Neuroimaging Software
Neuroimaging Software - Related Videos
06:35
Basics of Multivariate Analysis in Neuroimaging Data
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Cited by 37 •
2010
The current article describes the basics of multivariate analysis and contrasts it to the more commonly used voxel-wise univariate analysis. Both types of analysis are applied to a clinical-neuroscience data set. Supplementary split-half simulations show better replication of the multivariate results in independent data sets.
07:01
Head Implants for the Neuroimaging of Awake, Head-Fixed Rats
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2022
A detailed new procedure for functional imaging of awake, head-fixed rats is described.
15:18
Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure
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Cited by 134 •
2009
Despite an increase in the use of structural and functional magnetic resonance imaging (fMRI) in humans, the study of young pediatric populations remains a challenge. We present a hands-on, step-by-step video protocol including guidelines for clinicians and researchers intending to perform (f)MRI in young children.
05:05
Religious Chanting and Self-Related Brain Regions: A Multi-Modal Neuroimaging Study
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Cited by 2 •
2024
Here, we present a protocol to investigate the neurophysiological correlates of various meditation forms, including religious chanting. This method uniquely integrates fMRI eigenvector results for region selection in electroencephalogram (EEG) source analysis using k-means clustering. The results provide an in-depth understanding of the neural processes involved in repetitive religious chanting.
14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Cited by 73 •
2013
Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.
FAQs
Preprocessing improves interpretability by reducing distortions and aligning image data. Motion correction addresses movement-related variation, spatial registration places scans into a common coordinate framework, artifact reduction limits unwanted signal effects, and tissue segmentation separates anatomical regions for measurement. These operations prepare MRI, fMRI, PET, or diffusion-weighted data for statistical modeling and comparison across people or experimental conditions.
The scientific question determines which output matters: anatomical analysis emphasizes brain structure, functional analysis maps activity, and connectivity analysis examines relationships among brain regions. Neuroimaging software supports these distinct goals while also enabling comparisons across individuals or experimental conditions. Selecting the analysis focus helps researchers interpret imaging findings in relation to neuroscience questions.
Spatial registration is important because scans or participants may not be aligned in the same spatial arrangement. By bringing images into a shared reference space, the workflow can support comparisons of neural patterns across individuals or conditions. This alignment complements segmentation and statistical modeling, helping measurements correspond to comparable anatomical locations.
A typical workflow begins with raw MRI, fMRI, PET, or diffusion-weighted scans and applies preprocessing suited to the dataset. Motion correction, artifact reduction, registration, and tissue segmentation can then prepare the images for statistical modeling. The resulting analyses may measure structure, map activity, examine connectivity, or compare patterns, followed by visualization of interpretable results.
Researchers apply these tools when they need to connect imaging measurements with broader neuroscience outcomes. Studies may examine brain development or disease, relate neural patterns to behavior and cognition, or investigate treatment outcomes. The software helps transform imaging data into measurements and visualizations that can be compared across participants or experimental conditions.
Reliable workflows matter because consistent computational processing strengthens reproducibility, allowing imaging findings to be examined across analyses, participants, or conditions. In neuroscience, this supports more dependable links between brain structure, activity, connectivity, and behavioral or cognitive measures. The same principle is relevant to clinical investigation, where imaging results may be considered alongside treatment outcomes.