Resting state functional connectivity in SLE patients and association with cognitive impairment and blood-brain barrier permeability.

OBJECTIVE: Extensive blood-brain barrier (BBB) leakage has been linked to cognitive impairment in SLE. This study aimed to examine the associations of brain functional connectivity (FC) with cognitive impairment and BBB dysfunction among patients with SLE. METHODS: Cognitive function was assessed by neuropsychological testing (n = 77). Resting-state FC (rsFC) between brain regions, measured by functional MRI (n = 78), assessed coordinated neural activation in 131 regions across five canonical brain networks. BBB permeability was measured by dynamic contrast-enhanced MRI (n = 61). Differences in rsFC were compared between SLE patients with cognitive impairment (SLE-CI) and those with normal cognition (SLE-NC), between SLE patients with and without extensive BBB leakage, and with healthy controls. RESULTS: A whole-brain rsFC comparison found significant differences in intra-network and inter-network FC in SLE-CI vs SLE-NC patients. The affected connections showed a reduced negative rsFC in SLE-CI compared with SLE-NC and healthy controls. Similarly, a reduced number of brain-wide connections was found in SLE-CI patients compared with SLE-NC (P = 0.030) and healthy controls (P = 0.006). Specific brain regions had a lower total number of brain-wide connections in association with extensive BBB leakage (P = 0.011). Causal mediation analysis revealed that 64% of the association between BBB leakage and cognitive impairment in SLE patients was mediated by alterations in FC. CONCLUSION: SLE patients with cognitive impairment had abnormalities in brain rsFC which accounted for most of the association between extensive BBB leakage and cognitive impairment.

Mapping of fatty acid composition with free-breathing MR spectroscopic imaging and compressed sensing.

Nonalcoholic fatty liver disease (NAFLD) is a growing health problem, and a major challenge in NAFLD management is identifying which patients are at risk of progression to more serious disease. Simple measurements of liver fat content are not strong predictors of clinical outcome, but biomarkers related to fatty acid composition (ie, saturated vs. unsaturated fat) may be more effective. MR spectroscopic imaging (MRSI) methods allow spatially resolved, whole-liver measurements of chemical composition but are traditionally limited by slow acquisition times. In this work we present an accelerated MRSI acquisition based on spin echo single point imaging (SE-SPI), which, using appropriate sampling and compressed sensing reconstruction, allows free-breathing acquisition in a mouse model of fatty liver disease. After validating the technique's performance in oil/water phantoms, we imaged mice that had received a normal diet or a methionine and choline deficient (MCD) diet, some of which also received supplemental injections of iron to mimic hepatic iron overload. SE-SPI was more resistant to the line-broadening effects of iron than single-voxel spectroscopy measurements, and was consistently able to measure the amplitudes of low-intensity spectral peaks that are important to characterizing fatty acid composition. In particular, in the mice receiving the MCD diet, SE-SPI showed a significant decrease in a metric associated with unsaturated fat, which is consistent with the literature. This or other related metrics may therefore offer more a specific biomarker of liver health than fat content alone. This preclinical study is an important precursor to clinical testing of the proposed method. MR-based quantification of fatty acid composition may allow for improved characterization of non-alcoholic fatty liver disease. A spectroscopic imaging method with appropriate sampling strategy allows whole-liver mapping of fat composition metrics in a free-breathing mouse model. Changes in metrics like the surrogate unsaturation index (UIs) are visible in mice receiving a diet which induces fat accumulation in the liver, as compared to a normal diet; such metrics may prove useful in future clinical studies of liver disease.

Blood-brain barrier leakage in systemic lupus erythematosus is associated with gray matter loss and cognitive impairment.

OBJECTIVES: To examine the association between blood-brain barrier (BBB) integrity, brain volume and cognitive dysfunction in adult patients with systemic lupus erythematosus (SLE). METHODS: A total of 65 ambulatory patients with SLE and 9 healthy controls underwent dynamic contrast-enhanced MRI scanning, for quantitative assessment of BBB permeability. Volumetric data were extracted using the VolBrain pipeline. Global cognitive function was evaluated using a screening battery consisting of tasks falling into five broad cognitive domains, and was compared between patients with normal versus extensive BBB leakage. RESULTS: Patients with SLE had significantly higher levels of BBB leakage compared with controls (p=0.04). Extensive BBB leakage (affecting over >9% of brain volume) was identified only in patients with SLE (16/65; 24.6%), who also had smaller right and left cerebral grey matter volumes compared with controls (p=0.04). Extensive BBB leakage was associated with lower global cognitive scores (p=0.02), and with the presence of impairment on one or more cognitive tasks (p=0.01). CONCLUSION: Our findings provide evidence for a link between extensive BBB leakage and changes in both brain structure and cognitive function in patients with SLE. Future studies should investigate the mechanisms underlying BBB-mediated cognitive impairment, validate the diagnostic utility of BBB imaging, and determine the potential of targeting the BBB as a therapeutic strategy in patients with SLE.

3D single point imaging with compressed sensing provides high temporal resolution R 2* mapping for in vivo preclinical applications.

OBJECTIVE: Purely phase-encoded techniques such as single point imaging (SPI) are generally unsuitable for in vivo imaging due to lengthy acquisition times. Reconstruction of highly undersampled data using compressed sensing allows SPI data to be quickly obtained from animal models, enabling applications in preclinical cellular and molecular imaging. MATERIALS AND METHODS: TurboSPI is a multi-echo single point technique that acquires hundreds of images with microsecond spacing, enabling high temporal resolution relaxometry of large-R 2* systems such as iron-loaded cells. TurboSPI acquisitions can be pseudo-randomly undersampled in all three dimensions to increase artifact incoherence, and can provide prior information to improve reconstruction. We evaluated the performance of CS-TurboSPI in phantoms, a rat ex vivo, and a mouse in vivo. RESULTS: An algorithm for iterative reconstruction of TurboSPI relaxometry time courses does not affect image quality or R 2* mapping in vitro at acceleration factors up to 10. Imaging ex vivo is possible at similar acceleration factors, and in vivo imaging is demonstrated at an acceleration factor of 8, such that acquisition time is under 1 h. CONCLUSIONS: Accelerated TurboSPI enables preclinical R 2* mapping without loss of data quality, and may show increased specificity to iron oxide compared to other sequences.

Improving fMRI reliability in presurgical mapping for brain tumours.

PURPOSE: Functional MRI (fMRI) is becoming increasingly integrated into clinical practice for presurgical mapping. Current efforts are focused on validating data quality, with reliability being a major factor. In this paper, we demonstrate the utility of a recently developed approach that uses receiver operating characteristic-reliability (ROC-r) to: (1) identify reliable versus unreliable data sets; (2) automatically select processing options to enhance data quality; and (3) automatically select individualised thresholds for activation maps. METHODS: Presurgical fMRI was conducted in 16 patients undergoing surgical treatment for brain tumours. Within-session test-retest fMRI was conducted, and ROC-reliability of the patient group was compared to a previous healthy control cohort. Individually optimised preprocessing pipelines were determined to improve reliability. Spatial correspondence was assessed by comparing the fMRI results to intraoperative cortical stimulation mapping, in terms of the distance to the nearest active fMRI voxel. RESULTS: The average ROC-r reliability for the patients was 0.58±0.03, as compared to 0.72±0.02 in healthy controls. For the patient group, this increased significantly to 0.65±0.02 by adopting optimised preprocessing pipelines. Co-localisation of the fMRI maps with cortical stimulation was significantly better for more reliable versus less reliable data sets (8.3±0.9 vs 29±3 mm, respectively). CONCLUSIONS: We demonstrated ROC-r analysis for identifying reliable fMRI data sets, choosing optimal postprocessing pipelines, and selecting patient-specific thresholds. Data sets with higher reliability also showed closer spatial correspondence to cortical stimulation. ROC-r can thus identify poor fMRI data at time of scanning, allowing for repeat scans when necessary. ROC-r analysis provides optimised and automated fMRI processing for improved presurgical mapping.

NEMA NU 1-2018 performance characterization and Monte Carlo model validation of the Cubresa Spark SiPM-based preclinical SPECT scanner.

BACKGROUND: The Cubresa Spark is a novel benchtop silicon-photomultiplier (SiPM)-based preclinical SPECT system. SiPMs in SPECT significantly improve resolution and reduce detector size compared to preclinical cameras with photomultiplier tubes requiring highly magnifying collimators. The NEMA NU 1 Standard for Performance Measurements of Gamma Cameras provides methods that can be readily applied or extended to characterize preclinical cameras with minor modifications. The primary objective of this study is to characterize the Spark according to the NEMA NU 1-2018 standard to gain insight into its nuclear medicine imaging capabilities. The secondary objective is to validate a GATE Monte Carlo simulation model of the Spark for use in preclinical SPECT studies. METHODS: NEMA NU 1-2018 guidelines were applied to characterize the Spark's intrinsic, system, and tomographic performance with single- and multi-pinhole collimators. Phantoms were fabricated according to NEMA specifications with deviations involving high-resolution modifications. GATE was utilized to model the detector head with the single-pinhole collimator, and NEMA measurements were employed to tune and validate the model. Single-pinhole and multi-pinhole SPECT data were reconstructed with the Software for Tomographic Image Reconstruction and HiSPECT, respectively. RESULTS: The limiting intrinsic resolution was measured as 0.85 mm owing to a high-resolution SiPM array combined with a 3 mm-thick scintillation crystal. The average limiting tomographic resolution was 1.37 mm and 1.19 mm for the single- and multi-pinhole collimators, respectively, which have magnification factors near unity at the center of rotation. The maximum observed count rate was 15,400 cps, and planar sensitivities of 34 cps/MBq and 150 cps/MBq were measured at the center of rotation for the single- and multi-pinhole collimators, respectively. All simulated tests agreed well with measurement, where the most considerable deviations were below 7%. CONCLUSIONS: NEMA NU 1-2018 standards determined that a SiPM detector mitigates the need for highly magnifying pinhole collimators while preserving detailed information in projection images. Measured and simulated NEMA results were highly comparable with differences on the order of a few percent, confirming simulation accuracy and validating the GATE model. Of the collimators initially provided with the Spark, the multi-pinhole collimator offers high resolution and sensitivity for organ-specific imaging of small animals, and the single-pinhole collimator enables high-resolution whole-body imaging of small animals.

White versus gray matter: fMRI hemodynamic responses show similar characteristics, but differ in peak amplitude.

BACKGROUND: There is growing evidence for the idea of fMRI activation in white matter. In the current study, we compared hemodynamic response functions (HRF) in white matter and gray matter using 4 T fMRI. White matter fMRI activation was elicited in the isthmus of the corpus callosum at both the group and individual levels (using an established interhemispheric transfer task). Callosal HRFs were compared to HRFs from cingulate and parietal activation. RESULTS: Examination of the raw HRF revealed similar overall response characteristics. Finite impulse response modeling confirmed that the WM HRF characteristics were comparable to those of the GM HRF, but had significantly decreased peak response amplitudes. CONCLUSIONS: Overall, the results matched a priori expectations of smaller HRF responses in white matter due to the relative drop in cerebral blood flow (CBF) and cerebral blood volume (CBV). Importantly, the findings demonstrate that despite lower CBF and CBV, white matter fMRI activation remained within detectable ranges at 4 T.

Role of autoantibodies and blood-brain barrier leakage in cognitive impairment in systemic lupus erythematosus.

OBJECTIVE: Cognitive impairment is common in patients with SLE but the cause is unknown. The current cross-sectional study examined the association between select SLE-related autoantibodies, other serological biomarkers and extensive blood-brain barrier (BBB) leakage in patients with SLE with and without cognitive impairment. In addition, we determined whether the relationship between SLE autoantibodies, other biomarkers and cognitive impairment differed depending on the presence or absence of concurrent extensive BBB leakage. METHODS: Consecutive patients with SLE, recruited from a single academic medical centre, underwent formal neuropsychological testing for assessment of cognitive function. On the same day, BBB permeability was determined using dynamic contrast-enhanced MRI scanning. SLE autoantibodies and other serological biomarkers were measured. Regression modelling was used to determine the association between cognitive impairment, extensive BBB leakage and autoantibodies/biomarkers. RESULTS: There were 102 patients with SLE; 90% were female and 88% were Caucasian, with a mean±SD age of 48.9±13.8 years. The mean±SD SLE disease duration was 14.8±11.0 years. Impairment in one or more cognitive tests was present in 47 of 101 (47%) patients and included deficits in information processing speed (9%), attention span (21%), new learning (8%), delayed recall (15%) and executive abilities (21%). Extensive BBB leakage was present in 20 of 79 (25%) patients and was associated with cognitive impairment (15 of 20 (75%) vs 24 of 59 (41%); p=0.01) and shorter disease duration (median (IQR): 7 (8-24 years) vs 15 (2-16 years); p=0.02). No serological parameters were associated with extensive BBB leakage and there was no statistically significant association between cognitive impairment and circulating autoantibodies even after adjusting for BBB leakage. CONCLUSIONS: Extensive BBB leakage alone was associated with cognitive impairment. These findings suggest that BBB leakage is an important contributor to cognitive impairment, regardless of circulating SLE-related autoantibodies.

Functional mapping in the corpus callosum: a 4T fMRI study of white matter.

INTRODUCTION: The idea of fMRI activation in white matter (WM) is controversial. Our recent work has used two different approaches to investigate whether there is evidence for WM fMRI. The first approach used words and faces to elicit interhemispheric transfer activation in the posterior corpus callosum (Sperry task). The second approach used checkerboard stimuli to elicit similar activation in the anterior corpus callosum (Poffenberger task). Using these different tasks, it has been possible to detect WM activation in different regions. In the current study, we report the results of a critical experiment: demonstrating that callosal activation can be experimentally manipulated within the same set of individuals. METHODS: All subjects completed both the Sperry and Poffenberger tasks. Functional MRI data were acquired at 4T, using an asymmetric spin echo spiral sequence. Data were analyzed with FSL using a model-based approach. Analyses focused on group and individual activations in WM. RESULTS AND DISCUSSION: Corpus callosum activation was elicited for both tasks, with activation varying according to task type. A statistical contrast of the two tasks revealed posterior callosal activation for the Sperry task and anterior callosal activation for the Poffenberger task. The Sperry task showed activation in the isthmus and middle body of the corpus callosum at the group level and in 100% of subjects. The Poffenberger task showed activation in the genu and middle body of the corpus callosum at the group level and in 94% of subjects. The WM activation replicated prior results, with the additional strength of functional mapping within the same group of individuals.

Investigation of fMRI activation in the internal capsule.

BACKGROUND: Functional magnetic resonance imaging (fMRI) in white matter has long been considered controversial. Recently, this viewpoint has been challenged by an emerging body of evidence demonstrating white matter activation in the corpus callosum. The current study aimed to determine whether white matter activation could be detected outside of the corpus callosum, in the internal capsule. Data were acquired from a 4 T MRI using a specialized asymmetric spin echo spiral sequence. A motor task was selected to elicit activation in the posterior limb of the internal capsule. RESULTS: White matter fMRI activation was examined at the individual and group levels. Analyses revealed that activation was present in the posterior limb of the internal capsule in 80% of participants. These results provide further support for white matter fMRI activation. CONCLUSIONS: The ability to visualize functionally active tracts has strong implications for the basic scientific study of connectivity and the clinical assessment of white matter disease.

Tracking cognitive changes in new-onset epilepsy: functional imaging challenges.

Functional imaging has potential for tracking changes in cognition during the onset and evolution of epilepsy. Although the concept of imaging such changes over time is an exciting new direction, feasibility remains an open question. The current article outlines a case example in which functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) were used to monitor memory changes before and after selective temporal lobe resection. From this example, three key methodologic challenges for new-onset epilepsy are identified and discussed. The first challenge relates to the interpretation of results in regions near epileptogenic tissue. We argue that this is best addressed by collecting information from multiple modalities to test for convergent evidence. The second challenge relates to optimizing the methods for sensitivity to detecting changes. In this case, enhanced imaging methods and a region-of-interest approach provide necessary focus. The third and final challenge relates to the practical difficulties of conducting research in new-onset epilepsy cases. We suggest that greater integration of imaging research within the clinical setting is needed.

Combined Transfer Learning and Test-Time Augmentation Improves Convolutional Neural Network-Based Semantic Segmentation of Prostate Cancer from Multi-Parametric MR Images.

PURPOSE: Multiparametric MRI (mp-MRI) is a widely used tool for diagnosing and staging prostate cancer. The purpose of this study was to evaluate whether transfer learning, unsupervised pre-training and test-time augmentation significantly improved the performance of a convolutional neural network (CNN) for pixel-by-pixel prediction of cancer vs. non-cancer using mp-MRI datasets. METHODS: 154 subjects undergoing mp-MRI were prospectively recruited, 16 of whom subsequently underwent radical prostatectomy. Logistic regression, random forest and CNN models were trained on mp-MRI data using histopathology as the gold standard. Transfer learning, unsupervised pre-training and test-time augmentation were used to boost CNN performance. Models were evaluated using Dice score and area under the receiver operating curve (AUROC) with leave-one-subject-out cross validation. Permutation feature importance testing was performed to evaluate the relative value of each MR contrast to CNN model performance. Statistical significance (p<0.05) was determined using the paired Wilcoxon signed rank test with Benjamini-Hochberg correction for multiple comparisons. RESULTS: Baseline CNN outperformed logistic regression and random forest models. Transfer learning and unsupervised pre-training did not significantly improve CNN performance over baseline; however, test-time augmentation resulted in significantly higher Dice scores over both baseline CNN and CNN plus either of transfer learning or unsupervised pre-training. The best performing model was CNN with transfer learning and test-time augmentation (Dice score of 0.59 and AUROC of 0.93). The most important contrast was apparent diffusion coefficient (ADC), followed by Ktrans and T2, although each contributed significantly to classifier performance. CONCLUSIONS: The addition of transfer learning and test-time augmentation resulted in significant improvement in CNN segmentation performance in a small set of prostate cancer mp-MRI data. Results suggest that these techniques may be more broadly useful for the optimization of deep learning algorithms applied to the problem of semantic segmentation in biomedical image datasets. However, further work is needed to improve the generalizability of the specific model presented herein.

Confirming white matter fMRI activation in the corpus callosum: co-localization with DTI tractography.

Recently, functional magnetic resonance imaging (fMRI) activation has been detected in white matter, despite the widely-held belief that fMRI activation is restricted to gray matter. The objective of the current study was to determine whether the regions of white matter fMRI activation were structurally connected to the functional network in gray matter. To do this, we used fMRI-guided tractography to evaluate whether tracts connecting regions of gray matter fMRI activation were co-localized with white matter fMRI activation. An established interhemispheric transfer task was employed to elicit activation in the corpus callosum. Diffusion tensor imaging (DTI) tractography was used to determine the existence of tracts that connected regions of gray matter fMRI activation to regions of activation in the corpus callosum. Corpus callosum activation was detected in the majority of participants. While there was individual variability in the location of corpus callosum activation, activation was commonly observed in the callosal mid-body, isthmus/splenium, or both. Despite the variability, gray matter fMRI-guided tractography identified tracts that were co-localized with corpus callosum fMRI activation in all instances. In addition, callosal activation had tracts to bilateral gray matter fMRI activation for 7/8 participants. The results confirmed that the activated regions of the corpus callosum were structurally connected to the functional network of gray matter regions involved in the task. These findings are an important step towards establishing the functional significance of white matter fMRI, and provide the foundation for future work combining white matter fMRI and DTI tractography to study brain connectivity.

Blood-brain barrier imaging as a potential biomarker for bipolar disorder progression.

Bipolar disorder affects approximately 2% of the population and is typically characterized by recurrent episodes of mania and depression. While some patients achieve remission using mood-stabilizing treatments, a significant proportion of patients show progressive changes in symptomatology over time. Bipolar progression is diverse in nature and may include a treatment-resistant increase in the frequency and severity of episodes, worse psychiatric and functional outcomes, and a greater risk of suicide. The mechanisms underlying bipolar disorder progression remain poorly understood and there are currently no biomarkers for identifying patients at risk. The objective of this study was to explore the potential of blood-brain barrier (BBB) imaging as such a biomarker, by acquiring the first imaging data of BBB leakage in bipolar patients, and evaluating the potential association between BBB dysfunction and bipolar symptoms. To this end, a cohort of 36 bipolar patients was recruited through the Mood Disorders Clinic (Nova Scotia Health Authority, Canada). All patients, along with 14 control subjects (matched for sex, age and metabolic status), underwent contrast-enhanced dynamic MRI scanning for quantitative assessment of BBB leakage as well as clinical and psychiatric evaluations. Outlier analysis has identified a group of 10 subjects with significantly higher percentages of brain volume with BBB leakage (labeled the "extensive BBB leakage" group). This group consisted exclusively of bipolar patients, while the "normal BBB leakage" group included the entire control cohort and the remaining 26 bipolar subjects. Among the bipolar cohort, patients with extensive BBB leakage were found to have more severe depression and anxiety, and a more chronic course of illness. Furthermore, all bipolar patients within this group were also found to have co-morbid insulin resistance, suggesting that insulin resistance may increase the risk of BBB dysfunction in bipolar patients. Our findings demonstrate a clear link between BBB leakage and greater psychiatric morbidity in bipolar patients and highlight the potential of BBB imaging as a mechanism-based biomarker for bipolar disorder progression.

Optimizing the detection of white matter fMRI using asymmetric spin echo spiral.

The majority of functional magnetic resonance imaging (fMRI) studies restrict their focus to gray matter regions because this tissue is highly perfused relative to white matter. However, an increasing number of studies are reporting fMRI activation in white matter. The current study had two objectives: 1) to evaluate whether it is possible to detect white matter fMRI activation and 2) to determine whether certain MRI contrast mechanisms are more sensitive to white matter activation (i.e., BOLD contrast- versus T(2)-weighting). Data were acquired from a 4 T MRI using an asymmetric spin echo spiral sequence (ASE spiral). This technique collected three images with equal BOLD contrast weighting and increasing T(2)-weighting. An interhemispheric transfer task was used to elicit activation in the corpus callosum. White matter fMRI activation was examined for the averaged ASE spiral data and for each image separately. Callosal activation was present in all subjects as well as in the group analysis. Analyses revealed that increasing T(2) contrast improved sensitivity as measured by percent signal change. The results suggest that it is possible to detect white matter activation in fMRI and that ASE spiral showed increasing sensitivity to this activation as a function of T(2)-weighting. The findings provide further support for the investigation of white matter fMRI.

Asymmetric spin-echo (ASE) spiral improves BOLD fMRI in inhomogeneous regions.

Functional MRI (fMRI) is of limited use in areas such as the orbitofrontal and inferior temporal lobes due to the presence of local susceptibility-induced field gradients (SFGs), which result in severe image artifacts. Several techniques have been developed to reduce these artifacts, the most common being the dual-echo spiral sequences (spiral-in/out and spiral-in/in). In this study, a new multiple spiral acquisition technique was developed, in which the later spiral acquisitions are acquired asymmetrically with the peak of a spin-echo causing increased R(2)-weighting but matched R(2)'-weighting. This sequence, called asymmetric spin-echo (ASE) spiral, has demonstrated significant improvements in minimizing the signal loss and increasing the image quality as well as optimal blood-oxygen-level-dependent (BOLD)-weighting. The ASE spiral is compared to conventional spiral-out using both signal-to-noise ratio (SNR) and whole brain fMRI activation volumes from a breath-hold task acquired at 4 Tesla. The ASE dual spiral has exhibited SNR increases of up to 300% in areas where strong SFGs are present. As a result, the ASE spiral is highly efficient for recovering lost activation in areas of SFGs, as demonstrated by a 16% increase in the total number of activated voxels over the whole brain. Post spin-echo ASE spiral images have decreasing SNR due to R(2) signal losses, however the increase in R(2)-weighting leads to a higher percentage of signal changes producing ASE spiral images with equivalent contrast-to-noise ratio (CNR) for each echo. The use of this sequence allows for recovery of BOLD activation in areas of SFG without sacrificing the CNR over the whole brain.

Review: Nervous System Disease in Systemic Lupus Erythematosus: Current Status and Future Directions.

The American College of Rheumatology's case definitions for 19 neuropsychiatric syndromes in systemic lupus erythematosus (SLE) constitute a comprehensive classification of nervous system events in this disease. However, additional strategies are needed to determine whether a neuropsychiatric syndrome is attributable to SLE versus a competing comorbidity. Cognitive function is a clinical surrogate of overall brain health, with applications in both diagnosis and determination of clinical outcomes. Ischemic and inflammatory mechanisms are both key components of the immunopathogenesis of neuropsychiatric SLE (NPSLE), including abnormalities of the blood-brain barrier and autoantibody-mediated production of proinflammatory cytokines. Advances in neuroimaging provide a platform to assess novel disease mechanisms in a noninvasive way. The convergence of more rigorous clinical characterization, validation of biomarkers, and brain neuroimaging provides opportunities to determine the efficacy of novel targeted therapies in the treatment of NPSLE.

Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.

Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detection of PCa will use an engineered scalar image called Ktrans to summarize the information in the DCE time-series images. This work proposes a new model that amalgamates the U-net and the convGRU neural network architectures for the purpose of interpreting DCE time-series in a temporal and spatial basis for segmenting PCa in MR images. Ultimately, experiments show that the proposed model using the DCE time-series images can outperform a baseline U-net segmentation model using Ktrans. However, when other types of scalar MR images are considered by the models, no significant advantage is observed for the proposed model.

Detecting functional magnetic resonance imaging activation in white matter: interhemispheric transfer across the corpus callosum.

BACKGROUND: It is generally believed that activation in functional magnetic resonance imaging (fMRI) is restricted to gray matter. Despite this, a number of studies have reported white matter activation, particularly when the corpus callosum is targeted using interhemispheric transfer tasks. These findings suggest that fMRI signals may not be neatly confined to gray matter tissue. In the current experiment, 4 T fMRI was employed to evaluate whether it is possible to detect white matter activation. We used an interhemispheric transfer task modelled after neurological studies of callosal disconnection. It was hypothesized that white matter activation could be detected using fMRI. RESULTS: Both group and individual data were considered. At liberal statistical thresholds (p < 0.005, uncorrected), group level activation was detected in the isthmus of the corpus callosum. This region connects the superior parietal cortices, which have been implicated previously in interhemispheric transfer. At the individual level, five of the 24 subjects (21%) had activation clusters that were located primarily within the corpus callosum. Consistent with the group results, the clusters of all five subjects were located in posterior callosal regions. The signal time courses for these clusters were comparable to those observed for task related gray matter activation. CONCLUSION: The findings support the idea that, despite the inherent challenges, fMRI activation can be detected in the corpus callosum at the individual level. Future work is needed to determine whether the detection of this activation can be improved by utilizing higher spatial resolution, optimizing acquisition parameters, and analyzing the data with tissue specific models of the hemodynamic response. The ability to detect white matter fMRI activation expands the scope of basic and clinical brain mapping research, and provides a new approach for understanding brain connectivity.