Brain age as a surrogate marker for cognitive performance in multiple sclerosis

Background: Data from neuro-imaging techniques allow us to estimate a brain's age. Brain age is easily interpretable as "how old the brain looks", and could therefore be an attractive communication tool for brain health in clinical practice. This study aimed to investigate its clinical utility by investigating the relationship between brain age and cognitive performance in multiple sclerosis (MS). Methods: A linear regression model was trained to predict age from brain MRI volumetric features and sex in a healthy control dataset (HC_train, n=1673). This model was used to predict brain age in two test sets: HC_test (n=50) and MS_test (n=201). Brain-Predicted Age Difference (BPAD) was calculated as BPAD=brain age minus chronological age. Cognitive performance was assessed by the Symbol Digit Modalities Test (SDMT). Results: Brain age was significantly related to SDMT scores in the MS_test dataset (r=-0.46, p<.001), and contributed uniquely to variance in SDMT beyond chronological age, reflected by a significant correlation between BPAD and SDMT (r=-0.24, p<.001) and a significant weight (-0.25, p=0.002) in a multivariate regression equation with age. Conclusions: Brain age is a candidate biomarker for cognitive dysfunction in MS and an easy to grasp metric for brain health

The squares test as a measure of hand function in multiple sclerosis

Deterioration of hand function can be important in multiple sclerosis (MS). The standard way of assessing hand function in MS is the 9-hole peg test (9HPT), one of the three components of the MS functional composite measure. In this study we examine the squares test (ST), a test of hand function that is used extensively in handedness research. We evaluated reproducibility of the ST in 49 healthy controls, and both discriminatory power and concurrent validity of the ST in 38 MS patients and 18 age and gender matched controls. The ST proved to be a reliable and easy to administrate paper-and-pencil test of hand function. The ST showed a high and highly significant correlation with the standard 9HPT over a broad range of Expanded Disability Status Scale (EDSS) scores, and had high discriminatory power, also comparable to the 9HPT. Therefore, the ST is a candidate test for use in composite measures of MS related functional deficits for clinical practice and in clinical trials. (C) 2014 Elsevier B.V. All rights reserved

Do advanced statistical techniques really help in the diagnosis of the Metabolic Syndrome in patients treated with second-generation anti-psychotics?

Objective Metabolic and cardiovascular diseases in patients with schizophrenia have gained a lot of interest in recent years. Developing an algorithm to detect the metabolic syndrome based on readily available variables would eliminate the need for blood sampling, which is considered as expensive and inconvenient in this population. Methods We used logistic regression and optimized artificial neural networks and support vector machines to detect the metabolic syndrome in a cohort of schizophrenia patients of the UPC Kortenberg, KU Leuven. Testing was done on one third of the included cohort (202 patients), training was performed using a tenfold stratified cross-validation scheme. Results All three methods yielded similar results with satisfying accuracies of about 80 %. However, none of the advanced statistical methods could improve on the results obtained using a very simple and naïve model including only central obesity and information on blood pressure. Conclusion Although so-called patter recognition techniques bear high promise in improving clinical decision making, the results should be presented with caution and preferably in comparison with some lowtech technique. Runningstatus: publishe

Cortical mapping of painful electrical stimulation by quantitative electroencephalography: unraveling the time&ndash;frequency&ndash;channel domain

Lisa Goudman,1&ndash;3 Jorne Laton,4 Raf Brouns,4,5 Guy Nagels,4&ndash;6 Eva Huysmans,2,3,7,8 Ronald Buyl,7,9 Kelly Ickmans,2,3,10 Jo Nijs,2,3,10 Maarten Moens,1,2,4,11 1Department of Neurosurgery, Universitair Ziekenhuis Brussel, 2Pain in Motion International Research Group, 3Department of Physiotherapy, Human Physiology and Anatomy, Faculty of Physical Education and Physiotherapy, 4Center for Neurosciences (C4N), Vrije Universiteit Brussel (VUB), 5Department of Neurology, Universitair Ziekenhuis Brussel, 6National MS Center, 7Department of Public Health (GEWE), Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, 8Interuniversity Centre for Health Economics Research (I-CHER), 9Department of Biostatistics and Medical Informatics, Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, 10Department of Physical Medicine and Physiotherapy, 11Department of Radiology, Universitair Ziekenhuis Brussel, Brussels, Belgium Abstract: The goal of this study was to capture the electroencephalographic signature of experimentally induced pain and pain-modulating mechanisms after painful peripheral electrical stimulation to determine one or a selected group of electrodes at a specific time point with a specific frequency range. In the first experiment, ten healthy participants were exposed to stimulation of the right median nerve while registering brain activity using 32-channel electroencephalography. Electrical stimulations were organized in four blocks of 20 stimuli with four intensities &ndash; 100%, 120%, 140%, and 160% &ndash; of the electrical pain threshold. In the second experiment, 15 healthy participants received electrical stimulation on the dominant median nerve before and during the application of a second painful stimulus. Raw data were converted into the time&ndash;frequency domain by applying a continuous wavelet transform. Separated domain information was extracted by calculating Parafac models. The results demonstrated that it is possible to capture a reproducible cortical neural response after painful electrical stimulation, more specifically at 250 milliseconds poststimulus, at the midline electrodes Cz and FCz with predominant &delta;-oscillations. The signature of the top-down nociceptive inhibitory mechanisms is &delta;-activity at 235 ms poststimulus at the prefrontal electrodes. This study presents a methodology to overcome the a priori determination of the regions of interest to analyze the brain response after painful electrical stimulation. Keywords: electroencephalography, Parafac model, painful electrical stimulation, conditioned pain modulatio

Geophysical, remote sensing, GIS, and isotopic applications for a better understanding of the structural controls on groundwater flow in the Mojave Desert, California

Study region: Mojave Desert, USA. Study focus: An integrated (near-surface geophysics, remote sensing, isotopic analyses) study was conducted in the Mojave River Basin and Morongo Groundwater Basin to investigate potential effects that the Helendale Fault [HF] and basement uplifts might have on groundwater flow in the Mojave Desert. New hydrological insights for the region: The HF traces were mapped using LiDAR and Geoeye-1 imagery (surface) and magnetic profiles (subsurface). Shallow basement parallel to and west of the HF was detected using the Vertical Electrical Soundings (VESs). Conductive water-saturated breccia was detected along the HF using the Very Low Frequency (VLF) electromagnetic measurements. Isotopic analyses (δD and δ18O) for groundwater samples from productive shallow wells, and springs sampled west of the HF and the basement uplift are less depleted (Group I: Fifteenmile Valley Groundwater sub-basin [FVGS]; average δD: −86.8‰; δ18O: −11.8‰) than samples east of the basement uplift (Group II: Lucerne Valley Groundwater sub-basin [LVGS]; average δD: −95.0‰; δ18O: −12.1‰), whereas samples proximal to, the fault have compositions similar to Group I but show evidence for mixing with Group II compositions (Group III; average δD: −88.8‰; δ18O: −11.5‰). Findings are consistent with the HF channeling groundwater from the San Bernardino Mountains with basement uplifts acting as barriers to lateral groundwater flow and could be applicable to similar settings across the Mojave Desert and elsewhere worldwide. Keywords: Mojave desert, Groundwater flow, Structural controls, Geophysics (VLF, Magnetic, VES), Isotopic analyses (O, H), Remote sensing (LiDAR, GeoEye-1

The role of hippocampal theta oscillations in working memory impairment in multiple sclerosis

Working memory (WM) problems are frequently present in people with multiple sclerosis (MS). Even though hippocampal damage has been repeatedly shown to play an important role, the underlying neurophysiological mechanisms remain unclear. This study aimed to investigate the neurophysiological underpinnings of WM impairment in MS using magnetoencephalography (MEG) data from a visual‐verbal 2‐back task. We analysed MEG recordings of 79 MS patients and 38 healthy subjects through event‐related fields and theta (4–8 Hz) and alpha (8–13 Hz) oscillatory processes. Data was source reconstructed and parcellated based on previous findings in the healthy subject sample. MS patients showed a smaller maximum theta power increase in the right hippocampus between 0 and 400 ms than healthy subjects (p = .014). This theta power increase value correlated negatively with reaction time on the task in MS (r = −.32, p = .029). Evidence was provided that this relationship could not be explained by a ‘common cause’ confounding relationship with MS‐related neuronal damage. This study provides the first neurophysiological evidence of the influence of hippocampal dysfunction on WM performance in MS