Abstract
We obtain a personal signature of a person’s learning progress in a self-neuromodulation task, guided by functional MRI (fMRI). The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-derived brain state in the first session. The prediction is made by a deep neural network, which is trained on the entire training cohort of patients. This signal, which is indicative of a person’s progress in performing the task of Amygdala modulation, is aggregated across multiple prototypical brain states and then classified by a linear classifier to various personal and clinical indications. The predictive power of the obtained signature is stronger than previous approaches for obtaining a personal signature from fMRI neurofeedback and provides an indication that a person’s learning pattern may be used as a diagnostic tool. Our code has been made available, (Our code is available via https://github.com/MICCAI22/fmri_nf.) and data would be shared, subject to ethical approvals.
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References
Ackerman, P.L.: Determinants of individual differences during skill acquisition: cognitive abilities and information processing. J. Exp. Psychol. Gen. 117(3), 288 (1988)
Asano, Y., Rupprecht, C., Vedaldi, A.: A critical analysis of self-supervision, or what we can learn from a single image. In: International Conference on Learning Representations (2019)
Bagby, R.M., Parker, J.D., Taylor, G.J.: The twenty-item Toronto alexithymia scale-i. item selection and cross-validation of the factor structure. J. Psychosomatic Res. 38(1), 23–32 (1994)
Bleich-Cohen, M., Jamshy, S., Sharon, H., Weizman, R., Intrator, N., Poyurovsky, M., Hendler, T.: Machine learning fmri classifier delineates subgroups of schizophrenia patients. Schizophr. Res. 160(1–3), 196–200 (2014)
Calhoun, V.D., Miller, R., Pearlson, G., Adalı, T.: The chronnectome: time-varying connectivity networks as the next frontier in FMRI data discovery. Neuron 84(2), 262–274 (2014)
Crump, M.J., Vaquero, J.M., Milliken, B.: Context-specific learning and control: the roles of awareness, task relevance, and relative salience. Conscious. Cogn. 17(1), 22–36 (2008)
Dvornek, N.C., Ventola, P., Pelphrey, K.A., Duncan, J.S.: Identifying Autism from resting-State fMRI using long short-term memory networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 362–370. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67389-9_42
Fruchtman-Steinbok, T., et al.: Amygdala electrical-finger-print (amygefp) neurofeedback guided by individually-tailored trauma script for post-traumatic stress disorder: Proof-of-concept. NeuroImage: Clinical 32, 102859 (2021)
Gal, S., Tik, N., Bernstein-Eliav, M., Tavor, I.: Predicting individual traits from unperformed tasks. NeuroImage, 118920 (2022)
Hendler, T., et al.: Social affective context reveals altered network dynamics in schizophrenia patients. Transl. Psychiatry 8(1), 1–12 (2018)
Jacob, Y., Shany, O., Goldin, P., Gross, J., Hendler, T.: Reappraisal of interpersonal criticism in social anxiety disorder: a brain network hierarchy perspective. Cereb. Cortex 29(7), 3154–3167 (2019)
Keynan, J.N., et al.: Electrical fingerprint of the amygdala guides neurofeedback training for stress resilience. Nat. Hum. Behav. 3(1), 63–73 (2019)
Lerner, Y., et al.: Abnormal neural hierarchy in processing of verbal information in patients with schizophrenia. NeuroImage: Clinical 17, 1047–1060 (2018)
Lubianiker, N., et al.: Process-based framework for precise neuromodulation. Nat. Hum. Behav. 3(5), 436–445 (2019)
Marxen, M., et al.: Amygdala regulation following fMRI-neurofeedback without instructed strategies. Front. Hum. Neurosci. 10, 183 (2016)
Oksuz, I., et al.: Magnetic resonance fingerprinting using recurrent neural networks. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1537–1540. IEEE (2019)
Osin, J., et al.: Learning personal representations from fMRI by predicting neurofeedback performance. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 469–478. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59728-3_46
Paret, C., et al.: Current progress in real-time functional magnetic resonance-based neurofeedback: methodological challenges and achievements. Neuroimage 202, 116107 (2019)
Raz, G., Shpigelman, L., Jacob, Y., Gonen, T., Benjamini, Y., Hendler, T.: Psychophysiological whole-brain network clustering based on connectivity dynamics analysis in naturalistic conditions. Hum. Brain Mapp. 37(12), 4654–4672 (2016)
Sitaram, R., et al.: Closed-loop brain training: the science of neurofeedback. Nat. Rev. Neurosci. 18(2), 86–100 (2017)
Spielberger, C.D., Gorsuch, R.L.: State-trait anxiety inventory for adults: Manual and sample: Manual, instrument and scoring guide. Consulting Psychologists Press (1983)
Sulzer, J., et al.: Real-time fMRI neurofeedback: progress and challenges. Neuroimage 76, 386–399 (2013)
Taschereau-Dumouchel, V., Cushing, C., Lau, H.: Real-time functional MRI in the treatment of mental health disorders. Ann. Rev. Clin. Psychol. 18 (2022)
Utman, C.H.: Performance effects of motivational state: a meta-analysis. Pers. Soc. Psychol. Rev. 1(2), 170–182 (1997)
Weathers, F., Blake, D., Schnurr, P., Kaloupek, D., Marx, B., Keane, T.: The clinician-administered ptsd scale for dsm-5 (caps-5). interview available from the national center for ptsd (2013)
Whitfield-Gabrieli, S., Nieto-Castanon, A.: Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2(3), 125–141 (2012)
Yan, W., et al.: Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site fMRI data. EBioMedicine 47, 543–552 (2019)
Acknowledgments
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant ERC CoG 725974), and the ISRAEL SCIENCE FOUNDATION (grant No. 2923/20) within the Israel Precision Medicine Partnership program.
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Structural and functional scans were obtained with a 3.0T Siemens PRISMA MRI system. The CONN MATLAB toolbox [26] was used for functional volumes realignment, motion correction, normalization to MNI space and spatial smoothing with an isotropic 6-mm FWHM Gaussian kernel. Subsequently de-noising and de-trending regression algorithms were applied, followed by bandpass filtering in the range of 0.008–0.09 Hz. The frequencies in the bandpass filter reflect the goal of modeling the individual throughout the session, while removing the effects of the fast paced events that occur during the neurofeedback session. This filtering follows previous work [17] for a fair comparison.
Architecture of f, our Amygdala prediction Neural Network. We trained the network f, until convergence of its validation loss for each of the three datasets. The hyper-parameters of the network f were selected according to a grid search using the cross validation scores on the validation set. For training, we used an Adam optimizer, with initial learning rate of 0.001, and a batch sizes of 16.
(a) Mid-sagittal slice of Rest of brain mask; and (b) a coronal slice of it.
Visualization of the cluster centroids which represent the prototypical brain states found during the NF task for the Healthy subgroup. The resulting maps show the main activated nodes in each state. Clusters are distinct in their spatial arrangement, which supports the relevance of using clustering for this purpose. In this figure, the five prototypical clusters obtained on the healthy individuals dataset are presented. (a) Sensorimotor Network and Visual Cortex. (b) Main nodes of the Default Mode network (c) Main nodes of the Salience Network. (d) Dorsal attention network. (e) High visual areas (Ventral and Dorsal Stream).
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Leibovitz, R., Osin, J., Wolf, L., Gurevitch, G., Hendler, T. (2022). fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_27
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