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

Reward Versus Nonreward Sensitivity of the Medial Versus Lateral Orbitofrontal Cortex Relates to the Severity of Depressive Symptoms

BackgroundThe orbitofrontal cortex (OFC) is implicated in depression. The hypothesis investigated was whether the OFC sensitivity to reward and nonreward is related to the severity of depressive symptoms.MethodsActivations in the monetary incentive delay task were measured in the IMAGEN cohort at ages 14 years (n = 1877) and 19 years (n = 1140) with a longitudinal design. Clinically relevant subgroups were compared at ages 19 (high-severity group: n = 116; low-severity group: n = 206) and 14.ResultsThe medial OFC exhibited graded activation increases to reward, and the lateral OFC had graded activation increases to nonreward. In this general population, the medial and lateral OFC activations were associated with concurrent depressive symptoms at both ages 14 and 19 years. In a stratified high-severity depressive symptom group versus control group comparison, the lateral OFC showed greater sensitivity for the magnitudes of activations related to nonreward in the high-severity group at age 19 (p = .027), and the medial OFC showed decreased sensitivity to the reward magnitudes in the high-severity group at both ages 14 (p = .002) and 19 (p = .002). In a longitudinal design, there was greater sensitivity to nonreward of the lateral OFC at age 14 for those who exhibited high depressive symptom severity later at age 19 (p = .003).ConclusionsActivations in the lateral OFC relate to sensitivity to not winning, were associated with high depressive symptom scores, and at age 14 predicted the depressive symptoms at ages 16 and 19. Activations in the medial OFC were related to sensitivity to winning, and reduced reward sensitivity was associated with concurrent high depressive symptom scores

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

La stimulation magnétique transcrânienne dans la dépression (Analyse critique de la littérature et illustrations cliniques concernant son efficasité thérapeutique)

LE KREMLIN-B.- PARIS 11-BU Méd (940432101) / SudocPARIS-BIUM (751062103) / SudocSudocFranceF

Schizophrenia as a Network Disease: Disruption of Emergent Brain Function in Patients with Auditory Hallucinations

<div><p>Schizophrenia is a psychiatric disorder that has eluded characterization in terms of local abnormalities of brain activity, and is hypothesized to affect the collective, “emergent” working of the brain. Indeed, several recent publications have demonstrated that functional networks in the schizophrenic brain display disrupted topological properties. However, is it possible to explain such abnormalities just by alteration of local activation patterns? This work suggests a negative answer to this question, demonstrating that significant disruption of the topological and spatial structure of functional MRI networks in schizophrenia (a) cannot be explained by a disruption to area-based task-dependent responses, i.e. indeed relates to the emergent properties, (b) is global in nature, affecting most dramatically long-distance correlations, and (c) can be leveraged to achieve high classification accuracy (93%) when discriminating between schizophrenic vs control subjects based just on a single fMRI experiment using a simple auditory task. While the prior work on schizophrenia networks has been primarily focused on discovering statistically significant differences in network properties, this work extends the prior art by exploring the generalization (prediction) ability of network models for schizophrenia, which is not necessarily captured by such significance tests.</p></div

Stability of feature subset selection over cross-validation (CV) folds.

<p>Stability is measured as the percent of voxels in common among the subsets of <i>k</i> top variables selected at all CV folds: (a) activations and degrees; (b,c) edge weights (correlations), clustering coefficients, strength, absolute strength, positive strength, and local efficiency: (b) linear scale on x-axis, (c) log-scale on x-axis (focusing on small number of features selected.</p

9 stable edges common to all subsets of 30 top-ranked (lowest-pvalue) edges that survived Bonferroni correction, over 22 different cross-validation folds (leave-subject-out data subsets).

<p>(a) All views and (b) enlarged saggital view. Edge density is proportional to their absolute value. The network includes several areas not picked up by the degree maps, i.e. other than BA 22 and BA 21, mainly the cerebellum (declive) and the occipital cortex (BA 19).</p

Classification results: degree vs. activation features.

<p>Three classifiers, Gaussian Naive Bayes (GNB) in panel (a), SVM in panel (b) and sparse MRF in panel (c) are compared on two types of features, degrees and activation contrasts; (d) all three classifiers compared on long-distance degree maps (best-performing for MRF).</p

Graphical models of voxel interactions.

<p>Simple probabilistic graphical models capturing interactions among voxel-level BOLD signals and observed stimulus: (a) Markov network (undirected graph) over a pair of voxels and the task; (b) Bayesian network (directed graph) that includes an unobserved variable capturing other brain processes, besides the response to the observed stimulus, that can affect the BOLD signals. Note that directed links in Baysian networks are often (though not always) used to depict potential causal dependencies among the variables.</p