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Qinghao Liang Profile
Qinghao Liang

@liang_qinghao

Followers
74
Following
263
Media
1
Statuses
31

PhD student @Yale @YaleBme, BS of Physics @USTC

New Haven, CT
Joined September 2018
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@RosenblattMatt
Matt Rosenblatt
2 years
Excited to share that our paper "Data leakage inflates prediction performance in connectome-based machine learning models" is now out in @NatureComms https://t.co/ZCwD2TGuqu (with @LinkTejavibulya, @925452096Andy, @sNeuroble, and @DScheinost )
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nature.com
Nature Communications - The effects of data leakage on predictive models in neuroimaging studies are not well understood. Here, the authors show that data leakage via feature selection and repeated...
@RosenblattMatt
Matt Rosenblatt
2 years
Ever wondered how data leakage can affect neuroimaging (specifically functional/structural connectivity) predictive models? Check out our new preprint⬇️ https://t.co/hjC4ncR1yt
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@ImagingNeurosci
Imaging Neuroscience
2 years
New paper in Imaging Neuroscience by Qinghao Liang, Dustin Scheinost, et al: Rescuing missing data in connectome-based predictive modeling https://t.co/NRteWwPLCg
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@liang_qinghao
Qinghao Liang
2 years
🧠 Excited to share our latest research in @ImagingNeurosci ! 🎓 "Rescuing Missing Data in Connectome-Based Predictive Modeling" 📈 Strategic imputation of missing data can significantly enhance prediction performance in brain-phenotype studies. 🔗 https://t.co/ligUgkarx5
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direct.mit.edu
Abstract. Recent evidence suggests brain-phenotype predictions may require very large sample sizes. However, as the sample size increases, missing data also increase. Conventional methods, like...
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@RongtaoJiang
Rongtao Jiang
2 years
Curious about how liver fibrosis affects cognitive functioning and brain structure? Check out our new paper published in @eBioMedicine, showing the liver-brain axis and highlighting the mediating effect of systemic inflammation. https://t.co/kzRK71q0Wj
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@DScheinost
Dustin Scheinost
2 years
A bit late in posting this … Now out (and on the cover!) of the June issue of AJP: “Transdiagnostic Connectome-Based Prediction of Craving“ Using task-based functional connectomes, we predicted self-reported cravings in 274 individuals with CPM. https://t.co/h1wovxAOl9
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psychiatryonline.org
Objective:Craving is a central construct in the study of motivation and human behavior and is also a clinical symptom of substance and non-substance-related addictive disorders. Thus, craving...
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@JDadashkarimi
Javid Dadashkarimi
2 years
In new work out in Medical Image Analysis, we present  CAROT: Cross Atlas Remapping via Optimal Transport. CAROT touches on some of the vibrant areas of functional MRI, predictive modeling, neuroimaging, & connectomics including privacy & open science 1/16 https://t.co/mVhmCZarfB
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@DScheinost
Dustin Scheinost
3 years
@925452096Andy’s talk on physical frailty, mental health, and brain structure at 2:15 PM Friday. We show links between frailty and >300 health, outcomes, especially mental health ones, which is mediated by brain structure Full paper @LancetDigitalH:
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thelancet.com
Physical frailty is associated with diverse unfavourable health-related outcomes, which can be mediated by differences in brain structure. Our findings offer a framework for guiding preventative...
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@RosenblattMatt
Matt Rosenblatt
3 years
Use machine learning in🧠research or for other high-dimensional data? Curious about its robustness to data manipulations ("trustworthiness")? Check out our new article in @Patterns_CP and thread ⬇️
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cell.com
Imperceptible data manipulations can drastically increase or decrease performance in machine learning models that use high-dimensional neuroimaging data. These manipulations could achieve nearly any...
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@sNeuroble
Stephanie Noble (@sneuroble.bsky.social)
3 years
Do you do: 👶 fetal/infant/toddler (FIT) neuroimaging? 💻 machine learning? Want to do both? If so, we’re excited to announce a new primer and review on FIT-specific machine learning —a reflection on 100+ studies from the past decade https://t.co/uVAMP1v3vL 1/5
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@FIT_NGIn
Fetal, Infant, & Toddler Neuroimaging Group—FIT’NG
3 years
Have you wondered how predictive ML modelling has been used in the #FIT research? Freshly published work by @DScheinost et al. summarizes the existing studies and outlines the methodological & ethical considerations! #MachineLearning #fitng
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@DScheinost
Dustin Scheinost
3 years
Neuro friends, How much do you cite yourself? We looked at 150k articles and 8 million citations from 63 journals between 2000-2020 to find out! https://t.co/8UvW8N9Dyv 1/9
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biorxiv.org
Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the neurosciences, such as the under-citation of women. However,...
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@GRAIL2025
GRAIL workshop @ MICCAI 2025
3 years
With the conclusion of our 4th workshop on "GRaphs in biomedicAl Image anaLysis (#GRAIL2022)", we would like to congratulate Qinghao Liang et al. for winning the Best Paper Award. The authors will receive a GeForce RTX 3080 Ti, sponsored by @nvidia.
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@RosenblattMatt
Matt Rosenblatt
3 years
Are you in a neuroscience-related field? We are seeking online survey participants for the study: “Opinions on self-citation rates in neuroscience research”. It will only take about 10 mins if you are interested in participating:
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@RongtaoJiang
Rongtao Jiang
3 years
Excited to share our latest work in Cardiovascular Research which predicted individual differences in blood pressure from whole-brain functional connectivity using machine learning. @DScheinost @kittysj @vdcalhoun @sNeuroble @liang_qinghao https://t.co/XVRRDo4eDr
academic.oup.com
AbstractAims. Elevated blood pressure (BP) is a prevalent modifiable risk factor for cardiovascular diseases and contributes to cognitive decline in late l
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@YaleCellBio
Yale Cell Biology
4 years
The faculty in @YaleCellBio and @YaleSCC strongly support our colleague Haifan Lin and his entire research group in the wake of his administrative suspension by @Yale, a step taken without any formal charges or due process. See our statement 👇👇
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@FIT_NGIn
Fetal, Infant, & Toddler Neuroimaging Group—FIT’NG
4 years
Starting to plan your 2022 calendar? Don't forget to add our next two FIT'NG Together events! First a WIP Session on Jan. 25, and then a paper discussion on Feb. 22 featuring Dr. Tomoki Arichi. Register now! Jan 25: https://t.co/0OOURBmtA1 Feb. 22: https://t.co/bpAI8GaM2K
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@FIT_NGIn
Fetal, Infant, & Toddler Neuroimaging Group—FIT’NG
4 years
FIT'NG Trainees! Do you have an analysis, talk, or project in-progress? Would you like to get feedback from experts and trainees across FIT'NG? Sign up to present a Work in Progress at an upcoming FIT'NG Together Event. FIT neuroimaging topics only pls! https://t.co/O2JDNXfX63
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@DScheinost
Dustin Scheinost
4 years
Functional Connectivity for the Language Network in the Developing Brain: 30 Weeks of Gestation to 30 Months of Age https://t.co/jPtsuc5Uij TLDR: We used fetal, infant, and toddler fMRI to look at longitudinal changes in functional connectivity for Broca’s and Wernicke’s areas.
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