Neuroscience

Applications and tailor-made methods

Neuroscience

• NEW J. L. Pini, S. Moaveninejad, M. Corbetta, C. Porcaro. Fractal Dynamics of the Healthy Human Brain: A Subcortical-to-Cortical Gradient of Temporal Complexity. iScience, 29: 115858, 2026.
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• A. Moreno, Y. Zhang, Q. Huang, F. Vazquez, J. A. Nunez, E. Enriquez, D. Kim, K. Zhou, H. Gao, P. Gu, L. Zhan, H. Tang. Modeling Causal Interactions Across Brain Functional Subnetworks for Population-specific Disease Analysis. arXiv:2511.05548, 2025.
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• Y. Zhu, L. Parkkonen, A. Hyvärinen. Second-­order instantaneous causal analysis of spontaneous MEG. Imaging Neuroscience, 3: imag_a_00553, 2025.
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• Y. Li, H. Li, ans S. K. Zhou. Causal Longitudinal Image Synthesis. arXiv:2410.17691, 2024.
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• J. Ji, Z. Zhang, L. Han, J. Liu. MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation. Computers in Biology and Medicine, 170: 107940, 2024.
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• S. Chiyohara, J. Furukawa, T. Noda, J. Morimoto, and H. Imamizu. Proprioceptive short-term memory in passive motor learning. Scientific Reports, 13: 20826, 2023.
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• Z. Xia, T. Zhou, S. Mamoon, A. Alfakih, J. Lu A Structure-guided Effective and Temporal-lag Connectivity Network for Revealing Brain Disorder Mechanisms.  Arxiv preprint arXiv:2212.00555, 2022.
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• V. Lam, R. Clarnette, R. Francis, M. Bynevelt, G. Watts, L. Flicker, C. F. Orr, P. Loh, N. Lautenschlager, C. M. Reid, J. K. Foster, S. S Dhaliwal, S. Robinson, E. Corti, M. Vaccarezza, B. Horgan, R. Takechi, and J. Mamo. Efficacy of probucol on cognitive function in Alzheimer’s disease: study protocol for a double-blind, placebo-controlled, randomised phase II trial (PIA study). BMJ Open, 12(2): e058826, 2022.
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• K. Bo,  S. Yin,  Y. Liu,  Z. Hu,  S. Meyyappan,  S. Kim, A. Keil,  M. Ding. Decoding Neural Representations of Affective Scenes in Retinotopic Visual Cortex. Cerebral Cortex, bhaa411, 2021.
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• G. Zhang, A. Zhang, B. Cai, Z. Tu, V. D. Calhoun, Y. P. Wang. Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model.  Arxiv preprint arXiv:2010.13029, 2022.
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• T. Ogawa, H. Shimobayashi, J. Hirayama, M. Kawanabe. Asymmetric effective connectivity within frontoparietal motor network underlying motor imagery and motor execution. bioRxiv 2020.10.22.3511068.
  [pdf] [Google scholar]

• R. Tu, K. Zhang, B. C. Bertilson, H.  Kjellstöm, C,  Zhang. Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation. In Advances in Neural Information Processing Systems 33 (NIPS2019), pp. xx-xx, 2019.
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• V. Fleischer, M. Muthuraman, A. R. Anwar, G. Gonzalez-Escamilla, A. Radetz, R.-M. Gracien, S. Bittner, F. Luessi, S. G. Meuth, F.  Zipp, and S. Groppa  . Continuous reorganization of cortical information flow in multiple sclerosis: A longitudinal fMRI effective connectivity study. Scientific Reports, 10: 806, 2020.
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• A. Zhang, G. Zhang, B. Cai, T. W. Wilson, J. M. Stephen, V. D. Calhoun, Y.-P. Wang. A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence. Arxiv preprint arXiv:2006.12618, 2020. 
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• A. Zhang, G. Zhang, B. Cai, W. Hu, L. Xiao, T. W. Wilson, J. M. Stephen, V. D. Calhoun, Y.-P. Wang. Causal inference of brain connectivity from fMRI with ψ-Learning Incorporated Linear non-Gaussian Acyclic Model (ψ-LiNGAM). Arxiv preprint arXiv:2006.09536, 2020. 
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• B. Huang, K. Zhang, R. Sanchez-Romero, J. Ramsey, M. Glymour, C. Glymour. Diagnosis of Autism Spectrum Disorder by Causal Influence Strength Learned from Resting-State fMRI Data. Arxiv preprint arXiv:1902.10073, 2019. 
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• J. Ji, J. Liu, A. Zou, A. Zhang. ACOEC-FD: Ant Colony Optimization for Learning Brain Effective Connectivity Networks From Functional MRI and Diffusion Tensor Imaging. Frontiers in Neuroscience, 13: 1290, 2019.
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• R. Sanchez-Romero, J. D. Ramsey, K. Zhang, M. R. K. Glymour, B. Huang, and C. Glymour. Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods. Network Neuroscience, pp. xx--xx, 2018.
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• R. Sanchez-Romero, J. D. Ramsey, K. Zhang, M. R. K. Glymour, B. Huang, and C. Glymour. Causal discovery of feedback networks with functional magnetic resonance imaging. bioRxiv 245936, 2018.
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• C. Mills-Finnerty, C. Hanson, S. J. Hanson. Brain network connectivity underlying decisions between the" lesser of two evils". PeerJ Preprints 5:e3340v1, 2017.
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• N. Bielczyk, A. Llera, J. Buitelaar, J. Glennon, and C. Beckmann. Momentum: a new approach to causality in functional Magnetic Resonance Imaging. Arxiv preprint arXiv:1606.08724, 2016.
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• A. Manelis, J. R. C. Almeida, R. Stiffler, J. C. Lockovich, H. A. Aslam, and M. L. Phillips. Anticipation-related brain connectivity in bipolar and unipolar depression: a graph theory approach. Brain, pp. xx--xx, 2016.
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• I. Hettiarachchi, S. Mohamed, S. Nahavandi, and S. Nahavandi. Application of extended multivariate modeling for information flow analysis of event related responses. In Proc. 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC2015), pp. 1845-1851, Kowloon Tong, Hong Kong, 2015.
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• Y. Liu, X. Wu, J. Zhang, X. Guo, Z. Long, and L. Yao. Altered effective connectivity model in the default mode network between bipolar and unipolar depression based on resting-state fMRI. Journal of Affective Disorders, xx: xx--xx, 2015. 
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• J. M. Spielberg, R. E. McGlinchey and W. P. Milberg and D. H. Salat. Brain network disturbance related to posttraumatic stress & traumatic brain injury in veterans. Biological Psychiatry, xx: xx--xx, 2015. 
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• E. Dobryakova, O. Boukrina and G. R Wylie. Investigation of information flow during a novel working memory task in individuals with traumatic brain injury. Brain Connectivity, xx: xx--xx, 2015.
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• C. Mills-Finnerty, C. Hanson and S. J. Hanson. Brain network response underlying decisions about abstract reinforcers. NeuroImage, 103: 48--54, 2014.
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• L. Xu, T. Fan, X. Wu, K. Chen, X. Guo, J. Zhang and L. Yao. A pooling-LiNGAM algorithm for effective connectivity analysis of fMRI data. Frontiers in Computational Neuroscience, 8:125, 2014.
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• L. Schiatti, G. Nollo, G. Rossato and L. Faes. Investigating cardiovascular and cerebrovascular variability in postural syncope by means of extended Granger causality. In Proc. 8th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO2014), pp.43-44, Trento, Italy, 2014.
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• A. Manelis and L. M. Reder. Effective connectivity among the working memory regions during preparation for and during performance of the n-back task. Frontiers in Human Neuroscience, 8:593, 2014.
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• J. D. Ramsey, R. Sanchez-Romero and C. Glymour. Non-Gaussian methods and high-pass filters in the estimation of effective connections. NeuroImage, 84(1): 986--1006, 2014.
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• O. Boukrina and W. W. Graves. Neural networks underlying contributions from semantics in reading aloud. Frontiers in Human Neuroscience, 7:518, 2013.
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• D. A. Dawson, K. Cha, L. B. Lewis, J. D. Mendola, A. Shmuel. Evaluation and calibration of functional network modeling methods based on known anatomical connections. NeuroImage, 67: 331-343, 2013.
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• J. D. Ramsey, S. J. Hanson and C. Glymour. Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. NeuroImage, 58(3): 838--848, 2011.
  [pdf] [TETRAD IV] 

• S. M. Smith, K. L. Miller, G. Salimi-Khorshidi, M. Webster, C. F. Beckmann, T. E. Nichols, J. D. Ramsey and M. W. Woolrich. Network modelling methods for FMRI. NeuroImage, 54(2): 875--891, 2011.
  [pdf] [simulated fMRI data]