Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease.

Introduction: Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods: Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results: Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion: We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.

Age-Related Brain Atrophy and the Positive Effects of Behavioral Enrichment in Middle-Aged Beagles.

Aging dogs serve as a valuable preclinical model for Alzheimer's disease (AD) due to their natural age-related development of ß-amyloid (Aß) plaques, human-like metabolism, and large brains that are ideal for studying structural brain aging trajectories from serial neuroimaging. Here we examined the effects of chronic treatment with the calcineurin inhibitor (CNI) tacrolimus or the nuclear factor of activated T cells (NFAT)-inhibiting compound Q134R on age-related canine brain atrophy from a longitudinal study in middle-aged beagles (36 females, 7 males) undergoing behavioral enrichment. Annual MRI was analyzed using modern, automated techniques for region-of-interest-based and voxel-based volumetric assessments. We found that the frontal lobe showed accelerated atrophy with age, while the caudate nucleus remained relatively stable. Remarkably, the hippocampus increased in volume in all dogs. None of these changes were influenced by tacrolimus or Q134R treatment. Our results suggest that behavioral enrichment can prevent atrophy and increase the volume of the hippocampus but does not prevent aging-associated prefrontal cortex atrophy.

A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging.

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme.

Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.

Development of white matter fiber covariance networks supports executive function in youth.

During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging.

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Using Advanced Diffusion-Weighted Imaging to Predict Cell Counts in Gray Matter: Potential and Pitfalls.

Recent advances in diffusion imaging have given it the potential to non-invasively detect explicit neurobiological properties, beyond what was previously possible with conventional structural imaging. However, there is very little known about what cytoarchitectural properties these metrics, especially those derived from newer multi-shell models like Neurite Orientation Dispersion and Density Imaging (NODDI) correspond to. While these diffusion metrics do not promise any inherent cell type specificity, different brain cells have varying morphologies, which could influence the diffusion signal in distinct ways. This relationship is currently not well-characterized. Understanding the possible cytoarchitectural signatures of diffusion measures could allow them to estimate important neurobiological properties like cell counts, potentially resulting in a powerful clinical diagnostic tool. Here, using advanced diffusion imaging (NODDI) in the mouse brain, we demonstrate that different regions have unique relationships between cell counts and diffusion metrics. We take advantage of this exclusivity to introduce a framework to predict cell counts of different types of cells from the diffusion metrics alone, in a region-specific manner. We also outline the challenges of reliably developing such a model and discuss the precautions the field must take when trying to tie together medical imaging modalities and histology.

Higher-order multi-shell diffusion measures complement tensor metrics and volume in gray matter when predicting age and cognition.

Recent advances in diffusion-weighted imaging have enabled us to probe the microstructure of even gray matter non-invasively. However, these advanced multi-shell protocols are often not included in large-scale studies as they significantly increase scan time. In this study, we investigated whether one set of multi-shell diffusion metrics commonly used in gray matter (as derived from Neurite Orientation Dispersion and Density Imaging, NODDI) provide enough additional information over typical tensor and volume metrics to justify the increased acquisition time, using the cognitive aging framework in the human hippocampus as a testbed. We first demonstrated that NODDI metrics are robust and reliable by replicating previous findings from our lab in a larger population of 79 younger (20.41 ± 1.89 years, 46 females) and 75 older (73.56 ± 6.26 years, 45 females) adults, showing that these metrics in the hippocampal subfields are sensitive to age and memory performance. We then asked how these subfield specific hippocampal NODDI metrics compared with standard tensor metrics and volume in predicting age and memory ability. We discovered that both NODDI and tensor measures separately predicted age and cognition in comparable capacities. However, integrating these modalities together considerably increased the predictive power of our logistic models, indicating that NODDI and tensor measures may be capturing independent microstructural information. We use these findings to encourage neuroimaging data collection consortiums to include a multi-shell diffusion sequence in their protocols since existing NODDI measures (and potential future multi-shell measures) may be able to capture microstructural variance that is missed by traditional approaches, even in studies exclusively examining gray matter.

Tacrolimus Protects against Age-Associated Microstructural Changes in the Beagle Brain.

The overexpression of calcineurin leads to astrocyte hyperactivation, neuronal death, and inflammation, which are characteristics often associated with pathologic aging and Alzheimer's disease. In this study, we tested the hypothesis that tacrolimus, a calcineurin inhibitor, prevents age-associated microstructural atrophy, which we measured using higher-order diffusion MRI, in the middle-aged beagle brain (n = 30, male and female). We find that tacrolimus reduces hippocampal (p = 0.001) and parahippocampal (p = 0.002) neurite density index, as well as protects against an age-associated increase in the parahippocampal (p = 0.007) orientation dispersion index. Tacrolimus also protects against an age-related decrease in fractional anisotropy in the prefrontal cortex (p < 0.0001). We also show that these microstructural alterations precede cognitive decline and gross atrophy. These results support the idea that calcineurin inhibitors may have the potential to prevent aging-related pathology if administered at middle age.SIGNIFICANCE STATEMENT Hyperactive calcineurin signaling causes neuroinflammation and other neurobiological changes often associated with pathologic aging and Alzheimer's disease (AD). Controlling the expression of calcineurin before gross cognitive deficits are observable might serve as a promising avenue for preventing AD pathology. In this study, we show that the administration of the calcineurin inhibitor, tacrolimus, over 1 year prevents age- and AD-associated microstructural changes in the hippocampus, parahippocampal cortex, and prefrontal cortex of the middle-aged beagle brain, with no noticeable adverse effects. Tacrolimus is already approved by the Food and Drug Administration for use in humans to prevent solid organ transplant rejection, and our results bolster the promise of this drug to prevent AD and aging-related pathology.

Microstructural Alterations in Hippocampal Subfields Mediate Age-Related Memory Decline in Humans.

Aging, even in the absence of clear pathology of dementia, is associated with cognitive decline. Neuroimaging, especially diffusion-weighted imaging, has been highly valuable in understanding some of these changes in live humans, non-invasively. Traditional tensor techniques have revealed that the integrity of the fornix and other white matter tracts significantly deteriorates with age, and that this deterioration is highly correlated with worsening cognitive performance. However, traditional tensor techniques are still not specific enough to indict explicit microstructural features that may be responsible for age-related cognitive decline and cannot be used to effectively study gray matter properties. Here, we sought to determine whether recent advances in diffusion-weighted imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI) and Constrained Spherical Deconvolution, would provide more sensitive measures of age-related changes in the microstructure of the medial temporal lobe. We evaluated these measures in a group of young (ages 20-38 years old) and older (ages 59-84 years old) adults and assessed their relationships with performance on tests of cognition. We found that the fiber density (FD) of the fornix and the neurite density index (NDI) of the fornix, hippocampal subfields (DG/CA3, CA1, and subiculum), and parahippocampal cortex, varied as a function of age in a cross-sectional cohort. Moreover, in the fornix, DG/CA3, and CA1, these changes correlated with memory performance on the Rey Auditory Verbal Learning Test (RAVLT), even after regressing out the effect of age, suggesting that they were capturing neurobiological properties directly related to performance in this task. These measures provide more details regarding age-related neurobiological properties. For example, a change in fiber density could mean a reduction in axonal packing density or myelination, and the increase in NDI observed might be explained by changes in dendritic complexity or even sprouting. These results provide a far more comprehensive view than previously determined on the possible system-wide processes that may be occurring because of healthy aging and demonstrate that advanced diffusion-weighted imaging is evolving into a powerful tool to study more than just white matter properties.