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A roadmap towards standardized neuroimaging approaches for human thalamic nuclei

Nature Reviews Neuroscience volume 25, pages 792–808 (2024)Cite this article

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Abstract

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.

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Fig. 1: Visualization of thalamic nuclei with differing field strengths and MRI pulse sequences, highlighting current segmentation challenges.
Fig. 2: Variability in shapes and volumes of thalamic parcellations across individuals and imaging methods.
Fig. 3: Alterations to thalamic nuclei across healthy ageing and disease.

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Acknowledgements

The Thalamus Nuclei Neuroimaging Group (TANGO) consortium can be followed and reached via our website. S.S. was supported by the French National Institute for Health and Medical Research (INSERM), Label Excellence de la Région Normandie, the French National Agency for Research (ANR), the Fondation pour la Recherche Médicale (FRM; ING20140129160). R.A.M.H. was supported by H2020 Marie Skłodowska Curie Actions, Grant/Award Number: 101061988. V.J.K. was supported by the Deutsche Forschungsgemeinschaft, DFG SCHE 658/17. G.P. and A.L. were supported by RIPARTI – “assegni di RIcerca per riPARTire con le Imprese” initiative, APULIA REGION (POC PUGLIA FESR-FSE 2014 / 2020), Project Code 79ed97ad. G.P. was supported by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), “MNESYS, A multiscale integrated approach to the study of the nervous system in health and disease”) (PE0000006) – (DN. 1553 11.10.2022). T.T. was supported by University of Bordeaux’s IdEx “Investments for the Future” RRI programme “IMPACT” (IMaging for Precision medicine within A Collaborative Translational programme), and IHU Precision & Global Vascular Brain Health Institute, ANR-23-IAHU-000, which received financial support from the France 2030 programme. M.B.C. is funded by CIBM Center for Biomedical Imaging, a Swiss research centre of excellence founded and supported by CHUV, UNIL, EPFL, UNIGE and HUG, and also by Swiss National Science Foundation grants 205321-157040. A.-L.P. was supported by the INSERM, Label Excellence de la Région Normandie, the ANR, the FRM, the French Universitary Institute. A.A. was supported by ZonMW Open competition Grant 09120012110015 JPND/ZonMW (grant 73305113). M.S. was supported by National Institutes of Health NIBIB R01 EB032674. M.H. was supported by the National Institute for Health Research and the Medical Research Council.

Author information

Authors and Affiliations

  1. Normandie University, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France

    Shailendra Segobin

  2. Aix-Marseille University, CRMBM CNRS UMR 7339, Marseille, France

    Roy A. M. Haast

  3. APHM, La Timone Hospital, CEMEREM, Marseille, France

    Roy A. M. Haast

  4. Max Planck Institute for Biological Cybernetics, Tuebingen, Germany

    Vinod Jangir Kumar

  5. Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy

    Annalisa Lella & Giulio Pergola

  6. Integrative Model-based Cognitive Neuroscience Unit, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands

    Anneke Alkemade

  7. CIBM Center for Biomedical Imaging, Lausanne, Switzerland

    Meritxell Bach Cuadra

  8. Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland

    Meritxell Bach Cuadra

  9. Centre de recherche Cerveau et Cognition (Cerco), UMR5549, CNRS – Université de Toulouse, Toulouse, France

    Emmanuel J. Barbeau

  10. Aix Marseille Université, INSERM INS UMR 1106, APHM, Marseille, France

    Olivier Felician

  11. Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA

    Giulio Pergola

  12. Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Giulio Pergola

  13. Normandie University, UNICAEN, INSERM, U1237, PhIND “Physiopathology and Imaging of Neurological Disorders”, NeuroPresage Team, Cyceron, Caen, France

    Anne-Lise Pitel

  14. Department of Radiology, UMass Chan Medical School, Worcester, MA, USA

    Manojkumar Saranathan

  15. Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, Bordeaux, France

    Thomas Tourdias

  16. Neurocentre Magendie, University of Bordeaux, INSERM U1215, Bordeaux, France

    Thomas Tourdias

  17. Norwich Medical School, University of East Anglia, Norwich, UK

    Michael Hornberger

Authors
  1. Shailendra Segobin
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  2. Roy A. M. Haast
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  3. Vinod Jangir Kumar
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  4. Annalisa Lella
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  5. Anneke Alkemade
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  6. Meritxell Bach Cuadra
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  7. Emmanuel J. Barbeau
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  8. Olivier Felician
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  9. Giulio Pergola
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  10. Anne-Lise Pitel
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  11. Manojkumar Saranathan
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  12. Thomas Tourdias
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  13. Michael Hornberger
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Contributions

All authors researched data for the article, contributed substantially to discussion of the content, and reviewed and/or edited the manuscript before submission. M.H. and S.S. wrote the article.

Corresponding authors

Correspondence to Shailendra Segobin or Michael Hornberger.

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Nature Reviews Neuroscience thanks Rosanna Olsen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

FMRIB Software Library: https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases

FreeSurfer software suite: https://surfer.nmr.mgh.harvard.edu/

Harmonized Hippocampal Protocol: http://www.hippocampal-protocol.net/SOPs/index.php

Hippocampal Subfields Group: https://hippocampalsubfields.com/

Human Brain Atlas: https://hba.neura.edu.au/methods/

Human Brainnetome Atlas: https://atlas.brainnetome.org/

Lead-DBS: https://www.lead-dbs.org/

TANGO: https://thalamicsegmentation.github.io/

THOMAS: https://github.com/thalamicseg

Zenodo: https://doi.org/10.5281/zenodo.1253021

Glossary

Bayesian inference

An analysis technique that involves the use of probabilities to infer a hypothesized outcome.

Blood oxygen level-dependent (BOLD) signal

BOLD variations occur due to changes in deoxyhaemoglobin (which is paramagnetic) that are in turn caused by local changes in blood flow due to neuronal activity.

Diffusion MRI

Refers to imaging the microscopic motion of water molecules. When magnetic field gradients are applied on either side of the refocusing pulse in a spin-echo experiment, motion will result in reduction of the refocused MRI signal, which can be quantified and related to the diffusion coefficient.

Diffusion tensor imaging

(DTI). In diffusion MRI, by applying gradients in specific directions, information on preferential direction of motion can be inferred. One such method involves representing it as a tensor with the ellipsoid representing the three principal directions.

Echo-planar imaging

An ultra-fast variant of gradient or spin-echo MRI, in which multiple (or even all) k-space lines are acquired within a single repetition time. Echo-planar imaging is used when very fast acquisitions are required. Its high temporal resolution allows imaging of rapid physiological processes with decreased motion artefacts.

Fractional anisotropy

A scalar measurement from DTI that reflects the microstructural integrity of white matter tracts.

Functional MRI

(fMRI). A means for depicting brain activity by measuring regional BOLD variations in the brain.

Image registration

The mathematical operation that warps one image, called the source, to a target image. Registration can be rigid body, when the source and target are from the same brain, or non-linear, when matching a source to a template.

Mesh-based representation

A method that generally involves partitioning an image into tiny polygons in a process called tessellation. This term is used in the field of computer vision to describe the representation of 3D objects.

Nulling

The process of eliminating the magnetic signal coming from a particular tissue.

Partial volume effects

Effects that occur when a voxel contains signals from two or more tissues. The resultant signal is therefore an average of the signal arising from these tissues. Qualitatively, the image appears blurred and is quantitatively biased. Partial volume effects are particularly prominent in small regions or along borders of regions.

Proton density

The concentration of protons in each voxel, indicated by the voxel intensity.

Quantitative susceptibility mapping

Uses both magnitude and phase as a combined process to highlight the presence of compounds that could be diamagnetic (for example, calcification), paramagnetic (for example, deoxyhaemoglobin, due to fewer red blood cells causing anaemia) or ferromagnetic (for example, high iron content, making it behave as a magnet on its own).

Relaxation

The spin from hydrogen atoms aligns with the scanner’s main magnetic field (B0) in the longitudinal plane. When a radiofrequency pulse is applied, the spins absorb energy and their magnetization flips from the longitudinal into the transverse plane. When removed, the spins undergo relaxation to align again with the main B0 magnetization.

Relaxation times

The time taken by the spins from the hydrogen atoms to lose their energy. Transverse and longitudinal relaxation times are labelled T1 and T2, respectively.

Resting-state fMRI

(rs-fMRI). Refers to measuring the fluctuations occurring in the brain when not subject to a specific task. Studies can be hypothesis driven, directly looking at the synchronicity between regions in either static (time invariant) or dynamic (observing switching or transitions) models, at a whole-brain level, through independent component analysis or graph theory methods.

Susceptibility

Each hydrogen atom has a local magnetic field associated to it. The human body also contains other compounds (for example, calcium) that have magnetic properties, hence distorting the local magnetic field. This leads to a change in the phase of local tissue and hence to a change in the signal measured.

Susceptibility-weighted imaging

Shows the presence (through magnitude only) of tissue susceptibility by weighting the resulting MRI images.

T1-weighted (T1w) imaging

Imaging in which the contrast between tissues is due to the differences in their T1 relaxation times. The longer the T1 relaxation time, the darker the signal (for example, cerebrospinal fluid has much longer T1 relaxation times than white or grey matter and is dark in T1w-MRI).

T2-weighted imaging

Imaging in which the contrast between tissues is due to the differences in their T2 relaxation times.

T2*-weighted imaging

Imaging in which the contrast between tissues is due to the differences in their T2* relaxation times. This method takes into account magnetic field inhomogeneities in addition to T2 relaxation and is therefore faster than T2.

Tractography

Modelling of the pathway of white matter tracts using scalar and vector measurements from diffusion tensor imaging.

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Segobin, S., Haast, R.A.M., Kumar, V.J. et al. A roadmap towards standardized neuroimaging approaches for human thalamic nuclei. Nat. Rev. Neurosci. 25, 792–808 (2024). https://doi.org/10.1038/s41583-024-00867-1

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