Original contributionNon-Gaussian diffusion MRI assessment of brain microstructure in mild cognitive impairment and Alzheimer’s disease☆
Introduction
Diffusion MRI imaging (dMRI) plays an important role in brain aging and dementia research due to its sensitivity to tissue microstructure. In mild cognitive impairment (MCI) and Alzheimer’s disease (AD), diffusion tensor imaging (DTI) has become an important tool in the study of white matter (WM) alterations associated with disease status and progression. To date, most studies have focused on the diffusion metrics of fractional anisotropy (FA) and mean diffusivity (MD), but several recent studies have also employed measurements of axial and radial diffusivity, showing that these parameters may relate more closely to the underlying pathology and as such may be markers of disease progression [1], [2], [3], [4], [5], [6], [7], [8], [9], [10]. Indeed, a recent meta-analysis [9] of case-controlled studies of DTI demonstrated an overall decrease of FA and increase of MD in AD and MCI groups compared with controls. Despite the large heterogeneity in the anatomy of the regions considered, these studies support the idea that dMRI is a potentially sensitive neuroimaging technique for the detection of WM changes in AD.
Even with these advances in characterizing WM changes with dMRI in normal aging, MCI and AD, it is well recognized that DTI yields only a fraction of the information potentially accessible by dMRI. This is mainly due to the fact that DTI is unable to quantify non-Gaussian diffusion [11]. In the brain, non-Gaussian diffusion is known to be substantial [12] and is believed to arise from diffusion barriers, such as cell membranes and organelles, as well as water-containing compartments (both extracellular and intracellular) with differing diffusion properties. Therefore, exploring non-Gaussian diffusion effects in AD could potentially provide a better understanding of microstructural tissue changes associated with disease pathology thereby improving our ability to evaluate disease progression.
Diffusional kurtosis imaging (DKI) is a minimal extension of DTI that enables the precise quantification of the diffusional kurtosis, a measure of diffusional non-Gaussianity, which naturally leads to metrics related to tissue microstructural complexity [13], [14], [15], [16]. Aside from providing all of the diffusion indices conventionally obtained with DTI, DKI also provides the non-Gaussian metrics of mean kurtosis (MK) and axial (KII) and radial (K⊥) kurtosis. These additional metrics can further help in our understanding of tissue microstructure. In addition, it has been shown that the extra information provided by DKI can be used to resolve intravoxel fiber crossings [[13], [14], [15], [16]], which is not possible with DTI. Although a relatively new method, DKI is already yielding promising preliminary results for several brain diseases including stroke [17], [18], [19], brain cancer [20], [21], attention-deficit hyperactivity disorder [22], traumatic brain injury [23], [24], Huntington’s disease [25], epilepsy [26], as well as normal aging [27].
This is the first study to investigate brain tissue microstructural integrity using DKI in a cross-sectional investigation of both MCI and AD patients and in cognitively intact elderly controls. Our hypothesis is that the kurtosis metrics may contribute additional information about brain tissue microstructure beyond that provided by conventional DTI parameters (i.e., FA and MD) and that this additional information may ultimately improve the characterization of the brain tissue microstructural changes occurring prior to the onset of clinically relevant cognitive deficits and cerebral atrophy.
Section snippets
Subjects
The protocol was approved by the Institutional Review Board of New York University (NYU) School of Medicine and all subjects gave written informed consent before participating in the study. All subjects were recruited from the Clinical Core of the NYU Alzheimer’s disease Center. The control group (n = 16) fulfilled the following criteria: a) no evidence for dementia or MCI and b) a Global Deterioration Scale (GDS) = 1–2 [28]. The MCI group (n = 13) were defined as: a) mild memory impairment reported
Results
As shown in Table 1, there were no significant differences in age, gender or education level between all three patient groups. As expected, there were significant differences in GDS and MMSE scores for MCI and AD groups compared with control group. There was also a significant volume reduction in the hippocampus of the AD patients.
Fig. 2 shows the means (+/− SD) of the diffusivity metrics for the three groups (control, MCI and AD) for each ROI; it also indicates the indices that were found to
Discussion
Several studies have demonstrated regional increased rates of cerebral atrophy several years before elderly people reach the stage known as MCI [40], [41]. While these observations are consistent with the presence of prodromal AD, the mechanism(s) responsible are still poorly understood. More important, the differentiation of normal elderly from patients with MCI appears to be the most challenging task. Very little has been published about the diagnostic utility of dMRI in distinguishing
Limitations
Some limitations of the present study should be noted. First, replication of the DKI method in a larger sample is needed. Second, this work was performed using an early version of our DKI protocol that did not cover the whole brain. Finally, due to our limited sample size, WM hyperintensities were not included as a covariant in our analysis.
Conclusion
In summary, the present study demonstrates, for the first time, the ability to characterize tissue microstructural changes in MCI and AD patients based upon the assessment of non-Gaussian brain water diffusion, and it suggests that kurtosis parameters are useful additions to other diffusion measurements that may help to establish reliable biomarkers for AD diagnosis and progression. The possibility of assessing the clinical status of subjects at a single point in time represents a promising
Acknowledgments
The authors wish to acknowledge and thank Drs. Henry Rusinek, James B. Golomb and Stephen D. Ginsberg for their helpful discussions and input, Robyn Waters, Jane Kwon and Edgar Suan for patient coordination and Amanda Allen for technical assistance.
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This work was supported in part by research grants from the Werner Dannheisser Trust (JAH), the Litwin Fund for Alzheimer’s Research (JAH), Institute for the Study of Aging (JAH), NIH Grant 1R01AG027852 (JAH) and NIH Grant P30 AG08051 (SHF).