Definition Of Hotspots Research Articles

Defining plastic pollution hotspots

Plastic pollution in the natural environment poses a growing threat to ecosystems and human health, prompting urgent needs for monitoring, prevention and clean-up measures, and new policies. To effectively prioritize resource allocation and mitigation strategies, it is key to identify and define plastic hotspots. UNEP's draft global agreement on plastic pollution mandates prioritizing hotspots, suggesting a potential need for a defined term. Yet, the delineation of hotspots varies considerably across plastic pollution studies, and a definition is often lacking or inconsistent without a clear purpose and boundaries of the term. In this paper, we applied four common definitions of hotspot locations to plastic pollution datasets ranging from urban areas to a global scale. Our findings reveal that these hotspot definitions encompass between 0.8 % to 93.3 % of the total plastic pollution, covering <0.1 % to 50.3 % of the total locations. Given this wide range of results and the possibility of temporal inconsistency in hotspots, we emphasize the need for fit-for-purpose criteria and a unified approach to defining plastic hotspots. Therefore, we designed a step-wise framework to define hotspots by determining the purpose, units, spatial scale, temporal scale, and threshold values. Incorporating these steps in research and policymaking yields a harmonized definition of hotspots, facilitating the development of effective plastic pollution prevention and reduction measures.

Development of prediction models to identify hotspots of schistosomiasis in endemic regions to guide mass drug administration

Schistosomiasis is a neglected tropical disease affecting over 150 million people. Hotspots of Schistosoma transmission-communities where infection prevalence does not decline adequately with mass drug administration-present a key challenge in eliminating schistosomiasis. Current approaches to identify hotspots require evaluation 2-5 y after a baseline survey and subsequent mass drug administration. Here, we develop statistical models to predict hotspots at baseline prior to treatment comparing three common hotspot definitions, using epidemiologic, survey-based, and remote sensing data. In a reanalysis of randomized trials in 589 communities in five endemic countries, a regression model predicts whether Schistosoma mansoni infection prevalence will exceed the WHO threshold of 10% in year 5 ("prevalence hotspot") with 86% sensitivity, 74% specificity, and 93% negative predictive value (NPV; assuming 30% hotspot prevalence), and a regression model for Schistosoma haematobium achieves 90% sensitivity, 90% specificity, and 96% NPV. A random forest model predicts whether S. mansoni moderate and heavy infection prevalence will exceed a public health goal of 1% in year 5 ("intensity hotspot") with 92% sensitivity, 79% specificity, and 96% NPV, and a boosted trees model for S. haematobium achieves 77% sensitivity, 95% specificity, and 91% NPV. Baseline prevalence is a top predictor in all models. Prediction is less accurate in countries not represented in training data and for a third hotspot definition based on relative prevalence reduction over time ("persistent hotspot"). These models may be a tool to prioritize high-risk communities for more frequent surveillance or intervention against schistosomiasis, but prediction of hotspots remains a challenge.

Time to refine the geography of biodiversity hotspots by integrating molecular data: The Mediterranean Basin as a case study

Three decades ago, worldwide biodiversity hotspots were founded on the distributions of continental plants and vertebrates. Here, we question the timeliness of refining the geography of hotspots by basing their definition on more taxa, thanks to the molecular data available for hyper-diverse organisms such as insects, fungi and marine biota.To do so, we assess the temporal dynamic of molecular data acquisition and the geography of knowledge about lineages currently included or not into hotspot definition. Using the Mediterranean Basin hotspot as a case study, we examine the taxonomic and geographical facets of 175,828 DNA sequences distributed over 21,552 species, and 13,001 indexed biodiversity publications. We reveal a deeply fractured repartition of biodiversity research efforts within the hotspot regarding both barcoding efforts and publication activity, the northern side of the Mediterranean concentrating 84.16 % of the publications and 75.99 % of the public DNA sequences. In addition, 57.55 % of the sequences belong to lineages which were excluded from hotspots definition, with highly congruent geographical patterns among marine and continental lineages.Based on this analysis, we suggest 1) using the uneven geography of knowledge to rebalance sampling efforts towards poorly known regions within the Mediterranean hotspot, 2) handling the molecular corpus of orphan lineages to feed forthcoming multi-taxa biodiversity assessment initiatives, in order to 3) refine the geography of conservation stakes within the hotspot by identifying sub-hotspots of global diversity and 4) fusing a broad range of marine and continental taxa in global biodiversity mapping.

Automated field-in-field whole brain radiotherapy planning.

We developed and tested an automatic field-in-field (FIF) solution for whole-brain radiotherapy (WBRT) planning that creates a homogeneous dose distribution by minimizing hotspots, resulting in clinically acceptable plans. A configurable auto-planning algorithm was developed to automatically generate FIF WBRT plans independent of the treatment planning system. Configurable parameters include the definition of hotspots, target volume, maximum number of subfields, and minimum number of monitor units per field. This algorithm iteratively identifies a hotspot, creates two opposing subfields, calculates the dose, and optimizes the beam weight based on user-configured constraints of dose-volume histogram coverage and least-squared cost functions. The algorithm was retrospectively tested on 17 whole-brain patients. First, an in-house landmark-based automated beam aperture technique was used to generate the treatment fields and initial plans. Second, the FIF algorithm was employed to optimize the plans using physician-defined goals of 99.9% of the brain volume receiving 100% of the prescription dose (30 Gy in 10 fractions) and a target hotspot definition of 107% of the prescription dose. The final auto-optimized plans were assessed for clinical acceptability by an experienced radiation oncologist using a five-point scale. The FIF algorithm reduced the mean (± SD) plan hotspot percentage dose from 35.0 Gy (116.6%) ± 0.6 Gy (2.0%) to 32.6 Gy (108.8%) ± 0.4 Gy (1.2%). Also, it decreased the mean (± SD) hotspot V107% [cm3 ] from 959 ± 498 cm3 to 145 ± 224 cm3 . On average, plans were produced in 16 min without any user intervention. Furthermore, 76.5% of the auto-plans were clinically acceptable (needing no or minor stylistic edits), and all of them were clinically acceptable after minor clinically necessary edits. This algorithm successfully produced high-quality WBRT plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow.

Definition of hot-spots to reduce the nitrogen losses from agricultural land to groundwater in Slovakia

Abstract Agriculture is a significant contributor to nitrate pollution of groundwater which in many cases serves as a source of drinking water. Therefore, targeted reduction of nitrogen leaching losses is fully justified to address this issue. The aim of the study was to define the areas of utilized agricultural land (UAL) in Slovakia, where a nitrogen surplus needs to be reduced. Using the average values of leachable nitrogen in the period 2015-2018 and the long-term amount of percolated water, the nitrate concentration in leachate was calculated. To ensure that agricultural activities will contribute to the gradual reduction of nitrate concentration in groundwater, the nitrate concentration in leachate of 40 mg L-1 was chosen as the target value. This concentration was exceeded at 11.7% of the UAL area. The average indicative amount of nitrogen in industrial fertilizers that needs to be reduced to achieve a stricter nitrate concentration in the leachate in these hot-spots is 16 kg ha-1 with the proviso that in two districts this value exceeds 30 kg ha-1.

Digital mapping of soil organic carbon hotspots in nature conservation areas in the region of Flanders, Belgium

Climate-smart land management offers an opportunity to reconcile biodiversity goals with climate change mitigation. An important first step towards this, is an accurate estimate of the soil organic carbon (SOC) stock and the identification of locations with a large share in storage. General-purpose, regional assessments are not always useful to identify such hotspots, since they tend to even out extremes of SOC stocks in the landscape. The objectives of this study were to (i) develop and validate a digital soil mapping procedure to assess the SOC stock of nature conservation areas in Flanders, N. Belgium, and (ii) identify soil carbon hotspots in these areas. To this end, different soil inventories, encompassing 864 soil profiles, were combined in a digital soil mapping approach.The resulting boosted regression trees model was able to explain 75% of the variability in the training dataset (R2 = 0.75, RMSE = 9.83 kg m−2) and 59% after 10-fold cross-validation (R2 = 0.59, RMSE = 12.38 kg m−2). Reference Soil Group according to World Reference Base (WRB), vegetation type, highest groundwater level and clay fraction were identified as key predictors of the SOC stock in the upper 100 cm. The total SOC stock stored under 86,828 ha of nature conservation area was estimated to be 21,565 kton OC, or 24.8 kg m−2 on average. According to the presented hotspot definition, taking the SOC stock of the surrounding landscape into account, 10.5–11.3% of the nature conservation areas was classified as a hotspot. However, validation of the results in five study sites showed the local variability in predictive performance, with relatively accurate predictions in the two data-rich sites in the eastern part of the territory and poor predictions in the three remaining sites. Local, site-specific variations, together with uncertainties in the input data and unmapped elements can hamper the accurate estimation of SOC stocks and hotspots.

Community Response to Noise from Hot-Spots at a Major Road in Quito (Ecuador) and Its Application for Identification and Ranking These Areas.

Environmental legislation in Ecuador is advancing with the legitimate aspiration of providing citizens with new standards of quality and environmental health. In the context of environmental noise, these legislative advances are based on the experience accumulated in other countries, which is an advantage that must be managed with caution by incorporating local factors into noise management procedures. This study advances two lines of work. The first is to survey the population about their attitude towards noise from a major road to try to detect local factors in the annoyance and sleep disturbances. The second uses this information to compare noise indicators for the detection and ranking of hot-spots from major roads. The interviewees exhibited a high level of annoyance and sleep disturbance due to noise compared with the results of other studies. Results show that there are small differences in the definition of hot-spots when using WHO’s dose–response curves for Lden ≥ 68 dB for and for Lnight ≥ 58 dB, in comparison with the curves generated in this study (CS). Regarding the application of both dose–response curves (WHO vs. CS) to the estimation of the population at risk of the harmful effect of nighttime traffic noise (HSD), small oscillations are also observed even when Lnight ≥ 58 dB and Lnoche ≥ 60 dB are used.

Effectiveness of spatially targeted interventions for control of HIV, tuberculosis, leprosy and malaria: a systematic review

BackgroundAs infectious diseases approach global elimination targets, spatial targeting is increasingly important to identify community hotspots of transmission and effectively target interventions. We aimed to synthesise relevant evidence to define...

Randomness in Cancer Breakpoint Prediction

Cancer genomes are susceptible to multiple rearrangements by deleting, inserting, and translocating genomic regions. Recently, the problem of finding determinants of breakpoint formations was approached with machine learning methods; however, unlike cancer point mutations, breakpoint prediction appeared to be a more difficult task, and various machine learning models did not achieve high prediction power often slightly exceeding the threshold of random guessing. This raised the question of whether the breakpoints are random noise in cancer mutagenesis or there exist determinants in structural mutagenesis. In the present study, we investigated randomness in cancer breakpoint genome distributions through the power of machine learning models to predict breakpoint hot spots. We divided all cancer types into three groups by degree of randomness in their breakpoint formation. We tested different density thresholds and explored the bias in hot spot definition. We also compared prediction of hot spots versus individual breakpoints. We found that hot spots are considerably better predicted than individual breakpoints; however, some individual breakpoints can also be predicted with a satisfactory power, and thus, it is not proper to filter them from analyses. We demonstrated that positive-unlabeled learning can provide insights into insufficiency of cancer data sets, which are not always reflected by data set sizes. Overall, the present results support the view that cancer breakpoint landscape can be represented by predictable dense breakpoint regions and scattered individual breakpoints, which are not all random noise, but some are generated by detectable mechanism.

Estimating Electric Motor Temperatures With Deep Residual Machine Learning

Most traction drive applications lack accurate temperature monitoring capabilities, ensuring safe operation through expensive oversized motor designs. Classic thermal modeling requires expertise in model parameter choice, which is affected by motor geometry, cooling dynamics, and hot spot definition. Moreover, their major advantage over data-driven approaches, which is physical interpretability, tends to deteriorate as soon as their degrees of freedom are curtailed in order to meet the real-time requirement. In this article, deep recurrent and convolutional neural networks (NNs) with residual connections are empirically evaluated for their feasibility on predicting latent high-dynamic temperatures continuously inside permanent magnet synchronous motors. Here, the temperature profile in the stator teeth, winding, and yoke as well as the rotor's permanent magnets are estimated while their ground truth is available as test bench data. With an automated hyperparameter search through Bayesian optimization and a manual merge of target estimators into a multihead architecture, lean models are presented that exhibit a strong estimation performance at minimal model sizes. It has been found that the mean squared error and maximum absolute deviation performances of both, deep recurrent and convolutional NNs with residual connections, meet those of classic thermodynamics-based approaches, without requiring domain expertise nor specific drive train specifications for their topological design. Finally, learning curves for varying training set sizes and interpretations of model estimates through expected gradients are presented.

Spatially congruent sites of importance for global shark and ray biodiversity.

Many important areas identified for conservation priorities focus on areas of high species richness, however, it is unclear whether these areas change depending on what aspect of richness is considered (e.g. evolutionary distinctiveness, endemicity, or threatened species). Furthermore, little is known of the extent of spatial congruency between biodiversity measures in the marine realm. Here, we used the distribution maps of all known marine sharks, rays, and chimaeras (class Chondrichthyes) to examine the extent of spatial congruency across the hotspots of three measures of species richness: total number of species, evolutionarily distinct species, and endemic species. We assessed the spatial congruency between hotspots considering all species, as well as on the subset of the threatened species only. We consider three definitions of hotspot (2.5%, 5%, and 10% of cells with the highest numbers of species) and three levels of spatial resolution (1°, 4°, and 8° grid cells). Overall, we found low congruency among all three measures of species richness, with the threatened species comprising a smaller subset of the overall species patterns irrespective of hotspot definition. Areas of congruency at 1° and 5% richest cells contain over half (64%) of all sharks and rays and occurred off the coasts of: (1) Northern Mexico Gulf of California, (2) USA Gulf of Mexico, (3) Ecuador, (4) Uruguay and southern Brazil, (5) South Africa, southern Mozambique, and southern Namibia, (6) Japan, Taiwan, and parts of southern China, and (7) eastern and western Australia. Coarsening resolution increases congruency two-fold for all species but remains relatively low for threatened measures, and geographic locations of congruent areas also change. Finally, for pairwise comparisons of biodiversity measures, evolutionarily distinct species richness had the highest overlap with total species richness regardless of resolution or definition of hotspot. We suggest that focusing conservation attention solely on areas of high total species richness will not necessarily contribute efforts towards species that are most at risk, nor will it protect other important dimensions of species richness.

Prognostic potential of automated Ki67 evaluation in breast cancer: different hot spot definitions versus true global score

PurposeThe proliferation-associated biomarker Ki67 has potential utility in breast cancer, including aiding decisions based on prognosis, but has unacceptable inter- and intralaboratory variability. The aim of this study was to compare the prognostic potential for Ki67 hot spot scoring and global scoring using different digital image analysis (DIA) platforms.MethodsAn ER+/HER2− breast cancer cohort (n = 139) with whole slide images of sequential sections stained for hematoxylin–eosin, pancytokeratin and Ki67, was analyzed using two DIA platforms. For hot spot analysis virtual dual staining was applied, aligning pancytokeratin and Ki67 images and 22 hot spot algorithms with different features were designed. For global Ki67 scoring an automated QuPath algorithm was applied on Ki67-stained whole slide images. Clinicopathological data included overall survival (OS) and recurrence-free survival (RFS) along with PAM50 molecular subtypes.ResultsWe show significant variations in Ki67 hot spot scoring depending on number of included tumor cells, hot spot size, shape and location. The higher the number of scored tumor cells, the higher the reproducibility of Ki67 proliferation values. Hot spot scoring had greater prognostic potential for RFS in high versus low Ki67 subgroups (hazard ratio (HR) 6.88, CI 2.07–22.87, p = 0.002), compared to global scoring (HR 3.13, CI 1.41–6.96, p = 0.005). Regarding OS, global scoring (HR 7.46, CI 2.46–22.58, p < 0.001) was slightly better than hot spot scoring (HR 6.93, CI 1.61–29.91, p = 0.009). In adjusted multivariate analysis, only global scoring was an independent prognostic marker for both RFS and OS. In addition, global Ki67-based surrogate subtypes reached higher concordance with PAM50 molecular subtype for luminal A and B tumors (66.3% concordance rate, κ = 0.345), than using hot spot scoring (55.8% concordance rate, κ = 0.250).ConclusionsWe conclude that the automated global Ki67 scoring is feasible and shows clinical validity, which, however, needs to be confirmed in a larger cohort before clinical implementation.

Glioma grading by dynamic susceptibility contrast perfusion and 11C-methionine positron emission tomography using different regions of interest.

The use of dynamic susceptibility contrast (DSC) perfusion and 11C-methionine positron emission tomography (MET-PET) for glioma grading is currently not standardized. The purpose of this study was to identify regions of interest (ROIs) that enable the best performance and clinical applicability in both methods, as well as to evaluate the complementarity of DSC perfusion and MET-PET in spatial hotspot definition. In 41 patient PET/MRI datasets, different ROIs were drawn: in T2-hyperintense tumour, in T2-hyperintense tumour and adjacent oedema and in tumour areas with contrast enhancement, altered perfusion or pathological radiotracer uptake. The performance of DSC perfusion and MET-PET using the different ROIs to distinguish high- and low-grade gliomas was assessed. The spatial overlap of hotspots identified by DSC perfusion and MET-PET was assessed visually. ROIs in T2 fluid attenuated inversion recovery (FLAIR) sequence-hyperintense tumour revealed the most significant differences between high- and low-grade gliomas and reached the highest diagnostic performance in both DSC perfusion (p = 0.046; area under the curve = 0.74) and MET-PET (p = 0.007; area under the curve = 0.80). The combination of methods yielded an area under the curve of 0.80. Hotspots were completely overlapped in one half of the patients, partially overlapped in one third of the patients and present in only one method in approximately 20% of the patients. For multi-parametric examinations with DSC perfusion and MET-PET, we recommend an ROI definition based on T2-hyperintense tumour. DSC perfusion and MET-PET contain complementary information concerning the spatial hotspot definition.

Economic impact of organic agriculture hotspots in the United States

AbstractIn this paper, we assess whether or not organic agriculture has a positive impact on local economies. We first identify organic agriculture hotspots (clusters of counties with positively correlated high numbers of organic operations) using spatial statistics. Then, we estimate a treatment effects model that classifies a county's membership in an organic hotspot as an endogenous treatment variable. By modeling what a hotspot county's economic indicators would have been had the county not been part of a hotspot, this model captures the effect of being in a hotspot on a county's economic indicators. We perform the same analysis for general agricultural farm hotspots to confirm that the benefits associated with organic production hotspots are, in fact, due to the organic component. Our results show that organic hotspot membership leads to a lower county-level poverty rate and a higher median household income. A similar result is not found when investigating the impact of general agriculture hotspots. On the other hand, our result is robust to alternative hotspot definitions based on type of organic operations to alternative methods of estimating average treatment effects on the treated. These results provide strong motivation for considering hotspots of organic handling operations, which refers to middlemen such as processors, wholesalers and brokers, and hotspots of organic production to be local economic development tools, and may be of interest to policymakers whose objective is to promote rural development. Our results may incentivize policymakers to specifically focus on organic development, rather than the more general development of agriculture, as a means to promote economic growth in rural areas, and may further point them in the direction of not only encouraging the presence of organic operations, but of fostering the development of clusters or hotspots of these operations.

Modelling the spatial baseline for amphibian conservation in West Africa

To answer questions such as whether the existing network of protected areas is sufficient, conservation needs data covering complete taxonomic groups and large geographic areas. However, most distributional data sets are either coarse, patchy and/or based solely on expert opinion which is often hard to verify. In addition, not all regions are equally well studied. For example sub-Saharan Africa remains comparatively under-sampled for most taxa, especially Central and Western Africa. However, these regions contain many threatened species, including a high diversity of highly threatened vertebrates - amphibians. To fill this knowledge gap, we extrapolated species occurrence records (n = 15,944) on a 30 arc-seconds grid for most known West African amphibian taxa (92%), using environmental niche modelling and employing relevant environmental parameters (climate, vegetation, elevation & distance to rivers).We provide, for the first time, a fine scale distribution map of amphibian alpha diversity for the entire West African region. Already known centres of high biodiversity were confirmed (e.g. south-western Ghana and south-eastern Côte d’Ivoire) and new ones were identified (e.g. northern Liberia and the borders of Liberia with Guinea and Sierra Leone). Diversity analyses focusing on unique amphibians, i.e. threatened, endemic and evolutionary distinct species', revealed that areas of high diversity also contained many high conservation-priority species. Herewith, we offer a comprehensive baseline for identifying those areas which are important for amphibian conservation for one of the most periled regions on the continent. Those areas of high diversity were only partly in accordance with previous analyses such as the hotspot definitions, the ecoregion analyses, or analyses of other taxa, highlighting the added new value of our approach. The most outstanding areas of amphibian diversity were only partly covered by the existing network of protected areas. Thus there is an urgent need to devise a regional conservation concept to protect West African amphibians from extinction.

Thermal symmetry of healthy feet: a precursor to a thermal study of diabetic feet prior to skin breakdown

Early identification of areas of inflammation may aid prevention of diabetic foot ulcers. A new bespoke thermal camera system has been developed to thermally image feet at risk. Hotspots (areas at least 2.2 °C hotter than the contralateral site) may indicate areas of inflammation prior to any apparent visual signs. This article describes the thermal pattern and symmetry of 103 healthy pairs of feet. 68% of participants were thermally symmetric at the 33 foot sites measured. 32% of participants had at least one hotspot, but hotspots overall only accounted for 5% of the measurements made. Refinements to the definition of hotspots are proposed when considering feet at risk of ulceration.

Dynamic programming-based hot spot identification approach for pedestrian crashes

Network screening techniques are widely used by state agencies to identify locations with high collision concentration, also referred to as hot spots. However, most of the research in this regard has focused on identifying highway segments that are of concern to automobile collisions. In comparison, pedestrian hot spot detection has typically focused on analyzing pedestrian crashes in specific locations, such as at/near intersections, mid-blocks, and/or other crossings, as opposed to long stretches of roadway. In this context, the efficiency of the some of the widely used network screening methods has not been tested. Hence, in order to address this issue, a dynamic programming-based hot spot identification approach is proposed which provides efficient hot spot definitions for pedestrian crashes. The proposed approach is compared with the sliding window method and an intersection buffer-based approach. The results reveal that the dynamic programming method generates more hot spots with a higher number of crashes, while providing small hot spot segment lengths. In comparison, the sliding window method is shown to suffer from shortcomings due to a first-come-first-serve approach vis-à-vis hot spot identification and a fixed hot spot window length assumption.

Economic Impact of Organic Agriculture Hotspots in the United States

After identifying organic agriculture hotspots using spatial statistics, we estimate a treatment effects model that classifies a county’s membership in an organic hotspot as an endogenous treatment variable. We find that organic hotspot membership leads to a lower county-level poverty rate and a higher median household income. A similar result is not found when investigating the impact of general agriculture hotspots. On the other hand, our result is robust to alternative hotspot definitions based on type of organic operations to alternative methods of estimating average treatment effects on the treated.

Hotspot identification in the clothing industry using social life cycle assessment—opportunities and challenges of input-output modelling

A cradle-to-gate, input/output-based social life cycle assessment (SLCA) was conducted using the Swedish clothing consumption as a case study. The aim was to investigate the influence of the cut-off rule and the definition of "hotspots" in social hotspot assessment. A second aim was to identify social hotspots of Swedish clothing on a national level. The case study was based on the SLCA methodology provided in the Guidelines for Social Life Cycle Assessment of Products (BenoIt and Mazijn 2009). An input/output model was used to define the product system from cradle to gate. The negative social hotspots were evaluated for a set of social indicators that were selected by consumers. The impact assessment was conducted on a sector and country level by using the Social Hotspots Database. The identified sectors of the economy with high and very high levels of risk were listed for each social indicator. The results pinpointed some hotspots throughout the supply chain for Swedish clothing consumption. Some unexpected sectors such as commerce and business services in Bangladesh were identified as important hotspots as well as main sectors in the production phase such as plant fibres, textiles and garments that would be expected also on the bases of a traditional process analysis. A sensitivity analysis on different cut-off values showed the extent to which the choice of cut-off rule can directly affect the results via influence over the number of country-specific sectors (CSSs) in the product system. The influence of the hotspot definition was investigated by evaluating the working hour intensity for low- and medium-risk levels for three different indicators. The results show that for child labour, 92 % of the share of working hours was associated with low- and medium-risk levels. Therefore, the evaluation of risk levels other than high and very high can provide a more complete picture of the hotspots. The application of input/output-based SLCA on the clothing production supply chain provided a more complete picture of the social hotspots than with traditional process-based SLCA. Some unexpected sectors related to commerce and business appeared as social hotspots in the clothing industry. The study explored some important parameters in applying an input/output-based SLCA. The results show that the cut-off values and definition of hotspots in relation to risk levels can directly influence the results.

Automated TMS hotspot-hunting using a closed loop threshold-based algorithm

BackgroundAlthough neuronavigation is increasingly used for optimizing coil positioning, the inter-session reliability of hotspot location remains unsatisfactory, probably due to the variability of motor evoked potentials (MEPs) and residual investigator bias. PurposeTo increase the reliability and accuracy of hotspot location we introduce a novel automated hotspot-hunting procedure (AHH). MethodsAHH is based on resting motor thresholds (RMTs) instead of MEP amplitudes. By combining robotic coil positioning with a closed loop target search algorithm AHH runs independently from the investigator. AHH first identifies all targets with an RMT below a defined intensity of stimulator output (MEP-positive) and then locates the motor hotspot of a target muscle by measuring RMTs at all identified MEP-positive targets. Results were compared to robotic MEP amplitude TMS mapping (MAM) using a 7×7 predefined target grid and suprathreshold intensities and manual hotspot search (MHS). Sequence of stimulation was randomized from pulse to pulse in AHH and MAM. Each procedure was tested in 8 subjects. ResultsInter-session CoG shift was significantly reduced with AHH (1.4mm (SEM: 0.4)) as compared to MAM (7.0mm (SEM: 1.8)) (p=0.018) and MHS (9.6mm (SEM: 2.2)) (p=0.007). No statistical difference was observed between MAM and MHS. RMTs were reliable between sessions. ConclusionOur method represents the first fully automated, i.e. investigator-independent, TMS hotspot-hunting procedure. Measuring RMTs instead of MEP amplitudes leads to significantly increased accuracy and reliability of CoG locations. Moreover, by assessing thresholds AHH is the first procedure to fulfill the original hotspot definition.