Auditory repetition suppression alterations in relation to cognitive functioning in fragile X syndrome: a combined EEG and machine learning approach

Time-frequency analyses of repetition suppression and change detection in children with neurofibromatosis type 1

Children with neurofibromatosis type 1 (NF1) are at increased risk of developing cognitive problems, including attention deficits and learning difficulties. Alterations in brain response to repetition and change have been evidenced in other genetic conditions associated with cognitive dysfunctions. Whether the integrity of these fundamental neural responses is compromised in school-aged children with NF1 is still unknown. In this study, we examined the repetition suppression (RS) and change detection responses in children with NF1 (n = 36) and neurotypical controls (n = 41) aged from 4 to 13 years old, using a simple sequence of vowels. We performed time-frequency analyses to compare spectral power and phase synchronization between groups, in the theta, alpha and beta frequency bands. Correlational analyses were performed between the neural responses and the level of intellectual functioning, as well as with behavioral symptoms of comorbid neurodevelopmental disorders measured through parental questionnaires. Children with NF1 showed preserved RS, but increased spectral power in the change detection response. Correlational analyses performed with measures of change detection revealed a negative association between the alpha-band spectral power and symptoms of inattention and hyperactivity. These findings suggest atypical neural response to change in children with NF1. Further studies should be conducted to clarify the interaction with comorbid neurodevelopmental disorders and the possible role of altered inhibitory mechanisms in this enhanced neural response.

Olfactive short-term habituation in children and young people with profound intellectual and multiple disabilities

BACKGROUND

Despite its importance for learning, the existence of the habituation process and its characteristics in people with profound intellectual and multiple disabilities (PIMD) remains understudied. Habituation is, however, considered the simplest form of learning, and a significant neuroadaptive mechanism. Even though habituation occurs in all sensory modalities, the olfactory system is where it manifests itself very visibly.

AIM

This study explores the olfactory short-term habituation abilities of children and young people with PIMD.

METHOD

Twenty children and young people with PIMD (7-18 years) were presented six times successively with a 30-second habituating olfactory stimulus. The interstimulus interval was 15 s. A new odour was presented on the seventh trial. The scenario was carried out two times with two pairs of stimuli. The participants' head alignment duration on the odour was measured.

RESULTS

Seventeen participants out of 20 manifested a decline in response, which reached about 50 % between the first and sixth presentation of the habituation odour. All habituators also showed a distinctive response when exposed to a novel odour. The participants who did not habituate showed a strong, non-fluctuating response to the stimulus throughout the presentations. Three participants only habituated to one of the two habituation stimuli.

CONCLUSION AND IMPLICATIONS

The results raise theoretical, scientific, and practical issues. They question the factors explaining olfactory habituation mechanisms, namely the stimulus properties and the severity of impairment, reveal the need for points of comparison for interpreting this population's responses, and point to the consequences of stimuli repetition and or variety in therapeutic or educational settings for these individuals' learning and cognitive functioning.

Working memory and processing speed abilities are related to habituation and change detection in school-aged children: An ERP study

High cognitive performance is related to efficient brain processing while accomplishing complex cognitive tasks. This efficiency is observed through a rapid engagement of the brain regions and the cognitive processes required for task accomplishment. However, it is unclear if this efficiency is also present in basic sensory processes such as habituation and change detection. We recorded EEG with 85 healthy children (51 males) aged between 4 and 13 years old, while they listened to an auditory oddball paradigm. Cognitive functioning was evaluated using the Weschler Intelligence Scales for Children Fifth Edition and the Weschler Preschool & Primary School for Intelligence Fourth Edition. Auditory evoked potentials (AEPs) analyses and repeated measure analysis of covariance as well as regression models were performed. The analysis revealed that P1 and N1 repetition effects were observed across levels of cognitive functioning. Further, working memory abilities were related to repetition suppression on the auditory P2 component amplitude, while faster processing speed was related to repetition enhancement on the N2 component amplitude. Also, Late Discriminative Negativity (LDN) amplitude, a neural correlate of change detection, increased with working memory abilities. Our results confirm that efficient repetition suppression (i.e. greater reduction in amplitudes with greater levels of cognitive functioning) and more sensitive change detection (greater amplitude changes of the LDN) are related to the level of cognitive functioning in healthy children. More specifically, working memory and processing speed abilities are the cognitive domains related to efficient sensory habituation and change detection.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

EEG repetition and change detection responses in infancy predict adaptive functioning in preschool age: a longitudinal study

Neurodevelopmental disorders (NDDs) are mostly diagnosed around the age of 4-5 years, which is too late considering that the brain is most susceptive to interventions during the first two years of life. Currently, diagnosis of NDDs is based on observed behaviors and symptoms, but identification of objective biomarkers would allow for earlier screening. In this longitudinal study, we investigated the relationship between repetition and change detection responses measured using an EEG oddball task during the first year of life and at two years of age, and cognitive abilities and adaptive functioning during preschool years (4 years old). Identification of early biomarkers is challenging given that there is a lot of variability in developmental courses among young infants. Therefore, the second aim of this study is to assess whether brain growth is a factor of interindividual variability that influences repetition and change detection responses. To obtain variability in brain growth beyond the normative range, infants with macrocephaly were included in our sample. Thus, 43 normocephalic children and 20 macrocephalic children were tested. Cognitive abilities at preschool age were assessed with the WPPSI-IV and adaptive functioning was measured with the ABAS-II. Time-frequency analyses were conducted on the EEG data. Results indicated that repetition and change detection responses in the first year of life predict adaptive functioning at 4 years of age, independently of head circumference. Moreover, our findings suggested that brain growth explains variability in neural responses mostly in the first years of life, so that macrocephalic children did not display repetition suppression responses, while normocephalic children did. This longitudinal study demonstrates that the first year of life is an important period for the early screening of children at risk of developing NDDs.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Genetics, molecular control and clinical relevance of habituation learning

Habituation is the most fundamental form of learning. As a firewall that protects our brain from sensory overload, it is indispensable for cognitive processes. Studies in humans and animal models provide increasing evidence that habituation is affected in autism and related monogenic neurodevelopmental disorders (NDDs). An integrated application of habituation assessment in NDDs and their animal models has unexploited potential for neuroscience and medical care. With the aim to gain mechanistic insights, we systematically retrieved genes that have been demonstrated in the literature to underlie habituation. We identified 258 evolutionarily conserved genes across species, describe the biological processes they converge on, and highlight regulatory pathways and drugs that may alleviate habituation deficits. We also summarize current habituation paradigms and extract the most decisive arguments that support the crucial role of habituation for cognition in health and disease. We conclude that habituation is a conserved, quantitative, cognition- and disease-relevant process that can connect preclinical and clinical work, and hence is a powerful tool to advance research, diagnostics, and treatment of NDDs.

Impact of brain overgrowth on sensorial learning processing during the first year of life

Macrocephaly is present in about 2–5% of the general population. It can be found as an isolated benign trait or as part of a syndromic condition. Brain overgrowth has been associated with neurodevelopmental disorders such as autism during the first year of life, however, evidence remains inconclusive. Furthermore, most of the studies have involved pathological or high-risk populations, but little is known about the effects of brain overgrowth on neurodevelopment in otherwise neurotypical infants. We investigated the impact of brain overgrowth on basic perceptual learning processes (repetition effects and change detection response) during the first year of life. We recorded high density electroencephalograms (EEG) in 116 full-term healthy infants aged between 3 and 11 months, 35 macrocephalic (14 girls) and 81 normocephalic (39 girls) classified according to the WHO head circumference norms. We used an adapted oddball paradigm, time-frequency analyses, and auditory event-related brain potentials (ERPs) to investigate differences between groups. We show that brain overgrowth has a significant impact on repetition effects and change detection response in the 10–20 Hz frequency band, and in N450 latency, suggesting that these correlates of sensorial learning processes are sensitive to brain overgrowth during the first year of life.

Facial Emotions Are Accurately Encoded in the Neural Signal of Those With Autism Spectrum Disorder: A Deep Learning Approach

BACKGROUND

Individuals with autism spectrum disorder (ASD) exhibit frequent behavioral deficits in facial emotion recognition (FER). It remains unknown whether these deficits arise because facial emotion information is not encoded in their neural signal or because it is encodes but fails to translate to FER behavior (deployment). This distinction has functional implications, including constraining when differences in social information processing occur in ASD, and guiding interventions (i.e., developing prosthetic FER vs. reinforcing existing skills).

METHODS

We utilized a discriminative and contemporary machine learning approach-deep convolutional neural networks-to classify facial emotions viewed by individuals with and without ASD (N = 88) from concurrently recorded electroencephalography signals.

RESULTS

The convolutional neural network classified facial emotions with high accuracy for both ASD and non-ASD groups, even though individuals with ASD performed more poorly on the concurrent FER task. In fact, convolutional neural network accuracy was greater in the ASD group and was not related to behavioral performance. This pattern of results replicated across three independent participant samples. Moreover, feature importance analyses suggested that a late temporal window of neural activity (1000-1500 ms) may be uniquely important in facial emotion classification for individuals with ASD.

CONCLUSIONS

Our results reveal for the first time that facial emotion information is encoded in the neural signal of individuals with (and without) ASD. Thus, observed difficulties in behavioral FER associated with ASD likely arise from difficulties in decoding or deployment of facial emotion information within the neural signal. Interventions should focus on capitalizing on this intact encoding rather than promoting compensation or FER prostheses.

Developmental course of the repetition effect and change detection responses from infancy through childhood: a longitudinal study

Neuronal repetition effect (repetition suppression and repetition enhancement) and change detection responses are fundamental brain responses that have implications in learning and cognitive development in infants and children. Studies have shown altered neuronal repetition and change detection responses in various clinical populations. However, the developmental course of these neuronal responses from infancy through childhood is still unknown. Using an electroencephalography oddball task, we investigate the developmental peculiarities of repetition effect and change detection responses in 43 children that we followed longitudinally from 3 months to 4 years of age. Analyses were conducted on theta (3-5 Hz), alpha (5-10 Hz), and beta (10-30 Hz) time-frequency windows. Results indicated that in the theta time-frequency window, in frontocentral and frontal regions of the brain, repetition and change detection responses followed a U-shaped pattern from 3 months to 4 years of age. Moreover, the change detection response was stronger in young infants compared to older children in frontocentral regions, regardless of the time-frequency window. Our findings add to the evidence of top-down modulation of perceptual systems in infants and children.

Infant repetition effects and change detection: Are they related to adaptive skills?

Repetition effects and change detection response have been proposed as neuro-electrophysiological correlates of fundamental learning processes. As such, they could be a good predictor of brain maturation and cognitive development. We recorded high density EEG in 71 healthy infants (32 females) aged between 3 and 9 months, while they listened to vowel sequences (standard /a/a/a/i/ [80%] and deviant /a/a/a/a/ [20%]). Adaptive skills, a surrogate of cognitive development, were measured via the parent form of the Adaptive Behavior Assessment System Second Edition (ABAS-II). Cortical auditory-evoked potentials (CAEPs) analyses, time-frequency analyses and a statistical approach using linear mixed models (LMMs) and linear regression models were performed. Age and adaptive skills were tested as predictors. Age modulation of repetition effects and change detection response was observed in theta (3-5 Hz), alpha (5-10 Hz) and high gamma (80-90 Hz) oscillations and in all CAEPs. Moreover, adaptive skills modulation of repetition effects was evidenced in theta (3-5 Hz), high gamma oscillations (80-90 Hz), N250/P350 peak-to-peak amplitude and P350 latency. Finally, adaptive skills modulation of change detection response was observed in the N250/P350 peak-to-peak amplitude. Our results confirm that repetition effects and change detection response evolve with age. Moreover, our results suggest that repetition effects and change detection response vary according to adaptive skills displayed by infants during the first year of life, demonstrating their predictive value for neurodevelopment.

Early differences in auditory processing relate to Autism Spectrum Disorder traits in infants with Neurofibromatosis Type I

BACKGROUND

Sensory modulation difficulties are common in children with conditions such as Autism Spectrum Disorder (ASD) and could contribute to other social and non-social symptoms. Positing a causal role for sensory processing differences requires observing atypical sensory reactivity prior to the emergence of other symptoms, which can be achieved through prospective studies.

METHODS

In this longitudinal study, we examined auditory repetition suppression and change detection at 5 and 10 months in infants with and without Neurofibromatosis Type 1 (NF1), a condition associated with higher likelihood of developing ASD.

RESULTS

In typically developing infants, suppression to vowel repetition and enhanced responses to vowel/pitch change decreased with age over posterior regions, becoming more frontally specific; age-related change was diminished in the NF1 group. Whilst both groups detected changes in vowel and pitch, the NF1 group were largely slower to show a differentiated neural response. Auditory responses did not relate to later language, but were related to later ASD traits.

CONCLUSIONS

These findings represent the first demonstration of atypical brain responses to sounds in infants with NF1 and suggest they may relate to the likelihood of later ASD.

Increased aperiodic gamma power in young boys with Fragile X Syndrome is associated with better language ability

Background

The lack of robust and reliable clinical biomarkers in Fragile X Syndrome (FXS), the most common inherited form of intellectual disability, has limited the successful translation of bench-to-bedside therapeutics. While numerous drugs have shown promise in reversing synaptic and behavioral phenotypes in mouse models of FXS, none have demonstrated clinical efficacy in humans. Electroencephalographic (EEG) measures have been identified as candidate biomarkers as EEG recordings of both adults with FXS and mouse models of FXS consistently exhibit alterations in resting state and task-related activity. However, the developmental timing of these EEG differences is not known as thus far EEG studies have not focused on young children with FXS. Further, understanding how EEG differences are associated with core symptoms of FXS is crucial to successful use of EEG as a biomarker, and may improve our understanding of the disorder.

Methods

Resting-state EEG was collected from FXS boys with full mutation of Fmr1 (2.5–7 years old, n = 11) and compared with both age-matched (n = 12) and cognitive-matched (n = 12) typically developing boys. Power spectra (including aperiodic and periodic components) were compared using non-parametric cluster-based permutation testing. Associations between 30 and 50 Hz gamma power and cognitive, language, and behavioral measures were evaluated using Pearson correlation and linear regression with age as a covariate.

Results

FXS participants showed increased power in the beta/gamma range (~ 25–50 Hz) across multiple brain regions. Both a reduction in the aperiodic (1/f) slope and increase in beta/gamma periodic activity contributed to the significant increase in high-frequency power. Increased gamma power, driven by the aperiodic component, was associated with better language ability in the FXS group. No association was observed between gamma power and parent report measures of behavioral challenges, sensory hypersensitivities, or adaptive behaviors.

Limitations

The study sample size was small, although comparable to other human studies in rare-genetic disorders. Findings are also limited to males in the age range studied.

Conclusions

Resting-state EEG measures from this study in young boys with FXS identified similar increases in gamma power previously reported in adults and mouse models. The observed positive association between resting state aperiodic gamma power and language development supports hypotheses that alterations in some EEG measures may reflect ongoing compensatory mechanisms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-021-00425-x.

EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome

Introduction: Fragile X syndrome (FXS) is a genetic disorder caused by a mutation of the fragile X mental retardation 1 gene (FMR1). FXS is associated with neurophysiological abnormalities, including cortical hyperexcitability. Alterations in electroencephalogram (EEG) resting-state power spectral density (PSD) are well-defined in FXS and were found to be linked to neurodevelopmental delays. Whether non-linear dynamics of the brain signal are also altered remains to be studied. Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5-28 years), and 7 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site. Results: PSD results confirmed the increased gamma, decreased alpha power and APF in FXS participants compared to NT controls. No alterations in TBR were found. Importantly, results revealed reduced signal complexity in FXS participants, specifically in higher scales, suggesting that altered signal complexity is sensitive to brain alterations in this population. The replication study mostly confirmed these results and suggested critical points of stagnation in the neurodevelopmental curve of FXS. Conclusion: Signal complexity is a powerful feature that can be added to the electrophysiological biomarkers of brain maturation in FXS.

Distinct patterns of repetition suppression in Fragile X syndrome, down syndrome, tuberous sclerosis complex and mutations in SYNGAP1

Sensory processing is the gateway to information processing and more complex processes such as learning. Alterations in sensory processing is a common phenotype of many genetic syndromes associated with intellectual disability (ID). It is currently unknown whether sensory processing alterations converge or diverge on brain responses between syndromes. Here, we compare for the first time four genetic conditions with ID using the same basic sensory learning paradigm. One hundred and five participants, aged between 3 and 30 years old, composing four clinical ID groups and one control group, were recruited: Fragile X syndrome (FXS; n = 14), tuberous sclerosis complex (TSC; n = 9), Down syndrome (DS; n = 19), SYNGAP1 mutations (n = 8) and Neurotypical controls (NT; n = 55)). All groups included female and male participants. Brain responses were recorded using electroencephalography (EEG) during an audio-visual task that involved three repetitions of the pronunciation of the phoneme /a/. Event Related Potentials (ERP) were used to: 1) compare peak-to-peak amplitudes between groups, 2) evaluate the presence of repetition suppression within each group and 3) compare the relative repetition suppression between groups. Our results revealed larger overall amplitudes in FXS. A repetition suppression (RS) pattern was found in the NT group, FXS and DS, suggesting spared repetition suppression in a multimodal task in these two ID syndromes. Interestingly, FXS presented a stronger RS on one peak-to-peak value in comparison with the NT. The results of our study reveal the distinctiveness of ERP and RS brain responses in ID syndromes. Further studies should be conducted to understand the molecular mechanisms involved in these patterns of responses.

Development of Neural Response to Novel Sounds in Fragile X Syndrome: Potential Biomarkers

Auditory processing abnormalities in fragile X syndrome (FXS) may contribute to difficulties with language development, pattern identification, and contextual updating. Participants with FXS (N = 41) and controls (N = 27) underwent auditory event-related potentials during presentation of an oddball paradigm. Data was adequate for analysis for 33 participants with FXS and 27 controls (age 4-51 y, 13 females [FXS]; 4-54 y, 11 females [control]). Participants with FXS showed larger N1 and P2 amplitudes, abnormal lack of modulation of P1 and P2 amplitudes and P2 latency in response to oddball stimuli ) relative to controls: Females with FXS were more similar to controls. Participants with FXS showed a marginal speeding of the P2 latency, suggesting potentiation to oddball stimuli rather than habituation. Participants with FXS showed a heightened N1 habituation effect compared to controls. Gamma power was significantly higher for participants with FXS. Groups did not differ on mismatch negativity. Both controls and participants with FXS showed similar developmental trajectories in P1 and N1 amplitude, P2 latency, and gamma power, but not for P2 amplitude. One month retest analyses performed in 14 participants suggest strong test-retest reliability for most measures. Individuals with FXS show previously demonstrated increased response amplitude and high frequency neural activity. Despite an overall normal developmental trajectory for most measures, individuals with FXS show age-independent but gender-dependent decreases in complex processing of novel stimuli. Many markers show strong retest reliability even in children and thus are potential biomarkers for clinical trials in FXS.

Beneficial effects of sound exposure on auditory cortex development in a mouse model of Fragile X Syndrome

BACKGROUND

Fragile X syndrome (FXS) is the most common genetic cause of autism and intellectual disability. Fragile X mental retardation gene (Fmr1) knock-out (KO) mice display core deficits of FXS, including abnormally increased sound-evoked responses, and show a delayed development of parvalbumin (PV) cells. Here, we present the surprising result that sound exposure during early development reduces correlates of auditory hypersensitivity in Fmr1 KO mice.

METHODS

Fmr1 KO and wild-type (WT) mice were raised in a sound-attenuated environment (AE) or sound-exposed (SE) to 14 kHz tones (5 Hz repetition rate) from P9 until P21. At P21-P23, event-related potentials (ERPs), dendritic spine density, PV expression and phosphorylation of tropomyosin receptor kinase B (TrkB) were analyzed in the auditory cortex of AE and SE mice.

RESULTS

Enhanced N1 amplitude of ERPs, impaired PV cell development, and increased spine density in layers (L) 2/3 and L5/6 excitatory neurons were observed in AE Fmr1 KO compared to WT mice. In contrast, developmental sound exposure normalized ERP N1 amplitude, density of PV cells and dendritic spines in SE Fmr1 KO mice. Finally, TrkB phosphorylation was reduced in AE Fmr1 KO, but was enhanced in SE Fmr1 KO mice, suggesting that BDNF-TrkB signaling may be regulated by sound exposure to influence PV cell development.

CONCLUSIONS

Our results demonstrate that sound exposure, but not attenuation, during early developmental window restores molecular, cellular and functional properties in the auditory cortex of Fmr1 KO mice, and suggest this approach as a potential treatment for sensory phenotypes in FXS.

Intellectual disability and autism spectrum disorders 'on the fly': insights from Drosophila

Intellectual disability (ID) and autism spectrum disorders (ASD) are frequently co-occurring neurodevelopmental disorders and affect 2-3% of the population. Rapid advances in exome and genome sequencing have increased the number of known implicated genes by threefold, to more than a thousand. The main challenges in the field are now to understand the various pathomechanisms associated with this bewildering number of genetic disorders, to identify new genes and to establish causality of variants in still-undiagnosed cases, and to work towards causal treatment options that so far are available only for a few metabolic conditions. To meet these challenges, the research community needs highly efficient model systems. With an increasing number of relevant assays and rapidly developing novel methodologies, the fruit fly Drosophila melanogaster is ideally positioned to change gear in ID and ASD research. The aim of this Review is to summarize some of the exciting work that already has drawn attention to Drosophila as a model for these disorders. We highlight well-established ID- and ASD-relevant fly phenotypes at the (sub)cellular, brain and behavioral levels, and discuss strategies of how this extraordinarily efficient and versatile model can contribute to 'next generation' medical genomics and to a better understanding of these disorders.

Conserved regulation of neurodevelopmental processes and behavior by FoxP in Drosophila

FOXP proteins form a subfamily of evolutionarily conserved transcription factors involved in the development and functioning of several tissues, including the central nervous system. In humans, mutations in FOXP1 and FOXP2 have been implicated in cognitive deficits including intellectual disability and speech disorders. Drosophila exhibits a single ortholog, called FoxP, but due to a lack of characterized mutants, our understanding of the gene remains poor. Here we show that the dimerization property required for mammalian FOXP function is conserved in Drosophila. In flies, FoxP is enriched in the adult brain, showing strong expression in ~1000 neurons of cholinergic, glutamatergic and GABAergic nature. We generate Drosophila loss-of-function mutants and UAS-FoxP transgenic lines for ectopic expression, and use them to characterize FoxP function in the nervous system. At the cellular level, we demonstrate that Drosophila FoxP is required in larvae for synaptic morphogenesis at axonal terminals of the neuromuscular junction and for dendrite development of dorsal multidendritic sensory neurons. In the developing brain, we find that FoxP plays important roles in α-lobe mushroom body formation. Finally, at a behavioral level, we show that Drosophila FoxP is important for locomotion, habituation learning and social space behavior of adult flies. Our work shows that Drosophila FoxP is important for regulating several neurodevelopmental processes and behaviors that are related to human disease or vertebrate disease model phenotypes. This suggests a high degree of functional conservation with vertebrate FOXP orthologues and established flies as a model system for understanding FOXP related pathologies.