Imaging chronic active lesions in multiple sclerosis: a consensus statement.

Chronic active lesions (CAL) are an important manifestation of chronic inflammation in multiple sclerosis and have implications for non-relapsing biological progression. In recent years, the discovery of innovative MRI and PET-derived biomarkers has made it possible to detect CAL, and to some extent quantify them, in the brain of persons with multiple sclerosis, in vivo. Paramagnetic rim lesions on susceptibility-sensitive MRI sequences, MRI-defined slowly expanding lesions on T1-weighted and T2-weighted scans, and 18-kDa translocator protein-positive lesions on PET are promising candidate biomarkers of CAL. While partially overlapping, these biomarkers do not have equivalent sensitivity and specificity to histopathological CAL. Standardization in the use of available imaging measures for CAL identification, quantification and monitoring is lacking. To fast-forward clinical translation of CAL, the North American Imaging in Multiple Sclerosis Cooperative developed a consensus statement, which provides guidance for the radiological definition and measurement of CAL. The proposed manuscript presents this consensus statement, summarizes the multistep process leading to it, and identifies the remaining major gaps in knowledge.

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.

A multicenter pilot study evaluating simplified central vein assessment for the diagnosis of multiple sclerosis.

BACKGROUND: The central vein sign (CVS) is a proposed magnetic resonance imaging (MRI) biomarker for multiple sclerosis (MS); the optimal method for abbreviated CVS scoring is not yet established. OBJECTIVE: The aim of this study was to evaluate the performance of a simplified approach to CVS assessment in a multicenter study of patients being evaluated for suspected MS. METHODS: Adults referred for possible MS to 10 sites were recruited. A post-Gd 3D T2*-weighted MRI sequence (FLAIR*) was obtained in each subject. Trained raters at each site identified up to six CVS-positive lesions per FLAIR* scan. Diagnostic performance of CVS was evaluated for a diagnosis of MS which had been confirmed using the 2017 McDonald criteria at thresholds including three positive lesions (Select-3*) and six positive lesions (Select-6*). Inter-rater reliability assessments were performed. RESULTS: Overall, 78 participants were analyzed; 37 (47%) were diagnosed with MS, and 41 (53%) were not. The mean age of participants was 45 (range: 19-64) years, and most were female (n = 55, 71%). The area under the receiver operating characteristic curve (AUROC) for the simplified counting method was 0.83 (95% CI: 0.73-0.93). Select-3* and Select-6* had sensitivity of 81% and 65% and specificity of 68% and 98%, respectively. Inter-rater agreement was 78% for Select-3* and 83% for Select-6*. CONCLUSION: A simplified method for CVS assessment in patients referred for suspected MS demonstrated good diagnostic performance and inter-rater agreement.

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.

Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning.

PURPOSE: To develop a deep learning method to synthesize conventional contrast-weighted images in the brain from MR multitasking spatial factors. METHODS: Eighteen subjects were imaged using a whole-brain quantitative T1 -T2 -T1ρ MR multitasking sequence. Conventional contrast-weighted images consisting of T1 MPRAGE, T1 gradient echo, and T2 fluid-attenuated inversion recovery were acquired as target images. A 2D U-Net-based neural network was trained to synthesize conventional weighted images from MR multitasking spatial factors. Quantitative assessment and image quality rating by two radiologists were performed to evaluate the quality of deep-learning-based synthesis, in comparison with Bloch-equation-based synthesis from MR multitasking quantitative maps. RESULTS: The deep-learning synthetic images showed comparable contrasts of brain tissues with the reference images from true acquisitions and were substantially better than the Bloch-equation-based synthesis results. Averaging on the three contrasts, the deep learning synthesis achieved normalized root mean square error = 0.184 ± 0.075, peak SNR = 28.14 ± 2.51, and structural-similarity index = 0.918 ± 0.034, which were significantly better than Bloch-equation-based synthesis (p < 0.05). Radiologists' rating results show that compared with true acquisitions, deep learning synthesis had no notable quality degradation and was better than Bloch-equation-based synthesis. CONCLUSION: A deep learning technique was developed to synthesize conventional weighted images from MR multitasking spatial factors in the brain, enabling the simultaneous acquisition of multiparametric quantitative maps and clinical contrast-weighted images in a single scan.

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.

An Update on Diagnostic Laboratory Biomarkers for Multiple Sclerosis.

PURPOSE: For many patients, the multiple sclerosis (MS) diagnostic process can be lengthy, costly, and fraught with error. Recent research aims to address the unmet need for an accurate and simple diagnostic process through discovery of novel diagnostic biomarkers. This review summarizes recent studies on MS diagnostic fluid biomarkers, with a focus on blood biomarkers, and includes discussion of technical limitations and practical applicability. RECENT FINDINGS: This line of research is in its early days. Accurate and easily obtainable biomarkers for MS have not yet been identified and validated, but several approaches to uncover them are underway. Continue efforts to define laboratory diagnostic biomarkers are likely to play an increasingly important role in defining MS at the earliest stages, leading to better long-term clinical outcomes.

Deep grey matter injury in multiple sclerosis: a NAIMS consensus statement.

Although multiple sclerosis has traditionally been considered a white matter disease, extensive research documents the presence and importance of grey matter injury including cortical and deep regions. The deep grey matter exhibits a broad range of pathology and is uniquely suited to study the mechanisms and clinical relevance of tissue injury in multiple sclerosis using magnetic resonance techniques. Deep grey matter injury has been associated with clinical and cognitive disability. Recently, MRI characterization of deep grey matter properties, such as thalamic volume, have been tested as potential clinical trial end points associated with neurodegenerative aspects of multiple sclerosis. Given this emerging area of interest and its potential clinical trial relevance, the North American Imaging in Multiple Sclerosis (NAIMS) Cooperative held a workshop and reached consensus on imaging topics related to deep grey matter. Herein, we review current knowledge regarding deep grey matter injury in multiple sclerosis from an imaging perspective, including insights from histopathology, image acquisition and post-processing for deep grey matter. We discuss the clinical relevance of deep grey matter injury and specific regions of interest within the deep grey matter. We highlight unanswered questions and propose future directions, with the aim of focusing research priorities towards better methods, analysis, and interpretation of results.

Three-dimensional whole-brain simultaneous T1, T2, and T1ρ quantification using MR Multitasking: Method and initial clinical experience in tissue characterization of multiple sclerosis.

PURPOSE: To develop a 3D whole-brain simultaneous T1/T2/T1ρ quantification method with MR Multitasking that provides high quality, co-registered multiparametric maps in 9 min. METHODS: MR Multitasking conceptualizes T1/T2/T1ρ relaxations as different time dimensions, simultaneously resolving all three dimensions with a low-rank tensor image model. The proposed method was validated on a phantom and in healthy volunteers, comparing quantitative measurements against corresponding reference methods and evaluating the scan-rescan repeatability. Initial clinical validation was performed in age-matched relapsing-remitting multiple sclerosis (RRMS) patients to examine the feasibility of quantitative tissue characterization and to compare with the healthy control cohort. The feasibility of synthesizing six contrast-weighted images was also examined. RESULTS: Our framework produced high quality, co-registered T1/T2/T1ρ maps that closely resemble the reference maps. Multitasking T1/T2/T1ρ measurements showed substantial agreement with reference measurements on the phantom and in healthy controls. Bland-Altman analysis indicated good in vivo repeatability of all three parameters. In RRMS patients, lesions were conspicuously delineated on all three maps and on four synthetic weighted images (T2-weighted, T2-FLAIR, double inversion recovery, and a novel "T1ρ-FLAIR" contrast). T1 and T2 showed significant differences for normal appearing white matter between patients and controls, while T1ρ showed significant differences for normal appearing white matter, cortical gray matter, and deep gray matter. The combination of three parameters significantly improved the differentiation between RRMS patients and healthy controls, compared to using any single parameter alone. CONCLUSION: MR Multitasking simultaneously quantifies whole-brain T1/T2/T1ρ and is clinically promising for quantitative tissue characterization of neurological diseases, such as MS.

New imaging approaches for precision diagnosis and disease staging of MS?

Magnetic resonance imaging (MRI) has revolutionized the diagnosis and management of people living with multiple sclerosis (MS). However, conventional MRI sequences and measures currently used in clinical practice have limitations in the appropriate diagnosis, prediction of future disability, and monitoring of disease activity in MS. A specific challenge is the accurate and timely diagnosis of progressive subtypes of MS. This article will summarize emerging MRI measures that may be of utility as clinical tools in diagnosis and prediction in MS. Although a wide range of MRI techniques have different strengths and weaknesses, those that will be discussed in this article include the "central vein sign," leptomeningeal inflammation/enhancement, conventional and quantitative spinal cord imaging, susceptibility-weighted imaging, and high-field MRI techniques. There are a number of novel and emerging MRI techniques that hold promise in improving diagnosis, prediction, and disease monitoring in MS.

Multisite reliability and repeatability of an advanced brain MRI protocol.

BACKGROUND: MRI is the imaging modality of choice for diagnosis and intervention assessment in neurological disease. Its full potential has not been realized due in part to challenges in harmonizing advanced techniques across multiple sites. PURPOSE: To develop a method for the assessment of reliability and repeatability of advanced multisite-multisession neuroimaging studies and specifically to assess the reliability of an advanced MRI protocol, including multiband fMRI and diffusion tensor MRI, in a multisite setting. STUDY TYPE: Prospective. POPULATION: Twice repeated measurement of a single subject with stable relapsing-remitting multiple sclerosis (MS) at seven institutions. FIELD STRENGTH/SEQUENCE: A 3 T MRI protocol included higher spatial resolution anatomical scans, a variable flip-angle longitudinal relaxation rate constant (R1 ≡ 1/T1 ) measurement, quantitative magnetization transfer imaging, diffusion tensor imaging, and a resting-state fMRI (rsFMRI) series. ASSESSMENT: Multiple methods of assessing intrasite repeatability and intersite reliability were evaluated for imaging metrics derived from each sequence. STATISTICAL TESTS: Student's t-test, Pearson's r, and intraclass correlation coefficient (ICC) (2,1) were employed to assess repeatability and reliability. Two new statistical metrics are introduced that frame reliability and repeatability in the respective units of the measurements themselves. RESULTS: Intrasite repeatability was excellent for quantitative R1 , magnetization transfer ratio (MTR), and diffusion-weighted imaging (DWI) based metrics (r > 0.95). rsFMRI metrics were less repeatable (r = 0.8). Intersite reliability was excellent for R1 , MTR, and DWI (ICC >0.9), and moderate for rsFMRI metrics (ICC∼0.4). DATA CONCLUSION: From most reliable to least, using a new reliability metric introduced here, MTR > R1 > DWI > rsFMRI; for repeatability, MTR > DWI > R1 > rsFMRI. A graphical method for at-a-glance assessment of reliability and repeatability, effect sizes, and outlier identification in multisite-multisession neuroimaging studies is introduced. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:878-888.

Gradient nonlinearity effects on upper cervical spinal cord area measurement from 3D T1 -weighted brain MRI acquisitions.

PURPOSE: To explore (i) the variability of upper cervical cord area (UCCA) measurements from volumetric brain 3D T1 -weighted scans related to gradient nonlinearity (GNL) and subject positioning; (ii) the effect of vendor-implemented GNL corrections; and (iii) easily applicable methods that can be used to retrospectively correct data. METHODS: A multiple sclerosis patient was scanned at seven sites using 3T MRI scanners with the same 3D T1 -weighted protocol without GNL-distortion correction. Two healthy subjects and a phantom were additionally scanned at a single site with varying table positions. The 2D and 3D vendor-implemented GNL-correction algorithms and retrospective methods based on (i) phantom data fit, (ii) normalization with C2 vertebral body diameters, and (iii) the Jacobian determinant of nonlinear registrations to a template were tested. RESULTS: Depending on the positioning of the subject, GNL introduced up to 15% variability in UCCA measurements from volumetric brain T1 -weighted scans when no distortion corrections were used. The 3D vendor-implemented correction methods and the three proposed methods reduced this variability to less than 3%. CONCLUSIONS: Our results raise awareness of the significant impact that GNL can have on quantitative UCCA studies, and point the way to prospectively and retrospectively managing GNL distortions in a variety of settings, including clinical environments. Magn Reson Med 79:1595-1601, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

The NAIMS cooperative pilot project: Design, implementation and future directions.

The North American Imaging in Multiple Sclerosis (NAIMS) Cooperative represents a network of 27 academic centers focused on accelerating the pace of magnetic resonance imaging (MRI) research in multiple sclerosis (MS) through idea exchange and collaboration. Recently, NAIMS completed its first project evaluating the feasibility of implementation and reproducibility of quantitative MRI measures derived from scanning a single MS patient using a high-resolution 3T protocol at seven sites. The results showed the feasibility of utilizing advanced quantitative MRI measures in multicenter studies and demonstrated the importance of careful standardization of scanning protocols, central image processing, and strategies to account for inter-site variability.

Detection of altered hippocampal morphology in multiple sclerosis-associated depression using automated surface mesh modeling.

Depression is very common in multiple sclerosis (MS) but the underlying biological mechanisms are poorly understood. The hippocampus plays a key role in mood regulation and is implicated in the pathogenesis of depression. This study utilizes volumetric and shape analyses of the hippocampus to characterize neuroanatomical correlates of depression in MS. A cross-section of 109 female patients with MS was evaluated. Bilateral hippocampi were segmented from MRI scans (volumetric T1 -weighted, 1 mm(3) ) using automated tools. Shape analysis was performed using surface mesh modeling. Depression was assessed using the Center for Epidemiologic Studies-Depression (CES-D) scale. Eighty-three subjects were classified as low depression (CES-D 0-20) versus 26 subjects with high depression (CES-D ≥ 21). Right hippocampal volumes (P = 0.04) were smaller in the high depression versus the low depression groups, but there was no significant difference in left hippocampal volumes. Surface rendering analysis revealed that hippocampal shape changes in depressed patients with MS were clustered in the right hippocampus. Significant associations were found between right hippocampal shape and affective symptoms but not vegetative symptoms of depression. Our results suggested that regionally clustered reductions in hippocampal thickness can be detected by automated surface mesh modeling and may be a biological substrate of MS depression in female patients.

Lecanemab Planning: Blueprint for Safe and Effective Management of Complex Therapies.

Background: Approximately 6.9 million American individuals have Alzheimer dementia and 50% have mild disease. Lecanemab, an approved antiamyloid antibody, is associated with modest reduction in functional decline in patients with mild dementia or mild cognitive impairment. In Clarity-AD, 239 (26.6%) of patients experienced amyloid-related imaging abnormalities (ARIAs) overall (i.e., ARIAs associated with hemorrhages or edema). The complexity of treatment and risks of adverse events necessitate a multidisciplinary collaborative approach. Recent Findings: With limited treatment options, lecanemab approval generated significant interest among clinicians, patients, and families. Lecanemab treatment requires biweekly infusions along with ongoing imaging tests, laboratory monitoring, patient assessment, drug interaction screening, and cognitive function monitoring. Processes to support patient selection, access, and safety are important given the monitoring requirements and total cost of care. Implications for Practice: The planning process for lecanemab can serve as a blueprint to support safe and effective management of therapeutic innovation in neurology and other areas.

Alerting providers to hospitalized persons with dementia using the electronic health record.

BACKGROUND: While patients with dementia entering the hospital have worse outcomes than those without dementia, early detection of dementia in the inpatient setting is less than 50%. We developed and assessed the positive predictive value (PPV) and feasibility of a novel electronic health record (EHR) banner to identify patients with dementia who present to the inpatient setting using data from the medical record. METHODS: We developed and implemented an EHR algorithm to flag hospitalized patients age ≥65 years with potential cognitive impairment in the Epic EHR system using dementia ICD-10 codes, FDA-approved medications, and the use of the term "dementia" in the emergency department physician note. Medical records were reviewed for all patients who were flagged with an EHR banner from October 2022 to May 2023. RESULTS: A total of 344 individuals were identified who had a banner on their chart of which 280 (81.4%) were either diagnosed with dementia or were on an FDA-approved dementia medication. Forty-three individuals who had confirmed dementia were identified by a medication only (15.4%). Of the patients without confirmed dementia, the majority (N = 33, 9.6%) had a diagnosis of altered mental status, cognitive dysfunction, or mild cognitive impairment. Only 31 individuals (9.0%) had no indication of dementia or cognitive decline in their problem list, past medical history, or medication list. CONCLUSIONS: We found that it was feasible to implement an EHR algorithm for prospective dementia identification with a high PPV. These types of algorithms provide an opportunity to accurately identify hospitalized older individuals for inclusion in quality improvement projects, clinical trials, pay-for-performance programs, and other initiatives.

Pragmatic implementation of comprehensive dementia care management: The Cedars-Sinai C.A.R.E.S. Program preliminary data.

BACKGROUND: The United States faces a growing challenge with over 6.5 million people living with dementia (PLwD). PLwD and their caregivers struggle with cognitive, functional, behavioral, and psychosocial issues. As dementia care shifts to home settings, caregivers receive inadequate support but bear increasing responsibilities, leading to higher healthcare costs. In response, the Centers for Medicare & Medicaid Services (CMS) introduced the Guiding an Improving Dementia Experience (GUIDE) Model. The study explores the real-world implementation of the Cedars-Sinai C.A.R.E.S. Program, a pragmatic dementia care model, detailing its recruitment process and initial outcomes. METHODS: The Cedars-Sinai C.A.R.E.S. Program was integrated into the Epic electronic health record system and focused on proactive patient identification, engagement, interdisciplinary collaboration, care transitions, and ongoing care management. Eligible patients with a dementia diagnosis were identified through electronic health record and invited to join the program. Nurse practitioners with specialized training in dementia care performed comprehensive assessments using the CEDARS-6 tool, leading to personalized care plans developed in consultation with primary care providers. Patients benefited from a multidisciplinary team and support from care navigators. RESULTS: Of the 781 eligible patients identified, 431 were enrolled in the C.A.R.E.S. PROGRAM: Enrollees were racially diverse, with lower caregiver strain and patient behavioral and psychological symptoms of dementia (BPSD) severity compared to other programs dementia care programs. Healthcare utilization, including hospitalizations, emergency department (ED) admissions, and urgent care visits showed a downward trend over time. Completion of advanced directives and Physician Order of Life-Sustaining Treatment (POLST) increased after enrollment. CONCLUSION: The Cedars-Sinai C.A.R.E.S. Program offers a promising approach to dementia care. Its real-world implementation demonstrates the feasibility of enrolling a diverse population and achieving positive outcomes for PLwD and their caregivers, supporting the goals of national dementia care initiatives.

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.

Internet-delivered cognitive behavioural therapy programme to reduce depressive symptoms in patients with multiple sclerosis: a multicentre, randomised, controlled, phase 3 trial.

BACKGROUND: Depression is three to four times more prevalent in patients with neurological and inflammatory disorders than in the general population. For example, in patients with multiple sclerosis, the 12-month prevalence of major depressive disorder is around 25% and it is associated with a lower quality of life, faster disease progression, and higher morbidity and mortality. Despite its clinical relevance, there are few treatment options for depression associated with multiple sclerosis and confirmatory trials are scarce. We aimed to evaluate the safety and efficacy of a multiple sclerosis-specific, internet-based cognitive behavioural therapy (iCBT) programme for the treatment of depressive symptoms associated with the disease. METHODS: This parallel-group, randomised, controlled, phase 3 trial of an iCBT programme to reduce depressive symptoms in patients with multiple sclerosis was carried out at five academic centres with large outpatient care units in Germany and the USA. Patients with a neurologist-confirmed diagnosis of multiple sclerosis and depressive symptoms were randomly assigned (1:1:1; automated assignment, concealed allocation, no stratification, no blocking) to receive treatment as usual plus one of two versions of the iCBT programme Amiria (stand-alone or therapist-guided) or to a control condition, in which participants received treatment as usual and were offered access to the iCBT programme after 6 months. Masking of participants to group assignment between active treatment and control was not possible, although raters were masked to group assignment. The predefined primary endpoint, which was analysed in the intention-to-treat population, was severity of depressive symptoms as measured by the Beck Depression Inventory-II (BDI-II) at week 12 after randomisation. This trial is registered at ClinicalTrials.gov, NCT02740361, and is complete. FINDINGS: Between May 3, 2017, and Nov 4, 2020, we screened 485 patients for eligibility. 279 participants were enrolled, of whom 101 were allocated to receive stand-alone iCBT, 85 to receive guided iCBT, and 93 to the control condition. The dropout rate at week 12 was 18% (50 participants). Both versions of the iCBT programme significantly reduced depressive symptoms compared with the control group (BDI-II between-group mean differences: control vs stand-alone iCBT 6·32 points [95% CI 3·37-9·27], p<0·0001, effect size d=0·97 [95% CI 0·64-1·30]; control vs guided iCBT 5·80 points [2·71-8·88], p<0·0001, effect size d=0·96 [0·62-1·30]). Clinically relevant worsening of depressive symptoms was observed in three participants in the control group, one in the stand-alone iCBT group, and none in the guided iCBT group. No occurrences of suicidality were observed during the trial and there were no deaths. INTERPRETATION: This trial provides evidence for the safety and efficacy of a multiple sclerosis-specific iCBT tool to reduce depressive symptoms in patients with the disease. This remote-access, scalable intervention increases the therapeutic options in this patient group and could help to overcome treatment barriers. FUNDING: National Multiple Sclerosis Society (USA).

Fornix damage limits verbal memory functional compensation in multiple sclerosis.

Selective atrophy of the hippocampus, in particular the left CA1 subregion, is detectable in relapsing-remitting MS (RRMS) and is correlated with verbal memory performance. We used novel high-resolution imaging techniques to assess the role that functional compensation and/or white matter integrity of mesial temporal lobe (MTL) structures may play in mediating verbal memory performance in RRMS. High-resolution cortical unfolding of structural MRI in conjunction with functional magnetic resonance imaging (fMRI) was used to localize MTL activity in 18 early RRMS patients and 16 healthy controls during an unrelated word-pairs memory task. Diffusion tensor imaging (DTI) and Tract-Based Spatial Statistics (TBSS) were used to assess the integrity of the fornix and the parahippocampal white matter (PHWM), the major efferents and afferents of the hippocampus. RRMS patients showed greater activity in hippocampal and extra-hippocampal areas during unrelated word-pair learning and recall. Increased hippocampal activity, particularly in the right anterior hippocampus and left anterior CA1 was associated with higher verbal memory scores. Furthermore, increased fractional anisotropy (FA) in the fornix was correlated with both greater fMRI activity in this region and better memory performance. Altered hippocampal fMRI activity in RRMS patients during verbal learning may result from both structural damage and compensatory mechanisms. Successful functional compensation for hippocampal involvement in RRMS may be limited in part by white matter damage to the fornix, consistent with the critical role of this pathway in the clinical expression of memory impairment in MS.