Journal of Neuroscience最新文献

Birdsong is hierarchically organized in time, like speech and other communication behaviors. Syllables are produced in sequences to form song motifs and bouts. While syllables are copied from tutors, the factors that determine song temporal organization, including syllable sequencing (syntax), are unknown. Here, we tested the roles of learning and species genetics in song organization. We manipulated juvenile song experience and genetics in three species of estrildid finches (zebra finches: Taeniopygia guttata castanotis; long-tailed finches: Poephila acuticauda; Bengalese finches: Lonchura striata var. domestica). We analyzed the adult songs of male birds that were: 1) tutored by conspecifics; 2) untutored; 3) tutored by heterospecifics; and 4) genetic hybrids. Song macrostructure, syllable sequencing, and syllable timing were quantified and compared within and across species. Results showed that song organization was consistent within a species and differed across species, regardless of experience. Temporal features did not differ between tutored and untutored birds of the same species. The songs of birds tutored by other species were composed of heterospecific syllables produced in sequences typical of conspecific song. The songs of genetic hybrids showed the organization of both parental species, despite the fact that only males sing. Results indicate that song organization is predicted by species rather than experience.Significance statement Like speech, birdsong is a complex and learned behavior that is hierarchically organized in time. Previous work suggests that species identity influences song temporal organization. We tested the roles of genetics and learning in song organization in three songbird species and genetic hybrids. Birds were either tutored, untutored, or tutored by another species. Results showed that song organization was consistent within a species and differed across species, regardless of experience. Our findings suggest that the organization of behavioral sequences is shaped by both genes and experience, with the influence of experience acting at the level of units in a sequence and the influence of species genetics acting at the level of sequence organization.

The cAMP response element binding protein (CREB) is required for regulation of daily sleep amount, whereas gain of function of CREB-regulated transcription coactivator 1 (CRTC1) causes severe insomnia in mice. However, the physiological functions of CRTCs and their downstream target genes in the regulation of sleep amount remain unclear. Here, we use an adult brain chimeric (ABC)-expression/knock-out platform for somatic genetic analysis of sleep in adult male mice. ABC expression of constitutively active mutant CRTC1/2^CA in the mouse brain neurons significantly reduces the amount of non-rapid eye movement sleep (NREMS) and/or rapid eye movement sleep (REMS). Consistent with the fact that SIK3 phosphorylates and inhibits CRTCs, ABC expression of CRTC1/2/3^CA rescues the hypersomnia phenotype of Sleepy (Sik3^Slp ) mice. While ABC-Crtc2^KO or Crtc3^KO causes no sleep phenotype, ABC-Crtc1^KO or ABC expression of dominant-negative CRTC (dnCRTC) results in a modest reduction of NREMS amount accompanied with elevated NREMS delta power. Moreover, ABC expression of CRTC1^CA or dnCRTC in the excitatory neurons causes bidirectional changes of NREMS/REMS amount and/or NREMS delta power. The ability of CRTC1^CA to regulate sleep requires its transactivation domain and CREB-binding domain and is dependent on CREB. Furthermore, we showed that inducible ABC expression of corticotropin-releasing hormone (Crh) and brain-derived neurotrophic factor (Bdnf)-two target genes of CRTCs-significantly reduces daily sleep amount. Notably, ABC-Crh^KO , but not Bdnf^KO , rescues the insomnia phenotype of ABC-CRTC1^CA mice. Taken together, these results indicate that the CREB-CRTC1 complex regulates daily sleep amount by modulating the transcription of Crh in the mouse brain neurons.

Neurodegenerative diseases of both the central and peripheral nervous system are characterized by selective neuronal vulnerability, i.e., pathology that affects particular types of neurons. While much of this cell type selectivity may be driven by intrinsic differences among the neuron subpopulations, neuron-extrinsic mechanisms such as the selective malfunction of glial support cells may also play a role. Recently, we identified a population of Schwann cells (SCs) expressing Adamtsl1, Cldn14, and Pmp2 (a.k.a. PMP2+ SCs) that preferentially myelinate large-caliber motor axons. PMP2+ SCs are decreased in both ALS model mice and ALS patient nerves. Thus, PMP2+ SC dysfunction could contribute to motor-selective neuropathies. We engineered a tamoxifen-inducible Pmp2-CreERT2 mouse and expressed diphtheria toxin in PMP2+ SCs to assess the consequences of ablating this SC subtype in male and female mice. Loss of PMP2+ SCs led to significant loss of large-caliber motor axons with concomitant behavioral, electrophysiological, and ultrastructural defects. Subsequent withdrawal of tamoxifen restored both PMP2+ SCs and large-caliber motor axons and improved behavioral and electrophysiological readouts. Together, our findings highlight that the survival of large-caliber motor axons relies on PMP2+ SCs, demonstrating that malfunction of a specific SC subtype can lead to selective neuronal vulnerability.Significance Statement A hallmark of neurodegenerative disease is the differential vulnerability of neuron subtypes. While differences between neurons explain some differential sensitivity of neuronal subtypes, neuron-extrinsic mechanisms likely also contribute to selective neuronal vulnerability. Building on the recent identification of genetically distinct subtypes of myelinating Schwann cells, we test the hypothesis that Schwann cell subtypes support distinct classes of peripheral axons. To examine this, we ablated the PMP2+ subclass of myelinating Schwann cells and found preferential loss of large-caliber motor axons. These findings demonstrate that disrupting a specific SC subtype results in selective axonal vulnerability, highlighting the importance of considering neuron-extrinsic mechanisms when dissecting selective neuronal vulnerability in neurodegenerative disorders.

Predictive coding mechanisms facilitate detection and perceptual recognition, thereby influencing recognition judgements, and, broadly, perceptual decision-making. The anterior insula (AI) has been shown to be involved in reaching a decision about discrimination and recognition, as well as to coordinate brain circuits related to reward-based learning. Yet, experimental studies in the context of recognition and decision-making, targeting this area and based on formal trial-by-trial predictive coding computational quantities, are sparse. The present study goes beyond previous investigations and provides a predictive coding computational account of the role of the AI in recognition-related decision-making, by leveraging Zaragoza-Jimenez et al. (2023) open fMRI dataset (17 female, 10 male participants) and computational modeling, characterized by a combination of view-independent familiarity learning and contextual learning. Using model-based fMRI, we show that, in the context a two-option forced-choice identity recognition task, the AI engages in feature-level (i.e., view-independent familiarity) updating and error signaling processes and context-level familiarity updating to reach a recognition judgment. Our findings highlight that an important functional property of the AI is to update the level of familiarity of a given stimulus while also adapting to task-relevant, contextual information. Ultimately, these expectations, combined with input visual signals through reciprocally interconnected feedback and feedforward processes, facilitate recognition judgments, thereby guiding perceptual decision-making.

Corticocortical (CC) projections in the visual system facilitate hierarchical processing of sensory information. In addition to direct CC connections, indirect cortico-thalamo-cortical (CTC) pathways through the pulvinar nucleus of the thalamus can relay sensory signals and mediate cortical interactions according to behavioral demands. While the pulvinar connects extensively to the entire visual cortex, it is unknown whether transthalamic pathways link all cortical areas or whether they follow systematic organizational rules. Because mouse pulvinar neurons projecting to different areas are spatially intermingled, their input/output relationships have been difficult to characterize using traditional anatomical methods. To determine the organization of CTC circuits, we mapped the higher visual areas (HVAs) of male and female mice with intrinsic signal imaging and targeted five pulvinar→HVA pathways for projection-specific rabies tracing. We aligned postmortem cortical tissue to in vivo maps for precise quantification of the areas and cell types projecting to each pulvinar→HVA population. Layer 5 corticothalamic (L5CT) "driver" inputs to the pulvinar originate predominantly from primary visual cortex (V1), consistent with the CC hierarchy. L5CT inputs from lateral HVAs specifically avoid driving reciprocal connections, consistent with the "no-strong-loops" hypothesis. Conversely, layer 6 corticothalamic (L6CT) "modulator" inputs are distributed across areas and are biased toward reciprocal connections. Unlike previous studies in primates, we find that every HVA receives disynaptic input from the superior colliculus. CTC circuits in the pulvinar thus depend on both target HVA and input cell type, such that driving and modulating higher-order pathways follow complementary connection rules similar to those governing first-order CT circuits.

The human brain exhibits a high degree of individual variability in both its structure and function, which underlies intersubject differences in cognition and behavior. It was previously shown that functional connectivity is more variable in the heteromodal association cortex but less variable in the unimodal cortices. Structural connectivity (SC) is the anatomical substrate of functional connectivity, but the spatial and temporal patterns of individual variability in SC (IVSC) remain largely unknown. In the present study, we discovered a detailed and robust chart of IVSC obtained by applying diffusion MRI and tractography techniques to 1,724 adults (770 males and 954 females) from multiple imaging datasets. Our results showed that the SC exhibited the highest and lowest variability in the limbic regions and the unimodal sensorimotor regions, respectively. With increased age, higher IVSC was observed across most brain regions. Moreover, the specific spatial distribution of IVSC is related to the cortical laminar differentiation and myelination content. Finally, we proposed a modified ridge regression model to predict individual cognition and generated idiographic brain mapping, which was significantly correlated with the spatial pattern of IVSC. Overall, our findings further contribute to the understanding of the mechanisms of individual variability in brain SC and link to the prediction of individual cognitive function in adult subjects.

Risk is a fundamental factor affecting individual and social economic decisions, but its neural correlates are largely unexplored in the social domain. The amygdala, together with the dorsal anterior cingulate cortex (dACC), is thought to play a central role in risk-taking. Here, we investigated in human volunteers (n = 20; 11 females) how risk (defined as the variance of reward probability distributions) in a social situation affects decisions and concomitant neural activity as measured with fMRI. We found separate variance-risk signals for social and nonsocial outcomes in the amygdala. Specifically, amygdala activity increased parametrically with social reward variance of presented choice options and on separate trials with nonsocial reward variance. Behaviorally, 75% of participants were averse to social risk as estimated in a Becker-DeGroot-Marschak auction-like procedure. The stronger this aversion, the more negative the coupling between risk-related amygdala regions and dACC. This negative relation was significant for social risk attitude but not for the attitude toward variance-risk in juice outcomes. Our results indicate that the amygdala and its coupling with dACC process objective and subjectively evaluated social risk. Moreover, while social risk can be captured with a framework originally established by finance theory for nonsocial risk, the amygdala appears to process social risk largely separately from nonsocial risk.

When individuals make a movement that produces an unexpected outcome, they learn from the resulting error. This process, essential in both acquiring new motor skills and adapting to changing environments, critically relies on error sensitivity, which governs how much behavioral change results from a given error. Although behavioral and computational evidence suggests error sensitivity can change in response to task demands, neural evidence regarding the flexibility of error sensitivity in the human brain is lacking. Here, we tested whether the nervous system's sensitivity to errors, as measured by prediction-driven suppression of auditory cortical activity, can be modulated by altering participants' (both males and females) perceived variability during speech. Our results showed that error sensitivity, as measured by this suppression, was increased after exposure to an auditory perturbation that increased speakers' perceived variability. The current study establishes the validity of behaviorally modulating the nervous system's sensitivity to errors, which has significant potential to enhance motor learning and rehabilitation.

Macaque area V4 includes neurons that exhibit exquisite selectivity for visual form and surface texture, but their functional organization across laminae is unknown. We used high-density Neuropixels probes in two awake monkeys (one female and one male) to characterize the shape and texture tuning of dozens of neurons simultaneously across layers. We found sporadic clusters of neurons that exhibit similar tuning for shape and texture: ∼20% exhibited similar tuning with their neighbors. Importantly, these clusters were confined to a few layers, seldom "columnar" in structure. This was the case even when neurons were strongly driven and exhibited robust contrast invariance for shape and texture tuning. We conclude that functional organization in area V4 is not columnar for shape and texture stimulus features and in general organization may be at a coarser stimulus category scale (e.g., selectivity for stimuli with vs without 3D cues) and a coarser spatial scale (assessed by optical imaging), rather than at a fine scale in terms of similarity in single-neuron tuning for specific features. We speculate that this may be a direct consequence of the great diversity of inputs integrated by V4 neurons to build variegated tuning manifolds in a high-dimensional space.