Hasil untuk "Neurosciences. Biological psychiatry. Neuropsychiatry"

S. Rutherford, S. M. Kia, T. Wolfers et al.

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior, which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus ‘healthy’ control analytic approaches, probably owing to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case–control comparisons that rely on potentially noisy clinical labels. Here we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices and conclude by demonstrating several examples of downstream analyses that the normative model may facilitate, such as stratification of high-risk individuals, subtyping and behavioral predictive modeling. The protocol takes ~1–3 h to complete. This protocol guides the user through normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit), enabling individual differences to be mapped at the level of a single subject or observation in relation to a reference model.

A. Navarro-Ballester

Abstract Background Molecular imaging plays a key role in advancing understanding of neuropsychiatric disorders. However, the conceptual structure of this interdisciplinary field remains poorly mapped from a bibliometric perspective. The objective of this study was to explore the intellectual structure and thematic development of research on molecular imaging applied to neuropsychiatric disorders using co-citation network analysis. Methods A bibliometric co-citation analysis was conducted using data retrieved from Scopus. A targeted search strategy identified articles from 2014 to 2023 focused on MRS, fMRI, PET, and SPECT in the context of neuropsychiatric disorders. Bibliographic data were exported, and cited references were analyzed using VOSviewer. A manually curated thesaurus was applied to unify variant citations and reduce duplication. Co-citation networks were generated, and thematic clusters were identified and interpreted based on total link strength and citation density. Results The co-citation network included 51 documents and revealed six major thematic clusters encompassing automated anatomical labeling and brain segmentation, functional and structural connectivity, affective neuroscience, clinical biomarkers, and methodological standardization. Notable references included foundational works on resting-state functional connectivity, motion correction, and diagnostic criteria for neuropsychiatric disorders. The clustering structure highlighted the convergence of radiology, neuroscience, and psychiatry around shared methodological tools and conceptual frameworks. Conclusion Co-citation analysis revealed a well-defined and maturing intellectual landscape in molecular imaging applied to neuropsychiatry. The identified clusters represent distinct yet interconnected research lines, reflecting methodological innovation and translational potential. These findings offer a roadmap for future research, emphasizing methodological rigor, interdisciplinary collaboration, and clinical applicability.

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.

Konstantin M. Evdokimov, Ekaterina O. Ivanova, Amayak G. Brutyan et al.

Introduction. Functional tremor (FT) is the most common phenotype of functional movement disorders. Electrophysiological assessment is included in the diagnostic criteria for tremor; however, there is currently no consensus criteria for the differential diagnosis of FT. The objective of this study was to evaluate the utility of tremor frequency characteristics derived from accelerometry for the differential diagnosis between FT and organic tremor (OT). Materials and methods. Nineteen patients with FT, 20 patients with essential tremor, and 20 patients with Parkinson's disease were enrolled in the study and underwent electrophysiological examination with a two-channel accelerometer and subsequent data processing. Results. The study results revealed the differences in the frequency peak widths in patients with FT and OT, predominantly while performing a cognitive load task. This criterion showed a high sensitivity (100%) and a high specificity (97.5%) for the diagnosis of FT in the study population. Conclusion. Tremor characteristics recorded during accelerometry combined with cognitive load task can serve as an additional testing aid for differential diagnosis between functional and organic tremor.

Gabriel Feiler, Eugen Koh, D. Grant

Background/Purpose In the decades since George Engel proposed his Bio-Psycho-Social model in 1977, psychiatry has been increasingly divided into those that emphasised either the brain or the mind. This paper recognises Edwin (Ed) Harari’s clinical work and teaching at St Vincent’s Hospital, Melbourne, which was exemplary in the integration of the brain and mind, and the practice of Engel’s model in this most challenging environment. Conclusions Ed symbolised the applicability and value of integrating psychodynamic thinking to general psychiatry rather than encouraging clinicians to be dogmatic in any one approach. Ed successfully bridged the tension between the brain and mind by integrating the subjectivity of the individual with the objectivity of neuroscience, in the wider context of the system. He practiced and embodied a truly biopsychosocial approach due to his real interest to integrate multiple fields. Ed’s contribution to medicine as a psychiatrist epitomised what modern psychiatry has so often strived for: the integration of the mind and brain in the broader sociocultural context whilst remaining mindful of the biological essential for psychiatrists wherever they work.

E. Nestler

Eric J. Nestler is the Anne and Joel Ehrenkranz Dean of the Icahn School of Medicine at Mount Sinai in New York, where he also serves as Chief Scientific Officer. He received his BA, PhD, and MD degrees, and psychiatry residency training, from Yale University, where he performed his doctoral research in the laboratory of Nobel laureate Paul Greengard. He served on the Yale faculty from 1987 to 2000 as the Elizabeth Mears and House Jameson Professor of Psychiatry and Neurobiology and founding Director of the Division of Molecular Psychiatry. He moved to Dallas in 2000, where he was the Lou and Ellen McGinley Distinguished Professor and Chair of the Department of Psychiatry at The University of Texas Southwestern Medical Center until moving to New York in 2008, where he served as the inaugural Director of The Friedman Brain Institute until 2025, building it into a powerhouse of neuroscience research. Dr. Nestler is a member of the National Academy of Sciences (2025) and the National Academy of Medicine (1998) and a Fellow of the American Academy of Arts and Sciences (2005). He is a past President of the American College of Neuropsychopharmacology (2011) and the Society for Neuroscience (2017). He is a founder and SAB chair for PsychoGenics and chairs the SABs for One Mind and the Hope for Depression Research Foundation. The author of more than 800 publications, including the definitive textbooks Charney and Nestler's Neurobiology of Mental Illness (6th edition) and Nestler, Hyman, and Malenka's Molecular Neuropharmacology (4th edition), Dr. Nestler's research has been cited over 177,000 times with an h-index of 210, placing him among the top neuroscientists globally. His research studies the molecular basis of drug addiction and depression, with pioneering work on transcriptional and epigenetic mechanisms, including the molecular switch ΔFosB, revealing how drugs and stress fundamentally rewire the brain. His groundbreaking work on the biological basis of resilience has created a paradigm shift in psychiatric treatment, moving the field from symptom management toward prevention, with several pro-resilience mechanisms now in clinical testing for depression. His numerous honors include the Julius Axelrod Prize for Mentorship, the Gold Medal Award from the Society of Biological Psychiatry, the Peter Seeburg Integrative Neuroscience Prize, the Patricia S. Goldman-Rakic Prize, the Falcone Prize for Outstanding Achievement in Affective Disorders Research, the Rhoda and Bernard Sarnat International Prize in Mental Health, and honorary doctorates from Uppsala University and Concordia University.

Paul B. Badcock, C. Davey

The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible ‘process theories’ of cognition and behaviour, which fall under the banner of ‘active inference’. Despite the promise this theory holds for theorising, research and practical applications in psychology and psychiatry, its impact on these disciplines has only now begun to bear fruit. The aim of this treatment is to consider the extent to which active inference has informed theoretical progress in psychology, before exploring its contributions to our understanding and treatment of psychopathology. Despite facing persistent translational obstacles, progress suggests that active inference has the potential to become a new paradigm that promises to unite psychology’s subdisciplines, while readily incorporating the traditionally competing paradigms of evolutionary and developmental psychology. To date, however, progress towards this end has been slow. Meanwhile, the main outstanding question is whether this theory will make a positive difference through applications in clinical psychology, and its sister discipline of psychiatry.

Daniel L. Bowling

Efforts to integrate music into healthcare systems and wellness practices are accelerating but the biological foundations supporting these initiatives remain underappreciated. As a result, music-based interventions are often sidelined in medicine. Here, I bring together advances in music research from neuroscience, psychology, and psychiatry to bridge music’s specific foundations in human biology with its specific therapeutic applications. The framework I propose organizes the neurophysiological effects of music around four core elements of human musicality: tonality, rhythm, reward, and sociality. For each, I review key concepts, biological bases, and evidence of clinical benefits. Within this framework, I outline a strategy to increase music’s impact on health based on standardizing treatments and their alignment with individual differences in responsivity to these musical elements. I propose that an integrated biological understanding of human musicality—describing each element’s functional origins, development, phylogeny, and neural bases—is critical to advancing rational applications of music in mental health and wellness.

J. Harris, Joseph T. Coyle

Harris' Developmental Neuropsychiatry is an update of the First Edition, which introduced the integration of developmental neuroscience into the understanding of the pathophysiology and treatment of children with neuropsychiatric conditions. It opens with a comprehensive review of methods of assessment including behavior rating scales and neuropsychological testing moving from the scientific underpinnings to clinical application. The developmental aspects of components of cognition including attention, emotion, language, memory and consciousness are reviewed. It addresses how current brain imaging techniques have transformed our ability to link specific cognitive/emotional states to brain structure and function in health and disease. Historically, discussion of social and emotional development did not generally include the role of the brain. The Harris textbook commits significant space to connecting attachment, social development and temperament to normal brain maturation and pathologies associated with genetic disorders and environmental risk factors. The textbook closes by reviewing the diagnosis and treatment of several childhood neuropsychiatric disorders that can now be viewed through a lens informed by the prior basic chapters. The late James Harris, MD was a pioneer in bringing the brain into Child and Adolescent Psychiatry, and this textbook demonstrates how radically the advances in neuroscience and genetics have transformed the field and improved the care of this vulnerable population.

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.

Kumiko Hayashi, Nobumichi Takamatsu, Shunki Takaramoto

Extreme value analysis (EVA) is a statistical method that studies the properties of extreme values of datasets, crucial for fields like engineering, meteorology, finance, insurance, and environmental science. EVA models extreme events using distributions such as Fréchet, Weibull, or Gumbel, aiding in risk prediction and management. This review explores EVA's application to nanoscale biological systems. Traditionally, biological research focuses on average values from repeated experiments. However, EVA offers insights into molecular mechanisms by examining extreme data points. We introduce EVA's concepts with simulations and review its use in studying motor protein movements within cells, highlighting the importance of in vivo analysis due to the complex intracellular environment. We suggest EVA as a tool for extracting motor proteins' physical properties in vivo and discuss its potential in other biological systems. While there have been only a few applications of EVA to biological systems, it holds promise for uncovering hidden properties in extreme data, promoting its broader application in life sciences.

Kunihiko Kaneko

Life systems are complex and hierarchical, with diverse components at different scales, yet they sustain themselves, grow, and evolve over time. How can a theory of such complex biological states be developed? Here we note that for a hierarchical biological system to be robust, it must achieve consistency between micro-scale (e.g. molecular) and macro-scale (e.g. cellular) phenomena. This allows for a universal theory of adaptive change in cells based on biological robustness and consistency between cellular growth and molecular replication. Here, we show how adaptive changes in high-dimensional phenotypes (biological states) are constrained to low-dimensional space, leading to the derivation of a macroscopic law for cellular states. The theory is then extended to evolution, leading to proportionality between evolutionary and environmental responses, as well as proportionality between phenotypic variances due to noise and due to genetic changes. The universality of the results across several models and experiments is demonstrated. Then, by further extending the theory of evolutionary dimensional reduction to multicellular systems, the relationship between multicellular development and evolution, in particular the developmental hourglass, is demonstrated. Finally, the possibility of collapse of dimensional reduction under nutrient limitation is discussed.

Evan J. Kyzar, George H. Denfield

D. Oshevsky, T. Solokhina

Background: the complex process of transition to the new International Classification of Diseases 11th revision and intensive research in the field of clinical, biological and social psychiatry involves the integration of acquired knowledge about the patient on the basis of a holistic approach. The functional diagnosis of mental disorders is becoming more important as well as the possibility of formulating a functional diagnosis as a system of holistic assessment of the patient’s condition.Objective: to present the overview of domestic and foreign modern research on the evolution of conceptual views on functional diagnosis in psychiatry and the possibility of its practical applying.Material and method: a search of scientific publications in the databases of MedLine/PubMed, Scopus, Web of Science, eLibrary, Google Scholar was made over the past 20 years using the keywords “mental disorders”, “functional diagnostics”, “biopsychosocial model”. As a result 97 authors in accordance with criteria were selected.Results: Analysis of literature testifies that systematic approach to solving the problems of people with mental disorders, in despite of declare is not used in practice. A functional diagnosis is a tool that provides an opportunity to synthesize various information about a patient. The evolution of views on functional diagnostics in psychiatry based on the analysis of various diagnostic concepts is considered in a historical perspective. Taking into account new knowledge in the field of psychiatry, clinical psychology and neuroscience, modern methodological approaches to the substantiation of a functional diagnosis are presented. The role of an integrative dynamic biopsychosocial approach in the treatment and psychosocial rehabilitation of people with mental disorders is shown. The expediency of using a functional diagnosis in planning, implementing and evaluating the effectiveness of team methods of work in psychiatric practice is substantiated.Conclusion: the term “functional diagnosis” is a reliable framework model that allows a holistic and systematic approach to the patient’s problems, setting and solving new scientific and practical problems.

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.

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.

Sutirna Sutirna, Safuri Musa

The objective study is to know students' level of ability, understanding, skills, and attitudes in practice service guidance and counseling in schools. The approach research used is a study survey of guidance and counseling teachers who become tutors in accompaniment student practice guidance and counseling. Instruments in questionnaires closed as many as 25 items with indicator understanding, skills and attitudes students in implementation activity practice guidance and counseling. While processing techniques results survey uses percentages from many answer respondents compared amount whole respondents multiplied by 100%, the results percentage categorized as very good, good, well enough, less well, and very less. Research results conclude that students' level of ability in understanding, skills, and attitudes in implementation service guidance and counseling. The research results are concluded (1) the level of ability to understand guidance and counseling for students who carry out practices in schools is included in the sufficient category (very good 29.17% and good 56.25% ), (2) the level of students' skills in providing guidance and counseling services to students in the aspects of attending, responding, personalizing, and initiating is included in the sufficient category (very good 33.16% and good 56.88%), and (3) the level of ability of students' attitudes in carrying out guidance and counseling services in schools is categorized as sufficient (for very good 51.49% and good 41.96%).