Age-related delay in visual and auditory evoked responses is mediated by white- and grey-matter differences

Price, Darren; Tyler, Lorraine K.; Henriques, Rafael Neto; Campbell, Karen L.; Williams, Nitin; Treder, Matthias S.; Taylor, Jason R.; Cam‐CAN,; Brayne, Carol; Bullmore, Edward T.; Calder, Andrew C.; Cusack, Rhodri; Dalgleish, Tim; Duncan, John A.; Matthews, Fiona E.; Marslen–Wilson, William D.; Rowe, James B.; Shafto, Meredith A.; Cheung, Teresa; Davis, Simon W.; Geerligs, Linda; Kievit, Rogier A.; McCarrey, Anna C.; Mustafa, A.; Samu, Dávid; Tsvetanov, Kamen A.; Belle, Janna van; Bates, Lauren; Emery, Tina; Erzinglioglu, Sharon; Gadie, Andrew; Gerbase, Sofia; Georgieva, Stanimira; Hanley, Claire; Parkin, Beth; Troy, David; Auer, Tibor; Correia, Marta; Gao, Lu; Green, Emma; Allen, Jodie; Amery, Gillian; Amunts, Liana; Barcroft, Anne; Castle, Amanda; Dias, Cheryl; Dowrick, Jonathan; Fair, Melissa; Fisher, Hayley; Goulding, Anna; Grewal, Adarsh; Hale, Geoff; Hilton, Andrew; Johnson, Frances L.; Johnston, Patricia; Kavanagh-Williamson, Thea; Kwaśniewska, Magdalena; McMinn, Alison M.; Norman, Kim; Penrose, Jessica; Roby, Fiona; Rowland, Diane; Sargeant, John; Squire, Maggie; Stevens, Beth; Stoddart, Aldabra; Stone, Cheryl; Thompson, Tracy; Yazlik, Ozlem; Barnes, Dan; Dixon, Marie; Hillman, Jaya; Mitchell, Joanne; Villis, Laura; Henson, Richard N.

doi:10.1038/ncomms15671

Cited by 56 publications

(68 citation statements)

References 60 publications

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“…Although feature representation was qualitatively similar across young and older adults, it was delayed by 40 ms and weaker in older adults, in the absence of generic delays in the onset of visual cortical activity in older participants. This suggests that the reported delay occurred at the stages of cortical information processing, and was not due to precortical neural factors; thereby adding to the evidence that processing speed delays are unlikely to be due to bottom‐up optical factors, such as senile miosis, contrast sensitivity (Bieniek, Bennett, Sekuler, & Rousselet, ; Bieniek, Frei, & Rousselet, ), or visual acuity (Price et al, ). Furthermore, we believe that bottom‐up optical factors were unlikely contributors to the observed differences at the neural level, because any bottom‐up factors should affect all neural responses irrespectively of their category, whereas we observed much larger N170 to noise textures in older than in young participants.…”

Section: Discussionmentioning

confidence: 73%

“…Although feature representation was qualitatively similar across young and older adults, it was delayed by 40 ms and weaker in older adults, in the absence of generic delays in the onset of visual cortical activity in older participants. This suggests that the reported delay occurred at the stages of cortical information processing, and was not due to precortical neural factors; thereby adding to the evidence that processing speed delays are unlikely to be due to bottom-up optical factors, such as senile miosis, contrast sensitivity (Bieniek, Bennett, Sekuler, & Rousselet, 2016;Bieniek, Frei, & Rousselet, 2013), or visual acuity (Price et al, 2017). Although our stimuli were only frontal views of faces, it would be interesting to test side views, in line with previous studies showing age-related behavioral decrement on perception of faces across viewpoints (Habak, Wilkinson, & Wilson, 2008;Wilson, Mei, Habak, & Wilkinson, 2011).…”

Section: Discussionmentioning

confidence: 75%

“…Processing of facial/head viewpoints follows a distinct sequence of encoding that reflects different levels of computational complexity (Kietzmann, Gert, Tong, & König, 2017). Whether aging affects different stages of viewpoint processing similarly would inform whether processing speed is delayed in a constant or cumulative manner along the visual cortical hierarchy (Price et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

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Healthy aging delays the neural processing of face features relevant for behavior by 40 ms

Jaworska

Ince

et al. 2019

Human Brain Mapping

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Fast and accurate face processing is critical for everyday social interactions, but it declines and becomes delayed with age, as measured by both neural and behavioral responses. Here, we addressed the critical challenge of understanding how aging changes neural information processing mechanisms to delay behavior. Young (20–36 years) and older (60–86 years) adults performed the basic social interaction task of detecting a face versus noise while we recorded their electroencephalogram (EEG). In each participant, using a new information theoretic framework we reconstructed the features supporting face detection behavior, and also where, when and how EEG activity represents them. We found that occipital‐temporal pathway activity dynamically represents the eyes of the face images for behavior ~170 ms poststimulus, with a 40 ms delay in older adults that underlies their 200 ms behavioral deficit of slower reaction times. Our results therefore demonstrate how aging can change neural information processing mechanisms that underlie behavioral slow down.

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Section: Discussionmentioning

confidence: 73%

“…Although feature representation was qualitatively similar across young and older adults, it was delayed by 40 ms and weaker in older adults, in the absence of generic delays in the onset of visual cortical activity in older participants. This suggests that the reported delay occurred at the stages of cortical information processing, and was not due to precortical neural factors; thereby adding to the evidence that processing speed delays are unlikely to be due to bottom-up optical factors, such as senile miosis, contrast sensitivity (Bieniek, Bennett, Sekuler, & Rousselet, 2016;Bieniek, Frei, & Rousselet, 2013), or visual acuity (Price et al, 2017). Although our stimuli were only frontal views of faces, it would be interesting to test side views, in line with previous studies showing age-related behavioral decrement on perception of faces across viewpoints (Habak, Wilkinson, & Wilson, 2008;Wilson, Mei, Habak, & Wilkinson, 2011).…”

Section: Discussionmentioning

confidence: 75%

Section: Discussionmentioning

confidence: 99%

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Healthy aging delays the neural processing of face features relevant for behavior by 40 ms

Jaworska

Ince

et al. 2019

Human Brain Mapping

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“…peak evoked latency Sensory processing may slow down in the course of aging (Price et al, 2017). Here, we assessed the evoked response latency during auditory, visual and simultaneous audiovisual stimulation (index 1, Table 3).…”

Section: Meg Featuresmentioning

confidence: 99%

Combining electrophysiology with MRI enhances learning of surrogate-biomarkers

Engemann¹,

Kozynets²,

Sabbagh³

et al. 2019

Preprint

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Electrophysiological methods, i.e., M/EEG provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as surrogate biomarker in 674 subjects from the Cam-CAN. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by source-topography of power spectra between 8 and 30 Hz. Finally, we demonstrate that the model maintains benefits of stacking when data is missing. The proposed framework hence enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.

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“…source reconstruction. It has been shown that these 31 methods can be cast into the generalised model 32 s(t) = C s L C −1 x(t) =: w x(t) (2) where C ∈ R N ×N is the sensor covariance matrix, C s ∈ R M ×M is the source 33 covariance matrix,ŝ is the estimated source amplitude (a.k.a. virtual channel), and 34 w ∈ R N is the spatial filter [12].…”

mentioning

confidence: 99%

Source reconstruction of broadband EEG/MEG data using the frequency-adaptive broadband (FAB) beamformer

Treder

Nolte

2018

Preprint

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A beamformer enhances the signal from a voxel of interest by minimising interference from all other locations represented in the sensor covariance matrix. However, the presence of narrowband oscillations in EEG/MEG implies that the spatial structure of the covariance matrix, and hence also the optimal beamformer, depends on the frequency. The frequency-adaptive broadband (FAB) beamformer introduced here exploits this fact in the Fourier domain by partitioning the covariance matrix into cross-spectra corresponding to different frequencies. For each frequency bin, an individual spatial filter is constructed. This assures optimal noise suppression across the frequency spectrum. After applying the spatial filters in the frequency domain, the broadband source signal is recovered using the inverse Fourier transform. MEG simulations using artificial data and real resting-state measurements were used to compare the FAB beamformer to the LCMV beamformer and MNE. The FAB beamformer significantly outperforms both methods in terms of the quality of the reconstructed time series. To our knowledge, the FAB beamformer is the first beamforming approach tailored for the analysis of broadband neuroimaging data. Due to its frequency-adaptive noise suppression, the reconstructed source time series is suited for further time-frequency or connectivity analysis in source space. Introduction 1 Electroencephalography (EEG) and magnetoencephalography (MEG), jointly 2 abbreviated as MEEG here, measure the electrical currents or magnetic fields associated 3 with synchronised activity of neuronal populations [1-3]. Compared to hemodynamic 4 methods such as functional magnetic resonance imaging (fMRI), the Achilles' heel of 5 MEEG is the low accuracy in localising the sources. Since the sensors are located at a 6 distance to the brain, the activity of brain sources needs to be statistically 7 reconstructed. The inverse problem of estimating the sources from the sensor 8 measurements is underconstrained because the number of sources exceeds the number of 9 sensors. Furthermore, neurons in some subcortical areas are aligned such that they 10 constitute a closed field, producing little measurable electrical activity [1]. Consequently, 11 in order to obtain a unique solution, additional assumptions about the sources (such as 12 their uncorrelatedness) are required to constrain the inverse model [4, 5]. The linear 13 forward model of MEEG, specifying how activity in the brain projects into the sensors, 14 can be formulated as 15 December 17, 2018 1/15 42 The principal assumption for beamforming is that all sources are uncorrelated and 43 hence the source covariance matrix C s is diagonal. Indeed, inserting a diagonal source 44 covariance matrix into Eq 2 leads to a scaled version of the beamformer solution in Eq 3. 45 Recently, it has been shown that LCMV beamforming is formally equivalent to Linear 46 Discriminant Analysis (LDA) [16]. 47 318 Fourier domain representation of a Morlet wavelet. The time-frequency trade-off was 319determined by setti...

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Age-related delay in visual and auditory evoked responses is mediated by white- and grey-matter differences

Cited by 56 publications

References 60 publications

Healthy aging delays the neural processing of face features relevant for behavior by 40 ms

Healthy aging delays the neural processing of face features relevant for behavior by 40 ms

Combining electrophysiology with MRI enhances learning of surrogate-biomarkers

Source reconstruction of broadband EEG/MEG data using the frequency-adaptive broadband (FAB) beamformer

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