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BACKGROUND: hysical activity exerts a variety of long-term health benefits in older adults. In particular, it is assumed to be a protective factor against cognitive decline and dementia.
METHODS/DESIGN: Randomised controlled assessor blinded 2-armed trial (n = 60) to explore the exercise- induced neuroprotective and metabolic effects on the brain in cognitively healthy older adults. Participants (age ≥ 65), recruited within the setting of assisted living facilities and newspaper advertisements are allocated to a 12-week individualised aerobic exercise programme intervention or a 12-week waiting control group. Total follow-up is 24 weeks. The main outcome is the change in cerebral metabolism as assessed with Magnetic Resonance Spectroscopic Imaging reflecting changes of cerebral N-acetyl-aspartate and of markers of neuronal energy reserve. Imaging also measures changes in cortical grey matter volume. Secondary outcomes include a broad range of psychometric (cognition) and movement-related parameters such as nutrition, history of physical activity, history of pain and functional diagnostics. Participants are allocated to either the intervention or control group using a computer-generated randomisation sequence. The exercise physiologist in charge of training opens sealed and opaque envelopes and informs participants about group allocation. For organisational reasons, he schedules the participants for upcoming assessments and exercise in groups of five. All assessors and study personal other than exercise physiologists are blinded.
DISCUSSION: Magnetic Resonance Spectroscopic Imaging gives a deeper insight into mechanisms of exercise-induced changes in brain metabolism. As follow-up lasts for 6 months, this study is able to explore the mid-term cerebral metabolic effects of physical activity assuming that an individually tailored aerobic ergometer training has the potential to counteract brain ageing.
NCT02343029 (clinicaltrials.gov; 12 January 2015).
There is mounting evidence that aerobic exercise has a positive effect on cognitive functions in older adults. To date, little is known about the neurometabolic and molecular mechanisms underlying this positive effect. The present study used magnetic resonance spectroscopy and quantitative MRI to systematically explore the effects of physical activity on human brain metabolism and grey matter (GM) volume in healthy aging. This is a randomised controlled assessor-blinded two-armed trial (n=53) to explore exercise-induced neuroprotective and metabolic effects on the brain in cognitively healthy older adults. Participants (age >65) were allocated to a 12-week individualised aerobic exercise programme intervention (n=29) or a 12-week waiting control group (n=24). The main outcomes were the change in cerebral metabolism and its association to brain-derived neurotrophic factor (BDNF) levels as well as changes in GM volume. We found that cerebral choline concentrations remained stable after 12 weeks of aerobic exercise in the intervention group, whereas they increased in the waiting control group. No effect of training was seen on cerebral N-acetyl-aspartate concentrations, nor on markers of neuronal energy reserve or BDNF levels. Further, we observed no change in cortical GM volume in response to aerobic exercise. The finding of stable choline concentrations in the intervention group over the 3 month period might indicate a neuroprotective effect of aerobic exercise. Choline might constitute a valid marker for an effect of aerobic exercise on cerebral metabolism in healthy aging.
Quantitative T1 mapping indicates tumor infiltration beyond the enhancing part of glioblastomas
(2019)
The aim of this study was to evaluate whether maps of quantitative T1 (qT1) differences induced by a gadolinium‐based contrast agent (CA) are better suited than conventional T1‐weighted (T1w) MR images for detecting infiltration inside and beyond the peritumoral edema of glioblastomas. Conventional T1w images and qT1 maps were obtained before and after gadolinium‐based CA administration in 33 patients with glioblastoma before therapy. The following data were calculated: (i) absolute qT1‐difference maps (qT1 pre‐CA ‐ qT1 post‐CA), (ii) relative qT1‐difference maps, (iii) absolute and (iv) relative differences of conventional T1w images acquired pre‐ and post‐CA. The values of these four datasets were compared in four different regions: (a) the enhancing tumor, (b) the peritumoral edema, (c) a 5 mm zone around the pathology (defined as the sum of regions a and b), and (d) the contralateral normal appearing brain tissue. Additionally, absolute qT1‐difference maps (displayed with linear gray scaling) were visually compared with respective conventional difference images. The enhancing tumor was visible both in the difference of conventional pre‐ and post‐CA T1w images and in the absolute qT1‐difference maps, whereas only the latter showed elevated values in the peritumoral edema and in some cases even beyond. Mean absolute qT1‐difference values were significantly higher (P < 0.01) in the enhancing tumor (838 ± 210 ms), the peritumoral edema (123 ± 74 ms) and in the 5 mm zone around the pathology (81 ± 31 ms) than in normal appearing tissue (32 ± 35 ms). In summary, absolute qT1‐difference maps—in contrast to the difference of T1w images—of untreated glioblastomas appear to be able to visualize CA leakage, and thus might indicate tumor cell infiltration in the edema region and beyond. Therefore, the absolute qT1‐difference maps are potentially useful for treatment planning.
Purpose: In the clinical routine, detection of focal cortical dysplasia (FCD) by visual inspection is challenging. Still, information about the presence and location of FCD is highly relevant for prognostication and treatment decisions. Therefore, this study aimed to develop, describe and test a method for the calculation of synthetic anatomies using multiparametric quantitative MRI (qMRI) data and surface-based analysis, which allows for an improved visualization of FCD.
Materials and Methods: Quantitative T1-, T2- and PD-maps and conventional clinical datasets of patients with FCD and epilepsy were acquired. Tissue segmentation and delineation of the border between white matter and cortex was performed. In order to detect blurring at this border, a surface-based calculation of the standard deviation of each quantitative parameter (T1, T2, and PD) was performed across the cortex and the neighboring white matter for each cortical vertex. The resulting standard deviations combined with measures of the cortical thickness were used to enhance the signal of conventional FLAIR-datasets. The resulting synthetically enhanced FLAIR-anatomies were compared with conventional MRI-data utilizing regions of interest based analysis techniques.
Results: The synthetically enhanced FLAIR-anatomies showed higher signal levels than conventional FLAIR-data at the FCD sites (p = 0.005). In addition, the enhanced FLAIR-anatomies exhibited higher signal levels at the FCD sites than in the corresponding contralateral regions (p = 0.005). However, false positive findings occurred, so careful comparison with conventional datasets is mandatory.
Conclusion: Synthetically enhanced FLAIR-anatomies resulting from surface-based multiparametric qMRI-analyses have the potential to improve the visualization of FCD and, accordingly, the treatment of the respective patients.
The detection of cortical malformations in conventional MR images can be challenging. Prominent examples are focal cortical dysplasias (FCD), the most common cause of drug‐resistant focal epilepsy. The two main MRI hallmarks of cortical malformations are increased cortical thickness and blurring of the gray (GM) and white matter (WM) junction. The purpose of this study was to derive synthetic anatomies from quantitative T1 maps for the improved display of the above imaging characteristics in individual patients.
On the basis of a T1 map, a mask comprising pixels with T1 values characteristic for GM is created from which the local cortical extent (CE) is determined. The local smoothness (SM) of the GM‐WM junctions is derived from the T1 gradient. For display of cortical malformations, the resulting CE and SM maps serve to enhance local intensities in synthetic double inversion recovery (DIR) images calculated from the T1 map.
The resulting CE‐ and/or SM‐enhanced DIR images appear hyperintense at the site of cortical malformations, thus facilitating FCD detection in epilepsy patients. However, false positives may arise in areas with naturally elevated CE and/or SM, such as large GM structures and perivascular spaces.
In summary, the proposed method facilitates the detection of cortical abnormalities such as cortical thickening and blurring of the GM‐WM junction which are typical FCD markers. Still, subject motion artifacts, perivascular spaces, and large normal GM structures may also yield signal hyperintensity in the enhanced synthetic DIR images, requiring careful comparison with clinical MR images by an experienced neuroradiologist to exclude false positives.
The thickness of the cerebral cortex can provide valuable information about normal and abnormal neuroanatomy. High resolution MRI together with powerful image processing techniques has made it possible to perform these measurements automatically over the whole brain. Here we present a method for automatically generating voxel-based cortical thickness (VBCT) maps. This technique results in maps where each voxel in the grey matter is assigned a thickness value. Sub-voxel measurements of thickness are possible using sub-sampling and interpolation of the image information. The method is applied to repeated MRI scans of a single subject from two MRI scanners to demonstrate its robustness and reproducibility. A simulated data set is used to show that small focal differences in thickness between two groups of subjects can be detected. We propose that the analysis of VBCT maps can provide results that are complementary to other anatomical analyses such as voxel-based morphometry.
Background: Network science provides powerful access to essential organizational principles of the brain. The aim of this study was to investigate longitudinal evolution of gray matter networks in early relapsing–remitting MS (RRMS) compared with healthy controls (HCs) and contrast network dynamics with conventional atrophy measurements.
Methods: For our longitudinal study, we investigated structural cortical networks over 1 year derived from 3T MRI in 203 individuals (92 early RRMS patients with mean disease duration of 12.1 ± 14.5 months and 101 HCs). Brain networks were computed based on cortical thickness inter-regional correlations and fed into graph theoretical analysis. Network connectivity measures (modularity, clustering coefficient, local efficiency, and transitivity) were compared between patients and HCs, and between patients with and without disease activity. Moreover, we calculated longitudinal brain volume changes and cortical atrophy patterns.
Results: Our analyses revealed strengthening of local network properties shown by increased modularity, clustering coefficient, local efficiency, and transitivity over time. These network dynamics were not detectable in the cortex of HCs over the same period and occurred independently of patients’ disease activity. Most notably, the described network reorganization was evident beyond detectable atrophy as characterized by conventional morphometric methods.
Conclusion: In conclusion, our findings provide evidence for gray matter network reorganization subsequent to clinical disease manifestation in patients with early RRMS. An adaptive cortical response with increased local network characteristics favoring network segregation could play a primordial role for maintaining brain function in response to neuroinflammation.
Purpose: In secondary progressive Multiple Sclerosis (SPMS), global neurodegeneration as a driver of disability gains importance in comparison to focal inflammatory processes. However, clinical MRI does not visualize changes of tissue composition outside MS lesions. This quantitative MRI (qMRI) study investigated cortical and deep gray matter (GM) proton density (PD) values and T1 relaxation times to explore their potential to assess neuronal damage and its relationship to clinical disability in SPMS.
Materials and Methods: 11 SPMS patients underwent quantitative T1 and PD mapping. Parameter values across the cerebral cortex and deep GM structures were compared with 11 healthy controls, and correlation with disability was investigated for regions exhibiting significant group differences.
Results: PD was increased in the whole GM, cerebral cortex, thalamus, putamen and pallidum. PD correlated with disability in the whole GM, cerebral cortex, putamen and pallidum. T1 relaxation time was prolonged and correlated with disability in the whole GM and cerebral cortex.
Conclusion: Our study suggests that the qMRI parameters GM PD (which likely indicates replacement of neural tissue with water) and cortical T1 (which reflects cortical damage including and beyond increased water content) are promising qMRI candidates for the assessment of disease status, and are related to disability in SPMS.
Multimodal quantitative mri reveals no evidence for tissue pathology in idiopathic cervical dystonia
(2019)
Background: While in symptomatic forms of dystonia cerebral pathology is by definition present, it is unclear so far whether disease is associated with microstructural cerebral changes in idiopathic dystonia. Previous quantitative MRI (qMRI) studies assessing cerebral tissue composition in idiopathic dystonia revealed conflicting results.
Objective: Using multimodal qMRI, the presented study aimed to investigate alterations in different cerebral microstructural compartments associated with idiopathic cervical dystonia in vivo.
Methods: Mapping of T1, T2, T∗2, and proton density (PD) was performed in 17 patients with idiopathic cervical dystonia and 29 matched healthy control subjects. Statistical comparisons of the parametric maps between groups were conducted for various regions of interest (ROI), including major basal ganglia nuclei, the thalamus, white matter, and the cerebellum, and voxel-wise for the whole brain.
Results: Neither whole brain voxel-wise statistics nor ROI-based analyses revealed significant group differences for any qMRI parameter under investigation.
Conclusions: The negative findings of this qMRI study argue against the presence of overt microstructural tissue change in patients with idiopathic cervical dystonia. The results seem to support a common view that idiopathic cervical dystonia might primarily resemble a functional network disease.
Magnetic resonance imaging (MRI) is the gold standard imaging technique for diagnosis and monitoring of many neurological diseases. However, the application of conventional MRI in clinical routine is mainly limited to the visual detection of macroscopic tissue pathology since mixed tissue contrasts depending on hardware and protocol parameters hamper its application for the assessment of subtle or diffuse impairment of the structural tissue integrity. Multiparametric quantitative (q)MRI determines tissue parameters quantitatively, enabling the detection of microstructural processes related to tissue remodeling in aging and neurological diseases. In contrast to measuring tissue atrophy via structural imaging, multiparametric qMRI allows for investigating biologically distinct microstructural processes, which precede changes of the tissue volume. This facilitates a more comprehensive characterization of tissue alterations by revealing early impairment of the microstructural integrity and specific disease-related patterns. So far, qMRI techniques have been employed in a wide range of neurological diseases, including in particular conditions with inflammatory, cerebrovascular and neurodegenerative pathology. Numerous studies suggest that qMRI might add valuable information, including the detection of microstructural tissue damage in areas appearing normal on conventional MRI and unveiling the microstructural correlates of clinical manifestations. This review will give an overview of current qMRI techniques, the most relevant tissue parameters and potential applications in neurological diseases, such as early (differential) diagnosis, monitoring of disease progression, and evaluating effects of therapeutic interventions.