371 research outputs found

    Support Vector Machine Analysis of Functional Magnetic Resonance Imaging of Interoception Does Not Reliably Predict Individual Outcomes of Cognitive Behavioral Therapy in Panic Disorder with Agoraphobia

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    Background: The approach to apply multivariate pattern analyses based on neuro imaging data for outcome prediction holds out the prospect to improve therapeutic decisions in mental disorders. Patients suffering from panic disorder with agoraphobia (PD/AG) often exhibit an increased perception of bodily sensations. The purpose of this investigation was to assess whether multivariate classification applied to a functional magnetic resonance imaging (fMRI) interoception paradigm can predict individual responses to cognitive behavioral therapy (CBT) in PD/AG. Methods: This analysis is based on pretreatment fMRI data during an interoceptive challenge from a multicenter trial of the German PANIC-NET. Patients with DSM-IV PD/AG were dichotomized as responders (n = 30) or non-responders (n = 29) based on the primary outcome (Hamilton Anxiety Scale Reduction ≥50%) after 6 weeks of CBT (2 h/week). fMRI parametric maps were used as features for response classification with linear support vector machines (SVM) with or without automated feature selection. Predictive accuracies were assessed using cross validation and permutation testing. The influence of methodological parameters and the predictive ability for specific interoception-related symptom reduction were further evaluated. Results: SVM did not reach sufficient overall predictive accuracies (38.0–54.2%) for anxiety reduction in the primary outcome. In the exploratory analyses, better accuracies (66.7%) were achieved for predicting interoception-specific symptom relief as an alternative outcome domain. Subtle information regarding this alternative response criterion but not the primary outcome was revealed by post hoc univariate comparisons. Conclusion: In contrast to reports on other neurofunctional probes, SVM based on an interoception paradigm was not able to reliably predict individual response to CBT. Results speak against the clinical applicability of this technique

    Deconstructing Bipolar Disorder: A Critical Review of its Diagnostic Validity and a Proposal for DSM-V and ICD-11

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    The development of Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and International Classification of Diseases, Eleventh Edition, deserves a significant conceptual step forward. There is a clear need to improve and refine the current diagnostic criteria, but also to introduce dimensions, perhaps not as an alternative but rather as a useful complement to categorical diagnosis. Laboratory, family, and treatment response data should also be systematically included in the diagnostic assessment when available. We have critically reviewed the content, concurrent, discriminant, and predictive validity of bipolar disorder, and to overcome the validity problems of the current classifications of mental disorders, we propose a modular system which may integrate categorical and dimensional issues, laboratory data, associated nonpsychiatric medical conditions, psychological assessment, and social issues in a comprehensive and nevertheless practical approach

    The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

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    The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns.DFG, GRK 1589, Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1006, Modulation von Bewertungsprozessen beim menschlichen Entscheidungsverhalten: ein neurocomputationaler Ansat

    Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data.

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    IntroductionDeficits in visual perception are well-established in schizophrenia and are linked to abnormal activity in the lateral occipital complex (LOC). Related deficits may exist in bipolar disorder. LOC contains neurons tuned to object features. It is unknown whether neural tuning in LOC or other visual areas is abnormal in patients, contributing to abnormal perception during visual tasks. This study used multivariate pattern analysis (MVPA) to investigate perceptual tuning for objects in schizophrenia and bipolar disorder.MethodsFifty schizophrenia participants, 51 bipolar disorder participants, and 47 matched healthy controls completed five functional magnetic resonance imaging (fMRI) runs of a perceptual task in which they viewed pictures of four different objects and an outdoor scene. We performed classification analyses designed to assess the distinctiveness of activity corresponding to perception of each stimulus in LOC (a functionally localized region of interest). We also performed similar classification analyses throughout the brain using a searchlight technique. We compared classification accuracy and patterns of classification errors across groups.ResultsStimulus classification accuracy was significantly above chance in all groups in LOC and throughout visual cortex. Classification errors were mostly within-category confusions (e.g., misclassifying one chair as another chair). There were no group differences in classification accuracy or patterns of confusion.ConclusionsThe results show for the first time MVPA can be used successfully to classify individual perceptual stimuli in schizophrenia and bipolar disorder. However, the results do not provide evidence of abnormal neural tuning in schizophrenia and bipolar disorder

    Can Emotional and Behavioral Dysregulation in Youth Be Decoded from Functional Neuroimaging?

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    High comorbidity among pediatric disorders characterized by behavioral and emotional dysregulation poses problems for diagnosis and treatment, and suggests that these disorders may be better conceptualized as dimensions of abnormal behaviors. Furthermore, identifying neuroimaging biomarkers related to dimensional measures of behavior may provide targets to guide individualized treatment. We aimed to use functional neuroimaging and pattern regression techniques to determine whether patterns of brain activity could accurately decode individual-level severity on a dimensional scale measuring behavioural and emotional dysregulation at two different time points

    Ventral striatal fMRI in affective and psychotic disorders: a transdiagnostic approach using resting state and task functional resonance imaging, clinical and genetic data

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    The effective clinical management of psychotic and affective disorders still represents a major challenge in psychiatry. Due to the high prevalence of these disorders and the subjective suffering, they cause a massive burden for the health system and society, and improvement in diagnostic and treatment strategies is urgently sought. In consideration of the literature, there are two promising avenues for this endeavour: On the one hand, particularly regarding schizophrenia (SCZ), early detection of high risk states or disease manifestation is crucial for the eventual treatment success. On the other hand, the heterogeneity of psychotic and affective disorders as well as blurry boundaries between the associated clinical syndromes often leave the diagnosis, which is the foundation of an evidence based treatment selection, on shaky ground. At the neurobiological level, several lines of evidence underline the role of the ventral striatum, particularly the nucleus accumbens (NAcc), for the pathophysiology of psychosis and more generally reward processing. Disturbed reward processing in turn is related to anhedonia, a core symptom of major depressive disorder (MDD), bipolar disorder (BD) and also SCZ. Against this background, this thesis aimed to unravel the potential of ventral striatal brain circuits as a source of biomarkers of psychotic and affective disorders. For this purpose, two sub-studies were performed: Firstly, we studied the impact of a validated polygenic risk score (PGRS) for SCZ, childhood adversity (CA) as widespread environmental factor and their interaction on resting state (RS) fMRI measures and NAcc seed connectivity in 253 healthy controls (HC) and compared these patterns with fully expressed disease in 23 patients with SCZ. Consistent with previous reports, SCZ patients showed strong regional functional connectivity density (FCD) increases in subcortical nuclei, particularly in the NAcc, compared with HC. Furthermore, in the HC sample, a a positive association between the FCD of the NAcc and both the PGRS and the interaction between PGRS and CA was found. Fine-mapping exhibited increased connectivity between the NAcc and visual association cortices for high levels of both PGRS and the PGRS-by-CA interaction. Taken together, this study showed that in HC, high PGRS for SCZ affects both global and regionally specific connectivity of the NAcc in a similar pattern as observed in SCZ patients, and that this effect was already amplified even by a history of very mild CA. This latter observation strengthened the notion that environmental factors need consideration in imaging genetics studies. Secondly, we examined the neural underpinnings of reward anticipation (RA) in MDD, BD and SCZ as studied by fMRI. This study revealed that aberrantly low striatal activation during RA is typical of SCZ, whereas the response of this network appeared to be preserved in MDD and BD. Interestingly, two further large-scale brain networks involved in RA – the salience network and the default mode network showed both transdiagnostic and further disease-specific alterations: While the salience network was found to be impaired primarily in SCZ patients, all patient groups revealed deficits in the suppression of the default mode network. Among hub regions of all three networks that were further differentiated in an early and a late response period, levels of anhedonia were correlated with the extent of the (early) hippocampal deactivation failure across diagnostic boundaries. In sum, both investigations confirm the possibility to use fMRI to probe the functional status of the ventral striatum. The first study underlines the centrality of striatal regions in the pahophysiology of psychosis as these alterations already emerged in healthy individuals at high genetic risk for developing SCZ, particularly when including unspecific environmental risk to the model. Hyperconnectivity of this region in SCZ during the resting state matched with a blunted response during the RA task. The latter studyshowed that at least two further large-scale brain networks are impaired in both psychotic and affective disorders during RA, indicating a potential of reward processing as a source of imaging phenotypes or biomarkers to characterize patients of the respective disease spectrum

    Multimodal Neuroimaging-Informed Clinical Applications in Neuropsychiatric Disorders

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    Recent advances in neuroimaging data acquisition and analysis hold the promise to enhance the ability to make diagnostic and prognostic predictions and perform treatment planning in neuropsychiatric disorders. Prior research using a variety of types of neuroimaging techniques has confirmed that neuropsychiatric disorders are associated with dysfunction in anatomical and functional brain circuits. We first discuss current challenges associated with the identification of reliable neuroimaging markers for diagnosis and prognosis in mood disorders and for neurosurgical treatment planning for deep brain stimulation (DBS). We then present data on the use of neuroimaging for the diagnosis and prognosis of mood disorders and for DBS treatment planning. We demonstrate how multivariate analyses of functional activation and connectivity parameters can be used to differentiate patients with bipolar disorder from those with major depressive disorder and non-affective psychosis. We also present data on connectivity parameters that mediate acute treatment response in affective and non-affective psychosis. We then focus on precision mapping of functional connectivity in native space. We describe the benefits of integrating anatomical fiber reconstruction with brain functional parameters and cortical surface measures to derive anatomically-informed connectivity metrics within the morphological context of each individual brain. We discuss how this approach may be particularly promising in psychiatry, given the clinical and etiological heterogeneity of the disorders, and particularly in treatment response prediction and planning. Precision mapping of connectivity is essential for DBS. In DBS, treatment electrodes are inserted into positions near key grey matter nodes within the circuits considered relevant to disease expression. However, targeting white matter tracts that underpin connectivity within these circuits may increase treatment efficacy and tolerability therefore relevant for effective treatment. We demonstrate how this approach can be validated in the treatment of Parkinson’s disease by identifying connectivity patterns that can be used as biomarkers for treatment planning and thus refine the traditional approach of DBS planning that uses only grey matter landmarks. Finally we describe how this approach could be used in planning DBS treatment of psychiatric disorders

    Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers

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    Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia

    Abnormal left and right amygdala-orbitofrontal cortical functional connectivity to emotional faces:state versus trait vulnerability markers of depression in bipolar disorder

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    Background - Amygdala-orbitofrontal cortical (OFC) functional connectivity (FC) to emotional stimuli and relationships with white matter remain little examined in bipolar disorder individuals (BD). Methods - Thirty-one BD (type I; n = 17 remitted; n = 14 depressed) and 24 age- and gender-ratio-matched healthy individuals (HC) viewed neutral, mild, and intense happy or sad emotional faces in two experiments. The FC was computed as linear and nonlinear dependence measures between amygdala and OFC time series. Effects of group, laterality, and emotion intensity upon amygdala-OFC FC and amygdala-OFC FC white matter fractional anisotropy (FA) relationships were examined. Results - The BD versus HC showed significantly greater right amygdala-OFC FC (p = .001) in the sad experiment and significantly reduced bilateral amygdala-OFC FC (p = .007) in the happy experiment. Depressed but not remitted female BD versus female HC showed significantly greater left amygdala-OFC FC (p = .001) to all faces in the sad experiment and reduced bilateral amygdala-OFC FC to intense happy faces (p = .01). There was a significant nonlinear relationship (p = .001) between left amygdala-OFC FC to sad faces and FA in HC. In BD, antidepressants were associated with significantly reduced left amygdala-OFC FC to mild sad faces (p = .001). Conclusions - In BD, abnormally elevated right amygdala-OFC FC to sad stimuli might represent a trait vulnerability for depression, whereas abnormally elevated left amygdala-OFC FC to sad stimuli and abnormally reduced amygdala-OFC FC to intense happy stimuli might represent a depression state marker. Abnormal FC measures might normalize with antidepressant medications in BD. Nonlinear amygdala-OFC FC–FA relationships in BD and HC require further study
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