Neuropsychological and morphometric biomarkers of poor prognosis in patients with mild cognitive impairment

Despite a high prevalence of mild cognitive impairment (MCI), there are no accepted algorithms of diff erentiating the syndrome and the prognosis evaluation of later cognitive decline at this time. Objective. To identify biomarkers of poor prognosis in the various MCI types by optimizing neuropsychological examination in combination with MRI morphometry of brain structures. Patients and methods. We examined 45 patients (9 men, 36 women, mean age 72 ± 6.7 years) with MCI according to the modifi ed Petersen’s criteria and the DSM-5 criteria. All patients underwent the MMSE scale, the Detailed Neuropsychological Testing (DNT), which included a Ten Words Test (TWT), a “Double Test” (DT), a visual acuity test, a high-fi eld magnetic resonance imaging (MRI) of the brain with morphometry of cerebral structures (FreeSurfer, FSL). Results. According to the MMSE score, MCI were found in 26 (58%) patients. During the DNT, depending on the state of memory, 14 participants of the study identifi ed a non-amnestic type of MCI (na-MCI), 15 — an amnestic variant with impaired reproduction (ar-MCI), and 16 people — an amnestic type with a primary memory defect (apm-MCI). Volume changes of the anterior corpus callosum segment (CCA) were signifi cantly associated with the Immediate Recall after 4th reading and the Delayed Recall in the general MCI group (rho = 0.58; 0.58; p < 0.05) and the apmMCI group (rho = 0.6; 0.56; p < 0.05). Kruskal–Wallis Test showed that there were signifi cant group diff erences in the volumes of the CCA, right caudate nucleus, left cerebellar hemisphere cortex, posterior corpus callosum segment and left thalamus. At the same time, the fi rst three structures were combined into a set of informative features for differentiating the type of MCI based on the results of Forward stepwise Discriminant Analysis with a 77.3% accurate classifi cation rate (Wilks’s Lambda: 0.35962; approx. F (6.78) = 8.678, p < 0.001). ROC-analysis established the threshold values of the CCA volumes of ≤ 0.05% and the right caudate nucleus volumes of ≤ 0.23% (81.25% sensitivity in both cases; 62.1% and 60.7% specifi city; AUC 0.787 and 0.767; 95% CI 0.639–0.865 and 0.615–0.881; OR 7.1 and 6.7 (95% CI 1.6–30.6 and 1.6–29), associated with a memory defect in persons with MCI, while the ORs are 7.1 and 6.7 (95% CI 1.6–30.6 and 1.6–29), respectively. When both cerebral structures were included in the logit model, 88.6% classifi cation accuracy, 92.6% sensitivity, and 82.4% specifi city of the method were achieved. Conclusion. It has been demonstrated that classifying patients into the various types of MCI based on the data of memory function refl ected by the DNT and supplemented with MRI morphometry of the brain areas may be used as a sensitive and specifi c instrument for determining the category of patients with a high risk of Alzheimer’s disease. A neuropsychological profi le with a defect in primary memory, atrophic changes in anterior segment of the corpus callosum and the right caudate nucleus have been proposed as biomarkers of poor prognosis. Further longitudinal studies are necessary to clarify the proposed biomarkers of poor prognosis information and to detail the mechanisms of the neurodegenerative process.

Multimodal phenotypic axes of Parkinson’s disease

Individuals with Parkinson’s disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the “average” patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method—similarity network fusion—to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinson’s disease from the Parkinson’s Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from the fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.

Black bone MRI morphometry for mandibular cortical bone measurement in head and neck cancer patients: Prospective method comparison with CT

Objectives To determine the utility of low-flip angle black bone magnetic resonance imaging (MRI) for cortical mandibular bone assessment by comparing interdentium cortical measurements and inter-observer morphometric variability in relation to computed tomography (CT). Methods Quantification of cortical mandible bone width was performed as per Hamada et al. at 15 cross-sectional interdentium locations on pre-treatment black bone MRI and CT for 15 oropharyngeal cancer patients, with inter-observer analyses on a subset of 3 patients by 11 observers. Bland-Altman limits of agreement and bias estimation, Lin s concordance correlation (LCC), and Deming orthogonal regression were used to compare CT and MRI measurements. The absolute variance and intraclass correlation coefficient (ICC) were implemented for the inter-observer error quantification. Results Both the Bland Altman and Deming regression analyses showed CT and black bone MRI measurements were comparable within ±0.85 mm limits of agreement, and systematically smaller for MRI. LCC (0.60[0.52;0.67]) showed moderate equivalence between modalities. The average absolute variance between the observers was similar on CT (1.13±0.06 mm) and MRI (1.15 ±0.06 mm). The ICC analyses showed that measurement consistency was significantly higher (p<0.001) for the black bone MRI (0.43[0.32;0.56]) than CT (0.22[0.13;0.35]); nonetheless, the ICC was poor for both modalities. Conclusions Black bone MR sequence is usable as an alternative to CT for cortical mandible bone measurements, allowing use for early detection of cortical alteration (e.g. osteonecrosis). The cortical bone measurements showed substantive but equivalent inter-observer variation on both CT and black bone MRI. (Semi)automated measurement may mitigate this in future work.