Article
Neurosciences
Shile Qi, Rogers F. Silva, Daoqiang Zhang, Sergey M. Plis, Robyn Miller, Victor M. Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D. Calhoun
Summary: This study introduces a novel three-way parallel group independent component analysis (pGICA) fusion method that effectively incorporates temporal information in multimodal data fusion, demonstrating high accuracy and comparability in estimating cross-modality links. Experimental results suggest the potential of this method in investigating brain disorders.
HUMAN BRAIN MAPPING
(2022)
Article
Economics
Christian M. Hafner, Helmut Herwartz
Summary: This study introduces a model for dynamic independent component analysis, where the dynamics are driven by the score of the pseudo likelihood with respect to the rotation angle of model innovations. The simulation study shows good finite sample properties of the estimator. In an application to exchange rate series, the model-implied conditional portfolio kurtosis aligns with narratives on financial stress, consistent with a recently proposed model.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2023)
Article
Biology
Ben Wu, Subhadip Pal, Jian Kang, Ying Guo
Summary: Recent advances in neuroimaging technologies have provided opportunities to study human brain organization through different modalities, with a new approach called Distributional Independent Component Analysis (DICA) being introduced for unified feature extraction. DICA successfully recovers established functional brain networks in fMRI images and discovers structural network components in DTI images, while also providing empirical validation and performance evaluation against existing methods.
Article
Neurosciences
Junlin Jing, Benjamin Klugah-Brown, Shiyu Xia, Min Sheng, Bharat B. Biswal
Summary: This study compared the applications of Group information-guided independent component analysis (GIG-ICA) and independent vector analysis (IVA-GL) in neuroimaging research and found similarities and differences in the patterns of functional networks. IVA-GL demonstrated greater sensitivity in networks with higher intersubject variability, while GIG-ICA identified functional networks with distinct modularity patterns.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Neurosciences
Monika Graumann, Lara A. Wallenwein, Radoslaw M. Cichy
Summary: This study aimed to investigate the influence of spatial attention on object location representations, and identified processing stages in time and space through EEG and fMRI experiments. The results showed that spatial attention modulated location representations during late processing stages ( > 150 ms, in middle and high ventral visual stream areas) independent of background condition. This study clarified the processing stage at which attention modulates object location representations in the ventral visual stream and demonstrated that attentional modulation is a cognitive process separate from recurrent processes related to the processing of objects on cluttered backgrounds.
Article
Computer Science, Interdisciplinary Applications
Yuxuan Zhao, David S. Matteson, Stewart H. Mostofsky, Mary Beth Nebel, Benjamin B. Risk
Summary: Independent component analysis (ICA) and linear non-Gaussian component analysis (LNGCA) are important methods for identifying biomarkers in neurological disorders using fMRI. LNGCA outperforms principal component analysis in preserving low-variance features. A novel group LNGCA model is proposed to extract group and individual components, revealing differences in brain network engagement between autism spectrum disorder and typical development.
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2022)
Article
Computer Science, Artificial Intelligence
Zahoor Uddin, Muhammad Altaf, Ayaz Ahmad, Aamir Qamar, Farooq Alam Orakzai
Summary: This article presents a technique using independent vector analysis (IVA) for eliminating various artifacts in electrocardiogram (ECG) signals. The technique combines canonical correlation analysis (CCA) and independent component analysis (ICA) to effectively separate mixed data. The proposed technique is practical and outperforms CCA and ICA by minimizing changes to the ECG signals while removing artifacts.
PEERJ COMPUTER SCIENCE
(2023)
Article
Neurosciences
Takuto Okuno, Junichi Hata, Yawara Haga, Kanako Muta, Hiromichi Tsukada, Ken Nakae, Hideyuki Okano, Alexander Woodward
Summary: This study advances non-invasive brain analysis through novel approaches, such as big data analytics and in silico simulation. The researchers developed a Group Surrogate Data Generating Model (GSDGM) to generate biologically plausible human brain dynamics and a Multivariate Time-series Ensemble Similarity Score (MTESS) to measure similarity between multivariate time-series. These techniques were successfully applied to fingerprint analysis of resting-state brain data, distinguishing normal and outlier sessions.
Editorial Material
Biology
Kan Keeratimahat, Thomas E. Nichols
Summary: Wu et al. have made a significant contribution to MRI data-driven analysis methodology, but need to consider new potential applications and the impact of random initialization. Reanalyzing the data multiple times revealed a wide variability in reliability of the discovered independent components.
Article
Computer Science, Information Systems
Zhongqiang Luo, Ruiming Guo, Chengjie Li
Summary: This paper proposes an independent vector analysis (WA) detection receiver for blindly deconvolving the convolutive mixtures of digitally modulated signals for wireless communications. The method jointly carries out separation work for different frequency bin data fusion, and solves the random permutation problem of separation signals by exploiting the dependencies of frequency bins. Simulation results and analysis demonstrate the effectiveness of the proposed detection method.
Article
Neurosciences
Jeff B. B. Dennison, Lindsey J. J. Tepfer, David V. V. Smith
Summary: This project uses tensorial independent component analysis (tensorial ICA) to study the functional brain connectivity patterns associated with major depressive disorder (MDD). The results show decreased coherence in three networks in MDD, and one network is related to the social task. These findings suggest that tensorial ICA is a valuable tool for understanding clinical differences in relation to network activation and connectivity.
HUMAN BRAIN MAPPING
(2023)
Review
Neurosciences
Gemma Mestre-Bach, Roser Granero, Fernando Fernandez-Aranda, Susana Jimenez-Murcia, Marc N. Potenza
Summary: This narrative review explored studies that used independent component analysis (ICA) to investigate different brain networks associated with internet gaming disorder (IGD). Most studies identified alterations in the default-mode network, executive-control network, and salience network in individuals with IGD, which may contribute to the development and maintenance of this disorder. However, further research is needed to better understand the specific role of each network in the symptomatology and treatment of IGD.
DIALOGUES IN CLINICAL NEUROSCIENCE
(2023)
Article
Neurosciences
Chao-Ying Zhang, Qiu-Hua Lin, Yan-Wei Niu, Wei-Xing Li, Xiao-Feng Gong, Fengyu Cong, Yu-Ping Wang, Vince D. Calhoun
Summary: This study proposes a mathematical SSP denoising method for magnitude-only fMRI data, avoiding the need for testing various amplitude thresholds. By leveraging the phase information derived from complex-valued fMRI data, a mathematical SSP map is generated and generalized to work with magnitude-only data. Experimental results demonstrate that this method is more effective than amplitude-based thresholding, as it retains more BOLD-related activity and fewer unwanted voxels.
HUMAN BRAIN MAPPING
(2023)
Article
Psychology, Clinical
Giuseppe A. Zito, Andreas Hartmann, Benoit Beranger, Samantha Weber, Selma Aybek, Johann Faouzi, Emmanuel Roze, Marie Vidailhet, Yulia Worbe
Summary: This study used a data-driven approach to investigate the functional connectivity of brain networks in patients with Tourette disorder (TD). The results showed that there are distinct differences in connectivity patterns between TD patients and controls, as well as between medicated and unmedicated TD patients. These findings hold potential for the development of imaging-based biomarkers for TD diagnosis and treatment evaluation.
PSYCHOLOGICAL MEDICINE
(2023)
Article
Neurosciences
Pengxu Wei, Tong Zou, Zeping Lv, Yubo Fan
Summary: This study used functional magnetic resonance imaging to investigate how the brain controls walking. The results revealed multiple brain networks that coordinate limb movements, control rhythm, differentiate speed, and function as basic actor networks.
JOURNAL OF INTEGRATIVE NEUROSCIENCE
(2021)
Article
Dermatology
Hee Joo Kim, Jae Beom Park, Jong Hwan Lee, Il-Hwan Kim
INTERNATIONAL JOURNAL OF DERMATOLOGY
(2016)
Article
Neurosciences
Hojin Jang, Sergey M. Plis, Vince D. Calhoun, Jong-Hwan Lee
Article
Computer Science, Information Systems
Wanjoo Park, Da-Hye Kim, Sung-Phil Kim, Jong-Hwan Lee, Laehyun Kim
Article
Ophthalmology
Ke Jia, Xin Xue, Jong-Hwan Lee, Fang Fang, Jiaxiang Zhang, Sheng Li
Article
Neurosciences
Hyun-Chul Kim, Peter A. Bandettini, Jong-Hwan Lee
Article
Neurosciences
Hyun-Chul Kim, Marion Tegethoff, Gunther Meinlschmidt, Esther Stalujanis, Angelo Belardi, Sungman Jo, Juhyeon Lee, Dong-Youl Kim, Seung-Schik Yoo, Jong-Hwan Lee
Article
Clinical Neurology
Gunther Meinlschmidt, Marion Tegethoff, Angelo Belardi, Esther Stalujanis, Minkyung Oh, Eun Kyung Jung, Hyun-Chul Kim, Seung-Schik Yoo, Jong-Hwan Lee
JOURNAL OF AFFECTIVE DISORDERS
(2020)
Article
Biochemical Research Methods
Hyun-Chul Kim, Hojin Jang, Jong-Hwan Lee
JOURNAL OF NEUROSCIENCE METHODS
(2020)
Article
Neurosciences
Dong-Youl Kim, Eun Kyung Jung, Jun Zhang, Soo-Young Lee, Jong-Hwan Lee
HUMAN BRAIN MAPPING
(2020)
Editorial Material
Biochemical Research Methods
Jing Sui, MingXia Liu, Jong-Hwan Lee, Jun Zhang, Vince Calhoun
JOURNAL OF NEUROSCIENCE METHODS
(2020)
Article
Neurosciences
Hanh Vu, Hyun-Chul Kim, Minyoung Jung, Jong-Hwan Lee
Article
Neurosciences
Sungman Jo, Hyun-Chul Kim, Niv Lustig, Gang Chen, Jong-Hwan Lee
Summary: The study demonstrates that ROIs associated with idiosyncratic individual behavior can be identified from fMRI data using statistical approaches like MEMA. The relationship between neuronal activation in fMRI and behavioral data can be modeled using CCA. A real-world dataset on the neuronal response to nicotine use was obtained using a custom MRI-compatible apparatus.
HUMAN BRAIN MAPPING
(2021)
Article
Neurosciences
Juhyeon Lee, Minyoung Jung, Niv Lustig, Jong-Hwan Lee
Summary: We studied neural representations for visual perception of handwritten digits and visual objects using fMRI and a CNN. The CNN model's neural representation showed a hierarchical topography mapping similar to the human visual system. Lower convolutional layers of the CNN had greater similarity with early visual areas, while higher convolutional layers were encoded in higher-order visual areas. The neural representations for human visual perception were more widely distributed across the whole brain compared to the CNN model.
HUMAN BRAIN MAPPING
(2023)
Article
Psychiatry
Jinwoo Hong, Jundong Hwang, Jong-Hwan Lee
Summary: Using data from multiple sites and scanners, the study explores the connection between psychopathology factors and functional networks in adolescents. A neural network model is proposed to predict individual psychopathology factors and successfully improves the accuracy compared to alternative models.
JOURNAL OF PSYCHIATRIC RESEARCH
(2023)
Article
Neurosciences
Yeji Kim, Juhyeon Lee, Marion Tegethoff, Gunther Meinlschmidt, Seung-Schik Yoo, Jong-Hwan Lee
Summary: This study investigated the link between trait mindfulness scores and functional connectivity or behavioral data, highlighting the importance of the reliability of self-report mindfulness scores. Sixty healthy young male participants underwent functional MRI runs with mindfulness or mind-wandering tasks. Associations between self-report mindfulness scores and task accuracy, functional connectivity edges, and task performance were only observed in the consistent group. These findings emphasize the importance of appropriate screening mechanisms for self-report-based dispositional mindfulness scores when combined with neuronal features and behavioral data.
BRAIN AND COGNITION
(2023)
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.
