Psychopathological and Neurobiological Overlap Between Anorexia Nervosa and Self-Injurious Behavior: A Narrative Review and Conceptual Hypotheses.

Empirical evidence and clinical observations suggest a strong -yet under acknowledged-link between anorexia nervosa (AN) and non-suicidal self-injurious behavior (NSSI). By reviewing the literature on the psychopathology and neurobiology of AN and NSSI, we shed light on their relationship. Both AN and NSSI are characterized by disturbances in affect regulation, dysregulation of the reward circuitry and the opioid system. By formulating a reward-centered hypothesis, we explain the overlap between AN and NSSI. We propose three approaches understanding the relationship between AN and NSSI, which integrate psychopathology and neurobiology from the perspective of self-destructiveness: (1) a nosographical approach, (2) a research domain (RDoC) approach and (3) a network analysis approach. These approaches will enhance our knowledge of the underlying neurobiological substrates and may provide groundwork for the development of new treatment options for disorders of self-destructiveness, like AN and NSSI. In conclusion, we hypothesize that self-destructiveness is a new, DSM-5-transcending concept or psychopathological entity that is reward-driven, and that both AN and NSSI could be conceptualized as disorders of self-destructiveness.

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease.

This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer's Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924-0.955) and CNN (0.933; 95%CI: 0.918-0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar's test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855-0.932) and CNN (0.876; 95%CI: 0.836-0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01). Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.

Frequency Band-Specific Electrical Brain Stimulation Modulates Cognitive Control Processes.

A large body of findings has tied midfrontal theta-band (4-8 Hz) oscillatory activity to adaptive control mechanisms during response conflict. Thus far, this evidence has been correlational. To evaluate whether theta oscillations are causally involved in conflict processing, we applied transcranial alternating current stimulation (tACS) in the theta band to a midfrontal scalp region, while human subjects performed a spatial response conflict task. Conflict was introduced by incongruency between the location of the target stimulus and the required response hand. As a control condition, we used alpha-band (8-12 Hz) tACS over the same location. The exact stimulation frequencies were determined empirically for each subject based on a pre-stimulation EEG session. Behavioral results showed general conflict effects of slower response times (RT) and lower accuracy for high conflict trials compared to low conflict trials. Importantly, this conflict effect was reduced specifically during theta tACS, which was driven by slower response times on low conflict trials. These results show how theta tACS can modulate adaptive cognitive control processes, which is in accordance with the view of midfrontal theta oscillations as an active mechanism for cognitive control.