Reduced Cerebral Blood Flow Within the Default-Mode Network and Within Total Gray Matter in Major Depression

The default-mode network (DMN) was shown to have aberrant blood oxygenation-level-dependent (BOLD) activity in major depressive disorder (MDD). While BOLD is a relative measure of neural activity, cerebral blood flow (CBF) is an absolute measure. Resting-state CBF alterations have been reported in MDD. However, the association of baseline CBF and CBF fluctuations is unclear in MDD. Therefore, the aim was to investigate the CBF within the DMN in MDD, applying a strictly data-driven approach. In 22 MDD patients and 22 matched healthy controls, CBF was acquired using arterial spin labeling (ASL) at rest. A concatenated independent component analysis was performed to identify the DMN within the ASL data. The perfusion of the DMN and its nodes was quantified and compared between groups. The DMN was identified in both groups with high spatial similarity. Absolute CBF values within the DMN were reduced in MDD patients (p<0.001). However, after controlling for whole-brain gray matter CBF and age, the group difference vanished. In patients, depression severity was correlated with reduced perfusion in the DMN in the posterior cingulate cortex and the right inferior parietal lobe. Hypoperfusion within the DMN in MDD is not specific to the DMN. Still, depression severity was linked to DMN node perfusion, supporting a role of the DMN in depression pathobiology. The finding has implications for the interpretation of BOLD functional magnetic resonance imaging data in MDD.

A global ”imaging” view on systems approaches in immunology

The immune system exhibits an enormous complexity. High throughput methods such as the "-omic'' technologies generate vast amounts of data that facilitate dissection of immunological processes at ever finer resolution. Using high-resolution data-driven systems analysis, causal relationships between complex molecular processes and particular immunological phenotypes can be constructed. However, processes in tissues, organs, and the organism itself (so-called higher level processes) also control and regulate the molecular (lower level) processes. Reverse systems engineering approaches, which focus on the examination of the structure, dynamics and control of the immune system, can help to understand the construction principles of the immune system. Such integrative mechanistic models can properly describe, explain, and predict the behavior of the immune system in health and disease by combining both higher and lower level processes. Moving from molecular and cellular levels to a multiscale systems understanding requires the development of methodologies that integrate data from different biological levels into multiscale mechanistic models. In particular, 3D imaging techniques and 4D modeling of the spatiotemporal dynamics of immune processes within lymphoid tissues are central for such integrative approaches. Both dynamic and global organ imaging technologies will be instrumental in facilitating comprehensive multiscale systems immunology analyses as discussed in this review.

The spatiotemporal pattern of auditory cortical responses during verbal hallucinations

Functional magnetic resonance imaging (fMRI) studies can provide insight into the neural correlates of hallucinations. Commonly, such studies require self-reports about the timing of the hallucination events. While many studies have found activity in higher-order sensory cortical areas, only a few have demonstrated activity of the primary auditory cortex during auditory verbal hallucinations. In this case, using self-reports as a model of brain activity may not be sensitive enough to capture all neurophysiological signals related to hallucinations. We used spatial independent component analysis (sICA) to extract the activity patterns associated with auditory verbal hallucinations in six schizophrenia patients. SICA decomposes the functional data set into a set of spatial maps without the use of any input function. The resulting activity patterns from auditory and sensorimotor components were further analyzed in a single-subject fashion using a visualization tool that allows for easy inspection of the variability of regional brain responses. We found bilateral auditory cortex activity, including Heschl's gyrus, during hallucinations of one patient, and unilateral auditory cortex activity in two more patients. The associated time courses showed a large variability in the shape, amplitude, and time of onset relative to the self-reports. However, the average of the time courses during hallucinations showed a clear association with this clinical phenomenon. We suggest that detection of this activity may be facilitated by examining hallucination epochs of sufficient length, in combination with a data-driven approach.

From Statistical Data to Spatial Knowledge - informing decision making in Vietnam

Vietnam has developed rapidly over the past 15 years. However, progress was not uniformly distributed across the country. Availability, adequate visualization and analysis of spatially explicit data on socio-economic and environmental aspects can support both research and policy towards sustainable development. Applying appropriate mapping techniques allows gleaning important information from tabular socio-economic data. Spatial analysis of socio-economic phenomena can yield insights into locally-specifi c patterns and processes that cannot be generated by non-spatial applications. This paper presents techniques and applications that develop and analyze spatially highly disaggregated socioeconomic datasets. A number of examples show how such information can support informed decisionmaking and research in Vietnam.

A disease model for wheezing disorders in preschool children based on clinicians' perceptions

BACKGROUND: Wheezing disorders in childhood vary widely in clinical presentation and disease course. During the last years, several ways to classify wheezing children into different disease phenotypes have been proposed and are increasingly used for clinical guidance, but validation of these hypothetical entities is difficult. METHODOLOGY/PRINCIPAL FINDINGS: The aim of this study was to develop a testable disease model which reflects the full spectrum of wheezing illness in preschool children. We performed a qualitative study among a panel of 7 experienced clinicians from 4 European countries working in primary, secondary and tertiary paediatric care. In a series of questionnaire surveys and structured discussions, we found a general consensus that preschool wheezing disorders consist of several phenotypes, with a great heterogeneity of specific disease concepts between clinicians. Initially, 24 disease entities were described among the 7 physicians. In structured discussions, these could be narrowed down to three entities which were linked to proposed mechanisms: a) allergic wheeze, b) non-allergic wheeze due to structural airway narrowing and c) non-allergic wheeze due to increased immune response to viral infections. This disease model will serve to create an artificial dataset that allows the validation of data-driven multidimensional methods, such as cluster analysis, which have been proposed for identification of wheezing phenotypes in children. CONCLUSIONS/SIGNIFICANCE: While there appears to be wide agreement among clinicians that wheezing disorders consist of several diseases, there is less agreement regarding their number and nature. A great diversity of disease concepts exist but a unified phenotype classification reflecting underlying disease mechanisms is lacking. We propose a disease model which may help guide future research so that proposed mechanisms are measured at the right time and their role in disease heterogeneity can be studied.

Preterm birth, infant weight gain, and childhood asthma risk: A meta-analysis of 147,000 European children

BACKGROUND Preterm birth, low birth weight, and infant catch-up growth seem associated with an increased risk of respiratory diseases in later life, but individual studies showed conflicting results. OBJECTIVES We performed an individual participant data meta-analysis for 147,252 children of 31 birth cohort studies to determine the associations of birth and infant growth characteristics with the risks of preschool wheezing (1-4 years) and school-age asthma (5-10 years). METHODS First, we performed an adjusted 1-stage random-effect meta-analysis to assess the combined associations of gestational age, birth weight, and infant weight gain with childhood asthma. Second, we performed an adjusted 2-stage random-effect meta-analysis to assess the associations of preterm birth (gestational age <37 weeks) and low birth weight (<2500 g) with childhood asthma outcomes. RESULTS Younger gestational age at birth and higher infant weight gain were independently associated with higher risks of preschool wheezing and school-age asthma (P < .05). The inverse associations of birth weight with childhood asthma were explained by gestational age at birth. Compared with term-born children with normal infant weight gain, we observed the highest risks of school-age asthma in children born preterm with high infant weight gain (odds ratio [OR], 4.47; 95% CI, 2.58-7.76). Preterm birth was positively associated with an increased risk of preschool wheezing (pooled odds ratio [pOR], 1.34; 95% CI, 1.25-1.43) and school-age asthma (pOR, 1.40; 95% CI, 1.18-1.67) independent of birth weight. Weaker effect estimates were observed for the associations of low birth weight adjusted for gestational age at birth with preschool wheezing (pOR, 1.10; 95% CI, 1.00-1.21) and school-age asthma (pOR, 1.13; 95% CI, 1.01-1.27). CONCLUSION Younger gestational age at birth and higher infant weight gain were associated with childhood asthma outcomes. The associations of lower birth weight with childhood asthma were largely explained by gestational age at birth.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

VIRTOPSY--scientific documentation, reconstruction and animation in forensic: individual and real 3D data based geo-metric approach including optical body/object surface and radiological CT/MRI scanning.

Until today, most of the documentation of forensic relevant medical findings is limited to traditional 2D photography, 2D conventional radiographs, sketches and verbal description. There are still some limitations of the classic documentation in forensic science especially if a 3D documentation is necessary. The goal of this paper is to demonstrate new 3D real data based geo-metric technology approaches. This paper present approaches to a 3D geo-metric documentation of injuries on the body surface and internal injuries in the living and deceased cases. Using modern imaging methods such as photogrammetry, optical surface and radiological CT/MRI scanning in combination it could be demonstrated that a real, full 3D data based individual documentation of the body surface and internal structures is possible in a non-invasive and non-destructive manner. Using the data merging/fusing and animation possibilities, it is possible to answer reconstructive questions of the dynamic development of patterned injuries (morphologic imprints) and to evaluate the possibility, that they are matchable or linkable to suspected injury-causing instruments. For the first time, to our knowledge, the method of optical and radiological 3D scanning was used to document the forensic relevant injuries of human body in combination with vehicle damages. By this complementary documentation approach, individual forensic real data based analysis and animation were possible linking body injuries to vehicle deformations or damages. These data allow conclusions to be drawn for automobile accident research, optimization of vehicle safety (pedestrian and passenger) and for further development of crash dummies. Real 3D data based documentation opens a new horizon for scientific reconstruction and animation by bringing added value and a real quality improvement in forensic science.

Reporting and handling missing outcome data in mental health: a systematic review of Cochrane systematic reviews and meta-analyses

OBJECTIVES The purpose of the study was to provide empirical evidence about the reporting of methodology to address missing outcome data and the acknowledgement of their impact in Cochrane systematic reviews in the mental health field. METHODS Systematic reviews published in the Cochrane Database of Systematic Reviews after January 1, 2009 by three Cochrane Review Groups relating to mental health were included. RESULTS One hundred ninety systematic reviews were considered. Missing outcome data were present in at least one included study in 175 systematic reviews. Of these 175 systematic reviews, 147 (84%) accounted for missing outcome data by considering a relevant primary or secondary outcome (e.g., dropout). Missing outcome data implications were reported only in 61 (35%) systematic reviews and primarily in the discussion section by commenting on the amount of the missing outcome data. One hundred forty eligible meta-analyses with missing data were scrutinized. Seventy-nine (56%) of them had studies with total dropout rate between 10 and 30%. One hundred nine (78%) meta-analyses reported to have performed intention-to-treat analysis by including trials with imputed outcome data. Sensitivity analysis for incomplete outcome data was implemented in less than 20% of the meta-analyses. CONCLUSIONS Reporting of the techniques for handling missing outcome data and their implications in the findings of the systematic reviews are suboptimal.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.

Brain electric microstates and momentary conscious mind states as building blocks of spontaneous thinking: I. Visual imagery and abstract thoughts

Prompted reports of recall of spontaneous, conscious experiences were collected in a no-input, no-task, no-response paradigm (30 random prompts to each of 13 healthy volunteers). The mentation reports were classified into visual imagery and abstract thought. Spontaneous 19-channel brain electric activity (EEG) was continuously recorded, viewed as series of momentary spatial distributions (maps) of the brain electric field and segmented into microstates, i.e. into time segments characterized by quasi-stable landscapes of potential distribution maps which showed varying durations in the sub-second range. Microstate segmentation used a data-driven strategy. Different microstates, i.e. different brain electric landscapes must have been generated by activity of different neural assemblies and therefore are hypothesized to constitute different functions. The two types of reported experiences were associated with significantly different microstates (mean duration 121 ms) immediately preceding the prompts; these microstates showed, across subjects, for abstract thought (compared to visual imagery) a shift of the electric gravity center to the left and a clockwise rotation of the field axis. Contrariwise, the microstates 2 s before the prompt did not differ between the two types of experiences. The results support the hypothesis that different microstates of the brain as recognized in its electric field implement different conscious, reportable mind states, i.e. different classes (types) of thoughts (mentations); thus, the microstates might be candidates for the `atoms of thought'.

Epoetin and Darbepoetin for Managing Anemia in Patients Undergoing Cancer Treatment: Comparative Effectiveness Update

Objectives: To update the 2006 systematic review of the comparative benefits and harms of erythropoiesis-stimulating agent (ESA) strategies and non-ESA strategies to manage anemia in patients undergoing chemotherapy and/or radiation for malignancy (excluding myelodysplastic syndrome and acute leukemia), including the impact of alternative thresholds for initiating treatment and optimal duration of therapy. Data sources: Literature searches were updated in electronic databases (n=3), conference proceedings (n=3), and Food and Drug Administration transcripts. Multiple sources (n=13) were searched for potential gray literature. A primary source for current survival evidence was a recently published individual patient data meta-analysis. In that meta-analysis, patient data were obtained from investigators for studies enrolling more than 50 patients per arm. Because those data constitute the most currently available data for this update, as well as the source for on-study (active treatment) mortality data, we limited inclusion in the current report to studies enrolling more than 50 patients per arm to avoid potential differential endpoint ascertainment in smaller studies. Review methods: Title and abstract screening was performed by one or two (to resolve uncertainty) reviewers; potentially included publications were reviewed in full text. Two or three (to resolve disagreements) reviewers assessed trial quality. Results were independently verified and pooled for outcomes of interest. The balance of benefits and harms was examined in a decision model. Results: We evaluated evidence from 5 trials directly comparing darbepoetin with epoetin, 41 trials comparing epoetin with control, and 8 trials comparing darbepoetin with control; 5 trials evaluated early versus late (delay until Hb ≤9 to 11 g/dL) treatment. Trials varied according to duration, tumor types, cancer therapy, trial quality, iron supplementation, baseline hemoglobin, ESA dosing frequency (and therefore amount per dose), and dose escalation. ESAs decreased the risk of transfusion (pooled relative risk [RR], 0.58; 95% confidence interval [CI], 0.53 to 0.64; I2 = 51%; 38 trials) without evidence of meaningful difference between epoetin and darbepoetin. Thromboembolic event rates were higher in ESA-treated patients (pooled RR, 1.51; 95% CI, 1.30 to 1.74; I2 = 0%; 37 trials) without difference between epoetin and darbepoetin. In 14 trials reporting the Functional Assessment of Cancer Therapy (FACT)-Fatigue subscale, the most common patient-reported outcome, scores decreased by −0.6 in control arms (95% CI, −6.4 to 5.2; I2 = 0%) and increased by 2.1 in ESA arms (95% CI, −3.9 to 8.1; I2 = 0%). There were fewer thromboembolic and on-study mortality adverse events when ESA treatment was delayed until baseline Hb was less than 10 g/dL, in keeping with current treatment practice, but the difference in effect from early treatment was not significant, and the evidence was limited and insufficient for conclusions. No evidence informed optimal duration of therapy. Mortality was increased during the on-study period (pooled hazard ratio [HR], 1.17; 95% CI, 1.04 to 1.31; I2 = 0%; 37 trials). There was one additional death for every 59 treated patients when the control arm on-study mortality was 10 percent and one additional death for every 588 treated patients when the control-arm on-study mortality was 1 percent. A cohort decision model yielded a consistent result—greater loss of life-years when control arm on-study mortality was higher. There was no discernible increase in mortality with ESA use over the longest available followup (pooled HR, 1.04; 95% CI, 0.99 to 1.10; I2 = 38%; 44 trials), but many trials did not include an overall survival endpoint and potential time-dependent confounding was not considered. Conclusions: Results of this update were consistent with the 2006 review. ESAs reduced the need for transfusions and increased the risk of thromboembolism. FACT-Fatigue scores were better with ESA use but the magnitude was less than the minimal clinically important difference. An increase in mortality accompanied the use of ESAs. An important unanswered question is whether dosing practices and overall ESA exposure might influence harms.

Three decades of global methane sources and sinks

Methane is an important greenhouse gas, responsible for about 20 of the warming induced by long-lived greenhouse gases since pre-industrial times. By reacting with hydroxyl radicals, methane reduces the oxidizing capacity of the atmosphere and generates ozone in the troposphere. Although most sources and sinks of methane have been identified, their relative contributions to atmospheric methane levels are highly uncertain. As such, the factors responsible for the observed stabilization of atmospheric methane levels in the early 2000s, and the renewed rise after 2006, remain unclear. Here, we construct decadal budgets for methane sources and sinks between 1980 and 2010, using a combination of atmospheric measurements and results from chemical transport models, ecosystem models, climate chemistry models and inventories of anthropogenic emissions. The resultant budgets suggest that data-driven approaches and ecosystem models overestimate total natural emissions. We build three contrasting emission scenarios � which differ in fossil fuel and microbial emissions � to explain the decadal variability in atmospheric methane levels detected, here and in previous studies, since 1985. Although uncertainties in emission trends do not allow definitive conclusions to be drawn, we show that the observed stabilization of methane levels between 1999 and 2006 can potentially be explained by decreasing-to-stable fossil fuel emissions, combined with stable-to-increasing microbial emissions. We show that a rise in natural wetland emissions and fossil fuel emissions probably accounts for the renewed increase in global methane levels after 2006, although the relative contribution of these two sources remains uncertain.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.