Subtyping Attention-Deficit/Hyperactivity Disorder using temperament dimensions: Toward biologically...

Copyright 2014 American Medical Association. All rights reserved.

Subtyping Attention-Deficit/Hyperactivity DisorderUsing Temperament DimensionsToward Biologically Based Nosologic CriteriaSarah L. Karalunas, PhD; Damien Fair, PhD; Erica D. Musser, PhD; Kamari Aykes, BS;Swathi P. Iyer, MS; Joel T. Nigg, PhD

IMPORTANCE Psychiatric nosology is limited by behavioral and biological heterogeneitywithin existing disorder categories. The imprecise nature of current nosologic distinctionslimits both mechanistic understanding and clinical prediction. We demonstrate an approachconsistent with the National Institute of Mental Health Research Domain Criteria initiative toidentify superior, neurobiologically valid subgroups with better predictive capacity thanexisting psychiatric categories for childhood attention-deficit/hyperactivity disorder (ADHD).

OBJECTIVE To refine subtyping of childhood ADHD by using biologically based behavioraldimensions (ie, temperament), novel classification algorithms, and multiple externalvalidators.

DESIGN, SETTING, AND PARTICIPANTS A total of 437 clinically well-characterized,community-recruited children, with and without ADHD, participated in an ongoinglongitudinal study. Baseline data were used to classify children into subgroups based ontemperament dimensions and examine external validators including physiological andmagnetic resonance imaging measures. One-year longitudinal follow-up data are reported fora subgroup of the ADHD sample to address stability and clinical prediction.

MAIN OUTCOMES AND MEASURES Parent/guardian ratings of children on a measure oftemperament were used as input features in novel community detection analyses to identifysubgroups within the sample. Groups were validated using 3 widely accepted externalvalidators: peripheral physiological characteristics (cardiac measures of respiratory sinusarrhythmia and pre-ejection period), central nervous system functioning (via resting-statefunctional connectivity magnetic resonance imaging), and clinical outcomes (at 1-yearlongitudinal follow-up).

RESULTS The community detection algorithm suggested 3 novel types of ADHD, labeled asmild (normative emotion regulation), surgent (extreme levels of positive approach-motivation), and irritable (extreme levels of negative emotionality, anger, and poorsoothability). Types were independent of existing clinical demarcations including DSM-5presentations or symptom severity. These types showed stability over time and weredistinguished by unique patterns of cardiac physiological response, resting-state functionalbrain connectivity, and clinical outcomes 1 year later.

CONCLUSIONS AND RELEVANCE Results suggest that a biologically informedtemperament-based typology, developed with a discovery-based community detectionalgorithm, provides a superior description of heterogeneity in the ADHD population thandoes any current clinical nosologic criteria. This demonstration sets the stage for moreaggressive attempts at a tractable, biologically based nosology.

JAMA Psychiatry. doi:10.1001/jamapsychiatry.2014.763Published online July 9, 2014.

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Author Affiliations: Division ofPsychology, Department ofPsychiatry, Oregon Health andScience University, Portland(Karalunas, Fair, Nigg); Department ofBehavioral Neuroscience, OregonHealth and Science University,Portland (Fair, Aykes, Nigg);Advanced Imaging Research Center,Oregon Health and ScienceUniversity, Portland (Fair); Division ofClinical Science, Department ofPsychology, Florida InternationalUniversity, Miami (Musser);Functional Neuroimaging Laboratory,Brigham and Women’s Hospital,Harvard Medical School, Boston,Massachusetts (Iyer).

Corresponding Author: Sarah L.Karalunas, PhD, Division ofPsychology, Department ofPsychiatry, Oregon Health andScience University, 3181 SW SamJackson Park Rd, Portland, OR 97239([email protected]).

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P sychiatric nosology remains exclusively syndromic, de-pending on clusters of signs and symptoms rather thanbiologically based measures. This dependence intro-

duces fundamentally confounding factors limiting better un-derstanding of mental illness.1-4 For these reasons, the Na-tional Institute of Mental Health, under the Research DomainCriteria (RDoC) initiative,2 has emphasized broad neurobio-logically based dimensions (eg, positive/negative valence, cog-nitive control) to refine existing nosologic criteria. The long-term goal for this initiative, and the field in general, is toimprove neurobiological validity, clinical prediction, and treat-ment matching.2 Although it is clear that eventually the no-sologic criteria will need to be revised, a tractable approach toadvance the current classification system is not agreed upon.For example, no study of alternative nosologic criteria has dem-onstrated both biological validity and clinical prediction withinthe same sample.

We address this problem for the case of attention-deficit/hyperactivity disorder (ADHD). Like other psychiatric catego-ries, ADHD is suspected of identifying children with diverseetiologies5,6 but DSM-IV subtypes (and thus DSM-5 presenta-tions) fail to elucidate clear differences in pathophysiology orclinical course, in part because of their own instability overtime.5,7-9 To better parse heterogeneity some experts have ad-vised looking beyond existing symptom lists toward pheno-typic measures that can be represented dimensionally andhave well-theorized relationships with neurobiologicalsystems,2,10-12 which is the core of the RDoC approach. At thesame time, phenotype measures that retain clinical applica-bility are desirable. Attention-deficit/hyperactivity disorder isa good starting point for achieving RDoC aims because chil-dren with the disorder can be characterized in terms of sev-eral features that are best represented as dimensions and havewell-theorized relationships to biological systems.

How are novel types to be empirically identified and vali-dated? Clustering algorithms can be expected to yield vary-ing results depending on the algorithm used and featuresselected as input13 and therefore are, in themselves,exploratory14; the main question concerns whether clusters areclinically useful. Our key considerations were (1) input fea-tures, (2) choice of clustering method, and (3) multiple exter-nal validators.

Phenotypic FeaturesFor the features, we used a well-accepted, low-cost, parent-rated measure of child temperament. Temperament traits canbe described as a set of biologically based behavioral and emo-tional tendencies. Although not the only approach available,these traits extend beyond single-disorder clinical symptomsto expand coverage of negative and positive emotion sys-tems, as well as attentional capacities.15-17 These in turn are di-rectly related to the RDoC domains of negative valence sys-tems, positive valence systems/approach motivation, andcognitive control, respectively. These traits also are related to,although not identical with, personality traits in adults and areassessed relatively easily via parent-report questionnaires,which can be easily translated into clinical applications. Tem-perament traits also have distinct neurobiological correlates

with a substantial amount of literature on humans andanimals.18-20 In humans, temperament domains are differen-tially related to peripheral nervous system indicators,21-25 aswell as to specific neural circuitry (eg, amygdala, hippocam-pus, insula, striatum, anterior cingulate cortex, and prefron-tal cortex).26,27 The amygdala, in particular, plays a key role inemotion and motivation and so is one important brain corre-late of temperament domains.17,28,29

Clustering MethodsIn the present report we follow up on earlier work10 that useda clustering method known as community detection, whichstems from the mathematical discipline of graph theory.30

Graph theory concerns the study of networks, with networksbeing sets of nodes or vertices joined in pairs by lines or edges.Community structure in networks refers to the existence ofdensely connected groups of nodes, with only sparse connec-tions between the groups. In our case, individuals with ADHDwho share similar temperament traits may cluster to form spe-cific subtypes of the disorder. Community detection is a widelyused optimization clustering method. As a clustering ap-proach community detection has the attractive features ofbeing data driven (in that the number of communities does nothave to be prespecified in the model) and provides a quanti-tative measure of group robustness to chance variations in thedata structure.

External ValidationFor external validation, we considered peripheral nervous sys-tem indicators of emotional regulation and brain connectiv-ity of the amygdala. We also examined cross-time stability ofour novel groupings and clinical usefulness–based concur-rent and longitudinal clinical outcomes.

MethodsParticipants and Diagnostic ProceduresData are reported for 437 children between the ages of 7 and11 years: 190 individuals (46.3% male) served as the controlgroup, and 247 (71.6% male) were in the ADHD group. Partici-pants were drawn from the Oregon ADHD Program, a child co-hort for which the community-based recruitment and enroll-ment procedures and multi-informant assessment proceduresfor ADHD diagnosis are published elsewhere31-33 and de-scribed in the eAppendix in the Supplement. Sample charac-teristics for background on the ADHD and control groups arepresented in eTable 1 in the Supplement. Ethics approval wasobtained from the institutional review board at Oregon Healthand Science University. A parent or legal guardian providedwritten informed consent, and children provided written as-sent. Participants received financial compensation.

Temperament RatingsA parent or legal guardian completed the widely used Tem-perament in Middle Childhood Questionnaire (TMCQ).34 Itemsform 16 subscales and 3 higher-order factors that are hypoth-esized to be influenced by underlying neurobehavioral sys-

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tems. A list of scales and reliabilities in the current sample(which were all satisfactory) are provided in the eAppendix inthe Supplement.

Community Detection AnalysesCommunity detection30,35 is an optimization clusteringmethod. As used here, a matrix of child-by-child correlationswas created based on the 16 TMCQ subscales, standardizedto the sample mean and SD. Community detection wasapplied to the child matrix using Rubinov and Sporns’35

weight-conserving modularity algorithm.36 This algorithmstarts with each node (ie, child) being the sole member of acommunity and iteratively produces partitions whose con-junction produces the largest increase in the quality index(Q) until Q reaches a maximum. In our case, Q is a weightedcombination of the modularity for positive relationships (Q+)and negative relationships (Q−) introduced by Rubinov andSporns.35 We provide the formal definition in the eAppendixin the Supplement. Conceptually, Q represents the overallsegregation between identified communities, with highervalues indicating stronger separation of communities. Thisand other modularity algorithms are not deterministic, andthus can yield slightly different solutions on different runs.To generate a single, stable depiction of structure, finalgroup assignments for each child were based on the modalgroup assignment across 10 runs of the modularity algo-rithm. A secondary validation method (variation of informa-tion algorithm37) was also used to evaluate internal validity(group robustness to chance data variation).

External ValidationPhysiological RecordingPhysiological response data were available for 178 childrenwith ADHD and 128 control children. Temperament traits areconceptually related to positive and negative emotiondomains; therefore, recording was completed during anemotion induction and suppression task with positive andnegative emotion conditions, as well as during an emotion-ally neutral baseline condition, exactly as described in priorpublications.31 Respiratory sinus arrhythmia (RSA), a mea-sure of heart rate variability strongly influenced by parasym-pathetic regulation and hypothesized to be related to emo-tion regulation,38 was derived from the electrocardiogramsignal (MindWare Heart Rate Variability software, version2.6)39 and was quantified as the high-frequency component(>0.15 Hz) of the R-R peak time series (time between R spikesin the electrocardiogram in 60-second epochs). Standardmethods for artifact detection were followed as published inprior work40 and are also provided in the eAppendix in theSupplement.

The cardiac pre-ejection period (PEP) was derived fromelectrocardiogram and impedance cardiography in 60-second epochs (MindWare, version 2.6).41 It was calculated asthe time interval in milliseconds from the onset of the Q-wave to the B-point of the derived impedance signal (dZ/dt)using published methods.42 Pre-ejection period reactivity isa generally agreed upon measure of sympathetic influence onthe heart; the interpretation of tonic PEP is more complex.43,44

Neural FunctioningResting-state functional connectivity magnetic resonanceimaging (MRI) data (420-600 seconds) are reported for 39 chil-dren with ADHD and 15 typically developing children. Partici-pants were scanned using a 3.0-T scanner (Magnetom Trio Timsystem; Siemens Healthcare) with a 12-channel head-coil usingpreviously published methods.45,46

Functional images were processed to reduce artifacts andconnectivity was processed following published methods47 re-produced in the eAppendix in the Supplement. Motion, aknown confound in functional MRI studies, was corrected andquantified several ways45,48 (details provided in the eAppen-dix in the Supplement).

As noted above, one major candidate for neural involve-ment in temperament is the amygdala, so it was chosen as oursingle seed region for this demonstration analysis. Left andright amygdala regions of interest were obtained for partici-pants using FMRIB’s Integrated Registration and Segmenta-tion Tool, distributed with the FMRIB Software Library.49 Theregions of interest were created in the Montreal NeurologicalInstitute atlas space and then converted to Talairach atlas space.All subsequent operations were performed on the Talairach at-las-transformed regions of interest.

For all participants, the resting blood oxygen level–dependent time series for each region of interest was corre-lated with all other voxels in the brain, generating voxelwisefunctional connectivity maps. We performed 2-sample, 2-tailedt tests on all potential connections (Fisher transformed r val-ues) between community-defined subgroups (assuming un-equal variance; P ≤ .05), as well as between each community-defined subgroup and controls. One-sample tests were run forwithin-group connectivity maps. To account for multiple com-parisons, thresholding based on Monte Carlo simulation wasimplemented.50 To obtain multiple comparisons–correctedvoxel clusters (P < .05), a threshold of 53 contiguous voxels witha z value greater than 2.25 was used.

Longitudinal Follow-upOne-year longitudinal follow-up data on clinical featuresand TMCQ ratings are reported for 101 children with ADHD.The longitudinal study is ongoing, and the sample of 101children is 98% of those due for follow-up at the time ofdata analysis.

ResultsCommunity Detection AnalysesSubgrouping was done in a multistep fashion using commu-nity detection. At the first step, including all 437 children inthe sample, community detection identified 3 profiles of chil-dren (Q = 0.55) with sizes of 192, 181, and 64. Profile 1 in-cluded 82% of the control children and 13% of the ADHD chil-dren. Profiles 2 and 3 combined included 87% of the ADHDchildren and 18% of the non-ADHD control children. Separa-tion of the ADHD and non-ADHD samples was not attribut-able to sample differences in age or sex (see the eAppendix inthe Supplement). We concluded that our initial clustering

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analysis recovered a primarily control profile and 2 ADHD pro-files. Thus, at the second step, the analysis was conducted onthe ADHD and control samples separately with the aim of bet-ter characterizing variation within those 2 populations. Re-sults from the control participants revealed weakly divided,marginal subgroups and therefore were not subjected to fur-ther validation (see the eAppendix in the Supplement).

Community detection using the 247 children with ADHDidentified 3 profiles (referred to herein as types) as the modalsolution with type 1 including 64 (25.9%); type 2, 85 (34.4%);and type 3, 98 (39.7%) children based on modal type assign-ments. The quality quotient suggested strong separation ofthe groups (Q = 0.50). Variation of information algorithmanalyses indicated that the types were robust to modest ran-dom perturbations in the data (see the eFigure in the Supple-ment). Types reproduced with similar quality scores after arandom split replication of the ADHD population (Q = 0.46and Q = 0.50).

Clinically, type 1 children were more impulsive and inat-tentive (all P < .01) than controls, as well as less affiliative (allP < .05), but otherwise did not differ significantly from con-trols on any temperament domain. Type 1 children hadmilder impulsivity, inhibition, and attentional dyscontrolcompared with the other 2 ADHD types (all P < .001). Welabeled type 1 as mild ADHD. Type 2 children had moresevere impulsivity compared with type 1 children, and alsodemonstrated less shyness and more assertiveness/dominance, high-intensity pleasure seeking, and activitylevel than either of the other 2 types or the controls (allP < .001); we labeled this type surgent ADHD. Type 3 chil-dren, in addition to greater impulsivity and attentional dys-control than those categorized as having the mild type ofADHD, also had more negative emotionality than the other 2types or the controls (all P < .001). This included higher lev-els of anger, discomfort, fear, and sadness, and lower levelsof soothability (all P < .001), suggesting that this is a group ofchildren prone to anger, tantrums, and irritable behavior asdescribed in recent literature.51 Type 3 was thus labeled irri-table ADHD. Figure 1 provides a spring-embedding graph fordepiction of the degree of group separation in terms of thedistance between the participants. Figure 2 depicts the pro-file for each group on the 16 temperament scales.

The DSM-5–based presentations of ADHD were split acrossthe types suggesting the temperament types did not reduceto DSM presentations. Additional clinical and demographic de-scription can be found in eTable 2 in the Supplement.

External Validity I: Peripheral PhysiologyPhysiological measurements of peripheral nervous system re-sponse were recorded for 53 mild, 61 surgent, and 64 irritablechildren with ADHD and 128 controls. Group differences wereexamined in 2(valence: positive/negative) × 2(condition: in-duction/suppression) × 4(group) repeated-measures analy-ses of variance with sex covaried.52 The analysis of variancematrix was decomposed following procedures recom-mended by Keppel53 with embedded correction for type I er-ror using a Fisherian decomposition strategy and a least sig-nificant difference test for simple comparisons.

Pre-Ejection PeriodThere were baseline differences in PEP between groups(F1,305 = 2.73; P = .044; η2 = 0.03). Post hoc tests indicated thatthe surgent group had significantly shorter PEP values than didtypically developing children or the other ADHD types. Simi-larly, tonic PEP scores in each of the 4 emotion task condi-tions were compared. Only the group main effect was signifi-cant (F3,303 = 3.95; P = .009; η2 = 0.05). Post hoc tests againconfirmed that the surgent type had shorter PEP values thandid the typically developing controls, the irritable ADHD type,and the mild ADHD type. None of the groups showed signifi-cant change in PEP from baseline to task (ie, PEP reactivityscores did not differ significantly from zero; all P > .05), andthere were no significant main or interaction effects when PEPreactivity scores were examined for the emotion task (allP > .05). These results are summarized in Figure 3 and in eTable3 in the Supplement.

Respiratory Sinus ArrhythmiaThe RSA change from baseline to task was assessed, with posi-tive scores (increases) interpreted as increased parasympa-thetic activity and negative scores (decreases) as reduced para-sympathetic activity.54 A main effect of group (F3,303 = 3.97;P = .047; η2 = 0.03) was qualified by a 3-way interaction of va-lence × condition × group (F3,303 = 4.09; P = .018; η2 = 0.03). Thefollow-up simple comparisons revealed that the irritable type

Figure 1. Spring-Embedded Visualization of Temperament Groups

Graphical representation of the community detection results in theattention-deficit/hyperactivity disorder (ADHD) sample, which shows manystrong correlations among individuals in the same temperament type and fewerbetween-type connections. Nodes represent individuals in each temperamentgroup and connecting edges indicate correlations between individuals. Blueindicates the mild; red, surgent; and green, irritable types of ADHD. Correlationsare thresholded at .50 for purposes of visual representation.






group had a smaller increase in RSA compared with the con-trol and mild type groups during the 2 negative emotion con-ditions (all P < .05).

The surgent type group differed from the other 3 groupsduring the negative suppression condition (P = .04;η2 = 0.04) because they showed no significant change in RSA

in this condition from baseline. In the positive emotion con-ditions, the surgent group also differed significantly fromthe other ADHD types and non-ADHD controls (P = .01;η2 = 0.05). Where other ADHD types and typically develop-ing children demonstrated increased RSA, the surgent grouphad decreased RSA (P = .01; η2 = 0.05), consistent with with-drawal of parasympathetic activity (see Figure 2 and eTable 3in the Supplement).

Thus, data suggest that children with the surgent and ir-ritable types of ADHD have distinct peripheral physiologicalprofiles compared with each other and with typically devel-oping children. Children with the mild type, in contrast, showeda physiological profile similar to that of typically developingchildren.

External Validity II: Neural ConnectivityWe examined functional connectivity of the amygdala for 18mild, 11 surgent, and 10 irritable type children with ADHD and15 controls. Findings related to each group were distributedthroughout the cortex. We highlight a few of these differ-ences here and they are reported fully in the Table and shownin Figure 4.

The irritable type group displayed weaker connectivity(in the form of reduced negative relationships) from theamygdala to the anterior insula relative to all other groups

Figure 2. Temperament Type Profiles

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Group 3, irritable (n = 98)Group 2, surgent (n = 85)

–1.00Anger Soothability Fear Sadness

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Negative emotionC

Temperament in Middle Childhood Questionnaire scores for each of the 3temperament types identified in the attention-deficit/hyperactivity disordersample: cognitive control (A), surgency domains (B), and negative emotiondomains (C). Scores are shown as z scores relative to the control sample mean(0 on the y-axis is the mean of the typically developing sample). Limit linesindicate SEs. Scores were reversed for some scales as follows: for inhibition,high scores indicate less inhibitory control; for attentional focus, high scoresmean poorer focus; for shyness, high scores mean less shy; for soothability, highscores indicate less soothability. Three scales that only weakly differentiategroups (openness, perceptual sensitivity, and low-intensity pleasure) are notshown, but scores for these scales are reported in eTable 2 in the Supplement.HIP indicates high-intensity pleasure.

Figure 3. Cardiac Physiological Response

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Raw pre-ejection period (PEP) (A) and mean respiratory sinus arrhythmia (RSA)change from baseline (B) for each of the emotion task epochs: negativeinduction (NI), negative suppression (NS), positive induction (PI), and positivesuppression (PS), shown by temperament type. Limit lines indicate SE.






Table. ROI Table for Between-Group Comparisons

ROI Comparison

ROI Coordinatesa

Effectx y z

Surgent vs irritable type

Anterior insula −36 21 5 Surgent < irritable

−44 15 4 Surgent < irritable

36 17 −4 Surgent < irritable

36 18 6 Surgent < irritable

Posterior insula −36 −24 −11 Surgent > irritable

Midcingulate/posterior cingulate 7 −38 59 Surgent > irritable

−8 −42 65 Surgent > irritable

Temporal lobe

Anterior/superior −58 3 4 Surgent > irritable

47 15 −9 Surgent > irritable

Inferior −40 −9 −35 Surgent > irritable

Medial −41 −10 −20 Surgent > irritable

Superior 61 −53 8 Irritable > surgent

Occipital lobe 8 −85 32 Irritable < surgent

13 −84 20 Irritable < surgent

Parahippocampus −33 −20 −24 Surgent > irritable

Parietal lobe −20 −36 56 Surgent > irritable

Mild vs irritable type

Anterior insula −29 20 8 Mild < irritable

−37 14 7 Mild < irritable

Temporal lobe, inferior −65 −20 −16 Mild > irritable

Occipital lobe −1 −90 8 Irritable < mild

13 −87 13 Irritable < mild

Mild vs surgent type

Dorsal medial frontal 1 −22 56 Surgent < mild

Midcingulate/posterior cingulate 8 −40 56 Surgent > mild

−10 −44 59 Mild < surgent

Precuneus/posterior cingulate 15 −54 50 Mild < surgent

Ventral medial prefrontal cortex 2 34 6 Surgent < mild

Control vs mild type

Occipital lobe −3 −92 0 Control < mild

−19 −81 −5 Control < mild


−18 −99 −4 Control > mild

21 −82 5 Control < mild

26 −94 −3 Control < mild

−24 −100 5 Control < mild

Lateral frontal 49 21 28 Control > mild

Precuneus/posterior cingulate 4 −52 52 Control < mild


Inferior parietal lobe −55 −30 39 Control < mild


Anterior insula −22 −7 18 Control < mild

Cerebellum −22 −79 −28 Control > mild


Caudate 19 −17 22 Control < mild

(continued)






(including non-ADHD controls) and weaker connectivity tothe posterior cingulate (a key hub in the default network)relative to typically developing and surgent type children.The mild type showed the reduced connectivity profile ofthe posterior cingulate/precuneus vs the typically develop-ing and surgent type groups. The surgent type evidencedincreased connectivity from the amygdala to the cingulate/precuneus compared with the mild and irritable type groups,but was similar relative to control population. All groups haddifferential patterns of connectivity to unique dorsolateralprefrontal cortex regions compared with at least one othergroup (Table). Overall, each ADHD type had a distinct pat-tern of central nervous system connectivity.

External Validity III: Longitudinal Stabilityand Clinical OutcomeCommunity detection was repeated using time 2 data. Again,the modal solution suggested 3 ADHD profiles (Q = 0.51), whichcould be characterized as mild (n = 18), surgent (n = 42), andirritable (n = 41).

Multinomial logistic regression55 indicated that tempera-ment types were more stable than expected by chance(χ2

4 = 71.84; P < .001). In both the surgent and irritable types,more than 70% of the children were assigned to the same typeat follow-up. In contrast, the mild type often changed profile:at the time 2 evaluation, 43.6% of the children remained in themild type, 41.0% were assigned to the surgent type, and 15.4%were assigned to the irritable type (see the Video for depic-tion of stability of types over time).

With regard to clinical outcomes, children in the irritabletype at time 1 were more likely than those in the other 2 ADHDtypes to develop a new comorbid disorder over the course oflongitudinal follow-up (χ2

2 = 10.11; P = .006), with more thandouble the rates of onset compared with either of the othertypes or the non-ADHD control participants (11.1%, 18.2%,11.1%, and 39.5% in controls, mild type, surgent type, and ir-ritable type, respectively). This prediction was superior to othernosologic designations at baseline that may be related to prog-nosis: neither DSM-5 presentations nor groups based on thepresence or absence of oppositional defiant disorder symp-toms (ie, 0 symptoms vs 1+ symptoms) differed in their ratesof new comorbid disorder onsets at time 2 (χ2

1 = 0.68; P = .41and χ2

1 = 2.54, P = .11, respectively).

Furthermore, when temperament types, DSM-5 presen-tations, and oppositional defiant disorder symptom status wereentered into a logistic regression model, the temperament typeswere the best predictor of comorbidity onsets (P = .019; oddsratio, 2.46). Neither the presence of time 1 oppositional defi-ant disorder symptoms (P = .56) nor ADHD DSM-5 presenta-tion (P = .11) uniquely predicted new onsets at time 2. Futurework can examine other clinical designations that were lesscommon in the sample.

DiscussionIn the present study, we demonstrated an approach to revis-ing psychiatric nosologic criteria that (1) goes beyond clinicalsymptoms, in this case choosing temperament measuresthat are closely related to RDoC domains; (2) uses a novel,discovery-based clustering algorithm; (3) demonstratesexternal validation concurrently using peripheral and cen-tral physiology; and (4) shows superior clinical prediction vsexisting clinical categories related to ADHD or ADHD withcomorbidity. The results of the study suggest that revisingthe nosologic criteria in the case of ADHD is tractable andwill be biologically meaningful. The present results add toprior research on schizophrenia,56 suggesting that the use ofbiologically influenced indicators, optimization-based clus-tering techniques, and comparison of groups on externalvalidators is an informative approach for understandingheterogeneity in psychiatric disorders.

We identified 3 distinct types of ADHD based on tempera-ment profiles: (1) a mild type characterized only by deficits incore ADHD symptom domains; (2) a surgent type character-ized by high levels of positive approach–motivated behaviorsand activity level, shorter PEP, parasympathetic withdrawalin response to positive emotions, and atypical amygdala con-nectivity to medial frontal areas; and (3) an irritable type char-acterized by high levels of negative emotionality, weak para-sympathetic response to negative emotional stimuli, reducedamygdala-insula connectivity, and a doubling of risk for on-set of new behavioral or emotional disorders.

Crucially, we considered multiple validators, choosing a sub-set from the classic validators suggested by Robins and Guze57

and Cantwell58 including biological correlates and clinical

Table. ROI Table for Between-Group Comparisons (continued)

ROI Comparison

ROI Coordinatesa

Effectx y z

Control vs surgent

Medial frontal −18 53 10 Control < surgent

−30 54 1 Control < surgent

Cerebellum 41 −81 −34 Control > surgent

Control vs irritable

Anterior insula −43 14 4 Control > irritable

−35 22 3 Control > irritable

Precuneus −1 −69 24 Control > irritable

Medial frontal −33 44 27 Control > irritable

Abbreviation: ROI, region of interest.a The ROIs that differed between

groups.






course. We found promising results suggesting that these typesoutperform the existing nosologic criteria of ADHD. In the pres-ent study, the surgent and irritable types survived every test ofvalidation, including distinct physiology and functional con-

nectivity, stability over time, and better clinical prediction thanDSM presentations or the most common comorbid grouping, op-positional defiant disorder. Thus, they emerge as promising,novel types of ADHD categorization that support the promisein this approach generally and warrant further validation.

Although children with mild type ADHD showed distinctpatterns of functional connectivity compared with children withother types, they showed normative peripheral physiology andassignment to this type was not particularly stable over time.Thus, the mild type was not as well validated as the others were.From the perspective of Wakefield et al’s59 definition of men-tal disorders as including a psychological or physiological dys-function, these youth did not as clearly have a disorder, eventhough they just as clearly met DSM-5 ADHD criteria.

Cardiac physiology measures suggest that the neurobiol-ogy of the irritable type of ADHD is related to weak parasym-pathetic response to negative emotionality. This pattern is con-sistent with high levels of negative valence emotions and lowsoothability captured in the temperament profile, as well as withhigh rates of comorbidity in this group. In contrast, the neuro-biology of the surgent type is related to parasympathetic with-drawal to positive valence emotionality, which is potentially con-sistent with high sensation-seeking and approach-motivatedbehaviors in this group. Although no significant differences wereseen in PEP reactivity, the surgent type had shortened tonic PEPscores. Tonic PEP may be influenced by many factors, includ-ing sympathetic nervous system activity, preload, afterload, pe-ripheral resistance, and biomechanical processes. It will be in-teresting in future studies to examine whether tonic PEPdifferences are attributable to abnormal sympathetic arousal,owing to its historical interest in externalizing disorders, orwhether tonic PEP differences reflect other factors.

The irritable type ADHD group, which had abnormal para-sympathetic response to negative emotions, also had promi-nent atypical connectivity between the amygdala and the an-terior insula. The imaging results were based on a smallsubsample of participants and should be considered prelimi-nary; however, the results are promising because the insula isa region that has been long known to be important for moni-toring and adjusting sympathetic and parasympathetic tone.More recently, the insula has also been shown60,61 to be heav-ily involved in higher order control processing. Together, thesedata may highlight the region’s importance for emotional regu-lation and impulse control.

Several of the neural regions (eg, dorsolateral prefrontalcortex, anterior insula) differentiating the ADHD tempera-ment types are also implicated in the executive control ofattention.62 Thus, our results, although preliminary becauseof the small sample size, are consistent with those of Pe-tersen and Posner’s62 recent elaboration of their attentionmodel suggesting a close relationship between the anatomiccorrelates of executive attention and the broader constructsof behavioral and emotional self-regulation.62-64 Examina-tion of the cognitive profiles of each temperament-based group,as well as the relationships between attention and self-regulation models,65 are important areas for future research.

Perhaps most encouraging in the present study was thatour new temperament types of ADHD predicted clinical out-

Figure 4. Resting-State Functional Connectivity Data MagneticResonance Imaging Conjunction Maps

No.0.0 3.0

No.0.0 3.0

No.0.0 3.0

Mild typeA

Surgent typeB

Irritable typeC

Amygdala connectivity maps for each temperament type were directlycompared with the 2 other types and a matched control population. Resultsfrom each of these comparisons are provided in the Table. The figure is aconjunction map for the comparisons. For each comparison (eg, mild vssurgent, mild vs irritable), a voxel was coded as either 0 (not significantlydifferent between groups) or 1 (significantly different between groups). Mapswere summed such that voxels that never differ between groups have a value ofNo. = 0.0 and voxels that differ in all comparisons for that group have a value ofNo. = 3.0. A, Mild type differed from other types in areas in the posteriorcingulate/precuneus (black arrow). B, As with the mild type, the surgent typeshowed areas in the posterior cingulate and precuneus (black arrow) where itwas distinct from at least 2 other groups. C, The irritable type was distinct fromthe mild, surgent, and control populations in the anterior insula (blackarrows)—a region important for emotional regulation and task level control. No.indicates the number of comparisons in which the voxel significantly differedbetween groups. The colors range from 0.0 (never different) to 3.0 (different inall comparisons).






comes. In this case, we focused on the onset of new comorbidpsychiatric disorders at the 1 year follow-up as an index of clini-cal deterioration. The new categories provided improved pre-diction vs existing clinical indicators.

With regard to implications for ADHD, these findings arebroadly consistent with, but suggest some modifications to,prior theory of ADHD temperament variation that inspired thiswork,66,67 while being mostly consistent with and building onprevious work68 exploring temperament subgroups in ADHDin a different sample. Although the subgroups found in the pre-sent study were largely stable over time, future work will needto address issues related to family history, genetics, replica-bility in other samples, and treatment response.57,58

In addition, despite well-described limitations of parental-report measures,69 the results that we have reported suggest thatparent-reported temperament characteristics (at least as mea-sured in the present study) may be related to important indi-vidual differences in underlying physiology, suggesting somevalidity and clinical applicability for these measures. Tempera-ment traits have been investigated67 in many other disorders,and further exploration of temperament types in other DSM-defined disorders may be informative for understanding sharedand unique risk profiles and revising psychiatric nosologic cri-teria. Similarly, detailed comparison and integration of resultsobtained using alternative clustering algorithms or based on dif-ferent input features will be important.

In terms of the proposed RDoC research framework,parents’ reports on the TMCQ broadly capture RDoCdomains of negative valence systems, positive valencesystems/approach motivation, and cognitive control. Addi-tional studies examining subconstructs within thesedomains (eg, frustrative nonreward, reward valuation, orresponse selection and inhibition, respectively) may furtherrefine the picture provided in the present study. In addition,we focused on relationships between parent-report mea-sures, reflecting the self-report unit of analysis in the RDoCmatrix, and peripheral and central nervous system indica-tors, reflecting the physiology and circuit units of analysis,respectively. Additional work examining relationships withother units of analysis, such as cells or genes, will also beinformative.

ConclusionsOverall, progress toward neurobiologically based nosologic cri-teria, as envisioned in the National Institute of Mental HealthRDoC initiative,2 is tractable. In the illustration provided in thepresent study, temperament profiles in children with ADHDcan be related to meaningful differences in both the central andperipheral nervous systems and the longitudinal course, set-ting the stage for improving nosology.

ARTICLE INFORMATION

Submitted for Publication: September 23, 2013;final revision received March 5, 2014; acceptedMarch 6, 2014.

Published Online: July 9, 2014.doi:10.1001/jamapsychiatry.2014.763.

Author Contributions: Drs Fair and Nigg had fullaccess to all the data in the study and takeresponsibility for the integrity of the data and theaccuracy of the data analysis.Study concept and design: Karalunas, Fair, Nigg.Acquisition, analysis, or interpretation of data: Allauthors.Drafting of the manuscript: Karalunas, Fair, Musser.Critical revision of the manuscript for importantintellectual content: All authors.Statistical analysis: Karalunas, Fair, Musser, Aykes.Obtained funding: Karalunas, Fair, Nigg.Administrative, technical, or material support:Karalunas, Fair, Iyer.Study supervision: Nigg.

Conflict of Interest Disclosures: None reported.

Funding/Support: Funding for the study wasprovided by National Institute of Mental Healthgrants R01 MH059105 (Dr Nigg), R01 MH086654(Dr Nigg), R01 MH096773 (Dr Fair), R00MH091238 (Dr Fair), and F32 MH098632 (DrKaralunas), and ADHD Research Study UHN80R1.

Role of the Sponsor: The sponsors had no role inthe design and conduct of the study; collection,management, analysis, and interpretation of thedata; preparation, review, or approval of themanuscript; and decision to submit the manuscriptfor publication.

Additional Contributions: Colleen Schmitt, MS,and Hilary Galloway-Long, BA (Oregon Health and

Science University), helped with data and projectmanagement. No financial compensation wasprovided.

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