Neuroscience informatics最新文献

Background and Purpose

With an increased need for standardized methodology in accurate diagnosis of Traumatic Brain Injury (TBI), Diffusion Tensor Imaging (DTI) has provided promising diagnostic results as an adjunct modality yet remains underutilized. The purpose of this study was to validate the use of DTI with Statistical Parametric Mapping (SPM) for Traumatic Brain Injury (TBI) supporting its use as a diagnostic tool.

Materials and Methods

This study was retrospective and compared controls to patients clinically diagnosed with TBI. Forty-two controls (mean age = 34.1; range, 19 - 58; 28 Males and 13 Females) were screened (n = 41) for cognitive impairment and neurological abnormality. Two cohorts, each of eighteen patients (first cohort: mean age, 41.8; range, 23 - 70; 9 Males and 9 Females; second cohort: mean age, 45.7; range, 23 - 68; 9 Males and 9 Females) clinically diagnosed with TBI (n = 36) were pooled. DTI image acquisition was obtained using a 3 Tesla MRI scanner. DTI images were analyzed through voxel-based t-tests using SPM comparing each individual to the normative control group to generate z-maps for each individual control and each individual patient with a TBI. Test statistics were ranged for p-values (0.001-0.05) and cluster extent values (0, 30, 60, 65, 70, 75). Area Underneath A Receiver Operating Characteristic Curve (AUCROC) was used to validate diagnostic capability. AUCROC analysis was conducted on all sets of p-value and extent threshold values. Significance of results was determined by examining the AUCROC values.

Results and Conclusions

A maximal AUCROC of 1.000 was obtained across the p-value range and cluster extent thresholding values specified across the two cohorts. The high AUCROC supports validation of the methodology presented and the use of diffusion tensor imaging and SPM as an adjunct diagnostic tool for TBI.

Automated brain tumor segmentation from magnetic resonance (MR) images plays a crucial role in precise diagnosis and treatment monitoring in brain tumor care. Leveraging the Brain Tumor Segmentation Challenge (BraTS) dataset, this paper introduces an extended version of the nnU-Net architecture for brain tumor segmentation, addressing both adult (Glioma) and pediatric tumors. Our methodology integrates innovative approaches to enhance segmentation accuracy. We incorporate residual blocks to capture complex spatial features, attention gates to emphasize informative regions and implement the Hausdorff distance (HD) loss for boundary-based segmentation refinement. The effectiveness of each enhancement is systematically evaluated through an ablation study using different configurations on the BraTS dataset. Results from our study highlight the significance of combining residual blocks, attention gates, and HD loss, achieving the best performance with a mean Dice and HD score of 83%, 3.8 and 71%, and 8.7 for Glioma and Pediatrics datasets, respectively. This advanced nnU-Net showcases the promising potential for accurate and robust brain tumor segmentation, offering valuable insights for enhanced clinical decision-making in pediatric brain tumor care.

Introduction

While linguistic retrogenesis has been extensively investigated in the neuroscientific and behavioral literature, there has been little work on retrogenesis using computerized approaches to language analysis.

Methods

We bridge this gap by introducing a method based on comparing output of a pre-trained neural language model (NLM) with an artificially degraded version of itself to examine the transcripts of speech produced by seniors with and without dementia and healthy children during spontaneous language tasks. We compare a range of linguistic characteristics including language model perplexity, syntactic complexity, lexical frequency and part-of-speech use across these groups.

Results

Our results indicate that healthy seniors and children older than 8 years share similar linguistic characteristics, as do dementia patients and children who are younger than 8 years.

Discussion

Our study aligns with the growing evidence that language deterioration in dementia mirrors language acquisition in development using computational linguistic methods based on NLMs. This insight underscores the importance of further research to refine its application in guiding developmentally appropriate patient care, particularly in early stages.

EEGLAB is one of the most famous software for processing, analyzing, and researching experiments that have Electroencephalography (EEG) datasets. Due to the numerous add-ins along with global, widespread communications and online free YouTube channel, its popularity increased every year. To address this phenomenon from a bibliographic perspective, we found 20,464 citations in Google Scholar since 8/27/2023. Then, only the Web of Science (WOS) articles were 12,700 that they were extracted. The results were analyzed with Bibliometrix package from CRAN R software. The time span of these articles is from 2004 to 2023 with 12,700 documents in 1,125 sources (journals, books, etc.), 29,125 authors, 19,062 author's keywords, 13,707 keywords PLUS, 279,617 references. The annual growth rate is 28.12%, international Co-authorship is 37.27%, Co-authors per document is 4.89 and the average citations per document is 22.51. The most relevant sources are Neuroimage, Frontiers in Human Neurosciences, Scientific Reports, Psychophysiology, and PLOS One with 780, 526, 446,425, and 371 articles, respectively. The most cited countries are the USA, Germany, and the United Kingdom with 93,093, 32,621, and 20,748 total citations, respectively. The ERPLAB, ADJUST, and ICLabel add-ins have the local to global citation ratios equal to 85.4%, 65.1%, and 78.2% respectively. The collaboration network university, trend topic plot of keyword plus, thematic map trigram word in abstract and co-citation network of published papers after 2018 are presented. EEGLAB is among the most cited MATLAB toolboxes in computational neuroscience. Many developed and developing countries use it in their research publications.

Recently, characterizing the dynamics of brain functional networks at task free or cognitive tasks has developed different research efforts in the field of neuroscience. Epilepsy is an electrophysiological brain disease which is accompanied by recurrent seizures. Seizure and epilepsy detection is a main challenge in the field of neuroscience. Understanding the underlying mechanism of epilepsy and transition from a normal brain to epileptic brain crucial for the diagnosis and treatment purposes. To understand the organization of epileptic brain network functions at large scales, electroencephalogram (EEG) signals measure and record the changes in electrical activity and functional connectivity. Time frequency analysis and continuous spectral entropy are well developed methods which reveal dynamical aspects of brain activity and can analyze the transitions in intrinsic brain activity. In this work, we aim to model the dynamics of EEG signals of epileptic brain and characterize their dynamical patterns. We use Time frequency analysis to capture the alterations in the structure of EEG signals from patients with seizure. Continuous spectral entropy is used to detect the start of seizures. The main purpose of the current is to explore the changes in the organization of epileptic brain networks. Using time frequency techniques, we are able to draw a big picture of how the brain functions before and during seizure and step forward to classify seizure and corresponding brain activity during different stages of epilepsy. The present study may contribute to characterizing the complex non-linear dynamics of EEG signals of epileptic brain and further assists with biomarker detection for different clinical applications. This finding helps towards effective diagnosis and better treatment of epilepsy.

Epilepsy is the neurological disorder affecting around 50 million people worldwide. It is characterized by recurrent and unpredictable seizures. Correctly counting seizure occurrences is crucial for diagnosis and treatment of epilepsy, which will lower the risk of SUDEP (sudden unexpected deaths in epilepsy). Many previous researches on patient-specific seizure detection have obtained a good performance but with limited practicability in clinical setting. On the other hand, patient non-specific detection is clinically practicable but with limited performance. This study aims to improve the performance of patient non-specific seizure detection by comparing performances among one modality based models and multi-modal based model. The study was based on clinical data from the open source Siena Scalp EEG Database, which consist of simultaneous EEG (Electroenchephalography) and ECG (electrocardiography) recording from 14 patients with focal epilepsy. The seizures were annotated by an epilepsy expert after a careful review of the clinical and EEG data of each patient. First, relevant signal pre-processing were performed, followed by features extraction. Then, machine learning approach based on random forest was employed for seizure detection with leave-one-patient-out cross validation scheme. EEG detector and ECG detector were separately trained with each signal. Multi-modal detector was based on combining EEG detector and ECG detector by the late integration approach with the Boolean operation “OR” strategy. The performances were compared among those three detectors and with the state of the art. The result has shown that the multi-modal detector achieved a sensitivity of 87.62% and outperformed the ECG detector (41.55%), the EEG detector (81.43%), and the state-of-the-art non-specific detectors. Notably, the ECG detector detected some seizures which EEG detector failed to detect. This indicated that the ECG signal was beneficial for increasing sensitivity. However, due to the “OR” fusion strategy, the multi-modal detector also inherited the false detections resulted from either EEG detector or ECG detector. The findings of the study demonstrate the potential of improving performance of patient non-specific seizure detection by multimodal data. It shows that the proposed method should be further validated on large database and further development should focus on lowering false detections before clinical application.

Recent advances in brain network analysis are largely based on graph theory methods to assess brain network organization, function, and malfunction. Although, functional magnetic resonance imaging (fMRI) has been frequently used to study brain activity, however, the nonlinear dynamics in resting-state (fMRI) data have not been extensively characterized. In this work, we aim to model the dynamics of resting-state (fMRI) and characterize the dynamical patterns in resting-state (fMRI) time series data in left and right hippocampus and inferior frontal gyrus. We use Sliding Window Embedding (SWE) method to reconstruct the phase space of resting-state (fMRI) data from left and right hippocampus and orbital part of inferior frontal gyrus. The complexity of resting-state MRI data is examined using fractal analysis. The main purpose of the current study is to explore the topological organization of hippocampus and frontal gyrus and consequently, memory. By constructing resting-state functional network from resting-state (fMRI) time series data, we are able to draw a big picture of how brain functions and step forward to classify brain activity between normal control people and patients with different brain disorders.

Traumatic Brain Injuries (TBIs), including mild TBI (mTBI) and concussions, affect an estimated 69 million individuals annually with significant cognitive, physical, and psychosocial consequences. The Sport Concussion Assessment Tool 5th Edition (SCAT5) is pivotal for diagnosing these conditions but possesses inherent subjectivity. Conversely, eye-tracking systems provide objective data, capturing subtle disruptions in ocular and cognitive functions often missed by traditional measures. Yet, the concurrent use of these promising tools for neurotrauma diagnostics is relatively unexplored. This paper proposes integrating eye-tracking with SCAT5 to enhance mTBI and concussion diagnostics. We introduce a model that synergistically combines the strengths of both techniques into an ‘ocular score’, adding objectivity to SCAT5. This union promises improved clinical decision-making, impacting return-to-play, fitness-to-drive, and return-to-work judgments, providing a novel landscape in the neurotrauma scenario. However, our theoretical framework requires empirical validation. We advocate for future large-scale collaborative research databases, and exploration of eye-tracking-based diagnostic markers. Our methodology highlights the potential of this integrated approach to redefine neurotrauma management and diagnostics, addressing a critical global health concern with proven utility in high-risk settings like sports and the military.

Background and Objective

Tissue pulsatility imaging is an ultrasonic technique that can be used to map regional changes in blood flow in the brain. Classification of regional differences in pulsatility signals can be optimized by restricting the analysis to brain tissue. For 2D transcranial ultrasound imaging, we have implemented an automated image analysis procedure to specify a region of interest in the field of view that corresponds to brain.

Methods

Our segmentation method applies an initial K-means clustering algorithm that incorporates both echo strength and tissue displacement to identify skull in ultrasound brain scans. The clustering step is followed by processing steps that use knowledge of the scan format and anatomy to create an image mask that designates brain tissue. Brain regions were extracted from the ultrasound data using different numbers of K-means clusters and multiple combinations of ultrasound data. Masks generated from ultrasound data were compared with reference masks derived from Computed Tomography (CT) data.

Results

A segmentation algorithm based on ultrasound intensity with two K-means clusters achieves an accuracy better than 80% match with the CT data. Some improvement in the match is found with an algorithm that uses ultrasound intensity and displacement data, three K-means clusters, and addition of an algorithm to identify shallow sources of ultrasound shadowing.

Conclusions

Several segmentation algorithms achieve a match of over 80% between the ultrasound and Computed Tomography brain masks. A final tradeoff can be made between processing complexity and the best match of the two data sets.

Well-being (WB) is defined as a healthy state of mind and body. It is a state in which an individual is able to contribute to its society, able to work productively and overcome the normal stress of life. WB is a multi-dimensional concept and covers different aspects, including life satisfaction and quality of life. Little is known as to whether there are differences in connectivity patterns between healthy individuals with different WB states. We evaluated the WB state of healthy individuals with no prior diagnosis of any psychological disorder using the “General habitual WB questionnaire”, covering mental, physical and social domains. Subjects with mean age 25±4 years were divided into two groups, high WB state (n = 18) and low WB state (n = 14). We investigated and compared the groups for their resting state (rs-fMRI) functional connectivity (FC) patterns using DPARSF compiled with SPM12 toolbox. WB specific seeds were chosen for FC analysis. In the high WB group we found significantly increased connectivity between bilateral angular gyrus and frontal regions comprising the orbitofrontal cortex (OFC), right frontal superior gyrus and left precuneus. The low-WB group showed increased connectivity between the bilateral amygdala and the occipital lobe and the right anterior OFC. To conclude connectivity results with a quantitative approach, suggest differences in cognitive and decision-making processing between people with varying WB states. The high-WB group when compared to low-WB group had higher cognitive processing and decision making based on their internal mental processes and self-referential processing, whereas connectivity between amygdala and OFC relates to decreased attentional processing and promotes effective emotional regulation that may be a lead to rumination.