Cannabinoid type 1 receptor (CB1R), as the major member of the endocannabinoid system, is among the most abundant receptors expressed in the central nervous system. CB1R is mainly located on the axon terminals of presynaptic neurons and participate in the modulation of neuronal excitability and synaptic plasticity, playing an important role in the pathogenesis of various neuropsychiatric diseases. In recent years, the consistent development of CB1R radioligands and the maturity of molecular imaging techniques, particularly positron emission tomography (PET) may help to visualize the expression and distribution of CB1R in central nervous system . At present, CB1R PET imaging can effectively evaluate the changes of CB1R levels in neuropsychiatric diseases such as Huntington's disease and schizophrenia, and its correlation with the disease severity, therefore providing new insights for the diagnosis and treatment of neuropsychiatric diseases. This article reviews the application of CB1R PET imaging in Alzheimer's disease, Parkinson's disease, Huntington's disease, schizophrenia, post-traumatic stress disorder, cannabis use disorder and depression.
Cannabinoids ; Humans ; Mental Disorders/diagnostic imaging* ; Neurodegenerative Diseases/diagnostic imaging* ; Neurons ; Positron-Emission Tomography ; Receptor, Cannabinoid, CB1

Adult ; Brain Diseases ; diagnostic imaging ; genetics ; Calcinosis ; diagnostic imaging ; genetics ; Calcium ; metabolism ; Humans ; Middle Aged ; Neurodegenerative Diseases ; diagnostic imaging ; genetics ; Retrospective Studies ; Tomography, X-Ray Computed

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease with high morbidity and mortality. There are about 5%-15% of ALS patients combining with frontotemporal lobe degeneration (FTLD) at the same time and 50% of patients combing with cognitive function changes. The analysis of cortical thickness based on MRI is an important imaging method to evaluate brain structure. The aim of the study was to explore the changes of brain structure in ALS patients by cortical thickness analysis, and to explore the correlation between the brain structure and cognitive function. METHODS: In the study, 18 ALS patients treated in Department of Neurology, Peking University Third Hospital and 18 normal controls (age, gender and education level matched) were included. 3D magnetization prepared rapid gradient echo imaging (MPRAGE) sequence MRI was performed and the cortical thickness was analyzed. At the same time, all the ALS patients took neuropsychology assessments, including: mini-mental state examination (MMSE), verbal fluency test (VFT), Stroop color word test (SCWT), prospective memory (PM), emotional picture perception and recognition, and faux pas story test. RESULTS: After cognitive assessment, two ALS patients had cognitive impairment. One was in accordance with ALS-frontotemporal dementia (FTD) diagnosis and the other one was in accordance with ALS cognitive impairment (ALSci) diagnosis. In all the 18 ALS patients and 18 normal controls, the cortical thickness of the left medial orbitofrontal lobe and the medial temporal lobe were significantly reduced (P < 0.05) in ALS group by the vertex-wise comparison. Cortical thickness of the left entorhinal cortex, the left inferior temporal gyrus, the left medial orbitofrontal lobe and the left insular lobe was significantly reduced (P < 0.05) by the region-wise comparison. However, when only concluded the 16 ALS non-cognitive impairment patients, there was no significant difference between the two groups (P>0.05). There were correlations between the scores of prospective memory, emotional picture perception and recognition, faux pas story test and the cortical thickness of their corresponding regions (P < 0.05). CONCLUSION The cortical thickness of ALS patients are correlated with neuropsychological scores which may reflect the changes of cortical structure corresponding to the cognitive assessment, and may provide help for the early diagnosis of cognitive changes in ALS patients.
Humans ; Amyotrophic Lateral Sclerosis/diagnostic imaging* ; Neurodegenerative Diseases ; Frontotemporal Dementia/psychology* ; Neuropsychological Tests ; Cognitive Dysfunction/etiology* ; Magnetic Resonance Imaging/methods*

The pathogenesis of Alzheimer's disease (AD), a common neurodegenerative disease, is still unknown. It is difficult to determine the atrophy areas, especially for patients with mild cognitive impairment (MCI) at different stages of AD, which results in a low diagnostic rate. Therefore, an early diagnosis model of AD based on 3-dimensional convolutional neural network (3DCNN) and genetic algorithm (GA) was proposed. Firstly, the 3DCNN was used to train a base classifier for each region of interest (ROI). And then, the optimal combination of the base classifiers was determined with the GA. Finally, the ensemble consisting of the chosen base classifiers was employed to make a diagnosis for a patient and the brain regions with significant classification capability were decided. The experimental results showed that the classification accuracy was 88.6% for AD
Alzheimer Disease/diagnosis* ; Brain/diagnostic imaging* ; Cognitive Dysfunction/diagnosis* ; Early Diagnosis ; Humans ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Neurodegenerative Diseases

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease. Neuroimaging based on magnetic resonance imaging (MRI) is one of the most intuitive and reliable methods to perform AD screening and diagnosis. Clinical head MRI detection generates multimodal image data, and to solve the problem of multimodal MRI processing and information fusion, this paper proposes a structural and functional MRI feature extraction and fusion method based on generalized convolutional neural networks (gCNN). The method includes a three-dimensional residual U-shaped network based on hybrid attention mechanism (3D HA-ResUNet) for feature representation and classification for structural MRI, and a U-shaped graph convolutional neural network (U-GCN) for node feature representation and classification of brain functional networks for functional MRI. Based on the fusion of the two types of image features, the optimal feature subset is selected based on discrete binary particle swarm optimization, and the prediction results are output by a machine learning classifier. The validation results of multimodal dataset from the AD Neuroimaging Initiative (ADNI) open-source database show that the proposed models have superior performance in their respective data domains. The gCNN framework combines the advantages of these two models and further improves the performance of the methods using single-modal MRI, improving the classification accuracy and sensitivity by 5.56% and 11.11%, respectively. In conclusion, the gCNN-based multimodal MRI classification method proposed in this paper can provide a technical basis for the auxiliary diagnosis of Alzheimer's disease.
Humans ; Alzheimer Disease/diagnostic imaging* ; Neurodegenerative Diseases ; Magnetic Resonance Imaging/methods* ; Neural Networks, Computer ; Neuroimaging/methods* ; Cognitive Dysfunction/diagnosis*