Resequencing of global Lotus corniculatus accessions reveals population distribution and genetic loci, associated with cyanogenic glycosides accumulation and growth traits.

BACKGROUND: Lotus corniculatus is a widely distributed perennial legume whose great adaptability to different environments and resistance to barrenness make it an excellent forage and ecological restoration plant. However, its molecular genetics and genomic relationships among populations are yet to be uncovered. RESULT: Here we report on a genomic variation map from worldwide 272 L. corniculatus accessions by genome resequencing. Our analysis suggests that L. corniculatus accessions have high genetic diversity and could be further divided into three subgroups, with the genetic diversity centers were located in Transcaucasia. Several candidate genes and SNP site associated with CNglcs content and growth traits were identified by genome-wide associated study (GWAS). A non-synonymous in LjMTR was responsible for the decreased expression of CNglcs synthesis genes and LjZCD was verified to positively regulate CNglcs synthesis gene CYP79D3. The LjZCB and an SNP in LjZCA promoter were confirmed to be involved in plant growth. CONCLUSION: This study provided a large number of genomic resources and described genetic relationship and population structure among different accessions. Moreover, we attempt to provide insights into the molecular studies and breeding of CNglcs and growth traits in L. corniculatus.

Subthalamic Nucleus Deep Brain Stimulation Modulates 2 Distinct Neurocircuits.

OBJECTIVE: Current understanding of the neuromodulatory effects of deep brain stimulation (DBS) on large-scale brain networks remains elusive, largely due to the lack of techniques that can reveal DBS-induced activity at the whole-brain level. Using a novel 3T magnetic resonance imaging (MRI)-compatible stimulator, we investigated whole-brain effects of subthalamic nucleus (STN) stimulation in patients with Parkinson disease. METHODS: Fourteen patients received STN-DBS treatment and participated in a block-design functional MRI (fMRI) experiment, wherein stimulations were delivered during "ON" blocks interleaved with "OFF" blocks. fMRI responses to low-frequency (60Hz) and high-frequency(130Hz) STN-DBS were measured 1, 3, 6, and 12 months postsurgery. To ensure reliability, multiple runs (48 minutes) of fMRI data were acquired at each postsurgical visit. Presurgical resting-state fMRI (30 minutes) data were also acquired. RESULTS: Two neurocircuits showed highly replicable, but distinct responses to STN-DBS. A circuit involving the globus pallidus internus (GPi), thalamus, and deep cerebellar nuclei was significantly activated, whereas another circuit involving the primary motor cortex (M1), putamen, and cerebellum showed DBS-induced deactivation. These 2 circuits were dissociable in terms of their DBS-induced responses and resting-state functional connectivity. The GPi circuit was frequency-dependent, selectively responding to high-frequency stimulation, whereas the M1 circuit was responsive in a time-dependent manner, showing enhanced deactivation over time. Finally, activation of the GPi circuit was associated with overall motor improvement, whereas M1 circuit deactivation was related to reduced bradykinesia. INTERPRETATION: Concurrent DBS-fMRI using 3T revealed 2 distinct circuits that responded differentially to STN-DBS and were related to divergent symptoms, a finding that may provide novel insights into the neural mechanisms underlying DBS. ANN NEUROL 2020;88:1178-1193.

Focused ultrasound thalamotomy for tremor treatment impacts the cerebello-thalamo-cortical network.

High-intensity Magnetic Resonance-guided Focused Ultrasound (MRgFUS) is a recent, non-invasive line of treatment for medication-resistant tremor. We used MRgFUS to produce small lesions in the thalamic ventral intermediate nucleus (VIM), an important node in the cerebello-thalamo-cortical tremor network, in 13 patients with tremor-dominant Parkinson's disease or essential tremor. Significant tremor alleviation in the target hand ensued (t(12) = 7.21, p < 0.001, two-tailed), which was strongly associated with the functional reorganization of the brain's hand region with the cerebellum (r = 0.91, p < 0.001, one-tailed). This reorganization potentially reflected a process of normalization, as there was a trend of increase in similarity between the hand cerebellar connectivity of the patients and that of a matched, healthy control group (n = 48) after treatment. Control regions in the ventral attention, dorsal attention, default, and frontoparietal networks, in comparison, exhibited no association with tremor alleviation and no normalization. More broadly, changes in functional connectivity were observed in regions belonging to the motor, limbic, visual, and dorsal attention networks, largely overlapping with regions connected to the lesion targets. Our results indicate that MRgFUS is a highly efficient treatment for tremor, and that lesioning the VIM may result in the reorganization of the cerebello-thalamo-cortical tremor network.

Reconstructing lost BOLD signal in individual participants using deep machine learning.

Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles governing BOLD activity in one dataset and reconstruct artificially compromised regions in an independent dataset, frame by frame. Intriguingly, BOLD time series extracted from reconstructed frames are correlated with the original time series, even though the frames do not independently carry any temporal information. Moreover, reconstructed functional connectivity maps exhibit good correspondence with the original connectivity maps, indicating that the model recovers functional relationships among brain regions. We replicated this result in two healthy datasets and in patients whose scans suffered signal loss due to intracortical electrodes. Critically, the reconstructions capture individual-specific information. Deep machine learning thus presents a unique opportunity to reconstruct compromised BOLD signal while capturing features of an individual's own functional brain organization.