Cognitive and Affective Empathy in Huntington’s Disease

Abstract

Background:

Empathy is a multidimensional construct and a key component of social cognition. In Huntington’s disease (HD), little is known regarding the phenomenology and the neural correlates of cognitive and affective empathy, and regarding how empathic deficits interact with other behavioral and cognitive manifestations.

Objective:

To explore the cognitive and affective empathy disturbances and related behavioral and neural correlates in HD.

Methods:

Clinical and sociodemographic data were obtained from 36 healthy controls (HC) and 54 gene-mutation carriers (17 premanifest and 37 early-manifest HD). The Test of Cognitive and Affective Empathy (TECA) was used to characterize cognitive (CE) and affective empathy (AE), and to explore their associations with grey matter volume (GMV) and cortical thickness (Cth).

Results:

Compared to HC, premanifest participants performed significantly worse in perspective taking (CE) and empathic distress (AE). In symptomatic participants, scores were significantly lower in almost all the TECA subscales. Several empathy subscales were associated with the severity of apathy, irritability, and cognitive deficits. CE was associated with GMV in thalamic, temporal, and occipital regions, and with Cth in parietal and temporal areas. AE was associated with GMV in the basal ganglia, limbic, occipital, and medial orbitofrontal regions, and with Cth in parieto-occipital areas.

Conclusion:

Cognitive and affective empathy deficits are detectable early, are more severe in symptomatic participants, and involve the disruption of several fronto-temporal, parieto-occipital, basal ganglia, and limbic regions. These deficits are associated with disease severity and contribute to several behavioral symptoms, facilitating the presentation of maladaptive patterns of social interaction.

Keywords

Huntington’s disease social cognition empathy magnetic resonance imaging voxel-based morphometry

INTRODUCTION

Huntington’s disease (HD) is a monogenetic, autosomal dominant neurodegenerative disorder caused by CAG polyglutamine expansion in the HTT gene [1]. Clinical diagnosis is based on the presence of unequivocal motor abnormalities, but cognitive and behavioral changes are also indissociable from HD and may precede motor onset by up to 15 years [2].

Some of the cognitive domains most prototypically affected in HD are frontal-executive functions, attention and psychomotor speed [3]. Depressive mood, anxiety, irritability, perseverative behavior, and apathy are also prominent behavioral features of the disease [4]. Deficits in emotion recognition and in Theory of mind (ToM) can be also detected from the premanifest stage and throughout the course of the disease [5 –12]. These disturbances of social cognition have been associated with reduced functional capacity [13] and with negative consequences in both personal and professional life [14]. However, with the exception of basic emotion recognition and expression, and ToM, studies addressing other social cognition components in HD are scarce, and just few of them have contemplated the role of cognitive and affective empathy, but none have explored the neural correlates [10 , 15–17].

Empathy is a key component of social cognition that refers to the capacity to experience the feelings, thoughts, intentions, and attitudes of others. Conceptually, empathy is recognized as a multidimensional construct subdivided into a “cognitive” and an “affective” domain [18]. Cognitive empathy is the capacity to infer and understand another’s feelings (e.g., I can understand how you feel after being fired), and affective empathy is the ability to experience another’s feelings and emotions (e.g., I feel sad when I see you sad) [19, 20].

The study of cognitive and affective empathy has gained increasing interest, leading to the development of questionnaires to assess these processes [21] and to a better understanding of the neural correlates subserving them [15 , 23]. The exact neural mechanisms subserving complex social cognition abilities are partially understood. Several studies among healthy populations align cognitive empathy abilities with medial and dorsolateral prefrontal cortex areas, the temporo-parietal junction, and the temporal pole [24 –27], whereas affective empathy has been associated with fronto-temporo-parietal and limbic structures such as the precuneus, the inferior frontal gyrus, the anterior cingulate cortex, the somatosensory cortex, the amygdala, and the insula [26 , 28–30].

The set of brain structures that seem to play a central role in social cognition are all regions highly vulnerable to neurodegeneration and dysfunction in HD. In parallel, alterations related to social cognition have been frequently described in HD. However, although social cognition deficits are part of the cognitive and behavioral phenotype of HD, little is known regarding specific cognitive and affective empathy disturbances and related neural correlates in this population.

METHODS

We explored the profile—and the association between these profiles and other clinical indicators—of cognitive and affective empathy in healthy controls (HC), premanifest gene-mutation carriers (preHD), and early-stage manifest patients (HD). Secondly, we focused on a subset of gene-mutation carriers for which 3-Tesla magnetic resonance imaging (MRI) sequences were available, and we examined structural brain correlates of empathy by measuring grey-matter volume (GMV) and cortical thickness (Cth).

Participants

Fifty-four gene-mutation carriers with CAG > 39 and twenty-seven healthy controls were recruited from the outpatient clinic of the Movement Disorders Unit at Hospital de la Santa Creu i Sant Pau in Barcelona. The group of gene-mutation carriers included seventeen preHD and thirty-seven early-to-mild HD. The sample of HC was divided into seventeen participants matched for age, gender, and education with the preHD group and nineteen participants matched with the HD group. All participants were free of any neurological disorder other than HD and had no history of brain surgery, traumatic brain injury, epilepsy, or drug abuse. These criteria also applied to the HC. The subset of participants included in the neuroimaging analysis consisted of sixteen preHD and seventeen HD participants.

Assessments

The severity of motor symptoms was rated using the Unified Huntington’s Disease Rating Scale –Total Motor Score (UHDRS-TMS) [31]. All patients in the HD group had a diagnostic confidence level (DCL) of 4, and according to the total functional capacity (TFC), were in disease stage I (TFC > 11) or II (TFC > 6 and <11) [32]. PreHD participants had a UHDRS-TMS below 4, a DCL < 3, and a TFC = 13. The disease burden score (DBS) was calculated using the formula based on age and CAG repeat length: [age×(CAG-33.66)] [33]. We also recorded socio-demographic and clinical data including age, sex, education, global cognitive functioning, and severity of neuropsychiatric symptoms.

In all the sample, the Mini-Mental State Examination (MMSE) was administered to rate global cognition [34]. Behavior was assessed using caregiver report on Starkstein’s apathy scale (AS) and the short form of the Problem Behaviors Assessment scale for HD (PBA-s) [35, 36]. The cognitive tests comprising the UHDRS cognitive assessment (Cogscore) and tests comprising the cognitive protocol of the Enroll-HD study were administered to preHD and HD participants [31, 37].

To assess cognitive and affective empathy we used the Cognitive and Affective Empathy Test (TECA) [21]. This 33-item questionnaire is a 5-point validated Likert scale, and provides a total score for empathy and for the four subscales of empathy: two for cognitive empathy (“Perspective taking” or the ability to put oneself in someone else’s place; “Emotional understanding” or the ability to understand and infer emotional states from the intentions and impressions of others) and two for affective empathy (“Empathic distress” or the ability of sharing and connecting with the negative emotions of others; “Empathic happiness” or the ability of sharing and connecting with others’ positive emotions). Based on the scoring procedure, higher TECA scores mean better performance. Raw TECA scores were transformed into T scores using the normative data provided in the TECA manual [21] and then were standardized to Z scores. Based on the normative data of the test, clinically relevant performance was set at a Z score < –1.5. Because lack of awareness is a common feature of HD, and because the assessment of behavioral symptoms in HD is usually done with a caregiver or companion who can provide accurate information, the TECA was administered to a reliable informant and the scores obtained from this informant were those used in the set of analyses.

Written informed consent was obtained from all participants and all procedures were performed in accordance with the standards of the Ethics Committee at Hospital de la Santa Creu i Sant Pau in Barcelona and with the ethical standards of Helsinki Declaration of 1975, as revised in 2008.

Neuroimaging acquisition and preprocessing

T1-weighted MRI images were acquired on a 3T Philips Achieva using an MP-RAGE sequence (TR/TE = 12.66/7.11 milliseconds, flip-angle = 8°, field of view = 23 cm, matrix = 256×256 and slice thickness = 1 mm). We applied standard voxel-based morphometry (VBM) and cortical thickness (Cth) pipelines. VBM procedures were performed using the statistical parametric mapping software package (SPM12, http://www.fil.ion.ucl.ac.uk/spm) as previously reported [38]. Briefly, GMV tissue probability maps were computed from T1-MRI scans. These maps were then normalized to the Montreal Neurological Institute (MNI) space by applying the DARTEL algorithm. The normalized GMV maps were smoothed using an isotropic spatial filter of 8×8×8 mm full-width at half-maximum (FWHM) to reduce inter-individual variability. Cth analysis was performed using the FreeSurfer 6.0 software package (https://surfer.nmr.mgh.harvard.edu). The specific methods used for cortical reconstruction have been fully described elsewhere [39]. In short, Cth was computed at each vertex of the resulting surfaces from optimized surface deformation models. The resulting vertex-wise Cth data were normalized to average space and smoothed using a Gaussian kernel of 15 mm FWHM.

Statistical analysis

Socio-demographic and clinical variables were compared among the three groups. Data is expressed as means±standard deviation for continuous variables and as percentages for categorical variables. Post-hoc independent t-test comparisons were performed between the three groups for continuous variables and χ² for categorical variables. To calculate the effect size of the significant differences observed between groups we used Cohen’s d coefficient (d values: 0 –0.2, small effect size; 0.6, moderate effect size; >0.8, large effect size). A p-value < 0.05 was considered significant. Linear regression analyses controlling the effect of age and CAG were performed. Because of the large number of comparisons, False Discovery Rate (FDR) correction using a p-value < 0.05 was applied. Accordingly, Benjamini-Hochberg Adjusted p-values are provided. The SPSS v.25 software was used for the statistical analysis.

For the neuroimaging analysis, voxel-wise and vertex-wise measures derived from VBM and Cth analyses were introduced into a general lineal model (GLM) to explore the structural brain correlates of TECA performance. Again, these GLM included age and CAG length as nuisance covariates. Only clusters surviving p < 0.05 and family-wise error (FWE) correction for multiple comparisons were considered significant. FWE correction was performed using cluster-level RFT for the VBM analyses as implemented in SPM12, and a Monte Carlo simulation with 10,000 repeats was used for the Cth analysis as implemented in FreeSurfer.

Finally, we computed quantitative GMV and mean Cth values at the identified clusters to perform linear regression analysis between these measures, the raw scores in the TECA subdomains, and other clinical variables of interest, always using age and CAG as covariates. The data that support the findings of this study are available from the corresponding author upon reasonable request.

RESULTS

Clinic and socio-demographic data

The sample of gene-mutation carriers consisted of 17 preHD participants (mean age = 40.1±8 standard deviation (SD); mean CAG repeat length = 43.5±2 SD) and 37 HD patients (mean age = 51.6±10 SD; mean CAG repeat length = 44±3 SD). Healthy controls (HC) were grouped into those matched with the preHD group and those matched with the HD group. Accordingly, two different groups of HC were compared. Seventeen controls were matched with the preHD group (mean age = 36.6±10 SD) and 19 with the HD group (mean age = 53.7±10 SD).

The preHD group scored significantly lower than the HC group on the MMSE [t(32) = 2.8, p = 0.008; d = 1.02]. However, global cognitive status in both groups was within the range of normality (MMSE > 26). Conversely, the HD group obtained a mean MMSE score in the range of probable impairment (MMSE = 25±3.3 SD) and significantly lower than that in the HC group [t(54) = 7.3, p < 0.001; d = 1.82]. Regarding the Cogscore and related assessments, the HD group scored significantly lower than the preHD group and with large effect sizes (Cohen’s d > 6) in all the assessments (Table 1).

Table 1

Clinic and sociodemographic characteristics of the sample

	Controls	Controls	preHD	HD	p ^*	p ^†	p ^‡
	(for preHD)	(for HD)	N = 17	N = 37
	N = 17	N = 19
Age	36.6±10.3	53.7±10.7	40.1±8.2	51.6±10.4	0.298	0.309	<0.005
Gender (f/m)	7/10	10/9	11/6	20/17	–	–	–
Education	14.3±4.1	11.2±3.8	14.4±4	11.4±4.2	0.921	0.871	0.017
CAG	–	–	43.5±2.1	44±2.8	–	–	0.267
DBS	–	–	386±67	527±117	–	–	<0.001
Years-to-onset	–	–	11.3±4.7	–	–	–	–
UHDRS-TMS	0	0	2±2	33.2±21.1	0.005	<0.001	<0.001
TFC	13	13	13	10.6±2.4	–	<0.001	<0.001
FIS	100	100	100	88.8±13	–	<0.001	<0.005
MMSE	29.6±0.7	29.4±0.9	28±2.1	25±3.3	0.010	<0.001	<0.001
Apathy scale	0.5±0.9	0.5±1.2	7.1±8.3	12.5±8.7	0.007	<0.001	0.041
PBA-s Total	1.4±2.2	3.1±3.9	8.7±7.6	14.2±9.4	0.002	<0.001	0.045
Depression	0.3±7.8	1.1±1.3	1.2±1.9	1.6±2	0.089	0.300	0.506
Suicidality	0	0	0.3±1.2	0.3±1.1	0.333	0.065	0.877
Anxiety	0.8±1.4	1.1±1.6	1.5±2.3	1.5±1.8	0.371	0.418	0.966
Irritability	0.1±0.4	0.6±1.3	1.6±1.8	2.5±2.5	0.004	<0.001	0.238
Aggression	0	0.1±0.6	0.06±0.2	0.8±1.6	0.333	0.028	0.006
Apathy	0	0	2±2.6	2.7±2.4	0.007	<0.001	0.313
Perseverative beh.	0	0	1.4±1.7	3.1±2.4	0.005	<0.001	0.016
OCB	0.1±0.4	0.1±0.4	0.5±1.3	0.8±1.7	0.303	0.016	0.479
Delusions	0	0	0	0.3±1	–	0.033	0.033
Hallucinations	0	0	0	0	–	–	–
Disorientation	0	0	0	0.2±0.7	–	0.103	0.103
UHDRS cogscore	–	–	309±54	174±71	–	–	<0.001
SDMT	–	–	51.3±11	13.9±14	–	–	<0.001
Stroop color naming	–	–	70.6±12	43.4±16	–	–	<0.001
Stroop word reading	–	–	101.8±24	62.9±23	–	–	<0.001
Stroop interference	–	–	45.7±10	24±11	–	–	<0.001
FAS	–	–	40±12	19.7±13	–	–	<0.001
TMT-A	–	–	25.1±7	88.7±51	–	–	<0.001
TMT-B	–	–	55.3±20	175.3±79	–	–	<0.001
Category fluency	–	–	20.8±3	12.5±5	–	–	<0.001

*HC vs preHD; ^†HC vs HD; ^‡preHD vs HD.

On the neuropsychiatric assessments, the preHD group scored significantly higher than the HC group in the PBA-s subscores for irritability [t(32) = 3.2, p = 0.004; d = 1.15], apathy [t(32) = 3, p = 0.003; d = 1.13], perseverative behavior [t(32) = 3.2, p = 0.005; d = 1.16], total PBA-s score [t(32) = 3.6, p = 0.002; d = 1.30], and in the AS [t(32) = 3.1, p = 0.007; d = 1.12]. Similarly, the HD group scored significantly higher than the HC group in the PBA-s subscores for irritability [t(54) = 3.6, p < 0.001; d = 0.953], aggressive behavior [t(54) = 2.2, p = 0.028; d = 0.55]; apathy [t(54) = 6.8, p < 0.001; d = 1.46], perseverative behavior [t(56) = 7.9, p < 0.001; d = 1.10], obsessive-compulsive behavior [t(54) = 2.4, p = 0.016; d = 0.599], delusions [t(54) = 2.2, p = 0.033; d = 0.523], and in the AS [t(54) = 8.2, p < 0.001; d = 1.910]. Between the preHD and HD groups, higher scores were found in the HD group for aggressive behavior [t(52) = 2.8, p < 0.01; d = 0.701], perseverative behavior [t(52) = 2.8, p < 0.01; d = 0.817], delusions [t(52) = 2.2, p < 0.05; d = 0.424], and apathy in the apathy scale [t(52) = 2.2, p < 0.05; d = 0.635]. Almost all these significant differences showed large effect sizes (Cohen’s d > 6).

Cognitive and affective empathy

ANOVA revealed significant group differences in the form of lower scores in the gene-mutation carriers group in the raw total TECA score [F(1,78) = 19.7, p < 0.001; d = 1.119] and in all raw TECA subtests except empathic happiness [F(1,78) = 0.70, p = 0.498]. The differences remained significant when we included the global cognitive performance (MMSE) as covariate [F(1,78) = 26.7; p < 0.001]. Post-hoc t-test comparisons revealed the persistence of significantly lower scores between preHD patients and HC in perspective taking [t(32) = 2.7, p < 0.01] and in empathic distress [t(32) = 3.2, p < 0.005]. Moreover, 25% of preHD scored below Z < –1.5 in empathic distress, and 12.5% in perspective taking vs 0% in HC (Table 2).

Table 2

Cognitive and Affective Empathy Test performance

	Controls	Controls	preHD	HD	p ^*	p ^†	p ^‡
	(for preHD)	(for HD)	N = 17	N = 37
	N = 17	N = 19	N = 17	N = 37
Cognitive Empathy	33.3±4	33.4±2.5	31±4	24.1±6.1	0.126	<0.001	<0.001
Perspective taking	31.8±4.7	30±3.9	27.3±4.6	19.1±6	<0.01	<0.001	<0.001
Z < –1.5 (%)	0	0	12.5	62.2	χ² = 0.227	χ² < 0.001	χ² <0.001
Emotional understanding	34.8±5	36.8±4	34.8±4	29±7	0.997	<0.001	<0.001
Z < –1.5 (%)	0	0	0	18.9	–	χ² = 0.044	χ² = 0.058
Affective empathy	29.5±4	29.3±3	27±3.6	25±3.4	<0.05	<0.001	0.390
Empathic distress	25.1±6	23.3±4.8	18.9±4.3	16.5±4.5	<0.005	<0.001	0.082
Z < –1.5 (%)	0	0	25	54.1	<0.05	χ² < 0.001	χ² = 0.051
Empathic happiness	34±3.7	35.3±3.1	33±4	33.6±4.4	0.507	0.154	0.660
Z < –1.5 (%)	0	0	6.3	2.7	χ² = 0.485	χ² = 0.661	χ² = 0.659
TECA total	119±13	125±9	114±13	98.4±15	0.261	<0.001	<0.005
Z < –1.5 (%)	0	0	6.3	37.8	χ² = 0.485	χ² < 0.001	<0.05

Data are shown as mean±standard deviation. *HC vs preHD; ^†HC vs HD; ^‡preHD vs HD.

Post-hoc comparisons between HC and HD patients revealed significantly lower scores in the HD group for all the raw TECA measures except empathic happiness [t(54) = 1.4, p = 0.154]. Again, differences remained significant when the MMSE score was included as covariate [F(1,53) = 21.8; p < 0.001]. Comparing the percentage of cases scoring below Z < –1.5 in these two groups, we found significant differences for perspective taking (HC = 0% vs HD = 62%), emotional understanding (HC = 0% vs HD = 18.9%), empathic distress (HC = 0% vs HD = 54.1%), and total TECA score (HC = 0% vs HD = 37.8%). Comparing preHD with HD, raw scores were significantly lower in HD for perspective taking [t(52) = 4.8, p < 0.001; d = 1.53], emotional understanding [t(52) = 3.4, p = 0.005; d = 0.96], and total TECA score [t(52) = 3.3, p = 0.002; d = 1.04] but not for empathic distress and empathic happiness (Fig. 1).

Fig. 1

A) TECA total raw score in each group. B) Percentage of cases scoring below the clinical range (z score < 1.5). Significant differences between groups are expressed with “***”.

In the linear regression analysis, significant (FDR corrected) associations were found between perspective taking and AS (r = –0.366; p = 0.048), irritability (r = –0.429; p = 0.024), and delusions (r = –0.335; p = 0.045). Emotional understanding was significantly associated with irritability (r = –0.393; p = 0.039). Empathic happiness was significantly associated with PBA-s total (r = –0.439; p = 0.013), and apathy (r = –0.374; p = 0.042). The total TECA score was significantly associated with the MMSE (r = 0.280; p < 0.05), the AS (r = –0.413; p = 0.026), irritability (r = –0.391; p = 0.026), and apathy (r = –0.370; p = 0.030).

Regarding the cognitive measures, the perspective taking raw subscore was significantly associated with the Cogscore (r = 0.489; p = 0.005) and with all of the individual tests comprising the cognitive assessment. The strongest correlations were found with the SDMT (r = 0.544; p = 0.0001). Significant correlations were also found with the TMT-A (r = –0.332; p = 0.03) and the TMT-B (r = –0.433; p = 0.005). Emotional understanding was significantly associated with the Cogscore (r = 0.324; p = 0.038), and the SDMT (r = 0.327; p = 0.037). Empathic happiness was associate with the SDMT (r = 0.420; p = 0.04). Overall, the total TECA raw score was significantly associated with all the cognitive measures with the exception of semantic fluency. In the total HD sample, and within the preHD group, TECA scores were significantly correlated with the DBS (r = –0.389; p = 0.004).

Neuroimaging analyses

The subsample of patients included in the neuroimaging analyses consisted of 33 gene-mutation carriers (mean age = 46.1±11.8; mean CAG repeat length = 43.4±2.7; mean DBS = 435±103). Supplementary data provides the clinical and sociodemographic characteristics of these samples.

Voxel-wise VBM analysis revealed a significant association between the TECA total score and GMV in several cortical-subcortical clusters (Table 3). Specifically, lower TECA total scores were significantly associated with reduced GMV in the right subgenual area (BA 25), the left and right precentral gyrus, the left pulvinar nucleus, the left medial-dorsal thalamus, the left amygdala, the left calcarine gyrus, the left lingual gyrus (BA 18), the left superior temporal gyrus, and the left and right caudate nucleus. In the Cth analysis, lower TECA total scores were significantly associated with cortical thinning in several parietal-temporal and occipital regions, including the bilateral superior and inferior parietal gyrus, the bilateral lateral occipital gyrus, the left paracentral and postcentral gyrus, the left precuneus, and the left superior frontal gyrus (Fig. 2).

Table 3

Cluster description table of the VBM-GMV and Cth analyses (p < 0.05 FWE corrected)

	Peak MNI coordinates (x, y, z)	Cluster size	Peak T value
VBM-GMV
Left precentral gyrus	57 –5 44	3388	5.22
Left thalamus		7235
Pulvinar nucleus	–17 –32 2		5.61
Medial-dorsal thalamus	–6 –20 –3		4.56
Occipital cortex		4993
Left calcarine	–12 –78 6		4.77
Left lingual gyrus	5 –86 –9		4.62
Left superior temporal gyrus	–57 –3 2
Left amygdala	–20 –12 –11	2364	4.72
Basal ganglia		2311
Left caudate	–9 9 11		3.92
Right caudate nucleus	11 8 8		3.81
Right precentral gyrus	–59 –11 41	2263	4.40
Right subgenual area	3 11 –11	2375	4.71
Cth
Left inferior parietal gyrus	–28 –68 23	765	3.09
Left superior parietal gyrus	–22 –76 31	2578	4.42
Left lateral occipital gyrus	–32 –83 1	3076	4.03
Left paracentral gyrus	–15 –36 51	848	3.82
Left postcentral gyrus	–59 –15 36	2311	3.92
Left precuneus	–17 –75 21	659	3.28
Left superior frontal gyrus	–19 –1 63	1169	3.22
Right inferior parietal gyrus	43 –66 26	1054	3.08
Right superior parietal gyrus	24 –79 34	772	3.57
Right lateral occipital gyrus	16 –88 19	1802	3.15

Fig. 2

A) VBM-GMV and B) Cth correlates of total TECA scores in HD. C) Data from the linear regression analysis between measures of GMV and Cth in the clusters of interest and the TECA values for affective and cognitive empathy.

Linear regression analyses showed that performance on the subtests assessing cognitive empathy was significantly associated with GMV in the lingual/calcarine gyrus (r = 0.515; p = 0.002), the precentral gyrus (r = 0.510; p = 0.002), the superior temporal gyrus (r = 0.393; p = 0.023), and the pulvinar nucleus (r = 0.456; p = 0.007). Performance on the subtests assessing affective empathy was significantly associated with GMV in the caudate (r = 0.406; p = 0.019), lingual/calcarine (r = 0.599; p < 0.001), and the subgenual gyrus (r = 0.362; p = 0.038). Cognitive empathy was also associated with Cth in the left postcentral gyrus (r = 0.498, p = 0.003), the left superior parietal cortex (r = 0.464; p = 0.006), and the left superior temporal cortex (r = 0.475; p = 0.005), and affective empathy was significantly associated with Cth in the right superior parietal cortex (r = 0.455; p = 0.007) and right cuneus (r = 0.530; p = 0.001).

DISCUSSION

In the present study, we show that significant deficits in cognitive and affective empathy are inherent to HD, and that difficulties at the level of perspective taking and empathic distress are already identifiable in the premanifest stage. These deficits showed significant associations with several cognitive and behavioral parameters, as well as with GMV and Cth in several cortical-subcortical clusters. Our results also highlight a significant association between total TECA scores and DBS. This association remained significant when restricted to the preHD group, suggesting empathy disruption as an early measure of disease progression.

Phenomenologically, neither preHD nor HD participants showed difficulties in empathic happiness, that is, the ability to share and to connect with positive emotions of other people. This highlights that gene-mutation carriers maintain the ability to process and integrate their own positive experiences and those of others at an affective level. Conversely, deficits in perspective taking and empathic distress as assessed through ratings from caregivers or companions were prominent signs even in preHD participants, and both processes were worse in HD. Moreover, deficits at the level of emotional understanding were also found in HD. Difficulties in perspective taking—or the capacity to assume the point of view of the other—can be mostly ascribed to frontal-executive processes that are strongly dependent on the dorsomedial and dorsolateral prefrontal cortex [40]. As these regions maintain reciprocal connections with the associative portion of the caudate nucleus, the characteristic pattern of progressive frontal-striatal degeneration occurring in HD may account for the early emergence and concurrent disruption of perspective-taking abilities in HD. In effect, previous studies have shown that preHD individuals have more difficulties in perspective taking than controls due to functional alterations that imply hypoactivation in fronto-parietal networks [41].

Empathic distress, or the ability to experience the negative emotions of others, deficits were observed in preHD patients and in HD patients but were considerably worse in the latter. Empathic distress is linked to limbic and mirror neurons systems that facilitate the automatic processing and interoceptive integration and feeling of negative emotions of others [42, 43]. In contrast with our findings, previous studies assessing affective empathy in HD did not find alterations in empathic distress [10 , 15]. This divergence may be explained due to that in previous studies the questionnaires were administered to the patients, and consequently, the results were subjected to possible bias due to lack of awareness. In our sample, the lack of significant differences between empathic distress scores in preHD and HD indicates that difficulties in sharing and connecting with negative emotions are present from the premanifest stage, but remain relatively stable throughout disease progression.

A deficit at the level of emotional understanding, or the ability to understand and infer the emotional states from the intentions and impression from others, was only found in HD. This ability requires the integrity of elemental processes of emotions recognition [44]. In HD, the progressive impairment in facial emotion recognition has been shown to affect both preHD and HD [8 , 46]. This deficit may account for difficulties in the ability to understand the emotional states of others, and has been associated with early damage within insular and heteromodal visual processing regions [47].

In the regression analyses, frontal-executive dependent processes such as psychomotor processing speed, attention and cognitive flexibility were significantly associated with performance on several empathy measures. Executive functions are known to be involved in empathic skills. However, it is widely assumed that empathy is subserved by several cognitive processes, that cannot be solely attributed to executive functions [17].

Empathy subscores were also associated with the severity of apathy and irritability. These associations coincide with previous works in HD and Alzheimer’s disease and suggest a possible causal relation between them [5, 48]. For instance, in a previous neuroimaging study [38], we found that irritability was related to grey matter compromise in a set of regions overlapping with the set presently found. These regions are critically involved in the integration and expression of affect. Accordingly, it seems conceptually plausible to assume that the cognitive processes allowing us to interpret affective signals from other people, and the affective integration processes allowing us to experience the emotional state of other people, are likely defective in HD. These defects may facilitate the engagement of non-premeditated emotionally-driven disruptive behavioral responses (i.e, irritability or aggressive outburst) [38], and may diminish the capacity to process, understand or feel potential consequences to other people, and to adjust the behavior accordingly.

Almost all of the subcomponents of empathy were associated with apathy. Other studies have found a relationship between ToM and apathy in HD [49, 50], and in Parkinson’s disease [51, 52]. However, although it is reasonable to assume a possible association between empathy and motivation, most studies assessing empathy in HD have not taken apathy into account as a clinical variable of interest [9–12 , 54]. The meaning of this association is unclear, but it is reasonable to claim that these coexisting phenomena share the participation of similar dysfunctional systems. Apathy per se might contribute to less effective cognitive and affective empathy due to the lack of motivation or interest in social interaction [55]. However, the severity of apathy in HD is strongly associated with the structural and metabolic disruption of several brain regions contributing to emotional processing [56]. Thus, part of the dysfunctional mechanisms contributing to apathy may also contribute of empathy deficits.

Concerning the neuroimaging data, lower TECA scores were associated with decreased GMV in several cortico-subcortical limbic, basal ganglia, occipital, frontal, and temporal areas, and Cth in extensive parieto-temporal and occipital areas. Limbic areas appearing with decreased GMV in association with lower TECA performance are associated with the expression and integration of emotions [57]. Lower GMV were also found in diencephalic relay regions such as the pulvinar nucleus and the left medial-dorsal thalamus, which both maintain direct connections with the amygdala and PFC [38 , 59], and are involved in the regulation of emotional signals and in response to internal and external cues. Thus, damage to these areas may contribute to difficulties in sharing and expressing an adequate emotion in a context-dependent situation, and inferring another’s emotional states [60 –62].

Decreased GMV in the bilateral caudate nucleus is a key neuropathological hallmark of HD which we found to be also associated with worse TECA performance. Caudate nucleus damage disrupts the architecture of the motor, associative and limbic basal ganglia-thalamo-cortical circuitry. Accordingly, damage at this level promotes cognitive and affective symptoms of a different nature, including disturbed executive functioning, ToM, decision-making, behavioral regulation, and social cognition [17, 55]. TECA-related decreases in GMV and Cth also involved extensive temporo-parieto-occipital heteromodal associative regions. All of these regions have been repeatedly reported to participate in the multisensory integration of inner and external motion-related and affective signals and to have a critical role in ToM and social cognition [26 , 64]. Specifically, the supramarginal gyrus is part of the mirror neuron system and has a cardinal role on empathic abilities, and the superior temporal gyrus also has core participation in facial emotion recognition and social cognition processes [65 –68]. Finally, the lingual gyrus is involved in processing and encoding complex visual information, in mentalizing and in visual imagery. All of these processes play key roles in the ability to recognize and mentalize about others. Altogether, the correlational analysis between the separate cognitive and affective components of empathy were consistent with previous works indicating a more prominent involvement of high-order processing areas in cognitive empathy, and more limbic areas with affective empathy [5 , 69].

Because of the cross-sectional nature of the present study, it is impossible to determine a pattern of possible worsening or stability over time in the obtained metrics about cognitive and affective empathy. In this sense, future studies should explore the progression pattern of these manifestations and the possible relationship with neurodegenerative changes.

In conclusion, defective cognitive and affective empathy is present early in the course of HD as a consequence of the disruption of a set of key brain regions and processes. The associations of this feature with the severity of behavioral symptoms and with disease burden suggest that the standardized measurement of empathy may be an early indicator of disease progression.

Footnotes

ACKNOWLEDGMENTS

The authors wish to thank all those at the Hospital de la Santa Creu i Sant Pau involved in the study. The authors also wish to extend their gratitude to the study participants and their families.

The present study was partially funded by a Spanish Government Grant (PI17/001885) and by the Huntington’s Disease Society of America (HD Human Biology Project).

CONFLICT OF INTEREST

The authors have no conflict of interest to report.

References

Walker

. Huntington’s disease. Lancet. 2007;369:218–28.

Paulsen

, Miller

, Hayes

, Shaw

. Cognitive and behavioral changes in Huntington disease before diagnosis. 1st ed. Vol. 144, Handbook of Clinical Neurology. Elsevier B.V.; 2017. pp. 69-91.

Peavy

, Jacobson

, Goldstein

, Hamilton

, Kane

, Gamst

, et al.Cognitive and functional decline in Huntington’s disease: Dementia criteria revisited. Mov Disord. 2010;25(9):1163–9.

Craufurd

, Thompson

, Snowden

. Behavioral changes in Huntington disease. Neuropsychiatry Neuropsychol Behav Neurol. 2001;14(4):219–26.

Dogan

, Saß

, Mirzazade

, Kleiman

, Werner

, Pohl

, et al.Neural correlates of impaired emotion processing in manifest Huntington’s disease. Soc Cogn Affect Neurosci. 2014;9(5):671–80.

Ille

, Schäfer

, Scharmüller

, Enzinger

, Schöggl

, Kapfhammer

, et al.Emotion recognition and experience in Huntington disease: A voxel-based morphometry study. J Psychiatry Neurosci. 2011;36(6):383–90.

Novak

MJU

, Warren

, Henley

SMD

, Draganski

, Frackowiak

, Tabrizi

. Altered brain mechanisms of emotion processing in pre-manifest Huntington’s disease. Brain. 2012;135:1165–79.

Johnson

, Stout

, Solomon

, Langbehn

, Aylward

, Cruce

, et al.Beyond disgust: Impaired recognition of negative emotions prior to diagnosis in Huntington’s disease. Brain. 2007;130:1732–44.

Eddy

, Rickards

. Theory of mind can be impaired prior to motor onset in Huntington’s disease. Neuropsychology. 2015;29(5):792–8.

10.

Adjeroud

, Besnard

, El Massioui

, Verny

, Prudean

, Scherer

, et al.Theory of mind and empathy in preclinical and clinical Huntington’s disease. Soc Cogn Affect Neurosci. 2016;11:89–99.

11.

Allain

, Havet-Thomassin

, Verny

, Benedicte

, Lancelot

, Besnard

, et al.Evidence for deficits on different components of theory of mind in Huntington’s disease. Neuropsychology. 2011;25(6):741–51.

12.

Larsen

, Vinther-jensen

, Gade

, Nielsen

, Vogel

. Do i misconstrue? Sarcasm detection, emotion recognition, and theory of mind in Huntington disease. Neuropsychology. 2016;30(2):181–9.

13.

Crauford

, Snowden

. Neuropsychological and Neuropsychiatric aspects of Huntington’s disease. In: Harper

, Bates

, Jones

, editors. Huntington’s Disease. Oxford: Oxford University Press; 2002. pp. 62–94.

14.

Ille

, Holl

, Kapfhammer

, Reisinger

, Schäfer

, Schienle

. Emotion recognition and experience in Huntington’s disease: Is there a differential impairment?Psychiatry Res. 2011;188(3):377–82.

15.

Maurage

, Lahaye

, Grynberg

, Jeanjean

, Guettat

, Verellen-Dumoulin

, et al.Dissociating emotional and cognitive empathy in pre-clinical and clinical Huntington’s disease. Psychiatry Res. 2016;237:103–8.

16.

Trinkler

, Cleret de Langavant

, Bachoud-Levi

. Joint recognition-expression impairment of facial emotions in Huntington’s disease despite intact understanding of feelings. Cortex. 2013;549-58.

17.

Bora

, Velakoulis

, Walterfang

. Social cognition in Huntington’s disease: A meta-analysis. Behav Brain Res. 2016;297:131–40.

18.

Blair

RJR

. Responding to the emotions of others: Dissociating forms of empathy through the study of typical and psychiatric populations. Conscious Cogn. 2005;14(4):698–718.

19.

Dvash

, Shamay-Tsoory

. Theory of mind and empathy as multidimensional constructs: Neurological foundations. Top Lang Disord. 2014;34(4):282–95.

20.

Walter

. Social cognitive neuroscience of empathy: Concepts, circuits and genes. Emot Rev. 2012;4(1):9–17.

21.

López-Pérez

, Fernández-Pinto

, Abad García

. TECA, Test de Empatía Cognitiva y Afectiva. Madrid: TEA ediciones; 2008.

22.

Uribe

, Puig-Davi

, Abos

, Baggio

HCHC

, Junque

, Segura

. Neuroanatomical and functional correlates of cognitive and affective empathy in young healthy adults. Front Behav Neurosci. 2019;13:85.

23.

Dermody

, Wong

, Ahmed

, Piguet

, Hodges

, Irish

. Uncovering the neural bases of cognitive and affective empathy deficits in Alzheimer’s disease and the behavioral-variant of frontotemporal dementia. J Alzheimers Dis. 2016;53(3):801–16.

24.

Lamm

, Decety

, Singer

. Meta-analytic evidence for common and distinct neural networks associated with directly experienced pain and empathy for pain. Neuroimage. 2011;54(3):2492–502.

25.

Van Overwalle

, Baetens

. Understanding others’ actions and goals by mirror and mentalizing systems: A meta-analysis. Neuroimage. 2009;48(3):564–84.

26.

Banissy

, Kanai

, Walsh

, Rees

. Inter-individual differences in empathy are reflected in human brain structure. Neuroimage. 2012;62(3):2034–9.

27.

Eres

, Decety

, Louis

, Molenberghs

. Individual differences in local gray matter density are associated with differences in affective and cognitive empathy. Neuroimage. 2015;117:305–10.

28.

Bernhardt

, Singer

. The Neural Basis of Empathy. Annu Rev Neurosci. 2012;35(1):1–23.

29.

Christov-Moore

, Simpson

, Coudé

, Grigaityte

, Iacoboni

, Ferrari

. Empathy: Gender effects in brain and behavior. Neurosci Biobehav Rev. 2016;46(Pt 4):604–27.

30.

Goerlich-Dobre

, Lamm

, Pripfl

, Habel

, Votinov

. The left amygdala: A shared substrate of alexithymia and empathy. Neuroimage. 2015;122:20–32.

31.

Huntington Study Group. Unified Huntington’s Disease Rating Scale: Reliability and consistency. Mov Disord. 1996;11(2):136–42.

32.

Shoulson

, Fahn

. Huntington disease: Clinical care and evaluation. Neurology. 1979;29(1):1–3.

33.

Zhang

, Long

, Mills

, Warner

, Lu

, Paulsen

, et al.Indexing disease progression at study entry with individuals at-risk for Huntington disease. Am J Med Genet B Neuropsychiatr Genet. 2011;156B(7):751–63.

34.

Folstein

, Folstein

, McHugh

. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98.

35.

Callaghan

, Stopford

, Arran

, Boisse

M-F

, Coleman

, Santos

, et al.Reliability and factor structure of the Short Problem Behaviors Assessment for Huntington’s disease (PBA-s) in the TRACK-HD and REGISTRY studies. J Neuropsychiatry Clin Neurosci. 2015;27(1):59–64.

36.

Starkstein

, Mayberg

, Preziosi

, Andrezejewski

, Leiguarda

, Robinson

. Reliability, validity, and clinical correlates of apathy in Parkinson’s disease. J Neuropsychiatry Clin Neurosci. 1992;4(2):134–9.

37.

Landwehrmeyer

, Fitzer-Attas

, Giuliano

, Gonçalves

, Anderson

, Cardoso

, et al.Data analytics from Enroll-HD, a global clinical research platform for Huntington’s disease. Mov Disord Clin Pract. 2017;4(2):212–24.

38.

Martinez-Horta

, Sampedro

, Horta-Barba

, Perez-Perez

, Pagonabarraga

, Gomez-Anson

, et al.Structural brain correlates of irritability and aggression in early manifest Huntington’s disease. Brain Imaging Behav. 2021;15(1):107–13.

39.

Fischl

, Dale

. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A. 2000;97(20):11050–5.

40.

Healey

, Grossman

. Cognitive and affective perspective-taking: Evidence for shared and dissociable anatomical substrates. Front Neurol. 2018;9:491.

41.

Eddy

, Rickards

, Hansen

. Through your eyes or mine? The neural correlates of mental state recognition in Huntington’s disease. Hum Brain Ma. 2018;39:1354–66.

42.

Spunt

, Lieberman

. An integrative model of the neural systems supporting the comprehension of observed emotional behavior. Neuroimage. 2012;59(3):3050–9.

43.

Lamm

, Batson

, Decety

. The neural substrate of human empathy: Effects of perspective-taking and cognitive appraisal. J Cogn Neurosci. 2007;19(1):42–58.

44.

Olsson

, Ochsner

. The role of social cognition in emotion. Trends Cogn Sci. 2008;12(2):65–71.

45.

Sprengelmeyer

, Young

, Calder

, Karnat

, Lange

, Hömberg

, et al.Loss of disgust. Perception of faces and emotions in Huntington’s disease. Brain. 1996;119(Pt 5):1647–65.

46.

Sprengelmeyer

, Young

, Sprenglemeyer

, Calder

, Rowland

, Perrett

, et al.Recognition of facial expressions: Selective impairment of specific emotions in Huntington’s disease. Cogn Neuropsychol. 1997;14(6):839–79.

47.

Hennenlotter

, Schroeder

, Erhard

, Haslinger

, Stahl

, Weindl

, et al.Neural correlates associated with impaired disgust processing in pre-symptomatic Huntington’s disease. Brain. 2004;127(Pt 6):1446–53.

48.

Wright

, Dickerson

, Feczko

, Negeira

, Williams

. A functional magnetic resonance imaging study of amygdala responses to human faces in aging and mild Alzheimer’s disease. Biol Psychiatry. 2007;62(12):1388–95.

49.

Eddy

, Rickards

. Interaction without intent: The shape of the social world in Huntington’s disease. Soc Cogn Affect Neurosci. 2015;10:1228–35.

50.

Philpott

, Andrews

, Staios

, Churchyard

, Fisher

. Emotion evaluation and social inference impairments in Huntington’s disease. J Huntingtons Dis. 2016;5:175–83.

51.

Santangelo

, Vitale

, Trojano

, Errico

, Amboni

, Barbarulo

, et al.Neuropsychological correlates of theory of mind in patients with early Parkinson’s disease. Mov Disord. 2012;27(1):98–105.

52.

Martínez-Corral

, Pagonabarraga

, Llebaria

, Pascual-Sedano

, García-Sánchez

, Gironell

, et al.Facial emotion recognition impairment in patients with Parkinson’s disease and isolated apathy. Parkinsons Dis. 2010;2010:930627.

53.

Eddy

, Mahalingappa

, Rickards

. Putting things into perspective: The nature and impact of theory of mind impairment in Huntington’s disease. Eur Arch Psychiatry Clin Neurosci. 2014;264:697–705.

54.

Henley

SMD

, Wild

, Hobbs

, Warren

, Frost

, Scahill

, et al.Defective emotion recognition in early HD is neuropsychologically and anatomically generic. Neuropsychologia. 2008;46:2152–60.

55.

Callaghan

, Bertoux

, Hornberger

. Beyond and below the cortex: The contribution of striatal dysfunction to cognition and behaviour in neurodegeneration. J Neurol Neurosurg Psychiatry. 2014;85:371–8.

56.

Martínez-Horta

, Perez-Perez

, Sampedro

, Pagonabarraga

, Horta-Barba

, Carceller-Sindreu

, et al.Structural and metabolic brain correlates of apathy in Huntington’s disease. Mov Disord. 2018;33(7):1151–9.

57.

Taylor

, Liberzon

. Neural correlates of emotion regulation in psychopathology. Trends Cogn Sci. 2007;11(10):413–8.

58.

Pessoa

, Adolphs

. Emotion processing and the amygdala: From a “low road” to “many roads” of evaluating biological significance. Nat Rev Neurosci. 2010;11(11):773–83.

59.

Benarroch

. Pulvinar: Associative role in cortical function and clinical correlations. Neurology. 2015;84(7):738–47.

60.

Bickart

, Dickerson

, Barrett

. The amygdala as a hub in brain networks that support social life. Neuropsychologia. 2014;63:235–48.

61.

Bechara

, Damasio

. Role of the amygdala in decision-making. Ann N Y Acad Sci. 2003;985:356–69.

62.

Mason

, Zhang

, Begeti

, Guzman

, Lazar

, Rowe

, et al.The role of the amygdala during emotional processing in Huntington’s disease: From pre-manifest to late stage disease. Neuropsychologia. 2015;70:80–9.

63.

Adolphs

, Damasio

, Tranel

, Cooper

, Damasio

. A role for somatosensory cortices in the visual recognition of emotion as revealed by three-dimensional lesion mapping. J Neurosci. 2000;20(7):2683–90.

64.

Saxe

, Moran

, Scholz

, Gabrieli

. Overlapping and non-overlapping brain regions for theory of mind and self reflection in individual subjects. Soc Cogn Affect Neurosci. 2006;1(3):229–34.

65.

Bigler

, Mortensen

, Neeley

, Ozonoff

, Krasny

, Johnson

, et al.Superior temporal gyrus, language function, and autism. Dev Neuropsychol. 2007;31(2):217–38.

66.

Jou

, Minshew

, Keshavan

, Vitale

, Hardan

. Enlarged right superior temporal gyrus in children and adolescents with autism. Brain Res. 2010;1360:205–12.

67.

Radua

, Phillips

, Russell

, Lawrence

, Marshall

, Kalidindi

, et al.Neural response to specific components of fearful faces in healthy and schizophrenic adults. Neuroimage. 2010;49(1):939–46.

68.

Adolphs

. Is the human amygdala specialized for processing social information?Ann N Y Acad Sci. 2003;985:326–40.

69.

Valk

, Bernhardt

, Böckler

, Trautwein

F-M

, Kanske

, Singer

. Socio-cognitive phenotypes differentially modulate large-scale structural covariance networks. Cereb Cortex. 2016;1-11.