Biology subjects

Dowdle, L.

Publications and source records attributed to Dowdle, L..

5 recordsLinked to original sources

bioRxiv2026

A probabilistic atlas of the human thalamic reticular nucleus derived from 7T MRI

The thalamic reticular nucleus (TRN) is a thin, inhibitory shell surrounding the thalamus. It regulates the thalamocortical information flow, and thereby plays a central role in attention, task switching, and the sleep-wake cycle. Despite its importance, the TRN remains poorly studied in the human brain. This is largely because its small size and deep anatomical location limit its visibility with conventional non-invasive neuroimaging techniques. Here, we assessed whether the human TRN can be reliably visualised and segmented in vivo using ultra-high field (UHF) magnetic resonance imaging (MRI) at 7 Tesla. High resolution (0.35 mm isotropic) partial-brain T2* and T1 scans were acquired from healthy individuals, followed by manual delineation of the TRN. These in vivo segmentations were compared with TRN estimates obtained from two high-quality postmortem datasets serving as an anatomical reference. In vivo segmentations of TRN volume and thickness closely matched measurements derived from the postmortem reference datasets, and quantitative comparisons showed high consistency in TRN shape and location across individuals while also capturing meaningful inter-individual variability. Using these segmentations, we constructed a publicly available probabilistic atlas of the human TRN. This atlas provides a new resource for incorporating TRN anatomy into functional, structural, and clinical neuroimaging studies. Our findings demonstrate that the human TRN can be robustly mapped in vivo at 7T and establish a foundation for future investigations into its structure and function.

neuroscience↗

bioRxiv2024

Spanning spatial scales with functional imaging in the human brain; initial experiences at 10.5 Tesla

One of the most important frontiers in the efforts to improve functional imaging of brain activity (fMRI) is the recent push to increase the magnetic fields for human imaging beyond 10 Tesla. Having established safety, large gains in signal-to-noise-ratio (SNR), and novel radiofrequency arrays that capture the higher SNR, here we demonstrate major gains in the spatial resolution, sensitivity, and functional contrast of BOLD (Blood Oxygenation Level Dependent) based human fMRI at 10.5 Tesla. Using image reconstruction methods developed to minimize blurring, we also demonstrate that ultrahigh resolutions achieved at 10.5 Tesla suppress large-vein confounds in gradient-recalled-echo BOLD fMRI, thus improving the fidelity of functional signals relative to neuronal activity and yielding accurate cortical depth-profiles for layer-specific activation. Together, these multiplicative benefits deliver much needed increases in precision and resolution for meso-scale fMRI applications and illustrate the transformative potential of human functional imaging at magnetic fields that exceed 10 Tesla.

neuroscience↗

bioRxiv2023

Locally low-rank denoising in transform domains

PurposeTo develop an extension to locally low rank (LLR) denoising techniques based on transform domain processing that reduces the number of images required in the MR image series for high-quality denoising. Theory and MethodsLLR methods with random matrix theory-based thresholds are successfully used in the denoising of MR image series in a number of applications. The performance of these methods depend on how well the LLR assumption is satisfied, which deteriorates with few numbers of images, as is commonly encountered in quantitative MRI applications. We propose a transform-domain approach for denoising of MR image series to represent the underlying signal with higher fidelity when using a locally low rank approximation. The efficacy of the method is demonstrated for fully-sampled k-space, undersampled k-space, DICOM images, and complex-valued SENSE-1 images in quantitative MRI applications with as few as 4 images. ResultsFor both MSK and brain applications, the transform domain denoising preserves local subtle variability, whereas the quantitative maps based on image domain LLR methods tend to be locally more homogeneous. ConclusionA transform domain extension to LLR denoising produces high quality images and is compatible with both raw k-space data and vendor reconstructed data. This allows for improved imaging and more accurate quantitative analyses and parameters obtained therefrom.

scientific communication and education↗

bioRxiv2020

A Paradigm Change in Functional Brain Mapping: Suppressing the Thermal Noise in fMRI

Functional magnetic resonance imaging (fMRI) has become one of the most powerful tools for investigating the human brain. However, virtually all fMRI studies have relatively poor signal-to-noise ratio (SNR). Here we introduce a novel fMRI denoising technique, which suppresses noise that is indistinguishable from zero-mean, Gaussian-distributed noise. Thermal noise, falling in this category, is a major source of noise in fMRI, particularly, but not exclusively, at high spatial and/or temporal resolutions. Using 7-Tesla high-resolution data, we demonstrate improvements in temporal-SNR, the detection of stimulus-induced signal changes, and functional maps, while leaving stimulus-induced signal change amplitudes, image spatial precision, and functional point-spread-function unaltered. We also show that the method is equally applicable when using supra-millimeter resolution 3- and 7-Tesla fMRI data, different cortical regions, stimulation/task paradigms, and acquisition strategies. This denoising approach improves key metrics of functional activation detection while preserving spatial precision.

neuroscience↗

bioRxiv2020

Domain-Specific Tasks Differentiate Cortical Areas with Similar Sensory Responses

The brain is organized into distinct, flexible networks. Within these networks, cognitive variables such as attention can modulate sensory representations in accordance with moment-to-moment behavioral requirements. These modulations can be studied by varying task demands; however, the tasks employed are often incongruent with the postulated functions of a sensory system, limiting the characterization of the system in relation to natural behaviors. Here we combine domain-specific task manipulations and ultra-high field fMRI to study the nature of top-down modulations. We exploited faces, a visual category underpinned by a complex cortical network, and instructed participants to perform either a stimulus-relevant/domain-specific or a stimulus-irrelevant task in the scanner. We found that 1. perceptual ambiguity (i.e. difficulty of achieving a stable percept) is encoded in top-down modulations from higher-level cortices; 2. the right inferior-temporal lobe is active under challenging conditions and uniquely encodes trial-by-trial variability in face perception.

neuroscience↗