NEMO Technical Document 2013-002

Created: 2013-07-25 (GF)Last Updated: 2013-07-26 (GF)

Representing and Reasoning over Named & Unnamed ERP EffectsGwen Frishkoff

1. GOALS

The overarching goal of the NEMO project is to enable cross-lab comparison of ERP experimental effects. Quantitative meta-analyses are essential in determining which experiment findings are reliable and robust and can also help to reveal unexpected and novel results. Given that ERP patterns are complex and multidimensional, these comparisons can even be viewed as necessary for validation of core constructs in ERP research (e.g., statistically significant patterns and effects).

In this TR, we consider how to represent and visualize well-known ("named") and novel ("unnamed") effects within a common ontological framework. Unnamed effects represent experiment results that are new, as opposed to well-known effects, such as the word N170 or the semantic N400. It is therefore important to capture these effects and to specify how they are related to effects that have been previously described.

Once we have quantitive meta-analysis results (NEMO Phase II), it will be possible to say which effects are robust and worth representing as named ERP effects within the ontology. It will also be possible to determine the relationship between these effects, i.e., whether they are (a) equivalent, (b) disjoint, or (c) hierarchically related (e.g., Effect A is a subclass of Effect B). Since these patterns are considered as possible endophenotypes, meta-analysis results can also inform theories of mental processes (emotion, cognitive and sensory-motor processes).

The rest of this TR is structured as follows.

Section 2 gives examples of rule definitions for named and unnamed effects in NEMO.

Section 3 lists all of the unnamed ERP effects and describes them in greater detail, i.e., w.r.t. anatomical regions of the skull and scalp, and corresponding channels in the International 10-10 electrode system. Unnamed patterns are defined in space (scalp distribution), rather than time, because it is the spatial dimension that captures unique neural microstates (configurations of brain activity).

Section 4 explains the relationship between classes such as mean_intensity_LFRONT (a descriptive statistic, or OBI "measurement datum") and BFO spatial regions, such as frontocentral_scalp_surface_region. The logical axioms that relate spatially defined metrics, spatial regions, and anatomical parts allow us to reason across distinct branches of the NEMO ontology.

Section 5 shows how effects can be visually represented in the NEMO portal for a particular ERP dataset.

2. NAMED AND UNNAMED ERP EFFECTS IN NEMO

In this section we consider the difference in pattern definitions for named and unnamed ERP effects. NEMO.owl (v. 3.00) currently includes definitions for ~40 named patterns ("components") and for ~20 named effects. A scalp_recorded_ERP_component (NEMO_0000093) is an ERP pattern for a particular experiment condition (e.g., episodically old items). A scalp_recorded_ERP_diffwave_component (NEMO_0877000) — an "ERP effect" for short — is also a pattern: specifically, it is a pattern that reflects a statistically significant difference in the amplitude or intensity of the ERP over a particular region of the scalp for a particular condition contrast (e.g., episodically old VS. episodically new items).

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NEMO.owl (v. 3.00) also includes definitions for 40 unnamed ERP effects. An unnamed pattern class represents a statistical difference (either positive or negative) over any one of the 20 sub-regions of the scalp (see Sec. 3 for details).

2.1. Named ERP Effects. Figure 1 gives an example of a named ERP effect in NEMO, the fN400_old_new_effect (NEMO_9909000), which represents a statistically greater negativity for episodically new stimuli (condition of interest) minus episodically old stimuli (baseline condition). The rule is expressed below in OWL (Manchester syntax).

fN400_old_new_effect EquivalentTo scalp_recorded_ERP_diffwave (1) that (has_proper_part some (peak_latency_measurement_datum TEMPORAL CRITERION

that (has_numeric_value some (decimal[>= "300"^^decimal] and decimal[<= "500"^^decimal]))))

(2) and (has_proper_part some (intensity_measurement_datum SPATIAL CRITERIONthat (is_quality_measurement_of some (intensity

that (inheres_in some (scalp_recorded_ERP that (unfolds_in some frontocentral_scalp_surface_region)))))

and (has_numeric_value some decimal[< "0.0"^^decimal])) POLARITY CRITERIONand (has_quality some statistical_significance) STATISTICAL CRITERION

(3) and (proper_part_of some (averaged_EEG_data_setthat (is_specified_output_of some (subtraction_data_transformation FUNCTIONAL CRITERION that (has_specified_input some (averaged_EEG_data_set

that (is_about some (scalp_recorded_ERP that (occurs_in_response_to some (onset_stimulus_presentation

that (has_object some (object and (has_role some stimulus_role)))

and (proper_part_of some (experimental_condition_execution that (is_realization_of some (plan

that (is_concretization_of some (episodically_new_condition that (has_role some condition_of_interest)))))))))))))

and (has_specified_input some (averaged_EEG_data_set that (is_about some (scalp_recorded_ERP

that (occurs_in_response_to some (onset_stimulus_presentation that (has_object some (object

and (has_role some stimulus_role))) and (proper_part_of some (experimental_condition_execution

that (is_realization_of some (plan that (is_concretization_of some (episodically_old_condition

that(has_role some condition_for_comparison)17

Figure 1. Example of named ERP effect rule (equivalent class description) in NEMO.owl.

Note that named ERP effects are defined in terms of space, time (and/or frequency band), and function (i.e., experiment contrast), as in Figure 1. By contrast, unnamed effects are defined mainly with respect to spatial distribution: they lack temporal or functional criteria. This will be important for reasoning over the data and ontology when we're ready to visualize results in the portal.

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2.2. Unnamed ERP Patterns. Figure 2 gives an example of a rule for one of the unnamed patterns in NEMO.owl, NEMO_8461000 (unnamed_negative_LFRONT_effect). This class represents a statistically greater negativity for the condition of interest vs. the baseline condition over the left frontal region of the scalp. The specific conditions (task, stimuli) and the temporal interval for the effect are left unspecified.

unnamed_negative_LFRONT_effect EquivalentTo scalp_recorded_ERP_diffwave (1) that (has_proper_part some (mean_intensity_LFRONT))))) SPATIAL CRITERION(2) and (has_numeric_value some decimal[< "0.0"^^decimal])) POLARITY CRITERION(3) and (has_quality some statistical_significance) STATISTICAL CRITERION

Figure 2. Example of unnamed ERP pattern rule in NEMO.owl. This class represents a statistically greater negativity in the condition of interest (vs. the baseline condition) over the left frontocentral region of the scalp.

3. TAXONOMY OF UNNAMED ERP EFFECTS

As noted in Section 2, the unnamed pattern classes are defined with respect to scalp distribution (and polarity). To understand how they are related, it is important to be familiar with the spatial dimensions that are used to characterized ERP patterns in NEMO.

3.1. NEMO Standardized ROI. Because NEMO data from different labs include different numbers of channels (electrodes), it was important that we develop a standardized schema for clustering measures across electrodes — i.e., a standard specification of scalp regions of interest (ROI). The development of such a schema addresses several issues. First, it gives us a simple way to represent all data within a common spatial framework, a step towards normalization of datasets for cross-lab meta-analysis. Second, it represents a simple and sensible method for data reduction in space. It makes little sense to define ERP patterns in terms of effects at single channels. EEG is volume-conducted, meaning that effects are always distributed ("smeared") across the scalp. Thus, collapsing across nearby channels does not merely simplify analysis: it better represents the observed data. Finally, averaging across channels increases the signal-to-noise ratio (SNR). NEMO ROI are depicted in Figure 3 below (see 'NEMO-TR-2012-008_ROIdefinitions.docx' for details).

Figure 3. International 10-10 schema (Oostenveld, REF), divided into 20 scalp ROI & two spatial factors: Laterality (5: mid, left, right, left inferior, right inferior) and Caudality (4: frontal, central, parietal, occipital)

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Table 1 lists the ROI, along with their NEMO URI and corresponding 10-10 channels.

Table 1. NEMO Scalp regions of interest (ROI) defined in 10-10 and 10-20 systems. These classes represent BFO spatial regions and are subsumed under NEMO_5003000 (regional_part_of_scalp_surface_region).

Caudal Regions of Interest (ROI) URI 10-20 Channelsfrontal(NEMO_0000005)

mid frontal (MFRONT) NEMO_0000014 Fz, Af1, Afz, Fpz, Af2, F1, F2left frontal (LFRONT) NEMO_0000013 F3, Af3, Af5, F5right frontal (RFRONT)left frontotemporal (LFTEMP)right frontotemporal (RFTEMP)

NEMO_0000015NEMO_0000011NEMO_0000012

F4, Af4, Af6, F6F7, Af7, Fp1, F9F8, Af8, Fp2, F10

central(NEMO_5088000)

mid central (MCENT) NEMO_4013000 Cz, C1, C2, Fcz, Fc1, Fc2left central (LCENT)right central (RCENT)

NEMO_2327000NEMO_2471000

C3, C5, Fc3, FC5C4, C6, Fc4, FC6

left centrotemporal (LCTEMP)right centrotemporal (RCTEMP)

NEMO_5937000NEMO_6141000

T7, T9, FT7, FT9T8, T10, FT8, FT10

parietal(NEMO_0000006)

mid parietal (MPAR) NEMO_0000022 Pz, P1, P2, Cpz, Cp1, Cp2left parietal (LPAR) NEMO_0000021 P3, P5, Cp3, Cp5right parietal (RPAR)left posterotemporal (LPTEMP)right posterotemporal (RPTEMP)

NEMO_0000023NEMO_0000024NEMO_0000025

P4, P6, Cp4, Cp6P7, P9, TP7, TP9P8, P10, TP8, TP10

occipital(NEMO_0000007)

mid occipital (MOCC) NEMO_0000017 Oz, POz, PO1, PO2, Izleft occipital (LOCC) NEMO_0000016 O1, PO3, PO5right occipital (ROCC)left occipitotemporal (LOTEMP)right occipitotemporal (ROTEMP)

NEMO_0000279NEMO_0596000NEMO_9172000

O2, PO4, PO6PO7, PO9, I1PO8, PO10, I2

The classes in Table 1 have the following features, which are represented with axioms in NEMO.owl:

1. They are all spatial regions (as opposed to anatomical parts).2. Each region is defined as the location_of a corresponding regional_part_of_scalp_surface (i.e., an

anatomical part).3. Each region has five subdivsions (subclasses) corresponding to the five levels of laterality (mid, left,

right, left inferior, right inferior).4. The four caudal divisions (frontal, central, parietal, and occipital) are disjoint.

3.2. NEMO Unnamed ERP Effects. Table 2 lists all unnamed pattern classes in NEMO and their corresponding NEMO URI. Note these classes represent OBI data items and are subsumed under NEMO_0877000 (scalp_recorded_ERP_diffwave_component).

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Table 2. Unnamed ERP effects in NEMO, corresponding to scalp ROI in Table 1. Positive-going ERP effects are represented in red, and negative-going effects in blue.

Negative-goingERP Effects

NEMOURI

Positive-goingERP Effects

NEMO URI

unnamed_negative_MFRONT_effect

NEMO_1715000 unnamed_positive_MFRONT_effect

NEMO_7691000

unnamed_negative_LFRONT_effect NEMO_8461000 unnamed_positive_LFRONT_effect NEMO_1985000unnamed_negative_RFRONT_effect unnamed_negative_LFTEMP_effect unnamed_negative_RFTEMP_effect

NEMO_1731000NEMO_4092000NEMO_1836000

unnamed_positive_RFRONT_effect unnamed_positive_LFTEMP_effect unnamed_positive_RFTEMP_effect

NEMO_3722000NEMO_3483000NEMO_6184000

unnamed_negative_MCENT_effect NEMO_7783000 unnamed_positive_MCENT_effect NEMO_0082390unnamed_negative_LCENT_effect unnamed_negative_RCENT_effect

NEMO_4567654NEMO_8243000

unnamed_positive_LCENT_effect unnamed_positive_RCENT_effect

NEMO_2390877NEMO_8923251

unnamed_negative_LCTEMP_effect unnamed_negative_RCTEMP_effect

NEMO_9203050NEMO_0106090

unnamed_positive_LCTEMP_effect unnamed_positive_RCTEMP_effect

NEMO_9840257NEMO_9802452

unnamed_negative_MPAR_effect NEMO_8071000 unnamed_positive_MPAR_effect NEMO_0541000unnamed_negative_LPAR_effect NEMO_9152000 unnamed_positive_LPAR_effect NEMO_7761000unnamed_negative_RPAR_effect unnamed_negative_LPTEMP_effectunnamed_negative_RPTEMP_effect

NEMO_1424000NEMO_3890000NEMO_9155000

unnamed_positive_RPAR_effect unnamed_positive_LPTEMP_effectunnamed_positive_RPTEMP_effect

NEMO_7045000NEMO_3375000NEMO_0822000

unnamed_negative_MOCC_effect NEMO_8206000 unnamed_positive_MOCC_effect NEMO_8206000unnamed_negative_LOCC_effect NEMO_2238000 unnamed_positive_LOCC_effect NEMO_2238000unnamed_negative_ROCC_effect unnamed_negative_LOTEMP_effectunnamed_negative_ROTEMP_effect

NEMO_1463000NEMO_5224000NEMO_1463000

unnamed_positive_ROCC_effect unnamed_positive_LOTEMP_effectunnamed_positive_ROTEMP_effect

NEMO_6555000NEMO_1100223NEMO_1100224

Notice there are separate named effects for positive- and negative-going components. The polarity of an ERP effect is a function of the experiment contrast, i.e., which experiment condition serves as the condition of interest (vs. the baseline). In this sense, IT is arbitrary (since the direction of the contrast is arbitrary). For well-known ("named") effects — such as the fN400 old-new effect— the direction of the contrast is often specified by convention (e.g., in the DM/old-new memory paradigm, familiar items are typically the condition of interest, so the fN400 effect is typically defined as a relative decrease in positivity for old vs. new stimuli, as opposed to being defined as a relative increase in negativity for new stimuli). In NEMO, we are building in axioms to infer that two inverse contrasts denote the same effect (although they are logically, semantically distinct): e.g., the fN400_old_new_contrast and the fN400_new_old_contrast reflect a statistical difference between the same two conditions (cf. Frege's "morning star"/"evening star" example).

More problematic is the arbitrary focus on only one part of the scalp in defining ERP effects. Historically, researchers have focused on effects near the vertex (top of head), because sparse electrode systems only measured effects within this region. High-density systems show that the polarity of these effects invert, such that the very same effect can be equally characterized as a vertex positivity or as an inferior temporal negativity (example: VPP–N170).

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5. REASONING ACROSS NAMED & UNNAMED PATTERN CLASSES

GOAL: link class:unnamed_negative_LFRONT_effect to class:mean_intensity_LFRONT

1. In NEMO.owl, we assert that instance:TESTEXPT_ERP_diffwave_375 of class:unnamed_negative_LFRONT_effect

2. We also assert that TESTEXPT_ERP_diffwave_375 has_proper_part TESTEXPT_DIFFPATT_peak_latency_value_375, which has value =375.

3. ...and that TESTEXPT_ERP_diffwave_375 has_proper_part TESTEXPT_DIFFPATT_Intensity_Measurement_value_375, which is an instance of class:mean_intensity_LFRONT

4. By definition of class:mean_intensity_LFRONT, this implies that mean_intensity_LFRONT is a quality measurement of some intensity

that inheres in some class:scalp_recorded_ERP that unfolds_in some (instance of) class:left_frontal_scalp_region

5. In NEMO.owl, class:left_frontal_scalp_region is defined as a subclass of class:frontal_scalp_surface_region

Now look at all pattern classes to which instance:TESTEXPT_ERP_diffwave_375 belongs. Let's say instance:TESTEXPT_ERP_diffwave_375 belongs to a second class:X.

6. CASE 1 : If X is a named pattern class, such as class:fN400_old_new_effect, THEN do as follows:

First, check to see whether class:left_frontal_scalp_surface_region is a subclass of class:frontal_scalp_surface_region, which is the defining spatial criterion for the fN400 effect. It is.

Then list the named effect on the top level List unnamed_negative_FRONT_effect on sublevel & remove "unnamed_" prefix

7. CASE 2 : If X is another unnnamed pattern class, e.g., class:unnamed_positive_MOCC_effect, THEN do as follows:

First, check to see whether class:mid_occipital_scalp_surface_region is a subclass of class:frontal_scalp_surface_region, which is the defining spatial criterion for the fN400 effect. It is NOT.

Then list unnamed_negative_FRONT_effect on top level

EXAMPLE: fN400_old_new_effect negative_LFRONT_effect [the fN400 effect is defined over

frontal sites and has peak at or around 375ms] -- CASE 1unnamed_positive_MOCC_effect [there is no named pattern class in NEMO that represents a positivity over mid-occipital electrodes and has peak at or around 375ms] -- CASE 2

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4. PORTAL REPRESENTATION OF ERP CLASSIFICATION RESULTS

Figure 4. Top, Representation of a hypothetical ERP effect that is characterized by a significantly greater negativity for the condition of interest vs. the baseline condition over left occipital, left parietal, and left occipito-

temporal regions and a greater positivity over right frontal, right central, and right centrotemporal regions. Bottom, timecourse of hypothetical ERP effect.

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