High-resolution Magnetic Resonance Imaging Data Research Articles

Feasibility and efficiency of a new classification based on high-resolution MRI for carotid artery pseudo-occlusion and occlusion: Hybrid revascularization pilot study

Background and purposeStudies on changes in the distal internal carotid artery based on high resolution magnetic resonance imaging (HRMRI) are scarce. Herein, we propose a histological classification system for patients with carotid artery pseudo-occlusion or occlusion based on preoperative HRMRI, for which we evaluated the feasibility and clinical implications. Materials and methodsFrom January 2017 to June 2021, 40 patients with Doppler ultrasound, CTA or MRA suggesting carotid artery occlusion were enrolled in this study. A new classification system based on HRMRI was established and subsequently verified by postoperative specimens. We recorded and analyzed patient characteristics, HRMRI data, recanalization rate, requirements of additional endovascular procedures, complications, and outcomes. ResultsFour histological classifications (type Ⅰ–Ⅳ) were identified. According to our classification system, 20 patients (50.00%) were type I, nine (22.50%) were type II, 7 (17.50%) were type III, and four (10.00%) were type Ⅳ. The success rate of recanalization was 88.89% (32/36) in type I–III patients. Four (44.44%) type Ⅱ patients and five (71.43%) type Ⅲ patients suffered from intraoperative dissection. ConclusionPatients identified as types I (pseudo-occlusion) and II (thrombotic-occlusion) were able to be treated via hybrid revascularization with relatively low risk, while patients identified as type III (fibrous-occlusion) required more careful treatment. Recanalization is not suitable for patients identified as type Ⅳ. Our proposed classification system based on HRMRI data can be used as an adjunctive guide to predict the technical feasibility and success of revascularization via a hybrid technique.

Identification of high-risk intracranial plaques with 3D high-resolution magnetic resonance imaging-based radiomics and machine learning.

Identifying high-risk intracranial plaques is significant for the treatment and prevention of stroke. To develop a high-risk plaque model using three-dimensional (3D) high-resolution magnetic resonance imaging (HRMRI) based radiomics features and machine learning. 136 patients with documented symptomatic intracranial artery stenosis and available HRMRI data were included. Among these patients, 136 and 92 plaques were identified as symptomatic and asymptomatic plaques, respectively. A conventional model was developed by recording and quantifying the radiological plaque characteristics. Radiomics features from T1-weighted images (T1WI) and contrast-enhanced T1WI (CE-T1WI) were used to construct a high-risk plaque model with linear support vector classification (linear SVC). The radiological and radiomics features were combined to build a combined model. Receiver operating characteristic (ROC) curves were used to evaluate these models. Plaque length, burden, and enhancement were independently associated with clinical symptoms and were included in the conventional model, which had an AUC of 0.853 vs. 0.837 in the training and test sets. While the radiomics and the combined model showed an improved AUC: 0.923 vs. 0.925 for the training sets and 0.906 vs. 0.903 in the test sets. Both the radiomics model (p = 0.024, p = 0.018) and combined model (p = 0.042, p = 0.049) outperformed the conventional model in the two sets, whereas the performance of the combined model was not significantly different from that of the radiomics model in the two sets (p = 0.583 and p = 0.606). The radiomics model based on 3D HRMRI can accurately differentiate symptomatic from asymptomatic intracranial arterial plaques and significantly outperforms the conventional model.

Single MR image super-resolution via channel splitting and serial fusion network

In magnetic resonance imaging (MRI), spatial resolution is an important and critical imaging parameter that represents how much information is contained in a unit space. Acquiring high-resolution MRI data usually takes a long scanning time and is subject to motion artifacts due to hardware, physical, and physiological limitations. Single image super-resolution (SISR) based on deep learning is an effective and promising alternative technique to improve the native spatial resolution of magnetic resonance (MR) images. However, because of the simple diversity and single distribution of training samples, the effective training of deep models with medical training samples and improvement of the tradeoff between model performance and computing overhead are major challenges. In addition, deeper networks are more difficult to effectively train since the information is gradually weakened as the network deepens. In this paper, a novel channel splitting and serial fusion network (CSSFN) is presented for single MR image super-resolution. The proposed CSSFN splits hierarchical features into a series of subfeatures, which are then integrated together in a serial manner. Hence, the network becomes deeper and can discriminatively and reasonably deal with the subfeatures. Moreover, a dense global feature fusion (DGFF) is adopted to integrate the intermediate features, which further promotes the information flow in the network and helps to stabilize model training. Extensive experiments on several typical MR images show the superiority of our CSSFN models to other advanced SISR methods.

A Holographic Augmented Reality Interface for Visualizing of MRI Data and Planning of Neurosurgical Procedures.

The recent introduction of wireless head-mounted displays (HMD) promises to enhance 3D image visualization by immersing the user into 3D morphology. This work introduces a prototype holographic augmented reality (HAR) interface for the 3D visualization of magnetic resonance imaging (MRI) data for the purpose of planning neurosurgical procedures. The computational platform generates a HAR scene that fuses pre-operative MRI sets, segmented anatomical structures, and a tubular tool for planning an access path to the targeted pathology. The operator can manipulate the presented images and segmented structures and perform path-planning using voice and gestures. On-the-fly, the software uses defined forbidden-regions to prevent the operator from harming vital structures. In silico studies using the platform with a HoloLens HMD assessed its functionality and the computational load and memory for different tasks. A preliminary qualitative evaluation revealed that holographic visualization of high-resolution 3D MRI data offers an intuitive and interactive perspective of the complex brain vasculature and anatomical structures. This initial work suggests that immersive experiences may be an unparalleled tool for planning neurosurgical procedures.

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder.

Individual‐based morphological brain networks built from T1‐weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio‐cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual‐based morphological networks were constructed by using high‐resolution structural MRI data from 40 young children with ASD (age range: 2–8 years) and 38 age‐, gender‐, and handedness‐matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small‐worldness (i.e., σ) of individual‐level morphological brain networks, increased morphological connectivity in cortico‐striatum‐thalamic‐cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico‐cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio‐cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically‐relevant biomarkers for ASD.

A Cadaveric and Magnetic Resonance Imaging Investigation of the Salpingopharyngeus.

Purpose The aim of the study was to update our information regarding the salpingopharyngeus (SP) muscle using cadaveric and in vivo magnetic resonance imaging (MRI) data. Primary objectives were to (a) observe the presence/absence of the muscle and (b) quantify and describe its dimensions and course. Method SP specimens from 19 cadavers (10 women, nine men) were analyzed. Following head bisection, measurements of SP, including width of the cartilaginous attachment (CW) and width of the superior muscle base (SMW), were taken before and after removal of the overlying mucosa. In addition, SP was analyzed in 15 healthy subjects (eight men, seven women) using high-resolution three-dimensional MRI data. CW and SMW measures were replicated in the paraxial MRI view. Results The presence of the salpingopharyngeal fold and muscle was confirmed bilaterally in all cadaveric and living subjects. Following mucosa removal, mean cadaveric CW and SMW measurements were 5.6 and 3.8 mm, respectively. Mean in vivo CW and SMW were 6.1 and 3.7 mm, respectively. Results from the hierarchical regression analyses revealed that, in both cadaveric and living groups, SMW is dependent on the relationship between age and body weight, after controlling for sex. Conclusions The salpingopharyngeal fold and SP muscle are always present bilaterally and can be quantified at the superior origin using both cadaveric and in vivo three-dimensional MRI data. Though both the superior origin and inferior course of SP are highly variable, the size of the SP muscle is dependent on characteristics known to affect muscle fibers, such as the relationship between age and body weight. Given the consistent and quantifiable presence of the SP muscle, its potential role in velopharyngeal function for speech and swallowing is reconsidered. Supplemental Material https://doi.org/10.23641/asha.14347859.

Association of Epigenetic Metrics of Biological Age With Cortical Thickness

Magnetic resonance imaging (MRI) studies of aging adults have shown substantial intersubject variability across various brain metrics, and some of this variability is likely attributable to chronological age being an imprecise measure of age-related change. Accurately quantifying one's biological age could allow better quantification of healthy and pathological changes in the aging brain. To investigate the association of DNA methylation (DNAm)-based biological age with cortical thickness and to assess whether biological age acceleration compared with chronological age captures unique variance in cortical thinning. This cross-sectional study used high-resolution structural brain MRI data collected from a sample of healthy aging adults who were participating in a larger ongoing neuroimaging study that began in May 2014. This population-based study accrued participants from the greater Omaha, Nebraska, metropolitan area. One hundred sixty healthy adults were contacted for the MRI component, 82 of whom participated in both DNAm and MRI study components. Data analysis was performed from March to June 2019. Vertexwise cortical thickness, DNAm-based biological age, and biological age acceleration compared with chronological age were measured. A pair of multivariable regression models were computed in which cortical thickness was regressed on DNAm-based biological age, controlling for sex in the first model and also controlling for chronological age in the second model. Seventy-nine adult participants (38 women; mean [SD] age, 43.82 [14.50] years; age range, 22-72 years) were included in all final analyses. Advancing biological age was correlated with cortical thinning across frontal, superior temporal, inferior parietal, and medial occipital regions. In addition, biological age acceleration relative to chronological age was associated with cortical thinning in orbitofrontal, superior and inferior temporal, somatosensory, parahippocampal, and fusiform regions. Specifically, for every 1 year of biological age acceleration, cortical thickness would be expected to decrease by 0.024 mm (95% CI, -0.04 to -0.01 mm) in the left orbitofrontal cortex (partial r, -0.34; P = .002), 0.014 mm (95% CI, -0.02 to -0.01 mm) in the left superior temporal gyrus (partial r, -0.36; P = .001), 0.015 mm (95% CI, -0.02 to -0.01 mm) in the left fusiform gyrus (partial r, -0.38; P = .001), 0.015 mm (95% CI, -0.02 to -0.01 mm) in the right fusiform gyrus (partial r, -0.43; P < .001), 0.019 mm (95% CI, -0.03 to -0.01 mm) in the right inferior temporal sulcus (partial r, -0.34; P = .002), and 0.011 mm (95% CI, -0.02 to -0.01 mm) in the right primary somatosensory cortex (partial r, -0.37; P = .001). To our knowledge, this is the first study to investigate vertexwise cortical thickness in relation to DNAm-based biological age, and the findings suggest that this metric of biological age may yield additional insight on healthy and pathological cortical aging compared with standard measures of chronological age alone.

Distinct topographic-anatomical patterns in primary and secondary brain tumors and their therapeutic potential

PurposeUnderstanding the topographic-anatomical patterns of brain tumors has the potential to improve our pathophysiological understanding and may allow for anatomical tailoring of surgery and radiotherapy. This study analyzed topographic-anatomical patterns underlying neuroepithelial tumors, primary CNS lymphoma and metastases.MethodsAny histologically confirmed supra- or infratentorial parenchymal neoplasia of one institution over a 4-year period was included. Using high-resolution magnetic resonance imaging data, a detailed analysis of the topographic-anatomical tumor features was performed. Differences between neuroepithelial tumors, primary central nervous system lymphoma (PCNSL) and metastases were assessed using pairwise comparisons adjusted for multiple testing, upon significance of the omnibus test.ResultsBased on image analysis of 648 patients—419 (65%) neuroepithelial tumors, 28 (5%) PCNSL and 201 (31%) metastases—entity-specific topographic-anatomical patterns were identified. Neuroepithelial tumors showed a radial ventriculo-cortical orientation, inconsistent with the current belief of a growth along white matter tracts, whereas the pattern in PCNSL corresponded to a growth along such. Metastases preferentially affected the cortex and subcortical white matter of large arteries’ terminal supply areas. This study provides a comprehensive anatomical description of the topography of NT, PCNSL and metastases intended to serve as a topographic reference for clinicians and neuroscientists.ConclusionsThe identified distinct anatomical patterns provide evidence for a specific interaction between tumor and anatomical structures, following a pathoclitic concept. Understanding differences in their anatomical behavior has the potential to improve our pathophysiological understanding and to tailor therapy of brain tumors.

Erratum: Hippocampal Subfields and Limbic White Matter Jointly Predict Learning Rate in Older Adults.

Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82 years (Mage = 69.66, SDage = 3.92 years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.

Relationship between rectal abdominis muscle position and physiological umbilical herniation and return: A morphological and morphometric study.

The herniation of the intestinal loop (IL) in the extraembryonic coelom and its return to abdominal cavity is in parallel with the formation of the rectal abdominis muscle (RAM). Using high-resolution magnetic resonance imaging data of human fetuses (n = 19, CRL22-69 mm; stored at Kyoto Collection), this study aimed to analyze the relationship between the development of RAM and phase of IL herniation. The RAM runs at the lateral part of the abdominal wall in the small samples in the herniation phase. The position was shifted to the midline area in the larger samples in the herniation phase. According to fetal growth, the caudal ends of the muscles extended along the umbilical ring towards the pubis, though the caudal part of the RAMs were thin and faint in most of the samples. Length measurements related with the growth of the abdominal wall including RAM and abdominal circumference showed positive correlation with fetal growth. On the contrary, diastasis of RAMs and the width and area of the umbilical ring were almost constant according to fetal growth. Such morphometric value showed no obvious changes regardless of the phases of herniation. The ratio of the width and diastasis of the RAMs to the circumference was decreased, indicating that the closure of the ventral body wall was influenced by growth differences. The present data indicate that the formation of the abdominal wall including RAM is independent of the phase of IL herniation, whether in the extraembryonic coelom or in the abdominal cavity.

Hippocampal Subfields and Limbic White Matter Jointly Predict Learning Rate in Older Adults.

Age-related memory impairments have been linked to differences in structural brain parameters, including cerebral white matter (WM) microstructure and hippocampal (HC) volume, but their combined influences are rarely investigated. In a population-based sample of 337 older participants aged 61-82years (Mage = 69.66, SDage = 3.92years), we modeled the independent and joint effects of limbic WM microstructure and HC subfield volumes on verbal learning. Participants completed a verbal learning task of recall over five repeated trials and underwent magnetic resonance imaging (MRI), including structural and diffusion scans. We segmented three HC subregions on high-resolution MRI data and sampled mean fractional anisotropy (FA) from bilateral limbic WM tracts identified via deterministic fiber tractography. Using structural equation modeling, we evaluated the associations between learning rate and latent factors representing FA sampled from limbic WM tracts, and HC subfield volumes, and their latent interaction. Results showed limbic WM and the interaction of HC and WM-but not HC volume alone-predicted verbal learning rates. Model decomposition revealed HC volume is only positively associated with learning rate in individuals with higher WM anisotropy. We conclude that the structural characteristics of limbic WM regions and HC volume jointly contribute to verbal learning in older adults.

Quantitative score of the vessel morphology in middle cerebral artery atherosclerosis

BackgroundWe aimed to quantitatively assess the vessel morphology of middle cerebral artery (MCA) atherosclerosis and explore its value in discriminating plaque types. MethodsPatients were selected from a high-resolution magnetic resonance imaging (HRMRI) study from January 2007 to December 2015. One hundred and three patients with acute cerebral infarcts due to MCA stenosis (>50%) and eighty-nine patients with asymptomatic MCA stenosis (>50%) were included. Quantitative measurements of MCA morphology, including lumen area, outer-wall and wall area at stenotic site and reference site, stenotic degree, plaque length, remodeling index and plaque eccentricity, were performed on HRMRI with observers blinded to clinical presentations. Firth's penalized logistic regression analysis was used to construct a symptomatic plaque score (SPS) model. Then, the HRMRI data of 39 patients prospectively enrolled from January 2016 to January 2017 were used to validate the SPS model. ResultsThe HRMRI data of 103 patients with symptomatic MCA stenosis and 89 patients with asymptomatic MCA stenosis in the construction cohort were analyzed. Four main factors were found to be associated with symptomatic plaques: stenotic lumen area ≥ 2.28 mm2, stenotic wall area ≥ 8.88 mm2, plaque length and presence of an eccentric plaque. Summation of each logistic regression coefficient multiplying the corresponding score produced the SPS with an area under curve (AUC) of 0.890 on receiver operating characteristics analysis. Validation of the score of 39 plaques (19 symptomatic and 20 asymptomatic) revealed an AUC of 0.862, confirming the continued diagnostic ability. When the data were pooled in all 235 plaques, the optimal cutoff score of discriminating symptomatic and asymptomatic plaques was 2.79 (SPS ≥ 2.79 indicating a symptomatic plaque) with AUC = 0.886, sensitivity 81.1% and specificity 80.5%. ConclusionsThe quantitative analysis of MCA morphology can independently and accurately discriminate plaque types, suggesting its close association with the underlying pathophysiology. Further prospective studies are required to verify whether the SPS model is clinically valuable in monitoring plaque progression and assessing the vulnerability.

18F]-florbetaben PET/CT Imaging in the Alzheimer's Disease Mouse Model APPswe/PS1dE9.

Positron-emission-tomography (PET) using 18F labeled florbetaben allows noninvasive in vivo-assessment of amyloid-beta (Aβ), a pathological hallmark of Alzheimer's disease (AD). In preclinical research, [18F]-florbetaben-PET has already been used to test the amyloid-lowering potential of new drugs, both in humans and in transgenic models of cerebral amyloidosis. The aim of this study was to characterize the spatial pattern of cerebral uptake of [18F]-florbetaben in the APPswe/ PS1dE9 mouse model of AD in comparison to histologically determined number and size of cerebral Aβ plaques. Both, APPswe/PS1dE9 and wild type mice at an age of 12 months were investigated by smallanimal PET/CT after intravenous injection of [18F]-florbetaben. High-resolution magnetic resonance imaging data were used for quantification of the PET data by volume of interest analysis. The standardized uptake values (SUVs) of [18F]-florbetaben in vivo as well as post mortem cerebral Aβ plaque load in cortex, hippocampus and cerebellum were analyzed. Visual inspection and SUVs revealed an increased cerebral uptake of [18F]-florbetaben in APPswe/ PS1dE9 mice compared with wild type mice especially in the cortex, the hippocampus and the cerebellum. However, SUV ratios (SUVRs) relative to cerebellum revealed only significant differences in the hippocampus between the APPswe/PS1dE9 and wild type mice but not in cortex; this differential effect may reflect the lower plaque area in the cortex than in the hippocampus as found in the histological analysis. The findings suggest that histopathological characteristics of Aβ plaque size and spatial distribution can be depicted in vivo using [18F]-florbetaben in the APPswe/PS1dE9 mouse model.

Improving the Predictions of Computational Models of Convection-Enhanced Drug Delivery by Accounting for Diffusion Non-gaussianity.

Convection-enhanced delivery (CED) is an innovative method of drug delivery to the human brain, that bypasses the blood-brain barrier by injecting the drug directly into the brain. CED aims to target pathological tissue for central nervous system conditions such as Parkinson's and Huntington's disease, epilepsy, brain tumors, and ischemic stroke. Computational fluid dynamics models have been constructed to predict the drug distribution in CED, allowing clinicians advance planning of the procedure. These models require patient-specific information about the microstructure of the brain tissue, which can be collected non-invasively using magnetic resonance imaging (MRI) pre-infusion. Existing models employ the diffusion tensor, which represents Gaussian diffusion in brain tissue, to provide predictions for the drug concentration. However, those predictions are not always in agreement with experimental observations. In this work we present a novel computational fluid dynamics model for CED that does not use the diffusion tensor, but rather the diffusion probability that is experimentally measured through diffusion MRI, at an individual-participant level. Our model takes into account effects of the brain microstructure on the motion of drug molecules not taken into account in previous approaches, namely the restriction and hindrance that those molecules experience when moving in the brain tissue, and can improve the drug concentration predictions. The duration of the associated MRI protocol is 19 min, and therefore feasible for clinical populations. We first prove theoretically that the two models predict different drug distributions. Then, using in vivo high-resolution diffusion MRI data from a healthy participant, we derive and compare predictions using both models, in order to identify the impact of including the effects of restriction and hindrance. Including those effects results in different drug distributions, and the observed differences exhibit statistically significant correlations with measures of diffusion non-Gaussianity in brain tissue. The differences are more pronounced for infusion in white-matter areas of the brain. Using experimental results from the literature along with our simulation results, we show that the inclusion of the effects of diffusion non-Gaussianity in models of CED is necessary, if reliable predictions that can be used in the clinic are to be generated by CED models.

Support Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging.

Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal “affective” circuit, the dorsolateral, prefronto-striatal “executive” circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.

Radiation-induced gray matter atrophy in patients with nasopharyngeal carcinoma after intensity modulated radiotherapy: a MRI magnetic resonance imaging voxel-based morphometry study.

Gray matter (GM) damage after radiotherapy (RT) in nasopharyngeal carcinoma (NPC) patients can result in cognitive impairment, while there may be no visible brain tissue change according to the conventional magnetic resonance imaging (MRI). This study investigated radiation-induced GM volume differences between NPC patients who received RT and those who did not. High-resolution brain structural MRI data from two groups of patients were acquired. The pre-RT group was composed of 56 newly diagnosed but not yet medically treated NPC patients, while the after-RT group consisted of 40 NPC patients who had completed RT more than 1 year ago. Voxel-based morphometry (VBM) was applied to assess GM volumes. Two sample t-test was used to analyze GM volumes voxel-by-voxel using the VBM8 toolbox built in the SPM software. Radiation-induced cortical volume alteration in all NPC patients after RT and dosimetry of 36 patients were analyzed. Compared to pre-treatment group, cortical volumes of GM were significantly smaller in the left hippocampus, the right pulvinar and the right middle temporal gyrus (MTG, P<0.001, AlphaSim correction, cluster size ≥157). The mean dose (Dmean) for bilateral hippocampal heads were significantly higher than other different parts of the brain (P<0.001). No significant correlations between the GM volume in any brain regions and the mean dose of corresponding position of these brain regions were observed (P>0.05). Radiation to the NPC patients can not only induce damage of the hippocampus, but also other secondary damages of GM.

P79. The effect of cognitive reserve in a subcohort of the EMPIR project NeuroMet ‘Innovative measurements for improved diagnosis and management of neurodegenerative diseases’

Background One of the most common neurodegenerative diseases is Alzheimer’s disease (AD). Maintaining a healthy life style can reduce the risk of AD due to cognitive and brain reserve, but the influence of reserve is often difficult to measure. In this ongoing study, we use 7 T high resolution MRI data from the EMPIR project NeuroMet to explore cognitive reserve in more detail. In NeuroMet, the diverse expertise of various national measurement institutes, including both clinicians and academics, joined together to improve accurate diagnostic and thereby approach better therapy. We present an overview of the project, which aims to (1) develop improved magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) methods, (2) understand bias and uncertainty of immunoassays and digital PCR methods for neurodegenerative disease protein and miRNA biomarkers, (3) develop reference methods for tau, and (4) improve cognitive assessment questionnaire for early diagnosis of Alzheimer’s disease. Methods In this project, a total of 90 participants are being recruited (30 Healthy Control, 30 Mild Cognitive Impairment and 30 AD patients). All subjects are undergoing a battery of neuropsychological tests. With a focus on reserve, the relationship between memory scores and hippocampal volumetric data obtained from T1 weighted MRI images of a 7T MRI scanner (Siemens Magnetom, Erlangen, Germany) will be analyzed. Additionally, participants are being asked to fill in a self-administered lifestyle questionnaire which assesses current physical activity and nutrition. The influence of lifestyle factors will be investigated and preliminary results will be presented. For general blood count and APOE genotyping, blood samples are being drawn. Immunoassays (Meso Scale Discovery) are being developed to overcome matrix effects and achieve duplexed detection of As40 and As42 within human plasma. Available cerebrospinal fluid (CSF) samples are being tested performing digital PCR for a panel of proteins and miRNA biomarkers, all of which might be used to obtain further insights into cognitive reserve. Furthermore, liquid chromatography mass spectrometry (LC-MS, triple quadrupole and quadrupole time of flight) is performed on blood and CSF samples, to investigate concentrations of the mentioned biomarkers. This diversity of different cutting-edge measures, like ultra-high field MRI and MRS, LC-MS, miRNA assays, new immunoassays, and digital PCR methods, will provide anatomical, functional, and metabolic information in order to improve sensitivity, resolution and delineation for volume, connectivity, and metabolite quantification. Conclusion The NeuroMet project aims to improve diagnostic measurements for AD, which are important to better understand the effects of cognitive and brain reserve in detail.

Quantitative prediction of individual cognitive flexibility using structural MRI.

Cognitive flexibility, a core dimension of executive functions, refers to one's ability to switch between multiple tasks and sets in a quick and flexible manner. However, whether objective neuroimaging can be used to quantitatively predict cognitive flexibility at the individual level remains largely unexplored. High-resolution magnetic resonance imaging data of 100 healthy young participants from the Human Connectome Project (HCP) dataset were used to calculate gray matter volume (GMV). Cognitive flexibility was assessed by the Dimensional Change Card Sort Test (DCCS). Using a multivariate machine learning technique known as relevance vector regression (RVR), we examined the relationship between GMV and cognitive flexibility performance. We found that the application of RVR to GMV allowed quantitative prediction of the DCCS scores with statistically significant accuracy (correlation = 0.41, P = 0.0001; mean squared error = 73.35, P = 0.0001). Accurate prediction was mainly based on GMV in the temporal regions. In addition, a univariate approach also revealed an inverse association between DCCS scores and GMV in the temporal areas. Our findings provide preliminary support to the development of neuroimaging techniques as a useful means to inform the cognitive assessment of individuals. Furthermore, the significant contribution of temporal regions suggests the prominent role of temporal cortex morphology in individual differences in cognitive flexibility.

Neurite imaging reveals microstructural variations in human cerebral cortical gray matter

We present distinct patterns of neurite distribution in the human cerebral cortex using diffusion magnetic resonance imaging (MRI). We analyzed both high-resolution structural (T1w and T2w images) and diffusion MRI data in 505 subjects from the Human Connectome Project. Neurite distributions were evaluated using the neurite orientation dispersion and density imaging (NODDI) model, optimized for gray matter, and mapped onto the cortical surface using a method weighted towards the cortical mid-thickness to reduce partial volume effects. The estimated neurite density was high in both somatosensory and motor areas, early visual and auditory areas, and middle temporal area (MT), showing a strikingly similar distribution to myelin maps estimated from the T1w/T2w ratio. The estimated neurite orientation dispersion was particularly high in early sensory areas, which are known for dense tangential fibers and are classified as granular cortex by classical anatomists. Spatial gradients of these cortical neurite properties revealed transitions that colocalize with some areal boundaries in a recent multi-modal parcellation of the human cerebral cortex, providing mutually supportive evidence. Our findings indicate that analyzing the cortical gray matter neurite morphology using diffusion MRI and NODDI provides valuable information regarding cortical microstructure that is related to but complementary to myeloarchitecture.

Evaluation of Cognitive Deficits and Structural Hippocampal Damage in Encephalitis With Leucine-Rich, Glioma-Inactivated 1 Antibodies

Limbic encephalitis with leucine-rich, glioma-inactivated 1 (LGI1) antibodies is one of the most frequent variants of autoimmune encephalitis with antibodies targeting neuronal surface antigens. However, the neuroimaging pattern and long-term cognitive outcome are not well understood. To study cognitive outcome and structural magnetic resonance imaging (MRI) alterations in patients with anti-LGI1 encephalitis. A cross-sectional study was conducted at the Departments of Neurology at Charité-Universitätsmedizin Berlin and University Hospital Schleswig-Holstein, Kiel, Germany. Data on 30 patients with anti-LGI1 encephalitis and 27 healthy control individuals matched for age, sex, and educational level were collected from June 1, 2013, through February 28, 2015. Clinical assessment, cognitive testing, and high-resolution MRI data, including whole-brain, hippocampal and basal ganglia volumetry; white matter integrity (diffusion tensor imaging); gray matter density (voxel-based morphometry); and hippocampal microstructural integrity (mean diffusivity and fractional anisotropy). Of the 30 patients included in the study, 19 were male (63%); mean (SD) age was 65.7 (12.3) years. Patients with anti-LGI1 encephalitis had incomplete recovery with significant and persisting verbal (mean [SE] Rey Auditory Verbal Learning Test [RAVLT], delayed recall: patients, 6.52 [1.05]; controls, 11.78 [0.56], P < .001) and visuospatial (Rey-Osterrieth Complex Figure Test [ROCF], delayed recall: patients, 16.0 [1.96]; controls, 25.86 [1.24]; P < .001) memory deficits. These deficits were accompanied by pronounced hippocampal atrophy, including subfields cornu ammonis 2/3 (CA2/3) and CA4/dentate gyrus (DG), as well as impaired hippocampal microstructural integrity. Higher disease severity correlated with larger verbal memory deficits (RAVLT delayed recall, r = -0.40; P = .049), decreased volumes of left hippocampus (r = -0.47; P = .02) and left CA2/3 (r = -0.41; P = .04) and CA4/DG (r = -0.43; P = .03) subfields, and impaired left hippocampal microstructural integrity (r = 0.47; P = .01). In turn, decreased volume of the left CA2/3 subfield (RAVLT delayed recall, r = 0.40; P = .047) and impaired left hippocampal microstructural integrity (RAVLT recognition, r = -0.41; P = .04) correlated with verbal memory deficits. Basal ganglia MRI signal abnormalities were observed in only 1 patient, but a longer duration of faciobrachial dystonic seizures correlated with a reduction of pallidum volume (r = -0.71; P = .03). In contrast, no abnormalities of cortical gray matter or white matter were found. The latency between disease onset and initiation of immunotherapy was significantly correlated with verbal (RAVLT recall after interference, r = -0.48; P = .02) and visuospatial (ROCF delayed recall, r = -0.46; P = .03) memory deficits. Anti-LGI1 encephalitis is associated with cognitive deficits and disability as a result of structural damage to the hippocampal memory system. This damage might be prevented by early immunotherapy.