Flexible behaviour requires cognitive-control mechanisms to efficiently mediate conflict between competing information and alternative actions. Whether a global neural resource mediates all forms of conflict or this is achieved within domain-specific systems remains unclear. We use a novel fMRI paradigm to orthogonally manipulate rule, response and stimulus-based conflict within a full-factorial design. Whole-brain voxelwise analyses show that activation patterns associated with these conflict types are distinct, but they partially overlap within the Multiple Demand Cortex (MDC) regions that are most commonly active during cognitive tasks. Region of interest analysis shows that most MDC sub-regions are activated for all conflict types, but to significantly varying levels. We propose that conflict resolution is an emergent property of distributed brain networks, the functional-anatomical components of which place on a continuous, not categorical, scale from domain-specialised to domain general. MDC brain regions place towards one end of that scale but still exhibit significant functional heterogeneity.

Abstract Arousal dysfunction contributes to impairments seen in Alzheimer’s disease. However, the nature and degree of this dysfunction have not been studied in detail. We investigated changes in tonic and phasic arousal using simultaneous pupillometry-EEG, relating these changes to locus coeruleus integrity, a key arousal nucleus. Forty Alzheimer’s disease participants and 30 controls underwent neuropsychological testing using the Alzheimer’s Disease Assessment Scale–Cognitive Subscale (ADAS-Cog), MRI designed to show contrast in the locus coeruleus as a measure of integrity and simultaneous pupillometry-EEG during 5 min of eyes-open resting-state. Pupillometry-EEG was then also applied during an oddball task which included a passive session and sessions in which responses to target stimuli were required, to test the effect of salience. Alzheimer’s disease had lower locus coeruleus integrity (b = −0.26, P = 0.02) and lower peak alpha frequency (tonic arousal) (b = −1.09, P < 0.001). Both were related to ADAS-Cog. There was a very strong relationship between pupil size and both periodic and aperiodic EEG power. Cortical slowing in Alzheimer’s disease affected this relationship, particularly at low frequencies. During the attentionally demanding oddball task, Alzheimer’s disease participants’ behavioural performance was impaired, with reduced accuracy and slower and more variable reaction times. They also had reduced pupil responses to salient stimuli (phasic arousal) (estimate = −0.19, P < 0.001). EEG and pupil measures of pre-stimulus tonic arousal were strongly correlated and predicted behavioural responses in both groups. Arousal fluctuations at rest and in response to stimuli are abnormal in Alzheimer’s disease as measured by combined pupillometry and EEG. Salient stimuli that require a behavioural response are accompanied by a phasic increase in arousal, demonstrated by pupil dilation to oddball stimuli. This response is slower and of smaller magnitude in Alzheimer’s disease patients. Cortical slowing (reduced peak alpha frequency) is seen in Alzheimer’s disease, and this is modulated by arousal level and relates to overall cognition. Pupil-linked arousal responses and alpha EEG fluctuations are tightly coupled, but cortical slowing in Alzheimer’s disease influences this coupling. The tools used here to measure neurophysiological arousal level have potential in understanding the nature of arousal system dysfunction in Alzheimer’s disease at the group level. These tools may also be used as biomarkers at the individual level in order to target patients most likely to benefit from arousal-modulating medications.

This short review illustrates, using two recent studies, the potential and challenges of using machine learning methods to identify phenotypes of wheezing and asthma from childhood onwards.

BACKGROUND Estimates for the prevalence of food allergy vary widely, with a paucity of data for adults. The aim of this analysis was to report trends in the incidence and prevalence of food allergy in England, using a national primary care dataset. METHODS We analysed data from Clinical Practice Research Datalink between 1998 and 2018, with linked data to relevant hospital encounters in England. The main outcomes were incidence and prevalence of food allergy, according to three definitions of food allergy: possible food allergy, probable food allergy, and probable food allergy with adrenaline autoinjectors prescription. We also evaluated the difference in proportion of patients prescribed adrenaline autoinjectors by English Index of Multiple Deprivation (IMD), age, and by previous food anaphylaxis, and explored differences in patient encounters (general practice vs emergency department setting). FINDINGS 7 627 607 individuals in the dataset were eligible for inclusion, of whom 150 018 (median age 19 years [IQR 4-34]; 82 614 [55·1%] female and 67 404 [44·9%] male) had a possible food allergy. 121 706 met diagnostic criteria for probable food allergy, of whom 38 288 were prescribed adrenaline autoinjectors. Estimated incidence of probable food allergy doubled between 2008 and 2018, from 75·8 individuals per 100 000 person-years (95% CI 73·7-77·9) in 2008 to 159·5 (156·6-162·3) individuals per 100 000 person-years in 2018. Prevalence increased from 0·4% (23 399 of 6 432 383) to 1·1% (82 262 of 7 627 607) over the same period and was highest in children under 5 years (11 951 [4·0%] of 296 406 in 2018) with lower prevalence in school-aged children (from 11 353 [2·4%] of 473 597 in 2018 for children aged 5-9 years to 6896 [1·7%] of 404 525 for those aged 15-19 years) and adults (42 848 [0·7%] of 5 992 454 in 2018). In those with previous food anaphylaxis, only 2321 (58·3%) of 3980 (975 [64·0%] of 1524 children and young people and 1346 [54·8%] of 2456 adults) had a prescription for adrenaline autoinjector. Adrenaline autoinjectors prescription was less common in those resident in more deprived areas (according to IMD). In the analysis of health-care encounters, 488 604 (97·1%) of 503 198 visits recorded for food allergy occurred in primary care, with 115 655 (88·4%) of 130 832 patients managed exclusively in primary care. INTERPRETATION These estimates indicate an important and increasing burden of food allergy in England. Our findings that most patients with food allergy are managed outside the hospital system, with low rates of adrenaline autoinjector prescription in those with previous anaphylaxis, highlight a need to better support those working in primary care to ensure optimal management of patients with food allergy. FUNDING UK Food Standards Agency and UK Medical Research Council.

Purpose of review To review the current state of knowledge on the relationship between allergic sensitization and asthma; to lay out a roadmap for the development of IgE biomarkers that differentiate, in individual sensitized patients, whether their sensitization is important for current or future asthma symptoms, or has little or no relevance to the disease. Recent findings The evidence on the relationship between sensitization and asthma suggests that some subtypes of allergic sensitization are not associated with asthma symptoms, whilst others are pathologic. Interaction patterns between IgE antibodies to individual allergenic molecules on component-resolved diagnostics (CRD) multiplex arrays might be hallmarks by which different sensitization subtypes relevant to asthma can be distinguished. These different subtypes of sensitization are associated amongst sensitized individuals at all ages, with different clinical presentations (no disease, asthma as a single disease, and allergic multimorbidity); amongst sensitized preschool children with and without lower airway symptoms, with different risk of subsequent asthma development; and amongst sensitized patients with asthma, with differing levels of asthma severity. Summary The use of machine learning-based methodologies on complex CRD data can help us to design better diagnostic tools to help practising physicians differentiate between benign and clinically important sensitization.

Preschool wheezing and childhood asthma create a heavy disease burden which is only exacerbated by the complexity of the conditions. Preschool wheezing exhibits both “curricular” and “aetiological” heterogeneity: that is, heterogeneity across patients both in the time‐course of its development and in its underpinning pathological mechanisms. Since these are not fully understood, but clinical presentations across patients may nonetheless be similar, current diagnostic labels are imprecise—not mapping cleanly onto underlying disease mechanisms—and prognoses uncertain. These uncertainties also make a identifying new targets for therapeutic intervention difficult. In the past few decades, carefully designed birth cohort studies have collected “big data” on a large scale, incorporating not only a wealth of longitudinal clinical data, but also detailed information from modalities as varied as imaging, multiomics, and blood biomarkers. The profusion of big data has seen the proliferation of what we term “modern data approaches” (MDAs)—grouping together machine learning, artificial intelligence, and data science—to make sense and make use of this data. In this review, we survey applications of MDAs (with an emphasis on machine learning) in childhood wheeze and asthma, highlighting the extent of their successes in providing tools for prognosis, unpicking the curricular heterogeneity of these conditions, clarifying the limitations of current diagnostic criteria, and indicating directions of research for uncovering the etiology of the diseases underlying these conditions. Specifically, we focus on the trajectories of childhood wheeze phenotypes. Further, we provide an explainer of the nature and potential use of MDAs and emphasize the scope of what we can hope to achieve with them.

Which population factors have predisposed people to disregard government safety guidelines during the COVID-19 pandemic and what justifications do they give for this non-compliance? To address these questions, we analyse fixed-choice and free-text responses to survey questions about compliance and government handling of the pandemic, collected from tens of thousands of members of the UK public at three 6-monthly timepoints. We report that sceptical opinions about the government and mainstream-media narrative, especially as pertaining to justification for guidelines, significantly predict non-compliance. However, free text topic modelling shows that such opinions are diverse, spanning from scepticism about government competence and self-interest to full-blown conspiracy theories, and covary in prevalence with sociodemographic variables. These results indicate that attempts to counter non-compliance through argument should account for this diversity in peoples’ underlying opinions, and inform conversations aimed at bridging the gap between the general public and bodies of authority accordingly.