section_path: Research
section_name: Research
paragraph_text: Research
  
  The field of neuroscience encompasses all approaches that seek to understand the brain and the rest of the nervous system. Psychology seeks to understand mind and behavior, and neurology is the medical discipline that diagnoses and treats diseases of the nervous system. The brain is also the most important organ studied in psychiatry, the branch of medicine that works to study, prevent, and treat mental disorders. Cognitive science seeks to unify neuroscience and psychology with other fields that concern themselves with the brain, such as computer science (artificial intelligence and similar fields) and philosophy.
  
  The oldest method of studying the brain is anatomical, and until the middle of the 20th century, much of the progress in neuroscience came from the development of better cell stains and better microscopes. Neuroanatomists study the large-scale structure of the brain as well as the microscopic structure of neurons and their components, especially synapses. Among other tools, they employ a plethora of stains that reveal neural structure, chemistry, and connectivity. In recent years, the development of immunostaining techniques has allowed investigation of neurons that express specific sets of genes. Also, functional neuroanatomy uses medical imaging techniques to correlate variations in human brain structure with differences in cognition or behavior.
  
  Neurophysiologists study the chemical, pharmacological, and electrical properties of the brain: their primary tools are drugs and recording devices. Thousands of experimentally developed drugs affect the nervous system, some in highly specific ways. Recordings of brain activity can be made using electrodes, either glued to the scalp as in EEG studies, or implanted inside the brains of animals for extracellular recordings, which can detect action potentials generated by individual neurons. Because the brain does not contain pain receptors, it is possible using these techniques to record brain activity from animals that are awake and behaving without causing distress. The same techniques have occasionally been used to study brain activity in human patients with intractable epilepsy, in cases where there was a medical necessity to implant electrodes to localize the brain area responsible for epileptic seizures. Functional imaging techniques such as fMRI are also used to study brain activity; these techniques have mainly been used with human subjects, because they require a conscious subject to remain motionless for long periods of time, but they have the great advantage of being noninvasive.
  
  Another approach to brain function is to examine the consequences of damage to specific brain areas. Even though it is protected by the skull and meninges, surrounded by cerebrospinal fluid, and isolated from the bloodstream by the blood–brain barrier, the delicate nature of the brain makes it vulnerable to numerous diseases and several types of damage. In humans, the effects of strokes and other types of brain damage have been a key source of information about brain function. Because there is no ability to experimentally control the nature of the damage, however, this information is often difficult to interpret. In animal studies, most commonly involving rats, it is possible to use electrodes or locally injected chemicals to produce precise patterns of damage and then examine the consequences for behavior.
  
  Computational neuroscience encompasses two approaches: first, the use of computers to study the brain; second, the study of how brains perform computation. On one hand, it is possible to write a computer program to simulate the operation of a group of neurons by making use of systems of equations that describe their electrochemical activity; such simulations are known as biologically realistic neural networks. On the other hand, it is possible to study algorithms for neural computation by simulating, or mathematically analyzing, the operations of simplified "units" that have some of the properties of neurons but abstract out much of their biological complexity. The computational functions of the brain are studied both by computer scientists and neuroscientists.
  
  Computational neurogenetic modeling is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes.
  
  Recent years have seen increasing applications of genetic and genomic techniques to the study of the brain and a focus on the roles of neurotrophic factors and physical activity in neuroplasticity. The most common subjects are mice, because of the availability of technical tools. It is now possible with relative ease to "knock out" or mutate a wide variety of genes, and then examine the effects on brain function. More sophisticated approaches are also being used: for example, using Cre-Lox recombination it is possible to activate or deactivate genes in specific parts of the brain, at specific times.
  
  Recent years have also seen rapid advances in single-cell sequencing technologies, and these have been used to leverage the cellular heterogeneity of the brain as a means of better understanding the roles of distinct cell types in disease and biology (as well as how genomic variants influence individual cell types). In 2024, investigators studied a large integrated dataset of almost 3 million nuclei from the human prefrontal cortext from 388 individuals. In doing so, they annotated 28 cell types to evaluate expression and chromatin variation across gene families and drug targets. They identified about half a million cell type–specific regulatory elements and about 1.5 million single-cell expression quantitative trait loci (ie, genomic variants with strong statistical associations with changes in gene expression within specific cell types), which were then used to build cell-type regulatory networks (the study also describes cell-to-cell communication networks). These networks were found to manifest cellular changes in aging and neuropsychiatric disorders. As part of the same investigation, a machine learning model was designed to accurately impute single-cell expression (this model prioritized ~250 disease-risk genes and drug targets with associated cell types).