Progress in Neurobiology

Theta (4-10 Hz) oscillations seem well-suited for coordinating neural activity. Many studies have focused on thetas role in long-range coordination across brain regions (e.g., connectivity between the prefrontal cortex and the hippocampus). It remains unclear how theta coordinates neural activity more locally within prefrontal subareas. We examined neural activity in three frontal areas (i.e., dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), and frontal eye fields (FEF)) as non-human primates categorized dot patterns. We found that theta flexibly coordinated spiking activity and higher-frequency oscillations within and between frontal areas. First, theta phase in all areas was coupled to spiking information in the FEF and seemed to gate information that was behaviorally relevant. Second, theta influences were routed in opposite directions depending on feedback. Theta flowed in a posterior direction to the FEF during choices and after correct outcomes. Theta influences reversed directionality and flowed in an anterior direction after incorrect outcomes. Third, theta organized nested cross-frequency, phase-amplitude interactions. Theta was coupled to beta (15-30Hz) oscillations, both within and between areas. Beta, in turn, was coupled to gamma (40-90Hz) oscillations, but mainly locally. Together, our results position theta as a critical mechanism that flexibly and dynamically coordinates neural activity within and across the frontal cortex. HIGHLIGHTS- Neural activity was recorded in three frontal areas as non-human primates categorized dot patterns. - Theta (4-10Hz) oscillations gated relevant information in spiking. - Theta influences were routed in opposite directions depending on feedback. - Theta organized nested cross-frequency interactions with beta (15-30Hz) and gamma (40-90Hz) oscillations. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/729810v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1b4a6a4org.highwire.dtl.DTLVardef@12886feorg.highwire.dtl.DTLVardef@1b6c08dorg.highwire.dtl.DTLVardef@11ba768_HPS_FORMAT_FIGEXP M_FIG C_FIG

The current model of the action inhibition network includes the prefrontal cortex and the subthalamic nucleus (STN) connected via the prefrontal hyperdirect pathway. Proactive inhibition refers to preparatory mechanisms that facilitate action inhibition (i.e. enables a person to act with restraint), while reactive inhibition is a sudden stopping triggered by an external stimulus. Most research has focused on the reactive paradigm, with more limited investigation of proactive inhibition. We studied electrophysiologic activity in multiple cortical and STN regions in 17 patients with Parkinsons disease using high-resolution intracranial electrodes. Subjects performed a Go/NoGo task and a simpler Go task. Proactive inhibition was assessed by contrasting Go trials in the context of two tasks. In the rostral middle frontal gyrus, we found increased beta oscillations during movement preparation in Go trials of the Go/NoGo task compared to the Go task. A similar but weaker, preparatory beta modulation was observed in dorsal STN, while central STN was associated with significant modulation in theta power prior to movement. We interpret this activity as a reflection of the role of these regions in proactively restraining anticipated responses. Conversely, inferior frontal gyrus and ventral STN were primarily engaged during rapid post-cue action control. Specifically, withholding of action after the NoGo signal was accompanied by increased theta activity in these regions. Beta modulation within the STN mirrored those of sensorimotor cortex during successful inhibition and movement execution. In both regions, beta activity decreased during movement and was higher when movement was withheld. We conclude that communication within the hypothesized motor control network is frequency dependent, with key nodes promoting specific functions.

Parkinsons disease (PD) is marked by impairments in voluntary movement, including prolonged movement preparation and execution, yet how parkinsonism alters neural processing to produce these deficits remains unresolved. Prior work examining M1 spiking activity in parkinsonian states has largely characterized firing-rate changes and motor representations at the level of individual neurons, with inconsistent results and limited insight into population-level organization. Here we investigated how parkinsonism reshapes the population-level organization of neural activity in M1 during movement. We simultaneously recorded large populations of neurons from M1 in two nonhuman primates performing reaching tasks before and after induction of parkinsonism with the neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). In the parkinsonian state, both preparation and reach durations were significantly prolonged. Population-level analyses revealed that parkinsonism increased the dimensionality of M1 activity during both preparation and movement and reduced the orthogonality between preparatory and movement-related subspaces. Moreover, trial-by-trial variability in reach duration was explained by the alignment of the preparatory and reach subspaces, indicating the functional role of subspace orthogonality. Together, these findings suggest that parkinsonism disrupts the population-level organization of cortical dynamics across computations, providing a population-level framework linking altered cortical dynamics to the movement-related dysfunction observed in PD.

It remains unknown whether oscillatory brain activity that shapes sensorimotor state is routed selectively to task-relevant muscles or broadcast across the motor system. Here we combined electroencephalography with large-scale recordings of spinal motor neurons innervating the tibialis anterior. Participants maintained a submaximal dorsiflexion while performing a Go/No-Go task in which the instructed response was either a ballistic dorsiflexion or a ballistic handgrip, making the tibialis anterior task-relevant or task-irrelevant, respectively. Alpha- and beta-band modulations observed at the cortical level were largely expressed in motor neuron output, including in the task-irrelevant motor neuron pool, indicating broad propagation of cortical dynamics to spinal motor neurons. The peripheral expression of these modulations differed across frequency bands: alpha was partly effector-dependent, consistent with more selective transmission to the task-relevant pool, whereas beta was largely effector-independent, consistent with broader expression across motor neuron pools. Using simulation-based inference, we found that task-related changes in motor output were best explained by modulations in net excitatory drive, whereas alpha- and beta-band inputs contributed primarily to motor neuron synchronization. A complementary simulation showed that this synchronization may facilitate the rapid build-up of motor output following a sudden increase in excitatory drive. These results support a parallel control architecture in which low-frequency drive determines motor output, whereas higher-frequency oscillatory inputs are broadly distributed and shape the functional state of motor neuron pools in preparation for action.

Recent studies report rotational population dynamics in spinal cord neuronal activity during rhythmic movements, suggesting computational principles shared with motor cortex. Here we show that primate cervical spinal cord activity does not exhibit rotational dynamics during an alternating single-joint isometric wrist task, instead it displays low-dimensional alternating population patterns. Positive controls confirm presence of rotational structure in motor cortex activity during the same task, indicating distinct computational strategies across the motor axis. Cortical neurons with post-spike effects on motoneurons had activity with dynamics resembling cortical rather than spinal populations.

Postoperative cognitive decline (POCD) after coronary artery bypass grafting (CABG) is increasingly conceptualized as a system-level disturbance of large-scale brain coordination rather than focal dysfunction. Here, we propose a multiscale neural engineering framework that combines static and dynamic information-theoretic connectivity with graph-theoretical analysis to characterize postoperative network vulnerability and its association with cognitive outcome. Resting-state fMRI was acquired in 14 male CABG patients at an early postoperative baseline (BL) and at 3-month follow-up (FU). Cognitive outcome at follow-up was assessed with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), classifying 7 patients as POCD (RBANS < 80) and 7 as NO POCD. Functional connectivity between 32 brain regions, grouped in 8 resting-state networks (RSN), was estimated using mutual information (MI; static dependence) and mutual information rate (MIR; dynamic information exchange), each computed with parametric Gaussian (linear) and model-free k-nearest neighbor estimators. Pairwise connections were validated via surrogate testing, and group differences in longitudinal connectivity change ({Delta} = FU-BL) were assessed with permutation tests at global, intra- and inter-RSN scales. Graph metrics were computed on statistically thresholded weighted networks and related to RBANS using permutation-based Spearman correlations. POCD was not associated with a uniform reduction in connectivity but with a structured pattern of network reorganization. Static connectivity showed widespread alterations, particularly within higher-order associative systems, including salience, dorsal attention, and default mode networks. Dynamic connectivity did not exhibit global group differences but revealed selective, network-specific alterations in temporal information exchange. Longitudinal analyses showed that better cognitive outcomes were associated with increased global efficiency and density and reduced modularity and small-worldness, indicating a greater brain integration. In contrast, poorer outcomes were associated with increased segregation and higher betweenness centrality, suggesting greater reliance on hub-mediated communication. Linear measures captured more widespread connectivity changes, whereas nonlinear estimators revealed more selective alterations in dynamic information flow. Combining static and dynamic information measures with complementary estimators and surrogate-validated graph analysis reveals dissociable signatures of postoperative network dysfunction. POCD is characterized by impaired restoration of distributed integration and a progressive shift toward hub-dependent communication, suggesting that large-scale integrative vulnerability may constitute a candidate biomarker of cognitive resilience after cardiac surgery.

Creativity fluctuates markedly from moment to moment, even within the same individual, yet the neural dynamics that determines whether a given attempt produces a highly creative idea remains poorly understood. Prior studies have identified static EEG correlates of creative thinking, but these do not explain how brain activity is dynamically organized before and during successful idea generation. Here, we model creative cognition as trajectories through a neural state space using energy landscape analysis (ELA) of EEG recorded during two complementary problem-solving paradigms: the divergent Alternative Uses Test (AUT) and the convergent, goal-directed Fusion Innovation Test (FIT). Across both tasks, creative success was associated with dissociable dynamical signatures in the resting and ideation stages. Before ideation, greater diversity of resting-state patterns of activity, corresponding to possible attractors, indexed a preparatory substrate of creative potential, showing weak trial-level effects but robust subject-level coupling with performance. During ideation, higher creativity was predicted by how the brain traversed its accessible state space: successful trials were characterized by traversal biased toward sustained exploration within stable attractor basins rather than frequent switching between basins of attraction ({beta} = 0.104, p = 0.004, trial-level). These findings identify a task-invariant, biologically grounded dynamical mechanism of creative cognition and show that creative performance depends not only on which neural states are available, but also on how neural activity traverses that state space.

The macroscale signaling of the brain emerges from the integration of connected areas, orchestrated by microscale, regional molecular processes. Despite growing multimodal neuroimaging data resources, the role of cortical chemoarchitecture in shaping inter-regional functional connectivity remains poorly understood. Here, we examine whether brain regions that share a chemoarchitectural signature exhibit strong frequency-resolved functional connectivity, and whether aging moderates this neurochemical-functional alignment. Using magnetoencephalography data from across the healthy adult lifespan (n = 569), we identify a frequency-dependent organization of functional connectivity by slow neuromodulator systems, with low-frequency bands ({theta}-) shaped most strongly by noradrenergic systems, and faster ({beta}) alignment dependent on serotonergic chemoarchitecture. Aging strengthens the influence of neurochemistry on inter-regional connectivity, with frequency-specific implications for age-related cognitive performance. Neurochemical influences on{theta} -band connectivity were associated with worse cognition in older adults, while the opposite was true for the low-{gamma} ({gamma}{downarrow}) band. This suggests that neuromodulatory preservation of high-frequency dynamics in older adults may reflect neural resilience. Together, our findings indicate that neurochemical-functional alignment is frequency-dependent, distinguishing between maladaptive and resilient modes of brain organization.

Adaptive decision-making under ambiguity requires constant integration of reward- and loss-related information to guide behaviour. In humans and rodents, not all individuals maximise gains in decision-making tasks, such as the Iowa Gambling task or its rodent version, the Rat Gambling task (rGT). While the prefrontal and insular cortices have each been shown independently to support optimal probabilistic decision-making, how they interact functionally to shape individual differences in performance remains unclear. Here, we investigated the consequences of bilateral baclofen/muscimol-mediated inactivation of the prelimbic cortex (PLC) or the anterior insular cortex (AIC) vs. their functional disconnection on the performance of Sprague Dawley rats identified as safe (SDMs) or risky decision makers (RDMs) in the RGT. AIC inhibition decreased advantageous choice in SDMs, whereas it increased win-stay responding in RDMs. In contrast, PLC inhibition primarily affected lose-shift behaviour, reducing sensitivity to losses in SDMs while enhancing adaptive switching in RDMs. Functionally disconnecting the PLC from the AIC, which had no effect on the performance of SDMs, improved decision-making in RDMs by increasing loss-guided behavioural adaptation. Together, these findings identify parallel versus serial AIC-PLC processing as a potential neural mechanism underlying the tendency some individuals have to make suboptimal decisions.

Working memory depends on gamma oscillations generated across sensory and prefrontal cortices. In sensory cortices such as primary visual cortex (V1), stimulus-locked gamma oscillations encode stimulus information, while in prefrontal cortex (PFC), persistent gamma oscillations maintain this information after the stimulus is removed. In schizophrenia (SZ), gamma power is reduced in both V1 and PFC, consistent with deficits in sensory encoding and working memory maintenance in the illness. These two regimes of gamma oscillations arise from a canonical microcircuit involving pyramidal neurons (PNs) and parvalbumin-expressing interneurons (PVIs). Yet, whether stimulus-locked and persistent gamma oscillations are similarly or differentially vulnerable to synaptic alterations within this circuit in SZ remains unknown. To investigate this question, we used a mean-field model of the PN-PVI circuit generating either stimulus-locked or persistent gamma oscillations. We then assessed the effects of three synaptic alterations found in SZ: lower excitatory drive to PVIs (E[-&gt;]I), lower inhibitory drive to PNs (I[-&gt;]E), and greater variability in E[-&gt;]I synaptic strength. Each alteration produced larger gamma power deficits in the persistent regime than in the stimulus-locked regime. When applied together, these alterations interacted synergistically to reduce gamma power in both regimes, with the persistent regime exhibiting a more pronounced deficit. Among the three parameters, E[-&gt;]I synaptic strength was the strongest contributor to the synergistic loss of gamma power. Two-dimensional bifurcation analyses further revealed that this differential vulnerability arises from a narrower margin of oscillatory stability in the persistent regime, where the parameter values producing maximum gamma power sit closer to the Hopf bifurcation boundary. Together, these findings identify the persistent regime as intrinsically more fragile than the stimulus-locked regime, with the implications for understanding regional patterns of synaptic pathology and cortical gamma oscillations with distinct dynamics in SZ. Author summaryWorking memory depends on stimulus-locked gamma oscillations in sensory cortices such as primary visual cortex (V1) for encoding stimulus information, and persistent gamma oscillations in prefrontal cortex (PFC) for maintaining this information after stimulus offset. In schizophrenia (SZ), gamma power is reduced in both V1 and PFC, and postmortem human brain studies suggest that the underlying synaptic alterations are more severe in V1 than in PFC. Our computational modeling results suggest that this regional pattern arises because persistent gamma oscillations are intrinsically more fragile than stimulus-locked gamma oscillations, so that smaller synaptic alterations are sufficient to disrupt gamma oscillations in PFC while larger alterations are required to produce comparable disruption in V1. Together, these findings give rise to a differential vulnerability model of cortical gamma oscillations in SZ, linking the regional patterns of synaptic pathology to the deficits in gamma oscillations observed across sensory and prefrontal cortices in the illness.

Throughout the preclinical phase of Alzheimers disease (AD) {beta}-amyloid (A{beta}) accumulates preferentially within the default mode network (DMN), yet the functional and behavioural consequences of this pathological burden remain poorly understood. Using task-based fMRI combined with A{beta}, tau, and dopamine PET in cognitively normal older adults, we show that A{beta} burden impairs learning independent of tau, but this learning performance is recovered with higher dorsolateral striatal dopamine synthesis capacity. Investigating the neural mechanisms that support this learning, we observe that A{beta} positive individuals show attenuated DMN activity to error related feedback, a metric that relates to poorer learning. When estimating the effective connectivity during feedback, computational modelling reveals that A{beta} induces dis-inhibition of the DMN during error processing. Critically, dopamine synthesis capacity in the dorsolateral striatum rebalances effective connectivity between the DMN and frontostriatal network, thereby opposing A{beta} related disruption. These findings establish a systems-level framework in which A{beta} impairs learning by disrupting dynamic DMN modulation during feedback, a disruption for which dopaminergic function can partially compensate. This suggests that learning in the presence of A{beta} may be subserved by dopamine-dependent network rebalancing, a candidate mechanism of cognitive resilience to support learning in preclinical AD.

The ability of the brain to encode information and control behavior depends on the coordinated activity of large and distributed neuronal populations. Correlations in neuronal spiking activity across trials of the same condition, or noise correlations (NCs), have been interpreted as a reflection of shared synaptic connectivity and as a contributing factor to the information capacity of neuronal populations. The impact of NCs on coding is most often considered in populations of neurons exhibiting robust condition-dependent information in their spike counts (SCs). However, theoretical work suggests that NCs could provide a source of condition-dependent information separate from SCs. We examined the activity of large neuronal populations in prefrontal cortex of macaques while they performed a spatial delayed response task composed of visual, memory, and motor epochs. We found that pairs of neurons that displayed visual, memory, and motor selectivity in their SCs often exhibited selectivity in their NCs, independent of spike count. However, we also found that pairs of neurons without SC selectivity during the different task epochs nonetheless exhibited condition-dependent NCs. Moreover, we found that the magnitude of condition-dependent NCs were largely comparable across neuronal pairs with or without SC selectivity. These results demonstrate that correlated variability in spiking activity can be condition-dependent even in the absence of condition-dependent SCs.

Neural activity inevitably produces waste, which promotes neurodegeneration with topographic features. The glymphatic system is important for waste clearance. However, the spatial characteristics of glymphatic clearance across cortex and whether it interplays with neural activity in contribution to amyloidosis in human remain unexplored. Here, by intrathecal administration of gadolinium-based contrast agents, glymphatic influx and clearance patterns across cortex in 96 participants are depicted via Glymphatic MRI. Analyses integrating post-mortem transcriptomic profiles from Allen Human Brain Atlas indicate that, genes related with excitatory and inhibitory neurons, and pathways engaging in synaptic function were enriched in regions with faster glymphatic clearance. FALFF was calculated from resting-state fMRI to represent neural activity. At the regional level, based on a subgroup with rs-fMRI (N = 15), regional glymphatic clearance was positively coupled with spontaneous neural activity. Mismatch index, reflecting decoupling between spontaneous neural activity and glymphatic clearance function, turned out to be positively associated with regional severity of amyloidosis using open-source 11C-PiB dataset. Together, this study for the first time demonstrates the intricate interplays between neural activity and glymphatic dynamics from transcriptional to physiological level. The mismatch between these two processes may serve as an undescribed comprehensive mechanism promoting regional vulnerability to proteopathy and subsequent neurodegeneration in cortex.

Value-based decision making emerges from coordinated neural dynamics across distributed brain networks. Recent studies using noninvasive whole-brain measurements in humans have highlighted the importance of neural activity in the 2-10 Hz frequency band for value-based decision making. Using magnetoencephalography and hidden Markov model (HMM) analysis, we examined whether and how whole-brain neural dynamics in this frequency band, evolving on a timescale of a few hundred milliseconds, reflect value-based decision processes. Thirty-five healthy adults (females and males) made binary choices between risky and sure options. Trial-wise subjective values were estimated using behavioral economic modeling based on prospect theory. We found that HMM-derived trial-by-trial whole-brain neural dynamics (defined by 2-10 Hz amplitude envelopes in distributed brain regions and their interregional coupling) were associated with the subjective values of choice options in a manner distinct from simple perceptual- or motor-evoked activity. Notably, these trial-by-trial whole-brain dynamics covaried with the difference in subjective values between the chosen and unchosen options when the neural data were time-locked to participants responses, but not when time-locked to option onset. These findings revealed a crucial link between subsecond whole-brain neural dynamics and trial-by-trial decision variables, providing insights into how value-based decision processes unfold over time in the human brain.

Attentional functions enable the nervous system to regulate incoming information, selecting what is behaviorally relevant while filtering out distractions. To investigate attentional control in rats, we developed a paradigm in which animals are required to ignore an irrelevant tactile stimulus (a vibration) and categorize the relevant one as weak or strong. Stimuli were separated in time but delivered to the same set of vibrissae, making this a temporal rather than spatial attention task. In the first task version, the irrelevant stimulus was presented first and the relevant second. Across animals, we observed substantial variability in how this task was learned and performed, consistent with emerging work showing that learning unfolds along idiosyncratic trajectories rather than converging on a single behavioral solution. While the irrelevant stimulus typically exerted an attractive bias on judgments, some rats progressively reduced this influence and achieved near-complete suppression, transitioning from a proficient to an expert stage of performance. Other animals, however, adopted alternative stable strategies and did not exhibit comparable levels of attentional filtering. To probe behavioral flexibility, we designed a version of the task in which the relevant stimulus could appear in either temporal position. Under these conditions, rats generally struggled to flexibly allocate attention across time, again with substantial variability across individuals. Importantly, behavioral analyses indicate that this variability is not random but reflects a small number of reproducible strategy classes, characterized by distinct patterns of sensitivity to stimulus order. These findings suggest that attentional control in this task does not rely on a single canonical algorithm, but instead emerges from a constrained set of alternative solutions. Electrophysiological recordings from a limited number of animals revealed neural correlates consistent with these behavioral differences. In expert animals performing the original task, neuronal populations in motor cortex showed differential encoding of the irrelevant and relevant stimuli. In a proficient animal implanted in both vS1 and M1/M2, outcome-dependent modulation was prominent in vS1, whereas M1/M2 transformed sensory inputs into categorical representations with partial suppression of the irrelevant stimulus. Local field potential analyses further indicated that effective task performance was associated with increased high-gamma synchronization between vS1 and M1/M2, alongside low-beta modulation consistent with top-down interactions. Taken together, these results suggest that learning to ignore irrelevant information reshapes interactions between sensory and motor cortices, but that this process unfolds heterogeneously across individuals. Rather than reflecting a single mechanism of attentional control, the data support a framework in which multiple, identifiable strategies coexist within a shared task structure, underscoring the importance of individual differences in understanding the neural basis of attention.

Cortical brain regions integrate information across different timescales, ranging from fast sensory processing to longer integration windows, allowing cognitive functions like working memory. At the large-scale, brain regions organize into transient network states that rapidly switch over time and similarly contribute to cognition. Both cortical timescales and large-scale network dynamics are proposed to be determined by the balance between recurrent synaptic excitation and GABAergic inhibition. Here, we pharmacologically manipulated synaptic transmission at GABAA and NMDA receptors in 60 healthy male participants and acquired resting-state magnetoencephalography. Neuronal timescales followed a hierarchical gradient with shorter timescales in early sensory regions. Increasing GABAergic activity prolonged neuronal timescales across cortical regions. This effect was most prominent in the frontal default mode and in the dorsal attention network. Notably, dynamic network analyses revealed that the occurrence probability of the frontal default mode network increased, whereas the occurrence of the dorsal attention network was reduced. NMDA receptor modulation resulted in no significant changes. Together, these findings provide causal evidence that GABAergic inhibition is a key regulator of cortical temporal organization, linking microscale synaptic mechanisms to neuronal timescales and network dynamics that support diverse cognitive function.

Stroke is the leading cause of chronic disability in the United States, and advancing age is associated with worse recovery. Despite this, relatively little is known about how aging influences the repair and reorganization of neural circuits and large-scale cortical networks after stroke. To address this question, we compared cortical network dynamics and behavioral recovery after focal photothrombotic stroke in forepaw somatosensory cortex in young (3-month-old) and aged (18-month-old) Thy1-GCaMP6f mice. Both young and aged mice developed significant somatomotor deficits after stroke; however, only young mice exhibited substantial behavioral recovery despite similar infarct volumes across groups. Two age-dependent effects on cortical network function emerged. First, somatosensory-evoked activity and somatosensory functional connectivity were disrupted in both cohorts early after stroke, but their trajectories diverged over time. Forepaw-evoked GCaMP responses in the affected cortex were similarly reduced in both groups early after stroke; yet by 7 weeks, responses recovered in young mice but remained persistently depressed in aged animals. Likewise, bihemispheric somatosensory functional connectivity was initially disrupted in both groups but improved between 1 and 7 weeks only in young mice. Second, global temporal measures of network function evolved differently after stroke. At baseline, stimulus-locked fidelity and interhemispheric coherence were higher in young than aged mice, but after stroke, these measures declined in young animals to levels comparable to aged mice and did not recover by 7 weeks. Stroke also altered large-scale cortical entrainment dynamics, and reductions in cortical entrainment area between baseline and 1-week post-stroke predicted long-term behavioral recovery across animals. Together, these findings indicate that impaired behavioral recovery in aged mice reflects a failure of damaged somatosensory networks to reorganize, whereas recovery in young mice occurs despite persistent degradation of global network fidelity and coherence. These results highlight age-dependent mechanisms of circuit repair after stroke and suggest a potential network-level basis for chronic deficits in stroke survivors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/720447v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1f23999org.highwire.dtl.DTLVardef@1a3a433org.highwire.dtl.DTLVardef@712dd5org.highwire.dtl.DTLVardef@780eaa_HPS_FORMAT_FIGEXP M_FIG C_FIG

Intelligent behavior depends on the brain's ability to represent multiple features of the environment simultaneously while keeping those representations independent [1,2]. Patients with Alzheimer's disease often mix up objects, people, and events [3-7], raising the possibility that disease mixes up the way that information is represented in the brain. Here we show that the independence of visual representations progressively breaks down during early stages of disease progression in a rhesus macaque model of Alzheimer's disease and related dementias [8-10]. In visual area V4, representations of different visual features become progressively less independent, such that the representation of one feature is increasingly influenced by the value of another. We term this loss of independence neuronal feature confusion. This neuronal change predicts a specific behavioral consequence: because feature representations become less independent, preferences associated with one visual feature increasingly influence visually guided choices associated with other, independent features. Using an analogous image-selection task, we found the same behavioral signature in people with mild cognitive impairment, distinguishing them from age-matched controls. These results identify a specific and measurable alteration in neuronal population representations that predicts a behavioral change observed across species. More broadly, these findings demonstrate that neuronal population representations can guide the development of sensitive, non-invasive behavioral methods for early detection of functional changes associated with Alzheimer's disease.

We employed a noninvasive high-density surface electromyography (HDsEMG) framework to track spinal motor neuron responses during repetitive transcranial magnetic stimulation (rTMS) and characterize corticospinal transmission of different stimulation frequencies and intensities to the alpha motor neuron pool. Eleven healthy individuals performed isometric thumb flexion at 10% of maximal voluntary contraction while rTMS was delivered over the motor cortex at five frequencies (5, 10, 20, 30, and 50 Hz) and three subthreshold intensities (50%, 60%, and 70% of resting motor threshold). Motor units were decomposed from HDsEMG signals before stimulation and tracked during rTMS. Input-output coupling was quantified using coherence between the rTMS train and individual motor unit spike trains or the cumulative spike train (CST), with shuffled spike trains used as surrogate controls. rTMS inputs were robustly transmitted to spinal motor neurons for all frequencies except 5 Hz, indicating widespread corticospinal coupling. Transmission behaved linearly, with CST output spectra reproducing input frequencies and scaling proportionally with stimulation intensity. The estimated transfer function revealed a bandpass-like profile, with maximal transmission between 10 and 60 Hz. Transmitted inputs also induced oscillatory components in the common synaptic input to motor neurons at stimulation frequency. Simulations indicated that this frequency selectivity emerges from balanced excitatory and inhibitory inputs to the motor neuron pool, with specific synaptic dynamics. These findings demonstrate that corticospinal transmission during rTMS acts as a frequency-selective linear system and provide a framework for assessing and modulating corticospinal pathways, with potential application as tool for tracking disease progression and neurorehabilitation. Highlights- HDsEMG decomposition tracks spinal motor neuron activity during rTMS. - Corticospinal transmission scales with rTMS stimulation intensity. - Corticospinal pathways act as a frequency-selective system. - rTMS transfer function shows maximal transmission at 10-60 Hz. - EPSP-IPSP interactions explain bandpass corticospinal transmission during rTMS.

Visuospatial attention enables the selective allocation of cognitive resources to relevant stimuli. A well-established neural signature of attentional shifts is the lateralised modulation of occipito-parietal alpha power, with decreases over the hemisphere contralateral to the attended location and increases over the ipsilateral hemisphere. However, growing evidence suggests that multiple oscillatory mechanisms contribute to attentional deployment, including beta-band activity. A key unresolved question that remains is whether the same neural rhythms support the deployment of attention and the perceptual decisions that follow. Here, we recorded EEG in 26 participants (22 females) during covert visuospatial orienting and investigated how alpha- and beta-band dynamics relate to behavioural measures, namely perceptual sensitivity (d') and decision criterion (c), and whether attended location could be preferentially decoded from alpha- or beta-band activity. We found that pre-target beta phase significantly predicted decision criterion at earlier pre-target intervals, whereas perceptual sensitivity was predicted closer to target onset, suggesting that beta is related to both sensory gain and the perceptual decision. In contrast, decoding analyses revealed that attended location was most strongly discriminable from alpha-band activity, as confirmed by time-frequency analysis of decoding accuracy. Together, these findings suggest a functional dissociation between oscillatory mechanisms supporting attentional orienting and perceptual decision-making. Whereas alpha-band activity primarily reflects the allocation of attention, beta-band dynamics predict trial-by-trial variability in perceptual decisions.