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Background: Mild Cognitive Impairment (MCI) is a transitional stage from normal aging to dementia, characterized by noticeable changes in cognitive function that do not significantly impact daily life. Diffusion MRI (dMRI) plays a crucial role in understanding MCI by assessing white matter integrity and revealing early signs of axonal degeneration and myelin breakdown before cognitive symptoms appear., Methods: This study utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to compare white matter microstructure in individuals with MCI to cognitively normal (CN) individuals, employing advanced dMRI techniques such as diffusion kurtosis imaging (DKI), mean signal diffusion kurtosis imaging (MSDKI), and free water imaging (FWI)., Results: Analyzing data from 55 CN subjects and 46 individuals with MCI, this study found significant differences in white matter integrity, particularly in free water levels and kurtosis values, suggesting neuroinflammatory responses and microstructural integrity disruption in MCI. Moreover, negative correlations between Mini-Mental State Examination (MMSE) scores and free water levels in the brain within the MCI group point to the potential of these measures as early biomarkers for cognitive impairment., Conclusion: In conclusion, this study demonstrates how a multimodal advanced diffusion imaging approach can uncover early microstructural changes in MCI, offering insights into the neurobiological mechanisms behind cognitive decline., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Nelson, Keeling, Stokes and Bergamino.)

Introduction: Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia., Methods: In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined., Results: Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions., Discussion: The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Bergamino, Burke, Sabbagh, Caselli, Baxter and Stokes.)

Purpose: The lack of validated surrogate biomarkers is still an unmet clinical need in the management of early breast cancer cases that do not achieve complete pathological response after neoadjuvant chemotherapy (NACT). Here, we describe and validate the use of SAMHD1 expression as a prognostic biomarker in residual disease in vivo and in vitro., Methods: SAMHD1 expression was evaluated in a clinical cohort of early breast cancer patients with stage II-III treated with NACT. Heterotypic 3D cultures including tumor and immune cells were used to investigate the molecular mechanisms responsible of SAMHD1 depletion through whole transcriptomic profiling, immune infiltration capacity and subsequent delineation of dysregulated immune signaling pathways., Results: SAMHD1 expression was associated to increased risk of recurrence and higher Ki67 levels in post-NACT tumor biopsies of breast cancer patients with residual disease. Survival analysis showed that SAMHD1-expressing tumors presented shorter time-to-progression and overall survival than SAMHD1 negative cases, suggesting that SAMHD1 expression is a relevant prognostic factor in breast cancer. Whole-transcriptomic profiling of SAMHD1-depleted tumors identified downregulation of IL-12 signaling pathway as the molecular mechanism determining breast cancer prognosis. The reduced interleukin signaling upon SAMHD1 depletion induced changes in immune cell infiltration capacity in 3D heterotypic in vitro culture models, confirming the role of the SAMHD1 as a regulator of breast cancer prognosis through the induction of changes in immune response and tumor microenvironment., Conclusion: SAMHD1 expression is a novel prognostic biomarker in early breast cancer that impacts immune-mediated signaling and differentially regulates inflammatory intra-tumoral response., (© 2023. The Author(s).)

Background: Dementia is characterized by a cognitive decline in memory and other domains that lead to functional impairments. As people age, subjective memory complaints (SMC) become common, where individuals perceive cognitive decline without objective deficits on assessments. SMC can be an early sign and may precede amnestic mild cognitive impairment (MCI), which frequently advances to Alzheimer's disease (AD)., Objective: This study aims to investigate white matter microstructure in individuals with SMC, in cognitively impaired (CI) cohorts, and in cognitively normal individuals using diffusion kurtosis imaging (DKI) and free water imaging (FWI). The study also explores voxel-based correlations between DKI/FWI metrics and cognitive scores to understand the relationship between brain microstructure and cognitive function., Methods: Twelve healthy controls (HCs), ten individuals with SMC, and eleven CI individuals (MCI or AD) were enrolled in this study. All participants underwent MRI 3T scan and the BNI Screen (BNIS) for Higher Cerebral Functions., Results: The mean kurtosis tensor and anisotropy of the kurtosis tensor showed significant differences across the three groups, indicating altered white matter microstructure in CI and SMC individuals. The free water volume fraction (f) also revealed group differences, suggesting changes in extracellular water content. Notably, these metrics effectively discriminated between the CI and HC/SMC groups. Additionally, correlations between imaging metrics and BNIS scores were found for CI and SMC groups., Conclusions: These imaging metrics hold promise in discriminating between individuals with CI and SMC. The observed differences indicate their potential as sensitive and specific biomarkers for early detection and differentiation of cognitive decline.