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Cortical Thickness of the Salience Network and Change in Apathy Following Antidepressant Treatment for Late-Life Depression
Authors:Monique A. Pimontel  Nili Solomonov  Lauren Oberlin  Theodora Kanellopoulos  Jennifer N. Bress  Matthew J. Hoptman  George S. Alexopoulos  Faith M. Gunning
Affiliation:1. Weill Cornell Institute of Geriatric Psychiatry, Weill Cornell Medicine (MAP, NS, LO, TK, JNB, GSA, FMG), White Plains, NY;2. The Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY;3. New York University School of Medicine, New York, NY
Abstract:ObjectiveApathy is common in late-life depression and is associated with poor response to antidepressant drugs. In depressed older adults, apathy may be characterized by neuroanatomical abnormalities of the salience network. The current study examined whether cortical thickness of select salience network structures predicted change in apathy following a 12-week treatment with escitalopram.MethodsA sample of 46 older adults with major depressive disorder received 12 weeks of escitalopram treatment at a daily target dose of 20 mg. All participants underwent a structural brain MRI scan at baseline, and cortical thickness was estimated in key cortical nodes of the salience network: the caudal anterior cingulate cortex and the insula. We measured baseline and post-treatment symptoms using the Apathy Evaluation Scale and the Hamilton Depression Rating Scale.ResultsA thicker insula at baseline predicted reduction in apathy symptoms following 12 weeks of treatment with escitalopram, even when controlling for age, baseline depression severity and change in depressive symptoms.ConclusionReduced insular thickness predicted residual apathetic symptoms following escitalopram treatment. These results converge with our previous findings of abnormal functional connectivity of the insular cortex in older depressed individuals with apathy. Older depressed adults with apathy may benefit from alternative treatment approaches or augmentative interventions that target abnormalities of the salience network.
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