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Abstract Unifying integration and segregation in the brain has been a fundamental puzzle in neuroscience ever since the conception of the “binding problem.” Here, we introduce a framework that places integration and segregation within a continuum based on a fundamental property of the brain–its structural connectivity graph Laplacian harmonics and a new feature we term the gap-spectrum. This framework organizes harmonics into three regimes–integrative, segregative, and degenerate–that together account for various group-level properties. Integrative and segregative harmonics occupy the ends of the continuum, and they share properties such as reproducibility across individuals, stability to perturbation, and involve “bottom-up” sensory networks. Degenerate harmonics are in the middle of the continuum, and they are subject-specific, flexible, and involve “top-down” networks. The proposed framework accommodates inter-subject variation, sensitivity to changes, and structure-function coupling in ways that offer promising avenues for studying cognition and consciousness in the brain.

Abstract The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.

Past studies have explored formant centering, a corrective behavior of convergence over the duration of an utterance toward the formants of a putative target vowel. In this study, we establish the existence of a similar centering phenomenon for pitch in healthy elderly controls and examine how such corrective behavior is altered in Alzheimer's Disease (AD). We found the pitch centering response in healthy elderly was similar when correcting pitch errors below and above the target (median) pitch. In contrast, patients with AD showed an asymmetry with a larger correction for the pitch errors below the target phonation than above the target phonation. These findings indicate that pitch centering is a robust compensation behavior in human speech. Our findings also explore the potential impacts on pitch centering from neurodegenerative processes impacting speech in AD.

Abstract Background Alzheimer’s disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). Methods SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). Results Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. Conclusions These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.

Abstract Introduction Progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD), are the most common four‐repeat tauopathies (4RT), and both frequently occur with varying degree of Alzheimer's disease (AD) copathology. Intriguingly, patients with 4RT and patients with AD are at opposite ends of the wakefulness spectrum—AD showing reduced wakefulness and excessive sleepiness whereas 4RT showing decreased homeostatic sleep. The neural mechanisms underlying these distinct phenotypes in the comorbid condition of 4RT and AD are unknown. The objective of the current study was to define the alpha oscillatory spectrum, which is prominent in the awake resting‐state in the human brain, in patients with primary 4RT, and how it is modified in comorbid AD‐pathology. Method In an autopsy‐confirmed case series of 4R‐tauopathy patients (n = 10), whose primary neuropathological diagnosis was either PSP (n = 7) or CBD (n = 3), using high spatiotemporal resolution magnetoencephalography (MEG), we quantified the spectral power density within alpha‐band (8–12 Hz) and examined how this pattern was modified in increasing AD‐copathology. For each patient, their regional alpha power was compared to an age‐matched normative control cohort (n = 35). Result Patients with 4RT showed increased alpha power but in the presence of AD‐copathology alpha power was reduced. Conclusions Alpha power increase in PSP‐tauopathy and reduction in the presence of AD‐tauopathy is consistent with the observation that neurons activating wakefulness‐promoting systems are preserved in PSP but degenerated in AD. These results highlight the selectively vulnerable impacts in 4RT versus AD‐tauopathy that may have translational significance on disease‐modifying therapies for specific proteinopathies.

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.

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.

Brain organoids replicate cellular organization found in the developing human brain. Here, the authors utilize microelectronics to map activity in brain organoids and assemble functional circuits that mirror complexity found in brain networks in vivo.

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.

Primary progressive aphasia (PPA) is a clinical syndrome in which patients progressively lose speech and language abilities. Three variants are recognized: logopenic (lvPPA), associated with phonology and/or short-term verbal memory deficits accompanied by left temporo-parietal atrophy; semantic (svPPA), associated with semantic deficits and anterior temporal lobe (ATL) atrophy; non-fluent (nfvPPA) associated with grammar and/or speech-motor deficits and inferior frontal gyrus (IFG) atrophy. Here, we set out to investigate whether the three variants of PPA can be dissociated based on error patterns in a single language task. We recruited 21 lvPPA, 28 svPPA, and 24 nfvPPA patients, together with 31 healthy controls, and analyzed their performance on an auditory noun-to-verb generation task, which requires auditory analysis of the input, access to and selection of relevant lexical and semantic knowledge, as well as preparation and execution of speech. Task accuracy differed across the three variants and controls, with lvPPA and nfvPPA having the lowest and highest accuracy, respectively. Critically, machine learning analysis of the different error types yielded above-chance classification of patients into their corresponding group. An analysis of the error types revealed clear variant-specific effects: lvPPA patients produced the highest percentage of “not-a-verb” responses and the highest number of semantically related nouns (production of baseball instead of throw to noun ball); in contrast, svPPA patients produced the highest percentage of “unrelated verb” responses and the highest number of light verbs (production of take instead of throw to noun ball). Taken together, our findings indicate that error patterns in an auditory verb generation task are associated with the breakdown of different neurocognitive mechanisms across PPA variants. Specifically, they corroborate the link between temporo-parietal regions with lexical processing, as well as ATL with semantic processes. These findings illustrate how the analysis of pattern of responses can help PPA phenotyping and heighten diagnostic sensitivity, while providing insights on the neural correlates of different components of language.

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.

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.

Magnetoencephalography (MEG) is increasingly used for presurgical planning in people with medically refractory focal epilepsy. Localization of interictal epileptiform activity, a surrogate for the seizure onset zone whose removal may prevent seizures, is challenging and depends on the use of multiple complementary techniques. Accurate and reliable localization of epileptiform activity from spontaneous MEG data has been an elusive goal. One approach toward this goal is to use a novel Bayesian inference algorithm—the Champagne algorithm with noise learning—which has shown tremendous success in source reconstruction, especially for focal brain sources. In this study, we localized sources of manually identified MEG spikes using the Champagne algorithm in a cohort of 16 patients with medically refractory epilepsy collected in two consecutive series. To evaluate the reliability of this approach, we compared the performance to equivalent current dipole (ECD) modeling, a conventional source localization technique that is commonly used in clinical practice. Results suggest that Champagne may be a robust, automated, alternative to manual parametric dipole fitting methods for localization of interictal MEG spikes, in addition to its previously described clinical and research applications.

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.

Neurodynamic Utility Toolbox for Magnetoencephalo- and Electroencephalography (NUTMEG) is an open-source MATLAB-based toolbox for the analysis and reconstruction of magnetoencephalography/electroencephalography data in source space. NUTMEG includes a variety of options for the user in data import, preprocessing, source reconstruction, and functional connectivity. A group analysis toolbox allows the user to run a variety of inferential statistics on their data in an easy-to-use GUI-driven format. Importantly, NUTMEG features an interactive five-dimensional data visualization platform. A key feature of NUTMEG is the availability of a large menu of interference cancelation and source reconstruction algorithms. Each NUTMEG operation acts as a stand-alone MATLAB function, allowing the package to be easily adaptable and scripted for the more advanced user for interoperability with other software toolboxes. Therefore, NUTMEG enables a wide range of users access to a complete “sensor-to- source-statistics” analysis pipeline.

Magnetoencephalographic imaging (MEGI) offers a non-invasive alternative for defining preoperative language lateralization in neurosurgery patients. MEGI indeed can be used for accurate estimation of language lateralization with a complex language task – auditory verb generation. However, since language function may vary considerably in patients with focal lesions, it is important to optimize MEGI for estimation of language function with other simpler language tasks. The goal of this study was to optimize MEGI laterality analyses for two such simpler language tasks that can have compliance from those with impaired language function: a non-word repetition (NWR) task and a picture naming (PN) task. Language lateralization results for these two tasks were compared to the verb-generation (VG) task. MEGI reconstruction parameters (regions and time windows) for NWR and PN were first defined in a presurgical training cohort by benchmarking these against laterality indices for VG. Optimized time windows and regions of interest (ROIs) for NWR and PN were determined by examining oscillations in the beta band (12–30 Hz) a marker of neural activity known to be concordant with the VG laterality index (LI). For NWR, additional ROIs include areas MTG/ITG and for both NWR and PN, the postcentral gyrus was included in analyses. Optimal time windows for NWR were defined as 650–850 ms (stimulus-locked) and −350 to −150 ms (response-locked) and for PN −450 to −250 ms (response-locked). To verify the optimal parameters defined in our training cohort for NWR and PN, we examined an independent validation cohort (n = 30 for NWR, n = 28 for PN) and found high concordance between VG laterality and PN laterality (82%) and between VG laterality and NWR laterality (87%). Finally, in a test cohort (n = 8) that underwent both the intracarotid amobarbital procedure (IAP) test and MEG for VG, NWR, and PN, we identified excellent concordance (100%) with IAP for VG + NWR + PN composite LI, high concordance for PN alone (87.5%), and moderate concordance for NWR alone (66.7%). These findings provide task options for non-invasive language mapping with MEGI that can be calibrated for language abilities of individual patients. Results also demonstrate that more accurate estimates can be obtained by combining laterality estimates obtained from multiple tasks. MEGI

Objective: The adult brain’s potential for plastic reorganization is an important mechanism for the preservation and restoration of function in patients with primary glial neoplasm. Patients with recurrent brain tumors requiring multiple interventions over time present an opportunity to examine brain reorganization. Magnetoencephalography (MEG) is a noninvasive imaging modality that can be used for motor cortical network mapping which, when performed at regular intervals, offers insight into this process of reorganization. Utilizing MEG-based motor mapping, we sought to characterize the reorganization of motor cortical networks over time in a cohort of 78 patients with recurrent glioma.Methods: MEG-based motor cortical maps were obtained by measuring event-related desynchronization (ERD) in ß-band frequency during unilateral index finger flexion. Each patient presented at our Department at least on two occasions for tumor resection due to tumor recurrence, and MEG-based motor mapping was performed as part of preoperative assessment before each surgical resection. Whole-brain activation patterns from first to second MEG scan (obtained before first and second surgery) were compared. Additionally, we calculated distances of activation peaks, which represent the location of the primary motor cortex (MC), to determine the magnitude of movement in motor eloquent areas between the first and second MEG scan. We also explored which demographic, anatomic, and pathological factors influence these shifts.Results: The whole-brain activation motor maps showed a subtle movement of the primary MC from first to second timepoint, as was confirmed by the determination of motor activation peaks. The shift of ipsilesional MC was directly correlated with a frontal-parietal tumor location (p < 0.001), presence of motor deficits (p = 0.021), and with a longer period between MEG scans (p = 0.048). Also, a disengagement of wide areas in the contralesional (ipsilateral to finger movement) hemisphere at the second time point was observed.Conclusions: MEG imaging is a sensitive method for depicting the plasticity of the motor cortical network. Although the location of the primary MC undergoes only subtle changes, appreciable shifts can occur in the setting of a stronger and longer impairment of the tumor on the MC. The ipsilateral hemisphere may serve as a reservoir for functional recovery.

Reality monitoring is defined as the ability to distinguish internally self-generated information from externally-derived information. Functional imaging studies have consistently found that the medial prefrontal cortex (mPFC) is a key brain region subserving reality monitoring. This study aimed to determine a causal role for mPFC in reality monitoring using navigated repetitive transcranial magnetic stimulation (nrTMS). In a subject-blinded sham-controlled crossover design, healthy individuals received either active or sham nrTMS targeting mPFC. Active modulation of mPFC using nrTMS at a frequency of 10 Hz, significantly improved identification of both self-generated and externally-derived information during reality monitoring, when compared to sham or baseline. Targeted excitatory modulation of mPFC also improved positive mood, reduced negative mood, and increased overall alertness/arousal. These results establish optimal nrTMS dosing parameters that maximized tolerability/comfort and induced significant neuromodulatory effects in the mPFC target. Importantly, this is a proof-of-concept study that establishes the mPFC as a novel brain target that can be stimulated with nrTMS to causally impact both higher-order reality monitoring and mood.

Single-sided deafness (SSD) or profound unilateral hearing loss is the condition where the transfer of acoustic information to the brain is restricted to one ear. SSD impairment is most evident under adverse acoustic environments with overlapping interference, which burdens cognitive resources. It is known that bilateral deafness induces cross-modal brain plasticity within visual cortical areas. Here we investigate whether similar cross-modal plasticity is observed in adult-onset SSD. In SSD patients (n ​= ​29) and matched controls (n ​= ​29) we estimated voxel level resting-state power and functional connectivity in the alpha band (8–12 ​Hz) from magnetoencephalography (MEG) data. We examined both global functional connectivity (mean functional connectivity of each voxel with the rest of the brain), and seeded functional connectivity of primary auditory cortices (A1), primary visual cortices (V1) and posterior cingulate cortex (PCC) of the default mode network (DMN). Power reduction was observed in left auditory cortex. Global functional connectivity showed reduction in frontal cortices and enhancement in visual cortex. Seeded functional connectivity of auditory cortices showed reduction in temporal, frontal and occipital regions, and enhancement in parietal cortex. Interestingly, seeded functional connectivity of visual cortices showed enhancement in visual cortices, inferior parietal lobe, post-central gyrus, and the precuneus, and reduction in auditory cortex. Seeded functional connectivity of PCC showed reduction in frontal cortical regions that are part of the DMN, attention, and working memory networks. Adult-onset SSD exhibited widespread cross-modal brain plasticity involving alterations in auditory, visual, attention, working memory and default mode networks.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Single-sided deafness (SSD) or profound unilateral hearing loss obligates the only serviceable ear to capture all acoustic information. This loss of binaural function taxes cognitive resources for accurate listening performance, especially under adverse environments or challenging tasks. We hypothesized that adults with SSD would manifest both functional and structural brain plasticity compared to controls with normal binaural hearing. We evaluated functional alterations using magnetoencephalographic imaging (MEGI) of brain activation during performance of a moderately difficult auditory syllable sequence reproduction task and assessed structural integrity using diffusion tensor imaging (DTI). MEGI showed the SSD cohort to have increased induced oscillations in the theta band over the left superior temporal cortex and decreased induced gamma band oscillations over the frontal and parietal cortices between 175 and 475 ms following stimulus onset. DTI showed the SSD cohort to have extensive fractional anisotropy (FA) reduction in both auditory and non-auditory tracts and regions. Overlaying functional and structural changes revealed by the two imaging techniques demonstrated close registration of cortical areas and white matter tracts that expressed brain plasticity. Hence, complete loss of input from one ear in adulthood triggers both functional and structural alterations to dorsal temporal and frontal-parietal areas.

Self-agency is the experience of being the agent of one’s own thoughts and motor actions. The intact experience of self-agency is necessary for successful interactions with the outside world (i.e., reality monitoring) and for responding to sensory feedback of our motor actions (e.g., speech feedback control). Reality monitoring is the ability to distinguish internally self-generated information from outside reality (externally-derived information). In the present study, we examined the relationship of self-agency between lower-level speech feedback monitoring (i.e., monitoring what we hear ourselves say) and a higher-level cognitive reality monitoring task. In particular, we examined whether speech feedback monitoring and reality monitoring were driven by the capacity to experience self-agency—the ability to make reliable predictions about the outcomes of self-generated actions. During the reality monitoring task, subjects made judgments as to whether information was previously self-generated (self-agency judgments) or externally derived (external-agency judgments). During speech feedback monitoring, we assessed self-agency by altering environmental auditory feedback so that subjects listened to a perturbed version of their own speech. When subjects heard minimal perturbations in their auditory feedback while speaking, they made corrective responses, indicating that they judged the perturbations as errors in their speech output. We found that self-agency judgments in the reality-monitoring task were higher in people who had smaller corrective responses (p = 0.05) and smaller inter-trial variability (p = 0.03) during minimal pitch perturbations of their auditory feedback. These results provide support for a unitary process for the experience of self-agency governing low-level speech control and higher level reality monitoring.

Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.

This study compared magnetoencephalographic (MEG) imaging-derived indices of auditory and somatosensory cortical processing in children aged 8–12 years with autism spectrum disorder (ASD; N = 18), those with sensory processing dysfunction (SPD; N = 13) who do not meet ASD criteria, and typically developing control (TDC; N = 19) participants. The magnitude of responses to both auditory and tactile stimulation was comparable across all three groups; however, the M200 latency response from the left auditory cortex was significantly delayed in the ASD group relative to both the TDC and SPD groups, whereas the somatosensory response of the ASD group was only delayed relative to TDC participants. The SPD group did not significantly differ from either group in terms of somatosensory latency, suggesting that participants with SPD may have an intermediate phenotype between ASD and TDC with regard to somatosensory processing. For the ASD group, correlation analyses indicated that the left M200 latency delay was significantly associated with performance on the WISC-IV Verbal Comprehension Index as well as the DSTP Acoustic-Linguistic index. Further, these cortical auditory response delays were not associated with somatosensory cortical response delays or cognitive processing speed in the ASD group, suggesting that auditory delays in ASD are domain specific rather than associated with generalized processing delays. The specificity of these auditory delays to the ASD group, in addition to their correlation with verbal abilities, suggests that auditory sensory dysfunction may be implicated in communication symptoms in ASD, motivating further research aimed at understanding the impact of sensory dysfunction on the developing brain.

Understanding neural network dysfunction in neurodegenerative disease is imperative to effectively develop network-modulating therapies. In Alzheimer’s disease (AD), cognitive decline associates with deficits in resting-state functional connectivity of diffuse brain networks. The goal of the current study was to test whether specific cognitive impairments in AD spectrum correlate with reduced functional connectivity of distinct brain regions. We recorded resting-state functional connectivity of alpha-band activity in 27 patients with AD spectrum − 22 patients with probable AD (5 logopenic variant primary progressive aphasia, 7 posterior cortical atrophy, and 10 early-onset amnestic/dysexecutive AD) and 5 patients with mild cognitive impairment due to AD. We used magnetoencephalographic imaging (MEGI) to perform an unbiased search for regions where patterns of functional connectivity correlated with disease severity and cognitive performance. Functional connectivity measured the strength of coherence between a given region and the rest of the brain. Decreased neural connectivity of multiple brain regions including the right posterior perisylvian region and left middle frontal cortex correlated with a higher degree of disease severity. Deficits in executive control and episodic memory correlated with reduced functional connectivity of the left frontal cortex, whereas visuospatial impairments correlated with reduced functional connectivity of the left inferior parietal cortex. Our findings indicate that reductions in region-specific alpha-band resting-state functional connectivity are strongly correlated with, and might contribute to, specific cognitive deficits in AD spectrum. In the future, MEGI functional connectivity could be an important biomarker to map and follow defective networks in the early stages of AD.

Focal hand dystonia (FHd) is a recalcitrant, disabling movement disorder, characterized by involuntary co-contractions of agonists and antagonists, that can develop in patients who overuse or misuse their hands. The aim of this study was to document clinical neuromusculoskeletal performance and somatosensory responses (magnetoencephalography) in healthy controls and in FHd subjects with mild versus severe hand dystonia. The performance of healthy subjects (n = 17) was significantly better than that of FHd subjects (n = 17) on all clinical parameters. Those with mild dystonia (n = 10) demonstrated better musculoskeletal skills, task-specific motor performance, and sensory discrimination, but the performance of sensory and fine motor tasks was slower than that of patients with severe dystonia. In terms of somatosensory evoked field responses (SEFs), FHd subjects demonstrated a significant difference in the location of the hand representation on the x and y axes, lower amplitude of SEFs integrated across latency, and a higher ratio of mean SEF amplitude to latency than the controls. Bilaterally,. those with FHd (mild and severe) lacked progressive sequencing of the digits from inferior to superior. On the affected digits, subjects with severe dystonia had a significantly higher ratio of SEF amplitude to latency and a significantly smaller mean volume of the cortical hand representation than those with mild dystonia. Severity of dystonia positively correlated with the ratio of SEF mean amplitude to latency (0.9029 affected, 0.8477 unaffected; p

Uncovering brain activity from MEG data requires solving an ill-posed inverse problem, greatly confounded by noise, interference, and correlated sources. Sparse reconstruction algorithms, such as Champagne, show great promise in that they provide focal brain activations robust to these confounds. In this paper, we address the technical considerations of statistically thresholding brain images obtained from sparse reconstruction algorithms. The source power distribution of sparse algorithms makes this class of algorithms ill-suited to "conventional" techniques. We propose two nonparametric resampling methods hypothesized to be compatible with sparse algorithms. The first adapts the maximal statistic procedure to sparse reconstruction results and the second departs from maximal statistics, putting forth a less stringent procedure that protects against spurious peaks. Three MEG data sets are utilized to demonstrate the efficacy of the proposed methods. Two sparse algorithms, Champagne and generalized minimum current estimation (G-MCE), are compared to two non-sparse algorithms, a variant of minimum-norm estimation, sLORETA, and an adaptive beamformer. The results, in general, demonstrate that the already sparse images obtained from Champagne and G-MCE are further thresholded by both proposed statistical thresholding procedures. While non-sparse algorithms are thresholded by the maximal statistic procedure, they are not made sparse. The work presented here is one of the first attempts to address the problem of statistically thresholding sparse reconstructions, and aims to improve upon this already advantageous and powerful class of algorithm.

Spoken language exists because of a remarkable neural process. Inside a speaker’s brain, an intended message gives rise to neural signals activating the muscles of the vocal tract. The process is remarkable because these muscles are activated in just the right way that the vocal tract produces sounds a listener understands as the intended message. What is the best approach to understanding the neural substrate of this crucial motor control process? One of the key recent modeling developments in neuroscience has been the use of state feedback control (SFC) theory to explain the role of the CNS in motor control. SFC postulates that the CNS controls motor output by (1) estimating the current dynamic state of the thing (e.g., arm) being controlled, and (2) generating controls based on this estimated state. SFC has successfully predicted a great range of non-speech motor phenomena, but as yet has not received attention in the speech motor control community. Here, we review some of the key characteristics of speech motor control and what they say about the role of the CNS in the process. We then discuss prior efforts to model the role of CNS in speech motor control, and argue that these models have inherent limitations – limitations that are overcome by an SFC model of speech motor control which we describe. We conclude by discussing a plausible neural substrate of our model.

Plasticity after stroke has traditionally been studied by observing changes only in the spatial distribution and laterality of focal brain activation during affected limb movement. However, neural reorganization is multifaceted and our understanding may enhanced by examining dynamics of activity within large-scale networks involved in sensorimotor control of the limbs. Here, we review functional connectivity as a promising means of assessing the consequences of a stroke lesion on the transfer of activity within large-scale neural networks. We first provide a brief overview of techniques used to assess functional connectivity in subjects with stroke. Next, we review task-related and resting-state functional connectivity studies that demonstrate a lesion-induced disruption of neural networks, the relationship of the extent of this disruption with motor performance, and the potential for network reorganization in the presence of a stroke lesion. We conclude with suggestions for future research and theories that may enhance the interpretation of changing functional connectivity. Overall findings suggest that a network level assessment provides a useful framework to examine brain reorganization and to potentially better predict behavioral outcomes following stroke.