Unraveling neural and behavioral mechanisms of cognitive self-attenuation and alienation

Giedre Stripeikyte
EPFL, 2020
Thèse EPFL

Résumé

The present thesis aimed at investigating behavioral and neural underpinnings of the mechanisms related to the different aspects of self-monitoring. The first aim was to study functional connectivity alterations related to the mechanisms accounting for the alienation in psychotic patients and those at high-risk to develop it. The second aim was to extend the fundamental knowledge of whether higher-level cognitive processes beyond sensorimotor involvement are subject to attenuation and whether it is affected in psychotic patients with thought insertion.

In Part I of the thesis, I have studied whether the neural mechanisms (PH-network) accounting for the presence hallucination (PH), the experience that someone is here when no one is around, could also be relevant to understand alienation experienced in psychotic patients. In Study 1, I have investigated psychotic patients with passivity experiences where one's own actions, thoughts and emotions are perceived as not self-generated and caused by an external entity. In Study 2, individuals with 22q11 deletion syndrome who are at high-risk for psychosis development were investigated. In both populations, reduced functional connectivity between fronto-temporal connections within the PH-network was revealed. Taken together, these studies show that PH-network is affected not only when psychosis manifests but also in individuals prone to develop psychosis. The current findings strengthen the relevance of PH mechanisms to explain the occurrence of psychotic symptoms, specifically the alienation, and potentially could provide a biomarker for the disease development.

In Part II of the thesis, I have investigated self-attenuation, an important aspect for the sense of agency, during the cognitive function of numerosity estimations in healthy individuals and psychotic patients with thought insertion. In Study 3, a novel fMRI task was designed, allowing a controlled comparison of numerosity estimations for self and externally generated words in healthy volunteers. For the first time, self-attenuation during cognitive function beyond sensorimotor processing was reported. It was linked to the functional network involving intraparietal sulcus (key numerosity region) and extended areas more generally associated with attenuation processes. This work was continued in Study 4, where psychotic patients with thought insertion were studied. I showed that cognitive self-attenuation during numerosity estimations can be observed in patients with thought insertion and is related to altered executive functioning. Importantly, increased functional connectivity within the network related to attenuation processing during numerosity estimations was found in patients with thought insertion suggesting insufficient attenuation at the neural level. Lastly, I found the association between the altered functional connectivity within the PH-network and cognitive self-attenuation, providing a link between two different aspects related to the self-monitoring in patients with thought insertion.

To summarize, I present new insights into the different mechanisms related to self-monitoring, which would help to understand the manifestation of psychotic symptoms and develop prevention strategies.

Official source

https://infoscience.epfl.ch/record/282099?ln=fr

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Giedre Stripeikyte
EPFL, 2020
Thèse EPFL

Résumé

Official source

https://infoscience.epfl.ch/record/282099?ln=fr

À propos de ce résultat

Concepts associés (21)

Concepts associés

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Publications associées (16)

Publications associées

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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

Chargement

Brain-machine interfaces (BMIs) allow the user to control an external device such as a robotic arm, a cursor, or an avatar in a virtual world through the real-time decoding of brain signals and without the involvement of the musculoskeletal system. Although BMIs hold great promise for providing motor-impaired patients with means of control and interaction with the external world, the neurocognitive mechanisms that are involved in the use of a BMI remain poorly understood. In addition, BMIs allow researchers to investigate and separate the brain processes underlying cognitive functions from those related to motor control and afferent sensory signals that reliably accompany movements. Thus, BMIs are powerful tools for basic and applied neuroscience research. The present work investigates different stages of a BMI-mediated action and targets the subjective, behavioral, and neural signals of BMI control based on electroencephalography (EEG) signals. More specifically, the main study of this thesis has focused on the development of a multimodal imaging platform that allows EEG-based BMI control inside the magnetic resonance imaging (MRI) scanner and during the acquisition of functional MRI (fMRI). This platform has enabled us to exploit both the high temporal resolution of EEG and the high spatial resolution of fMRI to precisely identify the brain mechanisms underlying BMI control and those reflecting the subjective feeling of being in control over a BMI-action (i.e., the sense of agency, SoA). In this study we found an extended cortico-subcortical network involved in operating a motor-imagery BMI. Overall BMI performance was associated with activity in a set of regions including contralateral premotor cortex and the posterior cingulate cortex. Finally, cortical midline regions and the basal ganglia were involved in the subjective sense of controlling the BMI. In a second study, we further investigated whether the ability to control a BMI relates to the ability to perform motor imagery. Despite decades of technical advances, effective BMI-control remains limited to a subset of users. We show that inter-subject variability in BMI proficiency is associated with differences in motor imagery accuracy as captured by subjective and behavioral measurements, pointing to a prominent role of kinesthetic rather than visual imagery. We also identified enhanced lateralized ÎŒ-band oscillations over sensorimotor cortices during motor imagery in high- versus low-aptitude BMI users. Finally, we developed a novel paradigm for joint BMI actions, allowing two users to be jointly engaged in BMI control; our preliminary data provide evidence in support of the hypothesis that joint actions yield BMI-performance improvement even in absence of a physical connection. Our findings also show that during joint BMI-control the SoA over the imagery-mediated actions is significantly enhanced, and is affected by BMI abilities. This work show the potential of applying a multimodal imaging approach to the field of BMI: exploiting our EEG-BMI-fMRI platform we were able to identify, the neural mechanisms involved in motor and cognitive aspects of imagery-mediated BMI. Furthermore, our results enable us to better understand the subjective and behavioral aspects of BMI-actions, and reveal strategies that could potentially reinforce BMI control. Our findings can ultimately be of relevance for the field of neuroprosthetics and make BMI more accessible to a broader range of users.

EPFL2016

Chargement

The application of Bayesian modeling techniques is increasingly common in neuroscience due to the coherent and principled way in which the paradigm deals with uncertainty. The Bayesian framework is particularly valuable in the context of complex, ill-posed generative problems, in which case the incorporation of prior knowledge is optimal and practical. If one wants to take full advantage of joint imaging and behavioral data, the need for comprehensive generative models is evident. Here, I exploited the versatility afforded by Bayesian modeling approaches to investigate a series of questions from clinical, systems, and cognitive neuroscience. First, I investigated the lateralization of the cortical motor network in Parkinson's disease patients on and off dopaminergic medication with fMRI and dynamic causal modeling (DCM). Group-level model comparison revealed that disease and medication effects differentially involve homotopic cortical motor connections, but medication does not appear to have a restorative effect at the systems level. Our findings suggest the presence of maladaptive mechanisms, resulting from disease progression and long-term dopamine replacement. In a second project, I considered the way in which humans handle uncertainty in the parameters of the generative model of a task at hand. Using a novel psychophysics paradigm, participants' responses to fitting a parabola to a set of noisy points were compared to the predictions of a set of regression models, including Bayesian regression and several sub-optimal models. Only Bayesian regression could explain participants' response distributions across various levels of sensory uncertainty, suggesting that humans process parameter uncertainty in accordance with Bayesian regression. This finding deepens our understanding of the way in which humans learn and generalize from sparse, ambiguous data. The topic investigated in the greatest depth in this thesis was visual crowding - the deterioration of target discrimination due to the presence of flanking objects. Traditionally, vision is modeled as a feedforward, hierarchical network. Thus, crowding is explained as a pooling of neighboring elements' features, leading to a "bottleneck" at the earliest stages of vision. However, additional flankers can paradoxically improve performance (uncrowding). First, I used DCM and fMRI to show that recurrent processing is crucial for crowding and un-crowding alike. Higher visual areas modulate the lower areas, suggesting that explicit object representation plays a key role. I then used population receptive field (pRF) mapping to investigate the effects of context on pRF size. In accordance with pooling model predictions, the pRF size in crowding is larger than in the case of no crowding. However, in uncrowding, the pRF size is smaller than in crowding and no crowding, providing further evidence against purely feedforward models. Overall, my findings provide strong empirical evidence of context-dependent feedback modulation in vision. I propose a possible implementation which integrates the observed findings into a generative model of crowding: context-agnostic feedforward mechanisms transmit the information about the target and flankers retinotopically to higher visual areas; a subsequent context-dependent feedback pRF determines whether the target will be enhanced - by decreasing the pRF size - or suppressed - by enlarging the pRF size to encompass flankers.

EPFL2020

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EPFL2020

EPFL2016