A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei

Wolfgang M Pauli, Amanda N Nili, J Michael Tyszka, Wolfgang M Pauli, Amanda N Nili, J Michael Tyszka

Abstract

Recent advances in magnetic resonance imaging methods, including data acquisition, pre-processing and analysis, have benefited research on the contributions of subcortical brain nuclei to human cognition and behavior. At the same time, these developments have led to an increasing need for a high-resolution probabilistic in vivo anatomical atlas of subcortical nuclei. In order to address this need, we constructed high spatial resolution, three-dimensional templates, using high-accuracy diffeomorphic registration of T1- and T2- weighted structural images from 168 typical adults between 22 and 35 years old. In these templates, many tissue boundaries are clearly visible, which would otherwise be impossible to delineate in data from individual studies. The resulting delineations of subcortical nuclei complement current histology-based atlases. We further created a companion library of software tools for atlas development, to offer an open and evolving resource for the creation of a crowd-sourced in vivo probabilistic anatomical atlas of the human brain.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1. Example tissue boundaries.
Figure 1. Example tissue boundaries.
Example explicit (solid lines) and implicit (dashed lines) boundaries between the red nucleus (RN), parabrachial pigmented nucleus (PBP), substantia nigra (SNc and SNr), and subthalamic nucleus overlayed on the CIT168 T1w and T2w templates. The isotropic voxel size is 700 μm. See Table 1 for label acronyms.
Figure 2. Representative sections from atlas.
Figure 2. Representative sections from atlas.
Axial sections centered on the red nucleus (left column) and substantia nigra (middle column), and a coronal section centered on the ventral pallidum (right column). T1w and T2w sections without overlays show the original tissue contrast seen by observers during delineation. The deterministic (P>0.5) and original probabilistic labels are shown in the right two columns. Note the softening of label edges in the probabilistic overlay, indicating inter- and intra-observer variability in boundary location. The color key indicates which labels are visible or out-of-plane (gray).
Figure 3. Sections through smaller nuclei.
Figure 3. Sections through smaller nuclei.
A central coronal section through the VTA, PBP, SNc, and SNr (top row) and axial section through the HN (bottom row). T1w and T2w sections without overlays show the original tissue contrast seen by observers during delineation. The deterministic (P>0.5) and original probabilistic labels are shown in the right two columns. The color key indicates which labels are visible or out-of-plane (gray).
Figure 4. Cumulative relative frequencies of label…
Figure 4. Cumulative relative frequencies of label probabilities.
We visualize the uncertainty in defining each subcortical nucleus label by calculating the cumulative relative frequency (CRF) for each probabilistic label over all non-zero voxels. Pu and Ca are examples of labels with a high degree of both inter- and intra-rater similarity, while the more convex cumulative distributions observed for PBP and VTA reflect increased inter-rater variance as the label volume decreases and the tissue boundaries become less reliably defined.

References

Data Citations

    1. Tyszka J.M., Pauli W., Nili A. 2017. Open Science Framework.
References
    1. Christensen G. E, Rabbitt R. D. & Miller M. I. 3d brain mapping using a deformable neuroanatomy. Physics in Medicine & Biology 39, 609 (1994).
    1. Thirion J. P. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical Image Analysis 2, 243–260 (1998).
    1. Ashburner J. & Friston K. J. Nonlinear spatial normalization using basis functions. Human Brain Mapping 7, 254–266 (1999).
    1. Avants B. B., Duda J. T., Zhang H. & Gee J. C. Multivariate normalization with symmetric diffeomorphisms for multivariate studies. Medical image computing and computer-assisted intervention: MICCAI. International Conference on Medical Image Computing and Computer-Assisted Intervention 10, 359–366 (2007).
    1. Tyszka J. M. & Pauli W. M. In vivo delineation of subdivisions of the human amygdaloid complex in a high-resolution group template. Human Brain Mapping 37, 3979–3998 (2016).
    1. Hazy T. E., Frank M. J.. & O'Reilly R. C. Banishing the homunculus: making working memory work. Neuroscience 139, 105–118 (2006).
    1. Brown J., Bullock D. & Grossberg S. How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected rewarding cues. Journal of Neuroscience 19, 10502–10511 (1999).
    1. Haber S. N. The primate basal ganglia: parallel and integrative networks. Journal of Chemical Neuroanatomy 26, 317–330 (2003).
    1. Hazy T. E., Frank M. J. & O'Reilly R. C. Neural mechanisms of acquired phasic dopamine responses in learning. Neuroscience & Biobehavioral Reviews 34, 701–720 (2010).
    1. Hochreiter S. & Schmidhuber J. Long short-term memory. Neural Computation 9, 1735–1780 (1997).
    1. Schultz W., Dayan P. & Montague P. R. A neural substrate of prediction and reward. Science 275, 1593–1599 (1997).
    1. Shen W., Flajolet M., Greengard P. & Surmeier D. J. Dichotomous dopaminergic control of striatal synaptic plasticity. Science 321, 848–851 (2008).
    1. Montague P. R., Dolan R. J., Friston K. J. & Dayan P. Computational psychiatry. Trends in Cognitive Sciences 16, 72–80 (2012).
    1. Düzel E. et al. Functional imaging of the human dopaminergic midbrain. Trends in Neurosciences 32, 321–328 (2009).
    1. O'Doherty J. P., Cockburn J. & Pauli W. M. Learning, reward, and decision making. Annual Review of Psychology 68, 73–100 (2017).
    1. Balleine B. W. & O'Doherty J. P. Human and rodent homologies in action control: corticostriatal determinants of goal-directed and habitual action. Neuropsychopharmacology 35, 48–69 (2009).
    1. Wiecki T. V., Poland J. & Frank M. J. Model-based cognitive neuroscience approaches to computational psychiatry clustering and classification. Clinical Psychological Science 3, 378–399 (2015).
    1. Kamiński J. et al. Persistently active neurons in human medial frontal and medial temporal lobe support working memory. Nature Neuroscience 20, 590–601 (2017).
    1. Wang S. et al. The human amygdala parametrically encodes the intensity of specific facial emotions and their categorical ambiguity. Nature Communications 8 (2017).
    1. Colas J. T., Pauli W. M., Larsen T., Tyszka J. M. & O'Doherty J. P. Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI. PLOS Computational Biology 13, e1005810 (2017).
    1. Eickhoff S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. NeuroImage 25, 1325–1335 (2005).
    1. Frazier J. A. et al. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. The American Journal of Psychiatry 162, 1256–1265 (2005).
    1. Desikan R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980 (2006).
    1. Keuken M. C. et al. Quantifying inter-individual anatomical variability in the subcortex using 7 T structural MRI. NeuroImage 94, 40–46 (2014).
    1. Van Essen D. C. et al. The WU-Minn Human Connectome Project: an overview. NeuroImage 80, 62–79 (2013).
    1. Glasser M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013).
    1. Avants B. et al. Multivariate analysis of structural and diffusion imaging in traumatic brain injury. Academic Radiology 15, 1360–1375 (2008).
    1. Avants B. B., Epstein C. L., Grossman M. & Gee J. C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis 12, 26–41 (2008).
    1. Kovačević N. et al. A Three-dimensional MRI atlas of the mouse brain with estimates of the average and variability. Cerebral Cortex 15, 639–645 (2005).
    1. Lyttelton O., Boucher M., Robbins S. & Evans A. An unbiased iterative group registration template for cortical surface analysis. NeuroImage 34, 1535–1544 (2007).
    1. Reuter M., Schmansky N. J., Rosas H. D. & Fischl B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage 61, 1402–1418 (2012).
    1. Yushkevich P. A. et al. Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: towards a harmonized segmentation protocol. NeuroImage 111, 526–541 (2015).
    1. Van Leemput K. et al. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19, 549–557 (2009).
    1. Hawrylycz M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
    1. Mai J., Paxinos G. & Voss T. Atlas of the human brain. Elsevier: New York, (2008)3 edn.
    1. Yushkevich P. A. et al. User-guided 3d active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage 31, 1116–1128 (2006).
    1. Gerfen C. R. The neostriatal mosaic: multiple levels of compartmental organization. Trends in Neurosciences 15, 133–139 (1992).
    1. Frank M. J., Seeberger L. C. & O'Reilly R. C. By carrot or by stick: cognitive reinforcement learning in parkinsonism. Science (New York, N.Y.) 306, 1940–1943 (2004).
    1. O'Reilly R. C. Biologically based computational models of high-level cognition. Science 314, 91–94 (2006).
    1. Eshel N. et al. Arithmetic and local circuitry underlying dopamine prediction errors. Nature 525, 243–246 (2015).
    1. Haber S. N., Fudge J. L. & McFarland N. R. Striatonigrostriatal pathways in primates form an ascending spiral from the shell to the dorsolateral striatum. The Journal of Neuroscience 20, 2369–2382 (2000).
    1. Alexander G. E., DeLong M. R. & Strick P. L. Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual Review Neuroscience 9, 357–381 (1986).
    1. Middleton F. A. & Strick P. L. Basal ganglia and cerebellar loops: motor and cognitive circuits. Brain Research Reviews 31, 236–250 (2000).
    1. Levy R., Friedman H. R., Davachi L. & Goldman-Rakic P. S. Differential activation of the caudate nucleus in primates performing spatial and nonspatial working memory tasks. Journal of Neuroscience 17, 3870–3882 (1997).
    1. Robinson J. L., Laird A. R., Glahn D. C., Lovallo W. R. & Fox P. T. Metaanalytic connectivity modeling: delineating the functional connectivity of the human amygdala. Human Brain Mapping 31, 173–184 (2010).
    1. Pauli W. M., O'Reilly R. C., Yarkoni T. & Wager T. D. Regional specialization within the human striatum for diverse psychological functions. Proceedings of the National Academy of Sciences 113, 1907–1912 (2016).
    1. Mink J. W. The basal ganglia: focused selection and inhibition of competing motor programs. Progress in Neurobiology 50, 381–425 (1996).
    1. Brown J. W., Bullock D. & Grossberg S. How laminar frontal cortex and basal ganglia circuits interact to control planned and reactive saccades. Neural Networks 17, 471–510 (2004).
    1. Humphries M. D., Stewart R. D. & Gurney K. N. A physiologically plausible model of action selection and oscillatory activity in the basal ganglia. Journal of Neuroscience 26, 12921–12942 (2006).
    1. Frank M. J., Samanta J., Moustafa A. A. & Sherman S. J. Hold your horses: impulsivity, deep brain stimulation, and medication in parkinsonism. Science 318, 1309–1312 (2007).
    1. Wickens J. Basal ganglia: structure and computations. Network: Computation in Neural Systems 8, R77–R109 (1997).
    1. Neafsey E. J., Hull C. D. & Buchwald N. A. Preparation for movement in the cat. I. Unit activity in the cerebral cortex. Electroencephalography and Clinical Neurophysiology 44, 706–713 (1978).
    1. Deniau J. M. & Chevalier G. Disinhibition as a basic process in the expression of striatal functions. II. The striato-nigral influence on thalamocortical cells of the ventromedial thalamic nucleus. Brain Research 334, 227–233 (1985).
    1. Chevalier G. & Deniau J. M. Disinhibition as a basic process in the expression of striatal functions. Trends in Neurosciences 13, 277–280 (1990).
    1. Bullock D. & Grossberg S. Neural dynamics of planned arm movements: emergent invariants and speed-accuracy properties during trajectory formation. Psychological Review 95, 49–90 (1988).
    1. Ono T., Nakamura K., Nishijo H. & Fukuda M. Hypothalamic neuron involvement in integration of reward, aversion, and cue signals. Journal of Neurophysiology 56, 63–79 (1986).
    1. Matsumoto M. & Hikosaka O. Lateral habenula as a source of negative reward signals in dopamine neurons. Nature 447, 1111–1115 (2007).
    1. Lawson R. P., Drevets W. C. & Roiser J. P. Defining the habenula in human neuroimaging studies. Neuroimage 64, 722–727 (2013).
    1. Lammel S. et al. Input-specific control of reward and aversion in the ventral tegmental area. Nature 491, 212–217 (2012).
    1. Ji H. & Shepard P. D. Lateral habenula stimulation inhibits rat midbrain dopamine neurons through a GABAα receptor-mediated mechanism. Journal of Neuroscience 27, 6923–6930 (2007).
    1. Hong S. & Hikosaka O. The globus pallidus sends reward-related signals to the lateral habenula. Neuron 60, 720–729 (2008).
    1. Kim J. -w. et al. Human habenula segmentation using myelin content. NeuroImage 130, 145–156 (2016).
    1. Watabe-Uchida M., Zhu L., Ogawa S. K., Vamanrao A. & Uchida N. Whole-Brain Mapping of Direct Inputs to Midbrain Dopamine Neurons. Neuron 74, 858–873 (2012).
    1. Olszewski J. & Baxter D. Cytoarchitecture of the human brain stem 3rd edn (Karger Publishers 2013).
    1. Ikemoto S. Dopamine reward circuitry: two projection systems from the ventral midbrain to the nucleus accumbens–olfactory tubercle complex. Brain Research Reviews 56, 27–78 (2007).
    1. Fudge J. L. & Haber S. N. The central nucleus of the amygdala projection to dopamine subpopulations in primates. Neuroscience 97, 479–494 (2000).
    1. Dice L. R. Measures of the amount of ecologic association between species. Ecology 26, 297–302 (1945).
    1. Taha A. A. & Hanbury A. An efficient algorithm for calculating the exact Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 37, 2153–2163 (2015).
    1. Huttenlocher D. P., Klanderman G. A. & Rucklidge W. J. Comparing images using the Hausdorff distance. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 850–863 (1993).
    1. Eskildsen S. F. et al. BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage 59, 2362–2373 (2012).
    1. Amunts K. et al. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anatomy and Embryology 210, 343–352 (2005).
    1. Amunts K. et al. BigBrain: an ultrahigh-resolution 3d human brain model. Science (New York, N.Y.) 340, 1472–1475 (2013).
    1. Fischl B. et al. Whole brain segmentation: Automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).
    1. Iglesias J. E. et al. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. NeuroImage 115, 117–137 (2015).
    1. Saygin Z. M. et al. High-resolution magnetic resonance imaging reveals nuclei of the human amygdala: manual segmentation to automatic atlas. NeuroImage 155, 370–382 (2017).
    1. Choi E. Y., Yeo B. T. T. & Buckner R. L. The organization of the human striatum estimated by intrinsic functional connectivity. Journal of Neurophysiology 108, 2242–2263 (2012).
    1. Menke R. A., Jbabdi S., Miller K. L., Matthews P. M. & Zarei M. Connectivity-based segmentation of the substantia nigra in human and its implications in Parkinson's disease. NeuroImage 52, 1175–1180 (2010).
    1. Thomas C. et al. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proceedings of the National Academy of Sciences of the United States of America 111, 16574–16579 (2014).
    1. Maier-Hein K. H. et al. The challenge of mapping the human connectome based on diffusion tractography. Nature Communications 8 (2017).
    1. de Hollander G., Keuken M. C., van der Zwaag W., Forstmann B. U. & Trampel R. Comparing functional MRI protocols for small, iron-rich basal ganglia nuclei such as the subthalamic nucleus at 7 T and 3 T. Human Brain Mapping 38, 3226–3248 (2017).
    1. Poldrack R. A. & Yarkoni T. From brain maps to cognitive ontologies: informatics and the search for mental structure. Annual Review of Psychology 67, 587–612 (2016).
    1. Herrington J. D. et al. Amygdala volume differences in autism spectrum disorder are related to anxiety. Journal of Autism and Developmental Disorders 1–10 (2017).
    1. Tillman R. M. et al. Intrinsic functional connectivity of the central extended amygdala. Human Brain Mapping 39, 1291–1312 (2017).
    1. Gogtay N. et al. Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences of the United States of America 101, 8174–8179 (2004).
    1. Deoni S. C. L. et al. Mapping infant brain myelination with magnetic resonance imaging. Journal of Neuroscience 31, 784–791 (2011).

Source: PubMed

Sponsors et collaborateurs

Les conditions médicales

Interventions en matière de drogue

3
S'abonner