Imaging Neuroscience

11C-Raclopride (RAC) positron emission tomography (PET) is used to study dopamine response to pharmacological and behavioral challenges. Behavioral challenges produce smaller responses than pharmacological challenges and are more susceptible to sources of bias, including motion bias. The purpose of this study was to characterize the effect of motion bias within the context of a behavioral task challenge, examining the impact of different motion correction strategies, different task response magn...

Understanding the complex mechanism of human brain function requires innovative approaches that capture the intricate interplay of electrical, chemical, and hemodynamic activities. We developed the PMEEN system, named for its concurrent integration of PET, MRI, EEG, Eye Tracking, and fNIRS modalities by successfully addressing the electromagnetic and gamma ray interference among these modalities and incorporation of centralized clock control to allow simultaneous spatial registration and tempora...

PurposeNeurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI ...

BackgroundSpeech cortical mapping (SCM) conducted with widely available functional MRI (fMRI) can yield divergent results compared to the more commonly used navigated TMS (nTMS). The impact of specific fMRI task paradigms and preprocessing choices on reaching similarity with nTMS has not been explored before. ObjectiveTo test how the fMRI experimental task and spatial smoothing of the data compare with nTMS-based results, to subsequently increase the reliability of object naming fMRI for SCM. ...

We present an open, vendor-neutral BOLD SMS-EPI protocol tailored for multi-site fMRI studies, intended as a drop-in replacement for conventional vendor-specific, black-box acquisition and reconstruction pipelines. Built on Pulseq--an emerging standard for cross-platform MRI pulse sequence development--our protocol ensures identical SMS-EPI pulse sequences and image reconstruction across scanner vendors. This provides, for the first time, known and consistent experimental conditions across sites...

PurposeMulti-echo gradient-echo (ME-GRE) imaging in the spinal cord is susceptible to breathing-induced B0 field fluctuations due to the proximity of the lungs, leading to ghosting artifacts. A navigator readout can be used to monitor the fluctuations; however, standard navigator processing often fails in the spinal cord. Here, we introduce navigator processing tailored specifically for spinal cord imaging. MethodsME-GRE data covering all spinal cord regions were acquired in six healthy volunte...

The BrainAGE method is used to estimate biological brain age using structural neuroimaging. However, the stability of the model across different scan parameters and races/ethnicities has not been thoroughly investigated. Estimated brain age was compared within- and across-MRI field strength and across voxel sizes. Estimated brain age gap (BAG) was compared across demographically matched groups of different self-reported races and ethnicities in ADNI and IMAS cohorts. Longitudinal ComBat was used...

PurposePrior work has shown that whole-band linear regression of nuisance signals can introduce artifactual connectivity in high-frequency resting-state fMRI. Errors of motion regressors and non-stationarity of nuisance signals exacerbate artifacts. Here, we introduce spectral-temporal segmentation of regression vectors to decouple regression in different frequency bands to reduce motion artifacts. MethodsAn alternative approach to whole-band linear nuisance regression is introduced in the pres...

Quantification of the myelin content of the white matter is important for studying demyelination in neurodegenerative diseases such as Multiple Sclerosis (MS), particularly for longitudinal monitoring. A novel noninvasive MRI method, called Microstructure-Informed Myelin Mapping (MIMM), is developed to quantify the myelin volume fraction (MVF) by utilizing a multi gradient echo sequence (mGRE) and a detailed biophysical model of tissue microstructure. Myelin is modeled as anisotropic negative su...

IntroductionAutomatic whole brain and lesion segmentation at 7T presents challenges, primarily from bias fields and susceptibility artifacts. Recent advances in segmentation methods, namely using atlas-free and multi-contrast (for example, using T1-weighted, T2-weighted, fluid attenuated inversion recovery or FLAIR images) can enhance segmentation performance, however perfect registration at high fields remain a challenge primarily from distortion effects. We sought to use deep-learning algorith...

ObjectiveFunctional MRI (fMRI) is sensitive to changes in the blood oxygen level-dependent (BOLD) signal, which originates from neurovascular coupling, the mechanism that links neuronal activity to changes in cerebral blood flow. Isolating the native spontaneous neuronal fluctuations from the BOLD signal of the resting-state is challenging, as the signal induced by neuronal activity represents only a small part of this signal. Furthermore, many other non-neuronal (systemic) oscillations contribu...

IntroductionPortable low-field strength (64mT) MRI scanners promise to increase access to neuroimaging for clinical and research purposes, however these devices produce lower quality images compared to high-field scanners. In this study, we developed and evaluated a deep learning architecture to generate high-field quality brain images from low-field inputs using a paired dataset of multiple sclerosis (MS) patients scanned at 64mT and 3T. MethodsA total of 49 MS patients were scanned on portabl...

Arterial pulsation is crucial for promoting fluid circulation and for influencing neuronal activity. Previous studies assessed the pulsatility index based on blood flow velocity pulsatility in relatively large cerebral arteries of human. Here, we introduce a novel method to quantify the volumetric pulsatility of cerebral microvasculature across cortical layers and in white matter (WM), using high-resolution 4D vascular space occupancy (VASO) MRI with simultaneous recording of pulse signals at 7T...

Magnetic resonance spectroscopy (MRS) enables non-invasive assessment of brain metabolites and is commonly implemented using single-voxel spectroscopy (SVS) or magnetic resonance spectroscopic imaging (MRSI). This study directly compares the reproducibility of SVS-based semi-localization by adiabatic selective refocusing (sLASER) and 3D-Concentric Ring Trajectory-based Free Induction Decay MRSI (3D-CRT-FID-MRSI) at 3T and 7T in the same cohort of healthy participants. To explore MRSIs capabiliti...

Functional magnetic resonance spectroscopy (fMRS) extends conventional MRS by acquiring data while participants receive stimuli or are engaged in a task, with analysis focused on segmenting data to align with stimulus- or task-related metabolite changes. In this study, we propose and systematically evaluate three fMRS analysis pipelines: block, event-related, and sliding window approaches, to optimise parameter selection and assess reproducibility with respect to data quality. Using empirical an...

Head motion is a persistent challenge in positron emission tomography (PET) brain imaging, reducing quantitative accuracy, degrading time-activity curves (TACs), and complicating kinetic modeling. We evaluated a fully automated, multi-stage motion compensation framework, COMBRA (Correction of Motion and BRain Alignment), across more than one hundred scans and four independent PET studies using different radiotracers (18F-MK-6240, 11C-Raclopride, 11C-PBR28, and 11C-Martinostat). COMBRA implements...

Quantitative diffusion MRI (dMRI) is a promising technique for evaluating the spinal cord in health and disease. However, low signal-to-noise ratio (SNR) can impede interpretation and quantification of these images. The purpose of this study is to evaluate a denoising approach, Patch2Self, to improve the quality, reliability, and accuracy of quantitative diffusion MRI of the spinal cord. Patch2Self is a self-supervised learning-based denoising method that leverages statistical independence of no...

IntroductionMagnetic resonance imaging (MRI) at 7 Telsa (7T) has superior signal-to-noise ratio to 3 Telsa (3T) but also presents higher signal inhomogeneities and geometric distortions. A key knowledge gap is to robustly investigate the sensitivity and accuracy of 3T and 7T MRI in assessing brain morphometrics. This study aims to (a) aggregate a large number of paired 3T and 7T scans to evaluate their differences in quantitative brain morphological assessment using a widely available brain segm...

PurposeTo develop a deep learning tool for the automatic segmentation of T2-weighted intramedullary lesions in spinal cord injury (SCI). Material and MethodsThis retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The data consisted of T2-weighted MRI acquir...

BackgroundMultimodal MRI (sMRI, dMRI, rsfMRI) encodes complementary aspects of brain structure and function; principled joint representations promise more sensitive and interpretable markers of brain health than single-modality features. MethodsWe evaluate Normative Neurological Health Embedding (NNHEmbed), a flexible multi-view framework that uses constrained cross-modal similarity objectives to learn low-dimensional embeddings. Models were trained and tested on the UK Biobank (n = 21,300) and...