followed by a marked and prolonged decrease in twitching and wake movements. Altogether, these data support a causal role for the RN in infant motor behavior. Furthermore, they indicate that twitching, which is characterized by discrete motor output and reafferent input, provides an opportunity for sensorimotor integration and activity-dependent development of topography within the newborn RN.

A valuable and promising method for recording brain activity in behaving newborn rodents.

Neurophysiological recording of brain activity has been critically important to the field of neuroscience, but has contributed little to the field of developmental psychobiology. The reasons for this can be traced largely to methodological difficulties associated with recording neural activity in behaving newborn rats and mice. Over the last decade, however, the evolution of methods for recording from head-fixed newborns has heralded a new era in developmental neurophysiology. Here, we review these recent developments and provide a step-by-step primer for those interested in applying the head-fix method to their own research questions. Until now, this method has been used primarily to investigate spontaneous brain activity across sleep and wakefulness, the contributions of the sensory periphery to brain activity, or intrinsic network activity. Now, with some ingenuity, the uses of the head-fix method can be expanded to other domains to benefit our understanding of brain-behavior relations under normal and pathophysiological conditions across early development.

Self-generated movements with "unexpected" sensory consequences.

The nervous systems of diverse species, including worms and humans, possess mechanisms for distinguishing between sensations arising from self-generated (i.e., expected) movements from those arising from other-generated (i.e., unexpected) movements [1-3]. To make this critical distinction, animals generate copies, or corollary discharges, of motor commands [4, 5]. Corollary discharge facilitates the selective gating of reafferent signals arising from self-generated movements, thereby enhancing detection of novel stimuli [6-10]. However, for a developing nervous system, such sensory gating would be counterproductive if it impedes transmission of the very activity upon which activity-dependent mechanisms depend [11]. In infant rats during active (or REM) sleep--a behavioral state that predominates in early infancy [12-16]--neural circuits within the brainstem [17, 18] trigger hundreds of thousands of myoclonic twitches each day [19]. The putative contribution of these self-generated movements to the activity-dependent development of the sensorimotor system is supported by the observation that reafference from twitching limbs reliably and substantially triggers brain activity [20-23]. In contrast, under identical testing conditions, even the most vigorous wake movements reliably fail to trigger reafferent brain activity [21-23]. One hypothesis that accounts for this paradox is that twitches, uniquely among self-generated movements, lack corollary discharge [23]. Here, we test this hypothesis in newborn rats by manipulating the degree to which self-generated movements are expected and, therefore, their presumed recruitment of corollary discharge. We show that twitches, although self-generated, are processed as if they are unexpected.

Chronotype-dependent circadian rhythmicity of driving safety.

Among the factors associated with driving safety, sleep-related variables constitute a leading cause of road accidents. Circadian fluctuations of driver's somnolence has been previously linked to road safety. However, the role of chronotype in this relationship has been poorly investigated. Thus, the aim of the present work was to address whether driving performance is influenced by circadian patterns, in turn modulated by the driver's chronotype and the time of day (i.e. synchrony effect). We assessed 47 healthy young adults with specific chronotypes in several simulated driving sessions, both in the morning and in the evening. We collected driving performance data, along with self-reported levels of activation prior to each driving session and other sleep-related variables. Participants drove less safely when testing times took place outside their optimal time of day, as determined by their chronotype and confirmed by self-reported levels of activation. These differences were more pronounced in the morning, when morning types shown a better driving performance. Our results suggest that chronotype plays an important role as a modulator of the relationship between the time of day and driving safety. Therefore, it is necessary to acknowledge this variable in theoretical models of driving behavior, and for the improvement of occupational accidents prevention programs.