Acute nicotinamide riboside supplementation increases human cerebral NAD+ levels in vivo.

PURPOSE: The purpose of this study was to determine the effect of acute nicotinamide riboside (NR) supplementation on cerebral nicotinamide adenine dinucleotide (NAD+) levels in the human brain in vivo by means of downfield proton MRS (DF 1H MRS). METHODS: DF 1H MRS was performed on 10 healthy volunteers in a 7.0 T MRI scanner with spectrally selective excitation and spatially selective localization to determine cerebral NAD+ levels on two back-to-back days: once after an overnight fast (baseline) and once 4 h after oral ingestion of nicotinamide riboside (900 mg). Additionally, two more baseline scans were performed following the same paradigm to assess test-retest reliability of the NAD+ levels in the absence of NR. RESULTS: NR supplementation increased mean NAD+ concentration compared to the baseline (0.458 ± 0.053 vs. 0.392 ± 0.058 mM; p < 0.001). The additional two baseline scans demonstrated no differences in mean NAD+ concentrations (0.425 ± 0.118 vs. 0.405 ± 0.082 mM; p = 0.45), and no difference from the first baseline scan (F(2, 16) = 0.907; p = 0.424). CONCLUSION: These preliminary results confirm that acute NR supplementation increases cerebral NAD+ levels in healthy human volunteers and shows the promise of DF 1H MRS utility for robust detection of NAD+ in humans in vivo.

Early Magnetic Resonance Imaging Features of New Paramagnetic Rim Lesions in Multiple Sclerosis.

OBJECTIVE: To determine early magnetic resonance imaging (MRI) features of new multiple sclerosis (MS) lesions that will develop into paramagnetic rim lesions (PRLs), which have been associated with progressive tissue injury in MS. METHODS: New contrast-enhancing lesions observed on routine clinical MRI were imaged at 7 T within 4 weeks of observation, and 3 and 6 months later. The 6-month MRI was used to classify PRL status (PRL or non-PRL). The relationship between early lesion characteristics and subsequent PRL status was assessed using generalized linear mixed effects models. Random forest classification was performed to classify early predictors of subsequent PRL status. RESULTS: From 93 contrast-enhancing lesions in 23 MS patients, 37 lesions developed into a PRL. In lesions that developed into PRLs compared with those that did not, the average lesion T1 on the initial 7 T MRI was 1994 ms compared with 1,670 ms (p-value <0.001), and the average volume was 168.7 mL compared with 44 mL (p-value <0.001) in lesions that did not. These volume differences were also found on 3 T scans (p-value <0.001), and for intensity-normalized T1 -w (p-value = 0.011) and fluid-attenuated inversion recovery (p-value = 0.005). The area under the receiver operating characteristic curve for the random forest classification with leave-one-out cross-validation was found to be 0.86 using initial 7 T features. INTERPRETATION: New MS lesions that evolve into PRLs can be identified early in lesion evolution. These findings suggest that biological mechanisms underlying PRL development begin early, which has important implications for clinical trials targeting PRLs development and subsequent therapeutics. ANN NEUROL 2023;94:736-744.

Choroid plexus volume differentiates MS from its mimics.

This study aimed to determine whether choroid plexus volume (CPV) could differentiate multiple sclerosis (MS) from its mimics. A secondary analysis of two previously enrolled studies, 50 participants with MS and 64 with alternative diagnoses were included. CPV was automatically segmented from 3T magnetic resonance imaging (MRI), followed by manual review to remove misclassified tissue. Mean normalized choroid plexus volume (nCPV) to intracranial volume demonstrated relatively high specificity for MS participants in each cohort (0.80 and 0.76) with an area under the receiver-operator characteristic curve of 0.71 (95% confidence interval (CI) = 0.55-0.87) and 0.65 (95% CI = 0.52-0.77). In this preliminary study, nCPV differentiated MS from its mimics.

Diagnostic performance of central vein sign versus oligoclonal bands for multiple sclerosis.

BACKGROUND: Cerebrospinal fluid (CSF) oligoclonal bands (OCB) are a diagnostic biomarker in multiple sclerosis (MS). The central vein sign (CVS) is an imaging biomarker for MS that may improve diagnostic accuracy. OBJECTIVES: The objective of the study is to examine the diagnostic performance of simplified CVS methods in comparison to OCB in participants with clinical or radiological suspicion for MS. METHODS: Participants from the CentrAl Vein Sign in MS (CAVS-MS) pilot study with CSF testing were included. Select-3 and Select-6 (counting up to three or six CVS+ lesions per scan) were rated on post-gadolinium FLAIR* images. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value for Select-3, Select-6, OCB, and combinations thereof were calculated for MS diagnosis at baseline and at 12 months. RESULTS: Of 53 participants, 25 were OCB+. At baseline, sensitivity for MS diagnosis was 0.75 for OCB, 0.83 for Select-3, and 0.71 for Select-6. Specificity for MS diagnosis was 0.76 for OCB, 0.48 for Select-3, and 0.86 for Select-6. At 12 months, PPV for MS diagnosis was 0.95 for Select-6 and 1.00 for Select-6 with OCB+ status. DISCUSSION: Results suggest similar diagnostic performance of simplified CVS methods and OCB. Ongoing studies will refine whether CVS could be used in replacement or in conjunction with OCB.

Familial aggregation of seizure outcomes in four familial epilepsy cohorts.

OBJECTIVE: To assess the possible effects of genetics on seizure outcome by estimating the familial aggregation of three outcome measures: seizure remission, history of ≥4 tonic-clonic seizures, and seizure control for individuals taking antiseizure medication. METHODS: We analyzed families containing multiple persons with epilepsy in four previously collected retrospective cohorts. Seizure remission was defined as being 5 and 10 years seizure-free at last observation. Total number of tonic-clonic seizures was dichotomized at <4 and ≥4 seizures. Seizure control in patients taking antiseizure medication was defined as no seizures for 1, 2, and 3 years. We used Bayesian generalized linear mixed-effects model (GLMM) to estimate the intraclass correlation coefficient (ICC) of the family-specific random effect, controlling for epilepsy type, age at epilepsy onset, and age at last data collection as fixed effects. We analyzed each cohort separately and performed meta-analysis using GLMMs. RESULTS: The combined cohorts included 3644 individuals with epilepsy from 1463 families. A history of ≥4 tonic-clonic seizures showed strong familial aggregation in three separate cohorts and meta-analysis (ICC .28, 95% confidence interval [CI] .21-.35, Bayes factor 8 × 1016). Meta-analyses did not reveal significant familial aggregation of seizure remission (ICC .08, 95% CI .01-.17, Bayes factor 1.46) or seizure control for individuals taking antiseizure medication (ICC .13, 95% CI 0-.35, Bayes factor 0.94), with heterogeneity among cohorts. SIGNIFICANCE: A history of ≥4 tonic-clonic seizures aggregated strongly in families, suggesting a genetic influence, whereas seizure remission and seizure control for individuals taking antiseizure medications did not aggregate consistently in families. Different seizure outcomes may have different underlying biology and risk factors. These findings should inform the future molecular genetic studies of seizure outcomes.

CD137+ tumor infiltrating lymphocytes predicts ovarian cancer survival.

OBJECTIVE: Ovarian cancer (OC) is the leading cause of death from gynecologic malignancy in the United States, and biomarkers of patient outcomes are limited. Data using immunohistochemical (IHC) analysis are mixed regarding whether and which tumor infiltrating lymphocytes (TILs) impact survival, and IHC does not adequately quantify rare cell populations, including CD137+ (4-1BB) tumor-reactive TILs. Our study investigates if a higher percentage of CD3+ CD137+ TILs is associated with improved overall survival (OS) in OC. METHODS: Flow cytometry was performed on viably banked OC digests. Chart review and statistical analysis were performed. Forty-seven patients were included, 40 of whom were diagnosed with high-grade serous ovarian carcinoma (HGSOC), papillary serous carcinoma, or undifferentiated histology. RESULTS: A high percentage of CD3+ CD137+ TILs correlated with improved OS (n = 40, r = 0.48, P = 0.0016). Subjects were divided into CD3+ CD137+ TIL high and low groups by the median. Subjects with high CD3+CD137+ TIL frequencies (>9.6%) had longer OS (Wilcoxon rank-sum test; P = 0.0032) and improved OS (logrank test; P = 0.007). Differences in CD3+ or CD3+ CD8+ TILs did not impact survival. CD3+ CD137+ TILs were predictive of OS regardless of germline mutation or debulking status. Analysis of subgroups including late stage HGSOC and late stage HGSOC with primary optimal cytoreduction indicated CD3+ CD137+ TILs correlated with improved OS after adjusting for age and PARP inhibitor use (P = 0.034 and P = 0.016, respectively). CONCLUSIONS: Prevalence of CD3+ CD137+ TILs in digested OC specimens is associated with improved OS, while general TIL markers are not. CD137 has the potential to be a novel biomarker for survival in OC.

Sex Differences in Perihematomal Edema Volume and Outcome After Intracerebral Hemorrhage.

BACKGROUND: Although larger hematoma volume is associated with worse outcome after intracerebral hemorrhage (ICH), the association between perihematomal edema (PHE) volume and outcome remains uncertain, as does the impact of sex on PHE and outcome. Here we aimed to determine whether larger PHE volume is associated with worse outcome and whether PHE volume trajectories differ by sex. METHODS: We conducted a post hoc analysis of the Factor VIIa for Acute Hemorrhagic Stroke Treatment (FAST) trial, which randomized patients with ICH to receive recombinant activated factor VIIa or placebo. Computerized planimetry calculated PHE and ICH volumes on serial computed tomography (CT) scans (at baseline [within 3 h of onset], at 24 h, and at 72 h). Generalized estimating equations examined interactions between sex, CT time points, and FAST treatment arm on PHE and ICH volumes. Mixed and multivariable logistic models examined associations between sex, PHE, and outcomes. RESULTS: A total of 781 patients with supratentorial ICH (mean age 65 years) were included. Compared to women (n = 296), men (n = 485) had similar median ICH (14.9 vs. 13.6 mL, p = 0.053) and PHE volumes (11.1 vs. 10.5 mL, p = 0.56) at baseline but larger ICH and PHE volumes at 24 h (19.0 vs. 14.0 mL, p < 0.001; 22.2 vs. 15.7 mL, p < 0.001) and 72 h (16.0 vs. 11.8 mL, p < 0.001; 28.7 vs. 19.9 mL, p < 0.001). Men had higher absolute early PHE expansion (p < 0.001) and more hematoma expansion (growth ≥ 33% or 6 mL at 24 h, 33% vs. 22%, p < 0.001). An interaction between sex and CT time points on PHE volume (p < 0.001), but not on ICH volume, confirmed a steeper PHE trajectory in men. PHE expansion (per 5 mL, odds radio 1.19, 95% confidence interval 1.10-1.28), but not sex, was associated with poor outcome. CONCLUSIONS: Early PHE expansion and trajectory in men were significantly higher. PHE expansion was associated with poor outcomes independent of sex. Mechanisms leading to sex differences in PHE trajectories merit further investigation.

Detection of sex-specific glutamate changes in subregions of hippocampus in an early-stage Alzheimer's disease mouse model using GluCEST MRI.

INTRODUCTION: Regional glucose hypometabolism resulting in glutamate loss has been shown as one of the characteristics of Alzheimer's disease (AD). Because the impact of AD varies between the sexes, we utilized glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) for high-resolution spatial mapping of cerebral glutamate and investigated subregional changes in a sex-specific manner. METHODS: Eight-month-old male and female AD mice harboring mutant amyloid precursor protein (APPNL-F/NL-F: n = 36) and wild-type (WT: n = 39) mice underwent GluCEST MRI, followed by proton magnetic resonance spectroscopy (1H-MRS) in hippocampus and thalamus/hypothalamus using 9.4T preclinical MR scanner. RESULTS: GluCEST measurements revealed significant (p ≤ 0.02) glutamate loss in the entorhinal cortex (% change ± standard error: 8.73 ± 2.12%), hippocampus (11.29 ± 2.41%), and hippocampal fimbriae (19.15 ± 2.95%) of male AD mice. A similar loss of hippocampal glutamate in male AD mice (11.22 ± 2.33%; p = 0.01) was also observed in 1H-MRS. DISCUSSIONS: GluCEST MRI detected glutamate reductions in the fimbria and entorhinal cortex of male AD mice, which was not reported previously. Resilience in female AD mice against these changes indicates an intact status of cerebral energy metabolism. HIGHLIGHTS: Glutamate levels were monitored in different brain regions of early-stage Alzheimer's disease (AD) and wild-type male and female mice using glutamate-weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI). Male AD mice exhibited significant glutamate loss in the hippocampus, entorhinal cortex, and the fimbriae of the hippocampus. Interestingly, female AD mice did not have any glutamate loss in any brain region and should be investigated further to find the probable cause. These findings demonstrate previously unreported sex-specific glutamate changes in hippocampal sub-regions using high-resolution GluCEST MRI.

B 1 + $$ {\mathrm{B}}_1

PURPOSE: Nuclear Overhauser effect magnetization transfer ratio (NOEMTR ) is a technique used to investigate brain lipids and macromolecules in greater detail than other techniques and benefits from increased contrast at 7 T. However, this contrast can become degraded because of B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities present at ultra-high field strengths. High-permittivity dielectric pads (DP) have been used to correct for these inhomogeneities via displacement currents generating secondary magnetic fields. The purpose of this work is to demonstrate that dielectric pads can be used to mitigate B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities and improve NOEMTR contrast in the temporal lobes at 7 T. METHODS: Partial 3D NOEMTR contrast images and whole brain B 1 + $$ {\mathrm{B}}_1^{+} $$ field maps were acquired on a 7 T MRI across six healthy subjects. Calcium titanate DP, having a relative permittivity of 110, was placed next to the subject's head near the temporal lobes. Pad corrected NOEMTR images had a separate postprocessing linear correction applied. RESULTS: DP provided supplemental B 1 + $$ {\mathrm{B}}_1^{+} $$ to the temporal lobes while also reducing the B 1 + $$ {\mathrm{B}}_1^{+} $$ magnitude across the posterior and superior regions of the brain. This resulted in a statistically significant increase in NOEMTR contrast in substructures of the temporal lobes both with and without linear correction. The padding also produced a convergence in NOEMTR contrast toward approximately equal mean values. CONCLUSION: NOEMTR images showed significant improvement in temporal lobe contrast when DP were used, which resulted from an increase in B 1 + $$ {\mathrm{B}}_1^{+} $$ homogeneity across the entire brain slab. DP-derived improvements in NOEMTR are expected to increase the robustness of the brain substructural measures both in healthy and pathological conditions.

Orbital magnetic resonance imaging of giant cell arteritis with ocular manifestations: a systematic review and individual participant data meta-analysis.

OBJECTIVES: We conducted a systematic review and individual participant data meta-analysis of publications reporting the ophthalmologic presentation, clinical exam, and orbital MRI findings in patients with giant cell arteritis and ocular manifestations. METHODS: PubMed and Cochrane databases were searched up to January 16, 2022. Publications reporting patient-level data on patients with ophthalmologic symptoms, imaged with orbital MRI, and diagnosed with biopsy-proven giant cell arteritis were included. Demographics, clinical symptoms, exam, lab, imaging, and outcomes data were extracted. The methodological quality and completeness of reporting of case reports were assessed. RESULTS: Thirty-two studies were included comprising 51 patients (females = 24; median age, 76 years). Vision loss (78%) and headache (45%) were commonly reported visual and cranial symptoms. Ophthalmologic presentation was unilateral (41%) or bilateral (59%). Fundus examination most commonly showed disc edema (64%) and pallor (49%). Average visual acuity was very poor (2.28 logMAR ± 2.18). Diagnoses included anterior (61%) and posterior (16%) ischemic optic neuropathy, central retinal artery occlusion (8%), and orbital infarction syndrome (2%). On MRI, enhancement of the optic nerve sheath (53%), intraconal fat (25%), and optic nerve/chiasm (14%) was most prevalent. Among patients with monocular visual symptoms, 38% showed pathologic enhancement in the asymptomatic orbit. Six of seven cases reported imaging resolution after treatment on follow-up MRIs. CONCLUSIONS: Vision loss, pallid disc edema, and optic nerve sheath enhancement are the most common clinical, fundoscopic, and imaging findings reported in patients diagnosed with giant cell arteritis with ocular manifestations, respectively. MRI may detect subclinical inflammation and ischemia in the asymptomatic eye and may be an adjunct diagnostic tool. CLINICAL RELEVANCE STATEMENT: Brain and orbital MRIs may have diagnostic and prognostic roles in patients with suspected giant cell arteritis who present with ophthalmic symptoms.

Effect of GBCA Use on Detection and Diagnostic Performance of the Central Vein Sign: Evaluation Using a 3-T FLAIR* Sequence in Patients With Suspected Multiple Sclerosis.

BACKGROUND. The central vein sign (CVS) is a proposed MRI biomarker of multiple sclerosis (MS). The impact of gadolinium-based contrast agent (GBCA) administration on CVS evaluation remains poorly investigated. OBJECTIVE. The purpose of this study was to assess the effect of GBCA use on CVS detection and on the diagnostic performance of the CVS for MS using a 3-T FLAIR* sequence. METHODS. This study was a secondary analysis of data from the pilot study for the prospective multicenter Central Vein Sign: A Diagnostic Biomarker in Multiple Sclerosis (CAVS-MS), which recruited adults with suspected MS from April 2018 to February 2020. Participants underwent 3-T brain MRI including FLAIR and precontrast and post-contrast echo-planar imaging T2*-weighted acquisitions. Postprocessing was used to generate combined FLAIR and T2*-weighted images (hereafter, FLAIR*). MS diagnoses were established using the 2017 McDonald criteria. Thirty participants (23 women, seven men; mean age, 45 years) were randomly selected from the CAVS-MS pilot study cohort. White matter lesions (WMLs) were marked using FLAIR* images. A single observer, blinded to clinical data and GBCA use, reviewed marked WMLs on FLAIR* images for the presence of the CVS. RESULTS. Thirteen of 30 participants had MS. Across participants, on precontrast FLAIR* imaging, 218 CVS-positive and 517 CVS-negative WMLs were identified; on post-contrast FLAIR* imaging, 269 CVS-positive and 459 CVS-negative WMLs were identified. The fraction of WMLs that were CVS-positive on precontrast and postcontrast images was 48% and 58% in participants with MS and 7% and 10% in participants without MS, respectively. The median patient-level CVS-positivity rate on precontrast and postcontrast images was 43% and 67% for participants with MS and 4% and 8% for participants without MS, respectively. In a binomial model adjusting for MS diagnoses, GBCA use was associated with an increased likelihood of at least one CVS-positive WML (odds ratio, 1.6; p < .001). At a 40% CVS-positivity threshold, the sensitivity of the CVS for MS increased from 62% on precontrast images to 92% on postcontrast images (p = .046). Specificity was not significantly different between precontrast (88%) and postcontrast (82%) images (p = .32). CONCLUSION. GBCA use increased CVS detection on FLAIR* images, thereby increasing the sensitivity of the CVS for MS diagnoses. CLINICAL IMPACT. The postcontrast FLAIR* sequence should be considered for CVS evaluation in future investigational trials and clinical practice.

Repeatability of B1 + inhomogeneity correction of volumetric (3D) glutamate CEST via High-permittivity dielectric padding at 7T.

PURPOSE: Ultra-high field MR imaging lacks B1 + inhomogeneity due to shorter RF wavelengths used at higher field strengths compared to human anatomy. CEST techniques tend to be highly susceptible to B1 + inhomogeneities due to a high and uniform B1 + field being necessary to create the endogenous contrast. High-permittivity dielectric pads have seen increasing usage in MR imaging due to their ability to tailor the spatial distribution of the B1 + field produced. The purpose of this work is to demonstrate that dielectric materials can be used to improve glutamate weighted CEST (gluCEST) at 7T. THEORY AND METHODS: GluCEST images were acquired on a 7T system on six healthy volunteers. Aqueous calcium titanate pads, with a permittivity of approximately 110, were placed on either side in the subject's head near the temporal lobes. A post-processing correction algorithm was implemented in combination with dielectric padding to compare contrast improvement. Tissue segmentation was performed to assess the effect of dielectric pads on gray and white matter separately. RESULTS: GluCEST images demonstrated contrast enhancement in the lateral temporal lobe regions with dielectric pad placement. Tissue segmentation analysis showed an increase in correction effectiveness within the gray matter tissue compared to white matter tissue. Statistical testing suggested a significant difference in gluCEST contrast when pads were used and showed a difference in the gray matter tissue segment. CONCLUSION: The use of dielectric pads improved the B1 + field homogeneity and enhanced gluCEST contrast for all subjects when compared to data that did not incorporate padding.

Assessing the Stability of Photon-Counting CT: Insights from a Two-Year Longitudinal Study.

Background: Among the advancements in computed tomography (CT) technology, photon-counting computed tomography (PCCT) stands out as a significant innovation, providing superior spectral imaging capabilities while simultaneously reducing radiation exposure. Its long-term stability is important for clinical care, especially longitudinal studies, but is currently unknown. Purpose: This study sets out to comprehensively analyze the long-term stability of a first-generation clinical PCCT scanner. Materials and Methods: Over a two-year period, from November 2021 to November 2023, we conducted weekly identical experiments utilizing the same multi-energy CT protocol. These experiments included various tissue-mimicking inserts to rigorously assess the stability of Hounsfield Units (HU) and image noise in Virtual Monochromatic Images (VMIs) and iodine density maps. Throughout this period, notable software and hardware modifications were meticulously recorded. Each week, VMIs and iodine density maps were reconstructed and analyzed to evaluate quantitative stability over time. Results: Spectral results consistently demonstrated the quantitative stability of PCCT. VMIs exhibited stable HU values, such as variation in relative error for VMI 70 keV measuring 0.11% and 0.30% for single-source and dual-source modes, respectively. Similarly, noise levels remained stable with slight fluctuations linked to software changes for VMI 40 and 70 keV that corresponded to changes of 8 and 1 HU, respectively. Furthermore, iodine density quantification maintained stability and showed significant improvement with software and hardware changes, especially in dual-source mode with nominal errors decreasing from 1.44 to 0.03 mg/mL. Conclusion: This study provides the first long-term reproducibility assessment of quantitative PCCT imaging, highlighting its potential for the clinical arena. This study indicates its long-term utility in diagnostic radiology, especially for longitudinal studies.

Quantitative EEG Spectral Features Differentiate Genetic Epilepsies and Predict Neurologic Outcomes.

EEG plays an integral part in the diagnosis and management of children with genetic epilepsies. Nevertheless, how quantitative EEG features differ between genetic epilepsies and neurological outcomes remains largely unknown. Here, we aimed to identify quantitative EEG biomarkers in children with epilepsy and a genetic diagnosis in STXBP1, SCN1A, or SYNGAP1, and to assess how quantitative EEG features associate with neurological outcomes in genetic epilepsies more broadly. We analyzed individuals with pathogenic variants in STXBP1 (95 EEGs, n=20), SCN1A (154 EEGs, n=68), and SYNGAP1 (46 EEGs, n=21) and a control cohort of individuals without epilepsy or known cerebral disease (847 EEGs, n=806). After removing artifacts and epochs with excess noise or altered state from EEGs, we extracted spectral features. We validated our preprocessing pipeline by comparing automatically-detected posterior dominant rhythm (PDR) to annotations from clinical EEG reports. Next, as a coarse measure of pathological slowing, we compared the alpha-delta bandpower ratio between controls and the different genetic epilepsies. We then trained random forest models to predict a diagnosis of STXBP1, SCN1A, and SYNGAP1. Finally, to understand how EEG features vary with neurological outcomes, we trained random forest models to predict seizure frequency and motor function. There was strong agreement between the automatically-calculated PDR and clinical EEG reports (R 2=0.75). Individuals with STXBP1-related epilepsy have a significantly lower alpha-delta ratio than controls (P<0.001) across all age groups. Additionally, individuals with a missense variant in STXBP1 have a significantly lower alpha-delta ratio than those with a protein-truncating variant in toddlers (P<0.001), children (P=0.02), and adults (P<0.001). Models accurately predicted a diagnosis of STXBP1 (AUC=0.91), SYNGAP1 (AUC=0.82), and SCN1A (AUC=0.86) against controls and from each other in a three-class model (accuracy=0.74). From these models, we isolated highly correlated biomarkers for these respective genetic disorders, including alpha-theta ratio in frontal, occipital, and parietal electrodes with STXBP1, SYNGAP1, and SCN1A, respectively. Models were unable to predict seizure frequency (AUC=0.53). Random forest models predicted motor scores significantly better than age-based null models (P<0.001), suggesting spectral features contain information pertinent to gross motor function. In summary, we demonstrate that STXBP1-, SYNGAP1-, and SCN1A-related epilepsies have distinct quantitative EEG signatures. Furthermore, EEG spectral features are predictive of some functional outcome measures in patients with genetic epilepsies. Large-scale retrospective quantitative analysis of clinical EEG has the potential to discover novel biomarkers and to quantify and track individuals' disease progression across development.

Combined Orbital and Cranial Vessel Wall Magnetic Resonance Imaging for the Assessment of Disease Activity in Giant Cell Arteritis.

OBJECTIVE: Acute visual impairment is the most feared complication of giant cell arteritis (GCA) but is challenging to predict. Magnetic resonance imaging (MRI) evaluates orbital pathology not visualized by an ophthalmologic examination. This study combined orbital and cranial vessel wall MRI to assess both orbital and cranial disease activity in patients with GCA, including patients without visual symptoms. METHODS: Patients with suspected active GCA who underwent orbital and cranial vessel wall MRI were included. In 14 patients, repeat imaging over 12 months assessed sensitivity to change. Clinical diagnosis of ocular or nonocular GCA was determined by a rheumatologist and/or ophthalmologist. A radiologist masked to clinical data scored MRI enhancement of structures. RESULTS: Sixty-four patients with suspected GCA were included: 25 (39%) received a clinical diagnosis of GCA, including 12 (19%) with ocular GCA. Orbital MRI enhancement was observed in 83% of patients with ocular GCA, 38% of patients with nonocular GCA, and 5% of patients with non-GCA. MRI had strong diagnostic performance for both any GCA and ocular GCA. Combining MRI with a funduscopic examination reached 100% sensitivity for ocular GCA. MRI enhancement significantly decreased after treatment (P < 0.01). CONCLUSION: In GCA, MRI is a sensitive tool that comprehensively evaluates multiple cranial structures, including the orbits, which are the most concerning site of pathology. Orbital enhancement in patients without visual symptoms suggests that MRI may detect at-risk subclinical ocular disease in GCA. MRI scores decreased following treatment, suggesting scores reflect inflammation. Future studies are needed to determine if MRI can identify patients at low risk for blindness who may receive less glucocorticoid therapy.

Multicenter automated central vein sign detection performs as well as manual assessment for the diagnosis of multiple sclerosis.

BACKGROUND AND PURPOSE: The central vein sign (CVS) is a proposed diagnostic imaging biomarker for multiple sclerosis (MS). The proportion of white matter lesions exhibiting the CVS (CVS+) is higher in patients with MS compared to its radiological mimics. Evaluation for CVS+ lesions in prior studies have been performed by manual rating, an approach that is time-consuming and has variable inter-rater reliability. Accurate automated methods would facilitate efficient assessment for CVS. The objective of this study was to compare the performance of an automated CVS detection method with manual rating for the diagnosis of MS. MATERIALS AND METHODS: 3T MRI was acquired in 86 participants undergoing evaluation for MS in a 9-site multicenter study. Participants presented with either typical or atypical clinical syndromes for MS. An automated CVS detection method was employed and compared to manual rating, including total CVS+ proportion and a simplified counting method in which experts visually identified up to 6 CVS+ lesions using FLAIR* contrast (a voxel-wise product of T2 FLAIR and post-contrast T2*-EPI images). RESULTS: Automated CVS processing was completed in 79 of 86 participants (91%), of whom 28 (35%) fulfilled the 2017 McDonald criteria at the time of imaging. The area under the receiver-operator characteristic curve (AUC) for discrimination between participants with and without MS for the automated CVS approach was 0.78 (95% confidence interval: [0.67,0.88]). This was not significantly different from simplified manual counting methods (select6*) (0.80 [0.69,0.91]) or manual assessment of total CVS+ proportion (0.89 [0.82,0.96]). In a sensitivity analysis excluding 11 participants whose MRI exhibited motion artifact, the AUC for the automated method was 0.81 [0.70,0.91], which was not statistically different from that for select6* (0.79 [0.68,0.92]) or manual assessment of total CVS+ proportion (0.89 [0.81,0.97]). CONCLUSIONS: Automated CVS assessment was comparable to manual CVS scoring for differentiating patients with MS from those with other diagnoses. Large, prospective, multicenter studies utilizing automated methods and enrolling the breadth of disorders referred for suspicion of MS are needed to determine optimal approaches for clinical implementation of an automated CVS detection method. ABBREVIATIONS: CVS= central vein sign; CVS+ = white matter lesions exhibiting the CVS; MRI = magnetic resonance imaging; MS = multiple sclerosis; T2 FLAIR = T2 fluid-attenuated inversion recovery; T2*-EPI = T2*-weighted 3D echo planar imaging; FLAIR* = a voxel-wise product of T2 FLAIR and post-contrast T2*-EPI images; select6* = simplified counting method in which experts visually identified up to 6 CVS+ lesions on FLAIR* imaging.

Multicenter validation of automated detection of paramagnetic rim lesions on brain MRI in multiple sclerosis.

BACKGROUND AND PURPOSE: Paramagnetic rim lesions (PRLs) are an MRI biomarker of chronic inflammation in people with multiple sclerosis (MS). PRLs may aid in the diagnosis and prognosis of MS. However, manual identification of PRLs is time-consuming and prone to poor interrater reliability. To address these challenges, the Automated Paramagnetic Rim Lesion (APRL) algorithm was developed to automate PRL detection. The primary objective of this study is to evaluate the accuracy of APRL for detecting PRLs in a multicenter setting. METHODS: We applied APRL to a multicenter dataset, which included 3-Tesla MRI acquired in 92 participants (43 with MS, 14 with clinically isolated syndrome [CIS]/radiologically isolated syndrome [RIS], 35 without RIS/CIS/MS). Subsequently, we assessed APRL's performance by comparing its results with manual PRL assessments carried out by a team of trained raters. RESULTS: Among the 92 participants, expert raters identified 5637 white matter lesions and 148 PRLs. The automated segmentation method successfully captured 115 (78%) of the manually identified PRLs. Within these 115 identified lesions, APRL differentiated between manually identified PRLs and non-PRLs with an area under the curve (AUC) of .73 (95% confidence interval [CI]: [.68, .78]). At the subject level, the count of APRL-identified PRLs predicted MS diagnosis with an AUC of .69 (95% CI: [.57, .81]). CONCLUSION: Our study demonstrated APRL's capability to differentiate between PRLs and lesions without paramagnetic rims in a multicenter study. Automated identification of PRLs offers greater efficiency over manual identification and could facilitate large-scale assessments of PRLs in clinical trials.

Impact of Artificial Intelligence-Assisted Indication Selection on Appropriateness Order Scoring for Imaging Clinical Decision Support.

PURPOSE: The aim of this study was to assess appropriateness scoring and structured order entry after the implementation of an artificial intelligence (AI) tool for analysis of free-text indications. METHODS: Advanced outpatient imaging orders in a multicenter health care system were recorded 7 months before (March 1, 2020, to September 21, 2020) and after (October 20, 2020, to May 13, 2021) the implementation of an AI tool targeting free-text indications. Clinical decision support score (not appropriate, may be appropriate, appropriate, or unscored) and indication type (structured, free-text, both, or none) were assessed. The χ2 and multivariate logistic regression adjusting for covariables with bootstrapping were used. RESULTS: In total, 115,079 orders before and 150,950 orders after AI tool deployment were analyzed. The mean patient age was 59.3 ± 15.5 years, and 146,035 (54.9%) were women; 49.9% of orders were for CT, 38.8% for MR, 5.9% for nuclear medicine, and 5.4% for PET. After deployment, scored orders increased to 52% from 30% (P < .001). Orders with structured indications increased to 67.3% from 34.6% (P < .001). On multivariate analysis, orders were more likely to be scored after tool deployment (odds ratio [OR], 2.7, 95% CI, 2.63-2.78; P < .001). Compared with physicians, orders placed by nonphysician providers were less likely to be scored (OR, 0.80; 95% CI, 0.78-0.83; P < .001). MR (OR, 0.84; 95% CI, 0.82-0.87) and PET (OR, 0.12; 95% CI, 0.10-0.13) were less likely to be scored than CT (; P < .001). After AI tool deployment, 72,083 orders (47.8%) remained unscored, 45,186 (62.7%) with free-text-only indications. CONCLUSIONS: Embedding AI assistance within imaging clinical decision support was associated with increased structured indication orders and independently predicted a higher likelihood of scored orders. However, 48% of orders remained unscored, driven by both provider behavior and infrastructure-related barriers.

Imaging Biomarkers and Prevalence of Complex Aortic Plaque in Cryptogenic Stroke: A Systematic Review.

BACKGROUND: Complex aortic plaque (CAP) is a potential embolic source in patients with cryptogenic stroke (CS). We review CAP imaging criteria for transesophageal echocardiogram (TEE), computed tomography angiography (CTA), and magnetic resonance imaging and calculate CAP prevalence in patients with acute CS. METHODS AND RESULTS: PubMed and EMBASE databases were searched up to December 2022 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. Two independent reviewers extracted data on study design, imaging techniques, CAP criteria, and prevalence. The Cochrane Collaboration tool and Guideline for Reporting Reliability and Agreement Studies were used to assess risk of bias and reporting completeness, respectively. From 2293 studies, 45 were reviewed for CAP imaging biomarker criteria in patients with acute CS (N=37 TEE; N=9 CTA; N=6 magnetic resonance imaging). Most studies (74%) used ≥4 mm plaque thickness as the imaging criterion for CAP although ≥1 mm (N=1, CTA), ≥5 mm (N=5, TEE), and ≥6 mm (N=2, CTA) were also reported. Additional features included mobility, ulceration, thrombus, protrusions, and assessment of plaque composition. From 23 prospective studies, CAP was detected in 960 of 2778 patients with CS (0.32 [95% CI, 0.24-0.41], I2=94%). By modality, prevalence estimates were 0.29 (95% CI, 0.20-0.40; I2=95%) for TEE; 0.23 (95% CI, 0.15-0.34; I2=87%) for CTA and 0.22 (95% CI, 0.06-0.54; I2=92%) for magnetic resonance imaging. CONCLUSIONS: TEE was commonly used to assess CAP in patients with CS. The most common CAP imaging biomarker was ≥4 mm plaque thickness. CAP was observed in one-third of patients with acute CS. However, high study heterogeneity suggests a need for reproducible imaging methods.

Detection of Medication Taking Using a Wrist-Worn Commercially Available Wearable Device.

PURPOSE: Medication nonadherence is a persistent and costly problem across health care. Measures of medication adherence are ineffective. Methods such as self-report, prescription claims data, or smart pill bottles have been used to monitor medication adherence, but these are subject to recall bias, lack real-time feedback, and are often expensive. METHODS: We proposed a method for monitoring medication adherence using a commercially available wearable device. Passively collected motion data were analyzed on the basis of the Movelet algorithm, a dictionary learning framework that builds person-specific chapters of movements from short frames of elemental activities within the movements. We adapted and extended the Movelet method to construct a within-patient prediction model that identifies medication-taking behaviors. RESULTS: Using 15 activity features recorded from wrist-worn wearable devices of 10 patients with breast cancer on endocrine therapy, we demonstrated that medication-taking behavior can be predicted in a controlled clinical environment with a median accuracy of 85%. CONCLUSION: These results in a patient-specific population are exemplar of the potential to measure real-time medication adherence using a wrist-worn commercially available wearable device.