Order of statistical learning depends on perceptive uncertainty

Statistical learning (SL) is an innate mechanism by which the brain automatically encodes the n-th order transition probability (TP) of a sequence and grasps the uncertainty of the TP distribution. Through SL, the brain predicts a subsequent event (e _n+1 ) based on the preceding events (e _n ) that have a length of "n". It is now known that uncertainty modulates prediction in top-down processing by the human predictive brain. However, the manner in which the human brain modulates the order of SL strategies based on the degree of uncertainty remains an open question. The present study examined how uncertainty modulates the neural effects of SL and whether differences in uncertainty alter the order of SL strategies. It used auditory sequences in which the uncertainty of sequential information is manipulated based on the conditional entropy. Three sequences with different TP ratios of 90:10, 80:20, and 67:33 were prepared as low-, intermediate, and high-uncertainty sequences, respectively (conditional entropy: 0.47, 0.72, and 0.92 bit, respectively). Neural responses were recorded when the participants listened to the three sequences. The results showed that stimuli with lower TPs elicited a stronger neural response than those with higher TPs, as demonstrated by a number of previous studies. Furthermore, we found that participants adopted higher-order SL strategies in the high uncertainty sequence. These results may indicate that the human brain has an ability to flexibly alter the order based on the uncertainty. This uncertainty may be an important factor that determines the order of SL strategies. Particularly, considering that a higher-order SL strategy mathematically allows the reduction of uncertainty in information, we assumed that the brain may take higher-order SL strategies when encountering high uncertain information in order to reduce the uncertainty. The present study may shed new light on understanding individual differences in SL performance across different uncertain situations.

Automated pancreatic islet viability assessment for transplantation using bright-field deep morphological signature

Islets transplanted for type-1 diabetes have their viability reduced by warm ischemia, dimethyloxalylglycine (DMOG; hypoxia model), oxidative stress and cytokine injury. This results in frequent transplant failures and the major burden of patients having to undergo multiple rounds of treatment for insulin independence. Presently there is no reliable measure to assess islet preparation viability prior to clinical transplantation. We investigated deep morphological signatures (DMS) for detecting the exposure of islets to viability compromising insults from brightfield images. Accuracies ranged from 98 % to 68 % for; ROS damage, pro-inflammatory cytokines, warm ischemia and DMOG. When islets were disaggregated to single cells to enable higher throughput data collection, good accuracy was still obtained (83-71 %). Encapsulation of islets reduced accuracy for cytokine exposure, but it was still high (78 %). Unsupervised modelling of the DMS for islet preparations transplanted into a syngeneic mouse model was able to predict whether or not they would restore glucose control with 100 % accuracy. Our strategy for constructing DMS' is effective for the assessment of islet pre-transplant viability. If translated into the clinic, standard equipment could be used to prospectively identify non-functional islet preparations unable to contribute to the restoration of glucose control and reduce the burden of unsuccessful treatments.

Rich-club in the brain's macrostructure: Insights from graph theoretical analysis

The brain is a complex network. Growing evidence supports the critical roles of a set of brain regions within the brain network, known as the brain’s cores or hubs. These regions require high energy cost but possess highly efficient neural information transfer in the brain’s network and are termed the rich-club. The rich-club of the brain network is essential as it directly regulates functional integration across multiple segregated regions and helps to optimize cognitive processes. Here, we review the recent advances in rich-club organization to address the fundamental roles of the rich-club in the brain and discuss how these core brain regions affect brain development and disorders. We describe the concepts of the rich-club behind network construction in the brain using graph theoretical analysis. We also highlight novel insights based on animal studies related to the rich-club and illustrate how human studies using neuroimaging techniques for brain development and psychiatric/neurological disorders may be relevant to the rich-club phenomenon in the brain network.

Neuromodulation with electromagnetic stimulation for seizure suppression: From electrode to magnetic coil

Non-invasive brain tissue stimulation with a magnetic coil provides several irreplaceable advantages over that with an implanted electrode, in altering neural activities under pathological situations. We reviewed clinical cases that utilized time-varying magnetic fields for the treatment of epilepsy, and the safety issues related to this practice. Animal models have been developed to foster understanding of the cellular/molecular mechanisms underlying magnetic control of epileptic activity. These mechanisms include (but are not limited to) (1) direct membrane polarization by the magnetic field, (2) depolarization blockade by the deactivation of ion channels, (3) alteration in synaptic transmission, and (4) interruption of ephaptic interaction and cellular synchronization. Clinical translation of this technology could be improved through the advancement of magnetic design, optimization of stimulation protocols, and evaluation of the long-term safety. Cellular and molecular studies focusing on the mechanisms of magnetic stimulation are of great value in facilitating this translation.

Anatomo-electro-clinical correlations of hypermotor seizures with amygdala enlargement: Hippocampal seizure origin identified using stereoelectroencephalography

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A drug-resistant epilepsy case showed hypermotor seizures and amygdala enlargement.

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Seizure onset zone was the hippocampus, not amygdala, as revealed by SEEG.

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The enlarged amygdala pathology was classified as FCD type I.

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Selective amygdalohippocampectomy led to good outcomes.

Beta-band oscillations in the supplementary motor cortex are modulated by levodopa and associated with functional activity in the basal ganglia

We investigated the effect of acute levodopa administration on movement-related cortical oscillations and movement velocity in Parkinson's disease (PD). Patients with PD on and off medication and age- and sex-matched healthy controls performed a ballistic upper limb flexion movement as fast and accurately as possible while cortical oscillations were recorded with high-density electroencephalography. Patients off medication were also studied using task-based functional magnetic resonance imaging (fMRI) using a force control paradigm. Percent signal change of functional activity during the force control task was calculated for the putamen and subthalamic nucleus (STN) contralateral to the hand tested. We found that patients with PD off medication had an exaggerated movement-related beta-band (13–30 Hz) desynchronization in the supplementary motor area (SMA) compared to controls. In PD, spectral power in the beta-band was correlated with movement velocity. Following an acute dose of levodopa, we observed that the beta-band desynchronization in the SMA was reduced in PD, and was associated with increased movement velocity and increased voltage of agonist muscle activity. Further, using fMRI we found that the functional activity in the putamen and STN in the off medication state, was related to how responsive that cortical oscillations in the SMA of PD were to levodopa. Collectively, these findings provide the first direct evaluation of how movement-related cortical oscillations relate to movement velocity during the ballistic phase of movement in PD and demonstrate that functional brain activity in the basal ganglia pathways relate to the effects of dopaminergic medication on cortical neuronal oscillations during movement.

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Acute levodopa decreased beta-band desynchronization in the SMA, while improving movement velocity and muscle activity.

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Beta-band cortical activity during movement is positively correlated with upper limb movement velocity.

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fMRI in basal ganglia predicted the response of beta-band cortical activity to levodopa.

Diminished neural resources allocation to time processing in Autism Spectrum Disorders

Background

Interval timing, the ability to judge the duration of short events, has been shown to be compromised in Autism Spectrum Disorders (ASD). Timing abilities are ubiquitous and underlie behaviours as varied as sensory integration, motor coordination or communication. It has been suggested that atypical temporal processing in ASD could contribute to some of the disorder's symptoms, in particular motor clumsiness and difficulties in social interaction and communication. Recent behavioural investigations have suggested that interval timing in ASD is characterised by intact sensitivity but reduced precision in duration judgements.

Methods

In this study we investigated the processing of duration as compared to pitch in a group of high-functioning individuals with ASD using magnetoencephalography (MEG). 18 adolescents and adults with ASD and 18 age- and IQ-matched typically-developing control (TDC) individuals compared two consecutive tones according to their duration or pitch in separate experimental blocks. The analysis was carried out exclusively on physically identical stimuli (500 Hz tones lasting 600 ms), which served, according to instruction, as standard or probe in a Duration or Pitch task respectively.

Results

Our results suggest that compared to TDC individuals, individuals with ASD are less able to predict the duration of the standard tone accurately, affecting the sensitivity of the comparison process. In addition, contrary to TDC individuals who allocate resources at different times depending on the nature of the task (pitch or duration discrimination), individuals with ASD seem to engage less resources for the Duration task than for the Pitch task regardless of the context. Although individuals with ASD showed top-down adaptation to the context of the task, this neuronal strategy reflects a bias in the readiness to perform different types of tasks, and in particular a diminished allocation of resources to duration processing which could have cascading effect on learning and development of other cognitive functions.

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We investigated MEG response associated with duration or pitch comparison in ASD.

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We found lower sensitivity for duration discrimination behaviourally in ASD.

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ASD adults are less able to predict the offset of a standard tone.

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ASD adults engage less neural resources in duration than pitch discrimination task.

Alzheimer's disease: The state of the art in resting-state magnetoencephalography

Alzheimer's disease (AD) is accompanied by functional brain changes that can be detected in imaging studies, including electromagnetic activity recorded with magnetoencephalography (MEG). Here, we systematically review the studies that have examined resting-state MEG changes in AD and identify areas that lack scientific or clinical progress. Three levels of MEG analysis will be covered: (i) single-channel signal analysis, (ii) pairwise analyses over time series, which includes the study of interdependencies between two time series and (iii) global network analyses. We discuss the findings in the light of other functional modalities, such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Overall, single-channel MEG results show consistent changes in AD that are in line with EEG studies, but the full potential of the high spatial resolution of MEG and advanced functional connectivity and network analysis has yet to be fully exploited. Adding these features to the current knowledge will potentially aid in uncovering organizational patterns of brain function in AD and thereby aid the understanding of neuronal mechanisms leading to cognitive deficits.

'Tickling' seizures originating in the left frontoparietal region

We report a 10-year-old boy with mild developmental delay and epilepsy with new events of right back tickling and emotional upset. These initially appeared behavioral, causing postulation of habit behaviors or psychogenic nonepileptic seizures. Several ictal and interictal EEGs were unrevealing. Continuous EEG revealed only poorly localized frontal ictal activity. Given that his clinical symptoms suggested a parietal localization, double-density EEG electrodes were placed to better localize the epileptogenic and symptomatogenic zones. These revealed evolution of left greater than right frontoparietal discharges consistent with seizures at the time of the attacks. Medical management has significantly reduced the patient's seizures.

Synchrony of auditory brain responses predicts behavioral ability to keep still in children with autism spectrum disorder: Auditory-evoked response in children with autism spectrum disorder

The auditory-evoked P1m, recorded by magnetoencephalography, reflects a central auditory processing ability in human children. One recent study revealed that asynchrony of P1m between the right and left hemispheres reflected a central auditory processing disorder (i.e., attention deficit hyperactivity disorder, ADHD) in children. However, to date, the relationship between auditory P1m right-left hemispheric synchronization and the comorbidity of hyperactivity in children with autism spectrum disorder (ASD) is unknown. In this study, based on a previous report of an asynchrony of P1m in children with ADHD, to clarify whether the P1m right-left hemispheric synchronization is related to the symptom of hyperactivity in children with ASD, we investigated the relationship between voice-evoked P1m right-left hemispheric synchronization and hyperactivity in children with ASD. In addition to synchronization, we investigated the right-left hemispheric lateralization. Our findings failed to demonstrate significant differences in these values between ASD children with and without the symptom of hyperactivity, which was evaluated using the Autism Diagnostic Observational Schedule, Generic (ADOS-G) subscale. However, there was a significant correlation between the degrees of hemispheric synchronization and the ability to keep still during 12-minute MEG recording periods. Our results also suggested that asynchrony in the bilateral brain auditory processing system is associated with ADHD-like symptoms in children with ASD.

Computational analysis in epilepsy neuroimaging: A survey of features and methods

Epilepsy affects 65 million people worldwide, a third of whom have seizures that are resistant to anti-epileptic medications. Some of these patients may be amenable to surgical therapy or treatment with implantable devices, but this usually requires delineation of discrete structural or functional lesion(s), which is challenging in a large percentage of these patients.

Advances in neuroimaging and machine learning allow semi-automated detection of malformations of cortical development (MCDs), a common cause of drug resistant epilepsy. A frequently asked question in the field is what techniques currently exist to assist radiologists in identifying these lesions, especially subtle forms of MCDs such as focal cortical dysplasia (FCD) Type I and low grade glial tumors. Below we introduce some of the common lesions encountered in patients with epilepsy and the common imaging findings that radiologists look for in these patients. We then review and discuss the computational techniques introduced over the past 10 years for quantifying and automatically detecting these imaging findings. Due to large variations in the accuracy and implementation of these studies, specific techniques are traditionally used at individual centers, often guided by local expertise, as well as selection bias introduced by the varying prevalence of specific patient populations in different epilepsy centers. We discuss the need for a multi-institutional study that combines features from different imaging modalities as well as computational techniques to definitively assess the utility of specific automated approaches to epilepsy imaging. We conclude that sharing and comparing these different computational techniques through a common data platform provides an opportunity to rigorously test and compare the accuracy of these tools across different patient populations and geographical locations. We propose that these kinds of tools, quantitative imaging analysis methods and open data platforms for aggregating and sharing data and algorithms, can play a vital role in reducing the cost of care, the risks of invasive treatments, and improve overall outcomes for patients with epilepsy.

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We introduce common epileptogenic lesions encountered in patients with drug resistant epilepsy.

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We discuss state of the art computational techniques used to detect lesions.

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There is a need for multi-institutional studies that combine these techniques.

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Clinically validated pipelines alongside the advances in imaging and electrophysiology will improve outcomes.

What graph theory actually tells us about resting state interictal MEG epileptic activity

Graph theory provides a useful framework to study functional brain networks from neuroimaging data. In epilepsy research, recent findings suggest that it offers unique insight into the fingerprints of this pathology on brain dynamics. Most studies hitherto have focused on seizure activity during focal epilepsy, but less is known about functional epileptic brain networks during interictal activity in frontal focal and generalized epilepsy. Besides, it is not clear yet which measures are most suitable to characterize these networks. To address these issues, we recorded magnetoencephalographic (MEG) data using two orthogonal planar gradiometers from 45 subjects from three groups (15 healthy controls (7 males, 24 ± 6 years), 15 frontal focal (8 male, 32 ± 16 years) and 15 generalized epileptic (6 male, 27 ± 7 years) patients) during interictal resting state with closed eyes. Then, we estimated the total and relative spectral power of the largest principal component of the gradiometers, and the degree of phase synchronization between each sensor site in the frequency range [0.5–40 Hz]. We further calculated a comprehensive battery of 15 graph-theoretic measures and used the affinity propagation clustering algorithm to elucidate the minimum set of them that fully describe these functional brain networks. The results show that differences in spectral power between the control and the other two groups have a distinctive pattern: generalized epilepsy presents higher total power for all frequencies except the alpha band over a widespread set of sensors; frontal focal epilepsy shows higher relative power in the beta band bilaterally in the fronto-central sensors. Moreover, all network indices can be clustered into three groups, whose exemplars are the global network efficiency, the eccentricity and the synchronizability. Again, the patterns of differences were clear: the brain network of the generalized epilepsy patients presented greater efficiency and lower eccentricity than the control subjects for the high frequency bands, without a clear topography. Besides, the frontal focal epileptic patients showed only reduced eccentricity for the theta band over fronto-temporal and central sensors. These outcomes indicate that functional epileptic brain networks are different to those of healthy subjects during interictal stage at rest, with a unique pattern of dissimilarities for each type of epilepsy. Further, when properly selected, three network indices suffice to provide a comprehensive description of these differences. Yet, since such uniqueness in the pattern of differences is also evident in the power spectrum, we conclude that the added value of the graph theory approach in this context should not be overestimated.

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We study MEG activity during interictal resting state with closed eyes.

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Generalized epilepsy presents higher total power over a widespread set of sensors.

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Frontal epilepsy shows higher relative power in beta band on fronto-central sensors.

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We also found altered functional brain networks in epilepsy using graph theory.

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The pattern of differences from control subjects is unique for each type of epilepsy.

Targeted training modifies oscillatory brain activity in schizophrenia patients

Effects of both domain-specific and broader cognitive remediation protocols have been reported for neural activity and overt performance in schizophrenia (SZ). Progress is limited by insufficient knowledge of relevant neural mechanisms. Addressing neuronal signal resolution in the auditory system as a mechanism contributing to cognitive function and dysfunction in schizophrenia, the present study compared effects of two neuroplasticity-based training protocols targeting auditory–verbal or facial affect discrimination accuracy and a standard rehabilitation protocol on magnetoencephalographic (MEG) oscillatory brain activity in an auditory paired-click task. SZ were randomly assigned to either 20 daily 1-hour sessions over 4 weeks of auditory–verbal training (N = 19), similarly intense facial affect discrimination training (N = 19), or 4 weeks of treatment as usual (TAU, N = 19). Pre-training, the 57 SZ showed smaller click-induced posterior alpha power modulation than did 28 healthy comparison participants, replicating Popov et al. (2011b). Abnormally small alpha decrease 300–800 ms around S2 improved more after targeted auditory–verbal training than after facial affect training or TAU. The improvement in oscillatory brain dynamics with training correlated with improvement on a measure of verbal learning. Results replicate previously reported effects of neuroplasticity-based psychological training on oscillatory correlates of auditory stimulus differentiation, encoding, and updating and indicate specificity of cortical training effects.

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Induced posterior alpha power modulation in auditory paired-click design is abnormally small in schizophrenia patients.

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Abnormal alpha power modulation improved after neuroplasticity-based auditory training.

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Results confirm targeted training effects on oscillatory correlates of auditory stimulus discrimination, encoding, updating.

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No similar effects of visual affect discrimination training on alpha power indicate specificity of cortical training effects.

The temporal unfolding of face processing in social anxiety disorder--a MEG study

The current study is the first to use magnetoencephalography (MEG) to examine how individuals with social anxiety disorder (SAD) process emotional facial expressions (EFEs). We expected that, compared to healthy controls (HCs), participants with SAD will show an early (<200 ms post-stimulus) over-activation in the insula and the fusiform gyrus (FG, associated with the N170/M170 component), and later (>200 ms post-stimulus) over-activation in the dorsolateral prefrontal cortex (DLPFC). Individuals with SAD (n = 12) and healthy controls (HCs, n = 12) were presented with photographs of facial displays during MEG recording. As compared to the HC group, the SAD group showed a reduced M170 (right FG under-activation around 130–200 ms); early reduced activation in the right insula, and lower insular sensitivity to the type of EFE displayed. In addition, the SAD group showed a late over-activation in the right DLPFC. This unique EFE processing pattern in SAD suggests an early under-activation of cortical areas, possibly related to reduced emphasis on high spatial frequency information and greater early emphasis on low spatial frequency information. The late DLPFC over-activation in the SAD group may correlate to failures of cognitive control in this disorder. The importance of a temporal perspective for the understanding of facial processing in psychopathology is underlined.

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This study is the first to use MEG to study social anxiety disorder (SAD).

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SADs and controls viewed emotional facial expressions during MEG.

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Compared to controls, SADs showed reduced M170 (early fusiform gyrus activity).

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SADs presented a late over-activation in the right dorsolateral prefrontal cortex.

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The late frontal over-activity may correlate to failures of cognitive control in SAD.

Lessons of ALS imaging: Pitfalls and future directions - A critical review

Background

While neuroimaging in ALS has gained unprecedented momentum in recent years, little progress has been made in the development of viable diagnostic, prognostic and monitoring markers.

Objectives

To identify and discuss the common pitfalls in ALS imaging studies and to reflect on optimal study designs based on pioneering studies.

Methods

A “PubMed”-based literature search on ALS was performed based on neuroimaging-related keywords. Study limitations were systematically reviewed and classified so that stereotypical trends could be identified.

Results

Common shortcomings, such as relatively small sample sizes, statistically underpowered study designs, lack of disease controls, poorly characterised patient cohorts and a large number of conflicting studies, remain a significant challenge to the field. Imaging data of ALS continue to be interpreted at a group-level, as opposed to meaningful individual-patient inferences.

Conclusions

A systematic, critical review of ALS imaging has identified stereotypical shortcomings, the lessons of which should be considered in the design of future prospective MRI studies. At a time when large multicentre studies are underway a candid discussion of these factors is particularly timely.

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Stereotypical shortcomings can be identified in ALS neuroimaging studies.

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A systematic discussion of ALS study limitations is particularly timely.

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Individual patient data meta-analyses and multicentre studies are urgently required.

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The gaps identified in ALS imaging indicate exciting research opportunities.

Impaired evoked and resting-state brain oscillations in patients with liver cirrhosis as revealed by magnetoencephalography

A number of studies suggest that the clinical manifestation of neurological deficits in hepatic encephalopathy results from pathologically synchronized neuronal oscillations and altered oscillatory coupling. In the present study spontaneous and evoked oscillatory brain activities were analyzed jointly with established behavioral measures of altered visual oscillatory processing. Critical flicker and fusion frequencies (CFF, FUF) were measured in 25 patients diagnosed with liver cirrhosis and 30 healthy controls. Magnetoencephalography (MEG) data were collected at rest and during a visual task employing repetitive stimulation. Resting MEG and evoked fields were analyzed. CFF and FUF were found to be reduced in patients, providing behavioral evidence for deficits in visual oscillatory processing. These alterations were found to be related to resting brain activity in patients, namely that the lower the dominant MEG frequency at rest, the lower the CFF and FUF. An analysis of evoked fields at sensor level indicated that in comparison to normal controls, patients were not able to dynamically adapt to flickering visual stimulation. Evoked activity was also analyzed based on independent components (ICs) derived by independent component analysis. The similarity between the shape of each IC and an artificial sine function representing the stimulation frequency was tested via magnitude squared coherence. In controls, we observed a small number of components that correlated strongly with the sine function and a high number of ICs that did not correlate with the sine function. Interestingly, patient data were characterized by a high number of moderately correlating components. Taken together, these results indicate a fundamental divergence of the cerebral resonance activity in cirrhotic patients.

Frontal and superior temporal auditory processing abnormalities in schizophrenia

BACKGROUND

Although magnetoencephalography (MEG) studies show superior temporal gyrus (STG) auditory processing abnormalities in schizophrenia at 50 and 100 ms, EEG and corticography studies suggest involvement of additional brain areas (e.g., frontal areas) during this interval. Study goals were to identify 30 to 130 ms auditory encoding processes in schizophrenia (SZ) and healthy controls (HC) and group differences throughout the cortex.

METHODS

The standard paired-click task was administered to 19 SZ and 21 HC subjects during MEG recording. Vector-based Spatial-temporal Analysis using L1-minimum-norm (VESTAL) provided 4D maps of activity from 30 to 130 ms. Within-group t-tests compared post-stimulus 50 ms and 100 ms activity to baseline. Between-group t-tests examined 50 and 100 ms group differences.

RESULTS

Bilateral 50 and 100 ms STG activity was observed in both groups. HC had stronger bilateral 50 and 100 ms STG activity than SZ. In addition to the STG group difference, non-STG activity was also observed in both groups. For example, whereas HC had stronger left and right inferior frontal gyrus activity than SZ, SZ had stronger right superior frontal gyrus and left supramarginal gyrus activity than HC.

CONCLUSIONS

Less STG activity was observed in SZ than HC, indicating encoding problems in SZ. Yet auditory encoding abnormalities are not specific to STG, as group differences were observed in frontal and SMG areas. Thus, present findings indicate that individuals with SZ show abnormalities in multiple nodes of a concurrently activated auditory network.

Changes in the location of cortico-muscular coherence following stroke

Stroke results in reorganization of residual brain networks. The functional role of brain regions within these networks remains unclear, particularly those in the contralesional hemisphere. We studied 25 stroke patients with a range of motor impairment and 23 healthy age-matched controls using magnetoencephalography (MEG) and electromyography (EMG) to measure oscillatory signals from the brain and affected muscles simultaneously during a simple isometric hand grip, from which cortico-muscular coherence (CMC) was calculated. Peaks of cortico-muscular coherence in both the beta and gamma bands were found in the contralateral sensorimotor cortex in all healthy controls, but were more widespread in stroke patients, including some peaks found in the contralesional hemisphere (7 patients for beta coherence and 5 for gamma coherence). Neither the coherence value nor the distance of the coherence peak from the mean of controls correlated with impairment. Peak CMC in the contralesional hemisphere was found not only in some highly impaired patients, but also in some patients with good functional recovery. Our results provide evidence that a wide range of cortical brain regions, including some in the contralesional hemisphere, may have influence over EMG activity in the affected muscles after stroke thereby supporting functional recovery.