Cervical artery tortuosity-a reliable semi-automated magnetic resonance-based method.

Background: Assessments of subclinical connective tissue disorders depend on complex approaches, emphasizing the need for more accessible methods applicable to clinical routine. Therefore, we aimed to establish a reliable approach assessing cervical vessel tortuosity, which is known to be associated with such disorders. Methods: Magnetic resonance angiography (MRA) images of ReSect study participants [single-center prospective cohort of spontaneous cervical artery dissection (sCeAD) patients] were used. Each patient underwent the same magnetic resonance imaging (MRI) protocol. The segmentation procedure was done using MATrix LABoratory 9.4 [up-sampling of raw MRA images, distance metric (DM) calculation], ITK-SNAP [region of interest (ROI) determination, vessel segmentation] and Vascular Modelling ToolKit (centerline determination). To assess inter-user variability and validity, we (I) had two blinded independent users segment all arteries and we (II) compared the results of our method to visual appraisal of vessel tortuosity done by two blinded expert neuro-radiologists. Results: A total of 526 extracranial cervical arteries were available for analysis. The inter-user variability of our method users was below 0.5% throughout. Overall, our method outperformed the visual tortuosity appraisal, as the visual grading underestimated the DM in 38.8% subjects when tasked to assess overall cervical artery tortuosity (both vertebral and internal carotid arteries) and in 16.6% and 33.3% respectively if tasked to grade anterior or posterior circulation separately. Conclusions: We present a reliable method to assess cervical artery tortuosity derived from MRA images applicable in clinical routine and future research investigating the potential correlation of sCeAD and connective tissue disorder.

Short-term meditation training influences brain energy metabolism: A pilot study on 31 P MR spectroscopy.

BACKGROUND: Meditation is increasingly attracting interest among neuroimaging researchers for its relevance as a cognitive enhancement technique and several cross-sectional studies have indicated cerebral changes. This longitudinal study applied a distinct and standardized meditative technique with a group of volunteers in a short-term training program to analyze brain metabolic changes. METHODS: The effect of 7 weeks of meditation exercises (focused attention meditation, FAM) was assessed on 27 healthy volunteers. Changes in cerebral energy metabolism were investigated using 31 P-MR spectroscopy. Metabolite ratios were compared before (T1) and after training (T2). Additional questionnaire assessments were included. RESULTS: The participants performed FAM daily. Depression and anxiety scores revealed a lower level of state anxiety at T2 compared to T1. From T1 to T2, energy metabolism ratios showed the following differences: PCr/ATP increased right occipitally; Pi/ATP decreased bilaterally in the basal ganglia and temporal lobe on the right; PCr/Pi increased in occipital lobe bilaterally, in the basal ganglia and in the temporal lobe on the right side. The pH decreased temporal on the left side and frontal in the right side. The observed changes in the temporal areas and basal ganglia may be interpreted as a higher energetic state, whereas the frontal and occipital areas showed changes that may be related to a down-regulation in ATP turnover, energy state, and oxidative capacity. CONCLUSIONS: The results of the current study indicate for the first time in a longitudinal study that even short-term training in FAM may have considerable effects on brain energy state with different local energy management in specific brain regions. Especially higher energetic state in basal ganglia may represent altered function in their central role in complex cerebral distributed networks including frontal and temporal areas. Further studies including different forms of relaxation techniques should be performed for more specific and reliable insights.

Novel decision algorithm to discriminate parkinsonism with combined blood and imaging biomarkers.

INTRODUCTION: To determine an exploratory multimodal approach including serum NFL and MR planimetric measures to discriminate Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). METHODS: MR planimetric measurements and NFL serum levels, with a mean time interval of 60 months relative to symptom onset, were assessed in a retrospective cohort of 11 progressive supranuclear palsy (PSP), 22 Parkinson's disease (PD), 16 multiple system atrophy (MSA) patients and 42 healthy controls (HC). A decision tree model to discriminate PD, PSP, and MSA was constructed using receiver operating characteristic curve analysis and Classification and Regression Trees algorithm. RESULTS: Our multimodal decision tree provided accurate differentiation of PD versus MSA and PSP patients using a serum NFL cut-off of 14.66 ng/L. The pontine-to-midbrain-diameter-ratio (Pd/Md) discriminated MSA from PSP at a cut-off value of 2.06. The combined overall diagnostic yield was an accuracy of 83.7% (95% CI 69.8-90.8%). CONCLUSION: We provide a clinically feasible decision algorithm which combines serum NFL levels and a planimetric MRI marker to differentiate PD, MSA and PSP with high diagnostic accuracy. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that the combination of serum NFL levels und MR planimetric measurements discriminates between PD, PSP and MSA.

Adaptive multi-parameter regularization approach to construct the distribution function of relaxation times.

Determination of the distribution function of relaxation times (DFRT) is an approach that gives us more detailed insight into system processes, which are not observable by simple electrochemical impedance spectroscopy (EIS) measurements. DFRT maps EIS data into a function containing the timescale characteristics of the system under consideration. The extraction of such characteristics from noisy EIS measurements can be described by Fredholm integral equation of the first kind that is known to be ill-posed and can be treated only with regularization techniques. Moreover, since only a finite number of EIS data may actually be obtained, the above-mentioned equation appears as after application of a collocation method that needs to be combined with the regularization. In the present study, we discuss how a regularized collocation of DFRT problem can be implemented such that all appearing quantities allow symbolic computations as sums of table integrals. The proposed implementation of the regularized collocation is treated as a multi-parameter regularization. Another contribution of the present work is the adjustment of the previously proposed multiple parameter choice strategy to the context of DFRT problem. The resulting strategy is based on the aggregation of all computed regularized approximants, and can be in principle used in synergy with other methods for solving DFRT problem. We also report the results from the experiments that apply the synthetic data showing that the proposed technique successfully reproduced known exact DFRT. The data obtained by our techniques is also compared to data obtained by well-known DFRT software (DRTtools).