ArticleNeuroImage2016
Northwestern University schizophrenia data sharing for SchizConnect: A longitudinal dataset for large-scale integration.
Article in NeuroImage, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed.
- Motivation and pleasure deficits reflect altered network embedding of normal brain deviations in schizophrenia.Schizophrenia research · 2026Article
- Machine learning-based morphological brain analysis in schizophrenia and unaffected siblings: a multisite study of potential risk markers.Frontiers in neuroscience · 2026Article
- Longitudinal changes in hippocampal morphology before and after temporal lobe epilepsy surgery.Brain communications · 2025Article
- Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm.Nature communications · 2024Article
- Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia.Science advances · 2024Article
- Two neurostructural subtypes: results of machine learning on brain images from 4,291 individuals with schizophrenia.medRxiv : the preprint server for health sciences · 2023Article
- neuromaps: structural and functional interpretation of brain maps.Nature methods · 2022Article
- Clustering of cognitive subtypes in schizophrenia patients and their siblings: relationship with regional brain volumes.Schizophrenia (Heidelberg, Germany) · 2022Article
- Relationships between cognitive performance, clinical insight and regional brain volumes in schizophrenia.Schizophrenia (Heidelberg, Germany) · 2022Article
- Enabling Scientific Reproducibility through FAIR Data Management: An ontology-driven deep learning approach in the NeuroBridge Project.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Dissecting autism and schizophrenia through neuroimaging genomics.Brain : a journal of neurology · 2021Review
- Resective surgery prevents progressive cortical thinning in temporal lobe epilepsy.Brain : a journal of neurology · 2020Article
- Latent Clinical-Anatomical Dimensions of Schizophrenia.Schizophrenia bulletin · 2020Article
- Identifying Schizophrenia Using Structural MRI With a Deep Learning Algorithm.Frontiers in psychiatry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
In this paper, we describe an instance of the Northwestern University Schizophrenia Data and Software Tool (NUSDAST), a schizophrenia-related dataset hosted at XNAT Central, and the SchizConnect data portal used for accessing and sharing the dataset. NUSDAST was built and extended upon existing, standard schemas available for data sharing on XNAT Central (http://central.xnat.org/). With the creation of SchizConnect, we were able to link NUSDAST to other neuroimaging data sources and create a powerful, federated neuroimaging resource.
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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.