Hasil untuk "Psychiatry"

R. Gueorguieva, J. Krystal

P. Vidal-Ribas, M. Brotman, Isabel Valdivieso et al.

Objective Research and clinical interest in irritability have been on the rise in recent years. Yet several questions remain about the status of irritability in psychiatry, including whether irritability can be differentiated from other symptoms, whether it forms a distinct disorder, and whether it is a meaningful predictor of clinical outcomes. In this article, we try to answer these questions by reviewing the evidence on how reliably irritability can be measured and its validity. Method We combine a narrative and systematic review and meta-analysis of studies. For the systematic review and meta-analysis, we searched studies in PubMed and Web of Science based on preselected criteria. A total of 163 articles were reviewed, and 24 were included. Results We found that irritability forms a distinct dimension with substantial stability across time, and that it is specifically associated with depression and anxiety in longitudinal studies. Evidence from genetic studies reveals that irritability is moderately heritable, and its overlap with depression is explained mainly by genetic factors. Behavioral and neuroimaging studies show that youth with persistent irritability exhibit altered activations in the amygdala, striatum, and frontal regions compared with age-matched healthy volunteers. Most knowledge about the treatment of irritability is based on effects of treatment on related conditions or post hoc analyses of trial data. Conclusion We identify a number of research priorities including innovative experimental designs and priorities for treatment studies, and conclude with recommendations for the assessment of irritability for researchers and clinicians.

D. Quintana, T. Elvsåshagen, Nathalia Zak et al.

The number of publications investigating heart rate variability (HRV) in psychiatry and the behavioral sciences has increased markedly in the last decade. In addition to the significant debates surrounding ideal methods to collect and interpret measures of HRV, standardized reporting of methodology in this field is lacking. Commonly cited recommendations were designed well before recent calls to improve research communication and reproducibility across disciplines. In an effort to standardize reporting, we propose the Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH), a checklist with four domains: participant selection, interbeat interval collection, data preparation and HRV calculation. This paper provides an overview of these four domains and why their standardized reporting is necessary to suitably evaluate HRV research in psychiatry and related disciplines. Adherence to these communication guidelines will help expedite the translation of HRV research into a potential psychiatric biomarker by improving interpretation, reproducibility and future meta-analyses.

R. Uher, A. Zwicker

T. Insel

In 2050, when psychiatrists look back at the first two decades of the 21st century, what will they recognize as having the greatest impact? No doubt the revolution in genomics, which has given us new insights into the risk architecture of mental illness, and the revolution in neuroscience, which has given us a new view of mental illnesses as circuit disorders, will be considered important. But perhaps the revolution in technology and information science will prove more consequential for global mental health. If this sounds like hyperbole, consider two supportive data points. First, in the past decade smartphones have become nearly ubiquitous. There are over three billion smartphone Internet subscriptions, each device with the information processing capacity of the supercomputers of the 1990s. In many parts of the world that lack credit cards, phones have become the primary way to conduct commerce. Second, broadband access to social media and search platforms is becoming global. In 2016, 3.3 billion people had Internet access, one third of whom were in India and China. Even in areas without easy access to clean water, ownership of a smartphone and rapid access to information have become the symbols of modernity. The smartphone and the Internet can solve specific problems that we face in psychiatry, but their clinical use also raises new ethical challenges. What specific problems can be addressed by the smartphone? Our lack of objective measurement has handicapped both diagnosis and treatment in psychiatry. As just one example, our assessment of depression depends largely on selfreports of sleep, appetite and emotional state, although we recognize that people with depression are biased in their assessments. The smartphone offers us an objective and ecological source of measurement. This approach, now called digital phenotyping, is based on sensors (activity and location), voice and speech (sentiment and prosody), and, perhaps most important, human-computer interaction. Human-computer interaction measures not what you type but how you type. Subtle aspects of typing and scrolling, such as the latency between space and character or the interval between scroll and click, are surprisingly good surrogates for cognitive traits and affective states. If this seems improbable, remember that many of our neuropsychological tests, such as the Trails A and B tests or the Digit Symbol Substitution, are not substantially different from the psychomotor requirements of operating a smartphone. In a sense, those gold standard tests of cognitive control and information processing are attempting to assess how we function. In a world where we spend so much of our lives on our smartphones, could it become possible to assess how we function directly and continuously rather than using laboratory measures at a single point in time? The promise of digital phenotyping is that this objective measure happens in the context of a patient’s lived experience, reflecting how he/she functions in his/her world, not in our clinic. Signals from a new mother struggling with depression may look quite different during a 3 am feeding compared to what she reports to her clinician the next day. This kind of ecological and continuous measurement addresses some of the central issues that challenge our field. We know that most people with a mental illness do not seek help, and those who do seek help usually arrive after considerable delay. For populations at risk, such as post-partum women or victims of trauma, could digital phenotyping signal the transition from risk to the need for care? For people in care, too often we fail to preempt relapse. For patients in treatment, could digital phenotyping serve as a “smoke alarm” providing early signals of relapse or recovery? Digital phenotyping is still being developed as a clinical tool. It seems clear from the early results that, although activity and geolocation data are non-specific and noisy, for some people changes in activity can be an early sign ofmania or depression. Speech and voice may also yield clinically relevant signals. We have known for decades that when people are depressed their pronouns shift to first person singular. But again, the sensitivity and specificity of these findings still need to be defined. Putting sensor data, speech and voice data, and human-computer interaction together might provide a digital phenotype that could do for psychiatry what HgbA1c or serum cholesterol has done for other areas of medicine, giving precision to diagnosis and accuracy to outcomes. The opportunity of this new approach to measurement is matched by an ethical challenge.When doesmeasurement become surveillance? Is tracking geolocation or collecting speech too intrusive? How can patients trust that digital phenotyping data will be protected? Even if patients consent to have their smartphone monitored, is there full transparency and a deep understanding of what data will be collected and how these data will be used? Who owns the data? For psychiatry, one of the most informative phone signals might reside in the “digital exhaust”, such as search history or social media posts. Those signalsmight confess suicidal intent or early signs of psychosis. Does the value of this information outweigh the intrusion of privacy required to obtain it? All of these issues are part of an active debate, as merits any new promising technology. To be clear, digital phenotyping is still a research project conducted on small numbers of consented volunteers. While researchers hope this approach will solve global mental health problems, the scientific and ethical issues need to be resolved before digital phenotyping becomes a tool for population health. Some of the most vexing issues may have technical solutions. For instance, human-computer interaction is “contentfree”. This approach collects how you type, not what you type and, therefore, might be less intrusive than monitoring geolocation or search history. Tools that can analyze smartphone

P. Lewczuk, P. Riederer, S. O'Bryant et al.

Abstract In the 12 years since the publication of the first Consensus Paper of the WFSBP on biomarkers of neurodegenerative dementias, enormous advancement has taken place in the field, and the Task Force takes now the opportunity to extend and update the original paper. New concepts of Alzheimer’s disease (AD) and the conceptual interactions between AD and dementia due to AD were developed, resulting in two sets for diagnostic/research criteria. Procedures for pre-analytical sample handling, biobanking, analyses and post-analytical interpretation of the results were intensively studied and optimised. A global quality control project was introduced to evaluate and monitor the inter-centre variability in measurements with the goal of harmonisation of results. Contexts of use and how to approach candidate biomarkers in biological specimens other than cerebrospinal fluid (CSF), e.g. blood, were precisely defined. Important development was achieved in neuroimaging techniques, including studies comparing amyloid-β positron emission tomography results to fluid-based modalities. Similarly, development in research laboratory technologies, such as ultra-sensitive methods, raises our hopes to further improve analytical and diagnostic accuracy of classic and novel candidate biomarkers. Synergistically, advancement in clinical trials of anti-dementia therapies energises and motivates the efforts to find and optimise the most reliable early diagnostic modalities. Finally, the first studies were published addressing the potential of cost-effectiveness of the biomarkers-based diagnosis of neurodegenerative disorders.

Anna Carolyna Gianlorenço, Paulo Eduardo Portes Teixeira, Valton Costa et al.

<b>Background/Objectives:</b> Speech and motor control share overlapping neural mechanisms, yet their quantitative relationships in Parkinson’s disease (PD) remain underexplored. This study investigated bidirectional associations between acoustic voice features and objective motor metrics to better understand how vocal and motor systems relate in PD. <b>Methods:</b> Cross-sectional baseline data from participants in a randomized neuromodulation trial were analyzed (n = 13). Motor performance was captured using an Integrated Motion Analysis Suite (IMAS), which enabled quantitative, objective characterization of motor performance during balance, gait, and upper- and lower-limb tasks. Acoustic analyses included harmonic-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPS), jitter, shimmer, median fundamental frequency (F0), F0 standard deviation (SD F0), and voice intensity. Univariate linear regressions were conducted in both directions (voice ↔ motor), as well as partial correlations controlling for PD motor symptom severity. <b>Results:</b> When modeling voice outcomes, faster motor performance and shorter movement durations were associated with acoustically clearer voice features (e.g., higher elbow flexion-extension peak speed with higher voice HNR, β = 8.5, R² = 0.56, <i>p</i> = 0.01). Similarly, when modeling motor outcomes, clearer voice measures were linked with faster movement speed and shorter movement durations (e.g., higher voice HNR with higher peak movement speed in elbow flexion/extension, β = 0.07, R² = 0.56, <i>p</i> = 0.01). <b>Conclusions:</b> Voice and motor measures in PD showed significant bidirectional associations, suggesting shared sensorimotor control. These exploratory findings, while limited by sample size, support the feasibility of integrated multimodal assessment for future longitudinal studies.

Bhargav Teja Nallapu, Ali Ezzati, Helena M. Blumen et al.

ABSTRACT INTRODUCTION Understanding the heterogeneity of brain structure in individuals with the Motoric Cognitive Risk Syndrome (MCR) may improve the current risk assessments of dementia. METHODS We used data from six cohorts from the MCR consortium (N = 1987). A weakly‐supervised clustering algorithm called HYDRA (Heterogeneity through Discriminative Analysis) was applied to volumetric magnetic resonance imaging (MRI) measures to identify distinct subgroups in the population with gait speeds lower than one standard deviation (1SD) above mean. RESULTS Three subgroups (Groups A, B, and C) were identified through MRI‐based clustering with significant differences in regional brain volumes, gait speeds, and performance on Trail Making (Part‐B) and Free and Cued Selective Reminding Tests. DISCUSSION Based on structural MRI, our results reflect heterogeneity in the population with moderate and slow gait, including those with MCR. Such a data‐driven approach could help pave new pathways toward dementia at‐risk stratification and have implications for precision health for patients. Highlights Different patterns of brain atrophy were observed among the people with moderate and slow gait speeds Slower gait speeds were associated with substantial cortical atrophy, higher rates of Motoric Cognitive Risk Syndrome (MCR), and worse cognitive performance This approach can aid patient stratification at early asymptomatic stages and have implications for precision health.

Tsion Michael, Solomon Moges Demeke

IntroductionCommon mental disorders (CMDs) and suicidality are two of the most common psychological and mental health issues associated with acute and chronic sexual and gender-based violence (SGBV). Thus, the purpose of this study was to determine the magnitude of symptoms of CMDs, and suicidality among females experienced SGBV in Ethiopia.MethodA cross-sectional study was conducted among 407 female survivors of SGBV in the One Stop Centers of the Amhara region. Data analysis was performed using SPSS version 25. The odds ratio at a p-value of 0.05 was used to determine the strength of the association of the independent variables with CMDs and suicidality.ResultsA total of 407 women participated in the study. Suicidality was reported by a quarter of the survivors (24.1%), while CMDs were reported by nearly two-thirds (61.7%). Being widowed (AOR = 3.0, 95% CI = 3.0 [1.22, 7.66]), having a family history of mental illnesses (AOR = 7.1, 95% CI = 7.1 [4.07, 12.39)], being low-income (AOR = 2.8, 95% CI = 2.8 [1.64, 5.06]), and current drug use (AOR = 2.9, 95% CI = 2.9 [1.63, 5.16]) were all linked with CMDs. Having a history of abortion (AOR = 4.1, 95% CI = 4.1 [1.9, 8.5]), CMDs (AOR = 4.6, 95% CI = 4.6 [2.0, 10.74]), and history of suicide (AOR = 3.41, 95% CI = 3.41 [1.22, 9.55]) were some of the characteristics that were substantially linked with suicidality.ConclusionFemales with SGBV had a high prevalence of CMDs and suicidality and calls for comprehensive remedies.

P. Crichton, H. Carel, I. Kidd

Summary It has been argued that those who suffer from medical conditions are more vulnerable to epistemic injustice (a harm done to a person in their capacity as an epistemic subject) than healthy people. This editorial claims that people with mental disorders are even more vulnerable to epistemic injustice than those with somatic illnesses. Two kinds of contributory factors are outlined, global and specific. Some suggestions are made to counteract the effects of these factors, for instance, we suggest that physicians should participate in groups where the subjective experience of patients is explored, and learn to become more aware of their own unconscious prejudices towards psychiatric patients.

Chenlong Yang, Xiaohui Lou, Xiaohui Lou et al.

ObjectiveThis study aimed to develop an arbitrary-dimensional nerve root reconstruction magnetic resonance imaging (ANRR-MRI) technique for identifying the leakage orificium of sacral meningeal cysts (SMCs) without spinal nerve root fibres (SNRFs).MethodsThis prospective study enrolled 40 consecutive patients with SMCs without SNRFs between March 2021 and March 2022. Magnetic resonance neural reconstruction sequences were performed for preoperative evaluation. The cyst and the cyst-dura intersection planes were initially identified based on the original thin-slice axial T2-weighted images. Sagittal and coronal images were then reconstructed by setting each intersecting plane as the centre. Then, three-dimensional reconstruction was performed, focusing on the suspected leakage point of the cyst. Based on the identified leakage location and size of the SMC, individual surgical plans were formulated.ResultsThis cohort included 30 females and 10 males, with an average age of 42.6 ± 12.2 years (range, 17–66 years). The leakage orificium was located at the rostral pole of the cyst in 23 patients, at the body region of the cyst in 12 patients, and at the caudal pole in 5 patients. The maximum diameter of the cysts ranged from 2 cm to 11 cm (average, 5.2 ± 1.9 cm). The leakage orificium was clearly identified in all patients and was ligated microscopically through a 4 cm minimally invasive incision. Postoperative imaging showed that the cysts had disappeared.ConclusionANRR-MRI is an accurate and efficient approach for identifying leakage orificium, facilitating the precise diagnosis and surgical treatment of SMCs without SNRFs.

Masanobu Kogure, Nobuhisa Kanahara, Atsuhiro Miyazawa et al.

BackgroundMost genetic analyses that have attempted to identify a locus or loci that can distinguish patients with treatment-resistant schizophrenia (TRS) from those who respond to treatment (non-TRS) have failed. However, evidence from multiple studies suggests that patients with schizophrenia who respond well to antipsychotic medication have a higher dopamine (DA) state in brain synaptic clefts whereas patients with TRS do not show enhanced DA synthesis/release pathways.Patients and methodsTo examine the contribution (if any) of genetics to TRS, we conducted a genetic association analysis of DA-related genes in schizophrenia patients (TRS, n = 435; non-TRS, n = 539) and healthy controls (HC: n = 489).ResultsThe distributions of the genotypes of rs3756450 and the 40-bp variable number tandem repeat on SLC6A3 differed between the TRS and non-TRS groups. Regarding rs3756450, the TRS group showed a significantly higher ratio of the A allele, whereas the non-TRS group predominantly had the G allele. The analysis of the combination of COMT and SLC6A3 yielded a significantly higher ratio of the putative low-DA type (i.e., high COMT activity + high SLC6A3 activity) in the TRS group compared to the two other groups. Patients with the low-DA type accounted for the minority of the non-TRS group and exhibited milder psychopathology.ConclusionThe overall results suggest that (i) SLC6A3 could be involved in responsiveness to antipsychotic medication and (ii) genetic variants modulating brain DA levels may be related to the classification of TRS and non-TRS.

M. Angermeyer, S. Van der Auwera, M. Carta et al.

T. Bastiaanssen, C. Cowan, M. Claesson et al.

Abstract Microorganisms can be found almost anywhere, including in and on the human body. The collection of microorganisms associated with a certain location is called a microbiota, with its collective genetic material referred to as the microbiome. The largest population of microorganisms on the human body resides in the gastrointestinal tract; thus, it is not surprising that the most investigated human microbiome is the human gut microbiome. On average, the gut hosts microbes from more than 60 genera and contains more cells than the human body. The human gut microbiome has been shown to influence many aspects of host health, including more recently the brain. Several modes of interaction between the gut and the brain have been discovered, including via the synthesis of metabolites and neurotransmitters, activation of the vagus nerve, and activation of the immune system. A growing body of work is implicating the microbiome in a variety of psychological processes and neuropsychiatric disorders. These include mood and anxiety disorders, neurodevelopmental disorders such as autism spectrum disorder and schizophrenia, and even neurodegenerative disorders such as Alzheimer’s and Parkinson’s diseases. Moreover, it is probable that most psychotropic medications have an impact on the microbiome. Here, an overview will be provided for the bidirectional role of the microbiome in brain health, age-associated cognitive decline, and neurological and psychiatric disorders. Furthermore, a primer on the common microbiological and bioinformatics techniques used to interrogate the microbiome will be provided. This review is meant to equip the reader with a primer to this exciting research area that is permeating all areas of biological psychiatry research.

P. Fusar-Poli, Z. Hijazi, D. Ståhl et al.

Importance Prognosis is a venerable component of medical knowledge introduced by Hippocrates (460-377 BC). This educational review presents a contemporary evidence-based approach for how to incorporate clinical risk prediction models in modern psychiatry. The article is organized around key methodological themes most relevant for the science of prognosis in psychiatry. Within each theme, the article highlights key challenges and makes pragmatic recommendations to improve scientific understanding of prognosis in psychiatry. Observations The initial step to building clinical risk prediction models that can affect psychiatric care involves designing the model: preparation of the protocol and definition of the outcomes and of the statistical methods (theme 1). Further initial steps involve carefully selecting the predictors, preparing the data, and developing the model in these data. A subsequent step is the validation of the model to accurately test its generalizability (theme 2). The next consideration is that the accuracy of the clinical prediction model is affected by the incidence of the psychiatric condition under investigation (theme 3). Eventually, clinical prediction models need to be implemented in real-world clinical routine, and this is usually the most challenging step (theme 4). Advanced methods such as machine learning approaches can overcome some problems that undermine the previous steps (theme 5). The relevance of each of these themes to current clinical risk prediction modeling in psychiatry is discussed and recommendations are given. Conclusions and Relevance Together, these perspectives intend to contribute to an integrative, evidence-based science of prognosis in psychiatry. By focusing on the outcome of the individuals, rather than on the disease, clinical risk prediction modeling can become the cornerstone for a scientific and personalized psychiatry.

F. Jacka

The nascent field of ‘Nutritional Psychiatry’ offers much promise for addressing the large disease burden associated with mental disorders. A consistent evidence base from the observational literature confirms that the quality of individuals' diets is related to their risk for common mental disorders, such as depression. This is the case across countries and age groups. Moreover, new intervention studies implementing dietary changes suggest promise for the prevention and treatment of depression. Concurrently, data point to the utility of selected nutraceuticals as adjunctive treatments for mental disorders and as monotherapies for conditions such as ADHD. Finally, new studies focused on understanding the biological pathways that mediate the observed relationships between diet, nutrition and mental health are pointing to the immune system, oxidative biology, brain plasticity and the microbiome-gut-brain axis as key targets for nutritional interventions. On the other hand, the field is currently limited by a lack of data and methodological issues such as heterogeneity, residual confounding, measurement error, and challenges in measuring and ensuring dietary adherence in intervention studies. Key challenges for the field are to now: replicate, refine and scale up promising clinical and population level dietary strategies; identify a clear set of biological pathways and targets that mediate the identified associations; conduct scientifically rigorous nutraceutical and ‘psychobiotic’ interventions that also examine predictors of treatment response; conduct observational and experimental studies in psychosis focused on dietary and related risk factors and treatments; and continue to advocate for policy change to improve the food environment at the population level.

Rehanguli Maimaitituerxun, Wenhang Chen, Jingsha Xiang et al.

Abstract Background Depression and diabetes are major health challenges, with heavy economic social burden, and comorbid depression in diabetes could lead to a wide range of poor health outcomes. Although many descriptive studies have highlighted the prevalence of comorbid depression and its associated factors, the situation in Hunan, China, remains unclear. Therefore, this study aimed to identify the prevalence of comorbid depression and associated factors among hospitalized type 2 diabetes mellitus (T2DM) patients in Hunan, China. Methods This cross-sectional study involved 496 patients with T2DM who were referred to the endocrinology inpatient department of Xiangya Hospital affiliated to Central South University, Hunan. Participants’ data on socio-demographic status, lifestyle factors, T2DM-related characteristics, and social support were collected. Depression was evaluated using the Hospital Anxiety and Depression Scale-depression subscale. All statistical analyses were conducted using the R software version 4.2.1. Results The prevalence of comorbid depression among hospitalized T2DM patients in Hunan was 27.22% (95% Confidence Interval [CI]: 23.3–31.1%). Individuals with depression differed significantly from those without depression in age, educational level, per capita monthly household income, current work status, current smoking status, current drinking status, regular physical activity, duration of diabetes, hypertension, chronic kidney disease, stroke, fatty liver, diabetic nephropathy, diabetic retinopathy, insulin use, HbA1c, and social support. A multivariable logistic regression model showed that insulin users (adjusted OR = 1.86, 95% CI: 1.02–3.42) had a higher risk of depression, while those with regular physical activity (adjusted OR = 0.48, 95% CI: 0.30–0.77) or greater social support (adjusted OR = 0.20, 95% CI: 0.11–0.34) had a lower risk of depression. The area under the curve of the receiver operator characteristic based on this model was 0.741 with a sensitivity of 0.785 and specificity of 0.615. Conclusions Depression was moderately prevalent among hospitalized T2DM patients in Hunan, China. Insulin treatment strategies, regular physical activity, and social support were significantly independently associated with depression, and the multivariable model based on these three factors demonstrated good predictivity, which could be applied in clinical practice.

Felicia A. Hardi, Leigh G. Goetschius, Scott Tillem et al.

Unstable and unpredictable environments are linked to risk for psychopathology, but the underlying neural mechanisms that explain how instability relate to subsequent mental health concerns remain unclear. In particular, few studies have focused on the association between instability and white matter structures despite white matter playing a crucial role for neural development. In a longitudinal sample recruited from a population-based study (N = 237), household instability (residential moves, changes in household composition, caregiver transitions in the first 5 years) was examined in association with adolescent structural network organization (network integration, segregation, and robustness of white matter connectomes; Mage = 15.87) and young adulthood anxiety and depression (six years later). Results indicate that greater instability related to greater global network efficiency, and this association remained after accounting for other types of adversity (e.g., harsh parenting, neglect, food insecurity). Moreover, instability predicted increased depressive symptoms via increased network efficiency even after controlling for previous levels of symptoms. Exploratory analyses showed that structural connectivity involving the left fronto-lateral and temporal regions were most strongly related to instability. Findings suggest that structural network efficiency relating to household instability may be a neural mechanism of risk for later depression and highlight the ways in which instability modulates neural development.

G. Ortega Suero, M.J. Abenza Abildúa, C. Serrano Munuera et al.

Introduction: Ataxia and hereditary spastic paraplegia are rare neurodegenerative syndromes. We aimed to determine the prevalence of these disorders in Spain in 2019. Patients and methods: We conducted a cross-sectional, multicentre, retrospective, descriptive study of patients with ataxia and hereditary spastic paraplegia in Spain between March 2018 and December 2019. Results: We gathered data from a total of 1933 patients from 11 autonomous communities, provided by 47 neurologists or geneticists. Mean (SD) age in our sample was 53.64 (20.51) years; 938 patients were men (48.5%) and 995 were women (51.5%). The genetic defect was unidentified in 920 patients (47.6%). A total of 1371 patients (70.9%) had ataxia and 562 (29.1%) had hereditary spastic paraplegia. Prevalence rates for ataxia and hereditary spastic paraplegia were estimated at 5.48 and 2.24 cases per 100 000 population, respectively. The most frequent type of dominant ataxia in our sample was SCA3, and the most frequent recessive ataxia was Friedreich ataxia. The most frequent type of dominant hereditary spastic paraplegia in our sample was SPG4, and the most frequent recessive type was SPG7. Conclusions: In our sample, the estimated prevalence of ataxia and hereditary spastic paraplegia was 7.73 cases per 100 000 population. This rate is similar to those reported for other countries. Genetic diagnosis was not available in 47.6% of cases. Despite these limitations, our study provides useful data for estimating the necessary healthcare resources for these patients, raising awareness of these diseases, determining the most frequent causal mutations for local screening programmes, and promoting the development of clinical trials. Resumen: Introducción: Las ataxias (AT) y paraparesias espásticas hereditarias (PEH) son síndromes neurodegenerativos raros. Nos proponemos conocer la prevalencia de las AT y PEH (APEH) en España en 2019. Pacientes y métodos: Estudio transversal, multicéntrico, descriptivo y retrospectivo de los pacientes con AT y PEH, desde Marzo de 2018 a Diciembre de 2019 en toda España. Resultados: Se obtuvo información de 1.933 pacientes procedentes de 11 Comunidades Autónomas, de 47 neurólogos o genetistas. Edad media: 53,64 años ± 20,51 desviación estándar (DE); 938 varones (48,5%), 995 mujeres (51,1%). En 920 pacientes (47,6%) no se conoce el defecto genético. Por patologías, 1.371 pacientes (70,9%) diagnosticados de AT, 562 diagnosticados de PEH (29,1%). La prevalencia estimada de AT es 5,48/100.000 habitantes, y la de PEH es 2,24 casos/100.000 habitantes. La AT dominante más frecuente es la SCA3. La AT recesiva más frecuente es la ataxia de Friedreich (FRDA). La PEH dominante más frecuente es la SPG4, y la PEH recesiva más frecuente es la SPG7. Conclusiones: La prevalencia estimada de APEH en nuestra serie es de 7,73 casos/100.000 habitantes. Estas frecuencias son similares a las del resto del mundo. En el 47,6% no se ha conseguido un diagnóstico genético. A pesar de las limitaciones, este estudio puede contribuir a estimar los recursos, visibilizar estas enfermedades, detectar las mutaciones más frecuentes para hacer los screenings por comunidades, y favorecer los ensayos clínicos.

Shaorong Zhang, Shaorong Zhang, Qihui Wang et al.

IntroductionThe time, frequency, and space information of electroencephalogram (EEG) signals is crucial for motor imagery decoding. However, these temporal-frequency-spatial features are high-dimensional small-sample data, which poses significant challenges for motor imagery decoding. Sparse regularization is an effective method for addressing this issue. However, the most commonly employed sparse regularization models in motor imagery decoding, such as the least absolute shrinkage and selection operator (LASSO), is a biased estimation method and leads to the loss of target feature information.MethodsIn this paper, we propose a non-convex sparse regularization model that employs the Cauchy function. By designing a proximal gradient algorithm, our proposed model achieves closer-to-unbiased estimation than existing sparse models. Therefore, it can learn more accurate, discriminative, and effective feature information. Additionally, the proposed method can perform feature selection and classification simultaneously, without requiring additional classifiers.ResultsWe conducted experiments on two publicly available motor imagery EEG datasets. The proposed method achieved an average classification accuracy of 82.98% and 64.45% in subject-dependent and subject-independent decoding assessment methods, respectively.ConclusionThe experimental results show that the proposed method can significantly improve the performance of motor imagery decoding, with better classification performance than existing feature selection and deep learning methods. Furthermore, the proposed model shows better generalization capability, with parameter consistency over different datasets and robust classification across different training sample sizes. Compared with existing sparse regularization methods, the proposed method converges faster, and with shorter model training time.