A systematic review of machine learning findings in PTSD and their relationships with theoretical models
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Stress-induced changes in the molecular processes underlying fear memories: implications for PTSD and relevant animal models
Most of the fear literature on humans and animals tests healthy individuals. However, fear memories can differ between healthy individuals and those previously exposed to traumatic stress, such as a car accident, sexual abuse, military combat and personal assault. Traumatic stress can lead to post-traumatic stress disorder (PTSD) which presents alterations in fear memories, such as an impairment of fear extinction and extinction recall. PTSD-like animal models are exposed to a single highly stressful experience in the laboratory, such as stress immobilization or single-prolonged stress. Some days later, animals exposed to a PTSD-like model can be tested in fear procedures that help uncover molecular mechanisms of fear memories. In this review, there are discussed the molecular mechanisms in stress-induced fear memories of patients with PTSD and PTSD-like animal models. The focus is on the effects of estradiol and cortisol/corticosterone hormones and of different genes, such as FKBP prolyl isomerase 5 gene (FKBP5) – FK506 binding protein 51 (FKBP51), pituitary adenylate cyclase-activating peptide (PACAP) – pituitary adenylate cyclase-activating polypeptide type I receptor (PAC1R), endocannabinoid (eCB) system and the tropomyosin receptor kinase B (TrkB) – brain-derived neurotrophic factor (BDNF). The conclusion is that greater emphasis should be placed on investigating the molecular mechanisms of fear memories in PTSD, through direct testing of patients with PTSD or the use of relevant PTSD-like models.
The case for hemispheric lateralization of the human amygdala in fear processing
Amygdala activity during fear conditioning A systematic review by Sehlmeyer et al. (2009) suggests that findings on the laterality of amygdala activity in fear conditioning…
Predictive learning as the basis of the testing effect
A prominent learning phenomenon is the testing effect, meaning that testing enhances retention more than studying. Emergent frameworks propose fundamental (Hebbian and predictive) learning principles as its basis. Predictive learning posits that learning occurs based on the contrast (error) between a prediction and the feedback on that prediction (prediction error). Here, we propose that in testing (but not studying) scenarios, participants predict potential answers, and its contrast with the subsequent feedback yields a prediction error, which facilitates testing-based learning. To investigate this, we developed an associative memory network incorporating Hebbian and/or predictive learning, together with an experimental design where human participants studied or tested English-Swahili word pairs followed by recognition. Three behavioral experiments (N = 80, 81, 62) showed robust testing effects when feedback was provided. Model fitting (of 10 different models) suggested that only models incorporating predictive learning can account for the breadth of data associated with the testing effect. Our data and model suggest that predictive learning underlies the testing effect.
Understanding learning through uncertainty and bias
Learning allows humans and other animals to make predictions about the environment that facilitate adaptive behavior. Casting learning as predictive inference can shed light on normative cognitive mechanisms that improve predictions under uncertainty. Drawing on normative learning models, we illustrate how learning should be adjusted to different sources of uncertainty, including perceptual uncertainty, risk, and uncertainty due to environmental changes. Such models explain many hallmarks of human learning in terms of specific statistical considerations that come into play when updating predictions under uncertainty. However, humans also display systematic learning biases that deviate from normative models, as studied in computational psychiatry. Some biases can be explained as normative inference conditioned on inaccurate prior assumptions about the environment, while others reflect approximations to Bayesian inference aimed at reducing cognitive demands. These biases offer insights into cognitive mechanisms underlying learning and how they might go awry in psychiatric illness.
Machine learning empowered coherent Raman imaging and analysis for biomedical applications
In situ and in vivo visualization and analysis of functional, endogenous biomolecules in living systems have generated a pivotal impact in our comprehension of biology and medicine. An increasingly adopted approach involves the utilization of molecular vibrational spectroscopy, which delivers notable advantages such as label-free imaging, high spectral density, high sensitivity, and molecule specificity. Nonetheless, analyzing and processing the intricate, multi-dimensional imaging data to extract interpretable and actionable information poses a fundamental obstacle. In contrast to conventional multivariate methods, machine learning has recently gained considerable attention for its capability of discerning essential features from massive datasets. Here, we present a comprehensive review of the latest advancements in the application of machine learning in the molecular spectroscopic imaging fields. We also discuss notable attributes of spectroscopic imaging modalities and explore their broader impact on other imaging techniques.
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