2,073 research outputs found
Using neurobiological measures to predict and assess trauma-focused psychotherapy outcome in youth with posttraumatic stress disorder
In this thesis we examined different predictive neurobiological measures of traumafocused psychotherapy response and investigated the biological mechanisms underlying trauma-focused psychotherapy response in youth with PTSD. Our results suggest that activity of the major neuroendocrine stress response systems and brain functional connectivity before treatment are indeed associated with trauma-focused treatment response. Moreover, trauma-focused psychotherapy response seems to be related to longitudinal changes in autonomic nervous system activity during stress and brain structure. Together, these findings improve our understanding of the relationship between neurobiological measures and traumafocused psychotherapy response in youth with PTSD. However, these insights have currently limited to no clinical value because the current state of evidence does not support implementation of neurobiological biomarkers for treatment selection and necessary trials of (augmentation) treatments targeting neurobiological mechanisms related to treatment response have not been performed yet. The way forward now, is to perform individual prediction studies in less heterogeneous patient samples and to perform developmentally informed long-term studies examining (neuro) developmental trajectories related to PTSD and treatment response. These studies are necessary to address whether neurobiological measures can eventually improve treatment outcome and reduce the burden of PTSD in affected youth
The aging trajectories of brain functional hierarchy and its impact on cognition across the adult lifespan
IntroductionThe hierarchical network architecture of the human brain, pivotal to cognition and behavior, can be explored via gradient analysis using restingstate functional MRI data. Although it has been employed to understand brain development and disorders, the impact of aging on this hierarchical architecture and its link to cognitive decline remains elusive.MethodsThis study utilized resting-state functional MRI data from 350 healthy adults (aged 20–85) to investigate the functional hierarchical network using connectome gradient analysis with a cross-age sliding window approach. Gradient-related metrics were estimated and correlated with age to evaluate trajectory of gradient changes across lifespan.ResultsThe principal gradient (unimodal-to-transmodal) demonstrated a significant non-linear relationship with age, whereas the secondary gradient (visual-to-somatomotor) showed a simple linear decreasing pattern. Among the principal gradient, significant age-related changes were observed in the somatomotor, dorsal attention, limbic and default mode networks. The changes in the gradient scores of both the somatomotor and frontal–parietal networks were associated with greater working memory and visuospatial ability. Gender differences were found in global gradient metrics and gradient scores of somatomotor and default mode networks in the principal gradient, with no interaction with age effect.DiscussionOur study delves into the aging trajectories of functional connectome gradient and its cognitive impact across the adult lifespan, providing insights for future research into the biological underpinnings of brain function and pathological models of atypical aging processes
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
Reduced emergent character of neural dynamics in patients with a disrupted connectome
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as “Integrated Information Decomposition,” which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems — including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients’ structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence
Multimodal view on resting-state brain activity in Parkinson’s disease: examining the relation between functional resting-state networks and metabolic network activity
Research focusing on the pathophysiology of neurodegenerative disorders has undergone a fundamental shift towards a network perspective in the last decades. Besides regional aggregation of misfolded proteins and changes in cellular metabolism, accompanying changes of synaptic activity evolve and evoke dysregulation within neural circuits including remote brain regions. Modern theories of neurodegeneration propose a stereotypic pattern of these cerebral pathologies, which partly are in vivo accessible by multimodal neuroimaging techniques. The most often used indirect measurement of functional network integrity is resting-state functional magnetic resonance imaging, which depends on a complex interplay of hemodynamics, blood volume, and blood flow. Less is known about a potential metabolic component underlying resting-state networks in healthy brains and changes thereof in neurodegeneration and the influence of different transmitter systems. The current work therefore sought to investigate the association between functional resting-state networks and metabolic network activity and focused on metabolic consequences of nigrostriatal and striatocortical dysfunction in Parkinson’s disease. In the current work, a multimodal data set of the TP10 KFO219 cohort was analyzed regarding 1) the impact of nigrostriatal dopamine depletion on resting-state networks and 2) the relation between changes in functional connectivity and metabolic network activity. The first study addressed the subset of the KFO219 TP10 cohort who completed the trimodal imaging protocol (42 patients vs. 14 controls). Dopamine deficiency in Parkinson’s patients was examined by voxel-wise comparison of 6-[18F]fluoro-L-Dopa positron emission tomography scans. Resulting clusters served as seeds for restingstate functional connectivity maps that were compared between both groups by voxelwise t-tests. Metabolic activity was extracted from 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography scans for respective cortical clusters with striatocortical dysconnectivity and the relation to functional connectivity values was analyzed. In a separate study, functional and metabolic resting-state networks were obtained by performing spatial independent component analyses in a subset of the same cohort who underwent 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography and functional magnetic resonance imaging (56 vs 16) and completed neuropsychological testing. Multimodally obtained regions of interest in the default mode network were defined and metabolic activity as well as metabolic connectivity compared to functional connectivity differences between patients without or with mild cognitive impairment and healthy controls. Moreover, a third study was initiated in the context of the present work with the aim of establishing a dynamic 2-[18F]fluoro-2-deoxy-D-glucose positronemission tomography acquisition with a constant infusion protocol for examining interregional metabolic connectivity on single subject level and enable comparable analysis of hemodynamic and metabolic fluctuations in Parkinson’s disease. In the first study, a significant association between striatocortical functional connectivity changes of the data-driven defined dopamine depleted posterior putamen and metabolic activity of the cortical target area in the inferior parietal cortex was found in Parkinson’s disease. Interestingly, striatocortical connectivity of the inferior parietal cortex was associated with motor and cognitive impairment. In a second study, the multivariate approach revealed a moderate spatial convergence for the posterior default mode network in functional and metabolic data. For all multimodally obtained default mode network regions, a significant trend towards an increment of metabolic deficits from healthy controls via unimpaired patients to patients with mild cognitive impairment was identified. In addition, posterior default mode network regions with the strongest metabolic deficits and gradual decline in comparison to controls, also showed the strongest increases in both metabolic and functional connectivity compared to controls. The verification of the applicability of a constant infusion dynamic 2-[18F]fluoro-2-deoxy- D-glucose positron emission tomography protocol in Parkinson’s disease patients was started in a self-initiated study, which finished the acquisition phase with 10 participants per group by the time the current work was submitted. Together the first two studies highlight the added value of multimodal imaging in investigating human brain function and the pathophysiology of neurodegenerative disorders, in particular their great potential for identifying links between individual pathologies. The second study partly continued, and answered questions raised in response to the first study, which hinted at an involvement of default mode network regions in cognitive symptoms of Parkinson’s disease and a relation between functional network degeneration and metabolic activity. The current work shows exemplary the complementarity of both measures of brain network activity and their individual significance for cognitive symptoms in Parkinson’s disease. The presented work highlights how multimodal resting-state studies can provide new insights into the (patho-)physiological network organization of brain activity by confirming insights obtained by one modality and deepen our understanding of disease processes. The selfinitiated study further laid the ground for multimodal characterization of metabolic and hemodynamic network changes on single-subject level and the evaluation of dynamic positron emission tomography-based connectivity as metabolic network marker for Parkinson’s disease
Simultaneous Multiparametric and Multidimensional Cardiovascular Magnetic Resonance Imaging
No abstract available
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Advancing the Study of Functional Connectome Development
A better understanding of functional changes in the brain across childhood offers the potential to better support neurodevelopmental and learning challenges. However, neuroimaging tools such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are vulnerable to head motion and other artifacts, and studies have had limited reproducibility. To accomplish research goals, we need to understand the reliability and validity of data collection, processing, and analysis strategies. Neuroimaging datasets contain individually unique information, but identifiability is reduced by noise or lack of signal, suggesting it can be a measure of validity. The goal of this thesis was to use identifiability to benchmark different methodologies, and describe how identifiability associates with age across early childhood. I first compared several different fMRI preprocessing pipelines for data collected from young children. Preprocessing techniques are often controversial due to specific drawbacks and have typically been assessed with adult datasets, which have much less head motion. I found benefits to the use of global signal regression and temporal censoring, but overly strict censoring can impact identifiability, suggesting noise removed must be balanced against signal retained. I also compared several different EEG measures of functional connectivity (FC). EEG can be vulnerable to volume conduction artifacts that can be mitigated by only considering shared information with a time delay between signals. However, I found that mitigation strategies result in lower identifiability, suggesting that while removing confounding noise they also discard substantial signal of interest. Individual experiences may shape development in an individually unique way, which is supported by evidence that adults have more individually identifiable patterns of FC than children. I found that across 4 to 8 years of age, identifiability increased via increased self-stability, but without changes in similarity-to-others.
In the absence of ground truth, it is difficult to argue for or against analysis decisions based solely on a theoretical framework and need to also be validated. My work highlights the importance of not thinking about techniques in a valid-invalid dichotomy; certain methods may be sub-optimal while still being preferable to alternatives if they better manage the trade off between noise removed and signal retained
Commonalities and distinctions between the type 2 diabetes mellitus and Alzheimer’s disease: a systematic review and multimodal neuroimaging meta-analysis
BackgroundAlzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) are aging related diseases with high incidence. Because of the correlation of incidence rate and some possible mechanisms of comorbidity, the two diseases have been studied in combination by many researchers, and even some scholars call AD type 3 diabetes. But the relationship between the two is still controversial.MethodsThis study used seed-based d mapping software to conduct a meta-analysis of the whole brain resting state functional magnetic resonance imaging (rs-fMRI) study, exploring the differences in amplitude low-frequency fluctuation (ALFF) and cerebral blood flow (CBF) between patients (AD or T2DM) and healthy controls (HCs), and searching for neuroimaging evidence that can explain the relationship between the two diseases.ResultsThe final study included 22 datasets of ALFF and 22 datasets of CBF. The results of T2DM group showed that ALFF increased in both cerebellum and left inferior temporal gyrus regions, but decreased in left middle occipital gyrus, right inferior occipital gyrus, and left anterior central gyrus regions. In the T2DM group, CBF increased in the right supplementary motor area, while decreased in the middle occipital gyrus and inferior parietal gyrus. The results of the AD group showed that the ALFF increased in the right cerebellum, right hippocampus, and right striatum, while decreased in the precuneus gyrus and right superior temporal gyrus. In the AD group, CBF in the anterior precuneus gyrus and inferior parietal gyrus decreased. Multimodal analysis within a disease showed that ALFF and CBF both decreased in the occipital lobe of the T2DM group and in the precuneus and parietal lobe of the AD group. In addition, there was a common decrease of CBF in the right middle occipital gyrus in both groups.ConclusionBased on neuroimaging evidence, we believe that T2DM and AD are two diseases with their respective characteristics of central nervous activity and cerebral perfusion. The changes in CBF between the two diseases partially overlap, which is consistent with their respective clinical characteristics and also indicates a close relationship between them.Systematic review registrationPROSPERO [CRD42022370014]
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