Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling

Yasemin Hosgören Alıcı

doi:10.4274/atfm.galenos.2023.63497

Derleme

Etkin Bağlantısallık ve Analiz Yöntemlerine Giriş: Psikofizyolojik Etkileşim ve Dinamik Nedensel Modelleme

Yıl 2023, Cilt: 76 Sayı: 2, 84 - 92, 31.07.2023

Yasemin Hosgören Alıcı

Öz

Beyni mekansal olarak dağılmış ancak birbirleriyle sürekli iletişim halinde olan, bilgi alışverişinde bulunan işlevsel bağlantılı bölgeler olarak ele alma fikri uzun süredir gündemde olan konulardan biridir. Son dönemde teknolojideki gelişmeler etkilerini nörogörüntüleme yöntemlerinde de göstermiş, beynin bağlantılarını anlamak için yeni tekniklerin gelişmesine sebep olmuştur. Bu tekniklerden biri de etkin bağlantısallık olup bir nöronal sistemin diğerine uyguladığı etkiyi açıklar böylece aktive olan beyin bölgeleri arasındaki nedenselliği inceleyebilir. Etkin bağlantısallığın genellikle anatomik
temelli tahminlerde kullanılması beraberinde yapısal parametrelerle bir model oluşturulmasına ihtiyaç duyulmaktadır. Bu derlemede bağlantısallığın amaçları ve etkin bağlantısallık yönteminden bahsedildikten sonra etkin bağlantısallık için kullanılan dinamik nedensel modelleme ve psikofizyolojik modellemeden bahsedilecektir. Nörogörüntüleme çalışmaları ile ilgilenenlere konu hakkında temel terimlerin ve tekniklerin açıklaması amaçlanmıştır.

Anahtar Kelimeler

Kelimeler: Etkin Bağlantısallık, Dinamik Nedensel Modelleme, Psikofizyolojik Etkileşim, Nörogörüntüleme

Etik Beyan

Editörler kurulunun dışında olan kişiler tarafından değerlendirilmiştir.

Destekleyen Kurum

Proje Numarası

Teşekkür

Kaynakça

1. Horwitz B. The elusive concept of brain connectivity. Neuroimage. 2003;19(2):466-70.
2. Lang EW, Tomé AM, Keck IR, Górriz-Sáez J, Puntonet CG. Brain connectivity analysis: A short survey. Computational intelligence and neuroscience.2012.
3. Li K, Guo L, Nie J, Li G, Liu T. Review of methods for functional brain connectivity detection using fMRI. Comput Med Imaging Graph. 2009;33:131-139.
4. Sporns O. Networks of the Brain: MIT press; 2010.
5. Friston KJ. Functional and effective connectivity: a review. Brain Connectivity. 2011;1:13-36.
6. Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 2007;447:83-86.
7. Brodmann K. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues: Barth; 1909.
8. Swanson LW, Bota M. Foundational model of structural connectivity in the nervous system with a schema for wiring diagrams, connectome, and basic plan architecture. PNAS. 2010;107:20610-20617.
9. Sporns O, Tononi G, Kötter R. The human connectome: a structural description of the human brain. PLoS Comput Biol. 2005;1:e42.
10. Staum M. Physiognomy and phrenology at the Paris Athénée. Journal of the History of Ideas. 1995;56:443-462.
11. Phillips C, Zeki S, Barlow H. Localization of function in the cerebral cortex: past, present and future. Brain. 1984;107:328-361.
12. Bargmann CI, Marder E. From the connectome to brain function. Nature Methods. 2013;10:483.
13. Bushong S. Magnetic Resonance Imaging (MRI) Physical and Biological Principles. Mosby, 4th Ed, Philadelphia. 2003.
14. Avena-Koenigsberger A, Misic B, Sporns O. Communication dynamics in complex brain networks. Nature Reviews Neuroscience. 2018;19:17.
15. Passingham RE, Stephan KE, Kötter R. The anatomical basis of functional localization in the cortex. Nature Reviews Neuroscience. 2002;3:606-616.
16. Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis. Human Brain Mapping. 1994;2:56-78.
17. Biggs N, Lloyd EK, Wilson RJ. Graph Theory, 1736-1936: Oxford University Press; 1986.
18. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. 2009;10:186-198.
19. Bullmore ET, Bassett DS. Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol. 2011;7:113-140.
20. Guye M, Bettus G, Bartolomei F, Cozzone PJ. Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks. MAGMA. 2010;23:409-421.
21. Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. The application of graph theoretical analysis to complex networks in the brain. Clinical Neurophysiology. 2007;118:2317-31.
22. Jirsa VK. Connectivity and dynamics of neural information processing. Neuroinformatics. 2004;2:183-204.
23. Stephan KE. On the role of general system theory for functional neuroimaging. Journal of Anatomy. 2004;205:443-470.
24. Leunberger D. Introduction to Dynamic Systems: Theory, models, and Applications. 1979.
25. Smith LB, Thelen E. Development as a dynamic system. Trends Cogn Sci. 2003;7:343-348.
26. Stephan KE, Friston KJ. Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdiscip Rev Cogn Sci. 2010;1:446- 459.
27. David O, Kiebel SJ, Harrison LM, Mattout J, Kilner JM, Friston KJ. Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage. 2006;30:1255-1272.
28. Friston KJ, Trujillo-Barreto N, Daunizeau J. DEM: a variational treatment of dynamic systems. Neuroimage. 2008;41:849-885.
29. Büchel C, Friston KJ. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cerebral cortex (New York, NY: 1991). 1997;7:768-778.
30. Bullmore E, Horwitz B, Honey G, Brammer M, Williams S, Sharma T. How good is good enough in path analysis of fMRI data? NeuroImage. 2000;11:289-301.
31. Friston K, Buechel C, Fink G, Morris J, Rolls E, Dolan RJ. Psychophysiological and modulatory interactions in neuroimaging. Neuroimage. 1997;6:218- 229.
32. McIntosh A, Gonzalez-Lima F. Structural modeling of functional neural pathways mapped with 2-deoxyglucose: effects of acoustic startle habituation on the auditory system. Brain Research. 1991;547:295-302.
33. Aertsen A. Dynamics of activity and connectivity in physiological neuronal networks. Nonlinear Dynamics And Neuronal Networks. 1991.
34. Moran RJ, Pinotsis DA, Friston KJ. Neural masses and fields in dynamic causal modeling. Front Comput Neurosci. 2013;7:57.
35. Stephan KE, Penny WD, Moran RJ, den Ouden HE, Daunizeau J, Friston KJ. Ten simple rules for dynamic causal modeling. Neuroimage. 2010;49:3099-3109.
36. Daunizeau J, David O, Stephan KE. Dynamic causal modelling: a critical review of the biophysical and statistical foundations. Neuroimage. 2011;58:312-22.
37. Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19:1273-1302.
38. Friston KJ, Preller KH, Mathys C, et al. Dynamic causal modelling revisited. Neuroimage. 2019;199:730-744.
39. Hailperin T. Sentential probability logic: Origins, development, current status, and technical applications: Lehigh University Press; 1996.
40. Ly A, Verhagen J, Wagenmakers E-J. Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology. Journal of Mathematical Psychology. 2016;72:19-32.
41. Bastos-Leite AJ, Ridgway GR, Silveira C, Norton A, Reis S, Friston KJ. Dysconnectivity within the default mode in first-episode schizophrenia: a stochastic dynamic causal modeling study with functional magnetic resonance imaging. Schizophr Bull. 2015;41:144-153.
42. Dirkx MF, den Ouden H, Aarts E, et al. The cerebral network of Parkinson’s tremor: an effective connectivity fMRI study. Journal of Neuroscience. 2016;36:5362-5372.
43. Goulden N, Khusnulina A, Davis NJ, et al. The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM. Neuroimage. 2014;99:180-190.
44. Daunizeau J, Stephan KE, Friston KJ. Stochastic dynamic causal modelling of fMRI data: should we care about neural noise? Neuroimage. 2012;62:464- 481.
45. Kloeden P, Platen E. Numerical Solution of Stochastic Differential Equations, (Springer‐Verlag, Berlin, 1999).
46. Sharaev MG, Zavyalova VV, Ushakov VL, Kartashov SI, Velichkovsky BM. Effective connectivity within the default mode network: dynamic causal modeling of resting-state fMRI data. Frontiers in human neuroscience. 2016;10:14.
47. Zeidman P, Jafarian A, Seghier ML, et al. A guide to group effective connectivity analysis, part 2: Second level analysis with PEB. NeuroImage. 2019;200:12-25.
48. Kahan J, Foltynie T. Understanding DCM: ten simple rules for the clinician. Neuroimage. 2013;83:542-549.
49. Frässle S, Paulus FM, Krach S, Jansen A. Test-retest reliability of effective connectivity in the face perception network. Hum Brain Mapp. 2016;37:730- 744.
50. Khoshnejad M, Piché M, Saleh S, Duncan G, Rainville P. Serial processing in primary and secondary somatosensory cortex: A DCM analysis of human fMRI data in response to innocuous and noxious electrical stimulation. Neurosci Lett. 2014;577:83-88.
51. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273-289.
52. Talairach J. Co-planar stereotaxic atlas of the human brain. 3-D proportional system: An approach to cerebral imaging. 1988.
53. Nieto-Castanon A, Ghosh SS, Tourville JA, Guenther FH. Region of interest based analysis of functional imaging data. Neuroimage. 2003;19:1303-1016.
54. Hammers A, Allom R, Koepp MJ, et al. Three‐dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Human Brain Mapping. 2003;19:224-247.
55. Shattuck DW, Mirza M, Adisetiyo V, et al. Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage. 2008;39:1064-1080.
56. Eickhoff SB, Heim S, Zilles K, Amunts K. Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps. Neuroimage. 2006;32:570-582.
57. Poldrack RA. Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience. 2007;2:67-70.
58. Friston KJ, Litvak V, Oswal A, et al. Bayesian model reduction and empirical Bayes for group (DCM) studies. Neuroimage. 2016;128:413-431.
59. Stephan KE, Penny WD, Daunizeau J, Moran RJ, Friston KJ. Bayesian model selection for group studies. Neuroimage. 2009;46:1004-1017.
60. Lohmann G, Erfurth K, Müller K, Turner R. Critical comments on dynamic causal modelling. Neuroimage. 2012;59:2322-2329.
61. Friston K, Zeidman P, Litvak V. Empirical Bayes for DCM: a group inversion scheme. Front Syst Neurosci. 2015;9:164.
62. McLaren DG, Ries ML, Xu G, Johnson SC. A generalized form of contextdependent psychophysiological interactions (gPPI): a comparison to standard approaches. Neuroimage. 2012;61:1277-1286.
63. Di X, Huang J, Biswal BB. Task modulated brain connectivity of the amygdala: a meta-analysis of psychophysiological interactions. Brain Structure and Function. 2017;222:619-634.
64. Kim J, Horwitz B. Investigating the neural basis for fMRI-based functional connectivity in a blocked design: application to interregional correlations and psycho-physiological interactions. Magn Reson Imaging. 2008;26:583-593.
65. O’Reilly JX, Woolrich MW, Behrens TE, Smith SM, Johansen-Berg H. Tools of the trade: psychophysiological interactions and functional connectivity. Social cognitive and affective neuroscience. 2012;7:604-609.
66. Gitelman DR, Penny WD, Ashburner J, Friston KJ. Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. Neuroimage. 2003;19:200-207.
67. Di X, Reynolds RC, Biswal BB. Imperfect (de) convolution may introduce spurious psychophysiological interactions and how to avoid it. Hum Brain Mapp. 2017;38:1723-1740.
68. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology. 1986;51:1173.
69. Di X, Zhang Z, Biswal BB. Understanding psychophysiological interaction and its relations to beta series correlation. Brain Imaging and Behavior. 2020:1-16.

Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling

Yıl 2023, Cilt: 76 Sayı: 2, 84 - 92, 31.07.2023

Yasemin Hosgören Alıcı

Öz

The idea of treating the brain as spatially distributed but functionally connected regions that are in constant communication with each other and
transport information has been on the agenda for a long time. Recent advances in technology have also shown their effects on neuroimaging
methods, leading to the development of new techniques to understand brain connections. One of these techniques is effective connectivity, which
explains the effect that one neuronal system exerts on another so that it can examine the causality between activated brain regions. Although it is
generally used in anatomically based predictions, it is often necessary to create a model with structural parameters. After mentioning the purposes
of connectivity prior to effective connectivity, dynamic causal modelling and psychophysiological modelling used for effective connectivity will be
discussed in this review. It is aimed to explain basic terms and techniques for investigators that interest in neuroimaging.

Anahtar Kelimeler

Effective Connectivity, Dynamic Causal Modelling, Psychophysiological Modelling, Neuroimaging

Etik Beyan

Destekleyen Kurum

Proje Numarası

Teşekkür

Kaynakça

1. Horwitz B. The elusive concept of brain connectivity. Neuroimage. 2003;19(2):466-70.
2. Lang EW, Tomé AM, Keck IR, Górriz-Sáez J, Puntonet CG. Brain connectivity analysis: A short survey. Computational intelligence and neuroscience.2012.
3. Li K, Guo L, Nie J, Li G, Liu T. Review of methods for functional brain connectivity detection using fMRI. Comput Med Imaging Graph. 2009;33:131-139.
4. Sporns O. Networks of the Brain: MIT press; 2010.
5. Friston KJ. Functional and effective connectivity: a review. Brain Connectivity. 2011;1:13-36.
6. Vincent JL, Patel GH, Fox MD, Snyder AZ, Baker JT, Van Essen DC, et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 2007;447:83-86.
7. Brodmann K. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues: Barth; 1909.
8. Swanson LW, Bota M. Foundational model of structural connectivity in the nervous system with a schema for wiring diagrams, connectome, and basic plan architecture. PNAS. 2010;107:20610-20617.
9. Sporns O, Tononi G, Kötter R. The human connectome: a structural description of the human brain. PLoS Comput Biol. 2005;1:e42.
10. Staum M. Physiognomy and phrenology at the Paris Athénée. Journal of the History of Ideas. 1995;56:443-462.
11. Phillips C, Zeki S, Barlow H. Localization of function in the cerebral cortex: past, present and future. Brain. 1984;107:328-361.
12. Bargmann CI, Marder E. From the connectome to brain function. Nature Methods. 2013;10:483.
13. Bushong S. Magnetic Resonance Imaging (MRI) Physical and Biological Principles. Mosby, 4th Ed, Philadelphia. 2003.
14. Avena-Koenigsberger A, Misic B, Sporns O. Communication dynamics in complex brain networks. Nature Reviews Neuroscience. 2018;19:17.
15. Passingham RE, Stephan KE, Kötter R. The anatomical basis of functional localization in the cortex. Nature Reviews Neuroscience. 2002;3:606-616.
16. Friston KJ. Functional and effective connectivity in neuroimaging: a synthesis. Human Brain Mapping. 1994;2:56-78.
17. Biggs N, Lloyd EK, Wilson RJ. Graph Theory, 1736-1936: Oxford University Press; 1986.
18. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. 2009;10:186-198.
19. Bullmore ET, Bassett DS. Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol. 2011;7:113-140.
20. Guye M, Bettus G, Bartolomei F, Cozzone PJ. Graph theoretical analysis of structural and functional connectivity MRI in normal and pathological brain networks. MAGMA. 2010;23:409-421.
21. Reijneveld JC, Ponten SC, Berendse HW, Stam CJ. The application of graph theoretical analysis to complex networks in the brain. Clinical Neurophysiology. 2007;118:2317-31.
22. Jirsa VK. Connectivity and dynamics of neural information processing. Neuroinformatics. 2004;2:183-204.
23. Stephan KE. On the role of general system theory for functional neuroimaging. Journal of Anatomy. 2004;205:443-470.
24. Leunberger D. Introduction to Dynamic Systems: Theory, models, and Applications. 1979.
25. Smith LB, Thelen E. Development as a dynamic system. Trends Cogn Sci. 2003;7:343-348.
26. Stephan KE, Friston KJ. Analyzing effective connectivity with functional magnetic resonance imaging. Wiley Interdiscip Rev Cogn Sci. 2010;1:446- 459.
27. David O, Kiebel SJ, Harrison LM, Mattout J, Kilner JM, Friston KJ. Dynamic causal modeling of evoked responses in EEG and MEG. NeuroImage. 2006;30:1255-1272.
28. Friston KJ, Trujillo-Barreto N, Daunizeau J. DEM: a variational treatment of dynamic systems. Neuroimage. 2008;41:849-885.
29. Büchel C, Friston KJ. Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Cerebral cortex (New York, NY: 1991). 1997;7:768-778.
30. Bullmore E, Horwitz B, Honey G, Brammer M, Williams S, Sharma T. How good is good enough in path analysis of fMRI data? NeuroImage. 2000;11:289-301.
31. Friston K, Buechel C, Fink G, Morris J, Rolls E, Dolan RJ. Psychophysiological and modulatory interactions in neuroimaging. Neuroimage. 1997;6:218- 229.
32. McIntosh A, Gonzalez-Lima F. Structural modeling of functional neural pathways mapped with 2-deoxyglucose: effects of acoustic startle habituation on the auditory system. Brain Research. 1991;547:295-302.
33. Aertsen A. Dynamics of activity and connectivity in physiological neuronal networks. Nonlinear Dynamics And Neuronal Networks. 1991.
34. Moran RJ, Pinotsis DA, Friston KJ. Neural masses and fields in dynamic causal modeling. Front Comput Neurosci. 2013;7:57.
35. Stephan KE, Penny WD, Moran RJ, den Ouden HE, Daunizeau J, Friston KJ. Ten simple rules for dynamic causal modeling. Neuroimage. 2010;49:3099-3109.
36. Daunizeau J, David O, Stephan KE. Dynamic causal modelling: a critical review of the biophysical and statistical foundations. Neuroimage. 2011;58:312-22.
37. Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19:1273-1302.
38. Friston KJ, Preller KH, Mathys C, et al. Dynamic causal modelling revisited. Neuroimage. 2019;199:730-744.
39. Hailperin T. Sentential probability logic: Origins, development, current status, and technical applications: Lehigh University Press; 1996.
40. Ly A, Verhagen J, Wagenmakers E-J. Harold Jeffreys’s default Bayes factor hypothesis tests: Explanation, extension, and application in psychology. Journal of Mathematical Psychology. 2016;72:19-32.
41. Bastos-Leite AJ, Ridgway GR, Silveira C, Norton A, Reis S, Friston KJ. Dysconnectivity within the default mode in first-episode schizophrenia: a stochastic dynamic causal modeling study with functional magnetic resonance imaging. Schizophr Bull. 2015;41:144-153.
42. Dirkx MF, den Ouden H, Aarts E, et al. The cerebral network of Parkinson’s tremor: an effective connectivity fMRI study. Journal of Neuroscience. 2016;36:5362-5372.
43. Goulden N, Khusnulina A, Davis NJ, et al. The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM. Neuroimage. 2014;99:180-190.
44. Daunizeau J, Stephan KE, Friston KJ. Stochastic dynamic causal modelling of fMRI data: should we care about neural noise? Neuroimage. 2012;62:464- 481.
45. Kloeden P, Platen E. Numerical Solution of Stochastic Differential Equations, (Springer‐Verlag, Berlin, 1999).
46. Sharaev MG, Zavyalova VV, Ushakov VL, Kartashov SI, Velichkovsky BM. Effective connectivity within the default mode network: dynamic causal modeling of resting-state fMRI data. Frontiers in human neuroscience. 2016;10:14.
47. Zeidman P, Jafarian A, Seghier ML, et al. A guide to group effective connectivity analysis, part 2: Second level analysis with PEB. NeuroImage. 2019;200:12-25.
48. Kahan J, Foltynie T. Understanding DCM: ten simple rules for the clinician. Neuroimage. 2013;83:542-549.
49. Frässle S, Paulus FM, Krach S, Jansen A. Test-retest reliability of effective connectivity in the face perception network. Hum Brain Mapp. 2016;37:730- 744.
50. Khoshnejad M, Piché M, Saleh S, Duncan G, Rainville P. Serial processing in primary and secondary somatosensory cortex: A DCM analysis of human fMRI data in response to innocuous and noxious electrical stimulation. Neurosci Lett. 2014;577:83-88.
51. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15:273-289.
52. Talairach J. Co-planar stereotaxic atlas of the human brain. 3-D proportional system: An approach to cerebral imaging. 1988.
53. Nieto-Castanon A, Ghosh SS, Tourville JA, Guenther FH. Region of interest based analysis of functional imaging data. Neuroimage. 2003;19:1303-1016.
54. Hammers A, Allom R, Koepp MJ, et al. Three‐dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Human Brain Mapping. 2003;19:224-247.
55. Shattuck DW, Mirza M, Adisetiyo V, et al. Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage. 2008;39:1064-1080.
56. Eickhoff SB, Heim S, Zilles K, Amunts K. Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps. Neuroimage. 2006;32:570-582.
57. Poldrack RA. Region of interest analysis for fMRI. Social Cognitive and Affective Neuroscience. 2007;2:67-70.
58. Friston KJ, Litvak V, Oswal A, et al. Bayesian model reduction and empirical Bayes for group (DCM) studies. Neuroimage. 2016;128:413-431.
59. Stephan KE, Penny WD, Daunizeau J, Moran RJ, Friston KJ. Bayesian model selection for group studies. Neuroimage. 2009;46:1004-1017.
60. Lohmann G, Erfurth K, Müller K, Turner R. Critical comments on dynamic causal modelling. Neuroimage. 2012;59:2322-2329.
61. Friston K, Zeidman P, Litvak V. Empirical Bayes for DCM: a group inversion scheme. Front Syst Neurosci. 2015;9:164.
62. McLaren DG, Ries ML, Xu G, Johnson SC. A generalized form of contextdependent psychophysiological interactions (gPPI): a comparison to standard approaches. Neuroimage. 2012;61:1277-1286.
63. Di X, Huang J, Biswal BB. Task modulated brain connectivity of the amygdala: a meta-analysis of psychophysiological interactions. Brain Structure and Function. 2017;222:619-634.
64. Kim J, Horwitz B. Investigating the neural basis for fMRI-based functional connectivity in a blocked design: application to interregional correlations and psycho-physiological interactions. Magn Reson Imaging. 2008;26:583-593.
65. O’Reilly JX, Woolrich MW, Behrens TE, Smith SM, Johansen-Berg H. Tools of the trade: psychophysiological interactions and functional connectivity. Social cognitive and affective neuroscience. 2012;7:604-609.
66. Gitelman DR, Penny WD, Ashburner J, Friston KJ. Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. Neuroimage. 2003;19:200-207.
67. Di X, Reynolds RC, Biswal BB. Imperfect (de) convolution may introduce spurious psychophysiological interactions and how to avoid it. Hum Brain Mapp. 2017;38:1723-1740.
68. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology. 1986;51:1173.
69. Di X, Zhang Z, Biswal BB. Understanding psychophysiological interaction and its relations to beta series correlation. Brain Imaging and Behavior. 2020:1-16.

Toplam 69 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Psikiyatri
Bölüm	Makaleler
Yazarlar	Yasemin Hosgören Alıcı 0000-0003-3384-8131
Proje Numarası	-
Yayımlanma Tarihi	31 Temmuz 2023
Yayımlandığı Sayı	Yıl 2023 Cilt: 76 Sayı: 2

Kaynak Göster

APA	Hosgören Alıcı, Y. (2023). Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling. Ankara Üniversitesi Tıp Fakültesi Mecmuası, 76(2), 84-92. https://doi.org/10.4274/atfm.galenos.2023.63497
AMA	Hosgören Alıcı Y. Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling. Ankara Üniversitesi Tıp Fakültesi Mecmuası. Temmuz 2023;76(2):84-92. doi:10.4274/atfm.galenos.2023.63497
Chicago	Hosgören Alıcı, Yasemin. “Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling”. Ankara Üniversitesi Tıp Fakültesi Mecmuası 76, sy. 2 (Temmuz 2023): 84-92. https://doi.org/10.4274/atfm.galenos.2023.63497.
EndNote	Hosgören Alıcı Y (01 Temmuz 2023) Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling. Ankara Üniversitesi Tıp Fakültesi Mecmuası 76 2 84–92.
IEEE	Y. Hosgören Alıcı, “Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling”, Ankara Üniversitesi Tıp Fakültesi Mecmuası, c. 76, sy. 2, ss. 84–92, 2023, doi: 10.4274/atfm.galenos.2023.63497.
ISNAD	Hosgören Alıcı, Yasemin. “Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling”. Ankara Üniversitesi Tıp Fakültesi Mecmuası 76/2 (Temmuz 2023), 84-92. https://doi.org/10.4274/atfm.galenos.2023.63497.
JAMA	Hosgören Alıcı Y. Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling. Ankara Üniversitesi Tıp Fakültesi Mecmuası. 2023;76:84–92.
MLA	Hosgören Alıcı, Yasemin. “Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling”. Ankara Üniversitesi Tıp Fakültesi Mecmuası, c. 76, sy. 2, 2023, ss. 84-92, doi:10.4274/atfm.galenos.2023.63497.
Vancouver	Hosgören Alıcı Y. Introduction to Effective Connectivity and Analysis Methods: Psychophysiological Interaction and Dynamic Causal Modeling. Ankara Üniversitesi Tıp Fakültesi Mecmuası. 2023;76(2):84-92.

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