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Jauhiainen S, Kauppi JP, Krosshaug T, Bahr R, Bartsch J, Äyrämö S. Predicting ACL Injury Using Machine Learning on Data From an Extensive Screening Test Battery of 880 Female Elite Athletes. Am J Sports Med 2022; 50:2917-2924. [PMID: 35984748 PMCID: PMC9442771 DOI: 10.1177/03635465221112095] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Injury risk prediction is an emerging field in which more research is needed to recognize the best practices for accurate injury risk assessment. Important issues related to predictive machine learning need to be considered, for example, to avoid overinterpreting the observed prediction performance. PURPOSE To carefully investigate the predictive potential of multiple predictive machine learning methods on a large set of risk factor data for anterior cruciate ligament (ACL) injury; the proposed approach takes into account the effect of chance and random variations in prediction performance. STUDY DESIGN Case-control study; Level of evidence, 3. METHODS The authors used 3-dimensional motion analysis and physical data collected from 791 female elite handball and soccer players. Four common classifiers were used to predict ACL injuries (n = 60). Area under the receiver operating characteristic curve (AUC-ROC) averaged across 100 cross-validation runs (mean AUC-ROC) was used as a performance metric. Results were confirmed with repeated permutation tests (paired Wilcoxon signed-rank-test; P < .05). Additionally, the effect of the most common class imbalance handling techniques was evaluated. RESULTS For the best classifier (linear support vector machine), the mean AUC-ROC was 0.63. Regardless of the classifier, the results were significantly better than chance, confirming the predictive ability of the data and methods used. AUC-ROC values varied substantially across repetitions and methods (0.51-0.69). Class imbalance handling did not improve the results. CONCLUSION The authors' approach and data showed statistically significant predictive ability, indicating that there exists information in this prospective data set that may be valuable for understanding injury causation. However, the predictive ability remained low from the perspective of clinical assessment, suggesting that included variables cannot be used for ACL prediction in practice.
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Affiliation(s)
- Susanne Jauhiainen
- Faculty of Information Technology,
University of Jyväskylä, Jyväskylä, Finland,Susanne Jauhiainen, MSc,
Faculty of Information Technology, University of Jyväskylä, PO Box 35, FI-40014,
Jyväskylä, Finland (
)
| | - Jukka-Pekka Kauppi
- Faculty of Information Technology,
University of Jyväskylä, Jyväskylä, Finland
| | - Tron Krosshaug
- Oslo Sports Trauma Research Center,
Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo,
Norway
| | - Roald Bahr
- Oslo Sports Trauma Research Center,
Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo,
Norway
| | - Julia Bartsch
- Oslo Sports Trauma Research Center,
Department of Sports Medicine, Norwegian School of Sport Sciences, Oslo,
Norway
| | - Sami Äyrämö
- Faculty of Information Technology,
University of Jyväskylä, Jyväskylä, Finland
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2
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Kotila A, Tohka J, Kauppi JP, Gabbatore I, Mäkinen L, Hurtig TM, Ebeling HE, Korhonen V, Kiviniemi VJ, Loukusa S. Neural-level associations of non-verbal pragmatic comprehension in young Finnish autistic adults. Int J Circumpolar Health 2021; 80:1909333. [PMID: 34027832 PMCID: PMC8158210 DOI: 10.1080/22423982.2021.1909333] [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] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/12/2021] [Accepted: 03/23/2021] [Indexed: 11/18/2022] Open
Abstract
This video-based study examines the pragmatic non-verbal comprehension skills and corresponding neural-level findings in young Finnish autistic adults, and controls. Items from the Assessment Battery of Communication (ABaCo) were chosen to evaluate the comprehension of non-verbal communication. Inter-subject correlation (ISC) analysis of the functional magnetic resonance imaging data was used to reveal the synchrony of brain activation across participants during the viewing of pragmatically complex scenes of ABaCo videos. The results showed a significant difference between the ISC maps of the autistic and control groups in tasks involving the comprehension of non-verbal communication, thereby revealing several brain regions where correlation of brain activity was greater within the control group. The results suggest a possible weaker modulation of brain states in response to the pragmatic non-verbal communicative situations in autistic participants. Although there was no difference between the groups in behavioural responses to ABaCo items, there was more variability in the accuracy of the responses in the autistic group. Furthermore, mean answering and reaction times correlated with the severity of autistic traits. The results indicate that even if young autistic adults may have learned to use compensatory resources in their communicative-pragmatic comprehension, pragmatic processing in naturalistic situations still requires additional effort.
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Affiliation(s)
- Aija Kotila
- Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
| | - Jussi Tohka
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland
| | - Jukka-Pekka Kauppi
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Ilaria Gabbatore
- Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
- Department of Psychology, University of Turin, Turin, Italy
| | - Leena Mäkinen
- Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
| | - Tuula M. Hurtig
- Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
- Research Unit of Clinical Neuroscience, Psychiatry, University of Oulu
| | - Hanna E. Ebeling
- Clinic of Child Psychiatry, Oulu University Hospital and PEDEGO Research Unit, University of Oulu, Oulu, Finland
| | - Vesa Korhonen
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital and Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Vesa J. Kiviniemi
- Department of Diagnostic Radiology, Medical Research Center, Oulu University Hospital and Research Unit of Medical Imaging, Physics and Technology, the Faculty of Medicine, University of Oulu, Oulu, Finland
- Oulu Functional NeuroImaging-lab, Medical Research Center, Oulu University Hospital, Oulu, Finland
| | - Soile Loukusa
- Faculty of Humanities, Research Unit of Logopedics, University of Oulu, Oulu, Finland
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3
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Joensuu L, Rautiainen I, Äyrämö S, Syväoja HJ, Kauppi JP, Kujala UM, Tammelin TH. Precision exercise medicine: predicting unfavourable status and development in the 20-m shuttle run test performance in adolescence with machine learning. BMJ Open Sport Exerc Med 2021; 7:e001053. [PMID: 34104475 PMCID: PMC8144034 DOI: 10.1136/bmjsem-2021-001053] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/07/2021] [Indexed: 11/09/2022] Open
Abstract
Objectives To assess the ability to predict individual unfavourable future status and development in the 20m shuttle run test (20MSRT) during adolescence with machine learning (random forest (RF) classifier). Methods Data from a 2-year observational study (2013‒2015, 12.4±1.3 years, n=633, 50% girls), with 48 baseline characteristics (questionnaires (demographics, physical, psychological, social and lifestyle factors), objective measurements (anthropometrics, fitness characteristics, physical activity, body composition and academic scores)) were used to predict: (Task 1) unfavourable future 20MSRT status (identification of individuals in the lowest 20MSRT tertile after 2 years), and (Task 2) unfavourable 20MSRT development (identification of individuals with 20MSRT development in the lowest tertile among adolescents with baseline 20MSRT below median level). Results Prediction performance for future 20MSRT status (Task 1) was (area under the receiver operating characteristic curve, AUC) 83% and 76%, sensitivity 80% and 60%, and specificity 78% and 79% in girls and boys, respectively. Twenty variables showed predictive power in boys, 14 in girls, including fitness characteristics, physical activity, academic scores, adiposity, life enjoyment, parental support, social status in school and perceived fitness. Prediction performance for future development (Task 2) was lower and differed statistically from random level only in girls (AUC 68% and 40% in girls and boys). Conclusion RF classifier predicted future unfavourable status in 20MSRT and identified potential individuals for interventions based on a holistic profile (14‒20 baseline characteristics). The MATLAB script and functions employing the RF classifier of this study are available for future precision exercise medicine research.
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Affiliation(s)
- Laura Joensuu
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.,LIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland
| | - Ilkka Rautiainen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Sami Äyrämö
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Heidi J Syväoja
- LIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland
| | - Jukka-Pekka Kauppi
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Urho M Kujala
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Tuija H Tammelin
- LIKES Research Centre for Physical Activity and Health, Jyväskylä, Finland
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4
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Kujala MV, Kauppi JP, Törnqvist H, Helle L, Vainio O, Kujala J, Parkkonen L. Publisher Correction: Time-resolved classification of dog brain signals reveals early processing of faces, species and emotion. Sci Rep 2021; 11:6885. [PMID: 33742006 PMCID: PMC7979922 DOI: 10.1038/s41598-021-85718-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Affiliation(s)
- Miiamaaria V Kujala
- Department of Psychology, Faculty of Education and Psychology, University of Jyväskylä, PO Box 35, 40014, Jyväskylä, Finland. .,Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57, 00014, Helsinki, Finland. .,Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076, Aalto, Finland.
| | - Jukka-Pekka Kauppi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076, Aalto, Finland.,Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
| | - Heini Törnqvist
- Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57, 00014, Helsinki, Finland
| | - Liisa Helle
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076, Aalto, Finland
| | - Outi Vainio
- Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57, 00014, Helsinki, Finland
| | - Jan Kujala
- Department of Psychology, Faculty of Education and Psychology, University of Jyväskylä, PO Box 35, 40014, Jyväskylä, Finland
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076, Aalto, Finland
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Kujala MV, Kauppi JP, Törnqvist H, Helle L, Vainio O, Kujala J, Parkkonen L. Time-resolved classification of dog brain signals reveals early processing of faces, species and emotion. Sci Rep 2020; 10:19846. [PMID: 33199715 PMCID: PMC7669855 DOI: 10.1038/s41598-020-76806-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 10/30/2020] [Indexed: 11/22/2022] Open
Abstract
Dogs process faces and emotional expressions much like humans, but the time windows important for face processing in dogs are largely unknown. By combining our non-invasive electroencephalography (EEG) protocol on dogs with machine-learning algorithms, we show category-specific dog brain responses to pictures of human and dog facial expressions, objects, and phase-scrambled faces. We trained a support vector machine classifier with spatiotemporal EEG data to discriminate between responses to pairs of images. The classification accuracy was highest for humans or dogs vs. scrambled images, with most informative time intervals of 100–140 ms and 240–280 ms. We also detected a response sensitive to threatening dog faces at 30–40 ms; generally, responses differentiating emotional expressions were found at 130–170 ms, and differentiation of faces from objects occurred at 120–130 ms. The cortical sources underlying the highest-amplitude EEG signals were localized to the dog visual cortex.
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Affiliation(s)
- Miiamaaria V Kujala
- Department of Psychology, Faculty of Education and Psychology, University of Jyväskylä, PO Box 35, 40014, Jyväskylä, Finland. .,Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57, 00014, Helsinki, Finland. .,Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076, Aalto, Finland.
| | - Jukka-Pekka Kauppi
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076, Aalto, Finland.,Faculty of Information Technology, University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland
| | - Heini Törnqvist
- Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57, 00014, Helsinki, Finland
| | - Liisa Helle
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076, Aalto, Finland
| | - Outi Vainio
- Department of Equine and Small Animal Medicine, Faculty of Veterinary Medicine, University of Helsinki, PL 57, 00014, Helsinki, Finland
| | - Jan Kujala
- Department of Psychology, Faculty of Education and Psychology, University of Jyväskylä, PO Box 35, 40014, Jyväskylä, Finland
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, P.O. Box 12200, 00076, Aalto, Finland
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6
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Iljukov S, Kauppi JP, Uusitalo ALT, Peltonen JE, Schumacher YO. Association Between Implementation of the Athlete Biological Passport and Female Elite Runners' Performance. Int J Sports Physiol Perform 2020; 15:1231-1236. [PMID: 32084627 DOI: 10.1123/ijspp.2019-0643] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 11/24/2019] [Accepted: 12/10/2019] [Indexed: 10/27/2023]
Abstract
The purpose of this research was to evaluate the performances of female middle- and long-distance runners before and after the implementation of a new antidoping strategy (the Athlete Biological Passport [ABP]) in a country accused of systematic doping. A retrospective analysis of the results of Russian National Championships from 2008 to 2017 was performed. The 8 best female performances for the 800-m, 1500-m, 3000-m steeplechase, 5000-m, and 10,000-m events from the semifinals and finals were analyzed. The yearly number of athletes fulfilling standard qualifications for international competitions was also evaluated. Overall, numbers of athletes banned for doping in 2008-2017 were calculated. As a result, 4 events (800, 1500, 5000 [all P < .001], and 10,000 m [P < .01]) out of 5 showed statistically significant deterioration in the performances when comparing before and after the introduction of the ABP. The 3000-m steeplechase was the only event that did not show statistically significant change. The highest relative decrease in the number of runners who met standard qualification for international competition was for the 5000-m event (46%), followed by 1500-m (42%), 800-m (38%), 10,000-m (17%), and 3000-m steeplechase (1%). In conclusion, implementation of the ABP was followed by a significant reduction in the performance of female runners in a country accused of systematic doping. It can be reasonably speculated that more stringent antidoping testing, more specifically the introduction of the ABP, is a key reason for this reduction.
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Jauhiainen S, Kauppi JP, Leppänen M, Pasanen K, Parkkari J, Vasankari T, Kannus P, Äyrämö S. New Machine Learning Approach for Detection of Injury Risk Factors in Young Team Sport Athletes. Int J Sports Med 2020; 42:175-182. [PMID: 32920800 DOI: 10.1055/a-1231-5304] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The purpose of this article is to present how predictive machine learning methods can be utilized for detecting sport injury risk factors in a data-driven manner. The approach can be used for finding new hypotheses for risk factors and confirming the predictive power of previously recognized ones. We used three-dimensional motion analysis and physical data from 314 young basketball and floorball players (48.4% males, 15.72±1.79 yr, 173.34±9.14 cm, 64.65±10.4 kg). Both linear (L1-regularized logistic regression) and non-linear methods (random forest) were used to predict moderate and severe knee and ankle injuries (N=57) during three-year follow-up. Results were confirmed with permutation tests and predictive risk factors detected with Wilcoxon signed-rank-test (p<0.01). Random forest suggested twelve consistent injury predictors and logistic regression twenty. Ten of these were suggested in both models; sex, body mass index, hamstring flexibility, knee joint laxity, medial knee displacement, height, ankle plantar flexion at initial contact, leg press one-repetition max, and knee valgus at initial contact. Cross-validated areas under receiver operating characteristic curve were 0.65 (logistic regression) and 0.63 (random forest). The results highlight the difficulty of predicting future injuries, but also show that even with models having relatively low predictive power, certain predictive injury risk factors can be consistently detected.
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Affiliation(s)
- Susanne Jauhiainen
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Jukka-Pekka Kauppi
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
| | - Mari Leppänen
- Tampere Research Centre of Sports Medicine, UKK Institute, Tampere, Finland
| | - Kati Pasanen
- Tampere Research Centre of Sports Medicine, UKK Institute, Tampere, Finland.,Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada.,McCaig Institute for Bone and Joint Health, University of Calgary, Calgary, Alberta, Canada
| | - Jari Parkkari
- Tampere Research Centre of Sports Medicine, UKK Institute, Tampere, Finland.,Tampere University Hospital, Tampere, Finland
| | - Tommi Vasankari
- Tampere Research Centre of Sports Medicine, UKK Institute, Tampere, Finland
| | - Pekka Kannus
- Tampere Research Centre of Sports Medicine, UKK Institute, Tampere, Finland.,Tampere University Hospital, Tampere, Finland
| | - Sami Äyrämö
- Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland
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8
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Celikkanat H, Moriya H, Ogawa T, Kauppi JP, Kawanabe M, Hyvarinen A. Decoding emotional valence from electroencephalographic rhythmic activity. Annu Int Conf IEEE Eng Med Biol Soc 2018; 2017:4143-4146. [PMID: 29060809 DOI: 10.1109/embc.2017.8037768] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We attempt to decode emotional valence from electroencephalographic rhythmic activity in a naturalistic setting. We employ a data-driven method developed in a previous study, Spectral Linear Discriminant Analysis, to discover the relationships between the classification task and independent neuronal sources, optimally utilizing multiple frequency bands. A detailed investigation of the classifier provides insight into the neuronal sources related with emotional valence, and the individual differences of the subjects in processing emotions. Our findings show: (1) sources whose locations are similar across subjects are consistently involved in emotional responses, with the involvement of parietal sources being especially significant, and (2) even though the locations of the involved neuronal sources are consistent, subjects can display highly varying degrees of valence-related EEG activity in the sources.
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Tei S, Fujino J, Kawada R, Jankowski KF, Kauppi JP, van den Bos W, Abe N, Sugihara G, Miyata J, Murai T, Takahashi H. Collaborative roles of Temporoparietal Junction and Dorsolateral Prefrontal Cortex in Different Types of Behavioural Flexibility. Sci Rep 2017; 7:6415. [PMID: 28743978 PMCID: PMC5526981 DOI: 10.1038/s41598-017-06662-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 06/15/2017] [Indexed: 01/20/2023] Open
Abstract
Behavioural flexibility is essential for everyday life. This involves shifting attention between different perspectives. Previous studies suggest that flexibility is mainly subserved by the dorsolateral prefrontal cortex (DLPFC). However, although rarely emphasized, the temporoparietal junction (TPJ) is frequently recruited during flexible behaviour. A crucial question is whether TPJ plays a role in different types of flexibility, compared to its limited role in perceptual flexibility. We hypothesized that TPJ activity during diverse flexibility tasks plays a common role in stimulus-driven attention-shifting, thereby contributing to different types of flexibility, and thus the collaboration between DLPFC and TPJ might serve as a more appropriate mechanism than DLPFC alone. We used fMRI to measure DLPFC/TPJ activity recruited during moral flexibility, and examined its effect on other domains of flexibility (economic/perceptual). Here, we show the additional, yet crucial role of TPJ: a combined DLPFC/TPJ activity predicted flexibility, regardless of domain. Different types of flexibility might rely on more basic attention-shifting, which highlights the behavioural significance of alternatives.
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Affiliation(s)
- Shisei Tei
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan.,Institute of Applied Brain Sciences, Waseda University, 2-579-15 Mikajima, Tokorozawa, Saitama, 359-1192, Japan.,School of Human and Social Sciences, Tokyo International University, Saitama, 350-1198, Japan
| | - Junya Fujino
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan.,Medical Institute of Developmental Disabilities Research, Showa University Karasuyama Hospital, Tokyo, 157-8577, Japan
| | - Ryosaku Kawada
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan
| | | | - Jukka-Pekka Kauppi
- Department of Mathematical Information Technology, University of Jyväskylä, Jyväskylä, Finland.,Department of Computer Science and HIIT, University of Helsinki, P.O. 68 (Gustaf Hällströmin katu 2b), FI-00014, Helsinki, Finland
| | - Wouter van den Bos
- Center for Adaptive Rationality, Max Planck Institute for Human Development, 14197, Berlin, Germany
| | - Nobuhito Abe
- Kokoro Research Center, Kyoto University, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Genichi Sugihara
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Jun Miyata
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Toshiya Murai
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Sakyo-ku, Kyoto, 606-8507, Japan.
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10
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Reason M, Jola C, Kay R, Reynolds D, Kauppi JP, Grobras MH, Tohka J, Pollick FE. Spectators’ aesthetic experience of sound and movement in dance performance: A transdisciplinary investigation. ACTA ACUST UNITED AC 2016. [DOI: 10.1037/a0040032] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Herbec A, Kauppi JP, Jola C, Tohka J, Pollick FE. Differences in fMRI intersubject correlation while viewing unedited and edited videos of dance performance. Cortex 2015; 71:341-8. [PMID: 26298503 DOI: 10.1016/j.cortex.2015.06.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [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: 11/21/2014] [Revised: 04/29/2015] [Accepted: 06/26/2015] [Indexed: 11/16/2022]
Abstract
Intersubject correlation (ISC) analysis of functional magnetic resonance imaging (fMRI) data provides insight into how continuous streams of sensory stimulation are processed by groups of observers. Although edited movies are frequently used as stimuli in ISC studies, there has been little direct examination of the effect of edits on the resulting ISC maps. In this study we showed 16 observers two audiovisual movie versions of the same dance. In one experimental condition there was a continuous view from a single camera (Unedited condition) and in the other condition there were views from different cameras (Edited condition) that provided close up views of the feet or face and upper body. We computed ISC maps for each condition, as well as created a map that showed the difference between the conditions. The results from the Unedited and Edited maps largely overlapped in the occipital and temporal cortices, although more voxels were found for the Edited map. The difference map revealed greater ISC for the Edited condition in the Postcentral Gyrus, Lingual Gyrus, Precentral Gyrus and Medial Frontal Gyrus, while the Unedited condition showed greater ISC in only the Superior Temporal Gyrus. These findings suggest that the visual changes associated with editing provide a source of correlation in maps obtained from edited film, and highlight the utility of using maps to evaluate the difference in ISC between conditions.
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Affiliation(s)
| | - Jukka-Pekka Kauppi
- Department of Computer Science and Helsinki Institute for Information Technology, University of Helsinki, Finland
| | - Corinne Jola
- Division of Psychology, Abertay University, Dundee, UK
| | - Jussi Tohka
- Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañon, Madrid, Spain
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Kauppi JP, Hahne J, Müller KR, Hyvärinen A. Three-way analysis of spectrospatial electromyography data: classification and interpretation. PLoS One 2015; 10:e0127231. [PMID: 26039100 PMCID: PMC4454601 DOI: 10.1371/journal.pone.0127231] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Accepted: 04/12/2015] [Indexed: 12/02/2022] Open
Abstract
Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results.
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Affiliation(s)
- Jukka-Pekka Kauppi
- Dept. of Computer Science/HIIT, University of Helsinki, Helsinki, Finland
- Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, Espoo, Finland
| | - Janne Hahne
- Dept. of Computer Science, Machine Learning Group, Berlin Institute of Technology, Berlin, Germany
- Dept. of Neurorehabilitation Engineering, Universitätsmedizin Göttingen, Göttingen, Germany
| | - Klaus-Robert Müller
- Dept. of Computer Science, Machine Learning Group, Berlin Institute of Technology, Berlin, Germany
- Dept. of Brain and Cognitive Engineering, Korea University, Seoul, Korea
| | - Aapo Hyvärinen
- Dept. of Computer Science/HIIT, University of Helsinki, Helsinki, Finland
- Dept. of Dynamic Brain Imaging, Advanced Telecommunication Research Institute International (ATR), Kyoto, Japan
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Kauppi JP, Kandemir M, Saarinen VM, Hirvenkari L, Parkkonen L, Klami A, Hari R, Kaski S. Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals. Neuroimage 2015; 112:288-298. [PMID: 25595505 DOI: 10.1016/j.neuroimage.2014.12.079] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2014] [Revised: 11/25/2014] [Accepted: 12/31/2014] [Indexed: 10/24/2022] Open
Abstract
We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements.
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Affiliation(s)
- Jukka-Pekka Kauppi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
| | - Melih Kandemir
- Heidelberg University HCI/IWR, Heidelberg, Germany; Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
| | - Veli-Matti Saarinen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Lotta Hirvenkari
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Lauri Parkkonen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; MEG Core, Aalto NeuroImaging, Aalto University, Espoo, Finland.
| | - Arto Klami
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland.
| | - Riitta Hari
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; MEG Core, Aalto NeuroImaging, Aalto University, Espoo, Finland.
| | - Samuel Kaski
- Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki, Finland; Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, Espoo, Finland.
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Kauppi JP, Pajula J, Tohka J. A versatile software package for inter-subject correlation based analyses of fMRI. Front Neuroinform 2014; 8:2. [PMID: 24550818 PMCID: PMC3907702 DOI: 10.3389/fninf.2014.00002] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2013] [Accepted: 01/09/2014] [Indexed: 12/04/2022] Open
Abstract
In the inter-subject correlation (ISC) based analysis of the functional magnetic resonance imaging (fMRI) data, the extent of shared processing across subjects during the experiment is determined by calculating correlation coefficients between the fMRI time series of the subjects in the corresponding brain locations. This implies that ISC can be used to analyze fMRI data without explicitly modeling the stimulus and thus ISC is a potential method to analyze fMRI data acquired under complex naturalistic stimuli. Despite of the suitability of ISC based approach to analyze complex fMRI data, no generic software tools have been made available for this purpose, limiting a widespread use of ISC based analysis techniques among neuroimaging community. In this paper, we present a graphical user interface (GUI) based software package, ISC Toolbox, implemented in Matlab for computing various ISC based analyses. Many advanced computations such as comparison of ISCs between different stimuli, time window ISC, and inter-subject phase synchronization are supported by the toolbox. The analyses are coupled with re-sampling based statistical inference. The ISC based analyses are data and computation intensive and the ISC toolbox is equipped with mechanisms to execute the parallel computations in a cluster environment automatically and with an automatic detection of the cluster environment in use. Currently, SGE-based (Oracle Grid Engine, Son of a Grid Engine, or Open Grid Scheduler) and Slurm environments are supported. In this paper, we present a detailed account on the methods behind the ISC Toolbox, the implementation of the toolbox and demonstrate the possible use of the toolbox by summarizing selected example applications. We also report the computation time experiments both using a single desktop computer and two grid environments demonstrating that parallelization effectively reduces the computing time. The ISC Toolbox is available in https://code.google.com/p/isc-toolbox/
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Affiliation(s)
- Jukka-Pekka Kauppi
- Department of Computer Science and HIIT, University of Helsinki Helsinki, Finland ; Brain Research Unit, O.V. Lounasmaa Laboratory, School of Science, Aalto University Espoo, Finland
| | - Juha Pajula
- Department of Signal Processing, Tampere University of Technology Tampere, Finland
| | - Jussi Tohka
- Department of Signal Processing, Tampere University of Technology Tampere, Finland
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15
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Kauppi JP, Parkkonen L, Hari R, Hyvärinen A. Decoding magnetoencephalographic rhythmic activity using spectrospatial information. Neuroimage 2013; 83:921-36. [DOI: 10.1016/j.neuroimage.2013.07.026] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2013] [Revised: 06/29/2013] [Accepted: 07/05/2013] [Indexed: 10/26/2022] Open
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Kauppi JP, Martikainen K, Ruotsalainen U. Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns. Neural Netw 2010; 23:1226-37. [PMID: 20685076 DOI: 10.1016/j.neunet.2010.06.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2008] [Revised: 04/13/2010] [Accepted: 06/24/2010] [Indexed: 10/19/2022]
Abstract
The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns.
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Affiliation(s)
- Jukka-Pekka Kauppi
- Tampere University of Technology, Department of Signal Processing, Tampere, Finland.
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Kauppi JP, Jääskeläinen IP, Sams M, Tohka J. Inter-subject correlation of brain hemodynamic responses during watching a movie: localization in space and frequency. Front Neuroinform 2010; 4:5. [PMID: 20428497 PMCID: PMC2859808 DOI: 10.3389/fninf.2010.00005] [Citation(s) in RCA: 89] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2009] [Accepted: 03/01/2010] [Indexed: 11/16/2022] Open
Abstract
Cinema is a promising naturalistic stimulus that enables, for instance, elicitation of robust emotions during functional magnetic resonance imaging (fMRI). Inter-subject correlation (ISC) has been used as a model-free analysis method to map the highly complex hemodynamic responses that are evoked during watching a movie. Here, we extended the ISC analysis to frequency domain using wavelet analysis combined with non-parametric permutation methods for making voxel-wise statistical inferences about frequency-band specific ISC. We applied these novel analysis methods to a dataset collected in our previous study where 12 subjects watched an emotionally engaging movie “Crash” during fMRI scanning. Our results suggest that several regions within the frontal and temporal lobes show ISC predominantly at low frequency bands, whereas visual cortical areas exhibit ISC also at higher frequencies. It is possible that these findings relate to recent observations of a cortical hierarchy of temporal receptive windows, or that the types of events processed in temporal and prefrontal cortical areas (e.g., social interactions) occur over longer time periods than the stimulus features processed in the visual areas. Software tools to perform frequency-specific ISC analysis, together with a visualization application, are available as open source Matlab code.
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Affiliation(s)
- Jukka-Pekka Kauppi
- Department of Signal Processing, Tampere University of Technology Tampere, Finland
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Kauppi JP, Jaaskelainen I, Sams M, Tohka J. A new software tool for analyzing BOLD fMRI during movie watching. Front Neuroinform 2009. [DOI: 10.3389/conf.neuro.11.2009.08.032] [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] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
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