Transformer-based classification of visceral pain-related local field potential patterns in the brain

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Recent advances in machine learning approaches may enable more effective methods for analyzing LFP patterns across multiple brain areas than conventional time-frequency analysis. In this study, we tested the performance of two machine learning algorithms, AlexNet and the Transformer models, to classify LFP patterns in eight pain-related brain regions before and during acetic acid-induced visceral pain behaviors. Over short time windows lasting several seconds, applying AlexNet to LFP power datasets, but not to raw time-series LFP traces from multiple brain areas, successfully achieved superior classification performance compared with simple LFP power analysis. Furthermore, applying the Transformer directly to the raw LFP traces achieved significantly superior classification performance than AlexNet when using LFP power datasets. These results demonstrate the utility of the Transformer in the analysis of neurophysiological signals, and pave the way for its future applications in the decoding of more complex neuronal activity patterns. Biological sciences/Physiology/Neurophysiology Biological sciences/Neuroscience/Computational neuroscience/Neural decoding Biological sciences/Neuroscience/Somatosensory system/Pain visceral pain electrophysiological recordings machine learning Transformer Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION The organized activity patterns of the neuronal ensembles that control brain functions present as diverse electrophysiological features in the extracellular electroencephalogram (EEG) and local field potential (LFP) signals 1 . Time-frequency analysis has been widely applied to quantify changes in the intensity of oscillatory electrophysiological signals in each frequency band, including theta (5–8 Hz) and gamma (30–100 Hz) power, unveiling a variety of neuronal mechanisms relevant to emotions and behaviors. Especially, wavelet analysis is a useful method to analyze short-term LFP power fluctuations on the second scale. Additionally, recent advances in machine learning techniques such as Convolutional Neural Networks (CNNs) have garnered significant attention owing to their ability to simultaneously process large-scale physiological datasets spanning multiple brain regions and frequency bands, rather than being limited to single-dimensional LFP power data. Indeed, recent studies have demonstrated the effectiveness of CNN in estimating dynamically changing EEG/LFP patterns 2 – 4 . In addition, the development of a Transformer architecture, which incorporates self-attention and parallelization mechanisms, represents a significant breakthrough in machine learning, particularly in natural language processing 5 . This model is capable of capturing meaningful relationships within dynamically and sequentially changing datasets more precisely, and could potentially achieve superior performance across a wide range of tasks. Given that EEG/LFP signals are time-series datasets that include intricate temporal relationships, the Transformer model has the potential to effectively process these signals to estimate brain states from short time windows. Although several studies have explored this possibility 6 , such investigations remain in the early stage. To explore this potential, we compared the performances of the CNN and Transformer models in analyzing the LFP patterns recorded from multiple brain areas in mice. Further, we employed an acetic acid-induced model that causes visceral pain through inflammation and distention of internal organs to induce LFP patterns distinct from normal activity states in mice 7 , 8 . This model has the advantage of inducing long-lasting and stable distinct brain states in mice, including changes in LFP power. From these mice, we simultaneously recorded LFP signals from eight brain regions, 9 – 13 and subsequently compared performance of classification of the collective LFP patterns within short time windows (i.e., lasting several seconds) using conventional power analysis, AlexNet (CNN)-based machine learning, and Transformer-based machine learning approaches. Results Acetic acid-induced increases in c-Fos expressions in the ACC and PAG neurons We first verified how neurons in pain-related brain regions 9 – 14 are activated in a mouse model of visceral pain. To induce writhing reflexes, an aversive behavioral sign of visceral pain, mice were administered an intraperitoneal injection of 0.7% acetic acid (Fig. 1 a). Writhing occurred at an average frequency of 1.09 ± 0.33 /min (ranging from 0.43 to 2.98 /min) (Fig. 1 b; n = 7 mice). Coronal brain slices were subsequently prepared and immunohistochemical staining for c-Fos, an immediate early marker gene of neuronal activation, was performed from eight brain regions: the primary somatosensory cortex (S1), anterior cingulate cortex (ACC), prelimbic cortex (PL), periaqueductal gray (PAG), infralimbic cortex (IL), thalamus (THL), nucleus accumbens (NAc), and amygdala (AMY) (Fig. 1 c). In the ACC and PAG, the proportions of c-Fos-positive neurons in acetic acid-injected mice were significantly larger than those in saline-injected mice (Fig. 1 d; n = 7 mice; ACC, t 13 = 2.60, P = 0.022; PAG, t 13 = 2.38, P = 0.033, Student’s t -test), while no significant differences were observed in the other regions (Fig. 1 d; S1, t 12 = 0.51, P = 0.62; PL, t 13 = 1.13, P = 0.28; IL, t 13 = 0.45, P = 0.66; THL, t 13 = 0.41, P = 0.69; NAc, t 13 = 0.74, P = 0.47; AMY, t 13 = 0.51, P = 0.62). These results indicate that the ACC and PAG neurons were activated during acetic acid-induced visceral pain. No significant correlations were found between the proportion of c-Fos-positive neurons in the ACC or PAG and the writhing count (Supplementary Fig. S1 a; ACC, r = 0.61, P = 0.14; PAG, r = 0.19, P = 0.43). These results further indicate that neuronal c-Fos expression, a widely used marker of neuronal activity, partly represents changes in brain activity associated with visceral pain behaviors. Acetic acid-induced attenuation of LFP power over a long time window Next, we implanted electrodes into the aforementioned target brain regions and measured the LFP patterns for 40 min before and after acetic acid injection, termed the pre- and post-periods, respectively (Fig. 2 a). Using time-frequency analysis, the averaged changes in LFP power were examined over a long time (5 min) window in six frequency bands: delta (1–4 Hz), theta (5–8 Hz), alpha (9–13 Hz), beta (14–30 Hz), slow gamma (31–49 Hz), and fast gamma (51–100 Hz). In the ACC, acetic acid injection induced significant decreases in LFP power in all frequency bands, except for the fast gamma band (Fig. 2 b; delta, t 8 = 2.86, P = 0.021; theta, t 8 = 3.88, P = 0.0047; alpha, t 8 = 5.43, P = 6.2 × 10 − 4 ; beta, t 8 = 4.72, P = 0.0015; slow gamma, t 8 = 2.41, P = 0.042; t 8 = 1.08, P = 0.31, paired t -test). The same statistical analyses were applied to LFP signals from all brain regions, confirming similar decreases in the majority of the analyzed brain regions (Fig. 2 c). Mice injected with saline did not exhibit any significant changes in LFP power (Supplementary Fig. S3, P > 0.05, paired t -test, n = 3–4 mice). These results demonstrate that the overall LFP power in these brain regions was attenuated during acetic acid-induced visceral pain. In addition, significant negative correlations between LFP power changes and writhing counts were found in the ACC (Supplementary Fig. S1 b and 1c; delta, r = − 0.84, P = 0.0043; theta, r = − 0.72, P = 0.030; alpha, r = − 0.68, P = 0.044), suggesting that the ACC LFP power changes, rather than the proportion of c-Fos-positive neurons, could serve as a physiological marker of the intensity of visceral pain. Although we simultaneously recorded electrocardiogram (ECG) and respiratory (Resp) signals from the olfactory bulb and electromyogram (EMG) signals from the dorsal neck muscle, no significant differences were found in these physiological parameters before and after acetic acid injection (Supplementary Fig. S2; n = 9 mice; EMG, t 8 = 1.38, P = 0.20; heart rate, t 8 = 2.28, P = 0.052; respiratory frequency, t 8 = 1.28, P = 0.24, paired t -test). Classification of LFP patterns based on LFP power After identifying the overall decrease in LFP power from a time window of 5 min using the same datasets, we evaluated whether similar changes in LFP patterns could be captured over a shorter time (5 s) window using time-frequency analysis (Fig. 2 d, Left top). Recording periods of 40 min before and after the injection were divided into 5-s bins, termed pre- and post-bins, respectively, yielding 480 of each. Assuming that the LFP power following acetic acid injection (post) was lower than that before injection (pre), for each frequency band in each brain region, we defined the discriminant threshold that best estimated LFP patterns as high in pre-bins and low in post-bins (Fig. 2 d, Left bottom; see Methods). In each dataset, a confusion matrix was constructed to evaluate the accuracy of classifying the pre/post-bins, while a d -prime value was computed for each matrix (an example from theta LFP in the ACC is shown in Fig. 2 d, Central). For each mouse, the average d -prime value was computed for all of the six frequency bands (Fig. 2 d, Right). In all nine mice tested, d -prime values ranged from 0.1–1.8. Classification performance was subsequently evaluated using a stratified ten-fold cross-validation in each computational trial, maintaining a train-to-test ratio of 9:1. In this approach, 432 pre-bins and 432 post-bins were randomly selected as the training dataset while the remaining 48 pre- and 48 post-bins constituted the test dataset. Although 66% of datasets (from all brain regions and frequency bands) had d -prime values that significantly exceeded chance levels (defined as the lower limit of the 95% confidence interval being greater than 0, determined by 10-fold cross-validation), their practical significance was limited, as they had relatively higher false alarm rates (~ 40%) and lower hit rates (~ 60%) (as shown in the confusion matrix in Fig. 2 d). These results demonstrate that simple discriminant analysis of LFP power from single brain regions computed from 5-s bins is insufficient to distinguish the periods before and after acetic acid injection. Classification of LFP patterns by the AlexNet Next, we investigated whether machine learning approaches could more effectively discriminate the LFP patterns induced by acetic acid administration. First, we employed AlexNet, a convolutional neural network (CNN) architecture, to analyze sequential vectors/tensors composed of the LFP power, similar to the techniques used in image processing 15 . Using the same analytical approach, we subsequently applied the supervised AlexNet model to a six-dimensional vector composed of the LFP powers in six frequency bands in a single brain region in each time bin of each mouse (Fig. 3 a). Classification performance was also evaluated using a stratified ten-fold cross-validation in each computational trial (432 pre-bins and 432 post-bins as the training dataset and 48 pre- and 48 post-bins as the test dataset). In each computation, we initially adapted the pre-trained AlexNet architecture by replacing the last classification layer, and subsequently applied transfer learning using the training dataset to differentiate between the pre- and post-bins. Next, the modified architecture was employed to classify each bin in the test dataset as either a pre-bin or post-bin. As an example, the averaged d -prime values in the ACC using AlexNet were found to be significantly higher than those obtained from the discrimination analysis of the ACC LFP power presented in Fig. 2 d (Fig. 3 b; n = 9 mice, t 8 = 9.19, P = 1.6 × 10 − 5 , paired t -test). Overall, all datasets from all brain regions and frequency bands had d -prime values that significantly exceeded chance levels (defined as the lower limit of the 95% confidence interval being greater than 0, determined by 10-fold cross-validation). These results demonstrate that the AlexNet-based classification using combined LFP powers across multiple frequency bands can discriminate acetic acid-induced LFP patterns more effectively than the threshold-based discrimination of LFP power from single frequency bands. To investigate whether incorporating data from all recorded brain regions could improve the classification performance, we subsequently applied the same analysis to the LFP powers obtained from all eight brain regions. An 8 × 6-dimensional tensor was defined as the LFP power in the six frequency bands of the eight brain regions in each time bin for each mouse (Fig. 3 c). Similarly, the AlexNet architecture trained with the training dataset (comprising 432 pre- and post-bins) was used to classify each bin in the test datasets (consisting of 48 pre- and post-bins). Overall, d -prime values computed from all brain regions were found to be significantly higher than those computed from single brain regions presented in Fig. 3 b (Fig. 3 c– 3 f; n = 9 mice, t 8 = 4.51, P = 2.5 × 10 − 5 , Student’s t -test). These results demonstrate that integrating LFP power from multiple brain regions enables a more effective AlexNet-based classification of acetic acid-induced LFP patterns than using data from single brain regions. The analyses described above all involved a degree of subjectivity, because the frequency bands for computing the LFP power were determined by the experimenters. Furthermore, the AlexNet architecture was originally designed for image processing, and may thus not be optimally suited for time-series data, such as LFP traces. To address this limitation, we directly applied the original LFP traces without subjectively converting them into LFP powers within specific frequency bands (Fig. 3 g). Original LFP traces from eight brain regions at a sampling rate of 2000 Hz were downsampled to 200 Hz, defining an 8 × 1000-dimensional tensor in each 5-s bin. When applied to the downsampled LFP traces, AlexNet yielded significantly lower d -prime values (Fig. 3 h) compared to its application to tensors comprising LFP power across all frequency bands from all brain regions, as presented in Fig. 3 d ( n = 9 mice, t 8 = 7.62, P = 6.2 × 10 − 5 , paired t -test). These results indicate that AlexNet achieves better performance when LFP patterns are converted into low-dimensional, image-like datasets of LFP power in specific frequency bands through the power analysis of LFP signals, rather than when the raw LFP time-series traces are directly input to AlexNet. Classification of LFP patterns by the Transformer Subsequently, we evaluated the classification performance of the Transformer model using the same LFP dataset. Similar to the AlexNet analysis shown in Fig. 3 h, we used the downsampled LFP traces with an 8 × 1000-dimensional tensor, and the Transformer trained with the training dataset (consisting of 432 pre-and 432 post-bins) to classify each bin in the test datasets (consisting of 48 pre-and 48 post-bins) (Fig. 4 a). Seven of the nine mice exhibited prominently higher d -prime values with the Transformer compared to the AlexNet analysis of LFP power presented in Fig. 3 f, whereas the remaining two mice showed nearly comparable d -prime values using both approaches (Fig. 4 b and 4 c). Overall, the d -prime values obtained using the Transformer were significantly higher than those computed using AlexNet ( n = 9 mice, t 8 = 3.36, P = 0.0099, paired t -test), indicating that the Transformer exhibits superior performance in classifying LFP patterns related to acetic acid-induced visceral pain compared to AlexNet. All previous analyses were conducted with a fixed bin size of 5 s. To investigate the classification performance with other bin sizes, the same analyses with the Transformer were performed by varying the bin size from 1 to 20 s (Fig. 4 d). We found that the 5-s bin size, which we had been using for our analyses, yielded the highest d -prime values. While the 2-s bin size produced nearly equivalent performance, the d -prime values for the 5-s bin were significantly higher than those with bin sizes of 1, 10, and 20 s ( P < 0.05, Tukey’s test after ANOVA). These results confirm that, in our LFP analysis using Transformers, a bin size of approximately 5 s is most appropriate. DISCUSSION In this study, we recorded the LFP patterns in eight pain-related brain regions in mice exhibiting acetic acid-induced visceral pain (represented as the writhing reflex), a widely used mouse model of nociception. We first confirmed that the overall LFP power in frequency bands lower than the slow gamma range decreased in most brain regions following acetic acid injection when analyzed over a long time window (5 min). However, discriminant analysis with a one-dimensional threshold of LFP power changes within a shorter time window (5 s) could not accurately distinguish the acetic acid-induced LFP patterns. However, applying the AlexNet convolutional neural network to LFP powers, particularly when integrating data from all recorded brain regions, yielded an improved classification performance of the LFP patterns. Furthermore, we demonstrated that the Transformer model, when applied to the original LFP time-series traces without conversion into LFP power, achieved superior classification accuracy compared with AlexNet when using LFP power datasets. Pain processing involves diverse brain regions; the sensory aspect is primarily represented in the S1 16 , while the affective component is predominantly processed by the ACC and AMY 17 , 18 . Somatic pain has further been shown to alter brain LFP oscillations in the ACC, AMY, S1, and VTA in both rodents 19 – 25 and humans 26 . We demonstrated that visceral pain induced significant reductions in the overall LFP power in the ACC, which correlated better with painful behaviors than with neuronal c-Fos expression. However, the accuracy of the power-based classification was substantially reduced when analyzed over short (5-s) time windows. This is presumably because the LFP power fluctuates rapidly over short periods, and is more sensitive to biological noise in these time periods. Through supervised machine learning using AlexNet, we demonstrated that integrating LFP powers across six frequency bands and further integrating LFP powers from eight brain regions significantly improved classification accuracy. These results confirmed the utility of machine learning approaches in the analysis of multidimensional EEG/LFP data from the brain. From a biological perspective, these results indicate that LFP patterns related to visceral pain are not merely evoked by single brain regions, but rather emerge from widespread neural networks across multiple brain areas 27 . In addition, the Transformer model provided a better classification accuracy than AlexNet. This superior performance of the Transformer is presumably because it is well-suited for analyzing sequential time-series data with intricate temporal dependencies, similar to its applications in natural language processing 5 . Although the internal structure of the Transformer model obscures the specific features or time points to which it attends, our results highlight its efficacy in analyzing neuronal signal data, including EEG and LFP signals, as well as in reading more intricate neuronal ensemble patterns related to emotions, stimulus responses, and higher-order cognitive functions in various animal models. Furthermore, a significant advantage of the Transformer is not only its superior classification performance, but also its ability to process raw LFP signals directly without requiring subjective conversion into LFP power. This capability facilitates the real-time decoding of recorded neuronal signals, which holds promise for practical applications in various functions. Methods Ethical approvals All experiments were approved by the Committee on Animal Experiments at Tohoku University (approval number: 2022 PhA-004). The experiments were performed in accordance with the NIH guidelines for the care and use of animals. This study followed ARRIVE guidelines. Animals Male 8-to-10-week-old C57BL/6 mice (SLC, Shizuoka, Japan) with a preoperative weight of 20–30 g were housed under conditions of controlled temperature and humidity (22 ± 1ºC, 55 ± 5%) in a vivarium, maintained on a 12:12-h light/dark cycle (lights off from 8 am to 8 pm) with ad libitum access to food and water. All mice were housed individually. No mice were excluded from the analyses. Acetic acid writhing test Acetic writhing test was conducted as described 28 , 29 . After mice were acclimatized in a Plexiglas chamber for 30 min, they were intraperitoneally injected with 10 ml/kg saline or 0.7% acetic acid. Abdominal constriction responses were visually counted for 40 min after the injection. Immunochemistry The mice received an overdose of urethane 160 min after the injection, and they were perfused intracardially with cold 8% paraformaldehyde (PFA) in 25 mM PBS and decapitated. The brains were placed in 30% sucrose until equilibrated and coronally sectioned at a thickness of 50 µm. To obtain brain slices, the fixed samples were rinsed with PBS and then permeabilized in 100 mM PBS with 0.3% Triton X-100 and 5% serum bovine albumin at room temperature for 60 min. The slices were then incubated with a primary rabbit c-Fos antibody (1:5000; Cell Signaling Technology) in 100 mM PBS with 0.3% Triton X-100 and 5% serum bovine albumin for one overnight period at 4°C. After rinsing with PBS, they were then labeled with a secondary anti-rabbit IgG antibody Alexa 594 (1:500; Thermo Fisher Scientific) in 100 mM PBS with 0.3% Triton X-100 for 90 min. The samples were mounted using Fluoro-KEEPER Antifade Reagent with DAPI (Nacalai Tesque). Images were acquired using a fluorescence microscope (BZ-X800; Keyence, Osaka, Japan) with a water immersion objective lens (×20, 0.75 NA). The c-Fos and DAPI positive cells were counted using an analysis application (BZ-H4C; Keyence, Osaka, Japan). Surgery Standard surgical procedures were similar to those described previously 30 – 33 . Mice were anesthetized with 1–2% of isoflurane gas in air. Two electrocardiogram (ECG) electrodes (stainless steel wires; AS633, Cooner Wire Company) were sutured to tissue underneath the skin of the upper chest, and the animal was then fixed in a stereotaxic instrument with two ear bars and a nose clamp. Two incisions (1 cm) were made on both sides of the upper chest. First, two craniotomies were made; one covering the coordinates for the anterior cingulate cortex (ACC; 1.1 mm anterior and 0.2 mm lateral to the bregma), the prelimbic cortex (PL; 1.7 mm anterior and 0.2 mm lateral to the bregma), the infralimbic cortex (IL; 1.7 mm anterior and 0.2 mm lateral to the bregma), the nucleus accumbens (NAc; 0.8 mm anterior and 0.8 mm lateral to the bregma), the amygdala (AMY; 0.8 mm posterior and 3.0 mm lateral to the bregma), the primary somatosensory cortex (S1; 1.4 mm posterior and 2.1 mm lateral to the bregma), the thalamus (THL; 2.1 mm posterior and 1.3 mm lateral to the bregma), and the periaqueductal gray (PAG; 3.5 mm posterior and 0.2 mm lateral to the bregma). The electrode array was directly implanted into the cortical tissue in the left hemisphere with electrodes inserted 0.8 mm into S1, 2.0 mm into the ACC, 2.5 mm into the PL, 2.8 mm into the PAG, 2.9 mm into the IL, 3.0 mm into the THL, 4.4 mm into the NAc and AMY. Two electromyogram (EMG) electrodes were implanted into the dorsal neck area. For the olfactory bulb and cerebellum craniotomies, stainless steel screws were implanted on the skull attached to the brain surface, serving as Resp electrodes and ground/reference electrodes, respectively. Finally, all of the wires and the electrode array were secured to the skull using dental cement. After completing all surgical procedures, the anesthesia was terminated and the animals were spontaneously allowed to awake from the anesthesia. Following surgery, each animal was housed with free access to water and food, with daily observation. Electrophysiological recording The mouse was connected to the recording equipment via Cereplex M (Blackrock), a digitally programmable amplifier, which was placed close to the animal’s head. The output of the headstage was conducted to the Cereplex Direct recording system (Blackrock), a data acquisition system, via a lightweight multiwire tether and a commutator. For recording electrophysiological signals, the electrical interface board of the tetrode assembly was connected to a Cereplex M digital headstage (Blackrock Microsystems), and the digitized signals were transferred to a Cereplex Direct data acquisition system (Blackrock Microsystems). Electrical signals were sampled at 2 kHz and low-pass filtered at 500 Hz. In each mouse, recordings were performed for each 40 min before and after an acetic acid injection, termed pre- and post-periods, respectively. The animal’s moment-to-moment position was tracked at 15 Hz using a video camera attached to the ceiling. All recordings from a behavioral task were performed once so that all the tasks were novel for the mice and no duplications of samples were thus included. Histological analysis to confirm electrode placement The mice were overdosed with isoflurane, perfused intracardially with 8% paraformaldehyde in phosphate-buffered saline (pH 7.4) and decapitated. After dissection, the brains were fixed overnight in 4% PFA and equilibrated with 20 and 30% sucrose in phosphate-buffered saline for an overnight each. Frozen coronal sections (50 µm) were cut using a microtome, and serial sections were mounted and processed for cresyl violet staining. For cresyl violet staining, the slices were rinsed in water, stained with cresyl violet, and coverslipped with Permount. The positions of all electrodes were confirmed by identifying the corresponding electrode tracks in histological tissue. In the two mice out of nine mice, electrodes were located in all eight regions. In the five mice, electrodes were located in seven regions except the IL, AMY or PAG. In the one mouse, electrodes were located in the six regions except the PL and IL. Of the one mouse, electrodes were located in the six regions except the NAc and PAG. LFP power To compute the time-frequency representation of the LFP power change with time, LFP signals were downsampled to 200 Hz and convolved using a Morlet’s wavelet family defined by a constant ratio of f 0 /σ f = 5, where f 0 represents the frequency of interest and σ f represents the bandwidth of the wavelet in the frequency domain chosen to ensure optimal time-frequency localization properties of the wavelet. The frequency bands were defined as follows: delta: 1–4 Hz, theta: 5–8 Hz, alpha: 9–13 Hz, beta: 14–30 Hz, slow gamma: 31–49 Hz, and fast gamma: 51–100 Hz. Evaluation of classification performance based on d-prime values LFP power or LFP downsampled traces were concatenated in each 5-s bin before (gray, pre) and after (red, post) acetic acid injection, yielding 480 pre- and post-bins, respectively. In the classification analyses, d -prime values were employed as a measure to assess how accurately our analysis could discriminate between the two conditions (pre- and post-periods). The numbers of hit bins ( n hit , where both the prediction and the real data were post-bins), miss bins ( n miss , where the prediction was a pre-bin, but the real data were post-bin), false alarm bins ( n FA , where the prediction was a post-bin, but the real data were pre-bin), and correct rejection bins ( n CR , where both the prediction and the real data were pre-bins) were computed. The hit rate ( dHit = n hit /( n hit + n miss )) and false alarm rate ( pFA = n FA /( n FA + n CR )) were further computed, while the d -prime value was calculated as Z ( dHit ) × Z ( pFA ). Classification based on a discriminant threshold on LFP power The LFP power from all bins was classified as either pre- or post-bins by setting a discrimination threshold. Classification was further performed using different discrimination thresholds ranging from zero to maximum power, while the threshold yielding the highest d -prime value was selected as the final threshold for classification. Classification based on the AelxNet AlexNet, a convolutional neural network (CNN) primarily employed for image recognition and classification tasks 15 , was adapted to analyze the LFP power datasets or LFP traces by replacing the classification layer. This adaptation involved the application of transfer learning in the customized model. The architecture of the modified CNN comprised five convolutional layers, each followed by a local response normalization layer, a rectified linear unit (ReLU) activation layer, and a max-pooling layer. A fully-connected layer complemented the configuration. Detailed specifications of each layer of the CNN are provided in Supplementary Table S1 . To evaluate the performance and robustness of AlexNet, we employed stratified ten-fold cross-validation during the training procedure. In each fold, all bins were randomly split into training and test datasets at a 9:1 ratio, such that 432 bins were used for training and 48 bins for testing. This cross-validation process was repeated ten times, each time with different training and test dataset splits. The d -prime value was computed for each fold, and all values from 10 folds were averaged for each mouse. Classification based on the Transformer The downsampled LFP traces from all brain regions were concatenated into 5-s bins and directly input into the vision Transformer (Python 3.10.12 and TensorFlow 2.14.0) using Emotion-Recognition-Transformers 6 . A learning rate of 0.00001 was used in the study. Patch embedding was applied with a shape of [1, patch size]. The performance of the Transformer was evaluated using d -prime values obtained from stratified ten-fold cross-validation, similar to the method used for AlexNet. Statistics All data are presented as the mean ± SEM, and were analyzed using MATLAB. Normally distributed data are displayed as the sample mean and SEM with individual data points. Comparisons of two-sample data were performed using paired or Student’s t -tests. Multiple group comparisons were performed using post-hoc Bonferroni correction. Pearson’s correlation coefficients were used to statistically evaluate correlations. The null hypothesis was rejected at P < 0.05 level. Declarations COMPETING INTERESTS The authors declare no competing interests. Author Contribution T.K. and T.S. designed the study. T.K. acquired the biological and electrophysiological data. T.K., A.T., and T.X. performed the analyses and prepared the figures. K.T. and K.K. surpervised the project. T.K. and T.S. wrote the main manuscript text, and all the authors reviewed the main manuscript text. Acknowledgement This work was supported by KAKENHI (21H05243) from the Japan Society for the Promotion of Science (JSPS), a grant (JP21zf0127004) from the Japan Agency for Medical Research and Development (AMED), a grant (JPMJCR21P1) from the Japan Science and Technology Agency (JST) to T. Sasaki; and a grant (JPMJMS2292) from the JST to K. Kitajo, K. Tsutsui, and T. Sasaki; and a JSPS Research Fellowship (23KJ0084) to T. Kayama. Data Availability The data that support the findings of this study are available from the corresponding authors upon reasonable request. References Buzsaki, G. Rhythms of the Brain. New York: Oxford University Press (2006). Metzger, S. L. et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 620, 1037–1046, doi: 10.1038/s41586-023-06443-4 (2023). Willett, F. R. et al. A high-performance speech neuroprosthesis. 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Supplementary Files KayamaetalSupplementaryinformation.pdf Cite Share Download PDF Status: Published Journal Publication published 17 Oct, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 09 Aug, 2024 Reviews received at journal 22 Jul, 2024 Reviews received at journal 20 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers agreed at journal 10 Jul, 2024 Reviewers invited by journal 09 Jul, 2024 Editor assigned by journal 09 Jul, 2024 Editor invited by journal 04 Jul, 2024 Submission checks completed at journal 04 Jul, 2024 First submitted to journal 03 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4677672","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":330338437,"identity":"fa7e05d9-0699-473e-95a9-2b6919d7ea64","order_by":0,"name":"Tasuku Kayama","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Tasuku","middleName":"","lastName":"Kayama","suffix":""},{"id":330338438,"identity":"5bb70061-7235-46e6-8466-964195ba65e7","order_by":1,"name":"Atsushi Tamura","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Atsushi","middleName":"","lastName":"Tamura","suffix":""},{"id":330338439,"identity":"07675fca-d0ce-420c-849b-227b0efd39b0","order_by":2,"name":"Tuo Xiaoying","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Tuo","middleName":"","lastName":"Xiaoying","suffix":""},{"id":330338440,"identity":"e50e6b41-1ed1-46d9-8194-7a110209bcd1","order_by":3,"name":"Ken-Ichiro Tsutsui","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Ken-Ichiro","middleName":"","lastName":"Tsutsui","suffix":""},{"id":330338441,"identity":"510a47cd-1dd3-4407-bb42-75884f6a7368","order_by":4,"name":"Keiichi Kitajo","email":"","orcid":"","institution":"National Institute for Physiological Sciences","correspondingAuthor":false,"prefix":"","firstName":"Keiichi","middleName":"","lastName":"Kitajo","suffix":""},{"id":330338442,"identity":"995f4044-bb22-4bc9-9c86-6b9cd3f92709","order_by":5,"name":"Takuya Sasaki","email":"data:image/png;base64,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","orcid":"","institution":"Tohoku University","correspondingAuthor":true,"prefix":"","firstName":"Takuya","middleName":"","lastName":"Sasaki","suffix":""}],"badges":[],"createdAt":"2024-07-03 05:08:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4677672/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4677672/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-75616-6","type":"published","date":"2024-10-17T15:57:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61335637,"identity":"aa9a91e5-6cad-412c-8f80-970b3279f79e","added_by":"auto","created_at":"2024-07-29 15:29:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":269817,"visible":true,"origin":"","legend":"\u003cp\u003ec-Fos expression was increased in the ACC and PAG of mice with acetic acid-induced visceral pain.\u003c/p\u003e\n\u003cp\u003e(a) (Left) Mice were intraperitoneally injected with 0.7% acetic acid. (Right) An exemplar image of a mouse showing the writhing reflex. (b) (Left) Changes in writhing count every 5 min after acetic acid injection (\u003cem\u003en \u003c/em\u003e= 7 mice). (Right) The total number of writhing counts in the 40 min following an acetic acid injection in individual mice. (c) Representative fluorescent images of the ACC and PAG neurons labeled with an anti-c-Fos (magenta) antibody and DAPI (blue) following injection of saline or acetic acid. (d) The percentages of c-Fos-positive cells in each brain region (saline, \u003cem\u003en\u003c/em\u003e = 7 mice; acetic acid, \u003cem\u003en\u003c/em\u003e = 6–7 mice). *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, Student’s \u003cem\u003et\u003c/em\u003e-test.\u003c/p\u003e","description":"","filename":"FIgure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4677672/v1/5de9d08d89775b65b392d4ea.jpg"},{"id":61335635,"identity":"916787c5-3d75-4406-9398-300d7b7375aa","added_by":"auto","created_at":"2024-07-29 15:29:39","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":631775,"visible":true,"origin":"","legend":"\u003cp\u003eLFP power changes were observed in the eight brain regions.\u003c/p\u003e\n\u003cp\u003e(a) (Left) Histological confirmation of LFP electrode locations in the S1, ACC, PL, PAG, IL, THL, NAc, and AMY. Arrowheads represent the tips of electrodes. (Right) Representative electrophysiological traces (filtered at 40–500 Hz). (b) Acetic acid-induced time changes in LFP power (bin = 5 min) in the ACC following acetic acid injection (\u003cem\u003en \u003c/em\u003e= 9 mice). The LFP power was normalized by an average of the LFP power over 40 min before acetic acid injection. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, paired \u003cem\u003et\u003c/em\u003e-test. (c) The percentages of changes in LFP power at each frequent band in each brain region 40 min following acetic acid injection relative to those 40 min prior to acetic acid injection (\u003cem\u003en\u003c/em\u003e = 6–9 mice). *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, paired \u003cem\u003et\u003c/em\u003e-test. (d) Classification of acetic acid-induced brain states based on LFP power from individual brain regions. (Left) Schematic of the analysis. The ACC LFP power was concatenated in each 5-s bin before (gray, pre) and after (red, post) an acetic acid injection. Each bin was subsequently estimated as a pre or a post-bin based on a discrimination threshold. (Central top) Confusion matrix showing the number of bins in 10-fold cross-validation from a mouse. A \u003cem\u003ed\u003c/em\u003e-prime value was defined from each matrix. (Central bottom) For the ACC delta power in this mouse, a real d-prime value was indicated by the red arrow, which was compared to corresponding 1000 shuffled datasets (gray histgram). The dot line indicates chance level. (Right) For individual mice, d-prime values were computed from single LFP patterns. The averaged \u003cem\u003ed\u003c/em\u003e-prime value in each mouse was computed from all the six frequency bands. Gray line shows chance level.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4677672/v1/2537f555b7ce38efdaae5e8d.jpg"},{"id":61336171,"identity":"455fa562-13f7-44b0-99d0-b2fcf1abebcf","added_by":"auto","created_at":"2024-07-29 15:37:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":409684,"visible":true,"origin":"","legend":"\u003cp\u003eAlexNet-based classification of the acetic acid-induced brain states based on LFP patterns from multiple brain regions.\u003c/p\u003e\n\u003cp\u003e(a) The ACC LFP power in the six frequency bands was concatenated in each 5-s bin before (gray, pre) and after (red, post) acetic acid injection. After training of the datasets with single LFP power in the AlexNet with a supervised learning of correct labels, each bin without training was classified as a pre or post-bin. (b) (Top) Confusion matrix showing the number of bins in 10-fold cross-validation from an individual mouse. A \u003cem\u003ed\u003c/em\u003e-prime value was defined in each matrix. (Bottom) For individual mice, \u003cem\u003ed\u003c/em\u003e-prime values were computed from single LFP patterns. (c,d) The same as a and b, but LFP powers in all of the recorded eight brain regions were used in the analysis. (g) All \u003cem\u003ed\u003c/em\u003e-prime values obtained when single brain regions (left) or all brain regions recorded (rightmost) were included in the analysis. (f) Corresponding with e, comparison of \u003cem\u003ed\u003c/em\u003e-prime values from individual brain regions and all brain regions. ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, Student’s \u003cem\u003et\u003c/em\u003e-test between single regions vs all regions. (g) Original LFP traces from all brain regions concatenated in each 5-s bin before (gray, pre) and after (red, post) acetic acid injection were trained in the AlexNet with a supervised learning of correct labels, and each bin without training was classified as a pre or post-bin. (h) A confusion matrix from a mouse (top) and \u003cem\u003ed\u003c/em\u003e-prime values from all mice (bottom).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4677672/v1/d92b85d032c3c9a489b5d356.jpg"},{"id":61335632,"identity":"46e3f2ed-3ca8-4c84-8fc0-01c41d81590a","added_by":"auto","created_at":"2024-07-29 15:29:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":299752,"visible":true,"origin":"","legend":"\u003cp\u003eTransformer-based classification of acetic acid-induced brain states based on LFP patterns from multiple brain regions.\u003c/p\u003e\n\u003cp\u003e(a) Original LFP traces from all brain regions concatenated in each 5-s bin before (gray, pre) and after (red, post) acetic acid injection were trained in the Transformer with a supervised learning of correct labels, and each bin without training was classified as a pre or post-bin. (b) A confusion matrix from an individual mouse (top) and \u003cem\u003ed\u003c/em\u003e-prime values from all mice (bottom). (c) Comparison of \u003cem\u003ed\u003c/em\u003e-prime values computed from the AlexNet (corresponding with Figure 3f, all) and the Transformer. Each line represents an individual mouse. **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, Paired \u003cem\u003et\u003c/em\u003e-test bet. (d) (Left) The same analysis with the Transformer was performed with different bin sizes. (Right) \u003cem\u003ed\u003c/em\u003e-prime values from all mice plotted against different bin sizes. *\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001, ****\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.0001, Tukey’s test after ANOVA.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4677672/v1/f7b47d2b593a37ca7049493c.jpg"},{"id":67149528,"identity":"e31925ac-5569-4a9b-a06d-09e6cad4522f","added_by":"auto","created_at":"2024-10-21 16:13:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2227002,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4677672/v1/4835d1fc-acc4-47c5-99cb-642a71ddfaec.pdf"},{"id":61335633,"identity":"9d6be3ab-8587-4d47-83d0-8633a1bfdec2","added_by":"auto","created_at":"2024-07-29 15:29:39","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":338124,"visible":true,"origin":"","legend":"","description":"","filename":"KayamaetalSupplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4677672/v1/debed01967f24c9457ee8e11.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transformer-based classification of visceral pain-related local field potential patterns in the brain","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe organized activity patterns of the neuronal ensembles that control brain functions present as diverse electrophysiological features in the extracellular electroencephalogram (EEG) and local field potential (LFP) signals \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Time-frequency analysis has been widely applied to quantify changes in the intensity of oscillatory electrophysiological signals in each frequency band, including theta (5\u0026ndash;8 Hz) and gamma (30\u0026ndash;100 Hz) power, unveiling a variety of neuronal mechanisms relevant to emotions and behaviors. Especially, wavelet analysis is a useful method to analyze short-term LFP power fluctuations on the second scale. Additionally, recent advances in machine learning techniques such as Convolutional Neural Networks (CNNs) have garnered significant attention owing to their ability to simultaneously process large-scale physiological datasets spanning multiple brain regions and frequency bands, rather than being limited to single-dimensional LFP power data. Indeed, recent studies have demonstrated the effectiveness of CNN in estimating dynamically changing EEG/LFP patterns \u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, the development of a Transformer architecture, which incorporates self-attention and parallelization mechanisms, represents a significant breakthrough in machine learning, particularly in natural language processing \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This model is capable of capturing meaningful relationships within dynamically and sequentially changing datasets more precisely, and could potentially achieve superior performance across a wide range of tasks. Given that EEG/LFP signals are time-series datasets that include intricate temporal relationships, the Transformer model has the potential to effectively process these signals to estimate brain states from short time windows. Although several studies have explored this possibility \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, such investigations remain in the early stage.\u003c/p\u003e \u003cp\u003eTo explore this potential, we compared the performances of the CNN and Transformer models in analyzing the LFP patterns recorded from multiple brain areas in mice. Further, we employed an acetic acid-induced model that causes visceral pain through inflammation and distention of internal organs to induce LFP patterns distinct from normal activity states in mice \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This model has the advantage of inducing long-lasting and stable distinct brain states in mice, including changes in LFP power. From these mice, we simultaneously recorded LFP signals from eight brain regions, \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and subsequently compared performance of classification of the collective LFP patterns within short time windows (i.e., lasting several seconds) using conventional power analysis, AlexNet (CNN)-based machine learning, and Transformer-based machine learning approaches.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAcetic acid-induced increases in c-Fos expressions in the ACC and PAG neurons\u003c/h2\u003e \u003cp\u003eWe first verified how neurons in pain-related brain regions \u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e are activated in a mouse model of visceral pain. To induce writhing reflexes, an aversive behavioral sign of visceral pain, mice were administered an intraperitoneal injection of 0.7% acetic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Writhing occurred at an average frequency of 1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 /min (ranging from 0.43 to 2.98 /min) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7 mice). Coronal brain slices were subsequently prepared and immunohistochemical staining for c-Fos, an immediate early marker gene of neuronal activation, was performed from eight brain regions: the primary somatosensory cortex (S1), anterior cingulate cortex (ACC), prelimbic cortex (PL), periaqueductal gray (PAG), infralimbic cortex (IL), thalamus (THL), nucleus accumbens (NAc), and amygdala (AMY) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). In the ACC and PAG, the proportions of c-Fos-positive neurons in acetic acid-injected mice were significantly larger than those in saline-injected mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7 mice; ACC, \u003cem\u003et\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.60, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022; PAG, \u003cem\u003et\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033, Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test), while no significant differences were observed in the other regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed; S1, \u003cem\u003et\u003c/em\u003e\u003csub\u003e12\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62; PL, \u003cem\u003et\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.13, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28; IL, \u003cem\u003et\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.45, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66; THL, \u003cem\u003et\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69; NAc, \u003cem\u003et\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.47; AMY, \u003cem\u003et\u003c/em\u003e\u003csub\u003e13\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62). These results indicate that the ACC and PAG neurons were activated during acetic acid-induced visceral pain. No significant correlations were found between the proportion of c-Fos-positive neurons in the ACC or PAG and the writhing count (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea; ACC, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.61, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14; PAG, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.43). These results further indicate that neuronal c-Fos expression, a widely used marker of neuronal activity, partly represents changes in brain activity associated with visceral pain behaviors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eAcetic acid-induced attenuation of LFP power over a long time window\u003c/h2\u003e \u003cp\u003eNext, we implanted electrodes into the aforementioned target brain regions and measured the LFP patterns for 40 min before and after acetic acid injection, termed the pre- and post-periods, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Using time-frequency analysis, the averaged changes in LFP power were examined over a long time (5 min) window in six frequency bands: delta (1\u0026ndash;4 Hz), theta (5\u0026ndash;8 Hz), alpha (9\u0026ndash;13 Hz), beta (14\u0026ndash;30 Hz), slow gamma (31\u0026ndash;49 Hz), and fast gamma (51\u0026ndash;100 Hz). In the ACC, acetic acid injection induced significant decreases in LFP power in all frequency bands, except for the fast gamma band (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb; delta, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.86, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021; theta, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.88, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0047; alpha, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e; beta, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0015; slow gamma, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.41, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042; \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31, paired \u003cem\u003et\u003c/em\u003e-test). The same statistical analyses were applied to LFP signals from all brain regions, confirming similar decreases in the majority of the analyzed brain regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Mice injected with saline did not exhibit any significant changes in LFP power (Supplementary Fig. S3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, paired \u003cem\u003et\u003c/em\u003e-test, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3\u0026ndash;4 mice). These results demonstrate that the overall LFP power in these brain regions was attenuated during acetic acid-induced visceral pain. In addition, significant negative correlations between LFP power changes and writhing counts were found in the ACC (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb and 1c; delta, \u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.84, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0043; theta, \u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030; alpha, \u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044), suggesting that the ACC LFP power changes, rather than the proportion of c-Fos-positive neurons, could serve as a physiological marker of the intensity of visceral pain.\u003c/p\u003e \u003cp\u003eAlthough we simultaneously recorded electrocardiogram (ECG) and respiratory (Resp) signals from the olfactory bulb and electromyogram (EMG) signals from the dorsal neck muscle, no significant differences were found in these physiological parameters before and after acetic acid injection (Supplementary Fig. S2; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9 mice; EMG, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20; heart rate, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;2.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052; respiratory frequency, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;1.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24, paired \u003cem\u003et\u003c/em\u003e-test).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eClassification of LFP patterns based on LFP power\u003c/h2\u003e \u003cp\u003eAfter identifying the overall decrease in LFP power from a time window of 5 min using the same datasets, we evaluated whether similar changes in LFP patterns could be captured over a shorter time (5 s) window using time-frequency analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Left top). Recording periods of 40 min before and after the injection were divided into 5-s bins, termed pre- and post-bins, respectively, yielding 480 of each. Assuming that the LFP power following acetic acid injection (post) was lower than that before injection (pre), for each frequency band in each brain region, we defined the discriminant threshold that best estimated LFP patterns as high in pre-bins and low in post-bins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Left bottom; see Methods). In each dataset, a confusion matrix was constructed to evaluate the accuracy of classifying the pre/post-bins, while a \u003cem\u003ed\u003c/em\u003e-prime value was computed for each matrix (an example from theta LFP in the ACC is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Central). For each mouse, the average \u003cem\u003ed\u003c/em\u003e-prime value was computed for all of the six frequency bands (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Right). In all nine mice tested, \u003cem\u003ed\u003c/em\u003e-prime values ranged from 0.1\u0026ndash;1.8. Classification performance was subsequently evaluated using a stratified ten-fold cross-validation in each computational trial, maintaining a train-to-test ratio of 9:1. In this approach, 432 pre-bins and 432 post-bins were randomly selected as the training dataset while the remaining 48 pre- and 48 post-bins constituted the test dataset. Although 66% of datasets (from all brain regions and frequency bands) had \u003cem\u003ed\u003c/em\u003e-prime values that significantly exceeded chance levels (defined as the lower limit of the 95% confidence interval being greater than 0, determined by 10-fold cross-validation), their practical significance was limited, as they had relatively higher false alarm rates (~\u0026thinsp;40%) and lower hit rates (~\u0026thinsp;60%) (as shown in the confusion matrix in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). These results demonstrate that simple discriminant analysis of LFP power from single brain regions computed from 5-s bins is insufficient to distinguish the periods before and after acetic acid injection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eClassification of LFP patterns by the AlexNet\u003c/h2\u003e \u003cp\u003eNext, we investigated whether machine learning approaches could more effectively discriminate the LFP patterns induced by acetic acid administration. First, we employed AlexNet, a convolutional neural network (CNN) architecture, to analyze sequential vectors/tensors composed of the LFP power, similar to the techniques used in image processing \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Using the same analytical approach, we subsequently applied the supervised AlexNet model to a six-dimensional vector composed of the LFP powers in six frequency bands in a single brain region in each time bin of each mouse (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Classification performance was also evaluated using a stratified ten-fold cross-validation in each computational trial (432 pre-bins and 432 post-bins as the training dataset and 48 pre- and 48 post-bins as the test dataset). In each computation, we initially adapted the pre-trained AlexNet architecture by replacing the last classification layer, and subsequently applied transfer learning using the training dataset to differentiate between the pre- and post-bins. Next, the modified architecture was employed to classify each bin in the test dataset as either a pre-bin or post-bin. As an example, the averaged \u003cem\u003ed\u003c/em\u003e-prime values in the ACC using AlexNet were found to be significantly higher than those obtained from the discrimination analysis of the ACC LFP power presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9 mice, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;9.19, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.6 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, paired \u003cem\u003et\u003c/em\u003e-test). Overall, all datasets from all brain regions and frequency bands had \u003cem\u003ed\u003c/em\u003e-prime values that significantly exceeded chance levels (defined as the lower limit of the 95% confidence interval being greater than 0, determined by 10-fold cross-validation). These results demonstrate that the AlexNet-based classification using combined LFP powers across multiple frequency bands can discriminate acetic acid-induced LFP patterns more effectively than the threshold-based discrimination of LFP power from single frequency bands.\u003c/p\u003e \u003cp\u003eTo investigate whether incorporating data from all recorded brain regions could improve the classification performance, we subsequently applied the same analysis to the LFP powers obtained from all eight brain regions. An 8 \u0026times; 6-dimensional tensor was defined as the LFP power in the six frequency bands of the eight brain regions in each time bin for each mouse (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Similarly, the AlexNet architecture trained with the training dataset (comprising 432 pre- and post-bins) was used to classify each bin in the test datasets (consisting of 48 pre- and post-bins). Overall, \u003cem\u003ed\u003c/em\u003e-prime values computed from all brain regions were found to be significantly higher than those computed from single brain regions presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec\u0026ndash;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9 mice, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;4.51, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test). These results demonstrate that integrating LFP power from multiple brain regions enables a more effective AlexNet-based classification of acetic acid-induced LFP patterns than using data from single brain regions.\u003c/p\u003e \u003cp\u003eThe analyses described above all involved a degree of subjectivity, because the frequency bands for computing the LFP power were determined by the experimenters. Furthermore, the AlexNet architecture was originally designed for image processing, and may thus not be optimally suited for time-series data, such as LFP traces. To address this limitation, we directly applied the original LFP traces without subjectively converting them into LFP powers within specific frequency bands (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg). Original LFP traces from eight brain regions at a sampling rate of 2000 Hz were downsampled to 200 Hz, defining an 8 \u0026times; 1000-dimensional tensor in each 5-s bin. When applied to the downsampled LFP traces, AlexNet yielded significantly lower \u003cem\u003ed\u003c/em\u003e-prime values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh) compared to its application to tensors comprising LFP power across all frequency bands from all brain regions, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9 mice, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;7.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e, paired \u003cem\u003et\u003c/em\u003e-test). These results indicate that AlexNet achieves better performance when LFP patterns are converted into low-dimensional, image-like datasets of LFP power in specific frequency bands through the power analysis of LFP signals, rather than when the raw LFP time-series traces are directly input to AlexNet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eClassification of LFP patterns by the Transformer\u003c/h2\u003e \u003cp\u003eSubsequently, we evaluated the classification performance of the Transformer model using the same LFP dataset. Similar to the AlexNet analysis shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh, we used the downsampled LFP traces with an 8 \u0026times; 1000-dimensional tensor, and the Transformer trained with the training dataset (consisting of 432 pre-and 432 post-bins) to classify each bin in the test datasets (consisting of 48 pre-and 48 post-bins) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Seven of the nine mice exhibited prominently higher \u003cem\u003ed\u003c/em\u003e-prime values with the Transformer compared to the AlexNet analysis of LFP power presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef, whereas the remaining two mice showed nearly comparable \u003cem\u003ed\u003c/em\u003e-prime values using both approaches (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). Overall, the \u003cem\u003ed\u003c/em\u003e-prime values obtained using the Transformer were significantly higher than those computed using AlexNet (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9 mice, \u003cem\u003et\u003c/em\u003e\u003csub\u003e8\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;3.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0099, paired \u003cem\u003et\u003c/em\u003e-test), indicating that the Transformer exhibits superior performance in classifying LFP patterns related to acetic acid-induced visceral pain compared to AlexNet.\u003c/p\u003e \u003cp\u003eAll previous analyses were conducted with a fixed bin size of 5 s. To investigate the classification performance with other bin sizes, the same analyses with the Transformer were performed by varying the bin size from 1 to 20 s (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). We found that the 5-s bin size, which we had been using for our analyses, yielded the highest \u003cem\u003ed\u003c/em\u003e-prime values. While the 2-s bin size produced nearly equivalent performance, the \u003cem\u003ed\u003c/em\u003e-prime values for the 5-s bin were significantly higher than those with bin sizes of 1, 10, and 20 s (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Tukey\u0026rsquo;s test after ANOVA). These results confirm that, in our LFP analysis using Transformers, a bin size of approximately 5 s is most appropriate.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we recorded the LFP patterns in eight pain-related brain regions in mice exhibiting acetic acid-induced visceral pain (represented as the writhing reflex), a widely used mouse model of nociception. We first confirmed that the overall LFP power in frequency bands lower than the slow gamma range decreased in most brain regions following acetic acid injection when analyzed over a long time window (5 min). However, discriminant analysis with a one-dimensional threshold of LFP power changes within a shorter time window (5 s) could not accurately distinguish the acetic acid-induced LFP patterns. However, applying the AlexNet convolutional neural network to LFP powers, particularly when integrating data from all recorded brain regions, yielded an improved classification performance of the LFP patterns. Furthermore, we demonstrated that the Transformer model, when applied to the original LFP time-series traces without conversion into LFP power, achieved superior classification accuracy compared with AlexNet when using LFP power datasets.\u003c/p\u003e \u003cp\u003ePain processing involves diverse brain regions; the sensory aspect is primarily represented in the S1 \u003csup\u003e16\u003c/sup\u003e, while the affective component is predominantly processed by the ACC and AMY \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Somatic pain has further been shown to alter brain LFP oscillations in the ACC, AMY, S1, and VTA in both rodents \u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and humans \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. We demonstrated that visceral pain induced significant reductions in the overall LFP power in the ACC, which correlated better with painful behaviors than with neuronal c-Fos expression. However, the accuracy of the power-based classification was substantially reduced when analyzed over short (5-s) time windows. This is presumably because the LFP power fluctuates rapidly over short periods, and is more sensitive to biological noise in these time periods. Through supervised machine learning using AlexNet, we demonstrated that integrating LFP powers across six frequency bands and further integrating LFP powers from eight brain regions significantly improved classification accuracy. These results confirmed the utility of machine learning approaches in the analysis of multidimensional EEG/LFP data from the brain. From a biological perspective, these results indicate that LFP patterns related to visceral pain are not merely evoked by single brain regions, but rather emerge from widespread neural networks across multiple brain areas \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition, the Transformer model provided a better classification accuracy than AlexNet. This superior performance of the Transformer is presumably because it is well-suited for analyzing sequential time-series data with intricate temporal dependencies, similar to its applications in natural language processing \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Although the internal structure of the Transformer model obscures the specific features or time points to which it attends, our results highlight its efficacy in analyzing neuronal signal data, including EEG and LFP signals, as well as in reading more intricate neuronal ensemble patterns related to emotions, stimulus responses, and higher-order cognitive functions in various animal models. Furthermore, a significant advantage of the Transformer is not only its superior classification performance, but also its ability to process raw LFP signals directly without requiring subjective conversion into LFP power. This capability facilitates the real-time decoding of recorded neuronal signals, which holds promise for practical applications in various functions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEthical approvals\u003c/h2\u003e \u003cp\u003e All experiments were approved by the Committee on Animal Experiments at Tohoku University (approval number: 2022 PhA-004). The experiments were performed in accordance with the NIH guidelines for the care and use of animals. This study followed ARRIVE guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnimals\u003c/h2\u003e \u003cp\u003eMale 8-to-10-week-old C57BL/6 mice (SLC, Shizuoka, Japan) with a preoperative weight of 20\u0026ndash;30 g were housed under conditions of controlled temperature and humidity (22\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026ordm;C, 55\u0026thinsp;\u0026plusmn;\u0026thinsp;5%) in a vivarium, maintained on a 12:12-h light/dark cycle (lights off from 8 am to 8 pm) with ad libitum access to food and water. All mice were housed individually. No mice were excluded from the analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAcetic acid writhing test\u003c/h2\u003e \u003cp\u003eAcetic writhing test was conducted as described \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. After mice were acclimatized in a Plexiglas chamber for 30 min, they were intraperitoneally injected with 10 ml/kg saline or 0.7% acetic acid. Abdominal constriction responses were visually counted for 40 min after the injection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImmunochemistry\u003c/h2\u003e \u003cp\u003eThe mice received an overdose of urethane 160 min after the injection, and they were perfused intracardially with cold 8% paraformaldehyde (PFA) in 25 mM PBS and decapitated. The brains were placed in 30% sucrose until equilibrated and coronally sectioned at a thickness of 50 \u0026micro;m. To obtain brain slices, the fixed samples were rinsed with PBS and then permeabilized in 100 mM PBS with 0.3% Triton X-100 and 5% serum bovine albumin at room temperature for 60 min. The slices were then incubated with a primary rabbit c-Fos antibody (1:5000; Cell Signaling Technology) in 100 mM PBS with 0.3% Triton X-100 and 5% serum bovine albumin for one overnight period at 4\u0026deg;C. After rinsing with PBS, they were then labeled with a secondary anti-rabbit IgG antibody Alexa 594 (1:500; Thermo Fisher Scientific) in 100 mM PBS with 0.3% Triton X-100 for 90 min. The samples were mounted using Fluoro-KEEPER Antifade Reagent with DAPI (Nacalai Tesque). Images were acquired using a fluorescence microscope (BZ-X800; Keyence, Osaka, Japan) with a water immersion objective lens (\u0026times;20, 0.75 NA). The c-Fos and DAPI positive cells were counted using an analysis application (BZ-H4C; Keyence, Osaka, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSurgery\u003c/h2\u003e \u003cp\u003eStandard surgical procedures were similar to those described previously \u003csup\u003e\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Mice were anesthetized with 1\u0026ndash;2% of isoflurane gas in air. Two electrocardiogram (ECG) electrodes (stainless steel wires; AS633, Cooner Wire Company) were sutured to tissue underneath the skin of the upper chest, and the animal was then fixed in a stereotaxic instrument with two ear bars and a nose clamp. Two incisions (1 cm) were made on both sides of the upper chest. First, two craniotomies were made; one covering the coordinates for the anterior cingulate cortex (ACC; 1.1 mm anterior and 0.2 mm lateral to the bregma), the prelimbic cortex (PL; 1.7 mm anterior and 0.2 mm lateral to the bregma), the infralimbic cortex (IL; 1.7 mm anterior and 0.2 mm lateral to the bregma), the nucleus accumbens (NAc; 0.8 mm anterior and 0.8 mm lateral to the bregma), the amygdala (AMY; 0.8 mm posterior and 3.0 mm lateral to the bregma), the primary somatosensory cortex (S1; 1.4 mm posterior and 2.1 mm lateral to the bregma), the thalamus (THL; 2.1 mm posterior and 1.3 mm lateral to the bregma), and the periaqueductal gray (PAG; 3.5 mm posterior and 0.2 mm lateral to the bregma). The electrode array was directly implanted into the cortical tissue in the left hemisphere with electrodes inserted 0.8 mm into S1, 2.0 mm into the ACC, 2.5 mm into the PL, 2.8 mm into the PAG, 2.9 mm into the IL, 3.0 mm into the THL, 4.4 mm into the NAc and AMY. Two electromyogram (EMG) electrodes were implanted into the dorsal neck area. For the olfactory bulb and cerebellum craniotomies, stainless steel screws were implanted on the skull attached to the brain surface, serving as Resp electrodes and ground/reference electrodes, respectively. Finally, all of the wires and the electrode array were secured to the skull using dental cement. After completing all surgical procedures, the anesthesia was terminated and the animals were spontaneously allowed to awake from the anesthesia. Following surgery, each animal was housed with free access to water and food, with daily observation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eElectrophysiological recording\u003c/h2\u003e \u003cp\u003eThe mouse was connected to the recording equipment via Cereplex M (Blackrock), a digitally programmable amplifier, which was placed close to the animal\u0026rsquo;s head. The output of the headstage was conducted to the Cereplex Direct recording system (Blackrock), a data acquisition system, via a lightweight multiwire tether and a commutator. For recording electrophysiological signals, the electrical interface board of the tetrode assembly was connected to a Cereplex M digital headstage (Blackrock Microsystems), and the digitized signals were transferred to a Cereplex Direct data acquisition system (Blackrock Microsystems). Electrical signals were sampled at 2 kHz and low-pass filtered at 500 Hz. In each mouse, recordings were performed for each 40 min before and after an acetic acid injection, termed pre- and post-periods, respectively. The animal\u0026rsquo;s moment-to-moment position was tracked at 15 Hz using a video camera attached to the ceiling. All recordings from a behavioral task were performed once so that all the tasks were novel for the mice and no duplications of samples were thus included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHistological analysis to confirm electrode placement\u003c/h2\u003e \u003cp\u003eThe mice were overdosed with isoflurane, perfused intracardially with 8% paraformaldehyde in phosphate-buffered saline (pH 7.4) and decapitated. After dissection, the brains were fixed overnight in 4% PFA and equilibrated with 20 and 30% sucrose in phosphate-buffered saline for an overnight each. Frozen coronal sections (50 \u0026micro;m) were cut using a microtome, and serial sections were mounted and processed for cresyl violet staining. For cresyl violet staining, the slices were rinsed in water, stained with cresyl violet, and coverslipped with Permount. The positions of all electrodes were confirmed by identifying the corresponding electrode tracks in histological tissue.\u003c/p\u003e \u003cp\u003eIn the two mice out of nine mice, electrodes were located in all eight regions. In the five mice, electrodes were located in seven regions except the IL, AMY or PAG. In the one mouse, electrodes were located in the six regions except the PL and IL. Of the one mouse, electrodes were located in the six regions except the NAc and PAG.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLFP power\u003c/h2\u003e \u003cp\u003eTo compute the time-frequency representation of the LFP power change with time, LFP signals were downsampled to 200 Hz and convolved using a Morlet\u0026rsquo;s wavelet family defined by a constant ratio of f\u003csub\u003e0\u003c/sub\u003e/σ\u003csub\u003ef\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;5, where f\u003csub\u003e0\u003c/sub\u003e represents the frequency of interest and σ\u003csub\u003ef\u003c/sub\u003e represents the bandwidth of the wavelet in the frequency domain chosen to ensure optimal time-frequency localization properties of the wavelet. The frequency bands were defined as follows: delta: 1\u0026ndash;4 Hz, theta: 5\u0026ndash;8 Hz, alpha: 9\u0026ndash;13 Hz, beta: 14\u0026ndash;30 Hz, slow gamma: 31\u0026ndash;49 Hz, and fast gamma: 51\u0026ndash;100 Hz.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of classification performance based on d-prime values\u003c/h2\u003e \u003cp\u003eLFP power or LFP downsampled traces were concatenated in each 5-s bin before (gray, pre) and after (red, post) acetic acid injection, yielding 480 pre- and post-bins, respectively. In the classification analyses, \u003cem\u003ed\u003c/em\u003e-prime values were employed as a measure to assess how accurately our analysis could discriminate between the two conditions (pre- and post-periods). The numbers of hit bins (\u003cem\u003en\u003c/em\u003e\u003csub\u003ehit\u003c/sub\u003e, where both the prediction and the real data were post-bins), miss bins (\u003cem\u003en\u003c/em\u003e\u003csub\u003emiss\u003c/sub\u003e, where the prediction was a pre-bin, but the real data were post-bin), false alarm bins (\u003cem\u003en\u003c/em\u003e\u003csub\u003eFA\u003c/sub\u003e, where the prediction was a post-bin, but the real data were pre-bin), and correct rejection bins (\u003cem\u003en\u003c/em\u003e\u003csub\u003eCR\u003c/sub\u003e, where both the prediction and the real data were pre-bins) were computed. The hit rate (\u003cem\u003edHit\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003en\u003c/em\u003e\u003csub\u003ehit\u003c/sub\u003e/(\u003cem\u003en\u003c/em\u003e\u003csub\u003ehit\u003c/sub\u003e+\u003cem\u003en\u003c/em\u003e\u003csub\u003emiss\u003c/sub\u003e)) and false alarm rate (\u003cem\u003epFA\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003en\u003c/em\u003e\u003csub\u003eFA\u003c/sub\u003e/(\u003cem\u003en\u003c/em\u003e\u003csub\u003eFA\u003c/sub\u003e+\u003cem\u003en\u003c/em\u003e\u003csub\u003eCR\u003c/sub\u003e)) were further computed, while the \u003cem\u003ed\u003c/em\u003e-prime value was calculated as \u003cem\u003eZ\u003c/em\u003e(\u003cem\u003edHit\u003c/em\u003e) \u0026times; \u003cem\u003eZ\u003c/em\u003e(\u003cem\u003epFA\u003c/em\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eClassification based on a discriminant threshold on LFP power\u003c/h2\u003e \u003cp\u003eThe LFP power from all bins was classified as either pre- or post-bins by setting a discrimination threshold. Classification was further performed using different discrimination thresholds ranging from zero to maximum power, while the threshold yielding the highest \u003cem\u003ed\u003c/em\u003e-prime value was selected as the final threshold for classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eClassification based on the AelxNet\u003c/h2\u003e \u003cp\u003eAlexNet, a convolutional neural network (CNN) primarily employed for image recognition and classification tasks\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, was adapted to analyze the LFP power datasets or LFP traces by replacing the classification layer. This adaptation involved the application of transfer learning in the customized model. The architecture of the modified CNN comprised five convolutional layers, each followed by a local response normalization layer, a rectified linear unit (ReLU) activation layer, and a max-pooling layer. A fully-connected layer complemented the configuration. Detailed specifications of each layer of the CNN are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTo evaluate the performance and robustness of AlexNet, we employed stratified ten-fold cross-validation during the training procedure. In each fold, all bins were randomly split into training and test datasets at a 9:1 ratio, such that 432 bins were used for training and 48 bins for testing. This cross-validation process was repeated ten times, each time with different training and test dataset splits. The \u003cem\u003ed\u003c/em\u003e-prime value was computed for each fold, and all values from 10 folds were averaged for each mouse.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eClassification based on the Transformer\u003c/h2\u003e \u003cp\u003eThe downsampled LFP traces from all brain regions were concatenated into 5-s bins and directly input into the vision Transformer (Python 3.10.12 and TensorFlow 2.14.0) using Emotion-Recognition-Transformers \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. A learning rate of 0.00001 was used in the study. Patch embedding was applied with a shape of [1, patch size]. The performance of the Transformer was evaluated using \u003cem\u003ed\u003c/em\u003e-prime values obtained from stratified ten-fold cross-validation, similar to the method used for AlexNet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eAll data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM, and were analyzed using MATLAB. Normally distributed data are displayed as the sample mean and SEM with individual data points. Comparisons of two-sample data were performed using paired or Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-tests. Multiple group comparisons were performed using post-hoc Bonferroni correction. Pearson\u0026rsquo;s correlation coefficients were used to statistically evaluate correlations. The null hypothesis was rejected at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.K. and T.S. designed the study. T.K. acquired the biological and electrophysiological data. T.K., A.T., and T.X. performed the analyses and prepared the figures. K.T. and K.K. surpervised the project. T.K. and T.S. wrote the main manuscript text, and all the authors reviewed the main manuscript text.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by KAKENHI (21H05243) from the Japan Society for the Promotion of Science (JSPS), a grant (JP21zf0127004) from the Japan Agency for Medical Research and Development (AMED), a grant (JPMJCR21P1) from the Japan Science and Technology Agency (JST) to T. Sasaki; and a grant (JPMJMS2292) from the JST to K. Kitajo, K. Tsutsui, and T. Sasaki; and a JSPS Research Fellowship (23KJ0084) to T. Kayama.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBuzsaki, G. 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J Pharmacol Sci 139, 346\u0026ndash;351, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jphs.2019.02.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jphs.2019.02.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"visceral pain, electrophysiological recordings, machine learning, Transformer","lastPublishedDoi":"10.21203/rs.3.rs-4677672/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4677672/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNeuronal ensemble activity entrained by local field potential (LFP) patterns underlies a variety of brain functions, including emotion, cognition, and pain perception. Recent advances in machine learning approaches may enable more effective methods for analyzing LFP patterns across multiple brain areas than conventional time-frequency analysis. In this study, we tested the performance of two machine learning algorithms, AlexNet and the Transformer models, to classify LFP patterns in eight pain-related brain regions before and during acetic acid-induced visceral pain behaviors. Over short time windows lasting several seconds, applying AlexNet to LFP power datasets, but not to raw time-series LFP traces from multiple brain areas, successfully achieved superior classification performance compared with simple LFP power analysis. Furthermore, applying the Transformer directly to the raw LFP traces achieved significantly superior classification performance than AlexNet when using LFP power datasets. These results demonstrate the utility of the Transformer in the analysis of neurophysiological signals, and pave the way for its future applications in the decoding of more complex neuronal activity patterns.\u003c/p\u003e","manuscriptTitle":"Transformer-based classification of visceral pain-related local field potential patterns in the brain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-29 15:29:34","doi":"10.21203/rs.3.rs-4677672/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-09T04:26:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-22T17:41:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-20T13:46:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29739722732037145893759011136014805376","date":"2024-07-10T12:52:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206186068174046024277906959756184789298","date":"2024-07-10T09:38:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-10T01:10:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-10T01:03:47+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-04T10:26:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-04T10:22:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-03T05:05:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f401ec07-9299-4afb-9826-a06d34fbee39","owner":[],"postedDate":"July 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34968414,"name":"Biological sciences/Physiology/Neurophysiology"},{"id":34968415,"name":"Biological sciences/Neuroscience/Computational neuroscience/Neural decoding"},{"id":34968416,"name":"Biological sciences/Neuroscience/Somatosensory system/Pain"}],"tags":[],"updatedAt":"2024-10-21T16:08:22+00:00","versionOfRecord":{"articleIdentity":"rs-4677672","link":"https://doi.org/10.1038/s41598-024-75616-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-10-17 15:57:45","publishedOnDateReadable":"October 17th, 2024"},"versionCreatedAt":"2024-07-29 15:29:34","video":"","vorDoi":"10.1038/s41598-024-75616-6","vorDoiUrl":"https://doi.org/10.1038/s41598-024-75616-6","workflowStages":[]},"version":"v1","identity":"rs-4677672","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4677672","identity":"rs-4677672","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}