Stroke recoveryThe primary goals of stroke management are to reduce brain injury and promote maximum patient recovery. Rapid detection and appropriate emergency medical care are essential for optimizing health outcomes. When available, patients are admitted to an acute stroke unit for treatment. These units specialize in providing medical and surgical care aimed at stabilizing the patient's medical status. Standardized assessments are also performed to aid in the development of an appropriate care plan.
StrokeA stroke is a medical condition in which poor blood flow to the brain causes cell death. There are two main types of stroke: ischemic, due to lack of blood flow, and hemorrhagic, due to bleeding. Both cause parts of the brain to stop functioning properly. Signs and symptoms of a stroke may include an inability to move or feel on one side of the body, problems understanding or speaking, dizziness, or loss of vision to one side. Signs and symptoms often appear soon after the stroke has occurred.
Spontaneous recoverySpontaneous recovery is a phenomenon of learning and memory that was first named and described by Ivan Pavlov in his studies of classical (Pavlovian) conditioning. In that context, it refers to the re-emergence of a previously extinguished conditioned response after a delay. Such a recovery of "lost" behaviors can be observed within a variety of domains, and the recovery of lost human memories is often of particular interest. For a mathematical model for spontaneous recovery see Further Reading.
Traumatic brain injuryA traumatic brain injury (TBI), also known as an intracranial injury, is an injury to the brain caused by an external force. TBI can be classified based on severity ranging from mild traumatic brain injury (mTBI/concussion) to severe traumatic brain injury. TBI can also be characterized based on mechanism (closed or penetrating head injury) or other features (e.g., occurring in a specific location or over a widespread area). Head injury is a broader category that may involve damage to other structures such as the scalp and skull.
Lacunar strokeLacunar stroke or lacunar cerebral infarct (LACI) is the most common type of ischemic stroke, resulting from the occlusion of small penetrating arteries that provide blood to the brain's deep structures. Patients who present with symptoms of a lacunar stroke, but who have not yet had diagnostic imaging performed, may be described as having lacunar stroke syndrome (LACS). Much of the current knowledge of lacunar strokes comes from C. Miller Fisher's cadaver dissections of post-mortem stroke patients.
Transient ischemic attackA transient ischemic attack (TIA), commonly known as a mini-stroke, is a minor stroke whose noticeable symptoms usually end in less than an hour. TIA causes the same symptoms associated with strokes, such as weakness or numbness on one side of the body, sudden dimming or loss of vision, difficulty speaking or understanding language, slurred speech, or confusion. All forms of stroke, including TIA, result from a disruption in blood flow to the central nervous system.
Cross-validation (statistics)Cross-validation, sometimes called rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set. Cross-validation is a resampling method that uses different portions of the data to test and train a model on different iterations. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice.
AphasiaIn aphasia, a person may be unable to comprehend or unable to formulate language because of damage to specific brain regions. The major causes are stroke and head trauma; prevalence is hard to determine but aphasia due to stroke is estimated to be 0.1–0.4% in the Global North. Aphasia can also be the result of brain tumors, epilepsy, brain damage and brain infections, or neurodegenerative diseases (such as dementias). To be diagnosed with aphasia, a person's language must be significantly impaired in one (or more) of the four aspects of communication.
Expressive aphasiaExpressive aphasia, also known as Broca's aphasia, is a type of aphasia characterized by partial loss of the ability to produce language (spoken, manual, or written), although comprehension generally remains intact. A person with expressive aphasia will exhibit effortful speech. Speech generally includes important content words but leaves out function words that have more grammatical significance than physical meaning, such as prepositions and articles. This is known as "telegraphic speech".
Gene predictionIn computational biology, gene prediction or gene finding refers to the process of identifying the regions of genomic DNA that encode genes. This includes protein-coding genes as well as RNA genes, but may also include prediction of other functional elements such as regulatory regions. Gene finding is one of the first and most important steps in understanding the genome of a species once it has been sequenced. In its earliest days, "gene finding" was based on painstaking experimentation on living cells and organisms.
Statistical model validationIn statistics, model validation is the task of evaluating whether a chosen statistical model is appropriate or not. Oftentimes in statistical inference, inferences from models that appear to fit their data may be flukes, resulting in a misunderstanding by researchers of the actual relevance of their model. To combat this, model validation is used to test whether a statistical model can hold up to permutations in the data.
IschemiaIschemia or ischaemia is a restriction in blood supply to any tissue, muscle group, or organ of the body, causing a shortage of oxygen that is needed for cellular metabolism (to keep tissue alive). Ischemia is generally caused by problems with blood vessels, with resultant damage to or dysfunction of tissue i.e. hypoxia and microvascular dysfunction. It also implies local hypoxia in a part of a body resulting from constriction (such as vasoconstriction, thrombosis, or embolism).
Machine learningMachine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.
NeuroplasticityNeuroplasticity, also known as neural plasticity, or brain plasticity, is the ability of neural networks in the brain to change through growth and reorganization. It is when the brain is rewired to function in some way that differs from how it previously functioned. These changes range from individual neuron pathways making new connections, to systematic adjustments like cortical remapping. Examples of neuroplasticity include circuit and network changes that result from learning a new ability, information acquisition, environmental influences, practice, and psychological stress.
Training, validation, and test data setsIn machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets.
Protein structure predictionProtein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; and it is important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).
Vegetative stateA vegetative state (VS) or post-coma unresponsiveness (PCU), is a disorder of consciousness in which patients with severe brain damage are in a state of partial arousal rather than true awareness. After four weeks in a vegetative state, the patient is classified as being in a persistent vegetative state (PVS). This diagnosis is classified as a permanent vegetative state some months (three in the US and six in the UK) after a non-traumatic brain injury or one year after a traumatic injury.
HemiparesisHemiparesis, or unilateral paresis, is weakness of one entire side of the body (hemi- means "half"). Hemiplegia is, in its most severe form, complete paralysis of half of the body. Hemiparesis and hemiplegia can be caused by different medical conditions, including congenital causes, trauma, tumors, or stroke. Depending on the type of hemiparesis diagnosed, different bodily functions can be affected. Some effects are expected (e.g., partial paralysis of a limb on the affected side).
Model organismA model organism (often shortened to model) is a non-human species that is extensively studied to understand particular biological phenomena, with the expectation that discoveries made in the model organism will provide insight into the workings of other organisms. Model organisms are widely used to research human disease when human experimentation would be unfeasible or unethical. This strategy is made possible by the common descent of all living organisms, and the conservation of metabolic and developmental pathways and genetic material over the course of evolution.
Regression validationIn statistics, regression validation is the process of deciding whether the numerical results quantifying hypothesized relationships between variables, obtained from regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression, analyzing whether the regression residuals are random, and checking whether the model's predictive performance deteriorates substantially when applied to data that were not used in model estimation.
