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Hippocampal subfield imaging and fractional anisotropy show parallel changes in Alzheimer's disease tau progression using simultaneous tau‐PET/MRI at 3T

Abstract Introduction Alzheimer's disease (AD) is the most common form of dementia, characterized primarily by abnormal aggregation of two proteins, tau and amyloid beta. We assessed tau pathology and white matter connectivity changes in subfields of the hippocampus simultaneously in vivo in AD. Methods Twenty‐four subjects were scanned using simultaneous time‐of‐flight 18F‐PI‐2620 tau positron emission tomography/3‐Tesla magnetic resonance imaging and automated segmentation. Results We observed extensive tau elevation in the entorhinal/perirhinal regions, intermediate tau elevation in cornu ammonis 1/subiculum, and an absence of tau elevation in the dentate gyrus, relative to controls. Diffusion tensor imaging showed parahippocampal gyral fractional anisotropy was lower in AD and mild cognitive impairment compared to controls and strongly correlated with early tau accumulation in the entorhinal and perirhinal cortices. Discussion This study demonstrates the potential for quantifiable patterns of 18F‐PI2620 binding in hippocampus subfields, accompanied by diffusion and volume metrics, to be valuable markers of AD.


HIGHLIGHTS
• Tau standardized uptake value ration (SUVr) corresponded with known pathological progression of tau accumulation.
• Tau deposition is more strongly correlated to volume loss in ERC/PRC than in whole hippocampus. INTRODUCTION Alzheimer's disease (AD) is a progressive neurological disorder resulting in severe cognitive and behavioral deficits affecting nearly 6 million adults in the United States alone. 1 Two main pathologic features of AD are the aggregation of extracellular amyloid beta (Aβ) and intracellular hyperphosphorylated tau. Cross-sectional neuropathological studies have determined that tau has a characteristic pattern of accumulation in the brain throughout the progression of amnestic AD.
Accumulation begins in the transentorhinal region, progresses to the entorhinal region, then the hippocampal formation (including subiculum, cornu ammonis fields [CA1 -3], and dentate gyrus) while simultaneously spreading to the adjacent neocortex and beyond. 2 Studies have shown that connectivity and white matter integrity may be more closely associated with tau than Aβ deposition, 3,4 and tau aggregation correlates with severity of cognitive deficits and synaptic and neuronal loss. 5 Mounting evidence shows that the hippocampus is particularly atrophied during amnestic AD, but that functionally and cytoarchitecturally distinct subregions of the hippocampus may have differential sensitivity. [6][7][8] Tau in the entorhinal region may instigate volume loss, weakened connectivity, and metabolic reduction in spatially and functionally adjacent brain areas due to microtubule instability, which contributes to axonal degeneration and reduced synaptic transmission. 9,10 Ex vivo work has shown a characteristic progression of tau in AD and has allowed scientists to study tau seeding and AD onset. 2,11 However, in vivo work, especially including non-invasive magnetic resonance imaging (MRI) and positron emission tomography (PET) imaging, is critical for studying hippocampal subfield pathology throughout disease progression in living humans. Such studies could ultimately aid in earlier diagnosis and future tracking and treatment in human patients.
In vivo imaging studies have begun exploring region-specific pathologically induced changes using PET and MRI, but few have been able to demonstrate disease-related pathologic progression on the spatial scale of hippocampal subfields. Prior tau-PET imaging studies have shown that patterns of tracer uptake in the whole brain correspond to neuropathological staging, but this work has been limited to wholehippocampal analysis due to resolution limits of PET imaging. 12,13 Here, we use time-of-flight (TOF) imaging capability, 2D erosion for showing that tau corresponds to cognitive decline and is thought to be associated more closely with neurodegeneration and a loss of white matter connectivity than amyloid beta. These findings also build off previous imaging work on the whole hippocampal scale.
3. Future directions: These findings underline the potential for using PET/MRI to track AD pathology in vivo, which could help with earlier diagnosis, patient stratification, and treatment monitoring. minimizing partial volume effects, and a custom hippocampal atlas to enable subfield segmentation of PET images. 14 Diffusion tensor imaging (DTI) MRI has also been used to study connectivity and white matter degradation in the brain, complementary to structural MRI volume reductions in AD. 15,16 DTI is important to study in the context of AD to evaluate white matter connectivity within functionally connected regions of the medial temporal lobe. 17 Lower connectivity has been associated with reduced memory performance in amnestic AD and MCI subjects. 18 Our prior work has used simultaneous PET-MR and hippocampal segmentation techniques to analyze subfield-specific measures of volume and glucose metabolism. 14 However, few studies have investigated tau uptake in vivo on a hippocampal subfield-specific level in concert with volumetric and microstructural measurements whichgiven the characteristic progression of tau with disease severity-is important for the study of onset and real-time progression of AD.
We use a tracer with low choroid plexus binding, 18 F-PI-2620, which, in contradistinction to other tracers, does not obscure hippocampal uptake. 19,20 Thus, we aim to simultaneously analyze tau-PET uptake, volume, and connectivity of related subregions within and surrounding the hippocampus. By studying hippocampal subfield-specific differences in tau burden, white matter degradation, and volume, we may be able to better understand the relationship between the patterns of several disease-specific changes in vivo, which could ultimately lead to earlier diagnosis and future treatments for AD.
Here, we show that using simultaneous tau-PET/MRI and fine-tuned registration, we can detect subtle differences in tau tracer accumulation and volume within hippocampal subfields that is consistent with

Subjects
A total of 24 subjects (

Image acquisition
Subjects underwent a dynamic 90-minute tau-PET scan on a 3T PET-MR (SIGNA, GE) using time-of-flight (TOF) capability after a 5-10mCi intravenous injection of 18  2.0 mm isotropic, and an additional 3 to 6 blip-down b = 0 s/mm 2 image). 22 Note, voxel size was included as a covariate in initial analyses, which did not change the significance of any results. A LAVA-Flex (liver-accelerated volume acquisition-Flex) MRI series was used, along with an atlas-based segmentation algorithm, to classify fat, water, air, and bone in the head and construct subject-specific attenuation correction maps for PET reconstruction. 22

PET image processing
Standardized uptake value (SUV) maps were reconstructed from static images by summing the 30-minute interval between 60 and 90 minutes of dynamic PET data using TOF optimized subset expectation maximization (TOF-OSEM) with three iterations, 28 subsets, 2.78 × 1.17 × 1.17 mm voxel size, and zero-TE MR attenuation correction. SUV maps were normalized to the inferior cerebellar cortex (to account for any off-target binding effects 23 ) of each subject using a modified FreeSurfer segmentation to produce SUV ratio (SUVr) maps.
SUVr maps were precisely registered to coronal T2 MR image space using the T1-weighted IRFSPGR as an intermediate registration volume using niftireg. 24 Coronal T2 space was used for analysis to preserve accuracy of hippocampal subfield segmentations.

Subfield segmentation
Hippocampal subfields were segmented using Automated Segmenta- together. 27 The fornix was included from a FreeSurfer 6.0 segmentation. Hippocampal subfield volumes were calculated in coronal T2 MR space, and DTI and PET images were mapped into the same space to quantify mean fractional anisotropy (FA) and mean diffusivity (MD), and SUVr (respectively). Subfields were eroded by one voxel in the 2D oblique coronal plane, using the command c3d with the "-erode" flag and 1 × 1 × 0 option, to reduce partial volume effects in PET imaging. 28 SUV was computed by taking the average SUV across CA1, DG, SUB, and ERC/PRC. FA/MD was similarly computed across the subfields and PHG. We have previously validated these methods with analysis of FDG-PET and volume measures across hippocampal subfields in a separate AD cohort 14 (Figure 1).

DTI image processing
Diffusion images were pre-processed with FDT (FMRIB's Diffusion Toolbox) using top-up susceptibility-induced distortion correction, brain extraction, and eddy distortion correction. 29 Weighted-least-squares tensor fitting using DTIFIT generated FA and mean diffusivity MDmaps, 31 which indicate the degree of asymmetry of water diffusion directionality (which is sensitive to microstructural changes) and measure overall diffusion (which is increased with decreased cellularity/cellular processes), respectively. Diffusion images were also fitted to the neurite orientation dispersion and density imaging (NODDI) algorithm to generate free water volume fraction (FISO) and orientation dispersion index (ODI) maps. 32 FICVF was not computed as we had only one non-zero b shell. The b = 0s/mm 2 images for each subject were registered to coronal-oblique T2-weighted images using reg_aladin, 24 then FA, MD, FISO, and ODI images were resampled to coronal oblique T2-weighted image space using the b = 0-to-T2 image transformation matrix.
We then performed a tract-based analysis: tractography was computed using bedpostx in FMRIB's diffusion toolbox, which implements a probabilistic multi-fiber ball-and-stick diffusion model, followed by probtrackx. 33 The -opd, -l, and -f flags, which, respectively, out- Bilateral DTI results were combined using a volume-weighted average.

Statistical analysis
Volume subjects were combined to better balance the groups and compared to controls (n = 13). Analyses were also repeated using combined amnestic AD (n = 3) and MCI (n = 5) subjects. Finally, we compared all AD to all MCI, and all MCI to controls using the same model.
PET: Separate linear regression analyses were performed for tau SUVr using the same whole hippocampus and subfield combinations.
SUVr was the dependent variable and disease status was the independent variable, with age as an independent regressor. We similarly com- Multiple comparison correction was performed using Li and Ji's method 35 to account for both covariance and independence between subregions, using the eigenvalues of the subfield covariance matrix to compute an effective number of degrees of freedom for each metric, and providing a P-value significance threshold for each test.
All reported significant statistics are below this threshold unless otherwise specified. Regression residuals for all analyses performed showed normalcy. All statistical analyses were completed using STATA v15.0.

Volume
The combined AD/MCI group had smaller whole hippocampal volume compared to controls (P = .002). All subfields except SUB had significantly smaller volume in AD/MCI than controls ( Figure 2, Table 2). When non-amnestic MCI and AD subjects were excluded, volume differences between AD/MCI and controls were significant in ERC/PRC (P = .004) and whole hippocampus (P = .009). Volume dif-ferences between AD and MCI subjects was significant in ERC/PRC (P = .015) and whole hippocampus (P = .025) and trending in DG (P = .048; Li and Ji threshold was P < .025). Volumes were not significantly different between MCI and controls for any subfield or whole hippocampus.

Tau SUVr
Whole hippocampus SUVr was higher in AD/MCI than controls (
There were no other significant differences in ROI or tractographic FA, MD, ODI, or FISO in other subfields, the fornix, or the whole hippocampus.

DISCUSSION
In this study, we used simultaneous TOF PET and high-resolution MRI acquisition along with fine-tuned segmentations to analyze tau tracer accumulation and white matter connectivity in distinct hippocampal subfields. Our PET/MR image-based measurements correlated with known pathological progression of tau in the medial temporal lobe, 2 with the most significant involvement in ERC/PRC, and progressively less involvement further within the hippocampal formation. This finding parallels neuropathologic staging on a small scale that has not been previously demonstrated in vivo and could be useful in detecting subtle changes in tau accumulation during longitudinal studies in the future.
ERC/PRC tau also correlated with PHG white matter integrity, as measured by FA, and was observable alongside hippocampal atrophy.
Our work expands upon existing tau-PET and MRI AD literature that has demonstrated correlations between tau deposition and AD status. 18 F-AV-1451 tau-PET binding patterns across larger regions of the brain, including the whole medial temporal lobe, correspond with known tau progression and Braak staging of disease. 36,37 Other studies using 18 F-AV-1451 have shown comparable results and spatial distributions of tracer uptake throughout the brain corresponding to amyloid status, cognition, and neuropathologic staging, beginning with heaviest involvement surrounding the transentorhinal area. 13,38 Existing models of AD suggest that tau pathology, along with Aβ plaques, contribute to neuronal loss. 39 Our data support this, with ERC/PRC tau correlating with volume loss. Notably, the volume differences in DG, one of the last regions to accumulate tau within the hippocampus, 2,8 are not accompanied by significant tau tracer accumulation in AD/MCI in the same subregion, supporting the validity of the methods used in this study.
The tau tracer used, 18 F-PI-2620, is known to have lower off-target binding to Aβ and monoamine oxidase A and higher sensitivity and selectivity for pathologic tau aggregates than other tau tracers, including 18 F-AV-1451. 40 Previous work using different tau PET tracers has not found significant correlations between tau deposition and volume loss in hippocampal subfields; 41 however, this could be explained by differences in specific binding of tracers used, single-timepoint imaging, choroid plexus uptake, and limited resolution, but may still point toward the benefit of our method in evaluating hippocampal subfieldspecific changes that may go undetected when evaluating the whole hippocampus. 42 Subfield tau SUVr also correlated with diffusion MRI measurements.
ERC/PRC tau was closely negatively correlated with ROI-based and tract-based PHG FA, and closely positively correlated to PHG tractographic ODI. Because the PHG is a region of crossing fibers, a weakening of the primary parahippocampal cingulum bundle would be expected to reduce the FA (with less uniform diffusion along the parahippocampal cingulum) and increase the ODI (with more relative dispersion). 43 This is consistent with FA alterations in parahippocampal white matter accompanying progression of AD. 15,44 We observed higher MD only between amnestic AD/MCI and controls, corroborating other work in which MD increases alongside memory decline. 45,46 These observations are consistent with work showing that tau accumu-lation contributes to axonal damage, which may also result in functional isolation of the hippocampus. 12 We also saw tract-based CA1 to SUB MD was higher in AD/MCI and accompanied by increased tau accumulation in both regions, suggesting that tau may mediate reductions in subregional cellularity. In the same tract, we found ODI is negatively correlated with tau uptake, which may be explained by there being a larger gray matter overlap of this smaller tract, with similar dendritic degradation reducing overall dispersion as measured by ODI. 43 Alternatively, given that this is in a region of extensive crossing fibers, much more so than the central parahippocampal gyrus in which the parahippocampal cingulum resides, 47 the decrease in ODI may simply reflect fewer overall crossing fibers (e.g., a weakening of the crossing perforant pathway).
To precisely correlate diffusion characteristics, volume loss, and tau deposition with disease severity in vivo, it is important to evaluate small subregions, which often requires long PET and MRI scan times. Thus, we used a TOF-PET/MR imaging acquisition method because it offers several benefits that help minimize image misregistration errors due to subject motion, 48 including that signal-to-noise ratio is increased compared to non-TOF images, and simultaneous acquisition enhances spatial alignment between PET and MRI image series. 49 Partial volume effects were minimized by doing a subfield erosion. Partial volume correction was not applied as research has shown that this does not significantly impact results. 50 Motion correction was not performed on this cohort, because our group had not, at the time of acquisition, developed an optical tracking technique to prospectively correct for motion in list mode PET; however, our motion correction approach will be used in future work. MRI in the future, which would allow us to probe even smaller structural regions.

CONCLUSION
We have used TOF-PET/MRI methods and a second-generation tau tracer to reveal subfield-level relationships between AD pathological hallmarks, connectivity, and volume effects patients, which has previously only been demonstrated using post mortem histology. Using these methods, we separated both amnestic and non-amnestic AD, MCI, and control subject groups. Our results indicate that tau accumulation and volumetric changes in the hippocampus are more pronounced than white matter degradation. Moreover, the effect size is larger in certain AD hippocampal subfields compared to whole hippocampus, indicating that this type of analysis may enable earlier detection of tau pathology before it is apparent throughout the whole hippocampus. Combined with DTI, this approach reveals intricacies of subfield and subfield pathway degeneration intermingled with tau accumulation. Altogether, these results underline the potential value of subfield-specific imaging analyses using PET/MRI to track pathological hallmarks of AD in vivo. Quantifiable patterns of 18 F-PI2620 binding could be a valuable marker of AD progression or response to novel therapies in clinical trials, as well as help separate AD from other tauopathies.

CONFLICTS OF INTEREST
No potential conflicts of interest relevant to this article exist. Roche, and co-chairs the NIA ADRC Imaging Core steering committee.