Music and the brain - design of an MEG compatible piano

Magnetoencephalography (MEG) neuroimaging has been used to study subjects' responses when listening to music, but research into the effects of playing music has been limited by the lack of MEG compatible instruments that can operate in a magnetically shielded environment without creating electromagnetic interference. This paper describes the design and preliminary testing of an MEG compatible piano keyboard with 25 full size keys that employs a novel 3-state optical encoder design and electronics to provide realistic velocity-controlled volume modulation. This instrument will allow researchers to study musical performance on a finer timescale than fMRI and enable a range of MEG studies.

Decoding the orientation of contrast edges from MEG evoked and induced responses

Visual gamma oscillations have been proposed to subserve perceptual binding, but their strong modulation by diverse stimulus features confounds interpretations of their precise functional role. Overcoming this challenge necessitates a comprehensive account of the relationship between gamma responses and stimulus features. Here we used multivariate pattern analyses on human MEG data to characterize the relationships between gamma responses and one basic stimulus feature, the orientation of contrast edges. Our findings confirmed we could decode orientation information from induced responses in two dominant frequency bands at 24-32 Hz and 50-58 Hz. Decoding was higher for cardinal than oblique orientations, with similar results also obtained for evoked MEG responses. In contrast to multivariate analyses, orientation information was mostly absent in univariate signals: evoked and induced responses in early visual cortex were similar in all orientations, with only exception an inverse oblique effect observed in induced responses, such that cardinal orientations produced weaker oscillatory signals than oblique orientations. Taken together, our results showed multivariate methods are well suited for the analysis of gamma oscillations, with multivariate patterns robustly encoding orientation information and predominantly discriminating cardinal from oblique stimuli.

Dynamics of scene representations in the human brain revealed by magnetoencephalography and deep neural networks

Human scene recognition is a rapid multistep process evolving over time from single scene image to spatial layout processing. We used multivariate pattern analyses on magnetoencephalography (MEG) data to unravel the time course of this cortical process. Following an early signal for lower-level visual analysis of single scenes at ~100ms, we found a marker of real-world scene size, i.e. spatial layout processing, at ~250ms indexing neural representations robust to changes in unrelated scene properties and viewing conditions. For a quantitative model of how scene size representations may arise in the brain, we compared MEG data to a deep neural network model trained on scene classification. Representations of scene size emerged intrinsically in the model, and resolved emerging neural scene size representation. Together our data provide a first description of an electrophysiological signal for layout processing in humans, and suggest that deep neural networks are a promising framework to investigate how spatial layout representations emerge in the human brain.

Systematic review on T3 laryngeal squamous cell carcinoma; still far from a consensus on the optimal organ preserving treatment

OBJECTIVE

The optimal treatment of patients with T3 laryngeal carcinoma is controversially challenged by open partial laryngectomies (OPL), transoral laser microsurgery (TLM) and radiation therapy alone (RT) or combined with chemotherapy (ChRT). Treatment guidelines, experts' opinions and clinical studies are highly contradictory. The aim of this study is to compare the primary outcomes of the available treatment methods and identify the sources of variance among studies.

METHODS

A review of the literature published in the time period 2003-2015 was conducted via the PubMed database (www.pubmed.org) and Scopus database (www.scopus.com) with the search terms "T3 laryngeal squamous cell cancer treatment". Data from clinical studies involving patients with T3 laryngeal cancer (n > 10) subjected to TLM, OPL, ChRT or RT, were pooled. In the absence of controlled studies, prospective and retrospective clinical trials with minimum 5-year follow-up were acceptable, provided that they included a description of patient eligibility criteria, so as to exclude studies with serious selection bias.

RESULTS

Literature lacks studies with homogenous populations regarding TNM staging, preoperative/postoperative treatment or anatomical subsite. This raises substantial controversies and prohibits the conduction of a meta-analysis. Data for qualitative analysis were pooled from 8 studies (n = 1226). OPL and TLM both offer patients high survival and organ preservation rates. Preoperative induction chemotherapy seems to significantly compromise overall survival.

CONCLUSIONS

Multicenter studies referring to homogenous populations, at least regarding staging and anatomical subsite, are needed. No safe conclusions can be drawn given the heterogeneity in patient cohorts, study design and evaluation of results in the existing literature.

Similarity-Based Fusion of MEG and fMRI Reveals Spatio-Temporal Dynamics in Human Cortex During Visual Object Recognition

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brain's spatio-temporal dynamics, because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed an integration approach that uses representational similarities to combine measurements of magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to 2 independent MEG-fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50-80 ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. Further region-of-interest analyses established that dorsal and ventral regions showed MEG-fMRI correspondence in representations later than early visual cortex. Together, these results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity-based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions.

Autism as a disorder of prediction

A rich collection of empirical findings accumulated over the past three decades attests to the diversity of traits that constitute the autism phenotypes. It is unclear whether subsets of these traits share any underlying causality. This lack of a cohesive conceptualization of the disorder has complicated the search for broadly effective therapies, diagnostic markers, and neural/genetic correlates. In this paper, we describe how theoretical considerations and a review of empirical data lead to the hypothesis that some salient aspects of the autism phenotype may be manifestations of an underlying impairment in predictive abilities. With compromised prediction skills, an individual with autism inhabits a seemingly "magical" world wherein events occur unexpectedly and without cause. Immersion in such a capricious environment can prove overwhelming and compromise one's ability to effectively interact with it. If validated, this hypothesis has the potential of providing unifying insights into multiple aspects of autism, with attendant benefits for improving diagnosis and therapy.

To cut or not to cut? Assessing the modular structure of brain networks

A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks.

Chondrosarcoma treated with transoral near total resection of the cricoid

INTRODUCTION

Chondrosarcomas of the larynx are usually slow-growing tumours. Their prognosis is reportedly unaffected by local tumour recurrence. Nevertheless, total laryngectomy is often performed in fear that resection of the posterior lamina of the cricoid cartilage may cause laryngeal collapse and stenosis. Transoral laser surgery (TLS) is not considered among the treatment options. This case report supports the feasibility of a radical yet organ- and function-preserving tumour excision using TLS.

CASE REPORT

A female patient presented with dyspnoea due to an extensive low-grade laryngeal chondrosarcoma. TLS treatment involved total resection of the posterior lamina of the cricoid cartilage. The patient was decannulated 8 months later with normal swallowing and satisfactory voice quality, which allowed her to have a normal personal and social life.

CONCLUSION

TLS excision of the posterior lamina of the cricoid cartilage seems to be a feasible and radical yet function- and organ-preserving technique with minimal morbidity.

A note on the phase locking value and its properties

We investigate the properties of the Phase Locking Value (PLV) and the Phase Lag Index (PLI) as metrics for quantifying interactions in bivariate local field potential (LFP), electroencephalography (EEG) and magnetoencephalography (MEG) data. In particular we describe the relationship between nonparametric estimates of PLV and PLI and the parameters of two distributions that can both be used to model phase interactions. The first of these is the von Mises distribution, for which the sample PLV is a maximum likelihood estimator. The second is the relative phase distribution associated with bivariate circularly symmetric complex Gaussian data. We derive an explicit expression for the PLV for this distribution and show that it is a function of the cross-correlation between the two signals. We compare the bias and variance of the sample PLV and the PLV computed from the cross-correlation. We also show that both the von Mises and Gaussian models are suitable for representing relative phase in application to LFP data from a visually-cued motor study in macaque. We then compare results using the two different PLV estimators and conclude that, for this data, the sample PLV provides equivalent information to the cross-correlation of the two complex time series.