hpcwiki - User contributions [en]

MMS: Programming and Numerical Analysis 2014-II

2014-08-09T11:13:57Z

Fgomez: /* Course contents */

== Syllabus ==

* [[Media:Sibanalysis.pdf| Syllabus]]

== Course contents ==

'''1. Algoritmos (3 Semanas)'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]
* [[Media:Tallerprog2.pdf| Taller programación parte I y II]]
* Scilab [http://www.scilab.org/]
* Aprenda matlab como si estuviera en primero [http://www.fiwiki.org/images/d/db/Aprenda_Matlab_7_como_si_estuviera_en_primero.pdf]

File:Tallerprog2.pdf

2014-08-09T11:12:21Z

Fgomez: Fgomez uploaded a new version of "File:Tallerprog2.pdf"

MMS: Programming and Numerical Analysis 2014-II

2014-08-09T11:11:12Z

Fgomez: /* Course contents */

== Syllabus ==

* [[Media:Sibanalysis.pdf| Syllabus]]

== Course contents ==

'''1. Algoritmos (3 Semanas)'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]
* [[Tallerprog2.pdf| Taller programación parte I y II]]
* Scilab [http://www.scilab.org/]
* Aprenda matlab como si estuviera en primero [http://www.fiwiki.org/images/d/db/Aprenda_Matlab_7_como_si_estuviera_en_primero.pdf]

File:Tallerprog2.pdf

2014-08-09T11:10:12Z

Fgomez:

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:36:41Z

Fgomez: /* Course contents */

== Syllabus ==

* [[Media:Sibanalysis.pdf| Syllabus]]

== Course contents ==

'''1. Algoritmos (3 Semanas)'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]
* Scilab [http://www.scilab.org/]
* Aprenda matlab como si estuviera en primero [http://www.fiwiki.org/images/d/db/Aprenda_Matlab_7_como_si_estuviera_en_primero.pdf]

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:32:39Z

Fgomez: /* Course contents */

== Syllabus ==

* [[Media:Sibanalysis.pdf| Syllabus]]

== Course contents ==

'''1. Algoritmos (3 Semanas)'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:32:18Z

Fgomez: /* Syllabus */

== Syllabus ==

* [[Media:Sibanalysis.pdf| Syllabus]]

== Course contents ==

'''1. Algoritmos'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:29:55Z

Fgomez: /* Syllabus */

== Syllabus ==

* [[Media:silab.pdf| Syllabus]]

== Course contents ==

'''1. Algoritmos'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

File:Sibanalysis.pdf

2014-08-04T16:29:34Z

Fgomez:

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:26:40Z

Fgomez: /* Syllabus */

== Syllabus ==

* [[Media:silaboanalisisnumerico.pdf| Syllabus]]

== Course contents ==

'''1. Algoritmos'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:26:01Z

Fgomez: /* Syllabus */

== Syllabus ==

* [[Media:silaboanalisisnumerico.pdf| Syllabus]]

== Syllabus ==

* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

== Course contents ==

'''1. Algoritmos'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

File:SílaboAnálisisNumérico.pdf

2014-08-04T16:25:07Z

Fgomez:

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:24:45Z

Fgomez: /* Course contents */

== Syllabus ==

* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

== Course contents ==

'''1. Algoritmos'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:24:07Z

Fgomez: /* Course contents */

== Course contents ==

'''1. Algoritmos'''
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:23:45Z

Fgomez: /* Course contents */

== Course contents ==

'''1. Algoritmos''' [[Media:Notas1PAN.png| Notas ]]
* [[Media:tallerProgramacionI.pdf| Taller programación parte I]]

MMS: Programming and Numerical Analysis 2014-II

2014-08-04T16:23:21Z

Fgomez: Created page with "== Course contents == '''1. Algoritmos''' Notas * Class 1"

== Course contents ==

'''1. Algoritmos''' [[Media:Notas1PAN.png| Notas ]]
* [[Media:tallerProgramacionI.pdf| Class 1]]

File:TallerProgramacionI.pdf

2014-08-04T16:22:13Z

Fgomez:

MMS:Courses

2014-08-04T15:25:34Z

Fgomez: /* 2014 - III */

== 2014 - I ==
* [[MMS: M&S Principles]]
* [[MMS: Mathematical Foundations of M&S]]
* [[MMS: Programming and Numerical Analysis]]
== 2014 - III ==
* [[MMS: M&S Principles]]
* [[MMS: Dynamical Systems]]
* [[MMS: Computer programming for M&S]]
* [[MMS: M&S for Natural Systems]]
* [[MMS: Programming and Numerical Analysis 2014-II]]

MMS:Courses

2014-08-04T15:21:28Z

Fgomez: /* 2014 - III */

Machine learning based multimodal classification for Disorder of Consciousness

2013-09-09T14:17:43Z

Fgomez:

== People ==

* Francisco Gómez

== Summary ==

Multimodal information has been proved to be a powerful tool to study brain diseases. Very challenging conditions such as disorder of consciousness (DOC) has been greatly benefited of the multidimensional perspective of these measurements. For instance, by measuring different information related with metabolic activity (PET), white and gray matter structural preservation (DTI and MRI) and brain dynamics (fMRI), an interesting set of biomarkers related with the necessary conditions to observe brain activity in these conditions has been proposed. In other hand, machine learning based analysis has also attracted the attention of the neuroimages community. This tool allows the study of multivariate patterns of occurrence in these signals. In general, machine learning analysis of neuroimages has been performed at single modality level. In this work we propose to use machine learning tools to study the multivariate relationships across multiple modalities related with the level of consciousness in DOC patients. By using this approach we expect two main advances: 1) improving our understanding of the relationships between modalities, in particular, commonalities and particularities and 2) improving the characterization capacity in these diseases by exploiting weighted information coming from different modalities.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Model proposal.
* Journal article. Complete model and application to clinical data.

== References ==

"Relevance vector machine" consciousness classifier applied to cerebral metabolism of vegetative and locked-in patients. Phillips CL, Bruno MA, Maquet P, Boly M, Noirhomme Q, Schnakers C, Vanhaudenhuyse A, Bonjean M, Hustinx R, Moonen G, Luxen A, Laureys S, NeuroImage, 2011

Extended dual regression for account for inidivual variabilites in groupICA

2013-09-09T14:15:10Z

Fgomez:

== People ==

* Francisco Gómez

== Summary ==

Independent component analysis (ICA) is one of most popular methods to study fMRI resting state activity. In group studies, a common strategy is to concat individual data along temporal dimension and then looking for maximally independent spatial sources that summarize the functional connectivity at group level, this approach is called group spatial ICA. This group data decomposition is typically
followed by an estimation of specific individual information extracted out of these averages maps.
The most common method to perform this task is dual regression that consists in two stages: 1) regressing out the average spatial maps into the 4D data subject data to obtain a set of subject-timecourses, followed by a 2) a regresing out of these timecourses into the same 4D dataset to obtain subject specific spatial maps. Following, these individual spatial maps are tipically entered to a second level analysis. Individual extraction information is critical in order to characterize subject specific information. Specially, in conditions where subjects will have subject-specific sources of noise, which will violate the underlaying assumptions of homogeneity behind spatial group ICA. In addition, the usual approach to perform this dual regression based on univariate regression may ignore important information about spatial relationships typically present at subject level. In this work, we propose to extend the dual regression approach for group ICA to account for specific subject information related with spatial distribution and specific noise sources. We expect to test the new method in disorder of consciousness patients where large neighborhoods of spatial relationships has been previously exploited for the signal characterization and where is expected to have large heterogeneity in the individual noise sources.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Framework proposal.
* Journal article. Complete framework report.

== References ==

http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/DualRegression?action=AttachFile&do=get&target=CB09.pdf

Data driven determination of the level of consciousness from EEG

2013-09-09T14:14:23Z

Fgomez:

== People ==

* Jorge Victorino
* Francisco Gómez

== Summary ==

Development of objective assessment tools to determine the level of consciousness in severe disabled patients (such as vegetative state/unresponsive wakefulness and minimally conscious state) is fundamental to reduce the uncertainly of behavioral evaluations. Preliminary studies suggest that resting state EEG from these patients tend to show high levels of complexity. This is in accordance with the information integration theory that relates consciousness to the possibility for the brain to access a wide range of states with large amounts of information. These studies are focused mainly in two aspects: 1) Brain connectivity and 2) Signal complexity. In both cases, the EEG signal is represented on Fourier basis and some subbands are further studied. Importantly, this bases selection seems arbitrary and not necessarily is informative for the problem. This limitation may be reflected in the lack of sensitivity of the proposed approaches to capture the level of consciousness at individual level. In this work, we hypothesized that data driven representations adapted of the EEG resting state signal will better capture the concept of “informative” dynamics expected for high levels of consciousness. Specifically, we propose to develop a new dictionary learning sparse based representation for the EEG signal. We aim to use the “informative” concept to guide the construction of our dictionary. We will to test our hypothesis in the determination of the level of consciousness for VS/UWS and MCS patients and pharmacologically induced loss of consciousness.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Model proposal.
* Journal article. Complete model and application to clinical data.

== References ==

Histopathology. 1989 Jul;15(1):49-59. Diffuse axonal injury in head injury: definition, diagnosis and grading.
Adams JH, Doyle D, Ford I, Gennarelli TA, Graham DI, McLellan DR.

Resting state brain activity system identification framework (RestSYSID)

2013-09-09T14:12:26Z

Fgomez:

== People ==

* Javier Guaje
* Francisco Gómez

== Summary ==

Resting state provides a flexible protocol to investigate cerebral activity. The study of this dynamic has provided relevant information for several pathological/pharmacological brain conditions such as, Alzheimer, Parkinson and disorders of consciousness and anesthesia, among others. Despite of their extended use of this experimental protocol, the underlying bases that rules this large brain scale coordinated activity remain poorly understood. In the last years, some models based of the synchronization of neuronal mass units have been proposed to explain this emergent phenomenon. Nevertheless, the parameterization of these models and the data manipulation need to reproduce empirical observations are quite complex. In addition, the exact mechanism that supports or suppresses coordinated activity is not clear in this kind of models. These limitations difficult considerably their to formally study altered brain conditions. Recently, a series of models based on mechanical statistics have been proposed to explain the emergent phenomena behind resting state activity. The main idea is to use mechanical statistical models with very simple interactions, for instance the Ising models, to reproduce the emergent coordinated activity, typically observed in resting activity. The main advantage of this modeling strategy is their simplicity for the parameterization and the computational implementation. Additionally, these models operate on binary state representations of the brain activity, which simplify considerably the data treatment to reproduce experimental observations. In other hand, the study of brain activity has been greatly influenced by system identification approaches, such as dynamic causal modeling. This approach provides a standard framework to compare brain dynamic hypotheses linking brain models parameters and experimental data. Unfortunately, these system identification approaches have not been implemented yet for resting state activity, mainly, because of the computational problem of exploring milliards of dynamics based on classical neuronal mass models. In this work, we will propose a novel framework to study brain activity based on resting state mechanical statistical models. The framework is going to allow 1) system identification, 2) model comparison and 3) family model comparison on empirical data and is going be tested in the scenario of anesthesia mechanism understanding.
== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Framework proposal.
* Journal article. Complete framework report.

== References ==

Changes in Effective Connectivity by Propofol Sedation. F. Gómez*, C. Phillips*, A. Soddu, M. Boly, P. Boveroux, A. Vanhaudenhuyse, M-A Bruno, O. Gosseries, V Bonhomme, S. Laureys, Q. Noirhomme. PLOS One. (2013).

Ising-like dynamics in large-scale functional brain networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2009 June; 79(6 Pt 1): 061922.

http://www.scholarpedia.org/article/Dynamic_causal_modeling

RestLib: A library for rsFMRI analysis

2013-09-09T14:08:50Z

Fgomez:

== People ==

* Javier Guaje
* Andrea Soddu (London Ontario)
* Christophe Phillips (Liege)
* Francisco Gómez

== Summary ==

In the recent years, fMRI resting state activity has provided a set of powerful biomarkers for many pathological altered brain conditions such as, Alzheimer, Parkinson and disorders of consciousness, among others. These studies aim to establish changes in the resting state activity correlating with specific brain conditions. Usually, different dynamical aspects of the signal are exploited (functional connectivity, resting state network integrity or graph properties) to construct the corresponding biomarkers of the disease. Several efforts have been performed to mix individual subject information in group maps that capture the main common patterns of the studied populations. These maps provide valuable information about the studied condition. However, the use of this knowledge in clinical settings will require an individual subject resting state analysis. This is a challenging task, especially in severely affected brain conditions, because different patients can have quite specific noise sources. In this work, we propose an automated single subject resting state analysis tool that aims to isolate individual source noises and provide an individual characterization of multiple Resting State Networks at single subject level. The analysis performed by this tool will be used in the construction of diagnostic tool that operate at single subject level, but also as an input for group based analysis. We will test our approach in severely affected brain conditions such as disorder of consciousness and congenitally affected brains.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Journal article. Complete library report.

== References ==

Global breakdown of fMRI resting state network connectivity in patients with disorders of consciousness. A. Demertzi, A. Soddu, A. Vanhaudenhuyse, F. Gómez, C. Chatelle, L. Tshibanda, M. Boly, M. Thonnard, V. Charland-Verville, O. Gosseries, M-A. Bruno, A. Thibaut, M. Kirsch and S. Laureys. Belgian Brain Council. (2012).

Simulating metabolic activity out of resting state functional connectivity. Soddu A*, Gómez F*, Voss H, Bruno MA , Vanhaudenhuyse A, Demertzi A, Chatelle C, Truong J, Charland V, Noirhomme Q, Tshibanda JF, Salmon E, Shiff N, Laureys S. (* Equal contribution). Human Brain Mapping Conference. (2012).

Projects

2013-09-04T14:49:27Z

Fgomez:

[[Category:Projects]]
[[Category:BrainProjects]]
[[Category:ModelSimProjects]]
[[Category:DevelProjects]]
[[Category:CompVisProjects]]
==Current projects ==
===Modeling and Simulation===
* [[Atmospheric Structure Analysis and Retrieval (ASAR)]]
* [[Climate Change and Land Cover Effects in Bogota (Region) Water Supply]]
* [[Tomato Production System Simulation (ABM)]]
* [[Modeling and Simulation Foundations]] (Book)
* [[Evaluación de la rigidez y rangos de una prótesis de tobillo en la contracción muscular mediante simuladores dinámicos]] -Open SIMM. Tesis de pregrado - Manuel Fonseca -Universidad Central
* [[Planeación virtual de la transferencia de tendón radial en miembro superior]]
* [[Modelado del reclutamiento muscular para diferentes tipos de fibras en contracciones isométricas, concéntricas y excéntricas]] (Carlos Posada)- Semillero de Investigación. - Elementos finitos
* [[Modelado de la fatiga muscular en fibras de contracción rápida]] (Rodrigo Argothy) - UNAL- Elementos finitos.

===Brain===
* [[Comparative Morphological Analysis of Brain Structures]]

===Data Analysis ===
* [[Framework for Web Access to Multimodal Datasets]]
* [[Botany Data Characterization for Machine Learning]] Supported Tasks
* [[Data Mining on Patent Databases]]

=== Design and Manufacture===
* [[ Manufactura y réplica de Tina para baño de niños mayores con IMOC - Proyecto de extension 20101002]]
* [[ órtesis de antebrazo]] tipo yeso inhibitorio para manejo de la espasticidad y prevención de deformación el radio - Proyecto a realizar con la Universidad de Illinois (Elizabeth Thiao) - PIMIII (Este proyecto podría integrarse con la toma de EEG). Si hay evidencia que no hay cambio muscular co nel uso de la férula, dónde está? en el cerebro?

===Análisis de movimiento===

* [[Evaluación postural de niños con parálisis cerebral en una tina de baño diseñada en la universidad Central]].Aprobado como proyecto de extensión - Universidad del Rosario
* [[Efecto del uso de férula Milgram en pacientes displásicos durante el proceso de aprendizaje de la marcha]]

== Project Formulation==

=== Brain ===
* [[Biomechanical Brain Simulation by FEA]]
* [[RestLib: A library for rsFMRI analysis]]
* [[Resting state brain activity system identification framework (RestSYSID)]]
* [[Coocurrence and non-linear characterization of multiple RSN in Disorder of Consciousness]]
* [[Learning hemometabolic maps from PET]]
* [[Data driven determination of the level of consciousness from EEG]]
* [[Extended dual regression for account for inidivual variabilites in groupICA]]
* [[Machine learning based multimodal classification for Disorder of Consciousness]]

=== Natural Systems ===
* [[Atmospheric structure characterization and description in Satellite and Radar images by Scalar and Vector Field Analysis]]
* [[Characterization and detection of phytopathologies by visual descriptors in natural images]]
* [[Data characterization| Land cover reconstruction and characterization]]
* [[Leaf morphology adaptations in response to enviromental stress conditions in tropical vegetation]]
* [[Drone based surveillance for catastrophic events]]
* [[Automatic classification of morphometric features in leaves]]

=== Neuromotor System ===
* [[Cerebral palsy severity assessment by Neuroimage and EEG]]
* [[Gait Pattern Discovery in Young Dysplasia Patients]]

* [[Reconstrucción y seguimiento del músculo Tibial anterior durante ensayos de tracción y contracción isométrica]]

* [[Localización y distribución de los nervios en el músculo Tibial Anterior]]

* [[Estudio del efecto de la posición y longitud de la fasciotomía en el compartimento Tibial Anterior]]

* [[Análisis viscoelástico del músculo en ensayos isométricos]]

<!-- p> Prototipo de marco de trabajo para el acceso vía web a conjuntos de datos multimodales mediante dispositivos móviles<-- /p>

Extended dual regression for account for inidivual variabilites in groupICA

2013-09-04T14:43:21Z

Fgomez: Created page with "== People == * Francisco Gómez == Summary == Independent component analysis (ICA) is one of most popular methods to study fMRI resting state activity. In group studies, a..."

Projects

2013-09-04T14:38:16Z

Fgomez:

[[Category:Projects]]
[[Category:BrainProjects]]
[[Category:ModelSimProjects]]
[[Category:DevelProjects]]
[[Category:CompVisProjects]]
==Current projects ==
===Modeling and Simulation===
* [[Atmospheric Structure Analysis and Retrieval (ASAR)]]
* [[Climate Change and Land Cover Effects in Bogota (Region) Water Supply]]
* [[Tomato Production System Simulation (ABM)]]
* [[Modeling and Simulation Foundations]] (Book)
* [[Evaluación de la rigidez y rangos de una prótesis de tobillo en la contracción muscular mediante simuladores dinámicos]] -Open SIMM. Tesis de pregrado - Manuel Fonseca -Universidad Central
* [[Planeación virtual de la transferencia de tendón radial en miembro superior]]
* [[Modelado del reclutamiento muscular para diferentes tipos de fibras en contracciones isométricas, concéntricas y excéntricas]] (Carlos Posada)- Semillero de Investigación. - Elementos finitos
* [[Modelado de la fatiga muscular en fibras de contracción rápida]] (Rodrigo Argothy) - UNAL- Elementos finitos.

===Brain===
* [[Comparative Morphological Analysis of Brain Structures]]

===Data Analysis ===
* [[Framework for Web Access to Multimodal Datasets]]
* [[Botany Data Characterization for Machine Learning]] Supported Tasks
* [[Data Mining on Patent Databases]]

=== Design and Manufacture===
* [[ Manufactura y réplica de Tina para baño de niños mayores con IMOC - Proyecto de extension 20101002]]
* [[ órtesis de antebrazo]] tipo yeso inhibitorio para manejo de la espasticidad y prevención de deformación el radio - Proyecto a realizar con la Universidad de Illinois (Elizabeth Thiao) - PIMIII (Este proyecto podría integrarse con la toma de EEG). Si hay evidencia que no hay cambio muscular co nel uso de la férula, dónde está? en el cerebro?

===Análisis de movimiento===

* [[Evaluación postural de niños con parálisis cerebral en una tina de baño diseñada en la universidad Central]].Aprobado como proyecto de extensión - Universidad del Rosario
* [[Efecto del uso de férula Milgram en pacientes displásicos durante el proceso de aprendizaje de la marcha]]

== Project Formulation==

=== Brain ===
* [[Biomechanical Brain Simulation by FEA]]
* [[RestLib: A library for rsFMRI analysis]]
* [[Resting state brain activity system identification framework (RestSYSID)]]
* [[Coocurrence and non-linear characterization of multiple RSN in Disorder of Consciousness]]
* [[Learning hemometabolic maps from PET]]
* [[Data driven determination of the level of consciousness from EEG]]
* [[Extended dual regression for account for inidivual variabilites in groupICA]]
* [[Machine learning based multimodal classification for Disorder of Consciousness]]

=== Natural Systems ===
* [[Atmospheric structure characterization and description in Satellite and Radar images by Scalar and Vector Field Analysis]]
* [[Characterization and detection of phytopathologies by visual descriptors in natural images]]
* [[Data characterization| Land cover reconstruction and characterization]]

=== Neuromotor System ===
* [[Cerebral palsy severity assessment by Neuroimage and EEG]]
* [[Gait Pattern Discovery in Young Dysplasia Patients]]

* [[Reconstrucción y seguimiento del músculo Tibial anterior durante ensayos de tracción y contracción isométrica]]

* [[Localización y distribución de los nervios en el músculo Tibial Anterior]]

* [[Estudio del efecto de la posición y longitud de la fasciotomía en el compartimento Tibial Anterior]]

* [[Análisis viscoelástico del músculo en ensayos isométricos]]

<!-- p> Prototipo de marco de trabajo para el acceso vía web a conjuntos de datos multimodales mediante dispositivos móviles<-- /p>

Machine learning based multimodal classification for Disorder of Consciousness

2013-09-04T11:38:26Z

Fgomez: Created page with "== People == * Francisco Gómez == Summary == Multimodal information has been proved to be a powerful tool to study brain diseases. Very challenging conditions such as diso..."

Data driven determination of the level of consciousness from EEG

2013-09-04T11:37:33Z

Fgomez: Created page with "== People == * Jorge Victorino * Francisco Gómez == Summary == Development of objective assessment tools to determine the level of consciousness in severe disabled patient..."

== People ==

* Jorge Victorino
* Francisco Gómez

== Summary ==

Development of objective assessment tools to determine the level of consciousness in severe disabled patients (such as vegetative state/unresponsive wakefulness and minimally conscious state) is fundamental to reduce the uncertainly of behavioral evaluations []. Preliminary studies suggest that resting state EEG from these patients tend to show high levels of complexity [5,6]. This is in accordance with the information integration theory that relates consciousness to the possibility for the brain to access a wide range of states with large amounts of information [2]. These studies are focused mainly in two aspects: 1) Brain connectivity and 2) Signal complexity. In both cases, the EEG signal is represented on Fourier basis and some subbands are further studied. Importantly, this bases selection seems arbitrary and not necessarily is informative for the problem. This limitation may be reflected in the lack of sensitivity of the proposed approaches to capture the level of consciousness at individual level. In this work, we hypothesized that data driven representations adapted of the EEG resting state signal will better capture the concept of “informative” dynamics expected for high levels of consciousness. Specifically, we propose to develop a new dictionary learning sparse based representation for the EEG signal. We aim to use the “informative” concept to guide the construction of our dictionary. We will to test our hypothesis in the determination of the level of consciousness for VS/UWS and MCS patients and pharmacologically induced loss of consciousness.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Model proposal.
* Journal article. Complete model and application to clinical data.

== References ==

Histopathology. 1989 Jul;15(1):49-59. Diffuse axonal injury in head injury: definition, diagnosis and grading.
Adams JH, Doyle D, Ford I, Gennarelli TA, Graham DI, McLellan DR.

Coocurrence and non-linear characterization of multiple RSN in Disorder of Consciousness

2013-09-04T11:36:35Z

Fgomez: Created page with "== People == * Francisco Gómez * Andrea Soddu (London Ontario) == Summary == In the recent years, fMRI resting state activity has provided a set of powerful biomarkers for..."

== People ==

* Francisco Gómez
* Andrea Soddu (London Ontario)

== Summary ==

In the recent years, fMRI resting state activity has provided a set of powerful biomarkers for many pathological altered brain conditions such as, Alzheimer, Parkinson and disorders of consciousness, among others. The main idea is to register BOLD fluctuations under no stimuli conditions. This protocol provides a simple experimental alternative to study brain activity. In contrasts, data analysis of these brain measurements represents an important methodological challenge. The most popular alternative to study these autonomous signals is the use functional connectivity tools. These methods allow characterizing sets of brain regions with similar dynamic behaviors. Large amount functional connectivity evidence in resting state suggests that the brain activity in this state is organized in resting state networks (RSN) of behavioral/cognitive relevance. In the last years, at least ten RSNs have been consistently identified in Controls. The study of the activation patterns of these networks, in control subjects and pathological subjects is an active problem in neurosciences. Recently, we proposed a novel method to characterize multiple of these RSNs at individual level. The method was successfully used in the discrimination of disorder of consciousness (DOC) patients and healthy controls. Interestingly, this study suggested than networks maybe differently affected during these brain altered conditions. In this work, we hypothesized than these differences in RNSs alteration will be reflected in changes of the RSN relationships. Specifically, we expect changes in the coocurrence patterns and the connectivity patterns between networks. By exploiting these relationships we aim to improve the characterization capacities in DOC patients.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Model proposal.
* Journal article. Complete model and application to clinical data.

== References ==

Projects

2013-09-04T11:35:21Z

Fgomez:

[[Category:Projects]]
[[Category:BrainProjects]]
[[Category:ModelSimProjects]]
[[Category:DevelProjects]]
[[Category:CompVisProjects]]
==Current projects ==
===Modeling and Simulation===
* [[Atmospheric Structure Analysis and Retrieval (ASAR)]]
* [[Climate Change and Land Cover Effects in Bogota (Region) Water Supply]]
* [[Tomato Production System Simulation (ABM)]]
* [[Modeling and Simulation Foundations]] (Book)
* [[Evaluación de la rigidez y rangos de una prótesis de tobillo en la contracción muscular mediante simuladores dinámicos]] -Open SIMM. Tesis de pregrado - Manuel Fonseca -Universidad Central
* [[Planeación virtual de la transferencia de tendón radial en miembro superior]]
* [[Modelado del reclutamiento muscular para diferentes tipos de fibras en contracciones isométricas, concéntricas y excéntricas]] (Carlos Posada)- Semillero de Investigación. - Elementos finitos
* [[Modelado de la fatiga muscular en fibras de contracción rápida]] (Rodrigo Argothy) - UNAL- Elementos finitos.

===Brain===
* [[Comparative Morphological Analysis of Brain Structures]]

===Data Analysis ===
* [[Framework for Web Access to Multimodal Datasets]]
* [[Botany Data Characterization for Machine Learning]] Supported Tasks
* [[Data Mining on Patent Databases]]

=== Design and Manufacture===
* [[ Manufactura y réplica de Tina para baño de niños mayores con IMOC - Proyecto de extension 20101002]]
* [[ órtesis de antebrazo]] tipo yeso inhibitorio para manejo de la espasticidad y prevención de deformación el radio - Proyecto a realizar con la Universidad de Illinois (Elizabeth Thiao) - PIMIII (Este proyecto podría integrarse con la toma de EEG). Si hay evidencia que no hay cambio muscular co nel uso de la férula, dónde está? en el cerebro?

===Análisis de movimiento===

* [[Evaluación postural de niños con parálisis cerebral en una tina de baño diseñada en la universidad Central]].Aprobado como proyecto de extensión - Universidad del Rosario
* [[Efecto del uso de férula Milgram en pacientes displásicos durante el proceso de aprendizaje de la marcha]]

== Project Formulation==

=== Brain ===
* [[Biomechanical Brain Simulation by FEA]]
* [[RestLib: A library for rsFMRI analysis]]
* [[Resting state brain activity system identification framework (RestSYSID)]]
* [[Coocurrence and non-linear characterization of multiple RSN in Disorder of Consciousness]]
* [[Learning hemometabolic maps from PET]]
* [[Data driven determination of the level of consciousness from EEG]]
* [[Kernel based dual regression for rsFMRI]]
* [[Machine learning based multimodal classification for Disorder of Consciousness]]

=== Natural Systems ===
* [[Atmospheric structure characterization and description in Satellite and Radar images by Scalar and Vector Field Analysis]]
* [[Characterization and detection of phytopathologies by visual descriptors in natural images]]
* [[Data characterization| Land cover reconstruction and characterization]]

=== Neuromotor System ===
* [[Cerebral palsy severity assessment by Neuroimage and EEG]]
* [[Gait Pattern Discovery in Young Dysplasia Patients]]

* [[Reconstrucción y seguimiento del músculo Tibial anterior durante ensayos de tracción y contracción isométrica]]

* [[Localización y distribución de los nervios en el músculo Tibial Anterior]]

* [[Estudio del efecto de la posición y longitud de la fasciotomía en el compartimento Tibial Anterior]]

* [[Análisis viscoelástico del músculo en ensayos isométricos]]

<!-- p> Prototipo de marco de trabajo para el acceso vía web a conjuntos de datos multimodales mediante dispositivos móviles<-- /p>

Machine learning based multimodal classification for Dissorder of Conscioussness

2013-09-04T11:32:05Z

Determination of the level of conscioussness based on EEG Dictonary learning

2013-09-04T11:30:49Z

Fgomez: Created page with "== People == * Jorge Victorino * Quentin Noirhomme (Liege) * Francisco Gómez == Summary == Development of objective assessment tools to determine the level of consciousnes..."

== People ==

* Jorge Victorino
* Quentin Noirhomme (Liege)
* Francisco Gómez

== Summary ==

Development of objective assessment tools to determine the level of consciousness in severe disabled patients (such as vegetative state/unresponsive wakefulness and minimally conscious state) is fundamental to reduce the uncertainly of behavioral evaluations []. Preliminary studies suggest that resting state EEG from these patients tend to show high levels of complexity [5,6]. This is in accordance with the information integration theory that relates consciousness to the possibility for the brain to access a wide range of states with large amounts of information [2]. These studies are focused mainly in two aspects: 1) Brain connectivity [] and 2) Signal complexity []. In both cases, the EEG signal is represented on Fourier basis and some subbands are further studied. Importantly, this bases selection seems arbitrary and not necessarily is informative for the problem. This limitation may be reflected in the lack of sensitivity of the proposed approaches to capture the level of consciousness at individual level. In this work, we hypothesized that data driven representations adapted of the EEG resting state signal will better capture the concept of “informative” dynamics expected for high levels of consciousness. Specifically, we propose to develop a new dictionary learning sparse based representation for the EEG signal. We aim to use the “informative” concept to guide the construction of our dictionary. We will to test our hypothesis in the determination of the level of consciousness for VS/UWS and MCS patients and pharmacologically induced loss of consciousness.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Model proposal.
* Journal article. Complete model and application to clinical data.

== References ==

Learning hemometabolic maps from PET

2013-09-04T11:29:11Z

Fgomez: Created page with "== People == * Francisco Gómez == Summary == 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well established imaging technique to measure the global me..."

== People ==

* Francisco Gómez

== Summary ==

18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well established imaging technique to measure the global metabolic consumption of glucose in the brain. Nevertheless, it remains a mildly invasive approach due to the intravenous injection. Recently, we explored the possibility to construct metabolic activity maps out of functional connectivity maps as extracted from resting state fMRI activity (1). These maps were obtained by applying independent component analysis (ICA) to resting state fMRI and subsequently combining only components of neuronal origin. The obtained maps showed significant correlation with PET data from the same subjects. Our original approach used the square root of the ICA-maps before summing up all neuronal components. This step aims to reduce the spatial sparsity initially requested by the ICA signal decomposition to obtain a least sparse signal as expect for the FDGPET metabolic maps. In this work, we propose to improve this previous work by formulating a generalized combination model, which parameters will be estimated using machine learning from samples of both resting state activity and FDG pet. By using this approach we expect to improve our understanding of the neuronal basis of the resting state activity.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Model proposal.
* Journal article. Complete model and application to clinical data.

== References ==

Coocurrence and non-linear characterization of multiple RSN in Dissorder of Conscioussness

2013-09-04T11:28:23Z

Fgomez: Created page with "== People == * Francisco Gómez == Summary == In the recent years, fMRI resting state activity has provided a set of powerful biomarkers for many pathological altered brain..."

== People ==

* Francisco Gómez

== Summary ==

In the recent years, fMRI resting state activity has provided a set of powerful biomarkers for many pathological altered brain conditions such as, Alzheimer, Parkinson and disorders of consciousness, among others. The main idea is to register BOLD fluctuations under no stimuli conditions. This protocol provides a simple experimental alternative to study brain activity. In contrasts, data analysis of these brain measurements represents an important methodological challenge. The most popular alternative to study these autonomous signals is the use functional connectivity tools. These methods allow characterizing sets of brain regions with similar dynamic behaviors. Large amount functional connectivity evidence in resting state suggests that the brain activity in this state is organized in resting state networks (RSN) of behavioral/cognitive relevance. In the last years, at least ten RSNs have been consistently identified in Controls. The study of the activation patterns of these networks, in control subjects and pathological subjects is an active problem in neurosciences. Recently, we proposed a novel method to characterize multiple of these RSNs at individual level. The method was successfully used in the discrimination of disorder of consciousness (DOC) patients and healthy controls. Interestingly, this study suggested than networks maybe differently affected during these brain altered conditions. In this work, we hypothesized than these differences in RNSs alteration will be reflected in changes of the RSN relationships. Specifically, we expect changes in the coocurrence patterns and the connectivity patterns between networks. By exploiting these relationships we aim to improve the characterization capacities in DOC patients.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Journal article. Complete study report.

== References ==

Resting state brain activity system identification framework (RestSYSID)

2013-09-04T11:27:14Z

Fgomez: Created page with "== People == * Javier Guaje * Francisco Gómez == Summary == Resting state provides a flexible protocol to investigate cerebral activity. The study of this dynamic has prov..."

Projects

2013-09-04T11:26:16Z

Fgomez:

[[Category:Projects]]
[[Category:BrainProjects]]
[[Category:ModelSimProjects]]
[[Category:DevelProjects]]
[[Category:CompVisProjects]]
==Current projects ==
===Modeling and Simulation===
* [[Atmospheric Structure Analysis and Retrieval (ASAR)]]
* [[Climate Change and Land Cover Effects in Bogota (Region) Water Supply]]
* [[Tomato Production System Simulation (ABM)]]
* [[Modeling and Simulation Foundations]] (Book)
* [[Evaluación de la rigidez y rangos de una prótesis de tobillo en la contracción muscular mediante simuladores dinámicos]] -Open SIMM. Tesis de pregrado - Manuel Fonseca -Universidad Central
* [[Planeación virtual de la transferencia de tendón radial en miembro superior]]
* [[Modelado del reclutamiento muscular para diferentes tipos de fibras en contracciones isométricas, concéntricas y excéntricas]] (Carlos Posada)- Semillero de Investigación. - Elementos finitos
* [[Modelado de la fatiga muscular en fibras de contracción rápida]] (Rodrigo Argothy) - UNAL- Elementos finitos.

===Brain===
* [[Comparative Morphological Analysis of Brain Structures]]

===Data Analysis ===
* [[Framework for Web Access to Multimodal Datasets]]
* [[Botany Data Characterization for Machine Learning]] Supported Tasks
* [[Data Mining on Patent Databases]]

=== Design and Manufacture===
* [[ Manufactura y réplica de Tina para baño de niños mayores con IMOC - Proyecto de extension 20101002]]
* [[ órtesis de antebrazo]] tipo yeso inhibitorio para manejo de la espasticidad y prevención de deformación el radio - Proyecto a realizar con la Universidad de Illinois (Elizabeth Thiao) - PIMIII (Este proyecto podría integrarse con la toma de EEG). Si hay evidencia que no hay cambio muscular co nel uso de la férula, dónde está? en el cerebro?

===Análisis de movimiento===

* [[Evaluación postural de niños con parálisis cerebral en una tina de baño diseñada en la universidad Central]].Aprobado como proyecto de extensión - Universidad del Rosario
* [[Efecto del uso de férula Milgram en pacientes displásicos durante el proceso de aprendizaje de la marcha]]

== Project Formulation==

=== Brain ===
* [[Biomechanical Brain Simulation by FEA]]
* [[RestLib: A library for rsFMRI analysis]]
* [[Resting state brain activity system identification framework (RestSYSID)]]
* [[Coocurrence and non-linear characterization of multiple RSN in Dissorder of Conscioussness]]
* [[Learning hemometabolic maps from PET]]
* [[Determination of the level of conscioussness based on EEG Dictonary learning]]
* [[Kernel based dual regression for rsFMRI]]
* [[Machine learning based multimodal classification for Dissorder of Conscioussness]]

=== Natural Systems ===
* [[Atmospheric structure characterization and description in Satellite and Radar images by Scalar and Vector Field Analysis]]
* [[Characterization and detection of phytopathologies by visual descriptors in natural images]]
* [[Data characterization| Land cover reconstruction and characterization]]

=== Neuromotor System ===
* [[Cerebral palsy severity assessment by Neuroimage and EEG]]
* [[Gait Pattern Discovery in Young Dysplasia Patients]]

* [[Reconstrucción y seguimiento del músculo Tibial anterior durante ensayos de tracción y contracción isométrica]]

* [[Localización y distribución de los nervios en el músculo Tibial Anterior]]

* [[Estudio del efecto de la posición y longitud de la fasciotomía en el compartimento Tibial Anterior]]

* [[Análisis viscoelástico del músculo en ensayos isométricos]]

<!-- p> Prototipo de marco de trabajo para el acceso vía web a conjuntos de datos multimodales mediante dispositivos móviles<-- /p>

RestLib: A library for rsFMRI analysis

2013-09-04T11:25:27Z

Fgomez: Created page with "== People == * Javier Guaje * Andrea Soddu (London Ontario) * Christophe Phillips (Liege) * Francisco Gómez == Summary == In the recent years, fMRI resting state activity ..."

Biomechanical Brain Simulation by FEA

2013-08-26T17:24:49Z

Fgomez:

== People ==

* Manuel Mejia
* Angelica Ramirez
* Francisco Gómez
* Hugo Franco

== Summary ==

Traumatic brain injuries (TBI) are related to strong angular accelerations/deaccelerations causing extreme strain/stress values along brain tissues. This can result in the affectation of wide intracraneal structures by mechanical damage, hemorragies and inflamation processes, particulary, in the axonal tracks. This set of alterations are called diffuse axonal injury that is behind severe conditions such as loss-of-consciousness in vegetative state/unresponsive wakefulness and severe disability after mild TBI.

The assesment of the injury and the patient prognosis in these conditions could be enhanced by using computational models reproducing the dynamics of the cerebral tissue damage (ref kleiven). In this work, we propose an specific finite element-based (FEA) model to study the biomechanical properties of axonal track damage related to the severe grading of diffuse axonal injury (Adams 1989 et al.). In particular, we aim to model (1) grey-white matter interfaces, (2) corpus callosum and (3) brain stem damages.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Model proposal.
* Journal article. Complete model and application to clinical data.

== References ==

Histopathology. 1989 Jul;15(1):49-59. Diffuse axonal injury in head injury: definition, diagnosis and grading.
Adams JH, Doyle D, Ford I, Gennarelli TA, Graham DI, McLellan DR.

Biomechanical Brain Simulation by FEA

2013-08-26T17:23:42Z

Fgomez:

== People ==

* Manuel Mejia
* Angelica Ramirez
* Francisco Gómez
* Hugo Franco

== Summary ==

Traumatic brain injuries (TBI) are related to strong angular accelerations/deaccelerations causing extreme strain/stress values along brain tissues. This can result in the affectation of wide intracraneal structures by mechanical damage, hemorragies and inflamation processes, particulary, in the axonal tracks. This set of alterations are called diffuse axonal injury that is behind severe conditions such as loss-of-consciousness in vegetative state/unresponsive wakefulness and severe disability after mild TBI.

The assesment of the injury and the patient prognosis in these conditions could be enhanced by using computational models reproducing the dynamics of the cerebral tissue damage (ref). In this work, we propose an specific finite element-based (FEA) model to study the biomechanical properties of axonal track damage related to the severe grading of diffuse axonal injury (Adams 1989 et al.). In particular, we aim to model (1) grey-white matter interfaces, (2) corpus callosum damage and (3) brain stem damage.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Model proposal.
* Journal article. Complete model and application to clinical data.

== References ==

Histopathology. 1989 Jul;15(1):49-59. Diffuse axonal injury in head injury: definition, diagnosis and grading.
Adams JH, Doyle D, Ford I, Gennarelli TA, Graham DI, McLellan DR.

Comparative Morphological Analysis of Brain Structures

2013-08-26T16:51:31Z

Fgomez:

== People ==

* Darwin Martinez
* Hugo Franco
* Francisco Gómez

== Summary ==

Commonly, brain injuries in these patients are followed by complex physiological processes that can lead to inflammations and neuronal and axonal death, all of which contribute to dramatic changes in local and global morphological brain properties (Graham et al., 2005). Dissorder of Consciousness patients (DOC) usually present macrostructural lesions (Graham et al., 2005; Sidaros et al., 2009) that will locally change the geometrical brain conﬁguration (Graham et al., 2005). Typical structures involved in these degenerative processes include: thalami and white matter. The characterization of the structural changes in these patients is usually computed in a local way by focusing on local geometric changes intra-structure. Nevertheless, it will be expected that these local variations may also change spatial relationships among different cortical and subcortical structures.

In this work, we hypothesized that a proper characterization of the morphological changes, accounting for both intra-structure and inter-structure, will help to characterize morphological changes linked to altered states of consciousness. To test this hypothesis, we developed a novel structural brain characterization based on the distribution of distances among voxels in different brain regions. This distribution accounts for both local geometrical properties, such as, size and region shape, but also distance relationships between different brain regions. We tested the proposed biomarker for the characterization of structural alterations between left and right thalamus, two structures where structural damage maybe expected in these patients. Using the proposed feature we obtained significant differences in the distance distribution for the discrimination between healthy controls to DOC patients.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Clinical conference. Morphological changes on dissorder of conscioussness patients.
* Method conference. Initial method description and case study.
* Journal article. Robust experimental study and application to clinical data.

== References ==

* D. Graham, J. Adams, L. Murray, and B. Jennett. Neuropathology of the vegetative state after head injury. Neuropsychological Rehabilitation, 15(3-4):198–213, 2005.

* A. Sidaros, M. Liptrot A. Skimminge, A. Engberg K. Sidaros, M. Herning, O. Paulson, T. Jernigan, and E Rostrup. Long-term global and regional brain volume changes following severe traumatic brain injury: a longitudinal study with clinical correlates. NeuroImage, 44(1):1–8, 2009.

Comparative Morphological Analysis of Brain Structures

2013-08-26T16:48:10Z

Fgomez:

== People ==

* Darwin Martinez
* Hugo Franco
* Francisco Gómez

== Summary ==

Commonly, brain injuries in these patients are followed by complex physiological processes that can lead to inflammations and neuronal and axonal death, all of which contribute to dramatic changes in local and global morphological brain properties (Graham et al., 2005). Dissorder of Consciousness patients (DOC) usually present macrostructural lesions (Graham et al., 2005; Sidaros et al., 2009) that will locally change the geometrical brain conﬁguration (Graham et al., 2005). Typical structures involved in these degenerative processes include: thalami and white matter. The characterization of the structural changes in these patients is usually computed in a local way by focusing on local geometric changes intra-structure. Nevertheless, it will be expected that these local variations may also change spatial relationships among different cortical and subcortical structures.

In this work, we hypothesized that a proper characterization of the morphological changes, accounting for both intra-structure and inter-structure, will help to characterize morphological changes linked to altered states of consciousness. To test this hypothesis, we developed a novel structural brain characterization based on the distribution of distances among voxels in different brain regions. This distribution accounts for both local geometrical properties, such as, size and region shape, but also distance relationships between different brain regions. We tested the proposed biomarker for the characterization of structural alterations between left and right thalamus, two structures where structural damage maybe expected in these patients (Jellinger, 1994; Kinney et al., 1994). Using the proposed feature we obtained significant differences in the distance distribution for the discrimination between healthy controls to DOC patients.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Clinical conference. Morphological changes on dissorder of conscioussness patients.
* Method conference. Initial method description and case study.
* Journal article. Robust experimental study and application to clinical data.

== References ==

Biomechanical Brain Simulation by FEA

2013-08-26T16:45:13Z

Fgomez:

== People ==

* Manuel Mejia
* Angelica Ramirez
* Francisco Gómez
* Hugo Franco

== Summary ==

.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Method conference. Initial simulation setting.
* Journal article. Complete simulation model and application to clinical data.

Comparative Morphological Analysis of Brain Structures

2013-08-26T16:41:48Z

Fgomez:

Comparative Morphological Analysis of Brain Structures

2013-08-26T16:40:47Z

Fgomez:

== People ==

* Darwin Martinez
* Hugo Franco
* Francisco Gómez

== Summary ==

We propose a novel morphological brain structural characterization method based on relative morphological measures.

== Method overview ==

== Data sources ==

== Results (Expected) ==

* Clinical conference. Morphological changes on dissorder of conscioussness patients.
* Method conference. Initial method description and case study.
* Journal article. Robust experimental study and application to clinical data.

fully automatic framework to detect and extract arbitrary human motion volumes from real-world videos collected from YouTube. Our system is composed of two stages. A person detector is first applied to provide crude information about the possible locations of humans. Then a constrained clustering algorithm groups the detections and rejects false positives based on the appearance similarity and spatio-temporal coherence. In the second stage, we apply a top-down pictorial structure model to complete the extraction of the humans in arbitrary motion. During this procedure, a density propagation technique based on a mixture of Gaussians is employed to propagate temporal information in a principled way. This method reduces greatly the search space for the measurement in the inference stage. We demonstrate the initial success of this framework both quantitatively and qualitatively by using a number of YouTube videos.

<!--[[Category:Projects]]
[[Category:BrainProjects]]
''To start editing''
== Conscious State Characterization ==
* [[Structural changes as conscious state (VS, MCS and control) biomarkers]]
* [[Structure Analysis for Conscious State Characterization]]

== Applications ==
* [[Affectations of the Neuromotor System by Preterm Brain Injury]]

== Documents and Tutorials ==
* [[FSL]]
* [[SPM]]
* [[Freesurfer]]

== References ==
http://en.wikibooks.org/wiki/SPM/Programming_intro
--!>

Projects

2013-08-26T16:25:26Z

Fgomez:

Conferences

2013-08-14T15:48:15Z

Fgomez:

[http://iris.usc.edu/information/Iris-Conferences.html Computer Vision]
----
[http://www.wikicfp.com/cfp/call?conference=pattern%20recognition Pattern recognition]
----
[http://www.isi.uu.nl/Conferences/ Medical Imaging]
----
[http://www.conferencealerts.com/topic-listing?topic=Ecology Ecology]

Conferences

2013-08-14T15:44:25Z

Fgomez:

[http://iris.usc.edu/information/Iris-Conferences.html Computer Vision]
----
[http://www.wikicfp.com/cfp/call?conference=pattern%20recognition Pattern recognition]

----
[http://www.isi.uu.nl/Conferences/ Medical Imaging]

Conferences

2013-08-14T15:42:18Z

Fgomez:

[http://iris.usc.edu/information/Iris-Conferences.html Computer Vision]
----
[http://www.wikicfp.com/cfp/call?conference=pattern%20recognition Pattern recognition]

Conferences

2013-08-14T15:42:00Z

Fgomez:

[http://iris.usc.edu/information/Iris-Conferences.html Computer Vision]
[http://www.wikicfp.com/cfp/call?conference=pattern%20recognition Pattern recognition]

Conferences

2013-08-14T15:39:50Z

Fgomez: Created page with "[http://iris.usc.edu/information/Iris-Conferences.html Computer Vision Conference Lists]"

[http://iris.usc.edu/information/Iris-Conferences.html Computer Vision Conference Lists]