Continuous Automated Analysis Workflow for MRS Studies.

Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review.The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps.The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory.Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans.

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design: Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations: Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions: BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

Impact of non-brain anatomy and coil orientation on inter- and intra-subject variability in TMS at midline.

OBJECTIVE: To investigate inter-subject variability with respect to cerebrospinal fluid thickness and brain-scalp distance, and to investigate intra-subject variability with different coil orientations. METHODS: Simulations of the induced electric field (E-Field) using a figure-8 coil over the vertex were conducted on 50 unique head models and varying orientations on 25 models. Metrics exploring stimulation intensity, spread, and localization were used to describe inter-subject variability and effects of non-brain anatomy. RESULTS: Both brain-scalp distance and CSF thickness were correlated with weaker stimulation intensity and greater spread. Coil rotations show that for the dorsal portion of the stimulated brain, E-Field intensities are highest when the anterior-posterior axis of the coil is perpendicular to the longitudinal fissure, but highest for the medial portion of the stimulated brain when the coil is oriented parallel to the longitudinal fissure. CONCLUSIONS: Normal anatomical variation in healthy individuals leads to significant differences in the site of TMS, the intensity, and the spread. These variables are generally neglected but could explain significant variability in basic and clinical studies. SIGNIFICANCE: This is the first work to show how brain-scalp distance and cerebrospinal fluid thickness influence focality, and to show the disassociation between dorsal and medial TMS.

Resting state functional connectivity in primary insomnia, generalized anxiety disorder and controls.

Sleep abnormalities are extremely common in anxiety disorders and may contribute to their development and persistence. Their shared pathophysiological mechanisms could thus serve as biomarkers or targets for novel therapeutics. Individuals with Primary Insomnia were age- and sex-matched to controls and to persons with Generalized Anxiety Disorder. All underwent fMRI resting-state scans at 3-T. In Primary Insomnia and controls, sleep was recorded for 2 weeks using diaries and actigraphy. All participants completed state-anxiety and neuroticism inventories. Whole-brain connectivity of 6 fear- and extinction-related seeds were compared between the 3 groups using ANOVA. The only significant between-group main effect was seen for connectivity between the left amygdala seed and a bilateral cluster in the rostral anterior cingulate cortex. The latter is believed to exert top-down control over amygdala activity and their interaction may thus constitute an emotion regulatory circuit. This connectivity was significantly greatest in controls while Primary Insomnia was intermediate between that of controls and Generalized Anxiety Disorder. Across Primary Insomnia and control subjects, mean connectivity decreased with poorer sleep. Across all 3 groups, connectivity decreased with greater neuroticism and pre-scan anxiety. Decreased top-down control of the amygdala may increase risk of developing an anxiety disorder with preexisting Primary Insomnia.