Browsing by Author "Yue, Qiuhai"

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    Brain Modularity Mediates the Relation between Task Complexity and Performance
    (The MIT Press, 2017) Yue, Qiuhai; Martin, Randi; Fischer-Baum, Simon; Ramos-Nuñez, Aurora I.; Ye, Fengdan; Deem, Michael W.; Center for Theoretical Biological Physics
    Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than as a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases and other tasks showing worse performance. A recent theoretical model [Chen, M., & Deem, M. W. 2015. Development of modularity in the neural activity of children's brains. Physical Biology, 12, 016009] suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on more complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of simple and complex behavioral tasks. Complex and simple tasks were defined on the basis of whether they did or did not draw on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on a composite measure combining scores from the complex tasks but a positive correlation with performance on a composite measure combining scores from the simple tasks. These results and theory presented here provide a framework for linking measures of whole-brain organization from network neuroscience to cognitive processing.
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    Evaluating the Buffer vs. Embedded Processes Accounts of Verbal Short-term Memory by Using Multivariate Neuroimaging and Brain Stimulation Approaches
    (2018-11-15) Yue, Qiuhai; Martin, Randi C.
    Buffer versus embedded processes accounts of short-term memory (STM) have been under debate for decades. Buffer models propose dedicated storage for maintaining different types of information, where these buffers are separate from long-term memory (LTM) for those kinds of information. In contrast, embedded processes models argue against the existence of buffers, and claim STM consists of the activated portion of LTM. This thesis tested two competing models for verbal STM and obtained evidence from three experiments with novel multivariate neuroimaging and non-invasive brain stimulation approaches. From the perspective of cognitive neuroscience, buffer models predict that storage buffers depend on brain regions different from the LTM regions used to represent permanent knowledge, whereas embedded processes models predict that STM recruits the same neural substrate as LTM. Experiment 1 used functional magnetic resonance imaging with representational similarity analysis (RSA) to examine the correspondence of multi-voxel neural activation patterns with the theoretical representations for both phonological and semantic STM. In the phonological domain, a speech processing region in the left superior temporal gyrus (STG) showed RSA evidence of phonological coding during the encoding period, but not during the delay period. In contrast, the left supramarginal gyrus (SMG) showed RSA evidence of phonological storage during the delay period. In the semantic domain, the triangular part of the left inferior frontal gyrus showed RSA evidence of semantic coding during the encoding period, but not during the delay period. In contrast, some posterior regions such as the left angular gyrus, the left posterior middle temporal gyrus and an anterior region in the left middle frontal gyrus showed RSA evidence of semantic retention during the delay period, with the angular gyrus allowing for decoding of either phonological and semantic STM, depending on the task context. Results of Experiment 1 illustrated that different regions were involved in encoding and maintenance for phonological and semantic STM respectively. Experiment 2, using nonword stimuli, tested distractor interference effects on phonological STM. Although the speech processing region in the left STG showed RSA evidence of phonological storage for these materials during the delay period, such evidence was absent when distractors were presented. However, the proposed buffer region in the left SMG showed RSA evidence of phonological retention even in the presence of distractors during the delay period. Results of Experiment 2 suggested that the buffer region played a more important role in phonological STM in the face of distracting stimuli. Experiment 3 used transcranial magnetic stimulation (TMS) to test the necessity of the brain regions implicated in phonological STM from Experiments 1 and 2. An effect of TMS on response time for a STM recognition task was observed when the left SMG was stimulated during the delay period, whereas stimulation at the left STG or an occipital control region had no effect on behavioral performance. Results of Experiment 3 confirmed the causal role of the left SMG in phonological STM. Taken together, converging evidence from three experiments provided greater support for a buffer account than an embedded processes account of verbal STM. General implications for the buffer vs. embedded processes debate, as well as implications for theories of the neural basis of working memory are discussed.
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    Phonological Working Memory Representations in the Left Inferior Parietal Lobe in the Face of Distraction and Neural Stimulation
    (Frontiers Media S.A., 2022) Yue, Qiuhai; Martin, Randi C.
    The neural basis of phonological working memory (WM) was investigated through an examination of the effects of irrelevant speech distractors and disruptive neural stimulation from transcranial magnetic stimulation (TMS). Embedded processes models argue that the same regions involved in speech perception are used to support phonological WM whereas buffer models assume that a region separate from speech perception regions is used to support WM. Thus, according to the embedded processes approach but not the buffer approach, irrelevant speech and TMS to the speech perception region should disrupt the decoding of phonological WM representations. According to the buffer account, decoding of WM items should be possible in the buffer region despite distraction and should be disrupted with TMS to this region. Experiment 1 used fMRI and representational similarity analyses (RSA) with a delayed recognition memory paradigm using nonword stimuli. Results showed that decoding of memory items in the speech perception regions (superior temporal gyrus, STG) was possible in the absence of distractors. However, the decoding evidence in the left STG was susceptible to interference from distractors presented during the delay period whereas decoding in the proposed buffer region (supramarginal gyrus, SMG) persisted. Experiment 2 examined the causal roles of the speech processing region and the buffer region in phonological WM performance using TMS. TMS to the SMG during the early delay period caused a disruption in recognition performance for the memory nonwords, whereas stimulations at the STG and an occipital control region did not affect WM performance. Taken together, results from the two experiments are consistent with predictions of a buffer model of phonological WM, pointing to a critical role of the left SMG in maintaining phonological representations.
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    Static and Dynamic Measures of Human Brain Connectivity Predict Complementary Aspects of Human Cognitive Performance
    (Frontiers Media S.A., 2017) Ramos-Nuñez, Aurora I.; Fischer-Baum, Simon; Martin, Randi C.; Yue, Qiuhai; Ye, Fengdan; Deem, Michael W.
    In cognitive network neuroscience, the connectivity and community structure of the brain network is related to measures of cognitive performance, like attention and memory. Research in this emerging discipline has largely focused on two measures of connectivity—modularity and flexibility—which, for the most part, have been examined in isolation. The current project investigates the relationship between these two measures of connectivity and how they make separable contribution to predicting individual differences in performance on cognitive tasks. Using resting state fMRI data from 52 young adults, we show that flexibility and modularity are highly negatively correlated. We use a Brodmann parcellation of the fMRI data and a sliding window approach for calculation of the flexibility. We also demonstrate that flexibility and modularity make unique contributions to explain task performance, with a clear result showing that modularity, not flexibility, predicts performance for simple tasks and that flexibility plays a greater role in predicting performance on complex tasks that require cognitive control and executive functioning. The theory and results presented here allow for stronger links between measures of brain network connectivity and cognitive processes.