Diffusion MRI detects basal forebrain cholinergic abnormalities in the 3xTg‐AD mouse model of Alzheimer's disease
Open accessAbstract
Degeneration of the basal forebrain (BF) is detected early in the course of Alzheimer's disease (AD). Reduction in the number of BF cholinergic (ChAT) neurons associated with age-related hippocampal cholinergic neuritic dystrophy is described in the 3xTg-AD mouse model; however, no prior diffusion MRI (dMRI) study has explored the presence of BF alterations in this model. Here we investigated the ability of diffusion MRI (dMRI) to detect abnormalities in BF microstructure for the 3xTg-AD mouse model, along with related pathology in the hippocampus (HP) and white matter (WM) tracks comprising the septo-hippocampal pathway. 3xTg-AD and normal control (NC) mice were imaged in vivo using the specific dMRI technique known as diffusional kurtosis imaging (DKI) at 2, 8, and 15 months of age, and 8 dMRI parameters were measured at each time point. Our results revealed significant lower dMRI values in the BF of 2 months-old 3xTg-AD mice compared with NC mice, most likely related to the increased number of ChAT neurons seen in this AD mouse model at this age. They also showed significant age-related dMRI changes in the BF of both groups between 2 and 8 months of age, mainly a decrease in fractional anisotropy and axial diffusivity, and an increase in radial kurtosis. These dMRI changes in the BF may be reflecting the complex aging and pathological microstructural changes described in this region. Group differences and age-related changes were also observed in the HP, fimbria (Fi) and fornix (Fx). In the HP, diffusivity values were significantly higher in the 2 months-old 3xTg-AD mice, and the HP of NC mice showed a significant increase in axial kurtosis after 8 months, reflecting a normal pattern of increased fiber density complexity, which was not seen in the 3xTg-AD mice. In the Fi, mean and radial diffusivity values were significantly higher, and fractional anisotropy, radial kurtosis and kurtosis fractional anisotropy were significantly lower in the 2 months-old 3xTg-AD mice. The age trajectories for both NC and TG mice in the Fi and Fx were similar between 2 and 8 months, but after 8 months there was a significant decrease in diffusivity metrics associated with an increase in kurtosis metrics in the 3xTg-AD mice. These later HP, Fi and Fx dMRI changes probably reflect the growing number of dystrophic neurites and AD pathology progression in the HP, accompanied by WM disruption in the septo-hippocampal pathway. Our results demonstrate that dMRI can detect early cytoarchitectural abnormalities in the BF, as well as related aging and neurodegenerative changes in the HP, Fi and Fx of the 3xTg-AD mice. Since DKI is widely available on clinical scanners, these results also support the potential of the considered dMRI parameters as in vivo biomarkers for AD disease progression.
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Within the hippocampus, significant changes of diffusion metrics (FA, MD, AxD, and RD) were observed in 3×Tg-AD mice compared with controls, with decreased FA and AxD at both 2 and 8 months, and increased RD at 12 and 14 months.
“Within the hippocampus, significant changes of diffusion metrics (FA, MD, AxD, and RD) were observed in 3×Tg-AD mice compared with controls, with decreased FA and AxD at both 2 and 8 months (Falangola et al., 2021), and increased RD at 12 and 14 months (Snow et al., 2017).”
From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Falangola et al., 2021
The Results (Group analysis; page unavailable) confirm significant hippocampal diffusion-metric differences at 2 and 8 months. But the Discussion (page unavailable) says all hippocampal diffusion-tensor metrics, including axial diffusivity, were significantly higher—not decreased—in 2-month-old 3xTg-AD mice. The 12- and 14-month RD finding is attributed to Snow et al., not this source. (LLM, conf=0.86)
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Experimental — accuracy not yet measuredAdvanced diffusion models-including DKI, NODDI, and SANDI-offer improved sensitivity to microstructural complexity and may provide more biologically informative markers than DTI.
“Advanced diffusion models—including diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and soma and neurite density imaging (SANDI) —offer improved sensitivity to microstructural complexity and may provide more biologically informative markers than DTI.”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [21]
The Introduction (page unavailable) says DKI adds measures associated with tissue microstructural complexity beyond DTI, and Methods says it enables a more comprehensive assessment of the diffusion microenvironment. The paper does not address NODDI or SANDI, so it does not support the claim for all three named models. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe authors demonstrated that dMRI measures capture the larger number of cholinergic neurons in the basal forebrain of young 3xTg-AD mice.
“Additionally, we demonstrated that dMRI measures are able to capture the larger number of cholinergic neurons reported in the BF of these young 3xTg-AD mice [16].”
From Brain microstructure abnormalities in the 3xTg-AD mouse – A diffusion MRI and morphology correlation study (2022) · cites this paper as [16]
The Discussion (page not provided) reports significant basal-forebrain dMRI differences in two-month-old 3xTg-AD mice and says these may be related to an increased number of cholinergic neurons. It also says the study lacks a direct correlation with morphological data, so it does not demonstrate that dMRI measures capture the larger neuron number specifically. (LLM, conf=0.87)
Experimental — accuracy not yet measuredSimilar DTI changes have been reported in other regions such as the fimbria, basal forebrain, external capsule, and internal capsule, characterized by decreased fractional anisotropy and axial diffusion and increased radial diffusion.
“Similar DTI changes have also been reported in other regions such as the fimbria, basal forebrain, external capsule, and internal capsule, characterized by decreased fractional anisotropy (FA) and axial diffusion (AxD) and increased radial diffusion (RD) (Falangola et al., 2020; Falangola et al., 2021; Ni, 2021; Bergamino et al., 2023).”
From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Falangola et al., 2021
The Results (Group analysis; page unavailable) report lower fractional anisotropy and higher radial diffusivity in the fimbria of 3xTg-AD mice, but do not report lower axial diffusivity there. The Abstract (page unavailable) reports age-related decreases in fractional anisotropy and axial diffusivity in the basal forebrain, but an increase in radial kurtosis, not radial diffusivity. The Methods (DKI post-processing and Image analysis; page unavailable) list the studied regions as basal forebrain, hippocampus, fimbria, and fornix—not the external or internal capsule. (LLM, conf=0.88)
Experimental — accuracy not yet measuredA prior investigation discovered decreased AxD and FA values within the basal forebrain of 2-month-old 3×Tg-AD mice, with progressive temporal declines in AxD and FA at 8 and 15 months of age.
“For instance, a prior investigation discovered decreased AxD and FA values within the basal forebrain of 2-month-old 3×Tg-AD mice, with progressive temporal declines in AxD and FA at 8 and 15 months of age (Falangola et al., 2021).”
From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Falangola et al., 2021
The Discussion reports lower basal-forebrain D∥ (AxD) and FA in 2-month-old 3xTg-AD mice than in controls, and lower values for both metrics at 8 months. However, the Results (Age-related trajectories; page unavailable) report no significant basal-forebrain changes between 8 and 15 months, so the claimed progressive decline through 15 months is not supported. (LLM, conf=0.90)
Experimental — accuracy not yet measured
The authors found that dMRI metrics change longitudinally with aging and AD pathology progression in several brain regions.
“Moreover, we found that dMRI metrics change longitudinally with aging [16] and AD pathology progression [17] in several brain regions.”
From Brain microstructure abnormalities in the 3xTg-AD mouse – A diffusion MRI and morphology correlation study (2022) · cites this paper as [16]
Other studies conducted on the same model in mice at ages 2, 12, and 14 months identified significant WM alterations.
“Conversely, other studies conducted on the same model in mice at ages 2, 12, and 14 months identified significant WM alterations (Manno et al., 2019; Nie et al., 2019; Falangola et al., 2021).”
From Multimodal MRI combined with RNA sequencing reveals pathological signatures in the 9-month-old 3×Tg-AD mouse brain (2026) · cites this paper as Falangola et al., 2021
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Neuroimaging of mouse models of Alzheimer’s disease
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Magnetic Resonance Imaging in Animal Models of Alzheimer’s Disease Amyloidosis
Ni, R. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222312768 · Cited by 33 · see citing works
Lisgaras, C. P., & Scharfman, H. E. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106294 · Cited by 26 · see citing works
Evidence of disturbed insulin signaling in animal models of Alzheimer’s disease
Alves, S. S., Servilha‐Menezes, G., Rossi, L., da Silva-Júnior, R. M. P., & Garcia‐Cairasco, N. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105326 · Cited by 24 · see citing works
Gamage, R. V., Rossetti, I., Niedermayer, G., Münch, G. W., Buskila, Y., & Gyengési, E. (2023) · Journal of Neuroinflammation · doi:10.1186/s12974-023-02897-5 · Cited by 23 · see citing works
Tian, S. L., Ye, T., & Cheng, X. (2023) · Experimental Neurology · doi:10.1016/j.expneurol.2023.114505 · Cited by 19 · see citing works
The 3xTg-AD Mouse Model: A Comprehensive Tool for Understanding Alzheimer’s Disease
Pattanayak, A., & Firdous, S. M. (2026) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-025-01655-w · Cited by 11 · see citing works
Lv, J., Zhang, L., Wang, J., Shao, J.-J., Xin-gui, G., Wang, L., Kang, L.-Q., Zheng, W., Xin, Y., Song, R., et al. (2023) · Behavioural Brain Research · doi:10.1016/j.bbr.2023.114328 · Cited by 8 · see citing works
Power, S., Venkatesan, S., Qu, S., McLaurin, J., & Lambe, E. (2024) · bioRxiv · doi:10.1186/s40035-024-00452-7 · Cited by 5 · see citing works
Murdy, T. J., Dunn, A. R., Singh, S., Telpoukhovskaia, M. A., Zhang, S., White, J. K., Kahn, I., Febo, M., & Kaczorowski, C. C. (2023) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.1033975 · Cited by 6 · see citing works
Falangola, M. F., Nie, X., Voltin, J., Ward, R., Dhiman, S., Nietert, P. J., & Jensen, J. H. (2022) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.09.002 · Cited by 6 · see citing works
Falangola, M. F., Dhiman, S., Voltin, J. R., & Jensen, J. H. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2023.06.017 · Cited by 6 · see citing works
Bergamino, M., Nelson, M. R., Numani, A., Scarpelli, M., Healey, D., Fuentes, A., Turner, G., & Stokes, A. M. (2023) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2023.03.024 · Cited by 4 · see citing works
Diffusion MRI in 2‐Month‐Old Mouse Brain Predicts Alzheimer's Pathology Genotype
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Mean Kärger model water exchange rate in brain
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Serial reversal learning in an olfactory discrimination task in 3xTg-AD micePreprint
Roddick, K. M., Schellinck, H. M., & Brown, R. E. (2023) · Learning & Memory · doi:10.1101/lm.053840.123 · Cited by 4 · see citing works
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Kitchigina, V. F., Popova, I. Y., & Shubina, L. (2025) · Neuroscience and Behavioral Physiology · doi:10.1007/s11055-025-01784-1 · Cited by 2 · see citing works
Han, X., Hu, R., Ping, X., Chen, J., Liu, J., Liu, Z., Jin, X., Gao, X., Lin, M., Zhao, Y., et al. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71908
Li, Y., Tang, Z., Yao, M., Ran, Y., & Qiu, Z. (2026) · Neural Regeneration Research · doi:10.4103/nrr.nrr-d-25-00006
The role of the basal forebrain in the pathogenesis of Alzheimer’s disease
Kitchigina, V. F., Popova, I. Y., & Shubina, L. (2024) · Журнал высшей нервной деятельности им И П Павлова · doi:10.31857/s0044467724050026
Johnson, G. A., Tian, Y., Hornburg, K. J., Cook, J. J., Qi, Y. P., WHITE, L. E., KILLMAR, J. T., Kaczorowski, C. C., Ashbrook, D. G., & Williams, R. W. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.632011
Gamage, R. V., Rossetti, I., Niedermayer, G., Münch, G. W., Buskila, Y., & Gyengési, E. (2024) · Figshare · doi:10.6084/m9.figshare.26626864
Falangola, M. F., Voltin, J. R., Cole, M., Nietert, P., Liu, F., Iqbal, K., & Jensen, J. H. (2026) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2026.110641
An efficient Alzheimer’s disease detection by NV classifier with BWTDL approach using MRI image
Dwarkanth, P. S., Anitha, R., Babu, G. H., Ahammad, S. K. H., Karamti, H., Kumar, A., Oza, A. D., & Moges, D. M. (2025) · BMC Medical Imaging · doi:10.1186/s12880-026-02201-w
Lei, L., Meng, Q., Hu, X., Yang, J., Du, N., Su, S., Hashimoto, K., Cao, J., & Yang, J. (2026) · Aging Cell · doi:10.1111/acel.70574
- Authors
- Falangola, M. F., Nie, X., Ward, R., Dhiman, S., Voltin, J., Nietert, P. J., & Jensen, J. H.
- Affiliations
- Medical University of South Carolina (US)
- Corresponding author
- Falangola, Maria Fatima
- Venue
- Magnetic Resonance Imaging
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 83
- Pages
- 1-13
- ISSN
- 0730-725X (ISSN-L)
- PMC
- PMC8449815
- OpenAlex
- W3182752071
- Cited by
- 25
- References
- 74
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 4 · 2023: 9 · 2022: 3 · 2021: 1
- FWCI
- 2.24
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (1RF1AG057602-01)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, 3xTg-AD mice, basal forebrain, cholinergic neurons, choline acetyltransferase, septohippocampal pathway, hippocampus, fornix, fimbria, white matter microstructure, diffusion kurtosis imaging, fractional anisotropy, axial diffusivity, radial kurtosis
- MeSH terms
- Alzheimer Disease, Animals, Cholinergic Agents, Diffusion Magnetic Resonance Imaging, Mice, Diffusion Tensor Imaging, White Matter, Basal Forebrain
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.
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