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.

Electroretinography and contrast sensitivity, complementary translational biomarkers of sensory deficits in the visual system of individuals with Fragile X Syndrome

Background: Disturbances in sensory function are considered as an important clinical feature of individuals with neurodevelopmental disorders such as Fragile X syndrome (FXS). Evidence also directly connects sensory abnormalities with the clinical expression of behavioral impairments in individuals with FXS, thus elevating interest in sensory function as a clinical target for therapeutics development. Using electroretinography (ERG) and contrast sensitivity (CS), we previously reported the presence of sensory deficits in the visual system of the Fmr1-/y genetic mouse model of FXS. The goals of this study were two-fold: 1) assess the feasibility of measuring ERG and CS as a biomarker in individuals with FXS, and 2) investigate whether the deficits in ERG and CS originally discovered in Fmr1-/y mice were translatable to humans with FXS. Methods: Both ERGs and CS were measured in a cohort of individuals with FXS (n=20, 18-45 yrs) and age-matched healthy controls (n=20, 18-45 yrs). Under light-adapted conditions, and using both single flash and flicker (repeated train of flashes) stimulation protocols, retinal function was recorded from individual subjects using a portable, handheld, full field flash ERG device (RETeval®, LKC Technologies Inc, Gaithersburg, MD, USA). CS was assessed in each subject using the LEA SYMBOLS® low-contrast test (Good-Lite, Elgin, IL, USA). Results: Data recording was successfully completed for ERG and assessment of CS in most individuals from both cohorts demonstrating the feasibility of these methods for use in the FXS population. Similar to previously reported findings from the Fmr1-/y genetic mouse model of FXS, abnormalities in both ERG waveform and CS were observed in FXS. Conclusions: This study demonstrates the feasibility of using ERG and CS for assessing the visual system in FXS and establishes the translatability of the Fmr1-/y mice phenotype to individuals with FXS. By including electrophysiological and functional readouts, the results of this study suggest the utility of both ERG and CS (ERG-CS) as complimentary translational biomarkers for characterizing sensory abnormalities found in FXS, with potential applications to the clinical development of novel therapeutics that target sensory function abnormalities to treat core symptomatology in FXS.Trial Registration: ID-RCB number 2019-A01015-52 registered on the 05/17/2019.

A simple, high-throughput method of protein and label removal from extracellular vesicle samples

Proposed pipeline to increase of the clinical utility extracellular vesicles (EVs) as translational biomarkers.

Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach

The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.