Deep learning of spontaneous arousal fluctuations detects early cholinergic defects across neurodevelopmental mouse models and patients

Abstract

Neurodevelopmental spectrum disorders like autism (ASD) are diagnosed, on average, beyond age 4 y, after multiple critical periods of brain development close and behavioral intervention becomes less effective. This raises the urgent need for quantitative, noninvasive, and translational biomarkers for their early detection and tracking. We found that both idiopathic (BTBR) and genetic (CDKL5- and MeCP2-deficient) mouse models of ASD display an early, impaired cholinergic neuromodulation as reflected in altered spontaneous pupil fluctuations. Abnormalities were already present before the onset of symptoms and were rescued by the selective expression of MeCP2 in cholinergic circuits. Hence, we trained a neural network (ConvNetACh) to recognize, with 97% accuracy, patterns of these arousal fluctuations in mice with enhanced cholinergic sensitivity (LYNX1-deficient). ConvNetACh then successfully detected impairments in all ASD mouse models tested except in MeCP2-rescued mice. By retraining only the last layers of ConvNetACh with heart rate variation data (a similar proxy of arousal) directly from Rett syndrome patients, we generated ConvNetPatients, a neural network capable of distinguishing them from typically developing subjects. Even with small cohorts of rare patients, our approach exhibited significant accuracy before (80% in the first and second year of life) and into regression (88% in stage III patients). Thus, transfer learning across species and modalities establishes spontaneous arousal fluctuations combined with deep learning as a robust noninvasive, quantitative, and sensitive translational biomarker for the rapid and early detection of neurodevelopmental disorders before major symptom onset.

Keywords: CDKL5 disorder; LYNX1; MECP2; Rett syndrome; transfer learning.

Fig. 1.

Idiopathic and monogenic mouse models of ASD show prolonged and frequent high-arousal states. (A) Schematic of the experiment. Pupil traces are recorded from an awake mouse moving freely on a spherical treadmill, during a 30-min session. (B) Pupil diameter trace from a WT mouse. (C) Example of normalized pupil diameter traces from adult idiopathic (BTBR) and monogenic mouse lines (CDKL5 disorder and Rett syndrome), and their respective controls (C57BL6 and WT littermates). (D) Mean histogram of normalized pupil states across different mice. (E) Autocorrelation of the normalized pupil diameter traces, between ASD model and control mice. Shaded lines indicate SEM. Number of mice: P90 C57BL6 = 8, BTBR = 5, CDKL5^+/y = 5, CDKL5^−/y = 5; P100 MeCP2^+/x = 6, MeCP2^−/x = 8; >P60 MeCP2^+/y = 9, P90 MeCP2^Stop/y = 4.

Fig. 2.

Presymptomatic MeCP2 deficiency is reliably detected by spontaneous arousal oscillations but not by classical pupillary light reflex. (A) Normalized pupil fluctuations in MeCP2-deficient female (MeCP2^−/x, light magenta) and male (MeCP2^Stop/y, light red) mouse models of Rett syndrome at presymptomatic stage (postnatal day P45 and P30, respectively). (B) Mean histogram of normalized pupil states. (C) Autocorrelation traces of normalized pupil states. (D) Schematic of pupillary light reflex. Visible LED is briefly pulsed onto the eye of an awake mouse in total darkness. (E) Minimum pupil diameter and (F) redilation speed for MeCP2-deficient mice with respect to WT littermates (dashed line, WT baseline for each genotype). Each data point indicates average value measured from each mouse. Whiskers indicate the minimum and maximum values of the data, while boxes indicate the SD. Number of mice (each): P45 MeCP2^+/x and MeCP2^−/x = 5; P30 MeCP2^+/y and MeCP2^Stop/y = 7.

Fig. 3.

Altered cholinergic tone prolongs high-arousal states in LYNX1 KO and MeCP2^Stop/y mice. (A) Normalized pupillometry traces of P90 LYNX1 KO mice (cyan), age-matched C57BL6 controls (black), and adult (P60) MeCP2^Stop/y::ChatCre^+/− mice (green), in which MeCP2 expression is rescued only in cholinergic cells. Two MeCP2 control littermates are shown: MeCP2^+/y::ChatCre^+/− positive (black) and MeCP2^Stop/y::ChatCre^−/− null (light red). (B) Mean histogram of normalized pupil states. (C) Autocorrelation traces of normalized pupil states. Number of mice: P90 C57BL6 = 8, LYNX1 KO = 6; P60 MeCP2^+/y::ChatCre^+/− = 5, MeCP2^Stop/y::ChatCre^−/− = 3, MeCP2^Stop/y::ChatCre^+/− = 4.

Fig. 4.

Deep learning detects arousal alterations in mouse models of ASD. (A) Schematic of the model. A convolutional neural network for measuring cholinergic alterations (ConvNetACh), made of 4 convolutional layers and 2 fully connected layers, is fed with 32-s-long pupil traces, randomized in order, and shuffled in type for training (WT, black; LYNX1 KO, cyan). (B) The ConvNet develops receptive fields necessary for the discrimination of control from altered pupil fluctuations. (C) Measure of accuracy and loss during the training process (300 epochs), using 30 WT (∼30,000 traces) and 6 LYNX1 KO mice (∼8,000 traces). (D) Representation of individual learned WT and LYNX1 KO traces generated by the ConvNetACh using the DeepDream algorithm. (E) Representation of the average trace for one WT and LYNX1 KO mouse. (F) Validation of the model. Histogram of the responses of the network when it is fed with new data. Horizontal scale is detection probability of “altered ACh” output (WT = 0; LYNX1 KO = 1). (G) Measurement of cholinergic alteration in mouse models of ASD (circles, individual mice). Kolmogorov–Smirnov nonparametric test: *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Number of mice: P90 LYNX1 KO = 6, All WT = 45, P45 MeCP2^−/x = 5, P100 MeCP2^−/x = 8, P30 MeCP2^Stop/y = 7, P90 MeCP2^Stop/y = 4, P60 MeCP2^Stop/y::ChatCre^−/− = 3, P60 MeCP2^Stop/y::ChatCre^+/− = 4, P90 CDKL5^−/y = 5, P90 BTBR = 5.

Fig. 5.

Early detection of altered spontaneous arousal fluctuations in RTT patients using the neural network ConvNetPatients. (A) Example of normalized traces of spontaneous HRV in TD (black) and RTT (purple) subjects, across their regressive developmental trajectory. (B) Mean histogram of normalized HRV states in TD and RTT subjects. (C) Autocorrelation of normalized HRV traces, in TD and RTT subjects. Autocorrelation in the 0.5- to 3-s regime originates from a moving average in the algorithm that extracts HRV from the electrocardiogram. (D) Prediction of neuromodulatory alteration in TD and RTT subjects, using the ConvNetACh. Number of subjects: TD = 40, RTT Stage II = 15, RTT Stage III = 18; Mann−Whitney U test P values: TD-RTT Stage II ****P = 4.5E-6; TD-RTT Stage III ****P = 8.9E-5; RTT Stage II−RTT Stage III ns = 0.90. Black and pink dashed lines indicate average predictions for WT and P100 MeCP2^+/x mice, respectively. (E) Transfer learning for training ConvNetPatients, consisting of a fast and selective retraining of only the very last layer of ConvNetACh. (F) Training strategy, consisting of training ConvNetPatients from HRV data of a randomly selected number of subjects (pairs of TD and RTT trainers), and validating accuracy on the remaining individuals (validators). (G) Validation accuracy (i.e., percent of TD and RTT patients properly identified by ConvNetPatients as a function of training size). Accuracy is shown in the case of transfer learning from ConvNetACh (green), and in the case of a clear and untrained network (black), repeating the training process for the same number of epochs. For each training size, an equal number of TD and RTT patients were randomly chosen as trainers for ConvNetPatients, which was then tested on the remaining subjects (validators). The random selection was iterated 10 times for each training size (among 40 TD and 35 RTT subjects), in consideration of the variability arising from the choice of trainers. (H) Validation accuracy by age (of validators), evaluated from a training size of 20 TD and RTT trainers each, randomly selected 50 times. Dashed line is fit to data across different age groups.