INTRODUCTION RESULTS METHODS CONCLUSIONS Research partially supported by CONICET, CONICYT/FONDECYT

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Transcript of INTRODUCTION RESULTS METHODS CONCLUSIONS Research partially supported by CONICET, CONICYT/FONDECYT

  • INTRODUCTION

    RESULTSCONCLUSIONS

    www.fundacionfavaloro.orgwww.fundacionineco.orgwww.ineco.org.arMMN results: The cluster analysis identified significantly larger early MMN activation generated by deviants in the three groups. Likewise, no significant differences were found between groups in this deviant condition in the groups. These results suggest that prediction error generated in the early stages of auditory processing is normally developed in control participants and both patient groups.

    1.Friston, K., Prediction, perception and agency. Int J Psychophysiol, 2012. 83(2): p. 248-52.2.Fogelson, N., et al., The functional anatomy of schizophrenia: A dynamic causal modeling study of predictive coding. Schizophr Res, 2014. 158(1-3): p. 204-12.3.Limongi, R., A. Tomio, and A. Ibanez, Dynamical predictions of insular hubs for social cognition and their application to stroke. Front Behav Neurosci, 2014. 8.4.Chennu, S., et al., Expectation and attention in hierarchical auditory prediction. J Neurosci, 2013. 33(27): p. 11194-205.5.Bekinschtein, T.A., et al., Neural signature of the conscious processing of auditory regularities. Proc Natl Acad Sci U S A, 2009. 106(5): p. 1672-7.6.Oostenveld, R., et al., FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci, 2011. 2011: p. 156869.7.Tadel, F., et al., Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci, 2011. 2011: p. 879716.

    Predictive coding in Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder

    Gonzalez-Gadea, Maria Luz1,2,3#; Chennu, Srivas 4, 5#; Bekinschtein, Tristan A.,6,5;Rattazzi, Alexia7; Beraudi, Ana1; Trippichio, Paula1; Moyano, Beatriz8; Soffita, Yamila 9,8; Steinberg, Laura9; Adolfi, Federico1;Sigman, Mariano10;Marino, Julian12; Manes, Facundo1,2,3,13;Ibanez, Agustin1,2,3,11,13

    1. Institute of Cognitive Neurology (INECO); 2. National Scientific and Technical Research Council (CONICET).; 3. UDP-INECO Foundation Core on Neuroscience (UIFCoN) Diego Portales University, Santiago, Chile ; 4. Department of Clinical Neurosciences, University of Cambridge, UK.; 5. Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK; 6. Department of Psychology, University of Cambridge, UK; 7. Programa Argentino para Nios, Adolescentes y Adultos con Condiciones del Espectro Autista (PANAACEA), Buenos Aires, Argentina.; 8. Centro Interdisciplinario de Tourette, TOC , TDAH , y Trastornos Asociados ( CITTTA), Buenos Aires, Argentina.; 9. Institute of Neurosciences, FavaloroUniversity, Buenos Aires, Argentina; 10. Torcuato di Tella University, Buenos Aires,Argentina; 11. Universidad Autonoma del Caribe, Barranquilla, Colombia; 12. Facultad de Psicologia. Universidad Nacional de Cordoba.; 13. Centre of Excellence in Cognition and its Disorders, Australian Research Council (ACR), New South Wales, Australia.

    Experimental task:

    Auditory stimuli consisted of sequences of five complex 50-ms-duration sounds spaced 150 ms apart. Three sequences of complex sounds were included (see Figure 1): (1) standard sequences that contained five identical tones (AAAAA or BBBBB); (2) expected deviant sequences (monaural) which included four identical sounds and a tone of the other type (AAAAB or BBBBA); and (3) unexpected deviant sequences (interaural) in which all tones were identical, but the first four were presented in one ear and the fifth tone was presented in the opposite ear (AAAAA or BBBBB).

    Figure 3: Source reconstruction for expected deviant sequences. Figure 4: Top-down expectation for unexpected deviant sequences indexed by the P300. Predictive coding has emerged as a framework to disentangle the neural processes underlying cognitive impairments in neuropsychiatric disorders [1-3]. Although expectation biases favor anticipated task-relevant stimuli in neurotypical subjects [4], this process could be affected in individuals with top-down processing abnormalities, such as Autism Spectrum Disorders (ASD) or Attention-Deficit/Hyperactivity Disorder (ADHD). In this study, we assessed the influences of top-down expectation alongside bottom-up stimuli predictability in ASD and ADHD children using high-density electroencephalography (hdEEG) markers of predictive coding as measured with an event related potential (ERP) paradigm.

    We used a modified auditory task previously used to test the predictive coding model [4, 5]. This task included simple tones that were contextually grouped into sequences to create and then deviate from stimulus patterns. Participants were instructed to attend to stimuli deviating on frequency (expected) while stimuli deviating on laterality (unexpected) were also presented. We first investigated the ERP markers of predictive coding [4, 5]: the early prediction error indexed by the mismatch negativity (MMN), followed by the later P300 responsible for higher-order attentional processing.

    Based on task manipulation, we predicted that group differences would manifest in this last component. ASD children would show reduced P300 responses to unexpected deviants but enhanced responses to expected stimuli. In contrast, ADHD children would exhibit reduced P300s to expected deviants and stronger responses to unexpected deviants. Second, to explore the frontal mechanisms underlying this pattern of disassociation, we reconstructed cortical sources of P300s. Finally, we investigated the control mechanisms associated with group differences in top-down processing by exploring the associations between P300 markers and performance in executive function (EF) tasks.

    We have drawn upon the current neuroscientific understanding of predictive coding in cortical information processing to provide a collective account of atypical attention responses in both ASD and ADHD. Children with neuro-developmental disorders exhibited a double dissociated neural pattern; ASD children were strongly influenced by prior explicit task instructions and less affected by novel and unexpected contextual stimuli, whereas ADHD children were more influenced by task-irrelevant stimuli than explicitly expected stimuli. These findings could help us understand the various symptoms in each disorder. In ASD individuals, strong priors and expectations may account for restricted interest and hypo-reactivity to novel input, whereas in ADHD individuals, attenuated priors could explain distractibility symptoms.

    From a theoretical perspective, predictive coding could help to unravel the neural underpinnings of abnormal information processing in neuro-developmental disorders. As predictive coding theories are further developed to provide broad-based explanations of cortical information processing, they could provide a valuable, theoretically motivated method to reconcile seemingly conflicting low versus high level interpretations of ASD and ADHD.

    EF tasks:

    Participants completed a brief EF assessment of classical tasks that included (1) Digit Span sub-tests from the WISC-IV to assess working memory. (2) The childrens version of the Hayling test to evaluate inhibitory control. (3) The Trail Making Test (TMT) to assess set-shifting. Table 1 shows the mean, SD, and statistical comparisons of these tasks for all groups.

    hdEEG:

    128-channel hdEEG signal was recorded using a Biosemi amplifier, sampled at 1024 Hz and referenced to linked mastoids. Data were downsampled to 256 Hz and bandpass filtered at 0.5 and 20 Hz. The epochs were extracted between -200 ms and 1300 ms relative to the start of the presentation of each sequence. Additionally, the epochs were baseline-corrected relative to the mean activity during the -200 ms to 0 ms window before the onset of the fifth tone. control.

    Associations between cortical markers of top-down expectation and EF tasks:In control children, higher working memory was associated with cortical markers of attention to expected or task-relevant stimuli (rs= .47, p = .043). Regarding ASD children, greater inhibitory control was related to increased neural responses to expected stimuli (rs= .62, p = .002). Finally, in ADHD individuals, higher set-shifting skills were associated with enhanced cortical responses to unexpected sequences (rs= 61, p = .035)- Figure 2: Top-down expectation for expected deviant sequences indexed by the P300REFERENCES

    ACKNOWLEDGMENTSParticipants: Figure 1: Experimental task. Participants were asked to listen to the auditory stimulation and count the monaural deviant sequences. Through these instructions, expectation was manipulated using two types of deviant stimuli. The task included two blocks of stimuli. In each block, 71.5% of the sequences were standard, 14.25% were expected deviants, and the remaining 14.25% were unexpected deviants.

    Data analysis

    The MMN and P300 components were compared using spatiotemporal clustering analysis implemented in FieldTrip [6]. Cortical sources of both P300 markers of deviant conditions (expected and unexpected) were reconstructed with Brainstorm [7].P300 results: Expected deviant sequences

    P300s evoked by expected deviant sequences were significantly larger than standard sequences in the three groups (Figure 2A).However, between-group comparisons of this deviant condition revealed that ADHD individuals showed lower P300 responses than controls (Figure 2B). Figure 3A shows bilateral superior frontal cortex (FC) activation for expected deviants at the P300 peak in the control and ASD group. However, no such activation was observed in ADHD individuals. Additionally, left dorsolateral prefrontal cortex (DLPFC) activation was observed in the late phase of the P300 component only in the ASD group (Figure 3C).

    P300 results: Unexpected deviant sequences

    Higher P300 markers for this deviant condition compared to standard sequences were observed in th