Temporal Changes of Diffusion Patterns in Mild Traumatic Brain Injury via Group Based Semi-Blind Source Separation

Min Jing, TM McGinnity, SA Coleman, Armin Fuchs, Scott Kelso

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2 Citations (Scopus)

Abstract

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioural impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and thus are less suitable for individual case studies. In this work, we introduce an approach based on group spatial independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA.The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.
LanguageEnglish
JournalIEEE Journal of Biomedical and Health Informatics
Volumen/a
DOIs
Publication statusPublished - 2014

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Blind source separation
Brain
Independent component analysis
Diffusion tensor imaging
Recovery
Statistical methods
Decomposition

Cite this

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title = "Temporal Changes of Diffusion Patterns in Mild Traumatic Brain Injury via Group Based Semi-Blind Source Separation",
abstract = "Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioural impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and thus are less suitable for individual case studies. In this work, we introduce an approach based on group spatial independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA.The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.",
author = "Min Jing and TM McGinnity and SA Coleman and Armin Fuchs and Scott Kelso",
year = "2014",
doi = "10.1109/JBHI.2014.2352119",
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T1 - Temporal Changes of Diffusion Patterns in Mild Traumatic Brain Injury via Group Based Semi-Blind Source Separation

AU - Jing, Min

AU - McGinnity, TM

AU - Coleman, SA

AU - Fuchs, Armin

AU - Kelso, Scott

PY - 2014

Y1 - 2014

N2 - Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioural impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and thus are less suitable for individual case studies. In this work, we introduce an approach based on group spatial independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA.The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.

AB - Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioural impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and thus are less suitable for individual case studies. In this work, we introduce an approach based on group spatial independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior information into the GICA decomposition process thus taking available knowledge of mTBI into account. The proposed method is evaluated based on DTI data collected from American football players including eight controls and three mTBI subjects (at three time points post injury). The results show that common spatial patterns within the diffusion maps were extracted as spatially independent components (ICs) by GICA.The temporal change of diffusion patterns during recovery is revealed by the time course of the selected IC. The results also demonstrate that the temporal change can be further influenced by incorporating the prior knowledge of mTBI (if available) based on the proposed CGICA model. Although a small sample of mTBI subjects is studied, as a proof of concept, the preliminary results provide promising insight for applications of DTI to study recovery from mTBI and may have potential for individual case studies in practice.

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