Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation

Noradrenergic alterations in Parkinson's disease: a combined 11C-yohimbine PET/neuromelanin MRI study

Degeneration of the noradrenergic system is now considered a pathological hallmark of Parkinson's disease, but little is known about its consequences in terms of parkinsonian manifestations. Here, we evaluated two aspects of the noradrenergic system using multimodal in vivo imaging in patients with Parkinson's disease and healthy controls: the pigmented cell bodies of the locus coeruleus with neuromelanin sensitive MRI; and the density of α2-adrenergic receptors (ARs) with PET using 11C-yohimbine. Thirty patients with Parkinson's disease and 30 age- and sex-matched healthy control subjects were included. The characteristics of the patients' symptoms were assessed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Patients showed reduced neuromelanin signal intensity in the locus coeruleus compared with controls and diminished 11C-yohimbine binding in widespread cortical regions, including the motor cortex, as well as in the insula, thalamus and putamen. Clinically, locus coeruleus neuronal loss was correlated with motor (bradykinesia, motor fluctuations, tremor) and non-motor (fatigue, apathy, constipation) symptoms. A reduction of α2-AR availability in the thalamus was associated with tremor, while a reduction in the putamen, the insula and the superior temporal gyrus was associated with anxiety. These results highlight a multifaceted alteration of the noradrenergic system in Parkinson's disease since locus coeruleus and α2-AR degeneration were found to be partly uncoupled. These findings raise important issues about noradrenergic dysfunction that may encourage the search for new drugs targeting this system, including α2-ARs, for the treatment of Parkinson's disease.

CT-based volumetric measures obtained through deep learning: Association with biomarkers of neurodegeneration

INTRODUCTION

Cranial computed tomography (CT) is an affordable and widely available imaging modality that is used to assess structural abnormalities, but not to quantify neurodegeneration. Previously we developed a deep-learning-based model that produced accurate and robust cranial CT tissue classification.

MATERIALS AND METHODS

We analyzed 917 CT and 744 magnetic resonance (MR) scans from the Gothenburg H70 Birth Cohort, and 204 CT and 241 MR scans from participants of the Memory Clinic Cohort, Singapore. We tested associations between six CT-based volumetric measures (CTVMs) and existing clinical diagnoses, fluid and imaging biomarkers, and measures of cognition.

RESULTS

CTVMs differentiated cognitively healthy individuals from dementia and prodromal dementia patients with high accuracy levels comparable to MR-based measures. CTVMs were significantly associated with measures of cognition and biochemical markers of neurodegeneration.

DISCUSSION

These findings suggest the potential future use of CT-based volumetric measures as an informative first-line examination tool for neurodegenerative disease diagnostics after further validation.

HIGHLIGHTS

Computed tomography (CT)-based volumetric measures can distinguish between patients with neurodegenerative disease and healthy controls, as well as between patients with prodromal dementia and controls. CT-based volumetric measures associate well with relevant cognitive, biochemical, and neuroimaging markers of neurodegenerative diseases. Model performance, in terms of brain tissue classification, was consistent across two cohorts of diverse nature. Intermodality agreement between our automated CT-based and established magnetic resonance (MR)-based image segmentations was stronger than the agreement between visual CT and MR imaging assessment.

Comparison of quantitative [11C]PE2I brain PET studies between an integrated PET/MR and a stand-alone PET system

PET/MR systems demanded great efforts for accurate attenuation correction (AC) but differences in technology, geometry and hardware attenuation may also affect quantitative results. Dedicated PET systems using transmission-based AC are regarded as the gold standard for quantitative brain PET. The study aim was to investigate the agreement between quantitative PET outcomes from a PET/MR scanner against a stand-alone PET system. Nine patients with Parkinsonism underwent two 80-min dynamic PET scans with the dopamine transporter ligand [11C]PE2I. Images were reconstructed with resolution-matched settings using 68Ge-transmission (stand-alone PET), and zero-echo-time MR (PET/MR) scans for AC. Non-displaceable binding potential (BPND) and relative delivery (R1) were evaluated using volumes of interest and voxel-wise analysis. Correlations between systems were high (r ≥ 0.85) for both quantitative outcome parameters in all brain regions. Striatal BPND was significantly lower on PET/MR than on stand-alone PET (-7%). R1 was significantly overestimated in posterior cortical regions (9%) and underestimated in striatal (-9%) and limbic areas (-6%). The voxel-wise evaluation revealed that the MR-safe headphones caused a negative bias in both parametric BPND and R1 images. Additionally, a significant positive bias of R1 was found in the auditory cortex, most likely due to the acoustic background noise during MR imaging. The relative bias of the quantitative [11C]PE2I PET data acquired from a SIGNA PET/MR system was in the same order as the expected test-retest reproducibility of [11C]PE2I BPND and R1, compared to a stand-alone ECAT PET scanner. MR headphones and background noise are potential sources of error in functional PET/MR studies.

Subject-Specific Automatic Reconstruction of White Matter Tracts

MRI-based tractography is still underexploited and unsuited for routine use in brain tumor surgery due to heterogeneity of methods and functional-anatomical definitions and above all, the lack of a turn-key system. Standardization of methods is therefore desirable, whereby an objective and reliable approach is a prerequisite before the results of any automated procedure can subsequently be validated and used in neurosurgical practice. In this work, we evaluated these preliminary but necessary steps in healthy volunteers. Specifically, we evaluated the robustness and reliability (i.e., test-retest reproducibility) of tractography results of six clinically relevant white matter tracts by using healthy volunteer data (N = 136) from the Human Connectome Project consortium. A deep learning convolutional network-based approach was used for individualized segmentation of regions of interest, combined with an evidence-based tractography protocol and appropriate post-tractography filtering. Robustness was evaluated by estimating the consistency of tractography probability maps, i.e., averaged tractograms in normalized space, through the use of a hold-out cross-validation approach. No major outliers were found, indicating a high robustness of the tractography results. Reliability was evaluated at the individual level. First by examining the overlap of tractograms that resulted from repeatedly processed identical MRI scans (N = 10, 10 iterations) to establish an upper limit of reliability of the pipeline. Second, by examining the overlap for subjects that were scanned twice at different time points (N = 40). Both analyses indicated high reliability, with the second analysis showing a reliability near the upper limit. The robust and reliable subject-specific generation of white matter tracts in healthy subjects holds promise for future validation of our pipeline in a clinical population and subsequent implementation in brain tumor surgery.

Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example

BACKGROUND

Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21-78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23-65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13-48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12).

RESULTS

Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11).

CONCLUSIONS

As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation.

Unsupervised deep learning registration model for multimodal brain images

Multimodal image registration is a key for many clinical image-guided interventions. However, it is a challenging task because of complicated and unknown relationships between different modalities. Currently, deep supervised learning is the state-of-theart method at which the registration is conducted in end-to-end manner and one-shot. Therefore, a huge ground-truth data is required to improve the results of deep neural networks for registration. Moreover, supervised methods may yield models that bias towards annotated structures. Here, to deal with above challenges, an alternative approach is using unsupervised learning models. In this study, we have designed a novel deep unsupervised Convolutional Neural Network (CNN)-based model based on computer tomography/magnetic resonance (CT/MR) co-registration of brain images in an affine manner. For this purpose, we created a dataset consisting of 1100 pairs of CT/MR slices from the brain of 110 neuropsychic patients with/without tumor. At the next step, 12 landmarks were selected by a well-experienced radiologist and annotated on each slice resulting in the computation of series of metrics evaluation, target registration error (TRE), Dice similarity, Hausdorff, and Jaccard coefficients. The proposed method could register the multimodal images with TRE 9.89, Dice similarity 0.79, Hausdorff 7.15, and Jaccard 0.75 that are appreciable for clinical applications. Moreover, the approach registered the images in an acceptable time 203 ms and can be appreciable for clinical usage due to the short registration time and high accuracy. Here, the results illustrated that our proposed method achieved competitive performance against other related approaches from both reasonable computation time and the metrics evaluation.

Distribution of α2-Adrenergic Receptors in the Living Human Brain Using [11C]yohimbine PET

The neurofunctional basis of the noradrenergic (NA) system and its associated disorders is still very incomplete because in vivo imaging tools in humans have been missing up to now. Here, for the first time, we use [¹¹C]yohimbine in a large sample of subjects (46 healthy volunteers, 23 females, 23 males; aged 20-50) to perform direct quantification of regional alpha 2 adrenergic receptors' (α₂-ARs) availability in the living human brain. The global map shows the highest [¹¹C]yohimbine binding in the hippocampus, the occipital lobe, the cingulate gyrus, and the frontal lobe. Moderate binding was found in the parietal lobe, thalamus, parahippocampus, insula, and temporal lobe. Low levels of binding were found in the basal ganglia, the amygdala, the cerebellum, and the raphe nucleus. Parcellation of the brain into anatomical subregions revealed important variations in [¹¹C]yohimbine binding within most structures. Strong heterogeneity was found in the occipital lobe, the frontal lobe, and the basal ganglia, with substantial gender effects. Mapping the distribution of α₂-ARs in the living human brain may prove useful not only for understanding the role of the NA system in many brain functions, but also for understanding neurodegenerative diseases in which altered NA transmission with specific loss of α₂-ARs is suspected.

Automatic and manual segmentation of the piriform cortex: Method development and validation in patients with temporal lobe epilepsy and Alzheimer's disease

The piriform cortex (PC) is located at the junction of the temporal and frontal lobes. It is involved physiologically in olfaction as well as memory and plays an important role in epilepsy. Its study at scale is held back by the absence of automatic segmentation methods on MRI. We devised a manual segmentation protocol for PC volumes, integrated those manually derived images into the Hammers Atlas Database (n = 30) and used an extensively validated method (multi-atlas propagation with enhanced registration, MAPER) for automatic PC segmentation. We applied automated PC volumetry to patients with unilateral temporal lobe epilepsy with hippocampal sclerosis (TLE; n = 174 including n = 58 controls) and to the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI; n = 151, of whom with mild cognitive impairment (MCI), n = 71; Alzheimer's disease (AD), n = 33; controls, n = 47). In controls, mean PC volume was 485 mm³ on the right and 461 mm³ on the left. Automatic and manual segmentations overlapped with a Jaccard coefficient (intersection/union) of ~0.5 and a mean absolute volume difference of ~22 mm³ in healthy controls, ~0.40/ ~28 mm³ in patients with TLE, and ~ 0.34/~29 mm³ in patients with AD. In patients with TLE, PC atrophy lateralised to the side of hippocampal sclerosis (p < .001). In patients with MCI and AD, PC volumes were lower than those of controls bilaterally (p < .001). Overall, we have validated automatic PC volumetry in healthy controls and two types of pathology. The novel finding of early atrophy of PC at the stage of MCI possibly adds a novel biomarker. PC volumetry can now be applied at scale.

Evaluation of eight registration algorithms applied to the insula and insular gyri

BACKGROUND AND PURPOSE

Spatial registration is crucial in establishing correspondence between anatomic brain regions for research and clinical purposes. The insular cortex (IC) and gyri (IG) are implicated in various functions and pathologies including epilepsy. Optimizing registration of the insula to a common atlas can improve the accuracy of group-level analyses. Here, we compared six nonlinear, one linear, and one semiautomated registration algorithms (RAs) for registering the IC and IG to the Montreal Neurologic Institute standard space (MNI152).

METHODS

3T images acquired from 20 controls and 20 temporal lobe epilepsy patients with mesial temporal sclerosis underwent automated segmentation of the insula. This was followed by manual segmentation of the entire IC and six individual IGs. Consensus segmentations were created at 75% agreement for IC and IG before undergoing registration to MNI152 space with eight RAs. Dice similarity coefficients (DSCs) were calculated between segmentations after registration and the IC and IG in MNI152 space. Statistical analysis involved the Kruskal-Wallace test with Dunn's test for IC and two-way analysis of variance with Tukey's honest significant difference test for IG.

RESULTS

DSCs were significantly different between RAs. Based on multiple pairwise comparisons, we report that certain RAs performed better than others across population groups. Additionally, registration performance differed according to specific IG.

CONCLUSION

We compared different methods for registering the IC and IG to MNI152 space. We found differences in performance between RAs, which suggests that algorithm choice is important factor in analyses involving the insula.

[18F]F13640: a selective agonist PET radiopharmaceutical for imaging functional 5-HT1A receptors in humans

PURPOSE

F13640 (a.k.a. befiradol, NLX-112) is a highly selective 5-HT_1A receptor ligand that was selected as a PET radiopharmaceutical-candidate based on animal studies. Due to its high efficacy agonist properties, [¹⁸F]F13640 binds preferentially to functional 5-HT_1A receptors, which are coupled to intracellular G-proteins. Here, we characterize brain labeling of 5-HT_1A receptors by [¹⁸F]F13640 in humans and describe a simplified model for its quantification.

METHODS

PET/CT and PET-MRI scans were conducted in a total of 13 healthy male volunteers (29 ± 9 years old), with arterial input functions (AIF) (n = 9) and test-retest protocol (n = 8). Several kinetic models were compared (one tissue compartment model, two-tissue compartment model, and Logan); two models with reference region were also evaluated: simplified reference tissue model (SRTM) and the logan reference model (LREF).

RESULTS

[¹⁸F]F13640 showed high uptake values in raphe nuclei and cortical regions. SRTM and LREF models showed a very high correlation with kinetic models using AIF. As concerns test-retest parameters and the prolonged binding kinetics of [¹⁸F]F13640, better reproducibility, and reliability were found with the LREF method. Cerebellum white matter and frontal lobe white matter stand out as suitable reference regions.

CONCLUSION

The favorable brain labeling and kinetic profile of [¹⁸F]F13640, its high receptor specificity and its high efficacy agonist properties open new perspectives for studying functionally active 5-HT_1A receptors, unlike previous radiopharmaceuticals that act as antagonists. [¹⁸F]F13640's kinetic properties allow injection outside of the PET scanner with delayed acquisitions, facilitating the design of innovative longitudinal protocols in neurology and psychiatry.

TRIAL REGISTRATION

Trial Registration EudraCT 2017-002,722-21.

DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.

Fitting Contralateral Neuroanatomical Asymmetry into the Amyloid Cascade Hypothesis

Alzheimer's Disease (AD) is the most common cause of dementia. Due to the progressive nature of the neurodegeneration associated with the disease, it is of clinical interest to achieve an early diagnosis of AD. In this study, we analyzed the viability of asymmetry-related measures as potential biomarkers to facilitate the early diagnosis of AD. These measures were obtained from MAPER-segmented MP-RAGE MRI studies available at the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and by analyzing these studies at the level of individual segmented regions. The temporal evolution of these measures was obtained and then analyzed by generating spline regression models. Data imputation was performed where missing information prevented the temporal analysis of each measure from being realized, using additional information provided by ADNI for each patient. The temporal evolution of these measures was compared to the evolution of other commonly used markers for the diagnosis of AD, such as cognitive function, concentrations of Phosphorylated-Tau, Amyloid-β, and structural MRI volumetry. The results of the regression models showed that asymmetry measures, in particular regions such as the parahippocampal gyrus, differentiated themselves temporally before most of the other evaluated biomarkers. Further studies are suggested to corroborate these results.

A Longitudinal Study of Medial Temporal Lobe Volumes in Graves Disease

CONTEXT

Neuropsychiatric symptoms are common features of Graves disease (GD) in hyperthyroidism and after treatment. The mechanism behind these symptoms is unknown, but reduced hippocampal volumes have been observed in association with increased thyroid hormone levels.

OBJECTIVE

This work aimed at investigating GD influence on regional medial temporal lobe (MTL) volumes.

METHODS

Sixty-two women with newly diagnosed GD underwent assessment including magnetic resonance (MR) imaging in hyperthyroidism and 48 of them were followed up after a mean of 16.4 ± 4.2 SD months of treatment. Matched thyroid-healthy controls were also assessed twice at a 15-month interval. MR images were automatically segmented using multiatlas propagation with enhanced registration. Regional medial temporal lobe (MTL) volumes for amygdalae and hippocampi were compared with clinical data and data from symptom questionnaires and neuropsychological tests.

RESULTS

Patients had smaller MTL regions than controls at inclusion. At follow-up, all 4 MTL regions had increased volumes and only the volume of the left amygdala remained reduced compared to controls. There were significant correlations between the level of thyrotropin receptor antibodies (TRAb) and MTL volumes at inclusion and also between the longitudinal difference in the levels of free 3,5,3'-triiodothyronine and TRAb and the difference in MTL volumes. There were no significant correlations between symptoms or test scores and any of the 4 MTL volumes.

CONCLUSION

Dynamic alterations in the amygdalae and hippocampi in GD reflect a previously unknown level of brain involvement both in the hyperthyroid state of the condition and after treatment. The clinical significance, as well as the mechanisms behind these novel findings, warrant further study of the neurological consequences of GD.

Lung tissue biomechanics imaged with synchrotron phase contrast microtomography in live rats

The magnitude and distribution of strain imposed on the peripheral airspaces by mechanical ventilation at the microscopic level and the consequent deformations are unknown despite their importance for understanding the mechanisms occurring at the onset of ventilator-induced lung injury. Here a 4-Dimensional (3D + time) image acquisition and processing technique is developed to assess pulmonary acinar biomechanics at microscopic resolution. Synchrotron radiation phase contrast CT with an isotropic voxel size of 6 µm³ is applied in live anesthetized rats under controlled mechanical ventilation. Video animations of regional acinar and vascular strain are acquired in vivo. Maps of strain distribution due to positive-pressure breaths and cardiovascular activity in lung acini and blood vessels are derived based on CT images. Regional strain within the lung peripheral airspaces takes average values of 0.09 ± 0.02. Fitting the expression S = kVⁿ, to the changes in peripheral airspace area (S) and volume (V) during a positive pressure breath yields an exponent n = 0.82 ± 0.03, suggesting predominant alveolar expansion rather than ductal expansion or alveolar recruitment. We conclude that this methodology can be used to assess acinar conformational changes during positive pressure breaths in intact peripheral lung airspaces.

Limbic Serotonergic Plasticity Contributes to the Compensation of Apathy in Early Parkinson's Disease

BACKGROUND

De novo Parkinson's disease (PD) patients with apathy exhibit prominent limbic serotonergic dysfunction and microstructural disarray. Whether this distinctive lesion profile at diagnosis entails different prognosis remains unknown.

OBJECTIVES

To investigate the progression of dopaminergic and serotonergic dysfunction and their relation to motor and nonmotor impairment in PD patients with or without apathy at diagnosis.

METHODS

Thirteen de novo apathetic and 13 nonapathetic PD patients were recruited in a longitudinal double-tracer positron emission tomography cohort study. We quantified the progression of presynaptic dopaminergic and serotonergic pathology using [¹¹ C]PE2I for dopamine transporter and [¹¹ C]DASB for serotonin transporter at baseline and 3 to 5 years later, using linear mixed-effect models and mediation analysis to compare the longitudinal evolution between groups for clinical impairment and region-of-interest-based analysis.

RESULTS

After the initiation of dopamine replacement therapy, apathy, depression, and anxiety improved at follow-up in patients with apathy at diagnosis (n = 10) to the level of patients without apathy (n = 11). Patients had similar progression of motor impairment, whereas mild impulsive behaviors developed in both groups. Striato-pallidal and mesocorticolimbic presynaptic dopaminergic loss progressed similarly in both groups, as did serotonergic pathology in the putamen, caudate nucleus, and pallidum. Contrastingly, serotonergic innervation selectively increased in the ventral striatum and anterior cingulate cortex in apathetic patients, contributing to the reversal of apathy besides dopamine replacement therapy.

CONCLUSION

Patients suffering from apathy at diagnosis exhibit compensatory changes in limbic serotonergic innervation within 5 years of diagnosis, with promising evidence that serotonergic plasticity contributes to the reversal of apathy. The relationship between serotonergic plasticity and dopaminergic treatments warrants further longitudinal investigations. © 2022 International Parkinson and Movement Disorder Society.

Differences Between MR Brain Region Segmentation Methods: Impact on Single-Subject Analysis

For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, depending on the segmentation method and underlying brain atlas. This potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation–maximization (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions, the PCC-v was good (>0.75), indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis, we also applied the methods to 42 patients with Alzheimer’s disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient’s distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which showed to be consistent across methods. The absolute agreement on the AD patients’ z-scores was high for regions of thalamus and putamen. This is encouraging as it indicates that the studied methods are interchangeable for these regions. For regions such as the hippocampus, amygdala, caudate nucleus and accumbens, and globus pallidus, not all method combinations showed a high ICC-z. Whether two methods are indeed interchangeable should be confirmed for the specific application and dataset of interest.

Technical and clinical validation of commercial automated volumetric MRI tools for dementia diagnosis-a systematic review

Developments in neuroradiological MRI analysis offer promise in enhancing objectivity and consistency in dementia diagnosis through the use of quantitative volumetric reporting tools (QReports). Translation into clinical settings should follow a structured framework of development, including technical and clinical validation steps. However, published technical and clinical validation of the available commercial/proprietary tools is not always easy to find and pathways for successful integration into the clinical workflow are varied. The quantitative neuroradiology initiative (QNI) framework highlights six necessary steps for the development, validation and integration of quantitative tools in the clinic. In this paper, we reviewed the published evidence regarding regulatory-approved QReports for use in the memory clinic and to what extent this evidence fulfils the steps of the QNI framework. We summarize unbiased technical details of available products in order to increase the transparency of evidence and present the range of reporting tools on the market. Our intention is to assist neuroradiologists in making informed decisions regarding the adoption of these methods in the clinic. For the 17 products identified, 11 companies have published some form of technical validation on their methods, but only 4 have published clinical validation of their QReports in a dementia population. Upon systematically reviewing the published evidence for regulatory-approved QReports in dementia, we concluded that there is a significant evidence gap in the literature regarding clinical validation, workflow integration and in-use evaluation of these tools in dementia MRI diagnosis.

Signal and Texture Features from T2 Maps for the Prediction of Mild Cognitive Impairment to Alzheimer's Disease Progression

Early detection of Alzheimer's disease (AD) is crucial to preserve cognitive functions and provide the opportunity for patients to enter clinical trials. In recent years, some studies have reported that features related to the signal and texture of MRI images can be an effective biomarker of AD. To test these claims, a study was conducted using T2 maps, a sequence not previously studied, of 40 patients with mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative database, who either progressed to AD (18) or remained stable (22). From these maps, the mean value and absolute difference of 37 signal and texture imaging features for 40 contralateral pairs of regions were measured. We used seven machine learning methods to analyze whether, by adding these imaging features to the neuropsychological studies currently used for diagnosis, we could more accurately identify patients who will progress to AD. The predictive models improved with the addition of signal and texture features. Additionally, features related to the signal and texture of the images were much more relevant than volumetric ones. Our results suggest that contralateral signal and texture features should be further investigated as potential biomarkers for the prediction of AD.

Modeling [11C]yohimbine PET human brain kinetics with test-retest reliability, competition sensitivity studies and search for a suitable reference region

Previous work introduced the [¹¹C]yohimbine as a suitable ligand of central α2-adrenoreceptors (α2-ARs) for PET imaging. However, reproducibility of [¹¹C]yohimbine PET measurements in healthy humans estimated with a simplified modeling method with reference region, as well as sensitivity of [¹¹C]yohimbine to noradrenergic competition were not evaluated. The objectives of the present study were therefore to fill this gap.

METHODS

Thirteen healthy humans underwent two [¹¹C]yohimbine 90-minute dynamic scans performed on a PET-MRI scanner. Seven had arterial blood sampling with metabolite assessment and plasmatic yohimbine free fraction evaluation at the first scan to have arterial input function and test appropriate kinetic modeling. The second scan was a simple retest for 6 subjects to evaluate the test-retest reproducibility. For the remaining 7 subjects the second scan was a challenge study with the administration of a single oral dose of 150 µg of clonidine 90 min before the PET scan. Parametric images of α2-ARs distribution volume ratios (DVR) were generated with two non-invasive models: Logan graphical analysis with Reference (LREF) and Simplified Reference Tissue Method (SRTM). Three reference regions (cerebellum white matter (CERWM), frontal white matter (FLWM), and corpus callosum (CC)) were tested.

RESULTS

We showed high test-retest reproducibility of DVR estimation with LREF and SRTM regardless of reference region (CC, CERWM, FLWM). The best fit was obtained with SRTM_CC (r²=0.94). Test-retest showed that the SRTM_CC is highly reproducible (mean ICC>0.7), with a slight bias (-1.8%), whereas SRTM_CERWM had lower bias (-0.1%), and excellent ICC (mean>0.8). Using SRTM_CC, regional changes have been observed after clonidine administration with a significant increase reported in the amygdala and striatum as well as in several posterior cortical areas as revealed with the voxel-based analysis.

CONCLUSION

The results add experimental support for the suitability of [¹¹C]yohimbine PET in the quantitative assessment of α2-ARs occupancy in vivo in the human brain. Trial registration EudraCT 2018-000380-82.

Tissue Probability Based Registration of Diffusion-Weighted Magnetic Resonance Imaging

BACKGROUND

Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types.

PURPOSE

To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs).

STUDY TYPE

Retrospective longitudinal study.

POPULATION

Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n = 16) and a test-set (n = 16), and 35 randomly-selected participants from the Human Connectome Project.

FIELD STRENGTH/SEQUENCE

3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence.

ASSESSMENT

A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools.

STATISTICAL TESTS

Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools.

RESULTS

The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P < 0.05).

DATA CONCLUSION

A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 1.

Αlpha 5 subunit-containing GABAA receptors in temporal lobe epilepsy with normal MRI

GABAA receptors containing the α5 subunit mediate tonic inhibition and are widely expressed in the limbic system. In animals, activation of α5-containing receptors impairs hippocampus-dependent memory. Temporal lobe epilepsy is associated with memory impairments related to neuron loss and other changes. The less selective PET ligand [11C]flumazenil has revealed reductions in GABAA receptors. The hypothesis that α5 subunit receptor alterations are present in temporal lobe epilepsy and could contribute to impaired memory is untested. We compared α5 subunit availability between individuals with temporal lobe epilepsy and normal structural MRI ('MRI-negative') and healthy controls, and interrogated the relationship between α5 subunit availability and episodic memory performance, in a cross-sectional study. Twenty-three healthy male controls (median ± interquartile age 49 ± 13 years) and 11 individuals with MRI-negative temporal lobe epilepsy (seven males; 40 ± 8) had a 90-min PET scan after bolus injection of [11C]Ro15-4513, with arterial blood sampling and metabolite correction. All those with epilepsy and six controls completed the Adult Memory and Information Processing Battery on the scanning day. 'Bandpass' exponential spectral analyses were used to calculate volumes of distribution separately for the fast component [V F; dominated by signal from α1 (α2, α3)-containing receptors] and the slow component (V S; dominated by signal from α5-containing receptors). We made voxel-by-voxel comparisons between: the epilepsy and control groups; each individual case versus the controls. We obtained parametric maps of V F and V S measures from a single bolus injection of [11C]Ro15-4513. The epilepsy group had higher V S in anterior medial and lateral aspects of the temporal lobes, the anterior cingulate gyri, the presumed area tempestas (piriform cortex) and the insulae, in addition to increases of ∼24% and ∼26% in the ipsilateral and contralateral hippocampal areas (P < 0.004). This was associated with reduced V F:V S ratios within the same areas (P < 0.009). Comparisons of V S for each individual with epilepsy versus controls did not consistently lateralize the epileptogenic lobe. Memory scores were significantly lower in the epilepsy group than in controls (mean ± standard deviation -0.4 ± 1.0 versus 0.7 ± 0.3; P = 0.02). In individuals with epilepsy, hippocampal V S did not correlate with memory performance on the Adult Memory and Information Processing Battery. They had reduced V F in the hippocampal area, which was significant ipsilaterally (P = 0.03), as expected from [11C]flumazenil studies. We found increased tonic inhibitory neurotransmission in our cohort of MRI-negative temporal lobe epilepsy who also had co-morbid memory impairments. Our findings are consistent with a subunit shift from α1/2/3 to α5 in MRI-negative temporal lobe epilepsy.

A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison

BACKGROUND

The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer's disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the "gold standard" is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods.

PURPOSE

The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms.

METHODS

The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years.

CONCLUSION

Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [11C]PE2I PET-MR brain imaging

BACKGROUND

A valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study, we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain.

METHODS

Nine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain.

RESULTS

For BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions. Atlas-MRAC exhibited a significant bias in caudate nucleus (- 12%) while MaxProb-MRAC revealed a substantial, non-significant bias in the putamen (9%). R1 estimates had a marginal bias for all MRAC methods (- 1.0-3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest intersubject variability. Atlas-MRAC had highest variation in bias over time (+10 to - 10%), followed by MaxProb-MRAC (+5 to - 5%), but MaxProb showed the lowest mean bias. For the cerebellum, MaxProb-MRAC showed the highest variability while bias was constant over time for Atlas- and ZTE-MRAC.

CONCLUSIONS

Both Maxprob- and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.

An Association Between Serotonin 1A Receptor, Gray Matter Volume, and Sociability in Healthy Subjects and in Autism Spectrum Disorder

Central serotonin is an important molecular pathway, involved in the regulation of social behavior and gray matter volume (GMV). In men with autism spectrum disorders (ASD), the serotonergic system and the GMV have been found disrupted. Here, we investigated the relation between serotonin, GMV, and social personality in men with typical development (TD) and in men with ASD. We combined anatomical magnetic resonance imaging, Positron emission tomography scan with 2'-methoxyphenyl-(N-2'-pyridinyl)-p-18F-fluoro-benzamidoethylpiperazine radioligand and revised NEO personality inventory personality questionnaire to examine the association between serotonin 1A receptor (5-HT_1A R) binding potential, GMV and social personality in 24 adult male TD subjects and 18 male men with ASD. In both groups, we found a positive correlation between 5-HT_1A R binding potential and GMV in a region dependent manner. In the TD group, we observed a negative correlation between 5-HT_1A R and GMV in the left and right posterior putamen. 5HT_1A R binding and GMV in the putamen further correlated with social personality scores in the TD group. None of these associations were found in men with ASD, although no differences were observed for 5-HT_1A R concentration among the two groups. Our findings point to a deregulation of 5-HT_1A R density in the striatum of men with ASD, a failure that might contribute to their social disturbances. Serotonin is suspected to be involved in the pathophysiology of autism. We provide evidence for a role of serotonin 1A receptor in social behavior through a specific regulation of GMV in the putamen region in neurotypical subjects but not in men with autism. This suggests a potential impairment of the serotonergic system in men with autism which may contribute to patients' social disturbances. Our findings suggest further investigation on the role of serotonin 1A receptor and its activity in the striatum to regulate social behavior. Autism Res 2020, 13: 1843-1855. © 2020 International Society for Autism Research and Wiley Periodicals LLC LAY SUMMARY: Serotonin is suspected to be involved in the pathophysiology of autism. We provide evidence for a role of serotonin 1A receptor in social behavior through a specific regulation of gray matter volume in the putamen region in neurotypical subjects but not in men with autism. This suggests a potential impairment of the serotonergic system in men with autism which may contribute to patients' social disturbances. Our findings suggest further investigation on the role of serotonin 1A receptor and its activity in the striatum to regulate social behavior.

Small vessel disease lesion type and brain atrophy: The role of co-occurring amyloid

INTRODUCTION

It is unknown whether different types of small vessel disease (SVD), differentially relate to brain atrophy and if co-occurring Alzheimer's disease pathology affects this relation.

METHODS

In 725 memory clinic patients with SVD (mean age 67 ± 8 years, 48% female) we compared brain volumes of those with moderate/severe white matter hyperintensities (WMHs; n = 326), lacunes (n = 132) and cerebral microbleeds (n = 321) to a reference group with mild WMHs (n = 197), also considering cerebrospinal fluid (CSF) amyloid status in a subset of patients (n = 488).

RESULTS

WMHs and lacunes, but not cerebral microbleeds, were associated with smaller gray matter (GM) volumes. In analyses stratified by CSF amyloid status, WMHs and lacunes were associated with smaller total brain and GM volumes only in amyloid-negative patients. SVD-related atrophy was most evident in frontal (cortical) GM, again predominantly in amyloid-negative patients.

DISCUSSION

Amyloid status modifies the differential relation between SVD lesion type and brain atrophy in memory clinic patients.

Impaired connectivity within neuromodulatory networks in multiple sclerosis and clinical implications

There is mounting evidence regarding the role of impairment in neuromodulatory networks for neurodegenerative diseases, such as Parkinson's and Alzheimer's disease. However, the role of neuromodulatory networks in multiple sclerosis (MS) has not been assessed. We applied resting-state functional connectivity and graph theory to investigate the changes in the functional connectivity within neuromodulatory networks including the serotonergic, noradrenergic, cholinergic, and dopaminergic systems in MS. Twenty-nine MS patients and twenty-four age- and gender-matched healthy controls performed clinical and cognitive assessments including the expanded disability status score, symbol digit modalities test, and Hamilton Depression rating scale. We demonstrated a diffuse reorganization of network topography (P < 0.01) in serotonergic, cholinergic, noradrenergic, and dopaminergic networks in patients with MS. Serotonergic, noradrenergic, and cholinergic network functional connectivity derangement was associated with disease duration, EDSS, and depressive symptoms (P < 0.01). Derangements in serotonergic, noradrenergic, cholinergic, and dopaminergic network impairment were associated with cognitive abilities (P < 0.01). Our results indicate that functional connectivity changes within neuromodulatory networks might be a useful tool in predicting disability burden over time, and could serve as a surrogate endpoint to assess efficacy for symptomatic treatments.

On brain atlas choice and automatic segmentation methods: a comparison of MAPER & FreeSurfer using three atlas databases

Several automatic image segmentation methods and few atlas databases exist for analysing structural T1-weighted magnetic resonance brain images. The impact of choosing a combination has not hitherto been described but may bias comparisons across studies. We evaluated two segmentation methods (MAPER and FreeSurfer), using three publicly available atlas databases (Hammers_mith, Desikan-Killiany-Tourville, and MICCAI 2012 Grand Challenge). For each combination of atlas and method, we conducted a leave-one-out cross-comparison to estimate the segmentation accuracy of FreeSurfer and MAPER. We also used each possible combination to segment two datasets of patients with known structural abnormalities (Alzheimer's disease (AD) and mesial temporal lobe epilepsy with hippocampal sclerosis (HS)) and their matched healthy controls. MAPER was better than FreeSurfer at modelling manual segmentations in the healthy control leave-one-out analyses in two of the three atlas databases, and the Hammers_mith atlas database transferred to new datasets best regardless of segmentation method. Both segmentation methods reliably identified known abnormalities in each patient group. Better separation was seen for FreeSurfer in the AD and left-HS datasets, and for MAPER in the right-HS dataset. We provide detailed quantitative comparisons for multiple anatomical regions, thus enabling researchers to make evidence-based decisions on their choice of atlas and segmentation method.

Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus

Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer's disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.

Structural brain changes in hyperthyroid Graves' disease: protocol for an ongoing longitudinal, case-controlled study in Göteborg, Sweden-the CogThy project

INTRODUCTION

Cognitive impairment and reduced well-being are common manifestations of Graves' disease (GD). These symptoms are not only prevalent during the active phase of the disease but also often prevail for a long time after hyperthyroidism is considered cured. The pathogenic mechanisms involved in these brain-derived symptoms are currently unknown. The overall aim of the CogThy study is to identify the mechanism behind cognitive impairment to be able to recognise GD patients at risk.

METHODS AND ANALYSIS

The study is a longitudinal, single-centre, case-controlled study conducted in Göteborg, Sweden on premenopausal women with newly diagnosed GD. The subjects are examined: at referral, at inclusion and then every 3.25 months until 15 months. Examinations include: laboratory measurements; eye evaluation; neuropsychiatric and neuropsychological testing; structural MRI of the whole brain, orbits and medial temporal lobe structures; functional near-infrared spectroscopy of the cerebral prefrontal cortex and self-assessed quality of life questionnaires. The primary outcome measure is the change in medial temporal lobe structure volume. Secondary outcome measures include neuropsychological, neuropsychiatric, hormonal and autoantibody variables. The study opened for inclusion in September 2012 and close for inclusion in October 2019. It will provide novel information on the effect of GD on medial temporal lobe structures and cerebral cortex functionality as well as whether these changes are associated with cognitive and affective impairment, hormonal levels and/or autoantibody levels. It should lead to a broader understanding of the underlying pathogenesis and future treatment perspectives.

ETHICS AND DISSEMINATION

The study has been reviewed and approved by the Regional Ethical Review Board in Göteborg, Sweden. The results will be actively disseminated through peer-reviewed journals, national and international conference presentations and among patient organisations after an appropriate embargo time.

TRIAL REGISTRATION NUMBER

44321 at the public project database for research and development in Västra Götaland County, Sweden (https://www.researchweb.org/is/vgr/project/44321).

[18F]Florbetapir PET/MR imaging to assess demyelination in multiple sclerosis

Purpose

We evaluated myelin changes throughout the central nervous system in Multiple Sclerosis (MS) patients by using hybrid [¹⁸F]florbetapir PET-MR imaging.

Methods

We included 18 relapsing-remitting MS patients and 12 healthy controls. Each subject performed a hybrid [¹⁸F]florbetapir PET-MR and both a clinical and cognitive assessment. [¹⁸F]florbetapir binding was measured as distribution volume ratio (DVR), through the Logan graphical reference method and the supervised cluster analysis to extract a reference region, and standard uptake value (SUV) in the 70–90 min interval after injection. The two quantification approaches were compared. We also evaluated changes in the measures derived from diffusion tensor imaging and arterial spin labeling.

Results

[¹⁸F]florbetapir DVRs decreased from normal-appearing white matter to the centre of T2 lesion (P < 0.001), correlated with fractional anisotropy and with mean, axial and radial diffusivity within T2 lesions (coeff. = −0.15, P < 0.001, coeff. = −0.12, P < 0.001 and coeff. = −0.16, P < 0.001, respectively). Cerebral blood flow was reduced in white matter damaged areas compared to white matter in healthy controls (−10.9%, P = 0.005). SUV_70–90 and DVR are equally able to discriminate between intact and damaged myelin (area under the curve 0.76 and 0.66, respectively; P = 0.26).

Conclusion

Our findings demonstrate that [¹⁸F]florbetapir PET imaging can measure in-vivo myelin damage in patients with MS. Demyelination in MS is not restricted to lesions detected through conventional MRI but also involves the normal appearing white matter. Although longitudinal studies are needed, [¹⁸F]florbetapir PET imaging may have a role in clinical settings in the management of MS patients.

Imidazoline 2 binding sites reflecting astroglia pathology in Parkinson's disease: an in vivo11C-BU99008 PET study

Astroglia are multifunctional cells that regulate neuroinflammation and maintain homeostasis within the brain. Astroglial α-synuclein-positive cytoplasmic accumulations have been shown post-mortem in patients with Parkinson's disease and therefore astroglia may play an important role in the initiation and progression of Parkinson's disease. Imidazoline 2 binding sites are expressed on activated astroglia in the cortex, hippocampus, basal ganglia and brainstem; therefore, by measuring imidazoline 2 binding site levels we can indirectly evaluate astrogliosis in patients with Parkinson's disease. Here, we aimed to evaluate the role of astroglia activation in vivo in patients with Parkinson's disease using 11C-BU99008 PET, a novel radioligand with high specificity and selectivity for imidazoline 2 binding sites. Twenty-two patients with Parkinson's disease and 14 healthy control subjects underwent 3 T MRI and a 120-min 11C-BU99008 PET scan with volume of distribution (VT) estimated using a two-tissue compartmental model with a metabolite corrected arterial plasma input function. Parkinson's disease patients were stratified into early (n = 8) and moderate/advanced (n = 14) groups according to disease stage. In early Parkinson's disease, increased 11C-BU99008 VT uptake was observed in frontal (P = 0.022), temporal (P = 0.02), parietal (P = 0.026) and occipital (P = 0.047) cortical regions compared with healthy controls. The greatest 11C-BU99008 VT increase in patients with early Parkinson's disease was observed in the brainstem (52%; P = 0.018). In patients with moderate/advanced Parkinson's disease, loss of 11C-BU99008 VT was observed across frontal (P = 0.002), temporal (P < 0.001), parietal (P = 0.039), occipital (P = 0.024), and insula (P < 0.001) cortices; and in the subcortical regions of caudate (P < 0.001), putamen (P < 0.001) and thalamus (P < 0.001); and in the brainstem (P = 0.018) compared with healthy controls. In patients with Parkinson's disease, loss of 11C-BU99008 VT in cortical regions, striatum, thalamus and brainstem correlated with longer disease duration (P < 0.05) and higher disease burden scores, measured with Movement Disorder Society Unified Parkinson's Disease Rating Scale (P < 0.05). In the subgroup of patients with moderate/advanced Parkinson's disease, loss of 11C-BU99008 VT in the frontal (r = 0.79; P = 0.001), temporal (r = 0.74; P = 0.002) and parietal (r = 0.89; P < 0.001) cortex correlated with global cognitive impairment. This study demonstrates in vivo the role of astroglia in the initiation and progression of Parkinson's disease. Reactive astroglia observed early in Parkinson's disease could reflect a neuroprotective compensatory mechanisms and pro-inflammatory upregulation in response to α-synuclein accumulation. However, as the disease progresses and significant neurodegeneration occurs, astroglia lose their reactive function and such loss in the cortex has clinical relevance in the development of cognitive impairment.

Serotonergic pathology and disease burden in the premotor and motor phase of A53T α-synuclein parkinsonism: a cross-sectional study

BACKGROUND

Because of the highly penetrant gene mutation and clinical features consistent with idiopathic Parkinson's disease, carriers of the autosomal dominant Ala53Thr (A53T; 209G→A) point mutation in the α-synuclein (SNCA) gene are an ideal population to study the premotor phase and evolution of Parkinson's pathology. Given the known neurochemical changes in the serotonergic system and their association with symptoms of Parkinson's disease, we hypothesised that carriers of the A53T SNCA mutation might show abnormalities in the serotonergic neurotransmitter system before the diagnosis of Parkinson's disease, and that this pathology might be associated with measures of Parkinson's burden.

METHODS

In this cross-sectional study, we recruited carriers of the A53T SNCA mutation from specialist Movement Disorders clinics in Athens, Greece, and Salerno, Italy, and a cohort of healthy controls with no personal or family history of neurological or psychiatric disorders from London, UK (recruited via public advertisement) who were age matched to the A53T SNCA carriers. We also recruited one cohort of patients with idiopathic Parkinson's disease (cohort 1) from Movement Disorders clinics in London, UK, and retrieved data on a second cohort of such patients (cohort 2; n=40) who had been scanned with a different scanner. 7-day continuous recording of motor function was used to determine the Parkinson's disease status of the A53T carriers. To assess whether serotonergic abnormalities were present, we used [¹¹C]DASB PET non-displaceable binding to quantify serotonin transporter density. We constructed brain topographic maps reflecting Braak stages 1-6 and used these as seed maps to calculate [¹¹C]DASB non-displaceable binding potential in our cohort of A53T SNCA carriers. Additionally, all participants underwent a battery of clinical assessments to determine motor and non-motor symptoms and cognitive status, and [¹²³I]FP-CIT single-photon emission CT (SPECT) to assess striatal dopamine transporter binding and MRI for volumetric analyses to assess whether pathology is associated with measures of Parkinson's disease burden.

FINDINGS

Between Sept 1, 2016, and Sept 30, 2018, we recruited 14 A53T SNCA carriers, 25 healthy controls, and 25 patients with idiopathic Parkinson's disease. Seven (50%) of 14 A53T SCNA carriers were confirmed to have motor symptoms and confirmed to have Parkinson's disease, and the absence of motor symptoms was confirmed in seven (50%) A53T SCNA carriers (ie, premotor), in whom [¹²³I]FP-CIT SPECT confirmed the absence of striatal dopaminergic deficits. Compared with healthy controls, premotor A53T SNCA carriers showed loss of [¹¹C]DASB non-displaceable binding potential in the ventral (p<0·0001) and dorsal (p=0·0002) raphe nuclei, caudate (p=0·00015), putamen (p=0·036), thalamus (p=0·00074), hypothalamus (p<0·0001), amygdala (p=0·0041), and brainstem (p=0·046); and in A53T SNCA carriers with Parkinson's disease this loss was extended to the hippocampus (p=0·0051), anterior (p=0·022) and posterior cingulate (p=0·036), insula (p=0·0051), frontal (p=0·0016), parietal (p=0·019), temporal (p<0·0001), and occipital (p=0·0053) cortices. A53T SNCA carriers with Parkinson's disease showed a loss of striatal [¹²³I]FP-CIT-specific binding ratio compared with healthy controls (p<0·0001). Premotor A53T SNCA carriers had loss of [¹¹C]DASB non-displaceable binding potential in brain areas corresponding to Braak stages 1-3, whereas [¹¹C]DASB non-displaceable binding potential was largely preserved in areas corresponding to Braak stages 4-6. Except for one participant who was diagnosed with Parkinson's disease in the past year, all A53T SNCA carriers with Parkinson's disease had decreases in [¹¹C]DASB non-displaceable binding potential in brain areas corresponding to Braak stages 1-6. Decreases in [¹¹C]DASB non-displaceable binding potential in the brainstem were associated with increased Movement Disorder Score-Unified Parkinson's Disease Rating Scale total scores in all A53T SNCA carriers (r -0·66, 95% CI -0·88 to -0·20; p=0·0099), idiopathic Parkinson's disease cohort 1 (r -0·66, -0·84 to -0·36; p=0·00031), and idiopathic Parkinson's disease cohort 2 (r -0·71, -0·84 to -0·52; p<0·0001).

INTERPRETATION

The presence of serotonergic pathology in premotor A53T SNCA carriers preceded development of dopaminergic pathology and motor symptoms and was associated with disease burden, highlighting the potential early role of serotonergic pathology in the progression of Parkinson's disease. Our findings provide evidence that molecular imaging of serotonin transporters could be used to visualise premotor pathology of Parkinson's disease in vivo. Future work might establish whether serotonin transporter imaging is suitable as an adjunctive tool for screening and monitoring progression for individuals at risk or patients with Parkinson's disease to complement dopaminergic imaging, or as a marker of Parkinson's burden in clinical trials.

FUNDING

Lily Safra Hope Foundation and National Institute for Health Research (NIHR) Biomedical Research Centre at King's College London.

A Multi-Atlas Label Fusion Tool for Neonatal Brain MRI Parcellation and Quantification

Structure-by-structure analysis, in which the brain magnetic resonance imaging (MRI) is parcellated based on its anatomical units, is widely used to investigate chronological changes in morphology or signal intensity during normal development, as well as to identify the alterations seen in various diseases or conditions. The multi-atlas label fusion (MALF) method is considered a highly accurate parcellation approach, and anticipated for clinical application to quantitatively evaluate early developmental processes. However, the current MALF methods, which are designed for neonatal brain segmentations, are not widely available. In this study, we developed a T1-weighted, neonatal, multi-atlas repository and integrated it into the MALF-based brain segmentation tools in the cloud-based platform, MRICloud. The cloud platform ensures users instant access to the advanced MALF tool for neonatal brains, with no software or installation requirements for the client. The Web platform by braingps.mricloud.org will eliminate the dependence on a particular operating system (eg, Windows, Macintosh, or Linux) and the requirement for high computational performance of the user's computers. The MALF-based, fully automated, image parcellation could achieve excellent agreement with manual parcellation, and the whole and regional brain volumes quantified through this method demonstrated developmental trajectories comparable to those from a previous publication. This solution will make the latest MALF tools readily available to users, with minimum barriers, and will expedite and accelerate advancements in developmental neuroscience research, neonatology, and pediatric neuroradiology.

Brain structure segmentation in the presence of multiple sclerosis lesions

Intensity-based multi-atlas segmentation strategies have shown to be particularly successful in segmenting brain images of healthy subjects. However, in the same way as most of the methods in the state of the art, their performance tends to be affected by the presence of MRI visible lesions, such as those found in multiple sclerosis (MS) patients. Here, we present an approach to minimize the effect of the abnormal lesion intensities on multi-atlas segmentation. We propose a new voxel/patch correspondence model for intensity-based multi-atlas label fusion strategies that leads to more accurate similarity measures, having a key role in the final brain segmentation. We present the theory of this model and integrate it into two well-known fusion strategies: Non-local Spatial STAPLE (NLSS) and Joint Label Fusion (JLF). The experiments performed show that our proposal improves the segmentation performance of the lesion areas. The results indicate a mean Dice Similarity Coefficient (DSC) improvement of 1.96% for NLSS (3.29% inside and 0.79% around the lesion masks) and, an improvement of 2.06% for JLF (2.31% inside and 1.42% around lesions). Furthermore, we show that, with the proposed strategy, the well-established preprocessing step of lesion filling can be disregarded, obtaining similar or even more accurate segmentation results.

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We present an approach to improve multi-atlas brain parcellation of MS patients.

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We integrate our model into 2 well-known segmentation strategies.

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Our model improves the segmentation on the lesion areas.

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The improvement on the lesion areas is also reflected in the global performance.

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With our model, lesion filling can be omitted, obtaining at least similar results.

Inflammation in the hippocampus affects IGF1 receptor signaling and contributes to neurological sequelae in rheumatoid arthritis

Aberrant insulin-like growth factor 1 receptor (IGF1R)/insulin receptor signaling in brain has recently been linked to neurodegeneration in diabetes mellitus and in Alzheimer’s disease. In this study, we demonstrate that functional disability and pain in patients with rheumatoid arthritis (RA) and in experimental RA are associated with hippocampal inflammation and inhibition of IGF1R/insulin receptor substrate 1 (IRS1) signal, reproducing an IGF1/insulin-resistant state. This restricts formation of new neurons in the hippocampus, reduces hippocampal volume, and predisposes RA patients to develop neurological symptoms. Improving IRS1 function through down-regulation of IGF1R disinhibits neurogenesis and can potentially ameliorate neurological symptoms. This opens perspectives for drugs that revert IGF1/insulin resistance as an essential complement to the antirheumatic and antiinflammatory arsenal.

Rheumatoid arthritis (RA) is an inflammatory joint disease with a neurological component including depression, cognitive deficits, and pain, which substantially affect patients’ quality of daily life. Insulin-like growth factor 1 receptor (IGF1R) signaling is one of the factors in RA pathogenesis as well as a known regulator of adult neurogenesis. The purpose of this study was to investigate the association between IGF1R signaling and the neurological symptoms in RA. In experimental RA, we demonstrated that arthritis induced enrichment of IBA1⁺ microglia in the hippocampus. This coincided with inhibitory phosphorylation of insulin receptor substrate 1 (IRS1) and up-regulation of IGF1R in the pyramidal cell layer of the cornus ammoni and in the dentate gyrus, reproducing the molecular features of the IGF1/insulin resistance. The aberrant IGF1R signaling was associated with reduced hippocampal neurogenesis, smaller hippocampus, and increased immobility of RA mice. Inhibition of IGF1R in experimental RA led to a reduction of IRS1 inhibition and partial improvement of neurogenesis. Evaluation of physical functioning and brain imaging in RA patients revealed that enhanced functional disability is linked with smaller hippocampus volume and aberrant IGF1R/IRS1 signaling. These results point to abnormal IGF1R signaling in the brain as a mediator of neurological sequelae in RA and provide support for the potentially reversible nature of hippocampal changes.

Structural Magnetic Resonance Imaging in Huntington's Disease

Huntington's disease (HD) is an autosomal dominant neurodegenerative disorder, caused by expansion of the CAG repeat in the huntingtin gene. HD is characterized clinically by progressive motor, cognitive and neuropsychiatric symptoms. There are currently no disease modifying treatments available for HD, and there is a great need for biomarkers to monitor disease progression and identify new targets for therapeutic intervention. Neuroimaging techniques provide a powerful tool for assessing disease pathology and progression in premanifest stages, before the onset of overt motor symptoms. Structural magnetic resonance imaging (MRI) is non-invasive imaging techniques which have been employed to study structural and microstructural changes in premanifest and manifest HD gene carriers. This chapter described structural imaging techniques and analysis methods employed across HD MRI studies. Current evidence for structural MRI abnormalities in HD, and associations between atrophy, structural white matter changes, iron deposition and clinical performance are discussed; together with the use of structural MRI measures as a diagnostic tool, to assess longitudinal changes, and as potential biomarkers and endpoints for clinical trials.

Structural brain imaging in Alzheimer's disease and mild cognitive impairment: biomarker analysis and shared morphometry database

Magnetic resonance (MR) imaging is a powerful technique for non-invasive in-vivo imaging of the human brain. We employed a recently validated method for robust cross-sectional and longitudinal segmentation of MR brain images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Specifically, we segmented 5074 MR brain images into 138 anatomical regions and extracted time-point specific structural volumes and volume change during follow-up intervals of 12 or 24 months. We assessed the extracted biomarkers by determining their power to predict diagnostic classification and by comparing atrophy rates to published meta-studies. The approach enables comprehensive analysis of structural changes within the whole brain. The discriminative power of individual biomarkers (volumes/atrophy rates) is on par with results published by other groups. We publish all quality-checked brain masks, structural segmentations, and extracted biomarkers along with this article. We further share the methodology for brain extraction (pincram) and segmentation (MALPEM, MALPEM4D) as open source projects with the community. The identified biomarkers hold great potential for deeper analysis, and the validated methodology can readily be applied to other imaging cohorts.

Simplifying [18F]GE-179 PET: are both arterial blood sampling and 90-min acquisitions essential?

INTRODUCTION

The NMDA receptor radiotracer [18F]GE-179 has been used with 90-min scans and arterial plasma input functions. We explored whether (1) arterial blood sampling is avoidable and (2) shorter scans are feasible.

METHODS

For 20 existing [18F]GE-179 datasets, we generated (1) standardised uptake values (SUVs) over eight intervals; (2) volume of distribution (VT) images using population-based input functions (PBIFs), scaled using one parent plasma sample; and (3) VT images using three shortened datasets, using the original parent plasma input functions (ppIFs).

RESULTS

Correlations with the original ppIF-derived 90-min VTs increased for later interval SUVs (maximal ρ = 0.78; 80-90 min). They were strong for PBIF-derived VTs (ρ = 0.90), but between-subject coefficient of variation increased. Correlations were very strong for the 60/70/80-min original ppIF-derived VTs (ρ = 0.97-1.00), which suffered regionally variant negative bias.

CONCLUSIONS

Where arterial blood sampling is available, reduction of scan duration to 60 min is feasible, but with negative bias. The performance of SUVs was more consistent across participants than PBIF-derived VTs.

Role of iodine-containing multivitamins during pregnancy for children's brain function: protocol of an ongoing randomised controlled trial: the SWIDDICH study

INTRODUCTION

Iodine is essential for normal brain development. Moderate and severe fetal iodine deficiency results in substantial to serious developmental delay in children. Mild iodine deficiency in pregnancy is associated with neurodevelopmental deficits in the offspring, but evidence from randomised trials is lacking. The aim of the Swedish Iodine in Pregnancy and Development in Children study is to determine the effect of daily supplementation with 150 µg iodine during pregnancy on the offspring's neuropsychological development up to 14 years of age.

METHODS AND ANALYSIS

Thyroid healthy pregnant women (n=1275: age range 18-40 years) at ≤12 weeks gestation will be randomly assigned to receive multivitamin supplements containing 150 µg iodine or non-iodine-containing multivitamin daily throughout pregnancy. As a primary outcome, IQ will be measured in the offspring at 7 years (Wechsler Intelligence Scale for Children-V). As secondary outcomes, IQ will be measured at 3.5 and 14 years, psychomotor development at 18 months and 7 years, and behaviour at 3.5, 7 and 14 years. Iodine status (urinary iodine concentration) will be measured during pregnancy and in the offspring at 3.5, 7 and 14 years. Thyroid function (thyroid hormones, thyroglobulin), and deiodinase type 2 polymorphisms will be measured during pregnancy and in the offspring at 7 and 14 years. Structural MRI or other relevant structural or functional brain imaging procedures will be performed in a subgroup of children at 7 and 14 years. Background and socioeconomic information will be collected at all follow-up times.

ETHICS AND DISSEMINATION

This study is approved by the Ethics Committee in Göteborg, Sweden (Diary numbers: 431-12 approved 18 June 2012 (pregnancy part) and 1089-16 approved 8 February 2017 (children follow-up)). According to Swedish regulations, dietary supplements are governed by the National Food Agency and not by the Medical Product Agency. Therefore, there is no requirement for a monitoring committee and the National Food Agency does not perform any audits of trial conduct. The trial will be conducted in accordance with the Declaration of Helsinki. The participating sites will be contacted regarding important protocol changes, both orally and in writing, and the trial registry database will be updated accordingly. Study results will be presented at relevant conferences, and submitted to peer-reviewed journals with open access in the fields of endocrinology, paediatrics and nutrition. After the appropriate embargo period, the results will be communicated to participants, healthcare professionals at the maternal healthcare centres, the public and other relevant groups, such as the national guideline group for thyroid and pregnancy and the National Food Agency.

TRIAL REGISTRATION NUMBER

NCT02378246; Pre-results.

Regional brain morphometry in patients with traumatic brain injury based on acute- and chronic-phase magnetic resonance imaging

Traumatic brain injury (TBI) is caused by a sudden external force and can be very heterogeneous in its manifestation. In this work, we analyse T1-weighted magnetic resonance (MR) brain images that were prospectively acquired from patients who sustained mild to severe TBI. We investigate the potential of a recently proposed automatic segmentation method to support the outcome prediction of TBI. Specifically, we extract meaningful cross-sectional and longitudinal measurements from acute- and chronic-phase MR images. We calculate regional volume and asymmetry features at the acute/subacute stage of the injury (median: 19 days after injury), to predict the disability outcome of 67 patients at the chronic disease stage (median: 229 days after injury). Our results indicate that small structural volumes in the acute stage (e.g. of the hippocampus, accumbens, amygdala) can be strong predictors for unfavourable disease outcome. Further, group differences in atrophy are investigated. We find that patients with unfavourable outcome show increased atrophy. Among patients with severe disability outcome we observed a significantly higher mean reduction of cerebral white matter (3.1%) as compared to patients with low disability outcome (0.7%).

Comparison of image intensity, local, and multi-atlas priors in brain tissue classification

PURPOSE

Automated and accurate tissue classification in three-dimensional brain magnetic resonance images is essential in volumetric morphometry or as a preprocessing step for diagnosing brain diseases. However, noise, intensity in homogeneity, and partial volume effects limit the classification accuracy of existing methods. This paper provides a comparative study on the contributions of three commonly used image information priors for tissue classification in normal brains: image intensity, local, and multi-atlas priors.

METHODS

We compared the effectiveness of the three priors by comparing the four methods modeling them: K-Means (KM), KM combined with a Markov Random Field (KM-MRF), multi-atlas segmentation (MAS), and the combination of KM, MRF, and MAS (KM-MRF-MAS). The key parameters and factors in each of the four methods are analyzed, and the performance of all the models is compared quantitatively and qualitatively on both simulated and real data.

RESULTS

The KM-MRF-MAS model that combines the three image information priors performs best.

CONCLUSIONS

The image intensity prior is insufficient to generate reasonable results for a few images. Introducing local and multi-atlas priors results in improved brain tissue classification. This study provides a general guide on what image information priors can be used for effective brain tissue classification.

Gyri of the human parietal lobe: Volumes, spatial extents, automatic labelling, and probabilistic atlases

Accurately describing the anatomy of individual brains enables interlaboratory communication of functional and developmental studies and is crucial for possible surgical interventions. The human parietal lobe participates in multimodal sensory integration including language processing and also contains the primary somatosensory area. We describe detailed protocols to subdivide the parietal lobe, analyze morphological and volumetric characteristics, and create probabilistic atlases in MNI152 stereotaxic space. The parietal lobe was manually delineated on 3D T1 MR images of 30 healthy subjects and divided into four regions: supramarginal gyrus (SMG), angular gyrus (AG), superior parietal lobe (supPL) and postcentral gyrus (postCG). There was the expected correlation of male gender with larger brain and intracranial volume. We examined a wide range of anatomical features of the gyri and the sulci separating them. At least a rudimentary primary intermediate sulcus of Jensen (PISJ) separating SMG and AG was identified in nearly all (59/60) hemispheres. Presence of additional gyri in SMG and AG was related to sulcal features and volumetric characteristics. The parietal lobe was slightly (2%) larger on the left, driven by leftward asymmetries of the postCG and SMG. Intersubject variability was highest for SMG and AG, and lowest for postCG. Overall the morphological characteristics tended to be symmetrical, and volumes also tended to covary between hemispheres. This may reflect developmental as well as maturation factors. To assess the accuracy with which the labels can be used to segment newly acquired (unlabelled) T1-weighted brain images, we applied multi-atlas label propagation software (MAPER) in a leave-one-out experiment and compared the resulting automatic labels with the manually prepared ones. The results showed strong agreement (mean Jaccard index 0.69, corresponding to a mean Dice index of 0.82, average mean volume error of 0.6%). Stereotaxic probabilistic atlases of each subregion were obtained. They illustrate the physiological brain torque, with structures in the right hemisphere positioned more anteriorly than in the left, and right/left positional differences of up to 10 mm. They also allow an assessment of sulcal variability, e.g. low variability for parietooccipital fissure and cingulate sulcus. Illustrated protocols, individual label sets, probabilistic atlases, and a maximum-probability atlas which takes into account surrounding structures are available for free download under academic licences.

Test-retest reproducibility of quantitative binding measures of [11C]Ro15-4513, a PET ligand for GABAA receptors containing alpha5 subunits

INTRODUCTION

Alteration of γ-aminobutyric acid "A" (GABAA) receptor-mediated neurotransmission has been associated with various neurological and psychiatric disorders. [11C]Ro15-4513 is a PET ligand with high affinity for α5-subunit-containing GABAA receptors, which are highly expressed in limbic regions of the human brain (Sur et al., 1998). We quantified the test-retest reproducibility of measures of [11C]Ro15-4513 binding derived from six different quantification methods (12 variants).

METHODS

Five healthy males (median age 40 years, range 38-49 years) had a 90-min PET scan on two occasions (median interval 12 days, range 11-30 days), after injection of a median dose of 441 MegaBequerels of [11C]Ro15-4513. Metabolite-corrected arterial plasma input functions (parent plasma input functions, ppIFs) were generated for all scans. We quantified regional binding using six methods (12 variants), some of which were region-based (applied to the average time-activity curve within a region) and others were voxel-based: 1) Models requiring arterial ppIFs - regional reversible compartmental models with one and two tissue compartments (2kbv and 4kbv); 2) Regional and voxelwise Logan's graphical analyses (Logan et al., 1990), which required arterial ppIFs; 3) Model-free regional and voxelwise (exponential) spectral analyses (SA; (Cunningham and Jones, 1993)), which also required arterial ppIFs; 4) methods not requiring arterial ppIFs - voxelwise standardised uptake values (Kenney et al., 1941), and regional and voxelwise simplified reference tissue models (SRTM/SRTM2) using brainstem or alternatively cerebellum as pseudo-reference regions (Lammertsma and Hume, 1996; Gunn et al., 1997). To compare the variants, we sampled the mean values of the outcome parameters within six bilateral, non-reference grey matter regions-of-interest. Reliability was quantified in terms of median absolute percentage test-retest differences (MA-TDs; preferentially low) and between-subject coefficient of variation (BS-CV, preferentially high), both compounded by the intraclass correlation coefficient (ICC). These measures were compared between variants, with particular interest in the hippocampus.

RESULTS

Two of the six methods (5/12 variants) yielded reproducible data (i.e. MA-TD <10%): regional SRTMs and voxelwise SRTM2s, both using either the brainstem or the cerebellum; and voxelwise SA. However, the SRTMs using the brainstem yielded a lower median BS-CV (7% for regional, 7% voxelwise) than the other variants (8-11%), resulting in lower ICCs. The median ICCs across six regions were 0.89 (interquartile range 0.75-0.90) for voxelwise SA, 0.71 (0.64-0.84) for regional SRTM-cerebellum and 0.83 (0.70-0.86) for voxelwise SRTM-cerebellum. The ICCs for the hippocampus were 0.89 for voxelwise SA, 0.95 for regional SRTM-cerebellum and 0.93 for voxelwise SRTM-cerebellum.

CONCLUSION

Quantification of [11C]Ro15-4513 binding shows very good to excellent reproducibility with SRTM and with voxelwise SA which, however, requires an arterial ppIF. Quantification in the α5 subunit-rich hippocampus is particularly reliable. The very low expression of the α5 in the cerebellum (Fritschy and Mohler, 1995; Veronese et al., 2016) and the substantial α1 subunit density in this region may hamper the application of reference tissue methods.

Quantitative longitudinal imaging of activated microglia as a marker of inflammation in the pilocarpine rat model of epilepsy using [11C]-( R)-PK11195 PET and MRI

Inflammation may play a role in the development of epilepsy after brain insults. [¹¹C]-( R)-PK11195 binds to TSPO, expressed by activated microglia. We quantified [¹¹C]-( R)-PK11195 binding during epileptogenesis after pilocarpine-induced status epilepticus (SE), a model of temporal lobe epilepsy. Nine male rats were studied thrice (D0-1, D0 + 6, D0 + 35, D0 = SE induction). In the same session, 7T T2-weighted images and DTI for mean diffusivity (MD) and fractional anisotropy (FA) maps were acquired, followed by dynamic PET/CT. On D0 + 35, femoral arterial blood was sampled for rat-specific metabolite-corrected arterial plasma input functions (AIFs). In multiple MR-derived ROIs, we assessed four kinetic models (two with AIFs; two using a reference region), standard uptake values (SUVs), and a model with a mean AIF. All models showed large (up to two-fold) and significant TSPO binding increases in regions expected to be affected, and comparatively little change in the brainstem, at D0 + 6. Some individuals showed increases at D0 + 35. AIF models yielded more consistent increases at D0 + 6. FA values were decreased at D0 + 6 and had recovered by D0 + 35. MD was increased at D0 + 6 and more so at D0 + 35. [¹¹C]-( R)-PK11195 PET binding and MR biomarker changes could be detected with only nine rats, highlighting the potential of longitudinal imaging studies.

Accurate simulation of transcranial ultrasound propagation for ultrasonic neuromodulation and stimulation

Non-invasive, focal neurostimulation with ultrasound is a potentially powerful neuroscientific tool that requires effective transcranial focusing of ultrasound to develop. Time-reversal (TR) focusing using numerical simulations of transcranial ultrasound propagation can correct for the effect of the skull, but relies on accurate simulations. Here, focusing requirements for ultrasonic neurostimulation are established through a review of previously employed ultrasonic parameters, and consideration of deep brain targets. The specific limitations of finite-difference time domain (FDTD) and k-space corrected pseudospectral time domain (PSTD) schemes are tested numerically to establish the spatial points per wavelength and temporal points per period needed to achieve the desired accuracy while minimizing the computational burden. These criteria are confirmed through convergence testing of a fully simulated TR protocol using a virtual skull. The k-space PSTD scheme performed as well as, or better than, the widely used FDTD scheme across all individual error tests and in the convergence of large scale models, recommending it for use in simulated TR. Staircasing was shown to be the most serious source of error. Convergence testing indicated that higher sampling is required to achieve fine control of the pressure amplitude at the target than is needed for accurate spatial targeting.