TY - GEN
T1 - Sparse dictionary learning for fMRI analysis using autocorrelation maximization
AU - Khalid, Muhammad Usman
AU - Shah, Adnan
AU - Seghouane, Abd Krim
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - In this paper, the effect of temporal autocorrelations in functional magnetic resonance imaging (fMRI) data on sparse dictionary learning (SDL) is addressed. For sparse general linear model (sGLM), the fMRI time-series is modeled as a linear mixture of several signals such as neural dynamics, structured noise, random noise and unexplained signal variations on the basis of spatial sparseness. These signals are considered as underlying sources and SDL is used to estimate them. However, the sparse GLM model does not take into account the autocorrelations in fMRI data. To address this shortcoming, a new model is proposed to incorporate the prior knowledge about lag-1 autocorrelation into dictionary update stage. This helps improve the sensitivity and specificity of the fMRI data during statistical analysis. Using a simulation study, the effect of the proposed dictionary update on sGLM is compared to conventional sGLM by utilizing various detrending techniques. Furthermore, the proposed update is validated in an sGLM framework for real fMRI datasets, which shows its better capability to estimate neural dynamics in presence of spatiotemporal dependencies.
AB - In this paper, the effect of temporal autocorrelations in functional magnetic resonance imaging (fMRI) data on sparse dictionary learning (SDL) is addressed. For sparse general linear model (sGLM), the fMRI time-series is modeled as a linear mixture of several signals such as neural dynamics, structured noise, random noise and unexplained signal variations on the basis of spatial sparseness. These signals are considered as underlying sources and SDL is used to estimate them. However, the sparse GLM model does not take into account the autocorrelations in fMRI data. To address this shortcoming, a new model is proposed to incorporate the prior knowledge about lag-1 autocorrelation into dictionary update stage. This helps improve the sensitivity and specificity of the fMRI data during statistical analysis. Using a simulation study, the effect of the proposed dictionary update on sGLM is compared to conventional sGLM by utilizing various detrending techniques. Furthermore, the proposed update is validated in an sGLM framework for real fMRI datasets, which shows its better capability to estimate neural dynamics in presence of spatiotemporal dependencies.
KW - SDL
KW - autocorrelations
KW - fMRI
KW - sGLM
KW - sparsity
KW - statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=84953261730&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2015.7319342
DO - 10.1109/EMBC.2015.7319342
M3 - Conference contribution
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 4286
EP - 4289
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -