Identification of the Language Network from Resting-State fMRI in Patients with Brain Tumors: How Accurate Are Experts?

BACKGROUND AND PURPOSE: Resting-state fMRI helps identify neural networks in presurgical patients who may be limited in their ability to undergo task-fMRI. The purpose of this study was to determine the accuracy of identifying the language network from resting-state-fMRI independent component analysis (ICA) maps. MATERIALS AND METHODS: Through retrospective analysis, patients who underwent both resting-state-fMRI and task-fMRI were compared by identifying the language network from the resting-state-fMRI data by 3 reviewers. Blinded to task-fMRI maps, these investigators independently reviewed resting-state-fMRI ICA maps to potentially identify the language network. Reviewers ranked up to 3 top choices for the candidate resting-state-fMRI language map. We evaluated associations between the probability of correct identification of the language network and some potential factors. RESULTS: Patients included 29 men and 14 women with a mean age of 41 years. Reviewer 1 (with 17 years' experience) demonstrated the highest overall accuracy with 72%; reviewers 2 and 3 (with 2 and 7 years' experience, respectively) had a similar percentage of correct responses (50% and 55%). The highest accuracy used ICA50 and the top 3 choices (81%, 65%, and 60% for reviewers 1, 2, and 3, respectively). The lowest accuracy used ICA50, limiting each reviewer to the top choice (58%, 35%, and 42%). CONCLUSIONS: We demonstrate variability in the accuracy of blinded identification of resting-state-fMRI language networks across reviewers with different years of experience.

Slow-5 dynamic functional connectivity reflects the capacity to sustain cognitive performance during pain.

Some individuals are more distracted by pain during a cognitive task than others, representing poor pain coping. We have characterized individuals as A-type (attention dominates) or P-type (pain dominates) based on how pain interferes with task speed. The ability to optimize behavior during pain may relate to the flexibility in communication at rest between the dorsolateral prefrontal cortex (DLPFC) of the executive control network, and the anterior mid-cingulate cortex (aMCC) of the salience network (SN) - regions involved in cognitive-interference. The aMCC and aIns (SN hub) also signify pain salience; flexible communication at rest between them possibly allowing prioritizing task performance during pain. We tested the hypotheses that pain-induced changes in task performance are related to resting-state dynamic functional connectivity (dFC) between these region pairs (DLPFC-aMCC; aMCC-aIns). We found that 1) pain reduces task consistency/speed in P-type individuals, but enhances performance in A-type individuals, 2) task consistency is related to the FC dynamics within DLPFC-aMCC and aMCC-aIns pairs, 3) brain-behavior relationships are driven by dFC within the slow-5 (0.01-0.027Hz) frequency band, and 4) dFC across the brain decreases at higher frequencies. Our findings point to neural communication dynamics at rest as being associated with prioritizing task performance over pain.

Presurgical Brain Mapping of the Ventral Somatomotor Network in Patients with Brain Tumors Using Resting-State fMRI.

BACKGROUND AND PURPOSE: Resting-state fMRI readily identifies the dorsal but less consistently the ventral somatomotor network. Our aim was to assess the relative utility of resting-state fMRI in the identification of the ventral somatomotor network via comparison with task-based fMRI in patients with brain tumor. MATERIALS AND METHODS: We identified 26 surgically naïve patients referred for presurgical fMRI brain mapping who had undergone both satisfactory ventral motor activation tasks and resting-state fMRI. Following standard preprocessing for task-based fMRI and resting-state fMRI, general linear model analysis of the ventral motor tasks and independent component analysis of resting-state fMRI were performed with the number of components set to 20, 30, 40, and 50. Visual overlap of task-based fMRI and resting-state fMRI at different component levels was assessed and categorized as full match, partial match, or no match. Rest-versus-task-fMRI concordance was calculated with Dice coefficients across varying fMRI thresholds before and after noise removal. Multithresholded Dice coefficient volume under the surface was calculated. RESULTS: The ventral somatomotor network was identified in 81% of patients. At the subject level, better matches between resting-state fMRI and task-based fMRI were seen with an increasing order of components (53% of cases for 20 components versus 73% for 50 components). Noise-removed group-mean volume under the surface improved as component numbers increased from 20 to 50, though ANOVA demonstrated no statistically significant difference among the 4 groups. CONCLUSIONS: In most patients, the ventral somatomotor network can be identified with an increase in the probability of a better match at a higher component number. There is variable concordance of the ventral somatomotor network at the single-subject level between resting-state and task-based fMRI.

Quantifying the reliability of image replication studies: the image intraclass correlation coefficient (I2C2).

This article proposes the image intraclass correlation (I2C2) coefficient as a global measure of reliability for imaging studies. The I2C2 generalizes the classic intraclass correlation (ICC) coefficient to the case when the data of interest are images, thereby providing a measure that is both intuitive and convenient. Drawing a connection with classical measurement error models for replication experiments, the I2C2 can be computed quickly, even in high-dimensional imaging studies. A nonparametric bootstrap procedure is introduced to quantify the variability of the I2C2 estimator. Furthermore, a Monte Carlo permutation is utilized to test reproducibility versus a zero I2C2, representing complete lack of reproducibility. Methodologies are applied to three replication studies arising from different brain imaging modalities and settings: regional analysis of volumes in normalized space imaging for characterizing brain morphology, seed-voxel brain activation maps based on resting-state functional magnetic resonance imaging (fMRI), and fractional anisotropy in an area surrounding the corpus callosum via diffusion tensor imaging. Notably, resting-state fMRI brain activation maps are found to have low reliability, ranging from .2 to .4. Software and data are available to provide easy access to the proposed methods.

Granulocyte-monocyte adsorptive apheresis in pediatric inflammatory bowel disease: results, practical issues, safety, and future perspectives.

BACKGROUND: The purpose of the study was to collect data on granulocyte-monocyte adsorptive apheresis (GMA) for the treatment of corticosteroid-dependent (SD) or corticosteroid-resistant (SR) inflammatory bowel disease (IBD) in children from 3 Nordic countries to evaluate its efficacy and safety and to assess practical issues. METHODS: Retrospective data on 37 children treated with GMA were collected. In all, 22 children had ulcerative colitis (UC), 13 Crohn's disease (CD), and 2 had indeterminate colitis (IC). Their mean age was 13.2 years, range 5-17 years, and mean duration of disease was 2.4 years, range 1 month to 6 years. Indication for treatment in the UC group was SD in 11 cases, SR in 6 cases, and other reasons in 5 cases. The corresponding numbers in the CD group were SD in 8 cases, SR in 2 cases, and other reasons in 3 cases. In the IC group, 1 had SD and 1 was refractory to steroids, azathioprine, and infliximab. Efficacy was evaluated by severity indices: the Pediatric Ulcerative Colitis Activity Index (PUCAI) and the Pediatric Crohn's Disease Activity Index (PCDAI) and tapering of corticosteroids. RESULTS: PUCAI and PCDAI decreased significantly in both groups after 3 months (P = 0.0007, P = 0.025). The dosage of corticosteroid was significantly reduced in the UC group by the end of GMA (P = 0.004) and this response continued after 3 months. Relapse was seen in 2 patients with UC and 3 patients with CD after 3 months follow-up. CONCLUSIONS: GMA seems to be an effective and safe treatment in 81% of the SD or SR pediatric IBD patients, especially in those with UC.

The application of knowledge discovery in databases to post-marketing drug safety: example of the WHO database.

After market launch, new information on adverse effects of medicinal products is almost exclusively first highlighted by spontaneous reporting. As data sets of spontaneous reports have become larger, and computational capability has increased, quantitative methods have been increasingly applied to such data sets. The screening of such data sets is an application of knowledge discovery in databases (KDD). Effective KDD is an iterative and interactive process made up of the following steps: developing an understanding of an application domain, creating a target data set, data cleaning and pre-processing, data reduction and projection, choosing the data mining task, choosing the data mining algorithm, data mining, interpretation of results and consolidating and using acquired knowledge. The process of KDD as it applies to the analysis of spontaneous reports can be exemplified by its routine use on the 3.5 million suspected adverse drug reaction (ADR) reports in the WHO ADR database. Examples of new adverse effects first highlighted by the KDD process on WHO data include topiramate glaucoma, infliximab vasculitis and the association of selective serotonin reuptake inhibitors (SSRIs) and neonatal convulsions. The KDD process has already improved our ability to highlight previously unsuspected ADRs for clinical review in spontaneous reporting, and we anticipate that such techniques will be increasingly used in the successful screening of other healthcare data sets such as patient records in the future.

Introducing triage logic as a new strategy for the detection of signals in the WHO Drug Monitoring Database.

PURPOSE: An important role for the WHO Programme for International Drug Monitoring is to identify signals of international drug safety problems as early as possible. The signal detection strategy, operated at the Uppsala Monitoring Centre (UMC), gave too many drug-adverse drug reaction (ADR) combinations for individual review. Therefore additional selection strategies were needed to improve the likely signal-to-noise ratio and for the UMC to complement the efforts of national centres in an efficient way. METHODS: The combinations database of the first quarter of 2001 was analysed using algorithms representing different strategies for finding relevant signals using triage logic. RESULTS: The strategies that together gave a manageable number of combinations, i.e. around 600, for further consideration in a single quarter were the algorithms for 'Rapid reporting increase', 'Serious reaction and new drug' and 'Special interests'. These filters began to be used routinely on the combinations database in late 2001. CONCLUSIONS: While stressing that human review is essential, triage strategies are useful when attempting analysis of large amounts of data. By definition, the use of triage strategies may exclude some potential signals from consideration, although the intention is to improve the chances of detection by focussing on areas of greatest importance.

Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs.

OBJECTIVE: The aim of this paper is to demonstrate the usefulness of the Bayesian Confidence Propagation Neural Network (BCPNN) in the detection of drug-specific and drug-group effects in the database of adverse drug reactions of the World Health Organization Programme for International Drug Monitoring. METHODS: Examples of drug-adverse reaction combinations highlighted by the BCPNN as quantitative associations were selected. The anatomical therapeutic chemical (ATC) group to which the drug belonged was then identified, and the information component (IC) was calculated for this ATC group and the adverse drug reaction (ADR). The IC of the ATC group with the ADR was then compared with the IC of the drug-ADR by plotting the change in IC and its 95% confidence limit over time for both. RESULTS: The chosen examples show that the BCPNN data-mining approach can identify drug-specific as well as group effects. In the known examples that served as test cases, beta-blocking agents other than practolol are not associated with sclerosing peritonitis, but all angiotensin-converting enzyme inhibitors are associated with coughing, as are antihistamines with heart-rhythm disorders and antipsychotics with myocarditis. The recently identified association between antipsychotics and myocarditis remains even after consideration of concomitant medication. CONCLUSION: The BCPNN can be used to improve the ability of a signal detection system to highlight group and drug-specific effects.

Enhancing traditional, televised, and videotaped courses with Web-based technologies: a comparison of student satisfaction.

BACKGROUND: Varied distance learning strategies can be used to deliver nursing courses, including interactive television, videotape, and Web-based approaches. PURPOSES: (1) To assess student assess student satisfaction with a critical care elective course offered simultaneously via traditional and distance learning formats in which Web-based strategies were added, and (2) to compare satisfaction of students taking the traditional course versus those taking the class via distance technology. METHODS: Students (n = 113) who took the course during the spring 1998 and 1999 semesters completed a teacher-constructed evaluation at the end of the semester. FINDINGS: Mean ratings on the evaluation were positive. Ratings of interaction, communication with instructor, and facilitation of learning were higher from students who took the traditional course. CONCLUSIONS: The application of Web-based technologies may be one factor for the overall course satisfaction. However, it is important to continue to evaluate strategies that work best for students taking courses via distance technology.

Time saved with the use of emergency warning lights and siren while responding to requests for emergency medical aid in a rural environment.

OBJECTIVE: To determine whether the use of warning lights and siren saves a significant amount of time for ambulances responding to requests for emergency medical aid in a rural emergency medical services (EMS) setting. METHODS: A prospective design was used to determine run times for ambulances responding to calls with lights and siren (code 3) and for a similarly equipped "chase" ambulance traveling to the same destination via the same route without lights and siren, while obeying all traffic laws (code 2) within a rural setting. Data were collected for run time intervals, distance traveled, visibility, road surface conditions, time of day, and day of the week. Descriptive statistics, a paired Student's t-test, and analysis of variance were used to test for significant differences between code 2 and code 3 operations, as well as the other variables listed above. RESULTS: Sixty-seven runs were timed during a 21-month period. The average code 3 response interval was 8.51 minutes. The average code 2 response interval was 12.14 minutes. The 3.63 minutes saved on average represents significant time savings of 30.9% (p < 0.01). Shorter runs had higher time savings per mile than the longer runs. Run distance was the only variable that was statistically significant in affecting time saved during a code 3 response. CONCLUSION: Code 3 operation by EMS personnel in a rural EMS setting saved significant time over code 2 operation when traveling to a call.

Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study.

OBJECTIVES: To examine the relation between antipsychotic drugs and myocarditis and cardiomyopathy. DESIGN: Data mining using bayesian statistics implemented in a neural network architecture. SETTING: International database on adverse drug reactions run by the World Health Organization programme for international drug monitoring. MAIN OUTCOME MEASURES: Reports mentioning antipsychotic drugs, cardiomyopathy, or myocarditis. RESULTS: A strong signal existed for an association between clozapine and cardiomyopathy and myocarditis. An association was also seen with other antipsychotics as a group. The association was based on sufficient cases with adequate documentation and apparent lack of confounding to constitute a signal. Associations between myocarditis or cardiomyopathy and lithium, chlorpromazine, fluphenazine, haloperidol, and risperidone need further investigation. CONCLUSIONS: Some antipsychotic drugs seem to be linked to cardiomyopathy and myocarditis. The study shows the potential of bayesian neural networks in analysing data on drug safety.

The WHO Programme for International Drug Monitoring, its database, and the technical support of the Uppsala Monitoring Center.

We describe the development of an international adverse reaction database. The operational responsibility for technical aspects of international drug monitoring are run by the Uppsala Monitoring Center (UMC). The system is based on interchange of adverse reaction information between national drug monitoring centers in 60 countries. Collectively these centers provide more than 150,000 individual reports annually of reactions suspected of being drug induced. The cumulative database constructed from these reports now comprises over 2 million records. Compatibility of different data collection systems that need to communicate with each other has been achieved through harmonization rather than standardization. The design of the new system was driven by the needs of existing and prospective users in terms of data fields and functionality. The data set required in the original WHO case reports form was the lowest common denominator consistent with being useful for signal generation and evaluation. The new database has an unlimited number of data fields. The WHO system relies on information being transferred, stored, and retrieved in a timely and secure way. Through the use of sophisticated exchange server technology, the Internet can be used as a transport medium for data and document transfer with guaranteed security and client authentication.

The value of reporting therapeutic ineffectiveness as an adverse drug reaction.

Therapeutic ineffectiveness is a frequent drug-related problem that can occur in a variety of different situations and be caused by different mechanisms. Examples are inappropriate use, interactions or metabolic abnormalities. Observations in patients of unexpected ineffectiveness can provide important information with regard to such situations. Therefore, ineffectiveness--especially when unexpected or unexplained--is a potentially important reportable event in pharmacovigilance. The terms regarding ineffectiveness in the WHO Adverse Reaction Terminology (WHOART) have been recently revised in order to enable optimal coding of such case reports.

An ABC of drug-related problems.

The problems relating to the use of medicines are manifold. They may differ in pharmacological, pathological, epidemiological and legal respects, and may have different consequences, for example, as regards scientific study, regulation or rational use. Pharmacovigilance is concerned with all such problems: adverse effects and interactions as well as problems relating to ineffectiveness, inappropriate use, counterfeiting, dependence or poisoning. Practically all medicine-related problems can be classified in one basic system, taking into account their characteristics and distinctions. This system distinguishes between appropriate and inappropriate drug use; dose-related and dose-unrelated problems [corrected]; and types A ('drug actions'), B ('patient reactions') and C ('statistical') adverse effects. This classification may serve as an educational tool and may be useful in when choosing a study method and for the design of effective strategies in pharmacovigilance.

A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database.

BACKGROUND: The detection of new drug safety signals is of growing importance with ever more new drugs becoming available and exposure to medicines increasing. The task of evaluating information relating to safety lies with national agencies and, for international data, with the World Health Organization Programme for International Drug Monitoring. RATIONALE: An established approach for identifying new drug safety signals from the international database of more than 2 million case reports depends upon clinical experts from around the world. With a very large amount of information to evaluate, such an approach is open to human error. To aid the clinical review, we have developed a new signalling process using Bayesian logic, applied to data mining, within a confidence propagation neural network (Bayesian Confidence Propagation Neural Network; BCPNN). Ultimately, this will also allow the evaluation of complex variables. METHODS: The first part of this study tested the predictive value of the BCPNN in new signal detection as compared with reference literature sources (Martindale's Extra Pharmacopoeia in 1993 and July 2000, and the Physicians Desk Reference in July 2000). In the second part of the study, results with the BCPNN method were compared with those of the former signalling procedure. RESULTS: In the study period (the first quarter of 1993) 107 drug-adverse reaction combinations were highlighted as new positive associations by the BCPNN, and referred to new drugs. 15 drug-adverse reaction combinations on new drugs became negative BCPNN associations in the study period. The BCPNN method detected signals with a positive predictive value of 44% and the negative predictive value was 85%. 17 as yet unconfirmed positive associations could not be dismissed with certainty as false positive signals. Of the 10 drug-adverse reaction signals produced by the former signal detection system from data sent out for review during the study period, 6 were also identified by the BCPNN. These 6 associations have all had a more than 10-fold increase of reports and 4 of them have been included in the reference sources. The remaining 4 signals that were not identified by the BCPNN had a small, or no, increase in the number of reports, and are not listed in the reference sources. CONCLUSION: Our evaluation showed that the BCPNN approach had a high and promising predictive value in identifying early signals of new adverse drug reactions.

International monitoring of adverse health effects associated with herbal medicines.

Herbal medicines are used in health care around the world and may increase in importance. There is much uncertainty, however, with regard to their composition, efficacy and safety. There is substantial evidence that herbal medicines can cause serious adverse reactions, but more data are needed as regard their nature, frequency and preventability. In this respect the Uppsala Monitoring Centre of the World Health Organization can play a crucial role. Better reporting of adverse reactions to herbal medicines is needed, in particular with regard to the precise identity and composition of these products. A consistent use by producers, regulators and reporters of the international Latin binomial nomenclature and the use of the new Herbal Anatomical Therapeutic Chemical (ATC) classification are recommended. Copyright (c) 2000 John Wiley & Sons, Ltd.

Spontaneous reporting of suspected adverse reactions to antihistamines: a national and international perspective.

Twenty-one sedative and five non-sedating antihistamines are presently available on the UK market. Analysis of spontaneous reports of suspected adverse drug reactions was performed for those drugs with more than 90 reported reactions on the UK ADROIT database to the period ending 31 December 1997. Thus nine antihistamines, (four sedative antihistamines and five non-sedating antihistamines) were included. For each of the four sedative agents (azatidine, chlorpheniramine, diphenhydramine and trimeprazine), reactions associated with the cardiovascular, gastrointestinal, central nervous systems, the skin, general and psychiatric reactions made up more than 70% of total reported reactions. For all four agents, fatal reactions constituted less than 2.5% of total reactions. For each of the five non-sedating agents (acrivastine, astemizole, cetirizine, loratidine and terfenadine), reactions associated with the cardiovascular, gastrointestinal central nervous systems, the skin, general and psychiatric disorders together made up more than 75% of total reported reactions. For all five non-sedating agents, fatal reactions constituted less than 1% of total reactions. However, there were 21 reports of fatality in association with terfenadine, 11 (52%) of which were either sudden deaths or those associated with a cardiac rate of rhythm reaction. Analysis of the WHO database for non-sedating drugs showed a similar pattern, with terfenadine being associated with the highest frequency of reports of potentially serious arrhythmias and of sudden death and death related to disturbances of cardiac rate and rhythm combined. Despite the limitations of spontaneous reporting systems, comparison of the benefit-risk profiles of drugs using this data within a class of drugs can provide valuable information, and pharmacovigilance of antihistamines (and all other agents) using this and other means should continue for the lifetime of their use in humans.

From association to alert--a revised approach to international signal analysis.

From the inception of the WHO international drug monitoring programme, the main aim has been to detect signals of adverse reaction problems as early as possible. The Uppsala Monitoring Centre (UMC), is now in a better position to fulfil this mission. Using the latest technology, new tools have been developed which allow for rapid, robust and comprehensive data mining of the WHO database. Based on retrospective time scans made during the pilot phase the current threshold used is the 97.5% confidence level of difference from the generality of the database. To maximize the capacity for picking up signals, we intend to extend today's panel of expert consultants, as well as doing our own review. The new system includes an enhanced follow-up list of signals, a 're-signalling' procedure and a cumulative historical file of all drug-ADR associations. Already we produce some 50 signals per year, cisapride and tachycardia being an example of a controversial signal only recently accepted. With the addition of new tools for follow-up of important signals such as complex variable data mining techniques, and the combination of WHO ADR data with sales and prescription figures from the IMS, we will be able to provide more information that should benefit regulators, producers, prescribers, and most importantly, the users of medicines.

A Bayesian neural network method for adverse drug reaction signal generation.

OBJECTIVE: The database of adverse drug reactions (ADRs) held by the Uppsala Monitoring Centre on behalf of the 47 countries of the World Health Organization (WHO) Collaborating Programme for International Drug Monitoring contains nearly two million reports. It is the largest database of this sort in the world, and about 35,000 new reports are added quarterly. The task of trying to find new drug-ADR signals has been carried out by an expert panel, but with such a large volume of material the task is daunting. We have developed a flexible, automated procedure to find new signals with known probability difference from the background data. METHOD: Data mining, using various computational approaches, has been applied in a variety of disciplines. A Bayesian confidence propagation neural network (BCPNN) has been developed which can manage large data sets, is robust in handling incomplete data, and may be used with complex variables. Using information theory, such a tool is ideal for finding drug-ADR combinations with other variables, which are highly associated compared to the generality of the stored data, or a section of the stored data. The method is transparent for easy checking and flexible for different kinds of search. RESULTS: Using the BCPNN, some time scan examples are given which show the power of the technique to find signals early (captopril-coughing) and to avoid false positives where a common drug and ADRs occur in the database (digoxin-acne; digoxin-rash). A routine application of the BCPNN to a quarterly update is also tested, showing that 1004 suspected drug-ADR combinations reached the 97.5% confidence level of difference from the generality. Of these, 307 were potentially serious ADRs, and of these 53 related to new drugs. Twelve of the latter were not recorded in the CD editions of The physician's Desk Reference or Martindale's Extra Pharmacopoea and did not appear in Reactions Weekly online. CONCLUSION: The results indicate that the BCPNN can be used in the detection of significant signals from the data set of the WHO Programme on International Drug Monitoring. The BCPNN will be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs.

How does cystitis affect a comparative risk profile of tiaprofenic acid with other non-steroidal antiinflammatory drugs? An international study based on spontaneous reports and drug usage data. ADR Signals Analysis Project (ASAP) Team.

Series of well-documented case reports strongly suggest a causal association between tiaprofenic acid and a form of aseptic cystitis, which can cause serious and long-term morbidity if the drug is not withdrawn promptly. These findings are supported in the Australian and UK spontaneous reporting data-bases. Using sales data as the denominator, a comparison of NSAIDs in the WHO drug monitoring data-base indicates that the reaction is specific to tiaprofenic acid and cannot be accounted for by changes in reporting patterns in certain countries or years. Delayed recognition is an important feature of this reaction and possible reasons for this are discussed. Comparison of the risk profiles of seven NSAIDs indicated that tiaprofenic acid had the poorest risk profile, compared with NSAIDs of similar efficacy, when cystitis reports were included. The results suggest that combining spontaneous reports, classified according to severity, with sales data may enhance the ability of drug monitoring data-bases to contribute to risk benefit appraisals.