Effects of aging on the neuromagnetic mismatch detection to speech sounds

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Contents lists available at ScienceDirect

Biological Psychology journal homepage: www.elsevier.com/locate/biopsycho

Effects of aging on the neuromagnetic mismatch detection to speech sounds

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Chia-Hsiung Cheng a,e,h , Sylvain Baillet h , Fu-Jung Hsiao a,g , Yung-Yang Lin a,b,c,d,e,f,∗ a

Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan Department of Neurology, National Yang-Ming University, Taipei, Taiwan Institute of Physiology, National Yang-Ming University, Taipei, Taiwan d Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan e Laboratory of Neurophysiology, Taipei Veterans General Hospital, Taipei, Taiwan f Department of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan g Department of Education and Research, Taipei City Hospital, Taipei, Taiwan h McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Canada b c

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Article history: Received 25 December 2012 Accepted 9 November 2014 Available online xxx

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Keywords: Aging Phonetic processing Speech sound Magnetic mismatch negativity (MMNm) Magnetoencephalography (MEG)

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1. Introduction

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The ability to discriminate speech sounds is crucial for higher language functions in humans. However, it remains unclear whether physiological aging affects the functional integrity of pre-attentive phonological discrimination. The neuromagnetic cortical responses during automatic change detection of speech sounds (/ba/versus/da/) were recorded in 24 young and 21 aged male adults. We used minimum norm estimate of source reconstruction to characterize the spatiotemporal dynamics of magnetic mismatch responses (MMNm). Distributed activations to phonetic changes were identified in the temporal, frontal and parietal regions. Compared to younger participants, elderly volunteers exhibited a significant reduction of cortical responses to phonetic-MMNm, exception for the left orbitofrontal cortex and anterior inferior temporal gyrus. However, among the identified regions of interest, we did not observe significant between-group differences in the hemispheric asymmetry of phonetic-MMNm. Conclusively, our results suggest an altered phonetic processing at the perception level during physiological aging. © 2014 Published by Elsevier B.V.

The emerging evidence of cognitive neuroscience has shown age-related declines in many aspects (Hedden & Gabrieli, 2004; Rajah & D’Esposito, 2005). Among these cognitive functioning, language-related ability is much more clinically and scientifically important because of its intense association with other integrated cerebral function, such as verbal memory, thoughts and reasoning. The ability to discriminate speech sounds is crucial for higher language functions in humans (Naatanen, Paavilainen, Rinne, & Alho, 2007; Price, Thierry, & Griffiths, 2005). More specifically, phonemes are considered as the most fundamental units for obtaining meaningful contrasts before semantic processing. Hence, a clear understanding of basic level of language comprehension could help us probe the underlying mechanisms of language-related

∗ Corresponding author at: Institute of Brain Science, National Yang-Ming University, and Department of Neurology, Taipei Veterans General Hospital, No.201, Sec.2, Shih-Pai Rd., Taipei 112, Taiwan. Tel.: +886 2 28757398; fax: +886 2 28757579. E-mail addresses: [email protected], [email protected] (Y.-Y. Lin).

dysfunction.In several behavioral studies, significant differences have been found between younger and elderly adults in the discrimination task. For example, it has been shown that the elderly needed a higher contrast threshold to discriminate consonant syllables involving rapid spectrotemporal changes, such as/da-ga/, suggesting a declined performance in the categorization of speech sounds (Bellis, Nicol, & Kraus, 2000). However, the requirements of concentration, motivation and memory span in these tasks have been shown disturbed in the elderly population (Grady & Craik, 2000; Hedden & Gabrieli, 2004). Thus, a question emerges as to whether the speech discrimination defect in aged adults occurs at the level of pre-attentive, attention-independent perception. Mismatch negativity (MMN) and its magnetic counterpart (MMNm) are useful electrophysiological markers to evaluate the integrity of automatic central auditory processing (Hsiao, Cheng, Liao, & Lin, 2010; Kujala, Tervaniemi, & Schroger, 2007; Naatanen et al., 2011; Yabe et al., 2004). A number of studies have reported a lower amplitude of MMN/MMNm in response to pure-tone stimuli in aged adults (Alain, McDonald, Ostroff, & Schneider, 2004; Cheng, Baillet, Hsiao, & Lin, 2013; Cheng, Hsu, & Lin, 2013; Cheng, Wang, Hsu, & Lin, 2012; Cooper, Todd, McGill, & Michie, 2006; Czigler, Csibra,

http://dx.doi.org/10.1016/j.biopsycho.2014.11.003 0301-0511/© 2014 Published by Elsevier B.V.

Please cite this article in press as: Cheng, C. -H., et al. Effects of aging on the neuromagnetic mismatch detection to speech sounds. Biol. Psychol. (2014), http://dx.doi.org/10.1016/j.biopsycho.2014.11.003

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& Csontos, 1992; Kiang, Braff, Sprock, & Light, 2009), while to our knowledge, little is known about the effects of aging on the early cortical processes of phonetic discrimination. Up to the date, only two event-related potential (ERP) reports target this issue. One study revealed similar MMN amplitudes and latencies between young and elderly groups (Bellis et al., 2000), whereas the other demonstrated aging-associated amplitude reduction and latency prolongation of MMN responses (Aerts et al., 2013). These contradicting findings impeded us to reach a precise conclusion. Furthermore, Bellis and colleagues reported that topography of MMN responses was symmetrical over temporal regions and did not vary with age (Bellis et al., 2000), in contrast to previous reports showing left-lateralized cerebral processing of speech sounds (Naatanen et al., 1997; Rinne et al., 1999). Due to the previous controversial observation and little understanding about the neural substrates for aging-related changes in pre-attentive phonetic discrimination, we proposed to clarify the spatiotemporal neural dynamics of basic speech perception and to identify the possibly affected cortical regions in aged adults. In addition, we proposed to investigate the hemispheric asymmetry of speech-related MMNm (phonetic-MMNm). To solve these questions, we capitalized on the excellent temporal resolution and good spatial resolution of magnetoencephalography (MEG) to elucidate the neural signature of early-phase phonetic discrimination. Distributed source modeling method, minimum norm estimate (MNE), was applied to reconstruct the sources of neural activation. MNE has been proved to be the preferred method when analyzing multi-source evoked activities compared to other inverse solution (Lin, Belliveau, Dale, & Hamalainen, 2006). Specifically, the objectives of the present study were (1) to determine the aging-associated alterations of the spatiotemporal neural dynamics of MMNm due to phonetic changes, and (2) to investigate whether the hemispheric topographic asymmetries of phoneticMMNm components were modulated by aging.

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2. Materials and methods

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2.1. Subjects

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Due to the possible effects of gender differences on the cerebral responses (Matsubayashi et al., 2008), 24 young male adults (20–34 years of age, mean 24.04) and 21 healthy elderly male adults (59–82 years of age, mean 69.4) participated in this study. Careful examination verified that every subject had normal hearing ability without history of neurological deficits. The older participants scored ≥27 on the Mini-Mental Status Exam. All subjects were right-handed (handedness score >80%) as evaluated by the Edinburg Inventory (Oldfield, 1971). Written informed consent was obtained from all subjects.

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2.2. Stimuli and magnetic recording procedures

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The subjects were presented with auditory oddball stimulations consisting of standard (probability = 85%) and deviant stimuli (probability = 15%), with at least two standard events between each deviant. The hearing thresholds of 1000 Hz-tone were measured in each participant, and the absolute values of sounds intensity were about 70–80 dB and 75–85 dB in the young and old adults, respectively. Thereafter, we adopted the individual’s absolute intensity in the presentation of speech sounds. The stimuli were delivered binaurally through plastic tubes with 1000-ms stimulus onset asynchrony (SOA). The ∼350 ms consonant-vowel syllables/ba/(standards) and/da/(deviants) were naturally pronounced by a female native speaker of Chinese (Lin et al., 2005). These speech sounds were digitally recorded using a laptop computer (Thinkpad 390, IBM), with a sampling rate of 44.1 kHz and a resolution of 16 bits. We used the GoldWave Digital Audio Editor (GoldWave Inc.) to adjust the peak amplitude, duration, and the fade-in (15 ms) and fade-out (200 ms) times of the stimuli. The magnetic responses were recorded with a 306-channel whole-head MEG instrument (Vectorview, Elekta-Neuromag, Helsinki, Finland) in a magneticallyshielded room. The epoch duration was 900 ms, including a 100 ms prestimulus baseline. The online bandpass filter and sampling rate were set to [0.1, 130] Hz and 400 Hz, respectively. Electro-oculogram (EOG) electrodes were attached above the left orbit and below the right orbit, to monitor eye movements and blinks. Epochs contaminated by eye blinks (EOGs > 150 ␮V) were discarded. In each condition, at least 100 artifact-free deviants and a minimum of 500 standards were collected for further analyses. The order of the pure-tone and phonetic conditions was counterbalanced across subjects. Parts of data pertaining to frequency-MMNm have been published elsewhere (Cheng, Baillet, et al., 2013; Cheng, Hsu, et al., 2013). All the participants were instructed to focus on watching a silent movie they had selected and to ignore the auditory stimuli.

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2.3. Source estimation

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The averaged data were off-line filtered with a bandpass of [1, 30] Hz (Cheng et al., 2010; Kujala et al., 2007). The MMNm component in the event-related MEG average was determined by subtracting the responses to standards from those to deviants. The modeling of the cortical spatiotemporal dynamics of phonetic processing was obtained with Brainstorm (Tadel, Baillet, Mosher, Pantazis, & Leahy, 2011). The segmentation of head tissues from individual T1-weighted Magnetic Resonance Imaging (MRI, GE Discovery MR750 3T with TR 9.4 ms, TE 4 ms, recording matrix 256 × 256 pixels, field of view 256 mm, slice thickness 1 mm) volume data was obtained with BrainVisa (http://brainvisa.info/). The forward modeling of MEG measures was completed using an overlapping-sphere analytical model (Huang, Mosher, & Leahy, 1999). For each participant, cortically-constrained source imaging was performed using the depth-weighted minimum norm estimate (MNE) (Baillet, Mosher, & Leahy, 2001; Hamalainen & Ilmoniemi, 1994) model of Brainstorm, with default parameter settings, e.g., “kernel only” as the output mode, “constrained” as source orientation, and whitening PCA was applied (http://neuroimage.usc.edu/brainstorm/Tutorials/TutSourceEstimation), over a set of ∼7500 elementary current dipoles distributed over the individual cortical envelope. The individual source maps were geometrically registered to the Montreal Neurological Institute brain template (Colin27) using Brainstorm’s multilinear registration technique, with default parameters, e.g., the individual cortex to match the Colin27 cortex was deformed with an iterative closest point algorithm. The time-resolved magnitude of each elementary source was normalized to its fluctuations over baseline, yielding a set of Z-scored time series at each cortical

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Fig. 1. Selection of region of interests (4–5 cm2 ) on Montreal Neurological Institute Colin27 brain template. STG, superior temporal gyrus; STS, superior temporal sulcus; IFG, inferior frontal gyrus; IPL, inferior parietal lobule; OFC, orbitofrontal cortex; ITG, inferior temporal gyrus.

Please cite this article in press as: Cheng, C. -H., et al. Effects of aging on the neuromagnetic mismatch detection to speech sounds. Biol. Psychol. (2014), http://dx.doi.org/10.1016/j.biopsycho.2014.11.003

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location. The Z-scored values were rectified to detect absolute magnitude changes above baseline levels, and their peak responses were extracted for subsequent analysis (Senot, Baillet, Renault, & Berthoz, 2008). The comparisons of source profiles between original amplitude and Z-score revealed that Z-normalization not only preserved the main features of source waveforms but also corrected the baseline activity more effectively (Supplementary data).

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2.4. Selection of regions of interest

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The MNE source maps were obtained for each participant and group-averaged onto the aligned cortical surface of the Colin27 brain template. Based on the grandaveraged waveform time series, a temporal window between 100 and 250 ms was selected in each region of interest (ROI) for further analysis. The definition of the anatomical ROIs was based on the prediction that MMNm generators would be located primarily in the temporal, frontal and parietal regions (Lappe, Steinstrater, & Pantev, 2013a; Lin et al., 2007; Muller, Juptner, Jentzen, & Muller, 2002; Naatanen et al., 2007; Opitz, Rinne, Mecklinger, von Cramon, & Schroger, 2002; Rinne, Degerman, & Alho, 2005; Tse & Penney, 2008). A cluster of 30 cortical vertices corresponding to 4–5 cm2 , was manually selected to define each ROI (Fig. 1).

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2.5. Statistical analysis

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For the responses to repetitive standard sounds, two-way analysis of variances (group by hemisphere) were carried out to assess the peak amplitudes of M50, M100 and M200 cortical responses extracted from bilateral superior temporal regions. A

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Bonferroni correction was used as the post hoc analysis. For the phonetic-MMNm, between-group amplitude differences at each time point were compared using independent, two-tailed t-tests (Brainstorm) for each defined ROI. To test the group effects on hemispheric asymmetries of M50, M100 and M200 to standards and phonetic-MMNm, laterality index [L − R/L + R] was evaluated by independent, two-tailed t-tests (SPSS, version 13.0). p values<0.05 were taken as the significant threshold.

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3. Results

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3.1. Cortical responses to standard sounds

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Fig. 2 shows the grand-averaged magnetic responses to standard sounds and source imaging of bilateral superior temporal regions in each group. The statistical results revealed that the elderly demonstrated a significantly larger M50 amplitude compared to younger participants (p < 0.001). Although amplitudes of M100 and M200 were larger in the older adults according to the grand-averaged plots, the statistics did not reveal significant between-group differences. Furthermore, inspection from the sensor waveforms, an augmented and sustained component peaking ∼300 ms (M300) was observed in the younger group.

Fig. 2. (A) Grand-averaged sensor waveforms to repetitive standards/ba/and the cortical activation as a function of time in bilateral superior temporal gyrus (STG) in young and elderly groups. Clear deflections and magnetic field patterns were observed around 50 ms (M50), 100 ms (M100) and 200 ms (M200) after the stimulus onset. Noticeably, a sustained component peaking ∼300 ms (M300) was only observed in the younger group. (B) Cortical strengths of M50, M100 and M200 components were extracted from each subject followed by group comparisons. The elderly exhibited a significantly higher amplitude of M50 component to repetitive auditory inputs. The laterality index of either M50, M100 or M200, defined as [L − R/L + R], did not show significant group differences. L, left hemisphere; R, right hemisphere.

Please cite this article in press as: Cheng, C. -H., et al. Effects of aging on the neuromagnetic mismatch detection to speech sounds. Biol. Psychol. (2014), http://dx.doi.org/10.1016/j.biopsycho.2014.11.003

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Fig. 3. The upper panel shows butterfly plots of subtracted MMNm responses (deviants − standards) in each group. The lower panel shows spatiotemporal dynamics of minimum norm estimation (MNE) of phonetic-MMNm in the 100–300 ms time window. The grand-averaged MNE activation is mapped onto the Montreal Neurological Institute Colin27 brain template. The cortical surfaces have been smoothed for better visualization of activation (dark gray = sulci; light gray = gyri). L, left hemisphere; R, right hemisphere.

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Hemispheric topographic asymmetries in each individual were evaluated with the equation [L − R/L + R], where a positive value indicates a left-ward lateralization, while a negative value reflects a right-ward lateralization. In general, the registration and identification of speech stimuli was right-lateralized in both young and elderly groups. We did not find any significant group difference in terms of laterality index, although a trend to significance (p = 0.07) was observed in the M100 responses. 3.2. Source distribution of phonetic MMNm Fig. 3 shows the grand-averaged waveforms of phonetic-MMNm recorded from MEG sensor arrays and the corresponding spatiotemporal cortical source maps in young and elderly groups. In young adults, the superior temporal and frontal cortices were activated at ∼125 ms and sustained throughout the whole epoch

(125–250 ms). The activation of the right inferior parietal lobule emerged at 150 ms following temporal and frontal activity, and persisted for about 50 ms. The responses over the right hemisphere were larger than those over the left hemisphere. Neural activation in the elderly group demonstrated similar patterns, although with decreased activation strength and prolonged latencies compared to young participants. We also identified additional activated regions in the inferior temporal cortex. Specifically, activity in the inferior temporal cortex was prominent in the 150–250 ms range in the young group. Neural activation of this region was less obvious in the elderly group. 3.3. ROI-based analysis of phonetic-MMNm Seven ROIs in each hemisphere were identified: superior temporal gyrus (STG), superior temporal sulcus (STS), inferior frontal

Please cite this article in press as: Cheng, C. -H., et al. Effects of aging on the neuromagnetic mismatch detection to speech sounds. Biol. Psychol. (2014), http://dx.doi.org/10.1016/j.biopsycho.2014.11.003

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Q4 Fig. 4. MEG source strength as a function of time in the young (red trace) and elderly (blue trace) groups. The central MNE map shows peak activation of averaged phoneticMMNm responses across all subjects. STG, superior temporal gyrus; STS, superior temporal sulcus; ITG, inferior temporal gyrus; IFG, inferior frontal gyrus, OFC, orbitofrontal cortex; IPL, inferior parietal lobule. *p < 0.05 for the significant larger responses from the young group compared to the elderly. # p < 0.05 for the significant larger responses from the elderly group compared to the young. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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gyrus (IFG), orbitofrontal cortex (OFC), inferior frontal lobule (IPL), anterior inferior temporal gyrus (ITG) and posterior ITG. Fig. 4 displays the normalized MNE source waveforms of each ROI in the young and elderly groups. Apart from the left anterior ITG and left OFC, aged adults showed significantly lower MMNm responses to phonetic changes (p < 0.05). Further, compared to younger participants, the elderly demonstrated higher activation in the left OFC (p < 0.05). We further examined the effects of aging on hemispheric asymmetries in phonetic-MMNm responses. We did not find the statistical differences between young and elderly groups in terms of laterality index underlying phonetic MMNm (Fig. 5).

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4. Discussion

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To ascertain the aging-related alterations of pre-attentive discrimination of speech sounds, we studied the phonetic-MMNm response components through a MEG source imaging approach. The results indicated that the distinct neural substrates in temporal, frontal and parietal regions underlying automatic auditory phonetic comparisons were engaged. Reduced responses to phonetic deviants were observed in the aged-adults group, suggesting an alteration in change discrimination to speech sounds due to aging. Interestingly, our findings also showed a larger standard-evoked M50 amplitude in older adults compared to younger subjects, suggesting an inadequate cortical inhibition to redundant information in the early stage of auditory processing.

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4.1. Aging-related changes in phonetic-MMNm

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Although speech-related MMN responses have been widely assessed in patients with schizophrenia (Kasai et al., 2002), autism (Ceponiene et al., 2003), stroke (Ilvonen et al., 2003), epilepsy (Hara et al., 2012) and dyslexia (Kujala & Naatanen, 2001), there are only two ERP reports addressing the aging-related changes in the phonetic-MMN, with one showing similar responses while the

other demonstrating significant differences between young and elderly participants (Aerts et al., 2013; Bellis et al., 2000). Here, by using a whole-head MEG, we identified a distributed frontotemporo-parietal network of brain regions underlying automatic speech perception, which has been evident to be engaged in speech sound representation (Hickok & Poeppel, 2007; Pulvermuller & Shtyrov, 2006, 2009; Travis et al., 2011; Zatorre, Evans, Meyer, & Gjedde, 1992). To the best of our knowledge, this is the first study to report on utilizing distributed source imaging to examine aging-related changes of MMNm to phonetic processing. A remarkable decline of phonetic-MMNm amplitude in the STG, STS, ITG,

Fig. 5. Laterality index, defined as [L − R/L + R], of phonetic-MMNm in each region of interest. The bars above each column indicate the standard error of the mean. STG, superior temporal gyrus; STS, superior temporal sulcus; IFG, inferior frontal gyrus; IPL, inferior parietal lobule; OFC, orbitofrontal cortex; ITG, inferior temporal gyrus. L, left hemisphere; R, right hemisphere.

Please cite this article in press as: Cheng, C. -H., et al. Effects of aging on the neuromagnetic mismatch detection to speech sounds. Biol. Psychol. (2014), http://dx.doi.org/10.1016/j.biopsycho.2014.11.003

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IFG, IPL and right OFC was observed in the older adults. Generation of MMNm relies on the formation of sensory memory traces and the process of comparing deviants with previously stored representation of standards. The deactivation of temporal MMNm components in the elderly adults might be attributed to the less efficient maintenance of phoneme-specific memory traces (Naatanen et al., 2007). The frontal components reflect automatically attentional switching to rare stimuli or top-down prediction based on regularity representation (Winkler, Denham, & Nelken, 2009). The observed aging-associated reduction of frontal activity to phonetic discrimination might be a possible account for higher-order speech difficulties in older people. The involvement of orbitofrontal regions in phonetic-MMNm was a significant finding. A previous MEG study by using beamformer analyses has localized orbitofrontal generators of the musically elicited MMNm (Lappe et al., 2013a). Moreover, an agingassociated reduction of MMN amplitude to temporal changes has been linked to poorer verbal memory and executive function (Foster et al., 2013). Our results further indicated the participation of OFC in the distributed neural circuitry of phonetic-MMNm, and the elderly demonstrated an under-activation in the right OFC. In contrast to our expectation, the older subjects showed a larger left OFC activation than younger participants. Right frontal activations could be considered to be driven by involuntary attention switching, one of the neural functions underlying mismatch responses (Naatanen et al., 2007). In aged brains, the capacity of relevant neural assemblies tend to decline and might recruit additional, non-selective cerebral areas (e.g., left frontal areas) to subserve or support this function.

4.2. Hemispheric asymmetry of neural representation to speech sounds According to previous literature, it is generally expected that tone-elicited MMN/MMNm is right-hemispheric dominant in terms of STG sources. However, whether the speech-sound-elicited MMN/MMNm is lateralized to the left hemisphere remains debatable (Bellis et al., 2000; Naatanen et al., 2007). Our present data did not demonstrate any significant left-hemispheric dominance in this ROI. The symmetrical processing of phonetic-MMN was also documented in previous reports (Bellis et al., 2000; Joanisse, Robertson, & Newman, 2007), though some others showed contradicting results (Doeller et al., 2003; Naatanen et al., 1997; Rinne et al., 1999). These discrepancies might be attributed to different stimulus features, such as vowels vs. consonant–vowel syllables, or natural sounds vs. synthetic sounds. Additionally, compared to automatic responses, attentional tasks might engage phonological processing at a greater level, which may allow researchers to capture neural differences between speech and non-speech perception. In addition to STG, MMNm-related responses from more brain regions were identified with MEG source imaging. Like the patterns of superior temporal regions, most of other ROIs, such as ITG and IFG, did not show significant left-ward lateralization to speech sounds, except for IPL. One previous lesion study has demonstrated that compared to Broca’s aphasia, recovery of Wernicke’s aphasia additionally recruited the neural reorganization between STG and IPL (Abo et al., 2004). Recent neuroimaging research has also indicated the role of IPL, particularly angular gyrus, in the phonological processing (Cousin et al., 2007; Jardri et al., 2007). In the present study, a more left-ward lateralization of IPL to speech discrimination might highlight the specific role of this neural substrate in the early-phase, automatic phonological discrimination, though the hemispheric asymmetry pattern in this ROI was not modulated by physiological aging.

4.3. Aging-related alterations in responses to standards A higher activation strength of M50 responses to repetitive standard sounds was observed in the elderly group, suggesting an aging-associated decline in the cortical inhibition to sensory inputs. This less-attenuated cortical activity to redundant information was not only seen in the auditory modality (Alain & Woods, 1999; Cheng & Lin, 2012; Cheng et al., 2012; Golob, Irimajiri, & Starr, 2007), but also in the somatosensory system (Cheng & Lin, 2013). Although the aforementioned findings have been attributed to a defect of bottom-up loops due to the early phase of information processing, it is also striking to note that inhibition ability is regulated through top-down modulation and shown dysfunctional in older adults. Gazzaley and colleagues, by using functional MRI, have demonstrated an aging-related deficit in the suppression of cortical responses to task-irrelevant representations. Of great importance, such suppression-specific defect was profoundly associated with impaired working, memory (Gazzaley, Cooney, Rissman, & D’Esposito, 2005). Taken together, our study indicated that the elderly exhibited an inadequate cortical inhibition to repetitive phonetic sounds in the pre-attentive stage. Moreover, we found a long-lasting component peaking ∼300 ms (M300) in the younger, while absent in the older participants. This between-group differences were not only shown in the sensor waveforms, but also in the MNE source reconstruction (Fig. 2). Consistent with our finding, a previous ERP study has also shown that younger adults exhibited a sustained response to speech sounds in the 270–450 ms range, particularly in the left temporal electrode (Bellis et al., 2000). Although the precise function of this late-latency component to repetitive standards sounds remained extremely unclear, we speculated that it might be related to attentional processing, by which the younger group, compared with the elderly, tended to gain more perceptual awareness to the phonetic stimuli and thus boost the discrimination ability to the deviant sounds. 4.4. Methodological considerations and limitations Different from equivalent current dipole (ECD) modeling, which is sensitive to tangential sources and yields a focal activation location, MNE is a distributed source imaging approach wherein a large number of dipoles across the cortical surface over time are taken into consideration. In addition to possessing as equally accurate localization as ECD (Komssi, Huttunen, Aronen, & Ilmoniemi, 2004), MNE method further overcomes the problems of insensitivity of radial sources and requires minimal assumption. One might, however, argue that the relatively wide-spread activation is due to modeling rather than physiological issues. Although MEN is considered useful for the localization of unknown activity distribution, we still carefully defined the ROIs based on the previous literature showing that the additional activation of inferior frontal (Opitz et al., 2002; Rinne et al., 2005), orbitofrontal (Lappe et al., 2013a; Lappe, Steinstrater, & Pantev, 2013b), inferior parietal (Lappe et al., 2013b; Lin et al., 2007; Molholm, Martinez, Ritter, Javitt, & Foxe, 2005) and inferior temporal regions (Ponton, Bernstein, & Auer, 2009) are also associated with MMN function. Moreover, we should notice that the selection of ROIs followed a strict strategy, wherein individual structural MRI was projected onto the Colin27 MNI template, and then each ROI was defined at the same anatomical region across subjects, yielding a non-biased group comparisons in terms of cortical activation. Finally, to reduce modeling biases we demonstrated cortical activation with Z-score values above 15, considered as extremely significant deviating from baseline (Papoulis, 1991). It has been conceptualized that MMN responses elicited by oddball paradigms consists of memory-based and non-memory-based components, which could be likely separated from each other by

Please cite this article in press as: Cheng, C. -H., et al. Effects of aging on the neuromagnetic mismatch detection to speech sounds. Biol. Psychol. (2014), http://dx.doi.org/10.1016/j.biopsycho.2014.11.003

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collecting another “reverse oddball” or “control” block (Jacobsen & Schroger, 2003; Schroger & Wolff, 1996). However, in order to maintain the subject’s vigilance and minimize the signal variability between different blocks, we conducted traditional oddball paradigms in the present study. Furthermore, our previous work has shown that the results from these two experimental designs (oddball and reverse oddball) were extremely similar not only in the averaged waveforms but also in the phase-synchronization behaviors (Hsiao et al., 2010).

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5. Conclusions

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In summary, this study aimed to characterize the spatiotemporal neural dynamics of mismatch responses to phonetic changes, and to investigate whether such automatic discrimination ability is modulated by physiological aging. Our results revealed a reduced fronto-temporo-parietal activation to pre-attentive discrimination of speech sounds in aged adults. Furthermore, compared to the younger people, the elderly showed an inadequate cortical inhibition to redundant auditory inputs, as reflected by an enlarged M50 response. However, among the identified ROIs, we did not observe significant between-group differences on hemispheric asymmetries in terms of phonetic-MMNm. Conclusively, these findings highlight an altered phonetic processing at the perception level during physiological aging. Future studies by using behavioralneurophysiological and acoustic-phonetic approaches are needed to ascertain the association between speech perception and underlying physiological processing in aging. Conflict of interest

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The authors report no conflict of interest. The protocol has received the approval by the Institutional Review Board of Taipei Veterans General Hospital, and the procedures were in accordance with the Helsinki Declaration.

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Acknowledgements

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The authors would like to thank Chih-Che Chou and Chou430 Ming Cheng for technical assistance in MEG signal calibration 431 and acquisition of MR images, as well as the participation of 432 all the subjects and their families in this study. This work was 433 Q3 supported by Taipei Veterans General Hospital (V96ER3-004, 434 V97ER3-006, VGHUST97-P6-24, V98ER3-002, V98S4-018, V99ER3435 006, V101C-023 and V102E9-003), the National Science Coun436 cil (NSC-95-2314-B-010-030-MY3, NSC-96-2628-B-010-030-MY3, 437 NSC-98-2321-B-010-007, NSC-99-2321-B-010-004, NSC-99-2628438 B-010-011-MY3, NSC-100-2321-B-010-004-MY3, NSC-101-2314439 B-010-068-MY3, NSC-101-2917-I-010-003, NSC-102-2628-B-010440 008-MY3 and NSC-102-2811-B-010-047), and the Brain Research 441 Center (102AC-B22 and 102AC-B7), National Yang-Ming University 442 and a grant from Ministry of Education, Aim for the Top University 443 Plan, Taipei, Taiwan. 429 Q2

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