The role of historical and contemporary processes on phylogeographic structure and genetic diversity in the Northern Cardinal, Cardinalis cardinalis

Background Earth history events such as climate change are believed to have played a major role in shaping patterns of genetic structure and diversity in species. However, there is a lag between the time of historical events and the collection of present-day samples that are used to infer contemporary population structure. During this lag phase contemporary processes such as dispersal or non-random mating can erase or reinforce population differences generated by historical events. In this study we evaluate the role of both historical and contemporary processes on the phylogeography of a widespread North American songbird, the Northern Cardinal, Cardinalis cardinalis. Results Phylogenetic analysis revealed deep mtDNA structure with six lineages across the species\u27 range. Ecological niche models supported the same geographic breaks revealed by the mtDNA. A paleoecological niche model for the Last Glacial Maximum indicated that cardinals underwent a dramatic range reduction in eastern North America, whereas their ranges were more stable in México. In eastern North America cardinals expanded out of glacial refugia, but we found no signature of decreased genetic diversity in areas colonized after the Last Glacial Maximum. Present-day demographic data suggested that population growth across the expansion cline is positively correlated with latitude. We propose that there was no loss of genetic diversity in areas colonized after the Last Glacial Maximum because recent high-levels of gene flow across the region have homogenized genetic diversity in eastern North America. Conclusion We show that both deep historical events as well as demographic processes that occurred following these events are critical in shaping genetic pattern and diversity in C. cardinalis. The general implication of our results is that patterns of genetic diversity are best understood when information on species history, ecology, and demography are considered simultaneously

The role of historical and contemporary processes on phylogeographic structure and genetic diversity in the Northern Cardinal, Cardinalis cardinalis

<p>Abstract</p> <p>Background</p> <p>Earth history events such as climate change are believed to have played a major role in shaping patterns of genetic structure and diversity in species. However, there is a lag between the time of historical events and the collection of present-day samples that are used to infer contemporary population structure. During this lag phase contemporary processes such as dispersal or non-random mating can erase or reinforce population differences generated by historical events. In this study we evaluate the role of both historical and contemporary processes on the phylogeography of a widespread North American songbird, the Northern Cardinal, <it>Cardinalis cardinalis</it>.</p> <p>Results</p> <p>Phylogenetic analysis revealed deep mtDNA structure with six lineages across the species' range. Ecological niche models supported the same geographic breaks revealed by the mtDNA. A paleoecological niche model for the Last Glacial Maximum indicated that cardinals underwent a dramatic range reduction in eastern North America, whereas their ranges were more stable in México. In eastern North America cardinals expanded out of glacial refugia, but we found no signature of decreased genetic diversity in areas colonized after the Last Glacial Maximum. Present-day demographic data suggested that population growth across the expansion cline is positively correlated with latitude. We propose that there was no loss of genetic diversity in areas colonized after the Last Glacial Maximum because recent high-levels of gene flow across the region have homogenized genetic diversity in eastern North America.</p> <p>Conclusion</p> <p>We show that both deep historical events as well as demographic processes that occurred following these events are critical in shaping genetic pattern and diversity in <it>C. cardinalis</it>. The general implication of our results is that patterns of genetic diversity are best understood when information on species history, ecology, and demography are considered simultaneously.</p

Exploring the Role of Explicit and Implicit Self-Esteem and Self-Compassion in Anxious and Depressive Symptomatology Following Acquired Brain Injury

[EN] Objectives Acquired brain injury (ABI) can lead to the emergence of several disabilities and is commonly associated with high rates of anxiety and depression symptoms. Self-related constructs, such as self-esteem and self-compassion, might play a key role in this distressing symptomatology. Low explicit (i.e., deliberate) self-esteem is associated with anxiety and depression after ABI. However, implicit (i.e., automatic) self-esteem, explicit-implicit self-discrepancies, and self-compassion could also significantly contribute to this symptomatology. The purpose of the present study was to examine whether implicit self-esteem, explicit-implicit self-discrepancy (size and direction), and self-compassion are related to anxious and depressive symptoms after ABI in adults, beyond the contribution of explicit self-esteem. Methods The sample consisted 38 individuals with ABI who were enrolled in a long-term rehabilitation program. All participants completed the measures of explicit self-esteem, implicit self-esteem, self-compassion, anxiety, and depression. Pearson's correlations and hierarchical regression models were calculated. Results Findings showed that both self-compassion and implicit self-esteem negatively accounted for unique variance in anxiety and depression when controlling for explicit self-esteem. Neither the size nor direction of explicit-implicit self-discrepancy was significantly associated with anxious or depressive symptomatology. Conclusions The findings suggest that the consideration of self-compassion and implicit self-esteem, in addition to explicit self-esteem, contributes to understanding anxiety and depression following ABI.Lorena Desdentado is supported by a FPU doctoral scholarship (FPU18/01690) from the Spanish Ministry of Universities. This work was supported by CIBEROBN, an initiative of the ISCIII (ISC III CB06 03/0052).Desdentado, L.; Cebolla, A.; Miragall, M.; Llorens Rodríguez, R.; Navarro, MD.; Baños, RM. (2021). Exploring the Role of Explicit and Implicit Self-Esteem and Self-Compassion in Anxious and Depressive Symptomatology Following Acquired Brain Injury. Mindfulness. 12(4):899-910. https://doi.org/10.1007/s12671-020-01553-wS899910124Anson, K., & Ponsford, J. (2006). Coping and emotional adjustment following traumatic brain injury. The Journal of Head Trauma Rehabilitation, 21(3), 248–259. https://doi.org/10.1097/00001199-200605000-00005.Baños, R. M., & Guillén, V. (2000). Psychometric characteristics in normal and social phobic samples for a Spanish version of the Rosenberg Self-Esteem Scale. Psychological Reports, 87(1), 269–274. https://doi.org/10.2466/pr0.2000.87.1.269.Beadle, E. J., Ownsworth, T., Fleming, J., & Shum, D. (2016). The impact of traumatic brain injury on self-identity: a systematic review of the evidence for self-concept changes. The Journal of Head Trauma Rehabilitation, 31(2), E12–E25. https://doi.org/10.1097/HTR.0000000000000158.Beck, A. T. (1979). Cognitive therapy of depression. New York: Guilford Press.Beevers, C. G. (2005). Cognitive vulnerability to depression: A dual process model. Clinical Psychology Review, 25(7), 975–1002. https://doi.org/10.1016/j.cpr.2005.03.003.Bos, A. E. R., Huijding, J., Muris, P., Vogel, L. R. R., & Biesheuvel, J. (2010). Global, contingent and implicit self-esteem and psychopathological symptoms in adolescents. Personality and Individual Differences, 48(3), 311–316. https://doi.org/10.1016/j.paid.2009.10.025.Bowerman, B. L., & O’Connell, R. T. (1990). Linear statistical models: An applied approach (2nd ed.). Belmont, CA: Duxbury.Brenner, R. E., Heath, P. J., Vogel, D. L., & Credé, M. (2017). Two is more valid than one: examining the factor structure of the self-compassion scale (SCS). Journal of Counseling Psychology, 64(6), 696–707. https://doi.org/10.1037/cou0000211.Brysbaert, M. (2019). How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. Journal of Cognition, 2(1), 1–38. https://doi.org/10.5334/joc.72.Carroll, E., & Coetzer, R. (2011). Identity, grief and self-awareness after traumatic brain injury. Neuropsychological Rehabilitation, 21(3), 289–305. https://doi.org/10.1080/09602011.2011.555972.Corrigan, P. W., & Watson, A. C. (2002). The paradox of self-stigma and mental illness. Clinical Psychology: Science and Practice, 9(1), 35–53. https://doi.org/10.1093/clipsy/9.1.35.Creemers, D. H. M., Scholte, R. H. J., Engels, R. C. M. E., Prinstein, M. J., & Wiers, R. W. (2012). Implicit and explicit self-esteem as concurrent predictors of suicidal ideation, depressive symptoms, and loneliness. Journal of Behavior Therapy and Experimental Psychiatry, 43(1), 638–646. https://doi.org/10.1016/j.jbtep.2011.09.006.Creemers, D. H. M., Scholt, R. H. J., Engels, R. C. M. E., Prinstein, M. J., & Wiers, R. W. (2013). Damaged self-esteem is associated with internalizing problems. Frontiers in Psychology, 4, 152. https://doi.org/10.3389/fpsyg.2013.00152.Curvis, W., Simpson, J., & Hampson, N. (2018). Factors associated with self-esteem following acquired brain injury in adults: a systematic review. Neuropsychological Rehabilitation, 28(1), 142–183. https://doi.org/10.1080/09602011.2016.1144515.Elbaum, J., & Benson, D. (Eds.). (2007). Acquired brain injury: an integrative neuro-rehabilitation approach. New York: Springer. https://doi.org/10.1007/978-0-387-37575-5.Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149.FEDACE. (2015). Las personas con daño cerebral adquirido en España. Ministerio de Sanidad, Servicios Sociales e Igualdad. Retrieved May 21, 2020, from: https://fedace.org/index.php?V_dir=MSC&V_mod=download&f=2016-9/26-16-4-11.admin.Informe_FEDACE_RPD_para_DDC-1.pdf.Feigin, V. L., Forouzanfar, M. H., Krishnamurthi, R., Mensah, G. A., Connor, M., Bennett, D. A., Moran, A. E., Sacco, R. L., Anderson, L., Truelsen, T., O’Donnell, M., Venketasubramanian, N., Barker-Collo, S., Lawes, C. M. M., Wang, W., Shinohara, Y., Witt, E., Ezzati, M., & Naghavi, M. (2014). Global and regional burden of stroke during 1990-2010: findings from the Global Burden of Disease Study 2010. The Lancet, 383(9913), 245–254. https://doi.org/10.1016/S0140-6736(13)61953-4.Fennell, M. J. V. (1997). Low self-esteem: a cognitive perspective. Behavioural and Cognitive Psychotherapy, 25(1), 1–26. https://doi.org/10.1017/s1352465800015368.Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. https://doi.org/10.1016/0022-3956(75)90026-6.Garcia-Campayo, J., Navarro-Gil, M., Andrés, E., Montero-Marin, J., López-Artal, L., Marcos, M., & Demarzo, P. (2014). Validation of the Spanish versions of the long (26 items) and short (12 items) forms of the Self-Compassion Scale (SCS). Health and Quality of Life Outcomes, 12(4). https://doi.org/10.1186/1477-7525-12-4.GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators. (2018). Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology, 18(1), 56–87. https://doi.org/10.1016/S1474-4422(18)30415-0.Gould, K. R., Ponsford, J. L., Johnston, L., & Schönberger, M. (2011). Relationship between psychiatric disorders and 1-year psychosocial outcome following traumatic brain injury. The Journal of Head Trauma Rehabilitation, 26(1), 79–89. https://doi.org/10.1097/HTR.0b013e3182036799.Gracey, F., Palmer, S., Rous, B., Psaila, K., Shaw, K., O’Dell, J., Cope, J., & Mohamed, S. (2008). “Feeling part of things”: personal construction of self after brain injury. Neuropsychological Rehabilitation, 18(5–6), 627–650. https://doi.org/10.1080/09602010802041238.Gracey, F., Evans, J. J., & Malley, D. (2009). Capturing process and outcome in complex rehabilitation interventions: a “Y-shaped” model. Neuropsychological Rehabilitation, 19(6), 867–890. https://doi.org/10.1080/09602010903027763.Greenwald, A. G., & Farnham, S. D. (2000). Using the Implicit Association Test to measure self-esteem and self-concept. Journal of Personality and Social Psychology, 79(6), 1022–1038. https://doi.org/10.1037/0022-3514.79.6.1022.Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998). Measuring individual differences in implicit cognition: the Implicit Association Test. Journal of Personality and Social Psychology, 74(6), 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464.Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the Implicit Association Test: I. An improved scoring algorithm. Journal of Personality and Social Psychology, 85(2), 197–216. https://doi.org/10.1037/0022-3514.85.2.197.Hackett, M. L., Yapa, C., Parag, V., & Anderson, C. S. (2005). Frequency of depression after stroke: a systematic review of observational studies. Stroke, 36(6), 1330–1340. https://doi.org/10.1161/01.STR.0000165928.19135.35.Haeffel, G. J., Abramson, L. Y., Brazy, P. C., Shah, J. Y., Teachman, B. A., & Nosek, B. A. (2007). Explicit and implicit cognition: a preliminary test of a dual-process theory of cognitive vulnerability to depression. Behaviour Research and Therapy, 45(6), 1155–1167. https://doi.org/10.1016/j.brat.2006.09.003.Ingram, R. E. (1984). Toward an information-processing analysis of depression. Cognitive Therapy and Research, 8(5), 443–477. https://doi.org/10.1007/BF01173284.Izuma, K., Kennedy, K., Fitzjohn, A., Sedikides, C., & Shibata, K. (2018). Neural activity in the reward-related brain regions predicts implicit self-esteem: a novel validity test of psychological measures using neuroimaging. Journal of Personality and Social Psychology, 114(3), 343–357. https://doi.org/10.1037/pspa0000114.Khan-Bourne, N., & Brown, R. G. (2003). Cognitive behaviour therapy for the treatment of depression in individuals with brain injury. Neuropsychological Rehabilitation, 13(1–2), 89–107. https://doi.org/10.1080/09602010244000318.Kim, H. S., & Moore, M. T. (2019). Symptoms of depression and the discrepancy between implicit and explicit self-esteem. Journal of Behavior Therapy and Experimental Psychiatry, 63, 1–5. https://doi.org/10.1016/j.jbtep.2018.12.001.Lane, K. A., Banaji, M. R., Nosek, B. A., & Greenwald, A. G. (2007). Understanding and using the Implicit Association Test: IV. What we know (so far) about the method. In B. Wittenbrink & N. Schwarz (Eds.), Implicit measures of attitudes (pp. 59–102). New York: The Guildford Press.Leary, M. R., Tate, E. B., Adams, C. E., Batts Allen, A., & Hancock, J. (2007). Self-compassion and reactions to unpleasant self-relevant events: the implications of treating oneself kindly. Personality Processes and Individual Differences, 92(5), 887–904. https://doi.org/10.1037/0022-3514.92.5.887.Lennon, A., Bramham, J., Carroll, À., McElligott, J., Carton, S., Waldron, B., Fortune, D., Burke, T., Fitzhenry, M., & Benson, C. (2014). A qualitative exploration of how individuals reconstruct their sense of self following acquired brain injury in comparison with spinal cord injury. Brain Injury, 28(1), 27–37. https://doi.org/10.3109/02699052.2013.848378.Longworth, C., Deakins, J., Rose, D., & Gracey, F. (2018). The nature of self-esteem and its relationship to anxiety and depression in adult acquired brain injury. Neuropsychological Rehabilitation, 28(7), 1078–1094. https://doi.org/10.1080/09602011.2016.1226185.MacBeth, A., & Gumley, A. (2012). Exploring compassion: a meta-analysis of the association between self-compassion and psychopathology. Clinical Psychology Review, 32(6), 545–552. https://doi.org/10.1016/j.cpr.2012.06.003.McDonald, S., Saad, A., & James, C. (2011). Social dysdecorum following severe traumatic brain injury: loss of implicit social knowledge or loss of control? Journal of Clinical and Experimental Neuropsychology, 33(6), 619–630. https://doi.org/10.1080/13803395.2011.553586.Milne, E., & Grafman, J. (2001). Ventromedial prefrontal cortex lesions in humans eliminate implicit gender stereotyping. The Journal of Neuroscience, 21(12), 1–6.Moors, A., & De Houwer, J. (2006). Automaticity: a theoretical and conceptual analysis. Psychological Bulletin, 132(2), 297–326. https://doi.org/10.1037/0033-2909.132.2.297.Muris, P., & Petrocchi, N. (2017). Protection or vulnerability? A meta-analysis of the relations between the positive and negative components of self-compassion and psychopathology. Clinical Psychology & Psychotherapy, 24(2), 373–383. https://doi.org/10.1002/cpp.2005.Myers, R. (2000). Classical and modern regression with applications (2nd ed.). Belmont, CA: Duxbury.Neff, K. D. (2003). Self-compassion: an alternative conceptualization of a healthy attitude toward oneself. Self and Identity, 2(2), 85–101. https://doi.org/10.1080/15298860309032.Neff, K. D., & Vonk, R. (2009). Self-compassion versus global self-esteem: two different ways of relating to oneself. Journal of Personality, 77, 23–50. https://doi.org/10.1111/j.1467-6494.2008.00537.x.Neff, K. D., Tóth-Király, I., Yarnell, L. M., Arimitsu, K., Castilho, P., Ghorbani, N., Guo, H. X., Hirsch, J. K., Hupfeld, J., Hutz, C. S., Kotsou, I., Lee, W. K., Montero-Marin, J., Sirois, F. M., De Souza, L. K., Svendsen, J. L., Wilkinson, R. B., & Mantzios, M. (2019). Examining the factor structure of the Self-Compassion Scale in 20 diverse samples: support for use of a total score and six subscale scores. Psychological Assessment, 31(1), 27–45. https://doi.org/10.1037/pas0000629.Norton, P. J., & Paulus, D. J. (2017). Transdiagnostic models of anxiety disorder: theoretical and empirical underpinnings. Clinical Psychology Review, 56, 122–137. https://doi.org/10.1016/j.cpr.2017.03.004.Nosek, B. A., & Banaji, M. R. (2001). The go/no-go association task. Social Cognition, 19(6), 625–664. https://doi.org/10.1521/soco.19.6.625.20886.Oddy, M., & Herbert, C. (2003). Intervention with families following brain injury: evidence-based practice. Neuropsychological Rehabilitation, 13(1–2), 259–273. https://doi.org/10.1080/09602010244000345.Ouimet, A. J., Gawronski, B., & Dozois, D. J. A. (2009). Cognitive vulnerability to anxiety: a review and an integrative model. Clinical Psychology Review, 29(6), 459–470. https://doi.org/10.1016/j.cpr.2009.05.004.Ponsford, J., Kelly, A., & Couchman, G. (2014). Self-concept and self-esteem after acquired brain injury: a control group comparison. Brain Injury, 28(2), 146–154. https://doi.org/10.3109/02699052.2013.859733.Raes, F., Pommier, E., Neff, K. D., & Van Gucht, D. (2011). Construction and factorial validation of a short form of the Self-Compassion Scale. Clinical Psychology & Psychotherapy, 18(3), 250–255. https://doi.org/10.1002/cpp.702.Romero, 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. https://doi.org/10.1016/j.nrleng.2011.06.001.Rosenberg, M. (1965). Rosenberg Self-Esteem Scale (RSE). Acceptance and Commitment Therapy. Measures Package, 61, 52 /S0034-98872009000600009.Sandstrom, M. J., & Jordan, R. (2008). Defensive self-esteem and aggression in childhood. Journal of Research in Personality, 42(2), 506–514. https://doi.org/10.1016/j.jrp.2007.07.008.Schönberger, M., & Ponsford, J. (2010). The factor structure of the Hospital Anxiety and Depression Scale in individuals with traumatic brain injury. Psychiatry Research, 179(3), 342–349. https://doi.org/10.1016/j.psychres.2009.07.003.Schröder-Abé, M., Rudolph, A., & Schütz, A. (2007). High implicit self-esteem is not necessarily advantageous: discrepancies between explicit and implicit self-esteem and their relationship with anger expression and psychological health. European Journal of Personality, 21(3), 319–339. https://doi.org/10.1002/per.626.Scoglio, A. A. J., Rudat, D. A., Garvert, D., Jarmolowski, M., Jackson, C., & Herman, J. L. (2018). Self-compassion and responses to trauma: the role of emotion regulation. Journal of Interpersonal Violence, 33(13), 2016–2036. https://doi.org/10.1177/0886260515622296.Sloan, E., Hall, K., Moulding, R., Bryce, S., Mildred, H., & Staiger, P. K. (2017). Emotion regulation as a transdiagnostic treatment construct across anxiety, depression, substance, eating and borderline personality disorders: a systematic review. Clinical Psychology Review, 57, 141–163. https://doi.org/10.1016/j.cpr.2017.09.002.Smeijers, D., Vrijsen, J. N., van Oostrom, I., Isaac, L., Speckens, A., Becker, E. S., & Rinck, M. (2017). Implicit and explicit self-esteem in remitted depressed patients. Journal of Behavior Therapy and Experimental Psychiatry, 54, 301–306. https://doi.org/10.1016/j.jbtep.2016.10.006.Smith, E. R., & DeCoster, J. (2000). Dual-process models in social and cognitive psychology: conceptual integration and links to underlying memory systems. Personality and Social Psychology Review, 4(2), 108–131. https://doi.org/10.1207/S15327957PSPR0402_01.Sowislo, J. F., & Orth, U. (2013). Does low self-esteem predict depression and anxiety? A meta-analysis of longitudinal studies. Psychological Bulletin, 139(1), 213–240. https://doi.org/10.1037/a0028931.Strack, F., & Deutsch, R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8(3), 220–247. https://doi.org/10.1207/s15327957pspr0803_1.Terol-Cantero, M. C., Cabrera-Perona, V., & Martín-Aragón, M. (2015). Hospital Anxiety and Depression Scale (HADS) review in Spanish samples. Anales de Psicología, 31(2), 494–503. https://doi.org/10.6018/analesps.31.2.172701.Tóth-Király, I., & Neff, K. D. (2020). Is self-compassion universal? Support for the measurement invariance of the Self-Compassion Scale across populations. Assessment. Advance online publication. https://doi.org/10.1177/1073191120926232.Turner-Stokes, L., & Wade, D. (2003). Rehabilitation following acquired brain injury: National Clinical Guidelines. Clinical Medicine, 4(1), 61–65. https://doi.org/10.7861/clinmedicine.4-1-61.Tyerman, A., & Humphrey, M. (1984). Changes in self-concept following severe head injury. International Journal of Rehabilitation Research, 7(1), 11–23. https://doi.org/10.1097/00004356-198403000-00002.Valiente, C., Cantero, D., Vázquez, C., Sanchez, Á., Provencio, M., & Espinosa, R. (2011). Implicit and explicit self-esteem discrepancies in paranoia and depression. Journal of Abnormal Psychology, 120(3), 691–699. https://doi.org/10.1037/a0022856.Vickery, C. D., Sepehri, A., & Evans, C. C. (2008). Self-esteem in an acute stroke rehabilitation sample: a control group comparison. Clinical Rehabilitation, 22(2), 179–187. https://doi.org/10.1177/0269215507080142.Whelan-Goodinson, R., Ponsford, J., & Schönberger, M. (2009). Validity of the Hospital Anxiety and Depression Scale to assess depression and anxiety following traumatic brain injury as compared with the Structured Clinical Interview for DSM-IV. Journal of Affective Disorders, 114(1–3), 94–102. https://doi.org/10.1016/j.jad.2008.06.007.Zeigler-Hill, V. (2006). Discrepancies between implicit and explicit self-esteem: Implications for narcissism and self-esteem instability. Journal of Personality, 74(1), 119–144. https://doi.org/10.1111/j.1467-6494.2005.00371.x.Zessin, U., Dickhäuser, O., & Garbade, S. (2015). The relationship between self-compassion and well-being: a meta-analysis. Applied Psychology. Health and Well-Being, 7(3), 340–364. https://doi.org/10.1111/aphw.12051.Zhang, J. W., Chen, S., & Tomova Shakur, T. K. (2020). From me to you: Self-compassion predicts acceptance of own and others’ imperfections. Personality and Social Psychology Bulletin, 46(2), 228–242. https://doi.org/10.1177/0146167219853846.Zigmond, A. S., & Snaith, R. P. (1983). The Hospital Anxiety and Depression Scale. Acta Psychiatrica Scandinavica, 67(6), 361–370. https://doi.org/10.1111/j.1600-0447.1983.tb09716.x

Lymnaea schirazensis, an Overlooked Snail Distorting Fascioliasis Data: Genotype, Phenotype, Ecology, Worldwide Spread, Susceptibility, Applicability

BACKGROUND: Lymnaeid snails transmit medical and veterinary important trematodiases, mainly fascioliasis. Vector specificity of fasciolid parasites defines disease distribution and characteristics. Different lymnaeid species appear linked to different transmission and epidemiological patterns. Pronounced susceptibility differences to absolute resistance have been described among lymnaeid populations. When assessing disease characteristics in different endemic areas, unexpected results were obtained in studies on lymnaeid susceptibility to Fasciola. We undertook studies to understand this disease transmission heterogeneity. METHODOLOGY/PRINCIPAL FINDINGS: A ten-year study in Iran, Egypt, Spain, the Dominican Republic, Mexico, Venezuela, Ecuador and Peru, demonstrated that such heterogeneity is not due to susceptibility differences, but to a hitherto overlooked cryptic species, Lymnaea schirazensis, confused with the main vector Galba truncatula and/or other Galba/Fossaria vectors. Nuclear rDNA and mtDNA sequences and phylogenetic reconstruction highlighted an old evolutionary divergence from other Galba/Fossaria species, and a low intraspecific variability suggesting a recent spread from one geographical source. Morphometry, anatomy and egg cluster analyses allowed for phenotypic differentiation. Selfing, egg laying, and habitat characteristics indicated a migration capacity by passive transport. Studies showed that it is not a vector species (n = 8572 field collected, 20 populations): snail finding and penetration by F. hepatica miracidium occur but never lead to cercarial production (n = 338 experimentally infected). CONCLUSIONS/SIGNIFICANCE: This species has been distorting fasciolid specificity/susceptibility and fascioliasis geographical distribution data. Hence, a large body of literature on G. truncatula should be revised. Its existence has henceforth to be considered in research. Genetic data on livestock, archeology and history along the 10,000-year post-domestication period explain its wide spread from the Neolithic Fertile Crescent. It is an efficient biomarker for the follow-up of livestock movements, a crucial aspect in fascioliasis emergence. It offers an outstanding laboratory model for genetic studies on susceptibility/resistance in F. hepatica/lymnaeid interaction, a field of applied research with disease control perspectives

Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

[EN] Affective Computing has emerged as an important field of study that aims to develop systems that can automatically recognize emotions. Up to the present, elicitation has been carried out with nonimmersive stimuli. This study, on the other hand, aims to develop an emotion recognition system for affective states evoked through Immersive Virtual Environments. Four alternative virtual rooms were designed to elicit four possible arousal-valence combinations, as described in each quadrant of the Circumplex Model of Affects. An experiment involving the recording of the electroencephalography (EEG) and electrocardiography (ECG) of sixty participants was carried out. A set of features was extracted from these signals using various state-of-the-art metrics that quantify brain and cardiovascular linear and nonlinear dynamics, which were input into a Support Vector Machine classifier to predict the subject's arousal and valence perception. The model's accuracy was 75.00% along the arousal dimension and 71.21% along the valence dimension. Our findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.This work was supported by the Ministerio de Economia y Competitividad. Spain (Project TIN2013-45736-R).Marín-Morales, J.; Higuera-Trujillo, JL.; Greco, A.; Guixeres Provinciale, J.; Llinares Millán, MDC.; Scilingo, EP.; Alcañiz Raya, ML.... (2018). Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors. Scientific Reports. 8:1-15. https://doi.org/10.1038/s41598-018-32063-4S1158Picard, R. W. Affective computing. (MIT press, 1997).Picard, R. W. Affective Computing: Challenges. Int. J. Hum. Comput. Stud. 59, 55–64 (2003).Jerritta, S., Murugappan, M., Nagarajan, R. & Wan, K. Physiological signals based human emotion Recognition: a review. Signal Process. its Appl. (CSPA), 2011 IEEE 7th Int. Colloq. 410–415, https://doi.org/10.1109/CSPA.2011.5759912 (2011).Harms, M. B., Martin, A. & Wallace, G. L. Facial emotion recognition in autism spectrum disorders: A review of behavioral and neuroimaging studies. Neuropsychol. Rev. 20, 290–322 (2010).Koolagudi, S. G. & Rao, K. S. Emotion recognition from speech: A review. Int. J. Speech Technol. 15, 99–117 (2012).Gross, J. J. & Levenson, R. W. Emotion elicitation using films. Cogn. Emot. 9, 87–108 (1995).Lindal, P. J. & Hartig, T. Architectural variation, building height, and the restorative quality of urban residential streetscapes. J. Environ. Psychol. 33, 26–36 (2013).Ulrich, R. View through a window may influence recovery from surgery. Science (80-.). 224, 420–421 (1984).Fernández-Caballero, A. et al. Smart environment architecture for emotion detection and regulation. J. Biomed. Inform. 64, 55–73 (2016).Ekman, P. Basic Emotions. Handbook of cognition and emotion 45–60, https://doi.org/10.1017/S0140525X0800349X (1999).Posner, J., Russell, J. A. & Peterson, B. S. The circumplex model of affect: an integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17, 715–34 (2005).Russell, J. A. & Mehrabian, A. Evidence for a three-factor theory of emotions. J. Res. Pers. 11, 273–294 (1977).Calvo, R. A. & D’Mello, S. Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Trans. Affect. Comput. 1, 18–37 (2010).Valenza, G. et al. Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 374, 20150176 (2016).Valenza, G., Lanata, A. & Scilingo, E. P. The role of nonlinear dynamics in affective valence and arousal recognition. IEEE Trans. Affect. Comput. 3, 237–249 (2012).Valenza, G., Citi, L., Lanatá, A., Scilingo, E. P. & Barbieri, R. Revealing real-time emotional responses: a personalized assessment based on heartbeat dynamics. Sci. Rep. 4, 4998 (2014).Valenza, G. et al. Wearable monitoring for mood recognition in bipolar disorder based on history-dependent long-term heart rate variability analysis. IEEE J. Biomed. Heal. Informatics 18, 1625–1635 (2014).Piwek, L., Ellis, D. A., Andrews, S. & Joinson, A. The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Med. 13, 1–9 (2016).Xu, J., Mitra, S., Van Hoof, C., Yazicioglu, R. & Makinwa, K. A. A. Active Electrodes for Wearable EEG Acquisition: Review and Electronics Design Methodology. IEEE Rev. Biomed. Eng. 3333, 1–1 (2017).Kumari, P., Mathew, L. & Syal, P. Increasing trend of wearables and multimodal interface for human activity monitoring: A review. Biosens. Bioelectron. 90, 298–307 (2017).He, C., Yao, Y. & Ye, X. An Emotion Recognition System Based on Physiological Signals Obtained by Wearable Sensors. In Wearable Sensors and Robots: Proceedings of International Conference on Wearable Sensors and Robots 2015 (eds Yang, C., Virk, G. S. & Yang, H.) 15–25. https://doi.org/10.1007/978-981-10-2404-7_2 (Springer Singapore, 2017).Nakisa, B., Rastgoo, M. N., Tjondronegoro, D. & Chandran, V. Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors. Expert Syst. Appl. 93, 143–155 (2018).Kory Jacqueline, D. & Sidney, K. Affect Elicitation for Affective Computing. In The Oxford Handbook of Affective Computing 371–383 (2014).Ekman, P. The directed facial action task. In Handbook of emotion elicitation and assessment 47–53 (2007).Harmon-Jones, E., Amodio, D. M. & Zinner, L. R. Social psychological methods of emotion elicitation. Handb. Emot. elicitation Assess. 91–105, https://doi.org/10.2224/sbp.2007.35.7.863 (2007)Roberts, N. A., Tsai, J. L. & Coan, J. A. Emotion elicitation using dyadic interaction task. Handbook of Emotion Elicitation and Assessment 106–123 (2007).Nardelli, M., Valenza, G., Greco, A., Lanata, A. & Scilingo, E. P. Recognizing emotions induced by affective sounds through heart rate variability. IEEE Trans. Affect. Comput. 6, 385–394 (2015).Kim, J. Emotion Recognition Using Speech and Physiological Changes. Robust Speech Recognit. Underst. 265–280 (2007).Soleymani, M., Pantic, M. & Pun, T. Multimodal emotion recognition in response to videos (Extended abstract). 2015 Int. Conf. Affect. Comput. Intell. Interact. ACII 2015 3, 491–497 (2015).Baños, R. M. et al. Immersion and Emotion: Their Impact on the Sense of Presence. CyberPsychology Behav. 7, 734–741 (2004).Giglioli, I. A. C., Pravettoni, G., Martín, D. L. S., Parra, E. & Raya, M. A. A novel integrating virtual reality approach for the assessment of the attachment behavioral system. Front. Psychol. 8, 1–7 (2017).Marín-Morales, J., Torrecilla, C., Guixeres, J. & Llinares, C. Methodological bases for a new platform for the measurement of human behaviour in virtual environments. DYNA 92, 34–38 (2017).Vince, J. Introduction to virtual reality. (Media, Springer Science & Business, 2004).Alcañiz, M., Baños, R., Botella, C. & Rey, B. The EMMA Project: Emotions as a Determinant of Presence. PsychNology J. 1, 141–150 (2003).Vecchiato, G. et al. Neurophysiological correlates of embodiment and motivational factors during the perception of virtual architectural environments. Cogn. Process. 16, 425–429 (2015).Slater, M. & Wilbur, S. A Framework for Immersive Virtual Environments (FIVE): Speculations on the Role of Presence in Virtual Environments. Presence Teleoperators Virtual Environ. 6, 603–616 (1997).Riva, G. et al. Affective Interactions Using Virtual Reality: The Link between Presence and Emotions. CyberPsychology Behav. 10, 45–56 (2007).Baños, R. M. et al Changing induced moods via virtual reality. In International Conference on Persuasive Technology (ed. Springer, Berlin, H.) 7–15, https://doi.org/10.1007/11755494_3 (2006).Baños, R. M. et al. Positive mood induction procedures for virtual environments designed for elderly people. Interact. Comput. 24, 131–138 (2012).Gorini, A. et al. Emotional Response to Virtual Reality Exposure across Different Cultures: The Role of the AttributionProcess. CyberPsychology Behav. 12, 699–705 (2009).Gorini, A., Capideville, C. S., De Leo, G., Mantovani, F. & Riva, G. The Role of Immersion and Narrative in Mediated Presence: The Virtual Hospital Experience. Cyberpsychology, Behav. Soc. Netw. 14, 99–105 (2011).Chirico, A. et al. Effectiveness of Immersive Videos in Inducing Awe: An Experimental Study. Sci. Rep. 7, 1–11 (2017).Blascovich, J. et al. Immersive Virtual Environment Technology as a Methodological Tool for Social Psychology. Psychol. Inq. 7965, 103–124 (2012).Peperkorn, H. M., Alpers, G. W. & Mühlberger, A. Triggers of fear: Perceptual cues versus conceptual information in spider phobia. J. Clin. Psychol. 70, 704–714 (2014).McCall, C., Hildebrandt, L. K., Bornemann, B. & Singer, T. Physiophenomenology in retrospect: Memory reliably reflects physiological arousal during a prior threatening experience. Conscious. Cogn. 38, 60–70 (2015).Hildebrandt, L. K., Mccall, C., Engen, H. G. & Singer, T. Cognitive flexibility, heart rate variability, and resilience predict fine-grained regulation of arousal during prolonged threat. Psychophysiology 53, 880–890 (2016).Notzon, S. et al. Psychophysiological effects of an iTBS modulated virtual reality challenge including participants with spider phobia. Biol. Psychol. 112, 66–76 (2015).Amaral, C. P., Simões, M. A., Mouga, S., Andrade, J. & Castelo-Branco, M. A novel Brain Computer Interface for classification of social joint attention in autism and comparison of 3 experimental setups: A feasibility study. J. Neurosci. Methods 290, 105–115 (2017).Eudave, L. & Valencia, M. Physiological response while driving in an immersive virtual environment. 2017 IEEE 14th Int. Conf. Wearable Implant. Body Sens. Networks 145–148, https://doi.org/10.1109/BSN.2017.7936028 (2017).Sharma, G. et al. Influence of landmarks on wayfinding and brain connectivity in immersive virtual reality environment. Front. Psychol. 8, 1–12 (2017).Bian, Y. et al. A framework for physiological indicators of flow in VR games: construction and preliminary evaluation. Pers. Ubiquitous Comput. 20, 821–832 (2016).Egan, D. et al. An evaluation of Heart Rate and Electrodermal Activity as an Objective QoE Evaluation method for Immersive Virtual Reality Environments. 3–8, https://doi.org/10.1109/QoMEX.2016.7498964 (2016).Meehan, M., Razzaque, S., Insko, B., Whitton, M. & Brooks, F. P. Review of four studies on the use of physiological reaction as a measure of presence in stressful virtual environments. Appl. Psychophysiol. Biofeedback 30, 239–258 (2005).Higuera-Trujillo, J. L., López-Tarruella Maldonado, J. & Llinares Millán, C. Psychological and physiological human responses to simulated and real environments: A comparison between Photographs, 360° Panoramas, and Virtual Reality. Appl. Ergon. 65, 398–409 (2016).Felnhofer, A. et al. Is virtual reality emotionally arousing? Investigating five emotion inducing virtual park scenarios. Int. J. Hum. Comput. Stud. 82, 48–56 (2015).Anderson, A. P. et al. Relaxation with Immersive Natural Scenes Presented Using Virtual Reality. Aerosp. Med. Hum. Perform. 88, 520–526 (2017).Higuera, J. L. et al. Emotional cartography in design: A novel technique to represent emotional states altered by spaces. In D and E 2016: 10th International Conference on Design and Emotion 561–566 (2016).Kroenke, K., Spitzer, R. L. & Williams, J. B. W. The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001).Bradley, M. M. & Lang, P. J. Measuring emotion: The self-assessment manikin and the semantic differential. J. Behav. Ther. Exp. Psychiatry 25, 49–59 (1994).Lang, P. J., Bradley, M. M. & Cuthbert, B. N. International Affective Picture System (IAPS): Technical Manual and Affective Ratings. NIMH Cent. Study Emot. Atten. 39–58, https://doi.org/10.1027/0269-8803/a000147 (1997).Nanda, U., Pati, D., Ghamari, H. & Bajema, R. Lessons from neuroscience: form follows function, emotions follow form. Intell. Build. Int. 5, 61–78 (2013).Russell, J. A. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980).Sejima, K. Kazuyo Sejima. 1988–1996. El Croquis 15 (1996).Ochiai, H. et al. Physiological and Psychological Effects of Forest Therapy on Middle-Aged Males with High-NormalBlood Pressure. Int. J. Environ. Res. Public Health 12, 2532–2542 (2015).Noguchi, H. & Sakaguchi, T. Effect of illuminance and color temperature on lowering of physiological activity. Appl. Hum. Sci. 18, 117–123 (1999).Küller, R., Mikellides, B. & Janssens, J. Color, arousal, and performance—A comparison of three experiments. Color Res. Appl. 34, 141–152 (2009).Yildirim, K., Hidayetoglu, M. L. & Capanoglu, A. Effects of interior colors on mood and preference: comparisons of two living rooms. Percept. Mot. Skills 112, 509–524 (2011).Hogg, J., Goodman, S., Porter, T., Mikellides, B. & Preddy, D. E. Dimensions and determinants of judgements of colour samples and a simulated interior space by architects and non‐architects. Br. J. Psychol. 70, 231–242 (1979).Jalil, N. A., Yunus, R. M. & Said, N. S. Environmental Colour Impact upon Human Behaviour: A Review. Procedia - Soc. Behav. Sci. 35, 54–62 (2012).Jacobs, K. W. & Hustmyer, F. E. Effects of four psychological primary colors on GSR, heart rate and respiration rate. Percept. Mot. Skills 38, 763–766 (1974).Jin, H. R., Yu, M., Kim, D. W., Kim, N. G. & Chung, A. S. W. Study on Physiological Responses to Color Stimulation. In International Association of Societies of Design Research (ed. Poggenpohl, S.) 1969–1979 (Korean Society of Design Science, 2009).Vartanian, O. et al. Impact of contour on aesthetic judgments and approach-avoidance decisions in architecture. Proc. Natl. Acad. Sci. 110, 1–8 (2013).Tsunetsugu, Y., Miyazaki, Y. & Sato, H. Visual effects of interior design in actual-size living rooms on physiological responses. Build. Environ. 40, 1341–1346 (2005).Stamps, A. E. Physical Determinants of Preferences for Residential Facades. Environ. Behav. 31, 723–751 (1999).Berlyne, D. E. Novelty, Complexity, and Hedonic Value. Percept. Psychophys. 8, 279–286 (1970).Krueger, R. A. & Casey, M. Focus groups: a practical guide for applied research. (Sage Publications, 2000).Acharya, U. R., Joseph, K. P., Kannathal, N., Lim, C. M. & Suri, J. S. Heart rate variability: A review. Med. Biol. Eng. Comput. 44, 1031–1051 (2006).Tarvainen, M. P., Niskanen, J. P., Lipponen, J. A., Ranta-aho, P. O. & Karjalainen, P. A. Kubios HRV - Heart rate variability analysis software. Comput. Methods Programs Biomed. 113, 210–220 (2014).Pan, J. & Tompkins, W. J. A real-time QRS detection algorithm. Biomed. Eng. IEEE Trans. 1, 230–236 (1985).Tarvainen, M. P., Ranta-aho, P. O. & Karjalainen, P. A. An advanced detrending method with application to HRV analysis. IEEE Trans. Biomed. Eng. 49, 172–175 (2002).Valenza, G. et al. Predicting Mood Changes in Bipolar Disorder Through HeartbeatNonlinear Dynamics. IEEE J. Biomed. Heal. Informatics 20, 1034–1043 (2016).Pincus, S. & Viscarello, R. Approximate Entropy A regularity measure for fetal heart rate analysis. Obstet. Gynecol. 79, 249–255 (1992).Richman, J. & Moorman, J. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Hear. Circ Physiol 278, H2039–H2049 (2000).Peng, C.-K., Havlin, S., Stanley, H. E. & Goldberger, A. L. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5, 82–87 (1995).Grassberger, P. & Procaccia, I. Characterization of strange attractors. Phys. Rev. Lett. 50, 346–349 (1983).Delorme, A. & Makeig, S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004).Colomer Granero, A. et al. A Comparison of Physiological Signal Analysis Techniques and Classifiers for Automatic Emotional Evaluation of Audiovisual Contents. Front. Comput. Neurosci. 10, 1–14 (2016).Kober, S. E., Kurzmann, J. & Neuper, C. Cortical correlate of spatial presence in 2D and 3D interactive virtual reality: An EEG study. Int. J. Psychophysiol. 83, 365–374 (2012).Hyvärinen, A. & Oja, E. Independent component analysis: Algorithms and applications. Neural Networks 13, 411–430 (2000).Welch, P. D. The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Aver. aging Over Short, Modified Periodograms. IEEE Trans. AUDIO Electroacoust. 15, 70–73 (1967).Mormann, F., Lehnertz, K., David, P. & Elger, E. C. Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients. Phys. D Nonlinear Phenom. 144, 358–369 (2000).Jolliffe, I. T. Principal Component Analysis, Second Edition. Encycl. Stat. Behav. Sci. 30, 487 (2002).Schöllkopf, B., Smola, A. J., Williamson, R. C. & Bartlett, P. L. New support vector algorithms. Neural Comput 12, 1207–1245 (2000).Yan, K. & Zhang, D. Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors Actuators, B Chem. 212, 353–363 (2015).Chang, C.-C. & Lin, C.-J. Libsvm: A Library for Support Vector Machines. ACM Trans. Intell. Syst. Technol. 2, 1–27 (2011).Lewis, P. A., Critchley, H. D., Rotshtein, P. & Dolan, R. J. Neural correlates of processing valence and arousal in affective words. Cereb. Cortex 17, 742–748 (2007).McCall, C., Hildebrandt, L. K., Hartmann, R., Baczkowski, B. M. & Singer, T. Introducing the Wunderkammer as a tool for emotion research: Unconstrained gaze and movement patterns in three emotionally evocative virtual worlds. Comput. Human Behav. 59, 93–107 (2016).Blake, J. & Gurocak, H. B. Haptic glove with MR brakes for virtual reality. IEEE/ASME Trans. Mechatronics 14, 606–615 (2009).Heydarian, A. et al. Immersive virtual environments versus physical built environments: A benchmarking study for building design and user-built environment explorations. Autom. Constr. 54, 116–126 (2015).Kuliga, S. F., Thrash, T., Dalton, R. C. & Hölscher, C. Virtual reality as an empirical research tool - Exploring user experience in a real building and a corresponding virtual model. Comput. Environ. Urban Syst. 54, 363–375 (2015).Yeom, D., Choi, J.-H. & Zhu, Y. Investigation of the Physiological Differences between Immersive Virtual Environment and Indoor Enviorment in a Building. Indoor adn Built Enviornment 0, Accept (2017).Combrisson, E. & Jerbi, K. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods 250, 126–136 (2015).He, C., Yao, Y. & Ye, X. An Emotion Recognition System Based on Physiological Signals Obtained by Wearable Sensors. In Wearable Sensors and Robots: Proceedings of International Conference on Wearable Sensors and Robots 2015 (eds. Yang, C., Virk, G. S. & Yang, H.) 15–25, https://doi.org/10.1007/978-981-10-2404-7_2 (Springer Singapore, 2017)

Virtual reality exposure-based therapy for the treatment of post-traumatic stress disorder: a review of its efficacy, the adequacy of the treatment protocol, and its acceptability

Cristina Botella,1 Berenice Serrano,1 Rosa M Ba&ntilde;os,2 Azucena Garcia-Palacios1 1Universitat Jaume I, Castell&oacute;n de la Plana, Spain; 2Universitat de Valencia, Valencia, Spain Introduction: The essential feature of post-traumatic stress disorder (PTSD) is the development of characteristic symptoms following exposure to one or more traumatic events. According to evidence-based intervention guidelines and empirical evidence, one of the most extensively researched and validated treatments for PTSD is prolonged exposure to traumatic events; however, exposure therapy can present some limitations. Virtual reality (VR) can help to improve prolonged exposure because it creates fictitious, safe, and controllable situations that can enhance emotional engagement and acceptance. Objective: In addition to carrying out a review to evaluate the efficacy of VR exposure-based therapy (VR-EBT) for the treatment of PTSD, the aim of this study was to contribute to analyzing the use of VR-EBT by: first, evaluating the adequacy of psychological treatment protocols that use VR-EBT to treat PTSD; and second, analyzing the acceptability of VR-EBT. Method: We performed a replica search with descriptors and databases used in two previous reviews and updated to April 2015. Next, we carried out an evaluation of the efficacy, adequacy, and acceptability of VR-EBT protocols. Results: Results showed that VR-EBT was effective in the treatment of PTSD. The findings related to adequacy showed that not all studies using VR-EBT reported having followed the clinical guidelines for evidence-based interventions in the treatment of PTSD. Regarding acceptability, few studies evaluated this subject. However, the findings are very promising, and patients reported high acceptability and satisfaction with the inclusion of VR in the treatment of PTSD. Conclusion: The main weaknesses identified in this review focus on the need for more controlled studies, the need to standardize treatment protocols using VR-EBT, and the need to include assessments of acceptability and related variables. Finally, this paper highlights some directions and future perspectives for using VR-EBT in PTSD treatment. Keywords: evidence-based intervention, prolonged exposure, treatment efficac

An Internet-based program for depressive symptoms using human and automated support: a randomized controlled trial

Adriana Mira,1 Juana Bret&oacute;n-L&oacute;pez,1,2 Azucena Garc&iacute;a-Palacios,1,2 Soledad Quero,1,2 Rosa Mar&iacute;a Ba&ntilde;os,2,3 Cristina Botella1,2 1Department of Basic, Clinical Psychology and Psychobiology, Labpsitec, Universitat Jaume I, Castell&oacute;n de la Plana, Spain; 2CIBER&nbsp;of Physiopathology of Obesity and Nutrition CIBERobn, CB06/03 Instituto de Salud Carlos III, Santiago de Compostela, Spain; 3Department of Personality, Evaluation and Psychological Treatment, Universidad de Valencia, Valencia, Spain Purpose: The purpose of this study was to analyze the efficacy of an Internet-based program for depressive symptoms using automated support by information and communication technologies (ICTs) and human support. Patients and methods: An Internet-based program was used to teach adaptive ways to cope with depressive symptoms and daily problems. A total of 124 participants who were experiencing at least one stressful event that caused interference in their lives, many of whom had clinically significant depressive symptoms, were randomly assigned into either an intervention group with ICT support (automated mobile phone messages, automated emails, and continued feedback through the program); an intervention group with ICT support plus human support (brief weekly support phone call without clinical content); or a waiting-list control. At pre-, post-, and 12-month follow-up, they completed depression, anxiety, positive and negative effect, and perceived stress measures. Results were analyzed using both intention-to-treat and completers data. The majority were women (67.7%), with a mean age of 35.6 years (standard deviation =9.7). Results: The analysis showed that the two intervention groups improved significantly pre- to posttreatment, compared with the control group. Furthermore, improvements were maintained at the 12-month follow-up. Adherence and satisfaction with the program was high in both conditions. Conclusion: The Internet-based program was effective and well accepted, with and without human support, showing that ICT-based automated support may be useful. It is essential to continue to study other ICT strategies for providing support. Keywords: online intervention, types of support, depressive symptomatology, adherence, satisfactio

Pedagogical Practices to Teacher Education for Gerontology Education

This article discusses possible educational strategies for teaching cyberseniors in Distance Education. The objective of this study was to delineate pedagogical practices that can contribute to teacher training in gerontology education. This need resulted from the need to discuss the increasing longevity of the population. This change in society poses new challenges for education. In this sense, distance learning can become a way to social inclusion, because of its many possibilities. Unfortunately, there are a few related studies, especially considering didactic and pedagogical activity for teachers. Thus, we conducted a study using both qualitative and quantitative approaches. It is based on offering extension courses for the training of professionals and individuals 60 years or older. For data collection we conducted participant observation, interviews, questionnaires and survey of technological productions of participants in a virtual learning environment. From the reports of the participants it is possible to map strategies for teaching and teacher training, including professionals who work or intend to work in distance education with elderly adults