Uncertainty Promotes Neuroreductionism: A Behavioral Online Study on Folk Psychological Causal Inference from Neuroimaging Data.

INTRODUCTION: Increased efforts in neuroscience try to understand mental disorders as brain disorders. In the present study, we investigate how common a neuroreductionist inclination is among highly educated people. In particular, we shed light on implicit presuppositions of mental disorders little is known about in the public, exemplified here by the case of body integrity dysphoria (BID) that is considered a mental disorder for the first time in ICD-11. METHODS: Identically graphed, simulated data of mind-brain correlations were shown in 3 contexts with presumably different presumptions about causality. 738 highly educated lay people rated plausibility of causality attribution from the brain to mind and from mind to the brain for correlations between brain structural properties and mental phenomena. We contrasted participants' plausibility ratings of causality in the contexts of commonly perceived brain lesion-induced behavior (aphasia), behavior-induced training effects (piano playing), and a newly described mental disorder (BID). RESULTS: The findings reveal the expected context-dependent modulation of causality attributions in the contexts of aphasia and piano playing. Furthermore, we observed a significant tendency to more readily attribute causal inference from the brain to mind than vice versa with respect to BID. CONCLUSION: In some contexts, exemplified here by aphasia and piano playing, unidirectional causality attributions may be justified. However, with respect to BID, we critically discuss presumably unjustified neuroreductionist inclinations under causal uncertainty. Finally, we emphasize the need for a presupposition-free approach in psychiatry.

Reliability and comparability of human brain structural covariance networks.

Structural covariance analysis is a widely used structural MRI analysis method which characterises the co-relations of morphology between brain regions over a group of subjects. To our knowledge, little has been investigated in terms of the comparability of results between different data sets of healthy human subjects, as well as the reliability of results over the same subjects in different rescan sessions, image resolutions, or FreeSurfer versions. In terms of comparability, our results show substantial differences in the structural covariance matrix between data sets of age- and sex-matched healthy human adults. These differences persist after univariate site correction, they are exacerbated by low sample sizes, and they are most pronounced when using average cortical thickness as a morphological measure. Down-stream graph theoretic analyses further show statistically significant differences. In terms of reliability, substantial differences were also found when comparing repeated scan sessions of the same subjects, image resolutions, and even FreeSurfer versions of the same image. We could further estimate the relative measurement error and showed that it is largest when using cortical thickness as a morphological measure. Using simulated data, we argue that cortical thickness is least reliable because of larger relative measurement errors. Practically, we make the following recommendations (1) combining subjects across sites into one group should be avoided, particularly if sites differ in image resolutions, subject demographics, or preprocessing steps; (2) surface area and volume should be preferred as morphological measures over cortical thickness; (3) a large number of subjects (n≫30 for the Desikan-Killiany parcellation) should be used to estimate structural covariance; (4) measurement error should be assessed where repeated measurements are available; (5) if combining sites is critical, univariate (per ROI) site-correction is insufficient, but error covariance (between ROIs) should be explicitly measured and modelled.