Artificial intelligence has recently made a disruptive impact in medical imaging by successfully automatizing expert-level diagnostic tasks. However, replicating human-made decisions may inherently be biased by the fallible and dogmatic nature of human experts, in addition to requiring prohibitive amounts of training data. In this paper, we introduce an unsupervised deep learning architecture particularly designed for OCT representations for unbiased, purely data-driven biomarker discovery. We developed artificial intelligence technology that provides biomarker candidates without any restricting input or domain knowledge beyond raw images. Analyzing 54,900 retinal optical coherence tomography (OCT) volume scans of 1094 patients with age-related macular degeneration, we generated a vocabulary of 20 local and global markers capturing characteristic retinal patterns. The resulting markers were validated by linking them with clinical outcomes  using correlation and machine learning regression. The newly identified features correlated well with specific biomarkers traditionally used in clinical practice (r up to 0.73), and outperformed them in correlating with visual acuity ( Medical imaging for precision medicine relies on biomarkers that capture patient and disease characteristics accurately, efficiently, reproducibly and interpretably. By tradition, the process to establish a biomarker starts with hypothesis generation based on professional experience or theoretical motivation, and concludes with hypothesis testing in specifically designed experiments, for instance by demonstrating the linkage between a marker and clinical outcomes. However, human experts are limited in discovering novel biomarkers because current dogmas may hinder unbiased hypothesis generation, or simply because they may not comprehend the phenotypes of patients and diseases in their full complexity.1, triage referable retinal diseases2 and provide automated diagnoses of chest x-rays3 or retinal images4. When deep learning was targeted to clinical endpoints, it even enabled prediction of systemic cardiovascular parameters from photographs of the back of the eye5. However, these so-called supervised deep learning approaches have critical disadvantages because they can only find what is defined a priori by human experts, thus being limited to known biomarkers, and they scale poorly due to the need for labor- or cost-intensive ground-truth training data in the range of several tens of thousands of known samples.Recently, artificial intelligence (AI) has made a powerful entry into medical imaging by automatically replicating specific human tasks of biomarker identification and quantification with superhuman accuracy. For instance, artificial neural networks could autonomously diagnose skin cancer6. We introduce an AI algorithm that teaches itself to capture the most characteristic local structural markers in retinal images, which represent the main patterns of light-tissue interaction as these images are acquired. In a second step, we achieve an extremely compact description of the impractically complex three-dimensional retinal scans. To validate our method, we demonstrate that the obtained biomarker candidates correlate better with clinical outcomes than conventional retinal features.To surpass these limitations, in this paper we explore a paradigm-shifting concept of mining complex high-dimensional medical imaging data. Instead of manual labeling and supervised learning, we propose an unsupervised deep learning architecture particularly designed for OCT representations for unbiased biomarker discovery7. It has become the most important diagnostic test in ophthalmology, with approximately 30 million procedures annually or an OCT scan taken every few seconds worldwide8. A major issue is that OCT volumes (similar to computed tomography or magnetic resonance imaging scans) contain several hundreds of sections, showing a wide range of tiny retinal changes. These giant datasets have to be interpreted manually by ophthalmologists as part of their daily practice. Moreover, as a relatively new technology, the specific relevance of many of these potential biomarkers remains unclear. This leaves researchers and practitioners overwhelmed by millions of images and a lacking consensus regarding the relevant imaging biomarkers for an efficient management of the leading eye diseases of modern times such as diabetic retinopathy and age-related macular degeneration. This provides a suitable setting for the development and validation of the presented biomarker discovery method.In the field of ophthalmology, modern retinal imaging is performed by optical coherence tomography (OCT), an affordable, non-invasive imaging technique that acquires high-resolution three-dimensional images within instantsThe proposed AI pipeline Fig.  consistsIn principle, an auto-encoder comprises two sequential deep neural networks. The first (encoding) network is trained to produce high-level low-dimensional descriptors of input data , while the second (decoding) network is trained to reconstruct the original input data from the high-level description provided by the encoding network. If the reconstruction is accurate (i.e. if the output of the decoder matches the input of the encoder), we can assume that a meaningful high-level representation (or embedding) of the input data has been learned. These learned features serve as novel biomarker candidates in our experiments.OCT images are acquired by scanning a laser beam tomographically across the retina and sampling the light-tissue interaction at each individual scanning location. Thus, we applied the first auto-encoder on these individual scanning locations resembling vertical signal columns , markers of retinal morphology as conventionally measured from OCT  as well as measures of disease activity obtained by fluorescein angiography, a conventional, invasive dye-based investigation 10. We also evaluated qualitatively whether any of the learned, new markers corresponded with established clinical markers as reported previously10.The auto-encoders were trained on a clinical data set consisting of 54,900 OCT volume scans . Secondly, the global feature vector (20 dimensions) was used. We computed the Pearson correlation coefficient of the detected features with the conventional biomarkers and clinical outcomes described above. The correlation coefficients were computed utilizing the available information of all 1,094 patients. Additionally, we conducted hypothesis tests to evaluate if the correlation coefficients were significantly different from zero. Since this was an explorative study, we did not perform correction for multiplicity testing in order not to increase the type II error (missing an effect that is present). Results are presented in Fig. For the evaluation using regression models, the treatment naive baseline study eye OCTs described above where randomly divided into training and test sets of 985 and 109 patients, respectively. For each of the above-mentioned known markers, we trained a multiple linear regression model, using the learned 20-dimensional features as input. Elastic net regularization with 5-fold cross-validation was used to determine the optimal hyper-parameters  captured the local morphologic patterns in the OCT data to a high degree and corresponded well to conventional OCT features, but also provided previously unknown features, i.e. subclinical biomarker candidates that had not been considered yet in clinical practice  correlation with conventional features obtained by fluorescein angiography. It also surprisingly revealed a markedly positive correlation with retinal function, i.e. 10. Thus, our deep learning network identified a hitherto unconsidered subclinical biomarker candidate with a high relevance for visual function. The marker captured the typical pattern of the large choroidal vasculature, and in patients with lower visual acuity showed central punched out regions of low activation . The univariate correlation of the features with functional and morphologic metadata is shown in supplementary Table The second autoencoder provided 20 global features per OCT volume scan , while the same linear regression model framework was used for prediction. Using the conventional variables, the quantitative results were We further evaluated the descriptive power of features obtained by the newly developed image analysis approach against conventional biomarkers. For this experiment, we compared the prediction model for visual function based on the new features Table  against 1, triaging retinal OCT scans2 or outlining particular lesions on medical images11. However, it has critical limitations, including (1) bias introduced by the underlying domain knowledge used to generate the man-made training data, and (2) limited scalability due to often prohibitively large amounts of annotated data required. This conventional AI approach also does not allow to discover hidden, subclinical biomarkers in the data. Some of the limits of supervised deep learning have been elegantly circumvented by reinforcement learning, where, for instance, the computer program AlphaGo Zero achieved superhuman performance in playing the game Go by solely being taught the game rules13. In medical imaging however, diagnostic procedures and decisions are not nearly as clear as the rules of a board game, and novel approaches are required particularly as therapeutic implications are often controversial and real world outcomes (particularly in the field of retina) are generally poor. By introducing unsupervised deep learning to medical image analysis, we create a rigorously data-driven analytical tool that is (1) unbiased because it does not rely on human-defined features or hypotheses, and (2) scalable at will because it does not require annotated training data. Our unsupervised deep learning pipeline identified subclinical biomarker candidates in a large-scale dataset that were as good as, or better, in representing the visual acuity of patients than conventional manually defined features measured by state-of-the-art image segmentation methods. We believe our method produces biomarkers that are characteristic of the data, unbiased, compact, task-independent, and easy to obtain.Supervised deep learning based on manually labelled input data can successfully replicate the behavior of human experts in relatively simple, but labor-intensive tasks such as in diagnosing skin cancer10. In addition, it recognized subretinal hyperreflective lesions (marker a5), which are thought to represent incipient fibrosis, as an important feature unrelated to exudation14. In fact, this particular feature showed the strongest correlation with visual function in our cohort; albeit currently not being considered as an endpoint in trials for retinal therapeutics. Thus, our disruptive approach of an unbiased biomarker search may be useful in identifying, defining and prioritizing targets and endpoints for the development of new compounds and interventions. In addition to representing the main known characteristics, our method may also be used to discover new marker candidates in image data. To complement the “usual suspects”, the deep learning algorithm identified a new feature (a4), which demonstrated a pronounced positive correlation with visual acuity in our cohort of patients. Further research may be directed at identifying anatomical correlates for this subclinical marker, such as intact neurosensory structures which do not attract any attention in current clinical trials despite morphological appearance. Currently, we are unable to pinpoint individual local markers to a particular anterior-posterior location in OCT A-scans, and therefore the exact origin of the feature activation is yet unknown. Nevertheless, biomarker discovery such as reported here may become an important aspect in medical image interpretation as conventional markers are regarded to have substantial weaknesses in reflecting patient-centered outcomes , and pivotal drug developments fail presumably also due to the lack of reliable endpoints16.One major advantage of unsupervised learning is that it automatically detects the most characteristic image features in a dataset, while remaining invariant to any prefabricated and hence biased medical hypotheses. In our experiments, the deep learning algorithm captured the main local biomarkers conventionally used in OCT interpretation, including retinal thickness, intraretinal cystoid fluid, subretinal fluid and pigment epithelial detachmentObviously, not all local markers represent clinically relevant information—similar to the OCT image itself. For instance, feature a11 showed very low correlations with the provided meta-information, while showing homogenous activation patterns across images. We may speculate that some of our features, including a11, capture image characteristics such as noise, that are part of the image, but do not relate to individual biomarkers. Future work may address the automated establishment of the number of dimensions in the auto-encoder embedding and thus analyze how many individual features are required to represent an image dataset comprehensively.The second step of the unsupervised embedding resulted in a characteristic, compact representation of complex three-dimensional medical imaging datasets. Without human-made limitation to particular variables or measurements, our algorithm provides 20 quantified global features for each volume that represent the major morphologic patterns in the image data, as opposed to an unmanageable amount of 67m (17. When applied on an external test set, prediction accuracy was reduced to In our experiments, we achieved a coefficient of determination of 20. Possibly, morphologic changes on OCT correspond better to low-luminance visual acuity because it is a more sensitive measurement of visual dysfunction in the macular area.Interestingly, the correlation with low-luminance visual acuity was consistently larger than with best-corrected visual acuity. Previous studies have shown impairment in low-luminance visual acuity in patients with age-related macular degeneration that exceeds the deficits seen in best-corrected visual acuity21. In the context of ophthalmic imaging, researchers have proposed algorithms to identify abnormal tissue patterns by learning the characteristic appearance of normal tissue23. In such an approach, anomalous regions can be analyzed further to establish clusters of biomarkers allowing to define marker categories. In contrast, we propose to learn both local and global high-level descriptions of images, which are not restricted to anomalous structures, to provide a compact representation of entire volumes, and omit the need to prepare a dataset of normal patients. We believe that this makes our approach a valuable tool for hypothesis generation and biomarker identification, supporting a critical shift of mindset in medical image analysis. Namely, it expands the conventional biomarker evaluation strategy from supervised automation of expert annotation in known anomalies to an unrestricted unsupervised exploration of large-scale datasets.Unsupervised deep learning has previously been leveraged in medical imaging. For instance, an unsupervised learned representation of local 2D image patches was used for the task of mammography risk scoring24. However, if doctors wish to augment their practice by artificial intelligence, these traditional paradigms may need to be revisited particularly as the hardware technology of image acquisition has long surpassed the feasibility of expert-based definition of features and assessment of imaging studies.Whenever image data are compressed to high level representations, topographic information of the represented biomarkers is reduced. In our global embedding, visualization of the features is not possible any longer because they do not contain any spatial information. Thus, it is challenging to interpret the individual biomarkers and their contributions to the variability of retinal morphology. These difficulties in interpreting the mechanisms of the deep learning model constitute the main limitation of our proposed algorithm. From a clinical perspective, it has always been desirable to clinically understand the steps taken by a model to reach a particular decisionIn this paper we introduced unsupervised deep learning to analyze high-resolution, three-dimensional retinal images without human-introduced bias. We presented novel auto-encoder based technology to capture the most relevant local structural biomarkers, including discovery of a new subclincial marker candidate. In a second embedding, we obtained a compact global description of the complex three-dimensional retinal scans, which nevertheless correlated better to visual acuity of patients than established artificial-intelligence based measurements. Once validated in additional, independent datasets, unsupervised machine learning and the resulting biomarkers may be employed in medical image analysis in retinal imaging and beyond.Supplementary Information.


The present study aimed to investigate the decision-making process and outcomes associated with downsizing to a smaller home in late life. Older adults who had downsized in the past year (n = 68) completed self-report measures of push-pull factors driving the decision to move, relocation controllability, sense of place (SOP), move satisfaction, and psychological well-being. It was hypothesized that the relation between push-pull factors and relocation outcomes  would be serially mediated by control and SOP. Haye’s PROCESS macro was used to test serial multiple mediator models for each of the relocation outcomes. Placing greater importance on push relative to pull factors was associated with lower levels of well-being in three domains: environmental mastery , purpose in life , and self-acceptance . Results of serial mediation analyses suggested that older adults whose downsizing decisions were more strongly influenced by push factors felt less control over relocation, found it more difficult to develop SOP in the new home, and, in turn, experienced lower levels of psychological well-being and move satisfaction. These findings can be used to inform older adults’ downsizing decisions and develop supports for relocating older adults.


Induction of immune tolerance for solid organ and vascular composite allografts is the Holy Grail for transplantation medicine. This would obviate the need for life-long immunosuppression which is associated with serious adverse outcomes, such as infections, cancers, and renal failure. Currently the most promising means of tolerance induction is through establishing a mixed chimeric state by transplantation of donor hematopoietic stem cells; however, with the exception of living donor renal transplantation, the mixed chimerism approach has not achieved durable immune tolerance on a large scale in preclinical or clinical trials with other solid organs or vascular composite allotransplants (VCA). Ossium Health has established a bank of cryopreserved bone marrow (BM), termed “hematopoietic progenitor cell (HPC), Marrow,” recovered from deceased organ donor vertebral bodies. This new source for hematopoietic cell transplant will be a valuable resource for treating hematological malignancies as well as for inducing transplant tolerance. In addition, we have discovered and developed a large source of mesenchymal stem  cells (MSC) tightly associated with the vertebral body bone fragment byproduct of the HPC, Marrow recovery process. Thus, these vertebral bone adherent MSC (vBA-MSC) are matched to the banked BM obtained from each donor, as opposed to third-party MSC, which enhances safety and potentially efficacy. Isolation and characterization of vBA-MSC from over 30 donors has demonstrated that the cells are no different than traditional BM-MSC; however, their abundance is >1,000-fold higher than obtainable from living donor BM aspirates. Based on our own unpublished data as well as reports published by others, MSC facilitate chimerism, especially at limiting hematopoietic stem and progenitor cell (HSPC) numbers and increase safety by controlling and/or preventing graft-vs.-host-disease (GvHD). Thus, vBA-MSC have the potential to facilitate mixed chimerism, promote complementary peripheral immunomodulatory functions and increase safety of BM infusions. Both HPC, Marrow and vBA-MSC have potential use in current VCA and solid organ transplant (SOT) tolerance clinical protocols that are amenable to “delayed tolerance.” Current trials with HPC, Marrow are planned with subsequent phases to include vBA-MSC for tolerance of both VCA and SOT. SOT has become standard of care over the last half century, resulting in not only a significant life extension but also an enhancement of quality of life (QOL) for end-stage organ failure patients , 2. MoreThe continuing negative impact of chronic rejection, combined with the severe adverse effects of conventional immunosuppressive regimens, has spurred intense research into new and safer strategies to prevent allograft rejection. While chronic allograft failure is associated with more frequent hospitalization, higher morbidity and increased health care costs, chronic immunosuppression (IS) is linked to side effects that range from malignancy, infection, toxicities  to cardiovascular and metabolic diseases –15. MediBesides tolerance regimens, various alternative strategies to inhibit rejection are in development to replace or reduce the need for current mainstay IS drugs. These regimens seek to shift the balance of lymphocytes in favor of regulatory T (Treg) cells over effector/memory T (Tem) cells, as opposed to pan-T cell inactivation with calcineurin or mTOR inhibition , 28. Of The vascularized BM component of VCA has innate immunomodulatory properties; however, this is not sufficient to fully tolerize recipients to the highly immunogenic skin component of the composite tissue , 36, 40.Although superficially similar in that both approaches administer hematopoietic cells, there are fundamental differences with respect to safety and mechanisms between the mixed chimerism-based approaches that are currently used in clinical trials to promote tolerance in SOT and the immunomodulatory approach in clinical use for VCA. Induction of tolerance through mixed chimerism necessitates non-myeloablative conditioning in the form of irradiation , cytotoxic agents  and cell-depleting agents . LaCurrently, there are three U.S. centers , Stanford University, and Northwestern University) that are investigating clinical protocols for inducing SOT tolerance . The proVCA grafts are invariably from deceased donors, which are also a source of high quality BM obtained from the donor vertebrae that can be cryopreserved for subsequent infusion , 53, 54.The achievement of immunomodulation with BM infusion that clinically translates into significantly reduced need for IS in VCA demonstrates that (1) harvesting and cryostorage of deceased donor BM is feasible, (2) cryopreserved deceased donor BM can be safely infused, and (3) delaying infusion of previously cryopreserved deceased donor BM over a significant period following VCA still achieves desirable biological effects. This suggests that delayed tolerance with deceased donor SOT may be possible.Feasibility of delayed BMT for tolerance in SOT in fact has been demonstrated in rodent and non-human primate models of solid organ and vascularized composite allotransplantation . As has also been demonstrated over the last few decades with cryopreservation of cord blood, it is well-established that BM remains biologically active following long-term cryopreservation –81. The Efforts are currently underway in collaboration with the national Organ Procurement Organization (OPO) network in the U.S. to build the first bank of cryopreserved deceased donor BM. The U.S. OPO network provides an existing refined infrastructure for procuring and transporting bone tissue recovered from deceased donors. Approximately 10,000 deceased donor organs are recovered each year in the U.S., with a further 40,000 donations, yielding approximately 30,000 organs and over a million tissues recovered annuallyProtocols for isolation of HPC, Marrow from deceased donor vertebral bodies were based on original work at University of Pittsburgh and optimized at Johns Hopkins University , 53, 54.Our team has identified an abundant population of MSC associated with the vertebral body (VB) bone fragment byproduct of HPC, Marrow recovery. These MSC remain tightly adhered to cancellous bone fragments and can only be released by enzymatic treatment. We have determined that these vertebral bone adherent MSC (vBA-MSC) are identical to BM-MSC when cultured . The vBAThe potent immunoregulatory properties of MSC comprise a spectrum of secreted and cell-bound molecules that modulate a wide array of innate and adaptive immune responses. The multifaceted mechanisms of MSC immunomodulation have been detailed in numerous reviews and, therefore, will only be briefly introduced here. The MSC secretome includes both freely soluble factors as well as those encapsulated by extracellular vesicles. Mechanisms include metabolic inhibition , immunomodulatory cytokines , and checkpoint inhibitors . These myriad factors inhibit T cell activation and proliferation, as well as enhance proliferation of regulatory cells –88.Preclinical studies have demonstrated the therapeutic potential of MSC for inducing operational tolerance of SOT and VCA –102, proIn addition to potentially facilitating graft survival through ameliorating alloreactivity, MSC have demonstrated considerable potential for suppressing GvHD which could be an unintended consequence of SCT to induce tolerance , 113–126Another mechanism to minimize the risk of GvHD is titrating down the HSPC graft to a minimal efficacious dose, which correspondingly reduces the donor T cell load. MSCs have been reported to facilitate and enhance engraftment of allogeneic HSPC clinically, even after initial graft failure and rejection of conventional stem-cell grafts . PreclinBesides GvHD, engraftment syndrome (ES) that occurs in 7–90% of cases during neutrophil recovery after autologous and allogeneic HSCT poses a potential limitation . It is aEach of the current protocols for inducing tolerance in VCA and SOT lend themselves to deceased donor BM augmentation and BMT, respectively, with modification to accommodate donor availability. Inclusion of vBA-MSC either prophylactically or to treat GvHD is possible. Following BM isolation and quality control testing, HPC, Marrow would be cryogenically preserved until shipping under the same conditions for infusion into the patient 14 days following surgery, as described previously . SimultaIn regard to SOT, the MGH delayed tolerance protocol appears to be the most easily adaptable to HPC, Marrow, providing that encouraging results in NHP and early clinical trials in humans are repeated in future larger clinical trials , 146–1486 CD34+ cells from HPC, Marrow recovered from three donors. Importantly, these methods can be used on either freshly isolated or previously cryopreserved HPC, Marrow; thus, providing flexibility in cell production. The Stanford protocol infuses cryopreserved selected cells at 11 days after kidney transplant which would provide sufficient time to prepare HPC, Marrow as well as over a billion very early passage GMP vBA-MSC  or durable  mixed chimerism –154. TheDespite clinical realization of tolerance and preclinical evidence supporting the feasibility of delayed tolerance protocols as outlined above, tolerance induction is still limited to a few highly specialized centers . WidesprFuture broad success with BM and MSC induction of tolerance and potent immunoregulation will have profound effects on transplant patients. Achieving immune tolerance, in particular, will alleviate the burden of life-long IS and associated morbidity, avoid chronic rejection, and significantly improve overall outcomes. Furthermore, it will overcome current compliance and adherence-based limitations that negatively impact graft survival due to subsequent subliminal rejection and the development of donor-specific antibodies. Tolerance and immunoregulation will be especially impactful for patients by increasing accessibility to transplantation and through positively shifting the risk:benefit ratio by reducing associated long-term risk.In view of these potential opportunities and substantial benefits, the establishment of a bone marrow bank for delayed tolerance protocols marks a crucial step in making this resource available for present as well as future transplant patients. The complementary treatment with vBA-MSC could further increase safety with the added potential of enhanced efficacy. Furthermore, the ability of vBA-MSC to promote HSPC BM engraftment would allow lowering of HPC, Marrow doses which effectively extends the number of patients who receive organs from a single donor that are able to benefit from this procedure.All authors contributed to writing and editing the article, and approved the submitted version.BJ, GB, and EW have equity ownership in Ossium Health. BJ and EW are inventors on patents pending relevant to the subject matter. EW is a co-founder of Ossium Health. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The prevention of sudden cardiac death (SCD) in cardiomyopathies (CM) remains a challenge. The current guidelines still favor the implantation of devices for the primary prevention of SCD only in patients with severely reduced left ventricular ejection fraction (LVEF) and heart failure (HF) symptoms. The implantation of an implantable cardioverter-defibrillator (ICD) is a protective barrier against arrhythmic events in CMs, but the benefit does not outweigh the cost in low risk patients. The identification of high risk patients is the key to an individualized prevention strategy. Cardiac magnetic resonance (CMR) provides reliable and reproducible information about biventricular function and tissue characterization. Furthermore, late gadolinium enhancement (LGE) quantification and pattern of distribution, as well as abnormal T1 mapping and extracellular volume (ECV), representing indices of diffuse fibrosis, can enhance our ability to detect high risk patients. CMR can also complement electro-anatomical mapping (EAM), a technique already applied in the risk evaluation and in the ventricular arrhythmias ablation therapy of CM patients, providing a more accurate assessment of fibrosis and arrhythmic corridors. As a result, CMR provides a new insight into the pathological substrate of CM. CMR may help identify high risk CM patients and, combined with EAM, can provide an integrated evaluation of scar and arrhythmic corridors in the ablative therapy of ventricular arrhythmias. Cardiac plasticity permits the heart to adjust its function and structure in response to environmental stimuli such as the increase in the wall thickness or the dilatation of the ventricles due to pressure or volume overload conditions . The terPatients diagnosed with any kind of CM are threatened by a variety of complications including progressive heart failure, need for cardiac transplantation or sudden cardiac death (SCD). Even if manifestations such as an episode of heart failure decompensation give time to seek medical help and are manageable, SCD leaves no room for reaction. SCD can be divided into two major categories—deaths that occur as a consequence of a mechanical complication, for instance, myocardial rupture or left ventricular outflow tract obstruction in HCM and deaths that have a malignant arrhythmic origin . The latAccurate and straightforward arrhythmic risk stratification algorithms for the primary prevention of major arrhythmic events (MAEs)—including, in this article, SCD and both sustained ventricular tachycardia (VT) and fibrillation (Vf)—constitute the holy grail of clinical electrophysiology. An effective and efficient approach allows for the rational targeted allocation of life-saving implantable cardioverter defibrillators (ICDs), with the expected benefit outweighing any device related complication and adverse events  . Based oThe aforementioned studies were not designed to stratify patients according to arrhythmic risk but rather, based on their LVEF; consequently, they only demonstrated that moderately-to-severely impaired LVEFs were associated with high arrhythmic risk, without including patients with mildly-to-moderately impaired contractility. Not unexpectedly, a two-fold disparity between ICD allocation and MAE occurrence in the primary prevention population has been noted, with an ever increasing trend, given that the majority of sudden deaths in the community occurs in people with mildly impaired LVEF ,21, and As contemporary studies ,21,22 raThe aim of this review is to present the CMR imaging patterns of the arrhythmogenic substrate, studied up to date in CMs, and examine the role of CMR findings in the clinical decision making of the electrophysiologist. The diagnostic imaging of the human body, independently of the modality used, enables the creation of contrast between tissues. The signal source that serves as contrast for magnetic resonance imaging (MRI) is the response of hydrogen atoms to the magnetic field. Each hydrogen atom is composed of a proton which carries its own spin. The human body does not spontaneously create magnetic signal since spins are oriented randomly . By placThe heart, as a constantly beating organ, which is also affected by respiration movements, makes CMR measurements challenging because of multiple artifacts. Techniques such as the Look Locker (LL) sequence, the Modified Look Locker Inversion Recovery Sequence (MOLLI), the shortened MOLLI (shMOLLI) and the Saturation recovery single-shot acquisition sequence (SASHA) overcome these difficulties by using protocols of supplement excitation RF pulses to extract T1 measurements and have significantly improved the reproducibility of the method .Regarding T1 mapping, it is created by the accumulation of multiple T1 measurements from many heart beats, all at the same phase of the cardiac cycle. Each T1 value is represented by a pixel in the map. T1 mapping images the entire myocardium and does not separate extracellular from cellular environments. The deposition of lipids, water, proteins or iron change T1 measurements and reveal early histological changes ,39. NatiWhen CMR imaging is being supplemented with intravenous contrast agents, the information derived is multiplied. Gadolinium-based contrast agents (GBCAs) are the most widely used agents approved for use .They areThe paramagnetic properties of gadolinium lead to a shorten T1 relaxation time in these areas. The most valuable information extracted by the use of GBCA is its pattern of removal from tissues. Normally, after its first distribution, a GBCA is progressively washed out. In areas of myocardial fibrosis, the wash out is delayed since the extracellular space is expanded, the capillary density is reduced and the distance between myocardial cells is augmented. Imaging the heart ”late” after the gadolinium has been washed out from healthy tissue can identify these areas with pathological response indicative of replacement fibrosis. It is noticeable that, by definition, late gadolinium enhancement (LGE)  expresseLast but not least, extracellular volume (ECV) is another useful parameter reflecting histological changes early in the CMs’ course, independently of their cause. It is measured by the native and postcontrast T1 mapping also including the hematocrit level ,46 Figu.The quantification of ECV by CMR is an excellent noninvasive method as the measurements are highly correlated with the results of biopsies ,48. ECV A common issue affecting all contrast-based scar detection lies in the definition of scar and its subtypes, given that it hinges on relative signal intensity. There is not a universal cutoff over which the high signal intensity in LGE-CMR reflects scar tissue. Subsequently, the definition of scar varies between different studies and researchers. Two major approaches have been pursued, one defining scars based on increased signal intensity  and the other defining healthy tissue based on less pronounced signal intensity . A few of the most popular scar definitions in studies are the followings: areas with signal intensity >6 standard deviations (SD) higher than the average intensity of remote areas , or withThe most well established parameter of an arrhythmogenic substrate in NIDCM is the LGE imaging ,29,30,31Furthermore, T1 mapping and ECV are also valuable tools in the arrhythmic risk stratification of NIDCM ,62,63,64Finally, CMR derived left ventricular longitudinal strain is a newly proposed marker of prognosis in patients with NIDCM with conflicting results ,66. In oThe scar tissue following an acute coronary syndrome (ACS) constitutes the most common arrhythmogenic substrate in ICM ,69,70. AFocal myocardial fibrosis detected by LGE imaging substantially increases the risk for life threatening arrhythmias in ICM ,33,34,72Regarding the role of T1 mapping and ECV measurements in the arrhythmic risk stratification of ICM, there are a few studies, including both ischemic and nonischemic CM patients, suggesting a correlation between their pathological values and an increased arrhythmic burden ,80.Here, it should be highlighted that another highly effective risk stratification approach with absolute negative predictive value, and a positive predictive one of 22% over a mean follow up of 32 months, is that of PVS response in post-myocardial infarction (MI) patients with a LVEF ≥ 40% having at least one out of seven noninvasive risk factors (NIRFs) present on ambulatory and signal averaged electrocardiography . This apIn a rare divergence between American and European guidelines ,85, primIndeed, there has been considerable debate concerning the value of the latest European risk score ,88 regarThe presence of scar, imaged by LGE in HCM patients is considered to be a strong independent predictor for ventricular arrhythmias, ventricular remodeling, all-cause mortality and cardiac death ,99,100. A relatively common finding during CMR imaging of HCM patients is the appearance of areas of T2-high signal spotted within LGE. Although its clinical significance is not well established, a study of 81 HCM patients concluded that high T2 signal is another parameter for VT manifestation in this group . ConcernInflammatory CM is the result of an inflammatory process due either to infective or autoimmune causes. Myocardial oedema and fibrosis as a result of macro-, micro-vascular coronary artery disease, vasculitis and autoimmune myocarditis may lead to SCD in patients with autoimmune diseases, even if the left ventricular ejection fraction (LVEF) is preserved . These cBetween various ARDs, polymyalgia rheumatica, the commonest rheumaticdisease in elderly people, may present with SCD, as a result of chronic inflammation. Several studies demonstrated that besides promoting structural heart disease, inflammatory activation may also be per se arrhythmogenic, via cytokine-mediated effects on cardiac electrophysiologic properties. Furthermore, there is increasing evidence that inflammation is a risk factor for QTc prolongation and associated life-threatening arrhythmias, specifically torsade de pointes (TdP) . AdditioAutonomic dysfunction, potentially leading to SCD, even in the absence of relevant symptoms or reduced LVEF, was documented in systemic lupus erythematosus, idiopathic juvenile arthritis patients and children with type 1 diabetes mellitus ,108,109.A significant limitation of VT ablation is its variable success rate. The HELP-VT study evaluated VT ablation outcomes among patients with ICM and NIDCM, demonstrating similar success rates in the short term, but with significant reappearance of the arrhythmia in the long term in the latter . The absElectroanatomic mapping (EAM) is the currently used approach to identify the arrhythmiologic substrate. However, in EAM the arrhythmogenic substrate is identified indirectly by collecting local voltage amplitudes of the myocardium. This method has some drawbacks: (a) it is time consuming, (b) it lacks sensitivity for scar substrates deep in the mapped surface and (c) it lacks specificity for scar detection in the case of poor catheter contact or thin myocardium. Therefore, the need for new strategies to define arrhythmogenic scar substrates is relevant . CMR exaThe first necessary step in this algorithm is to incorporate CMR into 3D-EAM in order to transfer anatomical data from one software environment to the other. Potential causes of a mismatch include different rhythms during different image acquisition timings, as well as different loading conditions, leading to slight geometric alterations in chamber geometry , as it hIn one of the first studies, to evaluate the integration of CMR to achieve the demanding task of scar definition in a clinical setting , this imGenerate anatomic images during the procedure. Consequently, any registration mismatch errors are avoided. The operator uses the CMR scanner virtually in the same manner as a fluoroscope, with an attached pedalfor image acquisition whenever e.g., tissue characterization is needed .Locate indwelling ablation catheters ,123,124.Produce substrate maps to be compared with those derived from the “classical” EAM module and improve area of interest targeting. Evaluate the formation of permanent, irreversible lesions ,125,126 In a more radical approach, the field of magnetic resonance electrophysiology (MR-EP) , alternaA multidisciplinary approach for the risk stratification of ICM and NIDCM patients seems to be more suitable than the proposed oversimplified one based on LVEF so far, in order to better define their complex substrate and support individualization. Towards this direction, several efforts have been made to update primary prevention arrhythmic risk stratification in ICM and NIDCM with rather well maintained LVEF, either by assessing the presence of an arrhythmogenic substrate through a two-step approach with noninvasive risk factor presence leading to PVSin both diseases ,89,133 oIn this direction, two prospective observational studies are currently underway: The CMR-Guide trial  (NCT0191The question of which is the best risk stratification approach among HCM patients with a low European risk score and/or a single conventional noninvasive risk factor present, remains a highly debatable issue. On the contrary, there is solid evidence that, in the presence of multiple NIRFs and/or a high European risk score, the justification of ICD insertion is well supported . AlthougThere is an urgent need to update our risk stratification criteria for the appropriate and timely selection of the ICD candidates among CM patients at all LVEF spectrum values. In this direction, CMR imaging may play a significant role. Indeed, LGE quantification and pattern of distribution, as well as abnormal T1 mapping and ECV, can further enhance our ability to identify patients susceptible to life threatening arrhythmogenesis. Currently, more sophisticated approaches combining a variety of noninvasive and invasive modalities are underway in the new era of the arrhythmic risk stratification among CM patients even at early heart failure stages with less impaired LV function. Furthermore, CMR in combination with EAM may assist in the effectiveness of demanding interventional EP procedures not uncommonly applied among such seriously affected high risk patients.


Extensive heterogeneity in autism spectrum disorder (ASD) has hindered the characterization of consistent biomarkers, which has led to widespread negative results. Isolating homogenized subtypes could provide insight into underlying biological mechanisms and an overall better understanding of ASD. A total of 1093 participants from the population-based “Healthy Brain Network” cohort  were selected based on score availability in behaviors relevant to ASD, aged 6–18 and IQ >= 70. All participants underwent an unsupervised clustering analysis on behavioral dimensions to reveal subgroups with ASD traits, identified by the presence of social deficits. Analysis revealed three socially impaired ASD traits subgroups: (1) high in emotionally dysfunctional traits, (2) high in ADHD-like traits, and (3) high in anxiety and depressive symptoms. 527 subjects had good quality structural MRI T1 data. Site effects on cortical features were adjusted using the ComBat method. Neuroimaging analyses compared cortical thickness, gyrification, and surface area, and were controlled for age, gender, and IQ, and corrected for multiple comparisons. Structural neuroimaging analyses contrasting one combined heterogeneous ASD traits group against controls did not yield any significant differences. Unique cortical signatures, however, were observed within each of the three individual ASD traits subgroups versus controls. These observations provide evidence of ASD traits subtypes, and confirm the necessity of applying dimensional approaches to extract meaningful differences, thus reducing heterogeneity and paving the way to better understanding ASD traits. The heterogeneity of ASD, reflected in its etiology, development, and biological phenotypes, presents an enormous challenge in the delineation and understanding of the disorder. It is therefore fundamental to define distinct subgroups of ASD, and dimensional approaches have been proposed as one way to perform this.Autism spectrum disorder (ASD) is a complex array of neurodevelopmental conditions typically characterized by social interaction and communication impairments, and restricted and repetitive behaviors5. Core autistic traits are often concomitant with behavioral traits varying in type and degree, thus further complicating the characterization of ASD. Common symptoms reported in ASD patients include anxiety6, depressive symptoms7, aggression8, attention deficits9, hyperactivity10, and sleep difficulties11. ASD patients are also more likely to present medical issues including seizures12, immune system abnormalities13, and gastrointestinal disorders14. Behavioral symptoms often indicate the presence of comorbid psychiatric disorders such as attention-deficit/hyperactivity disorder (ADHD), major depressive disorder, anxiety disorders, and conduct disorders. This complexity enforces the legitimacy of implementing a dimensional approach to examine continuous autistic traits and fluctuating behavioral symptoms as inter-correlated constructs varying in expression. A dimensional approach can promote stratification according to behavioral and biological features as suggested by the Research Domain Criteria15.It has been suggested that autistic traits, in particular social and communication deficits, are distributed along a continuum extending into the general population17. Several case-control studies have reported various changes in thickness including increases18 and decreases20. Surface area in ASD has been less investigated, with most results reporting no differences22. Studies have also reported significant global increases in surface area at younger ages24, as well as decreases in later stages of life24. Gyrification observations within ASD on the other hand have greatly reported increases26, with few showing decreases28. Though neuroanatomical investigations in ASD have remained largely unreplicated, few observations have persisted. This includes early brain overgrowth in frontal and temporal lobes31, causing children to achieve a nearly developed brain volume earlier than controls. Also, longitudinal studies in cortical thickness have shown a general trajectory of accelerated thinning with age in ASD patients versus controls in frontal, temporal, and parietal areas32. We presume that the general variability and lack of reproducibility is due to the frequently encountered practice of combining heterogeneous ASD patients into one group within case-control studies.A prominently studied biological feature in ASD is cortical morphometry. In particular, ASD is accompanied by a range of aberrant cortical patterns present in both volumetric and surface-based morphology studies33. Extracted subgroups were subsequently combined into one autistic traits group and compared in cortical surface features  to the remainder of the population , thus mimicking a case-control study. We show that the case-control paradigm does not extract meaningful cortical features and that behavioral stratification is required. Therefore, in order to achieve clinically relevant morphometric signatures, we ran morphological analyses comparing each of our isolated socially impaired subgroups to controls. This should provide us with a better understanding of underlying heterogeneity present in the physiology of autistic traits, and ultimately ASD.The objective of the present study is to thus disentangle and better understand the behavioral heterogeneity in ASD by using subjects with autistic traits to extract refined cortical morphometry features. To observe how behavioral dimensions distribute in a general population of children and adolescents that vary in degree of social impairment spreading across the spectrum, we designed the following experiment. We chose a broad age range (5–18), including males and females, within a large-scale multidimensional population-based cohort in order to capture a larger effect variance (compared to a purely ASD cohort) by focusing on behavioral constructs, and not an ASD diagnosis. We then conducted an unsupervised clustering analysis on the z-scores of several behaviors, especially chosen due to their manifestation in ASD, in order to isolate data-driven subgroups high in our dimension of interest: social impairment, which is a surrogate of autistic traits34. It comprises a population of individuals at-risk for developing psychiatric disorders and typically developing participants. Subjects were recruited through flyer dissemination and subsequently assessed on clinical questionnaires at three sites in New York City, USA: Staten Island, Mobile Van, Midtown.The Healthy Brain Network (HBN) cohort initiative within the Child Mind Institute began in 2015 with the goal of collecting brain imaging, cognitive/behavioral, and genetic data for 10,000 children and adolescents (5–21 years old) to investigate the heterogeneity behind neuropsychiatric and neurocognitive development39. Full-scale IQ was measured using the Wechsler Adult Intelligence Scale  or the Wechsler Intelligence Scale for Children (WISC-III). Written informed consent was obtained from legal guardians and from participants themselves. This cohort study initiative was approved by the Chesapeake Institutional Review Board.In the status of the HBN cohort, consensus diagnostics are not available for most of the subjects enrolled; however, this does not preclude the possibility to carry out our dimensional study since subjects were not selected based on an ASD diagnosis, but rather on the presence of behavioral constructs relevant in the field of ASD. There were 1800 subjects participating at the time of this study, of which 1093 remained based on available overlap in behavioral scores assessing social deficits, hyperactivity, anxiety, irritability, depression, aggression, and attention problems, and having a full-scale Intelligence Quotient (FSIQ) >= 70. We selected these seven behaviors due to their presence in comorbid psychiatric disorders commonly reported in ASD patients, and therefore their frequent emergence along the autistic behavioral spectrum, implicating them in the understanding of ASD behavioral neuropathology44. Though not a diagnostic tool, the SRS exhibits high inter-rater and cross-cultural reliability, and correlates greatly with the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview—Revised (ADI-R) diagnostic assessments for ASD from the DSM-5, making it a robust measure to use in the dimensional study of ASD behaviors45. Similarly, for the remaining behaviors we did not use diagnostic assessments but rather scales measuring behavioral trait severity. Hyperactivity levels were determined using the hyperactivity subscale within the Strengths and Difficulties Questionnaire (SDQ)46; anxiety was measured using the total score from the Screen for Child Anxiety Related Disorders Parent-Report (SCARED-P)47; irritability was defined using the total score of the Affective Reactivity Index Parent-Report (ARI-P)48; and lastly, levels of depression, aggression, and attention problems were determined using subscales of the same names within the Child Behavioral Checklist (CBCL)49.One of the most prominent dimensions in ASD patients is social impairment. Here, we used data from the widely used 65-item parent social responsiveness scale (SRS) as a quantitative measure of clinical autistic traits, making it the central variable of interest in our study and in fact the score from which we separate out our autistic traits individuals. The SRS has been proven as a valid measure of autistic traits and thus has been used as a measure of autistic traits (for the purpose of understanding ASD) in several behavioral, genetic, and neuroimaging studies50. A substantial challenge in such analyses lies in determining the number of clusters, which is a user-defined parameter. To address this problem, the chosen number of clusters k was determined using a Bayesian Information Criterion (BIC) distribution , SciPy (version 1.1.0), and Matplotlib (version 1.5.1).Mean behavioral scores, FSIQ and age were compared between subgroups using non-parametric 2-sided Mann–Whitney 34).MRI acquisition took place at three different sites: mobile 1.5T Siemens Avanto in Staten Island, 3T Siemens Tim Trio at Rutgers University Brain Imaging Center, and 3T Siemens Prisma at the CitiGroup Cornell Brain Imaging Center . For more information on precise methods of image analysis and the construction of anatomical information for each individual done by this software, refer to53. Briefly, the FreeSurfer analysis stream includes intensity normalization, skull stripping, and segmentation of gray  and white matter surfaces52. Subsequent tessellation, as well as various topology corrections and inflation, leads to 3D meshes of cortical surfaces in different resolutions. Our work is based on a tessellation with ~160,000 vertices per hemisphere and used the FreeSurfer fsaverage template. We focused on three morphological measures of which the processing stream created vertex-wise maps for analysis: cortical thickness (CT), surface area (SA), and gyrification (lGI). The local gyrification index is measured as the ratio between buried and visible cortex54. All images were manually inspected in-house, in addition to using the Euler number as a metric of quality by retaining images at a threshold of -217, as specified in Rosen et al. 55.T1-weighted images were processed using the FreeSurfer software version 6.0.0 , and including the residual error:p = 0.01. We report clusters with cluster-wise p-value (cwp) of cwp <0.05. These p-values were corrected for multiple comparisons using the mri_glmfit-sim precomputed MonteCarlo simulation.Vertex-wise statistical analyses were conducted using the command-line group analysis stream in FreeSurfer. Cortical surfaces for each participant were first registered to a study-specific template, then smoothed using a full-width-at-half maximum (FWHM) kernel of 10 mm for CT and SA, and 5 for lGI. A general linear model was fit at each vertex k value of 9 was retained as our supervised partitioning for this study , thus indicating a high level of social impairment, providing us with greater confidence that subjects within these subgroups have “autistic-like” traits , n = 107 ; the second maintained normal levels in all behavioral scores except for attention problems and hyperactivity, n = 82 (described as attention problems—Attn); and the third showed high levels of anxiety and depression, as well as attention deficits, n = 61 (described as anxiety depression—AnxDep)  with the aim of creating a representative general population without autistic traits subjects. Studies often barely obtain additional behavioral information on their controls other than a “non-diagnosis” or “low SRS”. By combining these remaining six subgroups into one control group, we smooth out several behavioral heterogeneities and yield a control group composed of a wide behavioral spectrum, while still maintaining low mean levels of SRS  . We also compared each autistic traits subgroup to controls and observed significant differences in all behavioral scores, except for reactivity in the Attn subgroup. Lastly, we decided to combine all three high autistic traits subgroups into one “autistic-like” group  to compare against controls, which yielded highly significant differences in every behavioral score (Table Comparisons to controls”).Upon comparing the three socially impaired subgroups to one another, several significant differences in behavioral scores were found (Table Comparisons between subgroups”). Upon comparing each subgroup to controls, we again found no differences in gender. We did however find differences in age between all subgroups and controls, except for Emot. Although age differences were present, age ranges were again similar. FSIQ differed between all subgroups and controls, except for AnxDep, which is to be expected since autistic traits are generally accompanied by differences in FSIQ. Lastly, the comparison between the hSRS group and controls yielded significant differences in age and FSIQ, but not gender (Table Comparisons to controls”). Due to these differences, we deemed it important to control for FSIQ, age and gender in the subsequent neuroimaging analysis.With respect to demographic information, there were no significant differences in gender and FSIQ between all subgroups. However, there were reported differences in age, though age ranges were similar  . To delineate the interest and significance of subtyping in an autistic traits population, we first combined all three subgroups into one large group  and compared cortical thickness, local gyrification and surface area against controls. Indeed, this comparison did not yield significant differences in any of the measured surface features. We then compared the same surface features between each of our three subgroups against controls. After correction for multiple comparisons, the Emot subgroup exhibited decreases in gyrification in the right hemisphere in two separate clusters, one spanning the precuneus  , and another in the temporal lobe  (denoted as PC and pITG)  . Additionally, the Attn subgroup also exhibited two separate clusters in the left hemisphere showing increases in surface area in the precentral cortex  , and superiorfrontal regions  (denoted as PreC and SF)  , and decreases in thickness in the left posterior middletemporal gyrus lining the superior temporal sulcus (pMTG/STS)   Fig. . The AttSF) Fig. . Lastly,55) Fig.  (Table 3Emot), (2) high in attention deficits and hyperactivity (Attn), and (3) high in anxiety and depression, as well as attention deficits (AnxDep). Furthermore, upon neuroanatomical investigation, we found that comparing each subgroup to controls uncovers unique cortical signatures. Namely, after correcting for multiple comparisons, the Emot subgroup showed decreased gyrification in precuneus and posterior inferior temporal regions (PC and pITG); the Attn subgroup displayed increases in gyrification in the lateraloccipital (LO) and increases in surface area in the precentral and superiorfrontal regions (SF); and lastly, the AnxDep subgroup exhibited an increase in gyrification in the postcentral cortex (PostC), as well as a decrease in thickness in the posterior middletemporal and superior temporal sulcus area (pMTG/STS). Most interestingly, we found that by comparing the structural brain features of one heterogeneous autistic traits group  to controls, we were unable to uncover any cortical signatures. Simply comparing behaviorally diverse ASD-like cases to controls proved far too rudimentary to yield consistent features.ASD studies have unceasingly demonstrated heterogeneity, warranting a shift in focus towards initially characterizing these differences before subsequent analysis, and steering away from case-control studies. To this end, a dimensional approach proves most relevant. To the best of our knowledge, this is the first study using an unsupervised clustering analysis on a population-based cohort to investigate how autistic traits cluster with other behavioral dimensions into subgroups, with subsequent isolation of subgroup cortical signatures. Recent evidence advocates that autistic traits fall along a continuum within the general population, which was why this study was not limited to diagnosed individuals, but rather focused on autistic traits as absolute constructs in order to avoid potential selection or environmental biases often accompanying diagnosed patients. We obtained three autistic traits subgroups in our unsupervised clustering analysis with the following behavioral profiles: (1) high aggression, reactivity, and ADHD-like traits  as well as in visuospatial processing, empathy and memory, while the temporal lobe correlates to memory, audition, theory of mind and visual processes68. Considering that this group bears high in aggression, studies have also reported a general decrease in gyrification in aggressive patients70, as well as decreased functional connectivity between the precuneus and other brain regions in patients exhibiting higher aggression traits, possibly due to its role in the DMN and empathy72. Additionally, this subgroup exhibited high ADHD-like symptoms which have also shown links to precuneus regions of the brain74. In our second subgroup, Attn, we observed increases in surface area in the precentral (primary motor) cortex, which is involved in voluntary motor control76, and the superiorfrontal gyrus, which is part of the motor control network and also harbors functions in attention, working memory, executive functioning and in the default mode network78. A study has even suggested that early motor impairments are predictors of future social communication delays, further indicating the importance of understanding this region in relation to ASD risk79. Specifically, within ASD, atypical motor functioning has been measured in patients from infancy until well into adulthood81. Seeing as how we observed extensive structural alterations throughout the motor control network in the Attn subgroup, this warrants further investigation into the relationship between ADHD-like traits and motor control in ASD and autistic traits populations. The Attn subgroup also presented decreases in gyrification in the lateraloccipital region, which is heavily implicated in visual perception, and specifically in face recognition, which greatly influences social communication83. The last subgroup, AnxDep, exhibited increases in gyrification in the postcentral region (primary somatosensory cortex), which functions as the main sensory receptive area of the brain85. In ASD, atypical sensory reception, more specifically over-responsivity to tactile sensory inputs, is a very prevalent symptom88. This suggests that autistic individuals could easily be overwhelmed, perhaps forging a link to the development of anxious and depressive behaviors, as observed in this subgroup. The AnxDep subgroup additionally showed a decrease in thickness in the pMTG/STS region, which has been greatly implicated in language and social aspects, and thus an extremely important cortical region of interest in ASD behavioral studies91. Therefore, taking into account dimensional constructs of behavior in ASD can better prepare subgroups for the identification of biological mechanisms. Further investigation is warranted into the relationship between affected regions and corresponding subgroup behaviors in the context of ASD since these regions have been consistently reported within the ASD literature.Compared to our high vs. low/absent autistic traits contrast that yielded no results, by isolating behaviorally refined autistic traits subgroups we observed cortical signatures despite having lower statistical power than the combined sample. Decreased gyrification detected in the right PC and pITG region in the 39. Reported comorbid disorders include ADHD, depression disorders, anxiety disorders, obsessive compulsive disorder (OCD), and conduct disorders39. This high degree of comorbidity (based on diagnostic information) corresponds to our dimensional results, which describe these associations in an even more descriptive and spectral manner using behavioral constructs. Having access to a multidimensional cohort containing assessments of behaviors reported in ASD allowed us to explore how core autistic traits inherently distribute with other symptoms in a dimensionally continuous population. By running a data-driven clustering analysis on a population-based cohort, we isolated three main autistic traits subgroups. The AnxDep subgroup is composed of subjects high in anxiety, depression and attention deficits. This is in line with findings reported in the literature where anxiety and depression appear to be some of the most common psychiatric comorbidities in ASD patients39. The Attn subgroup could represent an isolated population consisting purely of ADHD and autistic traits in an otherwise behaviorally muted subclass. This may perhaps become the optimal subgroup for studying the overlap between ADHD and ASD. Lastly, the Emot subgroup has ADHD-like traits in combination with emotional regulation abnormalities as evidenced by high degrees of aggression and reactivity. The Emot subgroup suggests a third combination of behavioral traits showing that the aggressive behaviors often observed in autistic traits participants can in effect co-occur with ADHD-like traits. The diverse behavioral profile of each subgroup highlights the importance of combining independent behaviors into one multivariate analysis to observe how they distribute. For example, as mentioned previously, both the Attn and Emot subgroups show high levels of attention problems and hyperactivity, and remain relatively close in anxiety and depression levels. The Emot subgroup, however, exhibits exceptionally high levels of aggression and reactivity, a factor that separates one ADHD-like autistic traits subgroup into two (i.e. Attn and Emot), thus increasing behavioral homogeneity and the likelihood of extracting biological features from cortical images.The behaviors observed in our subgroups enforce the fact that ASD is highly concurrent with several psychiatric conditions in up to 80–95% of patients92. However, this gender disequilibrium is not entirely surprising as this difference can be explained by the fact that studies sampling from the general population often show a lower ratio (3:1)93, and that overall variability may play a role. Moreover, several studies have reported ratios ranging from 2:1 to 7:197, indicating a heterogeneity that warrants further exploration as well as a diversity in gender ratio that depends on how cohorts are built. Most importantly, within the current investigation gender differences were controlled for in the neuroimaging analysis.Notably, the unsupervised clustering analysis yielded high autistic traits subgroups with gender ratios  differing from those usually reported ASD populations 99. Also, the average age of subjects in the current study (~11.4 years old) could indicate that our cortical results are consequences of differential child development, a hypothesis however that can only be confirmed using a longitudinal, prospective design. Clinical diversity in autistic traits may be further explained by other modalities, thus next steps would involve considering genetic, volumetric, diffusion, and functional differences between the acquired subgroups.This study has several potential limitations. Firstly, though the SRS included a repetitive behavior subscale, it would have been interesting to include an independent repetitive behavior component within the clustering analysis. Concerning the unsupervised clustering, inherent limitations include the somewhat arbitrary determination of the number of clusters, and difficulties to reproduce the same partitioning in another dataset. Also, it is challenging to account for covariates in unsupervised clustering analyses. Alternative approaches could also have been applied on this dataset that would prove interesting in future studies including clustering based on SRS subscales , or clustering on a broader range of scales  with subsequent isolation of subgroups high in SRS. Additionally, the present study used a general population-based cohort, and not one tailored for ASD studies, thus warranting the careful isolation of behavioral variables relevant to our objective. Within the morphometric results, it is possible we did not observe further thickness differences due to the wide age range of our cohort. Thickness changes more with age and environment and may thus present larger heterogeneities than does gyrification , leading us to observe greater gyrification alterations within our resultsIn conclusion, we showed that subtypes of autistic traits yield refined signatures and therefore stress the importance of stratification using a dimensional approach. Several studies, including the current one, have demonstrated the difficulty in yielding significant biological features in case-control comparisons, leading to large-scale inconsistencies within ASD literature. Since several of the behavioral associations and affected cortical regions discussed in this study have similarly been implicated in ASD studies, our findings maintain the growing assumption that outcomes in autistic traits are related to variations observed in ASD patients. By uncovering better-defined subtypes of ASD, studies can finally begin to truly understand the underlying genetic, biological and behavioral mechanisms of this syndrome.Supplementary FiguresSupplementary Table


Head and neck cancer (HNC) is among the most common malignancy that has a profound impact on human health and life quality. The treatment for HNC, especially for the advanced cancer is stage-dependent and in need of combined therapies. Various forms of adjuvant treatments such as chemotherapy, phototherapy, hyperthermia, gene therapy have been included in the HNC therapy. However, there are still restrictions with traditional administration such as limited in situ therapeutic effect, systemic toxicity, drug resistance, etc. In recent years, stimuli-responsive drug delivery systems (DDSs) have attracted the great attention in HNC therapy. These intelligent DDSs could respond to unique tumor microenvironment, external triggers or dual/multi stimulus with more specific drug delivery and release, leading to enhanced treatment efficiency and less reduced side effects. In this article, recent studies on stimuli-responsive DDSs for HNC therapy were summarized, which could respond to endogenous and exogenous triggers including pH, matrix metalloproteinases (MMPs), reactive oxygen species (ROS), redox condition, light, magnetic field and multi stimuli. Their therapeutic remarks, current limits and future prospect for these intelligent DDSs were discussed. Furthermore, multifunctional stimuli-responsive DDSs have also been reviewed. With the modification of drug carriers or co-loading with therapeutic agents. Those intelligent DDSs showed more biofunctions such as combined therapeutic effects or integration of diagnosis and treatment for HNC. It is believed that stimuli-responsive drug delivery systems showed great potential for future clinic translation and application for the treatment of HNC. Head and neck cancer (HNC) refers to epithelial malignancies of the paranasal sinuses, nasal cavity, oral cavity, pharynx, and larynx. The high incidence and prevalence of HNC profoundly influence human health and reduce the quality of life  that could transport and release therapeutic agents to the tumor site have provide a promising strategy for the HNC therapy, which are usually consist of carriers and associated therapeutics for improved drug stability, local drug administration and controlled drug release , reducing environment . Also, tpH-responsive DDSs are among the most reported intelligent DDSs in HNC therapy considering the acidic tumor microenvironment (TME) distinguishing from normal tissue . Hypoxiad-(KLAKLAK)2 peptide. The DDS showed significant a significant increase in peptide release at acidic pH and KB cell ablation. Wei et al.  is widely utilized to functionalize the carriers since folate receptor (FR) have been reported highly expressed in many cancer cells but rarely in normal cells  of HNC tumor cells has become one of the major problems of chemotherapy , a family of proteolytic enzymes which could degrade extracellular matrix protein. They are in low quantity and activity in normal situation but could be upregulated in many kinds of cancers including HNC, which have been considered closely related to cancer initiation, growth, and metastasis , singlet oxygen (1O2), superoxide (O2−), and hydroxyl radicals (HO•), are oxygen-carrying active molecules produced by mammalian mitochondria, endoplasmic reticulum, and NADPH oxidase. In normal situation, ROS in tissue play an important role in modulating the of functions of proteins, regulating cell signaling, killing pathogens, etc. /glutathione disulfide (GSSG) couple has been reported the most abundant redox couple in cancer cells. The concentration of GSH in tumor tissues has been found much higher than that in normal tissue  fluorescence, etc.) represents as promising exogenous stimulus in the HNC tumor therapy, for its noninvasive feature and possibility of spatio-temporal control  has been recognized as an important therapy for HNC, with low side effect and little influence on the tissue structure and function of HNC patients  refers to induced hyperthermia in the targeted tissues under light irradiation for anti-neoplasm effect, which is considered as a promising strategy for HNC treatment , a kind of clinically used NIR-absorbing dye was reported able to convert light energy into heat due to internal conversion. Meanwhile, ICG was also reported cytotoxic effect on tumor cells with the generation of ROS  were mostly used strategy. Wang et al.  developePositive targeting was also reported in the light-responsive DDSs research. Ligands conjugated carriers were been fabricated for more specific drug delivery. For instance, Akbarzadeh et al.  reportedMultifunctional light-responsive DDSs has been applied in the design of integrated materials of diagnosis and treatment for HNC as well. Near-infrared fluorescence dyes could be co-loaded in the DDSs for tumor imaging and tumor ablation. Haedicke et al.  fabricat3O4 nanoparticles as a gene transfer vector to mediate transfection of OSCC by the targeting plasmids transport with an external magnetic field. The improved transfection efficiency and antitumor effect was verified in vitro and in vivo.The magnetic nanoparticle (MNPs)-based DDSs that could respond to external magnetic field have been extensively studied in diagnosis and treatment of the cancer . They coFurthermore, another important of MNPs is the heat generation induced by alternating magnetic field (LeBrun & Zhu, Other multifunctional magnetic field-responsive DDSs has also been reported with the development of the modification of MNPs, such as DDS with combination of diagnostics and treatment, ligand linked DDS and MNP-based multistimuli-responsive DDS which will be describe below.As is showed above, great progress has been made in the stimuli-responsive DDSs for the treatment of HNC in recent years. Researches have also made great efforts to modify stimuli-responsive DDSs with multiple functions for more controllable drug delivery, better biocompatibility and less drug resistance. The carriers were modified with functional segments for active tumor targeting, better stability of DDS, more drug loading, etc. Various kinds of diagnostic and/or therapeutic agents were co-loaded to improve the treatment efficiency, overcome drug resistance, simplify the diagnosis and treatment procedures, and so on.Moreover, another important strategy to further finetune drug release and enhance therapeutic efficacy of stimuli-responsive DDSs has gained great attention in recent years; that is, the development of novel dual or multi stimuli activated DDSs, which could respond to more than one internal and/or external trigger simultaneously or in sequence (Bhatnagar & Venuganti, More researches were conducted on the intelligent DDSs with combined response to internal and external triggers, since physical exogenous stimuli could be more controllable and flexible. Such dual stimuli-responsive DDSs were widely applied in the phototherapy for HNC. PDT or PTT agents were loaded in pH or MMP sensitive drug carriers for TME controlled drug release and light triggered antitumor treatment (Tarassoli et al., External magnetic field is another important physical stimulus applied in the research of multistimuli responsive DDSs. Magnetic nanoparticles have been widely studied as drug carriers in the diagnosis and treatment for HNC. In recent research, they were also reported combined with phototherapy, yielding magnetic field and light stimuli-responsive DDSs. For example, Bhana et al.  presenteBeside of dual-stimuli responsive DDSs, other triple or multistimuli responsive DDSs have also been reported in recent years. Jin et al.  conductein vivo condition. Additionally, multifunctional and multi stimuli-responsive DDSs have showed great potential in theranostic applications, combined antitumor therapy, inhibition of MDR and other kinds of treatments for NHC as mention before. The fabrication of stimuli-responsive DDSs with optimized structure, simplified preparation process and improved in vivo therapeutic effect might become one of the development directions.In this article, current progress of stimuli-responsive DDSs for the treatment of HNC were reviewed, which could respond to various kinds of triggers including the internal TME of HNC, external stimuli or multi stimuli. Their advantages and limitations were also summarized. It revealed that stimuli-responsive DDSs could be a promising nanomedicine platform for the anticancer drug delivery with improved therapeutic effect and systemic side effects over traditional drugs administration. Yet there are still many challenges which desire future studies are desirable. As mentioned before, although TME is quite different from normal, the condition is sometimes unstable and uncontrollable, which might limit the application of the internal stimuli-responsive DDSs. More strategies are desired to improve the sensitivity and controllability of stimuli-responsive DDSs. The combination of external stimuli responsive ability could be a useful option. Yet more research is still required for proper tissue penetration, more specific action range and less adverse effect on normal tissue around. Meanwhile, although the active targeting stimuli responsive DDSs has drawn growing attention in the HNC therapy, the research is still on the way to further exploration. Particularly, more preclinical tests are needed to investigated the antitumor effect and biocompatibility in the complex To sum up, stimuli-responsive DDSs have great prospect of clinical translation and more exploration, which provide a great alternative for NHC therapy. It is believed that with the development of fabrication techniques and further research, more stimuli-responsive DDSs will be applied for the treatment of HNC in clinic in the near future.


The molecular basis of plant immunity triggered by microbial pathogens is being well-characterized as a complex sequential process leading to the activation of defense responses at the infection site, but which may also be systemically expressed in all organs, a phenomenon also known as systemic acquired resistance (SAR). Some plant-associated and beneficial bacteria are also able to stimulate their host to mount defenses against pathogen ingress via the phenotypically similar, induced systemic resistance phenomenon. Induced systemic resistance resembles SAR considering its mechanistic principle as it successively involves recognition at the plant cell surface, stimulation of early cellular immune-related events, systemic signaling via a fine-tuned hormonal cross-talk and activation of defense mechanisms. It thus represents an indirect but efficient mechanism by which beneficial bacteria with biocontrol potential improve the capacity of plants to restrict pathogen invasion. However, according to our current vision, induced systemic resistance is specific considering some molecular aspects underpinning these different steps. Here we overview the chemical diversity of compounds that have been identified as induced systemic resistance elicitors and thereby illustrating the diversity of plants species that are responsive as well as the range of pathogens that can be controlled via this phenomenon. We also point out the need for further investigations allowing better understanding how these elicitors are sensed by the host and the diversity and nature of the stimulated defense mechanisms. These Pimulated . This PRimulated . Howeverimulated .Pseudomonas simiae strain WCS417 first detected as a pathogen via its flg22 epitope, but which then strongly attenuates the host immune response presumably via T3SS-mediated delivery of effectors  as quorum-sensing molecules involved in cell-to-cell communication in order to monitor their behavior according to population density. Some AHLs are not only the mean of communication between bacterial cells, but also may be used as signal in inter-kingdom interaction and act as plant growth promoting compounds and/or as immunity elicitors  and hemibiotroph (P. syringae DC3000) in barley and Arabidopsis, but not against the necrotrophs Botrytis cinerea and Plectosphaerella cucumerina BMM , surfactin demonstrated to induce ISR in bean, tomato, tobacco, against B. cinerea, in melon against Podosphaera fusca, and peanut, against Sclerotium rolfsii  are multifunctional secondary metabolites involved in developmental processes such as motility and biofilm formation and in biocontrol primarily based on their antimicrobial activity . However rolfsii . The str rolfsii . Despite rolfsii . Inductiviticola , while Cviticola  were shoviticola , cotton viticola , grapeviviticola  and Arabviticola . Additioviticola . The actviticola .Pseudomonas sp. CLPs in plant resistance stimulation on various pathosystems. Massetolide A was the first reported for its resistance-inducing activity in tomato against P. infestans  are amphiphilic glycolipids produced by various species including pathogenic isolates, but also some plant beneficial  species . They ar species . Mono- a species  tested ao 300 μM , 2020.N-alkylated benzylamine derivative  were as well-reported to induce systemic resistance in the host plant, showing that these metabolites can also act as infochemicals involved in inter-kingdom communication . The mosinactive . Bioacti viruses , where t viruses . HoweverBacillus CLP surfactin was fully conserved in protease-treated tobacco cells, and successive applications do not lead to some refractory state due to the saturation of high-affinity binding sites (as receptors). Based on these and the fact that surfactin readily interact with sphingolipid-enriched microdomains in the plasma membrane, it was suggested that this CLP is perceived by plant cells via a lipid-mediated process. This is supported by the strong structure-dependent activity showing that only long fatty acid chain homologs are active at triggering early immune-related events in tobacco cells because they should display stronger interaction with lipid bilayers organisms . Furtherbilayers . Rhamnolbilayers . As othebilayers . It may The way PGPR elicitors are perceived at the plasma membrane level is not clear, but there is still a clear convergence between PGPR-triggered immunity and pathogen-induced PTI in the early steps of downstream signaling. The best studied PGPR elicitors induce similar early immune events as observed upon pathogen perception, such as oxidative burst, ion fluxes, and phosphorylation cascade . Detectia priori conducive for development of resistance in pathogens (Pseudomonas spp. and Bacillus spp.), leaving elicitors from many other PGPR to be discovered. Moreover, in the last decades, tremendous advances have been done on understanding the basics of MAMP perception during PTI, but the mechanistic of PGPR elicitor recognition at the plant plasma membrane level and the molecular events underlying PGPR-induced priming remain largely obscure. As the lipid phase is suspected to act as docking platform for some of these elicitors, experimental biophysics and in silico dynamic modeling using appropriate biomimetic vesicles represent interesting approaches to get further insights into the physico-chemical basis of the interactions (From an agronomic perspective, ISR triggered by PGPR is interesting since the phenomenon is considered to provide long-lasting and broad-spectrum protection without causing growth cost and is not athogens . Howeverractions . It woulractions . Also, tin planta are sufficient to locally reach such threshold around the roots (Bacillus lipopeptides (Furthermore, PGPR elicitors in most instances are active at micromolar doses but only a few studies suggest that quantities produced by bacteria he roots . Determihe roots , but thepeptides . Biotic peptides . A betteBacillus lipopeptides (Integrating all this missing information should also lead to more rational determine the range of pathosystems, and environmental conditions in which PGPR-based bioproducts would be the most efficient. An alternative is to make from these bacterial immunogenic compounds microbial-derived products for the phytosanitary market provided that they can be produced and formulated in cost-effective industrial processes. This is feasible for some metabolites with high production rate in bioreactors, such as peptides , but theBoth authors conceived the idea, designed the outlines of the review, and wrote the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Compared to young men, Ugandan young women are disproportionately impacted by HIV. Childhood transactional sex may contribute to this disparity. Using data from the 2015 Uganda Violence Against Children Survey, we used logistic regression models to assess the association between childhood transactional sex and negative outcomes. Among 18-24-year-old young women who had sex prior to 18 (n = 982), those who ever engaged in transactional sex had 5.9 times  higher odds of having multiple sexual partners in the past year; 5.2 times  higher odds of infrequent condom use in the past year; 3.0 times  higher odds of hurting themselves intentionally; and 3.2 times  higher odds of having attitudes justifying spousal abuse than young women who never engaged in transactional sex. Interventions for transactional sex and HIV in Uganda should consider prioritizing prevention, harm-reduction and continued investment in adolescent girls’ and young women’s futures. Despite progress towards epidemic control, HIV remains a leading cause of death in sub-Saharan Africa (SSA) . Adolescrelationships are structured by economic uncertainty and gender inequality within the context of widely-held gender expectations in heterosexual relationships that men should provide for their partners, and that in exchange, women should provide sexual and domestic services  higher odds of having multiple sexual partners in the past year. Similarly, those who engaged in childhood transactional sex had 5.2 times  higher odds of never or infrequently using condoms in the past year. We also found that childhood transactional sex was significantly associated with 3.0 times  increased odds of intentional self-harm. Finally, compared to 18-24-year-olds who had never engaged in childhood transactional sex, those who had engaged in childhood transactional sex had 3.2 times  higher odds of attitudes supportive of wife-beating. Childhood transactional sex was not significantly associated with sexual or physical violence perpetration towards a non-partner in the multivariate analyses.This analysis examined the associations between childhood transactional sex and sex risk behaviors, attitudes supportive of wife-beating, substance misuse, violence, and mental health outcomes in Ugandan females ages 18–24. While previous studies have focused on identifying determinants of transactional sex and associations with HIV, this study highlights potential mechanisms explaining the association between early transactional sex and risk of HIV infection and other negative health outcomes in early adulthood. In summary, we found that one in seven 18–24 year old Ugandan young women who had ever had sex before age 18 had also engaged in transactional sex in childhood. The majority of these young women engaged in transactional sex for the first time between the ages of 16 and 17 and their first transactional sex partners were most often at least five years older. Bivariate analyses found associations between childhood transactional sex and having multiple sex partners in the past 12 months, no or infrequent condom use in the past 12 months, violence perpetration towards a non-partner, and attitudes supportive of wife-beating. After adjusting for demographic factors, childhood transactional sex was significantly associated with having multiple sex partners, no or infrequent condom use, attitudes supportive of wife-beating, and intentional self-harm.That reported early engagement in transactional sex is associated with more recent sexual risk behaviors (i.e. no or infrequent condom use and multiple sex partners in the past 12 months) may be in part explained by gender norms and beliefs that underlie transactional sex . TransacWhile being in relationships where material provision takes place is normative in this context , 50, newDetermined, Resilient, Empowered, AIDS-free, Mentored and Safe) in Uganda, which is focused on preventing HIV among AGYW. DREAMS uses a three-prong ecological model focused on the individual and her sexual partner(s), her family, and the community. The DREAMS model utilizes a multi-sectoral approach of implementing layered evidence-based interventions to reduce AGYW vulnerability to HIV and to increase AGYW agency. Given the focus on empowerment of AGYW and sexual risk reduction, DREAMS could reduce transactional sex and its associated risks, though this has not been evaluated [Transactional sex in adolescent girls and young children can be prevented by deliberate action to understand and respond to its root causes. Uganda has embraced broad violence prevention interventions, some of which are included in the INSPIRE technical package–a multi-sectoral approach of evidence-based strategies to prevent and respond to violence against children . PEPFAR valuated .Access to goods or money is a key motivator for engaging in transactional sex, and prevention programming must take into account the economic opportunity of sex for young people and offer an alternative . EconomiPrevention efforts need to be adapted for adolescent girls under 18 and take into consideration their cognitive development, life experience, and socioeconomic status, as well as societal gender norms. Parenting and caregiver support programs implemented in community settings can reduce risk for violence and risky sexual behaviors , 66. TheAs the results from this study demonstrate, childhood transactional sex is associated with self-harm in Uganda. Studies focused on transactional sex in adult populations have similarly documented high levels of trauma . HIV preThis study has several limitations. First, the VACS is a retrospective study based on self-reports. As such, both recall bias and social desirability may lead to possible under-reporting of key variables included in the present analysis. Additionally, VACS is a cross-sectional survey and therefore it is not possible to assume causation between transactional sex and the outcomes of interest. It is possible that there are common factors that underly both transactional sex in childhood and the outcomes. However, the analysis does have temporal separation between the exposure variable of transactional sex in childhood and time-bound outcome variables including experiences of adult IPV, mental distress, substance use, beliefs about wife beating, infrequent or low condom use and multiple sex partners in the past 12 months (consider a footnote here that it is possible experiences in the last 12 months for 18-year-olds could overlap with childhood TS). The outcome variables of violence perpetration, suicidal ideation and attempts, self-harm, substance use, HIV testing, and HIV status were measured for the lifetime and thus do not allow temporal separation. Future longitudinal research could allow for a more definite understanding of causality. Finally, the sample size may be a limiting factor in detecting significant relationships between childhood transactional sex and outcomes of interest.Despite considerable progress over the past few decades, HIV remains a public health crisis in Uganda and AGYW are at considerable risk. Transactional sex in childhood is not uncommon among girls in Uganda. This in turn exposes AGYW to sexual risk behaviors, endorsement of harmful gender norms, and poor mental health. Policies and programs aimed at preventing Ugandan girls from engaging in transactional sex may contribute to HIV prevention for AGYW. Further, giving AGYW currently engaged in transactional sex sexual and reproductive health education, economic and education opportunities, as well as skills and tools to reduce their risk may empower them to avoid transactional sex or reduce their risk. Engaging families and communities and promoting healthy gender norms may also contribute to reducing risk for transactional sex. Recognizing the significant harm for girls under 18 engaging in transactional sex and their increased risk for HIV, the integration of transactional sex prevention and response interventions into HIV programs is imperative.