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PubMed1999

[A computer-aided analysis system for hypertensive retinal image and its clinical application].

With the use of computer image processing technology and microcirculational network analysis technology, we have developed a new system to analyze the outlook of retinal vessles quantitatively. A multimedia structure has been designed for patient's case history file, which includes the basic information of patients, the operating information during sampling and processing patient's retinal image and the result. The system includes image sampling, image displaying, image processing, parameters measuring and analyzing, reports printing and atlas management. Clinical application indicates that the system is useful to hypertension diagnosis.

Aged↗
PubMed2002

Can we discover novel pathways using metabolomic analysis?

Metabolomic analysis aims at the identification and quantitation of all metabolites in a given biological sample. Current data acquisition and network analysis strategies are classified on the basis of pathway elucidation and characteristics of theoretical networks. The development of metabolomic methods and tools is progressing rapidly, but an understanding of the resulting data is limited owing to a fundamental lack of biochemical and physiological knowledge about network organization in plants.

Chromatography↗
PubMed2006

The computerized fetal heart rate analysis in post-term pregnancy identifies patients at risk for fetal distress in labour.

OBJECTIVE: To ascertain the diagnostic ability of a computerized fetal heart rate (FHR) analysis system in the identification of patients at risk of fetal distress in labour. STUDY DESIGN: Three hundred and two healthy post-term pregnancies were enrolled in a retrospective, cross-sectional study and subdivided into two groups, with (n=42) or without (n=260) fetal distress in labour. The last computerized FHR recording before onset of labour was analyzed. RESULTS: The two groups showed a significant difference only in FHR baseline and in percentage of small accelerations on total. The multivariate analysis showed that only the percentage of small accelerations was significantly related to the labour outcome. A higher diagnostic accuracy was obtained with use of neural network analysis, which allowed a sensitivity of 56%, specificity 91%, positive predictive value 53% and negative predictive value 92% with an overall accuracy of 86%. CONCLUSIONS: The increase in FHR baseline and in small FHR accelerations can be major factors in the prediction of subsequent fetal distress in healthy term fetuses. Use of neural networks seems to further improve the ability of computerized FHR analysis in the prediction of intrapartum distress.

Cardiotocography↗
PubMed2025

Bioinformatics identification and validation of pyroptosis-related gene for ischemic stroke.

BACKGROUND: Ischemic stroke (IS) is one of the common and frequent diseases with extremely high lethality and disability in the world, and there is no effective treatment at present. This study aimed to screen hub genes involved in cerebral ischemia/reperfusion injury (CIRI) and pyroptosis, and explore promising intervention targets. METHODS: CIRI-related genes (GSE202659 and GSE131193) and pyroptosis-related genes (PRGs) in mice were obtained from the Gene Expression Omnibus (GEO) and GeneCards database. We screened for LASSO regression to construct a prognostic model of GSE131193 and PRGs and examined by GSE137482. The functional enrichment analysis of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed on pyroptosis-related differentially expressed genes (PRDEGs) of GSE202659.The key modules for CIRI and pyroptosis were identified by Weight Gene Co-expression Network Analysis (WGCNA). Subsequently, Protein-protein Interaction (PPI) network and the Cytoscape was constructed to screen out hub genes. Used the starBase to predict miRNA interacting with hub genes and constructed mRNA-miRNA-lncRNA interaction networks. CIRI-related Molecular Subtypes were constructed for hub genes. The relationship between immune cells and hub genes was verified via CIBERSORT. Finally, we selected C57BL/6 mice to construct models to confirm hub genes by enzyme linked immunosorbent assay (ELISA), reverse transcription-polymerase chain reaction (RT-PCR), western blot, and Immunofluorescence. RESULTS: A total of 272 PRGs and 35 PRDEGs were screened. An eight-gene risk prediction models were established (AUC = 0.868). GO, KEGG, GSEA and GSVA analyses revealed that PRDEGs were mainly involved in positive regulation of cytokine production, and NOD-like receptor signaling pathway. And then, seven hub genes (Irf1, Icam1, Tlr2, Tnf, Cebpb, Il1rn, and Casp8) were identified by PPI. Icam1, Tnf, Cebpb, Il1rn, and Casp8 had high expression profiles in Cluster2 by hierarchical clustering. The immune infiltration analysis results showed that among the hub genes, Cebpb, Il1rn, and Casp8, showed a significant positive correlation with the degree of NK.Actived, and Icam1 showed a significant negative correlation with B.Cells.Memory. The results of animal experiments significantly demonstrated an upregulation of Irf1, Icam1, Tlr2, Cebpb, and Il1rn. CONCLUSION: Our finding indicated that Irf1, Icam1, Tlr2, Cebpb, and Il1rn are hub genes associated with pyroptosis, and these genes are all associated with different immune cells, so as to provide new targets for the prevention and treatment of IS from the perspective of pyroptosis.

Pyroptosis↗
PubMed2003

Analysis of a municipal wastewater treatment plant using a neural network-based pattern analysis.

This paper addresses the problem of how to capture the complex relationships that exist between process variables and to diagnose the dynamic behaviour of a municipal wastewater treatment plant (WTP). Due to the complex biological reaction mechanisms, the highly time-varying, and multivariable aspects of the real WTP, the diagnosis of the WTP are still difficult in practice. The application of intelligent techniques, which can analyse the multi-dimensional process data using a sophisticated visualisation technique, can be useful for analysing and diagnosing the activated-sludge WTP. In this paper, the Kohonen Self-Organising Feature Maps (KSOFM) neural network is applied to analyse the multi-dimensional process data, and to diagnose the inter-relationship of the process variables in a real activated-sludge WTP. By using component planes, some detailed local relationships between the process variables, e.g., responses of the process variables under different operating conditions, as well as the global information is discovered. The operating condition and the inter-relationship among the process variables in the WTP have been diagnosed and extracted by the information obtained from the clustering analysis of the maps. It is concluded that the KSOFM technique provides an effective analysing and diagnosing tool to understand the system behaviour and to extract knowledge contained in multi-dimensional data of a large-scale WTP.

Data Collection↗
PubMed2025

Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma.

BACKGROUND: One of the most common and prevalent cancers is laryngeal squamous cell carcinoma (LSCC), which poses a great threat to the life and health of the patient. Nonetheless, it has been demonstrated that ubiquitination is crucial for the development and course of LSCC. Therefore, it is particularly important to identify biomarkers for ubiquitination-related genes (UbRGs) in LSCC. METHODS: Differentially expressed genes (DEGs) in the LSCC versus controls were obtained by differential expression analysis. Also, key modular genes associated with LSCC were obtained using weighted gene co-expression network analysis (WGCNA). Next, DEGs, key module genes, and UbRGs were taken to intersect to obtain candidate genes. And then machine algorithms were to screen potential biomarkers, further their diagnostic value were analyzed and validated. Then, therapeutic agents for biomarkers were predict. In addition, the regulatory networks of the biomarkers were mapped. The expression levels of biomarkers were detected in clinical samples using reverse transcription-quantitative PCR (RT-qPCR). RESULTS: A total of eight candidate genes were acquired by the overlap 1,911 DEGs, the key modular genes of WGCNA, and 1,393 UbRGs. A sum of four biomarkers (WDR54, KAT2B, NBEAL2 and LNX1) were identified by two machine learning, then these four biomarkers were validated in GSE127165 and the expression trend was consistent with TCGA-LSCC, they were recorded as biomarkers. Moreover, the accuracy of the biomarkers in predicting clinical aspects of LSCC was confirmed by the receiver operating characteristic (ROC) curves. Subsequently, cancers such as malignant neoplasms, colorectal cancers, tumors, and primary malignant neoplasms were significantly associated with the biomarkers, which further suggests that these four biomarkers were strongly associated with cancer. Meanwhile, the drugs garcinol, cocaine, and triazolam, among others, used for LSCC treatment were predicted. Finally, transcription factors (TFs) (BRD4, MYC, AR, and CTCF) were predicted to regulate the biomarkers. RT-qPCR assays illustrated that the expression trends of KAT2B, LNX1 and NBEAL2 remained consistent with the dataset. CONCLUSION: The identification of four biomarkers (WDR54, KAT2B, NBEAL2 and LNX1) associated with UbRGs could ultimately serve as a predictive clinical diagnosis of LSCC and provide insight into the molecular mechanisms of LSCC.

Humans↗
PubMed2025

Transcriptional regulation reveals potent drought tolerance mechanisms in contrasting genotypes of Cajanus cajan (L.) Millspaugh.

Global warming severely impacts crop productivity, particularly in the Global South. Tropical pulse crops are nutritious staples and tolerant to harsh conditions, such as pigeonpea (Cajanus cajan). Two pigeonpea varieties have superior qualities, also with respect to abiotic stress tolerance: drought-tolerant Pusa Arhar 16 (PA16) and moderately drought-sensitive Pusa 992 (PA99). However, both are understudied at the molecular level. This study investigates molecular mechanisms of drought tolerance by investigating their responses to polyethylene glycol-induced drought. Superior drought tolerance in PA16 was characterized by enhanced shoot growth, photosynthetic characteristics and reduced oxidative stress as compared to PA992, while root length showed no significant difference between the varieties. Transcriptomic analysis identified differentially expressed genes among treatments and varieties, significantly upregulated under drought in PA16 versus PA992 with distinct patterns. For example, genes encoding terpenoid biosynthesis were up-regulated only in PA16, while those encoding LATE EMBRYOGENESIS ABUNDANT (LEA) proteins were drought-induced in both, PA16 and PA992. Functional enrichment analyses coupled with Weighted Correlation Network Analysis uncovered co-expression networks regulating drought-related pathways. Hence, the genotype and environment-specific gene regulation patterns suggest molecular and physiological mechanisms related to secondary metabolisms and LEA proteins underlying drought resilience in pigeonpea. This research offers potential targets for breeding drought-tolerant varieties of this important legume crop.

Cajanus↗
PubMed2005

Artificial intelligence in hematology.

Artificial intelligence (AI) is a computer based science which aims to simulate human brain faculties using a computational system. A brief history of this new science goes from the creation of the first artificial neuron in 1943 to the first artificial neural network application to genetic algorithms. The potential for a similar technology in medicine has immediately been identified by scientists and researchers. The possibility to store and process all medical knowledge has made this technology very attractive to assist or even surpass clinicians in reaching a diagnosis. Applications of AI in medicine include devices applied to clinical diagnosis in neurology and cardiopulmonary diseases, as well as the use of expert or knowledge-based systems in routine clinical use for diagnosis, therapeutic management and for prognostic evaluation. Biological applications include genome sequencing or DNA gene expression microarrays, modeling gene networks, analysis and clustering of gene expression data, pattern recognition in DNA and proteins, protein structure prediction. In the field of hematology the first devices based on AI have been applied to the routine laboratory data management. New tools concern the differential diagnosis in specific diseases such as anemias, thalassemias and leukemias, based on neural networks trained with data from peripheral blood analysis. A revolution in cancer diagnosis, including the diagnosis of hematological malignancies, has been the introduction of the first microarray based and bioinformatic approach for molecular diagnosis: a systematic approach based on the monitoring of simultaneous expression of thousands of genes using DNA microarray, independently of previous biological knowledge, analysed using AI devices. Using gene profiling, the traditional diagnostic pathways move from clinical to molecular based diagnostic systems.

Artificial Intelligence↗
PubMed2005

Network thinking in ecology and evolution.

Although pairwise interactions have always had a key role in ecology and evolutionary biology, the recent increase in the amount and availability of biological data has placed a new focus on the complex networks embedded in biological systems. The increased availability of computational tools to store and retrieve biological data has facilitated wide access to these data, not just by biologists but also by specialists from the social sciences, computer science, physics and mathematics. This fusion of interests has led to a burst of research on the properties and consequences of network structure in biological systems. Although traditional measures of network structure and function have started us off on the right foot, an important next step is to create biologically realistic models of network formation, evolution, and function. Here, we review recent applications of network thinking to the evolution of networks at the gene and protein level and to the dynamics and stability of communities. These studies have provided new insights into the organization and function of biological systems by applying existing techniques of network analysis. The current challenge is to recognize the commonalities in evolutionary and ecological applications of network thinking to create a predictive science of biological networks.

Journal Article↗
PubMed2005

Minor children and adult care exchanges with community-dwelling frail elders in a St. Lucian village.

OBJECTIVE: Research on care of community-dwelling frail elders typically includes formal health service providers and adult members of the informal care system. Involvement of children and adolescents with elder care is largely undocumented. The aim of this article is to describe children's involvement in elder care. These findings are part of an ethnographic community study that examined common Western assumptions about elder care in a St. Lucian village. METHODS: Data were obtained in a four-phase, 5-year, community-based ethnographic field study that included in-depth network analysis of elder households. RESULT: One hundred eighty-eight informal caregivers assisted 14 elder networks in obtaining the things they needed to live through provision of 355 care activities. Forty-five children (ages 3(1/2) to 16) provided 111 of 355 (31%) care activities. The frail elders gave adults and children community member caregivers 196 and 94 benefits, respectively. DISCUSSION: Minor children are integrally involved in reciprocal exchanges for elder care in this village. Although they do not provide all of the same care activities as adults, they clearly assist elders, especially with running errands. Elders emphasized different motivational mechanisms for involving minor children and adults in their care networks.

Activities of Daily Living↗
PubMed2006

The alliance for cellular signaling plasmid collection: a flexible resource for protein localization studies and signaling pathway analysis.

Cellular responses to inputs that vary both temporally and spatially are determined by complex relationships between the components of cell signaling networks. Analysis of these relationships requires access to a wide range of experimental reagents and techniques, including the ability to express the protein components of the model cells in a variety of contexts. As part of the Alliance for Cellular Signaling, we developed a robust method for cloning large numbers of signaling ORFs into Gateway entry vectors, and we created a wide range of compatible expression platforms for proteomics applications. To date, we have generated over 3000 plasmids that are available to the scientific community via the American Type Culture Collection. We have established a website at www.signaling-gateway.org/data/plasmid/ that allows users to browse, search, and blast Alliance for Cellular Signaling plasmids. The collection primarily contains murine signaling ORFs with an emphasis on kinases and G protein signaling genes. Here we describe the cloning, databasing, and application of this proteomics resource for large scale subcellular localization screens in mammalian cell lines.

Animals↗
PubMed2025

Identification of pathogenic variants in six Chinese families with keratoconus of autosomal dominant inheritance: pathogenicity analysis and variable phenotype.

PURPOSE: Keratoconus (KC) is a bilateral, asymmetric disease causing corneal thinning, irregular astigmatism, and vision decline, with unclear etiology. This study aims to investigate pathogenic variants of candidate genes in Chinese KC families via whole exome sequencing (WES). METHODS: The Pentacam 3D anterior segment analysis system was applied for keratectasia detection, and the Corvis ST was used for corneal biomechanics measurement. Probands from KC families were screened via WES and further verified in other family members through Sanger sequencing. Additionally, qPCR was used to validate copy number variants and identify pathogenic gene loci. The identified variants were then classified according to the Standards and Guidelines for the Interpretation of Sequence Variants published by the American College of Medical Genetics and Genomics (ACMG). Finally, STRING protein-protein interaction (PPI) networks analysis was performed to investigate interactions among candidate gene-related proteins. RESULTS: Using WES, four heterozygous missense variants were detected in the ZNF469, KRT12, COL8A2, and COL18A1 genes: c.4384G > A: p.Asp1462Asn, c.1229T > G:p.Val410Gly, c.505A > G:p.Ile169Val, and c.1159G > A:p.Gly387Arg. Additionally, a heterozygous frameshift variant was detected in the PMS2 gene: c.1551_1572del:p.Ser517Argfs*71. The affected parents carried the same variants as the probands verified by Sanger sequencing. A copy number variant was detected in the DPP6 gene: seq[GRCh38] dup(7)(q36.2q36.2) chr7:g.153782360_ 153982491dup. According to ACMG guidelines, ZNF469, KRT12, COL8A2, and COL18A1 gene variants are Likely Pathogenic; PMS2 and DPP6 gene variants are Pathogenic. STRING analysis highlights a tightly interconnected network centered on COL8A2, involving COL18A1, FN1, ZNF469, and KRT12. DPP6 was involved in KC via affecting FN1. In four of six autosomal dominant KC (adKC) families, affected parents had the same variants as probands but milder phenotypes. CONCLUSION: In this study, six novel variants in ZNF469, KRT12, COL8A2, COL18A1, PMS2, and DPP6 were linked to adKC. Family phenotypes showed variable expressivity with irregular dominance inheritance. Abnormal KC-related gene protein expression may contribute to corneal structural instability. This study broadened KC genetic screening candidates and suggested genetic testing could aid early KC diagnosis and intervention.

Adult↗
PubMed1991

Designing a neural network simulator--the MENS modelling environment for network systems: I.

During recent years, the field of neural network research has increasingly attracted the interest of workers from a large number of different disciplines. Current research topics include aspects as different as detailed simulations in brain physiology, predictions of protein structure in biochemistry, database organization in computer science, or various technical applications. The common scheme behind these different approaches is the use of distributed networks of simple computational elements that communicate with each other by means of weighted links. Computer simulations of neural networks require an appropriate software environment. Due to the computational similarities of many classes of such networks, simulation software can be structured into modular components that, to a large degree, are independent of specific applications. The aim of this and the following paper is to discuss some of the design considerations concerning software for neural network simulations. The aspects presented are interesting for both the development of new simulation software and the efficient use and modification of existing programs. Therefore, the general user as well as the software designer may hopefully benefit from this material. This paper briefly introduces some of the basic principles of neural networks. After a short discussion of different approaches to software design, two simple example applications are presented in order to demonstrate a conceptual framework common to many network simulations. The transfer of these considerations to the design of simulation software is then shown by example of the MENS network simulator developed in the Max-Planck-Institute for Brain Research. The paper gives a general introduction to the layout of data structures and different software components. Using the two introductory examples some aspects of network analysis are demonstrated. The following paper then considers further details of the design of a neural network simulator with respect to performance, implementation, and testing.

Animals↗
PubMed1997

Classification of low back pain from dynamic motion characteristics using an artificial neural network.

STUDY DESIGN: Data were collected from 183 subjects who were randomly assigned to the training and test groups. During testing of the classification system, knowledge of the low back pain condition or motion characteristics of the patients in the test group was not made available to the system. OBJECTIVES: To determine specific characteristics of trunk motion associated with different categories of spinal disorders and to determine whether a neural network analysis system can be effective in distinguishing patterns. SUMMARY OF BACKGROUND DATA: Numerous studies have established the difficulty of evaluating lower back pain. Imaging techniques are expensive and ineffective in many cases. A technique for evaluation of lower back pain was developed on the basis of analysis of such dynamic motion features as shape, velocity, and symmetry of movements, using a neural network classification system. METHODS: Dynamic motion data were collected from 183 subjects using a triaxial goniometer. Features of the movement were extracted and provided as input to a two-stage neural network classifier governed by a radial basis function architecture. After training, the output of the classifier was compared with Québec Task Force pain classifications obtained for the patients. Linear and nonlinear classification techniques were compared. RESULTS: The system could determine low back pain classification from motion characteristics. The neural network classifier produced the best results with up to 85% accuracy on novel "validation" data. CONCLUSIONS: A neural network based on kinematic data is an excellent predictive model for classification of lower back pain. Such a system could markedly improve the management of lower back pain in the individual patient.

Adult↗
PubMed2005

The yeast kinome displays scale free topology with functional hub clusters.

BACKGROUND: The availability of interaction databases provides an opportunity for researchers to utilize immense amounts of data exclusively in silico. Recently there has been an emphasis on studying the global properties of biological interactions using network analysis. While this type of analysis offers a wide variety of global insights it has surprisingly not been used to examine more localized interactions based on mechanism. In as such we have particular interest in the role of key topological components in signal transduction cascades as they are vital regulators of healthy and diseased cell states. RESULTS: We have used publicly available databases and a novel software tool termed Hubview to model the interactions of a subset of the yeast interactome, specifically protein kinases and their interaction partners. Analysis of the connectivity distribution has inferred a fat-tailed degree distribution with parameters consistent with those found in other biological networks. In addition, Hubview identified a functional clustering of a large group of kinases, distributed between three separate groupings. The complexity and average degree for each of these clusters is indicative of a specialized function (cell cycle propagation, DNA repair and pheromone response) and relative age for each cluster. CONCLUSION: Using connectivity analysis on a functional subset of proteins we have evidence that reinforces the scale free topology as a model for protein network evolution. We have identified the hub components of the kinase network and observed a tendency for these kinases to cluster together on a functional basis. As such, these results suggest an inherent trend to preserve scale free characteristics at a domain based modular level within large evolvable networks.

Multigene Family↗
PubMed2003

Hybrid canonical-correlation neural-network approach applied to nonnucleoside HIV-1 reverse transcriptase inhibitors (HEPT derivatives).

Beneficial antiviral HIV-1 chemotherapy is associated with adverse reactions. To optimize the desired actions and to lower the side effects of nonnucleoside HIV-1 reverse transcriptase (RT) inhibitors (NNRTIs), quantitative structure-activity relationships (QSARs) were studied by using a series of HEPT derivatives of NNRTIs. Hypothesis testing requires that certain assumptions are approximately satisfied in statistically based QSARs, however. A complementary approach is based on artificial neural network analysis. Model building can be made without the manifold assumptions of statistically based QSAR approaches but the problem is that the number of neural weights increase exponentially (danger of overfitting) under certain circumstances. A way to get more reliable results is to reduce the dimensionality of the two subsets (biological and chemical variables). A suitable method is the canonical correlation analysis. The two subsets of canonical variates are used as outputs (biologically derived variates) and inputs (chemically derived variates) of an optimized backpropagation neural network approach. The contribution summarizes the most recent results of this canonical-correlation backpropagation-neural network QSAR approach. It is shown that noncovalent interactions (lipophilic, steric, hydrogen-bonding, and inductive forces of the substituents) are responsible for the antiviral and cytotoxic actions. The outcome of this analysis produces an internally highly self-consistent result (model robustness). The predictive performance is tested. The butterfly-like conformation of the predicted compound is consistent with the butterfly-like model of other NNRTIs. Molecular simulation shows that the complexed drug interacts with the Tyr181 and Tyr188 residues of the RT. The uracil ring of the drug binds directly with Lys101, and the acyclic side chain (with an intact free hydroxyl function) binds with Lys103. The suggested noncovalent interaction forces are equivalent with that found by the QSAR analysis.

Anti-HIV Agents↗
PubMed2025

Identification of mitophagy-related biomarkers with immune cell infiltration in psoriasis.

BACKGROUND: Psoriasis is an inflammatory disorder characterized by scaly erythematous plaques and significant comorbidities. Recent studies have suggested that impaired mitophagy, the cellular mechanism for removing dysfunctional mitochondria, may contribute to the pathogenesis of psoriasis. METHODS: In this study, we analyzed bulk RNA sequencing data from 167 healthy individuals and 177 patients with psoriasis obtained from the Gene Expression Omnibus database (GSE30999 and GSE54456). Mitophagy-related genes were isolated using weighted gene co-expression network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed and protein-protein interaction networks were constructed for the functional enrichment of genes associated with mitophagy. The correlations between genes associated with mitophagy, signaling pathways, and immune cell infiltration were analyzed. The potential diagnostic value of genes associated with mitophagy was evaluated using receiver operating characteristic (ROC) curves, which were validated in imiquimod-induced psoriatic skin lesions in mice. RESULTS: We identified 3,839 differentially expressed genes between healthy individuals and patients with psoriasis, and 23 genes were selected as hub genes showing a high correlation with mitophagy in psoriasis. GO and KEGG analyses revealed that hub and associated genes were significantly correlated with skin functions, such as epidermal development and keratinocyte differentiation. In addition, mitophagy-related genes were negatively associated with pro-inflammatory and pro-proliferation pathways in psoriasis. Among the immune cells, CD4+ T cells were most significantly affected by mitophagy-related genes. ROC analysis demonstrated that mitophagy-related genes, especially ACER1, C1ORF68, CST6, FLG2, GJB3, GJB5, GPRIN2, KRT2, and SPRR4 were potential biomarkers of psoriasis for use in diagnosis or treatment. CONCLUSIONS: Mitophagy-related genes play crucial roles in psoriasis and have potential use as biomarkers, providing insights into disease mechanisms and therapeutic targets. Further research may lead to the development of new strategies for psoriasis management.

Psoriasis↗
PubMed2006

Brain imaging tools in neurosciences.

In this chapter brain imaging tools in neurosciences are presented. These include a brief overview on single-photon emission tomography (SPET) and a detailed focus on positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). In addition, a critical discussion on the advantages and disadvantages of the three diagnostic systems is added. Furthermore, this article describes the image analysis tools from visual analysis over region-of-interest technique up to statistical parametric mapping, co-registration methods, and network analysis. It also compares the newly developed combined PET/CT scanner approach with established image fusion software approaches. There is rapid change: Better scanner qualities, new software packages and scanner concepts are on the road paved for an amply bright future in neurosciences.

Animals↗