Academic literature on the topic 'Hemodynamic response function delays'
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Journal articles on the topic "Hemodynamic response function delays":
Wang, Xin, Caio Seguin, Andrew Zalesky, Wan-wa Wong, Winnie Chiu-wing Chu, and Raymond Kai-yu Tong. "Synchronization lag in post stroke: relation to motor function and structural connectivity." Network Neuroscience 3, no. 4 (January 2019): 1121–40. http://dx.doi.org/10.1162/netn_a_00105.
Braban, Andra, Robert Leech, Kevin Murphy, and Fatemeh Geranmayeh. "Cerebrovascular Reactivity Has Negligible Contribution to Hemodynamic Lag After Stroke: Implications for Functional Magnetic Resonance Imaging Studies." Stroke 54, no. 4 (April 2023): 1066–77. http://dx.doi.org/10.1161/strokeaha.122.041880.
Rindler, Tara N., Valerie M. Lasko, Michelle L. Nieman, Motoi Okada, John N. Lorenz, and Jerry B. Lingrel. "Knockout of the Na,K-ATPase α2-isoform in cardiac myocytes delays pressure overload-induced cardiac dysfunction." American Journal of Physiology-Heart and Circulatory Physiology 304, no. 8 (April 15, 2013): H1147—H1158. http://dx.doi.org/10.1152/ajpheart.00594.2012.
van Meer, Maurits PA, Kajo van der Marel, Jan Willem Berkelbach van der Sprenkel, and Rick M. Dijkhuizen. "MRI of bilateral sensorimotor network activation in response to direct intracortical stimulation in rats after unilateral stroke." Journal of Cerebral Blood Flow & Metabolism 31, no. 7 (April 27, 2011): 1583–87. http://dx.doi.org/10.1038/jcbfm.2011.61.
Medeiros, Júlio, Marco Simões, João Castelhano, Rodolfo Abreu, Ricardo Couceiro, Jorge Henriques, Miguel Castelo-Branco, Henrique Madeira, César Teixeira, and Paulo de Carvalho. "EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging study." PLOS ONE 19, no. 3 (March 7, 2024): e0299108. http://dx.doi.org/10.1371/journal.pone.0299108.
Yoshie, Koji, Pradeep S. Rajendran, Louis Massoud, OhJin Kwon, Vasudev Tadimeti, Siamak Salavatian, Jeffrey L. Ardell, Kalyanam Shivkumar, and Olujimi A. Ajijola. "Cardiac vanilloid receptor-1 afferent depletion enhances stellate ganglion neuronal activity and efferent sympathetic response to cardiac stress." American Journal of Physiology-Heart and Circulatory Physiology 314, no. 5 (May 1, 2018): H954—H966. http://dx.doi.org/10.1152/ajpheart.00593.2017.
Chen, Xiaoxiao, Javier A. Sala-Mercado, Robert L. Hammond, Masashi Ichinose, Soroor Soltani, Ramakrishna Mukkamala, and Donal S. O'Leary. "Dynamic control of maximal ventricular elastance via the baroreflex and force-frequency relation in awake dogs before and after pacing-induced heart failure." American Journal of Physiology-Heart and Circulatory Physiology 299, no. 1 (July 2010): H62—H69. http://dx.doi.org/10.1152/ajpheart.00922.2009.
Feige, Bernd, Klaus Scheffler, Fabrizio Esposito, Francesco Di Salle, Jürgen Hennig, and Erich Seifritz. "Cortical and Subcortical Correlates of Electroencephalographic Alpha Rhythm Modulation." Journal of Neurophysiology 93, no. 5 (May 2005): 2864–72. http://dx.doi.org/10.1152/jn.00721.2004.
Storti, S. F., E. Formaggio, A. Bertoldo, P. Manganotti, A. Fiaschi, and G. M. Toffolo. "Modelling hemodynamic response function in epilepsy." Clinical Neurophysiology 124, no. 11 (November 2013): 2108–18. http://dx.doi.org/10.1016/j.clinph.2013.05.024.
Lesser, Ronald P. "Functional MRI of Interictal EEG Activity." Epilepsy Currents 2, no. 1 (January 2002): 17. http://dx.doi.org/10.1111/j.1535-7597.2002.00006.x.
Dissertations / Theses on the topic "Hemodynamic response function delays":
Oota, Subba Reddy. "Modèles neurocomputationnels de la compréhension du langage : caractérisation des similarités et des différences entre le traitement cérébral du langage et les modèles de langage." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0080.
This thesis explores the synergy between artificial intelligence (AI) and cognitive neuroscience to advance language processing capabilities. It builds on the insight that breakthroughs in AI, such as convolutional neural networks and mechanisms like experience replay 1, often draw inspiration from neuroscientific findings. This interconnection is beneficial in language, where a deeper comprehension of uniquely human cognitive abilities, such as processing complex linguistic structures, can pave the way for more sophisticated language processing systems. The emergence of rich naturalistic neuroimaging datasets (e.g., fMRI, MEG) alongside advanced language models opens new pathways for aligning computational language models with human brain activity. However, the challenge lies in discerning which model features best mirror the language comprehension processes in the brain, underscoring the importance of integrating biologically inspired mechanisms into computational models. In response to this challenge, the thesis introduces a data-driven framework bridging the gap between neurolinguistic processing observed in the human brain and the computational mechanisms of natural language processing (NLP) systems. By establishing a direct link between advanced imaging techniques and NLP processes, it conceptualizes brain information processing as a dynamic interplay of three critical components: "what," "where," and "when", offering insights into how the brain interprets language during engagement with naturalistic narratives. This study provides compelling evidence that enhancing the alignment between brain activity and NLP systems offers mutual benefits to the fields of neurolinguistics and NLP. The research showcases how these computational models can emulate the brain’s natural language processing capabilities by harnessing cutting-edge neural network technologies across various modalities—language, vision, and speech. Specifically, the thesis highlights how modern pretrained language models achieve closer brain alignment during narrative comprehension. It investigates the differential processing of language across brain regions, the timing of responses (Hemodynamic Response Function (HRF) delays), and the balance between syntactic and semantic information processing. Further, the exploration of how different linguistic features align with MEG brain responses over time and find that the alignment depends on the amount of past context, indicating that the brain encodes words slightly behind the current one, awaiting more future context. Furthermore, it highlights grounded language acquisition through noisy supervision and offers a biologically plausible architecture for investigating cross-situational learning, providing interpretability, generalizability, and computational efficiency in sequence-based models. Ultimately, this research contributes valuable insights into neurolinguistics, cognitive neuroscience, and NLP
Akyol, Halime Iclal. "Blind Deconvolution Techniques In Identifying Fmri Based Brain Activation." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613869/index.pdf.
smoothness&rsquo
, our method successfully estimates all the components of our framework: the HRF, the stimulus and the noise process. Then, we propose to use a modified version of Hausdorff distance to detect similarities within the space of HRFs, spectrally transform the data using Laplacian Eigenmaps and finally cluster them through EM clustering. According to our simulations, our method proves to be robust to lag, sampling jitter, quadratic drift and AWGN (Additive White Gaussian Noise). In particular, we obtained 100% sensitivity and specificity in terms of detecting active and passive voxels in our real data experiments. To conclude with, we propose a new framework for a mathematical treatment for voxel-based fMRI data analysis and our findings show that even when the HRF is unpredictable due to variability in cognitive processes, one can still obtain very high quality activation detection through the method proposed in this thesis.
Adli, Yilmaz Emine. "Wavelet Based Deconvolution Techniques In Identifying Fmri Based Brain Activation." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613870/index.pdf.
Costantini, Isa. "Reconstruction régularisée et sans a-priori de l'activation cérébrale par IRMf." Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4022.
The advent of new brain imaging techniques such as resting-state functional MRI (fMRI), has led to the need for new approaches to recover brain functional activations without a prior knowledge on the experimental paradigm, as it was the case for task-fMRI. Conventional methods, i.e. the general linear model, requires the knowledge of the task paradigm to estimate the contribution of each voxel's time course to the given task. To overcome this limitation, approaches to deconvolve the blood-oxygen-leveldependent (BOLD) response and recover the underlying neural activations without necessity of prior information has been proposed. Supposing the brain activates in constant blocks, frst we propose a temporal regularized deconvolution technique which uses an exponential operator, whose shape and performance can be adjusted, into a least absolute shrinkage and selection operator (LASSO) model solved via the Least-Angle Regression (LARS) algorithm. We reduced the number of parameters to be set by the user, when compared with the state of the art. Second, we introduce a paradigm-free regularization algorithm that applies on the 4-D fMRI image, acting simultaneously in the 3-D space and the 1-D time dimensions. The approach is based on the idea that large image variations should be preserved as they occur during an activation, whereas small variations should be smoothed to remove noise. It allows to smooth the whole fMRI image with an anisotropic regularization, thus blindly recovering the location of the brain activations in space and their timing and duration.Both approaches were tested on phantom and real data and were demonstrated to improve the results obtained in the state of the art
Shao, Kuo-fang, and 邵國芳. "Investigating the Hemodynamic Response Function of Primary Visual Cortex in Hypercapnia." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/25369059707318641661.
逢甲大學
生醫資訊暨生醫工程碩士學位學程
100
Functional magnetic resonance imaging (fMRI) using blood oxygenation level-dependent (BOLD) contrast has appeared to measure vascular oxygenation change due to neuron activity. BOLD signals have been used to investigate the cerebral neuronal activities and widely applied to clinical examinations. Recently, it is found that BOLD signals could be altered solely by hypercapnia. Such phenomenon might cause misinterpretation of fMRI studies with concurrent hypercapnia. Carbon dioxide (CO2) is a potent vasodilator that could increase the cerebral blood flow prominently. Most fMRI studies used long-time box-car stimulus design for evaluating BOLD effect in hypercapnia. In this study, instead of long-time box-car stimulus; event-related fMRI (ER-fMRI) with short-time visual stimulus is applied to investigate the transient hemodynamic response function (HRF). We aim the HRF change by different CO2 concentration inhalation. The study consisted of ten healthy volunteers with sixteen experiments for each subject under different fractions of inhaled CO2 and stimulus duration. The time curve of HRF is investigated in different visual stimulus duration (1, 2, 4 and 8 sec) and various CO2 concentration (room air, 3%, 5% and 7%). Peak-BOLD, full-width-at-half-maximum (FWHM) and time-to-peak (TTP) were used to evaluate HRF shape. Our study showed that the Peak-BOLD percent changes decrease corresponding to the increase of the inhaled CO2 fractions. The duration for FWHM and TTP prolongs proportional to the increase of inhaled CO2 fractions. This finding is consistent with the concept of cerebrovascular reserve, which might remain unchanged in lower CO2 fractions but be damped in higher CO2 fractions. In conclusion, we provide important concepts in HRF by the influence of inhaled CO2.
Srikanth, R. "3D Coding Of MR Images And Estimation Of Hemodynamic Response Function From fMRI Data." Thesis, 2004. https://etd.iisc.ac.in/handle/2005/1175.
Srikanth, R. "3D Coding Of MR Images And Estimation Of Hemodynamic Response Function From fMRI Data." Thesis, 2004. http://etd.iisc.ernet.in/handle/2005/1175.
Batista, Catarina Guerra. "Brain response functions and neurovascular coupling in Type 2 Diabetes: insights from fMRI." Master's thesis, 2020. http://hdl.handle.net/10316/94050.
A Diabetes Mellitus Tipo 2 (T2DM) é uma doença metabólica de caráter epidémico que promove várias complicações vasculares e potencialmente altera a neurofisiologia humana, tendo evidências crescentes de uma associação com o risco acrescido de desenvolvimento de perda de função cerebral, danos cognitivos a longo prazo e demência. Alterações vasculares patofisiológicas podem influenciar a regulação do fluxo sanguíneo na microvasculatura cerebral, possivelmente danificando o acoplamento neurovascular. Como estudado de forma não invasiva com pela imagem por ressonância magnética funcional, decréscimos do sinal Dependente do Nível de Oxigenação Sanguínea (BOLD) podem refletir baixa atividade neuronal ou acoplamento neurovascular pouco eficiente, pelo que as deficiências subjacentes da função cerebral podem ser indistinguíveis. Deste modo, torna-se fulcral a compreensão das correlações neurobiológicas da disfunção cerebral precoce nesta patologia. Neste projeto questionou-se se a Função de Resposta Hemodinâmica (HRF) estaria comprometida em indivíduos com T2DM, se dependeria da região cerebral ou se representaria um fenómeno cortical geral e/ou se seria influenciada pelo tipo de estímulo exibido ao medir a resposta BOLD a estímulos performance-matched de movimento visual. Foram processados os dados anatómicos e funcionais de ressonância magnética pertencentes a 141 sujeitos (64 pacientes com Diabetes Tipo 2 e 77 controlos), relativos à tarefa de estimulação psicofísica mencionada, que foi implementada distintamente em duas classes de paradigmas - paradigmas de blocos e event-related. A análise dos dados processados referentes à tarefa de estimulação de blocos permitiu localizar as regiões do cérebro por esta ativadas, nas quais, por meio de uma desconvolução, se extraiu a HRF durante a tarefa de estimulação de eventos. Por fim, foram avaliadas as diferenças entre as HRFs das duas populações.De forma geral, e como esperado, os pacientes diabéticos apresentavam HRFs significativamente diferentes. Notavelmente, este resultado estendeu-se a todas as regiões do cérebro, independentemente do tipo de estímulo, sugerindo tratar-se de um fenómeno geral. Os pacientes diabéticos exibiam HRFs com maior variabilidade, mais lentas e com menor amplitude de pico. As HRFs também incluíam um initial dip, maior que nos controlos, e, quando observado, um undershoot menos evidente, porém mais tardio e longo. A maioria dos parâmetros da HRF eram significativamente diferentes entre as duas populações, sendo a dispersão e a variabilidade maiores nos pacientes diabéticos. Para além disso, também evidenciavam uma maior latência de pico e menores declive relativo até ao pico, área abaixo da curva, área da secção positiva da curva e área da secção negativa da curva.Em suma, os resultados revelam uma função de resposta hemodinâmica comprometida nos estádios iniciais da T2DM, que se poderá dever a um desacoplamento neurovascular sem défices neurossensoriais, como demonstrado pelos limiares percetuais preservados. Deste modo, a HRF mostra ser uma ferramenta importante em estudos funcionais acerca da patologia mencionada, e deve ser considerada como um biomarcador no desenvolvimento e teste de estratégias de terapêutica nestes pacientes. No entanto, deve ser feita mais investigação relativamente ao acoplamento neurovascular e aos seus mecanismos para melhor entender e potencialmente prevenir a deterioração da função cerebral na T2DM.
Type 2 Diabetes Mellitus (T2DM) is an epidemic metabolic disease that promotes multiple vascular complications and potentially alters human neurophysiology, with growing evidence of an association with the increased risk for brain function loss, long-term cognitive impairment and dementia. Pathophysiological vascular changes can influence the blood flow regulation in cerebral microvasculature, possibly impairing the neurovascular coupling. As non-invasively studied with functional magnetic resonance imaging, decreases in the Blood Oxygenation Level-Dependent (BOLD) signal may reflect low neuronal activity or inefficient neurovascular coupling, thus underlying brain function impairments might be undistinguishable. Therefore, it becomes crucial to understand the neurobiological correlates of early brain dysfunction in this pathology. In this project, it was questioned whether the Hemodynamic Response Function (HRF) would be compromised in individuals with T2DM, whether it would depend on the brain region or would instead represent a general cortical phenomenon and/or whether it would rely on the displayed type of stimulus by measuring the BOLD response to performance-matched visual motion stimuli. Anatomical and functional magnetic resonance data from 141 subjects (64 patients with T2DM and 77 controls) in response to the aforementioned psychophysical stimulation task, which was separately implemented in two classes of paradigms - block and event-related paradigms, were processed. The analysis of the processed data concerning the block stimulation task allowed to localize activated brain regions, in which, by a deconvolution, we extracted the HRF during the event-related stimulation task. Ultimately, the differences between the HRFs of the two populations were assessed.Overall, and as expected, diabetic patients revealed significantly different HRFs. Notably, this outcome extended to all brain regions, regardless of the type of stimulus, suggesting that this is a general phenomenon. Diabetic patients displayed HRFs with higher variability, more sluggish, and with lower peak amplitude. The HRFs in T2DM also included an initial dip, which was larger than in controls, and when it was witnessed, a less prominent, but a later and longer undershoot. Most HRF parameters were significantly different between the two populations, with diabetic patients presenting a higher dispersion and variability. Furthermore, they also displayed a higher peak latency and lower relative slope to peak, area under the curve, positive curve section area, and negative curve section area.In short, the results unveiled an impaired hemodynamic response function in the early stages of T2DM, which may be due to a neurovascular uncoupling without neurosensory deficits, as demonstrated by preserved perceptual thresholds. Therefore, the HRF is proven to be a relevant tool in functional studies about the mentioned pathology, and it should be considered as a biomarker in the development and testing of therapeutic strategies in these patients. However, further research regarding neurovascular coupling and its mechanisms is required to better understand and potentially halt the deterioration of the brain function in T2DM.
Outro - DoIT – Diamarker: a consortium for the discovery of novel biomarkers in diabetes – QREN- COMPETE INFARMED Research Fund for Health (FIS-FIS-2015-01_DIA) - DiaMarkData European Foundation for the Study of Diabetes (EFSD) 2019 - Innovative Measurement of Diabetes Outcomes 2019
Book chapters on the topic "Hemodynamic response function delays":
Srikanth, R., R. Muralishankar, and A. G. Ramakrishnan. "Wavelet-Based Estimation of Hemodynamic Response Function." In Neural Information Processing, 1285–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_200.
Choukroun, Yoni, Lior Golgher, Pablo Blinder, and Lior Wolf. "Reconstructing the Hemodynamic Response Function via a Bimodal Transformer." In Lecture Notes in Computer Science, 371–81. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-43895-0_35.
Zhang, Xiaoxu, and Jian Xu. "Time-Delay Identification for Linear Systems: A Practical Method Using the Frequency Response Function." In Advances in Delays and Dynamics, 333–48. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53426-8_22.
Bai, Ping, Young Truong, and Xuemei Huang. "Nonparametric Estimation of Hemodynamic Response Function: A Frequency Domain Approach." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 190–215. Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2009. http://dx.doi.org/10.1214/09-lnms5712.
Aggarwal, Priya, Anubha Gupta, and Ajay Garg. "Joint Estimation of Hemodynamic Response Function and Voxel Activation in Functional MRI Data." In Lecture Notes in Computer Science, 142–49. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24553-9_18.
Storti, S. F., E. Formaggio, A. Bertoldo, P. Manganotti, A. Fiaschi, and G. M. Toffolo. "Selection of Optimal Hemodynamic Response Function for FMRI Analysis on Acute Stroke Patients." In IFMBE Proceedings, 253–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03882-2_67.
Hryniewicz, Nikodem, Rafał Rola, Kamil Lipiński, Ewa Piątkowska-Janko, and Piotr Bogorodzki. "Optimization of the BOLD Hemodynamic Response Function for EEG-FMRI Studies in Epilepsy." In The Latest Developments and Challenges in Biomedical Engineering, 131–46. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38430-1_11.
Kim, Jung Hwan, Amanda Taylor, and David Ress. "Simple Signed-Distance Function Depth Calculation Applied to Measurement of the fMRI BOLD Hemodynamic Response Function in Human Visual Cortex." In Computational Modeling of Objects Presented in Images. Fundamentals, Methods, and Applications, 216–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54609-4_16.
Marrelec, Guillaume, Philippe Ciuciu, Mélanie Pélégrini-Issac, and Habib Benali. "Estimation of the Hemodynamic Response Function in Event-Related Functional MRI: Directed Acyclic Graphs for a General Bayesian Inference Framework." In Lecture Notes in Computer Science, 635–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45087-0_53.
Srinivasan, Shrikanth, and Riddhi Kundu. "Fluid Management in Cardiogenic Shock." In Rational Use of Intravenous Fluids in Critically Ill Patients, 315–28. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-42205-8_15.
Conference papers on the topic "Hemodynamic response function delays":
Shuvra, Lubna Tabassum, Sheikh Md Rabiul Islam, Nowrin Zaman, and Md Asif Hasan. "Analysis of Hemodynamic Response Function using fNIRS." In 2018 International Conference on Innovation in Engineering and Technology (ICIET). IEEE, 2018. http://dx.doi.org/10.1109/ciet.2018.8660933.
Shah, Adnan, and Abd-Krim Seghouane. "Consistent estimation of the hemodynamic response function in fNIRS." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6637857.
Xue, Kaiqing, and Xue Tang. "Hemodynamic Response Function in Absence Epilepsy: An EEG-fMRI Study." In 2016 5th International Conference on Measurement, Instrumentation and Automation (ICMIA 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/icmia-16.2016.65.
Afonso, David M., Joao M. Sanches, and Martin H. Lauterbach. "Neural physiological modeling towards a hemodynamic response function for fMRI." In 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2007. http://dx.doi.org/10.1109/iembs.2007.4352615.
Marrelec, G. "Bayesian estimation of the hemodynamic response function in functional MRI." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING. AIP, 2002. http://dx.doi.org/10.1063/1.1477050.
Forti, Rodrigo M., Andréa Alessio, and Rickson C. Mesquita. "Characterization of the NIRS Hemodynamic Response Function with Independent Component Analysis." In Biomedical Optics. Washington, D.C.: OSA, 2014. http://dx.doi.org/10.1364/biomed.2014.bs3a.21.
Seghouane, Abd-Krim, and Leigh A. Johnston. "Consistent hemodynamic response estimation function in fMRI using sparse prior information." In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6867941.
Vallenilla, Natalia A., Samuel Montero-Hernandez, Hyochol Ahn, Hongyu Miao, and Luca Pollonini. "Hemodynamic Response Function from Osteoarthritic Pain using functional Near-Infrared Spectroscopy." In Bio-Optics: Design and Application. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/boda.2021.jtu4a.32.
Seghouane, Abd-Krim, and Adnan Shah. "Consistent hemodynamic response function estimation in functional MRI by first order differencing." In 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013). IEEE, 2013. http://dx.doi.org/10.1109/isbi.2013.6556467.
Seghouane, Abd-Krim, and Adnan Shah. "Consistent estimation of the FMRI hemodynamic response function in AR(1) noise." In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015). IEEE, 2015. http://dx.doi.org/10.1109/isbi.2015.7163829.
Reports on the topic "Hemodynamic response function delays":
Hedrick, Jacob, and Timothy Jacobs. PR-457-14201-R02 Variable NG Composition Effects in LB 2S Compressor Engines Phase I Engine Response. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), August 2015. http://dx.doi.org/10.55274/r0010997.
Wideman, Jr., Robert F., Nicholas B. Anthony, Avigdor Cahaner, Alan Shlosberg, Michel Bellaiche, and William B. Roush. Integrated Approach to Evaluating Inherited Predictors of Resistance to Pulmonary Hypertension Syndrome (Ascites) in Fast Growing Broiler Chickens. United States Department of Agriculture, December 2000. http://dx.doi.org/10.32747/2000.7575287.bard.