Decoding degeneration: the implementation of machine learning for clinical detection of neurodegenerative disorders.

Machine learning represents a growing subfield of artificial intelligence with much promise in the diagnosis, treatment, and tracking of complex conditions, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. While no definitive methods of diagnosis or treatment exist for either disease, researchers have implemented machine learning algorithms with neuroimaging and motion-tracking technology to analyze pathologically relevant symptoms and biomarkers. Deep learning algorithms such as neural networks and complex combined architectures have proven capable of tracking disease-linked changes in brain structure and physiology as well as patient motor and cognitive symptoms and responses to treatment. However, such techniques require further development aimed at improving transparency, adaptability, and reproducibility. In this review, we provide an overview of existing neuroimaging technologies and supervised and unsupervised machine learning techniques with their current applications in the context of Alzheimer's and Parkinson's diseases.

Molecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer's disease.

Tau aggregation in neurofibrillary tangles (NFTs) is closely associated with neurodegeneration and cognitive decline in Alzheimer's disease (AD). However, the molecular signatures that distinguish between aggregation-prone and aggregation-resistant cell states are unknown. We developed methods for the high-throughput isolation and transcriptome profiling of single somas with NFTs from the human AD brain, quantified the susceptibility of 20 neocortical subtypes for NFT formation and death, and identified both shared and cell-type-specific signatures. NFT-bearing neurons shared a marked upregulation of synaptic transmission-related genes, including a core set of 63 genes enriched for synaptic vesicle cycling. Oxidative phosphorylation and mitochondrial dysfunction were highly cell-type dependent. Apoptosis was only modestly enriched, and the susceptibilities of NFT-bearing and NFT-free neurons for death were highly similar. Our analysis suggests that NFTs represent cell-type-specific responses to stress and synaptic dysfunction. We provide a resource for biomarker discovery and the investigation of tau-dependent and tau-independent mechanisms of neurodegeneration.

From cradle to grave: neurogenesis, neuroregeneration and neurodegeneration in Alzheimer's and Parkinson's diseases.

Two of the most common neurodegenerative disorders - Alzheimer's and Parkinson's diseases - are characterized by synaptic dysfunction and degeneration that culminate in neuronal loss due to abnormal protein accumulation. The intracellular aggregation of hyper-phosphorylated tau and the extracellular aggregation of amyloid beta plaques form the basis of Alzheimer's disease pathology. The major hallmark of Parkinson's disease is the loss of dopaminergic neurons in the substantia nigra pars compacta, following the formation of Lewy bodies, which consists primarily of alpha-synuclein aggregates. However, the discrete mechanisms that contribute to neurodegeneration in these disorders are still poorly understood. Both neuronal loss and impaired adult neurogenesis have been reported in animal models of these disorders. Yet these findings remain subject to frequent debate due to a lack of conclusive evidence in post mortem brain tissue from human patients. While some publications provide significant findings related to axonal regeneration in Alzheimer's and Parkinson's diseases, they also highlight the limitations and obstacles to the development of neuroregenerative therapies. In this review, we summarize in vitro and in vivo findings related to neurogenesis, neuroregeneration and neurodegeneration in the context of Alzheimer's and Parkinson's diseases.