Predição de sintomas de TEPT a partir da ativação cerebral em pessoas expostas a imagens de mutilação
Resumo
O objetivo do presente estudo foi verificar a possibilidade de predição dos sintomas do Transtorno de Estresse Pós-Traumático (TEPT) a partir dos padrões de atividade cerebral. Os participantes expostos a situações traumáticas foram submetidos a exames de Ressonância Magnética Funcional (RMf) enquanto eram expostos a fotos neutras e de corpos mutilados. Neste experimento, foram criados dois contextos de imagens aversivas (real e seguro). O modelo de aprendizado de máquina foi capaz de predizer sintomas de TEPT a partir de padrões de atividade cerebral em resposta às imagens de mutilação no contexto real, mas não no contexto seguro. As regiões cerebrais que apresentaram maior contribuição para o modelo foram as regiões occipitoparietais, incluindo o giro parietal superior e inferior, e o giro supramarginal.
Referências
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Bradley, M. M., Codispoti, M., Cuthbert, B. N., and Lang, P. J. (2001). Emotion and motivation I: defensive and appetitive reactions in picture processing. Emotion (Washington, D.C.), 1(3):276–298.
Cerqueira, D. R. d. C., Lima, R. S. d., Bueno, S., Valencia, L. I., Hanashiro, O., Machado, P. H. G., and Lima, A. d. S. (2017). Atlas da violência 2017. http://www.ipea.gov.br.
Cléry, J., Guipponi, O., Wardak, C., and Ben Hamed, S. (2015). Neuronal bases of peripersonal and extrapersonal spaces, their plasticity and their dynamics: Knowns and unknowns. Neuropsychologia, 70:313–326.
Cuthbert, B. N. and Insel, T. R. (2013). Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Medicine, 11(1):126.
de Oliveira, L., Portugal, L. C. L., Pereira, M., Chase, H. W., Bertocci, M., Stiffler, R., Greenberg, T., Bebko, G., Lockovich, J., Aslam, H., Mourao-Miranda, J., and Phillips, M. L. (2019). Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 4(8):726–733.
Fitzgerald, J. M., Belleau, E. L., Miskovich, T. A., Pedersen, W. S., and Larson, C. L. (2020). Multi-voxel pattern analysis of amygdala functional connectivity at rest predicts variability in posttraumatic stress severity. Brain and Behavior, 10(8):e01707.
Grefkes, C. and Fink, G. R. (2005). REVIEW: The functional organization of the intraparietal sulcus in humans and monkeys. Journal of Anatomy, 207(1):3–17.
Hoerl, A. E. and Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1):55–67.
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., and Wang, P. (2010). Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of Psychiatry, 167(7):748–751. Publisher: American Psychiatric Publishing.
Janssen, R. J., Mourão-Miranda, J., and Schnack, H. G. (2018). Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 3(9):798–808.
Jovanovic, T., Kazama, A., Bachevalier, J., and Davis, M. (2012). Impaired safety signal learning may be a biomarker of PTSD. Neuropharmacology, 62(2):695–704.
Koenen, K. C., Ratanatharathorn, A., Ng, L., McLaughlin, K. A., Bromet, E. J., Stein, D. J., Karam, E. G., Ruscio, A. M., Benjet, C., Scott, K., Atwoli, L., Petukhova, M., Lim, C. C. W., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., Bunting, B., Ciutan, M., Girolamo, G. d., Degenhardt, L., Gureje, O., Haro, J. M., Huang, Y., Kawakami, N., Lee, S., Navarro-Mateu, F., Pennell, B.-E., Piazza, M., Sampson, N., Have, M. t., Torres, Y., Viana, M. C., Williams, D., Xavier, M., and Kessler, R. C. (2017). Posttraumatic stress disorder in the World Mental Health Surveys. Psychological Medicine, 47(13):2260–2274.
Lilienfeld, S. O. and Treadway, M. T. (2016). Clashing Diagnostic Approaches: DSM-ICD Versus RDoC. Annual Review of Clinical Psychology, 12:435–463.
Lima, E. d. P., Vasconcelos, A. G., Berger, W., Kristensen, C. H., Nascimento, E. d., Figueira, I., and Mendlowicz, M. V. (2016). Cross-cultural adaptation of the post-traumatic stress disorder checklist 5 (pcl-5) and life events checklist 5 (lec-5) for the brazilian context. Trends in Psychiatry and Psychotherapy, 38(4):207–215.
Lobo, I., Campagnoli, R. R., Figueira, J. S., Andrade, I., Figueira, I., Gama, C., Gonçalves, R. M., Keil, A., Pereira, M. G., Volchan, E., Oliveira, L., and David, I. A. (2021). Hidden wounds of violence: Abnormal motor oscillatory brain activity is related to posttraumatic stress symptoms. NeuroImage, 224:117404.
McTeague, L. M., Lang, P. J., Laplante, M.-C., Cuthbert, B. N., Shumen, J. R., and Bradley, M. M. (2010). Aversive Imagery in Posttraumatic Stress Disorder: Trauma Recurrence, Comorbidity, and Physiological Reactivity. Biological Psychiatry, 67(4):346–356.
Michopoulos, V., Norrholm, S. D., and Jovanovic, T. (2015). Diagnostic Biomarkers for Posttraumatic Stress Disorder: Promising Horizons from Translational Neuroscience Research. Biological Psychiatry, 78(5):344–353.
Miller, G. A. and Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110(1):40–48.
Mocaiber, I., Sanchez, T. A., Pereira, M. G., Erthal, F. S., Joffily, M., Araujo, D. B., Volchan, E., and de Oliveira, L. (2011). Antecedent descriptions change brain reactivity to emotional stimuli: a functional magnetic resonance imaging study of an extrinsic and incidental reappraisal strategy. Neuroscience, 193:241–248.
Pereira, F., Mitchell, T., and Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial overview. NeuroImage, 45(1 Suppl):S199–209.
Portugal, L. C. L., Schrouff, J., Stiffler, R., Bertocci, M., Bebko, G., Chase, H., Lockovitch, J., Aslam, H., Graur, S., Greenberg, T., Pereira, M., Oliveira, L., Phillips, M., and Mourão-Miranda, J. (2019). Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach. NeuroImage. Clinical, 23:101813.
Rao, A., Monteiro, J. M., Mourao-Miranda, J., and Alzheimer’s Disease Initiative (2017). Predictive modelling using neuroimaging data in the presence of confounds. NeuroImage, 150:23–49.
Rasmussen, C. E. and Williams, C. K. I. (2005). Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning series. MIT Press, Cambridge, MA, USA.
Ribeiro, W. S., Mari, J. d. J., Quintana, M. I., Dewey, M. E., Evans-Lacko, S., Vilete, L. M. P., Figueira, I., Bressan, R. A., Mello, M. F. d., Prince, M., Ferri, C. P., Coutinho, E. S. F., and Andreoli, S. B. (2013). The Impact of Epidemic Violence on the Prevalence of Psychiatric Disorders in Sao Paulo and Rio de Janeiro, Brazil. PLOS ONE, 8(5):e63545.
Sarlo, M., Buodo, G., Poli, S., and Palomba, D. (2005). Changes in EEG alpha power to different disgust elicitors: the specificity of mutilations. Neuroscience Letters, 382(3):291–296.
Schienle, A., Schäfer, A., Hermann, A., Walter, B., Stark, R., and Vaitl, D. (2006). fMRI responses to pictures of mutilation and contamination. Neuroscience Letters, 393(2):174–178.
Schrouff, J. and Mourão-Miranda, J. (2018). Interpreting weight maps in terms of cognitive or clinical neuroscience: Nonsense? In 2018 international workshop on pattern recognition in neuroimaging (PRNI), pages 1–4.
Schrouff, J., Mourão-Miranda, J., Phillips, C., and Parvizi, J. (2016). Decoding intracranial EEG data with multiple kernel learning method. Journal of Neuroscience Methods, 261:19–28.
Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., and Mourão-Miranda, J. (2013). PRoNTo: Pattern Recognition for Neuroimaging Toolbox. Neuroinformatics, 11(3):319–337.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., and Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1):273–289.
Weathers, F., Blake, D., Schnurr, P., Kaloupek, D., Marx, B., and Keane, T. (2013a). The life events checklist for dsm-5 (lec-5). Instrument available from the National Center for PTSD at https://www.ptsd.va.gov/.
Weathers, F., Litz, B.T.and Keane, T., Palmieri, P., Marx, B., and Schnurr, P. (2013b). The ptsd checklist for dsm-5 (pcl-5). Scale available from the National Center for PTSD at www.ptsd.va.gov.
Yuan, K., Gong, Y.-M., Liu, L., Sun, Y.-K., Tian, S.-S., Wang, Y.-J., Zhong, Y., Zhang, A.-Y., Su, S.-Z., Liu, X.-X., Zhang, Y.-X., Lin, X., Shi, L., Yan, W., Fazel, S., Vitiello, M. V., Bryant, R. A., Zhou, X.-Y., Ran, M.-S., Bao, Y.-P., Shi, J., and Lu, L. (2021). Prevalence of posttraumatic stress disorder after infectious disease pandemics in the twenty-first century, including COVID-19: a meta-analysis and systematic review. Molecular Psychiatry, 26(9):4982–4998.
Zandvakili, A., Barredo, J., Swearingen, H. R., Aiken, E. M., Berlow, Y. A., Greenberg, B. D., Carpenter, L. L., and Philip, N. S. (2020). Mapping PTSD symptoms to brain networks: a machine learning study. Translational Psychiatry, 10(1):1–8.
Bastos, A. F., Silva, L. M. d., Oliveira, J. M. d., Oliveira, L., Pereira, M. G., Figueira, I., Mendlowicz, M. V., Berger, W., da Luz, M., Campos, B., Marques-Portella, C., Moll, J., Bramati, I., Volchan, E., and Erthal, F. S. (2022). Beyond fear: Patients with posttraumatic stress disorder fail to engage in safety cues. Journal of Affective Disorders Reports, 10:100380.
Bradley, M. M., Codispoti, M., Cuthbert, B. N., and Lang, P. J. (2001). Emotion and motivation I: defensive and appetitive reactions in picture processing. Emotion (Washington, D.C.), 1(3):276–298.
Cerqueira, D. R. d. C., Lima, R. S. d., Bueno, S., Valencia, L. I., Hanashiro, O., Machado, P. H. G., and Lima, A. d. S. (2017). Atlas da violência 2017. http://www.ipea.gov.br.
Cléry, J., Guipponi, O., Wardak, C., and Ben Hamed, S. (2015). Neuronal bases of peripersonal and extrapersonal spaces, their plasticity and their dynamics: Knowns and unknowns. Neuropsychologia, 70:313–326.
Cuthbert, B. N. and Insel, T. R. (2013). Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Medicine, 11(1):126.
de Oliveira, L., Portugal, L. C. L., Pereira, M., Chase, H. W., Bertocci, M., Stiffler, R., Greenberg, T., Bebko, G., Lockovich, J., Aslam, H., Mourao-Miranda, J., and Phillips, M. L. (2019). Predicting Bipolar Disorder Risk Factors in Distressed Young Adults From Patterns of Brain Activation to Reward: A Machine Learning Approach. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 4(8):726–733.
Fitzgerald, J. M., Belleau, E. L., Miskovich, T. A., Pedersen, W. S., and Larson, C. L. (2020). Multi-voxel pattern analysis of amygdala functional connectivity at rest predicts variability in posttraumatic stress severity. Brain and Behavior, 10(8):e01707.
Grefkes, C. and Fink, G. R. (2005). REVIEW: The functional organization of the intraparietal sulcus in humans and monkeys. Journal of Anatomy, 207(1):3–17.
Hoerl, A. E. and Kennard, R. W. (1970). Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics, 12(1):55–67.
Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., Sanislow, C., and Wang, P. (2010). Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. American Journal of Psychiatry, 167(7):748–751. Publisher: American Psychiatric Publishing.
Janssen, R. J., Mourão-Miranda, J., and Schnack, H. G. (2018). Making Individual Prognoses in Psychiatry Using Neuroimaging and Machine Learning. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 3(9):798–808.
Jovanovic, T., Kazama, A., Bachevalier, J., and Davis, M. (2012). Impaired safety signal learning may be a biomarker of PTSD. Neuropharmacology, 62(2):695–704.
Koenen, K. C., Ratanatharathorn, A., Ng, L., McLaughlin, K. A., Bromet, E. J., Stein, D. J., Karam, E. G., Ruscio, A. M., Benjet, C., Scott, K., Atwoli, L., Petukhova, M., Lim, C. C. W., Aguilar-Gaxiola, S., Al-Hamzawi, A., Alonso, J., Bunting, B., Ciutan, M., Girolamo, G. d., Degenhardt, L., Gureje, O., Haro, J. M., Huang, Y., Kawakami, N., Lee, S., Navarro-Mateu, F., Pennell, B.-E., Piazza, M., Sampson, N., Have, M. t., Torres, Y., Viana, M. C., Williams, D., Xavier, M., and Kessler, R. C. (2017). Posttraumatic stress disorder in the World Mental Health Surveys. Psychological Medicine, 47(13):2260–2274.
Lilienfeld, S. O. and Treadway, M. T. (2016). Clashing Diagnostic Approaches: DSM-ICD Versus RDoC. Annual Review of Clinical Psychology, 12:435–463.
Lima, E. d. P., Vasconcelos, A. G., Berger, W., Kristensen, C. H., Nascimento, E. d., Figueira, I., and Mendlowicz, M. V. (2016). Cross-cultural adaptation of the post-traumatic stress disorder checklist 5 (pcl-5) and life events checklist 5 (lec-5) for the brazilian context. Trends in Psychiatry and Psychotherapy, 38(4):207–215.
Lobo, I., Campagnoli, R. R., Figueira, J. S., Andrade, I., Figueira, I., Gama, C., Gonçalves, R. M., Keil, A., Pereira, M. G., Volchan, E., Oliveira, L., and David, I. A. (2021). Hidden wounds of violence: Abnormal motor oscillatory brain activity is related to posttraumatic stress symptoms. NeuroImage, 224:117404.
McTeague, L. M., Lang, P. J., Laplante, M.-C., Cuthbert, B. N., Shumen, J. R., and Bradley, M. M. (2010). Aversive Imagery in Posttraumatic Stress Disorder: Trauma Recurrence, Comorbidity, and Physiological Reactivity. Biological Psychiatry, 67(4):346–356.
Michopoulos, V., Norrholm, S. D., and Jovanovic, T. (2015). Diagnostic Biomarkers for Posttraumatic Stress Disorder: Promising Horizons from Translational Neuroscience Research. Biological Psychiatry, 78(5):344–353.
Miller, G. A. and Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110(1):40–48.
Mocaiber, I., Sanchez, T. A., Pereira, M. G., Erthal, F. S., Joffily, M., Araujo, D. B., Volchan, E., and de Oliveira, L. (2011). Antecedent descriptions change brain reactivity to emotional stimuli: a functional magnetic resonance imaging study of an extrinsic and incidental reappraisal strategy. Neuroscience, 193:241–248.
Pereira, F., Mitchell, T., and Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial overview. NeuroImage, 45(1 Suppl):S199–209.
Portugal, L. C. L., Schrouff, J., Stiffler, R., Bertocci, M., Bebko, G., Chase, H., Lockovitch, J., Aslam, H., Graur, S., Greenberg, T., Pereira, M., Oliveira, L., Phillips, M., and Mourão-Miranda, J. (2019). Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach. NeuroImage. Clinical, 23:101813.
Rao, A., Monteiro, J. M., Mourao-Miranda, J., and Alzheimer’s Disease Initiative (2017). Predictive modelling using neuroimaging data in the presence of confounds. NeuroImage, 150:23–49.
Rasmussen, C. E. and Williams, C. K. I. (2005). Gaussian Processes for Machine Learning. Adaptive Computation and Machine Learning series. MIT Press, Cambridge, MA, USA.
Ribeiro, W. S., Mari, J. d. J., Quintana, M. I., Dewey, M. E., Evans-Lacko, S., Vilete, L. M. P., Figueira, I., Bressan, R. A., Mello, M. F. d., Prince, M., Ferri, C. P., Coutinho, E. S. F., and Andreoli, S. B. (2013). The Impact of Epidemic Violence on the Prevalence of Psychiatric Disorders in Sao Paulo and Rio de Janeiro, Brazil. PLOS ONE, 8(5):e63545.
Sarlo, M., Buodo, G., Poli, S., and Palomba, D. (2005). Changes in EEG alpha power to different disgust elicitors: the specificity of mutilations. Neuroscience Letters, 382(3):291–296.
Schienle, A., Schäfer, A., Hermann, A., Walter, B., Stark, R., and Vaitl, D. (2006). fMRI responses to pictures of mutilation and contamination. Neuroscience Letters, 393(2):174–178.
Schrouff, J. and Mourão-Miranda, J. (2018). Interpreting weight maps in terms of cognitive or clinical neuroscience: Nonsense? In 2018 international workshop on pattern recognition in neuroimaging (PRNI), pages 1–4.
Schrouff, J., Mourão-Miranda, J., Phillips, C., and Parvizi, J. (2016). Decoding intracranial EEG data with multiple kernel learning method. Journal of Neuroscience Methods, 261:19–28.
Schrouff, J., Rosa, M. J., Rondina, J. M., Marquand, A. F., Chu, C., Ashburner, J., Phillips, C., Richiardi, J., and Mourão-Miranda, J. (2013). PRoNTo: Pattern Recognition for Neuroimaging Toolbox. Neuroinformatics, 11(3):319–337.
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., and Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1):273–289.
Weathers, F., Blake, D., Schnurr, P., Kaloupek, D., Marx, B., and Keane, T. (2013a). The life events checklist for dsm-5 (lec-5). Instrument available from the National Center for PTSD at https://www.ptsd.va.gov/.
Weathers, F., Litz, B.T.and Keane, T., Palmieri, P., Marx, B., and Schnurr, P. (2013b). The ptsd checklist for dsm-5 (pcl-5). Scale available from the National Center for PTSD at www.ptsd.va.gov.
Yuan, K., Gong, Y.-M., Liu, L., Sun, Y.-K., Tian, S.-S., Wang, Y.-J., Zhong, Y., Zhang, A.-Y., Su, S.-Z., Liu, X.-X., Zhang, Y.-X., Lin, X., Shi, L., Yan, W., Fazel, S., Vitiello, M. V., Bryant, R. A., Zhou, X.-Y., Ran, M.-S., Bao, Y.-P., Shi, J., and Lu, L. (2021). Prevalence of posttraumatic stress disorder after infectious disease pandemics in the twenty-first century, including COVID-19: a meta-analysis and systematic review. Molecular Psychiatry, 26(9):4982–4998.
Zandvakili, A., Barredo, J., Swearingen, H. R., Aiken, E. M., Berlow, Y. A., Greenberg, B. D., Carpenter, L. L., and Philip, N. S. (2020). Mapping PTSD symptoms to brain networks: a machine learning study. Translational Psychiatry, 10(1):1–8.
Publicado
27/06/2023
Como Citar
PORTUGAL, Liana C. L. et al.
Predição de sintomas de TEPT a partir da ativação cerebral em pessoas expostas a imagens de mutilação. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 23. , 2023, São Paulo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 174-185.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2023.229589.
