Background: Beta-band (13–30 Hz) oscillations in the cortico-basal ganglia-thalamic (CBT) network strongly correlate with motor deficits in Parkinson’s disease (PD), yet their synaptic origins remain unclear. Given that dopamine (DA) loss is necessary but not sufficient to produce sustained beta rhythms, we hypothesised that corticostriatal glutamatergic overdrive may function as a significant non-dopaminergic amplifier of pathological synchrony. Methods: Using an integrated experimental-computational approach, we combined 6-hydroxydopamine (6-OHDA) male rat models, ex vivo striatal patch-clamp recordings, chemogenetic modulation of corticostriatal projection, and multiscale computational network modelling to examine beta oscillation dynamics in the CBT network. Findings: Early DA denervation caused akinesia without beta elevation, while advanced degeneration triggered robust high-beta (25–40 Hz) oscillations and increased corticostriatal coherence. Ex vivo, medium spiny neurons (MSNs) exhibited heightened presynaptic glutamate release correlated with beta power. Computational modelling showed that excessive corticostriatal input under DA depletion increased MSN synchrony, disrupted striatal decorrelation, and was associated with the emergence of pathological beta rhythms, effects reversed by reducing glutamatergic input. In vivo chemogenetic silencing of corticostriatal projections suppressed beta synchrony and improved motor performance in 6-OHDA rats, whereas activation in DA-intact rats had no effect. Notably, striatal NMDA, not AMPA, receptor blockade reduced beta oscillations and motor deficits. Network simulations implicated the subthalamic → motor cortex feedback loop in the maintenance of this pathological beta state. Interpretation: Corticostriatal glutamatergic overdrive, through NMDA receptor-dependent signalling, is linked to the amplification and propagation of beta synchronisation across the CBT circuit, highlighting it as a potential biomarker and a promising therapeutic target in PD. Funding: This research was supported by the National Natural Science Foundation of China (32271173, 82371256) and the Natural Science Foundation of Beijing Municipality (7242214, 7252213). This study was also supported by the Swedish Research Council (VR-M-2020-01652), the Swedish e-Science Research Centre (SeRC), Science for Life Laboratory, KTH Digital Future, EU/Horizon 2020 No. 945539 (HBP 935 SGA3) and No. 101147319 (EBRAINS 2.0 Project), the European Union’s Research and Innovation Program Horizon Europe under grant agreement No. 101137289(the Virtual Brain Twin Project).