ReviewPsychopharmacology2015
The role of machine learning in neuroimaging for drug discovery and development.
Review in Psychopharmacology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study.Revista brasileira de psiquiatria (Sao Paulo, Brazil : 1999) · 2023Article
- Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature.NeuroImage · 2022Article
- NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data.Neuroinformatics · 2022Article
- Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Review
- Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning.Nature communications · 2021Article
- Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020Article
- Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.The Science of the total environment · 2020Review
- Post-acquisition processing confounds in brain volumetric quantification of white matter hyperintensities.Journal of neuroscience methods · 2019Article
- The neuropsychopharmacology of cannabis: A review of human imaging studies.Pharmacology & therapeutics · 2019Review
- Predicting personality from network-based resting-state functional connectivity.Brain structure & function · 2018Article
- Artificial intelligence, physiological genomics, and precision medicine.Physiological genomics · 2018Article
- Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1.PloS one · 2018Article
- Building better biomarkers: brain models in translational neuroimaging.Nature neuroscience · 2017Review
- Brain Metabolism Changes in Patients Infected with HTLV-1.Frontiers in molecular neuroscience · 2017Article
- The cortical thickness phenotype of individuals with DISC1 translocation resembles schizophrenia.The Journal of clinical investigation · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuroimaging has been identified as a potentially powerful probe for the in vivo study of drug effects on the brain with utility across several phases of drug development spanning preclinical and clinical investigations. Specifically, neuroimaging can provide insight into drug penetration and distribution, target engagement, pharmacodynamics, mechanistic action and potential indicators of clinical efficacy. In this review, we focus on machine learning approaches for neuroimaging which enable us to make predictions at the individual level based on the distributed effects across the whole brain. Crucially, these approaches can be trained on data from one study and applied to an independent study and, unlike group-level statistics, can be readily use to assess the generalisability to unseen data. In this review, we present examples and suggestions for how machine learning could help answer fundamental questions spanning the drug discovery pipeline: (1) Who should I recruit for this study? (2) What should I measure and when should I measure it? (3) How does the pharmacological agent behave using an experimental medicine model?, and (4) How does a compound differ from and/or resemble existing compounds? Specifically, we present studies from the literature and we suggest areas for the focus of future development. Further refinement and tailoring of machine learning techniques may help realise their tremendous potential for drug discovery and drug validation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.