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These methods are used in a wide range of areas including cognitive psychology and psychopathology. The studies are non-invasive and consist of measuring behavioral variables such as: reaction time, eye movements, arm movements, and responses selected.
Analyses of behavioral measures vary with the experimental factors controlled by the experimenter, and provide an indication of the way the brain controls that behavior. For example, researchers might study:
The outcomes of these experiments provide a framework to understand the way the brain controls behavior and, in addition, provides some guidance for using other research methods to explore the relationship between the brain and behavior more effectively.
The first fMRI study was performed in the early ‘90s, but since then the technology has evolved, with much more powerful, high-field magnets, such as 7 Tesla or 9 Tesla, now available. This technology allows neuroscientists, from a multitude of disciplines including: neuroscience, neurology, psychiatry and psychology, to continue to refine their studies and explorations into discovering the secrets behind the dynamic, functioning brain. The Brain Sciences Center retains a strong working relationship with the CMRR and MGH-MR Center through their partnership in The MIND Institute, a consortium of neuroimaging sites and scientists.
Functional magnetic resonance imaging (fMRI) provides a “snapshot” of the active, dynamic, functioning brain that allows neuroinvestigators to pinpoint the region where brain activity is taking place. fMRI uses powerfull magnets to measure small changes in the brain’s blood oxygenation level that occur while a task is performed.
Through this non-invasive technology, investigators can get a dynamic picture of what’s happening in specific areas of the active, working brain by looking at changes in the oxi-hemoglobin flow to the head. The hemoglobin has different magnetic properties depending on if it is oxygenated or not. These differences can be seen in a brain imaged by fMRI technology. This process relates to the energy expended by the brain’s neurons within a specific area of the brain. The powerful magnets stimulate the atoms and molecules within the blood flowing to the brain’s cells. The stronger the magnet used, the higher the resolution of brain images. In addition, MRI produces no ionizing radiation, so potential risks to subjects or patients are reduced.
Used in conjunction with structural magnetic resonance imaging (MRI), which provides an anatomical baseline, functional magnetic resonance imaging (fMRI) allows for optimal spatial resolution during an activity. These images allow researchers to compare a healthy subject to a patient with a neurological disease or disorder.
Investigators can then gain valuable insights from comparing the differences between the two groups. In addition, fMRI combined with magnetoencephalogray (MEG), which provides an excellent temporal dimension of brain activity, allows investigators further insights into the basic workings of the brain.
Functional MRI studies can also give researchers valuable insights into the dysfunctional brain with respect to neurological disorders and diseases including: schizophrenia, stroke, mental retardation, Apraxia, and Alzheimer’s Disease by measuring changes in the oxygenation of the blood flowing within the brain. This technology helps investigators view the interactions of neurons from different areas of the brain. For example: scientists can look for interactions from the motor cortex to the cerebellum or basal ganglia in the case of a movement disorder such Ataxia.
The brain is a very complex system, composed of many (billions) specialized cells, called neurons, that are dynamically interacting with each other via so called synaptic connections. The neurons in the brain, accordingly, form a very large network that ultimately processes peripheral sensory signals and performs various functions, such as control of movements. The synaptic connections in this network play a very important role because their specific organization in a given brain area determines a specific function performed by that area. Researchers in the Brain Sciences Center develop large-scale mathematical models of the brain network and then explicitly simulate these models using high-performance supercomputers.
Large-scale computer simulations are complementary to experimental studies of the brain. Mathematical models and computer simulations are important for understanding how the organization of synaptic connections shapes the underlying brain function, and also allow to decipher specific mechanisms of the function implementation. In addition to stimulating the building of unified theoretical frameworks of brain function, explicit computer simulations of large-scale realistic models also help researches screen out a number of working hypotheses much faster and at a lesser expense than testing them out in experimental studies. Predictions of simulations, in turn, provide guidance for future experiments.
The broader impact of large-scale modeling studies carried out at the Brain Sciences Center is two-fold.
First, the success of the current research would stimulate studies of functional disorders caused by focal brain lesions, such as stroke, resulting in the loss of cells and/or connections between them. The large-scale neural network simulations are able to model explicitly spatio-structural aspects of the underlying brain structure and should provide, therefore, appropriate theoretical means for the investigation of the cell/connection loss-induced neurological disorders.
Second, the current research should be also important for developing new generation of prosthetics that are driven by brain signals to assist, for example, paralyzed patients or amputees. Particularly, simulations of large-scale brain models could be important for pin-pointing the relevant brain signals and their spatial localization.
The uniquely powerful MEG machine at the Brain Science Center uses a non-invasive, whole-head, 248 channel, super-conducting-quantum-interference-device (SQUID) to measure small magnetic signals reflecting changes in the electrical signals in the human brain.The incorporation of liquid helium creates the incredibly-cold conditions (4.2 kelvin) necessary for the MEG’s SQUIDs to be able to measure fields that are literally billions of times weaker than the background magnetic field of the earth.
Investigators at the Center use the MEG to measure these magnetic changes in the active, functioning brain in the speed of milliseconds. Used in conjunction with magnetic resonance imaging (MRI), to relate the MEG sources to brain structures, and functional magnetic resonance imaging (fMRI), for optimal spatial resolution, researchers can now localize brain activity and measure it in the same temporal dimension as the functioning brain itself. This allows investigators to measure, in real-time, the integration and activity of neuronal populations while either working on a task, or at rest. The brains of healthy subjects and those suffering from dysfunction or disease are imaged and analyzed in these MEG studies.
MEG provides scientists a vital neuroimaging tool to gain critical perspectives into the basic mechanisms of the cognitive processes of the healthy, functioning brain in the same lightning speed at which the brain itself operates.
MEG studies also allow researchers valuable insights into the dysfunctional brain with respect to neurological disorders and diseases such as: schizophrenia, stroke, mental retardation, dyslexia and Alzheimer’s disease through measuring these changes in the brain’s electro-magnetic fields.
This ever-evolving technology began as a single-channel system in the 1970s. Since then, MEG technology has been constantly updated and refined into its current state-of-the-art status. The MEG instrument at the Brain Sciences Center, is one of the few of its caliber in existence. Its 248 SQUID sensors make this imaging machine one of the most powerful and technologically advanced in the world.
In a sense, scientists are looking to see how the brain works and communicates by measuring the electrical and magnetic potentials that occur in the brain. Some examples of neurophysiology techniques include: extra cellular single cell recordings, multiple-cell recordings, EEG/MEG, fMRI and PET.
Invasive: Extra cellular recordings are obtained by the implementation of electrodes—which consist of long strands of metal wire. An extension of this process is the collection of multiple-cell recordings gathered from a multi-electrode device.
Neuroscientists use these very sophisticated brain imaging and recording methods to see how the mind works and communicates. Structural brain imaging techniques, such as computerized axial tomography (CAT scans), are useful to clinical physicians to get an anatomical picture of an individual’s brain structure. It is common for researchers to use more than one of these techniques, such as fMRI and MEG, within the same experiment, by combining the unique strengths (fMRI-spatial, MEG-temporal) of each method.
Illustration: Preferred directions (unit vectors) of 475 motor cortical cells in three-dimensional space.(From Schwartz et al. J Neurosci 1988;8:2913-27)
Electrodes, consisting of thin wire strands, are implanted into specific areas of the brain. Single cell and multiple array micro-electrodes are used to obtain a precise, real time measure and recording of electrical activity in the brain. This method allows for finely detailed research in a localized area. The limitations of this method are that it is invasive and that brain activity can only be recorded from the area, or areas, where the electrodes are located.
Single cell recordings can help form the basis for neural control of prosthetic devices.
Two-dimensional visual charts are adequate displays for most data. However, when the data arrives in 248 channels, as it does from the magnetoencephalograph (MEG) at the Brain Sciences Center, and changes fluidly over time, two dimensions are not enough. Raw datastreams generated during MEG experiments can contain 1017 samples/second over 248 channels. For a 45-second experiment, that’s 11,349,720 data points! One way to study the datastreams is by listening to audio representations, or sonifications.
Sound is a unique medium for data representation. Not only does it occur over time, but sound exists in at least six other dynamic dimensions: frequency, amplitude, tone color, and physical location (up/down, front/back & left/right). Evolution has equipped humans with the ability to perceive very small changes in sounds. Some changes are smooth and subtle (e.g. speech inflections), while others are abrupt and alarming (e.g. dishes dropped in a restaurant).
Music is a very sophisticated refinement of the parameters of sound. The six natural dimensions listed above are organized into new categories that serve to define the qualities of musical instruments, ensembles, and styles. In Western musical tradition: Frequency becomes 12 pitch classes (C, C#, D, D#, E, F, F#, G, G#, A, A#). Pitch classes are organized into major and minor scales of different sizes (chromatic/diatonic/pentatonic). Amplitude becomes loudness (dynamics ppp - fff). Smooth changes in loudness take the form of crescendos (gradually louder) and decrescendos (gradually softer). Tone color becomes instrument timbre (strings, brass, woodwinds, percussion). Instruments are assigned parts to play based on their timbres and pitch range (e.g. the 88-note piano). Locations are translated in three dimensions: left/right becomes pan(orama), front/back becomes presence (reverberation), and elevation becomes height (only used in special circumstances). Instruments might be arranged to ‘level the playing field’, as in an orchestra where the violinists are seated in front of the louder trumpeters and drummers.
Individual data points are converted to integers, then to Musical Instrument Digital Interface (MIDI) events/notes. A MIDI sequencer application plays back these notes into synthesizers in much the same way as a player-piano mechanism sends note information to a piano. Each datastream is treated as a separate ‘track’ by the sequencer, but because the data is in digital form (and not an audio recording), playback can be slowed without affecting pitch. Other changes are possible: a unique instrument sound can be assigned to each datastream, pitches can be organized into musical scales, and accents can be derived from the data values. The result is a piece of ‘music’ that can sound like anything from a symphonic orchestra to a solo piano arrangement.