Parkinsonian symptoms arise due to over-activity of the indirect striatal output pathway, and under-activity of the direct striatal output pathway. l-DOPA-induced dyskinesia (LID) is caused when the opposite circuitry problems are established, with the indirect pathway becoming underactive, and the direct pathway becoming over-active. Here, we define synaptic plasticity abnormalities in these pathways associated with parkinsonism, symptomatic benefits of l-DOPA, and LID. We applied spike-timing dependent plasticity protocols to corticostriatal synapses in slices from 6-OHDA-lesioned mouse models of parkinsonism and LID, generated in BAC transgenic mice with eGFP targeting the direct or indirect output pathways, with and without l-DOPA present. In naïve mice, bidirectional synaptic plasticity, i.e. LTP and LTD, was induced, resulting in an EPSP amplitude change of approximately 50% in each direction in both striatal output pathways, as shown previously. In parkinsonism and dyskinesia, both pathways exhibited unidirectional plasticity, irrespective of stimulation paradigm. In parkinsonian animals, the indirect pathway only exhibited LTP (LTP protocol: 143.5 ± 14.6%; LTD protocol 177.7 ± 22.3% of baseline), whereas the direct pathway only showed LTD (LTP protocol: 74.3 ± 4.0% and LTD protocol: 63.3 ± 8.7%). A symptomatic dose of l-DOPA restored bidirectional plasticity on both pathways to levels comparable to naïve animals (Indirect pathway: LTP protocol: 124.4± 22.0% and LTD protocol: 52.1± 18.5% of baseline. Direct pathway: LTP protocol: 140.7 ± 7.3% and LTD protocol: 58.4 ± 6.0% of baseline). In dyskinesia, in the presence of l-DOPA, the indirect pathway exhibited only LTD (LTP protocol: 68.9 ± 21.3% and LTD protocol 52.0 ± 14.2% of baseline), whereas in the direct pathway, only LTP could be induced (LTP protocol: 156.6 ± 13.2% and LTD protocol 166.7 ± 15.8% of baseline). We conclude that normal motor control requires bidirectional plasticity of both striatal outputs, which underlies the symptomatic benefits of l-DOPA. Switching from bidirectional to unidirectional plasticity drives global changes in striatal pathway excitability, and underpins parkinsonism and dyskinesia.

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Purpose Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross‐sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions. Methods A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects. Results The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar. Conclusions The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large‐scale patient studies.