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The present study aims to investigate the relationship between cerebellar volumes and cognitive reserve in individuals with Mild Cognitive Impairment (MCI). A description of proxies of cerebellar cognitive reserve in terms of different volumes across lobules is also provided. 36 individuals with MCI underwent neuropsychological (MoCA, MMSE, Clock test, CRIq) assessment and neuroimaging acquisition with magnetic resonance imaging at 3 T. Simple linear correlations were applied between cerebellar volumes and cognitive measures. Multiple linear regression models were then used to estimate standardized regression coefficients and 95% confidence intervals. Simple linear correlations between cerebellar lobules volumes and cognitive features highlighted a significant association between CRIq_Working activity and specific motor cerebellar volumes: Left_V (ρ = 0.40, p = 0.02), Right_V (r = 0.42, p = 0.002), Vermis_VIIIb (ρ = 0.47, p = 0.003), Left_X (ρ = -0.46, p = 0.002) and Vermis_X (r = 0.35, p = 0.03). Furthermore, CRIq_Working activity scores correlated with certain cerebellar lobules implicated in cognition: Left_Crus_II, Vermis VIIb, Left_IX. MMSE was associated only with the Right_VIIB volume (r = 0.35, p = 0.02), while Clock Drawing Test scores correlated with both Left_Crus_I and Right_Crus_I (r = -0.42 and r = 0.42, p = 0.02, respectively). This study suggests that a higher cognitive reserve is associated with specific cerebellar lobule volumes and that Working activity may play a predominant role in this association. These findings contribute to the understanding of the relationship between cerebellar volumes and cognitive reserve, highlighting the potential modulatory role of Working activity on cerebellum response to cognitive decline.
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BACKGROUND: The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome. MAIN BODY: This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF. CONCLUSION: Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.
RESUMO
BACKGROUND AND OBJECTIVE: Magnetic resonance imaging of the brain allows to enrich the study of the relationship between cortical morphology, healthy ageing, diseases and cognition. Since manual segmentation of the cerebral cortex is time consuming and subjective, many software packages have been developed. FreeSurfer (FS) and Advanced Normalization Tools (ANTs) are the most used and allow as inputs a T1-weighted (T1w) image or its combination with a T2-weighted (T2w) image. In this study we evaluated the impact of different software and input images on cortical estimates. Additionally, we investigated whether the variation of the results depending on software and inputs is also influenced by age. METHODS: For 240 healthy subjects, cortical thickness was computed with ANTs and FreeSurfer. Estimates were derived using both the T1w image and adding the T2w image. Significant effects due to software, input images and age range were investigated with ANOVA statistical analysis. Moreover, the accuracy of the cortical thickness estimates was assessed based on their age-prediction precision. RESULTS: Using FreeSurfer and ANTs with T1w or T1w-T2w images resulted in significant differences in the cortical thickness estimates. These differences change with the age range of the subjects. Regardless of the images used, the more recent FS version tested exhibited the best performances in terms of age prediction. CONCLUSIONS: Our study points out the importance of i) consistently processing data using the same tool; ii) considering the software, input images and the age range of the subjects when comparing multiple studies.
