Diffusion tensor imaging‐based studies at the group‐level applied to animal models of neurodegenerative diseases
Open accessAbstract
Diffusion tensor imaging (DTI), as a technique of diffusion-weighted magnetic resonance imaging (MRI), has substantially contributed to the understanding of human and non-human microstructural brain alterations in the course of neurodegenerative diseases. Given that animal models including disease or knockout models allow for a broad spectrum of experimental manipulations not applicable to humans, the non-invasive DTI approach provides a promising tool for cross-species comparative and longitudinal investigations of the neurobiological targets and mechanisms of neurodegeneration. This overview with a systematic review focuses on the principles of DTI analysis as used in studies at the group level in living preclinical models of neurodegeneration. The translational aspect from in vivo animal models toward clinical applications in humans is covered as well as the DTI-based investigation of the non-human brains´ microstructure, the methodological issues in data postprocessing, and data interpretation at different abstraction levels. The aim of integrating DTI in multiparametric or multimodal imaging protocols will allow the interrogation of DTI data in terms of directional flow of information and may identify the microstructural underpinnings of neurodegeneration-related patterns.
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DTI changes precede anatomical changes on structural MRI for detecting brain impairment in different mice models.
“Several studies have demonstrated that DTI changes precede the anatomical changes on structural MRI for detecting the brain impairment in different mice models (Zhang et al., 2012; Muller et al., 2020).”
From Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging (2022) · cites this paper as Muller et al., 2020
The section on AD animal models reports that DTI detects abnormalities in several mouse models, including changes detected as early as 2.5 months in a tauopathy model (page unavailable). It does not establish that those DTI changes precede anatomical changes visible on structural MRI; the Discussion describes longitudinal DTI findings without making that comparison. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSignificant directional alignment between MRI and transcriptional changes likely reflects co-occurring neuronal loss, dendritic degeneration, axonal disruption, and myelin damage, together with cellular responses to neuroinflammation and neuropathology.
“Significant directional alignment between MRI and transcriptional changes likely reflects co‐occurring neuronal loss, dendritic degeneration, axonal disruption, and myelin damage, 85 , 86 , 87 together with cellular responses to neuroinflammation and neuropathology. 69”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [86]
Citation 69 is attached to the proposed cellular responses to neuroinflammation and neuropathology. The Introduction (Neuroimaging of Neurodegenerative Diseases in Humans and Preclinical Models; page unavailable) says cellular mechanisms are not completely understood, while the Applications section links DTI abnormalities to tissue pathology. Searches for neuroinflammation, cellular responses, and MRI–transcriptional alignment found no passage making the attributed connection. (LLM, conf=0.72)
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Flagged first, then by citations
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- Authors
- Müller, H.-P., Roselli, F., Rasche, V., & Kassubek, J.
- Affiliations
- Universität Ulm (DE)
- Corresponding author
- Müller, Hans-Peter
- Venue
- Frontiers in Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2020-08-31
- Volume
- 14
- Pages
- 734-734
- ISSN
- 1662-453X (ISSN-L)
- PMC
- PMC7487414
- OpenAlex
- W3081907146
- Cited by
- 12
- References
- 244
- Citations by year
- 2026: 3 · 2024: 2 · 2023: 1 · 2022: 4 · 2021: 2
- FWCI
- 0.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OPen Access Repositorium der Universität Ulm (OPARU) (Ulm University) (submittedVersion, cc-by)
- DZNE Pub (submittedVersion, other-oa)
- Universität Ulm, Kommunikations- und Informationszentrum (kiz) (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- diffusion tensor imaging, neurodegenerative diseases, animal models of neurodegeneration, brain microstructure, translational research, knockout models, multimodal imaging, white matter microstructure, cross-species comparison
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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