Social information and personal interests modulate neural activity during economic decision-making

In the present study we employed electrophysiological recordings to investigate the levels of processing at which positive and negative descriptions of other people bias social decision-making in a game in which participants accepted or rejected economic offers. Besides social information, we manipulated the fairness of the assets distribution, whether offers were advantageous or not for the participant and the uncertainty of the game context. Results show that a negative description of the interaction partner enhanced the medial frontal negativity (MFN) in an additive manner with fairness evaluations. The description of the partner interacted with personal benefit considerations, showing that this positive or negative information only biased the evaluation of offers when they did not favor the participant. P300 amplitudes were enhanced by advantageous offers, suggesting their heightened motivational significance at later stages of processing. Throughout all stages, neural activity was enhanced with certainty about the personal assignments of the split. These results provide new evidence on the importance of interpersonal information and considerations of self-interests relative to others in decision-making situations.Financial support to this research came from the Spanish Ministry of Science and Innovation through a “Ramón y Cajal” research fellowship (RYC-2008-03008) and grant PSI2010-16421 to María Ruz, and also from the European Commission through a “Leonardo da Vinci” fellowship (DE/10/LLP-LdV/PLM/282611) to Anna Moser

MVPAlab: A machine learning decoding toolbox for multidimensional electroencephalography data

This research was supported by the Spanish Ministry of Sci- ence and Innovation under the PID2019–111187GB-I00 grant, by the MCIN/AEI/10.13039/50110 0 011033/ and FEDER “Una manera de hacer Europa’’ under the RTI2018-098913-B100 project, by the Consejería de Economía, Innovación, Ciencia y Empleo (Junta de Andalucía) and FEDER under CV20-45250, A-TIC-080-UGR18, B- TIC-586-UGR20 and P20-00525 projects. The first author of this work is supported by a scholarship from the Spanish Ministry of Science and Innovation (BES-2017–079769). Funding for open ac- cess charge: Universidad de Granada / CBUA. The sample EEG dataset was extracted from an original experiment previously ap- proved by the Ethics Committee of the University of Granada.Background and Objective: The study of brain function has recently expanded from classical univariate to multivariate analyses. These multivariate, machine learning-based algorithms afford neuroscientists extracting more detailed and richer information from the data. However, the implementation of these procedures is usually challenging, especially for researchers with no coding experience. To address this problem, we have developed MVPAlab, a MATLAB-based, flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data. Methods: The MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contri- bution analyses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrial generation. To draw statisti- cal inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach. Results: A sample electroencephalography dataset was compiled to test all the MVPAlab main function- alities. Significant clusters (p < 0.01) were found for the proposed decoding analyses and different config- urations, proving the software capability for discriminating between different experimental conditions. Conclusions: This toolbox has been designed to include an easy-to-use and intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for users with few or no previous coding experience. In addition, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.Spanish Government PID2019-111187GB-I00 BES-2017-079769MCIN/AEIFEDER "Una manera de hacer Europa'' RTI2018-098913-B100Junta de AndalucíaEuropean Commission CV20-45250 A-TIC-080-UGR18 BTIC-586-UGR20 P20-00525Universidad de Granada/CBU

Neural representation of current and intended task sets during sequential judgements on human faces

Engaging in a demanding activity while holding in mind another task to be performed in the near future requires the maintenance of information about both the currently-active task set and the intended one. However, little is known about how the human brain implements such action plans. While some previous studies have examined the neural representation of current task sets and others have investigated delayed intentions, to date none has examined the representation of current and intended task sets within a single experimental paradigm. In this fMRI study, we examined the neural representation of current and intended task sets, employing sequential classification tasks on human faces. Multivariate decoding analyses showed that current task sets were represented in the orbitofrontal cortex (OFC) and fusiform gyrus (FG), while intended tasks could be decoded from lateral prefrontal cortex (lPFC). Importantly, a ventromedial region in PFC/OFC contained information about both current and delayed tasks, although cross-classification between the two types of information was not possible. These results help delineate the neural representations of current and intended task sets, and highlight the importance of ventromedial PFC/OFC for maintaining task-relevant information regardless of when it is needed.This work was supported by the Spanish Ministry of Science and Innovation (PSI2016-78236-P to M.R.) and the Spanish Ministry of Education, Culture and Sports (FPU2014/04272 to P.D.G.)

Atlas-based classification algorithms for identification of informative brain regions in fMRI data

Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Although a Searchlight strategy that locally sweeps all voxels in the brain is the most extended approach to assign functional value to different regions of the brain, this method does not offer information about the directionality of the results and it does not allow studying the combined patterns of more distant voxels. In the current study, we examined two different alternatives to searchlight. First, an atlas- based local averaging (ABLA, Schrouff et al., 2013a) method, which computes the relevance of each region of an atlas from the weights obtained by a whole-brain analysis. Second, a Multiple-Kernel Learning (MKL, Rakotomamonjy et al., 2008) approach, which combines different brain regions from an atlas to build a classification model. We evaluated their performance in two different scenarios where differential neural activity between conditions was large vs. small, and employed nine different atlases to assess the influence of diverse brain parcellations. Results show that all methods are able to localize informative regions when differences were large, demonstrating stability in the identification of regions across atlases. Moreover, the sign of the weights reported by these methods provides the sensitivity of multivariate approaches and the directionality of univariate methods. However, in the second context only ABLA localizes informative regions, which indicates that MKL leads to a lower performance when differences between conditions are small. Future studies could improve their results by employing machine learning algorithms to compute individual atlases fit to the brain organization of each participant.Spanish Ministry of Science and Innovation through grant PSI2016-78236-PSpanish Ministry of Economy and Competitiveness through grant BES-2014-06960

Short-term Prediction of MCI to AD conversion based on Longitudinal MRI analysis and neuropsychological tests

Nowadays, 35 million people worldwide su↵er from some form of dementia. Given the increase in life expectancy it is estimated that in 2035 this number will grow to 115 million. Alzheimer’s disease is the most common cause of dementia and it is of great importance diagnose it at an early stage. This is the main goal of this work, the de- velopment of a new automatic method to predict the mild cognitive im- pairment (MCI) patients who will develop Alzheimer’s disease within one year or, conversely, its impairment will remain stable. This technique will analyze data from both magnetic resonance imaging and neuropsycholog- ical tests by utilizing a t-test for feature selection, maximum-uncertainty linear discriminant analysis (MLDA) for classification and leave-one-out cross validation (LOOCV) for evaluating the performance of the meth- ods, which achieved a classification accuracy of 73.95%, with a sensitivity of 72.14% and a specificity of 73.77%.MICINN under the TEC2012-34306 projectConsejería de Innovación, Ciencia y Empresa (Junta de Andalucía, Spain) under the Excellence Project P11-TIC-710

Improving short-term prediction from MCI to AD by applying searchlight analysis

Alzheimer's disease (AD) is the most common cause of dementia. Nowadays, 44 million people worldwide suffer from this neurodegenerative disease. Fortunately, the use of new technologies can help doctors in diagnosing this disease in an increasingly early stage, which is vital to prevent its advance. In this work we have developed a new automatic method to predict if patients suffering from mild cognitive impairment (MCI) will develop AD within one year or, conversely, its impairment will remain stable. This technique is based on the so-called Searchlight, a widely known approach in fMRI but which has not been previously used with structural images. Besides analyzing the intensity of the voxels in each of the subregions defined by the Searchlight, data from two neuro-psychological tests were used during the classification process, achieving an accuracy of 84%

Data fusion based on Searchlight analysis for the prediction of Alzheimer's disease

This work was supported by the MINECO/FEDER, Spain under the RTI2018-098913-B-I00 project, the General Secretariat of Universities, Research and Technology, Junta de Andalucia, Spain under the Excellence FEDER Project A-TIC-117-UGR18, and University of Granada, Spain through grant "Contratos puente'' to J.E.A. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative, United States (ADNI; National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, United States, the National Institute of Biomedical Imaging and Bioengineering, United States, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org).The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro-Imaging at the University of California, Los Angeles. This research was also supported by NIH, Spain grants P30 AG010129, K01 AG030514, and the Dana Foundation, United States.Conceptualization, Methodology, Software, Investigation, Writing – original draft, Writing – review & editing. Javier Ramírez: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. Juan M. Górriz: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing. María Ruz: Conceptualization, Validation, Supervision, Investigation, Writing – original draft, Writing – review & editing.In recent years, several computer-aided diagnosis (CAD) systems have been proposed for an early identification of dementia. Although these approaches have mostly used the transformation of data into a different feature space, more precise information can be gained from a Searchlight strategy. The current study presents a data fusion classification system that employs magnetic resonance imaging (MRI) and neuropsychological tests to distinguish between Mild-Cognitive Impairment (MCI) patients that convert to Alzheimer's disease (AD) and those that remain stable. Specifically, this method uses a nested cross-validation procedure to compute the optimum contribution of each data modality in the final decision. The model employs Support-Vector Machine (SVM) classifiers for both data modalities and is combined with Searchlight when applied to neuroimaging. We compared the performance of our system with an alternative based on Principal Component Analysis (PCA) for dimensionality reduction. Results show that Searchlight outperformed PCA both for uni/multimodal classification, obtaining a maximum accuracy of 80.9% when combining data from six and twelve months before patients converted to AD. Moreover, Searchlight allowed the identification of the most informative regions at different stages of the longitudinal study, which can be crucial for a better understanding of the development of AD. Additionally, results do not depend on the parcellations provided by a specific brain atlas, which manifests the robustness and the spatial precision of the method proposed.MINECO/FEDER, Spain RTI2018-098913-B-I00General Secretariat of Universities, Research and TechnologyJunta de Andalucia A-TIC-117-UGR18University of Granada, Spain through grant "Contratos puente''Alzheimer's Disease Neuroimaging Initiative, United States (ADNI; National Institutes of Health) U01 AG024904National Institute on Aging, United StatesNational Institute of Biomedical Imaging and Bioengineering, United StatesAbbott LaboratoriesAstraZenecaBayer AGBristol-Myers SquibbEisai Co LtdElan CorporationRoche Holding GenentechGeneral Electric GE HealthcareGlaxoSmithKlineInnogeneticsJohnson & Johnson Johnson & Johnson USAEli LillyMedpace, Inc.Merck & CompanyNovartis AGPfizerF. Hoffman-La RocheMerck & Company Schering Plough CorporationSynarc, Inc.United States Department of Health & Human Services National Institutes of Health (NIH) - USANorthern California Institute for Research and EducationUnited States Department of Health & Human Services National Institutes of Health (NIH) - USA P30 AG010129Dana Foundation, United State

Near-peer Teaching in Histology Laboratory

Background: Near-peer teaching is an educational method based on being taught by one or more students who are more advanced in one specific area of the same curriculum. The aim of this study was to analyze outcomes and medical students’ reactions to near-peer teaching in Histology Laboratory session. Methods: Histology Laboratory session was firstly designed as a practical session driven by academic staff, while in our new approach was driven by Histology intern students, which are upper year students in Medicine curriculum. Our near-peer teaching was evaluated using a multiple choice test when half of students had attended the session, the results of which were compared with those from traditional teaching. A reaction evaluation survey was also administered at the end of the course. Results: Multiple choice test results did not showed statistical differences between near-peer and traditional teaching strategies. Results from the reaction evaluation were mostly positive, especially with regard to feeling comfortable in the session taught by intern students and how intern students managed to transmit the information properly.Conclusion: Near-peer teaching in Histology Laboratory practical session is an effective alternative teaching method, with outcomes equivalent to traditional design, and highly valued by undergraduate medical student

Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information

Genome-wide association studies have generated an increasing number of common genetic variants associated with neurological and psychiatric disease risk. An improved understanding of the genetic control of gene expression in human brain is vital considering this is the likely modus operandum for many causal variants. However, human brain sampling complexities limit the explanatory power of brain-related expression quantitative trait loci (eQTL) and allele-specific expression (ASE) signals. We address this, using paired genomic and transcriptomic data from putamen and substantia nigra from 117 human brains, interrogating regulation at different RNA processing stages and uncovering novel transcripts. We identify disease-relevant regulatory loci, find that splicing eQTLs are enriched for regulatory information of neuron-specific genes, that ASEs provide cell-specific regulatory information with evidence for cellular specificity, and that incomplete annotation of the brain transcriptome limits interpretation of risk loci for neuropsychiatric disease. This resource of regulatory data is accessible through our web server, http://braineacv2.inf.um.es/

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)