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Journal Photo for NeuroImage
Peer reviewed only Open Access

NeuroImage (NeuroImage)

Publisher : Elsevier Inc.
Radiology Imaging Cognitive Neuroscience
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e-ISSN 1095-9572
p-ISSN 1053-8119
Issue Frequency Monthly
Impact Factor 4.7
Est. Year 1992
Country United States
Language English
APC YES
Impact Factor Assignee Google Scholar

Journal Descriptions

NeuroImage is a gold open access journal that communicates important developments in understanding brain function, structure, and organization using all neuroimaging modalities, as well as advances in related imaging and analysis methodology. The journal focuses on the macroscopic level of the human brain but seeks to incorporate theoretical and technological innovations to investigate the brain at multiple levels of analysis. Submissions involving animal models or clinical populations are also welcome, provided that they contribute to a systems-level understanding of the human brain. NeuroImage publishes original research and review articles, as well as papers that focus on methods, models/resources, or emerging/controversial issues. A full list of article types is available in our Guide for Authors.

NeuroImage (NeuroImage) is :-

  • International, Peer-Reviewed, Open Access, Refereed, Radiology, Imaging, Cognitive Neuroscience, Neurology, Clinical Neurology , Online or Print , Monthly Journal

  • UGC Approved, ISSN Approved: P-ISSN P-ISSN: 1053-8119, E-ISSN: 1095-9572, Established: 1992, Impact Factor: 4.7
  • Does Not Provide Crossref DOI

  • Indexed in: DOAJ

  • Not indexed in Scopus, WoS, PubMed, UGC CARE

Indexing

Publications of NeuroImage

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex
Peter B Jones May, 2018
Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance ne...
Network-based statistic: Identifying differences in brain networks
Edward T. Bullmore December, 2010
Large-scale functional or structural brain connectivity can be modeled as a network, or graph. This paper presents a statistical approach to identify connections in such a graph that may be ...
Mapping Motor Inhibition: Conjunctive Brain Activations across Different Versions of Go/No-Go and Stop Tasks
Edward T. Bullmore February, 2001
Conjunctionanalysis methods were used in functional magnetic resonance imaging to investigate brain regions commonly activated in subjects performing different versions of go/no-go and stop ...
Whole-brain anatomical networks: Does the choice of nodes matter?
Edward T. Bullmore April, 2010
Whole-brain anatomical connectivity in living humans can be modeled as a network with diffusion-MRI and tractography. Network nodes are associated with distinct grey-matter regions, while wh...
Age-related changes in modular organization of human brain functional networks
Edward T. Bullmore February, 2009
Graph theory allows us to quantify any complex system, e.g., in social sciences, biology or technology, that can be abstractly described as a set of nodes and links. Here we derived human br...
Schizophrenia, neuroimaging and connectomics
Edward T. Bullmore October, 2012
Schizophrenia is frequently characterized as a disorder of brain connectivity. Neuroimaging has played a central role in supporting this view, with nearly two decades of research providing a...
On the use of correlation as a measure of network connectivity
Edward T. Bullmore May, 2012
Numerous studies have demonstrated that brain networks derived from neuroimaging data have nontrivial topological features, such as small-world organization, modular structure and highly con...
Differential neural responses to overt and covert presentations of facial expressions of fear and disgust
Edward T. Bullmore April, 2004
There is debate in cognitive neuroscience whether conscious versus unconscious processing represents a categorical or a quantitative distinction. The purpose of the study was to explore this...
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series
John Suckling July, 2014
The impact of in-scanner head movement on functional magnetic resonance imaging (fMRI) signals has long been established as undesirable. These effects have been traditionally corrected by me...