Investigating white matter association pathways in Alzheimer’s disease: a relaxometry-based analysis
Poster No:
185
Submission Type:
Abstract Submission
Authors:
Matteo Mancini1, Sabrina Bonarota2, Giulia Caruso2, Giovanni Calcagnini3, Laura Serra2, Federico Giove1
Institutions:
1Centro Ricerche Enrico Fermi, Rome, Italy, 2Fondazione Santa Lucia, Rome, Italy, 3Istituto Superiore di Sanità, Rome, Italy
First Author:
Co-Author(s):
Introduction:
Alzheimer's disease (AD) presents a heterogeneous course and stems from mild cognitive impairment (MCI). Transitioning from healthy ageing (HC) to MCI and AD involves several processes affecting brain structure and function. Grey matter (GM) neurodegeneration is a hallmark of the disease, but there is a growing interest towards white matter (WM) damage. Reduced structural integrity and potential demyelination have been observed in pathways interconnecting parietal and frontotemporal areas (Nasrabady et al. 2018), which could contribute to the anterior-posterior disconnection and the related disintegration of the default mode network (Dillen et al. 2017). The goal of this work is to characterise WM changes within the AD spectrum leveraging quantitative MRI.
Methods:
We acquired MRI data from 59 participants (12AD/21MCI/26HC), together with neuropsychological scores (Corsi Span (Monaco et al. 2013), Rey-Osterrieth Complex Figure Recall (Carlesimo et al. 2002)) for patients able to complete the tests. The protocol included a T1-weighted acquisition, an MR fingerprinting (MRF) sequence and a multi-shell diffusion-weighted acquisition. T1w data were processed with FreeSurfer for tissue segmentation, brain parcellation and volume estimation, while MRF data were reconstructed and used to estimate T1 and T2 relaxation time maps by dictionary learning (Cao et al. 2019). DWI data were pre-processed with FSL and MRtrix for denoising and distortion corrections (Gibbs ringing, phase-encoding, eddy currents).
After pre-processing, T1w data and T1 and T2 maps were rigidly aligned to diffusion data. DWI data were then used to estimate a fiber orientation distribution using multi-tissue multi-shell spherical deconvolution. The distribution peaks were processed with TractSeg to segment white matter bundles and reconstruct their tractograms (Wasserthal et al. 2018). Specifically, we focused on the cingulum (CG), the inferior longitudinal fasciculus (ILF) and the inferior front-occipital fasciculus (IFO), as they are involved in visuo-spatial memory abilities (Sasson et al. 2013).
To test for progressive changes in the metrics of interest for cortical and WM structures, we used the Jonckhere-Terpstra (Bewick et al. 2004) test (10000 permutations) for the trend HC>MCI>AD in GM parcel volumes, and the trend AD>MCI>HC for WM average T1 and T2. WM metrics were also correlated with clinical scores. Multiple comparisons correction was done with the Bonferroni approach (corrected p-threshold: 0.05).
Finally, we assessed each metric along the bundles of interest through tractometry (Yeatman et al. 2012): for each bundle, streamlines were resampled to a fixed number of points; a centroid was then found for all the streamlines, and for each streamline each segment was assigned to the closest segment in the centroid; then for each centroid segment, each metric's average was computed. AD-HC differences and MCI-HC ones were tested separately using two-sample t-tests, corrected for family-wise errors.
After pre-processing, T1w data and T1 and T2 maps were rigidly aligned to diffusion data. DWI data were then used to estimate a fiber orientation distribution using multi-tissue multi-shell spherical deconvolution. The distribution peaks were processed with TractSeg to segment white matter bundles and reconstruct their tractograms (Wasserthal et al. 2018). Specifically, we focused on the cingulum (CG), the inferior longitudinal fasciculus (ILF) and the inferior front-occipital fasciculus (IFO), as they are involved in visuo-spatial memory abilities (Sasson et al. 2013).
To test for progressive changes in the metrics of interest for cortical and WM structures, we used the Jonckhere-Terpstra (Bewick et al. 2004) test (10000 permutations) for the trend HC>MCI>AD in GM parcel volumes, and the trend AD>MCI>HC for WM average T1 and T2. WM metrics were also correlated with clinical scores. Multiple comparisons correction was done with the Bonferroni approach (corrected p-threshold: 0.05).
Finally, we assessed each metric along the bundles of interest through tractometry (Yeatman et al. 2012): for each bundle, streamlines were resampled to a fixed number of points; a centroid was then found for all the streamlines, and for each streamline each segment was assigned to the closest segment in the centroid; then for each centroid segment, each metric's average was computed. AD-HC differences and MCI-HC ones were tested separately using two-sample t-tests, corrected for family-wise errors.
Results:
Our analysis showed first that the areas with significantly decreasing volume trend included parietal, temporal and frontal areas (fig.1a), in line well-known patterns of atrophy observed in MCI and AD. We then observed significantly increasing trends for T1 in the the cingulum (bilaterally) and in the right IFO, as well as for T2 in IFO and ILF (bilaterally). For correlations with neuropsychological scores, we found significant negative correlations mainly with CG and IFO average T1 (fig.1c). For along-tract metrics, in addition to AD-HC differences in T1, in T2 MCI-HC and AD-HC differences in right ILF and IFO affected different sections of the tract (fig.2).
·Figure 1
·Figure 2
Conclusions:
Our results highlight the potential of assessing WM relaxometry in AD. As higher T1 and T2 have been associated with atrophy and amyloid burden (Tang et al. 2018), these results pinpoint to structural damage that could underlie cognitive impairment and potentially aid disease progression assessment.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Multivariate Approaches
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Keywords:
Aging
Degenerative Disease
Neurological
STRUCTURAL MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Dillen, K.N.H., Jacobs, H.I.L., Kukolja, J., et al. (2017) Functional Disintegration of the Default Mode Network in Prodromal Alzheimer’s Disease. Journal of Alzheimer’s Disease. 59(1):169-187.
3. Monaco, M., Costa, A., Caltagirone et al. (2013). Forward and backward span for verbal and visuo-spatial data: standardization and normative data from an Italian adult population. Neurol Sci. 34(5):749-54.
4. Carlesimo, A., Buccione, I., Fadda, L. et al. (2002) Standardizzazione di due test di memoria per l’uso clinico: breve racconto e figura di Rey. Nuova Rivista di Neurologia 12:1–13.
5. Cao, X., Ye, H., Liao, C., et al. (2019) Fast 3D brain MR fingerprinting based on multi-axis spiral projection trajectory. Magn Reson Med. 82(1):289-301.
6. Wasserthal, J., Neher, P., Maier-Hein, K.H. (2018) TractSeg - Fast and accurate white matter tract segmentation. NeuroImage. 183:239-253.
7. Bewick, V., Cheek, L., Ball, J. (2004) Statistics review 10: further nonparametric methods. Crit Care. 8(3):196-9.
8. Sasson, E., Doniger, G.M., Pasternak, O., et al. (2013) White matter correlates of cognitive domains in normal aging with diffusion tensor imaging. Front Neurosci. 7:32
9. Yeatman, J.D., Dougherty, R.F., Myall, N.J. et al. (2012) Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification. PLOS ONE. 7(11):e49790.
10. Tang, X., Cai, F., Ding, et al. (2018) Magnetic resonance imaging relaxation time in Alzheimer’s disease. Brain Research Bulletin. 140:176-189.