To extract biologically interpretable information, a common approach is to fit a microstructural tissue model to a set of signals acquired with different dMRI acquisition settings (Alexander et al., 2019; Lampinen et al., 2023; Jelescu et al., 2020). In the absence of diffusion time dependence, these typically include different combinations of gradient strengths (commonly quantified by the b-value), directions (b-vector), and B-tensor shape (Westin et al., 2014). Microstructural parameters estimated by these models - including compartmental signal fractions and diffusivities - have shown to be sensitive to changes in brain structure due to diseases like multiple sclerosis (Alotaibi et al., 2021), Alzheimer's disease (Parker et al., 2018) and Parkinson's disease (Kim et al., 2016), and can provide a more fundamental understanding of tissue microstructure in both healthy and pathological tissues (Zhang et al., 2012). The Standard Model of white matter (SM) (Novikov et al., 2019) describes the signal arising from white matter by a kernel consisting of three compartments (intra-axonal, extra-axonal, and free water (occasionally omitted)) convolved with a fiber orientation distribution (FOD) (Tournier et al., 2007b). Compartmental signal fractions and diffusivities can be estimated, alongside the parameters that describe the FOD (usually in the form of a spherical harmonics (SH) series). Nevertheless, the high-dimensional parameter space of the SM complicates the estimation of its parameters, potentially leading to low accuracy, precision, and degeneracy of estimates (Jelescu et al., 2016).

$\textbf{Purpose:}$ To develop and evaluate an operator learning framework for nonlinear inversion (NLI) of brain magnetic resonance elastography (MRE) data, which enables real-time inversion of elastograms with comparable spatial accuracy to NLI. $\textbf{Materials and Methods:}$ In this retrospective study, 3D MRE data from 61 individuals (mean age, 37.4 years; 34 female) were used for development of the framework. A predictive deep operator learning framework (oNLI) was trained using 10-fold cross-validation, with the complex curl of the measured displacement field as inputs and NLI-derived reference elastograms as outputs. A structural prior mechanism, analogous to Soft Prior Regularization in the MRE literature, was incorporated to improve spatial accuracy. Subject-level evaluation metrics included Pearson's correlation coefficient, absolute relative error, and structural similarity index measure between predicted and reference elastograms across brain regions of different sizes to understand accuracy. Statistical analyses included paired t-tests comparing the proposed oNLI variants to the convolutional neural network baselines. $\textbf{Results:}$ Whole brain absolute percent error was 8.4 $\pm$ 0.5 ($μ'$) and 10.0 $\pm$ 0.7 ($μ''$) for oNLI and 15.8 $\pm$ 0.8 ($μ'$) and 26.1 $\pm$ 1.1 ($μ''$) for CNNs. Additionally, oNLI outperformed convolutional architectures as per Pearson's correlation coefficient, $r$, in the whole brain and across all subregions for both the storage modulus and loss modulus (p < 0.05). $\textbf{Conclusion:}$ The oNLI framework enables real-time MRE inversion (30,000x speedup), outperforming CNN-based approaches and maintaining the fine-grained spatial accuracy achievable with NLI in the brain.

Diffusion-weighted magnetic resonance imaging (DWI) and fiber tractography are the only methods to measure the structure of the white matter in the living human brain. The diffusion signal has been modelled as the combined contribution from many individual fascicles of nerve fibers passing through each location in the white matter. Typically, this is done via basis pursuit, but estimation of the exact directions is limited due to discretization. The difficulties inherent in modeling DWI data are shared by many other problems involving fitting non-parametric mixture models. Ekanadaham et al. proposed an approach, continuous basis pursuit, to overcome discretization error in the 1-dimensional case (e.g., spike-sorting).

We propose Microscopic Propagator Imaging (MPI) as a novel method to retrieve the indices of the microscopic propagator which is the probability density function of water displacements due to diffusion within the nervous tissue microstructures. Unlike the Ensemble Average Propagator indices or the Diffusion Tensor Imaging metrics, MPI indices are independent from the mesoscopic organization of the tissue such as the presence of multiple axonal bundle directions and orientation dispersion. As a consequence, MPI indices are more specific to the volumes, sizes, and types of microstructures, like axons and cells, that are present in the tissue. Thus, changes in MPI indices can be more directly linked to alterations in the presence and integrity of microstructures themselves. The methodology behind MPI is rooted on zonal modeling of spherical harmonics, signal simulation, and machine learning regression, and is demonstrated on both synthetic and Human Diffusion MRI data.

In many pediatric fMRI studies, cardiac signals are often missing or of poor quality. A tool to extract Heart Rate Variation (HRV) waveforms directly from fMRI data, without the need for peripheral recording devices, would be highly beneficial. We developed a machine learning framework to accurately reconstruct HRV for pediatric applications. A hybrid model combining one-dimensional Convolutional Neural Networks (1D-CNN) and Gated Recurrent Units (GRU) analyzed BOLD signals from 628 ROIs, integrating past and future data. The model achieved an 8% improvement in HRV accuracy, as evidenced by enhanced performance metrics. This approach eliminates the need for peripheral photoplethysmography devices, reduces costs, and simplifies procedures in pediatric fMRI. Additionally, it improves the robustness of pediatric fMRI studies, which are more sensitive to physiological and developmental variations than those in adults.

If you've ever had an argument with the opposite sex, it may be tempting to conclude that men and women just aren't on the same wavelength. Now, a study not only suggests this is indeed the case, but that males and females really are wired differently from birth. In what's described as one of the biggest studies of newborn brain anatomy, scientists performed head scans of more than 500 babies. Overall, the female babies had more grey matter in their brains, while the males had more white matter. Grey matter is mostly found on the outer-most layer of the brain, or cortex, and plays a big role in mental functions, such as memory, emotions and processing information.

You can 3D-print nearly anything: rockets, mouse ovaries, and for some reason, lamps made of orange peels. Now, scientists at Monash University in Melbourne, Australia, have printed living neural networks composed of rat brain cells that seem to mature and communicate like real brains do. Researchers want to create mini-brains partly because they could someday offer a viable alternative to animal testing in drug trials and studies of basic brain function. At the start of 2023, the US Congress passed an annual spending bill pushing scientists to reduce their use of animals in federally funded research, following the signing of the US Food and Drug Administration's Modernization Act 2.0, which allowed high-tech alternatives in drug safety trials. Rather than testing new drugs on thousands of animals, pharmaceutical companies could apply them to 3D-printed mini-brains--in theory.

Fibre orientation distribution (FOD) reconstruction using deep learning has the potential to produce accurate FODs from a reduced number of diffusion-weighted images (DWIs), decreasing total imaging time. Diffusion acquisition invariant representations of the DWI signals are typically used as input to these methods to ensure that they can be applied flexibly to data with different b-vectors and b-values; however, this means the network cannot condition its output directly on the DWI signal. In this work, we propose a spherical deconvolution network, a model-driven deep learning FOD reconstruction architecture, that ensures intermediate and output FODs produced by the network are consistent with the input DWI signals. Furthermore, we implement a fixel classification penalty within our loss function, encouraging the network to produce FODs that can subsequently be segmented into the correct number of fixels and improve downstream fixel-based analysis. Our results show that the model-based deep learning architecture achieves competitive performance compared to a state-of-the-art FOD super-resolution network, FOD-Net. Moreover, we show that the fixel classification penalty can be tuned to offer improved performance with respect to metrics that rely on accurately segmented of FODs. Our code is publicly available at https://github.com/Jbartlett6/SDNet .

The brain's white matter (WM) undergoes developmental and degenerative processes during the human lifespan. To investigate the relationship between WM anatomical regions and age, we study diffusion magnetic resonance imaging tractography that is finely parcellated into fiber clusters in the deep, superficial, and cerebellar WM. We propose a deep-learning-based age prediction model that leverages large convolutional kernels and inverted bottlenecks. We improve performance using novel discrete multi-faceted mix data augmentation and a novel prior-knowledge-based loss function that encourages age predictions in the expected range. We study a dataset of 965 healthy young adults (22-37 years) derived from the Human Connectome Project (HCP). Experimental results demonstrate that the proposed model achieves a mean absolute error of 2.59 years and outperforms compared methods. We find that the deep WM is the most informative for age prediction in this cohort, while the superficial WM is the least informative. Overall, the most predictive WM tracts are the thalamo-frontal tract from the deep WM and the intracerebellar input and Purkinje tract from the cerebellar WM.

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.