Home Biology Music might be reconstructed from human auditory cortex exercise utilizing nonlinear decoding fashions

Music might be reconstructed from human auditory cortex exercise utilizing nonlinear decoding fashions

Music might be reconstructed from human auditory cortex exercise utilizing nonlinear decoding fashions


Quotation: Bellier L, Llorens A, Marciano D, Gunduz A, Schalk G, Brunner P, et al. (2023) Music might be reconstructed from human auditory cortex exercise utilizing nonlinear decoding fashions. PLoS Biol 21(8):


Educational Editor: David Poeppel, New York College, UNITED STATES

Obtained: September 19, 2022; Accepted: Could 30, 2023; Revealed: August 15, 2023

Copyright: © 2023 Bellier et al. That is an open entry article distributed beneath the phrases of the Artistic Commons Attribution License, which allows unrestricted use, distribution, and replica in any medium, offered the unique writer and supply are credited.

Knowledge Availability: All preprocessed information supporting the outcomes of this research can be found from the Zenodo repository (DOI: 10.5281/zenodo.7876019).

Funding: This work was supported by the Fondation Pour l’Audition (FPA RD-2015-2 to LB), the Nationwide Institutes of Well being’s Nationwide Institute of Biomedical Imaging and Bioengineering (R01-EB026439 and P41-EB018783 to PB), and the Nationwide Institutes of Well being’s Nationwide Institute of Neurological Issues and Stroke (U24-NS109103, U01-NS108916, and R13-NS118932 to PB; R01-NS21135 to RTK). The funders had no function in research design, information assortment and evaluation, determination to publish, or preparation of the manuscript.

Competing pursuits: The authors have declared that no competing pursuits exist.

mind–laptop interface; CAR,
frequent common reference; CI,
confidence interval; ECoG,
electrocorticography; HFA,
high-frequency exercise; ICA,
unbiased part evaluation; iEEG,
intracranial electroencephalography; IFG,
inferior frontal gyrus; MLP,
multilayer perceptron; MNI,
Montreal Neurological Institute; MSE,
imply squared error; SEM,
Normal Error of the Imply; SMC,
sensorimotor cortex; STG, superior temporal gyrus; STRF,
spectrotemporal receptive discipline


Music is a common expertise throughout all ages and cultures and is a core a part of our emotional, cognitive, and social lives [1,2]. Understanding the neural substrate supporting music notion, outlined right here because the processing of musical sounds from acoustics to neural representations to percepts and distinct from music manufacturing, is a central purpose in auditory neuroscience. The final many years have seen super progress in understanding the neural foundation of music notion, with a number of research assessing the neural correlates of remoted musical parts reminiscent of timbre [3,4], pitch [5,6], melody [7,8], concord [9,10], and rhythm [11,12]. It’s now effectively established that music notion depends on a broad community of subcortical and cortical areas, together with main and secondary auditory cortices, sensorimotor areas, and inferior frontal gyri (IFG) [1316]. Regardless of intensive overlap with the speech notion community [17,18], some mind areas of the temporal and frontal lobes are preferentially activated throughout music notion [15,1921]. Current research report selective musical activation of various neural populations inside the STG and the IFG [22]. Each hemispheres are concerned in music processing, with a relative choice for the fitting hemisphere in comparison with a left dominance for speech [23,24]. Nonetheless, an built-in view combining these musical parts and particular mind areas utilizing a single predictive modeling method utilized to a naturalistic and complicated auditory stimulus is missing. On this research, we aimed to specify which mind areas are preferentially engaged within the notion of various acoustic parts composing a music.

Right here, we used stimulus reconstruction to research the spatiotemporal dynamics underlying music notion. Stimulus reconstruction consists in recording the inhabitants neural exercise elicited by a stimulus after which evaluating how precisely this stimulus might be reconstructed from neural exercise by means of using regression-based decoding fashions. Reconstructing sensory inputs from recorded neuronal responses is proposed to be “a vital take a look at of our understanding of sensory coding” [25]. What details about the surface world might be extracted from analyzing the exercise elicited in a sensory circuit and which options are represented by completely different neural populations [2628]?

We adopted the methodological method utilized in speech reconstruction. Music and speech are each complicated acoustic indicators counting on a multiorder, hierarchical info construction—phonemes, syllables, phrases, semantics, and syntax for speech; notes, melody, chords, and concord for music [29]. The concept that music might be reconstructed utilizing the identical regression method as utilized to speech is additional supported by previous research exhibiting a practical overlap of mind constructions concerned in speech and music processing [30].

Vital advances have been made in reconstructing speech from the neural responses recorded with intracranial electroencephalography (iEEG). iEEG is especially effectively suited to review auditory processing as a consequence of its excessive temporal decision and glorious signal-to-noise ratio [31] and gives direct entry to the high-frequency exercise (HFA; 70 to 150 Hz), an index of nonoscillatory neural exercise reflecting native info processing and linked to single-unit firing [32] and the fMRI BOLD sign [33]. A number of research discovered that nonlinear fashions decoding from the auditory and sensorimotor cortices offered the best decoding accuracy [34,35] and success in reconstructing intelligible speech [36]. That is possible as a consequence of their skill to mannequin the nonlinear transformations undergone by the acoustic stimuli in increased auditory areas [37,38].

We obtained a singular iEEG dataset the place 29 neurosurgical sufferers passively listened to the favored rock music One other Brick within the Wall, Half 1 (by Pink Floyd), whereas their neural exercise was recorded from a complete of two,668 electrodes immediately mendacity on their cortical floor (electrocorticography (ECoG)). This dataset has been utilized in earlier research asking completely different analysis questions with out using decoding or encoding fashions [3943]. Passive listening is especially suited to our stimulus reconstruction method, as lively duties involving goal detection [3,7,8] or perceptual judgments [6,10], whereas mandatory to review key features of auditory cognition, can confound the neural processing of music with decision-making and motor exercise including noise to the reconstruction course of. The Pink Floyd music used on this dataset constitutes a wealthy and complicated auditory stimulus, in a position to elicit a distributed neural response together with mind areas encoding higher-order musical parts together with chords (i.e., at the least 3 notes performed collectively), concord (i.e., the connection between a system of chords), and rhythm (i.e., the temporal association of notes) [44,45].

We investigated to what extent the auditory spectrogram of the music stimulus might be reconstructed from the elicited HFA utilizing a regression method. We additionally quantified the impact of three components on reconstruction accuracy: (1) mannequin sort (linear versus nonlinear); (2) electrode density (the variety of electrodes used as inputs in decoding fashions); and (3) dataset length to offer each methodological and elementary insights into the reconstruction course of. We then examined whether or not the reconstructed music might be objectively recognized, following a classification-like method [34]. Given the same qualities of speech and music and the substantial overlap of their neural substrates, we hypothesized that we might encounter the identical limitations as noticed in speech reconstruction research, whereby solely nonlinear fashions present a recognizable reconstructed stimulus (i.e., a music {that a} listener might determine, with out the vocals being essentially intelligible), and that decoding accuracy has a logarithmic relationship with each electrode density and dataset length.

Be aware that earlier research have utilized decoding fashions to the music area, using a classification method. These research examined whether or not decoding fashions might determine completely different musical items [46] and genres [47,48] or estimate musical consideration [49] or experience degree of the listener [50]. A latest research tried to reconstruct music from EEG information and confirmed the feasibility of this method [51]. To our information, we current right here the primary iEEG research reporting music reconstruction by means of regression-based decoding fashions.

Along with stimulus reconstruction, we additionally adopted an encoding method to check whether or not latest speech findings generalize to music notion. Encoding fashions predict neural exercise at one electrode from a illustration of the stimulus. These fashions have been efficiently used to proof key neural properties of the auditory system [52,53]. Within the music area, encoding fashions have proven a partial overlap between the neural exercise underlying music imagery and music notion [54]. Current speech research have discovered that STG was parcellated alongside an antero-posterior axis. In response to speech sentences, posterior STG exhibited a transient improve of HFA on the onset of the sentence, whereas anterior STG exhibited a sustained HFA response all through the sentence [55,56]. Right here, we investigated whether or not we might observe comparable HFA exercise profiles, specifically onset and sustained, in response to a musical stimulus. Lastly, we carried out an ablation evaluation, a technique akin to creating digital lesions [57,58], by eradicating units of electrodes from the inputs of decoding fashions. This technique allowed us to evaluate the significance of anatomical and practical units of electrodes by way of how a lot info they contained in regards to the music stimulus, and if this info is exclusive or redundant throughout completely different elements of the music community. We hypothesized that the fitting STG would have a main function in representing acoustic info throughout music notion and that we might observe an identical antero-posterior STG parcellation with sustained and onset responses as noticed within the speech area. Additional, we anticipated that different elements, tuned to particular musical parts, may emerge and prolong this parcellation.

In abstract, we used regression-based decoding fashions to reconstruct the auditory spectrogram of a basic rock music from the neural exercise recorded from 2,668 ECoG electrodes implanted in 29 neurosurgical sufferers, we quantified the affect of three components on decoding accuracy, and we investigated the neural dynamics and areas underlying music notion by means of using encoding fashions and an ablation evaluation.


Distribution of song-responsive electrodes

To determine electrodes encoding acoustical details about the music, we fitted spectrotemporal receptive fields (STRFs) for all 2,379 artifact-free electrodes within the dataset, assessing how effectively the HFA recorded at these websites might be linearly predicted from the music’s auditory spectrogram (Fig 1). From a dense, bilateral, predominantly frontotemporal protection (Fig 2A), we recognized 347 electrodes with a big STRF (Fig 2B; see S1 Fig for an in depth view of every affected person’s protection and important electrodes). We discovered the next proportion of song-responsive electrodes in the fitting hemisphere. There have been 199 important electrodes out of 1,479 complete within the left hemisphere and 148 out of 900 in the fitting one (Fig 2B, 13.5% versus 16.4%, respectively; X2 (1, N = 2,379) = 4.01, p = .045).


Fig 1. Protocol, information preparation, and encoding mannequin becoming.

(A) High: Waveform of the complete music stimulus. Contributors listened to a 190.72-second rock music (One other Brick within the Wall, Half 1, by Pink Floyd) utilizing headphones. Backside: Auditory spectrogram of the music. Orange bars on high symbolize elements of the music with vocals. (B) X-ray exhibiting electrode protection of 1 consultant affected person. Every dot is an electrode, and the sign from the 4 highlighted electrodes is proven in (C). (C) HFA elicited by the music stimulus in 4 consultant electrodes. (D) Zoom-in on 10 seconds (black bars in A and C) of the auditory spectrogram and the elicited neural exercise in a consultant electrode. Every time level of the HFA (yi, crimson dot) is paired with a previous 750-ms window of the music spectrogram (Xi, black rectangle) ending right now level (proper fringe of the rectangle, in crimson). The set of all pairs (Xi, yi), with i starting from .75 to 190.72 seconds represent the examples (or observations) used to coach and consider the linear encoding fashions. Linear encoding fashions used right here consist in predicting the neural exercise (y) from the auditory spectrogram (X), by discovering the optimum intercept (a) and coefficients (w). (E) STRF for the electrode proven in crimson in (B), (C), and (D). STRF coefficients are z-valued and are represented as w within the earlier equation. Be aware that 0 ms (timing of the noticed HFA) is on the proper finish of the x-axis, as we predict HFA from the previous auditory stimulus. The information underlying this determine might be obtained at https://doi.org/10.5281/zenodo.7876019. HFA, high-frequency exercise; STRF, spectrotemporal receptive discipline.



Fig 2. Anatomical location of song-responsive electrodes.

(A) Electrode protection throughout all 29 sufferers proven on the MNI template (N = 2,379). All offered electrodes are freed from any artifactual or epileptic exercise. The left hemisphere is plotted on the left. (B) Location of electrodes considerably encoding the music’s acoustics (Nsig = 347). Significance was decided by the STRF prediction accuracy bootstrapped over 250 resamples of the coaching, validation, and take a look at units. Marker coloration signifies the anatomical label as decided utilizing the FreeSurfer atlas, and marker dimension signifies the STRF’s prediction accuracy (Pearson’s r between precise and predicted HFA). We use the identical coloration code within the following panels and figures. (C) Variety of important electrodes per anatomical area. Darker hue signifies a right-hemisphere location. (D) Common STRF prediction accuracy per anatomical area. Electrodes beforehand labeled as supramarginal, different temporal (i.e., aside from STG), and different frontal (i.e., aside from SMC or IFG) are pooled collectively, labeled as different and represented in white/grey. Error bars point out SEM. The information underlying this determine might be obtained at https://doi.org/10.5281/zenodo.7876019. HFA, high-frequency exercise; IFG, inferior frontal gyrus; MNI, Montreal Neurological Institute; SEM, Normal Error of the Imply; SMC, sensorimotor cortex; STG, superior temporal gyrus; STRF, spectrotemporal receptive discipline.


Nearly all of the 347 important electrodes (87%) have been concentrated in 3 areas: 68% in bilateral superior temporal gyri (STG), 14.4% in bilateral sensorimotor cortices (SMCs, on the pre- and postcentral gyri), and 4.6% in bilateral IFG (Fig 2C). The proportion of song-responsive electrodes per area was 55.7% for STG (236 out of 424 electrodes), 11.6% for SMC (45/389), and seven.4% for IFG (17/229). The remaining 13% of great electrodes have been distributed within the supramarginal gyri and different frontal and temporal areas. To look at whether or not the upper proportion of song-responsive electrodes in the fitting hemisphere was pushed by completely different nonuniform coverages between each hemispheres (e.g., by a denser protection of nonauditory areas within the left hemisphere than in the fitting hemisphere), we restricted our evaluation to the three primary song-responsive areas (STG, SMC, and IFG). We discovered the next proportion of song-responsive electrodes in these proper song-responsive areas, with 133 important electrodes out of 374 complete, in opposition to 165 out of 654 within the corresponding left areas (35.6% versus 25.3%, respectively; X2 (1, N = 1,026) = 12.34, p < .001).

Evaluation of STRF prediction accuracies (Pearson’s r) discovered a primary impact of laterality (additive two-way ANOVA with laterality and cortical areas as components; F(1, 346) = 7.48, p = 0.0065; Fig 2D), with increased correlation coefficients in the fitting hemisphere than within the left (MR = .203, SDR = .012; ML = .17, SDL = .01). We additionally discovered a primary impact of cortical areas (F(3, 346) = 25.09, p < .001), with the best prediction accuracies in STG (Tukey–Kramer submit hoc; MSTG = .266, SDSTG = .007; MSMC = .194, SDSMC = .017, pSTGvsSMC < .001; MIFG = .154, SDIFG = .027, pSTGvsSMC < .001; Mdifferent = .131, SDdifferent = .016, pSTGvsSMC < .001). As well as, we discovered increased prediction accuracies in SMC in comparison with the group not together with STG and IFG (MSMC = .194, SDSMC = .017; Mdifferent = .131, SDdifferent = .016, pSMCvsOther = .035).

Music reconstruction and methodological components impacting decoding accuracy

We examined music reconstruction from neural exercise and the way methodological components together with variety of electrodes included within the mannequin, the dataset length, or the mannequin sort used impacted decoding accuracy. We carried out a bootstrap evaluation by becoming linear decoding fashions on subsets of electrodes randomly sampled from all 347 important electrodes throughout the 29 sufferers, no matter anatomical location. This revealed a logarithmic relationship between what number of electrodes have been used as predictors within the decoding mannequin and the ensuing prediction accuracy (Fig 3A). For instance, 80% of one of the best prediction accuracy (utilizing all 347 important electrodes) was obtained with 43 (or 12.4%) electrodes. We noticed the identical relationship on the single-patient degree, for fashions skilled on every affected person’s important electrodes, though with decrease decoding accuracies (solid-colored circles in S2 Fig; for instance, 43 electrodes offered 66% of one of the best prediction accuracy). We noticed an identical logarithmic relationship between dataset length and prediction accuracy utilizing a bootstrap evaluation (Fig 3B). For instance, 90% of one of the best efficiency (utilizing the entire 190.72-second music) was obtained utilizing 69 seconds (or 36.1%) of information.


Fig 3. Music reconstruction and methodological issues.

(A) Prediction accuracy as a perform of the variety of electrodes included as predictors within the linear decoding mannequin. On the y-axis, 100% represents the utmost decoding accuracy, obtained utilizing all 347 important electrodes. The black curve reveals information factors obtained from a bootstrapping evaluation with 100 resamples for every variety of electrodes (with out alternative), whereas the crimson curve reveals a two-term energy collection match line. Error bars point out SEM. (B) Prediction accuracy as a perform of dataset length. (C) Auditory spectrograms of the unique music (high) and of the reconstructed music utilizing both linear (center) or nonlinear fashions (backside) decoding from all responsive electrodes. This 15-second music excerpt was held out throughout hyperparameter tuning by means of cross-validation and mannequin becoming and used solely as a take a look at set to judge mannequin efficiency. Corresponding audio waveforms have been obtained by means of an iterative phase-estimation algorithm and might be listened to in S1, S2, and S3 Audio information, respectively. Common efficient r-squared throughout all 128 frequency bins is proven above each decoded spectrograms. (D) Auditory spectrogram of the reconstructed music utilizing nonlinear fashions from electrodes of affected person P29 solely. Corresponding audio waveform might be listened to in S4 Audio. The information underlying this determine might be obtained at https://doi.org/10.5281/zenodo.7876019. SEM, Normal Error of the Imply.


Relating to mannequin sort, linear decoding offered a mean decoding accuracy of .325 (median of the 128 fashions’ efficient r-squared; IQR .232), whereas nonlinear decoding utilizing a two-layer, absolutely related neural community (multilayer perceptron (MLP)) yielded a mean decoding accuracy of .429 (IQR .222). This 32% improve in efficient r-squared (+.104 from .325) was important (two-sided paired t take a look at, t(127) = 17.48, p < .001). Consistent with this increased efficient r-squared for MLPs, the decoded spectrograms revealed variations between mannequin varieties, with the nonlinear reconstruction (Fig 3C, backside) exhibiting finer spectrotemporal particulars, relative to the linear reconstruction (Fig 3C, center). General, the linear reconstruction (S2 Audio) sounded muffled with sturdy rhythmic cues on the presence of foreground parts (vocals syllables and lead guitar notes); a way of spectral construction underlying timbre and pitch of lead guitar and vocals; a way of concord (chord development transferring from Dm to F, C, and Dm); however restricted sense of the rhythm guitar sample. The nonlinear reconstruction (S3 Audio) offered a recognizable music, with richer particulars as in comparison with the linear reconstruction. Perceptual high quality of spectral parts reminiscent of pitch and timbre have been particularly improved, and phoneme id was perceptible. There was additionally a stronger sense of concord and an emergence of the rhythm guitar sample.

Stimulus reconstruction was additionally relevant to a single affected person with high-density 3-mm electrode spacing protection. We used nonlinear fashions to reconstruct the music from the 61 important electrodes of affected person P29 (Fig 3D). These fashions carried out higher than the linear reconstruction based mostly on electrodes from all sufferers (efficient r-squared of .363), however decoding accuracy was decrease than that obtained with 347 important electrodes from all sufferers. On the perceptual aspect, these single-patient–based mostly fashions offered a degree of spectrotemporal particulars excessive sufficient to acknowledge the music (S4 Audio). To evaluate the decrease certain of single-patient–based mostly decoding, we reconstructed the music from the neural exercise of three extra sufferers (P28, P15, and P16), with fewer electrodes (23, 17, and 10, respectively, versus 61 in P29) and a decrease density (1 cm, 6 mm, and 1 cm center-to-center electrode distance, respectively, versus 3 mm in P29), however nonetheless protecting song-responsive areas (largely proper, left, and left STG, respectively) and with an excellent linear decoding accuracy (Pearson’s r = .387, .322, and .305, respectively, versus .45 in P29). Nonlinear fashions reconstructed the music spectrogram with an efficient r-squared of .207, .257, and .166, respectively (S3 Fig). Within the reconstructed waveforms (S5, S6, and S7 Audio information), we retrieved partial vocals (e.g., in P15, “all,” “was,” and “only a brick” have been the one recognizable syllables, as might be seen within the reconstructed spectrogram; S3 Fig, high) and a way of concord, though with various focus in recognizability.

We then quantified the decoded music recognizability by correlating excerpts of the unique versus decoded music spectrograms. Each linear (Fig 4A) and nonlinear (Fig 4B) reconstructions offered a excessive proportion of right identifications (32/38 and 36/38, respectively; Fig 4, left panels) and important identification imply percentiles (95.2% and 96.3%, respectively; Fig 4, proper panels; 1,000-iteration permutation take a look at, CI95 [.449 .582] for linear, [.447 .583] for nonlinear).


Fig 4. Music-excerpt identification rank evaluation.

After decoding the entire music by means of 12 distinct 15-second take a look at units, we divided each the unique music and the decoded spectrogram into 5-second excerpts and computed the correlation coefficient for all doable original-decoded pairs. (A) Decoding utilizing linear fashions. Left panel reveals the correlation matrix, with crimson dots indicating the row-wise most values (e.g., first decoded 5-second excerpt correlates most with thirty second unique music excerpt). Proper panel reveals a histogram of the excerpt identification rank, a measure of how shut the utmost original-decoded correlation coefficient landed from true excerpt determine (e.g., third original-decoded pair correlation coefficient, on the matrix diagonal, was the second highest worth on the third excerpt’s row, thus ranked 37/38). Grey shaded space represents the 95% confidence interval of the null distribution estimated by means of 1,000 random permutations of the unique music excerpt identities. The crimson vertical line reveals the typical identification rank throughout all music excerpts. (B) Similar panels for decoding utilizing nonlinear fashions. The information underlying this determine might be obtained at https://doi.org/10.5281/zenodo.7876019.


Encoding of musical parts

We analyzed STRF coefficients for all 347 important electrodes to evaluate how completely different musical parts have been encoded in numerous mind areas. This evaluation revealed quite a lot of spectrotemporal tuning patterns (Fig 5A; see S4 Fig for an in depth view of affected person P29’s STRFs computed for his or her 10-by-25, 3-mm-center-to-center grid of electrodes). To totally characterize the connection between the music spectrogram and the neural exercise, we carried out an unbiased part evaluation (ICA) on all important STRFs. We recognized 3 elements with distinct spectrotemporal tuning patterns, every explaining greater than 5% variance and collectively explaining 52.5% variance (Fig 5B).


Fig 5. Evaluation of the STRF tuning patterns.

(A) Consultant set of 10 STRFs (out of the 347 important ones) with their respective places on the MNI template utilizing matching markers. Coloration code is similar to the one utilized in Fig 1. (B) Three ICA elements every explaining greater than 5% variance of all 347 important STRFs. These 3 elements present onset, sustained, and late onset exercise. Percentages point out defined variance. (C) ICA coefficients of those 3 elements, plotted on the MNI template. Coloration code signifies coefficient amplitude, with in crimson the electrodes which STRFs symbolize the elements essentially the most. (D) To seize tuning to the rhythm guitar sample (Sixteenth notes at 100 bpm, i.e., 6.66 Hz), pervasive all through the music, we computed temporal modulation spectra of all important STRFs. Instance modulation spectrum is proven for a proper STG electrode. For every electrode, we extracted the utmost temporal modulation worth throughout all spectral frequencies round a price of 6.66 Hz (crimson rectangle). (E) All extracted values are represented on the MNI template. Electrodes in crimson present tuning to the rhythm guitar sample. The information underlying this determine might be obtained at https://doi.org/10.5281/zenodo.7876019. ICA, unbiased part evaluation; MNI, Montreal Neurological Institute; STG, superior temporal gyrus; STRF, spectro-temporal receptive discipline.


The primary part (28% defined variance) confirmed a cluster of optimistic coefficients (in crimson, in Fig 5B, high row) spreading over a broad frequency vary from about 500 Hz to 7 kHz and over a slim time window centered round 90 ms earlier than the noticed HFA (positioned at time lag = 0 ms, on the proper fringe of all STRFs). This temporally transient cluster revealed tuning to sound onsets. This part, known as the “onset part,” was discovered solely in electrodes positioned in bilateral posterior STG (Fig 5C, high row, electrodes depicted in crimson). Fig 6C, high row, confirmed in crimson the elements of the music eliciting the best HFA improve in electrodes possessing this onset part. These elements corresponded to onsets of lead guitar or synthesizer motifs (Fig 6A, blue and purple bars, respectively; see Fig 6E for a zoom-in) performed each 2 bars (inexperienced bars) and to onsets of syllable nuclei within the vocals (orange bars; see Fig 6D for a zoom-in).


Fig 6. Encoding of musical parts.

(A) Auditory spectrogram of the entire music. Orange bars above the spectrogram mark all elements with vocals. Blue bars mark lead guitar motifs, and purple bars mark synthesizer motifs. Inexperienced vertical bars delineate a collection of eight 4/4 bars (or measures). Thicker orange and blue bars mark places of the zoom-ins offered in (D) and (E), respectively. (B) Three STRF elements as offered in Fig 5B, specifically onset (high), sustained (center), and late onset (backside). (C) Output of the sliding correlation between the music spectrogram (A) and every of the three STRF elements (B). Optimistic Pearson’s r values are plotted in crimson, marking elements of the music that elicited a rise of HFA in electrodes exhibiting the given part. Be aware that for the sustained plot (center), optimistic correlation coefficients are particularly noticed throughout vocals. Additionally, observe for each the onset and late onset plots (high and backside, respectively), optimistic r values within the second half of the music correspond to steer guitar and synthesizer motifs, occurring each different 4/4 bar. (D) Zoom-in on the third vocals. Lyrics are offered above the spectrogram, decomposed into syllables. Most syllables triggered an HFA improve in each onset and late onset plots (high and backside, respectively), whereas a sustained improve of HFA was noticed throughout the complete vocals (center). (E) Zoom-in on a lead guitar motif. Sheet music is offered above the spectrogram. Most notes triggered an HFA improve in each onset and late onset plots (high and backside, respectively), whereas there was no HFA improve for the sustained part (center). The information underlying this determine might be obtained at https://doi.org/10.5281/zenodo.7876019. HFA, high-frequency exercise; STRF, spectrotemporal receptive discipline.


The second part (14.7% defined variance) confirmed a cluster of optimistic coefficients (in crimson, in Fig 5B, center row) spreading over the complete 750-ms time window and over a slim frequency vary from about 4.8 to 7 kHz. This part, known as the “sustained part,” was present in electrodes positioned in bilateral mid- and anterior STG and in bilateral SMC (Fig 5C, center row). Additionally, this part correlated greatest with elements of the music containing vocals, thus suggesting tuning to speech (Fig 6C, center row, in crimson; see Fig 6D for a zoom-in).

The third part (9.8% defined variance) confirmed an identical tuning sample because the onset part, solely with an extended latency of about 210 ms earlier than the noticed HFA (Fig 5B, backside row). This part, referred to any further because the “late onset part,” was present in bilateral posterior and anterior STG, neighboring the electrodes representing the onset part, and in bilateral SMC (Fig 5C, backside row). As with the onset part, this late onset part was most correlated with onsets of lead guitar and synthesizer motifs and of syllable nuclei within the vocals, solely with an extended latency (Fig 6C, backside row; see Fig 6D and 6E for zoom-ins).

A fourth part was discovered by computing the temporal modulations and extracting the utmost coefficient round a price of 6.66 Hz for all 347 STRFs (Fig 5D, crimson rectangle). This price corresponded to the Sixteenth notes of the rhythm guitar, pervasive all through the music, on the music tempo of 99 bpm (beats per minute). It was translated within the STRFs as small clusters of optimistic coefficients spaced by 150 ms (1/6.66 Hz) from one another (e.g., Fig 5A, electrode 5). This part, known as the “rhythmic part,” was present in electrodes positioned in bilateral mid-STG (Fig 5E).

Anatomo-functional distribution of the music’s acoustic info

To evaluate the function of those completely different cortical areas and practical elements in representing musical options, we carried out an ablation evaluation utilizing linear decoding fashions. We first computed linear decoding fashions for every of the 32 frequency bins of the music spectrogram, utilizing the HFA of all 347 important electrodes as predictors. This yielded a mean prediction accuracy of .62 (Pearson’s r; min .27—max .81). We then eliminated (or ablated) anatomically or functionally outlined units of electrodes and computed a brand new collection of decoding fashions to evaluate how every ablation would affect the decoding accuracy. We used prediction accuracies of the total, 347-electrode fashions as baseline values (Fig 7). We discovered a big primary impact of electrode units (one-way ANOVA; F [1, 24] = 78.4, p < .001). We then ran a collection of submit hoc analyses to look at the affect of every set on prediction accuracy.


Fig 7. Ablation evaluation on linear decoding fashions.

We carried out “digital lesions” within the predictors of decoding fashions, by ablating both anatomical (A) or practical (B) units of electrodes. Ablated units are proven on the x-axis, and their impacts on the prediction accuracy (Pearson’s r) of linear decoding fashions, as in comparison with the efficiency of a baseline decoding mannequin utilizing all 347 important electrodes, are proven on the y-axis. For every ablation, a notched field plot represents the distribution of the modifications in decoding accuracy for all 32 decoding fashions (one mannequin per frequency bin of the auditory spectrogram). For every field, the central mark signifies the median; the notch delineates the 95% confidence interval of the median; backside and high field edges point out the twenty fifth and seventy fifth percentiles, respectively; whiskers delineate the vary of nonoutlier values; and circles point out outliers. Crimson asterisks point out important affect from ablating a given set of electrodes. The information underlying this determine might be obtained at https://doi.org/10.5281/zenodo.7876019.



We utilized predictive modeling analyses to iEEG information obtained from sufferers listening to a Pink Floyd music. We have been in a position to reconstruct the music from direct human neural recordings with essentially the most sturdy results utilizing nonlinear fashions. By an integrative anatomo-functional method based mostly on each encoding and decoding fashions, we confirmed a right-hemisphere choice and a main function of the STG in music notion, evidenced a brand new STG subregion tuned to musical rhythm, and outlined an anterior–posterior STG group exhibiting sustained and onset responses to musical parts. Collectively, these outcomes additional our understanding of the neural dynamics underlying music notion.

In contrast with linear fashions, nonlinear fashions offered the best decoding accuracy (r-squared of 42.9%), a extra detailed decoded spectrogram, a recognizable music, and the next price of song-excerpt identification. This reveals that earlier methodological findings in speech decoding [34,35] additionally apply to music decoding. In distinction, linear fashions had decrease decoding accuracy (r-squared of 32.5%) and yielded a smoother decoded spectrogram missing tremendous particulars. That is possible as a consequence of the truth that acoustic info represented at STG is nonlinearly remodeled, thus requiring using nonlinear fashions to greatest analyze the electrophysiological information [37]. Be aware that the general correlation coefficient computed throughout all 29 contributors for linear decoding fashions in our current research (r = .29, from the nonnormalized values of S2 Fig) is corresponding to the general decoding accuracy of linear fashions for pure speech stimuli (r = .28, throughout 15 contributors) [34], suggesting that this limitation is shared between speech and music. Nonetheless, linear fashions yielded sturdy efficiency in our classification-like method, suggesting they might represent a method for some mind–laptop interface (BCI) functions, given they’re sooner to coach and simpler to interpret than nonlinear fashions.

We quantified the affect of the variety of electrodes used as inputs for the decoding fashions on their prediction accuracy and located that including electrodes past a certain quantity had diminishing returns, consistent with earlier literature for speech stimuli [34,35]. Decoding accuracy was additionally impacted by the practical and anatomical options of the electrodes included within the mannequin: Whereas eradicating 167 sustained electrodes didn’t affect decoding accuracy, eradicating 43 proper rhythmic electrodes decreased decoding accuracy (Fig 7B). That is greatest illustrated by the power to reconstruct a recognizable music from the info of a single affected person, with 61 electrodes positioned on the fitting STG.

This final consequence reveals the feasibility of this stimulus reconstruction method in a medical setting and means that future BCI functions ought to goal STG implantation websites along with practical localization slightly than solely counting on a excessive variety of electrodes. We additionally quantified the affect of the dataset length on decoding accuracy. We discovered that 80% of the utmost noticed decoding accuracy was achieved in 37 seconds, supporting the feasibility of utilizing predictive modeling approaches in comparatively small datasets.

Music notion relied on each hemispheres, with a choice for the fitting hemisphere. The suitable hemisphere had the next proportion of electrodes with important STRFs, increased STRF prediction accuracies, and the next affect of ablating proper electrode units (each anatomical and practical) from the decoding fashions. Left hemisphere electrodes additionally exhibited important STRFs and a decreased prediction accuracy when ablated. These outcomes are in accord with prior analysis, exhibiting that music notion depends on a bilateral community, with a relative proper lateralization [23,24,59]. We additionally discovered that the spatial distribution of musical info inside this community differed between hemispheres, as prompt by the ablation outcomes. Particularly, redundant musical info was distributed between STG, SMC, and IFG within the left hemisphere, whereas distinctive musical info was concentrated in STG in the fitting hemisphere. Such spatial distribution is paying homage to the dual-stream mannequin of speech processing [60].

Importantly, we discovered a vital function of bilateral STG in representing musical info, consistent with prior research [48,54,61,62]. As noticed in different research, STRFs obtained from the STG had wealthy, complicated tuning patterns. We recognized 4 elements: onset, sustained, late onset, and rhythmic. The onset and sustained elements have been just like these noticed for speech in prior work [55,56] and have been additionally noticed in anatomically distinct STG subregions, with the onset part in posterior STG and the sustained part in mid- and anterior STG. The onset part was tuned to a broad vary of frequencies however to a slim time window peaking at 90 ms, in keeping with the lag at which HFA tracked music depth profile [24]. This part was not speech particular because it was activated by each vocals and instrumental onsets, in keeping with prior speech work [56]. The sustained part, nonetheless, was solely activated by vocals. The late onset part was present in electrodes neighboring the onset part in STG and had comparable tuning properties because the onset part, solely peaking at a later latency of 210 ms. That is consistent with the findings of Nourski and colleagues [63], who, utilizing click on trains and a speech syllable, noticed a concentric spatial gradient of HFA onset latencies in STG, with shorter latencies in post-/mid-STG and longer latencies in surrounding tissue. We additionally noticed a rhythmic part positioned in mid-STG, which was tuned to the 6.66-Hz Sixteenth notes of the rhythm guitar. This uncovers a novel hyperlink between HFA and a selected rhythmic signature in a subregion of STG, increasing prior research that discovered an involvement of STG in a variety of rhythmic processes [6466]. Collectively, these 4 elements paint a wealthy image of the anatomo-functional group of complicated sound processing within the human STG.

Future analysis might goal extending electrode protection to extra areas, various the fashions’ options and targets, or including a behavioral dimension. Be aware we lacked protection within the main auditory cortex (A1), which might have improved efficiency of the linear decoding fashions. Importantly, the encoding fashions we used on this research to research the neural dynamics of music notion estimated the linear relationship between music’s acoustics and elicited HFA. It’s doable that areas not highlighted by our research reply to the music, both in different neural frequency bands [35,67] or encoding higher-order musical info (e.g., notes, chords, diploma of dissonance or of syncopation). Lastly, we lacked patient-related details about musicianship standing or diploma of familiarity with the music, stopping us from investigating interindividual variability.

Combining distinctive iEEG information and modeling-based analyses, we offered the primary recognizable music reconstructed from direct mind recordings. We confirmed the feasibility of making use of predictive modeling on a comparatively brief dataset, in a single affected person, and quantified the affect of various methodological components on the prediction accuracy of decoding fashions. Our outcomes affirm and prolong previous findings on music notion, together with a right-hemisphere choice and a significant function of bilateral STG. As well as, we discovered that the STG encodes the music’s acoustics by means of partially overlapping neural populations tuned to distinct musical parts and delineated a novel STG subregion tuned to musical rhythm. The anatomo-functional group reported on this research might have medical implications for sufferers with auditory processing issues. For instance, the musical notion findings might contribute to improvement of a common auditory decoder that features the prosodic parts of speech based mostly on comparatively few, well-located electrodes.

We restricted our investigation to the auditory spectrogram on the stimulus aspect and to HFA on the neural exercise aspect, given the complicated research design encompassing a number of higher-order analyses constructing upon encoding and decoding fashions. Future research ought to discover completely different higher-order representations of musical info within the auditory mind (i.e., notes, chords, sheet music), in addition to decrease neural oscillatory bands and spectral elements (e.g., theta, alpha, and beta energy, aperiodic part), identified to symbolize related acoustic info, including one other brick within the wall of our understanding of music processing within the human mind.



Sufferers passively listened to the music One other Brick within the Wall, Half 1, by Pink Floyd (launched on the album The Wall, Harvest Data/Columbia Data, 1979). They have been instructed to pay attention attentively to the music, with out specializing in any particular element. Whole music length was 190.72 seconds (waveform is represented in Fig 1A, high). The auditory stimulus was digitized at 44.1 kHz and delivered by means of in-ear monitor headphones (bandwidth 12 Hz to 23.5 kHz, 20 dB isolation from surrounding noise) at a cushty sound degree adjusted for every affected person (50 to 60 dB SL). Eight sufferers had multiple recording of the current job, wherein instances we chosen the cleanest one (i.e., containing the least epileptic exercise or noisy electrodes).

Preprocessing—Auditory stimulus

To review the connection between the acoustics of the auditory stimulus and the ECoG-recorded neural exercise, the music waveform was remodeled right into a magnitude-only auditory spectrogram utilizing the NSL MATLAB Toolbox [69]. This transformation mimics the processing steps of early levels of the auditory pathways, from the cochlea’s spectral filter financial institution to the midbrain’s decreased higher restrict of phase-locking skill, and outputs a psychoacoustic-, neurophysiologic-based spectrotemporal illustration of the music (just like the cochleagram) [70,71]. The ensuing auditory spectrogram has 128 frequency bins from 180 to 7,246 Hz, with attribute frequencies uniformly distributed alongside a logarithmic frequency axis (24 channels per octave), and a sampling price of 100 Hz. This full-resolution, 128-frequency bin spectrogram is used within the music reconstruction evaluation. For all different analyses, to lower the computational load and the variety of options, we output a decreased spectrogram with 32 frequency bins from 188 to six,745 Hz (Fig 1A, backside).

Preprocessing—ECoG information

We used the HFA (70 to 150 Hz) as an estimate of native neural exercise [72] (Fig 1C). For every dataset, we visually inspected uncooked recorded indicators and eliminated electrodes exhibiting noisy or epileptic exercise, with the assistance of a neurologist (RTK). General, from our beginning set of two,668 electrodes, we eliminated 106 noisy electrodes (absolute vary 0 to 22, imply 3.7 electrodes; relative vary 0% to twenty.2%, imply 3.7%) and 183 epileptic electrodes (absolute vary 0 to twenty-eight, imply 6.3; relative vary 0% to 27.6%, imply 7.6%) and obtained a set of two,379 artifact-free electrodes.

We then extracted information aligned with the music stimulus, including 10 seconds of information padding earlier than and after the music (to stop filtering-induced edge artifacts). We filtered out power-line noise, utilizing a variety of notch filters centered at 60 Hz and harmonics as much as 300 Hz (Butterworth, fourth order, 2 Hz bandwidth), and eliminated sluggish drifts with a 1-Hz high-pass filter (Butterworth, fourth order). We used a bandpass–Hilbert method [73] to extract HFA, with 20-Hz-wide sub-bands spanning from 70 to 150 Hz in 5-Hz steps (70 to 90, 75 to 95, … as much as 130 to 150 Hz). We selected a 20-Hz bandwidth to allow the commentary of temporal modulations as much as 10 Hz [74], encompassing the 6.66-Hz Sixteenth-note rhythm guitar sample, pervasive all through the music. This constitutes a vital methodological level, enabling the commentary of the rhythmic part (Fig 3D). For every sub-band, we first bandpass-filtered the sign (Butterworth, fourth order), then carried out median-based frequent common reference (CAR) [75], and computed the Hilbert rework to acquire the envelope. We standardized every sub-band envelope utilizing sturdy scaling on the entire time interval (subtracting the median and dividing by the interquartile vary between the tenth and ninetieth percentiles) and averaged them collectively to yield the HFA estimate. We carried out CAR individually for electrodes plugged on completely different splitter containers to optimize denoising in 14 contributors. Lastly, we eliminated the 10-second pads, down-sampled information to 100 Hz to match the stimulus spectrogram’s sampling price, and tagged outlier time samples exceeding 7 customary deviations for later removing within the modeling preprocessing. We used FieldTrip [76] (model from Could 11, 2021) and selfmade scripts to carry out all of the above preprocessing steps. Until specified in any other case, all additional analyses and computations have been applied in MATLAB (The MathWorks, Natick, MA, USA; model 2021a).

Preprocessing—Anatomical information

We adopted the anatomical information processing pipeline offered in Stolk and colleagues [77] to localize electrodes from a preimplantation MRI, a postimplantation CT scan, and protection info mapping electrodes to channel numbers within the practical information. After coregistration of the CT scan to the MRI, we carried out brain-shift compensation with a hull obtained utilizing scripts from the iso2mesh toolbox [78,79]. Cortical surfaces have been extracted utilizing the FreeSurfer toolbox [80]. We used volume-based normalization to transform patient-space electrode coordinates into MNI coordinates for illustration functions, and surface-based normalization utilizing the FreeSurfer’s fsaverage template to robotically acquire anatomical labels from the aparc+aseg atlas. Labels have been then confirmed by a neurologist (RTK).

Encoding—Mannequin becoming

To acquire a fitted STRF for a given electrode, we iterated by means of the next steps 250 instances.

We first break up the dataset into coaching, validation, and take a look at units (60–20–20 ratio, respectively) utilizing a customized group-stratified-shuffle-split algorithm (based mostly on the StratifiedShuffleSplit cross-validator in scikit-learn). We outlined comparatively lengthy, 2-second teams of consecutive samples as indivisible blocks of information. This ensured that coaching and take a look at units wouldn’t comprise neighboring, just about similar samples (as each music and neural information are extremely correlated over brief durations of time) and was vital to stop overfitting. We used stratification to implement equal splitting ratios between the vocal (13 to 80 seconds) and instrumental elements of the music. This ensured stability of mannequin efficiency throughout all 250 iterations, by avoiding {that a} mannequin might be skilled on the instrumentals solely and examined on the vocals. We used shuffle splitting, akin to bootstrapping with alternative between iterations, which permits us to find out take a look at set dimension independently from the variety of iterations (versus KFold cross-validation).

We then standardized the options by becoming a sturdy scaler to the coaching set solely (estimates the median and the two to 98 quantile vary; RobustScaler in sklearn bundle) and utilizing it to rework all coaching, validation, and take a look at units. This offers comparable significance to all options, i.e., each time lag and frequency of the auditory spectrogram.

We employed linear regression with RMSProp optimizer for environment friendly mannequin convergence, Huber loss value perform for robustness to outlier samples, and early stopping to additional stop overfitting. In early stopping, a generalization error is estimated on the validation set at every coaching step, and mannequin becoming ends after this error stops diminishing for 10 consecutive steps. This mannequin was applied in Tensorflow 1.6 and Python 3.6. The training price hyperparameter of the RMSProp optimizer was manually tuned to make sure quick mannequin convergence whereas additionally avoiding exploding gradients (overshooting of the optimization minimal).

We evaluated prediction accuracy of the fitted mannequin by computing each the correlation coefficient (Pearson’s r) and the r-squared between predicted and precise take a look at set goal (i.e., HFA at a given electrode). Together with these 2 efficiency metrics, we additionally saved the fitted mannequin coefficients.

Then, we mixed these 250 split-scale-fit-evaluate iterations in a bootstrap-like method to acquire 1 STRF and assess its significance (i.e., whether or not we are able to linearly predict HFA, at a given electrode, from the music spectrogram). For every STRF, we z-scored every coefficient throughout the 250 fashions (Fig 1E). For the prediction accuracy, we computed the 95% confidence interval (CI) from the 250 correlation coefficients and deemed an electrode as important if its 95% CI didn’t comprise 0. As a further criterion, we rejected important electrodes with a mean r-squared (throughout the 250 fashions) at or under 0.

Decoding—Parametric analyses

We quantified the affect of various methodological components (variety of electrodes, dataset length, and mannequin sort) on the prediction accuracy of decoding fashions. In a bootstrapping method, we randomly constituted subsets of 5, 10, 20, 40, 80, 160, and 320 electrodes (sampling with out alternative), no matter their anatomical location, for use as inputs of linear decoding fashions. We processed 100 bootstrap resamples (i.e., 100 units of 5 electrodes, 100 units of 10 electrodes…) and normalized for every of the 32 frequency bins the ensuing correlation coefficients by the correlation coefficients of the total, 347-electrode decoding mannequin. For every resample, we averaged the correlation coefficients from all 32 fashions (1 per frequency bin of the music spectrogram). This yielded 100 prediction accuracy estimates per variety of electrodes. We then fitted a two-term energy collection mannequin to those estimates to quantify the obvious power-law conduct of the obtained bootstrap curve. We adopted the identical method for dataset length, with excerpts of 15, 30, 60, 90, 120, 150, and 180 consecutive seconds.

To research the affect of mannequin sort on decoding accuracy and to evaluate the extent to which we might reconstruct a recognizable music, we skilled linear and nonlinear fashions to decode every of the 128 frequency bins of the total spectral decision music spectrogram from HFA of all 347 important electrodes. We used the MLP—a easy, absolutely related neural community—as a nonlinear mannequin (MLPRegressor in sklearn). We selected an MLP structure of two hidden layers of 64 items every, based mostly each on an extension of the Common Approximation Theorem stating {that a} 2 hidden layer MLP can approximate any steady multivariate perform [38] and on a earlier research with an identical use case [35]. Since MLP layers are absolutely related (i.e., every unit of a layer is related to all items of the following layer), the variety of coefficients to be fitted is drastically elevated comparatively to linear fashions (on this case, F * N + N * N + N vs. F, respectively, the place the overall variety of options F = E * L, with E representing the variety of important electrodes included as inputs of the decoding mannequin, and L the variety of time lags, and N represents the variety of items per layer). Given the restricted dataset length, we decreased time lags to 500 ms based mostly on the absence of great exercise past this level within the STRF elements and used this L worth in each linear and nonlinear fashions.

We outlined a set, 15-second steady take a look at set throughout which the music contained each vocals and instrumentals (from 61 to 76 seconds of the unique music, accessible on any streaming service) and held it out throughout hyperparameter tuning and mannequin becoming. We tuned mannequin hyperparameters (studying price for linear fashions, and L2-regularization alpha for MLPs) by means of 10-resample cross-validation. We carried out a grid search on every resample (i.e., coaching/validation break up) and saved for every resample the index of the hyperparameter worth yielding the minimal validation imply squared error (MSE). Candidate hyperparameter values ranged between .001 and 100 for the training price of linear fashions, and between .01 and 100 for the alpha of MLPs. We then rounded the imply of the ten ensuing indices to acquire the cross-validated, tuned hyperparameter. As a homogeneous presence of vocals throughout coaching, validation, and take a look at units was essential for correct tuning of the alpha hyperparameter of MLPs, we elevated group dimension to five seconds, equal to about 2 musical bars, within the group-stratified-shuffle-split step (see Encoding—Mannequin becoming for a reference), and used this worth for each linear and nonlinear fashions. For MLPs particularly, as random initialization of coefficients might result in convergence in direction of native optima, we adopted a best-of-3 technique the place we solely saved the “successful” mannequin (i.e., yielding the minimal validation MSE) amongst 3 fashions fitted on the identical resample.

As soon as we obtained the tuned hyperparameter, we computed 100 fashions on distinct coaching/validation splits, additionally adopting the best-of-3 technique for the nonlinear fashions (this time maintaining the mannequin yielding the utmost take a look at r-squared). We then sorted fashions by growing r-squared and evaluated the “efficient” r-squared by computing the r-squared between the take a look at set goal (the precise amplitude time course of the music’s auditory spectrogram frequency bin) and averages of n fashions, with n various from 100 to 1 (i.e., efficient r-squared for the typical of all 100 fashions, for the typical of the 99 greatest, …, of the two greatest, of one of the best mannequin). Lastly, we chosen n based mostly on the worth giving one of the best efficient r-squared and obtained a predicted goal together with its efficient r-squared as an estimate of decoding accuracy. The steps above have been carried out for all 128 frequency bins of the music spectrogram, each for linear and nonlinear fashions, and we in contrast the ensuing efficient r-squared utilizing a two-sided paired t take a look at.

Decoding—Music-excerpt identification rank evaluation

To judge the standard of the decoded music spectrogram, we used an goal method based mostly on correlation [34]. Firstly, we divided the music into twelve 15-second segments. We then decoded every one among these segments as held-out take a look at units, utilizing each linear and nonlinear fashions. Subsequent, we divided all predicted 15-second spectrograms into 5-second excerpts. We computed the 2D correlation coefficients between every of the 38 decoded excerpts and all 38 unique excerpts. We then carried out an excerpt identification rank evaluation by sorting these coefficients in ascending order and by figuring out the rank of the particular excerpt correlation. For instance, for decoded excerpt #4, if the correlation coefficient with the unique excerpt #4 is the third greatest, its rank will likely be 36/38. The ensuing metric ranges from 1/38 (worst doable identification, i.e., the given decoded excerpt matches greatest with all different music excerpts than with its corresponding one) to 38/38, and averaging ranks throughout all music excerpts provides a proxy for classification skill of the linear and nonlinear fashions. To evaluate statistical significance, we computed 1,000 iterations of the algorithm above whereas randomly permuting indices of the unique music excerpts to acquire a null distribution of the imply normalized rank. We deemed the imply normalized rank of our linear and nonlinear decoding fashions as important in the event that they have been outdoors of the 95% CI (i.e., exceeded the 97.5 percentile) of the null distribution.

Encoding—Evaluation of mannequin coefficients

We analyzed the STRF tuning patterns utilizing an ICA to focus on electrode populations tuned to distinct STRF options. Firstly, we ran an ICA with 10 elements on the centered STRF coefficients to determine elements individually explaining greater than 5% of variance. We computed defined variance by back-projecting every part and utilizing the next components: pvafi = 100–100 * imply(var(STRF − backproji)) / imply(var(STRF)), with i starting from 1 to 10 elements, pvafi being the proportion of variance accounted for by ICA part i, STRF being the centered STRF coefficients, and backproji being the back-projection of ICA part i in electrode area. We discovered 3 ICA elements, every explaining greater than 5% of variance, and collectively explaining 52.5% variance. To optimize the unmixing course of, we ran a brand new ICA asking for 3 elements. Then, we decided every part signal by setting as optimistic the signal of essentially the most salient coefficient (i.e., with the best absolute worth, or magnitude). Lastly, for every ICA part, we outlined electrodes as representing the part if their ICA coefficient was optimistic.

To have a look at rhythmic tuning patterns, we computed the temporal modulations of every STRF. Certainly, as a consequence of their various frequencies and latencies, they weren’t captured by the mixed part evaluation. We quantified temporal modulations between 1 and 16 Hz over the 32 spectral frequency bins of every STRF and extracted the utmost modulation worth throughout all 32 frequency bins between 6 and seven Hz of temporal modulations, akin to the music rhythmicity of Sixteenth notes at 99 bpm. We outlined electrodes as representing the part if their most modulation worth was above a manually outlined threshold of .3.

Decoding—Ablation evaluation

To evaluate the contribution of various mind areas and STRF elements in representing the music, we carried out an ablation evaluation. We quantified the affect of ablating units of electrodes on the prediction accuracy of a linear decoding mannequin computed utilizing all 347 important electrodes. Firstly, we constituted units of electrodes based mostly on anatomical or practical standards. We outlined 12 anatomical units by combining 2 components—space (entire hemisphere, STG, SMC, IFG, or different areas) and laterality (bilateral, left or proper). We outlined 12 practical units by combining 2 components—STRF part recognized within the STRF coefficient analyses (onset, sustained, late onset, and rhythmic) and laterality (bilateral, left or proper). See Fig 5 for the precise record of electrode units. Secondly, we computed the decoding fashions utilizing the identical algorithm as for the encoding fashions. Decoding fashions intention at predicting the music spectrogram from the elicited neural exercise. Right here, we used HFA from a set of electrodes as enter and a given frequency bin of the music spectrogram as output. For every of the 24 ablated units of electrodes, we obtained 32 fashions (1 per spectrogram frequency bin) and in contrast every one among them to the corresponding baseline mannequin computed utilizing all 347 important electrodes (repeated-measure one-way ANOVA). We then carried out a a number of comparability (submit hoc) take a look at to evaluate variations between ablations.

We based mostly our interpretation of ablation outcomes on the next assumptions. Collectively, as that they had important STRFs, all 347 important electrodes symbolize acoustic info on the music. If ablating a set of electrodes resulted in a big affect on decoding accuracy, we thought-about that this set represented distinctive info. Certainly, have been this info shared with one other set of electrodes, a compensation-like mechanism might happen and void the affect on decoding accuracy. If ablating a set of electrodes resulted in no important affect on decoding accuracy, we thought-about that this set represented redundant info, shared with different electrodes (because the STRFs have been important, we dominated out the chance that it might be as a result of this set didn’t symbolize any acoustic info). Additionally, evaluating the affect of a given set and one among its subsets of electrodes offered additional insights on the distinctive or redundant nature of the represented info.

Be aware that we carried out this ablation evaluation on linear decoding fashions to make sure interpretability of the outcomes. Certainly, as deep neural networks are in a position to mannequin any perform [38], they might reconstitute acoustic info (e.g., when ablating STG) from higher-order, nonlinear illustration of musical info (e.g., in SMC or IFG) and will thus alleviate, if not masks completely, any affect on decoding accuracy. Utilizing linear decoding fashions restricts compensation to the identical (or at most, linearly associated) info degree and allows drawing conclusions from the ablation evaluation outcomes. Additional, in comparison with linear fashions, nonlinear fashions require tuning extra hyperparameters, with most probably completely different optimum values between ablations, which might bias the outcomes.



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