Research Domain Criteria

RDoC
RDoC
Research Domain Criteria
Project type	Research framework
Sponsors	National Institute of Mental Health
Project coordinator	Bruce Cuthbert[1]
Duration	January 28, 2010[2] –
Website	NIMH.NIH.gov/Research-priorities/rdoc

The Research Domain Criteria (RDoC) project is an initiative of personalized medicine in psychiatry developed by US National Institute of Mental Health (NIMH). In contrast to the Diagnostic and Statistical Manual of Mental Disorders (DSM) maintained by the American Psychiatric Association (APA), RDoC aims to address the heterogeneity in the current nosology by providing a biologically-based, rather than symptom-based, framework for understanding mental disorders.[3] "RDoC is an attempt to create a new kind of taxonomy for mental disorders by bringing the power of modern research approaches in genetics, neuroscience, and behavioral science to the problem of mental illness."[4]

Call for creation

The National Institute of Mental Health oversees the RDoC initiative.

The 2008 NIMH Strategic Plan calls for NIMH to "Develop, for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures."[5] The strategic plan continues:

Currently, the diagnosis of mental disorders is based on clinical observation—identifying symptoms that tend to cluster together, determining when the symptoms appear, and determining whether the symptoms resolve, recur, or become chronic. However, the way that mental disorders are defined in the present diagnostic system does not incorporate current information from integrative neuroscience research, and thus is not optimal for making scientific gains through neuroscience approaches. It is difficult to deconstruct clusters of complex behaviors and attempt to link these to underlying neurobiological systems. Many mental disorders may be considered as falling along multiple dimensions (e.g., cognition, mood, social interactions), with traits that exist on a continuum ranging from normal to extreme. Co-occurrence of multiple mental disorders might reflect different patterns of symptoms that result from shared risk factors and perhaps the same underlying disease processes.

To clarify the underlying causes of mental disorders, it will be necessary to define, measure, and link basic biological and behavioral components of normal and abnormal functioning. This effort will require integration of genetic, neuroscience, imaging, behavioral, and clinical studies. By linking basic biological and behavioral components, it will become possible to construct valid, reliable phenotypes (measurable traits or characteristics) for mental disorders. This will help us elucidate the causes of the disorder, while clarifying the boundaries and overlap between mental disorders. In order to understand mental disorders in terms of dimensions and/or components of neurobiology and behaviors, it will be important to:

Initiate a process for bringing together experts in clinical and basic sciences to jointly identify the fundamental behavioral components that may span multiple disorders (e.g., executive functioning, affect regulation, person perception) and that are more amenable to neuroscience approaches.

Develop reliable and valid measures of these fundamental components of mental disorders for use in basic studies and in more clinical settings.

Determine the full range of variation, from normal to abnormal, among the fundamental components to improve understanding of what is typical versus pathological.

Integrate the fundamental genetic, neurobiological, behavioral, environmental, and experiential components that comprise these mental disorders.[5]

Contrast with DSM

On April 29, 2013, a few weeks before the publication of the DSM-5, NIMH director Thomas Insel published a blog post critical of the DSM methodology and highlighting the improvement offered by the RDoC project.[6]

Wrote Insel:

While DSM has been described as a 'Bible' for the field, it is, at best, a dictionary, creating a set of labels and defining each. The strength of each of the editions of DSM has been "reliability" – each edition has ensured that clinicians use the same terms in the same ways. The weakness is its lack of validity. Unlike our definitions of ischemic heart disease, lymphoma, or AIDS, the DSM diagnoses are based on a consensus about clusters of clinical symptoms, not any objective laboratory measure.[6]

In that post, Insel wrote: "Patients with mental disorders deserve better."[6] He would later elaborate on this point, saying "I look at the data and I'm concerned. ... I don't see a reduction in the rate of suicide or prevalence of mental illness or any measure of morbidity. I see it in other areas of medicine and I don't see it for mental illness. That was the basis for my comment that people with mental illness deserve better."[7]

In their effort to resolve their issues with the new DSM, the NIMH launched the Research Domain Criteria Project (RDoC), based on four assumptions:

A diagnostic approach based on the biology as well as the symptoms must not be constrained by the current DSM categories,

Mental disorders are biological disorders involving brain circuits that implicate specific domains of cognition, emotion, or behavior,

Each level of analysis needs to be understood across a dimension of function,

Mapping the cognitive, circuit, and genetic aspects of mental disorders will yield new and better targets for treatment.[6]

Insel stressed that the RDoC is not designed as diagnostic criteria to replace the DSM, but rather as a research framework, for future development. His argument centers around the claim that, "symptom-based diagnosis, once common in other areas of medicine, has been largely replaced in the past half century as we have understood that symptoms alone rarely indicate the best choice of treatment."[6] As a result of this position, the NIMH is no longer using the DSM as the criteria upon which they will evaluate funding of future clinic trials.[6]

DSM researcher Eric Hollander was quoted as saying "I do think it does represent a lack of interest and faith on behalf of NIMH for the DSM process and an investment in alternative diagnostic systems."[7]

A NIMH description of RDoC explained:

Currently, diagnosis in mental disorders is based on clinical observation and patients' phenomenological symptom reports ... However, in antedating contemporary neuroscience research, the current diagnostic system is not informed by recent breakthroughs in genetics; and molecular, cellular and systems neuroscience.[8]

RDoC matrix

The RDoC matrix is one way of organizing the concepts involved, with domains as tables, constructs as rows, sub-constructs as subrows and units of analysis often presented as columns.

Negative Valence Systems, as of January 2022[9]
Construct	Molecules	Cells	Neural Circuits	Physiology	Behavior	Self-reports	Paradigms
Acute Threat (Fear)	BDNF CCK Cortisol / Corticosterone CRF family Dopamine Endogenous cannabinoid FGF2 GABA Glutamate Neuropeptide S Neurosteroid NMDAR NPY OX Oxytocin Serotonin Vasopressin	GABAergic cells Glia Neuron Pyramidal cell	Amg basal aCeN lateral medial ACC dorsal rostral ANS Hi posterior anterior Hy ICMs Ins OFC PAG dorsal ventral Pons LC PFC vmPFC (il) dmPFC (pl) LPFC/Ins RPVM	BP EDA EMG facial Eye tracking Heart rate Pupillometry Breathing Response accuracy Startle context fear-potentiated	Analgesia (reduced pain perception) Early developmental approach Avoidance Facial expression Freezing Open field Inhibitory control Response time Risk assessment Social approach	Fear survey schedule SUDS	Behavioral approach test CO₂ challenge test Cold pressor test Fear conditioning Stranger tests Trier social stress test
Potential Threat (Anxiety)	Cortisol CRF family	Pituitary cells	BNST	ACTH Average cortisol levels Potentiated startle	n/a	Anxiety Sensitivity Index BIS Fear of Negative Evaluation Scale Intolerance of Uncertainty Scale LEDS	NPU Threat Task
Sustained Threat	ACTH CRF HPA axis hormones	Hi Microglia prefrontal	Attention network Dysregulation of Amg reactivity Dysregulation of cingulate reactivity Habit systems Striatum Caudate Accumbens Hy nuclei PVT Vigilance network	Dysregulated HPA axis Error-related negativity	Anhedonia/decreased appetitive behavior Anxious arousal Attentional bias to threat Avoidance Decreased libido Helplessness behavior Increased conflict detection Increased perseverative behavior Memory retrieval deficits Punishment sensitivity	Childhood Trauma Questionnaire LEDS Risky Families STRAIN TESI Youth Life Stress Interview	n/a
Loss	Androgen CRH Estrogen Glucocorticoid receptor Inflammatory molecules Oxt ADH	n/a	Amg Default mode network DLPFC Habit systems Striatum Caudate Accumbens Hi Ins OFC Parietal cortex PCC PVN Reward circuitry vmPFC	ANS HPA neuroimmune Prolonged psychophysiological reactivity	Amotivation Anhedonia Attentional bias to negative valenced information Crying Executive function Guilt Increased self-focus Loss of drive Loss-relevant recall bias Morbid thoughts Psychomotor retardation Rumination Sadness Shame Withdrawal Worry	LEDS STRAIN	Sadness eliciting film clips
Frustrative Nonreward	Dopamine GABA Glutamate Serotonin Steroids ADH	n/a	Amg Hy LC OFC PAG PNS Septum Striatum	n/a	Relational and physical aggression	Frustrative Nonreward Responsiveness Subscale Questionnaire of Daily Frustrations	Lab-TAB PSAP

Positive Valence Systems, as of January 2022[9]
Construct / Subconstruct		Molecules	Cells	Neural Circuits	Physiology	Behavior	Self-reports	Paradigms
Reward Responsiveness	Reward Anticipation	n/a	n/a	n/a	n/a	n/a	n/a	Monetary incentive delay
	Initial Response to Reward	CREB Endocannabinoids FosB Glutamate Mu and delta opioid Orexin	n/a	Anterior Ins Dorsal ACC LH Medial OFC Nucleus accumbens Ventral pallidum Ventromedial PFC VTA	n/a	Taste reactivity	Consummatory subscale of TEPS PANAS (state version)	Simple guessing task
	Reward Satiation	n/a	n/a	n/a	n/a	n/a	n/a	Fixed-ratio satiation schedule
Reward Learning	Probabilistic and Reinforcement Learning	n/a	n/a	n/a	n/a	n/a	n/a	Drifting double bandit Classical conditioning Probabilistic reward task Probabilistic stimulus selection task
	Reward Prediction Error	Dopamine Serotonin	n/a	Amg BG Dorsal ACC Lateral habenula OFC Rostral medial tegmentum Substantia nigra/VTA Ventral striatum	Cortical slow waves Heart rate change Skin conductance	Goal tracking Pavlovian approach Reward-related speeding Sign tracking	Affective forecasting ASAM scale Eating Expectancy Inventory Generalized reward and punishment expectancy scale Self-report of craving TEPS anticipatory scale	Drifting Double Bandit Rutledge Passive Lottery Task
	Habit - PVS	Elements (Correlates) This table is incomplete; you can help by expanding it.
Reward Valuation	Reward (probability)
	Delay
	Effort

Cognitive Systems, as of January 2022[9]
Construct / Subconstruct		Molecules	Cells	Neural Circuits	Physiology	Behavior	Self-reports	Paradigms
Attention		Implementation GABA Glutamate Control ACh Dopamine Glutamate Histamine Serotonin	Parvalbumin-positive interneurons	Balance between TPN vs DMN Implementation local circuit interactions pulvinar TRN Control Amg Ascending / descending information pathways Attentional systems Basal forebrain limbic system Dorsal network Ventral network	fMRI Sensory areas from peripheral to central Auditory ERP N1 N2 Neural oscillations P1 P300 Processing negativity Visual ERP N2pc Negativity (SN) Neural oscillations P300 Selection modulations of sensory ERP components Slow waves Peripheral physiology Heart rate deceleration Pupil dilation	ANT task distractibility Attentional lapses vs sustained attention Distractibility Object/feature attention Psychophysics Spatial attention		Attentional blink and psychological refractory period paradigm Blocked channel-selection tasks Dichotic listening distraction paradigms (capture) Dual-task paradigms inter-modal selective attention spatial and non-spatial cuing paradigms time-series of response times to extract variability and frequency domain analyses visual search
Perception	Visual	ACH Catecholamines GABA Glutamate NMDA Peptides Serotonin	Magnocellular (non-linear gain control) Parvalbumin-positive interneurons Parvo Pyramidal cells	Subcortical Koniocellular Magnocellular Parvocellular Cortical Cortico-cortical connections into supra- and infra-granular layers Dorsal/ventral streams Non-retinogeniculate SC SCN Local circuitry Lateral interactions Top-down interactions Implicated in contextual and association fields (responsible for the influence of spatial context on target processing)	Adaptation/habituation BOLD activation of cortical regions ERP components Ncl Oscillations (scalp EEG, LFP, and single/multi-unit recording) ssVEP transient VEP	Discrimination, identification and localization Perceptual learning Perceptual priming Reading Stimulus detection Visual acuity	Perceptual anomalies of schizophrenia and depression	Scheme 1: Stages of Vision Early Vision Local computations Retinotopic representations Intermediate Vision Nonlocal properties of images Transformations beyond retinotopic representations Late Vision Representations of external objects Scheme 2: Commonly Used Research Paradigms Backward masking Biological motion processing Bistability Coherent motion Contour integration/interpolation Contrast sensitivity Cross modality paradigms Emotion expression identification Face identification Figure ground Lateral facilitation Multistability Object perception Object recognition/perceptual closure/perceptual organization Parallel/serial search Reading Vernier discrimination Visual illusion susceptibility Other Schemes Action-Perception loops Re-entrant processing
	Auditory	Elements (Correlates) This table is incomplete; you can help by expanding it.
	Olfactory / Somatosensory / Multimodal	n/a	n/a	n/a	n/a	n/a	n/a	Manipulation of ISI and/or intensity Smell identification
Declarative Memory		Elements (Correlates) This table is incomplete; you can help by expanding it.
Language
Cognitive Control	1 of 2: Goal Selection; Updating, Representation, and Maintenance
	2 of 2: Goal Selection; Updating, Representation, and Maintenance
	1 of 2: Response Selection; Inhibition / Suppression
	2 of 2: Response Selection; Inhibition / Suppression
	Performance Monitoring
Working Memory	Active Maintenance
	Flexible Updating
	Limited Capacity
	Interference Control	D1 D2 Dopamine GABA Glutamate	Calbindin Calretinin Distinct types of inhibitory neurons Parvalbumin	DLPFC PFC-parietal-cingulate-dorsal Th-dorsal striatum	Neural waves Delta Gamma Theta	n/a	n/a	Complex Span tasks Delayed match to non-sample Delayed match to sample Keep track task Letter memory/running memory Letter number sequencing n-back Self-Ordered Pointing Simple Span Tasks Sternberg Item Recognition

Social Processes, as of January 2022[9]
Construct / Subconstruct		Molecules
Affiliation and Attachment		Elements (Correlates) This table is incomplete; you can help by expanding it.
Social Communication	Reception of Facial Communication
	Production of Facial Communication
	Reception of Non-Facial Communication
	Production of Non-Facial Communication
Perception and Understanding of Self	Agency
Perception and Understanding of Self	Self-Knowledge
Perception and Understanding of Others	Animacy Perception
	Action Perception
	Understanding Mental States

Arousal and Regulatory Systems, as of January 2022[9]
Construct	Molecules	Cells	Neural Circuits	Physiology	Behavior	Self-reports	Paradigms
Arousal	ACh CRF Cytokine Dopamine GABA Ghrelin Glutamate Histamine OX Leptin NPY NE/NA Opioid Oxt Serotonin ADH	Basal forebrain nuclei aCeN Dorsal raphe Hy LH perifornical dorsomedial LDT LC PPT TMN Ventral tegmental area	Basal nuclei to cortical circuits Cholinergic & monoaminergic nuclei / projections aCeN to monoaminergic and basal forebrain cholinergic nuclei brainstem projections to basal forebrain nuclei projections to Th and cortical projections to midbrain and Pn reciprocoal projection Circadian & sleep-related circuits modulate and are modulated by arousal Cortical circuits fronto-insular area 32 Hy to thalamic and cortical circuits Reciprocal Hy Reciprocal NTS-aCeN	EEG EMG ERP fMRI Neural activity Sex-specific differences in arousal Autonomic BP Breathing EDA Heart rate Pupil size HPA axis ACTH CRF Glucocorticoid	Affective state Agitation Cognition Emotional reactivity Blinking Motivated behavior Motor activity Sensory reactivity Startle Waking	ADACL POMS arousal subscale Self-assessment mannequin	Cardiac pre-ejection period EDA HRV Psychomotor vigilance task Pupillometry
Circadian Rhythms	Serotonin Input Dopamine GABA Glutamate Melanopsin NPY PACAP SP SCN synchronizing & modulating agents ADH Calbindin cAMP cGMP NO Steroids VIP Output ADH Cortisol GABA Melatonin VIP	Fibroblast ipRGC Medium spiny neuron Pars tuberalis cells Pinealocyte Rods and cones SCN "clock" cells	Input Raphe to SCN projection retinal cell RHT retinogeniculate tract Output BL / Hi central extended Amg (aCeN/BNST) HPA axis neuroendocrine cell groups Hy OX projections PVN, DMH, subparaventricular zone, PVT SCN / PVN / SCG / pineal SNS / PNS Intrinsic to SCN SCN core/shell Seasonal SCN / PVN / SCG / pineal	Gene expression Neural activity Neurotransmitter	Drive-regulated behavior Locomotor activity Masking Neurobehavioral function Sleep-rated and waking behavior Sleep-wake	Diary-based measures of daily regularity/rhythmicity (e.g., Social Rhythm Metric) Morningness–eveningness questionnaire Munich Chronotype Questionnaire Phase, diurnal preference, chronotype (e.g., Horne-Ostberg, CTQ) Sleepiness, alertness, well-being, mood	DLMO (phase estimate) Longitudinal Actigraphy Genetic approaches genome-wide association study candidate gene epigenomics circadian genomics (temporal gene expression) mutagenesis gene targeting quantitative trait locus
Sleep-Wakefulness	ACh Adenosine CRF Cytokine Dopamine GABA Galanin Glutamate Histamine OX NE/NA NPY Serotonin ADH	AH and basal forebrain Brainstem LC Raphe LDT/PPT VTA HACER Hy PHR (TMN) Th MD Rt	NREM sleep; forebrain basal forebrain and AH projections to arousal-promoting cell groups thalamocortical tract REM sleep; brainstem mesopontine nuclei Wakefulness Arousal and Circadian Rhythms circuits also subserve wakefulness	Brain metabolic activity Capacity for wakefulness under low stimulation EEG sleep spindle slow wave theta wave EMG EOG NREM sleep and REM sleep Physiologic measures of sleepiness, homeostatic sleep drive during waking Sex-specific sleep physiology Sleep hormones Sleep latency Temporal and topographic organization of... homeostatic sleep drive during sleep sleep dynamics Wakefulness	Intermediate/admixed sleep-wake states Rest-activity patterns Sensory arousal threshold Sleep co-sleeping deprivation and satiation inertia motor behaviors sex-specific behavior timing and variability Sleep-dependent neurobehavioral functions Wakefulness	Alertness Dream report Fatigue Insomnia severity index Sleep quality, restoration, quantity Sleep timing Sleep-modulated symptoms Sleepiness Specific sleep symptoms	Finger tapping motor sequence task Latency to persistent sleep Multiple sleep latency testing Non-REM sleep EEG slow wave activity Sleep spindle Total sleep time Wake time after sleep onset

Sensorimotor Systems, as of January 2022[9]
Construct / Subconstruct		Molecules	Cells	Neural Circuits	Physiology	Behavior	Self-reports	Paradigms
Motor Action	Action Planning and Selection	n/a	n/a	Parietal cortex inferior posterior Premotor cortex STS SMA proper	n/a	Apraxia conceptual ideational ideomotor limb-kinetic	n/a	Go-before-you-know
	Sensorimotor Dynamics	n/a	Granule cells Purkinje cells	BG Cerebello-olivary-pontine complex Cerebellum Parietal cortex Somatosensory cortex Substantia nigra Th	Short afferent inhibition	Dyspraxia Hyposensitivity Weakness	n/a	Sensory Motor Adaptation Tasks
	Initiation	n/a	n/a	BG Dorsal cingulate SMA proper	n/a	Apathy Catatonic stupor Negative symptoms Psychomotor retardation Stuttering	Lille Apathy Rating Scale	Libet's Temporal Judgement
	Execution	n/a	Alpha motor neurons Betz cells Pyramidal cells	Efferent and afferent spinal and peripheral pathways Motor cortex	Bereitschaftspotential Corticospinal tract excitability Hoffmann's reflex Movement-related cortical potentials Use-dependent plasticity	Activity level Ehlers–Danlos syndromes Psychomotor retardation	n/a	Motor evoked potential latency
	Inhibition and Termination	Dopamine GABA NE/NA	Intracortical inhibitory interneurons Striatal interneurons	ACC BG Cerebellum DLPFC FEF IFG IPL Lateral premotor cortex Mid-CgG PCC SMA proper Pre-SMA SPL	Cortical inhibition Oscillatory rhythms Prepulse inhibition	Activity level Automatic obedience Catatonia immobility rituals negativism Perseveration Stereotypy Tics Utilization behavior	n/a	Stop-signal reaction time
Agency and Ownership		n/a	Mirror neuron	Cerebellum Corpus callosum IPL Sensorimotor Th S1 SMA proper Pre-SMA	Efference copy Readiness potential	Alien hand syndrome Functional movement disorder Neglect Perception of external control Stereotypy Tic	n/a	n/a
Habit - Sensorimotor		Dopamine GABA Glutamate Serotonin	n/a	PtA Sensorimotor-BG	n/a	Compulsive behavior Stereotypy	Rush video-based tic rating scale Yale Global Tic Severity Scale	2-step task
Innate Motor Patterns		n/a	n/a	Brainstem Hy Motor cortex Occulomotor system	Corneal reflex	Disinhibition of early motor reflexes Incontinent affect Startle Stereotypy	n/a	n/a

The domains are tentative: "It is important to emphasize that these particular domains and constructs are simply starting points that are not definitive or set in concrete."[8] Also, subconstructs have been added to some constructs. For example, Visual Perception, Auditory Perception, and Olfactory/Somatosensory/Multimodal perception as subconstructs of the Perception construct.[9]

Methodology

The RDoC methodology distinguishes itself from traditional systems of diagnostic criteria.

Unlike conventional diagnostic systems (e.g. DSM) which use categorization, RDoC is a "dimensional system" — it relies on dimensions that "span the range from normal to abnormal."[8]

Whereas conventional diagnostic systems incrementally revise and build upon their pre-existing paradigms, "RDoC is agnostic about current disorder categories."[8] Official documents explain this feature, writing: "Rather than starting with an illness definition and seeking its neurobiological underpinnings, RDoC begins with current understandings of behavior-brain relationships and links them to clinical phenomena."[8]

Unlike conventional diagnostic systems, which typically rely on self-report and behavioral measures alone, the RDoC framework has the "explicit goal" of allowing investigators access to a wider range of data. In addition to self-report measures or measure of behavior, RDoC also incorporates units of analysis beyond those found in the DSM — allowing RDoC to be informed by insights into genes, molecules, cells, circuits, physiology, and large-scale paradigms.[8] Early data driven approaches to RDoC based continuous transdiagnostic psychiatric phenotypes predictict clinical prognosis across diagnosis and have genetic correlates that in not only clinical populations.[10][11]

References

"RDoC Unit and Work Group Members". National Institute of Mental Health. Archived from the original on 31 October 2016. Retrieved 31 October 2016.
Asher, Jules (28 January 2010). "Genes and Circuitry, Not Just Clinical Observation, to Guide Classification for Research". National Institute of Mental Health.
Cuthbert, Bruce N; Insel, Thomas R (Dec 2013). "Toward the future of psychiatric diagnosis: the seven pillars of RDoC". BMC Medicine. 11 (1): 126. doi:10.1186/1741-7015-11-126. ISSN 1741-7015. PMC 3653747. PMID 23672542.
Insel, Thomas R.; Lieberman, Jeffrey A. (13 May 2013). "DSM-5 and RDoC: Shared Interests" (Press release). National Institute of Mental Health.
"The National Institute of Mental Health Strategic Plan". National Institute of Mental Health. 6 November 2008. Strategy 1.4. Archived from the original on 17 December 2008.
Insel, Thomas (29 April 2013). "Director's Blog: Transforming Diagnosis". National Institute of Mental Health.
Szalavitz, Maia (7 May 2013). "Mental Health Researchers Reject Psychiatry's New Diagnostic 'Bible'". TIME.
"Research Domain Criteria (RDoC)". National Institute of Mental Health. 29 May 2013. Archived from the original on 1 June 2013.
"RDoC Matrix". National Institute of Mental Health. Archived from the original on 2022-01-21. Retrieved 16 January 2022.
McCoy, Thomas H.; Yu, Sheng; Hart, Kamber L.; Castro, Victor M.; Brown, Hannah E.; Rosenquist, James N.; Doyle, Alysa E.; Vuijk, Pieter J.; Cai, Tianxi (2018-06-15). "High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records". Biological Psychiatry. 83 (12): 997–1004. doi:10.1016/j.biopsych.2018.01.011. ISSN 1873-2402. PMC 5972065. PMID 29496195.
McCoy, Thomas H.; Castro, Victor M.; Hart, Kamber L.; Pellegrini, Amelia M.; Yu, Sheng; Cai, Tianxi; Perlis, Roy H. (2018-06-15). "Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records". Biological Psychiatry. 83 (12): 1005–1011. doi:10.1016/j.biopsych.2017.12.004. ISSN 1873-2402. PMC 5972060. PMID 29496196.

External links

NIMH page on RDoC
"Toward a new understanding of mental illness" a TED talk given by Director of the National Institute of Mental Health Thomas R. Insel
Video introduction to RDoC by NIMH

This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.

[RDoCunit-1] "RDoC Unit and Work Group Members". National Institute of Mental Health. Archived from the original on 31 October 2016. Retrieved 31 October 2016.

[RDoClaunch-2] Asher, Jules (28 January 2010). "Genes and Circuitry, Not Just Clinical Observation, to Guide Classification for Research". National Institute of Mental Health.

[3] Cuthbert, Bruce N; Insel, Thomas R (Dec 2013). "Toward the future of psychiatric diagnosis: the seven pillars of RDoC". BMC Medicine. 11 (1): 126. doi:10.1186/1741-7015-11-126. ISSN 1741-7015. PMC 3653747. PMID 23672542.

[4] Insel, Thomas R.; Lieberman, Jeffrey A. (13 May 2013). "DSM-5 and RDoC: Shared Interests" (Press release). National Institute of Mental Health.

[StrategicNIMH2008-5] "The National Institute of Mental Health Strategic Plan". National Institute of Mental Health. 6 November 2008. Strategy 1.4. Archived from the original on 17 December 2008.

[InselBlog20130429-6] Insel, Thomas (29 April 2013). "Director's Blog: Transforming Diagnosis". National Institute of Mental Health.

[Szalavitz2013-7] Szalavitz, Maia (7 May 2013). "Mental Health Researchers Reject Psychiatry's New Diagnostic 'Bible'". TIME.

[NIMH-RDoC2013-8] "Research Domain Criteria (RDoC)". National Institute of Mental Health. 29 May 2013. Archived from the original on 1 June 2013.

[RDoCmatrix-9] "RDoC Matrix". National Institute of Mental Health. Archived from the original on 2022-01-21. Retrieved 16 January 2022.

[10] McCoy, Thomas H.; Yu, Sheng; Hart, Kamber L.; Castro, Victor M.; Brown, Hannah E.; Rosenquist, James N.; Doyle, Alysa E.; Vuijk, Pieter J.; Cai, Tianxi (2018-06-15). "High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records". Biological Psychiatry. 83 (12): 997–1004. doi:10.1016/j.biopsych.2018.01.011. ISSN 1873-2402. PMC 5972065. PMID 29496195.

[11] McCoy, Thomas H.; Castro, Victor M.; Hart, Kamber L.; Pellegrini, Amelia M.; Yu, Sheng; Cai, Tianxi; Perlis, Roy H. (2018-06-15). "Genome-wide Association Study of Dimensional Psychopathology Using Electronic Health Records". Biological Psychiatry. 83 (12): 1005–1011. doi:10.1016/j.biopsych.2017.12.004. ISSN 1873-2402. PMC 5972060. PMID 29496196.