Sterling C. Johnson

Cognitive Neuroscience · 2002–2021

A researcher in the Science corpus: 13 papers; trusts 10 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Cognitive Neuroscience — 62 citations into it; #4,029 of 10,852 by trust here, #1,466 by citations
• Molecular Biology — 45 citations into it
• Radiology, Nuclear Medicine and Imaging — 40 citations into it
• Artificial Intelligence — 25 citations into it
• Computer Vision and Pattern Recognition — 25 citations into it
• Statistics and Probability — 12 citations into it
• Geometry and Topology — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Sterling C. Johnson trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Karl J. Friston
2. John Ashburner
3. Donald T. Stuss
4. Radford M. Neal
5. Hirotugu Akaike
6. D. V. Van Essen

Who trusts Sterling C. Johnson overall

1. Matthew D. Lieberman
2. Heung-Il Suk
3. Jason P. Mitchell
4. Krishna D. Singh
5. Peter Fransson
6. Brent A. Vogt
7. Demis Hassabis
8. Jessica R. Andrews-Hanna
9. Sarah-Jayne Blakemore
10. Jana Schaich Borg

Papers from Sterling C. Johnson's perspective

Ordered by how strongly the researchers Sterling C. Johnson trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet: A large-scale hierarchical image database
3. Dropout: a simple way to prevent neural networks from overfitting
4. Pattern Recognition and Machine Learning
5. Densely Connected Convolutional Networks
6. Visualizing and Understanding Convolutional Networks
7. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
8. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
9. Representation Learning: A Review and New Perspectives
10. TensorFlow: A system for large-scale machine learning

Their papers

• Gut microbiome alterations in Alzheimer’s disease (2017) — 1,842 citations
• Four distinct trajectories of tau deposition identified in Alzheimer’s disease (2021) — 636 citations
• MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide. (2020) — 356 citations
• Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan (2019) — 521 citations
• Early role of vascular dysregulation on late-onset Alzheimer's disease based on multifactorial data-driven analysis (2016) — 1,062 citations
• Imaging based enrichment criteria using deep learning algorithms for efficient clinical trials in MCI (2015) — 61 citations
• Speeding up Permutation Testing in Neuroimaging (2013) — 7 citations
• A Generalized Form of Context-Dependent Psychophysiological Interactions (gPPI): A Comparison to Standard Approaches (2012) — 1,411 citations
• Predictive Markers for AD in a Multi-Modality Framework: An Analysis of MCI Progression in the ADNI Population (2010) — 435 citations
• Longitudinal changes in patients with traumatic brain injury assessed with diffusion-tensor and volumetric imaging (2008) — 339 citations
• Metacognitive evaluation, self-relevance, and the right prefrontal cortex (2004) — 345 citations
• The neural substrates of writing: A functional magnetic resonance imaging study (2003) — 139 citations
• Neural correlates of self-reflection. (2002) — 903 citations