Yong Jeong

GABA from reactive astrocytes impairs memory in mouse models of Alzheimer's disease

In Alzheimers disease (AD), memory impairment is the most prominent feature that afflicts patients and their families. Although reactive astrocytes have been observed around amyloid plaques since the disease was first described, their role in memory impairment has been poorly understood. Here, we show that reactive astrocytes aberrantly and abundantly produce the inhibitory gliotransmitter GABA by monoamine oxidase-B (Maob) and abnormally release GABA through the bestrophin 1 channel. In the dentate gyrus of mouse models of AD, the released GABA reduces spike probability of granule cells by acting on presynaptic GABA receptors. Suppressing GABA production or release from reactive astrocytes fully restores the impaired spike probability, synaptic plasticity, and learning and memory in the mice. In the postmortem brain of individuals with AD, astrocytic GABA and MAOB are significantly upregulated. We propose that selective inhibition of astrocytic GABA synthesis or release may serve as an effective therapeutic strategy for treating memory impairment in AD.

A randomized trial of mesenchymal stem cells in multiple system atrophy

Neuroprotective or regenerative strategies are invaluable in multiple system atrophy (MSA) due to its rapid progression with fatal prognosis. We evaluated the efficacy of autologous mesenchymal stem cells (MSC) in patients with MSA‐cerebellar type (MSA‐C).

Wavelet minimum description length detrending for near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) can be employed to investigate brain activities associated with regional changes of the oxy- and deoxyhemoglobin concentration by measuring the absorption of near-infrared light through the intact skull. NIRS is regarded as a promising neuroimaging modality thanks to its excellent temporal resolution and flexibility for routine monitoring. Recently, the general linear model (GLM), which is a standard method for functional MRI (fMRI) analysis, has been employed for quantitative analysis of NIRS data. However, the GLM often fails in NIRS when there exists an unknown global trend due to breathing, cardiac, vasomotion, or other experimental errors. We propose a wavelet minimum description length (Wavelet-MDL) detrending algorithm to overcome this problem. Specifically, the wavelet transform is applied to decompose NIRS measurements into global trends, hemodynamic signals, and uncorrelated noise components at distinct scales. The minimum description length (MDL) principle plays an important role in preventing over- or underfitting and facilitates optimal model order selection for the global trend estimate. Experimental results demonstrate that the new detrending algorithm outperforms the conventional approaches.

Synergistic Effect of Graphene Oxide/MWCNT Films in Laser Desorption/Ionization Mass Spectrometry of Small Molecules and Tissue Imaging

Matrix-assisted laser desorption/ionization mass spectrometry has been considered an important tool for various biochemical analyses and proteomics research. Although addition of conventional matrix efficiently supports laser desorption/ionization of analytes with minimal fragmentation, it often results in high background interference and misinterpretation of the spatial distribution of biomolecules especially in low-mass regions. Here, we show design, systematic characterization, and application of graphene oxide/multiwalled carbon nanotube-based films fabricated on solid substrates as a new matrix-free laser desorption/ionization platform. We demonstrate that the graphene oxide/multiwalled carbon nanotube double layer provides many advantages as a laser desorption/ionization substrate, such as efficient desorption/ionization of analytes with minimum fragmentation, high salt tolerance, no sweet-spots for mass signal, excellent durability against mechanical and photoagitation and prolonged exposure to ambient conditions, and applicability to tissue imaging mass spectrometry. This platform will be widely used as an important tool for mass spectrometry-based biochemical analyses because of its outstanding performance, long-term stability, and cost effectiveness.

Individual subject classification for Alzheimer's disease based on incremental learning using a spatial frequency representation of cortical thickness data

Patterns of brain atrophy measured by magnetic resonance structural imaging have been utilized as significant biomarkers for diagnosis of Alzheimers disease (AD). However, brain atrophy is variable across patients and is non-specific for AD in general. Thus, automatic methods for AD classification require a large number of structural data due to complex and variable patterns of brain atrophy. In this paper, we propose an incremental method for AD classification using cortical thickness data. We represent the cortical thickness data of a subject in terms of their spatial frequency components, employing the manifold harmonic transform. The basis functions for this transform are obtained from the eigenfunctions of the Laplace-Beltrami operator, which are dependent only on the geometry of a cortical surface but not on the cortical thickness defined on it. This facilitates individual subject classification based on incremental learning. In general, methods based on region-wise features poorly reflect the detailed spatial variation of cortical thickness, and those based on vertex-wise features are sensitive to noise. Adopting a vertex-wise cortical thickness representation, our method can still achieve robustness to noise by filtering out high frequency components of the cortical thickness data while reflecting their spatial variation. This compromise leads to high accuracy in AD classification. We utilized MR volumes provided by Alzheimers Disease Neuroimaging Initiative (ADNI) to validate the performance of the method. Our method discriminated AD patients from Healthy Control (HC) subjects with 82% sensitivity and 93% specificity. It also discriminated Mild Cognitive Impairment (MCI) patients, who converted to AD within 18 months, from non-converted MCI subjects with 63% sensitivity and 76% specificity. Moreover, it showed that the entorhinal cortex was the most discriminative region for classification, which is consistent with previous pathological findings. In comparison with other classification methods, our method demonstrated high classification performance in both categories, which supports the discriminative power of our method in both AD diagnosis and AD prediction.

Cortical Thinning in Vascular Mild Cognitive Impairment and Vascular Dementia of Subcortical Type

Amnestic mild cognitive impairment (MCI) is known to be a preclinical stage of Alzheimers disease (AD). Similarly, MCI associated with small‐vessel disease (svMCI), might be a forme froste of subcortical vascular dementia (SVaD). Patterns of cortical thinning in addition to the ischemia rating on MRI may further elucidate the clinical characteristics and pathogenesis of SVaD and svMCI. We tried to determine if svMCI differs from SVaD in the distribution of cortical atrophy, which may help understand the hierarchy between svMCI and SVaD and possibly also how svMCI evolves into SVaD.

NIRS-SPM: statistical parametric mapping for near infrared spectroscopy

Even though there exists a powerful statistical parametric mapping (SPM) tool for fMRI, similar public domain tools are not available for near infrared spectroscopy (NIRS). In this paper, we describe a new public domain statistical toolbox called NIRS-SPM for quantitative analysis of NIRS signals. Specifically, NIRS-SPM statistically analyzes the NIRS data using GLM and makes inference as the excursion probability which comes from the random field that are interpolated from the sparse measurement. In order to obtain correct inference, NIRS-SPM offers the pre-coloring and pre-whitening method for temporal correlation estimation. For simultaneous recording NIRS signal with fMRI, the spatial mapping between fMRI image and real coordinate in 3-D digitizer is estimated using Horns algorithm. These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools.

Caregiver-Administered Neuropsychiatric Inventory (CGA-NPI):

The Neuropsychiatric Inventory (NPI) is used to assess neuropsychiatric symptoms in dementia patients. To reduce clinicians’ time taken to administer the NPI, the authors studied a caregiver-administered NPI (CGA-NPI), in which caregivers completed the written form of the NPI worksheet. After a brief presupervision session, the caregivers of 61 dementia patients completed the CGA-NPI by reading through the worksheet. This was followed by a postsupervision session to check if the caregivers had completed the form appropriately. The correlation between the prevalence rates of each neuropsychiatric symptom obtained by the CGA-NPI and those obtained by the NPI was fair to good (. = 0.57-0.90) in all domains. All frequency, severity, and caregivers’ distress scores of the CGA-NPI correlated significantly with those of the NPI (r> 0.6, P< .001). Total CGA-NPI scores also correlated highly with total NPI scores (r= 0.86, P< .001). These results suggest that the CGA-NPI can substitute for the NPI, saving administration time.

Quantitative analysis of hemodynamic and metabolic changes in subcortical vascular dementia using simultaneous near-infrared spectroscopy and fMRI measurements.

Subcortical vascular dementia (SVD) is a form of vascular dementia from small vessel disease with white matter lesions and lacunes. We hypothesized that hemodynamic and metabolic changes in the cortex during a simple motor task may reflect the impaired neurovascular coupling in SVD. We used fMRI and near-infrared spectroscopy (NIRS) simultaneously, which together provided multiple hemodynamic responses as well as a robust estimation of the cerebral metabolic rate of oxygen (CMRO(2)). During the task periods, the oxy-hemoglobin, total-hemoglobin, blood oxygenation level-dependent (BOLD) response, cerebral blood flow (CBF), and CMRO(2) decreased statistically significantly in the primary motor and somatosensory cortices of SVD patients, whereas the oxygen extraction fraction increased when compared with controls. Notably, the flow-metabolism coupling ratio, n representing the ratio of oxygen supply to its utilization, showed a robust reduction in the SVD patient group (n(Control)=1.99 ± 0.23; n(SVD)=1.08 ± 0.24), which implies a loss of metabolic reserve. These results support the pathological small vessel compromise, including an increased vessel stiffness, impaired vascular reactivity, and impaired neurovascular coupling in SVD. In conclusion, simultaneous measurement by NIRS and fMRI can reveal various hemodynamic and metabolic changes and may be used for as an early detection or monitoring of SVD.

Effects of disease duration on the clinical features and brain glucose metabolism in patients with mixed type multiple system atrophy

To study the effect of disease duration on the clinical, neuropsychological and [(18)F]-deoxyglucose (FDG) PET findings in patients with mixed type multiple system atrophy (MSA), this study included 16 controls and 37 mixed-type MSA patients with a shorter than a 3-year history of cerebellar or parkinsonian symptoms. We classified the patients into three groups according to the duration of parkinsonian or cerebellar symptoms (Group I = <or=1 year; II = 13-24 months; III = 25-36 months). We performed UPDRS, international cooperative ataxia rating scale (ICARS), and a neuropsychological test battery. We compared the FDG PET findings of each group of patients with controls. Group I patients frequently had memory and frontal executive dysfunction. They showed hypometabolism in the frontal cortex, anterior cerebellar hemisphere and vermis. They had parkinsonian motor deficits, but no basal ganglia hypometabolism. Group II and III patients frequently had multiple domain cognitive impairments, and showed hypometabolism in the frontal and parieto-temporal cortices. Hypometabolism of the bilateral caudate and the left posterolateral putamen was observed in Group II, and whole striatum in Group III. In summary, the cortical hypometabolism begins in the frontal cortex and spreads to the parieto-temporal cortex in MSA. This spreading pattern coincides with the progressive cognitive decline. Early caudate hypometabolism may also contribute to the cognitive impairment. Parkinsonian motor deficits precede putaminal hypometabolism that begins in its posterolateral part. Cerebellar hypometabolism occurs early in the clinical courses and seems to be a relevant metabolic descriptor of cerebellar deficits.