Subjective Cognitive Decline and Nighttime Sleep Alterations, a Longitudinal Analysis.

Objective: The aim of this study was to analyze quantitative sleep changes and their implication on subjective cognitive decline (SCD). Objective sleep patterns were investigated by an actigraph and recorded at the baseline and 2-year after in order to examine specific sleep alterations in SCD. Background: Sleep disorders are very common among average elderly adults and an altered sleep pattern is known to be a risk factor for future development of mild cognitive impairment (MCI) and dementia. Recent studies have shown how sleep is objectively altered in average senior adults with SCD, without any other significant change in cognition and behavior or brain structure. Considering that both SCD and disrupted sleep are risk factors for future MCI and dementia, with sleep only as a modifiable risk factor, further research is required to deeply investigate the interaction between sleep and SCD. Methods: Among 70 community-dwelling elderly individuals who had been enrolled at baseline, 35 (64.6 ± 5.6 years, 15 M/20 F) underwent a complete neuropsychological battery and 1-week wrist actigraphy recording 2 years later during the follow-up stage. Individuals were divided into two groups according to their SCD Questionnaire (SCD-Q) score. Sleep hours, sleep efficiency and onset latency, napping and time awake after sleep onset (WASO) were collected. All individuals underwent structural magnetic resonance imaging (MRI) examination to exclude brain disorders. Data collection was performed at baseline and after 2 years at the follow-up phase. Results: A significantly different night sleep time between the two groups was observed: SCD showed a lower total sleep time (TST) than non-SCD subjects. Moreover, a total time spent in bed (TIB) was significantly lower in SCD subjects over 2 years of observation. Conclusions: Objective changes over time of the sleep pattern, specifically TIB and TST, are present in SCD individuals. The results of the study show that sleep alterations are common in SCD and underline the clinical importance of screening in order to assess sleep alterations as well as improve sleep in average adults with SCD complaints.

Combined 3 Tesla MRI Biomarkers Improve the Differentiation between Benign vs Malignant Single Ring Enhancing Brain Masses.

PURPOSE: To evaluate whether the combination of imaging biomarkers obtained by means of different 3 Tesla (3T) Magnetic Resonance Imaging (MRI) advanced techniques can improve the diagnostic accuracy in the differentiation between benign and malignant single ring-enhancing brain masses. MATERIALS AND METHODS: 14 patients presenting at conventional 3T MRI single brain mass with similar appearance as regard ring enhancement, presence of peri-lesional edema and absence of hemorrhage signs were included in the study. All lesions were histologically proven: 5 pyogenic abscesses, 6 glioblastomas, and 3 metastases. MRI was performed at 3 Tesla and included Diffusion Weighted Imaging (DWI), Dynamic Susceptibility Contrast -Perfusion Weighted Imaging (DSC-PWI), Magnetic Resonance Spectroscopy (MRS), and Diffusion Tensor Imaging (DTI). Imaging biomarkers derived by those advanced techniques [Cerebral Blood Flow (CBF), relative Cerebral Blood Volume (rCBV), relative Main Transit Time (rMTT), Choline (Cho), Creatine (Cr), Succinate, N-Acetyl Aspartate (NAA), Lactate (Lac), Lipids, relative Apparent Diffusion Coefficient (rADC), and Fractional Anisotropy (FA)] were detected by two experienced neuroradiologists in joint session in 4 areas: Internal Cavity (IC), Ring Enhancement (RE), Peri-Lesional edema (PL), and Contralateral Normal Appearing White Matter (CNAWM). Significant differences between benign (n = 5) and malignant (n = 9) ring enhancing lesions were tested with Mann-Withney U test. The diagnostic accuracy of MRI biomarkers taken alone and MRI biomarkers ratios were tested with Receiver Operating Characteristic (ROC) analysis with an Area Under the Curve (AUC) ≥ 0.9 indicating a very good diagnostic accuracy of the variable. RESULTS: Five MRI biomarker ratios achieved excellent accuracy: IC-rADC/PL-NAA (AUC = 1), IC-rADC/IC-FA (AUC = 0.978), RE-rCBV/RE-FA (AUC = 0.933), IC-rADC/RE-FA (AUC = 0.911), and IC-rADC/PL-FA (AUC = 0.911). Only IC-rADC achieved a very good diagnostic accuracy (AUC = 0.909) among MRI biomarkers taken alone. CONCLUSION: Although the major limitation of the study was the small sample size, preliminary results seem to suggest that combination of multiple 3T MRI biomarkers is a feasible approach to MRI biomarkers in order to improve diagnostic accuracy in the differentiation between benign and malignant single ring enhancing brain masses. Further studies in larger cohorts are needed to reach definitive conclusions.

Data-driven grading of brain gliomas: a multiparametric MR imaging study.

PURPOSE: To grade brain gliomas by using a data-driven analysis of multiparametric magnetic resonance (MR) imaging, taking into account the heterogeneity of the lesions at MR imaging, and to compare these results with the most widespread current radiologic reporting methods. MATERIALS AND METHODS: One hundred eighteen patients with histologically confirmed brain gliomas were evaluated retrospectively. Conventional and advanced MR sequences (perfusion-weighted imaging, MR spectroscopy, and diffusion-tensor imaging) were performed. Three evaluations were conducted: semiquantitative (based on conventional and advanced sequences with reported cutoffs), qualitative (exclusively based on conventional MR imaging), and quantitative. For quantitative analysis, four volumes of interest were placed: regions with contrast material enhancement, regions with highest and lowest signal intensity on T2-weighted images, and regions of most restricted diffusivity. Statistical analysis included t test, receiver operating characteristic (ROC) analysis, discriminant function analysis (DFA), leave-one-out cross-validation, and Kendall coefficient of concordance. RESULTS: Significant differences were noted in age, relative cerebral blood volume (rCBV) in contrast-enhanced regions (cutoff > 2.59; sensitivity, 80%; specificity, 91%; area under the ROC curve [AUC] = 0.937; P = .0001), areas of lowest signal intensity on T2-weighted images (>2.45, 57%, 97%, 0.852, and P = .0001, respectively), restricted diffusivity regions (>2.61, 54%, 97%, 0.808, and P = .0001, respectively), and choline/creatine ratio in regions with the lowest signal intensity on T2-weighted images (>2.07, 49%, 88%, 0.685, and P = .0007, respectively). DFA that included age; rCBV in contrast-enhanced regions, areas of lowest signal intensity on T2-weighted images, and areas of restricted diffusivity; and choline/creatine ratio in areas with lowest signal intensity on T2-weighted images was used to classify 95% of patients correctly. Quantitative analysis showed a higher concordance with histologic findings than qualitative and semiquantitative methods (P < .0001). CONCLUSION: A quantitative multiparametric MR imaging evaluation that incorporated heterogeneity at MR imaging significantly improved discrimination between low- and high-grade brain gliomas with a very high AUC (ie, 0.95), thus reducing the risk of inappropriate or delayed surgery, respectively.