• Neuroinformatics
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AbstractNested oscillation occurs when the amplitude of a faster rhythm is coupled to the phase of a slower rhythm. It has been proposed to underlie the discrete nature of perception and the capacity of working memory and is a phenomenon observable in human brain imaging data. This paper compares three published methods for detecting nested oscillation and a fourth method proposed in this paper. These are: (i) the modulation index, (ii) the phase-locking value (PLV), (iii) the envelope-to-signal correlation (ESC) and (iv) a general linear model (GLM) measure derived from ESC. We applied the methods to electrocorticographic (ECoG) data recorded during a working-memory task and to data from a simulated hippocampal interneuron network. Further simulations were then made to address the dependence of each measure on signal to noise level, coupling phase, epoch length, sample rate, signal nonstationarity, and multi-phasic coupling. Our overall conclusion is that the GLM measure is the best all-round approach for detecting nested oscillation.

We summarize a theoretical framework based on global time-reparametrization invariance that explains the origin of dynamic fluctuations in glassy systems. We introduce the main ideas without getting into much technical details. We describe a number of consequences arising from this scenario that can be tested numerically and experimentally distinguishing those that can also be explained by other mechanisms from the ones that we believe, are special to our proposal. We support our claims by presenting some numerical checks performed on the 3d Edwards-Anderson spin-glass. Finally, we discuss up to which extent these ideas apply to super-cooled liquids that have been studied in much more detail up to present. Comment: 33 pages, 7 figs, contribution to JSTAT special issue `Principles of Dynamical Systems' work-shop at Newton Institute, Univ. of Cambridge, UK

Amyloid imaging with [(11)C]Pittsburgh Compound-B (PiB) provides in vivo data on plaque deposition in those with, or at risk for, Alzheimer's disease (AD). We performed a gene-based association analysis of 15 quality-controlled amyloid-pathway associated candidate genes in 103 Alzheimer's Disease Neuroimaging Initiative participants. The mean normalized PiB uptake value across four brain regions known to have amyloid deposition in AD was used as a quantitative phenotype. The minor allele of an intronic SNP within DHCR24 was identified and associated with a lower average PiB uptake. Further investigation at whole-brain voxel-wise level indicated that non-carriers of the minor allele had higher PiB uptake in frontal regions compared to carriers. DHCR24 has been previously shown to confer resistance against beta-amyloid and oxidative stress-induced apoptosis, thus our findings support a neuroprotective role. Pathway-based genetic analysis of targeted molecular imaging phenotypes appears promising to help elucidate disease pathophysiology and identify potential therapeutic targets.

AbstractViewing a sequence of faces of two different people results in a greater Blood Oxygenation Level Dependent (BOLD) response in FFA compared to a sequence of identical faces. Changes in identity, however, necessarily involve changes in the image. Is the release from adaptation a result of a change in face identity, per se, or could it be an effect that would arise from any change in the image of a face? Subjects viewed a sequence of two faces that could be of the same or different person, and in the same or different orientation in depth. Critically, the physical similarity of view changes of the same person was scaled, by Gabor-jet differences, to be equivalent to that produced by an identity change. Both person and orientation changes produced equivalent releases from adaptation in FFA (relative to identical faces) suggesting that FFA is sensitive to the physical similarity of faces rather than to the individuals depicted in the images.

AbstractWe investigated the relationship between body size, brain size and fibers in selected cranial nerves in shrews and moles. Species include tiny masked shrews (S. cinereus) weighing only a few grams and much larger mole species weighing up to 90 grams. It also includes closely related species with very different sensory specializations – such as the star-nosed mole and the common, eastern mole. We found that moles and shrews have tiny optic nerves with fiber counts not correlated with body or brain size. Auditory nerves were similarly small but increased in fiber number with increasing brain and body size. Trigeminal nerve number was by far the largest and also increased with increasing brain and body size. The star-nosed mole was an outlier, with more than twice the number of trigeminal nerve fibers than any other species. Despite this hypertrophied cranial nerve, star-nosed mole brains were not larger than predicted from body size, suggesting that magnification of their somatosensory systems does not result in greater overall CNS size.

Most previous studies investigating the neural correlates of reading have presented text using serial visual presentation (SVP), which may not fully reflect the underlying processes of natural reading. In the present study, eye movements and BOLD data were collected while subjects either read normal paragraphs naturally or moved their eyes through “paragraphs” of pseudo-text (pronounceable pseudowords or consonant letter strings) in two pseudo-reading conditions. Eye movement data established that subjects were reading and scanning the stimuli normally. A conjunction fMRI analysis across natural- and pseudo-reading showed that a common eye-movement network including frontal eye fields (FEF), supplementary eye fields (SEF), and intraparietal sulci was activated, consistent with previous studies using simpler eye movement tasks. In addition, natural reading versus pseudo-reading showed different patterns of brain activation: normal reading produced activation in a well-established language network that included superior temporal gyrus/sulcus, middle temporal gyrus (MTG), angular gyrus (AG), inferior frontal gyrus, and middle frontal gyrus, whereas pseudo-reading produced activation in an attentional network that included anterior/posterior cingulate and parietal cortex. These results are consistent with results found in previous single-saccade eye movement tasks and SVP reading studies, suggesting that component processes of eye-movement control and language processing observed in past fMRI research generalize to natural reading. The results also suggest that combining eyetracking and fMRI is a suitable method for investigating the component processes of natural reading in fMRI research.

Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find that category information implicitly emerges in the local similarity structure of this feature space. Further, these models learn hierarchical features which capture the structure of brain responses across the human ventral visual stream, on par with category-supervised models. These results provide computational support for a domain-general framework guiding the formation of visual representation, where the proximate goal is not explicitly about category information, but is instead to learn unique, compressed descriptions of the visual world. It is unknown whether object category learning can be formed purely through domain general learning of natural image structure. Here the authors show that human visual brain responses to objects are well-captured by self-supervised deep neural network models trained without labels, supporting a domain-general account.

Social plasticity, defined as the ability to adaptively change the expression of social behavior according to previous experience and to social context, is a key ecological performance trait that should be viewed as crucial for Darwinian fitness. The neural mechanisms for social plasticity are poorly understood, in part due to skewed reliance on rodent models. Fish model organisms are relevant in the field of social plasticity for at least two reasons: first, the diversity of social organization among fish species is staggering, increasing the breadth of evolutionary relevant questions that can be asked. Second, that diversity also suggests translational relevance, since it is more likely that “core” mechanisms of social plasticity are discovered by analyzing a wider variety of social arrangements than relying on a single species. We analyze examples of social plasticity across fish species with different social organizations, concluding that a “core” mechanism is the initiation of behavioral shifts through the modulation of a conserved “social decision-making network”, along with other relevant brain regions, by monoamines, neuropeptides, and steroid hormones. The consolidation of these shifts may be mediated via neurogenomic adjustments and regulation of the expression of plasticity-related molecules (transcription factors, cell cycle regulators, and plasticity products).