The performance of the subjects in the emotion recognition task (from facial expressions) is summarized in . (The scores are presented as both standard correlations and Fisher’s Z-transformed correlations; for the distribution of scores in the Fisher’s Z transform space, see also .) The correlation scores across all emotions had a mean Z-transformed value across subjects of Z = 1.17 (r = 0.81), and 42 of 103 subjects were considered impaired.
Impaired recognition of emotion in lesion subjects
Brain regions associated with emotion recognition impairment in the PM3 analysis
We performed a voxelwise lesion–deficit analysis to provide a standard description of the association between lesion location and impairment in emotion recognition from facial expressions (mean emotion recognition score). These analyses also served to inform the tractwise analyses, in the identification of potential cortical confounds and determination of white matter tract lesion–deficit associations.
We found lesion–deficit relationships in occipital, perisylvian, and frontal sectors of the right cerebral cortex and in the inferior frontal gyrus on the left (), consistent with previous findings by Adolphs et al. (2000)
. Specifically, the brain regions on the lateral surface in which lesions were found to be associated with deficits in emotion recognition in facial expressions included the following: bilateral frontal operculum (inferior frontal gyrus), right somatosensory cortices, right superior temporal gyrus, right inferior parietal lobule (including supramarginal gyrus), and right lateral occipital cortex. In the coronal sections, effects are apparent bilaterally in the anterior insula and portions of the caudate and putamen. The PM3 analysis also revealed extensive white matter involvement, particularly of the right hemisphere, extending from occipital cortices to the anterior frontal cortex. Statistical power was more limited for the left hemisphere, but it was sufficient to identify potential lesion–deficit associations on the left.
Main results of the fiber tract regression analyses
To investigate the role of fiber tract disconnection on the recognition of emotion from facial expressions, we first completed the main tractwise regression analyses including all 14 regressors (including for all tracts, gray matter, and lesion size).
In these analyses, only disconnection of the right IFOF significantly predicted impaired emotion recognition scores. This applied for the average emotion recognition score (t = 3.14; p < 0.0012; r = 0.32) (overall model: r 2 = 0.39; adjusted r 2 = 0.30; F(14,88) = 4.09; p < 0.00002), and, individually, for sadness (t = 3.66; p < 0.00022; r = 0.36) (overall model: r 2 = 0.38; adjusted r 2 = 0.28; F(14,88) = 3.79; p < 0.00005), anger (t = 3.02; p < 0.0016; r = 0.31) (overall model: r 2 = 0.43; adjusted r 2 = 0.34; F(14,88) = 4.80; p < 0.000002), and fear (t = 3.03; p < 0.0016; r = 0.31) (overall model: r 2 = 0.40; adjusted r 2 = 0.31; F(14,88) = 4.22; p < 0.00001). Descriptively, the IFOF tract in the right hemisphere appeared to follow clusters of local maxima in the PM3 map all along the tract (), also supporting the idea that long-range fibers are involved. Despite aforementioned limitations in statistical power for the left hemisphere, statistical power was equivalent for both hemispheres in the white matter compartments along the trajectory of the implicated tracts (). A post hoc test between left and right hemisphere of the estimated degree of disconnection of the IFOF in predicting performance was significant (t(88) = 2.68; p < 0.01), further supporting the finding. Thus, the laterality of our finding is likely not attributable to limitations in coverage and statistical power.
Figure 3 Voxelwise lesion deficit analysis (PM3) alone and with overlay of IFOF, ILF, and SLF. Fiber tract perimeter (after thresholding the tract probability at 0.15) is depicted in white and overlaid with unthresholded PM3 results mapped on coronal slices of (more ...)
To address possible confounds attributable to general visual (perception/recognition) or emotional (depression) impairments, we also performed the main regression analysis with additional covariates for basic visual perception, object recognition, and a composite measure for depression (BDI and MMPI). Critically, the results from this analysis did not differ from the original results, because damage to the IFOF on the right was still significantly associated with impaired emotion recognition (mean recognition score) from facial expressions (t
= 2.76; p
< 0.004). Thus, we have shown that basic visual object recognition does not appear to confound the finding of a significant association between IFOF disconnection and impairments in emotion recognition from facial expressions in our sample. These results are also consistent with those performed in the original study (Adolphs et al., 2000
As mentioned in Materials and Methods, we identified a subject (1981) from the list of 12 subjects with lesions most specific to the right IFOF or ILF and with the smallest involvement of gray matter. This subject had damage caused by an infarct, involving only 0.08 cm3 of gray matter (smallest among the 103 subjects) and 4.6 cm3 of white matter (total sample mean = 9.3 and 11.7 cm3, respectively). This subject’s lesion was primarily in the right IFOF () (68% of the lesion in the IFOF; 20% in the ILF) and followed quite precisely the posterior course of the right IFOF. We found clear impairments in the recognition of the facial expression of emotion in this subject (), with emotion recognition scores several SDs below the mean of the normative group on several emotions (happiness, 1.04 SDs; sadness, 2.93 SDs; anger, 1.48 SDs; fear, 4.22 SDs; disgust, 3.20 SDs; surprise, 1.75 SDs; and average emotion score, 3.47 SDs). documents the otherwise unremarkable neuropsychological profile for subject 1981, with normal perception, intelligence, attention, and memory. The subject’s impairment in the recognition of the facial expression of emotion thus appeared to be quite specific.
Additional analyses addressing null results for ILF and SLF
Neither ILF nor SLF was implicated in the main regression analyses, but, in each case, there is a rationale to wonder whether the findings are false negatives. We hypothesized the ILF to be critical to emotion recognition, in addition to the IFOF, and the case study was consistent with a role for the ILF, given the partial overlap between the ILF and the case study subject’s lesion. The SLF primarily overlapped with significant effects in the PM3 analysis: 69% (at p < 0.05) and 35% (at p < 0.01) of the voxels corresponding to the right SLF, excluding voxels overlapping with other fiber tracts.
We undertook supplementary analyses that addressed a potential role for colinearity (shared variance) in accounting for the absence of significant effects for these tracts. Shared variance arises from damage that tends to span anatomic structures attributable to either the characteristics of the lesions themselves and/or the use of a probabilistic atlas in which tracts can overlap.
Thus, anatomical proximity between structures (tracts, gray matter regions) and the relatively large size of the lesions on average lead to substantial correlations between the right SLF and IFOF regressors (0.72) and between the SLF and confounding perisylvian gray matter (0.89). Likewise, the right ILF and the right IFOF regressors showed substantial correlations (0.72), as did the ILF regressor and the cortical regressor (0.38).
We therefore performed supplemental regression analyses in which the 12 tracts were entered as the only independent variable, to assess the potential of the right SLF and ILF to predict average emotion score. In these analyses, only the right IFOF (t = 4.69; p < 0.000004; r = 0.42), the right ILF (t = 3.34; p < 0.0006; r = 0.32), and the right SLF (t = 4.28; p < 0.00002; r = 0.39) were associated with significant effects. Not surprisingly, the right IFOF presented the most significant effect.
We then performed additional analyses for the right SLF and right ILF with restricted combinations of regressors (see below) to further identify the factors potentially involved in the absence of significant effects for those two tracts in the main regression analyses with the full model. For the right SLF, the addition of the right IFOF regressor neutralized the significance of the SLF regressor (t = 1.16; p < 0.12). The addition of the right ILF or cortical regressor did not eliminate the significance of the SLF effects (respectively, t = 2.96, p < 0.002; t = 4.28; p < 0.00002). Similarly, when the right IFOF and right ILF were included together in the model, the ILF effect was no longer significant (t = −0.21; p < 0.58). When the right IFOF and the cortical regressor were included together, the right ILF effect was still significant (t = 3.34; p < 0.0006).
False-negative findings could also arise from lesion sampling effects because, in our subject sample, there were more lesions relatively specific to the IFOF. Twelve of the 25 subjects with lesions most specific to a given tract (based on the entropy analysis) had lesions primarily associated (highest peak in the probability distributions) with the IFOF; 23 of 25 had lesions associated with the IFOF when tracts with the second highest peak were also counted. For 8 of the 25 subjects, the highest peak in the probability distributions was for the SLF; 12 of 25 were counted when the second highest peak was also taken into account. Two of 25 subjects had highest peaks, and 6 of 25 had the first or second highest peaks, associated with the ILF. Thus, in these 25 subjects, lesions in the IFOF showed the highest specificity, in line with the ability of the regression to show significant effects for the IFOF, above and beyond the other tracts.
Supplementary analysis of the specificity of the IFOF in fear recognition impairments
We performed a supplementary analysis to further clarify the specificity of IFOF disconnection in fear recognition impairments. In particular, we were interested in assessing whether damage to the amygdala or disconnection of the amygdala could be contributing to the overall deficits in fear recognition observed in the group-level analysis and associated with disconnection of the IFOF, given the previous literature on the role of the amygdala in fear recognition (Adolphs et al., 1994
; Young et al., 1995
; Calder et al., 2001
). This supplementary analysis was also motivated by the following reasons: (1) subject 1981, who showed a dramatic impairment in fear recognition, had a lesion that also encompassed the ILF (which connects visual cortices with the amygdala) (Catani et al., 2003
); (2) in general, for the occipito-temporal components of the tract, damage affecting the IFOF is also likely to cause partial disconnection of the ILF, because of the proximity of the two tracts; and (3) the IFOF itself has a segment running near the amygdala, and thus it is likely that lesions to the amygdala will often involve the IFOF.
Accordingly, we reasoned that, if disconnection of, or lesions to, the amygdala are responsible for the fear recognition deficits, the lesion overlap of subjects with lesions tending to be maximally specific for the IFOF, weighted by the degree of fear recognition impairments (profound impairment associated with increased weight), should concentrate in occipito-temporal brain regions, which include the ILF and/or the amygdala. Conversely, if disconnections anywhere along the path of the IFOF can cause impairment in the recognition of fear, then substantial overlap should also be observed in the frontal sectors of this tract. In the list of 25 subjects with lesions maximally specific to a tract, we selected the six subjects with the lowest entropies associated with lesions in the IFOF (this corresponded to the first peak of the entropy distribution).We divided the binary lesion map of each subject by his/her emotion recognition score for fear, so that subjects with lower scores (in the direction of impairment) would have more weight. We then computed a weighted lesion overlap map by adding the values of weighted lesion maps across subjects for each voxel. shows the results of this analysis. The highest values in the weighted maps were found all along the tract, including in the frontal lobes, further supporting the causal role of lesions to the IFOF in fear recognition impairments.