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1.  Physiologically Evoked Neuronal Current MRI in a Bloodless Turtle Brain 
NeuroImage  2009;47(4):10.1016/j.neuroimage.2009.06.017.
Contradictory results from the efforts for detecting evoked neuronal currents have left the feasibility of neuronal current MRI (ncMRI) an open question. Most of the previous ncMRI studies in human subjects are suspect due to their inability to separate or eliminate the hemodynamic effects. In this study, we used a bloodless turtle brain that eliminates hemodynamic effects, to explore the feasibility of detecting visual-evoked ncMRI signals at 9.4T. The turtle brain, with its eyes attached, was dissected from the cranium and placed in artificial cerebral spinal fluid. Light flashes were delivered to the eyes, which produced visual-evoked neuronal activity in the brain. Local field potential (LFP) and MRI signals in the turtle brain were measured in an interleave fashion. Although robust neuronal responses to the visual stimulation were observed in the LFP signals, no significant signal changes synchronized with neuronal currents were found in the MRI images. Analysis of the temporal stability of the MRI time courses indicated that the detectable effect sizes are 0.11% and 0.09° for the magnitude and phase, respectively, and the visual-evoked ncMRI signals in the turtle brain are below these levels.
doi:10.1016/j.neuroimage.2009.06.017
PMCID: PMC3860745  PMID: 19539040
2.  General Multivariate Linear Modeling of Surface Shapes Using SurfStat 
NeuroImage  2010;53(2):491-505.
Although there are many imaging studies on traditional ROI-based amygdala volumetry, there are very few studies on modeling amygdala shape variations. This paper present a unified computational and statistical framework for modeling amygdala shape variations in a clinical population. The weighted spherical harmonic representation is used as to parameterize, to smooth out, and to normalize amygdala surfaces. The representation is subsequently used as an input for multivariate linear models accounting for nuisance covariates such as age and brain size difference using SurfStat package that completely avoids the complexity of specifying design matrices. The methodology has been applied for quantifying abnormal local amygdala shape variations in 22 high functioning autistic subjects.
doi:10.1016/j.neuroimage.2010.06.032
PMCID: PMC3056984  PMID: 20620211
Amygdala; Spherical Harmonics; Fourier Analysis; Surface Flattening; Multivariate Linear Model; SurfStat
3.  Impaired small-world efficiency in structural cortical networks in multiple sclerosis associated with white matter lesion load 
Brain  2009;132(12):3366-3379.
White matter tracts, which play a crucial role in the coordination of information flow between different regions of grey matter, are particularly vulnerable to multiple sclerosis. Many studies have shown that the white matter lesions in multiple sclerosis are associated with focal abnormalities of grey matter, but little is known about the alterations in the coordinated patterns of cortical morphology among regions in the disease. Here, we used cortical thickness measurements from structural magnetic resonance imaging to investigate the relationship between the white matter lesion load and the topological efficiency of structural cortical networks in multiple sclerosis. Network efficiency was defined using a ‘small-world’ network model that quantifies the effectiveness of information transfer within brain networks. In this study, we first classified patients (n = 330) into six subgroups according to their total white matter lesion loads, and identified structural brain networks for each multiple sclerosis group by thresholding the corresponding inter-regional cortical thickness correlation matrix, followed by a network efficiency analysis with graph theoretical approaches. The structural cortical networks in multiple sclerosis demonstrated efficient small-world architecture regardless of the lesion load, an organization that maximizes the information processing at a relatively low wiring cost. However, we found that the overall small-world network efficiency in multiple sclerosis was significantly disrupted in a manner proportional to the extent of total white matter lesions. Moreover, regional efficiency was also significantly decreased in specific brain regions, including the insula and precentral gyrus as well as regions of prefrontal and temporal association cortices. Finally, we showed that the lesions also altered many cortical thickness correlations in the frontal, temporal and parietal lobes. Our results suggest that the white matter lesions in multiple sclerosis might be associated with aberrant neuronal connectivity among widely distributed brain regions, and provide structural (morphological) evidence for the notion of multiple sclerosis as a disconnection syndrome.
doi:10.1093/brain/awp089
PMCID: PMC2792366  PMID: 19439423
cortical thickness; connectivity; MRI; multiple sclerosis; small-world networks
4.  Guidelines for reporting an fMRI study 
Neuroimage  2008;40(2):409-414.
In this editorial, we outline a set of guidelines for the reporting of methods and results in functional magnetic resonance imaging studies and provide a checklist to assist authors in preparing manuscripts that meet these guidelines.
doi:10.1016/j.neuroimage.2007.11.048
PMCID: PMC2287206  PMID: 18191585
5.  Comparing functional connectivity via thresholding correlations and singular value decomposition 
We compare two common methods for detecting functional connectivity: thresholding correlations and singular value decomposition (SVD). We find that thresholding correlations are better at detecting focal regions of correlated voxels, whereas SVD is better at detecting extensive regions of correlated voxels. We apply these results to resting state networks in an fMRI dataset to look for connectivity in cortical thickness.
doi:10.1098/rstb.2005.1637
PMCID: PMC1854921  PMID: 16087436
connectivity; correlation; singular value decomposition; fMRI

Results 1-5 (5)