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Delving Deeper into Diagnostics: The Current and Future Trends in Neurological Diagnostics

Neuroletter, Volume 2 Issue 1


This article explores the current and emerging technologies and solutions within

the field of neurological diagnostics. It provides an overview of the evolution of the field from its beginnings to its foreseeable future by delving into the innovations and technologies that shape its present.


Just like the nature of science itself, the field of neuroscience is an ever-evolving one. And

given the pace at which science and technology is evolving in this day and age, the evolution

of neuroscience is taking place at an accelerated pace. Every year, new knowledge,

discoveries, technology, and innovation emerge that is actively changing the workings of each and every aspect of the vast field of neuroscience. One such branch of neuroscience that has seen a rapid evolution is the field of neurological diagnostics. This area of neuroscience deals with the comprehensive identification, staging, and evaluation of neurological disorders. The field had its humble beginnings in 1929, when Hans Berger, a German neuropsychiatrist, stumbled upon the neurodiagnostic technique of electroencephalogram or EEG when he was able to obtain electrical potential readings from the scalps of patients with various skull defects. [1] However, today, neurological diagnostics has evolved into a field shaped by innovation and advanced technologies.

The current fabric of the field of diagnostics in neuroscience is dominated by three main

techniques - neurogenetics, neuro-modulation, and machine learning. [2] Neurogenetics, as the name itself suggests, is a field based on the intersection of neuroscience and genetics. Given that the overall performance and activity of the nervous system is fundamentally linked to the proper functioning of the genes and their products found within its constituent neurons, genetic disorders and defects form the basis of many neurological disorders. For example, Huntington’s disease is a neurological disorder with a genetic basis as it occurs as a result of an abnormal number of repeats of the CAG nucleotide sequence (cytosine, adenine, and guanine bases that make up our DNA) within the genome of the individual. Similarly, many genes have been identified to contain mutations within its coding regions in several neurological disorders such as neuropathies, myopathies, and ataxias. In addition to this, even complex neurological disorders, such as Parkinson’s disease and multiple sclerosis, that may not have a direct or obvious genetic link are also known to be caused by an interplay of environmental and genetic factors. Therefore, harnessing the benefits of genetic testing and applying it to the field of neurological diagnostics is essential for the effective management of neurological disorders. Current technologies and advances in the field of genetic testing that can also be utilized for the diagnosis of neurogenetic disorders include microarray-based techniques as well as the powerful next-generation DNA sequencing platforms. These techniques have been specifically developed for large-scale analysis of an individual’s whole genome, allowing for inspection of a large number of genes and, therefore the detection of any genetic disorders that contribute to neurological functioning with just a single test or reaction. [3]

Another method that is proving to be valuable for the efficient diagnosis and treatment of

neurological disorders is neuromodulation, which refers to any technology that affects the

nerves directly and alters – or rather, modulates – its activities through the direct use of

electrical and/or pharmaceutical agents, producing a measurable response from which the

functioning of the brain can be interpreted. The use of such technology in the field of

neurological diagnostics can be seen, especially in the diagnosis and even treatment of

neuromuscular disorders, multiple sclerosis, Parkinson’s disease, and Alzheimer’s disease.

Evaluating the brain’s response to its non-invasive stimulation through the mechanisms of

neuromodulation can be used to assess not only the presence or progression of neurological diseases but also track the patient’s response to certain treatments. [4]

Additionally, the application of machine learning, a branch of artificial intelligence, to the

field of neuroscience has been aiding the development of its various aspects from diagnostics to therapeutics. Until now, machine learning has been integrated with neuroimaging techniques such as CT and MRI scanning for the diagnosis of neurodegenerative diseases. When used in combination with neuroimaging, machine learning techniques can allow for the enhanced interpretation of neuroimaging data as well as aid with the identification of even the minute abnormalities in the data that may not be visible to the human eye. Additionally, machine learning is also being used to comprehensively analyze the large amount of data that is generated from genetic testing methods mentioned above, such as next-generation sequencing, allowing for a more well-refined identification of genetic disorders contributing to neurodegenerative diseases. [5]

Finally, considering the current trends in neurodiagnostic technology and its accelerating

evolution, certain predictions can be made about the future developments within this field. It is becoming increasingly clear that artificial intelligence and machine learning techniques

will play a dominant and critical role in neurological diagnostics. Additionally, it is also

evident that purely innovation-driven technologies and solutions will take centre stage in the faster, more reliable, and effective diagnosis and even treatment of neurological disorders. Therefore, from the most elementary methods of diagnosis to the most advanced techniques based on machine learning and other forms of neurotechnology, the evolution of the field of neurological diagnostics has been and will continue to be drastic, innovative, and complex –leading to a promising and exciting future for the field.


1. Stone, J. L. & Hughes, J. R. Early History of Electroencephalography and

Establishment of the American Clinical Neurophysiology Society. J. Clin. Neurophysiol. 30, 28–44 (2013).

2. Advances in Diagnostics and Treatment of Functional Neurological Disorders:

Neurogenomics, Neuromodulation and Machine-Learning. Frontiers

ent-of-functional-neurological-disorders-neurogenomics-neuromodulation (2020).

3. Toft, M. Advances in genetic diagnosis of neurological disorders. Acta Neurol. Scand.

129, 20–25 (2014).

4. Non-Invasive Neuromodulation of the Central Nervous System. (National Academies

Press, 2015). doi:10.17226/21767.

5. Myszczynska, M. A. et al. Applications of machine learning to diagnosis and

treatment of neurodegenerative diseases. Nat. Rev. Neurol. 16, 440–456 (2020).


About the author

Bhavana Pulipaka is an aspiring biomedical engineer who is currently pursuing

a BSc (Hons) degree in Biology at Ashoka University. Neuroscience has been a source of

curiosity for her since her early schooling years, and she continues to nurture her fascination for the field into her university years. Her main research interest in the field involves understanding the molecular basis of neurological diseases and disorders and developing novel, innovative and accessible technologies that can be used for the

effective diagnosis, treatment, and management of such neurological disorders.


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