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Mapping the Aphasia Connectome with BrainX3: Connectomic Predictions of TMS-Defined Eloquent Cortex

  • September 2016
DOI:10.13140/RG.2.2.13260.21128
Authors:
Gregory Zegarek at Hôpitaux Universitaires de Genève
Gregory Zegarek
David Dalmazzo at University Pompeu Fabra
David Dalmazzo
Santiago Brandi at University Pompeu Fabra
Santiago Brandi
Klaudia Grechuta at Boehringer Ingelheim
Klaudia Grechuta
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Abstract

BrainX3 is a large-scale simulation of human brain activity, rendered in 3D in a virtual reality environment.2 This simulation is grounded in the structural connectivity obtained from diffusion spectrum imaging data of five healthy right-handed male volunteers.4 BrainX3 serves as a data mining platform for visualization, analysis, and feature extraction of neuroscience data.1 As a proof of principle, we examine BrainX3’s ability to map the aphasia connectome, and assess its ability to predict Transcranial Magnetic Stimulation (TMS)-induced language deficits.
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Mapping the Aphasia Connectome with BrainX3:
Connectomic Predictions of TMS-Defined Eloquent Cortex
Gregory Zegarek, David Dalmazzo, Santiago Brandi,
Klaudia Grechuta, Xerxes Arsiwalla, Paul FMJ Verschure
Laboratory for Synthetic, Perceptive, Emotive and Cognitive Systems
Carrer de Roc Boronat, 138, 08018 Barcelona
BrainX3 is a large-scale simulation of human brain activity, rendered in 3D
in a virtual reality environment.2 This simulation is grounded in the
structural connectivity obtained from diffusion spectrum imaging data of
five healthy right-handed male volunteers.4 BrainX3 serves as a data
mining platform for visualization, analysis, and feature extraction of
neuroscience data.1 As a proof of principle, we examine BrainX3’s ability
to map the aphasia connectome, and assess its ability to predict
Transcranial Magnetic Stimulation (TMS)-induced language deficits.
In neurosurgery, one of the primary goals is to maximize the resection of
pathologic tissue while minimizing functional neurologic deficits. This
goal becomes especially salient when operating around eloquent cortical
areas, such as those involved in language function. Unfortunately, the
prediction of cortical areas involved in language through classic
anatomical topography is not sufficient due to interindividual variability
of cortical organization. The main solutions to this problem utilize
preoperative or intraoperative mapping of language function to ensure
that eloquent cortex will be spared.
The current gold-standard in language mapping for neurosurgical patients
is direct cortical stimulation. However, due to its invasive nature, DCS
cannot be used to map the healthy human brain. Furthermore, it is well
known that in epilepsy or brain tumor patients, there is more likely to be
an atypical language organization.3,5 Given these considerations, we
investigated the effect of TMS-induced “virtual lesions” in healthy
participants undergoing an object-naming task.6 We hypothesized that
sites that were more likely to produce object-naming errors were more
likely to have projections to language areas such as Broca’s and
Wernicke’s areas.
Introduction
Methods
1. Query Search. As a data mining platform, we employed BrainX3 to
search for sites within the connectome which are principally involved in
language function. BrainX3 is able to query by keyword or Brodmann Area.
In addition, the user can do an exploratory search by selecting individual
nodes to search the curated database for all functions related to that
node. We searched by keyword “language” for nodes that have language
as a primary function.
Figure 1. Literature search by keyword/Brodmann Area.
Figure 2. A. Percentage of all naming errors in male participants from
Krieg et al. B. Connectome projections from representative
points within regions with >25% naming errors.
Results
Figure 3. A. One-tailed T-test of connectivity strength for projections
from selected nodes to BA 22 (Wernicke’s Area) vs. all projections. p=.033
B. Linear regression of percentage of projections to Language Areas (BA 22,
44, or 45) vs. percentage of naming errors when selected area underwent
“virtual lesioning” with navigated TMS. p=.08
Conclusion
Through efferent mapping, BrainX3 was able to map all connections from
selected nodes known to be necessary for language function. Connectivity
strength from these selected nodes to Wernicke’s area was significantly
higher than averaged connectivity to all areas. In addition, the correlation
between the percentage of projections to language areas vs. the
percentage of naming errors is approaching significance. We believe that
our results represent a novel non-invasive method for mapping language
areas.
References:
1. Arsiwalla X, Dalmazzo D, Zucca R, Betella A, Brandi S, Martinez E, Omedas P, Verschure P (2015). Connectomics to Semantomics: Addressing the Brain’s Big Data Challenge. Procedia
Computer Science. doi:10.1016/j.procs.2015.07.278
2. Arsiwalla X, Zucca R, Betella A, Martinez E, Dalmazzo D, Omedas P, Deco G, Verschure P (2015). Network dynamics with BrainX3: a large-scale simulation of the human brain network with real-
time interaction. Frontiers in Neuroinformatics. doi: 10.3389/fninf.2015.00002
3. Caulo, M, Sestieri, C, Polito, R, Tartaro, A, Colosimo, C, Inter-Hemispheric Language Reorganization in Patients with Brain Tumors: An fMRI Study. Radiological Society of North America 2006
Scientific Assembly and Annual Meeting, November 26 - December 1, 2006 ,Chicago IL.
4. Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey C, Wedeen V, Sporns O (2008). Mapping the Structural Core of Human Cerebral Cortex. PLOS Biology. doi: 10.1371/journal.pbio.0060159
5. Hamberger M, Cole J (2011). Language Organization and Reorganization in Epilepsy. Neuropsychology Review. Doi: 10.1007/s11065-011-9180-z
6. Krieg S, Sollmann N, Tanigawa N, Foerschler A, Meyer B, Ringel F (2015). Cortical distribution of speech and language error investigated by visual object naming and navigated transcranial
magnetic stimulation. Brain Structure and Function. doi 10.1007/s00429-015-1042-7
2. Efferent Mapping. We selected nodes within regions that were most
likely to produce object-naming errors when “virtual lesioning” of these
areas was done with navigated TMS. Regions were selected if they
produced at least 25% object-naming errors.
3. Statistical Analysis. One-tailed T-tests were performed comparing
connectivity strength from each selected node to classic language areas
(Brodmann Area 22, 44, and 45) vs. non-language areas. Linear regression
analyses were performed for proportion of connectivity to language areas
vs. percentage of all naming errors as a proxy for language-involvement of
cortical area.
p=.033 R² = 0.39417
p=.08
A B
A B
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We selected nodes within regions that were most likely to produce object-naming errors when "virtual lesioning" of these areas was done with navigated TMS. Regions were selected if they produced at least 25% object-naming errors
  • Efferent Mapping
Efferent Mapping. We selected nodes within regions that were most likely to produce object-naming errors when "virtual lesioning" of these areas was done with navigated TMS. Regions were selected if they produced at least 25% object-naming errors.