Subjects and ethics statement. Eight adults (4 women) with pharmacoresistant focal epilepsy were included in this study. All subjects underwent chronic intracranial EEG (iEEG) monitoring at North Shore University Hospital to identify epileptogenic foci in the brain for later removal. Six subjects participated in initial experiments: five were implanted with stereoelectroencephalographic (sEEG) depth arrays (PMT, Chanhassen, MN) and one, with grid and strip arrays (see Fig. 1). See Table 1 for additional details on the anatomical region in which each electrode contact was located, following FreeSurfer automated parcellation (Fischl et al. 2004). A total of 1,137 electrodes from 6 patients were included in the analysis of natural breathing (see Figs. 2–8). An additional two subjects participated in the last (arousal) experiment, all implanted with sEEG depth arrays. Electrodes showing any sign of abnormal epileptiform discharges, as identified in epileptologists’ clinical reports, were excluded from the analysis. All included iEEG time series were manually inspected for signal quality and were free from interictal spikes. Respiration traces were also manually inspected and excluded if unstable. All research protocols were approved and monitored by the institutional review board at the Feinstein Institute for Medical Research, and informed written consent to participate in research studies was obtained from each subject before implantation of electrodes. The decision to implant, the electrode positions, and the timing of the electrode-removal procedure were made entirely on clinical grounds, without reference to this investigation. Table 1. Electrode sampling: anatomical region in which each electrode contact was located, following FreeSurfer automated parcellation Brain Area No. of Electrodes No. of Subjects Amygdala 33 4 Hippocampus 30 5 Entorhinal 4 2 Parahippocampal 9 3 Insula 55 5 Lateral orbitofrontal 55 4 Medial orbitofrontal 25 4 Pars orbitalis 9 3 Olfactory bulb* 10 3 Piriform* 9 3 Pars triangularis 24 5 Pars opercularis 25 6 Frontal-rostral middle 27 5 Frontal-caudal middle 27 4 Frontal-superior 22 4 Precentral 41 6 Paracentral 10 3 Postcentral 33 6 Cingulate-anterior 14 5 Cingulate-middle 10 5 Parietal-inferior 7 3 Parietal-superior 7 3 Precuneus 9 4 Cuneus-calcarine-occipital 12 2 Lingual 12 3 Fusiform 15 5 Temporal-middle 114 5 Temporal-inferior 43 4 Temporal-superior 93 6 Supramarginal 17 4 Transverse temporal 26 4 White matter 319 5 CSF 10 5 Total 1,137 6 Whereas the proposed studies are carried out in subjects who have brain abnormalities, their physiology is generalizable to nonpathological human brains because 1) all subjects included in this study had partial epilepsy, in which electrographic abnormalities were restricted to one portion of the brain; 2) a large subset of the electrodes (~90%) were implanted into normal brain areas to identify functional areas and other areas to exclude from surgical resection; 3) electrodes lying within pathological areas were excluded from all analyses; and 4) all eight patients had Full Scale IQ scores within the average range (>90) with spared attention, short-term memory, and interoceptive abilities, according to preoperative neuropsychological testing (NIH Toolbox; Gershon et al. 2013). The present study was carried out during the approximately 2-wk period following admission into the epilepsy monitoring unit for clinically indicated seizure-capture and mapping of sensorimotor and cognitive function.

Data collection and preprocessing. In six subjects, iEEG signals were acquired continuously at 3 kHz per channel (16-bit precision, range ±8 mV, DC) with a Tucker Davis PZ5M data acquisition module (Tucker Davis Technologies, Alachua, FL). In the other two subjects, a standard clinical recording system (XTELK EMU 256 LTM System; Natus Medical) was used, sampling at 512 Hz. iEEG data from both systems were extracted similarly by bandpass filtering (8-pole Butterworth filter, cutoffs at 0.01 and 300 Hz), and similar results were obtained across systems. Either subdural or skull electrodes were used as references, as dictated by recording quality at the bedside, and were subsequently re-referenced to a common average to ensure consistency across participants and to reduce line noise. We inspected the power spectrum of the signals online before the start of the experiment to ensure its physiological properties. The breathing cycle was assessed simultaneously by configuring a custom piezoelectric manometer within a nasal cannula device and recorded with the iEEG record for subsequent offline analysis. Subjects were not informed of the purpose of the cannula (i.e., they were simply asked to place it in the correct position before the start of the study). After the subjects firmly fixed the device in place, a 3-min period was allowed to get accustomed to wearing it. In the breath-counting experiments, the natural breathing blocks were run before the breath-counting blocks, to ensure that subjects did not attend to their breath more than usual during the natural breathing condition.

Experimental tasks. In the natural breathing experiment (see Figs. 2–6), subjects were instructed to rest wakefully (with their eyes open) during an 8.5-min period. We refer to natural breathing as the spontaneous or autonomic breathing of the subject when no cognitive task is performed. In the volitional breathing experiment (see Fig. 8), subjects were asked to slightly increase their nasal breathing rate from natural breathing. Subjects alternated between 2.5-min blocks of natural breathing and faster breathing (8 blocks total, 4 in each condition). We discarded the first 30 s of each block from the analysis, because the breathing rate usually became more regular after that initial period. Respiration signals were smoothed with a moving average filter and DC offset-corrected using standard MATLAB functions (“smooth.m” with a 10-point moving average window, and “detrend.m,” respectively). All blocks were visually inspected, and those with unstable respiration signals (e.g., containing low signal-to-noise ratio or brief periods of absent signal due to mouth respiration) were discarded. Remaining blocks were concatenated within each condition before coherence analysis (8-min total per condition). In the attention-to-breath (interoceptive) experiment (see Figs. 9 and 10), subjects were asked to count their breaths for periods of 2 min (8 blocks total), reporting their counts after each period (Levinson et al. 2014). A 30-s resting interval was given between blocks. Accurately reported blocks, where subjects correctly reported the number of breaths within ±1 breath, were compared with inaccurately reported blocks. The experiment was continued until four blocks of each condition (correct and incorrect) were collected. In total, subjects completed 36 correct blocks (median = 6, range = 5–7) and 25 incorrect blocks (median = 4, range = 4–5). In the attention-to-external-stimuli (exteroceptive) experiment (see Fig. 10A), a task adapted from sustained attention and vigilance paradigms was used (Greicius et al. 2003; Kajimura and Nomura 2015, Leth-Steensen et al. 2000). Subjects viewed a screen containing four circles at different fixed locations while wearing the spirometry apparatus. After 10 s, the center of a randomly assigned circle turned from black to white. Subjects were asked to press one of four corresponding keyboard keys to indicate which circle filled, as quickly and accurately as possible.

Coherence and power analysis. To calculate coherence between the iEEG and the breathing signals (iEEG-breath coherence), we computed the cross-spectrum. The magnitude of the cross-spectrum was squared, normalized by the power spectra of each signal at each respective frequency, and smoothed (Mitra and Pesaran 1999). Each 8-min data set was divided into overlapping 32-s windows (1/32 = 0.03-Hz resolution, sliding 5 s at a time). Statistical significance was evaluated by creating surrogate trials. The iEEG signal was shuffled with respect to the breathing signal (1,000 iterations) by circularly shifting the signals through consecutive random displacements, and the critical levels of the pointwise Wilcoxon-Mann-Whitney tests at each frequency were computed (see dashed magenta lines in Figs. 2C and 8A; α = 0.01). To calculate the breathing rate, the instantaneous frequency of the breathing signal was computed using a short-time Fourier transform. Computing the breathing rate by averaging the time between peaks in inhalation rate gave similar results. To ensure that the breathing signal was as stationary as possible, we excluded the initial 30 s from each block, because breathing rates usually stabilized within that time. All included breathing segments were also visually inspected to ensure signal quality. After computing iEEG-breath coherence values for all frequencies, we extracted the coherence value at the respiratory rate (see dashed green lines in Fig. 2C). Only the peak coherence at the respiratory rate was considered for further analysis. Coherence values at frequencies different from the respiratory rate were not considered in this study because breath manometry transduction is nonlinear, and higher-order components of the resultant signal (e.g., harmonics of the respiration signal and other mechanical artifacts that may appear in its power spectrum) are not necessarily reflective of physiological properties of the breath. For comparison, we also calculated coherence values with other methods, such as use of the wavelet transform (“globalcoher.m” function in MATLAB; The MathWorks, Natick, MA) and multitaper (“coherency.m” function in the Chronux toolbox; Mitra and Pesaran 1999). The results were nearly identical (data not shown). To determine the significance threshold for coherence among electrodes (e.g., see dark gray area in Fig. 3B), we used the 99% confidence interval (upper bound) of the averaged shuffled coherence across electrodes. Other methods to calculate a significance threshold (e.g., using the P-value method derived from the real data, which was well fitted by a bimodal distribution, P < 0.05, Hartigan’s significance test) yielded similar results. The critical value in Fig. 8C (thick red line) was calculated with bootstrapping statistics. First, iEEG and breathing signals for each electrode and condition (breathe normally vs. faster) were randomly shuffled (1,000 iterations). Second, coherence values were calculated for each iteration and then averaged to give rise to a surrogate distribution. The red line represents the 95th percentile of the median of the surrogate data after smoothing with a window size of 0.1 Hz and a slide of 0.01 Hz. The time-frequency coherence spectrum between the respiration and iEEG signals (see Fig. 8B) was computed using wavelets. To compute the difference in coherence values between breath-count correct and incorrect blocks (see Fig. 9B), we subtracted the coherence at the respiratory rate in the incorrect blocks from that in the correct blocks for each electrode and then normalized by their sum for each subject separately (see Fig. 9C). This normalization procedure was done to eliminate differences in overall coherence across subjects. To compare the alpha power between breath-count correct and incorrect blocks (see Fig. 9A, inset) alpha oscillations were extracted from the raw iEEG by bandpass filtering each 8-min data block between 8 and 13 Hz [150th-order finite impulse response (FIR) filter]. The instantaneous alpha-band amplitude was defined as the absolute value of the Hilbert transform of this signal. Data from each electrode were normalized (the difference in alpha power between conditions was divided by their sum), and the resultant distributions were compared using a Wilcoxon signed-rank test with a 95% confidence interval. In the exteroceptive attention task (see Fig. 10A), subjects completed 48 trials (~9 min, depending on reaction time). Coherence values were calculated as for the experiments described above. The initial and final ~30-s segments were removed so that data windows were identical in length (8 min) across breathing conditions (exteroceptive-attention, breath-count correct and incorrect). Resultant coherence values from each condition were normalized by subtracting the coherence from the natural breathing condition and dividing it by its sum [e.g., (breath-count correct − natural)/(breath-count correct + natural)]. Normalized coherence values were subjected to Kruskal-Wallis ANOVA test and subsequent post hoc Tukey’s test comparisons (95% confidence interval). To determine if iEEG-breath coherence was affected by reaction time (see Fig. 10C), time-frequency coherence spectrograms were computed for the entire data set (~9 min, 48 trials) using wavelets. Single-trial iEEG-breath coherence values were extracted from the time windows and frequencies of interest (e.g., the 0–10 s before the subject’s response at the respiratory frequency) for each electrode and plotted as a function of reaction time. Correlation and P values for Spearman’s ρ were computed using the exact permutation distributions. To estimate differences in breathing rate across conditions (see Fig. 10F, bottom), the intervals between peaks of inhalation rate were measured and the resultant distributions compared using Wilcoxon rank sum tests with a 99% confidence interval. The same criterion was used for estimating differences in breathing amplitude across conditions, after the peaks of the respiratory signal were measured.