Frontiers in Neuroscience最新文献

Introduction: Electroencephalographic (EEG) abnormalities, such as increased theta power, have been proposed as biomarkers for neurocognitive disorders. Advancements in amyloid positron emission tomography (PET) imaging have enhanced our understanding of the pathology of neurocognitive disorders, such as amyloid deposition. However, much remains unknown regarding the relationship between regional amyloid deposition and EEG abnormalities. This study aimed to explore the relationship between regional EEG abnormalities and amyloid deposition in patients with mild cognitive impairment (MCI).

Methods: We recruited 24 older adults with MCI from a community center for dementia prevention, and 21 participants were included in the final analysis. EEG was recorded using a 64-channel system, and amyloid deposition was measured using amyloid PET imaging. Magnetic resonance imaging (MRI) data were used to create individualized brain models for EEG source localization. Correlations between relative theta power and standardized uptake value ratios (SUVRs) in 12 brain regions were analyzed using Spearman's correlation coefficient.

Results: Significant positive correlations between relative theta power and SUVR values were found in several brain regions in the individualized brain model during the resting eyes-closed condition, including right temporal lobe (r = 0.581, p = 0.006), left hippocampus (r = 0.438, p = 0.047), left parietal lobe (r = 0.471, p = 0.031), right parietal lobe (r = 0.509, p = 0.018), left occipital lobe (r = 0.597, p = 0.004), and right occipital lobe (r = 0.590, p = 0.005). During the visual working memory condition, significant correlations were found in both cingulate lobes (left: r = 0.483, p = 0.027; right: r = 0.449, p = 0.041), left parietal lobe (r = 0.530, p = 0.010), right parietal lobe (r = 0.606, p = 0.004), left occipital lobe (r = 0.648, p = 0.001), and right occipital lobe (r = 0.657, p = 0.001).

Conclusion: The result suggests that regional increases in relative theta power are associated with regional amyloid deposition in patients with MCI. These findings highlight the potential of EEG in detecting amyloid deposition. Future large-scale studies are needed to validate these preliminary findings and explore their clinical applications.

Purpose: This study aimed to classify peripheral vitreoretinal interface (VRI) lesions using optical coherence tomography (OCT) and to compare these findings with those obtained by ultra-widefield (UWF) pseudocolor imaging.

Method: Peripheral OCT images of VRI lesions were obtained using spectral domain OCT system with a steerable probe guided by UWF images. Two independent investigators categorized the OCT images into four groups based on the extent of vitreoretinal traction and the presence of retinal breaks. Differences in OCT-based categorization between the same lesion types visualized by UWF imaging were also compared.

Results: Of the total 82 patients, 105 peripheral lesions were included in this study. The inter-observer agreement for the classification of UWF and OCT images demonstrated good consistency, with kappa values of 0.949 ± 0.025 and 0.836 ± 0.042, respectively. In the OCT classification of VRI lesions, 18 (17.1%) cases were category A, 28 (26.7%) cases were category B1, 30 (28.6%) cases were category B2, and 29 (27.6%) cases were category C. Of the 37 vitreoretinal tuft lesions, 32.4% were classified as category B2 and 16.2% as category C, according to peripheral OCT classification. Similarly, 37.8% of 40 snail track and lattice degeneration lesions were classified as category B2, and 16.2% as category C.

Conclusion: The VRI lesions can demonstrate considerable variability when visualized with peripheral OCT among the same lesion types visualized through UWF imaging. Classification of peripheral OCT images may provide a more effective evaluation of the risk of lesion progression.

The hypothalamus-pituitary-adrenal (HPA) and gut-brain axes are vital biological pathways in depression. The HPA axis regulates the body's stress response, and chronic stress can lead to overactivation of the HPA axis, resulting in elevated cortisol levels that contribute to neuronal damage, particularly in regions such as the hippocampus and prefrontal cortex, both of which are involved in mood regulation and mental disorders. In parallel, the gut-brain axis, a bidirectional communication network between the gut microbiota and the central nervous system, influences emotional and cognitive functions. Imbalances in gut microbiota can affect the HPA axis, promoting inflammation and increasing gut permeability. This allows endotoxins to enter the bloodstream, contributing to neuroinflammation and altering neurotransmitter production, including serotonin. Since the majority of serotonin is produced in the gut, disruptions in this pathway may be linked to depressive symptoms. This review explores the interplay between the HPA axis and the gut-brain axis in the context of depression.

Objectives: Neuroimaging investigations into chronic low back pain (CLBP) have detected functional abnormalities across a spectrum of brain regions, yet the findings have often been inconsistent. In this meta-analysis, we integrated the existing data, delineating a pattern of coherent results from the encompassed studies.

Methods: A systematic search of neuroimaging studies investigating the brain activity differences between CLBP and Healthy controls (HCs) was conducted in seven databases up to December 22, 2024. An anisotropic effect-size signed differential mapping (AES-SDM)-based meta-analysis was carried out to report the results and perform a multimodal analysis.

Results: A total of 20 publications reporting on 24 experiments in this meta-analysis. The ReHo meta-analysis showed abnormal spontaneous activity of left inferior temporal gyrus (ITG), left superior frontal gyrus (SFG), right middle frontal gyrus (MFG), right precuneus, right fusiform gyrus and bilateral postcentral gyrus (PoCG) in CLBP patients. The ALFF meta-analysis demonstrated functional alterations in the right rolandic operculum (extending to the right insula and right IFG), left ITG, left middle occipital gyrus (MOG), left paracentral lobule, left PoCG and bilateral cuneus cortex in CLBP patients. The results of the functional group meta-analysis revealed that patients with CLBP displayed new decreased functional activity in the right thalamus, right precentral gyrus (PreCG) and right lingual gyrus.

Conclusion: Patients with CLBP exhibit extensive multimodal functional neuroimaging abnormalities, involving brain regions related to pain perception, emotional processing, cognitive functions, and both the visual and motor cortices. These meta-analysis findings might provide a valuable reference for the identification of potential therapeutic targets for CLBP in the brain.

The neuropeptide pituitary adenylate cyclase-activating peptide (PACAP) plays a pivotal role in regulating stress, fear, and anxiety responses. Genetic and molecular studies investigating PACAP demonstrate sex-dimorphic characteristics, with females exhibiting increased reactivity of PACAP signaling in neuropsychiatric disorders. Studies expand the role of PACAP to substance use disorders (SUD) by demonstrating modulation of PACAP can lead to neurobiological changes induced by nicotine, ethanol, stimulants and opioids. Given that females with SUD exhibit distinct drug use, relapse, and withdrawal sensitivity relative to males, we hypothesize that the PACAP system contributes to these sex-specific differences. Therefore, we review the role of PACAP in SUD by characterizing the role of PACAP at the molecular, brain regional, and behavioral levels relevant to the addiction cycle. We present literature linking PACAP to neuropsychiatric disorders, which demonstrate the intricate role of PACAP within neuronal signaling and pathways modulating addiction. We hypothesize that females are more particularly susceptible to PACAP-related changes during the intoxication and withdrawal phases of the addiction cycle. Altogether understanding the sex-specific differences in the PACAP system offers a foundation for future studies aimed at developing tailored interventions for addressing SUD.

Objectives: Early diagnostic separation between glioblastoma (GBM) and solitary metastases (MET) is important for patient management but remains challenging when based on imaging only. The objective of this study was to assess whether amide proton transfer weighted (APTw) MRI alone or combined with dynamic susceptibility contrast (DSC) MRI parameters, including cerebral blood volume (CBV), cerebral blood flow (CBF), and leakage parameter (K2) measurements, can differentiate GBM from MET.

Methods: APTw MRI and DSC-MRI were performed on 18 patients diagnosed with GBM (N = 10) or MET (N = 8). Quantitative parameter maps were calculated, and regions-of-interest (ROIs) were placed in whole tumor, contrast-enhanced tumor (ET), edema, necrosis and normal-appearing white matter (NAWM). The mean and max of the APTw signal, CBF, leakage-corrected CBV and K2 were obtained from each ROI. Except for K2, all were normalized to NAWM (nAPTw_mean/max, nCBF_mean/max, ncCBV_mean/max,). Receiver Operating Characteristic (ROC) curves and area-under-the-curve (AUC) were assessed for different parameter combinations. Statistical analyses were performed using Mann-Whitney U test.

Results: When comparing GBM to MET, nAPT_max, nCBF_max, ncCBV_max and ncCBV_mean were significantly increased (p < 0.05) in ET with AUC being 0.81, 0.83, 0.85, and 0.83, respectively. Combinations of nAPTw_max + ncCBV_max, nAPTw_mean + ncCBV_mean, nAPTw_max + nCBF_max, nAPTw_max + K2_max and nAPTw_max + ncCBV_max + K2_max in ET showed significant prediction in differentiating GBM and MET (AUC = 0.92, 0.82, 0.92, 0.85, and 0.92 respectively).

Conclusion: When assessed in Gd-enhanced tumor areas, nAPTw MRI signal intensity alone or combined with DSC-MRI parameters, was an excellent predictor for differentiating GBM and MET. However, the small cohort warrants future studies.

This study investigated the impact of brain atlas selection on the classification accuracy of Autism Spectrum Disorder (ASD) models using functional Magnetic Resonance Imaging (fMRI) data. Brain atlases, such as AAL, CC200, Harvard-Oxford, and Yeo 7/17, are used to define regions of interest (ROIs) for fMRI analysis and play a crucial role in enabling researchers to study connectivity patterns and neural dynamics in ASD patients. Through a systematic review, we examined the performance of different atlases in various machine-learning and deep-learning frameworks for ASD classification. The results reveal that atlas selection significantly affects classification accuracy, with denser atlases, such as CC400, providing higher granularity, whereas coarser atlases such as AAL, offer computational efficiency. Furthermore, we discuss the dynamics of combining multiple atlases to enhance feature extraction and explore the implications of atlas selection across diverse datasets. Our findings emphasize the need for standardized approaches to atlas selection and highlight future research directions, including the integration of novel atlases, advanced data augmentation techniques, and end-to-end deep-learning models. This study provides valuable insights into optimizing fMRI-based ASD diagnosis and underscores the importance of interpreting atlas-specific features for an improved understanding of brain connectivity in ASD.

We present a novel software feature for the BrainScaleS-2 accelerated neuromorphic platform that facilitates the partitioned emulation of large-scale spiking neural networks. This approach is well suited for deep spiking neural networks and allows for sequential model emulation on undersized neuromorphic resources if the largest recurrent subnetwork and the required neuron fan-in fit on the substrate. We demonstrate the training of two deep spiking neural network models-using the MNIST and EuroSAT datasets-that exceed the physical size constraints of a single-chip BrainScaleS-2 system. The ability to emulate and train networks larger than the substrate provides a pathway for accurate performance evaluation in planned or scaled systems, ultimately advancing the development and understanding of large-scale models and neuromorphic computing architectures.