6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records.

BACKGROUND: Neurological and psychiatric sequelae of COVID-19 have been reported, but more data are needed to adequately assess the effects of COVID-19 on brain health. We aimed to provide robust estimates of incidence rates and relative risks of neurological and psychiatric diagnoses in patients in the 6 months following a COVID-19 diagnosis. METHODS: For this retrospective cohort study and time-to-event analysis, we used data obtained from the TriNetX electronic health records network (with over 81 million patients). Our primary cohort comprised patients who had a COVID-19 diagnosis; one matched control cohort included patients diagnosed with influenza, and the other matched control cohort included patients diagnosed with any respiratory tract infection including influenza in the same period. Patients with a diagnosis of COVID-19 or a positive test for SARS-CoV-2 were excluded from the control cohorts. All cohorts included patients older than 10 years who had an index event on or after Jan 20, 2020, and who were still alive on Dec 13, 2020. We estimated the incidence of 14 neurological and psychiatric outcomes in the 6 months after a confirmed diagnosis of COVID-19: intracranial haemorrhage; ischaemic stroke; parkinsonism; Guillain-Barré syndrome; nerve, nerve root, and plexus disorders; myoneural junction and muscle disease; encephalitis; dementia; psychotic, mood, and anxiety disorders (grouped and separately); substance use disorder; and insomnia. Using a Cox model, we compared incidences with those in propensity score-matched cohorts of patients with influenza or other respiratory tract infections. We investigated how these estimates were affected by COVID-19 severity, as proxied by hospitalisation, intensive therapy unit (ITU) admission, and encephalopathy (delirium and related disorders). We assessed the robustness of the differences in outcomes between cohorts by repeating the analysis in different scenarios. To provide benchmarking for the incidence and risk of neurological and psychiatric sequelae, we compared our primary cohort with four cohorts of patients diagnosed in the same period with additional index events: skin infection, urolithiasis, fracture of a large bone, and pulmonary embolism. FINDINGS: Among 236 379 patients diagnosed with COVID-19, the estimated incidence of a neurological or psychiatric diagnosis in the following 6 months was 33·62% (95% CI 33·17-34·07), with 12·84% (12·36-13·33) receiving their first such diagnosis. For patients who had been admitted to an ITU, the estimated incidence of a diagnosis was 46·42% (44·78-48·09) and for a first diagnosis was 25·79% (23·50-28·25). Regarding individual diagnoses of the study outcomes, the whole COVID-19 cohort had estimated incidences of 0·56% (0·50-0·63) for intracranial haemorrhage, 2·10% (1·97-2·23) for ischaemic stroke, 0·11% (0·08-0·14) for parkinsonism, 0·67% (0·59-0·75) for dementia, 17·39% (17·04-17·74) for anxiety disorder, and 1·40% (1·30-1·51) for psychotic disorder, among others. In the group with ITU admission, estimated incidences were 2·66% (2·24-3·16) for intracranial haemorrhage, 6·92% (6·17-7·76) for ischaemic stroke, 0·26% (0·15-0·45) for parkinsonism, 1·74% (1·31-2·30) for dementia, 19·15% (17·90-20·48) for anxiety disorder, and 2·77% (2·31-3·33) for psychotic disorder. Most diagnostic categories were more common in patients who had COVID-19 than in those who had influenza (hazard ratio [HR] 1·44, 95% CI 1·40-1·47, for any diagnosis; 1·78, 1·68-1·89, for any first diagnosis) and those who had other respiratory tract infections (1·16, 1·14-1·17, for any diagnosis; 1·32, 1·27-1·36, for any first diagnosis). As with incidences, HRs were higher in patients who had more severe COVID-19 (eg, those admitted to ITU compared with those who were not: 1·58, 1·50-1·67, for any diagnosis; 2·87, 2·45-3·35, for any first diagnosis). Results were robust to various sensitivity analyses and benchmarking against the four additional index health events. INTERPRETATION: Our study provides evidence for substantial neurological and psychiatric morbidity in the 6 months after COVID-19 infection. Risks were greatest in, but not limited to, patients who had severe COVID-19. This information could help in service planning and identification of research priorities. Complementary study designs, including prospective cohorts, are needed to corroborate and explain these findings. FUNDING: National Institute for Health Research (NIHR) Oxford Health Biomedical Research Centre.

Classification of neurological diseases using multi-dimensional CSF analysis.

Although CSF analysis routinely enables the diagnosis of neurological diseases, it is mainly used for the gross distinction between infectious, autoimmune inflammatory, and degenerative disorders of the CNS. To investigate, whether a multi-dimensional cellular blood and CSF characterization can support the diagnosis of clinically similar neurological diseases, we analysed 546 patients with autoimmune neuroinflammatory, degenerative, or vascular conditions in a cross-sectional retrospective study. By combining feature selection with dimensionality reduction and machine learning approaches we identified pan-disease parameters that were altered across all autoimmune neuroinflammatory CNS diseases and differentiated them from other neurological conditions and inter-autoimmunity classifiers that subdifferentiate variants of CNS-directed autoimmunity. Pan-disease as well as diseases-specific changes formed a continuum, reflecting clinical disease evolution. A validation cohort of 231 independent patients confirmed that combining multiple parameters into composite scores can assist the classification of neurological patients. Overall, we showed that the integrated analysis of blood and CSF parameters improves the differential diagnosis of neurological diseases, thereby facilitating early treatment decisions.

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorder in its earlier stages before status deterioration. OBJECTIVE: Motivated by the computation capabilities of natural evolution strategies (NES), this paper introduces an effective automatic classification approach denoted as natural evolution optimization-based deep learning (NEODL). The proposed classifier is an ingredient in a signal processing chain that comprises other state-of-the-art techniques in a consistent framework for the purpose of automatic EEG classification. METHODS: The proposed framework consists of four steps. First, the L1-principal component analysis technique is used to enhance the raw EEG signal against any expected artifacts or noise. Second, the purified EEG signal is decomposed into a number of sub-bands by applying the wavelet transform technique where a number of spectral and statistical features are extracted. Third, the extracted features are examined using the artificial bee colony approach in order to optimally select the best features. Lastly, the selected features are treated using the proposed NEODL classifier, where the input signal is classified according to the problem at hand. RESULTS: The proposed approach is evaluated using two benchmark datasets and addresses two neurological disorder applications: epilepsy disease and motor imagery. Several experiments are conducted where the proposed classifier outperforms other deep learning techniques as well as other existing approaches. CONCLUSION: The proposed framework, including the proposed classifier (NEODL), has a promising performance in the classification of EEG signals, including epilepsy disease and motor imagery. Based on the given results, it is expected that this approach will also be useful for the identification of the epileptogenic areas in the human brain. Accordingly, it may find application in the neuro-intensive care units, epilepsy monitoring units, and practical brain-computer interface systems in clinics.

Complicaciones neurológicas por coronavirus y COVID-19 / Neurological complications of coronavirus and COVID-19

INTRODUCCIÓN: Estudios clínicos y experimentales han demostrado que la familia de los coronavirus tiene un cierto tropismo por el sistema nervioso central. Siete tipos de coronavirus pueden contagiar al ser humano. DESARROLLO. Los coronavirus no siempre permanecen confinados en el tracto respiratorio, y en determinadas condiciones pueden invadir el sistema nervioso central y causar patologías neurológicas. La capacidad potencial de neuroinvasión está bien documentada en la mayor parte de los coronavirus humanos (OC-43, 229E, MERS y SARS) y en algunos coronavirus animales (coronavirus de la encefalomielitis hemaglutinante porcina). Se han descrito síntomas neurológicos en pacientes afectos por COVID-19, como cefalea, mareo, mialgias y anosmia, así como casos de encefalopatía, encefalitis, encefalopatía necrotizante hemorrágica, ictus, crisis epilépticas, rabdomiólisis y síndrome de Guillain-Barré, asociados a la infección por el SARS-CoV-2. CONCLUSIONES: Futuros estudios epidemiológicos y registros de casos deben elucidar la incidencia real de estas complicaciones neurológicas, sus mecanismos patogénicos y sus opciones terapéuticas

INTRODUCTION: Clinical and experimental studies have shown that the coronavirus family has a certain tropism for the central nervous system. Seven types of coronavirus can infect humans. DEVELOPMENT: Coronaviruses are not always confined to the respiratory tract, and under certain conditions they can invade the central nervous system and cause neurological pathologies. The potential for neuroinvasion is well documented in most human coronaviruses (OC-43, 229E, MERS and SARS) and in some animal coronaviruses (porcine haemagglutinating encephalomyelitis coronavirus). Neurological symptoms have been reported in patients affected by COVID-19, such as headache, dizziness, myalgia and anosmia, as well as cases of encephalopathy, encephalitis, necrotising haemorrhagic encephalopathy, stroke, epileptic seizures, rhabdomyolysis and Guillain-Barré syndrome, associated with SARS-CoV-2 infection. CONCLUSIONS: Future epidemiological studies and case records should elucidate the real incidence of these neurological complications, their pathogenic mechanisms and their therapeutic options

Validation of the pediatric stroke outcome measure for classifying overall neurological deficit.

BACKGROUND: The pediatric stroke outcome measure (PSOM) is a standardized, disease-specific outcome measure. We aimed to validate the overall classification of neurological deficit severity using PSOM. METHODS: We identified 367 neonates/children with arterial ischemic stroke (AIS) (Derivation Cohort). We analyzed the PSOM subscales (scored as 0 [no deficit], 0.5 [minimal/mild deficit; normal function], 1 [moderate deficit; slowing function], or 2 [severe deficit; missing function]) to derive severity levels using latent class analysis (LCA). We validated a severity classification scheme (PSOM-SCS) in: (a) children who had Pediatric Evaluation of Disability Inventory (PEDI; n = 63) and/or the Pediatric Quality-of-Life Inventory (PedsQL; n = 97) scored; and (b) an external cohort (AIS; n = 102) with concurrently scored modified Rankin Scale (mRS), King's Outcome Scale for Childhood Head-Injury (KOSCHI) and PSOM. RESULTS: Within the Derivation Cohort, LCA identified three severity levels: "normal/mild," "moderate," and "severe" (83.7%, 13.3%, and 3%, respectively). We developed severity classification based on PSOM subscale scores: "normal/mild"-normal function in all domains or slowing in one domain, "moderate"-slowing in ≥2 domains or missing function in one domain, and "severe"-missing function in ≥2 domains or slowing in ≥1 plus missing in one domain. PEDI and PedsQL both differed significantly across the severity groups. PSOM-SCS displayed high concordance with mRS (agreement coefficient [AC2] = 0.88) and KOSCHI (AC2 = 0.79). CONCLUSION: The PSOM-SCS constitutes a valid tool for classifying overall neurological severity emphasizing function and encompassing the full range of severity in pediatric stroke. IMPACT: Arithmetic summing of the PSOM subscales scores to assess severity classification is inadequate.The prior severity classification using PSOM overestimates poor outcomes.Three distinct severity profiles using PSOM subscales are identified.The PSOM-SCS is in moderate to excellent agreement with other disability measures.PSOM-SCS offers a valid tool for classifying the overall neurological deficit severity.

Trastorno paroxístico no epiléptico en paciente referido por sospecha de epilepsia / Paroxysmal non-epileptic events in patients referred for suspicion of epilepsy

Los Trastornos Paroxísticos No Epilépticos (TPNE), son diagnóstico diferencial de crisis epilépticas. En Chile no existen reportes de frecuencia. OBJETIVO: Determinar frecuencia de TPNE en pacientes derivados por sospecha de epilepsia a Unidad de Electroencefalografía, Hospital Roberto del Río. METODOLOGÍA: Estudio observacional, transversal. Se revisó registros clínicos de pacientes derivados por sospecha de epilepsia(2012- 2014). Inclusión: paciente con TPNE, >1 mes, sin epilepsia previa. Caracterización: sexo, edad, tipo/subtipo TPNE, comorbilidades, electroencefalograma (EEG), uso fármaco antiepiléptico (FAE). Aprobado por comité de ética. RESULTADOS: Derivados 913 pacientes por sospecha de epilepsia. 36% TPNE (2,3% con epilepsia concomitante), 22% epilepsia aislada. TPNE más frecuente: escolares (31%), adolescentes (29%), femenino (52%). 30,1% hipoxia cerebral (síncope, Espasmo Sollozo); 22,4% trastornos del comportamiento (Descontrol Episódico, Crisis Psicógena no Epiléptica). 32,8% con comorbilidades no epilépticas (Trastornos psiquiátricos/neurodesarrollo). 4,3 % recibieron FAE. CONCLUSIONES: La frecuencia de TPNE en niños/adolescentes supera a la de epilepsia. Es fundamental evaluación multidisciplinaria.

Paroxysmal non-epileptic events (PNE) are differential diagnosis of epileptic seizures. In Chile, there are no reports on its frequency. OBJECTIVE: To determine the frequency of PNE in patients referred for suspicion of epilepsy to the Electroencephalography Unit of Roberto del Río Hospital. METHODOLOGY: Observational, cross-sectional study. Clinical records of patients referred for suspicion of epilepsy (2012-2014) were reviewed. Inclusion: Patient with PNE, >1 month, without previous epilepsy. Characterization: gender, age, PNE type/subtype, comorbidities, electroencephalogram, use of antiepileptic drug (AED). Approved by the ethics committee. RESULTS: 913 patients were referred for suspected epilepsy: 36% PNE (2,3% with concomitant epilepsy), 22% isolated epilepsy. PNE were more frequent in children (31%) adolescents (29%), and in females (52%). 30.1% Cerebral hypoxia (syncope, breathholding-spells); 22,4% Behavioral disorders (Episodic loss of control, Non-Epileptic Psychogenic Seizures). 32.8% non-epileptic comorbidities (Psychiatric/ neurodevelopmental disorders). 4.3% received AEDs. CONCLUSIONS: The frequency of PNE in children and adolescents exceeds that of epilepsy. A multidisciplinary medical evaluation is of the outmost importance. Keywords: Paroxysmal Non-Epileptic Disorder, Non-epileptic episodes, Epilepsy.

Severe neurological impairment: a review of the definition.

Severe neurological impairment (SNI) is a term commonly used in the medical literature, though there is no agreed definition. This limits opportunities for research into healthcare needs, treatment opportunities, resource planning, and outcome. We reviewed the literature to establish consistency of use of the term and to place it in the context of other commonly employed terms used to describe children with severe, complex medical needs. Forty-two articles were included for full-text analysis, with 23 including a definition of SNI. Motor impairment, intellectual disability, communication difficulties, and increased care needs were included in the definition in 80%, 70%, 30%, and 13% of papers respectively. Dependence on others for decision-making, chronicity, and distinction between disorders of the central nervous system and peripheral nervous system were less frequently included. There is wide variation in the use of the term SNI. A consensus-based definition of this term would be useful to facilitate future research. WHAT THIS PAPER ADDS: There is inconsistency in use of the term severe neurological impairment (SNI), limiting research efforts. In defining SNI, considerations are mobility, intellectual disability, communication difficulties, and increased care needs. Distinction between acute and chronic, central and peripheral nervous system disorders, and dependence on others for decision-making were less significant.

DEFICIENCIA NEUROLÓGICO SEVERO: UNA REVISIÓN DE LA DEFINICIÓN: El deficiencia neurológico severo es un término comúnmente utilizado en la literatura médica, aunque no existe una definición acordada. Esto limita las oportunidades de investigación sobre necesidades de atención médica, oportunidades de tratamiento, planificación de recursos y resultados. Revisamos la literatura para establecer la coherencia en el uso del término y colocarlo en el contexto de otros términos empleados comúnmente que se utilizan para describir a los niños con necesidades médicas graves y complejas. Se incluyeron 42 artículos para el análisis de texto completo, de los cuales 23 incluían una definición de deficiencia neurológico severo. La discapacidad motora, la discapacidad intelectual, las dificultades de comunicación y el aumento de las necesidades de atención se incluyeron en la definición en 80%, 70%, 30% y 13% de los artículos, respectivamente. La dependencia de otros para la toma de decisiones, la cronicidad y la distinción entre los trastornos del sistema nervioso central y el sistema nervioso periférico se incluyeron con menos frecuencia. Existe una amplia variación en el uso del término deficiencia neurológico severo. Una definición basada en el consenso de este término sería útil para facilitar futuras investigaciones.

DEFICIÊNCIA NEUROLÓGICA SEVERA: UMA REVISÃO DA DEFINIÇÃO: A deficiência neurológica severa (DNS) é uma termo comumente usado na literatura médica, embora não tenha definição consensual. Isso limita as oportunidades de pesquisas sobre necessidades de saúde, oportunidades de tratamento, planejamento de recursos, e resultados. Nós revisamos a literature para estabelecer a consistência do uso do termo, e para colocá-lo no contexto de outros termos comumente empregados para descrever crianças com necessidades médicas severas e complexas. Quarenta e dois artigos foram incluídos para análise dos textos completes, com 23 includindo uma definição de DNS. Deficiência motora, deficiência intelectual, dificuldades de comunicação, e necessidades de cuidado aumentadas foram incluídas na definição 80%, 70%, 30%, e 13% dos artigos, respectivamente. Dependência de outros para tomada de decisões, cronicidade, e distinção entre desordens do sistema nervoso central e periférico foram menos frequentemente incluídas. Há grande variação no uso do termo DNS. Uma definição do termo baseada em consenso seria útil para facilitar futuras pesquisas.

Calculated Decisions: Modified Rankin Scale (mRS) for Neurologic Disability

The modified Rankin Scale (mRS) for neurologic disability measures the degree of disability or dependence in the daily activities of people who have suffered a stroke.

Improving the classification of neuropsychiatric conditions using gene ontology terms as features.

Although neuropsychiatric disorders have an established genetic background, their molecular foundations remain elusive. This has prompted many investigators to search for explanatory biomarkers that can predict clinical outcomes. One approach uses machine learning to classify patients based on blood mRNA expression. However, these endeavors typically fail to achieve the high level of performance, stability, and generalizability required for clinical translation. Moreover, these classifiers can lack interpretability because not all genes have relevance to researchers. For this study, we hypothesized that annotation-based classifiers can improve classification performance, stability, generalizability, and interpretability. To this end, we evaluated the models of four classification algorithms on six neuropsychiatric data sets using four annotation databases. Our results suggest that the Gene Ontology Biological Process database can transform gene expression into an annotation-based feature space that is accurate and stable. We also show how annotation features can improve the interpretability of classifiers: as annotations are used to assign biological importance to genes, the biological importance of annotation-based features are the features themselves. In evaluating the annotation features, we find that top ranked annotations tend contain top ranked genes, suggesting that the most predictive annotations are a superset of the most predictive genes. Based on this, and the fact that annotations are used routinely to assign biological importance to genetic data, we recommend transforming gene-level expression into annotation-level expression prior to the classification of neuropsychiatric conditions.

CRPS: what's in a name? Taxonomy, epidemiology, neurologic, immune and autoimmune considerations.

This account of the condition now termed complex regional pain syndrome (CRPS) spans approximately 462 years since a description embodying similar clinical features was described by Ambroise Paré in 1557. While reviewing its historical origins, the text describes why it became necessary to change the taxonomies of two clinical syndromes with similar pathophysiologies to one which acknowledges this aspect but does not introduce any mechanistic overtones. Discussed at length is the role of the sympathetic component of the autonomic nervous system (ANS) and why its dysfunction has both directly and indirectly influenced our understanding of the inflammatory aspects of CRPS. As the following article will show, our knowledge has expanded in an exponential fashion to include musculoskeletal, immune, autoimmune, central and peripheral nervous system and ANS dysfunction, all of which increase the complexity of its clinical management. A burgeoning literature is beginning to shed light on the mechanistic aspects of these syndromes and the increasing evidence of a genetic influence on such factors as autoimmunity, and its importance is also discussed at length. An important aspect that has been missing from the diagnostic criteria is a measure of disease severity. The recent validation of a CRPS Severity Score is also included.

Athletes with neurologic disease.

Neurologic disease does not discriminate, even among athletes. Common neurologic diseases among athletes include multiple sclerosis, seizures, headaches, and sleep disorders. Although concrete guidelines for sport participation among athletes with neurologic diseases do not exist, evidence-based and consensus statements can aid healthcare providers in determining whether and to what extent such athletes should participate in sports. Moreover, sport participation is important, since multiple studies indicate that exercise improves disease-specific symptoms, manifestations, and overall quality of life. Although some risk is involved for athletes with neurologic disease, risk is mitigated with proper supervision and neurologic oversight, disease-specific accommodations, and counseling of the athletic staff and the athletes. Neurologic oversight entails an initial comprehensive neurologic assessment by a neurologist followed by regular follow-up. Preparation for environmental conditions encountered by athletes with neurologic disease will further improve safety during their participation in sport. With sound recommendations, neurologic oversight, and proper supervision, most athletes with neurologic disease can participate in athletics. The health benefits that they will gain from participation in athletics outweigh the risks.

Automatic Musculoskeletal and Neurological Disorder Diagnosis With Relative Joint Displacement From Human Gait.

Musculoskeletal and neurological disorders are common devastating companions of ageing, leading to a reduction in quality of life and increased mortality. Gait analysis is a popular method for diagnosing these disorders. However, manually analyzing the motion data is a labor-intensive task, and the quality of the results depends on the experience of the doctors. In this paper, we propose an automatic framework for classifying musculoskeletal and neurological disorders among older people based on 3D motion data. We also propose two new features to capture the relationship between joints across frames, known as 3D Relative Joint Displacement (3DRJDP) and 6D Symmetric Relative Joint Displacement (6DSymRJDP), such that the relative movement between joints can be analyzed. To optimize the classification performance, we adapt feature selection methods to choose an optimal feature set from the raw feature input. Experimental results show that we achieve a classification accuracy of 84.29% using the proposed relative joint features, outperforming existing features that focus on the movement of individual joints. Considering the limited open motion database for gait analysis focusing on such disorders, we construct a comprehensive, openly accessible 3D full-body motion database from 45 subjects.

Functional neurological disorders: acute presentations and management.

Functional neurological disorders (FND) are common and associated with significant morbidity and healthcare costs. Patients with FND often present acutely, particularly with dissociative seizures (resembling epilepsy) or persistent weakness resembling a stroke. History and careful observation and examination are critical to diagnosis, as investigations will often be normal or non-contributory. The nature of convulsive movements in dissociative seizures often differs from that in epilepsy, and long duration of individual events, waxing and waning, closed eyes and high reported frequency in an apparently well individual are all suggestive. In those with stroke-like episodes, demonstration of normal power even briefly (eg Hoover's sign, 'give way' weakness) together with distractability are positive physical features indicating a functional disorder. A positive diagnosis and clear non-judgemental explanation, backed up by reliable information sources associated with prompt onward referral to a neurologist can greatly reduce distress and ultimately improve outcomes.

Proposed Standardized Neurological Endpoints for Cardiovascular Clinical Trials: An Academic Research Consortium Initiative.

Surgical and catheter-based cardiovascular procedures and adjunctive pharmacology have an inherent risk of neurological complications. The current diversity of neurological endpoint definitions and ascertainment methods in clinical trials has led to uncertainties in the neurological risk attributable to cardiovascular procedures and inconsistent evaluation of therapies intended to prevent or mitigate neurological injury. Benefit-risk assessment of such procedures should be on the basis of an evaluation of well-defined neurological outcomes that are ascertained with consistent methods and capture the full spectrum of neurovascular injury and its clinical effect. The Neurologic Academic Research Consortium is an international collaboration intended to establish consensus on the definition, classification, and assessment of neurological endpoints applicable to clinical trials of a broad range of cardiovascular interventions. Systematic application of the proposed definitions and assessments will improve our ability to evaluate the risks of cardiovascular procedures and the safety and effectiveness of preventive therapies.

Pathophysiology of the underactive bladder.

Underactive bladder (UAB), which has been described as a symptom complex suggestive of detrusor underactivity, is usually characterized by prolonged urination time with or without a sensation of incomplete bladder emptying, usually with hesitancy, reduced sensation on filling, and slow stream often with storage symptoms. Several causes such as aging, bladder outlet obstruction, diabetes mellitus, neurologic disorders, and nervous injury to the spinal cord, cauda equine, and peripheral pelvic nerve have been assumed to be responsible for the development of UAB. Several contributing factors have been suggested in the pathophysiology of UAB, including myogenic failure, efferent and/or afferent dysfunctions, and central nervous system dysfunction. In this review article, we have described relationships between individual contributing factors and the pathophysiology of UAB based on previous reports. However, many pathophysiological uncertainties still remain, which require more investigations using appropriate animal models.

Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis.

BACKGROUND: Deep sequencing of lymphocyte receptor repertoires has made it possible to comprehensively profile the clonal composition of lymphocyte populations. This opens the door for novel approaches to diagnose and prognosticate diseases with a driving immune component by identifying repertoire sequence patterns associated with clinical phenotypes. Indeed, recent studies support the feasibility of this, demonstrating an association between repertoire-level summary statistics (e.g., diversity) and patient outcomes for several diseases. In our own prior work, we have shown that six codons in VH4-containing genes in B cells from the cerebrospinal fluid of patients with relapsing remitting multiple sclerosis (RRMS) have higher replacement mutation frequencies than observed in healthy controls or patients with other neurological diseases. However, prior methods to date have been limited to focusing on repertoire-level summary statistics, ignoring the vast amounts of information in the millions of individual immune receptors comprising a repertoire. We have developed a novel method that addresses this limitation by using innovative approaches for accommodating the extraordinary sequence diversity of immune receptors and widely used machine learning approaches. We applied our method to RRMS, an autoimmune disease that is notoriously difficult to diagnose. RESULTS: We use the biochemical features encoded by the complementarity determining region 3 of each B cell receptor heavy chain in every patient repertoire as input to a detector function, which is fit to give the correct diagnosis for each patient using maximum likelihood optimization methods. The resulting statistical classifier assigns patients to one of two diagnosis categories, RRMS or other neurological disease, with 87% accuracy by leave-one-out cross-validation on training data (N = 23) and 72% accuracy on unused data from a separate study (N = 102). CONCLUSIONS: Our method is the first to apply statistical learning to immune repertoires to aid disease diagnosis, learning repertoire-level labels from the set of individual immune repertoire sequences. This method produced a repertoire-based statistical classifier for diagnosing RRMS that provides a high degree of diagnostic capability, rivaling the accuracy of diagnosis by a clinical expert. Additionally, this method points to a diagnostic biochemical motif in the antibodies of RRMS patients, which may offer insight into the disease process.

Nuevos tiempos para la neuroimagen de Medicina Nuclear en España: ¿de dónde partimos? / A new era for Nuclear Medicine neuroimaging in Spain: Where do we start from in Spain?

Objetivo. conocer la situación de los estudios de neuroimagen de Medicina Nuclear que se realizaron en España en el año 2013 y primer trimestre del 2014, con el fin de definir las actividades del grupo de trabajo de Neuroimagen de la Sociedad Española de Medicina Nuclear e Imagen Molecular (SEMNIM). Material y métodos. Se diseñó un cuestionario de 14 preguntas dividido en 3 partes: características de los servicios (equipamiento y profesionales involucrados), tipo de exploraciones e indicaciones clínicas y métodos de evaluación. El cuestionario se remitió a los 166 servicios de Medicina Nuclear que figuraban en la secretaría de la Sociedad Española de Medicina Nuclear e Imagen Molecular. Resultados. Respondieron a la encuesta un total de 54 centros distribuidos entre todas las comunidades autónomas. La mayoría de los centros realizaron entre 300 y 800 exploraciones de neuroimagen al año, representando más de 25 exploraciones al mes. La media de equipos por servicio era de 3, teniendo la mitad de ellos equipos PET/TC y SPECT/TC. Las exploraciones realizadas con más frecuencia son la SPECT cerebral con 123I-FP-CIT, seguida de la SPECT cerebral de perfusión y de la PET con 18F-FDG, siendo las indicaciones clínicas más frecuentes los estudios de deterioro cognitivo seguidos por los de trastornos del movimiento. Para la evaluación de las pruebas la mayoría de los centros utilizaron únicamente la valoración visual, en la valoración cuantitativa la cuantificación por regiones de interés fue la más utilizada. Conclusiones. Los resultados reflejan cuál fue la actividad clínica del año 2013 y primer trimestre del 2014, siendo las indicaciones principales los estudios de deterioro cognitivo y trastorno del movimiento. La variabilidad en la evaluación de los estudios PET y la colaboración con los especialistas clínicos que demandan las exploraciones de neuroimagen de Medicina Nuclear son algunos de los retos que debemos afrontar en los próximos años (AU)

Objective. To determine the status of neuroimaging studies of Nuclear Medicine in Spain during 2013 and first quarter of 2014, in order to define the activities of the neuroimaging group of the Spanish Society of Nuclear Medicine and Molecular Imaging (SEMNIM). Material and methods. A questionnaire of 14 questions was designed, divided into 3 parts: characteristics of the departments (equipment and professionals involved); type of scans and clinical indications; and evaluation methods. The questionnaire was sent to 166 Nuclear Medicine departments. Results. A total of 54 departments distributed among all regions completed the questionnaire. Most departments performed between 300 and 800 neuroimaging examinations per year, representing more than 25 scans per month. The average pieces of equipment were three; half of the departments had a PET/CT scanner and SPECT/CT equipment. Scans performed more frequently were brain SPECT with 123I-FP-CIT, followed by brain perfusion SPECT and PET with 18F-FDG. The most frequent clinical indications were cognitive impairment followed by movement disorders. For evaluation of the images most sites used only visual assessment, and for the quantitative assessment the most used was quantification by region of interest. Conclusions. These results reflect the clinical activity of 2013 and first quarter of 2014. The main indications of the studies were cognitive impairment and movement disorders. Variability in the evaluation of the studies is among the challenges that will be faced in the coming years (AU)