Neuroimaging Overview

Principles

Definition

Neuroimaging is a distinct field encompassing various techniques employed to visualize and analyze the structure, function, or pharmacological properties of the nervous system, particularly the brain. This methodological approach is fundamental for advancing understanding of neural processes, diagnosing neurological conditions, and supporting research across neuroscience, psychology, and clinical medicine.

Ontological type

Core Modalities

Neural Applications

Data Analysis Methods

Key developments

Key Figures

Atlas-Guided Structural Imaging era

Urban Ungerstedt [1] is associated with Karolinska Institutet [3] and University of Maryland, Baltimore [4] during the Atlas-Guided Structural Imaging era. Urban Ungerstedt [1] authored the 1971 Stereotaxic Mapping of the Monoamine Pathways in the Rat Brain [7], a landmark that advanced stereotaxic mapping by establishing standardized coordinates for monoaminergic pathways and enabling cross-subject localization within anatomical references. Torsten N. Wiesel [2] was affiliated with Rockefeller University [5] and Harvard University [6] during this era. Torsten N. Wiesel [2] contributed the 1965 RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT [8], revealing the functional architecture and receptive-field organization that underpinned later mapping of cortical specialization in the atlas-guided era.

Connectivity Integration and Standardization era

Olaf Sporns [1] is a leading figure in the Connectivity Integration and Standardization era, with affiliations at Rockefeller University [3] and Instituto Gulbenkian de Ciência [4]. His work, notably the Complex network measures of brain connectivity: Uses and interpretations [7], helped establish graph-based metrics for brain networks, while the 2009 paper Predicting human resting-state functional connectivity from structural connectivity [8] emphasized the link between structural scaffolds and functional organization, both shaping the standardization and comparability of connectomics in this era. James S. Hyde [2] is a prominent figure associated with Harvard University [5] and the University of Würzburg [6] during this era. His early contributions, as seen in Functional connectivity in the motor cortex of resting human brain using echo-planar MRI [9], and Processing strategies for time-course data sets in functional MRI of the human brain [10], laid groundwork for reliable resting-state measurements and time-series analysis—crucial steps toward reproducible functional mapping and integration with structural data.

Deep Learning and Automated Biomarkers era

Christian F. Beckmann [1] is a prominent neuroimaging researcher shaping the Deep Learning and Automated Biomarkers era, with affiliations at Radboud University Nijmegen [3] and Imperial College London [4]. Beckmann's contributions in this era include automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers [7], ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data [8], and ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging [9], which collectively advanced motion artifact mitigation and reliable resting-state analyses. Steven E. Petersen [2] has been influential in neuroimaging, with affiliations at University of California, San Francisco [5] and Stanford University [6]. His contributions include methods to detect, characterize, and remove motion artifact in resting-state fMRI [10] and analyses on the stability of BOLD fMRI correlations [11], which helped establish reliable benchmarking and interpretation of connectivity measures in high-throughput DL-based pipelines.