3066-280XISSN (Online)
Volume 4 , Issue 1 , PP: 21-30, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Abdelaziz Rabehi 1 *
Doi: https://doi.org/10.54216/MOR.040103
The progress of neuroimaging and the availability of big neuro data have brought both opportunities and difficulties in the fast-developing scientific area of computational neuroscience. As this review will show, new ways of managing and analyzing these large and layered datasets are emerging, highlighting the importance of various computational approaches to achieve valuable insights. We assess various methods for performing such analyses, among which we focus on machine learning algorithms like deep learning capable of addressing high-dimensional data characteristics for neuroimaging studies. The proposed method of analyzing multiple structural and functional MRI data in conjunction with electrophysiological and genetic data should help model neurological disorders more accurately. We also describe the preprocessing methods for dealing with data noise and variability, combined with statistical analysis that depends on existing databases to identify previously unknown patterns concerning brain functions and disorders. We also discussed the importance of open-source teamwork spaces and applications, which allow datasets and results to be shared and replicated. This review, therefore, aimed at reviewing the most effective strategies and filling the gaps within the current methodologies that may help enhance the strength and reliability of vast neurological datasets, hence diminishing diagnostic errors and helping formulate the right therapeutic intercessions in neurological disorders. This synthesis emphasizes the choice of a multidisciplinary approach when studying the neural tissues since the issue appears complex.
Neuroimaging , Big neuro data , Machine learning algorithms , Electrophysiological data , Genetic data , Vast neurological datasets
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