Early post-contrast T1 mapping yields maximal discriminatory capacity for detection of cardiac amyloid - influence of temporal T1 differences on MOLLI imaging

Background: Myocardial T1 mapping is increasingly used to diagnose and quantify disease burden in patients with known or suspected cardiac amyloid. Optimal timing for diagnostic application of T1 mapping is not established. Methods: Myocardial T1 mapping was performed in two cohorts - (1) "amyloid +" subjects, defined by biopsy-proven systemic amyloid with associated remodeling suggestive of cardiac involvement (left ventricular [LV] hypertrophy and/or atrial dilation); (2) normative controls without risk factors for amyloid or cardiovascular disease. CMR (1.5T) included 2 components - cine-CMR (SSFP) for cardiac structure/function, and T1 mapping for myocardial tissue characterization. T1 mapping was performed using a conventional modified look locker inversion recovery (MOLLI) sequence (flip angle = 30°; matrix 256 × 128; parallel imaging reduction factor =1.5; linear view ordering; 6 Kaiser-Bessel ramp preparation; 17 heart beat acquisition), with T1 calculated using an established formula (T1 = T1* (B/A-1), T1*, A, and B obtained via three-parameter exponential fit). To test time dependent differences in myocardial T1, MOLLI was acquired at sequential time points (3,5,10,14, 20 minutes) following intravenous administration of gadolinium (0.2 mmol/kg). Results: 10 subjects (5 amyloid, 5 controls) were studied (44 ± 21 years, 40% male); all amyloid affected subjects had biopsy-confirmed disease (4 light chain type, 1 transthyretin). Amyloid subjects had higher LV mass (200 ± 34 vs. 101 ± 34 gm, p = 0.004), lower myocardial contraction fraction (32 ± 8 vs. 88 ± 27, p = 0.002), and larger left atrial area (26 ± 5 vs. 18 ± 5 cm2, p = 0.03) than controls, but similar LV end-diastolic volume (120 ± 24 vs. 133 ± 45 ml, p = 0.61), stroke volume (66 ± 10 vs. 86 ± 30 ml, p = 0.20), and LVEF (56 ± 10 vs. 65 ± 4%, p = 0.11). MOLLI was successfully acquired in all subjects at each time point: T1 differed significantly (all p ≤ 0.05) between amyloid and control groups at all times (Figure 1). However, magnitude of difference temporally decreased following gadolinium administration (Figure 2): T1 differences between patients and controls were maximal at 3 minutes post-contrast (135 ± 15 vs. 310 ± 61 msec, p = 0.004) with progressive decrements thereafter, as evidenced by 57% relative difference between groups at 3 minutes and only a 36% difference at 20 minutes following gadolinium infusion. Figure 1 Mean +/- standard deviation T1 times for healthy controls and amyloid patients Mean +/- standard deviation T1 times for healthy controls and amyloid patients. Figure 2 Percentage T1 difference relative to the healthy mean T1 time Percentage T1 difference relative to the healthy mean T1 time. Conclusions: MOLLI-quantified myocardial T1 yields maximal difference between amyloid-affected subjects and normative controls within 3 minutes following gadolinium administration. Increased magnitude of T1 difference early post contrast may relate to altered gadolinium contrast kinetics due to amyloid-associated increases in cardiac extracellular volume, and/or pulse-sequence aspects of the MOLLI approach. Current findings support use of early post-contrast MOLLI T1 mapping for identification of cardiac amyloid. Funding: M. Cooper was supported by NSF GFRP DGE-0707428. J. Weinsaft was supported by NIH K23HL102249-01

Standardizing Single-Frame Phase Singularity Identification Algorithms and Parameters in Phase Mapping During Human Atrial Fibrillation

[EN] Purpose Recent investigations failed to reproduce the positive rotor-guided ablation outcomes shown by initial studies for treating persistent atrial fibrillation (persAF). Phase singularity (PS) is an important feature for AF driver detection, but algorithms for automated PS identification differ. We aim to investigate the performance of four different techniques for automated PS detection. Methods 2048-channel virtual electrogram (VEGM) and electrocardiogram signals were collected for 30 s from 10 patients undergoing persAF ablation. QRST-subtraction was performed and VEGMs were processed using sinusoidal wavelet reconstruction. The phase was obtained using Hilbert transform. PSs were detected using four algorithms: (1) 2D image processing based and neighbor-indexing algorithm; (2) 3D neighbor-indexing algorithm; (3) 2D kernel convolutional algorithm estimating topological charge; (4) topological charge estimation on 3D mesh. PS annotations were compared using the structural similarity index (SSIM) and Pearson's correlation coefficient (CORR). Optimized parameters to improve detection accuracy were found for all four algorithms usingF(beta)score and 10-fold cross-validation compared with manual annotation. Local clustering with density-based spatial clustering of applications with noise (DBSCAN) was proposed to improve algorithms 3 and 4. Results The PS density maps created by each algorithm with default parameters were poorly correlated. Phase gradient threshold and search radius (or kernels) were shown to affect PS detections. The processing times for the algorithms were significantly different (p< 0.0001). TheF(beta)scores for algorithms 1, 2, 3, 3 + DBSCAN, 4 and 4 + DBSCAN were 0.547, 0.645, 0.742, 0.828, 0.656, and 0.831. Algorithm 4 + DBSCAN achieved the best classification performance with acceptable processing time (2.0 +/- 0.3 s). Conclusion AF driver identification is dependent on the PS detection algorithms and their parameters, which could explain some of the inconsistencies in rotor-guided ablation outcomes in different studies. For 3D triangulated meshes, algorithm 4 + DBSCAN with optimal parameters was the best solution for real-time, automated PS detection due to accuracy and speed. Similarly, algorithm 3 + DBSCAN with optimal parameters is preferred for uniform 2D meshes. Such algorithms - and parameters - should be preferred in future clinical studies for identifying AF drivers and minimizing methodological heterogeneities. This would facilitate comparisons in rotor-guided ablation outcomes in future works.This work was supported by the NIHR Leicester Biomedical Research Centre, UK. XL received research grants from Medical Research Council UK (MRC DPFS Ref: MR/S037306/1). TA received research grants from the British Heart Foundation (BHF Project Grant No. PG/18/33/33780), BHF Research Accelerator Award funding and Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP, Brazil, Grant No. 2017/00319-8). MG research was funded by a research grant from the Instituto de Salud Carlos III (Ministry of Economy and Competitiveness, Spain: PI13-00903). GN received funding from the British Heart Foundation (BHF Programme Grant, RG/17/3/32774).Li, X.; Almeida, TP.; Dastagir, N.; Guillem Sánchez, MS.; Salinet, J.; Chu, GS.; Stafford, PJ.... (2020). Standardizing Single-Frame Phase Singularity Identification Algorithms and Parameters in Phase Mapping During Human Atrial Fibrillation. Frontiers in Physiology. 11:1-16. https://doi.org/10.3389/fphys.2020.00869S11611ALHUSSEINI, M., VIDMAR, D., MECKLER, G. L., KOWALEWSKI, C. A., SHENASA, F., WANG, P. J., … RAPPEL, W.-J. (2017). Two Independent Mapping Techniques Identify Rotational Activity Patterns at Sites of Local Termination During Persistent Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 28(6), 615-622. doi:10.1111/jce.13177Allessie, M. A., de Groot, N. M. S., Houben, R. P. M., Schotten, U., Boersma, E., Smeets, J. L., & Crijns, H. J. (2010). Electropathological Substrate of Long-Standing Persistent Atrial Fibrillation in Patients With Structural Heart Disease. Circulation: Arrhythmia and Electrophysiology, 3(6), 606-615. doi:10.1161/circep.109.910125Benharash, P., Buch, E., Frank, P., Share, M., Tung, R., Shivkumar, K., & Mandapati, R. (2015). Quantitative Analysis of Localized Sources Identified by Focal Impulse and Rotor Modulation Mapping in Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 8(3), 554-561. doi:10.1161/circep.115.002721BRAY, M.-A., LIN, S.-F., ALIEV, R. R., ROTH, B. J., & WIKSWO, J. P. (2001). Experimental and Theoretical Analysis of Phase Singularity Dynamics in Cardiac Tissue. Journal of Cardiovascular Electrophysiology, 12(6), 716-722. doi:10.1046/j.1540-8167.2001.00716.xBray, M.-A., & Wikswo, J. P. (2002). Use of topological charge to determine filament location and dynamics in a numerical model of scroll wave activity. IEEE Transactions on Biomedical Engineering, 49(10), 1086-1093. doi:10.1109/tbme.2002.803516Buch, E., Share, M., Tung, R., Benharash, P., Sharma, P., Koneru, J., … Shivkumar, K. (2016). Long-term clinical outcomes of focal impulse and rotor modulation for treatment of atrial fibrillation: A multicenter experience. Heart Rhythm, 13(3), 636-641. doi:10.1016/j.hrthm.2015.10.031Canny, J. (1986). A Computational Approach to Edge Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-8(6), 679-698. doi:10.1109/tpami.1986.4767851Clayton, R. H., & Nash, M. P. (2015). Analysis of Cardiac Fibrillation Using Phase Mapping. Cardiac Electrophysiology Clinics, 7(1), 49-58. doi:10.1016/j.ccep.2014.11.011Davis, J., & Goadrich, M. (2006). The relationship between Precision-Recall and ROC curves. Proceedings of the 23rd international conference on Machine learning - ICML ’06. doi:10.1145/1143844.1143874De Groot, N. M. S., Houben, R. P. M., Smeets, J. L., Boersma, E., Schotten, U., Schalij, M. J., … Allessie, M. A. (2010). Electropathological Substrate of Longstanding Persistent Atrial Fibrillation in Patients With Structural Heart Disease. Circulation, 122(17), 1674-1682. doi:10.1161/circulationaha.109.910901Earley, M. J., Abrams, D. J. R., Sporton, S. C., & Schilling, R. J. (2006). Validation of the Noncontact Mapping System in the Left Atrium During Permanent Atrial Fibrillation and Sinus Rhythm. Journal of the American College of Cardiology, 48(3), 485-491. doi:10.1016/j.jacc.2006.04.069Gianni, C., Mohanty, S., Di Biase, L., Metz, T., Trivedi, C., Gökoğlan, Y., … Natale, A. (2016). Acute and early outcomes of focal impulse and rotor modulation (FIRM)-guided rotors-only ablation in patients with nonparoxysmal atrial fibrillation. Heart Rhythm, 13(4), 830-835. doi:10.1016/j.hrthm.2015.12.028GOJRATY, S., LAVI, N., VALLES, E., KIM, S. J., MICHELE, J., & GERSTENFELD, E. P. (2009). Dominant Frequency Mapping of Atrial Fibrillation: Comparison of Contact and Noncontact Approaches. Journal of Cardiovascular Electrophysiology, 20(9), 997-1004. doi:10.1111/j.1540-8167.2009.01488.xGrandi, E., Pandit, S. V., Voigt, N., Workman, A. J., Dobrev, D., Jalife, J., & Bers, D. M. (2011). Human Atrial Action Potential and Ca 2+ Model. Circulation Research, 109(9), 1055-1066. doi:10.1161/circresaha.111.253955Gray, R. A., Pertsov, A. M., & Jalife, J. (1998). Spatial and temporal organization during cardiac fibrillation. Nature, 392(6671), 75-78. doi:10.1038/32164Guillem, M. S., Climent, A. M., Millet, J., Arenal, Á., Fernández-Avilés, F., Jalife, J., … Berenfeld, O. (2013). Noninvasive Localization of Maximal Frequency Sites of Atrial Fibrillation by Body Surface Potential Mapping. Circulation: Arrhythmia and Electrophysiology, 6(2), 294-301. doi:10.1161/circep.112.000167Guillem, M. S., Climent, A. M., Rodrigo, M., Fernández-Avilés, F., Atienza, F., & Berenfeld, O. (2016). Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications. Cardiovascular Research, 109(4), 480-492. doi:10.1093/cvr/cvw011Gurevich, D. R., & Grigoriev, R. O. (2019). Robust approach for rotor mapping in cardiac tissue. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(5), 053101. doi:10.1063/1.5086936HAISSAGUERRE, M., HOCINI, M., SHAH, A. J., DERVAL, N., SACHER, F., JAIS, P., & DUBOIS, R. (2013). Noninvasive Panoramic Mapping of Human Atrial Fibrillation Mechanisms: A Feasibility Report. Journal of Cardiovascular Electrophysiology, 24(6), 711-717. doi:10.1111/jce.12075Iyer, A. N., & Gray, R. A. (2001). An Experimentalist’s Approach to Accurate Localization of Phase Singularities during Reentry. Annals of Biomedical Engineering, 29(1), 47-59. doi:10.1114/1.1335538Jalife, J. (2002). Mother rotors and fibrillatory conduction: a mechanism of atrial fibrillation. Cardiovascular Research, 54(2), 204-216. doi:10.1016/s0008-6363(02)00223-7Jalife, J., Filgueiras Rama, D., & Berenfeld, O. (2015). Letter by Jalife et al Regarding Article, «Quantitative Analysis of Localized Sources Identified by Focal Impulse and Rotor Modulation Mapping in Atrial Fibrillation». Circulation: Arrhythmia and Electrophysiology, 8(5), 1296-1298. doi:10.1161/circep.115.003324Jarman, J. W. E., Wong, T., Kojodjojo, P., Spohr, H., Davies, J. E., Roughton, M., … Peters, N. S. (2012). Spatiotemporal Behavior of High Dominant Frequency During Paroxysmal and Persistent Atrial Fibrillation in the Human Left Atrium. Circulation: Arrhythmia and Electrophysiology, 5(4), 650-658. doi:10.1161/circep.111.967992Kuklik, P., Zeemering, S., Maesen, B., Maessen, J., Crijns, H. J., Verheule, S., … Schotten, U. (2015). Reconstruction of Instantaneous Phase of Unipolar Atrial Contact Electrogram Using a Concept of Sinusoidal Recomposition and Hilbert Transform. IEEE Transactions on Biomedical Engineering, 62(1), 296-302. doi:10.1109/tbme.2014.2350029Identification of Rotors during Human Atrial Fibrillation Using Contact Mapping and Phase Singularity Detection: Technical Considerations. (2017). IEEE Transactions on Biomedical Engineering, 64(2), 310-318. doi:10.1109/tbme.2016.2554660Lee, Y.-S., Song, J.-S., Hwang, M., Lim, B., Joung, B., & Pak, H.-N. (2016). A New Efficient Method for Detecting Phase Singularity in Cardiac Fibrillation. PLOS ONE, 11(12), e0167567. doi:10.1371/journal.pone.0167567Li, X., Chu, G. S., Almeida, T. P., Salinet, J. L., Dastagir, N., Mistry, A. R., … André Ng, G. (2017). 5Characteristics of ablated rotors in terminating persistent atrial fibrillation using non-contact mapping. EP Europace, 19(suppl_1), i3-i3. doi:10.1093/europace/eux283.145Li, X., Salinet, J. L., Almeida, T. P., Vanheusden, F. J., Chu, G. S., Ng, G. A., & Schlindwein, F. S. (2017). An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping. Computer Methods and Programs in Biomedicine, 141, 83-92. doi:10.1016/j.cmpb.2017.01.011Mandapati, R., Skanes, A., Chen, J., Berenfeld, O., & Jalife, J. (2000). Stable Microreentrant Sources as a Mechanism of Atrial Fibrillation in the Isolated Sheep Heart. Circulation, 101(2), 194-199. doi:10.1161/01.cir.101.2.194Narayan, S. M., Baykaner, T., Clopton, P., Schricker, A., Lalani, G. G., Krummen, D. E., … Miller, J. M. (2014). Ablation of Rotor and Focal Sources Reduces Late Recurrence of Atrial Fibrillation Compared With Trigger Ablation Alone. Journal of the American College of Cardiology, 63(17), 1761-1768. doi:10.1016/j.jacc.2014.02.543NARAYAN, S. M., KRUMMEN, D. E., & RAPPEL, W.-J. (2012). Clinical Mapping Approach To Diagnose Electrical Rotors and Focal Impulse Sources for Human Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 23(5), 447-454. doi:10.1111/j.1540-8167.2012.02332.xNarayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W.-J., & Miller, J. M. (2012). Treatment of Atrial Fibrillation by the Ablation of Localized Sources. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022Nattel, S. (2002). New ideas about atrial fibrillation 50 years on. Nature, 415(6868), 219-226. doi:10.1038/415219aNattel, S. (2003). Atrial Electrophysiology and Mechanisms of Atrial Fibrillation. Journal of Cardiovascular Pharmacology and Therapeutics, 8(1_suppl), S5-S11. doi:10.1177/107424840300800102Ortigosa, N., Fernández, C., Galbis, A., & Cano, Ó. (2015). Phase information of time-frequency transforms as a key feature for classification of atrial fibrillation episodes. Physiological Measurement, 36(3), 409-424. doi:10.1088/0967-3334/36/3/409Pandit, S. V., & Jalife, J. (2013). Rotors and the Dynamics of Cardiac Fibrillation. Circulation Research, 112(5), 849-862. doi:10.1161/circresaha.111.300158VII. Mathematical contributions to the theory of evolution.—III. Regression, heredity, and panmixia. (1896). Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character, 187, 253-318. doi:10.1098/rsta.1896.0007Pertsov, A. M., Davidenko, J. M., Salomonsz, R., Baxter, W. T., & Jalife, J. (1993). Spiral waves of excitation underlie reentrant activity in isolated cardiac muscle. Circulation Research, 72(3), 631-650. doi:10.1161/01.res.72.3.631Podziemski, P., Zeemering, S., Kuklik, P., van Hunnik, A., Maesen, B., Maessen, J., … Schotten, U. (2018). Rotors Detected by Phase Analysis of Filtered, Epicardial Atrial Fibrillation Electrograms Colocalize With Regions of Conduction Block. Circulation: Arrhythmia and Electrophysiology, 11(10). doi:10.1161/circep.117.005858Wieser, L., Stühlinger, M. C., Hintringer, F., Tilg, B., Fischer, G., & Rantner, L. J. (2007). Detection of Phase Singularities in Triangular Meshes. Methods of Information in Medicine, 46(06), 646-654. doi:10.3414/me0427Ríos-Muñoz, G. R., Arenal, Á., & Artés-Rodríguez, A. (2018). Real-Time Rotational Activity Detection in Atrial Fibrillation. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.00208Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008Rodrigo, M., Guillem, M. S., Climent, A. M., Pedrón-Torrecilla, J., Liberos, A., Millet, J., … Berenfeld, O. (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm, 11(9), 1584-1591. doi:10.1016/j.hrthm.2014.05.013Roney, C. H., Cantwell, C. D., Bayer, J. D., Qureshi, N. A., Lim, P. B., Tweedy, J. H., … Ng, F. S. (2017). Spatial Resolution Requirements for Accurate Identification of Drivers of Atrial Fibrillation. Circulation: Arrhythmia and Electrophysiology, 10(5). doi:10.1161/circep.116.004899Salinet, J., Schlindwein, F. S., Stafford, P., Almeida, T. P., Li, X., Vanheusden, F. J., … Ng, G. A. (2017). Propagation of meandering rotors surrounded by areas of high dominant frequency in persistent atrial fibrillation. Heart Rhythm, 14(9), 1269-1278. doi:10.1016/j.hrthm.2017.04.031Salinet, J. L., Madeiro, J. P. V., Cortez, P. C., Stafford, P. J., André Ng, G., & Schlindwein, F. S. (2013). Analysis of QRS-T subtraction in unipolar atrial fibrillation electrograms. Medical & Biological Engineering & Computing, 51(12), 1381-1391. doi:10.1007/s11517-013-1071-4Salinet, J. L., Oliveira, G. N., Vanheusden, F. J., Comba, J. L. D., Ng, G. A., & Schlindwein, F. S. (2013). Visualizing intracardiac atrial fibrillation electrograms using spectral analysis. Computing in Science & Engineering, 15(2), 79-87. doi:10.1109/mcse.2013.37Schilling, R. J., Peters, N. S., & Davies, D. W. (1998). Simultaneous Endocardial Mapping in the Human Left Ventricle Using a Noncontact Catheter. Circulation, 98(9), 887-898. doi:10.1161/01.cir.98.9.887Schricker, A. A., Lalani, G. G., Krummen, D. E., & Narayan, S. M. (2014). Rotors as Drivers of Atrial Fibrillation and Targets for Ablation. Current Cardiology Reports, 16(8). doi:10.1007/s11886-014-0509-0Steinberg, J. S., Shah, Y., Bhatt, A., Sichrovsky, T., Arshad, A., Hansinger, E., & Musat, D. (2017). Focal impulse and rotor modulation: Acute procedural observations and extended clinical follow-up. Heart Rhythm, 14(2), 192-197. doi:10.1016/j.hrthm.2016.11.008THIAGALINGAM, A., WALLACE, E. M., BOYD, A. C., EIPPER, V. E., CAMPBELL, C. R., BYTH, K., … KOVOOR, P. (2004). Noncontact Mapping of the Left Ventricle:. Insights from Validation with Transmural Contact Mapping. Pacing and Clinical Electrophysiology, 27(5), 570-578. doi:10.1111/j.1540-8159.2004.00489.xUmapathy, K., Nair, K., Masse, S., Krishnan, S., Rogers, J., Nash, M. P., & Nanthakumar, K. (2010). Phase Mapping of Cardiac Fibrillation. Circulation: Arrhythmia and Electrophysiology, 3(1), 105-114. doi:10.1161/circep.110.853804WITTKAMPF, F. H. M., & NAKAGAWA, H. (2006). RF Catheter Ablation: Lessons on Lesions. Pacing and Clinical Electrophysiology, 29(11), 1285-1297. doi:10.1111/j.1540-8159.2006.00533.xWang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing, 13(4), 600-612. doi:10.1109/tip.2003.81986

An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping

Background and Objective: Optimal targets for persistent atrial fibrillation (persAF) ablation are still debated. Atrial regions hosting high dominant frequency (HDF) are believed to participate in the initiation and maintenance of persAF and hence are potential targets for ablation, while rotor ablation has shown promising initial results. Currently, no commercially available system offers the capability to automatically identify both these phenomena. This paper describes an integrated 3D software platform combining the mapping of both frequency spectrum and phase from atrial electrograms (AEGs) to help guide persAF ablation in clinical cardiac electrophysiological studies. Methods: 30 s of 2048 non-contact AEGs (EnSite Array, St. Jude Medical) were collected and analyzed per patient. After QRST removal, the AEGs were divided into 4 s windows with a 50% overlap. Fast Fourier transform was used for DF identification. HDF areas were identified as the maximum DF to 0.25 Hz below that, and their centers of gravity (CGs) were used to track their spatiotemporal movement. Spectral organization measurements were estimated. Hilbert transform was used to calculate instantaneous phase. Results: The system was successfully used to guide catheter ablation for 10 persAF patients. The mean processing time was 10.4 ± 1.5 min, which is adequate comparing to the normal electrophysiological (EP) procedure time (120∼180 min). Conclusions: A customized software platform capable of measuring different forms of spatiotemporal AEG analysis was implemented and used in clinical environment to guide persAF ablation. The modular nature of the platform will help electrophysiological studies in understanding of the underlying AF mechanisms