AUTHOR=Nickel Alina , Thomalla Götz TITLE=Post-Stroke Depression: Impact of Lesion Location and Methodological Limitations—A Topical Review JOURNAL=Frontiers in Neurology VOLUME=8 YEAR=2017 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2017.00498 DOI=10.3389/fneur.2017.00498 ISSN=1664-2295 ABSTRACT=

Post-stroke depression (PSD) affects approximately one-third of all stroke patients. It hinders rehabilitation and is associated with worse functional outcome and increased mortality. Since the identification of PSD is a significant clinical problem, clinicians and researchers have tried to identify predictors that indicate patients at risk of developing PSD. This also includes the research question whether there is an association between PSD and stroke lesion characteristics, e.g., lesion size and lesion location. Early studies addressing this question are largely limited by technical constraints and, thus, focused on simple lesion characteristics such as lesion side or proximity of the lesion to the frontal pole of the brain. More recent studies have addressed the impact of involvement of specific neuronal circuits in the stroke lesion. State-of-the-art methods of lesion symptom mapping to study PSD have only been applied to small patient samples. Overall, results are controversial and no clear pattern of stroke lesions associated with PSD has emerged, though there are findings suggesting that more frontal stroke lesions are associated with higher incidence of PSD. Available studies are hampered by methodological limitations, including drawbacks of lesion analysis methods, small sample size, and the issue of patient selection. These limitations together with differences in approaches to assess PSD and in methods of image analysis limit the comparability of results from different studies. To summarize, as of today no definite association between lesion location and PSD can be ascertained and the understanding of PSD rests incomplete. Further insights are expected from the use of modern lesion inference analysis methods in larger patient samples taking into account standardized assessment of possible confounding parameters, such as stroke treatment and reperfusion status.