The behavioral results from individual praxis tasks and comparisons with other cognitive functions were used to identify the prevalence of deficits in each subtask in this cohort of patients. Note that this analysis was not used to inform the lesion-mapping analyses reported below. Instead, the behavioral data for each task were entered as a continuous variable. The reason for reporting the behavioral results below was to provide an indication of the number of patients who were deemed to be performing below the cut-off for praxis on these screening tasks. This was not used to inform our imaging analyses.

In the behavioral analyses, cut-off scores for normal performance (two standard deviations below the mean of agematched healthy controls) were 11.5 on Gesture Production, 5.8 Gesture Recognition, and 11.5 in Imitation, based on normative data published previously (see **Table 2** and Chapter 6 and 7 of Humphreys et al., 2012). Based on these criteria, 204 out of 387 patients performed abnormally on Gesture Production, 248 out of 387 on Gesture Recognition, and 252 out of 387 Imitation (on average, 235 patients out of 387 scored below range for apraxia). Of the left-handed patients, 12 out of 28 patients with left-hemisphere damage had no praxis deficits, whereas 16 patients with left-hemisphere damage scored below the cut-off in at least two of the praxis tasks, indicating they were most likely left-hemisphere dominant (Goldenberg, 2013b).

```
2http://aphasialab.org/vlsm
```
The average patient results on the three praxis tasks are outlined in **Table 3**.

In addition to praxis scores, we computed patients' general orientation in time and space and aphasia (using a picturenaming task from the BCoS). A total of 63 out of 387 (16%) of patients performed below the cut-off score for the orientation task, and 212 patients out of 387 (55%) performed below the cutoff score for picture naming, indicating language deficits adjusted for age.

We ran correlation analyses to identify whether our covariates of no interest were significantly correlated with a composite measure of apraxia, incorporating the scores of each of the three praxis tasks. Orientation in time and space correlated significantly with the composite Apraxia score (r387 = 0.396, p < 0.0001), as did lesion size (r387 = −0.120, p = 0.018) and age (r387 = −0.200, p < 0.0001).

### Imaging Results

Lesion overlap is shown in **Figure 1**. **Figure 2** shows the lesionsymptom maps for each of the three tasks, FDR-corrected at p < 0.05, in axial and rendered views; **Table 4** provides the coordinates for each area and each task.

Deficits in the Gesture Production task were associated with lesions in a network of areas involving the left superior temporal sulcus (x = −50, y = −36, z = −12; t = 3.99), the left uncinate fasciculus (x = −28, y = −4, z = −16; t = 4.41) (which connects the temporal lobe with the inferior frontal cortex including Broca's area), and the white matter beneath the left primary motor cortex, within the superior longitudinal fasciculus (x = −34, y = −25, z = 31; t = 4.05). The lesions identified disconnections between the temporal and parietal lobes with the frontal lobe, leading to impairment in converting gestures into motor commands.

TABLE 4 | Coordinates of lesion-symptom mapping results, FDR-corrected at p < 0.05, based on HCP (Andreas, 2016; Glasser et al., 2016) and Catani white matter (Thiebaut de Schotten et al., 2011) atlases.

Deficits in the Gesture Recognition task revealed significant associations with lesions in the left superior temporal sulcus (x = −54, y = −44, z = −6; t = 4.24).

Finally, regions significantly associated with the meaningless gesture imitation task comprised the left visual striate and prestriate cortices (x = −29, y = −88, z = −8; t = 5.74), PGi parietal area (x = −42, y = −56, z = 13; t = 4.97), and parahippocampal area (x = −32, y = −39, z = −15; t = 5.13). We report the results for all patients combined in **Table 4**.

Subgroup analyses were performed to identify lesions pertaining to right- versus left-handed patients with rightversus left-hemisphere lesions, separately. Only the analyses pertaining to right-handed patients with both left- and righthemisphere lesions combined revealed significant results (FDRcorrected at p < 0.05). No significant results were identified in the other subgroups. Nevertheless, we identified the lesion locations at p < 0.005 uncorrected for left-, followed by righthemisphere lesions in right-handed patients, which are reported in section "Subgroup VLSM Analyses" of the **Supplementary Material**. Of note, unlike other reports (Goldenberg, 2013b), in our data, there were no significant differences in performance of the BCOS praxis tasks between subgroups of patients, as reported in this dataset previously (Bickerton et al., 2012; Humphreys et al., 2012).

A follow-up analysis was performed to identify lesionsymptom mapping that isolated praxis deficits from screened in the BCoS from language (picture naming). This was done by re-running the VLSM analyses outlined above with scores from the Picture Naming task in the BCOS (Humphreys et al., 2012) as an additional covariate. No significant results were found in this analysis.

We explored this result further by correlating the separate praxis with the picture-naming task. Picture Naming significantly correlated with Gesture Production (r387 = 0.501, p < 0.0001), Gesture Recognition (r387 = 0.368, p < 0.0001), and Meaningless Gesture Imitation (r387 = 0.407, p < 0.0001). Moreover, we implemented VLSM analyses for Picture Naming. The results identified the superior temporal gyrus and are provided in the **Supplementary Material** (section "VLSM Results of Picture Naming Task").

### DISCUSSION

We conducted VLSM analyses for apraxia based on a large cohort of acute and subacute stroke patients. A validated battery of cognitive tasks for praxis (BCoS) was used (Bickerton et al., 2012) and analyzed alongside clinical CT images in which stroke lesions were automatically delineated. Our findings relate specifically to the early stages after stroke. Left, right, and bilateral hemisphere lesions were included in a VLSM analysis, in which the patients' scores in three praxis tasks from the BCoS were entered as continuous variables, creating an unbiased data sample.

Our results confirmed that deficits leading to apraxia result from left-hemisphere lesions (Goldenberg, 2013a). The lesion locations identified involved a network of areas comprising extrastriate visual areas, superior and medial temporal gyri, inferior parietal and inferior frontal areas, and white matter connections between the latter. As in recent VLSM studies of apraxia, our findings challenge traditional theories, which describe a prominent role of the parietal lobe in the disorder (Goldenberg, 2014). We identified instead ventral stream regions that pertain to the action-observation network. We discuss our results in relation to previous studies of apraxia, drawing parallels with the literature on language disorders after stroke. The last section highlights the implications of using clinical CT imaging in lesion-symptom mapping of apraxia.

### Neural Correlates of Apraxia Identified in Our Study

Our results identified an association of left-hemisphere lesions affecting the superior and medial temporal areas with all praxis tasks, namely gesture production, recognition, and meaningless gesture imitation. In addition, damage to the underlying white matter connections between the temporal cortex and the inferior frontal gyrus (the uncinate fascicle), as well as the superior longitudinal fasciculus underlying the primary motor cortex (Pandya et al., 2015), were associated with deficits in gesture production. Damage in the inferior parietal region PGi (Andreas, 2016; Glasser et al., 2016), prestriate, and parahippocampal area 2 (as identified in the HCP atlas; fusiform area, in other atlases) were associated with deficits in meaningless gesture imitation.

TABLE 3 | Patients' average results in the three praxis tasks.

Voxel-based lesion-symptom mapping studies of apraxia report wide networks of brain regions in the disorder, parallelling ours. These include inferior frontal (Pazzaglia et al., 2008), parietal, and temporal (Buxbaum et al., 2014; Hoeren et al., 2014) and also subcortical areas (Pramstaller and Marsden, 1996; Haaland et al., 2000; Leiguarda and Marsden, 2000). An important factor determining the outcome of patient studies relates to the tasks used to elicit conceptual and production deficits in apraxia, as well as the imaging modalities used to study these brain functions. We discuss the impact of these in the sections below.

### Traditional Brain Networks Identified in Apraxia and the Role of Tasks Used in Understanding the Neural Correlates of the Disorder

Lesions of the parietal lobe, particularly affecting the dominant hemisphere, have traditionally dominated neuropsychological models of apraxia (Liepmann, 1908, 1920). Much of our understanding of the role of parietal areas in action has come from anatomical and physiological studies of nonhuman primates. A dorsal visual stream has been subdivided into dorso-dorsal and ventro-dorsal streams, subserving motor representations allowing the implementation of reach and grasp actions, respectively (Rizzolatti and Matelli, 2003; Daprati and Sirigu, 2006). The ventral stream, which was originally proposed to mediate perceptual information (Goodale and Milner, 1992), has also been shown to play a role in the selection of actions (Milner and Goodale, 2008; Weiller et al., 2009; Rijntjes et al., 2012). Recent literature suggests there are connections between the two, supporting a role for ventral stream structures in both action observation and object use (Borra et al., 2010; Ramayya et al., 2010; Passingham et al., 2014; van Polanen and Davare, 2015).

### Parietal Cortex Contribution to Apraxia

The role of the inferior parietal cortex in limb apraxia has been reported in studies that used both real objectuse tasks (Goldenberg and Hagmann, 1998; Osiurak et al., 2008; Goldenberg and Spatt, 2009) and pantomime of object use (Buxbaum et al., 2014; Hoeren et al., 2014). Functional neuroimaging studies report a prominent role for the left inferior parietal cortex in the actual use of objects (Lewis, 2006; Osiurak and Badets, 2016; Reynaud et al., 2016). Our study did not involve the use of functional neuroimaging, and the tasks used for screening for apraxia involved pantomime of both transitive (with objects) and intransitive (with no objects) gestures. In particular, it did not include the use of real objects.

The lack of significant lesions in inferior parietal areas in our pantomime tasks could be due to task-related factors (reported below) and the imaging modality used (namely lesion-symptom mapping rather than fMRI, reported in greater detail in sections "Materials and Methods," "Settings Used for Our Voxel Based Lesion Symptom Mapping" of the **Supplementary Material**, and "Interpretation of Our Imaging Results Based on CT Imaging").

In relation to the former, it is noteworthy that there are anatomical connections between the superior temporal areas identified in our study and the inferior parietal areas reported in non-human primates (Rozzi et al., 2006). One possibility is that an effect of a lesion in the superior temporal sulcus could be to disconnect flow of information relating to biological motion (see below) from the inferior parietal cortex. This could elicit behavioral deficits in tool use. Lesion-mapping studies are descriptive. Unlike functional neuroimaging studies, they do not give an appreciation of how lesions in one area might impact activation or function in another area connected to it.

### Role of the Temporal Cortex in Apraxia

There is an increasing amount of evidence for a communicative component to pantomiming gestures, even those that pertain to object-use. A lesion-symptom mapping study involving pantomiming of object-use identified two networks implicated in the task: a "posterior" network of brain regions, comprising inferior parietal and dorsal stream areas, representing the motor aspects of object use and an "anterior" network of brain regions, comprising inferior frontal and temporal areas, relating to the communicative components of the task (Finkel et al., 2018). The Gesture Recognition task in the BCOS requires comprehension of gestures and what they represent when choosing among a multiple-choice set of options in writing. What is more, the scores we obtained from Gesture Recognition and Gesture Production tasks combined both transitive and intransitive gestures, possibly emphasizing a role for communication as in Finkel et al.'s (2018) study. The lesions identified in our tasks were located predominantly in superior temporal rather than parietal areas, corresponding to the "anterior" network, which was attributed to communication in Finkel et al's., 2018 study.

Nevertheless, our results on the Meaningless Gesture Imitation task, which did not require any verbal comprehension, also implicated both the superior and infero-temporal cortex, as well as inferior parietal area PGi. The study by Buxbaum et al. (2014) also identified lesions in the posterior temporal lobe and temporo-occipital areas as significant both in gesture representations of tools and in abstract movement representations when tested with meaningless gesture imitation. Both our results and theirs challenge the traditional model of apraxia in which the parietal lobe plays a central role, revealing the involvement of a wider network that comprises the left temporal lobe in the disorder (Goldenberg, 2009).

In the sub-sections below, we argue for a possible role of the temporal cortex in understanding action intentions, either through comprehension or through action observation.

### **A role for the temporal cortex in praxis and comprehension**

In our study, we found no voxels pertaining to apraxia alone when covarying for language deficits measured using a Picture Naming task. Moreover, the two deficits co-existed in approximately 50% of our patient cohort. Lesions involving superior temporal areas, identified in Gesture Production, were also present in Picture Naming. Taken together our findings suggest that the two deficits might overlap (Goldenberg and Randerath, 2015; Finkel et al., 2018). In another study by Weiss et al. (2016), praxis and language were differentiable.

One reason for the discrepancy between our and Weiss et al.'s (2016) results could relate to the behavioral tasks used in this study. The tasks used in our study were part of a cognitive screening program developed to test stroke patients (BCOS, Humphreys et al., 2012) in which language is tested using Picture Naming. This task involves the naming of a large number of graspable objects (Bub et al., 2018). Previous studies using fMRI have identified a role for dorsal stream structures in identifying manipulable objects (Chao and Martin, 2000; Creem and Proffitt, 2001). There is evidence that naming manipulable objects influences actions (Bub et al., 2018; Masson, 2018). One possible explanation of our inability to differentiate between these two disorders in our data might relate to the fact that ventral stream networks to "name" and "use" objects may overlap (Mahon et al., 2007). Another possibility relates to the fact that both Gesture Recognition and Production tasks in the BCOS involve comprehension and that this may overlap with language functions (Goldenberg and Randerath, 2015). Our measure of these praxis tasks combined transitive and intransitive gestures, which have been shown to test for communication (Johnen et al., 2016; Dressing et al., 2018; Finkel et al., 2018).

Nevertheless, these factors still fail to explain the fact we identified the superior temporal gyrus in a meaningless gesture imitation task that involved no communication. We outline below a possible explanation for this latter result.

### **Regions identified in our task that form part of the action observation network**

The involvement of the superior temporal area in the gesture imitation task in our study, which did not involve any verbal or semantic interpretations, parallels the roles described for these areas in action observation, which have been identified in nonhuman primates.

Studies have demonstrated the presence of cells in the superior temporal sulcus that code for action observation and are sensitive to biological motion stimuli (Jellema and Perrett, 2003; Barraclough et al., 2009). This region is anatomically connected to inferior parietal regions, which in turn connect to central premotor areas (Rozzi et al., 2006; Borra et al., 2008). The latter network of areas has been described as the "mirror neuron" network (Bonini et al., 2011), which is involved in understanding actions. Similar areas have been described in human fMRI studies, with evidence that the inferior parietal region is activated when healthy subjects are required to understand the meaning of gestures (Passingham et al., 2014) or when experts are asked to observe skillful actions that are familiar (Calvo-Merino et al., 2005). In our study, both the prestriate and inferior temporal cortices were involved in the imitation of meaningless gestures. This could relate to the role of the inferior temporal cortex in the discrimination between shapes (Huxlin et al., 2000). It may be that patients have to understand the shape of the hand that has to be copied.

A patient study by Achilles et al. (2016) provides some support for the above. Left-hemisphere stroke patients with and without apraxia were asked to rate the familiarity of meaningless gestures, which they imitated. Patients with apraxia were found to have better performance when copying meaningless gestures that were judged as being familiar by the whole patient cohort, suggesting that they were able to recognize familiarity in meaningless gestures.

Our results support a role for temporal lobe and prestriate areas in understanding the meaning of actions in meaningless gesture imitation tasks, even when language functions are not implicated (Buxbaum et al., 2014; Passingham et al., 2014). This might provide a non-verbal network sub-serving both the understanding of action intentions and communication.

### **"Domain-general" and "domain-specific" deficits after stroke and interpretation of our lesion-mapping results**

The presence of similar areas sub-serving functions such as praxis and language skills might indicate that their involvement in these could be generic to both tasks (Geranmayeh et al., 2014). This has been demonstrated in the case of parietal lobe involvement, which is implicated in a large range of cognitive functions (Humphreys and Lambon Ralph, 2015). In the language literature, the parietal cortex has been shown to influence both "domain-general" and "domain-specific" deficits. An example of the former is the "Multiple Demand" system, which exerts top-down control on a wide range of tasks and involves processes such as cognitive flexibility, behavioral inhibition, and attentional control (Duncan, 2010; Hampshire et al., 2012).

The same is likely to be true for the role of the temporal lobe in praxis. Based on the literature, the role of the temporal cortex in praxis may be "domain specific", in providing knowledge of tool function (Campanella et al., 2010; Buxbaum et al., 2014; Hoeren et al., 2014), or "domain general", in understanding action meaning and "theory of mind" (Allison et al., 2000; Saygin, 2007). The former system may be used for naming and using tools (Mahon et al., 2007), whilst the latter system would be used for understanding others' intentions through actions and non-verbal communication cues (Allison et al., 2000; Finkel et al., 2018).

Our study, like others, highlights a relationship between language and apraxia (Goldenberg and Randerath, 2015). However, we cannot draw conclusive evidence of the influence of one on the other. Some authors have tried to achieve such a differentiation with novel imaging analyses in lesionsymptom mapping, allowing the subtraction of one effect from the other (Dressing et al., 2018). However, to formally differentiate the relative contribution of the temporal lobe between the two cognitive domains, a systematic comparison between language and praxis skills would require more dedicated tasks, which would include tasks for biological motion targeted at differentiating between speech and hand gestures. This would need to be supplemented with converging evidence from fMRI and lesion-mapping techniques (Mahon et al., 2007).

### Interpretation of Our Imaging Results Based on Clinical CT Imaging

This study is one of a few to have implemented lesionsymptom mapping techniques on the clinical CT scans of a retrospective cohort of stroke patients (Rorden et al., 2012b; Gillebert et al., 2014; de Haan and Karnath, 2018). Clinical CT is

the imaging method of choice in patients admitted to hyperacute stroke units in the United Kingdom.

Recent advances (Ripolles et al., 2012; Rorden et al., 2012b) have made the identification of both ischemic and hemorrhagic lesions possible on the same CT scan (Chawla et al., 2009; Gillebert et al., 2014). The lesion delineation technique we used compares CT image intensity from a single patient with a group of images from control participants to identify outlier voxels (Crawford et al., 2009; Gillebert et al., 2014). In effect, this approach resembles the analysis of MR images (Stamatakis and Tyler, 2005). The use of standardized preprocessing techniques for CT (Rorden et al., 2012a) allowed us to obtain comparable results, in terms of lesion localization, to those reported in MRI studies (Buxbaum et al., 2014; Hoeren et al., 2014). Nevertheless, our lesion sizes and the number of patients required to obtain these results did differ significantly from lesion-mapping techniques that have used MRI (Manuel et al., 2013; Buxbaum et al., 2014; Weiss et al., 2016). This may have occurred due to the following methodological caveats. (1) The use of automated lesion delineation in our study may have underestimated lesion sizes, particularly for ischemic strokes, which are often difficult to detect on CT. The technique might benefit from more refined information that could be provided with complementary perfusion CT (Wing and Markus, 2019), which was not available at the time of data collection. (2) A study investigating the impact of sample size on the reproducibility of lesion-symptom mapping results (Lorca-Puls et al., 2018) reported striking differences in terms of either under- or overestimated effect sizes. An additional shortcoming of lesionsymptom mapping techniques called "the partial injury problem" (Rorden et al., 2009) is that they may fail to consider the contribution of anatomically distributed areas in producing a behavioral deficit. This is because patients may present with different lesions in a distributed network, for which mass univariate analyses may miss the critical regions involved, due once again, to low statistical power (Herbet et al., 2015; Gajardo-Vidal et al., 2018). Some authors have proposed ways of mitigating the biological constraints of lesion distributions with the use of multivariate pattern analysis techniques (Smith et al., 2013; Mah et al., 2014). (3) Patient selection: although we tried to obtain an unbiased data-sample, the majority of our patients had strokes affecting the middle cerebral artery, with lesions located in the convexity of the hemisphere. This led to low numbers of patients with more superior lesions, probably reducing the statistical power to detect effects in these cortical regions (Kroliczak and Frey, 2009; Agnew et al., 2012; Buxbaum et al., 2014). (4) Lesion localization: the use of CT imaging had the caveat of requiring different anatomical atlases for gray and white matter localization. The review by de Haan and Karnath (2018) outlines significant differences in the interpretation of lesion mapping results based on which atlas is used for anatomical localization. Atlases such as the AAL (Tzourio-Mazoyer et al., 2002) and Harvard-Oxford atlases (Desikan et al., 2006), which are widely available in statistical analysis packages, underrepresent the number of cortical areas (Van Essen et al., 2012). To avoid the mislabeling of areas (Passingham and Rowe, 2015), the anatomical localization of significant regions in this study were identified using separate atlases for white and gray matter regions (see section "Materials and Methods" and **Table 4**, above). For localization of gray matter areas, we selected to use a more detailed atlas, namely the HCP atlas (Andreas, 2016; Glasser et al., 2016).

## CONCLUSION

We have conducted a lesion mapping study on praxis deficits with the largest cohort studied to date. The patients were in the early stages after a stroke (Bernhardt et al., 2017). Our results suggest an important role for temporal lobe structures in the disorder. This area was not only implicated in the knowledge of tool functions when testing patients on pantomime tasks but was also present in the imitation of meaningless gestures. This finding concurs with other VLSM studies of the disorder in stroke (Buxbaum et al., 2014; Hoeren et al., 2014) as well as with previous literature involving praxis deficits in neurodegenerative disorders (Crutch et al., 2007; Johnen et al., 2016).

The implication of ventral stream areas in praxis, even when no object recognition is required, such as in the meaningless gesture imitation task, has been largely overlooked (Goldenberg, 2014). It is likely that the network implicated in apraxia evolved to sub-serve parallel functions for praxis and language in humans (Badets and Osiurak, 2017). New tasks are being developed that provide evidence that skillful tool use may support linguistic abilities (Brozzoli et al., 2019). Our results support recent studies designed to use action observation tasks for the rehabilitation of this devastating disorder (Pazzaglia and Galli, 2019). Further work is required to identify the granularity of the contributions of the temporal lobe and its connections in praxis and language deficits in patients with stroke and neurodegenerative conditions.

The adoption of analysis techniques borrowed from MRI (Seghier et al., 2008) that help the automated normalization into standard space and, therefore, inter-individual comparisons of CT images provides a window of opportunity for lesion-symptom mapping in larger patient cohorts (Gillebert et al., 2014). This will pave the way for a better understanding of cognitive deficits after stroke, such as apraxia.

### DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author and contingent on the approval of sharing this dataset by the BCOS team and local ethics committee.

### ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the National Research Ethics Service (NRES): Essex 1 Research Ethics Committee (REC). The patients/participants provided their written informed consent to participate in this study.

### AUTHOR CONTRIBUTIONS

fnhum-13-00422 December 18, 2019 Time: 16:0 # 11

ER conceptualized the study with DM. DM provided the data analysis techniques, which GP and ZZ implemented. JR was part of the original BCOS team who collected the data and together with the late Professor Humphreys provided access to it to complete this study. ZZ re-analyzed data with covariates of no interest (of aphasia and neglect) and created the **Figure 1**. JK and ZZ created the supplementary figures. JK and RP provided the anatomy and atlas support. ER and RP wrote up the manuscript. All authors reviewed and edited the manuscript.

### FUNDING

This work was supported by the Stroke Association UK (grant to Prof. G. W. Humphreys), and an Oxfordshire Health Services

### REFERENCES

Research Committee research grant to ER (Ref. 1227). JK holds a Wellcome Trust Sir Henry Wellcome Postdoctoral Fellowship (204696/Z/16/Z).

### ACKNOWLEDGMENTS

We would like to thank Prof. Glyn Humphreys' team and the clinical teams in Birmingham, who were involved in the data collection for the Birmingham Cognitive Screening Program.

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnhum. 2019.00422/full#supplementary-material

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Pizzamiglio, Zhang, Kolasinski, Riddoch, Passingham, Mantini and Rounis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# "Fake it till You Make it"! Contaminating Rubber Hands ("Multisensory Stimulation Therapy") to Treat Obsessive-Compulsive Disorder

Baland Jalal 1,2\*, Richard J. McNally 2 , Jason A. Elias 3,4 , Sriramya Potluri 3,4 and Vilayanur S. Ramachandran5

1Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom, 2Department of Psychology, Harvard University, Cambridge, MA, United States, 3Obsessive-Compulsive Disorder Institute, McLean Hospital, Belmont, MA, United States, 4Department of Psychiatry, Harvard Medical School, Boston, MA, United States, 5Center for Brain and Cognition, University of California, San Diego, San Diego, CA, United States

### Edited by:

Mariella Pazzaglia, Sapienza University of Rome, Italy

#### Reviewed by:

Daniela Rabellino, University of Western Ontario, Canada Jane Elizabeth Aspell, Anglia Ruskin University, United Kingdom

> \*Correspondence: Baland Jalal<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Sensory Neuroscience, a section of the journal Frontiers in Human Neuroscience

> Received: 04 June 2019 Accepted: 07 November 2019 Published: 09 January 2020

#### Citation:

Jalal B, McNally RJ, Elias JA, Potluri S and Ramachandran VS (2020) "Fake it till You Make it"! Contaminating Rubber Hands ("Multisensory Stimulation Therapy") to Treat Obsessive-Compulsive Disorder. Front. Hum. Neurosci. 13:414. doi: 10.3389/fnhum.2019.00414 Obsessive-compulsive disorder (OCD) is a deeply enigmatic psychiatric condition associated with immense suffering worldwide. Efficacious therapies for OCD, like exposure and response prevention (ERP), are sometimes poorly tolerated by patients. As many as 25% of patients refuse to initiate ERP mainly because they are too anxious to follow exposure procedures. Accordingly, we proposed a simple and tolerable (immersive yet indirect) low-cost technique for treating OCD that we call "multisensory stimulation therapy." This method involves contaminating a rubber hand during the so-called "rubber hand illusion" (RHI) in which tactile sensations may be perceived as arising from a fake hand. Notably, Jalal et al. (2015) showed that such fake hand contamination during the RHI provokes powerful disgust reactions in healthy volunteers. In the current study, we explored the therapeutic potential of this novel approach. OCD patients (n = 29) watched as their hidden real hand was being stroked together with a visible fake hand; either synchronously (inducing the RHI; i.e., the experimental condition; n = 16) or asynchronously (i.e., the control condition; n = 13). After 5 min of tactile stimulation, the rubber hand was contaminated with fake feces, simulating conventional exposure therapy. Intriguingly, results suggested sensory assimilation of contamination sensations into the body image via the RHI: patients undergoing synchronous stimulation did not report greater contamination sensations when the fake hand was initially contaminated relative to asynchronous stroking. But contrary to expectations, they did so after the rubber hand had been contaminated for 5 min, as assessed via disgust facial expressions (a secondary outcome) and in vivo exposure (upon discontinuing the illusion). Further, to our surprise, synchronous and asynchronous stroking induced an equally vivid and fast-emerging illusion, which helps explain why both conditions initially (5 min after initiating tactile stimulation) provoked contamination reactions of equal magnitude. This study is the first to suggest heightened malleability of body image in OCD. Importantly, it may pave the way for a tolerable technique for the treatment of OCD—highly suitable for poorly resourced and emergency settings, including low-income and developing countries with minimal access to high-tech solutions like virtual reality.

Keywords: obsessive-compulsive disorder (OCD), rubber hand illusion, therapy, contamination fears, exposure and response prevention (ERP), multisensory integration

### INTRODUCTION

Obsessive-compulsive disorder (OCD) is a deeply enigmatic psychiatric condition that afflicts 2% to 3% of the general population (Robins et al., 1984; Ruscio et al., 2010). One variant of OCD is characterized by severe contamination fears and excessive cleansing rituals (Rachman, 2004; Markarian et al., 2010). These patients may feel anxious even after incidents of slight ''contamination'' (e.g., touching a door knob) and might spend hours painstakingly washing and scrubbing their hands—sometimes until they bleed. The primary treatment for OCD is called exposure and response prevention (ERP; Meyer, 1966). During ERP, the patient is first ''contaminated'' (e.g., touches a toilet bowl), which can trigger an acute spike in anxiety, and then prevented from performing the compulsive ritual (e.g., washing hands). This procedure may help the patient experience a subsequent decrease in anxiety, resulting in habituation (Abramowitz et al., 2009). But unsurprisingly, many OCD patients do not benefit from ERP (Kozak, 1999); the notion of being contaminated in this crude fashion is simply too unbearable. Alarmingly, 50% of patients who start ERP do not improve, 20% drop out prematurely, and 25% refuse to initiate therapy (Kozak, 1999; Schruers et al., 2005; Abramowitz, 2006), mainly due to fear of treatment (Maltby and Tolin, 2005). As such, developing gentler (less distressing) interventions for OCD represents an unmet need.

To overcome challenges of existing exposure therapies, we recently proposed a simple and tolerable (immersive yet indirect) low-cost technique for the treatment of OCD (Jalal et al., 2015) that we call ''multisensory stimulation therapy.'' Healthy volunteers watched as their occluded real hand was being stroked together with a visible fake hand in precise synchrony, producing the so-called ''rubber hand illusion'' (RHI; Botvinick and Cohen, 1998). After 5 min of such tactile stimulation, we contaminated the dummy with fake feces, in effect, mimicking traditional exposure therapy. To our astonishment, participants reported disgust sensations—as if arising from the rubber hand! This finding with potential clinical utility (discussed in more detail below) has since been replicated in a large Japanese sample, suggesting the effect is both robust and cross-culturally reliable (Nitta et al., 2018).

One interpretation for the emergence of the RHI evokes the ''Bayesian logic'' of perceptual systems (e.g., Armel and Ramachandran, 2003; Ramachandran et al., 2011; Jalal et al., 2015). The brain's sensory system is hardwired to detect statistical correlations that provide the basis for making predictions and, ultimately, visual representations of the external world, including one's body (see also Corlett et al., 2011). The brain considers it highly unlikely that the random stroking seen on the fake hand and felt on the real hand is due simply to chance; it infers therefore that the sensations must be arising from the rubber hand, however absurd. As such, the illusion is driven by bottom-up mechanisms (i.e., statistical correlations between senses) and any object in theory could become part of one's body image including a table (Armel and Ramachandran, 2003). Consistent with this account, the RHI does not occur (or is greatly diminished) following asynchronous stimulation of the real and rubber hand. This ''gold standard'' control procedure shows the importance of spatial and temporal congruence of the tactile and visual inputs in driving the illusion (e.g., Shimada et al., 2009).

To date, research has explored various measures and versions of the RHI (e.g., Armel and Ramachandran, 2003; Costantini and Haggard, 2007; Ehrsson et al., 2007; Capelari et al., 2009; Kammers et al., 2009; Ramachandran et al., 2011). The basic effect emerges fairly quickly, in most healthy volunteers usually around 10–30 s after the synchronized stroking begins (Ehrsson, 2012). In our own studies, we have found that the illusion is reliably induced in healthy individuals within 2.5–5 min of tactile stimulation (e.g., in approximately 73% of subjects across two separate experiments; see Jalal et al., 2015; see also Armel and Ramachandran, 2003). The illusion is most commonly assessed with a subjective measure of limb ownership and an objective test of proprioceptive drift, where participants after the illusion onset close their eyes and point to the direction of their real hand. Botvinick and Cohen (1998) showed that after RHI induction, participants point to the artificial hand instead of their real hand unlike in the asynchronous control condition, and that the degree of this displacement is associated with the prevalence of the RHI over time (i.e., as measured within a 30-min stimulation period). In line with this, Tsakiris and Haggard (2005) demonstrated that continuous tactile stimulation during the RHI gradually increases such proprioceptive drift, suggesting a gradual intensifying of the illusion over time. This proprioceptive drift test correlates with the subjective vividness of the illusion (e.g., Longo et al., 2008).

The RHI has also been examined in psychiatric groups: for example, one study found a stronger illusion and faster onset in schizophrenia, suggesting a malleable self-representation in this population (Peled et al., 2000). Comparable results were reported in patients with eating disorders, who likewise have a pronounced RHI compared to healthy volunteers (Eshkevari et al., 2012). Other studies have revealed a more complex picture vis-à-vis body-related processing in psychopathology. For instance, although patients with posttraumatic stress disorder (PTSD; i.e., with dissociative symptoms) initially have a more intense illusion than do healthy controls, after three consecutive trials (over the course of 2 weeks), a comparable intensity to that of healthy subjects was reported (Lev-Ari and Hirschmann, 2016). Kaplan et al. (2014) did not find the intensity of the RHI to differ in patients with body dysmorphic disorder (BDD) and healthy controls, yet surprisingly, the BDD group displayed proprioceptive drift towards the rubber hand in both the synchronous and asynchronous control condition, unlike healthy individuals, who only did so in the RHI condition as expected. Finally, children with autism spectrum disorders (ASD) have a delayed susceptibility to the illusion (i.e., exhibit a later illusion onset compared to non-autistic children). Notably, children with ASD who have lower levels of empathy are less likely to experience the RHI (Cascio et al., 2012). Taken together, these studies suggest that some forms of psychopathology are associated with aberrant self-referential processing as assessed on the RHI.

To date, no studies have examined the RHI in OCD. The illusion may be particularly pertinent to OCD given the role of dopamine in the pathophysiology of the disorder (e.g., Denys et al., 2004; Koo et al., 2010). Although the function of dopamine in OCD is multifaceted (e.g., Fineberg et al., 2007), research has shown that dopamine antagonists [as an adjunct to selective serotonin reuptake inhibitor (SSRI) drugs] can reduce OCD symptoms (i.e., augment the effects of SSRIs; Vulink et al., 2009). In contrast, dopamine agonists can generate OCD-like behaviors in animals (Szechtman et al., 1998) and humans (Borcherding et al., 1990), providing clues about the functional role of dopamine in OCD.

Interestingly, research suggests that dopamine is a key modulator of multisensory integration as assessed via the RHI. For instance, the dopamine releaser drugs ketamine and dexamphetamine (with potential to trigger schizophrenia-like symptoms; Angrist and Gershon, 1970; Pomarol-Clotet et al., 2006) augment the illusion during regular synchronous stroking, but curiously also, in the (illusion-attenuating) asynchronous control condition (Albrecht et al., 2011; Morgan et al., 2011). Analogously, patients with Parkinson's disease (receiving dopaminergic drugs) fail to reject the RHI in the asynchronous condition as strongly as healthy control participants do, according to the authors, possibly due to dopamine dysregulation (Ding et al., 2017). Collectively, this research is in keeping with findings that schizophrenia (a disorder of dopamine abnormality; e.g., Howes et al., 2015) results in heightened illusory effects, and points to the pervasive role of dopamine in self-referential processing.

Research should disclose whether OCD is associated with multisensory processing abnormalities. By beginning to probe the corporeal self in OCD, one may eventually clarify how the processes that produce a sense of body ownership differ in this disorder vs. other psychiatric conditions. Indeed, if research reveals aberrant somatosensory integration in OCD, efforts to establish specificity could elucidate OCD etiology and differentially inform novel treatments (e.g., drug and behavioral interventions) aiming at restoring aspects of self-referential processing (also see Eshkevari et al., 2012).

The illusion may be of special interest to contaminationrelated OCD, i.e., provide an experimental probe for exploring pathological disgust and novel therapeutic techniques. As noted, we have shown that contaminating the fake hand during the RHI provokes OCD-like disgust reactions in healthy volunteers (Jalal et al., 2015): in this study, 81% of participants reported greater disgust during synchronous stroking vs. the asynchronous control condition, and, on overage, those undergoing the RHI reported significantly higher levels of disgust. In a ''direct replication study,'' Nitta et al. (2018) likewise showed that such ''exposure'' during the RHI triggered greater disgust reactions than asynchronous stroking in healthy individuals from Japan.

Notably, disgust plays a key role in OCD and is a strong predictor of contamination fears (e.g., Olatunji et al., 2005; see also Deacon and Olatunji, 2007; Olatunji et al., 2007; for reviews, see Ludvik et al., 2015; Knowles et al., 2018). Although disgust and contamination aversion overlap, they are indeed distinct concepts. Disgust is a basic emotion that induces a unique response (e.g., a facial expression; Rozin and Fallon, 1987), whereas contamination fears arise from post hoc interpretive processes, e.g., triggered by disgust or related emotions like anxiety (Rachman, 2004; also see Ludvik et al., 2015). Like disgust, anxiety is an independent driver of contamination fears (but may interact with disgust to trigger contamination concerns; Cisler et al., 2007). Interestingly, although traditional ERP triggers and degrades anxiety and washing urges (Rachman, 2004; Cougle et al., 2007), research suggests that disgust is also amenable to exposure therapy in OCD (McKay, 2006).

Although the results of Jalal et al. (2015) comport with the literature on ERP (i.e., disgust induced by ''fake hand exposure'' mirrors the effects of in vivo exposure; e.g., McKay, 2006), several issues remain vis-à-vis the clinical utility of this RHI contamination procedure. First, research should extend this work to a clinical population to assess the therapeutic use of the RHI; i.e., it is important to establish the presence of this basic ''RHI contamination effect'' in OCD patients. Second, to the extent that such rubber hand exposure evokes clinically relevant contamination reactions in OCD, research should examine whether this eventually leads to habituation.

Such research may have important treatment implications: if contaminating a fake hand during the RHI provokes contamination reactions (akin to ERP) via an immersive multisensory mechanism, this may pave the way for a novel (tolerable) intervention. As noted, such dummy contamination may eventually (after an extended period and/or repeated trials) lead to habituation, i.e., overall global reduction in contamination fears, analogous to conventional ERP. Another possibility is that contaminating a fake hand during the RHI, minimally, is useful during the initial stages of ERP (e.g., in an ''exposure hierarchy''; Wolpe, 1958; see also Abramowitz et al., 2003). This technique might sufficiently desensitize patients such that they are willing to undertake ERP, providing a convenient ''transitional link'' (Jalal et al., 2015).

### Primary Study Aims

In the current study, the key aim was to explore the therapeutic potential of the RHI for OCD. We examined whether ''contaminating'' the rubber hand during the illusion would result in greater contamination sensations as compared to the asynchronous control condition. We also tested whether such dummy contamination eventually resulted in habituation, assessed both during the illusion and during an in vivo exposure procedure immediately upon discontinuing the illusion (i.e., ceasing the stimulation of the real and rubber hand).

### Hypotheses

If contaminating the fake hand during the RHI (5 min after initiating stroking) provokes greater disgust than asynchronous stroking in healthy individuals (Jalal et al., 2015; Nitta et al., 2018)—given the role of disgust in OCD—this should also hold for patients with contamination obsessions. Moreover, considering that ERP targets both anxiety and washing urges (Rachman, 2004), RHI exposure should likewise evoke such contamination sensations overall (i.e., in addition to disgust). Finally, given that OCD patients dependably experience habituation following prolonged exposure to ''contaminants'' during ERP (on habituation see, e.g., Foa et al., 1983; Rachman, 2004; Abramowitz, 2006), RHI exposure should after an extended period lead to habituation [This latter hypothesis is partly grounded in research showing that the RHI emerges quickly and does not wane with time (e.g., Tsakiris and Haggard, 2005; Ehrsson, 2012), preserving the realistic nature of the exposure procedure].

Assuming that: (1) contaminating the fake hand during the RHI results in greater contamination sensations than does asynchronous stroking in OCD; and that (2) such exposure over time leads to habituation, we advanced the following hypotheses.

RHI contamination: OCD patients in the RHI condition would report greater contamination sensations (disgust, anxiety, and handwashing urges), and be more likely to exhibit a disgust facial expression, when the fake hand is contaminated (i.e., 5 min upon initiating the real and rubber hand stroking), compared to those in the asynchronous control condition.

RHI habituation: OCD patients in the RHI condition would report lower contamination sensations (disgust, anxiety, and handwashing urges), and be less likely to exhibit a disgust facial expression, 5 min after contaminating the dummy (i.e., 10 min upon initiating the real and rubber hand stroking), compared to those in the asynchronous condition.

In vivo exposure (habituation assessment): OCD patients in the RHI condition would report lower contamination sensations (disgust, anxiety, and handwashing urges) when their real hand is contaminated (i.e., immediately upon ceasing the stimulation of the real and rubber hand) compared to those in the asynchronous condition.

### Secondary (Exploratory) Aims

A secondary aim was to broadly explore multisensory processing in OCD. In view of research: (1) indicating that dopamine, implicated in OCD (e.g., Denys et al., 2004; Koo et al., 2010), is a modulator of multisensory processing (e.g., Albrecht et al., 2011; Morgan et al., 2011); and (2) suggesting aberrant somatosensory integration in psychiatric disorders more generally (see above), we tentatively hypothesized that OCD would be associated with atypical multisensory processing. For example, OCD patients would show high susceptibility to the illusion (indexed by illusion onset and intensity measures) compared to healthy populations (e.g., as reported in our own studies; Jalal et al., 2015). Given the exploratory (open-ended) nature of this inquiry, no directional hypothesis was made a priori.

### MATERIALS AND METHODS

### Participant Selection and Clinical Characteristics

Study participants included 29 OCD patients recruited from the McLean Hospital Obsessive-Compulsive Disorder Institute (OCDI), an intensive residential treatment (IRT) program affiliated with Harvard Medical School. At the OCDI, patients receive intensive (2–4 h daily) cognitive–behavioral therapy and psychopharmacological management, i.e., by a team of behavioral and family therapists, psychiatrists, etc. Medications are used on a case-to-case basis (i.e., determined during weekly psychiatric assessment) and often include SSRIs (e.g., venlafaxine and clomipramine) and antipsychotics (i.e., as an adjunct to SSRIs). Although treatment duration is based on individual need, patients on average remain at the OCDI for 45 days, with 25% of patients for at least 12 weeks (Athey et al., 2015). Inclusion criteria for admission to the OCDI include major OCD-related functional impairment and lack of response to treatment in other settings. The program does not have official exclusion criteria, but patients are not admitted if they have a condition that would interfere with treatment; e.g., severe intellectual disability (mental retardation or neurodevelopmental disorders etc.), current substance abuse and active psychosis (for details on McLean Hospital's IRT program, see also Stewart et al., 2005).

In the current study, all participants were diagnosed with OCD by an expert clinician on staff as part of standard clinical procedures based on DSM-IV or DSM-5 criteria and had disgust- and/or contamination-related obsessions. The presence of disgust- and contamination-related symptoms were defined by elevated scores on the Disgust Propensity and Sensitivity Scale-Revised (DPSS-R; van Overveld et al., 2006) and endorsement of contamination obsessions on the Dimensional Obsessive–Compulsive Scale (DOCS; Abramowitz et al., 2010; completed as part of an admission's battery of questionnaires). This clinical assessment was not based on a specific cutoff score but whether such symptoms were present [i.e., akin to the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) symptom checklist; Goodman et al., 1989]. As the main aim of this study was to explore a novel clinical approach, no strict selection criteria were applied (aside from the general OCDI selection criteria, noted above), ensuring that our sample was representative of this patient population. As such, medicated patients were not excluded. Given all patients were undergoing IRT, they were only selected for participation insofar that it would not interfere with their treatment.

Information regarding comorbid psychiatric diagnoses was available for 27 patients [i.e., out of 29; two patients did not complete an elaborate semi-structured diagnostic interview and/or a clinician administered intake interview to determine co-occurring conditions, due to logistic reasons (e.g., unavailability of clinical staff to conduct such interviews) or interference of OCD symptoms etc.]. Of these 27 patients, 92.6% (25/27) had OCD as a primary diagnosis and 3.7% (1/27) had OCD as a secondary diagnosis (data regarding whether OCD or a related mood disorder was primary was unavailable for one patient). Individuals who did not have a primary diagnosis of OCD were diagnosed with an obsessive-compulsive-related disorder (e.g., BDD: 3.7%; 1/27) or a related mood disorder (e.g., bipolar disorder I: 3.7%; 1/27).

Moreover, 74.1% (20/27) of participants had at least one comorbid axis I diagnosis. Frequencies of most co-occurring disorders were major depressive disorder (29.6%; 8/27), dysthymic disorder/persistent depressive disorder (18.5%; 5/27), post-traumatic stress disorder (18.5%; 5/27), and generalized anxiety disorder (14.8%; 4/27), followed by eating disorder NOS/other specified feeding or eating disorder (11.1%; 3/27), specific phobia (11.1%; 3/27), excoriation/skin-picking disorder (7.4%; 2/27), panic disorder (7.4%; 2/27), hoarding disorder (7.4%/ 2/27), bulimia nervosa (3.7%; 1/27), illness anxiety disorder (3.7%; 1/27), BDD (3.7%; 1/27), depressive disorder NOS (3.7%; 1/27), and trichotillomania (3.7%; 1/27). Participants' past diagnoses (i.e., prior to attending the OCDI), included (but were not restricted to) alcohol abuse, eating disorder NOS, major depressive disorder, specific phobia, anorexia nervosa, excoriation/skin-picking disorder, stimulant use disorder, etc. Finally, for these 27 patients for which comorbidity information was available, no patient endorsed autism spectrum disorder (i.e., on a self-reported diagnosis checklist).

Participation was restricted to those aged between 18 and 65 years old (M = 26.93, SD = 6.74, range = 18–43), and who were proficient in English. Seventy-six percent (22/29) of participants were female and 21% (6/29) were male (one participant did not provide consent for their demographic data to be shown).

### Procedure

Harvard University's Committee on the Use of Human Subjects approved the study protocol and McLean Hospital's Institutional Review Board formally ceded review to Harvard's committee. Participants gave written informed consent prior to initiation of any study procedure and received monetary compensation (\$20) for their time.

The participant sat behind a desk with both hands resting on it. A vertical cardboard barrier (sagittal partition) was placed on the table, just to the left of the participant's right hand, occluding his view of his right hand. A rubber hand was placed on the left side of the cardboard. A sheet of cloth was wrapped around the wrist of the dummy extending up to the shoulder of the right arm. This arrangement prevented the participant from viewing his right hand, giving the illusion that the fake hand was his real right hand. The rubber hand was positioned in parallel to (i.e., mirrored) the real left hand. The palm of the left hand was facing down, and the left arm was positioned in an approximately 90◦ angle along the body with the elbow near the torso and the forearm resting on the table. The participant's right arm was slightly extended, with the elbow slightly away from the torso and shoulder raised, allowing for the proper placement of the partition, i.e., extending from the right collarbone onto the desk. The real right forearm and hand (palm down) likewise rested on the desk, as noted, completely out of sight during the entire stimulation period.

Next, the participant was instructed to indicate orally when he or she experienced touch sensations coming from the rubber hand (this onset rating was only reported if the participant felt the illusion; the participant was not further asked about the illusion onset). The experimenter then began to stroke the participant's right hand (i.e., dorsum, with slight fluctuation in speed and directionality) with a paintbrush while simultaneously and synchronously stroking the rubber hand with another paintbrush continuously for 10 min (i.e., without interruption to sustain the illusion). The simultaneous stroking of the rubber hand and the real right hand produces the illusion (to the participant) that the rubber one feels like his own right hand. After 5 min of such stroking, the experimenter asked the participant to rate how much the rubber hand felt like his own hand on a 20-point Likert scale (this was the only time point at which the illusion intensity was assessed). Next, the experimenter used a tissue to smear the disgust stimulus (fake feces) on the rubber hand while simultaneously dabbing a damp paper towel from a nearby water bowl on the participant's real right hand. The damp towel placed on the occluded right hand served the purpose of mimicking the sensation of having the contaminant smeared on the participant's real hand (see also Jalal et al., 2015). Immediately thereafter, the participant was asked to provide subjective contamination ratings (i.e., disgust, anxiety, and handwashing urge levels), and the experimenter rated the participant's facial expression of disgust (either present or not). The tissue that had been used to ''contaminate'' the rubber hand and the clean paper towel was then removed from the fake and real hand; the fake feces remained on the rubber hand. The rubber hand and the participant's real hand continued to be stroked for an additional 5 min, after which the participant again provided contamination ratings and the experimenter rated his facial expression. The stroking of the rubber hand and real hand then stopped (i.e., 10 min of uninterrupted rubber hand and real hand stimulation had elapsed). Immediately thereafter, the experimenter told the participant that he would place the disgust stimulus (referred to as the ''object'') on his right hand and, accordingly, took a piece of the disgust stimulus and put it on the participant's real right hand. At this point, the participant provided a final set of contamination ratings.

A second group of patients underwent the same procedure except that the stimulation of the rubber hand and real right hand was asynchronous (i.e., the stroking was temporally and spatially incongruent), thereby either greatly diminishing or preventing the illusion from developing (The setup of the experiment is shown in **Figure 1**).

### Materials and Measures

### Yale-Brown Obsessive-Compulsive Scale (Y-BOCS)

The Y-BOCS (Goodman et al., 1989) is widely considered the ''gold standard'' measure for assessing OCD symptomatology in clinical research. The Y-BOCS indexes severity of obsessions and

compulsions in the past week. Scores are generated from a total of 10 items, each rated on a five-point Likert scale, and scores range from 0 to 40. In the present study, patients completed the self-report version of the Y-BOCS (Steketee et al., 1996).

### Disgust Stimulus

The disgust stimulus visually resembled and smelled of genuine feces. It consisted of food items (a mixture of chocolate and peanut butter) and was sprayed with a joke-shop odor, and placed in a bedpan. Participants were told before the study began that the stimulus was not genuine feces (**Figure 2**).

#### Multisensory Integration

RHI onset and intensity: the time onset of the RHI (i.e., how soon after the stroking was initiated participants felt the presence of the illusion, if at all) constituted a measure of multisensory integration. Participants were asked to indicate verbally if and when they experienced touch sensations coming from the rubber hand.

The perceived intensity of the illusion provided another measure of multisensory processing (i.e., limb ownership). Participants were asked to rate how much the rubber hand felt like their own hand (5 min after initiating the stroking), on a 20-point Likert scale, ranging from 1 (''not at all'') to 20 (''exactly like my own hand''). A more rapid onset (measured in seconds) and higher intensity rating indicated greater susceptibility to the illusion.

FIGURE 2 | Disgust stimulus.

### RHI Contamination

Participants were asked to provide ratings of contamination sensations (i.e., their level of disgust, anxiety, and handwashing urges), when the rubber hand was first contaminated (i.e., 5 min after initiating the stroking), on a 10-point Likert scale ranging from 1 (''not at all'') to 10 (''extremely''). Higher ratings indicated greater assimilation of contamination sensations into their body image via the RHI.

### RHI Habituation

Participants were asked to provide contamination ratings (i.e., disgust, anxiety, and handwashing urge levels), 5 min after the dummy contamination procedure (i.e., 10 min after initiating the stroking), on a 10-point Likert scale ranging from 1 (''not at all'') to 10 (''extremely''). Lower ratings indicated greater habituation.

Disgust facial expressions: to further gauge participants' disgust reactions, we observed and noted whether their facial expression indicated disgust (or not) when: (1) the rubber hand was initially contaminated; and (2) when RHI habituation assessment took place (i.e., 5 min after the dummy contamination).

### In vivo Exposure Habituation

Participants were asked to provide contamination ratings (i.e., disgust, anxiety and handwashing urge levels), when the experimenter contaminated the participant's real hand (i.e., immediately after RHI habituation ratings were obtained), on a 10-point Likert scale ranging from 1 (''not at all'') to 10

(''extremely''). Lower ratings indicated greater habituation, an overview of the experimental procedures is shown in **Figure 3**.

### Statistical Analyses

The study included a quantitative between-subject crosssectional design comparing two conditions (experimental vs. control) on the following measures: RHI contamination sensations, RHI habituation, in vivo exposure habituation, and multisensory integration, focusing on the betweensubject effects. The study targeted the following primary outcome variables: self-reported ratings of disgust, anxiety, and handwashing urges (assessment of RHI contamination sensations and habituation effects), and RHI onset and intensity (assessment of multisensory integration). Participants' facial expression of disgust (i.e., present or non-present; rated by the experimenter) constituted a secondary outcome measure of RHI contamination sensations and habituation.

RHI onset and intensity-dependent variables were analyzed via one-way ANOVA. Disgust, anxiety, and handwashing urge rating dependent variables were analyzed using a one-way MANOVA test, followed up with ANOVA post hoc tests. A chi-squared test was used to analyze disgust facial expression dependent variables.

For all analyses testing a priori hypotheses, we applied the Benjamini and Hochberg (1995) false discovery rate (FDR; e.g., McDonald, 2014) to control for potential Type I errors. Congruent with related studies (e.g., Skandali et al., 2018) and general guidelines (e.g., Genovese et al., 2002), the FDR was set at q < 0.15. In the current study, the Benjamini–Hochberg corrected significance level was 0.06. P-values shown in the text are uncorrected (i.e., raw; e.g., McDonald, 2014). Exploratory analyses and post hoc tests (i.e., following a significant omnibus MANOVA) were not adjusted for multiple comparisons. Multiplicity correction is not required when analyses are labeled exploratory (Bender and Lange, 2001).

For all dependent variables, the distribution of residuals was checked with Q–Q plots and the Shapiro–Wilk test; residuals were often found to depart from normality. Such variables were transformed with a log10(x + 1) and a square-root transformation to test whether these improved matters (Myers and Well, 2003). As the F-test is robust to minor normality departures (Blanca et al., 2017), we report untransformed data (except when otherwise specified in the text; on all figures, error bars denote standard error of the mean).

## RESULTS

Twenty-nine OCD patients completed the study. Of these, 16 were assigned to the experimental condition (i.e., to undergo the RHI) and 13 were assigned to the control (i.e., to undergo asynchronous stroking of the real and rubber hand). One OCD patient failed to provide consent for their demographic and Y-BOCS data to be used; these were thus excluded. The final sample sizes were experimental condition n = 16 and control condition n = 13.

Additional data were missing for a few measures. Three participants did not provide an illusion time onset. One participant's data were excluded from the ''RHI contamination and habituation'' analyses due to an experimental error. Likewise, a participant was excluded from these analyses for not exhibiting an adequate contamination fear response throughout the experiment [e.g., with average contamination ratings as low as 1.3 out of 10 in intensity when directly exposed to the disgust stimulus during in vivo exposure; for a third participant, the tissues used to stimulate the real hand and contaminate the dummy were not removed after this experimental procedure. As this protocol deviation was trivial (i.e., unlikely to impact contamination sensations), the data were not excluded. As a precaution, the data were also analyzed while excluding this participant; the results remained unaltered]. For demographic and clinical characteristics of participants, see **Table 1**.

### Multisensory Integration in OCD

RHI survival rate: all participants in the experimental condition (n = 16) reported a robust RHI effect; except one participant who did not provide an illusion onset, but rated the illusion as 5 out of 20 in intensity, which suggested he had a diminished RHI (based on our previous cutoff where an intensity rating of less than 3 out of 20 indicates no illusion; see Jalal et al., 2015). Surprisingly, all patients in the control condition (n = 13) also reported the

aM, mean; SD, standard deviation; n, sample size; F, F statistic; χ 2 , chi-square statistic; df, degrees of freedom; p, p-value; NS, non-significant; Y-BOCS, Yale-Brown Obsessive-Compulsive Scale; (†) one participant did not provide consent for their demographic and Y-BOCS data to be shown. ∗Log10(x + 1) transformed Y-BOCS scores. These analyses were also conducted without the two participants excluded from the RHI contamination and habituation analyses (described above); the results remained unaltered: Age (F(1,24) < 1, NS), Y-BOCS (F(1,24) = 1.32, p = 0.26), and Sex (χ 2 1 < 1, NS).

RHI, except one who scored 2 out of 20 in intensity (another participant had a borderline illusion with an intensity rating of 5). Thus, the presence of the RHI did not differ in the two conditions (χ 2 1 = 1.27, p = 0.26).

Illusion onset: on average, participants in the experimental condition reported experiencing the illusion after 65.50 s (SD = 68.16) vs. 57.42 s (SD = 51.16) in the control condition (experimental n = 14, control n = 12). A one-way ANOVA was conducted on the illusion onset dependent variable [i.e., log10(x + 1) transformed scores] to compare ratings in the experimental condition and control condition. The onset of the illusion did not differ in the two conditions (F(1,24) < 1, NS; see **Figure 4**).

Illusion intensity: a one-way ANOVA was conducted on the illusion intensity-dependent variable to compare ratings in the experimental condition and control condition (experimental n = 16, control n = 13). The intensity of the illusion did not differ in the two conditions (F(1,27) < 1, NS; see **Figure 5**).

OCD symptoms and RHI onset and intensity: an exploratory Pearson's Correlation Test showed that OCD symptom severity was not associated with how soon participants experienced the RHI [i.e., log10(x + 1) transformed Y-BOCS and onset scores; r11 = −0.16, p = 0.61, two-tailed], in the experimental condition; similarly, such symptom severity was not associated with the strength of the illusion (r13 = 0.12, p = 0.67, two-tailed). However, in the control condition, while OCD symptom severity was not associated with the illusion onset (r10 = 0.15, p = 0.64, two-

tailed), Y-BOCS scores inversely correlated with the intensity of the illusion (r11 = −0.73, p = 0.004, two-tailed).

### RHI Contamination ("Fake Hand Exposure")

To examine contamination sensations when the fake hand was contaminated, we conducted a one-way MANOVA on the dependent variables (experimental n = 14, control n = 13). Contamination sensations (disgust, anxiety, and handwashing urges) did not differ in the two conditions when the fake hand was contaminated (F(3,23) < 1, NS, Benjamini–Hochberg corrected; see **Figure 6**). The proportion of participants in the experimental condition and control condition who exhibited a facial expression of disgust when the fake hand was contaminated did not differ (experimental n = 14, control n = 13; χ 2 1 < 1, NS, Benjamini–Hochberg corrected).

### RHI Habituation

To examine habituation 5 min after the fake hand was contaminated, we conducted a one-way MANOVA (experimental n = 14, control n = 13) that revealed that contamination sensations (disgust, anxiety, and handwashing urges) did not differ in the two conditions (F(3,23) = 1.22, p = 0.32, Benjamini–Hochberg corrected; see **Figure 7**). The proportion of participants who exhibited a facial expression of disgust was higher in the experimental condition vs. the control condition

(experimental n = 13, control n = 13; 64.7% vs. 35.3%; χ 2 1 = 4.25, p = 0.04, Benjamini–Hochberg corrected).

### In vivo Exposure Habituation

To examine in vivo exposure habituation immediately upon discontinuing the stimulation of the real and rubber hand, we conducted a one-way MANOVA (experimental n = 14, control n = 13), showing that participants in the experimental condition reported higher overall contamination sensations (disgust, anxiety, and handwashing urges) compared to those in the control condition (F(3,23) = 3.12, p = 0.046, Benjamini–Hochberg corrected; see **Figure 8**). The MANOVA was followed up with a discriminant function analysis that revealed one discriminant function, which significantly differentiated the experimental and control condition (Wilks' lambda λ = 0.71, χ 2 3 = 8.02, p = 0.046). A canonical correlation of 0.54 showed that the model explained 29.2% of the variation in the condition variable. The discriminant function analysis revealed that disgust ratings had the highest standardized canonical discriminant function coefficient (β = 2.40) indicating the greatest contribution to the model (i.e., the best discriminator between the two conditions), followed by anxiety (β = −1.80) and then washing urge ratings (β = −0.04).

### Dummy Exposure vs. in vivo Exposure

In an exploratory analysis, to compare contamination sensations during dummy exposure vs. in vivo exposure, we conducted two repeated measures one-way MANOVAs (experimental n = 14, control n = 13), showing that while in vivo exposure provoked more intense responses than dummy exposure in the experimental condition (F(3,11) = 3.92, p = 0.04), this was not the case in the control condition (F(3,10) < 1, NS; residuals showed moderate deviation from normality but were not improved with a log or square-root transformation and were thus analyzed with those caveats). Follow-up one-way ANOVAs showed that in the experimental condition in vivo contamination triggered marginally significantly greater disgust (F(1,13) = 3.84, p = 0.07) and significantly greater anxiety (F(1,13) = 7.60, p = 0.02) and handwashing urges (F(1,13) = 8.81, p = 0.01) than dummy exposure.

### DISCUSSION

This study yields important new findings with clinical implications. Intriguingly, our results suggest sensory assimilation of contamination sensations into the body image via the RHI—that such feelings were curiously referred to an alien hand in patients with OCD. Patients undergoing synchronous stimulation did not report greater contamination sensations when the fake hand was initially contaminated relative to asynchronous stroking. But contrary to expectations, they did so after the dummy had been contaminated for 5 min, as assessed via disgust facial expressions (a secondary outcome) and in vivo exposure (upon discontinuing the illusion). We also found that patients failed to reject the illusion during the ''gold standard'' control condition. To our surprise, synchronous and asynchronous stroking induced an equally vivid and

fast-emerging illusion, which helps explain why both conditions initially (5 min after initiating tactile stimulation) provoked contamination reactions of equal magnitude. This study is the first to suggest heightened malleability of body image in OCD. Collectively, these results argue against a sharply localized (''hierarchical'') approach to brain function and illustrate dynamic intersensory interactions and plasticity of brain modules (''holistic mediation'').

Our findings stress the importance of the temporal dimensions of the RHI, and crucially, how these can be perturbed by psychopathology. As noted, our chosen duration of tactile stimulation (i.e., 5 min) prior to dummy contamination was insufficient to initially differentiate the synchronous and asynchronous conditions in patients with severe OCD. By comparison, we have previously shown that 5 min of tactile stimulation differentiates the RHI and the control condition in healthy individuals (Jalal et al., 2015). In the current study, indeed, as both methods of stroking triggered an equally intense illusion at this time point, one would expect them to provoke comparable contamination reactions. But over time, these results suggest that synchronous stimulation more effectively assimilated the visibly contaminated rubber hand into the body image (than asynchronous stroking)—accounting for the relative rise in contamination sensations. Although we did not explicitly assess illusion intensity at a later stage, this provides a viable explanation for why synchronous stroking differentially impacted contamination reactions 10 min after initiating stimulation on two separate measures. As mentioned, research suggests that the RHI becomes more intense with time (i.e., duration of stimulation), as indexed on a key measure of the illusion (i.e., perceiving one's real hand drifting towards the fake one; Tsakiris and Haggard, 2005; on the prevalence of the RHI over time and degree of proprioceptive drift, see also Botvinick and Cohen, 1998).

The formulation of the initial hypothesis that contaminating the fake hand during the RHI results in greater contamination sensations than does asynchronous stroking in OCD, specifically 5 min after beginning the stroking, was based on prior work in healthy volunteers (Jalal et al., 2015; see also Nitta et al., 2018). Evidently, in this study, as the RHI triggered greater contamination reactions than did the control procedure, not 5 min but instead 10 min after stroking began (consistent with the overall hypothesis, but not the timeline in which the two conditions were differentiated), our study design was unable to capture any habituation effects. Nevertheless, given the literature on ERP (e.g., Foa et al., 1983; Rachman, 2004; Abramowitz, 2006; McKay, 2006; i.e., the basis for the second hypothesis), we can safely assume that such fake hand exposure would eventually lead to habituation (i.e., causes a gradual decrease in these sensations as extinction occurs). As our exposure method proved highly potent at evoking contamination reactions (surprisingly, irrespective of stroking approach), it may be that akin to ERP, at least 30–45 min of continuous exposure is needed for habituation to occur, bearing in mind

that patients vary in the rate of habituation (e.g., Simpson et al., 2010). Future research should further disentangle such habituation timeline.

That a higher proportion of patients exhibited a disgust facial expression during the RHI relative to the control condition (65% vs. 35%; i.e., 5 min after the fake hand was contaminated, 10 min after stroking began) is consistent with the key role of disgust in OCD (Ludvik et al., 2015), e.g., as a strong predictor of contamination fears (e.g., Olatunji et al., 2005; see also Deacon and Olatunji, 2007; Olatunji et al., 2007). This measure provides an objective assessment of disgust.

The results of the exploratory analysis are noteworthy. They emphasize the overall finding that synchronous stroking over time exerts selective sensitizing effects (i.e., vis-à-vis contamination reactions). But more strikingly, they imply that ''fake hand exposure'' during asynchronous stroking provokes contamination sensations as effectively as actual real hand exposure. This finding is highly counterintuitive. It dovetails with our related studies showing that both college students with OCD symptoms (Jalal and Ramachandran, 2017) and severe OCD patients (Jalal et al., under review) report indistinguishable levels of disgust when merely watching an experimenter contaminating his own hand and when their hand is contaminated. This research illustrates the cognitive impenetrability of contamination sensations (i.e., how such gut reactions can override logic and break down ''self-other'' barriers). Intriguingly, they also suggest that direct skin contamination may be unnecessary to gain the beneficial effects of exposure therapy. Contaminating proxy stimuli such as alien limbs (synthetic or biological) can potentially trigger clinically relevant contamination reactions (see also, Jalal et al., 2018).

In this study, we found an overall amplified RHI. For instance, all patients reported the illusion during synchronous stroking. In contrast, around 85% of healthy volunteers experience the effect (Jalal et al., 2015). But the finding that patients failed to reject the RHI during asynchronous stroking is more notable. It mirrors research showing that both Parkinson's disease and schizophrenia patients exhibit heightened illusory effects during asynchronous stroking compared to healthy volunteers (Peled et al., 2000; Ding et al., 2017), and that dopamine releaser drugs ketamine and dexamphetamine enhance the RHI during both synchronous and asynchronous stimulation (Albrecht et al., 2011; Morgan et al., 2011). Taken together, these data indicate that dopamine dysregulation may boost a sense of embodiment. As noted, although the role of dopamine in OCD is admittedly complex (Fineberg et al., 2007), research has shown that dopamine antagonists can be useful in reducing OCD symptoms (as an adjunct to SSRIs; Vulink et al., 2009) and that dopamine agonists can generate OCD-like behaviors [Borcherding et al., 1990; Szechtman et al., 1998; of interest, ketamine per se shows affinity for dopamine D2 in addition to serotonin 5-HT2 receptors (Kapur and Seeman, 2002) both blocked by quetiapine, an antipsychotic sometimes used in the treatment of refractory OCD (Gefvert et al., 2001)].

Notably, dopamine has been linked to learning (e.g., Centonze et al., 2001; Castner and Williams, 2007) and is found in brain areas underlying the RHI (Ehrsson et al., 2004; on dopaminergic projections to the prefrontal cortex, see Goldman-Rakic et al., 1990). It could, therefore, contribute to perceptual learning processes mediating corporeal awareness and possibly account for an amplified illusion in OCD. But how does dopamine induce the RHI in the face of contradictory input (i.e., asynchronous stimulation)? One explanation is that dopamine overactivity underlies salience attribution: ascribing causal importance to salient events (e.g., Howes et al., 2015). In the asynchronous condition, the patient focuses his attention on a dummy that resembles the patient's hand and it appears in its expected location. This attention-grabbing input violates expectations, rendering the event highly salient. As such, learning (''dopamine-encoding'') might ensue, i.e., driving the illusion of ownership (''the fake hand on the table must be mine'') even when incoming sensory information is incongruous, effectively overriding internally constructed models of reality (Albrecht et al., 2011; on Bayesian prediction error, see Fletcher and Frith, 2009). Together, these findings stress how a unified sense of self may rest on a delicate balance between top-down regulation and bottom-up processes.

Another noteworthy factor to consider is the idiosyncratic perceptual style in OCD, possibly exacerbating such dopamine-driven top-down visual processing and salience misattribution. As early as the 1960s, Shapiro described the obsessive-compulsive attentional style: a painstaking focus on minor details in a rigid manner, at the expense of the big picture—effectively ''missing the forest for the trees'' (Shapiro, 1965; see also Yovel et al., 2005). Research has since shown that patients with OCD indeed focus on local aspects of visual stimuli instead of holistic, organizational features (Savage et al., 1999); i.e., in line with neurocognitive models implicating frontal–striatal abnormalities in OCD (e.g., mediating cognitive inflexibility circuits; Vaghi et al., 2017; for reviews, see Menzies et al., 2008; Nakao et al., 2014) [Similar tendencies occur in students with OCD symptoms (Soref et al., 2008) and individuals with obsessive-compulsive personality disorder (Yovel et al., 2005)]. Accordingly, OCD patients in the asynchronous condition when asked to focus on the fake hand did so in an intensely focused and inflexible manner, conceivably causing them to ignore the overall conflicting sensory information, i.e., leading to global degradation in multisensory integration and overreliance on the salient visual input. This explanation dovetails with the finding that patients with the etiologically related OCD spectrum (''fronto-stratial'') disorder BDD (Grace et al., 2017) display proprioceptive drift bias towards the fake hand during both synchronous and asynchronous stimulation. Unsurprisingly, patients with BDD, like those with OCD, focus on perceptual details at the cost of global, holistic processing (Deckersbach et al., 2000), fittingly evoked as an explanation for such unusual proprioceptive drift bias in BDD (Kaplan et al., 2014).

Counterintuitively, Y-BOCS scores inversely correlated with the intensity of the illusion but only during asynchronous stimulation. One explanation for this is that top-down attention, possibly driving the illusion during asynchronous stroking (via salience misattribution), was perturbed by anxiety states in the most severe patients. Indeed, anxiety decreases attentional control (Eysenck et al., 2007) and is unsurprisingly associated with OCD symptoms (e.g., Foa et al., 1998). Anxiety overall may, therefore, have interfered with perceptual learning effects of dopamine (caused ''general blunting''), which might explain why OCD severity (irrespective of condition) did not intensify the illusion.

The primary aim of this study was to explore the therapeutic potential of the RHI. Our findings may pave the way for a novel therapeutic technique for OCD (see also Jalal et al., 2015). Practically (e.g., based on the current results), such an approach might entail 10 min of tactile stimulation, coupled with at least 5 min of continuous dummy contamination (as outlined in the ''Materials and Methods'' section). The procedure should be repeated (e.g., 3–4 times) until habituation occurs; for severe patients, possibly starting with asynchronous stroking followed by synchronous for a more immersive experience [Analogously, a session of ERP typically lasts around 90 min (van der Heiden et al., 2016)].

This method we have introduced may offer a tolerable alternative to ERP, with potential to trigger clinically relevant contamination reactions. Crucially, unlike ERP, it does not require patients to touch highly aversive ''contaminants.'' As such, it is conceivable that patients who are reluctant to engage in ERP due to fear of direct skin exposure (i.e., too frightened to confront contaminants head-on) would be more accepting of this approach. Also, as noted, it might be useful during the initial stages of exposure to help desensitize patients such that they are willing to eventually undertake ERP.

Because the RHI itself is engaging—fittingly labeled a ''mindblowing party trick'' (Lawton, 2009)—our method might appeal to a younger audience. During pilot work, volunteers often express astonishment (sometimes even slight giggling) at the uncanny sensation of touch arising from an obvious fake hand. This element of amusement (positive affect) could establish a frame for a less fearful outlook on exposure, i.e., create nonthreatening re-association to bodily contamination. All in all, this simple, immersive, and cost-effective intervention might result in higher treatment uptake and lower dropout and facilitate early intervention. It is eminently suitable for poorly resourced and emergency settings, including low-income and developing countries with minimal access to high-tech solutions like virtual reality.

Although this is the first investigation to explore the RHI in OCD, our assessment of multisensory integration per se (a secondary study-aim) was limited in several ways. For instance, we did not take into account the impact of comorbid psychiatric conditions that may have affected these results. Indeed, as noted, psychiatric disorders have been shown to differentially influence self-referential processing. Ideally, future studies should explore corporeal awareness in OCD using large samples of unmedicated patients without comorbidities (albeit severe OCD patients without comorbidities are rare). This is particularly important because of the role of dopamine as a modulator of multisensory integration, with dopaminergic agents sometimes used as an adjunct to SSRIs in the treatment of OCD.

In this study, we assessed the RHI with a subjective intensity measure in addition to the onset rating. Although a single-item intensity measure is limited compared to embodiment questionnaires (Botvinick and Cohen, 1998; Peled et al., 2000), it has proven reliable in studies by us (Jalal et al., 2015; see also Armel and Ramachandran, 2003) and others (Lev-Ari et al., 2015; Lev-Ari and Hirschmann, 2016; Nitta et al., 2018). Future research examining multisensory integration in OCD should include additional measures such as questionnaires and the objective ''proprioceptive drift'' test (e.g., Botvinick and Cohen, 1998; Tsakiris and Haggard, 2005; Longo et al., 2008; Marotta et al., 2016). Although we have provided evidence visà-vis multisensory processing in healthy volunteers from our previous research (i.e., serving as a comparison to the current findings; Jalal et al., 2015), future studies should include a healthy control group; that we did not include one constitutes a limitation.

Consistent with Botvinick and Cohen (1998) seminal investigation, in the current study, the asynchronous control condition was a between-subject factor. Using the same sample across conditions would have been optimal for assessing self-referential processing (i.e., due to reduced variance arising from individual differences, e.g., sensory suggestibility, see Marotta et al., 2016). However, the key aim of this study was to explore the clinical potential of the RHI in OCD (specifically in severe patients undergoing IRT, often refractory to treatment in other settings). As such, our design ensured that patients were not subjected to high stress by being exposed to aversive contaminants twice (while present at our treatment center for a limited period), and, importantly also, prevented carry-over effects from the exposure procedures (e.g., habituation). Indeed, with our main clinical objective in mind, our sample was suitable for the following reasons: (1) comorbid and secondary diagnoses are common in OCD patients, who often tend to be medicated. Thus, our sample was typical of this patient population; (2) severe OCD patients may be the most fearful of ERP (i.e., entailing direct contamination) and thus generally the most in need of gentler, more tolerable treatments.

### REFERENCES

Future double-blind placebo-controlled trials should directly compare our proposed ''dummy contamination'' procedure to ERP. Finally, ''multisensory stimulation therapy'' lends itself to other applications in psychiatry (Jalal et al., 2015)—like treating ''needle phobia.'' Conducting realistic exposures in this population is challenging: repeated needle injections into a real arm could result in punctured veins. Using a fake hand during the RHI, instead, may provide a clever and convenient alternative.

### DATA AVAILABILITY STATEMENT

The raw data will be made available by the authors to any qualified researcher upon request.

### ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Harvard University. The patients/participants provided their written informed consent to participate in this study.

### AUTHOR CONTRIBUTIONS

BJ, RM, JE, and VR designed the study. BJ performed the data analysis and wrote the manuscript. RM contributed to data interpretation and edited the manuscript. JE and SP recruited and tested patients, and edited the manuscript. VR edited the manuscript. All authors read and approved the submitted version.

### FUNDING

The study was supported by discretionary funds awarded to VR. BJ was supported by Trinity College Cambridge, the Cambridge School of Clinical Medicine, and the Psychology Postgraduate Affairs Group.

### ACKNOWLEDGMENTS

We thank research assistants at Harvard University and McLean Hospital's OCDI for their remarkable assistance with recruitment and data collection, including Lauryn Garner, Kara Kelley, Eric Tifft, Mikaela Ingram, Zahra Dabirzadeh, and Arthur Grayson III.

improvement in contamination/washing symptoms in obsessive-compulsive disorder. J. Obsessive Compuls. Relat. Disord. 4, 20–24. doi: 10.1016/j.jocrd. 2014.11.001

**Conflict of Interest**: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Jalal, McNally, Elias, Potluri and Ramachandran. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation

Nadine Spychala1 , Stefan Debener 1 , Edith Bongartz 1 , Helge H. O. Müller 2 , Jeremy D. Thorne1 , Alexandra Philipsen2 and Niclas Braun1,2 \*

1Neuropsychology Lab, Department of Psychology, University of Oldenburg, Oldenburg, Germany, 2Department of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany

Neurofeedback-guided motor-imagery training (NF-MIT) has been proposed as a promising intervention following upper limb motor impairment. In this intervention, paretic stroke patients receive online feedback about their brain activity while conducting a motor-imagery (MI) task with the paretic limb. Typically, the feedback provided in NF-MIT protocols is an abstract visual signal based on a fixed trial. Here we developed a self-paced NF-MIT paradigm with an embodiable feedback signal (EFS), which was designed to resemble the content of the mental act as closely as possible. To this end, the feedback was delivered via an embodiable, anthropomorphic robotic hand (RH), which was integrated into a closed-looped EEG-based brain-computer interface (BCI). Whenever the BCI identified a new instance of a hand-flexion or hand-extension imagination by the participant, the RH carried out the corresponding movement with minimum delay. Nine stroke patients and nine healthy participants were instructed to control RH movements as accurately as possible, using mental activity alone. We evaluated the general feasibility of our paradigm on electrophysiological, subjective and performance levels. Regarding electrophysiological measures, individuals showed the predicted event-related desynchronization (ERD) patterns over sensorimotor brain areas. On the subjective level, we found that most individuals integrated the RH into their body scheme. With respect to RH control, none of our participants achieved a high level of control, but most managed to control the RH actions to some degree. Importantly, patients and controls achieved similar performance levels. The results support the view that self-paced embodiable NF-MIT is feasible for stroke patients and can complement classical NF-MIT.

Keywords: neurofeedback, motor imagery, brain computer interface, sense of ownership, sense of agency, stroke, rubber hand illusion

## INTRODUCTION

Motor impairments in the upper limbs are among the most prevalent symptoms following stroke (Grefkes and Ward, 2014). Neurofeedback-guided motor imagery training (NF-MIT) has been proposed as a promising intervention for treating upper limb motor impairments (Pichiorri et al., 2015; Zich et al., 2017). In this intervention, paretic stroke patients conduct a motor imagery (MI)

#### Edited by:

Mariella Pazzaglia, Sapienza University of Rome, Italy

#### Reviewed by:

J. Ignacio Serrano, Spanish National Research Council (CSIC), Spain Giulia Galli, Santa Lucia Foundation (IRCCS), Italy Yongtian He, University of Houston, United States

> \*Correspondence: Niclas Braun<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Brain-Computer Interfaces, a section of the journal Frontiers in Human Neuroscience

> Received: 08 March 2019 Accepted: 16 December 2019 Published: 20 January 2020

#### Citation:

Spychala N, Debener S, Bongartz E, Müller HHO, Thorne JD, Philipsen A and Braun N (2020) Exploring Self-Paced Embodiable Neurofeedback for Post-stroke Motor Rehabilitation. Front. Hum. Neurosci. 13:461. doi: 10.3389/fnhum.2019.00461

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task during which they receive online neurofeedback about their brain activity. The therapeutic idea behind NF-MIT is to provide feedback to the patients on how well they are performing, by showing a beneficial neuronal activation pattern thought to support motor recovery (Sitaram et al., 2017). Typically, the feedback is a rather abstract signal, such as a moving bar or ball presented on a computer screen. In the context of an embodied cognition view (Wilson, 2002; Foglia and Wilson, 2013; Wilson and Golonka, 2013), it can be argued that an embodiable feedback signal (EFS) is more natural and intuitive for the patient. A feedback signal that closely resembles the MI act performed, in both time and space, may be potentially better accepted by patients, in particular if they suffer from cognitive impairments, and may, eventually, lead to better performance.

A few studies have developed an EFS (Perez-Marcos et al., 2009; Alimardani et al., 2013; Ono et al., 2013; Pichiorri et al., 2015; Braun et al., 2016). Most of these studies were inspired by the active rubber hand illusion (aRHI; Kalckert and Ehrsson, 2012, 2014; Braun et al., 2014) or its VR-based derivatives (Slater et al., 2008, 2009; Sanchez-Vives et al., 2010; Kilteni et al., 2012; Ma and Hommel, 2013; Pichiorri et al., 2015; Ma et al., 2017). The aRHI is a special variant of the classical rubber hand illusion, in which a movable artificial robotic hand (RH), rather than a static rubber hand, is placed visibly, and in an anatomically-plausible position, in front of the individual, while the individual's own hand is hidden from view. If the RH is repeatedly moved in synchrony with the individual's real or imagined hand movements, an illusory sense of ownership (SoO) and sense of agency (SoA) for the artificial hand can typically be induced (Kalckert and Ehrsson, 2012, 2014; Braun et al., 2014, 2016). That is, individuals may then experience the RH as part of their own body (SoO) and its movements as under their voluntary movement control (SoA). In order to provide real-time feedback for the MI within an aRHI paradigm, imagined hand movements are decoded from electrical brain activity and the corresponding commands are issued to the RH. Ideally, the RH then executes the imagined movements with little temporal delay (Braun et al., 2016).

Several studies have indicated beneficial effects of EFS (Perez-Marcos et al., 2009; Slater et al., 2009; Alimardani et al., 2013, 2014). Ono et al. (2013), for instance, compared different feedback signals presented on a computer screen and found evidence for a more robust event-related desynchronization (ERD) pattern for natural as compared to abstract feedback signals. Also, in a previous aRHI study (Braun et al., 2016), we used a RH and investigated the role of feedback-signal embodiment. Individuals experienced RH movements as more self-related and self-caused if the RH was placed in a congruent position such that it could be embodied. Individuals were also able to induce RH movements more quickly. These findings suggest that natural feedback signals may help to improve the quality of NF-MIT. It is not clear, however, to what extent stroke patients in particular benefit from an EFS, since most paradigms have been tested only with healthy individuals. A recent meta-analysis conducted by Cervera et al. (2018) showed that brain-computer interface (BCI)-based neurorehabilitation on upper-limb motor function can lead to more improvement in motor performance than other conventional therapies, supporting the general effectiveness of classical NF-MIT in stroke patients. The specific effect of EFS, however, has not yet been studied.

Most NF-MIT paradigms employ cue-based designs, in which the timing and content of mental tasks are predefined by visual or auditory cues (Scherer et al., 2008). Such rigid training regimes are easy to control experimentally, but they suffer from poor ecological validity, in particular when voluntary actions are studied. Spontaneous, self-paced designs may be needed to allow individuals to develop self-control and increased acceptance (Lotte et al., 2013).

In the present study, we investigated whether a self-paced embodiable NF-MIT, in which the individuals can freely explore the consequences of their different MI acts on a RH, is feasible. We tested this RH neurofeedback paradigm with stroke patients (n = 9), since this is the major target group for which NF-MIT is ultimately intended, as well as with healthy participants matched in age and gender. We wanted to know whether stroke patients, in particular, are able to learn to control the RH. To evaluate neurofeedback performances, participants had to perform various tasks such as conducting as many RH movements as possible in some time periods and withholding any RH movements in others. Behavioral, subjective and electrophysiological measures were collected and analyzed.

### MATERIALS AND METHODS

### Participants

Nine chronic stroke patients (one female) aged 55–75 and nine healthy controls matched in age and sex were recruited for the study (see **Table 1**, for demographic and clinical data). All participants were required to have a normal or corrected-tonormal vision and no known history of a mental disorder. All patients suffered from a first-time monolateral stroke. Months since stroke ranged from 15 to 72 months, M = 41.44, SD = 22.6. Inclusion criterion was a moderate to severe right-hand paresis due to the stroke as assessed by the Fugl-Meyer test (see ''Stroke Patient Assessment'' section). Patients were required to have no dementia, no epileptic seizures, and no severe neglect or severe aphasia that would impair their ability to follow task instructions. All participants were compensated for their participation (8e per hour), gave written informed consent and were naive to the purpose of the study. The study was approved by the University of Oldenburg ethics committee.

TABLE 1 | Demographic data of stroke patients and healthy controls.

### Overview

The study was carried out over three sessions for stroke patients and two sessions for healthy controls. The additional session for the stroke patients served to conduct the cognitive, motor and sensory assessments. The other two sessions, in which the actual NF-MIT was conducted were identical for both groups. Here, we report NF-MIT data from the second of these two sessions. In the first session, participants had to kinesthetically imagine flexion/extension movements with both of their hands in spatiotemporal synchrony with the RHs flexion/extension movements, while in the second session, they imagined these movements with only their right hand. Thus, the first session implemented a different experimental task and will be reported elsewhere.

### Stroke Patient Assessment

Cognitive assessment was conducted using the current version of the Montreal cognitive assessment (MoCA; Nasreddine et al., 2005). This test covers different cognitive domains and is an established fast screening tool for detecting mild cognitive impairments. The MoCA score ranges from 0 to 30 and Germanspeaking participants with normal cognitive ability are expected to exceed a threshold between 18 and 24 (depending on age and gender; Thomann et al., 2018).

Motor assessment was carried out using an adapted version of the Fugl-Meyer test (Sanford et al., 1993). While the original Fugl-Meyer test assesses both upper and lower limb movements, we only focused on 29 upper-limb tasks in the present study. All movement tasks were first executed with the non-paretic and then with the paretic arm. For each task, the achieved motor performance scores of the paretic and the non-paretic arm were compared and their difference assessed on a 3-point Likert scale. The scale ranged from zero (clearly lower performance on the paretic side) to two (identical performance on the paretic and non-paretic side). A summation score was then calculated by adding up all 29 individual task scores. The maximum score achievable was thus 58. The cut-off criterion indicating mild to severe right-hand paresis was set to scores lower than, or equal to, 45.

The sensory assessment was based on a testing procedure adapted from the Nottingham Sensory Assessment (Lincoln et al., 1998). We tested six different sensory modalities (pressure, light touch, pain, temperature, proprioception and vibration) on three different upper limb locations (upper arm, forearm, back of the hand), while the patients' eyes were closed. For each modality and each position, sensibility performances of the paretic and the non-paretic body side were compared and assessed on a 3-point Likert scale, ranging from zero (no detection of respective sensory stimulus), to two (identical sensory detection performance on the paretic and the non-paretic side). A summation score was then calculated by adding up all 18 individual task scores. The maximum score achievable was thus 36.

### Apparatus

The NF-MIT took place in a sound-attenuated and dimly lit experiment room. The experimental setting was adapted from our previous study (Braun et al., 2016) and is depicted in **Figure 1A**. The participant sat in front of a rectangular table (50 × 60 cm) and placed the right hand into a black box. This box was upholstered on the inside, so as to allow for a comfortable placement, and covered both hand and lower arm, hiding them from view. The anthropomorphic RH was placed in an anatomically-congruent position next to the black box, such that it was positioned medially aside the hidden real right hand. The horizontal distance between the participant's real right hand and RH was around 7.5 cm. A green LED was placed in the middle of the table. Both the RH and the participant's right (unseen) hand were covered with a thin-gauge garden glove. A blanket covered shoulders and arm and the space between the RH and the participant's body. The aim of this was to facilitate the visual impression that the RH could be the participant's own hand. The RH closely resembled a typical large human hand in terms of shape and size, and could be controlled with Matlab R2012a (Mathworks, Natick, MA, USA) via a microcontroller (Arduino mega 2560). The RH realistically mimicked hand flexion and hand extension movements (for more details, see Braun et al., 2016). The time delay between a Matlab control command sent to the microcontroller and an actual RH movement onset was less than 200 ms. The NF-MIT itself consisted of two blocks, a training block, and a feedback block.

## Neurofeedback Motor-Imagery Training

### Training Block

The training block served to acquaint the participants with the MI task and to calibrate the classifiers used in the ensemble classification algorithm for the ensuing feedback block (see ''EEG Data Recording'' and ''EEG Data Analysis and Classifier Training'' sections for more detail). To enable a successful aRHI induction in the feedback block, RH movements were already included in the training. The training block lasted 12 min and was based on a fixed trial structure, and consisted of around 50 runs, each run lasting around 14 s and beginning with a 5 s rest period. During the initial rest period, the RH was in the open state and participants were instructed to relax and not to move. After that, a small LED indicated to the participants to prepare for the subsequent MI flexion trial, which began 300 ms after LED onset and lasted for 1.5 s. During this period, the RH flexed its fingers and the participants were required to concomitantly imagine the same movement with their right hand. The instruction was thus to kinesthetically imagine the same flexion movement, in spatiotemporal synchrony with the RH movement. The flexion period was finished by the offset of the LED and the participants were instructed to relax again for another 5 s. Then, the LED switched on again, preparing the participants for the extension trial, which began 300 ms after LED onset. The extension trial also lasted 1.5 s, during which the RH extended its fingers and participants were required to concomitantly imagine this extension movement with their right hand. The extension trial was finished by the offset of the LED and the next run began.

### Neurofeedback Block

During the neurofeedback block, the RH was connected to a BCI that was trained on the training block data (see ''EEG

FIGURE 1 | Study design. (A) Experimental apparatus. Participants placed their right hand into a black box, whereas the robotic hand (RH) was placed directly alongside in front of the participant. During the training block, participants kinesthetically imagined flexion and extension movements in spatio-temporal synchrony to the flexion and extension movements of the RH. (B) Brain-Computer Interface (BCI). For neurofeedback provision, the RH was connected to a BCI that was trained on the training block data and moved whenever the BCI detected the imagination of either a participant's flexion or extension. The BCI's classification algorithm was based on signal features of the 8–30 Hz sensorimotor rhythm (SMR). (C) Neurofeedback tasks. During the feedback block, participants attempted to control the RH's movements as accurately as possible, using their motor-imagery (MI) thoughts alone. To evaluate their achieved controllability over the RH, participants had to perform different "BCI Parcours" in which they attempted to carry out various computer-given, experimenter-given or self-given commands. Data privacy remark: The person shown on the figure agreed with the publication of this figure.

Data Recording'' section), and moved whenever the BCI's classification algorithm assumed an instance of flexion or extension imagination. The neurofeedback block consisted of an acquaintance phase and three neurofeedback tasks (**Figure 1C**), each of which lasted 4 min.

During the acquaintance phase, the participants had the opportunity to familiarize themselves with self-paced NF-MIT. That is, they were allowed to freely try different MI acts and to observe how these acts influenced the RH's movement behavior. The overall aim thereby was to gain as much control as possible over the RH's flexion, extension, and resting states, using MI thoughts alone. To help the participants to accomplish this aim, we suggested they try different mental strategies (e.g., ''If you want to flex the RH, try to imagine grasping something or recall the flexion imagination from the training phase'' or ''If you want to rest the RH, mentally count numbers''). Moreover, the participants were asked whether some threshold modifications within the classification algorithm needed to be done; for instance, whether they remained trapped within one RH state. The classifier thresholds of the logistic regression used in the ensemble classification algorithm were then adjusted according to which thresholds best enabled participants to control the RH using MI. That is, if for instance the RH opened more often than intended by the participant, we set the threshold of the respective classifier to a higher value, e.g., from 0.7 to 0.8 (see ''EEG Data Analysis and Classifier Training'' and ''Ensemble Classification Algorithm and Online Data Flow'' sections).

The first neurofeedback task was a ''rest vs. move task,'' during which the participants attempted to move the RH as often as possible during 30 s move phases and as little as possible during 30 s rest phases. The respective phases alternated eight times and were indicated by a small LED that switched on during move phases and switched off during rest phases.

The second neurofeedback task was a ''follow commands task,'' during which the participants attempted to carry out movement commands given by the experimenter. To this end, the experimenter sat next to the participants, equipped with a laptop, and time-marked each given command. The experimenter chose one from four different commands, three explicit commands and one implicit commands. The explicit commands were ''open,'' ''close'' and ''grasp,'' where grasp meant to open (extension) and immediately close (flexion) the RH. The implicit command was to keep the RH in its momentary resting state (i.e., either ''remaining opened'' or ''remaining closed''), as long as the experimenter gave no new explicit command.

The third neurofeedback task, the ''announce commands task,'' was identical to the follow commands tasks, with the exception that this time the participants themselves announced their next intended RH movement. Participants orally communicated the commands to the experimenter and thereafter aimed to initiate the announced movement. Commands were again time-marked by the experimenter. During the times in which the participant gave no command it was assumed that the participant currently did not intend to move the RH. Further, during the times in which the participant announced a command, but had not yet achieved the announced movement, it was assumed that the participant was still attempting to carry out the announced movement.

### EEG Data Recording

EEG data acquisition was done with a mobile, 24-channel EEG system (mBrainTrain GmbH, Belgrad, Serbia) using an elastic cap (EASYCAP, Herrsching, Germany). The cap's electrode montage was a subset of the 10–20 system. It included the following positions: FP1, FP2, F7, F8, FZ, FC1, FC2, T7, C3, CZ, C4, T8, TP9, CP5, CP1, CPz, CP2, CP6, TP10, P3, PZ, P4, O1, and O2. AFz served as ground (DRL) and FCz as reference (CMS). The continuous EEG signal was digitized via Lab Streaming Layer (LSL1 ) with a sampling rate of 500 Hz and 24-bit resolution.

### EEG Data Analysis and Classifier Training

To prepare the neurofeedback, EEG training data were analyzed on-site with EEGLAB (Delorme and Makeig, 2004) and BCILAB (Kothe and Makeig, 2013). More specifically, three probabilistic classifiers were derived and used as the basis for an ensemble-like classification algorithm (see details below). A first classifier was calibrated for discriminating flexion trials from extension trials, a second for discriminating flexion trials from rest trials, and a third for discriminating extension trials from rest trials. That is, the first classifier output referred to the probability of flexion as opposed to the extension, the second to the probability of flexion as opposed to remaining open, and the third to the probability of extension as opposed to remaining closed. The three classifiers were trained on different time segments but otherwise underwent the same signal processing steps. To derive each classifier, the EEG training data were first band-pass filtered from 8 to 28 Hz and then epoched into 1.5 s segments relative to the onsets of the extension, flexion, and rest trials. In total there were thus twice as many rest trials as extension or flexion trials. Next, the segments that included obvious non-stereotyped artifacts were identified and rejected using built-in EEGLAB functions (Delorme et al., 2007). The remaining segments were submitted to an adaptation of BCILAB's pre-built ParadigmCSP class (Kothe and Makeig, 2013). This paradigm detects classspecific changes in the sensorimotor rhythm (SMR) by means of common spatial pattern (CSP) analysis. Briefly described, given two time windows of a multivariate signal, CSP finds spatial filters that minimize the variance for one class and simultaneously maximize the variance for the other class (for reviews, see Ramoser et al., 2000; Blankertz et al., 2008). Using ParadigmCSP, 24 CSP filters were derived for each classifier. From these 24 spatial filters, the first four and last four filters (i.e., those filters who promised the highest classdiscriminability) were inspected with respect to their spatial topography and associated time course. All CSP-filters showing physiologically-plausible sensorimotor cortex activity were kept, and feature values were calculated by multiplying each EEG segment with each CSP-filter and then taking the log-variance from the CSP segments. For each classifier, the feature space was equal to the number of CSP filters used. To obtain probabilistic class estimates, a regularized logistic regression model as implemented in BCILAB was then trained on each of the three derived training sets (feature matrices). Given a training set of observations whose class membership is known, this machine learning algorithm learns to make probability estimates on the class membership of new observations (Dreiseitl and Ohno-Machado, 2002). These probability estimates served as the basis for our ensemble-like classification algorithm described below.

### Ensemble Classification Algorithm and Online Data Flow

To deliver the feedback, Matlab, LSL and BCILAB were used. The data processing and feature extraction followed the same procedure as during classifier training (**Figure 1B**). The classifiers always operated on the most current 1.5 s. EEG segment and were constantly updated every 50 ms. To control the RH's movements and resting states, an ensemble-like classification algorithm was implemented, in which the ultimate classifier output depended on the weighted estimates of the three individual classifiers. The usage of the three classifiers' estimates was determined by the RH's current state, being one of six different states: ''remaining opened,'' ''currently flexing,'' ''just closed,'' ''remaining closed,'' ''currently extending'' and ''just opened'' (**Figure 2**). For an illustration of one cycle of the RH's state transitions, first, assume that the RH is in the ''remaining opened'' state. In that state only the open vs. flexion

1https://github.com/sccn/labstreaminglayer

classifier is active and the RH remains in this state until the constantly-updated classifier output reaches a certain threshold (e.g., p = 0.7 for the flexion class), individually adjusted for the participant during the acquaintance phase. Once a threshold is reached, the RH starts flexing, i.e., it switches into the ''currently flexing'' state. During the ''currently flexing'' state, no classifier is active and the RH remains in this state for as long as the flexion movement is being completed (2 s). Next, the RH switches into the ''just closed'' state, during which the closed vs. extension classifier and the extension vs. flexion classifier are active. The RH remains in this state either until both classifier outputs reach their respective thresholds, or until 2 s have passed. In the first case, the classification algorithm assumes an extension has been imagined and thus immediately induces an extension movement, that is, it switches into the ''currently extending'' state. In the latter case, the RH switches into the ''remaining closed'' state, during which only the closed vs. extension classifier is active. The RH remains in this state until the closed vs. extension classifier output reaches its defined threshold and then it starts extending, that is, it switches into the ''currently extending'' state. During the ''currently extending'' state, no classifier is active and the RH remains in this state until the extension movement is completed (2 s). Next, the RH switches into the ''just opened'' state, during which the open vs. flexion classifier and the extension vs. flexion classifier are active. The RH remains in this state either until both classifier outputs meet their defined thresholds, or until 2 s have

passed. In the first case, the classification algorithm assumes a flexion has been imagined and thus immediately induces a flexion movement, that is, it switches into the ''currently flexing'' state. In the latter case, the RH switches into the ''remaining open'' state.

### EEG Offline Analysis

### Time-Frequency Analysis

MI-induced changes in the SMR served as the physiological basis for our classification algorithm. To further explore these oscillatory changes, an offline EEG time-frequency (TF) analysis was carried out on the training block data. EEG artifacts were attenuated using extended infomax independent component analysis (ICA; Bell and Sejnowski, 1995; Delorme and Makeig, 2004). Artifactual independent components were identified by visual inspection and excluded from the back projection. Next, the ICA-corrected continuous data were segmented from −2 to 3 s, relative to the onsets of the MI flexion and MI extension trials. EEG segments containing unique, non-stereotyped artifacts were identified by built-in EEGLAB functions and rejected. From each remaining EEG segment, a corresponding CSP segment was calculated by multiplying the data with a chosen CSPfilter. To obtain a unique CSP-component for each participant, all 24 potential CSP components were first derived from each segment and then the physiologically most plausible CSP component was kept. A TF analysis was carried out on the derived CSP segments by means of a continuous Morlet wavelet transform (Debener et al., 2005; Thorne et al., 2011). The hereby obtained frequency bins ranged from 5 to 50 Hz in 1 Hz frequency steps. The TF analysis was conducted for the time interval from −0.8 to 2.3 s.

To avoid edge artifacts, TF data were only analyzed from −0.5 s to 2 s, relative to the beginning of MI. Percent power change relative to baseline power was calculated. That is, for each frequency bin, the corresponding time series was first squared, then scaled to decibels (10 × log10) and finally its change in power (relative to the first 0.5 s mean baseline power) was calculated. For the statistical analysis, SMR power changes were separately extracted for flexion and extension trials. To this end, across trials, mean percent log power changes between 10 and 25 Hz were calculated for the 1 s time interval beginning 0.5 s after MI onset.

### ERD Latency Analysis

Based on the TF data, an ERD latency value was calculated, reflecting the time intervals between MI period onsets and ERD onsets. To this end, a threshold defined as a power reduction of at least 30% as a corresponding desynchronization was set. To counterbalance outliers, the 20th percentile of all trials exceeding the determined threshold was taken to calculate a mean ERD latency across trials for each participant.

### CSP Filter Analysis

To further evaluate the neurophysiological basis of our NF-MIT, we investigated the quality of the CSP filters that were calculated for the online analysis. For this purpose, six criteria were extracted from the heuristics of appropriate filter selection (see **Supplementary Material S1**). These criteria were, first, whether the signal in the CSP pattern appeared to originate from the sensorimotor areas; second, whether the signal in the CSP filter appeared to originate from the sensorimotor areas; third, whether there was a recognizable discriminability between the compared trial classes in the power value distributions; fourth, whether there was recognizable discriminability between trial classes on single-trial time course visualizations; fifth, whether a left-sided ERD and/or a rightsided ERD or ERS occurred; and finally, whether the power value distributions for each trial class were normally distributed. Two CSP filters (one for flexion, one for extension) for each participant were evaluated on these criteria. Each criterion could be either fulfilled or not. A sum value across the two CSP filters was calculated, leading to a maximum score of 12 points. We defined that scores ≥10 indicated plausible CSP filters.

### Questionnaire Data

A 15-item questionnaire (see **Table 2**), adapted from previous studies (Kalckert and Ehrsson, 2012; Braun et al., 2014, 2016), was used to assess the participant's subjective experiences. At the end of each block, the experimenter read each item to the participant, and the participant had to rate his or her level of agreement on a 7-point Likert scale. The scale ranged from −3 (''totally disagree'') to +3 (''totally agree''). SoO, SoA and two other phenomenal target properties—experiential realness (ER) and MI-action binding (MIAB)—were operationalized. SoA was defined as the amount of experienced authorship over the RH's movement behavior and SoO as the experienced level of ''mineness'' towards the RH. MIAB indicated the extent to which the self-induced MI percept and the RH motion percept felt bound together and ER the extent to which the MI act was felt as real and vivid. Three items were used for each phenomenal target property and later averaged to obtain a single value for each block. The remaining three items were control items, one relating to SoA, and two relating to SoO. These items entailed illusion-related statements but did not specifically capture the phenomenal experience of limb ownership or SoA. Hence, in the case of a successful SoO and SoA induction, items related to these two phenomenal constructs should have high affirmative ratings, whereas the control items should not be specifically affected by the experimental manipulation. As in former studies (Kalckert and Ehrsson, 2012; Braun et al., 2014, 2016), the illusion threshold to confirm

TABLE 2 | Questionnaire for assessment of subjective experiences.

Note. Three statements addressed each phenomenal target property. In addition, control statements were included that included illusion-related statements but which did not capture the phenomenal experience of agency or ownership. Statements were read in counterbalanced to the participants, and the participants had to rate their level of agreement on a 7-point Likert scale.

a successful SoO and SoA induction across participants was set to ≥ 1.

### Neurofeedback Performance Evaluation (Classification Accuracies)

### Training Accuracies

Online training accuracies were calculated for all three classifiers with a five-fold block-wise cross-validation procedure. The calculation relied on the training block, using the same time segments as during classifier calibration. To statistically test whether the training accuracies were above chance level, a binomial statistic with p = 0.05 was used (Combrisson and Jerbi, 2015).

### Feedback Accuracies

To evaluate performance during the feedback block, feedback accuracies were analyzed in each task. Additionally, the number of movements during rest phases was compared to the number of movements during move phases in the rest vs. move task. Since no fixed trial structure was given for the follow commands and announce commands task, we post hoc reconstructed a trial structure in order to calculate feedback accuracies (**Figure 3**). To this end, we segmented the data into 5 s intervals, relative to each command. We then defined two different trial types, intended movement trials and intended rest trials, and assigned each segment to one of the two types. A series of intended movement trials began as soon as a movement command was given by the experimenter or participant and lasted until the intended movement was finally carried out. A series of intended rest trials, in turn, started as soon as a movement was carried out and no further command was yet given. The intended movement trials in which a movement occurred were defined as true positive (TP) outcomes whereas those intended movement trials without any movement occurrence were defined as false negative (FN) outcomes. Likewise, the intended rest trials without any movement occurrences were defined as true negative (TN) outcomes whereas those intended rest trials with a movement occurrence were defined as false positive (FP) outcomes. To calculate overall feedback accuracies, the sum of all TP and TN trials was then divided by the total number of trials and multiplied by a hundred. The possible values thus ranged from zero, indicating that all trials failed, to 100, indicating only successful trials, that is, a perfect match of the participants' intentions and the RH behavior. Regarding the feedback accuracies in the rest vs. move tasks, data were equally segmented into 5 s intervals, with move phases comprising only intended movement trials, and rest phases comprising only intended rest trials. To statistically test whether feedback accuracies in the follow commands and announce commands task were above chance level, an established statistical procedure relying on the classification accuracies'' confidence intervals (CI) was used, with p = 0.05 (Billinger et al., 2012). Regarding the rest vs. move task, the same statistic as for the training accuracies was taken (Combrisson and Jerbi, 2015).

### Statistical Analyses