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).

Artificial Intelligence (AI) or Machine learning in present era, serves as the primary choice for data mining and big data analysis. With effective learning and adaptation model, it provides solutions to several engineering applications. These include techniques such as Artificial Neural Network modelling, Reasoning based decision algorithms, Simulation models, DNA computing and Quantum computing among several others. With the application of AI in Biomedical research, the fuzziness and randomness in handling such type of data has significantly reduced. Rapid technological advancements have helped AI techniques evolve in manner which promotes handling such fuzzy data effectively and much more conveniently. The review presents a comprehensive view of machine learning and AI computing models, advanced data analytics and optimisation approaches used in Bioengineering such as Drug Designing and Analysis, Medical imaging, biologically inspired learning and adaption for analytics, etc.

Alzheimer's Disease (AD) is the most prevalent non-reversible neurodegenerative disorder that affects the memory and cognitive centres of brain. It has been reported that, AD turns out to be prominent among the people aged ~65 or above and is regarded as the most common cause of dementia. Moreover, AD stands among the leading causes of death in the first world nations, accounting more than 60% incidence of progressive cognitive impairment in elderly people. Amyloid beta and neurofibrillary tangles are two putative cytotoxic entities that have been identified, aggregation of which has been associated with the pathological signature of AD. Beta secretases-an amyloid precursor protein cleavage enzyme, plays a pivotal role in such pathogenic process of AD. Several other enzymatic dysregulations have also been linked with AD. Involvement of enzymatic dysregulation is the most discussed pathological implication in AD and therapeutic approaches have been postulated targeting such anomalies. Together, global consequences of enzymatic dysregulation and related therapeutic possibilities in AD remain the prime focus of present time. Therefore, research and study for the eloquent insight into the AD pathology from enzymatic perspective is essential and the same endeavour has been carried out in the present study.

The pathobiology of neuronal cell loss due to various endogenous or exogenous influences is clinically termed as neurodegeneration. Neurodegeneration has been reported to be the major contributor to aging and central nervous system diseases. Apart from aging and endogenous involvement, neurodegeneration also has been reported from viral infection and prion diseases. Studies have shown that, chronic degeneration of neuronal cells initiate the pathology of Alzheimer's disease-the most prevalent neurodegenerative disorder in the world. Similar neurodegenerative pathology is also evident in Parkinson's disease, multiple sclerosis, and Amyotrophic Lateral Sclerosis. Neurodegeneration negatively affects the mental and physical functioning of the patient. Intriguingly, the involvement of inflammation has been linked as the most crucial entity in the mechanistic progress of neurodegeneration. Moreover, recent data also have shown that inflammatory biomarkers can prognosticate the silent progress of neurodegeneration through low-cost diagnostic approach. Mainly, Th17 and MDSCs are the particular immune cells, which have been reported to assist adequately to get a detailed insight into the underlying pathological process in neurodegeneration. Similarly, depression and dementia are also having a crucial association with pro-inflammatory cytokines, which in chronic spectrum indicates the degenerative pathology. Together, available literatures are depicting a direct association between neuroinflammation and neurodegeneration. In the present review, we have summed up all the neuropathologies in light of inflammation and emphasized the possible diagnostic measures by using inflammatory cells and mediators as biomarkers for neurodegenerative diseases.