Neuroinformatics最新文献

This study used voxel- and surface-based morphometry to analyze the changes in gray matter structure in MS patients and their correlation with clinical scales. An analysis was conducted on the structural magnetic resonance imaging data of 30 patients with MS who met the inclusion criteria and 30 healthy controls (HCs). Clinical disability was evaluated using the Expanded Disability Status Scale (EDSS) and the timed 25-foot walk test (T25FW). Cognitive function was assessed with the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Psychiatric symptoms were measured via the Hamilton Anxiety Scale (HAMA) and Hamilton Depression Scale (HAMD). Imaging data were also collected from the MS and healthy control groups, and SPM12 and CAT12 analyzed the images. After controlling for age and gender, voxel- and surface-based morphometry were used to study inter-group differences. Finally, the discrepancy data were correlated with the clinical scales. Compared to the HC group, the gray matter volume reduction in the MS group was mainly concentrated in the deep gray matter, with a small portion located in the cortical gray matter (FWE-corrected p-value < 0.05). Cortical thickness was significantly reduced in multiple dispersed regions of the brain bilaterally in the MS group compared to hc (FWE-corrected p-value < 0.05), and there was no obvious anatomical connection between these regions. Correlation analyses revealed: A negative correlation between caudate nucleus volume and EDSS scores (R = -0.415, p = 0.031); a positive correlation between the right parahippocampal gyrus and HAMA scores (R = 0.392, p = 0.039); a positive correlations of the right postcentral gyrus with both MMSE (R = 0.433, p = 0.021) and MoCA scores (R = 0.431, p = 0.022); a positive correlation between the left paracentral lobule and MoCA scores (R = 0.389, p = 0.041). A pattern of multiple gray matter structural changes was identified in our study, and a clinical correlation between structural changes was found. Grey matter volume and cortical thickness hold substantial promise as markers of disease progression and have the potential to respond to neuroprotective treatments for MS neurodegeneration.

Non-invasive intracranial pressure (ICP) monitoring can help clinicians safely and efficiently monitor spaceflight-associated neuro-ocular syndrome (SANS), idiopathic intracranial hypertension, and traumatic brain injury in astronauts. Current invasive ICP measurement techniques are unsuitable for austere environments like spaceflight. In this study, we explore the potential of plasma-derived cell-free RNA (cfRNA) biomarkers as non-invasive alternatives to cerebrospinal fluid (CSF) markers for ICP assessment. We conducted a secondary analysis of NASA's Open Science Data Repository datasets 363-364, focusing on plasma and CSF biomarkers related to ICP and neurovascular health. An ensemble model combining Support Vector Machine, Gradient Boosting Regressor, and Ridge Regression was developed to capture plasma-CSF biomarker relationships. To address limited sample size, we employed a Generative Adversarial Network (GAN) to generate synthetic plasma-CSF biomarker pairs, expanding the dataset from 29 to 279 samples. The model's performance was evaluated using Mean Squared Error (MSE) and validated against real biomarker data. The GAN-augmented ensemble model achieved high predictive accuracy with an MSE of 0.0044. Synthetic plasma-CSF pairs closely aligned with actual biomarker distributions, demonstrating their effectiveness in reducing overfitting and enhancing model robustness. Strong correlations between plasma-derived RNA biomarkers and corresponding CSF indicators support their potential as non-invasive proxies for ICP assessment. This study establishes a novel framework for non-invasive ICP monitoring using plasma cfRNA profiles enriched with GAN-generated synthetic data. The approach shows promise for both spaceflight and clinical applications, potentially broadening diagnostic capabilities for ICP-related conditions. However, further validation across diverse populations is necessary, along with careful consideration of bioethical and data security issues associated with synthetic data use in clinical diagnostics.

Reproducibility of neuroimaging analyses and aggregation of heterogenous datasets are significant challenges in human subjects imaging research. This stems in part from a lack of an easy to use and universal data format that encompasses all steps of neuroimaging. The BIDS format has become widely adopted, however it is increasingly complex to implement as features are added, with the documentation now exceeding 500 pages. As such, there is a need for standards that can handle the complexity of the data while minimizing the complexity of the format. Here we present a simple but generalizable data sharing specification, called the squirrel format (not related to the squirrel programming language), to share imaging data in a simple, but flexible, specification. It is so named because squirrels are effective at storing significant quantities of food and knowing exactly where and when to find it. The design objectives of the format specification are to 1) store subject information, experimental parameters, raw data, analyzed data, and analysis methods 2) organize data in a human-readable hierarchy 3) enable easy sharing and dissemination of data packages. We developed a relational hierarchy with a structured representation of all steps of neuroimaging data collection and analysis, and a generalizable specification to store any modality of neuroimaging data, which satisfies the design objectives. Additionally, redundancy is minimized by using relational database principles. The specification allows all research data to be classified into one of ten object types, thus simplifying the sharing of neuroimaging data. Like how squirrels employ 'chunking', the squirrel format chunks data into a manageable number of object types. The squirrel format was developed to share neuroimaging data but can be generalized to share any imaging research.

Despite extensive cerebellar research, the functional role of individual cerebellar micro complexes (CmCs) in motor coordination remains debated. This study aimed to utilise a reductionist approach to model the CmC function in motor control using the Histologic Boolean Mapping (HBM-VNR) framework and validate it through replication of features observed in the literature. HBM-VNR modelled each neuron within the CmC as a Boolean expression derived from its architectural connectivity. The model incorporates the Variable Neuronal Response (VNR) synaptic model, introducing probabilistic post-synaptic firing to reflect physiological variability. Motor control dynamics follow the cerebellar brain inhibition phenomenon, where Deep Cerebellar Nucleus (DCN) firing activates the antagonist muscles. The model performed the task of feedback-control in an idealised joint following a desired sinusoidal position. HBM-VNR produced a minimalistic model that reproduced adaptive compensation to external forces and predicted intention tremor when CmC population was reduced, and the expected ethanol induced motor impairments. Simulated firing patterns of the DCN and Purkinje cell showed patterns resembling real recordings both in physiological and pathological situations. The Shifting Central Frequency Hypothesis (SCFH) was suggested to explain the CmC comparator functionality. This study presents HBM-VNR as a histologically grounded modelling approach for neural circuits. HBM-VNR simulated adaptive motor control and predicted neocerebellar syndrome symptomatology and alcohol intoxication effects. SCFH offers a computational mechanism consistent with the cerebellar internal model theories and places CmC as the basis for motor learning in line with the literature, positioning HBM-VNR as a scalable framework for neuroanatomical modelling.

Major Depressive Disorder (MDD) significantly impacts global health, impairing individual functioning and increasing socioeconomic burden. Developing innovative, interpretable approaches for its identification is essential for improving diagnosis and guiding treatment. This study introduces a novel framework designed to classify MDD using resting-state functional MRI (rs-fMRI) data. Our framework follows three stages: First, Node2Vec extracts rich, low-dimensional brain region embeddings from functional connectivity (FC) networks, capturing their complex topological information. Second, these informative embeddings feed a Graph Attention Network (GAT) which, via multi-head attention, identifies and weighs discriminative inter-regional functional connections, refining them into a potent graph representation. Third, these GAT-derived representations are classified by an ensemble classifier (Random Forest, SVM, MLP) for robust MDD identification. The model achieved classification accuracies of 78.73% and 92.94% on the REST-meta-MDD and SRPBS-MDD datasets, respectively. Moreover, the attention mechanism revealed that resting-state functional connectivity of regions within the Default Mode Network (DMN) and Frontoparietal Network (FPN) were among the most discriminative features for distinguishing MDD from healthy controls. The attention mechanism enhanced interpretability by highlighting significant brain regions linked to MDD. Compared to traditional methods, this GNN-based approach effectively captures complex brain connectivity patterns and offers improved interpretability, ultimately aiding healthcare professionals in diagnosing MDD more accurately.

Study of brain function often involves analyzing task-related switching between intrinsic brain networks, which connect various brain regions. Functional brain connectivity analysis methods aim to estimate these networks but are limited by the statistical constraints of windowing functions, which reduce temporal resolution and hinder explainability of highly dynamic processes. In this work, we propose a novel approach to functional connectivity analysis through the explainability of EEG classification. Unlike conventional methods that condense raw data into extracted features, our approach inflates raw EEG data by decomposition into meaningful components that explain processes in the application domain. To uncover the brain connectivity that affects classification decisions, we introduce a new method of dynamic influence data inflation (DIDI), which extracts signals representing interactions between electrode regions. These inflated data are then classified using an end-to-end neural network classifier architecture designed for raw EEG signals. Saliency map estimation from trained classifiers reveals the connectivity dynamics affecting classification decisions, which can be visualized as dynamic connectivity support maps for improved interpretability. The methodology is demonstrated on two publicly available datasets: one for imagined motor movement classification and the other for emotion classification. The results highlight the dual benefits of our approach: in addition to providing interpretable insights into connectivity dynamics it increases classification accuracy.

Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convolutional neural network (DCNN) model using resting-state EEG data from the EMBARC study, achieving a balanced accuracy of 69% in predicting placebo responses in patients with major depressive disorder (MDD). We then applied this model to two additional datasets, LEMON and CAN-BIND-which did not include placebo groups-to investigate potential relationships between the model's predictions and various clinical features in independent samples. Notably, the model's predictions correlated with factors previously linked to placebo response in MDD, including age, extraversion, and cognitive processing speed. These findings highlight several factors associated with placebo susceptibility, offering insights that could guide more efficient clinical trial designs. Future research should explore the broader applicability of such predictive models across different medical conditions, and replicate the current EEG-based model of placebo response in independent samples.

Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively), between SZ and controls. The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between gray matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.

Axonal beading is a key morphological indicator of axonal degeneration, which plays a significant role in various neurodegenerative diseases and drug-induced neuropathies. Quantification of axonal susceptibility to beading using neuronal cell culture can be used as a facile assay to evaluate induced degenerative conditions, and thus aid in understanding mechanisms of beading and in drug development. Manual analysis of axonal beading for large datasets is labor-intensive and prone to subjectivity, limiting the reproducibility of results. To address these challenges, we developed a semi-automated Python-based tool to track axonal beading in time-lapse microscopy images. The software significantly reduces human effort by detecting the onset of axonal swelling. Our method is based on classical image processing techniques rather than an AI approach. This provides interpretable results while allowing the extraction of additional quantitative data, such as bead density, coarsening dynamics, and morphological changes over time. Comparison of results obtained through human analysis and the software shows strong agreement. The code can be easily extended to analyze diameter information of ridge-like structures in branched networks of rivers, road networks, blood vessels, etc.