Informant-based tools for assessment and monitoring of cognition, behavior, and function in neurocognitive disorders: Systematic review and report from a CCCDTD5 Working Group.

OBJECTIVE: As part of the fifth Canadian Consensus Conference on the Diagnosis and Treatment of Dementia, we assessed the literature on informant-based tools for assessment and monitoring of cognition, behavior, and function in neurocognitive disorders (NCDs) to provide evidence-based recommendations for clinicians and researchers. METHODS: A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses standards guidelines. Publications that validated the informant-based tools or described their key properties were reviewed. Quality of the studies was assessed using the modified Quality Assessment tool for Diagnostic Accuracy Studies. RESULTS: Out of 386 publications identified through systematic search, 34 that described 19 informant-based tools were included in the final review. Most of these tools are backed by good-quality studies and are appropriate to use in clinical care or research. The tools vary in their psychometric properties, domains covered, comprehensiveness, completion time, and ability to detect longitudinal change. Based on these properties, we identify different tools that may be appropriate for primary care, specialized memory clinic, or research settings. We also identify barriers to use of these tools in routine clinical practice. CONCLUSION: There are several good-quality tools available to collect informant-report for assessment and monitoring of cognition, behavior, or function in patients with NCDs. Clinicians and researchers may choose a particular tool based on their specific needs such as domains of interest, desired psychometric properties, and feasibility. Further work is needed to make the tools more user-friendly and to adopt them into routine clinical care.

Cerebral vasomotor reactivity in reversible cerebral vasoconstriction syndrome.

Background Altered cerebrovascular tone is implicated in reversible cerebral vasoconstriction syndrome (RCVS). We evaluated vasomotor reactivity using bedside transcranial Doppler in RCVS patients. Methods In this retrospective case-control study, middle cerebral artery (MCA) blood flow velocities were compared at rest and in response to breath-hold in RCVS ( n = 8), Migraineurs ( n = 10), and non-headache Controls ( n = 10). Hyperventilation response was measured in RCVS. Results In RCVS, Breath Holding Index (BHI) was severely reduced in seven of eight patients and 14/16 MCAs; seven of 16 MCAs showed exhausted (BHI < 0.1) or inverted (BHI < 0) vasomotor reactivity. Mean BHI in RCVS (0.23 ± 0.5) was significantly lower than Migraine (1.52 ± 0.57) and Controls (1.51 ± 0.32), p < 0.001. Triphasic velocity responses were seen in all groups. The maximum Vmean decline during the middle negative phase was -15.5 ± 9.2% in RCVS, -15.4 ± 7% in Migraine, and -10.3 ± 5% in Controls ( p = 0.04). In the late positive phase, average Vmean increase was 6.2 ± 14% in RCVS, which was significantly lower ( p < 0.001) than Migraine (30.5 ± 11%) and Controls (30.2 ± 6%). With hyperventilation, RCVS patients showed 23% decrease in Vmean. Conclusion Cerebral arterial tone is abnormal in RCVS, with proximal vasoconstriction and abnormally reduced capacity for vasodilation. Further studies are needed to determine the utility of BHI to diagnose RCVS before angiographic reversibility is established, and to estimate prognosis.

Automated segmentation and quantification of white matter hyperintensities in acute ischemic stroke patients with cerebral infarction.

White matter hyperintensities (WMHs) of presumed vascular origin are common in ageing population, especially in patients with acute cerebral infarction and the volume has been reported to be associated with mental impairment and the risk of hemorrhage from antithrombotic agents. WMHs delineation can be computerized to minimize human bias. However, the presence of cerebral infarcts greatly degrades the accuracy of WMHs detection and thus limits the application of computerized delineation to patients with acute cerebral infarction. We propose a computer-assisted segmentation method to depict WMHs in the presence of cerebral infarcts in combined T1-weighted, fluid attenuation inversion recovery, and diffusion-weighted magnetic resonance imaging (MRI). The proposed method detects WMHs by empirical threshold and atlas information, with subtraction of white matter voxels affected by acute infarction. The method was derived using MRI from 25 hemispheres with WMHs only and 13 hemispheres with both WMHs and cerebral infarcts. Similarity index (SI) and correlation were utilized to assess the agreement between the new automated method and a gold standard visually guided semi-automated method done by an expert rater. The proposed WMHs segmentation approach produced average SI, sensitivity and specificity of 83.142±11.742, 84.154±16.086 and 99.988±0.029% with WMHs only and of 68.826±14.036, 74.381±18.473 and 99.956±0.054% with both WMHs and cerebral infarcts in the derivation cohort. The performance of the proposed method with an external validation cohort was also highly consistent with that of the experienced rater.

Automatic detection and quantification of acute cerebral infarct by fuzzy clustering and histographic characterization on diffusion weighted MR imaging and apparent diffusion coefficient map.

Determination of the volumes of acute cerebral infarct in the magnetic resonance imaging harbors prognostic values. However, semiautomatic method of segmentation is time-consuming and with high interrater variability. Using diffusion weighted imaging and apparent diffusion coefficient map from patients with acute infarction in 10 days, we aimed to develop a fully automatic algorithm to measure infarct volume. It includes an unsupervised classification with fuzzy C-means clustering determination of the histographic distribution, defining self-adjusted intensity thresholds. The proposed method attained high agreement with the semiautomatic method, with similarity index 89.9 ± 6.5%, in detecting cerebral infarct lesions from 22 acute stroke patients. We demonstrated the accuracy of the proposed computer-assisted prompt segmentation method, which appeared promising to replace the laborious, time-consuming, and operator-dependent semiautomatic segmentation.