deletions | additions       diff --git a/section_Background_The_antidepressant_efficacy__.tex b/section_Background_The_antidepressant_efficacy__.tex  index ec10e98..0fbb4a1 100644  --- a/section_Background_The_antidepressant_efficacy__.tex  +++ b/section_Background_The_antidepressant_efficacy__.tex 
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A major limitation to the widespread use of rTMS is the fact that it is difficult to predict which patients will improve, thereby necessitating significant financial and/or time investment despite uncertainty regarding potential efficacy for any given patient. As a result, such predictive factors have been investigated thoroughly. Baseline clinical characteristics associated with improved response rates include concurrent antidepressant pharmacotherapy\cite{22980479}, fewer prior treatment failures, shorter duration of the current episode, and lack of a baseline anxiety disorder\cite{18704101}. Impaired response is associated with the converses of these factors as well as benzodiazepine or anticonvulsant pharmacotherapy\cite{22980479}. Response in older patients is improved when using increased doses in order to overcome the higher coil-to-cortex distance caused by cerebral atrophy in these populations\cite{15274174}.  Several biomarkers have also been identified to predict some degree of treatment response. Algorithms involving various electroencephalographic parameters have been reported for this purpose, although these studies have yet to be replicated in a prospective design\cite{22410477,22255807}. Anterior cingulate cortex activity and prefrontal cortex activity have shown predictive capability in various neuroimaging studies, but these findings are not unique to rTMS and are also found in patients more likely to respond to other treatments\cite{17984932,Micoulaud_Franchi_2013,22417767,Kito_2012,Micoulaud_Franchi_2013,22417767,21647787,Weiduschat_2013}. Electromyography-based motor cortex excitability has also demonstrated some limited utility in predicting response\cite{Fitzgerald_2004}. More recent data have suggested predictive value of functional network connectivity, including activity of frontostriatal networks\cite{Salomons_2013} networks\cite{Salomons_2013,Avissar2015}  and the default mode network.