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Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models ("conv5-FC3", ResNet and "SECNN") as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM and QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the "conv5-FC3" network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization (acceptable performances on all tested cohorts including some that are not included in training). The trained models will be made publicly available should the manuscript be accepted., Competing Interests: 6.3.1Disclosure statement Competing financial interests related to the present article: none to disclose for all authors. Competing financial interests unrelated to the present article: OC reports having received consulting fees from AskBio (2020) and Therapanacea (2022–2024), and that his laboratory has received grants (paid to the institution) from Qynapse (2017–2022). Members from his laboratory have co-supervised a PhD thesis with Qynapse (2017–2022). OC’s spouse was an employee of myBrainTechnologies and is an employee of DiamPark. OC holds a patent registered at the International Bureau of the World Intellectual Property Organization (PCT/IB2016/0526993, Schiratti J-B, Allassonniere S, Colliot O, Durrleman S, A method for determining the temporal progression of a biological phenomenon and associated methods and devices) (2017). Tobias Banaschewski served in an advisory or consultancy role for eye level, Infectopharm, Lundbeck, Medice, Neurim Pharmaceuticals, Oberberg GmbH, Roche, and Takeda. He received conference support or speaker’s fee by Janssen, Medice and Takeda. He received royalities from Hogrefe, Kohlhammer, CIP Medien, Oxford University Press; the present work is unrelated to these relationships. Dr. Barker has received honoraria from General Electric Healthcare for teaching on scanner programming courses. Dr. Poustka served in an advisory or consultancy role for Roche and Viforpharm and received speaker’s fee by Shire. She received royalties from Hogrefe, Kohlhammer and Schattauer. The present work is unrelated to the above grants and relationships.The other authors report no biomedical financial interests or potential conflicts of interest.

Imaging genetics offers the possibility of detecting associations between genotype and brain structure as well as function, with effect sizes potentially exceeding correlations between genotype and behavior. However, study results are often limited due to small sample sizes and methodological differences, thus reducing the reliability of findings. The IMAGEN cohort with 2000 young adolescents assessed from the age of 14 onwards tries to eliminate some of these limitations by offering a longitudinal approach and sufficient sample size for analyzing gene-environment interactions on brain structure and function. Here, we give a systematic review of IMAGEN publications since the start of the consortium. We then focus on the specific phenotype 'drug use' to illustrate the potential of the IMAGEN approach. We describe findings with respect to frontocortical, limbic and striatal brain volume, functional activation elicited by reward anticipation, behavioral inhibition, and affective faces, and their respective associations with drug intake. In addition to describing its strengths, we also discuss limitations of the IMAGEN study. Because of the longitudinal design and related attrition, analyses are underpowered for (epi-) genome-wide approaches due to the limited sample size. Estimating the generalizability of results requires replications in independent samples. However, such densely phenotyped longitudinal studies are still rare and alternative internal cross-validation methods (e.g., leave-one out, split-half) are also warranted. In conclusion, the IMAGEN cohort is a unique, very well characterized longitudinal sample, which helped to elucidate neurobiological mechanisms involved in complex behavior and offers the possibility to further disentangle genotype × phenotype interactions.

Research using the Stop Signal Task employing an adaptive algorithm to accommodate individual differences often report inferior performance on the task in individuals with ADHD, OCD, and substance use disorders compared to non-clinical controls. Furthermore, individuals with deficits in inhibitory control tend to show reduced neural activity in key inhibitory regions during successful stopping. However, the adaptive algorithm systematically introduces performance-related differences in objective task difficulty that may influence the estimation of individual differences in stop-related neural activity. This report examines the effect that these algorithm-related differences have on the measurement of neural activity during the stop signal task. We compared two groups of subjects (n = 210) who differed in inhibitory ability using both a standard fMRI analysis and an analysis that resampled trials to remove the objective task difficulty confound. The results show that objective task difficulty influences the magnitude of between-group differences and that controlling for difficulty attenuates stop-related activity differences between superior and poor inhibitors. Specifically, group differences in the right inferior frontal gyrus, right middle occipital gyrus, and left inferior frontal gyrus are diminished when differences in objective task difficulty are controlled for. Also, when objective task difficulty effects are exaggerated, group differences in stop related activity emerge in other regions of the stopping network. The implications of these effects for how we interpret individual differences in activity levels are discussed., (© 2018 Wiley Periodicals, Inc.)

Background: It has been reported that mania may be associated with superior cognitive performance. In this study, we test the hypothesis that manic symptoms in youth separate along two correlated dimensions and that a symptom constellation of high energy and cheerfulness is associated with superior cognitive performance., Method: We studied 1755 participants of the IMAGEN study, of average age 14.4 years (SD = 0.43), 50.7% girls. Manic symptoms were assessed using the Development and Wellbeing Assessment by interviewing parents and young people. Cognition was assessed using the Wechsler Intelligence Scale For Children (WISC-IV) and a response inhibition task., Results: Manic symptoms in youth formed two correlated dimensions: one termed exuberance, characterized by high energy and cheerfulness and one of undercontrol with distractibility, irritability and risk-taking behavior. Only the undercontrol, but not the exuberant dimension, was independently associated with measures of psychosocial impairment. In multivariate regression models, the exuberant, but not the undercontrolled, dimension was positively and significantly associated with verbal IQ by both parent- and self-report; conversely, the undercontrolled, but not the exuberant, dimension was associated with poor performance in a response inhibition task., Conclusions: Our findings suggest that manic symptoms in youth may form dimensions with distinct correlates. The results are in keeping with previous findings about superior performance associated with mania. Further research is required to study etiological differences between these symptom dimensions and their implications for clinical practice., (© 2014 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.)

Objective: Adolescents are particularly vulnerable to addiction, and in the case of smoking, this often leads to long-lasting nicotine dependence. The authors investigated a possible neural mechanism underlying this vulnerability., Method: Functional MRI was performed during reward anticipation in 43 adolescent smokers and 43 subjects matched on age, gender, and IQ. The authors also assessed group differences in novelty seeking, impulsivity, and reward delay discounting., Results: In relation to the comparison subjects, the adolescent smokers showed greater reward delay discounting and higher scores for novelty seeking. Neural responses in the ventral striatum during reward anticipation were significantly lower in the smokers than in the comparison subjects, and in the smokers this response was correlated with smoking frequency. Notably, the lower response to reward anticipation in the ventral striatum was also observed in smokers (N=14) who had smoked on fewer than 10 occasions., Conclusions: The present findings suggest that a lower response to reward anticipation in the ventral striatum may be a vulnerability factor for the development of early nicotine use.