An Oracle Approach for Interaction Neighborhood Estimation in Random Fields

We consider the problem of interaction neighborhood estimation from the partial observation of a finite number of realizations of a random field. We introduce a model selection rule to choose estimators of conditional probabilities among natural candidates. Our main result is an oracle inequality satisfied by the resulting estimator. We use then this selection rule in a two-step procedure to evaluate the interacting neighborhoods. The selection rule selects a small prior set of possible interacting points and a cutting step remove from this prior set the irrelevant points. We also prove that the Ising models satisfy the assumptions of the main theorems, without restrictions on the temperature, on the structure of the interacting graph or on the range of the interactions. It provides therefore a large class of applications for our results. We give a computationally efficient procedure in these models. We finally show the practical efficiency of our approach in a simulation study.Comment: 36 pages, 10 figure

Information theoretic interpretation of frequency domain connectivity measures

To provide adequate multivariate measures of information flow between neural structures, modified expressions of Partial Directed Coherence (PDC) and Directed Transfer Function (DTF), two popular multivariate connectivity measures employed in neuroscience, are introduced and their formal relationship to mutual information rates are proved.Comment: 17 pages, 1 figur

Discriminating different classes of biological networks by analyzing the graphs spectra distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them on (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed

A revelação do diagnóstico de doença de Alzheimer: opiniões de cuidadores em uma amostra brasileira

BACKGROUND: Disclosure of the diagnosis of Alzheimer's disease (AD) remains a contentious issue, and has been little studied in developing countries. OBJECTIVE: To investigate the influence of socio-demographic factors and the experience of being a caregiver on opinion about disclosing AD diagnosis to the patient in a Brazilian sample. METHOD: Caregivers of 50 AD patients together with 50 control participants that did not have the experience of being a caregiver of AD patient were interviewed using a structured questionnaire. RESULTS: Most of the participants (73.0%) endorsed disclosure of the diagnosis, while caregivers were less prone to disclose (58.0%) than controls (88.0%; p=0.0007). Logistic regression confirmed that only the experience of being a caregiver was associated with a lesser tendency for disclosure endorsement. CONCLUSION: The majority of participants was in favor of disclosing the diagnosis, but caregivers were less willing to disclose the diagnosis to the AD patient.FUNDAMENTO: A revelação do diagnóstico de doença de Alzheimer (DA) tem sido tema polêmico e pouco estudado em países em desenvolvimento. OBJETIVO: Investigar a influência de fatores sócio-demográficos e a experiência de ter sido cuidador na opinião sobre a revelação do diagnóstico em uma amostra brasileira. MÉTODO: Cuidadores de 50 pacientes com DA e 50 indívíduos controle que não tinham tido experiência como cuidadores de pacientes com DA foram entrevistados com o uso de um questionário estruturado. RESULTADOS: A maioria dos participantes (73,0%) manifestou-se a favor da revelação diagnóstico aos pacientes, mas cuidadores foram menos favoráveis (58,0%) que controles (88,0%; p=0,0007). Regressão logística demonstrou que apenas a experiência como cuidador foi associada com menor tendência a apoiar a revelação do diagnóstico. CONCLUSÃO: A maioria dos participantes foi a favor da revelação do diagnóstico ao paciente, mas aqueles com experiência como cuidadores de pacientes com DA foram menos favoráveis

Measuring network's entropy in ADHD: A new approach to investigate neuropsychiatric disorders

The application of graph analysis methods to the topological organization of brain connectivity has been a useful tool in the characterization of brain related disorders. However, the availability of tools, which enable researchers to investigate functional brain networks, is still a major challenge. Most of the studies evaluating brain images are based on centrality and segregation measurements of complex networks. in this study, we applied the concept of graph spectral entropy (GSE) to quantify the complexity in the organization of brain networks. in addition, to enhance interpretability, we also combined graph spectral clustering to investigate the topological organization of sub-network's modules. We illustrate the usefulness of the proposed approach by comparing brain networks between attention deficit hyperactivity disorder (ADHD) patients and the brain networks of typical developing (TD) controls. the main findings highlighted that GSE involving sub-networks comprising the areas mostly bilateral pre and post central cortex, superior temporal gyrus, and inferior frontal gyri were statistically different (p-value = 0.002) between ADHD patients and TO controls. in the same conditions, the other conventional graph descriptors (betweenness centrality, clustering coefficient, and shortest path length) commonly used to identify connectivity abnormalities did not show statistical significant difference. We conclude that analysis of topological organization of brain sub-networks based on GSE can identify networks between brain regions previously unobserved to be in association with ADHD. (C) 2013 Elsevier Inc. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Pew Latin American FellowshipFed Univ ABC, Ctr Math Computat & Cognit, BR-09210170 Santo Andre, SP, BrazilPrinceton Univ, Dept Psychol, Princeton, NJ 08540 USAPrinceton Univ, Neurosci Inst, Princeton, NJ 08540 USAUniversidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilUniv Estadual Campinas, Ctr Mol Biol & Genet Engn, BR-13083875 Campinas, SP, BrazilUniv São Paulo, Dept Comp Sci, Inst Math & Stat, BR-05508090 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilWeb of Scienc