Maintaining wakefulness is associated with a progressive increase in the need for sleep. This phenomenon has been linked to changes in synaptic function. The synaptic adhesion molecule Neuroligin-1 (NLG1) controls the activity and synaptic localization of N-methyl-D-aspartate receptors, which activity is impaired by prolonged wakefulness. We here highlight that this pathway may underlie both the adverse effects of sleep loss on cognition and the subsequent changes in cortical synchrony. We found that the expression of specific Nlg1 transcript variants is changed by sleep deprivation in three mouse strains. These observations were associated with strain-specific changes in synaptic NLG1 protein content. Importantly, we showed that Nlg1 knockout mice are not able to sustain wakefulness and spend more time in nonrapid eye movement sleep than wild-type mice. These changes occurred with modifications in waking quality as exemplified by low theta/alpha activity during wakefulness and poor preference for social novelty, as well as altered delta synchrony during sleep. Finally, we identified a transcriptional pathway that could underlie the sleep/wake-dependent changes in Nlg1 expression and that involves clock transcription factors. We thus suggest that NLG1 is an element that contributes to the coupling of neuronal activity to sleep/wake regulation.

Non-invasive brain–computer interface (BCI) technology can restore communication for those unable to communicate due to loss of muscle control. Nonetheless, compared to augmentative and alternative communication (AAC) devices requiring muscular control, BCIs provide relatively slow communication. Therefore, implementing techniques improving BCI speed and accuracy is important. Previous studies indicate that facial stimuli elicit N170 and N400 components, in addition to the P300 component associated with P300 BCI. These additional components can increase speed and accuracy. Our study investigated the influence of image size and content using four conditions: large face, small face, large non-face, and small non-face. We predicted faces would provide higher accuracy than non-face stimuli and larger stimuli would provide higher accuracy than small stimuli. We found no significant difference in performance between conditions; however, significant waveform differences were found in each condition.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18500号 / 医博第3920号 / 新制||医||1005(附属図書館) / 31386 / 京都大学大学院医学研究科医学専攻 / (主査)教授 福山 秀直, 教授 河野 憲二, 教授 宮本 享 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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Mindfulness and cognitive control are overlapping constructs. Mindfulness involves maintaining awareness of the current experience by sustaining attention to relevant information and disengaging from irrelevant information. Cognitive control refers to the set of processes involved in selecting and monitoring information relevant to our goals, while ignoring or inhibiting information irrelevant to these goals. This dissertation contains three studies that examine the convergence between mindfulness and cognitive control. The first study examined the relationship between self-reported mindfulness and behavioural correlates of cognitive control using the Digit Stroop task within two experimental contexts: when task difficulty was not manipulated (non-titrated) and when task difficulty was increased (titrated). The results demonstrate that self-reported mindfulness predicted behavioural performance, but only when cognitive control processes were sufficiently challenged by increasing task difficulty. The second study examined the precise neural mechanisms underlying the relationship between mindfulness and cognitive control using electroencephalography (EEG) to identify changes to event-related potentials (ERPs) during the non-titrated Digit Stroop task after two weeks of daily training. By introducing a novel active control training condition (guided visual imagery meditation) that contrasted passive attention regulation with the focused attention regulation in mindfulness, the results isolated electrophysiological correlates of cognitive control that were uniquely tied to mindfulness training, including increased efficiency in conflict detection, delayed attentional capture by incongruent stimuli, faster conscious evaluation of all stimuli, and delayed automatic detection of all errors. The third study replicated and extended these findings by examining changes to ERPs when the cognitive control system was challenged using the titrated Digit Stroop task. Compared to the active control group, the mindfulness group showed enhanced sensory processing, resistance to stimulus-driven attentional capture and faster conscious evaluation of all stimuli after training. Taken together, this dissertation establishes an empirical relationship between behavioural and electrophysiological correlates of mindfulness and cognitive control. / Thesis / Doctor of Philosophy (PhD) / Mindfulness is a way of paying attention, on purpose, in the present-moment and nonjudgmentally. By focusing attention on present goals and redirecting attention from distractions, mindfulness enhances moment-to-moment awareness of fluctuations in cognitive demands. As a result, meditators can develop greater control over a set of cognitive processes that promote useful behavioural responses. This deliberate practice overlaps with a construct known as “cognitive control”—a set of cognitive processes that facilitate information processing and behaviour to vary adaptively from moment to moment depending on current goals. This dissertation examines the relationship between mindfulness and cognitive control using electroencephalography (EEG) to record ongoing brain activity during two variations of a cognitive control task designed to manipulate difficulty. The results show that self-reported mindfulness predicts cognitive control performance when task difficulty is increased and that two weeks of daily mindfulness training leads to changes in neural activity underlying this cognitive control performance.

Several major functional networks in the brain have been identified, based on sub-regions in the brain that display functionally correlated, synchronous activity and perform common cognitive functions. Three such brain networks (default mode network - DMN, central executive network - CEN, and salience network - SN) form a tri-network model of higher cognitive functioning and are found to be dysregulated in a number of psychopathologies, such as PTSD, autism, schizophrenia, anxiety, depression, bipolar disorder and fronto-temporal dementia (FTD). Current therapies that improve the patient’s cognitive and behavioural states are also found to re-normalize these dysregulated networks, suggesting a correlation between network dysfunction and behavioural dysregulation. Hence, assessing tri-network activity and its dynamics can be a powerful tool to objectively assess treatment response in such psychopathologies. Doing so would most likely rely on functional magnetic resonance imaging (fMRI), as one of the most commonly used modalities for studying such brain networks. While fMRI allows for superior spatial resolution, it poses serious challenges to widespread clinical adoption due to MRI's high operational costs and poor temporal resolution of the acquired signal. One potential strategy to overcome this shortcoming is by identifying the activity of these networks using their EEG-based temporal signatures, greatly reducing the cost and increasing accessibility of using such measures. This thesis takes a step towards improving the clinical accessibility of such brain network-based biomarkers. Doing so first required the exploration of a popular EEG-based method currently being used to study brain networks in mental health disorders - Microstates. This work uncovered flaws in the core assumptions made in assessing Microstates, necessitating the development of an alternate method to detect such network activity using EEG. To accomplish this, it was important to understand the healthy dynamics between the three brain networks constituting the tri-network model and test one of the core predictions of this model, i.e. the SN gates the DMN and CEN activation based on interoceptive and exteroceptive task demands. Probing this question next uncovered mechanistic details of this process, discovering that the SN co-activates with the task-relevant network. Using this information, a novel machine learning pipeline was developed that used simultaneous EEG-fMRI data to identify EEG-based signatures of the three networks within the tri-network model, and could use these signatures to predict network activation. Finally, the novel machine learning pipeline was trialed in a study investigating the effects of lifestyle interventions on the network dynamics, showing that CEN-SN synchrony can predict response to intervention, while DMN-SN synchrony can develop in those that fail to respond. The understanding of healthy network dynamics gathered from the earlier study helps interpret these results, suggesting that the non-responders persistently activated DMN as a maladaptive strategy. In conclusion, the studies discussed in this thesis have improved our understanding of healthy network dynamics, uncovered critical flaws in currently popular methods of EEG-based network analysis, provided an alternative methodology to assess network dynamics using EEG, and also validated its use in tracking changes in network synchrony. The identified EEG signatures of widely used functional networks, will greatly increase the clinical accessibility of such brain network measures as biomarkers for neuropathologies. Monitoring the level of network activity in affected subjects may also lead to the development of novel individualized treatments such as brain network-based neurofeedback interventions. / Thesis / Doctor of Philosophy (PhD) / Synergistic activity in specific brain regions gives rise to large-scale brain networks, linked to specific cognitive tasks. Interactions between three such brain networks are believed to underlie healthy behavior and cognition, and these are found to be disrupted in those with mental health disorders. The ability to cheaply and effectively detect these networks can enable routine network-based clinical assessments, improving diagnosis of mental health disorders and tracking their response to treatment. The first study in this thesis found major flaws in a popular method to assess these networks using a suitably cheap imaging method called electroencephalography(EEG). The remainder of the thesis addressed these issues by first identifying healthy patterns of network activity, followed by designing a novel method to identify network activity using EEG. The final study validates the developed method by tracking network changes after lifestyle interventions. In sum, this thesis takes a step towards improving the clinical accessibility of such brain network-based biomarkers.

Humans are social beings whose interaction with others constitutes an important part of our well-being. In these social interactions there are certain factors that are essential for us to feel comfortable. One of these factors is to keep a proper “breathing space”. A physical distance to whom we interact, to not have our personal space violated. This space we keep to others is called interpersonal distance (IPD) and might be altered depending on the situation. In the recent decade the neural correlates of IPD have been investigated. The current systematic review aimed to investigate the existing literature on the neural correlations of IPD and how it relates to IPD-behaviour. A systematic search was made in the electric databases Scopus and PubMed. Nine articles remained to be reviewed after screening and selection was done.The results showed the superior parietal cortex, the medial prefrontal cortex, motor areas, occipital areas, and the amygdala to be the most prominent structural brain areas to be involved in IPD. Some functional connections between mentioned brain areas were found but needs to be replicated for better knowledge. The review provides insight into the neural nature of IPD and its behavioural basis.