Neuroinformatics最新文献

{"title":"Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value.","authors":"Elham Ahmadi Moghadam, Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini, Mohammad Hossein Moattar","doi":"10.1007/s12021-024-09685-3","DOIUrl":"10.1007/s12021-024-09685-3","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"521-537"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of Sports Concussion in Female Athletes: A Role for Neuroinformatics?","authors":"Rachel Edelstein, Sterling Gutterman, Benjamin Newman, John Darrell Van Horn","doi":"10.1007/s12021-024-09680-8","DOIUrl":"10.1007/s12021-024-09680-8","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"607-618"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579174/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141793888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroinformatics Applications of Data Science and Artificial Intelligence.","authors":"Ivo D Dinov","doi":"10.1007/s12021-024-09692-4","DOIUrl":"10.1007/s12021-024-09692-4","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"403-405"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning.","authors":"Roman Peter, Petr Hrobar, Josef Navratil, Martin Vagenknecht, Jindrich Soukup, Keiko Tsuji, Nestor X Barrezueta, Anna C Stoll, Renee C Gentzel, Jonathan A Sugam, Jacob Marcus, Danny A Bitton","doi":"10.1007/s12021-024-09679-1","DOIUrl":"10.1007/s12021-024-09679-1","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"719-730"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141898673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stitcher: A Surface Reconstruction Tool for Highly Gyrified Brains.","authors":"Heitor Mynssen, Kamilla Avelino-de-Souza, Khallil Chaim, Vanessa Lanes Ribeiro, Nina Patzke, Bruno Mota","doi":"10.1007/s12021-024-09678-2","DOIUrl":"10.1007/s12021-024-09678-2","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"539-554"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142401776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Deep Learning-based Pipeline for Segmenting the Cerebral Cortex Laminar Structure in Histology Images.","authors":"Jiaxuan Wang, Rui Gong, Shahrokh Heidari, Mitchell Rogers, Toshiki Tani, Hiroshi Abe, Noritaka Ichinohe, Alexander Woodward, Patrice J Delmas","doi":"10.1007/s12021-024-09688-0","DOIUrl":"10.1007/s12021-024-09688-0","url":null,"abstract":"<p><p>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 <math><mrow><mn>1274.750</mn> <mo>±</mo> <mn>156.400</mn></mrow> </math> <math><mrow><mi>μ</mi> <mi>m</mi></mrow> </math> for the cortical labels acquisition, which was in the acceptable range by computing the half Euclidean distance of the average cortex thickness ( <math><mrow><mn>1800.630</mn> <mspace></mspace> <mi>μ</mi> <mi>m</mi></mrow> </math> ). 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 <math> <mrow><msup><mn>95</mn> <mrow><mi>th</mi></mrow> </msup> </mrow> </math> percentile Hausdorff distance (95HD) of  <math><mrow><mn>92.150</mn> <mspace></mspace> <mi>μ</mi> <mi>m</mi></mrow> </math> . Whereas a mean 95HD of  <math><mrow><mn>94.170</mn> <mspace></mspace> <mi>μ</mi> <mi>m</mi></mrow> </math> 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, <math><mrow><mn>85.318</mn> <mo>%</mo></mrow> </math> Jaccard Index acquired from our pipeline, while <math><mrow><mn>83.000</mn> <mo>%</mo></mrow> </math> was stated in their paper.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"745-761"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579130/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroinformatics and Analysis of Traumatic Brain Injury and Related Conditions.","authors":"Andrei Irimia","doi":"10.1007/s12021-024-09691-5","DOIUrl":"10.1007/s12021-024-09691-5","url":null,"abstract":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"569-572"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142331110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning","authors":"Ashima Tyagi, Vibhav Prakash Singh, Manoj Madhava Gore","doi":"10.1007/s12021-024-09684-4","DOIUrl":"https://doi.org/10.1007/s12021-024-09684-4","url":null,"abstract":"<p>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 <i>Electroencephalography (EEG)</i> signals from 28 subjects, leveraging statistical moments of <i>Mel-frequency Cepstral Coefficients (MFCC)</i> 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 <i>Support Vector Machine</i> based <i>Recursive Feature Elimination (SVM-RFE)</i> 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.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"51 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies","authors":"Wentao Jiang, Xinyi Liu, Ming Song, Zhengyi Yang, Lan Sun, Tianzi Jiang","doi":"10.1007/s12021-024-09687-1","DOIUrl":"https://doi.org/10.1007/s12021-024-09687-1","url":null,"abstract":"<p>Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample preparation altering brain morphology. The absence of an accessible toolbox for magnetic resonance imaging (MRI) data processing presents a challenge for assessing morphological changes in the mouse brain. To address this, we developed the MBV-Pipe (Mouse Brain Volumetric Statistics-Pipeline) toolbox, integrating the methods of Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL)-Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) to evaluate brain tissue volume and white matter integrity. To validate the reliability of MBV-Pipe, brain MRI data from seven mice at three time points (in vivo, post-perfusion, and post-fixation) were acquired using a 9.4T ultra-high MRI system. Employing the MBV-Pipe toolbox, we discerned substantial volumetric changes in the mouse brain following perfusion relative to the in vivo condition, with the fixation process inducing only negligible variations. Importantly, the white matter integrity was found to be largely stable throughout the sample preparation procedures. The MBV-Pipe source code is publicly available and includes a user-friendly GUI for facilitating quality control and experimental protocol optimization, which holds promise for advancing mouse brain research in the future.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"28 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Morphology and Texture-Guided Deep Neural Network for Intracranial Aneurysm Segmentation in 3D TOF-MRA","authors":"Maysam Orouskhani, Negar Firoozeh, Huayu Wang, Yan Wang, Hanrui Shi, Weijing Li, Beibei Sun, Jianjian Zhang, Xiao Li, Huilin Zhao, Mahmud Mossa-Basha, Jenq-Neng Hwang, Chengcheng Zhu","doi":"10.1007/s12021-024-09683-5","DOIUrl":"https://doi.org/10.1007/s12021-024-09683-5","url":null,"abstract":"<p>This study concentrates on the segmentation of intracranial aneurysms, a pivotal aspect of diagnosis and treatment planning. We aim to overcome the inherent instance imbalance and morphological variability by introducing a novel morphology and texture loss reweighting approach. Our innovative method involves the incorporation of tailored weights within the loss function of deep neural networks. Specifically designed to account for aneurysm size, shape, and texture, this approach strategically guides the model to focus on capturing discriminative information from imbalanced features. The study conducted extensive experimentation utilizing ADAM and RENJI TOF-MRA datasets to validate the proposed approach. The results of our experimentation demonstrate the remarkable effectiveness of the introduced methodology in improving aneurysm segmentation accuracy. By dynamically adapting to the variances present in aneurysm features, our model showcases promising outcomes for accurate diagnostic insights. The nuanced consideration of morphological and textural nuances within the loss function proves instrumental in overcoming the challenge posed by instance imbalance. In conclusion, our study presents a nuanced solution to the intricate challenge of intracranial aneurysm segmentation. The proposed morphology and texture loss reweighting approach, with its tailored weights and dynamic adaptability, proves to be instrumental in enhancing segmentation precision. The promising outcomes from our experimentation suggest the potential for accurate diagnostic insights and informed treatment strategies, marking a significant advancement in this critical domain of medical imaging.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"9 1","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142182159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}