Altered hierarchical auditory predictive processing after lesions to the orbitofrontal cortex

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   Version published to 10.7554/elife.86386 on eLife

   Feb 9, 2024
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   Version published to 10.1101/2023.01.04.521570v4 on bioRxiv

   Jan 16, 2024
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   eLife

   Oct 21, 2023

   Author Response

   Reviewer #1 (Public Review):

   I believe it is important for the authors to clarify how the time frames to test for group differences of ERP components were defined. Were the components defined based on a grand average across lesions and controls or based or on the maximum range for both groups? As the paper is written currently this is unclear to me. It is also unclear why the group comparisons between controls and lateral PFC group were based only on the control group. To ensure no inadvertent biases towards the larger control group were introduced and ensure the studies findings were reliable, it would be appreciated if the authors could clarify this.

   We thank the reviewer for the helpful comment. We recognize the need for a clearer definition of time frames for testing group differences in the ERP components and …

   Author Response

   Reviewer #1 (Public Review):

   I believe it is important for the authors to clarify how the time frames to test for group differences of ERP components were defined. Were the components defined based on a grand average across lesions and controls or based or on the maximum range for both groups? As the paper is written currently this is unclear to me. It is also unclear why the group comparisons between controls and lateral PFC group were based only on the control group. To ensure no inadvertent biases towards the larger control group were introduced and ensure the studies findings were reliable, it would be appreciated if the authors could clarify this.

   We thank the reviewer for the helpful comment. We recognize the need for a clearer definition of time frames for testing group differences in the ERP components and apologize for any ambiguity in the previous version of the manuscript.

   Regarding the time frames to test for group differences of ERP components for the OFC and control groups, they were determined based on the combined maximum range for both groups. The time range for each group and each ERP component was derived from the statistical analysis of the condition contrasts run for each group. For instance, for the Local Deviance MMN, the condition contrast (i.e., Control condition versus Local Deviance condition) for the CTR group revealed a MMN component from 67 to128 ms, while the same condition contrast for the OFC group revealed a MMN from 73 to131 ms. The time frame used for the group comparison on the MMN time window was 50 to 150 ms to capture component activity for both groups. In the same way, for the Local Deviance P3a, the condition contrast (i.e., Control condition versus Local Deviance condition) for the CTR group revealed a P3a component ranging from 141 to 313 ms, while the same condition contrast for the OFC group revealed a P3a from 145 to 344 ms. The time frame used for the group comparison on the P3a time window encompassed 140 to 350 ms to capture component activity for both groups.

   In the “Results” section of the main manuscript, together with the results from the cluster-based permutation independent samples t-tests, we provide the time frames in which the latter were computed for each ERP component. These segments have been highlighted with yellow in the revised manuscript. Moreover, in the section “Materials and methods - Statistical analysis of event-related potentials” of the main manuscript [page 37, paragraph 2], we provide a revised description of how the time frames for group differences of ERPs were defined. The revised description states: “In a second step, to check for differences in the ERPs between the two main study groups, we ran the same cluster-based permutation approach contrasting each of the four conditions of interest between the two groups using independent samples t-tests. The cluster-based permutation independent samples t-tests were computed in the latency range of each component, which was determined based on the maximum range for both groups combined. The latency range for each group and component was based on the time frames derived from the statistical analysis of task condition contrasts.”

   Regarding the comparisons between the lateral PFC and control groups, they were not based solely on the control group condition contrast. This was miswritten. The approach to define time frames to test for ERP differences between the CTR and the lateral PFC group was the same as the one used to test differences between CTR and OFC groups. We apologize for any confusion this may have caused. We have revised the erroneous statements in the Supplementary File 1 [highlighted text, page 9-10].

   An additional potential weakness of the paper, and one that if addressed would increase our confidence that neural differences arise because of the specific lesion effect, is the lack of evidence that the lesion and control groups do not differ on measures that could inadvertently bias the neural data. For example, while the groups did not differ on demographics and a range of broad cognitive functions, were there any differences between the number or distribution of bad/noisy channels in each subject between the two groups? Were there differences in the number of blinks/saccades or distribution of blinks or saccades across the conditions in each subject across the two groups.

   We thank the reviewer for this suggestion. We have completed a number of measurements and tests to ensure that the OFC lesion group and the control group did not differ on measures that could affect the neural data. First, we computed the number of bad/noisy channels for each subject and group, and found that the two groups did not differ significantly. Second, we computed the number of trials remaining after removing the noisy segments across conditions for each subject and group, and found no significant differences between the groups. Third, the number of blinks/saccades across conditions for each subject and group showed no significant group differences. Altogether, the results indicate that the neural differences observed in our study arose because of the specific lesion effect.

   These additional EEG measures and the statistical test results are included in the Supplementary File 1 [page 15-16] and Supplementary File 1g. We have also added text in the section “Materials and methods - EEG acquisition and pre-processing” of the main manuscript [page 35, paragraph 3], which states: “To ensure the validity of the neural data analysis, potential sources of bias were assessed between the healthy control participants and the OFC lesion patients. Specifically, no significant differences were observed between the two groups in terms of the number of noisy channels, the number of noisy trials, or the number of blinks across the task blocks and the experimental conditions.”

   On a similar note, while I appreciate this is a well established task could the authors clarify whether task difficulty is balanced across the different conditions? The authors appear to have used the counting task to ensure equal attention is paid across conditions although presumably the blocks differ in the number of deviant tones and therefore in the task difficulty. Typically, tasks to maintain attention are orthogonal to the main task and equally challenging across the different blocks. Is there a way to reassure readers that this has not affected the neural results?

   Thank you for pointing this out. Indeed, the experimental blocks differ in the number of deviant tones and therefore in the task difficulty. Thus, it is a very good suggestion to look for behavioral performance differences across the different blocks. In the present set of analyses, two block types were used: Regular (xX) and Irregular (xY). In regular blocks, where the repeated sequence is xxxxx, participants were required to count the rare/uncommon sequences, i.e., xxxxy and xxxxo. In irregular blocks, where the repeated sequence is xxxxy, participants were required to count the rare/uncommon sequences, i.e., xxxxx and xxxxo. We have now updated the behavioral analysis. First, by excluding the omission block’s counting performance, and second, by calculating the counting performance separately for the two blocks. The new behavioral analysis revealed that participants from both groups performed better in the irregular block compared to the regular block. However, there was no statistically significant difference between the counting performances of the two groups.

   The new results are reported on page 5 of the main manuscript, section “Results - Behavioral performance”, paragraph 1: “Participants from both groups performed the task properly with an average error rate of 9.54% (SD 8.97) for the healthy control participants (CTR) and 10.55% (SD 6.18) for the OFC lesion patients (OFC). There was no statistically significant difference between the counting performance of the two groups [F(24) = 0.11, P = 0.75]. Participants from both groups performed better in the irregular block (CTR: 8.39 ± 8.24%; OFC: 7.50 ± 7.34%) compared to the regular block (CTR: 10.69 ± 11.36%; OFC: 13.60 ± 10.97%) [F(24) = 3.55, P = 0.07]. There was no block X group interaction effect [F(24) = 0.73, P = 0.40].”

   As with many patient lesion studies, while the comparison directly against the healthy age matched controls is critical it would have strengthened the authors claims if they could show differences between the brain damaged control group. Given the previous literature that also links lateral PFC with prediction error detection, I understand that this region is potentially not the clearest brain damaged control group and therefore another lesion group might have strengthened claims of specificity. Furthermore, the authors do not offer an explanation for why no differences between lateral PFC and control groups were found when others have previously reported them. Identifying those differences would strengthen our understanding of the involvement of different structures in this task/function.

   We thank the reviewer for raising this crucial issue. We recognize the importance of addressing the lack of neurophysiological differences between the lateral PFC lesion group and the control group. First, it is important to clarify that the lateral PFC lesion control group was initially included not as a control for specific lateral PFC lesions but rather a broader control group to account for potentially general effects of frontal brain damage. However, considering that previous studies have implicated specific areas of the lateral PFC (e.g., inferior frontal gyrus; IFG) in predictive processing, we also think that a more thorough justification of these null findings is needed.

   Intracranial EEG studies examining local and global level prediction error detection pointed to the role of inferior frontal gyrus (IFG) as a frontal source supporting top-down predictions in MMN generation (Dürschmid et al., 2016; Nourski et al., 2018; Phillips et al., 2016; Rosburg et al., 2005). However, other intracranial studies reported unclear (Bekinschtein et al., 2009) or weak (Dürschmid et al., 2016) frontal MMN effects. El Karoui et al. (2015) observed late ERP responses in the lateral PFC related to global deviants but no MMN to local deviants, and it was not clear where in the PFC these responses occurred, not showing responses in the IFG. Additionally, studies employing dynamic causal modeling of MMN consistently modeled frontal sources in the IFG region (Garrido et al., 2008; Garrido et al., 2009; Phillips et al., 2015). A review by Deouell (2007) highlighted the potential contributions of both IFG and middle frontal gyrus to MMN generation, suggesting that the specific source might vary depending on characteristics of the deviant stimuli, such as pitch or duration.

   In Alho et al. (1994) lesion study, diminished MMN to local-level deviants was found after lesion to the lateral PFC, with the lesion cohort exhibiting a hemisphere ratio of 7/3 for left and right hemispheres, respectively, which is different from our cohort's ratio of 4/6. Furthermore, all individuals in that study had infarcts in the middle cerebral artery, resulting in a more uniform lesion location compared to our cohort. Notably, the lesions observed in our lateral PFC group appeared to be situated in more superior brain regions and towards the MFG compared to the predominantly reported involvement of the IFG in previous studies. Another factor that might contribute to the lack of significant effects is the heterogeneity of the lesions in our lateral PFC group (see Supplementary Figures 2, 3 and 4). Especially for the left hemisphere cohort, the individual lesions did not share a consistent anatomical location. The right hemisphere cohort had a greater lesion overlap, but overall, the lesions were not centered in the IFG area with highest overlap being in the MFG area. This distinction in lesion location might contribute to the absence of effects observed in our study.

   Regarding the global effect, often reflected in the P300 component, it appears that the neural sources responsible for processing global deviance exhibit a more distributed pattern. This means that the brain regions involved in detecting and processing global deviations may not be as localized or concentrated as those implicated in local deviance processing. Given that the neural mechanisms underlying global deviance detection and processing are likely to involve a wider network of brain regions, they may be less susceptible to disruptions caused by focal lesions in the lateral PFC.

   In response to your comment, we have expanded the “Discussion” to address this point by adding a new section titled “Lack of findings in the lateral PFC lesion group” [page 21]. In this section, we first present some of the findings implicating specific areas of the lateral PFC in the generation of MMN and in predictive processing, and then offer an account of the potential reasons behind the lack of neurophysiological differences between the lateral PFC and control groups.

   Finally, while the authors have already cited widely across multiple fields, again speaking to the likely large impact the study will make, there does appear to be an unexplored conceptual link between the conclusions here that the OFC supports "the formation of predictions that define the current task by using context and temporal structure to allow old rules to be disregarded so that new ones can be rapidly acquired" and that lesions of the lateral portions of the OFC disrupt the assignment of credit or value to a stimuli that occurred temporally close to the outcome (Walton et al 2010, Noonan et al 2010, PNAS, Rudebeck et al 2017 Neuron, Noonan et al 2017, JON, Wittmann et al 2023 PlosB, note the wider imaging literature in line with this work Jocham et al 2014 Neuron and Wang et al bioRxiv). Without the OFC monkeys and humans appear to rely on an alternative, global learning mechanism that spreads the reinforcing properties of the outcome to stimuli that occurred further back in time. Could the authors speculate on how these two strains of evidence might converge? For example, does the OFC only assign credit in the event of a prediction error or does one mechanism subsume another?

   We thank the reviewer for this comment regarding the unexplored conceptual link between our study’s conclusion, which suggests that the OFC facilitates the detection of prediction errors, and the findings of other research that delves into the OFC’s role in assignment of credit to stimuli. We find this comment very interesting and appreciate the opportunity to speculate on the potential functional convergence of these two processes within the OFC.

   The OFC is a critical neural hub implicated in learning, decision-making, and adaptive behavior. The detection of prediction errors and the assignment of credit to stimuli are mechanisms linked with the OFC, which play an important role in all these functions (Noonan et al., 2012; Schultz & Dickinson, 2000; Sul et al., 2010; Tobler et al., 2006; Walton et al., 2010; Walton et al., 2011). Prediction errors involve recognizing discrepancies between expected and actual outcomes, which engages the OFC in rapidly updating stimulus valuations to align with newfound information (Holroyd & Coles, 2002; Kakade & Dayan, 2002). Signaling of errors provides a powerful mechanism whereby OFC facilitates adaptive learning and enables the brain to adjust its expectations based on novel experiences (Schultz, 2015; Seymour et al., 2004). Credit assignment, on the other hand, refers to properly identifying the causes of prediction errors. Without proper credit assignment, one might have intact error signaling mechanisms, but lose the ability to learn appropriately. This is especially true when multiple possible antecedents may be related to the error or when past choices have been unpredictable. In such situations, it is important to assign credit to the most recent choice and not get distracted by previous alternatives (Stalnaker et al., 2015).

   These mechanisms within the OFC appear interrelated yet distinct. While prediction errors could trigger credit assignment, the OFC's ability to continually assess stimuli's values extends beyond instances of prediction errors. The OFC is involved in continuously evaluating and updating the values of stimuli based on ongoing experiences (Padoa-Schioppa & Assad, 2006; Tremblay & Schultz, 1999). This process enables the brain to learn from both unexpected outcomes and regular, predictable interactions with the environment. In situations where outcomes are not solely determined by prediction errors, the assignment of credit remains important. Complex decision-making involves considering a variety of factors beyond just prediction errors, such as contextual information and long-term consequences. Clarifying the convergence of these mechanisms within the OFC holds profound implications for understanding the intricacies of learning dynamics and the orchestration of adaptive responses to the environment.

   While we recognize the value of this discussion, we believe it extends beyond the primary focus of our study. Consequently, we have made the decision not to incorporate it into the current manuscript.

   One remaining weakness, which plagues all patient studies, is that of anatomical specificity. The authors have analysed what is, for the field, a large group of patients, and while the lesions appear to be relatively focused on the OFC the individuals vary in the degree to which different subregions within the OFC are damaged. This is increasingly important as evidence over the last 10 years has identified functional roles of these specific structures (Rushworth et al 2011, Neuron, Rudebeck et al 2017 Neuron). It would be important to ultimately know whether the detection of prediction errors was specific to a particular OFC subregion, a general mechanism across this area of cortex, or whether different subregions were more involved during different contexts or types of stimuli/contexts/tasks etc. Some comments on this would be appreciated.

   The reviewer raised an important point here. It would have been interesting to explore this aspect. However, one challenge with focal lesion studies is to establish large patient cohorts. The group size of our study, which is relatively large compared to other studies of focal PFC lesions, does not allow us to perform any exploratory lesion-symptom mapping analyses. A larger patient sample will provide a stronger basis for drawing conclusions about the critical role of a particular OFC subregion to the detection of prediction errors and allow statistical approaches to lesion subclassification and brain-behavior analysis (e.g., voxel-based lesion-symptom mapping (Bates et al., 2003; Lorca-Puls et al., 2018)).

   Considering the average percentage of damaged tissue in our study, the medial part of OFC or Brodmann area 11 is affected more by the lesion (approx. 33%), followed by the anterior-most region of the prefrontal cortex or Brodmann area 10 (approx. 25%), and the lateral portions of the OFC or Brodmann area 47 (approx. 12%). From our analysis, it is difficult to conclude whether the detection of prediction errors in our study was specific to a certain OFC area, or whether different subregions were involved more than others during different types of stimuli/contexts processing.

   To provide a more balanced interpretation of our findings, we incorporated a section in the “Discussion”, titled “Limitations and future directions” [page 24-25], which delves into the limitations of our study and lesion studies generally with respect to anatomical specificity and the challenge to establish large patient cohorts.

   Reviewer #2 (Public Review):

   The current version of the manuscript is overall very long and verbose, for example, the introduction is 5 pages long and includes up to 102 references. In my view this is way too much. I suppose authors wish to be very detailed, but somehow they get an opposite effect, the main message of the introduction and aims get diluted.

   We thank the reviewer for the feedback on our manuscript's length and content. This prompted us to carefully reconsider the balance between providing necessary context and ensuring the clarity of our main message. Our intention was to establish a strong foundation for our research by presenting relevant literature and setting the stage for our aims. In our revised manuscript, we have condensed the Introduction while retaining the key elements necessary to understand the context and motivations behind our research. Specifically, the current version of the “Introduction” is three pages long and includes 83 references.

   I wonder if the presentation rate used, SOA; 150 is too fast and the stimuli too short 50 ms. Please prove a rationale for this.

   We appreciate the reviewer's thoughtful consideration of the stimulus duration and presentation rate (SOA) used in our study. We understand the importance of providing a rationale for our choices to ensure the validity of our experimental design. The decision to use a SOA of 150 ms and stimuli of 50 ms duration was grounded in established practices and relevant literature in the field. Similar presentation rates and stimulus durations were employed in previous studies using similar auditory oddball paradigms, investigating rapid cognitive processes in combination with event-related potentials (ERPs). For instance, Bekinschtein et al. (2009) first introduced the task by using a SOA of 150 ms and stimulus duration of 50 ms, demonstrating that this combination is sensitive to detecting auditory deviations and eliciting early and late ERP components. Additionally, Wacongne et al. (2011), Chennu et al. (2013), Uhrig et al. (2014), and El Karoui et al. (2015) employed similar task designs with the same SOA and stimulus duration in combination with scalp EEG, fMRI and intracranial recordings, further supporting the validity of this approach. Other studies, employing the same paradigm, such as Chao et al. (2018) and Doricchi et al. (2021), used a SOA of 200 ms but kept the same stimulus duration of 50 ms.

   One of the conditions is 'omissions', but results are not reported, so either authors do not mention this at all, or they report these data, which would be probably interesting.

   We thank the reviewer for the nice reminder. The “omissions” condition is indeed an integral part of our study, and we acknowledge its potential significance. However, we have decided to publish the detailed analysis of the 'omissions' condition in a separate paper, because we think that such analysis and discussion would make the current paper quite dense and complicated. We apologize for any confusion that might arise from the absence of the 'omissions' results in this manuscript. On page 33 of the main manuscript, we state the reason for not including the “omissions” condition in the current analysis: “In the present set of analyses, the Omission blocks were not further examined, because such analysis and discussion would make the current paper overly dense and complicated.”

   The Discussion is very long and in some aspect even too speculative. For example, in the conclusions authors claim that the OFC contributes to a top-down predictive process that modulates the deviance detection system in the primary auditory cortices and may be involved in connecting PEs at lower hierarchical areas with predictions at higher areas. I am not sure the current data support this. This would-be probably more appropriate if they could compare results from OFC and AC etc. so it is a more dynamic study.

   We thank the reviewer for this observation. We have made revisions to shorten and refine the discussion, with a primary focus on presenting and interpreting the key results in a more concise and straightforward manner (See tracked changes in the revised manuscript).

   However, the overall length of the Discussion has not been reduced significantly because we have introduced two additional sections within the Discussion (i.e., “Lack of findings in the lateral PFC lesion group” and “Limitations and future directions”) in response to reviewers’ request to address the lack of finding in the lateral PFC lesion group and certain limitations associated with the employed lesion method.

   We also agree that the claim mentioned by the reviewer is overly too speculative and therefore revised the sentence as follows [page 38, “Conclusion”]: “We suggest that the OFC likely contributes to a top-down predictive process that modulates the deviance detection system in lower sensory areas.”

   At the beginning of Discussion, the authors mention that overall, these findings provide novel information about the role of the OFC in detecting violation of auditory prediction at two levels of stimuli abstraction/time scale. I think this needs to be detailed more specifically rather than mention they provide novel results.

   We understand the importance of providing readers with precise descriptions about the novelty of our study. Therefore, we have revised the statement to provide more detailed information about the novel contributions offered by our study. The revised text states as follows [“Discussion”, page 18,]: “These findings indicate that the OFC is causally involved in the detection of local and local + global auditory PEs, thus providing a novel perspective on the role of OFC in predictive processing.”

   I am not sure I like to have a section as a general discussion within the discussion itself, probably this heading should be reformatted to be more specific to what is discussed.

   As suggested by the reviewer, we reformatted the heading to “OFC and hierarchical predictive processing” [page 22-24] to better capture the essence of the content covered in this section of the “Discussion”. Here, we discuss the functional relevance of our EEG findings under the umbrella of the predictive coding framework and the potential role of OFC in predictive processes (See tracked changes in the revised manuscript).

   Reviewer #3 (Public Review):

   The central claim of the study is that hierarchical predictive processing is altered in OFC patients. However, OFC patients were able to identify global deviants as well as controls. Thus, hierarchical predictive processing itself seems to be unaltered, even though its neural correlates were different. This begs the question of what exactly the functional meaning of the EEG findings is. From the evidence presented this is difficult to determine for three reasons (See comments below).

   We thank the reviewer for the detailed observations and valuable comments. The reviewer points out that hierarchical predictive processing is unaltered even though the neural correlates were altered, because OFC patients were able to identify global deviants as accurately as control participants. We respectfully disagree with the reviewer’s claim for two reasons: 1) The primary purpose of the behavioral data in this study was not to measure the participants’ deviant detection performance, but to confirm that they were paying attention to the global rule of each block. However, we agree that an effect of lesion on behavioral performance would strengthen the claim of altered high-level predictive processing. Your point highlights the importance of looking more carefully at our behavioral results. In a follow up study, which we are currently running, we explore the behavioral nuances of our task by measuring reaction times of correct deviant detections. 2) Earlier lesion studies reported typical performance on simple oddball tasks for patients with focal frontal lesions that did not significantly differ from control participants. However, despite normal task execution and neuropsychological profiles, patients with LPFC and OFC lesions present distinct neurophysiological evidence of alterations in novelty processing (Knight, 1984, 1997; Knight & Scabini, 1998; Løvstad et al., 2012; Yamaguchi & Knight, 1991).

   Regarding the central claim of our study being that hierarchical predictive processing is altered in OFC patients, we have tried not to make strong claims about our results showing altered hierarchical predictive processing. For example, the conclusion of the abstract states: “the altered magnitudes and time courses of MMN/P3a responses after lesions to the OFC indicate that the neural correlates of detection of auditory regularity violation is impacted at two hierarchical levels of stimuli abstraction.” Thus, we do not claim that detection of regularity violation is directly impaired (e.g., OFC patients were able to identify global deviants as well as healthy controls) but that the neural correlates of deviants’ detection are altered, and therefore impaired.

   Finally, we have gone through all the comments/reasons, which the reviewer believes are difficult to determine the functional meaning of our EEG findings, and addressed them one by one (see comments below). We hope that the revised manuscript has been improved accordingly and provides a more critical view on the extent to which the findings support hierarchical predictive coding.

   It is possible that the shifts in scalp potentials are due to volume conduction differences linked to post-lesion changes in neural tissue and anatomy rather than differences in information processing per se.

   We appreciate your comment regarding the potential influence of volume conduction differences on the observed shifts in scalp potentials in our study. We acknowledge that there are special challenges in interpreting ERP findings in brain lesion populations (Kutas et al., 2012; Rugg, 1995). To reliably interpret changes in the ERPs in lesion patients as reflecting impairments in certain cognitive processes, it is necessary to identify factors that might possibly affect the results and to apply the appropriate control measures. As noted by the reviewer, structural pathology, and the replacement of neural tissue by cerebrospinal fluid following tumor resection, likely causes inhomogeneities in the volume conduction of electrical activity and resulting changes in current flow patterns. Moreover, post-craniotomy skull defects can cause local inhomogeneities in the resistive properties of the skull (Løvstad & Cawley, 2011; Rugg, 1995). Both types of biophysical changes might alter the amplitude levels and/or topography (by altering the configuration of the generators) of surface-recorded ERPs (e.g., Swick (2005)). Consequently, caution is warranted when comparing the ERPs and their scalp distributions of intact and brain-lesioned groups. It is difficult to directly quantify the consequences of brain lesions on tissue conductivity. To conclude that ERP differences between patients and controls reflect functional abnormalities in particular cognitive processes, and not primarily nonspecific effects of structural brain damage, it is helpful to demonstrate that they are specific to certain ERP components/stages of information processing and task conditions. Changes confined to one or a subset of ERP components, that additionally may not manifest across all task conditions, can give some indication concerning the specificity of ERP changes (Kutas et al., 2012; Swaab, 1998). In our study, group differences pertaining to ERP amplitudes were limited to specific task conditions and not across all data. This condition-dependent pattern suggests that the observed shifts are related to the specific cognitive processes engaged during those task conditions rather than being a global artifact of volume conduction. If volume conduction was the main driver, we would expect these group differences to be more uniformly present across task conditions. Another piece of evidence against volume conduction effects is the scalp potentials’ latency differences between the two groups observed for the Local + Global deviance detection. Group differences in the latencies of ERPs, such as the MMN and P3a, cannot be attributed to volume conduction alone (Hämäläinen et al., 1993). These differences in the timing of neural responses strongly indicate genuine variations in cognitive processing.

   To provide a more balanced interpretation of our findings, we have incorporated a section in the “Discussion” that delves into the limitations of our study and lesion studies generally with respect to volume conduction and amplitude changes, titled “Limitations and future directions” [page 24-25].

   It is unclear from the analyses whether the P3a amplitude differences are true amplitude differences or a byproduct of latency differences. The reason is that the statistical method used (cluster based permutations) might yield significant effects when the latency of a component is shifted, even if peak amplitudes are the same. Complementary analyses on mean or peak amplitudes could resolve this issue.

   We thank the reviewer for raising an important concern about the use of cluster-based permutation tests and their potential to yield significant effects when the latency of a component is shifted. We acknowledge this concern and recognize the need for complementary analyses to address this issue. To provide a clearer understanding of the nature of the observed ERP amplitude differences, we conducted complementary analyses on mean amplitudes of the MMN and P3a components on the midline sensors for the conditions where significant group differences were observed. For the MMN component elicited by the Local Deviance, we found group amplitude differences on the electrodes AFz (p = 0.021), Fz (p = 0.008), CPz (p = 0.015), and Pz (p < 0.001). Surprisingly, we also found amplitude differences for the P3a component elicited by the Local Deviance on the electrodes AFz (p < 0.001), Fz (p < 0.001), FCz (p < 0.001), and Cz (p = 0.002) that were not observed previously with the cluster-based permutation analysis. For the MMN component elicited by the Local+Global Deviance, our analysis showed group amplitude differences on the electrodes AFz (p = 0.007), FCz (p = 0.051), Cz (p = 0.004), CPz (p = 0.002), and Pz (p < 0.001). However, as the reviewer rightly pointed out, the group differences for the P3a elicited by the Local + Global Deviance seem to be a byproduct of latency differences, as we did not find amplitude differences on any of the midline electrodes. Overall, this complementary analysis shows that the OFC patients had an attenuated MMN/P3a to local level prediction violation, and an attenuated and delayed MMN followed by a delayed P3a to the combined local and global level prediction violation. The new analysis is added in the Supplementary File 1 [page 5-7] and Supplementary File 1c and 1d.

   The MMN, P3a and P3b components are difficult to map to the hierarchical PC theory. Traditionally, the MMN is ascribed to lower level processing while P3a and P3b are ascribed to higher level processing. However, the picture is more complicated. For example, the current results show that the MMN is enhanced in local + global surprise while the P3a is elicited by local surprise. Furthermore, the P3a is classically interpreted as reflecting attention reorientation and the P3b as reflecting the conscious detection of task-relevant targets. How attention and conscious awareness fit in hierarchical PC is not entirely clear.

   Indeed, the relationships between MMN, P3a and P3b components and the predictive coding (PC) framework can be intricate. However, numerous studies employed the PC theory to interpret these common electrophysiological signatures as prediction error (PE) signals (Garrido et al., 2007, 2009; Lieder et al., 2013) and dissociations between these ERPs supported that there are successive levels of predictive processing (Chennu et al., 2013; El Karoui et al., 2015; Wacongne et al., 2011).

   In terms of hierarchical PC (Friston, 2005), the temporally constrained MMN has been traditionally linked with first-level predictive processing, known as the local effect of short-term stimulus deviance. PE signals at this level feed forward to a temporally extended, attention-dependent system that extracts longer-term patterns. PE signals at the higher level are usually indexed by the P300, identified as the global effect of longer-term stimulus deviance. The P300 reflects a more attention-driven process, emerging in response to novel or low-probability “target” stimuli that violate broader contextual expectations (Polich, 2007), such as those that form over multiple trials. Because the MMN, P3a and P3b also appear to exhibit varying degrees of sensitivity to preconscious and conscious perceptual predictions (Sculthorpe et al., 2009), they could serve as measures for examining the concept of a predictive neural hierarchy.

   Indeed, the MMN has been viewed as sensitive to local violation and essentially blind to higher-order regularities. However, this is a simplified view. For example, Wacongne et al. (2011) showed that violating a low-level perceptual expectation triggers the MMN, violating contextual expectations triggers the higher-level P3, and when both expectations are simultaneously violated, a larger response is evoked compared to either one alone. These findings, which are consistent with the results of our study, show that the local and global effects are not fully independent but interact in an early time window, indexed by enhanced and temporally extended MMN responses. They provide support not just for a hierarchical model, but for a predictive rather than a feedforward one. Moreover, the MMN has been found to be relatively insensitive to attention, because it is elicited in situations in which the subjects’ attention is directed away from the stimuli and there are no task demands (Chennu et al., 2013). Given that early MMN is a pre-attentive automatic ERP component (Näätänen et al., 2001; Pegado et al., 2010; Tiitinen et al., 1994), and given that it has been observed in comatose and vegetative state patients (Bekinschtein et al., 2009; Fischer et al., 2004; Naccache et al., 2004), the finding that even early MMN is impaired in OFC patients indicate that patients may suffer from a deficit in sensory predictive processing that is independent of attention and conscious awareness.

   The picture is more complicated when it comes to the predictive roles of P3a and P3b components. Following the MMN, a positive polarity P300 complex, sensitive to the detection of unpredicted auditory events, has been reported (Chennu et al., 2013; Doricchi et al., 2021; Kompus et al., 2020; Liaukovich et al., 2022). However, the two types of P300 (P3a and P3b) have not been clearly fitted into the hierarchical PC theory. The P3a is considered to be part of the brain's mechanism for detecting PEs (Wessel et al., 2012; Wessel et al., 2014) and may indicate that the brain is reallocating attentional resources to process and learn from these unexpected events. The P3a is typically interpreted as reflecting an involuntary attentional reorienting process (Escera & Corral, 2007; Ungan et al., 2019), which may relate to the operations of the ventral attention network (Corbetta et al., 2008; Corbetta & Shulman, 2002; Nieuwenhuis et al., 2005). Predictive coding emphasizes the role of contextual information in generating predictions with P3a being influenced by the context in which an unexpected event occurs (Schomaker et al., 2014). In the hierarchy of predictive processing, the P3a may reflect PEs at different hierarchical levels, depending on the complexity of the prediction and the degree to which it deviates from the sensory input. On the other hand, the P3b is linked to higher-level cognitive processes that involve updating long-term predictions based on incoming sensory information. It is highly dependent on attention, conscious awareness and active engagement with the task (Bekinschtein et al., 2009; Del Cul et al., 2007; Sergent et al., 2005; Strauss et al., 2015). It is thought to play a role in integrating the unexpected sensory input into the current context, potentially leading to updates of predictions in working memory (Chao et al., 1995; Donchin & Coles, 1988; Polich, 2007).

   Hierarchical PC theory is continually evolving, and the relationship between these ERP components and attention or conscious awareness remains an active area of research. We acknowledge the need for further investigation to better understand how attention and conscious awareness fit within this framework. In light of your comment, we provide a more comprehensive discussion about the functional meaning of the EEG findings in our “Discussion - OFC and hierarchical predictive processing” [page 22-24].

   The fact that lateral PFC patients show unaltered neural responses contradicts prominent views from PC identifying this region as a generator of the MMN and a source of predictions sent to temporal auditory areas.

   We appreciate the reviewer's comment and want to acknowledge that another reviewer raised this concern previously. We have provided a detailed response to this issue in our previous response (see Response to Reviewer #1 Comment 4). We have expanded the “Discussion” to address this point by adding a new section titled “Lack of findings in the lateral PFC lesion group” [page 21]. In this section, we first present some of the findings implicating specific areas of the lateral PFC in the generation of MMN and in predictive processing, and then offer an account of the potential reasons behind the lack of neurophysiological differences between the lateral PFC and control groups.

   For these reasons, a more critical view on the extent to which the findings support hierarchical predictive coding is needed.

   By responding to the reviewer’s previous comments (i.e., the reasons why the reviewer thinks it is difficult to determine the functional meaning of the EEG findings), we believe that we have offered a more critical view on this matter.

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4. 

   eLife

   Oct 15, 2023

   eLife assessment

   This important study demonstrates that the orbitofrontal cortex is causally involved in the detection of local auditory prediction errors. The methods and procedures are convincing, although the precise functional meaning of the reported effects remains to be specified. This work is of interest to neuropsychologists and cognitive neuroscientists working on the prefrontal cortex, predictive processing, auditory perception, and electrophysiology.

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5. 

   eLife

   Oct 15, 2023

   Reviewer #1 (Public Review):

   This study set out to test the causal involvement of the OFC in detecting auditory prediction errors at two levels of abstraction. The authors recorded EEG in patients with OFC damage and healthy age matched controls while they listened for deviations in sequences of tones in the Local-Global paradigm. This task can tease apart prediction errors at a local level (ie. within a sequence) and a global level (ie. between sequences). Focusing on the Mismatch Negativity (MMN) ERP component and the P3, which have both been previously linked to detecting violations in expectation and predictions, the study examined differences between neural responses elicited by patients and control subjects in four core conditions 1. standard sequences of tones (XXXXX XXXXX XXXXX XXXXX) that can be predicted both at local and …

   Reviewer #1 (Public Review):

   This study set out to test the causal involvement of the OFC in detecting auditory prediction errors at two levels of abstraction. The authors recorded EEG in patients with OFC damage and healthy age matched controls while they listened for deviations in sequences of tones in the Local-Global paradigm. This task can tease apart prediction errors at a local level (ie. within a sequence) and a global level (ie. between sequences). Focusing on the Mismatch Negativity (MMN) ERP component and the P3, which have both been previously linked to detecting violations in expectation and predictions, the study examined differences between neural responses elicited by patients and control subjects in four core conditions 1. standard sequences of tones (XXXXX XXXXX XXXXX XXXXX) that can be predicted both at local and global levels and should result in no prediction errors 2. Local deviations (XXXXY XXXXY XXXXY XXXXY) in which the final tone in the last sequence can only be predicted at the global level and which results in low-level prediction errors 3. Global deviations (XXXXY XXXXY XXXXY XXXXX) in which the final tone of the last sequence can be predicted only at the local level and results in higher level prediction errors. 4. Local+Global deviations (XXXXX XXXXX XXXXX XXXXY) in which the final tone of the last sequence is neither predicted at the global or local level and results in low and high prediction errors.

   The timely and well designed study combines casual and correlative experimental methods. This unique strength allows the authors to identify differences in neural processing and link them directly to a specific region of the brain. The task is simple and intuitive, having been well characterized in previous literature. Its use here to investigate prediction errors beyond the typical reward-guided paradigms is particularly novel. As is the focus on the OFC which is often understudied relative to nearby ACC more commonly associated with prediction error coding. These strengths ensure the paper will likely have a wide impact across a number of fields.

   The results suggest that OFC patients showed attenuated MMN to violations in local predictions as well as reduced and delayed MMN/P3a complex to combined violations in local and global predictions. By contrast, violations of prediction purely at the global level were preserved in the OFC group. No differences in processing local or global auditory prediction errors were observed in a brain damaged control group with lesions to the LPFC relative to controls. As these results stand they show a clear role of the OFC in the detection of prediction errors. This is particularly clear at the local level of processing.

   However, as with many patient lesion studies, while the comparison directly against the healthy age matched controls is critical it would have strengthened the authors claims if they could show differences between the brain damaged control group. Given the previous literature that also links lateral PFC with prediction error detection, I understand that this region is potentially not the clearest brain damaged control group and therefore another lesion group might have strengthened claims of specificity. Furthermore, the authors do not offer an explanation for why no differences between lateral PFC and control groups were found when others have previously reported them. Identifying those differences would strengthen our understanding of the involvement of different structures in this task/function.

   Furthermore, I believe it is important for the authors to clarify how the time frames to test for group differences of ERP components were defined. Were the components defined based on a grand average across lesions and controls or based or on the maximum range for both groups? As the paper is written currently this is unclear to me. It is also unclear why the group comparisons between controls and lateral PFC group were based only on the control group. To ensure no inadvertent biases towards the larger control group were introduced and ensure the studies findings were reliable, it would be appreciated if the authors could clarify this.

   An additional potential weakness of the paper, and one that if addressed would increase our confidence that neural differences arise because of the specific lesion effect, is the lack of evidence that the lesion and control groups do not differ on measures that could inadvertently bias the neural data. For example, while the groups did not differ on demographics and a range of broad cognitive functions, were there any differences between the number or distribution of bad/noisy channels in each subject between the two groups? Were there differences in the number of blinks/saccades or distribution of blinks or saccades across the conditions in each subject across the two groups. On a similar note, while I appreciate this is a well established task could the authors clarify whether task difficulty is balanced across the different conditions? The authors appear to have used the counting task to ensure equal attention is paid across conditions although presumably the blocks differ in the number of deviant tones and therefore in the task difficulty. Typically, tasks to maintain attention are orthogonal to the main task and equally challenging across the different blocks. Is there a way to reassure readers that this has not affected the neural results.

   Finally, one remaining weakness, which plagues all patient studies, is that of anatomical specificity. The authors have analysed what is, for the field, a large group of patients, and while the lesions appear to be relatively focused on the OFC the individuals vary in the degree to which different subregions within the OFC are damaged. This is increasingly important as evidence over the last 10 years has identified functional roles of these specific structures (Rushworth et al 2011, Neuron, Rudebeck et al 2017 Neuron). It would be important to ultimately know whether the detection of prediction errors was specific to a particular OFC subregion, a general mechanism across this area of cortex, or whether different subregions were more involved during different contexts or types of stimuli/contexts/tasks etc. Some comments on this would be appreciated.

   In spite of the concerns raised above I believe that the authors have achieved their aims. I hope that by expanding and clarifying the sections outlined above the authors can be even more confident that their results support their conclusions.

   As noted above, given the combination of methods and generalisability of the results the study will have a significant impact in a number of fields. I believe the use of an auditory paradigm will remind the community of the value of examining the generalisability of mechanisms across other sensory domains beyond vision. Unfortunately, though the data can not easily be shared (as is typical of patient data). However the authors explain in detail how permission could be sought by individual members of the community if needed.

   Finally, while the authors have already cited widely across multiple fields, again speaking to the likely large impact the study will make, there does appear to be an unexplored conceptual link between the conclusions here that the OFC supports "the formation of predictions that define the current task by using context and temporal structure to allow old rules to be disregarded so that new ones can be rapidly acquired" and that lesions of the lateral portions of the OFC disrupt the assignment of credit or value to a stimuli that occurred temporally close to the outcome (Walton et al 2010, Noonan et al 2010, PNAS, Rudebeck et al 2017 Neuron, Noonan et al 2017, JON, Wittmann et al 2023 PlosB, note the wider imaging literature in line with this work Jocham et al 2014 Neuron and Wang et al bioRxiv). Without the OFC monkeys and humans appear to rely on an alternative, global learning mechanism that spreads the reinforcing properties of the outcome to stimuli that occurred further back in time. Could the authors speculate on how these two strains of evidence might converge? For example, does the OFC only assign credit in the event of a prediction error or does one mechanism subsume another?

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6. 

   eLife

   Oct 15, 2023

   Reviewer #2 (Public Review):

   In this study authors study how OFP operates in control healthy humans and people that suffered of lesions of the OFP. Authors used a variation of the local vs. global oddball paradigm to study different levels of regularity violations. Overall the data is very interesting and having the study based on healthy and lesion humans make the results much more valuable than, other studies on healthy subject or even in animal studies.

   However, the current version of the manuscript is overall very long and verbose, for example, the introduction is 5 pages long and includes up to 102 references. In my view this is way too much. I suppose authors wish to be very detailed, but somehow they get an opposite effect, the main message of the introduction and aims get diluted.

   I wonder if the presentation rate used, SOA; 150 …

   Reviewer #2 (Public Review):

   In this study authors study how OFP operates in control healthy humans and people that suffered of lesions of the OFP. Authors used a variation of the local vs. global oddball paradigm to study different levels of regularity violations. Overall the data is very interesting and having the study based on healthy and lesion humans make the results much more valuable than, other studies on healthy subject or even in animal studies.

   However, the current version of the manuscript is overall very long and verbose, for example, the introduction is 5 pages long and includes up to 102 references. In my view this is way too much. I suppose authors wish to be very detailed, but somehow they get an opposite effect, the main message of the introduction and aims get diluted.

   I wonder if the presentation rate used, SOA; 150 is too fast and the stimuli too short 50 ms. Please prove a rationale for this. Also, one of the conditions is 'omissions', but results are not reported, so either authors do not mention this at all, or they report these data, which would be probably interesting.
   The results are complex themselves and difficult to follow for a non-specialist in the field and there is not much to simplify here, but again, the Discussion is very long and in some aspect even too speculative. For example, in the conclusions authors claim that the OFC contributes to a top-down predictive process that modulates the deviance detection system in the primary auditory cortices and may be involved in connecting PEs at lower hierarchical areas with predictions at higher areas. I am not sure the current data support this. This would-be probably more appropriate if they could compare results from OFP and AC etc. so it is a more dynamic study.

   At the beginning of Discussion, the authors mention that overall, these findings provide novel information about the role of the OFC in detecting violation of auditory prediction at two levels of stimuli abstraction/time scale. I think this needs to be detailed more specifically rather than mention they provide novel results

   I am not sure I like to have a section as a general discussion within the discussion itself, probably this heading should be reformatted to be more specific to what is discussed.

   In sum, while I find that this paper is potentially very interesting, it needs to be recast and shortened to make it more direct and appealing.

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7. 

   eLife

   Oct 15, 2023

   Reviewer #3 (Public Review):

   This study reports how human OFC lesions impact neural responses to sounds that are surprising with respect to local (sequences of sounds) and global expectations (sequences of sequences). The authors have used a clever global-local paradigm that dissociates hierarchical levels of expectations. The results are interpreted under the framework of predictive coding. A comparison with healthy controls and a group of lateral prefrontal cortex patients highlights the specific role of OFC in the reported effects.

   Strengths

   This study is methodologically sound, employing the well-established global-local paradigm and a set of classical event related analyses to disentangle different types of auditory expectations and answer the research question. The use of EEG in OFC patients provides causal evidence linking this …

   Reviewer #3 (Public Review):

   This study reports how human OFC lesions impact neural responses to sounds that are surprising with respect to local (sequences of sounds) and global expectations (sequences of sequences). The authors have used a clever global-local paradigm that dissociates hierarchical levels of expectations. The results are interpreted under the framework of predictive coding. A comparison with healthy controls and a group of lateral prefrontal cortex patients highlights the specific role of OFC in the reported effects.

   Strengths

   This study is methodologically sound, employing the well-established global-local paradigm and a set of classical event related analyses to disentangle different types of auditory expectations and answer the research question. The use of EEG in OFC patients provides causal evidence linking this area with altered evoked responses. Furthermore, the comparison with another lesion group (lateral PFC) provides evidence for a specific role of the OFC in the reported effects. The study contributes an interesting piece of evidence and does a good job placing the findings in the landscape of the relevant literature.

   Weaknesses

   The central claim of the study is that hierarchical predictive processing is altered in OFC patients. However, OFC patients were able to identify global deviants as well as controls. Thus, hierarchical predictive processing itself seems to be unaltered, even though its neural correlates were different. This begs the question of what exactly the functional meaning of the EEG findings is. From the evidence presented this is difficult to determine for three reasons.

   First, it is possible that the shifts in scalp potentials are due to volume conduction differences linked to post-lesion changes in neural tissue and anatomy rather than differences in information processing per se. Second, it is unclear from the analyses whether the P3a amplitude differences are true amplitude differences or a byproduct of latency differences. The reason is that the statistical method used (cluster based permutations) might yield significant effects when the latency of a component is shifted, even if peak amplitudes are the same. Complementary analyses on mean or peak amplitudes could resolve this issue.

   The third reason is that the MMN, P3a and P3b components are difficult to map to the hierarchical PC theory. Traditionally, the MMN is ascribed to lower level processing while P3a and P3b are ascribed to higher level processing. However, the picture is more complicated. For example, the current results show that the MMN is enhanced in local + global surprise while the P3a is elicited by local surprise. Furthermore, the P3a is classically interpreted as reflecting attention reorientation and the P3b as reflecting the conscious detection of task-relevant targets. How attention and conscious awareness fit in hierarchical PC is not entirely clear. Moreover, the fact that lateral PFC patients show unaltered neural responses contradicts prominent views from PC identifying this region as a generator of the MMN and a source of predictions sent to temporal auditory areas.

   For these reasons, a more critical view on the extent to which the findings support hierarchical predictive coding is needed.

   Read the original source
8. 

   Version published to 10.1101/2023.01.04.521570v3 on bioRxiv

   Mar 17, 2023
9. 

   Version published to 10.1101/2023.01.04.521570v2 on bioRxiv

   Jan 10, 2023
10. 

    Version published to 10.1101/2023.01.04.521570v1 on bioRxiv

    Jan 4, 2023