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Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet [article]

Po-Yu Kao, Angela Zhang, Michael Goebel, Jefferson W. Chen, B.S. Manjunath
2019 arXiv   pre-print
In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents.  ...  Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence.  ...  Acknowledgement This research was partially supported by a National Institutes of Health (NIH) award # 5R01NS103774-02.  ... 
arXiv:1904.07387v2 fatcat:agl4qxuupbfyhbjca5vrjrjprm

A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction [chapter]

Yeeleng S. Vang, Yingxin Cao, Xiaohui Xie
2019 Lecture Notes in Computer Science  
These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score.  ...  The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs.  ...  Our method combines the state-of-the-art approach of deep learning to find a good, non-linear compression of the high dimensional 3D MRI data and uses the superior performance of GBM to learn an ensemble  ... 
doi:10.1007/978-3-030-31901-4_1 fatcat:fuknspmndzay3oqchyghjaarcq

Novel age-dependent cortico-subcortical morphologic interactions predict fluid intelligence: A multi-cohort geometric deep learning study [article]

Yunan Wu, Pierre Besson, Emanuel A. Azcona, S. Kathleen Bandt, Todd B Parrish, Hans C Breiter, Aggelos K. Katsaggelos
2020 bioRxiv   pre-print
In this work, we developed graph convolutional neural networks (gCNNs) to predict Fluid Intelligence (Gf) from shapes of cortical ribbons and subcortical structures.  ...  Across both datasets, the morphometry of the amygdala and hippocampus, along with temporal, parietal and cingulate cortex consistently drove the prediction of Gf, suggesting a novel reframing of the morphometry  ...  More studies have attempted to predict fluid intelligence using the HCP dataset.  ... 
doi:10.1101/2020.10.14.331199 fatcat:2vhowa7c5fhhjciuk72i5tpcam

Bootstrap aggregating improves the generalizability of Connectome Predictive Modelling [article]

David O'Connor, Evelyn MR Lake, Dustin Scheinost, Robert T Constable
2020 bioRxiv   pre-print
Here we investigate the use of bagging when generating predictive models of fluid intelligence (fIQ) using functional connectivity (FC).  ...  As a solution, this study proposes an ensemble learning method, in this case bootstrap aggregating, or bagging, encompassing both model parameter estimation and feature selection.  ...  The remainder of the data used in this study were provided by the Philadelphia Neurodevelopmental Cohort (Principal Investigators: Hakon Hakonarson and Raquel Gur; phs000607.v1.p1).  ... 
doi:10.1101/2020.07.08.193664 fatcat:mh6ushpqmrfhdm33n5b4dlmxae

An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Jeffrey Liu, Bino Varghese, Farzaneh Taravat, Liesl S. Eibschutz, Ali Gholamrezanezhad
2022 Diagnostics  
quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow.  ...  making, including outcome prediction and treatment planning.  ...  More recently, a random forest-based predictive model of pediatric appendicitis was created and validated on a dataset obtained from 430 children and adolescents.  ... 
doi:10.3390/diagnostics12061351 pmid:35741161 pmcid:PMC9221728 fatcat:chuolsu2ufbfpnswaxca3mjonm

Integrative clinical, genomics and metabolomics data analysis for mainstream precision medicine to investigate COVID-19

Zeeshan Ahmed, Saman Zeeshan, David J Foran, Lawrence C Kleinman, Fredric E Wondisford, XinQi Dong
2020 BMJ Innovations  
Innovative and intelligent solutions are required to improve the traditional symptom-driven practice, and allow earlier interventions using predictive diagnostics and tailor better personalised treatments  ...  ) and making medically relevant predictions.  ...  Innovative and intelligent solutions are required to improve the traditional symptom-driven practice, and allow earlier interventions using predictive diagnostics and tailor better personalised treatments  ... 
doi:10.1136/bmjinnov-2020-000444 fatcat:sgshdmrjsff37glivsjctr7pz4

Selectively Enhanced Development of Working Memory in Musically Trained Children and Adolescents

Katri Annukka Saarikivi, Minna Huotilainen, Mari Tervaniemi, Vesa Putkinen
2019 Frontiers in Integrative Neuroscience  
In the current longitudinal study, we investigated the development of working memory in musically trained and nontrained children and adolescents, aged 9-20.  ...  These tests all primarily require active maintenance of a rule in memory or immediate recall.  ...  For instance, working memory skills and working memory capacity are tightly related to fluid intelligence (Kane et al., 2005; Kail, 2007; Demetriou et al., 2014; Salthouse, 2014; Heinzel et al., 2016)  ... 
doi:10.3389/fnint.2019.00062 pmid:31780907 pmcid:PMC6851266 fatcat:jmnknag3cfbwnkmke6gqzktdce

Predicting verbal reasoning from virtual community membership in a sample of Russian young adults

Pavel Kiselev, Valeriya Matsuta, Artem Feshchenko, Irina Bogdanovskaya, Boris Kiselev
2022 Heliyon  
Inspired by earlier studies, which investigated whether abstract-thinking ability are predictable by social networking sites data, we used supervised machine learning to predict verbal-reasoning ability  ...  We experimented with binary classification machine-learning models for verbal-reasoning prediction. Prediction performance was tested on isolated control subsamples for men and women.  ...  The Cattell and Horn Fluid-Crystalized (Gf-Gc) theory is probably the best known and most widely used theory of intelligence (Stankov et al., 1995; Kaya et al., 2015) .  ... 
doi:10.1016/j.heliyon.2022.e09664 pmid:35721677 pmcid:PMC9198326 fatcat:b4yilbqbpnephkqflmciipoa4u

Sex differences in predictors and regional patterns of brain-age-gap estimates [article]

Nicole Sanford, Ruiyang Ge, Mathilde Antoniades, Amirhossein Modabbernia, Shalaila S Haas, Heather C Whalley, Liisa Galea, Sebastian Gabriel Popescu, James H Cole, Sophia Frangou
2022 bioRxiv   pre-print
The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data, and is considered a biomarker of brain  ...  Conclusions: The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate  ...  RF regression is an ensemble machine learning method, which involves construction of multiple decision trees (i.e., forests) via bootstrap (bagging) and aggregates the predictions from these multiple trees  ... 
doi:10.1101/2022.01.19.476964 fatcat:lzct64aj7rbkvbbabjxqndjbo4

Shared and distinct resting functional connectivity in children and adults with attention-deficit/hyperactivity disorder

Xiaojie Guo, Dongren Yao, Qingjiu Cao, Lu Liu, Qihua Zhao, Hui Li, Fang Huang, Yanfei Wang, Qiujin Qian, Yufeng Wang, Vince D. Calhoun, Stuart J. Johnstone (+2 others)
2020 Translational Psychiatry  
We aim to explore shared and distinct FC patterns in ADHDchild and ADHDadult, and further investigated the cross-cohort predictability using the identified FCs.  ...  Moreover, the cross-cohort predictability using the identified FCs were tested. The ADHD-HC classification accuracies were 84.4% and 81.0% for children and male adults, respectively.  ...  To overcome this drawback, a new feature selection method based on relative importance and ensemble learning (FS_RIEL) we proposed was used to identify both shared and age-specific FC patterns impaired  ... 
doi:10.1038/s41398-020-0740-y pmid:32066697 fatcat:aaunhdsduvdldh7neeofwpxfmq

A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining

Mahsa Mansourian, Sadaf Khademi, Hamid Reza Marateb
2021 Diagnostics  
We discussed how balancing the dataset and not using external validation could hinder the generalization of the CAD methods. We provided the list of the critical issues to consider in such studies.  ...  Moreover, critical information related to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines was analyzed.  ...  [117] used ensemble learning to predict suicide attempts/death following a visit to psychiatric specialty care.  ... 
doi:10.3390/diagnostics11030393 pmid:33669114 pmcid:PMC7996506 fatcat:zynohu6szjc2rd4kwnd4jc4nje

Personalized Medicine Using Neuroimmunological Biomarkers in Depressive Disorders

Suhyuk Chi, Moon-Soo Lee
2021 Journal of Personalized Medicine  
This review focuses on the current knowledge of neuroimmunology and depression and the possible use of machine learning to widen our understanding of the topic.  ...  The machine learning approach is a powerful tool for pattern recognition and has been used in psychiatry for finding useful patterns in data that have translational meaning and can be incorporated in daily  ...  Diagnosis of Depressive Disorder for Specific Populations Using Machine Learning Walss-Bass et al. used data-driven exploration of depression and anxiety in adolescents [97] .  ... 
doi:10.3390/jpm11020114 pmid:33578686 fatcat:sraw54tqtjcshh33wbulsotwfe

How musical training affects cognitive development: rhythm, reward and other modulating variables

Ewa A. Miendlarzewska, Wiebke J. Trost
2014 Frontiers in Neuroscience  
Learning to play an instrument as a child may even predict academic performance and IQ in young adulthood.  ...  Further, we introduce the notion of rhythmic entrainment and suggest that it may represent a mechanism supporting learning and development of executive functions.  ...  ACKNOWLEDGMENTS The authors thank the National Center of Competence in Research (NCCR) in Affective Sciences (No. 51NF40-104897) at the University of Geneva for supporting this publication. Ewa A.  ... 
doi:10.3389/fnins.2013.00279 pmid:24672420 pmcid:PMC3957486 fatcat:w2jqc7fewrb5dgekxecuu6jqlq

Mapping and predicting literacy and reasoning skills from early to later primary school

Andreas Demetriou, Christine Merrell, Peter Tymms
2017 Learning and Individual Differences  
2017) 'Mapping and predicting literacy and reasoning skills from early to later primary school.', Learning and individual dierences., 54 . pp. 217-225.  ...  General ability at the start of school highly predicted G in the third year of primary school at age 6 -7 years.  ...  This is general intelligence or g that is closely reflected in measures of intelligence, such as the IQ, captured by various intelligence tests.  ... 
doi:10.1016/j.lindif.2017.01.023 fatcat:q7yokn6xnnbaxgg4ifijmh3yvm

Exploring Cognitive Processes of Knowledge Acquisition to Upgrade Academic Practices

Deepa Cherukunnath, Anita Puri Singh
2022 Frontiers in Psychology  
As brain development progresses toward adolescence, meta-awareness and social-emotional cognition influence the student learning process.  ...  strengthen the knowledge ensemble through subject-domain allied training.  ...  FUNDING This research was funded by the Indian Council of Social Science Research New Delhi, India, Post-doctoral Fellowship grant reference no. 3-126/19-20/PDF/GEN (dated 3/12/2019).  ... 
doi:10.3389/fpsyg.2022.682628 pmid:35602694 pmcid:PMC9120965 fatcat:skumwon53zgpnnmkknc4x3omjq
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