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CAT: a computational anatomy toolbox for the analysis of structural MRI data.
Literature Information
| DOI | 10.1093/gigascience/giae049 |
|---|---|
| PMID | 39102518 |
| Journal | GigaScience |
| Impact Factor | 3.9 |
| JCR Quartile | Q1 |
| Publication Year | 2024 |
| Times Cited | 466 |
| Keywords | Alzheimer’s disease, CAT12, MRI, ROI, SPM12 |
| Literature Type | Journal Article |
| ISSN | 2047-217X |
| Issue | 13() |
| Authors | Christian Gaser, Robert Dahnke, Paul M Thompson, Florian Kurth, Eileen Luders, The Alzheimer's Disease Neuroimaging Initiative |
TL;DR
The article presents the Computational Anatomy Toolbox (CAT), a comprehensive and user-friendly suite for brain morphometric analyses that caters to users of all expertise levels. By offering a full analysis workflow—including preprocessing, statistical analysis, and visualization—CAT aims to standardize and enhance brain imaging analysis in neuroscience research.
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Alzheimer’s disease · CAT12 · MRI · ROI · SPM12
Abstract
A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.
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Primary Questions Addressed
- What specific morphometric analyses can be performed using CAT, and how do they compare to other available tools?
- How does CAT ensure the quality control of MRI data during the preprocessing stage?
- What are the advantages of using a graphical user interface in CAT for beginners versus scripting for advanced users?
- Can CAT be integrated with other neuroimaging software, and if so, what are the potential benefits of such integration?
- How does CAT handle longitudinal data analysis differently than cross-sectional data analysis in terms of workflow and statistical methods?
Key Findings
Research Background and Objectives
The study introduces the Computational Anatomy Toolbox (CAT), a sophisticated suite of tools designed for brain morphometric analyses, particularly focusing on structural MRI data. CAT aims to enhance the accessibility and functionality of neuroimaging analysis for users ranging from beginners to experts. The overarching goal is to provide a comprehensive tool that integrates preprocessing, statistical analysis, and visualization of MRI data.
Main Methods/Materials/Experimental Design
CAT operates primarily within the SPM (Statistical Parametric Mapping) environment and is compatible with MATLAB. The software is designed to facilitate both voxel-based and surface-based morphometric analyses, with the following key components:
Voxel-Based Processing:
- Tissue Segmentation: Involves denoising (using SANLM), unified segmentation, and local intensity transformation.
- Spatial Registration: Utilizes Geodesic Shooting for aligning tissue segments to standardized templates.
Surface-Based Processing:
- Surface Creation: Estimates cortical thickness and creates surface meshes.
- Surface Registration: Maps individual surfaces to standard templates.
Region-Based Morphometry (RBM): Analyzes regions of interest (ROIs) using standardized atlases.
The following Mermaid code illustrates the processing pipeline:
Key Results and Findings
- Performance: CAT demonstrated superior performance in processing speed and sensitivity in detecting neuroimaging effects, even in the presence of noise.
- Application Example: Analyzed data from Alzheimer’s disease patients, revealing significant atrophy in gray matter volume and cortical thickness, particularly in regions associated with the default mode network.
- Quality Control: Multiple quality control options were integrated, allowing for the assessment of image quality and outlier detection.
Main Conclusions/Significance/Innovation
CAT represents a significant advancement in neuroimaging analysis by providing a user-friendly interface coupled with powerful analytical capabilities. Its ability to handle both cross-sectional and longitudinal data, along with integrated quality control measures, positions it as a central tool in the field of neuroscience. The toolbox's flexibility and comprehensive nature facilitate a wide range of morphometric analyses, making it a valuable resource for both research and clinical applications.
Research Limitations and Future Directions
- Limitations: The study primarily focuses on structural imaging; while some features may extend to functional imaging, further development is needed for broader applications.
- Future Directions: Future enhancements could include improved integration with non-SPM software, additional features for functional MRI analysis, and the potential expansion of CAT’s capabilities to handle larger datasets effectively.
Summary Table
| Aspect | Details |
|---|---|
| Tool | Computational Anatomy Toolbox (CAT) |
| Main Features | Voxel-based and surface-based morphometry, quality control, longitudinal analysis, and user-friendly interface |
| Key Findings | Superior performance in sensitivity and processing speed; significant findings in Alzheimer’s disease data |
| Limitations | Primarily focused on structural imaging; further development needed for functional MRI analysis |
| Future Directions | Broader applications in functional imaging, integration with other software, and handling larger datasets |
References
- ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies. - Henk J M M Mutsaerts;Jan Petr;Paul Groot;Pieter Vandemaele;Silvia Ingala;Andrew D Robertson;Lena Václavů;Inge Groote;Hugo Kuijf;Fernando Zelaya;Owen O'Daly;Saima Hilal;Alle Meije Wink;Ilse Kant;Matthan W A Caan;Catherine Morgan;Jeroen de Bresser;Elisabeth Lysvik;Anouk Schrantee;Astrid Bjørnebekk;Patricia Clement;Zahra Shirzadi;Joost P A Kuijer;Viktor Wottschel;Udunna C Anazodo;Dasja Pajkrt;Edo Richard;Reinoud P H Bokkers;Liesbeth Reneman;Mario Masellis;Matthias Günther;Bradley J MacIntosh;Eric Achten;Michael A Chappell;Matthias J P van Osch;Xavier Golay;David L Thomas;Enrico De Vita;Atle Bjørnerud;Aart Nederveen;Jeroen Hendrikse;Iris Asllani;Frederik Barkhof - NeuroImage (2020)
- Current understanding of magnetic resonance imaging biomarkers and memory in Alzheimer's disease. - Ece Bayram;Jessica Z K Caldwell;Sarah J Banks - Alzheimer's & dementia (New York, N. Y.) (2018)
- Brainstorm: a user-friendly application for MEG/EEG analysis. - François Tadel;Sylvain Baillet;John C Mosher;Dimitrios Pantazis;Richard M Leahy - Computational intelligence and neuroscience (2011)
- A new improved version of the realistic digital brain phantom. - Berengere Aubert-Broche;Alan C Evans;Louis Collins - NeuroImage (2006)
- Accurate automatic estimation of total intracranial volume: a nuisance variable with less nuisance. - Ian B Malone;Kelvin K Leung;Shona Clegg;Josephine Barnes;Jennifer L Whitwell;John Ashburner;Nick C Fox;Gerard R Ridgway - NeuroImage (2015)
- A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex. - David C Van Essen - NeuroImage (2005)
- Influence of Processing Pipeline on Cortical Thickness Measurement. - Shahrzad Kharabian Masouleh;Simon B Eickhoff;Yashar Zeighami;Lindsay B Lewis;Robert Dahnke;Christian Gaser;Francois Chouinard-Decorte;Claude Lepage;Lianne H Scholtens;Felix Hoffstaedter;David C Glahn;John Blangero;Alan C Evans;Sarah Genon;Sofie L Valk - Cerebral cortex (New York, N.Y. : 1991) (2020)
- A fast diffeomorphic image registration algorithm. - John Ashburner - NeuroImage (2007)
- Algorithms to improve the reparameterization of spherical mappings of brain surface meshes. - Rachel A Yotter;Paul M Thompson;Christian Gaser - Journal of neuroimaging : official journal of the American Society of Neuroimaging (2011)
- A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. - Chloe Hutton;Bogdan Draganski;John Ashburner;Nikolaus Weiskopf - NeuroImage (2009)
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- Age-Related Gray and White Matter Changes in Normal Adult Brains. - Farnaz Farokhian;Chunlan Yang;Iman Beheshti;Hiroshi Matsuda;Shuicai Wu - Aging and disease (2017)
- Assessing the marks of change: how psychotherapy alters the brain structure in women with borderline personality disorder. - Falk Mancke;Ruth Schmitt;Dorina Winter;Inga Niedtfeld;Sabine C Herpertz;Christian Schmahl - Journal of psychiatry & neuroscience : JPN (2018)
- The association between brain volume, cortical brain infarcts, and physical frailty. - Ilse M J Kant;Jeroen de Bresser;Simone J T van Montfort;Ellen Aarts;Jorrit-Jan Verlaan;Norman Zacharias;Georg Winterer;Claudia Spies;Arjen J C Slooter;Jeroen Hendrikse; - Neurobiology of aging (2018)
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- Dream Recall Frequency Is Associated With Medial Prefrontal Cortex White-Matter Density. - Raphael Vallat;Jean-Baptiste Eichenlaub;Alain Nicolas;Perrine Ruby - Frontiers in psychology (2018)
- Apparent diffusion coefficient changes in human brain during sleep - Does it inform on the existence of a glymphatic system? - Şükrü Barış Demiral;Dardo Tomasi;Joelle Sarlls;Hedok Lee;Corinde E Wiers;Amna Zehra;Tansha Srivastava;Kenneth Ke;Ehsan Shokri-Kojori;Clara R Freeman;Elsa Lindgren;Veronica Ramirez;Gregg Miller;Peter Bandettini;Silvina Horovitz;Gene-Jack Wang;Helene Benveniste;Nora D Volkow - NeuroImage (2019)
- Empirical examination of the replicability of associations between brain structure and psychological variables. - Shahrzad Kharabian Masouleh;Simon B Eickhoff;Felix Hoffstaedter;Sarah Genon; - eLife (2019)
- HIV infection and age effects on striatal structure are additive. - Erin E O'Connor;Timothy Zeffiro;Oscar L Lopez;James T Becker;Thomas Zeffiro - Journal of neurovirology (2019)
- Higher cardiovascular fitness level is associated with lower cerebrovascular reactivity and perfusion in healthy older adults. - Brittany Intzandt;Dalia Sabra;Catherine Foster;Laurence Desjardins-Crépeau;Richard D Hoge;Christopher J Steele;Louis Bherer;Claudine J Gauthier - Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism (2020)
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