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

• Published in: IEEE journal of biomedical and health informatics Vol. 19; no. 4; pp. 1459 - 1471
• Main Authors: Min Jing, McGinnity, T. Martin, Coleman, Sonya, Fuchs, Armin, Kelso, J. A. Scott
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Brain Injuries - classification; Brain Injuries - physiopathology; Brain modeling; Case studies; Diffusion; Diffusion tensor imaging; Diffusion Tensor Imaging - methods; Head; Head injuries; Humans; Image Processing, Computer-Assisted - methods; Injuries; Integrated circuit modeling; Longitudinal Studies; Male; Recovery; Samples; Signal Processing, Computer-Assisted; Statistical analysis; Statistical methods; Temporal logic; Time Factors; Young Adult; longitudinal study; group independent component analysis (GICA); semi-blind source separation; Diffusion tensor imaging (DTI); mild traumatic brain injury (mTBI)
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2014.2352119

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 behavioral 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 paper, we introduce an approach based on spatial group 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.