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Perez, A. T.

Publications and source records attributed to Perez, A. T..

2 recordsLinked to original sources

Synaptic Dysfunction and Compensation After NMDA Receptor Ablation in the Mouse Medial Prefrontal Cortex

N-methyl-D-aspartate receptors (NMDARs) in the prefrontal cortex (PFC) are critical regulators of neuronal excitability, synaptic plasticity, and cognitive function. NMDAR disruptions, including pharmacological blockade and anti-NMDAR encephalitis, can mimic symptoms of schizophrenia. These observations support the glutamate hypothesis of schizophrenia, which posits that symptoms arise from abnormal corticolimbic glutamatergic signaling. Further evidence for this theory includes abnormal expression of NMDARs and decreased dendritic spine density in the PFC of individuals with schizophrenia, as well as altered spine density and synaptic transmission caused by genetic manipulation of NMDARs. However, it is unknown how progressive loss of NMDAR function in the PFC during adolescence - a developmental time period associated with significant synaptic pruning and symptom onset in schizophrenia - affects excitatory synaptic structure and function. In this study, we used in vivo genome editing to ablate expression of the Grin1 gene, which encodes the obligate GluN1 subunit of NMDARs, in medial PFC neurons of female and male adolescent mice. We assessed synaptic density and function in layer V pyramidal neurons at multiple time points using whole-cell patch-clamp electrophysiology, integrated with confocal imaging of dendritic spine architecture in recorded neurons. NMDAR ablation caused an early decrease in basilar dendritic spine density, followed by a rebound in spine density and corresponding increase in AMPAR-mediated synaptic transmission, suggesting that synaptic compensation maintains an allostatic set point. Our findings demonstrate that NMDAR ablation initially disrupts local PFC networks, followed by recovery via compensatory processes that could be impaired in disease states.

neuroscience↗

A method for analyzing physiological data with multiple non-independent observations

Physiological studies often involve recording multiple observations (e.g., repeated muscle contractions or cardiac cycles) from the same subject. To compare groups of subjects, observations from one subject are then sometimes pooled with observations from other subjects in the same group, and analyses are performed on these pooled data. This approach presents a number of potential problems, including over-or under-representing certain subjects in the sample and non-independence of measurements. There are statistical methods available to deal with repeated measures, but many make assumptions about the distribution and completeness of data. Instead, we developed a method to deal with multiple observations from physiological data that ensures that each subject is represented only once in the analysis (i.e. it avoids pseudoreplication), and makes no assumptions about the distribution of the data. We demonstrate this method using two different physiological datasets: (1) muscle recordings taken from Drosophila melanogaster larvae during fictive crawling, and (2) electrocardiogram recordings taken from human volunteers before, during, and after exercise. Our results show the broad applicability and validity of this method.

physiology↗