Using free-water diffusion to differentiate and measure disease in progressive supranuclear palsy
Poster No:
171
Submission Type:
Abstract Submission
Authors:
Cassandra Marotta1, Ben Sinclair1, Terence O'Brien1, Ella Rowsthorn1, Matthew Pase1, Lucy Vivash1
Institutions:
1Monash University, Melbourne, Victoria
First Author:
Co-Author(s):
Introduction:
Progressive supranuclear palsy (PSP) is a rare neurodegenerative movement disorder characterised by falls, oculomotor dysfunction and cognitive decline. Diagnosing and measuring disease in PSP relies on clinical criteria with no objective biomarkers available. Free-water (FW) diffusion MRI has previously been shown to differentiate PSP from other atypical Parkinsonian diseases. We evaluated the potential of FW diffusion as a biomarker of clinical disease severity and its relationship with CSF measures of neurodegeneration and neuroinflammation in PSP.
Methods:
Multi-shell diffusion MRI was acquired and b-values 13×0 and 48×1000s/mm2 were extracted for FW analysis with a bi-tensor model (Pasternak et al., 2009). FW and FW-corrected fractional anisotropy (FAt) were investigated in sensorimotor and transcallosal white matter tracts in PSP and healthy control (HC) subjects (Archer et al., 2018, 2019). Rank-based ANCOVA was used to determine differences in the FW measures between the two groups. In the PSP group the association between FW measures and disease duration, clinical disease severity (measured by the PSP rating scale (PSPRS)) and CSF measures (glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL) and tau) were investigated with Spearman's partial correlation. Mean and maximum framewise displacement (FD) were included as covariates in all analyses.
Results:
The total cohort included 56 subjects, the PSP and HC groups were age and sex matched (66.6±6.1years; 28M). The PSP mean disease duration was 1.9 years (0-6) and PSPRS score was 32.4 (11-56).
FW was significantly higher in the PSP group than HC in the sensorimotor ventral premotor area and primary sensory cortex (p<0.02). FAt was lower in PSP than HC in the superior cerebellar peduncles and transcallosal pre-supplemental motor area (p<0.007 and p<0.003) however was significantly higher in the corticospinal tract (p<0.005), transcallosal primary sensory cortex (p<0.007) and sensorimotor primary motor cortex, dorsal premotor area and supplemental motor area (p<0.01)(Fig 1).
In the PSP group, disease duration exhibited weak to moderate negative trends with FAt in the sensorimotor supplemental motor area and left and right superior cerebellar peduncles (r=-0.35, -0.32 and -0.27, p<0.05).
The PSPRS exhibited weak to moderate negative trends with FAt in the sensorimotor tracts, particularly the primary motor cortex, primary sensory cortex area and supplemental motor area (r =-0.41, -0.31 and -0.30, p<0.02)(Fig 2). The left superior cerebellar peduncle also exhibited a weak negative trend however the middle cerebellar peduncle exhibited a positive trend (r=-0.27 and 0.27, p<0.05). No trends were seen between FW and the PSPRS.
In the CSF measures (n=15) GFAP exhibited moderate positive trends with FW in the middle cerebellar peduncle, sensorimotor dorsal premotor area, primary sensory cortex, supplemental motor area transcallosal supplemental motor area (r=0.44, 0.49, 0.50, 0.37 and 0.41, p<0.05). NfL and tau exhibited moderate trends in FAt in the middle cerebellar peduncle and ventral premotor area, although these were positive (NfL: r=0.43 and 0.43, p<0.03; tau: r=0.42 and 0.38, p<0.05).
No correlations survived multiple comparisons correction using the false discovery rate.
FW was significantly higher in the PSP group than HC in the sensorimotor ventral premotor area and primary sensory cortex (p<0.02). FAt was lower in PSP than HC in the superior cerebellar peduncles and transcallosal pre-supplemental motor area (p<0.007 and p<0.003) however was significantly higher in the corticospinal tract (p<0.005), transcallosal primary sensory cortex (p<0.007) and sensorimotor primary motor cortex, dorsal premotor area and supplemental motor area (p<0.01)(Fig 1).
In the PSP group, disease duration exhibited weak to moderate negative trends with FAt in the sensorimotor supplemental motor area and left and right superior cerebellar peduncles (r=-0.35, -0.32 and -0.27, p<0.05).
The PSPRS exhibited weak to moderate negative trends with FAt in the sensorimotor tracts, particularly the primary motor cortex, primary sensory cortex area and supplemental motor area (r =-0.41, -0.31 and -0.30, p<0.02)(Fig 2). The left superior cerebellar peduncle also exhibited a weak negative trend however the middle cerebellar peduncle exhibited a positive trend (r=-0.27 and 0.27, p<0.05). No trends were seen between FW and the PSPRS.
In the CSF measures (n=15) GFAP exhibited moderate positive trends with FW in the middle cerebellar peduncle, sensorimotor dorsal premotor area, primary sensory cortex, supplemental motor area transcallosal supplemental motor area (r=0.44, 0.49, 0.50, 0.37 and 0.41, p<0.05). NfL and tau exhibited moderate trends in FAt in the middle cerebellar peduncle and ventral premotor area, although these were positive (NfL: r=0.43 and 0.43, p<0.03; tau: r=0.42 and 0.38, p<0.05).
No correlations survived multiple comparisons correction using the false discovery rate.
Conclusions:
We investigated the differences in diffusion measures between PSP and HC subjects and found that the PSP cohort exhibited higher FW, while FAt showed varying results, with some white matter tracts surprisingly exhibiting greater FAt in PSP than HC. When investigating the relationship between clinical disease severity and FW measures we found that increasing disease duration and severity were associated with FAt reductions. However, no associations were found with FW, therefore FAt may be a more sensitive measure of clinical disease severity in PSP. In contrast, FW may have stronger associations than FAt when investigating potential CSF biomarkers, particularly for GFAP, a measure of neuroinflammation.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
Cerebro Spinal Fluid (CSF)
Degenerative Disease
Movement Disorder
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
Archer, D. B., Vaillancourt, D. E., & Coombes, S. A. (2018). A Template and Probabilistic Atlas of the Human Sensorimotor Tracts using Diffusion MRI. Cerebral Cortex, 28(5), 1685–1699. https://doi.org/10.1093/cercor/bhx066
Pasternak, O., Sochen, N., Gur, Y., Intrator, N., & Assaf, Y. (2009). Free water elimination and mapping from diffusion MRI. Magnetic Resonance in Medicine, 62(3), 717–730. https://doi.org/10.1002/mrm.22055