Network localization of neurological symptoms from focal brain lesions

Abstract

A traditional and widely used approach for linking neurological symptoms to specific brain regions involves identifying overlap in lesion location across patients with similar symptoms, termed lesion mapping. This approach is powerful and broadly applicable, but has limitations when symptoms do not localize to a single region or stem from dysfunction in regions connected to the lesion site rather than the site itself. A newer approach sensitive to such network effects involves functional neuroimaging of patients, but this requires specialized brain scans beyond routine clinical data, making it less versatile and difficult to apply when symptoms are rare or transient. In this article we show that the traditional approach to lesion mapping can be expanded to incorporate network effects into symptom localization without the need for specialized neuroimaging of patients. Our approach involves three steps: (i) transferring the three-dimensional volume of a brain lesion onto a reference brain; (ii) assessing the intrinsic functional connectivity of the lesion volume with the rest of the brain using normative connectome data; and (iii) overlapping lesion-associated networks to identify regions common to a clinical syndrome. We first tested our approach in peduncular hallucinosis, a syndrome of visual hallucinations following subcortical lesions long hypothesized to be due to network effects on extrastriate visual cortex. While the lesions themselves were heterogeneously distributed with little overlap in lesion location, 22 of 23 lesions were negatively correlated with extrastriate visual cortex. This network overlap was specific compared to other subcortical lesions (P < 10(-5)) and relative to other cortical regions (P < 0.01). Next, we tested for generalizability of our technique by applying it to three additional lesion syndromes: central post-stroke pain, auditory hallucinosis, and subcortical aphasia. In each syndrome, heterogeneous lesions that themselves had little overlap showed significant network overlap in cortical areas previously implicated in symptom expression (P < 10(-4)). These results suggest that (i) heterogeneous lesions producing similar symptoms share functional connectivity to specific brain regions involved in symptom expression; and (ii) publically available human connectome data can be used to incorporate these network effects into traditional lesion mapping approaches. Because the current technique requires no specialized imaging of patients it may prove a versatile and broadly applicable approach for localizing neurological symptoms in the setting of brain lesions.

Keywords: central post-stroke pain; hallucination; lesion network mapping; lesion networks; subcortical aphasia.

© The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Figures

The power of traditional lesion mapping is limited when symptoms reflect network dysfunction. Boes et al. present a novel method that leverages normative human connectome data to link symptoms to lesion-associated networks. They validate the method in four lesion syndromes, linking subcortical lesions to cortical regions implicated in symptom expression.

Figure 1

Lesion network mapping method. Twenty-three lesions resulting in peduncular hallucinosis were identified, three of which are illustrated here (column 1) and mapped to a reference brain (column 2). The brain network associated with each lesion was identified using resting state functional connectivity from a large cohort of normal subjects (column 3). Positive correlations with the lesion are shown in hot colours while negative correlations (anticorrelations) are shown in cool colours. These lesion-based networks were overlapped to identify networks common to at least 21 of 23 lesions (right). The image in column 1 row 2 was reprinted with permission from John Wiley & Sons.

Figure 2

Traditional lesion mapping results – peduncular hallucinosis. Areas of overlap among 23 peduncular hallucinosis lesions are shown (from left to right) in the pontine tegmentum, paramedian mesencephalic tegmentum, substantia nigra pars reticulata and intralaminar/paramedian thalamus. The colour scale indicates the number of overlapping lesions. The location of all lesions, additional slices of the lesion overlap, and coordinates of lesion overlap are available (Supplementary Fig. 1 and 2, and Supplementary Table 4).

Figure 3

Lesion network mapping results – peduncular hallucinosis. Regions of common network overlap in at least 21 of 23 cases with negative correlation (top, in cool colours) and positive correlation (bottom, in warm colours) are displayed. Note significant anticorrelation in extrastriate visual cortex, within the a priori region of interest (outlined in black). The colour scale indicates the number of cases with common overlap. MNI coordinates of axial slices shown are, left to right: top −2, 0, 2, 4, bottom −13, −8, 0, 6. Additional brain slices are provided (Supplementary Fig. 3).

Figure 4

Lesion mapping results for other syndromes. Areas of lesion overlap are shown for auditory hallucinosis, central post-stroke pain, and subcortical aphasia. The colour scale indicates the number of overlapping lesions. The coordinates of lesion overlap sites are available (Supplementary Table 6).

Figure 5

Lesion network mapping results: summary of main findings. Column 1 shows the hypothesized site of network overlap for each lesion syndrome. Column 2 shows the network overlap results, with positive correlations displayed in warmer colours and negative correlations in cooler colours. Column 3 shows the results of the voxel-wise Liebermeister test that compared network overlap from actual lesions relative to that of randomized lesions. The bar graph on the far right shows quantitative data supporting the specificity of the network overlap in the a priori cortical region of interest relative to all other cortical regions, derived from the voxel-wise Liebermeiseter results. Coordinates of findings in column 3 and additional regions are available (Supplementary Table 7). *P ≤ 0.05, **P ≤ 0.01. ROI = region of interest.

Figure 6

Unexpected findings from lesion network mapping. (A) The lateral geniculate nucleus is on the left, taken from the Julich Histological Atlas (Bürgel et al., 2006), and the image on the right shows areas that are significantly positively correlated with peduncular hallucinosis lesions. (B) The lateral cerebellum language area is shown on the left, as identified in a meta-analysis of functional MRI studies, represented as a sphere at MNI coordinate 35, 62, 28 (Stoodley and Schmahmann, 2009). The image on the right shows areas positively correlated with subcortical aphasia lesions. (C) A node of the pain matrix on the left, as identified from a meta-analysis of functional MRI studies (Friebel et al., 2011). The image on the right shows a cortical region that is significantly positively correlated with central post-stroke pain lesions. All displayed voxels represent Z-scores from a voxel-wise Liebermeister test, significant at a false discovery rate of 5% or greater. The colour bar minimum and maximum values show Z-scores of 7–9 for A, 3.5–5 for B, and 4–6 for C. Peak coordinates of these sites are available (Supplementary Table 7).

Figure 7

Between-syndrome lesion network mapping results. Voxel-based lesion-symptom mapping of the lesions did not segregate between lesion syndromes using a false discovery rate of 5%. In contrast, applying the same statistical approach there were voxels that segregated between lesion syndromes. The colour scale denotes a voxel-wise Z-score from a Leibermeister test; 2.5 is statistically significant with a false discovery rate of 5%; 6 is significant at both a false discovery rate <1% and at P < 0.01 after applying Bonferroni correction for multiple comparisons.