Qinghao Liang
DownloadHi-Res Photo
About
Biography
Qinghao Liang is a PhD candidate in Biomedical Engineering at Yale University,
with a focus on machine learning in Neuroimaging.
His research aims to enhance data imputation and transfer learning for better prediction in brain connectomes, striving to conserve neuroimaging data and boost prediction accuracy. Qinghao previously completed his BS in Physics at University of Science and Technology of China.
Appointments
Departments & Organizations
- Multi-modal Imaging, Neuroinformatics, & Data Science (MINDS) Laboratory
Research
Research at a Glance
Yale Co-Authors
Frequent collaborators of Qinghao Liang's published research.
Javid Dadashkarimi
Max Rolison, MD
Brendan Adkinson
Dustin Scheinost, PhD, BS
Publications
2024
Rescuing missing data in connectome-based predictive modeling
Liang Q, Jiang R, Adkinson B, Rosenblatt M, Mehta S, Foster M, Dong S, You C, Negahban S, Zhou H, Chang J, Scheinost D. Rescuing missing data in connectome-based predictive modeling. Imaging Neuroscience 2024, 2: 1-16. DOI: 10.1162/imag_a_00071.Peer-Reviewed Original ResearchCitationsAltmetric
2022
Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer
Scheinost D, Pollatou A, Dufford A, Jiang R, Farruggia M, Rosenblatt M, Peterson H, Rodriguez R, Dadashkarimi J, Liang Q, Dai W, Foster M, Camp C, Tejavibulya L, Adkinson B, Sun H, Ye J, Cheng Q, Spann M, Rolison M, Noble S, Westwater M. Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer. Biological Psychiatry 2022, 93: 893-904. PMID: 36759257, PMCID: PMC10259670, DOI: 10.1016/j.biopsych.2022.10.014.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCitationsAltmetricA functional connectome signature of blood pressure in >30 000 participants from the UK biobank.
Jiang R, Calhoun VD, Noble S, Sui J, Liang Q, Qi S, Scheinost D. A functional connectome signature of blood pressure in >30 000 participants from the UK biobank. Cardiovascular Research 2022, 119: 1427-1440. PMID: 35875865, PMCID: PMC10262183, DOI: 10.1093/cvr/cvac116.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsBlood pressureBP levelsSystolic/diastolic blood pressurePrevalent modifiable risk factorFunctional connectivityMeaningful blood pressureDiastolic blood pressureElevated blood pressureModifiable risk factorsBody mass indexWhole-brain functional connectivityCentral autonomic networkAnterior cingulate cortexAntihypertensive medicationsMass indexMultiple confoundersPulse pressureRisk factorsCardiovascular diseaseIrreversible structural damageMedicated participantsMedication statusCingulate cortexCognitive declineAlzheimer's diseaseA Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity
Jiang R, Scheinost D, Zuo N, Wu J, Qi S, Liang Q, Zhi D, Luo N, Chung Y, Liu S, Xu Y, Sui J, Calhoun V. A Neuroimaging Signature of Cognitive Aging from Whole‐Brain Functional Connectivity. Advanced Science 2022, 9: 2201621. PMID: 35811304, PMCID: PMC9403648, DOI: 10.1002/advs.202201621.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsCognitive declineNormal agingFunctional connectivitySimilar neural correlatesWhole-brain functional connectivityDorsal attention networkBrain network organizationNeural dedifferentiationFluid intelligenceCognitive agingCognitive abilitiesNeural correlatesAttention networkCognitive functionNetwork organizationHuman ageNeuroimaging signaturesCognitionUnique patternAgingConnectivityIntelligenceCorrelatesConstructsHealthy cohortPredicting the future of neuroimaging predictive models in mental health
Tejavibulya L, Rolison M, Gao S, Liang Q, Peterson H, Dadashkarimi J, Farruggia MC, Hahn CA, Noble S, Lichenstein SD, Pollatou A, Dufford AJ, Scheinost D. Predicting the future of neuroimaging predictive models in mental health. Molecular Psychiatry 2022, 27: 3129-3137. PMID: 35697759, PMCID: PMC9708554, DOI: 10.1038/s41380-022-01635-2.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsCitationsAltmetric
2021
Connectome-Based Predictive Modelling With Missing Connectivity Data Using Robust Matrix Completion
Liang Q, Negahban S, Chang J, Zhou H, Scheinost D. Connectome-Based Predictive Modelling With Missing Connectivity Data Using Robust Matrix Completion. 2021, 00: 738-742. DOI: 10.1109/isbi48211.2021.9434138.Peer-Reviewed Original ResearchCitationsConceptsRobust Matrix CompletionMatrix completionMachine learning modelsPortion of dataFeature selection stepConnectivity dataDataset showPredictive modellingLearning modelMultiple tasksHuge amountTask increasesModelling pipelineSelection stepExperimental resultsComplementary informationPredictive performanceUseful informationComparison methodDownstream analysisSmall subsetInformationComplete casesModellingTask