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Hermans T, Khazaei M, Raeisi K, Croce P, Tamburro G, Dereymaeker A, De Vos M, Zappasodi F, Comani S. Microstate Analysis Reflects Maturation of the Preterm Brain. Brain Topogr 2024; 37:461-474. [PMID: 37823945 PMCID: PMC11026208 DOI: 10.1007/s10548-023-01008-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 09/11/2023] [Indexed: 10/13/2023]
Abstract
Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome.The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics.Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age.The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes.
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Affiliation(s)
- Tim Hermans
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Mohammad Khazaei
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Khadijeh Raeisi
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Gabriella Tamburro
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Anneleen Dereymaeker
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Neonatal Intensive Care Unit, UZ Leuven, Leuven, Belgium
| | - Maarten De Vos
- Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Filippo Zappasodi
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
- Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
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Tamburro G, Fiedler P, De Fano A, Raeisi K, Khazaei M, Vaquero L, Bruña R, Oppermann H, Bertollo M, Filho E, Zappasodi F, Comani S. An ecological study protocol for the multimodal investigation of the neurophysiological underpinnings of dyadic joint action. Front Hum Neurosci 2023; 17:1305331. [PMID: 38125713 PMCID: PMC10730734 DOI: 10.3389/fnhum.2023.1305331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 11/15/2023] [Indexed: 12/23/2023] Open
Abstract
A novel multimodal experimental setup and dyadic study protocol were designed to investigate the neurophysiological underpinnings of joint action through the synchronous acquisition of EEG, ECG, EMG, respiration and kinematic data from two individuals engaged in ecologic and naturalistic cooperative and competitive joint actions involving face-to-face real-time and real-space coordinated full body movements. Such studies are still missing because of difficulties encountered in recording reliable neurophysiological signals during gross body movements, in synchronizing multiple devices, and in defining suitable study protocols. The multimodal experimental setup includes the synchronous recording of EEG, ECG, EMG, respiration and kinematic signals of both individuals via two EEG amplifiers and a motion capture system that are synchronized via a single-board microcomputer and custom Python scripts. EEG is recorded using new dry sports electrode caps. The novel study protocol is designed to best exploit the multimodal data acquisitions. Table tennis is the dyadic motor task: it allows naturalistic and face-to-face interpersonal interactions, free in-time and in-space full body movement coordination, cooperative and competitive joint actions, and two task difficulty levels to mimic changing external conditions. Recording conditions-including minimum table tennis rally duration, sampling rate of kinematic data, total duration of neurophysiological recordings-were defined according to the requirements of a multilevel analytical approach including a neural level (hyperbrain functional connectivity, Graph Theoretical measures and Microstate analysis), a cognitive-behavioral level (integrated analysis of neural and kinematic data), and a social level (extending Network Physiology to neurophysiological data recorded from two interacting individuals). Four practical tests for table tennis skills were defined to select the study population, permitting to skill-match the dyad members and to form two groups of higher and lower skilled dyads to explore the influence of skill level on joint action performance. Psychometric instruments are included to assess personality traits and support interpretation of results. Studying joint action with our proposed protocol can advance the understanding of the neurophysiological mechanisms sustaining daily life joint actions and could help defining systems to predict cooperative or competitive behaviors before being overtly expressed, particularly useful in real-life contexts where social behavior is a main feature.
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Affiliation(s)
- Gabriella Tamburro
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Antonio De Fano
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Khadijeh Raeisi
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Mohammad Khazaei
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Lucia Vaquero
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Experimental Pschology, Cognitive Processes and Speech Therapy, Universidad Complutense de Madrid, Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Universidad Complutense de Madrid, Madrid, Spain
- Department of Radiology, Universidad Complutense de Madrid, IdISSC, Madrid, Spain
| | - Hannes Oppermann
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Maurizio Bertollo
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Department of Medicine and Sciences of Aging, “University G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Edson Filho
- Wheelock College of Education and Human Development, Boston University, Boston, MA, United States
| | - Filippo Zappasodi
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neuroscience Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
- Behavioral Imaging and Neural Dynamics Center, University “G. d’Annunzio” of Chieti–Pescara, Chieti, Italy
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Khazaei M, Raeisi K, Vanhatalo S, Zappasodi F, Comani S, Tokariev A. Neonatal cortical activity organizes into transient network states that are affected by vigilance states and brain injury. Neuroimage 2023; 279:120342. [PMID: 37619792 DOI: 10.1016/j.neuroimage.2023.120342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Revised: 08/11/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research.
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Affiliation(s)
- Mohammad Khazaei
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy.
| | - Khadijeh Raeisi
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy
| | - Sampsa Vanhatalo
- BABA center, Pediatric Research Center, Departments of Clinical Neurophysiology and Physiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Filippo Zappasodi
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy; Institute for Advanced Biomedical Technologies, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neurosciences, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, ITAB building, 3rd floor, room 314, Chieti, Via dei Vestini, Italy; Behavioral Imaging and Neural Dynamics Center, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Anton Tokariev
- BABA center, Pediatric Research Center, Departments of Clinical Neurophysiology and Physiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
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Raeisi K, Khazaei M, Tamburro G, Croce P, Comani S, Zappasodi F. A Class-Imbalance Aware and Explainable Spatio-Temporal Graph Attention Network for Neonatal Seizure Detection. Int J Neural Syst 2023; 33:2350046. [PMID: 37497802 DOI: 10.1142/s0129065723500466] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
Seizures are the most prevalent clinical indication of neurological disorders in neonates. In this study, a class-imbalance aware and explainable deep learning approach based on Convolutional Neural Networks (CNNs) and Graph Attention Networks (GATs) is proposed for the accurate automated detection of neonatal seizures. The proposed model integrates the temporal information of EEG signals with the spatial information on the EEG channels through the graph representation of the multi-channel EEG segments. One-dimensional CNNs are used to automatically develop a feature set that accurately represents the differences between seizure and nonseizure epochs in the time domain. By employing GAT, the attention mechanism is utilized to emphasize the critical channel pairs and information flow among brain regions. GAT coefficients were then used to empirically visualize the important regions during the seizure and nonseizure epochs, which can provide valuable insight into the location of seizures in the neonatal brain. Additionally, to tackle the severe class imbalance in the neonatal seizure dataset using under-sampling and focal loss techniques are used. Overall, the final Spatio-Temporal Graph Attention Network (ST-GAT) outperformed previous benchmarked methods with a mean AUC of 96.6% and Kappa of 0.88, demonstrating its high accuracy and potential for clinical applications.
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Affiliation(s)
- Khadijeh Raeisi
- Department of Neuroscience, Imaging and Clinical Sciences, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Mohammad Khazaei
- Department of Neuroscience, Imaging and Clinical Sciences, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Gabriella Tamburro
- Department of Neuroscience, Imaging and Clinical Sciences-Behavioral Imaging and Neural Dynamics Center, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences-Behavioral Imaging and Neural Dynamics Center, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Silvia Comani
- Department of Neuroscience, Imaging and Clinical Sciences-Behavioral Imaging and Neural Dynamics Center, Universita Gabriele d'Annunzio, Chieti 66100, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences-Behavioral, Imaging and Neural Dynamics Center-Institute for, Advanced Biomedical Technologies, Universita Gabriele d'Annunzio, Chieti 66100, Italy
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Raeisi K, Khazaei M, Croce P, Tamburro G, Comani S, Zappasodi F. A graph convolutional neural network for the automated detection of seizures in the neonatal EEG. Comput Methods Programs Biomed 2022; 222:106950. [PMID: 35717740 DOI: 10.1016/j.cmpb.2022.106950] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 06/09/2022] [Accepted: 06/09/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Neonatal seizures are the most common clinical presentation of neurological conditions and can have adverse effects on the neurodevelopment of the neonatal brain. Visual detection of these events from continuous EEG recordings is a laborious and time-consuming task. We propose a novel algorithm for the automated detection of neonatal seizures. METHODS In this study, we propose a novel deep learning model based on Graph Convolutional Neural Networks for the automated detection of neonatal seizures. Unlike other methods exploiting mainly the temporal information contained in EEG signals, our method also considers long-range spatial information, i.e., the interdependencies across EEG signals. The temporal information is embedded as graph signals in the graph representation of the EEG recordings and includes EEG features extracted from the EEG signals in the time and frequency domains. The spatial information is represented as functional connections among the EEG channels (calculated by the phase-locking value and the mean squared coherence) or as maps of Euclidean distances. These different spatial representations were evaluated to assess their efficiency in providing more discriminative features for an effective detection of neonatal seizures. The model performance was assessed on a publicly available dataset of continuous EEG signals recorded from 39 neonates by means of the area under the curve (AUC) and the AUC for specificity values greater than 90% (AUC90). RESULTS After applying post-processing, consisting in smoothing the output of the classifiers, the models based on the mean squared coherence, the phase-locking value, and the Euclidean distance respectively reached a median AUC of 99.1% (IQR: 96.8%-99.6%), 99% (IQR: 95.2%-99.7%), and 97.3% (IQR: 86.3%-99.6%), and a median AUC90 of 96%, 95.7%, and 94.9%. These values are superior or comparable to those reached by methods considered as state-of-the-art in this field. CONCLUSIONS Our results show that the EEG graph representations drawn from functional connectivity measures can effectively leverage interdependencies among EEG signals and lead to reliable detection of neonatal seizures. Furthermore, our model has the advantage of requiring only temporal annotations on seizures for the training phase, making it more appealing for clinical applications.
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Affiliation(s)
- Khadijeh Raeisi
- Department of Neuroscience, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy.
| | - Mohammad Khazaei
- Department of Neuroscience, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Pierpaolo Croce
- Department of Neuroscience, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy; Behavioral Imaging and Neural Dynamics Center, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Gabriella Tamburro
- Department of Neuroscience, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy; Behavioral Imaging and Neural Dynamics Center, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Silvia Comani
- Department of Neuroscience, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy; Behavioral Imaging and Neural Dynamics Center, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Filippo Zappasodi
- Department of Neuroscience, Imaging and Clinical Sciences, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy; Behavioral Imaging and Neural Dynamics Center, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy; Institute for Advanced Biomedical Technologies, University "Gabriele d'Annunzio" of Chieti-Pescara, Chieti, Italy
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Abend N, Adams E, Al Balushi A, Alburaki W, Appendino J, Barbosa VS, Birca A, Bonifacio S, Branagan A, Chang T, Chowdhury R, Christou H, Chu C, Cilio MR, Comani S, Corsi-Cabrera M, Croce P, Cubero-Rego L, Dawoud F, de Vries L, Dehaes M, Devane D, Duncan A, El Ters N, El-Dib M, Elshibiny H, Esser M, Fairchild K, Finucane E, Franceschini MA, Gallagher A, Ghosh A, Glass H, Venkata SKRG, Baillet TH, Herzberg E, Hildrey E, Hurley T, Inder T, Jacobs E, Jefferies K, Jermendy A, Khazaei M, Kilmartin K, King G, Lauronen L, Lee S, Leijser L, Lind J, Llaguno NS, Machie M, Magalhães M, Mahdi Z, Maluomi J, Marandyuk B, Massey S, McCulloch C, Metsäranta M, Mikkonen K, Mohammad K, Molloy E, Momin S, Munster C, Murthy P, Netto A, Nevalainen P, Nguyen J, Nieves M, Nyman J, Oliver N, Peeters C, Pietrobom RFR, Pijpers J, Pinchefksy E, Ping YB, Quirke F, Raeisi K, Ricardo-Garcell J, Robinson J, Rodrigues DP, Rosati J, Scott J, Scringer-Wilkes M, Shellhaas R, Smit L, Soul J, Srivastava A, Steggerda S, Sunwoo J, Szakmar E, Tamburro G, Thomas S, Toiviainen-Salo S, Toma AI, Vanhatalo S, Variane GFT, Vein A, Vesoulis Z, Vilan A, Volpe J, Weeke L, Wintermark P, Wusthoff C, Zappasodi F, Zein H, Zempel J. Proceedings of the 13th International Newborn Brain Conference: Neonatal Neurocritical Care, Seizures, and Continuous EEG monitoring. J Neonatal Perinatal Med 2022; 15:467-485. [PMID: 35431189 DOI: 10.3233/npm-229006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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Abdi K, Abramsky R, Andescavage N, Bambi J, Basu S, Bearer C, Benner EJ, Biselele T, Bliznyuk N, Breckpot J, Carey G, Chao A, Christiansen LI, Comani S, Croce P, De Vos M, Dereymaeker A, Dubois L, Eisch AJ, Epstein A, Geva N, Geva Y, Gewillig M, Gillis S, Goldberg RN, Gram M, Gregory S, Guez-Barber D, Hayakawa M, Henriksen NL, Hermans T, Hershkovitz R, Holgersen K, Holmqvist B, Jain V, Jansen K, Kandula V, Kapse K, Kawaguchi M, Khair A, Khazaei M, Kidokoro H, Kiffer FC, Kisilewicz K, Kumai S, Lacaille H, Ley D, Limperopoulos C, Lindholm SEH, Lukusa P, Lundberg R, MacFarlane P, Matak P, Mavinga L, Mayer C, Mbayabo G, Mitsumatsu T, Mubungu G, Murnick J, Nakata T, Narita H, Nataraj P, Natsume J, Naulaers G, Nikam R, Ortenlöf N, Ottolini K, Pan X, Pankratova S, Pegram K, Penn AA, Pradhan S, Raeisi K, Rickman N, Rikard B, Rotem R, Sangild PT, Sato Y, Sawamura F, Shany E, Shelef I, Shiraki A, Smets L, Sura L, Suzui R, Suzuki T, Tady BP, Taga G, Tamburro G, Thewissen L, Thompson JW, Thymann T, Tokat C, Vacher CM, Valdes C, Vallius S, Vatolin S, Watanabe H, Weintraub AY, Weiss M, Yamamoto H, Yaniv SS, Younge N, Yun S, Zappasodi F. Proceedings of the 13th International Newborn Brain Conference: Fetal and/or neonatal brain development, both normal and abnormal. J Neonatal Perinatal Med 2022; 15:411-426. [PMID: 35431185 DOI: 10.3233/npm-229002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
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8
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Naghan PA, Qeraati M, Raeisi K, Bagheri A. The effect of acetazolamide on the improvement of central apnea caused by abusing opioid drugs in the clinical trial. Sleep Med 2019. [DOI: 10.1016/j.sleep.2019.11.868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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9
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Khazaei M, Raeisi K, Goshvarpour A, Ahmadzadeh M. Early detection of sudden cardiac death using nonlinear analysis of heart rate variability. Biocybern Biomed Eng 2018. [DOI: 10.1016/j.bbe.2018.06.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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10
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Raeisi K, Shariat M, Nayeri F, Raji F, Dalili H. A single center study of the effects of trained fathers' participation in constant breastfeeding. Acta Med Iran 2014; 52:694-696. [PMID: 25421843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2013] [Accepted: 09/17/2013] [Indexed: 06/04/2023] Open
Abstract
Constant breastfeeding that depends on the family support. Fathers' involvement is as an important factor of successful breastfeeding. The aim of this study was to evaluate the influence of fathers' participation in constant breastfeeding in Vali-E-Asr Hospital, Tehran, Iran. This interventional study was piloted on spouses of pregnant women participating in pregnancy courses. The case group consisted of fathers attending training courses of breastfeeding during pregnancy (Group A), and the control group was made up of fathers who did not take part in training courses (Group B). The courses were held three times from the 30th week of gestation to the end of pregnancy in a family health research center. Fathers attended three training sessions where they were trained by brochures. After delivery newborns were weighted and examined for jaundice (3-5 days, 30 days, three and six months after birth). According to mothers' views, spouses' participation, encouragement and support in group A, was 11 times more than group B. It means that 47 (94%) of spouses in the group A participated in mothers' constant breastfeeding, but fathers' participation in group B was 60% (30 spouses). This study showed that breastfeeding was more constant in the group that fathers participated in breastfeeding training course. One of the reasons for such a significant difference was spouses' participation, encouragement and support in the trained group. This study showed that fathers' involvement in training programs may influence constancy of breastfeeding.
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Affiliation(s)
- Khadijeh Raeisi
- Breastfeeding Research Center, Tehran University of Medical Sciences, Tehran, Iran.
| | - Mamak Shariat
- Maternal Fetal & Neonatal Research Center, Tehran University of Medical Sciences, Tehran, Iran.
| | - Fatemeh Nayeri
- Maternal Fetal & Neonatal Research Center, Tehran University of Medical Sciences, Tehran, Iran.
| | - Farima Raji
- Breastfeeding Research Center, Tehran University of Medical Sciences, Tehran, Iran.
| | - Hosein Dalili
- Breastfeeding Research Center, Tehran University of Medical Sciences, Tehran, Iran.
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