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Meta-analytic Evidence for Volume Increases in the Medial Temporal Lobe After Electroconvulsive Therapy

      Since its introduction to psychiatric practice more than 8 decades ago, electroconvulsive therapy (ECT) is widely recognized as a highly effective treatment for severe psychiatric disorders, but the exact mechanisms underlying treatment response have remained elusive. Neuroimaging research has put a spotlight on structural brain changes (
      • Dukart J.
      • Regen F.
      • Kherif F.
      • Colla M.
      • Bajbouj M.
      • Heuser I.
      • et al.
      Electroconvulsive therapy-induced brain plasticity determines therapeutic outcome in mood disorders.
      ), initially inspired by the neurotrophic hypothesis of depression that postulates neuroneogenesis as essential for antidepressant treatment response (
      • Malberg J.E.
      • Eisch A.J.
      • Nestler E.J.
      • Duman R.S.
      Chronic antidepressant treatment increases neurogenesis in adult rat hippocampus.
      ,
      • Santarelli L.
      • Saxe M.
      • Gross C.
      • Surget A.
      • Battaglia F.
      • Dulawa S.
      • et al.
      Requirement of hippocampal neurogenesis for the behavioral effects of antidepressants.
      ). Intriguingly, some longitudinal magnetic resonance imaging studies indeed reported localized brain volume changes in patients after ECT. A straightforward interpretation of these findings, however, has been obscured, given the considerable heterogeneity of brain locations reported across studies. These inconsistent results might be attributable to several factors. Small sample sizes are a particular problem, as individuals undergoing ECT are among the most severely affected psychiatric patients (
      • Loh N.
      • Nickl-Jockschat T.
      • Sheldrick A.J.
      • Grözinger M.
      Accessibility, standards and challenges of electroconvulsive therapy in Western industrialized countries: A German example.
      ) and, hence, are hard to recruit. Intra- and interstudy inhomogeneity of clinical samples—e.g., owing to diagnosis, age, and chronicity—and different statistical approaches for data analysis might further obscure the picture (
      • Botvinik-Nezer R.
      • Holzmeister F.
      • Camerer C.F.
      • Dreber A.
      • Huber J.
      • Johannesson M.
      • et al.
      Variability in the analysis of a single neuroimaging dataset by many teams.
      ).
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