A longitudinal analysis of brain volume changes in myelin oligodendrocyte glycoprotein antibody-associated disease.

BACKGROUND AND PURPOSE: Myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) is a relapsing demyelinating condition. There are several cross-sectional studies showing evidence of brain atrophy in people with MOGAD (pwMOGAD), but longitudinal brain volumetric assessment is still an unmet need. Current recommendations do not include monitoring with MRI and assume distinct attacks. Evidence of ongoing axon loss will have diagnostic and therapeutic implications. In this study, we assessed brain volume changes in pwMOGAD over a mean follow-up period of 2 years and compared this to changes in people with multiple sclerosis (pwMS). METHODS: This is a retrospective single-center study over a 7-year period from 2014 to 2021. MRI brain scans at the time of diagnosis and follow-up in remission were collected from 14 Caucasian pwMOGAD, confirmed by serum myelin oligodendrocyte glycoprotein immunoglobulin G antibody presence, detected by live cell-based assays. Total brain volume (TBV), white matter (WM), gray matter (GM), and demyelinating lesion volumes were assessed automatically using the Statistical Parametric Mapping and FMRIB automated segmentation tools. MRI brain scans at diagnosis and follow-up on remission were collected from 32-matched pwMS for comparison. Statistical analysis was done using analysis of variance. RESULTS: There is evidence of TBV loss, affecting particularly GM, over an approximately 2-year follow-up period in pwMOGAD (p < .05), comparable to pwMS. WM and lesion volume change over the same period were not statistically significant (p > .1). CONCLUSION: We found evidence of loss of GM and TBV over time  in pwMOGAD, similar to pwMS, although the WM and lesion volumes were unchanged.

Optical microscopic imaging, manipulation, and analysis methods for morphogenesis research.

Morphogenesis is a developmental process of organisms being shaped through complex and cooperative cellular movements. To understand the interplay between genetic programs and the resulting multicellular morphogenesis, it is essential to characterize the morphologies and dynamics at the single-cell level and to understand how physical forces serve as both signaling components and driving forces of tissue deformations. In recent years, advances in microscopy techniques have led to improvements in imaging speed, resolution and depth. Concurrently, the development of various software packages has supported large-scale, analyses of challenging images at the single-cell resolution. While these tools have enhanced our ability to examine dynamics of cells and mechanical processes during morphogenesis, their effective integration requires specialized expertise. With this background, this review provides a practical overview of those techniques. First, we introduce microscopic techniques for multicellular imaging and image analysis software tools with a focus on cell segmentation and tracking. Second, we provide an overview of cutting-edge techniques for mechanical manipulation of cells and tissues. Finally, we introduce recent findings on morphogenetic mechanisms and mechanosensations that have been achieved by effectively combining microscopy, image analysis tools and mechanical manipulation techniques.

Active bone marrow segmentation based on computed tomography imaging in anal cancer patients: A machine-learning-based proof of concept.

PURPOSE: Different methods are available to identify haematopoietically active bone marrow (ActBM). However, their use can be challenging for radiotherapy routine treatments, since they require specific equipment and dedicated time. A machine learning (ML) approach, based on radiomic features as inputs to three different classifiers, was applied to computed tomography (CT) images to identify haematopoietically active bone marrow in anal cancer patients. METHODS: A total of 40 patients was assigned to the construction set (training set + test set). Fluorine-18-Fluorodeoxyglucose Positron Emission Tomography (18FDG-PET) images were used to detect the active part of the pelvic bone marrow (ActPBM) and stored as ground-truth for three subregions: iliac, lower pelvis and lumbosacral bone marrow (ActIBM, ActLPBM, ActLSBM). Three parameters were used for the correspondence analyses between 18FDG-PET and ML classifiers: DICE index, Precision and Recall. RESULTS: For the 40-patient cohort, median values [min; max] of the Dice index were 0.69 [0.20; 0.84], 0.76 [0.25; 0.89], and 0.36 [0.15; 0.67] for ActIBM, ActLSBM, and ActLPBM, respectively. The Precision/Recall (P/R) ratio median value for the ActLPBM structure was 0.59 [0.20; 1.84] (over segmentation), while for the other two subregions the P/R ratio median has values of 1.249 [0.43; 4.15] for ActIBM and 1.093 [0.24; 1.91] for ActLSBM (under segmentation). CONCLUSION: A satisfactory degree of overlap compared to 18FDG-PET was found for 2 out of the 3 subregions within pelvic bones. Further optimization and generalization of the process is required before clinical implementation.

Using a semi-automated approach to quantify Unidentified Bright Objects in Neurofibromatosis type 1 and linkages to cognitive and academic outcomes.

Neurofibromatosis type 1 (NF1) is an autosomal dominant neurocutaneous syndrome that affects multiple organ systems resulting in widespread symptoms, including cognitive deficits. In addition to the criteria required for an NF1 diagnosis, approximately 70% of children with NF1 present with Unidentified Bright Objects (UBOs) or Focal Areas of Signal Intensity, which are hyperintense bright spots seen on T2-weighted magnetic resonance images and seen more prominently on FLAIR magnetic resonance images (Sabol et al., 2011). Current findings relating the presence/absence, quantities, sizes, and locations of these bright spots to cognitive abilities are mixed. To contribute to and hopefully disentangle some of these mixed findings, we explored potential relationships between metrics related to UBOs and cognitive abilities in a sample of 28 children and adolescents with NF1 (M=12.52 years; SD=3.18 years; 16 male). We used the Lesion Segmentation Tool (LST) to automatically detect and segment the UBOs. The LST was able to qualitatively and quantitatively reliably detect UBOs in images of children with NF1. Using these automatically detected and segmented lesions, we found that while controlling for age, biological sex, perceptual IQ, study, and scanner, "total UBO volume", defined as the sum of all the voxels representing all of the UBOs for each participant, helped explain differences in word reading, phonological awareness, and visuospatial skills. These findings contribute to the emerging NF1 literature and help parse the specific deficits that children with NF1 have, to then help improve the efficacy of reading interventions for children with NF1.

MRI-based Alzheimer's disease-resemblance atrophy index in the detection of preclinical and prodromal Alzheimer's disease.

Alzheimer's Disease-resemblance atrophy index (AD-RAI) is an MRI-based machine learning derived biomarker that was developed to reflect the characteristic brain atrophy associated with AD. Recent study showed that AD-RAI (≥0.5) had the best performance in predicting conversion from mild cognitive impairment (MCI) to dementia and from cognitively unimpaired (CU) to MCI. We aimed to validate the performance of AD-RAI in detecting preclinical and prodromal AD. We recruited 128 subjects (MCI=50, CU=78) from two cohorts: CU-SEEDS and ADNI. Amyloid (A+) and tau (T+) status were confirmed by PET (11C-PIB, 18F-T807) or CSF analysis. We investigated the performance of AD-RAI in detecting preclinical and prodromal AD (i.e. A+T+) among MCI and CU subjects and compared its performance with that of hippocampal measures. AD-RAI achieved the best metrics among all subjects (sensitivity 0.74, specificity 0.91, accuracy 85.94%) and among MCI subjects (sensitivity 0.92, specificity 0.81, accuracy 86.00%) in detecting A+T+ subjects over other measures. Among CU subjects, AD-RAI yielded the best specificity (0.95) and accuracy (85.90%) over other measures, while hippocampal volume achieved a higher sensitivity (0.73) than AD-RAI (0.47) in detecting preclinical AD. These results showed the potential of AD-RAI in the detection of early AD, in particular at the prodromal stage.

Medio-dorsal thalamus and confabulations: Evidence from a clinical case and combined MRI/DTI study.

The Medio-Dorsal Nuclei (MDN) including the thalamic magnocellular and parvocellular thalamic regions has been implicated in verbal memory function. In a 77 year old lady, with a prior history of a clinically silent infarct of the left MDN, we observed the acute onset of spontaneous confabulations when an isolated new infarct occurred in the right MDN. The patient and five age-matched healthy subjects underwent Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI). The thalamic lesions were localized by overlapping Morel Thalamic Atlas with structural MRI data. DTI was used to assess: i) white matter alterations (Fractional Anisotropy, FA) within fibers connecting the ischemic areas to cortex; ii) the micro-structural damage (Mean Diffusivity) within the thalamic sub-regions defined by their structural connectivity to the Anterior Cingulate Cortex (ACC) and to the temporal lobes. These target regions were chosen because their damage is considered associated with the appearance of confabulations. Thalamic lesions were localized within the parvocellular regions of the right and left MDNs. The structural connectivity study showed that the fiber tracts, connecting the bilaterally damaged thalamic regions with the frontal cortex, corresponded to the anterior thalamic radiations (ATR). FA within these tracts was significantly lower in the patient as compared to controls. Mean diffusivity within the MDNs projecting to Broadman area (BA) 24, BA25 and BA32 of ACC was significantly higher in the patient than in control group. Mean diffusivity values within the MDN projecting to temporal lobes in contrast were not different between patient and controls. Our findings suggest the involvement of bilateral MDNs projections to ACC in the genesis of confabulations and help provide clarity to the longstanding debate on the origin of confabulations.

Differentiation of Fibroadenomas and Pure Mucinous Carcinomas on Dynamic Contrast-Enhanced MRI of the Breast Using Volume Segmentation for Kinetic Analysis: A Feasibility Study.

OBJECTIVE: The objective of this study was to retrospectively evaluate the diagnostic performance of volume-based kinetic analysis in dynamic contrast-enhanced MRI (DCE-MRI) of the breast for the differentiation of fibroadenomas (FAs) with high T2 signal intensity from pure mucinous carcinomas (PMCs). MATERIALS AND METHODS: A review of records from 2007 to 2013 that were stored in the pathology department database at our institution identified nine patients with PMCs (defined as tumor cells with a mucinous component ≥ 90%) who underwent preoperative breast MRI. The PMCs were compared with 15 biopsy-proven FAs from 13 patients. Characteristics noted on DCE-MRI were evaluated using computer-assisted diagnosis software. For each mass, the proportion of progressive enhancement in the lesion at the delayed phase was quantified. Both groups of masses were compared using a Wilcoxon signed rank test. A ROC curve was used to define an appropriate cutoff point. RESULTS: The median rate of progressive enhancement was 100% (range, 99-100%) for FAs and 97% (range, 87-99%) for PMCs (p = 0.0326). The AUC of the kinetic curve for progressive enhancement was 0.7519 (95% CI, 0.5258-0.9407). A more appropriate cutoff value to maximize sensitivity and specificity was 98.5%. With this cutoff, sensitivity was 66.7% (95% CI, 11.1-100%) and specificity was 80% (95% CI, 39.6-99.8%) for the diagnosis of PMCs. CONCLUSION: Volume-based kinetic analysis may aid in differentiating FAs from PMCs on DCE-MRI studies of the breast.

Thalamic-hippocampal-prefrontal disruption in relapsing-remitting multiple sclerosis.

BACKGROUND: Cortical, thalamic and hippocampal gray matter atrophy in relapsing-remitting MS (RRMS) is associated cognitive deficits. However, the role of interconnecting white matter pathways including the fornix, cingulum, and uncinate fasciculus (UF) is less well studied. OBJECTIVE: To assess MS damage to a hippocampal-thalamic-prefrontal network and the relative contributions of its components to specific cognitive domains. METHODS: We calculated diffusion tensor fractional anisotropy (FA) in the fornix, cingulum and UF as well as thalamic and hippocampal volumes in 27 RRMS patients and 20 healthy controls. A neuropsychological battery was administered and 4 core tests known to be sensitive to MS changes were used to assess cognitive impairment. To determine the relationships between structure and cognition, all tests were grouped into 4 domains: attention/executive function, processing speed, verbal memory, and spatial memory. Univariate correlations with structural measures and depressive symptoms identified potential contributors to cognitive performance and subsequent linear regression determined their relative effects on performance in each domain. For significant predictors, we also explored the effects of laterality and axial versus radial diffusivity. RESULTS: RRMS patients had worse performance on the Symbol Digit Modalities Test, but no significant impairment in the 4 cognitive domains. RRMS had reduced mean FA of all 3 pathways and reduced thalamic and hippocampal volumes compared to controls. In RRMS we found that thalamic volume and BDI predicted attention/executive function, UF FA predicted processing speed, thalamic volume predicted verbal memory, and UF FA and BDI predicted spatial memory. CONCLUSIONS: Hippocampal-thalamic-prefrontal disruption affects cognitive performance in early RRMS with mild to minimal cognitive impairment, confirming both white and gray matter involvement in MS and demonstrating utility in assessing functional networks to monitor cognition.

Subcortical brain segmentation of two dimensional T1-weighted data sets with FMRIB's Integrated Registration and Segmentation Tool (FIRST).

Brain atrophy has been identified as an important contributing factor to the development of disability in multiple sclerosis (MS). In this respect, more and more interest is focussing on the role of deep grey matter (DGM) areas. Novel data analysis pipelines are available for the automatic segmentation of DGM using three-dimensional (3D) MRI data. However, in clinical trials, often no such high-resolution data are acquired and hence no conclusions regarding the impact of new treatments on DGM atrophy were possible so far. In this work, we used FMRIB's Integrated Registration and Segmentation Tool (FIRST) to evaluate the possibility of segmenting DGM structures using standard two-dimensional (2D) T1-weighted MRI. In a cohort of 70 MS patients, both 2D and 3D T1-weighted data were acquired. The thalamus, putamen, pallidum, nucleus accumbens, and caudate nucleus were bilaterally segmented using FIRST. Volumes were calculated for each structure and for the sum of basal ganglia (BG) as well as for the total DGM. The accuracy and reliability of the 2D data segmentation were compared with the respective results of 3D segmentations using volume difference, volume overlap and intra-class correlation coefficients (ICCs). The mean differences for the individual substructures were between 1.3% (putamen) and -25.2% (nucleus accumbens). The respective values for the BG were -2.7% and for DGM 1.3%. Mean volume overlap was between 89.1% (thalamus) and 61.5% (nucleus accumbens); BG: 84.1%; DGM: 86.3%. Regarding ICC, all structures showed good agreement with the exception of the nucleus accumbens. The results of the segmentation were additionally validated through expert manual delineation of the caudate nucleus and putamen in a subset of the 3D data. In conclusion, we demonstrate that subcortical segmentation of 2D data are feasible using FIRST. The larger subcortical GM structures can be segmented with high consistency. This forms the basis for the application of FIRST in large 2D MRI data sets of clinical trials in order to determine the impact of therapeutic interventions on DGM atrophy in MS.