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MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ

Year 2020, Volume: 83 Issue: 1, 71 - 80, 13.01.2020
https://doi.org/10.26650/IUITFD.2019.0072

Abstract

Fonksiyonel Manyetik Rezonans Görüntüleme (fMRG) verilerine dayanan fonksiyonel bağlantısallık analizleri beyin araştırmalarında önemli bir yer kazanmıştır. Özellikle dinlenim durumunda beynin büyük ölçekli nörokognitif ağlarının ortaya koyulabilmesi ve bunların hastalıklardaki değişimlerinin gösterilebilmesi bu araştırma yöntemine olan ilgiyi artırmıştır. Öte yandan, genel hatlarıyla birbirine benzer olsa da detayda farklılaşan sonuçlar üreten alternatif fonksiyonel bağlantısallık hesaplama yaklaşımları mevcuttur. Fonksiyonel nörogörüntülemenin etkin kullanımı için bu farklı yaklaşımların, ele alınan problem ve incelenen popülasyona bağlı olarak beynin büyük ölçekli ağlarının yapısal örüntülerini ve işlevlerini ortaya koymaktaki güçlü ve zayıf yönlerinin anlaşılması gereklidir. Bu çerçevede, hipotez testi için literatürden kaynaklı anatomik ilgi alanlarının seçimine dayanan tohum temelli fonksiyonel bağlantısallık analizi daha güçlü bir yaklaşımken, keşifçi araştırmalarda tümüyle veri güdümlü olan bağımsız bileşen analizi (BBA) tüm beyin verisini tarafsız değerlendirme olanağı sunmaktadır. Yöntemler arasındaki diğer önemli ayrım, grup analizleri için incelenen beyinlerin anatomik olarak ortak bir şablon üzerinde çakıştırılması veya anatomik tanımlamaların her beynin kendi mekânsal koordinatlarında gerçekleştirilmesidir. Beyinde büyük ölçekli deformasyonlara yol açan patolojilerde ikinci yolun seçimi başarımı büyük ölçüde arttırırken, büyük ölçekli sağlıklı katılımcı veri kümelerinden normatif sonuçlar çıkartmak için ilk yaklaşım daha avantajlı olabilir. Son olarak, bireysel koordinatlarda kortekse ilişkin anatomik tanımlamaların gerçekleştirilmesinde hacim veya yüzeye dayalı yaklaşımlar da fonksiyonel bağlantısallık çalışmalarının sonuçlarını önemli ölçüde etkilemektedir. Bu derlemede, fonksiyonel bağlantısallık hesaplama yaklaşımları bu üç perspektiften ele alınarak karşılaştırılacaktır.

Supporting Institution

İstanbul Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi

Project Number

42362

References

  • 1. Bijsterbosch J, Smith SM, Beckmann C. Introduction to Resting State FMRI Functional Connectivity. Oxford University Press, 2017.
  • 2. Beckmann CF, Deluca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond, B, Biol Sci 2005;360(1457):1001-13.
  • 3. Leopold DA, Maier A. Ongoing physiological processes in the cerebral cortex. Neuroimage 2012;62(4):2190-200.
  • 4. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 2003;100(1):253-8.
  • 5. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007;8(9):700-11.
  • 6. Raichle ME, Macleod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci USA 2001;98(2):676-82.
  • 7. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995;34(4):53741.
  • 8. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA 2006;103(37):13848-53.
  • 9. Schmidt SA, Akrofi K, Carpenter-thompson JR, Husain FT. Default mode, dorsal attention and auditory resting state networks exhibit differential functional connectivity in tinnitus and hearing loss. PLoS ONE 2013;8(10):e76488.
  • 10. Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis. Neuroimage 2008;42(3):1178-84.
  • 11. Critchley HD, Wiens S, Rotshtein P, Ohman A, Dolan RJ. Neural systems supporting interoceptive awareness. Nat Neurosci 2004;7(2):189-95.
  • 12. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 2002;3(3):201-15.
  • 13. Menon V, Adleman NE, White CD, Glover GH, Reiss AL. Error-related brain activation during a Go/NoGo response inhibition task. Hum Brain Mapp 2001;12(3):131-43.
  • 14. Andrews-hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron 2010;65(4):550-62.15. Toga AW, Clark KA, Thompson PM, Shattuck DW, Van horn JD. Mapping the human connectome. Neurosurgery 2012;71(1):1-5.
  • 16. Vossel S, Geng JJ, Fink GR. Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist 2014;20(2):150-9.
  • 17. Androulakis XM, Krebs KA, Jenkins C, et al. Central Executive and Default Mode Network Intranet work Functional Connectivity Patterns in Chronic Migraine. J Neurol Disord 2018;6(5):393.
  • 18. Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007;27(9):2349-56.
  • 19. Dosenbach NU, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. A dual-networks architecture of top-down control. Trends Cogn Sci (Regul Ed) 2008;12(3):99-105.
  • 20. Takamura T, Hanakawa T. Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders. J Neural Transm (Vienna) 2017;124(7):821-39.
  • 21. Greicius MD, Srivastava G, Reiss AL, Menon V. Defaultmode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci USA 2004;101(13):4637-42.
  • 22. Sheline YI, Morris JC, Snyder AZ, et al. APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aß42. J Neurosci 2010;30(50):17035-40.
  • 23. Sheline YI, Raichle ME, Snyder AZ, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry 2010;67(6):584-7.
  • 24. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Erratum: Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017;23(2):264.
  • 25. Smitha KA, Akhil raja K, Arun KM, Rajesh PG, Thomas B, Kapilamoorthy TR, et al. Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J 2017;30(4):305-17.
  • 26. Poldrack RA. Region of interest analysis for fMRI. Soc Cogn Affect Neurosci 2007;2(1):67-70.
  • 27. Whitfield-gabrieli S, Nieto-castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2012;2(3):125-41. [CrossRef] 28. Bajic D, Craig MM, Mongerson CRL, Borsook D, Becerra L. Identifying Rodent Resting-State Brain Networks with Independent Component Analysis. Front Neurosci 2017;11:685.
  • 29. Ribeiro de paula D, Ziegler E, Abeyasinghe PM, Das TK, Cavaliere C, Aiello M, et al. A method for independent component graph analysis of resting-state fMRI. Brain Behav 2017;7(3):e00626.
  • 30. Calhoun VD, Adali T, Stevens MC, Kiehl KA, Pekar JJ. Semi-blind ICA of fMRI: A method for utilizing hypothesisderived time courses in a spatial ICA analysis. Neuroimage 2005;25(2):527-38.
  • 31. Griffanti L, Douaud G, Bijsterbosch J, Evangelisti S, AlfaroAlmagro F, Glasser MF, et al. Hand classification of fMRI ICA noise components. Neuroimage 2017;154:188-205.
  • 32. Erhardt EB, Rachakonda S, Bedrick EJ, Allen EA, Adali T, Calhoun VD. Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp. 2011;32(12):207595.
  • 33. Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 2001;14(3):140-51.
  • 34. Woolrich MW, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage 2009;45(1 Suppl):S173-86.
  • 35. Ferrarini L, Palm WM, Olofsen H, van der Landen R, van Buchem MA, Reiber JH, et al. Ventricular shape biomarkers for Alzheimer’s disease in clinical MR images. Magn Reson Med 2008;59(2):260-7.
  • 36. Laird AR, Robinson JL, Mcmillan KM, et al. Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform. Neuroimage 2010;51(2):677-83.
  • 37. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal. 2001;5(2):143-56.
  • 38. Aribisala BS, He J, Blamire AM. Comparative study of standard space and real space analysis of quantitative MR brain data. J Magn Reson Imaging 2011;33(6):1503-9.
  • 39. Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53(1):1-15.
  • 40. Pauli WM, Nili AN, Tyszka JM. A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Sci Data 2018;5:180063.
  • 41. Iglesias JE, Insausti R, Lerma-usabiaga G, et al. A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage 2018;183:314-26.
  • 42. Huang H, Lu J, Wu J, Ding Z, Chen S, Duan L, et al. Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis. Sci Rep 2018;8(1):1223.
  • 43. Fischl B, Van der kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14(1):11-22. 44. Fischl B. FreeSurfer. Neuroimage 2012;62(2):774-81.
  • 45. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31(3):96880.
  • 46. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011;106(3):1125-65.
  • 47. Colclough GL, Smith SM, Nichols TE, Winkler AM, Sotiropoulos SN, Glasser MF, et al. The heritability of multi-modal connectivity in human brain activity. Elife 2017;6:e20178.
  • 48. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999;9(2):195-207.
  • 49. Lee MH, Smyser CD, Shimony JS. Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol 2013;34(10):1866-72.

MAGNETIC RESONANCE IMAGING BASED FUNCTIONAL CONNECTIVITY METHODS

Year 2020, Volume: 83 Issue: 1, 71 - 80, 13.01.2020
https://doi.org/10.26650/IUITFD.2019.0072

Abstract

Functional connectivity analyses based on functional Magnetic Resonance Imaging (fMRI) data have gained an important place in brain research. There are alternative functional connectivity estimation approaches, which, despite the similarity of the overall results, produce significant differences in their details. For effective use of the functional connectivity metrics, the strengths and weaknesses of various approaches need to be well understood. While the seed-based functional connectivity analyses based on the selection of those anatomic regions of interest derived from the literature represent a stronger approach for hypothesis testing, the independent component analysis (ICA) as a data-driven approach provides an unbiased evaluation possibility for exploratory data analysis. Another difference between the methods is related to group analyses in terms of registering individual brains to a common template or implementing anatomical definitions on the spatial coordinates of individual brains. While the latter increases the success in studies on pathologies that lead to large-scale brain deformations, the former may be advantageous for deriving normative results from large data sets. Lastly, volume vs surface-based approaches for the definition of cortical anatomy in the individual space also significantly affect the results of functional connectivity analyses. In this review, functional connectivity estimation methods will be compared by evaluating them using these three perspectives.

Project Number

42362

References

  • 1. Bijsterbosch J, Smith SM, Beckmann C. Introduction to Resting State FMRI Functional Connectivity. Oxford University Press, 2017.
  • 2. Beckmann CF, Deluca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond, B, Biol Sci 2005;360(1457):1001-13.
  • 3. Leopold DA, Maier A. Ongoing physiological processes in the cerebral cortex. Neuroimage 2012;62(4):2190-200.
  • 4. Greicius MD, Krasnow B, Reiss AL, Menon V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci USA 2003;100(1):253-8.
  • 5. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007;8(9):700-11.
  • 6. Raichle ME, Macleod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci USA 2001;98(2):676-82.
  • 7. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 1995;34(4):53741.
  • 8. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ, Smith SM, et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA 2006;103(37):13848-53.
  • 9. Schmidt SA, Akrofi K, Carpenter-thompson JR, Husain FT. Default mode, dorsal attention and auditory resting state networks exhibit differential functional connectivity in tinnitus and hearing loss. PLoS ONE 2013;8(10):e76488.
  • 10. Fransson P, Marrelec G. The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: Evidence from a partial correlation network analysis. Neuroimage 2008;42(3):1178-84.
  • 11. Critchley HD, Wiens S, Rotshtein P, Ohman A, Dolan RJ. Neural systems supporting interoceptive awareness. Nat Neurosci 2004;7(2):189-95.
  • 12. Corbetta M, Shulman GL. Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 2002;3(3):201-15.
  • 13. Menon V, Adleman NE, White CD, Glover GH, Reiss AL. Error-related brain activation during a Go/NoGo response inhibition task. Hum Brain Mapp 2001;12(3):131-43.
  • 14. Andrews-hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron 2010;65(4):550-62.15. Toga AW, Clark KA, Thompson PM, Shattuck DW, Van horn JD. Mapping the human connectome. Neurosurgery 2012;71(1):1-5.
  • 16. Vossel S, Geng JJ, Fink GR. Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist 2014;20(2):150-9.
  • 17. Androulakis XM, Krebs KA, Jenkins C, et al. Central Executive and Default Mode Network Intranet work Functional Connectivity Patterns in Chronic Migraine. J Neurol Disord 2018;6(5):393.
  • 18. Seeley WW, Menon V, Schatzberg AF, et al. Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci 2007;27(9):2349-56.
  • 19. Dosenbach NU, Fair DA, Cohen AL, Schlaggar BL, Petersen SE. A dual-networks architecture of top-down control. Trends Cogn Sci (Regul Ed) 2008;12(3):99-105.
  • 20. Takamura T, Hanakawa T. Clinical utility of resting-state functional connectivity magnetic resonance imaging for mood and cognitive disorders. J Neural Transm (Vienna) 2017;124(7):821-39.
  • 21. Greicius MD, Srivastava G, Reiss AL, Menon V. Defaultmode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci USA 2004;101(13):4637-42.
  • 22. Sheline YI, Morris JC, Snyder AZ, et al. APOE4 allele disrupts resting state fMRI connectivity in the absence of amyloid plaques or decreased CSF Aß42. J Neurosci 2010;30(50):17035-40.
  • 23. Sheline YI, Raichle ME, Snyder AZ, et al. Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry 2010;67(6):584-7.
  • 24. Drysdale AT, Grosenick L, Downar J, Dunlop K, Mansouri F, Meng Y, et al. Erratum: Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med 2017;23(2):264.
  • 25. Smitha KA, Akhil raja K, Arun KM, Rajesh PG, Thomas B, Kapilamoorthy TR, et al. Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J 2017;30(4):305-17.
  • 26. Poldrack RA. Region of interest analysis for fMRI. Soc Cogn Affect Neurosci 2007;2(1):67-70.
  • 27. Whitfield-gabrieli S, Nieto-castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect 2012;2(3):125-41. [CrossRef] 28. Bajic D, Craig MM, Mongerson CRL, Borsook D, Becerra L. Identifying Rodent Resting-State Brain Networks with Independent Component Analysis. Front Neurosci 2017;11:685.
  • 29. Ribeiro de paula D, Ziegler E, Abeyasinghe PM, Das TK, Cavaliere C, Aiello M, et al. A method for independent component graph analysis of resting-state fMRI. Brain Behav 2017;7(3):e00626.
  • 30. Calhoun VD, Adali T, Stevens MC, Kiehl KA, Pekar JJ. Semi-blind ICA of fMRI: A method for utilizing hypothesisderived time courses in a spatial ICA analysis. Neuroimage 2005;25(2):527-38.
  • 31. Griffanti L, Douaud G, Bijsterbosch J, Evangelisti S, AlfaroAlmagro F, Glasser MF, et al. Hand classification of fMRI ICA noise components. Neuroimage 2017;154:188-205.
  • 32. Erhardt EB, Rachakonda S, Bedrick EJ, Allen EA, Adali T, Calhoun VD. Comparison of multi-subject ICA methods for analysis of fMRI data. Hum Brain Mapp. 2011;32(12):207595.
  • 33. Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 2001;14(3):140-51.
  • 34. Woolrich MW, Jbabdi S, Patenaude B, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage 2009;45(1 Suppl):S173-86.
  • 35. Ferrarini L, Palm WM, Olofsen H, van der Landen R, van Buchem MA, Reiber JH, et al. Ventricular shape biomarkers for Alzheimer’s disease in clinical MR images. Magn Reson Med 2008;59(2):260-7.
  • 36. Laird AR, Robinson JL, Mcmillan KM, et al. Comparison of the disparity between Talairach and MNI coordinates in functional neuroimaging data: validation of the Lancaster transform. Neuroimage 2010;51(2):677-83.
  • 37. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal. 2001;5(2):143-56.
  • 38. Aribisala BS, He J, Blamire AM. Comparative study of standard space and real space analysis of quantitative MR brain data. J Magn Reson Imaging 2011;33(6):1503-9.
  • 39. Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53(1):1-15.
  • 40. Pauli WM, Nili AN, Tyszka JM. A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Sci Data 2018;5:180063.
  • 41. Iglesias JE, Insausti R, Lerma-usabiaga G, et al. A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage 2018;183:314-26.
  • 42. Huang H, Lu J, Wu J, Ding Z, Chen S, Duan L, et al. Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis. Sci Rep 2018;8(1):1223.
  • 43. Fischl B, Van der kouwe A, Destrieux C, Halgren E, Ségonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14(1):11-22. 44. Fischl B. FreeSurfer. Neuroimage 2012;62(2):774-81.
  • 45. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 2006;31(3):96880.
  • 46. Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011;106(3):1125-65.
  • 47. Colclough GL, Smith SM, Nichols TE, Winkler AM, Sotiropoulos SN, Glasser MF, et al. The heritability of multi-modal connectivity in human brain activity. Elife 2017;6:e20178.
  • 48. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 1999;9(2):195-207.
  • 49. Lee MH, Smyser CD, Shimony JS. Resting-state fMRI: a review of methods and clinical applications. AJNR Am J Neuroradiol 2013;34(10):1866-72.
There are 46 citations in total.

Details

Primary Language Turkish
Subjects Health Care Administration
Journal Section Reviews
Authors

Emre Harı This is me 0000-0002-8329-5507

Ulaş Ay This is me 0000-0001-7896-3681

Hüden Neşe This is me 0000-0001-7646-2875

Ali Bayram This is me 0000-0002-6588-3479

Tamer Demiralp 0000-0002-6803-734X

Project Number 42362
Publication Date January 13, 2020
Submission Date September 5, 2019
Published in Issue Year 2020 Volume: 83 Issue: 1

Cite

APA Harı, E., Ay, U., Neşe, H., Bayram, A., et al. (2020). MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ. Journal of Istanbul Faculty of Medicine, 83(1), 71-80. https://doi.org/10.26650/IUITFD.2019.0072
AMA Harı E, Ay U, Neşe H, Bayram A, Demiralp T. MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ. İst Tıp Fak Derg. January 2020;83(1):71-80. doi:10.26650/IUITFD.2019.0072
Chicago Harı, Emre, Ulaş Ay, Hüden Neşe, Ali Bayram, and Tamer Demiralp. “MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ”. Journal of Istanbul Faculty of Medicine 83, no. 1 (January 2020): 71-80. https://doi.org/10.26650/IUITFD.2019.0072.
EndNote Harı E, Ay U, Neşe H, Bayram A, Demiralp T (January 1, 2020) MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ. Journal of Istanbul Faculty of Medicine 83 1 71–80.
IEEE E. Harı, U. Ay, H. Neşe, A. Bayram, and T. Demiralp, “MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ”, İst Tıp Fak Derg, vol. 83, no. 1, pp. 71–80, 2020, doi: 10.26650/IUITFD.2019.0072.
ISNAD Harı, Emre et al. “MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ”. Journal of Istanbul Faculty of Medicine 83/1 (January 2020), 71-80. https://doi.org/10.26650/IUITFD.2019.0072.
JAMA Harı E, Ay U, Neşe H, Bayram A, Demiralp T. MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ. İst Tıp Fak Derg. 2020;83:71–80.
MLA Harı, Emre et al. “MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ”. Journal of Istanbul Faculty of Medicine, vol. 83, no. 1, 2020, pp. 71-80, doi:10.26650/IUITFD.2019.0072.
Vancouver Harı E, Ay U, Neşe H, Bayram A, Demiralp T. MANYETİK REZONANS GÖRÜNTÜLEME TEMELLİ FONKSİYONEL BAĞLANTISALLIK YÖNTEMLERİ. İst Tıp Fak Derg. 2020;83(1):71-80.

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Addressi: İ.Ü. İstanbul Tıp Fakültesi Dekanlığı, Turgut Özal Cad. 34093 Çapa, Fatih, İstanbul, TÜRKİYE

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