Link Tejavibulya

发表

Predicting the future of neuroimaging predictive models in mental health

Alexander J. Dufford, D. Scheinost, M. C. Farruggia, 2022, Molecular Psychiatry.

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer

Alexander J. Dufford, Marisa N. Spann, D. Scheinost, 2022, Biological Psychiatry.

Leveraging edge-centric networks complements existing network-level inference for functional connectomes

D. Scheinost, Link Tejavibulya, S. Noble, 2022, NeuroImage.

Predicting the future of neuroimaging predictive models in mental health

Alexander J. Dufford, D. Scheinost, S. Lichenstein, 2022, Molecular Psychiatry.

Functional Connectome–Based Predictive Modeling in Autism

Abigail S. Greene, D. Scheinost, R. Constable, 2022, Biological Psychiatry.

Predicting the future of neuroimaging predictive models in mental health

Alexander J. Dufford, D. Scheinost, A. Dufford, 2022, Molecular Psychiatry.

Heightened sensitivity to high-calorie foods in children at risk for obesity: insights from behavior, neuroimaging, and genetics

B. J. Casey, Anfei Li, K. Rapuano, 2023, Brain Imaging and Behavior.

Foxa2 and Pet1 Direct and Indirect Synergy Drive Serotonergic Neuronal Differentiation

E. Mazzoni, Shaun Mahony, M. Sierk, 2022, Frontiers in Neuroscience.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for any atlas when raw data is not available

D. Scheinost, Amin Karbasi, B. Adkinson, 2022, bioRxiv.

The effects of data leakage on neuroimaging predictive models

D. Scheinost, Link Tejavibulya, R. Jiang, 2023, bioRxiv.

A protocol for working with open-source neuroimaging datasets

D. Scheinost, R. Constable, Kangjoo Lee, 2022, STAR protocols.

Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity

D. Scheinost, Max J Rolison, Link Tejavibulya, 2021, NeuroImage.

Predicting Transdiagnostic Social Impairments in Childhood Using Connectome-Based Predictive Modeling

D. Scheinost, A. Dufford, J. Dadashkarimi, 2022, Biological Psychiatry.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for different atlases when raw data is not available

D. Scheinost, Amin Karbasi, B. Adkinson, 2023, Medical Image Anal..

Big data approaches to identifying sex differences in long-term memory

D. Scheinost, Link Tejavibulya, 2020, Cognitive neuroscience.

Power and reproducibility in the external validation of brain-phenotype predictions

M. Westwater, Link Tejavibulya, M. Rosenblatt, 2023, bioRxiv.

Brain-handedness associations depend on how and when handedness is measured

M. Westwater, C. Horien, Link Tejavibulya, 2024, bioRxiv.

Data leakage inflates prediction performance in connectome-based machine learning models

Link Tejavibulya, M. Rosenblatt, S. Noble, 2024, Nature communications.

The effects of data leakage on connectome-based machine learning models

D. Scheinost, Link Tejavibulya, R. Jiang, 2023, bioRxiv.

Leveraging edge-centric networks complements existing network-level inference for functional connectomes

D. Scheinost, Link Tejavibulya, S. Noble, 2022, NeuroImage.

Power and reproducibility in the external validation of brain-phenotype predictions.

M. Westwater, Link Tejavibulya, M. Rosenblatt, 2024, Nature human behaviour.

Edge-centric network control on the human brain structural network

J. Dadashkarimi, Link Tejavibulya, M. Rosenblatt, 2024, Imaging Neuroscience.