Hakmook Kang

Mapping mean axon diameter and axonal volume fraction by MRI using temporal diffusion spectroscopy

Mapping mean axon diameter and intra-axonal volume fraction may have significant clinical potential because nerve conduction velocity is directly dependent on axon diameter, and several neurodegenerative diseases affect axons of specific sizes and alter axon counts. Diffusion-weighted MRI methods based on the pulsed gradient spin echo (PGSE) sequence have been reported to be able to assess axon diameter and volume fraction non-invasively. However, due to the relatively long diffusion times used, e.g. >20ms, the sensitivity to small axons (diameter<2μm) is low, and the derived mean axon diameter has been reported to be overestimated. In the current study, oscillating gradient spin echo (OGSE) diffusion sequences with variable frequency gradients were used to assess rat spinal white matter tracts with relatively short effective diffusion times (1-5ms). In contrast to previous PGSE-based methods, the extra-axonal diffusion cannot be modeled as hindered (Gaussian) diffusion when short diffusion times are used. Appropriate frequency-dependent rates are therefore incorporated into our analysis and validated by histology-based computer simulation of water diffusion. OGSE data were analyzed to derive mean axon diameters and intra-axonal volume fractions of rat spinal white matter tracts (mean axon diameter of ~1.27-5.54μm). The estimated values were in good agreement with histology, including the small axon diameters (<2.5μm). This study establishes a framework for the quantification of nerve morphology using the OGSE method with high sensitivity to small axons.

Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer.

ObjectivesThe purpose of this study was to determine whether multiparametric magnetic resonance imaging (MRI) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DWI), obtained before and after the first cycle of neoadjuvant chemotherapy (NAC), is superior to single-parameter measurements for predicting pathologic complete response (pCR) in patients with breast cancer. Materials and MethodsPatients with stage II/III breast cancer were enrolled in an institutional review board–approved study in which 3-T DCE-MRI and DWI data were acquired before (n = 42) and after 1 cycle (n = 36) of NAC. Estimates of the volume transfer rate (Ktrans), extravascular extracellular volume fraction (ve), blood plasma volume fraction (vp), and the efflux rate constant (kep = Ktrans/ve) were generated from the DCE-MRI data using the Extended Tofts-Kety model. The apparent diffusion coefficient (ADC) was estimated from the DWI data. The derived parameter kep/ADC was compared with single-parameter measurements for its ability to predict pCR after the first cycle of NAC. ResultsThe kep/ADC after the first cycle of NAC discriminated patients who went on to achieve a pCR (P < 0.001) and achieved a sensitivity, specificity, positive predictive value, and area under the receiver operator curve (AUC) of 0.92, 0.78, 0.69, and 0.88, respectively. These values were superior to the single parameters kep (AUC, 0.76) and ADC (AUC, 0.82). The AUCs between kep/ADC and kep were significantly different on the basis of the bootstrapped 95% confidence intervals (0.018–0.23), whereas the AUCs between kep/ADC and ADC trended toward significance (−0.11 to 0.24). ConclusionsThe multiparametric analysis of DCE-MRI and DWI was superior to the single-parameter measurements for predicting pCR after the first cycle of NAC.

Resting state functional connectivity of the hippocampus associated with neurocognitive function in left temporal lobe epilepsy.

The majority of patients with temporal lobe epilepsy (TLE) experience disturbances of episodic memory from structural damage or dysfunction of the hippocampus. The objective of this study was to use functional Magnetic Resonance Imaging (fMRI) to identify regions where resting state connectivity to the left hippocampus (LH) is correlated with neuropsychological measures of verbal memory retention in TLE patients. Eleven left TLE (LTLE) patients and 15 control subjects participated in resting state fMRI scans. All LTLE patients underwent neuropsychological testing. Resting state functional connectivity maps to the LH were calculated for each patient, and subsequently used in a multiple regression analysis with verbal memory retention scores as a covariate. The analysis identified brain regions whose connectivity to the LH was linearly related to memory retention scores across the group of patients. In LTLE patients, right sided (contralateral) clusters in the precuneus and inferior parietal lobule (IPL) exhibited increased connectivity to the LH with increased memory retention score; left sided (ipsilateral) regions in the precuneus and IPL showed increased connectivity to the LH with decreased retention score. Patients with high memory retention scores had greater connectivity between the LH–right parietal clusters than between the LH–left parietal clusters; in contrast, control subjects had significantly and consistently greater LH–left hemisphere than LH–right hemisphere connectivity. Our results suggest that increased connectivity in contralateral hippocampal functional pathways within the episodic verbal memory network represents a strengthening of alternative pathways in LTLE patients with strong verbal memory retention abilities. Hum Brain Mapp 35:735–744, 2014.

Longitudinal Progression of Cognitive Decline Correlates with Changes in the Spatial Pattern of Brain 18F-FDG PET

Evaluating the symptomatic progression of mild cognitive impairment (MCI) caused by Alzheimer disease (AD) is practically accomplished by tracking performance on cognitive tasks, such as the Alzheimer Disease Assessment Scale’s cognitive subscale (ADAS_cog), the Mini-Mental Status Examination (MMSE), and the Functional Activities Questionnaire (FAQ). The longitudinal relationships between cognitive decline and metabolic function as assessed using 18F-FDG PET are needed to address both the cognitive and the biologic progression of disease state in individual subjects. We conducted an exploratory investigation to evaluate longitudinal changes in brain glucose metabolism of individual subjects and their relationship to the subject’s changes of cognitive status. Methods: We describe a method to determine correlations in 18F-FDG spatial distribution over time. This parameter is termed the regional 18F-FDG time correlation coefficient (rFTC). By using linear mixed-effects models, we determined the difference in the rFTC decline rate between controls and subjects at high risk of developing AD, such as individuals with MCI or the presence of apolipoprotein E (APOE)–ε4 allele. The association between each subject’s rFTC and performance on cognitive tests (ADAS_cog, MMSE, and FAQ) was determined with 2 different correlation methods. All subject data were downloaded from the Alzheimer Disease Neuroimaging Initiative. Results: The rFTC values of controls remained fairly constant over time (−0.003 annual change; 95% confidence interval, −0.010–0.004). In MCI patients, the rFTC declined faster than in controls by an additional annual change of −0.02 (95% confidence interval, −0.030 to −0.010). In MCI patients, the decline in rFTC was associated with cognitive decline (ADAS_cog, P = 0.011; FAQ, P = 0.0016; MMSE, P = 0.004). After a linear effect of time was accounted for, visit-to-visit changes in rFTC correlated with visit-to-visit changes in all 3 cognitive tests. Conclusion: Longitudinal changes in rFTC detect subtle metabolic changes in individuals associated with variations in their cognition. This analytic tool may be useful for a patient-based monitoring of cognitive decline.

Spatio-Spectral Mixed-Effects Model for Functional Magnetic Resonance Imaging Data

The goal of this article is to model cognitive control related activation among predefined regions of interest (ROIs) of the human brain while properly adjusting for the underlying spatio-temporal correlations. Standard approaches to fMRI analysis do not simultaneously take into account both the spatial and temporal correlations that are prevalent in fMRI data. This is primarily due to the computational complexity of estimating the spatio-temporal covariance matrix. More specifically, they do not take into account multiscale spatial correlation (between-ROIs and within-ROI). To address these limitations, we propose a spatio-spectral mixed-effects model. Working in the spectral domain simplifies the temporal covariance structure because the Fourier coefficients are approximately uncorrelated across frequencies. Additionally, by incorporating voxel-specific and ROI-specific random effects, the model is able to capture the multiscale spatial covariance structure: distance-dependent local correlation (within an ROI), and distance-independent global correlation (between-ROIs). Building on existing theory on linear mixed-effects models to conduct estimation and inference, we applied our model to fMRI data to study activation in prespecified ROIs in the prefontal cortex and estimate the correlation structure in the network. Simulation studies demonstrate that ignoring the multiscale correlation leads to higher false positive error rates.

Functional Networks in Temporal-Lobe Epilepsy: A Voxel-Wise Study of Resting-State Functional Connectivity and Gray-Matter Concentration

Temporal-lobe epilepsy (TLE) involves seizures that typically originate in the hippocampus. There is evidence that seizures involve anatomically and functionally connected brain networks within and beyond the temporal lobe. Many studies have explored the effect of TLE on gray matter and resting-state functional connectivity in the brain. However, the relationship between structural and functional changes has not been fully explored. The goal of this study was to investigate the relationship between gray matter concentration (GMC) and functional connectivity in TLE at the voxel level. A voxel-wise linear regression analysis was performed between GMC maps and whole-brain resting-state functional connectivity maps to both the left thalamus (Lthal) and the left hippocampus (LH) in a group of 15 patients with left TLE. Twenty regions were found that exhibited GMC decreases linearly correlated with resting-state functional connectivity to either the LH or the Lthal in the patient group only. A subset of these regions had significantly reduced GMC, and one of these regions also had reduced functional connectivity to the LH in TLE compared to the controls. These results suggest a network of impairment in left TLE where more severe reductions in GMC accompany decreases (LH, Lthal, right midcingulate gyrus, left precuneus, and left postcentral gyrus) or increases (LH to right thalamus) in resting functional connectivity. However, direct relationships between these imaging parameters and disease characteristics in these regions have yet to be established.

DXA-measured visceral adipose tissue predicts impaired glucose tolerance and metabolic syndrome in obese Caucasian and African-American women.

Background/Objectives:New methods to measure visceral adipose tissue (VAT) by dual-energy X-ray absorptiometry (DXA) may help discern sex, race and phenotype differences in the role of VAT in cardiometabolic risk. This study was designed (1) to compare relationships of DXA-VAT, anthropometric and body composition variables with cardiometabolic risk factors in obese women; (2) to determine which variables most robustly predict impaired glucose tolerance (IGT) and metabolic syndrome (MetSx); and (3) to determine thresholds for DXA-VAT by race.Subjects/Methods:VAT mass (g) and volume (cm3) were measured in 229 obese (body mass index (BMI), 30–49.9) women aged 21–69 years of European-American (EA=123) and African-American (AA=106) descent using the CoreScan algorithm on a Lunar iDXA scanner. Linear regression modeling and areas under the curve (AUC of ROC (receiver operating characteristic) curves) compared relationships with cardiometabolic risk. Bootstrapping with LASSO (least absolute shrinkage and selection operator) regression modeling determined thresholds and predictors of IGT and MetSx.Results:DXA-VAT explained more of the variance in triglycerides, blood pressure, glucose and homeostatic model assessment-insulin resistance (HOMA-IR) compared with anthropometric and other body composition variables. DXA-VAT also had the highest AUC for IGT (0.767) and MetSx (0.749). Including race as a variable and the interaction between VAT and race in modeling did not significantly change the results. Thresholds at which the probability of developing IGT or MetSx was⩾50% were determined separately for AA women (IGT: 2120 cm3; MetSx: 1320 cm3) and EA women (IGT: 2550 cm3; MetSx: 1713 cm3). The odds for IGT or MetSx were fourfold greater with each standard deviation increase in DXA-VAT.Conclusions:DXA-VAT provides robust clinical information regarding cardiometabolic risk in AA and EA obese women and offers potential utility in the risk reduction interventions.

Consuming a balanced high fat diet for 16 weeks improves body composition, inflammation and vascular function parameters in obese premenopausal women

OBJECTIVE Inflammation, insulin resistance and vascular dysfunction characterize obesity and predict development of cardiovascular disease (CVD). Although women experience CVD events at an older age, vascular dysfunction is evident 10years prior to coronary artery disease. Questions remain whether replacing SFA entirely with MUFA or PUFA is the optimal approach for cardiometabolic benefits. This study tested the hypotheses that: a) body composition, inflammation and vascular function would improve with a high fat diet (HFD) when type of fat is balanced as 1/3 SFA, 1/3 MUFA and 1/3 PUFA; and b) body composition, inflammation and vascular function would improve more when balanced HFD is supplemented with 18C fatty acids, in proportion to the degree of 18C unsaturation. METHODS Obese premenopausal women were stabilized on balanced HFD and randomized to consume 9g/d of encapsulated stearate (18:0), oleate (18:1), linoleate (18:2) or placebo. RESULTS Significant improvements occurred in fat oxidation rate (↑6%), body composition (%fat: ↓2.5±2.1%; %lean: ↑2.5±2.1%), inflammation (↓ IL-1α, IL-1β, 1L-12, Il-17, IFNγ, TNFα, TNFβ) and vascular function (↓BP, ↓PAI-1, ↑tPA activity). When compared to HFD+placebo, HFD+stearate had the greatest effect on reducing IFNγ (↓74%) and HFD+linoleate had the greatest effect on reducing PAI-1 (↓31%). CONCLUSIONS Balancing the type of dietary fat consumed (SFA/MUFA/PUFA) is a feasible strategy to positively affect markers of CVD risk. Moreover, reductions in inflammatory molecules involved in vascular function might be enhanced when intake of certain 18C fatty acids is supplemented. Long term effects need to be determined for this approach.

Cortical asymmetry in Parkinson's disease: early susceptibility of the left hemisphere.

Clinically, Parkinsons disease (PD) presents with asymmetric motor symptoms. The left nigrostriatal system appears more susceptible to early degeneration than the right, and a left‐lateralized pattern of early neuropathological changes is also described in several neurodegenerative conditions, including Alzheimers disease, frontotemporal dementia, and Huntingtons disease. In this study, we evaluated hemispheric differences in estimated rates of atrophy in a large, well‐characterized cohort of PD patients.

Increasing structural atrophy and functional isolation of the temporal lobe with duration of disease in temporal lobe epilepsy.

BACKGROUND Due to pharmacoresistant seizures and the underutilization of surgical treatments, a large number of temporal lobe epilepsy (TLE) patients experience seizures for years or decades. The goal of this study was to generate a predictive model of duration of disease with the least number of parameters possible in order to identify and quantify the significant volumetric and functional indicators of TLE progression. METHODS Two cohorts of subjects including 12 left TLE, 21 right TLE and 20 healthy controls (duration = 0) were imaged on a 3T MRI scanner using high resolution T1-weighted structural MRI and 20 min of resting functional MRI scanning. Multivariate linear regression methods were used to compute a predictive model of duration of disease using 49 predictors including functional connectivity and gray matter volumes computed from these images. RESULTS No model developed from the full set of data accurately predicted the duration of disease across the entire range from 3 to 50 years. We then performed the regression on 35 subjects with durations of disease in the range 10 to 35 years. The resulting predictive model showed that longer durations were associated with reductions in functional connectivity from the ipsilateral temporal lobe to the contralateral temporal lobe, precuneus and mid cingulate, and with decreases in volume of the ipsilateral hippocampus and pallidum. CONCLUSIONS Functional and volumetric parameters accurately predicted duration of disease in TLE. The findings suggest that TLE is associated with a gradual functional isolation and significant progressive structural atrophy of the ipsilateral temporal lobe over years of duration in the range of 10-35 years. Furthermore, these changes can also be detected in the contralateral hemisphere in these patients, but to a lesser degree.