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. Author manuscript; available in PMC: 2015 Jul 1.
Published in final edited form as: Clin Neurophysiol. 2013 Dec 1;125(7):1312–1323. doi: 10.1016/j.clinph.2013.11.026

Evaluating Signal-Correlated Noise as a Control Task with Language-Related Gamma Activity on Electrocorticography

Erik C Brown e,f, Otto Muzik a,b, Robert Rothermel d, Csaba Juhász a,b, Aashit K Shah b, Darren Fuerst b, Sandeep Mittal c, Sandeep Sood c, Eishi Asano a,b,*
PMCID: PMC4035421  NIHMSID: NIHMS557907  PMID: 24412331

Abstract

Objective

Our recent electrocorticography (ECoG) study suggested reverse speech, a widely used control task, to be a poor control for non-language-related auditory activity. We hypothesized that this may be due to retained perception as a human voice. We report a follow-up ECoG study in which we contrast forward and reverse speech with a signal-correlated noise (SCN) control task that cannot be perceived as a human voice.

Methods

Ten patients were presented 90 audible stimuli, including 30 each of corresponding forward speech, reverse speech, and SCN trials, during ECoG recording with evaluation of gamma activity between 50–150 Hz.

Results

Sites of the lateral temporal gyri activated throughout speech stimuli were generally less activated by SCN, while some temporal sites seemed to process both human and non-human sounds. Reverse speech trials were associated with activities across the temporal lobe similar to those associated with forward speech.

Conclusions

Findings herein externally validate functional neuroimaging studies utilizing SCN as a control for non-language-specific auditory function. Our findings are consistent with the notion that stimuli perceived as originating from a human voice are poor controls for non-language auditory function.

Significance

Our findings have implications in functional neuroimaging research as well as improved clinical mapping of auditory functions.

Keywords: High-frequency oscillations (HFOs), ripples, epilepsy surgery, video EEG

1. Introduction

Auditory language function is studied with a range of methodologies in humans (McNelly et al., 2009), including positron emission tomography (PET), functional magnetic resonance imaging (fMRI), near-infrared spectroscopy (NIRS), scalp electroencephalography (EEG), and magnetoencephalography (MEG). A well designed task to elicit cortical activity, including a control task to isolate task-specific activity, is critical. We previously attempted to validate reverse (or backward) speech as a control task in studies of auditory language with electrocorticography (ECoG) (Brown et al., 2012), the intracranial counterpart to EEG. In many fMRI studies, such time reversed speech is utilized to control for non-language-specific auditory activities (Gherri and Eimer, 2011, Moore-Parks et al., 2010, Perani et al., 1996, Redcay and Courchesne, 2008, Redcay et al., 2008, Sato et al., 2012). These studies commonly report that the blood-oxygen-level-dependent signal detected by fMRI is enhanced within peri-Sylvian regions during forward speech compared to reverse speech, supporting the notion that reverse speech controls for non-language-specific auditory functions. However, with ECoG we found that language-related activations of the temporal lobe, in particular those of the superior temporal gyrus with early-onset gamma (50–150Hz) activity, showed similar or even greater augmentation during reverse speech compared to forward speech (Brown et al., 2012). Taking into account that gamma-augmentation is generally considered as an excellent summary measure of cortical activation (Kojima et al., 2013, Lachaux et al., 2012, Ray et al., 2008), this ECoG observation suggests that reverse speech is a rather poor control for non-language-specific auditory activity in the brain. We hypothesized that the problem lies in the fact that reverse speech is still perceived as originating from a human voice, although it is largely unintelligible.

Various other types of control tasks have also been described in the literature and used for similar purposes. Several of these can be generated to match normal speech sounds for certain characteristics, such as duration or amplitude envelope, but do not retain those essential to create the perception of a human voice. These are considered as non-speech sounds. Noise-vocoded speech is essentially a normal speech signal with reduced spectral complexity generated by restricting the output sound to a finite set of frequency components without altering the amplitude envelope (Davis and Johnsrude, 2003, Millman et al., 2011, Shannon et al., 1995). Noise-vocoded speech shows the curious effect of enhancing the blood-oxygen-level-dependent signal on fMRI relative to normal speech when the degree of frequency degradation is not severe (Davis and Johnsrude, 2003), such as when a great number of frequency bands are utilized to reconstruct the speech. Musical rain can be generated from frequency formants otherwise used to produce synthetic voice sounds but with a randomly varying carrier frequency and formant onset to produce a rapid spatter of ‘pips’ with a pleasant, rain-like quality (Uppenkamp et al., 2006). Signal-correlated noise (SCN) is essentially a noise signal that is modulated by the amplitude envelope of the original speech signal (Davis and Johnsrude, 2003, Schroeder, 1968). Both musical rain and SCN have been shown on fMRI to activate superior temporal regions less robustly than normal speech sounds, suggesting them to be possible controls for non-language-specific auditory function.

We set out to determine whether a sound that did not create the perception of a human voice may better control for non-language-specific auditory activty as measured on ECoG in the temporal lobe compared to reverse speech. We chose to test SCN because it can be generated directly from the original forward speech sound (Davis and Johnsrude, 2003). Like reverse speech, SCN retains the exact same duration and intensity. However, unlike reverse speech, SCN is not perceived as a human voice. While reverse speech does retain some potential for intelligibility (Cowan et al., 1982), SCN cannot be understood in any circumstance (Davis and Johnsrude, 2003).

In our previous study comparing ECoG activities associated with forward and reverse speech, we noted two distinct sub-classes within the class of sites showing augmentation of gamma activity during auditory stimuli (Brown et al., 2012). In one sub-class, gamma-augmentation was restricted to only the earliest portions of the auditory stimulus. In the other sub-class, gamma-augmentation extended throughout the stimulus from beginning to end. In this follow-up study, we distinguished these sub-classes as ‘Early Auditory’ and ‘Full Auditory’ and analyzed them separately. We tested the following hypotheses for ECoG sites of the temporal lobe: 1) ECoG sites with augmented gamma activity spanning the entire duration of an auditory stimulus (i.e.: ‘Full Auditory’ sites) will show enhanced augmentation during reverse speech compared to forward speech and reduced augmentation during SCN. Such sites are responding to the entirety of the language stimuli and may be involved in the initial extraction of language-related auditory information prior to semantic processing. 2) ECoG sites with augmented gamma activity occurring only very early during an auditory stimulus (i.e.: ‘Early Auditory’ sites) will not show any preference for sounds perceived as human speech. Because these sites show only brief auditory responses upon stimulus deliver, they are not likely to be involved in the extraction of language-related auditory information.

2. Materials and Methods

2.1 Study Patients

Patients were selected by using the following inclusion criteria: (i) a history of intractable focal epilepsy scheduled for extraoperative subdural ECoG recording as part of presurgical evaluation at Children’s Hospital of Michigan or Harper University Hospital, Detroit, between December 2011 and March 2013, (ii) age of 5 years or older, and (iii) measurement of ECoG amplitude augmentations driven by a language task described in section 2.3. Our ECoG study performed prior to the current study period reported that even a 4-year-old child cooperatively and accurately named objects in an auditory-naming task and naming-related gamma-augmentation was observed in both temporal and frontal regions (Kojima et al., 2013).

Exclusion criteria consisted of: (i) presence of massive brain malformations (such as large perisylvian polymicrogyria or hemimegalencephaly) which confound anatomical landmarks for the central sulcus and Sylvian fissure, (ii) history of hearing impairment, (iii) right language dominance as determined by Wada testing (i.e. intracarotid sodium amobarbital procedure) or left-handedness when Wada test results are not available (Knecht et al., 2000), (iv) multiple seizure foci involving both hemispheres, (v) Verbal Comprehension Index (VCI) or Verbal Intelligence Quotient (VIQ) less than 70, (vi) inability to complete the language task described in section 2.3 due to lack of adequate vocabulary or cooperation, and (vii) history of previous neurological surgery. We studied a consecutive series of 10 patients satisfying all criteria (age range: 5–30 years; five females; Table 1). This study has been approved by the Institutional Review Board at Wayne State University, and written informed consent was obtained from all patients or their legal parent or guardian.

Table 1.

Patient Data

Patient Gender Age at Surgery (years) Dominant Hand Age at Epilepsy Onset Antiepileptic mediations PSI† VCI† VIQ† Schooling Wada Test†(Language) Seizure type ECoG Electrode placement Seizure Onset Zone ECoG contacts (total) Histology
1 Male 28 Rt 27 CBZ N/A N/A 99 College Degree Both Complex
Focal Sz 		Rt FPT Rt medial T 86 Tumor
2 Female 27 Rt 26 LEV N/A N/A 100 High School Diploma Lt Complex
Focal Sz 		Lt FPT Lt F 86 Tumor
3 Female 12 Rt 9 LAM, LAC 97 98 N/A Normal 7th Grade N/A Complex
Focal Sz 		Lt FPTO Lt anterior medial T 112 Gliosis
4 Female 5 Rt 1 LEV, LAC 97 N/A 129 Normal Kindergarten N/A Complex
Focal Sz 		Lt FPTO Lt medial T 108 Gliosis
5 Female 9 Rt 8 OXC 112 91 N/A Normal 4th Grade N/A Focal Sz Lt FPTO Lt anterior medial T 108 Tumor
6 Male 30 Rt 29 CBZ, LAC N/A N/A 108 College Lt Focal Sz
w/sGTC 		Lt & RT FPTO Rt inferior T Rt 94
Lt 30 		Tumor
7 Female 21 Rt 19 LEV, Phenytoin N/A N/A 83 High School Diploma Lt Complex
Focal Sz 		Lt FPTO Lt medial T 98 Gliosis
8 Male 28 Rt 5 CBZ, VAL N/A N/A 95 College Lt Complex
Focal Sz 		Rt FPT Rt medial T 112 Gliosis and Dysplasia
9 Male 13 Rt 9 LEV, OXC 115 114 N/A Normal 8th Grade N/A Complex
Focal Sz 		Rt FPT Rt F 128 Dysplasia
10 Male 12 Rt 7 LEV, VAL 73 73 N/A Homebound, Normal 7th Grade N/A Focal Sz
w/sGTC 		Lt FPTO Lt Inferior P 104 Dysplasia and Gliosis
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LEV:Levetiracetam. LAM:Lamotrigine. LAC:Lacosamide. CBZ:Carbamazepine. OXC:Oxcarbazepine. TPM:Topiramate. VAL:Valproate. RT:Right. Lt:Left. Sz:Seizure. sGTC:Secondarily Generalized Tonic-Clonic Sz. F:Frontal. T:Temporal. O:Occipital. P:Parietal.

†

PSI:Processing Speed Index. VCI:Verbal Comprehension Index. VIQ:Verbal Intelligence Quotient. Neuropsychological testing was performed based on clinical necessity. Wada testing (i.e. intracarotid sodium amobarbital procedure) results are provided to indentify the language-dominant hemisphere. Due to use of an auditory language task, we include the measures VIQ and VCI, when available..

Subdural platinum grid electrode (10 mm inter-contact distance; 4 mm diameter) placement was as described previously by our team (Kojima et al., 2013). Extraoperative video-ECoG recordings were obtained for 3 to 5 days, using a 192-channel Nihon Kohden Neurofax 1100A Digital System (Nihon Kohden America Inc., Foothill Ranch, CA, USA) at a sampling frequency of 1000 Hz as previously described (Kojima et al., 2013). Total electrode contact number ranged from 86 to 128 (Table 1). Seizure onset zones were clinically determined (Asano et al., 2009a) and excluded from subsequent analysis.

2.2 Coregistration of Electrodes on Individual Three-Dimensional MRI

MRI, including a volumetric T1-weighted spoiled gradient echo image as well as fluid-attenuated inversion recovery image of the entire head, was obtained preoperatively using a previously described protocol (Nagasawa et al., 2010a). Planar X-ray images (lateral and antero-posterior) were acquired with subdural electrodes in place for localization on the brain surface; three metallic fiducial markers at anatomically well-defined locations aided coregistration with MRI. A three-dimensional MRI brain surface image was created with electrode sites delineated (Alkonyi et al., 2009, Muzik et al., 2007, von Stockhausen et al., 1997). Accuracy was confirmed by intraoperative digital photographs showing in situ electrode locations (Asano et al., 2005, Nagasawa et al., 2010a, Wu et al., 2011).

2.3 Auditory Naming Task

Language mapping by measurement of auditory naming-related gamma activity was performed using an auditory naming task similar to that previously reported (Brown et al., 2012, Kojima et al., 2013). None of the patients had a seizure within two hours prior to or during task performance. While awake and comfortably seated on a bed in a room with unwanted noises minimized, patients received 30 question-and-answer trials. Question stimuli ranged from 1 to 2.5 s in duration. All questions were delivered via playback of an audio recording of the author’s (E.C.B.) voice using Presentation version 9.81 software (Neurobehavioral Systems Inc., Albany, CA, USA) and were designed to elicit 1 or 2 word answers with nouns; e.g. “What flies in the sky?”

In this study, we also delivered reverse speech and SCN trials during the task. To generate reverse speech trials, audio recordings of the 30 forward speech question trials were duplicated and then reversed in time with Cool Edit Pro version 2.00 (Syntrillium Software Corp., Phoenix, AZ, USA). The 30 SCN trials were generated by a method identical to that employed in previous fMRI studies (Davis and Johnsrude, 2003, Rodd et al., 2005) using a script obtained from Dr. Matt H. Davis (University of Cambridge, Cambridge, United Kingdom) for Praat version 5.3.03 software (University of Amsterdam, Amsterdam, The Netherlands). The script employs an algorithm slightly different than what was first described as SCN (Schroeder, 1968) to create a speech-spectrum SCN. Specifically, the low-frequency amplitude envelope of the forward speech trial is applied to a computer-generated noise with the same duration and frequency spectrum as the forward speech trial but with random phase. The amplitude modulated speech-spectrum noise is then scaled to match the power of the forward speech trial. The resulting SCN trial is an unintelligible noise signal with a rhythm similar to the corresponding forward speech trial, but not interpretable as originating from a human voice (listen to the Supplementary Audio files). See Supplementary Table S1 for a list of the forward speech stimuli used.

The audible session of 90 total stimuli in random order was recorded and integrated with ECoG as previously described (Brown et al., 2012, Kojima et al., 2013). Subsequently, the onset and offset of auditory stimuli as well as the onset of the patient’s vocalization of the response were marked for each trial. Cool Edit Pro was used to visually and audibly aid in the manual determination of these time-points. The response time was defined as the period between offset of stimulus presentation and onset of the respective overt response. Patients were instructed to answer “I don’t know” when they did not know the answer to or did not understand a stimulus.

2.4 Evaluation of ECoG Amplitude Changes

Each ECoG trace was transformed into the time-frequency domain, and we determined ‘when’ and ‘where’ gamma activity at 50–150 Hz was augmented. The time-frequency analysis used in the present study (Brown et al., 2012, Hoechstetter et al., 2004) was previously validated by the results of electrical stimulation (Kojima et al., 2012, Nagasawa et al., 2010a, Nagasawa et al., 2010b) as well as postoperative functional impairment (Kojima et al., 2013). In short, the primary measures of interest were the percent change in amplitude of gamma activity relative to that during the reference period (i.e.: the resting baseline) as well as statistical significance of task-related augmentation of gamma activity. The details of analytic methods are described below. The secondary measures included evaluation of low frequency band oscillations at 8–24 Hz (Brown et al., 2012, Miller et al., 2007), as described in our previous study.

2.4.1 Analysis of ECoG Amplitude Changes Relative to Stimulus Onset

A maximum of 90 trials were considered for analysis: 30 forward speech trials, 30 corresponding reverse speech, and 30 corresponding SCN trials. Forward speech, reverse speech, and SCN trial sets were analyzed separately. The inclusion criteria defining ECoG epochs suitable for this time-frequency analysis included: (i) a period of silence serving as a reference period of 400 ms duration was available between 600 to 200 ms prior to the onset of stimulus presentation. The exclusion criteria included: (i) ECoG trace was affected by movement artifacts, (ii) ECoG trace was affected by electrographic seizures, (iii) the corresponding forward speech, reverse speech, or SCN trial was excluded due to failure to satisfy criteria, and (iv) ECoG trace from the superior temporal gyrus was affected by runs of interictal epileptiform discharges lasting 3 seconds or longer.

Time-frequency analysis was performed using BESA® EEG V.5.1.8 software (MEGIS Software GmbH, Gräfelfing, Germany). Each suitable ECoG trial was transformed into the time-frequency domain using a previously described complex demodulation technique (Hoechstetter et al., 2004, Papp and Ktonas, 1977). A given ECoG channel was assigned amplitude values as a function of frequency and time. For evaluation of high frequency gamma activity, time-frequency transformation was performed for frequencies between 10 and 200 Hz and latencies between −600 ms and +4,000 ms relative to the onset of stimulus presentation, in steps of 5 Hz and 10 ms as previously reported (Brown et al., 2008); the method used to evaluate low frequency alpha- and beta-range oscillations is also previously described, presented herein as a secondary measure. At each time-frequency bin, we analyzed the percent change in amplitude (averaged across trials) relative to the grand mean amplitude of the reference period for each frequency epoch. Results are referred to as “event-related synchronization and desynchronization” (Pfurtscheller and Lopes da Silva, 1999) or “temporal spectral evolution” (TSE) (Salmelin and Hari, 1994).

To test for statistical significance in obtained TSE values, a two-step statistical analysis was performed using BESA software (Brown et al., 2012, Kojima et al., 2013, Nagasawa et al., 2010a). Initially, a studentized bootstrap statistic (Davidson and Hinkley, 1999) was applied to obtain an uncorrected p-value independently for each time-frequency bin. In a second step, correction for multiple testing was performed, accounting for the partial correlation between neighboring TSE values. The following modified Bonferroni correction was used (Auranen, 2002, Simes, 1986): p-values derived for a particular frequency band were sorted in ascending order (pi, i = 1, …, N, where N is the number of bins) and the maximum index, m, for which pi < α*i/N was determined. The corrected significance level, α, was set to 0.05. All TSE values corresponding to indices i < m were considered statistically significant. This is less conservative than classical Bonferroni correction but well suited for multiple correlated items (Simes, 1986).

As has been described repeatedly (Asano et al., 2009b, Brown et al., 2012, Fukuda et al., 2010, Nagasawa et al., 2010a, Nagasawa et al., 2010b), an additional manual correction was employed. TSE values in a given electrode were declared significant only if, after the modified Bonferroni correction, a minimum of eight time-frequency bins contained within the gamma range from 50 to 150 Hz were arranged in a continuous array spanning (i) at least 20 Hz in width and (ii) at least 20 ms in duration. All electrodes identified herein have exhibited statistically significant augmentation by this method for the forward speech, reverse speech, or SCN trials. In all charts of the present study, a positive deflection indicates augmentation.

2.4.2 Analysis of ECoG Amplitude Changes Relative to Stimulus Offset

We maintained a pre-stimulus reference period that was jittered based upon stimulus duration but always within a silent period. For each patient, we determined the longest stimulus duration, referred to here as tstim in milliseconds (ms). The inclusion criteria for defining trials suitable for this time-frequency analysis included: (i) patient provides a correct response and (ii) a period of silence serving as a reference period lasting 400-ms immediately preceding the time point −tstim − 200 ms, with stimulus-offset defined as 0 ms. Time-frequency transformation was performed for latencies between −tstim − 200 ms and −tstim + 5,000 ms relative to the offset of stimulus presentation. The exclusion criteria, waveform evaluation, and statistics were as described in section 2.4.1.

2.4.3 Analysis of ECoG Amplitude Changes Relative to Response Onset

We maintained a pre-stimulus reference period that was jittered based upon the combined stimulus and response-time duration, but likewise within a silent period. For each patient, we determined the longest stimulus + response time, here referred to as tresp in milli-seconds. The inclusion criteria for defining ECoG epochs suitable for this time-frequency analysis included: (i) patient provided a correct response, (ii) the response-time variability must be within 1000-ms across trials (Brown et al., 2008), and (iii) a period of silence serving as a reference period of 400-ms immediately preceding the time point −tresp − 200 ms, with response-onset defined as 0-ms. Time-frequency transformation was performed for latencies between −tresp − 200 ms and −tresp + 5,000 ms relative to the offset of stimulus presentation. The exclusion criteria, waveform evaluation, and statistics were as described in section 2.4.1.

2.5 Categorization of Electrode Sites with Significant Gamma-Augmentation

We utilized an algorithm for classification similar to what we have previously published (Brown et al., 2012). Specifically, we categorized electrode sites based solely on the temporal characteristics of gamma-augmentations. A given electrode is defined as an Auditory site if (i) significant gamma-augmentation begins within 300 ms following stimulus onset (Flinker et al., 2010) and (ii) ends prior to 300 ms following stimulus offset (Brown et al., 2012) during forward speech, reverse speech, or SCN trial sets. Thus, Auditory sites are those that are temporally ‘locked’ to the stimuli; a stimulus refers to the entire auditory question.

In this study, we further subdivided Auditory sites into Early-Auditory and Full-Auditory sites based upon our observations in a previous dataset (Brown et al., 2012). Full Auditory sites are those that possess significant gamma augmentation extending past 850 ms after stimulus-onset on any of forward, reverse, or SCN trial sets; with ‘850 ms’ representing half of the average duration of stimulus trials. All other Auditory sites are defined as Early Auditory, since the gamma-augmentation is restricted to only the earliest portions of the auditory stimuli. All other sites with significant gamma-augmentation were treated as ‘Non-Auditory’ sites. These Non-Auditory sites were further sub-categorized based upon the temporal domain in which peak gamma augmentation occurred. That is, ‘Late Stimulus’ sites exhibited peak augmentation during the stimulus, ‘Pre-Response’ sites exhibited peak augmentation after stimulus-offset but prior to response-onset, and ‘Post-Response’ sites exhibited peak augmentation following response-onset.

This analytic approach does not suffer from circular analysis (Kriegeskorte et al., 2009), since the aforementioned ECoG site classification was developed based upon observations from other datasets (Brown et al., 2012), rather than the current dataset. Furthermore, classification was not exclusively driven by the temporal pattern of gamma activity elicited by a ‘specific’ auditory stimulus type, but fairly performed according to the systematic rule treating the three stimulus types equally.

2.6 Comparing Outcome Measures between Task Types

We compared forward speech, reverse speech, and SCN stimulus sets with identical durations and overall auditory characteristics (e.g., volume). Statistics were generated using IBM SPSS Statistics version 20 software (SPSS Inc., Chicago, IL, USA). For behavioral measures, an ANOVA was performed to obtain a 95% confidence interval (C.I.) of the means. For comparisons of electrographic activity, we considered sites of the temporal and frontal lobe classified as described in section 2.5. Electrodes ambiguously located over the Sylvian fissure were determined to be sampling temporal cortex if classified as Auditory; otherwise, surgical photographs were inspected to determine if an electrode was associated more with the temporal or frontal cortex. If a patient contributed more than one site to a classification within the temporal or frontal lobe, the values associated with those sites were averaged such that each patient contributed only one sample for each classification. For comparison of signal changes at temporal lobe sites, we focused on the first 2.5 s following stimulus-onset, since the longest included stimuli were 2.5 s in duration. We averaged the percent gamma-augmentation, relative to the reference, across the frequency range 50 to 150 Hz, as described previously (Brown et al., 2012). We utilized the related measures of (i) peak augmentation/attenuation, and (ii) area under the gamma-augmentation curve (AUC), determined using the trapezoidal numerical integration function (trapz) in MatLab (The MathWorks Inc., Natick, MA, USA); this procedure for calculating the AUC was also done similarly for low frequency alpha- and beta-attenuations, 8 to 24 Hz, as described previously (Brown et al., 2012). The AUC takes both augmentation amplitude as well as duration into account, thus creating a more complete measure of ‘activity’. For comparison of signal changes at frontal lobe sites, we focused on results of response-onset analysis from 2 s prior to response-onset to 1 s after; peak augmentation/attenuation but not AUC was considered for frontal lobe sites due to variability of response-times and response durations (Brown et al., 2012). Friedman’s Test was applied across patients in order to test the hypothesis that forward speech, reverse speech, and SCN stimuli elicit differential distributions of cortical activity within a given classification. Any significant result yielded by this nonparametric analysis of variance by ranks was followed by pairwise post-hoc comparisons between trial sets. Uncorrected p-values are presented for Friedman’s tests. For pairwise post-hoc comparisons, Bonferroni corrected p-values are presented. Alongside statistical results of post-hoc ECoG signal comparisons is provided the pairwise median difference between the forward speech, reverse speech, and SCN trials; forward minus reverse, forward minus SCN, or reverse minus SCN.

3. Results

3.1 Behavioral Data

All subjects satisfying the criteria described in section 2.1 were able to complete the task. Appropriate responses were recorded for both trial types.

Behavioral results are summarized in Table 2. For trials included in stimulus-onset analysis, the grand-mean response-time across subjects was not significantly different (ANOVA p-value = 0.500) between forward speech trials (1304 ms; 95% C.I.: 1171 to 1436 ms), reverse speech trials (1198 ms; 95% C.I.: 1094 to 1303 ms), and SCN trials (1251 ms; 95% C.I.: 1118 to 1384 ms).

Table 2.

Behavioral Results

Patient Response Time Forward Speech Trials mean (95% CI) Response Time Reverse Speech Trials mean (95% CI) SCN Time Reverse Speech Trials mean (95% CI) Correct Responses %Forward,%Reverse,%SCN
1 719 (603–836) msec 648 (587–708) msec 1079 (436–1721) msec 96.7%,93.3%,96.7%
2 1276 (977–1574) msec* 760 (649–871) msec* 595 (513–678) msec* 96.7%, 100%,100%
3 932 (791–1074) msec 893 (781–1006) msec 856 (713–998) msec 96.7%, 100%,96.7%
4 2019 (1526–2512) msec 1611 (1373–1848) msec 1570 (1011–2129) msec 90%,90%,86.7
5 1621 (1049–2194) msec* 611 (504–718) msec* 989 (645–1332) msec 96.7%, 100%,96.7%
6 1271 (1007–1534) msec 1618 (1414–1821) msec 1458 (1242–1674) msec 100%,100%,100%
7 1436 (989–1883) msec 1910 (1287–2532) msec 1744 (1251–2237) msec 96.7%, 90%,96.7%
8 1610 (1023–2197) msec 1844 (1371–2317) msec 2182 (1648–2717) msec 100%,96.7%,66.7%
9 617 (540–694) msec* 931 (848–1015) msec*,*** 730 (622–838) msec*** 100%,100%,100%
10 1809 (987–2630) msec 1358 (848–1867) msec 1603 (1022–2183) msec 80.0%,86.7%,83.3%
Grand Average 1304 (1171–1436) msec 1198 (1094–1303) msec 1251 (1118–1384) msec 95.3%,95.7%,92.3%
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All response times averaged from Stimulus Onset analysis trials.

*

t-test indicates difference between forward and reverse speech trials; α = 0.05.;

**

difference between forward speech and SCN;

***

difference between reverses speech and SCN.

On average, 95.3% (95% C.I.: 91.0 to 99.7%) of the forward speech naming questions were answered correctly while the response “I don’t know” or equivalent was appropriately elicited by 95.7% (95% C.I.: 91.9 to 99.4%) of the corresponding reverse speech stimuli and by 92.3% (95% C.I.: 84.7 to 100%) of the corresponding SCN stimuli. Patients occasionally chose to say “no idea” or “I don’t understand”. These alternative responses to reverse speech and SCN stimuli were considered equivalent to the response “I don’t know” (Brown et al., 2012).

3.2 Temporal Lobe

Over the temporal lobe, 9 patients contributed a total of 38 sites with significant gamma-augmentation. Of these sites, 15 were classified as Early-Auditory, 15 were classified as Full-Auditory, 6 as Late Stimulus, 2 as Pre-Response, and 0 as Post-Response. The time-frequency analysis of representative Early-Auditory and Full-Auditory sites can be found in Figure 1. Results from gamma-range analyses are summarized in Figure 2.

Figure 1. Time-Frequency Analysis at Two Auditory Sites.

Figure 1

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Depicted here are the time-frequency results for forward speech, reverse speech, and SCN trials at two electrode sites of Patient 10 that were classified as Auditory; with one further classified as Full Auditory (top set) and the other as Early Auditory (bottom set). STG = Superior Temporal Gyrus.

Figure 2. Relative Gamma-Augmentations across All Patients.

Figure 2

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We created combined images across all patients with a previously described landmark constrained conformal cortical mapping approach using in-house neuroimaging software (Brown et al., 2012, Muzik et al., 2007). The size of provided electrode locations depicts the difference between forward and reverse speech trials (top) or forward speech and SCN trials (bottom) in peak gamma-augmentations, as averaged across the frequency range 50- to 150-Hz, in percent above baseline. Red electrodes are those for which reverse speech elicited the larger peak gamma-augmentation, blue electrodes are those for which that of forward speech was larger, and green electrodes are those for which that of SCN was larger. Only electrodes of the frontal and temporal lobes for which forward speech, reverse speech, or SCN elicited significant gamma augmentation are displayed on this MNI152 template brain atlas.

3.2.1 Early-Auditory Temporal Lobe Sites

Of the 15 temporal lobe sites classified as Early-Auditory, 8 sites (3 right hemisphere and 5 left) were located over the posterior superior temporal gyrus, 2 sites over the left posterior superior temporal sulcus, 3 sites (2 right hemisphere and 1 left) over the middle portion of the superior temporal gyrus, and 1 site over the middle portion of the left superior temporal sulcus. These sites were contributed by 7 different patients; sites contributed by the same patient were averaged together such that each patient contributed only one sample to statistical analysis. On stimulus-onset analysis, no significant difference in the distributions of gamma-band AUC measures was observed (Friedman’s p = 0.066). No significant difference was found between the distributions of peak gamma-augmentations (Friedman’s p = 0.867). Analysis of the low frequency alpha and beta range yielded no significant differences in either AUC (Friedman’s p = 0.180) or peak-attenuations (Friedman’s p = 0.565). Representative results obtained from patient 3 can be found on electrodes 8, 9, and 10 in Figure 3. Results from the remaining 12 electrodes are depicted graphically in the Supplementary Figure S1.

Figure 3. Gamma Comparisons at Temporal Sites in Patient 3.

Figure 3

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Complete analysis revealed 10 temporal lobe electrode sites with significant gamma-augmentation. Electrodes 2 thru 4 and 6 thru 10 are situated over or facing the superior temporal gyrus and classified as Auditory, based upon temporal characteristics; electrodes 2 thru 4 and 6 and 7 are Full Auditory while 8 thru 10 are Early Auditory. Electrode 1 is situated over the anterior portion of the superior temporal gyrus and classified as Pre-Response, based upon temporal characteristics. Electrode 5 is situated over the posterior portion of the middle temporal gyrus and classified as Pre-Response, based upon temporal characteristics. The pink curve in the figure denotes the central sulcus. The vertical, dashed black lines depict our method of dividing the temporal lobe into ‘anterior’, ‘middle’, and ‘posterior’ portions; each line is drawn down perpendicular to the axis of the temporal lobe from the inferior points of the pre- and post-central sulci, respectively. Results shown are those of stimulus-onset analysis. The seizure onset zone this patient resided in the medial temporal regions.

3.2.2 Full-Auditory Temporal Lobe Sites

Of the 15 temporal lobe sites classified as Full-Auditory, 3 sites (1 right hemisphere and 2 left) were located over the posterior superior temporal gyrus, 1 site over the left posterior superior temporal sulcus, 10 sites (3 right hemisphere and 7 left) over the middle portion of the superior temporal gyrus, and 1 site over the middle portion of the right superior temporal sulcus. These sites were contributed by 7 different patients. On stimulus-onset analysis, a significant difference in the distributions of gamma-band AUC measures was observed (Friedman’s p = 0.002). The SCN trials were associated with a gamma-band ECoG waveform possessing an AUC smaller than that associated with the corresponding reverse speech trials (pcorr = 0.003; median difference [reverse − SCN] 108.91 %-s) but not forward speech (pcorr = 0.099; [forward − SCN] = 82.56 %-s). Differences between forward speech and reverse speech trials failed to reach significance (pcorr = 0.543; [forward − reverse] −26.35 %-s). A significant difference in the distributions of peak gamma-augmentation was also observed (Friedman’s p = 0.004). The SCN trials were associated with a gamma-band ECoG waveform possessing a peak smaller than that associated with either the corresponding forward speech (pcorr = 0.048; 67.65%) or reverse speech trials (pcorr = 0.003; 96.90%). Difference between forward speech and reverse speech trials failed to reach significance (pcorr = 1; −29.25%). Analysis of the low frequency alpha and beta range yielded no significant differences in either AUC (Friedman’s p = 0.156) or peak-attenuations (Friedman’s p = 0.565). Representative results obtained from patient 3 can be found on electrodes 2 through 4 and 6 and 7 in Figure 3. Results from the remaining 10 electrodes are depicted graphically in the Supplementary Figure S1.

3.2.3 Non-Auditory Temporal Lobe Sites

Of the 8 temporal lobe sites classified as Non-Auditory (6 Late Stimulus, 2 Pre-Response, 0 Post-Response), 2 sites (1 right hemisphere and 1 left) were located over the anterior superior temporal gyrus, 3 sites over the middle portion of the left superior temporal gyrus, 1 site over the right posterior superior temporal gyrus, and 2 sites over the left posterior middle temporal gyrus. These sites were contributed by 6 different patients. On stimulus-onset analysis, a significant difference in the distributions of gamma-band AUC measures was observed (Friedman’s p = 0.009). The SCN trials were associated with a gamma-band ECoG waveform possessing an AUC smaller than that associated with the corresponding forward speech (pcorr = 0.012; 42.38 %-s) but not reverse speech trials (pcorr = 0.063; 40.69 %-s). No significant difference was observed between forward speech and reverse speech trials (pcorr = 1; 1.69 %-s). A significant difference in the distributions of peak gamma-augmentation was observed (Friedman’s p = 0.009). The SCN trials were associated with a gamma-band ECoG waveform possessing a peak smaller than that associated with the corresponding forward speech (pcorr = 0.012; 33.31%) but not reverse speech trials (pcorr = 0.063; 32.78%). No significant difference was observed between forward speech and reverse speech trials (pcorr = 1; 0.53%). Analysis of the low frequency alpha and beta range yielded no significant differences in either AUC (Friedman’s p = 0.607) or peak-attenuations (Friedman’s p = 0.513). A representative result from patient 4 can be found on electrodes 1 and 5 in Figure 3. Results from the remaining 6 electrodes are depicted graphically in the Supplementary Figure S1.

3.3 Frontal Lobe

Across all 11 patients, a total of 42 sites with significant gamma-augmentation were noted in the frontal lobe. Of these, 5 were classified as Early-Auditory, 4 as Full-Auditory, 5 as Late Stimulus, 14 as Pre-Response, and 14 as Post-Response. Results from gamma-range analyses are summarized in Figure 2.

3.3.1 Pre-Response Frontal Lobe Sites

Of the 14 frontal lobe sites classified as Pre-Response, 3 sites were located over the left inferior frontal gyrus, 4 sites over the left precentral gyrus, 3 sites (1 right hemisphere and 2 left) over the precentral sulcus, 2 sites (1 right hemisphere and 1 left) over the middle frontal gyrus, 1 site over the left orbital frontal region, and 1 site over the right medial superior frontal gyrus. These sites were contributed by 6 different patients. On response-onset analysis, a significant difference in the distributions of peak gamma-augmentation was observed (Friedman’s p = 0.009). Forward speech trials elicited a larger peak gamma-augmentation in these frontal Pre-Response sites as compared to the corresponding SCN trials (pcorr = 0.012; 32.87%) but not to the corresponding reverse speech trials (pcorr = 0.063; 21.26%). No difference was observed between reverse speech and SCN trials (pcorr = 1; 11.61%). Analysis of the low frequency alpha and beta range showed a significant difference in the distributions of peak-attenuation (Friedman’s p = 0.042). However, none of the post-hoc comparisons reached significance (all pcorr > 0.05). Representative results from patient 3 can be found in Figure 4. Results from the remaining 8 electrodes are depicted graphically in the Supplementary Figure S2.

Figure 4. Gamma Comparison at Frontal Sites in Patient 3.

Figure 4

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Complete analysis revealed 12 lateral frontal lobe electrode sites with significant gamma-augmentation. Electrodes 1, 4, 6, 7, 9, and 10 were classified as Post-Response, based upon temporal characteristics, each located over a portion of the precentral gyrus, with exception of electrode 1. Electrode 12 was classified as Late Stimulus, based upon temporal characteristics, and is located over the precentral sulcus. Electrodes 2, 3, 5, 8, and 11 were classified as Pre-Response, based upon temporal characteristics. Electrodes 2 and 3 are located over portions of the inferior frontal gyrus. Electrodes 5, 8, and 11 are situated over portions of the precentral gyrus or sulcus. Results shown are those of response-onset analysis; with a vertical yellow line identifying the onset of overt responses. The pink curve in the figure denotes the central sulcus.

3.3.2 Late Stimulus Frontal Lobe Sites

Of the 5 frontal lobe sites classified as Late Stimulus, 1 site over the left precentral sulcus, 1 site over the left medial superior frontal gyrus, 1 site over the right middle frontal gyrus, 1 site over the right inferior frontal gyrus, and 1 site over the right inferior frontal sulcus. These sites were contributed by 4 different patients. On response-onset analysis, a significant difference in the distributions of peak gamma-augmentation was observed (Friedman’s p = 0.039). Forward speech trials elicited a larger peak gamma-augmentation in these frontal Late Stimulus sites as compared to the corresponding reverse speech trials (pcorr = 0.039; 48.93%) but not corresponding SCN trials (pcorr = 0.231; 40.96%). No difference was observed between reverse speech and SCN trials (pcorr = 1; −7.97%). Analysis of the low frequency alpha and beta range yielded no significant difference in peak-attenuations (Friedman’s p = 0.779). A representative result from patient 3 can be found in Figure 4. Results from the remaining 4 electrodes are depicted graphically in the Supplementary Figure S2.

3.3.3 Post-Response Frontal Lobe Sites

Of the 14 frontal lobe sites classified as Post-Response, 13 sites (3 right hemisphere and 10 left) were located over the precentral gyrus or sulcus while 1 site was located over the left orbitofrontal cortex. These sites were contributed by 5 different patients. On response-onset analysis, no significant difference in the distributions of peak gamma-augmentation was observed (Friedman’s p = 0.549). Similarly, the low frequency alpha and beta analysis yielded no significant difference in peak-attenuation between forward and reverse speech trials (Friedman’s p = 0.247). Representative results derived from patient 3 can be found in Figure 4. Results from the remaining 8 electrodes are depicted graphically in the Supplementary Figure S2.

3.3.4 Auditory Frontal Lobe Sites

A total of 9 sites of the frontal lobe were classified as Auditory; 5 as Early Auditory and 4 as Full Auditory. Of the 5 Early Auditory sites found across 4 different patients, 3 sites (1 right hemisphere and 2 left) were located over the inferior frontal gyrus, 1 over the right precentral sulcus, and 1 over the medial surface of the right superior frontal gyrus. Of the 4 Full Auditory sites found across 3 different patients, 3 sites (1 right hemisphere and 2 left) were located over the precentral gyrus and 1 site over the right precentral sulcus. Due to the small number of frontal electrodes with Auditory activity and the apparent heterogeneity in spatial-temporal profiles within the groups, comparison tests of significance between the tasks were not performed. Results from each of these sites are displayed in Figure 5.

Figure 5. Frontal Sites with Auditory Activity.

Figure 5

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Several frontal sites exhibited Auditory activity, similar to that observed in the preceding study. In this study, we further divided Auditory sites into Early and Full Auditory. Language-specific processing may occur at such sites.

3.4 Correction for Multiple Comparisons

The above analyses included a total of 18 Friedman’s tests comparing the distributions of forward speech, reverse speech, and SCN trial types. After applying a modified Bonferroni correction for multiple comparisons more suitable for highly correlated data, i.e., Simes correction (Simes, 1986), only the findings related to the AUC of gamma-augmentations at temporal lobe Full-Auditory sites remained significant (corrected p < 0.05), where SCN trials were associated with a less robust gamma-augmentation.

4. Discussion

4.1 Primary Findings

We were able to validate findings from non-invasive imaging studies suggesting SCN to less intensely engage temporal lobe sites associated with language processing (Davis and Johnsrude, 2003, Rodd et al., 2005); see summary Figure 2. This was not simply due to a general lack of engagement of temporal lobe sites. Indeed, SCN augmented gamma activity in temporal sites classified as Early Auditory to a degree similar to that of forward as well as reverse speech. Only temporal lobe sites classified as Full Auditory or Non-Auditory displayed evidence of reduced gamma-augmentation during SCN trials. It is unlikely that this finding is simply due to a lesser cognitive demand imposed by SCN trials or due to Type I error. Rather, while the forward speech trials required a unique and appropriate response, our subjects were instructed to provide a generic response to both reverse speech and SCN trials. Indeed, reverse speech was not associated with such a reduced gamma-augmentation at Full Auditory and Non-Auditory sites of the temporal lobe, supporting our previously published findings suggesting reverse speech to be a poor control task for non-language auditory activity (Brown et al., 2012). SCN trials were also associated with response times similar to both forward speech and reverse speech. Thus, we have demonstrated on ECoG that a control task not perceivable as a human voice (i.e. SCN) will engage language-related temporal lobe sites to a lesser degree than stimuli that are perceived as such (i.e., forward and reverse speech). Taken together, temporal lobe sites classified as Early Auditory may be associated with a non-specific auditory process that can be engaged by either stimuli perceived as a human voice or stimuli not perceivable as such.

Sites of the lateral temporal lobe classified as Full Auditory may be associated with a language-specific auditory process that is engaged only by stimuli perceived as a human voice (i.e., forward and reverse speech). Lateral temporal sites classified as Non-Auditory may provide further analysis of incoming human voice characteristics even when the language information is confused (Brown et al., 2012). Reduced gamma-augmentation at Full-Auditory sites of the superior temporal gyrus and sulcus during SCN trials may indicate decreased attention to stimuli not perceivable as a human voice (Crone et al., 2011, Deco and Thiele, 2009).

Our ECoG study compared the effects of forward speech stimuli to that of corresponding reverse speech and SCN stimuli based on the frequency range from 50 to 150 Hz, as in our previous studies (Brown et al., 2012); a well validated, reliable, and direct measure of task-related cortical activity (Crone et al., 2006b, Kojima et al., 2013). We reproduced our previous result that reverse speech may engage language-specific cortex of the lateral temporal lobe, whereas the difference in the magnitude of gamma augmentation elicited by reverse and forward speech stimuli failed to reach significance in the present study. This may be related to a reduced power in the present study due to two differences between the present and our previous study: 1) in the present study, we split the Auditory category into Early Auditory and Full Auditory categories based upon the temporal profiles of gamma-augmentation observed in our previous study (Brown et al., 2012); and 2) our statistical analysis in the present study was more rigorous in that each subject was permitted to contribute a maximum of one sample to each classification whereas the previous study considered each individual electrode site as a separate and independent sample point (Brown et al., 2012). Even at the reduced statistical power of the present study, we were able to identify a robust finding suggesting SCN to have reduced activation at Full-Auditory sites of the lateral temporal lobe relative to reverse speech.

4.2 Secondary Findings

Our secondary measures did not yield significant findings of interest. In addition to gamma range high-frequency activity, we considered low-frequency oscillations across the alpha and beta ranges at 8–24 Hz. Attenuations at these frequencies were originally described as being closely related to gamma-augmentations (Crone et al., 2011, Crone et al., 1998, Pfurtscheller and Lopes da Silva, 1999). However, the underlying functional meaning of amplitude changes in these low frequency components is less well understood compared to that of high frequency gamma (Brown et al., 2012, Engel and Fries, 2010). Activity at these low frequencies has generally been found to be more spatially distributed and less dynamic (e.g.: lingering) than those of the gamma range (Brown et al., 2012, Crone et al., 2011, Crone et al., 2006a, Fukuda et al., 2010, Hermes et al., 2011, Miller et al., 2007) and such low frequency changes may be functionally independent of those in the gamma range (Cardin et al., 2009, Conner et al., 2011). Indeed, at temporal lobe sites classified as Full-Auditory and Non-Auditory, we found that low frequency oscillations in the alpha/beta range exhibited similar attenuations between forward speech, reverse speech, and SCN trials even though the gamma-band was less strongly augmented during SCN stimuli. The only low frequency finding of note was that of the Pre-Response sites of the frontal lobe in which low frequency attenuations were somewhat greater during reverse speech compared to the other stimuli, while the effect size was rather small (only 11% difference). This finding is difficult to interpret, as it is contrary to our previously reported findings (Brown et al., 2012) and did not survive correction for multiple comparisons.

4.3 Non-Language-Specific Processing in Wernicke’s Region

Posterior regions of the superior temporal gyrus are often included into a well known concept of the cortical organization of language processing known as Wernicke’s region (Boatman and Krauss, 2000, Bogen and Bogen, 1976, Harpaz et al., 2009, Luria, 1980, Simos et al., 2005). This is the brain region thought to engage in language-specific processing as it is well known historically that lesions in this region are capable of severely disrupting language comprehension; “Investigation of patients with a lesion of the posterior third of the first temporal gyrus of the left hemisphere and with sensory aphasia shows that, as a rule, they have no permanent hearing disturbance in the sphere of sounds not concerned with speech” (Luria, 1980). Therefore, it is interesting that many of our Early Auditory sites, which we have demonstrated here to be involved in a non-language-specific activity, are located in the posterior portions of the superior temporal gyrus. At these sites, forward speech, reverse speech, and SCN were associated with similar, short augmentations of gamma activity early during the stimulus. Indeed, it is unlikely that such early ECoG gamma augmentation reflects semantic processing at these sites since this gamma-augmentation, by definition, is not extending throughout the stimulus even when the stimulus contains comprehensible language (i.e. forward speech). Rather we demonstrated human-voice-specific processing at sites classified as Full Auditory, which were largely located in middle portions of the superior temporal gyrus. This emergent pattern begs the question of how lesions of the posterior superior temporal gyrus disrupt language when the more specific processing of the human voice is not located there. A plausible hypothesis may be that some such lesions may be disrupting a process of human voice detection, rather than language processing; not very unlike the ‘auditory analyzer’ proposed by Luria to reside here (Luria, 1980). That is, these Early Auditory sites may be engaged in a process of determining the presence of a human voice. If a human voice is detected, processing proceeds toward sites classified here as Full Auditory sites, and the processing of language-related information proceeds; e.g. semantic processing. Further work is needed to confirm the validity of this hypothesis.

4.4 Auditory Processing in the Precentral Frontal Eye Field

An auxiliary finding for our previous publication (Brown et al., 2012) in which auditory activity occurred in the frontal lobe were was reproduced here. Frontal sites classified as Early Auditory appeared highly variable in both time and space and it is difficult to draw conclusions regarding their nature; they appear quite different than similarly classified sites of the temporal lobe. However, frontal sites classified as Full Auditory exhibited a pattern of activity resembling that of similarly classified sites of the temporal lobe. Furthermore, three of the four frontal sites classified as Full Auditory resided in a region midway up the lateral surface of the precentral gyrus near what is known as the frontal eye field (Brown et al., 2012, Kirchner et al., 2009). The detailed evaluation of these Auditory sites was beyond the scope of this study, but it is interesting to note that these precentral sites exhibit activity not drastically different from that of the superior temporal gyrus. Indeed, evidence has suggested that some precentral sites maybe have direct white matter connectivity with sites of the superior temporal gyrus (Anwander et al., 2007, Bizzi et al., 2012, Brown et al., 2013, Martino et al., 2012, Perani et al., 2011), supporting old and new theories that the precentral gyrus and language-related motor functions are in tight communication with auditory processing of language in the temporal lobe (Bernal and Ardila, 2009, Luria, 1980). Further work is required to gain a further understanding of this interesting phenomenon of auditory processing near the frontal eye field of the precentral gyrus.

5. Conclusion

Stimuli that cannot be perceived as a human voice may be more appropriate for the control of non-language-specific auditory function of the lateral temporal lobe. SCN is an excellent candidate control task in the study of large-scale language networks because it can be generated directly from the corresponding speech to maintain the rhythm, duration, power, and frequency information while removing the essential characteristics of the human voice. We also found that two subclasses of activity along the superior temporal regions may carry out differential functions in the processing of speech; one being specific to the human voice and one being non-specific. Reverse speech was again shown to poorly control for non-language-related activities of the lateral temporal lobe.

Supplementary Material

01. Supplementary Figure S1: Remaining Temporal Sites of Augmentation.

The remaining temporal lobe sites of significant augmentation are shown: 12 Early Auditory, 10 Full Auditory, 6 Late-Stimulus. The pink curves in the figure denote the central sulcus on each patient’s 3D-MRI. The vertical, dashed black lines depict our method of dividing the temporal lobe into ‘anterior’, ‘middle’, and ‘posterior’ portions; each line is drawn down perpendicular to the axis of the temporal lobe from the inferior points of the pre- and post-central sulci, respectively. Red colored electrode sites indicate the seizure onset zone. In this figure, patients without red colored electrodes do not have their seizure focus located on the lateral surface of the brain.

02. Supplementary Figure S2: Remaining Frontal Sites of Augmentation.

The remaining frontal lobe sites of significant augmentation are shown: 4 Late Stimulus, 8 Pre-Response, and 8 Post-Response. The pink curves in the figure denote the central sulcus on each patient’s 3D-MRI. Red colored electrode sites indicate the seizure onset zone. In this figure, patients without red colored electrodes do not have their seizure focus located on the lateral surface of the brain.

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Highlights.

  • Forward speech activated frontal sites more than reverse speech and signal correlated noise (SCN).

  • SCN activated temporal sites responding throughout stimuli less than forward and reverse speech.

  • Sites of non-language-specific auditory activity were observed within Wernicke’s region.

Acknowledgments

This work was supported by NIH grant NS64033 (to E. Asano). We are grateful to Katsuaki Kojima, M.D., Harry T. Chugani, M.D., Carol Pawlak, R.EEG./EP.T., Sarah Minarik, R.N., B.S.N., Alanna Marie Carlson, M.A., and the staff of the Division of Electroneurodiagnostics at Children’s Hospital of Michigan, Wayne State University’s School of Medicine for the collaboration and assistance in performing the studies described above. Matthew H. Davis, PhD, Senior Medical Research Council Research Scientist, University of Cambridge, graciously provided the Praat script used for the generation of SCN trials as well a consultation on its operation. Cynthia L. Arfken, PhD, Associate Professor of Psychiatry and Behavioral Neurosciences at Wayne State University, graciously provided statistical consultation. Angelos Katramados, MD, staff Neurologist at Henry Ford Hospital, provided access to the text of A.R. Luria as well as stimulating clinical and scientific discussion. Two anonymous Reviewers provided excellent feedback to help to improve this text. The first author thanks the Translational Neuroscience Program and the MD/PhD Program of Wayne State University’s School of Medicine for providing an environment in which to perform translational research and grow as an investigator and Kristen Kingzett, M.D., for clinical skills mentoring and creating space to balance research with clinical obligations.

Footnotes

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References

  1. Alkonyi B, Juhász C, Muzik O, Asano E, Saporta A, Shah A, et al. Quantitative brain surface mapping of an electrophysiologic/metabolic mismatch in human neocortical epilepsy. Epilepsy Res. 2009;87:77–87. doi: 10.1016/j.eplepsyres.2009.08.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Anwander A, Tittgemeyer M, von Cramon DY, Friederici AD, Knosche TR. Connectivity-Based Parcellation of Broca’s Area. Cereb Cortex. 2007;17:816–25. doi: 10.1093/cercor/bhk034. [DOI] [PubMed] [Google Scholar]
  3. Asano E, Juhász C, Shah A, Muzik O, Chugani DC, Shah J, et al. Origin and propagation of epileptic spasms delineated on electrocorticography. Epilepsia. 2005;46:1086–97. doi: 10.1111/j.1528-1167.2005.05205.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Asano E, Juhasz C, Shah A, Sood S, Chugani HT. Role of subdural electrocorticography in prediction of long-term seizure outcome in epilepsy surgery. Brain. 2009a;132:1038–47. doi: 10.1093/brain/awp025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Asano E, Nishida M, Fukuda M, Rothermel R, Juhász C, Sood S. Differential visually-induced gamma-oscillations in human cerebral cortex. Neuroimage. 2009b;45:477–89. doi: 10.1016/j.neuroimage.2008.12.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Auranen T. Master’s Thesis. Espoo, Finland: Helsinki University of Technology; 2002. Nonparametric statistical analysis of time-frequency representations of magnetoencephalographic data. [Google Scholar]
  7. Bernal B, Ardila A. The role of the arcuate fasciculus in conduction aphasia. Brain. 2009;132:2309–16. doi: 10.1093/brain/awp206. [DOI] [PubMed] [Google Scholar]
  8. Bizzi A, Nava S, Ferre F, Castelli G, Aquino D, Ciaraffa F, et al. Aphasia induced by gliomas growing in the ventrolateral frontal region: assessment with diffusion MR tractography, functional MR imaging and neuropsychology. Cortex. 2012;48:255–72. doi: 10.1016/j.cortex.2011.11.015. [DOI] [PubMed] [Google Scholar]
  9. Boatman D, Krauss G. Language lateralisation and early right ear deafness: was Wernicke right? J Neurol Neurosurg Psychiatry. 2000;69:538–40. doi: 10.1136/jnnp.69.4.538. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bogen JE, Bogen GM. Wernicke’s region--Where is it? Ann N Y Acad Sci. 1976;280:834–43. doi: 10.1111/j.1749-6632.1976.tb25546.x. [DOI] [PubMed] [Google Scholar]
  11. Brown EC, Jeong J, Muzik O, Rothermel R, Matsuzaki N, Juhasz C, et al. Evaluating the Arcuate Fasciculus With Combined Diffusion-Weighted MRI Tractography and Electrocorticography. Hum Brain Mapp. 2013 doi: 10.1002/hbm.22331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Brown EC, Muzik O, Rothermel R, Matsuzaki N, Juhasz C, Shah AK, et al. Evaluating reverse speech as a control task with language-related gamma activity on electrocorticography. Neuroimage. 2012;60:2335–45. doi: 10.1016/j.neuroimage.2012.02.040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Brown EC, Rothermel R, Nishida M, Juhász C, Muzik O, Hoechstetter K, et al. In vivo animation of auditory-language-induced gamma-oscillations in children with intractable focal epilepsy. Neuroimage. 2008;41:1120–31. doi: 10.1016/j.neuroimage.2008.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cardin JA, Carlen M, Meletis K, Knoblich U, Zhang F, Deisseroth K, et al. Driving fast-spiking cells induces gamma rhythm and controls sensory responses. Nature. 2009;459:663–7. doi: 10.1038/nature08002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Conner CR, Ellmore TM, Pieters TA, DiSano MA, Tandon N. Variability of the relationship between electrophysiology and BOLD-fMRI across cortical regions in humans. J Neurosci. 2011;31:12855–65. doi: 10.1523/JNEUROSCI.1457-11.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Cowan N, Leavitt LA, Massaro DW, Kent RD. A fluent backward talker. J Speech Hear Res. 1982;25:48–53. doi: 10.1044/jshr.2501.48. [DOI] [PubMed] [Google Scholar]
  17. Crone NE, Korzeniewska A, Franaszczuk PJ. Cortical gamma responses: searching high and low. Int J Psychophysiol. 2011;79:9–15. doi: 10.1016/j.ijpsycho.2010.10.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Crone NE, Miglioretti DL, Gordon B, Sieracki JM, Wilson MT, Uematsu S, et al. Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. I. Alpha and beta event-related desynchronization. Brain. 1998;121:2271–99. doi: 10.1093/brain/121.12.2271. [DOI] [PubMed] [Google Scholar]
  19. Crone NE, Sinai A, Korzeniewska A. High-frequency gamma oscillations and human brain mapping with electrocorticography. Prog Brain Res. 2006a;159:275–95. doi: 10.1016/S0079-6123(06)59019-3. [DOI] [PubMed] [Google Scholar]
  20. Crone NE, Sinai A, Korzeniewska A. High-frequency gamma oscillations and human brain mapping with electrocorticography. Prog Brain Res. 2006b;159:275–95. doi: 10.1016/S0079-6123(06)59019-3. [DOI] [PubMed] [Google Scholar]
  21. Davidson AC, Hinkley DV. Studentized bootstrap method. Cambridge: Cambridge University Press; 1999. Bootstrap Methods and their Application; pp. 161–75. [Google Scholar]
  22. Davis MH, Johnsrude IS. Hierarchical processing in spoken language comprehension. J Neurosci. 2003;23:3423–31. doi: 10.1523/JNEUROSCI.23-08-03423.2003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Deco G, Thiele A. Attention: oscillations and neuropharmacology. Eur J Neurosci. 2009;30:347–54. doi: 10.1111/j.1460-9568.2009.06833.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Engel AK, Fries P. Beta-band oscillations--signalling the status quo? Curr Opin Neurobiol. 2010;20:156–65. doi: 10.1016/j.conb.2010.02.015. [DOI] [PubMed] [Google Scholar]
  25. Flinker A, Chang EF, Kirsch HE, Barbaro NM, Crone NE, Knight RT. Single-trial speech suppression of auditory cortex activity in humans. J Neurosci. 2010;30:16643–50. doi: 10.1523/JNEUROSCI.1809-10.2010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Fukuda M, Juhász C, Hoechstetter K, Sood S, Asano E. Somatosensory-related gamma-, beta- and alpha-augmentation precedes alpha- and beta-attenuation in humans. Clin Neurophysiol. 2010;121:366–75. doi: 10.1016/j.clinph.2009.10.036. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gherri E, Eimer M. Active listening impairs visual perception and selectivity: an ERP study of auditory dual-task costs on visual attention. J Cogn Neurosci. 2011;23:832–44. doi: 10.1162/jocn.2010.21468. [DOI] [PubMed] [Google Scholar]
  28. Harpaz Y, Levkovitz Y, Lavidor M. Lexical ambiguity resolution in Wernicke’s area and its right homologue. Cortex. 2009;45:1097–103. doi: 10.1016/j.cortex.2009.01.002. [DOI] [PubMed] [Google Scholar]
  29. Hermes D, Miller KJ, Vansteensel MJ, Aarnoutse EJ, Leijten FS, Ramsey NF. Neurophysiologic correlates of fMRI in human motor cortex. Hum Brain Mapp. 2011;33:1689–99. doi: 10.1002/hbm.21314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Hoechstetter K, Bornfleth H, Weckesser D, Ille N, Berg P, Scherg M. BESA source coherence: a new method to study cortical oscillatory coupling. Brain Topogr. 2004;16:233–8. doi: 10.1023/b:brat.0000032857.55223.5d. [DOI] [PubMed] [Google Scholar]
  31. Kirchner H, Barbeau EJ, Thorpe SJ, Regis J, Liegeois-Chauvel C. Ultra-rapid sensory responses in the human frontal eye field region. J Neurosci. 2009;29:7599–606. doi: 10.1523/JNEUROSCI.1233-09.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Knecht S, Deppe M, Drager B, Bobe L, Lohmann H, Ringelstein E, et al. Language lateralization in healthy right-handers. Brain. 2000;123:74–81. doi: 10.1093/brain/123.1.74. [DOI] [PubMed] [Google Scholar]
  33. Kojima K, Brown EC, Rothermel R, Carlson A, Fuerst D, Matsuzaki N, et al. Clinical significance and developmental changes of auditory-language-related gamma activity. Clin Neurophysiol. 2013;124:857–69. doi: 10.1016/j.clinph.2012.09.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kojima K, Brown EC, Rothermel R, Carlson A, Matsuzaki N, Shah A, et al. Multimodality language mapping in patients with left-hemispheric language dominance on Wada test. Clin Neurophysiol. 2012;123:1917–24. doi: 10.1016/j.clinph.2012.01.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Kriegeskorte N, Simmons WK, Bellgowan PS, Baker CI. Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci. 2009;12:535–40. doi: 10.1038/nn.2303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Lachaux JP, Axmacher N, Mormann F, Halgren E, Crone NE. High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research. Prog Neurobiol. 2012;98:279–301. doi: 10.1016/j.pneurobio.2012.06.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Luria AR. Higher cortical functions in man. 2. New York: Basic Books: Consultants Bureau; 1980. [Google Scholar]
  38. Martino J, De Witt Hamer PC, Berger MS, Lawton MT, Arnold CM, de Lucas EM, et al. Analysis of the subcomponents and cortical terminations of the perisylvian superior longitudinal fasciculus: a fiber dissection and DTI tractography study. Brain Struct Funct. 2012;218:105–21. doi: 10.1007/s00429-012-0386-5. [DOI] [PubMed] [Google Scholar]
  39. McNelly K, Dapretto M, Bookheimer S. Language and the developing brain: insights from neuroimaging. 1. New York, NY: Cambridge University Press; 2009. [Google Scholar]
  40. Miller KJ, Leuthardt EC, Schalk G, Rao RP, Anderson NR, Moran DW, et al. Spectral changes in cortical surface potentials during motor movement. J Neurosci. 2007;27:2424–32. doi: 10.1523/JNEUROSCI.3886-06.2007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Millman RE, Woods WP, Quinlan PT. Functional asymmetries in the representation of noise-vocoded speech. Neuroimage. 2011;54:2364–73. doi: 10.1016/j.neuroimage.2010.10.005. [DOI] [PubMed] [Google Scholar]
  42. Moore-Parks EN, Burns EL, Bazzill R, Levy S, Posada V, Muller RA. An fMRI study of sentence-embedded lexical-semantic decision in children and adults. Brain Lang. 2010;114:90–100. doi: 10.1016/j.bandl.2010.03.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Muzik O, Chugani DC, Zou G, Hua J, Lu Y, Lu S, et al. Multimodality data integration in epilepsy. Int J Biomed Imaging. 2007:13963. doi: 10.1155/2007/13963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Nagasawa T, Rothermel R, Juhasz C, Fukuda M, Nishida M, Akiyama T, et al. Cortical gamma-oscillations modulated by auditory-motor tasks-intracranial recording in patients with epilepsy. Hum Brain Mapp. 2010a;31:1627–42. doi: 10.1002/hbm.20963. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Nagasawa T, Rothermel R, Juhasz C, Nishida M, Sood S, Asano E. Cortical gamma-oscillations modulated by visuomotor tasks: Intracranial recording in patients with epilepsy. Epilepsy Behav. 2010b;18:254–61. doi: 10.1016/j.yebeh.2010.02.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Papp N, Ktonas P. Critical evaluation of complex demodulation techniques for the quantification of bioelectrical activity. Biomed Sci Instrum. 1977;13:135–45. [PubMed] [Google Scholar]
  47. Perani D, Dehaene S, Grassi F, Cohen L, Cappa SF, Dupoux E, et al. Brain processing of native and foreign languages. Neuroreport. 1996;7:2439–44. doi: 10.1097/00001756-199611040-00007. [DOI] [PubMed] [Google Scholar]
  48. Perani D, Saccuman MC, Scifo P, Anwander A, Spada D, Baldoli C, et al. Neural language networks at birth. Proc Natl Acad Sci U S A. 2011;108:16056–61. doi: 10.1073/pnas.1102991108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110:1842–57. doi: 10.1016/s1388-2457(99)00141-8. [DOI] [PubMed] [Google Scholar]
  50. Ray S, Crone NE, Niebur E, Franaszczuk PJ, Hsiao SS. Neural correlates of high-gamma oscillations (60–200 Hz) in macaque local field potentials and their potential implications in electrocorticography. J Neurosci. 2008;28:11526–36. doi: 10.1523/JNEUROSCI.2848-08.2008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Redcay E, Courchesne E. Deviant functional magnetic resonance imaging patterns of brain activity to speech in 2–3-year-old children with autism spectrum disorder. Biol Psychiatry. 2008;64:589–98. doi: 10.1016/j.biopsych.2008.05.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Redcay E, Haist F, Courchesne E. Functional neuroimaging of speech perception during a pivotal period in language acquisition. Dev Sci. 2008;11:237–52. doi: 10.1111/j.1467-7687.2008.00674.x. [DOI] [PubMed] [Google Scholar]
  53. Rodd JM, Davis MH, Johnsrude IS. The neural mechanisms of speech comprehension: fMRI studies of semantic ambiguity. Cereb Cortex. 2005;15:1261–9. doi: 10.1093/cercor/bhi009. [DOI] [PubMed] [Google Scholar]
  54. Salmelin R, Hari R. Spatiotemporal characteristics of sensorimotor neuromagnetic rhythms related to thumb movement. Neuroscience. 1994;60:537–50. doi: 10.1016/0306-4522(94)90263-1. [DOI] [PubMed] [Google Scholar]
  55. Sato H, Hirabayashi Y, Tsubokura H, Kanai M, Ashida T, Konishi I, et al. Cerebral hemodynamics in newborn infants exposed to speech sounds: a whole-head optical topography study. Hum Brain Mapp. 2012;33:2092–103. doi: 10.1002/hbm.21350. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Schroeder MR. Reference Signal for Signal Quality Studies. J Acoust Soc Am. 1968;44:1735–6. [Google Scholar]
  57. Shannon RV, Zeng FG, Kamath V, Wygonski J, Ekelid M. Speech recognition with primarily temporal cues. Science. 1995;270:303–4. doi: 10.1126/science.270.5234.303. [DOI] [PubMed] [Google Scholar]
  58. Simes RJ. An improved Bonferroni procedure for multiple tests of significance. Biometrika. 1986;73:751–4. [Google Scholar]
  59. Simos PG, Sarkari S, Castillo EM, Billingsley-Marshall RL, Pataraia E, Clear T, et al. Reproducibility of measures of neurophysiological activity in Wernicke’s area: a magnetic source imaging study. Clin Neurophysiol. 2005;116:2381–91. doi: 10.1016/j.clinph.2005.06.019. [DOI] [PubMed] [Google Scholar]
  60. Uppenkamp S, Johnsrude IS, Norris D, Marslen-Wilson W, Patterson RD. Locating the initial stages of speech-sound processing in human temporal cortex. Neuroimage. 2006;31:1284–96. doi: 10.1016/j.neuroimage.2006.01.004. [DOI] [PubMed] [Google Scholar]
  61. von Stockhausen HM, Thiel A, Herholz K, Pietrzyk U. A convenient method for topographical localization of intracranial electrodes with MRI and a conventional radiograph. Neuroimage. 1997;5:S514. [Google Scholar]
  62. Wu HC, Nagasawa T, Brown EC, Juhasz C, Rothermel R, Hoechstetter K, et al. Gamma-oscillations modulated by picture naming and word reading: Intracranial recording in epileptic patients. Clin Neurophysiol. 2011;122:1929–42. doi: 10.1016/j.clinph.2011.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

01. Supplementary Figure S1: Remaining Temporal Sites of Augmentation.

The remaining temporal lobe sites of significant augmentation are shown: 12 Early Auditory, 10 Full Auditory, 6 Late-Stimulus. The pink curves in the figure denote the central sulcus on each patient’s 3D-MRI. The vertical, dashed black lines depict our method of dividing the temporal lobe into ‘anterior’, ‘middle’, and ‘posterior’ portions; each line is drawn down perpendicular to the axis of the temporal lobe from the inferior points of the pre- and post-central sulci, respectively. Red colored electrode sites indicate the seizure onset zone. In this figure, patients without red colored electrodes do not have their seizure focus located on the lateral surface of the brain.

NIHMS557907-supplement-01.tif (2.8MB, tif)
02. Supplementary Figure S2: Remaining Frontal Sites of Augmentation.

The remaining frontal lobe sites of significant augmentation are shown: 4 Late Stimulus, 8 Pre-Response, and 8 Post-Response. The pink curves in the figure denote the central sulcus on each patient’s 3D-MRI. Red colored electrode sites indicate the seizure onset zone. In this figure, patients without red colored electrodes do not have their seizure focus located on the lateral surface of the brain.

NIHMS557907-supplement-02.tif (2.3MB, tif)
03
NIHMS557907-supplement-03.xlsx (11.9KB, xlsx)
04
Download audio file (154.6KB, wav)
05
Download audio file (154.6KB, wav)
06
Download audio file (154.6KB, wav)

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