Biology subjects

Garrido, M. I.

Publications and source records attributed to Garrido, M. I..

8 recordsLinked to original sources

bioRxiv2018

Individuals with 22q11.2 deletion syndrome show intact prediction but reduced adaptation in responses to repeated sounds: evidence from Bayesian mapping

Background: One of the most common copy number variants, the 22q11.2 microdeletion, confers a increased risk for schizophrenia. Since schizophrenia has been associated with an aberrant neural response to repeated stimuli through both reduced adaptation and prediction, we here hypothesized that this may also be the case in nonpsychotic individuals with a 22q11.2 deletion. Methods: We recorded high-density EEG from 19 individuals with 22q11.2 deletion syndrome (12-25 years), as well as 27 healthy volunteers with comparable age and sex distribution, while they listened to a sequence of sounds arranged in a roving oddball paradigm. Using posterior probability maps and dynamic causal modelling we tested three different models accounting for repetition dependent changes in cortical responses as well as in effective connectivity; namely an adaptation model, a prediction model, and a model including both adaptation and prediction. Results: Repetition-dependent changes were parametrically modulated by a combination of adaptation and prediction and were apparent in both cortical responses and in the underlying effective connectivity. This effect was reduced in individuals with a 22q11.2 deletion and was negatively correlated with negative symptom severity. Follow-up analysis showed that the reduced effect of the combined adaptation and prediction model seen in individuals with 22q11.2 deletion was driven by reduced adaptation rather than prediction failure. Conclusions: Our findings suggest that adaptation is reduced in individuals with a 22q11.2 deletion, which can be interpreted in light of the framework of predictive coding as a failure to suppress prediction errors.

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bioRxiv2018

Predicting Individual Psychotic Experiences on a Continuum Using Machine Learning

Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict subclinical psychotic-like experiences on a continuum between these two extremes in otherwise healthy people. We applied two different approaches to an auditory oddball regularity learning task obtained from N = 73 participants: O_LIA feature extraction and selection routine incorporating behavioural measures, event-related potential components and effective connectivity parameters; C_LIO_LIRegularisation of spatiotemporal maps of event-related potentials. C_LI Using the latter approach, optimal performance was achieved using the response to frequent, predictable sounds. Features within the P50 and P200 time windows had the greatest contribution toward lower Prodromal Questionnaire (PQ) scores and the N100 time window contributed most to higher PQ scores. As a proof-of-concept, these findings demonstrate that EEG data alone are predictive of individual psychotic-like experiences in healthy people. Our findings are in keeping with the mounting evidence for altered sensory responses in schizophrenia, as well as the notion that psychosis may exist on a continuum expanding into the non-clinical population.

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bioRxiv2018

Bayesian Model Selection Maps for group studies using M/EEG data

Predictive coding postulates that we make (top-down) predictions about the world and that we continuously compare incoming (bottom-up) sensory information with these predictions, in order to update our models and perception so as to better reflect reality. That is, our so-called Bayesian brains continuously create and update generative models of the world, inferring (hidden) causes from (sensory) consequences. Neuroimaging datasets enable the detailed investigation of such modelling and updating processes, and these datasets can themselves be analysed with Bayesian approaches. These offer methodological advantages over classical statistics. Specifically, any number of models can be compared, the models need not be nested, and the null model can be accepted (rather than only failing to be rejected as in frequentist inference). This methodological paper explains how to construct posterior probability maps (PPMs) for Bayesian Model Selection (BMS) at the group level using electroencephalography (EEG) or magnetoencephalography (MEG) data. The method has only recently been used for EEG data, after originally being developed and applied in the context of functional magnetic resonance imaging (fMRI) analysis. Here, we describe how this method can be adapted for EEG using the Statistical Parametric Mapping (SPM) software package for MATLAB. The method enables the comparison of an arbitrary number of hypotheses (or explanations for observed responses), at each and every voxel in the brain (source level) and/or in the scalp-time volume (scalp level), both within participants and at the group level. The method is illustrated here using mismatch negativity (MMN) data from a group of participants performing an audio-spatial oddball attention task. All data and code are provided in keeping with the Open Science movement. In so doing, we hope to enable others in the field of M/EEG to implement our methods so as to address their own questions of interest.

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bioRxiv2018

Reduced top-down connectivity as an underlying mechanism for psychotic experiences in healthy people

Our perceptions result from the brains ability to make inferences, or predictive models, of sensory information. Recently, it has been proposed that psychotic traits may be linked to impaired predictive processes. Here, we examine the brain dynamics underlying sensory learning and inference in stable and volatile environments, in a population of healthy individuals (N=75) with a range of psychotic-like experiences. We measured prediction error responses to sound sequences with electroencephalography, gauged sensory inference explicitly by behaviourally recording sensory regularity learning errors, and used dynamic causal modelling to tap into the underlying neural circuitry. We discuss the findings that were robust to replication across the two experiments (N=31 and N=44 for the discovery and the validation datasets, respectively). First, we found that during stable conditions, participants demonstrated a stronger predictive model, reflected in a larger prediction error response to unexpected sounds, and decreased regularity learning errors. Moreover, individuals with attenuated prediction errors in stable conditions were found to make greater incorrect predictions about sensory information. Critically, we show that greater errors in sensory learning and inference are related to increased psychotic-like experiences. These findings link neurophysiology to behaviour during sensory learning and prediction formation, as well as providing further evidence for the idea of a continuum of psychosis in the healthy, non-clinical population. Significance StatementWhilst perceiving the world, we make inferences by learning the regularities present in the sensory environment. It has been argued that psychosis may emerge due to a failure to learn sensory regularities, resulting in an impaired representation of the world. Recently it has been proposed that psychosis exists on a continuum; however, there is conflicting evidence on whether sensory learning deficits align on the non-clinical end of the psychosis continuum. We found that sensory learning is associated with brain prediction errors, and critically, it is impaired in healthy people who report more psychotic-like experiences. We replicated these findings in an independent sample, demonstrating strengthened credibility to support that the continuum of psychosis extends into the non-clinical population.

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bioRxiv2018

An afferent subcortical white matter pathway to the amygdala facilitates fear recognition

Our ability to rapidly detect threats is thought to be subserved by a subcortical pathway that quickly conveys visual information to the amygdala. This neural shortcut has been demonstrated in animals but has rarely been shown in the human brain. Importantly, it remains unclear whether such a pathway might influence neural activity and behaviour. We conducted a multimodal neuroimaging study of 622 participants from the Human Connectome Project. We applied probabilistic tractography to diffusion-weighted images, reconstructing a subcortical pathway to the amygdala from the superior colliculus via the pulvinar. We then computationally modelled the flow of neural activity during a face-viewing task and found strong evidence for a functionally-afferent subcortical pathway. Critically, individuals with greater fibre density in this pathway also had stronger dynamic coupling and enhanced fearful face recognition. Our findings provide converging evidence for the recruitment of an afferent subcortical route to the amygdala in the human brain that facilitates fear recognition.

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bioRxiv2017

Dysconnection of right parietal and frontal cortex in neglect syndrome

A lesion to the right hemisphere of the brain often leads to perceptual neglect of the left side of the sensorium. The fact that lesions to different cortical regions lead to the same symptoms points to neglect as a dysconnection syndrome that may result from the dysconnection of a distributed network, rather than a disruption of computation in any particular brain region. To test this hypothesis, we used Bayesian analysis of effective connectivity based on electroencephalographic recordings in patients with left-sided neglect after a right-hemisphere lesion. While age-matched healthy controls showed a contralateral increase in connection strength between parietal and frontal cortex with respect to the laterality of the stimuli, neglect patients showed a dysconnection between parietal and frontal cortex in the right hemisphere when stimuli appeared on their neglected side, but preserved connectivity in the left hemisphere when stimuli appeared on their right. Crucially, this parieto-frontal feedback connectivity was aggravated in patients with more severe symptoms. In contrast, patients and controls did not show differences in the local connectivity within regions. These findings suggest that the aetiology of neglect may lie in the dysconnection of a distributed network, rather than the disruption of any particular brain region.

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bioRxiv2017

The functional anatomy of self-generated and predictable speech

Sensory attenuation refers to the cortical suppression of self-generated sensations relative to externally-generated sensations. This attenuation of cortical responsiveness is the result of internal forward models which make precise predictions about forthcoming sensations. Forward models of sensory attenuation in the auditory domain are thought to operate along auditory white matter pathways such as the arcuate fasciculus and the frontal aslant. The aim of this study was to investigate whether brain regions that are structurally connected via these white matter pathways are also effectively connected during overt speech, as well as as when listening to externally-generated speech that is temporally predictable via a visual cue. Using Electroencephalography (EEG) and Dynamic Causal Modelling (DCM) we investigated network models that link the primary auditory cortex to Wernickes and Brocas area either directly or indirectly through Geschwinds territory, which are structurally connected via the arcuate fasciculus. Connections between Brocas and supplementary motor area, which are structurally connected via the frontal aslant, were also included. Our results revealed that bilateral areas interconnected by indirect and direct pathways of the arcuate fasciculus, in addition to regions interconnected by the frontal aslant best explain the EEG responses to both self-generated speech, and speech that is externally-generated but temporally predictable. These findings indicate that structurally connected brain regions thought to be involved in auditory attenuation are also effectively connected. Critically, our findings expand on the notion of internal forward models, whereby sensory consequences of our actions are internally predicted and reflected in reduced cortical responsiveness to these sensations.\n\nHighlightsO_LIEffective connectivity of auditory attenuation to self-generated and predictable speech\nC_LIO_LIEEG and DCM were used to investigate several plausible network models\nC_LIO_LIStructurally connected brain areas of auditory attenuation are effectively connected\nC_LIO_LIInternal forward models modulate self-generated and predictable speech\nC_LI

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bioRxiv2017

Auditory prediction errors as individual biomarkers of schizophrenia

Schizophrenia is a complex psychiatric disorder, typically diagnosed through symptomatic evidence collected through patient interview. We aim to develop an objective biologically-based computational tool which aids diagnosis and relies on accessible imaging technologies such as electroencephalography (EEG). To achieve this, we used machine learning techniques and a combination of paradigms designed to elicit prediction errors or Mismatch Negativity (MMN) responses. MMN, an EEG component elicited by unpredictable changes in sequences of auditory stimuli, has previously been shown to be reduced in people with schizophrenia and this is arguably one of the most reproducible neurophysiological markers of schizophrenia.\n\nEEG data were acquired from 21 patients with schizophrenia and 22 healthy controls whilst they listened to three auditory oddball paradigms comprising sequences of tones which deviated in 10% of trials from regularly occurring standard tones. Deviant tones shared the same properties as standard tones, except for one physical aspect: 1) duration-the deviant stimulus was twice the duration of the standard; 2) monaural gap-deviants had a silent interval omitted from the standard, or 3) inter-aural timing difference, which caused the deviant location to be perceived as 90{degrees} away from the standards.\n\nWe used multivariate pattern analysis, a machine learning technique implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo) to classify images generated through statistical parametric mapping (SPM) of spatiotemporal EEG data, i.e. event-related potentials measured on the two-dimensional surface of the scalp over time. Using support vector machine (SVM) and Gaussian processes classifiers (GPC), we were able classify individual patients and controls with balanced accuracies of up to 80.48% (p-values = 0.0326, FDR corrected) and an ROC analysis yielding an AUC of 0.87. Crucially, a GPC regression revealed that MMN predicted global assessment of functioning (GAF) scores (correlation = 0.73, R2 = 0.53, p = 0.0006)

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