Visual Misperceptions and Behavioral Variability in Parkinson’s Disease

The objective of this thesis are visual misperceptions and behavioral variability, their neural correlates and their potential for differential diagnosis of non-motor symptoms profiles in Parkinson’s disease (PD). This thesis investigates 1) objective and quantifiable measures of visual misperceptions and behavioral variability in PD and 2) functional connectivity correlates of visual misperceptions and behavioral variability 3) neural and cardiac correlates of behavioral variability in cognitive aging. In Chapter 2, we investigated task derived objective and quantifiable measures of visual misperceptions and behavioral variability in PD patients reporting visual hallucinations (VH) and PD patients without VH. First, PD patients with and without VH were compared with age matched healthy control group. Second, we compared PD patients reporting VH with PD patient who did not report VH. In addition, we investigated the relation between severity of perceptual errors, intra-individual variability of recognition times and fuctional connectivity estimates of Resting state networks in PD patients. We could show that PD patients who reported VH make more percpetual errors and show more intra-individual variability in recogntion time. In a subsample of PD patients, who participated in an functional Magnetic Resonance Imaging (fMRI) resting state experiment, a higher perceptual error score was related to hypoconnectivity between dorsal attention- and salience network. Increased intra-individual variability with respect to recognition time was related to hypoconnectivity between the somatomotor- and right fronto-parietal network. Perceptual errors and the hypoconnectivity between attentional networks might be a promising objective measurement to estimate the risk for VH of PD patients. The 7 determination of behavioral variability and hypoconnectivity between the somatomotor- and fronto-parietal network might be a useful to determine cogntive decline. Behavioral variability is increased in different disorders like attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), impulse control disorder (ICD) (Abramovitch, McCormack, Brunner, Johnson, & Wofford, 2019; Kertzman et al., 2018; Kofler et al., 2013), schizophrenia (Rentrop et al., 2010), dementia (Costa, Dogan, Schulz, & Reetz, 2019), Huntington Disease (Musso et al., 2015) and in untreated PD patients (Burton, Strauss, Hultsch, Moll, & Hunter, 2006; Camicioli, Wieler, de Frias, & Martin, 2008) which are characterized by cognitive decline. Until now the source of behavioral variability is not fully understood. Behavioral variability might be explained by variability quenching, a novel measurement which is until now investigated in healthy young individuals (Arazi, Censor, & Dinstein, 2017; Arazi, Gonen-Yaacovi, & Dinstein, 2017). In Chapter 3 we investigated age related cognitive decline. First, we compared behavioral variability, event-related potentials such as P1 and P3, change in power of alpha, beta, gamma and theta frequency, neural variability (variability quenching) and cardiac variability (HRV) between healthy elderly and healthy young volunteers. Second we tested the relation between standardized cognitive measures, task derived measures such as intra-individual variability, neural measures and cardiac measures in healthy young and healthy elderly. Healthy elderly show higher behavioral variability, lower P1 and P3 amplitude, lower theta power and lower cardiac variability. Lower cognitive performance in healthy elderly is related to higher behavioral variability and to lower neural variabiltiy. To conclude the measurement of behavioral- and neural variability are promising marker of cognitive decline. Perceptual errors in combination with hypoconnectivity between the dorsal attention network and the salience network 8 might be a promising tool to indicate PD patients at risk for experience of visual misperceptions and visual hallucinations. The analysis of behavioral variability combined with hypoconnectivity between somatomotor- and the fronto-parietal network might be a promising tool to identify PD patients at risk for cognitive decline