The Imperial College Cambridge Manchester (ICCAM) platform study: An experimental medicine platform for evaluating new drugs for relapse prevention in addiction. Part A: Study description.

Drug and alcohol dependence are global problems with substantial societal costs. There are few treatments for relapse prevention and therefore a pressing need for further study of brain mechanisms underpinning relapse circuitry. The Imperial College Cambridge Manchester (ICCAM) platform study is an experimental medicine approach to this problem: using functional magnetic resonance imaging (fMRI) techniques and selective pharmacological tools, it aims to explore the neuropharmacology of putative relapse pathways in cocaine, alcohol, opiate dependent, and healthy individuals to inform future drug development. Addiction studies typically involve small samples because of recruitment difficulties and attrition. We established the platform in three centres to assess the feasibility of a multisite approach to address these issues. Pharmacological modulation of reward, impulsivity and emotional reactivity were investigated in a monetary incentive delay task, an inhibitory control task, and an evocative images task, using selective antagonists for µ-opioid, dopamine D3 receptor (DRD3) and neurokinin 1 (NK1) receptors (naltrexone, GSK598809, vofopitant/aprepitant), in a placebo-controlled, randomised, crossover design. In two years, 609 scans were performed, with 155 individuals scanned at baseline. Attrition was low and the majority of individuals were sufficiently motivated to complete all five sessions (n=87). We describe herein the study design, main aims, recruitment numbers, sample characteristics, and explain the test hypotheses and anticipated study outputs.

Sexual dimorphism in healthy aging and mild cognitive impairment: a DTI study.

Previous PET and MRI studies have indicated that the degree to which pathology translates into clinical symptoms is strongly dependent on sex with women more likely to express pathology as a diagnosis of AD, whereas men are more resistant to clinical symptoms in the face of the same degree of pathology. Here we use DTI to investigate the difference between male and female white matter tracts in healthy older participants (24 women, 16 men) and participants with mild cognitive impairment (21 women, 12 men). Differences between control and MCI participants were found in fractional anisotropy (FA), radial diffusion (DR), axial diffusion (DA) and mean diffusion (MD). A significant main effect of sex was also reported for FA, MD and DR indices, with male control and male MCI participants having significantly more microstructural damage than their female counterparts. There was no sex by diagnosis interaction. Male MCIs also had significantly less normalised grey matter (GM) volume than female MCIs. However, in terms of absolute brain volume, male controls had significantly more brain volume than female controls. Normalised GM and WM volumes were found to decrease significantly with age with no age by sex interaction. Overall, these data suggest that the same degree of cognitive impairment is associated with greater structural damage in men compared with women.

Body mass index is associated with biological CSF markers of core brain pathology of Alzheimer's disease.

Weight changes are common in aging and Alzheimer's disease (AD) and postmortem findings suggest a relation between lower body mass index (BMI) and increased AD brain pathology. In the current multicenter study, we tested whether lower BMI is associated with higher core AD brain pathology as assessed by cerebrospinal fluid (CSF)-based biological markers of AD in 751 living subjects: 308 patients with AD, 296 subjects with amnestic mild cognitive impairment (MCI), and 147 elderly healthy controls (HC). Based upon a priori cutoff values on CSF concentration of total tau and beta-amyloid (Aß(1-42)), subjects were binarized into a group with abnormal CSF biomarker signature (CSF+) and those without (CSF-). Results showed that BMI was significantly lower in the CSF+ when compared with the CSF- group (F = 27.7, df = 746, p < 0.001). There was no interaction between CSF signature and diagnosis or apolipoprotein E (ApoE) genotype. In conclusion, lower BMI is indicative of AD pathology as assessed with CSF-based biomarkers in demented and nondemented elderly subjects.

Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment.

Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer's disease where the early identification of subjects with MCI is critical.

Multiple indices of diffusion identifies white matter damage in mild cognitive impairment and Alzheimer's disease.

The study of multiple indices of diffusion, including axial (DA), radial (DR) and mean diffusion (MD), as well as fractional anisotropy (FA), enables WM damage in Alzheimer's disease (AD) to be assessed in detail. Here, tract-based spatial statistics (TBSS) were performed on scans of 40 healthy elders, 19 non-amnestic MCI (MCIna) subjects, 14 amnestic MCI (MCIa) subjects and 9 AD patients. Significantly higher DA was found in MCIna subjects compared to healthy elders in the right posterior cingulum/precuneus. Significantly higher DA was also found in MCIa subjects compared to healthy elders in the left prefrontal cortex, particularly in the forceps minor and uncinate fasciculus. In the MCIa versus MCIna comparison, significantly higher DA was found in large areas of the left prefrontal cortex. For AD patients, the overlap of FA and DR changes and the overlap of FA and MD changes were seen in temporal, parietal and frontal lobes, as well as the corpus callosum and fornix. Analysis of differences between the AD versus MCIna, and AD versus MCIa contrasts, highlighted regions that are increasingly compromised in more severe disease stages. Microstructural damage independent of gross tissue loss was widespread in later disease stages. Our findings suggest a scheme where WM damage begins in the core memory network of the temporal lobe, cingulum and prefrontal regions, and spreads beyond these regions in later stages. DA and MD indices were most sensitive at detecting early changes in MCIa.

Using diffusion tensor imaging and mixed-effects models to investigate primary and secondary white matter degeneration in Alzheimer's disease and mild cognitive impairment.

White matter (WM) degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI) may be a key indicator of early damage in AD. Here, we analyzed WM diffusion tensor data using Tract-Based Spatial Statistics in conjunction with mixed-effects models. Four indices of diffusion were assessed in 61 healthy control, 19 non-amnestic MCIs, 14 amnestic MCIs, and 9 AD patients. The aim of the study was to use advanced mixed-effects models to investigate the retrogenesis hypothesis of AD, which suggests that tracts that are late to myelinate in ontogenetic development are the earliest to be affected in AD. Our results show that a number of late-myelinating pathways, including the parahippocampal region and the inferior longitudinal fasciculus, were predominantly affected by changes in WM volume. Conversely, early-myelinating pathways were found to be affected by a combination of both WM and gray matter (GM) atrophy. A model of the entire WM structure of the brain returned GM models for two indices of diffusion, suggesting that more complex regional landscapes of diffusion lie hidden beneath a global analysis of the entire brain. Our results warn against an explanation of white matter damage that points simply to one of two mechanisms: secondary degeneration or direct damage of myelin. We suggest that tracts may be affected by both mechanisms, with the balance depending on whether tracts are early or late-myelinating. A greater understanding of the pattern of WM changes in AD may prove useful for the early detection of AD.