Hypercholesterolemia is a known contributor to the pathogenesis of Alzheimer’s disease while its role in the occurrence of Parkinson’s disease (PD) is only conjecture and far from conclusive. Altered antioxidant homeostasis and mitochondrial functions are the key mechanisms in loss of dopaminergic neurons in the substantia nigra (SN) region of the midbrain in PD. Hypercholesterolemia is reported to cause oxidative stress and mitochondrial dysfunctions in the cortex and hippocampus regions of the brain in rodents. However, the impact of hypercholesterolemia on the midbrain dopaminergic neurons in animal models of PD remains elusive. We tested the hypothesis that hypercholesterolemia in MPTP model of PD would potentiate dopaminergic neuron loss in SN by disrupting mitochondrial functions and antioxidant homeostasis. It is evident from the present study that hypercholesterolemia in naïve animals caused dopamine neuronal loss in SN with subsequent reduction in striatal dopamine levels producing motor impairment. Moreover, in the MPTP model of PD, hypercholesterolemia exacerbated MPTP-induced reduction of striatal dopamine as well as dopaminergic neurons in SN with motor behavioral depreciation. Activity of mitochondrial complexes, mainly complex-I and III, was impaired severely in the nigrostriatal pathway of hypercholesterolemic animals treated with MPTP. Hypercholesterolemia caused oxidative stress in the nigrostriatal pathway with increased generation of hydroxyl radicals and enhanced activity of antioxidant enzymes, which were further aggravated in the hypercholesterolemic mice with Parkinsonism. In conclusion, our findings provide evidence of increased vulnerability of the midbrain dopaminergic neurons in PD with hypercholesterolemia.

Silymarin, a C25 containing flavonoid from the plant Silybum marianum, has been the gold standard drug to treat liver disorders associated with alcohol consumption, acute and chronic viral hepatitis, and toxin-induced hepatic failures since its discovery in 1960. Apart from the hepatoprotective nature, which is mainly due to its antioxidant and tissue regenerative properties, Silymarin has recently been reported to be a putative neuroprotective agent against many neurologic diseases including Alzheimer's and Parkinson's diseases, and cerebral ischemia. Although the underlying neuroprotective mechanism of Silymarin is believed to be due to its capacity to inhibit oxidative stress in the brain, it also confers additional advantages by influencing pathways such as β-amyloid aggregation, inflammatory mechanisms, cellular apoptotic machinery, and estrogenic receptor mediation. In this review, we have elucidated the possible neuroprotective effects of Silymarin and the underlying molecular events, and suggested future courses of action for its acceptance as a CNS drug for the treatment of neurodegenerative diseases.

Structural changes in different regions of the brain have become clinically relevant and regarded as the signature phenomenon for neurological diseases. Morphological changes in brain are also associated with neuronal and neurochemical alterations. Studies have showed that minute changes in neurochemical levels may have marked impact on the psychobehaviour of the subject. Several neurological disease profiles have been reported with such specific psychobehavioural expression. However, application of behavioural abnormalities as possible disease progress markers has not been considered and emphasised much. Reports suggested that-the subjects, who have already entered into the terminal stage of the disease, used to show cardinal behavioural signs for a specific disease profile. However, psychological expressions are comparatively early expressive. As most of the neurodegenerative disorders are unidirectional and progressive by nature, therapeutic intervention at the right time is essential for attaining the desired outcome. Moreover, early diagnosis can aid in managing the disease progression also. Psychobehavioural analysis could meet the expected outcome of disease diagnosis if implemented properly and timely. In the present review, we have amalgamated the reported behavioural anomalies with the supportive background from neurochemical basis. Further, we have concluded that behaviour centric studies could be a potential diagnostic tool for the early diagnosis of major neurological diseases such as Alzheimer disease, Parkinson's disease, Amyotrophic lateral sclerosis (ALS), Bipolar disorder, Schizophrenia, Impulse control disorder (ICD) and Obsessive-compulsive disorder (OCD).

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Alzheimer’s disease is incurable at the moment. If it can be appropriately diagnosed, the correct treatment can postpone the patient’s illness. To aid in the diagnosis of Alzheimer’s disease and to minimize the time and expense associated with manual diagnosis, a machine learning technique is employed, and a transfer learning method based on 3D MRI data is proposed. Machine learning algorithms can dramatically reduce the time and effort required for human treatment of Alzheimer’s disease. This approach extracts bottleneck features from the M-Net migration network and then adds a top layer to supervised training to further decrease the dimensionality and delete portions. As a consequence, the transfer network presented in this study has several advantages in terms of computational efficiency and training time savings when used as a machine learning approach for AD-assisted diagnosis. Finally, the properties of all subject slices are combined and trained in the classification layer, completing the categorization of Alzheimer’s disease symptoms and standard control. The results show that this strategy has a 1.5 percentage point better classification accuracy than the one that relies exclusively on VGG16 to extract bottleneck features. This strategy could cut the time it takes for the network to learn and improve its ability to classify things. The experiment shows that the method works by using data from OASIS. A typical transfer learning network’s classification accuracy is about 8% better with this method than with a typical network, and it takes about 1/60 of the time with this method.

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