Applications of open-source software ROAST in clinical studies: A review

Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning

Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields, particularly in non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community at https://github.com/lab-smile/GRACE.

Transcranial direct current stimulation (tDCS) over the orbitofrontal cortex reduces delay discounting

Delay discounting (DD) is a quantifiable psychological phenomenon that regulates decision-making. Nevertheless, the neural substrates of DD and its relationship with other cognitive domains are not well understood. The orbitofrontal cortex (OFC) is a potential candidate for supporting the expression of DD, but due to its wide involvement in several psychological functions and neural networks, its central role remains elusive. In this study, healthy subjects underwent transcranial direct current stimulation (tDCS) while performing an intertemporal choice task for the quantification of DD and a working memory task. To selectively engage the OFC, two electrode configurations have been tested, namely, anodal Fp1-cathodal Fp2 and cathodal Fp1-anodal Fp2. Our results show that stimulation of the OFC reduces DD, independently from electrode configuration. In addition, no relationship was found between DD measures and either working memory performance or baseline impulsivity assessed through established tests. Our work will direct future investigations aimed at unveiling the specific neural mechanisms underlying the involvement of the OFC in DD, and at testing the efficacy of OFC tDCS in reducing DD in psychological conditions where this phenomenon has been strongly implicated, such as addiction and eating disorders.

A map of evidence using transcranial direct current stimulation (tDCS) to improve cognition in adults with traumatic brain injury (TBI)

Introduction

Cognition impairments often occur after a traumatic brain injury and occur at higher rates in military members. Cognitive symptoms impair daily function, including balance and life quality, years after the TBI. Current treatments to regain cognitive function after TBI, including medications and cognitive rehabilitation, have shown limited effectiveness. Transcranial direct current stimulation (tDCS) is a low-cost, non-invasive brain stimulation intervention that improves cognitive function in healthy adults and people with neuropsychologic diagnoses beyond current interventions. Despite the available evidence of the effectiveness of tDCS in improving cognition generally, only two small TBI trials have been conducted based on the most recent systematic review of tDCS effectiveness for cognition following neurological impairment. We found no tDCS studies that addressed TBI-related balance impairments.

Methods

A scoping review using a peer-reviewed search of eight databases was completed in July 2022. Two assessors completed a multi-step review and completed data extraction on included studies using a priori items recommended in tDCS and TBI research guidelines.

Results

A total of 399 results were reviewed for inclusion and 12 met the criteria and had data extracted from them by two assessors using Google Forms. Consensus on combined data results included a third assessor when needed. No studies using tDCS for cognition-related balance were found.

Discussion

Guidelines and technology measures increase the identification of brain differences that alter tDCS effects on cognition. People with mild-severe and acute-chronic TBI tolerated and benefited from tDCS. TBI-related cognition is understudied, and systematic research that incorporates recommended data elements is needed to advance tDCS interventions to improve cognition after TBI weeks to years after injury.

A Systematic Review and Large-Scale tES and TMS Electric Field Modeling Study Reveals How Outcome Measure Selection Alters Results in a Person- and Montage-Specific Manner

Background

Electric field (E-field) modeling is a potent tool to examine the cortical effects of transcranial magnetic and electrical stimulation (TMS and tES, respectively) and to address the high variability in efficacy observed in the literature. However, outcome measures used to report E-field magnitude vary considerably and have not yet been compared in detail.

Objectives

The goal of this two-part study, encompassing a systematic review and modeling experiment, was to provide an overview of the different outcome measures used to report the magnitude of tES and TMS E-fields, and to conduct a direct comparison of these measures across different stimulation montages.

Methods

Three electronic databases were searched for tES and/or TMS studies reporting E-field magnitude. We extracted and discussed outcome measures in studies meeting the inclusion criteria. Additionally, outcome measures were compared via models of four common tES and two TMS modalities in 100 healthy younger adults.

Results

In the systematic review, we included 118 studies using 151 outcome measures related to E-field magnitude. Structural and spherical regions of interest (ROI) analyses and percentile-based whole-brain analyses were used most often. In the modeling analyses, we found that there was an average of only 6% overlap between ROI and percentile-based whole-brain analyses in the investigated volumes within the same person. The overlap between ROI and whole-brain percentiles was montage- and person-specific, with more focal montages such as 4Ã-1 and APPS-tES, and figure-of-eight TMS showing up to 73%, 60%, and 52% overlap between ROI and percentile approaches respectively. However, even in these cases, 27% or more of the analyzed volume still differed between outcome measures in every analyses.

Conclusions

The choice of outcome measures meaningfully alters the interpretation of tES and TMS E-field models. Well-considered outcome measure selection is imperative for accurate interpretation of results, valid between-study comparisons, and depends on stimulation focality and study goals. We formulated four recommendations to increase the quality and rigor of E-field modeling outcome measures. With these data and recommendations, we hope to guide future studies towards informed outcome measure selection, and improve the comparability of studies.