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Background: Huntington's disease (HD) is marked by irreversible loss of neuronal function for which currently no availability for disease‐modifying treatment exists. Advances in the understanding of disease progression can aid biomarker development, which in turn can accelerate therapeutic discovery. Methods: We characterised the progression of altered dynamics of whole‐brain network states in the zQ175DN mouse model of HD using a dynamic functional connectivity (FC) approach to resting‐state fMRI and identified quasi‐periodic patterns (QPPs) of brain activity constituting the most prominent resting‐state networks. Results: The occurrence of the normative QPPs, as observed in healthy controls, was reduced in the HD model as the phenotype progressed. This uncovered progressive cessation of synchronous brain activity with phenotypic progression, which is not observed with the conventional static FC approaches. To better understand the potential underlying cause of the observed changes in these brain states, we further assessed how mutant huntingtin (mHTT) protein deposition affects astrocytes and pericytes – one of the most important effectors of neurovascular coupling, along phenotypic progression. Increased cell‐type dependent mHTT deposition was observed at the age of onset of motor anomalies, in the caudate putamen, somatosensory and motor cortex, regions that are prominently involved in HD pathology as seen in humans. Conclusion: Our findings provide meaningful insights into the development and progression of altered functional brain dynamics in this HD model and open new avenues in assessing the dynamics of whole brain states, through QPPs, in clinical HD research. Highlights: Hyperactivity in the LCN‐linked regions within short QPPs observed before motor impairment onset.DMLN QPP presents a progressive decrease in DMLN activity and occurrence along HD‐like phenotype development.Breakdown of the LCN DMLN state flux at motor onset leads to a subsequent absence of the LCN DMLN QPP at an advanced HD‐like stage. [ABSTRACT FROM AUTHOR]

The present study aims to investigate whether begging calls elicit specific auditory responses in non-parenting birds, whether these responses are influenced by the hormonal status of the bird, and whether they reflect biparental care for offspring in the European starling (Sturnus vulgaris). An fMRI experiment was conducted to expose non-parenting male and female European starlings to recordings of conspecific nestling begging calls during both artificially induced breeding and non-breeding seasons. This response was compared with their reaction to conspecific individual warbling song motifs and artificial pure tones, serving as social species-specific and artificial control stimuli, respectively. Our findings reveal that begging calls evoke a response in non-parenting male and female starlings, with significantly higher responsiveness observed in the right Field L and the Caudomedial Nidopallium (NCM), regardless of season or sex. Moreover, a significant seasonal variation in auditory brain responses was elicited in both sexes exclusively by begging calls, not by the applied control stimuli, within a ventral midsagittal region of NCM. This heightened response to begging calls, even in nonparenting birds, in the right primary auditory system (Field L), and the photoperiod induced hormonal neuromodulation of auditory responses to offspring's begging calls in the secondary auditory system (NCM), bears resemblance to mammalian responses to hunger calls. This suggests a convergent evolution aimed at facilitating swift adult responses to such calls crucial for offspring survival. [ABSTRACT FROM AUTHOR]

Purpose: To introduce a novel imaging and parameter estimation framework for accurate multi‐shot diffusion MRI. Theory and Methods: We propose a new framework called ADEPT (Accurate Diffusion Echo‐Planar imaging with multi‐contrast shoTs) that enables fast diffusion MRI by allowing diffusion contrast settings to change between shots in a multi‐shot EPI acquisition (i.e., intra‐scan modulation). The framework estimates diffusion parameter maps directly from the acquired intra‐scan modulated k‐space data, while simultaneously accounting for shot‐to‐shot phase inconsistencies. The performance of the estimation framework is evaluated using Monte Carlo simulation studies and in‐vivo experiments and compared to that of reference methods that rely on parallel imaging for shot‐to‐shot phase correction. Results: Simulation and real‐data experiments show that ADEPT yields more accurate and more precise estimates of the diffusion metrics in multi‐shot EPI data in comparison with the reference methods. Conclusion: ADEPT allows fast multi‐shot EPI diffusion MRI without significantly degrading the accuracy and precision of the estimated diffusion maps. [ABSTRACT FROM AUTHOR]

Purpose: Echo planar imaging (EPI) is commonly used to acquire the many volumes needed for high angular resolution diffusion Imaging (HARDI), posing a higher risk for artifacts, such as distortion and deformation. An alternative to EPI is fast spin echo (FSE) imaging, which has fewer artifacts but is inherently slower. The aim is to accelerate FSE such that a HARDI data set can be acquired in a time comparable to EPI using compressed sensing. Methods: Compressed sensing was applied in either q‐space or simultaneously in k‐space and q‐space, by undersampling the k‐space in the phase‐encoding direction or retrospectively eliminating diffusion directions for different degrees of undersampling. To test the replicability of the acquisition and reconstruction, brain data were acquired from six mice, and a numerical phantom experiment was performed. All HARDI data were analyzed individually using constrained spherical deconvolution, and the apparent fiber density and complexity metric were evaluated, together with whole‐brain tractography. Results: The apparent fiber density and complexity metric showed relatively minor differences when only q‐space undersampling was used, but deteriorate when k‐space undersampling was applied. Likewise, the tract density weighted image showed good results when only q‐space undersampling was applied using 15 directions or more, but information was lost when fewer volumes or k‐space undersampling were used. Conclusion: It was found that acquiring 15 to 20 diffusion directions with a full k‐space and reconstructed using compressed sensing could suffice for a replicable measurement of quantitative measures in mice, where areas near the sinuses and ear cavities are untainted by signal loss. [ABSTRACT FROM AUTHOR]

In quantitative MR T1 mapping, the spin-lattice relaxation time T1 of tissues is estimated from a series of T1 -weighted images. As the T1 estimation is a voxel-wise estimation procedure, correct spatial alignment of the T1 -weighted images is crucial. Conventionally, the T1 -weighted images are first registered based on a general-purpose registration metric, after which the T1 map is estimated. However, as demonstrated in this paper, such a two-step approach leads to a bias in the final T1 map. In our work, instead of considering motion correction as a preprocessing step, we recover the motion-free T1 map using a unified estimation approach. In particular, we propose a unified framework where the motion parameters and the T1 map are simultaneously estimated with a Maximum Likelihood (ML) estimator. With our framework, the relaxation model, the motion model as well as the data statistics are jointly incorporated to provide substantially more accurate motion and T1 parameter estimates. Experiments with realistic Monte Carlo simulations show that the proposed unified ML framework outperforms the conventional two-step approach as well as state-of-the-art model-based approaches, in terms of both motion and T1 map accuracy and mean-square error. Furthermore, the proposed method was additionally validated in a controlled experiment with real T1 -weighted data and with two in vivo human brain T1 -weighted data sets, showing its applicability in real-life scenarios. [ABSTRACT FROM AUTHOR]