Intact auditory N100 and mismatch negativity in undergraduates with a history of mild traumatic brain injury

Intact auditory N100 and mismatch negativity in undergraduates with a history of mild traumatic brain injury | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL European Journal of Neuroscience This is a preprint and has not been peer reviewed. Data may be preliminary. 6 October 2025 V1 Latest version Share on Intact auditory N100 and mismatch negativity in undergraduates with a history of mild traumatic brain injury Authors : Jenna Pablo 0000-0003-3766-310X [email protected] , Lena Kemmelmeier , Hector Arciniega , Jorja Shires , Wendy Torrens , Marian Berryhill , and Sarah Haigh 0000-0003-2400-4412 Authors Info & Affiliations https://doi.org/10.22541/au.175971798.86492420/v1 294 views 151 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Most traumatic brain injuries (TBIs) are classified as mild (mTBI), yet they can still be associated with lasting behavioral, sensory, cognitive, and neural changes. Recent studies show that individuals with a history of mTBI (hmTBI) experience auditory sensitivities (e.g., noise annoyance and hyperacusis). Here, we tested whether we could detect early auditory processing alterations in individuals with a hmTBI using well-characterized event-related potentials (ERPs) sensitive to auditory sensory responses, specifically the N100, and the mismatch negativity (MMN) waveform. Eighteen participants with a self-reported hmTBI and 25 control participants completed a passive oddball task in which infrequent pitch-deviant tones were interleaved amidst frequent standard tones, during which EEG was recorded. We examined N100 and MMN amplitude and latency, using frequentist and Bayesian analyses. Across analyses, there were no significant group differences. The Bayes factors provided anecdotal to moderate support for the null hypothesis. These results show that undergraduates with hmTBI exhibit intact early auditory processes. Future research should examine whether later, top-down processes contribute to enduring auditory symptoms. Intact auditory N100 and mismatch negativity in undergraduates with a history of mild traumatic brain injury Pablo, J. N. 1 *, Kemmelmeier, L. L. 1,2 *, Arciniega, H. 3,4,5 , Shires, J. 1 , Torrens, W. A. 1 , Berryhill, M. E. 1 , & Haigh, S. M. 1 *Co-First Authors 1 Programs in Cognitive & Brain Sciences, and Neuroscience, University of Nevada, Reno, NV 89557 2 Department of Psychology, University of California, San Diego, La Jolla, CA 92093 3 Department of Rehabilitation Medicine, NYU Grossman School of Medicine, New York, NY, USA 10016 4 NYU Langone Concussion Center, NYU Langone Health, New York, NY, USA 10016 5 Institute for Translational Neuroscience, NYU Grossman School of Medicine, New York, NY, USA 10016 Corresponding authors: Jenna N. Pablo & Marian E. Berryhill 1664 N. Virginia St., MS 296 Reno, NV 89557 [email protected] & [email protected] (775)682-8667 Acknowledgements: This material is based upon work supported by NIMH R15MH122935 to SMH and MEG, Research Supplements to Promote Diversity in Health-Related Research to JNP (MH122935-01S1) and WAT (MH122935-01S2), and core facilities supported by NIH COBRE (P30 GM145646). Abstract Most traumatic brain injuries (TBIs) are classified as mild (mTBI), yet they can still be associated with lasting behavioral, sensory, cognitive, and neural changes. Recent studies show that individuals with a history of mTBI (hmTBI) experience auditory sensitivities (e.g., noise annoyance and hyperacusis). Here, we tested whether we could detect early auditory processing alterations in individuals with a hmTBI using well-characterized event-related potentials (ERPs) sensitive to auditory sensory responses, specifically the N100, and the mismatch negativity (MMN) waveform. Eighteen participants with a self-reported hmTBI and 25 control participants completed a passive oddball task in which infrequent pitch-deviant tones were interleaved amidst frequent standard tones, during which EEG was recorded. We examined N100 and MMN amplitude and latency, using frequentist and Bayesian analyses. Across analyses, there were no significant group differences. The Bayes factors provided anecdotal to moderate support for the null hypothesis. These results show that undergraduates with hmTBI exhibit intact early auditory processes. Future research should examine whether later, top-down processes contribute to enduring auditory symptoms. Keywords: concussion, mTBI, mismatch negativity, n100 Introduction Approximately 4.8 million cases of traumatic brain injury (TBI) occur annually in the United States. Of these, ~85% are classified as mild traumatic brain injury (mTBI), or concussion (Taylor et al., 2017). Although most people recover fully, an mTBI can lead to long-term sensory, cognitive, and emotional impairments (Åhman et al., 2013; Dean & Sterr, 2013; Mavroudis et al., 2024; McInnes et al., 2017; Miller et al., 2024; Shepherd et al., 2020, 2021; Tuersunjiang et al., 2025). Given the high prevalence of mTBI, these findings raise a pressing public health concern. There are currently no approved treatments for mTBI beyond symptom management, and conventional neuroimaging often fails to reveal abnormalities. This gap underscores the urgent need for reliable biomarkers that can detect enduring alterations, ultimately guiding diagnosis, prognosis, and treatment. Among the symptoms post-mTBI, auditory sensitivity has emerged as a persistent yet understudied phenomenon. Recent findings highlight the durability of auditory problems after TBI and mTBI. Noise annoyance is reported 4-15 years post-injury, with many individuals with TBI reporting feeling overwhelmed by auditory stimuli (Landon et al., 2012). Even in mTBI, self-reported noise sensitivity (i.e., general intolerance to everyday sounds; Theodoroff et al., 2022) is commonly reported, with estimates ranging from 28% (Shepherd et al., 2021) to 42% of individuals with mTBI reporting noise sensitivity one year post-injury (Shepherd et al., 2020). Noise sensitivities affect daily functioning for individuals with mTBI, as they relate to social withdrawal (Shepherd et al., 2020), anxiety (Miller et al., 2024; Shepherd et al., 2021), and depression (Shepherd et al., 2021). Further evidence of lasting auditory discomfort comes from sports-related concussion, with individuals reporting higher hyperacusis scores compared to controls at 5 weeks post-injury (Assi et al., 2018). These post-mTBI auditory symptoms have prompted calls for clinical practice guidelines to incorporate auditory symptom assessments in patients with head injury (Theodoroff et al., 2022). Given the high prevalence and impact of auditory sensitivities following mTBI, documenting robust auditory biomarkers is important to understand the neural basis of enduring symptoms in such a heterogeneous condition as mTBI. Most research on auditory sensitivity focuses on clinical or athletic participants. However, undergraduates with a self-reported history of mTBI (hmTBI) exhibit behavioral cognitive and neural differences, making them a conservative starting point to re-examine whether early auditory processing is impaired. For instance, hmTBI samples (mean ~4 years post-injury) demonstrated significant visual working memory deficits (Arciniega et al., 2019, 2020, 2021) accompanied by significantly weaker resting-state connectivity (Arciniega et al., 2021). Undergraduate hmTBI samples can reveal subtle, enduring neural differences, underscoring their value in evaluating which auditory biomarkers would be most apparent across mTBI populations. One approach for identifying biomarkers is via event-related potentials (ERPs). ERP biomarkers are temporally aligned with different stages of processing and can characterize immediate and lasting neural changes following mTBI. Additionally, measuring ERPs is relatively cost-effective, fast, and semi-portable compared to other neuroimaging techniques. Moreover, auditory ERPs have been widely studied in mTBI. These include the: N100 (Duncan et al., 2003, 2005; Gosselin et al., 2006; Manning Franke et al., 2021; Ruiter et al., 2019, 2020), mismatch negativity (MMN; Ewers, 2020; Lu, 2024; Ruiter et al., 2019; Wynn & Green, 2024), and P300 (Bernstein, 2002; Gosselin et al., 2006; Lavoie et al., 2004; Segalowitz et al., 2001; Vander Werff & Rieger, 2019). However, results are mixed across mTBI samples and tasks, highlighting the need for additional evidence. 1.1 N100 as a Biomarker of MTBI Given the prevalence of enduring auditory sensitivities in mTBI, early auditory ERPs provide a meaningful starting point for identifying potential biomarkers. One commonly collected auditory biomarker is the N100, a frontocentral ERP component that reflects stimulus detection (Joos et al., 2014; Näätänen & Picton, 1987). The existing research on N100 in mTBI is mixed. Attenuated N100 amplitudes have been reported in several populations, including closed head injury survivors (Duncan et al., 2003, 2005), athletes with mTBI (Gosselin et al., 2006), and mid-life veterans with a history of repeat (3+) mTBI (Manning Franke et al., 2021). N100 reductions in passive auditory oddball tasks (i.e., participants are not required to attend to the deviant stimuli), but not in active oddball tasks (i.e., participants are typically requested to respond to a deviant stimulus), were found in retired athletes with hmTBI (Ruiter et al., 2019), and adolescents with mTBI (Ruiter et al., 2020). Additionally, two papers reported that undergraduates with hmTBI exhibited typical N100 amplitudes across four active oddball tasks, which required participants to respond to low-probability stimuli (Bernstein, 2002; Segalowitz et al., 2001). Overall, the mixed findings suggest the need to examine N100 amplitudes in undergraduate hmTBI samples using passive auditory oddball tasks, which may be more sensitive to early sensory impairments than active tasks. 1.2 MMN as a Biomarker of MTBI A second widely studied biomarker is the auditory MMN, a frontocentral negative-deflecting ERP that occurs 150-200 ms post-stimulus (Näätänen et al., 1978). It is thought to reflect the predictive coding of sensory information (Friston, 2012; Garrido et al., 2009) or to be an index of sensory memory (Bartha‐Doering et al., 2015). The MMN is elicited during passive oddball tasks and does not require attention (Sussman, 2007), making MMN a valuable tool for determining whether individuals with hmTBI exhibit early sensory disruptions. In severe TBI, the MMN amplitude predicts coma recovery (Zhou et al., 2021), with lower amplitude associated with worse neurocognition and daily-life/social functional outcomes (Sun et al., 2015). The MMN amplitude is reduced in athletes with repeated hmTBI (Lu, 2024; Ruiter et al., 2019) and in contact athletes after a sports season (Ewers, 2020). In contrast, typical MMNs have been reported in acutely (≤21 days post-injury) concussed adolescents (Ruiter et al., 2020), and in veterans with mTBI (Wynn & Green, 2024). 1.3 Current Study In summary, prior work suggests that auditory sensitivity is a disruptive symptom of mTBI. Additionally, we have demonstrated that undergraduate hmTBI samples exhibit subtle yet lasting neural alterations. Examining N100 and MMN ERPs permits detection of early sensory disruptions; however, the mTBI literature varies widely depending on the type of task and sample. Here, we directly test whether early auditory processing is impaired in undergraduates with a hmTBI by examining the N100 and MMN in a passive auditory oddball task. Atypical N100 or MMNs would support an early processing explanation for the auditory sensitivity, suggesting that mTBI patients should be assessed for sensory deficits over time. Alternatively, null findings would suggest early processing is intact, and efforts should focus on more top-down processing explanations. 2. Methods 2.1 Participants We analyzed data from 18 participants with a hmTBI (time since injury: 4 wk–17 yr, M (SD)=5.72(5.13) years) and 25 healthy control participants (see Table 1). Inclusion criteria consisted of being at least 18 years old, self-reporting no history of neurological or psychiatric diagnoses, not taking any psychiatric medications, no history of epilepsy, and having normal hearing and normal/corrected-to-normal vision. Our sample size is similar to previous work from Arciniega et al. (2019) identifying behavioral effects of hmTBI in an undergraduate population (control n=20-25, hmTBI n=18-25). Further, previous ERP studies on undergraduates with a hmTBI utilized even smaller samples (control: n=13, hmTBI: n=10: Bernstein, 2002; control: n=12, hmTBI: n=10: Segalowitz et al., 2001) and were sensitive to group-level differences. The University of Nevada Institutional Review Board approved all procedures. Participants signed a consent form and received their choice of class credit or a $15 Amazon gift card. Table 1 Control and HmTBI Sample Characteristics Control hmTBI p -value Gender (Female/Male) 21/4 10/8 .09 Age 20.2(3.0) 20.7(4.4) .64 Handedness (R/L) 22/3 15/3 .43 GPA 3.6(0.3) 3.5(0.5) .53 EEG Interpolated Channels 2.2(1.3) 2.3(1.0) .74 EEG Trials Analyzed 545.2(46.2) 510.1(75.9) .09 Sport Accidents (n) - 10 - Falls (n) - 6 - Car Accident (n) - 1 - Head collision (n) - 1 - Note. Data depict group means (standard deviations) unless otherwise noted. Welch’s t-tests and Chi-square tests compared measures between control and hmTBI participants. R=right; L=left; 2.2 Materials 2.2.1 HmTBI Questionnaire HmTBI status was based on participants’ response to the following question, “Do you have a history of concussion or TBI (traumatic brain injury)?” Participants who indicated ‘yes’ were classified as having a hmTBI and provided the date of their most recent injury, along with a brief description of what happened and how they felt afterward. Previous protocols defined hmTBI status based on self-report too (Arciniega et al., 2019, 2020, 2021; Bernstein, 2002; Segalowitz et al., 2001). Etiology included: sports accidents (n=10), falls (n=6), a car accident (n=1), or hitting heads with another person (n=1). 2.2.2 Apparatus Stimulus presentation was conducted using MATLAB (R2022b version, The MathWorks, Natick, MA) and the Psychophysics Toolbox 3.0.18 extension (Kleiner et al., 2007; Pelli, 1997) on a 32” Display++ LCD Monitor from Cambridge Research Systems and Dell Precision 5810 Tower X-Series computer. Auditory stimuli were presented binaurally using Etymotic ER2 insert earphones. 2.2.3 Auditory Oddball Task During the passive auditory oddball task (see Figure 1), we presented a total of 600 auditory stimuli, comprising standard tones (1046.5 Hz (C6), 80%) and infrequent pitch-deviant tones (1108.73 Hz (C#6), 20%). Tones lasted 50 ms with a random inter-trial interval (ITI) of 450-950 ms. Participants engaged in an attention task while the tones played over the earphones. They were instructed to maintain fixation on a black central cross and press the spacebar when the cross flashed white (4-5% of trials; 100 ms) and to ignore the tones. Figure 1. Auditory oddball task schematic. Participants viewed a central fixation cross while auditory stimuli (80% standard, 20% pitch-deviant tones) played over earphones. The tones lasted 50 ms and were separated by a random inter-trial interval (ITI) of 450-950 ms. Occasionally, the fixation cross turned white (100ms), signaling participants to make a keypress. 2.3 EEG Data Acquisition and Preprocessing We recorded EEG using a 32-channel Biosemi ActiveTwo system (Amsterdam, Netherlands) at a sampling rate of 512 Hz. We re-referenced to the average of two additional mastoid electrodes. Electrooculogram electrodes were placed below the outer canthi of both eyes to detect blinks and horizontal eye movements, and an electrocardiogram electrode was positioned on the left collarbone. Preprocessing and subsequent analyses were conducted with custom scripts using the EEGLAB (version 2024.2.1; Delorme & Makeig, 2004) and ERPLAB (version 12.0; Lopez-Calderon & Luck, 2014) toolboxes in MATLAB (R2024b version, MathWorks, Natick, MA). Raw EEG data were bandpass filtered (0.1-100 Hz). Noisy channels, defined as >3 standard deviations from the mean amplitude, were interpolated using the mean of the nearest neighboring channels. HmTBI and control participants did not differ in the number of interpolated channels (see Table 1). 2.4 Data Analysis 2.4.1 Behavioral Data We extracted each participant’s median reaction time to the onset of the white cross during the oddball task. Reaction times that occurred over two trials after the presentation of the flash were considered invalid and excluded from this calculation. Accuracies were recorded as the proportion of hits. To compare reaction times and accuracies between groups, we performed Welch’s t-tests. 2.4.2 ERP Data EEG data were first segmented into epochs from 50 ms before to 330 ms after stimulus onset, and were baseline corrected to the pre-stimulus interval. Epochs with activity exceeding \(\pm\)100 μV were automatically rejected, and the remaining data were low-pass filtered at 20 Hz. Groups did not differ in the number of total epochs kept (see Table 1). To calculate N100 response amplitudes, we averaged the EEG signal voltage over 115-135 ms and 131-151 ms for standard and deviant epochs, respectively. To derive MMN responses, we computed difference waveforms by subtracting standard tone ERPs from the deviant tone ERPs. We calculated MMN amplitude by averaging the voltage 185-205 ms post-stimulus. All time windows used for amplitude calculation are centered on the peaks of the grand-averaged waveforms (see Supplementary Figure 1). We conducted a mixed ANOVA with group (hmTBI, control) and component (standard, deviant) to assess N100 amplitude differences at electrode Fz. A Welch’s t-test assessed group-wise differences in MMN amplitude at Fz. We analyzed N100 and MMN amplitudes separately, as MMN is a subtraction waveform (deviant minus standard) derived from the same ERP signal as N100, rather than a distinct experimental condition. We conducted frequentist statistical analyses in R Studio (version 2025.05.1.4513, Posit team, 2025) with R version 4.5.1, using the afex (Singmann et al., 2012), emmeans (Lenth, 2017), and effectsize (Ben-Shachar et al., 2020) packages. We ran Bayesian analyses to evaluate evidence supporting the null hypothesis using JASP (version 0.95.1, JASP Team, 2025). We report the null model on top and compare it to all other models using BF 10 . To assess evidence of interactions in the Bayesian ANOVAs, we evaluated the inclusion Bayes factor, BF incl (Van Den Bergh et al., 2020). We interpreted Bayes factors according to standard guidelines, such that values between 1-3 indicate anecdotal evidence, 3-10 indicate moderate evidence, and values greater than 10 indicate strong evidence (Van Doorn et al., 2021). 2.4.3 Exploratory Analyses We conducted an exploratory analysis of N100 and MMN response latency. Three raters, blind to participant identity and group membership, independently viewed all N100 and MMN waveforms and marked the time of relevant peaks at Fz. When the majority of raters identified the same peak, the time of the minimum amplitude point within that peak’s window was recorded as the latency (see Supplementary Figures 2-7). One hmTBI participant was excluded from latency analyses due to a lack of consensus (see Supplementary Figure 8). We adopted this approach after selection using a fixed time window failed, and captured non-N100 and non-MMN signals. A mixed ANOVA with group (hmTBI, control) and component (standard, deviant) tested N100 latency differences, and a Welch’s t-test examined groupwise differences in MMN latency. Lastly, we conducted an exploratory analysis of mTBI subgroups. We separated the sports incident hmTBI (n=10) and fall hmTBI (n=6) participants, then compared their N100 and MMN amplitudes to controls following the analysis plan described previously. We conducted mixed ANOVAs to assess N100 amplitudes and used Welch’s t-tests to compare MMN amplitudes between the control and hmTBI subgroups (separately for sports incidents and falls). Results 3.1 Behavioral Results A Welch’s t-test found that hmTBI (M(SD)=393.2(42.8) ms) and control (M(SD)=397.5(35.0) ms) participants responded at a similar speed on the attention task, t (32.04)=0.35, p =.73, Hedge’s g =0.11. Further, a Welch’s t-test revealed that hmTBI (M(SD)=0.83(0.03)) and control (M(SD)=0.83(0.05)) participants were equally accurate, t (32.04)=-0.34, p =.73, Hedge’s g =-0.10. These analyses verify that all participants were awake during the recording and do not allude to any attention-based differences. 3.2 N100 and MMN Amplitude Because we observed the N100 and MMN responses to be maximal at frontocentral locations in the grand-averaged data (see Supplementary Figure 9), we localized analyses to electrode Fz. The N100 amplitude data revealed a within-subjects main effect, but no between-group differences. A mixed ANOVA revealed an expected main effect of component (standard, deviant) on N100 amplitudes, F (1,41)=8.89, p =.005, η²ₚ=.18, such that deviant tones elicited larger amplitude N100 responses than standard tones. The Bayesian analysis supported this (BF 10 =4.88), indicating moderate evidence for the alternative over the null hypothesis. There was no main effect of group, F (1,41)=1.66, p =.21, η²ₚ=.04, suggesting no overall amplitude differences between control and hmTBI groups (see Figure 2A-B; 2D). The corresponding Bayes factor, BF 10 =0.69, provided anecdotal support for the null hypothesis. Further, there was no significant group×component interaction, F (1,41)=1.84, p =.18, η²ₚ=.04, BF incl =0.84, confirming that there were no between-groups differences in the pattern of N100 amplitudes across tone conditions. Visual inspection of the deviant ERPs caused us to revisit the numerical difference between groups. To ensure we were not missing this difference due to our a priori time window, we conducted a running t-test with 20 ms non-overlapping windows. Again, there were no group differences in amplitude for the deviant waveforms. The MMN data revealed no group differences either. A Welch’s t-test found no between-groups differences in MMN amplitude, t (39.22)=-1.46, p =.15, Hedge’s g =-0.44, BF 10 =.68 (see Figure 2C-D). Figure 2. Auditory ERP responses across hmTBI and control groups. Mean waveforms to the (A) standard and (B) pitch-deviant tones in control participants (black) and participants with a history of mild traumatic brain injury (hmTBI; red) at electrode Fz. (C) Mean mismatch negativity (MMN) waveforms, calculated by subtracting the standard response from the deviant response, across groups. Shaded regions indicate ± 1 standard error of the mean. Dashed gray boxes represent the time windows used to extract amplitude. (D) Group comparisons of standard N100, deviant N100, and MMN amplitude. Deviant N100 responses were significantly more negative than standard responses. Symbols depict group means, and error bars represent 95% confidence intervals. * p =.005 3.3 N100 and MMN Latency Visual inspection of the waveforms suggested a slight latency difference, with the hmTBI group being delayed; therefore, we conducted a latency analysis to confirm this. However, the analysis of N100 and MMN latencies identified no significant between-groups differences (N100 latency: F (1,40)=607.97, p =.55, η²ₚ=9.04×10 -3 , BF 10 =0.39; MMN latency: t (-27.31)=-0.66, p =.51, Hedge’s g =-0.22, BF 10 =0.37; see Figure 3). Deviant N100 responses had significantly longer latencies than standard N100 responses, F (1,40)=106.82, p <.001, η²ₚ=.51, BF 10 =2.49×10 5 . There was no significant group×component interaction, F (1,40)=1.03, p =.32, η²ₚ=.03, BF incl =0.64. Figure 3. Auditory ERP latencies across hmTBI and control groups. Mean latencies for the N100 responses to standard and pitch-deviant tones, and the mismatch negativity (MMN) response in control participants (black) and participants with a history of mild traumatic brain injury (hmTBI; red) at electrode Fz. Deviant N100 responses occurred later than standard responses. Symbols depict group means, and error bars represent 95% confidence intervals. ** p <.001 3.4 N100 and MMN Amplitude for Etiology Differences To examine potential etiology differences, we compared hmTBI subgroups (sports incidents and fall-related injuries) to controls on N100 and MMN amplitudes. For the sports subgroup, a mixed ANOVA revealed a main effect of component (standard, deviant), F (1,32)=7.57, p =.01, η²ₚ=.19, BF 10 =4.48 with deviants eliciting larger N100 responses than standard tones. There was no main effect of group, F (1,32)=0.41, p =.53, η²ₚ=.01, BF 10 =.42, and no significant group×component interaction, F (1,32)=1.45, p =.24, η²ₚ=.04, BF incl =0.44. Similarly, a Welch’s t-test found no between-groups differences in MMN amplitude, t (18.13)=-1.08, p =.29, Hedge’s g =-0.39, BF 10 =.52 (see Supplementary Figure 10A). For the fall subgroup, no significant effects emerged (component: F (1,28)=2.87, p =.10, η²ₚ=.09, BF 10 =1.65; group: F (1,28)=1.09, p =.31, η²ₚ=.04l, BF 10 =.62; interaction: F (1,28)=0.12, p =.73, η²ₚ=.001, BF incl =0.37). The MMN analysis also showed no group difference, t (9.65)=-2.16, p =.06, Hedge’s g =-0.83, BF 10 =1.24 (see Supplementary Figure 10B). Discussion Auditory sensitivities are becoming more frequently reported as lasting symptoms after mTBI (Miller et al., 2024; Shepherd et al., 2020, 2021). Additionally, previous studies have examined auditory ERPs as a way to assess early, sensory processing differences in a variety of convenience mTBI samples (e.g., athletes and veterans). Here, we investigated whether early auditory ERPs (N100, MMN) collected during a passive oddball task are impaired in hmTBI. This approach tapped into a participant sample who are otherwise considered healthy and past the typical recovery stage of a mTBI. We previously found that these undergraduates with hmTBI exhibit weaker resting state connectivity (Arciniega et al., 2021). Therefore, our interest in studying undergraduates with hmTBI allowed us to test whether there are durable, reliable, sensitive biomarkers for detecting neural differences long after mTBI. Instead, the current data revealed no group differences in N100 and MMN amplitudes and peak latencies, suggesting that there are no lasting deficits in early-stage auditory processing in undergraduates with hmTBI. Our results align with some earlier work, identifying typical N100 amplitudes in undergraduates with hmTBI for active oddball tasks (Bernstein, 2002; Segalowitz et al., 2001) and typical MMN amplitudes in younger, more recently injured mTBI groups, including young veterans (Wynn & Green, 2024) and adolescents (Ruiter et al., 2020), despite the high prevalence of auditory complaints in mTBI populations (Shepherd et al., 2020, 2021; Theodoroff et al., 2022). That said, the mixed findings in the mTBI ERP literature may be an issue of age at the time of injury, time since injury, mTBI biomechanics or severity, or contributions of prior head injuries. For instance, attenuated N100 amplitudes have been observed in mid-life veterans (Manning Franke et al., 2021), and atypical MMN amplitudes have been observed in those with repeat hmTBI (Lu, 2024; Ruiter et al., 2019) and in high-contact athletes (Ewers, 2020). Given that atypical MMN amplitudes have been reported after high-contact sports (Ewers, 2020) but not in all mTBI populations, we also conducted an exploratory analysis comparing mTBI subgroups (sports incidents and fall-related injuries). These underpowered exploratory analyses yielded no tantalizing distinctions across all measures. Thus, it does not appear to be the case that even a subset of hmTBI participants demonstrates atypical ERPs, consistent with the interpretation that early auditory processing is intact in undergraduates with hmTBI. Additional research is needed to determine whether later (top-down) processing biomarkers can be detected in hmTBI and whether they relate to self-reported, enduring auditory symptoms. 4.1 Future Directions Our sample was heterogeneous, including mTBI etiology (e.g., sports incidents, falls, etc.) and time since injury (4 wk-17 y). Larger samples and greater homogeneity would reduce variability and reveal more nuanced patterns and permit detection of smaller effects. Although we asked participants to describe how they felt at the time of their injury and provide an account of the incident, many descriptions lacked key details. Additionally, we were unable to distinguish between the effects of repetitive versus non-repetitive hmTBI, nor to disentangle relationships driven by various symptom profiles on ERP measures. Future work should take a more fine-grained approach to determine which injury-related factors best predict long-term outcomes. Lastly, we did not assess whether participants experienced auditory sensitivity. Combining ERP biomarkers with behavioral or subjective measures of auditory sensitivity will provide a more comprehensive account of lasting changes after mTBI. 4.2 Conclusions Undergraduate hmTBI participants exhibited intact N100 and MMN responses, suggesting preserved early auditory processing. These typical early responses suggest that alterations in later, top-down processing may occur after mTBI, providing a possible explanation for the clinical and behavioral complaints of auditory sensitivities. Moreover, our study contributes to a larger body of literature demonstrating the heterogeneous effects of mTBI, which is possibly shaped by age, injury severity, and etiology. Identifying the conditions under which neural alterations emerge is necessary and ongoing for developing reliable biomarkers for mTBI. Conflicts of Interest: The authors have no conflicts of interest. 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Collection European Journal of Neuroscience Keywords auditory concussion eeg erp mmn n100 Authors Affiliations Jenna Pablo 0000-0003-3766-310X [email protected] University of Nevada Reno View all articles by this author Lena Kemmelmeier University of Nevada Reno View all articles by this author Hector Arciniega NYU Grossman School of Medicine View all articles by this author Jorja Shires University of Nevada Reno View all articles by this author Wendy Torrens University of Nevada Reno View all articles by this author Marian Berryhill University of Nevada Reno View all articles by this author Sarah Haigh 0000-0003-2400-4412 University of Nevada Reno View all articles by this author Metrics & Citations Metrics Article Usage 294 views 151 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jenna Pablo, Lena Kemmelmeier, Hector Arciniega, et al. Intact auditory N100 and mismatch negativity in undergraduates with a history of mild traumatic brain injury. 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