Neurotransmitters and Their Functions

There are different types of neurotransmitters. For example, glutamate (glu) is involved in the excitation of the central nervous system (CNS). Aspartate (asp) is involved in the excitation of the brain and spinal cord. Gama-aminobutyric acid (GABA) is involved in the inhibition of the CNS. Glycine (gly) is involved in the inhibition in the spinal cord. In addition, acetylcholine (ACh) is involved in attention and autonomic activation. Dopamine (DA) is involved in movement and reward. Serotonin (5-HT) is involved in mood control, sensory processing, and relaxation.

The neurotransmitter noradrenaline (NA) is also known as norepinephrine (NE), and is involved in the control of smooth muscles and arousal. Substance P (SP) is involved in many functions including the signaling of pain. Opioids (Enk) are also involved in the regulation of pain, as well as satiety. Neuropeptide Y (NPY) is involved in the control of appetite. Adenosine triphosphate (ATP) is important for various functions.

The release of neurotransmitters can have ionotropic and/or metabotropic effects on the post-synaptic cell. The ionotropic effect means that upon activation the receptor can let the ions across the membrane. The metabotropic effect means that upon activation the receptor can stimulate biochemical signaling internally. A synapse can have both types of receptors, and some receptors have both functions. Metabotropic receptors may influence the gene expression and protein production, as well as the oscillating cycles of enzyme activities.

Ionotropic receptors associated with sodium channels will enable a neuron to fire. Those receptors associated with chloride channels may inhibit the firing to set the membrane potential back to the resting state. Post-synaptic potentials are the ultimate alterations in the membrane potentials, which can be excitatory (EPSP) or inhibitory (IPSP). Various synapses may have competing effects by reinforcing or opposing each other’s influences. The synaptic influences may be affected by the distance between a synapse and the axon hillock, as well as the thickness and shape of the dendrites.

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Stress Responses and Meditation

The fight-or-flight reaction is the body’s response to mental stress including job deadlines and major life changes. During this response, the stress hormones including adrenaline and cortisol are produced to promote your response capability. Your heart rate is faster to prepare for physical reactions by pumping more blood and nutrients to the muscles. Your breathing rate is faster too to provide more oxygen.

In the meantime, the blood is moved away from your gastrointestinal system and into the muscles so that you may feel a loss of appetite. To see the condition more clearly, your pupils dilate. In addition, the skin is more sensitive, and the hair becomes standing on end.

Long term or chronic stress may lead to the strain of the autonomic nervous system and result in problems such as high blood pressure and digestive disorders. Maladaptive coping ways dealing with stress include the denial of the problem, becoming workaholic to avoid conflicts in the personal life, and even the use of chemicals such as alcohol, cigarettes, stimulants, and sleeping pills. Other maladaptive ways include eating disorders such as binge eating and bulimia. These reactions are very harmful to health and need to be avoided.

The life-force energy that animates the human body has different names in different cultures. It is called “prana” in Yogis, and “chi” or “qi” in traditional Chinese medicine. The mechanism of acupuncture is based on the adjustment of the life-force energy by inserting needles into acupuncture points for relieving pain and healing other diseases. Meditation can also help tune the life-force energy. The practice of mindfulness may help with stress reduction in a healthier way.

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Structure of the Neurons: Dendrites, Axons, and Membrane Potentials

Types of cells in the nervous system

There are two types of cells in the nervous system. One type of cells are called neurons, the other type of cells are called glia. Studies have found that there are about 86 billion neurons in the human brain. In comparison, there are about 200 million neurons in the rat brain.

Dendrites and axons

The sizes and shapes of neurons can be very different, from 10 mm (micrometers) to 50 mm. A neuron is composed of dendrites and an axon. Dendrites get information from stimuli or input from other neurons or receptors. Membranes on the dendrites have small channels controlling the traffic of ions across the membrane. Chemical or physical stimuli can influence the ion channels and cause voltage changes across the membrane.

Stimulation of the dendrites make the neuron produces an electronic signal. The signal is transmitted by the axon and brought to other neurons or cells by the axon. Axons can carry away information and have many terminals.

Membrane potentials and communications among neurons

To keep the balanced chemical environment in the cells, neurons can pump ions and molecules across the membranes. The electrical potential caused by the voltage difference as a result of this pumping is named the membrane potential. A ‘spike’ is also called an action potential, which is a short and big jump in the membrane potential upon the stimulation on a neuron. Action potentials on the surface of a neuron can cause chain reactions and transmit down to the axon and across the whole cell, enabling the communication among the neurons in the nervous system.

Axons, neurotransmitters, and action potentials

Axons have terminal branches with boutons on them. Upon action potentials, the boutons can release neurotransmitters, the signaling molecules that have receptors on the membrane of the target cells. Neurotransmitters are molecules that can signal neurons. The receptors of neurotransmitters can locate on the dendrites of neurons and membranes of the glial cells, muscle cells, and gland cells. The binding of the neurotransmitters and their corresponding receptors can lead to changes including the opening of ion channels.

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Lucid Dreaming and Meditation

Lucid dreaming is having the awareness consciously when you are dreaming. Only about 20% of people have reported to have lucid dreams frequently. Having lucid dreams is a skill that can be trained and learned. It can also be considered a kind of meditation. In fact, those who practice meditation may be more likely to have lucid dreams. Women may be more likely to have lucid dreams than men do, although men can have frequent lucid dreams also.

Similar to meditation, lucid dreams may help you control fears and promote personal growth. In lucid dreams, you may control your reactions such as fear but not the dream itself (such as the environment in the dream). To train to have lucid dreams, you can visualize a symbol or image such as a star or a flower as your lucidity object, so that you will be aware of your dreams when you see the symbol when you are dreaming.

If you wake up from a dream, you can try to return to your dream by imagining getting back, while being aware that you are dreaming. You can tell yourself to watch for things that are unrealistic before going to sleep. Having some reflective time before sleep is also helpful. On your first awakening, try to promote your consciousness and awareness. Getting enough sleep is also the key to have lucid dreams.

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The Sleep Cycles, Meditation, and Brain Wave Patterns

We exist in our own subjective minds that cannot be separated from our personal perspective, which make it impossible to reach real objectivity. The brain waves during dream sleep are not much different from those during awake. Dream sleep is dominated by the unconscious mind with the unaware feelings, thoughts, impulses, and desires. The rapid eye movement (REM) sleep is also called “paradoxical sleep”, the stage with dreams. Freud and Jung thought that dreams represent the unconscious mind. The meditation processes have similar features with the sleep cycles such as the brain wave patterns.

The sleep cycle has four stages, from drowsy and drifts to sleep, from random and fast brain waves to regular alpha brain waves. The slower theta brain waves then follow. In the next stage of deeper sleep, the EEGs show regular brain waves with sporadic burst of energy and brain activity. The third stage of sleep comes with the slow delta brain waves and lower body temperature. The fourth stage is the deepest level with delta waves, usually about one hour after the first stage.

Following this stage, the sleeper enters stage 1 or 2 and the rapid eye movement (REM) sleep, or dream sleep. At this time, faster, active, and random brain waves occur similar to the waking brain waves. Meanwhile, the blood pressure and heart rate arise, but the muscles are paralyzed temporarily. A brief awaken period may occur after the REM sleep, and the cycle begins once more.

Meditation is considered to have four cycles with deeper focus but without dreams, a state different from sleep or waking. EEG studies of Zen meditators showed mixed brain waves of alpha and theta similar to the light sleep pattern, a mix of sleeping and waking consciousness. Meditation may be a conscious way to discover our unconscious mind and learn more about ourselves.

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Meditation, Brain Waves, and Brain Activities

Electroencephalogram (EEG) is an important method measuring the brain waves and the electrical activities of the brain. The slowest waves (about 0.5-2 cycles/second) are called delta waves. Those that cycle about 8-12 times/second are alpha waves. Those that cycle about 4-7 times/second are theta waves. Those that cycle about 14-30 times/second are beta waves. Those that cycle about 30-50 times/second are gamma waves.

Different mental conditions may have different brain waves. For example, relaxed and attentive conditions are related to alpha waves. A state in deep sleep may have delta waves. During day-dreams and relaxed states, theta waves may be shown. The states of stress or excitement may be shown as beta waves. Quick responses may be related to the gamma waves. During meditation, the brain waves are likely to synchronize, referring to a more ordered and tranquilized state.

Studies using single photon emission computed tomography (SPECT) imaging on experienced Buddhist meditators have shown that the frontal lobe of the brain has elevated activities during meditation. This region of the brain is usually related to concentration and focused attention. On the other hand, reduced activities have been observed in the parietal lobe, the region that is related to the sense of space and time. However, this does not mean the loss of sense of space and time during meditation.

Meditation can also influence the neurotransmitters by improving the response time. These findings are consistent with the idea that meditation can be a good method for the cultivation of concentration. Even though some meditation practice such as focusing on the breath may sound boring to some people, continuous practice can improve the ability of concentration and make it interesting. However, studies about meditation may have problems such as being subjective. More objective methods to study meditation are still needed.

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Meditation, the Brain, Neurons, and Neurotransmitters

Meditation can help promote health in various dimensions, including physical, mental, spiritual, emotional, intellectual, and social. The scientific studies and the scans of the meditating brain found that the blood flow condition may be improved in the brain, with improved connections between various regions of the brain. With lower stress hormone levels, meditation can lead to stress reduction and relaxation. Most importantly, meditation can help strengthen your brain power.

The brain has neurons, the nerve cells, and neurotransmitters, the molecules that sending messages among neurons. There are about 12 billion neurons in the brain. Neurons have dendrites as branch-like parts extending from the cell body, and axons as single long fiber-like parts.  Messages carried by neurotransmitters can be received by the dendrites and sent away by the axons.

Aging, injuries, and diseases such as stroke can lead to the damage or even loss of neurons. On the other hand, meditation can be a mental workout for keeping the neurons active and slowing down the aging process. For instance, Transcendental Meditation (TM) has been found to promote the flow of the blood to the brain.

Meditation is not the same as resting your body or slowing down your responses or senses. Instead, it can awaken your senses and train your attention. “Kensho” is an experience of insight-wisdom, a clear-thinking moment similar to pre-enlightenment with the understanding of the essence of things. Meditation practices have the potential to control many body functions such as the body temperature.

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Breathing Exercises, Concentration, and Meditation

Breathing exercises can be the beginning step of meditation. Breath control is called “pranayama” in Sanskrit. Here “prana” refers to the life force in the universe (in yoga philosophy). Breathing can infuse this life force into your body, while giving you the things to concentrate on. The breath is closely associated with the mind. The practice of breathing exercises or the pranayama exercises can help promote the flow of the life force or “prana”, to coordinate the breath and the mind, and to promote the nourishing and healing power of the life force.

Paying attention to the breath is the basic step in many meditation practices. Hearing and feeling your breath can help bring your attention to the breath, as well as focus and calm your mind. Deep breathing or abdominal breathing can be used at the beginning or in the process of meditation. To do a deep breathing exercise, you can put your hands on your abdomen, and expand your abdomen when you inhale. In the meantime, your hands feel being pushed outward. When you exhale, your hands feel being pushed inward with your abdomen relaxing down.

Paying attention to your breath and breathing exercises can help concentrate your mind. Meditation is not to “think of nothing.” Instead, many of the meditation techniques emphasize on focusing your mind on just one thing, or “using one thought to replace thousands of thoughts.” The cultivation of concentration or the focusing of the mind on one point is also called “dharana.”

In addition to the breath, other objects can also be used as the one point to focus on, such as a flower, a symbol, a candle flame, ocean sound, or even an idea or a word such as “peace.” However, your own breath is probably the easiest thing to access and the most commonly used object. When you have a harmonized breath, a concentrated mind, your mind will become free from illusions, distractions, and attachments. You may get closer to the highest stage in meditation, the pure consciousness or so called “Samadhi.”

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Meditation and the Yin-Yang Harmony

If you feel that it is hard to achieve your life goals, you may need to be more focused, being more self-disciplined, being more visionary, with a better self-esteem. Meditation is a method that may help you build these inside. You don’t have to be religious or spiritual to practice meditation. Meditation is not about religions, or beliefs, or philosophy. It is not about a specific format or body position. It is not about certain words, sentences, images, objects, or slogans.

Meditation is more about relaxation and relief. It is more about having a clearer mind, and living in the now, the current moment, the present space and time. It’s more about improving your concentration and looking inward. It’s more about seeking a deep acceptance, being aware of the current space and time, and being aware of your own mind and body. It’s more about getting happier and healthier. It can even help you improve you sleep quality.

Meditation can help improve the homeostasis or the connections between your mind and body. According to Chinese philosophy, the human body can be thought as a microcosm of the whole world, while the world we live in can be considered as a microcosm of the universe. Just as Yin and Yang are harmoniously balanced in nature, they should be harmoniously balanced in the human body. In addition, Yang exists in things that are primarily Yin, and Yin exists in things that are primarily Yang. They can help each other grow, and they can change into each other.

Meditation can help reach the harmonious connections between Yin and Yang. The common purpose of the methods used in Chinese medicine is to help achieve such harmonious state between Yin and Yang, including the methods of Qi Gong, martial arts, acupuncture, dietary therapy, and herbal medicine. This is how health, well-being, and longevity can be achieved.

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Practicing Mindfulness: A Mind-Body Scan Meditation

It may not be easy to practice meditation and mindfulness. However, this can be done step-by-step. You can begin with a mind-body scan. To do this, you can lie down comfortably and relax. You can close your eyes and have a deep breath. Try to listen to your own breath and feel the in and out. Imagine that energy is brought in with your inhalation, and stress is going out and released with each exhalation. Imagine that your body is getting heavier.

Then, focus on the toes of your left foot, try to move them a little and feel them. Then follow this sequence to move your attention: left foot – left ankle – left leg – left shin – left calf – left knee – left thigh – left hipbone – hips – buttocks – lower back. Then do the same thing with the right part of your body, from right toes – right foot – right ankle – right shin – right calf – right knee – right thigh – right hipbone – hips – lower back.

From there, bring your attention to your abdomen, and then upper chest – upper back – shoulders – spine – neck – the base of the skull – skull – ears – scalp – hair roots – forehead – eyes – nose – cheeks – mouth –jaw – teeth – tongue – lips – chin – throat – forehead – head crown. Keep breathing that bringing in the positives and releasing out the negative parts. Next, you can bring your focus to your whole body, your mind, and your feelings.

When you have finished, you can open your eyes slowly, sitting up then standing up slowly. The mind-body scan meditation can help you become more relaxed, less stressed, and with more mindfulness.

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Meditation while Living: A Mindfulness Way for Prevention of Illnesses

We have learned to pay attention since childhood. However, most of us hardly pay attention to our mind-body connection. Mindfulness, a form of meditation that helps you become aware of each moment, can be achieved by paying attention to your feelings, your senses, your thoughts, and the details of your experience. Thus you can meditate in your daily life.

“Meditation while living” and paying attention to everything in your experience and your inner life can be eye-opening. Instead of wasting of time in worries, you can be happier, more efficient, and more productive. However, unlike many other practices, you cannot fail at meditation as long as you keep trying.

Meditation is not a sport or a skill. It’s a personal journey and experience that can help you grow, improve, and evolve. By paying attention to the details of your life such as each bite when you are eating, you can enjoy more of your life with healthier life styles such as a healthier eating habit with more preferences for fresh foods and proper quantities. You would consciously avoid eating junk foods, or eating too much or too less.

Meditation is not only beneficial to your mind but also to your body, such as relieving pain and reducing stress. As the science psychoneuroimmunology (PNI) has proven, there is a strong connection between your thoughts, emotions, the nervous system, and the immune system. Too much stress can cause symptoms including depression, anxiety, nervousness, restlessness and irritability, too much worrying, and fear or panic attack. Other problems that can be caused by stress include concentration or attention difficulties, fatigue, insomnia, and other sleep disorders.

Stress symptoms also include dizziness, forgetfulness, nausea, tremors, sweating, headaches, joint inflammation, eating disorders, weight gain or loss, constipation, vomiting, high or low blood pressure, and heart problems. By relieving stress, meditation can help relief these mental and physical symptoms too. In this way, meditation can help prevent illnesses that may be the consequences from these problems caused by stress.

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Enlightenment and Buddhism Meditation

What is enlightenment? In Hinduism, the word “enlightenment” is “Samadhi.” In Buddhism, it is called “nirvana.” It refers to the perfect condition that the self is completely integrated into “a sense of oneness with the universe.” It is a state in which the mind is awakened. The word “satori” means a “glimpse” of enlightenment, but not the full and continued enlightenment. Literally, the word “Buddha” actually means the “enlightened one.”

The “Buddha” that people commonly referring to is Siddhattha Gotama, who lived in the 16th century B.C. and who achieved enlightenment when he was 35. He founded Buddhism. The Buddha taught that all beings have the ability to achieve enlightenment, and men and women have equal capacity to reach enlightenment.

Around 475 A.D., the Buddhist Bodhidharma traveled and spread Buddhism to China. The mingling of Buddhism and Taoism, the popular philosophy in China at the time, resulted in the Ch’an Buddhism. The spread of the Ch’an Buddhism to Japan resulted in Zen Buddhism, which then spread to other parts of the world. One of the features of the Ch’an Buddhism is to prompt your mind to see things in a different and enlightened way, e.g., via koan, which usually uses an apparently irrational question to help you achieve a different state of mind.

Different cultures have different forms of meditation. Meditation is also called “zazen” in Zen Buddhism, which means the path to attain enlightenment. Via meditation techniques, Buddhism intends to free the mind from the illusion of the self, ultimately reach a union or integration with the universal consciousness.

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Meditation as a Path toward Homeostasis and Happiness

As a useful tool for promoting the mind-body fitness, meditation can be integrated into your fitness program to train your mind and body concurrently just the same way as you train your muscles. While improving the connections between the mind and the body, meditation can also help keep the balance between them. In this busy world, stress can cause the imbalance between the mind and the body, while meditation can help restore such balance toward homeostasis, a harmonious state between the mind and the body. By promoting your own mind power, meditation provides a better path toward homeostasis than medication pills such as aspirin and antacids, in a natural way without the adverse side effects.

Most importantly, meditation can help you achieve happiness. As many studies have pointed out, those who were happy and satisfied with their lives had much lower rates of serious diseases compared to those who were “thoroughly dissatisfied.” Happiness is not only important for your mind, but also critical for your body. At least half of the physical diseases have been considered to be related to stress. Long term or chronic stress has been associated with headaches, back pain, fatigue, chronic indigestion, ulcers, heart problems, and immune dysfunctions including suppressed immunity and autoimmune diseases such as rheumatoid arthritis.

Meditation is not to make you out of touch with or get away from reality. Instead, it is to help you more focused on the present moment. Most meditation techniques can promote concentration and increase the focus of attention. Many of us are so absorbed in stressful events that we are not aware of what we are thinking, what we are doing, or even where we are.

In addition to meditation sessions, we can also practice mindfulness when we are doing our daily jobs, being aware and awake, paying attention to our mind and our body, paying attention to the people around us and our environment, and paying attention to the work we are doing. We do not need to control or worry about these things. We don’t have to judge these things. We just need to observe and experience them. We can just watch or observe our thoughts as a third person, observe how they come and go, without any judgments, and without being engaged.

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Meditation for Stress Reduction and Healing Diseases

Although stress response is an adaptive reaction, long-term and chronic stress is harmful to health. Different people may have different symptoms and signs caused by stress. Adrenal glands are usually involved in stress responses and generate hormones including adrenaline and cortisol for stimulated senses and reflexes, such as the reactions of “fight or flight.”

Stress can lead to physical changes such as muscle contraction, higher blood pressure, and faster heart rate. In the meantime, the blood is flowing away from the digestive organs and into the muscles and nervous system, with higher levels of molecules for coagulating the blood. Fluids are accumulated in the kidneys, and the immune system is getting prepared for anti-inflammatory actions. These changes are adaptive responses to emergent changes. However, staying in such condition for a long term would be damaging to your health.

On the other hand, meditation can help lower the level of cortisol that is elevated in the stress response. Even though stress is hard to avoid, in today’s society stresses are mostly from our own mind. Meditation can help you relief stress from two ways, by promoting clear thinking for stress reduction, and by working on the physical part directly.

While promoting awareness and confidence with a tranquil mind, meditation can help lower the levels of the cholesterol, blood pressure, and reverse the processes of arteriosclerosis. Meditation can help reduce the onset of chest pain, reduce the risks of heart disease, relief depression, and change the brain wave toward a calmer pattern.

With the power of stress relief, meditation can be used not only for preventive medicine but also for healing ailments including pain and heart disease. As a matter of fact, the three words “meditation,” “medicine,” and “medication” share the same Latin root “medicus” that means “to cure” and “to measure” in its original meaning.

When you can think more clearly and feel better, you would take better care of your own health. Meditation can serve as a jump start toward a healthier lifestyle including healthier eating habits, regular physical exercises, and better sleep quality. All of these can help improve health.

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Meditation Techniques and the Power of Meditation

What is the power of meditation? Meditation can boost the power of your mind, the power of your fitness and health, and the power for stress reduction. Meditation is not just sitting there and doing nothing, it is not a waste of time. Rather, it is a workout for the mind-body connection, an exercise. Meditation can promote the integration of the mind and the body toward the wholeness of you. Having been practiced for thousands of years, different cultures and traditions may have different definitions of meditation.

There are different types and techniques of meditation, including mindfulness meditation, breath meditation, visual or mandala meditation, guided imagery, creative visualization, sound or mantra meditation, classic Zen Buddhist meditation, Chinese Taoist meditation, yoga meditation, and new age meditation. Meditation can also be done with movements, such as those practiced in Qi Gong, Tai Chi, and Hatha Yoga. Medical meditation can be applied for stress reduction and for healing illnesses.

Some arts, such as sculpting and Chinese calligraphy, can also be used as means and techniques for meditation. A common feature of all of these different techniques is that they all emphasizes on the focus of attention to avoid scattered or chaotic thoughts. The focused thought, such as on one-pointedness including mantras, provides a path toward super-consciousness rather than non-consciousness or “non-thinking.”

You can build your muscles by lifting weights and resting, while the resting time is the critical repairing time for the muscle building. Similarly, you can build a stronger mental power by taking breaks from the busy thinking process, via the practice of meditation. Meditation can bring you more awareness, with a clearer, more effective, and more efficient mind. Such a powerful mind can help you move away from the negative thoughts that have troubled you, which you may become aware that those are just trivial things that you should not be worried about. You can then feel refreshed and joyful.

With more awareness and clearer thinking power, your mind would move away from attachments and illusions. If you become not too attached to anything, not too involved in anything, if you realize that you cannot really “own” anything, you would feel less pressure, you would be less stressed or worried, and more relaxed and happier.

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Neuroscience Data and Neuroinformatics

Neuroscience Data and Neuroinformatics

 

Neuroscience Data

 

At least 50% genes of the mammalian genome are expressed by the nervous system (Sutcliffe et al., 1991). To understand brain-related diseases, it is necessary to map and study the expression patterns of these genes in the brain. Challenges remain in the collection, management, and analyses of neuroanatomic data. It is also important to compare data between different mouse strains from various aspects including stress reactions and drug responses (Moskowitz et al., 1985; Lucki et al., 2001).

 

To achieve these goals, neuroinformatics supports are needed for data management and gene expression analysis about the brain. Such data include those from the laboratory experiments, as well as the published reports from the literature including peer-reviewed journals and textbooks. Knowledge bases and encyclopedic collection of key concepts and facts would be very helpful (Bloom et al., 2006).

 

To facilitate systems biology studies of the brain, neuroscience data at various levels should be included in such knowledge bases or databases. At the molecular level, data about these fields would be needed: neurotransmitters; relevant enzymes, receptors, and ion channels; drug binding sites; structural and functional biomarkers (Bloom et al., 2006). At the cellular level, data about these fields would be needed: neurons, glial cells, and brain areas in nuclei or laminar brain macro-regions; structural data of shapes, sizes; functional data of classes, developmental stages, and aging conditions (Bloom et al., 2006).

 

Three-dimensional brain maps and atlases would also be useful. Data should be organized for circuitry and connections, including the bidirectional connections between nuclear or laminar neuronal areas, synaptic connections, the pathways, and detailed descriptions of structures (e.g., densities, numbers) and functions (e.g., excitatory and inhibitory) (Bloom et al., 2006). Data about the functional systems such as the endocrine and emotional systems, as well as the physiological and behavioral data about the cells and circuits should also be included. In addition, it is necessary to study the associations between nuclei or cortical areas and behaviors. Images should be included to analyze the structural and functional aspects of the mental activities in humans.

 

Neuroinformatics and the Wiki Technology

 

Databases play an important role in neuroinformatics. New Web 2.0 technologies such as the wiki technology can be very helpful for building universal coordinate databases (Nielsen, 2009), knowledge bases, and encyclopedias. The Wiki technology allows the editing of the database at different locations.

 

Many developments have been made with such efforts. For example, the MediaWiki-based Brede Wiki (http://neuro.imm.dtu.dk/wiki/) provides links to various databases including MaND (http://neuro.imm.dtu.dk/wiki/MaND), the neuroimaging database on major depressive disorder. It also has links to BiND (http://neuro.imm.dtu.dk/wiki/BiND), the neuroimaging database on bipolar disorder, and the neuroimaging database Brede Database (http://neuro.imm.dtu.dk/services/brededatabase/).

 

The common wiki technology provides simple text searches. To support more complicated and semantic studies, semantic wikis in neuroinformatics have been built, such as NeuroLex (http://neurolex.org/), a dynamic lexicon of more than 20,000 neuroscience terms. The site can be searched and also browsed by hierarchies such as behavioral activity and brain regions. More work would still be needed.

 

References:

 

Bloom FE, Morrison JH, Young WG. Neuroinformatics: a new tool for studying the brain. J Affect Disord. 2006 May;92(1):133-8.

 

Lucki I, Dalvi A, Mayorga AJ. Sensitivity to the effects of pharmacologically

selective antidepressants in different strains of mice. Psychopharmacology

(Berl). 2001 May;155(3):315-22.

 

Moskowitz AS, Terman GW, Liebeskind JC. Stress-induced analgesia in the mouse: strain comparisons. Pain. 1985 Sep;23(1):67-72.

 

Nielsen FA. Lost in localization: a solution with neuroinformatics 2.0? Neuroimage. 2009 Oct 15;48(1):11-3. doi: 10.1016/j.neuroimage.2009.05.073.

 

Sutcliffe JG, Travis GH, Danielson PE, Wong KK, Ottiger HP, Burton FH, Hasel KW, Bloom FE, Forss-Petter S. Molecular approaches to genes of the CNS. Epilepsy Res Suppl. 1991;4:213-23.

 

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The Applications and Combination of Bioinformatics, Medical informatics, and Neuroinformatics

The Applications and Combination of Bioinformatics, Medical informatics, and Neuroinformatics

 

Mental disorders and other brain disorders such as anxiety, depression, schizophrenia, and visual impairments may account for at least one third of the worldwide burden of diseases (Wittchen et al., 2011). Neuroinformatics provides the supports for neuroscience studies via the applications of databases, neuroimaging analysis tools, modeling tools, and tools to support clinical trials and data analysis. Resources for neuroinformatics organizations and communities include International Neuroinformatics Coordinating Facility (http://www.incf.org/) and The Organization for Human Brain Mapping (OHBM) (http://www.humanbrainmapping.org).

 

Neuroinformatics needs to be integrated with other methods such as bioinformatics, medical informatics, and public health informatics to provide efficient support for the analysis, prevention, and treatment of brain-related diseases. Medical informatics is the usage of computational technologies in healthcare and the processing of clinical data to support clinical decision making. Its major applications include clinical decision support systems. Medical informatics can be used for neurophysiological modeling. Bioinformatics utilizes mathematical and computational methods to manage and analyze biological data including genetics data. Neuroinformatics is the integration of informatics and neuroscience to support the studies of the structure and function of the brain. Major neuroinformatics applications include the processing of neuroimages, neurophysiological modeling, and artificial neural networks.

 

The combination of neuroinformatics and medical informatics can lead to the development of novel therapies. For example, neuroinformatics and medical informatics can be used to analyze neuroimages and cortical maps. By applying neuroinformatics concepts such as “self-organization”, computational simulations and predictions can be made for analyzing the relationships between systematic spatio-temporal tactile stimuli and the somatosensory homunculus (Wiemer et al., 2003). Such analysis may be helpful for the development of novel therapies.

 

Another example is the application of neuroinformatics and medical informatics methods in analyzing the functional topology and complex dynamics of the cell nucleus using three dimensional (3D) datasets (Tvarusko et al., 1999). These methods are useful for analyzing both biological and medical imaging data, such as the analyses in the field of medical computer vision (Wiemer et al., 2003).

 

Neuroinformatics is especially useful for data classification to provide medical decision support. In the example of cancer and clinical oncological research, the self-organizing map (SOM) method that is commonly used in neuroinformatics to analyze gene expression data and data from comparative genomic hybridization (CGH) to classify cancer subtypes (Dougherty et al., 2002; Wiemer et al., 2003).

 

For data mining, model prediction, and decision support, neuroinformatics and bioinformatics methods can be  combined for data collection, data preprocessing, subset selection, design of classifier, model selection, neural network establishment, model training, and receiver-operating characteristic (ROC) analysis (Wiemer et al., 2003).

 

Bioinformatics, medical informatics, and neuroinformatics share some common technologies. They also have specific methods and features in each field. The combination and integration of these methods and technologies would be helpful for applications in both laboratory and clinical decision support, in the analysis of gene expression profiles, and in the development of personalized medicine.

 

References

 

Dougherty ER, Barrera J, Brun M, Kim S, Cesar RM, Chen Y, et al. Inference from clustering with application to gene-expression microarrays. J Comput Biol 2002; 9 (1): 105-26.

International Neuroinformatics Coordinating Facility [Internet]. Stockholm (SE): International Neuroinformatics Coordinating Facility; 2011 [cited 2011 Jan 3]. Available from: http://www.incf.org/

 

Tvarusko W, Bentele M, Misteli T, Rudolf R, Kaether C, Spector DL, et al. Time-resolved analysis and visualization of dynamic processes in living cells. Proc Natl Acad Sci USA 1999; 96 (14): 7950-5.

 

Wiemer J, Schubert F, Granzow M, Ragg T, Fieres J, Mattes J, Eils R. Informatics united: exemplary studies combining medical informatics, neuroinformatics and bioinformatics. Methods Inf Med. 2003;42(2):126-33.

 

Wittchen HU, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jonsson B, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21(9):655–679.

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Neuroinformatics Software and Ontology Tools

Neuroinformatics Software and Ontology Tools

 

 

Category

Source Name

Purposes/Features/Methods

Contents

Applications

URL

References

Neuroimaging Informatics Tools BrainVisa (BV) For high resolution MR images

To quantify global and regional variances

About cortical anatomy

A plug-in application for structural analysis software

To measure gyrification, gray matter thickness and sulcal, and gyral white matter spans

For investigations in cortical folding process in antenatal development

For recapitulation of the process in cerebral aging

For translation studies

For genetics studies of the cerebral gyrification

http://www.nitrc.org/projects/brainvisa_ext Kochunov et al., 2012
Expression patterns The Brain Architecture Knowledge Management System (BAMS) To study the structure-function of the rat bed nuclei of the stria terminalis (BST) in the cerebral nuclei Neuron populations Refined parcellation ventrally

Cholinergic neurons in the BST

Neuron  populations

http://brancusi.usc.edu/bkms Bota et al., 2012a
Connection matrices For connection matrices (connectomes)

For qualitative comparisons between regions and tracts

For building interrelated connectomes at various levels of the central nervous system

Connectivity reports

For making global connection matrices with data mapping and annotation Bota et al., 2012b
Genome-wide 3D gene expression The Allen Brain Atlas web site

 

For gene expression mapping analyses using situ hybridization (ISH)

For

quantifying gene expression

For finding unique genes in the brain circuits

For identifying behavior-controlling regions

For automatically mapping expression profiles

For the genomic scale 3-D mapping of gene expression

Genomic scale search

High-resolution images

Visualization tools

http://www.brain-map.org Ng et al., 2007
MEG analysis MEG-SIM MEG and EEG are used for detecting electrophysiological activity in the brain

The hemodynamic and electrophysiological techniques are used in the Human Connectome Project

With realistic simulated data

With the basic types of inverse processes For direct comparisons of techniques

To evaluate multiple analysis techniques

 

For functional connectivity (e.g., oscillatory activity) analyses

For independent component analysis (ICA)

For single-trial analysis

http://cobre.mrn.org/megsim Aine et al., 2012
Ontology BrainInfo An ontology for neuroanatomical nomenclature

 

Multiple concepts of entities

Synonyms and homonyms in different languages

Identifies complex structures as models with primary structures

For applications in verbal communication

For computerized knowledge management

Applies NeuroNames for indexing information

http://braininfo.org Bowden et al., 2012
Vision The International Neuroinformatics Coordinating Facility (INCF) Visiome Brain-related studies involve different fields and global collaborations To facilitate the classification of the contents and resources
An automatic tool to filter the possible terms
Sources:

The abstracts of the Vision Research Journal (VR)

Investigative Ophthalmology

Visual Science Journal (IOVS)

http://www.platform.visiome.org Usui et al., 2007

 

References

 

Kochunov, P., Rogers, W., Mangin, J.-F., & Lancaster, J. (2012). A library of cortical morphology analysis tools to study development, aging and genetics of cerebral cortex. Neuroinformatics, 10(1), 81–96. doi:10.1007/s12021-011-9127-9

Bota, M., Sporns, O., & Swanson, L. W. (2012a). Neuroinformatics analysis of molecular expression patterns and neuron populations in gray matter regions: the rat BST as a rich exemplar. Brain research, 1450, 174–193. doi:10.1016/j.brainres.2012.02.034

Bota, M., Dong, H.-W., & Swanson, L. W. (2012b). Combining collation and annotation efforts toward completion of the rat and mouse connectomes in BAMS. Frontiers in neuroinformatics, 6, 2. doi:10.3389/fninf.2012.00002

Ng, L., Pathak, S. D., Kuan, C., Lau, C., Dong, H., Sodt, A., … Hawrylycz, M. (2007). Neuroinformatics for genome-wide 3D gene expression mapping in the mouse brain. IEEE/ACM transactions on computational biology and bioinformatics / IEEE, ACM, 4(3), 382–393. doi:10.1109/tcbb.2007.1035

Aine, C. J., Sanfratello, L., Ranken, D., Best, E., MacArthur, J. A., Wallace, T., … Stephen, J. M. (2012). MEG-SIM: a web portal for testing MEG analysis methods using realistic simulated and empirical data. Neuroinformatics, 10(2), 141–158. doi:10.1007/s12021-011-9132-z

Bowden, D. M., Song, E., Kosheleva, J., & Dubach, M. F. (2012). NeuroNames: an ontology for the BrainInfo portal to neuroscience on the web. Neuroinformatics, 10(1), 97–114. doi:10.1007/s12021-011-9128-8

Usui, S., Palmes, P., Nagata, K., Taniguchi, T., & Ueda, N. (2007). Keyword extraction, ranking, and organization for the neuroinformatics platform. Bio Systems, 88(3), 334–342. doi:10.1016/j.biosystems.2006.08.015

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Neuroinformatics Databases about the Brain: Different Species

Neuroinformatics Databases about the Brain: Different Species

 

Species

Source Name

Purposes/Features/Methods

Contents

Applications

URL

References

Mouse 3D digital atlas database of the mouse brain Lower spatial resolution images were utilized to enhance time efficiency for morphological phenotyping

The in vivo data were compared to available in vitro C57BL/6J mouse brain atlases from literatures

The morphological differences were described

Analyzed unexpected geometrical differences between the in vivo and in vitro brain groups

From magnetic resonance microscopy (MRM)

About C57BL/6J adult mouse brains

Used sufficient signal-to-noise (SNR) and contrast-to-noise ratio (CNR)

Multiple atlases were built

Average geometrical deformation maps

A probabilistic atlas

In vivo atlases provide a better geometric match than in vitro datasets

In vivo atlases are needed for longitudinal analysis and for functional brain activation analyses

 

http://brainatlas.mbi.ufl.edu

 

Ma et al., 2008
Avian The Avian Brain Circuitry Database (ABCD) To support avian connectivity studies

To support the nomenclature

To support brain network research

For collecting, querying, and referencing neuroscience information

Focuses on the connectivity of the avian brain

The terminology of the areas

The hierarchy of the vocabularies

Collection of data on connections between brain areas

A functional keyword platform with associations to brain areas and connections

Data were gathered from the literature and textbooks

An online submission platform available for data collection from researchers

http://www.behav.org/abcd

 

Schrott & Kabai, 2008
Drosophila BrainTrap To analyze the 3D-expression patterns of the proteins and protein complexes of the Drosophila melanogaster brain Reporter gene expression

Protein localization

Full size images of the entire brain

Interactive visualization

Annotations that can be queried

Connected to the FlyBase Drosophila anatomy ontology

Anatomical searches have features of automatic completion

A hierarchical browser for the ontology

Annotations are kept

The annotator locations are highlighted

http://fruitfly.inf.ed.ac.uk/braintrap Knowles-Barley et al., 2010
Flybrain Neuron Database (Flybrain NDB) To track the known neurons in a specific nervous system Projection patterns of the neurons were documented with text-based descriptions, images, and videos

Information of the labeling patterns to visualize neurons

The possible olfactory sensory map in the lateral horn

 

 

A visualization tool for interactive analysis of 3D reconstruction of the confocal serial section images

Search functions enable the analyses of the associations between various brain regions

The links among optic lobe layers

http://flybrain-ndb.iam.u-tokyo.ac.jp

 

Shinomiya et al., 2011
MidExDB (Drosophila CNS Midline Gene Expression Database) The Drosophila CNS midline cells as a model system

For analyzing neuronal and glial development

Datasets of midline gene expression patterns from in situ hybridization experiments

Images and data about midline gene expression from in situ hybridization experiments

Information about each midline cell type and the development

Integration of large-scale gene expression data with the identification of individual cell types

Search tools for data visualization and comparison

Analyses of CNS midline cell neuronal and glial development and functions

 

http://midline.bio.unc.edu/MDB_Home.aspx Wheeler et al., 2009
Platygyra carnosus PcarnBase To sequence the transcriptome

To organize the sequence data

For the brain coral Platygyra carnosus (a species in southern China)

Healthy and tumorous coral tissues

A cDNA library

De novo assembly of the sequence to generate Unigenes

The transcriptome dataset for a species with genome sequences

BLASTx searches

Unigenes with protein annotations, GO annotations, and KEGG pathways

Functional genomic analyses of P. carnosus

Biomarker findings

Genetic basis of stress resistance

http://www.comp.hkbu.edu.hk/~db/PcarnBase

 

Sun et al., 2012

 

References

 

Ma, Y., Smith, D., Hof, P. R., Foerster, B., Hamilton, S., Blackband, S. J., … Benveniste, H. (2008). In Vivo 3D Digital Atlas Database of the Adult C57BL/6J Mouse Brain by Magnetic Resonance Microscopy. Frontiers in neuroanatomy, 2, 1. doi:10.3389/neuro.05.001.2008

Schrott, A., & Kabai, P. (2008). ABCD: a functional database for the avian brain. Journal of neuroscience methods, 167(2), 393–395. doi:10.1016/j.jneumeth.2007.08.007

Knowles-Barley, S., Longair, M., & Armstrong, J. D. (2010). BrainTrap: a database of 3D protein expression patterns in the Drosophila brain. Database: the journal of biological databases and curation, 2010, baq005. doi:10.1093/database/baq005

Shinomiya, K., Matsuda, K., Oishi, T., Otsuna, H., & Ito, K. (2011). Flybrain neuron database: a comprehensive database system of the Drosophila brain neurons. The Journal of comparative neurology, 519(5), 807–833. doi:10.1002/cne.22540

Wheeler, S. R., Stagg, S. B., & Crews, S. T. (2009). MidExDB: a database of Drosophila CNS midline cell gene expression. BMC developmental biology, 9, 56. doi:10.1186/1471-213X-9-56

Sun, J., Chen, Q., Lun, J. C. Y., Xu, J., & Qiu, J.-W. (2012). PcarnBase: Development of a Transcriptomic Database for the Brain Coral Platygyra carnosus. Marine biotechnology (New York, N.Y.). doi:10.1007/s10126-012-9482-z

 

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Neuroinformatics Databases about the Brain-Related Diseases

Neuroinformatics Databases about the Brain-Related Diseases

Disease

Source Name

Purposes/Features/Methods

Contents

Applications

URL

References

Autism National Database for Autism Research (NDAR) A secure database for data sharing, data harmonization, and collaboration in autism spectrum disorder researchers Data from more than 25,000 research participants Available to investigators via the NDAR portal http://ndar.nih.gov Hall et al., 2012
Traumatic brain injury

 

 

 

 

BrainIT To analyze the application of hyperventilation and the compliance to Brain Trauma Foundation-Guidelines (BTF-G) after traumatic brain injury (TBI) Patients: 151 patients with trauma and recorded arterial blood-gas (ABG) analysis Analyzed ABGs

Ventilation episodes (VE)

Patients without increased intracranial pressure (ICP) showed higher P(a)CO(2)

Intensified forced hyperventilation without the elevated ICP was shown in VE

Early prophylactic hyperventilation was applied in VE

http://brainit.org.uk/bit2/bit2 Neumann et al., 2008
The Traumatic Brain Injury Model Systems National Data and Statistical Center (TBINDSC) To study the traumatic brain injury Secondary studies of the data sets

Measurements of demographic features, functional conditions, and hospital stay time

Patients’ data in the TBIMS-NDB

The significant variance in distribution was age

The TBIMS-NDB cohort did not have lots of patients older than 65

Statistical adjustment may be needed for the lower portions of patients older than 65, and patients with stays shorter than 10 days

https://www.tbindsc.org Corrigan et al., 2011
The Traumatic Brain Injury Model Systems (TBIMS) Built by the National Institute on Disability and Rehabilitation Research (NIDRR)

 

The NIDRR Long Range Plan

Websites

Manuals

Publications

 

Promotes health and function

Promotes independent living

Improves community integration

http://www.biausa.org/tbims.htm Hammond & Malec, 2010
HIV HIVBrainSeqDB: The HIV Brain Sequence Database To study:

HIV replications in a host

HIV independently evolving

Genetic differences

HIV replication in the brain

Neurocognitive disorders in about 1/3 of patients

Drug resistance problems

The basic determinants of HIV neurotropism

Macrophage tropism and the viral envelope (env) gene

HIV sequence databases needed to collect data on neurotropic virus

Neurocognitive and neuropathological information needed

An online database of HIV envelope sequences

Sequences are from the brain and tissues from the same individuals

Sequences are annotated with clinical data and curated from publications

An anatomical ontology – The Foundational Model of Anatomy

Tissue types are divided into several categories (Brain, brainstem, and spinal cord; Meninges, choroid plexus, and CSF; Blood and lymphoid; etc.)

Envelope sequences from individuals

Patient coding is related

Sequences from the same individual can be grouped

Cytoscape was used

Interconnections across studies, patient number, and tissue representation

The visualization of the associations between studies, patients and sequences

Real-time searches, multiple parameters

Studies of the genetic aspects of HIV macrophage tropism

Analyses of HIV compartmentalization and evolution in the brain

Associations with HIV-associated neurological disorders

 

http://www.hivbrainseqdb.org Holman et al., 2010

 

References

 

Hall, D., Huerta, M. F., McAuliffe, M. J., & Farber, G. K. (2012). Sharing Heterogeneous Data: The National Database for Autism Research. Neuroinformatics. doi:10.1007/s12021-012-9151-4

Neumann, J.-O., Chambers, I. R., Citerio, G., Enblad, P., Gregson, B. A., Howells, T., … Kiening, K. (2008). The use of hyperventilation therapy after traumatic brain injury in Europe: an analysis of the BrainIT database. Intensive care medicine, 34(9), 1676–1682. doi:10.1007/s00134-008-1123-7

Corrigan, J. D., Cuthbert, J. P., Whiteneck, G. G., Dijkers, M. P., Coronado, V., Heinemann, A. W., … Graham, J. E. (2011). Representativeness of the Traumatic Brain Injury Model Systems National Database. The Journal of head trauma rehabilitation. doi:10.1097/HTR.0b013e3182238cdd

Hammond, F. M., & Malec, J. F. (2010). The Traumatic Brain Injury Model Systems: a longitudinal database, research, collaboration and knowledge translation. European journal of physical and rehabilitation medicine, 46(4), 545–548. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21224786

Holman, A. G., Mefford, M. E., O’Connor, N., & Gabuzda, D. (2010). HIVBrainSeqDB: a database of annotated HIV envelope sequences from brain and other anatomical sites. AIDS research and therapy, 7, 43. doi:10.1186/1742-6405-7-43

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Neuroinformatics Databases for the Structure-Function Studies of the Brain

Neuroinformatics Databases for the Structure-Function Studies of the Brain

 

 

Category

Source Name

The Needs/Challenges

Purposes/Features/Methods

Contents

Applications

URL

References

Neuroinformatics Databases about the Brain Structures The Internet Brain Volume Database (IBVD) Available data from neuroanatomic volumetric measurements;

Various data from different species;

Data with different features of gender, age, and pathology

For data integrative and access Data about various species, different diseases Meta-analysis;

Quality assurance

http://www.nitrc.org/projects/ibvd Kennedy et al., 2012
Brainpeps Peptides that can cross the blood-brain barrier (BBB) need to be collected and organized To support diagnostic and therapeutic applications of the peptides Available BBB data from the literature;

BBB transport information

Peptide selections for analyzing BBB responses;

Quantitative analyses of peptide structure-function (BBB behavior) associations;

Analyzing the BBB behaviors and responses to various compounds;

Analysis of the associations between the different BBB transport methods

 

http://brainpeps.ugent.be/ Van Dorpe, et al., 2012
The OASIS (Open Access Series of Imaging Studies) Brain Database       To test if the average of the midsagittal corpus callosum cross-sectional area (CCA) in females is larger than those in males in average http://www.oasis-brains.org Ardekani et al., 2012
Neuroinformatics Databases about the Brain Functions and Functional Analyses The Brainmap Database

 

Human functional brain mapping studies have produced a large volume of data Supports the application of the activation likelihood estimation (ALE) method

 

 

 

Published neuroimaging data;

The metadata about the experimental design;

To build a probabilistic atlas

The meta-analysis of the published neuroimaging studies;

To develop an ontological system for studying function-structure relationships

http://www.brainmap.org Laird et al., 2009
BrainKnowledge To connect fMRI image datasets to the literature Methods:

Literature extraction and mining;

Automatic extractions from fMRI literatures;

Co-occurrence models and brain association patterns;

The association between brain structures and functions

Indexed literature, Medline abstracts;

fMRI experimental results;

The comparison between experimental data with the data in the literature

To query for brain activation models from a brain function;

To search functions from brain structures;

To compare the fMRI data with those from the literature

http://brainknowledge.ee.ntu.edu.tw Hsiao et al., 2011
The Allen Brain Atlas To study the midbrain dopaminergic neurons that are associated with

the control of emotion, motivation,

and motor behavior

To study the connections between the loss of a subpopulation substantia nigra pars compacta and Parkinson’s disease The collection of in situ hybridization data A linked database of the expressed genes in the neuronal population http://www.brain-map.org

 

 

 

 

 

 

Alavian et al., 2009
MethylomeDB Genome-wide brain DNA methylation profiles are needed to analyze the epigenetic mark in the mammalian brain;

To study how aberrant DNA methylation changes are connected to many neurodevelopmental and neuropsychiatric disorders including schizophrenia and depression

For studies of brain functions and behaviors;

Brain methylome data;

Whole-genome DNA methylation profiles of human and mouse brain specimens;

Supports cross-species comparative epigenomic analyses;

Supports studies of schizophrenia and depression methylomes

Methylation profiles of samples of non-psychiatric controls, schizophrenia, and depression;

Mouse forebrain sample specimen for cross-species analyses;

Published DNA methylation data associated with brain development and function

 

 

Data visualization with at single-CpG resolution;

Wiggle and microarray formats;

Data download for specific samples

 

 

http://epigenomics.columbia.edu/methylomedb/index.html

 

 

 

 

 

 

Xin et al., 2012
The Stanley Neuropathology Consortium Integrative Database (SNCID) To study the atypical antipsychotics bind receptor, e.g., dopamine D(2) receptors (DRD2);

5-HT(2) receptors (HTR2A);

α-2 adrenergic receptors (ADRA2A);

muscarinic receptors (CHRM1/4)

To study:

Deficits in antipsychotic receptors;

Related pathways, e.g., Immune and inflammatory reactions and apoptosis networks were related to group II metabotropic glutamate receptors (GRM2);

Potential target for future antipsychotics;

 

 

 

 

Applications: The associations between the targets, e.g., Associations with the neurotrophic factor BDNF mRNA levels;

Myelination associated with DRD2 mRNA levels and ADRA2A activity in the frontal cortex

 

Potential antipsychotics may affect pathways different from current ones;

Data mining approaches may be useful for the studies of the efficacy and side-effects of the antipsychotics

http://sncid.stanleyresearch.org

 

 

 

 

 

 

Kim et al., 2012

 

References

 

Kennedy, D. N., Hodge, S. M., Gao, Y., Frazier, J. A., & Haselgrove, C. (2012). The internet brain volume database: a public resource for storage and retrieval of volumetric data. Neuroinformatics, 10(2), 129–140. doi:10.1007/s12021-011-9130-1

Van Dorpe, S., Bronselaer, A., Nielandt, J., Stalmans, S., Wynendaele, E., Audenaert, K., … De Spiegeleer, B. (2012). Brainpeps: the blood-brain barrier peptide database. Brain structure & function, 217(3), 687–718. doi:10.1007/s00429-011-0375-0

Ardekani, B. A., Figarsky, K., & Sidtis, J. J. (2012). Sexual Dimorphism in the Human Corpus Callosum: An MRI Study Using the OASIS Brain Database. Cerebral cortex (New York, N.Y.: 1991). doi:10.1093/cercor/bhs253

Laird, A. R., Eickhoff, S. B., Kurth, F., Fox, P. M., Uecker, A. M., Turner, J. A., …Fox, P. T. (2009). ALE Meta-Analysis Workflows Via the Brainmap Database: Progress Towards A Probabilistic Functional Brain Atlas. Frontiers in neuroinformatics, 3, 23. doi:10.3389/neuro.11.023.2009

Hsiao, M.-Y., Chen, C.-C., & Chen, J.-H. (2011). BrainKnowledge: a human brain function mapping knowledge-base system. Neuroinformatics, 9(1), 21–38. doi:10.1007/s12021-010-9083-9

Alavian, K. N., & Simon, H. H. (2009). Linkage of cDNA expression profiles of mesencephalic dopaminergic neurons to a genome-wide in situ hybridization database. Molecular neurodegeneration, 4, 6. doi:10.1186/1750-1326-4-6

Xin, Y., Chanrion, B., O’Donnell, A. H., Milekic, M., Costa, R., Ge, Y., & Haghighi, F. G. (2012). MethylomeDB: a database of DNA methylation profiles of the brain. Nucleic acids research, 40(Database issue), D1245–1249. doi:10.1093/nar/gkr1193

Kim, S., Zavitsanou, K., Gurguis, G., & Webster, M. J. (2012). Neuropathology markers and pathways associated with molecular targets for antipsychotic drugs in postmortem brain tissues: Exploration of drug targets through the Stanley Neuropathology Integrative Database. European neuropsychopharmacology: the journal of the European College of Neuropsychopharmacology, 22(10), 683–694. doi:10.1016/j.euroneuro.2012.01.010

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Neuroinformatics Databases about the Brain Structures

Neuroinformatics Databases about the Brain Structures

 

The Internet Brain Volume Database (IBVD) (Kennedy et al., 2012)

  • The URL: http://www.nitrc.org/projects/ibvd
  • The background
    • Publications available about
      • Neuroanatomic volumetric observations
      • Large data sets of volumetric measurements with variables of
        • Specific species
        • Structures
        • Subject features
          • Gender
          • Age
          • Pathology
          • The Internet Brain Volume Database (IBVD)
            • About neuroanatomic volumetric observations from available publications
            • For integrating, sharing, and accessing data
            • Includes data about various species
            • Includes data about different diseases
            • To support quality assurance evaluations
            • To support meta-analysis

 

 

Brainpeps: a database about the blood-brain barrier peptide (Van Dorpe et al., 2012)

  • The URL: http://brainpeps.ugent.be/
  • The background
    • Peptides can cross the blood-brain barrier (BBB) via different mechanisms
    • These peptides can be useful for diagnostic and therapeutic purposes
    • The BBB transport data have not been previously organized or collected
    • Brainpeps: a comprehensive BBB peptide database
      • To gather the BBB data from the literature
      • Contents: BBB transport information
      • Applications
        • To prioritize peptide selections for analyzing various BBB responses
        • For quantitative analyses of peptide structure-function (BBB behavior) associations
        • To classify the different methods in analyzing the BBB behaviors and responses to various compounds
        • To analyze the associations between the different BBB transport methods

 

 

The OASIS Brain Database: An application (Ardekani et al., 2012)

  • The URL: http://www.oasis-brains.org/
  • The background
    • Studies have shown that  the midsagittal corpus callosum cross-sectional area (CCA) in females may be larger than those in males in average
    • Such observation may be caused by spurious differences in the CCA-to-brain-size ratio
    • The study
      • Data source:
        • The OASIS (Open Access Series of Imaging Studies)
  • The CCAs were analyzed on magnetic resonance imaging (MRI) scans
  • The method
    • Multiple regression analysis
  • The null hypothesis
    • The average CCA is the same between genders
  • An additional study was done on a subset of young adults
  • The results
    • The null hypothesis was found false in both analyses
    • The average CCA was found larger in females, especially in the young adults
    • The results were reached based on the correction for the confounding factor of brain sizes

 

 

References

 

Ardekani, B. A., Figarsky, K., & Sidtis, J. J. (2012). Sexual Dimorphism in the Human Corpus Callosum: An MRI Study Using the OASIS Brain Database. Cerebral cortex (New York, N.Y.: 1991). doi:10.1093/cercor/bhs253

 

Kennedy, D. N., Hodge, S. M., Gao, Y., Frazier, J. A., & Haselgrove, C. (2012). The internet brain volume database: a public resource for storage and retrieval of volumetric data. Neuroinformatics, 10(2), 129–140. doi:10.1007/s12021-011-9130-1

 

Van Dorpe, S., Bronselaer, A., Nielandt, J., Stalmans, S., Wynendaele, E., Audenaert, K., … De Spiegeleer, B. (2012). Brainpeps: the blood-brain barrier peptide database. Brain structure & function, 217(3), 687–718. doi:10.1007/s00429-011-0375-0

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Neuroinformatics: An Introduction

Neuroinformatics: An Introduction

The challenges in neuroinformatics

 

  • Challenges in making neuroinformatics databases and tools (Morse, 2008)
    • The variety in spatial scales
    • Various data types
    • Traditional bioinformatics concentrates on
      • Genomic and proteomic data
      • Sequences (DNA, RNA, and protein molecules)
      • Development of the databases of
        • Interactions between proteins
        • The evolution of genes
        • Systems biology
  •  Other challenges (Bloom et al., 2006)
    • To improve the mapping gene expression patterns in the mouse brain
    • To analyze the phenotypic alterations in the genetic models of human brain diseases

The goals of neuroinformatics studies

  • Neuroinformatics goals (Morse, 2008)
    • To support the study of the nervous system
      • Normal functions
      • Neurological disorders
  • To develop web-accessible databases
  • To develop software tools
  • The requirements for neuroinformatics (Eckersley et al., 2003)
    • Technical requirements
      • The hardware
      • The software
      • The protocols
  • The legal and policy frameworks
  • To solve the cultural and legal problems
  • Legal strategies by the Free Software community
  • “Open science” uses for data
  • Flexible licensing for secondary commercial studies

The contents in neuroinformatics

  • Neuroinformatics studies include (Morse, 2008)
    • Traditional bioinformatics analyses of the brain gene and protein sequences
    • The localization of genes and proteins in the brain
    • The brain anatomy
    • Imaging of the brain cells
    • Brain imaging analyses via
      • Positron emission tomography (PET)
      • Functional magnetic resonance imaging (fMRI)
      • Electroencephalography (EEG)
      • Magnetoencephalography (MEG)
  • The analyses via electrophysiological recording approaches
  • The mining of clinical neurological data
  • The tools for studying the brain-related diseases (Bloom et al., 2006)
    • Central nervous system diseases
      • A main category of drug targets
  • Mouse models of Amyotrophic Lateral Sclerosis and Alzheimer’s disease
    • May be helpful for drug development
    • Can help with the study of
      • Abnormal behaviors
      • Cognition
      • Emotions
  • The analyses of the genes expressed in the nervous system
    • At least half of the mammalian genome
  • Applications of neuroinformatics (Grisham et al., 2010)
    • From the phenotypic level to the molecular level
    • Neuroanatomy and histology
    • Quantitative trait locus analysis
    • In situ hybridization and microarray analysis
    • Data mining of
      • The region(s) of chromosome(s) involving the phenotypic traits
      • Candidate genes
      • The in situ patterns
      • The nucleotide sequences

Neuroinformatics: the integration and associations with medical informatics and bioinformatics (Wiemer et al., 2003)

  • Intersections among
    • Neuroinformatics
    • Medical informatics
    • Bioinformatics
    • Common grounds in the techniques and solutions
      • The modeling of neurophysiological systems for the development of medical treatment
      • Medical computer vision for the image processing applications
      • For the studies of the dynamics of cell nuclei
      • Using neuroinformatics tools for bioinformatics data mining
      • For the studies in clinical oncology
      • The bidirectional transfers of knowledge and techniques
        • To improve efficiency and accuracy
        • Clinical decision support systems from neuroinformatics and bioinformatics may lead to
          • Better diagnostics
          • Personalized medicine

Relevant bioinformatics resources (Grisham et al., 2010)

 

References:

 

Bloom, F. E., Morrison, J. H., & Young, W. G. (2006). Neuroinformatics: a new tool for studying the brain. Journal of affective disorders, 92(1), 133–138. doi:10.1016/j.jad.2005.12.043

 

Eckersley, P., Egan, G. F., Amari, S., Beltrame, F., Bennett, R., Bjaalie, J. G., Dalkara, T., et al. (2003). Neuroscience data and tool sharing: a legal and policy framework for neuroinformatics. Neuroinformatics, 1(2), 149–165. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15046238

 

Grisham, W., Schottler, N. A., Valli-Marill, J., Beck, L., & Beatty, J. (2010). Teaching bioinformatics and neuroinformatics by using free web-based tools. CBE life sciences education, 9(2), 98–107. doi:10.1187/cbe.09-11-0079

 

Morse, T. M. (2008). Neuroinformatics: from bioinformatics to databasing the brain. Bioinformatics and biology insights, 2, 253–264. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19812780

 

Wiemer, J., Schubert, F., Granzow, M., Ragg, T., Fieres, J., Mattes, J., & Eils, R. (2003). Informatics united: exemplary studies combining medical informatics, neuroinformatics and bioinformatics. Methods of information in medicine, 42(2), 126–133. doi:10.1267/METH03020126

 

 

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Treatment of Mental Problems: A Summary of Therapies and Features

Treatment of Mental Problems: A Summary of Therapies and Features

 

Therapies

Contents

Features/Examples

Explanations/Targets

Side Effect Examples

Psychoanalysis/psychodynamic therapy Free association      
Catharsis
Transference
Behavior therapies Classical conditioning-based therapies Exposure therapies Systematic desensitization  
Flooding  
Aversion therapies    
Operant conditioning-based therapies Behavior modification (applied behavior analysis) Reinforcers  
Token economy    
Social learning Participant modeling Observational learning  
Cognitive therapies Rational-emotive therapy (RET) (Ellis) Changing irrational thoughts    
Cognitive-behavior therapy (CBT) (Beck) Changing  interpretation of thoughts    
Humanistic therapies Person-centered therapy (Rogers) Unconditional positive regard    
Existential humanistic therapy (EHT) Finding meanings in life    
Group therapy A small group      
Family systems therapy Whole families      
Pharmacotherapy Antipsychotic E.g., Haldol Targets: dopamine (reduced), serotonin (increased) Anxiety, appetite changes
Mood stabilizer E.g., Lithium Targets: Glutamate (reduced), gray matter (increased) Toxic to organs
Anticonvulsant E.g., Phenobarbital Targets: Cortical neuron firing (reduced) Nausea, dizziness
Antidepressant E.g., Prozac Targets: Serotonin, epinephrine/norepinephrine (increased) Nausea, appetite changes
Antianxiety E.g., Valium, Librium Targets: GABA (increased); sympathetic nervous system (inhibited) Sleep problems
Stimulant E.g., Ritalin Targets: Dopamine, norepinephrine (increased) Sleep problems, appetite changes

 

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