Evaluating the effect of rapamycin treatment in Alzheimer's disease and aging using in vivo imaging: the ERAP phase IIa clinical study protocol.

BACKGROUND: Rapamycin is an inhibitor of the mechanistic target of rapamycin (mTOR) protein kinase, and preclinical data demonstrate that it is a promising candidate for a general gero- and neuroprotective treatment in humans. Results from mouse models of Alzheimer's disease have shown beneficial effects of rapamycin, including preventing or reversing cognitive deficits, reducing amyloid oligomers and tauopathies and normalizing synaptic plasticity and cerebral glucose uptake. The "Evaluating Rapamycin Treatment in Alzheimer's Disease using Positron Emission Tomography" (ERAP) trial aims to test if these results translate to humans through evaluating the change in cerebral glucose uptake following six months of rapamycin treatment in participants with early-stage Alzheimer's disease. METHODS: ERAP is a six-month-long, single-arm, open-label, phase IIa biomarker-driven study evaluating if the drug rapamycin can be repurposed to treat Alzheimer's disease. Fifteen patients will be included and treated with a weekly dose of 7 mg rapamycin for six months. The primary endpoint will be change in cerebral glucose uptake, measured using [18F]FDG positron emission tomography. Secondary endpoints include changes in cognitive measures, markers in cerebrospinal fluid as well as cerebral blood flow measured using magnetic resonance imaging. As exploratory outcomes, the study will assess change in multiple age-related pathological processes, such as periodontal inflammation, retinal degeneration, bone mineral density loss, atherosclerosis and decreased cardiac function. DISCUSSION: The ERAP study is a clinical trial using in vivo imaging biomarkers to assess the repurposing of rapamycin for the treatment of Alzheimer's disease. If successful, the study would provide a strong rationale for large-scale evaluation of mTOR-inhibitors as a potential disease-modifying treatment in Alzheimer's disease. TRIAL REGISTRATION: ClinicalTrials.gov ID NCT06022068, date of registration 2023-08-30.

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET-derived features.

To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimer's disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to (1) predict brain age using 5-HT2AR binding outcomes, (2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and (3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean = 38, std = 18) and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE = 6.63 years, std = 0.74 years) and GM volume (mean MAE = 6.95 years, std = 0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE = 5.54 years, std = 0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.

Prediction of brain age using structural magnetic resonance imaging: A comparison of accuracy and test-retest reliability of publicly available software packages.

Brain age prediction algorithms using structural magnetic resonance imaging (MRI) aim to assess the biological age of the human brain. The difference between a person's chronological age and the estimated brain age is thought to reflect deviations from a normal aging trajectory, indicating a slower or accelerated biological aging process. Several pre-trained software packages for predicting brain age are publicly available. In this study, we perform a comparison of such packages with respect to (1) predictive accuracy, (2) test-retest reliability, and (3) the ability to track age progression over time. We evaluated the six brain age prediction packages: brainageR, DeepBrainNet, brainage, ENIGMA, pyment, and mccqrnn. The accuracy and test-retest reliability were assessed on MRI data from 372 healthy people aged between 18.4 and 86.2 years (mean 38.7 ± 17.5 years). All packages showed significant correlations between predicted brain age and chronological age (r = 0.66-0.97, p < 0.001), with pyment displaying the strongest correlation. The mean absolute error was between 3.56 (pyment) and 9.54 years (ENIGMA). brainageR, pyment, and mccqrnn were superior in terms of reliability (ICC values between 0.94-0.98), as well as predicting age progression over a longer time span. Of the six packages, pyment and brainageR consistently showed the highest accuracy and test-retest reliability.

In vivo correlation of serotonin transporter and 1B receptor availability in the human brain: a PET study.

Synaptic serotonin levels in the brain are regulated by active transport into the bouton by the serotonin transporter, and by autoreceptors, such as the inhibitory serotonin (5-HT) 1B receptor which, when activated, decreases serotonin release. Animal studies have shown a regulatory link between the two proteins. Evidence of such coupling could translate to an untapped therapeutic potential in augmenting the effect of selective serotonin reuptake inhibitors through pharmacological modulation of 5-HT1B receptors. Here we will for the first time in vivo examine the relationship between 5-HT1B receptors and serotonin transporters in the living human brain. Seventeen healthy individuals were examined with PET twice, using the radioligands [11C]AZ10419369 and [11C]MADAM for quantification of the 5-HT1B receptor and the 5-HT transporter, respectively. The binding potential was calculated for a set of brain regions, and the correlations between the binding estimates of the two radioligands were studied. [11C]AZ10419369 and [11C]MADAM binding was positively correlated in all examined brain regions. In most cortical regions the correlation was strong, e.g., frontal cortex, r(15) = 0.64, p = 0.01 and parietal cortex, r(15) = 0.8, p = 0.0002 while in most subcortical regions, negligible correlations was observed. Though the correlation estimates in cortex should be interpreted with caution due to poor signal to noise ratio of [11C]MADAM binding in these regions, it suggests a link between two key proteins involved in the regulation of synaptic serotonin levels. Our results indicate a need for further studies to address the functional importance of 5-HT1B receptors in treatment with drugs that inhibit serotonin reuptake.

Serotonin transporter availability increases in patients recovering from a depressive episode.

Molecular imaging studies have shown low cerebral concentration of serotonin transporter in patients suffering from depression, compared to healthy control subjects. Whether or not this difference also is present before disease onset and after remission (i.e. a trait), or only at the time of the depressive episode (i.e. a state) remains to be explored. We examined 17 patients with major depressive disorder with positron emission tomography using [11C]MADAM, a radioligand that binds to the serotonin transporter, before and after treatment with internet-based cognitive behavioral therapy. In all, 17 matched healthy control subjects were examined once. Cerebellum was used as reference to calculate the binding potential. Differences before and after treatment, as well as between patients and controls, were assessed in a composite cerebral region and in the median raphe nuclei. All image analyses and confirmatory statistical tests were preregistered. Depression severity decreased following treatment (p < 0.001). [11C]MADAM binding in patients increased in the composite region after treatment (p = 0.01), while no change was observed in the median raphe (p = 0.51). No significant difference between patients at baseline and healthy controls were observed in the composite region (p = 0.97) or the median raphe (p = 0.95). Our main finding was that patients suffering from a depressive episode show an overall increase in cerebral serotonin transporter availability as symptoms are alleviated. Our results suggest that previously reported cross-sectional molecular imaging findings of the serotonin transporter in depression most likely reflect the depressive state, rather than a permanent trait. The finding adds new information on the pathophysiology of major depressive disorder.

Early stopping in clinical PET studies: How to reduce expense and exposure.

Clinical positron emission tomography (PET) research is costly and entails exposing participants to radioactivity. Researchers should therefore aim to include just the number of subjects needed to fulfill the purpose of the study. In this tutorial we show how to apply sequential Bayes Factor testing in order to stop the recruitment of subjects in a clinical PET study as soon as enough data have been collected to make a conclusion. By using simulations, we demonstrate that it is possible to stop a study early, while keeping the number of erroneous conclusions low. We then apply sequential Bayes Factor testing to a real PET data set and show that it is possible to obtain support in favor of an effect while simultaneously reducing the sample size with 30%. Using this procedure allows researchers to reduce expense and radioactivity exposure for a range of effect sizes relevant for PET research.

Low convergent validity of [11C]raclopride binding in extrastriatal brain regions: A PET study of within-subject correlations with [11C]FLB 457.

Dopamine D2 receptors (D2-R) in extrastriatal brain regions are of high interest for research in a wide range of psychiatric and neurologic disorders. Pharmacological competition studies and test-retest experiments have shown high validity and reliability of the positron emission tomography (PET) radioligand [11C]FLB 457 for D2-R quantification in extrastriatal brain regions. However, this radioligand is not available at most research centers. Instead, the medium affinity radioligand [11C]raclopride, which has been extensively validated for quantification of D2-R in the high-density region striatum, has been applied also in studies on extrastriatal D2-R. Recently, the validity of this approach has been questioned by observations of low occupancy of [11C]raclopride in extrastriatal regions in a pharmacological competition study with quetiapine. Here, we utilise a data set of 16 healthy control subjects examined with both [11C]raclopride and [11C]FLB 457 to assess the correlation in binding potential (BPND) in extrastriatal brain regions. BPND was quantified using the simplified reference tissue model with cerebellum as reference region. The rank order of mean regional BPND values were similar for both radioligands, and corresponded to previously reported data, both post-mortem and using PET. Nevertheless, weak to moderate within-subject correlations were observed between [11C]raclopride and [11C]FLB 457 BPND extrastriatally (Pearson's R: 0.30-0.56), in contrast to very strong correlations between repeated [11C]FLB 457 measurements (Pearson's R: 0.82-0.98). In comparison, correlations between repeated [11C]raclopride measurements were low to moderate (Pearson's R: 0.28-0.75). These results are likely related to low signal to noise ratio of [11C]raclopride in extrastriatal brain regions, and further strengthen the recommendation that extrastriatal D2-R measures obtained with [11C]raclopride should be interpreted with caution.

Validity and reliability of extrastriatal [11C]raclopride binding quantification in the living human brain.

[11C]raclopride is a well established PET tracer for the quantification of dopamine 2/3 receptors (D2/3R) in the striatum. Outside of the striatum the receptor density is up to two orders of magnitude lower. In contrast to striatal binding, the characteristics of extrastriatal [11C]raclopride binding quantification has not been thoroughly described. Still, binding data for e.g., neocortex is frequently reported in the scientific literature. Here we evaluate the validity and reliability of extrastriatal [11C]raclopride binding quantification. Two sets of healthy control subjects were examined with HRRT and [11C]raclopride: (i) To assess the validity of extrastriatal [11C]raclopride binding estimates, eleven subjects were examined at baseline and after dosing with quetiapine, a D2/3R antagonist. (ii) To assess test-retest repeatability, nine subjects were examined twice. Non displaceable binding potential (BPND) was quantified using the simplified reference tissue model with cerebellum as reference. Quetiapine dosing was associated with decrease in [11C]raclopride BPND in temporal cortex (18 ±â€¯17% occupancy) and thalamus (20 ±â€¯17%), but not in frontal cortex. Extrastriatal occupancy was lower than in putamen (51 ±â€¯4%). The mean absolute variation was 4-7% in the striatal regions, 17% in thalamus, and 13-59% in cortical regions. Our data indicate that [11C]raclopride PET, quantified using cerebellum as reference, is not a suitable tool to measure D2/3R in extrastriatal regions.