The functional neuroanatomy of language localization in dyslexia has primarily been studied in the context of reading. However, dyslexia is sometimes referred to as a “language-based learning disability,” yet the functional signature of the core language comprehension network in dyslexia is far less understood. This thesis presents a series of studies designed to compare the functionality of the brain regions supporting linguistic processing between typical and impaired readers in order to characterize the cortical language network in dyslexia. First, we investigate the extent to which the efficiency (or quality of data vs. amount of scan time) of a functional language localizer based on passive spoken language comprehension can be maximized in Chapter 2. By demarcating the language network based on smaller amounts of data and testing stability and reliability within this framework, we found that scan time can be substantially reduced without sacrificing functional specialization for language. In Chapter 3, we apply the spoken language localizer to determine differences in functional organization of language in dyslexia and provide evidence that the core spoken language comprehension network is not markedly different between typical readers and those with dyslexia. We compared the individual activations from whole-brain analysis and functional profiles in the canonical language-selective regions and found that the functional response of localized language regions in individuals with dyslexia was as selective as in typically reading adults. Chapter 4 follows up on the functional evidence reported in Chapter 3 to examine the structural connectivity within the same functional language network and additionally found essentially no differences between controls and dyslexia, further supporting the observations made in Chapter 3 that core linguistic processing is intact in dyslexia. All together, these findings converge on the suggestion that individuals with dyslexia do not rely on a separate cognitive architecture for language, potentially revealing important new insight into the dissociation of language specialization abilities and reading difficulty in dyslexia.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第18876号 / 医博第3987号 / 新制||医||1008(附属図書館) / 31827 / 京都大学大学院医学研究科医学専攻 / (主査)教授 髙橋 良輔, 教授 小泉 昭夫, 教授 宮本 享 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20098号 / 情博第630号 / 新制||情||109(附属図書館) / 33214 / 京都大学大学院情報学研究科システム科学専攻 / (主査)教授 石井 信, 教授 松田 哲也, 教授 加納 学 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(文学) / 甲第20828号 / 文博第758号 / 新制||文||655(附属図書館) / 京都大学大学院文学研究科行動文化学専攻 / (主査)教授 蘆田 宏, 教授 板倉 昭二, 教授 Anderson James Russell, 准教授 ALTMANN Christian / 学位規則第4条第1項該当 / Doctor of Letters / Kyoto University / DFAM

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The purpose of this project was to compare the Attention to Memory (AtoM) and the Episodic Buffer (EB) Models. The AtoM model proposes that the ventral parietal cortex (VPC) and dorsal parietal cortex (DPC) are responsive to bottom-up and top- down attention to memory, respectively, (Cabeza, 2008; Cabeza et al., 2008; Ciaramelli et al., 2008). The EB model, on the other hand, proposes that the VPC is involved in the episodic buffer component of Baddeley's working memory model (Vilberg & Rugg, 2008). Using objective (source) and subjective (Remember/Know) retrieval tasks, specific patterns of PPC activity were posited based on the propositions of the AtoM model. These expectations included greater VPC activity for Remember and False Alarms compared to Correct Rejections and Subjective Know, greater DPC activity for Know and Objective Remember compared to Subjective Remember and correct rejections, and no difference in VPC activity for remembering both font and color compared to remembering only one contextual detail. During encoding participants saw words in one of two colors, red or yellow, and in one of two fonts, curvy or straight, and were required to indicate the color the word was presented in. Following each encoding scan participants performed either an Objective or Subjective retrieval task. During Objective retrieval task, participants performed a forced-choice source memory test choosing the word with the correct fontand color or the "new" option. During Subjective retrieval participants were presented with the word in a neutral font and white color and performed a Remember/Know test. On the Subjective retrieval task both VPC and DPC were active for recollection compared to familiar items and Correct Rejections. On the Objective retrieval task the DPC was active for all correct old responses. Neither the VPC nor the DPC were significantly active for False Alarms on both the Subjective and Objective tasks. Both VPC and DPC were more active for Subjective Remember compared to Objective Remember response. Neither PPC region was more active for remembering font and color compared to remembering only font or color. Memory load effects for retrieval of information from long-term memory were only seen in the hippocampus on the Subjective retrieval task. These patterns of activity support the role of the VPC in recollection, as seen on the Subjective task, and the role of the DPC in familiarity, as shown in both the Subjective and Objective tasks. The role of the VPC and DPC during recollection and familiarity processing supports both the AtoM and the EB model. The key predictor of the Episodic Buffer model, memory load effects, was not supported and provides the only evidence against one of the two proposed models. Future work should examine the role of the posterior parietal cortex in spontaneous episodic retrieval to assess the validity of the AtoM model. Advanced imaging analysis techniques should be used to determine functional connectivity between the PPC and frontal and temporal memory regions.

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.

Part 1 - Background: Maternal prenatal adversity often results in changes to the hypothalamic- pituitary-adrenal axis (HPA axis) function, such as greater cortisol secretion. Recent evidence suggests that fetal exposure to elevated cortisol levels may cause structural changes to key limbic regions integral to regulation of the HPA axis such as the amydala and hippocampus in children. In the early postnatal months these same structures are particularly vulnerable to the quality of maternal care and parenting styles. However, the relative impact and interaction of such factors is still underreported. Methods: 24 healthy 7-8 year old children (male:female=13:11) underwent an MRI. Amygdala and hippocampal volumes were assessed and used in multiple regression models to determine the impact of prenatal cortisol and postnatal maternal sensitivity. Results: Larger right hippocampal volumes were associated with increases in late gestation cortisol levels (4.6 mm3/nmol of cortisol; FDR corrected p<0.005). Increases in 6th month maternal sensitivity predicted a decrease in right hippocampal volumes at a trend level (FDR corrected p=0.09). There was no interaction effect between cortisol and sensitivity. There were no significant effects on left hippocampus or bilateral amygdala volumes. No sex differences were noted. Discussion: Given previous work we had expected greater amygdala volume and reduced hippocampal volumes to associate with increases in cortisol and decreases in sensitivity. Our results suggest that there may indeed be a programming effect on children’s hippocampi by prenatal cortisol. Findings may be reflective of a positive adaptive response or resilience to adverse prenatal environments. Part 2 - Introduction: Emotion regulation (ER) is an integral component to mental health. ER is thought to incorporate limbic as well prefrontal regions in several cognitive top-down circuits to utilize higher-order executive functions to adequately monitor and inhibit emotion when necessary. However, only recently has research targeted the developmental trajectories of these circuits from childhood. Methods: 29 healthy children aged 7-8 years (mean 7.34 ± 0.48) underwent functional magnetic resonance imaging (fMRI) with an implicit emotion go/nogo cognitive task to assess the developmental state and interaction between cognitive and emotional circuitry using functional connectivity (FC) in this age group. Results: Central executive networks (CEN) and salience networks (SN) showed more diffuse FC than mature networks, with greater inter-network connectivity. During exposure to fearful stimuli, there was greater connectivity within CEN and SN during go trials. Nogo trials were associated with more limbic-cognitive network interaction during concurrent exposure to fearful stimuli than neutral stimuli, Connectivity with the dACC was found to be common between limbic and CEN seeded networks. Discussion: Results indicate that cognitive networks are present but generally less mature than previous results from adult populations. Particularly, diffuse connectivity between the insula and PCC was negatively correlated indicating a developing switch between resting and salience networks. Additionally, greater connectivity for response inhibition tasks (nogo) during fearful stimuli exposure in the dACC, amygdala, anterior prefrontal, and DLPFC, suggests a maturing emotion regulation network, capable of managing cognitive tasks during emotional stimuli presentation. / Thesis / Master of Science (MSc)

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.