TRNP1, as it controls proliferation of neural progenitors in mice and ferrets. Here, we investigate the contribution of both regulatory and coding sequences of TRNP1 to brain size and cortical folding in over 30 mammals. We find that the rate of TRNP1 protein evolution (ω) significantly correlates with brain size, slightly less with cortical folding and much less with body size. This brain correlation is stronger than for >95% of random control proteins. This co-evolution is likely affecting TRNP1 activity, as we find that TRNP1 from species with larger brains and more cortical folding induce higher proliferation rates in neural stem cells. Furthermore, we compare the activity of putative cis-regulatory elements (CREs) of TRNP1 in a massively parallel reporter assay and identify one CRE that likely co-evolves with cortical folding in Old World monkeys and apes. Our analyses indicate that coding and regulatory changes that increased TRNP1 activity were positively selected either as a cause or a consequence of increases in brain size and cortical folding. They also provide an example how phylogenetic approaches can inform biological mechanisms, especially when combined with molecular phenotypes across several species.Brain size and cortical folding have increased and decreased recurrently during mammalian evolution. Identifying genetic elements whose sequence or functional properties co-evolve with these traits can provide unique information on evolutionary and developmental mechanisms. A good candidate for such a comparative approach is  Understanding the genetic basis of complex phenotypes within and across species is central for biology. Brain phenotypes – even when as simple as size or folding – are of particular interest to many fields, because they are linked to cognitive abilities, which are of particular interest to humans .Brain size and cortical folding show extensive variation across mammals, including recurrent independent increases and decreases . For exaThe underlying genetic and cellular factors that are associated with these evolutionary variations in brain size and folding have not been studied across such large phylogenies. However, observational and experimental studies, especially in mice, but increasingly also in other systems like the ferret, macaques and humans, have led to major insights into the genetic and cellular mechanisms of cortical development . BrieflyTKTL1  isolated from mouse cortices induces phase separation, accelerates mitosis, and increases proliferation (Trnp1 expression by in utero electroporation in mice and ferrets (embryonic day 13 [E13] in mice) leads to increased proliferation of aRGCs (Trnp1 expression levels in mice or ferrets (E13) reduces aRGC proliferation, increases their differentiation into BPs, and induces cortical folding  and the synonymous substitution rates, itations . In the tic tree . This alody size . For exaelection . With puTRNP1 sequences and phenotypic data. We started with all human autosomal genes that – as TRNP1 – have only one coding exon . For 133 (12.3%) of these we could find full-length high-quality one-to-one orthologous sequences for all 30 species , GI , and also body mass  and that these correlations are stronger than those of the average control protein , followed by GI (r=0.34), while the partial correlation with body mass (r=0.19) has a much larger drop compared to the marginals . Eight codons with a selection signature could be pinpointed with high confidence , using massively parallel reporter assays (MPRAs). To this end, a library of putative regulatory sequences is cloned into a reporter vector and their activity is quantified simultaneously by the expression levels of element-specific barcodes  from human foetal brain  CRE sequence tiles . Next, wactivity .Next, we tested whether CRE activity is associated with either brain size or GI across the 45 of the 75 mammalian species for which these phenotypes were available . None ofTRNP1 was expressed , we analysed the sequence evolution of putative TF binding sites . First, xpressed , supportTRNP1 in the germinal zone of the developing human brain  to be the best candidates to explain the variation in the intron CRE activity and one TF (CTCF) to co-vary with GI  and ferrets (E30), aRGC proliferation increases  cerebral cortices were dissected, removing the ganglionic eminence, the olfactory bulb, the hippocampal anlage, and the meninges. Cells were mechanically dissociated with a fire polish Pasteur pipette. Cells were then seeded onto poly-D-lysine (PDL)-coated glass coverslips in DMEM-GlutaMAX (Dulbeccos’s modified Eagles’s medium) supplemented with 10% foetal calf serum (FCS) and 100 µg/mL Pen. Strep. and cultured at 37°C in a 5% CO2 incubator.E14 mouse (H. sapiens) were grown in DMEM supplemented with 10% FCS and 1% Pen. Strep. Cells were cultured in 10 cm flat-bottom dishes at 37°C in a 5% CO2 environment and split every 2–3 days in a 1:10 ratio using 5 mL PBS to wash and 0.5 mL 0.25% Trypsin to detach the cells.HEK 293T cells (M. musculus) were cultured in Eagle’s minimum essential medium (Thermo Fisher Scientific) with 10% FCS (Thermo Fisher Scientific) at 37°C in a 5% CO2 incubator and split every 2–3 days in a 1:5 ratio using 5 mL PBS (Thermo Fisher Scientific) to wash and 0.5 mL 0.25% Trypsin (Thermo Fisher Scientific) to detach the cells.Neuro-2A cells (N2A)  and one cynomolgus monkey (M. fascicularis) cell line (2 incubator on Geltrex (Thermo Fisher Scientific) in DMEM F12 (Fisher Scientific) supplemented with 2 mM GlutaMAX-I (Fisher Scientific), 20 ng/µL bFGF (Peprotech), 20 ng/µL hEGF (Miltenyi Biotec), 2% B-27 supplement (50×) minus vitamin A (Gibco), 1% N2 supplement 100× (Gibco), 200 μM L-ascorbic acid 2-phosphate (Sigma), and 100 μg/mL penicillin-streptomycin (Pen. Strep.) with medium change every second day. For passaging, NPCs were washed with PBS and then incubated with TrypLE Select (Thermo Fisher Scientific) for 5 min at 37°C. Culture medium was added and cells were centrifuged at 200 × g for 5 min. Supernatant was replaced by fresh culture medium and cells were transferred to a new Geltrex-coated dish. The cells were split every 2–3 days in a ratio of 1:3. All cell lines have been authenticated using RNA sequencing (RNA-seq), see Neural progenitor cells of two human  were identified in human foetal brain and mouse embryonic brain DNase-seq datasets   . FirstlyMustela putorius furo) where the sequence was incomplete . For the ferret sequence, the genome sequence  contained a gap in the TRNP1 coding sequence leading to a truncated protein. To recover the full sequence of TRNP1 we used the conserved sequence 5’ of the gap and 3’ of the gap as input for primer blast .We sequenced TRNP1 coding sequences in six primates for which reference genome assemblies were either unavailable or very sparse and the ferret  dataset for human GRCh38.p12 genome  . RBB Ke strategyi to p is alignment position, i, where For all evolutionary analyses, the pruned mammalian tree from dS and dN. dN/dS was calculated as the ratio between the two parameters. Branch free-ratio model was ran on TRNP1 and 133 control protein-coding sequences using the following settings seqtype = 1, CodonFreq = 2, clock = 0, aaDist = 0, model = 1. We required the log(dS) tree length to be <3× SD away from the average, leading to the exclusion of one protein CCDS34575.1, resulting in a set containing 132 control sequence alignments and TRNP1.Program codeml from PAML software   according to the manufacturer’s instruction 2 hr after seeding the cells onto PDL-coated coverslips. One day later cells were washed with phosphate buffered saline (PBS) and then fixed in 4% paraformaldehyde (PFA) in PBS and processed for immunostaining.Cells plated on PDL-coated glass coverslips were blocked with 2% BSA, 0.5% Triton-X (in PBS) for 1 hr prior to immunostaining. Primary antibodies  were applied in blocking solution overnight at 4°C. Fluorescent secondary antibodies were applied in blocking solution for 1 hr at room temperature. DAPI  was used to visualize nuclei. Stained cells were mounted in Aqua Polymount (Polysciences). All secondary antibodies were purchased from Life Technologies. Representative high-quality images were taken using an Olympus FV1000 confocal laser‐scanning microscope using 20×/0.85 NA water immersion objective. Images used for quantification were taken using an epifluorescence microscope  equipped with a 20×/0.8 NA and 63×/1.25 NA oil immersion objectives. Postimage processing with regard to brightness and contrast was carried out where appropriate to improve visualization, in a pairwise manner.The proportion of successfully transfected cells that proliferate under each condition (Ki67-positive/GFP-positive) was modeled using logistic regression (R-package stats (version 4.0.3), glm function) with logit link function e models .To back-calculate the absolute proliferation probability  under each condition, intercept of the respective model was set to zero and the inverse logit function ondition . Two-sidondition .The association between the induced proliferation rates for each TRNP1 orthologue and the brain size or GI of the respective species was analysed using generalized least squares , while correcting for the expected correlation structure due to phylogenetic relation between the species. The expected correlation matrix for the continuous trait was generated using a Brownian motion  , in all other cloning steps chemically competent E. coli  were used.Primer sequences and plasmids used in the MPRA can be found in the analysis GitHub (see Data availability). To ensure maximum library complexity, transformations that involved the CRE library were performed using electroporation  to add a library-specific index sequence and Illumina Flow Cell adapters. The Adapter PCR was performed in triplicates using DreamTaq polymerase (Thermo Fisher Scientific). Subsequently 1–5 ng of the Adapter PCR product were subjected to the Index PCR using Q5 polymerase.Total RNA from NPCs was extracted using the Direct-zol RNA Microprep Kit (Zymo Research). Five hundred ng of RNA were subjected to reverse transcription using Maxima H Minus RT (Thermo Fisher Scientific) with oligo-dT primers. Fifty ng of cDNA were used for library preparation and processed as described for plasmid DNA.Plasmid and cDNA libraries were pooled and quality was evaluated using capillary gel electrophoresis . Sequencing was performed on an Illumina HiSeq 1500 instrument using a single-index, 50 bp, paired-end protocol.TCTAGAGTCGCGGCCTTACT). The remaining reads that matched one of the known CRE-tile barcodes were tallied up resulting in a count table. Next, we filtered out CRE tiles that had been detected in only one of the three input plasmid library replicates (4202/4950). Counts per million were calculated per CRE tile per library (median counts: ∼900k range: 590–1050k). Macaque replicate 3 was excluded due to its unusually low correlation with the other samples (Pearson’s r). The final regulatory activity for each CRE tile per cell line was calculated as:MPRA reads were demultiplexed with deML  using i5where Given that each tile was overlapping with two other tiles upstream and two downstream, we calculated the total regulatory activity per CRE region in a coverage-sensitive manner, that is, for each position in the original sequence, mean per-bp-activity across the detected tiles covering it was calculated. The final CRE region activity is the sum across all base positions.ar is regulatory activity of CRE region ai is tile activity from li is tile length. CRE activity and brain phenotypes were associated with one another using PGLS analysis (see above). The number of species varied for each phenotype-CRE pair , therefore the activity of each of the seven CRE regions was used separately to predict either GI or brain size of the respective species.where protocols.io (https://www.protocols.io/view/prime-seq-s9veh66). Here, we used 10 ng of the isolated RNA from the MPRA experiment and subjected it to the prime-seq protocol. Sequencing was performed on an Illumina HiSeq 1500 instrument with the following setup: read 1 16 bases, read 2 50 bases, and i7 index read 8 bases.RNA-seq was performed using the prime-seq method . The fulBulk RNA-seq data was generated from the same nine samples  that were assayed in the MPRA. Raw read fastq files were pre-processed using zUMIs (version 2.4.5b)  togetherTF position frequency matrices were retrieved from JASPAR CORE 2020 , includiA hidden Markov model-based program Cluster-Buster  . These motifs were distinct from one another (mean pairwise distance 0.72). Gene set enrichment analysis contrasting the TFs with the highest binding potential with the other expressed TFs was conducted using the Bioconductor package topGO  was retrieved from ENCODE  (doi:10.Human Hi-C data  on TAD pn), type of statistical test, significance thresholds, degrees of freedom, as well as standard deviations can be found in the figure legends.Data visualizations and statistical analysis was performed using R (version 4.0) . DetailsFurther information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Wolfgang Enard (enard@bio.lmu.de).Plasmids and cell lines used in this work will be available upon request. This is an important paper that combines comparative analysis and experimental assays to investigate the role of protein-coding and regulatory changes at TRNP1 in mammalian brain evolution. The evidence supporting a contribution of TRNP1 is convincing, although the strength of the link between protein-coding changes and trait evolution is stronger and more readily interpretable than the data on gene regulation. The work will be of interest to researchers interested in mammalian evolution, brain evolution, and evolutionary genetics. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Detlef Weigel as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Regulatory and coding sequences of TRNP1 co-evolve with brain size and cortical folding in mammals" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The reviewers appreciated the effort to investigate both coding and regulatory changes and feel the MPRA study was well-designed. However, because the results of the MPRA are somewhat unclear (and necessarily incomplete as a test of regulatory function), please temper your conclusions accordingly. Additionally, clarify how you dealt with the multiple testing burden.2) Please address the outstanding questions about the appropriateness of the background set of genes used for comparison of brain phenotype-evolutionary rate correlations. This could take the form of additional analysis, or minimally a discussion of the potential limitations and/or biases of the set you used.Reviewer #1 (Recommendations for the authors):I think this paper makes an important contribution to the literature. Genomic analyses that control for phylogenetic context/non-independence remain rare (at least in primates), and the integration of functional genomic analysis and experimentation is an important strength. I have a few concerns, however:1. I am unclear on your interpretation of the correlation between brain size/gyrification and the rate of protein evolution-specifically, why a positive correlation should necessarily be expected. While comparative analyses often analyze correlated evolution between two traits , this analysis seems more like analyzing the correlation between brain size and lineage-specific change in diet. If we assume that TRNP1 is a major driver of brain size, and selection on brain size fully accounts for selection on the gene, then the big-brained descendants of a big-brained ancestor might show strongly conserved patterns of evolution  rather than additional evidence for lineage-specific positive selection. In other words, whether selection for big brains translates to evidence for positive versus purifying selection on the sequence  would seem to depend on ancestral state. It would be helpful to readers if you can outline your hypotheses and predictions here more explicitly, including your expectations related to lineage-specific rates.2. On a related note, I appreciate the attempt to find a matched set of control genes for the protein molecular evolution analysis; clearly this is difficult. However, I am not entirely convinced that the control genes are an appropriate background set . Because one of the main filters for identifying these genes was to find genes where homologues can be readily identified, without the additional curation/sequencing done for TRNP1, they are likely to be enriched for genes evolving under constraint within mammals-so they may control for demographic differences, but not for the interaction of Ne with positive selection. This appears to be the case based on Figure 1—figure supplement 3, where most genes show no evidence of sites under positive selection. The comparison therefore works as a null for testing for positive selection, but not as an ideal null for testing whether patterns of positive selection correlate with a brain size/gyrification, as intended here (since most genes don't show the same pattern of positive selection).3. In Figure 1 C and D and lines 104-105, the strongly attenuated slope in the control genes line is because you are taking the average across control genes for each species. The comparison I think you want is whether the slope for TRNP1 is an outlier relative to the slopes calculated for each of the control genes, not relative to the slope calculated for their average, which will inevitably be attenuated. I think this is the result you report in lines 106-109, but it should also be what is visualized in Figure 1C-1D to avoid overemphasizing the difference between TRNP1 and the control gene distribution . On a related note: are the results in these figures and text controlled for phylogenetic non-independence? I ask in part because it seems like a lot of the signal is being driven by haplorrhine primates.4. You show, in a very nice result, that neural stem cell proliferation rate is correlated with brain size and gyrification index. Given that your evidence for the protein evolution-trait link is based on lineage-specific omega, is proliferation rate therefore correlated with omega?5. Line 189  draws on the MPRA results to argue that the expression regulation of TRNP1 co-evolves with cortical folding. Is TRNP1 indeed more highly expressed in catarrhines with higher GI values .Reviewer #3 (Recommendations for the authors):I would suggest that the authors address the following points:1. Protein evolution rate analyses: I found Fig1S3 surprising, as it seems that about half of all control proteins used by the authors also evolve under positive selection. This seems unlikely high, and suggests that either the alignments contain errors and require cleaning, or the false discovery rate is inadequately corrected for in this analysis. Additionally, it was not always clear to me when the authors use either branch or site tests – I assume that the evolutionary rate analyses use unconstrained branch models, but this does not appear in the methods. In this case, can branch length confound the signal? I would expect that the estimation of omega in human, for example, is less reliable than e.g. dolphin, where the terminal branch is much longer.2. Throughout the paper, it is unclear how multiple testing was corrected for. In some cases this does not matter, but it does for example when the authors investigate correlations between CRE activity and brain phenotypes at multiple sites. The likelihood of finding one spurious correlation increases rapidly when multiple CREs are tested, and in this case the authors cannot fall back on control regions to estimate the probability of observing such a correlation from background data, as they do for the gene evolution analyses. It may not be possible to control for this, but this should be explicitly acknowledged, and the conclusions toned down as a consequence.3. I do not think it is surprising that an enhancer active in brain contains an excess of binding sites for TFs involved in neuronal proliferation, especially as I don't fully understand the display of Figure 4C . The evidence that CTCF binding sites are stronger in catarrhines is weak – again, it is not clear to me how multiple testing was corrected for here, and the probability of spuriously finding one TF with a correlation out of 22 is high. I would suggest discussing this more explicitly, and toning down the discussion on CTCF as I am unconvinced that this signal is specific.4. Spotted a few typos that need correcting e.g. line 111 "showed higher a significant"; line 122 "orthologoues"; also some in the methods which should be caught by a spellcheck editor. Essential revisions:1) The reviewers appreciated the effort to investigate both coding and regulatory changes and feel the MPRA study was well-designed. However, because the results of the MPRA are somewhat unclear (and necessarily incomplete as a test of regulatory function), please temper your conclusions accordingly. Additionally, clarify how you dealt with the multiple testing burden.We thank the reviewers for their constructive assessment. We agree with the mentioned caveats regarding the MPRA results and have improved the manuscript accordingly: we have tempered our conclusions in the abstract, the results and the discussion. We also have made clear how we deal with the multiple testing burden and have added additional analyses. We think that our interpretation of the MPRA data is now much more transparent. Specifically, the changes include the following points:Thus, while the statistical evidence from our MPRA data alone is limited, we consider the GI association in catarrhines together with the additional evidence from Reilly et al. strong enough to warrant a more detailed analysis of the intron CRE.”)We rewrote the relevant result section and describe the statistical testing more clearly, explicitly acknowledge the multiple testing burden and explain why additional evidence makes our MPRA results interesting  and plugging these in the same PGLS setting. Only 8/1000 (0.8%) of the permuted activities showed similar association strength .As suggested by Reviewer 1, we have added that we find TRNP1 to have a significantly lower expression in cynomolgus macaque than in human neural progenitor cells , providing additional evidence for our interpretation of the MPRA data.To validate a possible role of CTCF for the regulation of TRNP1 activity, we gathered available experimental data from a ChIP-seq experiment in human neural progenitor cell lines, information about topologically associated domains (TADs) and histone marks for human gestational zone in the relevant developmental phase . This data confirms CTCF binding in the intron CRE in vivo and further supports a role of this element in brain development .2) Please address the outstanding questions about the appropriateness of the background set of genes used for comparison of brain phenotype-evolutionary rate correlations. This could take the form of additional analysis, or minimally a discussion of the potential limitations and/or biases of the set you used.We think that a large part of the outstanding questions come from a misunderstanding of our analysis of TRNP1 protein evolution due to a suboptimal description on our side. We have completely restructured the relevant result section, revised Figure 1 and its supplements, so that in this new version the logic of each analysis step is made explicit. With this extensive re-write we also describe the appropriate scope of the use of our set of control proteins. We provide specific answers to the reviewers concerns below, but would like to make the following points here that we also tried to stress in the revised manuscript:We now explain in more detail the creation of the control protein set in the main text. In brief, we took all genes we could find that – as TRNP1 – have only one coding exon, have a similar length and full length orthologous sequences in the 30 analyzed species. We manually inspected the alignments and find, that they match TRNP1 with respect to total tree length, alignment quality and average omega . Furthermore, because our control proteins are matched in size and total tree length, the statistical power to detect an association between omega and the rate of brain size/ GI evolution should be comparable. So neither from our selection procedure nor from their omega distribution, there is a reason to assume that the control genes are biased.There is good reason to assume that all investigated phenotypes are indirectly linked to the effective population size (Ne), and thus to the efficacy of selection. This would induce a positive correlation between the phenotypes and omega. The main purpose of the control proteins here is to quantify the effect that variation in the global efficacy of selection has on our Coevol correlation estimates. Although, as expected, there is a slight positive correlation between changes in brain size/gyrification for all control proteins, the one for TRNP1 is stronger and thus must have additional causes. That TRNP1 is among 4.0% and 6.4% of the most correlated proteins further strengthens this view and suggests that even though TRNP1 probably was relatively important for mammalian brain evolution, many other proteins also played a role.The PAML site model identifies eight codons in TRNP1 that evolve under positive selection and hence are worth further functional investigations, but does not indicate brain-related TRNP1 evolution by itself. We hope that placing this analysis now after the decisive Coevol analysis helps to separate these two different aspects of protein evolution analysis.Reviewer #1 (Recommendations for the authors):I think this paper makes an important contribution to the literature. Genomic analyses that control for phylogenetic context/non-independence remain rare (at least in primates), and the integration of functional genomic analysis and experimentation is an important strength. I have a few concerns, however:1. I am unclear on your interpretation of the correlation between brain size/gyrification and the rate of protein evolution-specifically, why a positive correlation should necessarily be expected. While comparative analyses often analyze correlated evolution between two traits , this analysis seems more like analyzing the correlation between brain size and lineage-specific change in diet. If we assume that TRNP1 is a major driver of brain size, and selection on brain size fully accounts for selection on the gene, then the big-brained descendants of a big-brained ancestor might show strongly conserved patterns of evolution  rather than additional evidence for lineage-specific positive selection. In other words, whether selection for big brains translates to evidence for positive versus purifying selection on the sequence  would seem to depend on ancestral state. It would be helpful to readers if you can outline your hypotheses and predictions here more explicitly, including your expectations related to lineage-specific rates.We are sorry that this has not been clear in the previous version. We have now rewritten this section extensively to clarify our expectations and what the Coevol algorithm is testing.Briefly, the Coevol algorithm takes the observed trait (e.g. brain size) from extant species, reconstructs the ancestral states and infers the change that has occurred on all branches. Thus, if a big brained descendant species is inferred to have had a big brained ancestor, the phenotype-based estimate of the branch length will be short. Then, Coevol correlates the phenotype-based branch length with the omega estimate for that branch. Hence, Coevol tests the correlation of phenotype change and protein sequence change and also takes uncertainties of the estimates into account. It is capable of detecting both positive as well as negative correlations. A positive correlation, as we find it here, suggests positive selection on an increase in brain size, while a negative correlation would suggest selection on a decrease.2. On a related note, I appreciate the attempt to find a matched set of control genes for the protein molecular evolution analysis; clearly this is difficult. However, I am not entirely convinced that the control genes are an appropriate background set . Because one of the main filters for identifying these genes was to find genes where homologues can be readily identified, without the additional curation/sequencing done for TRNP1, they are likely to be enriched for genes evolving under constraint within mammals-so they may control for demographic differences, but not for the interaction of Ne with positive selection. This appears to be the case based on Figure 1—figure supplement 3, where most genes show no evidence of sites under positive selection. The comparison therefore works as a null for testing for positive selection, but not as an ideal null for testing whether patterns of positive selection correlate with a brain size/gyrification, as intended here (since most genes don't show the same pattern of positive selection).Initially, a bias towards higher conservation in our set of control proteins was also one of our biggest worries. However, the fact that the distribution of our omega estimates and total tree length is not biased and that TRNP1 falls somewhere in the middle of this distribution has alleviated this concern. All these checks as well as the selection process of the proteins is now described in detail in the main part of the results.Moreover, we agree that the efficacy of selection boosts both purifying and directional (positive) selection. However, they have opposite effects on omega. Under purifying selection such a boost would decrease omega, while it would increase under positive selection as the dominant force. Because purifying selection is much more prevalent, it is unlikely that we detect the overall relatively mild effects due to Ne modulated variation in the efficacy of selection due to positive selection.As a side note, we want to point out again that we extensively restructured the results part of the protein selection analysis. We mainly use the PAML site model to find which part of TRNP1 protein has been the target of selection to facilitate further functional analysis. Hence the results of the site model for the control proteins are not very relevant and were rather distracting from the real evidence. Therefore, we removed the old Figure 1 —figure supplement 3.3. In Figure 1 C and D and lines 104-105, the strongly attenuated slope in the control genes line is because you are taking the average across control genes for each species. The comparison I think you want is whether the slope for TRNP1 is an outlier relative to the slopes calculated for each of the control genes, not relative to the slope calculated for their average, which will inevitably be attenuated. I think this is the result you report in lines 106-109, but it should also be what is visualized in Figure 1C-1D to avoid overemphasizing the difference between TRNP1 and the control gene distribution . On a related note: are the results in these figures and text controlled for phylogenetic non-independence? I ask in part because it seems like a lot of the signal is being driven by haplorrhine primates.We agree with the reviewer that the distribution of the control genes is very relevant and we have now changed Figure 1 to also show this distribution of partial correlations across control proteins relative to TRNP1 . As these are calculated as for TRNP1 by Coevol, they are also controlled for phylogenetic non-independence. This analysis stresses the relative importance of TRNP1 for brain evolution, however, the average lines that we show in panels D-F show that the detected correlation of TRNP1 cannot be explained by Ne effects alone.4. You show, in a very nice result, that neural stem cell proliferation rate is correlated with brain size and gyrification index. Given that your evidence for the protein evolution-trait link is based on lineage-specific omega, is proliferation rate therefore correlated with omega?Yes, given the agreement in the direction of all considered associations, we see it as implied. However, as we assayed only 5 constructs we chose to investigate the main relationship of interest (proliferation vs. traits) directly.5. Line 189  draws on the MPRA results to argue that the expression regulation of TRNP1 co-evolves with cortical folding. Is TRNP1 indeed more highly expressed in catarrhines with higher GI values .We thank the reviewer for this suggestion. Indeed, as expected we find that TRNP1 has a significantly lower expression in cynomolgus macaque than in human neural progenitor cells. We added this in Figure 4—figure supplement 1A-C and mention it in the main text (line 264): “Moreover, TRNP1 expression was significantly higher in human NPCs than those of cynomolgus macaque’s , consistent with higher intron CRE activity.”Reviewer #3 (Recommendations for the authors):I would suggest that the authors address the following points:1. Protein evolution rate analyses: I found Fig1S3 surprising, as it seems that about half of all control proteins used by the authors also evolve under positive selection. This seems unlikely high, and suggests that either the alignments contain errors and require cleaning, or the false discovery rate is inadequately corrected for in this analysis. Additionally, it was not always clear to me when the authors use either branch or site tests – I assume that the evolutionary rate analyses use unconstrained branch models, but this does not appear in the methods. In this case, can branch length confound the signal? I would expect that the estimation of omega in human, for example, is less reliable than e.g. dolphin, where the terminal branch is much longer.We rewrote the results part “TRNP1 amino acid substitution rates co-evolve with rates of change in brain size and cortical folding in mammals” to clarify the logic that led to our main conclusion. To begin with, we now also describe our quality checks of the alignments in detail. Briefly, all multiple sequence alignments were manually checked, none of them contain excessive amounts of missing data and we removed one protein alignment because it was an outlier with respect to the total tree length. We also provide all multiple alignments on the accompanying github-page https://github.com/Hellmann-Lab/Co-evolution-TRNP1-and-GI . In the old Fig1S3, we plotted the results of the PAML site model comparison between 2 modes of evolution (M8 vs M7), which is not a very stringent model and was originally only intended to prioritize sites within TRNP1 for future functional analysis. We now removed this misleading plot and only discuss the site model within the scope of our original intent.S), ω and the continuous traits, using a Brownian motion process. Hence, the variation of the reliability due to branch lengths is explicitly factored into the model and does not bias our estimates of co-evolution.The analysis that lead us to conclude that TRNP1 protein coding evolution is linked to the evolution of brain size, is based on Coevol, “A Phylogenetic Model for Investigating Correlated Evolution of Substitution Rates and Continuous Phenotypic Characters” . Briefly, Coevol is a Bayesian MCMC that optimizes a co-variance matrix that includes the branch lengths  orthologous regulatory sequences in species that are phylogenetically far from the human. This can be seen in the reduced orthologous CRE length beyond primates . And indeed, as you state, generating data for a large number of control regions is difficult. To generate a type of a null-distribution for the observed correlation across catarrhines, we have permuted the activities of all other CREs of this data set and only 8/1000 (0.8%) show higher or equal significance in the association. So we think we have now explicitly acknowledge these limitations, the multiple testing issue, gathered additional corroborating evidence e.g. from Reilly et al. and toned down our conclusions e.g. by saying:“Moreover, we find tentative evidence that the activity of a regulatory element in the intron of TRNP1 might be associated with gyrification in catarrhines.” (line 294)“In summary, we find a suggestive correlation between the activity of the intron CRE and gyrification in catarrhines, indicating that also regulatory changes of TRNP1 might have contributed to the evolution of gyrification.” (line 286)3. I do not think it is surprising that an enhancer active in brain contains an excess of binding sites for TFs involved in neuronal proliferation, especially as I don't fully understand the display of Figure 4C . The evidence that CTCF binding sites are stronger in catarrhines is weak – again, it is not clear to me how multiple testing was corrected for here, and the probability of spuriously finding one TF with a correlation out of 22 is high. I would suggest discussing this more explicitly, and toning down the discussion on CTCF as I am unconvinced that this signal is specific.We have described this analysis better in the text and changed the axis label in Figure 4C to “intron-binding expr. TFs/expr. TFs” .This plot summarizes a gene-set enrichment analysis on GO categories, where we only consider expressed TFs in NPCs and look for categories with an enrichment of TFs that also have a binding site in the intron CRE. To test the significance of such an enrichment, we use topGO  with a prioritization of more specific categories  and the reported p-values are indeed from a Fisher’s-exact test. Because ontology groups are inherently inter-dependent, multiple testing corrections would be overly stringent and hence are not commonly applied in this context.We are unsure how surprising it is to find an enrichment of TFs involved in particular Gene Ontology categories like neuronal proliferation, but we think it is relevant enough to be reported, also because TRNP1 is expressed in other tissues, such as the stomach, liver or kidney; and, as mentioned, we only use NPC-expressed TFs as the background.Regarding the binding sites, we agree that testing 22 TF binding site variation across only 10 species is underpowered. We stress now that this analysis serves to pinpoint the best candidates and explicitly states that the p-value is uncorrected. To strengthen the possible involvement of the strongest candidate CTCF, we have gathered experimental ChIP-seq and Topologically Associated Domain (TAD) data from relevant cell types , showing that intron CRE is indeed bound by CTCF, which is associated with a nearby TAD boundary. Hence, this CTCF binding site indeed seems functional and the binding strength could affect the binding of other TFs in vivo and in vitro, and TRNP1 expression and TAD boundaries in vivo. But we agree that this is still not conclusive and requires experimental evidence to really understand its potential role (as stated in the discussion).4. Spotted a few typos that need correcting e.g. line 111 "showed higher a significant"; line 122 "orthologoues"; also some in the methods which should be caught by a spellcheck editor.Thanks, we corrected this.


Laryngeal cancer most frequently develops in males aged 60-70 years with a history of tobacco and/or alcohol use, while fewer cases occur in young patients in which tobacco and alcohol are often absent or less significant, highlighting the importance of other etiologies. We present cases of human papillomavirus (HPV)-associated laryngeal cancer in two previously healthy young women. A retrospective case review was carried out for both patients. DNA was extracted from the primary tumors and matched to normal tissue or blood, HPV genotype was determined by PCR and whole exome sequencing was carried out. Genomic results were pooled with laryngeal cancer patients from the cancer genome atlas (TCGA) dataset. The first patient was an 18-year-old female who underwent laryngectomy followed by adjuvant radiation. The second was a 24-year-old female who received chemoradiation. The first patient has remained disease-free for 16 years and the second for two years; both continue to be monitored. One tumor was positive for HPV45 and had mutations in FAT1 and FAT2; the other was positive for HPV31 and had mutations at NOTCH1, MAPK1, and HIST1H2AK. Both tumors had wild-type TP53 alleles. We bring attention to HPV as an etiology of laryngeal carcinoma in young patients, which may have implications for the treatment and prognosis of similar patients. Laryngeal squamous cell carcinoma (LSCC) is one of the most common head and neck cancers, with 184,404 new cases and 88,840 deaths in 2020 worldwide . FurtherHPV has been associated with an increased incidence of all head and neck cancers and is more frequently associated with oropharyngeal squamous cell cancers (OPSCC) . PatientCase 1An 18-year-old female presented with a six-year history of hoarseness and a one-year history of sore throat, difficulty swallowing, and weight loss. She had no other comorbid medical conditions, was a non-drinker, and non-smoker, and had no significant exposure to second-hand smoke. She had no history of acid reflux, no previous HPV diagnosis, no history of high-risk sexual behavior, no previous pap tests were completed, and she had not received a previous HPV vaccine. Although her grandmother had a history of thyroid cancer, there were no other instances of head and neck cancers in the family.Computed tomographic (CT) scan of the head and neck showed a 5.0 cm mass arising below the hyoid bone and extending to the isthmus of the thyroid and the anterior vocal cords with destruction of the thyroid cartilage Figure . There wThe patient underwent a total laryngectomy, bilateral neck dissection, and total thyroidectomy. Pathology described a 1.5 x 1.5 x 1.0 cm invasive squamous cell carcinoma of the right vocal cord that involved the soft tissue anterior to the thyroid and the thyroid capsule, the submucosal layer of the right and left subglottic and supraglottic regions, and suprahyoid soft tissues. There were three positive lymph nodes in the right neck, with the largest measuring 2.0 cm with no extranodal extension. The final pathological stage was IVA (pT4N2bM0) according to the AJCC 8th edition (AJCCV8) staging system.2 IV) given every three weeks with the third cycle omitted due to intolerable nausea and vomiting. Overall, the patient recovered well from surgery without complications and tolerated radiation therapy with only a modest weight loss. The patient has been on surveillance without recurrence for the past 16 years.The patient received postoperative chemoradiation. Radiation therapy consisted of 6600 cGy in 31 fractions using intensity-modulated radiation therapy with three cycles of concurrent cisplatin chemotherapy (100mg/mMolecular testing including genomic sequencing and HPV subtyping (see Appendix for details on methodology). Samples obtained from this patient were positive for HPV45 and confirmed by Sanger sequencing. A total of 312 mutations were identified in the tumor’s whole exome sequences when compared to the patient’s control DNA, including mutations in the FAT1 and FAT2 genes, and the tumor was wild type for TP53.Case 2A 24-year-old female presented with a one-year history of persistent and progressive hoarseness. She had a history of anxiety, depression, tonsillectomy in childhood, and tubal ligation at the age of 21. She had no previous HPV diagnosis, no history of high-risk sexual behavior, no previous pap tests were completed, and she had not received a previous HPV vaccine. There was no known family history of cancer, although she had a 10-pack-year smoking history.The patient underwent nasopharyngoscopy which revealed a lesion with an epicenter of the left posterior half of the true vocal fold extending to the anterior commissure Figure . No subg2 IV) given every three weeks concurrent with external beam radiotherapy 70 Gy in 35 fractions. The patient has experienced no recurrences in the three years after treatment and continues to do well.A panendoscopy and biopsy of the lesion on the left vocal revealed p16-positive squamous cell cancer Figures , 4B. PETMolecular testing including genomic sequencing and HPV subtyping was done (details on Methods can be found in appendices). Samples obtained from this patient were positive for HPV31 by PCR and confirmed by Sanger sequencing and alignment to HPV31 genome sequences in Genbank. A total of 95 mutations were identified in the tumor when compared to the patient’s control DNA, including mutations in the following genes: NOTCH1, MAPK1 (ERK2), and HIST1H2AK, but was wild-type for TP53.Statistical analysesWe have previously examined the LSCC cohort of the TCGA . After rHPV is recognized as an important etiological factor in OPSCC, and HPV-associated OPSCC has a more favorable response to chemoradiation that is now recognized with a separate staging system for these cancers in the 8th edition of the American Joint Committee on Cancer (AJCC) staging system ,10,11. FAlthough HPV status is widely recognized as an etiology and prognostic factor for OPSCC, the same is not clearly recognized for LSCC. A meta-analysis conducted by Torrente et al. showed that approximately 25% of laryngeal cancers may be HPV-related . It seemIt is well recognized that different HPV strains pose different risks for developing malignancy. Although HPV16 is the most common subtype of HPV associated with head and neck cancers, the cases presented in this case report highlight the role of other HPV subtypes in laryngeal carcinoma. Ndiaye et al. conducted a meta-analysis on HPV-associated laryngeal carcinomas and found that HPV16 was the most common subtype associated with these carcinomas worldwide followed by HPV18 and HPV6; however, stratification by age was not reported . InteresIn addition to tumor HPV status being discussed as a prognostic factor in head and neck cancers, the role of the specific HPV subtype on prognosis in HNC has also been explored in the literature to only a limited degree, likely because HPV types may not be routinely collected in pathology testing. There currently is no consensus on the impact of subtypes on prognosis. Bratman et al. found that patients with tumors associated with HPV16 had a better prognosis than those infected with other subtypes, with patients with HPV16-associated tumors having a three-year survival rate of 88% vs. 49% in those associated with other HPV subtypes (p=0.003) . ConversWhile the mutational burden of HPV-negative and HPV-positive head and neck squamous cell carcinomas (HNSCC) are similar, the genomics of the two groups differ in mutational makeup , which iIn addition to ingested carcinogens, the risk of developing laryngeal carcinoma is higher in males and with advancing age. HPV-related head and neck cancers tend to be more common in men than in women; however, it is thought that gender differences in lifestyle and sexual behavior alone are not sufficient to explain the difference in the prevalence of HPV-related head and neck cancers between sexes . It is hAs with other cancers, the risk increases with age, likely due to the accumulation of mutations over time. However, in HPV-associated cervical cancer, it was found that age alone was not an important factor, but rather, the duration of HPV infection and HPV subtype influenced the risk of progressing to cancer . These dThis case report highlights the importance of considering HPV as an etiological factor in laryngeal carcinoma in young patients and emphasizes the importance of considering relatively rare HPV subtypes. The HPV subtypes implicated in both of our cases are included in newer HPV vaccinations, and therefore vaccination at an early age is likely beneficial in preventing HPV-associated laryngeal carcinomas. It is important to consider routinely testing all laryngeal carcinomas for HPV status and subtype, particularly in patients who are younger or do not have a history of carcinogen exposure, to further knowledge about the biology of this disease. Further work assessing tumor HPV status and subtype as a prognostic factor and potential factor in staging is warranted.


Recent research has shown promising results for the therapeutic benefits of psychedelics. One popular view claims that these benefits are mediated by the subjective experiences induced by these substances. Based on this, we designed a virtual reality experience, Psyrreal, that mimics the phenomenological components of psychedelic experiences.We aimed to investigate the therapeutic efficacy of Psyrreal and psychedelic VR experiences in treating depressive symptoms as well as explore the effect of Psyrreal on subjective factors which have been suggested to mediate the therapeutic benefits of psychedelics.In this open-label feasibility study, thirteen participants with mild-to-moderate depression underwent a 2-day therapeutic intervention implementing Psyrreal. Depressive symptoms were evaluated by the Emotional State Questionnaire (EST-Q2) at the start of the intervention and 2 weeks after. A thematic analysis of semi-structured interviews after Psyrreal was also conducted as an additional assessment of the method.n = 10, p = 0.007, Hedges’ g = 1.046) measured by the Emotional State Questionnaire (EST-Q2). The analysis of semi-structured interviews suggests that Psyrreal could lead to insight and alterations in the sense of self in some people.A 2-day intervention implementing Psyrreal led to significant decreases in depressive symptoms at the 2-week follow-up (This work proposes a novel method using virtual reality to augment the treatment of psychological disorders as well as to precisely investigate the mediating subjective factors of the therapeutic effects of psychedelic substances. Our preliminary results suggest that VR experiences combined with psychological support show potential in treating depressive symptoms and further research into similar methods is warranted. Indeed, my first experiences with LSD were very reminiscent of these early mystical encounters I had had as a child in nature. So, you see that it is even possible to have these experiences without drugs.” Albert Hoffman, cited in Grob  4.27 was used as the development platform and the experience was written fully in Blueprint visual scripting language. The final software is distributed as open source upon requestPsyrreal imitates the audiovisual and narrative phenomenology reported during psychedelic experiences , 64–74 . CertainN,N-dimethyltryptamine (DMT) or Ayahuasca. Participants in the validation study were recruited via social media. Five people rated at least one scene from Psyrreal to be visually very similar  to psychedelic experiences. Though the experience is purely audiovisual and did not include any physical stimulation, six people also rated the physical sensations induced by Psyrreal to be very similar to those experienced under the effects of psychedelics.The participants begin the experience in a virtual “real world” which starts to acquire psychedelic phenomenology and continues to progress through a total of 19 distinct visual levels incorporating and combining various psychedelic effects. The visuals for the most part consist of abstract shapes and geometric patterns that would allow participants to project their own meaning to the experience. The duration of levels varies from approximately 30 s to 2 min. A specific soundtrack was composed for Psyrreal implementing narrative  and perceptual  aspects of psychedelic phenomenology matching the visual levels. Psyrreal was validated among participants with extensive psychedelic experiences who evaluated its similarity to psychedelic experiences  of ages 20–58 (The study was approved by the ethics committee of the University of Tartu and performed in accordance with relevant guidelines and regulations. Participants gave written informed consent prior to participation. All 13 registered people finished the study. One participant (P6) was excluded from the analyses due to receiving antidepressant therapy at the time of the study and another was excluded due to their updated score of depressive symptoms being below the cut-off, thus leaving the final sample at 11 participants. Note that an estimated effect size of 1.11 (based on a relatively similar approach using VR to treat depression) should be detectable with a sample size of 9 .The Emotional State Questionnaire  (EST-Q2)One important facet of subjective experiences that could be instrumental for therapeutic benefits is the insight experience , 40, 41.MEQ30, PIQ, and EDI were administered at the end of the first day and again at the end of the second day. EST-Q2 was administered during registration and 2 weeks after the experiments. More detailed descriptions of the questionnaires can be found in This open-label feasibility study used a one-group, uncontrolled, longitudinal design to investigate the effects of psychedelic phenomenology in VR. Experiments were conducted on two consecutive days . ParticiThe second day had a similar structure starting with a short discussion with the psychologist to evaluate the effects of the first day and to prepare the participant for the VR experience. During this, the participant was also instructed to look around freely during the experience, sit however they felt comfortable, and not to overly focus on their questions but rather to relax and focus on the experience itself. Then, the participant underwent the same guided meditation in VR as the day before which was directly followed by the 45 min long VR experience. The psychologist was waiting in the next room in case of any psychological emergencies, and a technician was present in the room with the participant to monitor the VR system. After the experience, the participant was allowed to rest for as long as they preferred and then had an integrative session (20–30 min) with the psychologist and answered the questionnaires . Follow-up EST-Q2 questionnaire was sent to the participants 2 weeks after their participation in the experiment.The experiments were conducted in the virtual reality laboratory at the Institute of Computer Science of the University of Tartu between 29th October 2021 and 11th December 2021. For more additional details on setting and study procedure see also t-tests were conducted on the before and after scores of the questionnaires . All tests were two-tailed with α = 0.05. Shapiro–Wilk test confirmed approximately normal distribution for the before-after differences of all datasets. All statistical analyses were conducted with JASP  with mild to moderate depressive symptoms. Note that n = 10 for the EST-Q2 measure, as one participant did not complete the follow up questionnaire. The experiments took place on two consecutive days with Psyrreal being implemented on the second day (see We conducted an open-label feasibility study in adults ( day see . No adveM = 15.20 (SD = 2.66) and showed good consistency with the MADRS questionnaire results . There was a significant decrease in EST-Q2 scores at the 2 week follow-up  = 3.50, p = 0.007, Hedges’ g = 1.046; M = 10.10 (SD = 4.65) to M = 7.60 (SD = 4.14) after 2 weeks .PIQ score results were M = 35.76 (SD = 21.14) after the control condition to M = 39.83 (SD = 27.77) post-experiment, but the change was not statistically significant . Two participants  did show a large increase (>25 points) in EDI scores.EDI scores increased from M = 39.58 (SD = 19.57) to 42.73 (σ = 18.43). The change was not statistically significant . One participant (P3) reported a complete mystical experience  on day two. One participant (P7) reported a complete mystical experience on the first day, but not on the second.The MEQ30 average score increased from From this place where I got in touch with this emotion [sadness] these other questions also started to unravel and I understood many of my [behavioural and cognitive] patterns,” (P12). Six participants mentioned that they did not reach any new understandings, while three did arrive at new beneficial understandings: “[.] this novel understanding that I can reprogram myself, that this essence of myself doesn’t exist—it’s very liberating. It gives me, in some sense, a vitality that I’m looking for,” (P7).While the changes of scores on PIQ, MEQ30 and EDI did not reach statistical significance, the results were highly varied. A thematic analysis  was condIt was a different kind of insight, like a new insight. Like a physical insight,” (P7). Five people reported changes in perspective: “[.] Like a shift in perspective - in the beginning, I was looking more at the details, afterward I was looking at the whole,” (P3). Six participants reported alterations in their sense of self with three reporting alterations in the narrative sense of self and four participants reporting alterations in the embodied sense of self. For example: “Did you experience any change or loss of your self-image or sense of self? Yes, like dissolution, yes. [.] The whole experience was so immersive that you melted into it,” (P2). A total of 36% of participants reported somatic effects during the experience: “Specifically the parts with strong motion, these almost physical experiences, those were the most impactful,” (P12).Three participants specifically highlighted a physical feeling of insight as illustrated by the following quote from participant seven: “We created a novel VR experience based on psychedelic and mystical experiences as well as meditative states and implemented it in a therapeutic intervention for people with mild-to-moderate depression.The results of the study suggest that using VR experiences in a therapeutic setting could be beneficial in treating depressive symptoms, with participants showing a significant reduction in depressive symptoms 2 weeks after the experiments. As this was an open-label feasibility study with a relatively small number of participants, strong conclusions about the efficacy of such interventions cannot be made. Nevertheless, as 73% of participants showed a decrease in their depressive symptoms 2 weeks after a short 2-day intervention at a 60% remission rate, further research into such methods is warranted. Additionally, a previous study using a VR experience to treat depression also reported a comparable effect , offerinOverall, these results add some tentative support to the hypothesis that subjective experiences mediate the benefits of psychedelics. Psyrreal incorporates a wide range of elements common to psychedelic experiences such as specific audiovisual phenomenology ,” (P7).Our augmented therapy intervention combined a therapeutic environment and discussion about one’s problems with an engaging VR experience. One potential mechanism how this intervention yields therapeutic benefits could be that the intense and different virtual experience amplifies the therapeutic process and provides an experiential route for the participant to get in touch with their emotions –106 as a“[.] Then, at one moment, I let go, thinking that it doesn’t work like this [focusing on finding answers]. [.] Then it actually started to work, yes. [.] I think that the first half of the experience you kind of get into it or start going along with it and then the answers arrive very clearly somehow,” (P12). In our study, participants were explicitly instructed to accept and let go of any emotions that might arise during the experience. While Psyrreal does not include any explicitly affective visual stimuli  most participants mentioned experiencing a wide range of emotions with some explicitly reporting “getting in touch” with their emotions (see above). Therefore, we suggest that another potential therapeutic mechanism for this type of therapy could be combining a virtual emotion-eliciting experience with the preparation of “letting go.” Somewhat similar ideas have been implemented in VR-augmented exposure therapy where people are exposed to aversive virtual stimuli to facilitate habituation, inhibition or cognitive reappraisal of the psychological reaction  would be unlikely to reflect true effects. Hence, the main comparison in depression scores could only be evaluated at least 2 weeks after the intervention. The design was chosen after careful consideration of how to best optimize resources for the purpose of evaluating preliminary results and feasibility for conducting more expensive future studies – which we now hope to conduct. Therefore, it is also possible that an expectancy bias in the participants, initial discussion with the psychologist, guided meditation or even a brief experience of non-psychedelic virtual reality might be responsible for the decrease of depressive symptoms. This is highlighted by a few participants reporting quite high results on the Psychological Insight Questionnaire and Mystical Experience Questionnaire even on the first day. While the psychologist did not apply usual therapeutic techniques and assumed the role of an observer, simply an opportunity to discuss their problems could already have had a beneficial effect.While MEQ30 has been validated for psychedelic compounds and it has seen some use in investigating non-pharmacological methods for inducing mystical experiences [e.g., , 131–134The guided meditation and short demonstrative VR (used on the first day) could also be conducive to reporting high scores on the MEQ30, PIQ, and EDI. Additionally, the results of these questionnaires were compared between a 5 min long control VR and 45 min long Psyrreal, which limits the comparability of the conditions. The effects of meditation on inducing altered states of consciousness, insight, self-alterations and mystical experiences are well documented –84, 132,Although virtual reality is likely a less powerful tool than psychedelic substances  [but seen = 12, SD = 2.30).Forms of meditation, audiovisual media, engaging in physical activities  could also be used as therapeutic “amplifiers” , but virThe current study highlights multiple important directions for further research. First, some participants in our study mentioned that the computer graphics and lack of interactivity led to a loss of immersion in the experience. Next iterations of Psyrreal might benefit from adding a guided narration to make certain implemented concepts  clearer, and from incorporating more interactive elements which could be beneficial for increasing immersion in the experience . TechnicWe developed Psyrreal, a somatoaudiovisual virtual reality experience based on psychedelic and mystical phenomenology to aid the treatment of different psychological disorders and help participants to see things from new perspectives. We observed that using this novel therapeutic tool in an augmented therapy intervention alleviates mild-to-moderate depressive symptoms. The results of the study suggest that psychedelic subjective experiences implemented through virtual reality could have therapeutically beneficial effects  and that further research into similar novel tools is warranted. Implementing elements of psychedelic experiences that potentially mediate therapeutic effects in virtual reality can also help to precisely investigate and elucidate the mechanisms underlying psychedelic therapy.The original contributions presented in this study are included in this article/The studies involving human participants were reviewed and approved by Ethics Committee of the University of Tartu. The participants provided their written informed consent to participate in this study.KK, MV, and JA designed the software. KK, MV, JP, and KT conducted the experiments. JA conceived the idea. All authors contributed to the writing of the manuscript, designing the experiments, and approved the submitted version.


TM monitor for distinguishing postures and measuring stepping activity of 6–12-year-old children. Thirteen children (8.5 ± 1.8 years) engaged in pre-determined standardised (12 min) and non-standardised (6 min) activities. Agreement, specificity and positive predictive value were assessed between the activPAL4TM and direct observation (DO) (nearest 0.1 s). Between-activPAL4TM (inter-device) and between-observer (inter-rater) reliability were determined. Detection of sitting and stepping time and forward purposeful step count were all within 5% of DO. Standing time was slightly overestimated (+10%) and fast walking/jogging steps underestimated (−20%). For non-standardised activities, activPAL4TM step count matched most closely to combined backward and forward purposeful steps; however, agreement varied widely. The activPAL4TM demonstrated high levels of reliability  > 0.976), which were higher in some instances than could be achieved through direct observation  > 0.851 for non-standardised activities). Overall, the activPAL4TM recorded standardised activities well. However, further work is required to establish the exact nature of steps counted by the activPAL4TM.A link between inappropriate physical behaviour patterns  and poor health outcomes has been observed. To provide evidence to quantify this link, it is important to have valid and reliable assessment tools. This study aimed to assess the validity and reliability of the activPAL4 Physical activity (PA) has a prophylactic effect against many major non-communicable diseases . AdditioTM family of monitors  are thigh-worn devices that allow the objective measurement of both PA and SB [TM is a research-grade piezoelectric accelerometer allowing detection of stepping activity and postural change [TM family of monitors has been summarised by Wu et al. and O’Brien et al. [TM devices, suggesting that they view all versions of the monitor as equivalent. Validity and reliability have been reported for particular versions of the monitor for specific populations during a variety of activity protocols  and actults [TM ). When cults [TM ).TM iteration of the activPALTM has been presented as a device with hardware adaptation from previous versions of the monitor. While changes to posture and step detection are not explicitly detailed in the device documentation, it is possible that reliability and validity of the monitor have been altered compared to previous versions. Therefore, it is important to examine the monitor’s ability to measure accurately and reproducibly in various populations to ensure that the possible errors fall within an acceptable range.When new versions of devices are introduced, it is important to establish their validity and reliability. The activPAL4Children’s physical behaviour, particularly of younger children, is generally considered to be more variable in nature than adolescents or adults. It is possible that differences in movement patterns may affect reliability and validity of monitors used to measure that activity. Assessment of devices under both standardised and more challenging conditions would provide reassurance that outcomes are reasonable representations of actual physical behaviour within this younger population.TM activity monitor in detecting sitting postures, upright postures and stepping activity in children aged 6–12 years old under both standardised and non-standardised conditions.The aim of the study was to evaluate the reliability and validity of the activPAL4A convenience sample of typically developing children between the ages of 6–12 years was recruited into the study through personal contacts of the lead author in Kuwait City, Kuwait (October 2020–August 2021). This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of the School of Health and Life Sciences at Glasgow Caledonian University, August 2020, ref: HLS/PSWAHS/19/232.The parents/guardians of potential participants were provided with study information and asked to contact the research team if the child under their care might consider taking part. After any questions had been answered, potential participants were assessed against the inclusion/exclusion criteria and written informed consent/assent taken appropriately from the parent/guardian and child by the lead author. Participants were assured that they could withdraw from the study at any point without giving reasons. Children with cognitive impairments and children who had an acute lower limb injury (excluding bruises and scrapes) were excluded from the study.TM’s ability to adequately characterise that activity. Differences in body proportions between different age groups may affect outcomes from the monitor, so it is important to assess the validity and reliability across all ages.Children aged 6–12 years were chosen for this study as it was felt likely that they would perform movements that were more variable than other age groups and so present a robust challenge to the activPAL4TM monitors was used with outcomes developed using software version PALanalysis v8.10.8.32 with settings of minimum non-upright period and minimum upright period set to 2 s [A pool of 14 activPAL4t to 2 s .TM monitors were selected non-randomly from the pool of 14 and arbitrarily allocated to one of three locations. Two monitors were placed one on top of the other, with the third monitor placed just distally to these, all on the front of the thigh of the dominant leg at approximately the mid-point of the thigh  was used. This format reports a particular ‘bout’ of activity as an ‘event’ (to the nearest 10th of a second in duration). This would be either a period of sitting (or lying), a period of standing without stepping or a stride (2 steps). Therefore, for continuous periods of stepping activity, a sequence of stride events is reported.For the current study, the event-based output of the activPAL4The participants were video recorded, using a single video recorder, while performing a set of given tasks. Videos were recorded in MP4 format and analysed using VLC media player . Interpretation of the video recording (direct observation (DO)) of physical behaviour was considered the gold standard for this study.Participants were asked to complete two types of activities: standardised activities (duration 12 min) and non-standardised activities (duration 6 min)  similarlThe standardised and non-standardised activities chosen for this study may not fully represent real-life scenarios and, therefore, potentially limit generalizability of the findings. Nonetheless, the authors selected a wide range of activities that required various types of movements and stepping types, hoping that the findings would be applicable to real-life conditions.Standardised activities were selected to provide examples of sitting, standing without stepping and stepping activity in blocks of time. Standardised activities were performed in an indoor area approximately 5 × 5 m. When sitting, children were allowed to choose a preferred seat type . Activities were performed at the child’s self-selected speed. Instructions were given to sit, stand, walk at a normal self-selected speed or to run for the designated time period. Children were sometimes not cooperative, resulting in change over times between activities not being precise and delays in response to performing activities as requested. All activities had to be performed within the laboratory area, resulting in non-linear motion for stepping activities.Non-standard activities were selected to provide examples of a variety of stepping modalities interspersed with periods of standing without stepping. Three non-standard activities were selected to provide situations in which children could perform in a self-selected manner with changes in direction, slowing down, speeding up and stepping in different directions. The three activities were ‘keep up the balloon’, ‘throw hoops over a post’ and ‘musical chairs’  independently evaluated all video records of all participants. Definitions of posture and stepping activity were used to attempt to develop a consistent interpretation of the video records .TM, it is necessary to define what constitutes a step. This is because different step definitions can result in different step counts, which can impact the apparent validity and reliability of the monitor. The step count definitions used in this work were pragmatically defined based on observations of movement patterns and what the authors wished to characterise as a step.In order to accurately assess the reliability and validity of step detection using the activPAL4DO was used to characterise the posture (sitting or upright), stepping time and number of steps of the standardised activities. For the non-standardised activities, the movement patterns of the children were expected to be complex such that distinguishing time periods of standing from stepping would be difficult and extremely time consuming. Therefore, for the non-standardised activities, only steps taken were characterised and total steps across all three activities were analysed together. Timings were taken from the video to the nearest 10th of a second.TM results. For step count, simple means were taken. For posture changes, the mean time point of the change was utilised. Where short periods of intermediate standing posture between sitting and stepping were identified, these were included in the average DO record if 2/3 observers recorded this.The average results from the three DO characterisations were used to compare with the activPAL4TM output.To prepare a continuous record of posture allocation from the DO record, data were interpolated across the whole time of the study to tenths of a second, which could be directly compared with activPAL4TM posture and step count, Bland–Altman plots [TM attached lower on the thigh were used for the agreement analysis. The percentage difference between measures (Equation (1)) was plotted against the mean of measurements (Equation (2)):To measure the agreement between DO and activPAL4an plots  were useTM outcome measurements in relation to DO.An a priori upper limit of 5% difference was chosen as being acceptable to evaluate the activPAL4TM were also calculated [Second-by-second analysis of postural agreement, sensitivity and positive predictive value (PPV) between DO and activPAL4lculated .TM were identical (Equation (3)):Percentage of agreement was calculated for each participant across standardised activities to characterise the proportion of time posture classification  of DO and activPAL4TM agreed with this classification (Equation (4)):Within the time that a participant was observed (DO) to be in a certain posture, sensitivity was determined as the proportion of time that the activPAL4TM determined a particular posture, PPV characterises the proportion of this that agrees with DO (Equation (5)):Within the time that the activPAL4TM was evaluated by examining the outcomes of the sets of three devices worn simultaneously. Additionally, to establish the reliability of the DO measurements, a comparison between the three observers’ outcomes was made.The reliability of the activPAL4First overall percentage agreement was calculated within monitors and observers based on the interpolated tenth of a second data for sitting, standing and stepping for the standardised activities. To interpret levels of agreement, a priori values were set: >90% as excellent, >70% as good.TM for determining step count in standardised normal and fast walking and the non-standardised activities, the interclass correlation was calculated ) [To assess reliability of the activPAL4C) . The ICCTo assess reliability of the DO observers for determining step counts  during both standardised and non-standardised activities, the ICC was calculated as observers were consistent but from a larger possible pool.ICC score ratings of <0.40 were considered poor, 0.40 to 0.75 fair to good, >0.75 very good and >0.90 excellent.TM was malfunctioning. Data from 13 participants (6M/7F) remained in the study . Eight of the children attended private schools and five attended public schools. The data collection from these participants took place in the summer vacation of 2021.Fifteen healthy Kuwaiti children took part in the study. Data of two children were removed from the analysis as one of them had incomplete video recordings, and for the other, the activPAL4The sample size was relatively small due to the intensive data analysis required in DO. The sample covered the age range of interest and the activity protocol allowed a high degree of self-selection of movement patterns within the activities. Therefore, while a larger sample would have provided a more robust test of the validity and reliability of the activPAL4TM, the data recorded should have highlighted key elements of device performance.TM devices, each was used between one and five times overall, with each monitor being used a maximum of four times in any one location. Across all participants, nine or 10 different monitors were used in each of the three allocated locations.Of the 14 activPAL4TM sitting and stepping times during the standardised activities (TM than DO.There was acceptable agreement (<5% difference) between DO and activPAL4tivities . HoweverTM step count was within acceptable agreement levels to DO forward purposeful step count. However, when all steps, or purposeful forward and backward steps included together, the activPAL4TM demonstrated higher levels of disagreement with DO.During normal speed walking, the activPAL4TM underestimated the step count during fast walking/jogging compared to DO by approximately 20%.The activPAL4TM step count was on average 10% higher than DO forward purposeful step count. When backward steps were added, the overall agreement of activPAL4TM and DO was within acceptable limits. ActivPAL4TM considerably underestimated step count compared to DO ‘all steps’ count (−30%).During non-standardised activities, the activPAL4TM step counts between individuals, especially within the non-standardised activities.Overall, there was considerable variation in agreement between activPAL4TM than DO.Second-by-second agreement for time allocated to sitting, standing and stepping was excellent (98.6%) . SimilarTM was similar to that of the DO (TM compared to 99.1% for DO. Similar excellent levels of reliability (ICC) were demonstrated for step count across both standardised and non-standardised activities for the activPAL4TM. Of note was a lower reliability for step count by DO during non-standardised activities  0.851–0.944).Generally, the reliability of the activPAL4f the DO . Across TM family of monitors demonstrate high levels of validity in measuring standardised activities with high levels of reliability [TM iteration of the monitor has been presented as a new device and it is important to establish if this version has acceptable levels of validity and reliability under conditions in which it might be typically used. Children’s diverse movement patterns pose particular challenges to activity classification. For these reasons, this study was carried out to assess the validity and reliability of the activPAL4TM in children 6–12 years of age. Generally, for standardised activities, the monitor performed very well for the detection of sitting and stepping time and purposeful forward-stepping activity. In comparison to direct observation, performance appears to be poorer in detecting faster steps and when activities become non-standard.There have been previous publications suggesting that the activPALiability ,11,16,17TM’s validity in counting steps against DO. The volume of stepping activity characterised through DO  = 0.862 for forward purposeful steps). This suggests that even with a standardised definition of what constitutes a step, it was not possible for observers to consistently characterise the activity of 6–12-year-olds.Children responded to instructions with varying levels of urgency and compliance. Participants all completed all sections of the protocol but at points had to be encouraged and reminded of the parameters of the particular activity. For example, speed of movement varied within tasks for some children.TM during standardised activities than DO . In com [TM [TM  showed tTM [The sensitivity results presented in the current study appear to be superior to those previously reported in 5–12-year-old children for the activPAL3TM . Van looTM  reportedTM step count matched the forward purpose step volume recorded by DO better than the ‘all steps’ count or combined forward and backward step count. This suggests that the activPAL4TM provides a valid measure of step count for purposeful stepping activity and does not include smaller foot movements as step counts. The excellent validity of the activPAL4TM step count does not appear to extend to fast walking/jogging as overall these steps were underestimated by 20%. The reduction in step count for faster movements has been reported for previous versions of the monitor. For example, Aminian and Hinckson [TM step count. Sellers et al. [TM monitor.From the step count results  it appeaHinckson  reporteds et al.  also higTM is gained from the non-standardised activities where the best agreement was recorded by combining forwards and backward purposeful steps (relatively large foot movements). The ‘all steps’ count recorded by DO was considerably higher (30%) than the activPAL4TM count, reinforcing the observation that the activPAL4TM does not count all foot movements  values of 0.86 and above have been reported for posture classification and step count in young people [Differences in the exact internal configuration of activity monitors may affect interpretation of movement patterns. If, for example, the accelerometers within the devices are aligned in slightly different ways, this may result in changes in timing and type of activity classification. Assessing reliability is therefore important. Categorisation to sitting, standing and stepping times showed agreement for over 98% of the time . Step cog people .TM in different real-life conditions.As part of the evaluation of methods performed in this study, the reliability of observers was also assessed. The children who took part in this study were able to choose how they moved within each activity. This led to a range of different sitting postures and stepping movement patterns. It was challenging to be able to reliably characterise the movements using direct observation. This was particularly the case for stepping activity during the non-standardised activities. The lower level of reliability and the variation of this reliability between participants  highlighTM.Only a relatively small sample of participants was recruited to this study due to the extensive manual data analysis required in DO video assessment. However, the sample was sufficient to include a wide range of movement and stepping types within both standardised and non-standardised activity protocols, thus providing a suitably rigorous assessment of validity and reliability of the activPAL4TM under real-life conditions, it would have been necessary to study real-life scenarios. It is acknowledged that the standardised and non-standardised activities chosen within this study can only provide evidence of validity and reliability for these specific activities. However, by selecting activities that required a wide range of movements and stepping types, it was hoped that characteristics of validity and reliability would be relevant to real-world contexts.To establish the validity and reliability of the activPAL4TM.For the assessment of reliability and validity of step detection, it is necessary to define what constitutes a step. As different step definitions will result in different step counts, the outcomes presented in this study must be considered in relation to the specific step definitions used. There is scope for additional work to explore how different step definitions influence outcomes of validity and reliability for the activPAL4TM (inter-device) reliability, activPAL4TMs were allocated to the three positions on the leg in an arbitrary way. To improve the assessment of reliability, this could have been carried out randomly. However, a range of activPAL4TM devices were placed in each of the locations across participants, providing a suitable assessment of inter-device reliability.To establish the between-activPAL4The scope of the current study was limited to establishing the validity and reliability of the activPAL4TM in 6–12-year-old children. Further work is required to establish concurrent validity with previous versions of the monitor and to establish validity and reliability in other populations, e.g., adults or people with movement disorders.TM provided high validity against direct observation for detection of sitting and stepping time but slightly overestimated standing time. Purposeful forward stepping was detected with a high degree of accuracy for normal walking speeds, but steps were underestimated for faster walking/jogging. The characterisation of non-standardised activity stepping was challenging for DO. In general, it appeared that the activPAL4TM recorded only larger stepping movements (forward and backwards) as steps but did not record smaller movements as steps. The activPAL4TM demonstrated a high level of reliability between monitors as good and in some cases better than DO.The activPAL4TM appears to perform to a similar standard to earlier versions of the activPALTM device, recording standardised activities well but underestimating faster stepping. Further work is required to establish the exact nature of steps counted by the activPAL4TM under non-standardised activity protocols.Overall, the activPAL4


Moreover, as a diagnostic classifier of PD, 4DFD obtained an area under curve value of 0.97 for a receiver operating characteristic curve analysis. These results suggest that 4DFD could be a promising method for characterizing the specific changes in the brain dynamics associated with PD.Complexity analysis of electroencephalogram (EEG) signals has emerged as a valuable tool for characterizing Parkinson’s disease (PD). Fractal dimension (FD) is a widely employed method for measuring the complexity of shapes with many applications in neurodegenerative disorders. Nevertheless, very little is known on the fractal characteristics of EEG in PD measured by FD. In this study we performed a spatio-temporal analysis of EEG in PD using FD in four dimensions (4DFD). We analyzed 42 resting-state EEG recordings comprising two groups: 27 PD patients without dementia and 15 healthy control subjects (HC). From the original resting-state EEG we derived the cortical activations defined by a source reconstruction at each time sample, generating point clouds in three dimensions. Then, a sliding window of one second (the fourth dimension) was used to compute the value of 4DFD by means of the box-counting algorithm. Our results showed a significantly higher value of 4DFD in the PD group ( The fractal dimension (FD)  is a quaParkinson’s disease (PD) is a neurodegenerative disorder which affects approximately 1% of the world’s elderly population . PatientPrevious studies have shown that non-linear measures of EEG complexity can be of relevance in characterizing early neurodegeneration processes in PD. Müller et al.  showed tTo this aim, using fractal dimension we analyzed the complexity of resting-state EEG signal in PD patients without dementia. As described above, most previous work in this topic has analyzed EEG using several measures of entropy, but very little is known about the fractal characteristics of EEG in PD. In our study, we followed a similar approach to the one presented in , adaptedA total of 42 subjects participated in the study: 27 patients diagnosed with Parkinson’s disease (PD group) , and 15 healthy control subjects (HC group) with no symptoms or history of any neuropsychiatric disorder . Demographics of both groups and clinical data of patients see  were colFor each PD patient, the disease was diagnosed on the basis of their medical history and neurological examinations. Specifically, the diagnosis of PD was made following the Movement Disorder Society (MDS) clinical diagnostic criteria . All patAll subjects gave their informed consent for participation in the study .During EEG recordings, both patients and healthy controls were seated in an upright or slightly reclined position in a quiet room. Spontaneous resting EEG recordings were acquired while at rest with their eyes open, not engaging in any task for a minimum of 5 min. The EEG session took place approximately 1–2 h after the administration of morning medications.EEG was recorded with a Brainamp DC  equipped with 62 channels following the standard 10–20 montage. In all the recordings, reference and ground electrodes were located on the forehead and two additional channels were used to record the electrooculogram (EOG) in a diagonal montage to monitor for eye-blinking and saccades. Impedance at all electrodes was kept below 5 kOhm. Spontaneous EEG data were collected at 1000 Hz sampling rate and with a hardware filtering between 0.016 and 250 Hz. Due to the computational restrictions of further analysis, a resampling process to 500 Hz was performed.EEG preprocessing was performed using MATLAB R2016b  and custom-made scripts based on the EEGLab toolbox [http://neuroimage.usc.edu/brainstorm, accessed on 1 July 2023). First, a forward EEG model was computed using the boundary element method implemented in OpenMEEG [Source modelling was performed using the Brainstorm software , which iOpenMEEG . We usedOpenMEEG . Dipole S n(r) is the number of boxes of scale dr covering the set S. If S is not an ideal fractal, then the FD is estimated as the slope of the linear regression of log(n(r)) vs. log(1/r).A 4D fractal dimension (4DFD) approach was used for measuring the spatio-temporal complexity of brain activations. We used the well-known box-counting algorithm  to estimS is composed of the 4D points defined by the 3D locations of cortical activations at each time sample (time is the fourth dimension). In order to obtain this 4D set, firstly, a binary 3D representation of the cortical activations at each time sample was obtained. As an example, In our study, the set Once the 3D point clouds for all the time samples in the 120 s epoch had been computed, the epoch was divided using a sliding window of 1 s without overlapping. Then, the box-counting algorithm was used to obtain the 4DFD value for the 4D set defined in each window by putting together the 3D point clouds of the time samples for the corresponding second. In this way, a total of 120 4DFD values were obtained for each epoch. Computing the box-counting algorithm in 4D is a highly time-consuming task, so we used the CUDA/C++ source code of the GPU-optimized parallel version of the algorithm provided in . BinarizU test [U tests were performed. Correlations between 4DFD and neuropsychological scores in the PD group were assessed using the nonparametric Spearman’s rank correlation test (ρ) [Demographic variables were compared between groups using the nonparametric Mann–Whitney U test  (age andU test  (sex). Itest (ρ) . Since stest (ρ) . In thisp-value obtained was below 0.05. All statistical analyses were performed in IBM SPSS 28.Results of statistical tests were considered significant when the The demographic and baseline clinical information are summarized in U = 393, p < 0.001).Comparison between HC and PD groups based on 4DFD is shown in U = 87, p = 0.239). Moreover, significant differences in 4DFD values were also found when comparing healthy subjects and PD patients without tremor .Six patients in the PD group exhibited tremor-related symptoms, so we conducted additional analyses in order to check whether the 4DFD increase in the PD group could be affected by this factor. Our analyses revealed that there was no significant difference in 4DFD values between PD patients with tremor and those without .We also compared the evolution of the 4DFD for the 120 s analyzed in each epoch see . Each poIn order to assess the performance of 4DFD as a diagnostic classifier for PD we used an ROC curve analysis see . In thatp = 0.55).Finally, in order to analyze the relationship between neuropsychological scores and 4DFD in the PD group, we calculated the Spearman correlation coefficient between all these variables with a post-hoc Bonferroni correction for multiple comparisons see . No signIn the last few years, several studies have proposed complexity measures of EEG as potential neurophysiological markers of early neurodegeneration in PD. These complexity measures were mainly related to the concept of entropy ,31,32,33We found that 4DFD was significantly higher in the PD group compared to the HC see , suggestNevertheless, our approach based on the fractal dimension adds a spatial component which is not taken into account in those previous studies based on entropy. Roughly speaking, the higher the fractal dimension of a shape is, the more complex the object covers the space in which it is defined. In our study, the shape being analyzed was the 4D point cloud defined by the cortical brain activations over time. That means that the dynamics of brain activations in PD patients are distributed in a more complex  way throughout the brain over time than for healthy controls. A possible explanation for this finding is that PD patients show an early impairment in their ability to regulate the flow of information and to allocate attention to filtering stimuli . TherefoThere are also some other studies where PD patients presented reduced complexity of EEG compared to control subjects, such as the study of Yi et al.  and the 4DFD also performed very well as a classifier see , separatNo significant correlations were found between 4DFD and the severity of motor impairment in the PD group, nor were any correlations found with overall cognitive impairment scores. Thus, based on these findings, the spatio-temporal fractal dimension does not appear to be a suitable descriptor for the progression of motor and cognitive symptoms in PD. We hypothesize that, although 4DFD can detect general changes in brain dynamics between PD patients and healthy subjects, the subtle progressive neurodegeneration in PD may not be adequately captured by this whole-brain complexity measure. This could be attributed to the fact that the patients do not exhibit major cognitive impairments. Targeted follow-up studies could help clarify the role of fractal dimension as a predictor of progression into MCI and dementia.Regarding previous studies using fractal analysis for assessing the complexity of EEG signal in PD, to the best of our knowledge there is only one study, the one presented by Muller and collaborators , based oOne requirement of the FD computation based on the box-counting algorithm is the necessity of binarizing the signal in order to construct the boxes at different scales . This prWhile our study has provided valuable insights into the complexity of resting-state EEG signals in PD patients, we acknowledge that our analysis of brain dynamics was confined to the resting-state. Future research would benefit from an extended analysis that includes active-state EEG, possibly during specifically designed cognitive tasks. Such an approach could provide insights into brain activity dynamics and complexity in PD and help unravel whether the observed changes in complexity are state-dependent and to what extent they are influenced by engagement in cognitive tasks.In conclusion, in this study we have presented a novel methodology for performing the complexity analysis of resting-state EEG signals in PD. Our method is based on computing the 4DFD of the brain activations induced from the EEG signal, allowing us to perform in this way a spatio-temporal analysis. This complexity analysis based on the 4DFD of resting-state EEG obtained excellent performance in differentiating PD patients from healthy subjects. Our findings indicate that 4DFD is a promising tool for gaining deeper insights into the early cortical neurodegeneration characteristics of PD and to better plan rehabilitation treatment.