Maria Bjerke

Purpose: To assess the performance of the inferior lateral ventricle (ILV) to hippocampal (Hip) volume ratio on brain MRI, for Alzheimer’s disease (AD) diagnostics, comparing it to individual automated ILV and hippocampal volumes, and visual medial temporal lobe atrophy (MTA) consensus ratings. Methods: One-hundred-twelve subjects (mean age ± SD, 66.85 ± 13.64 years) with varying degrees of cognitive decline underwent MRI using a Philips Ingenia 3T. The MTA scale by Scheltens, rated on coronal 3D T1-weighted images, was determined by three experienced radiologists, blinded to diagnosis and sex. Automated volumetry was computed by icobrain dm (v. 5.10) for total, left, right hippocampal, and ILV volumes. The ILV/Hip ratio, defined as the percentage ratio between ILV and hippocampal volumes, was calculated and compared against a normative reference population (n = 1903). Inter-rater agreement, association, classification accuracy, and clinical interpretability on patient level were reported. Results: Visual MTA scores showed excellent inter-rater agreement. Ordinal logistic regression and correlation analyses demonstrated robust associations between automated brain segmentations and visual MTA ratings, with the ILV/Hip ratio consistently outperforming individual hippocampal and ILV volumes. Pairwise classification accuracy showed good performance without statistically significant differences between the ILV/Hip ratio and visual MTA across disease stages, indicating potential interchangeability. Comparison to the normative population and clinical interpretability assessments showed commensurability in classifying MTA “severity” between visual MTA and ILV/Hip ratio measurements. Conclusion: The ILV/Hip ratio shows the highest correlation to visual MTA, in comparison to automated individual ILV and hippocampal volumes, offering standardized measures for diagnostic support in different stages of cognitive decline.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and degenerative disorder of the central nervous system. Accelerated brain volume loss (BVL) has emerged as a promising magnetic resonance imaging marker (MRI) of neurodegeneration, correlating with present and future clinical disability. We have systematically selected MS patients fulfilling ‘no evidence of disease activity-3′ (NEDA-3) criteria under high-efficacy disease-modifying treatment (DMT) from the database of two Belgian MS centers. BVL between both MRI scans demarcating the NEDA-3 period was assessed and compared with a group of prospectively recruited healthy volunteers who were matched for age and gender. Annualized whole brain volume percentage change was similar between 29 MS patients achieving NEDA-3 and 24 healthy controls (−0.25 ± 0.49 versus −0.24 ± 0.20, p = 0.9992; median follow-up 21 versus 33 months; respectively). In contrast, we found a mean BVL increase of 72%, as compared with the former, in a second control group of MS patients (n = 21) whom had been excluded from the NEDA-3 group due to disease activity (p = 0.1371). Our results suggest that neurodegeneration in MS can slow down to the rate of normal aging once inflammatory disease activity has been extinguished and advocate for an early introduction of high-efficacy DMT to reduce the risk of future clinical disability.

Little is known about the interplay between the autonomic nervous system and disease activity in multiple sclerosis (MS). We examined the relationship between heart rate variability (HRV), a reliable measure of vagal nerve function, and disease characteristics in a prospective MS cohort. Standard deviation of each normal-to-normal inter-beat interval (SDNN) and root mean square of successive differences (RMSSD), global indices of HRV, were measured in 114 MS patients, which included four predefined subgroups, and 30 age and sex-matched healthy controls (HC). We assessed group differences at baseline, HRV reproducibility at month 3, and used logistic regression modeling to relate baseline HRV with relapse occurrence. No significant HRV differences were found between MS and HC and between MS subgroups. In MS patients, both HRV indices correlated with age (r = −0.278, p = 0.018 and r = −0.319, p < 0.001, respectively) and with month 3 assessments (r = 0.695 and r = 0.760, p < 0.001). Higher SDNN and RMSSD at baseline were associated with self-reported relapses at month 3 (OR = 1.053, 95% CI (1.013–1.095), p = 0.009 and OR = 1.065, 95% CI (1.016–1.117), p = 0.009), and SDNN at baseline with relapses at month 12 (OR = 1.034, 95% CI (1.009–1.059), p = 0.008; ROC, AUC = 0.733, p = 0.002). There were no baseline HRV differences between MS and HC or between subgroups. Post-hoc analysis showed an association with an increased relapse risk.

Introduction: Distinguishing between two of the most common causes of dementia, Alzheimer disease (AD) and frontotemporal lobar degeneration (FTLD), on clinical diagnostic criteria alone has poor diagnostic accuracy. Promising tools to increase the diagnostic accuracy of AD are the use of cerebrospinal fluid (CSF) biomarkers and electroencephalography (EEG), which is being investigated as a diagnostic tool for neurodegenerative brain disorders. In this study, we investigated the utility of EEG-based biomarkers in comparison and in addition to established neurochemical biomarkers in the AD versus FTLD differential diagnosis. Methods: Our study cohort comprised 37 AD and 32 FTLD patients, of which 19 AD and 11 FTLD had definite diagnoses. All these participants had CSF biomarker analyses resulting in four neurochemical (NCM) biomarkers (Aβ _1-42 , T-tau, P-tau ₁₈₁ and Nf-L) and underwent 19-channel resting-state EEG. From the EEG spectra, dominant frequency peaks (DFP) were extracted in four regions resulting in four dominant frequencies (in left-temporal, frontal, right-temporal and parieto-occipital regions). This yielded a total of eight features (4 NCM + 4 EEG), which we used to train and test a classifier and assess the diagnostic value of the markers separately (using only the NCM or EEG subset) and combined. Results: The classification accuracies were much higher when training and testing on the definite subgroup than on the whole group. Furthermore, we found that the NCM feature subset allowed a better accuracy than the EEG feature subset, both when training and testing on the whole group (NCM 82% vs EEG 72%), as on the definite group only (92% vs 86%). Using both feature subsets together increased the accuracy to 86% in the whole group and 94% in the definite subgroups. Another interesting finding was the presence of two spectral peaks in a considerable number of patients in both groups. Conclusion: Combining EEG with neurochemical biomarkers resulted in differential diagnostic accuracies of 86% in clinically diagnosed AD and FTD patients and of 94% in patients having a definite diagnosis. Furthermore, we found evidence that the slowing down of the dominant EEG rhythm might be a gradual appearance of a slow rhythm and fading out of the normal ground rhythm, rather than a gradual slowing down of the ground rhythm. Finally, we have discovered two differences in the occurrence of the dominant EEG frequency: people lacking a clear dominant peak almost all had definite AD, while people with two peaks more often had FTLD. These unexpected but interesting findings need to be explored further.