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Structural integrity of the insula and emotional facial recognition performance following stroke
(2023)
The role of the human insula in facial emotion recognition is controversially discussed, especially in relation to lesion-location-dependent impairment following stroke. In addition, structural connectivity quantification of important white-matter tracts that link the insula to impairments in facial emotion recognition has not been investigated. In a case–control study, we investigated a group of 29 stroke patients in the chronic stage and 14 healthy age- and gender-matched controls. Lesion location of stroke patients was analysed with voxel-based lesion-symptom mapping. In addition, structural white-matter integrity for tracts between insula regions and their primarily known interconnected brain structures was quantified by tractography-based fractional anisotropy. Our behavioural analyses showed that stroke patients were impaired in the recognition of fearful, angry and happy but not disgusted expressions. Voxel-based lesion mapping revealed that especially lesions centred around the left anterior insula were associated with impaired recognition of emotional facial expressions. The structural integrity of insular white-matter connectivity was decreased for the left hemisphere and impaired recognition accuracy for angry and fearful expressions was associated with specific left-sided insular tracts. Taken together, these findings suggest that a multimodal investigation of structural alterations has the potential to deepen our understanding of emotion recognition impairments after stroke.
Background and purpose
The insula has important functions in monitoring and integrating physiological responses to a personal experience of multimodal input. The experience of chills in response to auditory stimuli is an important example for a relevant arousing experience coupled with bodily response. A group study about altered chill experiences in patients with insula lesions is lacking.
Methods
Twenty-eight stroke patients with predominantly insula lesions in the chronic stage and 14 age-matched controls were investigated using chill stimuli of both valences (music, harsh sounds). Group differences were analyzed in subjective chill reports, associated bodily responses (skin conductance response), lesion mapping, diffusion-weighted imaging and functional magnetic resonance imaging. Other neuropsychological deficits were excluded by comprehensive testing. Diffusion-weighted imaging was quantified for four insula tracts using fractional anisotropy.
Results
The frequency of chill experiences was comparable between participant groups. However, bodily responses were decreased for the stroke group. Whereas there was no association of lesion location, a positive association was found for the skin conductance response during aversive sounds and the tract connecting anterior inferior insula and left temporal pole in the stroke group. Similarly, functional magnetic resonance imaging activation in areas hypothesized to compensate for damage was increased with bodily response.
Conclusions
A decoupling of felt arousal and bodily response after insula lesion was observed. Impaired bodily response was related to an impaired interaction of the left anterior insula and the temporal pole.
Objective: The instrument THERapy-related InterACTion (THER-I-ACT) was developed to document therapeutic interactions comprehensively in the human therapist–patient setting. Here, we investigate whether the instrument can also reliably be used to characterise therapeutic interactions when a digital system with a humanoid robot as a therapeutic assistant is used.
Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis [n = 9] or arm ability training (AAT) for mild arm paresis [n = 8]) using the digital therapy system E-BRAiN over a course of nine sessions. Analysis of the therapeutic interaction: A total of 34 therapy sessions were videotaped. All therapeutic interactions provided by the humanoid robot during the first and the last (9th) session of daily training were documented both in terms of their frequency and time used for that type of interaction using THER-I-ACT. Any additional therapeutic interaction spontaneously given by the supervising staff or a human helper providing physical assistance (ABT only) was also documented. All ratings were performed by two trained independent raters.
Statistical analyses: Intraclass correlation coefficients (ICCs) were calculated for the frequency of occurrence and time used for each category of interaction observed.
Results: Therapeutic interactions could comprehensively be documented and were observed across the dimensions provision of information, feedback, and bond-related interactions. ICCs for therapeutic interaction category assessments from 34 therapy sessions by two independent raters were high (ICC ≥0.90) for almost all categories of the therapeutic interaction observed, both for the occurrence frequency and time used for categories of therapeutic interactions, and both for the therapeutic interaction performed by the robot and, even though much less frequently observed, additional spontaneous therapeutic interactions by the supervisory staff and a helper being present. The ICC was similarly high for an overall subjective rating of the concentration and engagement of patients (0.87).
Conclusion: Therapeutic interactions can comprehensively and reliably be documented by trained raters using the instrument THER-I-ACT not only in the traditional patient–therapist setting, as previously shown, but also in a digital therapy setting with a humanoid robot as the therapeutic agent and for more complex therapeutic settings with more than one therapeutic agent being present.
Objective: To characterize a socially active humanoid robot’s therapeutic interaction as a therapeutic assistant when providing arm rehabilitation (i.e., arm basis training (ABT) for moderate-to-severe arm paresis or arm ability training (AAT) for mild arm paresis) to stroke survivors when using the digital therapeutic system Evidence-Based Robot-Assistant in Neurorehabilitation (E-BRAiN) and to compare it to human therapists’ interaction.
Methods: Participants and therapy: Seventeen stroke survivors receiving arm rehabilitation (i.e., ABT [n = 9] or AAT [n = 8]) using E-BRAiN over a course of nine sessions and twenty-one other stroke survivors receiving arm rehabilitation sessions (i.e., ABT [n = 6] or AAT [n = 15]) in a conventional 1:1 therapist–patient setting. Analysis of therapeutic interaction: Therapy sessions were videotaped, and all therapeutic interactions (information provision, feedback, and bond-related interaction) were documented offline both in terms of their frequency of occurrence and time used for the respective type of interaction using the instrument THER-I-ACT. Statistical analyses: The therapeutic interaction of the humanoid robot, supervising staff/therapists, and helpers on day 1 is reported as mean across subjects for each type of therapy (i.e., ABT and AAT) as descriptive statistics. Effects of time (day 1 vs. day 9) on the humanoid robot interaction were analyzed by repeated-measures analysis of variance (rmANOVA) together with the between-subject factor type of therapy (ABT vs. AAT). The between-subject effect of the agent (humanoid robot vs. human therapist; day 1) was analyzed together with the factor therapy (ABT vs. AAT) by ANOVA.
Main results and interpretation: The overall pattern of the therapeutic interaction by the humanoid robot was comprehensive and varied considerably with the type of therapy (as clinically indicated and intended), largely comparable to human therapists’ interaction, and adapted according to needs for interaction over time. Even substantially long robot-assisted therapy sessions seemed acceptable to stroke survivors and promoted engaged patients’ training behavior.
Conclusion: Humanoid robot interaction as implemented in the digital system E-BRAiN matches the human therapeutic interaction and its modification across therapies well and promotes engaged training behavior by patients. These characteristics support its clinical use as a therapeutic assistant and, hence, its application to support specific and intensive restorative training for stroke survivors.