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Predictive coding theories posit that neural networks learn statistical regularities in the environment for comparison with actual outcomes, signaling a prediction error (PE) when sensory deviation occurs. PE studies in audition have capitalized on low-frequency event-related potentials (LF-ERPs), such as the mismatch negativity. However, local cortical activity is well-indexed by higher-frequency bands [high-γ band (Hγ): 80–150 Hz]. We compared patterns of human Hγ and LF-ERPs in deviance detection using electrocorticographic recordings from subdural electrodes over frontal and temporal cortices. Patients listened to trains of task-irrelevant tones in two conditions differing in the predictability of a deviation from repetitive background stimuli (fully predictable vs. unpredictable deviants). We found deviance-related responses in both frequency bands over lateral temporal and inferior frontal cortex, with an earlier latency for Hγ than for LF-ERPs. Critically, frontal Hγ activity but not LF-ERPs discriminated between fully predictable and unpredictable changes, with frontal cortex sensitive to unpredictable events. The results highlight the role of frontal cortex and Hγ activity in deviance detection and PE generation.

A growing body of evidence shows that epileptic activity is frequently observed in patients with Alzheimer’s disease (AD), implicating underlying excitatory-inhibitory imbalance. The distinction of whether the AD-epileptic phenotype represents a subset of patients or an underdiagnosed manifestation holds major therapeutic implications. Here, we quantified the excitatory-inhibitory imbalance in AD patients using magnetoencephalography and examined the relationships to AD pathophysiology—amyloid-beta and tau, and to epileptic activity. We used two metrics to quantify regional excitatory-inhibitory imbalance distinguishing between local hyperexcitability ( Neural excitability, quantified by regional aperiodic spectral slope ) and aberrant long-range synaptic input integration ( Neural fragility, quantified by regional linear dynamic instability ). We found that amyloid-beta correlated with higher neural fragility and higher neural excitability, while tau and hypometabolism uniquely correlated with higher neural excitability. Importantly, the AD-epileptic phenotype showed a distinctive increase in neural fragility. Our findings demonstrate that AD pathophysiology is associated with diverse mechanisms of excitatory-inhibitory imbalance and that AD-epileptic phenotype represents a distinct group of patients with greater impairments in long-range synaptic input integration. This study reveals that excitatory–inhibitory imbalance in Alzheimer’s disease is diverse including both impaired long-range synaptic integration and altered local excitability, with distinct links to β-amyloid and tau pathology.

One hundred and fifty years of neurolinguistic research has identified the key structures in the human brain that support language. However, neither the classic neuropsychological approaches introduced by Broca (1861) and Wernicke (1874), nor modern neuroimaging employing PET and fMRI has been able to delineate the temporal flow of language processing in the human brain. We recorded the electrocorticogram (ECoG) from indwelling electrodes over left hemisphere language cortices during two common language tasks, verb generation and picture naming. We observed that the very high frequencies of the ECoG (high-gamma, 70–160 Hz) track language processing with spatial and temporal precision. Serial progression of activations is seen at a larger timescale, showing distinct stages of perception, semantic association/selection, and speech production. Within the areas supporting each of these larger processing stages, parallel (or “incremental”) processing is observed. In addition to the traditional posterior vs. anterior localization for speech perception vs. production, we provide novel evidence for the role of premotor cortex in speech perception and of Wernicke's and surrounding cortex in speech production. The data are discussed with regards to current leading models of speech perception and production, and a “dual ventral stream” hybrid of leading speech perception models is given.

Alzheimer’s disease (AD) is characterized by the accumulation of amyloid-β and misfolded tau proteins causing synaptic dysfunction, and progressive neurodegeneration and cognitive decline. Altered neural oscillations have been consistently demonstrated in AD. However, the trajectories of abnormal neural oscillations in AD progression and their relationship to neurodegeneration and cognitive decline are unknown. Here, we deployed robust event-based sequencing models (EBMs) to investigate the trajectories of long-range and local neural synchrony across AD stages, estimated from resting-state magnetoencephalography. The increases in neural synchrony in the delta-theta band and the decreases in the alpha and beta bands showed progressive changes throughout the stages of the EBM. Decreases in alpha and beta band synchrony preceded both neurodegeneration and cognitive decline, indicating that frequency-specific neuronal synchrony abnormalities are early manifestations of AD pathophysiology. The long-range synchrony effects were greater than the local synchrony, indicating a greater sensitivity of connectivity metrics involving multiple regions of the brain. These results demonstrate the evolution of functional neuronal deficits along the sequence of AD progression.

RationaleInvestigation of associated risk factors of valproic acid (VPA)–induced tremor helped in increasing tolerance and optimizing treatment scheme individually.ObjectivesTo determine the risk factors of VPA-induced tremor, with particular attention on identifying tremor-susceptible gene mutations.MethodsEpileptic patients taking VPA were divided into a tremor and a non-tremor groups. A mutation of rs9652490 in the leucine-rich repeat and immunoglobulin domain-containing Nogo-receptor-interacting protein 1 (LINGO-1) gene was determined by Sanger sequencing. Cerebellar atrophy was assessed, and various cerebellar dimensions were measured on magnetic resonance imaging (MRI) scans.ResultsOne hundred and eighty-one of 200 subjects were included. Multivariate regression analysis indicated several VPA-induced tremor-related factors: females (OR = 2.718, p = 0.014), family history of tremor (OR = 7.595, p = 0.003), treatment duration (> 24 months; OR = 3.294, p = 0.002), and daily dosage (> 1,000 mg/d; OR = 19.801, p = 0.008) of VPA. Chi-square tests revealed that treatment with VPA magnesium-ER (p = 0.030) and carbamazepine combination (p = 0.040) reduced the incidence of tremor. One hundred and seventy-six gene sequencing and 86 MRI results excluded any significant difference between the two groups in the mutation of rs9652490 within LINGO-1, the ratio of cerebellar atrophy or the cerebellar-dimension values (p > 0.05). However, mutation of rs9652490 within LINGO-1 was correlated with increased cerebellar atrophy (p = 0.001), reduced cerebellar hemisphere thickness (p = 0.025), and right cerebellar hemisphere longitudinal diameter (p = 0.047).ConclusionsOur cohort indicated risk (female, positive family history of tremor, daily dosage > 1000 mg and treatment duration > 24 months of VPA) and protective factors (VPA magnesium-ER and combination with CBZ) of VPA-induced tremor. Mutation of rs9652490 within LINGO-1 correlated with cerebellar atrophy, neither was correlated with VPA-induced tremor.

The spatiotemporal dynamics of cortical oscillations across human brain regions remain poorly understood because of a lack of adequately validated methods for reconstructing such activity from noninvasive electrophysiological data. In this paper, we present a novel adaptive spatial filtering algorithm optimized for robust source time–frequency reconstruction from magnetoencephalography (MEG) and electroencephalography (EEG) data. The efficacy of the method is demonstrated with simulated sources and is also applied to real MEG data from a self-paced finger movement task. The algorithm reliably reveals modulations both in the beta band (12–30 Hz) and high gamma band (65–90 Hz) in sensorimotor cortex. The performance is validated by both across-subjects statistical comparisons and by intracranial electrocorticography (ECoG) data from two epilepsy patients. Interestingly, we also reliably observed high frequency activity (30–300 Hz) in the cerebellum, although with variable locations and frequencies across subjects. The proposed algorithm is highly parallelizable and runs efficiently on modern high-performance computing clusters. This method enables the ultimate promise of MEG and EEG for five-dimensional imaging of space, time, and frequency activity in the brain and renders it applicable for widespread studies of human cortical dynamics during cognition.

Abstract BACKGROUND: The removal of brain tumors in perieloquent or eloquent cortex risks causing new neurological deficits in patients. The assessment of the functionality of perilesional tissue is essential to avoid postoperative neurological morbidity. OBJECTIVE: To evaluate preoperative magnetoencephalography-based functional connectivity as a predictor of short- and medium-term neurological outcome after removal of gliomas in perieloquent and eloquent areas. METHODS: Resting-state whole-brain magnetoencephalography recordings were obtained from 79 consecutive subjects with focal brain gliomas near or within motor, sensory, or language areas. Neural activity was estimated using adaptive spatial filtering. The mean imaginary coherence between voxels in and around brain tumors was compared with contralesional voxels and used as an index of their functional connectivity with the rest of the brain. The connectivity values of the tissue resected during surgery were correlated with the early (1 week postoperatively) and medium-term (6 months postoperatively) neurological morbidity. RESULTS: Patients undergoing resection of tumors with decreased functional connectivity had a 29% rate of a new neurological deficit 1 week after surgery and a 0% rate at 6-month follow-up. Patients undergoing resection of tumors with increased functional connectivity had a 60% rate of a new deficit at 1 week and a 25% rate at 6 months. CONCLUSION: Magnetoencephalography connectivity analysis gives a valuable preoperative evaluation of the functionality of the tissue surrounding tumors in perieloquent and eloquent areas. These data may be used to optimize preoperative patient counseling and surgical strategy.

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.

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 ( = 30 for NWR, = 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 ( = 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.

We examined the spatiotemporal dynamics of word processing by recording the electrocorticogram (ECoG) from the lateral frontotemporal cortex of neurosurgical patients chronically implanted with subdural electrode grids. Subjects engaged in a target detection task where proper names served as infrequent targets embedded in a stream of task-irrelevant verbs and nonwords. Verbs described actions related to the hand (e.g, throw) or mouth (e.g., blow), while unintelligible nonwords were sounds which matched the verbs in duration, intensity, temporal modulation, and power spectrum. Complex oscillatory dynamics were observed in the delta, theta, alpha, beta, low, and high gamma (HG) bands in response to presentation of all stimulus types. HG activity (80-200 Hz) in the ECoG tracked the spatiotemporal dynamics of word processing and identified a network of cortical structures involved in early word processing. HG was used to determine the relative onset, peak, and offset times of local cortical activation during word processing. Listening to verbs compared to nonwords sequentially activates first the posterior superior temporal gyrus (post-STG), then the middle superior temporal gyrus (mid-STG), followed by the superior temporal sulcus (STS). We also observed strong phase-locking between pairs of electrodes in the theta band, with weaker phase-locking occurring in the delta, alpha, and beta frequency ranges. These results provide details on the first few hundred milliseconds of the spatiotemporal evolution of cortical activity during word processing and provide evidence consistent with the hypothesis that an oscillatory hierarchy coordinates the flow of information between distinct cortical regions during goal-directed behavior.