Neuroscience bulletin最新文献

Synapse organizers are essential for the development, transmission, and plasticity of synapses. Acting as rare synapse suppressors, the MAM domain containing glycosylphosphatidylinositol anchor (MDGA) proteins contributes to synapse organization by inhibiting the formation of the synaptogenic neuroligin-neurexin complex. A previous analysis of MDGA2 mice lacking a single copy of Mdga2 revealed upregulated glutamatergic synapses and behaviors consistent with autism. However, MDGA2 is expressed in diverse cell types and is localized to both excitatory and inhibitory synapses. Differentiating the network versus cell-specific effects of MDGA2 loss-of-function requires a cell-type and brain region-selective strategy. To address this, we generated mice harboring a conditional knockout of Mdga2 restricted to CA1 pyramidal neurons. Here we report that MDGA2 suppresses the density and function of excitatory synapses selectively on pyramidal neurons in the mature hippocampus. Conditional deletion of Mdga2 in CA1 pyramidal neurons of adult mice upregulated miniature and spontaneous excitatory postsynaptic potentials, vesicular glutamate transporter 1 intensity, and neuronal excitability. These effects were limited to glutamatergic synapses as no changes were detected in miniature and spontaneous inhibitory postsynaptic potential properties or vesicular GABA transporter intensity. Functionally, evoked basal synaptic transmission and AMPAR receptor currents were enhanced at glutamatergic inputs. At a behavioral level, memory appeared to be compromised in Mdga2 cKO mice as both novel object recognition and contextual fear conditioning performance were impaired, consistent with deficits in long-term potentiation in the CA3-CA1 pathway. Social affiliation, a behavioral analog of social deficits in autism, was similarly compromised. These results demonstrate that MDGA2 confines the properties of excitatory synapses to CA1 neurons in mature hippocampal circuits, thereby optimizing this network for plasticity, cognition, and social behaviors.

As a noninvasive technique, ultrasound stimulation is known to modulate neuronal activity both in vitro and in vivo. The latest explanation of this phenomenon is that the acoustic wave can activate the ion channels and further impact the electrophysiological properties of targeted neurons. However, the underlying mechanism of low-intensity pulsed ultrasound (LIPUS)-induced neuro-modulation effects is still unclear. Here, we characterize the excitatory effects of LIPUS on spontaneous activity and the intracellular Ca²⁺ homeostasis in cultured hippocampal neurons. By whole-cell patch clamp recording, we found that 15 min of 1-MHz LIPUS boosts the frequency of both spontaneous action potentials and spontaneous excitatory synaptic currents (sEPSCs) and also increases the amplitude of sEPSCs in hippocampal neurons. This phenomenon lasts for > 10 min after LIPUS exposure. Together with Ca²⁺ imaging, we clarified that LIPUS increases the [Ca²⁺]_cyto level by facilitating L-type Ca²⁺ channels (LTCCs). In addition, due to the [Ca²⁺]_cyto elevation by LIPUS exposure, the Ca²⁺-dependent CaMKII-CREB pathway can be activated within 30 min to further regulate the gene transcription and protein expression. Our work suggests that LIPUS regulates neuronal activity in a Ca²⁺-dependent manner via LTCCs. This may also explain the multi-activation effects of LIPUS beyond neurons. LIPUS stimulation potentiates spontaneous neuronal activity by increasing Ca²⁺ influx.

Functional networks (FNs) hold significant promise in understanding brain function. Independent component analysis (ICA) has been applied in estimating FNs from functional magnetic resonance imaging (fMRI). However, determining an optimal model order for ICA remains challenging, leading to criticism about the reliability of FN estimation. Here, we propose a SMART (splitting-merging assisted reliable) ICA method that automatically extracts reliable FNs by clustering independent components (ICs) obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders. We extend SMART ICA to multi-subject fMRI analysis, validating its effectiveness using simulated and real fMRI data. Based on simulated data, the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters. Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects, the resulting reliable group-level FNs are greatly similar between the two cohorts, and interestingly the subject-specific FNs show progressive changes while age increases. Furthermore, both small-scale and large-scale brain FN templates are provided as benchmarks for future studies. Taken together, SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data, while also providing linkages between different FNs.