MRC Brain Network Dynamics Unit

Abstract Background High-dose high-intensity upper limb neurorehabilitation can lead to meaningful clinical gains even in chronic stroke, yet substantial variability in recovery remains unexplained. Identifying neurophysiological markers linked to neuroplasticity and recovery could provide mechanistic insights and guide personalised rehabilitation. Objective To characterise stroke-related alterations in β-activity during movement and neural activity at rest and explore associations between brain activity and changes in upper limb clinical outcomes in chronic stroke survivors undergoing three-week high-dose rehabilitation. Methods Electroencephalography (EEG) was recorded during the three-week rehabilitation programme in 40 chronic stroke survivors participating in the Queen Square Upper Limb (QSUL) Programme, as well as in 26 healthy controls. Recordings were taken during passive movement of the affected and unaffected index fingers (∼70 movements per hand) and at rest (∼7 min). Clinical assessments included the Fugl-Meyer Upper Limb Assessment (FM-UE), reflecting impairment-level deficits, and the Chedoke Arm and Hand Activity Inventory (CAHAI-13), capturing real-world upper limb activity, to examine their differential relationships with movement-related β-activity. Results Stroke survivors showed significant improvements in FM-UE and CAHAI scores following the rehabilitation programme (Mean Δ: FM-UE = 7.5, CAHAI = 7.4), exceeding minimum clinically important differences. Compared to controls, stroke survivors exhibited less strong β-event-related desynchronization/synchronization (β-ERD/ERS) during passive movement of the affected and unaffected index finger, with effects lateralised to the lesioned hemisphere. No significant differences at rest were observed between stroke participants and healthy controls. Only improvements in CAHAI, but not FM-UE, were associated with stronger β-ERD (more negative) and stronger β-ERS (more positive) responses during passive movement. Conclusions Stronger movement-related β-activity is associated with improvements in upper limb activity following high-dose high-intensity neurorehabilitation, suggesting β-activity as a potential marker of neuroplasticity.

Abstract Non-invasive measurement of neurotransmitter-specific glucose metabolism in the human brain remains a major challenge, limiting mechanistic insight into excitatory–inhibitory imbalance across neurological and psychiatric disorders. Current methods lack the ability to selectively and precisely resolve neurotransmitter-specific metabolic pathways, particularly GABA, while remaining compatible with clinically feasible acquisitions. Here, we introduce a clinically compatible approach which enables targeted and non-invasive detection of glutamate, GABA, and lactate metabolism in the human brain. Called proton-observed proton-edited ¹³C-magnetic resonance spectroscopy (POPE-¹³C-MRS), the method uses an exogenous ¹³C-glucose probe combined with standard proton radiofrequency hardware and widely available MR pulse sequences. We use a cross-species validation framework to first calibrate POPE-¹³C-MRS in mice and then demonstrate its feasibility in humans at ultra-high field. While in vivo GABA labelling has been previously reported, POPE-¹³C-MRS provides, for the first time, robust access to GABAergic metabolism using standard MRI hardware, feasible within clinical constraints, including applicability to deep brain regions. By refining existing indirect ¹H-¹³C-MRS strategies and enabling targeted probing of excitatory and inhibitory metabolic pathways, POPE-¹³C-MRS opens new opportunities for studying neurometabolic coupling and excitatory–inhibitory balance in vivo , with broad implications for translational and clinical neuroscience.

The brain's memory function involves patterns of neural population spiking activity, shaped by experience and recurring over time. These neural population patterns are typically studied with respect to the three stages of acquisition, retention, and retrieval. Despite intensive investigation, the relationship between the features of population activity and the properties, computations, and codes for memory remains elusive. In this perspective, we synthesize recent advances in the study of memory from the viewpoint of brain network physiology, aiming for a comprehensive mapping between the properties and computations of memory and the features of population-activity codes. We propose that brain memory circuits implement trade-offs between conflicting demands on population codes. We anticipate that an important challenge for both discovery and translational neuroscience of memory is to study these trade-offs, delineating a safe zone in the population-activity space where neuronal circuits operate efficiently.

Abstract Freezing of gait in Parkinson’s disease (PD) is a major cause of disability, often resistant to dopaminergic therapy and deep brain stimulation (DBS). Its underlying mechanisms remain unclear, mainly because the roles of the human mesencephalic locomotor region (MLR) nuclei are not well understood. Here, we combined rare local field potentials (LFP) recordings from the cuneiform (CuN) and pedunculopontine nuclei (PPN) with biomechanical markers of gait initiation (GI) in four PD patients. We identified functional differences: increases in CuN alpha-band activity precede anticipatory postural adjustments (APA) and correlate with the rhythm of upcoming steps, whereas decreases in PPN beta-band activity occur during APA just before the lead foot lifts off. Imminent freezing is characterized by a breakdown of this organization, marked by mistimed alpha-band surges across the MLR and abnormal PPN beta-band modulation. CuN stimulation selectively improved the stepping rhythm, while PPN stimulation worsened pace or forward vigor. Furthermore, exaggerated mesencephalic alpha-band power was associated with poor clinical responses. These results clarify the individual roles of MLR nuclei in human locomotion and identify pathological alpha dynamics as a biomarker for advancing adaptive neurostimulation.

BackgroundPhase‐amplitude coupling (PAC) in the beta‐gamma range has emerged as a promising electrophysiological biomarker of Parkinson's disease (PD).ObjectiveThis study aims to investigate how levodopa and locomotion modulate cortical (central electroencephalogram [cEEG]) and corticomuscular (cEEG‐gEMG [gastrocnemius electromyography]) beta‐gamma PAC in patients with PD.MethodsThirty patients with PD underwent simultaneous cEEG and gEMG recordings during sitting, standing, and free walking in both off and on dopaminergic states. Spectral features and PAC analyses were conducted to assess the effects of levodopa, locomotion, and their associations with motor symptoms.ResultsIn the off levodopa state, patients showed prolonged gait cycle intervals and shorter step lengths, correlating with higher Movement Disorder Society–revised Unified Parkinson's Disease Rating Scale Part III (UPDRS‐III) scores. The cEEG beta‐gamma PAC during sitting and standing, and cEEG‐gEMG beta‐gamma PAC during walking, positively correlated with UPDRS‐III in the off levodopa state. The cEEG alpha/low beta‐gamma and cEEG‐gEMG low beta‐gamma PAC increased from on to off levodopa while walking, with the latter correlating with reduced step length. Step event–related PAC analysis unveiled a dynamic enhancement of alpha/beta cEEG‐gamma gEMG PAC around heel strikes in on levodopa compared with off.ConclusionsBoth cortical and corticomuscular beta‐gamma PACs are modulated by levodopa and locomotion, with low beta‐gamma corticomuscular PAC specifically linked to gait dysfunction. Moreover, the levodopa‐related enhancement of alpha/beta–gamma PAC during heel strikes highlights the functional relevance of dopaminergic modulation during gait. These findings highlight the potential of PAC as a biomarker for PD, particularly in the development of gait phase‐locked adaptive deep brain stimulation strategies for patients with PD guided by noninvasive PAC monitoring. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Background: Bradykinesia in Parkinson′s disease (PD) may arise due to transient, network–wide neural dynamics that extend beyond beta–band oscillatory activity within the motor cortical-subthalamic nucleus (STN) circuit. Methods: We address this question by using Hidden Markov Models (HMMs) to identify neural states from chronic motor cortical and STN recordings in five PD patients (1,046 hours from 10 hemispheres), with concurrent measurements of bradykinesia using wearable sensors. Findings: We identified four neural states with distinct spectral and temporal features. Two states exhibited spectral signatures—particularly STN low and high gamma, STN delta/alpha, cortical beta, and cortico-STN beta coherence—that predicted worsening bradykinesia. However, STN beta power alone was not consistently predictive, challenging traditional beta-centric views. These states also displayed compensatory features associated with bradykinesia amelioration, including cortical delta/alpha activity, cortical high gamma, and cortico–STN high gamma coherence. Two additional states affected bradykinesia through temporal rather than spectral properties. Prolonged lifetimes of one of these state worsened symptoms, whereas increased occurrences of another, marked by local beta without cortico–STN beta coherence, improved motor function. Interpretation: Our findings highlight the multidimensional nature of bradykinesia and suggest that state-aware, adaptive interventions targeting state features—rather than single frequency bands—may offer new opportunities for improved deep brain stimulation in PD. Keywords: Hidden Markov Model (HMM), Neural states, Bradykinesia, Parkinson′s disease

Abstract Parkinson’s disease (PD) is a complex neurodegenerative disorder characterised by selective neuronal loss. We integrate deep full-length single-nuclei sequencing of the human substantia nigra with novel genome-wide association studies (GWAS) identifying genetic and cellular drivers of PD. Genetic risk converges on AGTR1+ dopaminergic neurons and perineuronal oligodendrocytes (pODCs), both reduced in PD, as well as oligodendrocyte precursor cells, enriched among disease-disrupted intercellular interactions. AGTR1+ neurons represent a metabolically stressed state, characterised by renin-angiotensin system (RAS) and MAPK activation, oxidative stress, and mitochondrial dysfunction, rather than a distinct subtype. AGTR1+ neurons and pODCs link PD risk to metabolic traits; in pODCs, this association reflects insulin resistance with downregulated PI3K–AKT signalling. GWAS of comorbid PD and type 2 diabetes (T2D) identifies loci in AGTR1 and TCF7L2, while AGTR1+ neurons specifically upregulate RAS and T2D drug targets. Familial PD genes associated to comorbid PD/T2D associate with non-Lewy body PD, stratifying disease mechanisms.

S ummary Hippocampal ripples are highly synchronized neuronal population patterns reactivating past waking experiences in the offline brain. Whether the level, structure, and content of ripple-nested activity are consistent across consecutive events or are tuned in each event remains unclear. By profiling individual ripples using laminar currents in the mouse hippocampus during sleep/rest, we identified Rad sink and LM sink ripples featuring current sinks in stratum radiatum versus stratum lacunosum-moleculare , respectively. These two ripple profiles recruit neurons differently. Rad sink ripples integrate recent motifs of waking coactivity, combining superficial and deep CA1 principal cells into denser, higher-dimensional patterns that undergo hour-long stable reactivation. In contrast, LM sink ripples contain core motifs of prior coactivity, engaging deep cells into sparser, lower-dimensional patterns that undergo a reactivation drift, gradually updating their pre-structured content for recent wakefulness. We propose that ripple-by-ripple diversity instantiates parallel reactivation channels for stable integration of recent wakefulness and flexible updating of prior internal representations.

Background: Tremor is the most common movement disorder and a prevalent symptom of neurodegenerative conditions such as Parkinson’s disease (PD). Given the limitations of medication, which may not effectively treat tremor, and the limited availability of surgical treatments such as deep brain stimulation, there is a pressing clinical need for non-invasive therapeutic alternatives, including peripheral electrical stimulation. The high variability of PD tremor poses a challenge to such therapies and calls for person-specific stimulation parameters. Methods: We developed a wrist-worn system incorporating an adaptable phase-tracking algorithm designed for real-time estimation of Parkinsonian rest tremor phase. The algorithm dynamically adapts to tremor variability, including changes in the axis of maximum excursion and center frequency. The system was first validated offline, followed by in-clinic feasibility testing in three individuals with PD. The system triggered the delivery of both phasic and open-loop electrical stimulation to the participant’s wrist. Results: Robust phase estimation was achieved both offline and in all participants. The system adapted to changes in tremor dominant axis and center frequency. Modest tremor modulation was observed at select person-specific settings. Conclusion: This work provides a novel platform for research involving tremor phase tracking, accounting for PD tremor variability, and a foundation for developing personalized, non-invasive tremor management strategies. Clinical and Translational Impact Statement—This study presents a wearable system for adaptive tremor phase tracking validated in individuals with Parkinson’s disease and establishes a foundation for further development of personalized non-invasive tremor management strategies. Category: Clinical Research