The present fMRI study investigated which subregions of the visual word recognition network contribute to morphologically structured word form processing in Korean. Participants performed two separate lexical decision tasks in the scanner. In the Eojeol task, stimuli were three-syllable words composed of a disyllabic noun plus a case particle, whereas in the Noun task, stimuli were disyllabic (DSN) and trisyllabic (TSN) nouns. Behaviorally, reaction time was significantly longer in the Eojeol condition than in both DSN and TSN conditions, indicating greater processing demands for morphologically complex words, while accuracy did not differ across conditions. At the neural level, ROI analyses revealed that Eojeol processing elicited stronger activation than simple nouns in specific nodes of the visual word recognition related system, including the left inferior frontal gyrus (pars opercularis), anterior perisylvian cortex, anterior superior temporal sulcus, and anterior inferior temporal gyrus. These findings suggest that morphologically complex word recognition in Korean recruits additional resources within the visual word recognition network, highlighting the role of anterior fronto-temporal subregions in combining content and functional morphemes into unified lexical representations.

Synaptic NMDA-type glutamate receptors (NMDARs) are central components of all neural computations. NMDAR levels critically vary between synapses but how the synaptic localization of NMDARs is controlled remains unclear. Here we show that SIK3, a serine-threonine protein kinase, localizes to synapses where it phosphorylates the obligatory NMDAR subunit GluN1 at a single site. This single phosphorylation event controls NMDAR recruitment to synapses. Genetic deletion of SIK3 severely and selectively decreases synaptic GluN1 levels by ~60% without altering AMPA-type glutamate receptor levels or inducing major transcriptional changes. Pharmacological inhibition of SIK3 impairs synaptic NMDAR responses within 45 minutes. Mutating the SIK3-phosphorylation site inhibits GluN1 synaptic recruitment. 'Sleepy', a gain-of-function mutation in SIK3 that increases sleep, selectively enhances synaptic NMDAR levels by increasing GluN1 phosphorylation. Thus, SIK3 phosphorylation of GluN1 provides a key signal for recruiting NMDARs to synapses.

Maternal caregiving requires sensory vigilance to ensure offspring survival, particularly during sleep. Infant cries are salient sensory cues that reliably trigger maternal arousal and caregiving responses in both humans and dams, but the underlying neural circuitry remains poorly defined. Here we identify a corticohypothalamic circuit in mice that mediates maternal awakening in response to pup calls. We show that layer 6 (L6) neurons in the temporal association cortex and ectorhinal cortex (TeA/Ect) projecting to the preoptic area (POA) exhibit enhanced responsiveness to pup calls after the transition to motherhood. This enhanced responsiveness is mediated by increased expression of dopamine receptor D1 (Drd1) in TeA/Ect → POA neurons. Additionally, we reveal a pathway from the auditory thalamus to L6 TeA/Ect neurons that transmits pup cues during sleep. Together, these findings identify a dedicated neural circuit that grants infant cries privileged access to arousal systems, likely representing a fundamental adaptation for caregiving across mammals.

Neuroimmunology is a field of neurology focused on the diagnosis and treatment of diseases where the immune system causes inflammation or injury to the nervous system. This includes diseases such as multiple sclerosis, myelin oligodendrocyte glycoprotein antibody-associated disease, neuromyelitis optica spectrum disorder, autoimmune encephalitis, autoimmune ataxia, stiff person syndrome spectrum disorder, and neuro-rheumatologic diseases such as neuropsychiatric lupus. Over the past couple decades, there have been significant advancements in the diagnostic methods for several neuroimmunologic diseases. Despite these achievements, major needs remain for additional methods to facilitate earlier diagnosis, monitor disease activity and treatment, and elucidate disease mechanisms to aid in the development of novel or tailored treatment strategies. Currently, there is often a trade-off between the invasiveness of neurodiagnostic testing and the level of detail that can be obtained from the test without causing an iatrogenic injury. Here, we discuss the potential of extracellular vesicles (EVs), especially CNS-derived EVs obtained from peripheral blood, as a promising avenue for advancing research and clinical care in neuroimmunology. This article provides a review of the key clinical features and approaches to care for the most common neuroimmunologic diseases. We identify gaps that remain in our current understanding of these conditions. We then highlight how ongoing research in the EV field may address these existing gaps. Finally, we examine the challenges that must be overcome before EV analysis can be more widely adopted in both research and clinical care in this field.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in social interaction and behavioral functioning. ASD diagnosis primarily relies on behavioral assessments and can vary across individuals. In this study, an ASD diagnostic model was developed using diffusion tensor imaging (DTI) derived inter-regional correlations, graph-theoretical analysis and artificial intelligence. Initially, the DTI data from 283 individuals (154 ASD and 129 typically developing) were preprocessed using a standard pipeline. Subsequently, DTI-derived parameters were extracted from 50 white matter (WM) regions. Inter-regional correlations were computed between DTI-derived parameters using Pearson correlation coefficient (PCC), Biweight midcorrelation coefficient, and Spearman rank correlation coefficient. The resulting fully correlated matrices were subsequently decomposed into positive and negative correlation matrices. These matrices were sparsified at different threshold levels to construct graph networks. Nine graph-theoretical features were calculated for each WM region, yielding 450 features, which were fed as input to artificial intelligence models, including logistic regression, support vector machine (SVM), random forest, extreme gradient boosting, and a one-dimensional convolutional neural network. The highest average 5-fold classification accuracy of 90.47% was achieved using 75 features derived from a positively correlated PCC network thresholded at 0.2 and SVM. The most discriminative features were eigenvector and pagerank centrality of the cingulum (cingulate gyrus) left, and eigenvector centrality of the superior corona radiata left, inferior cerebellar peduncle right, and cingulum (cingulate gyrus) right. These findings indicate that positively sparsified DTI inter-regional correlation networks combined with graph-theoretical analysis and artificial intelligence provide a promising framework for accurate ASD classification.

Cognitive impairment is a debilitating aspect of multiple sclerosis (MS), and reliable blood biomarkers to assess it are still scarce. MicroRNAs such as miR-21-5p and miR-195-5p, together with clusterin, are involved in neuroinflammation and synaptic plasticity, which suggests a potential role in MS. The aim of the study was to investigate the relationship between plasma clusterin, miR-21-5p, and miR-195-5p expression and cognitive performance in patients with relapsing-remitting multiple sclerosis (pwRRMS). The study included 30 treatment-naïve pwRRMS, 30 pwRRMS on disease-modifying therapy (DMT), and 30 healthy controls. Plasma clusterin was determined by enzyme-linked immunosorbent assay, relative expression levels of the miRNAs were determined using real-time quantitative PCR, and cognitive performance was assessed using the Brief International Cognitive Assessment for MS. In silico network analysis was performed to explore the regulatory architecture of the miRNA-CLU axis. miR-21-5p expression was significantly elevated in treatment-naïve pwRRMS, while miR-195-5p was downregulated across all pwRRMS compared with controls and these differences persisted after adjustment for age and sex. Plasma clusterin levels were similar across groups. Neither the miRNAs nor clusterin significantly correlated with cognitive impairment status or adjusted BICAMS scores. Cognitive impairment in the pwRRMS on DMT group was associated with higher lesion burdens. In silico analysis positioned miR-21-5p, miR-195-5p, and clusterin among genes involved in neuroinflammatory pathways, generating cellular-level hypotheses; their circulating levels primarily reflected systemic inflammatory activity. In this cohort, magnetic resonance imaging lesion burden showed a stronger association with cognitive impairment than circulating biomarkers, and these findings warrant validation in larger multi-center studies.

Although brain cholesterol dysregulation is implicated in Parkinson's disease (PD), targeting cholesterol synthesis has shown limited efficacy. Here we identify ATP-binding cassette transporter A1 (ABCA1) as a critical regulator of cholesterol homeostasis in PD. We show that ABCA1 expression is elevated in the substantia nigra of postmortem PD brains and in a PD mouse model, with regional analyses revealing preferential alterations in nigral and striatal circuits. Mechanistically, astrocytic ABCA1 drives excessive cholesterol efflux in mice, promoting neuronal cholesterol overload. In neurons, ABCA1 localizes to lysosomes, where it facilitates cholesterol import, leading to cholesterol accumulation, autophagy impairment and α-synuclein pathology. Genetic silencing of Abca1 in either astrocytes or dopaminergic neurons attenuates neurodegeneration and behavioral deficits in vivo. Furthermore, we identify magnesium citrate as an ABCA1 inhibitor and show that it reduces dopaminergic neuronal loss in a PD mouse model. Our findings identify an ABCA1-dependent cholesterol pathway underlying PD pathogenesis and support ABCA1 inhibition as a potential disease-modifying strategy.

The thalamus integrates sensory, motor and associative functions through distinct nuclei, yet the developmental principles driving their specification remain unclear. Here we generate a spatiotemporal single-cell multiomic and spatial transcriptomic atlas of the embryonic mouse thalamus, combined with barcoding-based lineage tracing. We identify two major glutamatergic lineages, sensory-related and motor-associative, each governed by different gene regulatory programs and temporal dynamics. These lineages arise from spatially and molecularly distinct progenitor domains, revealing an early segregation of functional identities. Within the sensory lineage, we resolve branching sensory trajectories leading to first-order visual and somatosensory neurons, both emerging from a shared higher-order-like state. Using in silico predictions and in vivo perturbation, we identify Sp9 as a key regulator of visual thalamic fate. Together, our findings define the molecular architecture and developmental trajectories that underpin thalamic modality specification, providing a framework to understand how functional circuits emerge in the mammalian brain.

Obesity is a major metabolic disorder linked to neuroinflammation and cognitive decline. Beyond hypothalamic alterations, emerging evidence indicates that extra-hypothalamic regions, including the cerebellum, are vulnerable to obesity-induced neurotoxicity. This study investigated whether high-fat diet (HFD)-induced obesity causes cerebellar oxidative stress, impairs Na[+],K[+]-ATPase (NKA) activity, and triggers molecular features resembling brain aging in female Balb/C mice. Chronic 10-week HFD intake established a marked obese phenotype, characterized by increased adiposity, fasting hyperglycemia, hyperlipidemia, and elevated serum TNF-α. In the cerebellum, obesity induced a selective, expression-independent reduction in α2,3-NKA isoform activity, accompanied by a marked depletion of tissue cGMP levels. This functional impairment was driven by an oxidative microenvironment, evidenced by elevated H2O2 and TBARS levels and increased 4-HNE accumulation. Concomitantly, obese animals mounted an adaptive antioxidant response characterized by increased GST, GR, and GPx activities, reduced glutathione (GSH) consumption, and ERK1/2-mediated nuclear translocation of Nrf2, alongside structural membrane lipid remodeling (elevated cholesterol and reduced phospholipids). In summary, chronic obesity induces robust lipoxidative stress and suppresses cerebellar cGMP signaling and α2,3-NKA activity in female mice, recapitulating key molecular hallmarks of natural brain aging.

While traditional view posed SMA as a pure motor neuron disorder, accumulating evidence pointed to the broader involvement of central nervous system. This study aimed to combined connectome-wide resting-state functional connectivity with machine learning algorithms to investigate the underlying brain mechanisms in SMA and capture the potential neuroimaging biomarker. Resting-state functional magnetic resonance imaging were acquired from 43 SMA (26 SMA type 2 and 17 type 3) and 45 healthy controls (HCs). Connectome-wide functional connectivity, defined based on the Automated Anatomical Labeling 116 atlas (AAL116), combined with machine learning algorithms including logistic regression (LR), support vector machine (SVM) and random forest (RF) were conducted for SMA vs HCs and SMA type 2 vs 3 classification. SHapley Additive exPlanations (SHAP) algorithm was used to quantify the contribution of individual connections. Connectome-wide functional connectivity allowed individual classification of SMA and HCs with stable performance across machine learning models. The majority of the most discriminative functional connectivity involved the high-level associative regions and cerebellum, subserving sensory and motor processing. Furthermore, the functional connectivity between right superior parietal gyrus and right inferior temporal gyrus was positively correlated with motor function. In conclusion, our study provided connectome-wide evidence that functional connectivity patterns involving high-level associative cortices and the cerebellum could distinguish SMA from HCs using machine learning. The identified connectivity features offer new insight into the distributed brain pathology of SMA beyond the traditional classical motor neuron view, and may serve as promising neuroimaging marker for this disease.

Mutual cooperation (MC) requires independent decision-makers to coordinate under uncertainty about each other's intentions. Using simultaneous intracranial electroencephalography from epilepsy-patient dyads playing a real-time iterated prisoner's dilemma, we identified neurocognitive mechanisms that enable interacting brains to align during mutual cooperation. Amygdala interbrain representational alignment (IBRA), similarity in fine-grained neural spectral patterns across partners, emerged selectively during MC and predicted trial-by-trial outcomes. Early θ-suppression-characterized IBRA reflected rapid, history-sensitive alignment, whereas later IBRA, characterized by low-frequency suppression and γ increases, supported coordinated commitment and action. Furthermore, mutual cooperation relied on an amygdala-mediated, simulation-to-decision cascade, operationalized here as a model-based prospective estimate of partner cooperation that guided self-choice. Amygdala low-frequency suppression tracked this prospective estimate of partner cooperation and θ-γ coupling linked this partner estimate to self-decision processes. Conditional cooperators strategically relied on partner tracking, whereas unconditional cooperators leveraged dispositional tolerance of uncertainty to transition into anticipatory neural alignment. These findings reveal the neural interdependency underpinning MC, redefining cooperation not as isolated decision-making but as a dynamic, interbrain, cognitive alignment.

Chronic stress has lasting adverse consequences that can lead, over time, to depression, yet the optimal timeframe for effective therapies remains unclear. The lateral habenula (LHb) has been implicated in stress-related behavioral repercussions, but the precise timing of its contribution is not understood. Here, using spatial transcriptomics, we generated a spatially resolved molecular atlas of the LHb of male mice, revealing a topographically organized and temporally defined remodeling of gene expression after chronic stress. Longitudinal in vivo two-photon calcium imaging of individual LHb neurons revealed progressively heightened excitatory responses to aversive stimuli. These neuronal adaptations mirrored both the progression of transcriptional changes and the emergence of depression-related behavioral phenotypes and were reversed by the antidepressant ketamine. Moreover, chemogenetically limiting LHb neuronal activity or administering ketamine at the onset of these changes exerted antidepressant effects. These findings link spatiotemporal signatures of LHb neuronal ensembles and genetic landscape with the onset of stress-induced depression-related states.