A roadmap towards standardized neuroimaging approaches for human thalamic nuclei.
Journal
Nature reviews. Neuroscience
ISSN: 1471-0048
Titre abrégé: Nat Rev Neurosci
Pays: England
ID NLM: 100962781
Informations de publication
Date de publication:
17 Oct 2024
17 Oct 2024
Historique:
accepted:
11
09
2024
medline:
18
10
2024
pubmed:
18
10
2024
entrez:
17
10
2024
Statut:
aheadofprint
Résumé
The thalamus has a key role in mediating cortical-subcortical interactions but is often neglected in neuroimaging studies, which mostly focus on changes in cortical structure and activity. One of the main reasons for the thalamus being overlooked is that the delineation of individual thalamic nuclei via neuroimaging remains controversial. Indeed, neuroimaging atlases vary substantially regarding which thalamic nuclei are included and how their delineations were established. Here, we review current and emerging methods for thalamic nuclei segmentation in neuroimaging data and consider the limitations of existing techniques in terms of their research and clinical applicability. We address these challenges by proposing a roadmap to improve thalamic nuclei segmentation in human neuroimaging and, in turn, harmonize research approaches and advance clinical applications. We believe that a collective effort is required to achieve this. We hope that this will ultimately lead to the thalamic nuclei being regarded as key brain regions in their own right and not (as often currently assumed) as simply a gateway between cortical and subcortical regions.
Identifiants
pubmed: 39420114
doi: 10.1038/s41583-024-00867-1
pii: 10.1038/s41583-024-00867-1
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. Springer Nature Limited.
Références
Usrey, W. M. & Sherman, S. M. In: The Cerebral Cortex and Thalamus (eds Usrey, W. M. & Sherman, S. M.) 3–10 (Oxford Univ. Press, 2023). An updated and comprehensive volume on thalamic nuclei and their contributions to cortical mechanisms.
Jones, E. G. The Thalamus (Cambridge Univ. Press, 2007).
Halassa, M. M. & Sherman, S. M. Thalamocortical circuit motifs: a general framework. Neuron 103, 762–770 (2019).
doi: 10.1016/j.neuron.2019.06.005
Krauth, A. et al. A mean three-dimensional atlas of the human thalamus: generation from multiple histological data. Neuroimage 49, 2053–2062 (2010).
doi: 10.1016/j.neuroimage.2009.10.042
Tourdias, T., Saranathan, M., Levesque, I. R., Su, J. & Rutt, B. K. Visualization of intra-thalamic nuclei with optimized white-matter-nulled MPRAGE at 7T. Neuroimage 84, 534–545 (2014).
doi: 10.1016/j.neuroimage.2013.08.069
Segobin, S. & Pitel, A. L. The specificity of thalamic alterations in Korsakoff’s syndrome: implications for the study of amnesia. Neurosci. Biobehav. Rev. 130, 292–300 (2021).
doi: 10.1016/j.neubiorev.2021.07.037
Kumar, V. J., Scheffler, K., Hagberg, G. E. & Grodd, W. Quantitative susceptibility mapping of the basal ganglia and thalamus at 9.4 Tesla. Front. Neuroanat. 15, 725731 (2021).
doi: 10.3389/fnana.2021.725731
Morel, A., Magnin, M. & Jeanmonod, D. Multiarchitectonic and stereotactic atlas of the human thalamus. J. Comp. Neurol. 387, 588–630 (1997). Most probabilistic atlases that incorporate histological data are derived from this histological atlas.
doi: 10.1002/(SICI)1096-9861(19971103)387:4<588::AID-CNE8>3.0.CO;2-Z
Niemann, K., Mennicken, V. R., Jeanmonod, D. & Morel, A. The Morel stereotactic atlas of the human thalamus: atlas-to-MR registration of internally consistent canonical model. Neuroimage 12, 601–616 (2000).
doi: 10.1006/nimg.2000.0650
Schaltenbrand, G. & Wahren, W. Atlas for Stereotaxy of the Human Brain (Thieme, 1977).
Chakravarty, M. M., Bertrand, G., Hodge, C. P., Sadikot, A. F. & Collins, D. L. The creation of a brain atlas for image guided neurosurgery using serial histological data. Neuroimage 30, 359–376 (2006).
doi: 10.1016/j.neuroimage.2005.09.041
Sadikot, A. et al. Creation of computerized 3D MRI-integrated atlases of the human basal ganglia and thalamus. Front. Syst. Neurosci. 5, 71 (2011).
doi: 10.3389/fnsys.2011.00071
Iglehart, C., Monti, M., Cain, J., Tourdias, T. & Saranathan, M. A systematic comparison of structural-, structural connectivity-, and functional connectivity-based thalamus parcellation techniques. Brain Struct. Funct. 225, 1631–1642 (2020).
doi: 10.1007/s00429-020-02085-8
Hwang, K., Shine, J. M., Cole, M. W. & Sorenson, E. Thalamocortical contributions to cognitive task activity. eLife 11, e81282 (2022).
doi: 10.7554/eLife.81282
Antonucci, L. A. et al. Flexible and specific contributions of thalamic subdivisions to human cognition. Neurosci. Biobehav. Rev. 124, 35–53 (2021).
doi: 10.1016/j.neubiorev.2021.01.014
Kumar, V. J., Beckmann, C. F., Scheffler, K. & Grodd, W. Relay and higher-order thalamic nuclei show an intertwined functional association with cortical-networks. Commun. Biol. 5, 1187 (2022).
doi: 10.1038/s42003-022-04126-w
Wen, H. et al. Pulvinar response profiles and connectivity patterns to object domains. J. Neurosci. 43, 812–826 (2023).
doi: 10.1523/JNEUROSCI.0613-22.2022
Dadar, M., Fonov, V. S. & Collins, D. L. A comparison of publicly available linear MRI stereotaxic registration techniques. Neuroimage 174, 191–200 (2018).
doi: 10.1016/j.neuroimage.2018.03.025
Klein, A. et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46, 786–802 (2009).
doi: 10.1016/j.neuroimage.2008.12.037
Mai, J. K. & Majtanik, M. Toward a common terminology for the thalamus. Front. Neuroanat. 12, 114 (2019). A paper that underlines the importance of a common nomenclature and discusses how it can be achieved.
doi: 10.3389/fnana.2018.00114
pubmed: 30687023
Deoni, S. C. L., Josseau, M. J. C., Rutt, B. K. & Peters, T. M. Visualization of thalamic nuclei on high resolution, multi-averaged T1 and T2 maps acquired at 1.5T. Hum. Brain Mapp. 25, 353–359 (2005).
doi: 10.1002/hbm.20117
pubmed: 15852386
Deoni, S. C. L., Rutt, B. K., Parrent, A. G. & Peters, T. M. Segmentation of thalamic nuclei using a modified k-means clustering algorithm and high-resolution quantitative magnetic resonance imaging at 1.5T. Neuroimage 34, 117–126 (2007).
doi: 10.1016/j.neuroimage.2006.09.016
pubmed: 17070073
Traynor, C. R., Barker, G. J., Crum, W. R., Williams, S. C. R. & Richardson, M. P. Segmentation of the thalamus in MRI based on T1 and T2. Neuroimage 56, 939–950 (2011).
doi: 10.1016/j.neuroimage.2011.01.083
pubmed: 21310246
Mulder, M. J., Keuken, M. C., Bazin, P.-L., Alkemade, A. & Forstmann, B. U. Size and shape matter: the impact of voxel geometry on the identification of small nuclei. PLoS One 14, e0215382 (2019).
doi: 10.1371/journal.pone.0215382
pubmed: 30978242
Iglesias, J. E. & Sabuncu, M. R. Multi-atlas segmentation of biomedical images: a survey. Med. Image Anal. 24, 205–219 (2015).
doi: 10.1016/j.media.2015.06.012
pubmed: 26201875
Iglesias, J. E. et al. A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. Neuroimage 183, 314–326 (2018).
doi: 10.1016/j.neuroimage.2018.08.012
pubmed: 30121337
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).
doi: 10.1016/j.neuroimage.2012.01.021
Wang, H. et al. Multi-atlas segmentation with joint label fusion. IEEE Trans. Pattern Anal. Mach. Intell. 35, 611–623 (2013).
doi: 10.1109/TPAMI.2012.143
Su, J. H. et al. Thalamus Optimized Multi Atlas Segmentation (THOMAS): fast, fully automated segmentation of thalamic nuclei from structural MRI. Neuroimage 194, 272–282 (2019).
doi: 10.1016/j.neuroimage.2019.03.021
Saranathan, M., Iglehart, C., Monti, M., Tourdias, T. & Rutt, B. In vivo high-resolution structural MRI-based atlas of human thalamic nuclei. Sci. Data 8, 275 (2021).
pmcid: 8553748
doi: 10.1038/s41597-021-01062-y
Saranathan, M., Tourdias, T., Bayram, E., Ghanouni, P. & Rutt, B. K. Optimization of white-matter-nulled magnetization prepared rapid gradient echo (MP-RAGE) imaging. Magn. Reson. Med. 73, 1786–1794 (2015).
doi: 10.1002/mrm.25298
Sudhyadhom, A., Haq, I. U., Foote, K. D., Okun, M. S. & Bova, F. J. A high resolution and high contrast MRI for differentiation of subcortical structures for DBS targeting: the Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR). Neuroimage 47, T44–T52 (2009).
doi: 10.1016/j.neuroimage.2009.04.018
Brun, G. et al. Automatic segmentation of deep grey nuclei using a high-resolution 7 T magnetic resonance imaging atlas-Quantification of T1 values in healthy volunteers. Eur. J. Neurosci. 55, 438–460 (2022).
doi: 10.1111/ejn.15575
Datta, R. et al. Fast automatic segmentation of thalamic nuclei from MP2RAGE acquisition at 7 Tesla. Magn. Reson. Med. 85, 2781–2790 (2021).
doi: 10.1002/mrm.28608
Weiskopf, N. et al. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3 T: a multi-center validation. Front. Neurosci. 7, 95 (2013).
pmcid: 3677134
doi: 10.3389/fnins.2013.00095
Caan, M. W. A. et al. MP2RAGEME: T1, T2*, and QSM mapping in one sequence at 7 tesla. Hum. Brain Mapp. 40, 1786–1798 (2019).
doi: 10.1002/hbm.24490
Alkemade, A. et al. 7 Tesla MRI followed by histological 3D reconstructions in whole-brain specimens. Front. Neuroanat. 14, 536838 (2020).
pmcid: 7574789
doi: 10.3389/fnana.2020.536838
Alkemade, A. et al. A unified 3D map of microscopic architecture and MRI of the human brain. Sci. Adv. 8, eabj7892 (2022).
pmcid: 9045605
doi: 10.1126/sciadv.abj7892
Duan, Y., Li, X. & Xi, Y. Thalamus segmentation from diffusion tensor magnetic resonance imaging. Int. J. Biomed. Imaging 2007, 90216 (2007).
pmcid: 2234355
doi: 10.1155/2007/90216
Jonasson, L. et al. A level set method for segmentation of the thalamus and its nuclei in DT-MRI. Signal. Process. 87, 309–321 (2007).
doi: 10.1016/j.sigpro.2005.12.017
Rittner, L., Lotufo, R. A., Campbell, J. & Pike, G. B. In 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1173–1176 (2010).
Wiegell, M. R., Tuch, D. S., Larsson, H. B. W. & Wedeen, V. J. Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging. Neuroimage 19, 391–401 (2003).
doi: 10.1016/S1053-8119(03)00044-2
Kumar, V., Mang, S. & Grodd, W. Direct diffusion-based parcellation of the human thalamus. Brain Struct. Funct. 220, 1619–1635 (2015).
doi: 10.1007/s00429-014-0748-2
Mang, S. C., Busza, A., Reiterer, S., Grodd, W. & Klose, A. U. Thalamus segmentation based on the local diffusion direction: a group study. Magn. Reson. Med. 67, 118–126 (2012).
doi: 10.1002/mrm.22996
Ziyan, U., Tuch, D. & Westin, C.-F. Segmentation of thalamic nuclei from DTI using spectral clustering. Med. Image Comput. Comput. Interv. 9, 807–814 (2006).
Ziyan, U. & Westin, C.-F. Joint segmentation of thalamic nuclei from a population of diffusion tensor MR images. Med. Image Comput. Comput. Interv. 11, 279–286 (2008).
Najdenovska, E. et al. In-vivo probabilistic atlas of human thalamic nuclei based on diffusion-weighted magnetic resonance imaging. Sci. Data 5, 180270 (2018).
pmcid: 6257045
doi: 10.1038/sdata.2018.270
Battistella, G. et al. Robust thalamic nuclei segmentation method based on local diffusion magnetic resonance properties. Brain Struct. Funct. 222, 2203–2216 (2017).
doi: 10.1007/s00429-016-1336-4
Behrens, T. E. J. et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750–757 (2003).
doi: 10.1038/nn1075
O’Muircheartaigh, J. et al. Clustering probabilistic tractograms using independent component analysis applied to the thalamus. Neuroimage 54, 2020–2032 (2011).
doi: 10.1016/j.neuroimage.2010.09.054
Calamante, F. et al. Super-resolution track-density imaging of thalamic substructures: comparison with high-resolution anatomical magnetic resonance imaging at 7.0T. Hum. Brain Mapp. 34, 2538–2548 (2013).
doi: 10.1002/hbm.22083
Basile, G. A. et al. In vivo super-resolution track-density imaging for thalamic nuclei identification. Cereb. Cortex 31, 5613–5636 (2021).
doi: 10.1093/cercor/bhab184
Stough, J. V. et al. Automatic method for thalamus parcellation using multi-modal feature classification. Med. Image Comput. Comput. Interv. 17, 169–176 (2014).
Semedo, C. et al. In Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 (eds Frangi, A. F., Schnabel, J. A., Davatzikos, C., Alberola-López, C. & Fichtinger, G.) 383–391 (Springer International Publishing, 2018).
Huang, S. Y. et al. Connectome 2.0: developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome. Neuroimage 243, 118530 (2021).
doi: 10.1016/j.neuroimage.2021.118530
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S. & Woolrich, M. W. Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34, 144–155 (2007).
doi: 10.1016/j.neuroimage.2006.09.018
Johansen-Berg, H. et al. Functional-anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus. Cereb. Cortex 15, 31–39 (2005).
doi: 10.1093/cercor/bhh105
Basile, G. A. et al. In vivo probabilistic atlas of white matter tracts of the human subthalamic area combining track density imaging and optimized diffusion tractography. Brain Struct. Funct. 227, 2647–2665 (2022).
pmcid: 9618529
doi: 10.1007/s00429-022-02561-3
Maier-Hein, K. H. et al. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8, 1349 (2017).
pmcid: 5677006
doi: 10.1038/s41467-017-01285-x
Shine, J. M., Lewis, L. D., Garrett, D. D. & Hwang, K. The impact of the human thalamus on brain-wide information processing. Nat. Rev. Neurosci. 24, 416–430 (2023). Highlights the role of the thalamus in a large number of human brain functional signatures.
doi: 10.1038/s41583-023-00701-0
Mezer, A., Yovel, Y., Pasternak, O., Gorfine, T. & Assaf, Y. Cluster analysis of resting-state fMRI time series. Neuroimage 45, 1117–1125 (2009).
doi: 10.1016/j.neuroimage.2008.12.015
O’Muircheartaigh, J., Keller, S. S., Barker, G. J. & Richardson, M. P. White matter connectivity of the thalamus delineates the functional architecture of competing thalamocortical systems. Cereb. Cortex 25, 4477–4489 (2015).
doi: 10.1093/cercor/bhv063
Toulmin, H. et al. Specialization and integration of functional thalamocortical connectivity in the human infant. Proc. Natl Acad. Sci. USA 112, 6485–6490 (2015).
doi: 10.1073/pnas.1422638112
Yuan, R. et al. Functional topography of the thalamocortical system in human. Brain Struct. Funct. 221, 1971–1984 (2016).
doi: 10.1007/s00429-015-1018-7
Zhang, D. et al. Intrinsic functional relations between human cerebral cortex and thalamus. J. Neurophysiol. 100, 1740–1748 (2008).
doi: 10.1152/jn.90463.2008
Zhang, D., Snyder, A. Z., Shimony, J. S., Fox, M. D. & Raichle, M. E. Noninvasive functional and structural connectivity mapping of the human thalamocortical system. Cereb. Cortex 20, 1187–1194 (2010).
doi: 10.1093/cercor/bhp182
Ji, B. et al. Dynamic thalamus parcellation from resting‐state fMRI data. Hum. Brain Mapp. 37, 954–967 (2016).
doi: 10.1002/hbm.23079
Kumar, V. J., van Oort, E., Scheffler, K., Beckmann, C. F. & Grodd, W. Functional anatomy of the human thalamus at rest. Neuroimage 147, 678–691 (2017). Compares and contrasts parcellations of the thalamus obtained from structural versus functional connectivity data and shows that there is no one-to-one mapping between them.
doi: 10.1016/j.neuroimage.2016.12.071
Kim, D., Park, B. & Park, H. Functional connectivity‐based identification of subdivisions of the basal ganglia and thalamus using multilevel independent component analysis of resting state fMRI. Hum. Brain Mapp. 34, 1371–1385 (2013).
doi: 10.1002/hbm.21517
Hale, J. R. et al. Comparison of functional thalamic segmentation from seed-based analysis and ICA. Neuroimage 114, 448–465 (2015).
doi: 10.1016/j.neuroimage.2015.04.027
Zhang, S. & Li, C.-S. R. Functional connectivity parcellation of the human thalamus by independent component analysis. Brain Connect. 7, 602–616 (2017).
doi: 10.1089/brain.2017.0500
Setzer, B. et al. A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state. Nat. Commun. 13, 5442 (2022).
doi: 10.1038/s41467-022-33010-8
Tregidgo, H. F. J. et al. Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas. Neuroimage 274, 120129 (2023). An optimized segmentation procedure using T1w MRI and DWI data that is available within the new FreeSurfer pipeline.
doi: 10.1016/j.neuroimage.2023.120129
Yan, C. et al. Segmenting thalamic nuclei from manifold projections of multi-contrast MRI. Proceedings of SPIE https://doi.org/10.48550/arXiv.2301.06114 (2023).
Majdi, M. S. et al. Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks. Magn. Reson. Imaging 73, 45–54 (2020).
doi: 10.1016/j.mri.2020.08.005
Umapathy, L., Keerthivasan, M. B., Zahr, N. M., Bilgin, A. & Saranathan, M. Convolutional neural network based frameworks for fast automatic segmentation of thalamic nuclei from native and synthesized contrast structural MRI. Neuroinformatics 20, 651–664 (2022). Describes the use of deep-learning procedures to enhance thalamic nuclei segmentation procedures, arguably the method of the future.
doi: 10.1007/s12021-021-09544-5
Shao, M. et al. Evaluating the impact of MR image harmonization on thalamus deep network segmentation. Proc. SPIE Int. Soc. Opt. Eng. 12032, 120320H (2022).
Setsompop, K., Feinberg, D. A. & Polimeni, J. R. Rapid brain MRI acquisition techniques at ultra-high fields. NMR Biomed. 29, 1198–1221 (2016).
doi: 10.1002/nbm.3478
Williams, B., Nguyen, D., Vidal, J. P. & Saranathan, M. Thalamic nuclei segmentation from T1-weighted MRI: unifying and benchmarking state-of-the-art methods. Imaging Neurosci. 2, 1–16 (2024).
doi: 10.1162/imag_a_00166
Jaimes, C. et al. Probabilistic tractography-based thalamic parcellation in healthy newborns and newborns with congenital heart disease. J. Magn. Reson. Imaging 47, 1626–1637 (2018).
doi: 10.1002/jmri.25875
Jakab, A. et al. Mental development is associated with cortical connectivity of the ventral and nonspecific thalamus of preterm newborns. Brain Behav. 10, e01786 (2020).
doi: 10.1002/brb3.1786
Lidauer, K. et al. Subcortical and hippocampal brain segmentation in 5-year-old children: validation of FSL-FIRST and FreeSurfer against manual segmentation. Eur. J. Neurosci. 56, 4619–4641 (2022).
doi: 10.1111/ejn.15761
Hashempour, N. et al. A novel approach for manual segmentation of the amygdala and hippocampus in neonate MRI. Front. Neurosci. 13, 1025 (2019).
doi: 10.3389/fnins.2019.01025
Tutunji, R. et al. Thalamic volume and dimensions on MRI in the pediatric population: normative values and correlations: (a cross sectional study). Eur. J. Radiol. 109, 27–32 (2018).
doi: 10.1016/j.ejrad.2018.10.018
Turesky, T. K., Vanderauwera, J. & Gaab, N. Imaging the rapidly developing brain: current challenges for MRI studies in the first five years of life. Dev. Cogn. Neurosci. 47, 100893 (2021).
doi: 10.1016/j.dcn.2020.100893
Gousias, I. S. et al. Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants. Neuroimage 62, 1499–1509 (2012).
doi: 10.1016/j.neuroimage.2012.05.083
Morey, R. A. et al. A comparison of automated segmentation and manual tracing for quantifying hippocampal and amygdala volumes. Neuroimage 45, 855–866 (2009).
doi: 10.1016/j.neuroimage.2008.12.033
Pomponio, R. et al. Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. Neuroimage 208, 116450 (2020).
doi: 10.1016/j.neuroimage.2019.116450
Choi, E. Y. et al. Thalamic nuclei atrophy at high and heterogenous rates during cognitively unimpaired human aging. Neuroimage 262, 119584 (2022).
doi: 10.1016/j.neuroimage.2022.119584
Pfefferbaum, A., Sullivan, E. V., Zahr, N. M., Pohl, K. M. & Saranathan, M. Multi-atlas thalamic nuclei segmentation on standard T1-weighed MRI with application to normal aging. Hum. Brain Mapp. 44, 612–628 (2023).
doi: 10.1002/hbm.26088
Schmahmann, J. D. Vascular syndromes of the thalamus. Stroke 34, 2264–2278 (2003).
doi: 10.1161/01.STR.0000087786.38997.9E
Carlesimo, G. A., Lombardi, M. G. & Caltagirone, C. Vascular thalamic amnesia: a reappraisal. Neuropsychologia 49, 777–789 (2011).
doi: 10.1016/j.neuropsychologia.2011.01.026
Golden, E. C., Graff-Radford, J., Jones, D. T. & Benarroch, E. E. Mediodorsal nucleus and its multiple cognitive functions. Neurology 87, 2161–2168 (2016).
doi: 10.1212/WNL.0000000000003344
Pergola, G. et al. Quantitative assessment of chronic thalamic stroke. AJNR Am. J. Neuroradiol. 34, E51–E55 (2013).
doi: 10.3174/ajnr.A2897
Percheron, G. The anatomy of the arterial supply of the human thalamus and its use for the interpretation of the thalamic vascular pathology. Z. Neurol. 205, 1–13 (1973).
Danet, L. et al. Thalamic amnesia after infarct: the role of the mammillothalamic tract and mediodorsal nucleus. Neurology 85, 2107–2115 (2015).
doi: 10.1212/WNL.0000000000002226
Hwang, K., Shine, J. M., Bruss, J., Tranel, D. & Boes, A. Neuropsychological evidence of multi-domain network hubs in the human thalamus. eLife 10, e69480 (2021).
doi: 10.7554/eLife.69480
Harding, A., Halliday, G., Caine, D. & Kril, J. Degeneration of anterior thalamic nuclei differentiates alcoholics with amnesia. Brain 123, 141–154 (2000).
doi: 10.1093/brain/123.1.141
Segobin, S. et al. Dissociating thalamic alterations in alcohol use disorder defines specificity of Korsakoff’s syndrome. Brain 142, 1458–1470 (2019).
doi: 10.1093/brain/awz056
Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).
doi: 10.1007/BF00308809
pubmed: 1759558
Bernstein, A. S., Rapcsak, S. Z., Hornberger, M. & Saranathan, M.; Alzheimer’s Disease Neuroimaging Initiative.Structural changes in thalamic nuclei across prodromal and clinical Alzheimer’s disease. J. Alzheimers Dis. 82, 361–371 (2021).
doi: 10.3233/JAD-201583
pubmed: 34024824
Forno, G. et al. Thalamic nuclei changes in early and late onset Alzheimer’s disease. Curr. Res. Neurobiol. 4, 100084 (2023).
doi: 10.1016/j.crneur.2023.100084
pubmed: 37397807
Azevedo, C. J. et al. Thalamic atrophy in multiple sclerosis: a magnetic resonance imaging marker of neurodegeneration throughout disease. Ann. Neurol. 83, 223–234 (2018).
doi: 10.1002/ana.25150
pubmed: 29328531
Planche, V. et al. White-matter-nulled MPRAGE at 7T reveals thalamic lesions and atrophy of specific thalamic nuclei in multiple sclerosis. Mult. Scler. 26, 987–992 (2020).
doi: 10.1177/1352458519828297
pubmed: 30730233
Alemán-Gómez, Y. et al. Multimodal magnetic resonance imaging depicts widespread and subregion specific anomalies in the thalamus of early-psychosis and chronic schizophrenia patients. Schizophr. Bull. 49, 196–207 (2023).
doi: 10.1093/schbul/sbac113
pubmed: 36065156
Henry, R. G. et al. Connecting white matter injury and thalamic atrophy in clinically isolated syndromes. J. Neurol. Sci. 282, 61–66 (2009).
doi: 10.1016/j.jns.2009.02.379
pubmed: 19394969
Ontaneda, D. et al. Deep grey matter injury in multiple sclerosis: a NAIMS consensus statement. Brain 144, 1974–1984 (2021).
doi: 10.1093/brain/awab132
pubmed: 33757115
Kuchcinski, G. et al. Thalamic alterations remote to infarct appear as focal iron accumulation and impact clinical outcome. Brain 140, 1932–1946 (2017).
doi: 10.1093/brain/awx114
pubmed: 28549087
Linck, P. A. et al. Neurodegeneration of the substantia nigra after ipsilateral infarct: MRI R2* mapping and relationship to clinical outcome. Radiology 291, 438–448 (2019).
doi: 10.1148/radiol.2019182126
pubmed: 30860451
Tamura, A. et al. Thalamic atrophy following cerebral infarction in the territory of the middle cerebral artery. Stroke 22, 615–618 (1991).
doi: 10.1161/01.STR.22.5.615
Moon, Y., Han, S.-H. & Moon, W.-J. Patterns of brain iron accumulation in vascular dementia and Alzheimer’s dementia using quantitative susceptibility mapping imaging. J. Alzheimers Dis. 51, 737–745 (2016).
doi: 10.3233/JAD-151037
Blyau, S. et al. Differential vulnerability of thalamic nuclei in multiple sclerosis. Mult. Scler. J. 29, 295–300 (2023).
doi: 10.1177/13524585221114247
Magliozzi, R. et al. “Ependymal‐in” gradient of thalamic damage in progressive multiple sclerosis. Ann. Neurol. 92, 670–685 (2022).
doi: 10.1002/ana.26448
Lee, J.-S., Heo, D.-Y., Choi, K.-H. & Kim, H.-J. Impact of the ventricle size on alzheimer’s disease progression: a retrospective longitudinal study. Dement. Neurocogn. Disord. 23, 95–106 (2024).
doi: 10.12779/dnd.2024.23.2.95
Oliveira, L. M., Nitrini, R. & Román, G. C. Normal-pressure hydrocephalus: a critical review. Dement. Neuropsychol. 13, 133–143 (2019).
doi: 10.1590/1980-57642018dn13-020001
Johnstone, E., Frith, C. D., Crow, T. J., Husband, J. & Kreel, L. Cerebral ventricular size and cognitive impairment in chronic schizophrenia. Lancet 308, 924–926 (1976).
doi: 10.1016/S0140-6736(76)90890-4
Van Erp, T. G. M. et al. Subcortical brain volume abnormalities in 2028 individuals with schizophrenia and 2540 healthy controls via the ENIGMA consortium. Mol. Psychiatry 21, 547–553 (2016).
doi: 10.1038/mp.2015.63
Pergola, G., Selvaggi, P., Trizio, S., Bertolino, A. & Blasi, G. The role of the thalamus in schizophrenia from a neuroimaging perspective. Neurosci. Biobehav. Rev. 54, 57–75 (2015).
doi: 10.1016/j.neubiorev.2015.01.013
Pergola, G. et al. Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia. Schizophr. Res. 180, 13–20 (2017).
doi: 10.1016/j.schres.2016.07.005
Honea, R. A. et al. Is gray matter volume an intermediate phenotype for schizophrenia? A voxel-based morphometry study of patients with schizophrenia and their healthy siblings. Biol. Psychiatry 63, 465–474 (2008).
doi: 10.1016/j.biopsych.2007.05.027
Cooper, D., Barker, V., Radua, J., Fusar-Poli, P. & Lawrie, S. M. Multimodal voxel-based meta-analysis of structural and functional magnetic resonance imaging studies in those at elevated genetic risk of developing schizophrenia. Psychiatry Res. Neuroimaging 221, 69–77 (2014).
doi: 10.1016/j.pscychresns.2013.07.008
Akudjedu, T. N. et al. Progression of neuroanatomical abnormalities after first-episode of psychosis: a 3-year longitudinal sMRI study. J. Psychiatr. Res. 130, 137–151 (2020).
doi: 10.1016/j.jpsychires.2020.07.034
Cobia, D. J., Smith, M. J., Wang, L. & Csernansky, J. G. Longitudinal progression of frontal and temporal lobe changes in schizophrenia. Schizophr. Res. 139, 1–6 (2012).
doi: 10.1016/j.schres.2012.05.002
Gaser, C., Nenadic, I., Buchsbaum, B. R., Hazlett, E. A. & Buchsbaum, M. S. Ventricular enlargement in schizophrenia related to volume reduction of the thalamus, striatum, and superior temporal cortex. Am. J. Psychiatry 161, 154–156 (2004).
doi: 10.1176/appi.ajp.161.1.154
Guo, J. Y. et al. Longitudinal regional brain volume loss in schizophrenia: relationship to antipsychotic medication and change in social function. Schizophr. Res. 168, 297–304 (2015).
doi: 10.1016/j.schres.2015.06.016
Borghei, A., Piracha, A. & Sani, S. Prevalence and anatomical characteristics of the human massa intermedia. Brain Struct. Funct. 226, 471–480 (2021).
doi: 10.1007/s00429-020-02193-5
Trzesniak, C. et al. Adhesio interthalamica alterations in schizophrenia spectrum disorders: a systematic review and meta-analysis. Prog. Neuropsychopharmacol. Biol. Psychiatry 35, 877–886 (2011).
doi: 10.1016/j.pnpbp.2010.12.024
Cassel, J.-C. et al. The reuniens and rhomboid nuclei of the thalamus: a crossroads for cognition-relevant information processing? Neurosci. Biobehav. Rev. 126, 338–360 (2021).
doi: 10.1016/j.neubiorev.2021.03.023
Schiff, N. D. et al. Thalamic deep brain stimulation in traumatic brain injury: a phase 1, randomized feasibility study. Nat. Med. 29, 3162–3174 (2023).
doi: 10.1038/s41591-023-02638-4
Wong, J. K. et al. Deep brain stimulation in essential tremor: targets, technology, and a comprehensive review of clinical outcomes. Expert. Rev. Neurother. 20, 319–331 (2020).
doi: 10.1080/14737175.2020.1737017
Middlebrooks, E. H., He, X., Grewal, S. S. & Keller, S. S. Neuroimaging and thalamic connectomics in epilepsy neuromodulation. Epilepsy Res. 182, 106916 (2022).
doi: 10.1016/j.eplepsyres.2022.106916
Aggleton, J. P., Pralus, A., Nelson, A. J. D. & Hornberger, M. Thalamic pathology and memory loss in early Alzheimer’s disease: moving the focus from the medial temporal lobe to Papez circuit. Brain 139, 1877–1890 (2016).
doi: 10.1093/brain/aww083
Craig, A. D. How do you feel? Interoception: the sense of the physiological condition of the body. Nat. Rev. Neurosci. 3, 655–666 (2002).
doi: 10.1038/nrn894
Steullet, P. et al. The thalamic reticular nucleus in schizophrenia and bipolar disorder: role of parvalbumin-expressing neuron networks and oxidative stress. Mol. Psychiatry 23, 2057–2065 (2018).
doi: 10.1038/mp.2017.230
El Khoueiry, C. et al. Developmental oxidative stress leads to T-type Ca
pmcid: 9126813
doi: 10.1038/s41380-021-01425-2
Viviano, J. D. & Schneider, K. A. Interhemispheric interactions of the human thalamic reticular nucleus. J. Neurosci. 35, 2026–2032 (2015).
pmcid: 6705358
doi: 10.1523/JNEUROSCI.2623-14.2015
Schira, M. M. et al. HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations. Brain Struct. Funct. 228, 1849–1863 (2023).
pmcid: 10516788
doi: 10.1007/s00429-023-02653-8
Boccardi, M. et al. Delphi definition of the EADC-ADNI harmonized protocol for hippocampal segmentation on magnetic resonance. Alzheimers Dement. 11, 126–138 (2015).
doi: 10.1016/j.jalz.2014.02.009
Baumeister, H. et al. Comparison of histological delineation of the entorhinal, perirhinal, ectorhinal, and parahippocampal cortices by different neuroanatomy laboratories. Alzheimers Dement 19, e076135 (2023).
doi: 10.1002/alz.076135
Carter, P. et al. A demonstration of using formal consensus methods within guideline development; a case study. BMC Med. Res. Methodol. 21, 73 (2021).
pmcid: 8052943
doi: 10.1186/s12874-021-01267-0
Nasa, P., Jain, R. & Juneja, D. Delphi methodology in healthcare research: how to decide its appropriateness. World J. Methodol. 11, 116–129 (2021).
pmcid: 8299905
doi: 10.5662/wjm.v11.i4.116
Vidal, J. P. et al. Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation. Brain Struct. Funct. 229, 1087–1101 (2024).
doi: 10.1007/s00429-024-02777-5
Oxenford, S. et al. Lead-OR: a multimodal platform for deep brain stimulation surgery. eLife 11, e72929 (2022).
doi: 10.7554/eLife.72929
Fan, L. et al. The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016).
doi: 10.1093/cercor/bhw157
van Oort, E. S. B. et al. Functional parcellation using time courses of instantaneous connectivity. Neuroimage 170, 31–40 (2018).
doi: 10.1016/j.neuroimage.2017.07.027
Danet, L. et al. Medial thalamic stroke and its impact on familiarity and recollection. eLife 6, e28141 (2017).
doi: 10.7554/eLife.28141
Pitel, A.-L. et al. Macrostructural abnormalities in Korsakoff syndrome compared with uncomplicated alcoholism. Neurology 78, 1330–1333 (2012).
doi: 10.1212/WNL.0b013e318251834e
Alegro, M. et al. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 634–642 (IEEE Computer Society, 2016).
Alho, A. T. D. L. et al. Magnetic resonance diffusion tensor imaging for the pedunculopontine nucleus: proof of concept and histological correlation. Brain Struct. Funct. 222, 2547–2558 (2017).
doi: 10.1007/s00429-016-1356-0
Alho, E. J. L. et al. High thickness histological sections as alternative to study the three-dimensional microscopic human sub-cortical neuroanatomy. Brain Struct. Funct. 223, 1121–1132 (2018).
doi: 10.1007/s00429-017-1548-2
Mollink, J. et al. Evaluating fibre orientation dispersion in white matter: comparison of diffusion MRI, histology and polarized light imaging. Neuroimage 157, 561–574 (2017).
doi: 10.1016/j.neuroimage.2017.06.001
Sitek, K. R. et al. Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T. eLife 8, e48932 (2019).
doi: 10.7554/eLife.48932
Jorge, J. et al. Improved susceptibility-weighted imaging for high contrast and resolution thalamic nuclei mapping at 7T. Magn. Reson. Med. 84, 1218–1234 (2020).
doi: 10.1002/mrm.28197
Abosch, A., Yacoub, E., Ugurbil, K. & Harel, N. An assessment of current brain targets for deep brain stimulation surgery with susceptibility-weighted imaging at 7 tesla. Neurosurgery 67, 1745–1756 (2010).
doi: 10.1227/NEU.0b013e3181f74105
Deshmane, A., Gulani, V., Griswold, M. A. & Seiberlich, N. Parallel MR imaging. J. Magn. Reson. Imaging 36, 55–72 (2012).
pmcid: 4459721
doi: 10.1002/jmri.23639
Marques, J. P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49, 1271–1281 (2010).
doi: 10.1016/j.neuroimage.2009.10.002