Towards Deciphering the Fetal Foundation of Normal Cognition and Cognitive Symptoms From Sulcation of the Cortex.

Growing evidence supports that prenatal processes play an important role for cognitive ability in normal and clinical conditions. In this context, several neuroimaging studies searched for features in postnatal life that could serve as a proxy for earlier developmental events. A very interesting candidate is the sulcal, or sulco-gyral, patterns, macroscopic features of the cortex anatomy related to the fold topology-e.g., continuous vs. interrupted/broken fold, present vs. absent fold-or their spatial organization. Indeed, as opposed to quantitative features of the cortical sheet (e.g., thickness, surface area or curvature) taking decades to reach the levels measured in adult, the qualitative sulcal patterns are mainly determined before birth and stable across the lifespan. The sulcal patterns therefore offer a window on the fetal constraints on specific brain areas on cognitive abilities and clinical symptoms that manifest later in life. After a global review of the cerebral cortex sulcation, its mechanisms, its ontogenesis along with methodological issues on how to measure the sulcal patterns, we present a selection of studies illustrating that analysis of the sulcal patterns can provide information on prenatal dispositions to cognition (with a focus on cognitive control and academic abilities) and cognitive symptoms (with a focus on schizophrenia and bipolar disorders). Finally, perspectives of sulcal studies are discussed

Origin of symbol-using systems: speech, but not sign, without the semantic urge

Natural language—spoken and signed—is a multichannel phenomenon, involving facial and body expression, and voice and visual intonation that is often used in the service of a social urge to communicate meaning. Given that iconicity seems easier and less abstract than making arbitrary connections between sound and meaning, iconicity and gesture have often been invoked in the origin of language alongside the urge to convey meaning. To get a fresh perspective, we critically distinguish the origin of a system capable of evolution from the subsequent evolution that system becomes capable of. Human language arose on a substrate of a system already capable of Darwinian evolution; the genetically supported uniquely human ability to learn a language reflects a key contact point between Darwinian evolution and language. Though implemented in brains generated by DNA symbols coding for protein meaning, the second higher-level symbol-using system of language now operates in a world mostly decoupled from Darwinian evolutionary constraints. Examination of Darwinian evolution of vocal learning in other animals suggests that the initial fixation of a key prerequisite to language into the human genome may actually have required initially side-stepping not only iconicity, but the urge to mean itself. If sign languages came later, they would not have faced this constraint

A Comparison Study of Using Origami as a Teaching Tool in Middle-School Mathematics Class in North America and China

This Major Paper compares origami-based mathematics school activities in North America and China. It introduces the current situation of the use of origami in mathematics classes to identify the similarities and differences of using origami as a teaching approach in these two regions. The Paper also attempts the analysis from various perspectives, including mathematics reform, mathematics learning system and environment, as well as the benefits of using origami in mathematics class, and how it relates to students’ mathematics achievement. Both North American and Chinese mathematics educators’ focus on integrating origami into middle schools’ mathematics classes can be found in the study.Some same or similar origami activities are used in the two regions; however, origami in Chinese classes is more often used as an auxiliary teaching activity, while teachers in North America also teach origami itself such as modular origami

Quantifying the Microvascular Origin of BOLD-fMRI from First Principles with Two-Photon Microscopy and an Oxygen-Sensitive Nanoprobe

The blood oxygenation level-dependent (BOLD) contrast is widely used in functional magnetic resonance imaging (fMRI) studies aimed at investigating neuronal activity. However, the BOLD signal reflects changes in blood volume and oxygenation rather than neuronal activity per se. Therefore, understanding the transformation of microscopic vascular behavior into macroscopic BOLD signals is at the foundation of physiologically informed noninvasive neuroimaging. Here, we use oxygen-sensitive two-photon microscopy to measure the BOLD-relevant microvascular physiology occurring within a typical rodent fMRI voxel and predict the BOLD signal from first principles using those measurements. The predictive power of the approach is illustrated by quantifying variations in the BOLD signal induced by the morphological folding of the human cortex. This framework is then used to quantify the contribution of individual vascular compartments and other factors to the BOLD signal for different magnet strengths and pulse sequences.National Institutes of Health (U.S.) (Grant P41RR14075)National Institutes of Health (U.S.) (Grant R01NS067050)National Institutes of Health (U.S.) (Grant R01NS057198)National Institutes of Health (U.S.) (Grant R01EB000790)American Heart Association (Grant 11SDG7600037)Advanced Multimodal NeuroImaging Training Program (R90DA023427

Knowledge Discovery Through Large-Scale Literature-Mining of Biological Text-Data

The aim of this study is to develop scalable and efficient literature-mining framework for knowledge discovery in the field of medical and biological sciences. Using this scalable framework, customized disease-disease interaction network can be constructed. Features of the proposed network that differentiate it from existing networks are its 1) flexibility in the level of abstraction, 2) broad coverage, and 3) domain specificity. Empirical results for two neurological diseases have shown the utility of the proposed framework. The second goal of this study is to design and implement a bottom-up information retrieval approach to facilitate literature-mining in the specialized field of medical genetics. Experimental results are being corroborated at the moment

Quantumlike Chaos in the Frequency Distributions of the Bases A, C, G, T in Drosophila DNA

Continuous periodogram power spectral analyses of fractal fluctuations of frequency distributions of bases A, C, G, T in Drosophila DNA show that the power spectra follow the universal inverse power-law form of the statistical normal distribution. Inverse power-law form for power spectra of space-time fluctuations is generic to dynamical systems in nature and is identified as self-organized criticality. The author has developed a general systems theory, which provides universal quantification for observed self-organized criticality in terms of the statistical normal distribution. The long-range correlations intrinsic to self-organized criticality in macro-scale dynamical systems are a signature of quantumlike chaos. The fractal fluctuations self-organize to form an overall logarithmic spiral trajectory with the quasiperiodic Penrose tiling pattern for the internal structure. Power spectral analysis resolves such a spiral trajectory as an eddy continuum with embedded dominant wavebands. The dominant peak periodicities are functions of the golden mean. The observed fractal frequency distributions of the Drosophila DNA base sequences exhibit quasicrystalline structure with long-range spatial correlations or self-organized criticality. Modification of the DNA base sequence structure at any location may have significant noticeable effects on the function of the DNA molecule as a whole. The presence of non-coding introns may not be redundant, but serve to organize the effective functioning of the coding exons in the DNA molecule as a complete unit.Comment: 46 pages, 9 figure

From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field