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OBJECTIVE: Childhood maltreatment (CM) is a significant risk factor for the development and severity of bipolar disorder (BD) with increased risk of suicide attempts (SA). This study evaluated whether a machine learning algorithm could be trained to predict if a patient with BD has a history of CM or previous SA based on brain metabolism measured by positron emission tomography. METHODS: Thirty-six euthymic patients diagnosed with BD type I, with and without a history of CM were assessed using the Childhood Trauma Questionnaire. Suicide attempts were assessed through the Mini International Neuropsychiatric Interview (MINI-Plus) and a semi-structured interview. Resting-state positron emission tomography with 18F-fluorodeoxyglucose was conducted, electing only grey matter voxels through the Statistical Parametric Mapping toolbox. Imaging analysis was performed using a supervised machine learning approach following Gaussian Process Classification. RESULTS: Patients were divided into 18 participants with a history of CM and 18 participants without it, along with 18 individuals with previous SA and 18 individuals without such history. The predictions for CM and SA were not significant (accuracy = 41.67%; p = 0.879). CONCLUSION: Further investigation is needed to improve the accuracy of machine learning, as its predictive qualities could potentially be highly useful in determining histories and possible outcomes of high-risk psychiatric patients.

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Objective: Childhood maltreatment (CM) is a significant risk factor for the development and severity of bipolar disorder (BD) with increased risk of suicide attempts (SA). This study evaluated whether a machine learning algorithm could be trained to predict if a patient with BD has a history of CM or previous SA based on brain metabolism measured by positron emission tomography. Methods: Thirty-six euthymic patients diagnosed with BD type I, with and without a history of CM were assessed using the Childhood Trauma Questionnaire. Suicide attempts were assessed through the Mini International Neuropsychiatric Interview (MINI-Plus) and a semi-structured interview. Resting-state positron emission tomography with 18F-fluorodeoxyglucose was conducted, electing only grey matter voxels through the Statistical Parametric Mapping toolbox. Imaging analysis was performed using a supervised machine learning approach following Gaussian Process Classification. Results: Patients were divided into 18 participants with a history of CM and 18 participants without it, along with 18 individuals with previous SA and 18 individuals without such history. The predictions for CM and SA were not significant (accuracy = 41.67%; p = 0.879). Conclusion: Further investigation is needed to improve the accuracy of machine learning, as its predictive qualities could potentially be highly useful in determining histories and possible outcomes of high-risk psychiatric patients.

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The deposition of amyloid plaques is considered one of the main microscopic features of Alzheimer's disease (AD). Since plaque formation can precede extensive neurodegeneration and it is the main clinical manifestation of AD, it constitutes a relevant target for new treatment and diagnostic approaches. Micro-Raman spectroscopy, a label-free technique, is an accurate method for amyloid plaque identification and characterization. Here, we present a high spatial resolution micro-Raman hyperspectral study in transgenic APPswePS1ΔE9 mouse brains, showing details of AD tissue biochemical and histological changes without staining. First we used stimulated micro-Raman scattering to identify the lipid-rich halo surrounding the amyloid plaque, and then proceeded with spontaneous (conventional) micro-Raman spectral mapping, which shows a cholesterol and sphingomyelin lipid-rich halo structure around dense-core amyloid plaques. The detailed images of this lipid halo relate morphologically well with dystrophic neurites surrounding plaques. Principal Component Analysis (PCA) of the micro-Raman hyperspectral data indicates the feasibility of the optical biomarkers of AD progression with the potential for discriminating transgenic groups of young adult mice (6-month-old) from older ones (12-month-old). Frequency-specific PCA suggests that plaque-related neurodegeneration is the predominant change captured by Raman spectroscopy, and the main differences are highlighted by vibrational modes associated with cholesterol located majorly in the lipid halo.

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Given the long subclinical stage of Alzheimer's disease (AD), the study of biomarkers is relevant both for early diagnosis and the fundamental understanding of the pathophysiology of AD. Biomarkers provided by Amyloid-ß (Aß) plaques have led to an increasing interest in characterizing this hallmark of AD due to its promising potential. In this work, we characterize Aß plaques by label-free multimodal imaging: we combine two-photon excitation autofluorescence (TPEA), second harmonic generation (SHG), spontaneous Raman scattering (SpRS), coherent anti-Stokes Raman scattering (CARS), and stimulated Raman scattering (SRS) to describe and compare high-resolution images of Aß plaques in brain tissues of an AD mouse model. Comparing single-laser techniques images, we discuss the origin of the SHG, which can be used to locate the plaque core reliably. We study both the core and the halo with vibrational microscopy and compare SpRS and SRS microscopies for different frequencies. We also combine SpRS spectroscopy with SRS microscopy and present two core biomarkers unexplored with SRS microscopy: phenylalanine and amide B. We provide high-resolution SRS images with the spatial distribution of these biomarkers in the plaque and compared them with images of the amide I distribution. The obtained spatial correlation corroborates the feasibility of these biomarkers in the study of Aß plaques. Furthermore, since amide B enables rapid imaging, we discuss its potential as a novel fingerprint for diagnostic applications.

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ABSTRACT Objective: To investigate the psychometric properties of the short or multimodal treatment study version of the Swanson, Nolan, and Pelham, Version IV (SNAP-IV) scale, which measures attention-deficit/hyperactivity disorder and oppositional defiant disorder symptoms. Methods: Participants were 765 parents of children from 4 to 16 years old (641 non-attention-deficit/hyperactivity disorder and 124 attention-deficit/hyperactivity disorder children) from Belo Horizonte, Brazil, who reported sociodemographic characteristics and answered the SNAP-IV. Parents of the clinical sample also underwent the K-SADS-PL interview. Results: Age was significantly associated with SNAP-IV hyperactivity-impulsivity problems (r = −0.14), but not with inattention or oppositional defiant disorder. Sex was a significant influence on attention-deficit/hyperactivity disorder and oppositional defiant disorder severity (all p < 0.001), with boys showing higher scores in the full sample, but not within the attention-deficit/hyperactivity disorder group. Exploratory and confirmatory factor analysis supports a three-factor structure of the SNAP-IV scale. Moderate-to-strong correlations were found between SNAP-IV and K-SADS-PL measures. All SNAP-IV scales showed very high internal consistency coefficients (all above 0.91). SNAP-IV inattention scores were the most predictive of attention-deficit/hyperactivity disorder diagnosis (AUC: 0.877 for the averaging rating method and the raw sum method, and 0.874 for the symptom presence/absence method). Conclusion: The parent SNAP-IV showed good psychometric properties in a Brazilian school and clinical sample.

RESUMO Objetivo: Investigar as propriedades psicométricas da versão curta ou MTA da escala Swanson, Nolan e Pelham, versão IV (SNAP-IV), que mede os sintomas do transtorno de déficit de atenção/hiperatividade e transtorno desafiador de oposição. Métodos: Os participantes incluíram 765 pais de crianças de 4 a 16 anos (641 crianças sem transtorno de déficit de atenção/hiperatividade e 124 com transtorno de déficit de atenção/hiperatividade) de Belo Horizonte, Brasil, que relataram características sociodemográficas e responderam o SNAP-IV. Os pais da amostra clínica também foram submetidos à entrevista com K-SADS-PL. Resultados: A idade foi significativamente associada aos problemas de hiperatividade-impulsividade no SNAP-IV (r = −0,14), mas não à desatenção ou aos transtornos desafiadores de oposição. O sexo foi uma influência significativa na gravidade do transtorno de déficit de atenção/hiperatividade e transtorno desafiador de oposição (todos os p < 0,001), os meninos apresentaram escores mais altos na amostra completa, mas não no grupo de transtorno de déficit de atenção/hiperatividade. A análise fatorial exploratória e confirmatória apoia uma estrutura de três fatores da escala SNAP-IV. Foram encontradas correlações moderadas a fortes entre as medidas dos instrumentos SNAP-IV e K-SADS-PL. Todas as escalas do SNAP-IV mostraram coeficientes de consistência interna muito altos (todos acima de 0,91). Os escores de desatenção do SNAP-IV foram os mais preditivos do diagnóstico de transtorno de déficit de atenção/hiperatividade (AUC - área sob a curva ROC: 0,877 para o método de classificação da média e o método da soma bruta e 0,874 para o método de presença ou ausência de sintomas). Conclusão: A avaliação do SNAP-IV pelos pais apresentou boas propriedades psicométricas em uma escola brasileira e amostra clínica.

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The global prevalence of Alzheimer's disease (AD) points to endemic levels, especially considering the increase of average life expectancy worldwide. AD diagnosis based on early biomarkers and better knowledge of related pathophysiology are both crucial in the search for medical interventions that are able to modify AD progression. In this study we used unsupervised spectral unmixing statistical techniques to identify the vibrational spectral signature of amyloid ß aggregation in neural tissues, as early biomarkers of AD in an animal model. We analyzed spectral images composed of a total of 55 051 Raman spectra obtained from the frontal cortex and hippocampus of five bitransgenic APPswePS1ΔE9 mice, and colocalized amyloid ß plaques by other fluorescence techniques. The Raman signatures provided a multifrequency fingerprint consistent with the results of synthesized amyloid ß fibrils. The fingerprint obtained from unmixed analysis in neural tissues is shown to provide a detailed image of amyloid plaques in the brain, with the potential to be used as biomarkers for non-invasive early diagnosis and pathophysiology studies in AD on the retina.

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