Gamma Frequency Bands Research Articles

Effect of Loranthus longiflorus ethanolic Extract on Neuronal Damage Induced by Electromagnetic Radiation in Wistar Rats

Background: The effect of electromagnetic radiation (EMR) on brain is of serious concern as it causes adverse effects on memory and learning besides causing stress. Earlier studies indicated that EMR exposure could lead to recognized pathologic consequences including increased permeability of blood brain barrier, distressed neuronal function and increased activity of the alpha, beta and gamma frequency bands in electroencephalograph. Oxidant-antioxidant and neurotransmitter imbalances were also noted in previous studies. In the present study, the neuroprotective effect of Loranthus longiflorus extract (LLE) on EMR-induced rat brain has been investigated. Methods: Four groups of rats (n=6) were considered for the study. Group 1 consisted of normal animals that received only feed and water while group 2 rats received only exposure to EMR for 28 consecutive days. Groups 3 and 4 were treated respectively with LLE (500 mg/kg bw) and standard drug melatonin (10 mg/kg bw) daily for 28 days together with EMR exposure for the same period. After 10, 20 and 28 days, Y- maze test was carried out to investigate the cognitive functions in experimental animals. Result: It was observed that chronic exposure to EMR decreased cognitive characteristics in rats as revealed by significant changes in their behaviour as well as neurotransmitters such as GABA, ACh and dopamine. Treatment with LLE and melatonin reversed these changes to near normal values weindicating the efficacy of the plant extract in combating the neuronal changes in animals. A normal expression of tyrosine hydroxylase in LLE treated animals as compared to melatonin group was also recorded that further confirmed reversal of brain activity.

Succinic Semialdehyde Dehydrogenase Deficiency: Review of the Natural History Study.

The SSADHD Natural History Study was initiated in 2019 to define the natural course and identify biomarkers correlating with severity. The study is conducted by 4 institutions: BCH (US clinical), WSU (bioanalytical core), USF (biostatistical core), and Heidelberg (iNTD), with support from the family advocacy group (SSADH Association). Recruitment goals were to study 20 patients on-site at BCH, 10 with iNTD, and 25 as a standard-of care cohort. At this half-way point of this longitudinal study, 28 subjects have been recruited (57% female, mean 9 years, range 18 months-40 years). Epilepsy is present in half and increases in incidence and severity, as do psychiatric symptoms, in adolescence and adulthood. The average Full Scale IQ (FSIQ) was 53 (Verbal score of 56, Non Verbal score of 49), and half scored as having ASD. Although there was no correlation between gene variant and phenotypic severity, there were extreme cases of lowest functioning in one individual and highest in another that may have genotype-phenotype correlation. The most common EEG finding was mild background slowing with rare epileptiform activity, whereas high-density EEG and magnetoencephalography showed reduction in the gamma frequency band consistent with GABAergic dysfunction. MR spectroscopy showed elevations in the GABA/NAA ratio in all regions studied with no crossover between subjects and controls. The SSADH Natural History Study is providing a unique opportunity to study the complex pathophysiology longitudinally and derive electrophysiologic, neuroimaging, and laboratory data for correlation and to serve as biomarkers for clinical trials and prognostic assessments in this ultra-rare inherited disorder of GABA metabolism.

Extremely preterm children demonstrate hyperconnectivity during verb generation: A multimodal approach

Children born extremely preterm (EPT, <28 weeks gestation) are at risk for delays in development, including language. We use fMRI-constrained magnetoencephalography (MEG) during a verb generation task to assess the extent and functional connectivity (phase locking value, or PLV) of language networks in a large cohort of EPT children and their term comparisons (TC). 73 participants, aged 4 to 6 years, were enrolled (42 TC, 31 EPT). There were no significant group differences in age, sex, race, ethnicity, parental education, or family income. There were significant group differences in expressive language scores (p < 0.05). Language representation was not significantly different between groups on fMRI, with task-specific activation involving bilateral temporal and left inferior frontal cortex. There were group differences in functional connectivity seen in MEG. To identify a possible subnetwork contributing to focal spectral differences in connectivity, we ran Network Based Statistics analyses. For both beta (20–25 Hz) and gamma (61–70 Hz) bands, we observed a subnetwork showing hyperconnectivity in the EPT group (p < 0.05). Network strength was computed for the beta and gamma subnetworks and assessed for correlation with language performance. For the EPT group exclusively, strength of the subnetwork identified in the gamma frequency band was positively correlated with expressive language scores (r = 0.318, p < 0.05). Thus, hyperconnectivity is positively related to language for EPT children and might represent a marker for resiliency in this population.

Graph energy based centrality measures to detect epileptogenic focal invasive EEG electrodes

PurposeMedically intractable epilepsy can be treated with surgical interventions, which require localization of the cortical region where seizures start. This region is referred to as the epileptogenic zone (EZ). Good surgical outcomes depend on an exact localization of the EZ. MethodsWe propose a graph theoretical approach providing a novel method to localize the epileptogenic zone using invasive electroencephalogram (EEG) data. The proposed methods employ centrality determination using three graph energies, namely simple graph energy, Laplacian energy, and distance energy. Centrality values of invasive EEG electrodes from 19 patients were analyzed at different frequency bands and at different time points. K-means clustering was used to distinguish focal (electrodes placed in the epileptogenic zone) from non-focal electrodes using the centrality values obtained. ResultsFocal electrodes show higher centrality values when compared to non-focal electrodes. All three graph energy based centrality measures proposed show maximum f-score and accuracy during the early seizure phase in the gamma frequency band. Among the three proposed methods, simple graph energy based centrality outperforms Laplacian centrality and distance energy based centrality and also other related and competitive methods available in the literature in terms of accuracy and f-score. ConclusionGraph energy based centrality measures are useful parameters for the delineation of the epileptogenic zone. Among the three centrality measures examined, simple graph energy based centrality proved best suited for this purpose.

Frequency-specific network activity predicts bradykinesia severity in Parkinson’s disease

ObjectiveBradykinesia has been associated with beta and gamma band interactions in the basal ganglia-thalamo-cortical circuit in Parkinson’s disease. In this present cross-sectional study, we aimed to search for neural networks with electroencephalography whose frequency-specific actions may predict bradykinesia. MethodsTwenty Parkinsonian patients treated with bilateral subthalamic stimulation were first prescreened while we selected four levels of contralateral stimulation (0: OFF, 1–3: decreasing symptoms to ON state) individually, based on kinematics. In the screening period, we performed 64-channel electroencephalography measurements simultaneously with electromyography and motion detection during a resting state, finger tapping, hand grasping tasks, and pronation-supination of the arm, with the four levels of contralateral stimulation. We analyzed spectral power at the low (13–20 Hz) and high (21–30 Hz) beta frequency bands and low (31–60 Hz) and high (61–100 Hz) gamma frequency bands using the dynamic imaging of coherent sources. Structural equation modelling estimated causal relationships between the slope of changes in network beta and gamma activities and the slope of changes in bradykinesia measures. ResultsActivity in different subnetworks, including predominantly the primary motor and premotor cortex, the subthalamic nucleus predicted the slopes in amplitude and speed while switching between stimulation levels. These subnetwork dynamics on their preferred frequencies predicted distinct types and parameters of the movement only on the contralateral side. DiscussionConcurrent subnetworks affected in bradykinesia and their activity changes in the different frequency bands are specific to the type and parameters of the movement; and the primary motor and premotor cortex are common nodes.

Event-Related Phase-Amplitude Coupling During Working Memory of Musical Chords

Phase-amplitude coupling (PAC) is a well-established concept for evaluating the strength of memory coding within brain regions, and has been shown to possess the characteristic of presenting memory mechanisms. It has been demonstrated that oscillations of theta and gamma brain waves can represent the neural coding structure of memory retrieval. However, most previous studies have presented PAC-related memory mechanisms with visual modalities, and little is known about the influence of auditory stimuli. In this study, 18 participants were recruited and 36-channels electroencephalography (EEG) signals were recorded while they were performing an n-back auditory working memory task. There were three experimental conditions with different levels of working memory load. Event-related phase-amplitude coupling (ERPAC) with the advantage of better temporal resolution was used to evaluate the coupling phenomenon from the reconstructed dipole brain sources. We primarily focused on independent components from the frontal and parietal regions, which were reported to be related to memory mechanisms. The results suggest that significant ERPAC was observed in both the frontal and parietal regions. In addition to the coupling between theta (4-7 Hz) and low gamma (30-40 Hz) frequency bands, pronounced high beta oscillations (20-30 Hz) were also observed to be modulated by the phases of theta oscillations. These findings suggest the existence of phase-amplitude coupling in the neocortex during auditory working memory, and provide a highly resolved timeline to evaluate brain dynamics. In addition, the ERPAC results also support the involvement of theta-gamma and theta-beta neural coding mechanisms in cognitive and memory tasks. Collectively, these findings demonstrate the existence of ERPAC within the frontal and parietal regions during an auditory working memory task using complex chords as stimuli, and prompt the use of complex stimuli in studies that are closer to the real-life applications of cognitive evaluations, mental treatments, and brain-computer interfaces.

Transforming Imagined Thoughts into Speech Using a Covariance-Based Subset Selection Method

With the advancement of engineering solutions in the medical domain, the patient’s life can become comfortable. This work recognizes the silent speech of three words. The decoding of silent speech can be useful for patients who are in a locked-in syndrome state. Moreover, it is also applicable to entertainment, cognitive biometrics, and brain-computer interfacing. Brain waves of these imagined words in the delta, theta, alpha, beta, gamma, and high gamma frequency bands are analysed. Covariance based connectivity features are extracted in each frequency band. The principal features which represent more than 95% of the variance are selected as a subset of the covariance connectivity matrix. This sub-set is tested on five classifiers. The maximum accuracy achieved is 76.4% in the theta band. Also, theta and high gamma band contain maximum information about imagined speech with average accuracies of 68.32% and 62.09% respectively.

Self-Weighted Semi-Supervised Classification for Joint EEG-Based Emotion Recognition and Affective Activation Patterns Mining

In Electroencephalography (EEG)-based affective brain-computer interfaces (aBCIs), there is a consensus that EEG features extracted from different frequency bands and channels have different abilities in emotion expression. Besides, EEG is so weak and non-stationary that easily causes distribution discrepancies for EEG data collected at different times; therefore, it is necessary to explore the affective activation patterns in cross-session emotion recognition. To address these two problems, we propose a self-weighted semi-supervised classification (SWSC) model in this paper for joint EEG-based cross-session emotion recognition and affective activation patterns mining, whose merits include 1) using both the labeled and unlabeled samples from different sessions for better capturing data characteristics, 2) introducing a self-weighted variable to learn the importance of EEG features adaptively and quantitatively, and 3) mining the activation patterns including the critical EEG frequency bands and channels automatically based on the learned self-weighted variable. Extensive experiments are conducted on the benchmark SEED_IV emotional data set and SWSC obtained excellent average accuracies of 77.40%, 79.55% and 81.52% in three cross-session emotion recognition tasks. Moreover, SWSC identifies that the Gamma frequency band contributes the most and the EEG channels in prefrontal, left/right temporal and (central) parietal lobes are more important for cross-session emotion recognition.

Brain rhythm bursts are enhanced by multiplicative noise.

Many healthy and pathological brain rhythms, including beta and gamma rhythms and essential tremor, are suspected to be induced by noise. This yields randomly occurring, brief epochs of higher amplitude oscillatory activity known as "bursts," the statistics of which are important for proper neural function. Here, we consider a more realistic model with both multiplicative and additive noise instead of only additive noise, to understand how state-dependent fluctuations further affect rhythm induction. For illustrative purposes, we calibrate the model at the lower end of the beta band that relates to movement; parameter tuning can extend the relevance of our analysis to the higher frequency gamma band or to lower frequency essential tremors. A stochastic Wilson-Cowan model for reciprocally as well as self-coupled excitatory (E) and inhibitory (I) populations is analyzed in the parameter regime where the noise-free dynamics spiral in to a fixed point. Noisy oscillations known as quasi-cycles are then generated by stochastic synaptic inputs. The corresponding dynamics of E and I local field potentials can be studied using linear stochastic differential equations subject to both additive and multiplicative noises. As the prevalence of bursts is proportional to the slow envelope of the E and I firing activities, we perform an envelope-phase decomposition using the stochastic averaging method. The resulting envelope dynamics are uni-directionally coupled to the phase dynamics as in the case of additive noise alone but both dynamics involve new noise-dependent terms. We derive the stationary probability and compute power spectral densities of envelope fluctuations. We find that multiplicative noise can enhance network synchronization by reducing the magnitude of the negative real part of the complex conjugate eigenvalues. Higher noise can lead to a "virtual limit cycle," where the deterministically stable eigenvalues around the fixed point acquire a positive real part, making the system act more like a noisy limit cycle rather than a quasi-cycle. Multiplicative noise can thus exacerbate synchronization and possibly contribute to the onset of symptoms in certain motor diseases.

Large-Scale Desynchronization During Interictal Epileptic Discharges Recorded With Intracranial EEG.

It is increasingly recognized that deep understanding of epileptic seizures requires both localizing and characterizing the functional network of the region where they are initiated, i. e., the epileptic focus. Previous investigations of the epileptogenic focus' functional connectivity have yielded contrasting results, reporting both pathological increases and decreases during resting periods and seizures. In this study, we shifted paradigm to investigate the time course of connectivity in relation to interictal epileptiform discharges. We recruited 35 epileptic patients undergoing intracranial EEG (iEEG) investigation as part of their presurgical evaluation. For each patient, 50 interictal epileptic discharges (IEDs) were marked and iEEG signals were epoched around those markers. Signals were narrow-band filtered and time resolved phase-locking values were computed to track the dynamics of functional connectivity during IEDs. Results show that IEDs are associated with a transient decrease in global functional connectivity, time-locked to the peak of the discharge and specific to the high range of the gamma frequency band. Disruption of the long-range connectivity between the epileptic focus and other brain areas might be an important process for the generation of epileptic activity. Transient desynchronization could be a potential biomarker of the epileptogenic focus since 1) the functional connectivity involving the focus decreases significantly more than the connectivity outside the focus and 2) patients with good surgical outcome appear to have a significantly more disconnected focus than patients with bad outcomes.

Phase Lag Analyses on Ictal Scalp Electroencephalography May Predict Outcomes of Corpus Callosotomy for Epileptic Spasms.

Objective: We aimed to clarify the patterns of ictal power and phase lag among bilateral hemispheres on scalp electroencephalography (EEG) recorded pre-operatively during epileptic spasms (ESs) and the correlation with the outcomes following corpus callosotomy.Methods: We enrolled 17 patients who underwent corpus callosotomy for ESs before 20 years of age. After corpus callosotomy, seven patients did not experience further ESs (favorable outcome group), and the remaining 10 patients had ongoing ESs (unfavorable outcome group). We used pre-operative scalp EEG data from monopolar montages using the average reference. The relative power spectrum (PS), ictal power laterality (IPL) among the hemispheres, and phase lag, calculated by the cross-power spectrum (CPS) among symmetrical electrodes (i.e., F3 and F4), were analyzed in the EEG data of ESs from 143 pre-operative scalp video-EEG records. Analyses were conducted separately in each frequency band from the delta, theta, alpha, beta, and gamma range. We compared the means of those data in each patient between favorable and unfavorable outcome groups.Results: Among all frequency bands, no significant differences were seen in the individual mean relative PSs in the favorable and unfavorable outcome group. Although the mean IPLs in each patient tended to be high in the unfavorable outcome group, no significant differences were found. The mean CPSs in the delta, theta, and gamma frequency bands were significantly higher in the unfavorable than in the favorable outcome group. Using the Youden index, the optimal cutoff points of those mean CPS values for unfavorable outcomes were 64.00 in the delta band (sensitivity: 100%, specificity: 80%), 74.20 in the theta band (100, 80%), and 82.05 in the gamma band (100, 80%). Subanalyses indicated that those CPS differences originated from pairs of symmetrical electrodes in the bilateral frontal and temporal areas.Significance: Ictal power and laterality of the ictal power in each frequency band were not associated with the outcomes of CC; however, the phase lags seen in the delta, theta, and gamma frequency bands were larger in the unfavorable than in the favorable outcome group. The phase lags may predict outcomes of CC for ESs on pre-surgical scalp-ictal EEGs.

MEG detects abnormal hippocampal activity in amyloid‐positive MCI

AbstractBackgroundEarly Alzheimer’s disease (AD) is characterized by pathological hippocampal hyperactivity1,2. Non‐invasive detection of hippocampal hyperactivity is relevant for diagnostic and therapeutic purposes, but no reliable method is available. We tested magneto‐encephalographical (MEG) detection of hippocampal activity, hypothesizing that in amyloid‐positive MCI patients resting‐state hippocampal activity would be higher than in both controls and AD patients.MethodWe used resting‐state MEG data from 18 mild/moderate AD patients, 18 amyloid‐positive (CSF) amnestic MCI patients, and 18 persons with subjective cognitive decline (SCD). MEG source space signals were reconstructed for 80 AAL regions using beamforming3. We obtained 20 non‐selected, eyes‐closed, 13‐second epochs per person. Global spectral power group differences were identified for common frequency bands using ANOVA and t‐tests. Classification was tested with ROC curves.ResultPeak frequency was similar in SCD and MCI groups, but lower in AD patients (P&lt;0.001). In MCI and AD, hippocampal total power increased, with a significant rise in the right hippocampus in MCI (p&lt;0.01). In MCI, this was mainly driven by high relative lower alpha (8‐10 Hz) power, significant only in the right hippocampus (P&lt;0.01). Relative gamma (30‐45 Hz) power was lowest in the MCI patient group, hippocampal as well as overall (p&lt;0.01). Classification between MCI and SCD was most accurate using right hippocampal gamma power (Area under the curve = 0.821).ConclusionResting‐state MEG detects abnormal hippocampal activity in preclinical AD patients. This may be a macroscale 'signature' of underlying neuronal hyperactivity1,4. Relevant findings in these subcortical regions and in the gamma frequency band show the added value of MEG compared to EEG and fMRI. The present approach may facilitate diagnostic and therapeutic strategies.

Cortical Oscillatory Signatures Reveal the Prerequisites for Tinnitus Perception: A Comparison of Subjects With Sudden Sensorineural Hearing Loss With and Without Tinnitus.

Just as the human brain works in a Bayesian manner to minimize uncertainty regarding external stimuli, a deafferented brain due to hearing loss attempts to obtain or “fill in” the missing auditory information, resulting in auditory phantom percepts (i.e., tinnitus). Among various types of hearing loss, sudden sensorineural hearing loss (SSNHL) has been extensively reported to be associated with tinnitus. However, the reason that tinnitus develops selectively in some patients with SSNHL remains elusive, which led us to hypothesize that patients with SSNHL with tinnitus (SSNHL-T) and those without tinnitus (SSNHL-NT) may exhibit different cortical activity patterns. In the current study, we compared resting-state quantitative electroencephalography findings between 13 SSNHL-T and 13 SSNHL-NT subjects strictly matched for demographic characteristics and hearing thresholds. By performing whole-brain source localization analysis complemented by functional connectivity analysis, we aimed to determine the as-yet-unidentified cortical oscillatory signatures that may reveal potential prerequisites for the perception of tinnitus in patients with SSNHL. Compared with the SSNHL-NT group, the SSNHL-T group showed significantly higher cortical activity in Bayesian inferential network areas such as the frontopolar cortex, orbitofrontal cortex (OFC), and pregenual anterior cingulate cortex (pgACC) for the beta 3 and gamma frequency bands. This suggests that tinnitus develops in a brain with sudden auditory deafferentation only if the Bayesian inferential network updates the missing auditory information and the pgACC-based top-down gatekeeper system is actively involved. Additionally, significantly increased connectivity between the OFC and precuneus for the gamma frequency band was observed in the SSNHL-T group, further suggesting that tinnitus derived from Bayesian inference may be linked to the default mode network so that tinnitus is regarded as normal. Taken together, our preliminary results suggest a possible mechanism for the selective development of tinnitus in patients with SSNHL. Also, these areas could serve as the potential targets of neuromodulatory approaches to preventing the development or prolonged perception of tinnitus in subjects with SSNHL.

Impaired speed encoding and grid cell periodicity in a mouse model of tauopathy.

Dementia is associated with severe spatial memory deficits which arise from dysfunction in hippocampal and parahippocampal circuits. For spatially sensitive neurons, such as grid cells, to faithfully represent the environment these circuits require precise encoding of direction and velocity information. Here, we have probed the firing rate coding properties of neurons in medial entorhinal cortex (MEC) in a mouse model of tauopathy. We find that grid cell firing patterns are largely absent in rTg4510 mice, while head-direction tuning remains largely intact. Conversely, neural representation of running speed information was significantly disturbed, with smaller proportions of MEC cells having firing rates correlated with locomotion in rTg4510 mice. Additionally, the power of local field potential oscillations in the theta and gamma frequency bands, which in wild-type mice are tightly linked to running speed, was invariant in rTg4510 mice during locomotion. These deficits in locomotor speed encoding likely severely impact path integration systems in dementia.

A MEG Study of Visual Repetition Priming in Schizophrenia: Evidence for Impaired High-Frequency Oscillations and Event-Related Fields in Thalamo-Occipital Cortices.

Background: Cognitive dysfunctions represent a core feature of schizophrenia and a predictor for clinical outcomes. One possible mechanism for cognitive impairments could involve an impairment in the experience-dependent modifications of cortical networks.Methods: To address this issue, we employed magnetoencephalography (MEG) during a visual priming paradigm in a sample of chronic patients with schizophrenia (n = 14), and in a group of healthy controls (n = 14). We obtained MEG-recordings during the presentation of visual stimuli that were presented three times either consecutively or with intervening stimuli. MEG-data were analyzed for event-related fields as well as spectral power in the 1–200 Hz range to examine repetition suppression and repetition enhancement. We defined regions of interest in occipital and thalamic regions and obtained virtual-channel data.Results: Behavioral priming did not differ between groups. However, patients with schizophrenia showed prominently reduced oscillatory response to novel stimuli in the gamma-frequency band as well as significantly reduced repetition suppression of gamma-band activity and reduced repetition enhancement of beta-band power in occipital cortex to both consecutive repetitions as well as repetitions with intervening stimuli. Moreover, schizophrenia patients were characterized by a significant deficit in suppression of the C1m component in occipital cortex and thalamus as well as of the late positive component (LPC) in occipital cortex.Conclusions: These data provide novel evidence for impaired repetition suppression in cortical and subcortical circuits in schizophrenia. Although behavioral priming was preserved, patients with schizophrenia showed deficits in repetition suppression as well as repetition enhancement in thalamic and occipital regions, suggesting that experience-dependent modification of neural circuits is impaired in the disorder.

EEG differentiates left and right imagined Lower Limb movement

BackgroundIdentifying which EEG signals distinguish left from right leg movements in imagined lower limb movement is crucial to building an effective and efficient brain-computer interface (BCI). Past findings on this issue have been mixed, partly due to the difficulty in collecting and isolating the relevant information. The purpose of this study was to contribute to this new and important literature. Research QuestionCan left versus right imagined stepping be differentiated using the alpha, beta, and gamma frequencies of EEG data at four electrodes (C1, C2, PO3, and PO4)? MethodsAn experiment was conducted with a sample of 16 healthy male participants. They imagined left and right lower limb movements across 60 trials at two time periods separated by one week. Participants were fitted with a 64-electrode headcap, lay supine on a specially designed device and then completed the imagined task while observing a customized computer-generated image of a human walking to signify the left and right steps, respectively. ResultsFindings showed that eight of the twelve frequency bands from 4 EEG electrodes were significant in differentiating imagined left from right lower limb movement. Using these data points, a neural network analysis resulted in an overall participant average test classification accuracy of left versus right movements at 63 %. SignificanceOur study provides support for using the alpha, beta and gamma frequency bands at the sensorimotor areas (C1 and C2 electrodes) and incorporating information from the parietal/occipital lobes (PO3 and PO4 electrodes) for focused, real-time EEG signal processing to assist in creating a BCI for those with lower limb compromised mobility.

Altered supraspinal motor networks in survivors of poliomyelitis: A cortico-muscular coherence study

ObjectivePoliomyelitis results in changes to the anterior horn cell. The full extent of cortical network changes in the motor physiology of polio survivors has not been established. Our aim was to investigate how focal degeneration of the lower motor neurons (LMN) in infancy/childhood affects motor network connectivity in adult survivors of polio. MethodsSurface electroencephalography (EEG) and electromyography (EMG) were recorded during an isometric pincer grip task in 25 patients and 11 healthy controls. Spectral signal analysis of cortico-muscular (EEG-EMG) coherence (CMC) was used to identify the cortical regions that are functionally synchronous and connected to the periphery during the pincer grip task. ResultsA pattern of CMC was noted in polio survivors that was not present in healthy individuals. Significant CMC in low gamma frequency bands (30–47 Hz) was observed in frontal and parietal regions. ConclusionThese findings imply a differential engagement of cortical networks in polio survivors that extends beyond the motor cortex and suggest a disease-related functional reorganisation of the cortical motor network. SignificanceThis research has implications for other similar LMN conditions, including spinal muscular atrophy (SMA). CMC has potential in future clinical trials as a biomarker of altered function in motor networks in post-polio syndrome, SMA, and other related conditions.

Gamma power abnormalities in a Fmr1-targeted transgenic rat model of fragile X syndrome

Fragile X syndrome (FXS) is characteristically displayed intellectual disability, hyperactivity, anxiety, and abnormal sensory processing. Electroencephalography (EEG) abnormalities are also observed in subjects with FXS, with many researchers paying attention to these as biomarkers. Despite intensive preclinical research using Fmr1 knock out (KO) mice, an effective treatment for FXS has yet to be developed. Here, we examined Fmr1-targeted transgenic rats (Fmr1-KO rats) as an alternative preclinical model of FXS. We characterized the EEG phenotypes of Fmr1-KO rats by measuring basal EEG power and auditory steady state response (ASSR) to click trains of stimuli at a frequency of 10–80 Hz. Fmr1-KO rats exhibited reduced basal alpha power and enhanced gamma power, and these rats showed enhanced locomotor activity in novel environment. While ASSR clearly peaked at around 40 Hz, both inter-trial coherence (ITC) and event-related spectral perturbation (ERSP) were significantly reduced at the gamma frequency band in Fmr1-KO rats. Fmr1-KO rats showed gamma power abnormalities and behavioral hyperactivity that were consistent with observations reported in mouse models and subjects with FXS. These results suggest that gamma power abnormalities are a translatable biomarker among species and demonstrate the utility of Fmr1-KO rats for investigating drugs for the treatment of FXS.

Power spectral density and coherence analysis of eye disease with and without visual hallucination

ObjectivesCharles Bonnet Syndrome (CBS) is a rare clinical condition which has been defined as complex visual hallucinations (CVH) due to visual loss. This study investigated differences in the EEG power spectral density (PSD) and magnitude-squared coherences between patients with eye disease and hallucinations (VH+), and the control subjects with eye disease without hallucinations (VH-). Methods19 scalp channels EEG was recorded in four VH+ (CBS) and four VH- subjects during an eyes-closed resting condition. Artefact-free epochs were analyzed to obtain PSD values in the delta, theta, alpha1, alpha2, beta1, beta2 and gamma frequency bands. Coherence values were calculated through inter-hemispheric and intra-hemispheric electrodes pairs of interest. All subjects were performed with neuropsychological and behavioral assessments to evaluate cognitive functions. ResultsThe VH + group had increase PSD in theta, beta2 and gamma bands in central, parietal and occipital (O2) areas. The synchronicity was altered particularly in parietal and frontal-parietal regions especially at theta and alpha1 respectively. ConclusionsThe aberrant activity in occipital and parietal regions suggest the mechanism of CBS. This is a major electrophysiological study of understanding CBS and visual hallucinations.

Neurophysiological Correlates of Dynamic Beat Tracking in Individuals With Williams Syndrome

BackgroundWilliams syndrome (WS) is a neurodevelopmental disorder characterized by hypersociability, heightened auditory sensitivities, attention deficits, and strong musical interests despite differences in musical skills. Behavioral studies have reported that individuals with WS exhibit variable beat and rhythm perception skills. MethodsWe sought to investigate the neural basis of beat tracking in individuals with WS using electroencephalography. Twenty-seven adults with WS and 16 age-matched, typically developing control subjects passively listened to musical rhythms with accents on either the first or second tone of the repeating pattern, leading to distinct beat percepts. ResultsConsistent with the role of beta and gamma oscillations in rhythm processing, individuals with WS and typically developing control subjects showed strong evoked neural activity in both the beta (13–30 Hz) and gamma (31–55 Hz) frequency bands in response to beat onsets. This neural response was somewhat more distributed across the scalp for individuals with WS. Compared with typically developing control subjects, individuals with WS exhibited significantly greater amplitude of auditory evoked potentials (P1-N1-P2 complex) and modulations in evoked alpha (8–12 Hz) activity, reflective of sensory and attentional processes. Individuals with WS also exhibited markedly stable neural responses over the course of the experiment, and these responses were significantly more stable than those of control subjects. ConclusionsThese results provide neurophysiological evidence for dynamic beat tracking in WS and coincide with the atypical auditory phenotype and attentional difficulties seen in this population.