Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

Abstract

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

Enigma of Pyramidal Neurons: Chirality-Centric View on Biological Evolution. Congruence to Molecular, Cellular, Physiological, Cognitive, and Psychological Functions

“…Such structures serve as enclosures for viral genomes and potentially can serve as internal determinants of cell shape. In addition to ferritins [208,216], well-known examples of tetrahedral protein assembly are small heat shock proteins (sHSPs) [217] and globular hemoglobin (Hb) [218][219][220][221][222]. Hb is a highly conserved globular protein in all life forms and functionally tied to aerobic organisms utilizing oxygen from the atmosphere and delivering it to cells.…”

Problems of Connectionism

“…This distinction between healthy individuals and pathological ones is also evident in adults, where differences in connectivity have been spotted in cases of insomnia disorder [77], attention deficit disorder, hyperactivity disorder, and bipolar disorder [78], also in Alzheimer's disease [79], and in cases of schizophrenia [80]. For instance, in schizophrenic patients, there has been a reduction in the functional connectivity of the insula and an overall abnormality of the entire area [81], and in Alzheimer's disease and aging, the reduction of both gray and white matters implies a reduction in the brain's connectivity [82].…”

Integrating Artificial Intelligence and Organoids: A New Horizon in Mental Health Research

“…All neurons feed into a single processor, which performs calculations and outputs a binary result. During the learning process, the weights of the neurons are adjusted based on the output error, allowing the ANN to improve its accuracy over time (Surianarayanan et al, 2023). The human brain is designed similarly but is exponentially more complex, with billions of neurons interconnected to form intricate circuitries.…”

“…ML focuses on the development of algorithms that can learn from and make predictions or decisions based on data. ANNs are computational models inspired by the human brain's structure and function (Surianarayanan et al, 2023). They enable a learning system that can deeply analyze experiments and raw data.…”