Gallagher, RL; Koscik, RL; Moody, JF; Vogt, NM; Adluru, N; Kecskemeti, SR; Van Hulle, CA; ... Bendlin, BB; + view all Gallagher, RL; Koscik, RL; Moody, JF; Vogt, NM; Adluru, N; Kecskemeti, SR; Van Hulle, CA; Chin, NA; Asthana, S; Kollmorgen, G; Suridjan, I; Carlsson, CM; Johnson, SC; Dean, DC; Zetterberg, H; Blennow, K; Alexander, AL; Bendlin, BB; - view fewer (2023) Neuroimaging of tissue microstructure as a marker of neurodegeneration in the AT(N) framework: defining abnormal neurodegeneration and improving prediction of clinical status. Alzheimer's Research and Therapy , 15 (1) , Article 180. 10.1186/s13195-023-01281-y.

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Abstract

Background: Alzheimer’s disease involves accumulating amyloid (A) and tau (T) pathology, and progressive neurodegeneration (N), leading to the development of the AD clinical syndrome. While several markers of N have been proposed, efforts to define normal vs. abnormal neurodegeneration based on neuroimaging have been limited. Sensitive markers that may account for or predict cognitive dysfunction for individuals in early disease stages are critical. Methods: Participants (n = 296) defined on A and T status and spanning the AD-clinical continuum underwent multi-shell diffusion-weighted magnetic resonance imaging to generate Neurite Orientation Dispersion and Density Imaging (NODDI) metrics, which were tested as markers of N. To better define N, we developed age- and sex-adjusted robust z-score values to quantify normal and AD-associated (abnormal) neurodegeneration in both cortical gray matter and subcortical white matter regions of interest. We used general logistic regression with receiver operating characteristic (ROC) and area under the curve (AUC) analysis to test whether NODDI metrics improved diagnostic accuracy compared to models that only relied on cerebrospinal fluid (CSF) A and T status (alone and in combination). Results: Using internal robust norms, we found that NODDI metrics correlate with worsening cognitive status and that NODDI captures early, AD neurodegenerative pathology in the gray matter of cognitively unimpaired, but A/T biomarker-positive, individuals. NODDI metrics utilized together with A and T status improved diagnostic prediction accuracy of AD clinical status, compared with models using CSF A and T status alone. Conclusion: Using a robust norms approach, we show that abnormal AD-related neurodegeneration can be detected among cognitively unimpaired individuals. Metrics derived from diffusion-weighted imaging are potential sensitive markers of N and could be considered for trial enrichment and as outcomes in clinical trials. However, given the small sample sizes, the exploratory nature of the work must be acknowledged.

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