Progress in Neurobiology

The basal ganglia (BG), thalamus and cortex form the BG-thalamo-cortical (BGTC) network, which is essential for voluntary movement and centrally involved in Parkinson's disease (PD). In both patients and animal models, BG neuronal activity exhibits exaggerated oscillatory synchronization in the beta frequencies (13-30 Hz). Theoretical studies have proposed multiple network mechanisms for the generation of these abnormal beta oscillations. However, key properties, such as frequency and power distribution across BG nuclei, vary substantially among patients and between animal models (rodents vs. non-human primates). This variability complicates direct comparisons between theoretical models and experimental data, and questions whether distinct neuronal mechanisms may underlie beta oscillations across species. In an experimentally constrained BGTC network model with species-specific parameters (synaptic, neuronal, and network properties), we evaluate the features of abnormal beta oscillatory activity generated by different mechanisms. The network's negative feedback loops serve as potential sources of spontaneous oscillations. Using rodent- or primate-constrained models, we compare the spectral properties of oscillatory activity across network populations for each loop and derive the expected phase relationships between populations to align predictions with existing rodent data and propose testable hypotheses for primates. We also demonstrate how oscillation frequency can be modulated when multiple generation mechanisms interact as coupled oscillators in the full network. Our results, combined with observed cross-species beta oscillations characteristics, suggest that abnormal beta oscillations likely arise from distinct mechanisms in rodents and primates.

Hippocampal ripples, accompanied by highly synchronous neuronal firing, are critical for immediate offline memory consolidation, learning, and planning. However, the cellular mechanisms that determine which individual neurons are recruited to participate in action potential (AP) firing during ripples remain unclear. We investigated the membrane potential (Vm) dynamics of CA1 pyramidal cells during ripples using whole-cell recordings in awake, head-fixed mice. We found that ripple participation was strongly associated with concurrent membrane-potential depolarization on an event-to-event basis, whereas between-cell differences in overall participation propensity were jointly explained by ripple-associated depolarization and intrinsic excitability. Applying this framework across CA1 cell populations revealed that differences in recruitment between superficial and deep pyramidal cells were primarily driven by ripple-associated depolarization, counteracted by excitability. Differences between place and non-place cells were similarly driven by ripple-associated depolarization, though this effect acted in the same direction as excitability. Finally, we identified the post-ripple LFP rebound as a prominent marker of ripple diversity linked to post-ripple membrane-potential hyperpolarization at the single-neuron level. Together, these findings provide a mechanistic understanding of how CA1 pyramidal cells are selectively recruited during hippocampal ripples.

Hypothalamic supramammillary nucleus (SuM) neurons project to the dentate gyrus and co-release glutamate and GABA, thereby regulating hippocampal information processing. However, how synapses mediating glutamate/GABA co-transmission are established during development remains unclear. Here, we investigated the functional and structural maturation of SuM inputs onto adult-born granule cells (GCs). We found that most newborn GCs received GABAergic responses without detectable glutamatergic responses, whereas a smaller population exhibited glutamate/GABA co-transmission. Newborn GCs with glutamate/GABA co-transmission showed more mature anatomical and physiological properties than GABA-only neurons, with greater dendritic complexity, longer dendritic length, lower input resistance, and stronger medial perforant path inputs. Postsynaptic depolarization failed to induce synapse unsilencing in GABA-only neurons but induced robust long-term potentiation in newborn GCs with glutamatergic transmission, indicating that plasticity at SuM inputs emerges after functional glutamatergic transmission is established. Morphological analysis showed that SuM terminals apposed to newborn GCs contained both VGluT2 and VIAAT, whereas postsynaptic receptor organization remained immature. Moreover, environmental enrichment and chemogenetic activation of SuM neurons increased the proportion of newborn GCs receiving glutamate/GABA co-transmission, suggesting that SuM activity promotes the emergence of glutamatergic transmission. Together, these results suggest that SuM inputs onto newborn GCs undergo sequential maturation from initial GABAergic transmission to glutamate/GABA co-transmission, a process likely supported by SuM activity-dependent postsynaptic maturation.

Cognitive stability, or task focus, enables goal-directed behavior through representational dynamics: by prioritizing task-relevant representations over competing task-irrelevant ones. Beyond corrective reactive mechanisms operating after conflict arises, stability can be deployed predictively through the statistical learning of conflict regularities, either proactively, guided by global conflict expectations, or through a predictive reactive mode guided by item-specific ones. Yet, the representational mechanisms enabling such predictive adjustments remain underspecified. We addressed this by re-analyzing EEG data from a spatial Stroop task concurrently manipulating list-wide (LWPC) and item-specific (ISPC) proportion congruency. Moving beyond existing analytical approaches, we developed a framework combining trial-by-trial Bayesian conflict-expectancy modeling with a novel spatio-temporo-spectral searchlight Representational Similarity Analysis, and leveraged directional representational modeling complemented by ridge-regression decoding. We first identified the necessary computational substrate for stability predictive reconfigurations: the spatio-temporo-spectral patterns encoding task (Target, Distractor) and conflict-expectancy (LWPC, ISPC) representations, the latter representations encoded already in the pre-stimulus windows. Crucially, conflict expectations modulated Target and Distractor encoding strength with distinct spatio-temporal and spectral dynamics, revealing a functional dissociation. LWPC-induced proactive stability follows a "Target-first" policy, primarily strengthening the Target early post-stimulus via Alpha and lower-Beta patterns, weakening Distractor encoding only later on; conversely, ISPC-induced predictive reactive stability follows a "Distractor-first" policy, rapidly weakening Distractor encoding via response-locked posterior Beta patterns, strengthening the Target only downstream. Our study refines theoretical cognitive control models, incorporating a mechanistic explanation for predictive stability, while also establishing a multidimensional methodological template for interrogating the underlying substrates of adaptive behavior and beyond.

Cortical alpha and beta oscillations are traditionally conceptualized as spatially distinct, with alpha rhythms constrained to the occipital cortex and beta rhythms to frontal cortices. Little work has tested this spatial separability empirically and explored whether the distribution of these cortical rhythms may be continuous in space and frequency. Using task-free magnetoencephalography of 604 participants and density-based unsupervised clustering of individual spatio-spectral peaks, we show that the distribution of cortical oscillatory activity across the alpha-to-beta frequency range is structured along a continuous spatio-spectral gradient from occipitotemporal to frontal cortices. Across individuals, the expression of this gradient correlates positively with cognition and decays with age. This work introduces a continuous spatio-spectral organizational structure linking alpha- and beta-band activity across all lobes of the neocortex, with a potential role in flexible segregation and integration of communication between sensorimotor regions of the brain.

Working memory (WM) and decision-making (DM) are closely linked cognitive processes that rely on frontoparietal regions, yet their distinct and shared neural dynamics remain unclear. Using intracranial EEG recordings from 24 human subjects performing a delayed match-to-sample task, we examined high-frequency broadband (HFB) activity to dissociate WM- and DM-related processes with high spatiotemporal resolution. Principal component analysis of frontoparietal HFB activity revealed multiple temporally distinct patterns spanning stimulus presentation and delay phases. The most dominant component reflected sustained WM-related activity across task phases, consistent with persistent maintenance of internal representations. Additional components captured transient WM encoding across trial phases, as well as both stimulus-linked and ramping DM-related activity suggesting dual early and late stages of evaluative processing. These processes were distributed across frontoparietal regions but showed systematic differences in anatomical focus, with WM-related activity concentrated in dorsolateral prefrontal cortex (PFC) and DM-related activity concentrated in ventrolateral PFC, while inferior parietal cortex preferentially tracked the behavioral significance of incoming stimuli and superior parietal cortex tracked the appearance of new information that may require updating ongoing processing. Together, these findings demonstrate that WM and DM processes are separable yet interleaved over time within distributed cortical networks, revealing coordinated but functionally distinct contributions to goal-directed behavior.

Human brain function emerges from the interplay between recurrent local activity and distributed inter-regional interactions. However, a biologically interpretable framework for quantifying this balance between the two factors at the whole-brain level remains lacking. Here, we extended an established biophysical model to quantify inter-regional excitation and newly introduced the recurrent ratio (R-ratio), a measure of the relative balance between intra- and inter-regional excitation. Dynamical analyses showed that an optimal R-ratio supports a trade-off between network stability and flexibility. Applying this framework to healthy aging and Alzheimer's disease revealed progressively increased R-ratios with advancing age and disease severity. In healthy individuals, higher R-ratios were associated with age-related alterations in brain morphology, molecular pathology, and cognitive function, whereas Alzheimer's disease was characterized by increased R-ratios accompanied by reduced dynamical persistence. Together, these findings establish the R-ratio as a biologically interpretable marker of whole-brain excitation balance and demonstrate its utility for linking biophysical mechanisms with large-scale brain dynamics, aging, and neurodegeneration.

Hippocampal sharp-wave ripple (SWR) events occur during post-experience rest and sleep and play a significant role in memory consolidation. Recent ex vivo studies have found orexin-driven neuromodulatory effects on SWR parameters and proposed that they can influence memory consolidation. Additionally, behavioral studies demonstrate contradictory results regarding orexinergic effects on memory acquisition depending on task structure and intervention time. This inconsistency, along with the diffuse spread of orexin receptors (OXR) in the CA1 and CA3 hippocampus, challenges theoretical interpretation of orexinergic effects on memory. To reconcile this problem, we propose a rate-based model linking orexinergic tone-dependent SWR statistics to memory performance. The model combines an encoding module whose gain scales with the global orexinergic tone with a consolidation module driven by OX2R-specific replay dynamics. The model is based on a plasticity rule that considers SWR events as the unit of induction. Therefore, weight change requires sufficient replay content and depolarizing drive. We observed that, depending on the task structure, encoding-dominant learning benefits discrimination as the orexin drive increases. Consolidation-dominant learning shows a non-monotonic relationship with an internal optimum level of orexinergic tone. This interaction highlights that stronger truncation at higher receptor engagement shortens memory replay and reduces the efficacy of the consolidation process. The model predicts that the optimal regime depends on task complexity reflected in sequence length. Together, these results demonstrate how task demands determine whether increased orexinergic drive enhances or impairs effective consolidation and SWR-dependent replay in the hippocampus.

Astrocytes actively regulate synaptic transmission and neuronal excitability, yet their role in orchestrating macroscopic cortical network regimes and slow-wave oscillations remains an active area of reasearch. This study investigates how bidirectional neuron astrocyte interactions shape emergent population dynamics using a computational network model of excitatory and inhibitory neurons coupled to an astrocyte. The results identify astrocytic feedback topology, rather than astrocytic coupling strength alone, as a key determinant of emergent cortical network dynamics. By systematically dissecting pathway-specific connectivity, it has been shown that the neuronal population driving astrocytic activation and the neuronal population receiving gliotransmission jointly determine whether the network occupies asynchronous irregular (AI), synchronous irregular (SI), synchronous regular(SR), asynchronous regular(AR) or quiescent regimes.Directing gliotransmission selectively onto excitatory neurons consistently promotes population synchrony regardless of the population influencing astrocytic dynamics, whereas selective modulation of inhibitory interneurons induces network quiescence via strong suppression. Under dual-target gliotransmission, network synchrony is dictated by the population driving astrocytic dynamics: excitatory-only drive promotes synchrony, while combined or inhibitory-specific drive preserves asynchronous states. Furthermore, the model reveals that astrocytic signaling kinetics provide an additional temporal control mechanism that regulates the frequency and persistence of self sustained up states.

Perceptual decisions engage distributed brain networks, yet how decision-related signals are organized across and within brain areas remains unclear. Mesoscale functional domains are established in visual cortex, but whether spatially distinct domains organize perceptual decisions across the brain is unknown. Here we used 7-T functional MRI at 1-mm isotropic resolution in two awake macaques performing a contrast-discrimination task to map brain-wide activity during sensation, decision formation and saccadic report. Voxel timecourses separated into three groups with distinct temporal profiles, associated with sensory, decision-related and action-related activity. Their spatial distributions formed a broad progression from visual cortex to parietal, prefrontal and motor regions, while revealing distinct response domains within these regions. Choice-related signals were distributed across sensory, associative, and motor systems before movement, including in visual cortex responses to physically identical stimuli that differed according to the animal's eventual choice. Moreover, within the intraparietal sulcus and other regions, mesoscale domains preferentially responsive to near-threshold conditions carried choice information under uncertainty, whereas domains responsive to high-contrast conditions carried choice information when evidence was strong. These findings reveal that perceptual decisions are organized across the brain through spatially differentiated mesoscale networks whose activity varies with task phase and sensory certainty. This spatial organization provides a framework for investigating how distributed neural signals support flexible choice.

Phosphorylated -synuclein at S129 (pS129) is the predominant form of -synuclein in the aggregates known as Lewy bodies (LB) and Lewy neurites (LN), the pathological hallmarks of Parkinson's disease (PD) and dementia with Lewy bodies (DLB), collectively known as synucleinopathies. The toxic nature of these inclusions remains contentious. To improve our understanding of these aggregates and their role in the structural and functional changes observed in synucleinopathies, we combined non-invasive structural and functional MRI in awake head-fixed M83 mice with whole-brain light-sheet fluorescence microscopy (LSFM) in the same animals following a unilateral injection of either human preformed fibrils of -synuclein (PFF) or PBS into the dorsal striatum. We developed a multimodal framework integrating the non-invasive, mesoscopic nature of MRI with the cellular-level details of LSFM to further our understanding of the underlying pathology of synucleinopathies. Using this framework, we show that four months after injection, animals injected with PFFs showed ubiquitous spread of pS129 pathology throughout the whole brain and spinal cord consistent with anatomical connectivity from the seeding region, with some structures such as the midbrain more susceptible to pS129 accumulation. Compared to controls, PFF-injected animals showed widespread atrophy as well as a decrease in functional connectivity, while evoked activity in the somatosensory/somatomotor areas in response to spontaneous paw movements showed significant hypoactivation. These cellular, structural and functional changes were significantly associated with the proportion of pS129 accumulation determined in the same animals. Our results demonstrate the deleterious effects of pS129 accumulation and argue in favor of a toxic role played by these aggregates.

The neural mechanisms underlying spontaneous perceptual switches during bistable perception, particularly the dynamic interplay between large-scale brain networks, remain incompletely understood. Here, using functional magnetic resonance imaging (fMRI) in six participants and a bistable kinetic depth effect (KDE) stimulus, we investigated the spatiotemporal dynamics of brain activity associated with spontaneous perceptual reversals compared to physically induced (replayed) switches. Leveraging a fine-grained functional brain parcellation (MD758) and time-resolved analysis including noise correlations, we identified a distributed network encompassing occipital (MT/MST, LOC), parietal (IPS), and frontal (FEF, IFC, DLPFC) regions showing differential activity during spontaneous versus replayed switches. This network topography qualitatively overlapped a prior review-derived synthesis of perceptual-switching regions. Temporal decomposition of the differential fMRI signal further revealed a characteristic network cascade after the perceptual report: positive dorsal attention (DAN) and visual (VIS) network contrasts peaked approximately 4 s after the report alongside a negative default mode network (DMN) contrast; a slower, more modest ventral attention (VAN)-frontoparietal (FPN) component was observed around 10 s post-report but was weak and is treated as preliminary. The early 4 s profile was recovered across raw-signal, PCA-denoised, and noise-correlation analyses (three related, not fully independent analyses), with the noise-correlation analysis indicating that it is not fully attributable to shared motor-report signals. These findings delineate the temporal sequence of network recruitment following spontaneous perceptual transitions and suggest that interplay between attentional control and default mode networks accompanies the resolution of perceptual ambiguity.

In the striatum, interneurons tightly control the activity of output neurons, the striatal projection neurons (SPN). Among these striatal interneurons, parvalbumin-expressing (PV) and somatostatin-expressing (SOM) cells exhibit markedly distinct electrophysiological and anatomical properties, suggesting differential integration into striatal circuits and distinct forms of feedforward inhibition onto SPN. However, it remains unclear whether differences already emerge at the level of corticostriatal connectivity and if they shape specific interactions with cortical inputs to SPN. Here, we investigated the spatial organization of the projections from the primary and secondary somatosensory areas (S1 and S2), onto striatal PV and SOM interneurons and SPN. With a combination of anatomical tracing and functional circuit mapping, we characterized key features of corticostriatal connectivity, including input number, origin, spacing, overlap and strength. We found that input spacing and strength, at both the individual cell and population levels, were the principal features distinguishing PV and SOM interneurons. In contrast, connectivity differed primarily in input number and laminar organization across S1 and S2. Our findings suggest two potential modes of interaction between interneurons and SPN connectivity: one in which PV interneurons receive S1 inputs structured in large clusters, that densely overlap with those of neighboring SPN, providing an anatomical substrate for potential neuronal assemblies. And another in which SOM interneurons also receive abundant cortical inputs, but thinly disseminated across S1 and S2 and that overlap little with those of SPN, potentially shaping these cell integration through input cross-interaction. These cell-type principles of corticostriatal wiring may support distinct forms of striatal computation.

Prior information, such as predictive cues, shape perceptual decisions. Predictive theories make conflicting claims about whether the brain preferentially processes expected, as proposed by Bayesian accounts, or unexpected information, as proposed by cancellation accounts. These conflicting mechanisms may co-exist if they differ across sensory and action systems, or if their influence changes as the decision unfolds. To test these alternatives, we tracked sensory and action representations in EEG during a motion-discrimination task with changing cue validity. Cross-temporal decoding revealed distinct stabilization of sensory and action representations. Under strong priors, stabilization of unexpected actions predicted stronger bias by the cue, consistent with cancellation accounts. Unlike action, stabilization of expected sensory information was both positively and negatively correlated with precision-weighted prior bias, depending on the decision stage (before or after stimulus onset), compatible with both Bayesian and cancellation accounts. Together, these findings suggest that predictive processing does not uniformly favor expected or unexpected information. Instead, its effects depend on representational domain and decision stage.

Human working memory enables us to temporarily store and flexibly utilize information. It is thought that there are two distinct neural mechanisms by which information is retained in working memory: persistent firing of working memory-relevant neurons, or short-term synaptic plasticity in the absence of ongoing neuronal firing. However, both these mechanisms can lead to strong neural interference when working memory contents are updated with novel information. As an alternative, here we propose that information can be flexibly held in working memory in the form of traveling waves - spatiotemporal patterns of neural activity that propagate across brain tissue. In an artificial neural network, we simulated wave propagation by passing transient neural firing patterns representative of single-item information across different network layers. In comparison to sustained neural firing, we demonstrate that these transient traveling waves result in minimal synaptic plasticity, thereby facilitating working memory updating without significant neural interference. We then identified such traveling waves in intracranial recordings from the human temporal lobe. Traveling waves started precisely when information was being encoded and persisted uniformly throughout maintenance and recall, aligning with the activation pattern seen in the artificial neural network. In trials where participants committed memory errors, no such continuous traveling wave activity in the temporal lobe was observed. Our data from artificial neural networks and human intracranial brain recordings converge to indicate that neural traveling waves support information processing in working memory while minimizing interference during updating. This reported mechanism, together with the classical working memory models, serves as a mutually complementary, multi-mechanistic framework for working memory storage.

Selective vulnerability is a hallmark of Parkinson's disease (PD): post-mortem, dopamine-containing cell loss in the substantia nigra first and most severely affects calbindin-poor compartments termed nigrosomes. Testing this in vivo has been limited by circularity, as MRI studies have delineated nigrosomes from the same disease-related contrast they then measure. Here, we warp histology-derived, calbindin-validated probabilistic masks of nigrosomes 1-5 into a cohort-specific multimodal 7 Tesla quantitative MRI template, providing a degeneration-independent anatomical scaffold, in 28 patients with PD and 28 age- and sex-matched controls. In controls, nigrosomes differ from the surrounding nigral matrix in magnetic susceptibility and R1. PD shifts nigrosomal values upward relative to the matrix in susceptibility, R2*, and R1, concentrated in nigrosomes 1, 2, and 4, whereas whole-nigra analysis shows no group difference. Within the matrix, disease effects are largest caudally and emerge dorsally from the ventral tier, and susceptibility alterations in nigrosomes 1 and 4 track contralateral motor symptoms asymmetry. The multimodal pattern suggests that the detectable signal reflects predominantly secondary, iron-associated glial processes rather than neuronal loss itself. These findings recover the post-mortem spatial pattern of nigral vulnerability in vivo and establish a degeneration-independent framework for subregional nigral imaging in PD.

Brain white matter (WM) microstructure undergoes extensive changes with age, yet the regional and cellular heterogeneity of age-related WM alterations and their impact on cognition remain unclear. Leveraging Neurite Orientation Dispersion and Density Imaging (NODDI), an advanced microstructural modelling to capture specific WM cellular properties, we investigated age-dependent relationships between advanced microstructural markers and cognitive performance, indexed by six cognitive domains derived from principal component analysis. Using a suite of standard diffusion tensor imaging (DTI) and NODDI techniques in eighty-six cognitively normal adults (Young: n=33, Older: n=53), we show that age group differences are widespread across whole brain white matter, with converging region-based and voxel-level evidence implicating commissural, association, and limbic pathways. Older adults exhibited significant and widespread reductions in global fractional anisotropy (FA) and neurite density index (NDI), concomitant with increases in mean diffusivity (MD), orientation density index (ODI), and free water fraction (FWF). Critically, age moderated WM-cognition associations in a domain- and tract-specific manner: younger adults exhibited stronger coupling between commissural and association tract microstructure and language and visual memory, whereas older adults showed greater dependence on projection and limbic tracts for executive and language function. The observed age-dependent coupling between specific microstructural metrics and cognition underscores the value of advanced diffusion models for capturing heterogeneity in brain aging, reveals age-specific biomarkers of cognitive health. These patterns align with phylogenetic and ontogenetic patterns of white matter vulnerability in callosal, long-range association, and limbic pathways, and suggest cellular specificity in white matter changes associated with cognitive decline.

Memory reactivation is thought to support memory consolidation, but how ongoing brain states shape awake reactivation and its relationship to memory remain poorly understood. Here, we asked whether spontaneous hippocampal-cortical reactivation during awake rest is embedded in ongoing brain state dynamics by examining its coupling with whole-brain activity patterns and infra-slow global signal (GS) fluctuations, and whether these dynamics shape its relationship to memory retention. Reactivation preferentially occurred during an internally oriented brain state characterized by default mode network activation and dorsal attention network suppression, as well as states that reinstated encoding-related visual activity. Reactivation also clustered around the peak of infra-slow GS fluctuations and in high GS amplitude time windows, features previously associated with lower arousal. Importantly, the relationship between reactivation and memory depended on the ongoing brain state: distinct whole-brain activity patterns expressed during reactivation were associated with opposing memory outcomes, while reactivation during high GS amplitude windows was positively associated with retention. Together, these findings show that awake memory reactivation is embedded in dynamic whole-brain states that may influence its memory outcome, suggesting that recurring brain states may provide favorable windows for awake memory consolidation.

Even professional athletes sometimes fail to perform when it matters most. This phenomenon, known as choking under pressure, has been observed in a variety of motor and cognitive tasks. Can inordinately large rewards influence even sensory processing, and if so, what might be its neural basis? To probe this, we studied rhesus monkeys performing a challenging color change detection task and recorded from populations of neurons in visual area V4. We found that monkeys underperformed when offered rare "jackpot" rewards and uncovered associated detrimental alterations in V4 population activity. These findings were consistent with misallocated spatial attention, providing a potential neural basis for perceptual choking under pressure.

Motor learning transforms variable actions into reliable skilled behavior, but how premotor cortex supports this transformation remains unclear. We longitudinally recorded bilateral local field potentials and single-unit activity from the rat rostral forelimb area while animals acquired a single-pellet retrieval task. Here, we show that improved skill was accompanied by reduced, rather than increased, task-related premotor recruitment. Despite this attenuation, firing remained dominated by suppression around pellet retrieval, and stronger pre-retrieval suppression was associated with greater odds of successful performance. Population neural state analysis further showed that successful actions were most strongly coupled to sustained pre-retrieval engagement early in learning, whereas later success occurred with less population-level engagement. These findings reveal premotor cortical constraint as a population-level process that is progressively refined as motor learning transforms variable actions into reliable skilled behavior.