Stephanie Barsoum

Despite the presence of significant Alzheimer's disease pathology, characterized by amyloid β (Aβ) plaques and phosphorylated tau (pTau) tangles, some cognitively unimpaired elderly individuals do not inevitably develop dementia. Cortical astroglial inflammation, a ubiquitous feature of symptomatic Alzheimer's disease, shows a strong correlation with cognitive impairment severity, highlighting the influence of factors beyond classical pathology. However, non-invasively imaging neuroinflammation, particularly astrogliosis, using MRI remains a significant challenge. Here we sought to address this challenge and to leverage multidimensional (MD) MRI, a powerful approach that combines relaxation with diffusion MR contrasts, to map cortical astrogliosis in the human brain by accessing sub-voxel information. Our goal was to investigate whether MD-MRI can map astroglial pathology in the cerebral cortex, and if so, whether it can distinguish cognitively normal state from dementia in the presence of hallmark Alzheimer's disease neuropathological changes. We adopted a multimodal approach by integrating histological and MRI analyses using human postmortem brain samples from two independent discovery and replication cohorts. Ex vivo cerebral cortical tissue specimens were derived from two groups—non-demented individuals with varying levels of postmortem Alzheimer's disease pathology and individuals with both Alzheimer's disease pathology and dementia—and scanned using 7 T MRI. We acquired and processed MD-MRI, diffusion tensor, and quantitative T1 and T2 MRI data, followed by histopathology on the same tissue. By co-registering MRI and microscopy data, we performed quantitative multimodal analyses, leveraging targeted immunostaining to assess MD-MRI sensitivity and specificity towards Aβ, pTau, and glial fibrillary acidic protein (GFAP), a marker for astrogliosis. Our discovery analysis reveals a distinct MD-MRI signature of cortical astrogliosis, enabling the creation of predictive maps for cognitive state amid Alzheimer's disease neuropathological changes. Multiple linear regression analysis linked histological values to MRI changes, revealing that the MD-MRI cortical astrogliosis biomarker was significantly associated with GFAP burden (standardized β = 0.658/0.709, pFDR < 0.0001), but not with Aβ (standardized β = 0.009/0.120, pFDR = 0.913/0.274) or pTau (standardized β = −0.196/0.158, pFDR = 0.051/0.251), for the discovery/replication groups, respectively. Conversely, none of the conventional MRI parameters showed significant associations with GFAP burden in the cortex. Finally, we showed that the MD-MRI-derived astrogliosis biomarker is the only MRI measure capable of predicting cognitive state. While the extent to which pathological glial activation contributes to neuronal damage and cognitive impairment in Alzheimer's disease is uncertain, developing a non-invasive imaging method to see its effects holds promise from a mechanistic perspective and as a potential predictor of cognitive outcomes.

Gray matter (GM) alterations play a role in aging‐related disorders like Alzheimer's disease and related dementias, yet MRI studies mainly focus on macroscopic changes. Although reliable indicators of atrophy, morphological metrics like cortical thickness lack the sensitivity to detect early changes preceding visible atrophy. Our study aimed at exploring the potential of diffusion MRI in unveiling sensitive markers of cortical and subcortical age‐related microstructural changes and assessing their associations with cognitive and behavioral deficits. We leveraged the Human Connectome Project‐Aging cohort that included 707 participants (394 female; median age = 58, range = 36–90 years) and applied the powerful mean apparent diffusion propagator model to measure microstructural parameters, along with comprehensive behavioral and cognitive test scores. Both macro‐ and microstructural GM characteristics were strongly associated with age, with widespread significant microstructural correlations reflective of cellular morphological changes, reduced cellular density, increased extracellular volume, and increased membrane permeability. Importantly, when correlating MRI and cognitive test scores, our findings revealed no link between macrostructural volumetric changes and neurobehavioral performance. However, we found that cellular and extracellular alterations in cortical and subcortical GM regions were associated with neurobehavioral performance. Based on these findings, it is hypothesized that increased microstructural heterogeneity and decreased neurite orientation dispersion precede macrostructural changes, and that they play an important role in subsequent cognitive decline. These alterations are suggested to be early markers of neurocognitive performance that may distinctly aid in identifying the mechanisms underlying phenotypic aging and subsequent age‐related functional decline.

Human evolution has seen the development of higher-order cognitive and social capabilities in conjunction with the unique laminar cytoarchitecture of the human cortex. Moreover, early-life cortical maldevelopment has been associated with various neurodevelopmental diseases. Despite these connections, there is currently no noninvasive technique available for imaging the detailed cortical laminar structure. This study aims to address this scientific and clinical gap by introducing an approach for imaging human cortical lamina. This method combines diffusion–relaxation multidimensional MRI with a tailored unsupervised machine learning approach that introduces enhanced microstructural sensitivity. This new imaging method simultaneously encodes the microstructure, the local chemical composition and importantly their correlation within complex and heterogenous tissue. To validate our approach, we compared the intra-cortical layers obtained using our ex vivo MRI-based method with those derived from Nissl staining of postmortem human brain specimens. The integration of unsupervised learning with diffusion–relaxation correlation MRI generated maps that demonstrate sensitivity to areal differences in cytoarchitectonic features observed in histology. Significantly, our observations revealed layer-specific diffusion–relaxation signatures, showing reductions in both relaxation times and diffusivities at the deeper cortical levels. These findings suggest a radial decrease in myelin content and changes in cell size and anisotropy, reflecting variations in both cytoarchitecture and myeloarchitecture. Additionally, we demonstrated that 1D relaxation and high-order diffusion MRI scalar indices, even when aggregated and used jointly in a multimodal fashion, cannot disentangle the cortical layers. Looking ahead, our technique holds the potential to open new avenues of research in human neurodevelopment and the vast array of disorders caused by disruptions in neurodevelopment.