Decoding the diagnostic potential of T cell repertoires in peripheral blood of patients from amnestic mild cognitive impairment to Alzheimer's disease.

Alzheimer's disease (AD) is currently an incurable neurodegenerative disorder and is the most common etiological cause of dementia. Consequently, it has severe burden on its patients and on their caregivers and represents a global health concern. Clinical investigations have indicated that a dysregulation of peripheral T cell immune homeostasis may be involved in the pathogenesis of AD, as well as in the early stages of AD, characterized by mild cognitive impairment (MCI). However, the characteristics and concomitant feasibility of the use of T-cell receptor (TCR) typing for disease diagnosis remains largely unknown. We employed a high-throughput sequencing and multidimensional bioinformatics analyses for the identification of TCR repertoires present in peripheral blood samples of 10 patients with amnestic MCI (aMCI), 10 patients with AD, and 10 healthy controls (HCs). Based on the characteristics of the TCR repertoires in the amount and diversity of combinations of V-J, the spectrum of immune defense, and differentially expressed genes (DEGs), single and specific TCR profiles were observed in the patient samples of aMCI and AD compared to profiles of HCs. In particular, the diversity of TCR clonotypes manifested a pattern of "decreased first and then increased" pattern during the progression from aMCI to AD, a pattern that was not observed in HC samples. Additionally, a total of 46 and 35 amino acid CDR3 sequences with consistent and reverse expressive abundance with diversity of TCR clonotypes were identified, respectively. Taken together, we provide novel and essential preliminary evidence demonstrating the presence of diversity of T cell repertoires from differentially expressed V-J gene segments and amino acid clonotypes using peripheral blood samples from patients with AD, aMCI, and from HC. Such findings have the potential to reveal potential mechanisms through which aMCI progresses to AD and provide a reference for the future development of immune-related diagnoses and therapies for AD.

Brain inflammation co-localizes highly with tau in mild cognitive impairment due to early-onset Alzheimer's disease.

Brain inflammation, with an increased density of microglia and macrophages, is an important component of Alzheimer's disease (AD) and a potential therapeutic target. However, it is incompletely characterized, particularly in patients whose disease begins before the age of 65 years and, thus, have few co-pathologies. Inflammation has been usefully imaged with translocator protein (TSPO) positron emission tomography (PET), but most inflammation PET tracers cannot image subjects with a low-binder TSPO rs6971 genotype. In an important development, participants with any TSPO genotype can be imaged with a novel tracer, [11C]ER176, that has a high binding potential and a more favorable metabolite profile than other TSPO tracers currently available. We applied [11C]ER176 to detect brain inflammation in mild cognitive impairment (MCI) caused by early-onset AD. Furthermore, we sought to correlate the brain localization of inflammation, volume loss, elevated Aß and tau. We studied brain inflammation in 25 patients with early-onset amnestic MCI (average age 59 ± 4.5 years, 10 women) and 23 healthy controls (average age 65 ± 6.0 years, 12 women), both groups with a similar proportion of all three TSPO-binding affinities. [11C]ER176 total distribution volume (VT), obtained with an arterial input function, was compared across patients and controls using voxel-wise and region-wise analyses. In addition to inflammation PET, most MCI patients had Aß (n=23), and tau PET (n=21). For Aß and tau tracers, standard uptake value ratios (SUVRs) were calculated using cerebellar grey matter as region of reference. Regional correlations among the three tracers were determined. Data were corrected for partial volume effect. Cognitive performance was studied with standard neuropsychological tools. In MCI caused by early-onset AD, there was inflammation in the default network, reaching statistical significance in precuneus and lateral temporal and parietal association cortex bilaterally, and in the right amygdala. Topographically, inflammation co-localized most strongly with tau (r= 0.63 ± 0.24). This correlation was higher than the co-localization of Aß with tau (r= 0.55±0.25) and of inflammation with Aß (0.43±0.22). Inflammation co-localized least with atrophy (-0.29±0.26). These regional correlations could be detected in participants with any of the three rs6971 TSPO polymorphisms. Inflammation in AD-related regions correlated with impaired cognitive scores. Our data highlight the importance of inflammation, a potential therapeutic target, in the AD process. Furthermore, they support the notion that, as shown in experimental tissue and animal models, the propagation of tau in humans is associated with brain inflammation.

Single-value brain activity scores reflect both severity and risk across the Alzheimer's continuum.

Single-value scores reflecting the deviation from (FADE score) or similarity with (SAME score) prototypical novelty-related and memory-related functional magnetic resonance imaging (fMRI) activation patterns in young adults have been proposed as imaging biomarkers of healthy neurocognitive aging. Here, we tested the utility of these scores as potential diagnostic and prognostic markers in Alzheimer's disease (AD) and risk states like mild cognitive impairment (MCI) or subjective cognitive decline (SCD). To this end, we analyzed subsequent memory fMRI data from individuals with SCD, MCI, and AD dementia as well as healthy controls (HC) and first-degree relatives of AD dementia patients (AD-rel) who participated in the multi-center DELCODE study (N = 468). Based on the individual participants' whole-brain fMRI novelty and subsequent memory responses, we calculated the FADE and SAME scores and assessed their association with AD risk stage, neuropsychological test scores, CSF amyloid positivity, and ApoE genotype. Memory-based FADE and SAME scores showed a considerably larger deviation from a reference sample of young adults in the MCI and AD dementia groups compared to HC, SCD and AD-rel. In addition, novelty-based scores significantly differed between the MCI and AD dementia groups. Across the entire sample, single-value scores correlated with neuropsychological test performance. The novelty-based SAME score further differed between Aß-positive and Aß-negative individuals in SCD and AD-rel, and between ApoE ε4 carriers and non-carriers in AD-rel. Hence, FADE and SAME scores are associated with both cognitive performance and individual risk factors for AD. Their potential utility as diagnostic and prognostic biomarkers warrants further exploration, particularly in individuals with SCD and healthy relatives of AD dementia patients.

Late-onset temporal lobe epilepsy: insights from brain atrophy and Alzheimer's disease biomarkers.

Considering the growing age of the world population, the incidence of epilepsy in older adults is expected to increase significantly. It has been suggested that late-onset temporal lobe epilepsy (LO-TLE) may be neurodegenerative in origin and overlap with Alzheimer's Disease (AD). Herein, we aimed to characterize the pattern of cortical atrophy and cerebrospinal fluid (CSF) biomarkers of AD (total and phosphorylated tau, and ß-amyloid) in a selected population of LO-TLE of unknown origin. We prospectively enrolled individuals with temporal lobe epilepsy onset after the age of 50 and no cognitive impairment. They underwent a structural MRI scan and CSF biomarkers measurement. Imaging and biomarkers data were compared to three retrospectively collected groups: (i) age-sex-matched healthy controls, (ii) patients with Mild Cognitive Impairment (MCI) and abnormal CSF AD biomarkers (MCI-AD), and (iii) patients with MCI and normal CSF AD biomarkers (MCI-noAD). From a pool of 52 patients, twenty consecutive eligible LO-TLE patients with a mean disease duration of 1.8 years were recruited. As control populations, 25 patients with MCI-AD, 25 patients with MCI-noAD, and 25 healthy controls were enrolled. CSF biomarkers returned normal values in LO-TLE, significantly different from patients with MCI due to AD. There were no differences in cortico-subcortical atrophy between epilepsy patients and healthy controls, while patients with MCI demonstrated widespread injuries of cortico-subcortical structures. Individuals with a late-onset form of temporal lobe epilepsy, characterized by short disease duration and normal CSF ß-amyloid and tau protein levels, showed patterns of cortical thickness and subcortical volumes not significantly different from healthy controls, but highly different from patients with MCI, either due to Alzheimer's Disease or not.

Serum biomarkers and disease progression in CT-negative mild traumatic brain injury.

Blood proteins are emerging as potential biomarkers for mild traumatic brain injury (mTBI). Molecular pathology of mTBI underscores the critical roles of neuronal injury, neuroinflammation, and vascular health in disease progression. However, the temporal profile of blood biomarkers associated with the aforementioned molecular pathology after CT-negative mTBI, their diagnostic and prognostic potential, and their utility in monitoring white matter integrity and progressive brain atrophy remain unclear. Thus, we investigated serum biomarkers and neuroimaging in a longitudinal cohort, including 103 CT-negative mTBI patients and 66 matched healthy controls (HCs). Angiogenic biomarker vascular endothelial growth factor (VEGF) exhibited the highest area under the curve of 0.88 in identifying patients from HCs. Inflammatory biomarker interleukin-1ß and neuronal cell body injury biomarker ubiquitin carboxyl-terminal hydrolase L1 were elevated in acute-stage patients and associated with deterioration of cognitive function from acute-stage to 6-12 mo post-injury period. Notably, axonal injury biomarker neurofilament light (NfL) was elevated in acute-stage patients, with higher levels associated with impaired white matter integrity in acute-stage and progressive gray and white matter atrophy from 3- to 6-12 mo post-injury period. Collectively, our findings emphasized the potential clinical value of serum biomarkers, particularly NfL and VEGF, in diagnosing mTBI and monitoring disease progression.

Distinguishable neural circuit mechanisms associated with the clinical efficacy of rTMS in aMCI patients.

Repetitive transcranial magnetic stimulation is used in early-stage Alzheimer's disease to slow progression, but heterogeneity in response results in different treatment outcomes. The mechanisms underlying this heterogeneity are unclear. This study used resting-state neuroimaging to investigate the variability in episodic memory improvement from angular gyrus repetitive transcranial magnetic stimulation and tracked the neural circuits involved. Thirty-four amnestic mild cognitive impairment patients underwent angular gyrus repetitive transcranial magnetic stimulation (4 weeks, 20 Hz, 100% resting motor threshold) and were divided into high-response and low-response groups based on minimal clinically important differences in auditory verbal learning test scores. Baseline and pre/post-treatment neural circuit activities were compared. Results indicated that the orbital middle frontal gyrus in the orbitofrontal cortex network and the precuneus in the default mode network had higher local activity in the low-response group. After treatment, changes in local and remote connectivity within brain regions of the orbitofrontal cortex, default mode network, visual network, and sensorimotor network showed opposite trends and were related to treatment effects. This suggests that the activity states of brain regions within the orbitofrontal cortex and default mode network could serve as imaging markers for early cognitive compensation in amnestic mild cognitive impairment patients and predict the aftereffects of repetitive transcranial magnetic stimulation response.

Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

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.

Utilizing graph convolutional networks for identification of mild cognitive impairment from single modal fMRI data: a multiconnection pattern combination approach.

Mild cognitive impairment plays a crucial role in predicting the early progression of Alzheimer's disease, and it can be used as an important indicator of the disease progression. Currently, numerous studies have focused on utilizing the functional brain network as a novel biomarker for mild cognitive impairment diagnosis. In this context, we employed a graph convolutional neural network to automatically extract functional brain network features, eliminating the need for manual feature extraction, to improve the mild cognitive impairment diagnosis performance. However, previous graph convolutional neural network approaches have primarily concentrated on single modes of brain connectivity, leading to a failure to leverage the potential complementary information offered by diverse connectivity patterns and limiting their efficacy. To address this limitation, we introduce a novel method called the graph convolutional neural network with multimodel connectivity, which integrates multimode connectivity for the identification of mild cognitive impairment using fMRI data and evaluates the graph convolutional neural network with multimodel connectivity approach through a mild cognitive impairment diagnostic task on the Alzheimer's Disease Neuroimaging Initiative dataset. Overall, our experimental results show the superiority of the proposed graph convolutional neural network with multimodel connectivity approach, achieving an accuracy rate of 92.2% and an area under the Receiver Operating Characteristic (ROC) curve of 0.988.

Diffusion MRI-guided theta burst stimulation enhances memory and functional connectivity along the inferior longitudinal fasciculus in mild cognitive impairment.

Mild cognitive impairment (MCI) during aging is often a harbinger of Alzheimer's disease, and, therefore, early intervention to preserve cognitive abilities before the MCI symptoms become medically refractory is particularly critical. Functional MRI­guided transcranial magnetic stimulation is a promising approach for modulating hippocampal functional connectivity and enhancing memory in healthy adults. Here, we extend these previous findings to individuals with MCI and leverage theta burst stimulation (TBS) and white matter tractography derived from diffusion-weighted MRI to target the hippocampus. Our preliminary findings suggested that TBS could be used to improve associative memory performance and increase resting-state functional connectivity of the hippocampus and other brain regions, including the occipital fusiform, frontal orbital cortex, putamen, posterior parahippocampal gyrus, and temporal pole, along the inferior longitudinal fasciculus in MCI. Although the sample size is small, these results shed light on how TBS propagates from the superficial cortex around the parietal lobe to the hippocampus.

Identification of differentially expressed genes in the medial prefrontal cortex of rats subjected to chronic unpredictable mild stress and treated with electroacupuncture.

Major depressive disorder is a chronic mental health condition that seriously impacts afflicted individuals. Although electroacupuncture has proven to be an effective therapy for depression, its underlying biological mechanism remains largely unknown. In this study, we aimed to investigate the effects of electroacupuncture on depression-like behavior and to identify potential target genes related to those effects. To achieve this, we subjected rats to chronic unpredictable mild stress (CUMS) and used sucrose preference, forced swimming, and open-field tests to determine their depression-like behavior in the absence or after receipt of electroacupuncture treatment. RNA sequencing technology was then used to reveal the differentially expressed genes associated with depression and electroacupuncture treatment effects in the medial prefrontal cortex (mPFC). Repeated electroacupuncture treatments at the Baihui (GV20) and Taichong (LR3) acupoints significantly alleviated depression-like behavioral defects in the animals. Genomic RNA sequencing revealed several significant changes in the mPFC transcriptome of rats that received treatment. Through differential gene expression analysis, we found that electroacupuncture reversed the CUMS-induced downregulation of 46 genes and upregulation of 13 genes. Among the differentially expressed genes, Casr, Bdkrb2, Gnb3, and Ccl1 were found to be associated with depression and electroacupuncture treatment effects. In conclusion, we verified that electroacupuncture treatment has an effective antidepressant effect, and the underlying mechanism involves multiple systems and targets.

Accelerated Solar-Driven Polyolefin Degradation via Self-Activated Hydroxy-Rich ZnIn2S4.

Degradation of polyolefin (PE) plastic by a traditional chemical method requires a high pressure and a high temperature but generates complex products. Here, sulfur vacancy-rich ZnIn2S4 and hydroxy-rich ZnIn2S4 were rationally fabricated to realize photocatalytic degradation of PE in an aqueous solution under mild conditions. The results reveal that the optimized photocatalyst could degrade PE into CO2 and CO, and PE had a weight loss of 84.5% after reaction for 60 h. Systematic experiments confirm that the synergetic effect of hydroxyl groups and S vacancies contributes to improve the photocatalytic degradation properties of plastic wastes. In-depth investigation illustrates that the active radicals attack (h+ and •OH) weak spots (C-H and C-C bonds) of the PE chain to form CO2, which is further selectively photoreduced to CO. Multimodule synergistic tandem catalysis can further improve the utilization value of plastic wastes; for example, product CO2/CO in the plastic degradation process can be converted in situ into HCOOH by coupling with electrocatalytic technology.

An Injectable Photothermal-Fusing Hydrogel: Achieving Temperature-Controllable Mild Photothermal Therapy to Reverse Chemotherapy-Induced Immune Tolerance.

Mild photothermal therapy (M-PTT) can induce immunogenic cell death (ICD) to reverse the immune tolerance caused by low-dose chemotherapy. However, it still needs convenient strategies to control temperature during M-PTT. In this work, the phase change material lauric acid (LA, melting point 43 °C) was introduced to construct nanoparticles loaded with deferoxamine mesylate (DFO) and cisplatin (CDDP), which were mixed into a supramolecular hydrogel formed by polyvinylpyrrolidone (PVP)/tannic acid (TA)/Fe3+ to obtain FeTP@DLD/DLC. When the temperature reached 43 °C under laser irradiation, DFO was released from melted LA and destroyed the interaction between Fe3+ and TA to cut off the temperature increase, achieving a "photothermal fusing effect". Meanwhile, CDDP was released for low-dose chemotherapy, while the resulting immune tolerance was reversed by M-PTT-induced ICD. Finally, through a single administration, FeTP@DLD/DLC-mediated M-PTT synergized with chemotherapy achieved a potent antitumor effect. This work provided a convenient solution for the revitalization of these traditional antitumor therapies.

Temperature Self-Limited Intelligent Thermo-chemotherapeutic Lipid Nanosystem for P-gp Reversal Time Window Matched Pulse Treatment of MDR Tumor.

Mild photothermal therapy (PTT) shows the potential for chemosensitization by tumor-localized P-glycoprotein (P-gp) modulation. However, conventional mild PTT struggles with real-time uniform temperature control, obscuring the temperature-performance relationship and resulting in thermal damage. Besides, the time-performance relationship and the underlying mechanism of mild PTT-mediated P-gp reversal remains elusive. Herein, we developed a temperature self-limiting lipid nanosystem (RFE@PD) that integrated a reversible organic heat generator (metal-phenolic complexes) and metal chelator (deferiprone, DFP) encapsulated phase change material. Upon NIR irradiation, RFE@PD released DFP for blocking ligand-metal charge transfer to self-limit temperature below 45 °C, and rapidly reduced P-gp within 3 h via Ubiquitin-proteasome degradation. Consequently, the DOX·HCl-loaded thermo-chemotherapeutic lipid nanosystem (RFE@PD-DOX) led to dramatically improved drug accumulation and 5-fold chemosensitization in MCF-7/ADR tumor models by synchronizing P-gp reversal and drug pulse liberation, achieving a tumor inhibition ratio of 82.42%. This lipid nanosystem integrated with "intrinsic temperature-control" and "temperature-responsive pulse release" casts new light on MDR tumor therapy.

Strategic Lacunes Associated With Mild Cognitive Impairment in Rural Chinese Older Adults: A Population-Based Study.

BACKGROUND: Lacunes are associated with cognitive impairment. We sought to identify strategic lacune locations associated with mild cognitive impairment (MCI) and subtypes of MCI among older adults, and further to examine the role of white matter hyperintensities and perivascular spaces in the association. METHODS: This population-based cross-sectional study included 1230 dementia-free participants in the brain magnetic resonance imaging substudy (2018-2020) in MIND-China (Multimodal Interventions to Delay Dementia and Disability in Rural China). Lacunes were visually identified in frontal lobe, parieto-occipital lobe, temporal lobe, insula, basal ganglia, thalamus, cerebellum, and brainstem. MCI, amnestic MCI (aMCI), and nonamnestic MCI (naMCI) were defined following the Petersen's criteria. Data were analyzed using logistic regression models. RESULTS: Of the 1230 participants (age, ≥60 years; mean age, 69.40; SD, 4.30 years; 58.5% women), lacunes were detected in 357 people and MCI was defined in 286 individuals, including 243 with aMCI and 43 with naMCI. Lacunes in the supratentorial area, internal capsula, putamen/pallidum, and insula was significantly associated with increased odds ratio of MCI (multivariable-adjusted odds ratio ranged 1.40-3.21; P<0.05) and aMCI (multivariable-adjusted odds ratio ranged 1.46-3.36; P<0.05), whereas lacunes in the infratentorial area and brainstem were significantly associated with naMCI (multivariable-adjusted odds ratio ranged 2.68-3.46; P<0.01). Furthermore, the associations of lacunes in insula and internal capsula with MCI and aMCI, as well as the associations of lacunes in infratentorial area and brainstem with naMCI were present independent of white matter hyperintensities volume and perivascular spaces number. CONCLUSIONS: Lacunes in the internal capsula, putamen/pallidum, insula, and brainstem may represent the strategic lacunes that are independently associated with MCI, aMCI, or naMCI in Chinese older adults. REGISTRATION: URL: https://www.chictr.org.cn; Unique identifier: ChiCTR1800017758.

Brain region specific regulation of anandamide (down) and sphingosine-1-phosphate (up) in association with anxiety (AEA) and resilience (S1P) in a mouse model of chronic unpredictable mild stress.

Chronic unpredictable and unavoidable stress is associated with mental health problems such as depression and anxiety, whereas cycles of stress and stress relief strengthen resilience. It has been suggested that increased breakdown of brain endocannabinoids (eCB) promotes a feeling of adversity. To assess the impact of stress on bioactive lipid homeostasis, we analyzed eCB, sphingolipids, and ceramides in seven brain regions and plasma in a mouse model of chronic unpredictable mild stress. Chronic unpredictable mild stress (CUMS) was associated with low levels of anandamide in hippocampus and prefrontal cortex in association with indicators of anxiety (elevated plus maze). Oppositely, CUMS caused elevated levels of sphingosine-1-phosphate (S1P d18:1) and sphinganine-1-phosphate (S1P d18:0) in the midbrain and thalamus, which was associated with readouts of increased stress resilience, i.e., marble burying and struggling in the tail suspension tests. In the periphery, elevated plasma levels of ceramides revealed similarities with human major depression and suggested unfavorable effects of stress on metabolism, but plasma lipids were not associated with body weight, sucrose consumption, or behavioral features of depression or anxiety. The observed brain site-specific lipid changes suggest that the forebrain succumbs to adverse stress effects while the midbrain takes up defensive adjustments.

Meso-scale reorganization of local-global brain networks under mild sedation of propofol anesthesia.

The fragmentation of the functional brain network has been identified through the functional connectivity (FC) analysis in studies investigating anesthesia-induced loss of consciousness (LOC). However, it remains unclear whether mild sedation of anesthesia can cause similar effects. This paper aims to explore the changes in local-global brain network topology during mild anesthesia, to better understand the macroscopic neural mechanism underlying anesthesia sedation. We analyzed high-density EEG from 20 participants undergoing mild and moderate sedation of propofol anesthesia. By employing a local-global brain parcellation in EEG source analysis, we established binary functional brain networks for each participant. Furthermore, we investigated the global-scale properties of brain networks by estimating global efficiency and modularity, and examined the changes in meso-scale properties of brain networks by quantifying the distribution of high-degree and high-betweenness hubs and their corresponding rich-club coefficients. It is evident from the results that the mild sedation of anesthesia does not cause a significant change in the global-scale properties of brain networks. However, network components centered on SomMot L show a significant decrease, while those centered on Default L, Vis L and Limbic L exhibit a significant increase during the transition from wakefulness to mild sedation (p<0.05). Compared to the baseline state, mild sedation almost doubled the number of high-degree hubs in Vis L, DorsAttn L, Limbic L, Cont L, and reduced by half the number of high-degree hubs in SomMot R, DorsAttn R, SalVentAttn R. Further, mild sedation almost doubled the number of high-betweenness hubs in Vis L, Vis R, Limbic R, Cont R, and reduced by half the number of high-betweenness hubs in SomMot L, SalVentAttn L, Default L, and SomMot R. Our results indicate that mild anesthesia cannot affect the global integration and segregation of brain networks, but influence meso-scale function for integrating different resting-state systems involved in various segregation processes. Our findings suggest that the meso-scale brain network reorganization, situated between global integration and local segregation, could reflect the autonomic compensation of the brain for drug effects. As a direct response and adjustment of the brain network system to drug administration, this spontaneous reorganization of the brain network aims at maintaining consciousness in the case of sedation.

Age dictates brain functional connectivity and axonal integrity following repetitive mild traumatic brain injuries in mice.

Traumatic brain injuries (TBI) present a major public health challenge, demanding an in-depth understanding of age-specific symptoms and risk factors. Aging not only significantly influences brain function and plasticity but also elevates the risk of hospitalizations and death following TBIs. Repetitive mild TBIs (rmTBI) compound these issues, resulting in cumulative and long-term brain damage in the brain. In this study, we investigate the impact of age on brain network changes and white matter properties following rmTBI by employing a multi-modal approach that integrates resting-state functional magnetic resonance imaging (rsfMRI), graph theory analysis, diffusion tensor imaging (DTI), and neurite orientation dispersion and density imaging (NODDI). Our hypothesis is that the effects of rmTBI are worsened in aged animals, with this group showing more pronounced alterations in brain connectivity and white matter structure. Utilizing the closed-head impact model of engineered rotational acceleration (CHIMERA) model, we conducted rmTBIs or sham (control) procedures on young (2.5-3-months-old) and aged (22-months-old) male and female mice to model high-risk groups. Functional and structural imaging unveiled age-related reductions in communication efficiency between brain regions, while injuries induced opposhigh-risking effects on the small-world index across age groups, influencing network segregation. Functional connectivity analysis also identified alterations in 79 out of 148 brain regions by age, treatment (sham vs. rmTBI), or their interaction. Injuries exerted pronounced effects on sensory integration areas, including insular and motor cortices. Age-related disruptions in white matter integrity were observed, indicating alterations in various diffusion directions (mean diffusivity, radial diffusivity, axial diffusivity, and fractional anisotropy) and density neurite properties (dispersion index, intracellular and isotropic volume fraction). Neuroinflammation, assessed through Iba-1 and GFAP markers, correlated with higher dispersion in the optic tract, suggesting a neuroinflammatory response in injured aged animals compared to sham aged. These findings offer insight into the interplay between age, injuries, and brain connectivity, shedding light on the long-term consequences of rmTBI.

Latent diffusion model-based MRI superresolution enhances mild cognitive impairment prognostication and Alzheimer's disease classification.

INTRODUCTION: Timely diagnosis and prognostication of Alzheimer's disease (AD) and mild cognitive impairment (MCI) are pivotal for effective intervention. Artificial intelligence (AI) in neuroradiology may aid in such appropriate diagnosis and prognostication. This study aimed to evaluate the potential of novel diffusion model-based AI for enhancing AD and MCI diagnosis through superresolution (SR) of brain magnetic resonance (MR) images. METHODS: 1.5T brain MR scans of patients with AD or MCI and healthy controls (NC) from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1) were superresolved to 3T using a novel diffusion model-based generative AI (d3T*) and a convolutional neural network-based model (c3T*). Comparisons of image quality to actual 1.5T and 3T MRI were conducted based on signal-to-noise ratio (SNR), naturalness image quality evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). Voxel-based volumetric analysis was then conducted to study whether 3T* images offered more accurate volumetry than 1.5T images. Binary and multiclass classifications of AD, MCI, and NC were conducted to evaluate whether 3T* images offered superior AD classification performance compared to actual 1.5T MRI. Moreover, CNN-based classifiers were used to predict conversion of MCI to AD, to evaluate the prognostication performance of 3T* images. The classification performances were evaluated using accuracy, sensitivity, specificity, F1 score, Matthews correlation coefficient (MCC), and area under the receiver-operating curves (AUROC). RESULTS: Analysis of variance (ANOVA) detected significant differences in image quality among the 1.5T, c3T*, d3T*, and 3T groups across all metrics. Both c3T* and d3T* showed superior image quality compared to 1.5T MRI in NIQE and BRISQUE with statistical significance. While the hippocampal volumes measured in 3T* and 3T images were not significantly different, the hippocampal volume measured in 1.5T images showed significant difference. 3T*-based AD classifications showed superior performance across all performance metrics compared to 1.5T-based AD classification. Classification performance between d3T* and actual 3T was not significantly different. 3T* images offered superior accuracy in predicting the conversion of MCI to AD than 1.5T images did. CONCLUSIONS: The diffusion model-based MRI SR enhances the resolution of brain MR images, significantly improving diagnostic and prognostic accuracy for AD and MCI. Superresolved 3T* images closely matched actual 3T MRIs in quality and volumetric accuracy, and notably improved the prediction performance of conversion from MCI to AD.