Active transportation  has been associated with decreased morbidity and mortality; however, low-cost and scalable intervention programs are lacking.The goal of the research was to determine the effectiveness of a 3-month behavior change program delivered via a mobile phone app to promote AT (TravelVu Plus) on time spent in moderate-to-vigorous physical activity (MVPA).For this 2-arm parallel randomized controlled trial, we recruited a population-based sample of 254 adults from Stockholm County who were aged 20 to 65 years and had access to a smartphone. On completion of 1-week baseline measures, the 254 participants were randomized to either the control or intervention group (1:1 ratio). Both groups had access to the standard TravelVu app (Trivector AB) for monitoring their AT for 6 months. The intervention group also received a 3-month behavior change program to promote AT (TravelVu Plus app). Assessors of outcomes were blinded to group allocation. Outcomes were objectively measured MVPA at 3 (primary) and 6 months. Secondary outcomes were AT, attitudes toward AT, and health-related quality of life at 3 and 6 months.P=.29); however, at 6 months the intervention group had a greater improvement in MVPA than the controls . A Bayesian analyses showed that there was a 98% probability that the intervention had any effect at 6 months, and a 63% probability that this effect was >5 minute MVPA per day.No effect on MVPA was observed after 3 months (No effect on MVPA immediately after the intervention period (at 3 months) was observed; however, there was a delayed effect on MVPA (6 minutes per day) at 6 months, which corresponds to approximately 30% of the weekly MVPA recommendation. Our findings suggest that a behavior change program promoting AT delivered via an app may have a relevant effect on PA.ClinicalTrials.gov NCT03086837; https://clinicaltrials.gov/ct2/show/NCT03086837RR2-10.1186/s12889-018-5658-4  Physical inactivity  is a major risk factor for noncommunicable diseases, including cardiovascular disease, type 2 diabetes, and premature death  and contMobile health  interventions are increasingly being used to promote healthy lifestyle behaviors ,14 and iThis paper reports the results of the Smart City Active Mobile Phone Intervention (SCAMPI) trial. The aim of this trial was to determine the effectiveness of a 3-month mobile phone–based behavior change program promoting AT on moderate-to-vigorous physical activity (MVPA) in Swedish adults . The priA 2-arm parallel design RCT was conducted between September 2017 and September 2018. The study was approved by the regional research ethics committee in Stockholm  and registered at ClinicalTrials.gov [NCT03086837]. Details on the development of the app and design of the SCAMPI trial are published elsewhere [Participants were recruited from a random sample of 4995 adults provided by Statistics Sweden. Two waves of invitation letters were sent out (September 2017 [n=2000] and January 2018 [n=2995]) in order to capture different seasons and weather. Participants were eligible if they were aged 20 to 65 years, understood written Swedish, lived in the county of Stockholm, and had access to a smartphone compatible with the app. Exclusion criteria was not being able to perform MVPA. People who wanted to participate in the study signed up at the study website or sent an email or a letter to the research group. After providing informed consent , a web-based questionnaire was administrated to collect self-reported sociodemographic variables , height, weight, attitude toward AT, neighborhood walkability, and health-related quality of life. Thereafter, baseline measures of PA (using the wGT3X-BT accelerometer [ActiGraph LLC]) as well as AT  were assessed simultaneously during 7 consecutive days. The details of the TravelVu app have been provided in the study protocol [A total of 250 participants (125 per group) was estimated to provide 80% power (α=.05) to detect a 10-minute difference in MVPA per day assuming a standard deviation of 25 minutes  and lossAfter baseline measures, participants in the control condition were encouraged to continue with their normal travel routines during the 6-month study period. During this time they were able to continue to monitor their daily travels using the standard TravelVu app (without a behavior change program) if they chose to. At 3 and 6 months, participants in the control group (as well as in the intervention group) were asked to monitor their travel behavior in the app during the same seven days as they wore the accelerometer for follow-up outcome assessment.In addition to the standard TravelVu app, intervention participants received a 3-month behavior change program (TravelVu Plus), aimed at increasing PA through AT. The program was delivered as extra features to the standard TravelVU app. The development of the TravelVu Plus program and its features are described in more detail in our study protocol . BrieflyPA was measured objectively using the wGT3X-BT triaxial accelerometer (ActiGraph LLC), which was worn on the hip over seven consecutive 24-hour periods. Raw acceleration data (at 90 Hz) were uploaded and processed using the ActiLife software version 6.13.3 (ActiGraph LLC) into filtered sum of vector magnitudes (VM). Nonwear time was detected and excluded using a Troiano algorithm . A day wTo complement the accelerometer data, we also evaluated the number of minutes spent cycling and walking for transport assessed using the TravelVu app during seven consecutive 24-hour periods as a secondary outcome. Days marked as valid were used to assess mode and duration (minutes per day) of AT . Days with unreasonably high levels of AT for the Stockholm area (>4 hours) were not included. Attitudes toward AT were assessed using the psychosocial items in section B of the validated Transport and Physical Activity Questionnaire . Mean vaPerceived neighborhood walkability was assessed at baseline using the Neighborhood Environment Walkability Scale questionnaire ,30. To cAll statistical analyses were conducted in accordance with the study protocol  and follThree-way interaction analyses were performed to assess if the following characteristics at baseline moderated the intervention effect on the primary outcome: sex, age, educational attainment, BMI, foreign background, season of randomization, perceived walkability index, attitude toward AT, or general health. We also examined whether the effect on MVPA was associated with engagement with the app .Due to relatively few missing values in the outcome measures  and since we could not rule out the possibility that data were missing at random, we followed the recommendations to report completers only as the primary analyses -33. A seP values and confidence intervals are not well defined in linear mixed models [We also conducted the following analyses that were added to the statistical analysis plan before data analysis but not reported in the protocol . First, d models  and shoud models . UniformStatistical analyses were performed with a significance level of .05 using R version 3.6.1, and Bayesian inference was done using the probabilistic programming language Stan  and SPSS Statistics version 24 (IBM Corporation).P=.29); however, at 6 months, the intervention group had a greater improvement in MVPA than the control group . As shown in P=.007). Sensitivity analysis (imputed data) revealed comparable results (P=.01), indicating the intervention was more effective in men than in women. There was no interaction effect for any of the other investigated covariates . AT (minutes per day) was statistically significantly associated with accelerometer MVPA  at baseline, and this association remained similar at the two follow-ups. Pre-post differences of MVPA did not differ by app engagement . Results showed no statistically significant difference between groups on the primary outcome at 3 months ( results . There wObjective measures of engagement with the self-monitoring feature of AT in the standard TravelVu app for the two groups are shown in P=.01) during the first 3 months. During months 3 to 6 after baseline, the number of days were comparable .In the control group (TravelVu), as seen in This is the first RCT to determine the effect of a stand-alone behavior change program promoting AT delivered through a mobile phone app  on MVPA among adults. Access to the TravelVu Plus app did not lead to any statistically significant difference in MVPA at 3 months (primary outcome); however, relevant differences in MVPA were observed at 6 months. This difference was driven predominantly by an increase in MPA, which is reasonable since the intervention targeted cycling and walking. Bayesian analyses provided further support for an effect of the intervention.To the best of our knowledge, only one previous study evaluated the effectiveness of an app designed to solely promote AT . In thatIt is also relevant to compare our results with other app-based PA interventions. Systematic reviews and meta-analyses have concluded that mobile phone–based interventions may have small to moderate effect sizes when it comes to improving PA in free-living adults -44. A reTo the best of our knowledge, this is the first trial that evaluated objectively assessed AT via GPS monitoring instead of using self-report as a secondary outcome. This approach relied on participants’ approval of trips taken and making corrections to these as necessary . Notwithstanding, participants in the intervention group approved 71% of their travel trips (60 out of 84 days), indicating high use of this feature to self-monitor their AT. Furthermore, although the control group only had access to the standard version of the app, they used it for an average of 39 days, which supports that they also appreciated monitoring AT. Still, given the required level of interaction, only 65% of participants provided complete data on AT for baseline and the two follow-up assessments. This loss of data likely contributed to not being able to detect an effect on AT, and thus findings for AT as outcome should be interpreted with caution. Our original preference was to passively record AT by a mobile app for the assessment period only ; however, this was not technically feasible at that time. Future studies should consider how to address these issues to optimize objectively assessed AT.Strengths of the SCAMPI trial include the RCT design, as well as the objectively measured primary outcome (MVPA). Previous studies on AT and PA are mostly observational, and systematic reviews have called for well-designed interventions targeting AT ,11,46. TThe main limitation of this study is that the primary outcome (MVPA) was assessed by accelerometry which does not capture cycling behavior; however, this choice was carefully considered when designing the study. Notwithstanding its limitations, an objective measure of PA was considered preferable to self-report as there is no well-established and objective measure to assess AT . We also complemented the accelerometer data with GPS data to assess AT  as the secondary outcome. The GPS data indicated that in our study approximately 90% of AT was walking, which was captured with accelerometry. Thus, it is reasonable to conclude that most likely our effects on MVPA are slightly underestimated due to the fact that the accelerometer could not capture cycling. Also, we cannot exclude that some AT could be light PA instead of MVPA; however, no difference was found when contrasting light PA between groups. Another important limitation to consider is seasonality. Sweden has a relatively cold climate with a winter period between December and February; however, the autumn and spring months can also be quite cold and snowy, which does not facilitate AT. As described above, we recruited in two waves (early autumn and early spring) in order to spread the intervention period out throughout different seasons, and we did not find any evidence that the effectiveness of the intervention differed upon which season participants entered the trial. Finally, other limitations of this trial include that automated feedback messages were used instead of personalized, that the review and correction of the automatically captured AT was considered time-consuming by some participants, and that the app consumed quite a lot of battery power .To minimize the risk for selection bias, recruitment was population-based with a sample randomly drawn by Statistics Sweden. Nevertheless, as is common in research, the willingness to participate was higher among women and well-educated people. However, the effect was not moderated by baseline educational attainment. Furthermore, both groups had on average 60 minutes MVPA at baseline. No comparable population-based data regarding MVPA exists in Sweden, and comparisons between studies are difficult due to different protocols and accelerometer cut points; however, previous studies in Swedish adult populations have shown approximately 35 minutes of MVPA per day -49. ThusThe SCAMPI trial provides several important findings and insights for future interventions targeting AT. First, objective measures showed that the TravelVu Plus app was used frequently for 3 months, indicating its potential for promoting AT. This finding was supported by the in-depth interviews with a subsample (reported separately). Furthermore, this qualitative data suggested the need for improvements such as personalized messages and an improved function for registrations, preferably automatized if possible, for correction of travel trips in the app. These improvements may enhance the effectiveness of the app. Second, we were able to observe a 6-minute difference in MVPA in the intervention group compared with the control group at 6 months. The observed effect is modest; however, considering that 6 minutes per day adds up to 42 minutes per week which corresponds to nearly 30% of the recommendation (150 minutes MVPA per week)  and alsoWe observed no effect on MVPA immediately after the intervention period (at 3 months); however, there was a delayed effect on MVPA (6 minutes per day) at 6 months, which corresponds to almost 30% of the weekly MVPA recommendation. Our findings coupled with the high engagement in the app suggest that a behavior change program promoting AT delivered through an app may have a relevant effect on PA, motivating further research on mHealth AT interventions.


On June 16, 2020, a recreational ice hockey game was played at an ice rink in the Tampa Bay, Florida, metropolitan area. Teams A and B, each consisting of 11 players , included men aged 19–53 years. During the 5 days after the game, 15 persons (14 of the 22 players and a rink staff member) experienced signs and symptoms compatible with coronavirus disease 2019 (COVID-19)https://www.quidel.com/immunoassays/coronavirus). An investigation by the Florida Department of Health revealed that eight of 10 team A players (excluding the index patient), five of 11 players from team B, and one rink staff member experienced COVID-19 signs and symptoms during June 18–21  who experienced fever, cough, sore throat, and a headache beginning on June 17, the day after he had participated in an evening game; 2 days later, a nasal specimen was obtained, which tested positive for SARS-CoV-2 by Sofia SARS Antigen Fluorescent Immunoassay ; some players do not wear face protection. Cloth face masks for disease control were not used in the locker rooms or during the game. A standard ice rink in the United States measures 200 feet (61 meters) by 85 feet (26 meters). Boards and plexiglass, extending upward to approximately 10 feet (3 meters), surround the ice surface creating a physically segregated playing area. In addition to the 60-minute game time on the ice, during which players frequently came within 6 feet of one another, each team used a separate locker room, typically for 20 minutes before and after the game. Players from the teams did not have other common exposures in the week before the game. The median incubation period for SARS-CoV-2 is 4–5 days from exposure to symptom onset and ranges from 2–14 days.6). The higher proportion of infected players on the index patient’s team might result from additional exposures to the index patient in the locker room and on the player bench, where players sit close to one another.The ice rink provides a venue that is likely well suited to COVID-19 transmission as an indoor environment where deep breathing occurs, and persons are in close proximity to one another. An Italian study estimating the rate of SARS-CoV-2 emission by infectious persons based on viral load in the mouth showed that during heavy exercise, a high viral emission rate can be reached during oral breathing (7). This game involved a relatively limited number of players and only one spectator, who remained symptom-free and was not tested (the limited number of spectators was not related to rink policy); however, hockey games can include up to 20 players on each of the two teams and many spectators in the arena.A limitation of this investigation was that not all players from the game sought testing, and asymptomatic infections were possibly not identified. The indoor space and close contact between players during a hockey game increase infection risk for players and create potential for a superspreader event, especially with ongoing community COVID-19 transmission. Superspreader events, in which one infectious person infects many others, can lead to explosive growth at the beginning of an outbreak and facilitate sustained transmission later in an outbreak (The high proportion of infections that occurred in this outbreak provides evidence for SARS-CoV-2 transmission during an indoor sporting activity where intense physical activity is occurring. In response, Florida Department of Health staff members provided isolation and quarantine recommendations to the persons in the rink during the game and advised ice rink management on COVID-19 risk and disease control.


While extragonadal seminomas resulting in spinal cord compression are rarely reported in the literature, most have been treated with surgical decompression followed by radiation therapy. In this report, we present the unique and interesting case of a 38-year-old man who initially presented as an outpatient with a chief complaint of axial neck pain and lateral thoracic wall pain. After an extensive malignancy workup, he was diagnosed with a primary cervical spine seminoma and was treated with a C6–T1 laminectomy with posterior spinal instrumentation from C5 to T2. He has since undergone chemotherapy with cisplatin, vinblastine, and bleomycin, and at 24-month follow-up, he remains asymptomatic with no signs of recurrent disease. This is most commonly caused by metastatic disease from a primary lesion including tumors of the prostate, lung, and breast. Primary germ cell tumors of the central nervous system are uncommon, typically intramedullary, and affect the pineal region. Although extremely rare, there have been several reports of primary germinomas of the spine and therefore they should be considered in the differential diagnosis of a cervical spinal mass in a young patient.Our patient is a 38-year-old man with no significant medical history who initially presented to the outpatient clinic with a 1-month history of axial neck pain and right lateral thoracic pain. The pain was localized to the right 11th intercostal space and was exacerbated by inspiration. He reported no trauma or prior inciting event. On physical examination, he had tenderness to palpation about his posterior neck. Motor and sensory exams were within normal limits with no obvious neurologic deficit. Initial plain AP and lateral radiographs of the cervical spine were normal (Figure MRI of the cervical spine was obtained and demonstrated a nongeographic, destructive lesion in the posterior elements of the cervical spine arising from the spinous process of C7 and evidence of spinal cord compression Figure . CT scanA C6–T1 laminectomy was performed to gain access to the mass. The tumor was identified and wide en bloc resection of the mass was performed. The specimen consisted of the lesion surrounded by healthy appearing tissue Figure . Frozen 5Seminomas represent 50% of all germ cell tumors and most commonly affect white men between the ages of 20 to 40. There are approximately 4,000 new cases per year in the United States, representing a small fraction of new oncologic diagnoses. The prevalence of these tumors is greater in populations with a history of radiation exposure. Although classically found in the testes, extragonadal lesions have been described. Extragonadal seminomas represent 2% to 5% of all germ cell tumors and primarily arise from structures within the mediastinum, retroperitoneum, and pineal gland. Within the mediastinum, primary extragonadal seminomas have been found to arise from cardiovascular and pulmonary structures.6 Most commonly, seminomas metastasize via the lymphatic system to the viscera such as the lungs. Although rare, both metastatic seminomas and primary extragonadal seminomas can involve the spine and result in spinal cord compression.7Patients with primary testicular seminomas typically present with a painless testicular mass. At the time of diagnosis, 80% of these patients have disease limited to the testes without evidence of metastasis. The treatment of primary testicular seminomas includes resection and radiation therapy. With orchiectomy and radiation therapy, patients can expect cure rates up to 99% without recurrence.7 reported 2 cases of thoracic spinal metastases. The first describes a 33-year-old man who had previously undergone right inguinal orchiectomy and was under surveillance for recurrence. The patient developed metastases to the spine and suffered a pathologic T12 burst fracture requiring decompression and fusion from T10 to L2. The second case describes a 44-year-old man who presented with back pain and lower extremity weakness. He was found to have metastatic lesions to T4 and T5 with spinal cord compression. After left inguinal orchidectomy, combination cisplatin, etoposide, and bleomycin chemotherapy, external beam radiation therapy, his motor function and sensation improved. He did not require surgical decompression. Ng et al.8 reported a third case of spinal cord compression due to recurrent metastatic testicular seminoma. The authors described a case of a 38-year-old man who presented 14 months' status post right inguinal orchiectomy with back pain, lower extremity weakness, and acute urinary retention. He underwent posterior decompression from T7 to T9 and posterior instrumentation from T6 to T11. He regained full neurological function postoperatively. Chiba et al9 also reported a case of a patient who developed gait disturbances secondary to metastatic seminoma 6 years after radical orchiectomy. The patient was found to have a new T8 lesion on routine surveillance and experienced full recovery of his symptoms after surgical decompression and fusion.Four cases of spinal cord compression due to metastasis from a primary testicular seminoma have been previously reported in the literature. Yee et al7 In a case series of 48 patients with primary extragonadal germ cell tumors, 27 arose from the central nervous system, 16 from the mediastinum, and 5 from the retroperitoneum, none of which presented with spinal cord compression.10 Another case series of extragonadal manifestations of seminomas in the head and neck area included 16 patients, but none involved the cervical spine or resulted in spinal compression.11 There was one case of spinal cord compression due to a primary posterior mediastinal germ cell tumor reported in the literature in a 7-year-old boy. The mass extended from T5 to T11 with involvement of the vertebral bodies of T8–T9. The patient was treated with total laminectomy from T6 to T10 with postoperative bracing treatment. The patient recovered postoperatively with mild kyphotic curvature and 4/5 strength in his lower extremities at 3 years postoperatively.12In patients with primary retroperitoneal and mediastinal seminomas, extensive mass effect and destruction of the anterior spinal elements must occur before spinal cord compression results.13 30 primary intradural spinal germinomas were reported, only 13% of which were purely extramedullary. All four of the cases of primary extramedullary spinal germinomas were intradural, requiring posterior laminectomy and durotomy to access the lesions, and careful and extensive dissection of the tumor from the nerve roots with variable clinical success (Table 14 A second case describes a 28-year-old man who died secondary to the cardiopulmonary complications of transverse myelitis from ascending metastases to the cervical spine.5 Finally, two case reports, one of a 43-year-old man and another of a 20-year-old woman, describe near full neurologic recovery after posterior decompression and dissection of the nerve roots.15Primary spinal germinomas have also been described although they typically arise from the medulla of the spinal cord itself. In a literature review by Loya et al,To the best of our knowledge, this is the first report of a primary extragonadal, extradural seminoma in the posterior elements of the cervical spine. This highlights the importance of the unique location of the tumor, which presents a diagnostic challenge as to the identity of the tumor prior to biopsy. Surgical management is also dependent on the location of the tumor because durotomy was not required to access our patient's tumor due to its extradural nature. Certainly, it is imperative to do a thorough oncologic workup before surgical resection. If a primary seminoma is identified, primary resection followed by an oncological consultation should be highly considered because previous case reports have supported the benefits of surgical decompression and subsequent radiation and chemotherapy.


Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is often accompanied by impaired emotion regulation (ER). There has been increasing emphasis on developing evidence-based approaches to improve ER in ASD. Electroencephalography (EEG) has shown success in reducing ASD symptoms when used in neurofeedback-based interventions. Also, certain EEG components are associated with ER. Our overarching goal is to develop a technology that will use EEG to monitor real-time changes in ER and perform intervention based on these changes. As a first step, an EEG-based brain computer interface that is based on an Affective Posner task was developed to identify patterns associated with ER on a single trial basis, and EEG data collected from 21 individuals with ASD. Accordingly, our aim in this study is to investigate EEG features that could differentiate between distress and non-distress conditions. Specifically, we investigate if the EEG time-locked to the visual feedback presentation could be used to classify between WIN (non-distress) and LOSE (distress) conditions in a game with deception. Results showed that the extracted EEG features could differentiate between WIN and LOSE conditions (average accuracy of 81%), LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (average accuracy 94.9%). Accumulating evidence has indicated differences in processing and connectivity across brain regions in individuals with ASD, relative to non-autistic peers7. These pervasive impairments have a significant impact on emotional functioning and emotion regulation (ER), which is defined as the person's ability to modify their arousal and emotional state to promote adaptive behavior9. Compared to typically developing individuals, individuals with ASD have more impaired ER, which may cause them to react maladaptively to emotional stimuli with tantrums, temper outbursts, aggression and self-injurious behavior11. ER impairment contributes to negative social outcomes, which in turn leads to negative expectancies for future interactions and a cycle of social avoidance, missed opportunities, and failure to learn new social skills as well as, ultimately, poor quality of life marked by limited independence17. Given the pervasive impact of impaired ER across functional domains, it should be considered while developing ASD treatment protocols19.Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by impaired social interaction and communication, restricted interests, and repetitive behaviors23. It also provides high temporal resolution, allowing real time experience to its users while interacting with virtual social environment that involves emotion expression, recognition and regulation. Furthermore, it can be used to engage children with ASD in life-like social situations through augmented reality, in the safety of one’s own home. Augmented neurofeedback can also be used to provide augmented traditional therapy sessions similar to the in-person sessions, which would reduce the cost and increase the accessibility to more children24.There has been an increasing interest in using neurofeedback technology in the treatment and study of several neurodevelopmental conditions such as ASD. Electroencephalography (EEG) based neurofeedback can provide an assessment and intervention tool that is safe, portable, and easy to use without the need for verbal or physical response27. EEG-based neurofeedback has been successfully used to improve cognitive flexibility, and enhanced social and communicative skills in individuals with ASD30. EEG-guided neurofeedback studies demonstrated changes in the EEG activity in individuals with ASD compared to neurotypical individuals4. Murias et al.31, found an increase in the theta and beta band powers in individuals with ASD’s EEG activity compared to their neurotypical peers during resting state. Another study by Kouijzer et al.29 showed a significant improvement in social and communication skills when the power in the theta band in individuals with ASD was reduced. Cowan et al.30 found a substantial improvement in attention and socialization after suppressing the excessive alpha and theta power. Furthermore, Wang et al.27 showed a linear decrease of the theta to beta power ratio, and an increase in the gamma power after applying the neurofeedback intervention.Various studies have assessed the effectiveness of using EEG in neurofeedback-based interventions for individuals with ASD28; a summary of these EEG features is shown in Table 5. It has been found that ER induced by negative pictures showed an increase in frontal theta oscillations5. Moreover, a positive correlation between theta oscillations and successful usage of cognitive reappraisal to decrease negative emotions has been shown26. Another study by Noordt et al.1 showed that inter-trial coherence of medial frontal theta oscillations was significantly lower for individuals with ASD. They also showed that the medial frontal theta power and phase coherence were greater following LOSE compared to WIN feedback. Moreover, they found that individuals with ASD were more sensitive to the valence of the reward as compared to their neurotypical peers. Another study by Stavropoulos et al.2, investigated the power in alpha and theta bands in response to a reward prediction task in both individuals with ASD and neurotypical individuals. They observed suppression in the alpha band power after presenting the feedback in individuals with ASD, while more theta activity was observed in neurotypical individuals.Several EEG components have been identified which are associated with emotional processing and regulation in individuals with ASD and neurotypical individuals10 and late positive potential (LPP)32 in neurotypical individuals. P300 is attention dependent, and therefore reflects higher cognitive processing of stimuli33, while LPP is more sustained positivity. Thiruchselvam et al.8 studied the effects of negative response inhibition and reappraisal on amplitude and latency of P300 and LPP in neurotypical individuals. Findings of this study showed a decrease in the amplitude of P300 and the duration and amplitude of LPP. Another ERP component is the feedback-related negativity (FRN), which reflects the neural activity associated with task performance and outcome monitoring34. Larson et al4. demonstrated that the amplitude of the FRN in response to distress conditions is different from the non-distress conditions both for individuals with ASD and neurotypical individuals during the monetary loss- gain feedback4. Therefore, FRN amplitude could be utilized to identify conditions associated with distress35.Overall, it is well established that earlier responses and larger amplitudes of ERP reflect more intact functioning in both ASD and neurotypical individuals37. Hileman et al.28 examined the difference in the P1and N170 in response to emotion stimuli in children with ASD and their neurotypical peers. Findings indicated that neurotypical individuals showed a larger amplitude and shorter latency of P1 and N170 in response to emotion stimuli compared to their peers with ASD. These studies show that several EEG components could be used to detect and monitor changes in brain activity related to ER in neurotypical and individuals with ASD.Other studies of changes in the event related potentials (ERPs) have shown that emotional stimuli induce P30039 task to analyze the effect of distress on the brain activity of individuals with ASD. Identifying brain responses to distress is a foundational step towards developing BCI technology tools to support ER-focused interventions. The Affective Posner task with deception has been used previously in functional magnetic resonance imaging studies for ER through the participation of chronically irritable children44, but not in EEG-based studies. Our preliminary results showed that the extracted set of temporal and spectral EEG features showed high accuracies in differentiating between distress (LOSE) and non-distress (WIN) conditions (average accuracy of 81%), and the LOSE and rest-EEG conditions (average accuracy 94.8%), and WIN and rest-EEG conditions (94.9%), with low variance across participants.In this study, we developed an EEG-based BCI system based on an Affective PosnerThe weighted sequential forward feature selection (WSFS) algorithm selects a subset of features that significantly contribute to the accurate classification of each problem. In this work, we have three main classification problems as shown in Fig. The percentage of the occurrence of feature subsets of each classification problem generated by applying the WSFS algorithm is shown in Fig. For the classification problem of differentiating between the LOSE and rest-EEG conditions, the LPP, the P300 and total power in the frontal channels showed high contribution towards the overall accuracy, with 20.37%, 16.67% 14.87% respectively. Moreover, the WSFS algorithm showed that the P300, FRN and the total power in the frontal channels in the EEG frequency range (4–30 Hz) contribute significantly towards the classification of WIN versus rest-EEG conditions. The late positive potential, P300 followed by the FRN and the total power in the frontal channels in the EEG frequency range (4–30 Hz) are the EEG features most contributed in classifying between LOSE and rest-EEG conditions.40. These results were calculated using the subset of features that resulted in the highest classification rate after cross validation for each participant. The overall average classification accuracy is 94.79% (chance level of 50%), while the average sensitivity of LOSE is 92.6% with low variance as shown in Table The performance measurement of LOSE versus rest-EEG (baseline-EEG) classification, for each participant are presented in Table The accuracy, sensitivity and F1 scores for the 2-class classification, WIN versus rest-EEG (baseline-EEG) conditions, for each participant are presented in Table The performance measurement of distress (LOSE) versus non-distress (WIN) condition classification for each participant are presented in Table p value of 8.74e−04 and 0.0234) were significant for Win versus lose classification while P300 was not significant . For Lose versus rest-EEG classification, total frontal power and LPP were significant features ; however, P300 did not show any significance . In the classification of Win versus rest-EEG, only total frontal power was significant . The results for the Wilcoxon rank sum test performed over SVM scores of Win versus Lose, win versus rest-EEG and Lose versus rest-EEG classification showed that SVM scores were significant in each classification problem . Note that, zero is the observed p value in MATLAB when the p value is smaller than the smallest number that could be presented in MATLAB.The results of applying Wilcoxon rank statistical test on the feature vectors show that the total frontal power and absolute power difference of F3 and F4  on the classification performance, we applied a permutation test within each classification problem. In this permutation test, we randomly assign features to each participant for each classification problem, and this assignment is repeated 100 times. The results of the permutation test are shown in Table 28. This set of features includes spectral features as follows, the total power in alpha, theta, beta frequency bands and in the frequency range (4–30 Hz). Moreover, a number of temporal features have been also calculated, the P300, the LPP and the FRN. Then, a subset of these features was chosen based on their ability to provide information about each classification problem. More specifically, we analyzed the EEG recorded during the task where feedback was predetermined to result in LOSE feedback  or WIN (correct). The WSFS algorithm started with a single feature and added other features satisfying high overall accuracy, as explained in Fig. The research objective of this study is to identify and analyze EEG features that are able to differentiate between the distress and non-distress conditions of individuals with ASD. For this purpose, the study protocol involved a card game with deception. We analyzed and extracted temporal and spectral EEG features from the collected EEG time-locked to the feedback presentation. Our approach includes a systematic feature selection, the WSFS algorithm, that selects a subset of EEG features that contributes towards the overall accuracy of each classification problem. Therefore, an initial set of temporal and spectral features were selected based on previous studies of cortical activity related to ER of neurotypical individuals and individuals with ASDcy range –30 Hz. M42, where it was shown that positive emotional stimuli introduces P300 and LPP, and the negative response inhibition and reappraisal decreases the amplitude of P300 and affect the duration and amplitude of LPP. Therefore, we argue that P300 and LPP are both ERPs of EEG that could be utilized as a measure of ER43.The findings of this study suggest that both the power in frontal channels in the frequency range (4–30 Hz) and the P300 EEG features are very informative and contribute significantly towards the classification of distress and non-distress conditions for individuals with ASD. The P300 showed the highest contribution towards the classification of rest-EEG versus LOSE conditions, and rest-EEG versus WIN conditions. These findings align with previous studies in neurotypical populations44 and visual stimuli classification45. Moreover, the absolute power difference between the EEG channels F3 and F4 showed high sensitivity towards the classification of LOSE conditions in both WIN versus LOSE and LOSE versus rest-EEG classification problems. Absolute power difference between the frontal channels46 have been used in emotion studies and these studies48 showed that anxiety and distress levels are reduced by increasing left compared to right prefrontal power. Therefore, in addition to P300 and LPP, absolute power difference between F3 and F4 is also a feature to be further investigated for ER. Moreover, the results of the Wilcoxon rank test applied on the set of features selected by the WSFS algorithm to assess their significance in the classification of Win versus rest-EEG, Lose versus rest-EEG and Win versus Lose showed that only certain features that appeared in the list of top 3 selected EEG features reached statistical significance. However, these selected features go through nonlinear transformation where SVM scores are extracted during classification and they contribute to the classification between conditions after this nonlinear transformation. We argue that such a classification approach and extracting new nonlinear combinations of these selected features to obtain SVM scores is very informative. Therefore, we performed Wilcoxon rank sum test over SVM scores of Win versus Lose, Win versus rest-EEG and Lose versus rest-EEG classification with significance level of 0.05. As a result, SVM scores were significant in each classification procedure. Furthermore, we applied permutation task where the features randomly assign to each participant in each classification problem. It is also important to note that during the random assignments, each participant had a subset of features different from the features selected by WSFS algorithm. The Wilcoxon rank sum test revealed that the features selected though WSFS algorithm were more significant than the features obtained through permutation task for each classification. Overall, these findings confirm that the features selected by WSFS algorithm was significant for each classification problem.On the other hand, we found that in addition to the P300 and total frontal power, the FRN showed a high significance in classifying the WIN versus rest-EEG and LOSE versus rest-EEG conditions. These ERPs are sensitive to visual stimuli and they have been used for stimulus identification49 so it is plausible that the EEG features reactive to LOSE trials are still reactive during the quick transition to a WIN trial. Further, while receiving “correct” cues during the WIN trial, it is the case that participants still may be below the overall amount required to win their bonus, therefore leading to sustained negative emotion. Taken together, it is not surprising to find lower sensitivity to differentiating WIN versus LOSE and high sensitivity to differentiating both WIN and LOSE from rest-EEG. In summary, the introduced set of features showed a high contribution towards the classification of distress, non-distress and rest-EEG conditions on a single trial basis which suggests that these features can be monitored in real-time through this EEG-based BCI to detect changes in distress for ER intervention.Changes in the above-discussed EEG features provided very high performance  of WIN and LOSE condition detection compared to rest-EEG as also was shown in Tables 50. One promising manualized psychotherapy intervention to improve ER in those with ASD is the Emotion Awareness and Skills Enhancement (EASE) program51. EASE targets impaired ER during the high-risk transitional period of adolescence into young adulthood. Evaluation of EASE to date has found that it is feasible to implement, is acceptable to consumers, and results in medium to large effects for the reduction of ER impairments as well as associated depression, anxiety, and problem behaviors51 As evidence-based treatments for ER in ASD become available, there is need to consider how to translate these tools into clinical practice and support dissemination of key therapeutic ingredients. As such, there is a growing opportunity and need for complimenting such behavioral clinical treatments with various low-cost and easy-to-access technology-based tools. The proposed EEG-based BCI system is portable, cost effective and it also has very high temporal resolution. Our next steps will be to directly integrate EASE strategies in the EEG-based BCI and provide users with opportunities to repeat the learned clinical skills during simulated training for ER and distress tolerance. The classifier developed and presented in this manuscript will be the foundation of real-time  monitoring of potential increase in the distress level of the individuals with ASD, and when an increase in distress level is detected, the envisioned BCI system will operate to provide stimuli/cues  to trigger the use of emotion regulation strategies learned through EASE. The overarching goal of this envisioned EEG-based BCI is to improve EASE’s generalization effects to activities outside of the therapy environment for adolescents and adults with ASD. Accordingly, this will be the first EEG-based BCI specifically designed for ER intervention for individuals with ASD, which will hopefully increase accessibility to intervention where specialists may be harder to find, in a format that is likely to appeal to users and that is based on underlying biology.Our overarching goal is to eventually utilize this EEG-based BCI system as an ER intervention tool to tightly complement psychotherapeutic clinical treatments. While medications are considered a common and effective treatment for ER impairments, the use of medication presents with adverse side effects such as sedation and a significant relapse rate52. During the consent process, the participants were informed that their safety and the confidentiality of the collected data are the primary consideration. Participants were told that at any point during the experimental procedure, if they feel any discomfort, they could stop the experiment. Moreover, all the experimental procedures described below is approved by the University of Pittsburgh Institutional Review Board. All participants received $25 for participation, plus an additional of $50 for winning.A total of twenty-one participants with ASD were recruited and their guardians provided written informed Consent approved by the University of Pittsburgh Institutional Review Board (IRB #STUDY17070496). Inclusion criteria as follows (1) ages 12–21, inclusive; (2) a clinical diagnosis of ASD, confirmed by research reliable administration of the Autism Diagnostic Observation, Second Edition (ADOS-2)39 as shown in Fig. N number of trials. The first two introductory tasks were completed without EEG data collection and without any deception, and each of the first two tasks consisted of 50 trials. The first task was developed to learn the response time of each participant, where only Correct or Wrong feedback were presented. The average response time per each participant was estimated and used in task 2 which included the three feedbacks . In this second task, the Too Slow feedback is presented if the player took longer time to decide the location of the star than their averaged response time learned in task 1. The third task includes deception and EEG data collection. Two-minutes of resting-state EEG was collected as baseline before task 3. Third task has a deception component where 60% of all the correct responses led to “Too Slow” feedback, independent of the actual participant’s response time. To induce an emotional reaction, participants were motivated by a potential bonus of $50 which was dependent on earning a sufficient number of points greater than zero. Participants started with a total of 150 points, and this task is composed of 100 trials. If the participant received a Correct response during the third task, 10 points were added to the total points. If Wrong or Too Slow feedback was presented, 10 points were taken off the total points. If the total number of points was greater than 0, the participant was considered a winner and qualified for the $50 bonus. The third task was designed such that the total number of points was always less than zero at the end of this task. One-minute of resting-state EEG followed task 3, followed by a task set similar to task three but without deception, to ensure that the participant win a sufficient number of trials (points) to earn the $50 bonus. Figure T, consists of a sequence of presented screens as shown in Fig. Participants were seated in a comfortable chair facing a computer screen and were asked to play a card game based on an Affective Posner taskEEG data were collected using DSI-24 system  using 24 channels. Channel locations are P3, C3, F3, Fz, F4, C4, P4, Cz, A1, Fp1, Fp2, T3, T5, O1, O2, F7, F8, A2, T6 and T4 according to the international 10–20 system. The reference sensor is placed at the nominal Pz position, while the ground at the earlobes. The EEG data were digitized with 300 Hz sampling rate.54 was used to calculate frequency-based features. A total of eight spectral and temporal features were calculated for each trial. The spectral EEG features were calculated as follows; for the frontal EEG channels F3 and F4 combined we calculated (1) the total power, (2) the absolute power difference between F3 and F4 in the frequency range (4–30 Hz). Moreover, for the EEG frontal channels, F3, Fz and F4, we calculated the following features, the total power in the (3) Theta (4–7 Hz), (4) Alpha (8- 15 Hz), and (5) Beta (16- 30 Hz) frequency bands for each channel separately.A FIR band-pass filter with cut-off frequencies 0.1 and 40 Hz was used to filter the data. Then, three seconds of multichannel EEG data time-locked to the visual feedback  presentation was extracted for each trial. For the baseline EEG data, the 2 min of rest-EEG were divided into three seconds non-overlapping windows of EEG, and each 3 s window is considered as one trial with a total number of 40 trials. Temporal and spectral features were calculated from the EEG signals collected from the frontal EEG channels; F3, Fz and F4. Welch’s periodogram method of power spectral estimationMoreover, for the EEG frontal channels, F3, Fz and F4, we calculated the following temporal features; (6) the P300 which is a positive change in the EEG around 300 ms after the stimuli, (7) the FRN which is a negative deflection occurring between 200 and 300 ms and (8) the late positive potential (LPP), which is the average EEG calculated over a window (850–1600 ms) time-locked to the feedback presentation during this game. Finally, these features were normalized using z-score normalization and concatenated to form a feature vector.A Support Vector Machine classifier (SVM) was used to evaluate the effectiveness of the extracted features in discriminating between three classes of data as shown in Fig. N is the total number of features. The selection algorithm forms an initial empty subset More specifically, given a feature N iterations, th classification problem, as can be seen in Fig. p and q are the participant and classification problem number, respectively, where The final selected subset of features after mentclass2pt{minim40 score is also used to assess the performance of the three developed classifiers. This metric considers both the precision and recall of the test, precision is the ratio of correctly predicted patterns to the total predicted patterns; and recall is the ratio of accurately-recognized observations to the total actual observations55.This score results in value of 1, at perfect precision and recall values, and zero at worst.The analysis introduced in this work is based on the EEG data collected during the third task. For each classification problem presented in Fig. p value for the null hypothesis. The null hypothesis states that both feature vectors, representing both classes in each problem, come from the same distribution56. While, the alternative hypothesis states that both vectors are different, which means that the two classes are separable. Moreover, we applied the Wilcoxon rank test on the generated SVM scores from each classification problem. This was done to see whether these scores are significant to separate between each pair of classes for the three classification problems shown in Fig. In order to validate the significance of the selected set of features in separating between (1) Win versus rest-EEG, (2) Lose versus rest-EEG and 3) Win versus Lose conditions, we performed one-sided Wilcoxon rank statistical test for each one of these classification problems. For each one of these problems, we selected three features which showed the highest percentage of selections by the WSFS algorithm. Then for each class, in each classification problem, we generated a feature vector containing the values of these selected three features for all the trials in this class. After that, we used the Wilcoxon rank statistical test to assess whether the difference between these two classes represented by these features, is significant or not. Therefore, for the Win versus rest-EEG, the total frontal power, FRN and P300 were selected. For the lose versus rest-EEG, the total frontal power, LPP and P300 were chosen, and finally for the Lose versus Win, the total frontal power, the absolute power difference of F3 and F4 and P300 were selected. The Wilcoxon rank test is a nonparametric hypothesis test, which returns the  Win versAnother test of validation was done to determine the significance of the selected set of features by randomly permuting the features and accordingly assigned randomly selected features to each participant assuring that each participant will have different subset of features than the features selected through feature selection algorithm. We repeated this permutation 100 times, and for each repetition we performed classification. Then, average performance measures (accuracy and F1 score) were calculated for Win versus Lose, Lose versus rest-EEG and Win versus rest-EEG classifications for each participant. Moreover, we used the right-side Wilcoxon rank test with significance level of 0.05, to compare the accuracy and F1 score values obtained through permutation with the accuracy and F1 score values obtained through classifications that used features selected based on feature selection algorithm.An EEG-based BCI system based on Affective Posner task was introduced. This system was designed to identify the changes in brain activity of individuals with ASD during rest, non-distress (WIN) and distress (LOSE) conditions. Furthermore, the analysis of the recorded EEG during the third task resulted in extracting EEG features that significantly contributed towards the classification between these conditions on a single trial basis. The P300 and total power in the (4–30 Hz) EEG frequency range, showed high accuracies in differentiating between distress and non-distress conditions. the P300, FRN and total frontal power contributed significantly in the WIN versus rest-EEG classification. In addition to these three EEG features, the LPP showed high contribution towards the classification of LOSE and rest-EEG conditions with low variance across participants. These results align with earlier findings about EEG features that can be extracted from neurotypical and individuals with ASD in response to emotional stimuli. This work is a first step towards building a real-time BCI system that could distinguish between non-distress (WIN) and distress (LOSE) on a trial by trial basis. Now that we have shown that EEG can reliably distinguish between distress and non-distress conditions), in future work, we aim to add capabilities to the presented BCI system such that it could monitor brain responses through EEG related to ER activity to work along with current clinical behavioral treatment methods, such as EASE, as a technological intervention tool. Also, this system could be integrated with virtual reality setup to simulate distressing activities in real-life settings, to provide opportunities to practice ER strategies and get real-time, automated feedback. Through such an integration, the proposed EEG-guided BCI technology could be used to complement clinical treatments that focus on ER to supplement in-person sessions with a therapist, decrease clinician time spent with each patient, or provide exposure to key components of treatments for those who do not have access to a therapist. Moreover, such a BCI could support all the existing technological approaches to monitor and analyze the brain responses during technology-driven interventions. As changes in the EEG features indicate high distressed conditions, visual cues that will enforce the participants to use ER strategies will be presented through the EEG-based BCI system during real-time ER intervention.Supplementary information


Relapsed or refractory diffuse large B-cell lymphoma (DLBCL) is an aggressive disease with poor outcomes in patients ineligible for autologous stem cell transplantation. In this setting, novel treatment approaches are urgently required and the innovative agent pixantrone has shown some promising results in terms of disease-free and overall survival (OS). The present study retrospectively analyzed 12 patients routinely treated with pixantrone in monotherapy or in combinations at the Institute of Oncology Ljubljana, Slovenia, between January 2016 and October 2018. All 12 patients had refractory lymphoma to last treatment and a large proportion of them had other high risk features  score, high percentage of primary refractory disease and high percentage of refractoriness to anthracyclines) at initiation of pixantrone. All patients progressed during treatment and none of the patients were alive at the time of analysis due to progressive lymphoma. Pixantrone specific median OS was 3.5 months . A somewhat superior median OS (P=0.065) was observed in patients primarily sensitive to anthracyclines. Pixantrone has shown only limited efficacy in the present real world study comparable to the results of another real world UK retrospective analysis and substantially worse than the efficacy observed in the PIX301 registration trial. Therefore, an appropriate selection of patients for this treatment is crucial. Despite the limited experience due to a small number of patients, it was recommended to consider only patients with relapsed (and not refractory) disease, patients with non-primary refractory disease and those with fewer lines of prior therapy. The diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin's lymphoma (NHL). Its first line treatment usually consists of an anthracycline and rituximab containing regimen while second line salvage therapy in relapsing and refractory patients habitually comprises a platinum and/or gemcitabine based regimen followed by consolidative high dose therapy and autologous stem cell transplantation in younger, fit patients. However, there is no consensus regarding the third and further line therapies in multiply relapsed or refractory aggressive B-cell NHL which typically consist of polychemotherapy combinations excluding the anthracyclines or monotherapy with oxaliplatin, etoposide, gemcitabine and vinorelbine –4.Pixantrone is a novel aza-anthracenedione developed to reduce the risk of cardiotoxicity while maintaining efficacy in the treatment of aggressive lymphomas –8. It taIn a phase II study including patients with relapsed aggressive NHL, an overall response rate of 27% has been shown with pixantrone in monotherapy demonstrating the longest response duration of 24 months . A randoHowever, the real world post-approval data on the efficacy of pixantrone are, firstly, more than scarce and, secondly, less promising for non-select every day relapsed and refractory patients  than theWe retrospectively analyzed all the patients routinely treated with pixantrone at the Institute of Oncology Ljubljana, Slovenia. Pixantrone is being administered in Slovenia since January 2016. Our analysis included patients receiving monotherapy with pixantrone as well as those treated with a pixantrone based multidrug regimen . Data r2 on days 1, 8 and 15 or with pixantrone 50 mg/m2 on days 1 and 8, combined with rituximab 375 mg/m2 on day 1, etoposide 100 mg/m2 on day 1, bendamustin 90 mg/m2 on day 1 and methylprednisolone, according to the PREBEN protocol  and activated B-cell (ABC) subtypes and re-determined CD20 and BCL2 expression as well as the proliferative activity defined with Ki67 expression.All procedures followed in this study were in accordance with the ethical standards of the responsible committee on human experimentation  and the Helsinki Declaration of 1975, as revised in 2000. Individual patient consent was not collected for this study as this was a retrospective database analysis and the institutional informed consent form for treatment included consent to use the patient's data, materials and/or test results for research purposes. The study was approved as such as a part of treatment result evaluation  by the institutional review board of the Institute of Oncology Ljubljana.Survival data were analyzed using the Kaplan-Meier survival curves and log-rank test was applied to compare the survival distributions between groups. P<0.05 was considered to indicate a statistically significant difference. The GraphPad Prism program  was used for statistical analysis.c-myc rearrangement was found in 2 samples . As much as 75% of the patients presented with constitutional symptoms. The IPI score at lymphoma diagnosis was ≥2 in all 12 patients and ≥3 prior to pixantrone treatment.Eight patients were initially treated with R-CHOP , 1 patient with R-CHOEP , the patient with follicular lymphoma received only CHOP in 1999, 1 patient was treated with a combination of R-CHOP and MD MTX (middle dose methotrexate) and 1 patient received one cycle of R-CHOP with MD MTX and continued her first treatment with R-EPOCH  . The patWith aforementioned first line treatments 7 patients achieved complete remission (58%), 2 patients partial remission (17%) and 3 patients progressed during the first line treatment (25%). The median duration of response to first line treatment was 4 months . Regarding the duration of response to first line anthracycline containing regimen, altogether 7 patients (58%) had primary refractory disease. Only 3 patients fulfilled the criteria for primary anthracycline sensitivity according to the PIX301 study.The prognostic features of our group of patients, which was also the smallest, were the worst compared both to the UK retrospective analysis and especially to the PIX301 study cohort as given in Pixantrone was applied as the third line treatment in 3 patients (25%), the forth line treatment in 6 patients (50%), the fifth line treatment in 1 patient (8%) and the eighth line treatment in 2 patients (17%) . Eight pAll patients underwent cardiac investigation with cardiac ultrasound or radionuclide ventriculography prior to introduction of pixantrone therapy. There were no episodes of cardiac toxicity observed during the pixantrone treatment. One patient experienced arrhythmia after the last pixantrone infusion, which was attributed to electrolyte imbalance (hypophosphatemia and hypomagnesemia).Despite the applications of granulocyte colony-stimulating factors in all patients, we observed 4 episodes of grade IV neutropenia and 1 episode of febrile neutropenia. Two patients without significant neutropenia presented with bacterial pneumonia and one with urosepsis. As to the hematological toxicity, we observed 6 episodes of thrombocytopenia grade III and IV in 4 patients and grade III anemia in 1 patient, requiring supplementation of blood products. Chemotherapy had to be delayed for 1 week in only 3 patients, and in 1 patient for 2 weeks, due to unresolved hematological toxicity.Our results present another perspective of pixantrone treatment and confirm the observations of the retrospective UK study . In PIX3It is unquestionably noteworthy that in the PIX301 study, the inclusion criteria was a complete or relative response to anthracycline treatment lasting at least 24 weeks . Our groOn the other hand, a recent extended survival analysis of the PIX301 trial showed that the outcome of treatment with pixantrone is not related to prior treatment outcome . Still, When comparing the median OS rates, it was correspondingly longer in the PIX301 study-10.2 months  parallelThe distribution of histological subtypes in our group of patients was somewhat against favor of the ABC subtype compared to the data from the literature . This suTherefore, to improve the survival outcomes, it would be most beneficial if patients, predicted to have a worse outcome , were treated differently upfront-potentially with an inclusion of a BCL2 inhibitor combined to chemotherapy.Most common adverse effects of pixantrone include cardiac toxicity and bone marrow suppression, particularly of the neutrophil lineage. The incidence of severe infections, however, is usually low and other toxicities, such as nausea, vomiting, and diarrhea, are rather infrequent and usually mild . Both thIn conclusion, relapsed or refractory DLBCL is an aggressive disease where long term remissions are hard to achieve and every line of treatment brings shorter responses. Novel agents like pixantrone have been shown to improve disease-free and OS, but only in a highly selected population of patients. According to our real life outcomes an appropriate selection of patients for this treatment is crucial and we recommend considering patients with relapsed (and not refractory) disease, patients with non-primary refractory disease and those with fewer lines of prior therapy. Bearing in mind also the still existent (yet reduced) risk of cardiotoxicity, as well as hematological toxicity of pixantrone, we would advise against using it to treat patients with primary anthracycline response lasting <24 weeks and those who were refractory to last treatment. Nevertheless, our conclusions need to be considered with caution due to our limited patient sample. Still, histology needs to be considered when deciding about the first (and future line) treatments.


The inability of current video-based eye trackers to reliably detect very small eye movements has led to confusion about the prevalence or even the existence of monocular microsaccades . As current methods often rely on precisely localizing the pupil and/or corneal reflection on successive frames, current microsaccade-detection algorithms often suffer from signal artifacts and a low signal-to-noise ratio. We describe a new video-based eye tracking methodology which can reliably detect small eye movements over 0.2 degrees (12 arcmins) with very high confidence. Our method tracks the motion of iris features to estimate velocity rather than position, yielding a better record of microsaccades. We provide a more robust, detailed record of miniature eye movements by relying on more stable, higher-order features  instead of lower-order features , which are sensitive to noise and drift. To maintain vision and compensate for the intersaccadic drift between large eye movements, humans continuously make small (miniature) eye movements including tremor, drift, and microsaccades, 2, 3.main sequence[microsaccades.Microsaccades are the small, jerk-like motionswhich aequenceand havequence. Folloequence, 23, 24Currently, there is disagreement in the literature about the presence of monocular microsaccades during everyday vision. Gautier et al.has argOne of the limitations of the current generation of video-based eye trackers is their inability to precisely detect microsaccades because the size of those eye movements is similar to the noise level of the trackers which rely on precisely localizing the pupil and/or corneal reflection (CR) on successive frames. Moreover, some of these methods use only pupil information to acquire a higher sampling rate, but in these methods, relative motion between the head and camera are not taken into account, whichNew algorithms that take advantage of modern high-resolution cameras and recent advances in computer vision can overcome some of these issues. High-resolution cameras make it possible to extract fine local features such as iris textures. Using populations of such features in place of large, single features such as the pupil allows for a higher degree of confidence in microsaccade detection. Additionally, tracking the motion of those features across consecutive frames allows for the study of motion distributions, simplifying the analysis of small eye movements. We demonstrate that microsaccades less than 0.20 degrees of visual angle can be reliably detected with these techniques and a new velocity tracking algorithm. The method is validated with two types of experimental tasks; reading characters on a test target and watching videos.Eye movements are monitored by extracting the motion vectors of multiple iris features. A high frame-rate, high-resolution camera is used to capture a video of an observer’s face containing the eyes and surrounding regions. A trained convolutional network (CNN)is usedThe region of each frame containing the iris must be isolated from the rest of the frame before velocity estimation can begin. A number of approaches have been used for iris segmentation including ellipse fitting, geodesic active contours, HoughNetwork Architecture. A U-Net based architecture[tectureallows tecture. FigurData labelling. Instead of training on the full-resolution (1920x1080) video, each frame was partitioned into three regions: left and right eye (960x540 each) and the lower part of the face (960x1080). Binary labels of iris and non-iris regions were generated for training the model by manually clicking six – ten points on the border of the iris on each image as shown in Figure 3, and an ellipse was fit to those points with a least square ellipse fitting method[ method. SinceTraining procedure. Training images were re-sized to 224x224. The R, G, and B channels were used as input to the model for the desired two (iris and non-iris) output classes. The Adam Optimizer[timizerwas apptimizeron an UPerformance Metrics Pixel-wise cross entropy of class probabilities was used during training as a loss function. The accuracy of iris detection was measured with the Intersection over Union (IoU) metric. The IoU metric varies from 0 – 1 and is defined as the ratio of the intersection of the ground-truth and predicted regions to the union of those regions[regions. PerfeIf P is the predicted label and G is the ground truth, IoU is computed as shown in Figure 4.Even when an observer’s head is stabilized by a chin rest, head movements on the scale of small eye movements still occur, so it is crucial to compensate for this motion when detecting microsaccades. Complex solutions using 3D head models or external hardware trackers could be used, but in this work, we propose an image-based method that compensates with a simple planar image transformation based on regions detected on the face.The human face is non-planar and deformable. The eyes are not necessarily in the same plane as the nose, cheek or forehead, making planar transformation models like homography (8 degrees of freedom (DOF)), affine (6 DOF) or similarity (4 DOF) transforms imperfect approximations. Here, we make the simplifying assumption that the regions selected in Figure 5 fall approximately in the same plane as that of the two irises being tracked. Relatively planar regions that do not move with normal eye movements were manually selected for each subject.thframe using the L2 distance norm.Head movements were compensated by automatically aligning each frame of a video to the initial frame of that video. Computing the homography transformation between frames based on good feature matches across adjacent frames allows a sequence of frames to be aligned. A list of good matches between pairs of adjacent frames was determined with Lowe's ratio testfolloweMotion compensation can be accomplished for an entire video by aligning each frame in this way to the initial reference frame by cascading the homography transformations as proposed by Dutta et al.. We foInstead, we selected features in rectangular regions as seen in Figure 6, then computed good matches between consecutive frames for each of the regions. Computation of the geometric median of those matches provided an estimate of the best value of the distribution of the rectangular patch in the source and destination images. Using one point from each of the four regions we computed the transformation between image pairs. The homography transformation is the least-constrained plane-to-plane mapping, with eight degrees of freedom (DOF) which can be described as allowing translation (2 DOF), scaling (2 DOF), rotation (2 DOF), and keystoning . Other transformations are more constrained. The affine transform, for example, allows only six degrees of freedom which can be described as translation (2 DOF), scaling (2 DOF), rotation (1 DOF), and shearing . The similarity transformation has only four degrees of freedom, allowing only translation (2 DOF), uniform scaling (1 DOF), and rotation (1 DOF). As shown by Grundmann et al., the ebreaks in a video sequence when the video starts panning or scale changes become substantial. Information from prior frames is not considered after a break and a new reference frame is introduced. Grundmann et al.[Dutta et al.identif et al.proposeWe elected to realign to a new keyframe once every 480 frames (5 s). Less frequent realignment increased the aggregated skew and perspective errors, and more frequent realignment increased the frequency of the jitter. The overall model is shown in Figure 7.After the segmentation of irises in the stabilized video, the next crucial step is to extract the iris features. These features were extracted in a Contrast Limited Adaptive Histogram equalized grayscale representation of the imageas thisThese motion vectors can be used to compute horizontal, vertical and torsional eye movements. A number of researchers have used iris features to measure torsional eye movements, 48, 49Because we are concerned with microsaccades in this work, we ignored torsional movements and computed the geometric median of the shift of all matched keypoint pairs between frames. The relative velocity of each eye was calculated independently as the product of the scaled motion vector and the sampling rate, and integrating those values gave a relative position signal for each eye. Calibration was performed by simple linear regression of the relative position signal and the known gaze position during calibration. Position cannot be estimated during blinks, so the data reported here include only trials completed without blinks during the calibration routine.The raw signal was filtered using 1D total variation denoising (TVD)which mA number of methods have been proposed for microsaccade detection, 53, 54To automatically adapt to individual observers, we implemented an adaptive-threshold method based on a Gaussian mixture model (GMM) with two velocity distributions representing noise and microsaccades. To eliminate larger saccades, only absolute velocities below 20 deg/sec were passed to the model. A thresholding parameter based on estimated mean and standard deviation using 99.7% of each distribution was used. Based on observations of the noise floor, a lower bound of 3.84 deg/sec was selected to minimize false alarms. Figure 8 shows the block diagram from the iris feature extraction to microsaccade detection.Binocular eye movements were recorded using a Panasonic Lumix DMC-GH4 mirrorless digital camera modified by removing the IR-rejection filter. Video recordings of the eyes and the region surrounding the eyes were recorded at a frame rate of 96 frames per second (fps). Head movements were restricted with a UHCOTech HeadSpot chin and forehead rest.  Filtered tungsten and LED infrared light sources were used to illuminate the eyes and the region surrounding the eyes as shown in Figure 9. Subjects were asked to sit comfortably to minimize head movements. Trials were repeated until calibration was completed without blinks.Apparatus. The Lumix DMC-GH4 was set to ISO 400, and F/8. The camera was placed at a distance of 50 cm from the observer, and the center of the lens was positioned 4.5 cm below the eyes at an angle of approximately 4 degrees with the horizontal axis (the frame was not centered on the eyes). A focal length of approximately 70 mm was used to capture an appropriate region including the eyes and surrounding areas.Light source. The experiments were conducted in a lab with indirect fluorescent illumination. A tungsten-halogen source with a bifurcated fiber-optic light guide was filtered with a near-infrared high-pass (750nm) filter. As seen in Figure 9, the light guides were placed at an angle ~25-30 degrees above the horizontal, separated by 88mm. The sources provided an irradiance of 0.020 – 0.037 W/cm[Eye movements of seven participants  with normal or corrected-to-normal vision were recorded. Participants with light and dark irises and with and without prescription glasses were selected. Subjects were undergraduate and graduate students with a mean age of 25 years (σ=3). The experiment was conducted with the approval of the Institutional Review Board and with the informed consent of all participants.Each observer performed three tasks, each preceded by a task-specific 9-point calibration routine.Microsaccades can be evoked when observers read small, isolated characters, as in the Snellen eye chart. Calibfigure-8 pattern. In the second video, they were instructed to track the cup containing a marble in a ‘shell game.’ Both videos induced smooth pursuit eye movements. The laptop was placed on a table so that the display center was 100 cm from the observer and 10 cm above the subject’s eyes.  The display field of view was 13.5° X 7.4°.To examine the detection of microsaccades while viewing a moving stimulus, observers viewed two short videos fromon a 12Figure 10 shows the loss function for the training and the testing datasets. The model starts to reach its asymptote at 35 epochs for both sets. For our model, the average IoU values were 0.898 for the training set, 0.891 for the correlated test data, and 0.866 for the uncorrelated test data.Figure 11 shows images with iris regions predicted by the model and ground truth indicated in yellow and green colors respectively. For proper visualization, only the border predicted by the model is shown. The large white regions in the upper-right image are specular reflections in the observer’s eyeglasses. A spike in the loss function at epochs 16-17 evident in Figure 10 is because of inconsistency in some of the labeled data such as when the eyelids cover the iris as seen in the lower left image in Figure 11.We evaluated the video stabilization for two cases; a rigid Styrofoam model head and for real human faces. For the rigid head model, we tracked the four points labeled A, B, C and D in Figure 12 (left). The rigid head was rotated slightly about the base, resulting in horizontal, vertical and diagonal movements of approximately 2 mm at a frequency of approximately 1 Hz, and the video was stabilized using our compensation model. The original motion for point B is shown in solid red in Figure 13 and the motion-compensated output is shown in dashed blue.Table 1 shows the mean squared change (MSE) from the initial starting reference frame and standard deviation (STD) of points before and after stabilization in pixels. The overall mean per class (MPC) across all points shows a dramatic improvement in performance. The test points were tracked by locating the brightest spot (a specular reflection) on the black marker as indicated in Figure 12 (right). A variation of one pixel is expected because the maximum location algorithm in OpenCVreturnsWe also examined the performance of the video stabilization algorithm for a seated observer in a chinrest looking at nine calibration points for about a second each. Head motion was tracked by attaching two small stickers to the observer’s face and tracking each sticker in the video by computing the mean of the central votes of matched features by using consensus-based matching and tracking. BeforIn the first task, observers were instructed to read a line on the pocket Snellen eye chart whose characters subtended an angle of approximately 5 arcminutes . Because of the size and distance of the target, the eye movements necessary to foveate each target constitute microsaccades. FigurFigure 16 plots the absolute velocity of the cyclopean gaze (with its amplitude), X-t, Y-t and YX graphs for both eyes when microsaccades were detected. The amplitude of each microsaccade was calculated based on the gaze position between the local minima of the cyclopean velocity before and after the microsaccades. The two local minima are indicated in the absolute velocity plot by the vertical light blue lines. There is a close relationship between the two eyes for most of the cases. The center column shows a case where the eyes were moving in opposite directions.Results for all seven subjects are shown in Table 3. The table shows the number of microsaccades per observer: (A) found when the eye video was inspected visually; (B) detected by the algorithm; (C) detected by the algorithm when separated by an interval equal to at least half of the average duration available for each character; and (D) detected by the algorithm but not present .The events for 0.15 degree microsaccades in Table 3 (C) should be  as the instructed targets are set in that order as seen in Figure 14. Initially, two events of 0.28-degree and 0.32-degree microsaccades are determined. Then, the number of events before 0.28-degree microsaccades, between 0.28 degree and 0.32 degree and after 0.32 degree are detected with a consideration that only one unique microsaccade must be a voluntary movement made for the target in the interval. All the other movements in the interval are neglected as our interest lies in detecting movements for the instructed target rather than observing the number of overall detected microsaccades. Note that the method identified all small movements ≥ 0.2 degrees.Identifying small movements with a magnitude of 0.15 degree is possible for some events when a lower threshold is selected, but this increased the false alarm rate. Subjects 5 and 6 were two cases of noisy data. For Subject 6, using an initial regularization parameter of 0.1 for filtering resulted in eight false alarms and a miss rate of 11.1% . It was seen that signal was noisy even after head motion compensation thus changing filtering regularization parameter to 0.20 and 0.15 for the right and left eye respectively there was an improvement in the result with only two false alarms. However, higher values of filtering regularization parameter increased the miss rate to 33.3%.-1. Microsaccades are defined in this context as all movements with a peak velocity <50 deg/sec.The importance of compensating for small head movements can be seen in Figure 17. Without compensation (top panel), a threshold of 12.0 deg/sec was necessary to exclude noise. After compensation (bottom panel), a threshold of 5.1 deg/sec allowed microsaccades to be detected more accurately. The one false alarm detected for the compensated data was also detected in the uncompensated data indicating a false alarm resulting from large head motion. Without compensating for head motion, a total of nine microsaccades were missed. Figure 18 shows the ‘main sequence’ (peak velocity vs. amplitude) for microsaccades and saccades detected by our method. Both saccades and microsaccades fall on the main sequence with a slope of 47 sIn the first video stimulus task, the observers were instructed to fixate on a moving car, leading to smooth pursuit, fixations, saccades, and microsaccades. The box plot with the number of microsaccades detected per second and the variation of the rate of microsaccades among different subjects is plotted for all subjects in Figure 19 (top row). The median number of microsaccades in the video is approximately one per second for most of the trial.In the second video task, the observers viewed a ‘shell game’ with three cups and one marble and were instructed to follow the cup that contained the marble. The visual task induced smooth pursuit, saccades, fixations, and microsaccades. The box plot with the number of microsaccades detected per second and the variation of the rate of microsaccades among different subjects is plotted for all subjects in Figure 19 (bottom row). Figure 20 compares the average amplitude of the microsaccades over the entire video for the two video tasks.Current video-based eye-tracking methods can lead to ambiguities and potential errors when tracking very small eye movements. Our work represents an advance in analyzing microsaccades by tracking iris textures using a high frame rate, high-resolution camera. We segmented the iris from the video frame with a trained CNN and measured the model accuracy using an IoU metric that rewards correct matches and penalizes false matches. The IoU metric was appropriate because we calculated frame-to-frame velocity based on the geometric median of the population of iris feature matches rather than relying on extracting the precise iris boundary. Our CNN model was able to generalize for a varying set of data, though some labeling errors  resulted in inconsistencies. The CNN was trained with labels from a single human labeler and might be improved further if labels with multiple-labeler agreement were used for training.thof the original value.  The model also improved the detection of microsaccades for human subjects as the number of false alarms decreased significantly after compensating for head motion. However, some false alarms remained in cases where a large jitter was observed because of transformation issues (warping). For some subjects, head movements were comparatively large causing more noise in the signal resulting in higher thresholding value obtained from GMMs for microsaccade detection. The current implementation can miss some microsaccades if they occur while updating to a new reference keyframe. When a new reference keyframe was updated there was usually a significant apparent movement, and the motion was misinterpreted as a saccade. Since our major concern lies in detection of microsaccades when a person fixates the instructed targets at a regular interval (approx. 1 sec) and we did not wish to include post-saccadic oscillations or movements compensating for overshoots or undershoots as microsaccade events, we required a minimum period of 52ms (5 frames) between detected velocity peaks. In our tests, one microsaccade was missed during that time interval as the microsaccade timing was aligned to the timestamp of keyframe update.Head motion impacts the number of detectable microsaccades, so we introduced a head compensation model based on simple planar transformations. The model achieved excellent performance with a rigid head model, reducing the overall MSE from 3.59 pixels from the initial reference frame to just 0.42 pixels with standard deviation decreasing to 1/8We have demonstrated the robust detection of microsaccades by tracking the velocity signal generated from iris textures, extracting microsaccades with GMM-based adaptive velocity threshold parameter with a mean of 5.48 deg/sec (σ=1.16). In four of 21 trials in three tasks, the threshold value used was the lower bound of 3.84 deg/sec. Note that for head-fixed datareferreManually inspecting the video showed that the number of microsaccades was less than the number of instructed targets for the smallest (0.15 degree) microsaccades. It is possible that not all instructed eye movements were actually made; note that an entire grouping of characters fell within the fovea. For three out of seven subjects, all of the 0.15° microsaccades that were identified via visual inspection of the video record were also detected by the algorithm. For one subject, two microsaccades that were not detected by the algorithm were observed to be smaller than the remaining microsaccades for the same observer, perhaps because the preceding microsaccades ‘overshot’ the previous character. It is likely that individual fixations fall on different positions within each character, and some of the characters subtended a horizontal angle of only 0.04 degrees, so it is possible that the individual microsaccades were less than 0.15-degrees if the eye movements were not between character centers. Thus, the actual magnitude of the microsaccades were not exactly the expected value of 0.15, 0.28 and 0.32 degrees calculated from the original target angles. Note too that our algorithm is best suited for event detection rather than providing an exact estimate of position.Figure 16 (second column) showed one of the sample events where the two eyes moved in opposite directions. This could be the result of true uncorrelated motion (such as vergence movements) or errors in the head compensation model. Vergence was detected by our method especially before and after the blinks as the eyes converged before a blink and diverged after.The results for our study for the number of microsaccades per second for the two video stimuli tasks are in agreement with Martinez-Conde et al.and thefigure-8 car stimulus suggest that the person is making more microsaccades in the later part of the video. This may be because the car was closer to the camera at that point and the number of catch-up saccades less than 50 deg/sec is expected during smooth pursuit[Analysis of the video pursuit. We alIn the video tasks, the threshold was set for each trial by a GMM. As the smooth pursuit velocity induced by the moving targets was less than the microsaccade velocity threshold, the static GMM was sufficient. In the future, we will implement a continually variable adaptive-threshold in which the GMM will be based on a sliding window so that microsaccades superimposed over higher velocity smooth pursuit can be reliably detected without increasing false alarms during the pursuit.While the Snellen chart offers a convenient target to induce microsaccades, it presents several challenges. At this scale, several targets fall well within the fovea, so an observer doesn’t need to make saccades to read each character. In addition, significant undershoots and/or overshoots could occur that would make it difficult to identify individual eye movements, without affecting an observer’s ability to read each character. We used the finite interval in an attempt to reduce errors for localizing individual eye movements, but it is still possible that we were not able to accurately parse each microsaccade perfectly.This experiment could be improved by using a high-resolution digital display in place of the printed Snellen target, and display only one character at a time. This would simplify the identification of each microsaccade by synchronizing presentation and detection times. A higher frame-rate camera could also improve performance. The temporal resolution of the camera impacts the estimate of the amplitude, as it is obtained by integration of velocity over time. With more discrete velocity samples during brief microsaccades we can estimate the amplitude of microsaccades more accurately. Additionally, the signal-to-noise ratio both in the head compensation model and the iris feature tracking method can be improved by using a higher frame rate camera with all-intraframe (ALL-I) encoding. We are also exploring hybrid algorithms that merge velocity and position signals to gain the precision benefit of the iris motion tracking while maintaining the traditional positional information.In summary, the main contributions of this paper are the use of trained CNNs to provide a more robust solution for iris segmentation across observers with different iris and skin pigmentation; an image-based model for head motion compensation using planar transformations which can be applied in various applications like video stabilization; extracting high quality iris images rich in textures; and an algorithm to reliably detect small eye movements over 0.2 degrees with very high confidence by extracting motion signals with a high signal-to-noise ratio by computing motion distributions rather than relying on precise pupil boundary localization as in pupil and Pupil-CR systems. This method can identify even smaller movements if the velocity of the movement is higher than the threshold parameter (>3.84 deg/sec).http://biblio.unibe.ch/portale/elibrary/BOP/jemr/ethics.html.The author(s) declare(s) that the contents of the article are in agreement with the ethics described inJBP is co-inventor of the base method used to extract velocity signals from iris texture features.The authors wish to acknowledge the helpful discussions with Michele Rucci and Martina Poletta, who proposed the use of the Snellen target to initiate microsaccades; Emmett Ientilucci who offered valuable guidance and assistance in radiometric measurements; Christye Sisson who shared her expertise on extracting textured iris images; and Anjali Jogeshwar who provided enthusiastic support throughout.