Shantanu Majumdar

Alzheimer's disease and amnestic mild cognitive impairment weaken connections within the default-mode network: a multi-modal imaging study.

We applied a multi-modal imaging approach to examine structural and functional alterations in the default-mode network (DMN) that are associated with Alzheimers disease (AD) and amnestic mild cognitive impairment (aMCI), a transitional phase between healthy cognitive aging and dementia. Subjects included 10 patients with probable AD, 11 patients with aMCI, and 12 age- and education-matched normal controls (NC). Whole-brain resting-state functional, diffusion-weighted, and volumetric magnetic resonance imaging (MRI) data as well as 18F-fluorodeoxyglucose-based positron emission tomography (FDG-PET) data were acquired. We carried out resting-state functional MRI-based functional connectivity and diffusion MRI-based structural connectivity analyses using isthmus of the cingulate cortex (ICC) and the subjacent white matter as the seeds. Whole-brain group and region of interest-based analyses demonstrated that AD weakens the structural and functional connections between ICC and other regions within the DMN, consistent with regional reduction of metabolic activity and atrophy within the DMN. A progressive weakening trend of these connections was also observed from NC to aMCI and then AD, although significant differences between aMCI and the other two groups were not found. Overall, based on both FDG-PET and MRI results, the DMN appears to serve as a window to understanding structural and functional brain changes associated with AD and aMCI.

Integration of resting-state FMRI and diffusion-weighted MRI connectivity analyses of the human brain: limitations and improvement.

Integration of functional connectivity analysis based on resting‐state functional Magnetic Resonance Imaging (fMRI) and structural connectivity analysis based on Diffusion‐Weighted Imaging (DWI) has shown great potential to improve understanding of the neural networks in the human brain. However, there are sensitivity and specificity‐related interpretation issues that must be addressed.

A diffusion gradient optimization framework for spinal cord diffusion tensor imaging

The uncertainty in the estimation of diffusion model parameters in diffusion tensor imaging (DTI) can be reduced by optimally selecting the diffusion gradient directions utilizing some prior structural information. This is beneficial for spinal cord DTI, where the magnetic resonance images have low signal-to-noise ratio and thus high uncertainty in diffusion model parameter estimation. Presented is a gradient optimization scheme based on D-optimality, which reduces the overall estimation uncertainty by minimizing the Rician Cramer-Rao lower bound of the variance of the model parameter estimates. The tensor-based diffusion model for DTI is simplified to a four-parameter axisymmetric DTI model where diffusion transverse to the principal eigenvector of the tensor is assumed isotropic. Through simulations and experimental validation, we demonstrate that an optimized gradient scheme based on D-optimality is able to reduce the overall uncertainty in the estimation of diffusion model parameters for the cervical spinal cord and brain stem white matter tracts.

Alzheimer's disease and mild cognitive impairment weaken connections within the default-mode network: A multimodal study with resting-state fMRI, diffusion MRI and FDG-PET

Alzheimer’s disease (AD) is important from the therapeutic point of view. Despite many neuropsychological studies to differentiate VaD from AD, only a few studies focused on subcortical VaD (SVaD), among the heterogeneous VaD. Furthermore, these studies on SVaD did not eliminate confounding effects of mixed Alzheimer and vascular pathology. We aimed to investigate neuropsychological differences between patients with Pittsburgh compound-B (PIB) negative SVaD and those with PIB-positive AD. Methods: We recruited patients who were clinically diagnosed with SVaD or AD, and underwent a 11 C-PIB PET scan and MRI at Samsung Medical Center or Asan Medical Center, Seoul Korea, between September 2008 and May 2011. All patients met SVaD (N1⁄469) or AD (N1⁄467) criteria as described in the study from our group. The final patient sample consisted of 44/69 (63.8%) SVaD patients who tested negative for PIB retention [PIB (-) SVaD] and 59/67 (88.1%) AD patients who tested positive for PIB retention [PIB (+) AD]. Results: Neuropsychological profile differences among the groups were analyzed by applying an analysis of covariance (ANCOVA) with Bonferroni post hoc analysis. Age and education were entered as covariates. As shown in Table 1, post hoc comparisons showed that both PIB (+) AD and PIB (-) SVaD patients were significantly more impaired than NC on every neuropsychological test. Patients with PIB (-) SVaD performed better than PIB (+) AD patients on both verbal and visual memory tests including delayed recalls of the verbal learning test and Rey Complex Figure test. In contrast, PIB (-) SVaD patients were worse than PIB (+) AD patients on frontal executive tests including the semantic/phonemic fluency of the Controlled Oral Word Association Test (COWAT) and Stroop word/color tests. The two patient groups performed comparably in attention, language, calculation, praxis, and visuospatial domains. Domain score using SNSB-D showed the same trend. Conclusions: Our study is unique in selecting each patient group based on amyloid imaging, which minimized the possibility of contaminating mixed vascular and AD pathologies.

An electromagnetic acoustic transduction technique for detecting strut fractures in artificial heart valves

This paper discusses a completely noninvasive technique to detect single leg separation in Bjork-Shiley convexo-concave (BSCC) heart valves. Prior studies have shown that the resonant frequencies of the valve depend on the condition of the strut. The technique described here uses electromagnetic methods to mechanically excite the valve across a range of frequencies of interest and determine the resonant frequency of the valve. Results obtained with a prototype setup are presented which demonstrate the usefulness of the technique in a clinical setting for detecting single leg separated (SLS) condition in patients with BSCC heart valves

A Modified FFT Algorithm for Efficient Computation of Narrow Band Spectrum

In many applications, we are interested in computing the energy contained in a narrow band of a bandpass signal given its bandwidth and carrier frequency. The technique implemented in this paper can be used to compute the Fourier transform (FT) in a narrow band without having to calculate the entire frequency spectrum. It uses the decimation in time method for computing the FFT, exploiting some of the symmetry properties of the transform technique