Electrophysiological and Haemodynamic Correlates of Face Perception, Recognition and Priming

Henson, Richard N.; Goshen‐Gottstein, Yonatan; Ganel, Tzvi; Otten, Leun J.; Quayle, Angela H.; Rugg, Michael D.

doi:10.1093/cercor/13.7.793

Cited by 359 publications

(316 citation statements)

References 73 publications

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“…The KDEF has been validated as a reliable facial set and used in a number of emotion studies (Goeleven et al, 2008). Additionally, 63 scrambled versions of the KDEF faces were used in the task as non-facial controls (Henson et al, 2003). Overall, the task consisted of 315 trials, substantially more than previous emotion perception studies, but a necessary requirement for ERP analyses, divided into 3 blocks of 105 trials each, with a brief break in between blocks.…”

Section: Facial Expression Taskmentioning

confidence: 99%

“…Conversely their origins may lie in the neural structures underlying perceptual and emotional processing of facial expression stimuli (Gray et al, 2007, Henson et al, 2003. Self-report questionnaires provide insight into self-assessed emotional responsiveness, which in some HD studies have shown trends and significance for lower self-assessed emotion (Sprengelmeyer et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

“…Of particular relevance is that following more generic visual processing (0-100ms), specific structural encoding of the face occurs at circa 170ms (termed the N170), followed by valancedependent processing of the face at circa 250ms (termed the N250). The N170 is primarily contributed to by fusiform area (FFA) and superior temporal gyrus (STG), whereas the N250 has less discreet sources (Bentin et al, 1996, Campanella et al, 2002, Henson et al, 2003. Assessment of the ERPs underlying emotional face processing can thus help delineate the disparate processes involved in this ecologically relevant function, and in particular can separate them from the motor-related functions required in performance measures such as reaction time and accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Emotion perception and electrophysiological correlates in Huntington’s disease

Croft¹,

McKernan²,

Gray³

et al. 2014

Clinical Neurophysiology

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Objective This study aimed to characterise, emotion perception deficits in symptomatic Huntington's disease (HD) via the use of event-related potentials (ERPs). Methods ERP data were recorded during a computerised facial expression task in 11 HD participants and 11 matched controls. Expression (scrambled, neutral, happy, angry, disgust) classification accuracy and intensity were assessed. Relationships between ERP indices and clinical disease characteristics were also examined. Results Accuracy was significantly lower for HD relative to controls, due to reduced performance for neutral, angry and disgust (but not happy) faces. Intensity ratings did not differ between groups. HD participants displayed significantly reduced visual processing amplitudes extending across pre-face (P100) and face-specific (N170) processing periods, whereas subsequent emotion processing amplitudes (N250) were similar across groups. Face-specific and emotionspecific derivations of the N170 and N250 ('neutral minus scrambled' and 'each emotion minus neutral', respectively) did not differ between groups. Conclusions Our data suggest that the facial emotion recognition performance deficits in HD are primarily related to neural degeneration underlying 'generalised' visual processing, rather than face or emotional specific processing. Significance ERPs are a useful tool to separate functionally discreet impairments in HD, and provide an important avenue for biomarker application that could more-selectively track disease progression.

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Section: Facial Expression Taskmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emotion perception and electrophysiological correlates in Huntington’s disease

Croft¹,

McKernan²,

Gray³

et al. 2014

Clinical Neurophysiology

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“…We used data from a single subject and from one channel, PO8, which showed an N170 component in response to face stimuli [Henson et al, 2003]. We computed the average (ERP) and the singular value decomposition (SVD) of the sample residual covariance matrix.…”

Section: Synthetic Datamentioning

confidence: 99%

Parametric analysis of oscillatory activity as measured with EEG/MEG

Kiebel

Tallon‐Baudry

Friston

2005

Human Brain Mapping

129

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Abstract:We assess the suitability of conventional parametric statistics for analyzing oscillatory activity, as measured with electroencephalography/magnetoencephalography (EEG/MEG). The approach we consider is based on narrow-band power time-frequency decompositions of single-trial data. The ensuing power measures have a 2 -distribution. The use of the general linear model (GLM) under normal error assumptions is, therefore, difficult to motivate for these data. This is unfortunate because the GLM plays a central role in classical inference and is the standard estimation and inference framework for neuroimaging data. The key contribution of this work is to show that, in many circumstances, one can appeal to the central limit theorem and assume normality for generative models of power. If this is not appropriate, one can transform the data to render the error terms approximately normal. These considerations allow one to analyze induced and evoked oscillations using standard frameworks like statistical parametric mapping. We establish the validity of parametric tests using synthetic and real data and compare its performance to established nonparametric procedures. Hum Brain Mapp 26:170 -177, 2005.

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“…We tested our algorithm on the EEG response to 380 seeing faces as compared to scrambled faces. In this paradigm we know from fMRI studies (Henson et al, 2003) and combined EEG/MEG studies (Henson et al, 2009) that the activated areas include the FFA and OFA. Studies of patients with lesions in the FFA and OFA further validate the importance of these areas in face perception (Eimer and McCarthy, 1999;Dalrymple et al, 2011).…”

mentioning

confidence: 99%

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

Hansen

2017

NeuroImage

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Electroencephalography (EEG) can capture brain dynamics in high temporal resolution.By projecting the scalp EEG signal back to its origin in the brain also high spatial resolution can be achieved. Source localized EEG therefore has potential to be a very powerful tool for understanding the functional dynamics of the brain. Solving the inverse problem of EEG is however highly ill-posed as there are many more potential locations of the EEG generators than EEG measurement points. Several well-known properties of brain dynamics can be exploited to alleviate this problem. More short ranging connections exist in the brain than long ranging, arguing for spatially focal sources. Additionally, recent work (Delorme et al., 2012) argues that EEG can be decomposed into components having sparse source distributions. On the temporal side both short and long term stationarity of brain activation are seen. We summarize these insights in an inverse solver, the so-called "Variational Garrote" (Kappen and Gómez, 2013). Using a Markov prior we can incorporate flexible degrees of temporal stationarity. Through spatial basis functions spatially smooth distributions are obtained. Sparsity of these are inherent to the Variational Garrote solver. We name our method the MarkoVG and demonstrate its ability to adapt to the temporal smoothness and spatial sparsity in simulated EEG data.Finally a benchmark EEG dataset is used to demonstrate MarkoVG's ability to recover non-stationary brain dynamics.

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Electrophysiological and Haemodynamic Correlates of Face Perception, Recognition and Priming

Cited by 359 publications

References 73 publications

Emotion perception and electrophysiological correlates in Huntington’s disease

Emotion perception and electrophysiological correlates in Huntington’s disease

Parametric analysis of oscillatory activity as measured with EEG/MEG

Spatio-temporal reconstruction of brain dynamics from EEG with a Markov prior

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