Using near-infrared spectroscopy to assess neural activation during object processing in infants

Abstract

The capacity to represent the world in terms of numerically distinct objects (i.e., object individuation) is a milestone in early cognitive development and forms the foundation for more complex thought and behavior. Over the past 10 to 15 yr, infant researchers have expended a great deal of effort to identify the origins and development of this capacity. In contrast, relatively little is known about the neural mechanisms that underlie the ability to individuate objects, in large part because there are a limited number of noninvasive techniques available to measure brain functioning in human infants. Recent research suggests that near-IR spectroscopy (NIRS), an optical imaging technique that uses relative changes in total hemoglobin concentration and oxygenation as an indicator of neural activation, may be a viable procedure for assessing the relation between object processing and brain function in human infants. We examine the extent to which increased neural activation, as measured by NIRS, could be observed in two neural areas known to be involved in object processing, the primary visual cortex and the inferior temporal cortex, during an object processing task. Infants aged 6.5 months are presented with a visual event in which two featurally distinct objects emerge successively to opposite sides of an occluder and neuroimaging data are collected. As predicted, increased neural activation is observed in both the primary visual and inferior cortex during the visual event, suggesting that these neural areas support object processing in the young infant. The outcome has important implications for research in cognitive development, developmental neuroscience, and optical imaging.

Assessing infants' cortical response to speech using near-infrared spectroscopy

“…HbO 2 and HbR were first plotted across the four 60 s time blocks composed of the following components: the entire 20 s audiovisual trial, the 10 s baseline period, the entire 20 s visual only trial, and the 10 s baseline period. Artifacts originating in infant physiology and movement were spatially filtered using a principle components analysis (PCA) of the signals across the four channels (Zhang et al, 2005;Wilcox et al, 2005). This approach is based on the assumption that systemic components of interference are spatially global and have higher energy than the signal changes evoked by the perceptual stimuli themeselves (Zhang et al, 2005).…”

“…The NIRS technique used in the present research utilizes changes in blood volume and hemoglobin oxygenation (i.e., hemodynamics) as an index of neural activation (see Meek, 2002;Grinvald et al, 1991;Strangman et al, 2002a;Villringer and Chance, 1997;Villringer and Dirnagl, 1995). This technique has been used with medically at-risk infants in the clinical setting (Chen et al, 2000;Hintz et al, 2001;Meek et al, 1999b;Meek et al, 1998;Meek et al, 1999a;Sakatani et al, 1999;Soul and du Plessis, 1999;Zaramella et al, 2001) and, more recently, its applicability in the experimental setting has been explored (Baird et al, 2002;Peña et al, 2003); Taga et al, 2003;Wilcox et al, 2005). The rationale for this approach rests on the concept that neural activation in response to a stimulus results in increased energy demands in the area activated.…”

Diffuse optical tomography of pain and tactile stimulation: Activation in cortical sensory and emotional systems

“…The analysis has been described in detail elsewhere (Franceschini et al 2006). Here, data was corrected for motion artifacts using principal component analysis in a similar manner to the procedure previously outlined in Wilcox et al andZhang et al, 2005 (Wilcox et al, 2005;Zhang et al, 2005). Single trial averages (STA's) were calculated for each detector for the oxy-(HbO) and deoxyhemoglobin (Hb) changes detected…”

A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data

“…These methods remove MAs by examining the characteristics of the signal [26][27][28][29][30][31][32][33]. One of these methods was developed by our group, the "movement artifact reduction algorithm" (MARA) [34].…”