This study was supported by funds from the Benesse Corporation, Japan Society for the Promotion of Science (JSPS) Funding Program for Next Generation World-Leading Researchers (LZ008), the Organization for Promoting Research in Neurodevelopmental Disorders, and the JSPS KAKENHI (15K04185; 18K03174).

Our study was approved by the local institutional ethics committee.
1. Basic Information
NOTE: The analytical protocols can be applied to various types of data without any restrictions as to specific participants, electrodes, reference methods, or electrode locations. In our example, we tested six patients suffering from pharmacologically intractable focal epilepsy. We tested patients who had no epileptic foci in the regions of interest.
<ol>
	<li>Record intracran...

<ol>
	<li>Lachaux, J. P., Rudrauf, D., Kahane, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intracranial+EEG+and+human+brain+mapping.">Intracranial EEG and human brain mapping.</a> Journal of Physiology - Paris. 97 (4-6), 613-628 (2003).</li><li>Kilner, J. M., Kiebel, S. J., Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Applications+of+random+field+theory+to+electrophysiology.">Applications of random field theory to electrophysiology.</a> Neuroscience Letters. 374 (3), 174-178 (2005).</li><li>Canolty, R. T., Knight, R. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+functional+role+of+cross-frequency+coupling.">The functional role of cross-frequency coupling.</a> Trends in Cognitive Sciences. 14 (11), 506-515 (2010).</li><li>Chen, C. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+dynamic+causal+model+for+evoked+and+induced+responses.">A dynamic causal model for evoked and induced responses.</a> Neuroimage. 59 (1), 340-348 (2012).</li><li>Friston, K. J., Harrison, L., Penny, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+causal+modelling.">Dynamic causal modelling.</a> Neuroimage. 19 (4), 1273-1302 (2003).</li><li>Canolty, R. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+gamma+power+is+phase-locked+to+theta+oscillations+in+human+neocortex.">High gamma power is phase-locked to theta oscillations in human neocortex.</a> Science. 313, 1626-1628 (2006).</li><li>Tort, A. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+cross-frequency+couplings+of+local+field+potential+oscillations+in+rat+striatum+and+hippocampus+during+performance+of+a+T-maze+task.">Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task.</a> Proceedings of the National Academy of Sciences of the United States of America. 105 (51), 20517-20522 (2008).</li><li>Voytek, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shifts+in+gamma+phase-amplitude+coupling+frequency+from+theta+to+alpha+over+posterior+cortex+during+visual+tasks.">Shifts in gamma phase-amplitude coupling frequency from theta to alpha over posterior cortex during visual tasks.</a> Frontiers in Human Neuroscience. 4, 191 (2010).</li><li>Mukamel, R., Fried, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+intracranial+recordings+and+cognitive+neuroscience.">Human intracranial recordings and cognitive neuroscience.</a> Annual Review of Psychology. 63, 511-537 (2012).</li><li>Parvizi, J., Kastner, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Promises+and+limitations+of+human+intracranial+electroencephalography.">Promises and limitations of human intracranial electroencephalography.</a> Nature Neuroscience. 21, 474-483 (2018).</li><li>Hill, N. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recording+human+electrocorticographic+(ECoG)+signals+for+neuroscientific+research+and+real-time+functional+cortical+mapping.">Recording human electrocorticographic (ECoG) signals for neuroscientific research and real-time functional cortical mapping.</a> Journal of Visualized Experiments. (64), 3993 (2012).</li><li>Herrmann, C. S., Rach, S., Vosskuhl, J., Str&#252;ber, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time-frequency+analysis+of+event-related+potentials:+A+brief+tutorial.">Time-frequency analysis of event-related potentials: A brief tutorial.</a> Brain Topography. 27 (4), 438-450 (2014).</li><li>Litvak, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+and+MEG+data+analysis+in+SPM8.">EEG and MEG data analysis in SPM8.</a> Computational Intelligence and Neuroscience. 2011, 852961 (2011).</li><li>Mihara, T., Baba, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combined+use+of+subdural+and+depth+electrodes.">Combined use of subdural and depth electrodes.</a> Epilepsy Surgery. , 613-621 (2001).</li><li>Kilner, J., Bott, L., Posada, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modulations+in+the+degree+of+synchronization+during+ongoing+oscillatory+activity+in+the+human+brain.">Modulations in the degree of synchronization during ongoing oscillatory activity in the human brain.</a> European Journal of Neuroscience. 21, 2547-2554 (2005).</li><li>Lachaux, J. P., Rodriguez, E., Martinerie, J., Varela, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+phase+synchrony+in+brain+signals.">Measuring phase synchrony in brain signals.</a> Human Brain Mapping. 8 (4), 194-208 (1999).</li><li>Stephan, K. E., Penny, W. D., Daunizeau, J., Moran, R. J., Friston, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bayesian+model+selection+for+group+studies.">Bayesian model selection for group studies.</a> Neuroimage. 46 (4), 1004-1017 (2009).</li><li>Sato, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid,+high-frequency,+and+theta-coupled+gamma+oscillations+in+the+inferior+occipital+gyrus+during+face+processing.">Rapid, high-frequency, and theta-coupled gamma oscillations in the inferior occipital gyrus during face processing.</a> Cortex. 60, 52-68 (2014).</li><li>Sato, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bidirectional+electric+communication+between+the+inferior+occipital+gyrus+and+the+amygdala+during+face+processing.">Bidirectional electric communication between the inferior occipital gyrus and the amygdala during face processing.</a> Human Brain Mapping. 38 (2), 4511-4524 (2017).</li><li>Bartlett, J. C., Searcy, J., Abdi, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=What+are+the+routes+to+face+recognition?.">What are the routes to face recognition?.</a> Perception of faces, objects, and scenes: Analytic and holistic processing. , 21-52 (2003).</li><li>Bouvier, S. E., Engel, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Behavioral+deficits+and+cortical+damage+loci+in+cerebral+achromatopsia.">Behavioral deficits and cortical damage loci in cerebral achromatopsia.</a> Cerebral Cortex. 16 (2), 183-191 (2006).</li><li>Pitcher, D., Walsh, V., Duchaine, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+the+occipital+face+area+in+the+cortical+face+perception+network.">The role of the occipital face area in the cortical face perception network.</a> Experimental Brain Research. 209 (4), 481-493 (2011).</li><li>Latini, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+insights+in+the+limbic+modulation+of+visual+inputs:+The+role+of+the+inferior+longitudinal+fasciculus+and+the+Li-Am+bundle.">New insights in the limbic modulation of visual inputs: The role of the inferior longitudinal fasciculus and the Li-Am bundle.</a> Neurosurgical Review. 38 (1), 179-189 (2015).</li><li>Davies-Thompson, J., Andrews, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intra-+and+interhemispheric+connectivity+between+face-selective+regions+in+the+human+brain.">Intra- and interhemispheric connectivity between face-selective regions in the human brain.</a> Journal of Neurophysiology. 108 (11), 3087-3095 (2012).</li><li>Jerbi, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Saccade+related+gamma-band+activity+in+intracerebral+EEG:+dissociating+neural+from+ocular+muscle+activity.">Saccade related gamma-band activity in intracerebral EEG: dissociating neural from ocular muscle activity.</a> Brain Topography. 22, 18-23 (2009).</li><li>Buzs&#225;ki, G., Silva, F. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+frequency+oscillations+in+the+intact+brain.">High frequency oscillations in the intact brain.</a> Progress in Neurobiology. 98, 241-249 (2012).</li><li>Benayoun, M., Kohrman, M., Cowan, J., van Drongelen, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG,+temporal+correlations,+and+avalanches.">EEG, temporal correlations, and avalanches.</a> Journal of Clinical Neurophysiology. 27 (6), 458-464 (2010).</li><li>Herrmann, C. S., Grigutsch, M., Busch, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EEG+oscillations+and+wavelet+analysis.">EEG oscillations and wavelet analysis.</a> Event-related potentials: A methods handbook. , 229-259 (2005).</li><li>Pigorini, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Time-frequency+spectral+analysis+of+TMS-evoked+EEG+oscillations+by+means+of+Hilbert-Huang+transform.">Time-frequency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform.</a> Journal of Neuroscience Methods. 198 (2), 236-245 (2011).</li><li>Holdgraf, C. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+tuning+shifts+in+human+auditory+cortex+enhance+speech+intelligibility.">Rapid tuning shifts in human auditory cortex enhance speech intelligibility.</a> Nature communications. 7, 13654 (2016).</li><li>Aru, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Untangling+cross-frequency+coupling+in+neuroscience.">Untangling cross-frequency coupling in neuroscience.</a> Current Opinion in Neurobiology. 31, 51-61 (2015).</li><li>Gerber, E. M., Sadeh, B., Ward, A., Knight, R. T., Deouell, L. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-sinusoidal+activity+can+produce+cross-frequency+coupling+in+cortical+signals+in+the+absence+of+functional+interaction+between+neural+sources.">Non-sinusoidal activity can produce cross-frequency coupling in cortical signals in the absence of functional interaction between neural sources.</a> PLoS One. 11 (12), e0167351 (2016).</li><li>Cole, S. R., Voytek, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Brain+oscillations+and+the+importance+of+waveform+shape.">Brain oscillations and the importance of waveform shape.</a> Trends in Cognitive Sciences. 21 (2), 137-149 (2017).</li><li>Mikulan, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intracranial+high-&#947;+connectivity+distinguishes+wakefulness+from+sleep.">Intracranial high-&#947; connectivity distinguishes wakefulness from sleep.</a> Neuroimage. 169, 265-277 (2018).</li><li>Zheng, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Amygdala-hippocampal+dynamics+during+salient+information+processing.">Amygdala-hippocampal dynamics during salient information processing.</a> Nature communications. 8, 14413 (2017).</li><li>Maris, E., Oostenveld, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonparametric+statistical+testing+of+EEG-+and+MEG-data.">Nonparametric statistical testing of EEG- and MEG-data.</a> Journal of Neuroscience Methods. 164 (1), 177-190 (2007).</li><li>Stolk, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrated+analysis+of+anatomical+and+electrophysiological+human+intracranial+data.">Integrated analysis of anatomical and electrophysiological human intracranial data.</a> Nature Protocols. 13, 1699-1723 (2018).</li><li>Friston, K. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+causal+modelling+revisited.">Dynamic causal modelling revisited.</a> NeuroImage. , (2017).</li></ol>

The analytical protocols for intracranial EEG data using the SPM software introduced herein have several advantages compared with functional MRI. First, the protocols can depict neural activation at a high temporal resolution. Therefore, the results indicate whether the cognitive correlates of neural activation are implemented at early or late stages of processing. In our example, the face effect was identified during the very early stages (i.e., 110 ms) of visual processing. In addition, the comparison of the t...

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is one of the primary goals of cognitive neuroscience. However, accomplishing this goal is not easy. One popular method used to record neural activity is functional magnetic resonance imaging (MRI). Although functional MRI offers several advantages, such as a high spatial resolution at the millimeter level and non-invasive recording, a clear disadvantage of functional MRI is its low temporal resolution. In addition, functional MRI measures blood-oxygen-level-dependent signals, which only indirectly reflect electric neural activity. Popular electrophysiological methods, including electroencephalography (EEG) and magnetoencephalography (MEG), have high temporal resolutions at the millisecond level. However, they have relatively low spatial resolutions, because they record electric/magnetic signals at the scalp and must solve difficult inverse problems to depict brain activity.
Intracranial EEG can directly record electrical neural activity at high temporal (millisecond) and spatial (centimeter) resolutions1. This measure can provide valuable opportunities to understand neural activity and connectivity, although it has clear limitations (e.g., measurable regions are restricted to clinical criteria). Several intracranial EEG studies have applied traditional averaging analysis to depict neural activity. Although averaging analysis can sensitively detect time-locked and low-frequency band activation, it cannot detect non-phase-locked and/or high-frequency (e.g., gamma band) activation. In addition, functional neural coupling has not been analyzed in depth in the literature on intracranial EEG recordings. Several new techniques have been recently developed to depict neural activity and intra- and inter-regional connectivity in functional MRI and EEG/MEG recordings, which can be applied to analyze intracranial EEG data.
Here, we introduce analytical protocols that we have recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software. First, to reveal when, and at which frequency, the brain regions could be activated, we performed time-frequency SPM analysis2. This analysis decomposes the time and frequency domains simultaneously using a continuous wavelet transform and appropriately corrects the family-wise error (FWE) rate in the time-frequency maps using the random field theory. Second, to reveal how brain regions communicate, we applied dynamic causal modeling (DCM) of induced responses4. DCM enables the investigation of effective connectivity (i.e., the causal and directional influences among brain regions5). Although DCM was originally proposed as a tool for analyzing functional MRI data5, DCM of induced responses has been extended to analyze the time-varying power spectra of electrophysiological signals4. This analysis allows the depiction of both intra- and inter-regional neural connectivity. Several neurophysiological studies have suggested that local intra-regional computations and long-range inter-regional communication mainly use gamma- and theta-band oscillations, respectively, and their interactions (e.g., entrainments) can be reflected by theta-gamma cross-frequency coupling3,6,7,8. This report focuses on the data analytical protocol; for an overview of background information9,10 and recording protocols11 of intracranial EEG, please refer to the literature.

analyzing neural activity and connectivity using intracranial eeg data with spm software

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive neuroscience. Intracranial electroencephalography (EEG) can directly record electrical neural activity and has the unique potential to accomplish this goal. Traditionally, averaging analysis has been applied to analyze intracranial EEG data; however, several new techniques are available for depicting neural activity and intra- and inter-regional connectivity. Here, we introduce two analytical protocols we recently applied to analyze intracranial EEG data using the Statistical Parametric Mapping (SPM) software: time-frequency SPM analysis for neural activity and dynamic causal modeling of induced responses for intra- and inter-regional connectivity. We report our analysis of intracranial EEG data during the observation of faces as representative results. The results revealed that the inferior occipital gyrus (IOG) showed gamma-band activity at very early stages (110 ms) in response to faces, and both the IOG and amygdala showed rapid intra- and inter-regional connectivity using various types of oscillations. These analytical protocols have the potential to identify the neural mechanisms underlying cognitive functions with high spatial and temporal profiles.

Using the protocol presented herein, we analyzed intracranial EEG data in response to faces18,19. We recorded data from six patients during the passive viewing of faces, mosaics, and houses in upright and inverted orientations. The contrasts of upright faces versus upright mosaics and upright faces versus upright houses revealed the face effect (i.e., face-specific brain activity relative to other object...

Watch this Scientific Journal Video about Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software at JoVE.com

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

We present two analytical protocols that can be used to analyze intracranial electroencephalography data using the Statistical Parametric Mapping (SPM) software: time-frequency statistical parametric mapping analysis for neural activity, and dynamic causal modeling of induced responses for intra- and inter-regional connectivity.

Measuring neural activity and connectivity associated with cognitive functions at high spatial and temporal resolutions is an important goal in cognitive ...

This method can help answer key questions in the cognitive neuroscience field. The main advantage of this technique is that it can analyze neural activity and connectivity on high-spatial temporal evolutions. To begin analysis of intracranial EEG data, set up SPM12 and select the MEG EEG analytical menu.First, perform time-frequency analysis for the preprocessed intracranial EEG data of each trial using continuous wavelet decomposition with Morlet wavelets based on predefined parameters. To reveal the temporal evolution of spectral components, conduct wavelet decomposition using seven-cycle Morlet wavelets for the entire epoch of 1, 000 to 2, 000 milliseconds using the frequency range of four to 300 hertz. Next, determine the mother wavelet and the number of cycles and note that the number of cycles controls the time-frequency resolutions and should be greater than five to ensure estimation stability.Determine time and frequency ranges. Then crop the resultant time frequency maps automatically to remove edge effects. Here, the maps are cropped at 200 to 500 milliseconds.Perform data transformation if desired and baseline correction by selecting time frequency rescale for the time frequency maps to better visualize event-related power changes and improve normality. Next, convert the time frequency maps into two-dimensional images. Also smooth using a Gaussian kernel with a predefined full-width half-maximum value to compensate for inter-subject variability and to conform to assumptions of random field theory.Now select specify second level in the SPM menu and enter the 2D images that will be analyzed. Then run the general linear model by selecting model estimation. Finally, select results to perform statistical inferences on the time-frequency data based on random field theory.Detect significantly activated time-frequency clusters with predefined thresholds such as those seen here. Start dynamic causal modeling analysis by selecting DCM in the SPM menu. Then choose the IND option and select new data to import the preprocessed intracranial EEG data.Next, use the MEG EEG menu to specify the time window of interest, frequency window of interest, number of wavelet cycles that will be used, conditions of interest, and the contrasts for the conditions. Set the time window to one to 500 milliseconds. Use five-cycle Morlet wavelets of four to 100 hertz in one hertz steps.Use the default setting for the wavelet cycle. Determine the time-frequency ranges based on research interest. Note that a time window with an additional 512 milliseconds can be automatically used during computation to remove edge effects.Based on the DCM framework, define the driving inputs which represent sensory inputs on neural states and the intrinsic connections which embody baseline connectivity among neural states. Also, define the modulatory effects on the intrinsic connections via experimental manipulations for null and hypothesized models. Define the type of modulation as linear or nonlinear.Now specify the intrinsic linear and nonlinear connections, driving inputs and modulation inputs. Modify the default settings of related parameters if necessary such as prior stimulus onset time and duration. Then choose invert DCM to estimate the models.After that, select save results as image to save frequency-frequency modulatory coupling parameter images. Next, conduct a random effects Bayesian Model Selection analysis by selecting BMS to identify the optimal network model. Use the model expected probabilities and accedence probabilities as evaluation measures.Then make inferences regarding the cross-frequency patterns of the modulatory connections using the winning model parameters. Now smooth the modulatory coupling parameter images by selecting convert to images. Then use specify second level to perform general linear model analysis.Finally, select results to calculate the 2D SPMT values. Here, the full-width half-maximum was set at eight hertz and significant values were exploratorily identified using an uncorrected height threshold of P less than 0.05. Time-frequency analyses were conducted to investigate the temporal and frequency profiles of Inferior Occipital Gyrus or IOG activity during the processing of phases.Here we see time-frequency maps of the right IOG activity for the upright phase and upright mosaic conditions. The SPMT data for upright phase versus upright mosaic are also shown. Functional network models are shown here.Eight possible combinations of modulatory input of upright phase versus upright mosaic onto connections between the IOG and amygdala and self-connection onto each region were investigated. Frequency-frequency modulatory coupling parameters and SPMT values for upright phase versus upright mosaic for the IOG versus amygdala and amygdala versus IOG modulation are shown here. The red-yellow areas indicate significant excitatory connectivity while the blue-cyan areas indicate inhibitory connectivity.After watching this video, you should have a good understanding on how to analyze intracranial EEG data for detecting neural activity and connectivity using the SPM software.

analyzing-neural-activity-connectivity-using-intracranial-eeg-data

Research

JoVE Journal

Neuroscience

9.3K Views.  Kyoto University. Questo metodo può aiutare a rispondere a domande chiave nel campo delle neuroscienze cognitive. Il principale vantaggio di questa tecnica è che può analizzare l'attività neurale e la connettività su evoluzioni temporali ad alto spaziale. Per iniziare l'analisi dei dati EEG intracranici, impostare SPM12 e selezionare il menu analitico MEG EEG.In primo luogo, eseguire l'analisi della frequenza del tempo per i dati EEG intracranici pre-elaborazione di ogni prova utilizzando la decomp...