Background: Neuroinflammation may contribute to psychiatric symptoms in older people, in particular in the context of Alzheimer’s disease (AD). We sought to identify systemic and central nervous system (CNS) inflammatory alterations associated with neuropsychiatric symptoms (NPS); and to investigate their relationships with AD pathology and clinical disease progression. Methods: We quantified a panel of 38 neuroinflammation and vascular injury markers in paired serum and cerebrospinal fluid (CSF) samples in a cohort of cognitively normal and impaired older subjects. We performed neuropsychiatric and cognitive evaluations and measured CSF biomarkers of AD pathology. Multivariate analysis determined serum and CSF neuroinflammatory alterations associated with NPS, considering cognitive status, AD pathology, and cognitive decline at follow-up visits. Results: NPS were associated with distinct inflammatory profiles in serum, involving eotaxin-3, interleukin (IL)-6 and C-reactive protein (CRP); and in CSF, including soluble intracellular cell adhesion molecule-1 (sICAM-1), IL-8, 10-kDa interferon-γ-induced protein, and CRP. AD pathology interacted with CSF sICAM-1 in association with NPS. Presenting NPS was associated with subsequent cognitive decline which was mediated by CSF sICAM-1. Conclusions: Distinct systemic and CNS inflammatory processes are involved in the pathophysiology of NPS in older people. Neuroinflammation may explain the link between NPS and more rapid clinical disease progression.

Purpose: We aimed at assessing the potential of automated MR morphometry to assess individual basal ganglia and thalamus volumetric changes at the chronic phase after cortical stroke. Methods: Ninety-six patients (mean age: 65 ± 18 years, male 55) with cortical stroke at the chronic phase were retrospectively included. Patients were scanned at 1.5 T or 3 T using a T1-MPRAGE sequence. Resulting 3D images were processed with the MorphoBox prototype software to automatically segment basal ganglia and thalamus structures, and to obtain Z scores considering the confounding effects of age and sex. Stroke volume was estimated by manual delineation on T2-SE imaging. Z scores were compared between ipsi- and contralateral stroke side and according to the vascular territory. Potential relationship between Z scores and stroke volume was assessed using the Spearman correlation coefficient. Results: Basal ganglia and thalamus volume Z scores were lower ipsilaterally to MCA territory stroke (p values < 0.034) while they were not different between ipsi- and contralateral stroke sides in non-MCA territory stroke (p values > 0.37). In MCA territory stroke, ipsilateral caudate nucleus (rho = − 0.34, p = 0.007), putamen (rho = − 0.50, p < 0.001), pallidum (rho = − 0.44, p < 0.001), and thalamus (rho = − 0.48, p < 0.001) volume Z scores negatively correlated with the cortical stroke volume. This relation was not influenced by cardiovascular risk factors or time since stroke. Conclusion: Automated MR morphometry demonstrated atrophy of ipsilateral basal ganglia and thalamus at the chronic phase after cortical stroke in the MCA territory. The atrophy was related to stroke volume. These results confirm the potential role for automated MRI morphometry to assess remote changes after stroke.

BACKGROUND AND PURPOSE: Automated volumetric analysis of structural MR imaging allows quantitative assessment of brain atrophy in neurodegenerative disorders. We compared the brain segmentation performance of the AI-Rad Companion brain MR imaging software against an in-house FreeSurfer 7.1.1/Individual Longitudinal Participant pipeline. MATERIALS AND METHODS: T1-weighted images of 45 participants with de novo memory symptoms were selected from the OASIS-4 database and analyzed through the AI-Rad Companion brain MR imaging tool and the FreeSurfer 7.1.1/Individual Longitudinal Participant pipeline. Correlation, agreement, and consistency between the 2 tools were compared among the absolute, normalized, and standardized volumes. Final reports generated by each tool were used to compare the rates of detection of abnormality and the compatibility of radiologic impressions made using each tool, compared with the clinical diagnoses. RESULTS: We observed strong correlation, moderate consistency, and poor agreement between absolute volumes of the main cortical lobes and subcortical structures measured by the AI-Rad Companion brain MR imaging tool compared with FreeSurfer. The strength of the correlations increased after normalizing the measurements to the total intracranial volume. Standardized measurements differed significantly between the 2 tools, likely owing to differences in the normative data sets used to calibrate each tool. When considering the FreeSurfer 7.1.1/Individual Longitudinal Participant pipeline as a reference standard, the AI-Rad Companion brain MR imaging tool had a specificity of 90.6%–100% and a sensitivity of 64.3%–100% in detecting volumetric abnormalities. There was no difference between the rate of compatibility of radiologic and clinical impressions when using the 2 tools. CONCLUSIONS: The AI-Rad Companion brain MR imaging tool reliably detects atrophy in cortical and subcortical regions implicated in the differential diagnosis of dementia.

Purpose We suggest a motion correction concept that employs free‐induction‐decay (FID) navigator signals to continuously monitor motion and to guide the acquisition of image navigators for prospective motion correction following motion detection. Methods Motion causes out‐of‐range signal changes in FID time series that, and in this approach, initiate the acquisition of an image navigator. Co‐registration of the image navigator to a reference provides rigid‐body‐motion parameters to facilitate prospective motion correction. Both FID and image navigator are integrated into a prototype magnetization‐prepared rapid gradient‐echo (MPRAGE) sequence. The performance of the method is investigated using image quality metrics and the consistency of brain volume measurements. Results Ten healthy subjects were scanned (a) while performing head movements (nodding, shaking, and moving in z‐direction) and (b) to assess the co‐registration performance. Mean absolute errors of 0.27 ± 0.38 mm and 0.19 ± 0.24° for translation and rotation parameters were measured. Image quality was qualitatively improved after correction. Significant improvements were observed in automated image quality measures and for most quantitative brain volume computations after correction. Conclusion The presented method provides high sensitivity to detect head motion while minimizing the time invested in acquiring navigator images. Limits of this implementation arise from temporal resolution to detect motion, false‐positive alarms, and registration accuracy. Magn Reson Med 78:193–203, 2017. © 2016 International Society for Magnetic Resonance in Medicine