2,302 research outputs found
A neuroimaging investigation of bipolar disorder and the neurocognitive effects of 5-HT7 antagonists
Bipolar disorder is a psychiatric disorder characterised by pathological mood states, but there is growing recognition of the role of cognitive impairment and dysfunction of emotional processes, which has a profound impact on quality of life. Many people with bipolar disorders exhibit brain volume impairment associated with cognitive dysfunction and an increased risk of dementia. In this thesis, I conducted a systematic review to understand the relationships between mood disorders and the 5-HT7 receptor. The 5-HT7 receptor is related to depression and anxiety, but the relationship between 5-HT7 and mania remains unclear; in addition, sleep and memory were also related to the 5-HT7 receptor. Followed by these findings, in the next two chapters, I examined the effects of 5-HT7 antagonists, using JNJ-18038683, on emotional and cognitive functioning, as well as their neural substrates. I then reported on neuroimaging investigations examining the effects of 5-HT7 antagonists on emotional processing and cognitive function in healthy volunteers to gain insight into their potential mode of action and utility for bipolar disorder. In fMRI analyses, the drug acted on 5-HT7 receptors potentially improving cognitive performance by modulating the function of the Cognitive Control Network in healthy controls. In the above-mentioned chapters, I gained a better understanding of the 5-HT7 antagonist, JNJ-18038683, and the putative promising effects for pharmacological treatments. However, the approach taken has some limitations, including a small sample size, potential participant bias, and a lack of systematic control of medication dose and duration of administration. In addition, in Chapter 5, I explored the brain basis of bipolar disorder and its links to cognitive and emotional dysfunction using a new ‘brain age’ approach. Individuals with bipolar disorder were found to have increased brain age compared to healthy controls. I hope that these findings can be applied to pharmacological treatment for individuals with bipolar disorder, ultimately allowing patients to benefit from the drug in the future
Addiction in context
The dissertation provides a comprehensive exploration of the interplay between social and cultural factors in substance use, specifically focusing on alcohol use disorder (AUD) and cannabis use disorder (CUD). It begins by introducing the concept of social plasticity, which posits that adolescents' susceptibility to AUD is influenced by their heightened sensitivity to their social environment, but this sensitivity increases the potential for recovery in the transition to adulthood.A series of studies delves into how social cues impact alcohol craving and consumption. One study using functional magnetic resonance imaging (fMRI) investigated social alcohol cue reactivity and its relationship to social drinking behavior, revealing increased craving but no significant change in brain activity in response to alcohol cues. Another fMRI study compared social processes in alcohol cue reactivity between adults and adolescents, showing age-related differences in how social attunement affects drinking behavior. Shifting focus to cannabis, this dissertation discusses how cultural factors, including norms, legal policies, and attitudes, influence cannabis use and processes underlying CUD. The research presented examined various facets of cannabis use, including how cannabinoid concentrations in hair correlate with self-reported use, the effects of cannabis and cigarette co-use on brain reactivity, and cross-cultural differences in CUD between Amsterdam and Texas. Furthermore, the evidence for the relationship between cannabis use, CUD, and mood disorders is reviewed, suggesting a bidirectional relationship, with cannabis use potentially preceding the onset of bipolar disorder and contributing to the development and worse prognosis of mood disorders and mood disorders leading to more cannabis use
Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches
Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people.
Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy.
The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech.
In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection
Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts
Background: Personal recovery, ‘living a satisfying, hopeful and contributing lifeeven with the limitations caused by the illness’ (Anthony, 1993) is of particular value in bipolar disorder where symptoms often persist despite treatment. So far, personal recovery has only been studied in researcher-constructed environments (interviews, focus groups). Support forum posts can serve as a complementary naturalistic data source. Objective: The overarching aim of this thesis was to study personal recovery experiences that people living with bipolar disorder have shared in online support forums through integrating health research, NLP, and corpus linguistics in a mixed methods approach within a pragmatic research paradigm, while considering ethical issues and involving people with lived experience. Methods: This mixed-methods study analysed: 1) previous qualitative evidence on personal recovery in bipolar disorder from interviews and focus groups 2) who self-reports a bipolar disorder diagnosis on the online discussion platform Reddit 3) the relationship of mood and posting in mental health-specific Reddit forums (subreddits) 4) discussions of personal recovery in bipolar disorder subreddits. Results: A systematic review of qualitative evidence resulted in the first framework for personal recovery in bipolar disorder, POETIC (Purpose & meaning, Optimism & hope, Empowerment, Tensions, Identity, Connectedness). Mainly young or middle-aged US-based adults self-report a bipolar disorder diagnosis on Reddit. Of these, those experiencing more intense emotions appear to be more likely to post in mental health support subreddits. Their personal recovery-related discussions in bipolar disorder subreddits primarily focussed on three domains: Purpose & meaning (particularly reproductive decisions, work), Connectedness (romantic relationships, social support), Empowerment (self-management, personal responsibility). Support forum data highlighted personal recovery issues that exclusively or more frequently came up online compared to previous evidence from interviews and focus groups. Conclusion: This project is the first to analyse non-reactive data on personal recovery in bipolar disorder. Indicating the key areas that people focus on in personal recovery when posting freely and the language they use provides a helpful starting point for formal and informal carers to understand the concerns of people diagnosed with bipolar disorder and to consider how best to offer support
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning
Open Radio Access Network systems, with their virtualized base stations
(vBSs), offer operators the benefits of increased flexibility, reduced costs,
vendor diversity, and interoperability. Optimizing the allocation of resources
in a vBS is challenging since it requires knowledge of the environment, (i.e.,
"external'' information), such as traffic demands and channel quality, which is
difficult to acquire precisely over short intervals of a few seconds. To tackle
this problem, we propose an online learning algorithm that balances the
effective throughput and vBS energy consumption, even under unforeseeable and
"challenging'' environments; for instance, non-stationary or adversarial
traffic demands. We also develop a meta-learning scheme, which leverages the
power of other algorithmic approaches, tailored for more "easy'' environments,
and dynamically chooses the best performing one, thus enhancing the overall
system's versatility and effectiveness. We prove the proposed solutions achieve
sub-linear regret, providing zero average optimality gap even in challenging
environments. The performance of the algorithms is evaluated with real-world
data and various trace-driven evaluations, indicating savings of up to 64.5% in
the power consumption of a vBS compared with state-of-the-art benchmarks
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
30th European Congress on Obesity (ECO 2023)
This is the abstract book of 30th European Congress on Obesity (ECO 2023
“Have patients with chronic skin diseases needs been met?”:A thesis on psoriasis and eczema patient care in dermatology service
Background: Common chronic skin diseases such as eczema and psoriasis usually require long term medical care. They are often associated with psychological and metabolic comorbidities, which can impact on patient quality of life (QOL) and on the self-management of these diseases. Regular assessment of patient needs, comorbidities and feedback is a critical step in the development of decision-analytic models. Currently, no intervention is available to regularly assess such patients’ needs and comorbidities and support their involvement in the decision-making and self-management of their morbidity and comorbidities. The aim of this research is to involve the patients in decision making of their care and to support their self-management by the use of a paper questionnaire (study tool) at each consultation. Objective: To explore the acceptability and potential of a self-developed paper questionnaire that constituted a study tool for addressing the needs, comorbidities, and feedback of patients with psoriasis and eczema and supporting their involvement in decision making and self-management of their chronic conditions. Method: A mixed method study was conducted and included a postal survey on adult male and female patients with psoriasis and eczema, using the study tool, which is a paper questionnaire and contains the Dermatology Life Quality Index (DLQI) and seven supplementary open-ended questions to capture patients’ views, feedback, comorbidities, coping status and needs. The survey was followed by semi-structured face-to-face interviews with a sample of the patients who had participated in the survey. The aims of the interviews were two-fold: 1. to gain a deeper understanding of their experience of living with and managing their skin disease; and 2. to gather patient feedback on the service they received as well as their views on using the new study tool or any alternative intervention to address and support their self-management. The final study was a pilot which involved presenting a proposal of an online version of the study tool to a group of healthcare experts asking them to critically review the extent to which the online model responded to patients expressed needs. Results: Of the 114 patients who participated in the postal survey 108 (94.7%) of them expressed physical, metabolic and psychological comorbidities. Stress was identified as the dominant disease-triggering factor in 72 (63%) participants. Thirty-three (28.9%) of participants reported that they could not cope with their chronic illness. Eighteen (15.7%) participants suffered from anxiety, and 12 (10.5%) had depression and suicidal thoughts. Twenty-nine (25%) participants addressed their needs for support at home, and 16 (14%) of them asked for support at work. In the patient feedback section, 21 (18.4%) and 9 (7.8%) participants rated the service they received from their general practitioner (GP) and dermatologist as poor, respectively. In the interviews, all the participants 22 (100%) welcomed the use of the study tool on a regular basis to address their needs, comorbidities and feedback. Nineteen (86.3%) of them suggested that they would prefer using an online version of the tool or patient portal system as a convenient way of remote and interactive communication with the healthcare provider, particularly during the worsening of their skin condition. In the final pilot study, the healthcare experts agreed that the proposed online version of the study tool could be a convenient platform for such patients to support their self-management. They discussed the potential importance of such a tool if it provided them with access to supportive services such as patient information on skin diseases and self-management, access to local mental health service and other relevant psoriasis and eczema patients’ support groups and charities. Conclusion: This novel mixed method research identified knowledge gaps in managing patients with psoriasis and eczema. It provided a new tool that has the potential to regularly engage and assess patients’ unmet needs, comorbidities and feedback. The tool can involve patients in decision-making and offers them the autonomy to disclose heterogeneous needs that may support their self-management. All the interviewees welcomed regular use of the study tool and the majority of them suggested that they would prefer using an online version of the tool if it was available. Future research is needed to assess the impact of the study tool in filling important gaps in patient self-management and in health service improvement
An XRI Mixed-Reality Internet-of-Things Architectural Framework Toward Immersive and Adaptive Smart Environments
The internet-of-things (IoT) refers to the growing number of embedded
interconnected devices within everyday ubiquitous objects and environments,
especially their networks, edge controllers, data gathering and management,
sharing, and contextual analysis capabilities. However, the IoT suffers from
inherent limitations in terms of human-computer interaction. In this landscape,
there is a need for interfaces that have the potential to translate the IoT
more solidly into the foreground of everyday smart environments, where its
users are multimodal, multifaceted, and where new forms of presentation,
adaptation, and immersion are essential. This work highlights the synergetic
opportunities for both IoT and XR to converge toward hybrid XR objects with
strong real-world connectivity, and IoT objects with rich XR interfaces. The
paper contributes i) an understanding of this multi-disciplinary domain XR-IoT
(XRI); ii) a theoretical perspective on how to design XRI agents based on the
literature; iii) a system design architectural framework for XRI smart
environment development; and iv) an early discussion of this process. It is
hoped that this research enables future researchers in both communities to
better understand and deploy hybrid smart XRI environments
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