Mid-infrared photons relieve tinnitus-like behavior of mice by regulating HCN2 ion channels in the medial geniculate body

Xinmiao Xue; Peng Liu; Chi Zhang; Zhixin Zhang; Hanwen Zhou; Zhiwei Ding; Li Wang; Yuke Jiang; Shuhan Lu; Weidong Shen; Xiaoming Li; Lan Sun; Xiaoxia Jiang; Shiming Yang; Fangyuan Wang

doi:10.1080/07853890.2025.2584694

. 2025 Nov 10;57(1):2584694. doi: 10.1080/07853890.2025.2584694

Mid-infrared photons relieve tinnitus-like behavior of mice by regulating HCN2 ion channels in the medial geniculate body

Xinmiao Xue ^a,^b,^c,^d,^e,^#, Peng Liu ^f,^#, Chi Zhang ^a,^b,^c,^d,^e, Zhixin Zhang ^a,^b,^c,^d,^e, Hanwen Zhou ^a,^b,^c,^d,^e, Zhiwei Ding ^a,^b,^c,^d,^e, Li Wang ^a,^b,^c,^d,^e, Yuke Jiang ^a,^b,^c,^d,^e, Shuhan Lu ^a,^b,^c,^d,^e, Weidong Shen ^a,^b,^c,^d,^e, Xiaoming Li ^f, Lan Sun ^g,^✉, Xiaoxia Jiang ^h,^✉, Shiming Yang ^a,^b,^c,^d,^e,^✉, Fangyuan Wang ^a,^b,^c,^d,^e,^✉

PMCID: PMC12604138 PMID: 41208363

Abstract

Background

Subjective tinnitus is an auditory perception occurring without an external sound source. The medial geniculate body (MGB) plays a critical role in tinnitus pathology. The order of this study is to investigate whether mid-infrared (MIR) modulation of the MGB can mitigate tinnitus-like behavior in mice.

Methods

RNA sequencing was employed to analyze and compare gene expression levels in the MGB of mice with tinnitus to those of control mice. Golgi staining and transmission electron microscopy were used to confirm alterations in the structural plasticity of neurons. The whole-cell patch-clamp technique was applied to assess changes in neuronal functional plasticity. MIR optical fibers were employed to modulate MGB neuronal activity. Molecular dynamics simulations were performed to investigate the regulatory effect of MIR on hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channel function.

Results

The results of this study illustrates that MIR modulation reversed the abnormal electrophysiological properties of neurons associated with tinnitus-like behavior. Furthermore, molecular dynamics simulations revealed that MIR regulates HCN channel function, reducing the increased firing frequency of MGB neurons in tinnitus mice.

Conclusions

MIR intervention may alleviate the abnormal increase in neuronal firing frequency in the MGB of tinnitus mice by modulating HCN channel function. This regulatory mechanism involves influencing the secondary structure of HCN channels, enhancing hyperpolarization-activated current amplitude, and restoring reduced amplitudes observed in tinnitus mice.

Keywords: Otolaryngology, auditory health, physical therapy, light therapy, neuronal plasticity, whole-cell patch clamp, synaptic ultrastructure

Introduction

Subjective tinnitus is a pathological condition characterized by the perception of phantom sounds in the ears without any identifiable external source. This condition can lead to a variety of problems, including sleep deprivation, difficulty concentrating, and psychological distress [1]. Epidemiological studies indicate that approximately 14% of adults worldwide experience tinnitus symptoms [2]. The primary risk factors for tinnitus include hearing loss, anxiety, depression, sleep disorders, head and facial injuries, and the use of ototoxic medications [3,4].

The most widely accepted theoretical model for tinnitus development is the central gain mechanism hypothesis [5]. This model proposes that a decrease in auditory input (typically resulting from hearing loss) triggers an elevation in central auditory pathway gain. Specifically, neurons that experience reduced input compensate by amplifying their responsiveness to any given input strength, thereby restoring their baseline activity levels [6]. More precisely, when input signals to the auditory nerve are diminished, this leads to reduced activation of inhibitory interneurons within the cochlear nucleus. This reduction in inhibitory input results in increased excitability of projection neurons in the cochlear nucleus. The heightened excitability is then transmitted to higher auditory nuclei, including the inferior colliculus, medial geniculate body (MGB), and auditory cortex. When this compensatory increase in neural excitability becomes dysregulated, tinnitus symptoms may emerge [7]. Additionally, some researchers propose that the endogenous noise cancellation mechanism may play a role in tinnitus onset. Normally, abnormal central plasticity changes caused by hearing loss are mitigated through feedback connections between the limbic regions and the auditory pathway at the MGB level [8,9]. However, dysfunction in these regions can disrupt the endogenous noise cancellation mechanism, allowing tinnitus signals to propagate from the MGB to the auditory cortex. This disruption may lead to a permanent reorganization of auditory cortex function [10]. Moreover, Knipper et al. put forward the hypothesis that tinnitus arises following the stimulation of relevant synapse formation and network co-activation by fast auditory fiber activity. This particular activity serves to enhance inhibitory processes within specific neural circuits. However, noise exposure results in the loss of such activity, which subsequently causes a reduction in the sustained activity of parvalbumin-positive (PV⁺) interneurons. This, in turn, triggers a range of abnormalities that have been previously documented in tinnitus models [11]. Additionally, they have identified diminished evoked blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) responses in the medial geniculate body (MGB) as one of the most prominent functional biomarkers for tinnitus patients [12].

It is clear that, regardless of which of the aforementioned model classes is taken into account, the MGB is involved via either the classical or non-classical auditory pathway [13]. Furthermore, tinnitus models such as thalamocortical dysrhythmia suggest that compensatory plastic changes related to tinnitus may occur within MGB-associated networks [14]. The MGB receives ascending inputs from both lemniscal and extra-lemniscal pathways that project to auditory cortical and limbic centers, while also receiving descending inputs from reticular, limbic, auditory, and non-auditory cortices [15–19]. Given its distinctive position and role in integrating and processing multimodal upstream and downstream signals, it is reasonable to hypothesize that the MGB plays a key role in tinnitus pathology [20]. In previous studies, MGB neurons in rats and guinea pigs exhibiting behavioral signs of tinnitus following noise-induced damage demonstrated increased spontaneous firing [21,22]. Similarly, a study conducted on tinnitus patients reported an increase in gray matter concentration in the posterior thalamus (including the MGB) [23], although a subsequent independent study found no observe changes in gray matter concentration at the thalamic level [24].

The modulation of neuronal excitability is primarily linked to various ion channels in the cell membrane. Among these, hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are strongly associated with neuronal excitability and firing frequency. HCN channels are activated by membrane hyperpolarization and permit the passage of mixed Na+/K⁺ currents (Ih), thereby contributing to pacemaker depolarization and generating rhythmic activity in spontaneously firing neurons [25]. Studies on tinnitus animal models induced by salicylate have reported significant differences in Ih amplitude in MGB neurons between tinnitus and non-tinnitus groups, suggesting that HCN ion channels may be involved in tinnitus pathogenesis [26]. However, in animal models of noise-induced tinnitus, it has not yet been explored whether tinnitus-like behavior affects the Ih amplitude of neurons in the MGB nucleus. Furthermore, previous studies have reported that salicylate-induced tinnitus and noise trauma-induced tinnitus are not based on the same neurophysiological mechanisms [27].

The nervous system demonstrates notable sensitivity to an array of physical-stimulation-based neuromodulation techniques, including magnetic, electrical and optical stimulation, due to its distinctive electrical conduction characteristics. Among these methods, magnetic stimulation, as represented by transcranial magnetic stimulation (TMS), encounters two major limitations—high attenuation and imprecise localization—when it comes to stimulating deep brain nuclei, such as the MGB [28]. Conversely, research utilizing deep brain stimulation (DBS) targeted at the MGB for modulating tinnitus behavior has revealed its potential to alleviate tinnitus symptoms to a certain degree [29,30]. However, to date, despite the growing attention that optical infrared neural stimulation has garnered within the field of neuromodulation due to its promising potential for clinical applications [31], there remains a notable absence of documented reports specifically addressing the use of optical infrared for modulating the MGB with the objective of alleviating tinnitus symptoms. Here, we introduce a fundamentally different energy stimulation method, mid-infrared (MIR) modulation, which transmits MIR photons—electromagnetic waves with frequencies ranging from 12 to 120 THz—to influence biological processes [32].

Our previous research demonstrated that MIR stimulation of the auditory cortex in tinnitus mice regulated tinnitus-like behaviors [33]. Currently, it remains unclear whether the MGB can be targeted for MIR modulation to alleviate tinnitus-like behavior. Therefore, this study aims to evaluate whether MIR stimulation of the MGB can effectively reduce tinnitus-like behaviors in mice. Concurrently, we will evaluate whether there are excitability-increasing plastic changes in MGB neurons, and assess whether MIR modulation can reverse these abnormal plastic alterations. Additionally, we will compare the current amplitudes of HCN ion channels in MGB neurons between tinnitus-induced mice and control mice to identify any significant differences. Furthermore, we will assess whether MIR modulation can restore the current amplitudes of HCN ion channels to their normal levels.

Methods

Animals

Male C57BL/6J mice, aged 8 weeks (approximately two months), were procured from SPF (Beijing) Biotechnology Co., Ltd., located in Beijing, China. The number of animals used in the different sub-experiments illustrated in Table S1. All experimental animals in this study were maintained under specific-pathogen-free (SPF) conditions, with a strict 12-hour light/dark cycle, and provided ad libitum access to standard rodent chow and water. All procedures adhered to the guidelines for the Care and Use of Laboratory Animals set by the Chinese PLA General Hospital (Beijing, China) and received approval from the hospital’s Animal Care Committee (SQ2021284). The study adhered to the ARRIVE guidelines.

Noise exposure

The mice were randomly assigned to either the noise exposure group or the sham noise exposure group by the SPSS software. After anesthetizing all mice with 1% to 1.5% isoflurane, earplugs were inserted unilaterally into the right ears to provide maximum hearing protection for gap detection analysis [34]. Following this, mice in the noise exposure group were subjected to 1 h of narrowband noise centered at 16 kHz with a sound pressure level of 116 dB HL [35]. The sham noise exposure group underwent the identical process as the noise group but was not exposed to noise. No animal was excluded during the whole study. The loud noise was produced with a precision attenuator (PA5, TDT, Alachua, FL, USA) and then amplified via an MF-1201 MOSFET amplifier (A-Tech). The noise intensity was meticulously adjusted and measured with a sound level meter (Brüel & Kjær model 2250, Denmark), accompanied by a 4-channel preamplifier (RA4PA, TDT) and a condenser microphone (RA4LI, TDT).

Detection of tinnitus-like behavior in mice

To assess whether the mice exhibited tinnitus-like behavior following noise exposure, we conducted gap-induced pre-pulse inhibition of the acoustic startle response (GPIAS) and pre-pulse inhibition (PPI) experiments both before and one week after noise exposure. These experiments were performed using Xeye hardware and software (Beijing, China).

The GPIAS experiment consisted of two types of trials: no-gap trials and gap trials. In gap trials, a 50-millisecond (ms) gap without background noise was presented 130 ms before the startle sound. In contrast, no-gap trials involved continuous background noise both before and after the startle sound (Figure 1A). Narrowband sounds with center frequencies of 10, 12, 16, 20, 24, and 32 kHz, with a bandwidth of 1 kHz, were used as background noise at an intensity of 70 dB SPL. The startle sound was white noise at an intensity of 104 dB SPL, lasting 20 ms. During the experiment, the animals were positioned on a platform inside a soundproof enclosure, which had a piezoelectric transducer installed underneath its floor. The pressure signals generated by the mice’s startle responses were recorded to measure the amplitude of the acoustic startle response. The gap detection ability of the mice was assessed by calculating the GPIAS ratio, defined as the peak-to-peak value of the startle waveform in the gap trial divided by the peak-to-peak value of the startle waveform in the no-gap trial. Animals in both sham noise exposure and noise exposure groups were interspersed for experiments to exclude confounding factors caused by the tested order. A mouse was identified as exhibiting tinnitus-like behavior and assigned to the tinnitus group if its GPIAS ratio increased by ≥0.3 at least once across the detection frequencies when comparing pre- and post-noise exposure values [34,35]. Mice failing to meet this criterion were categorized into the non-tinnitus group. Meanwhile, mice exposed to sham noise were included in the control group. The PPI experiment is a reverse version of the GPIAS experiment, designed to rule out potential confounding factors such as temporal processing impairment or an inability to perceive background sounds, both of which could influence the GPIAS ratio. The PPI test consists of two types of trials: the startle-only trial and the pre-pulse trial. The key difference between these trials is that the pre-pulse trial includes a pre-pulse presented 130 ms before the onset of the startle sound, whereas the startle-only trial consists solely of a 20 ms white noise stimulus (Figure 1B).

Figure 1. — Establishment and validation of tinnitus-like behavior in mice. (A) (Left) Diagram illustrating the gap detection protocol (gap trial: a startle sound stimulus preceded by a constant background sound; no-gap trial: a startle sound stimulus preceded by a constant background sound with a brief gap). (Right) Startle responses elicited by the gap detection protocol were recorded as a downward pressing force on a mechanical platform. (B) (Left) Diagram illustrating the PPI protocol; (Right) startle responses elicited by a loud sound (startle-only trial) or by a loud sound preceded by a brief non-startling sound (pre-pulse trial). (C₁–E₁) Summary graphs of the GPIAS ratio (response to gap trial/response to no-gap trial) for six different frequency background sounds in (C₁) control, (D₁) non-tinnitus, and (E₁) tinnitus groups (control: n = 20, non-tinnitus: n = 19, tinnitus: n = 18). Blue bars represent the GPIAS ratio before sham or noise exposure; orange bars represent the GPIAS ratio one week after noise exposure. (C₂–E₂) Summary graphs of the PPI ratio (1 – response to pre-pulse trial/response to startle-only trial) at different frequency pre-pulse sounds for (C₂) control, (D₂) non-tinnitus, and (E₂) tinnitus groups. Error bars represent SEM. (N = 18 to 20 mice, ns indicates p > 0.05, *indicates p < 0.05, ***indicates p < 0.001).

The intensity and center frequency of the pre-pulse sound matched the parameters of the background noise used in the GPIAS experiment. The PPI ratio, which evaluates the PPI response, is defined as: 1 – (the peak-to-peak value of the startle waveform in the pre-pulse trial divided by the peak-to-peak value of the startle waveform in the startle-only trial).

Since three animals died due to anesthesia during the noise-exposure process, the pre-noise baseline GPIAS ratio and PPI ratio data of these three mice were excluded from the analysis.

RNA-seq

One week following exposure to noise, the mice were anesthetized with a 1.5% concentration of isoflurane. In a state of profound anesthesia, their brains were surgically excised, and tissue samples were harvested from both the control group and the tinnitus group, with a sample size of three mice per group. These samples were immediately immersed in TRIzol reagent sourced from Thermo Fisher Scientific for preservation. Subsequently, the RNA extracted from these samples underwent rigorous quality and concentration assessment using a NanoDrop 2000 spectrophotometer manufactured by Thermo Fisher Scientific. Additionally, the integrity of the RNA was meticulously evaluated using an RNA Nano 6000 Assay Kit on an Agilent Bioanalyzer 2100 system. Library preparation, clustering, and sequencing were carried out utilizing the advanced Illumina NovaSeq 6000 platform. The raw sequencing outputs were meticulously processed using proprietary Perl scripts, specifically designed to filter out reads containing poly-N stretches, adapters, and those of subpar quality, ultimately resulting in high-quality cleaned data. Quality control assessments were conducted, encompassing metrics such as Q20 and Q30 percentages, GC content, and sequence duplication levels. Following this, comprehensive downstream analyses were performed on these high-quality, cleaned datasets.

Bioinformatics analysis

Differential expression analysis of tissue samples from MGB was conducted using the EdgeR statistical package. Significant differentially expressed genes (DEGs) were identified based on stringent criteria, including a false discovery rate (FDR) of ≤ 0.05 and a fold change of ≥ 1.5. For gene ontology (GO) enrichment analysis, the GOseq R package was used, while KEGG pathway enrichment analysis was conducted with KOBAS software. A volcano plot was generated using the online platform https://www.bioinformatics.com.cn for data visualization and analysis [36]. The construction of the protein-protein interaction network was achieved by querying the STRING database (http://stringdb.org/) with the list of DEGs [37]. A threshold of 0.400 for the interaction score, representing a medium level of confidence, was used to determine significant protein-protein interactions [38]. Using the Cytoscape software, the genes with the highest connectivity within the PPI network were ranked according to their degree values [39].

Immunocytochemistry

To stain the tissues, mice were first anesthetized with 1.5% isoflurane and subsequently perfused with chilled 0.9% saline, followed by fixation with 4% paraformaldehyde. The extracted brains were post-fixed in the same fixative solution for 12 h and then sequentially immersed in 20% sucrose for 24 h and 30% sucrose for another 24 h. Using a cryotome, brain slices of 30 μm thickness, encompassing the MGB, were prepared and pre-treated with 5% donkey serum and 0.3% Triton X-100 for 2 h at room temperature. These slices were then incubated overnight at 4 °C with primary antibodies: rabbit anti-NeuN (dilution 1:500, Abcam, cat. no. ab104225), guinea pig anti-c-Fos (dilution 1:500, Synaptic Systems, cat. no. 226308), and rabbit anti-HCN2 (dilution 1:400, GeneTex, cat. no. GTX54824). Following primary antibody incubation, the tissue sections were stained with secondary antibodies: Alexa Fluor 488-conjugated donkey anti-rabbit IgG (dilution 1:500, Invitrogen, cat. no. R37118) and Alexa Fluor 594-conjugated goat anti-guinea pig IgG (dilution 1:500, Thermo Fisher Scientific, cat. no. A-11076). Finally, the stained slices were examined and imaged under a fluorescence confocal microscope (LEICA DMIL) to identify neurons.

Golgi staining

Mice were euthanized using isoflurane, after which their brains were promptly extracted. The brains were carefully rinsed multiple times with 0.9% saline solution (NaCl) and then immersed in Golgi–Cox staining solution (Servicebio, Wuhan, China). They were subsequently stored in a cool, well-ventilated, and dark environment for 14 days. Following the incubation period, the brains were rinsed three times with distilled water and treated overnight with 80% glacial acetic acid until the tissue softened. The brains were then placed in a 30% sucrose solution. Sections (100 µm thick) were obtained using a vibratome (LEICA, VT1000S). The slides were dried overnight in the dark on a gelatin-coated surface. After treatment with ammonia water and an acid-hardening fixation solution, the slides were dried again and subsequently sealed using glycerin gelatin. Golgi-stained neurons were observed using a Nikon Eclipse E100 microscope, and panoramic images of brain tissue were captured using the Pannoramic 250 multi-layer scanning system with a digital slice scanner. For Sholl analysis, two neurons per animal (3 animals per group) were selected. Concentric circles spaced 10 µm apart, centered around the cell body, were overlaid on the images using ImageJ software. The number of dendrite intersections with these circles was recorded [40,41]. The total dendritic length was quantified using the NeuroJ plugin in ImageJ software.

Transmission electron microscopy (TEM) study

Samples for TEM analysis were prepared following established protocols [42]. In brief, the mice were rendered unconscious through anesthesia and subsequently perfused with a 0.9% saline solution (NaCl), after which they were perfused with 4% paraformaldehyde. The brains were carefully dissected, and tissue blocks (<1 mm³) containing the MGB (coordinates: approximately bregma −2.80 mm to −3.60 mm) were obtained. The blocks were then post-fixed in 2.5% glutaraldehyde (Servicebio, Wuhan, China). The tissue samples were then immersed in 1% osmium tetroxide (sourced from Ted Pella, Inc., located in Redding, California), which was dissolved in phosphate buffer. Following dehydration through a series of increasingly concentrated ethanol and acetone solutions, the tissues were encapsulated in resin. To prevent redundant measurements of identical cells or features, sections that were not consecutive and measured 70 nm in thickness were obtained using an ultramicrotome (model Ultracut UCT, manufactured by Leica Microsystems in Germany) fitted with a diamond knife sourced from Diatome (Hatfield, PA). Each grid was mounted with three of these sections.

Three grids were randomly prepared and stained with 4% uranyl acetate and 0.4% lead citrate. Synaptic images were captured using a Hitachi 7800 electron microscope at a magnification of ×30,000. For each animal (n = 3 per group), four synaptic images were captured at ×30,000 magnification. A detailed analysis of the synapses was conducted as previously described to assess various structural parameters, including the thickness and area of the postsynaptic density (PSD), the width of the synaptic cleft, the curvature of the synaptic interface, and the length of the synaptic active zone [43,44].

Cannula implantation and virus injection

Mice were sedated with isoflurane and positioned in a stereotaxic apparatus (RWD Life Science, Wuhan, China). The MGB was targeted using stereotaxic coordinates based on the mouse brain atlas (relative to bregma: anterior −3.8 mm, medial-lateral 2.0 mm, and dorsal-ventral 2.8 mm) [45]. Surgically, a guide cannula provided by RWD Life Science was implanted into the right MGB and firmly attached to the skull with the aid of dental acrylic and skull screws. Following the surgery, the mice were given at least 5 days to recuperate before proceeding with subsequent experimental procedures. For viral injections, a Hamilton microsyringe was used to deliver 200 nL of viral constructs (AAV-hSyn-EGFP or AAV-hSyn-hM4Di-EGFP from RWD Life Science, Wuhan, China; AAV-shRNA-scramble-GFP or AAV-shRNA-HCN2-GFP from Genomeditech, Shanghai, China) into the right MGB at a rate of 20 nL/min. After the injection, the needle remained in position for 10 min to ensure sufficient viral dissemination before being gently removed. A week thereafter, brain sections were examined to confirm the efficacy of viral expression. Prior to any further experimentation, mice were allowed at least three weeks to recuperate. For chemogenetic activation, mice that had undergone microinjection with either AAV-hSyn-hM4Di-EGFP or AAV-hSyn-EGFP three weeks prior received an intraperitoneal dose of 0.5 mg/kg of Clozapine N-oxide (CNO).

Mid-infrared source

All surgical procedures were carried out following the protocols outlined in our previously published research [33]. For in vivo irradiation, we utilized a quantum cascade laser emitting in the MIR region, with a fixed wavelength of 8.8 micrometers. The MIR fiber featured a core diameter of 240 micrometers, plus or minus 15 micrometers, a numerical aperture of 0.3, with a tolerance of 0.03, and a consistent output power of 7.5 milliwatts, with a variation of 0.5 milliwatts. The laser settings included a pulse duration of 2 μs, a repetition frequency of 200 kHz, and a duty cycle of 40%. The MIR fiber was precisely aligned to the MGB using a pre-implanted cannula and delivered irradiation for 10 min per day at the same time for three consecutive days. For in vitro irradiation, the protocol closely followed the in vivo procedure. The cell culture dish was mounted onto a software-directed motorized stage, traveling at a velocity of 0.2 millimeters per second, with an exposure duration of 2 s per scanning location. Subsequent to irradiation, the cell culture dishes underwent a 24-hour incubation period prior to advancing to subsequent experimental procedures.

Preparation of brain slices

Initially, mice were anesthetized using a 1.5% concentration of isoflurane and subsequently underwent perfusion with pre-oxygenated artificial cerebrospinal fluid that had been chilled to ice-cold temperatures. This fluid was composed of 230 mM sucrose, 2.5 mM KCl, 1.22 mM Na₂HPO₄, 24 mM NaHCO₃, 10 mM MgSO₄, 0.5 mM CaCl₂, 10 mM glucose, and 2 mM sodium pyruvate, with the pH carefully maintained between 7.3 and 7.4. Before use, the solution was oxygenated for one hour with a 95% O₂/5% CO₂ gas mixture [33]. The whole brain was carefully excised, and coronal brain slices, each 300 μm thick, containing the MGB were prepared according to the Allen Brain Atlas guidelines [46]. The sections were cut using a vibratome (VT1000S, Leica Microsystems, Deerfield, IL, USA) and subsequently used for electrophysiological recordings. The tissue sections were subsequently incubated for one hour at 33 °C in a recovery solution comprising of 126 mM NaCl, 2.5 mM KCl, 1.25 mM Na₂HPO₄, 26 mM NaHCO₃, 2 mM MgCl₂, 2 mM CaCl₂, 10 mM glucose, and 2 mM sodium pyruvate, with the pH carefully maintained between 7.3 and 7.4.

Electrophysiological recordings of brain slices

Brain slices were positioned within an incubation chamber and immersed in a physiological recording solution that contained 115 mM NaCl, 5 mM KCl, 1.25 mM Na₂HPO₄, 10 mM glucose, 2 mM pyruvate, 25 mM NaHCO₃, 2 mM MgCl₂, and 2 mM CaCl₂, with the pH maintained between 7.3 and 7.4 at room temperature. This solution was continuously oxygenated with a 95% O₂ and 5% CO₂ gas mixture. MGB neurons were visualized through the application of infrared differential interference contrast (IR-DIC) microscopy. Patch pipettes, exhibiting resistances within the range of 5 to 7 megaohms (MΩ), were loaded with an intracellular solution consisting of 130 mM potassium gluconate, 10 mM KCl, 10 mM HEPES, 1 mM MgCl₂, 5 mM EGTA, 1 mM CaCl₂, 2 mM Na₂ATP, and 0.5 mM Na₃GTP, with the pH adjusted to 7.4 (all reagents obtained from Fluka, NY, USA). Whole-cell patch-clamp recordings were performed utilizing a MultiClamp 700B amplifier in conjunction with a Digidata 1440 A analog-to-digital converter. Data acquisition was meticulously managed and controlled by pClamp 10.2 software, developed by Axon Instruments (now Molecular Devices). The passive membrane properties recorded included resting membrane potential (RMP) and membrane capacitance (Cm). The rheobase, defined as the minimum current required to initiate an action potential (AP), was determined. The active properties of APs were assessed based on the first AP evoked by a stepwise increase in current (increments of 20 pA, ranging from 0 to 300 pA) over 500 ms. The voltage threshold was set at dV/dt = 10 mV/ms [47]. Spike amplitude was quantified as the voltage difference between the AP peak and threshold [47]. Additionally, the number of APs was recorded by gradually increasing the depolarizing step current in 20 pA increments from 20 to 400 pA over 500 ms. For voltage-clamp experiments, Ih currents were measured by holding the membrane at −60 mV, followed by hyperpolarizing voltage steps ranging from −60 mV to −120 mV in 10 mV decrements [48]. Input resistance (Rin) was evaluated by applying hyperpolarizing currents ranging from −200 pA to 0 pA in 20 mV decrements. Rin was calculated by fitting the current-voltage (I-V) relationship using Origin 8.0 software. Specifically, Rin was derived from the slope of the linear fit describing the correlation between the peak membrane potential change (ΔVm) and the injected current [49].

q-PCR

RNA extraction was performed following previously established protocols [50]. RNA was extracted using the RNA Extraction Reagent supplied by Servicebio (Wuhan, China) and subsequently reverse transcribed into complementary DNA (cDNA) employing the Servicebio RT First Strand cDNA Synthesis Kit. For quantitative polymerase chain reaction (qPCR) analysis, the reactions were conducted using the 2× SYBR Green qPCR Master Mix provided by Servicebio. The mRNA expression levels of the HCN2 gene were normalized to the expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA. The fold changes in gene expression were determined using the comparative CT (2 − ΔΔCT) method [51]. The oligonucleotide primer sequences (Servicebio) for HCN2 (NM_001194.4) were as follows: the forward primer was GAATTCGAAGTATACCTCGAGGCCA, and the reverse primer was ATGGTCTTTGTAGTCGGATCCCAGG.

Preparation of cultured cells

The PGMLV-CMV-MCS-3 × Flag-EF1-ZsGreen1-T2A-Puro vector and required packaging plasmids were obtained from JiMan (Shanghai, China). Primers were designed based on the HCN2 gene sequence (NM_001194.4) to construct an HCN2 overexpression viral vector. PCR amplification was conducted in a 50 μL reaction volume under the specified thermal cycling conditions: an initial denaturation step at 98 °C for 2 min, followed by 30 cycles comprising of denaturation at 98 °C for 10 s, annealing at 58 °C for 30 s, and extension at 72 °C for 2 min. A final extension step was performed at 72 °C for an additional 2 min to ensure complete extension of the amplified DNA fragments. After PCR completion, the amplified products were separated via agarose gel electrophoresis, and the target gene was extracted for further processing. For vector preparation, 1 μg of the pLV-SFFV-MCS-EF1-ZsGreen1-T2A-Puro plasmid was digested with Xba I and BamH I at 37 °C for 5 h. The linearized vector was then recombined with the target fragment using homologous recombination enzymes. Seamless cloning was performed to ligate the target fragment into the vector. To produce high-purity lentiviral vectors devoid of endotoxin contamination, the lentiviral vector and its corresponding packaging plasmids were co-transfected into HEK-293T cells utilizing the HG Transgene reagent. Approximately 10 to 12 h post-transfection, an enhancer buffer was introduced to the culture. Subsequently, after an additional 8-hour incubation period, the culture medium was replaced with fresh media to optimize lentivirus production. The cells were cultured for 48 h, after which the supernatant containing lentiviral particles was harvested, concentrated, and stored as a high-titer lentiviral stock solution. The viral titer was determined and calibrated in HEK-293 cells. The H_HCN2 lentivirus was then used to infect HEK-293 cells to generate a stable H_HCN2 HEK-293 strain. Puromycin (2 μg/mL) was used for selection, ensuring stable lentiviral cell line generation. Once stabilized, cells were maintained in complete culture medium composed of DMEM, 10% fetal bovine serum, 1% penicillin-streptomycin, and 0.75 μg/mL puromycin.

Electrophysiological recordings of cultured cells

For conducting electrophysiological recordings of cultured cells, a physiological recording solution maintained at pH 7.4 and composed of 160 mM NaCl, 2.5 mM KCl, 1 mM MgCl₂, 2 mM CaCl₂, and 10 mM glucose was employed. Following incubation within the recording chamber, transfected cells were identified under blue light illumination at 470 nm using a 40× objective lens from Olympus. Subsequently, an electrode filled with an internal solution consisting of 150 mM KCl, 5 mM MgCl₂, and 10 mM Hepes (adjusted to pH 7.4) was utilized to establish a seal with the fluorescently labeled cells. A step voltage protocol, ranging from −130 mV to −40 mV in increments of 10 mV, was applied to quantify the amplitude of the Ih current. The MIR fiber was positioned 300 μm from the recorded cell to capture Ih current both before and after the intervention.

Molecular dynamics simulations

Molecular dynamics simulations were conducted using the CHARMM force field and implemented through the GROMACS software suite, specifically version 2018 [52]. The model construction was facilitated by the CHARMM-GUI web server, which enabled the incorporation of proteins into DPPC membranes and the addition of TIP3 water molecules to the system. To simulate a physiological environment, 0.15 mmol of NaCl was added a [53]. To ensure computational precision, a spherical cut-off distance of 1.2 nanometers was imposed. The particle-mesh Ewald method was employed to compute van der Waals forces and short-range Coulombic interactions. Temperature regulation was achieved using the Nose-Hoover thermostat, while pressure control was maintained through the application of the Berendsen barostat [54]. Initially, energy minimization was conducted in small increments to stabilize initial molecular velocities. The system was then pre-equilibrated for 1 ns at 300 K using a leap-frog integrator in an NPT ensemble simulation, with a time step of 1.0 fs. Subsequently, an NVT ensemble simulation with a 2.0 fs time step was conducted at 300 K for 6 ns. This simulation was performed to analyze sodium ion movement across the membrane and assess the impact of terahertz wave intensities on protein secondary structure. Throughout the simulation, DSSP software was employed to track secondary structure alterations, and snapshot visualizations were generated using VMD [55].

Statistical analysis

All data were analyzed using GraphPad Prism software (version 9.0.1, San Diego, CA, USA). All data are presented as the mean ± standard error of the mean (SEM). Statistical comparisons of normally distributed data were performed using Student’s t-test. For analyses involving three or more groups, either a one-way or two-way analysis of variance (ANOVA) was conducted, as appropriate. A P-value < 0.05 was considered statistically significant, with a two-tailed test applied.

Results

Establishment and validation of tinnitus-like behavior in mice

In this study, a tinnitus animal model was established by evaluating the inhibition of an acoustic startle response through a silent gap embedded in continuous background noise. As illustrated in Figure 1A, the startle response in the gap trial was diminished in tinnitus mice, as the perceived tinnitus sound may fill the gap. Additionally, the PPI study, which serves as the inverse of GPIAS testing, demonstrated that a preceding startle pulse sound inhibits the startle response. And in the pre-pulse trial, the startle response did not exhibit a distinct difference between both the control mice and the tinnitus mice (Figure 1B). The results showed that no significant differences in GPIAS ratios were observed before and after sham or noise exposure in both the control (Figure 1C₁) and non-tinnitus (Figure 1D₁) groups across all tested frequencies. However, in tinnitus mice, a significant increase in the GPIAS ratio was observed at 10, 16, and 32 kHz (Figure 1E₁). Furthermore, across all three groups, there were no significant differences in the PPI ratio before and after noise exposure (Figure 1C₂, D₂, and E₂). Both the tinnitus and non-tinnitus groups exhibited similar auditory brainstem response (ABR) threshold shifts after noise exposure. Specifically, a marked elevation in ABR thresholds was observed at all frequencies in the left ears when comparing pre- and post-noise exposure, while effective hearing protection was maintained in the right ears of both groups (Figure S1A–D, Supporting Information). These results suggest that gap detection deficits in mice with tinnitus-like behavior are not attributable to temporal processing impairments or an inability to perceive background sounds.

Figure 2. — RNA-seq analysis of MGB between control and tinnitus groups. (A) Volcano plot of DEGs in the MGB. (B) The protein–protein interaction (PPI) network displaying hub genes ranked by degree value in the MGB. In the network, genes are represented by nodes, and interactions among them are indicated by edges. (C-E) GO enrichment analysis of DEGs, showing the top 10 functions in (C) biological processes, (D) cellular components, and (E) molecular functions. (F) KEGG pathway enrichment analysis of DEGs.

Analysis of the gene transcriptome in tinnitus mice

To investigate gene expression changes within the MGB of tinnitus and control mice, we performed RNA sequencing (RNA-seq) analysis. Differential expression analysis identified genes exhibiting a fold change > 1.5, resulting in the identification of 218 upregulated genes and 90 downregulated genes. The volcano plot (Figure 2A) visually represents the DEGs, where upregulated genes are displayed in orange, and downregulated genes are shown in blue. Additionally, the top 10 upregulated and downregulated genes in the MGB are listed in Table 1. Given the substantially higher number of upregulated genes compared to downregulated genes, we performed PPI network analysis and enrichment analysis specifically on the upregulated genes. Using Cytoscape software, the upregulated genes were ranked by their degree values, and the top 30 genes were visualized (Figure 2B). Among the upregulated genes, ten hub genes were identified, including Ppp1r1b, Sst, Egr1, Foxg1, Npy, Tac1, Drd2, Egr2, Gpr88, and Pde10a. The biological functions of the upregulated genes were assessed via GO enrichment analysis, with significantly enriched terms categorized into biological processes (BP), cellular components (CC), and molecular functions (Figure 2C–E). Additionally, the KEGG pathway enrichment analysis results are shown in Figure 2F. In the BP category, the top five enriched pathways were signal transduction, regulation of ion transmembrane transport, long-term memory, long-term synaptic potentiation, and regulation of dendritic spine morphogenesis. For the CC category, the top five enriched pathways included glutamatergic synapse, voltage-gated potassium channel complex, postsynaptic density, intracellular component, and post-synapse. The pathways enriched among upregulated genes were primarily associated with neuronal signaling and synaptic plasticity alterations. To further validate these findings, we conducted histomorphological and electrophysiological assessments of neurons.

Table 1.

The top ten upregulated and downregulated genes in MGB of tinnitus mice.

Upregulated genes	Log2FC	FDR	Downregulated genes	Log2FC	FDR
Gpr6	3.510615463	5.19E-10	Pmch	−3.678723298	0.195711213
Lrrc10b	3.505799578	3.80E-06	Pitx2	−2.813117154	8.42E-06
Ankrd63	3.024991384	4.33E-05	Lmx1b	−2.774396449	2.56E-06
Gm19410	2.958440913	1.41E-09	Slc6a3	−2.749983763	0.228897621
Sh3rf2	2.949519949	0.007113205	En1	−2.558294534	0.004242774
Cd4	2.931990485	7.36E-09	Rln3	−2.530971083	0.014012759
Drd1	2.915573333	6.86E-15	Zc3h11a	−2.454347247	0.081206893
Zbed6	2.88758631	4.87E-06	Th	−2.416097028	0.004238232
Egr2	2.767158465	4.34E-06	Chrna6	−2.338586201	6.26E-05
Ano2	2.693973858	9.83E-09	Pax7	−2.321777279	0.63434339

Open in a new tab

Morphological changes in the MGB neurons of mice with tinnitus-like behavior

To examine neuronal morphology in the MGB, we performed Golgi staining and observed significant structural changes in the MGB neurons of tinnitus mice, compared to control and non-tinnitus mice (Figure 3A–C). Using Sholl analysis, we identified a significant increase in dendritic branching in tinnitus mice (Figure 3D). Specifically, increased dendritic branching was observed both proximally (50–90 μm from the soma) and distally (220–270 μm from the soma), indicating enhanced neurite arborization in tinnitus mice. Additionally, Significant differences in total dendritic branching were observed among the control group, non-tinnitus group, and tinnitus group, as determined by one-way ANOVA (F(2, 15) = 4.633, p = 0.0271). Post-hoc Dunnett’s multiple comparisons test revealed that the total dendritic branching in tinnitus mice was significantly higher than that in control mice (Adjusted P Value = 0.0156). In contrast, no significant difference was detected between non-tinnitus mice and control mice (Adjusted P Value = 0.3548) (Figure 3E). Furthermore, significant differences in total dendrite lengths were identified among the control group, non-tinnitus group, and tinnitus group, as evidenced by one-way ANOVA analysis (F(2, 15) = 14.55, p = 0.0003). Post-hoc Dunnett’s multiple comparisons test indicated that there was no significant difference in total dendrite lengths between control and non-tinnitus mice (Adjusted P Value = 0.2308). However, a marked increase in total dendrite length was observed in tinnitus mice compared to control mice (Adjusted P Value = 0.0002) (Figure 3F).

Additionally, synaptic structures of neurons in the MGB were examined in all three groups using TEM, and Figure 3G presents representative images. The presynaptic membrane structure is shown in blue pseudo-color, while the postsynaptic membrane structure is depicted in orange pseudo-color. Arrows indicate the PSD, synaptic cleft, and vesicles.

Results showed that difference of the length of the active zone identified among the control group, non-tinnitus group, and tinnitus group, as evidenced by one-way ANOVA analysis (F (2, 33) = 59.80, p < 0.0001). was increased in tinnitus mice compared to control mice (Dunnett’s multiple comparisons test, Adjusted P Value <0.0001) (Figure 3H), 非耳鸣组与对照组相比则没有明显差异 (Adjusted P Value =0.0849) and both the thickness (one-way ANOVA analysis, F (2, 33) = 10.95, p = 0.0002, Adjusted P Value = 0.0002) and area of PSD (one-way ANOVA analysis,) were significantly larger in tinnitus mice compared to control mice (Figure 3I,J). These indices did not differ significantly between non-tinnitus and control mice. Additionally, there were no statistically significant variations in synaptic cleft distance or synaptic interface curvature among the three groups (Figure 3K,L).

Increased excitability of MGB neurons from tinnitus mice

To assess neuronal excitability in the MGB, immunofluorescence staining was performed in all three groups. NeuN was used to label neurons, while and c-Fos, a widely recognized molecular marker of neuronal activation [56], was used to examine changes in the number of activated (Figure 4A–C). Although no statistically meaningful differences were observed in the total number of neurons among the three groups (Figure 4D), tinnitus mice exhibited a notable increase in c-Fos⁺ neurons (Figure 4E).

Figure 4. — NeuN and c-Fos immunoreactivity in the MGB of mice across three experimental groups. (A–C) Low-magnification micrographs showing NeuN and c-Fos immunoreactivity in the control group (A₁), non-tinnitus group (B₁), and tinnitus group (C₁). (A₂–C₂) High-magnification micrographs of the areas marked by rectangles in A₁–C₁. Green fluorescence represents NeuN, red fluorescence represents c-Fos, and blue fluorescence represents DAPI. (D) Quantification of NeuN⁺ neuron counts in the MGB across the three groups. (E) Proportion of c-Fos⁺ neurons in slice samples from the three groups (data obtained from three mice, with two brain sections analyzed per mouse). (F) Representative traces of MGB neuron-evoked APs recorded from each experimental group. (G) Relationship between injected current and the count of action potentials among neurons across the three groups. (H) Number of evoked APs at 400 pA current in each experimental group. (I-L) Summary of passive membrane properties of MGB neurons across the three groups, including (I) rheobase current, (J) Cm, (K) RMP, and (L) Rin. (M-O) Summary of active membrane properties, including (M) spike amplitude, (N) spike threshold, and (O) difference between RMP and spike threshold. Error bars represent SEM. (N = 6 slices for immunofluorescent staining, N = 10 to 12 neurons for whole cell patch, ns, no significant difference, *p < 0.05, **p < 0.01, ***p < 0.001).

In our whole-cell patch-clamp experiment, we observed that the number of action potentials (spikes) induced by depolarizing current steps was significantly higher in MGB neurons from tinnitus mice compared to those from control and non-tinnitus mice (Figure 4F). The relationship between injected current and spike count is presented in Figure 4G, while Figure 4H shows the number of spikes at 400 pA for each experimental group. Additionally, the threshold current (rheobase) for AP generation was significantly lower in MGB neurons from tinnitus mice (Figure 4I). However, no significant changes were detected in Cm or RMP among the three groups (Figure 4J,K). In contrast, input resistance (Rin) was significantly increased in tinnitus mice (Figure 4L). In the AP waveform analysis, no notable variations were found in the amplitude of spikes (Figure 4M) or spike threshold (Figure 4N) among the three groups. However, compared to the other two groups, MGB neurons from tinnitus mice showed a reduced potential difference between their spike threshold and RMP (Figure 4O). These electrophysiological results demonstrate that the excitability of MGB neurons is elevated in tinnitus mice.

Chemogenetic modulation of MGB neuron activity and tinnitus-like behavior

To determine whether increased excitability of MGB neurons is causally linked to tinnitus-like behavior, we utilized chemogenetic techniques to inhibit MGB neuronal activity in tinnitus mice and assessed whether this intervention could alleviate their tinnitus-like symptoms. As illustrated in Figure 5A, tinnitus mice were identified based on their GPIAS and PPI ratios measured before and after noise exposure. The mice were subsequently divided at random into two groups. One group underwent a stereotaxic injection of an AAV vector carrying hSyn-hM4Di-EGFP, while the second group was injected with an AAV vector containing hSyn-EGFP into the MGB (Figure 5B). Following AAV viral injection, strong fluorescent expression was confirmed in the MGB using a fluorescent microscope (Figure 5C). The GPIAS ratios for both groups were documented both prior to and one week following noise exposure (Figure 5D,E), while the corresponding PPI ratios are presented in Figure 5F,G. After intraperitoneal injection of CNO, the GPIAS and PPI ratios were reassessed. The results indicated a notable decline in the GPIAS ratio at the tinnitus frequency in the hSyn-hM4Di group (Figure 5H), whereas the PPI ratio remained unchanged (Figure 5I). In contrast, the hSyn-EGFP group exhibited no significant changes in either the GPIAS or PPI ratios for the tinnitus frequency (Figure 5J,K). These findings suggest that inhibiting MGB neurons in tinnitus mice can effectively modulate tinnitus-like behaviors, supporting a causal link between MGB neuronal excitability and tinnitus.

Figure 5. — Chemogenetic modulation of MGB neurons regulates tinnitus-like behavior. (A) A visual representation outlining the experimental setup. (B) Illustration of viral injection into the MGB. (C) Fluorescence microscopy images showing viral expression in the MGB. (D-E) Summary graphs of GPIAS ratio across six different frequency background sounds for the hSyn-hM4Di group (D) and the hSyn-EGFP group (E). (F-G) Summary graphs of PPI ratio across six different frequency background sounds for the hSyn-hM4Di group (F) and the hSyn-EGFP group (G). (H) GPIAS ratios decreased after CNO injection in the hSyn-hM4Di group. (I) No significant differences in the PPI ratio corresponding to the tinnitus frequency before and after CNO injection in the hSyn-hM4Di group. (J-K) No significant differences in either the GPIAS ratio (J) or the PPI ratio (K) in the sham group before and after treatment in the hSyn-EGFP group. Error bars represent SEM. (N = 9 to 10 mice, ns, no significant difference, **p < 0.01).

MIR can alleviate tinnitus-like behavior in mice

To further investigate whether MIR stimulation has a regulatory effect on tinnitus-like behavior, we conducted MIR treatment in tinnitus mice. After selecting mice exhibiting tinnitus-like behavior, a metal cannula was surgically implanted into the right MGB nucleus (Figure 6A). Five days post-surgery, mice exhibiting tinnitus were divided at random into two groups: Sham group and MIR treatment group (Figure 6B). The GPIAS ratio and PPI ratio before and after noise exposure in both groups are shown in Figure 6C–F. An MIR fiber was used to deliver MIR photons to the MGB region, resembling the fiber-based optogenetics technique [57]. In the sham group, after anesthetizing the mice, the optical fiber was inserted through the cannula while blocking the light pathway (Figure 6B, upper), whereas in the MIR group, the optical fiber was inserted while keeping the light pathway open (Figure 6B, lower). Both groups underwent continuous treatment for three days, with each session lasting 10 min per day. After treatment, GPIAS and PPI ratios were re-evaluated in both groups. Results indicated that MIR treatment alleviated the increased GPIAS ratio at tinnitus frequencies (Figure 6G). However, there was no significant change in the PPI ratio corresponding to the tinnitus frequency before and after MIR stimulation (Figure 6H). Additionally, the sham group exhibited no significant differences in the GPIAS ratio (Figure 6I) or PPI ratio (Figure 6J) before and after treatment. In summary, MIR treatment alleviated tinnitus-like behaviors in mice. The temperature monitoring results indicate that the tissue temperature rise during the MIR regulation process is around 0.5 °C (Figure S3).

Figure 6. — MIR treatment relieves tinnitus-like behavior in mice. (A) Diagram illustrating the experimental procedure for selecting mice with tinnitus-like behavior and randomly assigning them to either the sham group or the MIR treatment group. (B) Schematic representation of treatment conditions: in the sham group, the light pathway was blocked (upper), while in the MIR group, the light pathway remained open (lower). (C-D) Summary graphs of the GPIAS ratio across six different frequency background sounds for the sham group (C) and the MIR group (D). (E–F) Summary graphs of the PPI ratio across six different frequency background sounds for the sham group (E) and the MIR group (F). (G) MIR treatment alleviates the increase in the GPIAS ratio at tinnitus frequencies. (H) No significant difference in the PPI ratio corresponding to the tinnitus frequency before and after MIR treatment. (M–N) No significant differences in the GPIAS ratio (M) or PPI ratio (N) in the sham group before and after treatment. Error bars represent SEM. (N = 9 to 10 mice, ns, no significant difference, **p < 0.01).

The inhibited effect of MIR on MGB neurons of tinnitus mice

Given that MGB neurons in tinnitus mice undergo morphological and electrophysiological plasticity, we further investigated whether these mechanisms contribute to MIR-induced regulation of tinnitus-like behavior. Firstly, we studied the morphological plastic changes of MGB neurons induced by MIR. TEM results indicated that no statistically meaningful variations were observed in the synaptic ultrastructure of MGB neurons when comparing the sham group to the MIR group (Figure S2A–F in supporting information). To determine whether MIR treatment influences neuronal morphology, we examined MGB neurons using TEM. The results showed no significant differences in synaptic ultrastructure between the sham and MIR groups (Figure S2A–F, Supporting Information). Additionally, MAP2 immunofluorescence staining (see Methods, Supplementary Material) revealed no significant differences in dendritic length or primary neuron branching between the control group and MIR-treated group (Figure S2G–J, Supporting Information). These findings indicate that MIR treatment does not significantly alter MGB neuronal morphology in tinnitus mice, suggesting that its regulatory effect may be mediated primarily through electrophysiological mechanisms rather than structural changes.

To assess whether MIR treatment reduces the increased excitability of MGB neurons observed in tinnitus mice, we measured the count of spikes elicited by depolarizing current steps in both the sham and MIR groups (Figure 7A). The results demonstrated that MIR treatment significantly reduced the elevated firing frequency of MGB neurons induced by tinnitus-like behavior (Figure 7B,C). Figure 7B illustrates the correlation between injected current and spike count. Meanwhile, Figure 7C presents the spike count at 400 pA for each experimental group. Additionally, the rheobase was notably increased in the MIR group, indicating a higher threshold for action potential generation (Figure 7D). While no significant difference was noted in Cm between the groups (Figure 7E), both RMP and input resistance (Rin) showed a decreasing trend following MIR treatment (Figure 7F,G). No statistically meaningful variation was seen in spike amplitude between the two groups (Figure 7H). However, in the MIR group, the spike threshold was decreased, while the difference between RMP and spike threshold was increased (Figure 7I,J).

Figure 7. — Electrophysiological properties of MGB neurons following MIR treatment. (A) Representative traces of MGB neuron-evoked APs recorded from the sham group and the MIR treatment group. (B) Relationship between injected current and the number of APs recorded from MGB neurons in the two groups. (C) Number of evoked APs at 400 pA current in each experimental group. (D–G) Comparison of the passive membrane attributes of MGB neurons across the two groups, including (D) rheobase current, (E) Cm, (F) RMP, and (G) Rin. (H–J) Summary of active membrane properties, including (H) spike amplitude, (I) spike threshold, and (J) difference between RMP and spike threshold. Error bars represent SEM. (N = 10–11 neurons, ns, no significant difference, *p < 0.05, **p < 0.01, ***p < 0.001).

Ih amplitude decrease in tinnitus mice

The elevated excitability of MGB neurons in tinnitus mice prompts inquiry into the molecular mechanisms responsible for this heightened activity. Previous research indicates that Ih currents are crucial in the onset of tinnitus [26,58]. Additionally, HCN channels are known to regulate input resistance (Rin) at the resting membrane potential [59]. In our experiments, we observed an increase in Rin in MGB neurons of tinnitus mice, whereas MIR treatment reduced Rin, indicating a potential role of HCN channels in mediating this effect. To further investigate this, we directed our attention to HCN2 channels and investigated their levels of expression in the MGB of all three groups using immunofluorescence (Figure 8A–C). The findings showed no notable variation in the expression of HCN2 protein across the three groups (Figure 8D). Additionally, qPCR analysis revealed no significant difference in HCN2 mRNA levels among the groups (Figure 8E).

Figure 8. — HCN2 expression and Ih current in MGB neurons across three experimental groups. (A–C) Low-magnification micrographs showing HCN2 immunoreactivity in the control group (A₁), non-tinnitus group (B₁), and tinnitus group (C₁). (A₂–C₂) High-magnification micrographs of the areas marked by rectangles in A₁–C₁. (D) Quantification of HCN2 fluorescence intensity in the MGB nucleus across the three groups. (E) The qPCR study found no statistically significant variations in the relative mRNA levels of HCN2 among the three groups. (F) Illustrative recordings of Ih currents obtained from MGB neurons within each experimental group are presented. (G) Stimulation protocol for Ih current recordings. (H) Ih amplitude measured from MGB neurons across the three groups at different voltage levels. (I) Averaged Ih amplitude recorded at −120 mV from MGB neurons across the three groups. Error bars represent SEM. (N = 18 areas for HCN2 fluorescence intensity, N = 6 mice for qPCR, N = 10 to 12 neurons for whole cell patch, ns, no significant difference, *p < 0.05, **p < 0.01, ***p < 0.001).

Subsequently, we conducted whole-cell patch-clamp recordings on MGB neurons to directly assess Ih currents by adjusting the membrane voltage in increments of 10 mV, ranging from −60 mV to −120 mV (Figure 8F,G). The findings revealed that the Ih amplitude in mice with tinnitus was notably decreased when compared to both the control group and the group without tinnitus (Figure 8H,I). Figure 8H illustrates the relationship between injected current and Ih amplitude, while Figure 8I displays the Ih amplitude recorded at −120 mV for each experimental group. These findings indicate a functional reduction in HCN channel activity, which is likely contributing to the increased excitability of MGB neurons and the development of tinnitus-like behavior.

The function of Ih current in the MIR regulation of tinnitus-like behavior in mice

After confirming that the reduction in Ih amplitude is associated with tinnitus-like behavior, we further investigated whether MIR exerts its regulatory effect on tinnitus-like behavior through Ih current modulation. To test this hypothesis, we randomly divided the selected tinnitus mice into two groups following screening (Figure 9A). Using stereotaxic injection, one group received AAV-shRNA-scramble-GFP injections in the MGB, while the other group received AAV-shRNA-HCN2-GFP injections (Figure 9B). The successful viral expression in the MGB was confirmed using fluorescence microscopy (Figure 9C). Additionally, qPCR analysis confirmed that AAV-shRNA-HCN2-GFP effectively knocked down HCN2 expression in the MGB (Figure 9D). Figure 9E displays the GPIAS ratios for both groups before and one week following exposure to noise, while Figure 9F presents their PPI ratios.

After 21 days of viral expression, both groups underwent MIR treatment, followed by re-evaluation of their GPIAS and PPI ratios to determine whether HCN2 knockdown influenced the MIR effect on tinnitus-like behavior (Figure 9A). The results showed that in the scramble group, the GPIAS ratio at tinnitus frequencies decreased significantly after MIR treatment, whereas the PPI ratio remained unchanged (Figure 9G). In contrast, in the shRNA-HCN2 group, no notable alterations were noted in either the GPIAS ratio or the PPI ratio at tinnitus frequencies after MIR treatment (Figure 9H). The results suggest that Ih current is essential in the regulation of tinnitus-like behavior in mice, mediated by MIR, suggesting that MIR exerts its effect by modulating HCN2 channel function rather than its expression levels.

We further tested the current amplitude of the HCN2 channel in HEK293 cells to verify the MIR-mediated effect on HCN channels (Figure 9I). To eliminate the potential influence of temperature rise, we collected data from cells fiber located 300 micrometers from the end of the laser fiber [60]. The results indicated that MIR significantly increased Ih amplitude at membrane voltages ranging from −100 to −130 mV (Figure 9J), suggesting that MIR may reduce neuronal excitability by modulating HCN channel function. In order to gain a deeper understanding of the mechanism responsible for the elevated Ih current amplitude, we performed molecular dynamics simulations of the HCN channel, utilizing its structural data from the Protein Data Bank (PDB ID: 6GYN), simulating its condition subsequent MIR exposure. The front view and top view of the HCN ion channel protein are shown in Figure 9K and Figure 9L, respectively. We analyzed alterations in the protein’s secondary structure and noted an elevation in the count of random coils (Figure 9M), coupled with a decrease in the number of α-helices (Figure 9N) and a reduction in total secondary structure elements (Figure 9O) with increasing electromagnetic field intensity. Furthermore, as the intensity of the electromagnetic photon field rose, the number of cations traversing the HCN ion channel augmented, leveling off once the field intensity exceeded 0.4 V/nm (Figure 9P). The results indicate that infrared photons enhance the passage of cations through HCN channels.

Discussion

In this study, we found that 48.6% (18/37) of the mice exhibited tinnitus-like behavior after noise exposure, a proportion that aligns closely with the success rates of tinnitus modeling in mice reported in previous studies [26,35]. Previous studies have demonstrated a strong correlation between increased excitability of MGB neurons and the occurrence of tinnitus-like behavior [61]. Therefore, we performed RNA-seq analysis on MGB nuclei from tinnitus and control mice. The results showed that, compared to the control group, the tinnitus group exhibited 218 upregulated genes in the MGB nuclei, which were primarily enriched in signaling pathways associated with neuronal electrophysiological plasticity and structural plasticity. Additionally, our in vitro experiments demonstrated that MGB neurons in tinnitus mice exhibited plastic changes in both structure and electrophysiological properties, compared to control and non-tinnitus mice. Golgi staining revealed that the morphological complexity of MGB neurons in tinnitus mice was increased. Previous research has shown that noise-induced hearing loss can lead to dendritic structural remodeling of neurons in the prefrontal cortex, potentially linked to changes in cortical electroencephalographic signals [62]. Consistent with this, we observed an increase in dendritic length and branching in MGB neurons of tinnitus mice. An increase in dendritic branches enhances a neuron’s ability to process and integrate complex signals more efficiently [63]. This structural plasticity in tinnitus mice may contribute to altered neuronal excitability, further supporting the hypothesis that abnormal MGB activity plays a key role in tinnitus pathophysiology. Moreover, existing literature suggests that tinnitus may lead to abnormal thalamocortical neural oscillations [64,65]. Based on this, we speculate that these electrophysiological changes may be associated with modifications in neuronal structural plasticity. Additionally, TEM analysis revealed that the synaptic ultrastructure of MGB neurons changed in a manner that favored signal transmission. Meanwhile, c-Fos immunofluorescence staining, a molecular marker of neuronal activation, showed an increased proportion of activated neurons in the MGB of tinnitus mice, along with a higher neuronal firing frequency. Previous studies have shown that 99% of neurons in the mouse MGB are glutamatergic [66]. Therefore, the increase in c-Fos + neurons observed in this study is likely due to heightened activation of excitatory neurons. Additionally, our whole-cell patch-clamp recordings revealed that MGB neurons in tinnitus mice exhibited a regular firing pattern characteristic of excitatory neurons [67]. Previous studies have shown that mere noise-exposure-induced damage may also cause a change in the relative contributions of thalamocortically relayed and intracortically relayed activity [68]. Nevertheless, in the present study, we did not detect the same situation in the non-tinnitus group, and this aspect still needs to be further validated by subsequent experiments. These findings suggest that abnormal neuronal plasticity may contribute to tinnitus-like behavior.

To further investigate associated with the regulatory effect of MIR on tinnitus-like behavior and MGB neuronal plasticity, we examined its impact on neuronal excitability and structural plasticity.

In this study, a mid-infrared wavelength of 8.8 μm, which exhibits low attenuation in artificial cerebrospinal fluid, was selected to minimize thermal effects [69]. To further prevent excessive heating of brain tissue during MIR modulation, the energy intensity was chosen in accordance with our previous study, with a power density at the laser fiber tip in air of approximately 7.5 mW/cm² [33]. Recent studies suggest that MIR influences neuronal plasticity and can even modulate cognitive functions in mice [60,70]. In this study, we applied MIR irradiation to the MGB of mice with tinnitus and found that they effectively modulated tinnitus-like behavior. Further investigations into MIR’s effects on neuronal structural and functional plasticity revealed that MIR reversed the increased firing frequency of MGB neurons, which may be a consequence of increased background activity downstream of the auditory pathway or due to adaptive stochastic resonance changes upstream [71]. This result aligns with a previous study demonstrating that MIR stimulation reduced neuronal responsiveness to current pulses below 200 pA and significantly increased the rheobase in mouse prefrontal cortex neurons [60]. In another study, infrared photon stimulation was applied directly above the cortical surface following craniotomy, and employed two-photon calcium imaging was used to monitor neuronal population activities during radiation application. The results indicated that some neurons exhibited increased excitability, while and others showed decreased excitability during infrared photons application [31]. Interestingly, within the same cell type, the pattern of change was consistent, with previously hyperactive neurons becoming suppressed and less active neurons becoming more responsive [31]. In our experiment, MGB neurons in tinnitus mice displayed increased excitability, and MIR treatment predominantly reduced their firing frequency. However, MIR did not significantly alter dendritic morphology or synaptic ultrastructure in our study. This lack of structural change be related to the duration and frequency of MIR exposure, suggesting that longer or more frequent stimulation protocols may be required for detectable morphological adaptations.

The above results suggest that MIR primarily restores the abnormal increase in neuronal firing frequency within the MGB, thereby modulating tinnitus-like behavior in mice. Since neuronal AP firing and rheobase are closely related to passive membrane properties, and given that passive properties—particularly neuronal Rin—differed among the experimental groups, these factors likely contribute to the observed changes in neuronal excitability. A decrease in Rin leads to reduced neuronal spiking activity, an effect known as shunting inhibition [72]. In this experiment, MGB neurons in the tinnitus group exhibited an increase in Rin, which coincided with elevated neuronal firing frequency. However, MIR treatment reduced Rin, and as a result, neuronal firing frequency in the MIR group showed a decreasing trend. Furthermore, a previous study suggested that MIR-induced reduction in Rin could explain changes in neuronal spiking activity, as artificially inserting leak conductance to decrease Rin produced a similar effect [60].

The electrical sensitivity of the nervous system serves as a fundamental basis for its modulation by magnetic and electrical stimuli. For instance, DBS and transcranial electrical stimulation directly apply electrical currents to the nervous system to modulate neural activity [73]. In contrast, TMS operates through the principle of electromagnetic induction, wherein a time-varying magnetic field generates an electrical current within the brain tissue, thereby achieving neural modulation without direct electrical contact [74,75]. Furthermore, MIR radiation has been demonstrated to interact linearly with various endogenous biomacromolecules, including proteins and lipids, through vibrational resonance effects [76]. This interaction can induce conformational changes or alter molecular dynamics, potentially influencing cellular function. Notably, ion channels—a vital class of macromolecular proteins embedded in neuronal cell membranes—are essential for maintaining the electrical properties of neurons. These channels mediate the selective flow of ions across the membrane, thereby governing action potential generation and synaptic transmission.

Therefore, we speculated that MIR may regulate ion channels involved in Rin modulation, thereby reversing abnormal neuronal hyperexcitability. Previous research has demonstrated that HCN channels play a key role in regulating Rin at RMP [59]. Moreover, multiple experimental studies have shown that HCN channel activity significantly influences the function of other subthreshold conductance channels co-expressed in specific neurons [77–80]. Notably, the HCN2 subunit is highly expressed in MGB nuclei [81]. To assess whether HCN2 expression levels contributed to tinnitus pathophysiology, we performed immunofluorescence and qPCR analysis. The results revealed no significant differences in HCN2 protein or mRNA expression among the control, non-tinnitus, and tinnitus groups. However, electrophysiological recordings demonstrated that Ih amplitude in MGB neurons of tinnitus mice was significantly lower than in both control and non-tinnitus mice. Given that Ih currents modulate Rin, this reduction in Ih could contribute to increased Rin and neuronal firing frequency in tinnitus mice. Our findings further indicate that MIR modulates the function of HCN ion channels by altering their secondary structure, thereby facilitating cation passage through the channels. Previous studies have shown that infrared photons can also influence potassium channel function by modifying the structural properties of the channel’s filtering region [76]. By restoring Ih amplitude, MIR can effectively regulate neuronal Rin, thereby normalizing the aberrantly increased excitability of MGB neurons in tinnitus mice.

One important constraint of photo-biomodulation therapy is the inherent thermal effects [82]. A previous study demonstrated that MIR wavelength with low light attenuation in water can effectively prevent excessive heat [83]. Our previous research indicates that a distance of 300 μm from the MIR optical fiber, the temperature increase in artificial cerebrospinal fluid was approximately 0.6 °C, while in in vivo experiments, the maximum temperature increase at the target area was less than 2 °C [33]. According to previous studies, such temperature changes do not significantly affect brain neurons [84–86]. Moreover, The PPI ratio of tinnitus mice remained unchanged after MIR treatment, suggesting that MIR radiation does not impair temporal processing, further supporting its safety [34]. Thus, the nonthermal effects of MIRS may prevent overheating of the target tissue during stimulation, making it a safe neuromodulatory approach.

Several limitations of our study warrant discussion. Firstly, the translational development of fiber-optic mediated mid-infrared neuromodulation is subject to several technical constraints. Although this approach provides spatially precise modulation, its dependency on invasive implantation presents challenges for clinical adoption. While minimally invasive strategies employing skull thinning have achieved neuronal activation with diminished tissue damage [87], the penetration depth remains constrained to approximately 0.3 mm irrespective of skull integrity [88], thereby limiting effective application to cortical regions. These technical boundaries indicate the value of exploring alternative delivery mechanisms in future, including MIR-responsive nanomaterials functioning as photothermal transducers and sophisticated wavefront engineering techniques, to enhance future translational potential. Secondly, the temporal dynamics of MIR-induced neuromodulatory effects on tinnitus-like behaviors remain insufficiently characterized. Future longitudinal studies are warranted to systematically evaluate the persistence and stability of these behavioral modifications. Additionally, a comprehensive parameter optimization investigating different MIR wavelengths and energy intensities would provide valuable insights for refining stimulation protocols and enhancing therapeutic efficacy. Thirdly, while our experimental findings suggest that alterations in ion channel function may be closely associated with neuronal excitability plasticity, emerging evidence from animal models of tinnitus indicates that extracellular matrix density (ECM) also plays a critical role in modulating cortical neuroplasticity and synaptic stability [89]. However, this study did not investigate changes in ECM or other extracellular components, leaving open questions about their potential contributions to MIR-mediated effects. Finally, interspecies divergence in neuronal circuitry poses challenges for translating these findings into clinical applications. A key consideration is the markedly lower proportion of inhibitory interneurons within the MGB of rodents compared to primates and humans [66]. While existing literature demonstrates that the majority of GABAergic projections to the MGB derive from extrinsic sources—primarily the thalamic reticular nucleus and inferior colliculus—rather than intrinsic interneurons [90], the current investigation did not focus specifically on inhibitory neuronal populations given their limited distribution in rodent models. Consequently, additional studies utilizing human stem cell-derived neuronal systems or primate models are required to substantiate the therapeutic potential of MIR for human tinnitus. These limitations underscore the need for cautious interpretation of our results and highlight directions for future research.

Conclusions

This study investigates the role of the MGB in the onset of tinnitus and explores its potential as a therapeutic target. The findings indicate that MIR intervention alleviates tinnitus-like behavior in mice. Tinnitus-like behavior may induce both structural and electrophysiological plasticity changes in MGB neurons. Our results demonstrate that MIR intervention reverses the abnormal increase in neuronal firing frequency in tinnitus mice by modulating the function of HCN ion channels on the neuronal cell membrane. The regulatory mechanism of MIR primarily involves altering the secondary structure of proteins, which enhances Ih amplitude and restores the reduced amplitude observed in MGB neurons of tinnitus mice. These findings suggest that targeting MGB neurons with infrared photons may represent a promising neuromodulatory strategy for alleviating refractory tinnitus. Future research should focus on optimizing MIR delivery methods and validating its efficacy in higher-order species to facilitate clinical translation.

Supplementary Material

Supplemental Material

IANN_A_2584694_SM3897.pdf^{(155.6KB, pdf)}

Supplemental Material

IANN_A_2584694_SM3889.zip^{(888.1KB, zip)}

Acknowledgments

We thank the National Clinical Research center for Otolaryngologic Diseases, Beijing 100853, PR China for the supporting of this study. X Xue conceptualized the study and wrote the original draft, P Liu and L Sun performed formal analysis, C Zhang conducted the investigation, Z Zhang and W Shen developed the methodology. H Zhou and S Lu curated the data, Y Jiang, Z Ding, and X Jiang validated the results and contributed to resource management, L Wang supervised project administration, X Li handled visualization, S Yang provided overall supervision, F Wang reviewed and edited the manuscript.

Funding Statement

This work was supported by the National Clinical Research center for Otolaryngologic Diseases and National Key Research and Development Program (grant numbers 2022YFC2402705 and 2022YFC2402701).

Ethics approval and consent to participate

All procedures adhered to the guidelines for the Care and Use of Laboratory Animals set by the Chinese PLA General Hospital (Beijing, China) and received approval from the hospital’s Animal Care Committee.

Publication statement

We hereby confirm that all authors have carefully reviewed and approved the final version of this manuscript. Each author has had the opportunity to provide input, make necessary revisions, and ensure that their contributions are accurately represented in the submitted work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The raw data supporting the conclusions of this manuscript will be made available by the corresponding author, without undue reservation, to any qualified researcher upon reasonable request.

References

1.Park M, Kang S H, Nari F, et al. Association between tinnitus and depressive symptoms in the South Korean population. PLoS One. 2021;16(12):e0261257. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Jarach CM, Lugo A, Scala M, et al. Global prevalence and incidence of tinnitus: a systematic review and meta-analysis. JAMA Neurol. 2022;79(9):888–900. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Dalrymple SN, Lewis SH, Philman S.. Tinnitus: diagnosis and management. Am Fam Physician. 2021;103(11):663–671. [PubMed] [Google Scholar]
4.Biswas R, Genitsaridi E, Trpchevska N, et al. Low evidence for tinnitus risk factors: a systematic review and meta-analysis. J Assoc Res Otolaryngol. 2023;24(1):81–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Hayes SH, Schormans AL, Sigel G, et al. Uncovering the contribution of enhanced central gain and altered cortical oscillations to tinnitus generation. Prog Neurobiol. 2021;196:101893. [DOI] [PubMed] [Google Scholar]
6.Sedley W. Tinnitus: does gain explain? Neuroscience. 2019;407:213–228. [DOI] [PubMed] [Google Scholar]
7.Shore SE, Wu C.. Mechanisms of noise-induced tinnitus: insights from cellular studies. Neuron. 2019;103(1):8–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Yu XJ, Xu XX, He S, et al. Change detection by thalamic reticular neurons. Nat Neurosci. 2009;12(9):1165–1170. [DOI] [PubMed] [Google Scholar]
9.Yu XJ, Xu XX, Chen X, et al. Slow recovery from excitation of thalamic reticular nucleus neurons. J Neurophysiol. 2009;101(2):980–987. [DOI] [PubMed] [Google Scholar]
10.Rauschecker JP, Leaver AM, MüHLAU M.. Tuning out the noise: limbic-auditory interactions in tinnitus. Neuron. 2010;66(6):819–826. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Knipper M, VAN Dijk P, Schulze H, et al. The neural bases of tinnitus: lessons from deafness and cochlear implants. J Neurosci. 2020;40(38):7190–7202. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Hofmeier B, Wertz J, Refat F, et al. Functional biomarkers that distinguish between tinnitus with and without hyperacusis. Clin Transl Med. 2021;11(5):e378. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Brinkmann P, Kotz SA, Smit JV, et al. Auditory thalamus dysfunction and pathophysiology in tinnitus: a predictive network hypothesis. Brain Struct Funct. 2021;226(6):1659–1676. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.LLINáS R, Urbano FJ, Leznik E, et al. Rhythmic and dysrhythmic thalamocortical dynamics: GABA systems and the edge effect. Trends Neurosci. 2005;28(6):325–333. [DOI] [PubMed] [Google Scholar]
15.Winer JA, Larue DT.. Patterns of reciprocity in auditory thalamocortical and corticothalamic connections: study with horseradish peroxidase and autoradiographic methods in the rat medial geniculate body. J Comp Neurol. 1987;257(2):282–315. [DOI] [PubMed] [Google Scholar]
16.Ledoux JE, Farb CR, Romanski LM.. Overlapping projections to the amygdala and striatum from auditory processing areas of the thalamus and cortex. Neurosci Lett. 1991;134(1):139–144. [DOI] [PubMed] [Google Scholar]
17.Bajo VM, Rouiller EM, Welker E, et al. Morphology and spatial distribution of corticothalamic terminals originating from the cat auditory cortex. Hear Res. 1995;83(1–2):161–174. [DOI] [PubMed] [Google Scholar]
18.Winer JA, Kelly JB, Larue DT.. Neural architecture of the rat medial geniculate body. Hear Res. 1999;130(1–2):19–41. [DOI] [PubMed] [Google Scholar]
19.Lee CC, Winer JA.. Connections of cat auditory cortex: I. Thalamocortical system. J Comp Neurol. 2008;507(6):1879–1900. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Leaver AM, Renier L, Chevillet MA, et al. Dysregulation of limbic and auditory networks in tinnitus. Neuron. 2011;69(1):33–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Cook JA, Barry KM, Zimdahl JW, et al. Spontaneous firing patterns in the medial geniculate nucleus in a guinea pig model of tinnitus. Hear Res. 2021;403:108190. [DOI] [PubMed] [Google Scholar]
22.Kalappa BI, Brozoski TJ, Turner JG, et al. Single unit hyperactivity and bursting in the auditory thalamus of awake rats directly correlates with behavioural evidence of tinnitus. J Physiol. 2014;592(22):5065–5078. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.MüHLAU M, Rauschecker JP, Oestreicher E, et al. Structural brain changes in tinnitus. Cereb Cortex. 2006;16(9):1283–1288. [DOI] [PubMed] [Google Scholar]
24.Landgrebe M, Langguth B, Rosengarth K, et al. Structural brain changes in tinnitus: grey matter decrease in auditory and non-auditory brain areas. Neuroimage. 2009;46(1):213–218. [DOI] [PubMed] [Google Scholar]
25.Chen SJ, Xu Y, Liang YM, et al. Identification and characterization of a series of novel HCN channel inhibitors. Acta Pharmacol Sin. 2019;40(6):746–754. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Li S, Kalappa BI, Tzounopoulos T.. Noise-induced plasticity of KCNQ2/3 and HCN channels underlies vulnerability and resilience to tinnitus. Elife. 2015:4:e07242. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Lanaia V, Tziridis K, Schulze H.. Salicylate-induced changes in hearing thresholds in mongolian gerbils are correlated with tinnitus frequency but not with tinnitus strength. Front Behav Neurosci. 2021;15:698516. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Ridding MC, Rothwell JC.. Is there a future for therapeutic use of transcranial magnetic stimulation? Nat Rev Neurosci. 2007;8(7):559–567. [DOI] [PubMed] [Google Scholar]
29.VAN Zwieten G, Janssen MLF, Smit JV, et al. Inhibition of experimental tinnitus with high frequency stimulation of the rat medial geniculate body. Neuromodulation. 2019;22(4):416–424. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Devos JVP, Smit JV, George ELJ, et al. Effective treatment of refractory tinnitus by bilateral deep brain stimulation of the medial geniculate body of the thalamus: a case report. Brain Stimul. 2023;16(5):1322–1324. [DOI] [PubMed] [Google Scholar]
31.Tan X, Zhong Y, Li R, et al. Neuromodulation of chemical synaptic transmission driven by THz photons. Research (Wash D C). 2022;2022:0010. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Wang P, Lou J, Fang G, et al. Progress on cutting-edge infrared-terahertz biophysics. IEEE Trans Microw Theory Techn. 2022. [Google Scholar]
33.Liu P, Xue X, Zhang C, et al. Mid-infrared photons alleviate tinnitus by activating the KCNQ2 channel in the auditory cortex. Research (Wash DC). 2024;7:0479. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Middleton JW, Kiritani T, Pedersen C, et al. Mice with behavioral evidence of tinnitus exhibit dorsal cochlear nucleus hyperactivity because of decreased GABAergic inhibition. Proc Natl Acad Sci USA. 2011;108(18):7601–7606. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Li S, Choi V, Tzounopoulos T.. Pathogenic plasticity of Kv7.2/3 channel activity is essential for the induction of tinnitus. Proc Natl Acad Sci USA. 2013;110(24):9980–9985. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Tang D, Chen M, Huang X, et al. SRplot: a free online platform for data visualization and graphing. PLoS One. 2023;18(11):e0294236. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–d13. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.ZEIDáN-CHULIá F, GüRSOY M, Neves DE Oliveira BH, et al. A systems biology approach to reveal putative host-derived biomarkers of periodontitis by network topology characterization of MMP-REDOX/NO and apoptosis integrated pathways. Front Cell Infect Microbiol. 2015;5:102. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Tang Y, Li M, Wang J, et al. CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 2015;127:67–72. [DOI] [PubMed] [Google Scholar]
40.Sholl DA. Dendritic organization in the neurons of the visual and motor cortices of the cat. J Anat. 1953;87(4):387–406. [PMC free article] [PubMed] [Google Scholar]
41.Jacobs S, Cheng C, Doering LC.. Hippocampal neuronal subtypes develop abnormal dendritic arbors in the presence of Fragile X astrocytes. Neuroscience. 2016;324:202–217. [DOI] [PubMed] [Google Scholar]
42.Konan LM, Song H, Pentecost G, et al. Multi-focal neuronal ultrastructural abnormalities and synaptic alterations in mice after low-intensity blast exposure. J Neurotrauma. 2019;36(13):2117–2128. [DOI] [PubMed] [Google Scholar]
43.Xiao Y, Fu H, Han X, et al. Role of synaptic structural plasticity in impairments of spatial learning and memory induced by developmental lead exposure in Wistar rats. PLoS One. 2014;9(12):e115556. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Xu X, Liu X, Zhang Q, et al. Sex-specific effects of bisphenol-A on memory and synaptic structural modification in hippocampus of adult mice. Horm Behav. 2013;63(5):766–775. [DOI] [PubMed] [Google Scholar]
45.Chen L, Wang X, Ge S, et al. Medial geniculate body and primary auditory cortex differentially contribute to striatal sound representations. Nat Commun. 2019;10(1):418. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Dong HW. The Allen Atlas: a digital brain atlas of C57BL/6J male mouse, (with CD-ROM). Wiley. 2007. [Google Scholar]
47.Li KX, Lu YM, Xu ZH, et al. Neuregulin 1 regulates excitability of fast-spiking neurons through Kv1.1 and acts in epilepsy. Nat Neurosci. 2011;15(2):267–273. [DOI] [PubMed] [Google Scholar]
48.Zhu M, Idikuda VK, Wang J, et al. Shank3-deficient thalamocortical neurons show HCN channelopathy and alterations in intrinsic electrical properties. J Physiol. 2018;596(7):1259–1276. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Robinson DW, Cameron WE.. Time-dependent changes in input resistance of rat hypoglossal motoneurons associated with whole-cell recording. J Neurophysiol. 2000;83(5):3160–3164. [DOI] [PubMed] [Google Scholar]
50.Fang J, Wang H, Zhou J, et al. Baicalin provides neuroprotection in traumatic brain injury mice model through Akt/Nrf2 pathway. Drug Des Devel Ther. 2018;12:2497–2508. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Livak KJ, Schmittgen TD.. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods (San Diego, Calif). 2001;25(4):402–408. [DOI] [PubMed] [Google Scholar]
52.Abraham MJ, Murtola T, Schulz R, et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015. [Google Scholar]
53.Jo S, Kim T, Iyer VG, et al. CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem. 2008;29(11):1859–65. [DOI] [PubMed] [Google Scholar]
54.Hoover WG. Canonical dynamics: equilibrium phase-space distributions. Phys Rev A Gen Phys. 1985;31(3):1695–1697. [DOI] [PubMed] [Google Scholar]
55.Humphrey W, Dalke A, Schulten K.. VMD: visual molecular dynamics. J Mol Graph. 1996;14(1):33–38. [DOI] [PubMed] [Google Scholar]
56.Pompeiano M, Colonnese MT.. cFOS as a biomarker of activity maturation in the hippocampal formation. Front Neurosci. 2023;17:929461. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Schiller M, Azulay-Debby H, Boshnak N, et al. Optogenetic activation of local colonic sympathetic innervations attenuates colitis by limiting immune cell extravasation. Immunity. 2021;54(5):1022–1036. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Tang ZQ, Trussell LO.. Serotonergic regulation of excitability of principal cells of the dorsal cochlear nucleus. J Neurosci. 2015;35(11):4540–4551. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Combe CL, Gasparini S.. I(h) from synapses to networks: HCN channel functions and modulation in neurons. Prog Biophys Mol Biol. 2021;166:119–132. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Liu X, Qiao Z, Chai Y, et al. Nonthermal and reversible control of neuronal signaling and behavior by midinfrared stimulation. Proc Natl Acad Sci USA. 2021;118(10):e2015685118. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Almasabi F, Janssen MLF, Devos J, et al. The role of the medial geniculate body of the thalamus in the pathophysiology of tinnitus and implications for treatment. Brain Res. 2022;1779:147797. [DOI] [PubMed] [Google Scholar]
62.Hayes SH, Patel SV, Arora P, et al. Neurophysiological, structural, and molecular alterations in the prefrontal and auditory cortices following noise-induced hearing loss. Neurobiol Dis. 2024;200:106619. [DOI] [PubMed] [Google Scholar]
63.Spruston N. Pyramidal neurons: dendritic structure and synaptic integration. Nat Rev Neurosci. 2008;9(3):206–221. [DOI] [PubMed] [Google Scholar]
64.Vianney-Rodrigues P, Auerbach BD, Salvi R.. Aberrant thalamocortical coherence in an animal model of tinnitus. J Neurophysiol. 2019;121(3):893–907. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Gault R, Mcginnity TM, Coleman S.. A computational model of thalamocortical dysrhythmia in people with tinnitus. IEEE Trans Neural Syst Rehabil Eng. 2018;26(9):1845–1857. [DOI] [PubMed] [Google Scholar]
66.Arcelli P, Frassoni C, Regondi MC, et al. GABAergic neurons in mammalian thalamus: a marker of thalamic complexity? Brain Res Bull. 1997;42(1):27–37. doi: 10.1016/S0361-9230(96)00107-4. [DOI] [PubMed] [Google Scholar]
67.Tottene A, Favero M, Pietrobon D.. Enhanced thalamocortical synaptic transmission and dysregulation of the excitatory-inhibitory balance at the thalamocortical feedforward inhibitory microcircuit in a genetic mouse model of migraine. J Neurosci. 2019;39(49):9841–9851. [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Jeschke M, Happel MFK, Tziridis K, et al. Acute and long-term circuit-level effects in the auditory cortex after sound trauma. Front Neurosci. 2020;14:598406. [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Song Z, Sun Y, Liu P, et al. Terahertz wave alleviates comorbidity anxiety in pain by reducing the binding capacity of nanostructured glutamate molecules to GluA2. Research (Wash D C). 2024;7:0535. [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Zhang J, He Y, Liang S, et al. Non-invasive, opsin-free mid-infrared modulation activates cortical neurons and accelerates associative learning. Nat Commun. 2021;12(1):2730. [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Schilling A, Sedley W, Gerum R, et al. Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception. Brain. 2023;146(12):4809–4825. [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Holt GR, Koch C.. Shunting inhibition does not have a divisive effect on firing rates. Neural Comput. 1997;9(5):1001–1013. [DOI] [PubMed] [Google Scholar]
73.VAN Zwieten G, Roberts MJ, Schaper F, et al. Noise-induced neurophysiological alterations in the rat medial geniculate body and thalamocortical desynchronization by deep brain stimulation. J Neurophysiol. 2021;125(2):661–671. [DOI] [PubMed] [Google Scholar]
74.Barker AT. An introduction to the basic principles of magnetic nerve stimulation. J Clin Neurophysiol. 1991;8(1):26–37. [DOI] [PubMed] [Google Scholar]
75.Jannati A, Oberman LM, Rotenberg A, et al. Assessing the mechanisms of brain plasticity by transcranial magnetic stimulation. Neuropsychopharmacology. 2023;48(1):191–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Xiao T, Wu K, Wang P, et al. Sensory input-dependent gain modulation of the optokinetic nystagmus by mid-infrared stimulation in pigeons. Elife. 2023:12:e78729. [DOI] [PMC free article] [PubMed] [Google Scholar]
77.George MS, Abbott LF, Siegelbaum SA.. HCN hyperpolarization-activated cation channels inhibit EPSPs by interactions with M-type K(+) channels. Nat Neurosci. 2009;12(5):577–584. [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Migliore M, Migliore R.. Know your current I(h): interaction with a shunting current explains the puzzling effects of its pharmacological or pathological modulations. PLoS One. 2012;7(5):e36867. [DOI] [PMC free article] [PubMed] [Google Scholar]
79.Amarillo Y, Zagha E, Mato G, et al. The interplay of seven subthreshold conductances controls the resting membrane potential and the oscillatory behavior of thalamocortical neurons. J Neurophysiol. 2014;112(2):393–410. [DOI] [PMC free article] [PubMed] [Google Scholar]
80.Hu W, Bean BP.. Differential control of axonal and somatic resting potential by voltage-dependent conductances in cortical layer 5 pyramidal neurons. Neuron. 2018;99(6):1355. [DOI] [PMC free article] [PubMed] [Google Scholar]
81.Notomi T, Shigemoto R.. Immunohistochemical localization of Ih channel subunits, HCN1-4, in the rat brain. J Comp Neurol. 2004;471(3):241–276. [DOI] [PubMed] [Google Scholar]
82.Wells J, Kao C, Mariappan K, et al. Optical stimulation of neural tissue in vivo. Opt Lett. 2005;30(5):504–506. [DOI] [PubMed] [Google Scholar]
83.MARéCHAL . The molecular structure of liquid water delivered by absorption spectroscopy in the whole IR region completed with thermodynamics data. [Google Scholar]
84.Stujenske JM, Spellman T, Gordon JA.. Modeling the spatiotemporal dynamics of light and heat propagation for in vivo optogenetics. Cell Rep. 2015;12(3):525–534. [DOI] [PMC free article] [PubMed] [Google Scholar]
85.Ait Ouares K, Beurrier C, Canepari M, et al. Opto nongenetics inhibition of neuronal firing. Eur J Neurosci. 2019;49(1):6–26. [DOI] [PubMed] [Google Scholar]
86.Owen SF, Liu MH, Kreitzer AC.. Thermal constraints on in vivo optogenetic manipulations. Nat Neurosci. 2019;22(7):1061–1065. [DOI] [PMC free article] [PubMed] [Google Scholar]
87.Aslih N, Dekel BZ, Malonek D, et al. Non-invasive evaluation of embryos using mid-infrared attenuated total reflection spectrometry of incubation medium: a preliminary study. Reprod Biomed Online. 2023;46(5):793–801. [DOI] [PubMed] [Google Scholar]
88.Sun C, Fan Q, Xie R, et al. Tetherless optical neuromodulation: wavelength from orange-red to mid-infrared. Neurosci Bull. 2024;40(8):1173–1188. [DOI] [PMC free article] [PubMed] [Google Scholar]
89.Tziridis K, Maul A, Rasheed J, et al. Tinnitus is associated with increased extracellular matrix density in the auditory cortex of Mongolian gerbils. BMC Neurosci. 2024;25(1):52. [DOI] [PMC free article] [PubMed] [Google Scholar]
90.Richardson BD, Sottile SY, Caspary DM.. Mechanisms of GABAergic and cholinergic neurotransmission in auditory thalamus: impact of aging. Hear Res. 2021;402:108003. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplemental Material

IANN_A_2584694_SM3897.pdf^{(155.6KB, pdf)}

Supplemental Material

IANN_A_2584694_SM3889.zip^{(888.1KB, zip)}

Data Availability Statement

The raw data supporting the conclusions of this manuscript will be made available by the corresponding author, without undue reservation, to any qualified researcher upon reasonable request.

[CIT0001] 1.Park M, Kang S H, Nari F, et al. Association between tinnitus and depressive symptoms in the South Korean population. PLoS One. 2021;16(12):e0261257. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0002] 2.Jarach CM, Lugo A, Scala M, et al. Global prevalence and incidence of tinnitus: a systematic review and meta-analysis. JAMA Neurol. 2022;79(9):888–900. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0003] 3.Dalrymple SN, Lewis SH, Philman S.. Tinnitus: diagnosis and management. Am Fam Physician. 2021;103(11):663–671. [PubMed] [Google Scholar]

[CIT0004] 4.Biswas R, Genitsaridi E, Trpchevska N, et al. Low evidence for tinnitus risk factors: a systematic review and meta-analysis. J Assoc Res Otolaryngol. 2023;24(1):81–94. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0005] 5.Hayes SH, Schormans AL, Sigel G, et al. Uncovering the contribution of enhanced central gain and altered cortical oscillations to tinnitus generation. Prog Neurobiol. 2021;196:101893. [DOI] [PubMed] [Google Scholar]

[CIT0006] 6.Sedley W. Tinnitus: does gain explain? Neuroscience. 2019;407:213–228. [DOI] [PubMed] [Google Scholar]

[CIT0007] 7.Shore SE, Wu C.. Mechanisms of noise-induced tinnitus: insights from cellular studies. Neuron. 2019;103(1):8–20. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0008] 8.Yu XJ, Xu XX, He S, et al. Change detection by thalamic reticular neurons. Nat Neurosci. 2009;12(9):1165–1170. [DOI] [PubMed] [Google Scholar]

[CIT0009] 9.Yu XJ, Xu XX, Chen X, et al. Slow recovery from excitation of thalamic reticular nucleus neurons. J Neurophysiol. 2009;101(2):980–987. [DOI] [PubMed] [Google Scholar]

[CIT0010] 10.Rauschecker JP, Leaver AM, MüHLAU M.. Tuning out the noise: limbic-auditory interactions in tinnitus. Neuron. 2010;66(6):819–826. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0011] 11.Knipper M, VAN Dijk P, Schulze H, et al. The neural bases of tinnitus: lessons from deafness and cochlear implants. J Neurosci. 2020;40(38):7190–7202. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0012] 12.Hofmeier B, Wertz J, Refat F, et al. Functional biomarkers that distinguish between tinnitus with and without hyperacusis. Clin Transl Med. 2021;11(5):e378. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0013] 13.Brinkmann P, Kotz SA, Smit JV, et al. Auditory thalamus dysfunction and pathophysiology in tinnitus: a predictive network hypothesis. Brain Struct Funct. 2021;226(6):1659–1676. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0014] 14.LLINáS R, Urbano FJ, Leznik E, et al. Rhythmic and dysrhythmic thalamocortical dynamics: GABA systems and the edge effect. Trends Neurosci. 2005;28(6):325–333. [DOI] [PubMed] [Google Scholar]

[CIT0015] 15.Winer JA, Larue DT.. Patterns of reciprocity in auditory thalamocortical and corticothalamic connections: study with horseradish peroxidase and autoradiographic methods in the rat medial geniculate body. J Comp Neurol. 1987;257(2):282–315. [DOI] [PubMed] [Google Scholar]

[CIT0016] 16.Ledoux JE, Farb CR, Romanski LM.. Overlapping projections to the amygdala and striatum from auditory processing areas of the thalamus and cortex. Neurosci Lett. 1991;134(1):139–144. [DOI] [PubMed] [Google Scholar]

[CIT0017] 17.Bajo VM, Rouiller EM, Welker E, et al. Morphology and spatial distribution of corticothalamic terminals originating from the cat auditory cortex. Hear Res. 1995;83(1–2):161–174. [DOI] [PubMed] [Google Scholar]

[CIT0018] 18.Winer JA, Kelly JB, Larue DT.. Neural architecture of the rat medial geniculate body. Hear Res. 1999;130(1–2):19–41. [DOI] [PubMed] [Google Scholar]

[CIT0019] 19.Lee CC, Winer JA.. Connections of cat auditory cortex: I. Thalamocortical system. J Comp Neurol. 2008;507(6):1879–1900. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0020] 20.Leaver AM, Renier L, Chevillet MA, et al. Dysregulation of limbic and auditory networks in tinnitus. Neuron. 2011;69(1):33–43. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0021] 21.Cook JA, Barry KM, Zimdahl JW, et al. Spontaneous firing patterns in the medial geniculate nucleus in a guinea pig model of tinnitus. Hear Res. 2021;403:108190. [DOI] [PubMed] [Google Scholar]

[CIT0022] 22.Kalappa BI, Brozoski TJ, Turner JG, et al. Single unit hyperactivity and bursting in the auditory thalamus of awake rats directly correlates with behavioural evidence of tinnitus. J Physiol. 2014;592(22):5065–5078. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0023] 23.MüHLAU M, Rauschecker JP, Oestreicher E, et al. Structural brain changes in tinnitus. Cereb Cortex. 2006;16(9):1283–1288. [DOI] [PubMed] [Google Scholar]

[CIT0024] 24.Landgrebe M, Langguth B, Rosengarth K, et al. Structural brain changes in tinnitus: grey matter decrease in auditory and non-auditory brain areas. Neuroimage. 2009;46(1):213–218. [DOI] [PubMed] [Google Scholar]

[CIT0025] 25.Chen SJ, Xu Y, Liang YM, et al. Identification and characterization of a series of novel HCN channel inhibitors. Acta Pharmacol Sin. 2019;40(6):746–754. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0026] 26.Li S, Kalappa BI, Tzounopoulos T.. Noise-induced plasticity of KCNQ2/3 and HCN channels underlies vulnerability and resilience to tinnitus. Elife. 2015:4:e07242. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0027] 27.Lanaia V, Tziridis K, Schulze H.. Salicylate-induced changes in hearing thresholds in mongolian gerbils are correlated with tinnitus frequency but not with tinnitus strength. Front Behav Neurosci. 2021;15:698516. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0028] 28.Ridding MC, Rothwell JC.. Is there a future for therapeutic use of transcranial magnetic stimulation? Nat Rev Neurosci. 2007;8(7):559–567. [DOI] [PubMed] [Google Scholar]

[CIT0029] 29.VAN Zwieten G, Janssen MLF, Smit JV, et al. Inhibition of experimental tinnitus with high frequency stimulation of the rat medial geniculate body. Neuromodulation. 2019;22(4):416–424. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0030] 30.Devos JVP, Smit JV, George ELJ, et al. Effective treatment of refractory tinnitus by bilateral deep brain stimulation of the medial geniculate body of the thalamus: a case report. Brain Stimul. 2023;16(5):1322–1324. [DOI] [PubMed] [Google Scholar]

[CIT0031] 31.Tan X, Zhong Y, Li R, et al. Neuromodulation of chemical synaptic transmission driven by THz photons. Research (Wash D C). 2022;2022:0010. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0032] 32.Wang P, Lou J, Fang G, et al. Progress on cutting-edge infrared-terahertz biophysics. IEEE Trans Microw Theory Techn. 2022. [Google Scholar]

[CIT0033] 33.Liu P, Xue X, Zhang C, et al. Mid-infrared photons alleviate tinnitus by activating the KCNQ2 channel in the auditory cortex. Research (Wash DC). 2024;7:0479. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0034] 34.Middleton JW, Kiritani T, Pedersen C, et al. Mice with behavioral evidence of tinnitus exhibit dorsal cochlear nucleus hyperactivity because of decreased GABAergic inhibition. Proc Natl Acad Sci USA. 2011;108(18):7601–7606. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0035] 35.Li S, Choi V, Tzounopoulos T.. Pathogenic plasticity of Kv7.2/3 channel activity is essential for the induction of tinnitus. Proc Natl Acad Sci USA. 2013;110(24):9980–9985. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0036] 36.Tang D, Chen M, Huang X, et al. SRplot: a free online platform for data visualization and graphing. PLoS One. 2023;18(11):e0294236. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0037] 37.Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–d13. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0038] 38.ZEIDáN-CHULIá F, GüRSOY M, Neves DE Oliveira BH, et al. A systems biology approach to reveal putative host-derived biomarkers of periodontitis by network topology characterization of MMP-REDOX/NO and apoptosis integrated pathways. Front Cell Infect Microbiol. 2015;5:102. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0039] 39.Tang Y, Li M, Wang J, et al. CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems. 2015;127:67–72. [DOI] [PubMed] [Google Scholar]

[CIT0040] 40.Sholl DA. Dendritic organization in the neurons of the visual and motor cortices of the cat. J Anat. 1953;87(4):387–406. [PMC free article] [PubMed] [Google Scholar]

[CIT0041] 41.Jacobs S, Cheng C, Doering LC.. Hippocampal neuronal subtypes develop abnormal dendritic arbors in the presence of Fragile X astrocytes. Neuroscience. 2016;324:202–217. [DOI] [PubMed] [Google Scholar]

[CIT0042] 42.Konan LM, Song H, Pentecost G, et al. Multi-focal neuronal ultrastructural abnormalities and synaptic alterations in mice after low-intensity blast exposure. J Neurotrauma. 2019;36(13):2117–2128. [DOI] [PubMed] [Google Scholar]

[CIT0043] 43.Xiao Y, Fu H, Han X, et al. Role of synaptic structural plasticity in impairments of spatial learning and memory induced by developmental lead exposure in Wistar rats. PLoS One. 2014;9(12):e115556. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0044] 44.Xu X, Liu X, Zhang Q, et al. Sex-specific effects of bisphenol-A on memory and synaptic structural modification in hippocampus of adult mice. Horm Behav. 2013;63(5):766–775. [DOI] [PubMed] [Google Scholar]

[CIT0045] 45.Chen L, Wang X, Ge S, et al. Medial geniculate body and primary auditory cortex differentially contribute to striatal sound representations. Nat Commun. 2019;10(1):418. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0046] 46.Dong HW. The Allen Atlas: a digital brain atlas of C57BL/6J male mouse, (with CD-ROM). Wiley. 2007. [Google Scholar]

[CIT0047] 47.Li KX, Lu YM, Xu ZH, et al. Neuregulin 1 regulates excitability of fast-spiking neurons through Kv1.1 and acts in epilepsy. Nat Neurosci. 2011;15(2):267–273. [DOI] [PubMed] [Google Scholar]

[CIT0048] 48.Zhu M, Idikuda VK, Wang J, et al. Shank3-deficient thalamocortical neurons show HCN channelopathy and alterations in intrinsic electrical properties. J Physiol. 2018;596(7):1259–1276. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0049] 49.Robinson DW, Cameron WE.. Time-dependent changes in input resistance of rat hypoglossal motoneurons associated with whole-cell recording. J Neurophysiol. 2000;83(5):3160–3164. [DOI] [PubMed] [Google Scholar]

[CIT0050] 50.Fang J, Wang H, Zhou J, et al. Baicalin provides neuroprotection in traumatic brain injury mice model through Akt/Nrf2 pathway. Drug Des Devel Ther. 2018;12:2497–2508. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0051] 51.Livak KJ, Schmittgen TD.. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods (San Diego, Calif). 2001;25(4):402–408. [DOI] [PubMed] [Google Scholar]

[CIT0052] 52.Abraham MJ, Murtola T, Schulz R, et al. GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015. [Google Scholar]

[CIT0053] 53.Jo S, Kim T, Iyer VG, et al. CHARMM-GUI: a web-based graphical user interface for CHARMM. J Comput Chem. 2008;29(11):1859–65. [DOI] [PubMed] [Google Scholar]

[CIT0054] 54.Hoover WG. Canonical dynamics: equilibrium phase-space distributions. Phys Rev A Gen Phys. 1985;31(3):1695–1697. [DOI] [PubMed] [Google Scholar]

[CIT0055] 55.Humphrey W, Dalke A, Schulten K.. VMD: visual molecular dynamics. J Mol Graph. 1996;14(1):33–38. [DOI] [PubMed] [Google Scholar]

[CIT0056] 56.Pompeiano M, Colonnese MT.. cFOS as a biomarker of activity maturation in the hippocampal formation. Front Neurosci. 2023;17:929461. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0057] 57.Schiller M, Azulay-Debby H, Boshnak N, et al. Optogenetic activation of local colonic sympathetic innervations attenuates colitis by limiting immune cell extravasation. Immunity. 2021;54(5):1022–1036. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0058] 58.Tang ZQ, Trussell LO.. Serotonergic regulation of excitability of principal cells of the dorsal cochlear nucleus. J Neurosci. 2015;35(11):4540–4551. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0059] 59.Combe CL, Gasparini S.. I(h) from synapses to networks: HCN channel functions and modulation in neurons. Prog Biophys Mol Biol. 2021;166:119–132. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0060] 60.Liu X, Qiao Z, Chai Y, et al. Nonthermal and reversible control of neuronal signaling and behavior by midinfrared stimulation. Proc Natl Acad Sci USA. 2021;118(10):e2015685118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0061] 61.Almasabi F, Janssen MLF, Devos J, et al. The role of the medial geniculate body of the thalamus in the pathophysiology of tinnitus and implications for treatment. Brain Res. 2022;1779:147797. [DOI] [PubMed] [Google Scholar]

[CIT0062] 62.Hayes SH, Patel SV, Arora P, et al. Neurophysiological, structural, and molecular alterations in the prefrontal and auditory cortices following noise-induced hearing loss. Neurobiol Dis. 2024;200:106619. [DOI] [PubMed] [Google Scholar]

[CIT0063] 63.Spruston N. Pyramidal neurons: dendritic structure and synaptic integration. Nat Rev Neurosci. 2008;9(3):206–221. [DOI] [PubMed] [Google Scholar]

[CIT0064] 64.Vianney-Rodrigues P, Auerbach BD, Salvi R.. Aberrant thalamocortical coherence in an animal model of tinnitus. J Neurophysiol. 2019;121(3):893–907. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0065] 65.Gault R, Mcginnity TM, Coleman S.. A computational model of thalamocortical dysrhythmia in people with tinnitus. IEEE Trans Neural Syst Rehabil Eng. 2018;26(9):1845–1857. [DOI] [PubMed] [Google Scholar]

[CIT0066] 66.Arcelli P, Frassoni C, Regondi MC, et al. GABAergic neurons in mammalian thalamus: a marker of thalamic complexity? Brain Res Bull. 1997;42(1):27–37. doi: 10.1016/S0361-9230(96)00107-4. [DOI] [PubMed] [Google Scholar]

[CIT0067] 67.Tottene A, Favero M, Pietrobon D.. Enhanced thalamocortical synaptic transmission and dysregulation of the excitatory-inhibitory balance at the thalamocortical feedforward inhibitory microcircuit in a genetic mouse model of migraine. J Neurosci. 2019;39(49):9841–9851. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0068] 68.Jeschke M, Happel MFK, Tziridis K, et al. Acute and long-term circuit-level effects in the auditory cortex after sound trauma. Front Neurosci. 2020;14:598406. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0069] 69.Song Z, Sun Y, Liu P, et al. Terahertz wave alleviates comorbidity anxiety in pain by reducing the binding capacity of nanostructured glutamate molecules to GluA2. Research (Wash D C). 2024;7:0535. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0070] 70.Zhang J, He Y, Liang S, et al. Non-invasive, opsin-free mid-infrared modulation activates cortical neurons and accelerates associative learning. Nat Commun. 2021;12(1):2730. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0071] 71.Schilling A, Sedley W, Gerum R, et al. Predictive coding and stochastic resonance as fundamental principles of auditory phantom perception. Brain. 2023;146(12):4809–4825. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0072] 72.Holt GR, Koch C.. Shunting inhibition does not have a divisive effect on firing rates. Neural Comput. 1997;9(5):1001–1013. [DOI] [PubMed] [Google Scholar]

[CIT0073] 73.VAN Zwieten G, Roberts MJ, Schaper F, et al. Noise-induced neurophysiological alterations in the rat medial geniculate body and thalamocortical desynchronization by deep brain stimulation. J Neurophysiol. 2021;125(2):661–671. [DOI] [PubMed] [Google Scholar]

[CIT0074] 74.Barker AT. An introduction to the basic principles of magnetic nerve stimulation. J Clin Neurophysiol. 1991;8(1):26–37. [DOI] [PubMed] [Google Scholar]

[CIT0075] 75.Jannati A, Oberman LM, Rotenberg A, et al. Assessing the mechanisms of brain plasticity by transcranial magnetic stimulation. Neuropsychopharmacology. 2023;48(1):191–208. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0076] 76.Xiao T, Wu K, Wang P, et al. Sensory input-dependent gain modulation of the optokinetic nystagmus by mid-infrared stimulation in pigeons. Elife. 2023:12:e78729. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0077] 77.George MS, Abbott LF, Siegelbaum SA.. HCN hyperpolarization-activated cation channels inhibit EPSPs by interactions with M-type K(+) channels. Nat Neurosci. 2009;12(5):577–584. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0078] 78.Migliore M, Migliore R.. Know your current I(h): interaction with a shunting current explains the puzzling effects of its pharmacological or pathological modulations. PLoS One. 2012;7(5):e36867. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0079] 79.Amarillo Y, Zagha E, Mato G, et al. The interplay of seven subthreshold conductances controls the resting membrane potential and the oscillatory behavior of thalamocortical neurons. J Neurophysiol. 2014;112(2):393–410. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0080] 80.Hu W, Bean BP.. Differential control of axonal and somatic resting potential by voltage-dependent conductances in cortical layer 5 pyramidal neurons. Neuron. 2018;99(6):1355. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0081] 81.Notomi T, Shigemoto R.. Immunohistochemical localization of Ih channel subunits, HCN1-4, in the rat brain. J Comp Neurol. 2004;471(3):241–276. [DOI] [PubMed] [Google Scholar]

[CIT0082] 82.Wells J, Kao C, Mariappan K, et al. Optical stimulation of neural tissue in vivo. Opt Lett. 2005;30(5):504–506. [DOI] [PubMed] [Google Scholar]

[CIT0083] 83.MARéCHAL . The molecular structure of liquid water delivered by absorption spectroscopy in the whole IR region completed with thermodynamics data. [Google Scholar]

[CIT0084] 84.Stujenske JM, Spellman T, Gordon JA.. Modeling the spatiotemporal dynamics of light and heat propagation for in vivo optogenetics. Cell Rep. 2015;12(3):525–534. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0085] 85.Ait Ouares K, Beurrier C, Canepari M, et al. Opto nongenetics inhibition of neuronal firing. Eur J Neurosci. 2019;49(1):6–26. [DOI] [PubMed] [Google Scholar]

[CIT0086] 86.Owen SF, Liu MH, Kreitzer AC.. Thermal constraints on in vivo optogenetic manipulations. Nat Neurosci. 2019;22(7):1061–1065. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0087] 87.Aslih N, Dekel BZ, Malonek D, et al. Non-invasive evaluation of embryos using mid-infrared attenuated total reflection spectrometry of incubation medium: a preliminary study. Reprod Biomed Online. 2023;46(5):793–801. [DOI] [PubMed] [Google Scholar]

[CIT0088] 88.Sun C, Fan Q, Xie R, et al. Tetherless optical neuromodulation: wavelength from orange-red to mid-infrared. Neurosci Bull. 2024;40(8):1173–1188. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0089] 89.Tziridis K, Maul A, Rasheed J, et al. Tinnitus is associated with increased extracellular matrix density in the auditory cortex of Mongolian gerbils. BMC Neurosci. 2024;25(1):52. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0090] 90.Richardson BD, Sottile SY, Caspary DM.. Mechanisms of GABAergic and cholinergic neurotransmission in auditory thalamus: impact of aging. Hear Res. 2021;402:108003. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Mid-infrared photons relieve tinnitus-like behavior of mice by regulating HCN2 ion channels in the medial geniculate body

Xinmiao Xue

Peng Liu

Chi Zhang

Zhixin Zhang

Hanwen Zhou

Zhiwei Ding

Li Wang

Yuke Jiang

Shuhan Lu

Weidong Shen

Xiaoming Li

Lan Sun

Xiaoxia Jiang

Shiming Yang

Fangyuan Wang

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Methods

Animals

Noise exposure

Detection of tinnitus-like behavior in mice

Figure 1.

RNA-seq

Bioinformatics analysis

Immunocytochemistry

Golgi staining

Transmission electron microscopy (TEM) study

Cannula implantation and virus injection

Mid-infrared source

Preparation of brain slices

Electrophysiological recordings of brain slices

q-PCR

Preparation of cultured cells

Electrophysiological recordings of cultured cells

Molecular dynamics simulations

Statistical analysis

Results

Establishment and validation of tinnitus-like behavior in mice

Figure 2.

Analysis of the gene transcriptome in tinnitus mice

Table 1.

Morphological changes in the MGB neurons of mice with tinnitus-like behavior

Figure 3.

Increased excitability of MGB neurons from tinnitus mice

Figure 4.

Chemogenetic modulation of MGB neuron activity and tinnitus-like behavior

Figure 5.

MIR can alleviate tinnitus-like behavior in mice

Figure 6.

The inhibited effect of MIR on MGB neurons of tinnitus mice

Figure 7.

Ih amplitude decrease in tinnitus mice

Figure 8.

The function of Ih current in the MIR regulation of tinnitus-like behavior in mice

Figure 9.

Discussion

Conclusions

Supplementary Material

Acknowledgments

Funding Statement

Ethics approval and consent to participate

Publication statement

Disclosure statement

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles