Limitations of multiexponential T1 mapping of cortical myeloarchitecture

. 2025 ; 20 (12) : e0338035. [epub] 20251204

Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid41343606

Neuropsychiatric malignancies frequently manifest at the level of individual cortical layers. The resolutions currently available for medical magnetic resonance imaging (MRI) prevent the study of these pathologies at clinically available field strengths of 3 T. Previous studies have claimed to have overcome these issues by extensions of quantitative MRI. Following this, the feasibility of multiexponential T1 relaxometry was assessed as a basis for in vivo delineation of cortical lamination. Three methods of non-linear least-squares-based multiexponential analysis were examined across key degrees of freedom identified in the literature. The methods employ a wide variety of ways to overcome the common pitfalls of multiexponential analysis, such as regularization, bound constraints, and repeated optimization from multiple starting points. A custom MRI phantom was 3D-printed and filled with various MnCL2 mixtures that represent the spin-lattice relaxation times that commonly occur in neocortical gray and white matter at 3 T. A 96 × 96-voxel image consisting of a single slice was acquired using a FLASH sequence and used to create 10 composite datasets with known distributions of T1 decay constants. The results showed that lowest relative error achieved across multiexponential models was approximately 20%. As achieving even this level of estimation accuracy requires either T1 ratios that rarely occur in the cerebral cortex or knowledge of the number of relaxation components and their expected values to a degree that is seldom feasible, the visualization of cortical layers based on these estimates is unlikely to represent their true distribution. In conclusion, the current methodological approaches do not allow for sufficiently precise estimation of T1 decay constants spanning the range of cortical gray and white matter.

Zobrazit více v PubMed

Brodmann K. Vergleichende lokalisationslehre der großhirnrinde in ihren prinzipeien dargestellt auf grund des zellenbaues. Leipzig: Barth. 1909.

Economo C, Triarhou LC. Cellular Structure of the Human Cerebral Cortex. 2009.

Vogt C, Vogt O. Allgemeine Ergebnisse unserer Hirnforschung. Leipzig: Verlag von Johann Ambrosius Barth. 1919.

Harrison PJ. The neuropathology of schizophrenia: A critical review of the data and their interpretation. Brain. 1999;122(4):593–624. PubMed

Wagstyl K, Ronan L, Whitaker KJ, Goodyer IM, Roberts N, Crow TJ, et al. Multiple markers of cortical morphology reveal evidence of supragranular thinning in schizophrenia. Transl Psychiatry. 2016;6(4):e780. doi: 10.1038/tp.2016.43 PubMed DOI PMC

Lewis DA, Campbell MJ, Terry RD, Morrison JH. Laminar and regional distributions of neurofibrillary tangles and neuritic plaques in Alzheimer’s disease: a quantitative study of visual and auditory cortices. J Neurosci. 1987;7(6):1799–808. doi: 10.1523/JNEUROSCI.07-06-01799.1987 PubMed DOI PMC

Romito-DiGiacomo RR, Menegay H, Cicero SA, Herrup K. Effects of Alzheimer’s Disease on Different Cortical Layers: The Role of Intrinsic Differences in Aβ Susceptibility. J Neurosci. 2007;27(32):8496–504. PubMed PMC

Stoner R, Chow ML, Boyle MP, Sunkin SM, Mouton PR, Roy S, et al. Patches of disorganization in the neocortex of children with autism. N Engl J Med. 2014;370(13):1209–19. doi: 10.1056/NEJMoa1307491 PubMed DOI PMC

Feinberg DA, Beckett AJS, Vu AT, Stockmann J, Huber L, Ma S, et al. Next-generation MRI scanner designed for ultra-high-resolution human brain imaging at 7 Tesla. Nat Methods. 2023;20(12):2048–57. doi: 10.1038/s41592-023-02068-7 PubMed DOI PMC

Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci. 2000;97(20):11050–5. PubMed PMC

Alkemade A, Mulder MJ, Groot JM, Isaacs BR, van Berendonk N, Lute N, et al. The Amsterdam Ultra-high field adult lifespan database (AHEAD): A freely available multimodal 7 Tesla submillimeter magnetic resonance imaging database. Neuroimage. 2020;221:117200. doi: 10.1016/j.neuroimage.2020.117200 PubMed DOI

Trampel R, Bazin PL, Pine K, Weiskopf N. In-vivo magnetic resonance imaging (MRI) of laminae in the human cortex. NeuroImage. 2019;197:707–15. PubMed

Edwards LJ, Kirilina E, Mohammadi S, Weiskopf N. Microstructural imaging of human neocortex in vivo. Neuroimage. 2018;182:184–206. doi: 10.1016/j.neuroimage.2018.02.055 PubMed DOI

Kundu S, Barsoum S, Ariza J, Nolan AL, Latimer CS, Keene CD, et al. Mapping the individual human cortex using multidimensional MRI and unsupervised learning. Brain Commun. 2023;5(6):fcad258. doi: 10.1093/braincomms/fcad258 PubMed DOI PMC

Eickhoff S, Walters NB, Schleicher A, Kril J, Egan GF, Zilles K, et al. High-resolution MRI reflects myeloarchitecture and cytoarchitecture of human cerebral cortex. Hum Brain Mapp. 2005;24(3):206–15. doi: 10.1002/hbm.20082 PubMed DOI PMC

Lifshits S, Tomer O, Shamir I, Barazany D, Tsarfaty G, Rosset S, et al. Resolution considerations in imaging of the cortical layers. Neuroimage. 2018;164:112–20. doi: 10.1016/j.neuroimage.2017.02.086 PubMed DOI

Istratov A, Vyvenko O. Exponential analysis in physical phenomena. Rev Sci Instrum. 1999;70:1233–57.

Spencer RG, Bi C. A Tutorial Introduction to Inverse Problems in Magnetic Resonance. NMR Biomed. 2020;33(12):e4315. doi: 10.1002/nbm.4315 PubMed DOI

Shrager RI, Hendler RW. Some pitfalls in curve-fitting and how to avoid them: a case in point. J Biochem Biophys Methods. 1998;36(2–3):157–73. doi: 10.1016/s0165-022x(98)00007-4 PubMed DOI

Tomer O, Barazany D, Baratz Z, Tsarfaty G, Assaf Y. In vivo measurements of lamination patterns in the human cortex. Hum Brain Mapp. 2022;43(9):2861–8. doi: 10.1002/hbm.25821 PubMed DOI PMC

Clayden NJ, Hesler BD. Multiexponential analysis of relaxation decays. J Magn Reson. 1992;98(2):271–82.

Jamárik J, Vojtíšek L, Churová V, Kašpárek T, Schwarz D. Identification of Laminar Composition in Cerebral Cortex Using Low-Resolution Magnetic Resonance Images and Trust Region Optimization Algorithm. Diagnostics (Basel). 2021;12(1):24. doi: 10.3390/diagnostics12010024 PubMed DOI PMC

Pohmann R, Speck O, Scheffler K. Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 tesla using current receive coil arrays. Magn Reson Med. 2016;75(2):801–9. doi: 10.1002/mrm.25677 PubMed DOI

Barral JK, Gudmundson E, Stikov N, Etezadi-Amoli M, Stoica P, Nishimura DG. A robust methodology for in vivo T1 mapping. Magn Reson Med. 2010;64(4):1057–67. PubMed PMC

Whittall KP, MacKay AL. Quantitative interpretation of NMR relaxation data. J Magn Reson. 1989;84(1):134–52.

Bojorquez JZ, Bricq S, Acquitter C, Brunotte F, Walker PM, Lalande A. What are normal relaxation times of tissues at 3 T?. Magn Reson Imaging. 2017;35:69–80. PubMed

Boudreau M, Karakuzu A, Cohen-Adad J, Bozkurt E, Carr M, Castellaro M, et al. Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge. Magn Reson Med. 2024;92(3):1115–27. doi: 10.1002/mrm.30111 PubMed DOI

Bjarnason TA, Mitchell JR. AnalyzeNNLS: Magnetic resonance multiexponential decay image analysis. J Magn Reson. 2010. Oct 1;206(2):200–4. PubMed

Fordham EJ, Venkataramanan L, Mitchell J, Valori A. What are, and what are not, inverse Laplace transforms. N/A. 2023;8.

Bromage GE. A quantification of the hazards of fitting sums of exponentials to noisy data. Comput Phys Commun. 1983;30(3):229–33.

Celik H, Bouhrara M, Reiter DA, Fishbein KW, Spencer RG. Stabilization of the inverse Laplace transform of multiexponential decay through introduction of a second dimension. J Magn Reson. 2013;236. PubMed PMC

Bi C, Fishbein K, Bouhrara M, Spencer RG. Stabilization of parameter estimates from multiexponential decay through extension into higher dimensions. Sci Rep. 2022;12(1):5773. doi: 10.1038/s41598-022-08638-7 PubMed DOI PMC

Laule C, Bjarnason TA, Vavasour IM, Traboulsee AL, Wayne Moore GR, Li DKB, et al. Characterization of brain tumours with spin–spin relaxation: pilot case study reveals unique T2 distribution profiles of glioblastoma, oligodendroglioma and meningioma. J Neurol. 2017;264(11):2205–14. PubMed

Najít záznam

Citační ukazatele

Pouze přihlášení uživatelé

Možnosti archivace

Nahrávání dat ...