Automated Fiber Quantification Identifies the Extent of White Matter Integrity Loss Concurrent to Hippocampal Atrophy
Joseph A Helpern1, George Russell Glenn1, Andreana A Benitez2, Fatima A Falangola3, Rachael A Deardorff1, and Jens A Jensen1

1Radiology, Medical University of South Carolina, Charleston, SC, United States, 2Neurology, Medical University of South Carolina, Charleston, SC, United States, 3Neuroscience, Medical University of South Carolina, Charleston, SC, United States


Automated fiber quantification with white matter tract integrity metrics identified subtle changes in brain microstructure occurring in the early stages of Alzheimer’s pathology. These metrics were found to be significantly different in the posterior section of the parahippocampal white matter in cognitively normal older adults with hippocampal atrophy as compared to those without. Axonal water fraction distinguished those with hippocampal atrophy versus those without with the largest effect size (Cohen’s d = 0.86, p = 0.004). As expected, the metrics for these two groups did not differ in the arcuate fasciculus, a tract typically unaffected in Alzheimer’s disease.


Automated fiber quantification (AFQ), a novel tractography-based image analysis technique1, used in conjunction with white matter tract integrity (WMTI) metrics2 is a promising method for identifying subtle loss of white matter (WM) integrity due to aging or early stages of disease. Here, we investigate the utility of AFQ in conjunction with WMTI in differentiating cognitively normal older adults with and without hippocampal atrophy, as this could provide non-invasive imaging biomarkers of changes in tissue microstructure that occur early in the course of Alzheimer’s disease (AD), thus improving detection of disease and its progression.


MR images were obtained with a 3T Siemens TIM Trio. MPRAGE and FLAIR images were acquired for anatomical and WM hyperintensity assessment. Diffusional kurtosis images (DKI)3 were acquired using a twice-refocused spin-echo EPI diffusion sequence with b-values=0,1000,2000 s/mm2 with 64 homogeneously distributed gradient directions for each b-value and with 24 additional b=0 images. Diffusion parametric maps were calculated with Diffusional Kurtosis Estimator (DKE) software [http://www.nitrc.org/projects/dke]. WMTI metrics were computed as previously described2,3 and included axonal water fraction (AWF) and axial and radial extra-axonal water diffusivity (De,ax and De,rad). DKI data were incorporated into the AFQ image processing pipeline (https://github.com/jyeatman/AFQ) using fully automated in-house MATLAB scripts4.

AFQ utilizes diffusion tractography data and performs a series of automated steps to identify and segment specific WM fiber bundles and isolate the core of each tract1,5. Fiber bundles are selected by specifying regions of interest (ROIs), chosen from a WM template, which are applied to define the start and end points of each tract. Once the core of a tract is identified, AFQ interpolates a fixed number of nodes along the tract and estimates the diffusion and kurtosis tensors at every node, enabling reconstruction of all tensor-derived metrics4.

Twenty seven cognitively healthy NC (age=70.59±8.26; 19F) were studied. We examined parahippocampal WM (Figure 1A), as it includes axons that relay information to the hippocampus and is critical for memory formation. We contrasted these results with the arcuate fasciculus (Figure 1A), as this tract is typically unaffected early in the course of AD. To facilitate statistical testing of differences in tract nodes between the two groups, the tract nodes in Figure 1A were averaged into five bins.


NC subjects with (n=12) and without (n=15) hippocampal atrophy did not differ in demographic characteristics or neuropsychological testing. Significant changes in WMTI metrics were found in the posterior section of the parahippocampal WM in cognitively normal older adults with hippocampal atrophy versus those without, across several diffusion and WMTI parameters (Figure 1B, top row). Of all these parameters, AWF distinguished the two groups with the largest effect size (Tract node bin 1, Cohen’s d = 0.86, p = 0.004). As expected, these groups did not differ in their diffusion/WMTI parameters in the arcuate fasciculus (Figure 1B, bottom row).


AD has a pathological cascade that long precedes its clinical manifestations. Disease-modifying therapies that address β-amyloid aggregation have thus far been relatively ineffective in ameliorating cognitive decline in the later stages of disease6. While amyloidosis is undoubtedly implicated in AD, we hypothesize that the earliest stages of AD consist of complementary pathological changes in WM on which alternative therapies and diagnostic tests can effectively focus. Indeed, WMTI metrics have previously been shown to be relevant for the assessment of AD2.

AFQ offers several advantages in circumventing the limitations of co-registration and manual tracings by using tractography to identify tracts within each individual and then quantifying the diffusion properties of equidistant nodes along these tracts. Additionally, AFQ is valuable in populations with widely varying brain volumes (as in aging and AD), where disease can significantly affect key sections but not the entirety of WM tracts. The results presented here, that were obtained utilizing a combination of AFQ with WMTI metrics, support the idea that loss of WM integrity may exist even prior to clinically manifest AD, and that WMTI metrics are potentially highly sensitive to detecting these subtle changes at the earliest stages of disease.


Although there is no clear consensus on the temporal order of brain microstructural changes that occur with AD7, diffusion-based MRI metrics remain viable biomarkers of AD8. Here we demonstrate that such metrics when used in combination with AFQ may provide useful information in the preclinical stages of AD and may provide evidence implicating the degeneration of WM in the earliest stages of AD9,10. This provides a strong rationale for developing biomarkers to detect WM changes in the pathogenesis of AD.


Supported in part by NIH grant R01-AG027852 and The Litwin foundation.


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Figure 1A. AFQ-derived tracts namely the parahippocampal white matter (green) and arcuate fasciculus (purple). AFQ uses tractography to identify tracts within each individual and then quantifies the diffusion properties of equidistant nodes along these tracts. Hippocampal atrophy was measured using NeuroQuant (i.e. z-score ≤ -1).

Figure 1B: Results of AFQ analyses of the parahippocampal WM (top row) and arcuate fasciculus (bottom row) tracts, where the y-axes represent the diffusion (MD,FA) or WMTI (AWF, De,^) parameters and the x-axes indicate the tract nodes.

Proc. Intl. Soc. Mag. Reson. Med. 25 (2017)