Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies
Open accessAbstract
BACKGROUND: Despite many successes, the case-control approach is problematic in biomedical science. It introduces an artificial symmetry whereby all clinical groups (e.g., patients and control subjects) are assumed to be well defined, when biologically they are often highly heterogeneous. By definition, it also precludes inference over the validity of the diagnostic labels. In response, the National Institute of Mental Health Research Domain Criteria proposes to map relationships between symptom dimensions and broad behavioral and biological domains, cutting across diagnostic categories. However, to date, Research Domain Criteria have prompted few methods to meaningfully stratify clinical cohorts. METHODS: We introduce normative modeling for parsing heterogeneity in clinical cohorts, while allowing predictions at an individual subject level. This approach aims to map variation within the cohort and is distinct from, and complementary to, existing approaches that address heterogeneity by employing clustering techniques to fractionate cohorts. To demonstrate this approach, we mapped the relationship between trait impulsivity and reward-related brain activity in a large healthy cohort (N = 491). RESULTS: We identify participants who are outliers within this distribution and show that the degree of deviation (outlier magnitude) relates to specific attention-deficit/hyperactivity disorder symptoms (hyperactivity, but not inattention) on the basis of individualized patterns of abnormality. CONCLUSIONS: Normative modeling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort. Instead, disease can be considered as an extreme of the normal range or as-possibly idiosyncratic-deviation from normal functioning. It also enables inferences over the degree to which behavioral variables, including diagnostic labels, map onto biology.
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Written from the paper’s full text.
TL;DR
The paper introduces normative modeling with Gaussian process regression to parse heterogeneity in clinical cohorts without dichotomizing into case-control groups. In 491 healthy Human Connectome Project participants, it maps delay discounting to reward-related brain activity and shows that deviation magnitude relates to ADHD hyperactivity but not inattention.
Key findings
- The normative model predicted that participants who discount rewards more strongly also more strongly engage a network of cortical and subcortical regions; this network overlaps with, but is not identical to, the gambling-task network.
- Most participants fit the normative model well, including many with high hyperactivity (>8); none of these showed brainordinates deviating from the norm (p < .05, false discovery rate corrected) or were outliers under extreme value distributions (all p > .05).
- Negative deviance correlated with hyperactivity for the top 1% of maximal deviation (r = .91, p = .03, n = 5), top 5% (r = .53, p = .007, n = 25), 10% (r = .31, p = .03, n = 50), 15% (r = .36, p = .002, n = 74), and 20% (r = .22, p = .03, n = 99); positive deviance did not correlate with hyperactivity (all p > .4).
- Deviance was not associated with inattention (all p > .2); 22 participants had normative probability maps with brain regions deviating from the normative model (p < .05, false discovery rate corrected), and no brain region deviated in more than three participants.
Methods
- Gaussian process regression was used to build normative models independently for each brain region, predicting reward-related functional MRI activity from delay discounting; normative probability map Z scores and extreme value statistics summarized subject-level abnormality.
- The sample was 491 healthy Human Connectome Project participants (288 females; mean age 27 years, range 22–36 years) who completed an incentive processing gambling task, the Achenbach Adult Self-Report, and a delay discounting task; ADHD symptom means were 3.03 for inattention, 2.37 for hyperactivity, and 5.40 for total scores.
- Model estimation used 10-fold cross-validation accommodating family structure; analyses related deviations to inattention and hyperactivity, with additional checks excluding high head motion and regressing out motion-related components.
Limitations
- The paper states three areas for future work: evaluating stability of abnormality indices over time, handling extremely large data sets because Gaussian process models scale poorly computationally, and improving model accuracy by modeling spatial correlations.
- The paper notes that head motion is an important source of artifactual variation in functional MRI; it reports excluding high-motion subjects and repeating analyses after motion correction, with conclusions remaining valid.
- Evident from the text: the demonstration uses a single healthy cohort and relates deviations to self-reported ADHD symptoms rather than a clinical ADHD cohort, so clinical validation and longitudinal stability are not established here.
Model-written summary. Check it against the paper before relying on it.
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Cites
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Claims the paper makes
0 of 29 cited sources checked · 0 supported · 0 flagged
- Findingown data
A strong positive correlation (r = .91, p = .03) was found between negative deviance and hyperactivity for the 1% of participants showing maximal deviation (n = 5).
“Furthermore, a strong positive correlation (r = .91, p = .03) was found between the negative deviance and hyperactivity for the 1% of participants showing maximal deviation (n = 5).”
Quote in the paperSubstantial claim - Findingown data
The correlation between negative deviance and hyperactivity persisted into the bulk of the population, remaining significant for participants having negative deviance in the top 5%, 10%, 15%, and 20% of the cohort.
“The degree of deviation was not only informative about the most extreme subjects because the correlation persisted into the bulk of the population, remaining significant for the participants having negative deviance in the top 5% (r = .53, p = .007, n = 25), 10% (r = .31, p = .03, n = 50), 15% (r = .36, p = .002, n = 74), and 20% (r = .22, p = .03, n = 99) of the cohort.”
Quote in the paperSubstantial claim - Findingown data
The network predicted by the normative model corresponds to the regions that the normative model predicted accurately under cross-validation.
“and, as expected, corresponds to the regions that the normative model predicted accurately under cross-validation (Figure 4C).”
Quote in the paperNot a substantial claim - Findingown data
Subjects with the highest absolute deviance did not show high inattention.
“Subjects with the highest absolute deviance did not show high inattention (Supplemental Figure S2)”
Quote in the paperSubstantial claim - Findingown data
The patterns of abnormality were highly individualized with low overlap between subjects.
“The patterns of abnormality were highly individualized with low overlap between subjects; no brain region deviated in more than three participants (Supplemental Figure S4), pointing toward significant heterogeneity of the imaging phenotype within this cohort.”
Quote in the paperSubstantial claim
- Findingown data
Deviance was not associated with inattention.
“In contrast, deviance was not associated with inattention.”
Quote in the paperSubstantial claim - Findingown data
The participants with high hyperactivity who deviate from the normative model score in the 99th and 97th percentiles for hyperactivity within the cohort.
“These participants score in the 99th and 97th percentiles for hyperactivity within the cohort.”
Quote in the paperNot a substantial claim - Findingown data
The study mapped the relationship between trait impulsivity and reward-related brain activity in a large healthy sample.
“To illustrate, we map the relationship between trait impulsivity and reward-related brain activity in a large, healthy sample.”
Quote in the paperSubstantial claim - Findingown data
None of the corresponding positive deviance scores correlated with hyperactivity.
“None of the corresponding positive deviance scores correlated with hyperactivity (all p > .4).”
Quote in the paperSubstantial claim - Findingcitation
The network predicted by the normative model is consistent with the network engaged by delay discounting.
“It is also consistent with the network engaged by delay discounting (41)”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [41] Wesley, 2014, Remember the future II: Meta-analyses and functional overlap of working memory and delay discounting, Biol Psychiatry source
Not checked in the free run.
- Findingown data
We did not find evidence for clearly defined clusters on the basis of symptoms alone.
“We also note that we did not find evidence for clearly defined clusters on the basis of symptoms alone (Supplemental Figure S1).”
Quote in the paperSubstantial claim - Findingown data
The normative probability maps (NPMs) for 22 participants contained brain regions deviating from the normative model at p < .05, false discovery rate corrected.
“The NPMs for 22 participants contained brain regions deviating from the normative model (p < .05, false discovery rate corrected).”
Quote in the paperSubstantial claim - Findingnone given
The normative model predicts that participants who discount rewards more strongly also more strongly engage a network of cortical and subcortical regions.
“The normative model predicts that participants who discount rewards more strongly also more strongly engage a network of cortical and subcortical regions (described in the Supplement).”
Quote in the paperNot a substantial claim - Findingown data
No brain region deviated in more than three participants.
“The patterns of abnormality were highly individualized with low overlap between subjects; no brain region deviated in more than three participants (Supplemental Figure S4), pointing toward significant heterogeneity of the imaging phenotype within this cohort.”
Quote in the paperSubstantial claim - Findingown data
No delay discounting measures were correlated with either hyperactivity or inattention.
“No delay discounting measures were correlated with either hyperactivity (AUC200, r = −.047; AUC40K, r = −.040, both p > .2) or inattention (AUC200, r = .079; AUC40K, r = .045, both p > .2).”
Quote in the paperNot a substantial claim - Findingown data
Our subjects that were outliers remained outliers after excluding high head motion subjects and regressing out motion-related components.
“All conclusions remained valid, and our subjects that were outliers remained outliers.”
Quote in the paperSubstantial claim - Premisecitation
Biological tests to assist diagnosis or predict outcome in psychiatry have not been developed.
“This problem is particularly acute in psychiatry because biological tests to assist diagnosis or predict outcome have not been developed (1).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [1] Kapur, 2012, Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it?, Mol Psychiatry source
Not checked in the free run.
- Premisenone given
Clustering has the problem that there are many different ways to partition clinical populations depending on the measures and clustering algorithm used.
“Clustering is useful for identifying subgroups of participants at a particular time point but also has problems: 1) there are many different ways to partition clinical populations depending on the measures and clustering algorithm used”
Quote in the paperSubstantial claim - Premisenone given
Nearly all published reports on RDoC heterogeneity have employed data-driven clustering methods aiming to fractionate clinical groups mostly on the basis of neuropsychological measures.
“Of the reports published, nearly all have employed data-driven clustering methods aiming to fractionate clinical groups mostly on the basis of neuropsychological measures.”
Quote in the paperSubstantial claim - Premisereasoning
The RDoC objectives are difficult to achieve within the case-control paradigm because it entails partitioning cohorts according to predefined labels, precluding later inferences about their validity.
“These objectives are difficult to achieve within the case-control paradigm, which, by definition, entails partitioning cohorts according to predefined labels, precluding later inferences about their validity.”
Quote in the paperSubstantial claim - Premisereasoning
The case-control approach becomes problematic in domains such as psychiatry, where disorders are diagnosed on the basis of overlapping symptoms, often yielding clinical groups that are heterogeneous and overlapping.
“However, it becomes problematic in domains such as psychiatry where disorders are diagnosed on the basis of symptoms that overlap between disorders, often yielding clinical groups that are heterogeneous and overlapping.”
Quote in the paperSubstantial claim - Premisenone given
The case-control paradigm induces an artificial symmetry such that both cases and controls are assumed to be well-defined entities.
“Moreover, the case-control paradigm induces an artificial symmetry such that both cases and controls are assumed to be well-defined entities (Figure 1).”
Quote in the paperSubstantial claim - Premisecitation
Patient subgroups identified by clustering may not be stable over time.
“3) patient subgroups may not be stable over time (14)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [14] Lahey, 2005, Instability of the DSM-IV subtypes of ADHD from preschool through elementary school, Arch Gen Psychiatry source
Not checked in the free run.
- Premisecitation
Impulsivity and impairments in reward processing are core features of many disorders, including ADHD and addiction.
“This relationship is of high clinical relevance because impulsivity and impairments in reward processing are core features of many disorders, including ADHD (19, 20) and addiction (21).”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [19] Tripp, 2008, Research review: Dopamine transfer deficit: A neurobiological theory of altered reinforcement mechanisms in ADHD, J Child Psychol Psychiatry source
Not checked in the free run.
- cites [20] Plichta, 2014, Ventral-striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: A meta-analytic review of the fMRI literature, Neurosci Biobehav Rev source
Not checked in the free run.
- cites [21] Reynolds, 2006, A review of delay-discounting research with humans: Relations to drug use and gambling, Behav Pharmacol source
Not checked in the free run.
- Premisecitation
Delay discounting is a stable measure of trait impulsivity.
“and is a stable measure of trait impulsivity (23).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [23] Kirby, 2009, One-year temporal stability of delay-discount rates, Psychon Bull Rev source
Not checked in the free run.
- Interpretationreasoning
The analytical framework allows making probabilistic statements about which participants deviate from the normative pattern.
“2) make probabilistic statements about which participants deviate from the normative pattern”
Quote in the paperSubstantial claim- The authors state this as an assumption: Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
- Interpretationreasoning
By mapping the full range of population variation, symptoms in individual patients can be considered as an extreme value within this distribution.
“The intuition is that by mapping the full range of population variation, we can consider symptoms in individual patients as an extreme value within this distribution.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Understanding healthy variation is a prerequisite to understanding disease variation.
- The authors state this as an assumption: Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
- Interpretationreasoning
The analytical framework allows statistically mapping the brain regions underlying deviations on a case-by-case basis.
“3) statistically map the brain regions underlying these deviations on a case-by-case basis”
Quote in the paperSubstantial claim- The authors state this as an assumption: Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
- Interpretationown data
The extreme clinical phenotype is characterized by mechanistic heterogeneity.
“In other words, the extreme clinical phenotype is characterized by mechanistic heterogeneity, which is a key feature of many psychiatric disorders, including ADHD (46).”
Quote in the paperSubstantial claim - Interpretationnone given
Referencing samples to a common normative distribution may yield more interpretable clusters than clustering the data directly.
“The benefit is that referencing samples to a common normative distribution may yield more interpretable clusters than clustering the data directly.”
Quote in the paperSubstantial claim - Interpretationnone given
Our approach provides probabilistic predictions.
“Most importantly, our approach provides probabilistic predictions and the ability to make statistical inferences about the manner in which individual subjects differ from the normative model.”
Quote in the paperSubstantial claim - Interpretationreasoning
Normative modeling provides a way to characterize the deviation statistically, enabling clinically relevant deviations to be distinguished from artifactual deviations based on the individualized pattern of abnormality.
“It is thus significant that normative modeling provides a way to characterize the deviation statistically, enabling clinically relevant deviations to be distinguished from artifactual deviations based on the individualized pattern of abnormality.”
Quote in the paperSubstantial claim - Interpretationreasoning
Normative modeling can indicate whether clustering is appropriate.
“Normative modeling complements clustering approaches; it can accommodate all scenarios in Figure 1 and can indicate whether clustering is appropriate (Figure 1B).”
Quote in the paperSubstantial claim - Interpretationreasoning
The two mechanisms reflect a convergence of different biological mechanisms on the same symptoms.
“These reflect a convergence of different biological mechanisms on the same symptoms.”
Quote in the paperSubstantial claim - Premisecitation
Mechanistic heterogeneity is a key feature of many psychiatric disorders, including ADHD.
“In other words, the extreme clinical phenotype is characterized by mechanistic heterogeneity, which is a key feature of many psychiatric disorders, including ADHD (46).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [46] Faraone, 2015, Attention deficit/hyperactivity disorder, Nat Rev Dis Primers source
Not checked in the free run.
- Interpretationreasoning
Normative models can be targeted specifically to detect abnormalities in multivariate phenotypes by making different choices for variables used for the covariates or responses.
“Normative models can also be targeted specifically to detect abnormalities in multivariate phenotypes by making different choices for variables used for the covariates or responses.”
Quote in the paperSubstantial claim - Interpretationown data
Normative modeling allows teasing apart symptom domains that are highly correlated.
“Therefore, normative modeling allows us to tease apart symptom domains that are highly correlated.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Understanding healthy variation is a prerequisite to understanding disease variation.
- The authors state this as an assumption: Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
- Interpretationreasoning
The normative modeling approach allowed performing statistical inferences at the level of the individual participant.
“This approach allowed us to 1) map the range of normal variation in reward-related brain responses, 2) perform statistical inferences at the level of the individual participant, and 3) identify participants deviating from the normative model.”
- Also stated in: § Discussion · plain
“Normative modeling provides the ability to make statistical inferences at the level of the individual.”
Quote in the paperSubstantial claim- Rests on an assumption: The healthy cohort used to construct the normative model is representative of the normal population.
- Also stated in: § Discussion · plain
- Interpretationreasoning
Normative models are useful for fractionating heterogeneous clinical groups, where the normative distribution can be fit to a large sample of healthy participants to learn a healthy normative pattern and then applied to a clinical cohort to determine where patients lie on the healthy continuum.
“Normative models are also useful for fractionating heterogeneous clinical groups, where the normative distribution can be fit to a large sample of healthy participants to learn a healthy normative pattern and then applied to a clinical cohort to determine where patients lie on the healthy continuum.”
Quote in the paperSubstantial claim - Interpretationown data
The normative modeling approach allowed identifying participants deviating from the normative model.
“This approach allowed us to 1) map the range of normal variation in reward-related brain responses, 2) perform statistical inferences at the level of the individual participant, and 3) identify participants deviating from the normative model.”
Quote in the paperSubstantial claim- Rests on an assumption: The healthy cohort used to construct the normative model is representative of the normal population.
- Interpretationreasoning
Normative modeling enables inferences over the degree to which behavioral variables, including diagnostic labels, map onto biology.
“It also enables inferences over the degree to which behavioral variables, including diagnostic labels, map onto biology.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
- Interpretationreasoning
Normative modeling complements clustering approaches.
“Normative modeling complements clustering approaches; it can accommodate all scenarios in Figure 1 and can indicate whether clustering is appropriate (Figure 1B).”
Quote in the paperSubstantial claim - Interpretationreasoning
Our approach has advantages over existing approaches to normative modeling based on multivariate regression and the one-class support vector machine.
“Moreover, our approach has advantages over existing approaches to normative modeling based on multivariate regression (18) and the one-class support vector machine (15).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [18] Erus, 2015, Imaging patterns of brain development and their relationship to cognition, Cereb Cortex source
Not checked in the free run.
- cites [15] Mourao-Miranda, 2011, Patient classification as an outlier detection problem: An application of the One-Class Support Vector Machine, Neuroimage source
Not checked in the free run.
- Interpretationown data
The normative modeling approach allowed mapping the range of normal variation in reward-related brain responses.
“This approach allowed us to 1) map the range of normal variation in reward-related brain responses, 2) perform statistical inferences at the level of the individual participant, and 3) identify participants deviating from the normative model.”
Quote in the paperSubstantial claim- Rests on an assumption: The healthy cohort used to construct the normative model is representative of the normal population.
- Interpretationcitation
A key feature of our approach that differentiates it from other approaches to studying heterogeneity in clinical populations is the primary focus on mapping variation across the cohort.
“A key feature of our approach that differentiates it from other approaches to studying heterogeneity in clinical populations (7, 8) is the primary focus on mapping variation across the cohort.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [7] Fair, 2012, Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD, Proc Natl Acad Sci U S A source
Not checked in the free run.
- cites [8] van Hulst, 2015, Distinct neuropsychological profiles within ADHD: A latent class analysis of cognitive control, reward sensitivity and timing, Psychol Med source
Not checked in the free run.
- Interpretationown data
The normative model captures variation across the full range of hyperactivity symptoms.
“Therefore, the normative model captures variation across the full range of hyperactivity symptoms.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Understanding healthy variation is a prerequisite to understanding disease variation.
- The authors state this as an assumption: Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
- Interpretationreasoning
Normative modeling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort.
“Normative modeling provides a natural framework to study disorders at the individual participant level without dichotomizing the cohort.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Understanding healthy variation is a prerequisite to understanding disease variation.
- The authors state this as an assumption: Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
- May combine two claims. 22
- Classified as probably not a substantial claim. 28
May combine two claims.
Findingown dataNeither the positive nor negative deviance correlated with inattention.
“and neither the positive nor negative deviance correlated with inattention (all p > .2).”
Quote in the paperSubstantial claimMay combine two claims.
FindingcitationThe network predicted by the normative model overlaps with, but is not identical to, the network activated by the gambling task.
“This network overlaps with, but is not identical to, the network activated by the gambling task [Figure 6 in Barch et al. (39); see also Figure 6A later on].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Barch et al. (39) Barch, 2013, Function in the human connectome: Task-fMRI and individual differences in behavior, Neuroimage source
Not checked in the free run.
May combine two claims.
Findingown dataNot all participants fit the pattern; other subjects have high hyperactivity but deviate from the normative model.
“Second, not all participants fit this pattern; other subjects have high hyperactivity but deviate from the normative model (Figure 5, red circles).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataParticipants scoring highly for hyperactivity were either at the extreme of a normal spectrum of functioning or had reward-related brain responses that differed from the normal pattern.
“We related the degree of deviance to ADHD symptoms and showed that participants scoring highly for hyperactivity were either at the extreme of a normal spectrum of functioning or had reward-related brain responses that differed from the normal pattern.”
- Also stated in: § Discussion · plain
“We identified two distinct mechanisms through which participants have high hyperactivity: participants either fit the normative model well, suggesting they are at the extreme of a normal axis of variation, or showed patterns of abnormality that were highly individualized but still meaningfully related to symptoms.”
Quote in the paperSubstantial claim- Rests on an assumption: The healthy cohort used to construct the normative model is representative of the normal population.
- Also stated in: § Discussion · plain
May combine two claims.
Findingown dataMost participants fit the normative model well, including many participants having high hyperactivity.
“First, most participants fit the normative model well, including many participants having high hyperactivity.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe degree of deviation correlated with hyperactivity, but not inattention, well into the middle of the population distribution on the basis of idiosyncratic patterns of abnormality.
“In the latter case, the degree of deviation correlated with hyperactivity, but not inattention, well into the middle of the population distribution on the basis of idiosyncratic patterns of abnormality.”
- Also stated in: § Discussion · plain
“We also showed domain specificity because the degree of abnormality correlated with hyperactivity but not inattention.”
Quote in the paperSubstantial claim- Rests on an assumption: The healthy cohort used to construct the normative model is representative of the normal population.
- Also stated in: § Discussion · plain
May combine two claims.
Findingown dataFor participants having hyperactivity >8, none showed brainordinates deviating from the norm at p < .05 false discovery rate corrected, and none would be considered an outlier under the extreme value distributions fit to the positive, negative, or absolute deviations.
“The normative model provided a good fit for these participants because none showed brainordinates deviating from the norm (p < .05, false discovery rate corrected), and none would be considered an outlier under the extreme value distributions fit to the positive, negative, or absolute deviations (all p > .05).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe cost for this data set was acceptable (a few seconds per brain location).
“However, the cost for this data set was acceptable (a few seconds per brain location).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataIn preliminary work, an alternative approach [Bayesian polynomial regression] scales well at a small cost to accuracy.
“For larger data sets, there are many solutions; in preliminary work, we found that an alternative approach [Bayesian polynomial regression (49)] scales well at a small cost to accuracy.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [49] Bishop, 2006
Not checked in the free run.
May combine two claims.
Interpretationnone givenThe analytical framework permits diagnostic labels to be used as predictor variables, enabling inferences over the labels just as any other variable.
“4) diagnostic labels to be used as predictor variables, enabling inferences over the labels just as any other variable.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
Probably not a substantial claim: may not pass S4 (based).
Premisenone givenIt is unclear whether healthy participants should be clustered separately or in combination with patients.
“5) finally, it is unclear whether healthy participants should be clustered separately or in combination with patients.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationThe National Institute of Mental Health Research Domain Criteria initiative encourages researchers to link symptom dimensions with biological systems, cutting across diagnostic classifications.
“In response to this problem, the National Institute of Mental Health launched the Research Domain Criteria (RDoC) initiative (2), which encourages researchers to link symptom dimensions with biological systems, cutting across diagnostic classifications.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] Insel, 2010, Research Domain Criteria (RDoC): Toward a new classification framework for research on mental disorders, Am J Psychiatry source
Not checked in the free run.
Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).
PremisecitationSome reports have suggested that disease variation may be nested within normal variation.
“Some reports have suggested that disease variation may be nested within normal variation (7).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] Fair, 2012, Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD, Proc Natl Acad Sci U S A source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationDelay discounting measures the degree to which individuals devalue future rewards relative to immediate rewards.
“We use delay discounting to quantify impulsivity, which measures the degree to which individuals devalue future rewards relative to immediate rewards (22)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [22] Ainslie, 1975, Specious reward—behavioral theory of impulsiveness and impulse control, Psychol Bull source
Not checked in the free run.
Classified as probably not a substantial claim. May combine two claims.
Interpretationnone givenA normative modeling approach models biological variation across either the entire study population, including all clinical groups, or a large healthy sample.
“In contrast to clustering approaches, we propose a normative modeling approach that models biological variation across either 1) the entire study population (including all clinical groups) or 2) a large healthy sample.”
Quote in the paperSubstantial claim- The authors state this as an assumption: Understanding healthy variation is a prerequisite to understanding disease variation.
Classified as probably not a substantial claim.
Premisenone givenIt may be difficult to choose a unique optimal number of clusters, because different metrics may yield different optimal numbers or may not identify a unique maximum.
“4) it may be difficult to choose a unique optimal number of clusters (e.g., different metrics may yield different optimal numbers of clusters or may not identify a unique maximum)”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Premisenone givenThe clinical view of disease is that disorders in individual patients manifest as deviations from a normal pattern of functioning.
“This does not match the clinical view of disease, where disorders in individual patients manifest as deviations from a normal pattern of functioning.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationIn clustering, some participants may not clearly belong to any class, or some classes may become unmanageably small.
“2) some participants may not clearly belong to any class, or some classes may become unmanageably small (8)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] van Hulst, 2015, Distinct neuropsychological profiles within ADHD: A latent class analysis of cognitive control, reward sensitivity and timing, Psychol Med source
Not checked in the free run.
May combine two claims.
InterpretationreasoningThe normative modeling approach is fundamentally different from, and complementary to, clustering.
“This approach is fundamentally different from, and complementary to, clustering (Figure 1).”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe RDoC initiative has prompted few methods to study heterogeneity within clinical cohorts or to meaningfully stratify clinical cohorts.
“However, to date, Research Domain Criteria have prompted few methods to meaningfully stratify clinical cohorts.”
- Also stated in: § Conclusions · plain
“The RDoC initiative has prompted considerable discussion (4, 5) but to date has led to few methods to study heterogeneity within clinical cohorts.”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [4] Cuthbert, 2014, The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology, World Psychiatry source
Not checked in the free run.
- cites [5] Weinberger, 2014, RDoCs redux, World Psychiatry source
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- Also stated in: § Conclusions · plain
Classified as probably not a substantial claim. May combine two claims.
PremisecitationThe ultimate aim of RDoC is to find new ways of classifying psychiatric diseases based on multiple dimensions of biology and behavior, reducing heterogeneity in clinical cohorts, improving the neurobiological validity of disease classifications, and enabling more effective, personalized treatments.
“The ultimate aim of RDoC is to find “new ways of classifying psychiatric diseases based on multiple dimensions of biology and behavior” (http://www.nimh.nih.gov/research-priorities/rdoc/index.shtml)—reducing heterogeneity in clinical cohorts; improving the neurobiological validity of disease classifications; and enabling more effective, personalized treatments.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [2] Insel, 2010, Research Domain Criteria (RDoC): Toward a new classification framework for research on mental disorders, Am J Psychiatry source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe RDoC objectives are consistent with the European roadmap for mental health research.
“These objectives are also consistent with the European roadmap for mental health research (3).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [3] Schumann, 2014, Stratified medicine for mental disorders, Eur Neuropsychopharmacol source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationreasoningThe abnormality indices we propose provide a way to assess clinical information quantitatively in relation to biology.
“The abnormality indices we propose provide a way to assess this information quantitatively in relation to biology.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Premisenone givenThe case-control approach to studying brain disorders has been successful for detecting group effects, for example, between patients and control subjects.
“The case-control approach to studying brain disorders has been successful for detecting group effects, for example, between patients and control subjects.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenNormative modeling might indicate that disease is nested within the healthy range.
“Alternatively, normative modeling might indicate that disease is nested within the healthy range (Figure 1C).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationGaussian process regression can be extended to directly predict multivariate phenotypes.
“Alternatively, Gaussian process regression can be extended to directly predict multivariate phenotypes, as we demonstrated previously (43, 44).”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [43] Marquand, 2014, Bayesian multi-task learning for decoding multi-subject neuroimaging data, Neuroimage source
Not checked in the free run.
- cites [44] Marquand AF, Williams SCR, Doyle OM, Rosa MJ (2014). Full Bayesian multi‐task learning for multi‐output brain decoding and accommodating missing data. In: Proceedings of the 2014 International Workshop on Pattern Recognition in Neuroimaging, Tübingen, Germany. New York: IEEE Press, 1–4. source
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based). May combine two claims.
InterpretationreasoningWe can begin to make statements about the quality of different measures, which can be compared in terms of predictive power.
“Second, we can begin to make statements about the quality of different measures, which can be compared in terms of predictive power.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationOne important source of artifactual variation in functional MRI is head motion.
“One important source of artifactual variation in functional MRI is head motion (47).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [47] Van Dijk, 2012, The influence of head motion on intrinsic functional connectivity MRI, Neuroimage source
Not checked in the free run.
Classified as probably not a substantial claim.
InterpretationreasoningExisting diagnostic labels can be included as covariates just as any other variable.
“Third, existing diagnostic labels can be included as covariates just as any other variable.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningDisease can be considered as an extreme of the normal range or as possibly idiosyncratic deviation from normal functioning.
“Instead, disease can be considered as an extreme of the normal range or as—possibly idiosyncratic—deviation from normal functioning.”
Quote in the paperNot a substantial claim- The authors state this as an assumption: Disease may, but may not necessarily, occur as an extreme deviation from a normal pattern.
May combine two claims.
InterpretationreasoningThis inclusion permits inference over the proportion of biological variance the labels explain and therefore how appropriate a case-control analysis is for the chosen measures.
“This inclusion permits inference over the proportion of biological variance the labels explain and therefore how appropriate a case-control analysis is for the chosen measures.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningThis mapping breaks the symmetry inherent in the case-control approach in that the primary interest is in how each individual differs from the population.
“This mapping breaks the symmetry inherent in the case-control approach in that the primary interest is in how each individual differs from the population.”
Quote in the paperSubstantial claimMay combine two claims.
InterpretationreasoningNormative modeling can integrate multiple measures characterizing different cognitive domains, quantifying the relationship between each domain with biology and symptoms.
“A particular strength is its flexibility; it can integrate multiple measures characterizing different cognitive domains, quantifying the relationship between each domain with biology and symptoms.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningModel estimation was performed under 10-fold cross-validation, repeatedly partitioning data into training and test sets across subjects in a way that accommodated the family structure of the data, to ensure unbiased estimates of generalizability.
“To ensure unbiased estimates of generalizability, model estimation was performed under 10-fold cross-validation, where data were repeatedly partitioned into training and test sets across subjects, in a way that accommodated the family structure of the data (Supplement).”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenThe extreme value distribution provides a mechanism to understand how deviations relate to clinical symptoms.
“The extreme value distribution provides a mechanism to understand how these deviations relate to clinical symptoms.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductown dataHead motion is unlikely to underlie the effects we report because we excluded subjects having a high degree of head motion and repeated the analysis after regressing out motion-related components.
“Head motion is unlikely to underlie the effects we report because we excluded subjects having a high degree of head motion and repeated the analysis after regressing out motion-related components using a similar approach to Pruim et al. (48).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [48] Pruim, 2015, ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data, Neuroimage source
Not checked in the free run.
Classified as probably not a substantial claim.
PremisereasoningThe case-control approach precludes inference over the validity of diagnostic labels.
“By definition, it also precludes inference over the validity of the diagnostic labels.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim.
InterpretationreasoningNormative modeling is compatible with the objectives of RDoC because it allows different axes of variation to be studied independently of the diagnostic labels.
“Normative modeling is compatible with the objectives of RDoC because it allows different axes of variation to be studied independently of the diagnostic labels.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenThe normative model can be fit directly to the heterogeneous cohort to find outliers within the cohort.
“Alternatively, the normative model can be fit directly to the heterogeneous cohort to find outliers within the cohort.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationnone givenA limitation for Gaussian process models is poor computational scaling to large numbers of data points.
“A limitation for Gaussian process models is a poor computational scaling to large numbers of data points.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe study demonstrated a normative modeling approach for mapping associations between brain function and behavior and the overall deviation of each subject from the normative model.
“In conclusion, we demonstrated a normative modeling approach for mapping 1) associations between brain function and behavior and 2) the overall deviation of each subject from the normative model.”
Quote in the paperSubstantial claim- Rests on an assumption: The healthy cohort used to construct the normative model is representative of the normal population.
Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.
InterpretationreasoningDisease can be considered as an extremum of the normal range or as-possibly idiosyncratic-deviation from normal functioning.
“Instead, disease can be considered as an extremum of the normal range or as—possibly idiosyncratic—deviation from normal functioning.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThere are many more recent innovations in machine learning for scaling Gaussian process regression to very large data sets.
“There are also many more recent innovations in machine learning for scaling Gaussian process regression to very large data sets (50, 51, 52).”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [50] Snelson E, Ghahramani Z (2006): Sparse Gaussian Processes using Pseudo-inputs. Proceedings of the 2005 Conference. In: Weiss Y, Scholkopf B, Platt J, editors. Advances in Neural Information Processing Systems 18. Cambridge, MA: The MIT Press.
Not checked in the free run.
- cites [51] Filippone M, Engler R (2015): Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE). ArXiv:1501.05427v3 [stat.ME].
Not checked in the free run.
- cites [52] Saatci, 2011
Not checked in the free run.
May combine two claims.
Study design and conductown dataAll conclusions remained valid after excluding high head motion subjects and regressing out motion-related components.
“All conclusions remained valid, and our subjects that were outliers remained outliers.”
Quote in the paperSubstantial claim
The authors state this as an assumption.
Understanding healthy variation is a prerequisite to understanding disease variation.
“Our approach is predicated on the assertions that 1) understanding healthy variation is a prerequisite to understanding disease variation”
The authors state this as an assumption.
Determining where each subject lies within the population range is required because variation associated with most disorders overlaps with normal variation.
“that this requires 2) the ability to determine where each subject lies within the population range because variation associated with most disorders overlaps with normal variation.”
The authors state this as an assumption.
Disease may, but may not necessarily, occur as an extreme deviation from a normal pattern.
“In this study, we consider that disease may—but may not necessarily—occur as an extreme deviation from a normal pattern.”
The paper relies on this without support.
The healthy cohort used to construct the normative model is representative of the normal population.
“We used normative models constructed from spectra of clinically relevant variables (delay discounting) to predict reward-related brain activity in a large, healthy cohort.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
From the homogeneity assumption, the comorbidity and heterogeneity problems emerge and limit psychiatric research.
“From the homo- geneity assumption, the comorbidity (17) and heterogeneity (18,19) problems emerge and limit psychiatric research (20–26).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [19]
That study found one subtype in which dorsolateral prefrontal activity predicted high hyperactive symptoms and another subtype not related to such activity.
“The investigators found one sub- type, where dorsolateral prefrontal activity predicted high hy- peractive symptoms and another subtype unrelated to such activity.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [19]
Several studies have used normative models to address comorbidity and heterogeneity in clinical and nonclinical cohorts.
“Several studies incorporated normative models to address comorbidity and heterogeneity in clinical and nonclinical co- horts (19,60,61).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [19]
Newer hybrid models such as normative modeling and the functional random forest may overcome the limitations of the bifactor framework and allow new inferences in psychopathology research.
“Newer hybrid models, such as normative modeling (19,59–61) or the functional random forest (FRF) (18,62), may overcome these limitations and enable new inferences from psychopathology studies.”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [19]
In a study using 491 Human Connectome Project subjects, investigators built normative models to identify impulsivity subtypes linked to self-reported hyperactive symptoms.
“Using data from 491 subjects within the Hu- man Connectome Project, investigators constructed normative models to identify impulsivity subtypes tied to self-reported hyperactive symptoms (19).”
From Methods and challenges for assessing heterogeneity (2020) · cites this paper as [19]
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Self-citation
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Self-citation
- Authors
- Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F.
- Affiliations
- Radboud University Nijmegen (NL)
- Venue
- Biological Psychiatry
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 80
- Issue
- 7
- Pages
- 552-561
- ISSN
- 0006-3223 (ISSN-L)
- PMC
- PMC5023321
- OpenAlex
- W2229782077
- Semantic Scholar
- 9b47bad4812d5be774f7917d33c0387cb880590c
- Cited by
- 683
- References
- 54
- Citations by year
- 2026: 125 · 2025: 131 · 2024: 91 · 2023: 63 · 2022: 62 · 2021: 89 · 2020: 36 · 2019: 32 · 2018: 29 · 2017: 17 · 2016: 8
- FWCI
- 40.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Biological Psychiatry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion, cc-by)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- Eli Lilly and Company
- Wellcome Trust (024.001.006, 098369/Z/12/Z, 098369)
- European Commission (278948, 602805)
- Servier
- H. Lundbeck A/S
- Horizon 2020 Framework Programme (H2020/2014–2020, 643051, 603016, 642996, 024.001.006)
- Cilag
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- normative modeling, clinical heterogeneity, Research Domain Criteria, personalized prediction, impulsivity, ADHD symptoms, hyperactivity, diagnostic labels, outlier detection
- MeSH terms
- Adult, Attention Deficit Disorder with Hyperactivity, Brain, Data Interpretation, Statistical, Female, Humans, Impulsive Behavior, Magnetic Resonance Imaging, Male, Reward, Models, Statistical, Cluster Analysis, Case-Control Studies, Young Adult, Functional Neuroimaging, Delay Discounting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-08.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-08.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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