Depression is a substantial health and economic burden. In approximately one-third of patients, depression is resistant to first-line treatment; therefore, it is essential to find alternative treatments. Transcranial magnetic stimulation (TMS) is a neuromodulatory treatment involving the application of magnetic pulses to the brain that is approved in the United Kingdom and the United States in treatment-resistant depression. This trial aims to compare the clinical effectiveness, cost-effectiveness, and mechanism of action of standard treatment repetitive TMS (rTMS) targeted at the F3 electroencephalogram site with a newer treatment—a type of TMS called theta burst stimulation (TBS) targeted based on measures of functional brain connectivity. This protocol outlines brain imaging acquisition and analysis for the Brain Imaging Guided Transcranial Magnetic Stimulation in Depression (BRIGhTMIND) study trial that is used to create personalized TMS targets and answer the proposed mechanistic hypotheses.The aims of the imaging arm of the BRIGhTMIND study are to identify functional and neurochemical brain signatures indexing the treatment mechanisms of rTMS and connectivity-guided intermittent theta burst TMS and to identify imaging-based markers predicting response to treatment.The study is a randomized double-blind controlled trial with 1:1 allocation to either 20 sessions of TBS or standard rTMS. Multimodal magnetic resonance imaging (MRI) is acquired for each participant at baseline (before TMS treatment) with T1-weighted and task-free functional MRI during rest used to estimate TMS targets. For participants enrolled in the mechanistic substudy, additional diffusion-weighted sequences are acquired at baseline and at posttreatment follow-up 16 weeks after treatment randomization. Core data sets of T1-weighted and task-free functional MRI during rest are acquired for all participants and are used to estimate TMS targets. Additional sequences of arterial spin labeling, magnetic resonance spectroscopy, and diffusion-weighted images are acquired depending on the recruitment site for mechanistic evaluation. Standard rTMS treatment is targeted at the F3 electrode site over the left dorsolateral prefrontal cortex, whereas TBS treatment is guided using the coordinate of peak effective connectivity from the right anterior insula to the left dorsolateral prefrontal cortex. Both treatment targets benefit from the level of MRI guidance, but only TBS is provided with precision targeting based on functional brain connectivity.Recruitment began in January 2019 and is ongoing. Data collection is expected to continue until January 2023.This trial will determine the impact of precision MRI guidance on rTMS treatment and assess the neural mechanisms underlying this treatment in treatment-resistant depressed patients.ISRCTN Registry ISRCTN19674644; https://www.isrctn.com/ISRCTN19674644DERR1-10.2196/31925 In addition to such anatomical variation, advances in brain connectomics highlight substantial interindividual variability of functional brain networks that is expected to have a direct impact of network-mediated downstream effects from superficial TMS. There is also a potential for target misalignment across multiple treatment sessions. Therefore, it is important to find alternative procedures to TMS treatment target estimation that are reproducible and to also consider precision-based approaches.Neuromodulation in TRD by means of transcranial magnetic stimulation (TMS) is cost-effective in comparison with standard care . This apNeuroimaging studies in depression have consistently demonstrated altered connectivity within, and between, canonical resting-state networks, such as the default mode, salience, and central executive networks, adding to our current understanding of the dysfunctional brain circuitry involved -11. ImpoThe Brain Imaging Guided Transcranial Magnetic Stimulation in Depression (BRIGhTMIND) study  is a ranThe objectives of the imaging arm of the BRIGhTMIND study are to identify functional and neurochemical brain signatures indexing the treatment mechanisms of rTMS and cgiTBS and to identify imaging-based markers predicting response to treatment.The primary hypotheses for the mechanistic imaging study are as follows:Treatment response, as measured using change in the HDRS-17, will correlate with posttreatment changes in DLPFC-dorsomedial prefrontal cortex (DMPFC) FC at 16 weeks.Treatment response (HDRS-17) to TMS treatment can be predicted using baseline insula-DLPFC effective connectivity.Connectivity-guided intermittent TBS treatment-related γ-aminobutyric acid (GABA) changes will be correlated with a reduction in HDRS-17 at 16 weeks.The exploratory aims for the mechanistic imaging study are as follows:To identify imaging-based biotypes that predict treatment response in TRD patientsTo further study the neural mechanisms underlying therapeutic efficacy by assessing interrelations of changes in complex brain network metrics with improvement of clinical symptomsTo determine the distance between the applied rTMS and TBS treatment targets with an in silico subgenual anterior cingulate cortex (sgACC) seeded DLPFC target, and its association with therapeutic efficacy (change in HDRS-17 at 16 weeks)Analysis of baseline MRI and clinical data will explore seven themes that will support and boost both the impact and inference of the main study outcomes. These themes are (1) treatment resistance, (2) comorbid anxiety, (3) cognitive impairment, (4) trauma, (5) medication and other confounds, (6) interlinking analyses, and (7) model building. Themes 1-5 will be driven by the testing of distinct hypotheses based on previous knowledge and literature. Themes 6 and 7 are exploratory as they are provided to allow expansion of the proposed analyses into further tests in different MRI modalities and measures, and to use any resulting findings from all the above themes into building a model-based brain signature of TRD. Given the limited literature published on TRD and the rich data set available from the BRIGhTMIND study, it is important that there is room to carry out both hypothesis-driven and exploratory analyses. All baseline analyses will remain blinded to treatment allocation (cgiTBS or rTMS) and trial outcomes (responder or nonresponder) until after the final database lock. The specifics of these planned analyses can be found in the study by Cottam et al .Ethical approval was granted by East Midlands Leicester Central Research Ethics Committee (ref: 18/EM/0232) on 30 August 2018.In the initial setting, there will be 4 recruitment sites from across the United Kingdom National Health Services (NHS) , which will deliver the TMS treatments locally. However, only 3 sites will perform imaging, as all Northampton participants will be scanned at the Nottingham site. The possibility of incorporating additional sites (either for recruitment and scanning or for recruitment only) will be kept open throughout the course of the BRIGhTMIND study and will be evaluated case by case, based on achieved recruitment. However, Northampton did not reopen to recruitment in August 2020 and was informed of the decision to close the site in December 2020 owing to low recruitment numbers. A new site is being opened in Oldham (Pennine-Care NHS Foundation Trust) in the summer of 2021 to increase recruitment. Both scanning and TMS treatments will be delivered locally at the Oldham site.MRI scans are acquired at 2 time points. First, a baseline scan is acquired within 14 days of the baseline assessment, after which treatment randomization occurs. Allocated TMS treatment is then initiated within 14 days of the MRI scan. The second MRI scan is acquired within 14 days of the 16 weeks follow-up assessment . Both MRThe participants will undergo multimodal MRI at 3T. We will acquire a core protocol across all treatment sites consisting of a structural T1-weighted scan and an eyes-open blood oxygenation level dependent (BOLD) echo-planar imaging (EPI) rsfMRI scan with additional positive and negative phase-encoded images to enable distortion correction. For the mechanistic substudy, all sites will also acquire a diffusion-weighted scan , whereas 2 sites (Nottingham and Northampton) will also acquire an arterial spin labeling (ASL) scan, and 3 sites  will acquire a Meshcher-Garwood Point Resolved Spectroscopy (MEGA-PRESS) magnetic resonance spectroscopy (MRS) scan. General descriptions of the scanning sequences are delineated in the next paragraph, with further details available in 3 isotropic voxels covering the whole head from the vertex to the neck. rsfMRI images will be acquired with the eyes open using a fixation cross. All sites are instructed to use a gradient echo EPI sequence aligned with the anterior commissure-posterior commissure line, with acquisition covering from the vertex downward . All rsfMRI images will have associated forward- and reverse-phase-encoded B0 images acquired to facilitate distortion correction. Importantly, these images will be acquired with the same image dimensions as the rsfMRI and will be acquired directly before the rsfMRI. Diffusion-weighted images will be acquired and aligned with the anterior commissure-posterior commissure line, with coverage beginning at the vertex and extending inferiorly . In addition, a reverse phase-encoded B0 image (posterior>anterior) will also be collected with all other parameters and coverage matched with the diffusion-weighted image acquisition to enable distortion correction. ASL on the Discovery MR750  will be carried out using the vendor-supplied 3D-pseudocontinuous ASL sequence with whole-head coverage, and the participants will be instructed to keep their eyes open . Further ASL sequences on the Ingenia (Philips) and Premier  will be set up to replicate the original acquisition  sequences with 1 mmSubject digital imaging and communications in medicine (DICOM) session files are uploaded onto an XNAT (Washington University School of Medicine)  databaseThe MRS data are handled differently from the rest of the imaging data, as it is uploaded separately onto XNAT for participants whose structural T1-weighted and rsfMRI data have passed QC only. The QC for MRS data is carried out during preprocessing and is described later in this paper.Both structural T1-weighted and BOLD rsfMRI data are preprocessed with the SPMIC-BRC pipeline v1.2.6 ,31. Thisrobustfov. The original and brain extracted images are then nonlinearly registered to the MNI152 1-mm template [Structural T1-weighted images are first coarsely brain extracted using the FSL brain extraction tool (BET)  and theitemplate  using FStemplate -39. The template . The resepi_reg, and then combined with the TOPUP spatial distortion correction transformation. The resulting combined transformation is then inverted to create a nonlinear transformation from the T1-weighted to  BOLD space. The previously computed binary WM and CSF masks are later warped into BOLD space using the T1-weighted to BOLD transformation to extract the WM and CSF time series from the BOLD data. To control for additional physiological or scanner-related noise, the WM and CSF time series are then regressed out of the rsfMRI time series.The BOLD rsfMRI images undergo EPI distortion correction by inputting the positive and negative phase-encoded acquisitions into TOPUP . Then, tAlthough the above pipeline will remain locked during the trial for the calculation of TMS target coordinates, any analyses carried out for final publication will seek to use further developed state-of-the-art pipelines to make the best use of the data at the time. Thus, later versions of software or new tools may be incorporated into this pipeline and those detailed below.The MRS data will be processed using an in-house routine developed in MATLAB (MathWorks Inc) before metabolite quantification. All routines used will be made available to other researchers at the end of the trial via GitHub.The GE Healthcare (P-file) MRS data will be coil-combined using the phase and maximum amplitude of the acquired unsuppressed water reference. Philips (.sdat or .spar) data are already coil-combined by the vendor software. Data will be split into 160 OFF and 160 ON spectra and eddy current corrected using the internal water reference. To minimize artifacts caused by subject motion or frequency drift, each spectrum will be frequency-and phase-corrected to the mean OFF spectrum using spectral registration ,45. SpecN-acetylaspartate (tNAA=NAA+N-acetyl aspartylglutamate), total creatine (tCr=Cr+phosphocreatine), total choline (tCho=glycerophosphoryl choline+phosphorylcholine), mIns (myo-inositol), Glx (glutamate and glutamine), and taurine (Tau) levels. The edited ON-OFF MEGA-PRESS spectra will be analyzed in the range of 0.2-4 ppm with LCModel, using sequence-specific basis sets simulated using the same techniques as outlined for the OFF spectra.Unedited OFF spectra will be analyzed between 0.2 ppm and 4.0 ppm with linear combination model  ,47. SequMetabolite concentration values will be reported as ratios relative to tCr and Glx. In addition, water-scaled values, relative to the unsuppressed water signal, will be reported. For the latter, corresponding structural T1-weighted images will be segmented via SPM12  into graInitial QC criteria will be applied to reject data based on the raw spectral linewidth of the unsuppressed water<13 Hz  and alsoZ-score map  is found. This most correlated component is subsequently masked with a left middle frontal gyrus (lMFG) region of interest  to the al atlas . SpecifiThe rTMS brain target is determined by taking the scalp F3 voxel coordinate in the MNI152 space defined in Tsuzuki et al  (x=−49.0mkheadsurf script with the processed T1-weighted image (converted into Freesurfer MGZ format) as input and 100 smoothing steps, and then transformed into T1-weighted space using the tkr2scanner transformation matrix of the MGZ file. Note that, as the lower head and neck are previously removed from the T1-weighted image, the resulting head model does not include enough facial features (such as the lips or chin) to uniquely identify the participant. Therefore, their anonymity is preserved (To ensure that TMS treatment is administered as close as possible to the brain target point (computed as either a cgiTBS or rTMS target), the scalp projection and angulation of the TMS wand is computed. The MNI152 brain mask is first transformed into the subject T1-weighted space and then all the background voxels in the T1-weighted image are set to zero. Background voxels are considered to be voxels that are outside the transformed brain mask or have an intensity value below a certain threshold set by visual inspection. A 3D mesh model of the head is then created using Freesurfer reserved .In the following stage, the nasion and both the left and right preauricular coordinates are manually annotated on the T1-weighted image. Then, the nasion and preauricular points on the head 3D mesh are defined as the vertices closest to the manually determined coordinates. This allows the construction of a nasion-left-right (NLR) coordinate system for the head mesh model, which is measured using the same units as the T1-weighted space . The idea behind the use of this coordinate system is that it is invariant to head positioning. The origin of the NLR coordinate system lies on the intersection between the line that passes through both preauricular points and the perpendicular line that passes through the nasion. The x-axis points toward the nasion, the y-axis points toward the left preauricular point, and the z-axis points toward the top of the head A. In ord correspond to the coordinates of landmark L in S space, and ‖∙‖ is the L2 vector norm. Finally, given the 2 sets of 4 points, the optimal rigid transformation that maps T1-weighted space points into their NLR coordinates is computed using a Least-Squares approach [Here, approach ,61. ThisTreatment delivery on the BRIGhTMIND study will be performed using a 70-mm figure-of-eight coil (Ez Cool coil) and a Magstim Horizon Performance Stimulator with StimGuide Navigated TMS Package (Magstim Company). Therefore, the final step in the process is to generate a StimGuide compatible file with the computed treatment targets and rotation matrices in quaternion form . The lasAlthough the clinical trial is outlined in full in the former protocol paper , it is iThe primary mechanistic outcomes are as follows:Correlation of change in depression symptoms following TMS with changes in DLPFC-DMPFC FC at 16 weeks (post minus pretreatment)Correlation of change in depression symptoms following TMS with baseline effective connectivity  between the rAI seed region and the DLPFC Correlation of change in depression symptoms following TMS with cgiTBS treatment-related GABA changes (post minus pretreatment) at 16 weeksThe exploratory outcomes are as follows:Exploratory outcomes include, but are not limited to, evaluation of FC-based biotypes in the prediction of individual treatment response in TRD patients (25% change in HDRS-17 score from baseline to 16 weeks) [Exploratory outcomes include, but are not limited to, change in depression symptoms following TMS (HDRS-17 score post minus pretreatment) with baseline peak negative FC between the sgACC-left DLPFC and correlation of treatment response with distance between coordinates of the peak negative sgACC-left DLPFC FC and the calculated cgiTBS target.Clinical, demographic, and psychometric data will be compared between groups and conditions using the latest version of appropriate statistical software such as SPSS (IBM Inc) or R (R Foundation).Imaging data will be analyzed using the latest versions of established toolboxes where possible, but as the T1-weighted and rsfMRI preprocessing pipelines have been defined to calculate TMS stimulation targets, this preprocessed data will also be used for further statistical analysis where appropriate. Toolboxes will include (but are not limited to) the following: FSL, SPM12, rsfMRI Data Analysis Toolkit, Brain Connectivity Toolbox, and LCModel for MRS data. Analysis of ASL and diffusion MRI data will be described in subsequent manuscripts as they are not used to create treatment targets, nor are they required to test the stated mechanistic hypotheses.Recruitment to the study was suspended on March 19, 2020, because of the SARS-CoV-2 pandemic. The study was reopened with a range of changes to protect participants and staff from SARS-CoV-2 on August 1, 2020, with recruitment at a slower permitted rate starting on September 1, 2020, at 3 of the 4 original study sites. The Northampton site was unable to restart, but another site at Oldham Greater Manchester has been opened to recruit for the study. Recruitment of the original sample of 368 participants was no longer possible within the resources and time-frame available.In-person assessments were minimized owing to SARS-CoV-2 upon the study restart in August 2020, and as such, all assessments except the THINC-Integrated Tool  task weUsing the expertise and independent advice of 2 independent committee monitoring and checking the progress of the study and in discussion with our funders who also sought external peer review, we changed the primary outcome measure from the binary variable of responder or nonresponder at 16 weeks (response was defined as a 50% drop or greater in the HDRS-17 from baseline to 16 weeks) to the mean change in the total score on the HDRS-17 across all follow-up time points .The National Institute of Clinical Excellence in its 2004 clinical guideline for depression defined a minimum clinically important difference in the total score on this scale of 3 points , which wUnder the assumptions mentioned above, a sample size of 266 participants will be required to achieve 89.3% power to detect the average difference of 3 points in the HDRS-17 at 8, 16, and 26 weeks at a 5% significance level (2-tailed); hence, 266 is the revised target for recruitment to the study. Recruitment of the sample will continue until January 31, 2022 and is limited by the resources available to the study. Given the uncertainties of recruitment to the study in the current pandemic, we note that under the same assumptions, a sample size of 232 would reassuringly still yield 85.1% power.If 266 participants are randomized into the study, we expect 116 participants to provide full data for the mechanistic substudy , based on the observed acquisition rate to date . The remaining 56.2% (118/210) without full mechanistic substudy data are because of a combination of participants being randomized at the London site (where only baseline scans are carried out), loss to follow-up at 16 weeks for HDRS-17 score, and loss to MRI follow-up. If we allow for 5% further loss owing to poor imaging quality, this will give us a total of 120 participants to assess the correlation of DLPFC-DMPFC FC change at 16 weeks and HDRS-17 change at 16 weeks.A sample size of 120 achieves 96.3% power to detect a difference of −0.3 between the null hypothesis correlation of 0.2 and the alternative hypothesis correlation of 0.5 using a 2-sided hypothesis test with α=.05. The null hypothesis of 0.2 represents a very weak or weak correlation, whereas the alternative of 0.5 represents a moderate correlation . The corno treatment arm to the trial.We must address 2 points regarding this aim. First, we previously had hypotheses 1 and 2 in reverse order, powering the substudy on the ability of the DLPFC-DMPFC FC change to discriminate between responders and nonresponders. The reason for moving away from this was because of the change in minimum clinically important difference criterion, such that by performing a correlation analysis, we assessed a noncategorized version of this test that removed the need to incorporate a response criterion but also improved statistical power owing to the use of the whole sample. Second, the proposed test assumes a weak correlation rather than no correlation. This approach was taken to make the test more robust against serial effects unrelated to treatment in the absence of a Regarding primary hypotheses 2 and 3, sensitivity analyses are presented to evidence that the revised sample size for the study will still allow for testing of these hypotheses. Sensitivity analysis based on a 1-tailed test with α=.05, 90% power, and a sample size of 232 for hypothesis 2 suggests a required effect size of |ρ|=0.19. This correlation of effective connectivity from the insula-DLPFC with changes in HDRS-17 at 12 weeks in our pilot data was −0.26. Sensitivity analysis following the same formula but with a lower expected sample size of 111 (as outlined for hypothesis 1) for hypothesis 3 suggests a required effect size of |ρ|=0.27. The correlation between GABA and HDRS-17 changes at 12 weeks in our pilot data was 0.68. Taken together, the study is expected to have the power to test the proposed hypotheses.The first participant was recruited on January 22, 2019, but recruitment was suspended on April 23, 2020, because of the SARS-CoV-2 pandemic. Recruitment began again in August 2020 and is ongoing. To date, 194 participants have been randomized for treatment.Neuromodulation via TMS has been shown to reduce the symptoms of TRD. However, using traditional identification of treatment locations, such as tape measurements or electroencephalography caps, may not be as effective as using personalized location estimation based on MRI. The BRIGhTMIND imaging substudy will assess the effectiveness and mechanistic effect of 2 variants of TMS, where treatment locations will be determined in a personalized manner using MRI. In addition, this study will evaluate whether treating a FC-based location is more effective than treatment based on a standard F3 location. If this hypothesis is found to be true, it may serve as evidence of the importance of using precision medicine in mental health.An important point within an imaging trial such as this is the QC and amalgamation of data from different imaging scanners and locations. Differences in acquisitions, scanner hardware, and geographic locations can lead to problems when combining data for analysis. To tackle these potential variations, this study has put in place standardized image acquisition protocols, QC protocols, and data analysis pipelines with all data analysis occurring through the lead center at Nottingham. The use of automated QC tools such as MRIQC  allows investigators to make clear decisions on whether the data are of acceptable quality to enter analyses while minimizing the risk of bias ,69. AfteTo take advantage of the outlined image standardization and large data set, the study will perform data harmonization with the aim of minimizing statistical bias from any sites or scanner vendors in addition to performing all analyses at the Nottingham site. Relevant work has been carried out to assess various approaches with differing MRI modalities via the Enhancing Neuroimaging Genetics through Meta-Analysis consortium . ComBat Although this trial aims to investigate the effect of precision MRI-guided TMS treatment, this precision must not be carried out at the expense of treatment tolerability, and thus, participant comfort. For instance, when the location of the calculated treatment site brings about adverse events, such as facial nerve twitching and jaw clamping, we follow the protocol set out by Morriss et al . This inIt is hoped that the sharing of such a detailed MRI study protocol provides clarity to the methods used for the BRIGhTMIND trial and, as such, will aid and accelerate future studies and replications. In addition, this transparency will lend greater weight and credence to the results of the trial upon its completion. An important factor when the study has the potential to provide important insights into the mechanisms of TRD and the effect of TMS treatment.


The COVID-19 pandemic has resulted in unparalleled global impacts on human mobility. In the ocean, ship-based activities are thought to have been impacted due to severe restrictions on human movements and changes in consumption. Here, we quantify and map global change in marine traffic during the first half of 2020. There were decreases in 70.2% of Exclusive Economic Zones but changes varied spatially and temporally in alignment with confinement measures. Global declines peaked in April, with a reduction in traffic occupancy of 1.4% and decreases found across 54.8% of the sampling units. Passenger vessels presented more marked and longer lasting decreases. A regional assessment in the Western Mediterranean Sea gave further insights regarding the pace of recovery and long-term changes. Our approach provides guidance for large-scale monitoring of the progress and potential effects of COVID-19 on vessel traffic that may subsequently influence the blue economy and ocean health. The COVID-19 response has led to unparalleled changes in the functioning of human society, from travel restrictions to changes in consumption. Here the authors use high resolution satellite data to track the global reduction in marine traffic during the pandemic, and more recent hints of recovery to pre-lockdown levels. To better understand the potential effects on the blue economy and the environment, there is an urgent need to quantify the magnitude and patterns of the changes in human activities at sea.The coronavirus disease (COVID-19) pandemic has emerged as both a global health and socioeconomic crisis, with many countries implementing unparalleled mobility restrictions to control the spread of the virus. This unprecedented event, which has been referred to as the “anthropause”, a period of reduced human mobility13; sectors which rely heavily on the movement of people and goods. As with previous economic recessions15, changes in vessel movement associated with COVID-19 are also likely to result in significant short- and long-term effects on multiple anthropogenic pressures, such as air pollution18, the spread of invasive alien species20, or collisions with marine animals22. Localised studies have already reported short-term declines in underwater noise23, water turbidity24 and fishing effort11 as a result of the reduction of the vessel activity during the first wave of the COVID-19 outbreak. However, as mobility restrictions vary among countries and maritime sectors, the spatio-temporal effects of COVID-19 on ship-based activities and their influence on the marine environment are still unclear at global and regional scales.Human activities in the ocean have been radically altered by the COVID-19 pandemic, with reports of port restrictions and changes in consumption patterns impacting multiple maritime sectors, most notably fisheries, passenger ferries and cruise ships30. AIS is a vessel identification system that transmits real-time information on routes of vessels via VHF radio transponders. AIS is required on all ships of 300 gross tonnage or more engaged on international voyages, all cargo ships of 500 gross tonnage or more, and all passenger ships, irrespective of size. In addition, individual countries may require AIS usage on additional vessels. For example, AIS is required for EU fishing vessels >15 m in length31. Moreover, AIS is also increasingly used on a voluntary basis by many other vessels, including smaller fishing and leisure craft, thereby providing a unique opportunity to monitor the location of a diversity of vessels across the world30. Despite some limitations of the system , historical and real-time AIS data have been shown to provide unparalleled insights into shipping-derived impacts and conservation planning at multiple spatial and temporal scales35. With regard to COVID-19, AIS has recently been employed to assess the potential spread of the virus37 and to describe the reductions in marine traffic at local scales11.Fortunately, recent technological advances associated with the Automatic Identification System (AIS), in particular the emerging constellations of microsatellites (S-AIS), now means that ship-based mobility patterns can be monitored globally at high spatio-temporal resolution38 and cruise tourism39, which includes three European countries heavily impacted by the COVID-19 outbreak . Our approach quantifies the magnitude and patterns of changes in ship-based activities, providing data that can inform large-scale investigation of the potential socioeconomic and environmental effects of COVID-19 on the world’s ocean.Here, we use AIS data to conduct a comprehensive assessment of the short-term changes on ship-based mobility patterns in response to COVID-19 across multiple sectors and at different spatio-temporal scales. First, we illustrate our approach by conducting a global assessment using monthly traffic density maps to evaluate changes in vessel activity across multiple regions and maritime sectors during the first half of 2020, relative to the same period in 2019. Then, we assess similar factors with high temporal resolution (i.e. daily basis) in the Western Mediterranean Sea, a key region for the global shipping network40. Lockdown measures across coastal countries analysed (n = 124) started after the World Health Organization pandemic declaration on 11 March 2020, although China, the reported source of the outbreak, started to establish confinement measures by late January ary Fig. .Fig. 1SpWe analysed global patterns in marine traffic during the first half of 2020 (January–June) using monthly density maps (at 0.25-degree resolution) from satellite AIS. An important characteristic of the AIS data is their stratification according to ship categories, thus allowing attribution of the spatial footprint of marine traffic to different maritime sectors. Merchant vessels (i.e. cargo and tankers) were widespread along major shipping lines, fishing and “other vessels”  were more dispersed between coastal and offshore waters, while passenger vessels presented a more limited distribution Fig. .Fig. 2GlIn order to assess potential disturbances of marine traffic in response to COVID-19, we quantified the absolute and relative changes of monthly density maps in comparison with the same reference period in 2019, thus accounting for general seasonal variability of ship-based activities. Average change in traffic density was unevenly distributed across the globe and varied by vessel category Fig. . Major cTemporal variation of global changes in marine traffic density was assessed by estimating the occupancy and the proportion of grid cells with decreases and increases on a given month Fig. . ConsideSpatial and temporal changes varied regionally Fig. . In Euro42. Maritime chokepoints constitute narrow passages that concentrate marine traffic, and thus are ideal locations to monitor synthetic changes in traffic density that may be otherwise masked. Temporal patterns were variable across chokepoints and between sectors . Temporal patterns were heterogeneous among countries  of the analysed countries  was lower than in other sectors directly influenced by the lockdown measures and restrictions on travel, which reinforces global interconnectedness and reliance on many goods being manufactured overseas rather than locally. In contrast, the most heavily impacted and least resilient to COVID-19 were the tourism and recreation sectors, with major declines and slower recovery rates in vessel activity at global and regional scales. Such disruptions have the potential to turn into far-reaching and significant social and economic impacts on tourism-dependent economies for years to come.47, plays an important role in the global economy and so was resilient to COVID-19. Our work was not able to monitor changes in small-scale fisheries due to limitations of deployment of AIS in this sector, but previous studies suggest these fisheries, which dominate in many low-income countries, are likely to be particularly vulnerable to socioeconomic effects derived from COVID-1949. Further work is needed to ascertain the impact of the COVID-19 outbreak on the behaviour of small-scale fisheries sector.For fisheries, the spatial heterogeneity of changes suggests that the impact of the outbreak has been uneven across fishing fleets. Regional analyses in the Western Mediterranean, however, reveal that fishing vessel activity was close to pre-lockdown levels by June, suggesting that the industrial fisheries sector, which is well-resourced and heavily subsidised in some countries43, with increasing rates of shipping in 92% of the EEZs34, and forecast increases of the global shipping network ranging from 240% to 1209% by 2050 under different economic scenarios20. Conversely, not all changes observed were necessarily related to COVID-19. Changes in maritime activities can be driven by multiple factors such as regulations , socioeconomic changes, piracy, environmental changes or by cultural and political events51. For example, the fall in oil price in early 2020 likely contributed to the observed increases of tanker vessel traffic before the declines in oil demand due to COVID-1953 and the re-routing of cargo ships to avoid Suez Canal tolls in favour of the longer journey via the Cape of Good Hope54. On the other hand, the shape of displacements in fishing vessels intensity suggests several annual shifts in the fishing grounds , to assess their interactions and potential effects on wildlife35 and quantify their cumulative impacts on marine ecosystems55. Several ongoing initiatives providing emerging AIS-derived products at multiple spatial and temporal scales  will prove essential to allow large-scale monitoring of the progress and potential effects of COVID-19 and other future shocks.Monitoring the movements of marine traffic in near real-time at a global scale is now possible as a result of unprecedented technological advances in the domains of big data and nano-satellite communication systems leading to increases in global AIS coverage. It is noteworthy that during the most recent comparable global shock, the 2008 financial crisis and associated recession, a study such as ours would not have been possible. In addition to gridded density maps, there are additional characteristics that can be derived from raw AIS data  that have already been used to assess possible transmission of COVID-198. For example, the reduction of air traffic as a consequence of 11 September 2001 terrorist attacks in the USA offered the possibility to discern the effect of condensation trails from jet aircraft on daily temperature ranges56. While the COVID-19 pandemic has brought a dramatic global health and socioeconomic crisis, the unprecedented disruption during lockdowns offers new opportunities for environmental research8. For instance, substantial decrease in noise resulting from confinement measures offered the chance to extract anthropogenic sources of noise from those of natural processes57 and assess responses of birds to recently vacated acoustic spaces58. The spatial and temporal heterogeneity of changes described in this study is highly relevant for further studies aiming to assess the environmental effects of COVID-19 on marine ecosystems. Commercial fishing and shipping, in fact, contribute significantly to overall cumulative human impacts on the ocean55 and information about their spatial patterns is of paramount importance for conservation planning60. The reduction of maritime activities in some affected regions and locations may provide some positive outcomes for the marine environment. For instance, after the 2008–2009 global financial crisis, decreases in bottom trawling fishing pressure and reductions in vessels speeds (i.e. due to increase of fuel price51), resulted in improvements in benthic ecological status14 and air quality15, respectively. Our global assessment is well aligned in time with two focal studies that reported reductions of marine traffic in the Port of Vancouver and Venice during earlier stages of the COVID-19 pandemic, documenting declines in levels of underwater sound23 and water turbidity24. Such agreement highlights the potential of our global dataset to identify impacted and control locations for comparison in further environmental studies. In addition, reductions found in vessel traffic around the Galapagos Islands or coastal areas of the Mediterranean also suggest that marine protected areas could benefit from a decrease in marine traffic associated with tourism and other human-derived pressures. Conversely, if an associated reduction of surveillance effort by maritime authorities occurs, this could present a higher risk of illicit activities , especially in lower-income countries61. In fact, our results suggest there were increases in industrial fishing activity in the national waters of some countries, a pattern worthy of further investigation; especially considering that illegal fishing or non-compliance with fisheries laws may have increased as a result of perceived or real reductions in enforcement efforts due to reduced logistical, personnel and financial resources during the COVID-19 outbreak.Unintended short-term cessation of human mobility due to natural catastrophes, electrical blackouts or terrorist attacks resulted in previous “natural experiments” that unmasked the effects of human activities on the Earth SystemChanges in marine traffic have been shaped by policy actions related to COVID-19 restrictions on human mobility and perturbations in consumer demand and supply chains. The response of marine ecosystems to COVID-19 will depend on the intensity and duration of the reduction of human pressures. There is, however, a degree of uncertainty around future scenarios and long-lasting impacts. The scientific community needs empirical observations in order to better understand the socioeconomic impacts on maritime sectors and the environmental consequences of COVID-19 on marine ecosystems. The pandemic has also constrained the capacities of research institutions to pursue monitoring programmes (e.g. research cruises) underscoring the need to advance implementation of real-time autonomous monitoring systems to survey the ocean, including anthropogenic impacts. As we continue to update changes in marine traffic density, we will be able to track longer-term changes due to COVID-19. Future AIS studies should address temporal variability of spatial patterns of more regional and sectoral focused studies. Such assessments will provide crucial insights into the effects of the current pandemic, or other global shocks, on the blue economy and ocean health.http://www.exactearth.com/), a space-based data service provider which operates a constellation of 65 microsatellites to provide global AIS coverage at a high-frequency rate (<5 min average update rate). The latest upgrade in the constellation entered into production in February 2019 with the addition of 6 new satellites (i.e. there were 59 satellites in January 2019), thus S-AIS coverage can be considered equivalent for the study period (exactEarth Ltd. pers comm.). Values represented the monthly number of vessel transits within grid cells of 0.25 × 0.25 degrees. Vessels were classified into five categories: cargo, tanker, passenger, fishing and “other”. The category “other” included any vessel not covered by the preceding categories . We calculated the vessel density as the number of vessel transits per unit area, considering the difference of cell size across the latitudinal gradient28. Further details regarding post-processing and quality control procedures for density maps can be found in the For global analyses, satellite AIS (S-AIS) data for January–June in both 2019 and 2020 were obtained from exactEarth Ltd (62) using a real-time operational system connected to a web-service provided by Marine Traffic (https://www.marinetraffic.com/). The database used in this study contained AIS data from 1 January 2016 until 30 November 2020 at 5-min intervals (comprising > 545 million AIS messages). In addition to the vessel tracks, the database also included information associated with each vessel, such as the vessel type or length. A first pre-processing of the raw data included the removal of duplicates, invalid identification numbers (i.e. Maritime Mobile Service Identity -MMSI- codes without 9 digits) and codes outside the correct numerical range . In order to address inconsistencies in the vessel and MMSI combinations , we selected the more frequent combination of MMSI and vessel characteristics (e.g. vessel name and vessel type) for each calendar year. We used a similar vessel categorization as the S-AIS dataset, but were able to derive a sixth category from the AIS metadata, separating “recreational” boats from “other” vessels. Therefore, vessels were classified into six categories: cargo, tanker, passenger (included high-speed craft and passenger vessels), fishing, recreational , and others . We excluded ship type codes 20–29 (i.e. wing-in-ground-effect and search and rescue aircraft), as well as codes that had an invalid value (i.e. empty or null) or those where the value was not listed in the previous type codes. We calculated the number of vessels per day considering only those that were underway, thus removing moored vessels inside ports that were inactive. T-AIS coverage was not homogenous in the study area due to lack of uniformity in the distribution of antennas , thus reducing potential bias due to spatial and temporal gaps in signal reception.In addition to satellite stations, AIS signals can also be detected by terrestrial antennas (T-AIS). Unlike S-AIS, with global coverage, land-based antennas have a horizontal range of about 40 nautical miles (74 km). Terrestrial AIS (T-AIS) data from the Western Mediterranean . In our study, this approach is consistent with S-AIS coverage (see above) and allows accounting for seasonal variability , we compared the unique number of vessels on a daily basis using T-AIS. Our dataset showed a marked annual cycle, reducing in the boreal winter and a year on year increasing annual trend for some sectors . In order to have the best possible alignment with the containment measures (i.e. provided at country level), we selected EEZs pertaining to coastal countries, hence excluding territorial regions (e.g. overseas territories), EEZs without traffic density data (e.g. those within the Caspian Sea), joint regimes (i.e. shared and jointly managed EEZs), as well as disputed or unclaimed areas. Furthermore, we selected regions larger than 2307 km2  to ensure we had at least three observations per EEZ. Then, we obtained information on income levels from the World Bank and containment measures from the Oxford COVID-19 Government Response Tracker (OxCGRT) for each of the selected countries (n = 124). OXCGRT provides a transparent, real-time monitoring system that allows comparison of government measures between countries40. We estimated the monthly median Stringency index (Index methodology version 3.1). This index is an additive score of nine policy decision indicators, rescaled to vary from 0 to 100, which records the strictness of the lockdown measures per country. To analyse the effect of confinement measures on the relative change of vessel traffic density we used linear mixed-effects models (LMM). Mixed-effect models are a useful tool in presence of repeated measurements for units of observation that are clustered (e.g. within geographic regions)64. LMMs, for instance, have been used previously to assess temporal changes in ocean health on EEZs65 or the effects of air pollution on severity of COVID-1966. We included the interaction of Stringency index and income levels as fixed effects, with country as random effect. We ran a separate model for each ship category. The total number of sampling units (i.e. countries) used in the models varied due to differences in occupancy between categories  estimation within the lme4 package68. Significance was calculated using the lmerTest package69, which applies Satterthwaite’s method to estimate degrees of freedom and generate p-values for mixed models.We evaluated the effect of containment measures on changes in marine traffic density at country level. Monthly gridded density values were averaged by Exclusive Economic Zones (EEZ)e i.e. 209. In ordSupplementary InformationPeer Review File


The probability of infection changes nonlinearly with the amount of respiratory matter to which a person is exposed. If most people in the wider community wear even simple surgical masks, then the probability of an encounter with a virus particle is even further limited. In indoor settings, it is impossible to avoid breathing in air that someone else has exhaled, and in hospital situations where the virus concentration is the highest, even the best-performing masks used without other protective gear such as hazmat suits will not provide adequate protection.The effectiveness of masks in preventing the transmission of severe acute respiratory syndrome coronavirus 2 has been debated since the beginning of the COVID-19 pandemic. One important question is whether masks are effective despite the forceful expulsion of respiratory matter during coughing and sneezing. Cheng Science, abg6296, this issue p. 1439 Face masks are highly effective at preventing SARS-CoV-2 transmission because, in most environments, airborne virus abundance is limited. Airborne transmission by droplets and aerosols is important for the spread of viruses. Face masks are a well-established preventive measure, but their effectiveness for mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is still under debate. We show that variations in mask efficacy can be explained by different regimes of virus abundance and are related to population-average infection probability and reproduction number. For SARS-CoV-2, the viral load of infectious individuals can vary by orders of magnitude. We find that most environments and contacts are under conditions of low virus abundance (virus-limited), where surgical masks are effective at preventing virus spread. More-advanced masks and other protective equipment are required in potentially virus-rich indoor environments, including medical centers and hospitals. Masks are particularly effective in combination with other preventive measures like ventilation and distancing. Airborne transmission is one of the main pathways for the transmission of respiratory viruses, including the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) , surgical and similar masks exhibit higher and more variable penetration rates (~30 to 70%)  has the same chance to infect a person, Pinf can be calculated by a single-hit model of infectionPsingle represents the infection probability for a single virus and Nv represents the total number of viruses to which the person is exposed . This very large number implies that indoor environments are usually in a respiratory particle–rich regime. Surgical masks with particle collection efficiencies of ~50% cannot prevent the release of millions of particles per person and their inhalation by others  are typically low, with mean values of ~53 for coronaviruses , ~38 for influenza viruses (A and B), and ~96 for rhinoviruses  for the number of potentially inhaled viruses , assuming a characteristic infectious dose of IDv,50 = 100 or 1000 viruses, respectively , influenza viruses (A and B), rhinoviruses, and SARS-CoV-2 . We find that usually just a minor fraction of exhaled respiratory particles contains viruses. In contrast to the high number of emitted respiratory particles, the number of viruses in 30-min samples of exhaled air . For this purpose, we modeled Nv for SARS-CoV-2 as lognormally distributed with standard deviations (σ) in the range of ~1 to 2 on the basis of recently reported distributions of the viral load of SARS-CoV-2 in respiratory fluids   and average daily numbers of human contacts (c ≈ 10 to 25 contacts per day)  is plotted against the infection probability without masks . It shows that surgical masking achieves large reductions in infection probability when the maskless infection probability is low but increasingly smaller reductions when the maskless infection probability is high. Pinf,pop/Pinf,pop = / Pinf,pop]. It decreases slowly with increasing Pinf,pop in the virus-limited regime, exhibits a steep decrease upon transition into the virus-rich regime as Pinf,pop approaches unity, and goes to zero at Pinf,pop = 1. The nonlinear dependence of mask efficacy on infection risk differs from the assumption that the percentage change of infection probability as a result of mask use would be proportional to the percentage change of inhaled particle number . Accordi8 per milliliter of respiratory fluid, which is realistic and higher than the estimated infectious dose for SARS-CoV-2   cannot really protect them from inhaling these particles. However, as only few respiratory particles contain viruses and most environments are in a virus-limited regime, wearing masks can keep the number of inhaled viruses in a low-


To report the course of ataxia in children up to 2 years post-operatively, following surgical resection of a posterior fossa tumour (PFT).Thirty-five children,  having resection of PFT, were assessed using the Scale for the Assessment and Rating of Ataxia (SARA), Brief Ataxia Rating Scale (BARS) and the mobility domain of the Paediatric Evaluation of Disability Index (PEDI-m) at initial post-operative period (baseline), 3 months, 1 year and 2 years post-operatively.Baseline median scores of the SARA and BARS were 8.5 (range 0–35.5), and 7 (0–25) respectively. Ataxia improved at 3 months . Additional gradual improvements in SARA were recorded at 1 (median reduction 2) and 2 years post-operatively (median reduction 0.5). Median baseline PEDI-m was 54.75 (range 15.2–100) with improvement at 3 months (median increase 36.95) and small improvement at 1 year (median increase 2.5) and 2 years (median increase 5.8). Children with medulloblastoma and midline tumours  demonstrated more severe ataxia than children with low-grade gliomas and unilateral tumours .The largest improvement in ataxia scores and functional mobility scores is demonstrated within the first 3 months post-operatively, but ongoing gradual improvement is observed at 2 years. Children with medulloblastoma and midline tumour demonstrated higher ataxia scores long term.The online version contains supplementary material available at 10.1007/s00381-021-05246-0. Posterior fossa tumours (PFT) account for approximately 50% of all childhood brain tumours . ManagemAtaxia is the predominant motor problem in children with PFT. Ataxia describes a number of related impairments including limb incoordination, tremor, gait disturbance, impaired balance and oculomotor and oromotor dysfunction . ApproxiEvolving evidence indicates that ataxia can persist in the long term in children with PFT. Robertson et al.  presenteRisk factors for ataxia are less well described than for other toxicity such as cerebellar mutism syndrome, where invasion of the brainstem is known to be a risk factor , and potIn this paper, we present a new insight into the longitudinal course of ataxia and functional mobility using standardised measures in children with PFT from the peri-operative period, initial post-operative period and up to 2 years post-operatively. Additionally, we explore the impact of tumour location, histology and adjuvant treatment on severity of ataxia.Cohort prospective single site longitudinal study.Children who had surgical resection of a PFT and were aged between 4 and 18 years (inclusive) were eligible for inclusion. Due to the need to be able to follow the instructions required for assessment of ataxia, the lower age limit was set as 4 years old.Children were recruited from a single tertiary neurosurgical/oncology unit in the UK between 2012 and 2018. The study was approved by the relevant local ethics and research and development committee (IRAS ID98449).Potential participants were identified and screened for eligibility by the neurosciences therapy team. Informed and written consent was obtained from all children and/or their parents (with child assent).Children were assessed initially as an inpatient within 1 week of surgical resection (baseline) and then in an outpatient clinic setting at 3 months, 1 year and 2 years post-operatively. Assessments were completed by a neuro-physiotherapist trained in the use of the standardised outcome measures. A subset of participants was also assessed pre-operatively; this occurred if children were stable pre-operatively and where timing of admission and surgery allowed for pre-operative assessment.Children were assessed using the Scale for the Assessment and Rating of Ataxia   and the The mobility domain of the Paediatric Evaluation of Disability Index (PEDI) was also used as a functional mobility measure . The PEDBaseline potential predictive factors of severity of ataxia were collected including tumour histology, age at diagnosis, adjuvant treatment (radiotherapy or chemotherapy), tumour location and surgical approach.The study was not designed as a comparative study; therefore, the data were summarised using descriptive statistics . Medians and interquartile ranges were used to summarise continuous data because of the small number of observations. For categorical data, counts are reported. The SARA and the BARS demonstrated similar patterns, and as the SARA is more widely used in clinical paediatrics, further analysis regarding ataxia was undertaken using the SARA only.Patterns of change in outcome measures were therefore explored graphically (using Box-Whisker plots and time series plots) for the SARA and the PEDI-m. Participants were stratified by putative predictive factors of tumour histology (medulloblastoma versus low-grade glioma), tumour location and adjuvant treatment The PEDI is normative-referenced between 6 months and 7.5 years. In line with recommendations, raw scores were converted to scaled scores to enable the use of the PEDI-m in children over the age of 7.5 years.Thirty-five participants were recruited to the study. Participant characteristics are detailed in Table Descriptive analysis of the outcome measures is presented in Table Twenty-two children were also assessed pre-operatively using the SARA scale. There was an initial increase in SARA post-operatively (worsening ataxia) followed by improvement on average for the group. The ataxia scores at 3 months post-operatively are lower (better) than the pre-operative assessment scores, indicating improvement in ataxia 3 months after surgery. At 2 years post-operatively, ataxia severity had reduced by half of the pre-operative level .The change in Bal-SARA represents a large proportion of the initial change in SARA score. The Bal-SARA includes gait, stance and sitting items of the SARA scale.Further analysis is completed to compare change over time for outcome measures dependent on tumour location Fig. . AlternaA similar trajectory for both tumour locations was noted; however, children with unilateral tumours demonstrated lower SARA scores than those with midline tumours, indicating less ataxia throughout all time points. The change in PEDI-m scores demonstrated the same trajectory as previously noted, with rapid increase in scores between baseline and 3 months, although there is less difference in scores between tumour location. The ceiling effect of the PEDI-m is observed.Further analysis of ataxia considering a more detailed breakdown of tumour location  angle, CP angle, cerebellar hemisphere and brain stem) is detailed in supplementary material (Online Resource n = 11) versus low-grade glioma (n = 17), are presented for all assessment timepoints.Further analysis was completed to determine whether changes in outcome measure score were dependent on tumour histology. The two most common tumour histologies, medulloblastoma  and lower PEDI-m scores (more mobility function impairment) than children with low-grade gliomas Fig. . The gapBoth the SARA and PEDI-m appear to be able to distinguish between the two tumour types. Again, there is a ceiling effect for the PEDI-m.n = 19) versus surgery and radiotherapy (n = 5) versus surgery, radiotherapy and chemotherapy (n = 11) are presented.Further analysis was completed to determine whether changes in outcome measure were dependent on adjuvant treatment. Surgery only  is presented in supplementary material  that may predict the severity and course of long-term ataxia were also identified.The most rapid improvement in ataxia was noted in the first three post-operative months. Much smaller change was noted thereafter up to 2 years post-operatively. Although the minimally clinically important difference (MCID) for the SARA in children has not been determined, the MCID for adults has been reported as 1.1 . The MCIFor the PEDI-m, a rapid improvement in mobility was also noted at the 3-month assessment ,Analysis of a subset of children who were assessed pre-operatively demonstrated an initial increase in ataxia post-operatively, followed by a drop below the pre-operative level by the 3-month assessment time point. Although it is known that approximately 60% of children will present with ataxia pre-operatively , this isSARA scores are age-dependent in healthy children , 20. HowThe pattern of change reported in this study is supported by results from a longitudinal study which measured function in 12 children with PFT (age 6–17 years) who were followed up to 1 year postoperatively . A reducKuper et al.  suggesteChildren with medulloblastoma and those with midline tumours demonstrated higher ataxia scores  throughout the trajectory of their recovery. This is in line with other studies reporting that children with medulloblastoma have a higher incidence of ataxia and cerebellar mutism syndrome than children with other tumour types , 22–25. It is difficult to determine the influence of tumour histology compared with tumour location on ataxia severity, as there is an inherent link between the two factors: medulloblastoma more often being midline than lateralised in the cerebellum and LGG showing the converse relationship. Lesions to the deep cerebellar nuclei and inferior vermis, which are structures at or close to the midline, and the involvement of the efferent cerebello-thalamo-cerebral tract have been linked with persistent motor deficit in children , 24, 27.Findings from this study show a wider gap in ataxia scores depending on tumour histology than observed due to tumour location, suggesting that histology differentiates the strata of participants better than tumour location. Despite not being independent factors, this indicates that anatomical predilection of tumour histology is not the whole answer, and other elements should be considered, the most obvious being the need for adjuvant oncology treatment, i.e. radiotherapy and chemotherapy for children with medulloblastomas. The results showed that children who had received both chemotherapy and radiotherapy demonstrated a sustained higher level of ataxia which is in keeping with previous research highlighting the impact of chemotherapy in addition to surgery and radiotherapy on motor function . This maOverall, tumour location, histology and adjuvant treatment are all interlinked, e.g. children with medulloblastoma because of the nature of the tumour type typically feature midline tumours and receive multimodal therapy . Therefore, in the future, a larger study which includes multivariate analysis would be of benefit to identify the impact of single variables to determine if any particular factor raises the risk of ataxia severity.The small number of children with severe ataxia (SARA greater than 14 ) in thisThis is the largest study to date reporting longitudinal ataxia severity in children with PFT. The strengths of this study include a prospective design using standardised validated outcome measures  over a 2Our study provides information about the long-term course of ataxia in children following surgical resection for PFT. Children often have worse ataxia after surgery, before improving to better than they were prior to surgery. Children with medulloblastoma and midline tumours have more long-term balance and coordination problems compared to children with LGG or unilateral tumours. The most rapid improvement was seen in the first 3 months postoperatively followed by a small change up to 2 years post-operatively. Any late changes do not appear to be explained simply by developmental maturation of cerebellar function especially in adolescents, though the impact of age should be examined further.Although the findings would benefit from further confirmation with a larger dataset, they demonstrate the need to examine the potential of targeted rehabilitation interventions more than 1 year post-operatively when some children otherwise typically appear to demonstrate minimal change.ESM 1(DOCX 88 kb)ESM 2(DOCX 59 kb)


Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards. With the recent surge in the development of machine learning (ML) techniques, particularly the success of machine learning in speech and image recognition ,2, ML-aiAs a result of continuing improvements in healthcare provision, life expectancy has been increasing in many countries. Consequently, the prevalence of Alzheimer’s disease (AD) has also been rising. AD is one of the brain malfunctions whose exact triggering reasons are still not fully established , and itsThe major signal processing phases, such as signal pre-processing, neuromarker (feature) extraction, and feature classification/recognition, should be carefully engineered while developing an ML-aided diagnosis system. Of all these important steps, the development of effective neuromarkers is possibly one of the most important tasks for a MEG-based MCI detection system, not only from the ML point of view but also from the clinical perspective. A good neuromarker developed based on Explainable AI should not only provide decent classification performance, but also reveal useful characteristics that reveal the changes accompanying a particular cognitive disorder, e.g., where the anatomical origins of MCI alterations at locations that MCI evidence stems from.A number of ML-aided diagnosis approaches using MEG source-level analyses for MCI detection have been reported recently . For exaDimitriadis et al.  recentlyMEG-based MCI detection using time-frequency analysis has also been explored by Poza et al. . FollowiConsidered as non-stationary measurements , both EE(1)A regional analysis of the effectiveness of different cortical regions for MCI detection is undertaken to examine the phenomena of hyper- and hypo-connections between regions of interest (ROIs) which may have a significant impact on MCI vs. HC classification with respect to ROI selections.(2)Based on the findings from the regional analysis, a system for MCI/HC classification is developed. In particular, the effectiveness of the novel wavelet-based neuromarkers for MCI detection is evaluated. The results show that the proposed method can achieve an average classification accuracy of about 84%, using only MEG data from the selected cortical regions.(3)A comparative analysis using two different evaluation procedures is undertaken. One is based on the well-known Monte Carlo repetition of random splits, i.e., into 95/5% of training/test subsets. The other test procedure is a subject-based 20-fold leave-one-out (LOO) cross-validation, in order to better mimic the actual clinical scenario with individual participants (each class contained 20 subjects).(4)The region selection and recognition problems are addressed from a practical perspective by exploring the statistical significance of the results, which is a crucial aspect that is often ignored in the performance evaluation for such recognition systems.As seen in the aforementioned studies, the range of ML techniques that have been used have shown high variability in MCI detection performance. In this work, we conducted a series of experimental evaluations using MEG data for MCI vs. HC classification, in order to establish which of these evaluation metrics could reflect real-world scenarios. The study reported in this paper covers the following four main aspects:The rest of the paper is organized as follows: In In this section, an objective approach is presented for finding a subset of ROIs, which may be relatively more effective for MCI detection. The analysis is based on a neuroimaging database  obtainedFor the source-level analysis, a sphere-based forward model was employed and was realigned with the individual’s MRI in the sensor space. The minimum norm estimation (MNE) technique was adopted for inverse modeling . One impThe intra-hemispheric and inter-hemispheric connectivity were also explored. The investigations addressed the problem of ROIs selection from three perspectives: (1) computation of connectivity indicator; (2) analysis of single ROI vs. rest of the regions; (3) ROI sensitivity analysis for MCI detection. The follow-up subsections are devoted to the discussion of the connectivity indicator , analysiThe aim of this section is to statistically evaluate and decide the region(s) that may be relatively more effective in ML-aided MCI diagnoses. Given the issues that typically affect source localization solutions, , we decided to group the source space into ten conventional surface regions within the left/right hemisphere to study their possible interactions. In detail, the cortical surface is partitioned into frontal , temporal , parietal , occipital , and central  regions. Another reason for choosing these partitions is the differentiated role(s) of these regions in AD/MCI pathology . In ordeAfter source-level reconstruction, data from the selected 10 regions were averaged and used to represent each region, which provided 10 parcels in the source space. It is worth mentioning that 3 min of recording was used for each participant. For connectivity analysis, we windowed the data into 180 one-second epochs, with each epoch being comprised of 200 samples (data was down-sampled to 200 Hz from 1000 Hz). Therefore, in total, 3600 epochs (20 × 180) were generated for each class per ROI. Each epoch (which is the mean of the parcels from its corresponding region) was then paired with every other epoch from other regions for the coherence analysis, and their pair-wise functional connectivity was estimated subsequently. As a result, we were able to estimate the pairwise functional connectivity (FCs) among the ten cortical regions, as illustrated in x and y, x and y, respectively. n <n <f selected ROIs.The broadband methods for functional connectivity analysis have been found to be effective for EEG signals for seizure detection . In our To leverage the imaginary part of coherence, and to mitigate the possible volume conduction effect in MEG signals while computing the absolute value of the coherence , we compThe results shown in (1)Each ROI contained hundreds of MEG values in the source space. First, the values of each ROI were averaged: this resulted in ten mean MEG values for the ten selected ROIs.(2)For each averaged MEG value per ROI, the coherences with respect to the remaining nine mean ROI values were computed, which produced nine coherences for each ROI.(3)The nine coherence values for each ROI were summed together and the resulting summation is defined as the degree of connectivity for each ROI.(4)Considering that there are 20 participants per class and 180 s per participant, 20 × 180 = 3600 epoch per ROI for each class are produced.(5)The max, min, mean, and SD for these 3600 epochs, with respect to the degree of connectivity for each ROI, are computed and shown in The detailed steps required for this computation are shown as follows:Using the FCs of HC participants as the baseline, the averaged FCs of eight regions were found to be reduced for MCI subjects, except for the left frontal (LF) and right central (RC) regions with increased FCs. Further exploring the statistics of MS-COHs in The data in It should be noted that the degree of connectivity per region  as suggested by the data in As is shown in Further exploring the results in Based on the above analysis, we propose two hypotheses: (1) ROIs showing hypo-connectivity may be reflecting the underlying anatomical changes due to progression towards AD; (2) hyper-connectivity of ROIs may be induced by some compensatory mechanism limited to adjacent areas in MCI groups. It would be interesting to know which group,  will be more sensitive to the MCI, hence to better facilitate the MCI vs. HC classification problem. In particular, the second hypothesis will be addressed in t-test [The database we explored comprised 40 subjects. To justify its representativeness, we examined the statistical significance of the connectivity analysis results obtained in the previous section. Certain differences in the degree of the regional connections  and stret-test  was usedt-test assumes data samples follow a normal distribution [t-test should therefore provide reliable evaluation [The ribution . Given ttributed ,34. The aluation .t-test [Based on the above results, the statistical significance of the coherence connectivity indicators for HC and MCI classes were further explored separately as well as the connectivity of ROIs for both inter- and intra-hemispheres. It was found that out of 45 ROI pairs, 25 belong to the inter-hemispheric group while the remaining 20 pairs were from the intra-hemispheric group. The standard two-sample t-test  was perfTo further validate the hypotheses proposed in The classification method is firstly introduced in Thanks to its advantageous characteristics in time-frequency analysis, WT has also been frequently used in EEG data analysis ,36. HoweFollowing the regional analysis in the previous section, three minutes of available raw MEG signals were first transformed into source space using the MNE method. The mean values of the ten cortical ROIs were computed, which subsequently produced 10 arrays of averaged source recordings. As illustrated in The first step of the feature extraction was to perform the wavelet packet decomposition (WPD) for each epoch . The Dau follows :(3)WTψ{xThe wavelet coefficients, (1)For the source space MEG data, in total, 180 s of the recording was preserved, which was then further segmented into a number of windows in the time domain.(2)The epochs (10 s per epoch) were then down-sampled to 200 Hz, followed by a band-pass filtering process extracting the signal between 0.1 Hz to 100 Hz, which cover all the typical bands  using a 4th-order Butterworth filter. Here, we purposely included the frequency content that was higher than 60 Hz, as in principle the MEG modality may retain useful information in the high-frequency range (high gamma band).(3)The wavelet packet decomposition was performed on the resulting time-domain signals. WPD was performed up to level 3; each level covers the selected full frequency range, which facilitates the multi-resolution analysis of the signal in question.(4)The approximate derivative ,41 was c(5)Finally, the multi-dimensional feature vector was constructed by concatenating the standard deviations (SDs) of the obtained approximate derivative coefficients from each band.(6)A range of classifiers that have shown to be effective for MEG/EEG signal classifications were employed to investigate the effectiveness of the features, including k-nearest neighbor (k-NN), linear discriminate analysis (LDA), and support vector machine (SVM).For the sake of completeness, the experimental steps in sequential order are presented as follows:It is worth noting that according to our preliminary exploration, the classifiers based on the neural network are not effective for MCI detection in this work. One possible reason could be the available data for training is not large enough to converge the training process. The motivation for inserting the Approx-Deriv into the wavelet algorithm was to capture/reflect the nonstationary characteristics of the brain signals . ComputaIn this section, the classification performance of the proposed wavelet neuromarkers is explored using both the MCR and LOO evaluation approaches. The two ROI groups proposed in The well-known MCR cross-validation was first employed for performance evaluation. For each class , 95% of the data (342 observations) was randomly selected for training the classifier, while the remaining 5% (18 observations) was used to test for performance. This process was repeated 20 times in order to guarantee: (1) the average accuracy was principally representing the true performance of the system, (2) in line with the subject-based LOOCV tests for 20 participants, which was repeated 20 times to exhaustively test all the subjects, each for once only. The 3-NN classifier provided the highest mean recognition rate amongst the three classifiers . It is worth noting that the employed classifiers are the conventional algorithms for benchmarking and have demonstrated good performances in previous MEG/EEG classification studies .As shown in To further investigate the impact of the ROI groups on MCI detection, a subject-based LOO cross-validation approach was adopted for imitating a real-world scenario, in which, for each test, the data of a participant that the classifier had never seen was, in turn, used for validation . The remTo further explore the effectiveness of the proposed method, t-test [In this section, we further conduct a number of statistical evaluations of the wavelet neuromarkers proposed in t-test  and Krust-test ) were emt-test and Kruskal–Wallis test were conducted independently for each dimension between the HC and MCI classes. As a rule of thumb, we set α = 0.05 for both types of tests, the resulting p-values are shown in The wavelet feature vectors proposed in this study contain fourteen dimensions. The p-values shown in p-value to be positively biased. Therefore, in order to battle against such overly optimistic inference, we adopted the Holm–Bonferroni method [p-values. The detailed p-values and their corrected values for the six frequency ranges with α < 0.05 are listed in p-values for the selected features were found to have increased after the correction, they were still well below the threshold of α = 0.05. The mean p-values of the six dimensions for the t-test and Kruskal–Wallis test were 0.01 and 0.02, respectively. These results suggest that the effective feature vector should contain at least six dimensions, which demonstrated statistical significance for both the t-test and Kruskal–Wallis test. It is worth noting that while training with only the features with p-value < 0.05 for both the t-test and Kruskal–Wallis tests  to reduce the dimensionality and leverage the useful information from multiple dimensions of the feature vector . A set ohe tests . It appeThis work explored the functional connectivity (FC) changes across brain regions  between participants with mild cognitive impairment (MCI) and healthy control (HC) subjects. The findings, based on MS-COH indicators, were then verified using a wavelet-based source-space neuromarker, tested through a MEG database containing 40 participants (20 MCIs and 20 HCs). We identified two distinct ROI groups: one with hyperconnected FCs and another with hypoconnected FCs. The proposed neuromarkers obtained using the data from the hyperconnectivity ROI group provided much-improved recognition performance compared to that using the hypoconnectivity ROI group for MCI detection. We also found that when the features from all the 10 ROIs (both hyper and hypo connected regions) were concatenated for classifier training, the recognition performance could not be improved further—the average accuracy rate was practically degraded to 74.72%, which was about 10% worse than using only the data from hyper-connectivity regions for the classification. Some of the findings from this work may be considered to extend the existing understanding of the functional roles of the selected ROIs. We noticed that the left-brain half (hemisphere) tends to be more effective in MCI detection: out of five ROIs, four of them demonstrated hyper-connectivity , whereas, in the right half, only two ROIs (RC and RT) showed such phenomenon. It is well-recognized that the left hemisphere of the cerebrum normally controls speech, comprehension, arithmetic, and writing; the right half controls creativity, spatial ability, artistic, and musical skills . In thisBased on the proposed implementation, our best mean MCI detection rate is 83.8% using the LOO cross-validation. A considerable difference in MCI detection performance was also noticed between using the MCR and LOO cross-validation approaches while using the same feature for classification. Such a difference deserves some further consideration. It is reported that the state-of-the-art MEG features have already been quite effective for MCI detection (more than 98% of recognition accuracy  indicateIt is, therefore, recommended that features and classification schemes should be tested in real-world clinical settings, in such a way that fair comparisons can be made amongst various neuromarkers. As the best performance was obtained after the PCA transform, additional work could be conducted to resolve one limitation that remains in this work: the proposed feature set spans across multiple frequency bands with different resolutions. It will be interesting to explore and find the most effective feature combination for MCI detection, as well as its clinical origin, so that further improvement of the recognition performance may be achieved. As indicated in our previous work ,51, we a


To investigate changes in retinal thickness, drusen volume, and visual acuity following subthreshold nanosecond laser (SNL) treatment in patients with age-related macular degeneration (ARMD).Retrospective chart review.Patients with intermediate ARMD treated with a single session of SNL  were included. Swept-source optical coherence tomography (OCT) imaging  was performed within 6 months before and after SNL treatment. Retinal layers were segmented using the artificial intelligence-enabled Orion® software . The macular region was analyzed according to the Early Treatment Diabetic Retinopathy Study map. Mean difference and standard deviation in baseline and post-treatment retinal layer thicknesses are reported.37 eyes from 25 patients were included in this study (mean age 74.7±9.2 years). An average of 51±6 spots were applied around the macula of each study eye, with a mean spot power of 0.33±0.04mJ. Increases in total retinal thickness were observed within the outer temporal and inferior sectors (P<0.05). Within the annulus, there was an increase in thickness of the sub-retinal pigment epithelial (RPE) space , defined between the RPE and Bruch’s membrane. An increase in thickness of 1.13±2.55μm (P = 0.01) was also noted in the inferior sector of the photoreceptor complex, defined from the inner and outer segment junction to the RPE. Decreases in thickness were observed within the superior sector of the inner nuclear layer (INL) , and within the annulus of the outer nuclear layer (ONL) .At 6 months post-SNL treatment, there were sectoral increases in OPL, photoreceptor complex, and sub-RPE space thicknesses and sectoral decreases in INL and ONL thicknesses. This pilot study demonstrates the utility of OCT combined with artificial intelligence-enabled software to track retinal changes that occur following SNL treatment in intermediate ARMD. Age-Related Macular Degeneration (ARMD) affects between 7.4–30% of those aged 45 to 85, and is a common cause of blindness . Early ANutritional supplementation and lifestyle interventions  can slow the progression of ARMD in its early and intermediate stages ,3. In itJobling et al. found a reduction in drusen area with no subsequent damage to photoreceptors following laser treatment. The Laser Intervention in Early Stages of Age-Related Macular Degeneration (LEAD) study was the first multi-centered randomized controlled trial to investigate the impact of SNL treatment in patients with intermediate ARMD. This study found that SNL treatment did not reduce the overall rate of progression to late ARMD. Post hoc subgroup analysis suggested significant treatment effect modification based on the presence of reticular pseudodrusen (RPD), where eyes without these subretinal drusenoid deposits exhibited a lower rate of progression to late ARMD [Recently, subthreshold nanosecond laser (SNL) was investigated as a potential treatment for decreasing ARMD progression. A preliminary study demonstrated laser-induced regulation of extracellular matrix remodeling pathways, which resulted in thinning of BM ,6. In huate ARMD . Furtherate ARMD ,9.Identification of RPD requires the use of at least two different imaging modalities, such as optical coherence tomography (OCT) and color fundus photography . OCT is This study presents a real-world evaluation of the structural and functional outcomes of SNL treatment, and uses artificial intelligence to maximize the utility and accuracy of OCT in tracking changes over time. The purpose of this investigation is to determine the effect of SNL treatment on retinal layer thickness, retinal volume, and visual acuity in patients with intermediate ARMD.This pilot study is a single-center retrospective chart review of all patients who underwent SNL treatment and completed their 6-month post-treatment follow up visit. Ethics approval for this study was obtained from the CIRBI Institutional Review Board and research adhered to the tenets of the Declaration of Helsinki. Consent was not obtained from patients included in this retrospective chart review as all data were analyzed anonymously.Patients were included in this study according to the following criteria: 1) age 50 to <95 years at the time of SNL treatment, 2) intermediate ARMD with at least one druse >125μm within 1500μm from the fovea, 3) pupil dilation of at least 5mm possible in treatment eye, 4) single session of SNL treatment, 5) OCT scans available within 6 months prior to SNL treatment, and 6) OCT scans available at 6 months post-treatment.Patients were excluded according to the following criteria: 1) presence of RPD, 2) any evidence of advanced ARMD, 3) other macular disease with subretinal deposits not typical of ARMD, 4) ocular disease other than ARMD that significantly compromised the ability to treat or visualize the fundus, 5) baseline OCT scan taken >6 months prior to SNL treatment, and 6) OCT scans taken with a device other than the specified device.A single session of SNL treatment was performed using the retinal rejuvenation therapy device . This device uses a 532nm laser with 3-nanosecond pulse duration to induce a non-thermal injury confined to the RPE. The laser was applied along the arcades as previously described with a target of 50 spots per eye . The powSwept-source OCT using a commercial OCT device  was performed within 6 months prior to SNL treatment and at 6 months post-SNL treatment. The macular region was captured with a 7mm x 7mm (512x256) volume scan centered on the macula.TM . This artificial intelligence-enabled software performed automated segmentation of the retina into individual layers. Retinal layer thickness and volume in the central macular area were calculated by the software. The retina was segmented into the following seven layers: 1) retinal nerve fiber layer (RNFL), 2) ganglion cell and inner plexiform layer (GC-IPL), 3) inner nuclear layer (INL), 4) outer plexiform layer (OPL), 5) outer nuclear layer (ONL), 6) photoreceptors (PR), and 7) the sub-retinal pigment epithelium (RPE) space, defined between the RPE and Bruch’s membrane complex. Total retinal thickness was defined from the inner limiting membrane to BM. This software was previously validated in eyes with early and intermediate ARMD [Following image acquisition, structural OCT layer segmentation was performed using Orionate ARMD .The software analyzed the retina according to the Early Treatment Diabetic Retinopathy Study (ETDRS) map, automatically centered on the fovea . RetinalDescriptive statistics, including means, mean differences, and standard deviations, were applied for all variables. Paired two-tailed t-tests were used to compare baseline and post-treatment data in the peri-foveal annular region and within each sector of the ETDRS map. Subgroup analysis for all variables was performed for eyes demonstrating BCVA ≥20/40 at baseline, as per the inclusion criteria of the LEAD study . A P-valA total of 37 eyes from 25 patients with intermediate ARMD were included in this retrospective chart review. Reasons for exclusion are listed in Overall, increases in retinal thickness was observed in the GC-IPL, OPL, PR, and sub-RPE layers. Retinal thinning was seen in the INL and ONL layers. There were mixed changes in the RNFL, with both increases and decreases in thickness observed in different areas. Mean differences in retinal thickness from baseline to post-treatment follow-up are reported in Subgroup analysis was performed for eyes demonstrating BCVA ≥20/40 at baseline . An incrChanges in retinal volume are reported in Subgroup analysis of retinal volumes in eyes with baseline BCVA ≥20/40 revealed a conservative, but significant, increase in volume within the inner annulus  of the sub-RPE space. An increase in the outer annulus volume of the sub-RPE space was also observed . There was no significant change in overall total retinal volume, which included the foveal area in the calculation.Post-treatment visual acuity (0.213 ± 0.214) did not significantly differ from baseline . This result was unchanged following subgroup analysis (P = 0.85).This study demonstrates that changes in the retina are detectable within 6 months post-SNL treatment in patients with intermediate ARMD. Sectoral increases in thickness were observed in the RNFL, as well as in the GC-IPL, OPL, PR, and sub-RPE layers. Sectoral thinning was observed in the INL and ONL. Further, sub-RPE volume was unchanged in this study, which implies no change in drusen volume. However, significant changes in retinal thickness and volumes were observed following exclusion of the central sub-field. Results of the subgroup analysis of patients with BCVA ≥20/40 showed fewer areas of retinal thinning in the INL and ONL, as well as an increase in sub-RPE volume within the peri-foveal region. This investigation highlights the utility of using artificial intelligence-enabled software to identify and track early structural changes on OCT imaging that occurred following SNL treatment.Additionally, there was no detectable change in visual acuity as a result of SNL treatment within this study period. The lack of change in functional outcomes indicates that SNL treatment was not harmful during the course of this short-term investigation. However, further study is warranted to determine if this treatment results in improvement, or at least, ongoing stability of visual acuity.Wu et al. investigated changes in retinal thickness prior to the development of drusen-associated atrophy in eyes with intermediate ARMD. This study described the features of nascent geographic atrophy, which include subsidence of the OPL and INL with or without the development of a wedge-shaped band within the limits of the OPL [The natural history of drusen and associated atrophy has been previously documented.  the OPL . Additio the OPL ,14,15. ILamin et al. have demonstrated that the accuracy of the Orion® software is sustained in the presence of macular pathology, such as ARMD, and changes in retinal layers are detectable in eyes with ARMD compared to control eyes [Previous work has demonstrated errors in existing automated segmentation algorithms, necessitating the development of an effective and reproducible method for measuring retinal thickness . This isrol eyes . As suchThe limitations of this investigation include the variation between patients in time elapsed between imaging and SNL treatment. While this is difficult to standardize due to the real-world nature of this study, a cut-off of 6 months between baseline imaging and SNL treatment was selected to avoid significant increases in drusen area that have been shown to occur over longer periods of time, as well as to evaluate any early responses to the treatment ,18. SincInterpretation of automated segmentation results of very thin layers such as the OPL should be considered with caution. Small errors can lead to seemingly large changes. Additionally, when the OCT beam is not directly incident on this layer, structure such as Henle’s fibers are revealed making the layer appear thicker on one side of the fovea and thinner on the other. Small changes in pupil entry position of the OCT beam can cause this, and the resulting retinal image appears tilted.While the RPE and BM were segmented accurately based on our review, the inner segment-outer segment junction was less visible with the pathology and the segmentation could look overly smooth. In all such cases, it may make more sense to analyze a retinal complex; for instance, analyzing the ONL and the PR complex as a single thickness, in much the same way the GC-IPL is used given that the interface of the ganglion cells and their dendrites in the IPL is barely visible using unaveraged OCT.At 6 months post-SNL treatment, there were sectoral increases in retinal thickness in most layers, with the exception of the INL and ONL, where retinal thinning occurred. This study contributes to the evolving understanding of SNL treatment in ARMD, through the use of OCT imaging enhanced with artificial intelligence-enabled segmentation. This allows for longitudinal assessment of retinal changes that occur in response to SNL treatment in eyes with intermediate ARMD. Greater understanding of the changes that occur in the retina over time can contribute to the evidence for the effectiveness of SNL as a potential intervention in ARMD.S1 Dataset(XLSX)Click here for additional data file.