Visual Overview
Visual abstract
Abstract
Planning and executing motor behaviors requires coordinated neural activity among multiple cortical and subcortical regions of the brain. Phase–amplitude coupling between the high-gamma band amplitude and the phase of low frequency oscillations (theta, alpha, beta) has been proposed to reflect neural communication, as has synchronization of low-gamma oscillations. However, coupling between low-gamma and high-gamma bands has not been investigated. Here, we measured phase–amplitude coupling between low- and high-gamma in monkeys performing a reaching task and in humans either performing finger-flexion or word-reading tasks. We found significant coupling between low-gamma phase and high-gamma amplitude in multiple sensorimotor and premotor cortices of both species during all tasks. This coupling modulated with the onset of movement. These findings suggest that interactions between the low and high gamma bands are markers of network dynamics related to movement and speech generation.
Significance Statement
Planning and executing motor behaviors requires coordinated neural activity among different brain regions. Activity in the low-gamma and (to a lesser extent) high-gamma frequency bands is thought to reflect neural information transfer among brain regions across many different behavioral contexts. In monkeys and humans performing different motor behaviors, we found phase–amplitude coupling, a marker of coordinated neural activity, between low-gamma phase and high-gamma amplitude in motor regions. Further, this coupling modulated with the onset of the behavior. This provides insight into underlying network dynamics fundamental to motor control and provides an additional tool for fundamental investigation of cross-area communication in many behavioral contexts and neuropsychiatric conditions.
Introduction
Local field potentials (LFPs) are generated largely by the ensemble postsynaptic activity of populations of neurons and reflect underlying network dynamics (Buzsáki et al., 2012). Traditionally categorized into several canonical frequency bands, modulation of activity in the theta (θ, 4–8 Hz), mu/alpha (μ/α, 8–13 Hz), beta (β, 13–30 Hz), and gamma (γ, 40–200 Hz) bands is linked to a wide range of brain functions, such as language perception and production (Crone et al., 2001b; Towle et al., 2008; Flinker et al., 2015) and movement and force production (Pfurtscheller et al., 1996, 2003; Crone et al., 1998; Pogosyan et al., 2009; Engel and Fries, 2010; Igarashi et al., 2013; Brinkman et al., 2016; Flint et al., 2016, 2017). Moreover, studies have demonstrated correlations between LFPs and neuronal spiking (Fries et al., 2001; Chalk et al., 2010; Igarashi et al., 2013; Hyafil et al., 2015) and between LFPs of different frequencies (Canolty et al., 2006; Voytek et al., 2010), the latter case being termed cross-frequency coupling (CFC).
The rhythmicity of LFP oscillations offers an elegant potential mechanism for coordinating neural activity over a wide range of spatial and temporal scales; thus, LFPs are hypothesized to have functional roles by influencing neural activity (Deans et al., 2007; Canolty et al., 2010; Engel and Fries, 2010; Fröhlich and McCormick, 2010; Anastassiou and Koch, 2015; Fries, 2015; Khanna and Carmena, 2017; Pinotsis et al., 2023). In particular, the γ band has received substantial attention due to consistent observations of event-related modulations in γ band activity and synchronization over a wide range of behaviors and cortical regions (Crone et al., 1998, 2006; Fries, 2009). Although variably defined in the literature (Buzsáki and Schomburg, 2015), in neocortex, the γ band is really two distinct bands, low gamma (Lγ, variably defined but here defined as 40–50 Hz) and high gamma (Hγ, 70–200 Hz). Oscillations mostly within the Lγ band are theorized to have an important role in neural communication (Fries, 2009, 2015), whereas Hγ activity is nonoscillatory, broadband activity, traditionally considered a proxy for ensemble spiking activity (Ray et al., 2008; Manning et al., 2009; Jia and Kohn, 2011; Ray and Maunsell, 2011; Miller et al., 2012; Donoghue et al., 2020).
Movement execution involves coordinated neural activity within higher-order motor cortices (premotor and posterior parietal areas), subcortical nuclei, and cerebellum, and the sensorimotor cortices [primary motor (M1) and primary somatosensory (S1)]. Hypothesized as a marker of coordinated neural activity and information transfer within and between cortical networks (Canolty and Knight, 2010), CFC describes the interactions between LFPs of different frequencies. One type of CFC is phase–amplitude coupling (PAC), in which the amplitude of higher frequency activity varies with the phase of a lower frequency rhythm. Many methods have been developed (Canolty et al., 2006; Tort et al., 2010; Nadalin et al., 2019) and used to detect different types of PAC between several different frequency band pairs during motor behaviors in animals and humans without and with motor disorders (Canolty et al., 2006; Miller et al., 2012; Yanagisawa et al., 2012; De Hemptinne et al., 2013; Igarashi et al., 2013). Two studies noted a decrease in μ/α–Hγ and β–Hγ PAC at movement onset, suggesting that these two types of PAC might suppress or “gate” movement (Miller et al., 2012; Yanagisawa et al., 2012). One study also found Lγ–spike coupling (Igarashi et al., 2013), suggesting that there might also be coupling between Lγ and Hγ. Moreover, PAC has been investigated as a biomarker for closed-loop deep brain stimulation in treatment of movement disorders (De Hemptinne et al., 2015; Swann et al., 2015; Habets et al., 2018; Bouthour et al., 2019).
Here, we describe a novel form of PAC between Lγ and Hγ across species and motor behaviors of varying complexity. We recorded LFPs in monkeys during a reaching task and humans during finger-flexion and word-reading tasks. For each task, we computed Lγ–Hγ PAC using two different methods: the modulation index (MI) (Tort et al., 2010) and a generalized linear model (GLM) framework (Nadalin et al., 2019). We found Lγ–Hγ PAC in many parts of the motor and premotor cortices of monkeys and humans and showed that it modulates during these motor behaviors. To our knowledge, this is the first study to investigate Lγ–Hγ PAC. This finding provides new insight into the roles of different gamma band activities in motor and premotor cortices. Additionally, Lγ–Hγ PAC could potentially serve as a biomarker for studies of motor control or movement disorders.
Materials and Methods
All experimental protocols were performed with approval from the Institutional Animal Care Use Committee and the Institutional Review Board of Northwestern University. All analyses were performed using custom scripts in MATLAB (MathWorks).
Reaching task subjects and data acquisition
The monkey experimental protocols and results are reported in detail elsewhere (Flint et al., 2012). To summarize, two rhesus monkeys (C and M) were trained to perform a center-out reaching task while grasping a two-link planar manipulandum. The center-out reaching task involved moving a computer cursor via the manipulandum to one of eight square, 2 cm outer targets spaced at 45° intervals around a circle of radius 10 cm. Each trial began with the monkey holding the cursor in the center target of the circle. After a random hold time of 0.5–0.6 s, a randomly selected outer target illuminated, signaling the monkey to reach to that target. The monkey needed to move the cursor into the outer target within 1.5 s and hold for a random time of 0.2–0.4 s to receive a liquid reward.
An intracortical 96-channel silicon microelectrode array (Blackrock Neurotech) was implanted in the proximal arm area of M1 contralateral to the tested arm in monkeys C and M. Another intracortical 96-channel array was previously implanted in the proximal arm area of S1 contralateral to the tested arm in monkey M. Intracortical arrays were grounded to the Cereport pedestal and referenced to a subdural platinum wire with 3 mm exposed length placed under the dura. Anesthesia and surgery details are described elsewhere (Pohlmeyer et al., 2007; Flint et al., 2012).
Neural signals were recorded using a 128-channel acquisition system (Cerebus, Blackrock Neurotech) while the monkeys performed the reaching task. LFPs were obtained by bandpass filtering between 0.5 and 500 Hz and sampling at 2 kHz for monkey C and 1 kHz for monkey M, with subsequent notch filtering at 60, 120, 180, and 240 Hz to remove line noise. Multiple data files of 5–20 min duration were recorded in each 60–90-min-long experimental session. Overall, we analyzed 32 data files over 10 experimental sessions from C M1, 58 data files recorded over 11 sessions from M M1, and 48 data files recorded over 10 sessions from M S1. Movement onset was detected from synchronized kinematics recorded from the manipulandum as described elsewhere (Pohlmeyer et al., 2007; Flint et al., 2012).
Finger-flexion task and data acquisition
All human participants were recruited at Northwestern Memorial Hospital and gave informed consent prior to participation. The experimental protocols and results are reported in detail elsewhere (Flint et al., 2020). Briefly, we analyzed recordings from five male human participants, four (FF1–FF4) undergoing awake intraoperative mapping prior to resection of low-grade gliomas and one (FF5) undergoing extraoperative intracranial monitoring before resection for medically refractory epilepsy.
Participants were instructed to execute repeated trials of a finger-flexion task that required isotonic movement and isometric force of a single finger in sequence. At the beginning of each trial, participants held their index finger in a neutral posture. After a cue on a monitor, they executed a flexion movement, bringing the palmar surface of the distal phalanx of the index finger into contact with a load cell. They then applied force to match a randomly generated force target presented on the monitor within 2 s. Following a successful match or failure, the participant returned the finger to the neutral position. The next trial began after a delay of 1 s. Target presentation and cursor feedback were conducted by the open-source BCI2000 software (Schalk et al., 2004). Finger kinematics were recorded with a 22-sensor CyberGlove (Immersion), sampled at 2 kHz. The time resolution for both kinematic data acquisition and force cursor control was 50 ms.
In FF1-4, ECoG arrays were placed over hand motor areas contralateral to the tested hand, which were defined using anatomical landmarks (i.e., “hand knob” in the precentral gyrus), preoperative fMRI, and/or direct electrocortical stimulation mapping to identify functional hand motor area. In FF5, arrays were placed according to clinical necessity. All participants had arrays covering M1 and premotor cortex, with all except for FF3 covering S1 as well. For FF1–FF4, 64-electrode (8 × 8) higher-density arrays (Integra), with 1.5 mm exposed recording site diameter and 4 mm interelectrode spacing, were used. For FF5, a 32-electrode (8 × 4) array, with the same electrode size and spacing as the 64-electrode arrays, was used. ECoG signals were bandpass filtered from 0.3 to 500 Hz and sampled at 2 kHz, and force and kinematics were synchronously recorded, using a NeuroPort Neural Signal Processor (Blackrock Microsystems).
Word-reading task and data acquisition
The experimental protocols and results are reported in detail elsewhere (Mugler et al., 2014, 2018). Briefly, we analyzed data from seven human participants, six (WR1–WR6) during awake intraoperative mapping for glioma resection and one (WR7) during extraoperative intracranial monitoring for medically refractory epilepsy. A monitor presented randomly selected, single words either every 2 s (WR1–6) or every 4 s (WR7). Participants read the word aloud as soon as it appeared. The displayed word was randomly selected from a set of monosyllabic words with primarily consonant–vowel–consonant structure. This set consisted mostly of words from the modified rhyme test [details elsewhere (House et al., 1965)], as well as several additional words containing American English phonemes not seen in the modified rhyme test. Stimuli were presented using BCI2000 (Schalk et al., 2004). Speech audio was sampled at either 48 kHz from a unidirectional lapel microphone (Sennheiser) placed near the participant's mouth connected to a recording computer (WR1–WR6) or at 44.1 kHz from a USB microphone (MXL) using a customized version of BCI2000 and a Tucker-Davis Bioamp system (WR7).
ECoG arrays were placed over areas related to motor speech production, namely, ventral M1, ventral premotor cortex, and frontal operculum (inferior frontal gyrus). All electrode arrays except for WR6 covered portions of ventral S1 as well. Array location was confirmed as described above. Recordings in WR1–WR6 used 64-electrode, higher-density arrays and were recorded using the methods described above. Recordings in WR7 used a 32-electrode (8 × 4) clinical array (PMT), with 2.3 mm exposed diameter and 10 mm interelectrode spacing, and were recorded with a Nihon Kohden system, bandpass filtering from 0.5 to 300 Hz, and sampling at 1 kHz. Audio recordings were synchronized to the ECoG recordings.
ECoG electrode localization
For intraoperative recordings, electrode locations were stereotactically registered at the time of grid placement using Brainlab Curve. We identified each electrode's functional anatomical position with regard to surrounding landmarks (i.e., central sulcus, precentral gyrus, frontal gyri) using the superposed electrode locations on the reconstructed cortical surface provided in the Brainlab software suite, as well as intraoperative photos. For extraoperative recordings, we used the Fieldtrip toolbox (Oostenveld et al., 2011) to reconstruct the patients’ cortical surface from the preimplantation MRI and coregistered it to the postimplantation CT scan. We verified our presumed electrode functional anatomical locations in both settings to be coherent with cortical stimulation mapping results. For ensemble visualization of electrodes from multiple participants, we translated our identified electrode positions to a template brain (Lalys et al., 2010) using LeGUI software (Davis et al., 2021).
Signal processing
All analyses were performed using custom MATLAB (MathWorks) scripts unless otherwise specified. All intracortical and ECoG signals were resampled to 1 kHz and notch filtered at 60, 120, and 180 Hz to remove line noise. Afterward, each electrode was visually inspected for noise or artifacts and excluded from subsequent analyses if noisy. The clean channels in each ECoG array were common average referenced (CAR).
Trials were aligned to event onsets of each task. For the reaching task, changes in the 2D cursor position were used to identify reach (i.e., movement) onset (see Flint et al., 2012 for details). For the finger-flexion task, principal component analysis was performed on the finger joint positions measured by the CyberGlove sensors. The dominant component reflected the position of the index finger, and the derivative of this component was used to identify movement onset (see Flint et al., 2017 for details). For the word-reading task, visual and auditory spectral changes in the audio signal were inspected to manually label the onset of each phoneme within each word. Speech onset was identified as the onset of the first phoneme in each word.
LFP and ECoG spectrograms were computed in a 2 s interval centered on event onset using 256 ms bins of data, shifted in 25 ms increments. For each bin, a Hanning window and fast Fourier transform were applied, and the resulting complex magnitudes were squared. Spectrograms were created by computing the log of the mean magnitude over trials for each bin and normalizing by subtracting the log of the mean power spectrum over the entire interval. For power spectra, the aperiodic component was estimated using an iterative method (Donoghue et al., 2020).
Estimation of phase–amplitude coupling
Phase–amplitude coupling (PAC) between the phase of the lower frequency band and the amplitude of the higher frequency band was computed using two methods: the modulation index (MI) (Tort et al., 2010) and a modified GLM framework (Nadalin et al., 2019). We selected the second method because it considers the power of the frequency-band–defining phase when estimating PAC, reducing the impact of an important confound and thus permitting a more valid interpretation of PAC changes with behavior (Aru et al., 2015; Nadalin et al., 2019). For each method, the CAR signals were first bandpass filtered with a two-way least-squares FIR filter using EEGLAB's eegfilt.m (Delorme and Makeig, 2004) to isolate activity within the Lγ (40–50 Hz) and Hγ (70–200 Hz) bands. We deliberately chose 40–50 Hz to represent Lγ to avoid any potential overlap with β and with line noise. Each band's filtered signal was then z-scored in time. The Hilbert transform was then applied to these signals to extract the instantaneous Lγ phase
To estimate PAC for each interval using the MI (Tort et al., 2010), the corresponding 200 ms bins of
Comodulograms were created using the MI by first defining two sets of frequency bands, one for the phase frequencies and the other for the amplitude frequencies. The phase frequency bands were centered on frequencies ranging from 4 to 56 Hz in steps of 4 Hz and had fixed bandwidths of 4 Hz. The amplitude frequency bands were centered on frequencies ranging from 10 to 200 Hz in steps of 10 Hz and had variable bandwidths. Specifically, the bandwidths of the amplitude frequency bands were twice the center frequency of the corresponding phase band, as this ensured that the passband encompassed the sidebands created by the assumed phase frequency (Berman et al., 2012). For each pair of phase and amplitude frequency bands,
To estimate PAC for each interval using the modified GLM framework (Nadalin et al., 2019), we concatenated and used the corresponding 200 ms bins of
For PAC, the second and third GLMs were used to create surfaces in the 3D space spanned by
Analysis of phase–amplitude coupling
To identify electrodes with significant PAC within the baseline and event-onset intervals using the MI,
For each interval during the reaching task, the degree of Lγ–Hγ PAC was determined by computing the proportion of electrodes with significant PAC per file. Differences in Lγ–Hγ PAC between the intervals were assessed by subtracting
Statistics
Statistical analyses were performed in MATLAB. For PAC results from the reaching task (monkeys), two-tailed paired and unpaired t tests were used to assess within and between electrode differences in PAC strength, respectively. For PAC results from the finger-flexion and word-reading tasks (humans), one-tailed Wilcoxon signed rank tests were used to assess within electrode differences in PAC strength.
Data and code accessibility
The code and datasets used during the current study are available from the corresponding author on reasonable request.
Results
We collected intracranial recordings in monkeys and humans performing different motor behaviors (Fig. 1a,c). Two rhesus monkeys (C and M) performed a reaching task with visual feedback, during which we recorded neural activity from two intracortical arrays in the primary motor cortices (M1) of both monkeys (CM1 and MM1) and from one array in the primary somatosensory cortex (S1) of one (MS1) over multiple experimental sessions spanning 4–9 weeks (Fig. 1a). Furthermore, 12 human participants performed either a finger-flexion task (5 participants; Fig. 1b) or a word-reading task (7 participants; Fig. 1c; see Extended Data Table 1-1 for demographics). In these participants, we recorded neural activity from electrocorticography (ECoG) arrays covering the posterior frontal lobe and postcentral gyrus.
Motor behaviors and the phase–amplitude coupling (PAC) methods. a, Monkey performing a reaching task using a planar manipulandum. b, Human participant performing a finger-flexion task. The participant flexed the index finger and used isometric force to move a computer cursor in 1D to randomly placed target (see Materials and Methods). c, Human participant reading single words from the screen (word-reading task). d–f, Plots generated from an example electrode from the reaching task. d, Example phase–amplitude plots of Hg amplitude at each Lg phase. Notable variation of the higher frequency amplitude with phase indicates PAC (blue), whereas little-to-no variation suggests no PAC (green). a.u., arbitrary units. e, f, Surfaces generated by the GLM framework demonstrating PAC (e) and no PAC (f). The orange surface (
Table 1-1
Participant demographics. Download Table 1-1, DOCX file.
Lγ–Hγ phase–amplitude coupling is a marker of rest and reaching in monkey M1
In the sensorimotor cortex, descriptions of θ–Lγ (Igarashi et al., 2013), μ/α–Hγ (Yanagisawa et al., 2012), and β–Hγ (Miller et al., 2012; De Hemptinne et al., 2013) PAC and their modulation by movement (Miller et al., 2012; Yanagisawa et al., 2012) have provided insight into the temporal gating of motor representation in the sensorimotor cortex during movement execution. To add to these previous findings, we investigated the existence of Lγ–Hγ PAC in the sensorimotor cortex of monkeys and whether it modulates with movement. For each experimental session during the reaching task, we aligned the trials to the outward reach onset, seeing typical modulation of Hγ power around reach onset in precentral and postcentral gyri (Fig. 2a,b). We defined two intervals: resting baseline in the center target (−500 to −300 ms) and reach onset (−100 to 100 ms). For each electrode and interval, we estimated the Lγ–Hγ PAC using the z-scored modulation index (
Low γ–high γ PAC in monkeys performing the reaching task. a–d, Illustrative plots from an example electrode and experimental session in M1 in monkey M (M M1). a, Spectrogram time-locked to reach onset with the baseline (−500 to −300 ms, dashed lines) and reach-onset (−100 to 100 ms, solid lines) intervals marked. b, Phase–amplitude plots during the baseline (red) and reach-onset (yellow) intervals. c, Comodulograms during the baseline (left) and reach-onset (right) intervals. d, Surfaces generated by the GLM framework during the baseline (left) and reach-onset (right) intervals. The greater the differences between the surfaces, the greater the PAC. Thus, PAC decreases from baseline to reach onset. e–h, Same as in a–d, except in monkey M S1 (M S1). i, Distributions of the differences in the z-scored modulation index
Figure 2-1
Comodulogram of example electrode demonstrating other types of phase-amplitude coupling (β-Hγ PAC here) in addition to Lγ-Hγ PAC during the reaching task. Download Figure 2-1, TIF file.
We observed a high degree of Lγ–Hγ PAC in all three intracortical arrays using both
Summary of results from the reaching task
We also found a differential modulation of Lγ–Hγ PAC with reaching based on brain region (Table 1). Across all electrodes with significant Lγ–Hγ PAC identified with
For MS1, we also observed significantly higher Lγ–Hγ PAC in baseline than in reach onset using both
Lγ–Hγ phase–amplitude coupling is a marker of finger flexion versus rest in humans
Our initial results confirm the existence of Lγ–Hγ PAC in the sensorimotor cortex of monkeys and indicate a movement- and region-related modulation of Lγ–Hγ PAC. To support and expand upon these initial findings, we investigated Lγ–Hγ PAC in humans performing a finger-flexion task. Briefly, the participants were visually cued to flex their index finger and then extend back to a neutral baseline (see Materials and Methods). We categorized electrodes as precentral gyrus (preCG), postcentral gyrus (postCG), or anterior to the precentral sulcus (aPreCS, including premotor and prefrontal cortices) electrodes depending on their estimated location. We defined baseline (−600 to −400 ms) and flexion-onset (−200 to 0 ms) intervals relative to the onset of finger flexion using slightly earlier times than for the monkeys because there were multiple electrodes in premotor cortex (which activates sooner) included in the analysis. We computed
We found Lγ–Hγ PAC in all three defined brain regions using both
Low γ–high γ PAC in humans performing finger flexion. a, Spatial distribution of all electrodes across all five participants plotted on a template brain. b–d, Illustrative plots from an example electrode in motor cortex (circled in black/arrow in g). b, Spectrogram time-locked to flexion onset (top, left) with baseline (−600 to −400 ms, dashed lines) and flexion-onset (−200 to 0 ms, solid lines) intervals marked. c, Comodulograms during the baseline (left) and flexion-onset (right) intervals. See Extended Data Figure 3-1 for an example electrode demonstrating other types of PAC. d, Phase–amplitude plots during the baseline (red) and flexion-onset (yellow) intervals. e, Surfaces generated by the GLM framework during the baseline (left) and flexion-onset (right) intervals from another example electrode (circled in black/arrow in i). f, Differences in
Figure 3-1
Comodulogram of example electrode demonstrating other types of phase-amplitude coupling (θ-Hγ, μ/α-Lγ, μ/α-Hγ, and β-Hγ PAC here) in addition to Lγ-Hγ PAC during the finger-flexion task. Download Figure 3-1, TIF file.
Summary of results from the finger-flexion task
In monkeys, we showed that Lγ–Hγ PAC modulates with movement greatly in M1 and less so in S1 (Fig. 2i,j). One advantage of ECoG over intracortical arrays is much broader coverage, allowing us to investigate Lγ–Hγ PAC in more areas. We computed the difference in the pooled
Lγ–Hγ phase–amplitude coupling discriminates between silence and speech onset in humans
Thus far, we have demonstrated that Lγ–Hγ PAC and its modulation patterns with simpler limb movements are consistently present and generalize across species. To examine whether these patterns were present in other types of movement, we investigated Lγ–Hγ PAC in humans performing a more complex motor behavior—speech. We categorized electrodes as preCG, postCG, or posterior inferior frontal gyrus (pIFG), depending on their estimated location, in participants who performed a word-reading task (Fig. 4a). As in the finger flexion participants, we computed
Low γ–high γ PAC in humans reading words. a, Spatial distribution of all electrodes across all seven participants on a template brain. b–e, Illustrative plots from an example electrode (circled in black in g). b, Spectrogram time-locked to voice onset (top, left) with baseline (−600 to −400 ms, dashed lines) and voice-onset (−200 to 0 ms, solid lines) intervals marked. c, Comodulograms during the baseline (left) and voice-onset (right) intervals. See Extended Data Figure 4-1 for an example electrode demonstrating other types of PAC. See Extended Data Figure 4-2 for a more detailed comparison between β–Hγ and Lγ–Hγ PAC in an example electrode. d, Phase–amplitude plots during the baseline (red) and voice-onset (yellow) intervals. e, Surfaces generated by the GLM framework during the baseline (left) and voice-onset (right) intervals from another example electrode (circled in black in i). f, Differences in
Figure 4-1
Comodulogram of example electrode demonstrating other types of phase-amplitude coupling (μ/α-Hγ, β-Lγ, and β-Hγ PAC here) in addition to Lγ-Hγ PAC during the word-reading task. Download Figure 4-1, TIF file.
Figure 4-2
Example comodulograms from the word-reading task during the baseline and event-onset (voice-onset) intervals. These demonstrate different modulation patterns of β-Hγ and Lγ-Hγ PAC, as measured by the mean z-scored modulation index () across the wide PAC bands encompassing both β (16-24 Hz, white) and Lγ (40-48 Hz, red) with Hγ (70-200 Hz). a Increase β-Hγ PAC from baseline (
We again found Lγ–Hγ PAC in all three speech-related brain regions using both
Summary of results from the word-reading task
As we found for monkeys and humans doing finger flexion, we found region-related modulation of Lγ–Hγ PAC around word vocalization (Fig. 4f–i). The pooled preCG
Discussion
Generating movement and speech requires the coordination and control of neurons within brain motor and speech networks. Here, we examined cortical recordings in monkeys and humans for Lγ–Hγ PAC during and before movement and speech. We confirmed that Lγ–Hγ PAC is widespread across different motor regions, behaviors, and species. Furthermore, we observed a consistent, region-related modulation of Lγ–Hγ PAC during these motor behaviors across species. We found that Lγ–Hγ PAC was high in resting states and decreased at the onset of movement in both monkeys and humans in primary motor cortex. These modulations were independent of Lγ amplitude modulations. This PAC was much less prevalent and remained relatively unchanged at movement onset, in postcentral gyrus in both species. Further, we observed similar, though less consistent, decreases in Lγ–Hγ PAC in higher-order motor regions of humans at the onset of movement. Moreover, these patterns were consistent across all three motor tasks. Collectively, these results suggest that modulation of Lγ–Hγ PAC is a motor-related phenomenon that reflects underlying network dynamics fundamental to the gating and activation of motor behaviors.
Event-related modulation of Lγ and Hγ activity has been observed in many brain regions, in several species, and during both motor and nonmotor behaviors (Crone et al., 1998, 2001a,b, 2006; Jia and Kohn, 2011). Although sometimes combined in analyses, Lγ and Hγ are distinct entities associated with different origins and neural processes (Crone et al., 1998; Edwards et al., 2005; Canolty et al., 2006; Ray et al., 2008; Jia and Kohn, 2011; Ray and Maunsell, 2011; Buzsáki et al., 2012; Igarashi et al., 2013). Lγ activity is thought to arise from rhythmic interactions between reciprocally connected inhibitory interneurons and excitatory pyramidal neurons (Buzsáki and Wang, 2012). In contrast, multiple studies have shown that Hγ activity is a broadband (nonoscillatory) phenomenon likely arising from summed postsynaptic potentials of many thousands of neurons (Manning et al., 2009; Buzsáki et al., 2012; Miller et al., 2012; Donoghue et al., 2020). Hγ is somewhat correlated with ensemble spiking activity (Ray et al., 2008; Jia and Kohn, 2011; Ray and Maunsell, 2011). Functionally, observations of spike–Lγ phase coupling (Fries et al., 2001; Pesaran et al., 2002; Womelsdorf et al., 2006, 2007; Canolty et al., 2010; Igarashi et al., 2013) and synchronization of Lγ phase across brain areas led to the communication through coherence (CTC) hypothesis (Fries, 2009, 2015), which posits that Lγ band has a mechanistic role in neural communication by helping to synchronize across brain areas. Although the ability for Lγ activity to directly influence neural activity is controversial (Fröhlich and McCormick, 2010; Engelhard et al., 2013; Buzsáki and Schomburg, 2015; Schneider et al., 2021), it appears clearer that γ activity, especially in the Lγ range, is at least a marker of engaged, cross-area neural networks (Jia and Kohn, 2011; Engelhard et al., 2013). For example, Lγ activity may coordinate spiking between hippocampus and rhinal cortices (Bauer et al., 2007), consistent with the observation that increasing Lγ activity via biofeedback correlates with increased spiking synchronization (Engelhard et al., 2013). It also plays a strong role in spatial memory consolidation, as shown by causal closed-loop control of Lγ (Kanta et al., 2019). Moreover, in an Alzheimer’s disease mice model, optogenetic Lγ stimulation restored previously diminished Lγ activity and improved spatial memory (Etter et al., 2019).
Lγ synchronization (CTC) and PAC are both thought to be indicative of information transfer in a cortical network (Fries, 2009, 2015; Canolty and Knight, 2010). Moreover, Lγ synchronization and PAC are related and may interact with each other (Gonzalez et al., 2020). One seminal study reported coupling between θ phase and Hγ amplitude (θ–Hγ) over a range of sensorimotor and cognitive tasks across the human cortex (Canolty et al., 2006). Additionally, several studies have observed θ–Lγ and θ–Hγ PAC in rat M1 (Igarashi et al., 2013), μ/α–Hγ PAC in human sensorimotor cortices (Yanagisawa et al., 2012), and β–Hγ PAC in human sensorimotor cortices (Miller et al., 2012; De Hemptinne et al., 2013) during upper extremity movements. Yet, to our knowledge, this study is the first to extensively investigate and report the presence of interactions between Lγ and Hγ activity via PAC. While we cannot definitively assign a mechanistic role to Lγ–Hγ PAC due to limitations of PAC analysis (Aru et al., 2015), one interpretation of our results is that this phenomenon is a signature of a fundamental neural process that suppresses motor-related activity on a more local scale. This is similar to reports that μ/α–Hγ (Yanagisawa et al., 2012) and β–Hγ (Miller et al., 2012) PAC decrease with movement, suggesting an inverse relationship with (sometimes called gating of) motor activity. Accordingly, local release from this suppressive process occurs in areas important for generating the desired movement—such as regions of M1 projecting to agonist muscles—which is reflected by a decrease in Lγ–Hγ PAC in electrodes recording from these areas. On a larger spatial scale, a more global reduction in this suppressive process, reflected by a net decrease in Lγ–Hγ PAC over a region, permits the transition from an inactive to active motor state.
What is the neural process that gives rise to Lγ–Hγ PAC? Since Hγ activity has been hypothesized to be a marker of ensemble spiking activity (Ray et al., 2008; Jia and Kohn, 2011; Ray and Maunsell, 2011), one possibility is that Lγ–Hγ PAC is the LFP representation of spike–Lγ correlative metrics, such as spike–Lγ coherence. This would relate Lγ–Hγ PAC to the theorized functions of Lγ activity (Fries, 2009, 2015). Indeed, in M1 of rats performing forelimb movements, spiking activity in shallow cortical layers preferentially occurred at specific Lγ phases (Igarashi et al., 2013). Since multiple animal studies have investigated spike–Lγ correlations (Fries et al., 2001; Womelsdorf et al., 2006, 2007; Engelhard et al., 2013; Igarashi et al., 2013; Zhou et al., 2016), especially in a sensory context, it would be interesting to see if Lγ–Hγ PAC is also present in similar scenarios to support this possibility. If so, Lγ–Hγ PAC as a surrogate for spike–Lγ correlative metrics could be a useful investigative tool, especially in humans. Spiking information is difficult to obtain in this population, and surface electrode arrays and depth electrodes provide opportunities to record neural activity across large spatial scales. Alternatively, Hγ has been shown to be correlated with underlying latent spiking dynamics (Gallego-Carracedo et al., 2022). It remains to be seen how Lγ–Hγ PAC may relate to the latent spiking dynamics.
Lγ amplitude has been shown to decrease with movement in M1 (Igarashi et al., 2013). Since modulations in the band activity defining phase can modulate PAC (Aru et al., 2015; Nadalin et al., 2019), a simpler explanation for our findings is that the decrease in Lγ–Hγ PAC reflects decreased Lγ amplitude. Although we cannot completely exclude this possibility, multiple pieces of evidence make it unlikely. Primarily, we utilized a modified GLM method that accounts for the amplitude of the band defining phase when estimating PAC strength, thus minimizing the effect of Lγ amplitude on the estimated Lγ–Hγ PAC (Nadalin et al., 2019). While the modulation index method does not directly account for Lγ amplitude, we observed an increase in Lγ–Hγ
Spectrograms of example ECoG electrodes during the finger-flexion and word-reading tasks. a, Decrease in Lγ (40–50 Hz, horizontal dashed black lines) power (by 0.80 dB) from the baseline (vertical dotted black lines) to motor-onset (vertical solid black lines) intervals in an electrode with significant Lγ–Hγ PAC. PAC was measured by the z-scored modulation index
Figure 5-1
Power spectra from an example electrode during the word-reading task. a Increase in Lγ (40-50 Hz, horizontal dashed black lines) power relative the estimated aperiodic component during the baseline interval. b Increase in Lγ (40-50 Hz, horizontal dashed black lines) power relative the estimated aperiodic component during the movement interval. Download Figure 5-1, TIF file.
Beyond providing insight on network dynamics, PAC may also have practical clinical applications. In patients with Parkinson's disease, a condition that includes bradykinesia, rigidity, and freezing of gait, there is exaggerated β–γ PAC in motor regions that decreases with therapeutic deep brain stimulation (DBS) of the subthalamic nucleus, suggesting that elevated β–γ PAC reflects a motor-suppressed state (De Hemptinne et al., 2013, 2015; Yin et al., 2022). Further, exaggerated α–γ PAC has been found in patients with essential tremor (Kondylis et al., 2016). In both disorders, this abnormal PAC was more widespread spatially than in people without movement disorders. In addition to being a biomarker for symptom severity in Parkinson's disease, β–γ PAC has potential use as a feedback signal for closed-loop (adaptive) DBS (De Hemptinne et al., 2013, 2015; Swann et al., 2015; Qasim et al., 2016; Habets et al., 2018; Malekmohammadi et al., 2018; Bouthour et al., 2019; Hwang et al., 2020). In addition to observing Lγ–Hγ sometimes being the predominant type of PAC, we noted that the pattern of modulation differed between β–Hγ and Lγ–Hγ PAC with motor onset (Extended Data Fig. 5-1). Moreover, our results suggest that Lγ–Hγ PAC contains different information about motor behavior activation than the well-described modulations in β and Hγ band powers (Fig. 5). Since our results suggest that modulation of Lγ–Hγ PAC is a unique motor-related phenomenon, it would be interesting to investigate its relative strength and modulation pattern in patients with movement disorders. Once characterized in this population, Lγ–Hγ PAC, along with other markers related to motor activity and activation, could potentially be used to develop a more sophisticated adaptive DBS scheme via a multi-input control system.
Additionally, β–Hγ PAC has been used to detect seizures during invasive monitoring for epilepsy surgery (Edakawa et al., 2016), and δ–Lγ PAC was able to identify the postictal generalized EEG state that tends to present in patients at risk for sudden unexpected death in epilepsy (Grigorovsky et al., 2020). Thus, investigating Lγ–Hγ PAC in this patient population might possibly provide another marker to improve seizure monitoring and predicting outcomes in epilepsy patients. Furthermore, abnormal PAC may represent promising neurophysiological markers of schizophrenia (Hirano et al., 2018; Won et al., 2018), obsessive compulsive disorder (Bahramisharif et al., 2016), and Alzheimer's disease (Etter et al., 2019). If these reports of abnormal PAC can be demonstrated to reliably correlate with symptom severity, they may be used to develop stimulation paradigms aimed at alleviating these symptoms in these often-debilitating psychiatric conditions. Additionally, several types of PAC, including Lγ–Hγ PAC, contain some information about speech that may be used for simple decoding tasks (Proix et al., 2022). Thus, there are a number of potential therapeutic applications for which Lγ–Hγ PAC may contribute to improved functional outcomes.
Footnotes
The authors declare no competing financial interests.
We thank Zachary Wright, Michael Scheid, and Lucas Jordan for assistance collecting monkey data and Stephan Schuele and our EEG technologists for assisting with recruitment and recording of ECoG data. This research was supported in part by National Institutes of Health grants K08NS060223 R01NS094748, R01 NS099210, R01NS112942, and F32-DC-015708 (to E.M.M.); the Dixon Translational Research Grants Initiative at Northwestern Medicine and the Northwestern University Clinical and Translational Sciences Institute (NIH UL1RR025741, UL1-TR-000150, and UL1-TR-001422), Paralyzed Veterans of America Research Grant #2728, Brain Research Foundation (BRF SG 2009-14), Doris Duke Charitable Foundation Clinical Scientist Development Award #2011039, and a Craig H. Neilsen Foundation Fellowship (to R.D.F).
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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