Attention Bias In Rumination And Depression: Cognitive .
HHS Public AccessAuthor manuscriptAuthor ManuscriptClin Psychol Sci. Author manuscript; available in PMC 2019 November 01.Published in final edited form as:Clin Psychol Sci. 2018 November ; 6(6): 765–782. doi:10.1177/2167702618797935.Attention Bias in Rumination and Depression: CognitiveMechanisms and Brain NetworksRoselinde H. Kaiser1,*, Hannah R. Snyder2, Franziska Goer3, Rachel Clegg3, ManonIronside3, and Diego A. Pizzagalli3,4,5Author Manuscript1Departmentof Psychology and Neuroscience, University of Colorado Boulder2Departmentof Psychology, Brandeis University3Center4Mcleanfor Depression, Anxiety and Stress Research, McLean HospitalImaging Center, McLean Hospital, Belmont, MA, USA5Departmentof Psychiatry, Harvard Medical SchoolAbstractAuthor ManuscriptDepressed individuals exhibit biased attention to negative emotional information. However, muchremains unknown about (1) the neurocognitive mechanisms of attention bias (e.g., qualities ofnegative information that evoke attention bias, or functional brain network dynamics that mayreflect a propensity for biased attention) and (2) distinctions in the types of attention bias related todifferent dimensions of depression (e.g., ruminative depression). Here, in 50 women, clinicaldepression was associated with facilitated processing of negative information only when suchinformation was self-descriptive and task-relevant. However, among depressed individuals, traitrumination was associated with biases towards negative self-descriptive information regardless oftask goals, especially when negative self-descriptive material was paired with self-referentialimages that should be ignored. Attention biases in ruminative depression were mediated bydynamic variability in frontoinsular resting-state functional connectivity. These findings highlightpotential cognitive and functional network mechanisms of attention bias specifically related to theruminative dimension of depression.Keywordscognitive bias; attention bias; rumination; depression; functional connectivityAuthor Manuscript*Corresponding author: Roselinde H. Kaiser, Ph.D., Research on Affective Disorders and Development (RADD) Lab, Department ofPsychology and Neuroscience, University of Colorado Boulder, Muenzinger D244, 345 UCB, Boulder CO 80309,Roselinde.Kaiser@colorado.edu.Author ContributionsR.H.K. developed the study concept. R.H.K., H.S., and D.A.P. contributed to the study design. Testing and data collection wereperformed by R.H.K., F.G., R.C, and M.I. R.H.K. performed the data analysis, and R.H.K., H.S., and D.A.P. performed interpretationof analyses. D.A.P and R.H.K. provided funding. R.H.K. drafted the paper, and all other authors provided critical revisions. Allauthors approved the final version of the paper for submission.Declaration of Conflicting InterestsThe authors declared no conflicts of interest with respect to the authorship or the publication of this article. Over the past three years,D.A.P received consulting fees from Akili Interactive Labs, BlackThorn Therapeutics, Boehringer Ingelheim, Pfizer, and Posit Sciencefor activities unrelated to the current study.
Kaiser et al.Page 2Author ManuscriptAuthor ManuscriptCognitive models of depression propose that negative beliefs about the self are central todepressive disorders, driving negative interpretations and automatic thinking that biasgoaldirected attention (Beck, 2008). Such models have received robust support, and haveinformed the development of psychosocial interventions focused on redirecting attention inthe presence of negative cognitions (Eisendrath et al., 2016; Hollon & Ponniah, 2010).However, clinical research has also revealed a lack of precision in our understanding ofcognitive or neural mechanisms of attention biases. In particular, evidence is mixedregarding the specific domains of attention that are biased in depression, the qualities ofnegative information that bias attention, and functioning of neural systems that may beassociated with a propensity towards biased attention. Furthermore, there is considerableheterogeneity in the cognitive biases exhibited by depressed individuals (Everaert, Koster, &Derakshan, 2012). One source of this heterogeneity in attention biases may be heterogeneityin depression phenotypes. That is, dimensional features such as trait rumination, which varyacross individuals with depression, may interact with mood to produce distinct profiles ofattention bias. The current study thus seeks to gain a better understanding of the specificityand nature of attention biases related to ruminative depression, a critical step for moreprecisely characterizing mood disorder at an individual level.Understanding Component Mechanisms of Attention Bias in DepressionAuthor ManuscriptMeta-analytic evidence indicates that depression (current or past diagnosis, or elevatedsymptoms of depression) is associated with slowed responses when naming the ink color ofemotionally negative words, and speeded responses to negative targets or cues that arespatially congruent with negative words or images (Epp, Dobson, Dozois, & Frewen, 2012;Peckham, McHugh, & Otto, 2010; Winer & Salem, 2016). Neuroimaging studies haveprovided converging evidence for attention biases in depression, e.g., showing that elevatedsymptoms of depression are related to increased activity and functional connectivity amongprefrontal cognitive systems and midline regions involved in self-directed attention inresponse to negative distractors on an emotion word Stroop (R. H. Kaiser, Andrews-Hanna,Spielberg, et al., 2015). These findings provide evidence for attention biases in depression,and reveal important distinctions in the impact of biases on performance (e.g., enhancedperformance when negative information is consistent with task goals but impairedperformance when negative information is inconsistent with goals). However, effect sizeshave been inconsistent across meta-analyses and individual studies, suggesting that attentionbiases may be less reliable or more complex than originally suspected.Author ManuscriptTo address mixed findings for attention bias in depression, more recent theories point toestablished models in cognitive neuroscience emphasizing that attention is not a unitaryconstruct, but includes subprocesses such as orienting, selecting, engaging, and disengagingfrom stimuli (Petersen & Posner, 2012; Posner & Boies, 1971), which may be differentiallyassociated with depression. One theory proposes that attention biases in depression arespecifically active at later stages of processing, e.g., facilitating elaboration of (and difficultydisengaging from) negative information once it has captured attention (De Raedt & Koster,2010). This idea is supported by evidence that individuals with depression show attentionbiases for negative information when such information is either presented at longer( 500ms) but not shorter ( 250ms) durations in the dot-probe paradigm, or is followed byClin Psychol Sci. Author manuscript; available in PMC 2019 November 01.
Kaiser et al.Page 3Author Manuscriptlonger ( 1300ms) but not shorter ( 250ms) delays to a target in the exogenous cueingparadigm (E. H. W. Koster, De Raedt, Goeleven, Franck, & Crombez, 2005; Ernst H. W.Koster, De Raedt, Leyman, & De Lissnyder, 2010; Mogg, Bradley, & Williams, 1995;Sylvester, Hudziak, Gaffrey, Barch, & Luby, 2016). In addition, the idea that attention biasesare specific to later stages of processing is supported by evidence that depression is relatedto increased dwell time looking at negative material but no differences in initial orienting(Caseras, Gamer, Bradley, & Mogg, 2007; Leyman, De Raedt, Vaeyens, & Philippaerts,2011; Matthews & Antes, 1992). However, metaanalyses have yielded equivocal support forattention biases at later (elaborated) as well as earlier (orienting) stages of processing indepression, or failed to find an association between attention bias and stimulus duration(Armstrong & Olatunji, 2012; Peckham et al., 2010). Thus, while important distinctions mayexist in the attention subprocesses that are biased in depression, evidence for thosedistinctions is not yet conclusive.Author ManuscriptAuthor ManuscriptIn addition to distinguishing which domains of attention are biased in depression, acomplementary goal is to distinguish the types of information that evoke such bias, i.e., whatis it about negative emotional information that captures or holds attention? At least threepotential answers exist for this question. One is that depressed individuals are drawn towardsnegative emotional content because it matches their current mood state. This moodcongruence hypothesis is supported by evidence that experimentally-induced negative moodin healthy individuals can induce attention biases towards negative material that are similarto those exhibited in depression (Bradley, Mogg, & Lee, 1997; Gilboa-Schechtman, Revelle,& Gotlib, 2000; Gotlib & McCann, 1984; Isaac et al., 2012; Ridout, Noreen, & Johal, 2009).However, these effects have not been consistently replicated (Chepenik, Cornew, & Farah,2007; McCabe, Gotlib, & Martin, 2000; Newman & Sears, 2015). In addition, attentionbiases in depression have been observed with other forms of negative information (e.g.,anger) that are putatively unrelated to mood state (Lonigan & Vasey, 2009; Mogg et al.,1995; Oehlberg, Revelle, & Mineka, 2012; Platt, Murphy, & Lau, 2015). Together, thesefindings suggest that depression-related attention biases are not exclusively explained bymood congruency.Author ManuscriptA second explanation is that depressed individuals are more sensitive to self-referentialinformation (regardless of emotional content), and biases towards negative information arecoincident to the fact that depressed individuals happen to have a self-concept that is morenegative than non-depressed individuals. Across clinical and non-clinical samples, selfrelatedness of stimuli has been shown to facilitate recall and perceptual integration ofinformation (reviewed in (Sui & Humphreys, 2015)) and boost activity and functionalconnectivity in brain systems including medial prefrontal cortex (MPFC), insula,hippocampus, and areas of anterior and posterior cingulate cortex (ACC, PCC) (Craik et al.,1999; Fossati et al., 2003; Macrae, Moran, Heatherton, Banfield, & Kelley, 2004; Murray,Debbane, Fox, Bzdok, & Eickhoff, 2015; Murray, Schaer, & Debbane, 2012). These neuralsystems, many of which are grouped in a functional network known as the default network,show increased functional connectivity during autobiographical thinking (Young, Siegle,Bodurka, & Drevets, 2016) and other forms of self-focused attention (reviewed in (Qin &Northoff, 2011)). Critically, default network and frontoinsular regions also exhibit restingstate hyperconnectivity in major depression (R. H. Kaiser, Andrews-Hanna, Wager, &Clin Psychol Sci. Author manuscript; available in PMC 2019 November 01.
Kaiser et al.Page 4Author ManuscriptPizzagalli, 2015). Although caution to avoid reverse inference is warranted wheninterpreting these converging patterns, one theory is that amplified activity and coordinationamong frontoinsular-default networks is a marker of attention biases towards self-focusedthinking in depressed individuals.Author ManuscriptA third explanation for negative attention biases in depression points to the interactionbetween self-relatedness and emotional valence of information: e.g., that attention is biasedtowards positive (but not negative) self-referential information in healthy people, andtowards negative (but not positive) self-referential information in depressed people.Consistent with this assumption, research in healthy individuals has demonstrated fasterjudgements of, and increased medial prefrontal activity in response to, positive as comparedwith negative self-referential information (Moran, Macrae, Heatherton, Wyland, & Kelley,2006; Watson, Dritschel, Obonsawin, & Jentzsch, 2007). In contrast, depressed individualsexhibit a reversed pattern of reduced prefrontal and hippocampal response to positive selfreferential information (Quevedo et al., 2016), and amplified response to negative selfreferential information (Macdonald & Kuiper, 1985; Shestyuk & Deldin, 2010). Thesefindings complement evidence for enhanced self-focused attention and frontoinsular-defaultnetwork activity in depression, but suggest that the combination of self-relatedness andnegative emotionality is responsible for evoking attention bias.Author ManuscriptThe hypotheses outlined above have been only partially tested, because although manyexperiments have manipulated the emotional content of stimuli, few have directlymanipulated the self-referential quality of stimuli (reviewed in (Wisco, 2009)). Instead,researchers have commonly defined the self-referential nature of stimuli post-hoc on thebasis of task performance (e.g., reaction time or neural response to unselected emotionalwords while words are being judged on their self-referential quality (Alloy, Abramson,Murray, Whitehouse, & Hogan, 1997; Connolly, Abramson, & Alloy, 2016; Gencoz, Voelz,Gencoz, Pettit, & Joiner, 2001)). Although this approach has merit, the same response biasesthat are of interest in this research can also confound the comparison of depressed and nondepressed participants on specific categories of self-referential information, e.g., healthyindividuals may endorse few negative words as self-descriptive, whereas depressedindividuals may endorse many negative words as self-descriptive, yielding unbalanced setsof stimuli for further experimentation or statistical analysis (discussion in (Connolly et al.,2016)). In sum, research optimized for testing attention to emotionally negative (or positive)and self-descriptive (or non-self-descriptive) information is needed to understand thequalities of emotional information that evoke attention bias.Author ManuscriptAttention Bias Across Clinical Phenotypes: Rumination and DepressionExperimental paradigms designed to unpack the neurocognitive mechanisms of attentionbias are necessary to refine our understanding of how and when attention biases occur indepression. However, such research, when conducted exclusively using categorical casecontrol designs, may be insufficient for understanding individual differences in attentionbias. Depression is a complex and heterogeneous family of disorders, with varying symptompresentations, etiologies, and functional impairments exhibited across individuals (R.H.Kaiser, 2017). Thus, attention biases may not characterize different depressed individuals toClin Psychol Sci. Author manuscript; available in PMC 2019 November 01.
Kaiser et al.Page 5Author Manuscriptthe same extent. Here, the dimension of trait rumination, defined by the tendency towardsnegative and repetitive self-focused thinking (Susan Nolen-Hoeksema, Wisco, &Lyubomirsky, 2008), may be particularly relevant. Consistent with this notion, nondepressed individuals prone to rumination exhibit attention biases that overlap with thoseobserved in depression (Beckwe & Deroost, 2016; Hilt & Pollak, 2013), and higher levels ofrumination among depressed individuals are associated with more extreme attention biases(Donaldson, Lam, & Mathews, 2007). This convergence suggests the possibility that traitrumination may explain or exacerbate attention biases associated with depression.Author ManuscriptHowever, not all depressed individuals are prone to rumination, and not all individuals proneto rumination are depressed. Accordingly, it is possible is that different symptom dimensionsinteract to produce distinct phenotypes of depression characterized by unique profiles ofattention bias. In particular, whereas both depression and trait rumination have beenseparately linked to biased elaboration (or difficulty disengaging from) negative information(Joormann, Levens, & Gotlib, 2011; Joormann, Nee, Berman, Jonides, & Gotlib, 2010; R.H. Kaiser, Andrews-Hanna, Metcalf, & Dimidjian, 2015), some evidence suggests thatdepressed ruminators also exhibit biases orienting to or selecting negative information (DeLissnyder, Derakshan, De Raedt, & Koster, 2011; Joormann, Dkane, & Gotlib, 2006;Whitmer & Banich, 2007). Thus, depressed ruminators may be uniquely characterized byboth preferential attention to, and elaboration of, negative self-referential thoughts (relateddiscussion in (Everaert et al., 2012)). Such biases match the clinical profile of ruminativedepression (i.e., elevated trait rumination co-occurring with depression), in whichrumination is experienced as intrusive and difficult to escape (Papageorgiou & Wells, 2001).Author ManuscriptAuthor ManuscriptOn the level of brain functioning, ruminative depression has been associated with increasedresting-state functional connectivity (RSFC) among regions of the default network (Bermanet al., 2011) and highly variable RSFC between medial prefrontal cortex (MPFC) regions ofdefault network and anterior insula (R.H. Kaiser et al., 2016). As noted above, the defaultnetwork comprises midline, inferior temporal, and parietal regions involved in selfgenerated, self-focused, or autobiographical thinking (Andrews-Hanna, Smallwood, &Spreng, 2014), whereas the anterior insula is considered to be a hub of the “saliencenetwork” involved in allocating resources to other networks (including default network) onthe basis of salient cues or thoughts (Menon & Uddin, 2010; Sridharan, Levitin, & Menon,2008). Prior research suggests that variability in cross-network RSFC may be related toregulatory relationships in which key regions of one network are engaged in up- or downregulating activity in a second network at rest or in response to cognitive demands(Hutchison & Morton, 2015; Hutchison et al., 2013). Thus, increased variability in RSFCbetween regions of the salience (insula) and default (MPFC) networks may reflect anindividual’s heightened tendency to recruit these cross-network regulatory systems, i.e.,increased tendency for insula to be engaged to up- or down-regulate activity in defaultnetwork (Sridharan et al., 2008). On the level of cognitive processing, such increasedfrontoinsular variability may reflect a tendency for biased allocation of resources towardsself-focused thinking, or efforts to regulate self-focused thinking. Together, this suggests amodel in which ruminative depression is related to attention biases via altered functioning offrontoinsular and default network regions.Clin Psychol Sci. Author manuscript; available in PMC 2019 November 01.
Kaiser et al.Page 6Author ManuscriptPresent StudyAuthor ManuscriptThe present study aimed to provide insight into the cognitive mechanisms and functionalnetwork correlates of depression, and in particular, the ruminative dimension of depression.Toward this goal, we developed a behavioral task designed to separately manipulate the selfreferential quality and emotional valence of information, and that varied the depth ofelaboration for (self-referential or emotional aspects of) information (see (Elliott,Rubinsztein, Sahakian, & Dolan, 2000; Etkin, Egner, Peraza, Kandel, & Hirsch, 2006) fordescriptions of other tasks with partially overlapping procedures). In this task, participantsjudged either the self-descriptiveness or the emotional valence of words (pre-selected to beself-descriptive or non-self-descriptive, crossed by negative or positive emotion), whileignoring background images that were selfreferential (own face) or non-self-referential(other face). Therefore, the content of the word must always be elaborated upon in order tocomplete the task, although the level of elaboration of specific features of the word (selfdescriptiveness or valence) depends on the relevance of that feature to task goals (e.g., selfdescriptiveness should be more deeply processed when the goal is to judge selfdescriptiveness than when the goal is to judge emotional valence). Meanwhile, the selfreferential content of the images s
Attention Bias in Rumination and Depression: Cognitive Mechanisms and Brain Networks Roselinde H. Kaiser1,*, Hannah R. Snyder2, Franziska Goer3, Rachel Clegg3, Manon Ironside3, and Diego A. Pizzagalli3,4,5 1Department of Psychology and Neuroscience, University of Colorado Boulder 2Department of Psychology, Brandeis University 3Center for Depression, Anxiety and Stress Research, McLean Hospital
rumination is an adaptive coping mechanism that might be part of the problem-solving process, or whether it is maladaptive and increases the risk for depression. Thus, in research on problem solving and goal attainment, rumination has been concep-tualized as an attempt at problem solving and as a self-regulatory strategy used to resolve unattained
The Rumination Cure I used to be a big time ruminator. I think I could have qualified for the rumination Olympics if there were such a thing. My ruminations would last for days after even the mildest incident. Worse, my ruminations were accompanied by visions of self-harm. Yes, I actually had mental pictures of
(4 Hours) Biasing of BJTs: Load lines (AC and DC); Operating Points; Fixed Bias and Self Bias, DC Bias with Voltage Feedback; Bias Stabilization; Examples. (4 Hours) Biasing of FETs and MOSFETs: Fixed Bias Configuration and Self Bias Configuration, Voltage Divider Bias and Design (4 Hours) MODULE - II (12 Hours) .
CHAPTER 11 Conservatism Bias 119 CHAPTER 12 Ambiguity Aversion Bias 129 CHAPTER 13 Endowment Bias 139 CHAPTER 14 Self-Control Bias 150 CHAPTER 15 Optimism Bias 163 Contents vii 00_POMPIAN_i_xviii 2/7/06 1:58 PM Page vii. CHAPTER 16 Mental Accounting Bias 171 CHAPTER 17 Confirmation Bias 187
DC Biasing BJT circuits There is numerous bias configuration of BJT circuits. Some of the common configuration of BJT circuit includes 1. Fixed-bias circuit 2. Emitter-bias circuit 3. Voltage divider bias circuit 4. Collector-feedback bias circuit 5. Emitter-follower bias circuit 6. Common base circuit Fixed Bias Configuration
ES-5: PREVENTING SOCIAL BIAS Controlling Social Bias involves understanding, identifying, and actively countering bias. It is important to reflect on the nature of bias and how it comes about before attempting to control social bias. Bias is a part of human nature because we all naturally prefer familiar things and familiar ways of thinking.
A sensor bias current will source from Sensor to Sensor- if a resistor is tied across R BIAS and R BIAS-. Connect a 10 kΩ resistor across Sensor and Sensor- when using an AD590 temperature sensor. See STEP 4 Sensor - Pins 13 & 14 on page 8. 15 16 R BIAS R BIAS-SENSOR BIAS CURRENT (SW1:7, 8, 9, 10)
ASTM C 1628 06ASTM C 1628 06 Standard Specification for Joints for Concrete Gravity Flow Sewer Pipe Using Rubber Gaskets AS C 16 09ASTM C 1677 09 Standard Specification for Joints for Concrete Box, Using Rubber Gaskets ASTM C 1619 05 Standard Specification for Elastomeric Seals for Joining Concrete Structures ASTM C 505 05a Standard Specification for Irrigation Pipe with Rubber Gasket Joints .
