Bearded capuchin monkeys as a model for Alzheimer's disease.

The absence of a natural animal model is one of the main challenges in Alzheimer's disease research. Despite the challenges of using nonhuman primates in studies, these animals can bridge mouse models and humans, as nonhuman primates are phylogenetically closer to humans and can spontaneously develop AD-type pathology. The capuchin monkey, a New World primate, has recently attracted attention due to its skill in creating and using instruments. We analyzed one capuchin brain using structural 7 T MRI and performed a neuropathological evaluation of three animals. Alzheimer-type pathology was found in the two of the capuchins. Widespread ß-amyloid pathology was observed, mainly in focal deposits with variable morphology and a high density of mature plaques. Notably, plaque-associated dystrophic neurites associated with disruption of axonal transport and early cytoskeletal alteration were frequently found. Unlike in other species of New World monkeys, cerebral arterial angiopathy was not the predominant form of ß-amyloid pathology. Additionally, abnormal aggregates of hyperphosphorylated tau, resembling neurofibrillary pathology, were observed in the temporal and frontal cortex. Astrocyte hypertrophy surrounding plaques was found, suggesting a neuroinflammatory response. These findings indicate that aged capuchin monkeys can spontaneously develop Alzheimer-type pathology, indicating that they may be an advantageous animal model for research in Alzheimer's disease.

Bearded capuchin monkey as a model for Alzheimer's disease research.

The absence of a natural animal model is one of the main challenges in Alzheimer's disease research. Despite the challenges of using non-human primates in studies, they can bridge mouse models and humans, as non-human primates are phylogenetically close to humans and can spontaneously develop AD-type pathology. The capuchin monkey, a New World primate, has recently attracted attention due to its skill in creating and using instruments. We analyzed three capuchin brains using structural 7T MRI and neuropathological evaluation. Alzheimer-type pathology was found in one case. Widespread ß-amyloid pathology mainly in the form of focal deposits with variable morphology and high density of mature plaques. Noteworthy, plaque-associated dystrophic neurites, associated with disrupted of axonal transport and early cytoskeletal alteration, were frequently found. Unlike other species of New World monkeys, cerebral arterial angiopathy was not the predominant form of ß-amyloid pathology. Additionally, abnormal aggregates of hyperphosphorylated tau, resembling neurofibrillary pathology, were observed in the temporal and frontal cortex. Besides, astrocyte hypertrophy surrounding plaques was found, suggesting a neuroinflammatory response. Aged capuchin monkeys can spontaneously develop Alzheimer-type pathology, indicating that they may be an advantageous animal model for research in Alzheimer's disease.

Cellular and Extracellular White Matter Abnormalities in Obsessive-Compulsive Disorder: A Diffusion Magnetic Resonance Imaging Study.

BACKGROUND: While previous studies have implicated white matter (WM) as a core pathology of obsessive-compulsive disorder (OCD), the underlying neurobiological processes remain elusive. This study used free-water (FW) imaging derived from diffusion magnetic resonance imaging to identify cellular and extracellular WM abnormalities in patients with OCD compared with control subjects. Next, we investigated the association between diffusion measures and clinical variables in patients. METHODS: We collected diffusion-weighted magnetic resonance imaging and clinical data from 83 patients with OCD (56 women/27 men, age 37.7 ± 10.6 years) and 52 control subjects (27 women/25 men, age 32.8 ± 11.5 years). Fractional anisotropy (FA), FA of cellular tissue, and extracellular FW maps were extracted and compared between patients and control subjects using tract-based spatial statistics and voxelwise comparison in FSL Randomise. Next, we correlated these WM measures with clinical variables (age of onset and symptom severity) and compared them between patients with and without comorbidities and patients with and without psychiatric medication. RESULTS: Patients with OCD demonstrated lower FA (43.4% of the WM skeleton), lower FA of cellular tissue (31% of the WM skeleton), and higher FW (22.5% of the WM skeleton) compared with control subjects. We did not observe significant correlations between diffusion measures and clinical variables. Comorbidities and medication status did not influence diffusion measures. CONCLUSIONS: Our findings of widespread FA, FA of cellular tissue, and FW abnormalities suggest that OCD is associated with microstructural cellular and extracellular abnormalities beyond the corticostriatothalamocortical circuits. Future multimodal longitudinal studies are needed to understand better the influence of essential clinical variables across the illness trajectory.

The role of artificial intelligence and machine learning in harmonization of high-resolution post-mortem MRI (virtopsy) with respect to brain microstructure.

Enhanced resolution of 7 T magnetic resonance imaging (MRI) scanners has considerably advanced our knowledge of structure and function in human and animal brains. Post-industrialized countries are particularly prone to an ever-increasing number of ageing individuals and ageing-associated neurodegenerative diseases. Neurodegenerative diseases are associated with volume loss in the affected brain. MRI diagnoses and monitoring of subtle volume changes in the ageing/diseased brains have the potential to become standard diagnostic tools. Even with the superior resolution of 7 T MRI scanners, the microstructural changes comprising cell types, cell numbers, and cellular processes, are still undetectable. Knowledge of origin, nature, and progression for microstructural changes are necessary to understand pathogenetic stages in the relentless neurodegenerative diseases, as well as to develop therapeutic tools that delay or stop neurodegenerative processes at their earliest stage. We illustrate the gap in resolution by comparing the identical regions of the post-mortem in situ 7 T MR images (virtual autopsy or virtopsy) with the histological observations in serial sections through the same brain. We also described the protocols and limitations associated with these comparisons, as well as the necessity of supercomputers and data management for "Big data". Analysis of neuron and/or glial number by using a body of mathematical tools and guidelines (stereology) is time-consuming, cumbersome, and still restricted to trained human investigators. Development of tools based on machine learning (ML) and artificial intelligence (AI) could considerably accelerate studies on localization, onset, and progression of neuron loss. Finally, these observations could disentangle the mechanisms of volume loss into stages of reversible atrophy and/or irreversible fatal cell death. This AI- and ML-based cooperation between virtopsy and histology could bridge the present gap between virtual reality and neuropathology. It could also culminate in the creation of an imaging-associated comprehensive database. This database would include genetic, clinical, epidemiological, and technical aspects that could help to alleviate or even stop the adverse effects of neurodegenerative diseases on affected individuals, their families, and society.

An fMRI normative database for connectivity networks using one-class support vector machines.

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal patterns and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored.