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PubMed2005

Network analysis of mild cognitive impairment.

We present a network analysis of a cross-sectional study of mild cognitive impairment (MCI). Network analysis, as opposed to univariate analysis, accounts for interactions among brain structures in explaining a clinical outcome. In this context, we analyze structural magnetic resonance (MR) data based on a Bayesian network representation of variables in the problem domain. The Bayesian network resulting from this analysis reveals complex, nonlinear multivariate associations among morphological changes in the left hippocampus and in the right thalamus and the presence of mild cognitive impairment. This Bayesian network could be used to predict the presence of mild cognitive impairment from structural MR scans.

Aged↗
PubMed1997

Assessment of quantitative artificial neural network analysis in a metabolically dynamic ex vivo 31P NMR pig liver study.

Quantitative artificial neural network analysis for 1550 ex vivo 31P nuclear magnetic resonance spectra from hypothermically reperfused pig livers was assessed. These spectra show wide ranges of metabolite concentrations and have been analyzed using metabolite prior knowledge based lineshape fitting analysis which had proved robust in its biochemical interpretation. This finding provided a good opportunity to assess the performance of artificial neural network analysis in a biochemically complex situation. The results showed high correlations (0.865 < or = R < or = 0.992) between the lineshape fitting and artificial neural network analysis for the metabolite values, and the artificial neural network analysis was able to fully represent the trends in the metabolic fluctuations during the experiments.

Animals↗
PubMed2005

[Applying social network analysis to the health system].

BACKGROUND: Social network analysis is a method for describing and contextualising the structural relationships amongst social entities. The aim of this study was to assess the relationships between hospitals through the use of an example of a hospital that takes care of myocardial infarction in the region of Provence Alpes Côte d'Azur (PACA). METHOD: This retrospective study included all patients hospitalised for acute coronary syndrome in the PACA region between January and June 2000. The description analysis consisted of making a graph describing the transfer of patients between hospitals to calculate indicators for the centrality of the hospital and its prestige. The graph was also used to determine and measure the sub-group cohesion (cliques) and the density of the hospital network. RESULTS: A total of 2049 patients suffering from myocardial infarction were admitted to 78 hospitals during the study period. There were 867 transfers from one hospital to another. 51 cliques were highlighted; 29 hospitals do not belong to a clique. Among the 49 hospitals who do belong to sub-groups, 50% are linked to two cliques. Eight hospitals belong to more than 8 sub-groups, and the maximum number of sub-groups to which a hospital is linked is 12. The density of the hospital network is 0.12. CONCLUSIONS: Social network analysis is a field of research open and adaptable to the public health scene. This method could be applied to the evaluation of health and hospital administration and management networks.

Community Networks↗
PubMed1994

Survival analysis of censored data: neural network analysis detection of complex interactions between variables.

Neural networks can be used as pattern recognition systems in complex data sets. We are exploring their utility in performing survival analysis to predict time to relapse or death. This technique has the potential to find easily some types of very complex interactions in data that would not be easily recognized by conventional statistical methods. In this paper we demonstrate that there are several ways neural networks can be used to find three-way interactions among variables. Thus, in data sets where such complex interactions exist, neural networks may find utility in detecting such interactions and in helping to produce predictive models.

Breast Neoplasms↗
PubMed1976

The application of network analysis to the study of branching patterns of large dendritic fields.

Network analysis of dendritic fields not only defines the topology and connectivity of segments of an arborescence, but offers a means of discovering how networks grow. An important theory has recently been formulated29 suggesting that dendritic branching patterns may be established by synaptogenic interaction of dendritic growth cones with growing axons. This thesis may be verified through network analysis since the theory predicts that growth at pendant vertices will predominate in dendritic networks, that dendritic growth will be directed into areas of maximal synaptogenic activity and that arc lengths will be inversely related, and the order of branching at vertices directly related, to the magnitude of the synaptogenic activity operating about growing dendritic terminals. The possibility of a preponderance of terminal growth may be detected by comparing the topologies in an observed dendritic network with those of a series of hypothetical growth models. This paper provides the frequency table for models grown by monochotomous, dichotomous and trichotomous branching on random pendant vertices and random arcs for large networks in which 'set theory' contingencies are included. The paper also describes a method of calculating branching probabilities from the measurement of segment lengths, which is a means of testing the last mentioned prediction of the synaptogenic theory of denddritic growth. The method of network analysis is then discussed in relation to probable dendritic growth patterns, the constancy of segment lengths and the interaction of extrinsic and intrinsic factors in determining branching probabilities.

Dendrites↗
PubMed2004

Interorganizational relationships within state tobacco control networks: a social network analysis.

INTRODUCTION: State tobacco control programs are implemented by networks of public and private agencies with a common goal to reduce tobacco use. The degree of a program's comprehensiveness depends on the scope of its activities and the variety of agencies involved in the network. Structural aspects of these networks could help describe the process of implementing a state's tobacco control program, but have not yet been examined. METHODS: Social network analysis was used to examine the structure of five state tobacco control networks. Semi-structured interviews with key agencies collected quantitative and qualitative data on frequency of contact among network partners, money flow, relationship productivity, level of network effectiveness, and methods for improvement. RESULTS: Most states had hierarchical communication structures in which partner agencies had frequent contact with one or two central agencies. Lead agencies had the highest control over network communication. Networks with denser communication structures had denser productivity structures. Lead agencies had the highest financial influence within the networks, while statewide coalitions were financially influenced by others. Lead agencies had highly productive relationships with others, while agencies with narrow roles had fewer productive relationships. Statewide coalitions that received Robert Wood Johnson Foundation funding had more highly productive relationships than coalitions that did not receive the funding. CONCLUSION: Results suggest that frequent communication among network partners is related to more highly productive relationships. Results also highlight the importance of lead agencies and statewide coalitions in implementing a comprehensive state tobacco control program. Network analysis could be useful in developing process indicators for state tobacco control programs.

Academies and Institutes↗
PubMed2006

Transmission network analysis to complement routine tuberculosis contact investigations.

OBJECTIVE: We examined the feasibility and value of network analysis to complement routine tuberculosis (TB) contact investigation procedures during an outbreak. METHODS: We reviewed hospital, health department, and jail records and interviewed TB patients. Mycobacterium tuberculosis isolates were genotyped. We evaluated contacts of TB patients for latent TB infection (LTBI) and TB, and analyzed routine contact investigation data, including tuberculin skin test (TST) results. Outcomes included number of contacts identified, number of contacts evaluated, and their TST status. We used network analysis visualizations and metrics (reach, degree, betweenness) to characterize the outbreak. RESULTS: secondary TB patients and more than 1200 contacts. Genotyping detected a 21-band pattern of a strain W variant. No HIV-infected patients were diagnosed. Contacts prioritized by network analysis were more likely to have LTBI than nonprioritized contacts (odds ratio=7.8; 95% confidence interval=1.6, 36.6). Network visualizations and metrics highlighted patients central to sustaining the outbreak and helped prioritize contacts for evaluation. CONCLUSIONS: A network-informed approach to TB contact investigations provided a novel means to examine large quantities of data and helped focus TB control.

Adolescent↗
PubMed1994

The systems approach to international migration: an application of network analysis methods.

"The objective of this article is to apply formal network analysis to the systems approach [to the study of international migration]. Network analysis will allow two of the four methodological issues to be considered. First, clique and centrality analysis methods will be used to examine the definition and development of ties within an international migration system over time. Second, quadratic assignment procedure (QAP) regression techniques will be used to address the influence of macro factors on international migration flows....[We use] the European Union as a network of migration interest...." (SUMMARY IN FRE AND SPA)

Demography↗
PubMedDate not supplied

Command and control in emergency services operations: a social network analysis.

There is increasing interest in the use of social network analysis as a tool to study the performance of teams and organizations. In this paper, processes of command and control in the emergency services are explored from the perspective of social network theory. We report a set of network analyses (comprising visualization, a selection of mathematical metrics, and a discussion of procedures) based on the observation of six emergency service incidents: three fire service operations involving the treatment of hazardous chemicals, and three police operations involving immediate response to emergency calls. The findings are discussed in terms of our attempts to categorize the network structures against a set of extant command and control network archetypes and the relationship between those structures; comments on the qualities the networks display are put into the contexts of the incidents reported. We suggest that social network analysis may have a valuable part to play in the general study of command and control.

Efficiency, Organizational↗
PubMed2006

Qualitative and quantitative investigation of chromium-polluted soils by laser-induced breakdown spectroscopy combined with neural networks analysis.

Laser-induced breakdown spectroscopy (LIBS) has been applied to the analysis of three chromium-doped soils. Two chemometric techniques, principal components analysis (PCA) and neural networks analysis (NNA), were used to discriminate the soils on the basis of their LIBS spectra. An excellent rate of correct classification was achieved and a better ability of neural networks to cope with real-world, noisy spectra was demonstrated. Neural networks were then used for measuring chromium concentration in one of the soils. We performed a detailed optimization of the inputs of the network so as to improve its predictive performances and we studied the effect of the presence of matrix-specific information in the inputs examined. Finally the inputs of the network--the spectral intensities--were replaced by the line areas. This provided the best results with a prediction accuracy and precision of about 5% in the determination of chromium concentration and a significant reduction of the data, too.

Journal Article↗
PubMed2004

Global network analysis of phenotypic effects: protein networks and toxicity modulation in Saccharomyces cerevisiae.

Using genome-wide information to understand holistically how cells function is a major challenge of the postgenomic era. Recent efforts to understand molecular pathway operation from a global perspective have lacked experimental data on phenotypic context, so insights concerning biologically relevant network characteristics of key genes or proteins have remained largely speculative. Here, we present a global network investigation of the genotype/phenotype data set we developed for the recovery of the yeast Saccharomyces cerevisiae from exposure to DNA-damaging agents, enabling explicit study of how protein-protein interaction network characteristics may be associated with phenotypic functional effects. We show that toxicity-modulating proteins have similar topological properties as essential proteins, suggesting that cells initiate highly coordinated responses to damage similar to those needed for vital cellular functions. We also identify toxicologically important protein complexes, pathways, and modules. These results have potential implications for understanding toxicity-modulating processes relevant to a number of human diseases, including cancer and aging.

Cluster Analysis↗
PubMed1998

European Functional Analysis Network (EUROFAN) and the functional analysis of the Saccharomyces cerevisiae genome.

Less than two yeras after the sequence of its genome was completed, the baker's yeast, Saccharomyces cerevisiae, is a leading organism in the rapidly growing field of functional genomics. Two thousands novel protein coding genes, nearly all of them "orphans", have already been disrupted by the coordinated efforts of a large consortium of European laboratories, EUROFAN, and other initiatives. The mutants are submitted to many specialized functional assays, and studies are performed in parallel at the transcriptome and the proteome levels. With a central repository of mutant yeast strains, and a centralized database, EUROFAN lays the foundations for the future of genomics with yeast serving both as a model and a tool.

DNA Transposable Elements↗
PubMed2001

Neural network analysis of the pattern of functional connectivity between cerebral areas in schizophrenia.

Recent evidence suggests that the core abnormality of cerebral function in schizophrenia is a disruption of functional connectivity between diverse cerebral sites. Functional connectivity is defined as the correlation between neuronal activity at remote sites. It can be measured using functional imaging techniques such as positron emission tomography (PET). This paper reports an analysis using a neural network to discriminate between the patterns of functional connectivity in schizophrenic patients and healthy subjects. The data was derived from a PET study of regional cerebral blood flow during word generation in 6 healthy subjects and 16 schizophrenic patients with established illness, in whom the clinical diagnosis could be made with confidence. After training on data from two healthy subjects and seven schizophrenic patients, the neural network successfully assigned all members of a test set of four healthy subjects and nine schizophrenic patients to the correct diagnostic category. While this result should be interpreted with caution on account of the small sample size, it indicates that neural network analysis is potentially of value in the diagnosis of schizophrenia.

Case-Control Studies↗
PubMed2004

Quantitative methods for ecological network analysis.

The analysis of networks of ecological trophic transfers is a useful complement to simulation modeling in the quest for understanding whole-ecosystem dynamics. Trophic networks can be studied in quantitative and systematic fashion at several levels. Indirect relationships between any two individual taxa in an ecosystem, which often differ in either nature or magnitude from their direct influences, can be assayed using techniques from linear algebra. The same mathematics can also be employed to ascertain where along the trophic continuum any individual taxon is operating, or to map the web of connections into a virtual linear chain that summarizes trophodynamic performance by the system. Backtracking algorithms with pruning have been written which identify pathways for the recycle of materials and energy within the system. The pattern of such cycling often reveals modes of control or types of functions exhibited by various groups of taxa. The performance of the system as a whole at processing material and energy can be quantified using information theory. In particular, the complexity of process interactions can be parsed into separate terms that distinguish organized, efficient performance from the capacity for further development and recovery from disturbance. Finally, the sensitivities of the information-theoretic system indices appear to identify the dynamical bottlenecks in ecosystem functioning.

Animals↗
PubMed1999

Headache interference as a function of affect and coping: an artificial neural network analysis.

Artificial neural networks present a technique for modeling relationships between variables in complex systems. The negative effects of headache are determined by many "biopsychosocial" elements that represent a complex system. Artificial neural networks may therefore be useful for examining psychological factors in headache. To test this hypothesis, we trained an artificial neural network to predict life-style interference attributed to headache from psychological measures of anger, depression, and coping appraisal and strategies. The artificial neural network demonstrated a better fit of the data than that obtained by multiple regression, and predicted interference levels to within 10% error for 80% of novel cases. Artificial neural networks may be a useful technique for examining psychological correlates of headache.

Adaptation, Psychological↗
PubMed1996

Fuel identification by neural network analysis of the response of vapor-sensitive sensor arrays.

Neural network analysis of the response of an array of vapor-sensitive detectors has been used to identify six different types of aviation fuel. The data set included 96 samples of JP-4, JP-5, JP-7, JP-8, JetA, and aviation gasoline (AvGas). A sample of each neat fuel was injected into a continuous stream of breathing air through an injection port from a gas chromatograph. The aspirated sample was then swept from the injection port to the chamber without separation. In the chamber, the sample was exposed to an array of eight vapor-sensitive detectors. The analog output of each detector was digitized and stored while the sample was swept into and through the chamber. The response of each detector was then averaged and stored as the final response or pattern of each sample. It was clear from a visual inspection of each of the radar plots that there was a characteristic pattern in the response of the array to five of the six different fuel types. This was confirmed using neural network analysis to study the entire data set. A two-step procedure was developed to separate the patterns of all six fuel tyes into their respective classes. In the first step, fuels were separated into one of five groups: JP-4, JP-5, JP-7, AvGas, or a combined JP-8/JetA group. In the second step, the fuels in the combined group were separated into either JP-8 or JetA groups.

Chromatography, Gas↗
PubMed2004

Comparison of logistic regression and neural network analysis applied to predicting living setting after hip fracture.

PURPOSE: Describe and compare the characteristics of artificial neural networks and logistic regression to develop prediction models in epidemiological research. METHODS: The sample included 3708 persons with hip fracture from 46 different states included in the Uniform Data System for Medical Rehabilitation. Mean age was 75.5 years (sd=14.2), 73.7% of patients were female, and 82% were non-Hispanic white. Average length of stay was 17.0 days (sd=10.6). The primary outcome measure was living setting (at home vs. not at home) at 80 to 180 days after discharge. RESULTS: Statistically significant variables (p <.05) in the logistic model included follow-up therapy, sphincter control, self-care ability, marital status, age, and length of stay. Areas under the receiver operating characteristic curves were 0.67 for logistic regression and 0.73 for neural network analysis. Calibration curves indicated a slightly better fit for the neural network model. CONCLUSIONS: Follow-up therapy and independent bowel and/or bladder function were strong predictors of living at home up to 6 months after hospitalization for hip fracture. No practical differences were found between the predictive ability of logistic regression and neural network analysis in this sample.

Aged↗
PubMed1994

Neural network analysis of the P300 event-related potential in multiple sclerosis.

Neural network analysis is sensitive to subtle changes in patterns of data. We hypothesized that a disease process which can cause impairment of cortical function such as multiple sclerosis (MS) would affect the P300 cognitive evoked potential (P300) in a manner detectable by a feedforward backpropagation neural network. Such a network was trained using a learning data set consisting of 101 P300 wave forms (from 26 MS patients and 26 normal controls). The network was then used to classify a randomly selected test data set of 20 studies (2 studies each of 5 MS patients and 5 controls) to which it had not been previously exposed, with an average accuracy (MS = abnormal, control = normal) of 81% for a single midline electrode, increasing to 90% using 3 midline electrodes in a jury system. Neural network analysis can be of help in distinguishing normal (control) P300 from abnormal (MS) P300.

Acoustic Stimulation↗