Within-subject test-retest reliability of the atlas-based cortical volume measurement in the rat brain: A voxel-based morphometry study

Age-related changes in the architecture and biochemical markers levels in motor-related cortical areas of SHR rats-an ADHD animal model

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder whose exact pathophysiology has not been fully understood yet. Numerous studies have suggested disruptions in the cellular architecture and neuronal activity within brain structures of individuals with ADHD, accompanied by imbalances in the immune system, oxidative stress, and metabolism.

Methods

This study aims to assess two functionally and histologically distinct brain areas involved in motor control and coordination: the motor cortex (MC) and prefrontal cortex (PFC). Namely, the morphometric analysis of the MC throughout the developmental stages of Spontaneously Hypertensive Rats (SHRs) and Wistar Kyoto Rats (WKYs). Additionally, the study aimed to investigate the levels and activities of specific immune, oxidative stress, and metabolic markers in the PFC of juvenile and maturing SHRs in comparison to WKYs.

Results

The most significant MC volume reductions occurred in juvenile SHRs, accompanied by alterations in neuronal density in these brain areas compared to WKYs. Furthermore, juvenile SHRs exhibit heightened levels and activity of various markers, including interleukin-1α (IL-1α), IL-6, serine/threonine-protein mammalian target of rapamycin, RAC-alpha serine/threonine-protein kinase, glucocorticoid receptor β, malondialdehyde, sulfhydryl groups, superoxide dismutase, peroxidase, glutathione reductase, glutathione S-transferase, glucose, fructosamine, iron, lactic acid, alanine, aspartate transaminase, and lactate dehydrogenase.

Discussion

Significant changes in the MC morphometry and elevated levels of inflammatory, oxidative, and metabolic markers in PFC might be associated with disrupted brain development and maturation in ADHD.

A radiomics-based brain network in T1 images: construction, attributes, and applications

T1 image is a widely collected imaging sequence in various neuroimaging datasets, but it is rarely used to construct an individual-level brain network. In this study, a novel individualized radiomics-based structural similarity network was proposed from T1 images. In detail, it used voxel-based morphometry to obtain the preprocessed gray matter images, and radiomic features were then extracted on each region of interest in Brainnetome atlas, and an individualized radiomics-based structural similarity network was finally built using the correlational values of radiomic features between any pair of regions of interest. After that, the network characteristics of individualized radiomics-based structural similarity network were assessed, including graph theory attributes, test-retest reliability, and individual identification ability (fingerprinting). At last, two representative applications for individualized radiomics-based structural similarity network, namely mild cognitive impairment subtype discrimination and fluid intelligence prediction, were exemplified and compared with some other networks on large open-source datasets. The results revealed that the individualized radiomics-based structural similarity network displays remarkable network characteristics and exhibits advantageous performances in mild cognitive impairment subtype discrimination and fluid intelligence prediction. In summary, the individualized radiomics-based structural similarity network provides a distinctive, reliable, and informative individualized structural brain network, which can be combined with other networks such as resting-state functional connectivity for various phenotypic and clinical applications.

Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks

The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.

Long-term ANT-DBS effects in pilocarpine-induced epileptic rats: A combined 9.4T MRI and histological study

Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) appears to be effective against seizures in animals and humans however, its therapeutic mechanisms remain elusive. This study aimed to combine 9.4T multimodal magnetic resonance imaging (MRI) with histology to investigate the longitudinal effects of long-term ANT-DBS in pilocarpine-induced epileptic rats. Status epilepsy (SE) was induced by LiCl-pilocarpine injection in 11 adult male Sprague-Dawley rats. Four weeks after SE, chronic epileptic rats underwent either ANT-DBS (n = 6) or sham-DBS (n = 5) surgery. Electroencephalography (EEG) and spontaneous recurrent seizures (SRS) were recorded for 1 week. The T2-weighted image and images from resting-state functional MRI (rs-fMRI) were acquired at three states: before SE, at 4 weeks post-SE, and at 5 weeks post-DBS. Volumes of the hippocampal subregions and hippocampal-related functional connectivity (FC) were compared longitudinally. Finally, antibodies against neuronal nuclei (NeuN) and glial fibrillary acidic proteins were used to evaluate neuronal loss and astrogliosis in the hippocampus. Long-term ANT-DBS significantly reduced seizure generalization in pilocarpine-induced epileptic rats. By analyzing the gray matter volume using T2-weighted images, long-term ANT-DBS displayed morphometric restoration of the hippocampal subregions. Neuronal protection of the hippocampal subregions and inhibition of astrogliosis in the hippocampal subregions were observed in the ANT-DBS group. ANT-DBS caused reversible regulation of FC in the insula-hippocampus and subthalamic nucleus-hippocampus. Long-term ANT-DBS provides comprehensive protection of hippocampal histology, hippocampal morphometrics, and hippocampal-related functional networks.

Phloretin decreases microglia-mediated synaptic engulfment to prevent chronic mild stress-induced depression-like behaviors in the mPFC

Background: Stress is an important risk factor to induce psychiatric disorders such as depression. Phloretin (PHL), a natural dihydrochalcone compound, has been shown to exhibit anti-inflammatory and anti-oxidative effects. However, the impact of PHL on the depression and the underlying mechanism remain unclear. Methods: The animal behavior tests were used to determine the protective of PHL on the chronic mild stress (CMS)-induced depression-like behaviors. The Magnetic Resonance Imaging (MRI), electron microscopy analysis, fiber photometry, electrophysiology, and Structure Illumination Microscopy (SIM) were used to investigate the protective of PHL on the structural and functional impairments induced by CMS exposure in the mPFC. The RNA sequencing, western blot, reporter gene assay, and chromatin immunoprecipitation were adopted to investigate the mechanisms. Results: We showed that PHL efficiently prevented the CMS-induced depressive-like behaviors. Moreover, PHL not only attenuated the decrease of synapse losses but also improved the dendritic spine density and neuronal activity in the mPFC after CMS exposure. Furthermore, PHL remarkably inhibited the CMS-induced microglial activation and phagocytic activity in the mPFC. In addition, we demonstrated that PHL decreased the CMS-induced synapse losses by inhibiting the deposition of complement C3 deposition onto synapses and subsequent microglia-mediated synaptic engulfment. Finally, we revealed that PHL inhibited the NF-κB-C3 axis to display neuroprotective effects. Conclusions: Our results indicate that PHL represses the NF-κB-C3 axis and subsequent microglia-mediated synaptic engulfment to protect against CMS-induced depression in the mPFC.

Test-retest and repositioning effects of white matter microstructure measurements in selected white matter tracts

We used intra-class effect decomposition (ICED) to evaluate the reliability of myelin water fraction (MWF) and geometric mean T2 relaxation time (geomT2IEW) estimated from a multi-echo MRI sequence. Our evaluation addressed test-retest reliability, with and without participant re-positioning, for seven commonly assessed white matter tracts: anterior and posterior limbs of the internal capsule, dorsal and ventral branches of the cingulum, the inferior fronto-occipital fasciculus, the superior longitudinal fasciculus, and the fornix in 20 healthy adults. We acquired two back-to-back scans in a single session, and a third after a break and repositioning the participant in the scanner. For both indices and for all white matter tracts assessed, reliability for an immediate retest, and after the participant's repositioning in the scanner was high. Variance partitioning revealed that in addition to measurement noise, which was significant in all regions, repositioning contributed to unreliability mainly in longer association fibers. Hemispheric location did not significantly contribute to unreliability in any region of interest (ROI). Thus, despite non-negligible error of measurement, for all ROIs, MWF and geomT2IEW have good test-retest reliability, regardless of the hemispheric location and are, therefore, suitable for longitudinal investigations in healthy adults.

A Reparametrized CNN Model to Distinguish Alzheimer's Disease Applying Multiple Morphological Metrics and Deep Semantic Features From Structural MRI

It is of potential clinical value to improve the accuracy of Alzheimer's disease (AD) recognition using structural MRI. We proposed a reparametrized convolutional neural network (Re-CNN) to discriminate AD from NC by applying morphological metrics and deep semantic features. The deep semantic features were extracted through Re-CNN on structural MRI. Considering the high redundancy in deep semantic features, we constrained the similarity of the features and retained the most distinguishing features utilizing the reparametrized module. The Re-CNN model was trained in an end-to-end manner on structural MRI from the ADNI dataset and tested on structural MRI from the AIBL dataset. Our proposed model achieves better performance over some existing structural MRI-based AD recognition models. The experimental results show that morphological metrics along with the constrained deep semantic features can relatively improve AD recognition performance. Our code is available at: https://github.com/czp19940707/Re-CNN.

Evolution of Brain Morphology in Spontaneously Hypertensive and Wistar-Kyoto Rats From Early Adulthood to Aging: A Longitudinal Magnetic Resonance Imaging Study

The influence of hypertension and aging alone on brain structure has been described extensively. Our understanding of the interaction of hypertension with aging to brain morphology is still limited. We aimed to detect the synergistic effects of hypertension and aging on brain morphology and to describe the evolution patterns of cerebral atrophy from spatial and temporal perspectives. In 8 spontaneously hypertensive rats (SHRs) and 5 Wistar-Kyoto rats, high-resolution magnetic resonance imaging scans were longitudinally acquired at 10, 24, 52, and 80 weeks. We analyzed the tissue volumes of gray matter, white matter, cerebral spinal fluid, and total intracranial volume (TIV), and then evaluated gray matter volume in detail using voxel-based morphometry (VBM) and region of interest-based methods. There were interactive effects on hypertension and aging in tissue volumes of gray matter, white matter, and TIV, of which gray matter atrophy was most pronounced, especially in elderly SHRs. We identified the vulnerable gray matter volume with combined effects of hypertension and aging in the septal region, bilateral caudate putamen, hippocampus, primary somatosensory cortex, cerebellum, periaqueductal gray, right accumbens nucleus, and thalamus. We automatically extracted the septal region, anterior cingulate cortex, primary somatosensory cortex, caudate putamen, hippocampus, and accumbens nucleus and revealed an inverted-U trajectory of volume change in SHRs, with volume increase at the early phase and decline at the late phase. Hypertension interacts with aging to affect brain volume changes such as severe atrophy in elderly SHRs.

Gray Matter Volume Variability in Young Healthy Adults: Influence of Gender Difference and Brain-Derived Neurotrophic Factor Genotype

Although brain gray matter (GM) plastically changes during short-term training, it is still unclear whether brain structures are stable for short periods (several months). Therefore, this study aimed to re-test the short-term variability of GM volumes and to clarify the effect of factors (gender and BDNF-genotype) expected to contribute to such variability. The subjects comprised 41 young healthy adults. T1-weighted images were acquired twice with an interval of approximately 4 months using a 3 T-MRI scanner. Voxel-based morphometry (VBM) was used to calculate GM volumes in 47 regions. The intraclass correlation coefficient (ICC) and Test–retest variability (%TRV) were used as indices of variability. As a result, the ICCs in 43 regions were excellent (ICC > 0.90) and those in 3 regions were good (ICC > 0.80), whereas the ICC in the thalamus was moderate (ICC = 0.694). Women had a higher %TRV than men in 5 regions, and %TRV of the Val66Val group was higher than that of the Met carrier group in 2 regions. Moreover, the Female-Val66Val group had a higher %TRV than the Male-Met carrier group in 3 regions. These results indicate that although the short-term variability of GM volumes is small, it is affected by within-subject factors.