Coefficient of variation

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Coefficient_of_variation

In probability theory and statistics, the coefficient of variation (COV), also known as Normalized Root-Mean-Square Deviation (NRMSD), Percent RMS, and relative standard deviation (RSD), is a standardized measure of dispersion of a probability distribution or frequency distribution. It is defined as the ratio of the standard deviation ${\displaystyle \sigma }$ to the mean ${\displaystyle \mu }$ (or its absolute value, ${\displaystyle |\mu |}$), and often expressed as a percentage ("%RSD"). The CV or RSD is widely used in analytical chemistry to express the precision and repeatability of an assay. It is also commonly used in fields such as engineering or physics when doing quality assurance studies and ANOVA gauge R&R, by economists and investors in economic models, and in neuroscience.

Definition

The coefficient of variation (CV) is defined as the ratio of the standard deviation ${\displaystyle \sigma }$ to the mean ${\displaystyle \mu }$, ${\displaystyle CV=\frac{\sigma }{\mu }.}$

It shows the extent of variability in relation to the mean of the population. The coefficient of variation should be computed only for data measured on scales that have a meaningful zero (ratio scale) and hence allow relative comparison of two measurements (i.e., division of one measurement by the other). The coefficient of variation may not have any meaning for data on an interval scale. For example, most temperature scales (e.g., Celsius, Fahrenheit etc.) are interval scales with arbitrary zeros, so the computed coefficient of variation would be different depending on the scale used. On the other hand, Kelvin temperature has a meaningful zero, the complete absence of thermal energy, and thus is a ratio scale. In plain language, it is meaningful to say that 20 Kelvin is twice as hot as 10 Kelvin, but only in this scale with a true absolute zero. While a standard deviation (SD) can be measured in Kelvin, Celsius, or Fahrenheit, the value computed is only applicable to that scale. Only the Kelvin scale can be used to compute a valid coefficient of variability.

Measurements that are log-normally distributed exhibit stationary CV; in contrast, SD varies depending upon the expected value of measurements.

A more robust possibility is the quartile coefficient of dispersion, half the interquartile range ${\displaystyle (Q_3-Q_1)/2}$ divided by the average of the quartiles (the midhinge), ${\displaystyle (Q_1+Q_3)/2}$.

In most cases, a CV is computed for a single independent variable (e.g., a single factory product) with numerous, repeated measures of a dependent variable (e.g., error in the production process). However, data that are linear or even logarithmically non-linear and include a continuous range for the independent variable with sparse measurements across each value (e.g., scatter-plot) may be amenable to single CV calculation using a maximum-likelihood estimation approach.

Examples

In the examples below, we will take the values given as randomly chosen from a larger population of values.

• The data set [100, 100, 100] has constant values. Its standard deviation is 0 and average is 100, giving the coefficient of variation as 0 / 100 = 0

• The data set [90, 100, 110] has more variability. Its standard deviation is 10 and its average is 100, giving the coefficient of variation as 10 / 100 = 0.1

• The data set [1, 5, 6, 8, 10, 40, 65, 88] has still more variability. Its standard deviation is 32.9 and its average is 27.9, giving a coefficient of variation of 32.9 / 27.9 = 1.18

In these examples, we will take the values given as the entire population of values.

• The data set [100, 100, 100] has a population standard deviation of 0 and a coefficient of variation of 0 / 100 = 0

• The data set [90, 100, 110] has a population standard deviation of 8.16 and a coefficient of variation of 8.16 / 100 = 0.0816

• The data set [1, 5, 6, 8, 10, 40, 65, 88] has a population standard deviation of 30.8 and a coefficient of variation of 30.8 / 27.9 = 1.10

Estimation

When only a sample of data from a population is available, the population CV can be estimated using the ratio of the sample standard deviation ${\displaystyle s}$ to the sample mean ${\displaystyle \overline{x}}$:

${\displaystyle \widehat{c_{\mathrm{v}}}=\frac{s}{\overline{x}}}$

But this estimator, when applied to a small or moderately sized sample, tends to be too low: it is a biased estimator. For normally distributed data, an unbiased estimator^[4] for a sample of size n is:

${\displaystyle {\widehat{c_{\mathrm{v}}}}^{\ast }=(1+\frac{1}{4n})\widehat{c_{\mathrm{v}}}}$

Log-normal data

In many applications, it can be assumed that data are log-normally distributed (evidenced by the presence of skewness in the sampled data). In such cases, a more accurate estimate, derived from the properties of the log-normal distribution, is defined as:

${\displaystyle {\widehat{cv}}_{\mathrm{r}\mathrm{a}\mathrm{w}}=\sqrt{{\mathrm{e}}^{s_{\mathrm{l}\mathrm{n}}^2}-1}}$

where ${\displaystyle s_{\mathrm{l}\mathrm{n}}}$ is the sample standard deviation of the data after a natural log transformation. (In the event that measurements are recorded using any other logarithmic base, b, their standard deviation ${\displaystyle s_b}$ is converted to base e using ${\displaystyle s_{\mathrm{l}\mathrm{n}}=s_b\ln (b)}$, and the formula for ${\displaystyle {\widehat{cv}}_{\mathrm{r}\mathrm{a}\mathrm{w}}}$ remains the same.) This estimate is sometimes referred to as the "geometric CV" (GCV) in order to distinguish it from the simple estimate above. However, "geometric coefficient of variation" has also been defined by Kirkwood as:

${\displaystyle \mathrm{G}\mathrm{C}{\mathrm{V}}_{\mathrm{K}}={\mathrm{e}}^{s_{\mathrm{l}\mathrm{n}}}-1}$

This term was intended to be analogous to the coefficient of variation, for describing multiplicative variation in log-normal data, but this definition of GCV has no theoretical basis as an estimate of ${\displaystyle c_{\mathrm{v}}}$ itself.

For many practical purposes (such as sample size determination and calculation of confidence intervals) it is ${\displaystyle s_{ln}}$ which is of most use in the context of log-normally distributed data. If necessary, this can be derived from an estimate of ${\displaystyle c_{\mathrm{v}}}$ or GCV by inverting the corresponding formula.

Comparison to standard deviation

Advantages

The coefficient of variation is useful because the standard deviation of data must always be understood in the context of the mean of the data. In contrast, the actual value of the CV is independent of the unit in which the measurement has been taken, so it is a dimensionless number. For comparison between data sets with different units or widely different means, one should use the coefficient of variation instead of the standard deviation.

Disadvantages

• When the mean value is close to zero, the coefficient of variation will approach infinity and is therefore sensitive to small changes in the mean. This is often the case if the values do not originate from a ratio scale.
• Unlike the standard deviation, it cannot be used directly to construct confidence intervals for the mean.
• CVs are not an ideal index of the certainty of measurement when the number of replicates varies across samples because CV is invariant to the number of replicates while the certainty of the mean improves with increasing replicates. In this case, standard error in percent is suggested to be superior.

Applications

The coefficient of variation is also common in applied probability fields such as renewal theory, queueing theory, and reliability theory. In these fields, the exponential distribution is often more important than the normal distribution. The standard deviation of an exponential distribution is equal to its mean, so its coefficient of variation is equal to 1. Distributions with CV < 1 (such as an Erlang distribution) are considered low-variance, while those with CV > 1 (such as a hyper-exponential distribution) are considered high-variance. Some formulas in these fields are expressed using the squared coefficient of variation, often abbreviated SCV. In modeling, a variation of the CV is the CV(RMSD). Essentially the CV(RMSD) replaces the standard deviation term with the Root Mean Square Deviation (RMSD). While many natural processes indeed show a correlation between the average value and the amount of variation around it, accurate sensor devices need to be designed in such a way that the coefficient of variation is close to zero, i.e., yielding a constant absolute error over their working range.

In actuarial science, the CV is known as unitized risk.

In Industrial Solids Processing, CV is particularly important to measure the degree of homogeneity of a powder mixture. Comparing the calculated CV to a specification will allow to define if a sufficient degree of mixing has been reached.

In Fluid Dynamics, the CV, also referred to as Percent RMS, %RMS, %RMS Uniformity, or Velocity RMS, is a useful determination of flow uniformity for industrial processes. The term is used widely in the design of pollution control equipment, such as electrostatic precipitators (ESPs), selective catalytic reduction (SCR), scrubbers, and similar devices. The Institute of Clean Air Companies (ICAC) references RMS deviation of velocity in the design of fabric filters (ICAC document F-7). The guiding principal is that many of these pollution control devices require "uniform flow" entering and through the control zone. This can be related to uniformity of velocity profile, temperature distribution, gas species (such as ammonia for an SCR, or activated carbon injection for mercury absorption), and other flow-related parameters. The Percent RMS also is used to assess flow uniformity in combustion systems, HVAC systems, ductwork, inlets to fans and filters, air handling units, etc. where performance of the equipment is influenced by the incoming flow distribution.

Laboratory measures of intra-assay and inter-assay CVs

CV measures are often used as quality controls for quantitative laboratory assays. While intra-assay and inter-assay CVs might be assumed to be calculated by simply averaging CV values across CV values for multiple samples within one assay or by averaging multiple inter-assay CV estimates, it has been suggested that these practices are incorrect and that a more complex computational process is required. It has also been noted that CV values are not an ideal index of the certainty of a measurement when the number of replicates varies across samples − in this case standard error in percent is suggested to be superior. If measurements do not have a natural zero point then the CV is not a valid measurement and alternative measures such as the intraclass correlation coefficient are recommended.

As a measure of economic inequality

The coefficient of variation fulfills the requirements for a measure of economic inequality. If x (with entries x_i) is a list of the values of an economic indicator (e.g. wealth), with x_i being the wealth of agent i, then the following requirements are met:

• Anonymity – c_v is independent of the ordering of the list x. This follows from the fact that the variance and mean are independent of the ordering of x.
• Scale invariance: c_v(x) = c_v(αx) where α is a real number.
• Population independence – If {x,x} is the list x appended to itself, then c_v({x,x}) = c_v(x). This follows from the fact that the variance and mean both obey this principle.
• Pigou–Dalton transfer principle: when wealth is transferred from a wealthier agent i to a poorer agent j (i.e. x_i > x_j) without altering their rank, then c_v decreases and vice versa.

c_v assumes its minimum value of zero for complete equality (all x_i are equal). Its most notable drawback is that it is not bounded from above, so it cannot be normalized to be within a fixed range (e.g. like the Gini coefficient which is constrained to be between 0 and 1). It is, however, more mathematically tractable than the Gini coefficient.

As a measure of standardisation of archaeological artefacts

Archaeologists often use CV values to compare the degree of standardisation of ancient artefacts. Variation in CVs has been interpreted to indicate different cultural transmission contexts for the adoption of new technologies. Coefficients of variation have also been used to investigate pottery standardisation relating to changes in social organisation. Archaeologists also use several methods for comparing CV values, for example the modified signed-likelihood ratio (MSLR) test for equality of CVs.

Examples of misuse

Comparing coefficients of variation between parameters using relative units can result in differences that may not be real. If we compare the same set of temperatures in Celsius and Fahrenheit (both relative units, where kelvin and Rankine scale are their associated absolute values):

Celsius: [0, 10, 20, 30, 40]

Fahrenheit: [32, 50, 68, 86, 104]

The sample standard deviations are 15.81 and 28.46, respectively. The CV of the first set is 15.81/20 = 79%. For the second set (which are the same temperatures) it is 28.46/68 = 42%.

If, for example, the data sets are temperature readings from two different sensors (a Celsius sensor and a Fahrenheit sensor) and you want to know which sensor is better by picking the one with the least variance, then you will be misled if you use CV. The problem here is that you have divided by a relative value rather than an absolute.

Comparing the same data set, now in absolute units:

Kelvin: [273.15, 283.15, 293.15, 303.15, 313.15]

Rankine: [491.67, 509.67, 527.67, 545.67, 563.67]

The sample standard deviations are still 15.81 and 28.46, respectively, because the standard deviation is not affected by a constant offset. The coefficients of variation, however, are now both equal to 5.39%.

Mathematically speaking, the coefficient of variation is not entirely linear. That is, for a random variable ${\displaystyle X}$, the coefficient of variation of ${\displaystyle aX+b}$ is equal to the coefficient of variation of ${\displaystyle X}$ only when ${\displaystyle b=0}$. In the above example, Celsius can only be converted to Fahrenheit through a linear transformation of the form ${\displaystyle ax+b}$ with ${\displaystyle b\ne 0}$, whereas Kelvins can be converted to Rankines through a transformation of the form ${\displaystyle ax}$.

Distribution

Provided that negative and small positive values of the sample mean occur with negligible frequency, the probability distribution of the coefficient of variation for a sample of size ${\displaystyle n}$ of i.i.d. normal random variables has been shown by Hendricks and Robey to be

$${\displaystyle \mathrm{d}{F}_{c_{\mathrm{v}}}=\frac{2}{{\pi }^{1/2}\mathrm{\Gamma }\left(\frac{n-1}{2}\right)}{\mathrm{e}}^{-\frac{n}{2{\left(\frac{\sigma }{\mu }\right)}^2}\frac{{c_{\mathrm{v}}}^2}{1+{c_{\mathrm{v}}}^2}}\frac{{c_{\mathrm{v}}}^{n-2}}{(1+{c_{\mathrm{v}}}^2{)}^{n/2}}\underset{i=0}{\overset{n-1}{\phantom{}{\sum }^{\mathrm{\prime }}}}\frac{(n-1)!\mathrm{\Gamma }\left(\frac{n-i}{2}\right)}{(n-1-i)!i!}\frac{n^{i/2}}{2^{i/2}{\left(\frac{\sigma }{\mu }\right)}^i}\frac{1}{(1+{c_{\mathrm{v}}}^2{)}^{i/2}}\mathrm{d}{c}_{\mathrm{v}},}$$

where the symbol $\phantom{}{\sum }^{\mathrm{\prime }}$ indicates that the summation is over only even values of ${\displaystyle n-1-i}$, i.e., if ${\displaystyle n}$ is odd, sum over even values of ${\displaystyle i}$ and if ${\displaystyle n}$ is even, sum only over odd values of ${\displaystyle i}$.

This is useful, for instance, in the construction of hypothesis tests or confidence intervals. Statistical inference for the coefficient of variation in normally distributed data is often based on McKay's chi-square approximation for the coefficient of variation. 

Alternative

According to Liu (2012), Lehmann (1986). "also derived the sample distribution of CV in order to give an exact method for the construction of a confidence interval for CV;" it is based on a non-central t-distribution.

Similar ratios

Standardized moments are similar ratios, ${\displaystyle {\mu }_k/{\sigma }^k}$ where ${\displaystyle {\mu }_k}$ is the k^th moment about the mean, which are also dimensionless and scale invariant. The variance-to-mean ratio, ${\displaystyle {\sigma }^2/\mu }$, is another similar ratio, but is not dimensionless, and hence not scale invariant. See Normalization (statistics) for further ratios.

In signal processing, particularly image processing, the reciprocal ratio ${\displaystyle \mu /\sigma }$ (or its square) is referred to as the signal-to-noise ratio in general and signal-to-noise ratio (imaging) in particular.

Other related ratios include:

• Efficiency, ${\displaystyle {\sigma }^2/{\mu }^2}$
• Standardized moment, ${\displaystyle {\mu }_k/{\sigma }^k}$
• Variance-to-mean ratio (or relative variance), ${\displaystyle {\sigma }^2/\mu }$
• Fano factor, ${\displaystyle {\sigma }_W^2/{\mu }_W}$ (windowed VMR)

Central nervous system disease

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Central_nervous_system_disease

 

Central nervous system disease
Central nervous system in yellow (brain and spinal cord)
Specialty	Psychiatry, Neurology, Neurosurgery

Central nervous system diseases, also known as central nervous system disorders, are a group of neurological disorders that affect the structure or function of the brain or spinal cord, which collectively form the central nervous system (CNS). These disorders may be caused by such things as infection, injury, blood clots, age related degeneration, cancer, autoimmune disfunction, and birth defects. The symptoms vary widely, as do the treatments.

Central nervous system tumors are the most common forms of pediatric cancer. Brain tumors are the most frequent and have the highest mortality.

Some disorders, such as substance addiction, autism, and ADHD may be regarded as CNS disorders, though the classifications are not without dispute.

Signs and symptoms

Every disease has different signs and symptoms. Some of them are persistent headache; pain in the face, back, arms, or legs; an inability to concentrate; loss of feeling; memory loss; loss of muscle strength; tremors; seizures; increased reflexes, spasticity, tics; paralysis; and slurred speech. One should seek medical attention if affected by these.

Causes

Trauma

Main article: Traumatic brain injury

Any type of traumatic brain injury (TBI) or injury done to the spinal cord can result in a wide spectrum of disabilities in a person. Depending on the section of the brain or spinal cord that experiences the trauma, the outcome may be anticipated.

Infections

Main article: List of central nervous system infections

Infectious diseases are transmitted in several ways. Some of these infections may affect the brain or spinal cord directly. Generally, an infection is a disease that is caused by the invasion of a microorganism or virus.

Degeneration

Degenerative spinal disorders involve a loss of function in the spine. Pressure on the spinal cord and nerves may be associated with herniation or disc displacement. Brain degeneration also causes central nervous system diseases (i.e. Alzheimer's, Parkinson's, and Huntington's diseases). Studies have shown that obese people may have severe degeneration in the brain due to loss of tissue affecting cognition.

Structural defects

Common structural defects include birth defects, anencephaly, and spina bifida. Children born with structural defects may have malformed limbs, heart problems, and facial abnormalities.

Defects in the formation of the cerebral cortex include microgyria, polymicrogyria, bilateral frontoparietal polymicrogyria, and pachygyria.

CNS Tumors

A tumor is an abnormal growth of body tissue. In the beginning, tumors can be noncancerous, but if they become malignant, they are cancerous. In general, they appear when there is a problem with cellular division. Problems with the body's immune system can lead to tumors.

Autoimmune disorders

An autoimmune disorder is a condition where in the immune system attacks and destroys healthy body tissue. This is caused by a loss of tolerance to proteins in the body, resulting in immune cells recognising these as 'foreign' and directing an immune response against them.

Stroke

Main article: Stroke

A stroke is an interruption of the blood supply to the brain. Approximately every 40 seconds, someone in the US has a stroke. This can happen when a blood vessel is blocked by a blood clot or when a blood vessel ruptures, causing blood to leak to the brain. If the brain cannot get enough oxygen and blood, brain cells can die, leading to permanent damage.

Functions

Spinal cord

Main article: Spinal cord

The spinal cord transmits sensory reception from the peripheral nervous system. It also conducts motor information to the body's skeletal muscles, cardiac muscles, smooth muscles, and glands. There are 31 pairs of spinal nerves along the spinal cord, all of which consist of both sensory and motor neurons. The spinal cord is protected by vertebrae and connects the peripheral nervous system to the brain, and it acts as a "minor" coordinating center.

Brain

Main article: Human brain

The brain serves as the organic basis of cognition and exerts centralized control over the other organs of the body. The brain is protected by the skull; however, if the brain is damaged, significant impairments in cognition and physiological function or death may occur.

Diagnosis

Types of CNS disorders

Addiction

Main article: Addiction

Addiction is a disorder of the brain's reward system which arises through transcriptional and epigenetic mechanisms and occurs over time from chronically high levels of exposure to an addictive stimulus (e.g., morphine, cocaine, sexual intercourse, gambling, etc.).

Arachnoid cysts

Main article: Arachnoid cysts

Arachnoid cysts are cerebrospinal fluid covered by arachnoidal cells that may develop on the brain or spinal cord. They are a congenital disorder, and in some cases may not show symptoms. However, if there is a large cyst, symptoms may include headache, seizures, ataxia (lack of muscle control), hemiparesis, and several others. Macrocephaly and ADHD are common among children, while presenile dementia, hydrocephalus (an abnormality of the dynamics of the cerebrospinal fluid), and urinary incontinence are symptoms for elderly patients (65 and older).

Attention deficit/hyperactivity disorder (ADHD)

Main article: Attention deficit-hyperactivity disorder

ADHD is an organic disorder of the nervous system. ADHD, which in severe cases can be debilitating, has symptoms thought to be caused by structural as well as biochemical imbalances in the brain; in particular, low levels of the neurotransmitters dopamine and norepinephrine, which are responsible for controlling and maintaining attention and movement. Many people with ADHD continue to have symptoms well into adulthood. Also of note is an increased risk of the development of Dementia with Lewy bodies, or (DLB), and a direct genetic association of Attention deficit disorder to Parkinson's disease two progressive, and serious, neurological diseases whose symptoms often occur in people over age 65.

Autism

Main article: Autism

Autism is a neurodevelopmental disorder that is characterized by repetitive patterns of behavior and persistent deficits in social interaction and communication.

Brain tumors

Main article: Brain tumor

Tumors of the central nervous system constitute around 2% of all cancer in the United States.

Catalepsy

Main article: Catalepsy

Catalepsy is a nervous disorder characterized by immobility and muscular rigidity, along with a decreased sensitivity to pain. Catalepsy is considered a symptom of serious diseases of the nervous system (e.g., Parkinson's disease, Epilepsy, etc.) rather than a disease by itself. Cataleptic fits can range in duration from several minutes to weeks. Catalepsy often responds to Benzodiazepines (e.g., Lorazepam) in pill and I.V. form.

Encephalitis

Main article: Encephalitis

Encephalitis is an inflammation of the brain. It is usually caused by a foreign substance or a viral infection. Symptoms of this disease include headache, neck pain, drowsiness, nausea, and fever. If caused by the West Nile virus, it may be lethal to humans, as well as birds and horses.

Epilepsy/Seizures

Main article: Epilepsy

Epilepsy is an unpredictable, serious, and potentially fatal disorder of the nervous system, thought to be the result of faulty electrical activity in the brain. Epileptic seizures result from abnormal, excessive, or hypersynchronous neuronal activity in the brain. About 50 million people worldwide have epilepsy, and nearly 80% of epilepsy occurs in developing countries. Epilepsy becomes more common as people age. Onset of new cases occurs most frequently in infants and the elderly. Epileptic seizures may occur in recovering patients as a consequence of brain surgery.

Infection

For a more comprehensive list, see List of infections of the central nervous system.

A number of different pathogens (i.e., certain viruses, bacteria, protozoa, fungi, and prions) can cause infections that adversely affect the brain or spinal cord.

Locked-in syndrome

A medical condition, Locked-in syndrome usually resulting from a stroke that damages part of the brainstem, in which the body and most of the facial muscles are paralysed but consciousness remains and the ability to perform certain eye movements is preserved.

Meningitis

Main article: Meningitis

Meningitis is an inflammation of the meninges (membranes) of the brain and spinal cord. It is most often caused by a bacterial or viral infection. Fever, vomiting, and a stiff neck are all symptoms of meningitis.

Migraine

Main article: Migraine

A chronic, often debilitating neurological disorder characterized by recurrent moderate to severe headaches, often in association with a number of autonomic nervous system symptoms.

Multiple sclerosis

Main article: Multiple sclerosis

Multiple sclerosis (MS) is a chronic, inflammatory demyelinating disease, meaning that the myelin sheath of neurons is damaged. Symptoms of MS include visual and sensation problems, muscle weakness, numbness and tingling all over, muscle spasms, poor coordination, and depression. Also, patients with MS have reported extreme fatigue and dizziness, tremors, and bladder leakage.

Myelopathy

Main article: Myelopathy

Myelopathy is an injury to the spinal cord due to severe compression that may result from trauma, congenital stenosis, degenerative disease or disc herniation. The spinal cord is a group of nerves housed inside the spine that runs almost its entire length.

Tourette's

Main article: Tourette's syndrome

Tourette's syndrome is an inherited neurological disorder. Early onset may be during childhood, and it is characterized by physical and verbal tics. Tourette's often also includes symptoms of both OCD and ADHD indicating a link between the three disorders. The exact cause of Tourette's, other than genetic factors, is unknown.

Neurodegenerative disorders

Alzheimer's

Main article: Alzheimer's disease

Alzheimer's is a neurodegenerative disease typically found in people over the age of 65 years. Worldwide, approximately 24 million people have dementia; 60% of these cases are due to Alzheimer's. The ultimate cause is unknown. The clinical sign of Alzheimer's is progressive cognition deterioration.

Huntington's disease

Main article: Huntington's disease

Huntington's disease is a degenerative neurological disorder that is inherited. Degeneration of neuronal cells occurs throughout the brain, especially in the striatum. There is a progressive decline that results in abnormal movements. Statistics show that Huntington's disease may affect 10 per 100,000 people of Western European descent.

Parkinson's

Main article: Parkinson's disease

Parkinson's disease, or PD, is a progressive illness of the nervous system. Caused by the death of dopamine-producing brain cells that affect motor skills and speech. Symptoms may include bradykinesia (slow physical movement), muscle rigidity, and tremors. Behavior, thinking, sensation disorders, and the sometimes co-morbid skin condition Seborrheic dermatitis are just some of PD's numerous nonmotor symptoms. Parkinson's disease, Attention deficit/hyperactivity disorder (ADHD) and Bi-polar disorder, all appear to have some connection to one another, as all three nervous system disorders involve lower than normal levels of the brain chemical dopamine (In ADHD, Parkinson's, and the depressive phase of Bi-polar disorder.) or too much dopamine (in Mania or Manic states of Bi-polar disorder) in different areas of the brain:

Treatments

There are a wide range of treatments for central nervous system diseases. These can range from surgery to neural rehabilitation or prescribed medications. The most valued companies worldwide whose leading products are in CNS Care include CSPC Pharma (Hong Kong), Biogen (United States), UCB (Belgium) and Otsuka (Japan) who are active in treatment areas like MS, Alzheimers, Epilepsy and Psychiatry.

Cerebral atrophy

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Cerebral_atrophy 

Cerebral atrophy is a common feature of many of the diseases that affect the brain. Atrophy of any tissue means a decrement in the size of the cell, which can be due to progressive loss of cytoplasmic proteins. In brain tissue, atrophy describes a loss of neurons and the connections between them. Brain atrophy can be classified into two main categories: generalized and focal atrophy. Generalized atrophy occurs across the entire brain whereas focal atrophy affects cells in a specific location. If the cerebral hemispheres (the two lobes of the brain that form the cerebrum) are affected, conscious thought and voluntary processes may be impaired.

Some degree of cerebral shrinkage occurs naturally with the dynamic process of aging. Structural changes continue during adulthood as brain shrinkage commences after the age of 35, at a rate of 0.2% per year. The rate of decline is accelerated when individuals reach 70 years old. By the age of 90, the human brain will have experienced a 15% loss of its initial peak weight. Besides brain atrophy, aging has also been associated with cerebral microbleeds.

Symptoms

Many diseases that cause cerebral atrophy are associated with dementia, seizures, and a group of language disorders called the aphasias. Dementia is characterized by a progressive impairment of memory and intellectual function that is severe enough to interfere with social and work skills. Memory, orientation, abstraction, ability to learn, visual-spatial perception, and higher executive functions such as planning, organizing and sequencing may also be impaired. Seizures can take different forms, appearing as disorientation, strange repetitive movements, loss of consciousness, or convulsions. Aphasias are a group of disorders characterized by disturbances in speaking and understanding language. Receptive aphasia causes impaired comprehension. Expressive aphasia is reflected in odd choices of words, the use of partial phrases, disjointed clauses, and incomplete sentences.

Causes

The pattern and rate of progression of cerebral atrophy depends on the disease involved.

Injury

• Stroke, loss of brain function due to a sudden interruption of blood supply in the brain
• Moderate-to-severe traumatic brain injury 

Diseases and disorders

Pick's disease showing brain atrophy,

• Alzheimer's disease (High resolution MRI scans have shown the progression of cerebral atrophy in Alzheimer's disease)
• Cerebral palsy, in which lesions (damaged areas) may impair motor coordination
• Senile dementia, fronto-temporal dementia, and vascular dementia
• Pick's disease, causes progressive destruction of nerve cells in the brain
• Huntington's disease, and other genetic disorders that cause build-up of toxic levels of proteins in neurons
• Leukodystrophies, such as Krabbe disease, which destroy the myelin sheath that protects axons
• Multiple sclerosis, which causes inflammation, myelin damage, and lesions in cerebral tissue
• Epilepsy, in which lesions cause abnormal electrochemical discharges that result in seizures
• GLUT1 deficiency syndrome
• Anorexia nervosa, bulimia nervosa, and other eating disorders
• Malnutrition, caused by lack or excess of nutrition from foods
• Type II diabetes, where the body does not use insulin properly resulting in high blood sugar
• Bipolar disorder, significant loss of brain tissue during manic episodes; however it's not verified whether the episodes cause brain tissue loss or vice versa
• Schizophrenia
• Mitochondrial encephalomyopathies, such as Kearns–Sayre syndrome, which interfere with the basic functions of neurons
• Posterior cortical atrophy: In the most posterior area of the brain lies the visual cortex, the area of the brain where visual information is received and processed. When cortical atrophy occurs in this brain area due to neurodegeneration, the first symptom is impairment in vision. A common vision impairment seen in patients with posterior cortical atrophy is simultanagnosia, where a person is unable to see multiple locations at once or to quickly shift attention between these locations. When looking at images of a brain with posterior cortical atrophy, one can see a loss in volume of the dorsal and ventral visual pathways, where visual stimuli is brought to the visual cortex and integrated information is sent back out to other areas of the brain. Because this disorder results in visual impairments, there is often a missed or delayed diagnosis, as the assumption is that there is a problem is in the eyes when the reality is that the problem is all the way in the back of the brain.
• Prion diseases, a group of invariably fatal encephalopathies that cause the progressive death of neurons.

Infections

Where an infectious agent or the inflammatory reaction to it destroys neurons and their axons, these include...

• Encephalitis, acute inflammation in the brain
• Neurosyphilis, an infection in the brain or spinal cord
• AIDS, disease of the immune system

Drug-induced

• Alcohol (partially reversible): Standardized MRI evidence suggest chronic alcoholism (alcohol use disorder) is associated with widespread cortical atrophy and major brain changes. In contrast to healthy controls, macrostructural findings indicate alcoholic brains are smaller in mass and volume. Neuroimaging studies also show that cortical shrinkage in "uncomplicated alcoholism" is most severe in the frontal lobe in comparison to the other divisions of the cerebral cortex. In addition, neurological diseases that co-occur with excessive alcohol consumption—such as Wernicke–Korsakoff syndrome (WKS)—are characterized by substantial volume deficits of the diencephalon structures. In comparison to unaffected, non-alcoholic participants, tissue degeneration in WKS patients is found in specific white matter structures including the corpus callosum, the hippocampus, the subcortical basal ganglia (hypothalamus, thalamus, putamen), and the mammillary bodies.
• Antipsychotic
• Corticosteroid use (There appears to be correlations between degree of dosing with corticosteroids and cerebral atrophy)

Diagnosis

Neurofilament light chain

Main article: Neurofilament light chain

Cerebrospinal fluid (CSF) is a fluid that is found exclusively in the brain and spinal cord that circulates between sections of the brain offering an extra layer of protection. Studies have shown that biomarkers in the CSF and plasma can be tracked for their presence in different parts of the brain—and their presence can tell us about cerebral atrophy. One study took advantage of biomarkers, namely one called neurofilament light chain (NFL), in patients with Alzheimer's disease. Neurofilament light chain is a protein that is important in the growth and branching of neurons—cells found in the brain. In Alzheimer's Disease, neurons will stop working or die in a process called neurodegeneration. By tracking NFL, researchers can see this neurodegeneration, which this study showed was associated with brain atrophy and later cognitive decline in Alzheimer's patients. Other biomarkers like Ng – a protein important in long-term potentiation and memory – have been tracked for their associations with brain atrophy as well, but NFL had the greatest association.

Measures

Brain CT with different grading systems of cerebral atrophy (seen as decreased size of gyri and secondary increased size of sulci):
- Medial temporal lobe atrophy (MTA)
- Posterior atrophy (PA)
- Frontal cortical atrophy (fGCA)

CT and MRI are most commonly used to observe the brain for cerebral atrophy. A CT scan takes cross sectional images of the brain using X-rays, while an MRI uses a magnetic field. With both measures, multiple images can be compared to see if there is a loss in brain volume over time.

Difference from hydrocephalus

Cerebral atrophy can be hard to distinguish from hydrocephalus because both cerebral atrophy and hydrocephalus involve an increase in cerebrospinal fluid (CSF) volume. In cerebral atrophy, this increase in CSF volume comes as a result of the decrease in cortical volume. In hydrocephalus, the increase in volume happens due to the CSF itself.

Typical imaging findings in normal pressure hydrocephalus versus brain atrophy.

	Normal pressure hydrocephalus	Brain atrophy
Preferable projection	Coronal plane at the level of the posterior commissure of the brain.
Modality in this example	CT	MRI
CSF spaces over the convexity near the vertex (red ellipse )	Narrowed convexity ("tight convexity") as well as medial cisterns	Widened vertex (red arrow) and medial cisterns (green arrow)
Callosal angle (blue V)	Acute angle	Obtuse angle
Most likely cause of leucoaraiosis (periventricular signal alterations, blue arrows )	Transependymal cerebrospinal fluid diapedesis	Vascular encephalopathy, in this case suggested by unilateral occurrence

Treatment

Cerebral atrophy is not usually preventable. However, there are steps that can be taken to reduce the risk:

• controlling blood pressure
• a healthy balanced diet including omega-3's and antioxidants
• staying active mentally, physically, and socially.

Reversibility of cerebral atrophy

While most cerebral atrophy is said to be irreversible, there are recent studies that show this is not always the case. A child who was treated with ACTH originally showed atrophy, but four months after treatment the brain was seemingly normal again.

As previously mentioned, chronic alcoholism is known to be associated with significant brain damage. The pronounced shrinkage in the frontal lobes and cerebellum of alcoholics correlates with serious impairments in executive and psychomotor functions. However, longitudinal studies suggest that some of these brain damages are partially reversible with abstinence. In response to drinking cessation, bodies of gray and white matter including the cerebral cortex, the limbic system (amygdala, hippocampus, thalamus), the cerebellum, and the brainstem all showed a general increase in brain volume. Similarly, ventricular enlargement—which reflects atrophy of surrounding brain regions—is also reduced in abstinent alcoholics. Following extended sobriety, the volume of the lateral and third ventricles was decreased, and abstainers showed an improvement in working memory and balance. Finally, evidence for the recovery of brain volume with continued sobriety is supported by the improvement in neuropsychological performance. Compared to the control participants, abstinent alcoholic patients scored significantly better on tests measuring cognitive, sensory, and motor functions including abstract reasoning, memory, visuospatial ability, and gait and balance. That being said, while short-term abstinence suffices to produce structural and functional recovery, some alcohol-induced brain changes may persist even after long-term sobriety.