Brainiacs 2023 Volume 4 Issue 2

@Article{Craig2023MLSHRFMA, abstract = {Current approaches to plagiarism detection often focus on finding lexical matches rather than semantic similarities in the text content that is compared. But the more important unanswered questions remain whether similar concepts expressed in related topical contexts are semantically equivalent as idea-laundering plagiarism by humans or algorithm-generated plagiarism by machines. Now publicly available and easily accessible, text-generating algorithms have automated the process of assembling a text derived from but not attributed to published content scraped from the web. The FAIR Metrics, with FAIR an acronym for Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records, measure how appropriately a document cites prior records based on whether they contain similar claims that are equivalent in meaning. We demonstrate herein a workflow with results for manual evaluation of the FAIR Metrics to quantify the extent of plagiarism in 8 articles retracted or reported for plagiarism. We also demonstrate use of the Nexus-PORTAL-DOORS-Scribe (NPDS) Cyberinfrastructure to manage semantic descriptions of the concept mappings and entity equivalence evaluations made using concepts and relationships from the PDP-DREAM Ontology.}, author = {Craig, Adam and Athreya, Anousha and Taswell, Carl}, date = {2023-12-27}, journaltitle = {Brainiacs Journal of Brain Imaging And Computing Sciences}, title = {Managing Lexical-Semantic Hybrid Records of {FAIR Metrics} Analyses with the {NPDS Cyberinfrastructure}}, doi = {10.48085/d5b2734f2}, issue = {2}, volume = {4}, keywords = {Plagiarism, bibliometrics, citation analysis, knowledge engineering, semantic web, equivalent entities, concept mapping, ontology.}, publisher = {Brain Health Alliance}, }

@Article{Hecker2023FPGWAS, author = {Hecker, Julian and Craig, Adam and Hughes, Andrew and Neidich, Julie and Taswell, Carl and Laird, Nan}, date = {2023-12-21}, journaltitle = {Brainiacs Journal of Brain Imaging And Computing Sciences}, title = {Fallacies and Pitfalls in Genome-Wide Association Studies}, doi = {10.48085/gfa4e8812}, issue = {2}, volume = {4}, abstract = {Since the first genome-wide association study (GWAS) identifying variants associated with myocardial infarction was published over 20 years ago, GWASs have emerged as a powerful tool for exploring the genetic basis of complex traits. To date, hundreds of thousands of statistically significant associations have been reported across thousands of human phenotypes. Nevertheless, the design, implementation, and analysis of GWASs remain complex, and the results are easily misinterpreted. Common mistakes include 1) assuming that variants with the strongest statistical associations are causal instead of correlative, 2) believing that associated loci act through nearby genes, and 3) overemphasizing the contribution of individual loci to the total variability of particular traits. Clinical assays have been designed using the results of GWAS that rely on the contribution of such erroneous data interpretations to predict clinical phenotypes, reactions to medications or foods, and/or propensity to develop diseases. The failure to recognize these errors due to fallacies in logical reasoning and statistical inference presents problems for both the scientific community when the wrong targets may be prioritized in future research studies, as well as for communication with the general public when our understanding of the genetic basis of important traits may be misrepresented and overstated. Here, we review statistical data quality, analysis, and meta-analysis, of GWAS results with an emphasis on accurate and reliable interpretation. Placed in the appropriate context, GWASs enable genome-wide discovery of loci associated with diverse traits, but they constitute only a first step towards understanding the biological mechanism(s) underlying the observed associations. Scientific elucidation of these biological mechanisms must be required to establish causality with biochemical and pathophysiological explanations for any putative statistical correlations.}, keywords = {Genome-wide association studies (GWAS), correlation-causation fallacy, meta-analysis, random effects model, fixed effects model, population stratification, family-based association studies (FBAS).}, publisher = {Brain Health Alliance}, }

@Article{Kristanto2023MVMRIA, abstract = {Multiverse analysis has been proposed as a powerful technique to disclose the large number of degrees of freedom in data preprocessing and analysis that strongly contribute to the current replication crisis in science. However, in the field of imaging neuroscience, where multidimensional, complex and noisy data are measured, multiverse analysis may be computationally infeasible. The number of possible forking paths given by different methodological decisions and analytical choices is immense. Recently, Dafflon et al. (2022) proposed an active learning approach as an alternative to exhaustively exploring all forking paths. Here, we aimed to extend their active learning pipeline by integrating latent underlying variables which are not directly observable. The extension to latent outcomes is particularly valuable for computational psychiatry and neurocognitive psychology, where latent traits are conceptualized as common cause of a variety of observable neural and behavioral symptoms. To illustrate our approach and to test its direct replicability, we analyzed the individual organization and topology of functional brain networks of two relatively large samples from the ABCD study dataset (\textit{N} = 1491) and HCP dataset (\textit{N} = 833). Graph-theoretical parameters that take into account both brain-wide and region-specific network properties were used as predictors of a latent variable reflecting general cognition. Our results demonstrate the ability of the extended method to selectively explore the multiverse when predicting a latent variable. First, the low-dimensional space created with the proposed approach was able to cluster the forking paths according to their similarity. Second, the active learning approach successfully estimated the prediction performance of all pipelines in both datasets. To interactively explore the multiverse of results, we developed a Shiny app to visualize the predictive accuracy resulting from each forking path and to illustrate the similarity between pipelines created by different combinations of data processing choice. The code for active learning and the app are available at the Github repository ExtendedAL.}, author = {Kristanto, Daniel and Gießing, Carsten and Marek, Merle and Zhou, Changsong and Debener, Stefan and Thiel, Christiane and Hildebrandt, Andrea}, date = {2023-12-21}, journaltitle = {Brainiacs Journal of Brain Imaging And Computing Sciences}, title = {An Extended Active Learning Approach to Multiverse Analysis: Predictions of Latent Variables from Graph Theory Measures of the Human Connectome and Their Direct Replication}, doi = {10.48085/j962e0f53}, issue = {2}, volume = {4}, keywords = {Multiverse analysis, latent variable modeling, active learning, Shiny application.}, publisher = {Brain Health Alliance}, }

@Article{Taswell2023BBNewt, author = {S. Koby Taswell and Aniruddh Anand and Max Montes-Soza and Carl Taswell}, date = {2023-12-18}, journaltitle = {Brainiacs Journal of Brain Imaging And Computing Sciences}, title = {BabbleNewt: A Simplified, Consistent, and Interoperable Citation Format for Bibliographic Metadata}, doi = {10.48085/k562cb81c}, issue = {2}, volume = {4}, abstract = {Of the diverse bibliographic metadata formats, BibTeX and BibLaTeX have been dominant across mathematics, computing, and engineering due to their use with the TeX and LaTeX typesetting compilers. Despite success in these fields as well as the publishing industry, both BibTeX and BibLaTeX have some deficiencies, notably inconsistencies in the format definitions and use of macros, pseudo-records, programs and processing methods across different software implementations and installations. These inconsistencies contribute to bibliography parsing and document typesetting errors especially problematic with difficult debugging for large bibliography files. A subproject within the PORTAL-DOORS Project (PDP), the BabbleNewt Project aims to address these concerns by designing a set of formats which iterate on the original \BibTeX and BibLaTeX formats while enabling easy conversion between them and a newly designed simplified, consistent, and interoperable format called BabbleNewt. The set of related formats implemented for bibliography processors by PDP BabbleNewt includes two formats PdpBibtex and PdpBiblatex corresponding to the original BibTeX and BibLaTeX, two generalized transition formats PdpBibtexgen and PdpBiblatexgen, and the novel format PdpBabblenewt.}, keywords = {Bibliographic metadata, interoperability, file formats, BibTeX, BibLaTeX, PdpBibtex, PdpBibtexgen, PdpBiblatex, PdpBiblatexgen, PdpBabblenewt.}, publisher = {Brain Health Alliance}, }

@Article{Taswell2023RVISR, abstract = {Commentary on accountability for willful disregard of reproducibility, validity, and integrity in scholarly research}, author = {Taswell, Carl}, date = {2023-12-31}, journaltitle = {Brainiacs Journal of Brain Imaging And Computing Sciences}, title = {Reproducibility, Validity, and Integrity in Scholarly Research: What Accountability for Willful Disregard?}, doi = {10.48085/l3570f30f}, issue = {2}, volume = {4}, keywords = {Repreoducibility, validity, integrity, accountability, willful disregard}, publisher = {Brain Health Alliance}, }

@Article{Taswell2023WAGTI, abstract = {On October 9th, Brain Health Alliance (BHA, a US 501c3 nonprofit organization) hosted Guardians 2023, our 2nd annual conference entitled "Who are the Guardians of Truth and Integrity?" The Guardians conferences focus on the global impact of information cyberwars on citizens of planet Earth. Internationally in media of many forms, information has been warped and twisted, resulting in disease, death, and destruction around the globe. To combat the spread of lies and extremified propaganda, the Guardians conferences strive to promote better understanding and awareness about the harm caused by information wars, and to advance learning and knowledge about how to support truth and integrity through technological and sociological research and education for communications in science, engineering, and medicine.}, author = {Taswell, S. Koby and Craig, Adam}, date = {2023-12-31}, journaltitle = {Brainiacs Journal of Brain Imaging And Computing Sciences}, title = {Who are the Guardians of Truth and Integrity?}, doi = {10.48085/y331839fb}, issue = {2}, volume = {4}, keywords = {Research integrity, citational justice, publishing ethics, scientific truth, GWAS, fake stuff, academic ghosting, FAIR Metrics.}, publisher = {Brain Health Alliance}, }