Background: Deficits in the processing of emotional stimuli have long been associated with major depressive disorder (MDD). These emotional biases are believed central to the symptomatology of MDD, with evidence growing that such biases can also be seen during remission. Although such changes are typical of psychiatric morbidity evidence is growing for the impact of ageing on emotional processing as well. Evidence shows that relative to younger adults, older adults demonstrate biases that favour positive information over negative. There is therefore overlap between MDD and normal ageing that has yet to be explored in the literature. Because positive biases are implicated in successful ageing it is important to consider the impact of previous MDD as individuals age. It is this question that is explored in this thesis. Study 1: A behavioural neuropsychological investigation was undertaken comparing older and younger adults with and without a history of MDD on a battery of affective cognitive tasks. Results suggested that the difference between the older adults with and without a history of MDD lay in their ability to disengage from negative information. Study 2: An fMRI investigation was undertaken in a subset of the study 1 sample using neuroimaging paradigms assessing memory encoding and attention for emotional stimuli. Broadly results suggested no influence of previous MDD on the processing of emotional information in the studied domains, with evidence seen in both tasks for the neural basis of the positivity effect of ageing. Study 3: A resting-state fMRI investigation of brain connectivity was undertaken to assess the influence of previous MDD and normal ageing on the communication structure of the brain. Results were largely suggestive of the influence of normal healthy ageing, with limited evidence of the influence of previous MDD or its interaction with ageing. Conclusions: Results were mixed across the investigations. Generally speaking the initial behavioural study was best powered to investigation the questions of interest, suggesting the potential for differential affective processing strategies in later life dependent on previous MDD. The subsequent imaging studies were perhaps less well placed to draw conclusions given limitations in terms of the domains investigated and the sample size. Evidence for the postulate that previous MDD impacts the development of the positivity effect has therefore been demonstrated, but for now remains limited to the behavioural domain.

Emotional intelligence (EI) is defined as an individual's capacity to accurately perceive, understand, reason about, and regulate emotions, and to apply that information to facilitate thought and achieve goals. Although EI plays an important role in mental health and success in academic, professional and social realms, the neurocircuitry underlying this capacity remains poorly characterized, and no study to date has yet examined the relationship between EI and intrinsic neural network function. Here, in a sample of 54 healthy individuals (28 women, 26 men), we apply independent components analysis (ICA) with dual regression to functional magnetic resonance imaging (fMRI) data acquired while subjects were resting in the scanner to investigate brain circuits (intrinsic resting state networks) whose activity is associated with greater self-reported (i.e. Trait) and objectively measured (i.e. Ability) EI. We show that higher Ability EI, but not Trait EI, is associated with stronger negatively correlated spontaneous fMRI signals between the basal ganglia/limbic network (BGN) and posterior default mode network (DMN), and regions involved in emotional processing and regulation. Importantly, these findings suggest that the functional connectivity within and between intrinsic networks associated with mentation, affective regulation, emotion processing, and reward are strongly related to ability EI.

Language areas of the brain can be mapped in individual participants with functional MRI. We investigated the validity and reliability of four language mapping paradigms that may be appropriate for individuals with acquired aphasia: sentence completion, picture naming, naturalistic comprehension, and narrative comprehension. Five neurologically normal older adults were scanned on each of the four paradigms on four separate occasions. Validity was assessed in terms of whether activation patterns reflected the known typical organization of language regions, that is, lateralization to the left hemisphere, and involvement of the left inferior frontal gyrus and the left middle and/or superior temporal gyri. Reliability (test-retest reproducibility) was quantified in terms of the Dice coefficient of similarity, which measures overlap of activations across time points. We explored the impact of different absolute and relative voxelwise thresholds, a range of cluster size cutoffs, and limitation of analyses to a priori potential language regions. We found that the narrative comprehension and sentence completion paradigms offered the best balance of validity and reliability. However, even with optimal combinations of analysis parameters, there were many scans on which known features of typical language organization were not demonstrated, and test-retest reproducibility was only moderate for realistic parameter choices. These limitations in terms of validity and reliability may constitute significant limitations for many clinical or research applications that depend on identifying language regions in individual participants. (C) 2016 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license

The feasibility of using real-time functional magnetic resonance imaging (fMRI) feedback regarding the level of activation in rostromedial prefrontal cortex (rMPFC) to learn improved regulation of this brain area was examined in a group of 5 young adults. Subjects received real-time feedback from the target brain region while engaging in a blocked-design task involving alternating blocks of attempted up-regulation and down-regulation of the target brain region. A transient negative emotional state was induced prior to each scanning session. Subjects completed 6 scanning sessions (a pre-training session, 4 feedback sessions and a post-training session - no feedback was provided for pre and post-training sessions). The guideline strategy provided to subjects of engaging in emotional awareness during up-regulation and bodily awareness during down-regulation was found to consistently regulate the region in the pre-training session prior to the fMRI feedback sessions. This finding is in line with the previously proposed role of the rMPFC in emotional awareness. In contrast to previous real-time fMRI findings, greater recruitment of the region was observed in the pre-training session compared to the post-training session, with a non-significant negative trend observed across feedback sessions. These results suggest that there may be limitations to which the feedback techniques successfully employed for other brain regions extend to yet unexplored brain regions. / Arts, Faculty of / Psychology, Department of / Graduate

Cognitive impairments and changes in the structure and function of related brain regions, namely the prefrontal cortex and striatum, have long been implicated in drug addiction. However, it is unknown whether these abnormalities predate substance abuse, potentially serving as risk factors for dependence, or if they are the consequence of protracted use. To address this question, endophenotype research using stimulant-dependent individuals’ biological siblings has been used to investigate traits implicated in the pathology of addiction. Impairments present in both groups suggest an underlying risk-state for dependence, while additional abnormalities present only in stimulant-dependent individuals reflect potential effects of the drugs themselves. Contrastingly, there are also individuals who use stimulant drugs in a controlled manner without developing dependence. These ‘recreational users’ may lack the underlying traits that comprise a greater risk for dependence, or they might maintain additional protective factors against the development of addiction. Experiments in the first half of this dissertation used functional magnetic resonance imaging to investigate neurocognitive similarities and differences between dependent stimulant users, their non-dependent siblings, recreational users of cocaine, and unrelated healthy control volunteers. In Chapter 2, performance on a colour-word Stroop task was impaired in both stimulant-dependent individuals and their siblings, suggesting an endophenotype of cognitive inefficiency. However, neural activity significantly differed between the groups, indicating additional changes specific to the use of stimulant drugs. In Chapter 3, dependent users showed significant attentional bias to salient stimuli on a cocaine-word Stroop task, with a concurrent increase in prefrontal activation. Conversely, recreational users showed resilience in the face of cocaine cues and a decrease in arousal. Finally, Chapter 4 explored differences in reward sensitivity to both generic and drug-specific reinforcers, comparing the effects of personal and family history of stimulant exposure on a monetary incentive delay task. It is also under debate whether the neurocognitive differences seen in stimulant-dependent individuals are unique to substance abuse, or if parallel changes in behaviour and neurobiology are present in similar addiction-spectrum disorders, such as binge eating leading to obesity. In Chapter 5, stimulant-dependent and obese individuals with binge-eating behaviours showed differences in their substance-specific and general reward responsivity on a novel reward-valuation task. However, in Chapter 6 a similar decline in orbitofrontal cortex grey matter volume in relation to both years of stimulant use and body mass index was identified, implicating an overlap in this area between both conditions. These findings are integrated in Chapter 7, discussing the neurocognitive risk and protective factors that underlie an individual’s vulnerability for addiction, not only to stimulant drugs, but also potentially for other addictive behaviours.

The aim of this investigation was to determine whether i) affective touch has a role in mediating beneficial social influences on resilience to depression and ii) whether affective touch acts through specific skin CT afferents to enhance central serotonin function. To develop and validate the Touch Experiences and Attitudes Questionnaire (TEAQ), 117 items about experiences and attitudes to touch were completed online by 618 participants. Principal components analysis reduced this to 57 items and 6 factors. Three factors concerned touch experienced; in social situations (CST), in intimate relationships (CIT) and during childhood (ChT) and 3 factors concerned attitude to touch; in intimate relationships (AIT), with unfamiliar people (AUT) and in Skin Care (ASkC). The shortened TEAQ was completed by a second sample of 704 participants. Confirmatory factor analysis found the 6 factor structure to be a good fit of the data, suggesting the TEAQ to be valid and reliable. Participants completed some demographic questions and some questionnaires to determine their current psychiatric symptoms, social circumstances, recent life events, childhood adversity and personality alongside the TEAQ. Currently depressed participants had lower touch scores for all factors compared to healthy controls. Remitted depressed participants had significantly lower touch scores on all factors except CST, ASkC and AIT compared to healthy controls. A multiple regression analysis found neuroticism, satisfaction with social support, recent life events, CIT and childhood adversity (CHA) to be predictive of depression, whereas extraversion, number of social supports, ChT and CST, did not significantly predict depression score. Logistic regression analysis found ChT, CHA and neuroticism to predict vulnerability to depression, but not AIT or AUT. It was concluded that CIT was the most important aspect of affective touch for promoting resilience to depression. The CNS effects of pleasant and unpleasant touch were investigated using fMRI in healthy female volunteers. It has been hypothesised that a novel class of CT afferent fibres in hairy skin encodes affective touch. Therefore, CNS responses to pleasant stroking of the forearm with stroking of the fingers were compared. No differential CNS effects of forearm stroking over finger stroking were seen. Indeed, more brain regions were activated by pleasant brush stroking of the fingers which lack CT afferents. Pleasant brush responses in left inferior frontal gyrus were attenuated by tryptophan depletion. However, the midbrain raphe was activated by unpleasant brush stroking and de-activated by pleasant and this effect was abolished by tryptophan depletion. This study found little evidence that CT afferents in hairy skin have a specific role in affective touch and serotonin cells of the raphe appear engaged by unpleasant stimuli rather than pleasant. In conclusion, the results of the questionnaire study indicated touch (hugs, kisses, stroking) in intimate relationships may promote resilience to depression whereas touch with other social contacts does not, suggesting type of affective touch to be important. It is suggested that future studies of the role of current social support and of early adversity in depression should include assessments of the correlated dimension of affective touch. The fMRI study found little evidence for a specific peripheral touch receptor encoding pleasant affective touch. The median raphe nucleus was inhibited by pleasant touch and this is in keeping with the idea that that aversive stimuli activate serotonin projections to the forebrain but not that this is strengthened by affective touch. Further investigation is required to identify CNS mechanisms of affective touch.

Performance in visual tasks improves with attention, and this improvement has been shown to stem, in part, from changes in sensory processing. However, the mechanism by which attention affects perception remains unclear. Considering that neurons within the visual areas are selective for basic image statistics, such as orientation or spatial frequency (SF), it is plausible that attention modulates these sensory preferences by altering their so-called ‘tuning curves’. The goal of this project is to investigate this possibility by measuring and comparing the SF tuning curves across a range of attentional states in humans. In Experiment 1, a model-driven approach to fMRI analysis was introduced that allows for fast and efficient estimation of population spatial frequency tuning (pSFT) for individual voxels within human visual cortices. Using this method, I estimated pSFTs within early visual cortices of 8 healthy, young adults. Consistent with previous studies, the estimated SF optima showed a decline with retinotopic eccentricity. Moreover, my results suggested that the bandwidth of pSFT depends on eccentricity, and that populations with lower SF peaks possess broader bandwidths. In Experiment 2, I proposed a new visual task, coined the Numerosity Judgement Paradigm (NJP), for fine-grained parametric manipulation of attentional load. Eight healthy, young adults performed this task in an MRI scanner, and the analysis of the BOLD signal indicated that the activity within the putative dorsal attention network was precisely modulated as a function of the attentional load of the task. In Experiment 3, I used the NJP to modulate attentional load, and exploited the model-based approach to estimate pSFTs under different attentional states. fMRI results of 9 healthy, young adults did not reveal any changes in either peak or the bandwidth of the pSFTs with attentional load. This study yields a full visuocortical map of spatial frequency sensitivity and introduces a new paradigm for modulating attentional load. Although under this paradigm I did not find any changes in SF preferences within human visual areas with attentional load, I cannot preclude the possibility that changes emerge under different attentional manipulations.

En imagerie médicale, des collaborations internationales ont lançé l'acquisition de centaines de Terabytes de données - et en particulierde données d'Imagerie par Résonance Magnétique fonctionelle (IRMf) -pour les mettre à disposition de la communauté scientifique.Extraire de l'information utile de ces données nécessite d'importants prétraitements et des étapes de réduction de bruit. La complexité de ces analyses rend les résultats très sensibles aux paramètres choisis. Le temps de calcul requis augmente plus vite que linéairement: les jeux de données sont si importants qu'il ne tiennent plus dans le cache, et les architectures de calcul classiques deviennent inefficaces.Pour réduire les temps de calcul, nous avons étudié le feature-grouping commetechnique de réduction de dimension. Pour ce faire, nous utilisons des méthodes de clustering. Nous proposons un algorithme de clustering agglomératif en temps linéaire: Recursive Nearest Agglomeration (ReNA). ReNA prévient la création de clusters énormes, qui constitue un défaut des méthodes agglomératives rapidesexistantes. Nous démontrons empiriquement que cet algorithme de clustering engendre des modèles très précis et rapides, et permet d'analyser de grands jeux de données avec des ressources limitées.En neuroimagerie, l'apprentissage statistique peut servir à étudierl'organisation cognitive du cerveau. Des modèles prédictifs permettent d'identifier les régions du cerveau impliquées dans le traitement cognitif d'un stimulus externe. L'entraînement de ces modèles est un problème de très grande dimension, et il est nécéssaire d'introduire un a priori pour obtenir un modèle satisfaisant.Afin de pouvoir traiter de grands jeux de données et d'améliorer lastabilité des résultats, nous proposons de combiner le clustering etl'utilisation d'ensembles de modèles. Nous évaluons la performance empirique de ce procédé à travers de nombreux jeux de données de neuroimagerie. Cette méthode est hautement parallélisable et moins coûteuse que l'état del'art en temps de calcul. Elle permet, avec moins de données d'entraînement,d'obtenir de meilleures prédictions. Enfin, nous montrons que l'utilisation d'ensembles de modèles améliore la stabilité des cartes de poids résultantes et réduit la variance du score de prédiction. / In medical imaging, collaborative worldwide initiatives have begun theacquisition of hundreds of Terabytes of data that are made available to thescientific community. In particular, functional Magnetic Resonance Imaging --fMRI-- data. However, this signal requires extensive fitting and noise reduction steps to extract useful information. The complexity of these analysis pipelines yields results that are highly dependent on the chosen parameters.The computation cost of this data deluge is worse than linear: as datasetsno longer fit in cache, standard computational architectures cannot beefficiently used.To speed-up the computation time, we considered dimensionality reduction byfeature grouping. We use clustering methods to perform this task. We introduce a linear-time agglomerative clustering scheme, Recursive Nearest Agglomeration (ReNA). Unlike existing fast agglomerative schemes, it avoids the creation of giant clusters. We then show empirically how this clustering algorithm yields very fast and accurate models, enabling to process large datasets on budget.In neuroimaging, machine learning can be used to understand the cognitiveorganization of the brain. The idea is to build predictive models that are used to identify the brain regions involved in the cognitive processing of an external stimulus. However, training such estimators is a high-dimensional problem, and one needs to impose some prior to find a suitable model.To handle large datasets and increase stability of results, we propose to useensembles of models in combination with clustering. We study the empirical performance of this pipeline on a large number of brain imaging datasets. This method is highly parallelizable, it has lower computation time than the state-of-the-art methods and we show that, it requires less data samples to achieve better prediction accuracy. Finally, we show that ensembles of models improve the stability of the weight maps and reduce the variance of prediction accuracy.

Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are two neurodevelopmental disorders with distinct diagnostic criteria that often co-occur. Although both ASD and ADHD are associated with structural and functional brain abnormalities when compared to typically developing (TD) populations, it is necessary to disentangle the shared and specific abnormalities between these disorders, potentially underlying similarities and differences in their clinical and neurocognitive profiles. The aim of this thesis was to explore the shared and disorder specific functional and structural brain abnormalities between ASD and ADHD. For this purpose, the neural underpinnings of a group of children with ADHD, a group of children with ASD and a group of TD children aged 8 to 12 years old were compared using different neuroimaging techniques. In Chapter 2, the experimental sample included in this thesis was described using multiple clinical and neurocognitive measures. In the first study (Chapter 3), total and regional brain volumes were compared between groups, using voxel-based morphometry. The results of this study showed larger grey matter volume (GMV) in the left precuneus and decreased GMV in the right thalamus in the ADHD group compared to either the TD or the ASD groups, and increased GMV in the right precentral gyrus in the ASD group compared to either the ADHD or the TD groups. In the second study (Chapter 4), white matter microstructure was compared between groups using diffusion tensor imaging derived indices (fractional anisotropy [FA] and mean diffusivity [MD]). Reduced FA (i.e. reduced diffusion directionality) in the genu of the corpus callosum (CC) was found in the ASD group compared to either children with ADHD or TD children, whereas lower FA in the body of the CC was a shared feature between the ADHD and ASD groups. Finally, in the last study (Chapter 5), inhibition-related brain activation was compared between groups during the execution of an inhibition stop-signal task. In children with ADHD, successful inhibition was associated with right inferior parietal activation, whereas right frontal regions were activated in children with ASD. Between-group comparisons disclosed higher middle frontal activation in the ASD group compared to the ADHD or the TD groups. Taken together, our findings provide further evidence contributing to disentangle the shared and specific brain structural and functional abnormalities between ASD and ADHD. / Doctorat en Sciences psychologiques et de l'éducation / info:eu-repo/semantics/nonPublished

京都大学 / 0048 / 新制・課程博士 / 博士(人間・環境学) / 甲第21860号 / 人博第889号 / 新制||人||213(附属図書館) / 2018||人博||889(吉田南総合図書館) / 京都大学大学院人間・環境学研究科共生人間学専攻 / (主査)教授 月浦 崇, 教授 齋木 潤, 教授 小村 豊, 教授 村井 俊哉 / 学位規則第4条第1項該当 / Doctor of Human and Environmental Studies / Kyoto University / DGAM