Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD
Open accessAbstract
Research and clinical investigations in psychiatry largely rely on the de facto assumption that the diagnostic categories identified in the Diagnostic and Statistical Manual (DSM) represent homogeneous syndromes. However, the mechanistic heterogeneity that potentially underlies the existing classification scheme might limit discovery of etiology for most developmental psychiatric disorders. Another, perhaps less palpable, reality may also be interfering with progress-heterogeneity in typically developing populations. In this report we attempt to clarify neuropsychological heterogeneity in a large dataset of typically developing youth and youth with attention deficit/hyperactivity disorder (ADHD), using graph theory and community detection. We sought to determine whether data-driven neuropsychological subtypes could be discerned in children with and without the disorder. Because individual classification is the sine qua non for eventual clinical translation, we also apply support vector machine-based multivariate pattern analysis to identify how well ADHD status in individual children can be identified as defined by the community detection delineated subtypes. The analysis yielded several unique, but similar subtypes across both populations. Just as importantly, comparing typically developing children with ADHD children within each of these distinct subgroups increased diagnostic accuracy. Two important principles were identified that have the potential to advance our understanding of typical development and developmental neuropsychiatric disorders. The first tenet suggests that typically developing children can be classified into distinct neuropsychological subgroups with high precision. The second tenet proposes that some of the heterogeneity in individuals with ADHD might be "nested" in this normal variation.
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Claims the paper makes
9 of 25 cited sources checked · 4 supported · 0 flagged · accuracy not yet measured
- Findingown data
Independently applying community detection to the ADHD cohort replicated the typically developing subgroup patterns.
“Independently applying the community detection algorithm to the ADHD cohort shows similar findings to those in A.”
- Also stated in: § Atypical Neuropsychological Patterns in ADHD Are Specific to Subgroup · plain
“independently applying Newman's community detection algorithm (29) on the ADHD cohort replicated this finding, showing similar subgroup patterns (Fig. 4).”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [29] 29. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577-8582. source
The method of optimal modularity (p. 2) describes the paper’s community-detection algorithm, supporting the methodological part of the sentence. The paper does not report an ADHD cohort or a comparison with typically developing subgroups, so it does not support the claimed replication of findings. (LLM, conf=0.84)
Experimental — accuracy not yet measured
- Also stated in: § Atypical Neuropsychological Patterns in ADHD Are Specific to Subgroup · plain
- Findingown data
Community detection on the typically developing control population identified four distinct subgroups.
“Although common practice would expect this investigation to yield one unitary group, the analysis instead yielded four unique communities or subgroups (Fig. 4).”
Quote in the paperSubstantial claim - Findingown data
The SVM classifier could not strongly distinguish individual ADHD from TDC cases using neuropsychological scores, with 65% accuracy, 86.3% sensitivity, and 38.5% specificity.
“Importantly, despite these highly reliable differences, the SVM classifier was unable to make strong distinctions in individual cases with regard to ADHD status (65% accuracy, 86.3% sensitivity, and 38.5% specificity).”
Quote in the paperSubstantial claim - Findingown data
Applying the same community detection procedure to the ADHD sample identified six groups.
“Independently testing the community structure in this sample revealed similar findings, albeit via six groups (Fig. 4).”
Quote in the paperSubstantial claim - Findingown data
In the typically developing control sample, subgroup 3, comprising 18% of the sample, had relatively inaccurate temporal information processing.
“The third group (18% of sample) had relatively inaccurate temporal information processing (subgroup 3).”
Quote in the paperSubstantial claim
- Findingown data
In the typically developing control sample, subgroup 2, comprising 20% of the sample, had reduced working memory, memory span, inhibition, and output speed.
“The second group (20% of sample) had reduced working memory, memory span, inhibition, and output speed (subgroup 2).”
Quote in the paperSubstantial claim - Findingown data
In the typically developing control sample, subgroup 1, comprising 43% of the sample, showed a pattern of greater response variability than peers.
“One group (43% of sample) appeared to have a pattern consistent with more response variability relative to their peers (subgroup 1).”
Quote in the paperSubstantial claim - Findingown data
A seven-factor model of cognitive abilities fit the neuropsychological data satisfactorily and comparably to five- and six-factor models.
“Fit was satisfactory and comparable for the best-fitting five-, six-, and seven-factor models as follows: five-factor χ 2 (63) = 103.4,CFI = 0.98, TLI = 0.97, RMSEA = 0.036; six-factor χ 2 (58) = 95.1,CFI = 0.98, TLI = 0.98, RMSEA = 0.035; and seven-factor χ 2 (52) = 89.9,CFI = 0.98, TLI = 0.97, RMSEA = 0.038.”
Quote in the paperSubstantial claim - Findingown data
In the typically developing control sample, subgroup 4, comprising 18% of the sample, had relatively weak signal detection, suggesting suboptimal or altered arousal.
“The last group (18% of sample) had relatively weak signal detection, suggesting suboptimal or altered arousal (subgroup 4).”
Quote in the paperSubstantial claim - Findingown data
A one-factor model of the neuropsychological measures showed inadequate fit.
“Results for the feature reduction for the neuropsychological measures showed that a one-factor model fit inadequately [χ 2 (70) = 386.2,CFI = 0.87, TLI = 0.84, RMSEA = 0.09], justifying our effort to create a multiconstruct model.”
Quote in the paperSubstantial claim - Findingown data
Comparisons between the full ADHD and TDC cohorts showed significant differences across all neuropsychological measures.
“The comparisons yielded significant differences across all measures (Fig. 3A and Table S3).”
Quote in the paperSubstantial claim - Findingown data
In the ADHD sample, subgroup 2A, comprising 17% of the sample, had reduced working memory, memory span, and output speed.
“As with TDC, the second group (17% of sample) appeared to have reduced working memory, memory span, and output speed (i.e., subgroup 2A).”
Quote in the paperSubstantial claim - Findingown data
Response variability was atypical in four of the six communities.
“Qualitatively stronger results appeared for response variability, which was atypical in four of the six communities, supporting theories of its importance in ADHD (8).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [8] 8. Castellanos FX, Tannock R (2002) Neuroscience of attention-deficit/hyperactivity dis- order: the search for endophenotypes. Nat Rev Neurosci 3:617-628. source
- Findingown data
Weaker but statistically significant contributions toward ADHD status were identified for spatial span and speed, and only in a subset of the identified profiles.
“weaker, although statistically significant, contributions toward ADHD status were also identified for spatial span and speed, but again in only a subset of the identified profiles (Fig. 3 and Table S3).”
Quote in the paperSubstantial claim - Findingown data
Applying community detection to the typically developing cohort yielded four distinct subgroups, i.e., cognitive profiles.
“After applying the community detection procedure to the typically developing cohort, four unique subgroups (i.e., cognitive profiles) emerge (y axis = z score).”
Quote in the paperSubstantial claim - Findingown data
Response inhibition is atypical in only three of the six ADHD subgroups relative to controls within the same profile.
“this measure is atypical in only three of the six ADHD subgroups relative to controls within the same profile.”
Quote in the paperSubstantial claim - Findingown data
The ADHD subgroups showed minimal differences in symptom scores, IQ, age, or sex ratios.
“Strikingly, even though these groups were all distinct in their neuropsychological profiles, they again showed minimal differences in symptom scores, IQ, age, or sex ratios (Tables S4 and S5).”
Quote in the paperSubstantial claim - Findingown data
In the ADHD sample, subgroup 4A, comprising 8% of the sample, was characterized by suboptimal arousal.
“The fifth group (8% of sample) was also similar to the fourth group of TDC youth in that they appeared to be characterized by suboptimal arousal (subgroup 4A).”
Quote in the paperNot a substantial claim - Findingown data
Diagnostic classification of ADHD status for individuals in most subgroups can be identified with higher accuracy when community assignments are taken into account.
“diagnostic classification of ADHD status for individuals in most of the subgroups, albeit modest in some, can be identified with higher accuracy when the community assignments were taken into account.”
Quote in the paperSubstantial claim - Findingown data
In the ADHD sample, subgroup 4B, comprising 8% of the sample, had low arousal with a slightly different profile in the remaining measures.
“The sixth group (8% of sample) was similar to the fifth with regard to low arousal, however, with a slightly different profile in the remaining measures (subgroup 4B).”
Quote in the paperNot a substantial claim - Findingown data
ADHD classification improved or remained the same for all but one subgroup compared with the initial all-ADHD versus all-TDC analysis.
“Interestingly, the ADHD classification improved or remained the same for all but one of the groups, relative to the initial All ADHD vs.All TDC analysis.”
Quote in the paperSubstantial claim - Findingown data
In the ADHD sample, subgroup 3, comprising 25% of the sample, had inaccurate temporal information processing.
“The fourth group (25% of sample) had inaccurate temporal information processing (subgroup 3).”
Quote in the paperNot a substantial claim - Findingown data
In the ADHD sample, subgroup 1, comprising 21% of the sample, showed high reaction time variability relative to peers.
“Similar to the TDC, the first group (21% of sample) appeared to have a pattern consistent with high levels of reaction time variability relative to their peers (subgroup 1).”
Quote in the paperSubstantial claim - Findingown data
In the ADHD sample, subgroup 2B, comprising 20% of the sample, had the same apparent weaknesses as subgroup 2A but with a slightly modified profile in the remaining measures.
“The third group (20% of sample) also had the same apparent weaknesses, but with a slightly modified profile in the remaining measures (i.e., subgroup 2B).”
Quote in the paperNot a substantial claim - Findingown data
Comparing the entire ADHD cohort with the typically developing population replicated the finding of atypical response inhibition.
“Our initial analysis comparing the entire ADHD cohort to the TDC population replicates this particular finding (Fig. 3).”
Quote in the paperNot a substantial claim - Findingown data
Comparing typically developing children with ADHD children within each distinct subgroup increases diagnostic accuracy based on neuropsychological measures.
“As illustrated by our single subject classification procedures, comparing typically developing children with ADHD children within each of these distinct subgroups increases diagnostic accuracy (i.e., ADHD vs. non-ADHD classification) on the basis of the neuropsychological measures.”
Quote in the paperSubstantial claim - Study design and conductown data
The ADHD community structure was robust according to a quality index of Q = 0.55 and variation of information analysis.
“Again, Q (Q = 0.55) and the VOI analysis (Fig. S2) highlighted the robustness of the communities.”
Quote in the paperSubstantial claim - Study design and conductown data
The four TDC subgroups were significantly different from random according to the quality index (Q = 0.45), variation of information, and a secondary randomization analysis.
“Importantly, the quality index (Q = 0.45) and variation of information (VOI) (two measures of community robustness), as well as a secondary randomization analysis (SI Text and Figs.S2 and S3), showed that the subgroups identified here were significantly different from random.”
Quote in the paperSubstantial claim - Interpretationreasoning
The ability to characterize individual subjects and identify ADHD status empirically from a cognitive battery opens the door for tractable genetic, functional, and clinical applications.
“The ability to characterize individual subjects and identify ADHD status empirically on the basis of a cognitive battery opens the door for tractable genetic, functional, and clinical applications.”
Quote in the paperSubstantial claim - Interpretationown data§ Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD
Typically developing children can be classified into distinct neuropsychological subgroups with high precision.
“The first tenet suggests that typically developing children can be classified into distinct neuropsychological subgroups with high precision.”
Quote in the paperSubstantial claim - Premisecitation
Nigg et al. showed that several neuropsychological measures central to ADHD had substantial distributional overlap between ADHD and control samples.
“For example, Nigg et al. (13) showed that several neuropsychological measures central to ADHD had substantial distributional overlap between ADHD and control samples.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13] 13. Nigg JT, Willcutt EG, Doyle AE, Sonuga-Barke EJ (2005) Causal heterogeneity in at- tention-deficit/hyperactivity disorder: Do we need neuropsychologically impaired subtypes? Biol Psychiatry 57:1224-1230. source
- Interpretationreasoning§ Distinct neuropsychological subgroups in typically developing youth inform heterogeneity in children with ADHD
Some of the heterogeneity observed in individuals with ADHD may be nested within normal variation.
“The second tenet proposes that some of the heterogeneity in individuals with ADHD might be "nested" in this normal variation.”
Quote in the paperSubstantial claim - Interpretationown data
The ADHD subgroups are not merely severity-based groupings but represent distinct cognitive profiles among children with similar ADHD severity.
“Thus, these are not simply more or less severe ADHD groups, but rather unique cognitive profiles within children who all have similar severity of ADHD.”
Quote in the paperSubstantial claim - Interpretationown data
Each of the numerous neuropsychological measures proposed as related to ADHD appears to apply to only a subset of those with the disorder.
“each of them appears to apply to only a subset of those with the disorder.”
Quote in the paperSubstantial claim - Interpretationown data
Heterogeneity within developmental neuropsychiatric disorders such as ADHD may be nested within normal variation.
“these data suggest that heterogeneity within developmental neuropsychiatric disorders, such as ADHD, might be nested within this normal variation.”
Quote in the paperSubstantial claim- Rests on an assumption: The typically developing and ADHD cohorts studied are representative of their respective populations, so that the observed subgroups reflect the broader population's neuropsychological variation.
- Interpretationown data
Some of the heterogeneity within ADHD appears to be nested within the variation found in typically developing populations.
“This particular result, again, suggests that some of the heterogeneity within ADHD appears to be nested within the variation found in typically developing populations.”
Quote in the paperSubstantial claim- Rests on an assumption: The typically developing and ADHD cohorts studied are representative of their respective populations, so that the observed subgroups reflect the broader population's neuropsychological variation.
- Premisecitation
Only a small minority of subjects with ADHD could be considered clinically affected on the basis of any one measure.
“The data suggested that only a small minority of subjects with the disorder could be considered clinically "affected" on the basis of any one measure (13).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [13] 13. Nigg JT, Willcutt EG, Doyle AE, Sonuga-Barke EJ (2005) Causal heterogeneity in at- tention-deficit/hyperactivity disorder: Do we need neuropsychologically impaired subtypes? Biol Psychiatry 57:1224-1230. source
- Premisecitation
All of the neuropsychological measures examined in this study have previously been reported as atypical in ADHD.
“In general all of the measures examined here have been previously reported as atypical in ADHD (ref.13 and Fig. 3).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites ref.13
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
There is considerable evidence that the DSM does not necessarily describe homogeneous conditions but reflects multiple unique independent mechanistic pathways within a given disorder.
“However, there is considerable evidence that suggests the DSM does not necessarily describe homogenous conditions, but rather reflects the end result of multiple unique independent mechanistic pathways within a given disorder (1,2).”
Quote in the paperSubstantial claim0 of 2 sources checked - Study design and conductown data
The applied cognitive battery contains enough information to make valid predictions of community assignment for individual subjects.
“Here we demonstrated that there is enough information in the applied cognitive battery to make valid predictions of community assignment for individual subjects.”
Quote in the paperSubstantial claim
- May combine two claims. 17
- Classified as probably not a substantial claim. 13
May combine two claims.
Findingown dataFor working memory and temporal information processing, the full-cohort ADHD versus typically developing comparison replicated previous findings, but the community-based comparisons yielded deficits in only a subset of profiles.
“whereas the comparison of the full ADHD and TDC cohort replicated previous findings in this regard (41), the comparisons based on community structure yielded only a subset of profiles with working memory and temporal information processing deficits.”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [41] 41. Martinussen R, Hayden J, Hogg-Johnson S, Tannock R (2005) A meta-analysis of working memory impairments in children with attention-deficit/hyperactivity disor- der. J Am Acad Child Adolesc Psychiatry 44:377-384. sourceExperimental — accuracy not yet measured
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationRapid and sizeable changes in environmental demands over time may have driven the emergence of multiple peaks in neurocognitive strategies or profiles in typical populations, as observed here.
“Rapid and sizeable changes in environmental demands across time may have served as the driving force toward multiple "peaks" with regard to neurocognitive strategies or profiles in typical populations (21,36), as found here.”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [21] 21. Goldman D, Oroszi G, Ducci F (2005) The genetics of addictions: Uncovering the genes. Nat Rev Genet 6:521-532. source
The Genes and neurobiologies and ANTAGONISTS passages describe a proposed balance between cognitive performance and stress resilience, with selection maintaining two COMT alleles in human populations. They do not say that rapid, sizeable changes in environmental demands produced multiple peaks in neurocognitive profiles in typical populations. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites [36] 36. Nettle D (2006) The evolution of personality variation in humans and other animals. Am Psychol 61:622-631. source
May combine two claims.
PremisecitationHeritable diversity can form along a continuous dimension or as multiple discrete strategies.
“With respect to our findings it should be noted that at times heritable diversity might form along a continuous dimension (i.e., unimodal distribution), whereas at others times it may form as multiple discrete strategies (i.e., multimodal distributions) (21,(36)(37)(38).”
Quote in the paperSubstantial claim1 of 3 sources checked- cites [21] 21. Goldman D, Oroszi G, Ducci F (2005) The genetics of addictions: Uncovering the genes. Nat Rev Genet 6:521-532. source
The RESILIENCY section discusses two possible genetic architectures for addiction vulnerability: many variants acting together and different individual variants sufficing in different people. It does not establish that heritable diversity forms a continuous, unimodal dimension or multiple discrete, multimodal strategies; those distributional claims go beyond the passage. (LLM, conf=0.72)
Experimental — accuracy not yet measured - cites [36] 36. Nettle D (2006) The evolution of personality variation in humans and other animals. Am Psychol 61:622-631. source
- cites [37] 37. Mier D, Kirsch P, Meyer-Lindenberg A (2010) Neural substrates of pleiotropic action of genetic variation in COMT: A meta-analysis. Mol Psychiatry 15:918-927. source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationSimilar arguments have been made for COMT and working memory, with an evolutionary trade-off between efficient working memory updating (val) and robust working memory maintenance (met).
“Similar arguments have been made with regard to COMT and working memory, with an evolutionary trade-off between efficient working memory updating (val) and robust working memory maintenance (met) (22).”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [22] 22. Braver TS, Cole MW, Yarkoni T (2010) Vive les differences! Individual variation in neural mechanisms of executive control. Curr Opin Neurobiol 20:242-250. source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationIn this proposal each COMT allele is maintained in the population because each provides an environment-specific selective advantage, one for cognitive efficiency under typical conditions and the other for emotional resilience under stress.
“In this proposal each allele is maintained in the population because each provides an environment-specific selective advantage-one for cognitive efficiency under typical conditions and the other for emotional resilience under stressful circumstances (21,37).”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [21] 21. Goldman D, Oroszi G, Ducci F (2005) The genetics of addictions: Uncovering the genes. Nat Rev Genet 6:521-532. source
- cites [37] 37. Mier D, Kirsch P, Meyer-Lindenberg A (2010) Neural substrates of pleiotropic action of genetic variation in COMT: A meta-analysis. Mol Psychiatry 15:918-927. source
Classified as probably not a substantial claim.
PremisecitationSubstantial evidence has accrued that individual differences in successful adaptive psychological styles are central to human development, functioning, social cohesion, and health outcomes.
“Although consensus remains elusive on defining specific personality or cognitive "types" (18,19), substantial evidence has accrued that individual differences in successful adaptive psychological styles are central to human development, functioning, social cohesion, and health outcomes (20)(21)(22)(23).”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [20] 20. Buss DM (1991) Evolutionary personality psychology. Annu Rev Psychol 42:459-491. source
- cites [21] 21. Goldman D, Oroszi G, Ducci F (2005) The genetics of addictions: Uncovering the genes. Nat Rev Genet 6:521-532. source
The “Progress through intermediate phenotypes” section says behavioural control supports long-term behaviour and that deficits in children are linked to problems including substance use. This supports a narrower point about development and addiction risk, but the paper does not establish that adaptive psychological styles broadly determine human functioning, social cohesion, and health outcomes. Page numbers were not available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measured - cites [22] 22. Braver TS, Cole MW, Yarkoni T (2010) Vive les differences! Individual variation in neural mechanisms of executive control. Curr Opin Neurobiol 20:242-250. source
The Abstract links individual differences in personality, emotion processing, and cognitive ability to executive control, and the Emotion regulation section discusses differences in regulation success and depression. These passages support a narrower claim about cognitive and emotional functioning, but do not establish that adaptive psychological styles are central to human development, social cohesion, or health outcomes broadly. Page numbers are unavailable in our copy. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Probably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).
PremisecitationThe COMT val158met polymorphism may relate to evolutionary trade-offs between efficient executive functioning (met) and improved emotional regulation (val).
“some have recently argued that the single-nucleotide polymorphism (val158met) of the catechol O-methyltransferase (COMT) gene, which codes for an enzyme that degrades dopamine in prefrontal cortex, may relate to evolutionary trade-offs between efficient executive functioning (met) and improved emotional regulation (val) (21,”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [21] 21. Goldman D, Oroszi G, Ducci F (2005) The genetics of addictions: Uncovering the genes. Nat Rev Genet 6:521-532. source
May combine two claims.
Findingown dataThe four typically developing control subgroups showed minimal differences in IQ, age, or sex ratios, with one exception.
“These groups show minimal differences in intelligence quotient (IQ), age, or sex ratios (with one exception, see Tables S4 and S5).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAtypical neuropsychological measures differed by profile, and ADHD classification improved within profiles.
“Atypical Neuropsychological Measures Are Unique on the Basis of Profile and ADHD Classification Improves Within Profiles.”
- Also stated in: § Atypical Neuropsychological Measures Are Unique on the Basis of Profile and ADHD Classification Improves Within Profiles. · plain
“Whereas the ANOVA in six of the seven factors showed a main effect of diagnosis, the post hoc comparisons showed that not all subgroups had the same atypical neuropsychological scores.”
Quote in the paperSubstantial claim- Also stated in: § Atypical Neuropsychological Measures Are Unique on the Basis of Profile and ADHD Classification Improves Within Profiles. · plain
May combine two claims.
Findingown dataSubgroup 4A showed the greatest improvement, with 84.1% classification accuracy for ADHD versus TDC youth, 83.3% sensitivity, and 84.6% specificity.
“Group 4A had the maximal improvement at 84.1% classification of ADHD vs. TCD youth (83.3% sensitivity and 84.6% specificity).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe remaining subgroups had classification accuracies as follows: group 1, 68% total, 49.2% sensitivity, 80.4% specificity; group 2A, 68.5%, 83.7%, 51.2%; group 2B, 64.7%, 80.4%, 44.2%; group 3, 73.6%, 88.73%, 46.2%; group 4A, 84.1%, 83.3%, 84.6%; and group 4B, 61.9%, 37%, 76.9%.
“The remaining groups were as follows: group 1, 68% total, 49.2% sensitivity, 80.4% specificity; group 2A, 68.5%, 83.7%, 51.2%; group 2B, 64.7%, 80.4%, 44.2%; group 3, 73.6%, 88.73%, 46.2%; group 4A, 84.1%, 83.3%, 84.6%; and group 4B, 61.9%, 37%, 76.9%.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
Findingown dataThe four typically developing subgroups were labeled by their standout lower factor as variability (subgroup 1), low executive (subgroup 2), low temporal information processing (subgroup 3), and low arousal (subgroup 4).
“This procedure left us with four groups, which we label as follows: a variability group (subgroup 1), a low executive group (subgroup 2), a low temporal information processing group (subgroup 3), and a low arousal group (subgroup 4).”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataAmong ADHD subgroups, atypical inhibitory control, speed, and working memory each appeared in three groups; atypical arousal and span each appeared in two groups; atypical response variability and temporal information priming each appeared in four groups.
“Among ADHD subgroups, atypical inhibitory control, speed, and working memory were each found in three groups.Atypical arousal and span were each identified in two groups.Atypical response variability and temporal information priming were each found in four groups.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataSVM classification of individuals into their neuropsychological profiles achieved 78% accuracy for the TDC population and 77% accuracy for the ADHD population.
“Classification generally was quite respectable across the groups with 78% accuracy for the TDC population (subgroup 1, 93%; subgroup 2, 71%; subgroup 3, 71%; subgroup 4, 81.25%) and 77% accuracy for the ADHD population (subgroup 1, 78%; subgroup 2A, 75%; subgroup 2B, 66.25%; subgroup 3, 77%; subgroup 4A, 88%; note that subgroup 4B was unable to be examined as it was not reproduced in large enough”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe main deviation of the ADHD cohort from the typically developing cohort was the presence of an additional low arousal subgroup (4B) and an additional low executive subgroup (2B).
“The difference between the two appears to be split in subgroup 2 and subgroup 4.”
- Also stated in: § Atypical Neuropsychological Patterns in ADHD Are Specific to Subgroup · plain
“The main deviation from the finding in TDC was the revelation of an additional low arousal group (subgroup 4B) and an additional low executive group (subgroup 2B).”
Quote in the paperSubstantial claim- Also stated in: § Atypical Neuropsychological Patterns in ADHD Are Specific to Subgroup · plain
May combine two claims.
Interpretationown dataThe findings better support the idea that unique neuropsychological profiles exist even among typically developing, well-adjusted individuals than the assumption that nonclinical control populations are uniform.
“This analysis shows that despite traditional assumptions that nonclinical control populations are uniform entities, our results fit better with the alternative supposition that there are unique neuropsychological profiles even in typically developing, well-adjusted samples.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningIdentifying a mechanism associated with a mental disorder may require comparing individuals to well-adjusted persons with the same cognitive style or profile.
“It may be that identifying a mechanism associated with a mental disorder requires comparing individuals to well-adjusted persons with the same cognitive style or profile.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPsychological heterogeneity is an important means of improving the robustness of a collective when facing shifting environmental demands.
“The modeling literature related to adaptive complex social systems also provides significant evidence that suggests psychological heterogeneity is an important means by which to improve the robustness of the collective when faced with shifting environmental demands (35).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] 35. Miller JH, Page SE (2007) Complex Adaptive Systems: An Introduction to Computa- tional Models of Social Life (Princeton Univ Press, Princeton).
Not checked in the free run.
Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).
InterpretationreasoningThe approach used here is not limited to ADHD and could be used to inform heterogeneity and perhaps etiology in other developmental or adult neuropsychiatric disorders.
“the approach used here is not limited to the study of ADHD, but could be used to inform heterogeneity and perhaps etiology in other developmental or adult neuropsychiatric disorders, and thus has broad appeal.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationPoor response inhibition has been proposed as a critical component of ADHD and is well established at the group level.
“poor response inhibition has been suggested to be a critical component of ADHD (39) and has been well established at the group level (40).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [39] 39. Barkley RA (1997) Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychol Bull 121:65-94. source
- cites [40] 40. Willcutt EG, et al. (2010) Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex 46(10):1345-1361. source
May combine two claims.
InterpretationcitationAlthough many neuropsychological measures have been proposed as related to ADHD, each may apply to only a subset of individuals with the disorder.
“In other words, whereas numerous unique neuropsychological measures have been proposed as related to ADHD, perhaps each of them applies to only a subset of those subjects with the disorder.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataThe robustness of the identified communities was tested in several ways, and the group assignments were found to be highly deviant from random.
“the robustness of the identified communities was tested in several ways (e.g., Figs.S2 and S3), indicating that the group assignments identified here are highly deviant from random.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe discrete subgroups observed in this study are a small representation of similar forms of neuropsychological diversity.
“It might be reasoned that the discrete subgroups observed here (Figs. 3 and 4) are a small representation of similar forms of neuropsychological diversity.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing). May combine two claims.
PremisecitationPsychological heterogeneity is a key feature in the stability of complex social systems and has likely been an important attribute of our evolving species.
“It is a key feature in the stability of complex social systems and thus has likely been an important attribute of our evolving species.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [35] 35. Miller JH, Page SE (2007) Complex Adaptive Systems: An Introduction to Computa- tional Models of Social Life (Princeton Univ Press, Princeton).
Not checked in the free run.
Classified as probably not a substantial claim.
PremisecitationMechanistic heterogeneity potentially underlying the existing classification scheme might limit the ability to clarify etiology and identify novel therapeutics for several psychiatric illnesses.
“The mechanistic heterogeneity that potentially underlies the existing classification scheme might be limiting our ability to clarify etiology and identify novel therapeutics for several psychiatric illnesses (3).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [3] 3. Agid Y, et al. (2007) How can drug discovery for psychiatric disorders be improved? Nat Rev Drug Discov 6:189-201. source
The paper relies on this without support.
The 498 children in the dataset are representative of typically developing youth and youth with ADHD.
“For the current investigation we apply community detection to a well-characterized dataset of 498 children who include both typically developing control youth (TDC) (n = 213) and youth with ADHD (n = 285) (Table S1).”
The paper relies on this without support.
The 20 neuropsychological measures used are valid indicators of the cognitive functions they are intended to assess.
“From these youth, some 20 neuropsychological measures were obtained that were intended to cover a wide domain of cognitive functions variously theorized to be involved in ADHD (Table S2 and Fig. 2).”
The paper relies on this without support.
The typically developing and ADHD cohorts studied are representative of their respective populations, so that the observed subgroups reflect the broader population's neuropsychological variation.
“Children who completed a full research psychiatric evaluation aged 6-17 y participated in this study (TDC, n = 213; ADHD, n = 285) (SI Text).”
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?
It reports that working memory impairments are found in 30% to 37% and inhibitory control deficits in 21% to 46% of pediatric ADHD cases.
“Working memory impairments are reported in 30% to 37% (Coghill et al., 2014; Fair et al., 2012), and inhibitory control deficits are detected in 21%–46% of pediatric ADHD cases (Coghill et al., 2014; Nigg et al., 2005; Solanto et al., 2001; Sonuga-Barke et al., 2010).”
From Executive Functioning Heterogeneity in Pediatric ADHD (2019) · cites this paper as Fair et al., 2012
In Results (page unavailable), Fair et al. identify two ADHD subgroups with reduced working memory, comprising 17% and 20% of their ADHD sample—37% combined. The paper does not report the 30% lower bound, so it supports only part of the cited range. Fair et al. are cited for the working-memory clause, not the separate inhibitory-control range. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIt reports that estimates of any executive dysfunction among pediatric ADHD cases range from 21% to 60% across studies.
“estimates for the proportion of pediatric ADHD cases who exhibit any form of executive dysfunction range from 21% to 60% across studies employing a wide range of tasks and impairment criteria (Biederman et al., 2004; Coghill et al., 2014; Fair, Bathula, Nikolas, & Nigg, 2012; Geurts, van der Oord, & Crone, 2006; Nigg et al., 2005; Solanto et al., 2001; Sonuga-Barke, Bitsalou, & Thompson, 2010).”
From Executive Functioning Heterogeneity in Pediatric ADHD (2019) · cites this paper as Fair, Bathula, Nikolas, & Nigg, 2012
The introduction says that only a subset of children with ADHD may be affected on any one neuropsychological measure, and the Results identify distinct ADHD subgroups. But the Results’ 21% refers to one subgroup with relatively high reaction-time variability; the paper does not report a 21%–60% range for any executive dysfunction across studies or impairment criteria. Page numbers were not available in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredTo the authors' knowledge, 100% of the available literature either omitted working memory tests or measured working memory with at least one test having poor construct specificity.
“Applying the construct specificity concerns raised by Sonuga-Barke et al. (2008) and Snyder et al. (2015) to evaluate the tests used to evaluate neuropsychological heterogeneity in ADHD indicates that, to our knowledge, 100% of the available literature either omitted tests of working memory or measured working memory using at least one test with poor construct specificity (Biederman et al., 2004;”
From Executive Functioning Heterogeneity in Pediatric ADHD (2019) · cites this paper as Fair et al., 2012
The Subjects and Demographics section identifies this as an ADHD heterogeneity study whose neuropsychological battery was designed to capture working memory; the Results also identify digit span backward among its measures. Our copy does not assess whether any working-memory test had poor construct specificity or establish the citing authors’ 100% estimate across the literature. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFour cognitive ADHD subtypes were revealed in a study based on a community of children with or without ADHD.
“Four cognitive ADHD subtypes were revealed in a study based on a community of children with or without ADHD 70 ; however, whether these subtypes predict treatment response or course remains unclear.”
From Attention-deficit/hyperactivity disorder (2015) · cites this paper as [70]
The Results identify four neuropsychological subgroups among typically developing children and similar profiles among children with ADHD. But the ADHD cohort yielded six groups, not four; the Atypical Neuropsychological Patterns in ADHD section explains that two additional ADHD groups appeared within the broader profiles. The citation supports the study’s finding of cognitive heterogeneity in both cohorts, but not the precise count of four ADHD subtypes. (LLM, conf=0.90)
Experimental — accuracy not yet measuredEtiological heterogeneity in biological and environmental factors is likely reflected in variation in neural correlates and results in diverse cognitive and behavioral profiles and developmental trajectories of ADHD.
“The etiological heterogeneity in terms of the biological and environmental factors is likely reflected in variation in neural correlates (Nigg and Casey, 2005; Mackie et al., 2007; Fair et al., 2012; Costa Dias et al., 2013, 2015; Karalunas et al., 2014), and results in the diverse cognitive and behavioral profiles and developmental trajectories of the disorder (Halperin and Schulz, 2006; Rajendr”
From A Review of Heterogeneity in Attention Deficit/Hyperactivity Disorder (ADHD) (2019) · cites this paper as Fair et al., 2012
In “Current Approach Can Be Extended Across Multiple Modalities” (page unavailable), Fair et al. hypothesize that the identified ADHD subgroups have distinct brain profiles, supporting likely variation in neural correlates. The paper does not establish that heterogeneity in biological and environmental causes produces that variation; it proposes neuroimaging and genetic analyses as future extensions. The later clause about developmental trajectories is attached to other citations in the citing sentence. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Community detection of performance measures in ADHD did not produce subgroups separated by inhibition and working memory problems.
“Indeed, community detection of performance measures in ADHD did not yield subgroups separated by inhibition and working memory problems (Fair et al., 2012), as observed here.”
From Data‐driven identification of subtypes of executive function across typical development, attention deficit hyperactivity disorder, and autism spectrum disorders (2020) · cites this paper as Fair et al., 2012
Working memory impairments are reported in 30% to 37% of pediatric ADHD cases.
“Working memory impairments are reported in 30% to 37% (Coghill et al., 2014; Fair et al., 2012)”
From Executive Functioning Heterogeneity in Pediatric ADHD (2019) · cites this paper as Fair et al., 2012
Only a subset of children with ADHD meets criteria for an executive function deficit.
“Given that only a subset of children with ADHD meets criteria for an executive function deficit [16-23]”
From Prevalence and diagnostic validity of motivational impairments and deficits in visuospatial short-term memory and working memory in ADHD subtypes (2015) · cites this paper as [16]
Experimental — accuracy not yet measuredVisuospatial WM deficits on group-level are probably carried by only a subset of children with ADHD.
“visuospatial WM deficits on group-level are probably carried by only a subset of children with ADHD [16].”
From Prevalence and diagnostic validity of motivational impairments and deficits in visuospatial short-term memory and working memory in ADHD subtypes (2015) · cites this paper as [16]
ADHD is a neuropsychologically heterogeneous disorder that probably is not characterized by any single core dysfunction.
“recent findings suggest that ADHD is a neuropsychologically heterogeneous disorder that probably is not characterized by any single core dysfunction [16-19].”
From Prevalence and diagnostic validity of motivational impairments and deficits in visuospatial short-term memory and working memory in ADHD subtypes (2015) · cites this paper as [16]
These results support models and previous findings that ADHD is a neuropsychologically heterogeneous disorder that cannot be characterized by a single core dysfunction.
“However, at the same time, these results support models and previous findings which suggest that ADHD is a neuropsychologically heterogeneous disorder that cannot be characterized by a single core dysfunction [16-22, 25].”
From Prevalence and diagnostic validity of motivational impairments and deficits in visuospatial short-term memory and working memory in ADHD subtypes (2015) · cites this paper as [16]
The neuropsychological heterogeneity in ADHD may be a derivative of normal variation.
“This suggests that the neuropsychological heterogeneity in ADHD may be a derivative of normal variation”
From Prevalence and diagnostic validity of motivational impairments and deficits in visuospatial short-term memory and working memory in ADHD subtypes (2015) · cites this paper as [16]
The absence of associations across motivational and memory domains supports recent evidence suggesting separable neuropsychological subtypes in ADHD.
“supports recent evidence suggesting separable neuropsychological subtypes in ADHD [e.g., 16, 19, 23]”
From Prevalence and diagnostic validity of motivational impairments and deficits in visuospatial short-term memory and working memory in ADHD subtypes (2015) · cites this paper as [16]
Other domains impaired in ADHD include temporal information processing and timing, speech and language, memory span, processing speed and response time variability, arousal and activation, and motor control.
“Other domains impaired in ADHD include temporal information processing and timing 67 ; speech and language 68 ; memory span, processing speed and response time variability 69 ; arousal and activation 70 ; and motor control 71 .”
From Attention-deficit/hyperactivity disorder (2015) · cites this paper as [70]
ADHD is likely heterogeneous in mediating cognitive mechanisms, comorbidity, severity, clinical course, temporal variation, environmental exposures, and probably genetic variation.
“While effects were relatively consistent across formulations of ADHD here, ADHD is likely to be heterogeneous with regard to mediating cognitive mechanisms64,65 as well as with regard to comorbidity, severity, clinical course, temporal variation, environmental exposures, and probably genetic variation.”
From Working Memory and Vigilance as Multivariate Endophenotypes Related to Common Genetic Risk for Attention-Deficit/Hyperactivity Disorder (2018) · cites this paper as [65]
Heterogeneity of ADHD limits the ability to identify the underlying neurobiology/etiology of ADHD.
“Heterogeneity limits our ability to identify the underlying neurobiology of ADHD.”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
Even within the same DSM ADHD subtype, variations in clinical presentation are remarkable, and variations in etiology and pathophysiology are expected.
“however, even within the same subtype, variations in clinical presentation are remarkable, and variations in etiology and pathophysiology are expected (Fair et al., 2012, Karalunas et al., 2015, Nigg et al., 2005).”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
Findings from these studies suggest that identifying mechanistic subpopulations across ADHD and typically developing children may inform heterogeneity and the clinical course of a given child.
“Findings from these studies suggest that identifying mechanistic subpopulations across ADHD and typically developing children may inform heterogeneity and the clinical course of a given child.”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
Fair and colleagues found that both children with ADHD and typically developing children could be classified into unique, but similar neuropsychological profiles, and children with ADHD from each profile showed atypical scores in very specific neuropsychological measures.
“The study found that both groups (children with ADHD and typically developing children) could be classified into unique, but similar neuropsychological profiles. Furthermore, children with ADHD from each profile showed atypical scores in very specific neuropsychological measures.”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
Three recent reports from the authors' team have highlighted the use of community detection as a way to characterize heterogeneity in ADHD.
“Three recent reports from our team have highlighted the use of community detection as a way to characterize heterogeneity in ADHD (Fair et al., 2012, Gates et al., 2014, Karalunas et al., 2015).”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
Variation across several neuropsychological measures does not always shape across a continuous dimension (unimodal distribution), but rather some behavioral features form multiple discrete subgroups (multimodal distribution).
“For example, in a recent work, we showed that variation across several neuropsychological measures does not always shape across a continuous dimension (i.e. unimodal distribution), but rather some behavioral features form multiple discrete subgroups (i.e. multimodal distribution) (Fair et al., 2012).”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
The finding that NAcc connectivity patterns vary by neurotype is similar in theme to previous reports.
“This particular finding is similar in theme to that of previous reports (Fair et al., 2012, Gates et al., 2014).”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
The data highlight variation not only in ADHD but also in typically developing children.
“These data highlight not only variation in ADHD, but in typically developing children as well (Fair et al., 2012, Gates et al., 2014).”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
Similar methods have been used to assess behavioral and temperamental variation in ADHD and typically developing populations.
“Similar methods have been used to assess behavioral and temperamental variation in ADHD and typically developing populations (Fair et al., 2012, Gates et al., 2014, Karalunas et al., 2015).”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Fair et al., 2012
Multiple neurobiological mechanisms or etiologies may lead to similar symptom clusters within a given mental disorder diagnostic category.
“It is possible, if not likely [2], [3], that as with insomnia multiple mechanisms may lead to similar symptom clusters for a given mental disorder as defined by the Diagnostic and Statistical Manual of Mental Disorders (currently, DSM-5) or International Classification of Diseases (ICD).”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [2]
Most evidence for this phenomenon has been from neuropsychological data and behavior or symptom report, with recent evidence suggesting heterogeneity in the biological components as well.
“Most evidence for this phenomenon has been from neuropsychological data and behavior or symptom report (e.g., [2]), with recent evidence suggesting heterogeneity in the biological components as well [48].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [2]
Prior work in fMRI using this modularity algorithm required that the researcher set an arbitrary threshold for similarity between individuals.
“Prior work in fMRI using this modularity algorithm required that the researcher set an arbitrary threshold, or cutoff point, for what constitutes similarity between any two given individuals. Researchers would then arrive at thresholds by looking for consistency in community detection results at multiple thresholds (e.g., [2], [21], [22]).”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [2]
Data in the ADHD literature examining functional differences in the diagnostic subtypes (i.e. Combined type, and Inattentive type) lend support for this particular finding.
“Data in the ADHD literature examining functional differences in the diagnostic subtypes (i.e. Combined type, and Inattentive type) lend support for this particular finding [2].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [2]
This further supports the notion that many mechanisms exist by which children may meet criteria for ADHD.
“This further supports the notion that many mechanisms exist by which children may meet criteria for ADHD [2].”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [2]
The authors identified seven functional domains implicated in ADHD from recent cognitive batteries.
“We identified seven functional domains implicated in ADHD (see Figure 1] that were described in the majority of several recent cognitive batteries [5,12,16,17]”
From Linking ADHD to the Neural Circuitry of Attention (2017) · cites this paper as [5]
Most children with ADHD show deficits on multiple EFs.
“However, evidence suggests that most children with ADHD show deficits on multiple EFs [24]”
Multiple developmental and pathophysiological pathways inform ADHD symptomatology.
“However, there is now considerable justification suggesting that multiple developmental and pathophysiological pathways inform ADHD symptomatology (Fair et al., 2012; Nigg et al., 2004; Sonuga-Barke et al., 2003).”
From Reward circuit connectivity relates to delay discounting in children with attention-deficit/hyperactivity disorder (2013) · cites this paper as Fair et al., 2012
Some 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).”
From Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies (2016) · cites this paper as [7]
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.”
From Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies (2016) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Subgroup 4B lowers rather than raises diagnostic accuracy
MinorThe discussion says that comparing children within each subgroup increases diagnostic accuracy. Yet subgroup 4B has 61.9% accuracy, below the reported 65% accuracy for the full ADHD-versus-control comparison.
“As illustrated by our single subject classification procedures, comparing typically developing children with ADHD children within each of these distinct subgroups increases diagnostic accuracy (i.e., ADHD vs. non-ADHD classification) on the basis of the neuropsychological measures.”
“The remaining groups were as follows: group 1, 68% total, 49.2% sensitivity, 80.4% specificity; group 2A, 68.5%, 83.7%, 51.2%; group 2B, 64.7%, 80.4%, 44.2%; group 3, 73.6%, 88.73%, 46.2%; group 4A, 84.1%, 83.3%, 84.6%; and group 4B, 61.9%, 37%, 76.9%.”
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Self-citationBeyond <i>DSM</i> : Seeking a Brain-Based Classification of Mental Illness
Miller, G. (2010) · Science · doi:10.1126/science.327.5972.1437 · Cited by 77 · see citing works
The Genetics of Addictions: Uncovering the Genes
Goldman, D. A., Oroszi, G., & Ducci, F. (2006) · FOCUS The Journal of Lifelong Learning in Psychiatry · doi:10.1176/foc.4.3.401 · Cited by 49 · see citing works
Evolutionary Psychology and Developmental Dynamics: Comment on Lickliter and Honeycutt (2003).
Buss, D. M., & Reeve, H. K. (2003) · Psychological Bulletin · doi:10.1037/0033-2909.129.6.848 · Cited by 39 · see citing works
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BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106 · see citing works
Attention-deficit/hyperactivity disorder
Faraone, S. V., Asherson, P., Banaschewski, T., Biederman, J., Buitelaar, J. K., Ramos-Quiroga, J. A., Rohde, L. A., Sonuga-Barke, E. J. S., Tannock, R., & Franke, B. (2015) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2015.20 · Cited by 1,479 · see citing works
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966 · see citing works
Understanding heterogeneity in clinical cohorts using normative models: Beyond case-control studies
Marquand, A. F., Rezek, I., Buitelaar, J., & Beckmann, C. F. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.12.023 · Cited by 683 · see citing works
“Some reports have suggested that disease variation may be nested within normal variation (7).”
BackgroundAn open resource for transdiagnostic research in pediatric mental health and learning disorders
Alexander, L. M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., Potler, N. V., Langer, N., Alexander, A., Kovacs, M., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.181 · Cited by 652 · see citing works
“Complete HBN Protocol Behavioral Measures Child Behavior Checklist (CBCL) (5-17) Youth Self Report (YSR) (11-18) Adult Self Report (ASR) (18+) Screen for Child Anxiety Related Disorders (SCARED) – Parent Report & Self Report (8-18) State Trait Anxiety Inventory (STAI) (18+) – Self Report Mood & Feelings Questionnaire…”
BackgroundCharacterizing a psychiatric symptom dimension related to deficits in goal-directed control
Gillan, C. M., Kosinski, M., Whelan, R., Phelps, E. A., & Daw, N. D. (2016) · eLife · doi:10.7554/elife.11305 · Cited by 586 · see citing works
“…has already been made by studies examining dissociable clusters of patients within groups diagnosed with the same disorder (Brodersen et al., 2014; Fair et al., 2012), the identification of robust, generalizable and specific markers that contribute to psychiatric co-morbidity has been curtailed…”
BackgroundThe Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.03.009 · Cited by 443 · see citing works
“A community detection approach [80] uses a similar bottom up approach to identify novel executive function [80], temperament [81], and neural [82,83] subtypes in an ADHD population.”
MethodSelf-citationGrayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411 · see citing works
“…consistently implicated in Schizophrenia (Baker et al., 2014; Khadka et al., 2013; Satterthwaite et al., 2015), ADHD (Castellanos and Aoki, 2016; Fair et al., 2012b), and Autism (Kessler et al., 2016; Moseley et al., 2015; Supekar et al., 2013) – though a thorough examination of this literature…”
BackgroundResultInfluentialSelf-citationFair, D. A., Nigg, J. T., Iyer, S., Bathula, D., Mills, K. L., Dosenbach, N. U. F., Schlaggar, B. L., Mennes, M., Gutman, D., Bangaru, S., et al. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00080 · Cited by 463 · see citing works
“…datasets, with coordinated recruitment, deep phenotyping, multimodal data acquisition (e.g., rs-fcMRI, diffusion imaging, ASL), and likely improved homogeneity in our subgrouping (Fair et al., 2012) that a fair assessment of the predictive potential of MR-based approaches will be realized.”
BackgroundSelf-citationADHD: Current Concepts and Treatments in Children and Adolescents
Drechsler, R., Brem, S., Brandeis, D., Grünblatt, E., Berger, G. E., & Walitza, S. (2020) · Neuropediatrics · doi:10.1055/s-0040-1701658 · Cited by 447 · see citing works
A Review of Heterogeneity in Attention Deficit/Hyperactivity Disorder (ADHD)
Luo, Y., Weibman, D., Halperin, J. M., & Li, X. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00042 · Cited by 394 · see citing works
“The etiological heterogeneity in terms of the biological and environmental factors is likely reflected in variation in neural correlates (Nigg and Casey, 2005; Mackie et al., 2007; Fair et al., 2012; Costa Dias et al., 2013, 2015; Karalunas et al., 2014), and results in the diverse cognitive and behavioral profiles an…”
BackgroundUnraveling the Miswired Connectome: A Developmental Perspective
Martino, A. D., Fair, D. A., Kelly, C. A. M., Satterthwaite, T. D., Castellanos, F. X., Thomason, M. E., Craddock, R. C., Luna, B., LEVENTHAL, B. L., Zuo, X., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.050 · Cited by 346 · see citing works
“Indeed, the changes in shortand long-range connectivity appear to be most prominent in the first 2 years of life (e.g., Gao et al., 2011; Yap et al., 2011), with more modest changes occurring later in development (Fair et al., 2012b; Satterthwaite et al., 2012, 2013a).”
BackgroundMethodInfluentialSelf-citationKaralunas, S. L., Fair, D., Musser, E. D., Aykes, K., Iyer, S. P., & Nigg, J. T. (2014) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2014.763 · Cited by 340 · see citing works
Self-citationRetractedWang, X., & Krystal, J. H. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.018 · Cited by 309 · see citing works
Vandenbroucke, L., Weeda, W. D., Lee, N. C., Baeyens, D., Westfall, J., Figner, B., & Huizinga, M. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01572 · Cited by 316 · see citing works
Executive Functioning Heterogeneity in Pediatric ADHD
Kofler, M. J., Irwin, L. N., Soto, E. F., Groves, N. B., Harmon, S. L., & Sarver, D. E. (2019) · Journal of Abnormal Child Psychology · doi:10.1007/s10802-018-0438-2 · Cited by 288 · see citing works
Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning
Chand, G. B., Dwyer, D., Erus, G., Sotiras, A., Varol, E., Srinivasan, D., Doshi, J. J., Pomponio, R., Pigoni, A., Dazzan, P., et al. (2020) · Brain · doi:10.1093/brain/awaa025 · Cited by 271 · see citing works
“HYDRA is fundamentally different from previous clustering methods (Fair et al. , 2012; Arnedo et al. , 2015; Du et al. , 2015; Clementz et al. , 2016) because it specifically seeks to cluster illness effects by modelling differences from healthy controls rather than clustering patients directly.”
MethodKaralunas, S. L., Geurts, H. M., Konrad, K., Bender, S., & Nigg, J. T. (2014) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12217 · Cited by 278 · see citing works
Self-citationDovis, S., Van der Oord, S., Wiers, R. W., & Prins, P. J. M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121651 · Cited by 272 · see citing works
“However, improvements on untrained EFs and behavior were mostly nonspecific.”
BackgroundInfluentialMarquand, A. F., Wolfers, T., Mennes, M., Buitelaar, J. K., & Beckmann, C. F. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.04.002 · Cited by 254 · see citing works
“, 2012 (15) ADHD (285) and TDC (213) Neuropsychologic scores CD (33) 6 for ADHD (determined implicitly by the algorithm) Response time variability (1) None”
BackgroundCoghill, D. R., Seth, S., Pedroso, S., Usala, T., Currie, J., & Gagliano, A. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.10.005 · Cited by 271 · see citing works
van Duijvenvoorde, A. C. K., Peters, S., Braams, B. R., & Crone, E. A. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.06.037 · Cited by 241 · see citing works
“Further research on the behavioral effects of reward-sensitivity should therefore take into account individual differences and subgroups (e.g., Fair et al., 2012).”
BackgroundCoghill, D. R., Seth, S., & Matthews, K. (2014) · Psychological Medicine · doi:10.1017/s0033291713002547 · Cited by 242 · see citing works
“In a landmark study, Fair et al. (2012) demonstrated differences between children with ADHD and controls on a range of cognitive factors (inhibition, working memory, memory span, arousal/ activation, response variability, temporal information processing and processing speed).”
BackgroundInfluentialConnectotyping: Model Based Fingerprinting of the Functional Connectome
Miranda-Dominguez, O., Mills, B. D., Carpenter, S. D., Grant, K. A., Kroenke, C. D., Nigg, J. T., & Fair, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111048 · Cited by 197 · see citing works
“Apinv~yx T xx {1 : ð6Þ While the pseudo-inverse method renders the solution with the lowest discrepancy between the predictions and measurements (in a least squares sense), its use often leads to over-fitting and its predictions are highly susceptible to noise on ill-posed systems [2].”
BackgroundSelf-citationRauh, V., & Margolis, A. E. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12537 · Cited by 228 · see citing works
“…Fair, Bathula, Nikolas, and Nigg (2012) propose that (a) typically developing children can be classified into distinct neuropsychological subgroups with high precision, and (b) some of the heterogeneity in individuals with ADHD might be ‘nested’ in this normal variation (Fair et al., 2012).”
BackgroundInfluentialDissecting the Heterogeneous Cortical Anatomy of Autism Spectrum Disorder Using Normative Models
Zabihi, M., Oldehinkel, M., Wolfers, T., Frouin, V., Goyard, D., Loth, E., Charman, T., Tillmann, J., Banaschewski, T., Dumas, G., et al. (2018) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2018.11.013 · Cited by 197 · see citing works
The molecular genetic architecture of attention deficit hyperactivity disorder
Hawi, Z., Cummins, T. D. R., Tong, J., Johnson, B., Lau, R., Samarrai, W., & Bellgrove, M. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.183 · Cited by 216 · see citing works
Prediction complements explanation in understanding the developing brain
Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018) · Nature Communications · doi:10.1038/s41467-018-02887-9 · Cited by 167 · see citing works
Executive function and developmental disorders: the flip side of the coin
Johnson, M. H. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.07.001 · Cited by 190 · see citing works
Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry
Wiecki, T. V., Poland, J. S., & Frank, M. J. (2015) · Clinical Psychological Science · doi:10.1177/2167702614565359 · Cited by 191 · see citing works
“One potential way to deal with this problem is to use an unsupervised clustering algorithm to find a new grouping that is hopefully more sensitive to the neurocognitive deficits (Fair et al., 2012).”
BackgroundMethodCharting the landscape of priority problems in psychiatry, part 1: classification and diagnosis
Stephan, K. E., Bach, D. R., Fletcher, P. C., Flint, J., Frank, M. J., Friston, K. J., Heinz, A., Huys, Q. J. M., Owen, M. J., Binder, E. B., et al. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00361-2 · Cited by 180 · see citing works
Ostlund, B. D., Alperin, B. R., Drew, T. W., & Karalunas, S. L. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.100931 · Cited by 159 · see citing works
Costa Dias, T. G., Wilson, V. B., Bathula, D. R., Iyer, S. P., Mills, K. L., Thurlow, B. L., Stevens, C. A., Musser, E. D., Carpenter, S. D., Grayson, D. S., et al. (2013) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2012.10.015 · Cited by 180 · see citing works
“However, there is now considerable justification suggesting that multiple developmental and pathophysiological pathways inform ADHD symptomatology (Fair et al., 2012; Nigg et al., 2004; Sonuga-Barke et al., 2003).”
BackgroundSelf-citationTowards a brain‐based predictome of mental illness
Rashid, B., & Calhoun, V. D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25013 · Cited by 160 · see citing works
Development of ADHD: Etiology, Heterogeneity, and Early Life Course
Nigg, J. T., Sibley, M. H., Thapar, A., & Karalunas, S. L. (2020) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-060320-093413 · Cited by 184 · see citing works
Self-citationInsomnia heterogeneity: Characteristics to consider for data-driven multivariate subtyping
Benjamins, J. S., Migliorati, F., Dekker, K., Wassing, R., Moens, S., Blanken, T. F., Lindert, B. H. W. T., Mook, J. S., & Someren, E. J. W. V. (2016) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2016.10.005 · Cited by 179 · see citing works
Electrophysiological biomarkers of diagnosis and outcome in neurodevelopmental disorders
Jeste, S., Frohlich, J., & Loo, S. K. (2015) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000181 · Cited by 168 · see citing works
“Efforts to identify latent subgroups using neuropsychological measures [4], temperament dimensions [5 & ], and resting state connectivity [6], in both ADHD and typical development, suggest that a reconceptualization of classification efforts may be needed.”
BackgroundLinking ADHD to the Neural Circuitry of Attention
Mueller, A., Hong, D. S., Shepard, S., & Moore, T. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.03.009 · Cited by 158 · see citing works
A trans-diagnostic perspective on obsessive-compulsive disorder
Gillan, C. M., Fineberg, N. A., & Robbins, T. W. (2017) · Psychological Medicine · doi:10.1017/s0033291716002786 · Cited by 158 · see citing works
“This can be achieved in many different ways, just a few of which have been outlined here (see other good examples: Fair et al. 2012; Brodersen et al. 2014), and at many units of analysis.”
Nejati, V., Salehinejad, M. A., Nitsche, M. A., Najian, A., & Javadi, A.-H. (2020) · Journal of Attention Disorders · doi:10.1177/1087054717730611 · Cited by 163 · see citing works
“Instead, ADHD is quite a heterogeneous disorder in terms of impaired deficits and neuropsychological measures central to ADHD (Fair et al., 2012).”
BackgroundImaging Functional and Structural Brain Connectomics in Attention-Deficit/Hyperactivity Disorder
Cao, M., Shu, N., Cao, Q., Wang, Y., & Yong He, A. (2014) · Molecular Neurobiology · doi:10.1007/s12035-014-8685-x · Cited by 153 · see citing works
“Moreover, there could be mechanistic heterogeneity that potentially underlies the existing classification scheme [86].”
Backgroundvon Rhein, D. T., Mennes, M., van Ewijk, H., Groenman, A. P., Zwiers, M. P., Oosterlaan, J., Heslenfeld, D. J., Franke, B., Hoekstra, P. J., Faraone, S. V., et al. (2014) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-014-0573-4 · Cited by 147 · see citing works
“Using empirical modelling techniques like latent class analysis (LCA) and recent extensions of these techniques, one may identify groups of participants who have very similar patterning of scores on cognitive and brain measures reflecting biologically relevant, distinct etiological pathways towards disease [93, 94].”
MethodKaralunas, S. L., Hawkey, E., Gustafsson, H., Miller, M., Langhorst, M., Cordova, M., Fair, D., & Nigg, J. T. (2018) · Journal of Abnormal Child Psychology · doi:10.1007/s10802-017-0394-2 · Cited by 148 · see citing works
“Although there is heterogeneity in the ADHD population (Fair et al. 2012; Nigg et al. 2005), at the group level relatively consistent impairments have been found in individuals with ADHD in each of these domains (Huang-Pollock et al. 2012; Kofler et al. 2013; Willcutt et al. 2005).”
Self-citationSarver, D. E., Rapport, M. D., Kofler, M. J., Raiker, J. S., & Friedman, L. M. (2015) · Journal of Abnormal Child Psychology · doi:10.1007/s10802-015-0011-1 · Cited by 149 · see citing works
“Taken together, this finding is consistent with the well-documented neurocognitive and behavioral heterogeneity in ADHD and TD populations (Fair et al. 2012), and implies that gross motor movement may be more likely to facilitate than impair cognitive functioning for children with ADHD.”
BackgroundResultMultidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder
Hong, S., Valk, S. L., Martino, A. D., Milham, M. P., & Bernhardt, B. C. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx229 · Cited by 131 · see citing works
Insights into multimodal imaging classification of ADHD
Colby, J. B., Rudie, J. D., Brown, J. A., Douglas, P. K., Cohen, M. S., & Shehzad, Z. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00059 · Cited by 144 · see citing works
Heritability of the human connectome: A connectotyping study
Miranda-Domínguez, Ó., Feczko, E., Grayson, D. S., Walum, H., Nigg, J. T., & Fair, D. A. (2017) · Network Neuroscience · doi:10.1162/netn_a_00029 · Cited by 120 · see citing works
Self-citationConsiderations for MRI study design and implementation in pediatric and clinical populations
Greene, D. J., BLACK, K. J., & Schlaggar, B. L. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2015.12.005 · Cited by 126 · see citing works
Mostert, J. C., Onnink, A. M. H., Klein, M., Dammers, J., Harneit, A., Schulten, T., van Hulzen, K. J. E., Kan, C. C., Slaats-Willemse, D., Buitelaar, J. K., et al. (2015) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2015.08.010 · Cited by 142 · see citing works
“The current findings provide a starting point to investigate individual differences in terms of impaired cognitive pathways, for instance by using clustering analyses on the neuropsychological data (Fair et al., 2012).”
MethodSalehinejad, M. A., Wischnewski, M., Nejati, V., Vicario, C. M., & Nitsche, M. A. (2019) · PLOS ONE · doi:10.1371/journal.pone.0215095 · Cited by 126 · see citing works
“A precise description of ADHD neuropathology is difficult to delineate due to its neuropsychological heterogeneity [12, 13], and substantial overlap between ADHD and typically developing children [14].”
BackgroundCharacterizing heterogeneity in children with and without ADHD based on reward system connectivity
Costa Dias, T. G., Iyer, S. P., Carpenter, S. D., Cary, R. P., Wilson, V. B., Mitchell, S. H., Nigg, J. T., & Fair, D. A. (2015) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2014.12.005 · Cited by 130 · see citing works
“Connectivity preprocessing followed prior methods, well described and widely used in the imaging literature (Fair et al., 2007, 2008, 2009; Fox et al., 2005).”
MethodSelf-citationKaralunas, S. L., Gustafsson, H., Fair, D. A., Musser, E. D., & Nigg, J. T. (2018) · Psychological Assessment · doi:10.1037/pas0000664 · Cited by 138 · see citing works
Self-citationGreen, B. C., Johnson, K. A., & Bretherton, L. (2013) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.12056 · Cited by 135 · see citing works
“Recently there has been increasing recognition amongst researchers that behavioural and cognitive features associated with ADHD are distributed in the population as a whole, with the syndrome of ADHD representing the extreme end of a continuum (Fair et al. 2012, Shaw et al. 2011).”
Toward a revised nosology for attention-deficit/hyperactivity disorder heterogeneity
Nigg, J. T., Karalunas, S. L., Feczko, E., & Fair, D. A. (2020) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2020.02.005 · Cited by 132 · see citing works
Self-citationHeterogeneity of executive functions among comorbid neurodevelopmental disorders
Dajani, D. R., Llabre, M. M., Nebel, M. B., Mostofsky, S. H., & Uddin, L. Q. (2016) · Scientific Reports · doi:10.1038/srep36566 · Cited by 126 · see citing works
Vaidya, C. J., You, X., Mostofsky, S., Pereira, F., Berl, M. M., & Kenworthy, L. (2020) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.13114 · Cited by 126 · see citing works
Salehinejad, M. A., Nejati, V., Mosayebi-Samani, M., Mohammadi, A., Wischnewski, M., Kuo, M.-F., Avenanti, A., Vicario, C. M., & Nitsche, M. A. (2020) · Neuroscience Bulletin · doi:10.1007/s12264-020-00501-x · Cited by 118 · see citing works
Are There Executive Dysfunction Subtypes Within ADHD?
Roberts, B. A., Martel, M. M., & Nigg, J. T. (2017) · Journal of Attention Disorders · doi:10.1177/1087054713510349 · Cited by 116 · see citing works
“Specific EF deficits have been shown to be dissociable such that subgroups of children with ADHD may be affected by problems in only one area of EF (Fair et al., 2012; Miller et al., 2010; Sonuga-Barke et al., 2010).”
BackgroundResultInfluentialSelf-citationEslami, T., Almuqhim, F., Raiker, J. S., & Saeed, F. (2021) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.575999 · Cited by 127 · see citing works
“…given the well-documented neurocognitive dysfunction and alterations in temperament characteristic of individuals with ADHD, graph theoretical and community detection algorithms have been applied to advance our understanding of these deficits in ADHD (Fair et al., 2012; Karalunas et al., 2014).”
MethodMetin, B., Roeyers, H., Wiersema, J. R., van der Meere, J., & Sonuga-Barke, E. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.08.023 · Cited by 121 · see citing works
Brain Subtyping Enhances The Neuroanatomical Discrimination of Schizophrenia
Dwyer, D., Cabral, C., Kambeitz‐Ilankovic, L., Sanfelici, R., Kambeitz, J. P., Calhoun, V. D., Falkai, P., Pantelis, C., Meisenzahl, E. M., & Koutsouleris, N. (2018) · Schizophrenia Bulletin · doi:10.1093/schbul/sby008 · Cited by 114 · see citing works
Finn, E., & Constable, R. T. (2016) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2016.18.3/efinn · Cited by 92 · see citing works
Classification and treatment of antisocial individuals: From behavior to biocognition
Brazil, I. A., van Dongen, J. D. M., Maes, J. H. R., Mars, R. B., & Baskin-sommers, A. R. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.10.010 · Cited by 118 · see citing works
What big data can do for treatment in psychiatry
Gillan, C. M., & Whelan, R. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.003 · Cited by 106 · see citing works
Remapping the cognitive and neural profiles of children who struggle at school
Astle, D. E., Bathelt, J., the CALM team, & Holmes, J. (2018) · Developmental Science · doi:10.1111/desc.12747 · Cited by 114 · see citing works
“Machine learning methods have rarely been applied to understanding developmental disorders (e.g. Fair et al., 2012).”
MethodOwens, M., Herbert, J., Jones, P. B., Sahakian, B. J., Wilkinson, P. O., Dunn, V. J., Croudace, T. J., & Goodyer, I. M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1318786111 · Cited by 113 · see citing works
“Etiological heterogeneity in another psychiatric disorder [attention deficit hyperactivity disorder (ADHD)] was recently revealed by recognizing subgroups nested within the normal population with distinctive cognitive profiles (34).”
BackgroundExecutive Functioning and Emotion Regulation in Children with and without ADHD
Groves, N. B., Wells, E. L., Soto, E. F., Marsh, C. L., Jaisle, E. M., Harvey, T. K., & Kofler, M. J. (2022) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-021-00883-0 · Cited by 110 · see citing works
“For example, working memory is impaired in 30% to 85% (Coghill et al., 2014; Fair et al., 2012; Fosco et al., 2019; Kofler et al., 2018; Karalunas et al., 2017), inhibitory control is impaired in 21% to 46% (Coghill et al.”
BackgroundKaralunas, S. L., Gustafsson, H. C., Dieckmann, N. F., Tipsord, J., Mitchell, S. H., & Nigg, J. T. (2017) · Journal of Abnormal Psychology · doi:10.1037/abn0000292 · Cited by 104 · see citing works
Self-citationAttention Deficit Hyperactivity Disorder
Matthews, M., Nigg, J. T., & Fair, D. A. (2013) · Current topics in behavioral neurosciences · doi:10.1007/978-3-662-45758-0_249 · Cited by 121 · see citing works
Self-citationKaralunas, S. L., Huang-Pollock, C. L., & Nigg, J. T. (2012) · Neuropsychology · doi:10.1037/a0029936 · Cited by 104 · see citing works
Self-citationGates, K. M., Molenaar, P. C. M., Iyer, S. P., Nigg, J. T., & Fair, D. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091322 · Cited by 107 · see citing works
“, [2]), with recent evidence suggesting heterogeneity in the biological components as well [48].”
BackgroundInfluentialSelf-citationWu, M., Mwangi, B., Bauer, I. E., Passos, I. C., Sanches, M., Zunta‐Soares, G. B., Meyer, T. D., Hasan, K. M., & Soares, J. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.016 · Cited by 108 · see citing works
“Most recently, several studies have successfully identified unique neurocognitive subgroups using data-driven machine learning algorithms (Brodersen et al., 2014; Burdick et al., 2014; Fair et al., 2012; Heinrichs & Awad, 1993; LEE et al., 2014).”
BackgroundIntegrating Impairments in Reaction Time and Executive Function Using a Diffusion Model Framework
Karalunas, S. L., & Huang‐Pollock, C. L. (2013) · Research on Child and Adolescent Psychopathology · doi:10.1007/s10802-013-9715-2 · Cited by 98 · see citing works
“Future studies that better characterize subgroups using methods such as latent class or graph theoretical approaches (Fair et al. 2012; Newman 2006) will be critical for understanding within-group heterogeneity.”
Francx, W., Oldehinkel, M., Oosterlaan, J., Heslenfeld, D., Hartman, C. A., Hoekstra, P. J., Franke, B., Beckmann, C. F., Buitelaar, J. K., & Mennes, M. (2015) · Cortex · doi:10.1016/j.cortex.2015.08.012 · Cited by 102 · see citing works
Blanken, T. F., Deserno, M. K., Dalege, J., Borsboom, D., Blanken, P., KERKHOF, G. A., & Cramer, A. O. J. (2018) · Scientific Reports · doi:10.1038/s41598-018-24224-2 · Cited by 103 · see citing works
Johnston, B. A., Mwangi, B., Matthews, K., Coghill, D., Konrad, K., & Steele, J. D. (2014) · Human Brain Mapping · doi:10.1002/hbm.22542 · Cited by 101 · see citing works
“Given the possible heterogeneity of the ADHD syndrome [Fair et al., 2012], the results are encouraging for the identification of consistent imaging biomarkers, that can inform future work into the aetiology, pathophysiology, and clinical management of ADHD.”
BackgroundNigg, J. T., Gustafsson, H. C., Karalunas, S. L., Ryabinin, P., McWeeney, S. K., Faraone, S. V., Mooney, M. A., Fair, D. A., & Wilmot, B. (2018) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2017.12.013 · Cited by 93 · see citing works
Self-citationComputer-based Cognitive Training for ADHD
Sonuga-Barke, E., Brandeis, D., Holtmann, M., & Cortese, S. (2014) · Child and Adolescent Psychiatric Clinics of North America · doi:10.1016/j.chc.2014.05.009 · Cited by 92 · see citing works
Heterogeneity in ADHD: Neurocognitive predictors of peer, family, and academic functioning
Kofler, M. J., Sarver, D. E., Spiegel, J. A., Day, T. N., Harmon, S. L., & Wells, E. L. (2017) · Child Neuropsychology · doi:10.1080/09297049.2016.1205010 · Cited by 94 · see citing works
“arousal (Fair et al., 2012), combinations of short-term memory, vigilance, set shifting, inhibition, and visual-motor integration (Biederman et al.”
BackgroundBrain connectivity in normally developing children and adolescents
Khundrakpam, B. S., Lewis, J. D., Zhao, L., Chouinard-Decorte, F., & Evans, A. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.03.062 · Cited by 91 · see citing works
van Rooij, D., Hartman, C. A., Mennes, M., Oosterlaan, J., Franke, B., Rommelse, N., Heslenfeld, D., Faraone, S. V., Buitelaar, J. K., & Hoekstra, P. J. (2015) · NeuroImage: Clinical · doi:10.1016/j.nicl.2015.01.004 · Cited by 89 · see citing works
Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels
Dam, N. T. V., O’Connor, D. A., Marcelle, E. T., Ho, E. J., Craddock, R. C., Tobe, R. H., Gabbay, V., Hudziak, J. J., Castellanos, F. X., LEVENTHAL, B. L., et al. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.06.027 · Cited by 83 · see citing works
van Hulst, B. M., de Zeeuw, P., & Durston, S. (2015) · Psychological Medicine · doi:10.1017/s0033291714001792 · Cited by 87 · see citing works
“Building on a multiple-pathway hypothesis, recent studies have found several subgroups with varying neuropsychological profiles in ADHD (Sonuga-Barke et al. 2010; de Zeeuw et al. 2012; Fair et al. 2012).”
ResultBecker, S. P., & Willcutt, E. G. (2019) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-018-1136-x · Cited by 85 · see citing works
“1 3 difference between a target (signal) and a nontarget (noise) that is interpreted as an indicator of low (suboptimal) arousal [81].”
Rudolph, M. D., Miranda-Domínguez, Ó., Cohen, A. O., Breiner, K., Steinberg, L. D., Bonnie, R. J., Scott, E. S., Taylor‐Thompson, K., Chein, J. M., Fettich, K. C., et al. (2017) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.01.010 · Cited by 76 · see citing works
Self-citationAttention Deficit/Hyperactivity Disorder (ADHD) and Entrepreneurship
Antshel, K. M. (2017) · Academy of Management Perspectives · doi:10.5465/amp.2016.0144 · Cited by 86 · see citing works
Toward Precision Medicine in ADHD
Buitelaar, J. K., Bölte, S., Brandeis, D., Caye, A., Christmann, N., Cortese, S., Coghill, D., Faraone, S. V., Franke, B., Gleitz, M., et al. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.900981 · Cited by 79 · see citing works
“Several approaches to stratification of ADHD with the aim of producing more homogenous neurocognitive subgroups have been described ADHD (Fair et al., 2012; Coghill et al., 2014; van Hulst et al., 2015; Leikauf et al., 2017; Mostert et al., 2018).”
MethodThe Role of Speed in ADHD-Related Working Memory Deficits
Weigard, A. S., & Huang‐Pollock, C. L. (2016) · Clinical Psychological Science · doi:10.1177/2167702616668320 · Cited by 75 · see citing works
Alabdulakareem, E., & Jamjoom, M. M. (2020) · Entertainment Computing · doi:10.1016/j.entcom.2020.100341 · Cited by 75 · see citing works
Automated Selection of Robust Individual-Level Structural Equation Models for Time Series Data
Lane, S., & Gates, K. M. (2017) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705511.2017.1309978 · Cited by 66 · see citing works
“Both theory (Lamiell, 1981; Molenaar, 2008) and emerging results (Anzman-Frasca et al., 2013; Fair, Bathula, Nikolas, & Nigg, 2012; Gates, Molenaar, Iyer, Nigg.”
Holmes, J., Bryant, A., the CALM Team, & Gathercole, S. E. (2019) · BMC Pediatrics · doi:10.1186/s12887-018-1385-3 · Cited by 67 · see citing works
Loo, S. K., McGough, J. J., McCracken, J. T., & Smalley, S. L. (2018) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12814 · Cited by 73 · see citing works
Rohr, C. S., Vinette, S. A., Parsons, K., Cho, I. Y. K., Dimond, D., Benischek, A., Lebel, C. A., Dewey, D., & Bray, S. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw236 · Cited by 67 · see citing works
Alaerts, K., Nayar, K., Kelly, C. A. M., Raithel, J., Milham, M. P., & Martino, A. D. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv029 · Cited by 65 · see citing works
“As in prior work (Fair et al., 2012; Satterthwaite et al., 2013), these findings were robustly detectable across motion-correction approaches (i.”
BackgroundMethodResultInfluentialThe definition and measurement of heterogeneity
Nunes, A., Trappenberg, T. P., & Alda, M. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00986-0 · Cited by 79 · see citing works
Deficits in Cognitive Control, Timing and Reward Sensitivity Appear to be Dissociable in ADHD
de Zeeuw, P., Weusten, J., van Dijk, S., van Belle, J., & Durston, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051416 · Cited by 71 · see citing works
Heidbreder, R. (2015) · ADHD Attention Deficit and Hyperactivity Disorders · doi:10.1007/s12402-015-0171-4 · Cited by 74 · see citing works
Cordova, M., Antovich, D., Ryabinin, P., Neighbor, C., Mooney, M. A., Dieckmann, N. F., Miranda-Domínguez, Ó., Nagel, B. J., Fair, D. A., & Nigg, J. T. (2021) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2022.03.030 · Cited by 65 · see citing works
Self-citationADHD and attentional control: Impaired segregation of task positive and task negative brain networks
Mills, B. D., Miranda-Domínguez, Ó., Mills, K. L., Earl, E., Cordova, M., Painter, J. G., Karalunas, S. L., Nigg, J. T., & Fair, D. A. (2017) · Network Neuroscience · doi:10.1162/netn_a_00034 · Cited by 61 · see citing works
Self-citationBaroni, A., & Castellanos, F. X. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.08.005 · Cited by 64 · see citing works
- Authors
- Fair, D. A., Bathula, D., Nikolas, M. A., & Nigg, J. T.
- Affiliations
- Oregon Health & Science University (US)
- Indian Institute of Technology Ropar (IN)
- University of Iowa (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2012-04-02
- Volume
- 109
- Issue
- 17
- Pages
- 6769-6774
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3340031
- OpenAlex
- W2136530770
- Semantic Scholar
- adf36af1b47715f99d3f580f47ce0801a1338b8e
- Cited by
- 464
- References
- 54
- Citations by year
- 2026: 17 · 2025: 25 · 2024: 29 · 2023: 23 · 2022: 36 · 2021: 30 · 2020: 34 · 2019: 41 · 2018: 40 · 2017: 41 · 2016: 43 · 2015: 36 · 2014: 35 · 2013: 21 · 2012: 13
- FWCI
- 23.86
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR000128)
- Oregon Clinical and Translational Research Institute
- National Institute of Mental Health (R00MH091238, R01MH096773, R01MH059105, R01MH086654)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Attention Deficit Hyperactivity Disorder
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Developmental and Educational Neuropsychology
- Keywords
- attention deficit hyperactivity disorder, typically developing children, developmental heterogeneity, community detection, graph theory, multivariate pattern analysis, support vector machine, diagnostic classification
- MeSH terms
- Adolescent, Attention Deficit Disorder with Hyperactivity, Child, Cognition, Humans, Neuropsychological Tests, Cohort Studies
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