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At least 163 records · Page 9Linked to original sources
PubMed2002

Functional connectivity in the resting brain: a network analysis of the default mode hypothesis.

Functional imaging studies have shown that certain brain regions, including posterior cingulate cortex (PCC) and ventral anterior cingulate cortex (vACC), consistently show greater activity during resting states than during cognitive tasks. This finding led to the hypothesis that these regions constitute a network supporting a default mode of brain function. In this study, we investigate three questions pertaining to this hypothesis: Does such a resting-state network exist in the human brain? Is it modulated during simple sensory processing? How is it modulated during cognitive processing? To address these questions, we defined PCC and vACC regions that showed decreased activity during a cognitive (working memory) task, then examined their functional connectivity during rest. PCC was strongly coupled with vACC and several other brain regions implicated in the default mode network. Next, we examined the functional connectivity of PCC and vACC during a visual processing task and show that the resultant connectivity maps are virtually identical to those obtained during rest. Last, we defined three lateral prefrontal regions showing increased activity during the cognitive task and examined their resting-state connectivity. We report significant inverse correlations among all three lateral prefrontal regions and PCC, suggesting a mechanism for attenuation of default mode network activity during cognitive processing. This study constitutes, to our knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network. Our findings also provide insight into how this network is modulated by task demands and what functions it might subserve.

Adult↗
PubMed2001

Neural network analysis in predicting 2-year survival in elderly people: a new statistical-mathematical approach.

We designed this study to test the usefulness of artificial neural networks (ANN) in assessing 2-year survival in elderly persons, and to understand the net's logical functioning, thus determining the relative importance of the single biological and clinical variables which influence survival. ANN are statistical-mathematical tools able to determine the existence of a correlation between series of data and, once 'trained', to predict output data given input data. Although ANN have been applied in various areas of medical research, they have only very recently been applied in geriatrics (Cacciafesta et al., 2000. Arch. Gerontol. Geriatr. 31 (in press)). We built up an ANN to investigate how 17 clinical variables relating to a sample of 159 elderly people affect survival, and the possibility of predicting 2-year survival or non-survival for each single subject. When tested on a sample of 20 elderly people, the trained network gave the correct answer in 85% of the cases. We then extracted the mathematical function that the net used for calculating the output (survival) for each set of input data (clinical variables). Using this formula, we investigated how some clinical variables influence 2-year survival: we found that a low serum cholesterol level is an unfavourable characteristic in relation to survival. We conclude-despite the fact that the sample studied was relatively small-that ANN are useful in predicting 2-year survival in elderly people. The mathematical function we obtained from the net seems useful in determining the relative importance of single variables related to survival.

Journal Article↗
PubMed2003

Process mapping as a tool for home health network analysis.

Process mapping is a qualitative tool that allows service providers, policy makers, researchers, and other concerned stakeholders to get a "bird's eye view" of a home health care organizational network or a very focused, in-depth view of a component of such a network. It can be used to share knowledge about community resources directed at the older population, identify gaps in resource availability and access, and promote on-going collaborative interactions that encourage systemic policy reassessment and programmatic refinement. This article is a methodological description of process mapping, which explores its utility as a practice and research tool, illustrates its use in describing service-providing networks, and discusses some of the issues that are key to successfully using this methodology.

Community Health Planning↗
PubMed1999

Neural network analysis of the volumetric capnogram to detect pulmonary embolism.

BACKGROUND: Pulmonary embolism (PE) produces ventilation/perfusion mismatch that may be manifested in various variables of the volume-based capnogram (VBC). We hypothesized that a neural network (NN) system could detect changes in VBC variables that reflect the presence of a PE. METHODS: A commercial VBC system was used to record multiple respiratory variables from consecutive expiratory breaths. Data from 12 subjects (n = 6 PE+ and n = 6 PE-) were used as input to a fully connected back-propagating NN for model development. The derived model was tested in a prospective, observational study at an urban teaching hospital. Volumetric capnograms were then collected on 53 test subjects: 30 subjects with PE confirmed by pulmonary angiography or diagnostic scintillation lung scan, and 23 subjects without PE based on pulmonary angiography. The derived NN model was applied to VBC data from the test population. RESULTS: Seventeen VBC variables were used by the derived NN model to generate a numeric probability of PE. When the derived NN model was applied to VBC data from the 53 test subjects, PE was detected with a sensitivity of 100% (95% CI = 89% to 100%) and a specificity of 48% (95% CI = 27% to 69%). The likelihood ratio positive [LR(+)] for the VBC-NN test was 1.82 and the LR (-) was 0.1. CONCLUSION: This study demonstrates the feasibility of developing a rapid, noninvasive breath test for diagnosing PE using volumetric capnography and NN analysis.

Angiography↗
PubMed2003

Determination of the composition of light thin films with artificial neural network analysis of Rutherford backscattering experiments.

AlO(x)N(y) ultrathin films are used as insulating layers in advanced microelectronic devices. Structural characterization of these films is often done by the Rutherford backscattering (RBS) analysis. The RBS analysis of these oxinitrides is a difficult task since the relevant signals of the spectrum are washed out by the large substrate background and a considerable time is required for an analyst to characterize the sample. In this work we developed specialized artificial neural networks that are able to perform a fast and efficient analysis of the data. The results are in good agreement with traditional methods.

Journal Article↗
PubMed2003

A metabolic network analysis & NMR experiment design tool with user interface-driven model construction for depth-first search analysis.

A Windows program for metabolic engineering analysis and experimental design has been developed. A graphical user interface enables the pictorial, "on-screen" construction of a metabolic network. Once a model is composed, balance equations are automatically generated. Model construction, modification and information exchange between different users is thus considerably simplified. For a given model, the program can then be used to predict all the extreme point flux distributions that optimize an objective function while satisfying balances and constraints by using a depth-first search strategy. One can also find the minimum reaction set that satisfies different conditions. Based on the identified flux distributions or linear combinations, the user can simulate the NMR and GC/MS spectra of selected signal molecules. Alternately, spectra vectorization allows for the automated optimization of labeling experiments that are intended to distinguish between different, yet plausible flux extreme point distributions. The example provided entails predicting the flux distributions associated with deleting pyruvate kinase and designing 13C NMR experiments that can maximally discriminate between the flux distributions.

Computer Simulation↗
PubMed2003

Neural network analysis of lymphoma microarray data: prognosis and diagnosis near-perfect.

BACKGROUND: Microarray chips are being rapidly deployed as a major tool in genomic research. To date most of the analysis of the enormous amount of information provided on these chips has relied on clustering techniques and other standard statistical procedures. These methods, particularly with regard to cancer patient prognosis, have generally been inadequate in providing the reduced gene subsets required for perfect classification. RESULTS: Networks trained on microarray data from DLBCL lymphoma patients have, for the first time, been able to predict the long-term survival of individual patients with 100% accuracy. Other networks were able to distinguish DLBCL lymphoma donors from other donors, including donors with other lymphomas, with 99% accuracy. Differentiating the trained network can narrow the gene profile to less than three dozen genes for each classification. CONCLUSIONS: Here we show that artificial neural networks are a superior tool for digesting microarray data both with regard to making distinctions based on the data and with regard to providing very specific reference as to which genes were most important in making the correct distinction in each case.

Algorithms↗
PubMed1998

Prediction of cirrhosis in patients with chronic hepatitis C infection by artificial neural network analysis of virus and clinical factors.

The diagnosis of cirrhosis in patients with hepatitis C virus (HCV) infection is currently made using a liver biopsy. In this study we have trained and validated artificial neural networks (ANN) with routine clinical host and viral parameters to predict the presence or absence of cirrhosis in patients with chronic HCV infection and assessed and interpreted the role of the different inputs on the ANN classification. Fifteen routine clinical and virological factors were collated from 112 patients who were HCV RNA positive by reverse transcriptase-polymerase chain reaction (RT-PCR). Standard and Ward-type feed-forward fully-connected ANN analyses were carried out both by training the networks with data from 82 patients and subsequently testing with data from 30 patients plus performing leave-one-out tests for the whole patient data set. The ANN results were also compared with those from multiple logistic regression. The performance of both ANN methods was superior compared with the logistic regression. The best performance was obtained with the Ward-type ANNs resulting in a sensitivity of 92% and a specificity of 98.9% together with a predictive value of a positive test of 95% and a predictive value of a negative test of 97% in the leave-one-out test. Hence, further validation of the ANN analysis is likely to provide a non-invasive test for diagnosing cirrhosis in HCV-infected patients.

Adult↗
PubMedDate not supplied

Artificial neural network analysis of 1H nuclear magnetic resonance spectroscopic data from human plasma.

Nuclear magnetic resonance (NMR) spectroscopy is finding increasing use in studies of plasma and lipoproteins in health and disease, including cancer. Analysis of the NMR data is not straightforward due to complex systems and also partly unknown underlying biochemistry. Here we demonstrate how artificial neural networks can be utilised in biomedical NMR. Their quantification power is illustrated by establishing lipoprotein lipid quantification directly from plasma 1H NMR data. The biochemical rationale for this example is elucidated on the basis of the relative weights of the spectral inputs in a trained network. A novel application of a Kohonen-type network architecture to classify plasma 1H NMR spectra is also presented.

Humans↗
PubMed2003

Prognosis in node-negative primary breast cancer: a neural network analysis of risk profiles using routinely assessed factors.

BACKGROUND: The present study investigated complex time-dependent effects of routinely assessed factors on the risk of breast cancer recurrence over follow-up time, with a partial logistic artificial neural network (PLANN) model. PATIENTS AND METHODS: PLANN was applied to data from 1793 patients with node-negative breast cancer, not submitted to any adjuvant treatment and with a minimal potential follow-up of 10 years. RESULTS: The shape of the hazard function changed according to histology, which showed a time-dependent effect, partly modulated by estrogen receptors (ERs). Age and progesterone receptors (PgR) showed protective effects; the latter was more evident for short follow-up and high ER values. Tumour size and ER content showed time-dependent unfavourable effects at early and long follow-up times, respectively. Predicted values of disease recurrence probability at 2 years of follow-up showed that low steroid-receptor content, young age and large tumour size were associated with the highest risk of relapse. Although the oldest patients with high ER content seem to be those most protected overall, high risk predictions tend to spread also to higher steroid-receptor contents, intermediate ages and small tumour size, with an increase in follow-up time. CONCLUSION: PLANN with suitable visualisation techniques provided thorough insights into the dynamics of breast cancer recurrence for improving individual risk staging of node-negative breast cancer patients.

Breast Neoplasms↗
PubMed1993

Network analysis of functional auditory pathways mapped with fluorodeoxyglucose: associative effects of a tone conditioned as a Pavlovian excitor or inhibitor.

The purpose of this study was to examine how opposite learned associative properties of the same auditory stimulus are represented by the pattern of network interactions between auditory system structures. [14C(U)]2-fluoro-2-deoxyglucose (FDG) autoradiography was used to compare mean auditory system activity and interregional correlations resulting from the presentation of a tone trained as either a Pavlovian conditioned excitor or inhibitor. Rats were trained with reinforced trials of the conditioned excitor (A+) intermixed with non-reinforced trials of a tone-light compound (AX-). For the Conditioned Excitor group, the tone was the excitor (A+), while for the Conditioned Inhibitor group the tone was the inhibitor (X-). After conditioning, both groups were injected with FDG and presented with the same tone. Structural equation models, constructed from the anatomical connections between auditory regions and their interregional correlations in FDG uptake, were used to calculate path coefficients representing the network interactions. The opposite associative significance of the tone was reflected as functional changes in the interactions between parallel auditory pathways. Direct covariance effects through lemniscal pathways from the ventral cochlear nucleus were similar in absolute magnitude but differed in sign between the Excitor and Inhibitor network models. Extra-auditory influences on the dorsal cochlear nucleus were greater for the tone-inhibitor, reflecting possible interactions of this nucleus with extra-auditory regions. The different associative effects of the tone suggest that central auditory pathways can code not only the physical qualities, but also the associative significance of auditory stimuli. These findings demonstrate that neural network interactions differentiate the associative effects of tones in the brain. It is proposed that associative learning is a distributed property of neural networks and that such a property can be understood by considering the interactions between component parts of the network.

Acoustic Stimulation↗
PubMed2026

Integrative Network Analysis of Bioactive Compounds from Punica granatum L. Peel: Multi-Target Mechanisms in Wound Healing.

BACKGROUND: Wound-healing agents often have limited efficacy and require prolonged recovery times, prompting growing interest in developing herbal-based formulations. Among these, Punica granatum L. has attracted considerable attention because of its high polyphenolic content. Despite its widespread use, the precise pharmacological targets underlying its wound-healing effects remain poorly understood and require systematic investigation. OBJECTIVES: This study aimed to elucidate the underlying pharmacological mechanisms of the topical wound-healing properties of P. granatum L. using a network pharmacology approach. METHODS: Bioactive compounds of P. granatum L. and their potential target genes were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Similarity Ensemble Approach (SEA), and SwissTargetPrediction databases. Wound healing-related genes were retrieved from the GeneCards database. Genes intersecting P. granatum L. targets and wound healing-associated genes were subjected to functional enrichment analyses, including protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The PPI network was further analyzed using Cytoscape, and the phytoconstituent-target interaction network was visualized using Gephi. These findings were validated using molecular docking. RESULTS: A total of 40 intersecting genes were identified as potential P. granatum L. targets involved in wound healing. Among these, EGFR, PTPN11, HRAS, IGF1R, and ESR1 were identified as key hub genes. Functional enrichment analysis indicated that the most significantly enriched signaling pathways included the MAPK, PI3K-Akt, EGFR tyrosine kinase inhibitor resistance, focal adhesion, and FoxO signaling pathways. Molecular docking analysis confirmed favorable binding of quercetin and ellagic acid to the hub targets EGFR, IGF1R, and ESR1. CONCLUSIONS: These findings elucidate the pharmacological pathways underlying P. granatum-mediated wound healing and suggest that P. granatum L. acts as a multi-target modulator in the wound-healing process.

Focal Adhesion↗
PubMed2025

DigiNet: Optimizing personalized care for patients with stage IV non-small cell lung cancer (NSCLC) through a digitally connected provider network-analysis plan of a prospective multicenter cohort trial.

PURPOSE: The German sector-based healthcare system poses a major challenge to continuous patient monitoring and long-term follow-up, both essential for generating high-quality, longitudinal real-world data. The national Network for Genomic Medicine (nNGM) bridges the inpatient and outpatient care sectors to provide comprehensive molecular diagnostics and personalized treatment for non-small cell lung cancer (NSCLC) patients in Germany. Building on the established nNGM infrastructure, the DigiNet study aims to evaluate the impact of digitally integrated, personalized care on overall survival (OS) and the optimization of treatment pathways, compared to routine care. METHODS: DigiNet is a prospective, controlled, non-randomized multicenter cohort study including patients with stage IV NSCLC in two study regions (East and West) in Germany. The results of molecular diagnostics and clinical information, along with the entire treatment data are documented in a shared database. A board of lung cancer specialists monitors critical events. Patients digitally complete quality of life questionnaires, with results visualized for physicians. To assess the impact of this personalized digital care, a population-based control group will be identified by matching cohorts within the involved cancer registries. The primary endpoint is OS, and secondary endpoints comprise time on first-line treatment and hospitalization rates. Furthermore, a health economic and business economic evaluation will be conducted. Qualitative interviews with patients and physicians will be performed to assess barriers and facilitating factors for implementing the DigiNet intervention. ETHICS: The study protocol was reviewed and approved by the Ethics Committee of the University Hospital of Cologne (21-1521). TRIAL REGISTRATION: NCT05818449, registered retrospectively on December 12, 2022.

Humans↗
PubMedDate not supplied

Nonlinear V1 responses to natural scenes revealed by neural network analysis.

A key goal in the study of visual processing is to obtain a comprehensive description of the relationship between visual stimuli and neuronal responses. One way to guide the search for models is to use a general nonparametric regression algorithm, such as a neural network. We have developed a multilayer feed-forward network algorithm that can be used to characterize nonlinear stimulus-response mapping functions of neurons in primary visual cortex (area V1) using natural image stimuli. The network is capable of extracting several known V1 response properties such as: orientation and spatial frequency tuning, the spatial phase invariance of complex cells, and direction selectivity. We present details of a method for training networks and visualizing their properties. We also compare how well conventional explicit models and those developed using neural networks can predict novel responses to natural scenes.

Action Potentials↗
PubMed1999

Genetic network analysis in light of massively parallel biological data acquisition.

Complementary DNA microarray and high density oligonucleotide arrays opened the opportunity for massively parallel biological data acquisition. Application of these technologies will shift the emphasis in biological research from primary data generation to complex quantitative data analysis. Reverse engineering of time-dependent gene-expression matrices is amongst the first complex tools to be developed. The success of reverse engineering will depend on the quantitative features of the genetic networks and the quality of information we can obtain from biological systems. This paper reviews how the (1) stochastic nature, (2) the effective size, and (3) the compartmentalization of genetic networks as well as (4) the information content of gene expression matrices will influence our ability to perform successful reverse engineering.

Computational Biology↗
PubMed1997

Neural network analysis of Doppler ultrasound blood flow signals: a pilot study.

It has been hypothesised that each artery in the human body has its own characteristic "signature" -a unique Doppler flow profile which can identify the artery and which may also be modified by the presence of disease. To test this hypothesis an artificial neural network (ANN) was trained to recognise three groups of maximum frequency envelopes derived from Doppler ultrasound spectrograms; these were the common carotid, common femoral and popliteal arteries. Data were collected from 24 subjects known to have no significant atheromatous disease. The maximum frequency envelopes were used to create sets of training and testing vectors for a backpropagation ANN. The ANN demonstrated a high success rate for appropriate classification of the test vectors: 100% for the carotid; 92% for the femoral; and 96% for the popliteal artery. This work has demonstrated the ability of the ANN to differentiate accurately between different and similar flow profiles, outlining the potential of this technology to identify subtle changes induced by the onset of arterial disease within a specific vessel. It should be noted that the ANN not only models the maximum frequency envelope but also, unlike standard indices, makes a decision as to which artery the maximum frequency envelope belongs to, thus providing the potential to obviate human subjective classification.

Adult↗