Forecasting fMRI images from video sequences: linear model analysis

Zhu C, Li H, Song Z, Jiang M, Song L, Li L, Wang X, Zheng Q. Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer’s disease on routinely acquired t1-weighted imaging-based brain network. Health Inf Sci Syst. 2024;12(1):19.

Article  Google Scholar 

Sudha G, Saravanan N, Muthalakshmi M, Birunda M. Dynamically stabilized recurrent neural network optimized with artificial gorilla troops espoused Alzheimer’s disorder detection using EEG signals. Health Inf Sci Syst. 2024;12(1):25.

Article  Google Scholar 

Samokhina AM, Goncharenko V, Gtigoryan R, Strijov V. Classification models for p300 evoked potentials. Sistemy i Sredstva Informatiki [Syst Means Inf]. 2022;32(3):36–49.

Google Scholar 

Tigga NP, Garg S. Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals. Health Inf Sci Syst. 2022;11(1):1.

Article  Google Scholar 

Glover GH. Overview of functional magnetic resonance imaging. Neurosurg Clin N Am. 2011;22(2):133–9. https://doi.org/10.1016/j.nec.2010.11.001.

Article  Google Scholar 

Ozcelik F, Van Rullen R. Natural scene reconstruction from fMRI signals using generative latent diffusion. 2023. arXiv:2303.05334.

Thirion B, Duchesnay E, Hubbard E, Dubois J, Poline J-B, Lebihan D, Dehaene S. Inverse retinotopy: inferring the visual content of images from brain activation patterns. Neuroimage. 2006;33(4):1104–16.

Article  Google Scholar 

Kamitani Y, Tong F. Decoding the visual and subjective contents of the human brain. Nat Neurosci. 2005;8(5):679–85.

Article  Google Scholar 

Haynes J-D, Rees G. Predicting the orientation of invisible stimuli from activity in human primary visual cortex. Nat Neurosci. 2005;8(5):686–91.

Article  Google Scholar 

Cox DD, Savoy RL. Functional magnetic resonance imaging (fMRI) “brain reading’’: detecting and classifying distributed patterns of fmri activity in human visual cortex. Neuroimage. 2003;19(2):261–70.

Article  Google Scholar 

Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science. 2001;293(5539):2425–30.

Article  Google Scholar 

Chen Z, Qing J, Zhou JH. Cinematic mindscapes: high-quality video reconstruction from brain activity, 2023. arXiv:2305.11675.

Lan Y-T, Ren K, Wang Y, Zheng W-L, Li D, Lu B-L, Qiu L. Seeing through the brain: image reconstruction of visual perception from human brain signals. 2023. arXiv arXiv:2308.02510.

Sun J, Li M, Chen Z, Moens M-F. Neurocine: decoding vivid video sequences from human brain activities. 2024. arXiv arXiv:2402.01590.

Berezutskaya J, Vansteensel MJ, Aarnoutse EJ, Freudenburg ZV, Piantoni G, Branco MP, Ramsey NF. Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film. Sci Data 2022;9(1). https://doi.org/10.1038/s41597-022-01173-0.

Anderson D, Fite K, Petrovich N, Hirsch J. Cortical activation while watching video montage: an fMRI study. Media Psychol. 2006;8:7–24. https://doi.org/10.1207/S1532785XMEP0801_2.

Article  Google Scholar 

Puras JV, Grigorieva E. The neurovisualization methods in diagnostics of head injury: part 1—computer tomography and magnetic resonance imaging. Russian J Neurosurg. 2014;2:7–16.

Google Scholar 

Bandettini PA, Wong EC, Hinks RS, Tikofsky RS, Hyde JS. Time course epi of human brain function during task activation. Magn Reson Med. 1992;25(2):390–7. https://doi.org/10.1002/mrm.1910250220.

Article  Google Scholar 

Ogawa S, Lee TM, Kay AR, Tank DW. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci. 1990;87(24):9868–72. https://doi.org/10.1073/pnas.87.24.9868.

Article  Google Scholar 

Le Bihan D, Karni A. Applications of magnetic resonance imaging to the study of human brain function. Curr Opin Neurobiol. 1995;5(2):231–7. https://doi.org/10.1016/0959-4388(95)80031-X.

Article  Google Scholar 

Logothetis NK. The underpinnings of the BOLD functional magnetic resonance imaging signal. J Neurosci. 2003;23(10):3963–71. https://doi.org/10.1523/jneurosci.23-10-03963.2003.

Article  Google Scholar 

Connelly A, Jackson GD, Frackowiak RS, Belliveau JW, Vargha-Khadem F, Gadian DG. Functional mapping of activated human primary cortex with a clinical MR imaging system. Radiology. 1993;188(1):125–30. https://doi.org/10.1148/radiology.188.1.8511285.

Article  Google Scholar 

Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, Poncelet BP, Kennedy DN, Hoppel BE, Cohen MS, Turner R. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci. 1992;89(12):5675–9. https://doi.org/10.1073/pnas.89.12.5675.

Article  Google Scholar 

Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, Merkle H, Ugurbil K. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci. 1992;89(13):5951–5. https://doi.org/10.1073/pnas.89.13.5951.

Article  Google Scholar 

Baudendistel K, Schad LR, Friedlinger M, Wenz F, Schröder J, Lorenz WJ. Postprocessing of functional MRI data of motor cortex stimulation measured with a standard 1.5 t imager. Magn Reson Imaging. 1995;13(5):701–7. https://doi.org/10.1016/0730-725X(95)00016-A.

Article  Google Scholar 

Cox RW. Afni: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996;29(3):162–73. https://doi.org/10.1006/cbmr.1996.0014.

Article  Google Scholar 

Menon RS, Kim S-G. Spatial and temporal limits in cognitive neuroimaging with fMRI. Trends Cogn Sci. 1999;3(6):207–16.

Article  Google Scholar 

Logothetis NK. What we can do and what we cannot do with fMRI. Nature. 2008;453(7197):869–78.

Article  Google Scholar 

Sharaev M, Andreev A, Artemov A, Bernstein A, Burnaev E, Kondratyeva E, Sushchinskaya S, Akzhigitov R. fMRI: preprocessing, classification and pattern recognition. 2018.

Roux FE, Ranjeva JP, Boulanouar K, Manelfe C, Sabatier J, Tremoulet M, Berry I. Motor functional MRI for pre-surgical evaluation of cerebral tumors. Stereotactic Funct Neurosurg. 1998;68(1–4):106–11. https://doi.org/10.1159/000099910.

Article  Google Scholar 

Papke K, Hellmann T, Renger B, Morgenroth C, Knecht S, Schuierer G, Reimer P. Clinical applications of functional MRI at 1.0 t: motor and language studies in healthy subjects and patients. Eur Radiol. 1999;9(2):211–20. https://doi.org/10.1007/s003300050658.

Article  Google Scholar 

Engel SA, Rumelhart DE, Wandell BA, Lee AT, Glover GH, Chichilnisky E-J, Shadlen MN. fMRI of human visual cortex. Nature. 1994;369(6481):525. https://doi.org/10.1038/369525a0.

Article  Google Scholar 

Schneider W, Casey BJ, Noll D. Functional MRI mapping of stimulus rate effects across visual processing stages. Hum Brain Mapp. 1994;1(2):117–33. https://doi.org/10.1002/hbm.460010205.

Article  Google Scholar 

Binder JR, Rao SM, Hammeke TA, Yetkin FZ, Jesmanowicz A, Bandettini PA, Wong EC, Estkowski LD, Goldstein MD, Haughton VM, Hyde JS. Functional magnetic resonance imaging of human auditory cortex. Ann Neurol. 1994;35(6):662–72. https://doi.org/10.1002/ana.410350606.

Article  Google Scholar 

Dymarkowski S, Sunaert S, Van Oostende S, Van Hecke P, Wilms G, Demaerel P, Nuttin B, Plets C, Marchal G. Functional MRI of the brain: localisation of eloquent cortex in focal brain lesion therapy. Eur Radiol. 1998;8(9):1573–80. https://doi.org/10.1007/s003300050589.

Article  Google Scholar 

Decety J, Grezes J, Costes N, Perani D, Jeannerod M, Procyk E, Grassi F, Fazio F. Brain activity during observation of actions. influence of action content and subject’s strategy. Brain: J Neurol. 1997;120(10):1763–77.

Article  Google Scholar 

Tran D, Bourdev L, Fergus R, Torresani L, Paluri M. Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision. 2015. pp. 4489–97.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. 2015.

Boersma P, Weenink D. Praat: doing phonetics by computer [computer program]. version 6.0. 37, 3 February 2018.

Berezutskaya J, Freudenburg ZV, Ambrogioni L, Güçlü U, Gerven MAJ, Ramsey NF. Cortical network responses map onto data-driven features that capture visual semantics of movie fragments. Sci Rep. 2020. https://doi.org/10.1038/s41598-020-68853-y.

Article  Google Scholar 

Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N. An image is worth 16x16 words: transformers for image recognition at scale. 2021. arXiv:2010.11929.

留言 (0)

沒有登入
gif