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Browse courses and booksModule 12
Chapter 12 · 2 h · 8 quiz items · pass at 80%
BCIA Domain VI requires the practitioner to read a brain map and turn it into a plan. This module walks the full QEEG workflow, compares the normative databases the field uses, fixes what a z-score means statistically and clinically, and connects map findings to the assessment-to-protocol chain that Domain VII builds on. The quiz proves the learner can interpret a deviation and explain how it informs protocol selection.
The intake gave you a clinical picture and a hypothesis. A quantitative EEG gives you a look at the brain that hypothesis is about. Where the interview tells you what the client experiences, the brain map tells you what their cortex is doing, measured in microvolts and compared to a reference population, and at its best the two converge into a sharper account than either alone. At its worst the map misleads, because a colorful topographic display reads like certainty when it is actually a statistical comparison built on assumptions you cannot see unless you go looking. This chapter is about reading the map with your eyes open: how the data are acquired and cleaned, what the database comparison actually does, what a z-score means and does not mean, what phenotype patterns and source analysis add, and how a finding becomes a protocol hypothesis without overreaching.
Hold one frame from the start, because it governs everything downstream. QEEG is not a diagnostic test. It is disciplined pattern recognition under explicit constraints. The EEG signal is the summed post-synaptic activity of synchronously firing cortical pyramidal cells, volume-conducted and substantially attenuated through skull and scalp before it reaches the electrodes, which makes it an indirect, spatially blurred, reference-dependent measure of mostly surface cortical activity. Every interpretive move in this chapter respects those three constraints. The companion volumes carry the depth this chapter condenses: Neurophysiology for Neurofeedback for the cell-to-scalp account of where the signal comes from, and The QEEG Field Guide for the full treatment of acquisition, artifacting, databases, and phenotypes that this chapter compresses to practitioner depth.
A brain map is a pipeline, and the output is only as good as the weakest stage. The sequence runs acquisition, artifact review, epoch selection, transform, database comparison, report, and a practitioner who jumps to the colorful report without trusting the stages behind it is being driven by software they cannot interrogate.
Acquisition. You record from a standard montage, typically the full nineteen channels of the international 10-20 system, against a defined reference (often linked ears or average), under standardized conditions: usually several minutes of eyes-closed and several minutes of eyes-open resting baseline, sometimes with task conditions. The single non-negotiable here is that the recording conditions must match the conditions of the database you will compare against. If the database was built on linked-ears reference with five minutes eyes closed and you record two minutes against a different reference, the comparison is distorted before any analysis runs. Chapters 6 and 7 covered acquisition and montages. The assessment point is that the brain map begins with a recording clean and standard enough to be comparable.
Artifact review. This is the stage that separates a usable map from a misleading one, and it is where the garbage-in problem lives. EEG is contaminated by signals that are not cortical: muscle tension (broadband fast activity, worst at frontal and temporal sites), eye blinks and movements (slow, high-amplitude, frontal), cardiac artifact, movement, electrode pop, and line noise. QEEG is more sensitive to artifact than visual clinical EEG, because the math will faithfully quantify muscle as "beta" and eye movement as "frontal delta" if you let it. The deviation a database flags is meaningless, or worse, actively misleading, if it is artifact the analysis treated as brain. Artifact review, whether by trained visual editing or validated automated pipelines with human oversight, is not optional preprocessing. It is the act that determines whether the numbers downstream describe a brain or a clenched jaw. The QEEG Field Guide devotes its longest chapter to this for a reason.
Epoch selection. From the cleaned record you select the segments to analyze: stretches of artifact-free, representative, stable activity in the condition of interest. You are choosing the data the statistics will run on, and the choice should represent the client's typical state in that condition, not a drowsy stretch (which piles posterior theta) or a moment of tension (which inflates beta).
Transform. The selected time-domain signal is converted to the frequency domain, classically by Fast Fourier Transform, which decomposes the wiggling voltage trace into power across frequencies: how much delta, theta, alpha, beta the signal contains, at each site. This is what makes the EEG "quantitative", the move from a tracing a neurologist reads by eye to numbers a database can compare.
Database comparison. The client's metrics are compared to an age-matched normative sample, and each is expressed as a z-score: how many standard deviations this client sits from the reference mean. This is the stage that turns "20 microvolts of alpha" into "z = +2.1," a number with statistical context. The rest of this chapter is largely about what that comparison does and does not license.
Report. The output is the familiar topographic map: heads colored by deviation, z-score tables, sometimes coherence and source displays. The report is a summary of the pipeline, not an oracle, and reading it well means holding in mind every stage that produced it.
Two ways of expressing power sit at the base of map interpretation, and they answer different questions. Absolute power is the raw amount of activity in a band at a site, in microvolts squared: how much theta is there. Relative power is that band as a proportion of the site's total power across all bands: what fraction of this site's activity is theta. The two can disagree, and the disagreement is informative.
A client can show high relative theta because theta itself is elevated (high absolute theta), or because everything else is low and theta is simply the largest slice of a small pie. A client can show normal relative power in a band whose absolute power is markedly abnormal, because the whole spectrum shifted together. The practical discipline is to read both and reconcile them. Relative power is convenient because it normalizes for overall amplitude differences between people, but it can hide an absolute shift. Absolute power is direct but varies with skull thickness, electrode contact, and individual amplitude in ways relative power partly controls for. When a finding shows up consistently in both, your confidence is higher. When they diverge, that divergence is a clue, often that a narrow-band abnormality is being masked or manufactured by a broadband shift, and it is worth resolving before you build a protocol on it.
A clean recording in isolation is just voltages. Is 20 microvolts of alpha high or low? Is 10.5 Hz alpha fast or slow? Without a reference population the numbers mean nothing, and the normative database is that reference: a collection of EEG recordings from a sample, stratified by age and often sex, processed into means and standard deviations for each metric at each site, against which an individual's z-scores are computed. The database supplies the context that makes "high" and "low" meaningful. It is a tool, not truth, and the choice of tool shapes what you see.
Several databases dominate clinical practice, and the practitioner point is not to memorize their sample sizes but to understand that they differ in ways that matter:
NeuroGuide (the Thatcher database) is the most established and most extensively cited in the peer-reviewed QEEG literature, with FDA 510(k) clearance and lifespan coverage from infancy through old age (Thatcher et al., 2003). Its depth at the extremes of the age range, infancy and advanced age, is unmatched, and its long publication record makes it the default reference for much of the field. Its core sample is predominantly North American.
BrainDx descends from the NYU lineage (Neurometrics and NxLink before it), built on the foundational normative work of John and Prichep at New York University, which independently cross-validated against other early databases and helped establish the multi-site replication standard for the field (John et al., 1988). It carries the historical discriminant-function framework and pairs spectral norms with event-related potential analysis in integrated reports.
Neurofield, qEEG-Pro, and other commercial systems each bring their own sample and processing choices. qEEG-Pro holds the largest resting-state normative sample of any commercial system and uses a fully automated, reproducible artifact-rejection pipeline, which removes operator variability from the cleaning stage. Modern entrants increasingly draw on open, harmonized, multinational data rather than a single lab's collection, a direction the field is moving toward as the limits of single-site samples become clearer.
The selection criteria that matter clinically: an age-matched sample (essential, and the reason age stratification is the first thing to check), a sample whose recording protocol matches yours (reference, conditions, montage), a sample whose demographic makeup is appropriate to your client, and a clearly documented artifact standard. The cross-database reality is reassuring for the core findings and cautionary at the edges: for resting-state spectral power in roughly the 1 to 20 Hz range, the major databases agree closely, with z-score correlations above 0.9 between systems (Keizer, 2019), and a frontal theta excess that shows up in one will almost certainly show up in another (Thatcher & Lubar, 2008). Divergence emerges in high-beta and EMG-adjacent frequencies (where artifact handling differs), in connectivity metrics, in task-state and ERP norms (which only some databases carry), and at the lifespan edges. The practical recommendation: choose one primary database, learn its characteristics well, use a second for cross-validation on complex cases, and trust findings that survive both over discrepancies between them.
One demographic caution belongs in every report. Most normative databases over-represent what the literature calls WEIRD populations, Western, educated, industrialized, rich, democratic (Henrich, Heine, & Norenzayan, 2010), and "normal" in one population is not automatically normal in another. There is a further, more insidious problem: gel-based electrode systems, the standard for historical normative data collection, are systematically harder to use with the coarse and protective hairstyles common among people of African descent, which produces non-random data loss and functionally under-represents these populations in the databases. When a client's background differs substantially from the database sample, weight the patterns more heavily than individual z-score magnitudes, name the sample's composition in the report, and consider a multinational normative set rather than a single-site one. This is not a reason to abandon database comparison. It is a reason to temper z-score confidence with demographic awareness.
The z-score is the unit of brain-map interpretation, and understanding it precisely is what keeps you from over-reading the map. A z-score is the distance of a measurement from the reference mean, expressed in standard deviations: z = (the client's value minus the database mean) divided by the database standard deviation. It standardizes every metric, power, frequency, asymmetry, coherence, onto a common scale, which is what lets you compare a theta value to an alpha value to a coherence value on the same map.
The translation to percentiles, if the distribution is Gaussian, is the part worth memorizing because the exam tests it and the chair depends on it: z = 0 is exactly average; z = +1 sits at the 84th percentile (one SD above the mean); z = +2 at roughly the 98th; z = -1 at the 16th; z = -2 at roughly the 2nd. The conventional threshold of ±2 SD captures about 95 percent of the reference population in the middle, which is why values beyond ±2 are flagged as deviating, and the NeuroGuide and Thatcher convention many platforms follow treats z ≥ |1.5| as borderline and z ≥ |2.0| as clearly deviating.
Three cautions keep the z-score from being read as more than it is.
Statistical significance is not clinical significance. A z-score beyond ±2 means the measurement falls in the outer few percent of the reference population. It does not mean dysfunction, it does not mean treatment is indicated, and it does not mean this pattern is causing the client's symptoms. An alpha frequency 0.5 Hz above expected might cross a statistical threshold and mean nothing functionally; a frontal theta elevation just short of the threshold might be clinically central when it sits alongside attention symptoms and performance deficits. The z-score identifies patterns worth investigating; clinical context decides whether they matter.
The multiple-comparison problem inflates false positives. A standard map computes hundreds to thousands of values: nineteen electrodes, several bands and sub-bands, absolute and relative power, coherence across many pairs, asymmetry, in eyes-open and eyes-closed conditions. At a p < .05 threshold, roughly five percent of those exceed it by chance in a neurotypical brain, so a single map might flag fifteen or twenty "significant" values when several are statistical noise. The defense is the governing principle of map reading: patterns over points. A single electrode with an extreme z-score and nothing else abnormal is probably a false positive or an artifact. Multiple adjacent electrodes deviating consistently, converging across metrics and conditions, in a way that makes physiological sense and matches the symptoms, is a finding you can trust.
Z-scores assume a distribution that EEG data only approximate. The percentile translation above holds only if the underlying metric is Gaussian after transformation, and EEG power distributions have heavier-than-Gaussian tails even after standard log transformation (Wood et al., 2024). The practical consequence is false-positive inflation at the extremes: a database claiming five percent of healthy subjects beyond ±2 SD may actually flag six to nine percent, and at the ±3 SD threshold, where theory predicts roughly one in 370, you may see closer to one in 30. A z-score of +3.5 feels extreme and is rarer than +2, but it is far less rare than the percentile table claims. This does not break z-score interpretation. It sharpens the same discipline. Weight |z| > 2.5 more heavily than |z| > 2.0, where the inflation is worst, and require anatomic and frequency coherence across multiple deviations before acting, because a frontal theta pattern at z = +2.0 across F3, F4, and Fz, consistent in both conditions and matched to symptoms, is worth more than a lone electrode at z = +3.0 with nothing else out of range.
Put the cautions together and the central interpretive move of this chapter follows: a statistical deviation is a question, not an answer. A z-score outside ±2 tells you a measurement falls in the outer few percent of the reference population for that metric. It does not tell you the pattern is dysfunctional, that it is causing the client's difficulty, or that training it will help. The reds and blues on a topographic map communicate urgency. None of that communicates functional significance.
Functional significance requires something the map cannot supply on its own: evidence that the pattern interferes with what the client needs to do. That evidence comes from the symptom report, the performance testing, and the clinical history you gathered at intake. A statistically deviant pattern in a high-functioning, asymptomatic client is individual variation, possibly a benign genetic trait, an adaptive specialization, or a medication effect, and training it toward the norm treats normality as a target when it may be irrelevant. The same pattern in a client with documented functional impairment that matches the phenotype's clinical correlates is a candidate training target. The deviation earns clinical weight only when it converges with the rest of the picture.
This is why the map never reads alone. A continuous performance test that shows poor sustained attention alongside a frontal theta elevation is convergent evidence; the same theta elevation with a normal CPT calls its clinical significance into question. The interpretive standard is convergence across modalities: brain map, performance testing, symptom report, and history pointing the same direction. Use the z-scores to find patterns worth investigating; use the clinical context to decide whether those patterns are the problem. When the map and the client tell the same story from different angles, you have a target. When they conflict, you have a question to resolve before you act, not a license to trust the map over the person.
Reading a map z-score by z-score is how beginners drown in false positives. Experienced practitioners read patterns, and the pattern vocabulary is the phenotype: a configuration of band, direction, and region that recurs across individuals, associates with functional characteristics, and points toward a training direction. A phenotype is not a diagnosis. It is a reproducible electrophysiological pattern with clinical correlates, and treating it as a pattern rather than a diagnosis is what keeps the interpretation honest.
The reasoning that generates phenotypes is more durable than any memorized list, and it is worth carrying as a habit. When you see a deviation, ask three questions: what band, what direction, what region. The combination tells a functional story. Elevated slow activity (delta, theta) at a site suggests underactivation or disinhibition: frontal theta excess means the prefrontal cortex is not maintaining the tonic activation that executive control requires. Elevated fast activity (beta, high beta) suggests hyperactivation, anxiety, or irritability: frontal high beta reads as worry and cognitive hyperarousal. Alpha is the brain's idling rhythm, so elevated alpha means a region is resting when it should work, deficient alpha means a region cannot rest when it should, and a slowed alpha peak frequency means the brain's fundamental processing clock is running slow. Region supplies the functional context: frontal sites for executive function and approach-withdrawal motivation, central strip for sensorimotor regulation, posterior for visual processing and the idling rhythm, midline for conflict monitoring (Fz) and the default-mode hub (Pz). This physiology-first reasoning lets you interpret a deviation the atlas never catalogued. Neurophysiology for Neurofeedback carries the full function-to-site bridge.
A handful of patterns recur often enough to anchor the vocabulary:
Elevated frontal theta. Slow-frequency excess over prefrontal cortex (F3, F4, Fz), the most replicated attention phenotype in QEEG, associated with inattentive and combined ADHD presentations and with executive dysfunction. It is the prototype underaroused pattern and points toward theta-down, beta-up training. One caution this pattern demands: confirm it is not an artifact of a slow individual alpha peak. When a client's alpha sits low (8 to 9 Hz instead of 10), it bleeds into the theta band and inflates apparent theta without reflecting true underarousal, and a fixed-band analysis will misread it. Check the individual alpha peak before committing.
Elevated high beta. Fast-activity excess, often frontal (20 to 30 Hz), the correlate of worry, rumination, and cognitive hyperarousal, common in anxious and obsessive presentations. It points toward beta-down training, with the standing caveat that frontal high beta is the band most often contaminated by muscle artifact, so exclude EMG before calling it cortical.
Frontal alpha asymmetry. A difference in alpha between left and right frontal sites, the most studied EEG correlate of depression, where relatively less left-frontal activation (more left alpha) maps in the Davidson model to withdrawal and reduced approach motivation (Davidson, 1998). The effects are small to moderate and heterogeneous, but when present this pattern changes the protocol, pointing toward an asymmetry approach that raises left-frontal activation rather than a generic arousal protocol. It is one of the clearest cases where a map alters the plan a symptom picture alone would have produced.
Diffuse slowing. Combined delta and theta excess, frontal or global, which raises concern for structural or acquired pathology (post-concussive, neurodegenerative) rather than a regulatory pattern, and warrants neurological workup before it is attributed to attention. The presence of delta on top of theta is the flag that distinguishes it from the benign inattentive phenotype.
These are entry points, not a complete atlas. The QEEG Field Guide catalogues dozens of phenotypes with detection rules, evidence grades, and medication and state confounds. The practitioner skill is to recognize the common patterns, reason from band-direction-region for the rest, and always treat the phenotype as an association to be confirmed against the clinical picture, not a label to be applied.
A scalp electrode has a resolution problem that limits everything above. The signal at any site is a blurred sum of activity from a region of cortex, smeared by the volume conductor of skull and scalp, so a deviation at a surface site does not tell you precisely where in the brain it is generated. Source analysis is the attempt to work backward from the surface to the source.
LORETA (low-resolution electromagnetic tomography) and its standardized variant sLORETA estimate the three-dimensional distribution of current sources inside the brain that best account for the scalp recording (Pascual-Marqui, 1994). Instead of nineteen surface values, you get an estimate of activity across thousands of voxels in cortical space, which can be compared to a normative database the same way surface metrics are and displayed as a volume map. The clinical addition is the ability to ask where, in source space, a deviation lives: whether a frontal slow-activity finding localizes to anterior cingulate, whether a deviation sits in a specific cortical region, which the surface map can only hint at.
The constraints are essential and the report's apparent precision will mislead you if you forget them. Source localization solves an inverse problem that has no unique solution: many source configurations can produce the same surface pattern, and LORETA imposes mathematical constraints to estimate the most likely one. It is low-resolution, on the order of centimeters with nineteen electrodes, it relies on a standardized head model rather than the individual's anatomy, it does not reach deep subcortical structures cleanly, and, like every stage of the pipeline, it will localize artifact as confidently as brain if the data were not cleaned first. The resolution of the picture is not the resolution of the knowledge. A colorful three-dimensional source map looks more certain than a surface topograph, and it is in fact two inferences deep, an inverse-problem estimate compared against a database of inverse-problem estimates, each layer adding assumptions. Used well, aiming at a clear, symptom-relevant source finding, sLORETA adds real anatomical specificity that informs where to train. Used as a more-advanced-is-always-better upgrade, it introduces more ways to be misled. Sometimes the simpler surface analysis is the more reliable one.
The map exists to inform a decision, and the chain that connects them is assessment to hypothesis to protocol. You do not read z-scores into a protocol mechanically. You reason from the map's findings, cross-checked against the clinical picture, to a hypothesis about what the brain is doing, and from that hypothesis to a protocol that tests it. Chapter 13 develops the full protocol selection framework, including the three starting points (symptom-based, EEG-based, QEEG-guided) and the decision trees for common presentations. This section sets up the handoff.
The map contributes three classes of finding that surface clinical observation cannot give you, and each suggests a kind of target. A power deviation (the frontal theta excess, the high-beta hot spot, the posterior alpha deficit) points toward a conventional amplitude protocol at the deviant site and band. A connectivity finding (coherence or phase values that are excessive or deficient between two regions) points toward coherence-style training of that specific site pair and direction, with the caution that coherence is sensitive to montage and reference and carries a thinner evidence base. A source finding (a deviation localized by LORETA to a region) points toward source-space targeting, aiming at the generator rather than the surface projection. And the phenotype reading ties these together: rather than chasing individual z-scores, you recognize the map as fitting a known pattern and adopt the protocol direction that pattern implies, which protects you from over-fitting to noise in any single value.
The governing discipline of the handoff is that the map guides and does not command. A QEEG-guided protocol is only as good as the agreement between the map and the clinical picture. When they point the same direction, confidence is high. When they conflict, a client reporting classic inattention whose map looks hyperaroused, do not reflexively trust the map over the person: re-examine the recording for artifact, re-check the medication history (a stimulant taken that morning shifts the map toward beta), and weigh both data streams before committing. The map is a powerful guide and a poor master, and the protocol decision belongs to the practitioner reasoning across all the evidence, not to the software that produced the prettiest display.
Between the map and the protocol sits a conversation, and how you frame it shapes whether the client becomes a partner in the work or a passive recipient of a verdict they half-understood. The brain-map conversation has its own discipline, and it follows directly from the scope-of-practice and psychoeducation rules of Chapter 11.
Describe, do not diagnose. The accurate and useful framing is description: "your map shows elevated frontal theta and a slowed alpha frequency, patterns that commonly go along with the attention-regulation difficulty you described, and patterns this training targets." The inaccurate and, for a non-licensed practitioner, out-of-scope framing is diagnosis from the map: "your QEEG shows ADHD." The first describes what is and connects it to the client's experience and the plan; the second commits the reverse-inference error (a pattern that appears in a condition does not mean the condition is present) and crosses into diagnostic territory the map cannot support and the credential may not permit. Describe the pattern, link it to the symptom, name the training direction.
Three honesty disciplines keep the conversation sound. Convey that the map is one data source among several, integrated with the intake, the inventories, and the performance testing, not a stand-alone truth. Convey that a deviation is a finding to be understood, not automatically a problem to be fixed, and that a pattern matters clinically only when it connects to how the client functions. And resist the map's persuasive power: clients find the colored heads persuasive, which is exactly why an honest practitioner does not let the display carry more certainty than the underlying statistics warrant. A client who understands their map as a useful, limited picture that informs a training hypothesis is better prepared for the realistic course ahead than one who was handed a brain scan and told what is wrong with them.
Not every client needs a full brain map, and part of assessment competence is knowing when the map earns its cost and when a briefer look suffices. A QEEG is an investment, in money for the client and in time and interpretive care for you, and it is justified when the added precision changes what you do.
Order a full QEEG when the presentation is complex, atypical, or treatment-resistant, where the population-average hypothesis a symptom picture provides is most likely to be wrong and an individualized map most likely to redirect you. Order it when you intend to run QEEG-guided or z-score protocols, which require a map to define their targets (Chapter 16). Order it when a connectivity or source-level question is clinically live, since those findings are invisible to surface observation. Order it when an objective brain-level baseline is valuable for tracking change across the course, or when a finding would meaningfully shift the plan, the frontal alpha asymmetry that turns a generic arousal protocol into an asymmetry protocol being the clearest example.
Brief surface clinical observation, a few minutes of eyes-open and eyes-closed activity at a handful of relevant sites, can be sufficient when the presentation is a clean fit for a well-evidenced category protocol, when a full map would only confirm what the symptoms already say, or when cost or access makes a full QEEG impractical and a reasoned symptom-and-surface approach is the responsible alternative. A clean symptom picture with a textbook presentation can yield a more reliable protocol choice than a noisy QEEG over-interpreted. More data is not automatically better data. The judgment is whether the map will change your decision. When it would, the QEEG is worth it. When it would only decorate a decision you have already reasoned your way to, the briefer assessment is the honest choice, and the cost belongs to the client's training instead.
Strip this chapter to what you do with a recording. You acquire it under conditions that match your database, and you clean it ruthlessly, because the math will quantify muscle as beta and eye movement as frontal delta if you let it. You transform the clean epochs to power, compare them to an age-matched normative sample, and read the z-scores as questions rather than answers: patterns over points, weighting convergent deviations across sites, metrics, and conditions over isolated extremes, and remembering that fat-tailed distributions make extreme z-scores less rare than the percentile table claims. You read phenotypes by band, direction, and region rather than chasing individual values, treating each as an association to confirm against the clinical picture, not a diagnosis. You use source analysis for anatomical specificity while holding its low resolution and inverse-problem uncertainty honestly. You hand the map to the protocol decision as a guide, not a command, never trusting the display over the person when they conflict. And you describe the findings to the client, never diagnose from them.
For the BCN exam, hold the structure. Know the workflow in order: acquisition, artifact review, epoch selection, transform, database comparison, report. Know that QEEG is descriptive, not diagnostic, and that the reverse-inference error (pattern to diagnosis) is the field's most common interpretive mistake. Know the z-score translation cold: z = +2 at roughly the 98th percentile, ±2 SD as the conventional flag, |1.5| borderline and |2.0| clearly deviating in the Thatcher convention. Know that statistical significance is not clinical significance, that the multiple-comparison problem manufactures false positives, and that the defense is convergent patterns. Know the difference between absolute and relative power, and between major databases (NeuroGuide's lifespan depth and citation record, BrainDx's NYU lineage and ERP integration, qEEG-Pro's large automated sample), and know that they agree on resting spectral power in the 1 to 20 Hz range and diverge at high-beta, connectivity, and the lifespan edges. Know that LORETA estimates sources by solving a non-unique inverse problem at centimeter resolution. Know the common phenotypes, frontal theta for inattention, frontal high beta for worry, frontal alpha asymmetry for depressed mood, diffuse slowing as a workup flag, and the slow-alpha-peak confound that masquerades as theta excess. The brain map is disciplined pattern recognition under explicit constraints, and on the exam as at the chair, it is graded on whether you cleaned the data, read patterns not points, and let the clinical picture decide what the deviation means.