Intrinsic functional brain networks in health and disease

6 Introduction   6  6.1   Imaging  cognitive  processes  with  functional  magnetic  resonance  imaging   7  6.2   Imaging  the  brain’s  resting  state   8  6.3   Intrinsic  connectivity  networks  in  the  resting  state   9  6.4   Investigating  modulations  and  plasticity  of  intrinsic  connectivity  networks   12 7 Paper  1:   Towards  discovery  science  of  human  brain  function  (PNAS  2010)   14 8 Paper  2:   Repeated  pain  induces  adaptations  of  intrinsic  brain  activity  to  reflect  past  and  predict future pain  (Neuroimage  2011)   30 9 Paper  3:   Intrinsic  network  connectivity  reflects  consistency  of  synesthetic  experience

Determination of Standard Gibbs Energies of Transfer of Organic Anions across the Water/Nitrobenzene Interface

The oxidation of decamethylferrocene dissolved in a nitrobenzene droplet and attached to a graphite electrode that is immersed in an aqueous electrolyte solution provides the basis to determine the standard Gibbs energies of transfer of simple organic anions, such as those of aliphatic and aromatic mono- and dicarboxylic acids, and phenols. The data are rationalized on the basis of the constitution of the compounds

Ongoing Slow Fluctuations in V1 Impact on Visual Perception

The human brain's ongoing activity is characterized by intrinsic networks of coherent fluctuations, measured for example with correlated functional magnetic resonance imaging signals. So far, however, the brain processes underlying this ongoing blood oxygenation level dependent (BOLD) signal orchestration and their direct relevance for human behavior are not sufficiently understood. In this study, we address the question of whether and how ongoing BOLD activity within intrinsic occipital networks impacts on conscious visual perception. To this end, backwardly masked targets were presented in participants' left visual field only, leaving the ipsi-lateral occipital areas entirely free from direct effects of task throughout the experiment. Signal time courses of ipsi-lateral BOLD fluctuations in visual areas V1 and V2 were then used as proxies for the ongoing contra-lateral BOLD activity within the bilateral networks. Magnitude and phase of these fluctuations were compared in trials with and without conscious visual perception, operationalized by means of subjective confidence ratings. Our results show that ipsilateral BOLD magnitudes in V1 were significantly higher at times of peak response when the target was perceived consciously. A significant difference between conscious and non-conscious perception with regard to the pre-target phase of an intrinsic-frequency regime suggests that ongoing V1 fluctuations exert a decisive impact on the access to consciousness already before stimulation. Both effects were absent in V2. These results thus support the notion that ongoing slow BOLD activity within intrinsic networks covering V1 represents localized processes that modulate the degree of readiness for the emergence of visual consciousness

More Consistently Altered Connectivity Patterns for Cerebellum and Medial Temporal Lobes than for Amygdala and Striatum in Schizophrenia

Background: Brain architecture can be divided into a cortico-thalamic system and modulatory "subcortical-cerebellar" systems containing key structures such as striatum, medial temporal lobes (MTLs), amygdala, and cerebellum. Subcortical-cerebellar systems are known to be altered in schizophrenia. In particular, intrinsic functional brain connectivity (iFC) between these systems has been consistently demonstrated in patients. While altered connectivity is known for each subcortical-cerebellar system separately, it is unknown whether subcortical-cerebellar systems' connectivity patterns with the cortico-thalamic system are comparably altered across systems, i.e., if separate subcortical-cerebellar systems' connectivity patterns are consistent across patients. Methods: To investigate this question, 18 patients with schizophrenia (3 unmedicated, 15 medicated with atypical antipsychotics) and 18 healthy controls were assessed by resting-state functional magnetic resonance imaging (fMRI). Independent component analysis of fMRI data revealed cortical intrinsic brain networks (NWs) with time courses representing proxies for cortico-thalamic system activity. Subcortical-cerebellar systems' activity was represented by fMRI-based time courses of selected regions-of interest (ROls;i.e., striatum, MTL, amygdala, cerebellum). Correlation analysis among ROI- and NWs time courses yielded individual connectivity matrices [i.e., connectivity between NW and ROls (alIROls-NW, separateROI-NW), only NWs (NWs-NWs), and only ROls (alIROls-alIROls)] as main outcome measures, which were classified by support-vector-machine-based (SVM) leave-one-out cross-validation. Differences in classification accuracy were statistically evaluated for consistency across subjects and systems. Results: Correlation matrices based on aIIROls-NWs yielded 91% classification accuracy, which was significantly superior to alIROls-alIROls and NWs-NWs (56 and 74%, respectively). Considering separate subcortical-cerebellar systems, cerebellum NWs and MTL-NWs reached highest accuracy values with 91 and 85%, respectively, while those of striatum-NW and amygdala-NW were significantly lower with about 65% classification accuracy. Conclusion: Results provide initial evidence for differential consistency of altered intrinsic connectivity patterns between subcortical-cerebellar systems and the corticothalamic system. Data suggest that differential dysconnectivity patterns between subcortical-cerebellar and cortical systems might reflect different disease states or patient subgroups

Insular dysfunction within the salience network is associated with severity of symptoms and aberrant inter-network connectivity in major depressive disorder

Major depressive disorder (MDD) is characterized by altered intrinsic functional connectivity within (intra-iFC) intrinsic connectivity networks (ICNs),such as the Default Mode- (DMN),Salience- (SN) and Central Executive Network (CEN). It has been proposed that aberrant switching between DMN-mediated self-referential and CEN-mediated goal-directed cognitive processes might contribute to MDD, possibly explaining patients' difficulties to disengage the processing of self-focused, often negatively biased thoughts. Recently, it has been shown that the right anterior insula (rAl) within the SN is modulating DMN/CEN interactions. Since structural and functional alterations within the Al have been frequently reported in MDD, we hypothesized that aberrant intra-iFC in the SN's rAl is associated with both aberrant iFC between DMN and CEN (inter-iFC) and severity of symptoms in MDD. Twenty-five patients with MDD and 25 healthy controls were assessed using resting-state fMRI (rs-fMRI) and psychometric examination. High-model-order independent component analysis (ICA) of rs-fMRI data was performed to identify ICNs including DMN, SN, and CEN. Intra-iFC within and inter-iFC between distinct subsystems of the DMN, SN, and CEN were calculated, compared between groups and correlated with the severity of symptoms. Patients with MDD showed (1) decreased intra-iFC within the SN's rAl,(2) decreased inter-iFC between the DMN and CEN, and (3) increased inter-iFC between the SN and DMN. Moreover, decreased intra-iFC in the SN's rAl was associated with severity of symptoms and aberrant DMN/CEN interactions, with the latter losing significance after correction for multiple comparisons. Our results provide evidence for a relationship between aberrant intra-iFC in the salience network's rAl, aberrant DMN/CEN interactions and severity of symptoms, suggesting a link between aberrant salience mapping, abnormal coordination of DMN/CEN based cognitive processes and psychopathology in MDD

Insular dysfunction reflects altered between-network connectivity and severity of negative symptoms in schizophrenia during psychotic remission

Schizophrenia is characterized by aberrant intrinsic functional connectivity (iFC) within and between intrinsic connectivity networks (ICNs), including the Default Mode- (DMN), Salience- (SN), and Central Executive Network (CEN). The anterior insula (AI) of the SN has been demonstrated to modulate DMN/CEN interactions. Recently, we found that the dependence of DMN/CEN interactions on SN's right AI activity is altered in patients with schizophrenia in acute psychosis and related to psychotic symptoms, indicating a link between aberrant AI, DMN, CEN, and psychosis. However, since structural alterations of the insula are also present during psychotic remission and associated with negative symptoms, impaired AI interaction might be relevant even for psychotic remission and corresponding symptoms. Twelve patients with schizophrenia during psychotic remission (SR) and 12 healthy controls were assessed using resting-state fMRI and psychometric examination. High-model-order independent component analysis of fMRI data revealed ICNs including DMN, SN, and CEN. Scores of iFC within (intra-iFC) and between (inter-iFC) distinct subsystems of the DMN, SN, and CEN were calculated, compared between groups and correlated with the severity of symptoms. Intra-iFC was altered in patients SN, DMN, and CEN, including decreased intra-iFC in the left AI within the SN. Patients' inter-iFC between SN and CEN was increased and correlated with the severity of negative symptoms. Furthermore, decreased intra-iFC of the left AI correlated with both severity of negative symptoms and increased inter-iFC between SN and CEN. Our result provides first evidence for a relationship between AI dysfunction and altered between-network interactions in schizophrenia during psychotic remission, which is related to the severity of negative symptoms. Together with our previous results, data suggest specific SN/DMN/CEN reorganization in schizophrenia with distinct insular pathways for distinct symptom dimensions

Impact of Global Mean Normalization on Regional Glucose Metabolism in the Human Brain

Because the human brain consumes a disproportionate fraction of the resting body’s energy, positron emission tomography (PET) measurements of absolute glucose metabolism (CMRglc) can serve as disease biomarkers. Global mean normalization (GMN) of PET data reveals disease-based differences from healthy individuals as fractional changes across regions relative to a global mean. To assess the impact of GMN applied to metabolic data, we compared CMRglc with and without GMN in healthy awake volunteers with eyes closed (i.e., control) against specific physiological/clinical states, including healthy/awake with eyes open, healthy/awake but congenitally blind, healthy/sedated with anesthetics, and patients with disorders of consciousness. Without GMN, global CMRglc alterations compared to control were detected in all conditions except in congenitally blind where regional CMRglc variations were detected in the visual cortex. However, GMN introduced regional and bidirectional CMRglc changes at smaller fractions of the quantitative delocalized changes. While global information was lost with GMN, the quantitative approach (i.e., a validated method for quantitative baseline metabolic activity without GMN) not only preserved global CMRglc alterations induced by opening eyes, sedation, and varying consciousness but also detected regional CMRglc variations in the congenitally blind. These results caution the use of GMN upon PET-measured CMRglc data in health and disease

Concept of the Munich/Augsburg Consortium Precision in Mental Health for the German Center of Mental Health

The Federal Ministry of Education and Research (BMBF) issued a call for a new nationwide research network on mental disorders, the German Center of Mental Health (DZPG). The Munich/Augsburg consortium was selected to participate as one of six partner sites with its concept “Precision in Mental Health (PriMe): Understanding, predicting, and preventing chronicity.” PriMe bundles interdisciplinary research from the Ludwig-Maximilians-University (LMU), Technical University of Munich (TUM), University of Augsburg (UniA), Helmholtz Center Munich (HMGU), and Max Planck Institute of Psychiatry (MPIP) and has a focus on schizophrenia (SZ), bipolar disorder (BPD), and major depressive disorder (MDD). PriMe takes a longitudinal perspective on these three disorders from the at-risk stage to the first-episode, relapsing, and chronic stages. These disorders pose a major health burden because in up to 50% of patients they cause untreatable residual symptoms, which lead to early social and vocational disability, comorbidities, and excess mortality. PriMe aims at reducing mortality on different levels, e.g., reducing death by psychiatric and somatic comorbidities, and will approach this goal by addressing interdisciplinary and cross-sector approaches across the lifespan. PriMe aims to add a precision medicine framework to the DZPG that will propel deeper understanding, more accurate prediction, and personalized prevention to prevent disease chronicity and mortality across mental illnesses. This framework is structured along the translational chain and will be used by PriMe to innovate the preventive and therapeutic management of SZ, BPD, and MDD from rural to urban areas and from patients in early disease stages to patients with long-term disease courses. Research will build on platforms that include one on model systems, one on the identification and validation of predictive markers, one on the development of novel multimodal treatments, one on the regulation and strengthening of the uptake and dissemination of personalized treatments, and finally one on testing of the clinical effectiveness, utility, and scalability of such personalized treatments. In accordance with the translational chain, PriMe’s expertise includes the ability to integrate understanding of bio-behavioral processes based on innovative models, to translate this knowledge into clinical practice and to promote user participation in mental health research and care

Neurobiological Divergence of the Positive and Negative Schizophrenia Subtypes Identified on a New Factor Structure of Psychopathology Using Non-negative Factorization:An International Machine Learning Study

ObjectiveDisentangling psychopathological heterogeneity in schizophrenia is challenging and previous results remain inconclusive. We employed advanced machine-learning to identify a stable and generalizable factorization of the “Positive and Negative Syndrome Scale (PANSS)”, and used it to identify psychopathological subtypes as well as their neurobiological differentiations.MethodsPANSS data from the Pharmacotherapy Monitoring and Outcome Survey cohort (1545 patients, 586 followed up after 1.35±0.70 years) were used for learning the factor-structure by an orthonormal projective non-negative factorization. An international sample, pooled from nine medical centers across Europe, USA, and Asia (490 patients), was used for validation. Patients were clustered into psychopathological subtypes based on the identified factor-structure, and the neurobiological divergence between the subtypes was assessed by classification analysis on functional MRI connectivity patterns.ResultsA four-factor structure representing negative, positive, affective, and cognitive symptoms was identified as the most stable and generalizable representation of psychopathology. It showed higher internal consistency than the original PANSS subscales and previously proposed factor-models. Based on this representation, the positive-negative dichotomy was confirmed as the (only) robust psychopathological subtypes, and these subtypes were longitudinally stable in about 80% of the repeatedly assessed patients. Finally, the individual subtype could be predicted with good accuracy from functional connectivity profiles of the ventro-medial frontal cortex, temporoparietal junction, and precuneus.ConclusionsMachine-learning applied to multi-site data with cross-validation yielded a factorization generalizable across populations and medical systems. Together with subtyping and the demonstrated ability to predict subtype membership from neuroimaging data, this work further disentangles the heterogeneity in schizophrenia

Penilaian Kinerja Keuangan Koperasi di Kabupaten Pelalawan

This paper describe development and financial performance of cooperative in District Pelalawan among 2007 - 2008. Studies on primary and secondary cooperative in 12 sub-districts. Method in this stady use performance measuring of productivity, efficiency, growth, liquidity, and solvability of cooperative. Productivity of cooperative in Pelalawan was highly but efficiency still low. Profit and income were highly, even liquidity of cooperative very high, and solvability was good