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Development of grouped icEEG for the study of cognitive processing.

Kadipasaoglu CM, Forseth K, Whaley M, Conner CR, Rollo MJ, Baboyan VG, Tandon N - Front Psychol (2015)

Bottom Line: To date, the contributions of icEEG have been limited to the individual-level analyses or cohorts whose data are not integrated in any way.Here we discuss how grouped approaches to icEEG overcome challenges related to sparse-sampling, correct for individual variations in response and provide statistically valid models of brain activity in a population.In this fashion, grouped icEEG analyses can provide significant advances in understanding the mechanisms by which cortical networks give rise to cognitive functions.

View Article: PubMed Central - PubMed

Affiliation: Vivian Smith Department of Neurosurgery, University of Texas Health Science Center at Houston Houston, TX, USA.

ABSTRACT
Invasive intracranial EEG (icEEG) offers a unique opportunity to study human cognitive networks at an unmatched spatiotemporal resolution. To date, the contributions of icEEG have been limited to the individual-level analyses or cohorts whose data are not integrated in any way. Here we discuss how grouped approaches to icEEG overcome challenges related to sparse-sampling, correct for individual variations in response and provide statistically valid models of brain activity in a population. By the generation of whole-brain activity maps, grouped icEEG enables the study of intra and interregional dynamics between distributed cortical substrates exhibiting task-dependent activity. In this fashion, grouped icEEG analyses can provide significant advances in understanding the mechanisms by which cortical networks give rise to cognitive functions.

No MeSH data available.


Frontal-ventral temporal interactions are evaluated using grouped icEEG collected during a word-completion task. Connectivity is evaluated using the Short-time direct Directed Transfer Function (SdDTF). Post-stimulus interregional flows were determined across post-stimulus windows (100 ms long, 50 ms shift) for high-frequency broadband gamma activity (60–120 Hz) and were compared to pre-stimulus flows computed over one pre-stimulus, baseline window (−700 ms to −200 ms). After normalizing across all patients, all post-stimulus interregional flows were tested for significance (FDR-corrected with a significance level of p = 0.05). Shown at right is the time course of percent change of flows (±1 standard error of the mean) from pars triangularis to word-preferential areas in fusiform gyrus (w-FG) that achieved significance. Electrodes for each region (colored spheres) have been identified using SB-MEMA (not shown). The cortical model to the left (lateral view at top, ventral view at bottom; left hemisphere) provides a snapshot of significant flows for the cortical reading network at 400 ms after stimulus onset (w-FG is shown in green and pars triangularis is shown in red). The ability to study long-distance cortical network interactions at millisecond resolution is a unique advantage of grouped icEEG, and enables the critical evaluation of hypotheses regarding functional network dynamics.
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Figure 2: Frontal-ventral temporal interactions are evaluated using grouped icEEG collected during a word-completion task. Connectivity is evaluated using the Short-time direct Directed Transfer Function (SdDTF). Post-stimulus interregional flows were determined across post-stimulus windows (100 ms long, 50 ms shift) for high-frequency broadband gamma activity (60–120 Hz) and were compared to pre-stimulus flows computed over one pre-stimulus, baseline window (−700 ms to −200 ms). After normalizing across all patients, all post-stimulus interregional flows were tested for significance (FDR-corrected with a significance level of p = 0.05). Shown at right is the time course of percent change of flows (±1 standard error of the mean) from pars triangularis to word-preferential areas in fusiform gyrus (w-FG) that achieved significance. Electrodes for each region (colored spheres) have been identified using SB-MEMA (not shown). The cortical model to the left (lateral view at top, ventral view at bottom; left hemisphere) provides a snapshot of significant flows for the cortical reading network at 400 ms after stimulus onset (w-FG is shown in green and pars triangularis is shown in red). The ability to study long-distance cortical network interactions at millisecond resolution is a unique advantage of grouped icEEG, and enables the critical evaluation of hypotheses regarding functional network dynamics.

Mentions: The neural substrates that comprise the reading network include cortical areas traditionally associated with language production (e.g., Broca's area), as well a ventrally positioned region in the fusiform gyrus, which demonstrates preferential responses to visually presented words and pseudowords (w-FG) (McCandliss et al., 2003). Cognitive approaches are divided on connectivity patterns during word reading that facilitate the visual processing of orthographic stimuli (Carreiras et al., 2014). While it is agreed upon that w-FG is crucial to word reading, some models predict strictly feed-forward connectivity patterns accompany word reading while other models stress the presence of bi-directional interactions between ventral visual and higher-level frontal cortex (Price and Devlin, 2011; Carreiras et al., 2014). Given that the anatomical sources and temporal evolution of top-down control are not well-established, a data-driven connectivity measure, such as SdDTF, is necessary to investigate the timing and directionality of information transmission during word reading. SdDTF quantifies connectivity across multi-dimensional networks, and can derive directed information flow between any two network nodes, while controlling for the contributions from all other sources (Korzeniewska et al., 2008, 2011). Applied to our icEEG data, patient-specific information flows were computed for subsets of task-relevant electrodes identified through SB-MEMA. It is important to note that connectivity between any two regions can only be derived in patients with electrodes recording simultaneously from both regions. In other words, connectivity measures must first be performed within subject, before individual connectivity estimates can be combined across subjects to yield a grouped connectivity estimate. In this fashion, flows derived from SdDTF were averaged over patient and region, and were able to isolate top-down information flow from Pars Triangularis to w-FG during a word completion task (Figure 2).


Development of grouped icEEG for the study of cognitive processing.

Kadipasaoglu CM, Forseth K, Whaley M, Conner CR, Rollo MJ, Baboyan VG, Tandon N - Front Psychol (2015)

Frontal-ventral temporal interactions are evaluated using grouped icEEG collected during a word-completion task. Connectivity is evaluated using the Short-time direct Directed Transfer Function (SdDTF). Post-stimulus interregional flows were determined across post-stimulus windows (100 ms long, 50 ms shift) for high-frequency broadband gamma activity (60–120 Hz) and were compared to pre-stimulus flows computed over one pre-stimulus, baseline window (−700 ms to −200 ms). After normalizing across all patients, all post-stimulus interregional flows were tested for significance (FDR-corrected with a significance level of p = 0.05). Shown at right is the time course of percent change of flows (±1 standard error of the mean) from pars triangularis to word-preferential areas in fusiform gyrus (w-FG) that achieved significance. Electrodes for each region (colored spheres) have been identified using SB-MEMA (not shown). The cortical model to the left (lateral view at top, ventral view at bottom; left hemisphere) provides a snapshot of significant flows for the cortical reading network at 400 ms after stimulus onset (w-FG is shown in green and pars triangularis is shown in red). The ability to study long-distance cortical network interactions at millisecond resolution is a unique advantage of grouped icEEG, and enables the critical evaluation of hypotheses regarding functional network dynamics.
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Related In: Results  -  Collection

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Show All Figures
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Figure 2: Frontal-ventral temporal interactions are evaluated using grouped icEEG collected during a word-completion task. Connectivity is evaluated using the Short-time direct Directed Transfer Function (SdDTF). Post-stimulus interregional flows were determined across post-stimulus windows (100 ms long, 50 ms shift) for high-frequency broadband gamma activity (60–120 Hz) and were compared to pre-stimulus flows computed over one pre-stimulus, baseline window (−700 ms to −200 ms). After normalizing across all patients, all post-stimulus interregional flows were tested for significance (FDR-corrected with a significance level of p = 0.05). Shown at right is the time course of percent change of flows (±1 standard error of the mean) from pars triangularis to word-preferential areas in fusiform gyrus (w-FG) that achieved significance. Electrodes for each region (colored spheres) have been identified using SB-MEMA (not shown). The cortical model to the left (lateral view at top, ventral view at bottom; left hemisphere) provides a snapshot of significant flows for the cortical reading network at 400 ms after stimulus onset (w-FG is shown in green and pars triangularis is shown in red). The ability to study long-distance cortical network interactions at millisecond resolution is a unique advantage of grouped icEEG, and enables the critical evaluation of hypotheses regarding functional network dynamics.
Mentions: The neural substrates that comprise the reading network include cortical areas traditionally associated with language production (e.g., Broca's area), as well a ventrally positioned region in the fusiform gyrus, which demonstrates preferential responses to visually presented words and pseudowords (w-FG) (McCandliss et al., 2003). Cognitive approaches are divided on connectivity patterns during word reading that facilitate the visual processing of orthographic stimuli (Carreiras et al., 2014). While it is agreed upon that w-FG is crucial to word reading, some models predict strictly feed-forward connectivity patterns accompany word reading while other models stress the presence of bi-directional interactions between ventral visual and higher-level frontal cortex (Price and Devlin, 2011; Carreiras et al., 2014). Given that the anatomical sources and temporal evolution of top-down control are not well-established, a data-driven connectivity measure, such as SdDTF, is necessary to investigate the timing and directionality of information transmission during word reading. SdDTF quantifies connectivity across multi-dimensional networks, and can derive directed information flow between any two network nodes, while controlling for the contributions from all other sources (Korzeniewska et al., 2008, 2011). Applied to our icEEG data, patient-specific information flows were computed for subsets of task-relevant electrodes identified through SB-MEMA. It is important to note that connectivity between any two regions can only be derived in patients with electrodes recording simultaneously from both regions. In other words, connectivity measures must first be performed within subject, before individual connectivity estimates can be combined across subjects to yield a grouped connectivity estimate. In this fashion, flows derived from SdDTF were averaged over patient and region, and were able to isolate top-down information flow from Pars Triangularis to w-FG during a word completion task (Figure 2).

Bottom Line: To date, the contributions of icEEG have been limited to the individual-level analyses or cohorts whose data are not integrated in any way.Here we discuss how grouped approaches to icEEG overcome challenges related to sparse-sampling, correct for individual variations in response and provide statistically valid models of brain activity in a population.In this fashion, grouped icEEG analyses can provide significant advances in understanding the mechanisms by which cortical networks give rise to cognitive functions.

View Article: PubMed Central - PubMed

Affiliation: Vivian Smith Department of Neurosurgery, University of Texas Health Science Center at Houston Houston, TX, USA.

ABSTRACT
Invasive intracranial EEG (icEEG) offers a unique opportunity to study human cognitive networks at an unmatched spatiotemporal resolution. To date, the contributions of icEEG have been limited to the individual-level analyses or cohorts whose data are not integrated in any way. Here we discuss how grouped approaches to icEEG overcome challenges related to sparse-sampling, correct for individual variations in response and provide statistically valid models of brain activity in a population. By the generation of whole-brain activity maps, grouped icEEG enables the study of intra and interregional dynamics between distributed cortical substrates exhibiting task-dependent activity. In this fashion, grouped icEEG analyses can provide significant advances in understanding the mechanisms by which cortical networks give rise to cognitive functions.

No MeSH data available.