Default Mode Network Efficiency Is Correlated With Deficits in Inhibition in Adolescents With Inhalant Use Disorder-Reference-Cited by-同舟云学术

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Default Mode Network Efficiency Is Correlated With Deficits in Inhibition in Adolescents With Inhalant Use Disorder

Published:2020-03-26 Issue: Volume:11 Page:
ISSN:1664-0640
Container-title:Frontiers in Psychiatry
language:
Short-container-title:Front. Psychiatry

Author:

Hernández-Álvarez Dailett M.,Pacheco Lucero,Velasco-Segura Roberto,Pérez de la Mora Miguel,Tejeda-Romero Claudia,González-García Nadia

Publisher

Frontiers Media SA

Subject

Psychiatry and Mental health

Reference67 articles.

1. United nations office on drugs and crime: World drug report 2017;Merz;SIRIUS-Zeitschrift für Strategische Analysen,2018

2. Transitions from first substance use to substance use disorders in adolescence: Is early onset associated with a rapid escalation;Behrendt;Drug Alcohol Depend,2009

3. Sensitive periods of substance abuse: Early risk for the transition to dependence;Jorsan;Dev Cogn Neurosci,2017

4. Earlier adolescent substance use onset predicts stronger connectivity between reward and cognitive control brain networks;Weissman;Dev Cogn Neurosci,2015

5. A transdiagnostic dimensional approach towards a neuropsychological assessment for addiction: an international Delphi consensus study;Yücel;Addiction,2019

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1. Sex-specific differences in brain activity dynamics of youth with a family history of substance use disorder;2024-09-04

2. Altered functional connectivity between cortical networks associated with inhibitory control in trauma-exposed females;Psychiatry Research: Neuroimaging;2023-08

3. Characterizing different cognitive and neurobiological profiles in a community sample of children using a non-parametric approach: An fMRI study;Developmental Cognitive Neuroscience;2023-04

4. Human immunodeficiency virus ‐related decreases in corpus callosal integrity and corresponding increases in functional connectivity;Human Brain Mapping;2021-08-12

5. Multi-modal biomarkers of low back pain: A machine learning approach;NeuroImage: Clinical;2021

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