![]() Effective connectivity determines which of the brain location are sending and/or receiving information this can be calculated using mathematical techniques such as Granger causality, Hilbert transform, transfer entropy and correlation. This type of connectivity analysis is based on brain signals recorded by Electroencephalography (EEG), Magnetoencephalography (MEG), Functional Magnetic Resonance imaging (fMRI) and Positron Emission Tomography (PET). It just shows that these regions have similar signal content and therefor are most likely connected. Functional connectivity does not determine the specific direction of information flow in the brain. Coherence and Phase synchrony are common mathematical methods for quantifying frequency and phase dependent correlations of brain activity measured by two or more brain sensors. Įffective and Functional Connectivity measurements can be analyzed in the Frequency Domain with methods such as Coherence and Phase synchrony or in the Time Domain with methods such as Correlation and Granger Causality. Effective connectivity uses the functional connectivity information and goes one step further and determines the direct or indirect influence that one neural system may have over another, more specifically the direction of the dynamic information flow in the brain. task dependent) that is required for sensory responses, motor responses and intellectual or emotional processing. task independent) or higher order information processing (i.e. These areas may be involved in the resting state (i.e. ![]() ![]() Functional connectivity identifies activity brain regions that have similar frequency, phase and/or amplitude of correlated activity. These are the anatomical network maps that indicate possible pathways that the signals can travel on in the brain. Structural connectivity is based on detection of the fiber tracts that physically connect the regions of the brain. Within these categories several different imaging hardware equipment and software programs are used to detect, measure and quantify the integrity of the network. These network connection types are categorized as Structural, Functional and Effective. There are three connectivity network types that are used to investigate communication within and across the brain. Neuroimaging connectivity techniques for quantifying the brain networks use signal processing techniques that have been around for many decades. This illustrates a more complex picture of the brain as a dynamic interconnected network, capable of plasticity and adaptation. Treatments and remediation have also been shown to change how the brain functions. Neurological disorders and tumors can disrupt brain functions. Sometimes these activations are linear and yet at other times these activations can be simultaneous. Over the past 10 years advances in brain imaging techniques have revealed that these regions are connected and communicate with other specialized regions across networks in the brain. A large number of neuroimaging brain studies in the past have found that there are specific regions in the brain that are specialized for processing certain types of information. Connectivity analyses of the brain are performed to map out the communication networks needed for the brain to function. The synchronized activity within this neuronal network can be detected by MEG and EEG then imaged using network connectivity analysis. The human brain is a vast network of connected pathways that communicate through synchronized electric brain activity along fiber tracts. In this review we highlight how functional brain connectivity is assessed in Source space using coherence technique measured by MEG. Statistical analysis can then be performed on the coherence results to verify evidence of normal or abnormal network activity in a patient. ![]() Recently coherence, after it has been imaged in the brain, has been used to assess how coherent or connected specific locations in the brain are networked together in several different neurological disorders. Since the 1960’s, coherence has generally been assessed on the similarity of the frequency content across EEG sensors. Coherence is one mathematical method that can be used to determine if two or more sensors, or brain regions, have similar neuronal oscillatory activity with each other. Well-connected highly synchronous functional activity can be measured by Electroencephalography (EEG) or Magnetoencephalography (MEG) and then analyzed with several types of mathematical algorithms. Detection of the synchronous activation of neurons can be used to determine the wellbeing or integrity of the functional connectivity in the human brain networks. The functional network communications across the brain networks dependent on neuronal oscillations. Brian connectivity describes the networks of functional and anatomical connections across the brain. ![]()
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