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

Wolfgang M. Pauli; Amanda N. Nili; J. Michael Tyszka

doi:10.1038/sdata.2018.63

journal article Open Access Apr 17, 2018

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

Wolfgang M. Pauli

Amanda N. Nili J. Michael Tyszka

Scientific Data Vol. 5 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1038/sdata.2018.63

Abstract

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
T
1
- and
T
2
- 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.

Topics

No keywords indexed for this article. Browse by subject →

References

86

[1]

Christensen, G. E, Rabbitt, R. D. & Miller, M. I. 3d brain mapping using a deformable neuroanatomy. Physics in Medicine & Biology 39, 609 (1994). 10.1088/0031-9155/39/3/022

[2]

Thirion, J. P. Image matching as a diffusion process: an analogy with Maxwell's demons. Medical Image Analysis 2, 243–260 (1998). 10.1016/s1361-8415(98)80022-4

[3]

Ashburner, J. & Friston, K. J. Nonlinear spatial normalization using basis functions. Human Brain Mapping 7, 254–266 (1999). 10.1002/(sici)1097-0193(1999)7:4<254::aid-hbm4>3.0.co;2-g

[4]

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).

[5]

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). 10.1002/hbm.23289

[6]

Hazy, T. E., Frank, M. J.. & O'Reilly, R. C. Banishing the homunculus: making working memory work. Neuroscience 139, 105–118 (2006). 10.1016/j.neuroscience.2005.04.067

[7]

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). 10.1523/jneurosci.19-23-10502.1999

[8]

The primate basal ganglia: parallel and integrative networks

SUZANNE N. HABER

Journal of Chemical Neuroanatomy 2003 10.1016/j.jchemneu.2003.10.003

[9]

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). 10.1016/j.neubiorev.2009.11.019

[10]

Long Short-Term Memory

Sepp Hochreiter, Jürgen Schmidhuber

Neural Computation 1997 10.1162/neco.1997.9.8.1735

[11]

A Neural Substrate of Prediction and Reward

Wolfram Schultz, Peter Dayan, P. Read Montague

Science 1997 10.1126/science.275.5306.1593

[12]

Shen, W., Flajolet, M., Greengard, P. & Surmeier, D. J. Dichotomous dopaminergic control of striatal synaptic plasticity. Science 321, 848–851 (2008). 10.1126/science.1160575

[13]

Montague, P. R., Dolan, R. J., Friston, K. J. & Dayan, P. Computational psychiatry. Trends in Cognitive Sciences 16, 72–80 (2012). 10.1016/j.tics.2011.11.018

[14]

Düzel, E. et al. Functional imaging of the human dopaminergic midbrain. Trends in Neurosciences 32, 321–328 (2009). 10.1016/j.tins.2009.02.005

[15]

O'Doherty, J. P., Cockburn, J. & Pauli, W. M. Learning, reward, and decision making. Annual Review of Psychology 68, 73–100 (2017). 10.1146/annurev-psych-010416-044216

[16]

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). 10.1038/npp.2009.131

[17]

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). 10.1177/2167702614565359

[18]

Kamiński, J. et al. Persistently active neurons in human medial frontal and medial temporal lobe support working memory. Nature Neuroscience 20, 590–601 (2017). 10.1038/nn.4509

[19]

Wang, S. et al. The human amygdala parametrically encodes the intensity of specific facial emotions and their categorical ambiguity. Nature Communications 8 (2017). 10.1038/ncomms14821

[20]

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). 10.1371/journal.pcbi.1005810

[21]

A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data

Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg et al.

NeuroImage 2005 10.1016/j.neuroimage.2004.12.034

[22]

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). 10.1176/appi.ajp.162.7.1256

[23]

An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest

Rahul S. Desikan, Florent Ségonne, Bruce Fischl et al.

NeuroImage 2006 10.1016/j.neuroimage.2006.01.021

[24]

Keuken, M. C. et al. Quantifying inter-individual anatomical variability in the subcortex using 7 T structural MRI. NeuroImage 94, 40–46 (2014). 10.1016/j.neuroimage.2014.03.032

[25]

The WU-Minn Human Connectome Project: An overview

David C. Van Essen, Stephen M. Smith, Deanna M. Barch et al.

NeuroImage 2013 10.1016/j.neuroimage.2013.05.041

[26]

The minimal preprocessing pipelines for the Human Connectome Project

Matthew F. Glasser, Stamatios N. Sotiropoulos, J. Anthony Wilson et al.

NeuroImage 2013 10.1016/j.neuroimage.2013.04.127

[27]

Multivariate Analysis of Structural and Diffusion Imaging in Traumatic Brain Injury

Brian Avants, Jeffrey T. Duda, Junghoon Kim et al.

Academic Radiology 2008 10.1016/j.acra.2008.07.007

[28]

Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain

B AVANTS, C EPSTEIN, M GROSSMAN et al.

Medical Image Analysis 2008 10.1016/j.media.2007.06.004

[29]

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). 10.1093/cercor/bhh165

[30]

Lyttelton, O., Boucher, M., Robbins, S. & Evans, A. An unbiased iterative group registration template for cortical surface analysis. NeuroImage 34, 1535–1544 (2007). 10.1016/j.neuroimage.2006.10.041

[31]

Within-subject template estimation for unbiased longitudinal image analysis

Martin Reuter, Nicholas J. Schmansky, H. Diana Rosas et al.

NeuroImage 2012 10.1016/j.neuroimage.2012.02.084

[32]

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). 10.1016/j.neuroimage.2015.01.004

[33]

Van Leemput, K. et al. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19, 549–557 (2009). 10.1002/hipo.20615

[34]

An anatomically comprehensive atlas of the adult human brain transcriptome

Michael J. Hawrylycz, Ed S. Lein, Angela L. Guillozet-Bongaarts et al.

Nature 2012 10.1038/nature11405

[35]

Mai, J., Paxinos, G. & Voss, T. Atlas of the human brain. Elsevier: New York, (2008)3 edn.

[36]

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett et al.

NeuroImage 2006 10.1016/j.neuroimage.2006.01.015

[37]

Gerfen, C. R. The neostriatal mosaic: multiple levels of compartmental organization. Trends in Neurosciences 15, 133–139 (1992). 10.1016/0166-2236(92)90355-c

[38]

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). 10.1126/science.1102941

[39]

O'Reilly, R. C. Biologically based computational models of high-level cognition. Science 314, 91–94 (2006). 10.1126/science.1127242

[40]

Eshel, N. et al. Arithmetic and local circuitry underlying dopamine prediction errors. Nature 525, 243–246 (2015). 10.1038/nature14855

[41]

Striatonigrostriatal Pathways in Primates Form an Ascending Spiral from the Shell to the Dorsolateral Striatum

SUZANNE N. HABER, Julie L. Fudge, NIKOLAUS R. McFARLAND

The Journal of Neuroscience 2000 10.1523/jneurosci.20-06-02369.2000

[42]

Parallel Organization of Functionally Segregated Circuits Linking Basal Ganglia and Cortex

G E Alexander, M R DeLong, P L Strick

Annual Review of Neuroscience 1986 10.1146/annurev.ne.09.030186.002041

[43]

Basal ganglia and cerebellar loops: motor and cognitive circuits

F Middleton

Brain Research Reviews 2000 10.1016/s0165-0173(99)00040-5

[44]

Differential Activation of the Caudate Nucleus in Primates Performing Spatial and Nonspatial Working Memory Tasks

Richard Levy, Harriet R. Friedman, Lila Davachi et al.

The Journal of Neuroscience 1997 10.1523/jneurosci.17-10-03870.1997

[45]

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). 10.1002/hbm.20854

[46]

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). 10.1073/pnas.1507610113

[47]

Mink, J. W. The basal ganglia: focused selection and inhibition of competing motor programs. Progress in Neurobiology 50, 381–425 (1996). 10.1016/s0301-0082(96)00042-1

[48]

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). 10.1016/j.neunet.2003.08.006

[49]

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). 10.1523/jneurosci.3486-06.2006

[50]

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). 10.1126/science.1146157

Showing 50 of 86 references

Cited By

486

Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale

Nhung Hoang, Neda Sardaripour · 2024

PLOS Biology

The Subcortical Atlas of the Rhesus Macaque (SARM) for neuroimaging

Renée Hartig, Daniel Glen · 2021

NeuroImage

High-resolution mapping and digital atlas of subcortical regions in the macaque monkey based on matched MAP-MRI and histology

Kadharbatcha S. Saleem, Alexandru V. Avram · 2021

NeuroImage

Metrics

486

Citations

86

References

Details

Published: Apr 17, 2018
Vol/Issue: 5(1)
License: View

Authors

Cite This Article

Wolfgang M. Pauli, Amanda N. Nili, J. Michael Tyszka (2018). A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Scientific Data, 5(1). https://doi.org/10.1038/sdata.2018.63

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

You May Also Like