ArticleThe Journal of comparative neurology2017
The significance of negative correlations in brain connectivity.
Article in The Journal of comparative neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- Brain Graph Sparsification for fMRI-based Connectome Analysis: A Methodological Review.Neuroinformatics · 2026Review
- Longitudinal Study of Adolescent Brain Connectivity Development Using Sign-Aware Graph Theory Metrics.Human brain mapping · 2026Article
- Deriving functional network topology frombioRxiv : the preprint server for biology · 2026Article
- Brain Myelin Covariance Networks: Gradients, Cognition, and Higher-Order Landscape.Behavioral sciences (Basel, Switzerland) · 2025Article
- Introduction to correlation networks: Interdisciplinary approaches beyond thresholding.Physics reports · 2025Article
- Quantifying the influence of biophysical factors in shaping brain communication through remnant functional networks.Network neuroscience (Cambridge, Mass.) · 2025Article
- Cerebellar functional connectivity alteration in individuals with lower limb amputation.PloS one · 2025Article
- Common Neural Correlates for Subjective and Objective Sleepiness Indices: A Functional Connectivity Study.Nature and science of sleep · 2025Article
- Identification of brain-like complex information architectures in embryonic tissue ofCommunicative & integrative biology · 2025Article
- Tell me why: A scoping review on the fundamental building blocks of fMRI-based network analysis.NeuroImage. Clinical · 2025Article
- The brain's first "traffic map" through Unified Structural and Functional Connectivity (USFC) modeling.Communications biology · 2024Article
- A Principled Framework to Assess the Information-Theoretic Fitness of Brain Functional Sub-Circuits.Mathematics (Basel, Switzerland) · 2024Article
- A principled framework to assess the information-theoretic fitness of brain functional sub-circuits.ArXiv · 2024Article
- Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model.IEEE transactions on neural networks and learning systems · 2024Article
- The Brain's First "Traffic Map" through Unified Structural and Functional Connectivity (USFC) Modeling.Research square · 2024Article
- Multiscale detrended cross-correlation coefficient: estimating coupling in non-stationary neurophysiological signals.Frontiers in neuroscience · 2024Article
- Neural substrates of verbal memory impairment in schizophrenia: A multimodal connectomics study.Human brain mapping · 2023Article
- A Postsynaptic Density Immediate Early Gene-Based Connectome Analysis of Acute NMDAR Blockade and Reversal Effect of Antipsychotic Administration.International journal of molecular sciences · 2023Article
- Modular segregation drives causality of the dynamic oscillatory network responses during threat processing.Brain communications · 2023Article
- Signed graph representation learning for functional-to-structural brain network mapping.Medical image analysis · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative. We validated this novel probability-based modularity approach on two independent publicly-available resting-state connectome data sets (the Human Connectome Project [HCP] and the 1,000 functional connectomes) and demonstrated that negative correlations alone are sufficient in understanding resting-state modularity. In fact, this approach (a) permits a dual formulation, leading to equivalent solutions regardless of whether one considers positive or negative edges; (b) is theoretically linked to the Ising model defined on the connectome, thus yielding modularity result that maximizes data likelihood. Additionally, we were able to detect novel and consistent sex differences in modularity in both data sets. As data sets like HCP become widely available for analysis by the neuroscience community at large, alternative and perhaps more advantageous computational tools to understand the neurobiological information of negative edges in fMRI connectomes are increasingly important.
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.