Neural correlates of the use of prior knowledge in predictive coding

Every day, we use our sensory organs to perceive the environment around us. However, our perception not only depends on sensory information, but also on information already present in our brains, i.e. prior knowledge acquired by previous experience. The idea that prior knowledge is required for efficient perception goes back to Hermann von Helmholtz (1867). He raised the hypothesis that perception is a knowledge-driven inference process, in which prior knowledge allows to infer the (uncertain) causes of our sensory inputs. According to the currently very prominent “predictive coding theory” (e. g. Rao and Ballard, 1999; Friston, 2005, 2010; Hawkins and Blakeslee, 2005; Clark, 2012; Hohwy, 2013) this inference process is realized in our brains by using prior knowledge to build internal predictions for incoming information. Despite the increasing popularity of predictive coding theory in the last decade (see Clark, 2012 and comments to his article), previous research in the field has left out several important aspects: 1. The neural correlates of the use of prior knowledge are still widely unexplored; 2. Neurophysiological evidence for the neural implementation of predictive coding is limited and 3. Assumption-free approaches to study predictive coding mechanism are missing. In the present work, I try to fill these gaps using three studies with magnetoencephalographic (MEG) recordings in human participants: Study 1 (n = 48) investigates how prior knowledge from life-long experience influences perception. The results demonstrate that prediction errors induced by the violation of predictions based on life-long experience with faces are reflected in increased high-frequency gamma band activity (> 68 Hz). For studies 2 and 3, neurophysiological analysis is combined with information-theoretic analysis methods. These allow investigating the neural correlates of predictive coding with only few prior assumptions. In particular, the information-theoretic measure active information storage (AIS; Lizier et al., 2012; Wibral et al., 2014) can quantify how much information is maintained in neural activity (predictable information). I use AIS in order to study the neural correlates of activated prior knowledge in study 2 and 3. Study 2 (n = 52) assesses how prior knowledge is pre-activated in task relevant states to become usable for predictions. I find that pre-activation of prior knowledge for predictions about faces increases alpha and beta band related predictable information as measured by AIS in content specific brain areas. Study 3 (n patients = 19; n controls = 19) explores whether predictive coding related mechanism are impaired in autism spectrum disorder (ASD). The results show that alpha and beta band related predictable information is reduced in the brain of ASD patients, in particular in the posterior part of the default mode network. These findings indicate reduced use or precision of prior knowledge in ASD. In summary, the results presented in the present work illustrate the neural correlates of the use of prior knowledge in the predictive coding framework. They provide neurophysiological evidence for the link of prediction errors and fast neural activity (study 1, gamma band) as well as predictions and slower neural activity (study 2 and 3, alpha and beta band). These findings are in line with a theoretical proposal for the neural implementation of predictive coding theory (Bastos et al., 2012). Further, by application of AIS analysis (study 2 and 3) the present work introduces the largely assumption-free usage of information-theoretic measures to study the neural correlates of predictive coding in the human brain. In future, analysis of predictable information as measured by AIS may be applied to a broad variety of experiments studying predictive coding and also for research on neuropsychiatric disorders as has been demonstrated for ASD

Significance of beta-band oscillations in Autism Spectrum Disorders during motor response inhibition tasks: a MEG study

In Autism Spectrum Disorders (ASD), impaired response inhibition and lack of adaptation are hypothesized to underlie core ASD symptoms, such as social communication and repetitive, stereotyped behavior. Thus, the aim of the present study was to compare neural correlates of inhibition, post-error adaptation, and reaction time variability in ASD and neuro-typical control (NTC) participants by investigating possible differences in error-related changes of oscillatory MEG activity. Twelve male NTC (mean age 20.3 ± 3.7) and fourteen male patients with ASD (mean age 17.8 ± 2.9) were included in the analysis. Subjects with ASD showed increased error-related reaction time variability. MEG analysis revealed decreased beta power in the ASD group in comparison to the NTC group over the centro-parietal channels in both, the pre-stimulus and post-response interval. In the ASD group, mean centro-parietal beta power negatively correlated with dimensional autism symptoms. In both groups, false alarms were followed by an early increase in temporo-frontal theta to alpha power; and by a later decrease in alpha to beta power at central and posterior sensors. Single trial correlations were additionally studied in the ASD group, who showed a positive correlation of pre-stimulus beta power with post-response theta, alpha, and beta power, particularly after hit trials. On a broader scale, the results deliver important insights into top-down control deficits that may relate to core symptoms observed in ASD

Patients with fibromyalgia show increased beta connectivity across distant networks and microstates alterations in resting-state electroencephalogram

Fibromyalgia (FM) is a chronic condition characterized by widespread pain of unknown etiology associated with alterations in the central nervous system. Although previous studies demonstrated altered patterns of brain activity during pain processing in patients with FM, alterations in spontaneous brain oscillations, in terms of functional connectivity or microstates, have been barely explored so far. Here we recorded the EEG from 43 patients with FM and 51 healthy controls during open-eyes resting-state. We analyzed the functional connectivity between different brain networks computing the phase lag index after group Independent Component Analysis, and also performed an EEG microstates analysis. Patients with FM showed increased beta band connectivity between different brain networks and alterations in some microstates parameters (specifically lower occurrence and coverage of microstate class C). We speculate that the observed alterations in spontaneous EEG may suggest the dominance of endogenous top-down influences; this could be related to limited processing of novel external events and the deterioration of flexible behavior and cognitive control frequently reported for FM. These findings provide the first evidence of alterations in long-distance phase connectivity and microstate indices at rest, and represent progress towards the understanding of the pathophysiology of fibromyalgia and the identification of novel biomarkers for its diagnosis.Spanish Government (Ministerio de Economía y Competitividad; grant number PSI2016-75313-R) and from the Galician Government (Consellería de Cultura, Educación e Ordenación Universitaria; axudas para a consolidación e Estruturación de unidades de investigación competitivas do Sistema universitario de Galicia; grant number GRC GI-1807-USC; REF: ED431-2017/27). A.G.V. was partially supported by a grant from Xunta de Galicia (Axudas de apoio á etapa de formación posdoutoral 2018) and by the Portuguese Foundation for Science and Technology within the scope of the Individual Call to Scientific Employment Stimulus 201

Neural correlates of the use of prior knowledge in predictive coding

Every day, we use our sensory organs to perceive the environment around us. However, our perception not only depends on sensory information, but also on information already present in our brains, i.e. prior knowledge acquired by previous experience. The idea that prior knowledge is required for efficient perception goes back to Hermann von Helmholtz (1867). He raised the hypothesis that perception is a knowledge-driven inference process, in which prior knowledge allows to infer the (uncertain) causes of our sensory inputs. According to the currently very prominent “predictive coding theory” (e. g. Rao and Ballard, 1999; Friston, 2005, 2010; Hawkins and Blakeslee, 2005; Clark, 2012; Hohwy, 2013) this inference process is realized in our brains by using prior knowledge to build internal predictions for incoming information. Despite the increasing popularity of predictive coding theory in the last decade (see Clark, 2012 and comments to his article), previous research in the field has left out several important aspects: 1. The neural correlates of the use of prior knowledge are still widely unexplored; 2. Neurophysiological evidence for the neural implementation of predictive coding is limited and 3. Assumption-free approaches to study predictive coding mechanism are missing. In the present work, I try to fill these gaps using three studies with magnetoencephalographic (MEG) recordings in human participants: Study 1 (n = 48) investigates how prior knowledge from life-long experience influences perception. The results demonstrate that prediction errors induced by the violation of predictions based on life-long experience with faces are reflected in increased high-frequency gamma band activity (> 68 Hz). For studies 2 and 3, neurophysiological analysis is combined with information-theoretic analysis methods. These allow investigating the neural correlates of predictive coding with only few prior assumptions. In particular, the information-theoretic measure active information storage (AIS; Lizier et al., 2012; Wibral et al., 2014) can quantify how much information is maintained in neural activity (predictable information). I use AIS in order to study the neural correlates of activated prior knowledge in study 2 and 3. Study 2 (n = 52) assesses how prior knowledge is pre-activated in task relevant states to become usable for predictions. I find that pre-activation of prior knowledge for predictions about faces increases alpha and beta band related predictable information as measured by AIS in content specific brain areas. Study 3 (n patients = 19; n controls = 19) explores whether predictive coding related mechanism are impaired in autism spectrum disorder (ASD). The results show that alpha and beta band related predictable information is reduced in the brain of ASD patients, in particular in the posterior part of the default mode network. These findings indicate reduced use or precision of prior knowledge in ASD. In summary, the results presented in the present work illustrate the neural correlates of the use of prior knowledge in the predictive coding framework. They provide neurophysiological evidence for the link of prediction errors and fast neural activity (study 1, gamma band) as well as predictions and slower neural activity (study 2 and 3, alpha and beta band). These findings are in line with a theoretical proposal for the neural implementation of predictive coding theory (Bastos et al., 2012). Further, by application of AIS analysis (study 2 and 3) the present work introduces the largely assumption-free usage of information-theoretic measures to study the neural correlates of predictive coding in the human brain. In future, analysis of predictable information as measured by AIS may be applied to a broad variety of experiments studying predictive coding and also for research on neuropsychiatric disorders as has been demonstrated for ASD

Cognitive effects of rhythmic auditory stimulation in Parkinson’s disease: A P300 study

Rhythmic auditory stimulation (RAS) may compensate dysfunctions of the basal ganglia (BG), involved with intrinsic evaluation of temporal intervals and action initiation or continuation. In the cognitive domain, RAS containing periodically presented tones facilitates young healthy participants’ attention allocation to anticipated time points, indicated by better performance and larger P300 amplitudes to periodic compared to random stimuli. Additionally, active auditory-motor synchronization (AMS) leads to a more precise temporal encoding of stimuli via embodied timing encoding than stimulus presentation adapted to the participants’ actual movements. Here we investigated the effect of RAS and AMS in Parkinson’s disease (PD). 23 PD patients and 23 healthy age-matched controls underwent an auditory oddball task. We manipulated the timing (periodic/random/adaptive) and setting (pedaling/sitting still) of stimulation. While patients elicited a general timing effect, i.e., larger P300 amplitudes for periodic versus random tones for both, sitting and pedaling conditions, controls showed a timing effect only for the sitting but not for the pedaling condition. However, a correlation between P300 amplitudes and motor variability in the periodic pedaling condition was obtained in control participants only. We conclude that RAS facilitates attentional processing of temporally predictable external events in PD patients as well as healthy controls, but embodied timing encoding via body movement does not affect stimulus processing due to BG impairment in patients. Moreover, even with intact embodied timing encoding, such as healthy elderly, the effect of AMS depends on the degree of movement synchronization performance, which is very low in the current study