BackgroundAnimal models of Alzheimer’s disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present.MethodsCognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats.ResultsThe results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes.ConclusionsIn this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics.Electronic supplementary materialThe online version of this article (10.1186/s13195-018-0346-2) contains supplementary material, which is available to authorized users.

5-Methoxy-N,N-dimethyltryptamine (5-MeO-DMT) is a natural hallucinogen component of Ayahuasca, an Amazonian beverage traditionally used for ritual, religious and healing purposes that is being increasingly used for recreational purposes in US and Europe. 5MeO-DMT is of potential interest for schizophrenia research owing to its hallucinogenic properties. Two other psychotomimetic agents, phencyclidine and 2,5-dimethoxy-4-iodo-phenylisopropylamine (DOI), markedly disrupt neuronal activity and reduce the power of low frequency cortical oscillations (<4 Hz, LFCO) in rodent medial prefrontal cortex (mPFC). Here we examined the effect of 5-MeO-DMT on cortical function and its potential reversal by antipsychotic drugs. Moreover, regional brain activity was assessed by blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI). 5-MeO-DMT disrupted mPFC activity, increasing and decreasing the discharge of 51 and 35% of the recorded pyramidal neurons, and reducing (-31%) the power of LFCO. The latter effect depended on 5-HT1A and 5-HT2A receptor activation and was reversed by haloperidol, clozapine, risperidone, and the mGlu2/3 agonist LY379268. Likewise, 5-MeO-DMT decreased BOLD responses in visual cortex (V1) and mPFC. The disruption of cortical activity induced by 5-MeO-DMT resembles that produced by phencyclidine and DOI. This, together with the reversal by antipsychotic drugs, suggests that the observed cortical alterations are related to the psychotomimetic action of 5-MeO-DMT. Overall, the present model may help to understand the neurobiological basis of hallucinations and to identify new targets in antipsychotic drug development.