Des arguments pour poser son smartphone après 21  heures . un conseil aux ados.Dr Claude jean Paris

FATIGUE. Des chercheurs de l'université du Michigan aux États-Unis ont étudié l'impact sur le sommeil d'une utilisation du smartphone après 21 h. Dans l'étude qu'ils ont publié dans le magazineOrganizational Behavior and Human Decision Process, les chercheurs affirment qu'une telle utilisation perturbe la qualité du sommeil et implique donc des lendemains difficiles...

L'étude s'est attachée à interroger 82 cadres supérieurs et 161 employés sur leur utilisation du smartphone et sur leur état de forme. Chaque jour, il était demandé à chacun des participants de donner des précisions sur l'heure à laquelle ils avaient utilisé leur téléphone pour la dernière fois la veille et pour quelle utilisation. Une première série de questions leur était posée à 6h le matin pour connaître la qualité de leur sommeil durant la nuit et leur éventuel sentiment d'épuisement au matin. De nouvelles questions étaient posées à 16h pour évaluer leur motivation au travail.

Une grande fatigue et une moins bonne motivation au travail

Il est ainsi apparu qu'une utilisation du smartphone après 21 h nuisait à la qualité du sommeil, impliquait une grande fatigue le matin et était assortie d'une moins bonne motivation au travail tout au long de la journée.

Le groupe de 161 employés a également été testé par rapport à son utilisation des ordinateurs - fixes ou portables - des tablettes et de la télévision. Mais c'est bien le téléphone portable qui a les effets les plus néfastes sur le sommeil. Ce résultat a d'ailleurs conduit l'un des auteurs de l'étude, Russel Johnson, a déclarer dans un communiqué que "les smartphones sont presque parfaitement conçus pour perturber le sommeil".

LUMIÈRE BLEUE. L'une des explications pouvant expliquer la plus grande nocivité des smartphones par rapport aux autres appareils serait le type de lumière qu'ils émettent. La fameuse "lumière bleue" (cf. encadré) dont l'intensité serait plus importante dans les écrans de smartphones. Ce type de lumière aurait une influence négative sur la production de mélatonine, une substance chimique sécrétée par notre organisme et favorisant le sommeil.

Les lumières intenses et bleues retardent le sommeil.
Des recherches ont montré que la rétine renferme des cellules ganglionnaires à mélanopsine, remplissant une fonction non visuelle. Selon la lumière qu'elles reçoivent, ces cellules activent le noyau responsable du sommeil. Ainsi, plus la lumière est de forte intensité et dans les bleus - comme les LED - plus elle retarde l'horloge biologique. "Si je travaille le soir avec une lampe de bureau très forte, mon horloge biologique va être décalée. Et mon sommeil déclenché plus tardivement", raconte Claude Gonfier, chercheur au département de chronobiologie de l'Inserm à Bron (Lyon). Un couche-tard/lève-tard ne doit donc pas s'exposer à ce genre de lumière le soir, sous peine d'accentuer soon jetlagsocial. Mieux vaut donc privilégier une lumière dans les tons orangés. Et adopter un style de vie et un métier en accord avec son chronotype. 

L'obésité infantile est liée au stress Dr Claude Jean PARIS

Obese children have higher stress hormone levels than normal-weight peers

Hair analysis found elevated cortisol concentrations in children as young as 8

Chevy Chase, MD—Obese children naturally produce higher levels of a key stress hormone than their normal weight peers, according to new research accepted for publication in The Endocrine Society's Journal of Clinical Endocrinology & Metabolism.

The body produces the hormone cortisol when a person experiences stress. When a person faces frequent stress, cortisol and other stress hormones build up in the blood and, over time, can cause serious health problems. This study measured cortisol in scalp hair, which reflects long-term exposure and has been proposed to be a biomarker for stress. The study is the first to show obese children have chronically elevated levels of cortisol.

"We were surprised to find obese children, as young as age 8, already had elevated cortisol levels," said one of the study's authors, Erica van den Akker, MD, PhD, of Erasmus MC-Sophia Children's Hospital in Rotterdam, The Netherlands. "By analyzing children's scalp hair, we were able to confirm high cortisol levels persisted over time."

The observational case-control study analyzed hair samples from 20 obese children and 20 normal weight children to measure long-term cortisol levels. Each group included 15 girls and 5 boys between the ages of 8 and 12.

Obese subjects had an average cortisol concentration of 25 pg/mg in their scalp hair, compared to an average concentration of 17 pg/mg in the normal weight group. The hormone concentrations found in hair reflect cortisol exposure over the course of about one month.

"Because this study took an observational approach, more research will determine the cause of this phenomenon," van den Akker said. "We do not know whether obese children actually experience more psychological stress or if their bodies handle stress hormones differently. Answering these key questions will improve our understanding of childhood obesity and may change the way we treat it."

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Other authors of the study include: M. Veldhorst, G. Noppe, C. Kok and S. Mekic of Erasmus MC-Sophia Children's Hospital; M. Jongejan of Sint Franciscus Gasthuis in Rotterdam; and J. Koper and E. van Rossum of Erasmus MC in Rotterdam.

The study, "Increased Scalp Hair Cortisol Concentrations in Obese Children," was published online, ahead of print.

Founded in 1916, The Endocrine Society is the world's oldest, largest and most active organization devoted to research on hormones and the clinical practice of endocrinology. Today, The Endocrine Society's membership consists of over 17,000 scientists, physicians, educators, nurses and students in more than 100 countries. Society members represent all basic, applied and clinical interests in endocrinology. The Endocrine Society is based in Chevy Chase, Maryland. To learn more about the Society and the field of endocrinology, visit our site at http://www.endocrine.org. Follow us on Twitter at https://twitter.com/#!/EndoMedia.

 Une decouvert scientifique qui permet d'expliquer le support cérébral du lacher prise .

 Pourquoi le cerveau a t il besoin de se mettre en mode automatique?

the structural–functional connectome and the default mode network of the human brain

• Andreas Horn^a, b, , , 
• Dirk Ostwald^a, 
• Marco Reisert^c, 
• Felix Blankenburg^a, d

• ^a Center for Adaptive Rationality (ARC), Max Planck Institute for Human Development, Berlin, Germany
• ^b Department of Neurology, Charité, University Medicine Berlin, Berlin, Germany
• ^c Department of Radiology, Medical Physics, University Hospital Freiburg, Germany
• ^d Dahlem Institute for Neuroimaging of Emotion, Freie Universität Berlin, Berlin, Germany

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Highlights

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Structure–function connectivity relationship

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Multi-modal data fusion

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Voxel-wise connectivity analysis

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Default mode network

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Global fiber-tracking

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Abstract

An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional–structural connectomes of the human brain. Probabilistic fiber-tracking and a novel global fiber-tracking technique were used to measure structural connectivity whereas for functional connectivity, full and partial correlations between each voxel pair's fMRI-timecourses were calculated. For every voxel, two vectors consisting of functional and structural connectivity estimates to all other voxels in the cortex were correlated with each other. In this way, voxels structurally and functionally connected to similar regions within the rest of the brain could be identified. Areas forming parts of the ‘default mode network’ (DMN) showed the highest agreement of structure–function connectivity. Bilateral precuneal and inferior parietal regions were found using all applied techniques, whereas the global tracking algorithm additionally revealed bilateral medial prefrontal cortices and early visual areas. There were no significant differences between the results obtained from full and partial correlations. Our data suggests that the DMN is the functional brain network, which uses the most direct structural connections. Thus, the anatomical profile of the brain seems to shape its functional repertoire and the computation of the whole-brain functional–structural connectome appears to be a valuable method to characterize global brain connectivity within and between populations.

Keywords

• Connectome; 
• Structure–function relationship; 
• Functional magnetic resonance imaging (fMRI); 
• Diffusion tensor imaging (DTI); 
• Resting-state fMRI; 
• Default mode network (DMN)

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Figures and tables from this article:

Fig. 1. 

Processing of functional time signals and structural data obtained from functional MRI and diffusion based imaging. Panel A: A group template as a best mean agreement between the individual anatomical features is parcellated into 40,000 voxels. Panel B: Structural and functional connectivity is obtained by using diffusion-weighted and fMRI data. Probabilistic and global tracking is performed between each point defined in panel A. Functional time courses are extracted from the same points, accordingly. Panel C: For each connectivity method, an adjacency matrix is obtained for each subject, showing the connectivity between each pair of the 40,000 voxels. Panel D: Each voxel's connectivity to the rest of the brain is represented in a 1 × 40,000 vector (i.e. one row or column extracted from the matrices in panel C). Agreement between vectors obtained from different methods within subjects can be combined by spatially correlating vectors for each voxel and by mapping resulting values back to the group-template space (panel E). Results can then be transformed to standard MNI space by applying the normalization transformation of the template to the resulting images.

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Fig. 2. 

Group results of correlations between functional and structural connectivity measures. Voxel-wise comparisons between each voxel's functional and structural connectivity to the rest of the brain were analyzed for each subject and analyzed in a t-test group analysis. Findings from spatial correlation analyses between one of the structural connectivity measures (probabilistic global fiber-tracking) and one of the functional connectivity measures (full or partial correlations) are shown. Areas in this figure are thresholded at p < 0.001 and corrected for multiple comparisons on a cluster-level (FWE < 0.05). 1: Precuneus and adjacent posterior cingulate/retrosplenial cortex (PCC/Rsp), 2. Bilateral Inferior parietal lobe/angular gyrus (IPL/AG), 3. Right Supramarginal gyrus (SMG), 4. Bilateral Medial prefrontal gyrus (MPFC), 5. Left middle/inferior frontal gyrus, 6. Occipital pole.

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Fig. 3. 

Contrasts between results from Fig. 2 further show the effect of the different structural tracking methods and the similarity between functional methods. Differential contrast for probabilistic > global tracking similarity with full and partial correlations show higher t-values in the middle temporal gyrus and precuneus. Global > probabilistic tracking similarity with full and partial correlations show higher t-values in frontal areas. Areas in this figure are thresholded at p < 0.001 and corrected for multiple comparisons on the cluster-level (FWE < 0.05). 1: Precuneus and adjacent posterior cingulate/retrosplenial cortex (PCC/Rsp), 2. Bilateral inferior parietal lobe/angular gyrus (IPL/AG), 3. Bilateral Medial prefrontal gyrus (MPFC), 4. Occipital pole.

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Fig. 4. 

Unthresholded maps of mean effect size from spatial correlation analyses for the different connectivity measures. Voxel-wise comparisons between each voxel's functional and structural connectivity to the rest of the brain were analyzed for each subject and then averaged over the group of subjects. Note that Pearson's R-values were Fisher-transformed to a gaussian distribution prior to averaging over subjects.

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Fig. 5. 

Influence of the identified regions as shown by their connectivity in tractography. Regions that showed the highest structure–function agreement in between probabilistic trackings and full correlations are visualized as the solid regions in yellow. Only fibers that connect to these regions are displayed and color-coded for their traversing direction (xyz mapping to rgb). Fibers of the cingulum bundle connect precuneal regions to the medial prefrontal cortex, inferior longitudinal fascicle to the temporal lobes. Interhemispheric connectivity is mediated via the corpus callosum.

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Table 1.

Connectivity agreement as measured by correlating whole connectivity matrices for different connectivity measures. Mean Pearson's R-values, as well as t- and p-values of a one-sample t-test over the group are given for the analyses with and without prior regression of Euclidean distance.

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Table 2.

Results from spatial correlations between different connectivity measures. Coordinates and t-values refer to peak-voxels in significant clusters.

View Within Article

Table 3.

Intensity values in brain-maps showing regions with high cross-modal connectivity agreement were correlated to intensity values in a template of the default mode network obtained from a classical independent component analysis study (Garrity et al., 2007). Mean correlation strength of single-subject images with the template and their standard deviation are displayed for the combinations between the two functional and structural methods applied.

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Du nouveau pour protéger le cerveau du cannabis

Pregnenolone Can Protect the Brain from Cannabis Intoxication

1. Monique Vallée1,2,†, 
2. Sergio Vitiello1,2,*, 
3. Luigi Bellocchio1,2,*, 
4. Etienne Hébert-Chatelain1,2,*,
5. Stéphanie Monlezun3,*, 
6. Elena Martin-Garcia4, 
7. Fernando Kasanetz1,2, 
8. Gemma L. Baillie5,7,
9. Francesca Panin1,2, 
10. Adeline Cathala1,2, 
11. Valérie Roullot-Lacarrière1,2, 
12. Sandy Fabre3, 
13. Dow P. Hurst6,
14. Diane L. Lynch6, 
15. Derek M. Shore6, 
16. Véronique Deroche-Gamonet1,2, 
17. Umberto Spampinato1,2,
18. Jean-Michel Revest1,2, 
19. Rafael Maldonado4, 
20. Patricia H. Reggio6, 
21. Ruth A. Ross5,7, 
22. Giovanni Marsicano1,2,
23. Pier Vincenzo Piazza1,2,†‡

+Author Affiliations

1. ¹INSERM, Neurocentre Magendie, Physiopathologie de la Plasticité Neuronale, U862, F-33000 Bordeaux, France.
2. ²Université de Bordeaux, Neurocentre Magendie, Physiopathologie de la Plasticité Neuronale, U862, F-33000 Bordeaux, France.
3. ³Alienor Farma Parc Scientifique Unitec1, 2 Allée du Doyen Georges Brus, 33600 Pessac, France.
4. ⁴Laboratory of Neuropharmacology, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomédica de Barcelona, Calle Dr. Aiguader 88, Barcelona 08003, Spain.
5. ⁵Kosterlitz Centre for Therapeutics, Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK.
6. ⁶Department of Chemistry and Biochemistry, University of North Carolina at Greensboro, Greensboro, NC, USA.
7. ⁷Present address: Department of Pharmacology and Toxicology, University of Toronto, Medical Sciences Building, 1 King's College Circle, Toronto, Ontario, Canada M5S 1A8.

1. ↵‡Corresponding author. E-mail: pier-vincenzo.piazza@inserm.fr

1. ↵* These authors contributed equally to this work.
2. ↵† These authors equally supervised this work.

• ABSTRACT
• EDITOR'S SUMMARY

Pregnenolone is considered the inactive precursor of all steroid hormones, and its potential functional effects have been largely uninvestigated. The administration of the main active principle of Cannabis sativa (marijuana), ∆⁹-tetrahydrocannabinol (THC), substantially increases the synthesis of pregnenolone in the brain via activation of the type-1 cannabinoid (CB₁) receptor. Pregnenolone then, acting as a signaling-specific inhibitor of the CB₁ receptor, reduces several effects of THC. This negative feedback mediated by pregnenolone reveals a previously unknown paracrine/autocrine loop protecting the brain from CB₁ receptor overactivation that could open an unforeseen approach for the treatment of cannabis intoxication and addiction.