检索结果-内蒙古大学图书馆

Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs)

Biological psychiatry. Cognitive neuroscience and neuroimaging 2025年

作者： Najme Soleimani Armin Iraji Godfrey Pearlson Adrian Preda Vince D Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta Georgia USA. Electronic address: nsoleimani1@gsu.edu. Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta Georgia USA. Departments of Psychiatry and Neuroscience Yale University School of Medicine New Haven Connecticut USA. Department of Psychiatry and Human Behavior University of California Irvine California USA.

BACKGROUND:Mental illnesses extract personal and societal costs, leading to significant challenges in cognitive function, emotional regulation, and social behavior. These disorders are thought to result from disruptions in how different brain regions communicate with each other. Despite advances in neuroimaging, current methods are not always precise enough to fully understand the complexity of these disruptions. More advanced approaches are needed to better identify and characterize the specific brain network alterations linked to different psychiatric conditions. METHODS:We employed a hierarchical approach to derive Double Functionally Independent Primitives (dFIPs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. dFIPs represent independent patterns of functional network connectivity (FNC) across the brain. Our study utilized a large multi-site dataset comprising 5805 individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and healthy controls. We analyzed how combinations of dFIPs differentiate psychiatric diagnoses. RESULTS:Distinct dFIP patterns emerged for each disorder. Schizophrenia was characterized by heightened cerebellar connectivity and reduced cerebellar-subcortical connectivity. In ASD, sensory domain hyperconnectivity was prominent. Some dFIPs displayed disorder-specific connectivity patterns, while others exhibited commonalities across multiple conditions. These findings underscore the utility of dFIPs in revealing neural connectivity alterations unique to each disorder, serving as unique fingerprints for different mental disorders. CONCLUSIONS:Our study demonstrates that dFIPs provide a novel, data-driven method for identifying disorder-specific functional connectivity patterns in psychiatric conditions. These distinct neural signatures offer potential biomarkers for mental illnesses, contributing to a deeper understanding of the neurobiologica

关键词： Double Functionally Independent Primitive (dFIP) Functional Network Connectivity (FNC) Independent Component Analysis (ICA) Mental Disorders

来源：评论

学校读者我要写书评

暂无评论

Transcriptome analysis of archived tumors by Visium, GeoMx DSP, and Chromium reveals patient heterogeneity

引用

Nature Communications 2025年第1期16卷 1-18页

作者： Dong, Yixing Saglietti, Chiara Bayard, Quentin Espin Perez, Almudena Carpentier, Sabrina Buszta, Daria Tissot, Stephanie Dubois, Rémy Kamburov, Atanas Kang, Senbai Haignere, Carla Sarkis, Rita Andre, Sylvie Alexandre Gaveta, Marina Lopez Lastra, Silvia Piazzon, Nathalie Santos, Rita von Loga, Katharina Hoffmann, Caroline Coukos, George Peters, Solange Soumelis, Vassili Durand, Eric Yves de Leval, Laurence Gottardo, Raphael Homicsko, Krisztian Madissoon, Elo Biomedical Data Science Center Lausanne University Hospital University of Lausanne Lausanne Switzerland Institute of Pathology Department of Laboratory Medicine and Pathology Lausanne University Hospital and University of Lausanne Lausanne Switzerland Owkin Paris France Department of Oncology Lausanne University Hospital Swiss Cancer Center Leman Lausanne Switzerland Ludwig Insitute for Cancer Research Lausanne Switzerland Agora Translational Research Center Lausanne Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland School of Life Sciences Ecole Polytechnique Fédérale de Lausanne Switzerland

Recent advancements in probe-based, full-transcriptome technologies for FFPE tissues, such as Visium CytAssist, Chromium Flex, and GeoMx DSP, enable analysis of archival samples, facilitating the generation of data from extensive cohorts. However, these methods can be labor-intensive and costly, requiring informed selection based on research objectives. We compare these methods on FFPE tumor samples in Breast, NSCLC and DLBCL showing 1) good-quality, highly reproducible data from all methods;2) GeoMx data containing cell mixtures despite marker-based preselection;3) Visium and Chromium outperform GeoMx in discovering tumor heterogeneity and potential drug targets. We recommend the use of Visium and Chromium for high-throughput and discovery projects, while the manually more challenging GeoMx platform with targeted regions remains valuable for specialized questions. © The Author(s) 2025.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Functionally-Adaptive Gray and White Matter Structural Basis Sets via Dynamic Fusion of Multimodal MRI data 46

Functionally-Adaptive Gray and White Matter Structural Basis...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Duda, Marlena Calhoun, Vince D. Georgia State University Georgia Tech Emory University Center for Translational Research in Neuroimaging and Data Science AtlantaGA United States

ISBN: (纸本)9798350371499

The exact nature of the coupling of brain structure and function has long been an open area of research. Often, this question is approached by first defining a single structural basis set, and then estimating functional brain activation time courses as a linear combination of these structural bases. However, knowing that functional brain activity and connectivity vary over time, so might the nature of these structural/functional couplings. Thus, a single rigidly defined, "functionally unaware"structural manifold may be insufficient to describe structure/function linkages across a whole functional time series. Here, we introduce dynamic fusion, an ICA-based symmetric fusion, and show evidence that challenges current approaches and suggests time-resolved structural basis sets can better represent changing functional manifolds. We perform dynamic fusion using measures of both gray matter (GM) and white matter (WM) structure and present results that may indicate a stronger link between WM structure and dynamic brain function than in GM. © 2024 IEEE.

关键词： data fusion

来源：评论

学校读者我要写书评

暂无评论

Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram data 46

Evaluating Augmentation Approaches for Deep Learning-based M...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. Georgia State University Emory University Georgia Institute of Technology Center for Translational Research in Neuroimaging and Data Science Atlanta United States

ISBN: (纸本)9798350371499

While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their potential for clinical translation. data augmentation (DA) could address this limitation, but the utility of EEG DA methods remains relatively underexplored in neuropsychiatric disorder diagnosis. In this study, we train a model for major depressive disorder diagnosis. We then evaluate the utility of 6 EEG DA approaches. Importantly, to remove the bias that could be introduced by comparing performance for models trained on larger augmented training sets to models trained on smaller baseline sets, we also introduce a new baseline trained on duplicate training data. We lastly examine the effects of the DA approaches upon representations learned by the model with a pair of explainability analyses. We find that while most approaches boost model performance, they do not improve model performance beyond that of simply using a duplicate training set without DA. The exception to this is channel dropout augmentation, which does improve model performance. These findings suggest the importance of comparing EEG DA methods to a baseline with a duplicate training set of equal size to the augmented training set. We also found that some DA methods increased model robustness to frequency (Fourier transform surrogates) and channel (channel dropout) perturbation. While our findings on EEG DA efficacy are restricted to our dataset and model, we hope that future studies on deep learning for small EEG datasets and on new EEG DA methods will find our findings helpful. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

引用

Biological Psychiatry: Cognitive Neuroscience and neuroimaging 2025年第4期10卷 425-436页

作者： Ganesan, Saampras Barrios, Fernando A. Batta, Ishaan Bauer, Clemens C.C. Braver, Todd S. Brewer, Judson A. Brown, Kirk Warren Cahn, Rael Cain, Joshua A. Calhoun, Vince D. Cao, Lei Chetelat, Gaël Ching, Christopher R.K. Creswell, J. David Dagnino, Paulina Clara Davanger, Svend Davidson, Richard J. Deco, Gustavo Dutcher, Janine M. Escrichs, Anira Eyler, Lisa T. Fani, Negar Farb, Norman A.S. Fialoke, Suruchi Fresco, David M. Garg, Rahul Garland, Eric L. Goldin, Philippe Hafeman, Danella M. Jahanshad, Neda Kang, Yoona Khalsa, Sahib S. Kirlic, Namik Lazar, Sara W. Lutz, Antoine McDermott, Timothy J. Pagnoni, Giuseppe Piguet, Camille Prakash, Ruchika S. Rahrig, Hadley Reggente, Nicco Saccaro, Luigi F. Sacchet, Matthew D. Siegle, Greg J. Tang, Yi-Yuan Thomopoulos, Sophia I. Thompson, Paul M. Torske, Alyssa Treves, Isaac N. Tripathi, Vaibhav Tsuchiyagaito, Aki Turner, Matthew D. Vago, David R. Valk, Sofie Zeidan, Fadel Zalesky, Andrew Turner, Jessica A. King, Anthony P. Department of Biomedical Engineering The University of Melbourne Carlton VIC Australia Contemplative Studies Centre Melbourne School of Psychological Sciences The University of Melbourne Melbourne VIC Australia Systems Lab of Neuroscience Neuropsychiatry and Neuroengineering The University of Melbourne Parkville VIC Australia Universidad Nacional Autónoma de México Instituto de Neurobiolgía México Querétaro Mexico Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology and Emory University Atlanta GA United States Department of Psychology Northeastern University Boston Massachusetts Brain and Cognitive Science McGovern Institute for Brain Research Massachusetts Institute of Technology Cambridge Massachusetts Department of Psychological and Brain Sciences Washington University St. Louis Missouri Department of Behavioral and Social Sciences Brown University School of Public Health Providence Rhode Island Department of Psychology Carnegie Mellon University Pittsburgh PA United States University of Southern California Department of Psychiatry & Behavioral Sciences Los Angeles California University of Southern California Center for Mindfulness Science Los Angeles California Institute for Advanced Consciousness Studies Santa Monica California Department of Psychiatry and Behavioral Health The Ohio State University College of Medicine Columbus OH United States Normandie University Université de Caen Normandie INSERM U1237 Neuropresage Team Cyceron Caen France Imaging Genetics Center Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of the University of Southern California Los Angeles California Desert-Pacific Mental Illness Research Education and Clinical Center VA San Diego Healthcare System San Diego California Computational Neuroscience Group Center for Brain and Cognition Department of Information and Communication Technologies Universitat Pompeu Fa

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)–Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the f

关键词： Consortium ENIGMA Magnetic resonance imaging (MRI) Meditation Mindfulness neuroimaging

来源：评论

学校读者我要写书评

暂无评论

A Novel Activation Maximization-based Approach for Insight into Electrophysiology Classifiers

A Novel Activation Maximization-based Approach for Insight i...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Ellis, Charles A. Sendi, Mohammad S.E. Miller, Robyn Calhoun, Vince Georgia State University Georgia Institute of Technology and Emory University Tri-institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States

ISBN: (纸本)9781665401265

Spectral analysis remains a hallmark approach for gaining insight into electrophysiology modalities like electroencephalography (EEG). As the field of deep learning has progressed, more studies have begun to train deep learning classifiers on raw EEG data, which presents unique problems for explainability. A growing number of studies have presented explainability approaches that provide insight into the spectral features learned by deep learning classifiers. However, existing approaches only attribute importance to different frequency bands. Most of the methods cannot provide insight into the actual spectral values or the relationship between spectral features that models have learned. Here, we present a novel adaptation of activation maximization for electrophysiology time-series that generates samples that indicate the features learned by classifiers by optimizing their spectral content. We evaluate our approach within the context of EEG sleep stage classification with a convolutional neural network, and we find that our approach is able to identify spectral patterns known to be associated with each sleep stage. We also find surprising results suggesting that our classifier may have prioritized the use of eye and motion artifact when identifying Awake samples. Our approach is the first adaptation of activation maximization to the domain of raw electrophysiology classification. Additionally, our approach has implications for explaining any classifier trained on highly dynamic, long time-series. © 2021 IEEE.

关键词： Electrophysiology

来源：评论

学校读者我要写书评

暂无评论

Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach 46

Identifying Reproducibly Important EEG Markers of Schizophre...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Sancho, Martina Lapera Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States

ISBN: (纸本)9798350371499

The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnostic biomarkers of SZ using explainable machine learning methods. However, the generalizability of identified biomarkers in many machine learning-based studies is highly questionable given that most studies only analyze explanations from a small number of models. In this study, we present (1) a novel feature interaction-based explainability approach and (2) several new approaches for summarizing multi-model explanations. We implement our approach within the context of electroencephalogram (EEG) spectral power data. We further analyze both training and test set explanations with the goal of extracting generalizable insights from the models. Importantly, our analyses identify effects of SZ upon the α, β, and θ frequency bands, the left hemisphere of the brain, and interhemispheric interactions across a majority of cross-validation folds. We hope that our analysis will provide helpful insights into SZ and inspire the development of robust approaches for identifying neuropsychiatric disorder biomarkers from explainable machine learning models. © 2024 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage data

Improving Multichannel Raw Electroencephalography-based Diag...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ellis, Charles A. Sattiraju, Abhinav Miller, Robyn L. Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States

ISBN: (纸本)9798350337488

As the field of deep learning has grown in recent years, its application to the domain of raw resting-state electroencephalography (EEG) has also increased. Relative to traditional machine learning methods or deep learning methods applied to manually engineered features, there are fewer methods for developing deep learning models on small raw EEG datasets. One potential approach for enhancing deep learning performance, in this case, is the use of transfer learning. While a number of studies have presented transfer learning approaches for manually engineered EEG features, relatively few approaches have been developed for raw resting-state EEG. In this study, we propose a novel EEG transfer learning approach wherein we first train a model on a large publicly available single-channel sleep stage classification dataset. We then use the learned representations to develop a classifier for automated major depressive disorder diagnosis with raw multichannel EEG. Statistical testing reveals that our approach significantly improves the performance of our model (p © 2023 IEEE.

关键词： deep learning electroencephalography explainable AI major depressive disorder transfer learning

来源：评论

学校读者我要写书评

暂无评论

Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis

Improving Explainability for Single-Channel EEG Deep Learnin...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States

ISBN: (纸本)9798350337488

Deep learning methods are increasingly being applied to raw electroencephalography (EEG) data. Relative to traditional machine learning methods, deep learning methods can increase model performance through automated feature extraction. Nevertheless, they are also less explainable. Existing raw EEG explainability methods identify relative feature importance but do not identify how features relate to model predictions. In this study, we combine well-characterized first layer filters with a novel post hoc statistical analysis of the filter activations that linearly relates properties of model activations with predictions. We implement our approach within the context of automated sleep stage classification, finding that the model uncovers positive relationships between waveforms resembling sleep spindles and NREM2, between high frequency filters and Awake samples, and between δ activity and NREM3. Our approach represents a significant step forward for raw EEG explainability and has the potential to provide many insights relating learned features and model predictions in future studies. © 2023 IEEE.

关键词： activation analysis electroencephalography explainable AI sleep stage classification deep learning

来源：评论

学校读者我要写书评

暂无评论

MPIDS: Multi-Group Perturbation Based Individualized Differential Structural Brain Network

MPIDS: Multi-Group Perturbation Based Individualized Differe...

引用

IEEE International Symposium on Biomedical Imaging

作者： Zixin Huang Yijie Zhang Tülay Adali Rongtao Jiang Daoqiang Zhang Vince D. Calhoun Shile Qi Department of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Department of CSEE University of Maryland Baltimore County Baltimore MD USA Department of Radiology and Biomedical Imaging Yale University New Haven CT USA Tri-institntional Center for Translational Research in Neuregionmaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

MRI-based brain structural network plays a key role in understanding the organization architecture of the human brain. However, current structural brain networks are inferred only at the group-level, missing individual heterogeneity at the single subject level. In this study, we propose a multi-group perturbation based individualized differential structural brain network (MPIDS), which reveals subject-specific deviations from a reference group in structural correlations among brain regions, enabling precision capture of individual differences. Each group is sequentially used as the reference network, with perturbations introduced by incorporating subjects from other groups. Results show that the proposed MPIDS consistently achieve the highest accuracy in predicting cognition across two datasets, three brain atlases, three prediction models comparing with voxel-based and region-based methods. Moreover, in diagnosis of SZ and ASD comparing to HC, MPIDS robustly outperformed voxel-based and region-based methods across three datasets, three brain atlases, and three classifiers. Overall, results demonstrate the effectiveness of the proposed MPIDS in both prediction and classification, which can be used to investigate the heterogeneity of brain disorders.

关键词： Correlation Accuracy Perturbation methods Organizations Predictive models Brain modeling Robustness Cognition Reproducibility of results Biomedical imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：