7 research outputs found

    Cortical Surface Reconstruction from High-Resolution MR Brain Images

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    Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the human brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction approaches are typically optimized for standard resolution (~1โ€‰mm) data and are not directly applicable to higher resolution images. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and scales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The method uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping, has nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow sulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained by topology-preserving level set approach. The method's performance is illustrated on exvivo images with 0.25โ€“0.35โ€‰mm isotropic voxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data sets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects

    Reading Your Own Mind: Dynamic Visualization of Real-Time Neural Signals

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    Brain Computer Interfaces: BCI) systems which allow humans to control external devices directly from brain activity, are becoming increasingly popular due to dramatic advances in the ability to both capture and interpret brain signals. Further advancing BCI systems is a compelling goal both because of the neurophysiology insights gained from deriving a control signal from brain activity and because of the potential for direct brain control of external devices in applications such as brain injury recovery, human prosthetics, and robotics. The dynamic and adaptive nature of the brain makes it difficult to create classifiers or control systems that will remain effective over time. However it is precisely these qualities that offer the potential to use feedback to build on simple features and create complex control features that are robust over time. This dissertation presents work that addresses these opportunities for the specific case of Electrocorticography: ECoG) recordings from clinical epilepsy patients. First, queued patient tasks were used to explore the predictive nature of both local and global features of the ECoG signal. Second, an algorithm was developed and tested for estimating the most informative features from naive observations of ECoG signal. Third, a software system was built and tested that facilitates real-time visualizations of ECoG signal patients and allows ECoG epilepsy patients to engage in an interactive BCI control feature screening process

    Cortical Orchestra Conducted by Purpose and Function

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋‡Œ๊ณผํ•™์ „๊ณต,2020. 2. ์ •์ฒœ๊ธฐ.์ด‰๊ฐ๊ณผ ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ์€ ์šฐ๋ฆฌ์˜ ์ƒ์กด ๋ฐ ์ผ์ƒ์ƒํ™œ์— ์ ˆ๋Œ€์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ค‘์š”ํ•œ ๊ฐ๊ฐ ๊ธฐ๋Šฅ์ด๋‹ค. ๋ง์ดˆ์‹ ๊ฒฝ๊ณ„์—์„œ ์ด ๋‘ ๊ฐ€์ง€ ๊ธฐ๋Šฅ๋“ค์— ํ•„์š”ํ•œ ์ •๋ณด๋ฅผ ์ˆ˜์ง‘ํ•˜๊ณ  ์ „๋‹ฌํ•˜๋Š” ๊ธฐ๊ณ„์  ์ˆ˜์šฉ๊ธฐ ๋ฐ ๊ทธ ๊ตฌ์‹ฌ์„ฑ ์‹ ๊ฒฝ๋“ค์— ๋Œ€ํ•œ ์‹ ํ˜ธ ์ „๋‹ฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜ ๋ฐ ๊ทธ ํŠน์ง•๋“ค์€ ์ƒ๋Œ€์ ์œผ๋กœ ์ž˜ ์•Œ๋ ค์ ธ ์žˆ๋Š” ํŽธ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜, ์ด‰๊ฐ๊ณผ ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ์„ ํ˜•์„ฑํ•˜๊ธฐ ์œ„ํ•œ ์ธ๊ฐ„ ๋‡Œ์˜ ํ”ผ์งˆ์—์„œ์˜ ์ •๋ณด ์ฒ˜๋ฆฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์— ๋Œ€ํ•˜์—ฌ ์šฐ๋ฆฌ๊ฐ€ ํ˜„์žฌ ์•Œ๊ณ  ์žˆ๋Š” ๋ฐ”๋Š” ๊ทนํžˆ ์ผ๋ถ€๋ถ„์ด๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•˜๋Š” ์ผ๋ จ์˜ ์—ฐ๊ตฌ๋“ค์€ ์ธ๊ฐ„ ๋‡Œ ํ”ผ์งˆ ๋‹จ๊ณ„์—์„œ ์ด‰๊ฐ๊ณผ ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ์˜ ์ง€๊ฐ์  ์ฒ˜๋ฆฌ๊ณผ์ •์— ๋Œ€ํ•œ ๊ฑฐ์‹œ์  ์‹ ๊ฒฝ๊ณ„ ์ •๋ณด์ฒ˜๋ฆฌ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋‹ค๋ฃฌ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‡Œํ”ผ์งˆ๋‡ŒํŒŒ๋ฅผ ์ด์šฉํ•˜์—ฌ ์ธ๊ฐ„ ์ผ์ฐจ ๋ฐ ์ด์ฐจ ์ฒด์„ฑ๊ฐ๊ฐ ํ”ผ์งˆ์—์„œ ์ธ๊ณต์ ์ธ ์ž๊ทน๊ณผ ์ผ์ƒ์ƒํ™œ์—์„œ ์ ‘ํ•  ์ˆ˜ ์žˆ๋Š” ์ž๊ทน์„ ํฌํ•จํ•˜๋Š” ๋‹ค์–‘ํ•œ ์ง„๋™์ด‰๊ฐ๊ฐ ๋ฐ ์งˆ๊ฐ ์ž๊ทน์— ๋Œ€ํ•œ ๊ฑฐ์‹œ์  ์‹ ๊ฒฝ๊ณ„ ์ •๋ณด์ฒ˜๋ฆฌ ํŠน์„ฑ์„ ๋ฐํ˜”๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ์ผ์ฐจ ๋ฐ ์ด์ฐจ ์ฒด์„ฑ๊ฐ๊ฐ ํ”ผ์งˆ์˜ ์ด‰๊ฐ๊ฐ ์ฃผํŒŒ์ˆ˜ ํŠน์ด์ ์ธ ํ•˜์ด-๊ฐ๋งˆ ์˜์—ญ ์‹ ๊ฒฝํ™œ๋™์ด ์ž๊ทน ์ฃผํŒŒ์ˆ˜์— ๋”ฐ๋ผ ๊ฐ๊ฐ ์ƒ์ดํ•œ ์‹œ๊ฐ„์  ๋‹ค์ด๋‚˜๋ฏน์Šค๋ฅผ ๊ฐ€์ง€๊ณ  ๋ณ€ํ™”ํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ด๋Ÿฌํ•œ ํ•˜์ด-๊ฐ๋งˆ ํ™œ๋™์€ ์„ฑ๊ธด ์งˆ๊ฐ๊ณผ ๋ฏธ์„ธํ•œ ์ž…์ž๊ฐ์„ ๊ฐ€์ง„ ์ž์—ฐ์Šค๋Ÿฌ์šด ์งˆ๊ฐ ์ž๊ทน์— ๋Œ€ํ•ด์„œ๋„ ์ง„๋™์ด‰๊ฐ๊ฐ์˜ ๊ฒฝ์šฐ์™€ ์œ ์‚ฌํ•œ ํŒจํ„ด์„ ๋ณด์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์€ ์ธ๊ฐ„์˜ ์ง„๋™์ด‰๊ฐ๊ฐ์ด ๋งค์šฐ ๋‹จ์ˆœํ•œ ํ˜•ํƒœ์— ์ž๊ทน์ผ์ง€๋ผ๋„ ๋Œ€๋‡Œ ์ฒด์„ฑ๊ฐ๊ฐ ์‹œ์Šคํ…œ์— ์žˆ์–ด ๊ฑฐ์‹œ์ ์ธ ๋‹ค์ค‘ ์˜์—ญ์—์„œ์˜ ๊ณ„์ธต์  ์ •๋ณด์ฒ˜๋ฆฌ๋ฅผ ๋™๋ฐ˜ํ•œ๋‹ค๋Š” ์ ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋‘ ๋ฒˆ์งธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ๊ฐ„์˜ ์›€์ง์ž„๊ณผ ๊ด€๋ จ๋œ ๋‘์ •์—ฝ ์˜์—ญ์—์„œ์˜ ํ•˜์ด-๊ฐ๋งˆ ๋‡Œํ™œ์„ฑ์ด ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ๊ณผ ๊ฐ™์€ ๋ง์ดˆ์‹ ๊ฒฝ๊ณ„๋กœ๋ถ€ํ„ฐ์˜ ์ฒด์„ฑ๊ฐ๊ฐ ํ”ผ๋“œ๋ฐฑ์„ ์ฃผ๋กœ ๋ฐ˜์˜ํ•˜๋Š”์ง€, ์•„๋‹ˆ๋ฉด ์›€์ง์ž„ ์ค€๋น„ ๋ฐ ์ œ์–ด๋ฅผ ์œ„ํ•œ ํ”ผ์งˆ ๊ฐ„ ์‹ ๊ฒฝ ํ”„๋กœ์„ธ์Šค์— ๋Œ€ํ•œ ํ™œ๋™์„ ๋ฐ˜์˜ํ•˜๋Š”์ง€๋ฅผ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์ž๋ฐœ์  ์šด๋™ ์ค‘ ๋Œ€๋‡Œ ์šด๋™๊ฐ๊ฐ๋ น์—์„œ์˜ ํ•˜์ด-๊ฐ๋งˆ ํ™œ๋™์€ ์ผ์ฐจ ์ฒด์„ฑ๊ฐ๊ฐํ”ผ์งˆ์ด ์ผ์ฐจ ์šด๋™ํ”ผ์งˆ๋ณด๋‹ค ๋” ์ง€๋ฐฐ์ ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ์ด ์—ฐ๊ตฌ์—์„œ๋Š”, ์›€์ง์ž„๊ณผ ๊ด€๋ จ๋œ ์ผ์ฐจ ์ฒด์„ฑ๊ฐ๊ฐํ”ผ์งˆ์—์„œ์˜ ํ•˜์ด-๊ฐ๋งˆ ๋‡Œํ™œ๋™์€ ๋ง์ดˆ์‹ ๊ฒฝ๊ณ„๋กœ๋ถ€ํ„ฐ์˜ ์ž๊ธฐ์ˆ˜์šฉ๊ฐ๊ฐ๊ณผ ์ด‰๊ฐ์— ๋Œ€ํ•œ ์‹ ๊ฒฝ๊ณ„ ์ •๋ณด์ฒ˜๋ฆฌ๋ฅผ ์ฃผ๋กœ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒƒ์„ ๋ฐํ˜”๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ, ๋งˆ์ง€๋ง‰ ์—ฐ๊ตฌ์—์„œ๋Š” ์ธ๊ฐ„ ๋Œ€๋‡Œ์—์„œ์˜ ์ฒด์„ฑ๊ฐ๊ฐ ์ง€๊ฐ ํ”„๋กœ์„ธ์Šค์— ๋Œ€ํ•œ ๊ฑฐ์‹œ์  ํ”ผ์งˆ ๊ฐ„ ๋„คํŠธ์›Œํฌ๋ฅผ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, 51๋ช…์˜ ๋‡Œ์ „์ฆ ํ™˜์ž์—๊ฒŒ์„œ ์ฒด์„ฑ๊ฐ๊ฐ์„ ์œ ๋ฐœํ–ˆ๋˜ ๋‡Œํ”ผ์งˆ์ „๊ธฐ์ž๊ทน ๋ฐ์ดํ„ฐ์™€ 46๋ช…์˜ ํ™˜์ž์—๊ฒŒ์„œ ์ด‰๊ฐ๊ฐ ์ž๊ทน ๋ฐ ์šด๋™ ์ˆ˜ํ–‰ ์ค‘์— ์ธก์ •ํ•œ ๋‡Œํ”ผ์งˆ๋‡ŒํŒŒ ํ•˜์ด-๊ฐ๋งˆ ๋งคํ•‘ ๋ฐ์ดํ„ฐ๋ฅผ ์ข…ํ•ฉ์ ์œผ๋กœ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ, ์ฒด์„ฑ๊ฐ๊ฐ ์ง€๊ฐ ํ”„๋กœ์„ธ์Šค๋Š” ๋Œ€๋‡Œ์—์„œ ๋„“์€ ์˜์—ญ์— ๊ฑธ์ณ ๋ถ„ํฌํ•˜๋Š” ์ฒด์„ฑ๊ฐ๊ฐ ๊ด€๋ จ ๋„คํŠธ์›Œํฌ์˜ ์‹ ๊ฒฝ ํ™œ์„ฑ์„ ์ˆ˜๋ฐ˜ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ์•„๋ƒˆ๋‹ค. ๋˜ํ•œ, ๋‡Œํ”ผ์งˆ์ „๊ธฐ์ž๊ทน์„ ํ†ตํ•œ ๋Œ€๋‡Œ ์ง€๋„์™€ ํ•˜์ด-๊ฐ๋งˆ ๋งคํ•‘์„ ํ†ตํ•œ ๋Œ€๋‡Œ ์ง€๋„๋Š” ์„œ๋กœ ์ƒ๋‹นํ•œ ์œ ์‚ฌ์„ฑ์„ ๋ณด์˜€๋‹ค. ํฅ๋ฏธ๋กญ๊ฒŒ๋„, ๋‡Œํ”ผ์งˆ์ „๊ธฐ์ž๊ทน๊ณผ ํ•˜์ด-๊ฐ๋งˆ ํ™œ๋™์„ ์ข…ํ•ฉํ•œ ๋‡Œ์ง€๋„๋“ค๋กœ๋ถ€ํ„ฐ ์ฒด์„ฑ๊ฐ๊ฐ ๊ด€๋ จ ๋‡Œ ์˜์—ญ์˜ ๊ณต๊ฐ„์  ๋ถ„ํฌ๊ฐ€ ์ฒด์„ฑ๊ฐ๊ฐ ๊ธฐ๋Šฅ์— ๋”ฐ๋ผ ์„œ๋กœ ๋‹ฌ๋ž๊ณ , ๊ทธ์— ํ•ด๋‹นํ•˜๋Š” ๊ฐ ์˜์—ญ๋“ค์€ ์„œ๋กœ ๋šœ๋ ทํ•˜๊ฒŒ ๋‹ค๋ฅธ ์‹œ๊ฐ„์  ๋‹ค์ด๋‚˜๋ฏน์Šค๋ฅผ ๊ฐ€์ง€๊ณ  ์ˆœ์ฐจ์ ์œผ๋กœ ํ™œ์„ฑํ™”๋˜์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์€ ์ฒด์„ฑ๊ฐ๊ฐ์— ๋Œ€ํ•œ ๊ฑฐ์‹œ์  ์‹ ๊ฒฝ๊ณ„ ํ”„๋กœ์„ธ์Šค๊ฐ€ ๊ทธ ์ง€๊ฐ์  ๊ธฐ๋Šฅ์— ๋”ฐ๋ผ ๋šœ๋ ท์ด ๋‹ค๋ฅธ ๊ณ„์ธต์  ๋„คํŠธ์›Œํฌ๋ฅผ ๊ฐ€์ง„๋‹ค๋Š” ์ ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋” ๋‚˜์•„๊ฐ€, ๋ณธ ์—ฐ๊ตฌ์—์„œ์˜ ๊ฒฐ๊ณผ๋“ค์€ ์ฒด์„ฑ๊ฐ๊ฐ ์‹œ์Šคํ…œ์˜ ์ง€๊ฐ-ํ–‰๋™ ๊ด€๋ จ ์‹ ๊ฒฝํ™œ๋™ ํ๋ฆ„์— ๊ด€ํ•œ ์ด๋ก ์ ์ธ ๊ฐ€์„ค์— ๋Œ€ํ•˜์—ฌ ์„ค๋“๋ ฅ ์žˆ๋Š” ์ฆ๊ฑฐ๋ฅผ ์ œ์‹œํ•˜๊ณ  ์žˆ๋‹ค.Tactile and proprioceptive perceptions are crucial for our daily life as well as survival. At the peripheral level, the transduction mechanisms and characteristics of mechanoreceptive afferents containing information required for these functions, have been well identified. However, our knowledge about the cortical processing mechanism for them in human is limited. The present series of studies addressed the macroscopic neural mechanism for perceptual processing of tactile and proprioceptive perception in human cortex. In the first study, I investigated the macroscopic neural characteristics for various vibrotactile and texture stimuli including artificial and naturalistic ones in human primary and secondary somatosensory cortices (S1 and S2, respectively) using electrocorticography (ECoG). I found robust tactile frequency-specific high-gamma (HG, 50โ€“140 Hz) activities in both S1 and S2 with different temporal dynamics depending on the stimulus frequency. Furthermore, similar HG patterns of S1 and S2 were found in naturalistic stimulus conditions such as coarse/fine textures. These results suggest that human vibrotactile sensation involves macroscopic multi-regional hierarchical processing in the somatosensory system, even during the simplified stimulation. In the second study, I tested whether the movement-related HG activities in parietal region mainly represent somatosensory feedback such as proprioception from periphery or primarily indicate cortico-cortical neural processing for movement preparation and control. I found that sensorimotor HG activities are more dominant in S1 than in M1 during voluntary movement. Furthermore, the results showed that movement-related HG activities in S1 mainly represent proprioceptive and tactile feedback from periphery. Given the results of previous two studies, the final study aimed to identify the large-scale cortical networks for perceptual processing in human. To do this, I combined direct cortical stimulation (DCS) data for eliciting somatosensation and ECoG HG band (50 to 150 Hz) mapping data during tactile stimulation and movement tasks, from 51 (for DCS mapping) and 46 patients (for HG mapping) with intractable epilepsy. The results showed that somatosensory perceptual processing involves neural activation of widespread somatosensory-related network in the cortex. In addition, the spatial distributions of DCS and HG functional maps showed considerable similarity in spatial distribution between high-gamma and DCS functional maps. Interestingly, the DCS-HG combined maps showed distinct spatial distributions depending on the somatosensory functions, and each area was sequentially activated with distinct temporal dynamics. These results suggest that macroscopic neural processing for somatosensation has distinct hierarchical networks depending on the perceptual functions. In addition, the results of the present study provide evidence for the perception and action related neural streams of somatosensory system. Throughout this series of studies, I suggest that macroscopic somatosensory network and structures of our brain are intrinsically organized by perceptual function and its purpose, not by somatosensory modality or submodality itself. Just as there is a purpose for human behavior, so is our brain.PART I. INTRODUCTION 1 CHAPTER 1: Somatosensory System 1 1.1. Mechanoreceptors in the Periphery 2 1.2. Somatosensory Afferent Pathways 4 1.3. Cortico-cortical Connections among Somatosensory-related Areas 7 1.4. Somatosensory-related Cortical Regions 8 CHAPTER 2: Electrocorticography 14 2.1. Intracranial Electroencephalography 14 2.2. High-Gamma Band Activity 18 CHAPTER 3: Purpose of This Study 24 PART II. EXPERIMENTAL STUDY 26 CHAPTER 4: Apparatus Design 26 4.1. Piezoelectric Vibrotactile Stimulator 26 4.2. Magnetic Vibrotactile Stimulator 29 4.3. Disc-type Texture Stimulator 33 4.4. Drum-type Texture Stimulator 36 CHAPTER 5: Vibrotactile and Texture Study 41 5.1. Introduction 42 5.2. Materials and Methods 46 5.2.1. Patients 46 5.2.2. Apparatus 47 5.2.3. Experimental Design 49 5.2.4. Data Acquisition and Preprocessing 50 5.2.5. Analysis 51 5.3. Results 54 5.3.1. Frequency-specific S1/S2 HG Activities 54 5.3.2. S1 HG Attenuation during Flutter and Vibration 62 5.3.3. Single-trial Vibration Frequency Classification 64 5.3.4. S1/S2 HG Activities during Texture Stimuli 65 5.4. Discussion 69 5.4.1. Comparison with Previous Findings 69 5.4.2. Tactile Frequency-dependent Neural Adaptation 70 5.4.3. Serial vs. Parallel Processing between S1 and S2 72 5.4.4. Conclusion of Chapter 5 73 CHAPTER 6: Somatosensory Feedback during Movement 74 6.1. Introduction 75 6.2. Materials and Methods 79 6.2.1. Subjects 79 6.2.2. Tasks 80 6.2.3. Data Acquisition and Preprocessing 82 6.2.4. S1-M1 HG Power Difference 85 6.2.5. Classification 86 6.2.6. Timing of S1 HG Activity 86 6.2.7. Correlation between HG and EMG signals 87 6.3. Results 89 6.3.1. HG Activities Are More Dominant in S1 than in M1 89 6.3.2. HG Activities in S1 Mainly Represent Somatosensory Feedback 94 6.4. Discussion 100 6.4.1. S1 HG Activity Mainly Represents Somatosensory Feedback 100 6.4.2. Further Discussion and Future Direction in BMI 102 6.4.3. Conclusion of Chapter 6 103 CHAPTER 7: Cortical Maps of Somatosensory Function 104 7.1. Introduction 106 7.2. Materials and Methods 110 7.2.1. Participants 110 7.2.2. Direct Cortical Stimulation 114 7.2.3. Classification of Verbal Feedbacks 115 7.2.4. Localization of Electrodes 115 7.2.5. Apparatus 116 7.2.6. Tasks 117 7.2.7. Data Recording and Processing 119 7.2.8. Mapping on the Brain 120 7.2.9. ROI-based Analysis 122 7.3. Results 123 7.3.1. DCS Mapping 123 7.3.2. Three and Four-dimensional HG Mapping 131 7.3.3. Neural Characteristics among Somatosensory-related Areas 144 7.4. Discussion 146 7.4.1. DCS on the Non-Primary Areas 146 7.4.2. Two Streams of Somatosensory System 148 7.4.3. Functional Role of ventral PM 151 7.4.4. Limitation and Perspective 152 7.4.5. Conclusion of Chapter 7 155 PART III. CONCLUSION 156 CHAPTER 8: Conclusion and Perspective 156 8.1. Perspective and Future Work 157 References 160 Abstract in Korean 173Docto

    Investigating structural plasticity in musiciansโ€™ brains using structural magnetic resonance and diffusion tensor imaging techniques

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    Neuroplasticity is the ability of the brain to change its structure and/or function in response to environmental stimuli. It is implicated in many processes, such as learning, maturation, skill acquisition, and rehabilitation following brain injury. With the advent of neuroimaging techniques, the study of neuroplasticity and its mechanisms have fascinated researchers given the wide scope with which this process is involved. Musicians have long been considered an ideal model to study neuroplasticity in humans. It has been shown that musicians with their early, intensive, and multimodal skilful practice have structural plasticity in different brain regions. The objective of this work was to extend these structural studies through examining different cohorts of musicians, using a multitude of imaging and morphometric techniques, and performing novel examinations of brain regions essential for enabling high level musical performance such as Brocaโ€™s area, corpus callosum (CC), and cerebellum. Three age-, gender- and handedness-matched cohorts were examined. The first cohort included 26 orchestral musicians and 26 non-musicians. High resolution T1-weighted structural MR images were acquired to measure gray and white matter volumes and cortical surface area of Brocaโ€™s area subparts: pars opercularis/BA44 and pars triangularis/BA45. The second cohort included 12/12/12 professional musicians/amateur musicians/non-musicians. High resolution T1-weighted MR images were acquired to measure cross-sectional areas of four regions of the midsagittal CC: CC1 (rostrum/ genu/anterior body), CC2 (anterior midbody), CC3 (posterior midbody), and CC4 (isthmus and splenium). In the third cohort, 12/12 musicians and non-musicians were examined. High resolution T1-weighted structural MR images were acquired to measure cross-sectional areas of CC1-CC4 regions; and diffusion tensor imaging-based tractography was used to measure average fractional anisotropy (FA), mean diffusivity (MD), tract volume, and number of streamlines of the same regions. In a subset (10/10) of this cohort, high resolution structural scans were used to measure gray and white matter volumes of cerebellar hemispheres; and diffusion tensor imaging-based tractography was used to measure average FA, tract volume, and number of streamlines of superior (SCP) and middle (MCP) cerebellar peduncles. Outcome measures were compared between groups. Compared to controls, musicians possessed greater gray matter volume and cortical surface area of left pars opercularis/BA44 in the first cohort. The volume of left pars opercularis was positively correlated with years of musical performance. Professional musicians possessed greater cross-sectional area of CC1 and CC4 regions compared to amateurs and non-musicians in the second cohort. In the third cohort, musicians possessed greater cross-sectional area, average FA/tract volume/number of streamlines, and lower MD in CC4 region. There was a negative correlation between cross-sectional area of CC4 region and age of starting musical training. There was a positive correlation between average FA values and cross-sectional area of CC4 region in all subjects. In addition, musicians had increased white matter volume of the right cerebellar hemisphere, increased tract volume and number of streamlines of right SCP, and tract volume of right MCP. I hypothesize that these findings represent use-dependent structural plasticity imposed by musical performance. At the microscopic level, these macroanatomical changes may reflect increased synaptogenesis and dendritic growth, generation of new axon collaterals, and formation of new neurons, which would support enhanced functional demands on musiciansโ€™ brains

    Diseases of the Brain, Head and Neck, Spine 2020โ€“2023

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    This open access book offers an essential overview of brain, head and neck, and spine imaging. Over the last few years, there have been considerable advances in this area, driven by both clinical and technological developments. Written by leading international experts and teachers, the chapters are disease-oriented and cover all relevant imaging modalities, with a focus on magnetic resonance imaging and computed tomography. The book also includes a synopsis of pediatric imaging. IDKD books are rewritten (not merely updated) every four years, which means they offer a comprehensive review of the state-of-the-art in imaging. The book is clearly structured and features learning objectives, abstracts, subheadings, tables and take-home points, supported by design elements to help readers navigate the text. It will particularly appeal to general radiologists, radiology residents, and interventional radiologists who want to update their diagnostic expertise, as well as clinicians from other specialties who are interested in imaging for their patient care

    Neuroimaging - Clinical Applications

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    Modern neuroimaging tools allow unprecedented opportunities for understanding brain neuroanatomy and function in health and disease. Each available technique carries with it a particular balance of strengths and limitations, such that converging evidence based on multiple methods provides the most powerful approach for advancing our knowledge in the fields of clinical and cognitive neuroscience. The scope of this book is not to provide a comprehensive overview of methods and their clinical applications but to provide a "snapshot" of current approaches using well established and newly emerging techniques
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