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Biology subjects

Chan, K.-S.

Publications and source records attributed to Chan, K.-S..

11 recordsLinked to original sources

Coordinated Brain-Network Dynamics of Maternal Adaptation

The maternal brain exhibits impressive plasticity, undergoing many anatomical and functional alterations over the transition to parenting. Such alterations can reflect maternal adaptation to offspring needs, aligning with the onset and maintenance of maternal care behaviors. These brain-wide neural changes occur across stages, starting during pregnancy and continuing through the peripartal transition. We therefore wanted to understand brain changes with pregnancy, parenting, and care behaviors over time. Many brain regions have been implicated in the coordination of maternal adaptation and corresponding care behaviors. We used computational modeling of multi-region neural oscillation data to identify distinct electrical networks during naturally-occurring care behaviors and across multiple maternal stages in outbred, highly maternal mice. This work demonstrates that multi-region oscillatory electrical dynamics at high spatiotemporal resolution reflect these functions. Distinct, discriminative networks predict maternal behavior and stage, showcasing network adaptation with parental transition. Maternal behavior and stage also had substantial effects on other distinct, multi-region oscillatory electrical networks relevant to stress susceptibility, further linking maternal adaptation to the neurophysiology of stress. While these stress networks were perturbed by maternal experience of early life stress, networks trained on maternal behavior and stage remained robust against such perturbation. This suggests a uniquely persistent strength of maternal electrical dynamics. Together, this work supports the role of oscillatory electrical dynamics as networks underlying fundamentally conserved behaviors and transitions, demonstrating wide relevance and significance to brain-behavior discovery.

neuroscience↗

In vivo human neurite exchange imaging (NEXI) at 500 mT/m diffusion gradients

Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion-water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength=500 mT/m, maximum slew rate=600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13-30 ms) and b-values up to 17.5 ms/m2. The anisotropic Karger model was applied to estimate the exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median{+/-}interquartile range) 13{+/-}8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the NEXI exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast ([≤]20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex and visual cortex exhibit longer exchange times compared to other cortical regions. Non-linear fitting for the anisotropic Karger model was accelerated 100 times using a GPU-based pipeline compared to the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in dMRI data, which can have a substantial impact on the estimated NEXI exchange time and require extra attention when comparing NEXI results between various hardware setups. Highlights- The Connectome 2.0 scanner equipped with 500 mT/m gradients enables high-sensitivity diffusion MRI for imaging exchange times in vivo - The global exchange time across the living human cortex was estimated to be about 13 ms on Connectome 2.0, faster than measured using a protocol compatible to Connectome 1.0 - Spatially varying exchange times were observed across the cortex - A GPU-accelerated tool was developed to speed up parameter estimation by 100 times - The narrow pulse approximation used in neurite exchange imaging does not affect estimation performance in high gradient performance MRI systems

neuroscience↗

Peripheral CGRP engages brain-wide electrical network activity of migraine

BackgroundMigraine is a disorder of severe, recurrent headaches and debilitating sensory, cognitive and affective symptoms, often triggered by stress. Early life stress in childhood has been shown to increase the likelihood of migraine in adulthood in humans. Calcitonin-gene relate peptide (CGRP) has been shown to reliably and acutely induce migraine or migraine-like behavior in both humans and rodent models. Here we investigate the impact of early life stress and CGRP on migraine-related neural circuitry, as well as the impact of early life stress on CGRP-mediated migraine-like activity in order to better understand the mechanisms by which early life stress predisposes neural circuitry to migraine brain activity. MethodsWe implemented an early life stress paradigm in the outbred strain of mice, CD1. We evaluated the impact of peripheral CGRP on migraine-like behavior and employed multi-site in vivo neurophysiology in freely behaving mice. A changepoint analysis was used to dissect differences in individual CGRP-induced responses. ResultsWe found that early life stress exacerbated migraine-related behavioral and network physiology. CGRP alone caused disruptions in neural oscillatory activity across a network of brain regions including the anterior cingulate cortex (ACC), amygdala (AMY), thalamus (Po, VPM, and MDthal), and parabrachial nucleus (PBN). We found that power across the network was lowered within 10 minutes of peripheral CGRP exposure, which was sustained for [~]40-50 min. Coherence was mostly disrupted in amygdalar brain region pairings, and took on a shorter timecourse, with partial rescue of these responses by migraine abortive, sumatriptan. We found that early life stress exacerbated most of these responses, especially AMY-thalamic coherence pairings, although early life stress in the absence of CGRP demonstrated no impact on the network overall. We further identified individual mice with brain-network activity hypersusceptible to migraine. ConclusionsOur findings demonstrate that early life stress confers vulnerability to migraine, simultaneously impacting behavior and brain network activity responses to peripheral CGRP.

neuroscience↗

Normative trajectories of R1, R2* and magnetic susceptibility in basal ganglia on healthy ageing

Quantitative MRI techniques, including R1, R2*, and magnetic susceptibility mapping, have emerged as promising tools for generating surrogate imaging markers of brain tissue microstructure, enabling non-invasive in vivo measurements associated with myelination and iron deposition. Gaining insights into how these quantitative measurements evolve throughout a normal lifespan can enhance our understanding of brain maturation processes and facilitate the study of disease-related microstructural changes by distinguishing pathological alterations from normal brain development. In this study, we established the normative trajectories of R1, R2*, and magnetic susceptibility in the basal ganglia at 3T. We used a healthy ageing cohort comprising 260 subjects with an evenly distributed age range and sex ratio throughout adulthood. Utilizing the non-parametric Gaussian Process Regression model to derive the normative trajectories, we found that R1 in these structures predominantly exhibit a quadratic shape over age, while R2* and magnetic susceptibility are primarily linear. We validated the normative trajectories of R2* and magnetic susceptibility using an independent cohort. Additionally, we demonstrated that the spatial distributions of the quantitative MRI parameters also change with age in the putamen and caudate nucleus. This study not only reinforces existing findings on the association between age and qMRI but also provides valuable resources for studying cognitive ageing, in conjunction with the behavioural data available in the same data collection.

neuroscience↗

The Advanced BRain Imaging on ageing and Memory (ABRIM) data collection: Study protocol and rationale

To understand the neurocognitive mechanisms that underlie heterogeneity in cognitive ageing, recent scientific efforts have led to a growing public availability of imaging cohort data. The Advanced BRain Imaging on ageing and Memory (ABRIM) project aims to add to these existing datasets by taking an adult lifespan approach to provide a cross-sectional, normative database with a particular focus on connectivity, myelinization and iron content of the brain in concurrence with cognitive functioning, mechanisms of reserve, and sleep-wake rhythms. ABRIM freely shares MRI and behavioural data from 295 participants between 18-80 years, stratified by age decade and sex (median age 52, IQR 36-66, 53.20% females). The ABRIM MRI collection consists of both the raw and pre-processed structural and functional MRI data to facilitate data usage among both expert and non-expert users. The ABRIM behavioural collection includes measures of cognitive functioning (i.e., global cognition, processing speed, executive functions, and memory), proxy measures of cognitive reserve (e.g., educational attainment, verbal intelligence, and occupational complexity), and various self-reported questionnaires (e.g., on depressive symptoms, pain, and the use of memory strategies in daily life and during a memory task). In a sub-sample (n = 120), we recorded sleep-wake rhythms with an actigraphy device for a period of 7 consecutive days. Here, we provide an in-depth description of our study protocol, pre-processing pipelines, and data availability. ABRIM provides a cross-sectional database on healthy participants throughout the adult lifespan, including numerous parameters relevant to improve our understanding of cognitive ageing. Therefore, ABRIM enables researchers to model the advanced imaging parameters and cognitive topologies as a function of age, identify the normal range of values of such parameters, and to further investigate the diverse mechanisms of reserve and resilience.

neuroscience↗

On the performance of multi-compartment relaxometry for myelin water imaging - Intra-subject and inter-protocol reproducibility

We evaluate the test-retest repeatability and study the tissue properties of multicompartment relaxometry-based myelin water imaging (MCR-MWI) derived from different gradient echo (GRE) acquisition settings. Additionally, the variable flip angle acquisition scheme is optimised based on numerical simulations to reduce the acquisition time of MCR-MWI in a clinically practical range without using advanced image acquisition methods. For the test-retest analysis, in vivo imaging was performed to collect data from three healthy volunteers in two identical sessions. Three GRE sequence settings with different echo times and repetition times imitating various scanner setups were evaluated. The in vivo data was also used to validate the optimal variable flip angle combination derived from simulations. Bundle-specific profiles of MCR-MWI derived microstructural parameters were investigated, as well as the cross-correlations of those parameters. Good cross-session repeatability is observed for MCR-MWI. While good correlations can also be found in myelin water fraction (MWF) across protocols, systematic differences, particularly for protocols with different repetition times, are observed. Numerical simulations indicate that MCR-MWI can be performed with a minimum of three flip angles covering a wide range of T1 weighting without adding significant measurement bias and the result is supported by the in vivo experiment allowing whole brain 1.5mm isotropic MWF maps to be acquired in 9 minutes. Bundles-specific MWF analysis reveals that certain white matter bundles are similar in all three participants. We also found that microstructure relaxation parameters have low correlations with MWF. MCR-MWI is a reproducible measure of myelin. However, attention should be paid to considering the protocol related MWF differences for comparison studies, especially when different repetition times are used as this can introduce biases up to 0.5% of MWF in our tested protocols. The optimised flip angle acquisition scheme can reduce the total scan time to 40% of the original implementation without significant quality degradation. Highlights- Multi-compartment relaxometry based myelin water imaging (MCR-MWI) can be performed with data comprising as few as 3 flip angles without introducing substantial bias or instability in the fitting procedure; - MCR-MWI is a reproducible measure of myelin water fraction (MWF) and incorporating DWI can further improve the measurement reproducibility; - MCR-MWI allows the acquisition of whole brain 1.5mm isotropic MWF maps in 9 minutes, even without the use of advanced model-based reconstructions; - Small MWF bias can present in cross-protocol comparison if the MT effect is not constant across GRE protocols (e.g., different TRs or flip angle combinations); - Compartmental relaxation parameters derived from MCR-MWI possess complimentary information beyond myelin water concentration.

neuroscience↗

Metabolic Derangement in Polycystic Kidney Disease Mouse Models Is Ameliorated by Mitochondrial-Targeted Antioxidants

Autosomal dominant polycystic kidney disease (ADPKD) is characterized by progressively enlarging cysts. Here we elucidate the interplay between oxidative stress, mitochondrial dysfunction, and metabolic derangement using two mouse models of PKD1 mutation, PKD1RC/null and PKD1RC/RC. Mouse kidneys with PKD1 mutation have decreased mitochondrial complexes activity. Targeted proteomics analysis shows a significant decrease in proteins involved in the TCA cycle, fatty acid oxidation (FAO), respiratory complexes, and endogenous antioxidants. Overexpressing mitochondrial-targeted catalase (mCAT) using adeno-associated virus reduces mitochondrial ROS, oxidative damage, ameliorates the progression of PKD and partially restores expression of proteins involved in FAO and the TCA cycle. In human ADPKD cells, inducing mitochondrial ROS increased ERK1/2 phosphorylation and decreased AMPK phosphorylation, whereas the converse was observed with increased scavenging of ROS in the mitochondria. Treatment with the mitochondrial protective peptide, SS31, recapitulates the beneficial effects of mCAT, supporting its potential application as a novel therapeutic for ADPKD.

pathology↗

Studying magnetic susceptibility, microstructural compartmentalisation and chemical exchange in a formalin-fixed ex vivo human brain specimen

PurposeEx vivo imaging is a preferable method to study the biophysical mechanism of white matter orientation-dependent signal phase evolution. Yet, how formalin fixation, commonly used for tissue preservation, affects the phase measurement is not fully known. We, therefore, study the impacts of formalin fixation on magnetic susceptibility, microstructural compartmentalisation and chemical exchange measurement on human brain tissue. MethodsA formalin-fixed, post-mortem human brain specimen was scanned with multiple orientations with respect to the main magnetic field direction for robust bulk magnetic susceptibility measurement with conventional quantitative susceptibility imaging models. Homogeneous white matter tissues were subsequently excised from the whole-brain specimen and scanned in multiple rotations on an MRI scanner to measure the anisotropic magnetic susceptibility and microstructure-related contributions in the signal phase. Electron microscopy was used to validate the MRI findings. ResultsThe bulk isotropic magnetic susceptibility of ex vivo whole-brain imaging is comparable to in vivo imaging, with noticeable enhanced non-susceptibility contributions. The excised specimen experiment reveals that anisotropic magnetic susceptibility and compartmentalisation phase effect were considerably reduced in formalin-fixed white matter tissue. ConclusionsDespite formalin-fixed white matter tissue has comparable bulk isotropic magnetic susceptibility to those measured via in vivo imaging, its orientation-dependent components in the signal phase related to the tissue microstructure is substantially weaker, making it less favourable in white matter microstructure studies using phase imaging.

biophysics↗

QSM Reconstruction Challenge 2.0 Part 1: A Realistic in silico Head Phantom for MRI data simulation and evaluation of susceptibility mapping procedures

PurposeTo create a realistic in-silico head phantom for the second Quantitative Susceptibility Mapping (QSM) Reconstruction Challenge and for future evaluations of processing algorithms for QSM. MethodsWe created a digital whole-head tissue property phantom by segmenting and post-processing high-resolution (0.64mm isotropic), multi-parametric MRI data acquired at 7T from a healthy volunteer. We simulated the steady-state magnetization at 7T using a Bloch simulator and mimicked a Cartesian sampling scheme through Fourier-based processing. Computer code for generating the phantom and performing the MR simulation was designed to facilitate flexible modifications of the phantom in the future, such as the inclusion of pathologies, as well as the simulation of a wide range of acquisition protocols. Specifically, the following parameters and effects were implemented: repetition time and echo time, voxel size, background fields, and RF phase biases. Diffusion weighted imaging phantom data is provided allowing future investigations of tissue microstructure effects in phase and QSM algorithms. ResultsThe brain-part of the phantom featured realistic morphology with spatial variations in relaxation and susceptibility values similar to the in vivo setting. We demonstrated some of the phantoms properties, including the possibility of generating phase data with non-linear evolution over echo time due to partial volume effects or complex distributions of frequency shifts within the voxel. ConclusionThe presented phantom and computer programs are publicly available and may serve as a ground truth in future assessments of the faithfulness of quantitative susceptibility reconstruction algorithms.

neuroscience↗

SEPIA - SuscEptibility mapping PIpeline tool for phAse images

Quantitative susceptibility mapping (QSM) is a physics-driven computational technique that has a high sensitivity in quantifying iron deposition based on MRI phase images. Furthermore, it has a unique ability to distinguish paramagnetic and diamagnetic contributions such as haemorrhage and calcification based on image contrast. These properties have contributed to a growing interest to use QSM not only in research but also in clinical applications. However, it is challenging to obtain high quality susceptibility map because of its ill-posed nature, especially for researchers who have less experience with QSM and the optimisation of its pipeline. In this paper, we present an open-source processing pipeline tool called SuscEptibility mapping PIpeline tool for phAse images (SEPIA) dedicated to the post-processing of MRI phase images and QSM. SEPIA connects various QSM toolboxes freely available in the field to offer greater flexibility in QSM processing. It also provides an interactive graphical user interface to construct and execute a QSM processing pipeline, simplifying the workflow in QSM research. The extendable design of SEPIA also allows developers to deploy their methods in the framework, providing a platform for developers and researchers to share and utilise the state-of-the-art methods in QSM.

neuroscience↗

White Matter microstructural property decoding from gradient echo data using realistic white matter models

Multi-echo gradient echo (ME-GRE) magnetic resonance signal evolution in white matter has a strong dependence on the orientation of myelinated axons with respect to the main static field. Although analytical solutions have been able to predict some of the white matter (WM) signal behaviour of the hollow cylinder model, it has been shown that realistic models of WM offer a better description of the signal behaviour observed. In this work, we present a pipeline to (i) generate realistic 2D WM models with their microstructure based on real axon morphology with adjustable fiber volume fraction (FVF) and g-ratio. We (ii) simulate their interaction with the static magnetic field to be able to simulate their MR signal. For the first time, we (iii) demonstrate that realistic 2D WM models can be used to simulate a MR signal that provides a good approximation of the signal obtained from a real 3D WM model derived from electron microscopy. We then (iv) demonstrate in silico that 2D WM models can be used to predict microstructural parameters in a robust way if ME-GRE multi-orientation data is available and the main fiber orientation in each pixel is known using DTI. A deep learning network was trained and characterized in its ability to recover the desired microstructural parameters such as FVF, g-ratio, free and bound water transverse relaxation and magnetic susceptibility. Finally, the network was trained to recover these micro-structural parameters from an ex vivo dataset acquired in 9 orientations with respect to the magnetic field and 12 echo times. We demonstrate that this is an overdetermined problem and that as few as 3 orientations can already provide comparable results for some of the decoded metrics. [Highlights] - A pipeline to generate realistic white models of arbitrary fiber volume fraction and g-ratio is presented; - We present a methodology to simulated the gradient echo signal from segmented 2D and 3D models of white matter, which takes into account the interaction of the static magnetic field with the anisotropic susceptibility of the myelin phospholipids; - Deep Learning Networks can be used to decode microstructural white matter parameters from the signal of multi-echo multi-orientation data;

neuroscience↗