Home-administered transcranial direct current stimulation is a feasible intervention for depression: an observational cohort study.

Transcranial direct current stimulation (tDCS) is an emerging treatment for major depression. We recruited participants with moderate-to-severe major depressive episodes for an observational clinical trial using Soterix Medical's tDCS telehealth platform as a standard of care. The acute intervention consisted of 28 sessions (5 sessions/week, 6 weeks) of the left anodal dorsolateral prefrontal cortex (DLPFC) tDCS (2.0 mA × 30 min) followed by a tapering phase of weekly sessions for 4 weeks (weeks 7-10). The n = 16 completing participants had a significant reduction in depressive symptoms by week 2 of treatment [Montgomery-Åsberg Depression Rating Scale (MADRS), Baseline: 28.00 ± 4.35 vs. Week 2: 17.12 ± 5.32, p < 0.001] with continual improvement across each biweekly timepoint. Acute intervention responder and remission rates were 75 and 63% and 88 and 81% following the taper period (week 10).

Mirabegron Versus Solifenacin in Multiple Sclerosis Patients With Overactive Bladder Symptoms: A Prospective Comparative Nonrandomized Study.

OBJECTIVE: To determine the patient-perceived effectiveness and tolerability of mirabegron compared to solifenacin in a multiple sclerosis (MS) population with overactive bladder (OAB) symptoms. MATERIALS AND METHODS: MS patients with OAB symptoms who were not on medication for their urinary symptoms at enrollment were prospectively recruited. Patients enrolled in years 1-2 were prescribed mirabegron, whereas patients enrolled in years 3-4 were prescribed solifenacin. At enrollment and 6-week follow-up, patients completed several patient reported outcome measures. The primary outcome was change in OAB Questionnaire Short Form (OAB-q SF) symptom severity and minimal clinically important difference (MCID) achievement. The Patient Assessment of Constipation Symptoms (PAC-SYM) was used to assess bowel function over the treatment period. RESULTS: Sixty-one patients were enrolled. The majority of the mirabegron (70%) and the solifenacin (69%) group achieved the OAB-q SF symptom severity MCID. The solifenacin group had a statistically significant greater decrease in its end of study OAB-q SF score (Δ = -37.87 vs -20.43, P = .02). Constipation improved in the mirabegron group and worsened in the solifenacin group (ΔPAC-SYM = -0.38 vs +0.22; P = .02), with 30% of patients prescribed solifenacin experiencing worsening above the MCID threshold. CONCLUSION: Among MS patients, we demonstrated similar response rates to mirabegron and solifenacin, with approximately 50%-70% achieving each patient reported outcome measure's MCID. Though this small study showed some short-term evidence that improvement in urinary symptom severity was greater with solifenacin, this potential benefit must be weighed against the observed risk of worsening constipation. Further studies are needed to confirm these findings.

Spatial distribution of multiple sclerosis lesions in the cervical spinal cord.

Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsing-remitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T2- and T2*-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publicly-available analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P < 0.001), which was further confirmed by absolute and normalized lesion volumes (P < 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P < 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P < 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P < 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework - robust to variability in both image parameters and clinical condition - for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of -15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

New rapid, accurate T2 quantification detects pathology in normal-appearing brain regions of relapsing-remitting MS patients.

INTRODUCTION: Quantitative T2 mapping may provide an objective biomarker for occult nervous tissue pathology in relapsing-remitting multiple sclerosis (RRMS). We applied a novel echo modulation curve (EMC) algorithm to identify T2 changes in normal-appearing brain regions of subjects with RRMS (N = 27) compared to age-matched controls (N = 38). METHODS: The EMC algorithm uses Bloch simulations to model T2 decay curves in multi-spin-echo MRI sequences, independent of scanner, and scan-settings. T2 values were extracted from normal-appearing white and gray matter brain regions using both expert manual regions-of-interest and user-independent FreeSurfer segmentation. RESULTS: Compared to conventional exponential T2 modeling, EMC fitting provided more accurate estimations of T2 with less variance across scans, MRI systems, and healthy individuals. Thalamic T2 was increased 8.5% in RRMS subjects (p < 0.001) and could be used to discriminate RRMS from healthy controls well (AUC = 0.913). Manual segmentation detected both statistically significant increases (corpus callosum & temporal stem) and decreases (posterior limb internal capsule) in T2 associated with RRMS diagnosis (all p < 0.05). In healthy controls, we also observed statistically significant T2 differences for different white and gray matter structures. CONCLUSIONS: The EMC algorithm precisely characterizes T2 values, and is able to detect subtle T2 changes in normal-appearing brain regions of RRMS patients. These presumably capture both axon and myelin changes from inflammation and neurodegeneration. Further, T2 variations between different brain regions of healthy controls may correlate with distinct nervous tissue environments that differ from one another at a mesoscopic length-scale.

Decision-making and outcomes in severe symptomatic aortic stenosis.

BACKGROUND AND AIM OF THE STUDY: Aortic stenosis (AS) remains the most common valvular disease of the elderly in the United States. Though valve replacement has proven effective among older adults, decision-making regarding surgery is difficult for these patients and their physicians. Herein, the clinical outcomes and decision-making process for elderly patients with severe symptomatic AS was assessed. METHODS: A retrospective cohort study of patients aged > or = 60 years with severe AS was conducted at two large urban teaching hospitals. Severe AS was defined by a mean valve gradient > or = 50 mmHg or valve area < 0.8 cm2 by echocardiogram, and associated symptoms (angina, congestive heart failure, dyspnea, fatigue, or exercise intolerance). Demographic and clinical data and information about decision-making were obtained from inpatient and outpatient medical records. RESULTS: Of the 124 patients studied, 49 (39.5%) had aortic valve replacement (AVR) surgery. In a logistic regression analysis adjusting for gender, comorbidity and baseline functional status, those patients aged < 80 years were significantly more likely to have surgery than older patients. Surgery was associated with a large reduction in mortality in all age groups. At one-year follow up, 87.8% of all patients (87.5% of those aged > or = 80 years) who had undergone surgery were alive, while only 54.7% (49.1% of those aged > or = 80 years) who did not receive surgery were alive. Postoperative complications were similar among older and younger elderly patients. Comorbidity and age were the most common reasons for not offering elderly patients valve replacement. CONCLUSION: The results of the present study showed that AVR surgery improves the survival of elderly patients with severe AS, and patients aged > 80 years experience benefits similar to younger patients. Nevertheless, these findings suggest that surgery may not always be offered to elderly patients who might benefit from it.