Sarah J Ordaz1, Meghan S Goyer2, Tiffany C Ho2, Manpreet K Singh3, Ian H Gotlib2. 1. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., MC, Stanford 5722, CA, USA. Electronic address: sjo22@stanford.edu. 2. Department of Psychology, Stanford University, Stanford, CA, USA. 3. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Rd., MC, Stanford 5722, CA, USA.
Abstract
BACKGROUND: Suicidal ideation rates rise precipitously in adolescence, contributing to risk for attempts. Although researchers are beginning to explore the brain basis of attempts in depressed adolescents, none have focused on the basis of ideation, which has implications for prevention. This study examined the association between intrinsic neural network coherence and the severity of suicidal ideation in depressed adolescents. METHODS: Forty adolescents diagnosed with Major Depressive Disorder were administered the Columbia-Suicide Severity Rating Scale and underwent resting-state fMRI. We quantified within-network coherence in the executive control (ECN), default mode (DMN), and salience (SN) networks, and in a non-relevant network consisting of noise signal. We associated coherence in each of these networks with the greatest lifetime severity of suicidal ideation experienced, covarying for motion, age of depression onset, and severity of current depressive and anxious symptoms. RESULTS: Lower coherence in the left ECN, anterior DMN, and SN were independently associated with greater lifetime severity of suicidal ideation. When including all three significant networks and covariates in a single model, only the left ECN significantly predicted suicidal ideation. LIMITATION: Studies with a larger sample size are needed to verify our findings. CONCLUSIONS: Our finding of hypoconnectivity in multiple networks extends emerging evidence for hypoconnectivity in adolescent suicidality and is consistent with theoretical conceptualizations of suicidal ideation as a complex set of cognitions associated with cognitive control, self-referential thinking, and processing salient information. While multiple networks could be targets for effective early interventions, those targeting ECN functionality (cognitive control) may be particularly beneficial.
BACKGROUND: Suicidal ideation rates rise precipitously in adolescence, contributing to risk for attempts. Although researchers are beginning to explore the brain basis of attempts in depressed adolescents, none have focused on the basis of ideation, which has implications for prevention. This study examined the association between intrinsic neural network coherence and the severity of suicidal ideation in depressed adolescents. METHODS: Forty adolescents diagnosed with Major Depressive Disorder were administered the Columbia-Suicide Severity Rating Scale and underwent resting-state fMRI. We quantified within-network coherence in the executive control (ECN), default mode (DMN), and salience (SN) networks, and in a non-relevant network consisting of noise signal. We associated coherence in each of these networks with the greatest lifetime severity of suicidal ideation experienced, covarying for motion, age of depression onset, and severity of current depressive and anxious symptoms. RESULTS: Lower coherence in the left ECN, anterior DMN, and SN were independently associated with greater lifetime severity of suicidal ideation. When including all three significant networks and covariates in a single model, only the left ECN significantly predicted suicidal ideation. LIMITATION: Studies with a larger sample size are needed to verify our findings. CONCLUSIONS: Our finding of hypoconnectivity in multiple networks extends emerging evidence for hypoconnectivity in adolescent suicidality and is consistent with theoretical conceptualizations of suicidal ideation as a complex set of cognitions associated with cognitive control, self-referential thinking, and processing salient information. While multiple networks could be targets for effective early interventions, those targeting ECN functionality (cognitive control) may be particularly beneficial.
Authors: Israel Garcia-Carachure; Francisco J Flores-Ramirez; Samuel A Castillo; Anapaula Themann; Miguel A Arenivar; Joshua Preciado-Piña; Arturo R Zavala; Mary Kay Lobo; Sergio D Iñiguez Journal: Neuropsychopharmacology Date: 2020-03-12 Impact factor: 7.853
Authors: Elizabeth T Cox Lippard; Jennifer A Y Johnston; Linda Spencer; Susan Quatrano; Siyan Fan; Anjali Sankar; Judah Weathers; Brian Pittman; Maria A Oquendo; Hilary P Blumberg Journal: J Affect Disord Date: 2018-11-28 Impact factor: 4.839
Authors: Jonathan P Stange; Lisanne M Jenkins; Stephanie Pocius; Kayla Kreutzer; Katie L Bessette; Sophie R DelDonno; Leah R Kling; Runa Bhaumik; Robert C Welsh; John G Keilp; K Luan Phan; Scott A Langenecker Journal: Psychol Med Date: 2019-10-10 Impact factor: 7.723
Authors: Martin J Lan; Mina M Rizk; Spiro P Pantazatos; Harry Rubin-Falcone; Jeffrey M Miller; M Elizabeth Sublette; Maria A Oquendo; John G Keilp; J John Mann Journal: Depress Anxiety Date: 2019-03-21 Impact factor: 6.505
Authors: Jaclyn S Kirshenbaum; Rajpreet Chahal; Tiffany C Ho; Lucy S King; Anthony J Gifuni; Dana Mastrovito; Saché M Coury; Rachel L Weisenburger; Ian H Gotlib Journal: J Child Psychol Psychiatry Date: 2021-08-27 Impact factor: 8.265
Authors: Randy P Auerbach; David Pagliaccio; Grace O Allison; Kira L Alqueza; Maria Fernanda Alonso Journal: Biol Psychiatry Date: 2020-06-10 Impact factor: 13.382