Spatio-temporal deep learning methods for motion estimation using 4D OCT image data

Purpose: Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution that has been used for intraoperative imaging and also for motion estimation, for example, in the context of ophthalmic surgery or cochleostomy. Recently, motion estimation between a template and a moving OCT image has been studied with deep learning methods to overcome the shortcomings of conventional, feature-based methods. Methods: We investigate whether using a temporal stream of OCT image volumes can improve deep learning-based motion estimation performance. For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach. Also, we propose a temporal regularization strategy at the model output. Results: Using a tissue dataset without additional markers, our deep learning methods using 4D data outperform previous approaches. The best performing 4D architecture achieves an correlation coefficient (aCC) of 98.58% compared to 85.0% of a previous 3D deep learning method. Also, our temporal regularization strategy at the output further improves 4D model performance to an aCC of 99.06%. In particular, our 4D method works well for larger motion and is robust toward image rotations and motion distortions. Conclusions: We propose 4D spatio-temporal deep learning for OCT-based motion estimation. On a tissue dataset, we find that using 4D information for the model input improves performance while maintaining reasonable inference times. Our regularization strategy demonstrates that additional temporal information is also beneficial at the model output.This work was partially funded by Forschungszentrum Medizintechnik Hamburg (grants 04fmthh16)

Neural networks underlying trait aggression depend on MAOA gene alleles

Low expressing alleles of the MAOA gene (MAOA-L) have been associated with an increased risk for developing an aggressive personality. This suggests an MAOA-L-specific neurobiological vulnerability associated with trait aggression. The neural networks underlying this vulnerability are unknown. The present study investigated genotype-specific associations between resting state brain networks and trait aggression (Buss-Perry Aggression Questionnaire) in 82 healthy Caucasian males. Genotype influences on aggression-related networks were studied for intrinsic and seed-based brain connectivity. Intrinsic connectivity was higher in the ventromedial prefrontal cortex (VMPFC) of MAOA-L compared to high expressing allele (MAOA-H) carriers. Seed-based connectivity analyses revealed genotype differences in the functional involvement of this region. MAOA genotype modulated the relationship between trait aggression and VMPFC connectivity with supramarginal gyrus (SMG) and areas of the default mode network (DMN). Separate analyses for the two groups were performed to better understand how the genotype modulated the relationship between aggression and brain networks. They revealed a positive correlation between VMPFC connectivity and aggression in right angular gyrus (AG) and a negative correlation in right SMG in the MAOA-L group. No such effect emerged in the MAOA-H carriers. The results indicate a particular relevance of VMPFC for aggression in MAOA-L carriers; in specific, a detachment from the DMN along with a strengthened coupling to the AG seems to go along with lower trait aggression. MAOA-L carriers may thus depend on a synchronization of emotion regulation systems (VMPFC) with core areas of empathy (SMG) to prevent aggression

MAOA-VNTR polymorphism modulates context-dependent dopamine release and aggressive behavior in males

A recent [18F]FDOPA-PET study reports negative correlations between dopamine synthesis rates and aggressive behavior. Since dopamine is among the substrates for monoamine oxidase A (MAOA), this investigation examines whether functional allelic variants of the MAOA tandem repeat (VNTR) promotor polymorphism, which is known to modulate aggressive behavior, influences dopamine release and aggression in response to violent visual stimuli.We selected from a genetic prescreening sample, strictly case-matched groups of 2 × 12 healthy male subjects with VNTRs predictive of high (MAOA-High) and low (MAOA-Low) MAOA expression. Subjects underwent pairs of PET sessions (dopamine D2/3 ligand [18F]DMFP) while viewing a movie of neutral content, versus violent content. Directly afterwards, aggressive behavior was assessed by the Point Subtraction Aggression Paradigm (PSAP). Finally, PET data of 23 participants and behavioral data of 22 participants were analyzed due to post hoc exclusion criteria.In the genetic prescreening sample MAOA-Low carriers had significantly increased scores on the Buss–Perry Aggression Questionnaire. In the PET-study-group, aggressive behavior under the emotional neutral condition was significantly higher in the MAOA-Low group. Interestingly, the two MAOA-groups showed inverse dopaminergic and behavioral reactions to the violent movie: The MAOA-High group showed higher dopamine release and increased aggression after the violent movie; MAOA-Low subjects showed decreases in aggressive behavior and no consistent dopamine release.These results indicate a possible impact of the MAOA-promotor polymorphism on the neurobiological modulation of aggressive behavior. However, the data do not support approaches stating that MAOA-Low fosters aggression by a simple pro-dopaminergic mechanism