9 research outputs found
Layer-specific connectivity revealed by diffusion-weighted functional MRI in the rat thalamocortical pathway
Investigating neural activity from a global brain perspective in-vivo has been in the domain of functional Magnetic Resonance Imaging (fMRI) over the past few decades. The intricate neurovascular couplings that govern fMRI's blood-oxygenation-level-dependent (BOLD) functional contrast are invaluable in mapping active brain regions, but they also entail significant limitations, such as non-specificity of the signal to active foci. Diffusion-weighted functional MRI (dfMRI) with relatively high diffusion-weighting strives to ameliorate this shortcoming as it offers functional contrasts more intimately linked with the underlying activity. Insofar, apart from somewhat smaller activation foci, dfMRI's contrasts have not been convincingly shown to offer significant advantages over BOLD-driven fMRI, and its activation maps relied on significant modelling. Here, we study whether dfMRI could offer a better representation of neural activity in the thalamocortical pathway compared to its (spin-echo (SE)) BOLD counterpart. Using high-end forepaw stimulation experiments in the rat at 9.4 T, and with significant sensitivity enhancements due to the use of cryocoils, we show for the first time that dfMRI signals exhibit layer specificity, and, additionally, display signals in areas devoid of SE-BOLD responses. We find that dfMRI signals in the thalamocortical pathway cohere with each other, namely, dfMRI signals in the ventral posterolateral (VPL) thalamic nucleus cohere specifically with layers IV and V in the somatosensory cortex. These activity patterns are much better correlated (compared with SE-BOLD signals) with literature-based electrophysiological recordings in the cortex as well as thalamus. All these findings suggest that dfMRI signals better represent the underlying neural activity in the pathway. In turn, these advanatages may have significant implications towards a much more specific and accurate mapping of neural activity in the global brain in-vivo
On the sensitivity of the diffusion MRI signal to brain activity in response to a motor cortex paradigm
Diffusion functional MRI (dfMRI) is a promising technique to map functional
activations by acquiring diffusion-weighed spin-echo images. In previous
studies, dfMRI showed higher spatial accuracy at activation mapping compared to
classic functional MRI approaches. However, it remains unclear whether dfMRI
measures result from changes in the intra-/extracellular environment, perfusion
and/or T2 values. We designed an acquisition/quantification scheme to
disentangle such effects in the motor cortex during a finger tapping paradigm.
dfMRI was acquired at specific diffusion weightings to selectively suppress
perfusion and free-water diffusion, then times series of the apparent diffusion
coefficient (ADC-fMRI) and of the perfusion signal fraction (IVIM-fMRI) were
derived. ADC-fMRI provided ADC estimates sensitive to changes in perfusion and
free-water volume, but not to T2/T2* values. With IVIM-fMRI we isolated the
perfusion contribution to ADC, while suppressing T2 effects. Compared to
conventional gradient-echo BOLD fMRI, activation maps obtained with dfMRI and
ADC-fMRI had smaller clusters, and the spatial overlap between the three
techniques was below 50%. Increases of perfusion fractions were observed during
task in both dfMRI and ADC-fMRI activations. Perfusion effects were more
prominent with ADC-fMRI than with dfMRI but were significant in less than 25%
of activation ROIs. Taken together, our results suggest that the sensitivity to
task of dfMRI derives from a decrease of hindered diffusion and an increase of
the pseudo-diffusion signal fraction, leading to different, more confined
spatial activation patterns compared to classic functional MRI.Comment: Submitted to peer-reviewed journa
MP-PCA denoising of fMRI time-series data can lead to artificial activation "spreading"
MP-PCA denoising has become the method of choice for denoising in MRI since
it provides an objective threshold to separate the desired signal from unwanted
thermal noise components. In rodents, thermal noise in the coils is an
important source of noise that can reduce the accuracy of activation mapping in
fMRI. Further confounding this problem, vendor data often contains zero-filling
and other effects that may violate MP-PCA assumptions. Here, we develop an
approach to denoise vendor data and assess activation "spreading" caused by
MP-PCA denoising in rodent task-based fMRI data. Data was obtained from N = 3
mice using conventional multislice and ultrafast acquisitions (1 s and 50 ms
temporal resolution, respectively), during visual stimulation. MP-PCA denoising
produced SNR gains of 64% and 39% and Fourier spectral amplitude (FSA)
increases in BOLD maps of 9% and 7% for multislice and ultrafast data,
respectively, when using a small [2 2] denoising window. Larger windows
provided higher SNR and FSA gains with increased spatial extent of activation
that may or may not represent real activation. Simulations showed that MP-PCA
denoising causes activation "spreading" with an increase in false positive rate
and smoother functional maps due to local "bleeding" of principal components,
and that the optimal denoising window for improved specificity of functional
mapping, based on Dice score calculations, depends on the data's tSNR and
functional CNR. This "spreading" effect applies also to another recently
proposed low-rank denoising method (NORDIC). Our results bode well for
dramatically enhancing spatial and/or temporal resolution in future fMRI work,
while taking into account the sensitivity/specificity trade-offs of low-rank
denoising methods
Double diffusion encoding and applications for biomedical imaging
Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important
contemporary non-invasive modalities for probing tissue structure at the
microscopic scale. The majority of dMRI techniques employ standard single
diffusion encoding (SDE) measurements, covering different sequence parameter
ranges depending on the complexity of the method. Although many signal
representations and biophysical models have been proposed for SDE data, they
are intrinsically limited by a lack of specificity. Advanced dMRI methods have
been proposed to provide additional microstructural information beyond what can
be inferred from SDE. These enhanced contrasts can play important roles in
characterizing biological tissues, for instance upon diseases (e.g.
neurodegenerative, cancer, stroke), aging, learning, and development.
In this review we focus on double diffusion encoding (DDE), which stands out
among other advanced acquisitions for its versatility, ability to probe more
specific diffusion correlations, and feasibility for preclinical and clinical
applications. Various DDE methodologies have been employed to probe compartment
sizes (Section 3), decouple the effects of microscopic diffusion anisotropy
from orientation dispersion (Section 4), probe displacement correlations, study
exchange, or suppress fast diffusing compartments (Section 6). DDE measurements
can also be used to improve the robustness of biophysical models (Section 5)
and study intra-cellular diffusion via magnetic resonance spectroscopy of
metabolites (Section 7). This review discusses all these topics as well as
important practical aspects related to the implementation and contrast in
preclinical and clinical settings (Section 9) and aims to provide the readers a
guide for deciding on the right DDE acquisition for their specific application