Brain Informatics最新文献

{"title":"Breakdown of the compositional data approach in psychometric Likert scale big data analysis: about the loss of statistical power of two-sample t-tests applied to heavy-tailed big data.","authors":"René Lehmann, Bodo Vogt","doi":"10.1186/s40708-025-00253-2","DOIUrl":"10.1186/s40708-025-00253-2","url":null,"abstract":"<p><p>Bipolar psychometric scale data play a crucial role in psychological healthcare and health economics, such as in psychotherapeutic profiling and setting standards. Creating an accurate psychological profile not only benefits the patient but also saves time and costs. The quality of psychotherapeutic measures directly impacts grant funding decisions, influencing managerial choices. Moreover, the accuracy of consumer data analyses affects costs, profits, and the long-term sustainability of decisions. Considering psychometric bipolar scale data as compositional data can enhance the statistical power of well-known paired and unpaired two-sample t-tests, supporting managerial decision-making and the development or implementation of health interventions. This increase in statistical power is observed when the central limit theorem (CLT) holds true in statistics. Through stochastic simulation, this study explores the impact of violating the CLT on statistical power of the unpaired t-test under heavy-tailed data generating processes (DGPs) with finite variance. The findings reveal a reduction in statistical power based on specific parameters like the psychometric limit of quantification, the number of items in a questionnaire, the response scale used, and the dispersion of the DGP.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.","authors":"Maryam Akhavan Aghdam, Serdar Bozdag, Fahad Saeed","doi":"10.1186/s40708-025-00252-3","DOIUrl":"10.1186/s40708-025-00252-3","url":null,"abstract":"<p><p>Clinical diagnosis of Alzheimer's disease (AD) is usually made after symptoms such as short-term memory loss are exhibited, which minimizes the intervention and treatment options. The existing screening techniques cannot distinguish between stable MCI (sMCI) cases (i.e., patients who do not convert to AD for at least three years) and progressive MCI (pMCI) cases (i.e., patients who convert to AD in three years or sooner). Delayed diagnosis of AD also disproportionately affects underrepresented and socioeconomically disadvantaged populations. The significant positive impact of an early diagnosis solution for AD across diverse ethno-racial and demographic groups is well-known and recognized. While advancements in high-throughput technologies have enabled the generation of vast amounts of multimodal clinical, and neuroimaging datasets related to AD, most methods utilizing these data sets for diagnostic purposes have not found their way in clinical settings. To better understand the landscape, we surveyed the major preprocessing, data management, traditional machine-learning (ML), and deep learning (DL) techniques used for diagnosing AD using neuroimaging data such as structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and positron emission tomography (PET). Once we had a good understanding of the methods available, we conducted a study to assess the reproducibility and generalizability of open-source ML models. Our evaluation shows that existing models show reduced generalizability when different cohorts of the data modality are used while controlling other computational factors. The paper concludes with a discussion of major challenges that plague ML models for AD diagnosis and biomarker discovery.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11928716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring multi-granularity balance strategy for class incremental learning via three-way granular computing.","authors":"Yan Xian, Hong Yu, Ye Wang, Guoyin Wang","doi":"10.1186/s40708-025-00255-0","DOIUrl":"10.1186/s40708-025-00255-0","url":null,"abstract":"<p><p>Class incremental learning (CIL) is a specific scenario in incremental learning. It aims to continuously learn new classes from the data stream, which suffers from the challenge of catastrophic forgetting. Inspired by the human hippocampus, the CIL method for replaying episodic memory offers a promising solution. However, the limited buffer budget restricts the number of old class samples that can be stored, resulting in an imbalance between new and old class samples during each incremental learning stage. This imbalance adversely affects the mitigation of catastrophic forgetting. Therefore, we propose a novel CIL method based on multi-granularity balance strategy (MGBCIL), which is inspired by the three-way granular computing in human problem-solving. In order to mitigate the adverse effects of imbalances on catastrophic forgetting at fine-, medium-, and coarse-grained levels during training, MGBCIL introduces specific strategies across the batch, task, and decision stages. Specifically, a weighted cross-entropy loss function with a smoothing factor is proposed for batch processing. In the process of task updating and classification decision, contrastive learning with different anchor point settings is employed to promote local and global separation between new and old classes. Additionally, the knowledge distillation technology is used to preserve knowledge of the old classes. Experimental evaluations on CIFAR-10 and CIFAR-100 datasets show that MGBCIL outperforms other methods in most incremental settings. Specifically, when storing 3 exemplars on CIFAR-10 with Base2 Inc2 setting, the average accuracy is improved by up to 9.59% and the forgetting rate is reduced by up to 25.45%.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143650755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Localization of epileptic foci from intracranial EEG using the GRU-GC algorithm.","authors":"Xiaojia Wang, Dayang Wu, Chunfeng Yang","doi":"10.1186/s40708-025-00254-1","DOIUrl":"10.1186/s40708-025-00254-1","url":null,"abstract":"<p><p>Epilepsy is one of the most common clinical diseases, which is caused by abnormal discharge of brain nerves. Around 30% of patients will develop drug-resistant epilepsy that are hard to be cured by anti-epileptic drug treatment. This patient cohort are ideal candidate for surgical resection of the epileptic focus. For safety and maximum effective rate, the key to success of the operation is to identify the focus area and normal functional area accurately in the preoperative evaluation stage. Intracranial EEG (iEEG) has attracted much attention for its precise capture of the state of rapid brain activity and its strong locality. To automate the process of iEEG inspection and surgical evaluation, this paper propose a Gated Recurrent Unit-Granger Causality (GRU-GC) algorithm to detect effective connectivity between channels and construct a directed graph. From six local features, the top five feature combinations were selected to differentiate between epileptic foci and non-epileptic regions. Experiments indicate that these features are most discriminative during the ictal phase, yielding superior classification accuracy. Compared to traditional time-series-based methods, this study shows that GRU-GC algorithm is efficient in building effective graph model for improving preoperative epilepsy evaluations.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11910482/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing the synergy of statistics and deep learning for BCI competition 4 dataset 4: a novel approach.","authors":"Gauttam Jangir, Nisheeth Joshi, Gaurav Purohit","doi":"10.1186/s40708-025-00250-5","DOIUrl":"10.1186/s40708-025-00250-5","url":null,"abstract":"<p><p>Human brain signal processing and finger's movement coordination is a complex mechanism. In this mechanism finger's movement is mostly performed for every day's task. It is well known that to capture such movement EEG or ECoG signals are used. In this order to find the patterns from these signals is important. The BCI competition 4 dataset 4 is one such standard dataset of ECoG signals for individual finger movement provided by University of Washington, USA. In this work, this dataset is, statistically analyzed to understand the nature of data and outliers in it. Effectiveness of pre-processing algorithm is then visualized. The cleaned dataset has dual polarity and gaussian distribution nature which makes Tanh activation function suitable for the neural network BC4D4 model. BC4D4 uses Convolutional neural network for feature extraction, dense neural network for pattern identification and incorporating dropout & regularization making the proposed model more resilient. Our model outperforms the state of the art work on the dataset 4 achieving 0.85 correlation value that is 1.85X (Winner of BCI competition 4, 2012) & 1.25X (Finger Flex model, 2022).</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"5"},"PeriodicalIF":0.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11829870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel machine learning-driven comparative analysis of CSP, STFT, and CSP-STFT fusion for EEG data classification across multiple meditation and non-meditation sessions in BCI pipeline.","authors":"Nalinda D Liyanagedera, Corinne A Bareham, Heather Kempton, Hans W Guesgen","doi":"10.1186/s40708-025-00251-4","DOIUrl":"10.1186/s40708-025-00251-4","url":null,"abstract":"<p><p>This study focuses on classifying multiple sessions of loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data. This novel study focuses on using multiple sessions of EEG data from a single individual to train a machine learning pipeline, and then using a new session data from the same individual for the classification. Here, two meditation techniques, LKM-Self and LKM-Others were compared with non-meditation EEG data for 12 participants. Among many tested, three BCI pipelines we built produced promising results, successfully detecting features in meditation/ non-meditation EEG data. While testing different feature extraction algorithms, a common neural network structure was used as the classification algorithm to compare the performance of the feature extraction algorithms. For two of those pipelines, Common Spatial Patterns (CSP) and Short Time Fourier Transform (STFT) were successfully used as feature extraction algorithms where both these algorithms are significantly new for meditation EEG. As a novel concept, the third BCI pipeline used a feature extraction algorithm that fused the features of CSP and STFT, achieving the highest classification accuracies among all tested pipelines. Analyses were conducted using EEG data of 3, 4 or 5 sessions, totaling 3960 tests on the entire dataset. At the end of the study, when considering all the tests, the overall classification accuracy using SCP alone was 67.1%, and it was 67.8% for STFT alone. The algorithm combining the features of CSP and STFT achieved an overall classification accuracy of 72.9% which is more than 5% higher than the other two pipelines. At the same time, the highest mean classification accuracy for the 12 participants was achieved using the pipeline with the combination of CSP STFT algorithm, reaching 75.5% for LKM-Self/ non-meditation for the case of 5 sessions of data. Additionally, the highest individual classification accuracy of 88.9% was obtained by the participant no. 14. Furthermore, the results showed that the classification accuracies for all three pipelines increased with the number of training sessions increased from 2 to 3 and then to 4. The study was successful in classifying a new session of EEG meditation/ non-meditation data after training machine learning algorithms using a different set of session data, and this achievement will be beneficial in the development of algorithms that support meditation.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"4"},"PeriodicalIF":0.0,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11807047/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143374904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease.","authors":"Colin Birkenbihl, Madison Cuppels, Rory T Boyle, Hannah M Klinger, Oliver Langford, Gillian T Coughlan, Michael J Properzi, Jasmeer Chhatwal, Julie C Price, Aaron P Schultz, Dorene M Rentz, Rebecca E Amariglio, Keith A Johnson, Rebecca F Gottesman, Shubhabrata Mukherjee, Paul Maruff, Yen Ying Lim, Colin L Masters, Alexa Beiser, Susan M Resnick, Timothy M Hughes, Samantha Burnham, Ilke Tunali, Susan Landau, Ann D Cohen, Sterling C Johnson, Tobey J Betthauser, Sudha Seshadri, Samuel N Lockhart, Sid E O'Bryant, Prashanthi Vemuri, Reisa A Sperling, Timothy J Hohman, Michael C Donohue, Rachel F Buckley","doi":"10.1186/s40708-024-00249-4","DOIUrl":"10.1186/s40708-024-00249-4","url":null,"abstract":"<p><p>Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals. We demonstrate that this approach makes specific, uncontrollable assumptions and likely leads to biased and erroneous resilience estimates. This is especially true when information about CR is contained in the data the linear model was fitted to, either through inclusion of CR-associated variables or due to correlation. We propose an alternative strategy which overcomes the standard approach's limitations using machine learning principles. Our proposed approach makes fewer assumptions about the data and CR and achieves better estimation accuracy on simulated ground-truth data.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11772644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143053883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CalciumZero: a toolbox for fluorescence calcium imaging on iPSC derived brain organoids.","authors":"Xiaofu He, Yian Wang, Yutong Gao, Xuchen Wang, Zhixiong Sun, Huixiang Zhu, Kam W Leong, Bin Xu","doi":"10.1186/s40708-024-00248-5","DOIUrl":"10.1186/s40708-024-00248-5","url":null,"abstract":"<p><p>Calcium plays an important role in regulating various neuronal activities in human brains. Investigating the dynamics of the calcium level in neurons is essential not just for understanding the pathophysiology of neuropsychiatric disorders but also as a quantitative gauge to evaluate the influence of drugs on neuron activities. Accessing human brain tissue to study neuron activities has historically been challenging due to ethical concerns. However, a significant breakthrough in the field has emerged with the advent of utilizing patient-derived human induced pluripotent stem cells (iPSCs) to culture neurons and develop brain organoids. This innovative approach provides a promising modeling system to overcome these critical obstacles. Many robust calcium imaging analysis tools have been developed for calcium activity analysis. However, most of the tools are designed for calcium signal detection only. There are limited choices for in-depth downstream applications, particularly in discerning differences between patient and normal calcium dynamics and their responses to drug treatment obtained from human iPSC-based models. Moreover, end-user researchers usually face a considerable challenge in mastering the entire analysis procedure and obtaining critical outputs due to the steep learning curve associated with these available tools. Therefore, we developed CalciumZero, a user-friendly toolbox to satisfy the unmet needs in calcium activity studies in human iPSC-based 3D-organoid/neurosphere models. CalciumZero includes a graphical user interface (GUI), which provides end-user iconic visualization and smooth adjustments on parameter tuning. It streamlines the entire analysis process, offering full automation with just one click after parameter optimization. In addition, it includes supplementary features to statistically evaluate the impact on disease etiology and the detection of drug candidate effects on calcium activities. These evaluations will enhance the analysis of imaging data obtained from patient iPSC-derived brain organoid/neurosphere models, providing a more comprehensive understanding of the results.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11746984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain-enabled digital twin system for brain stroke prediction.","authors":"Venkatesh Upadrista, Sajid Nazir, Huaglory Tianfield","doi":"10.1186/s40708-024-00247-6","DOIUrl":"10.1186/s40708-024-00247-6","url":null,"abstract":"<p><p>A digital twin is a virtual model of a real-world system that updates in real-time. In healthcare, digital twins are gaining popularity for monitoring activities like diet, physical activity, and sleep. However, their application in predicting serious conditions such as heart attacks, brain strokes and cancers remains under investigation, with current research showing limited accuracy in such predictions. Moreover, concerns around data security and privacy continue to challenge the widespread adoption of these models. To address these challenges, we developed a secure, machine learning powered digital twin application with three main objectives enhancing prediction accuracy, strengthening security, and ensuring scalability. The application achieved an accuracy of 98.28% for brain stroke prediction on the selected dataset. The data security was enhanced by integrating consortium blockchain technology with machine learning. The results show that the application is tamper-proof and is capable of detecting and automatically correcting backend data anomalies to maintain robust data protection. The application can be extended to monitor other pathologies such as heart attacks, cancers, osteoporosis, and epilepsy with minimal configuration changes.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11732804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable brain age prediction: a comparative evaluation of morphometric and deep learning pipelines.","authors":"Maria Luigia Natalia De Bonis, Giuseppe Fasano, Angela Lombardi, Carmelo Ardito, Antonio Ferrara, Eugenio Di Sciascio, Tommaso Di Noia","doi":"10.1186/s40708-024-00244-9","DOIUrl":"10.1186/s40708-024-00244-9","url":null,"abstract":"<p><p>Brain age, a biomarker reflecting brain health relative to chronological age, is increasingly used in neuroimaging to detect early signs of neurodegenerative diseases and support personalized treatment plans. Two primary approaches for brain age prediction have emerged: morphometric feature extraction from MRI scans and deep learning (DL) applied to raw MRI data. However, a systematic comparison of these methods regarding performance, interpretability, and clinical utility has been limited. In this study, we present a comparative evaluation of two pipelines: one using morphometric features from FreeSurfer and the other employing 3D convolutional neural networks (CNNs). Using a multisite neuroimaging dataset, we assessed both model performance and the interpretability of predictions through eXplainable Artificial Intelligence (XAI) methods, applying SHAP to the feature-based pipeline and Grad-CAM and DeepSHAP to the CNN-based pipeline. Our results show comparable performance between the two pipelines in Leave-One-Site-Out (LOSO) validation, achieving state-of-the-art performance on the independent test set ( <math><mrow><mi>M</mi> <mi>A</mi> <mi>E</mi> <mo>=</mo> <mn>3.21</mn></mrow> </math> with DNN and morphometric features and <math><mrow><mi>M</mi> <mi>A</mi> <mi>E</mi> <mo>=</mo> <mn>3.08</mn></mrow> </math> with a DenseNet-121 architecture). SHAP provided the most consistent and interpretable results, while DeepSHAP exhibited greater variability. Further work is needed to assess the clinical utility of Grad-CAM. This study addresses a critical gap by systematically comparing the interpretability of multiple XAI methods across distinct brain age prediction pipelines. Our findings underscore the importance of integrating XAI into clinical practice, offering insights into how XAI outputs vary and their potential utility for clinicians.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"33"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142847751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}