ArticleFrontiers in neuroinformatics2017
The Patient Repository for EEG Data + Computational Tools (PRED+CT).
Article in Frontiers in neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19.Sensors (Basel, Switzerland) · 2022Pooled it
- AI-assisted multi-modal information for the screening of depression: a systematic review and meta-analysis.NPJ digital medicine · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Graph convolution network-based eeg signal analysis: a review.Medical & biological engineering & computing · 2025Review
- Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2025Review
- Artificial intelligence for brain disease diagnosis using electroencephalogram signals.Journal of Zhejiang University. Science. B · 2024Review
- Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121.Brain sciences · 2024Article
- Support vector machine classification of patients with depression based on resting-state electroencephalography.Asian biomedicine : research, reviews and news · 2024Article
- Implications of Aperiodic and Periodic EEG Components in Classification of Major Depressive Disorder from Source and Electrode Perspectives.Sensors (Basel, Switzerland) · 2024Article
- Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology.Frontiers in neuroscience · 2024Article
- Distinguishing Parkinson's Disease with GLCM Features from the Hankelization of EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Quantitative High Density EEG Brain Connectivity Evaluation in Parkinson's Disease: The Phase Locking Value (PLV).Journal of clinical medicine · 2023Article
- Applying Deep Learning on a Few EEG Electrodes during Resting State Reveals Depressive States. A Data Driven Study.Brain sciences · 2022Article
- Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2022Review
- Article
- Timing variability and midfrontal ~4 Hz rhythms correlate with cognition in Parkinson's disease.NPJ Parkinson's disease · 2021Article
- Modulatory effects of positive mood and approach motivation on reward processing: Two sides of the same coin?Cognitive, affective & behavioral neuroscience · 2020Article
- Electrophysiology as a theoretical and methodological hub for the neural sciences.Psychophysiology · 2019Review
- Analysis of Decision-Making Process Using Methods of Quantitative Electroencephalography and Machine Learning Tools.Frontiers in neuroinformatics · 2019Article
- Mid-frontal theta activity is diminished during cognitive control in Parkinson's disease.Neuropsychologia · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Electroencephalographic (EEG) recordings are thought to reflect the network-wide operations of canonical neural computations, making them a uniquely insightful measure of brain function. As evidence of these virtues, numerous candidate biomarkers of different psychiatric and neurological diseases have been advanced. Presumably, we would only need to apply powerful machine-learning methods to validate these ideas and provide novel clinical tools. Yet, the reality of this advancement is more complex: the scale of data required for robust and reliable identification of a clinical biomarker transcends the ability of any single laboratory. To surmount this logistical hurdle, collective action and transparent methods are required. Here we introduce the Patient Repository of EEG Data + Computational Tools (PRED+CT: predictsite.com). The ultimate goal of this project is to host a multitude of available tasks, patient datasets, and analytic tools, facilitating large-scale data mining. We hope that successful completion of this aim will lead to the development of novel EEG biomarkers for differentiating populations of neurological and psychiatric disorders.
Indexed as
Identifiers
What OpenQuestion holds
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.