Development of a spontaneous pain indicator based on brain cellular calcium using deep learning
0
조회
0
다운로드
1
인용
0
참고문헌
초록
Chronic pain remains an intractable condition in millions of patients worldwide. Spontaneous ongoing pain is a major clinical problem of chronic pain and is extremely challenging to diagnose and treat compared to stimulus-evoked pain. Although extensive efforts have been made in preclinical studies, there still exists a mismatch in pain type between the animal model and humans (i.e., evoked vs. spontaneous), which obstructs the translation of knowledge from preclinical animal models into objective diagnosis and effective new treatments. Here, we developed a deep learning algorithm, designated AI-bRNN (Average training, Individual test-bidirectional Recurrent Neural Network), to detect spontaneous pain information from brain cellular Ca2+ activity recorded by two-photon microscopy imaging in awake, head-fixed mice. AI-bRNN robustly determines the intensity and time points of spontaneous pain even in chronic pain models and evaluates the efficacy of analgesics in real time. Furthermore, AI-bRNN can be applied to various cell types (neurons and glia), brain areas (cerebral cortex and cerebellum) and forms of somatosensory input (itch and pain), proving its versatile performance. These results suggest that our approach offers a clinically relevant, quantitative, real-time preclinical evaluation platform for pain medicine, thereby accelerating the development of new methods for diagnosing and treating human patients with chronic pain.
Abstract
Chronic pain remains an intractable condition in millions of patients worldwide. Spontaneous ongoing pain is a major clinical problem of chronic pain and is extremely challenging to diagnose and treat compared to stimulus-evoked pain. Although extensive efforts have been made in preclinical studies, there still exists a mismatch in pain type between the animal model and humans (i.e., evoked vs. spontaneous), which obstructs the translation of knowledge from preclinical animal models into objective diagnosis and effective new treatments. Here, we developed a deep learning algorithm, designated AI-bRNN (Average training, Individual test-bidirectional Recurrent Neural Network), to detect spontaneous pain information from brain cellular Ca2+ activity recorded by two-photon microscopy imaging in awake, head-fixed mice. AI-bRNN robustly determines the intensity and time points of spontaneous pain even in chronic pain models and evaluates the efficacy of analgesics in real time. Furthermore, AI-bRNN can be applied to various cell types (neurons and glia), brain areas (cerebral cortex and cerebellum) and forms of somatosensory input (itch and pain), proving its versatile performance. These results suggest that our approach offers a clinically relevant, quantitative, real-time preclinical evaluation platform for pain medicine, thereby accelerating the development of new methods for diagnosing and treating human patients with chronic pain.
참고문헌 (62)
- Backonja, M. M., "Neuropathic pain symptoms relative to overall pain rating", J. Pain, 5, pp.491~497 (2004)
- Enright, A., "The global burden of pain: The tip of the iceberg?", Anesth. Analg, 123, pp.529~530 (2016)
- Jamison, R. N., "Influence of opioid-related side effects on disability, mood, and opioid misuse risk among patients with chronic pain in primary care", Pain. Rep, 2, pp.e589~ (2017)
- Grosser, T., "Time for nonaddictive relief of pain", Science, 355, pp.1026~1027 (2017)
- Skolnick, P., "Re-energizing the development of pain therapeutics in light of the opioid epidemic", Neuron, 92, pp.294~297 (2016)
- Lovell, N., "Characteristics, symptom management, and outcomes of 101patients with COVID-19 referred for hospital palliative care", J. Pain. Symptom Manag, 60, pp.e77~e81 (2020)
- Borsook, D., "Lost but making progress-Where will new analgesic drugs come from?", Sci. Transl. Med, 6, pp.249sr3~ (2014)
- Mao, J., "Current challenges in translational pain research", Trends Pharmacol. Sci, 33, pp.568~573 (2012)
- Mogil, J. S., "The necessity of animal models in pain research", Pain, 151, pp.12~17 (2010)
- Tappe-Theodor, A., "Studying ongoing and spontaneous pain in rodents-challenges and opportunities", Eur. J. Neurosci, 39, pp.1881~1890 (2014)
- Hargreaves, K., "A new and sensitive method for measuring thermal nociception in cutaneous hyperalgesia", Pain, 32, pp.77~88 (1988)
- Langford, D. J., "Coding of facial expressions of pain in the laboratory mouse", Nat. Methods, 7, pp.447~449 (2010)
- King, T., "Unmasking the tonic-aversive state in neuropathic pain", Nat. Neurosci, 12, pp.1364~1366 (2009)
- Tappe-Theodor, A., "Pros and cons of clinically relevant methods to assess pain in rodents", Neurosci. Biobehav. Rev, 100, pp.335~343 (2019)
- Rocchi, L., "Somatosensory temporal discrimination threshold involves inhibitory mechanisms in the primary somatosensory area", J. Neurosci, 36, pp.325~335 (2016)
- Kim, Y. R., "S1 employs feature-dependent differential selectivity of single cells and distributed patterns of populations to encode mechanosensations", Front. Cell. Neurosci, 13, pp.132~ (2019)
- Bushnell, M. C., "Pain perception: is there a role for primary somatosensory cortex?", Proc. Natl Acad. Sci. USA, 96, pp.7705~7709 (1999)
- Dhand, A., "The neurology of itch", Brain, 137, pp.313~322 (2014)
- Kim, S. S., "Multimodal interactions between proprioceptive and cutaneous signals in primary somatosensory cortex", Neuron, 86, pp.555~566 (2015)
- Khasabov, S. G., "Responses of neurons in the primary somatosensory cortex to itch- and pain-producing stimuli in rats", J. Neurophysiol, 123, pp.1944~1954 (2020)
- Schuster, M., "Bidirectional recurrent neural networks", IEEE Trans. Signal Process, 45, pp.2673~2681 (1997)
- Chaplan, S. R., "Quantitative assessment of tactile allodynia in the rat paw", J. Neurosci. Methods, 53, pp.55~63 (1994)
- Eto, K., "Inter-regional contribution of enhanced activity of the primary somatosensory cortex to the anterior cingulate cortex accelerates chronic pain behavior", J. Neurosci, 31, pp.7631~7636 (2011)
- Kim, S. K., "Rapid synaptic remodeling in the adult somatosensory cortex following peripheral nerve injury and its association with neuropathic pain", J. Neurosci, 31, pp.5477~5482 (2011)
- Zhou, P., "Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data", eLife, 7, pp.e28728~ (2018)
- Deneux, T., "Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo", Nat. Commun, 7, pp.12190~ (2016)
- Oppenheim, S., "Discrete-Time Signal Processing, Vol. 1" (1999), Prentice-Hall, Inc.
- "Keras" (2015)[URL]
- Hochreiter, S., "Long short-term memory", Neural Comput, 9, pp.1735~1780 (1997)
- Glorot, X., , Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 15, pp.315~323 (2011)
- "Adam: A method for stochastic optimization" (2014)[URL]
- "SciPy: Open source scientific tools for Python" (2001)[URL]
- Dubuisson, D., "The formalin test : a quantitative study of the analgesic effects of morphine, meperidine, and brain stem stimulation in rats and cats", Pain, 4, pp.161~174 (1977)
- Xu, C., "Machine learning and complex biological data", Genome Biol, 20, pp.76~ (2019)
- Hunskaar, S., "Formalin test in mice, a useful technique for evaluating mild analgesics", J. Neurosci. Methods, 14, pp.69~76 (1985)
- Hunskaar, S., "The formalin test in mice : dissociation between inflammatory and non-inflammatory pain", Pain, 30, pp.103~114 (1987)
- Sotocinal, S. G., "The Rat Grimace Scale: a partially automated method for quantifying pain in the laboratory rat via facial expressions", Mol. Pain, 7, pp.55~ (2011)
- Kim, W., "Combined effects of bee venom acupuncture and morphine on oxaliplatin-induced neuropathic pain in mice", Toxins (Basel), 8, pp.33~ (2016)
- Rode, F., "Venlafaxine compromises the antinociceptive actions of gabapentin in rat models of neuropathic and persistent pain", Psychopharmacol. (Berl. ), 187, pp.364~375 (2006)
- Folkesson, A., "Co-administered gabapentin and venlafaxine in nerve injured rats : Effect on mechanical hypersensitivity, motor function and pharmacokinetics", Scand. J. Pain, 1, pp.91~97 (2010)
- Ahn, S. H., "Gabapentin effect on neuropathic pain compared among patients with spinal cord injury and different durations of symptoms", Spine (Philos. Pa 1976), 28, pp.341~346 (1976)
- Amr, Y. M., "Evaluation of efficacy of the perioperative administration of Venlafaxine or gabapentin on acute and chronic postmastectomy pain", Clin. J. Pain, 26, pp.381~385 (2010)
- Iadarola, M. J., "Neural activation during acute capsaicin-evoked pain and allodynia assessed with PET", Brain, 121, pp.931~947 (1998)
- Moulton, E. A., "The cerebellum and pain: passive integrator or active participator?", Brain Res. Rev, 65, pp.14~27 (2010)
- Urban, R., "Behavioral indices of ongoing pain are largely unchanged in male mice with tissue or nerve injury-induced mechanical hypersensitivity", Pain, 152, pp.990~1000 (2011)
- Kurejova, M., "An improved behavioural assay demonstrates that ultrasound vocalizations constitute a reliable indicator of chronic cancer pain and neuropathic pain", Mol. Pain, 6, pp.18~ (2010)
- Monroe, T. B., "Pain in people with Alzheimer disease: potential applications for psychophysical and neurophysiological research", J. Geriatr. Psychiatry Neurol, 25, pp.240~255 (2012)
- Ha, A. D., "Pain in Parkinson’s disease", Mov. Disord, 27, pp.485~491 (2012)
- Miranda, H. F., "Dexketoprofeninduced antinociception in animal models of acute pain: synergy with morphine and paracetamol", Neuropharmacology, 52, pp.291~296 (2007)
- Joshi, S. K., "Nonopioid actions of U50, 488 enantiomers contribute to their peripheral cutaneous antinociceptive effects", J. Pharmacol. Exp. Ther, 305, pp.919~924 (2003)
- Wiffen, P. J., "Gabapentin for chronic neuropathic pain in adults", Cochrane Database Syst. Rev, 6, pp.CD007938~ (2017)
- Rauck, R., "Intrathecal gabapentin to treat chronic intractable noncancer pain", Anesthesiology, 119, pp.675~686 (2013)
- Gallagher, H. C., "Venlafaxine for neuropathic pain in adults", Cochrane Database Syst. Rev, 18, pp.CD011091~ (2015)
- Kim, W., "Functional and structural plasticity in the primary somatosensory cortex associated with chronic pain", J. Neurochem, 141, pp.499~506 (2017)
- Prescott, S. A., "Conn’s Translational Neuroscience, Vol. 1" (2017), Elsevier Academic Press
- Hassan, U., "Reducing noise by repetition: introduction to signal averaging", Eur. J. Phys, 31, pp.453~465 (2010)
- Apaydin, H., "Comparative analysis of recurrent neural network architectures for reservoir inflow forecasting", Water, 12, pp.1500~ (2020)
- Arisoy, E., , Proc. 40th IEEE International Conference on Acoustics, Speech and Signal Processing (2015)
- Matsushita, K., "A fully implantable wireless ECoG 128-channel recording device for human brain-machine interfaces: W-HERBS", Front. Neurosci, 12, pp.511~ (2018)
- Hartley, C., "Nociceptive brain activity as a measure of analgesic efficacy in infants", Sci. Transl. Med, 9, pp.388~ (2017)
- Musk, E., "An integrated brain-machine interface platform with thousands of channels", J. Med. Internet Res, 21, pp.e16194~ (2019)
- Opri, E., "Chronic embedded cortico-thalamic closed-loop deep brain stimulation for the treatment of essential tremor", Sci. Transl. Med, 12, pp.eaay7680~ (2020)