Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

Background Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. Methods Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10‑20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). Results Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. Conclusions These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

The Electrophysiological Correlates of Phoneme Perception in Primary Progressive Aphasia: A Preliminary Case Series

Aims: This study aimed to investigate phoneme perception in patients with primary progressive aphasia (PPA) by using the event-related potential (ERP) technique. These ERP components might contribute to the diagnostic process of PPA and its clinical variants (NFV: nonfluent variant, SV: semantic variant, LV: logopenic variant) and reveal insights about phoneme perception processes in these patients.Method: Phoneme discrimination and categorization processes were investigated by the mismatch negativity (MMN) and P300 in eight persons with early- and late-stage PPA (3 NFV, 2 LV, 2 SV, and 1 PPA-NOS; not otherwise specified) and 30 age-matched healthy adults. The mean amplitude, the onset latency, and the topographic distribution of both components in each patient were compared to the results of the control group.Results: The MMN was absent or the onset latency of the MMN was delayed in the patients with the NFV, LV, and PPA-NOS in comparison to the control group. In contrast, no differences in mean amplitudes and onset latencies of the MMN were found between the patients with the SV and the control group. Concerning the P300, variable results were found in the patients with the NFV, SV, and PPA-NOS, but the P300 of both patients with the LV was delayed and prolonged with increased mean amplitude in comparison to the control group.Conclusion: In this preliminary study, phoneme discrimination deficits were found in the patients with the NFV and LV, and variable deficits in phoneme categorization processes were found in all patients with PPA. In clinical practice, the MMN might be valuable to differentiate the SV from the NFV and the LV and the P300 to differentiate the LV from the NFV and the SV. Further research in larger and independent patient groups is required to investigate the applicability of these components in the diagnostic process and to determine the nature of these speech perception deficits in the clinical variants of PPA.

Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

Background: Early identification of autism spectrum disorder (ASD) provides opportunity for early intervention and improved outcomes. Electroencephalography (EEG) use in infants has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates either during or after the first year may serve as early, accurate indicators of later autism diagnosis. Methods: Using longitudinal EEG data collected during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during phoneme learning) versus 12 months (after phoneme learning), and identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n=14 with later ASD; n= 40 without ASD). Features included a combination of power and nonlinear measures across 10-20 electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Results: Using a combination Pearson correlation feature selection and support vector machine classifier 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12- month data. At 6-months, predictive features were biased to measures from central electrodes, power measures, and measures in the alpha range. At 12-months, predictive features were more distributed between power and nonlinear measures, and biased toward measures in the beta range. Conclusions: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.

The Development of a Paediatric Phoneme Discrimination Test for Arabic Phonemic Contrasts

Objective: The aim of this project was to develop the Arabic CAPT (A-CAPT), a Standard Arabic version of the CHEAR auditory perception test (CAPT) that assesses consonant perception ability in children. Method: This closed-set test was evaluated with normal-hearing children aged 5 to 11 years. Development and validation of the speech materials were accomplished in two experimental phases. Twenty-six children participated in phase I, where the test materials were piloted to ensure that the selected words were age appropriate and that the form of Arabic used was familiar to the children. Sixteen children participated in phase II where test–retest reliability, age effects, and critical differences were measured. A computerized implementation was used to present stimuli and collect responses. Children selected one of four response options displayed on a screen for each trial. Results: Two lists of 32 words were developed with two levels of difficulty, easy and hard. Assessment of test–retest reliability for the final version of the lists showed a strong agreement. A within-subject ANOVA showed no significant difference between test and retest sessions. Performance improved with increasing age. Critical difference values were similar to the British English version of the CAPT. Conclusions: The A-CAPT is an appropriate speech perception test for assessing Arabic-speaking children as young as 5 years old. This test can reliably assess consonant perception ability and monitor changes over time or after an intervention.

Impaired Perception and Neural Processing of Rules in Developmental Dyslexia

Rules and regularities of language are typically processed in an implicit and effortless way in the human brain. Individuals with developmental dyslexia have problems in implicit learning of regularities in sequential stimuli but the neural basis of this deficit has not been studied. This study investigated extraction and utilization of a complex auditory rule at neural and perceptual levels in 18 adults with dyslexia and 20 typical readers. Mismatch negativity (MMN) and P3a responses to rule violations in speech stimuli, reflecting change detection and attention switch, respectively, were recorded with electroencephalogram. Both groups reported no or little explicit awareness of the rule, suggesting implicit processing. People with dyslexia showed deficient extraction of the rule evidenced by diminished MMNs estimated to originate particularly from the left perisylvian region. The group difference persisted in attentive condition after the participants were told about the rule, and behavioral detection of the rule violations was poor in people with dyslexia, possibly suggesting difficulties also in utilizing explicit information of the rule. Based on these results, the speech processing difficulties in dyslexia extend beyond phoneme discrimination and basic auditory feature extraction. Challenges in implicit extraction and effortless adoption of complex auditory rules may be central for language learning difficulties in dyslexia.

Listening to a Dysphonic Speaker in Noise May Impede Children's Spoken Language Processing in a Realistic Classroom Setting

Purpose The aim of this study was to investigate children's processing of dysphonic speech in a realistic classroom setting, under the influence of added classroom noise. Method Typically developing 6-year-old primary school children performed two listening tasks in their regular classrooms: a phoneme discrimination task to assess speech perception and a sentence–picture matching task to assess listening comprehension. Speech stimuli were played back in either a typical or an impaired voice quality. Children performed the tasks in the presence of induced classroom noise at signal-to-noise ratios between +2 and +9 dB. Results Children's performance in the phoneme discrimination task decreased significantly when the speaker's voice was impaired. The effect of voice quality on sentence–picture matching depended on task demands: Easy sentences were processed more accurately in the impaired-voice condition than in the typical-voice condition. Signal-to-noise ratio effects are discussed in light of methodological constraints. Conclusions Listening to a dysphonic teacher in a noisy classroom may impede children's perception of speech, particularly when phonological discrimination is needed to disambiguate the speech input. Future research regarding the interaction of voice quality and task demands is necessary.

Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months

Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved outcomes. Use of electroencephalography (EEG) in infants has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment in ASD, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly within the first postnatal year, so altered neural substrates either during or after the first year may serve as early, accurate indicators of later autism diagnosis. Using longitudinal EEG data collected during a passive phoneme task in infants with high familial risk for ASD, we compared predictive accuracy at 6-months (during phoneme learning) versus 12-months (after phoneme learning). Samples at both ages were matched in size and diagnoses (n=14 with later ASD; n= 40 without ASD). Using Pearson correlation feature selection and support vector machine with radial basis function classifier, 100% predictive diagnostic accuracy was observed at both ages. However, predictive features selected at the two ages differed and came from different scalp locations. We also report that performance across multiple machine learning algorithms was highly variable and declined when the 12-month sample size and behavioral heterogeneity was increased. These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes in order to develop clinically relevant classification algorithms.