Journal: bioRxiv

Article Title: Creativity and memory: Cortical representational change along with amygdala activation predict the insight memory effect

doi: 10.1101/2023.06.13.544774

Figure Lengend Snippet: Identification of VOTC regions showing Representational Change (RC) Note. Values = estimated marginal means ± SEM; asterisk = statistical significance; dot represents trend without statistical significance. HI-I = solved trials with high insight. Panel A. RC from pre to post solution: Multivoxel pattern similarity. Multivoxel patterns per ROI for each time point (pre- and post solution) are extracted and subsequently correlated. Pre= 0.5sec after stimulus presentation; post = during solution button press. Those Pre-Post Solution Similarity values (r) are subsequently estimated in a linear mixed model as a function of insight and ROI. Bar plots show change (Δ) in Multivoxel Pattern Similarity (MVPS) analysis. Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B. RC from pre to post solution: Representational strength - AlexNet. This RSA method employs four steps. ( B1 ) A brain activation pattern matrix (APM, size 120×120) is generated for each region-of-interest (ROI) and each time point (pre- post solution, see Panel A) where each cell is representing a multivoxel pattern similarity value for each Mooney image pair. ( B2 ) A conceptual stimuli model (here using AlexNet, size 120×120) is generated where each cell is representing a similarity value for each Mooney object pair. ( B3 ) For each brain region and each time point, the row of each stimulus (∼120) in the stimuli model and in the APM are correlated yielding a representational strength measure (i.e. brain-model fit) per region and time point. ( B4 ) The representational strength is used as a dependent variable in linear mixed models to investigate which ROIs exhibit an insight-related increase in representational strength from pre- to post solution (time). Barplots show Representational Strength [Rep-Str] = “second order correlation” between multivoxel patterns in respective ROI at pre and post response time point and conceptual stimuli models (AlexNet and Word2Vec [W2V]).

Article Snippet: Those results were replicated when using the Word2Vec instead of the AlexNet conceptual model: the linear trend of the insight-memory-factor was significant ( Chi 2 ( 2) = 12.39, p <.01) over both brain regions (pFusG, iLOC) suggesting that this linear trend is robust.

Techniques: Activation Assay, Generated

Journal: bioRxiv

Article Title: Creativity and memory: Cortical representational change along with amygdala activation predict the insight memory effect

doi: 10.1101/2023.06.13.544774

Figure Lengend Snippet: Insight memory factor predicts multivariate activity in VOTC, univariate activity in amygdala and Amygdala-VOTC functional connectivity. Note . Values represent estimated marginal means ± (between subject) SEM. Forgotten = subsequently forgotten trials; Rem_LO-I: subsequently remembered trials originally solved with low insight; Rem_HI-I = subsequently remembered trials originally solved with high insight. Panel A: change (Δ) in Multivoxel Pattern Similarity (MVPS). Change in MVPS = 1 minus the correlation between the post and pre solution multivoxel pattern in the respective ROI. Panel B: Representational strength of solution object (post solution) is measured via a conceptual model created out of the penultimate layer of AlexNet. Panel C: Representational strength is measured via a conceptual Word2Vec [W2V] model. Panel D: Amygdala mean activity at post solution divided by insight memory conditions. Panel E: Functional connectivity between Amygdala and VOTC. Values represent averaged correlation coefficients between left and right amygdala and left and right iLOC or pFusG.

Article Snippet: Those results were replicated when using the Word2Vec instead of the AlexNet conceptual model: the linear trend of the insight-memory-factor was significant ( Chi 2 ( 2) = 12.39, p <.01) over both brain regions (pFusG, iLOC) suggesting that this linear trend is robust.

Techniques: Activity Assay, Functional Assay

Journal: Briefings in Bioinformatics

Article Title: Text mining approaches for dealing with the rapidly expanding literature on COVID-19

doi: 10.1093/bib/bbaa296

Figure Lengend Snippet: Resources for text mining researchers and practitioners, including embeddings, reusable annotated datasets, knowledge graphs and pretrained COVID-19-domain language models. These resources can be incorporated into downstream systems. Under ‘Affiliation’, we use \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$^\dagger $\end{document} for industry, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$^*$\end{document} for nonprofit and no symbol for academic affiliations; if no affiliation is provided, the work is conducted by independent researchers

Article Snippet: , GreenCovidSQuADBERT [ ] , CORD-19, Word2vec, SQuADBERT , LMU Munich, Siemens \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$^\dagger $\end{document} , – , A cheap and performant way to achieve domain adaptation for BERT models; achieves by training Word2vec and aligning Word2vec embeddings to BERT wordpieces.

Techniques: Derivative Assay