ArticleHuman brain mapping2022
On predictability of individual functional connectivity networks from clinical characteristics.
Article in Human brain mapping, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Tell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis.NeuroImage. Clinical · 2025Article
- On predictability of individual functional connectivity networks from clinical characteristics.Human brain mapping · 2022Article
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
Abstract
In recent years, understanding functional brain connectivity has become increasingly important as a scientific tool with potential clinical implications. Statistical methods, such as graphical models and network analysis, have been adopted to construct functional connectivity networks for single subjects. Here we focus on studying the association between functional connectivity networks and clinical characteristics such as psychiatric symptoms and diagnoses. Utilizing machine learning algorithms, we propose a method to examine predictability of functional connectivity networks from clinical characteristics. Our methods can identify salient clinical characteristics predictive of the whole brain network or specific subnetworks. We illustrate our methods on the analysis of fMRI data in the Philadelphia Neurodevelopmental Cohort study, demonstrating clinically meaningful results.
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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.