1,129 research outputs found

    The relationship between corpus callosum size and forebrain volume

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    Using high-resolution in vivo magnetic resonance morphometry we measured forebrain volume (FBV), midsagittal size of the corpus callosum (CC) and four CC subareas in 120 young and healthy adults (49 women, 71 men). We found moderate linear and quadratic correlations, indicating that the CC and all CC subareas increase with FBV both in men and women (multiple r2 ranging from 0.10 to 0.28). Allometric equations revealed that these increases were less than proportional to FBV (r2 ranging from 0.02 to 0.30). Absolute CC measurements, as well as CC subareas relative to total CC or FBV (the latter measures termed the CC ratios), were further analyzed with regard to possible effects of handedness, gender, or handedness by gender interaction. Contrary to previous reports, left-handers did not show larger CC measurements compared to right-handers. The only apparent influence of gender was on the CC ratios, which were larger in women. However, smaller brains had larger CC ratios which were mainly independent of gender, a result of the less than proportional increase of callosal size with FBV. We suggest that the previously described gender differences in CC anatomy may be better explained by an underlying effect of brain size, with larger brains having relatively smaller callosa. This lends empirical support to the hypothesis that brain size may be an important factor influencing interhemispheric connectivity and lateralizatio

    The Harvard Beat Assessment Test (H-BAT): a battery for assessing beat perception and production and their dissociation

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    Humans have the abilities to perceive, produce, and synchronize with a musical beat, yet there are widespread individual differences. To investigate these abilities and to determine if a dissociation between beat perception and production exists, we developed the Harvard Beat Assessment Test (H-BAT), a new battery that assesses beat perception and production abilities. H-BAT consists of four subtests: (1) music tapping test (MTT), (2) beat saliency test (BST), (3) beat interval test (BIT), and (4) beat finding and interval test (BFIT). MTT measures the degree of tapping synchronization with the beat of music, whereas BST, BIT, and BFIT measure perception and production thresholds via psychophysical adaptive stair-case methods. We administered the H-BAT on thirty individuals and investigated the performance distribution across these individuals in each subtest. There was a wide distribution in individual abilities to tap in synchrony with the beat of music during the MTT. The degree of synchronization consistency was negatively correlated with thresholds in the BST, BIT, and BFIT: a lower degree of synchronization was associated with higher perception and production thresholds. H-BAT can be a useful tool in determining an individual's ability to perceive and produce a beat within a single session

    Guidelines for TMS/tES Clinical Services and Research through the COVID-19 Pandemic

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    BACKGROUND: The COVID-19 pandemic has broadly disrupted biomedical treatment and research including non-invasive brain stimulation (NIBS). Moreover, the rapid onset of societal disruption and evolving regulatory restrictions may not have allowed for systematic planning of how clinical and research work may continue throughout the pandemic or be restarted as restrictions are abated. The urgency to provide and develop NIBS as an intervention for diverse neurological and mental health indications, and as a catalyst of fundamental brain research, is not dampened by the parallel efforts to address the most life-threatening aspects of COVID-19; rather in many cases the need for NIBS is heightened including the potential to mitigate mental health consequences related to COVID-19. OBJECTIVE: To facilitate the re-establishment of access to NIBS clinical services and research operations during the current COVID-19 pandemic and possible future outbreaks, we develop and discuss a framework for balancing the importance of NIBS operations with safety considerations, while addressing the needs of all stakeholders. We focus on Transcranial Magnetic Stimulation (TMS) and low intensity transcranial Electrical Stimulation (tES) - including transcranial Direct Current Stimulation (tDCS) and transcranial Alternating Current Stimulation (tACS). METHODS: The present consensus paper provides guidelines and good practices for managing and reopening NIBS clinics and laboratories through the immediate and ongoing stages of COVID-19. The document reflects the analysis of experts with domain relevant expertise spanning NIBS technology, clinical services, and basic and clinical research - with an international perspective. We outline regulatory aspects, human resources, NIBS optimization, as well as accommodations for specific demographics. RESULTS: A model based on three phases (early COVID-19 impact, current practices, and future preparation) with an 11-step checklist (spanning removing or streamlining in-person protocols, incorporating telemedicine, and addressing COVID-19-associated adverse events) is proposed. Recommendations on implementing social distancing and sterilization of NIBS related equipment, specific considerations of COVID-19 positive populations including mental health comorbidities, as well as considerations regarding regulatory and human resource in the era of COVID-19 are outlined. We discuss COVID-19 considerations specifically for clinical (sub-)populations including pediatric, stroke, addiction, and the elderly. Numerous case-examples across the world are described. CONCLUSION: There is an evident, and in cases urgent, need to maintain NIBS operations through the COVID-19 pandemic, including anticipating future pandemic waves and addressing effects of COVID-19 on brain and mind. The proposed robust and structured strategy aims to address the current and anticipated future challenges while maintaining scientific rigor and managing risk

    Brain Extraction comparing Segment Anything Model (SAM) and FSL Brain Extraction Tool

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    Brain extraction is a critical preprocessing step in almost every neuroimaging study, enabling accurate segmentation and analysis of Magnetic Resonance Imaging (MRI) data. FSL's Brain Extraction Tool (BET), although considered the current gold standard, presents limitations such as over-extraction, which can be particularly problematic in brains with lesions affecting the outer regions, inaccurate differentiation between brain tissue and surrounding meninges, and susceptibility to image quality issues. Recent advances in computer vision research have led to the development of the Segment Anything Model (SAM) by Meta AI, which has demonstrated remarkable potential across a wide range of applications. In this paper, we present a comparative analysis of brain extraction techniques using BET and SAM on a variety of brain scans with varying image qualities, MRI sequences, and brain lesions affecting different brain regions. We find that SAM outperforms BET based on several metrics, particularly in cases where image quality is compromised by signal inhomogeneities, non-isotropic voxel resolutions, or the presence of brain lesions that are located near or involve the outer regions of the brain and the meninges. These results suggest that SAM has the potential to emerge as a more accurate and precise tool for a broad range of brain extraction applications.Comment: 9 pages, 4 figures, 2 tables, SI in the given ur

    Non-Invasive Brain Stimulation Applied to Heschl's Gyrus Modulates Pitch Discrimination

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    The neural basis of the human brain's ability to discriminate pitch has been investigated by functional neuroimaging and the study of lesioned brains, indicating the critical importance of right and left Heschl's gyrus (HG) in pitch perception. Nonetheless, there remains some uncertainty with regard to localization and lateralization of pitch discrimination, partly because neuroimaging results do not allow us to draw inferences about the causality. To address the problem of causality in pitch discrimination functions, we used transcranial direct current stimulation to downregulate (via cathodal stimulation) and upregulate (via anodal stimulation) excitability in either left or right auditory cortex and measured the effect on performance in a pitch discrimination task in comparison with sham stimulation. Cathodal stimulation of HG on the left and on the right hemispheres adversely affected pitch discrimination in comparison to sham stimulation, with the effect on the right being significantly stronger than on the left. Anodal stimulation on either side had no effect on performance in comparison to sham. Our results indicate that both left and right HG are causally involved in pitch discrimination, although the right auditory cortex might be a stronger contributor

    Automated Ensemble-Based Segmentation of Pediatric Brain Tumors: A Novel Approach Using the CBTN-CONNECT-ASNR-MICCAI BraTS-PEDs 2023 Challenge Data

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    Brain tumors remain a critical global health challenge, necessitating advancements in diagnostic techniques and treatment methodologies. In response to the growing need for age-specific segmentation models, particularly for pediatric patients, this study explores the deployment of deep learning techniques using magnetic resonance imaging (MRI) modalities. By introducing a novel ensemble approach using ONet and modified versions of UNet, coupled with innovative loss functions, this study achieves a precise segmentation model for the BraTS-PEDs 2023 Challenge. Data augmentation, including both single and composite transformations, ensures model robustness and accuracy across different scanning protocols. The ensemble strategy, integrating the ONet and UNet models, shows greater effectiveness in capturing specific features and modeling diverse aspects of the MRI images which result in lesion_wise dice scores of 0.52, 0.72 and 0.78 for enhancing tumor, tumor core and whole tumor labels respectively. Visual comparisons further confirm the superiority of the ensemble method in accurate tumor region coverage. The results indicate that this advanced ensemble approach, building upon the unique strengths of individual models, offers promising prospects for enhanced diagnostic accuracy and effective treatment planning for brain tumors in pediatric brains.Comment: 3 Figs, 3 Table

    Does stroke location predict walk speed response to gait rehabilitation?

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    Objectives Recovery of independent ambulation after stroke is a major goal. However, which rehabilitation regimen best benefits each individual is unknown and decisions are currently made on a subjective basis. Predictors of response to specific therapies would guide the type of therapy most appropriate for each patient. Although lesion topography is a strong predictor of upper limb response, walking involves more distributed functions. Earlier studies that assessed the cortico-spinal tract (CST) were negative, suggesting other structures may be important. Experimental Design: The relationship between lesion topography and response of walking speed to standard rehabilitation was assessed in 50 adult-onset patients using both volumetric measurement of CST lesion load and voxel-based lesion–symptom mapping (VLSM) to assess non-CST structures. Two functional mobility scales, the functional ambulation category (FAC) and the modified rivermead mobility index (MRMI) were also administered. Performance measures were obtained both at entry into the study (3–42 days post-stroke) and at the end of a 6-week course of therapy. Baseline score, age, time since stroke onset and white matter hyperintensities score were included as nuisance covariates in regression models. Principal Observations: CST damage independently predicted response to therapy for FAC and MRMI, but not for walk speed. However, using VLSM the latter was predicted by damage to the putamen, insula, external capsule and neighbouring white matter. Conclusions Walk speed response to rehabilitation was affected by damage involving the putamen and neighbouring structures but not the CST, while the latter had modest but significant impact on everyday functions of general mobility and gait

    Automated Ensemble-Based Segmentation of Adult Brain Tumors: A Novel Approach Using the BraTS AFRICA Challenge Data

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    Brain tumors, particularly glioblastoma, continue to challenge medical diagnostics and treatments globally. This paper explores the application of deep learning to multi-modality magnetic resonance imaging (MRI) data for enhanced brain tumor segmentation precision in the Sub-Saharan Africa patient population. We introduce an ensemble method that comprises eleven unique variations based on three core architectures: UNet3D, ONet3D, SphereNet3D and modified loss functions. The study emphasizes the need for both age- and population-based segmentation models, to fully account for the complexities in the brain. Our findings reveal that the ensemble approach, combining different architectures, outperforms single models, leading to improved evaluation metrics. Specifically, the results exhibit Dice scores of 0.82, 0.82, and 0.87 for enhancing tumor, tumor core, and whole tumor labels respectively. These results underline the potential of tailored deep learning techniques in precisely segmenting brain tumors and lay groundwork for future work to fine-tune models and assess performance across different brain regions.Comment: 3 figs and 3 table
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