 symptoms in early RTT trials and relevant studies in animal models [29][30][31][32][33][34][35].
These results have led to multiple clinical trials involving IGF1 and analogues, such as trofinetide [36] and recombinant IGF1. Safety trials have shown no effects on seizures, as patients treated before with anti-epileptic drugs (AED) tolerated the treatment, and it was not necessary to adjust the dosage [37]. Trials of IGF1 have shown conflicting results. An early trial demonstrated safety and clinical efficacy [37,38]. A follow-up phase 2 trial did not show clear benefit in the primary outcome measures; however, there were improvements in some secondary endpoints related to behaviour and communication, while some other measures worsened, and spectral power on EEG was shown to change over time [22]. Notably, these trials showed marked heterogeneity in treatment response, despite adequate subject matching at baseline. This response variability suggests that there may be a subpopulation of treatment-susceptible individuals which is not identifiable by routine clinical assessments.

Targeting Networks in Rett Syndrome
Considering that IGF1 can be beneficial to some individuals with RTT, and that IGF1 can modulate the maturation of cortical circuitry [39] and dendritic spine plasticity [35], we hypothesised that treatment with IGF1 would impact network profiles in RTT, and that there may be a relationship between network activity and clinical response. Further, we hypothesised that the characterisation of the network architecture at baseline in individuals with RTT would make it possible to identify treatment-susceptible patients and to predict treatment response.
To test our hypotheses, we used noninvasive EEG recording to investigate network features and their association with clinical response to IGF1. We found an association between IGF1 administration and specific changes in network architecture, and between network activity and clinical response to treatment. Based on the results obtained in this small group, we conclude that analyses of specific network activity parameters may identify treatment-responsive RTT subpopulations which would not be evident from clinical assessments. These findings have implications for the development of new treatments for RTT if borne out in larger cohorts.

Subjects
Patients were recruited from the Tuscany Rett Centre, Viareggio, Italy. All studies were conducted in accordance with the Declaration of Helsinki and approved by the Tuscany Child Ethical Committee (Approval ID: 12720). Patients' families consented to data collection and use for scientific purposes.
Ten individuals were recruited for a trial with recombinant human IGF1, as described by   [37]. One participant was excluded from the present analysis due to an incomplete electrophysiological dataset, so nine patients were analysed in this study. All participants had a classic phenotype and a verified pathogenic MECP2 mutation. Matched individuals with RTT were recruited as controls. These patients did not receive any investigational treatment, i.e., neither drug nor placebo. All participants were subjected to the same schedule of assessments and continued to receive standard treatment.

IGF1 Administration
Subcutaneous human recombinant IGF1 (mecasermin) was administered twice daily for 20-24 weeks. Six patients described in this study were treated with IGF1 for 24 weeks. Four additional patients were treated for 20 weeks [37]. There was no correlation between the duration of the treatment and outcomes. For all patients, a dose of 0.05 mg/kg was administered during the first and last weeks, and a dose of 0.1 mg/kg during the intervening weeks. This dose is in line with that recommended for IGF1's endocrine indications, and safety at this dosing level has been previously demonstrated [38]. For one of the patients treated with IGF1, the EEG recording were not suitable for the present study; hence, the study is based on data from nine patients.

Data Collection
Subjects were evaluated clinically and from EEGs performed at baseline (pretreatment) and at 12 months after treatment initiation.

Clinical Characterisation
Disease severity was assessed clinically using the International Scoring System (ISS) [40,41] at baseline and at 12 months. Clinical assessments were performed by two independent assessors and averaged for each patient. Clinical assessment scores were used to match IGF1 treated and untreated groups at baseline and for evaluating changes following treatment.
The treated group was subdivided into responders or nonresponders on the basis of changes in severity score from baseline to end of treatment (Table 1). Treatment response was defined as any reduction in ISS scores from baseline to 12 months, as determined by two independent assessors.

Electrophysiological Recordings
Electroencephalographic (EEG) data were recorded using an eight-electrode montage. Recordings were made for a minimum of 20 min on awake subjects, at rest in a dedicated recording environment with minimal sensory stimulation, following a period of adaptation to the environment. EEG data were preprocessed in order to ensure accurate feature extraction and to eliminate artefacts, including those created by motion and epileptiform activity. Details on electrode montage and data processing are given in Appendix B.

EEG Feature Extraction
Profiles of electrophysiological features were derived from recorded EEG data using custom MatLab scripts as described in Keogh et al. 2018 [23]. All scripts used in this analysis are available at https://github.com/conorkeogh/IGF_Response/.

Spectral Power
Power spectra were calculated using Fourier Transforms of preprocessed EEG data. Overall power, measured in decibels, was assessed using the mean of individual channel spectra. Individual frequency bands were assessed by isolating these bands and calculating relative power normalised to overall power [42].

Profiles
Distribution of activity was assessed by evaluating the relative activity at each hemisphere. This was calculated by subtracting spectral power measures in the right hemisphere from those in the left. Detailed profiles of power distribution were derived by analysing power across the whole hemisphere, at each individual electrode and within frequency bands.

Network Measures
Network function was analysed by evaluating interactions between electrodes [43]. Measures of interelectrode coherence were derived for each electrode pair through calculation of the cross-spectrum of the channels normalised by the power spectra of both channels, repeated for each unique pair: Dimensionality reduction of connectivity measures was performed via principal component analysis [44]. Network architectures were assessed and compared between groups by examining the covariance of interelectrode coherence measures and associated loadings of network measures (i.e., the principal components of the coherence measures) [45]. This allowed the overall architecture of cortical networks to be compared between groups while limiting the number of statistical tests required. Pairwise comparisons of individual electrode pairs were performed on a post hoc, exploratory basis to better characterise the nature of any differences seen in comparisons of principal components.
Patient recruitment and clinical evaluations were performed by personnel who were not involved in EEG analysis. Processing and feature extraction algorithms were applied to all recordings without consideration of clinical features, disease severity, treatment status, genetics or any other clinical parameter.
All data processing was performed blinded to patient characteristics.

Statistical Analyses
The effect of IGF1 treatment was investigated using a two-way, mixed-factor, repeated-measure ANOVA to assess whether an interaction between treatment group and timepoint existed. Nonparametric statistical tests were used in subsequent post hoc analyses to avoid assumptions of normality. Mann-Whitney U tests were used for pairwise comparisons. Wilcoxon signed-rank tests were employed for paired data in within-group analyses. All tests were two-tailed.
All statistics are reported as mean +/− standard deviation.

Effect of Treatment
Treated (n = 9) and untreated n = 9) groups were compared. Treatment status (treated vs. untreated) was used as a between-group measure, with timepoint (baseline vs. twelve months) as a within-subjects factor. The treated group was then subdivided into responders (n = 5) and nonresponders (n = 4), and these subgroups compared in order to assess whether electrophysiological measures varied with clinical response.

Predicting Treatment Response
Machine learning techniques were used to assess the predictive ability of EEG parameters identified in those who responded to IGF1 treatment.

Model Training
A support vector machine classifier was trained on all network features identified as differing to a statistically significant degree between responders and nonresponders before treatment on post hoc analysis. These features were used to define the planes of a potential biomarker feature space [46]. A training algorithm identified the plane within this space that best separated the two groups. This was then used to classify further data.

Validation
A five-fold cross validation approach was used to validate the model and to assess its performance when presented with a new set of data, as in a clinical scenario [47]. The data for the treated group was randomly partitioned into five subsets (i.e., two subjects were randomly excluded from model training and used for testing at each iteration). A classifier was trained on four subsets, and the fifth, not involved in model training, was used to assess the classifier's prediction accuracy. This was then repeated, with a model trained on each set of four subsets and tested on the remaining subset, and the overall accuracy of the modelling approach was tested.

Sequential Elimination
Model training and validation was repeated, eliminating the network feature with the least statistically significant difference from the model at each repetition; the resulting accuracies were then assessed. This allowed us to identify the features driving the predictive ability of the machine learning classifier. This could then be used to determine the minimum dataset required to accurately predict treatment response, and to provide insight into the mechanisms underlying this response.

Demographics
The overall group had a median age of 6 years (interquartile range 6), with no statistically significant difference between subgroups (treated: 4, IQR 5; untreated: 8, IQR 7; p = 0.35, Mann-Whitney U test). The overall group had a mean clinical severity score (