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Statistical methods for brain connectivity analysis

English title Statistical methods for brain connectivity analysis
Applicant Thiran Jean-Philippe
Number 144467
Funding scheme Project funding (Div. I-III)
Research institution Laboratoire de traitement des signaux 5 EPFL - STI - IEL - LTS5
Institution of higher education EPF Lausanne - EPFL
Main discipline Mathematics
Start/End 01.10.2012 - 30.06.2013
Approved amount 44'133.00
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All Disciplines (2)

Discipline
Mathematics
Information Technology

Keywords (10)

multivariate analysis; Brain connectivity; Random field theory; False discovery rate FDR; Diffusion imaging; Magnetic resonance imaging (MRI); Bonferroni; Multiple testing; Family-wise error rate (FWER); Multiple comparisons

Lay Summary (English)

Lead
Lay summary

The recent developments in medical imaging and image analysis allowed the determination of the interregional brain connectivity trough Diffusion Magnetic Resonance Imaging tractography. The brain connectivity is then represented by a connection matrix where each cell represents a certain measure of connectivity between two regions of interests of the brain. Our group is one of the pioneers in the domain of diffusion medical image processing and is being continuously improving the processing of such images to construct more robust connection matrices.

An important part of the project is the ability of inferring from the connection matrices and the ability to perform rigorous statistical analysis to derive new significant medical results. In particular, we focus on the brain regions of interest level of inference. Performing inference at the level of regions of interest involves the problem of multiplicity correction or the so-called multiple testing or multiple comparisons problem.

The problem of multiple comparisons in brain connectivity analysis is the subject of a current PhD thesis started in 05.09.2009, directed by Prof. Jean Philippe Thiran (EPFL – Signal Processing Laboratory 5) and co-directed by Prof. Stephan Morgenthaler (EPFL – Chair of Applied Statistics). The program of this PhD thesis can be divided into three essential parts.

1.The general problem of multiple comparisons.

The objective of this part is to develop new multiple comparisons procedure that has a certain property of optimality.

2.The multiple comparisons for positively dependent data.

By supposing that the brain regions of interest which are in the same vicinity or which belong to the same functional network are positively correlated, we develop new multiple comparison strategies that exploit this additional information in the aim of increasing the sensitivity of detecting real effects. We call this strategy the two-step methods.

3. The application of multiple comparisons on brain connectivity matrices.

This part is not only an application of the two precedent parts. In fact, we need to adapt it by estimating the distributions of the statistical tests when network measures are used and by estimating the influence of the MRI images noise on these measures. In addition, we need to estimate the structure of positive dependence to define brain sub-networks to use the two-step methods.  This part is crucial for the validation of the developed adaptive strategy.

The work done so far has been focused on the two first parts, which are more related to the problem of multiple comparisons. The third part, which is an adaptation of the previous work to the brain connectivity analysis, will be the main subject of this 9 months project, in order to complete the PhD thesis.

Considering the results of the previous project, at the end of this 9 months project we will have developed a framework of an adaptive strategy for the statistical analysis of both functional and structural connection matrices ready to be used in single subject analysis or group comparison. The framework will be general enough to be used in many neuroscientific research domains and will include a variety of network measures that permit an efficient investigation of a large range of pathologies where changes in brain connectivity is a key ingredient of medical interpretation, such as Alzheimer or Schizophrenia.  

Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
Brain network analysis of patients with Temporal Lobe Epilepsy
Lemkaddem Alia, Vulliemoz Serge, Griffa Alessandra, Daducci Alessandro, Meskaldji Djalel, et al. (2013), Brain network analysis of patients with Temporal Lobe Epilepsy, in 2013 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, San Francisco, California, USAIEEE, San Francisco, California, USA.
Extreme prematurity and intra uterine growth restriction effects in brain network topology at school age
Fischi Gomez Elda, Meskaldji Djalel, Vasung L., F. Lazeyras François, Thiran Jean-Philippe, et al. (2013), Extreme prematurity and intra uterine growth restriction effects in brain network topology at school age, in 21st Annual Meeting International Society for Magnetic Resonance in Medicine, Salt Lake City, Utah, USAISMRM, Salt Lake City.
Comparing connectomes across subjects and populations at different scales.
Meskaldji Djalel Eddine, Fischi-Gomez Elda, Griffa Alessandra, Hagmann Patric, Morgenthaler Stephan, Thiran Jean-Philippe, Comparing connectomes across subjects and populations at different scales., in NeuroImage, 80, 416-25.

Collaboration

Group / person Country
Types of collaboration
EPFL - Chair of Applied Statistics Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
CHUV - Department of Radiology Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Research Infrastructure

Associated projects

Number Title Start Funding scheme
121945 New methods for mapping and analysing large scale structural brain connectivity with MRI 01.08.2009 Project funding (Div. I-III)
121945 New methods for mapping and analysing large scale structural brain connectivity with MRI 01.08.2009 Project funding (Div. I-III)
150828 Development of Advanced Translational High-Field MRI 12.05.2014 R'EQUIP
124089 Imaging large scale neuronal networks in epilepsy 01.05.2009 SPUM
157063 Towards micro-structure-based tractography for quantitative brain connectivity analysis 01.10.2014 Project funding (Div. I-III)
130090 Imaging the connectome in the early phase of psychosis 01.01.2011 Project funding (Div. I-III)

Abstract

The recent developments in medical imaging and image analysis allowed the determination of the interregional brain connectivity trough Diffusion Magnetic Resonance Imaging tractography. The brain connectivity is then represented by a connection matrix where each cell represents a certain measure of connectivity between two regions of interests of the brain. Our group is one of the pioneers in the domain of diffusion medical image processing and is being continuously improving the processing of such images to construct more robust connection matrices.An important part of the project is the ability of inferring from the connection matrices and the ability to perform rigorous statistical analysis to derive new significant medical results. In particular, we focus on the brain regions of interest level of inference. Performing inference at the level of regions of interest involves the problem of multiplicity correction or the so-called multiple testing or multiple comparisons problem.The problem of multiple comparisons in brain connectivity analysis is the subject of a current PhD thesis started in 05.09.2009, directed by Prof. Jean Philippe Thiran (EPFL - Signal Processing Laboratory 5) and co-directed by Prof. Stephan Morgenthaler (EPFL - Chair of Applied Statistics). The program of this PhD thesis can be divided into three essential parts.1.The general problem of multiple comparisons. The objective of this part is to develop new multiple comparisons procedure that has a certain property of optimality. 2.The multiple comparisons for positively dependent data.By supposing that the brain regions of interest which are in the same vicinity or which belong to the same functional network are positively correlated, we develop new multiple comparison strategies that exploit this additional information in the aim of increasing the sensitivity of detecting real effects. We call this strategy the two-step methods.3.The application of multiple comparisons on brain connectivity matrices.This part is not only an application of the two precedent parts. In fact, we need to adapt it by estimating the distributions of the statistical tests when network measures are used and by estimating the influence of the MRI images noise on these measures. In addition, we need to estimate the structure of positive dependence to define brain sub-networks to use the two-step methods. This part is crucial for the validation of the developed adaptive strategy.The work done so far has been focused on the two first parts, which are more related to the problem of multiple comparisons. The third part, which is an adaptation of the previous work to the brain connectivity analysis, will be the main subject of this 9 months project, in order to complete the PhD thesis. Considering the results of the previous project, at the end of this 9 months project we will have developed a framework of an adaptive strategy for the statistical analysis of both functional and structural connection matrices ready to be used in single subject analysis or group comparison. The framework will be general enough to be used in many neuroscientific research domains and will include a variety of network measures that permit an efficient investigation of a large range of pathologies where changes in brain connectivity is a key ingredient of medical interpretation, such as Alzheimer or Schizophrenia.
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