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Cited by 196Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesTraumatic Brain Injury Research

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Mapping Connectivity Damage in the Case of Phineas Gage

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Van Horn, John Darrell, Irimia, Andrei, Torgerson, Carinna M., Chambers, Micah C., Kikinis, Ron, Toga, Arthur W.·2012·PLoS ONE
DOI 10.1371/journal.pone.0037454PMC PMC3353935
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Abstract

White matter (WM) mapping of the human brain using neuroimaging techniques has gained considerable interest in the neuroscience community. Using diffusion weighted (DWI) and magnetic resonance imaging (MRI), WM fiber pathways between brain regions may be systematically assessed to make inferences concerning their role in normal brain function, influence on behavior, as well as concerning the consequences of network-level brain damage. In this paper, we investigate the detailed connectomics in a noted example of severe traumatic brain injury (TBI) which has proved important to and controversial in the history of neuroscience. We model the WM damage in the notable case of Phineas P. Gage, in whom a "tamping iron" was accidentally shot through his skull and brain, resulting in profound behavioral changes. The specific effects of this injury on Mr. Gage's WM connectivity have not previously been considered in detail. Using computed tomography (CT) image data of the Gage skull in conjunction with modern anatomical MRI and diffusion imaging data obtained in contemporary right handed male subjects (aged 25-36), we computationally simulate the passage of the iron through the skull on the basis of reported and observed skull fiducial landmarks and assess the extent of cortical gray matter (GM) and WM damage. Specifically, we find that while considerable damage was, indeed, localized to the left frontal cortex, the impact on measures of network connectedness between directly affected and other brain areas was profound, widespread, and a probable contributor to both the reported acute as well as long-term behavioral changes. Yet, while significantly affecting several likely network hubs, damage to Mr. Gage's WM network may not have been more severe than expected from that of a similarly sized "average" brain lesion. These results provide new insight into the remarkable brain injury experienced by this noteworthy patient.

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Figures

Figure 1 Modeling the path of the tamping iron through the Gage skull and its effects on white matter structure. a) The skull of Phineas Gage on display at the Warren Anatomical Museum at Harvard Medical School. b) CT image volumes were reconstructed, spatially aligned, and manual segmentation of the individual pieces of bone dislodged by the tamping iron (rod), top of the cranium, and mandible was performed. Surface meshes for each individual element of the skull were created. Based upon observations from previous examinations of the skull as well as upon the dimensions of the iron itself, fiducial constraint landmarks were digitally imposed and a set of possible rod trajectories were cast through the skull. This figure shows the set of possible rod trajectory centroids which satisfied each of the anatomical constraints. The trajectory nearest the mean trajectory was considered the true path of the rod and was used in all subsequent calculations. Additionally, voxels comprising the interior boundary and volume of the cranial vault were manually extracted and saved as a digital endocast of Mr. Gage's brain cavity. c) A rendering of the Gage skull with the best fit rod trajectory and example fiber pathways in the left hemisphere intersected by the rod. Graph theoretical metrics for assessing brain global network integration, segregation, and efficiency [92] were computed across each subject and averaged to measure the changes to topological, geometrical, and wiring cost properties. d) A view of the interior of the Gage skull showing the extent of fiber pathways intersected by the tamping iron in a sample subject (i.e. one having minimal spatial deformation to the Gage skull). The intersection and density of WM fibers between all possible pairs of GM parcellations was recorded, as was average fiber length and average fractional anisotropy (FA) integrated over each fiber.
Figure 1 Modeling the path of the tamping iron through the Gage skull and its effects on white matter structure. a) The skull of Phineas Gage on display at the Warren Anatomical Museum at Harvard Medical School. b) CT image volumes were reconstructed, spatially aligned, and manual segmentation of the individual pieces of bone dislodged by the tamping iron (rod), top of the cranium, and mandible was performed. Surface meshes for each individual element of the skull were created. Based upon observations from previous examinations of the skull as well as upon the dimensions of the iron itself, fiducial constraint landmarks were digitally imposed and a set of possible rod trajectories were cast through the skull. This figure shows the set of possible rod trajectory centroids which satisfied each of the anatomical constraints. The trajectory nearest the mean trajectory was considered the true path of the rod and was used in all subsequent calculations. Additionally, voxels comprising the interior boundary and volume of the cranial vault were manually extracted and saved as a digital endocast of Mr. Gage's brain cavity. c) A rendering of the Gage skull with the best fit rod trajectory and example fiber pathways in the left hemisphere intersected by the rod. Graph theoretical metrics for assessing brain global network integration, segregation, and efficiency [92] were computed across each subject and averaged to measure the changes to topological, geometrical, and wiring cost properties. d) A view of the interior of the Gage skull showing the extent of fiber pathways intersected by the tamping iron in a sample subject (i.e. one having minimal spatial deformation to the Gage skull). The intersection and density of WM fibers between all possible pairs of GM parcellations was recorded, as was average fiber length and average fractional anisotropy (FA) integrated over each fiber.CC BY · PubMed Central
Figure 2 The circular representation of cortical anatomy and WM connectivity from N = 110 normal right-handed males (age 25–36). The outermost ring shows the various brain regions arranged by lobe (fr – frontal; ins – insula; lim – limbic; tem – temporal; par – parietal; occ- occipital; nc – non-cortical; bs – brain stem; CeB - cerebellum) and further ordered anterior-to-posterior based upon the centers-of-mass of these regions in the published Destrieux atlas [72] (see also Table 6 for complete region names, abbreviations, and FreeSurfer IDs, and Table 7 for the abbreviation construction scheme). The left half of the connectogram figure represents the left-hemisphere of the brain, whereas the right half represents the right hemisphere with the exception of the brain stem, which occurs at the bottom, 6 o'clock position of the graph. The lobar abbreviation scheme is given in the text. The color map of each region is lobe-specific and maps to the color of each regional parcellation as shown in Fig. S2. The set of five rings (from the outside inward) reflect average i) regional volume, ii) cortical thickness, iii) surface area, and iv) cortical curvature of each parcellated cortical region. For non-cortical regions, only average regional volume is shown. Finally, the inner-most ring displays the relative degree of connectivity of that region with respect to WM fibers found to emanate from this region, providing a measure of how connected that region is with all other regions in the parcellation scheme. The links represent the computed degrees of connectivity between segmented brain regions. Links shaded in blue represent DTI tractography pathways in the lower third of the distribution of fractional anisotropy, green lines the middle third, and red lines the top third. Circular “color bars” at the bottom of the figure describe the numeric scale for each regional geometric measurement and its associated color on that anatomical metric ring of the connectogram.
Figure 2 The circular representation of cortical anatomy and WM connectivity from N = 110 normal right-handed males (age 25–36). The outermost ring shows the various brain regions arranged by lobe (fr – frontal; ins – insula; lim – limbic; tem – temporal; par – parietal; occ- occipital; nc – non-cortical; bs – brain stem; CeB - cerebellum) and further ordered anterior-to-posterior based upon the centers-of-mass of these regions in the published Destrieux atlas [72] (see also Table 6 for complete region names, abbreviations, and FreeSurfer IDs, and Table 7 for the abbreviation construction scheme). The left half of the connectogram figure represents the left-hemisphere of the brain, whereas the right half represents the right hemisphere with the exception of the brain stem, which occurs at the bottom, 6 o'clock position of the graph. The lobar abbreviation scheme is given in the text. The color map of each region is lobe-specific and maps to the color of each regional parcellation as shown in Fig. S2. The set of five rings (from the outside inward) reflect average i) regional volume, ii) cortical thickness, iii) surface area, and iv) cortical curvature of each parcellated cortical region. For non-cortical regions, only average regional volume is shown. Finally, the inner-most ring displays the relative degree of connectivity of that region with respect to WM fibers found to emanate from this region, providing a measure of how connected that region is with all other regions in the parcellation scheme. The links represent the computed degrees of connectivity between segmented brain regions. Links shaded in blue represent DTI tractography pathways in the lower third of the distribution of fractional anisotropy, green lines the middle third, and red lines the top third. Circular “color bars” at the bottom of the figure describe the numeric scale for each regional geometric measurement and its associated color on that anatomical metric ring of the connectogram.CC BY · PubMed Central
Figure 3 Mean connectivity affected by the presence of the tamping iron combined across subjects. The lines in this connectogram graphic represent the connections between brain regions that were lost or damaged by the passage of the tamping iron. Fiber pathway damage extended beyond the left frontal cortex to regions of the left temporal, partial, and occipital cortices as well as to basal ganglia, brain stem, and cerebellum. Inter-hemispheric connections of the frontal and limbic lobes as well as basal ganglia were also affected. Connections in grayscale indicate those pathways that were completely lost in the presence of the tamping iron, while those in shades of tan indicate those partially severed. Pathway transparency indicates the relative density of the affected pathway. In contrast to the morphometric measurements depicted in Fig. 2, the inner four rings of the connectogram here indicate (from the outside inward) the regional network metrics of betweenness centrality, regional eccentricity, local efficiency, clustering coefficient, and the percent of GM loss, respectively, in the presence of the tamping iron, in each instance averaged over the N = 110 subjects.
Figure 3 Mean connectivity affected by the presence of the tamping iron combined across subjects. The lines in this connectogram graphic represent the connections between brain regions that were lost or damaged by the passage of the tamping iron. Fiber pathway damage extended beyond the left frontal cortex to regions of the left temporal, partial, and occipital cortices as well as to basal ganglia, brain stem, and cerebellum. Inter-hemispheric connections of the frontal and limbic lobes as well as basal ganglia were also affected. Connections in grayscale indicate those pathways that were completely lost in the presence of the tamping iron, while those in shades of tan indicate those partially severed. Pathway transparency indicates the relative density of the affected pathway. In contrast to the morphometric measurements depicted in Fig. 2, the inner four rings of the connectogram here indicate (from the outside inward) the regional network metrics of betweenness centrality, regional eccentricity, local efficiency, clustering coefficient, and the percent of GM loss, respectively, in the presence of the tamping iron, in each instance averaged over the N = 110 subjects.CC BY · PubMed Central
Figure 4 The distribution characteristics of affected white matter pathways. WM fiber pathways intersected by the rod were pooled across all N = 110 subjects and examined for a) the relative lengths (wij) of affected pathways and b) the relative percentages of lost fiber density (gij); c) the bivariate distribution of gij versus wij indicating that local fiber pathways were affected, e.g. relatively short pathways proximal to the injury site, as well as damaging dense, longer-range fiber pathways, e.g. innervating regions some distance from the tamping iron injury (see “Calculation of Pathology Effects upon GM/WM Volumetrics” for further details).
Figure 4 The distribution characteristics of affected white matter pathways. WM fiber pathways intersected by the rod were pooled across all N = 110 subjects and examined for a) the relative lengths (wij) of affected pathways and b) the relative percentages of lost fiber density (gij); c) the bivariate distribution of gij versus wij indicating that local fiber pathways were affected, e.g. relatively short pathways proximal to the injury site, as well as damaging dense, longer-range fiber pathways, e.g. innervating regions some distance from the tamping iron injury (see “Calculation of Pathology Effects upon GM/WM Volumetrics” for further details).CC BY · PubMed Central
Figure 5 Healthy region-specific graph theoretical metrics, the effects of systematic lesions, and the difference between the observed and simulated tamping iron lesions. A) Cortical maps of regional graph theoretical properties. Regions affected by the passage of the tamping iron include those having relatively high betweenness centrality and clustering coefficients but relatively low mean local efficiency and eccentricity. B) A cortical surface schematic of the relative effects of systematic lesions of similar WM/GM attributes over the cortex for both network integration (i) and segregation (ii). For each mapping, colors represent the Z-score difference between systematic lesions of that area relative the average change in integration taken across all simulated lesions. C) Cortical maps of the differences/similarity between the effects on integration and segregation observed from the tamping iron lesion with that of each simulated lesion. Here black is most similar (e.g. the observed lesion is most similar to itself) whereas white is least similar to (e.g. most different from) the tamping iron's effects on these measures of network architecture.
Figure 5 Healthy region-specific graph theoretical metrics, the effects of systematic lesions, and the difference between the observed and simulated tamping iron lesions. A) Cortical maps of regional graph theoretical properties. Regions affected by the passage of the tamping iron include those having relatively high betweenness centrality and clustering coefficients but relatively low mean local efficiency and eccentricity. B) A cortical surface schematic of the relative effects of systematic lesions of similar WM/GM attributes over the cortex for both network integration (i) and segregation (ii). For each mapping, colors represent the Z-score difference between systematic lesions of that area relative the average change in integration taken across all simulated lesions. C) Cortical maps of the differences/similarity between the effects on integration and segregation observed from the tamping iron lesion with that of each simulated lesion. Here black is most similar (e.g. the observed lesion is most similar to itself) whereas white is least similar to (e.g. most different from) the tamping iron's effects on these measures of network architecture.CC BY · PubMed Central

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Citations

Cites

Show all 95 references

Research works printed but not linked: 7

  1. E Dupuy, 1877, A critical review of the prevailing theories concerning the physiology and pathology of the brain., Medical Times and Gazette, ii, 11
  2. AM Dale, 1999, Cortical surface-based analysis - I. Segmentation and surface reconstruction., Neuro Image, 9, 179
  3. B Fischl, 2002, Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain., Neurotechnique, 33, 341
  4. B Fischl, 1999, Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system., Neuroimage, 9, 195
  5. J Alstott, 2009, Modeling the impact of lesions in the human brain., Plos Computational Biology, 5, e1000408, 10.1371/journal.pcbi.1000408 · DOI · Paper page
  6. I Dinov, 2010, Neuroimaging Study Designs, Computational Analyses and Data Provenance Using the LONI Pipeline., PLoS ONE
  7. I Dinov, 2010, Efficient, distributed and interactive neuroimaging data analysis using the LONI pipeline., Frontiers in Neuroinformatics, 3, 1

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Metadata

Authors
Van Horn, J. D., Irimia, A., Torgerson, C. M., Chambers, M. C., Kikinis, R., & Toga, A. W.
Affiliations
  • University of California, Los Angeles (US)
  • Brigham and Women's Hospital (US)
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2012-05-16
Volume
7
Issue
5
Pages
e37454
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0037454
PMC
PMC3353935
OpenAlex
W2043275179
Cited by
196
References
95
Citations by year
2026: 6 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 7 · 2021: 16 · 2020: 34 · 2019: 29 · 2018: 18 · 2017: 8 · 2016: 15 · 2015: 19 · 2014: 9 · 2013: 15 · 2012: 4
FWCI
11.35
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • University of California, Los Angeles
  • National Institute of Mental Health (RC1MH088194)
  • National Institute of Biomedical Imaging and Bioengineering (U54EB005149)
  • National Center for Research Resources (P41RR013642, P41RR013218)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Clinical Neurology › Traumatic Brain Injury Research
Keywords
Phineas Gage, traumatic brain injury, white matter injury, connectomics, diffusion MRI, brain network connectivity, frontal lobe lesions, network hubs, history of neuroscience
MeSH terms
Adult, Brain Injuries, Frontal Lobe, Humans, Magnetic Resonance Imaging, Male, Nerve Fibers, Myelinated, Personality Disorders, Tomography, X-Ray Computed, Vermont, History, 19th Century, Neuroimaging

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