Major depressive disorder (MDD) is considered the current leading cause of disability worldwide (Friedrich, 2017), yet the recovery process in MDD, including neurobiological underpinnings, clinical features and optimal approaches to treatment remains ambiguous. Current definitions of recovery are disputed and involve measures considered subjective in nature, such as thresholds for questionnaires and clinical interviews of symptoms and their duration (De Zwart and Jeronimus, 2019; Fava et al., 2007; Keller, 2003, 2004). Symptom-based measures, although informative of clinical presentation, are not informative of neurobiological underpinnings that may persist even when symptoms are reduced. Indeed, even after treatment, persistent residual symptoms, impairments in quality of life, and vulnerabilities for future return to more severe psychopathology persist (Gotlib and Hammen, 2008; IsHak et al., 2011; Judd et al., 1998a; Kennedy et al., 2004; Kennedy and Foy, 2005; Kennedy and Paykel, 2004). Without assessment of neural mechanisms of recovery in MDD, efforts toward developing novel treatment approaches that are able to address neural processes of illness and to provide sustained remission are slowed. The following collection of studies provide neural and clinical insights into MDD recovery and relate findings to potential treatment approaches that are optimized to individual differences in symptoms and neural functioning and able to address neural vulnerabilities to provide sustained remission. In pursuit of individualized treatment selection in MDD, study one involved a meta-analysis of prior prognostic fMRI studies of response to cognitive behavioral therapy (CBT) or a selective serotonin reuptake inhibitor (SSRI) in MDD. Study one also reported on the application of resulting meta-analytic regions (subgenual and perigenual anterior cingulate cortex) in a confirmatory MDD sample. Although regions showed some predictive potential in the confirmatory sample, when predicting SSRI response, effects were inconsistent with prior studies, suggesting methodological confounds may hinder ready translation. In an assessment of the course of MDD, the second study documented depression symptoms and quality of life across 9-14 years after acute treatment (CBT or SSRI) and found that persistent residual depression symptoms and quality of life deficits were common. In light of the normality of chronic symptoms and impairment, the third study evaluated neural features of treatment (CBT) resistance in MDD within the context of neural mechanisms of change. The third study found a vermis-centered cerebellar cluster that was unresponsive to CBT, whereas prefrontal and parietal cortical regions were responsive, providing support of prior theories that CBT directly affects cognitive control and cortical regulatory processes in contrast to salience-driven subcortical functioning (Clark and Beck, 2010; DeRubeis et al., 2008; Frewen et al., 2008; Mayberg, 2003). In consideration of findings, clinical recommendations that pertain to treating residual symptoms and associated neural features toward asymptomatic remission are provided. Future research directions are also provided regarding neuroscience informed precision medicine, current therapy and medication practices, and the larger picture of MDD chronicity broadly. / Doctor of Philosophy / Major depressive disorder (MDD) is considered the leading cause of disability worldwide (Friedrich, 2017), yet there are many aspects of MDD recovery that are unclear such as neural and clinical features and optimal treatment approaches. Current definitions of recovery involve questionnaires and interviews, which may not accurately represent all aspects of recovery (De Zwart and Jeronimus, 2019; Fava et al., 2007; Keller, 2003, 2004). For example, they do not assess neural or biological features of recovery that may continue even if symptoms improve. Indeed, even after treatment, often some minimal depression symptoms, impairments in quality of life, and risks for future more severe symptoms continue (Gotlib and Hammen, 2008; IsHak et al., 2011; Judd et al., 1998a; Kennedy et al., 2004; Kennedy and Foy, 2005; Kennedy and Paykel, 2004). Without assessing neural features of MDD and recovery, developing treatments that can address illness- related neural features and provide sustained recovery are slowed. The following studies report on neural and clinical features of MDD recovery to approach treatment and sustained recovery with consideration of individual differences in symptoms and neural functioning. Pursuing neuroimaging measures of individual differences to inform treatment selection, study one involved a statistical review of prior neuroimaging prediction studies of MDD treatment. Study one also reported on whether the regions suggested by the statistical review to inform treatment selection would be useful when applied to a prior MDD treatment study. Findings suggested functioning of the identified brain regions can help inform treatment selection, but method differences among studies included in the review hinder application of resulting regions. In an assessment of the course of MDD, the second study documented depression symptoms and quality of life across 9-14 years after treatment and found at least minimal depression symptoms as well as impairments in quality life commonly continued after treatment. In light of persistent symptoms and impairment, the third study aimed to identify neural features of MDD that did not respond to treatment, as well as neural features that were responsive to treatment. The third study found that therapy directly affects cognitive control processes, but may not affect brain regions associated more with emotion-driven processes. Clinical recommendations pertain to treating depression symptoms that continue after treatment toward asymptomatic recovery. Future research directions pertain to neuroscience informed treatment selection, current therapy and medication practices, and the larger picture of persistent depression symptoms broadly.

Sustainability is inherently a complex problem that requires new ways of thinking. To solve grand challenges such as climate change, environmental degradation, and poverty, engineers cannot rely on the same models of thinking that were used to create these problems. Engineering education is therefore critical to advance sustainable engineering solutions. Improving education relies on understanding of cognition of thinking and designing for sustainability. In this thesis, a nascent neuroimaging technology called functional near-infrared spectroscopy (fNIRS) was used to measure cognition among engineering students thinking about sustainability. fNIRS provides an opportunity to investigate how sustainability in design influences cognition, and how different concept generation techniques help students consider many aspects related to sustainability. The first manuscript provides evidence that engineering students perceive sustainability in design as a constraint, limiting the number of solutions for design and decreasing the cognitive efficiency to generate solutions. Senior engineering students generated fewer solutions than freshmen, however, seniors were better able to cognitively manage the sustainability parameter with higher cognitive efficiency. The second manuscript investigates the cognitive difference when generating concepts using concept listing or concept mapping. The results indicate that concept mapping (i.e. intentionally drawing relationships between concepts) leads to more concepts generated. An increase in concepts during concept mapping was also observed to shift cognitive load in the brain from regions associated with process sequencing to regions associated with cognitive flexibility. This research demonstrates the feasibility of fNIRS applied in engineering research and provides more understanding of the cognitive requirements for sustainability thinking. / M. S.

The Blood Oxygen Level Dependent (BOLD) signal provides indirect estimates of neural activity. The parameters of this BOLD signal can give information about the pathophysiological state of the brain. Most of the models for the BOLD signal are overparameterized which makes the unique identification of these parameters difficult. In this work, we use information from multiple neu- roimaging sources to get better estimates of these parameters instead of relying on the information from the BOLD signal only. The mulitmodal neuroimaging setup consisted of the information from Cerebral Blood Volume (CBV) ( VASO-Fluid-Attenuation-Inversion-Recovery (VASO-FLAIR)), and Cerebral Blood Flow (CBF) (from Arterial Spin Labelling (ASL)) in addition to the BOLD signal and the fusion of this information is achieved in a Particle Filter (PF) framework. The trace plots and the correlation coefficients of the parameter estimates from the PF reflect ill-posedness of the BOLD model. The means of the parameter estimates are much closer to the ground truth compared to the estimates obtained using only the BOLD information. These parameter estimates were also found to be more robust to noise and influence of the prior. / Master of Science

This dissertation investigates the effects of multisensory biophilic restorative experiences on how engineers feel, think, and design. While previous research on the restorative effects of biophilic experiences have mostly focused on the benefits of visual exposure, less is known about the potential of exposure to auditory and multisensory stimuli. Moreover, a knowledge gap exists in regards to how the cognitive benefits of biophilic restorative experiences influence performance in real-world cognitive tasks, like design. To address the identified knowledge gaps, a randomized controlled trial with 154 participants was conducted, exploring the restorative effects of biophilic auditory, visual, and multisensory (auditory + visual) experiences after induced psychosocial stress. To assess the potential influence on the performance of a real-world cognitive tasks, an open-ended design task was given to participants following the exposure period. Dependent variables tracked covered three key domains of the research question: (1) psychological and physiological responses (feel), (2) neurocognitive responses (think), and (3) design originality and incorporation of biophilia (design). Results showed that the biophilic auditory experience induced higher physiological arousal during and after exposure, while the visual and multisensory conditions presented evidence of increased neural efficiency. The biophilic conditions assisted in restoring cognitive resources and improved prefrontal cortical functional connectivity, specifically within main hubs of the Default Mode Network (DMN). However, better engagement of the DMN did not result in more original design products. No significant differences were found for exploration of the design space across conditions. Interestingly, the visual group incorporated significantly more biophilic design patterns, such as "Visual Connection with Nature" and "Presence of Water", in their design concepts. This finding suggests a potential priming effect, where exposure to biophilic stimuli influenced designers' choices towards more nature-connected ideas. The study here presented contributes to the understanding of biophilic restorative experiences' nuanced effects on physiology, neurocognition, and design cognition. Accessibility and availability of the interventions tested affords readily replication of the experiment design and application of findings to the general public. / Doctor of Philosophy / This dissertation explores how exposure to simulated nature experiences through different senses affects how engineers feel, think, and design. Two main environmental psychology theories propose that nature experiences can aid in the recovery from stressful states and mental fatigue. The Kaplans' Attention Restoration Theory suggests that looking at nature can help the brain recover from overuse by allowing it to restore attentional resources. Roger Ulrich's Stress Reduction Theory proposes that nature experiences can reduce stress by calming the body and the mind. While previous studies have mainly explored the effects of exposure to nature through visual experiences, the study presented here examines the effects of exposure to nature-based sounds (birdsong and water sounds), as well as exposure to the combination of sounds and visuals (indoor plants, nature-inspired art, and daylight). Additionally, it investigates how the potential benefits to the brain and mind influence performance in real-world tasks like designing. To explore these effects, 154 engineering students were randomly assigned to different groups and exposed to nature sounds, nature visuals, or a combination of both, after being induced to a stressful state. After the exposure period, participants were given an open-ended design task. Throughout the experiment, participants' bodily responses were tracked by a wrist-worn device and participants' brain activity was tracked by a brain-imaging headset. Design concepts produced in the design task were assessed for originality and for the incorporation of nature-inspired ideas. Results showed that listening to nature sounds increased arousal of the body both during and after the exposure period. Visual, as well as combined auditory and visual exposure improved brain efficiency. All nature experiences helped restore mental resources and improved brain connectivity, particularly in areas associated with mind wandering. Although better brain connectivity did not result in more original design concepts, interestingly, participants in the visual exposure group incorporated more nature-related features, like bodies of water and natural views, into their designs. This finding suggests that seeing nature might inspire more nature-connected design ideas. This study enhances our understanding of how nature experiences affect the body, brain, and mind. The interventions tested can be easily replicated and applied in everyday settings so that anyone can benefit off of their outcomes.

This paper presents the Social Phobia Psychotherapy Research Network. The research program encompasses a coordinated group of studies adopting a standard protocol and an agreed-on set of standardized measures for the assessment and treatment of social phobia (SP). In the central project (study A), a multicenter randomized controlled trial, refined models of manualized cognitive-behavioral therapy and manualized short-term psychodynamic psychotherapy are compared in the treatment of SP. A sample of 512 outpatients will be randomized to either cognitive-behavioral therapy, short-term psychodynamic psychotherapy or waiting list. Assessments will be made at baseline, at the end of treatment and 6 and 12 months after the end of treatment. For quality assurance and treatment integrity, a specific project using highly elaborated measures has been established (project Q). Study A is complemented by 4 interrelated add-on projects focusing on attachment style (study B1), on cost-effectiveness (study B2), on variation in the serotonin transporter gene in SP (study C1) and on structural and functional deviations of the hippocampus and amygdala (study C2). Thus, the Social Phobia Psychotherapy Research Network program enables a highly interdisciplinary research into SP. The unique sample size achieved by the multicenter approach allows for studies of subgroups (e.g. comorbid disorders, isolated vs. generalized SP), of responders and nonresponders of each treatment approach, for generalization of results and for a sufficient power to detect differences between treatments. Psychological and biological parameters will be related to treatment outcome, and variables for differential treatment indication will be gained. Thus, the results provided by the network may have an important impact on the treatment of SP and on the development of treatment guidelines for SP. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

This paper presents the Social Phobia Psychotherapy Research Network. The research program encompasses a coordinated group of studies adopting a standard protocol and an agreed-on set of standardized measures for the assessment and treatment of social phobia (SP). In the central project (study A), a multicenter randomized controlled trial, refined models of manualized cognitive-behavioral therapy and manualized short-term psychodynamic psychotherapy are compared in the treatment of SP. A sample of 512 outpatients will be randomized to either cognitive-behavioral therapy, short-term psychodynamic psychotherapy or waiting list. Assessments will be made at baseline, at the end of treatment and 6 and 12 months after the end of treatment. For quality assurance and treatment integrity, a specific project using highly elaborated measures has been established (project Q). Study A is complemented by 4 interrelated add-on projects focusing on attachment style (study B1), on cost-effectiveness (study B2), on variation in the serotonin transporter gene in SP (study C1) and on structural and functional deviations of the hippocampus and amygdala (study C2). Thus, the Social Phobia Psychotherapy Research Network program enables a highly interdisciplinary research into SP. The unique sample size achieved by the multicenter approach allows for studies of subgroups (e.g. comorbid disorders, isolated vs. generalized SP), of responders and nonresponders of each treatment approach, for generalization of results and for a sufficient power to detect differences between treatments. Psychological and biological parameters will be related to treatment outcome, and variables for differential treatment indication will be gained. Thus, the results provided by the network may have an important impact on the treatment of SP and on the development of treatment guidelines for SP. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease. Many researchers believe that an effective AD treatment will prevent the development of disease rather than treat the disease after a diagnosis. Therefore, the development of tools to detect AD-related pathology in early stages is an important goal. In this report, MRI-based markers of neurodegeneration are explored as biomarkers of AD. In the first chapter, the sensitivity of cross-sectional MRI biomarkers to neurodegenerative changes is evaluated in AD patients and in patients with a diagnosis of mild cognitive impairment (MCI), a prodromal stage of AD. The results in Chapter 1 suggest that cross-sectional MRI biomarkers effectively measure neurodegeneration in AD and MCI patients and are sensitive to atrophic changes in patients who convert from MCI to AD up to 1 year before clinical conversion. Chapter 2 investigates longitudinal MRI-based measures of neurodegeneration as biomarkers of AD. In Chapter 2a, measures of brain atrophy rate in a cohort of AD and MCI patients are evaluated; whereas in Chapter 2b, these measures are assessed in a pre-MCI stage, namely older adults with cognitive complaints (CC) but no significant deficits. The results from Chapter 2 suggest that dynamic MRI-based measures of neurodegeneration are sensitive biomarkers for measuring progressive atrophy associated with the development of AD. In the final chapter, a novel biomarker for AD, visual contrast sensitivity, was evaluated. The results demonstrated contrast sensitivity impairments in AD and MCI patients, as well as slightly in CC participants. Impaired contrast sensitivity was also shown to be significantly associated with known markers of AD, including cognitive impairments and temporal lobe atrophy on MRI-based measures. The results of Chapter 3 support contrast sensitivity as a potential novel biomarker for AD and suggest that future studies are warranted. Overall, the results of this report support MRI-based measures of neurodegeneration as effective biomarkers for AD, even in early clinical and preclinical disease stages. Future therapeutic trials may consider utilizing these measures to evaluate potential treatment efficacy and mechanism of action, as well as for sample enrichment with patients most likely to rapidly progress towards AD.

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La variabilité inter-individuelle est un obstacle majeur à l'analyse d'images médicales, en particulier en neuroimagerie. Il convient de distinguer la variabilité naturelle ou statistique, source de potentiels effets d'intérêt pour du diagnostique, de la variabilité artefactuelle, constituée d'effets de nuisance liés à des problèmes expérimentaux ou techniques, survenant lors de l'acquisition ou le traitement des données. La dernière peut s'avérer bien plus importante que la première : en neuroimagerie, les problèmes d'acquisition peuvent ainsi masquer la variabilité fonctionnelle qui est par ailleurs associée à une maladie, un trouble psychologique, ou à l'expression d'un code génétique spécifique. La qualité des procédures statistiques utilisées pour les études de groupe est alors diminuée car lesdites procédures reposent sur l'hypothèse d'une population homogène, hypothèse difficile à vérifier manuellement sur des données de neuroimagerie dont la dimension est élevée. Des méthodes automatiques ont été mises en oeuvre pour tenter d'éliminer les sujets trop déviants et ainsi rendre les groupes étudiés plus homogènes. Cette pratique n'a pas entièrement fait ses preuves pour autant, attendu qu'aucune étude ne l'a clairement validée, et que le niveau de tolérance à choisir reste arbitraire. Une autre approche consiste alors à utiliser des procédures d'analyse et de traitement des données intrinsèquement insensibles à l'hypothèse d'homogénéité. Elles sont en outre mieux adaptées aux données réelles en ce qu'elles tolèrent dans une certaine mesure d'autres violations d'hypothèse plus subtiles telle que la normalité des données. Un autre problème, partiellement lié, est le manque de stabilité et de sensibilité des méthodes d'analyse au niveau voxel, sources de résultats qui ne sont pas reproductibles.Nous commençons cette thèse par le développement d'une méthode de détection d'individus atypiques adaptée aux données de neuroimagerie, qui fournit un contrôle statistique sur l'inclusion de sujets : nous proposons une version regularisée d'un estimateur de covariance robuste pour le rendre utilisable en grande dimension. Nous comparons plusieurs types de régularisation et concluons que les projections aléatoires offrent le meilleur compromis. Nous présentons également des procédures non-paramétriques dont nous montrons la qualité de performance, bien qu'elles n'offrent aucun contrôle statistique. La seconde contribution de cette thèse est une nouvelle approche, nommée RPBI (Randomized Parcellation Based Inference), répondant au manque de reproductibilité des méthodes classiques. Nous stabilisons l'approche d'analyse à l'échelle de la parcelle en agrégeant plusieurs analyses indépendantes, pour lesquelles le partitionnement du cerveau en parcelles varie d'une analyse à l'autre. La méthode permet d'atteindre un niveau de sensibilité supérieur à celui des méthodes de l'état de l'art, ce que nous démontrons par des expériences sur des données synthétiques et réelles. Notre troisième contribution est une application de la régression robuste aux études de neuroimagerie. Poursuivant un travail déjà existant, nous nous concentrons sur les études à grande échelle effectuées sur plus de cent sujets. Considérant à la fois des données simulées et des données réelles, nous montrons que l'utilisation de la régression robuste améliore la sensibilité des analyses. Nous démontrons qu'il est important d'assurer une résistance face aux violations d'hypothèse, même dans les cas où une inspection minutieuse du jeu de données a été conduite au préalable. Enfin, nous associons la régression robuste à notre méthode d'analyse RPBI afin d'obtenir des tests statistiques encore plus sensibles.

INTRODUCTION: The association of Sleep-Related Movement Disorders (SRMDs) such as Restless Legs Syndrome (RLS) and Periodic Limb Movements (PLMs) with cerebrovascular disease is underexplored. Emerging evidence links them to vascular disease, for which white matter hyperintensities (WMHs) are a well-recognized biomarker. METHODS: We conducted a cross-sectional hospital-based observational study in which high-risk TIA and minor stroke patients were assessed for vascular risk factors, WMHs and polysomnography-determined sleep variables. RESULTS: Ninety-seven patients were enrolled, of whom 44 completed polysomnography. Twenty-five percent had RLS, which was associated with lower quality of life. Independent of the effect of classical vascular risk factors, PLMs (but not RLS) were associated with WMHs on linear regression analyses (p=0.016). CONCLUSIONS: SRMDs are prevalent after minor stroke/TIA. RLS is associated with poor quality of life, while PLMs are associated with WMHs. Whether PLMs are implicated in the pathogenesis of WMHs or whether WMHs exacerbate PLMs remains uncertain.