Machine Learning Neuroimaging
Machine Learning Neuroimaging - 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.
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.
07:13
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Cited by 1 •
2025
This study evaluates prognostic systems for colorectal signet-ring cell carcinoma patients using machine learning models and competing risk analyses. It identifies log odds of positive lymph nodes as a superior predictor compared to pN staging, demonstrating strong predictive performance and aiding clinical decision-making through robust survival prediction tools.
08:05
Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Cited by 16 •
2020
Presented here is a protocol introducing a set of child-friendly statistical learning tasks geared towards examining children’s learning of temporal statistical patterns across domains and sensory modalities. The developed tasks collect behavioral data using the web-based platform and task-based functional magnetic resonance imaging (fMRI) data for examining neural engagement during statistical learning.
06:22
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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2025
This study effectively accomplished the automated classification of two distinct categories by acquiring cough sound data from patients diagnosed with chronic obstructive pulmonary disease (COPD) and respiratory tract infections (RTI), utilizing an integration of speech signal processing techniques and machine learning algorithms.
FAQs
Preprocessing prepares MRI or functional MRI data so computational models can identify meaningful patterns rather than artifacts or inconsistent measurements. Its output becomes the basis for engineered features or features learned directly by the model. Because later classification, prediction, clustering, or reconstruction depends on these inputs, preprocessing choices can substantially affect the reliability and interpretation of findings.
Engineered features are selected or constructed from brain images before modeling, whereas learned features are derived by the algorithm during training. Both approaches provide representations that connect imaging data with structure, function, or behavior. The choice affects how much information is supplied explicitly and how easily investigators can interpret the patterns used for classification or prediction.
Validation tests whether computational findings remain reliable beyond the data used to develop a model. Interpretability helps investigators understand which imaging patterns support an output, while confounding control reduces the risk that unrelated factors explain an apparent association. Together, these safeguards strengthen biomarker development and help distinguish reproducible neurobiological signals from dataset-specific results.
Depending on the research objective, models can classify images into meaningful groups, predict clinical or behavioral measurements, cluster observations according to shared patterns, or reconstruct image information. These tasks use patterns extracted from MRI or functional MRI data and can address different questions, from organizing complex datasets to relating brain measurements to behavior or disease characteristics.
A typical workflow begins with MRI or functional MRI data preprocessing, followed by representation through engineered or learned features. Investigators then select a task such as classification, prediction, clustering, or reconstruction and evaluate the resulting model through careful validation. The final interpretation relates computational outputs to brain structure, function, behavior, or clinical measurements.
In bioengineering, these methods can connect complex imaging patterns with brain structure, brain function, clinical measurements, or behavior. Such relationships support brain mapping and the search for biomarkers that characterize disease or individual variation. Their value depends on whether the identified patterns remain reliable under validation and can be interpreted well enough to inform further research or assessment.
Individualized assessment uses computational analysis to relate a person’s imaging data to relevant clinical or behavioral measurements rather than relying only on broad group patterns. This application is important in bioengineering because MRI and functional MRI contain complex, multivariable information. Careful validation and confounding control are necessary before such outputs can support research or clinical tools.