Biology subjects

Mann, J. J.

Publications and source records attributed to Mann, J. J..

6 recordsLinked to original sources

bioRxiv2026

External validation and time-stability analysis of STARE, a blood-free quantification tool for irreversible PET tracers

Rationale"Gold standard" blood-based quantification of dynamic 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) data has limited practical clinical applications due to cost and complexity of data collection and analysis. We previously presented a blood-free quantification alternative, STARE (Source-to-Target Automatic Rotating Estimation), that was validated on 18F-FDG data acquired on a ECAT EXACT HR+ scanner. Here, we extend that initial work by externally validating STARE using within-subject data acquired with both a Siemens Biograph mCT scanner and a portable Brain Biosciences CerePET scanner. MethodsPerformance was assessed by comparing regional net influx rates (Ki) estimated using STARE and the standard blood-based Patlak approach. Twenty participants underwent 60-minute 18F-FDG scans, on two different days, once in each scanner. The time-stability of both STARE- and Patlak-based Ki estimates was evaluated by applying each method to the first 20 (STARE only), 30, 40, and 50 minutes of data. ResultsSTARE demonstrated high correlation with Patlak Ki estimates across both scanner types, particularly in the Biograph mCT (r = 0.93), with lower correlation in the CerePET (r = 0.71). In the Biograph dataset, STARE provided reliable Ki estimates at all evaluated scan durations (20 minutes and above), while in the CerePET dataset, only the 50-minute duration yielded STARE Ki estimates that were not significantly different from the full 60 minutes. The Patlak approach provided Ki estimates at 40 minutes scan duration and above that did not differ from the 60-min scan results in both datasets. ConclusionSTARE is a viable, noninvasive alternative to traditional blood-based quantification of dynamic 18F-FDG PET data, facilitating shorter, blood-free acquisition. This advancement could make dynamic 18F-FDG PET imaging more accessible and comfortable for patients, promoting broader clinical adoption.

neuroscience↗

bioRxiv2024

Connecting the Dots: Approaching a Standardized Nomenclature for Molecular Connectivity Combining Data and Literature

PET-based connectivity computation is a molecular approach that complements fMRI-derived functional connectivity. However, the diversity of methodologies and terms employed in PET connectivity analysis has resulted in ambiguities and confounded interpretations, highlighting the need for a standardized nomenclature. Drawing parallels from other imaging modalities, we propose "molecular connectivity" as an umbrella term to characterize statistical dependencies between PET signals across brain regions at the individual level (within-subject). Like fMRI resting-state functional connectivity, "molecular connectivity" leverages temporal associations in the PET signal to derive brain network associations. Another within-subject approach evaluates regional similarities of tracer kinetics, which are unique in PET imaging, thus referred to as "kinetic connectivity". On the other hand, "molecular covariance" denotes group-level computations of covariance matrices across-subject. Further specification of the terminology can be achieved by including the employed radioligand, such as "metabolic connectivity/covariance" for [18F]FDG as well as "tau/amyloid covariance" for [18F]flutemetamol / [18F]flortaucipir. To augment these distinctions, high-temporal resolution functional [18F]FDG PET scans from 17 healthy participants were analysed with common techniques of molecular connectivity and covariance, allowing for a data-driven support of the above terminology. Our findings demonstrate that temporal band-pass filtering yields structured network organization, whereas other techniques like 3rd order polynomial fitting, spatio-temporal filtering and baseline normalization require further methodological refinement for high-temporal resolution data. Conversely, molecular covariance from across-subject data provided a simple means to estimate brain region interactions through regularized or sparse inverse covariance estimation. A standardized nomenclature in PET-based connectivity research can reduce ambiguity, enhance reproducibility, and facilitate interpretability across radiotracers and imaging modalities. Via a data-driven approach, this work provides a transparent framework for categorizing and comparing PET-derived connectivity and covariance metrics, laying the foundation for future investigations in the field.

neuroscience↗

bioRxiv2024

PET Imaging of the Serotonin 1A Receptor in Major Depressive Disorder: Hierarchical Multivariate Analysis of 11CWAY100635 Overcomes Outcome Measure Discrepancies

The serotonin 1A receptor has been linked to both the pathophysiology of major depressive disorder (MDD) and the antidepressant action of serotonin reuptake inhibitors. Most PET studies of the serotonin 1A receptor in MDD used the receptor antagonist radioligand, [carbonyl-11C]WAY100635; however the interpretation of the combined results has been contentious owing to reports of higher or lower binding in MDD with different outcome measures. The reasons for these divergent results originate from several sources, including properties of the radiotracer itself, which complicate its quantification and interpretation; as well as from previously reported differences between MDD and healthy volunteers in both reference tissue binding and plasma free fraction, which are typically assumed not to differ. Recently, we have developed two novel hierarchical multivariate methods which we validated for the quantification and analysis of [11C]WAY100635, which show better accuracy and inferential efficiency compared to standard analysis approaches. Importantly, these new methods should theoretically be more resilient to many of the factors thought to have caused the discrepancies observed in previous studies. We sought to apply these methods in the largest [11C]WAY100635 sample to date, consisting of 160 individuals, including 103 MDD patients, of whom 50 were not-recently-medicated and 53 were antidepressant-exposed, as well as 57 healthy volunteers. While the outcome measure discrepancies were substantial using conventional univariate analysis, our multivariate analysis techniques instead yielded highly consistent results across PET outcome measures and across pharmacokinetic models, with all approaches showing higher serotonin 1A autoreceptor binding potential in the raphe nuclei of not-recently-medicated MDD patients relative to both healthy volunteers and antidepressant-exposed MDD patients. Moreover, with the additional precision of estimates afforded by this approach, we can show that while binding is also higher in projection areas in this group, these group differences are approximately half of those in the raphe nuclei, which are statistically distinguishable from one another. These results are consistent with the biological role of the serotonin 1A autoreceptor in the raphe nuclei in regulating serotonin neuron firing and release, and with preclinical and clinical evidence of deficient serotonin activity in MDD due to over expression of autoreceptors resulting from genetic and/or epigenetic effects. These results are also consistent with downregulation of autoreceptors as a mechanism of action of selective serotonin reuptake inhibitors. In summary, the results using multivariate analysis approaches therefore demonstrate both face and convergent validity, and may serve to provide a resolution and consensus interpretation for the disparate results of previous studies examining the serotonin 1A receptor in MDD.

neuroscience↗

bioRxiv2021

Source-to-Target Automatic Rotating Estimation (STARE) -- A publicly-available, blood-free quantification approach for PET tracers with irreversible kinetics: Theoretical framework and validation for FDG

IntroductionFull quantification of positron emission tomography (PET) data requires an input function. This generally means arterial blood sampling, which is invasive, labor-intensive and burdensome. There is no current, standardized method to fully quantify PET radiotracers with irreversible kinetics in the absence of blood data. Here, we present Source-to-Target Automatic Rotating Estimation (STARE), a novel, data-driven approach to quantify the net influx rate (Ki) of irreversible PET radiotracers, that requires only individual-level PET data and no blood data. We validate STARE with [18F]FDG PET and assess its performance using simulations. MethodsSTARE builds upon a source-to-target tissue model, where the tracer time activity curves (TACs) in multiple "target" regions are expressed at once as a function of a "source" region, based on the two-tissue irreversible compartment model, and separates target region Ki from source Ki by fitting the source-to-target model across all target regions simultaneously. To ensure identifiability, data-driven, subject-specific anchoring is used in the STARE minimization, which takes advantage of the PET signal in a vasculature cluster in the FOV that is automatically extracted and partial volume-corrected. To avoid the need for any a priori determination of a single source region, each of the considered regions acts in turn as the source, and a final Ki is estimated in each region by averaging the estimates obtained in each source rotation. ResultsIn a large dataset of [18F]FDG human scans (N=69), STARE Ki estimates were in good agreement with corresponding arterial blood-based estimates (regression slope=0.88, r=0.80), and were precisely estimated, as assessed by comparing STARE Ki estimates across several runs of the algorithm (coefficient of variation across runs=6.74 {+/-} 2.48%). In simulations, STARE Ki estimates were largely robust to factors that influence the individualized anchoring used within its algorithm. ConclusionThrough simulations and application to [18F]FDG PET data, feasibility is demonstrated for STARE blood-free, data-driven quantification of Ki. Future work will include applying STARE to PET data obtained with a portable PET camera and to other irreversible radiotracers.

molecular biology↗

bioRxiv2019

Early Life Adversity, but not suicide, is associated with less prefrontal cortex gray matter in adulthood

BackgroundSuicide and major depression (MDD) are more prevalent in individuals reporting early life adversity (ELA). Prefrontal cortex volume is reduced by stress acutely and progressively in vivo, and changes in neuron and glia density are reported in depressed suicide decedents. We previously found reduced levels of the neurotrophic factor BDNF in suicide decedents and with ELA, and in the present study we sought to determine whether cortex thickness, neuron density or glia density in the dorsolateral prefrontal (BA9) and anterior cingulate (BA24) cortex are associated with ELA or suicide. MethodsA total of 52 brains, constituting 13 quadruplets of nonpsychiatric nonsuicide controls and MDD suicide decedents with and without ELA (n=13/group), all with psychological autopsy, were matched for age, sex and postmortem interval. Brains were collected at autopsy and frozen and blocks containing BA9 and BA24 were later dissected, post-fixed and sectioned. Sections were immunostained for NeuN to label neurons and counterstained with thionin to stain glial cell nuclei. Cortex thickness, neuron and glial density and neuron volume were measured by stereology. ResultsCortical thickness was 6% less with an ELA history in BA9 and 12% less in BA24 (p<0.05), but not in depressed suicide decedents in either BA9 or BA24. Neuron density was not different in ELA or in suicide decedents, but glial density was 17% greater with ELA history in BA9 and 15% greater in BA24, but not in suicides. Neuron volume was not different with ELA or suicide. DiscussionReported ELA, but not the stress associated with suicide, is associated with thinner prefrontal cortex and greater glia density in adulthood. ELA may alter normal neurodevelopment and contribute to suicide risk.

neuroscience↗

bioRxiv2017

Association of BDNF Val66Met Polymorphism and Brain BDNF levels with Major Depression and Suicide

BACKGROUND: Brain-derived neurotrophic factor (BDNF) is implicated in the pathophysiology of major depressive disorder (MDD) and suicide. Both are partly caused by early life adversity (ELA) and ELA reduces both BDNF protein and gene expression. This study examines the association of BDNF Val66Met polymorphism and brain BDNF levels with depression and suicide. We hypothesized that both major depression and ELA would be associated with the Met allele and lower brain BDNF levels. Such an association would be consistent with low BDNF mediating the effect of ELA on adulthood suicide and MDD. METHODS: BDNF Val66Met polymorphism was genotyped in postmortem brains of 37 suicide decedents and 53 non-suicides. Additionally, BDNF protein levels were determined by Western blot in dorsolateral prefrontal cortex (Brodmann area 9; BA9), anterior cingulate cortex (ACC; BA24), caudal brainstem and rostral brainstem. The relationships between these measures and major depression, death by suicide and reported childhood adversity were examined. RESULTS: Depressed subjects had an excess of the Met allele and lower BDNF levels in ACC and caudal brainstem compared with non-depressed subjects. No effect of history of suicide death or early life adversity was observed with genotype, but lower BDNF levels in ACC were found in subjects who had been exposed to early life adversity and/or died by suicide compared to nonsuicide decedents and no reported childhood adversity. CONCLUSIONS: This study provides further evidence for low BDNF in major depression related to the BDNF met risk allele. Future studies should seek to determine how altered BDNF expression contributes to MDD and suicide.

neuroscience↗