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Background: Cannabis proofed to be effective in pain relief, but one major side effect is its influence on memory in humans. Therefore, the role of memory on central processing of nociceptive information was investigated in healthy volunteers.
Methods: In a placebo-controlled cross-over study including 22 healthy subjects, the effect of 20 mg oral Δ9-tetrahydrocannabinol (THC) on memory involving nociceptive sensations was studied, using a delayed stimulus discrimination task (DSDT). To control for nociceptive specificity, a similar DSDT-based study was performed in a subgroup of thirteen subjects, using visual stimuli.
Results: For each nociceptive stimulus pair, the second stimulus was associated with stronger and more extended brain activations than the first stimulus. These differences disappeared after THC administration. The THC effects were mainly located in two clusters comprising the insula and inferior frontal cortex in the right hemisphere, and the caudate nucleus and putamen bilaterally. These cerebral effects were accompanied in the DSDT by a significant reduction of correct ratings from 41.61% to 37.05% after THC administration (rm-ANOVA interaction "drug" by "measurement": F (1,21) = 4.685, p = 0.042). Rating performance was also reduced for the visual DSDT (69.87% to 54.35%; rm-ANOVA interaction of "drug" by "measurement": F (1,12) = 13.478, p = 0.003) and reflected in a reduction of stimulus-related brain deactivations in the bilateral angular gyrus.
Conclusions: Results suggest that part of the effect of THC on pain may be related to memory effects. THC reduced the performance in DSDT of nociceptive and visual stimuli, which was accompanied by significant effects on brain activations. However, a pain specificity of these effects cannot be deduced from the data presented.
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.
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.
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.
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.