Arthur Mikhno

Higher Serotonin 1A Binding in a Second Major Depression Cohort: Modeling and Reference Region Considerations

BACKGROUND Serotonin 1A receptors (5-HT(1A)) are implicated in major depressive disorder (MDD). We previously reported higher 5-HT(1A) binding potential (BP(F)) in antidepressant naive MDD subjects compared with control subjects, while other studies report lower BP(ND). Discrepancies can be related to differences in study population or methodology. We sought to replicate our findings in a novel cohort and determine whether choice of reference region and outcome measure could explain discrepancies. METHODS Nine new control subjects and 22 new not recently medicated (NRM) MDD subjects underwent positron emission tomography. BP(F) and BP(ND) were determined using a metabolite and free fraction corrected arterial input function. BP(ND) was also determined using cerebellar gray matter (CGM) and cerebellar white matter (CWM) reference regions as input functions. RESULTS BP(F) was higher in the new NRM cohort (p = .037) compared with new control subjects, comparable to the previous cohort (p = .04). Cohorts were combined to examine the reference region and outcome measure. BP(F) was higher in the NRM compared with control subjects (p = .0001). Neither BP(ND) using CWM (p = .86) nor volume of distribution (V(T)) (p = .374) differed between groups. When CGM was used, the NRM group had lower 5-HT(1A) BP(ND) compared with control subjects (p = .03); CGM V(T) was higher in NRM compared with control subjects (p = .007). CONCLUSIONS Choice of reference region and outcome measure can produce different 5-HT(1A) findings. Higher 5-HT(1A) BP(F) in MDD was found with the method with fewest assumptions about nonspecific binding and a reference region without receptors.

Pittsburgh Compound B (11C-PIB) and Fluorodeoxyglucose (18 F-FDG) PET in Patients With Alzheimer Disease, Mild Cognitive Impairment, and Healthy Controls

Amyloid load in the brain using Pittsburgh compound B (11C-PIB) positron emission tomography (PET) and cerebral glucose metabolism using fluorodeoxyglucose (18F-FDG) PET were evaluated in patients with mild Alzheimer disease (AD, n = 18), mild cognitive impairment (MCI, n = 24), and controls (CTR, n = 18). 11C-PIB binding potential (BPND) was higher in prefrontal cortex, cingulate, parietal cortex, and precuneus in AD compared to CTR or MCI and in prefrontal cortex for MCI compared to CTR. For 18F-FDG, regional cerebral metabolic rate for glucose (rCMRGlu) was decreased in precuneus and parietal cortex in AD compared to CTR and MCI, with no MCI—CTR differences. For the AD—CTR comparison, precuneus BPND area under the receiver operating characteristic (ROC) curve (AUC) was 0.938 and parietal cortex rCMRGlu AUC was 0.915; for the combination, AUC was 0.989. 11C-PIB PET BPND clearly distinguished diagnostic groups and combined with 18F-FDG PET rCMRGlu, this effect was stronger. These PET techniques provide complementary information in strongly distinguishing diagnostic groups in cross-sectional comparisons that need testing in longitudinal studies.

Voxel-Based Analysis of 11C-PIB Scans for Diagnosing Alzheimer's Disease

The positron emission tomography (PET) radioligand N-methyl-11C-2-(4-methylaminophenyl)-6-hydroxybenzothiazole (also known as 11C-6-OH-BTA-1 or 11C-PIB) binds to amyloid-β (Aβ), which accumulates pathologically in Alzheimers disease (AD). Although 11C-PIB accumulation is greater in patients with AD than in healthy controls at a group level, the optimal method for discriminating between these 2 groups has, to our knowledge, not been established. We assessed the use of data-determined standardized voxels of interest (VOIs) to improve the classification capability of 11C-PIB scans on patients with AD. Methods: A total of 16 controls and 14 AD age-matched patients were recruited. All subjects underwent a 11C-PIB scan and structural MRI. Binding potential (a measure of amyloid burden) was calculated for each voxel using the Logan graphical method with cerebellar gray matter as the reference region. Voxel maps were then partial-volume corrected and spatially normalized by MRI onto a standardized template. The subjects were divided into 2 cohorts. The first cohort (control, 12; AD, 9) was used for statistical parametric mapping analysis and delineation of data-based VOIs. These VOIs were tested in the second cohort (control, 4; AD, 5) of subjects. Results: Statistical parametric mapping analysis revealed significant differences between control and AD groups. The VOI map determined from the first cohort resulted in complete separation between the control and the AD subjects in the second cohort (P < 0.02). Binding potential values based on this VOI were in the same range as other reported individual and mean cortical VOI results. Conclusion: A standardized VOI template that is optimized for control or AD group discrimination provides excellent separation of control and AD subjects on the basis of 11C-PIB uptake. This VOI template can serve as a potential replacement for manual VOI delineation and can eventually be fully automated, facilitating potential use in a clinical setting. To facilitate independent analysis and validation with more and a broader variety of subjects, this VOI template and the software for processing will be made available through the Internet.

Hydrocortisone responsiveness in Gulf War veterans with PTSD: Effects on ACTH, declarative memory hippocampal [18F]FDG uptake on PET

Neuroendocrine, cognitive and hippocampal alterations have been described in Gulf War (GW) veterans, but their inter-relationships and significance for posttraumatic stress disorder (PTSD) have not been described. Hydrocortisone (Hcort) was administered to GW veterans with (PTSD+ n=12) and without (PTSD- n=8) chronic PTSD in a randomized, placebo-controlled, double-blind challenge. Changes in plasma ACTH, memory, and hippocampal [(18)F]FDG uptake on positron emission tomography were assessed. The low-dose dexamethasone suppression test was also administered. The PTSD+ group showed greater cortisol and ACTH suppression, reflecting greater peripheral glucocorticoid receptor (GR) responsiveness, and did not show an Hcort-induced decrement in delayed recall or retention. The groups had comparable relative regional hippocampal [(18)F]FDG uptake at baseline, but only the PTSD- group had an Hcort-associated decrease in hippocampal [(18)F]FDG uptake. Asymmetry in hippocampal hemispheric volumes differed between PTSD+ and PTSD- groups. This asymmetry was associated with cortisol, ACTH, retention and functional hippocampal asymmetry before, but not after, Hcort administration. Differences in brain metabolic responses between GW veterans with and without PTSD may reflect differences in peripheral and central GR responsiveness.

A new method for assessing PET-MRI coregistration

Positron emission tomography (PET) images are acquired for many purposes, from diagnostic assessment to aiding in the development of novel therapies. Whatever the intended use, it is often necessary to distinguish between different anatomical regions within these images. Because of this, magnetic resonance images (MRIs) are generally acquired to provide an anatomical reference. This reference will only be accurate if the PET image is properly coregistered to the MRI; yet currently, a method to evaluate PET-MRI coregistration accuracy does not exist. This problem is compounded by the fact that two visually indistinguishable coregistration results can produce estimates of ligand binding that vary significantly. Therefore, the focus of this work was to develop a method that can evaluate coregistration performance based on measured ligand binding within certain regions of the coregistered PET image. The evaluation method is based on the premise that a more accurate coregistration will result in higher ligand binding in certain anatomical regions defined by the MRI. This fully automated method was able to assess coregistration results within the variance of an expert manual rater and shows promise as a possible coregistration cost function.

Prediction of extubation failure for neonates with respiratory distress syndrome using the MIMIC-II clinical database

Extubation failure (EF) is an ongoing problem in the neonatal intensive care unit (NICU). Nearly 25% of neonates fail their first extubation attempt, requiring re-intubations that are associated with risk factors and financial costs. We identified 179 mechanically ventilated neonatal patients that were intubated within 24 hours of birth in the MIMIC-II intensive care database. We analyzed data from the patients 2 hours prior to their first extubation attempt, and developed a prediction algorithm to distinguish patients whose extubation attempt was successful from those that had EF. From an initial list of 57 candidate features, our machine learning approach narrowed down to six features useful for building an EF prediction model: monocyte cell count, rapid shallow breathing index, fraction of inspired oxygen (FiO2), heart rate, PaO2/FiO2 ratio where PaO2 is the partial pressure of oxygen in arterial blood, and work of breathing index. Algorithm performance had an area under the receiver operating characteristic curve (AUC) of 0.871 and sensitivity of 70.1% at 90% specificity.

Multimodal classification of Dementia using functional data, anatomical features and 3D invariant shape descriptors

Multimodality classification of Alzheimers disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region. Classification performance is evaluated on data from 17 healthy controls (CTR), 22 MCI, and 17 AD subjects. Zernike significantly outperforms volume, accuracy (Zernike to volume): CTR/AD (90.7% to 71.6%), CTR/MCI (76.2% to 60.0%), MCI/AD (84.3% to 65.5%). Zernike also provides comparable and complementary performance to PET. Optimal accuracy is achieved when Zernike and PET features are combined (accuracy, specificity, sensitivity), CTR/AD (98.8%, 99.5%, 98.1%), CTR/MCI (84.3%, 82.9%, 85.9%) and MCI/AD (93.3%, 93.6%, 93.3%).

Toward Noninvasive Quantification of Brain Radioligand Binding by Combining Electronic Health Records and Dynamic PET Imaging Data

Quantitative analysis of positron emission tomography (PET) brain imaging data requires a metabolite-corrected arterial input function (AIF) for estimation of distribution volume and related outcome measures. Collecting arterial blood samples adds risk, cost, measurement error, and patient discomfort to PET studies. Minimally invasive AIF estimation is possible with simultaneous estimation (SIME), but at least one arterial blood sample is necessary. In this study, we describe a noninvasive SIME (nSIME) approach that utilizes a pharmacokinetic input function model and constraints derived from machine learning applied to an electronic health record database consisting of “long tail” data (digital records, paper charts, and handwritten notes) that were collected ancillary to the PET studies. We evaluated the performance of nSIME on 95 [11C]DASB PET scans that had measured AIFs. The results indicate that nSIME is a promising alternative to invasive AIF measurement. The general framework presented here may be expanded to other metabolized radioligands, potentially enabling quantitative analysis of PET studies without blood sampling. A glossary of technical abbreviations is provided at the end of this paper.

Combining brain imaging data with electronic health records to non-invasively quantify [ 11 C]DASB binding

Quantitative analysis of PET data requires a metabolite-corrected arterial input function (AIF) for estimation of distribution volume and related outcome measures. Collecting arterial blood samples adds risk, cost, and patient discomfort to PET studies. Minimally invasive AIF estimation is possible with simultaneous estimation (SIME), but one arterial blood sample is necessary to be used as an anchor value to ensure identifiability of each individuals AIF. For [11C]DASB, a widely used serotonin transporter PET tracer, this blood sample is optimally taken 50 minutes after injection. We present here an approach for replacing such a single time-point anchor with a predicted value using brain imaging and electronic health record (EHR) data. Average bootstrap R2 > 0.8 in training data suggest that up to 80% of the variance in [11C]DASB SIME anchor may be explained by a model including heart rate, blood pressure, tracer dose, body size and cerebellar gray matter uptake. Preliminary results show that these models generalize well to a small test dataset. This may allow for quantitative analysis with no blood sampling.

Locally weighted total variation denoising for ringing artifact suppression in pet reconstruction using PSF modeling

Iterative reconstruction with point spread function (PSF) modeling improves contrast recovery in positron emission tomography (PET) images, but also introduces ringing artifacts and over enhancement that is contrast and object size dependent. Mitigation of these artifacts is crucial for clinical and research purposes. In this work we introduce a new iterative regularized reconstruction method that incorporates locally-weighted total variation denoising designed to suppress artifacts induced by PSF modeling. The reconstruction method is evaluated on a simulated cylindrical phantom and preliminary results show that ringing artifacts are suppressed while contrast recovery is maintained.