Neuroinformatics最新文献

Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing literature on using Bayesian scalar-on-tensor regression techniques that use tensor-based representations for high-dimensional and spatially distributed covariates to predict continuous outcomes. However surprisingly, there is limited development on corresponding Bayesian classification methods relying on tensor-valued covariates. Standard approaches that vectorize the image are not desirable due to the loss of spatial structure, and alternate methods that use extracted features from the image in the predictive model may suffer from information loss. We propose a novel data augmentation-based Bayesian classification approach relying on tensor-valued covariates, with a focus on imaging predictors. We propose two data augmentation schemes, one resulting in a support vector machine (SVM) type of classifier, and another yielding a logistic regression classifier. While both types of classifiers have been proposed independently in literature, our contribution is to extend such existing methodology to accommodate high-dimensional tensor valued predictors that involve low rank decompositions of the coefficient matrix while preserving the spatial information in the image. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed for implementing these methods. Simulation studies show significant improvements in classification accuracy and parameter estimation compared to routinely used classification methods. We further illustrate our method in a neuroimaging application using cortical thickness MRI data from Alzheimer's Disease Neuroimaging Initiative, with results displaying better classification accuracy throughout several classification tasks, including classification on pairs of the three diagnostic groups: normal control, AD patients, and MCI patients; gender classification (males vs females); and cognitive performance based on high and low levels of MMSE scores.

US Food and Drug Administration (FDA) cleared a Transcranial Magnetic Stimulation (TMS) system with functional Magnetic Resonance Imaging-guided (fMRI) individualized treatment protocol for major depressive disorder, which employs resting state-fMRI (RS-fMRI) functional connectivity (FC) to pinpoint the target individually to increase the accuracy and effeteness of the stimulation. Furthermore, task activation-guided TMS, as well as the use of RS-fMRI local metrics for targeted the specific abnormal brain regions, are considered a precise scheme for TMS targeting. Since 1.5 T MRI is more available in hospitals, systematic evaluation of the test-retest reliability and sensitivity of fMRI metrics on 1.5 T and 3 T MRI may provide reference for the application of fMRI-guided individualized-precise TMS stimulation. Twenty participants underwent three RS-fMRI scans and one scan of finger-tapping task fMRI with self-initiated (SI) and visual-guided (VG) conditions at both 3 T and 1.5 T. Then the location reliability derived by FC (with three seed regions) and peak activation were assessed by intra-individual distance. The test-retest reliability and sensitivity of five RS-fMRI local metrics were evaluated using intra-class correlation and effect size, separately. The intra-individual distance of peak activation location between 1.5 T and 3 T was 15.8 mm and 19 mm for two conditions, respectively. The intra-individual distance for the FC derived targets at 1.5 T was 9.6-31.2 mm, compared to that of 3 T (7.6-31.1 mm). The test-retest reliability and sensitivity of RS-fMRI local metrics showed similar trends on 1.5 T and 3 T. These findings hasten the application of fMRI-guided individualized TMS treatment in clinical practice.

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions. Through better data integration, feature identification, knowledge representation, validation, etc., neuroinformaticists, are ideally suited to bring clarity, context, and explainabilty to the study of sports-related head injuries in males and in females, and helping to define recovery.

Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~ 50%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts' histopathological assessment standards (> 85% of cases) and enables high-throughput image analysis workflows in digital pathology labs.

Leveraging vast neuroimaging and electrophysiological datasets, AI algorithms are uncovering patterns that offer unprecedented insights into brain structure and function. Neuroinformatics, the fusion of neuroscience and AI, is advancing technologies like brain-computer interfaces, AI-driven cognitive enhancement, and personalized neuromodulation for treating neurological disorders. These developments hold potential to improve cognitive functions, restore motor abilities, and create human-machine collaborative systems. Looking ahead, the convergence of neuroscience and AI is set to transform cognitive modeling, decision-making, and mental health interventions. This fusion mirrors the quest for nuclear fusion energy, both driven by the need to unlock profound sources of understanding. As STEM disciplines continue to drive core developments of foundational models of the brain, neuroinformatics promises to lead innovations in augmented intelligence, personalized healthcare, and effective decision-making systems.

Brain reconstruction, specially of the cerebral cortex, is a challenging task and even more so when it comes to highly gyrified brained animals. Here, we present Stitcher, a novel tool capable of generating such surfaces utilizing MRI data and manual segmentation. Stitcher makes a triangulation between consecutive brain slice segmentations by recursively adding edges that minimize the total length and simultaneously avoid self-intersection. We applied this new method to build the cortical surfaces of two dolphins: Guiana dolphin (Sotalia guianensis), Franciscana dolphin (Pontoporia blainvillei); and one pinniped: Steller sea lion (Eumetopias jubatus). Specifically in the case of P. blainvillei, two reconstructions at two different resolutions were made. Additionally, we also performed reconstructions for sub and non-cortical structures of Guiana dolphin. All our cortical mesh results show remarkable resemblance with the real anatomy of the brains, except P. blainvillei with low-resolution data. Sub and non-cortical meshes were also properly reconstructed and the spatial positioning of structures was preserved with respect to S. guianensis cerebral cortex. In a comparative perspective between methods, Stitcher presents compatible results for volumetric measurements when contrasted with other anatomical standard tools. In this way, Stitcher seems to be a viable pipeline for new neuroanatomical analysis, enhancing visualization and descriptions of non-primates species, and broadening the scope of compared neuroanatomy.

Characterizing the anatomical structure and connectivity between cortical regions is a critical step towards understanding the information processing properties of the brain and will help provide insight into the nature of neurological disorders. A key feature of the mammalian cerebral cortex is its laminar structure. Identifying these layers in neuroimaging data is important for understanding their global structure and to help understand the connectivity patterns of neurons in the brain. We studied Nissl-stained and myelin-stained slice images of the brain of the common marmoset (Callithrix jacchus), which is a new world monkey that is becoming increasingly popular in the neuroscience community as an object of study. We present a novel computational framework that first acquired the cortical labels using AI-based tools followed by a trained deep learning model to segment cerebral cortical layers. We obtained a Euclidean distance of $1274.750\pm 156.400$ $\mu m$ for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness ( $1800.630\mu m$ ). We compared our cortical layer segmentation pipeline with the pipeline proposed by Wagstyl et al. (PLoS biology, 18(4), e3000678 2020) adapted to 2D data. We obtained a better mean ${95}^{\mathrm{th}}$ percentile Hausdorff distance (95HD) of  $92.150\mu m$ . Whereas a mean 95HD of  $94.170\mu m$ was obtained from Wagstyl et al. We also compared our pipeline's performance against theirs using their dataset (the BigBrain dataset). The results also showed better segmentation quality, $85.318\%$ Jaccard Index acquired from our pipeline, while $83.000\%$ was stated in their paper.

Schizophrenia is a mental disorder characterized by neurophysiological dysfunctions that result in disturbances in thinking, perception, and behavior. Early identification of schizophrenia can help prevent potential complications and facilitate effective treatment and management of the condition. This paper proposes a computer aided diagnosis system for the early detection of schizophrenia using 19-channel Electroencephalography (EEG) signals from 28 subjects, leveraging statistical moments of Mel-frequency Cepstral Coefficients (MFCC) and ensemble learning. Initially, the EEG signals are passed through a high-pass filter to mitigate noise and remove extraneous data. The feature extraction technique is then employed to extract MFC coefficients from the filtered EEG signals. The dimensionality of these coefficients is reduced by computing their statistical moments, which include the mean, standard deviation, skewness, kurtosis, and energy. Subsequently, the Support Vector Machine based Recursive Feature Elimination (SVM-RFE) is applied to identify pertinent features from the statistical moments of the MFC coefficients. These SVM-RFE-based selected features serve as input for three base classifiers: Support Vector Machine, k-Nearest Neighbors, and Logistic Regression. Additionally, an ensemble learning approach, which combines the predictions of the three classifiers through majority voting, is introduced to enhance schizophrenia detection performance and generalize the results of the proposed approach. The study’s findings demonstrate that the ensemble model, combined with SVM-RFE-based selected statistical moments of MFCC, achieves encouraging detection performance, highlighting the potential of machine learning techniques in advancing the diagnostic process of schizophrenia.