Fast optical signal in visual cortex: Improving detection by General Linear Convolution Model

被引:25
作者
Chiarelli, Antonio Maria
Di Vacri, Assunta
Romani, Gian Luca
Merla, Arcangelo [1 ]
机构
[1] Univ G dAnnunzio, Dept Neurosci & Imaging, I-66100 Chieti, Italy
关键词
Optical imaging; Near infrared spectroscopy; Hemoglobin; General Linear Model; Fast optical signals; NEAR-INFRARED SPECTROSCOPY; INDEPENDENT COMPONENT ANALYSIS; CORTICAL ACTIVITY; LIGHT-SCATTERING; MOTOR CORTEX; NEURONAL-ACTIVITY; OCCIPITAL CORTEX; BRAIN-FUNCTION; HUMAN ADULT; ACTIVATION;
D O I
10.1016/j.neuroimage.2012.10.047
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
In this study we applied the General Linear Convolution Model to fast optical signals (FOS). We modeled the Impulse Response Function (IRF) as a rectangular function lasting 30 ms, with variable time delay with respect to the stimulus onset. Simulated data confirmed the feasibility of this approach and its capability of detecting simulated activations in case of very unfavorable Signal to Noise Ratio (SNR), providing better results than the grand average method. The model was tested in a cohort of 10 healthy volunteers who underwent to hemi-field visual stimulation. Experimental data quantified the IRF time delay at 80-100 ms after the stimulus onset, in agreement with classical visual evoked potential literature and previous optical imaging studies based on grand average approach and a larger number of trails. FOS confirmed the expected contralateral activation in the occipital region. Correlational analysis between hemodynamic intensity signal, phase and intensity FOS supports diffusive rather than optical absorption changes associated with neuronal activity in the activated cortical volume. Our study provides a feasible method for detecting fast cortical activations by means of FOS. (C) 2012 Published by Elsevier Inc.
引用
收藏
页码:194 / 202
页数:9
相关论文
共 55 条
[1]   Optical tomographic imaging of dynamic features of dense-scattering media [J].
Barbour, RL ;
Graber, HL ;
Pei, YL ;
Zhong, S ;
Schmitz, CH .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2001, 18 (12) :3018-3036
[2]  
Baringa M., 1697, SCIENCE, V278
[3]   NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy [J].
Chul, Jong ;
Tak, Sungho ;
Jang, Kwang Eun ;
Jung, Jinwook ;
Jang, Jaeduck .
NEUROIMAGE, 2009, 44 (02) :428-447
[4]   Effects of Prior Information on Decoding Degraded Speech: An fMRI Study [J].
Clos, Mareike ;
Langner, Robert ;
Meyer, Martin ;
Oechslin, Mathias S. ;
Zilles, Karl ;
Eickhoff, Simon B. .
HUMAN BRAIN MAPPING, 2014, 35 (01) :61-74
[5]  
COHEN LB, 1972, J PHYSIOL-LONDON, V224, P701, DOI 10.1113/jphysiol.1972.sp009919
[6]   Photon migration through a turbid slab described by a model based on diffusion approximation .2. Theory [J].
Contini, D ;
Martelli, F ;
Zaccanti, G .
APPLIED OPTICS, 1997, 36 (19) :4587-4599
[7]   Functional near infrared optical imaging in cognitive neuroscience: an introductory review [J].
Cutini, Simone ;
Moro, Sara Basso ;
Bisconti, Silvia .
JOURNAL OF NEAR INFRARED SPECTROSCOPY, 2012, 20 (01) :75-92
[8]   A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application [J].
Ferrari, Marco ;
Quaresima, Valentina .
NEUROIMAGE, 2012, 63 (02) :921-935
[9]   Noninvasive measurement of neuronal activity with near-infrared optical imaging [J].
Franceschini, MA ;
Boas, DA .
NEUROIMAGE, 2004, 21 (01) :372-386
[10]  
Friston K. J., 1994, HUM BRAIN MAPP, V2, P189, DOI [10.1002/hbm.460020402, DOI 10.1002/HBM.460020402]