Nature Reviews Neuroscience最新文献

Transient changes in the firing of midbrain dopamine neurons have been closely tied to the unidimensional value-based prediction error contained in temporal difference reinforcement learning models. However, whereas an abundance of work has now shown how well dopamine responses conform to the predictions of this hypothesis, far fewer studies have challenged its implicit assumption that dopamine is not involved in learning value-neutral features of reward. Here, we review studies in rats and humans that put this assumption to the test, and which suggest that dopamine transients provide a much richer signal that incorporates information that goes beyond integrated value.

Schemas are rich and complex knowledge structures about the typical unfolding of events in a context; for example, a schema of a dinner at a restaurant. In this Perspective, we suggest that reinforcement learning (RL), a computational theory of learning the structure of the world and relevant goal-oriented behaviour, underlies schema learning. We synthesize literature about schemas and RL to offer that three RL principles might govern the learning of schemas: learning via prediction errors, constructing hierarchical knowledge using hierarchical RL, and dimensionality reduction through learning a simplified and abstract representation of the world. We then suggest that the orbitomedial prefrontal cortex is involved in both schemas and RL due to its involvement in dimensionality reduction and in guiding memory reactivation through interactions with posterior brain regions. Last, we hypothesize that the amount of dimensionality reduction might underlie gradients of involvement along the ventral–dorsal and posterior–anterior axes of the orbitomedial prefrontal cortex. More specific and detailed representations might engage the ventral and posterior parts, whereas abstraction might shift representations towards the dorsal and anterior parts of the medial prefrontal cortex.

Fedorenko and coauthors argue that language is localized to a small static set of brain regions, in a single segregated network (Fedorenko, E., Ivanova, A. A. & Regev, T. I. The language network as a natural kind within the broader landscape of the human brain. Nat. Rev. Neurosci. 25, 289–312; 2024)¹. We challenge this traditional view of the neurobiology of language and argue that language is widely distributed throughout the brain.

We thank Drijvers, Small, and Skipper (Drijvers, L., Small, S. L. & Skipper, J. I. Language is widely distributed throughout the brain. Nat. Rev. Neurosci. https://doi.org/10.1038/s41583-024-00903-0; 2025)¹ for their comments on our Review (Fedorenko, E., Ivanova, A. A. & Regev, T. I. The language network as a natural kind within the broader landscape of the human brain. Nat. Rev. Neurosci. 25, 289–312; 2024)², which we respond to below.

The brain is always intrinsically active, using energy at high rates while cycling through global functional modes. Awake brain modes are tied to corresponding behavioural states. During goal-directed behaviour, the brain enters an action-mode of function. In the action-mode, arousal is heightened, attention is focused externally and action plans are created, converted to goal-directed movements and continuously updated on the basis of relevant feedback, such as pain. Here, we synthesize classical and recent human and animal evidence that the action-mode of the brain is created and maintained by an action-mode network (AMN), which we had previously identified and named the cingulo-opercular network on the basis of its anatomy. We discuss how rather than continuing to name this network anatomically, annotating it functionally as controlling the action-mode of the brain increases its distinctiveness from spatially adjacent networks and accounts for the large variety of the associated functions of an AMN, such as increasing arousal, processing of instructional cues, task general initiation transients, sustained goal maintenance, action planning, sympathetic drive for controlling physiology and internal organs (connectivity to adrenal medulla), and action-relevant bottom–up signals such as physical pain, errors and viscerosensation. In the functional mode continuum of the awake brain, the AMN-generated action-mode sits opposite the default-mode for self-referential, emotional and memory processing, with the default-mode network and AMN counterbalancing each other as yin and yang.

Cerebral small vessel disease (SVD) is a vascular disorder that increases the risk of stroke and dementia and is diagnosed through brain MRI. Current primary prevention and secondary treatment of SVD are focused on lifestyle interventions and vascular risk factor control, including blood pressure reduction. However, these interventions have limited effects, a proportion of individuals with sporadic SVD do not have hypertension, and SVD shows strong familial and genetic underpinnings. Here, we describe the increasing evidence that cerebral endothelial cell dysfunction is a key mechanism of SVD. Dysfunctional endothelial cells can cause cerebral blood vessel dysfunction, alter blood–brain barrier integrity and interfere with cell–cell interactions in the neuro-glial-vascular unit, thereby causing damage to adjacent brain tissue. Endothelial cells in SVD may become dysfunctional through intrinsic mechanisms via genetic vulnerability to SVD and/or via extrinsic factors such as hypertension, smoking and diabetes. Drugs that act on endothelial pathways are already looking promising in clinical trials, and understanding their action on endothelial cells and the surrounding brain may lead to the development of other therapies to limit disease progression and improve outcomes for individuals with SVD.

In their Review article earlier this year, Fedorenko, Ivanova & Regev (Fedorenko, E., Ivanova, A. A. & Regev, T. I. The language network as a natural kind within the broader landscape of the human brain. Nat. Rev. Neurosci. 25, 289–312 (2024))¹ propose a functional separation between the core language network and other perceptual, motor and higher-level cognitive components of communication-related networks in the left hemisphere of the human brain. In the ‘Open questions and a way forward’¹ section that ends their Review, the authors discuss the need for cross-species comparative research to disentangle how these brain networks came to support human language. Here, we suggest that the authors’ functional separation of a core language network and other components in the human brain is grounded in the evolution of two separate structural networks within primate brains.