Faculty Bibliography

SUMMARY: Diffusion tensor imaging (DTI) can provide information about brain white matter integrity. The results of DTI studies in schizophrenia are somewhat variable and could benefit from standardized image processing methods. Fourteen patients with schizophrenia or schizoaffective disorder and 14 healthy volunteers underwent DTI. Scans were analyzed using a rigorous voxelwise approach. The key dependent variable, fractional anisotropy, was lower for patients in the corpus callosum, left superior temporal gyrus, parahippocampal gyri, middle temporal gyri, inferior parietal gyri, medial occipital lobe, and the deep frontal perigenual region. Regions showing reduced white matter fractional anisotropy are known to be abnormal in schizophrenia. The voxelwise method used in the current study can provide the basis for hypothesis-driven research

Many fMRI analysis methods use a model for the hemodynamic response function (HRF). Common models of the HRF, such as the Gaussian or Gamma functions, have parameters that are usually selected a priori by the data analyst. A new method is presented that characterizes the HRF over a wide range of parameters via three basis signals derived using principal component analysis (PCA). Covering the HRF variability, these three basis signals together with the stimulation pattern define signal subspaces which are applicable to both linear and nonlinear modeling and identification of the HRF and for various activation detection strategies. Analysis of simulated fMRI data using the proposed signal subspace showed increased detection sensitivity compared to the case of using a previously proposed trigonometric subspace. The methodology was also applied to activation detection in both event-related and block design experimental fMRI data using both linear and nonlinear modeling of the HRF. The activated regions were consistent with previous studies, indicating the ability of the proposed approach in detecting brain activation without a priori assumptions about the shape parameters of the HRF. The utility of the proposed basis functions in identifying the HRF is demonstrated by estimating the HRF in different activated regions

An adaptive spatial filtering method is proposed that takes into account contextual information in fMRI activation detection. This filter replaces the time series of each voxel with a weighted average of time series of a small neighborhood around it. The filter coefficients at each voxel are derived so as to maximize a test statistic designed to indicate the presence of activation. This statistic is the ratio of the energy of the filtered time series in a signal subspace to the energy of the residuals. It is shown that the filter coefficients and the maximum energy ratio can be found through a generalized eigenproblem. This approach equates the filter coefficients to the elements of an eigenvector corresponding to the largest eigenvalue of a specific matrix, while the largest eigenvalue itself becomes the maximum energy ratio that can be used as a statistic for detecting activation. The distribution of this statistic under the null hypothesis is derived by a nonparametric permutation technique in the wavelet domain. Also, in this paper we introduce a new set of basis vectors that define the signal subspace. The space spanned by these basis vectors covers a wide range of possible hemodynamic response functions (HRF) and is applicable to both event related and block design fMRI signal analysis. This approach circumvents the need for a priori assumptions about the exact shape of the HRF. Resting-state experimental fMRI data were used to assess the specificity of the method, showing that the actual false-alarm rate of the proposed method is equal or less than its expected value. Analysis of simulated data and motor task fMRI datasets from six volunteers using the method proposed here showed an improved sensitivity as compared to a conventional test with a similar statistic applied to spatially smoothed data

A new method is proposed for activation detection in event-related functional magnetic resonance imaging (fMRI). The method is based on the analysis of selected resolution levels (a subspace) in translation invariant wavelet transform (TIWT) domain. Using a priori knowledge about the activation signal and trends, we analyze their power in different resolution levels in TIWT domain and select an optimal set of resolution levels. A randomization-based statistical test is then applied in the wavelet domain for activation detection. This approach suppresses the effects of trends and enhances the detection sensitivity. In addition, since TIWT is insensitive to signal translations, the power analysis is robust with respect to signal shifts. The randomization test alleviates the need for assumptions about fMRI noise. The method has been applied to simulated and experimental fMRI datasets. Comparisons have been made between the results of the proposed method, a similar method in the time domain and the cross-correlation method. The proposed method has shown superior sensitivity compared to the other methods

Abnormalities in the P300 ERP, elicited by the oddball task and measured using EEG, have been found in a number of central nervous system disorders including schizophrenia, Alzheimer's disease, and alcohol dependence. While electrophysiological studies provide high temporal resolution, localizing the P300 deficit has been particularly difficult because the measurements are collected from the scalp. Knowing which brain regions are involved in this process would elucidate the behavioral correlates of P300. The aim of this study was to determine the brain regions involved in a visual oddball task using fMRI. In this study, functional and high-resolution anatomical MR images were collected from seven normal volunteers. The data were analyzed using a randomization-based statistical method that accounts for multiple comparisons, requires no assumptions about the noise structure of the data, and does not require spatial or temporal smoothing. Activations were detected (P<0.01) bilaterally in the supramarginal gyrus (SMG; BA 40), superior parietal lobule (BA 7), the posterior cingulate gyrus, thalamus, inferior occipitotemporal cortex (BA 19/37), insula, dorsolateral prefrontal cortex (BA 9), anterior cingulate cortex (ACC), medial frontal gyrus (BA 6), premotor area, and cuneus (BA 17). Our results are consistent with previous studies that have observed activation in ACC and SMG. Activation of thalamus, insula, and the occipitotemporal cortex has been reported less consistently. The present study lends further support to the involvement of these structures in visual target detection

An important step in the analysis of fMRI time-series data is to detect, and as much as possible, correct for subject motion during the course of the scanning session. Several public domain algorithms are currently available for motion detection in fMRI. This paper compares the performance of four commonly used programs: AIR 3.08, SPM99, AFNI98, and the pyramid method of Thevenaz, Ruttimann, and Unser (TRU). The comparison is based on the performance of the algorithms in correcting a range of simulated known motions in the presence of various degrees of noise. SPM99 provided the most accurate motion detection amongst the algorithms studied. AFNI98 provided only slightly less accurate results than SPM99, however, it was several times faster than the other programs. This algorithm represents a good compromise between speed and accuracy. AFNI98 was also the most robust program in presence of noise. It yielded reasonable results for very low signal to noise levels. For small initial misalignments, TRU's performance was similar to SPM99 and AFNI98. However, its accuracy diminished rapidly for larger misalignments. AIR was found to be the least accurate program studied

Psychophysiologic studies use air puff as an aversive stimulus to document abnormal fear conditioning in children of parents with anxiety disorders. This study used functional magnetic resonance imaging (fMRI) to examine changes in amygdala activity during air-puff conditioning among adults. Blood oxygen level-dependent (BOLD) signal was monitored in seven adults during 16 alternating presentations of two different colored lights (CS+ vs. CS-), one of which was consistently paired with an aversive air puff. A region-of-interest analysis demonstrated differential change in BOLD signal in the right but not left amygdala across CS+ versus CS- viewing. The amygdala is engaged by pairing of a light with an air puff. Given that prior studies relate air-puff conditioning to risk for anxiety in children, these methods may provide an avenue for directly studying the developmental neurobiology of fear conditioning

Face-emotion processing has shown signs of developmental change during adolescence. Functional magnetic resonance imaging (fMRI) was used on 10 adolescents and 10 adults to contrast brain regions engaged by a masked emotional-face task (viewing a fixation cross and a series of masked happy and masked fearful faces), while blood oxygen level dependent signal was monitored by a 1.5-T MRI scanner. Brain regions differentially engaged in the 2 age groups were mapped by using statistical parametric mapping. Summed across groups, the contrast of masked face versus fixation-cross viewing generated activations in occipital-temporal regions previously activated in passive face-viewing tasks. Adolescents showed higher maxima for activations in posterior association cortex for 3 of the 4 statistical contrasts. Adolescents and adults differed in the degree to which posterior hemisphere brain areas were engaged by viewing masked facial displays of emotion

The cerebral deposition of amyloid beta-peptide, a central event in Alzheimer's disease (AD) pathogenesis, begins several years before the onset of clinical symptoms. Non-invasive detection of AD pathology at this initial stage would facilitate intervention and enhance treatment success. Here, we demonstrate the ability of high field strength MRI to detect regional brain volume reductions and ventricular enlargement in the PS-APP transgenic mouse model of AD more sensitively than histopathologic analysis by unbiased stereology. Moreover, the transverse relaxation time T2, an intrinsic MR parameter thought to reflect impaired cell physiology, was altered substantially in cortical regions containing beta-amyloid but only slightly in cerebellum, which contains little beta-amyloid. MR measures were also minimally altered in mice expressing mutant presenilin-1, which do not deposit beta-amyloid, supporting the view that the MR abnormalities in PS-APP mice are partly related to amyloid beta-peptide deposition. These results set the stage for MRI to aid in the early diagnosis of AD and the evaluation of potential therapies in transgenic animal models and in patients