Widespread Disruption Research Articles

The ITAlian rainfall-induced LandslIdes CAtalogue, an extensive and accurate spatio-temporal catalogue of rainfall-induced landslides in Italy

Abstract. Italy is frequently hit and damaged by landslides, resulting in substantial and widespread disruptions. In particular, slope failures have a high impact on the population, communication infrastructure, and economic and productive sectors. The hazard posed by landslides requires adequate responses for landslide risk mitigation, with special attention to the risk to the population. In 2006 the Italian Department of Civil Protection, an office of the Prime Minister, commissioned the Research Institute for Geo-Hydrological Protection (Istituto di Ricerca per la Protezione Idrogeologica), a research institute of the Italian National Research Council, to carry out operational forecasting of rainfall-induced landslides. Collecting landslide information in a catalogue is a preliminary action toward landslide forecasting. The use of spatially and temporally inaccurate landslide catalogues results in uncertain and unreliable operational landslide forecasting. Consequently, accurate catalogues are needed to reduce the uncertainties, which are to some extent unavoidable. To this end, over the last 15 years many researchers have been involved in compiling a catalogue called ITALICA (ITAlian rainfall-induced LandslIdes CAtalogue), which currently lists 6312 records with information on rainfall-induced landslides that occurred over the Italian territory between January 1996 and December 2021. Overall, more than one-third of the catalogue has very high geographic accuracy (less than 1 km2) and hourly temporal resolution. In contrast, less than 2 % of the catalogue has low and very low geographical accuracy and daily temporal resolution. This makes ITALICA the largest catalogue of rainfall-induced landslides accurately located in space and time available in Italy. Without this high level of accuracy, the precipitation responsible for the initiation of landslides cannot be reliably reconstructed, thus making the prediction of landslide occurrence ineffective. ITALICA can be accessed at https://doi.org/10.5281/zenodo.8009366 (Brunetti et al., 2023). ITALICA's information on rainfall-induced landslides in Italy places a special emphasis on their spatial and temporal locations, making the catalogue especially suitable for defining the rainfall conditions capable of triggering future landslides in the Italian territory. This information is fundamental for decision-making in landslide risk management.

Supply chain resilience to climate change inflicted extreme events in agri-food industry: The role of social capital and network complexity

In light of climate change inflicted extreme events—such as floods, fires, droughts, storms, and hurricanes, along with the consequent widespread business disruptions—supply chain resilience (SCR) has emerged as a critical tool to sustain business performance. However, little in-depth theoretical and empirical research has been conducted in relation to the link between exposure to climate change and SCR and the underlying mechanisms and boundary conditions that explicate this relationship. Using time-lagged data drawn from 260 firms in the Australian food supply chains, we examined how exposure to climate change influences SCR in the face of extreme events, and whether intra- and inter-firm social capital and network complexity affect this relationship. Our analysis revealed that exposure to climate change events has a direct positive—albeit not statistically significant—influence on SCR to extreme events. Nevertheless, we found that exposure to climate change significantly and positively influences SCR to extreme events through the mediation of intra- and inter-firm social capital. Additionally, we uncovered that network complexity does not influence the effect of intra-firm social capital on SCR to extreme events, whereas it does negatively influence that of inter-firm social capital. The reliability and validity of our results were confirmed by means of robustness tests. Our study, which has several theoretical and practical implications, makes specific contributions to the United Nations Development Goals.

Device-Measured Change in Physical Activity in Primary School Children During the UK COVID-19 Pandemic Lockdown: A Longitudinal Study.

Lockdown measures, including school closures, due to the COVID-19 pandemic have caused widespread disruption to children's lives. The aim of this study was to explore the impact of a national lockdown on children's physical activity using seasonally matched accelerometry data. Using a pre/post observational design, 179 children aged 8 to 11years provided physical activity data measured using hip-worn triaxial accelerometers worn for 5 consecutive days prepandemic and during the January to March 2021 lockdown. Multilevel regression analyses adjusted for covariates were used to assess the impact of lockdown on time spent in sedentary and moderate to vigorous physical activity. A 10.8-minute reduction in daily time spent in moderate to vigorous physical activity (standard error: 2.3min/d, P < .001) and a 33.2-minute increase in daily sedentary activity (standard error: 5.5min/d, P < .001) were observed during lockdown. This reflected a reduction in daily moderate to vigorous physical activity for those unable to attend school (-13.1 [2.3]min/d, P < .001) during lockdown, with no significant change for those who continued to attend school (0.4 [4.0]min/d, P < .925). These findings suggest that the loss of in-person schooling was the single largest impact on physical activity in this cohort of primary school children in London, Luton, and Dunstable, United Kingdom.

COVID-19 Pandemic Impacts on Community Connections and Third Place Engagement: A Qualitative Analysis of Older Americans

The COVID-19 pandemic caused widespread disruption and upheaval across communities. This qualitative study uses data from the national COVID-19 Coping Study to investigate how community engagement shifted during the pandemic among older Americans. We conducted reflexive thematic analysis of open-ended online survey responses from a random subsample of 500 participants. Our analysis identified ways in which participants altered sources of community and engagement, struggled with engagement, and increased engagement, and how experiences differed among bonding ties compared to bridging ties. Results reinforce the importance of third places for heterogeneous social connections and inform strategies to strengthen community among older Americans.

Nonlinear changes in delayed functional network topology in Alzheimer’s disease: relationship with amyloid and tau pathology

BackgroundAlzheimer’s disease is a neurodegenerative disorder associated with the abnormal deposition of pathological processes, such as amyloid-ß and tau, which produces nonlinear changes in the functional connectivity patterns between different brain regions across the Alzheimer’s disease continuum. However, the mechanisms underlying these nonlinear changes remain largely unknown. Here, we address this question using a novel method based on temporal or delayed correlations and calculate new whole-brain functional networks to tackle these mechanisms.MethodsTo assess our method, we evaluated 166 individuals from the ADNI database, including amyloid-beta negative and positive cognitively normal subjects, patients with mild cognitive impairment, and patients with Alzheimer’s disease dementia. We used the clustering coefficient and the global efficiency to measure the functional network topology and assessed their relationship with amyloid and tau pathology measured by positron emission tomography, as well as cognitive performance using tests measuring memory, executive function, attention, and global cognition.ResultsOur study found nonlinear changes in the global efficiency, but not in the clustering coefficient, showing that the nonlinear changes in functional connectivity are due to an altered ability of brain regions to communicate with each other through direct paths. These changes in global efficiency were most prominent in early disease stages. However, later stages of Alzheimer’s disease were associated with widespread network disruptions characterized by changes in both network measures. The temporal delays required for the detection of these changes varied across the Alzheimer’s disease continuum, with shorter delays necessary to detect changes in early stages and longer delays necessary to detect changes in late stages. Both global efficiency and clustering coefficient showed quadratic associations with pathological amyloid and tau burden as well as cognitive decline.ConclusionsThis study suggests that global efficiency is a more sensitive indicator of network changes in Alzheimer’s disease when compared to clustering coefficient. Both network properties were associated with pathology and cognitive performance, demonstrating their relevance in clinical settings. Our findings provide an insight into the mechanisms underlying nonlinear changes in functional network organization in Alzheimer’s disease, suggesting that it is the lack of direct connections that drives these functional changes.

Impact of COVID-19 on stage migration in bladder cancer: A single-centre retrospective comparative cohort analysis

Objectives: To evaluate whether the COVID-19 pandemic has increased the proportion of patients diagnosed with muscle invasive bladder cancer (MIBC) at first presentation and explore potential causes for this hypothesised stage migration. Subjects/Patients and Methods: All patients undergoing first transuretheral resection of bladder tumour (TURBT) between 2 January 2018 and 30 June 2021. The date of the lockdown restrictions was used to divide patients into pre-COVID and COVID cohorts. Data were collated retrospectively from electronic patient records. Variables included demographics data, TNM stage and presentation (emergent/elective). A comparative analysis of the two cohorts was undertaken using the chi-square test to evaluate for statistical significance. A p-value of &lt;0.05 was regarded as significant. Results: A total of 1064 patient underwent a TURBT for a suspected bladder cancer in the study period. The number of patients in the pre-COVID and COVID cohorts was 704 and 360, respectively. Mean age was comparable between cohorts (74.7 vs 74.2 ( p = 0.46)) as was sex ratio (F:M (1:2.84 vs 1:2.95) p = 0.80). The proportion of patients with MIBC pre-COVID was 18% compared to 23.6% in the COVID cohort. This represents a 5.6% increase in the prevalence of first presentation MIBC during the COVID era ( p = 0.03 [95% confidence interval (CI) = 0.2–10.4]). The odds of a patient presenting with MIBC was 38% higher during the COVID era versus pre-COVID (odds ratio (OR) [95% CI] = 1.38 [1.01–1.85]). A significantly higher proportion of patients had metastases at presentation in the COVID cohort versus pre-COVID (4.4% vs 2.1% ( p = 0.034)). Conclusion: Our data would support the hypothesis that the COVID pandemic has precipitated a stage migration in the presentation of bladder cancer towards muscle invasive disease. This has implications in terms of patient outcomes as well as an increased demand on services to provide radial treatment. Potential reasons for the stage migration are patient-related factors and pathway failure due to widespread service disruption caused by COVID-19. Level of evidence: 3

A Novel Approach for Prediction of Lung Disease Using Chest X-ray Images Based on DenseNet and MobileNet.

Covid19 corona virus has caused widespread disruption across the world, in terms of the health, economy, and society problems. X-ray images of the chest can be helpful in making an accurate diagnosis because the corona virus typically first manifests its symptoms in patients' lungs. In this study, a classification method based on deep learning is proposed as a means of identifying lung disease from chest X-ray images. In the proposed study, the detection of covid19 corona virus disease from chest X-ray images was made with MobileNet and Densenet models, which are deep learning methods. Several different use cases can be built with the help of MobileNet model and case modelling approach is utilized to achieve 96% accuracy and an Area Under Curve (AUC) value of 94%. According to the result, the proposed method may be able to more accurately identify the signs of an impurity from dataset of chest X-ray images. This research also compares various performance parameters such as precision, recall and F1-Score.

The good gifts program: evaluation of a positive youth development intervention for opportunity youth

ABSTRACT The hardships faced by youth experiencing or at-risk for experiencing homelessness, or opportunity youth, are well documented. Programs for these youth are often deficit-based, failing to recognize existing strengths to foster resilience. The Good Gifts Program is a positive youth development intervention created collaboratively with opportunity youth, service providers, and researchers to augment existing services with the goal of nurturing gratitude, generosity, and hope. We evaluated this pilot program during a period of wide-spread service disruption (in the summer of 2021 during the COVID-19 pandemic). Opportunity youth (n = 38; aged 16–24) completed up to four group sessions as well as pre- and post-test assessments, with daily diaries throughout. Results showed no overall evidence for program efficacy and, indeed, declines in gratitude, generosity, and hope with greater program attendance. The modest sample size and significant heterogeneity in program fidelity, participation, and context presented challenges to data interpretation and highlight considerations for future work.

What the geological past can tell us about the future of the ocean’s twilight zone

Paleontological reconstructions of plankton community structure during warm periods of the Cenozoic (last 66 million years) reveal that deep-dwelling ‘twilight zone’ (200–1000 m) plankton were less abundant and diverse, and lived much closer to the surface, than in colder, more recent climates. We suggest that this is a consequence of temperature’s role in controlling the rate that sinking organic matter is broken down and metabolized by bacteria, a process that occurs faster at warmer temperatures. In a warmer ocean, a smaller fraction of organic matter reaches the ocean interior, affecting food supply and dissolved oxygen availability at depth. Using an Earth system model that has been evaluated against paleo observations, we illustrate how anthropogenic warming may impact future carbon cycling and twilight zone ecology. Our findings suggest that significant changes are already underway, and without strong emissions mitigation, widespread ecological disruption in the twilight zone is likely by 2100, with effects spanning millennia thereafter.

Bipolar impact and phasing of Heinrich-type climate variability.

During the last ice age, the Laurentide Ice Sheet exhibited extreme iceberg discharge events that are recorded in North Atlantic sediments1. These Heinrich events have far-reaching climate impacts, including widespread disruptions to hydrological and biogeochemical cycles2-4. They occurred during Heinrich stadials-cold periods with strongly weakened Atlantic overturning circulation5-7. Heinrich-type variability is not distinctive in Greenland water isotope ratios, a well-dated site temperature proxy8, complicating efforts to assess their regional climate impact and phasing against Antarctic climate change. Here we show that Heinrich events have no detectable temperature impact on Greenland and cooling occurs at the onset of several Heinrich stadials, and that both types of Heinrich variability have a distinct imprint on Antarctic climate. Antarctic ice cores show accelerated warming that is synchronous with increases in methane during Heinrich events, suggesting an atmospheric teleconnection9, despite the absence of a Greenland climate signal. Greenland ice-core nitrogen stable isotope ratios, a sensitive temperature proxy, indicate an abrupt cooling of about three degrees Celsius at the onset of Heinrich Stadial 1 (17.8 thousand years before present, where present isdefined as 1950). Antarctic warming lags this cooling by 133 ± 93 years, consistent with an oceanic teleconnection. Paradoxically, proximal sites are less affected by Heinrich events than remote sites, suggesting spatially complex event dynamics.

Alone and Together: Resilience in a Fluid Socio-Technical-Natural System

Disruption to routines is an increasingly common part of everyday life. With the roots of some disruptions in the interconnectedness of the world and environmental and socio-political instability, there is good reason to believe that conditions that cause widespread disruption will persist. Individuals, communities, and systems are thus challenged to engage in resilience practices to deal with both acute and chronic disruption. Our interest is in chronic, everyday resilience, and the role of both technology and non-technical adaptation practices engaged by individuals and communities, with a specific focus on practices centered in nature. Foregrounding nature's role allows close examination of environmental adversity and nature as part of adaptivity. We add to the CSCW and HCI literature on resilience by examining long-distance hikers, for whom both the sources of adversity and the mitigating resilience processes cut across the social, the technical, and the environmental. In interviews with 12 long-distance hikers we find resilience practices that draw upon technology, writ large, and nature in novel assemblages, and leverage fluid configurations of the individual and the community. We place our findings in the context of a definition for resilience that emphasizes a systems view at multiple scales of social organization. We make three primary contributions: (1) we contribute an empirical account of resilience in a contextual setting that complements prior CSCW resilience studies, (2) we add nuance to existing models for resilience to reflect the role of technology as both a resilience tool and a source of adversity, and (3) we identify the need for new designs that integrate nature into systems as a way to foster collaborative resilience. This nuanced understanding of the role of technology in individual and community resilience in and with nature provides direction for technology design that may be useful for everyday disrupted life.

Playing with change: insights and lessons from researching play during the COVID-19 pandemic

ABSTRACT The COVID-19 pandemic presented widespread disruption to our day-to-day lives and our ongoing and planned research projects at the PEDAL Centre. As the situation unfolded, it was necessary to rapidly adapt our projects to ensure continuity of work and delivery of outcomes. These adaptations were initially quick-thinking responses to huge challenges; however, upon reflection, it became clear that important lessons would be learned from these experiences. This paper uses the 6Cs framework (Golinkoff & Hirsh-Pasek, 2016) to describe what we retrospectively learned from adjusting our research programmes to the unprecedented COVID-19 circumstances. We illustrate how a ‘playful’ mindset and its characteristics were key to overcoming the challenges faced. Furthermore, we provide insights that can be considered as we move forward after the pandemic.

Assessing the Impact of the COVID-19 Pandemic on Nursing Education: A National Study of Prelicensure RN Programs.

The COVID-19 pandemic has had a profound impact on prelicensure nursing education, leading to widespread disruptions that may have implications for nursing students' learning and engagement outcomes. Understanding how the rapid shift to online and simulation-based teaching methods has affected new graduates' clinical preparedness is critical to ensure patient safety moving forward. To assess the impact of institutional, academic, and demographic characteristics on prelicensure nursing students' academic, initial postgraduation, and early career outcomes during the COVID-19 pandemic. We conducted a mixed-methods longitudinal study focused on prelicensure registered nurse (RN) students entering the core of their didactic and clinical nursing coursework during the pandemic. This study uses a combination of real-time student and faculty self-report data, including externally validated instruments, within and end-of-program standardized test scores, and focus group findings. Various statistical methods, ranging from simpler descriptive and non-parametric methods to Generalized Estimating Equation (GEE) models and detailed textual analysis, are applied to assess student, faculty, and institution-level data. The final sample includes more than 1,100 student and faculty participants affiliated with 51 prelicensure RN programs located across 27 states. Leveraging more than 4,000 course observations collected from fall 2020 to spring 2022 and supplemented by the rich personal narratives of over 60 focus group participants, this study illuminates the breadth, scale, and ever-evolving nature of prelicensure RN programs' efforts to maintain the continuity of nursing students' education during the public health crisis. In doing so, it captures the many ways in which nursing administrators, faculty, and students sought to address the unparalleled challenges they confronted on a day-to-day basis. In particular, the findings provide critical insights into the efficacy of the changes nursing programs made to their course delivery formats to adjust to the confluence of rapidly evolving federal, state, and private restrictions to stem the spread of COVID-19. This study stands as the most comprehensive assessment of prelicensure nursing education in the United States since the onset of COVID-19. It extends knowledge by linking potential deficiencies in students' didactic and clinical education during the pandemic and their early career preparedness, clinical competence, and the patient safety implications therein.

SARSCoV2 Omicron (B. 1.1. 529) recent updates and challenges worldwide.

The current world is plagued by unpredictability as a result of various COVID-19 variants. The current variants of concern (VOCs) are B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma) and B.1.617/B.1.617.2(Delta). WHO classified two variants, delta (B.1.617.2) and omicron (B.1.1.529), as having highly mutable strikes. The WHO predicted that it would be more dangerous than previous variants due to its mutatable capability. The Omicron variant of coronavirus has caused widespread disruption, with countries struggling to manage the massive number of infections. Due to its unique properties, such as protein structure, symptoms, transmission, and epidemiology, this review aims to compare omicron to other variants. Furthermore, we have highlighted vaccines that have been used to combat this pandemic.

Online + International: Utilizing Theory to Maximize Intercultural Learning in Virtual Exchange Courses

Virtual exchanges (VEs) are course-based experiences designed to promote global learning, often by integrating cross-cultural interactions and collaborations with people from other areas of the world into coursework in a virtual format. Due to the widespread disruption of the COVID-19 pandemic, VEs have seen an increase in popularity. However, research findings on the effectiveness of VEs are mixed, and limited guidance is available to VE instructors on how to structure and facilitate these programs. The purpose of this paper is to highlight how theories and literature in two distinct areas of scholarship, Intergroup Contact theory and the Community of Inquiry model in online learning, could be applied to VEs to maximize student learning. We discuss each of these theories first and then highlight how they could be applied to VEs using illustrative examples from a pilot study of five VE courses offered at one institution during the summer of 2021.

The role of the salience network in cognitive and affective deficits.

Analysis and interpretation of studies on cognitive and affective dysregulation often draw upon the network paradigm, especially the Triple Network Model, which consists of the default mode network (DMN), the frontoparietal network (FPN), and the salience network (SN). DMN activity is primarily dominant during cognitive leisure and self-monitoring processes. The FPN peaks during task involvement and cognitive exertion. Meanwhile, the SN serves as a dynamic "switch" between the DMN and FPN, in line with salience and cognitive demand. In the cognitive and affective domains, dysfunctions involving SN activity are connected to a broad spectrum of deficits and maladaptive behavioral patterns in a variety of clinical disorders, such as depression, insomnia, narcissism, PTSD (in the case of SN hyperactivity), chronic pain, and anxiety, high degrees of neuroticism, schizophrenia, epilepsy, autism, and neurodegenerative illnesses, bipolar disorder (in the case of SN hypoactivity). We discuss behavioral and neurological data from various research domains and present an integrated perspective indicating that these conditions can be associated with a widespread disruption in predictive coding at multiple hierarchical levels. We delineate the fundamental ideas of the brain network paradigm and contrast them with the conventional modular method in the first section of this article. Following this, we outline the interaction model of the key functional brain networks and highlight recent studies coupling SN-related dysfunctions with cognitive and affective impairments.

Redefinition of War and the Weapons of Mass Destruction in the 21st Century

War needs science and advanced technology to evolve. Until the time of the technological revolution, the war did not have such a large-scale to cover the whole world. But nowadays, communication became a tool that determines the potential of expanding threats. Modern military forces can change an environment faster than ever, but in a decentralized area, the army is unnecessary to war. This article shows the concept of a new idea of decentralized war as a civil and hybrid warfare conducted in the countries in which human rights are repeatedly violated. As evidenced by recent studies, decentralized war uses new generation technology as a weapon of mass destruction based on globally available mass communication tools. Thus, the study concentrates on the issue of these weapons as not only nuclear weapons are likely to play a more significant role in the international security environment, and constraints on the proliferation and use of chemical and biological weapons seem to be inefficient. There will be greater scope for terrorism, though it is not possible to predict the frequency or severity of any future employment of the weapons of mass destruction. New forms of the weapons (beyond chemical, biological, radiological, and nuclear weapons) are likely to emerge. This is namely cyber weapons that is capable of inflicting a widespread disruption and generating high costs to deter large-scale cyber attacks. As a consequence of war redefinition, the article is also a trial of reconsidering the term of weapon of mass destruction, by explaining the role of cyber attacks, political fanaticism and hateful propaganda.

Commodity prices and global economic activity

Commodity prices provide useful information about current and future global economic activity. First, we show that overall commodity prices indeed tend to comove with economic activity. Second, we try to extract the global demand factor(s) using many commodity prices. While commodity prices reflect both demand and supply factors, by relying on a wide variety of commodity prices, supply shocks can be filtered out as they tend to be commodity-specific idiosyncratic shocks except for widespread supply disruptions confined to a few historical periods. In this paper, we then show that factors extracted from commodity prices movement contain useful information to nowcast and forecast global GDP and industrial production.

Simulating the GB power system frequency during underfrequency events 2018–19

AbstractLightning hit a transmission power line outside London, England on 9 August 2019. There followed a loss of power from a cascade of generator outages that exceeded contingency reserves, leading to an exceptional fall in grid frequency causing widespread transport disruptions and the disconnection of over 1 m households. Simulating such events typically involves a system of differential equations representing the overall generation and load present at the time. A standard model based on the swing equation assumes unlimited capacity in aggregated resources, and the availability of these services throughout the duration of the frequency deviation. In simulating the effect of outages on the GB Grid frequency on 2019/8/9, the effect of limiting these services to the capacity of resources engaged during the event is examined. It is shown that by taking these refinements into account the timing and extent of the frequency nadir can be successfully estimated. Insight is gained into the responses of various grid characteristics and how they interact with unplanned generation imbalances. Using this adapted model, further events on the GB grid are examined to validate the influence of these features. With the model’s effectiveness validated, novel mitigating measures to preserve the stability of a low-inertia grid can be evaluated.

The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy.

The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional-behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analysed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy and morphological and functional imaging network features. The concurrent validity of the behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness and education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic-clonic seizures) and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy.