以全腦全息希爾伯特頻譜探討視覺工作記憶及其容量之個別差異的神經機制;Investigating the Neural Correlates of Visual Working Memory and Individual Differences in Working Memory Capacity by Tomographical Holo-Hilbert Spectral Analysis

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Title:	以全腦全息希爾伯特頻譜探討視覺工作記憶及其容量之個別差異的神經機制;Investigating the Neural Correlates of Visual Working Memory and Individual Differences in Working Memory Capacity by Tomographical Holo-Hilbert Spectral Analysis
Authors:	梁偉光;鄭仕坤
Contributors:	國立中央大學認知與神經科學研究所
Keywords:	工作記憶;工作記憶廣度作業;全息希爾伯特頻譜;振幅調制;腦功能成像辨識的方法及系統;working memory;working memory span task;Holo-Hilbert Spectral Analysis;HHSA;Dynamic EEG Projected Brain Tomographic Imager;deepBTGI
Date:	2018-12-19
Issue Date:	2018-12-20 13:47:22 (UTC+8)
Publisher:	科技部
Abstract:	在我們的日常生活所接受的巨量視覺資訊中，大部份的物件同時包括了多種特徵。視覺工作記憶是一種介於感知記憶與長期記憶之間特殊的認知執行控制功能。在過去一年我與共同主持人已展開實驗以探討視覺工作記憶，並已取得初步成果，本計畫將延續並延伸前計畫的研究內容，聚焦於探討特徵連結視覺工作記憶 (Feature-Binding Visual Working Memory)，以及該特徵連結在視覺工作記憶背後的神經震盪 (neural oscillations) 機制，並以一種變化檢測作業以及數種複雜及簡單記憶廣度作業，通過操弄工作記憶容量及廣度來求得所要探討問題的解答。本計畫所提出的實驗與神經震盪特徵分析除了揭示視覺工作記憶中訊息連結的神經震盪機制，也可顯示在記憶廣度作業中”相關與非相關訊息”在神經震盪模式上的個別差異。本計畫所使用的數據分析技術-全息希爾伯特頻譜 (Holo-Hilbert Spectral Analysis, HHSA) (黃鍔院士發明，我為共同作者之一)，相較於傳統傅立葉分析更能正確解析工作記憶過程中腦波訊號中的非線性特徵，並且能更可靠有效的解析神經震盪的模式。另外，我們也利用腦訊號源重建的方式將全息希爾伯特頻譜轉換為全腦影像(Dynamic EEG Projected Brain Tomographic Imager, deepBTGI, 已獲台灣及中國專利，美國與印度專利申請中) 以進一步了解這些非線性現象源自哪一些腦區。由HHSA 以及deepBTGI 所解析的振幅調制的初步結果顯示，非線性特徵在不同的記憶廣度下均與視覺工作記憶能力及其容量呈現高度相關。更重要的是，藉由 HHSA 並結合特徵連結複雜記憶廣度實驗，我們預期本計畫所提出的研究將可顯示出受測者處理相關訊息與非相關訊息的個別差異。並預期相較於簡單特徵的複雜記憶廣度作業，這種特徵連結複雜記憶廣度作業連同其神經震盪特徵，與其他高階認知功能有更高的相關性。因此，本計畫的重要性不只在神經震盪機制分析方法上的突破，也在於對視覺工作記憶的運作機制更進一步的解析。 ;In our daily life, we receive a vast amount of visual information, and almost all of the visual information includes multi-feature objects. Visual working memory (VWM) is a specific memory system that bridges the gap between our information-rich but short-lived perceptual memory and high-capacity but effortful visual long-term memory. Building on the results from these studies and the preliminary findings from our previous project, the proposed study aims to extend the previous project, focuses on investigating the neural oscillatory mechanism of Feature-Binding Visual Working Memory (Binding-VWM) via a change detection task and several simple and complex span tasks. By manipulating the memory load (number of items to be remembered) as well as the extent to which some basic processes (e.g. rehearsal, maintenance, updating, controlled search) operate in these tasks, the proposed study may not only reveal the neural oscillatory mechanism of Binding-VWM, but also shed light on the oscillatory pattern that characterizes individual differences in solving a combination of irrelevant and relevant information in VWM. To better understand the neural oscillatory mechanism underlying this cognitive function, the proposed study employs an up-to-date and sophisticated data-analysis technique – the Holo-Hilbert Spectral Analysis (HHSA) proposed by Norden E. Huang (2016, in which I also share the co-authorship). HHSA has been found to show greater sensitivity than traditional approaches in probing nonlinear characteristics (revealed by amplitude and frequency modulations) for brain signals during both the resting state and task-related cognitive processes, especially those relating to VWM. We will also employ a newly patented method - Dynamic EEG Projected Brain Tomographic Imager (deepBTGI, Liang et al., 2015), which is a source-level extension to HHSA, to investigate Binding-VWM. Our preliminary results show that the amplitude modulation revealed by HHSA and deepBTGI is highly correlated with the capacity of VWM both in the proposed change detection task and an operation span task. More importantly, by employing HHSA with the proposed feature-binding complex span task, the proposed study may reveal individual differences in irrelevant information suppression and relevant information representation in VWM. Therefore, we expect that the working memory capacity as well as the oscillatory pattern of a feature-binding complex span task tend to correlate higher with measures of higher-order cognition than does single-feature complex span task. Because the proposed study will bring about advances in data-analysis methods for neural oscillatory mechanism in the field of cognitive neuroscience, it is not only theoretically important but also critical to improving the predictive ability of neural models of visual working memory.
Relation:	財團法人國家實驗研究院科技政策研究與資訊中心
Appears in Collections:	[College of Science Institute of Cognitive Neuroscience] Research Project

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