Trends in Neurosciences最新文献

The rise of large, publicly shared functional magnetic resonance imaging (fMRI) data sets in human neuroscience has focused on acquiring either a few hours of data on many individuals ('wide' fMRI) or many hours of data on a few individuals ('deep' fMRI). In this opinion article, we highlight an emerging approach within deep fMRI, which we refer to as 'intensive' fMRI: one that strives for extensive sampling of cognitive phenomena to support computational modeling and detailed investigation of brain function at the single voxel level. We discuss the fundamental principles, trade-offs, and practical considerations of intensive fMRI. We also emphasize that intensive fMRI does not simply mean collecting more data: it requires careful design of experiments to enable a rich hypothesis space, optimizing data quality, and strategically curating public resources to maximize community impact.

The search for neural markers of depression remains challenging. Despite progress, neuroimaging results have generally not yielded actionable findings that could transform how we understand and treat this disorder. However, in a recent study, Lynch and colleagues identified enlargement of the frontrostriatal salience network as a reproducible, trait-like marker of depression.

The monoaminergic nuclei are thought to be some of the earliest sites of Alzheimer's disease (AD) pathology in the brain, with tau-containing pretangles appearing in these nuclei decades before the onset of clinical impairments. It has increasingly been recognized that monoamine systems represent a critical target of investigation towards understanding the progression of AD and designing early detection and treatment approaches. This review synthesizes evidence across animal studies, human neuropathology, and state-of-the-art neuroimaging and daily life assessment methods in humans, which demonstrate robust relationships between monoamine systems and AD pathophysiology and behavior. Further, the review highlights the promise of multimethod, multisystem approaches to studying monoaminergic mechanisms of resilience to AD pathology.

Neuroinflammation is a feature of both neurodegenerative disease and normal brain aging. The roles of type I interferon (IFN-I) in the aged brain are incompletely understood. A recent article by Roy et al. reveals pervasive IFN-I activity in normal mouse brain aging, and highlights the importance of microglial IFN-I signaling in neuroinflammation.

A recent study by Wu, Podvalny, and colleagues investigated how ongoing spontaneous brain activity interacts with sensory input and shapes conscious perception. It reports diverse effects of prestimulus activity in several key networks, revealing new roles of the prefrontal cortex and the default mode network in perception and consciousness.

The capabilities of transformer networks such as ChatGPT and other large language models (LLMs) have captured the world's attention. The crucial computational mechanism underlying their performance relies on transforming a complete input sequence - for example, all the words in a sentence - into a long 'encoding vector' that allows transformers to learn long-range temporal dependencies in naturalistic sequences. Specifically, 'self-attention' applied to this encoding vector enhances temporal context in transformers by computing associations between pairs of words in the input sequence. We suggest that waves of neural activity traveling across single cortical areas, or multiple regions on the whole-brain scale, could implement a similar encoding principle. By encapsulating recent input history into a single spatial pattern at each moment in time, cortical waves may enable a temporal context to be extracted from sequences of sensory inputs, the same computational principle as that used in transformers.

Chirality is a fundamental trait of living organisms, encompassing the homochirality of biological molecules and the left-right (LR) asymmetry of visceral organs and the brain. The nervous system in bilaterian organisms displays a lateralized organization characterized by the presence of asymmetrical neuronal circuits and brain functions that are predominantly localized within one hemisphere. Although body asymmetry is relatively well understood, and exhibits robust phenotypic expression and regulation via conserved molecular mechanisms across phyla, current findings indicate that the asymmetry of the nervous system displays greater phenotypic, genetic, and evolutionary variability. In this review we explore the use of nematode, zebrafish, and Drosophila genetic models to investigate neuronal circuit asymmetry. We discuss recent discoveries in the context of body-brain concordance and highlight the distinct characteristics of nervous system asymmetry and its cognitive correlates.

A major aim of neuroscience is to identify and model the functional properties of neural cells whose dysfunction underlie neuropsychiatric illness. In this article, we propose that human-derived monoclonal autoantibodies (HD-mAbs) are well positioned to selectively target and manipulate neural subpopulations as defined by their protein expression; that is, cellular proteotypes. Recent technical advances allow for efficient cloning of autoantibodies from neuropsychiatric patients. These HD-mAbs can be introduced into animal models to gain biological and pathobiological insights about neural proteotypes of interest. Protein engineering can be used to modify, enhance, silence, or confer new functional properties to native HD-mAbs, thereby enhancing their versatility. Finally, we discuss the challenges and limitations confronting HD-mAbs as experimental research tools for neuroscience.

Many animal species use olfaction to extract information about objects in their environment. Yet, the specific molecular signature that any given object emits varies due to various factors. Here, we detail why such variability makes chemosensory-mediated object recognition such a hard problem, and we propose that a major function of the elaborate chemosensory network is to overcome it. We describe previous work addressing different elements of the problem and outline future research directions that we consider essential for a full understanding of object-oriented olfaction. In particular, we call for extensive representation of olfactory object variability in chemical, behavioral, and electrophysiological analyses. While written with an emphasis on macrosmatic mammalian species, our arguments apply to all organisms that employ chemosensation to navigate complex environments.

The human voice is a potent social signal and a distinctive marker of individual identity. As individuals go through puberty, their voices undergo acoustic changes, setting them apart from others. In this article, we propose that hormonal fluctuations in conjunction with morphological vocal tract changes during puberty establish a sensitive developmental phase that affects the monitoring of the adolescent voice and, specifically, self-other distinction. Furthermore, the protracted maturation of brain regions responsible for voice processing, coupled with the dynamically evolving social environment of adolescents, likely disrupts a clear differentiation of the self-voice from others' voices. This socioneuroendocrine framework offers a holistic understanding of voice monitoring during adolescence.