Faculty Bibliography

Although there is an increasing agreement that hypertension is associated with cerebrovascular compromise, relationships between blood pressure (BP) and cerebral blood flow are not fully understood. It is not known what BP level, and consequently what therapeutic goal, is optimal for brain perfusion. Moreover, there is limited data on how BP affects hippocampal perfusion, a structure critically involved in memory. We conducted a cross-sectional (n=445) and longitudinal (n=185) study of adults and elderly without dementia or clinically apparent stroke, who underwent clinical examination and brain perfusion assessment (age 69.2±7.5 years, 62% women, 45% hypertensive). Linear models were used to test baseline BP-blood flow relationship and to examine how changes in BP influence changes in perfusion. In the entire group, systolic BP (SBP) was negatively related to cortical (β=-0.13, P=0.005) and hippocampal blood flow (β=-0.12, P=0.01). Notably, this negative relationship was apparent already in subjects without hypertension. Hypertensive subjects showed a quadratic relationship between SBP and hippocampal blood flow (β=-1.55, P=0.03): Perfusion was the highest in subjects with mid-range SBP around 125 mm Hg. Longitudinally, in hypertensive subjects perfusion increased with increased SBP at low baseline SBP but increased with decreased SBP at high baseline SBP. Cortical and hippocampal perfusion decrease with increasing SBP across the entire BP spectrum. However, in hypertension, there seems to be a window of mid-range SBP which maximizes perfusion.

Cognitive models suggest that posttraumtic stress disorder (PTSD) is maintained, in part, as a result of an individual's maladaptive beliefs about one's ability to cope with current and future stress. These models are consistent with considerable findings showing a link between low levels of self-efficacy and PTSD. A growing body of work has demonstrated that perceptions of self-efficacy can be enhanced experimentally in healthy subjects and participants with PTSD, and increasing levels of self-efficacy improves performance on cognitive, affective, and problem-solving tasks. This study aimed to determine whether increasing perceptions of self-efficacy in participants with PTSD would be associated with changes in neural processing. Combat veterans (N = 34) with PTSD were randomized to either a high self-efficacy (HSE) induction, in which they were asked to recall memories associated with successful coping, or a control condition before undergoing resting state fMRI scanning. Two global network measures in four neural circuits were examined. Participants in the HSE condition showed greater right-lateralized path length and decreased right-lateralized connectivity in the emotional regulation and executive function circuit. In addition, area under receiver operating characteristics curve (AUC) analyses found that average connectivity (.71) and path length (.70) moderately predicted HSE group membership. These findings provide further support for the importance of enhancing perceived control in PTSD, and doing so may engage neural targets that could guide the development of novel interventions.

A novel approach is presented for group statistical analysis of diffusion weighted MRI datasets through voxelwise Orientation Distribution Functions (ODF). Recent advances in MRI acquisition make it possible to use high quality diffusion weighted protocols (multi-shell, large number of gradient directions) for routine in vivo study of white matter architecture. The dimensionality of these data sets is however often reduced to simplify statistical analysis. While these approaches may detect large group differences, they do not fully capitalize on all acquired image volumes. Incorporation of all available diffusion information in the analysis however risks biasing the outcome by outliers. Here we propose a statistical analysis method operating on the ODF, either the diffusion ODF or fiber ODF. To avoid outlier bias and reliably detect voxelwise group differences and correlations with demographic or behavioral variables, we apply the Low-Rank plus Sparse (L+S) matrix decomposition on the voxelwise ODFs which separates the sparse individual variability in the sparse matrix S whilst recovering the essential ODF features in the low-rank matrix L. We demonstrate the performance of this ODF L+S approach by replicating the established negative association between global white matter integrity and physical obesity in the Human Connectome dataset. The volume of positive findings (p<0.01, 227 cm³) agrees with and expands on the volume found by TBSS (17 cm³), Connectivity based fixel enhancement (15 cm³) and Connectometry (212 cm³). In the same dataset we further localize the correlations of brain structure with neurocognitive measures such as fluid intelligence and episodic memory. The presented ODF L+S approach will aid in the full utilization of all acquired diffusion weightings leading to the detection of smaller group differences in clinically relevant settings as well as in neuroscience applications.

PURPOSE/OBJECTIVE:F-THK5351. The conventional standardized uptake value ratio (SUVR) method relies on the average uptake from an unaffected tissue sample, and therefore is susceptible to biases from off-target binding as well as variability among individuals in the reference region. We propose a new method, standardized uptake value peak-alignment (SUVP), to reduce the bias of the SUVR, and improve the quantitative assessment of tau deposition. METHODS:F-AV-1451 on two independent cohorts (N = 18 and 32, respectively), each with cognitively normal (NL) subjects and Alzheimer's disease (AD) subjects. RESULTS:F-AV-1451). In the cerebellar cortex, an AD-NL group difference with effect size (Cohen's d) of 0.62 was observed for AV-1451, confirming the limitation of the SUVR approach using this region as a reference. A smaller cerebellar effect size (0.09) was observed for THK5351. CONCLUSION/CONCLUSIONS:The SUVP method reduces the bias of the reference region and improves the NL-AD classification compared to the SUVR approach.

Alzheimer's disease (AD) is known to be associated with loss of cholinergic neurons in the nucleus basalis of Meynert, located in the posterior basal forebrain. Structural changes of septal nuclei, located in the anterior basal forebrain, have not been well studied in AD. Using a validated algorithm, we manually traced septal nuclei on high-resolution coronal magnetic resonance imaging (MRI) in 40 subjects with mild cognitive impairment (MCI) or AD, 89 healthy controls, and 18 subjects who were cognitively normal at the time of MRI but went on to develop AD an average of 2.8 years later. We found that cognitively normal subjects destined to develop AD in the future had enlarged septal nuclei as compared to both healthy controls and patients with current MCI or AD. To our knowledge, this is the first time a brain structure has been found to be enlarged in association with risk of AD. Further research is needed to determine if septal enlargement reflects neuroplastic compensation, amyloid deposition, inflammation, or another process and to determine whether it can serve as an early MRI biomarker of AD.

Diagnosing illnesses, developing and comparing treatment methods, and conducting research on the organization of the peripheral nervous system often require the analysis of peripheral nerve images to quantify the number, myelination, and size of axons in a nerve. Current methods that require manually labeling each axon can be extremely time-consuming as a single nerve can contain thousands of axons. To improve efficiency, we developed a computer-assisted axon identification and analysis method that is capable of analyzing and measuring sub-images covering the nerve cross-section, acquired using a scanning electron microscope. This algorithm performs three main procedures - it first uses cross-correlation to combine the acquired sub-images into a large image showing the entire nerve cross-section, then identifies and individually labels axons using a series of image intensity and shape criteria, and finally identifies and labels the myelin sheath of each axon using a region growing algorithm with the geometric centers of axons as seeds. To ensure accurate analysis of the image, we incorporated manual supervision to remove mislabeled axons and add missed axons. The typical user-assisted processing time for a two-megapixel image containing over 2000 axons was less than 1h. This speed was almost eight times faster than the time required to manually process the same image. Our method has proven to be well suited for identifying axons and their characteristics, and represents a significant time savings over traditional manual methods.

We apply the initial momentum shape representation of diffeomorphic metric mapping from a template region of interest (ROI) to a given ROI as a morphometic marker in Parkinson's disease. We used a three-step segmentation-registration-momentum process to derive feature vectors from ROIs in a group of 42 subjects consisting of 19 Parkinson's Disease (PD) subjects and 23 normal control (NC) subjects. Significant group differences between PD and NC subjects were detected in four basal ganglia structures including the caudate, putamen, thalamus and globus pallidus. The magnitude of regionally significant between-group differences detected ranged between 34-75%. Visualization of the different structural deformation pattern between-groups revealed that some parts of basal ganglia structure actually hypertrophy, presumably as a compensatory response to more widespread atrophy. Our results of both hypertrophy and atrophy in the same structures further demonstrate the importance of morphological measures as opposed to overall volume in the assessment of neurodegenerative disease.