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Biotypes: The Tip of the Research Domain Criteria Iceberg
Biological Psychiatry: CNNI
Commentary Biotypes: The Tip of the Research Domain Criteria Iceberg Maria Jalbrzikowski and Carrie E. Bearden We have long known that current diagnostic classification systems in psychiatry fail to capture the significant clinical heterogeneity observed in psychotic disorders. This heterogeneity poses a significant barrier for determining underlying mechanisms, identifying biomarkers that map on to the disease pathology, and developing treatment targets in psychosis. Therefore, based on the proposition that measures based on dimensions and observable behaviors may be more informative than current diagnostic systems, the Research Domain Criteria (RDoC) initiative was created as a potentially new, neuroscience-based approach to the classification of mental disorders (1). RDoc holds promise for exciting discoveries and a potential to create a more meaningful nosology, but it also comes with multiple challenges. The study by Meda et al. (2) in this issue of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging simultaneously unveils the some of the possibilities and obstacles that we now face in the “RDoC era.” Meda et al. (2) apply an RDoC theoretical framework to data from the five Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium sites, with the goal of identifying neurobiologically based dimensional measures that might better relate to mechanism or prognosis. The authors investigate resting-state functional magnetic resonance imaging (rs-fMRI) connectivity patterns in psychosis spectrum patients, using both traditional DSM diagnoses of psychotic disorders (e.g., schizophrenia, schizoaffective disorder, and bipolar disorder with psychotic features) and a new dimensionally based classification using cognitive and neurophysiological data (i.e., Biotypes) (3). This approach highlights an important analytic step that is necessary to move the field forward while simultaneously building on the existing base of psychiatric research, which is grounded in the DSM categorical strongholds. In fact, it has been previously highlighted that when using an RDoC framework, the approach used by researchers should not focus on the distinction between a dimensional and categorical measure—of course, any dimensional metric can be dichotomized, and vice versa (4). Instead, the optimal choice must be determined from the purpose of the investigation (e.g., determining eligibility for a clinical trial vs. tracking symptom changes over time). This is an important step for all users implementing an RDoC approach, given concerns about the ability for RDoC to truly improve our existing classification systems (5) and recent evidence suggesting that a “hybrid” combination of categorical and dimensional measures may provide us with the most comprehensive understanding of psychiatric conditions (6,7). Meda et al. (2) uncover potentially important biologically based distinctions using their new Biotype approach.
Specifically, individuals who fall into the category of Biotype 1 exhibit impaired cognitive control and reduced sensorimotor reactivity, those in Biotype 2 have impaired cognitive control coupled with exaggerated sensorimotor reactivity, and those in Biotype 3 have relatively intact cognitive control and sensorimotor reactivity. Importantly, individuals across all diagnostic groups were found in each of the Biotype groups, indicating that these Biotypes are not driven by categorical diagnoses. This is an innovative approach but also brings forth many questions. How do we know when we have created an appropriate Biotype? Do we have enough evidence that these are robust Biotypes that will be recapitulated in independent samples, and are these Biotypes clinically meaningful? At what point do specific Biotypes, such as those implemented in this study, become the criterion standard and become ready for “prime time”? Therefore, in addition to intriguing results showing that both the DSM-based and Biotype classifications exhibit rs-fMRI network abnormalities, this report also invokes many follow-up questions about the variety of approaches that could be implemented when transitioning from DSM classifications to more biologically oriented distinctions. Within this large sample (258 healthy controls, 518 psychotic spectrum probands, and 349 relatives of probands), Meda et al. conducted an independent components analysis of rs-fMRI data to identify rs-fMRI networks and examine the extent to which these networks differentiate between probands (i.e., DSM based) and Biotypes (i.e., dimension based). The authors find that nine of the 13 examined rsfMRI networks have reduced connectivity in those with psychosis spectrum disorders (i.e., DSM based) and psychosis Biotypes (i.e., dimension based). Intriguingly, within these networks, there was only one between-proband difference: participants with a schizoaffective diagnosis exhibited reduced connectivity in the right frontoparietal network in comparison to individuals with a schizophrenia diagnosis. However, four of these networks differentiated between the psychosis Biotypes, which the authors interpret as supporting the view that Biotypes are more sensitive in capturing independent component analysis–derived rs-fMRI connectivity differences among psychosis probands relative to conventional DSM diagnoses. One may therefore speculate that these biological markers may generate more precise disease clusters for psychosis compared to conventional approaches. While these results suggest promise for implementing a dimensional approach, the study also made us ponder the alternative analyses that could be conducted with the same (or similar) data. Other complementary data-driven approaches (e.g., k-means clustering) could be implemented on the dataset to identify subgroups. Then, one could ask, to what
SEE CORRESPONDING ARTICLE ON PAGE 488
486 & 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.bpsc.2016.09.004 Biological Psychiatry: Cognitive Neuroscience and Neuroimaging November 2016; 1:486–487 www.sobp.org/BPCNNI ISSN: 2451-9022
Biological Psychiatry: CNNI
Commentary
extent do these subgroups map on to the originally identified Biotypes (3)? Could adding additional information, such as rs-fMRI connectivity profiles, better refine the original Biotypes? In addition, what does it mean that only one network differentiated between schizophrenia and schizoaffective disorder (but not bipolar with psychotic features) in the DSM-based analyses, while four rs-fMRI networks differentiated between the Biotypes at some level? Given that no rsfMRI network abnormalities were identified between Biotypes 1 and 2, does this mean that these two groups would be better merged? The results from this article highlight that we have only hit the “tip of the iceberg” when implementing RDoC approaches, comparing these results to traditional DSMbased results and determining the utility of dimensionalbased measures in psychiatry research. Given the strong genetic contribution to psychotic illness, this study also examined rs-fMRI networks in relatives of the probands/Biotypes to determine whether the relatives also showed distinct connectivity profiles in comparison to healthy controls. For the right frontoparietal network, only relatives of participants with schizophrenia exhibited reduced connectivity in comparison to the relatives of those with schizoaffective disorder and bipolar disorder with psychotic features. However, these results are difficult to interpret, given that individuals with schizoaffective disorder exhibited reduced connectivity to those with schizophrenia in the withinproband analyses. If we were to follow the traditional “rules” for an appropriate endophenotype based on DSM-based diagnoses (8), this network would not meet criteria, because the relatives who show altered connectivity profiles (i.e., relatives with schizophrenia) do not correspond to the psychiatric group with reduced connectivity (i.e., those with schizoaffective disorder). However, when using the dimensional, Biotype approach for the right frontoparietal network, relatives of Biotypes 1 and 2 show similar patterns of altered connectivity as individuals in these two Biotype groups. We can speculate that the identification of appropriate endophenotypes may be more fruitful when dimensional approaches are implemented. However, additional studies are necessary to back this provocative claim. Importantly, Meda et al. also show that psychosis-related connectivity deficits in multiple networks (seven of the nine that differentiated between controls and psychosis) were significantly related to lower cognitive control composite scores but not sensorimotor reactivity measures. These results suggest that identified rs-fMRI abnormalities are related to one’s ability to engage in flexible and goaldirected behavior. At the same time, given that multiple networks are related to the composite score, does this mean that the identified rs-fMRI networks are all tapping into a similar construct and do not need to be differentiated from one another? In addition, given that several of the identified rsfMRI networks (i.e., anterior default mode network, hemispherically separate frontoparietal network) have not been validated within large samples of healthy adults (9,10), the findings become difficult to interpret. The largest correlation identified only accounted for a small percentage (3%) in network variability, leaving open the question of to what the rest of the variability can be attributed.
In sum, the work by Meda et al. chips a brave first ridge into the iceberg, addressing a timely and promising question. However, much work remains. Importantly, we will need to understand the extent to which these identified rs-fMRI networks and Biotypes predict future clinical symptomatology and outcomes, and the extent to which these measures are amendable to targeted treatments. It is only then that we will begin to understand whether these measures can truly help us fulfill the RDoC promise: to provide personalized medicine that reduces the costly burden of mental health problems on our society.
Acknowledgments and Disclosures This work was supported by National Institute of Mental Health Grant Nos. R01MH085953 and U01MH087626 (to CEB) and National Institute on Drug Abuse Training Grant TT32DA031111 (to MJ). The authors report no biomedical financial interests or potential conflicts of interest.
Article Information From the Department of Psychiatry (MJ), University of Pittsburgh, Pittsburgh, Pennsylvania, and the Departments of Psychiatry and Biobehavioral Sciences and Psychology (CEB), University of California, Los Angeles, Los Angeles, California. Address correspondence to Carrie E. Bearden, Ph.D., University of California, Departments of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, 300 UCLA Medical Plaza, Room 2267, Los Angeles, CA 90095; E-mail: [email protected]. Received Sep 23, 2016; accepted Sep 23, 2016.
References 1.
2.
3.
4.
5.
6.
7.
8. 9.
10.
Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. (2010): Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. Am J Psychiatry 167: 748–751. Meda SA, Clementz BA, Sweeney JA, Keshavan MS, Tamminga CA, Ivleva EI, et al. (2016): Examining functional resting-state connectivity in psychosis and its subgroups in the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort. Biol Psychiatry Cogn Neurosci 1:488–497. Clementz BA, Sweeney JA, Hamm JP, Ivleva EI, Ethridge LE, Pearlson GD, et al. (2016): Identification of distinct psychosis biotypes using brain-based biomarkers. Am J Psychiatry 173:373–384. Kraemer HC (2015): Research Domain Criteria (RDoC) and the DSM— Two methodological approaches to mental health diagnosis. JAMA Psychiatry 72:1163–1164. Weinberger DR, Glick ID, Klein DF (2015): Whither Research Domain Criteria (RDoC)?: The good, the bad, and the ugly. JAMA Psychiatry 72:1161–1162. Jalbrzikowski M, Ahmed KH, Patel A, Jonas R, Kushan L, Chow C, Bearden CE (2016): Categorical versus dimensional approaches to autism-associated intermediate phenotypes in 22q11.2 microdeletion syndrome [published online ahead of print June 30]. Biol Psychiatry Cogn Neurosci Neuroimaging. Elton A, Di Martino A, Hazlett HC, Gao W (2016): Neural connectivity evidence for a categorical-dimensional hybrid model of autism spectrum disorder. Biol Psychiatry 80:120–128. Flint J, Munafò MR (2007): The endophenotype concept in psychiatric genetics. Psychol Med 37:163–180. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. (2011): Functional network organization of the human brain. Neuron 72:665–678. Gordon EM, Laumann TO, Adeyemo B, Huckins JF, Kelley WM, Petersen SE (2015): Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral Cortex 26: 288–303.
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging November 2016; 1:486–487 www.sobp.org/BPCNNI
487
Commentary Biotypes: The Tip of the Research Domain Criteria Iceberg Maria Jalbrzikowski and Carrie E. Bearden We have long known that current diagnostic classification systems in psychiatry fail to capture the significant clinical heterogeneity observed in psychotic disorders. This heterogeneity poses a significant barrier for determining underlying mechanisms, identifying biomarkers that map on to the disease pathology, and developing treatment targets in psychosis. Therefore, based on the proposition that measures based on dimensions and observable behaviors may be more informative than current diagnostic systems, the Research Domain Criteria (RDoC) initiative was created as a potentially new, neuroscience-based approach to the classification of mental disorders (1). RDoc holds promise for exciting discoveries and a potential to create a more meaningful nosology, but it also comes with multiple challenges. The study by Meda et al. (2) in this issue of Biological Psychiatry: Cognitive Neuroscience and Neuroimaging simultaneously unveils the some of the possibilities and obstacles that we now face in the “RDoC era.” Meda et al. (2) apply an RDoC theoretical framework to data from the five Bipolar-Schizophrenia Network on Intermediate Phenotypes consortium sites, with the goal of identifying neurobiologically based dimensional measures that might better relate to mechanism or prognosis. The authors investigate resting-state functional magnetic resonance imaging (rs-fMRI) connectivity patterns in psychosis spectrum patients, using both traditional DSM diagnoses of psychotic disorders (e.g., schizophrenia, schizoaffective disorder, and bipolar disorder with psychotic features) and a new dimensionally based classification using cognitive and neurophysiological data (i.e., Biotypes) (3). This approach highlights an important analytic step that is necessary to move the field forward while simultaneously building on the existing base of psychiatric research, which is grounded in the DSM categorical strongholds. In fact, it has been previously highlighted that when using an RDoC framework, the approach used by researchers should not focus on the distinction between a dimensional and categorical measure—of course, any dimensional metric can be dichotomized, and vice versa (4). Instead, the optimal choice must be determined from the purpose of the investigation (e.g., determining eligibility for a clinical trial vs. tracking symptom changes over time). This is an important step for all users implementing an RDoC approach, given concerns about the ability for RDoC to truly improve our existing classification systems (5) and recent evidence suggesting that a “hybrid” combination of categorical and dimensional measures may provide us with the most comprehensive understanding of psychiatric conditions (6,7). Meda et al. (2) uncover potentially important biologically based distinctions using their new Biotype approach.
Specifically, individuals who fall into the category of Biotype 1 exhibit impaired cognitive control and reduced sensorimotor reactivity, those in Biotype 2 have impaired cognitive control coupled with exaggerated sensorimotor reactivity, and those in Biotype 3 have relatively intact cognitive control and sensorimotor reactivity. Importantly, individuals across all diagnostic groups were found in each of the Biotype groups, indicating that these Biotypes are not driven by categorical diagnoses. This is an innovative approach but also brings forth many questions. How do we know when we have created an appropriate Biotype? Do we have enough evidence that these are robust Biotypes that will be recapitulated in independent samples, and are these Biotypes clinically meaningful? At what point do specific Biotypes, such as those implemented in this study, become the criterion standard and become ready for “prime time”? Therefore, in addition to intriguing results showing that both the DSM-based and Biotype classifications exhibit rs-fMRI network abnormalities, this report also invokes many follow-up questions about the variety of approaches that could be implemented when transitioning from DSM classifications to more biologically oriented distinctions. Within this large sample (258 healthy controls, 518 psychotic spectrum probands, and 349 relatives of probands), Meda et al. conducted an independent components analysis of rs-fMRI data to identify rs-fMRI networks and examine the extent to which these networks differentiate between probands (i.e., DSM based) and Biotypes (i.e., dimension based). The authors find that nine of the 13 examined rsfMRI networks have reduced connectivity in those with psychosis spectrum disorders (i.e., DSM based) and psychosis Biotypes (i.e., dimension based). Intriguingly, within these networks, there was only one between-proband difference: participants with a schizoaffective diagnosis exhibited reduced connectivity in the right frontoparietal network in comparison to individuals with a schizophrenia diagnosis. However, four of these networks differentiated between the psychosis Biotypes, which the authors interpret as supporting the view that Biotypes are more sensitive in capturing independent component analysis–derived rs-fMRI connectivity differences among psychosis probands relative to conventional DSM diagnoses. One may therefore speculate that these biological markers may generate more precise disease clusters for psychosis compared to conventional approaches. While these results suggest promise for implementing a dimensional approach, the study also made us ponder the alternative analyses that could be conducted with the same (or similar) data. Other complementary data-driven approaches (e.g., k-means clustering) could be implemented on the dataset to identify subgroups. Then, one could ask, to what
SEE CORRESPONDING ARTICLE ON PAGE 488
486 & 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.bpsc.2016.09.004 Biological Psychiatry: Cognitive Neuroscience and Neuroimaging November 2016; 1:486–487 www.sobp.org/BPCNNI ISSN: 2451-9022
Biological Psychiatry: CNNI
Commentary
extent do these subgroups map on to the originally identified Biotypes (3)? Could adding additional information, such as rs-fMRI connectivity profiles, better refine the original Biotypes? In addition, what does it mean that only one network differentiated between schizophrenia and schizoaffective disorder (but not bipolar with psychotic features) in the DSM-based analyses, while four rs-fMRI networks differentiated between the Biotypes at some level? Given that no rsfMRI network abnormalities were identified between Biotypes 1 and 2, does this mean that these two groups would be better merged? The results from this article highlight that we have only hit the “tip of the iceberg” when implementing RDoC approaches, comparing these results to traditional DSMbased results and determining the utility of dimensionalbased measures in psychiatry research. Given the strong genetic contribution to psychotic illness, this study also examined rs-fMRI networks in relatives of the probands/Biotypes to determine whether the relatives also showed distinct connectivity profiles in comparison to healthy controls. For the right frontoparietal network, only relatives of participants with schizophrenia exhibited reduced connectivity in comparison to the relatives of those with schizoaffective disorder and bipolar disorder with psychotic features. However, these results are difficult to interpret, given that individuals with schizoaffective disorder exhibited reduced connectivity to those with schizophrenia in the withinproband analyses. If we were to follow the traditional “rules” for an appropriate endophenotype based on DSM-based diagnoses (8), this network would not meet criteria, because the relatives who show altered connectivity profiles (i.e., relatives with schizophrenia) do not correspond to the psychiatric group with reduced connectivity (i.e., those with schizoaffective disorder). However, when using the dimensional, Biotype approach for the right frontoparietal network, relatives of Biotypes 1 and 2 show similar patterns of altered connectivity as individuals in these two Biotype groups. We can speculate that the identification of appropriate endophenotypes may be more fruitful when dimensional approaches are implemented. However, additional studies are necessary to back this provocative claim. Importantly, Meda et al. also show that psychosis-related connectivity deficits in multiple networks (seven of the nine that differentiated between controls and psychosis) were significantly related to lower cognitive control composite scores but not sensorimotor reactivity measures. These results suggest that identified rs-fMRI abnormalities are related to one’s ability to engage in flexible and goaldirected behavior. At the same time, given that multiple networks are related to the composite score, does this mean that the identified rs-fMRI networks are all tapping into a similar construct and do not need to be differentiated from one another? In addition, given that several of the identified rsfMRI networks (i.e., anterior default mode network, hemispherically separate frontoparietal network) have not been validated within large samples of healthy adults (9,10), the findings become difficult to interpret. The largest correlation identified only accounted for a small percentage (3%) in network variability, leaving open the question of to what the rest of the variability can be attributed.
In sum, the work by Meda et al. chips a brave first ridge into the iceberg, addressing a timely and promising question. However, much work remains. Importantly, we will need to understand the extent to which these identified rs-fMRI networks and Biotypes predict future clinical symptomatology and outcomes, and the extent to which these measures are amendable to targeted treatments. It is only then that we will begin to understand whether these measures can truly help us fulfill the RDoC promise: to provide personalized medicine that reduces the costly burden of mental health problems on our society.
Acknowledgments and Disclosures This work was supported by National Institute of Mental Health Grant Nos. R01MH085953 and U01MH087626 (to CEB) and National Institute on Drug Abuse Training Grant TT32DA031111 (to MJ). The authors report no biomedical financial interests or potential conflicts of interest.
Article Information From the Department of Psychiatry (MJ), University of Pittsburgh, Pittsburgh, Pennsylvania, and the Departments of Psychiatry and Biobehavioral Sciences and Psychology (CEB), University of California, Los Angeles, Los Angeles, California. Address correspondence to Carrie E. Bearden, Ph.D., University of California, Departments of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, 300 UCLA Medical Plaza, Room 2267, Los Angeles, CA 90095; E-mail: [email protected]. Received Sep 23, 2016; accepted Sep 23, 2016.
References 1.
2.
3.
4.
5.
6.
7.
8. 9.
10.
Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. (2010): Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. Am J Psychiatry 167: 748–751. Meda SA, Clementz BA, Sweeney JA, Keshavan MS, Tamminga CA, Ivleva EI, et al. (2016): Examining functional resting-state connectivity in psychosis and its subgroups in the Bipolar-Schizophrenia Network on Intermediate Phenotypes cohort. Biol Psychiatry Cogn Neurosci 1:488–497. Clementz BA, Sweeney JA, Hamm JP, Ivleva EI, Ethridge LE, Pearlson GD, et al. (2016): Identification of distinct psychosis biotypes using brain-based biomarkers. Am J Psychiatry 173:373–384. Kraemer HC (2015): Research Domain Criteria (RDoC) and the DSM— Two methodological approaches to mental health diagnosis. JAMA Psychiatry 72:1163–1164. Weinberger DR, Glick ID, Klein DF (2015): Whither Research Domain Criteria (RDoC)?: The good, the bad, and the ugly. JAMA Psychiatry 72:1161–1162. Jalbrzikowski M, Ahmed KH, Patel A, Jonas R, Kushan L, Chow C, Bearden CE (2016): Categorical versus dimensional approaches to autism-associated intermediate phenotypes in 22q11.2 microdeletion syndrome [published online ahead of print June 30]. Biol Psychiatry Cogn Neurosci Neuroimaging. Elton A, Di Martino A, Hazlett HC, Gao W (2016): Neural connectivity evidence for a categorical-dimensional hybrid model of autism spectrum disorder. Biol Psychiatry 80:120–128. Flint J, Munafò MR (2007): The endophenotype concept in psychiatric genetics. Psychol Med 37:163–180. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. (2011): Functional network organization of the human brain. Neuron 72:665–678. Gordon EM, Laumann TO, Adeyemo B, Huckins JF, Kelley WM, Petersen SE (2015): Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral Cortex 26: 288–303.
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging November 2016; 1:486–487 www.sobp.org/BPCNNI
487