A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure.

Identifying associations between interindividual variability in brain structure and behaviour requires large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on brain-behaviour associations should be analysed. We analysed associations between brain structure (grey matter volume, cortical thickness, and surface area) and behaviour (spanning cognition, emotion, and alertness) using regularized canonical correlation analysis and a machine learning framework that tests the generalisability and stability of such associations. The replicability of brain-behaviour associations was assessed in two large, independent cohorts. The load of genetic factors on these associations was analysed with heritability and genetic correlation. We found one heritable and replicable latent dimension linking cognitive-control/executive-functions and positive affect to brain structural variability in areas typically associated with higher cognitive functions, and with areas typically associated with sensorimotor functions. These results revealed a major axis of interindividual behavioural variability linking to a whole-brain structural pattern.

Canonical Correlation Analysis and Partial Least Squares for Identifying Brain-Behavior Associations: A Tutorial and a Comparative Study.

Canonical correlation analysis (CCA) and partial least squares (PLS) are powerful multivariate methods for capturing associations across 2 modalities of data (e.g., brain and behavior). However, when the sample size is similar to or smaller than the number of variables in the data, standard CCA and PLS models may overfit, i.e., find spurious associations that generalize poorly to new data. Dimensionality reduction and regularized extensions of CCA and PLS have been proposed to address this problem, yet most studies using these approaches have some limitations. This work gives a theoretical and practical introduction into the most common CCA/PLS models and their regularized variants. We examine the limitations of standard CCA and PLS when the sample size is similar to or smaller than the number of variables. We discuss how dimensionality reduction and regularization techniques address this problem and explain their main advantages and disadvantages. We highlight crucial aspects of the CCA/PLS analysis framework, including optimizing the hyperparameters of the model and testing the identified associations for statistical significance. We apply the described CCA/PLS models to simulated data and real data from the Human Connectome Project and Alzheimer's Disease Neuroimaging Initiative (both of n > 500). We use both low- and high-dimensionality versions of these data (i.e., ratios between sample size and variables in the range of ∼1-10 and ∼0.1-0.01, respectively) to demonstrate the impact of data dimensionality on the models. Finally, we summarize the key lessons of the tutorial.

A hierarchical Bayesian model to find brain-behaviour associations in incomplete data sets.

Canonical Correlation Analysis (CCA) and its regularised versions have been widely used in the neuroimaging community to uncover multivariate associations between two data modalities (e.g., brain imaging and behaviour). However, these methods have inherent limitations: (1) statistical inferences about the associations are often not robust; (2) the associations within each data modality are not modelled; (3) missing values need to be imputed or removed. Group Factor Analysis (GFA) is a hierarchical model that addresses the first two limitations by providing Bayesian inference and modelling modality-specific associations. Here, we propose an extension of GFA that handles missing data, and highlight that GFA can be used as a predictive model. We applied GFA to synthetic and real data consisting of brain connectivity and non-imaging measures from the Human Connectome Project (HCP). In synthetic data, GFA uncovered the underlying shared and specific factors and predicted correctly the non-observed data modalities in complete and incomplete data sets. In the HCP data, we identified four relevant shared factors, capturing associations between mood, alcohol and drug use, cognition, demographics and psychopathological measures and the default mode, frontoparietal control, dorsal and ventral networks and insula, as well as two factors describing associations within brain connectivity. In addition, GFA predicted a set of non-imaging measures from brain connectivity. These findings were consistent in complete and incomplete data sets, and replicated previous findings in the literature. GFA is a promising tool that can be used to uncover associations between and within multiple data modalities in benchmark datasets (such as, HCP), and easily extended to more complex models to solve more challenging tasks.

Multiple Holdouts With Stability: Improving the Generalizability of Machine Learning Analyses of Brain-Behavior Relationships.

BACKGROUND: In 2009, the National Institute of Mental Health launched the Research Domain Criteria, an attempt to move beyond diagnostic categories and ground psychiatry within neurobiological constructs that combine different levels of measures (e.g., brain imaging and behavior). Statistical methods that can integrate such multimodal data, however, are often vulnerable to overfitting, poor generalization, and difficulties in interpreting the results. METHODS: We propose an innovative machine learning framework combining multiple holdouts and a stability criterion with regularized multivariate techniques, such as sparse partial least squares and kernel canonical correlation analysis, for identifying hidden dimensions of cross-modality relationships. To illustrate the approach, we investigated structural brain-behavior associations in an extensively phenotyped developmental sample of 345 participants (312 healthy and 33 with clinical depression). The brain data consisted of whole-brain voxel-based gray matter volumes, and the behavioral data included item-level self-report questionnaires and IQ and demographic measures. RESULTS: Both sparse partial least squares and kernel canonical correlation analysis captured two hidden dimensions of brain-behavior relationships: one related to age and drinking and the other one related to depression. The applied machine learning framework indicates that these results are stable and generalize well to new data. Indeed, the identified brain-behavior associations are in agreement with previous findings in the literature concerning age, alcohol use, and depression-related changes in brain volume. CONCLUSIONS: Multivariate techniques (such as sparse partial least squares and kernel canonical correlation analysis) embedded in our novel framework are promising tools to link behavior and/or symptoms to neurobiology and thus have great potential to contribute to a biologically grounded definition of psychiatric disorders.

Brain-behaviour modes of covariation in healthy and clinically depressed young people.

Understanding how variations in dimensions of psychometrics, IQ and demographics relate to changes in brain connectivity during the critical developmental period of adolescence and early adulthood is a major challenge. This has particular relevance for mental health disorders where a failure to understand these links might hinder the development of better diagnostic approaches and therapeutics. Here, we investigated this question in 306 adolescents and young adults (14-24 y, 25 clinically depressed) using a multivariate statistical framework, based on canonical correlation analysis (CCA). By linking individual functional brain connectivity profiles to self-report questionnaires, IQ and demographic data we identified two distinct modes of covariation. The first mode mapped onto an externalization/internalization axis and showed a strong association with sex. The second mode mapped onto a well-being/distress axis independent of sex. Interestingly, both modes showed an association with age. Crucially, the changes in functional brain connectivity associated with changes in these phenotypes showed marked developmental effects. The findings point to a role for the default mode, frontoparietal and limbic networks in psychopathology and depression.

Early visual cortical structural changes in diabetic patients without diabetic retinopathy.

BACKGROUND: It is known that diabetic patients have changes in cortical morphometry as compared to controls, but it remains to be clarified whether the visual cortex is a disease target, even when diabetes complications such as retinopathy are absent. Therefore, we compared type 2 diabetes patients without diabetic retinopathy with control subjects using magnetic resonance imaging to assess visual cortical changes when retinal damage is not yet present. METHODS: We performed T1-weighted imaging in 24 type 2 diabetes patients without diabetic retinopathy and 27 age- and gender-matched controls to compare gray matter changes in the occipital cortex between groups using voxel based morphometry. RESULTS: Patients without diabetic retinopathy showed reduced gray matter volume in the occipital lobe when compared with controls. CONCLUSIONS: Reduced gray matter volume in the occipital cortex was found in diabetic patients without retinal damage. We conclude that cortical early visual processing regions may be affected in diabetic patients even before retinal damage occurs.

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

BACKGROUND: Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. OBJECTIVE: Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). METHOD: We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately - using standard univariate VBM - and simultaneously, with multivariate analyses. RESULTS: Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. CONCLUSION: While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities.

Avaliação da atividade antibacteriana dos extratos de Avicennia schaueriana Stapf & Leechm. ex Moldenke, Verbenaceae / Evaluation of the antibacterial activity of extracts of Avicennia schaueriana Stapf & Leechm. ex Moldenke, Verbenaceae

A flora tem sido uma fonte valiosa de constituintes bioativos para manutenção da saúde humana. Tal fato tem reforçado a crescente investigação do potencial terapêutico das plantas medicinais. Neste contexto, o presente trabalho teve como objetivo avaliar o potencial antibacteriano dos extratos hidro-alcoólicos da casca, folha, e raiz de Avicennia schaueriana Stapf & Leechm. ex Moldenke, Verbenaceae. As partes vegetais foram coletadas no povoado pesqueiro de Siribinha, Bahia e os extratos vegetais foram preparados pelo método de maceração. Todos os testes de sensibilidade dos extratos vegetais foram avaliados por meio de testes não-paramétricos. O extrato da raiz de A. schaueriana apresentou uma atividade inibitória do crescimento superior aos extratos da folha e casca, com halos de inibição que variaram entre 7 a 18 mm de acordo com as concentrações e as cepas bacterianas testadas. Já a concentração inibitória mínima dos extratos hidro-alcoólicos de A. schaueriana apresentaram uma média de valores entre 0,62 a 10,00 mg/mL para as bactérias analisadas. Os resultados estatísticos confirmaram a diferença de sensibilidade dos microrganismos estudados frente aos extratos de A. schaueriana.

Natural products have been a valuable bioactives constituent source for maintenance of the health human being. Such fact has strengthened the increasing inquiry of the therapeutical potential of medicinal plants. This work had as objective evaluate the antibacterial potential from leaf, root and bark hydro-alcoholics extracts of Avicennia schaueriana Stapf & Leechm. ex Moldenke, Verbenaceae. The plant parts had been collected in the fishing town of Siribinha, Bahia and the plant extracts had been prepared by the method of maceration. All the tests of sensitivity of plant extracts had been evaluated by means of notparametric tests. The results of the test of diffusion in disk showed that the extract of the root of the A. schaueriana had been higher to extracts of the leaf and bark, with inhibition rings from growth varying of 7 to 18 mm; in accordance with the tested concentrations. Already the minimum inhibitory concentration of hydro-alcoholic extracts of A. schaueriana had presented an average of values between 0,62 to 10,00 mg/mL for the analyzed bacteria. The statistical results had confirmed the difference of sensitivity of the studied microorganisms front to extracts of the A. schaueriana.

Atividade antimicrobiana in vitro do extrato de Abarema cochliocarpos (Gomes) Barneby & Grimes / In vitro antimicrobial activity of the extract of Abarema cochliocarpos (Gomes) Barneby & Grimes

O presente trabalho objetivou avaliar o potencial antimicrobiano in vitro do extrato hidro-alcoólico da casca de Abarema cochliocarpos (Gomes) Barneby & Grimes, conhecido como barbatimão, nas cepas de Staphylococcus aureus (ATCC 6538), Micrococcus luteus (ATCC 9341), Escherichia coli (ATCC 10536), Pseudomonas aeruginosa (ATCC 15442) e Staphylococcus aureus isolado de amostra clínica (SAIACLIN). Coletaram-se cascas de um espécime de barbatimão no Parque Ecológico INDES, localizado em Vila de Sauípe, Bahia, para preparação do extrato hidro-alcoólico. Os testes de susceptibilidade bacteriana foram realizados através do método de difusão em disco e da concentração inibitória mínima (CIM). O extrato hidro-alcoólico de A. cochliocarpos inibiu o crescimento somente das bactérias Gram-positivas. S. aureus (ATCC 6538), SAIACLIN e M. luteus apresentaram halos crescentes de inibição bacteriana de acordo com o aumento da concentração do extrato (1, 2 e 3 mg/disco), que variaram entre 8 a 15 mm, de 8 a 16 mm e de 13 a 22 mm para os respectivos microrganismos. Os resultados confirmaram a capacidade de inibição do crescimento bacteriano in vitro pelo extrato da casca de A. cochliocarpos, corroborando os relatos populares.

The present study aimed at evaluating the in vitro antimicrobial potential of the hydroalcoholic extract from the bark of Abarema cochliocarpos (Gomes) Barneby & Grimes, known as 'barbatimão', over the strains of Staphylococcus aureus (ATCC 6538), Micrococcus luteus (ATCC 9341), Escherichia coli (ATCC 6538), Pseudomonas aeruginosa (ATCC 15442) and over a Clinical Sample isolated from Staphylococcus aureus (SAIACLIN). Samples of 'barbatimão' bark were collected from a specimen at the INDES Ecological Park, located at Vila de Sauípe, Bahia to prepare the hydroalcoholic extract. The bacterial susceptibility tests were carried through the disk diffusion method and through the minimum inhibitory concentration (MIC). The hydroalcoholic extract of A. cochliocarpos only inhibited the growth of the Gram-positive bacteria. S. aureus (ATCC 6538), SAIACLIN and M. luteus had presented increasing zones of bacterial inhibition in accordance with the increase of the extract concentration (1, 2 and 3 mg/disco), that had varied between 8 to 15 mm, from 8 to 16 mm and from 13 to 22 mm for the respective microorganisms. The results had confirmed the capacity of inhibition of the in vitro bacterial growth for the extract of the bark of A. cochliocarpos, corroborating the popular stories.