- Email: [email protected]
Neurocognitive Markers of Depression
Early Career Investigator Commentary
Biological Psychiatry
Neurocognitive Markers of Depression Roselinde H. Kaiser Depressive disorders are among the most prevalent, debilitating, costly, and deadly illnesses in the world (1,2), but heterogeneity in how depression manifests for individuals has complicated attempts to identify a singular neurobiological substrate of depression or devise broadly effective therapies. In response to the challenge of heterogeneous disease presentation, an emerging field of psychiatric research capitalizes on individual differences in neural, cognitive, and emotional functioning to identify the constellation of neurocognitive features that mark a particular profile of mood disorder. One goal of such research is to develop neurocognitive markers that can be used on a person-specific basis to diagnose and predict depression and to select precise targets of treatment. However, to translate neurocognitive research to a precision medicine framework, it will first be necessary to carefully evaluate the validity, sensitivity, and specificity of multimodal neurocognitive markers in diverse samples. The investigation of Price et al. (3) reported in the current issue of Biological Psychiatry is an important step toward these objectives. Considerations when developing a neurocognitive marker include selection of functional domains to be targeted, the methods of evaluating those domains, and an analytic approach for defining the marker. Price et al. use a combination of task-based and resting-state functional magnetic resonance imaging, a behavioral index of emotional distractor inhibition, and self-report measures of affective reactivity and symptoms to characterize neurocognitive functioning in adults with or without major depression. Of note, this investigation combines a hypothesis-driven focus on positive affective functioning with a data-driven analytic approach for clustering individuals on the basis of functional network response to positive mood induction. Thus, Price et al. capitalize on both conceptual models that describe anhedonia as a core feature and potential subtype of depression (4) and the rich interindividual variability in functional network activity that can be mined for data-driven clustering. Results of the Price et al. clustering analysis revealed two groups of people defined by distinct profiles of network functioning during the positive mood manipulation, with one group exhibiting generally elevated functional connectivity, particularly in paths originating in ventral frontoinsular systems (Figure 1A), and the other group exhibiting relatively lower functional connectivity except in paths originating in parietal or pregenual regions. These data-derived clusters, in turn, were predictors of emotional functioning: the high-connectivity group captured the majority (80%) of depressed individuals, and depressed participants characterized by hyperconnectivity reported greater difficulty maintaining positive mood than participants characterized by hypoconnectivity. Of note, brain network responses to positive material also predicted behavioral responses to negative material: depressed
hyperconnectivity individuals exhibited greater attention bias toward negative emotional distractors than did depressed hypoconnectivity individuals. The finding that frontoinsular network dysfunction was associated with aberration at both ends of the emotional spectrum is an intriguing pattern that hints at the complexity of neurocognitive functioning in depression and highlights the potential for data-driven approaches to refine theories regarding subtypes of illness. Together, these results suggest that hyperconnectivity in ventral frontoinsular systems coupled with blunted positiveemotion processing and amplified negative-emotion processing may be a promising neurocognitive marker of depression. Identifying a pattern of cohesive individual differences across neurobiological and cognitive levels of analysis is the first step toward developing a neurocognitive marker; the second step is to critically evaluate the validity of such markers. Two important criteria are that the marker has adequate sensitivity (high likelihood that individuals with the disorder exhibit the marker) and specificity (low likelihood that individuals without the disorder exhibit the marker) (5). However, the goal of evaluating sensitivity and specificity is difficult to achieve when the disorder itself —e.g., a potential subtype of depression—is an emerging concept that cannot yet be validated against explicit symptom measures. Nevertheless, Price et al. provide compelling evidence that positive-mood–responsive ventral hyperconnectivity is a sensitive marker for a common strain of depression, with four out of five depressed subjects demonstrating the marker. Taskresponsive ventral hyperconnectivity was less successful in a test of specificity, as nondepressed participants were equally likely to cluster into the high or low task-responsive connectivity groups. However, by integrating information from a separate, resting-state paradigm, a stronger case for specificity may be made: healthy individuals who exhibited taskresponsive hyperconnectivity in ventral regions also tended to show reduced resting-state functional connectivity in a medial circuit of the default network originating in the posterior cingulate. In contrast, consistent with previous research (Figure 1B) (6), amplified resting-state hyperconnectivity in the same circuit characterized depressed individuals in this study regardless of task-related network response. One interesting possibility raised by Price et al. is that dampened intrinsic functional connectivity in the default network, a set of regions functionally implicated in self-referential and internally directed thinking, is a compensatory mechanism that counterbalances task-responsive hyperconnectivity and is related to emotional resilience. Future studies that explore this possibility may use neurocognitive markers that integrate not only multiple levels of functioning, but also multiple modalities within each level (e.g., indices of resting-state and task-responsive network activity) to predict mental health.
SEE CORRESPONDING ARTICLE ON PAGE 347
http://dx.doi.org/10.1016/j.biopsych.2016.11.007 ISSN: 0006-3223
& 2016 Society of Biological Psychiatry. e29 Biological Psychiatry February 15, 2017; 81:e29–e31 www.sobp.org/journal
Biological Psychiatry
Commentary
Figure 1. Potential neurocognitive markers of depression. A neurocognitive marker can be defined as the cohesive pattern of altered functioning across multiple levels of analysis that is reliably and specifically associated with a disorder. (A) The study by Price et al. (3) suggests that amplified functional connectivity (FC) of ventral frontoinsular systems (exhibited by Group B) together with impaired ability to maintain positive mood or ignore negative distractors may constitute a neurocognitive marker of a common variant of depression; regions of default network in red, of ventral attention (or salience) network in purple, of affective network in gold, and of frontoparietal network in green [Adapted from Price et al. (3)]. (B) Converging evidence that abnormal FC among frontoinsular and midline regions may represent a neurocognitive marker of depression comes from a meta-analysis of resting-state FC abnormalities in children and adults with major depressive disorder (MDD), which revealed reliable patterns of hyperconnectivity or hypoconnectivity among regions of the default, frontoparietal, and ventral attention networks [Adapted from Kaiser et al. (6)]. (C) Clinical interventions that target neurocognitive markers may illuminate causal relationships among levels of dysfunction; research by Hamilton et al. (9) provides interesting evidence that neurofeedback designed to modulate frontoinsular response to negative emotional images is related to decreased network reactivity and decreased affective response to negative images or negative self-descriptive material [Adapted with permission from Hamilton et al. (9)]. Amyg, amygdala; dACC, dorsal anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; HC, healthy controls; IAPS, International Affective Picture System; ROI, region of interest; L, left; NucAcc, nucleus accumbens; PCC, posterior cingulate cortex; pgACC, pregenual anterior cingulate cortex; R, right; sgACC, subgenual anterior cingulate cortex; SRET, Self-Referential Encoding Task; VLPFC, ventrolateral prefrontal cortex.
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Biological Psychiatry February 15, 2017; 81:e29–e31 www.sobp.org/journal
Biological Psychiatry
Commentary
One use for neurocognitive markers is to translate them into clinical settings where they may support more precise psychiatric diagnosis and treatment selection. However, for neurocognitive markers to be translatable, it is also important to understand how—and whether—they are causally associated with disease processes or mechanisms of treatment. For example, isolating discrete brain regions that drive network-level anomalies may be important when making decisions about targets for brain stimulation or pharmacotherapy (7). Price et al. use a method for evaluating effective connectivity that estimates directional associations among nodes of functional networks, a potential step toward understanding causal relationships between regions of large-scale networks that are disrupted in depression. A next step is to determine causal relationships not only on singular levels of functioning, but across levels of neurocognition: for example, determining whether a particular profile of network functioning causes or is consequent to mood dysregulation, or if no causal associations exist. Answering these questions may involve longitudinal studies that track the temporal sequence of neurocognitive and psychiatric dysfunction (8) or studies that test the cognitive consequences of interventions designed to manipulate network functioning, e.g., through neurofeedback (Figure 1C) (9). Making feasible the translation of neurocognitive markers also requires that we identify the most parsimonious, costeffective method of assessing that marker. One strategy may be to begin with low-cost methods of testing the marker on one level of analysis, e.g., using cognitive testing, to select individuals who may benefit from additional testing on additional levels such as neuroimaging. By scaffolding the pipeline of clinical evaluation in this way, we capitalize on the probability that abnormality on one level of functioning indicates a specified pattern of abnormality on other levels of functioning and thereby indicates specific targets for treatment. In conclusion, the search for neurocognitive markers is a growing field as investigators seek new strategies for understanding the heterogeneity of mood disorders. Price et al. contribute a candidate marker characterized by ventral hyperconnectivity in response to positive mood, which together with amplified resting-state functional connectivity in midline systems and altered behavioral response to emotional material may characterize a common subtype of depression. While reproducibility will be important, these efforts are a timely addition to our understanding of mood pathology and generate interesting directions for future neurocognitive research.
Acknowledgments and Disclosures Early Career Investigator Commentaries are solicited in partnership with the Education Committee of the Society of Biological Psychiatry. As part of the educational mission of the Society, all authors of such commentaries are mentored by a senior investigator. This work was mentored by Diego Pizzagalli, Ph.D. I am grateful to Dr. Diego Pizzagalli for valuable comments. The author reports no biomedical financial interests or potential conflicts of interest.
Article Information From the Department of Psychology, University of California, Los Angeles, Los Angeles, California. Address correspondence to Roselinde H. Kaiser, Ph.D., Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Los Angeles, CA 90095-1563; E-mail: [email protected]. Received Nov 15, 2016; accepted Nov 16, 2016.
References 1.
2.
3.
4. 5.
6.
7.
8.
9.
Kessler RC, Petukhova M, Sampson NA, Zaslavsky AM, Wittchen HU (2012): Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int J Methods Psychiatr Res 21:169–184. Walker ER, McGee RE, Druss BG (2015): Mortality in mental disorders and global disease burden implications: A systematic review and metaanalysis. Jama Psychiatry 72:334–341. Price RB, Lane S, Gates K, Kraynak TE, Horner MS, Thase ME, Siegle GJ (2017): Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biol Psychiatry 81:347–357. Pizzagalli DA (2014): Depression, stress, and anhedonia: Toward a synthesis and integrated model. Annu Rev Clin Psychol 10:393–423. Pencina MJ, D'Agostino RB, Vasan RS (2008): Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat Med 27:157–172. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA (2015): Largescale network dysfunction in major depressive disorder: A metaanalysis of resting-state functional connectivity. JAMA Psychiatry 72: 603–611. Fox MD, Buckner RL, White MP, Greicius MD, Pascual-Leone A (2012): Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol Psychiatry 72:595–603. Snyder HR, Hankin BL (2016): Spiraling out of control stress generation and subsequent rumination mediate the link between poorer cognitive control and internalizing psychopathology. Clin Psychol Sci 4: 1047–1064. Hamilton JP, Glover GH, Bagarinao E, Chang C, Mackey S, Sacchet MD, Gotlib IH (2016): Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder. Psychiatry Res 249:91–96.
Biological Psychiatry February 15, 2017; 81:e29–e31 www.sobp.org/journal
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Biological Psychiatry
Neurocognitive Markers of Depression Roselinde H. Kaiser Depressive disorders are among the most prevalent, debilitating, costly, and deadly illnesses in the world (1,2), but heterogeneity in how depression manifests for individuals has complicated attempts to identify a singular neurobiological substrate of depression or devise broadly effective therapies. In response to the challenge of heterogeneous disease presentation, an emerging field of psychiatric research capitalizes on individual differences in neural, cognitive, and emotional functioning to identify the constellation of neurocognitive features that mark a particular profile of mood disorder. One goal of such research is to develop neurocognitive markers that can be used on a person-specific basis to diagnose and predict depression and to select precise targets of treatment. However, to translate neurocognitive research to a precision medicine framework, it will first be necessary to carefully evaluate the validity, sensitivity, and specificity of multimodal neurocognitive markers in diverse samples. The investigation of Price et al. (3) reported in the current issue of Biological Psychiatry is an important step toward these objectives. Considerations when developing a neurocognitive marker include selection of functional domains to be targeted, the methods of evaluating those domains, and an analytic approach for defining the marker. Price et al. use a combination of task-based and resting-state functional magnetic resonance imaging, a behavioral index of emotional distractor inhibition, and self-report measures of affective reactivity and symptoms to characterize neurocognitive functioning in adults with or without major depression. Of note, this investigation combines a hypothesis-driven focus on positive affective functioning with a data-driven analytic approach for clustering individuals on the basis of functional network response to positive mood induction. Thus, Price et al. capitalize on both conceptual models that describe anhedonia as a core feature and potential subtype of depression (4) and the rich interindividual variability in functional network activity that can be mined for data-driven clustering. Results of the Price et al. clustering analysis revealed two groups of people defined by distinct profiles of network functioning during the positive mood manipulation, with one group exhibiting generally elevated functional connectivity, particularly in paths originating in ventral frontoinsular systems (Figure 1A), and the other group exhibiting relatively lower functional connectivity except in paths originating in parietal or pregenual regions. These data-derived clusters, in turn, were predictors of emotional functioning: the high-connectivity group captured the majority (80%) of depressed individuals, and depressed participants characterized by hyperconnectivity reported greater difficulty maintaining positive mood than participants characterized by hypoconnectivity. Of note, brain network responses to positive material also predicted behavioral responses to negative material: depressed
hyperconnectivity individuals exhibited greater attention bias toward negative emotional distractors than did depressed hypoconnectivity individuals. The finding that frontoinsular network dysfunction was associated with aberration at both ends of the emotional spectrum is an intriguing pattern that hints at the complexity of neurocognitive functioning in depression and highlights the potential for data-driven approaches to refine theories regarding subtypes of illness. Together, these results suggest that hyperconnectivity in ventral frontoinsular systems coupled with blunted positiveemotion processing and amplified negative-emotion processing may be a promising neurocognitive marker of depression. Identifying a pattern of cohesive individual differences across neurobiological and cognitive levels of analysis is the first step toward developing a neurocognitive marker; the second step is to critically evaluate the validity of such markers. Two important criteria are that the marker has adequate sensitivity (high likelihood that individuals with the disorder exhibit the marker) and specificity (low likelihood that individuals without the disorder exhibit the marker) (5). However, the goal of evaluating sensitivity and specificity is difficult to achieve when the disorder itself —e.g., a potential subtype of depression—is an emerging concept that cannot yet be validated against explicit symptom measures. Nevertheless, Price et al. provide compelling evidence that positive-mood–responsive ventral hyperconnectivity is a sensitive marker for a common strain of depression, with four out of five depressed subjects demonstrating the marker. Taskresponsive ventral hyperconnectivity was less successful in a test of specificity, as nondepressed participants were equally likely to cluster into the high or low task-responsive connectivity groups. However, by integrating information from a separate, resting-state paradigm, a stronger case for specificity may be made: healthy individuals who exhibited taskresponsive hyperconnectivity in ventral regions also tended to show reduced resting-state functional connectivity in a medial circuit of the default network originating in the posterior cingulate. In contrast, consistent with previous research (Figure 1B) (6), amplified resting-state hyperconnectivity in the same circuit characterized depressed individuals in this study regardless of task-related network response. One interesting possibility raised by Price et al. is that dampened intrinsic functional connectivity in the default network, a set of regions functionally implicated in self-referential and internally directed thinking, is a compensatory mechanism that counterbalances task-responsive hyperconnectivity and is related to emotional resilience. Future studies that explore this possibility may use neurocognitive markers that integrate not only multiple levels of functioning, but also multiple modalities within each level (e.g., indices of resting-state and task-responsive network activity) to predict mental health.
SEE CORRESPONDING ARTICLE ON PAGE 347
http://dx.doi.org/10.1016/j.biopsych.2016.11.007 ISSN: 0006-3223
& 2016 Society of Biological Psychiatry. e29 Biological Psychiatry February 15, 2017; 81:e29–e31 www.sobp.org/journal
Biological Psychiatry
Commentary
Figure 1. Potential neurocognitive markers of depression. A neurocognitive marker can be defined as the cohesive pattern of altered functioning across multiple levels of analysis that is reliably and specifically associated with a disorder. (A) The study by Price et al. (3) suggests that amplified functional connectivity (FC) of ventral frontoinsular systems (exhibited by Group B) together with impaired ability to maintain positive mood or ignore negative distractors may constitute a neurocognitive marker of a common variant of depression; regions of default network in red, of ventral attention (or salience) network in purple, of affective network in gold, and of frontoparietal network in green [Adapted from Price et al. (3)]. (B) Converging evidence that abnormal FC among frontoinsular and midline regions may represent a neurocognitive marker of depression comes from a meta-analysis of resting-state FC abnormalities in children and adults with major depressive disorder (MDD), which revealed reliable patterns of hyperconnectivity or hypoconnectivity among regions of the default, frontoparietal, and ventral attention networks [Adapted from Kaiser et al. (6)]. (C) Clinical interventions that target neurocognitive markers may illuminate causal relationships among levels of dysfunction; research by Hamilton et al. (9) provides interesting evidence that neurofeedback designed to modulate frontoinsular response to negative emotional images is related to decreased network reactivity and decreased affective response to negative images or negative self-descriptive material [Adapted with permission from Hamilton et al. (9)]. Amyg, amygdala; dACC, dorsal anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; HC, healthy controls; IAPS, International Affective Picture System; ROI, region of interest; L, left; NucAcc, nucleus accumbens; PCC, posterior cingulate cortex; pgACC, pregenual anterior cingulate cortex; R, right; sgACC, subgenual anterior cingulate cortex; SRET, Self-Referential Encoding Task; VLPFC, ventrolateral prefrontal cortex.
e30
Biological Psychiatry February 15, 2017; 81:e29–e31 www.sobp.org/journal
Biological Psychiatry
Commentary
One use for neurocognitive markers is to translate them into clinical settings where they may support more precise psychiatric diagnosis and treatment selection. However, for neurocognitive markers to be translatable, it is also important to understand how—and whether—they are causally associated with disease processes or mechanisms of treatment. For example, isolating discrete brain regions that drive network-level anomalies may be important when making decisions about targets for brain stimulation or pharmacotherapy (7). Price et al. use a method for evaluating effective connectivity that estimates directional associations among nodes of functional networks, a potential step toward understanding causal relationships between regions of large-scale networks that are disrupted in depression. A next step is to determine causal relationships not only on singular levels of functioning, but across levels of neurocognition: for example, determining whether a particular profile of network functioning causes or is consequent to mood dysregulation, or if no causal associations exist. Answering these questions may involve longitudinal studies that track the temporal sequence of neurocognitive and psychiatric dysfunction (8) or studies that test the cognitive consequences of interventions designed to manipulate network functioning, e.g., through neurofeedback (Figure 1C) (9). Making feasible the translation of neurocognitive markers also requires that we identify the most parsimonious, costeffective method of assessing that marker. One strategy may be to begin with low-cost methods of testing the marker on one level of analysis, e.g., using cognitive testing, to select individuals who may benefit from additional testing on additional levels such as neuroimaging. By scaffolding the pipeline of clinical evaluation in this way, we capitalize on the probability that abnormality on one level of functioning indicates a specified pattern of abnormality on other levels of functioning and thereby indicates specific targets for treatment. In conclusion, the search for neurocognitive markers is a growing field as investigators seek new strategies for understanding the heterogeneity of mood disorders. Price et al. contribute a candidate marker characterized by ventral hyperconnectivity in response to positive mood, which together with amplified resting-state functional connectivity in midline systems and altered behavioral response to emotional material may characterize a common subtype of depression. While reproducibility will be important, these efforts are a timely addition to our understanding of mood pathology and generate interesting directions for future neurocognitive research.
Acknowledgments and Disclosures Early Career Investigator Commentaries are solicited in partnership with the Education Committee of the Society of Biological Psychiatry. As part of the educational mission of the Society, all authors of such commentaries are mentored by a senior investigator. This work was mentored by Diego Pizzagalli, Ph.D. I am grateful to Dr. Diego Pizzagalli for valuable comments. The author reports no biomedical financial interests or potential conflicts of interest.
Article Information From the Department of Psychology, University of California, Los Angeles, Los Angeles, California. Address correspondence to Roselinde H. Kaiser, Ph.D., Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Los Angeles, CA 90095-1563; E-mail: [email protected]. Received Nov 15, 2016; accepted Nov 16, 2016.
References 1.
2.
3.
4. 5.
6.
7.
8.
9.
Kessler RC, Petukhova M, Sampson NA, Zaslavsky AM, Wittchen HU (2012): Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int J Methods Psychiatr Res 21:169–184. Walker ER, McGee RE, Druss BG (2015): Mortality in mental disorders and global disease burden implications: A systematic review and metaanalysis. Jama Psychiatry 72:334–341. Price RB, Lane S, Gates K, Kraynak TE, Horner MS, Thase ME, Siegle GJ (2017): Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biol Psychiatry 81:347–357. Pizzagalli DA (2014): Depression, stress, and anhedonia: Toward a synthesis and integrated model. Annu Rev Clin Psychol 10:393–423. Pencina MJ, D'Agostino RB, Vasan RS (2008): Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond. Stat Med 27:157–172. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA (2015): Largescale network dysfunction in major depressive disorder: A metaanalysis of resting-state functional connectivity. JAMA Psychiatry 72: 603–611. Fox MD, Buckner RL, White MP, Greicius MD, Pascual-Leone A (2012): Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biol Psychiatry 72:595–603. Snyder HR, Hankin BL (2016): Spiraling out of control stress generation and subsequent rumination mediate the link between poorer cognitive control and internalizing psychopathology. Clin Psychol Sci 4: 1047–1064. Hamilton JP, Glover GH, Bagarinao E, Chang C, Mackey S, Sacchet MD, Gotlib IH (2016): Effects of salience-network-node neurofeedback training on affective biases in major depressive disorder. Psychiatry Res 249:91–96.
Biological Psychiatry February 15, 2017; 81:e29–e31 www.sobp.org/journal
e31