The study of neonatal neuroanatomy is a complex field because of the rapid changes occurring continuously. The physical development of tissues is critical to understanding the cognitive development of behavior and learning in the neonate. These structures can be visualized and analyzed with Magnetic Resonance Imaging (MRI). The hippocampal structure in particular is vital to memory consolidation and is therefore key to understanding how newborns build upon acquired knowledge and how this affects their behavior as they grow. This strucure has been studied at many different ages with parallel work analyzing how changes in anatomy result in observable cognitive differences and developments. This study in particular has a rare cohort of scans that were taken shortly after birth. Prematurity is an especially important factor to consider here as it affects physical and behavioral growth. Here we looked at 42 premature MRI subjects and used these to build a temporal model of brain development. Our primary aims of the study are to: (1) develop a protocol for labeling the hippocampus in premature neonate scans, (2) create a growth over time model of the structure in order to automatically segment a larger number of scans and (3) observe how volume varies with behavior as projected by Bayley's Scale of Infant and Toddler Development. So far aim 1 has been achieved and current work is increasing the previous atlas size of 32 scans to 42 in order to improve automatic segmentation. The overarching goal of premature neonatal MRI segmentation is to provide further data for early diagnosis of behavior abnormalities that could develop later in the subject. Results here could produce a meaningful indicator of cognitive development through MRI analysis as well as assist in improving the accuracy of MRI automatic segmentation algorithms. 

Premature births, or births before 37 gestational weeks, are associated with structural and functional abnormalities in the brain. Magnetic Resonance Imaging (MRI) is used to examine brain injury in neonates. Previous work demonstrated correlations between neonatal cortical lobes volumes from MRI and scores on the Bayley Scales of Infant and Toddler Development-Third Edition (Bayley-III), which measures cognitive, motor, and language skills of infants. The occipital lobe is important to diagnosis of visual and neurodevelopmental delays. In this work, I examined the relationship between occipital lobe subdivision volumes and Bayley-III scores. In order to do this, I designed a parcellation or labeling protocol that divided the occipital lobe into two regions based on the calcarine sulcus, the cuneus region and the lingual region. I applied the protocol to the 32 MRI training dataset. Using this parcellation protocol, I created a spatio-temporal atlas of brain parcels, which is a time and space model of brain tissue and parcel probability estimates. This atlas was used to validate our protocol using a leave-one-out validation and to automatically parcellate the larger dataset using EM or Expectation-Maximization labeling. The dataset contained 270 T1-weighted MRI scans from premature neonates who took the Bayley-III at 18 months. Once the larger dataset was labeled, I used multi-variate linear regression, normalizing for head size, to examine the relationship between occipital lobe gray matter volumes and scores on the Bayley-III and analyzed the growth pattern of the regions of the occipital lobe. Both regions showed symmetric and exponential growth patterns. Statistically significant correlations between the lingual region of the occipital lobe and Bayley-III scores were found. This suggests that regional occipital lobe volumes are associated with developmental outcomes in premature neonates.

Cognitive impairment in the form of dementia is a hallmark of Alzheimer’s disease (AD), an aging-related neurodegenerative disorder that is characterized by increasing deficits in memory and cognition. To allow for targeted, early intervention, it is necessary to develop reliable prognostic tools that can identify individuals at risk of developing AD. Studies suggest that several neuroimaging markers may be predictive of cognitive decline; these include atrophy of medial temporal lobe structures, reduced cerebral blood flow to the hippocampus, reduced functional connectivity between memory-related structures, and lower density of cholinergic cells within the nucleus basalis. In this study, we modeled the relationship between episodic memory test scores from Wechsler’s Adult Intelligence Scale using both established and novel neuroimaging markers while controlling for age, sex and education. Standard neuropsychological battery test scores and structural, diffusion and resting-state functional MRI data were collected from 54 participants (n = 29 women) aged 65 and above (mean = 78.6 years) recruited from Group Health Research Institute’s Adult Changes in Thought study. We found left hippocampal and left entorhinal cortex (LEC) volumes to be significantly smaller in cognitively impaired subjects as compared to healthy controls (p <0.05), and episodic memory scores were most positively correlated with LEC volumes (r = 0.45, p < 0.05) as compared to other MRI measures. Stepwise regression analysis also showed that LEC volume explained a significant portion of variance in memory scores (R² = 0.31, F = 6.88, p <0.05). In comparison to structural MRI markers, functional connectivity measures did not add predictive value, suggesting that physiological measures play a more mediating role in the relationship between cortical structure and performance. Overall, volumetric MRI measures served as better predictors of cognitive performance than other examined MRI measures, and the inclusion of these measures in future AD risk assessments may help to predict cognitive decline with greater sensitivity.

The purpose of this study is to determine whether blood-oxygenation level dependent (BOLD) variability could be a marker of neurodegeneration caused by Parkinson's Disease (PD). BOLD variability is related to higher cognitive performance and it decreases with age. Also, studies have shown that BOLD variability is correlated with lower levels of dopamine, a neurotransmitter that is decreased in PD and contributes to cognitive and motor symptoms. This motivates our hypothesis that BOLD variability may be correlated with cerebrospinal fluid (CSF) levels of alpha synuclein (α-Syn) and amyloid-β1–42 (Aβ42), markers that decrease with advanced PD. We obtained resting state and structural MRI data from 31 de novo PD subjects from the Parkinson’s Progression Markers Initiative (PPMI) cohort, who were scanned within 3 months of lumbar puncture. We first regressed out sources of noise such as scanner instabilities, subject motion, and physiological artifacts (e.g. respiration and cardiac effects). Second, we coregistered (registering one person’s brain to other brain image modality (rest to T1)) each data and conducted spatial smoothing. Then, we calculated the standard deviation of the BOLD signal (SDbold) and registered all the subjects’ brains to standard Montreal Neurological Institute (MNI) template. Finally, we computed the correlation between each subject’s SDbold and CSF markers We observed a significant correlation between SDbold and α-Syn in anterior cingulate gyrus and premotor cortex, areas that are known to be affected by PD. We did not see the same relationship with Aβ42. Our results suggest a specific relationship between physiological disruption related to PD and α-Syn that might be mediated by dopamine.

Repeated mild traumatic brain injury (mTBI) from blast exposure is a common injury in the Veteran population, and is associated with memory and concentration impairment. Our lab has shown that Veterans exposed to multiple blast mTBI demonstrate cerebrocerebellar hypometabolism in infratentorial and medial temporal brain regions compared to normal controls. Apolipoprotein E (ApoE), a protein associated with Alzheimer disease risk and amyloid β protein deposition, is linked to decreased brain function. Therefore, we are investigating effect of ApoE genotype status on the degree of injury associated with repetitive mTBI. We hypothesize that severity of regional hypometabolism in mTBI-exposed Veterans is influenced by ApoE genetic polymorphism, and associated with persistent neurodegenerative processes. We performed ApoE genotyping and positron emission tomography with [18F]-fluorodeoxyglucose (FDG-PET) to investigate glucose metabolism in brains of blast-exposed Veterans and age-matched community controls. We used an automated program to segment FDG-PET brain images into 19 independent anatomical regions. Preliminary analysis suggests metabolism in left medial frontal and left parahippocampal cortex of mTBI subjects (n=12) were significantly lower (p<0.05) than controls (n=9). ApoE4 carriers (n=6) were significantly lower in 6 regions compared to non-ApoE4 carriers (n=6) within mTBI subjects: right frontal, right and left occipital, right medial parietal, left caudate, and left amygdala. ApoE4 carriers exposed to mTBI (n=6) were significantly lower in 6 regions compared to non-ApoE4 community controls (n=9): right and left frontal, right occipital, left medial frontal, right medial parietal and right caudate. However, there were no significant findings between mTBI group and controls when non-ApoE4 carrier status was controlled. Further analysis includes voxel-wise comparison, correlation between blast exposure (time since last blast and total number of blasts), and neuropsychological measures. These initial results suggest ApoE genotype patterns of decreased brain metabolism in Veterans exposed to mTBI.

Social deficits in autism spectrum disorder (ASD) have been linked to irregularities in amygdala activation. The amygdala is a brain structure important for emotional processing and has been a common focus in neuroimaging research on ASD. I intend to expand on this research by investigating the relationship between social anxiety in ASD and the correlated activation (functional connectivity) of the amygdala and the ventromedial prefrontal cortex (vmPFC), a brain structure shown to inhibit amygdala activation. I am performing secondary data analysis of functional magnetic resonance imaging (fMRI) task data on 31 ASD and 25 control adult subjects matched on intelligence quotient test scores and age. Subjects performed an emotional face and geometric figure matching fMRI task. The social avoidance and distress scale (SADS) was also administered to subjects to assess social anxiety level. I will perform a seed-based region of interest functional connectivity analysis to extract a correlation coefficient between amygdala and vmPFC activation while viewing emotional faces. Using these data, I will correlate vmPFC/amygdala functional connectivity with social anxiety level. I hypothesize that weaker functional connectivity between the vmPFC and the amygdala while viewing emotional faces will be related to higher social anxiety in ASD, but not in the control group. If the data support my hypothesis, this will provide evidence that the vmPFC is related to abnormal amygdala activation and social anxiety in ASD.