Susceptibility-Weighted MRI for Predicting NF-2 Mutations and S100 Protein Expression in Meningiomas.

S100 protein expression levels and neurofibromatosis type 2 (NF-2) mutations result in different disease courses in meningiomas. This study aimed to investigate non-invasive biomarkers of NF-2 copy number loss and S100 protein expression in meningiomas using morphological, radiomics, and deep learning-based features of susceptibility-weighted MRI (SWI). This retrospective study included 99 patients with S100 protein expression data and 92 patients with NF-2 copy number loss information. Preoperative cranial MRI was conducted using a 3T clinical MR scanner. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and subsequent registration of FLAIR to high-resolution SWI was performed. First-order textural features of SWI were extracted and assessed using Pyradiomics. Morphological features, including the tumor growth pattern, peritumoral edema, sinus invasion, hyperostosis, bone destruction, and intratumoral calcification, were semi-quantitatively assessed. Mann-Whitney U tests were utilized to assess the differences in the SWI features of meningiomas with and without S100 protein expression or NF-2 copy number loss. A logistic regression analysis was used to examine the relationship between these features and the respective subgroups. Additionally, a convolutional neural network (CNN) was used to extract hierarchical features of SWI, which were subsequently employed in a light gradient boosting machine classifier to predict the NF-2 copy number loss and S100 protein expression. NF-2 copy number loss was associated with a higher risk of developing high-grade tumors. Additionally, elevated signal intensity and a decrease in entropy within the tumoral region on SWI were observed in meningiomas with S100 protein expression. On the other hand, NF-2 copy number loss was associated with lower SWI signal intensity, a growth pattern described as "en plaque", and the presence of calcification within the tumor. The logistic regression model achieved an accuracy of 0.59 for predicting NF-2 copy number loss and an accuracy of 0.70 for identifying S100 protein expression. Deep learning features demonstrated a strong predictive capability for S100 protein expression (AUC = 0.85 ± 0.06) and had reasonable success in identifying NF-2 copy number loss (AUC = 0.74 ± 0.05). In conclusion, SWI showed promise in identifying NF-2 copy number loss and S100 protein expression by revealing neovascularization and microcalcification characteristics in meningiomas.

Developing blood-brain barrier arterial spin labelling as a non-invasive early biomarker of Alzheimer's disease (DEBBIE-AD): a prospective observational multicohort study protocol.

INTRODUCTION: Loss of blood-brain barrier (BBB) integrity is hypothesised to be one of the earliest microvascular signs of Alzheimer's disease (AD). Existing BBB integrity imaging methods involve contrast agents or ionising radiation, and pose limitations in terms of cost and logistics. Arterial spin labelling (ASL) perfusion MRI has been recently adapted to map the BBB permeability non-invasively. The DEveloping BBB-ASL as a non-Invasive Early biomarker (DEBBIE) consortium aims to develop this modified ASL-MRI technique for patient-specific and robust BBB permeability assessments. This article outlines the study design of the DEBBIE cohorts focused on investigating the potential of BBB-ASL as an early biomarker for AD (DEBBIE-AD). METHODS AND ANALYSIS: DEBBIE-AD consists of a multicohort study enrolling participants with subjective cognitive decline, mild cognitive impairment and AD, as well as age-matched healthy controls, from 13 cohorts. The precision and accuracy of BBB-ASL will be evaluated in healthy participants. The clinical value of BBB-ASL will be evaluated by comparing results with both established and novel AD biomarkers. The DEBBIE-AD study aims to provide evidence of the ability of BBB-ASL to measure BBB permeability and demonstrate its utility in AD and AD-related pathologies. ETHICS AND DISSEMINATION: Ethics approval was obtained for 10 cohorts, and is pending for 3 cohorts. The results of the main trial and each of the secondary endpoints will be submitted for publication in a peer-reviewed journal.

Pulmonary vasculature development in congenital diaphragmatic hernia: a novel automated quantitative imaging analysis.

PURPOSE: Impaired fetal lung vasculature determines the degree of pulmonary hypertension in the congenital diaphragmatic hernia (CDH). This study aims to demonstrate the morphometric measurements that differ in pulmonary vessels of fetuses with CDH. METHODS: Nitrofen-induced CDH Sprague-Dawley rat fetuses were scanned with microcomputed tomography. The analysis of the pulmonary vascular tree was performed with artificial intelligence. RESULTS: The number of segments in CDH was significantly lower than that in the control group on the left (U = 2.5, p = 0.004) and right (U = 0, p = 0.001) sides for order 1(O1), whereas there was a significant difference only on the right side for O2 and O3. The pooled element numbers in the control group obeyed Horton's law (R2 = 0.996 left and R2 = 0.811 right lungs), while the CDH group broke it. Connectivity matrices showed that the average number of elements of O1 springing from elements of O1 on the left side and the number of elements of O1 springing from elements of O3 on the right side were significantly lower in CDH samples. CONCLUSION: According to these findings, CDH not only reduced the amount of small order elements, but also destroyed the fractal structure of the pulmonary arterial trees.

Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas.

PURPOSE: Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations play crucial roles in glioma biology. Such genetic information is typically obtained invasively from excised tumor tissue; however, these mutations need to be identified preoperatively for better treatment planning. The relative cerebral blood volume (rCBV) information derived from dynamic susceptibility contrast MRI (DSC-MRI) has been demonstrated to correlate with tumor vascularity, functionality, and biology, and might provide some information about the genetic alterations in gliomas before surgery. Therefore, this study aims to predict IDH and TERTp mutational subgroups in gliomas using deep learning applied to rCBV images. METHOD: After the generation of rCBV images from DSC-MRI data, classical machine learning algorithms were applied to the features obtained from the segmented tumor volumes to classify IDH and TERTp mutation subgroups. Furthermore, pre-trained convolutional neural networks (CNNs) and CNNs enhanced with attention gates were trained using rCBV images or a combination of rCBV and anatomical images to classify the mutational subgroups. RESULTS: The best accuracies obtained with classical machine learning algorithms were 83 %, 68 %, and 76 % for the identification of IDH mutational, TERTp mutational, and TERTp-only subgroups, respectively. On the other hand, the best-performing CNN model achieved 88 % accuracy (86 % sensitivity, 91 % specificity) for the IDH-mutational subgroups, 70 % accuracy (73 % sensitivity and 67 % specificity) for the TERTp-mutational subgroups, and 84 % accuracy (86 % sensitivity, 81 % specificity) for the TERTp-only subgroup using attention gates. CONCLUSIONS: DSC-MRI can be utilized to noninvasively classify IDH- and TERTp-based molecular subgroups of gliomas, facilitating preoperative identification of these genetic alterations.

Optimized multi-voxel TE-averaged PRESS for glutamate detection in the human brain at 3T.

PURPOSE: To optimize possible combinations of echo times (TE) for multi-voxel TE-averaged Point RESolved Spectroscopy (PRESS) while reducing the total number of TEs required to separate glutamate (Glu) and glutamine (Gln) within a clinically feasible scan time. METHODS: General Approach to Magnetic resonance Mathematical Analysis (GAMMA) was used to implement 2D J-resolved PRESS technique, and the spectra of 14 individual brain metabolites were simulated at 64 different TEs. Monte Carlo simulations were used for selecting the best TE combinations to separate Glu and Gln using TE-averaged PRESS with a total number of two, three, four and five TEs. Single-voxel 1H-MRS data were acquired using 64 different TEs from a healthy volunteer on a clinical 3T MR scanner to validate the echo time combinations selected with simulations. Additionally, 2D 1H-MRSI data of eight healthy volunteers were acquired on a clinical 3T MR scanner using four different TEs that were determined by Monte Carlo simulations. Optimized TE-averaged PRESS spectra were created by averaging the spectra acquired at selected TEs. LCModel was used for spectral quantification. A Wilcoxon signed-rank test was used to detect statistically significant differences in Glu/Gln ratios between 35 ms PRESS and optimized TE-averaged PRESS data. RESULTS: Glu could be clearly separated from Gln at 2.35 ppm, using optimized TE-averaged PRESS with only four TEs (35, 37, 40, and 42 ms) that were selected through Monte Carlo simulations. Glu/Gln ratios were significantly higher in the optimized TE-averaged PRESS data of healthy volunteers than in the 35 ms PRESS data (P = 0.008). CONCLUSION: Optimized multi-voxel TE-averaged PRESS enabled faster and unobstructed quantification of Glu at multiple voxels in the human brain in vivo at 3T.

Shrinkage of olfactory amygdala connotes cognitive impairment in patients with Parkinson's disease.

During the caudo-rostral progression of Lewy pathology, the amygdala is involved relatively early in Parkinson's disease (PD). However, lesser is known about the volumetric differences at the amygdala subdivisions, although the evidence mainly implicates the olfactory amygdala. We aimed to investigate the volumetric differences between the amygdala's nuclear and sectoral subdivisions in the PD cognitive impairment continuum compared to healthy controls (HC). The volumes of nine nuclei of the amygdala were estimated with FreeSurfer (nuclear parcellation-NP) from T1-weighted images of PD patients with normal cognition (PD-CN), PD with mild cognitive impairment (PD-MCI), PD with dementia (PD-D), and HC. The appropriate nuclei were then merged to obtain three sectors of the amygdala (sectoral parcellation-SP). The nuclear and sectoral volumes were compared among the four groups and between the hyposmic and normosmic PD patients. There was a significant difference in the total amygdala volume among the four groups. In terms of nuclei, the bilateral cortico-amygdaloid transition area (CAT) and sectors superficial cortex-like region (sCLR) volumes of PD-MCI and PD-D were less than those of the PD-CN and HC. A linear discriminant analysis revealed that left CAT and left sCLR volumes classified the PD-CN and cognitively impaired PD (PD-CI: PD-MCI plus PD-D) with 90.7% accuracy according to NP and 85.2% accuracy to SP. Similarly, left CAT and sCLR volumes correctly identified the hyposmic and normosmic PD with 64.8% and 61.1% accuracies. Notably, the left olfactory amygdala volume successfully discriminated cognitive impairment in PD and could be used as neuroimaging-based support for PD-CI diagnosis. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09887-y.

Magnetic resonance spectroscopic correlates of progression free and overall survival in "glioblastoma, IDH-wildtype, WHO grade-4".

Background: The 2021 World Health Organization (WHO) Central Nervous System (CNS) Tumor Classification has suggested that isocitrate dehydrogenase wildtype (IDH-wt) WHO grade-2/3 astrocytomas with molecular features of glioblastoma should be designated as "Glioblastoma, IDH-wildtype, WHO grade-4." This study analyzed the metabolic correlates of progression free and overall survival in "Glioblastoma, IDH-wildtype, WHO grade-4" patients using short echo time single voxel 1H-MRS. Methods: Fifty-seven adult patients with hemispheric glioma fulfilling the 2021 WHO CNS Tumor Classification criteria for "Glioblastoma, IDH-wildtype, WHO grade-4" at presurgery time point were included. All patients were IDH1/2-wt and TERTp-mut. 1H-MRS was performed on a 3 T MR scanner and post-processed using LCModel. A Mann-Whitney U test was used to assess the metabolic differences between gliomas with or without contrast enhancement and necrosis. Cox regression analysis was used to assess the effects of age, extent of resection, presence of contrast enhancement and necrosis, and metabolic intensities on progression-free survival (PFS) and overall survival (OS). Machine learning algorithms were employed to discern possible metabolic patterns attributable to higher PFS or OS. Results: Contrast enhancement (p = 0.015), necrosis (p = 0.012); and higher levels of Glu/tCr (p = 0.007), GSH/tCr (p = 0.019), tCho/tCr (p = 0.032), and Glx/tCr (p = 0.010) were significantly associated with shorter PFS. Additionally, necrosis (p = 0.049), higher Glu/tCr (p = 0.039), and Glx/tCr (p = 0.047) were significantly associated with worse OS. Machine learning models differentiated the patients having longer than 12 months OS with 81.71% accuracy and the patients having longer than 6 months PFS with 77.41% accuracy. Conclusion: Glx and GSH have been identified as important metabolic correlates of patient survival among "IDH-wt, TERT-mut diffuse gliomas" using single-voxel 1H-MRS on a clinical 3 T MRI scanner.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 2.

Preoperative clinical MRI protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this second part, we review magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), susceptibility-weighted imaging (SWI), MRI-PET, MR elastography (MRE), and MR-based radiomics applications. The first part of this review addresses dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE) MRI, arterial spin labeling (ASL), diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting (MRF). EVIDENCE LEVEL: 3. TECHNICAL EFFICACY: Stage 2.

Advanced MR Techniques for Preoperative Glioma Characterization: Part 1.

Preoperative clinical magnetic resonance imaging (MRI) protocols for gliomas, brain tumors with dismal outcomes due to their infiltrative properties, still rely on conventional structural MRI, which does not deliver information on tumor genotype and is limited in the delineation of diffuse gliomas. The GliMR COST action wants to raise awareness about the state of the art of advanced MRI techniques in gliomas and their possible clinical translation or lack thereof. This review describes current methods, limits, and applications of advanced MRI for the preoperative assessment of glioma, summarizing the level of clinical validation of different techniques. In this first part, we discuss dynamic susceptibility contrast and dynamic contrast-enhanced MRI, arterial spin labeling, diffusion-weighted MRI, vessel imaging, and magnetic resonance fingerprinting. The second part of this review addresses magnetic resonance spectroscopy, chemical exchange saturation transfer, susceptibility-weighted imaging, MRI-PET, MR elastography, and MR-based radiomics applications. Evidence Level: 3 Technical Efficacy: Stage 2.

Assessment of myelin oligodendrocyte glycoprotein antibodies and magnetic resonance spectroscopy in childhood-onset systemic lupus erythematosus.

OBJECTIVES: Systemic lupus erythematosus (SLE) is a chronic inflammatory disease characterised by the presence of various autoantibodies. Mild cognitive impairment developing in patients without significant neuropsychiatric (NP) symptoms was thought to be the result of immune-mediated myelinopathy. We aimed to determine the role of myelin oligodendrocyte glycoprotein antibody (MOG-Ab) in the neurological manifestations of childhood-onset SLE (cSLE) and if there is a correlation between various metabolite peaks in magnetic resonance spectroscopy (MRS) and myelinopathy. METHODS: MOG-Ab levels were studied in all healthy subjects (n=28) and in all patients with (NPSLE=9) and without (non-NPSLE=36) overt neuropsychiatric manifestations. Twenty patients (all had a normal-appearing brain on plain magnetic resonance) in non-NPSLE and 20 subjects in healthy group met the MRS imaging standards for evaluation in which normal appearing brain on plain MR. RESULTS: A total of 45 cSLE (36 non-NPSLE and 9 NPSLE) subjects and 28 healthy children were recruited to the study. The mean age of the SLE patients at study time was 16.22±3.22 years. MOG-Ab was not detected in cSLE or in healthy group. There was no significant difference between the non-NPSLE group and healthy subjects in terms of choline, N-acetyl aspartate (NAA), creatine, NAA/creatine, and choline/creatine. CONCLUSIONS: There was no association of MOG-Ab with cSLE, whether NP manifestations were present or not. A causal relationship between immune-mediated myelinopathy and cognitive impairment could not be suggested, since there has been no patient with positive MOG-Ab and there has been no difference in choline, choline/creatine between groups.

Identifying overall survival in 98 glioblastomas using VASARI features at 3T.

PURPOSE: This study aims to evaluate qualitative and quantitative imaging metrics along with clinical features affecting overall survival in glioblastomas and to classify them into high survival and low survival groups based on 12, 19, and 24 months thresholds using machine learning. METHODS: The cohort consisted of 98 adult glioblastomas. A standard brain tumor magnetic resonance (MR) imaging protocol, was performed on a 3T MR scanner. Visually Accessible REMBRANDT Images (VASARI) features were assessed. A Kaplan-Meier survival analysis followed by a log-rank test and multivariate Cox regression analysis were used to investigate the effects of VASARI features along with the age, gender, the extent of resection, pre- and post-KPS, ki67 and P53 mutation status on overall survival. Supervised machine learning algorithms were employed to predict the survival of glioblastoma patients based on 12, 19, and 24 months thresholds. RESULTS: Tumor location (p<0.001), the proportion of non-enhancing component (p=0.0482), and proportion of necrosis (p=0.02) were significantly associated with overall survival based on Kaplan-Meier analysis. Multivariate Cox regression analysis revealed that increases in proportion of non-enhancing component (p=0.040) and proportion of necrosis (p=0.039) were significantly associated with overall survival. Machine-learning models were successful in differentiating patients living longer than 12 months with 96.40% accuracy (sensitivity=97.22%, specificity=95.55%). The classification accuracies based on 19 and 24 months survival thresholds were 70.87% (sensitivity=83.02%, specificity=60.11%) and 74.66% (sensitivity=67.58%, specificity=82.08%), respectively. CONCLUSION: Employing clinical and VASARI features together resulted in a successful classification of glioblastomas that would have a longer overall survival.

The effect of tumor shape irregularity on Gamma Knife treatment plan quality and treatment outcome: an analysis of 234 vestibular schwannomas.

The primary aim of Gamma Knife (GK) radiosurgery is to deliver high-dose radiation precisely to a target while conforming to the target shape. In this study, the effects of tumor shape irregularity (TSI) on GK dose-plan quality and treatment outcomes were analyzed in 234 vestibular schwannomas. TSI was quantified using seven different metrics including volumetric index of sphericity (VioS). GK treatment plans were created on a single GK-Perfexion/ICON platform. The plan quality was measured using selectivity index (SI), gradient index (GI), Paddick's conformity index (PCI), and efficiency index (EI). Correlation and linear regression analyses were conducted between shape irregularity features and dose plan indices. Machine learning was employed to identify the shape feature that predicted dose plan quality most effectively. The treatment outcome analysis including tumor growth control and serviceable hearing preservation at 2 years, were conducted using Cox regression analyses. All TSI features correlated significantly with the dose plan indices (P < 0.0012). With increasing tumor volume, vestibular schwannomas became more spherical (P < 0.05) and the dose plan indices varied significantly between tumor volume subgroups (P < 0.001 and P < 0.01). VioS was the most effective predictor of GK indices (P < 0.001) and we obtained 89.36% accuracy (79.17% sensitivity and 100% specificity) for predicting PCI. Our results indicated that TSI had significant effects on the plan quality however did not adversely affect treatment outcomes.

Identification of metabolic correlates of mild cognitive impairment in Parkinson's disease using magnetic resonance spectroscopic imaging and machine learning.

OBJECTIVE: To investigate metabolic changes of mild cognitive impairment in Parkinson's disease (PD-MCI) using proton magnetic resonance spectroscopic imaging (1H-MRSI). METHODS: Sixteen healthy controls (HC), 26 cognitively normal Parkinson's disease (PD-CN) patients, and 34 PD-MCI patients were scanned in this prospective study. Neuropsychological tests were performed, and three-dimensional 1H-MRSI was obtained at 3 T. Metabolic parameters and neuropsychological test scores were compared between PD-MCI, PD-CN, and HC. The correlations between neuropsychological test scores and metabolic intensities were also assessed. Supervised machine learning algorithms were applied to classify HC, PD-CN, and PD-MCI groups based on metabolite levels. RESULTS: PD-MCI had a lower corrected total N-acetylaspartate over total creatine ratio (tNAA/tCr) in the right precentral gyrus, corresponding to the sensorimotor network (p = 0.01), and a lower tNAA over myoinositol ratio (tNAA/mI) at a part of the default mode network, corresponding to the retrosplenial cortex (p = 0.04) than PD-CN. The HC and PD-MCI patients were classified with an accuracy of 86.4% (sensitivity = 72.7% and specificity = 81.8%) using bagged trees. CONCLUSION: 1H-MRSI revealed metabolic changes in the default mode, ventral attention/salience, and sensorimotor networks of PD-MCI patients, which could be summarized mainly as 'posterior cortical metabolic changes' related with cognitive dysfunction.

Detection of visual and frontoparietal network perfusion deficits in Parkinson's disease dementia.

Mild cognitive impairment of Parkinson's disease (PD) may be an early manifestation that may progressively worsen to dementia. Cognitive decline has been associated with changes in the brain perfusion pattern. This study aimed to evaluate cerebral blood flow (CBF) deficits specific to different stages of cognitive decline. Seventeen patients with cognitively normal PD (PD-CN), 18 patients with PD with mild cognitive impairment (PD-MCI), and 16 patients with PD with dementia (PDD) were included in this study. The participants were scanned using a 3 T Philips MRI scanner. Arterial spin labelling magnetic resonance (ASL-MR) images were acquired, followed by calculation of the CBF maps, and registration onto the MNI152 brain atlas. A whole-brain voxel-based CBF comparison was performed among the patient groups using age as a covariate. The mean age of patients with PDD was significantly higher than that of patients with PD-MCI (P = 0.015) and PD-CN (P = 0.001). The CBF values of the three groups were significantly different in the left cuneus of the visual network (VN), left inferior frontal gyrus of the frontoparietal network (FPN), and left dorsomedial nucleus of the thalamus. PDD had lower perfusion values than PD-MCI group in the same regions detected in the main group analysis. Additionally, comparison of PDD with PD-CN and non-demented groups revealed that the perfusion reduction extended into the bilateral cuneus of the VN, bilateral thalami, and left inferior frontal gyrus of the FPN. PDD could be separated from PD-MCI and PD-CN stages with CBF deficits in non-dopaminergically mediated posterior and dopaminergically mediated frontal networks.

Electrical status epilepticus in sleep: The role of thalamus in etiopathogenesis.

PURPOSE: In patients diagnosed with epilepsy, decreased ratio of N-acetyl aspartate to creatine (NAA/Cr) measured in magnetic resonance spectroscopy (MRS) has been accepted as a sign of neuronal cell loss or dysfunction. In this study, we aimed to determine whether a similar neuronal cell loss is present in a group of encephalopathy with electrical status epilepticus in sleep (ESES) patients METHODS: We performed this case-control study at a tertiary pediatric neurology center with patients with ESES. Inclusion criteria for the patient group were as follows: 1) a spike-wave index of at least 50%, 2) acquired neuropsychological regression, 3) normal cranial MRI. Eventually, a total of 21 patients with ESES and 17 control subjects were enrolled in the study. MRI of all control subjects was also within normal limits. 3D Slicer program was used for the analysis of thalamic and brain volumes. LCModel spectral fitting software was used to analyze single-voxel MRS data from the right and left thalamus of the subjects. RESULTS: The mean age was 8.0 ± 1.88 years and 8.3 ± 1.70 years in ESES patients and the control subjects. After correcting for the main potential confounders (age and gender) with a linear regression model, NAA/Creatine ratio of the right thalamus was significantly lower in the ESES patient group compared to the healthy control group (p = 0.026). Likewise, the left thalamus NAA/Cr ratio was significantly lower in the ESES patient group than the healthy control group (p = 0.007). After correcting for age and gender, right thalamic volume was not statistically significantly smaller in ESES patients than in healthy controls (p = 0.337), but left thalamic volume was smaller in ESES patients than in healthy controls (p = 0.024). CONCLUSION: In ESES patients, the NAA/Creatine ratio, which is an indicator of neuronal cell loss or dysfunction in the right and left thalamus, which appears regular on MRI, was found to be significantly lower than the healthy control group. This metabolic-induced thalamic dysfunction, which was reported for the first time up to date, may play a role in ESES epileptogenesis.

The Open Brain Consent: Informing research participants and obtaining consent to share brain imaging data.

Having the means to share research data openly is essential to modern science. For human research, a key aspect in this endeavor is obtaining consent from participants, not just to take part in a study, which is a basic ethical principle, but also to share their data with the scientific community. To ensure that the participants' privacy is respected, national and/or supranational regulations and laws are in place. It is, however, not always clear to researchers what the implications of those are, nor how to comply with them. The Open Brain Consent (https://open-brain-consent.readthedocs.io) is an international initiative that aims to provide researchers in the brain imaging community with information about data sharing options and tools. We present here a short history of this project and its latest developments, and share pointers to consent forms, including a template consent form that is compliant with the EU general data protection regulation. We also share pointers to an associated data user agreement that is not only useful in the EU context, but also for any researchers dealing with personal (clinical) data elsewhere.

The cerebral blood flow deficits in Parkinson's disease with mild cognitive impairment using arterial spin labeling MRI.

Parkinson's disease (PD) with mild cognitive impairment (PD-MCI) is currently diagnosed based on an arbitrarily predefined standard deviation of neuropsychological test scores, and more objective biomarkers for PD-MCI diagnosis are needed. The purpose of this study was to define possible brain perfusion-based biomarkers of not only mild cognitive impairment, but also risky gene carriers in PD using arterial spin labeling magnetic resonance imaging (ASL-MRI). Fifteen healthy controls (HC), 26 cognitively normal PD (PD-CN), and 27 PD-MCI subjects participated in this study. ASL-MRI data were acquired by signal targeting with alternating radio-frequency labeling with Look-Locker sequence at 3 T. Single nucleotide polymorphism genotyping for rs9468 [microtubule-associated protein tau (MAPT) H1/H1 versus H1/H2 haplotype] was performed using a Stratagene Mx3005p real-time polymerase chain-reaction system (Agilent Technologies, USA). There were 15 subjects with MAPT H1/H1 and 11 subjects with MAPT H1/H2 within PD-MCI, and 33 subjects with MAPT H1/H1 and 19 subjects with MAPT H1/H2 within all PD. Voxel-wise differences of cerebral blood flow (CBF) values between HC, PD-CN and PD-MCI were assessed by one-way analysis of variance followed by pairwise post hoc comparisons. Further, the subgroup of PD patients carrying the risky MAPT H1/H1 haplotype was compared with noncarriers (MAPT H1/H2 haplotype) in terms of CBF by a two-sample t test. A pattern that could be summarized as "posterior hypoperfusion" (PH) differentiated the PD-MCI group from the HC group with an accuracy of 92.6% (sensitivity = 93%, specificity = 93%). Additionally, the PD patients with MAPT H1/H1 haplotype had decreased perfusion than the ones with H1/H2 haplotype at the posterior areas of the visual network (VN), default mode network (DMN), and dorsal attention network (DAN). The PH-type pattern in ASL-MRI could be employed as a biomarker of both current cognitive impairment and future cognitive decline in PD.

Neuroinflammation Mediators are Reduced in Sera of Parkinson's Disease Patients with Mild Cognitive Impairment.

INTRODUCTION: Cognitive impairment is common in Parkinson's disease (PD) and PD patients with mild cognitive impairment (PD-MCI) are at increased risk of developing Parkinson's disease dementia (PDD). Reliable biomarkers are required for objective identification of cognitive decline in PD. In this pilot study, serum levels of well-known mediators of neuroinflammation were measured in PD patients with or without MCI to find out the involvement of neuroinflammation and microglial activation in PD-MCI. METHODS: 36 PD-MCI, 25 PD patients with normal cognition (PD-NC) and 19 healthy controls were recruited. Serum levels of NLR family pyrin domain containing 1 (NLRP1), NLRP3, caspase-1, NF-kB, IL-1b and IL-18 were measured by ELISA and a panel of neuropsychological tests was administered. RESULTS: PD-MCI patients showed significantly reduced levels of NF-kB, IL-1b and IL-18, whereas NLRP1, NLRP3 and caspase-1 levels were comparable among PD-NC and PD-MCI patients. IL-18 levels were positively correlated with Addenbrooke's Cognitive Examination-Revised and Symbol Digit Modalities Test scores. CONCLUSION: Levels of several microglial activation mediators are reduced in PD-MCI patients inferring a protective role to certain inflammation factors against cognitive decline in PD.

Identification of IDH and TERTp mutation status using 1 H-MRS in 112 hemispheric diffuse gliomas.

BACKGROUND: There is a growing interest in noninvasively defining molecular subsets of hemispheric diffuse gliomas based on the isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutation status, which correspond to distinct tumor entities, and differ in demographics, natural history, treatment response, recurrence, and survival patterns. PURPOSE: To investigate whether metabolite levels detected with short echo time (TE) proton MR spectroscopy (1 H-MRS) at 3T can be used for noninvasive molecular classification of IDH and TERTp mutation-based subsets of gliomas. STUDY TYPE: Retrospective. SUBJECTS: In all, 112 hemispheric diffuse gliomas (70 males/42 females, mean age: 42.1 ± 13.9 years). FIELD STRENGTH/SEQUENCE: Short-TE 1 H-MRS (repetition time (TR) = 2000 msec, TE = 30 msec, number of signal averages = 192) and routine clinical brain tumor MR protocols were acquired at 3T. ASSESSMENT: 1 H-MRS data were quantified using LCModel software. TERTp and IDH1 or IDH2 (IDH1/2) mutations in the tissue were determined by either minisequencing or Sanger sequencing. STATISTICAL TESTS: Metabolic differences between IDH mutant and IDH wildtype gliomas were assessed by a Mann-Whitney U-test. A Kruskal-Wallis test followed by a Tukey-Kramer test was used to analyze metabolic differences between IDH and TERTp mutational molecular subsets of gliomas. A Spearman rank correlation coefficient was used to assess the correlations of metabolite intensities with the Ki-67 index. Furthermore, machine learning was employed to classify the IDH and TERTp mutational status of gliomas, and the accuracy, sensitivity, and specificity values were estimated. RESULTS: Short-TE 1 H-MRS classified the presence of an IDH mutation with 88.39% accuracy, 76.92% sensitivity, and 94.52% specificity, and a TERTp mutation within primary IDH wildtype gliomas with 92.59% accuracy, 83.33% sensitivity, and 95.24% specificity. DATA CONCLUSION: Short-TE 1 H-MRS could be used to identify molecular subsets of hemispheric diffuse gliomas corresponding to IDH and TERTp mutations. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1799-1809.