174 research outputs found

    Functional Brain Differences Predict Challenging Auditory Speech Comprehension in Older Adults

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    abstract: Older adults often experience communication difficulties, including poorer comprehension of auditory speech when it contains complex sentence structures or occurs in noisy environments. Previous work has linked cognitive abilities and the engagement of domain-general cognitive resources, such as the cingulo-opercular and frontoparietal brain networks, in response to challenging speech. However, the degree to which these networks can support comprehension remains unclear. Furthermore, how hearing loss may be related to the cognitive resources recruited during challenging speech comprehension is unknown. This dissertation investigated how hearing, cognitive performance, and functional brain networks contribute to challenging auditory speech comprehension in older adults. Experiment 1 characterized how age and hearing loss modulate resting-state functional connectivity between Heschlโ€™s gyrus and several sensory and cognitive brain networks. The results indicate that older adults exhibit decreased functional connectivity between Heschlโ€™s gyrus and sensory and attention networks compared to younger adults. Within older adults, greater hearing loss was associated with increased functional connectivity between right Heschlโ€™s gyrus and the cingulo-opercular and language networks. Experiments 2 and 3 investigated how hearing, working memory, attentional control, and fMRI measures predict comprehension of complex sentence structures and speech in noisy environments. Experiment 2 utilized resting-state functional magnetic resonance imaging (fMRI) and behavioral measures of working memory and attentional control. Experiment 3 used activation-based fMRI to examine the brain regions recruited in response to sentences with both complex structures and in noisy background environments as a function of hearing and cognitive abilities. The results suggest that working memory abilities and the functionality of the frontoparietal and language networks support the comprehension of speech in multi-speaker environments. Conversely, attentional control and the cingulo-opercular network were shown to support comprehension of complex sentence structures. Hearing loss was shown to decrease activation within right Heschlโ€™s gyrus in response to all sentence conditions and increase activation within frontoparietal and cingulo-opercular regions. Hearing loss also was associated with poorer sentence comprehension in energetic, but not informational, masking. Together, these three experiments identify the unique contributions of cognition and brain networks that support challenging auditory speech comprehension in older adults, further probing how hearing loss affects these relationships.Dissertation/ThesisDoctoral Dissertation Neuroscience 201

    On Control Systems of the Brain: A Study of Their Connections, Activations, and Interactions

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    Implementation of daily functions in humans crucially relies on both the bottom-up moment-to- moment processing of relevant input and output information as well as the top-down controls that instantiate and regulate goal-directed strategies. The current dissertation focuses on different systems of brain regions related to task control. We are interested in investigating, in detail, some of the basic activity patterns that different control systems carry during simple tasks, and how differences in activity patterns may shed new insight onto the distinctions among the systems\u27 functional roles. In addition, carefully coordinated interactions between brain regions specialized for control-related activity and regions specialized for bottom-up information processing are essential for humans to adeptly undertake various goal-directed tasks. Hence, another goal is to explore how the relationships among regions related to control and regions related to processing will change as result of top-down control signals during tasks. In Chapter 2, we applied the graph theory method of link communities onto the brain\u27s resting-state intrinsic connectivity structure to identify possible points of interactions among the previously defined functional systems, including various control systems. In Chapter 3, we conducted a meta-analysis of tasks to examine the distinct functional characteristics of control systems in task activation. Using a data-driven clustering analysis, we identified two distinct trial-related response profiles that divided the regions of control systems into a right frontoparietal and cinguloopercular cluster, which may be engaged in fine-tuning task parameters and evaluating performance, and a left frontoparietal and dorsal attention cluster, which may be involved in timely updates of trial-wise parameters as well as information processing. In Chapter 4, we explored the changes in functional relationships among selected systems during individual trials of a goal-direct task and found the presence of complex and dynamic relationships that suggest changes among the various functional systems across a trial reflect both continuous as well as momentary effects of top-down signals. Collectively, the studies presented here both contributed to as well as challenged previous frameworks of task control in an effort to build better understanding of the basic organization and interactions among the brain\u27s functional systems

    Spatial and temporal characteristics of error-related activity in the human brain

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    A number of studies have focused on the role of specific brain regions, such as the dorsal anterior cingulate cortex during trials on which participants make errors, whereas others have implicated a host of more widely distributed regions in the human brain. Previous work has proposed that there are multiple cognitive control networks, raising the question of whether error-related activity can be found in each of these networks. Thus, to examine error-related activity broadly, we conducted a meta-analysis consisting of 12 tasks that included both error and correct trials. These tasks varied by stimulus input (visual, auditory), response output (button press, speech), stimulus category (words, pictures), and task type (e.g., recognition memory, mental rotation). We identified 41 brain regions that showed a differential fMRI BOLD response to error and correct trials across a majority of tasks. These regions displayed three unique response profiles: (1) fast, (2) prolonged, and (3) a delayed response to errors, as well as a more canonical response to correct trials. These regions were found mostly in several control networks, each network predominantly displaying one response profile. The one exception to this โ€œone network, one response profileโ€ observation is the frontoparietal network, which showed prolonged response profiles (all in the right hemisphere), and fast profiles (all but one in the left hemisphere). We suggest that, in the place of a single localized error mechanism, these findings point to a large-scale set of error-related regions across multiple systems that likely subserve different function

    Altered Alpha Oscillatory Power Dynamics Underlie Difficulties with Cognitive Flexibility

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    Cognitive flexibility is an important mental faculty, but there are certain populations that experience reduced flexibility, which may be associated with altered neural activity. Rumination is when an individual becomes mentally stuck on a thought, and they experience difficulty shifting their attention away from the ruminative thought demonstrating reduced cognitive flexibility. In a similar manner, individuals diagnosed with substance use disorder show varying degrees of attentional bias towards drug related stimuli. The drug cues capture attention, and it is difficult for these individuals to shift attention away from thoughts related to drug cues. Both populations experience difficulty shifting attention when they experience highly salient thoughts (high automatic constraints). Here we suggest and demonstrate that reduced cognitive flexibility in these populations is associated with altered activity of alpha oscillations, as alpha oscillations play an important role in supporting cognitive flexibility. In our first study, we assess the relationship between trait tendency to ruminate and resting state alpha power in left frontal and parietal located electrodes. Individuals higher in trait rumination exhibit higher alpha power in left frontal located electrodes. This finding suggests that higher alpha power may contribute to mental inflexibility associated with rumination. In our second study, we assess the relationship between attentional bias towards drug cues and alpha power while automatic constraints on thought are high during an emotional version of the Stroop task and when drug cues are not present and therefore automatic constraints are low, but flexibility is required during a probabilistic reversal learning task. The emotional version of the Stroop task includes traditional congruent and incongruent word meanings as well as drug related and neutral word meanings. Participants in this study were long-term nicotine smokers, therefore the emotional stimuli were smoking related. The probabilistic reversal learning task instructs participants to choose one of two presented stimuli on each trial. The stimuli have different probabilities of reward or punishment. If the participant chooses the stimulus with the higher probability of reward several trials in a row, the reward probabilities reverse, and the participant must adapt to the new reward contingencies. Participants demonstrate the traditional Stroop effect of lower accuracy and slower reaction time during incongruent trials compared to congruent trials. Additionally, participants show a slowed reaction time during drug trials compared to neutral trials suggesting attentional bias during drug trials. Greater attentional bias is associated with higher alpha power in left frontal electrodes during drug trials. No significant relationship between attentional bias and alpha power during the probabilistic reversal learning task was revealed. Together, these results suggest higher alpha power in left frontal regions may contribute to mental inflexibility prompted by attentional bias when automatic constraints are high, but when automatic constraints are low, flexibility may not be reduced. All together these results reveal a relationship between reduced cognitive flexibility when salient stimuli or thoughts are present and altered alpha power dynamics, which may offer new avenues for behavioral intervention to improve cognitive flexibility

    Intrinsic connectivity of left ventrolateral prefrontal cortex predicts individual differences in controlled semantic retrieval

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    Control processes allow us to constrain the retrieval of semantic information from long-term memory so that it is appropriate for the task or context. Control demands are influenced by the strength of the target information itself and by the circumstances in which it is retrieved, with more control needed when relatively weak aspects of knowledge are required and after the sustained retrieval of related concepts. To investigate the neurocognitive basis of individual differences in these aspects of semantic control, we used resting-state fMRI to characterise the intrinsic connectivity of left ventrolateral prefrontal cortex (VLPFC), implicated in controlled retrieval, and examined associations on a paced serial semantic task, in which participants were asked to detect category members among distractors. This task manipulated both the strength of target associations and the requirement to sustain retrieval within a narrow semantic category over time. We found that individuals with stronger connectivity between VLPFC and medial prefrontal cortex within the default mode network (DMN) showed better retrieval of strong associations (which are thought to be recalled more automatically). Stronger connectivity between the same VLPFC seed and another DMN region in medial parietal cortex was associated with larger declines in retrieval over the course of the category. In contrast, participants with stronger connectivity between VLPFC and cognitive control regions within the ventral attention network (VAN) had better controlled retrieval of weak associations and were better able to sustain their comprehension throughout the category. These effects overlapped in left insular cortex within the VAN, indicating that a common pattern of connectivity is associated with different aspects of controlled semantic retrieval induced by both the structure of long-term knowledge and the sustained retrieval of related information

    The Contribution of Functional Brain Networks and Oscillations to the Development of Cognitive Control

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    Adolescence is a qualitatively unique period of development when cognitive control abilities are available but are unreliably engaged, which can lead to risk-taking behavior impacting survival. The specific neural mechanisms contributing to the maturation of cognitive control remain poorly understood. To address this issue, we employed functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) to study brain networks and oscillations underlying cognitive control development in both the resting state and during a cognitive flexibility task. In the first study, we found that the organization of brain networks was established prior to adolescence. However, a network of brain regions anchored in the anterior cingulate cortex (ACC) and anterior insula (aIns) significantly increased its influence over other brain networks via increased network integration during the resting state, resulting in faster correct responses on a cognitive control task. In the second study, we leveraged increased temporal resolution using MEG to further probe resting state connectivity changes with age. We found similar medial prefrontal regions became less coupled in their interactions with the rest of the brain, specifically in the theta band (5-9 Hz oscillations), and were related to developmental decreases in impulsivity. As such, these results suggest there are developmental increases in the flexibility of resting state connectivity, which may afford less effortful instantiation of cognitive control. The third study directly tested age-related changes in brain oscillations during a cognitive flexibility paradigm. We found evidence of strong induction of theta band oscillations in the ACC when task switching that scaled positively with average reaction time. Similar to our resting state MEG findings, we found that the prominence of ACC theta band rhythms decreased with development, suggesting that during cognitive flexibility, adolescents need to engage greater cognitive control to switch between cognitive demands compared to adults. Taken together, these results inform a model of adolescent development such that the specialization of medial prefrontal systems plays a primary role in developmental improvements in cognitive control as they strengthen their integration with other networks. Increased network integration affords these regions the ability to more flexibly engage other brain regions, supporting the maturation of cognitive control

    ๋…ธ๋…„๊ธฐ ๊ณผ์ œ ๊ด€๋ จ ๋‡Œ ์—ฐ๊ฒฐ๋ง์˜ ํšจ์œจ์  ์žฌ์กฐ์งํ™”์™€ ์—ฐ๊ด€๋œ ์ธ์ง€ ํ†ต์ œ ์ˆ˜ํ–‰

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์‹ฌ๋ฆฌํ•™๊ณผ, 2021. 2. ์ตœ์ง„์˜.Appropriate reconfiguration of the brain functional network based on various given situations came to the fore as an important factor for the adaptive function in younger adults. Since the role of reconfiguration in older adults needs to be clarified, this study aimed to examine the relationship between brain network reconfiguration and adaptive function even in older adults who had experienced both structural and functional brain change over a lifetime. A total of 83 elderly people who participated in the Korean Social Life and Health Aging Project (KSHAP) completed the resting-state and multi-source interference task (MSIT) fMRI protocol. They underwent 10-minute resting state fMRI acquisition with their eyes open, and 6-minute MSIT state to measure their performance on the cognitive control task. Older people who reconfigured their task-positive networks less from the resting-state to the MSIT showed better performance both in the MSIT, and the neuropsychological tests measuring working memory function. These results were still significant even controlling age, sex, years of education, total gray matter volume, and the mean movement between two states. Especially, the less reconfiguration in the fronto-parietal network (FPN) was significantly associated with better performance on both the cognitive control task and the working memory tests. The MSIT performance was not affected by the individual difference in the configuration of both rest and task state. Yet, the working memory function was significantly affected by the individual difference in the configuration of task state. These results indicated that less and efficient reconfiguration was associated with better adaptive function even in elderly people. In addition, the FPN stability between two different states played a significant role in the cognitive function of elderly adults. Moreover, the cognitive control in older adults was associated with task switching rather than the optimization of the states. On the other hand, the working memory was still associated with the optimization of the task state. This study extended the analysis method of neuroimaging and suggested a novel approach to investigate the cognitive control of older adults.๋‡Œ์˜ ๊ธฐ๋Šฅ์  ์—ฐ๊ฒฐ๋ง (brain functional network)์„ ์ƒํ™ฉ์— ๋”ฐ๋ผ ํšจ์œจ์ ์œผ๋กœ ์žฌ์กฐ์งํ™”ํ•˜๋Š” ๋Šฅ๋ ฅ (network reconfiguration)์€ ์ Š์€ ์ธ๊ตฌ์—์„œ ์ ์‘์ ์ธ ๊ธฐ๋Šฅ๊ณผ ์—ฐ๊ด€์„ฑ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋‡Œ์˜ ๊ตฌ์กฐ์ , ๊ธฐ๋Šฅ์  ๋ณ€ํ™”๊ฐ€ ๋ฐœ์ƒํ•˜๋Š” ๋…ธ๋…„๊ธฐ์—๋„ ๊ทธ๋Ÿฌํ•œ ์—ฐ๊ด€์„ฑ์ด ๋‚˜ํƒ€๋‚  ์ˆ˜ ์žˆ๋Š”์ง€ ํƒ๊ตฌํ•˜๋Š” ๊ฒƒ์„ ๋ชฉ์ ์œผ๋กœ ํ•˜์˜€๋‹ค. Korean Social Life and Health Aging Project (KSHAP) ์—ฐ๊ตฌ์— ์ฐธ์—ฌํ•œ ๋†์ดŒ์ง€์—ญ L ์ง€์—ญ๊ณผ K ์ง€์—ญ์˜ ์ฐธ๊ฐ€์ž 83๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์•„๋ฌด ๊ณผ์ œ๋„ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๋Š” ํœด์ง€๊ธฐ์™€ ์ธ์ง€ ํ†ต์ œ๋ฅผ ์š”๊ตฌํ•˜๋Š” ๋‹ค์ค‘๊ฐ„์„ญ๊ณผ์ œ (MSIT) ๊ธฐ๋Šฅ์  ์ž๊ธฐ๊ณต๋ช…์˜์ƒ (fMRI)์„ ์–ป์—ˆ๋‹ค. ํœด์ง€๊ธฐ์—์„œ ์ธ์ง€์  ํ†ต์ œ๋ฅผ ์š”๊ตฌํ•˜๋Š” ๊ณผ์ œ๋กœ์˜ ๋‡Œ ๊ธฐ๋Šฅ์  ์—ฐ๊ฒฐ๋ง์˜ ์žฌ์กฐ์งํ™”๊ฐ€ ์ ์€ ์‚ฌ๋žŒ์ผ์ˆ˜๋ก (ํšจ์œจ์ ์ผ์ˆ˜๋ก) ๊ณผ์ œ ์ˆ˜ํ–‰ ์†๋„๊ฐ€ ๋นจ๋ผ์ง€๋ฉฐ, ๋†’์€ ์ž‘์—…๊ธฐ์–ต ์ง€์ˆ˜ ๋ฐ ์ž‘์—…๊ธฐ์–ต ์†Œ๊ฒ€์‚ฌ์—์„œ ๋›ฐ์–ด๋‚œ ์ˆ˜ํ–‰์„ ๋ณด์˜€์œผ๋ฉฐ, ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์—ฐ๋ น, ์„ฑ๋ณ„, ๊ต์œก ์—ฐํ•œ์— ๋”๋ถˆ์–ด ๋‡Œ์˜ ๋…ธํ™”๋ฅผ ๋ฐ˜์˜ํ•˜๋Š” ๋‡Œ ๊ตฌ์กฐ์  ๋ณ€์ˆ˜๋“ค์„ ํ†ต์ œํ•˜๊ณ ๋„ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ํŠนํžˆ, ์ „๋‘๋‘์ • ๋„คํŠธ์›Œํฌ (FPN)์˜ ์ ์€ ์žฌ์กฐ์งํ™”๋ฅผ ๋ณด์ด๋Š” ์‚ฌ๋žŒ๋“ค์€ ์ธ์ง€ ํ†ต์ œ ๊ธฐ๋Šฅ๊ณผ ์ž‘์—… ๊ธฐ์–ต ๊ธฐ๋Šฅ ๋ชจ๋‘์—์„œ ๋›ฐ์–ด๋‚œ ์ˆ˜ํ–‰์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋‹ค. ํ•œํŽธ, ๋…ธ๋…„๊ธฐ ์ธ์ง€ํ†ต์ œ ๊ธฐ๋Šฅ์—๋Š” ํœด์ง€๊ธฐ๋‚˜ ๊ณผ์ œ ์ˆ˜ํ–‰ ์ƒํƒœ ๊ฐ๊ฐ์˜ ์—ฐ๊ฒฐ๋ง ์กฐ์งํ™” (configuration)์˜ ๊ฐœ์ธ์ฐจ๋Š” ์˜ํ–ฅ์ด ์—†์—ˆ์ง€๋งŒ, ์ž‘์—…๊ธฐ์–ต ๊ธฐ๋Šฅ์—๋Š” ๊ณผ์ œ ์ˆ˜ํ–‰ ์ƒํƒœ ์—ฐ๊ฒฐ๋ง ์กฐ์งํ™”์˜ ๊ฐœ์ธ์ฐจ์˜ ์˜ํ–ฅ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ๋…ธ๋…„๊ธฐ์—๋„ ์ ์€, ํšจ์œจ์ ์ธ ์žฌ์กฐ์งํ™”๊ฐ€ ์ ์‘ ๊ธฐ๋Šฅ๊ณผ ์—ฐ๊ด€์ด ์žˆ์Œ์„ ๋“œ๋Ÿฌ๋‚ด๋ฉฐ, ์ƒํ™ฉ์— ๋”ฐ๋ฅธ ์ „๋‘๋‘์ • ๋„คํŠธ์›Œํฌ์˜ ์•ˆ์ •์„ฑ์ด ๋…ธ๋…„๊ธฐ ์ธ์ง€๊ธฐ๋Šฅ์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ์Œ์„ ์ž…์ฆํ•œ๋‹ค. ๋˜ํ•œ, ๋…ธ๋…„๊ธฐ ์ธ์ง€ ํ†ต์ œ ๊ธฐ๋Šฅ์—๋Š” ํœด์ง€๊ธฐ ์—ฐ๊ฒฐ๋ง ์กฐ์งํ™”์˜ ์ตœ์ ํ™”๊ฐ€ ์•„๋‹ˆ๋ผ ๊ณผ์ œ ๊ฐ„ ์ „ํ™˜์ด ๋ณด๋‹ค ์ ์‘์ ์ธ ๊ธฐ๋Šฅ๊ณผ ์—ฐ๊ด€๋˜์–ด ์žˆ์Œ์ด ํ™•์ธ๋˜์—ˆ์œผ๋ฉฐ, ์ž‘์—… ๊ธฐ์–ต ๊ธฐ๋Šฅ์—์„œ๋Š” ๊ณผ์ œ๋ฅผ ์ˆ˜ํ–‰ํ•  ๋•Œ์˜ ์—ฐ๊ฒฐ๋ง ์กฐ์งํ™”์˜ ์ตœ์ ํ™”๊ฐ€ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ์Œ์„ ํ™•์ธํ–ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋‡Œ ์—ฐ๊ฒฐ๋ง ์žฌ์กฐ์งํ™”๊ฐ€ ์ •์ƒ์ ์ธ ๋…ธํ™”๋ฅผ ํฌํ•จํ•˜๋Š” ์ผ๋ฐ˜์ ์ธ ๋ฐœ๋‹ฌ ๊ณผ์ • ๋‚ด์—์„œ ์ˆ˜ํ–‰๊ณผ ๋งบ๋Š” ๊ด€๊ณ„๋ฅผ ํ™•์ธํ•˜์—ฌ ์ถ”ํ›„ ์ž„์ƒ ์—ฐ๊ตฌ์˜ ๊ธฐํ‹€์ด ๋  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ, ๊ธฐ์กด์˜ ๋‡Œ ์˜์ƒ ๋ถ„์„๋ฒ•์„ ํ™•์žฅํ•˜์—ฌ ์ธ์ง€ ํ†ต์ œ ๊ธฐ๋Šฅ์„ ์—ฐ๊ตฌํ•  ๋˜๋‹ค๋ฅธ ๊ด€์ ์„ ์ œ์‹œํ•œ๋‹ค. ์ถ”ํ›„ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ทธ๋ž˜ํ”„ ์ด๋ก  ์ง€์ˆ˜๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๋‡Œ์˜ ๋‹ค์–‘ํ•œ ์ƒํƒœ์˜ ๊ตฌ์„ฑ๋ฐฉ์‹(topology)๊ณผ ์žฌ์กฐ์งํ™” ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์—ฐ๊ตฌํ•œ๋‹ค๋ฉด, ์ ์‘ ๊ธฐ๋Šฅ๊ณผ ์—ฐ๊ฒฐ๋ง ์žฌ์กฐ์งํ™”์˜ ๊ด€๊ณ„๊ฐ€ ๋”์šฑ ๋ถ„๋ช…ํ•˜๊ฒŒ ๋ฐํ˜€์งˆ ๊ฒƒ์ด๋‹ค.Chapter 1. Introduction 1 1.1. Cognitive Aging in Older adults 1 1.2. Cognitive Control Function in the Cognitive Aging 3 1.3. MSIT: an fMRI task to measure cognitive control 4 1.4. Brain Network Reconfiguration and the General Cognitive Ability 6 1.5. Brain Network Reconfiguration in the Aging 8 1.6. Objectives and Hypotheses 9 Chapter 2. Methods 11 2.1. Participants & Procedures 11 2.2. Multi-Source Interference Task 13 2.3. Neuropsychological Tests 16 2.4. MRI Acquisition and Preprocessing 19 2.5. Calculating the Network Similarity Index of the Brain Network 27 2.6. Individual Resting/Task State Functional Connectivity Configuration 29 2.7. Statistical Analysis 30 Chapter 3. Results 31 3.1. Behavioral Results 31 3.2. Brain Network Similarity Index & Cognitive Control Functions 36 3.3. Impact of Resting-State and Task Configuration on Brain Reconfiguration 43 3.4. MSIT activation & Cognitive Control Functions 46 Bibliography 60 ๊ตญ๋ฌธ์ดˆ๋ก 68Maste

    Decoding the processing of lying using functional connectivity MRI

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