In Vivo Detection of Changes Related to Cortical Columnar Organization and Neuroinflammation Across the AD Continuum.

BACKGROUND: Alzheimer's disease (AD) neuropathology reveals progressive microstructural alterations of cortical architecture. Recent studies reported intriguing biphasic trajectories of cortical structural changes in the early stages of Alzheimer's disease (AD), comprising decreased mean diffusivity (MD) and increased cortical thickness in cognitively normal amyloid-positive individuals, ahead of increases and decreases, respectively, in subsequent disease stages. OBJECTIVE: To better understand the cytoarchitectural correlates of these observations, we assessed novel cortical diffusion tensor imaging (DTI) metrics that are correlated with disruption of cortical minicolumns and protein deposition. DESIGN: Cross-sectional and longitudinal analysis of whole brain and temporal lobe cortical diffusivity measures. Investigation of associations between baseline cortical diffusivity values and 24-month longitudinal structural-MRI changes. Investigations of the relationships between cortical diffusivity measures and biomarkers of neuroinflammation. SETTING: Alzheimer's Disease Neuroimaging Initiative (ADNI). PARTICIPANTS: Twenty-four amyloid-negative controls (CN-), 28 amyloid-positive controls (CN+), 46 amyloid-positive subjects with mild cognitive impairment (MCI+) and 22 amyloid-positive subjects with AD were included. MEASUREMENTS: 3DT1 and DTI scans at baseline and approximately 24-month follow-up were used to calculate cortical MD and three novel cortical diffusivity measures: the angle between the radial minicolumnar axis and the principal diffusion direction (AngleR); the diffusion components perpendicular to the minicolumns (PerpPD+), and the principal diffusion component parallel with the minicolumns (ParlPD). Cortical macrostructural measurements (cortical volume fraction and cortical thickness), were used to test the hypothesis that baseline cortical diffusivity values can predict change in structural MRI outcomes over approximately 24 months. CSF soluble TREM2 and progranulin (PGRN) concentrations were used to investigate associations with microglial activity and potentially other aspects of neuroinflammation. RESULTS: Cortical diffusivity metrics revealed a dependence on disease stage, with AngleR and PerpPD+ displaying biphasic relationships and ParlPD a monotonic relationship with clinical severity. The novel metrics were able to differentiate between Amyloid+ and Amyloid- controls (AngleR) and to differentiate among disease stages along the AD continuum (PerpPD+). Linear regression revealed significant associations between baseline cortical diffusivity values and subsequent 24-month longitudinal structural-MRI changes. AngleR values were significantly associated with CSF sTREM2 and PGRN concentrations. CONCLUSIONS: Cortical diffusivity parameters reflecting minicolumnar organization and neuroinflammation may provide a sensitive and biologically interpretable measurement of cortex quality and microstructure across the AD continuum.

Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes.

Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main subtypes: behavioural-variant FTD, non-fluent/agrammatic variant primary progressive aphasia and semantic variant PPA. To better understand the FTD subtypes and develop more specific treatments, correct diagnosis is essential. This study aimed to test the discrimination power of a novel set of cortical Diffusion Tensor Imaging measures (DTI), on FTD subtypes. A total of 96 subjects with FTD and 84 healthy subjects (HS) were included in the study. A "selection cohort" was used to determine the set of features (measurements) and to use them to select the "best" machine learning classifier from a range of seven main models. The selected classifier was trained on a "training cohort" and tested on a third cohort ("test cohort"). The classifier was used to assess the classification power for binary (HS vs. FTD), and multiclass (HS and FTD subtypes) classification problems. In the binary classification, one of the new DTI features obtained the highest accuracy (85%) as a single feature, and when it was combined with other DTI features and two other common clinical measures (grey matter fraction and MMSE), obtained an accuracy of 88%. The new DTI features can distinguish between HS and FTD subgroups with an accuracy of 76%. These results suggest that DTI measures could support differential diagnosis in a clinical setting, potentially improve efficacy of new innovative drug treatments through effective patient selection, stratification and measurement of outcomes.