Cognitive decline and hallucinations are common and debilitating non-motor symptoms, occurring during later phases of Parkinson’s disease (PD). Minor hallucinations (MH), appear at early phases and have been suggested to predict cognitive impairment in PD, however, this has not been well-established by clinical research. Here, we investigated whether non-demented PD patients with MH (without differences in frontal-subcortical and posterior cognitive function) show altered brain oscillations and whether such MH-related electrophysiological changes are associated with cognitive impairments that increase over time. Combining model-driven EEG analysis with neuropsychiatric and neuropsychological examinations in 75 PD patients, we reveal enhanced frontal theta oscillations in PD patients suffering from MH and link these oscillatory changes with lower cognitive frontal-subcortical functions. Neuropsychological follow-up examinations five years later revealed a stronger decline in frontal-subcortical functions in MH-patients anticipated by stronger frontal theta alterations measured during the first assessment, defining an MH and theta oscillation-based early marker of a cognitive decline in PD.

Although hallucinations are important and frequent symptoms in major psychiatric and neurological diseases, little is known about their brain mechanisms. Hallucinations are unpredictable and private experiences, making their investigation, quantification and assessment highly challenging. A major shortcoming in hallucination research is the absence of methods able to induce specific and short-lasting hallucinations, which resemble clinical hallucinations, can be elicited repeatedly and vary across experimental conditions. By integrating clinical observations and recent advances in cognitive neuroscience with robotics, we have designed a novel device and sensorimotor method able to repeatedly induce a specific, clinically relevant hallucination: presence hallucination. Presence hallucinations are induced by applying specific conflicting (spatiotemporal) sensorimotor stimulation including an upper extremity and the torso of the participant. Another, MRI-compatible, robotic device using similar sensorimotor stimulation permitted the identification of the brain mechanisms of these hallucinations. Enabling the identification of behavioral and a frontotemporal neural biomarkers of hallucinations, under fully controlled experimental conditions and in real-time, this method can be applied in healthy participants as well as patients with schizophrenia, neurodegenerative disease or other hallucinations. The execution of these protocols requires intermediate-level skills in cognitive neuroscience and MRI processing, as well as minimal coding experience to control the robotic device. These protocols take ~3 h to be completed.

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.