The Internationalization of Psychology Journals in Brazil: A Bibliometric Examination Based on Four Indices

Abstract: There is considerable variability among psychology journals in Brazil, in terms of presence on the international stage. However, research as to why is very scarce. This study empirically examined the relationship between several indices of internationalization and real-world internationalization, among these journals. 661 articles from the top-17 psychology journals in Brazil were coded for: English-language text, editorial board makeup, lead author institution, and article type. Analyses revealed that successful internationalization was associated with: (i) lead author institution from a native English-speaking country; (ii) empirical articles; and (iii) editorial board members from a native English-speaking country. Use of English-language text was not associated with successful internationalization. These findings suggest that the path to internationalization for psychology journals in Brazil may depend on increased publishing of findings from English-speaking countries; or at the very least: increased collaboration between Brazilian and native English-speaking scholars

Density and Dichotomous Family History Measures of Alcohol Use Disorder as Predictors of Behavioral and Neural Phenotypes: A Comparative Study Across Gender and Race/Ethnicity

Background: Family history (FH) is an important risk factor for the development of alcohol use disorder (AUD). A variety of dichotomous and density measures of FH have been used to predict alcohol outcomes; yet, a systematic comparison of these FH measures is lacking. We compared 4 density and 4 commonly used dichotomous FH measures and examined variations by gender and race/ethnicity in their associations with age of onset of regular drinking, parietal P3 amplitude to visual target, and likelihood of developing AUD. Methods: Data from the Collaborative Study on the Genetics of Alcoholism (COGA) were utilized to compute the density and dichotomous measures. Only subjects and their family members with DSM-5 AUD diagnostic information obtained through direct interviews using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) were included in the study. Area under receiver operating characteristic curves were used to compare the diagnostic accuracy of FH measures at classifying DSM-5 AUD diagnosis. Logistic and linear regression models were used to examine associations of FH measures with alcohol outcomes. Results: Density measures had greater diagnostic accuracy at classifying AUD diagnosis, whereas dichotomous measures presented diagnostic accuracy closer to random chance. Both dichotomous and density measures were significantly associated with likelihood of AUD, early onset of regular drinking, and low parietal P3 amplitude, but density measures presented consistently more robust associations. Further, variations in these associations were observed such that among males (vs. females) and Whites (vs. Blacks), associations of alcohol outcomes with density (vs. dichotomous) measures were greater in magnitude. Conclusions: Density (vs. dichotomous) measures seem to present more robust associations with alcohol outcomes. However, associations of dichotomous and density FH measures with different alcohol outcomes (behavioral vs. neural) varied across gender and race/ethnicity. These findings have great applicability for alcohol research examining FH of AUD

Common Limitations of Image Processing Metrics:A Picture Story

While the importance of automatic image analysis is continuously increasing, recent meta-research revealed major flaws with respect to algorithm validation. Performance metrics are particularly key for meaningful, objective, and transparent performance assessment and validation of the used automatic algorithms, but relatively little attention has been given to the practical pitfalls when using specific metrics for a given image analysis task. These are typically related to (1) the disregard of inherent metric properties, such as the behaviour in the presence of class imbalance or small target structures, (2) the disregard of inherent data set properties, such as the non-independence of the test cases, and (3) the disregard of the actual biomedical domain interest that the metrics should reflect. This living dynamically document has the purpose to illustrate important limitations of performance metrics commonly applied in the field of image analysis. In this context, it focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.Comment: This is a dynamic paper on limitations of commonly used metrics. The current version discusses metrics for image-level classification, semantic segmentation, object detection and instance segmentation. For missing use cases, comments or questions, please contact [email protected] or [email protected]. Substantial contributions to this document will be acknowledged with a co-authorshi

Understanding metric-related pitfalls in image analysis validation

Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.Comment: Shared first authors: Annika Reinke, Minu D. Tizabi; shared senior authors: Paul F. J\"ager, Lena Maier-Hei

Parsing genetically influenced risk pathways: genetic loci impact problematic alcohol use via externalizing and specific risk

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White matter hyperintensities are significantly associated with cortical atrophy in Alzheimer's disease

Background and objective: Methodological variability in the assessment of white matter hyperintensities (WMH) in dementia may explain inconsistent reports of its prevalence and impact on cognition. We used a method of brain MRI segmentation for quantifying both tissue and WMH volumes in Alzheimer's disease (AD) and examined the association between WMH and structural and cognitive variables. Methods: A consecutive series of 81 patients meeting NINCDS-ADRDA criteria for probable AD was studied. Nineteen healthy volunteers of comparable age served as the control group. Patients had a complete neurological and neuropsychological evaluation, and a three dimensional MRI was obtained. Images were segmented into grey matter, white matter, and cerebrospinal fluid. WMH were edited on segmented images, and lobar assignments were based on Talairach coordinates. Results: Mild and moderate to severe AD patients had significantly more WMH than controls (p<0.05). WMH preferentially involved the frontal lobes (70%), were inversely correlated with grey matter cortical volume (R(2) = 0.23, p<0.001), and were significantly associated with vascular risk factors and with a worse performance on memory tasks. Conclusion: Objective measurements of tissue volumes in AD demonstrated that WMH are significantly related to cortical atrophy and neuropsychological impairment

White Matter Abnormalities in Veterans With Mild Traumatic Brain Injury

Objective: It has been estimated that 10%-20% of U.S. veterans of the wars in Iraq and Afghanistan experienced mild traumatic brain injury (TBI), mostly secondary to blast exposure. Diffusion tensor imaging (DTI) may detect subtle white matter changes in both the acute and chronic stages of mild TBI and thus has the potential to detect white matter damage in patients with TBI. The authors used DTI to examine white matter integrity in a relatively large group of veterans with a history of mild TBI. Method: DTI images from 72 veterans of the wars in Iraq and Afghanistan who had mild TBI were compared with DTI images from 21 veterans with no exposure to TBI during deployment. Conventional voxel-based analysis as well as a method of identifying spatially heterogeneous areas of decreased fractional anisotropy ("potholes") were used. Veterans also underwent psychiatric and neuropsychological assessments. Results: Voxel-based analysis did not reveal differences in DTI parameters between the veterans with mild TBI and those with no TBI. However, the veterans with mild TBI had a significantly higher number of potholes than those without TBI. The difference in the number of potholes was not influenced by age, time since trauma, a history of mild TBI unrelated to deployment, or coexisting psychopathology. The number of potholes was correlated with the severity of TBI and with performance in executive Conclusions: Veterans who had blast-related mild TBI showed evidence of multifocal white matter abnormalities that were associated with severity of the injury and with relevant functional measures. Overall, white matter potholes may constitute a sensitive biomarker of axonal injury that can be identified in mild TBI at acute and chronic stages of its clinical course. (Am J Psychiatry 2012; 169:1284-1291