Multi-centre analysis of networks and genes modulated by hypothalamic stimulation in patients with aggressive behaviours

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1. 

   Version published to 10.7554/elife.84566 on eLife

   May 22, 2023
2. 

   eLife

   May 15, 2023

   Author Response

   Reviewer #1 (Public Review):

   The authors have compiled and analysed a unique dataset of patients with treatment-resistant aggressive behaviours who received deep brain stimulation (DBS) of the posterior hypothalamic region. They used established analysis pipelines to identify local predictors of clinical outcomes and performed normative structural and functional connectivity analyses to derive networks associated with treatment response. Finally, Gouveia et al. perform spatial transcriptomics to determine the molecular substrates subserving the identified circuits. The inclusion of data from multiple centres is a notable strength of this retrospective study, but there are current limitations in the methodology and interpretation of findings that need to be addressed.

   1. The validation of findings is heterogeneous and …

   More

   Author Response

   Reviewer #1 (Public Review):

   The authors have compiled and analysed a unique dataset of patients with treatment-resistant aggressive behaviours who received deep brain stimulation (DBS) of the posterior hypothalamic region. They used established analysis pipelines to identify local predictors of clinical outcomes and performed normative structural and functional connectivity analyses to derive networks associated with treatment response. Finally, Gouveia et al. perform spatial transcriptomics to determine the molecular substrates subserving the identified circuits. The inclusion of data from multiple centres is a notable strength of this retrospective study, but there are current limitations in the methodology and interpretation of findings that need to be addressed.

   1. The validation of findings is heterogeneous and inconsistent across analysis pipelines. While the authors performed non-parametric permutation testing during sweet-spot mapping, structural and functional connectivity were validated using a 'four-fold consistency analysis'. The latter consists of a visual representation of streamlines and peak intensities after randomly dividing data into four groups, the findings were not validated quantitatively. If possible, the authors should apply permutation analysis in alignment with sweet-spot mapping and demonstrate the predictive ability of their identified networks in a LOO or k-fold cross-validation paradigm as carried out by similar studies. Given that the data has been derived from multiple centers, the prediction of left-out cohorts based on models generated by the remaining cohorts could be another means of validation. If validation is not possible, the authors should clearly state the limitations of their approach.

   We appreciate the comment. We have now improved the validation of our connectomics analyses and removed the four-fold consistency analysis. For the functional connectivity analysis, we performed a 1000 permutation test (p<0.05). Similar brain areas were detected in the corrected and uncorrected maps. For the structural connectivity analysis, we used False Discovery Rate (FDR) correction at a significant level of p<0.001, as it is not feasible to perform a 1000 permutation test with this data. The structural connectome is composed of 12 million fibres, and every single permutation takes approximately 4 hours to be completed using our most powerful computational system. To perform 1000 permutations, it would take at least 4000 hours (i.e. 167 days or 5.5 months) of uninterrupted analysis to complete the test. However, it is important to highlight that an FDR correction at the level of p<0.001 is an extremely stringent method. This means that of the 23,000 fibres detected as being touched by the VATs, only 23 would be incorrect, while the remaining 22,977 are correct. Here again, we observed many similarities between the uncorrected and corrected maps, with the main anatomical structures being detected in both. The Methods section and Figures 4 and 5 were revised to reflect these changes.

   1. In addition to a 'four-fold consistency analysis', functional connectivity was evaluated using LOOCV in a priori identified ROIs. Their network analysis, however, revealed a far more extensive network encompassing cortical, subcortical, and cerebellar structures. To avoid selection bias the authors should incorporate identified structures into their analysis and apply appropriate means of validation.

   We thank the reviewer for this valuable suggestion. We originally did not explore the various significant areas but performed a more focused analysis intended to demonstrate that regions of the known ‘aggression network’ are indeed implicated in our findings. We performed a new analysis exploring the correlation between symptom improvement and the functional connectivity of all the areas described in Figure 5 (i.e., functional connectivity map). To this aim, we extracted individual connectivity values from the peak within each significant region and performed the same additive linear model, incorporating the functional connectivity of each area as well as the age of the patients to estimate individual symptomatic improvement. In addition, we performed a complete exploratory analysis considering the connectivity of any 2 brain structures and age. The resulting matrix shows to what extent functional connectivity to any two areas can be used to estimate clinical outcomes. Interestingly, this new analysis revealed the Periaqueductal Grey matter (PAG) to be the most important functionally connected area when investigated alone or in combination with brain structures critically involved in the regulation of emotional responses, namely the amygdala, anterior cingulate cortex, bed nucleus of the stria terminalis, nucleus accumbens, orbitofrontal cortex and fusiform gyrus. Also, the significance of the PAG connectivity was retained during leave-one-out cross-validation (LOOCV). The Methods, Results, Discussion and Figure 6 were revised. In addition, we added a new Table 2 and Supplementary File 1 to describe the new analysis and results.

   1. Functional connectivity mapping: how were R-maps generated? The authors mention that patient-specific R-maps were p-thresholded and corrected for multiple comparisons, but it is not clear how group-level maps were generated. How did the authors perform regression on these maps? Were voxels that did not survive thresholding excluded?

   This is a multiple-step analysis. First, it is necessary to localize the electrodes in each patient’s brain and estimate the volume of activated tissue (VAT) observed when stimulation parameters associated with symptomatic improvement are used. The VATs are then used as seeds for the next steps, during which we investigate how much functional influence the VTAs have on the other areas of the brain (i.e., individual r-map). This is done by correlating the BOLD time course of the VAT’s seed with the BOLD time course of all other voxels in the brain. The individual r-maps are then corrected for multiple comparisons to exclude voxels with potentially spurious correlations, resulting in an individual r-map that only included voxels surviving Bonferroni correction at the level of p<0.05. Finally, to create group-level maps, a voxel-wise linear regression analysis was performed to investigate whether each voxel of the map exerts more or less influence (corrected individual r-map with the functional connectivity of the patient’s VAT) or is more or less related to the clinical outcome (i.e. individual improvement). The last step is a permutation correction resulting in a significant group-level functional connectivity map (ppermute<0.05). We modified the Methods section and added a new Figure 1-figure supplement 1 illustrating this analysis.

   1. The authors determined that age was a significant prédictor of the outcome, but it is unclear whether certain age groups presented with distinct etiologies underlying their aggressiveness. For example, aggression in epilepsy may show a better response to DBS as opposed to schizophrenia. How does patient outcome change when stratifying according to etiology? How does model performance change when controlling for etiology? The authors should include the etiology of aggressiveness in Table 1.

   This is an interesting point. We observed a similar distribution between the pediatric and adult populations in relation to the most common etiologies reported. Epilepsy was the most frequent diagnosis in both populations (pediatric: 50%, adult: 62%), followed by autism spectrum disorder (pediatric: 34%, adult: 24%). The remaining etiologies were largely composed of single cases. A similar proportion of intellectual disability was also observed in pediatric and adult populations. Severe cases were observed in 75% of pediatric and 85% of adult patients. Moderate disability was present in 25% of pediatric and 15% of adult patients. Since several diagnoses were unique to some patients, the addition of this information to Table 1 could result in the identification of the patient. Thus, to preserve anonymity, the diagnoses were added to the end of Table 1 from more to less frequent. We have also revised the Results and Discussion sections to address this concern.

   1. Stimulation parameters. The authors report average pulse widths of 219 µs and 142µs respectively, which is up to 4-fold higher as compared to DBS settings used conventionally in movement disorders and will significantly alter the volume of activated tissue. Did the authors account for the drastic increases in pulse width during VAT modeling?

   We thank the reviewer for raising this point about the volume of activated tissue (VAT) modelled and the unusual pulse width observed in some patients in this cohort. These patients presented stimulation-induced sympathetic side effects when DBS was set with higher frequencies (e.g. increased heart rate and blood pressure). The chosen final parameters were the ones associated with a clinical benefit without generating side effects. There are a multitude of ways to estimate the VATs, from advanced axon cable models – the gold standard, which simulate axon membrane dynamics and require patient-specific diffusion-weighted imaging and tremendous computing power 1 - to simple heuristics-based models that estimate the rough extent of a VAT based on stimulation parameters without constructing an actual spatial model 2–4. The model employed in our study (and in a number of previous publications by our group 5–10) was the FieldTripSimBio ‘E-field norm’ finite element method (FEM) model. This model, which was first described by Horn et al. 11 and is freely available in Lead-DBS (https://www.lead-dbs.org/), strikes a balance between the sophisticated axon cable models and the simpler heuristic models. In particular, it constructs an electric field (E-field, by applying an electric field strength threshold, or activation threshold) and calculates the VAT associated with specific voltage settings and contact configurations, taking into account the conductivity of surrounding brain tissue and electrode components. Notably, studies comparing VAT modelling techniques 12 showed that ‘E-field norm’ FEM models closely approximate (<0.1 mm difference) the gold standard axon cable models in terms of the size of VATs constructed for monopolar stimulation settings. However, it should be acknowledged that the FieldTripSimBio model in Lead-DBS does not allow the user to specifically enter values for pulse width. Instead, it employs a standard activation/electric field strength threshold (0.2 V/mm) that reflects a combination of commonly modelled axon diameters (roughly 3.5 μm) and pulse width values (i.e., 60-90 μs). This threshold is based on work by researchers such as Astrom et al. 13 and reflects a ‘middle ground’ value that takes into account the fact that any VAT model will necessarily be an imperfect approximation of how electrical stimulation interfaces with brain tissue, depending heavily on aspects such as the diameter of local axons. Nonetheless, it is certainly understood that increased pulse width does meaningfully increase the effective range of stimulation (thus translating to a larger VAT) by lowering the activation threshold of nearby axons 12.

   Given that our patient cohort included a small number of patients who were stimulated with higher pulse widths than the values assumed by our model (90 μs), it is reasonable to wonder whether we underestimated the size of these patients’ VATs. To address this aspect, we modelled these patients’ VATs using a simpler heuristic model 2 that does allow specific pulse width values to be selected by the user. More specifically, we computed a range of VATs for these patients using varied pulse width values (ranging from 90 μs up to their actual values). Not surprisingly, this endeavour did yield larger VATs when higher pulse widths were used. On average, the absolute difference in VAT diameter between 90 μs and 450 μs (the largest pulse width observed in this cohort) versions of these patients’ VATs was 2 mm. To check whether or not this difference could have potentially impacted our results, we repeated our probabilistic mapping analysis using altered VATs (specifically, VATs that were enlarged by 2 mm in diameter) for the patients with higher pulse widths. This new repeat analysis yielded a very similar average map to the original analysis: the overall map pattern and location/values of the peak corresponding to the most efficacious area for maximal symptom alleviation remaining unaltered, and only a few voxels on the periphery of the map changing in value by a couple of percentage points. This new supplementary analysis indicates that our results were not meaningfully altered by the unusual pulse width observed in these patients. We modified the Methods section to address some of these aspects and added a new Figure 3-figure supplement 2 illustrating both voxel efficacy maps.

   1. Imaging transcriptomics. The methods described lack detail: How did the authors account for differences in expression across donors, samples, and regions during preprocessing of the Allen Human Brain Atlas? How was expression data collapsed into regions of interest? Did the authors apply any normalization? Recent publications have introduced reproducible workflows for processing and preparing the AHBA expression data for analysis that is publicly available.

   1. 'genes with similar patterns of spatial distribution to the TFCE map were compiled in an extensive list'. It is unclear why authors used TFCE maps for spatial transcriptomics as opposed to the functional connectivity map featured in Figure 5. How was similarity measured between the TFCE map and the AHBA? How were candidate genes identified? Please provide a more comprehensive description of the analysis pipeline.

   We apologize for the short description of this analysis. We performed a gene set analysis using the abagen toolbox (https://abagen.readthedocs.io/en/stable/index.html) to investigate genes with a spatial pattern distribution similar to one of clinically relevant functional connectivity. For this analysis, we used the Allen Human Brain Atlas (https://alleninstitute.org/) microarray data describing the cortical, subcortical, brainstem and cerebellar localization of over 20,000 genes in the human brain (3702 anatomical locations from 6 neurotypical adult brains) 14–17, along with a cell-specific aggregate gene set 18. These data are provided preprocessed, with gene expression values normalized across all brains, and registered to standard MNI space, allowing for a direct comparison between the spatial pattern of gene expression and the functional connectivity map (https://human.brain-map.org/microarray/search) 15. The TFCE maps were used to create clusters of clinically relevant functional connectivity with a spatial extent that overlaps with the anatomical locations from which microarray data was obtained. We parcellated both datasets (results of functional connectivity analysis and Allen Gene Atlas) according to the Harvard-Oxford brain atlas and correlated the spatial distribution of gene expression with the spatial distribution of the results of the functional connectivity mapping. The resultant list of candidate genes was used as input in gene ontology tools to investigate the associated biological processes and cell types. It is important to highlight that this process involves 2 corrections for multiple comparisons using FDR at q<0.005; one correction occurs at the level of the gene list to include only the most significant genes in the gene ontology analysis; a second correction occurs at the level of the gene ontology analysis to consider only the most significant biological processes. We have included some of these details in the revised Methods section.

   1. What do the bar plots in Figure 7 (left) represent? P-values? The authors should label the axes to make this clear to the reader.

   1. Interprétation of imaging transcriptomics: The authors identify a therapeutic circuit associated with deep brain stimulation of the posterior hypothalamic area, however, it is unclear how to reconcile genes associated with hormones, inflammation, and plasticity in this context. The authors mention and discuss genes implicated in hormonal processing, specifically oxytocin. The results provided in Figure 7, however, do not support this finding and it is unclear how the authors identified genes linked to oxytocin. In addition, the authors identified reductions in the number of microglia and astrocytes, while oligodendrocytes were overexpressed relative to the expected distribution of genes per cell type. These findings were attributed to DBS effects, however, both connectomic and transcriptomic data are acquired from healthy subjects, which suggests a physiological deficit/enrichment in a therapeutic circuit. How do the authors interpret findings given that no electrode implantation and stimulation were performed?

   The analysis of normative datasets (functional and structural connectomics and spatial transcriptomics) is based on the idea of better understanding mechanisms of treatment considering our current knowledge of the average human brain. Unlike patient-specific studies in which imaging is acquired from a single patient or genetic profiles are extracted from tissue samples, these normative analyses rely on high-quality “atlases” derived from healthy subjects. In the case of functional and structural connectivity, these atlases are calculated from very large cohorts of subjects (around 1000 brain scans). Thus, imaging connectomics investigates the pattern of brain activity and structural connectivity related to a specific area of the brain (in this case, the volume of tissue activated (VATs) with DBS) and correlate these data with clinical outcomes to shed light on potential mechanisms of action. Similarly, the spatial transcriptomic analysis identifies spatial correlations between patterns of gene expression and brain characteristics detected by MRI 19 (in this case, the spatial pattern of functional connectivity) to investigate possible genetic underlying mechanisms. It is important to highlight that previous studies have shown that normative analyses yield results that are similar to the ones observed using patient-specific data 20–22. In the specific case of imaging connectomics, It has been shown that normative datasets can be used to create probabilistic models of optimal connectivity associated with patients’ outcomes that are meaningful to predict outcomes in patient-specific connectivity data 21. Thus, these exploratory data-driven approaches strive to simulate the presumed fingerprint that a particular patient’s individualized DBS intervention might modulate. They also allow the investigation of possible mechanisms of action in a large, previously inaccessible cohort of patients whose individual data are available. We apologize for the inaccuracy in Figure 7. Along with improving the Discussion section of the manuscript, we included the label for the bar plots in the left panel to improve the clarity of the graph and added the missing result from the KEGG 2021 Human Library that shows the oxytocin signalling pathway.

   1. Data availability. Code used for data processing should be made openly available or shared as source data along with the Figures that were generated using the code. Sweet-spot, structural, and functional connectivity maps should be shared openly.

   All tools and codes necessary for localizing the electrodes, estimating the volume of activated tissues, and analyzing imaging connectomics are freely available in Lead-DBS (https://www.lead-dbs.org/), a toolbox designed for DBS electrode reconstructions and computer simulations based on postoperative imaging. All codes for spatial transcriptomics are freely available in abagen (https://abagen.readthedocs.io/en/stable/), a toolbox designed to analyze the Allen Brain Atlas genetics data. Along with the codes, the websites for these tools provide manuals describing the step-by-step procedure for successful analysis. The datasets were made freely available at Zenodo (doi: 10.5281/zenodo.7344268). We improved our Data Availability Statement to address this concern.

   Reviewer #2 (Public Review):

   Deep brain stimulation (DBS) is an important, relatively new approach for treating refractory psychiatric illnesses including depression, addiction, and obsessive-compulsive disorder. This study examines the structural and functional connections associated with symptom improvement following DBS in the posterior hypothalamus (pHyp-DBS) for severe and refractory aggressive behavior. Behavioral assessments, outcome data, electrode placements, and structural and functional (resting-state) imaging data were collected from 33 patients from 5 sites. The results show structural connections of the effective electrodes (91% of patients responded positively) were with sensorimotor regions, emotional regulation areas, and monoamine pathways. Functional connectivity between the target, periaqueductal gray, and amygdala was highly predictive of treatment outcome.

   Strengths.

   This dataset is interesting and potentially valuable.

   Weaknesses.

   The figures seem to indicate that electrodes and symptom improvement is located lateral to the hypothalamus, perhaps in the subthalamic nucleus (STN). This is might explain why the streamlines from the tractography are strongest in motor regions. The inclusion of the monoaminergic based on the tractography is not warranted, as the resolution is not sufficient to demonstrate the distinction between the MFB (a relatively small bundle) and others flowing through this region to the brainstem.

   This is an interesting point. The sweet spot identified in this work is indeed located in the posterior-inferior-lateral region of the posterior hypothalamic area, reaching the most superior part of the red nucleus, without including the STN. It is important to highlight that the voxel-efficacy mapping only shows voxels associated with a minimum of 50% symptomatic improvement following treatment. Thus, the areas not touching the red nucleus are also associated with excellent symptom alleviation. Although the structural connectivity mapping revealed tracts involved in motor and sensory information, it also showed tracts known to be involved in the regulation of emotions, such as the MFB, the Amygdalofugal Pathway and the ALIC. It is worth noting that this analysis is excellent for segregating the fibre tracts as relevant or not associated with a clinical improvement, but it is not capable of tearing apart the system to determine which of those are necessary for symptom alleviation. As a result, it is not possible to determine whether the motor projections are stronger or more relevant than others. However, the structural connectivity analysis presented here contributes to the body of knowledge on the network of aggressive behaviour and provides clinically relevant data that can be useful to improve future patient outcomes.

   We agree with the reviewer that the engagement of the motor system is indeed highly relevant for the reduction of aggressive behaviours, as we have previously shown that aggressive behaviour is highly correlated with motor agitation 23,24. Additionally, in the context of ASD, self-injury behaviour is defined as a type of repetitive/stereotypic behaviour that results in physical injury to the patient’s own body. In relation to the involvement of the monoaminergic system, we would like to apologize for not being clear in the discussion of our findings. Although the functional and structural connectivity maps are related, they provide different means of exploring distinct aspects of the connectivity profile of each VAT. While the structural connectivity map may elucidate symptom improvement via direct fibre modulation (i.e. fibres that touch vs fibres that do not touch the VAT), the functional connectivity map investigates the functional dynamics of the network via BOLD signals (functional MRI). In this manuscript, we showed the functional connectivity (not fibre tracts) of the VATs with areas known to regulate monoamine production, such as the Raphe nuclei and the Substantia Nigra. Both serotonin and dopamine are critically involved in the control of aggressive behaviours, being the target of the main medications used to treat these symptoms in several patient populations. To address all the raised concerns, we incorporated a few sentences in the discussion, highlighting the relevance of the motor system and some limitations of our analysis. We also added a new Figure 3-figure supplement 1 and a discussion on the position of the sweet spot in relation to the red nucleus and subthalamic nucleus.

   REFERENCES:

   1. Gunalan, K., Howell, B. & McIntyre, C. C. Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation. Neuroimage 172, 263–277 (2018).
   2. Dembek, T. A. et al. Probabilistic mapping of deep brain stimulation effects in essential tremor. Neuroimage Clin 13, 164–173 (2017).
   3. Kuncel, A. M., Cooper, S. E. & Grill, W. M. A method to estimate the spatial extent of activation in thalamic deep brain stimulation. Clin. Neurophysiol. 119, 2148–2158 (2008).
   4. Mädler, B. & Coenen, V. A. Explaining clinical effects of deep brain stimulation through simplified target-specific modeling of the volume of activated tissue. AJNR Am. J. Neuroradiol. 33, 1072–1080 (2012).
   5. Elias, G. J. B. et al. Probabilistic Mapping of Deep Brain Stimulation: Insights from 15 Years of Therapy. Ann. Neurol. 89, 426–443 (2021).
   6. Germann, J. et al. Brain structures and networks responsible for stimulation-induced memory flashbacks during forniceal deep brain stimulation for Alzheimer’s disease. Alzheimers. Dement. 17, 777–787 (2021).
   7. Elias, G. J. B. et al. Mapping the network underpinnings of central poststroke pain and analgesic neuromodulation. Pain 161, 2805–2819 (2020).
   8. Gouveia, F. V. et al. Case report: 5 Years follow-up on posterior hypothalamus deep brain stimulation for intractable aggressive behaviour associated with drug-resistant epilepsy. Brain Stimul. 14, 1201–1204 (2021).
   9. Coblentz, A. et al. Mapping efficacious deep brain stimulation for pediatric dystonia. J. Neurosurg. Pediatr. 27, 346–356 (2021).
   10. M Oliveira, L. et al. Probabilistic characterisation of deep brain stimulation in patients with tardive syndromes. J. Neurol. Neurosurg. Psychiatry (2021) doi:10.1136/jnnp-2020-324270.
   11. Horn, A. et al. Connectivity Predicts deep brain stimulation outcome in Parkinson disease. Ann. Neurol. 82, 67–78 (2017).
   12. Duffley, G., Anderson, D. N., Vorwerk, J., Dorval, A. D. & Butson, C. R. Evaluation of methodologies for computing the deep brain stimulation volume of tissue activated. J. Neural Eng. 16, 066024 (2019).
   13. Astrom, M., Diczfalusy, E., Martens, H. & Wardell, K. Relationship between neural activation and electric field distribution during deep brain stimulation. IEEE Trans. Biomed. Eng. 62, 664–672 (2015).
   14. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
   15. Arnatkeviciute, A., Fulcher, B. D. & Fornito, A. A practical guide to linking brain-wide gene expression and neuroimaging data. Neuroimage 189, 353–367 (2019).
   16. Arnatkeviciute, A., Markello, R. D., Fulcher, B. D., Misic, B. & Fornito, A. Toward Best Practices for Imaging Transcriptomics of the Human Brain. Biol. Psychiatry 93, 391–404 (2023).
   17. Markello, R. D. et al. Standardizing workflows in imaging transcriptomics with the abagen toolbox. Elife 10, (2021).
   18. Seidlitz, J. et al. Transcriptomic and cellular decoding of regional brain vulnerability to neurogenetic disorders. Nat. Commun. 11, 3358 (2020).
   19. Fornito, A., Arnatkevičiūtė, A. & Fulcher, B. D. Bridging the Gap between Connectome and Transcriptome. Trends Cogn. Sci. 23, 34–50 (2019).
   20. Arnatkeviciute, A., Fulcher, B. D., Bellgrove, M. A. & Fornito, A. Imaging Transcriptomics of Brain Disorders. Biol Psychiatry Glob Open Sci 2, 319–331 (2022).
   21. Wang, Q. et al. Normative vs. patient-specific brain connectivity in deep brain stimulation. Neuroimage 224, 117307 (2021).
   22. Elias, G. J. B. et al. Normative connectomes and their use in DBS. in Connectomic Deep Brain Stimulation 245–274 (Elsevier, 2022).
   23. Gouveia, F. V. et al. Bilateral Amygdala Radio-Frequency Ablation for Refractory Aggressive Behavior Alters Local Cortical Thickness to a Pattern Found in Non-refractory Patients. Front. Hum. Neurosci. 15, 653631 (2021).
   24. Venetucci Gouveia, F. et al. Case report: 5 Years follow-up on posterior hypothalamus deep brain stimulation for intractable aggressive behaviour associated with drug-resistant epilepsy. Brain Stimul. (2021) doi:10.1016/j.brs.2021.07.062.
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   31. Gouveia, F. V. et al. Reduction of aggressive behaviour following hypothalamic deep brain stimulation: involvement of 5-HT1Aand testosterone. bioRxiv (2023) doi:10.1101/2023.03.20.533520.

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3. 

   eLife

   May 12, 2023

   eLife assessment

   This study presents useful structural and functional connectivity profiles of patients receiving deep brain stimulation in the posterior hypothalamus for severe and refractory aggressive behavior. The inclusion of data from multiple centers is compelling. However, the imaging analysis is incomplete and the interpretation of the findings is not solid.

   Read the original source
4. 

   eLife

   May 12, 2023

   Reviewer #1 (Public Review):

   The authors have compiled and analysed a unique dataset of patients with treatment-resistant aggressive behaviours who received deep brain stimulation (DBS) of the posterior hypothalamic region. They used established analysis pipelines to identify local predictors of clinical outcomes and performed normative structural and functional connectivity analyses to derive networks associated with treatment response. Finally, Gouveia et al. perform spatial transcriptomics to determine the molecular substrates subserving the identified circuits. The inclusion of data from multiple centres is a notable strength of this retrospective study, but there are current limitations in the methodology and interpretation of findings that need to be addressed.

   1. The validation of findings is heterogeneous and inconsistent across …

   More

   Reviewer #1 (Public Review):

   The authors have compiled and analysed a unique dataset of patients with treatment-resistant aggressive behaviours who received deep brain stimulation (DBS) of the posterior hypothalamic region. They used established analysis pipelines to identify local predictors of clinical outcomes and performed normative structural and functional connectivity analyses to derive networks associated with treatment response. Finally, Gouveia et al. perform spatial transcriptomics to determine the molecular substrates subserving the identified circuits. The inclusion of data from multiple centres is a notable strength of this retrospective study, but there are current limitations in the methodology and interpretation of findings that need to be addressed.

   1. The validation of findings is heterogeneous and inconsistent across analysis pipelines. While the authors performed non-parametric permutation testing during sweet-spot mapping, structural and functional connectivity were validated using a 'four-fold consistency analysis'. The latter consists of a visual representation of streamlines and peak intensities after randomly dividing data into four groups, the findings were not validated quantitatively. If possible, the authors should apply permutation analysis in alignment with sweet-spot mapping and demonstrate the predictive ability of their identified networks in a LOO or k-fold cross-validation paradigm as carried out by similar studies. Given that the data has been derived from multiple centers, the prediction of left-out cohorts based on models generated by the remaining cohorts could be another means of validation. If validation is not possible, the authors should clearly state the limitations of their approach.

   2. In addition to a 'four-fold consistency analysis', functional connectivity was evaluated using LOOCV in a priori identified ROIs. Their network analysis, however, revealed a far more extensive network encompassing cortical, subcortical, and cerebellar structures. To avoid selection bias the authors should incorporate identified structures into their analysis and apply appropriate means of validation.

   3. Functional connectivity mapping: how were R-maps generated? The authors mention that patient-specific R-maps were p-thresholded and corrected for multiple comparisons, but it is not clear how group-level maps were generated. How did the authors perform regression on these maps? Were voxels that did not survive thresholding excluded?

   4. The authors determined that age was a significant prédictor of the outcome, but it is unclear whether certain age groups presented with distinct etiologies underlying their aggressiveness. For example, aggression in epilepsy may show a better response to DBS as opposed to schizophrenia. How does patient outcome change when stratifying according to etiology? How does model performance change when controlling for etiology? The authors should include the etiology of aggressiveness in Table 1.

   5. Stimulation parameters. The authors report average pulse widths of 219 µs and 142µs respectively, which is up to 4-fold higher as compared to DBS settings used conventionally in movement disorders and will significantly alter the volume of activated tissue. Did the authors account for the drastic increases in pulse width during VAT modeling?

   6. Imaging transcriptomics. The methods described lack detail: How did the authors account for differences in expression across donors, samples, and regions during preprocessing of the Allen Human Brain Atlas? How was expression data collapsed into regions of interest? Did the authors apply any normalization? Recent publications have introduced reproducible workflows for processing and preparing the AHBA expression data for analysis that is publicly available.

   7. 'genes with similar patterns of spatial distribution to the TFCE map were compiled in an extensive list'. It is unclear why authors used TFCE maps for spatial transcriptomics as opposed to the functional connectivity map featured in Figure 5. How was similarity measured between the TFCE map and the AHBA? How were candidate genes identified? Please provide a more comprehensive description of the analysis pipeline.

   8. What do the bar plots in Figure 7 (left) represent? P-values? The authors should label the axes to make this clear to the reader.

   9. Interprétation of imaging transcriptomics: The authors identify a therapeutic circuit associated with deep brain stimulation of the posterior hypothalamic area, however, it is unclear how to reconcile genes associated with hormones, inflammation, and plasticity in this context. The authors mention and discuss genes implicated in hormonal processing, specifically oxytocin. The results provided in Figure 7, however, do not support this finding and it is unclear how the authors identified genes linked to oxytocin. In addition, the authors identified reductions in the number of microglia and astrocytes, while oligodendrocytes were overexpressed relative to the expected distribution of genes per cell type. These findings were attributed to DBS effects, however, both connectomic and transcriptomic data are acquired from healthy subjects, which suggests a physiological deficit/enrichment in a therapeutic circuit. How do the authors interpret findings given that no electrode implantation and stimulation were performed?

   10. Data availability. Code used for data processing should be made openly available or shared as source data along with the Figures that were generated using the code. Sweet-spot, structural, and functional connectivity maps should be shared openly.

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   Read the original source
5. 

   eLife

   May 12, 2023

   Reviewer #2 (Public Review):

   Deep brain stimulation (DBS) is an important, relatively new approach for treating refractory psychiatric illnesses including depression, addiction, and obsessive-compulsive disorder. This study examines the structural and functional connections associated with symptom improvement following DBS in the posterior hypothalamus (pHyp-DBS) for severe and refractory aggressive behavior. Behavioral assessments, outcome data, electrode placements, and structural and functional (resting-state) imaging data were collected from 33 patients from 5 sites. The results show structural connections of the effective electrodes (91% of patients responded positively) were with sensorimotor regions, emotional regulation areas, and monoamine pathways. Functional connectivity between the target, periaqueductal gray, and amygdala … More

   Reviewer #2 (Public Review):

   Deep brain stimulation (DBS) is an important, relatively new approach for treating refractory psychiatric illnesses including depression, addiction, and obsessive-compulsive disorder. This study examines the structural and functional connections associated with symptom improvement following DBS in the posterior hypothalamus (pHyp-DBS) for severe and refractory aggressive behavior. Behavioral assessments, outcome data, electrode placements, and structural and functional (resting-state) imaging data were collected from 33 patients from 5 sites. The results show structural connections of the effective electrodes (91% of patients responded positively) were with sensorimotor regions, emotional regulation areas, and monoamine pathways. Functional connectivity between the target, periaqueductal gray, and amygdala was highly predictive of treatment outcome.

   Strengths.
   This dataset is interesting and potentially valuable.

   Weaknesses.
   The figures seem to indicate that electrodes and symptom improvement is located lateral to the hypothalamus, perhaps in the subthalamic nucleus (STN). This is might explain why the streamlines from the tractography are strongest in motor regions. The inclusion of the monoaminergic based on the tractography is not warranted, as the resolution is not sufficient to demonstrate the distinction between the MFB (a relatively small bundle) and others flowing through this region to the brainstem.

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6. 

   Version published to 10.1101/2022.10.29.22281666v1 on medRxiv

   Nov 1, 2022