Sensorimotor hallucinations in Parkinson's Disease

Hallucinations in Parkinson’s disease (PD) are one of the most disturbing non-motor symptoms, affect half of the patients, and constitute a major risk factor for adverse clinical outcomes such as psychosis and dementia. Here we report a robotics-based approach, enabling the induction of a specific clinically-relevant hallucination (presence hallucination, PH) under controlled experimental conditions and the characterization of a PD subgroup with enhanced sensorimotor sensitivity for such robot-induced PH. Using MR-compatible robotics in healthy participants and lesion network mapping analysis in neurological non-PD patients, we identify a fronto-temporal network that was associated with PH. This common PH-network was selectively disrupted in a new and independent sample of PD patients and predicted the presence of symptomatic PH. These robotics-neuroimaging findings determine the behavioral and neural mechanisms of PH and reveal pathological cortical sensorimotor processes of PH in PD, identifying a more severe form of PD associated with psychosis and cognitive decline.

Hallucination Engineering: From Neuroscience Robotics to Hallucinations in Health and Disease

Eva Blondiaux

My thesis focuses on a psychotic symptom called the presence hallucination (PH). PHs are defined as the false perception that someone is nearby when no one is actually present. PHs can occur in various populations, ranging from healthy subjects (when exposed to extreme conditions) to neurological (e.g. epileptic, Parkinsonâs disease patients) and psychiatric patients (e.g. schizophrenia). Our group has designed a robotic device that allows to safely induce PH in healthy subjects, in a controlled manner, using different sensorimotor conflicts (Blanke et al., 2014). The aim of my thesis was to unravel the brain regions and mechanisms associated with PH and explore how the neuroscientific understanding of PH can improve diagnostics of different clinical populations with hallucinations. In Chapter 2, I investigated the brain networks associated with a category of complex phenomena named autoscopic phenomena (AP). AP include PHs and three other forms: autoscopic hallucinations, heautoscopies, and out-of-body experiences. AP are rare phenomena, defined as illusory reduplication involving a double of oneâs own body seen or felt in extrapersonal space. Lesion network mapping analysis was used to identify the brain networks associated to each AP. My data showed that each AP is characterised by specific connectivity networks consistent with their phenomenology. All AP also share common brain connectivity. In Chapter 3, using an MR-compatible robot able to induce PH, I investigated the brain regions and mechanisms of robot-induced PH in healthy subjects using fMRI. I combined these data with the results of my lesion network mapping analysis in neurological patients with focal brain damage associated with PH (Chapter 3). Both networks (i.e. robot-induced PH-network in healthy subjects and symptomatic PH-network) were merged together to identify the key regions involved in PH, defining a common PH-network. The relevance of this common PH-network was then assessed and confirmed in patients with Parkinsonâs disease (PD), revealing a difference in functional connectivity within this network between PD patients experiencing symptomatic PH compared to patients without such symptoms. Further, I studied the functional connectivity within the common PH-network in 22q11.2 deletion syndrome (DS) subjects (i.e. subjects with high risks (30%) of developing schizophrenia) (Chapter 4) and in psychotic patients with passivity experiences (Chapter 5). Studying PH in these population is relevant since PH is frequent in schizophrenia. In both populations, a reduced functional connectivity in the common PH-network was revealed. In Chapter 4, I also assessed the sensitivity to the sensorimotor conflicts and the proneness to experience robot-induced PH in 22q11DS subjects and age-matched controls. Results showed a lack of sensitivity in sensorimotor modulation for the sense of agency, further corroborated by a lack of delay dependency in experiencing robot-induced PH in 22q11DS subjects compared to controls. In summary, by coupling robotic technology with neuroimaging, a common PH-network was delineated and was found relevant in different clinical populations. These findings are important since PH can be present at early stages before the apparition of more severe symptoms. Therefore, our results can lead to more robust diagnostic tools and early treatment intervention, which might significantly improve prognosis in those patients.

EPFL2020

Robot-induced hallucinations in Parkinson’s disease depend on altered sensorimotor processing in fronto-temporal network

Michel Akselrod, Michela Bassolino, Fosco Bernasconi, Olaf Blanke, Eva Blondiaux, Nathan Quentin Faivre, Matteo Franza, Masayuki Hara, Stéphanie Konik, Jevita Potheegadoo, Giulio Rognini, Giedre Stripeikyte, Dimitri Nestor Alice Van De Ville

Hallucinations in Parkinson’s disease (PD) are disturbing and frequent non-motor symptoms and constitute a major risk factor for psychosis and dementia. We report a robotics-based approach applying conflicting sensorimotor stimulation, enabling the induction of presence hallucinations (PHs) and the characterization of a subgroup of patients with PD with enhanced sensitivity for conflicting sensorimotor stimulation and robot-induced PH. We next identify the fronto-temporal network of PH by combining MR-compatible robotics (and sensorimotor stimulation in healthy participants) and lesion network mapping (neurological patients without PD). This PH-network was selectively disrupted in an additional and independent cohort of patients with PD, predicted the presence of symptomatic PH, and associated with cognitive decline. These robotics-neuroimaging findings extend existing sensorimotor hallucination models to PD and reveal the pathological cortical sensorimotor processes of PH in PD, potentially indicating a more severe form of PD that has been associated with psychosis and cognitive decline.

2021

Hallucination Engineering: From Neuroscience Robotics to Hallucinations in Health and Disease

Eva Blondiaux

EPFL2020