On the neural basis of emotion processing in depression and anxiety : an fMRI study in outpatients

The wide implications of emotions in our social and private life, and the far-reaching consequences of dysfunctions in emotional processing, have made emotion one of the most widely studied psychological processes. During the last decades, there have been numerous attempts to formulate neurobiological and cognitive theories of emotion. Mental disorders, e.g., schizophrenia, bipolar disorders, mood disorder, anxiety, are associated with dysfunction of emotional processing. These dysfunctions may be caused by abnormalities at neural level. From all psychiatric disorders characterized by emotional disturbance, major depressive disorder and anxiety disorders are the most prevalent in our society. For outcome improvement, a clear delineation of the neural mechanism of emotional processing in community-based outpatients is of fundamental importance to understanding their underlying mechanisms. We use the functional magnetic resonance imaging (fMRI) method for studying different cognitive and emotional functions in major depressive disorder and anxiety disorders. The findings presented herein indicate that dysfunctions in the neural circuitry of emotional processing are different in depression and anxiety. Furthermore we find that comorbidity of depression and anxiety cannot be regarded as a summation of the two. We also show that even if there are no gross abnormalities at the neural level, abnormalities in the neural network may cause dysfunctions of emotional processes in mild-remitted patients and participants with high vulnerability for affective disorders. This finding unveils a much more complex picture of emotion perception than the present day theories account for. De brede implicaties van emoties in ons sociale en privéleven, en de verstrekkende gevolgen van problemen in de verwerking van emoties, hebben emotie één van de meest bestudeerde psychologische processen gemaakt. De laatste decennia zijn talrijke pogingen gedaan om neurobiologische en cognitieve theorieën van emotie te formuleren. Mentale stoornissen, zoals schizofrenie, en bipolaire, stemmings-, en angststoornissen, zijn geassocieerd met problemen in de verwerking van emoties. Deze disfuncties zouden op neuronaal niveau veroorzaakt kunnen worden. De meest voorkomende psychiatrische stoornissen in onze samenleving zijn depressie en angststoornissen. Voor het verbeteren van het behandelresultaat, is het van fundamenteel belang om inzicht te krijgen in de neurale mechanismen, die betrokken zijn bij de verwerking van emoties in poliklinische patiënten uit de gemeenschap. We gebruiken de methode van functionele magnetische resonantie (fMRI) om verschillende cognitieve en emotionele functies te onderzoeken in depressie en angststoornissen. De bevindingen, die hier worden gepresenteerd, geven aan dat disfuncties in het neurale circuit van emotieverwerking verschillend zijn in depressie en angst. Verder, vinden we dat comorbiditeit van depressie en angst niet kan worden opgevat als een simpele opsomming van de twee. Ook laten we zien dat, ondanks er geen grote verschillen met gezonde personen aanwezig zijn op neuronaal niveau, abnormaliteiten in het neurale netwerk disfuncties kunnen veroorzaken in de emotionele verwerking van licht verbeterde patiënten en personen met een hoge kwetsbaarheid voor affectieve stoornissen. Deze bevinding onthult een gecompliceerder beeld van de perceptie van emotie dan huidige theorieën aangeven.

On the connection between level of education and the neural circuitry of emotion perception

Through education, a social group transmits accumulated knowledge, skills, customs, and values to its members. So far, to the best of our knowledge, the association between educational attainment and neural correlates of emotion processing has been left unexplored. In a retrospective analysis of The Netherlands Study of Depression and Anxiety (NESDA) functional magnetic resonance imaging (fMRI) study, we compared two groups of fourteen healthy volunteers with intermediate and high educational attainment, matched for age and gender. The data concerned event-related fMRI of brain activation during perception of facial emotional expressions. The region of interest (ROI) analysis showed stronger right amygdala activation to facial expressions in participants with lower relative to higher educational attainment (HE). The psychophysiological interaction analysis revealed that participants with HE exhibited stronger right amygdala-right insula connectivity during perception of emotional and neutral facial expressions. This exploratory study suggests the relevance of educational attainment on the neural mechanism of facial expressions processing

Richness in Functional Connectivity Depends on the Neuronal Integrity within the Posterior Cingulate Cortex

The brain's connectivity skeleton-a rich club of strongly interconnected members-was initially shown to exist in human structural networks, but recent evidence suggests a functional counterpart. This rich club typically includes key regions (or hubs) from multiple canonical networks, reducing the cost of inter-network communication. The posterior cingulate cortex (PCC), a hub node embedded within the default mode network, is known to facilitate communication between brain networks and is a key member of the "rich club." Here, we assessed how metabolic signatures of neuronal integrity and cortical thickness influence the global extent of a functional rich club as measured using the functional rich club coefficient (fRCC). Rich club estimation was performed on functional connectivity of resting state brain signals acquired at 3T in 48 healthy adult subjects. Magnetic resonance spectroscopy was measured in the same session using a point resolved spectroscopy sequence. We confirmed convergence of functional rich club with a previously established structural rich club. N-acetyl aspartate (NAA) in the PCC is significantly correlated with age (p = 0.001), while the rich club coefficient showed no effect of age (p = 0.106). In addition, we found a significant quadratic relationship between fRCC and NAA concentration in PCC (p = 0.009). Furthermore, cortical thinning in the PCC was correlated with a reduced rich club coefficient after accounting for age and NAA. In conclusion, we found that the fRCC is related to a marker of neuronal integrity in a key region of the cingulate cortex. Furthermore, cortical thinning in the same area was observed, suggesting that both cortical thinning and neuronal integrity in the hub regions influence functional integration of at a whole brain level

Enhanced amygdala reactivity to emotional faces in adults reporting childhood emotional maltreatment

In the context of chronic childhood emotional maltreatment (CEM; emotional abuse and/or neglect), adequately responding to facial expressions is an important skill. Over time, however, this adaptive response may lead to a persistent vigilance for emotional facial expressions. The amygdala and the medial prefrontal cortex (mPFC) are key regions in face processing. However, the neurobiological correlates of face processing in adults reporting CEM are yet unknown. We examined amydala and mPFC reactivity to emotional faces (Angry, Fearful, Sad, Happy, Neutral) vs scrambled faces in healthy controls and unmedicated patients with depression and/or anxiety disorders reporting CEM before the age of 16 years (n = 60), and controls and patients who report no childhood abuse (n = 75). We found that CEM was associated with enhanced bilateral amygdala reactivity to emotional faces in general, and independent of psychiatric status. Furthermore, we found no support for differential mPFC functioning, suggesting that amygdala hyper-responsivity to emotional facial perception in adults reporting CEM may be independent from top-down influences of the mPFC. These findings may be key in understanding the increased emotional sensitivity and interpersonal difficulties, that have been reported in individuals with a history of CEM.</p

Whole-brain functional connectivity during emotional word classification in medication-free Major Depressive Disorder: abnormal salience circuitry and relations to positive emotionality

Major Depressive Disorder (MDD) has been associated with biased processing and abnormal regulation of negative and positive information, which may result from compromised coordinated activity of prefrontal and subcortical brain regions involved in evaluating emotional information. We tested whether patients with MDD show distributed changes in functional connectivity with a set of independently derived brain networks that have shown high correspondence with different task demands, including stimulus salience and emotional processing. We further explored if connectivity during emotional word processing related to the tendency to engage in positive or negative emotional states. In this study, 25 medication-free MDD patients without current or past comorbidity and matched controls (n=25) performed an emotional word-evaluation task during functional MRI. Using a dual regression approach, individual spatial connectivity maps representing each subject’s connectivity with each standard network were used to evaluate between-group differences and effects of positive and negative emotionality (extraversion and neuroticism, respectively, as measured with the NEO-FFI). Results showed decreased functional connectivity of the medial prefrontal cortex, ventrolateral prefrontal cortex, and ventral striatum with the fronto-opercular salience network in MDD patients compared to controls. In patients, abnormal connectivity was related to extraversion, but not neuroticism. These results confirm the hypothesis of a relative (para)limbic-cortical decoupling that may explain dysregulated affect in MDD. As connectivity of these regions with the salience network was related to extraversion, but not to general depression severity or negative emotionality, dysfunction of this network may be responsible for the failure to sustain engagement in rewarding behavior

Visual Behavior, Pupil Dilation, and Ability to Identify Emotions From Facial Expressions After Stroke

[EN] Social cognition is the innate human ability to interpret the emotional state of others from contextual verbal and non-verbal information, and to self-regulate accordingly. Facial expressions are one of the most relevant sources of non-verbal communication, and their interpretation has been extensively investigated in the literature, using both behavioral and physiological measures, such as those derived from visual activity and visual responses. The decoding of facial expressions of emotion is performed by conscious and unconscious cognitive processes that involve a complex brain network that can be damaged after cerebrovascular accidents. A diminished ability to identify facial expressions of emotion has been reported after stroke, which has traditionally been attributed to impaired emotional processing. While this can be true, an alteration in visual behavior after brain injury could also negatively contribute to this ability. This study investigated the accuracy, distribution of responses, visual behavior, and pupil dilation of individuals with stroke while identifying emotional facial expressions. Our results corroborated impaired performance after stroke and exhibited decreased attention to the eyes, evidenced by a diminished time and number of fixations made in this area in comparison to healthy subjects and comparable pupil dilation. The differences in visual behavior reached statistical significance in some emotions when comparing individuals with stroke with impaired performance with healthy subjects, but not when individuals post-stroke with comparable performance were considered. The performance dependence of visual behavior, although not determinant, might indicate that altered visual behavior could be a negatively contributing factor for emotion recognition from facial expressions.This study was funded by Conselleria de Educacion, Cultura y Deporte of Generalitat Valenciana of Spain (Project SEJI/2019/017), and Universitat Politecnica de Valencia (Grant PAID-10-18).Maza, A.; Moliner, B.; Ferri, J.; Llorens Rodríguez, R. (2020). Visual Behavior, Pupil Dilation, and Ability to Identify Emotions From Facial Expressions After Stroke. Frontiers in Neurology. 10:1-12. https://doi.org/10.3389/fneur.2019.01415S11210Nijsse, B., Spikman, J. M., Visser-Meily, J. M. A., de Kort, P. L. M., & van Heugten, C. M. (2019). Social cognition impairments are associated with behavioural changes in the long term after stroke. PLOS ONE, 14(3), e0213725. doi:10.1371/journal.pone.0213725Feldman, R. S., White, J. B., & Lobato, D. (1982). Social Skills and Nonverbal Behavior. Development of Nonverbal Behavior in Children, 259-277. doi:10.1007/978-1-4757-1761-7_10Vuilleumier, P., & Pourtois, G. (2007). Distributed and interactive brain mechanisms during emotion face perception: Evidence from functional neuroimaging. Neuropsychologia, 45(1), 174-194. doi:10.1016/j.neuropsychologia.2006.06.003Frith, C. D., & Frith, U. (2006). How we predict what other people are going to do. Brain Research, 1079(1), 36-46. doi:10.1016/j.brainres.2005.12.126Zinchenko, O., Yaple, Z. A., & Arsalidou, M. (2018). Brain Responses to Dynamic Facial Expressions: A Normative Meta-Analysis. Frontiers in Human Neuroscience, 12. doi:10.3389/fnhum.2018.00227Ekman, P. (1993). Facial expression and emotion. American Psychologist, 48(4), 384-392. doi:10.1037/0003-066x.48.4.384Srinivasan, R., & Martinez, A. M. (2021). Cross-Cultural and Cultural-Specific Production and Perception of Facial Expressions of Emotion in the Wild. IEEE Transactions on Affective Computing, 12(3), 707-721. doi:10.1109/taffc.2018.2887267Smith, M. L., Grühn, D., Bevitt, A., Ellis, M., Ciripan, O., Scrimgeour, S., … Ewing, L. (2018). Transmitting and decoding facial expressions of emotion during healthy aging: More similarities than differences. Journal of Vision, 18(9), 10. doi:10.1167/18.9.10Thompson, A. E., & Voyer, D. (2014). Sex differences in the ability to recognise non-verbal displays of emotion: A meta-analysis. Cognition and Emotion, 28(7), 1164-1195. doi:10.1080/02699931.2013.875889Doležal, J., & Fabian, V. (2015). 41. Application of eye tracking in neuroscience. Clinical Neurophysiology, 126(3), e44. doi:10.1016/j.clinph.2014.10.200Guo, K. (2012). Holistic Gaze Strategy to Categorize Facial Expression of Varying Intensities. PLoS ONE, 7(8), e42585. doi:10.1371/journal.pone.0042585Guo, K., Soornack, Y., & Settle, R. (2019). Expression-dependent susceptibility to face distortions in processing of facial expressions of emotion. Vision Research, 157, 112-122. doi:10.1016/j.visres.2018.02.001Eisenbarth, H., & Alpers, G. W. (2011). Happy mouth and sad eyes: Scanning emotional facial expressions. Emotion, 11(4), 860-865. doi:10.1037/a0022758Schurgin, M. W., Nelson, J., Iida, S., Ohira, H., Chiao, J. Y., & Franconeri, S. L. (2014). Eye movements during emotion recognition in faces. Journal of Vision, 14(13), 14-14. doi:10.1167/14.13.14Guo, K., & Shaw, H. (2015). Face in profile view reduces perceived facial expression intensity: An eye-tracking study. Acta Psychologica, 155, 19-28. doi:10.1016/j.actpsy.2014.12.001Guo, K. (2013). Size-Invariant Facial Expression Categorization and Associated Gaze Allocation within Social Interaction Space. Perception, 42(10), 1027-1042. doi:10.1068/p7552Sirois, S., & Brisson, J. (2014). Pupillometry. WIREs Cognitive Science, 5(6), 679-692. doi:10.1002/wcs.1323Eckstein, M. K., Guerra-Carrillo, B., Miller Singley, A. T., & Bunge, S. A. (2017). Beyond eye gaze: What else can eyetracking reveal about cognition and cognitive development? Developmental Cognitive Neuroscience, 25, 69-91. doi:10.1016/j.dcn.2016.11.001Ariel, R., & Castel, A. D. (2013). Eyes wide open: enhanced pupil dilation when selectively studying important information. Experimental Brain Research, 232(1), 337-344. doi:10.1007/s00221-013-3744-5Zekveld, A. A., & Kramer, S. E. (2014). Cognitive processing load across a wide range of listening conditions: Insights from pupillometry. Psychophysiology, 51(3), 277-284. doi:10.1111/psyp.12151De Gee, J. W., Knapen, T., & Donner, T. H. (2014). Decision-related pupil dilation reflects upcoming choice and individual bias. Proceedings of the National Academy of Sciences, 111(5), E618-E625. doi:10.1073/pnas.1317557111Bradley, M. M., Miccoli, L., Escrig, M. A., & Lang, P. J. (2008). The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology, 45(4), 602-607. doi:10.1111/j.1469-8986.2008.00654.xDuque, A., Sanchez, A., & Vazquez, C. (2014). Gaze-fixation and pupil dilation in the processing of emotional faces: The role of rumination. Cognition and Emotion, 28(8), 1347-1366. doi:10.1080/02699931.2014.881327Lanata, A., Armato, A., Valenza, G., & Scilingo, E. P. (2011). Eye tracking and pupil size variation as response to affective stimuli: a preliminary study. Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare. doi:10.4108/icst.pervasivehealth.2011.246056Peinkhofer, C., Knudsen, G. M., Moretti, R., & Kondziella, D. (2019). Cortical modulation of pupillary function: systematic review. PeerJ, 7, e6882. doi:10.7717/peerj.6882Grill-Spector, K., Knouf, N., & Kanwisher, N. (2004). The fusiform face area subserves face perception, not generic within-category identification. Nature Neuroscience, 7(5), 555-562. doi:10.1038/nn1224Ferretti, V., & Papaleo, F. (2018). Understanding others: emotion recognition abilities in humans and other animals. Genes, Brain and Behavior, e12544. doi:10.1111/gbb.12544Sergerie, K., Chochol, C., & Armony, J. L. (2008). The role of the amygdala in emotional processing: A quantitative meta-analysis of functional neuroimaging studies. Neuroscience & Biobehavioral Reviews, 32(4), 811-830. doi:10.1016/j.neubiorev.2007.12.002Rapcsak, S. Z., Galper, S. R., Comer, J. F., Reminger, S. L., Nielsen, L., Kaszniak, A. W., … Cohen, R. A. (2000). Fear recognition deficits after focal brain damage: A cautionary note. Neurology, 54(3), 575-575. doi:10.1212/wnl.54.3.575Radice-Neumann, D., Zupan, B., Tomita, M., & Willer, B. (2009). Training Emotional Processing in Persons With Brain Injury. Journal of Head Trauma Rehabilitation, 24(5), 313-323. doi:10.1097/htr.0b013e3181b09160Yuvaraj, R., Murugappan, M., Norlinah, M. I., Sundaraj, K., & Khairiyah, M. (2013). Review of Emotion Recognition in Stroke Patients. Dementia and Geriatric Cognitive Disorders, 36(3-4), 179-196. doi:10.1159/000353440Babbage, D. R., Yim, J., Zupan, B., Neumann, D., Tomita, M. R., & Willer, B. (2011). Meta-analysis of facial affect recognition difficulties after traumatic brain injury. Neuropsychology, 25(3), 277-285. doi:10.1037/a0021908Milders, M., Fuchs, S., & Crawford, J. R. (2003). Neuropsychological Impairments and Changes in Emotional and Social Behaviour Following Severe Traumatic Brain Injury. Journal of Clinical and Experimental Neuropsychology, 25(2), 157-172. doi:10.1076/jcen.25.2.157.13642Genova, H. M., Genualdi, A., Goverover, Y., Chiaravalloti, N. D., Marino, C., & Lengenfelder, J. (2016). An investigation of the impact of facial affect recognition impairments in moderate to severe TBI on fatigue, depression, and quality of life. Social Neuroscience, 12(3), 303-307. doi:10.1080/17470919.2016.1173584Rigon, A., Voss, M. W., Turkstra, L. S., Mutlu, B., & Duff, M. C. (2018). Different aspects of facial affect recognition impairment following traumatic brain injury: The role of perceptual and interpretative abilities. Journal of Clinical and Experimental Neuropsychology, 40(8), 805-819. doi:10.1080/13803395.2018.1437120Rosenberg, H., McDonald, S., Dethier, M., Kessels, R. P. C., & Westbrook, R. F. (2014). Facial Emotion Recognition Deficits following Moderate–Severe Traumatic Brain Injury (TBI): Re-examining the Valence Effect and the Role of Emotion Intensity. Journal of the International Neuropsychological Society, 20(10), 994-1003. doi:10.1017/s1355617714000940Lancelot, C., & Gilles, C. (2018). How does visual context influence recognition of facial emotion in people with traumatic brain injury? Brain Injury, 33(1), 4-11. doi:10.1080/02699052.2018.1531308McDonald, S. (2013). Impairments in Social Cognition Following Severe Traumatic Brain Injury. Journal of the International Neuropsychological Society, 19(3), 231-246. doi:10.1017/s1355617712001506Vallat-Azouvi, C., Azouvi, P., Le-Bornec, G., & Brunet-Gouet, E. (2018). Treatment of social cognition impairments in patients with traumatic brain injury: a critical review. Brain Injury, 33(1), 87-93. doi:10.1080/02699052.2018.1531309Godin, B., Oishi, K., Oishi, K., Davis, C., Gomez, Y., Trupe, L., … Tippett, D. (2018). Impaired Recognition of Emotional Faces after Stroke Involving Right Amygdala or Insula. Seminars in Speech and Language, 39(01), 087-100. doi:10.1055/s-0037-1608859Abbott, J. D., Cumming, G., Fidler, F., & Lindell, A. K. (2013). The perception of positive and negative facial expressions in unilateral brain-damaged patients: A meta-analysis. Laterality: Asymmetries of Body, Brain and Cognition, 18(4), 437-459. doi:10.1080/1357650x.2012.703206Abbott, J. D., Wijeratne, T., Hughes, A., Perre, D., & Lindell, A. K. (2014). The perception of positive and negative facial expressions by unilateral stroke patients. Brain and Cognition, 86, 42-54. doi:10.1016/j.bandc.2014.01.017Delazer, M., Sojer, M., Ellmerer, P., Boehme, C., & Benke, T. (2018). Eye-Tracking Provides a Sensitive Measure of Exploration Deficits After Acute Right MCA Stroke. Frontiers in Neurology, 9. doi:10.3389/fneur.2018.00359Lech, M., Kucewicz, M. T., & Czyżewski, A. (2019). Human Computer Interface for Tracking Eye Movements Improves Assessment and Diagnosis of Patients With Acquired Brain Injuries. Frontiers in Neurology, 10. doi:10.3389/fneur.2019.00006Spikman, J. M., Milders, M. V., Visser-Keizer, A. C., Westerhof-Evers, H. J., Herben-Dekker, M., & van der Naalt, J. (2013). Deficits in Facial Emotion Recognition Indicate Behavioral Changes and Impaired Self-Awareness after Moderate to Severe Traumatic Brain Injury. PLoS ONE, 8(6), e65581. doi:10.1371/journal.pone.0065581Knox, L., & Douglas, J. (2009). Long-term ability to interpret facial expression after traumatic brain injury and its relation to social integration. Brain and Cognition, 69(2), 442-449. doi:10.1016/j.bandc.2008.09.009Struchen, M. A., Clark, A. N., Sander, A. M., Mills, M. R., Evans, G., & Kurtz, D. (2008). Relation of executive functioning and social communication measures to functional outcomes following traumatic brain injury. NeuroRehabilitation, 23(2), 185-198. doi:10.3233/nre-2008-23208Ferro, J. M., Caeiro, L., & Santos, C. (2009). Poststroke Emotional and Behavior Impairment: A Narrative Review. Cerebrovascular Diseases, 27(1), 197-203. doi:10.1159/000200460Bortolon, C., Capdevielle, D., & Raffard, S. (2015). Face recognition in schizophrenia disorder: A comprehensive review of behavioral, neuroimaging and neurophysiological studies. Neuroscience & Biobehavioral Reviews, 53, 79-107. doi:10.1016/j.neubiorev.2015.03.006Harms, M. B., Martin, A., & Wallace, G. L. (2010). Facial Emotion Recognition in Autism Spectrum Disorders: A Review of Behavioral and Neuroimaging Studies. Neuropsychology Review, 20(3), 290-322. doi:10.1007/s11065-010-9138-6Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). «Mini-mental state». Journal of Psychiatric Research, 12(3), 189-198. doi:10.1016/0022-3956(75)90026-6Romero, M., Sánchez, A., Marín, C., Navarro, M. D., Ferri, J., & Noé, E. (2012). Clinical usefulness of the Spanish version of the Mississippi Aphasia Screening Test (MASTsp): validation in stroke patients. Neurología (English Edition), 27(4), 216-224. doi:10.1016/j.nrleng.2011.06.001Aguillon-Hernandez, N., Roché, L., Bonnet-Brilhault, F., Roux, S., Barthelemy, C., & Martineau, J. (2016). Eye Movement Monitoring and Maturation of Human Face Exploration. Medical Principles and Practice, 25(6), 548-554. doi:10.1159/000447971Mathôt, S., Fabius, J., Van Heusden, E., & Van der Stigchel, S. (2018). Safe and sensible preprocessing and baseline correction of pupil-size data. Behavior Research Methods, 50(1), 94-106. doi:10.3758/s13428-017-1007-2Green, C., & Guo, K. (2016). Factors contributing to individual differences in facial expression categorisation. Cognition and Emotion, 32(1), 37-48. doi:10.1080/02699931.2016.1273200Burley, D. T., Gray, N. S., & Snowden, R. J. (2017). As Far as the Eye Can See: Relationship between Psychopathic Traits and Pupil Response to Affective Stimuli. PLOS ONE, 12(1), e0167436. doi:10.1371/journal.pone.0167436Partala, T., & Surakka, V. (2003). Pupil size variation as an indication of affective processing. International Journal of Human-Computer Studies, 59(1-2), 185-198. doi:10.1016/s1071-5819(03)00017-xGotham, K. O., Siegle, G. J., Han, G. T., Tomarken, A. J., Crist, R. N., Simon, D. M., & Bodfish, J. W. (2018). Pupil response to social-emotional material is associated with rumination and depressive symptoms in adults with autism spectrum disorder. PLOS ONE, 13(8), e0200340. doi:10.1371/journal.pone.0200340Demenescu, L. R., Mathiak, K. A., & Mathiak, K. (2014). Age- and Gender-Related Variations of Emotion Recognition in Pseudowords and Faces. Experimental Aging Research, 40(2), 187-207. doi:10.1080/0361073x.2014.882210Grainger, S. A., Henry, J. D., Phillips, L. H., Vanman, E. J., & Allen, R. (2015). Age Deficits in Facial Affect Recognition: The Influence of Dynamic Cues. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, gbv100. doi:10.1093/geronb/gbv100Wagner, H. L. (1993). On measuring performance in category judgment studies of nonverbal behavior. Journal of Nonverbal Behavior, 17(1), 3-28. doi:10.1007/bf00987006Bradley, M. M., & Lang, P. J. (2015). Memory, emotion, and pupil diameter: Repetition of natural scenes. Psychophysiology, 52(9), 1186-1193. doi:10.1111/psyp.12442Larsen, R. S., & Waters, J. (2018). Neuromodulatory Correlates of Pupil Dilation. Frontiers in Neural Circuits, 12. doi:10.3389/fncir.2018.0002

Tracking the impact of depression in a perspective-taking task

Research has identified impairments in Theory of Mind (ToM) abilities in depressed patients, particularly in relation to tasks involving empathetic responses and belief reasoning. We aimed to build on this research by exploring the relationship between depressed mood and cognitive ToM, specifically visual perspective-taking ability. High and low depressed participants were eye-tracked as they completed a perspective-taking task, in which they followed the instructions of a ‘director’ to move target objects (e.g. a “teapot with spots on”) around a grid, in the presence of a temporarily-ambiguous competitor object (e.g. a “teapot with stars on”). Importantly, some of the objects in the grid were occluded from the director’s (but not the participant’s) view. Results revealed no group-based difference in participants’ ability to use perspective cues to identify the target object. All participants were faster to select the target object when the competitor was only available to the participant, compared to when the competitor was mutually available to the participant and director. Eye-tracking measures supported this pattern, revealing that perspective directed participants’ visual search immediately upon hearing the ambiguous object’s name (e.g. “teapot”). We discuss how these results fit with previous studies that have shown a negative relationship between depression and ToM

Piccolo genotype modulates neural correlates of emotion processing but not executive functioning

Major depressive disorder (MDD) is characterized by affective symptoms and cognitive impairments, which have been associated with changes in limbic and prefrontal activity as well as with monoaminergic neurotransmission. A genome-wide association study implicated the polymorphism rs2522833 in the piccolo (PCLO) gene—involved in monoaminergic neurotransmission—as a risk factor for MDD. However, the role of the PCLO risk allele in emotion processing and executive function or its effect on their neural substrate has never been studied. We used functional magnetic resonance imaging (fMRI) to investigate PCLO risk allele carriers vs noncarriers during an emotional face processing task and a visuospatial planning task in 159 current MDD patients and healthy controls. In PCLO risk allele carriers, we found increased activity in the left amygdala during processing of angry and sad faces compared with noncarriers, independent of psychopathological status. During processing of fearful faces, the PCLO risk allele was associated with increased amygdala activation in MDD patients only. During the visuospatial planning task, we found no genotype effect on performance or on BOLD signal in our predefined areas as a function of increasing task load. The PCLO risk allele was found to be specifically associated with altered emotion processing, but not with executive dysfunction. Moreover, the PCLO risk allele appears to modulate amygdala function during fearful facial processing in MDD and may constitute a possible link between genotype and susceptibility for depression via altered processing of fearful stimuli. The current results may therefore aid in better understanding underlying neurobiological mechanisms in MDD

Mood Modulates Auditory Laterality of Hemodynamic Mismatch Responses during Dichotic Listening

Hemodynamic mismatch responses can be elicited by deviant stimuli in a sequence of standard stimuli even during cognitive demanding tasks. Emotional context is known to modulate lateralized processing. Right-hemispheric negative emotion processing may bias attention to the right and enhance processing of right-ear stimuli. The present study examined the influence of induced mood on lateralized pre-attentive auditory processing of dichotic stimuli using functional magnetic resonance imaging (fMRI). Faces expressing emotions (sad/happy/neutral) were presented in a blocked design while a dichotic oddball sequence with consonant-vowel (CV) syllables in an event-related design was simultaneously administered. Twenty healthy participants were instructed to feel the emotion perceived on the images and to ignore the syllables. Deviant sounds reliably activated bilateral auditory cortices and confirmed attention effects by modulation of visual activity. Sad mood induction activated visual, limbic and right prefrontal areas. A lateralization effect of emotion-attention interaction was reflected in a stronger response to right-ear deviants in the right auditory cortex during sad mood. This imbalance of resources may be a neurophysiological correlate of laterality in sad mood and depression. Conceivably, the compensatory right-hemispheric enhancement of resources elicits increased ipsilateral processing