Feature selection and classification of imbalanced datasets. Application to PET images of children with Autistic Spectrum Disorders

Learning with discriminative methods is generally based on minimizing themisclassification of training samples, which may be unsuitable for imbalanceddatasets where the recognition might be biased in favor of the most numerousclass. This problem can be addressed with a generative approach, which typicallyrequires more parameters to be determined leading to reduced performances inhigh dimension. In such situations, dimension reduction becomes a crucial issue.We propose a feature selection / classification algorithm based on generativemethods in order to predict the clinical status of a highly imbalanced datasetmade of PET scans of forty-five low-functioning children with autism spectrumdisorders (ASD) and thirteen non-ASD low-functioning children. ASDs aretypically characterized by impaired social interaction, narrow interests, andrepetitive behaviours, with a high variability in expression and severity. Thenumerous findings revealed by brain imaging studies suggest that ASD isassociated with a complex and distributed pattern of abnormalities that makesthe identification of a shared and common neuroimaging profile a difficult task.In this context, our goal is to identify the rest functional brain imagingabnormalities pattern associated with ASD and to validate its efficiency inindividual classification. The proposed feature selection algorithm detected acharacteristic pattern in the ASD group that included a hypoperfusion in theright Superior Temporal Sulcus (STS) and a hyperperfusion in the contralateralpostcentral area. Our algorithm allowed for a significantly accurate (88\%),sensitive (91\%) and specific (77\%) prediction of clinical category. For thisimbalanced dataset, with only 13 control scans, the proposed generativealgorithm outperformed other state-of-the-art discriminant methods. The highpredictive power of the characteristic pattern, which has been automaticallyidentified on whole brains without any priors, confirms previous findingsconcerning the role of STS in ASD. This work offers exciting possibilities forearly autism detection and/or the evaluation of treatment response in individualpatients

Kinetic analysis of [11C]befloxatone in the human brain, a selective radioligand to image monoamine oxidase A.

International audienceBACKGROUND: [11C]Befloxatone measures the density of the enzyme monoamine oxidase A (MAO-A) in the brain. MAO-A is responsible for the degradation of different neurotransmitters and is implicated in several neurologic and psychiatric illnesses. This study sought to estimate the distribution volume (VT) values of [11C]befloxatone in humans using an arterial input function. METHODS: Seven healthy volunteers were imaged with positron emission tomography (PET) after [11C]befloxatone injection. Kinetic analysis was performed using an arterial input function in association with compartmental modeling and with the Logan plot, multilinear analysis (MA1), and standard spectral analysis (SA) at both the regional and voxel level. Arterialized venous samples were drawn as an alternative and less invasive input function. RESULTS: An unconstrained two-compartment model reliably quantified VT values in large brain regions. A constrained model did not significantly improve VT identifiability. Similar VT results were obtained using SA; however, the Logan plot and MA1 slightly underestimated VT values (about -10 %). At the voxel level, SA showed a very small bias (+2 %) compared to compartmental modeling, Logan severely underestimated VT values, and voxel-wise images obtained with MA1 were too noisy to be reliably quantified. Arterialized venous blood samples did not provide a satisfactory alternative input function as the Logan-VT regional values were not comparable to those obtained with arterial sampling in all subjects. CONCLUSIONS: Binding of [11C]befloxatone to MAO-A can be quantified using an arterial input function and a two-compartment model or, in parametric images, with SA

Irregular sleep habits, regional grey matter volumes, and psychological functioning in adolescents

Changing sleep rhythms in adolescents often lead to sleep deficits and a delay in sleep timing between weekdays and weekends. The adolescent brain, and in particular the rapidly developing structures involved in emotional control, are vulnerable to external and internal factors. In our previous study in adolescents at age 14, we observed a strong relationship between weekend sleep schedules and regional medial prefrontal cortex grey matter volumes. Here, we aimed to assess whether this relationship remained in this group of adolescents of the general population at the age of 16 (n = 101; mean age 16.8 years; 55% girls). We further examined grey matter volumes in the hippocampi and the amygdalae, calculated with voxel-based morphometry. In addition, we investigated the relationships between sleep habits, assessed with self-reports, and regional grey matter volumes, and psychological functioning, assessed with the Strengths and Difficulties Questionnaire and tests on working memory and impulsivity. Later weekend wake-up times were associated with smaller grey matter volumes in the medial prefrontal cortex and the amygdalae, and greater weekend delays in wake-up time were associated with smaller grey matter volumes in the right hippocampus and amygdala. The medial prefrontal cortex region mediated the correlation between weekend wake up time and externalising symptoms. Paying attention to regular sleep habits during adolescence could act as a protective factor against the emergence of psychopathology via enabling favourable brain development.Peer reviewe

Sleep habits, academic performance, and the adolescent brain structure

Here we report the first and most robust evidence about how sleep habits are associated with regional brain grey matter volumes and school grade average in early adolescence. Shorter time in bed during weekdays, and later weekend sleeping hours correlate with smaller brain grey matter volumes in frontal, anterior cingulate, and precuneus cortex regions. Poor school grade average associates with later weekend bedtime and smaller grey matter volumes in medial brain regions. The medial prefrontal anterior cingulate cortex appears most tightly related to the adolescents' variations in sleep habits, as its volume correlates inversely with both weekend bedtime and wake up time, and also with poor school performance. These findings suggest that sleep habits, notably during the weekends, have an alarming link with both the structure of the adolescent brain and school performance, and thus highlight the need for informed interventions.Peer reviewe

The empirical replicability of task-based fMRI as a function of sample size

Replicating results (i.e. obtaining consistent results using a new independent dataset) is an essential part of good science. As replicability has consequences for theories derived from empirical studies, it is of utmost importance to better understand the underlying mechanisms influencing it. A popular tool for non-invasive neuroimaging studies is functional magnetic resonance imaging (fMRI). While the effect of underpowered studies is well documented, the empirical assessment of the interplay between sample size and replicability of results for task-based fMRI studies remains limited. In this work, we extend existing work on this assessment in two ways. Firstly, we use a large database of 1400 subjects performing four types of tasks from the IMAGEN project to subsample a series of independent samples of increasing size. Secondly, replicability is evaluated using a multi-dimensional framework consisting of 3 different measures: (un)conditional test-retest reliability, coherence and stability. We demonstrate not only a positive effect of sample size, but also a trade-off between spatial resolution and replicability. When replicability is assessed voxelwise or when observing small areas of activation, a larger sample size than typically used in fMRI is required to replicate results. On the other hand, when focussing on clusters of voxels, we observe a higher replicability. In addition, we observe variability in the size of clusters of activation between experimental paradigms or contrasts of parameter estimates within these

Blunted ventral striatal responses to anticipated rewards foreshadow problematic drug use in novelty-seeking adolescents.

Novelty-seeking tendencies in adolescents may promote innovation as well as problematic impulsive behaviour, including drug abuse. Previous research has not clarified whether neural hyper- or hypo-responsiveness to anticipated rewards promotes vulnerability in these individuals. Here we use a longitudinal design to track 144 novelty-seeking adolescents at age 14 and 16 to determine whether neural activity in response to anticipated rewards predicts problematic drug use. We find that diminished BOLD activity in mesolimbic (ventral striatal and midbrain) and prefrontal cortical (dorsolateral prefrontal cortex) regions during reward anticipation at age 14 predicts problematic drug use at age 16. Lower psychometric conscientiousness and steeper discounting of future rewards at age 14 also predicts problematic drug use at age 16, but the neural responses independently predict more variance than psychometric measures. Together, these findings suggest that diminished neural responses to anticipated rewards in novelty-seeking adolescents may increase vulnerability to future problematic drug use

Overdominant effect of a CHRNA4 polymorphism on cingulo-opercular network activity and cognitive control

The nicotinic system plays an important role in cognitive control, and is implicated in several neuropsychiatric conditions. Yet, the contributions of genetic variability in this system to individuals' cognitive control abilities are poorly understood, and the brain processes that mediate such genetic contributions remain largely unidentified. In this first large-scale neuroimaging genetics study of the human nicotinic receptor system (two cohorts, males and females, fMRI total N=1586, behavioral total N=3650), we investigated a common polymorphism of the high-affinity nicotinic receptor α4β2 (rs1044396 on the CHRNA4 gene) previously implicated in behavioral and nicotine-related studies (albeit with inconsistent major/minor allele impacts). Based on our prior neuroimaging findings, we expected this polymorphism to impact neural activity in the cingulo-opercular network involved in core cognitive control processes including maintenance of alertness. Consistent across the cohorts, all cortical areas of the cingulo-opercular network showed higher activity in heterozygotes compared to both types of homozygotes during cognitive engagement. This inverted U-shaped relation reflects an overdominant effect, i.e. allelic interaction (cumulative evidence p=1.33*10-5). Furthermore, heterozygotes performed more accurately in behavioral tasks that primarily depend on sustained alertness. No effects were observed for haplotypes of the surrounding CHRNA4 region, supporting a true overdominant effect at rs1044396. As a possible mechanism, we observed that this polymorphism is an expression quantitative trait locus (eQTL) modulating CHRNA4 expression levels. This is the first report of overdominance in the nicotinic system. These findings connect CHRNA4genotype, cingulo-opercular network activation and sustained alertness, providing insights into how genetics shapes individuals' cognitive control abilities