William Seunghyun Sohn

Tool-use practice induces changes in intrinsic functional connectivity of parietal areas

Intrinsic functional connectivity from resting state functional magnetic resonance imaging (rsfMRI) has increasingly received attention as a possible predictor of cognitive function and performance. In this study, we investigated the influence of practicing skillful tool manipulation on intrinsic functional connectivity in the resting brain. Acquisition of tool-use skill has two aspects such as formation of motor representation for skillful manipulation and acquisition of the tool concept. To dissociate these two processes, we chose chopsticks-handling with the non-dominant hand. Because participants were already adept at chopsticks-handling with their dominant hand, practice with the non-dominant hand involved only acquiring the skill for tool manipulation with existing knowledge. Eight young participants practiced chopsticks-handling with their non-dominant hand for 8 weeks. They underwent functional magnetic resonance imaging (fMRI) sessions before and after the practice. As a result, functional connectivity among tool-use-related regions of the brain decreased after practice. We found decreased functional connectivity centered on parietal areas, mainly the supramarginal gyrus (SMG) and superior parietal lobule (SPL) and additionally between the primary sensorimotor area and cerebellum. These results suggest that the parietal lobe and cerebellum purely mediate motor learning for skillful tool-use. This decreased functional connectivity may represent increased efficiency of functional network.

Progressive changes in hippocampal resting-state connectivity across cognitive impairment: a cross-sectional study from normal to Alzheimer disease.

We investigate the changes in functional connectivity of the left and right hippocampus by comparing the resting-state low-frequency fluctuations in the blood oxygen level-dependent signal from these regions with relation to Alzheimer disease (AD) progression. AD patients were divided into subgroups based on the clinical dementia rating (CDR) scores. Patients with amnestic mild cognitive impairment (aMCI) were also analyzed as an intermediate stage between normal controls and AD. We found that the total functional connectivity of both the right and left hippocampus was maintained during aMCI and the early stages of AD and that it decreased in the later stages of AD. However, when total functional connectivity was broken down into specific regions of the brain, we observed increased or decreased connectivity to specific regions beginning with aMCI. Direct correlation analysis in seeding the left hippocampus revealed a significant decrease in the functional connectivity with the posterior cingulate cortex region and lateral parietal areas, and an increase in functional connectivity in and the anterior cingulate cortex beginning with aMCI. In this study, we were able to quantify the deterioration of resting-state hippocampal connectivity with disease severity and formation of compensatory recruitment in the early stages of AD.

Influence of ROI selection on resting state functional connectivity: an individualized approach for resting state fMRI analysis

The differences in how our brain is connected are often thought to reflect the differences in our individual personalities and cognitive abilities. Individual differences in brain connectivity has long been recognized in the neuroscience community however it has yet to manifest itself in the methodology of resting state analysis. This is evident as previous studies use the same region of interest (ROIs) for all subjects. In this paper we demonstrate that the use of ROIs which are standardized across individuals leads to inaccurate calculations of functional connectivity. We also show that this problem can be addressed by taking an individualized approach by using subject-specific ROIs. Finally we show that ROI selection can affect the way we interpret our data by showing different changes in functional connectivity with aging.

Degree-based statistic and center persistency for brain connectivity analysis.

Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass‐univariate hypothesis testing. Although, several cluster‐wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster‐forming threshold. In this study, we propose a novel method, degree‐based statistic (DBS), performing cluster‐wise inference. DBS is designed to overcome the above‐mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known “ground truth” simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165–181, 2017.

Higher extrinsic and lower intrinsic connectivity in resting state networks for professional Baduk (Go) players

Dedication and training to a profession results in a certain level of expertise. This expertise, like any other skill obtained in our lifetime, is encoded in the brain and may be reflected in our brains connectome. This property can be observed by mapping resting state connectivity. In this study, we examine the differences in resting state functional connectivity in four major networks between professional “Baduk” (Go) players and normal subjects.

Node Identification Using Inter-Regional Correlation Analysis for Mapping Detailed Connections in Resting State Networks

Brain function is often characterized by the connections and interactions between highly interconnected brain regions. Pathological disruptions in these networks often result in brain dysfunction, which manifests as brain disease. Typical analysis investigates disruptions in network connectivity based correlations between large brain regions. To obtain a more detailed description of disruptions in network connectivity, we propose a new method where functional nodes are identified in each region based on their maximum connectivity to another brain region in a given network. Since this method provides a unique approach to identifying functionally relevant nodes in a given network, we can provide a more detailed map of brain connectivity and determine new measures of network connectivity. We applied this method to resting state fMRI of Alzheimers disease patients to validate our method and found decreased connectivity within the default mode network. In addition, new measure of network connectivity revealed a more detailed description of how the network connections deteriorate with disease progression. This suggests that analysis using key relative network hub regions based on regional correlation can be used to detect detailed changes in resting state network connectivity.

Resting state brain networks and their implications in neurodegenerative disease

Neurons are the basic units of the brain, and form network by connecting via synapses. So far, there have been limited ways to measure the brain networks. Recently, various imaging modalities are widely used for this purpose. In this paper, brain network mapping using resting state fMRI will be introduced with several applications including neurodegenerative disease such as Alzheimer’s disease, frontotemporal lobar degeneration and Parkinson’s disease. The resting functional connectivity using intrinsic functional connectivity in mouse is useful since we can take advantage of perturbation or stimulation of certain nodes of the network. The study of brain connectivity will open a new era in understanding of brain and diseases thus will be an essential foundation for future research.