The neural architecture of theory-based reinforcement learning

Dynamic computational phenotyping of human cognition

Computational phenotyping has emerged as a powerful tool for characterizing individual variability across a variety of cognitive domains. An individual's computational phenotype is defined as a set of mechanistically interpretable parameters obtained from fitting computational models to behavioural data. However, the interpretation of these parameters hinges critically on their psychometric properties, which are rarely studied. To identify the sources governing the temporal variability of the computational phenotype, we carried out a 12-week longitudinal study using a battery of seven tasks that measure aspects of human learning, memory, perception and decision making. To examine the influence of state effects, each week, participants provided reports tracking their mood, habits and daily activities. We developed a dynamic computational phenotyping framework, which allowed us to tease apart the time-varying effects of practice and internal states such as affective valence and arousal. Our results show that many phenotype dimensions covary with practice and affective factors, indicating that what appears to be unreliability may reflect previously unmeasured structure. These results support a fundamentally dynamic understanding of cognitive variability within an individual.

Doubling down on dual systems: A cerebellum-amygdala route towards action- and outcome-based social and affective behavior

The amygdala and cerebellum are both evolutionary preserved brain structures containing cortical as well as subcortical properties. For decades, the amygdala has been considered the fear-center of the brain, but recent advances have shown that the amygdala acts as a critical hub between cortical and subcortical systems and shapes social and affective behaviors beyond fear. Likewise, the cerebellum is a dedicated control unit that fine-tunes motor behavior to fit contextual requirements. There is however increasing evidence that the cerebellum strongly influences subcortical as well as cortical processes beyond the motor domain. These insights broadened the view on the cerebellum's functions to also include social and affective behavior. Here we explore how the amygdala and cerebellum might interact in shaping social and affective behaviors based on their roles in threat reactivity and reinforcement learning. A novel mechanistic neural framework of cerebellum-amygdala interactions will be presented which provides testable hypotheses for future social and affective neuroscientific research in humans.

Midbrain signaling of identity prediction errors depends on orbitofrontal cortex networks

Outcome-guided behavior requires knowledge about the identity of future rewards. Previous work across species has shown that the dopaminergic midbrain responds to violations in expected reward identity and that the lateral orbitofrontal cortex (OFC) represents reward identity expectations. Here we used network-targeted transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) during a trans-reinforcer reversal learning task to test the hypothesis that outcome expectations in the lateral OFC contribute to the computation of identity prediction errors (iPE) in the midbrain. Network-targeted TMS aiming at lateral OFC reduced the global connectedness of the lateral OFC and impaired reward identity learning in the first block of trials. Critically, TMS disrupted neural representations of expected reward identity in the OFC and modulated iPE responses in the midbrain. These results support the idea that iPE signals in the dopaminergic midbrain are computed based on outcome expectations represented in the lateral OFC.

Naturalistic reinforcement learning

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

Functional neuroimaging as a catalyst for integrated neuroscience

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.

Artificial intelligence-based radiomics in bone tumors: Technical advances and clinical application

Radiomics is the extraction of predefined mathematic features from medical images for predicting variables of clinical interest. Recent research has demonstrated that radiomics can be processed by artificial intelligence algorithms to reveal complex patterns and trends for diagnosis, and prediction of prognosis and response to treatment modalities in various types of cancer. Artificial intelligence tools can utilize radiological images to solve next-generation issues in clinical decision making. Bone tumors can be classified as primary and secondary (metastatic) tumors. Osteosarcoma, Ewing sarcoma, and chondrosarcoma are the dominating primary tumors of bone. The development of bone tumor model systems and relevant research, and the assessment of novel treatment methods are ongoing to improve clinical outcomes, notably for patients with metastases. Artificial intelligence and radiomics have been utilized in almost full spectrum of clinical care of bone tumors. Radiomics models have achieved excellent performance in the diagnosis and grading of bone tumors. Furthermore, the models enable to predict overall survival, metastases, and recurrence. Radiomics features have exhibited promise in assisting therapeutic planning and evaluation, especially neoadjuvant chemotherapy. This review provides an overview of the evolution and opportunities for artificial intelligence in imaging, with a focus on hand-crafted features and deep learning-based radiomics approaches. We summarize the current application of artificial intelligence-based radiomics both in primary and metastatic bone tumors, and discuss the limitations and future opportunities of artificial intelligence-based radiomics in this field. In the era of personalized medicine, our in-depth understanding of emerging artificial intelligence-based radiomics approaches will bring innovative solutions to bone tumors and achieve clinical application.