Search bioRxiv⌕ Search

Biology subjects

Carreno-Munoz, M. I.

Publications and source records attributed to Carreno-Munoz, M. I..

2 recordsLinked to original sources

Sensory processing dysregulations as reliable translational biomarkers in SYNGAP1 haploinsufficiency

Amongst the numerous genes associated with intellectual disability, SYNGAP1 stands out for its frequency and penetrance of loss-of-function variants found in patients, as well as the wide range of co-morbid disorders associated with its mutation. Most studies exploring the pathophysiological alterations caused by Syngap1 haploinsufficiency in mouse models have focused on cognitive problems and epilepsy, however whether and to what extent sensory perception and processing are altered by Syngap1 haploinsufficiency is less clear. By performing EEG recordings in awake mice, we identified specific alterations in multiple aspects of auditory and visual processing, including increased baseline gamma oscillation power, increased theta/gamma phase amplitude coupling following stimulus presentation and abnormal neural entrainment in response to different sensory modality-specific frequencies. We also report lack of habituation to repetitive auditory stimuli and abnormal deviant sound detection. Interestingly, we found that most of these alterations are present in human patients as well, thus making them strong candidates as translational biomarkers of sensory-processing alterations associated with SYNGAP1/Syngap1 haploinsufficiency.

neuroscience↗

Detecting fine and elaborate movements with piezo sensors, from heartbeat to the temporal organization of behavior

Behavioral phenotyping devices have been successfully used to build ethograms, but studying the temporal dynamics of individual movements during spontaneous, ongoing behavior, remains a challenge. We now report on a novel device, the Phenotypix, which consists in an open-field platform resting on highly sensitive piezoelectric (electro-mechanical) pressure-sensors, with which we could detect the slightest movements from freely moving rats and mice. The combination with video recordings and signal analysis based on time-frequency decomposition, clustering and machine learning algorithms allowed to quantify various behavioral components with unprecedented accuracy, such as individual heartbeats and breathing cycles during rest, shaking in response to pain or fear, and the dynamics of balance within individual footsteps during spontaneous locomotion. We believe that this device represents a significant progress and offers new opportunities for the awaited advance of behavioral phenotyping.

animal behavior and cognition↗