2.1.

Participants

Eleven right-handed young adults (five females, age 18–30, mean age 23) participated in the study. All participants signed a consent form approved by the Georgetown University Institutional Review Board and reported as being in good health and without medications. All subjects had normal (or corrected-to-normal) vision and undertook a battery of behavioral tests that included measures of IQ (Weschler Abbreviated Scale of Intelligence; the average IQ score 121.7) and handedness before one experimental session lasting 2 h, during which they performed a target-detection task with simultaneous optical and EEG recording of brain activity. All subjects were compensated for their participation in these experiments.

2.2.

Optical Data Collection

Optical signals were recorded using a continuous-wave CW5 imaging system (TechEn, Inc., Milford, Massachusetts) that measures the amplitude of light traveling through the brain and surrounding tissue. The CW5 system has 32 frequency-division multiplexed sources emitting light at two wavelengths, 690 and 830 nm (i.e., 16 lasers at each wavelength), and 32 detector channels. Continuous parallel operation of all the sources and detectors allows rapid data collection. Each laser is modulated at a different frequency from 6.4 to 12.8 kHz, with intervals of 200 Hz between adjacent lasers to allow subsequent frequency demodulation and separation of individual source-detector pairs. Each detector is based on a Hamamatsu APD module (C5460–01) followed by signal amplification and collects light from all sources simultaneously. All optical signals are initially sampled at 41.7 kHz, using four NI6052E data acquisition cards (National Instruments Corporation, Austin, Texas). After the detected signals have been digitized and saved to disk, they are demodulated in software to determine the contribution at the detector from each source.

2.2.1.

Optical probes

The light sources and detectors of the CW5 instrument were connected to the subject's head by flexible optical fiber bundles. Their front ends (optodes) were arranged on a supporting plastic base in a geometrical configuration to allow detectors to “see” as many neighboring sources as possible (source-detector separation was 3 cm). For the specific goals of the current project, we used an optical probe that could be put on top of the 128-electrode net manufactured by Electrical Geodesic, Inc. [(EGI), Eugene, Oregon]. This allowed us to record optical and EEG signals simultaneously. Two 14×8-cm probes, each accommodating 11 optodes with three dual-wavelength (690 and 830 nm) laser sources and eight optical detectors for each hemisphere, were placed bilaterally using anatomical landmarks of the international 10–20 EEG system and aiming to cover the area between locations F3/4-F7/8-C3/4, that is, the inferior frontal gyrus (IFG) and the middle frontal gyrus (MFG) (Fig. 1).

Fig. 1

(a) Position of the left optical probe in reference to the locations of the 10–20 EEG electrode placement system. The right probe was placed symmetrically on the right side of the head. (b) Combined optical and EEG recording probe.

2.3.

Experimental Paradigms

The paradigm used in this study was based on the Animal-No Animal Go-NoGo task introduced by Thorpe ² to study fast object recognition using the ERP technique. In this paradigm, we used black-and-white pictures of natural scenes (size of 472×728 pixels), which were normalized in terms of brightness and contrast. Pictures were presented to the subjects for 26 ms at the center of a computer LCD monitor at a viewing distance of 75 cm and an angular size of 10 deg in 12 blocks, each containing 100 pictures. The intertrial interval within each block varied from 900 to 1700 ms, and there were 20-s breaks between successive blocks. Thus, 1200 pictures selected randomly from the same picture set were presented in each experiment. Approximately 50% of pictures contained animals (targets) and pictures without animals served as nontargets. The task was to simultaneously press two buttons on a button box in response to pictures containing targets as quickly as possible using the thumbs of both hands. During all interstimulus intervals, a crosshair was present in the middle of the screen and subjects were asked to permanently focus their gaze on this cross thus minimizing eye movements. Each experiment was preceded by a 2-min recording period in the state of quiescence, when subjects were sitting passively with their eyes open and looking at the crosshair in the center of the monitor (for 1 min) and then eyes closed for another minute. These periods of passive wakefulness were used to assess the baseline level of functional connectivity.

2.4.

Optical Data Analysis

After offline frequency demodulation, optical data were filtered <1 Hz, downsampled to 20 samples per second, and stored on an acquisition PC computer for further analysis. Independent component analysis (ICA) was performed, and artifactual components generated by superficial layers (scalp and skull) as well as cardiac and respiratory signals were removed using the approach as described previously.¹²

After the ICA procedure, optical signals from 1 s before and up to 30 s after the beginning of each block with picture presentations were used to calculate relative changes in concentration of oxygenated (HbO) and deoxygenated (Hb) hemoglobin using the open-source software HOMer (Photon Migration Laboratory, Massachusetts General Hospital, Charlestown, Massachusetts; http://www.nmr.mgh.harvard.edu/PMI/resources/homer/home.htm). Two-dimensional images of those changes were also reconstructed for each subject using the back-projection algorithm implemented in the HOMer package, which approximates the convexity of the scalp by flat surface and uses semi-infinite homogenous slab geometries. Thus, a resulting two-dimensional scalp map does not depend on an individual “realistic” head space and is based only on the flat-probe geometry, which may be considered as an anatomical template. This facilitates cross-subject averaging and group-level analysis because the individual data are automatically warped into the standard (flat) probe and then simply averaged across subjects. Scalp maps were created using 14×8 = 112 voxels (1×1 cm each) within a rectangular covering each (left or right) probe [Fig. 2a]. Hemodynamic activity reconstructed for each voxel and each time point (thus giving its time course) is referred to as an optical channel.

At the beginning of an experiment, head measurements were taken and the standard locations Cz, C3, F3, F7, and T3 in the left hemisphere (and the corresponding locations in the right hemisphere) from the international 10–20 system for the placement of EEG electrodes were determined for each individual subject. Then, a 128-channel EGI electrode sensor net was placed on the subject's head and the placement of the corresponding electrodes at the 10–20 locations was verified. Optical probes were positioned on top of the electrode net using electrodes C3/4, F3/4, F7/8, and T3/4 as reference points [Figs. 1 and 2a]. Thus, the left probe was positioned to have (i) electrode F7 between source 1 and detectors 2 and 4, (ii) electrode F3 between detectors 3 and 5, and (iii) electrode T3 just behind detector 8 [Fig. 2a]. Similarly, the right probe was placed using electrodes F8, F4, and T4 as a coordinate system. A conventional conceptual knowledge about the exact locality of the 10–20 electrodes has been confirmed in a recent study.¹³ Specifically, F3/F4 electrodes have been shown to be located on left/right middle frontal gyrus, between posterior 1/3 and 1/2; whereas F7/F8 electrodes are located on pars triangularis of left/right inferior frontal gyrus.¹³ Taking this information into account, we assumed that source-detector pairs from the lower half of the left probe (i.e., s1-d2, s1-d4, s1-d6, s2-d4, s2-d6, s3-d6, s3-d8) reflect the activity of the left IFG, whereas source-detector pairs from the upper half (i.e., s1-d1, s1-d3, s1-d5, s2-d3, s3-d5, s2-d7, s3-d7) reflect the activity of the left MFG [Fig. 2a]. Accordingly, the corresponding source-detector pairs from the lower/upper halves of the right probe were taken as reflecting the activities of the right IFG/MFG, respectively.

As an analog of functional connectivity based on correlation of the fMRI BOLD signals,¹ we calculated coefficients of correlation between the time courses of activities of all optical channels (that is, voxels) using the “corrcoef” Matlab function (The Mathworks Inc., Natick, Massachusetts) and used those correlations as measures of functional connectivity. Coefficients of correlation (here referred to as correlations) were calculated separately for oxy- and deoxygenated hemoglobin thus giving two separate measures of connectivity. Using original Matlab scripts, we calculated pairwise correlations between all optical channels in each subject and divided them into the following four groups: 1 and 2–unilateral correlations [all pairwise correlations between channels within the IFG and channels within the MFG, separately for the left (1) and the right (2) hemispheres]; and 3 and 4–bilateral correlations [between all homologous channels within the left/right IFG (3) and the left/right MFG (4)].

2.5.

Statistical Analysis

Changes in concentration (here referred to as “amplitudes of the hemodynamic response”) relative to the 1-s baseline periods before the beginning of each block were averaged over the first 30-s intervals within each block and either over all channels in each hemisphere or separately for the IFG and the MFG in the left and the right hemispheres. These values were analyzed by a two-way ANOVA test (p < 0.05) with regions of interest (left and right IFG, left and right MFG) as fixed effects and subjects as random effects. All correlations were subjected to Fisher's z-transform (making their statistical distributions close to normal distributions), and z-correlations during cognitive task (condition 3) were compared to z-correlations during baseline periods with eyes closed (condition 1) and eyes open (condition 2). These comparisons were made through a two-way ANOVA test (p < 0.05) with functional states (eyes closed, eyes open, or cognitive task) as fixed effects and subjects as random effects. In all cases, pairwise statistical comparisons were made with correction for multiple comparisons based on Dunn–Sidak's approach.¹⁴

2.6.

EEG Data Analysis

The standard preprocessing procedure for EEG included filtering (bandpass 1–100 Hz), artifact detection and removal, segmentation, and baseline correction. EEG data were also subjected to ICA, which allowed us to identify artifacts related to eye blinks and eye movements, motion-related as well as physiological artifacts (mostly heartbeat related). All these artifacts were removed during restoration of the EEG signal from the corresponding ICA components with artifactual components excluded. Segmentation was done using epochs from –200 ms before (baseline period) and up to 800 ms after stimulus presentation. Target- and nontarget-related signals for each subject were averaged over 10 groups of trials (each group containing ∼60 target- and ∼60 nontarget-related trials) resulting in 10 sample ERPs for each subject. These sample ERPs were then subjected to time-frequency decomposition through deconvolution with Mortlet wavelets as implemented in the Fieldtrip software.¹⁵ Such ERP-based time-frequency analysis preserves the phase-locked signal, and the resulting spectrograms are usually referred to as spectrograms of phase-locked or evoked oscillatory activity as opposed to the time-locked or induced oscillations when time-frequency decomposition is done on each trial first and then averaged.¹⁶ Statistical analysis for EEG data was done using nonparametric permutation tests using the false discovery rate to account for multiple comparisons as implemented within the Fieldtrip software using the algorithm by Genovese et al.¹⁷

2.7.

Scalp Maps of Hemodynamic Response (Amplitude and Connectivity) and EEG

To qualitatively represent changes in connectivity at the sublobar level (that is, between MFG and IFG within the same hemisphere, which we refer to as “unilateral connectivity”), for each voxel within the MFG we calculated its average correlation over all voxels within the IFG, and for each voxel in the IFG, we calculated its average correlation over all voxels within the MFG. These average correlations associated with each voxel (instead of pairs of voxels) could be mapped on a scalp map similar to the amplitude maps. To produce amplitude and correlation scalp maps, we used the normalized head template incorporated in the open-source EEGLAB software.¹⁸ Specifically, the time courses of hemodynamic responses of all voxels (either amplitude or average correlation) were exported into the EEGLAB as multivariate time series analogous to the EEG along with spatial coordinates of the corresponding voxels determined in reference to the standard 10–20 electrode system as described earlier [see also Fig. 2a]. Function headplot within the EEGLAB package was used to create scalp maps shown in Fig. 2b (for hemodynamic amplitudes) and Fig. 3b (for hemodynamic correlations). For example, the map on Fig. 2b is a projection of the two-dimensional map shown in Fig. 2a onto the surface of the normalized scalp template (with interpolation causing a smoothing effect). Similarly, scalp maps of frequency components within the high β–low γ range (20–40 Hz) were produced for EEG responses (Fig. 4).

Fig. 4

Group average differential (targets > nontargets) scalp maps of high frequency activity 20–40 Hz) at times of earliest significant differences between targets and nontargets. Only pixels with significant values are shown (p < 0.05 corrected). Note the earliest activation at high frequencies 20–40 Hz in the occipital and prefrontal cortices (t = 110 ms) as well as the right hemispheric dominance of the response at β–γ frequencies especially evident at t = 150 ms when the response was at its maximum.

3.1.

Independent Component Analysis of Optical Data and Removal of Systemic Artifacts

The procedure for removal of systemic artifacts from optical data using ICA is illustrated by two examples in Figs. 5 and 6. It is well known that the most prominent physiological artifact contaminating optical signals is a cardiac signal reflecting systemic transient changes in blood oxygenation with every heartbeat. In Fig. 5a, two consecutive one-second epochs of raw data sampled at 200 Hz are shown for two channels recorded from subject 8. Both optical channels are heavily contaminated with cardiac artifact seen as high amplitude slow waves at frequency ∼1.4 Hz. ICA was independently applied to NIRS and EEG data, and the components containing mostly the cardiac signal were identified for both NIRS and EEG [Fig. 5b]. The same optical channels shown in Fig. 5c after the removal of cardiac components are artifact-free and this demonstrates the effectiveness of ICA as a cardiac artifact-removing tool.

Fig. 5

(a) Raw optical signals are heavily contaminated by systemic physiological (cardiac) artifact. (b) Two independent components are shown that contain the cardiac artifact in the optical signal (thick line) as well as EEG (thin line). Those components have been removed during restoration of optical and EEG signals. (c) The same optical data as in (a) are shown after the removal of the cardiac artifact.

Fig. 6

(a) Raw optical signals (thin line) and the same signals after ICA-based artifact removal (thick line). (b) The corresponding power spectra of channels shown in (a).

The next example illustrates the removal of the contribution of superficial layers from optical data. A representative 150-s segment with nine channels of raw optical data from subject 1 is shown in Fig. 6a, and the corresponding power spectra are shown in Fig. 6b. Because of low-pass filtering at 1 Hz and the removal of cardiac independent components, the most prominent physiological artifact in the optical data related to cardiac pulsation has been already removed. The remaining slow oscillations in the signal represent Mayer's waves (∼0.1 Hz) related to autoregulation of vascular processes as well as respiratory pulsations identifiable as small spectral peaks at ∼0.25 Hz [see channels 1, 5, 7, 8, 9 in Fig. 6b]. After ICA decomposition of the raw data, artifactual components related to the activity of superficial layers were identified as described earlier¹² through visual inspection and spectral analysis by having “flat” spectra typical for white noise. Those ICA components were then excluded during restoration of the signal. As a result, optical signals became significantly less corrupted by noise and physiological rhythms could be seen even in originally “noisy” channels [see, for example, channels 5 and 7 in Fig. 6b, where the remaining respiratory spectral peak at 0.25 Hz can be clearly seen after noise removal]. This illustrates the effectiveness of ICA in removing artifacts and noise generated in the superficial layers and improving signal-to-noise ratio of optical recordings. The restored signal was then used to calculate event-related responses as well as signal correlations.

3.2.

Optical Hemodynamic Signal: Amplitude Analysis

An example of typical raw optical signals (after ICA-based denoising procedure) during quiescent periods and cognitive task are shown in Fig. 7. Cognitive tasks typically evoked a transient increase in oxygenated hemoglobin (ΔHbO = 0.5–2 μM) and a corresponding reduction in deoxygenated hemoglobin (ΔHb = 0.2–0.5 μM) (Fig. 7). The task-related response started within a few seconds after the beginning of the task and reached a peak at 10 s. After that time, the response was gradually decreasing toward the baseline but remained elevated (or reduced for Hb) over the whole period of task performance (Fig. 7).

Fig. 7

A representative example of hemodynamic signals from one subject during the cognitive task for oxygenated (HbO, top) and deoxygenated (Hb, bottom) hemoglobin. Onset of the task is marked by t = 0. Note a smaller amplitude scale for Hb responses.

The group average amplitude responses and the results of their statistical analysis are shown in Fig. 8. Scalp maps of group average responses obtained through image reconstruction and superimposed on a standard head template are presented in Fig. 2. The data clearly show a spatial pattern of task-related activation bilaterally within the inferior and the middle frontal gyri with a strong dominance of the right hemisphere (Fig. 8). The HbO response (an increase in concentration) appeared to have a greater subject-to-subject variation compared to the Hb response (a decrease in concentration). As a result, the average amplitudes of the HbO response were significantly different from baseline levels (assumed to be zero) only in the right hemisphere while the average amplitudes of the Hb response were significantly lower than the baseline level in both hemispheres and the level of significance was higher compared to the HbO response (Fig. 8). Although both HbO and Hb amplitude responses were greater in the right hemisphere, these hemispheric differences did not reach significance levels due to a relatively large variability between subjects.

Fig. 2

Scalp maps of the group average hemodynamic amplitude responses shown in Fig. 8. (a) Two-dimensional maps reconstructed within the Homer software; positions of sources (x) and detectors (o) are shown as well as positions of anatomical marks F3, F7, T3, and C3 from the 10–20 electrode coordinate system, which were used to place the optical probe at the approximately same location in every subject. (b) The same maps as in (a) interpolated and projected on the surface of the head template using the EEGLAB software.

Fig. 8

Group average hemodynamic amplitude responses. Changes in concentrations of HbO and Hb are averaged over a 30-s time period of the task and compared to baseline (1-s time period immediately preceding the onset of the task) for the left and right hemispheres. IFG and MFG are the inferior and the middle frontal gyrus, respectively. Significant changes are indicated by asterisks; p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***).

3.3.

Optical Hemodynamic Signal: Functional Connectivity Analysis

Functional connectivity based on interchannel correlation between hemodynamic responses showed clear changes related to the cognitive task while the baseline levels of connectivity were similar in both passive states [eyes closed and eyes open; Fig. 3a]. First, we compared task-related changes in unilateral functional connectivity between the IFG and the MFG to the baseline levels of connectivity during passive states. In both hemispheres, functional connectivity increased during task performance, but these increases were significant only in the right hemisphere [p < 0.05 for the increase in correlation of HbO activity and p < 0.01 for the increase in correlation of Hb activity; Fig. 3a, black asterisks]. Second, we compared task-related increases in unilateral connectivity in the left hemisphere versus the right hemisphere. For both forms of hemoglobin, an increase in connectivity was significantly greater in the right hemisphere [p < 0.05 for HbO and p < 0.001 for Hb; Fig. 3a, gray asterisks]. Thus, the data on unilateral functional connectivity show the dominance of the right hemisphere during the object detection task. The corresponding spatial maps of connectivity are presented in Fig. 3b, where the dominance of the right hemisphere is clearly seen. Here, each IFG/MFG voxel shows an average correlation of activity of that voxel with all MFG/IFG voxels within the same hemisphere thus representing unilateral connectivity within the region of interest (prefrontal cortex).

Third, we measured bilateral connectivity, that is, between the homologous areas in the left and right hemispheres. Baseline level of bilateral connectivity appeared to be relatively high (for HbO) and did not change during task performance (see top half of Fig. 9). The only significant increase in bilateral connectivity was observed for Hb in the middle frontal gyrus (see bottom half of Fig. 9). Thus, task-related changes in bilateral connectivity (i.e., across hemispheres) were much less pronounced compared to the changes in unilateral (specifically, right-hemispheric) connectivity.

Fig. 9

Group average values of bilateral (interhemispheric) functional connectivity during resting periods and during task performance. IFG and MFG are the inferior and the middle frontal gyrus, respectively. Significant changes (compared to the resting periods) are indicated by asterisk (p < 0.05).

3.4.

EEG Data

Here we present only a small selection of the EEG data in order to compare them to the hemodynamic response. To demonstrate lateralization of the brain response during object recognition, we present scalp maps calculated for phase-locked high β–low γ activity (20–40 Hz). Those scalp maps were calculated at every time point within an interval from 0.05 to 0.4 s after stimulus onset, and Fig. 4 represents maps at time points where the corresponding differential electrophysiological parameter attained the maximal value (i.e., β–γ band activity calculated as the difference between the target- and nontarget-related responses). High-frequency response (20–40 Hz) showed two major loci of activation during this cognitive task, one in the occipital areas and another in the frontal areas. This electrophysiological response developed earliest (0.11 s after stimulus onset) and showed a significant right-hemispheric lateralization for both the occipital and the prefrontal sources of activity especially prominent at t = 150 ms when the response was at its peak (Fig. 4).