Search

Insomnia symptoms, including difficulty falling asleep or falling back to sleep when awakened in the night or early morning, and daily dysfunctions are considered the most common sleep problem in adults, with an estimated prevalence of 30-35% in the general population (Morin et al., 2015). Chronic insomnia disorder (ID) has a prevalence of 10% worldwide. It is characterized as insomnia symptoms occurring at least three times per week and lasting for more than three months based on the third edition of the International Classification of Sleep Disorders (ICSD-3) criteria. ID can severely affect the quality of life (Léger et al., 2012) and is comorbid with various medical, neurological, and mental disorders like depression (Leerssen et al., 2020) and suicidal ideation (McCall et al., 2013). However, the underlying pathophysiology of insomnia is poorly understood. Previous evidence has shown that cortical and subcortical brain structures play key roles in the pathophysiology of ID (Altena et al., 2010; Emamian et al., 2021; Schiel et al., 2020; Zhao et al., 2015). Besides, previous functional MRI (fMRI) research has demonstrated that disruptions in functional brain connectivity may be related to insomnia (Lee et al., 2021; Li et al., 2018). However, all the mentioned studies were based on relatively small samples and from a single site, which limited the generalizability of results (Dang-Vu, 2013; Tahmasian et al., 2018). Recently, our large-scale collaborative study in the ENIGMA-Sleep group has investigated the effect of insomnia on different brain regions by assessing the group difference between subjects with and without insomnia (Weihs et al., 2022). However, the results still demonstrated no significant group differences in brain structure between subjects with and without insomnia. Although group difference studies could not identify the relationship between the presence of insomnia and brain morphology, here we propose a new approach to include i) UKBB (as the largest sample on subjective insomnia assessment), ii) seven ENIGMA-Sleep sites (in six of which, we got clinically diagnosed ID patients), iii) adding surface-based morphometry (SBM) analysis voxel-based morphometry (VBM) analysis, and iv) machine learning (ML) classification approach to distinguish subjects with and without insomnia. As a growing trend in neuroimaging studies has been raised to decode complex and linear or non-linear relations between brain regions in neuropsychiatric disorders using ML approach, ML-based individual classification approaches provide another point of view to discover the disease-related brain regions while doing diagnostic classification. The development of interpretable clinical decision tools can be regarded as an approximation of a neurobiological account of a psychiatric disorder (Nour et al., 2022). Analyzing complex and non-linear patterns in the brain and making decisions at the subject level instead of the group level are strong points of ML to address future challenges in precision psychiatry (Rashid & Calhoun, 2020). Previous research efforts have successfully applied machine learning (ML) methods to discriminate paradoxical and psychophysiological insomnia based on structural and functional brain images (Afshani et al., 2022), fMRI-based prediction of differential brain activity patterns in patients with insomnia (Lee et al., 2021) and EEG-based individual classification of Insomnia patients have been recently investigated (Abdullah et al., 2018). Thus, in this project, we propose a study to implement an individual classification framework for insomnia symptoms in a large-scale worldwide population-based cohort throughout the UK Biobank and the ENIGMA-Sleep consortium, which will take the first step to classify insomnia symptoms and identify biomarkers for insomnia symptoms using brain structure measurements based on structural MRI analysis including VBM and SBM calculated from T1 image as the input.

Sleep is essential for the maintenance of human physical health (Grandner et al., 2012; Reid et al., 2006), cognitive performance (Ferrie et al., 2011; Leng et al., 2017), and mental health (Freeman et al., 2017; Joao et al., 2018). Experimental sleep deprivation and different degrees of sleep restriction permit the characterization of human sleep behaviors and brain's responses to inadequate sleep (Durmer and Dinges, 2005; Elmenhorst et al., 2017; Elmenhorst et al., 2018; Van Dongen et al., 2003). Sleep disruption is known to impact not only brain functions but also affects brain anatomy; earlier studies found that sleep deprivation is associated with lower volumes of gray matter across various brain regions (Åkerstedt et al., 2020; Liu et al., 2014; Long et al., 2020; Sun et al., 2020), however with inconsistent localization of effects. Other studies reported alterations in white matter microstructure (Elvsåshagen et al., 2015; Voldsbekk et al., 2021), increased ventricles size, and cognitive decline (Lo et al., 2014). In addition, one-night sleep deprivation was found to increase the Aβ burden in the right hippocampus and thalamus of healthy controls (Shokri-Kojori 2018), suggesting an interrupted clearance pathway due to sleep deprivation. These prior studies suggest that sleep deprivation may impact anatomical features of the brain, but there is a need for large-scale replications and extensions of previous work. Increased brain age compared to chronological age is a risk factor for several neurodegenerative and psychiatric disorders (Wyss-Coray, 2016). Brain-age prediction is a widely studied topic that aims to estimate the trajectory of brain aging (Franke et al., 2019), and its association with pathological conditions. MRI-derived features used to predict individuals’ brain age have proven to constitute a proxy for overall health (Habes et al., 2016, Koutsouleris et al., 2014, Cole et al., 2018). Sleep patterns also change with aging. In particular, older adults tend to sleep less, and present a more fragmented sleep with lower slow-wave sleep. They also present a reduced sleep rebound following sleep deprivation, report less sleepiness under acute sleep deprivation conditions, and report a smaller increase in lapses of attention after sleep deprivation [23]. However, little is known on whether brain-age models reflect the impact of sleep deprivation on the brain structure - i.e., the difference between predicted age and real age (so-called brain-age delta) using MRI data. We hypothesized that the morphological alterations associated with sleep deprivation would lead to an increased brain-delta score.

Conduct disorder (CD) is one of the most common childhood psychiatric disorders and is characterised by a repetitive and pervasive pattern of aggressive or antisocial behaviours, such as physical aggression towards other humans or animals, theft, property damage and rule violations (APA, 2013). The worldwide prevalence of CD is estimated to be 2-2.5%, with a prevalence of 3-4% in boys and 1-2% in girls (Maughan et al., 2004). Overall, CD is a leading cause of referral to child and adolescent mental health services and places an enormous burden on the affected individuals, their families and society (Erksine et al., 2014; Rivenbark et al., 2018). Although neuroimaging studies have reported alterations in brain structure in CD and supported the view that CD is a neurodevelopmental disorder, a major concern of the diagnostic criteria for CD (e.g., initiating physical fights) is that they are entirely based on behavioural symptoms, and can be shared with other disorders (e.g., ADHD, Substance Use Disorder, Tourette's disorder or Bipolar Disorder), and are therefore less informative about the underlying psychological states/cognitions or neural substrates which drive the symptoms (Blair et al., 2005; Fairchild et al., 2019). A comprehensive and thorough understanding of the underlying pathophysiology of CD may help address this question and help refine diagnostic approaches. Neuroimaging studies on CD have shown altered neural responses in tasks involving emotional processing and executive function (Alegria et al., 2016; Noordermeer et al., 2016; Raschle et al., 2015) and reductions in grey matter volume (Noordermeer et al., 2016; Raschle et al., 2015; Rogers & De Brito, 2016) across a number of cortical (e.g., ventromedial prefrontal and insular cortices) and subcortical (e.g., amygdala and basal ganglia) regions critical for emotion processing and regulation, reinforcement-based decision-making, executive function and empathic responding (Fairchild et al., 2019). Although these discoveries marked pivotal advances in our understanding of CD, the evidence base suffers from two important limitations. First, inconsistent findings and a lack of replication are common; demographic and clinical features of the samples, as well as methodological factors, have likely contributed to this. For example, CD is a heterogeneous disorder with subtypes based on the age-of-onset of symptoms (childhood-onset versus adolescence-onset, which is defined by whether severe antisocial behaviours emerge before or after 10 years of age), and the presence or absence of callous-unemotional (CU) traits (encompassed by the ‘with limited prosocial emotions’ specifier in the DSM-5; APA, 2013). However, most primary magnetic resonance imaging (MRI) studies of CD have not examined the influence of these subtypes on their findings. Potential sex differences in the relationship between CD and brain structure might also have contributed to the inconsistent findings (Smaragdi et al., 2017). Most studies of CD have focused only on males and hence it is currently understudied/unclear whether females with CD show similar alterations. Relatedly, studies that have investigated mixed-sex samples were largely underpowered to test for sex-by-diagnosis interactions and may hence have contributed to the inconsistencies observed in the literature. Second, most of the neuroimaging studies had small sample sizes, increasing the risk for false positives and false negatives (Button et al., 2013), as well as resulting in limited power to test differences between CD subtypes or explore heterogeneity. In this context, our understanding of CD-related brain structural alterations could be improved through a large-scale coordinated and harmonised analysis of the vast amounts of existing data to map brain differences in heterogeneous CD patients worldwide. In 2009, the Enhancing NeuroImaging Genetics through Meta-Analyses (ENIGMA) consortium (http://enigma.ini.usc.edu) was established to pool brain imaging data from existing cohorts across the world in order to better understand brain structure, function, and disease, based on meta- and mega-analyses of brain imaging and genetic data (Bearden & Thompson, 2017; Thompson et al., 2020). The ENIGMA-Antisocial Behavior (ASB) working group aims to clarify associations between CD, conduct problems (CPs), psychopathy, or antisocial personality disorder and alterations in brain structure and function by using ENIGMA’s highly powered consortium approach to overcome the limitations of small studies with low power and uncertain reproducibility. Therefore, the proposed study will focus on examining the cortical and subcortical correlates of CD using a mega-analysis (i.e., individual participant data meta-analysis) of structural MRI (sMRI) data, considering the influence of subtypes and testing for possible sex-specific effects. In addition to those with a CD diagnosis, there is a considerable amount of youths demonstrating elevated CP (Ghandour et al., 2019; Deighton et al., 2019), but who have not been assessed with diagnostic instruments and/or have not received a clinical diagnosis of CD. It is of interest if the findings from case-control comparisons of brain structure can be generalised to undiagnosed youths with elevated CP. As youths with a CD diagnosis are often recruited from specialised settings (e.g., clinics, mental health services or youth offending services), studying youths with elevated CP recruited through non-specialised settings would reduce selection bias and increase the generalisability of findings to a broader population (Horga et al., 2014). Neuroimaging studies have demonstrated structural brain alterations in youths with elevated CP, including decreased grey matter volume in the insula, amygdala, frontal and temporal regions (Raschle et al., 2015; Rogers & De Brito, 2016). A longitudinal study also reported altered trajectories of cortical thickness development in the dorsolateral prefrontal cortex and the anterior cingulate cortex (ACC), and volume of the hippocampus in youths showing elevated CP (Oostermeijer et al., 2016). Hence, both CP and CD appear to be related to similar and overlapping MRI findings of lower volume in the prefrontal cortex and limbic system. However, to date, no study has directly investigated if and how the cortical and subcortical alterations in youths with elevated CP overlap with the findings in youths with CD. Comparing youths with CD, elevated CP and TD will help clarify the neural mechanisms underlying antisocial behaviours in the clinical population, and extend the results to a more heterogeneous group of undiagnosed individuals showing elevated CP. Based on the ENIGMA-ASB datasets and using large-scale mega-analytical methods, the proposed study will also include an elevated CP group to identify the shared brain alterations underlying elevated antisocial behaviours as well as the specific alterations limited to youths with CD through the large-scale mega-analysis. Research Questions: (1) Do youths with CD show differences in cortical thickness, surface area and/or subcortical volume compared to TD youths? (2) How are sex, age-of-onset and CU traits related to brain alterations in CD? (3) Can alterations detected in youths with CD be generalised to youths with elevated CP?

Sahil Bajaj,1,2 Karina S Blair,1,2 Ru Zhang,1,2 Johannah Bashford-Largo,1,2 Matthew Dobbertin,1,2 R James Blair1,2 1Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, USA; 2Program for Trauma and Anxiety (PTAC), Center for Neurobehavioral Research, Boys Town National Research Hospital, Boys Town, NE, USACorrespondence: Sahil BajajCenter for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USAEmail sahil.bajaj@boystown.org