Graph theory findings in the pathophysiology of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is the most common form of adult epilepsy. Accumulating evidence has shown that TLE is a disorder of abnormal epileptogenic networks, rather than focal sources. Graph theory allows for a network-based representation of TLE brain networks, and has potential to illuminate characteristics of brain topology conducive to TLE pathophysiology, including seizure initiation and spread. We review basic concepts which we believe will prove helpful in interpreting results rapidly emerging from graph theory research in TLE. In addition, we summarize the current state of graph theory findings in TLE as they pertain its pathophysiology. Several common findings have emerged from the many modalities which have been used to study TLE using graph theory, including structural MRI, diffusion tensor imaging, surface EEG, intracranial EEG, magnetoencephalography, functional MRI, cell cultures, simulated models, and mouse models, involving increased regularity of the interictal network configuration, altered local segregation and global integration of the TLE network, and network reorganization of temporal lobe and limbic structures. As different modalities provide different views of the same phenomenon, future studies integrating data from multiple modalities are needed to clarify findings and contribute to the formation of a coherent theory on the pathophysiology of TLE.     

Changes in whole-brain functional networks and memory performance in aging

We used resting-functional magnetic resonance imaging data from 98 healthy older adults to analyze how local and global measures of functional brain connectivity are affected by age, and whether they are related to differences in memory performance. Whole-brain networks were created individually by parcellating the brain into 90 cerebral regions and obtaining pairwise connectivity. First, we studied age-associations in interregional connectivity and their relationship with the length of the connections. Aging was associated with less connectivity in the long-range connections of fronto-parietal and fronto-occipital systems and with higher connectivity of the short-range connections within frontal, parietal, and occipital lobes. We also used the graph theory to measure functional integration and segregation. The pattern of the overall age-related correlations presented positive correlations of average minimum path length (r = 0.380, p = 0.008) and of global clustering coefficients (r = 0.454, p < 0.001), leading to less integrated and more segregated global networks. Main correlations in clustering coefficients were located in the frontal and parietal lobes. Higher clustering coefficients of some areas were related to lower performance in verbal and visual memory functions. In conclusion, we found that older participants showed lower connectivity of long-range connections together with higher functional segregation of these same connections, which appeared to indicate a more local clustering of information processing. Higher local clustering in older participants was negatively related to memory performance.     

Rapid Extraction of the Spatial Distribution of Physical Saliency and Semantic Informativeness from Natural Scenes in the Human Brain

Physically salient objects are thought to attract attention in natural scenes. However, research has shown that meaning maps, which capture the spatial distribution of semantically informative scene features, trump physical saliency in predicting the pattern of eye moments in natural scene viewing. Meaning maps even predict the fastest eye movements, suggesting that the brain extracts the spatial distribution of potentially meaningful scene regions very rapidly. To test this hypothesis, we applied representational similarity analysis to ERP data. The ERPs were obtained from human participants (N = 32, male and female) who viewed a series of 50 different natural scenes while performing a modified 1-back task. For each scene, we obtained a physical saliency map from a computational model and a meaning map from crowd-sourced ratings. We then used representational similarity analysis to assess the extent to which the representational geometry of physical saliency maps and meaning maps can predict the representational geometry of the neural response (the ERP scalp distribution) at each moment in time following scene onset. We found that a link between physical saliency and the ERPs emerged first (∼78 ms after stimulus onset), with a link to semantic informativeness emerging soon afterward (∼87 ms after stimulus onset). These findings are in line with previous evidence indicating that saliency is computed rapidly, while also indicating that information related to the spatial distribution of semantically informative scene elements is computed shortly thereafter, early enough to potentially exert an influence on eye movements.SIGNIFICANCE STATEMENT Attention may be attracted by physically salient objects, such as flashing lights, but humans must also be able to direct their attention to meaningful parts of scenes. Understanding how we direct attention to meaningful scene regions will be important for developing treatments for disorders of attention and for designing roadways, cockpits, and computer user interfaces. Information about saliency appears to be extracted rapidly by the brain, but little is known about the mechanisms that determine the locations of meaningful information. To address this gap, we showed people photographs of real-world scenes and measured brain activity. We found that information related to the locations of meaningful scene elements was extracted rapidly, shortly after the emergence of saliency-related information.     

From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction

Line drawings convey meaning with just a few strokes. Despite strong simplifications, humans can recognize objects depicted in such abstracted images without effort. To what degree do deep convolutional neural networks (CNNs) mirror this human ability to generalize to abstracted object images? While CNNs trained on natural images have been shown to exhibit poor classification performance on drawings, other work has demonstrated highly similar latent representations in the networks for abstracted and natural images. Here, we address these seemingly conflicting findings by analyzing the activation patterns of a CNN trained on natural images across a set of photographs, drawings, and sketches of the same objects and comparing them to human behavior. We find a highly similar representational structure across levels of visual abstraction in early and intermediate layers of the network. This similarity, however, does not translate to later stages in the network, resulting in low classification performance for drawings and sketches. We identified that texture bias in CNNs contributes to the dissimilar representational structure in late layers and the poor performance on drawings. Finally, by fine-tuning late network layers with object drawings, we show that performance can be largely restored, demonstrating the general utility of features learned on natural images in early and intermediate layers for the recognition of drawings. In conclusion, generalization to abstracted images, such as drawings, seems to be an emergent property of CNNs trained on natural images, which is, however, suppressed by domain-related biases that arise during later processing stages in the network.     

Decoding the information structure underlying the neural representation of concepts

The nature of the representational code underlying conceptual knowledge remains a major unsolved problem in cognitive neuroscience. We assessed the extent to which different representational systems contribute to the instantiation of lexical concepts in high-level, heteromodal cortical areas previously associated with semantic cognition. We found that lexical semantic information can be reliably decoded from a wide range of heteromodal cortical areas in the frontal, parietal, and temporal cortex. In most of these areas, we found a striking advantage for experience-based representational structures (i.e., encoding information about sensory-motor, affective, and other features of phenomenal experience), with little evidence for independent taxonomic or distributional organization. These results were found independently for object and event concepts. Our findings indicate that concept representations in the heteromodal cortex are based, at least in part, on experiential information. They also reveal that, in most heteromodal areas, event concepts have more heterogeneous representations (i.e., they are more easily decodable) than object concepts and that other areas beyond the traditional “semantic hubs” contribute to semantic cognition, particularly the posterior cingulate gyrus and the precuneus.

The capacity for conceptual knowledge is arguably one of the most defining properties of human cognition, and yet it is still unclear how concepts are represented in the brain. Recent developments in functional neuroimaging and computational linguistics have sparked renewed interest in elucidating the information structures and neural circuits underlying concept representation (1–5). Attempts to characterize the representational code for concepts typically involve information structures based on three qualitatively distinct types of information, namely, taxonomic, experiential, and distributional information. As the term implies, a taxonomic information system relies on category membership and intercategory relations. Our tendency to organize objects, events, and experiences into discrete categories has led most authors—dating back at least to Plato (6)—to take taxonomic structure as the central property of conceptual knowledge (7). The taxonomy for concepts is traditionally seen as a hierarchically structured network, with basic-level categories (e.g., “apple,” “orange”) grouped into superordinate categories (e.g., “fruit,” “food”) and subdivided into subordinate categories (e.g., “Gala apple,” “tangerine”) (8). A prominent account in cognitive science maintains that such categories are represented in the mind/brain as purely symbolic entities, whose semantic content and usefulness derive primarily from how they relate to each other (9, 10). Such representations are seen as qualitatively distinct from the sensory-motor processes through which we interact with the world, much like the distinction between software and hardware in digital computers.An experiential representational system, on the other hand, encodes information about the experiences that led to the formation of particular concepts. It is motivated by a view, often referred to as embodied, grounded, or situated semantics, in which concepts arise primarily from generalization over particular experiences, as information originating from the various modality-specific systems (e.g., visual, auditory, tactile, motor, affective) is combined and re-encoded into progressively more schematic representations that are stored in memory. Since, in this view, there is a degree of continuity between conceptual and modality-specific systems, concept representations are thought to reflect the structure of the perceptual, affective, and motor processes involved in those experiences (11–14).Finally, distributional information pertains to statistical patterns of co-occurrence between lexical concepts (i.e., concepts that are widely shared within a population and denoted by a single word) in natural language usage. As is now widely appreciated, these co-occurrence patterns encode a substantial amount of information about word meaning (15–17). Although word co-occurrence patterns primarily encode contextual associations, such as those connecting the words “cow,” “barn,” and “farmer,” semantic similarity information is indirectly encoded since words with similar meanings tend to appear in similar contexts (e.g., “cow” and “horse,” “pencil” and “pen”). This has led some authors to propose that concepts may be represented in the brain, at least in part, in terms of distributional information (15, 18).Whether, and to what extent, each of these types of information plays a role in the neural representation of conceptual knowledge is a topic of intense research and debate. A large body of evidence has emerged from behavioral studies, functional neuroimaging experiments, and neuropsychological assessments of patients with semantic deficits, with results typically interpreted in terms of taxonomic (19–24), experiential (13, 25–34), or distributional (2, 3, 5, 35, 36) accounts. However, the extent to which each of these representational systems plays a role in the neural representation of conceptual knowledge remains controversial (23, 37, 38), in part, because their representations of common lexical concepts are strongly intercorrelated. Patterns of word co-occurrence in natural language are driven in part by taxonomic and experiential similarities between the concepts to which they refer, and the taxonomy of natural categories is systematically related to the experiential attributes of the exemplars (39–41). Consequently, the empirical evidence currently available is unable to discriminate between these representational systems.Several computational models of concept representation have been proposed based on these structures. While earlier models relied heavily on hierarchical taxonomic structure (42, 43), more recent proposals have emphasized the role of experiential and/or distributional information (34, 44–46). The model by Chen and colleagues (45), for example, showed that graded taxonomic structure can emerge from the statistical coherent covariation found across experiences and exemplars without explicitly coding such taxonomic information per se. Other models propose that concepts may be formed through the combination of experiential and distributional information (44, 46), suggesting a dual representational code akin to Paivio’s dual coding theory (47).We investigated the relative contribution of each representational system by deriving quantitative predictions from each system for the similarity structure of a large set of concepts and then using representational similarity analysis (RSA) with high-resolution functional MRI (fMRI) to evaluate those predictions. Unlike the more typical cognitive subtraction technique, RSA focuses on the information structure of the pattern of neural responses to a set of stimuli (48). For a given stimulus set (e.g., words), RSA assesses how well the representational similarity structure predicted by a model matches the neural similarity structure observed from fMRI activation patterns (Fig. 1). This allowed us to directly compare, in quantitative terms, predictions derived from the three representational systems.Open in a separate windowFig. 1.Representational similarity analysis. (A) An fMRI activation map was generated for each concept presented in the study, and the activation across voxels was reshaped as a vector. (B) The neural RDM for the stimulus set was generated by computing the dissimilarity between these vectors (1 − correlation) for every pair of concepts. (C) A model-based RDM was computed from each model, and the similarity between each model’s RDM and the neural RDM was evaluated via Spearman correlation. (D) Anatomically defined ROIs. The dashed line indicates the boundary where temporal lobe ROIs were split into anterior and posterior portions (see main text for acronyms). (E) Cortical areas included in the functionally defined semantic network ROI (49).     

化学计量学结合信息熵赋权优选半夏白术天麻汤提取工艺

目的建立化学计量学与信息熵赋权相结合的提取工艺优化方法,并将其应用于半夏白术天麻汤水提工艺优化,以充分保证经典名方的有效性和质量一致性。方法以半夏白术天麻汤为模型药物,采用HPLC法建立指纹图谱,对共有峰峰面积进行主成分分析(PCA),以PCA总因子得分、指纹图谱相似度及干膏收率为评价指标,采用L9(34)正交设计法考察加水量、浸泡时间、煎煮时间、煎煮次数对提取效果的影响,信息熵赋权法确定各指标的客观权重,优化水提工艺参数。结果通过中药色谱指纹图谱相似度评价系统匹配26个共有峰,并确定其中7个化合物。根据综合评分结果,确定最佳提取工艺为12倍加水量,煎煮2次,每次1 h。3批验证综合评分均值为0.418 0,RSD为3.32%。结论优选后的工艺提取率高、稳定性和重复性好,适用于半夏白术天麻汤经典名方制剂的工业化生产。     

基于1H-NMR植物代谢组学技术分析青海产区枸杞子的化学特征

目的:采用核磁共振氢谱(~1H-NMR)植物代谢组学技术比较青海产区枸杞子与其他产区(宁夏、甘肃、新疆、内蒙古)枸杞子的化学成分差异。方法:收集5个产区共97份枸杞子样本,其中青海61个样本,采用50%甲醇提取,检测~1H-NMR图谱,结合多元统计分析,对比青海产区枸杞子与其他产区枸杞子的化学差异性,并对各产区样本的枸杞多糖进行含量测定(以无水葡萄糖计),检测波长490 nm。结果:枸杞子的~1H-NMR图谱共检测到32个化学成分,多元统计分析表明青海产区枸杞子与其他产区样本相比,无明显分离趋势;青海产区枸杞子与宁夏产区相比,以及青海省6个不同地区的枸杞子相比,重叠样品较多,均不能显著分开。相似度结果表明,大多数样品的相似度0.85;化合物的单变量分析结果显示,除了蔗糖、葡萄糖、脯氨酸等个别代谢物在各产区样本中存在显著差异外,其余代谢物在各产区样品中的含量分布基本一致。青海与其他产区样本中枸杞多糖含量无显著性差异,且枸杞多糖含量与~1H-NMR指认的小分子化合物的相关系数处于-0.2～0.4。结论:采用~1HNMR植物代谢组学技术从整体化学组成上分析了青海产区枸杞子的化学特征,并结合枸杞多糖含量测定,显示青海产区枸杞子与其他产区枸杞子的化学差异较小。建立的基于~1H-NMR的枸杞子质量评价方法可为其质控水平提升及种植产区选择提供科学依据。     

心脑静片的UPLC指纹图谱研究

目的：建立心脑静片超高效液相色谱法(UPLC)指纹图谱,为制剂的质量评价提供参考。方法：采用Waters ACQUITY UPLC BEH C₁₈色谱柱(100 mm×3.0 mm,1.7μm),以乙腈-0.1%甲酸为流动相梯度洗脱,流速为0.6 mL·min^–1,检测波长为280 nm。采用“中药色谱指纹图谱相似度评价系统”(2012版)建立共有模式,并进行相似度评价。采用UPLC-四级杆飞行时间质谱法,通过对照品比对和文献分析对共有峰进行指认。结果：通过2个厂家14批样品的测定,建立心脑静片指纹图谱,确定了24个共有峰,鉴定了其中的19个共有峰。14批心脑静片的相似度均大于0.99,表明不同厂家样品的质量差异较小。结论：建立的方法简便、可靠,可用于心脑静片的质量评价。     

不同检测方法在银杏叶缓释制剂处方设计中的比较研究

[目的] 比较采用紫外分光光度法和高效液相色谱法(HPLC法)测定银杏黄酮醇苷类成分含量的差异性,并为银杏叶缓释制剂处方设计建立一种简单、有效、可靠的方法。[方法] 紫外分光光度法以总黄酮醇苷为指标成分,于266 nm处测定吸光值。采用HPLC法测量黄酮苷元(槲皮素、山奈素、异鼠李素)3种指标成分,用FDA推荐的f₂相似因子法来评价两种检测方法的相似性。[结果] 两种检测方法的方法学验证均符合要求,且f₂因子值均大于50,两者相似性良好。[结论] 在银杏叶缓释制剂处方设计过程中,可以采用紫外分光光度法代替HPLC法来测定黄酮醇苷类成分的含量。该法操作省时、简便、准确、可靠。