Phase Alignment of Low-Frequency Neural Activity to the Amplitude Envelope of Speech Reflects Evoked Responses to Acoustic Edges, Not Oscillatory Entrainment.

The amplitude envelope of speech is crucial for accurate comprehension. Considered a key stage in speech processing, the phase of neural activity in the theta-delta bands (1-10 Hz) tracks the phase of the speech amplitude envelope during listening. However, the mechanisms underlying this envelope representation have been heavily debated. A dominant model posits that envelope tracking reflects entrainment of endogenous low-frequency oscillations to the speech envelope. Alternatively, envelope tracking reflects a series of evoked responses to acoustic landmarks within the envelope. It has proven challenging to distinguish these two mechanisms. To address this, we recorded MEG while participants (n = 12, 6 female) listened to natural speech, and compared the neural phase patterns to the predictions of two computational models: an oscillatory entrainment model and a model of evoked responses to peaks in the rate of envelope change. Critically, we also presented speech at slowed rates, where the spectro-temporal predictions of the two models diverge. Our analyses revealed transient theta phase-locking in regular speech, as predicted by both models. However, for slow speech, we found transient theta and delta phase-locking, a pattern that was fully compatible with the evoked response model but could not be explained by the oscillatory entrainment model. Furthermore, encoding of acoustic edge magnitudes was invariant to contextual speech rate, demonstrating speech rate normalization of acoustic edge representations. Together, our results suggest that neural phase-locking to the speech envelope is more likely to reflect discrete representation of transient information rather than oscillatory entrainment.SIGNIFICANCE STATEMENT This study probes a highly debated topic in speech perception: the neural mechanisms underlying the cortical representation of the temporal envelope of speech. It is well established that the slow intensity profile of the speech signal, its envelope, elicits a robust brain response that "tracks" these envelope fluctuations. The oscillatory entrainment model posits that envelope tracking reflects phase alignment of endogenous neural oscillations. Here the authors provide evidence for a distinct mechanism. They show that neural speech envelope tracking arises from transient evoked neural responses to rapid increases in the speech envelope. Explicit computational modeling provides direct and compelling evidence that evoked responses are the primary mechanism underlying cortical speech envelope representations, with no evidence for oscillatory entrainment.

Cortical Synchrony and Information Flow during Transition from Wakefulness to Light Non-Rapid Eye Movement Sleep.

Sleep is a highly stereotyped phenomenon, requiring robust spatiotemporal coordination of neural activity. Understanding how the brain coordinates neural activity with sleep onset can provide insights into the physiological functions subserved by sleep and the pathologic phenomena associated with sleep onset. We quantified whole-brain network changes in synchrony and information flow during the transition from wakefulness to light non-rapid eye movement (NREM) sleep, using MEG imaging in a convenient sample of 14 healthy human participants (11 female; mean 63.4 years [SD 11.8 years]). We furthermore performed computational modeling to infer excitatory and inhibitory properties of local neural activity. The transition from wakefulness to light NREM was identified to be encoded in spatially and temporally specific patterns of long-range synchrony. Within the delta band, there was a global increase in connectivity from wakefulness to light NREM, which was highest in frontoparietal regions. Within the theta band, there was an increase in connectivity in fronto-parieto-occipital regions and a decrease in temporal regions from wakefulness to Stage 1 sleep. Patterns of information flow revealed that mesial frontal regions receive hierarchically organized inputs from broad cortical regions upon sleep onset, including direct inflow from occipital regions and indirect inflow via parieto-temporal regions within the delta frequency band. Finally, biophysical neural mass modeling demonstrated changes in the anterior-to-posterior distribution of cortical excitation-to-inhibition with increased excitation-to-inhibition model parameters in anterior regions in light NREM compared with wakefulness. Together, these findings uncover whole-brain corticocortical structure and the orchestration of local and long-range, frequency-specific cortical interactions in the sleep-wake transition.SIGNIFICANCE STATEMENT Our work uncovers spatiotemporal cortical structure of neural synchrony and information flow upon the transition from wakefulness to light non-rapid eye movement sleep. Mesial frontal regions were identified to receive hierarchically organized inputs from broad cortical regions, including both direct inputs from occipital regions and indirect inputs via the parieto-temporal regions within the delta frequency range. Biophysical neural mass modeling revealed a spatially heterogeneous, anterior-posterior distribution of cortical excitation-to-inhibition. Our findings shed light on the orchestration of local and long-range cortical neural structure that is fundamental to sleep onset, and support an emerging view of cortically driven regulation of sleep homeostasis.

Mechanisms of sensorimotor adaptation in a hierarchical state feedback control model of speech.

Upon perceiving sensory errors during movements, the human sensorimotor system updates future movements to compensate for the errors, a phenomenon called sensorimotor adaptation. One component of this adaptation is thought to be driven by sensory prediction errors-discrepancies between predicted and actual sensory feedback. However, the mechanisms by which prediction errors drive adaptation remain unclear. Here, auditory prediction error-based mechanisms involved in speech auditory-motor adaptation were examined via the feedback aware control of tasks in speech (FACTS) model. Consistent with theoretical perspectives in both non-speech and speech motor control, the hierarchical architecture of FACTS relies on both the higher-level task (vocal tract constrictions) as well as lower-level articulatory state representations. Importantly, FACTS also computes sensory prediction errors as a part of its state feedback control mechanism, a well-established framework in the field of motor control. We explored potential adaptation mechanisms and found that adaptive behavior was present only when prediction errors updated the articulatory-to-task state transformation. In contrast, designs in which prediction errors updated forward sensory prediction models alone did not generate adaptation. Thus, FACTS demonstrated that 1) prediction errors can drive adaptation through task-level updates, and 2) adaptation is likely driven by updates to task-level control rather than (only) to forward predictive models. Additionally, simulating adaptation with FACTS generated a number of important hypotheses regarding previously reported phenomena such as identifying the source(s) of incomplete adaptation and driving factor(s) for changes in the second formant frequency during adaptation to the first formant perturbation. The proposed model design paves the way for a hierarchical state feedback control framework to be examined in the context of sensorimotor adaptation in both speech and non-speech effector systems.

A joint subspace mapping between structural and functional brain connectomes.

Understanding the connection between the brain's structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject's functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.

Bayesian inference of a spectral graph model for brain oscillations.

The relationship between brain functional connectivity and structural connectivity has caught extensive attention of the neuroscience community, commonly inferred using mathematical modeling. Among many modeling approaches, spectral graph model (SGM) is distinctive as it has a closed-form solution of the wide-band frequency spectra of brain oscillations, requiring only global biophysically interpretable parameters. While SGM is parsimonious in parameters, the determination of SGM parameters is non-trivial. Prior works on SGM determine the parameters through a computational intensive annealing algorithm, which only provides a point estimate with no confidence intervals for parameter estimates. To fill this gap, we incorporate the simulation-based inference (SBI) algorithm and develop a Bayesian procedure for inferring the posterior distribution of the SGM parameters. Furthermore, using SBI dramatically reduces the computational burden for inferring the SGM parameters. We evaluate the proposed SBI-SGM framework on the resting-state magnetoencephalography recordings from healthy subjects and show that the proposed procedure has similar performance to the annealing algorithm in recovering power spectra and the spatial distribution of the alpha frequency band. In addition, we also analyze the correlations among the parameters and their uncertainty with the posterior distribution which cannot be done with annealing inference. These analyses provide a richer understanding of the interactions among biophysical parameters of the SGM. In general, the use of simulation-based Bayesian inference enables robust and efficient computations of generative model parameter uncertainties and may pave the way for the use of generative models in clinical translation applications.

Dynamic functional connectivity MEG features of Alzheimer's disease.

Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.

Distinct neurophysiology during nonword repetition in logopenic and non-fluent variants of primary progressive aphasia.

Overlapping clinical presentations in primary progressive aphasia (PPA) variants present challenges for diagnosis and understanding pathophysiology, particularly in the early stages of the disease when behavioral (speech) symptoms are not clearly evident. Divergent atrophy patterns (temporoparietal degeneration in logopenic variant lvPPA, frontal degeneration in nonfluent variant nfvPPA) can partially account for differential speech production errors in the two groups in the later stages of the disease. While the existing dogma states that neurodegeneration is the root cause of compromised behavior and cortical activity in PPA, the extent to which neurophysiological signatures of speech dysfunction manifest independent of their divergent atrophy patterns remain unknown. We test the hypothesis that nonword deficits in lvPPA and nfvPPA arise from distinct patterns of neural oscillations that are unrelated to atrophy. We use a novel structure-function imaging approach integrating magnetoencephalographic imaging of neural oscillations during a non-word repetition task with voxel-based morphometry-derived measures of gray matter volume to isolate neural oscillation abnormalities independent of atrophy. We find reduced beta band neural activity in left temporal regions associated with the late stages of auditory encoding unique to patients with lvPPA and reduced high-gamma neural activity over left frontal regions associated with the early stages of motor preparation in patients with nfvPPA. Neither of these patterns of reduced cortical oscillations was explained by cortical atrophy in our statistical model. These findings highlight the importance of structure-function imaging in revealing neurophysiological sequelae in early stages of dementia when neither structural atrophy nor behavioral deficits are clinically distinct.

Neuronal synchrony abnormalities associated with subclinical epileptiform activity in early-onset Alzheimer's disease.

Since the first demonstrations of network hyperexcitability in scientific models of Alzheimer's disease, a growing body of clinical studies have identified subclinical epileptiform activity and associated cognitive decline in patients with Alzheimer's disease. An obvious problem presented in these studies is lack of sensitive measures to detect and quantify network hyperexcitability in human subjects. In this study we examined whether altered neuronal synchrony can be a surrogate marker to quantify network hyperexcitability in patients with Alzheimer's disease. Using magnetoencephalography (MEG) at rest, we studied 30 Alzheimer's disease patients without subclinical epileptiform activity, 20 Alzheimer's disease patients with subclinical epileptiform activity and 35 age-matched controls. Presence of subclinical epileptiform activity was assessed in patients with Alzheimer's disease by long-term video-EEG and a 1-h resting MEG with simultaneous EEG. Using the resting-state source-space reconstructed MEG signal, in patients and controls we computed the global imaginary coherence in alpha (8-12 Hz) and delta-theta (2-8 Hz) oscillatory frequencies. We found that Alzheimer's disease patients with subclinical epileptiform activity have greater reductions in alpha imaginary coherence and greater enhancements in delta-theta imaginary coherence than Alzheimer's disease patients without subclinical epileptiform activity, and that these changes can distinguish between Alzheimer's disease patients with subclinical epileptiform activity and Alzheimer's disease patients without subclinical epileptiform activity with high accuracy. Finally, a principal component regression analysis showed that the variance of frequency-specific neuronal synchrony predicts longitudinal changes in Mini-Mental State Examination in patients and controls. Our results demonstrate that quantitative neurophysiological measures are sensitive biomarkers of network hyperexcitability and can be used to improve diagnosis and to select appropriate patients for the right therapy in the next-generation clinical trials. The current results provide an integrative framework for investigating network hyperexcitability and network dysfunction together with cognitive and clinical correlates in patients with Alzheimer's disease.

Empirical Bayesian localization of event-related time-frequency neural activity dynamics.

Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations across human brain regions is an important problem in functional brain imaging and human cognitive neuroscience with magnetoencephalography (MEG) and electroencephalography (EEG). The problem is challenging not only in terms of localization of complex source configurations from sensor measurements with unknown noise and interference but also for reconstruction of transient event-related time-frequency dynamics of cortical oscillations. We recently proposed a robust empirical Bayesian algorithm for simultaneous reconstruction of complex brain source activity and noise covariance, in the context of evoked and resting-state data. In this paper, we expand upon this empirical Bayesian framework for optimal reconstruction of event-related time-frequency dynamics of regional cortical oscillations, referred to as time-frequency Champagne (TFC). This framework enables imaging of five-dimensional (space, time, and frequency) event-related brain activity from M/EEG data, and can be viewed as a time-frequency optimized adaptive Bayesian beamformer. We evaluate TFC in both simulations and several real datasets, with comparisons to benchmark standards - variants of time-frequency optimized adaptive beamformers (TFBF) as well as the sLORETA algorithm. In simulations, we demonstrate several advantages in estimating time-frequency cortical oscillatory dynamics compared to benchmarks. With real MEG data, we demonstrate across many datasets that the proposed approach is robust to highly correlated brain activity and low SNR data, and is able to accurately reconstruct cortical dynamics with data from just a few epochs.

Striatal networks for tinnitus treatment targeting.

Neuromodulation treatment effect size for bothersome tinnitus may be larger and more predictable by adopting a target selection approach guided by personalized striatal networks or functional connectivity maps. Several corticostriatal mechanisms are likely to play a role in tinnitus, including the dorsal/ventral striatum and the putamen. We examined whether significant tinnitus treatment response by deep brain stimulation (DBS) of the caudate nucleus may be related to striatal network increased functional connectivity with tinnitus networks that involve the auditory cortex or ventral cerebellum. The first study was a cross-sectional 2-by-2 factorial design (tinnitus, no tinnitus; hearing loss, normal hearing, n = 68) to define cohort level abnormal functional connectivity maps using high-field 7.0 T resting-state fMRI. The second study was a pilot case-control series (n = 2) to examine whether tinnitus modulation response to caudate tail subdivision stimulation would be contingent on individual level striatal connectivity map relationships with tinnitus networks. Resting-state fMRI identified five caudate subdivisions with abnormal cohort level functional connectivity maps. Of those, two connectivity maps exhibited increased connectivity with tinnitus networks-dorsal caudate head with Heschl's gyrus and caudate tail with the ventral cerebellum. DBS of the caudate tail in the case-series responder resulted in dramatic reductions in tinnitus severity and loudness, in contrast to the nonresponder who showed no tinnitus modulation. The individual level connectivity map of the responder was in alignment with the cohort expectation connectivity map, where the caudate tail exhibited increased connectivity with tinnitus networks, whereas the nonresponder individual level connectivity map did not.

Clinical Validation of the Champagne Algorithm for Evoked Response Source Localization in Magnetoencephalography.

Magnetoencephalography (MEG) is a robust method for non-invasive functional brain mapping of sensory cortices due to its exceptional spatial and temporal resolution. The clinical standard for MEG source localization of functional landmarks from sensory evoked responses is the equivalent current dipole (ECD) localization algorithm, known to be sensitive to initialization, noise, and manual choice of the number of dipoles. Recently many automated and robust algorithms have been developed, including the Champagne algorithm, an empirical Bayesian algorithm, with powerful abilities for MEG source reconstruction and time course estimation (Wipf et al. 2010; Owen et al. 2012). Here, we evaluate automated Champagne performance in a clinical population of tumor patients where there was minimal failure in localizing sensory evoked responses using the clinical standard, ECD localization algorithm. MEG data of auditory evoked potentials and somatosensory evoked potentials from 21 brain tumor patients were analyzed using Champagne, and these results were compared with equivalent current dipole (ECD) fit. Across both somatosensory and auditory evoked field localization, we found there was a strong agreement between Champagne and ECD localizations in all cases. Given resolution of 8mm voxel size, peak source localizations from Champagne were below 10mm of ECD peak source localization. The Champagne algorithm provides a robust and automated alternative to manual ECD fits for clinical localization of sensory evoked potentials and can contribute to improved clinical MEG data processing workflows.

Multiple Midfrontal Thetas Revealed by Source Separation of Simultaneous MEG and EEG.

Theta-band (∼6 Hz) rhythmic activity within and over the medial PFC ("midfrontal theta") has been identified as a distinctive signature of "response conflict," the competition between multiple actions when only one action is goal-relevant. Midfrontal theta is traditionally conceptualized and analyzed under the assumption that it is a unitary signature of conflict that can be uniquely identified at one electrode (typically FCz). Here we recorded simultaneous MEG and EEG (total of 328 sensors) in 9 human subjects (7 female) and applied a feature-guided multivariate source-separation decomposition to determine whether conflict-related midfrontal theta is a unitary or multidimensional feature of the data. For each subject, a generalized eigendecomposition yielded spatial filters (components) that maximized the ratio between theta and broadband activity. Components were retained based on significance thresholding and midfrontal EEG topography. All of the subjects individually exhibited multiple (mean 5.89, SD 2.47) midfrontal components that contributed to sensor-level midfrontal theta power during the task. Component signals were temporally uncorrelated and asynchronous, suggesting that each midfrontal theta component was unique. Our findings call into question the dominant notion that midfrontal theta represents a unitary process. Instead, we suggest that midfrontal theta spans a multidimensional space, indicating multiple origins, but can manifest as a single feature at the sensor level because of signal mixing.SIGNIFICANCE STATEMENT "Midfrontal theta" is a rhythmic electrophysiological signature of the competition between multiple response options. Midfrontal theta is traditionally considered to reflect a single process. However, this assumption could be erroneous because of "mixing" (multiple sources contributing to the activity recorded at a single electrode). We investigated the dimensionality of midfrontal theta by applying advanced multivariate analysis methods to a multimodal MEG/EEG dataset. We identified multiple topographically overlapping neural sources that drove response conflict-related midfrontal theta. Midfrontal theta thus reflects multiple uncorrelated signals that manifest with similar EEG scalp projections. In addition to contributing to the cognitive control literature, we demonstrate both the feasibility and the necessity of signal demixing to understand the narrowband neural dynamics underlying cognitive processes.

Emergence of canonical functional networks from the structural connectome.

How do functional brain networks emerge from the underlying wiring of the brain? We examine how resting-state functional activation patterns emerge from the underlying connectivity and length of white matter fibers that constitute its "structural connectome". By introducing realistic signal transmission delays along fiber projections, we obtain a complex-valued graph Laplacian matrix that depends on two parameters: coupling strength and oscillation frequency. This complex Laplacian admits a complex-valued eigen-basis in the frequency domain that is highly tunable and capable of reproducing the spatial patterns of canonical functional networks without requiring any detailed neural activity modeling. Specific canonical functional networks can be predicted using linear superposition of small subsets of complex eigenmodes. Using a novel parameter inference procedure we show that the complex Laplacian outperforms the real-valued Laplacian in predicting functional networks. The complex Laplacian eigenmodes therefore constitute a tunable yet parsimonious substrate on which a rich repertoire of realistic functional patterns can emerge. Although brain activity is governed by highly complex nonlinear processes and dense connections, our work suggests that simple extensions of linear models to the complex domain effectively approximate rich macroscopic spatial patterns observable on BOLD fMRI.

Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm.

Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from "baseline" or "control" measurements. However, in many scenarios, such baseline data is not available, or is unreliable, and it is unclear how best to estimate the noise covariance. In this technical note, we propose several robust methods to estimate the contributions to sensors from noise arising from outside the brain without the need for additional baseline measurements. The incorporation of these methods for diagonal noise covariance estimation improves the robust reconstruction of complex brain source activity under high levels of noise and interference, while maintaining the performance features of Champagne. Specifically, we show that the resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient. In simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning. We also demonstrate that, even without the use of any baseline data, Champagne with noise learning is able to reconstruct complex brain activity with just a few trials or even a single trial, demonstrating significant improvements in source reconstruction for electromagnetic brain imaging.

Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework.

Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theory and practice of SBL in three important ways. First, we reformulate three existing SBL algorithms under the majorization-minimization (MM) framework. This unification perspective not only provides a useful theoretical framework for comparing different algorithms in terms of their convergence behavior, but also provides a principled recipe for constructing novel algorithms with specific properties by designing appropriate bounds of the Bayesian marginal likelihood function. Second, building on the MM principle, we propose a novel method called LowSNR-BSI that achieves favorable source reconstruction performance in low signal-to-noise-ratio (SNR) settings. Third, precise knowledge of the noise level is a crucial requirement for accurate source reconstruction. Here we present a novel principled technique to accurately learn the noise variance from the data either jointly within the source reconstruction procedure or using one of two proposed cross-validation strategies. Empirically, we could show that the monotonous convergence behavior predicted from MM theory is confirmed in numerical experiments. Using simulations, we further demonstrate the advantage of LowSNR-BSI over conventional SBL in low-SNR regimes, and the advantage of learned noise levels over estimates derived from baseline data. To demonstrate the usefulness of our novel approach, we show neurophysiologically plausible source reconstructions on averaged auditory evoked potential data.

Taking the sublexical route: brain dynamics of reading in the semantic variant of primary progressive aphasia.

Reading aloud requires mapping an orthographic form to a phonological one. The mapping process relies on sublexical statistical regularities (e.g. 'oo' to |uː|) or on learned lexical associations between a specific visual form and a series of sounds (e.g. yacht to/jɑt/). Computational, neuroimaging, and neuropsychological evidence suggest that sublexical, phonological and lexico-semantic processes rely on partially distinct neural substrates: a dorsal (occipito-parietal) and a ventral (occipito-temporal) route, respectively. Here, we investigated the spatiotemporal features of orthography-to-phonology mapping, capitalizing on the time resolution of magnetoencephalography and the unique clinical model offered by patients with semantic variant of primary progressive aphasia (svPPA). Behaviourally, patients with svPPA manifest marked lexico-semantic impairments including difficulties in reading words with exceptional orthographic to phonological correspondence (irregular words). Moreover, they present with focal neurodegeneration in the anterior temporal lobe, affecting primarily the ventral, occipito-temporal, lexical route. Therefore, this clinical population allows for testing of specific hypotheses on the neural implementation of the dual-route model for reading, such as whether damage to one route can be compensated by over-reliance on the other. To this end, we reconstructed and analysed time-resolved whole-brain activity in 12 svPPA patients and 12 healthy age-matched control subjects while reading irregular words (e.g. yacht) and pseudowords (e.g. pook). Consistent with previous findings that the dorsal route is involved in sublexical, phonological processes, in control participants we observed enhanced neural activity over dorsal occipito-parietal cortices for pseudowords, when compared to irregular words. This activation was manifested in the beta-band (12-30 Hz), ramping up slowly over 500 ms after stimulus onset and peaking at ∼800 ms, around response selection and production. Consistent with our prediction, svPPA patients did not exhibit this temporal pattern of neural activity observed in controls this contrast. Furthermore, a direct comparison of neural activity between patients and controls revealed a dorsal spatiotemporal cluster during irregular word reading. These findings suggest that the sublexical/phonological route is involved in processing both irregular and pseudowords in svPPA. Together these results provide further evidence supporting a dual-route model for reading aloud mediated by the interplay between lexico-semantic and sublexical/phonological neurocognitive systems. When the ventral route is damaged, as in the case of neurodegeneration affecting the anterior temporal lobe, partial compensation appears to be possible by over-recruitment of the slower, serial attention-dependent, dorsal one.

Interhemispheric Auditory Cortical Synchronization in Asymmetric Hearing Loss.

OBJECTIVES: Auditory cortical activation of the two hemispheres to monaurally presented tonal stimuli has been shown to be asynchronous in normal hearing (NH) but synchronous in the extreme case of adult-onset asymmetric hearing loss (AHL) with single-sided deafness. We addressed the wide knowledge gap between these two anchoring states of interhemispheric temporal organization. The objectives of this study were as follows: (1) to map the trajectory of interhemispheric temporal reorganization from asynchrony to synchrony using magnitude of interaural threshold difference as the independent variable in a cross-sectional study and (2) to evaluate reversibility of interhemispheric synchrony in association with hearing in noise performance by amplifying the aidable poorer ear in a repeated measures, longitudinal study. DESIGN: The cross-sectional and longitudinal cohorts were comprised of 49 subjects (AHL; N = 21; 11 male, 10 female; mean age = 48 years) and NH (N = 28; 16 male, 12 female; mean age = 45 years). The maximum interaural threshold difference of the two cohorts spanned from 0 to 65 dB. Magnetoencephalography analyses focused on latency of the M100 peak response from auditory cortex in both hemispheres between 50 msec and 150 msec following monaural tonal stimulation at the frequency (0.5, 1, 2, 3, or 4 kHz) corresponding to the maximum and minimum interaural threshold difference for better and poorer ears separately. The longitudinal AHL cohort was drawn from three subjects in the cross-sectional AHL cohort (all male; ages 49 to 60 years; varied AHL etiologies; no amplification for at least 2 years). All longitudinal study subjects were treated by monaural amplification of the poorer ear and underwent repeated measures examination of the M100 response latency and quick speech in noise hearing in noise performance at baseline, and postamplification months 3, 6, and 12. RESULTS: The M100 response peak latency values in the ipsilateral hemisphere lagged those in the contralateral hemisphere for all stimulation conditions. The mean (SD) interhemispheric latency difference values (ipsilateral less contralateral) to better ear stimulation for three categories of maximum interaural threshold difference were as follows: NH (≤ 10 dB)-8.6 (3.0) msec; AHL (15 to 40 dB)-3.0 (1.2) msec; AHL (≥ 45 dB)-1.4 (1.3) msec. In turn, the magnitude of difference values were used to define interhemispheric temporal organization states of asynchrony, mixed asynchrony and synchrony, and synchrony, respectively. Amplification of the poorer ear in longitudinal subjects drove interhemispheric organization change from baseline synchrony to postamplification asynchrony and hearing in noise performance improvement in those with baseline impairment over a 12-month period. CONCLUSIONS: Interhemispheric temporal organization in AHL was anchored between states of asynchrony in NH and synchrony in single-sided deafness. For asymmetry magnitudes between 15 and 40 dB, the intermediate mixed state of asynchrony and synchrony was continuous and reversible. Amplification of the poorer ear in AHL improved hearing in noise performance and restored normal temporal organization of auditory cortices in the two hemispheres. The return to normal interhemispheric asynchrony from baseline synchrony and improvement in hearing following monoaural amplification of the poorer ear evolved progressively over a 12-month period.

Speech compensation responses and sensorimotor adaptation to formant feedback perturbations.

Control of speech formants is important for the production of distinguishable speech sounds and is achieved with both feedback and learned feedforward control. However, it is unclear whether the learning of feedforward control involves the mechanisms of feedback control. Speakers have been shown to compensate for unpredictable transient mid-utterance perturbations of pitch and loudness feedback, demonstrating online feedback control of these speech features. To determine whether similar feedback control mechanisms exist in the production of formants, responses to unpredictable vowel formant feedback perturbations were examined. Results showed similar within-trial compensatory responses to formant perturbations that were presented at utterance onset and mid-utterance. The relationship between online feedback compensation to unpredictable formant perturbations and sensorimotor adaptation to consistent formant perturbations was further examined. Within-trial online compensation responses were not correlated with across-trial sensorimotor adaptation. A detailed analysis of within-trial time course dynamics across trials during sensorimotor adaptation revealed that across-trial sensorimotor adaptation responses did not result from an incorporation of within-trial compensation response. These findings suggest that online feedback compensation and sensorimotor adaptation are governed by distinct neural mechanisms. These findings have important implications for models of speech motor control in terms of how feedback and feedforward control mechanisms are implemented.

Reduced synchrony in alpha oscillations during life predicts post mortem neurofibrillary tangle density in early-onset and atypical Alzheimer's disease.

INTRODUCTION: Neurophysiological manifestations selectively associated with amyloid beta and tau depositions in Alzheimer's disease (AD) are useful network biomarkers to identify peptide specific pathological processes. The objective of this study was to validate the associations between reduced neuronal synchrony within alpha oscillations and neurofibrillary tangle (NFT) density in autopsy examination, in patients with AD. METHODS: In a well-characterized clinicopathological cohort of AD patients (n = 13), we quantified neuronal synchrony within alpha (8-12 Hz) and delta-theta (2-8 Hz) oscillations, using magnetoencephalography during the disease course, within six selected neocortical and hippocampal regions, including angular gyrus, superior temporal gurus, middle frontal gyrus, primary motor cortex, CA1, and subiculum, and correlated these with regional NFT density quantified at histopathological examination. RESULTS: Abnormal synchrony in alpha, but not in delta-theta, significantly predicted the NFT density at post mortem neuropathological examination. DISCUSSION: Reduced alpha synchrony is a sensitive neurophysiological index associated with pathological tau, and a potential network biomarker for clinical trials, to gauge the extent of network dysfunction and the degree of rescue in treatments targeting tau pathways in AD.

Sensorimotor Cortical Oscillations during Movement Preparation in 16p11.2 Deletion Carriers.

Sensorimotor deficits are prevalent in many neurodevelopmental disorders like autism, including one of its common genetic etiologies, a 600 kb reciprocal deletion/duplication at 16p11.2. We have previously shown that copy number variations of 16p11.2 impact regional brain volume, white matter integrity, and early sensory responses in auditory cortex. Here, we test the hypothesis that abnormal cortical neurophysiology is present when genes in the 16p11.2 region are haploinsufficient, and in humans that this in turn may account for behavioral deficits specific to deletion carriers. We examine sensorimotor cortical network activity in males and females with 16p11.2 deletions compared with both typically developing individuals, and those with duplications of 16p11.2, using magnetoencephalographic imaging during preparation of overt speech or hand movements in tasks designed to be easy for all participants. In deletion carriers, modulation of beta oscillations (12-30 Hz) were increased during both movement types over effector-specific regions of motor cortices compared with typically developing individuals or duplication carriers, with no task-related performance differences between cohorts, even when corrected for their own cognitive and sensorimotor deficits. Reduced left hemispheric language specialization was observed in deletion carriers but not in duplication carriers. Neural activity over sensorimotor cortices in deletion carriers was linearly related to clinical measures of speech and motor impairment. These findings link insufficient copy number repeats at 16p11.2 to excessive neural activity (e.g., increased beta oscillations) in motor cortical networks for speech and hand motor control. These results have significant implications for understanding the neural basis of autism and related neurodevelopmental disorders.SIGNIFICANCE STATEMENT The recurrent ∼600 kb deletion at 16p11.2 (BP4-BP5) is one of the most common genetic etiologies of ASD and, more generally, of neurodevelopmental disorders. Here, we use high-resolution magnetoencephalographic imaging (MEG-I) to define with millisecond precision the underlying neurophysiological signature of motor impairments for individuals with 16p11.2 deletions. We identify significant increases in beta (12-30 Hz) suppression in sensorimotor cortices related to performance during speech and hand movement tasks. These findings not only provide a neurophysiological phenotype for the clinical presentation of this genetic deletion, but also guide our understanding of how genetic variation encodes for neural oscillatory dynamics.