Objective: The ability to detect frequency variation is a fundamental skill necessary for speech perception. It is known that musical expertise is associated with a range of auditory perceptual skills, including discriminating frequency change, which suggests the neural encoding of spectral features can be enhanced by musical training. In this study, we measured auditory cortical responses to frequency change in musicians to examine the relationships between N1/P2 responses and behavioral performance/musical training. Methods: Behavioral and electrophysiological data were obtained from professional musicians and age-matched non-musician participants. Behavioral data included frequency discrimination detection thresholds for no threshold-equalizing noise (TEN), +5, 0, and -5 signal-to-noise ratio settings. Auditory-evoked responses were measured using a 64-channel electroencephalogram (EEG) system in response to frequency changes in ongoing pure tones consisting of 250 and 4,000 Hz, and the magnitudes of frequency change were 10%, 25% or 50% from the base frequencies. N1 and P2 amplitudes and latencies as well as dipole source activation in the left and right hemispheres were measured for each condition. Results: Compared to the non-musician group, behavioral thresholds in the musician group were lower for frequency discrimination in quiet conditions only. The scalp-recorded N1 amplitudes were modulated as a function of frequency change. P2 amplitudes in the musician group were larger than in the non-musician group. Dipole source analysis showed that P2 dipole activity to frequency changes was lateralized to the right hemisphere, with greater activity in the musician group regardless of the hemisphere side. Additionally, N1 amplitudes to frequency changes were positively related to behavioral thresholds for frequency discrimination while enhanced P2 amplitudes were associated with a longer duration of musical training. Conclusions: Our results demonstrate that auditory cortical potentials evoked by frequency change are related to behavioral thresholds for frequency discrimination in musicians. Larger P2 amplitudes in musicians compared to non-musicians reflects musical training-induced neural plasticity.

Word acquisition could be mediated by the neurocognitive mechanism known as fast mapping (FM). It refers to a process of incidental exclusion-based learning and is believed to be a critical mechanism for the rapid build-up of lexicon, although its neural mechanisms are still poorly understood. To investigate the neural bases of this key learning skill, we used event-related potentials (ERPs) and employed an audio-visual paradigm that included a counterbalanced set of familiar and novel spoken word forms presented, in a single exposure, in conjunction with novel and familiar images. To define learning-related brain dynamics, passive auditory ERPs, known to index long-term memory trace activation, were recorded before and after the FM task. Following the single FM learning exposure, we found a significant enhancement in neural activation elicited by the newly trained word form, which was expressed at ~200-400 ms after the word onset. No similar amplitude increase was found either for the native familiar word used as a control stimulus in the same learning paradigm or for similar control stimuli which were not subject to training. Topographic analysis suggested a left-lateral shift of the ERP scalp distribution for the novel FM word form, underpinned by fronto-temporal cortical sources, which may indicate the involvement of pre-existing neurolinguistic networks for mastering new word forms with native phonology. Overall, the near-instant changes in neural activity after a single-shot novel word training indicate that FM could promote rapid integration of newly learned items into the brain's neural lexicon, even in adulthood.

We assessed how synchronous speech listening and lipreading affects speech recognition in acoustic noise. In simple audiovisual perceptual tasks, inverse effectiveness is often observed, which holds that the weaker the unimodal stimuli, or the poorer their signal-to-noise ratio, the stronger the audiovisual benefit. So far, however, inverse effectiveness has not been demonstrated for complex audiovisual speech stimuli. Here we assess whether this multisensory integration effect can also be observed for the recognizability of spoken words. To that end, we presented audiovisual sentences to 18 native-Dutch normal-hearing participants, who had to identify the spoken words from a finite list. Speech-recognition performance was determined for auditory-only, visual-only (lipreading), and auditory-visual conditions. To modulate acoustic task difficulty, we systematically varied the auditory signal-to-noise ratio. In line with a commonly observed multisensory enhancement on speech recognition, audiovisual words were more easily recognized than auditory-only words (recognition thresholds of -15 and -12 dB, respectively). We here show that the difficulty of recognizing a particular word, either acoustically or visually, determines the occurrence of inverse effectiveness in audiovisual word integration. Thus, words that are better heard or recognized through lipreading, benefit less from bimodal presentation. Audiovisual performance at the lowest acoustic signal-to-noise ratios (45%) fell below the visual recognition rates (60%), reflecting an actual deterioration of lipreading in the presence of excessive acoustic noise. This suggests that the brain may adopt a strategy in which attention has to be divided between listening and lipreading.

Auditory processing disorder (APD) affects about 2-5% of children. However, the nature of this disorder is poorly understood. Children with APD typically have difficulties in complex listening situations. One mechanism thought to aid in listening-in-noise is the medial olivocochlear (MOC) inhibition. The purpose of this review was to critically analyze the published data on MOC inhibition in children with APD to determine whether the MOC efferents are involved in these individuals. The otoacoustic emission (OAE) methods used to assay MOC reflex were examined in the context of the current understanding of OAE generation mechanisms. Relevant literature suggests critical differences in the study population and OAE methods. Variables currently known to influence MOC reflex measurements, for example, middle-ear muscle reflexes or OAE signal-to-noise ratio, were not controlled in most studies. The use of potentially weaker OAE methods and the remarkable heterogeneity across studies does not allow for a definite conclusion whether or not the MOC reflex is altered in children with APD. Further carefully designed studies are needed to confirm the involvement of MOC efferents in APD. Knowledge of efferent functioning in children with APD would be mechanistically and clinically beneficial.

Resting-state brain activity has been widely investigated using blood oxygenation level dependent (BOLD) contrast techniques. However, BOLD signal changes reflect a combination of the effects of cerebral blood flow (CBF), cerebral blood volume (CBV), as well as the cerebral metabolic rate of oxygen (CMRO2). In this study, resting-state brain activation was detected and compared using the following techniques: (a) BOLD, using a gradient-echo echo planar imaging (GE-EPI) sequence; (b) CBV-weighted signal, acquired using gradient and spin echo (GRASE) based vascular space occupancy (VASO); and (c) CBF, using pseudo-continuous arterial spin labeling (pCASL). Reliable brain networks were detected using VASO and ASL, including sensorimotor, auditory, primary visual, higher visual, default mode, salience and left/right executive control networks. Differences between the resting-state activation detected with ASL, VASO and BOLD could potentially be due to the different temporal signal-to-noise ratio (tSNR) and the short post-labeling delay (PLD) in ASL, along with differences in the spin-echo readout of VASO. It is also possible that the dynamics of spontaneous fluctuations in BOLD, CBV and CBF could differ due to biological reasons, according to their location within the brain.

In theory, miniaturized systems such as the around-the-ear electrode arrays (cEEGrids) enable mobile monitoring of the electroencephalogram (EEG) in a variety of real life situations without interfering with the natural setting. However, the research benefit of such cEEGrid recordings critically depends on their validity. To investigate whether visual and motor processing are reflected in the cEEGrid-EEG, a direct comparison of EEG that was concurrently recorded with the cEEGrids and with a high-density cap setup was conducted. Thirteen participants performed a classic Simon task in which letters were presented laterally and a lateralized choice response was executed. N1, P1 and P300 event-related potential (ERP) waveforms were extracted from cEEGrid-EEG: they were found to be strongly correlated with corresponding waveforms extracted from cap-EEG but with lower signal strength and lower signal-to-noise-ratio (SNR). Event-related lateralizations (ERLs) recorded at posterior scalp sites were well reflected in middle cEEGrid pairs. Moreover, the effect size of the Simon correspondence effect on the extracted ERLs was similar between the two systems. However, lateralizations at central cap sites were less well reflected in the cEEGrid-EEG indicating a difficulty in capturing motor response preparation and execution. These results show that well-described visual and cognitive ERPs and ERLs can be measured using the cEEGrids, while motor-related cortical potentials are not well captured. This study further demonstrates the potential and possible limitations of unobtrusive cEEGrid-EEG recordings.

High gamma oscillations (70-150 Hz; HG) are rapidly evolving, spatially localized neurophysiological signals that are believed to be the best representative signature of engaged neural populations. The HG band has been best characterized from invasive electrophysiological approaches such as electrocorticography because of the increased signal-to-noise ratio that results when by-passing the scalp and skull. Despite the recent observation that HG activity can be detected non-invasively by electroencephalography (EEG), it is unclear to what extent EEG can accurately resolve the spatial distribution of HG signals during active task engagement. We have overcome some of the limitations inherent to acquiring HG signals across the scalp by utilizing individual head anatomy in combination with an inverse modeling method. We applied a linearly constrained minimum variance (LCMV) beamformer method on EEG data during a motor imagery paradigm to extract a time-frequency spectrogram at every voxel location on the cortex. To confirm spatially distributed patterns of HG responses, we contrasted overlapping maps of the EEG HG signal with blood oxygen level dependence (BOLD) functional magnetic resonance imaging (fMRI) data acquired from the same set of neurologically normal subjects during a separate session. We show that scalp-based HG band activity detected by EEG during motor imagery spatially co-localizes with BOLD fMRI data. Taken together, these results suggest that EEG can accurately resolve spatially specific estimates of local cortical high frequency signals, potentially opening an avenue for non-invasive measurement of HG potentials from diverse sets of neurologically impaired populations for diagnostic and therapeutic purposes.

The attended speech stream can be detected robustly, even in adverse auditory scenarios with auditory attentional modulation, and can be decoded using electroencephalographic (EEG) data. Speech segmentation based on the relative root-mean-square (RMS) intensity can be used to estimate segmental contributions to perception in noisy conditions. High-RMS-level segments contain crucial information for speech perception. Hence, this study aimed to investigate the effect of high-RMS-level speech segments on auditory attention decoding performance under various signal-to-noise ratio (SNR) conditions. Scalp EEG signals were recorded when subjects listened to the attended speech stream in the mixed speech narrated concurrently by two Mandarin speakers. The temporal response function was used to identify the attended speech from EEG responses of tracking to the temporal envelopes of intact speech and high-RMS-level speech segments alone, respectively. Auditory decoding performance was then analyzed under various SNR conditions by comparing EEG correlations to the attended and ignored speech streams. The accuracy of auditory attention decoding based on the temporal envelope with high-RMS-level speech segments was not inferior to that based on the temporal envelope of intact speech. Cortical activity correlated more strongly with attended than with ignored speech under different SNR conditions. These results suggest that EEG recordings corresponding to high-RMS-level speech segments carry crucial information for the identification and tracking of attended speech in the presence of background noise. This study also showed that with the modulation of auditory attention, attended speech can be decoded more robustly from neural activity than from behavioral measures under a wide range of SNR.

Resting-state fMRI results in neurodegenerative diseases have been somewhat conflicting. This may be due to complex partial volume effects of CSF in BOLD signal in patients with brain atrophy. To encounter this problem, we used a coefficient of variation (CV) map to highlight artifacts in the data, followed by analysis of gray matter voxels in order to minimize brain volume effects between groups. The effects of these measures were compared to whole brain ICA dual regression results in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). 23 AD patients, 21 bvFTD patients and 25 healthy controls were included. The quality of the data was controlled by CV mapping. For detecting functional connectivity (FC) differences whole brain ICA (wbICA) and also segmented gray matter ICA (gmICA) followed by dual regression were conducted, both of which were performed both before and after data quality control. Decreased FC was detected in posterior DMN in the AD group and in the Salience network in the bvFTD group after combining CV quality control with gmICA. Before CV quality control, the decreased connectivity finding was not detectable in gmICA in neither of the groups. Same finding recurred when exclusion was based on randomization. The subjects excluded due to artifacts noticed in the CV maps had significantly lower temporal signal-to-noise ratio than the included subjects. Data quality measure CV is an effective tool in detecting artifacts from resting state analysis. CV reflects temporal dispersion of the BOLD signal stability and may thus be most helpful for spatial ICA, which has a blind spot in spatially correlating widespread artifacts. CV mapping in conjunction with gmICA yields results suiting previous findings both in AD and bvFTD.

Do task demands change the way we extract information from a stimulus, or only how we use this information for decision making? In order to answer this question for visual word recognition, we used EEG/MEG as well as fMRI to determine the latency ranges and spatial areas in which brain activation to words is modulated by task demands. We presented letter strings in three tasks (lexical decision, semantic decision, silent reading), and measured combined EEG/MEG as well as fMRI responses in two separate experiments. EEG/MEG sensor statistics revealed the earliest reliable task effects at around 150 ms, which were localized, using minimum norm estimates (MNE), to left inferior temporal, right anterior temporal and left precentral gyri. Later task effects (250 and 480 ms) occurred in left middle and inferior temporal gyri. Our fMRI data showed task effects in left inferior frontal, posterior superior temporal and precentral cortices. Although there was some correspondence between fMRI and EEG/MEG localizations, discrepancies predominated. We suggest that fMRI may be less sensitive to the early short-lived processes revealed in our EEG/MEG data. Our results indicate that task-specific processes start to penetrate word recognition already at 150 ms, suggesting that early word processing is flexible and intertwined with decision making.

The human auditory system is highly skilled at extracting and processing information from speech in both single-speaker and multi-speaker situations. A commonly studied speech feature is the amplitude envelope which can also be used to determine which speaker a listener is attending to in those multi-speaker situations. Non-invasive brain imaging (electro-/magnetoencephalography [EEG/MEG]) has shown that the phase of neural activity below 16 Hz tracks the dynamics of speech, whereas invasive brain imaging (electrocorticography [ECoG]) has shown that such processing is strongly reflected in the power of high frequency neural activity (around 70-150 Hz; known as high gamma). The first aim of this study was to determine if high gamma power scalp recorded EEG carries useful stimulus-related information, despite its reputation for having a poor signal to noise ratio. Specifically, linear regression was used to investigate speech envelope and attention decoding in low frequency EEG, high gamma power EEG, and in both EEG signals combined. The second aim was to assess whether the information reflected in high gamma power EEG may be complementary to that reflected in well-established low frequency EEG indices of speech processing. Exploratory analyses were also completed to examine how low frequency and high gamma power EEG may be sensitive to different features of the speech envelope. While low frequency speech tracking was evident for almost all subjects as expected, high gamma power also showed robust speech tracking in some subjects. This same pattern was true for attention decoding using a separate group of subjects who participated in a cocktail party attention experiment. For the subjects who showed speech tracking in high gamma power EEG, the spatiotemporal characteristics of that high gamma tracking differed from that of low-frequency EEG. Furthermore, combining the two neural measures led to improved measures of speech tracking for several subjects. Our results indicated that high gamma power EEG can carry useful information regarding speech processing and attentional selection in some subjects. Combining high gamma power and low frequency EEG can improve the mapping between natural speech and the resulting neural responses.

Objective: This study evaluated cortical encoding of voice onset time (VOT) in quiet and noise, and their potential associations with the behavioral categorical perception of VOT in children with auditory neuropathy spectrum disorder (ANSD). Design: Subjects were 11 children with ANSD ranging in age between 6.4 and 16.2 years. The stimulus was an /aba/-/apa/ vowel-consonant-vowel continuum comprising eight tokens with VOTs ranging from 0 ms (voiced endpoint) to 88 ms (voiceless endpoint). For speech in noise, speech tokens were mixed with the speech-shaped noise from the Hearing In Noise Test at a signal-to-noise ratio (SNR) of +5 dB. Speech-evoked auditory event-related potentials (ERPs) and behavioral categorization perception of VOT were measured in quiet in all subjects, and at an SNR of +5 dB in seven subjects. The stimuli were presented at 35 dB SL (re: pure tone average) or 115 dB SPL if this limit was less than 35 dB SL. In addition to the onset response, the auditory change complex (ACC) elicited by VOT was recorded in eight subjects. Results: Speech evoked ERPs recorded in all subjects consisted of a vertex positive peak (i.e., P1), followed by a trough occurring approximately 100 ms later (i.e., N2). For results measured in quiet, there was no significant difference in categorical boundaries estimated using ERP measures and behavioral procedures. Categorical boundaries estimated in quiet using both ERP and behavioral measures closely correlated with the most-recently measured Phonetically Balanced Kindergarten (PBK) scores. Adding a competing background noise did not affect categorical boundaries estimated using either behavioral or ERP procedures in three subjects. For the other four subjects, categorical boundaries estimated in noise using behavioral measures were prolonged. However, adding background noise only increased categorical boundaries measured using ERPs in three out of these four subjects. Conclusions: VCV continuum can be used to evaluate behavioral identification and the neural encoding of VOT in children with ANSD. In quiet, categorical boundaries of VOT estimated using behavioral measures and ERP recordings are closely associated with speech recognition performance in children with ANSD. Underlying mechanisms for excessive speech perception deficits in noise may vary for individual patients with ANSD.

Background: Proprioception is important for regaining motor function in the paretic upper extremity after stroke. However, clinical assessments of proprioception are subjective and require verbal responses from the patient to applied proprioceptive stimuli. Cortical responses evoked by robotic wrist perturbations and measured by electroencephalography (EEG) may be an objective method to support current clinical assessments of proprioception. Objective: To establish whether evoked cortical responses reflect proprioceptive deficits as assessed by clinical scales and whether they predict upper extremity motor function at 26 weeks after stroke. Methods: Thirty-one patients with stroke were included. In week 1, 3, 5, 12, and 26 after stroke, the upper extremity sections of the Erasmus modified Nottingham Sensory Assessment (EmNSA-UE) and the Fugl-Meyer Motor Assessment (FM-UE) and the EEG responses (64 channels) to robotic wrist perturbations were measured. The extent to which proprioceptive input was conveyed to the affected hemisphere was estimated by the signal-to-noise ratio (SNR) of the evoked response. The relationships between SNR and EmNSA-UE as well as SNR and time after stroke were investigated using linear regression. Receiver-operating-characteristic curves were used to compare the predictive values of SNR and EmNSA-UE for predicting whether patients regained some selective motor control (FM-UE > 22) or whether they could only move their paretic upper extremity within basic limb synergies (FM-UE ≤ 22) at 26 weeks after stroke. Results: Patients (N = 7) with impaired proprioception (EmNSA-UE proprioception score < 8) had significantly smaller SNR than patients with unimpaired proprioception (N = 24) [EmNSA-UE proprioception score = 8, t(29) = 2.36, p = 0.03]. No significant effect of time after stroke on SNR was observed. Furthermore, there was no significant difference in the predictive value between EmNSA-UE and SNR for predicting motor function at 26 weeks after stroke. Conclusion: The SNR of the evoked cortical response does not significantly change as a function of time after stroke and differs between patients with clinically assessed impaired and unimpaired proprioception, suggesting that SNR reflects persistent damage to proprioceptive pathways. A similar predictive value with respect to EmNSA-UE suggests that SNR may be used as an objective predictor next to clinical sensory assessments for predicting motor function at 26 weeks after stroke.

Recent advances in electroencephalographic (EEG) acquisition allow for recordings using wet and dry sensors during whole-body motion. The large variety of commercially available EEG systems contrasts with the lack of established methods for objectively describing their performance during whole-body motion. Therefore, the aim of this study was to introduce methods for benchmarking the suitability of new EEG technologies for that context. Subjects performed an auditory oddball task using three different EEG systems (Biosemi wet-BSM, Cognionics Wet-Cwet, Conionics Dry-Cdry). Nine subjects performed the oddball task while seated and walking on a treadmill. We calculated EEG epoch rejection rate, pre-stimulus noise (PSN), signal-to-noise ratio (SNR) and EEG amplitude variance across the P300 event window (CVERP) from a subset of 12 channels common to all systems. We also calculated test-retest reliability and the subject's level of comfort while using each system. Our results showed that using the traditional 75 μV rejection threshold BSM and Cwet epoch rejection rates are ~25% and ~47% in the seated and walking conditions respectively. However, this threshold rejects ~63% of epochs for Cdry in the seated condition and excludes 100% of epochs for the majority of subjects during walking. BSM showed predominantly no statistical differences between seated and walking condition for all metrics, whereas Cwet showed increases in PSN and CVERP, as well as reduced SNR in the walking condition. Data quality from Cdry in seated conditions were predominantly inferior in comparison to the wet systems. Test-retest reliability was mostly moderate/good for these variables, especially in seated conditions. In addition, subjects felt less discomfort and were motivated for longer recording periods while using wet EEG systems in comparison to the dry system. The proposed method was successful in identifying differences across systems that are mostly caused by motion-related artifacts and usability issues. We conclude that the extraction of the selected metrics from an auditory oddball paradigm may be used as a benchmark method for testing the performance of different EEG systems in mobile conditions. Moreover dry EEG systems may need substantial improvements to meet the quality standards of wet electrodes.

Single-nucleotide polymorphisms (SNPs) of the fat mass and obesity associated (FTO) gene are linked to obesity, but how these SNPs influence resting-state neural activation is unknown. Few brain-imaging studies have investigated the influence of obesity-related SNPs on neural activity, and no study has investigated resting-state connectivity patterns. We tested connectivity within three, main resting-state networks: default mode (DMN), sensorimotor (SMN), and salience network (SN) in 30 male participants, grouped based on genotype for the rs9939609 FTO SNP, as well as punishment and reward sensitivity measured by the Behavioral Inhibition (BIS) and Behavioral Activation System (BAS) questionnaires. Because obesity is associated with anomalies in both systems, we calculated a BIS/BAS ratio (BBr) accounting for features of both scores. A prominence of BIS over BAS (higher BBr) resulted in increased connectivity in frontal and paralimbic regions. These alterations were more evident in the obesity-associated AA genotype, where a high BBr was also associated with increased SN connectivity in dopaminergic circuitries, and in a subnetwork involved in somatosensory integration regarding food. Participants with AA genotype and high BBr, compared to corresponding participants in the TT genotype, also showed greater DMN connectivity in regions involved in the processing of food cues, and in the SMN for regions involved in visceral perception and reward-based learning. These findings suggest that neural connectivity patterns influence the sensitivity toward punishment and reward more closely in the AA carriers, predisposing them to developing obesity. Our work explains a complex interaction between genetics, neural patterns, and behavioral measures in determining the risk for obesity and may help develop individually-tailored strategies for obesity prevention.

Transcallosal motor fiber (TCMF) plays a role in interhemispheric inhibition (IHI) between two primary motor cortices. IHI has been an important concept in development of the motor system of the brain. Many studies have focused on the research of the topography of TCMF, however, little is known about development of TCMF. In the current study, we attempted to investigate development of TCMF from the corticospinal tract (CST) in the human brain using diffusion tensor tractography. A total of 76 healthy subjects were recruited for this study. We reconstructed the TCMF, which was derived from the CST, by selection of two regions of interest below the corpus callosum (upper and middle pons). Termination criteria used for fiber tracking were fractional anisotropy <0.2 and three tract turning angles of <45, 60, and 75(°). The subjects were classified into four groups according to age: group A (0-5 years), group B (6-10 years), group C (11-15 years), and group D (16-20 years). Significant differences in the incidence of TCMF were observed between group B and group C, and between group B and group D, with tract turning angles of 60 and 75(°) (p < 0.05). However, no significant differences in any tract turning angle were observed between group C and group D (p > 0.05). In addition, in terms of the incidence of TCMF, no significant differences were observed between the three tract turning angles (p > 0.05). We obtained visualized TCMF from the CST with development and found that the incidence of TCMF differed significantly around the approximate age of 10 years. As a result, we demonstrated structural evidence for development of TCMF in the human brain.

While the human medial prefrontal cortex (mPFC) is widely believed to be a key node of neural networks relevant for socio-emotional processing, its functional subspecialization is still poorly understood. We thus revisited the often assumed differentiation of the mPFC in social cognition along its ventral-dorsal axis. Our neuroinformatic analysis was based on a neuroimaging meta-analysis of perspective-taking that yielded two separate clusters in the ventral and dorsal mPFC, respectively. We determined each seed region's brain-wide interaction pattern by two complementary measures of functional connectivity: co-activation across a wide range of neuroimaging studies archived in the BrainMap database and correlated signal fluctuations during unconstrained ("resting") cognition. Furthermore, we characterized the functions associated with these two regions using the BrainMap database. Across methods, the ventral mPFC was more strongly connected with the nucleus accumbens, hippocampus, posterior cingulate cortex, and retrosplenial cortex, while the dorsal mPFC was more strongly connected with the inferior frontal gyrus, temporo-parietal junction, and middle temporal gyrus. Further, the ventral mPFC was selectively associated with reward related tasks, while the dorsal mPFC was selectively associated with perspective-taking and episodic memory retrieval. The ventral mPFC is therefore predominantly involved in bottom-up-driven, approach/avoidance-modulating, and evaluation-related processing, whereas the dorsal mPFC is predominantly involved in top-down-driven, probabilistic-scene-informed, and metacognition-related processing in social cognition.