24 results on '"Cattinelli,I"'
Search Results
2. Clu-B: a new toolbox for quantitative meta-analysis of neuroimaging data
- Author
-
BERLINGERI, MANUELA, DE MARCO, ROCCO, GALLUCCI, MARCELLO, BORGONI, RICCARDO, PAULESU, ERALDO, Cattinelli,I, Clemente, L, Borghese, NA, Berlingeri, M, Cattinelli, I, DE MARCO, R, Clemente, L, Gallucci, M, Borgoni, R, Borghese, N, and Paulesu, E
- Subjects
M-PSI/03 - PSICOMETRIA ,Hierarchical Clustering, neuroimaging, meta-analysis ,M-PSI/02 - PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA - Published
- 2015
3. Nouns and verb in the brain: a meta-analysis of 27 fMRI and PET studies
- Author
-
CREPALDI, DAVIDE, BERLINGERI, MANUELA, PAULESU, ERALDO, LUZZATTI, CLAUDIO GIUSEPPE, Cattinelli, I, Borghese, A, Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, and Luzzatti, C
- Subjects
clustering algorithm ,noun-verb dissociation ,task demand ,Neuroimaging ,left inferior frontal gyrus ,M-PSI/02 - PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA ,meta-analysi - Abstract
To date the evidence which has emerged from both anatomo‑correlative and neuroimaging studies investigating the brain areas responsible for noun and verb processing [e.g. 1, 2, 3] is inconsistent; this may be due to the lack of homogeneity in the tasks used in the various experiments. The metanalysis described in this study aims at disentangling brain regions that are systematically associated with a given grammatical class from those whose grammatical‑class specificity is modulated by the task used. We collected 441 activation peak coordinates associated with either nouns or verbs in simple effect analyses from 27 neuroimaging studies published from 1996 to 2008, and employed a hierarchical cluster algorithm adopting the Ward criterion [4] to automatically segregate groups of activation coordinates into separate clusters with mean standard deviations of less than 7.5 mm [5] in the three directions (x, y, z). This procedure produced a set of 37 clusters, which were tested with a chi‑square analysis for specificity for grammatical class and/or task. Nine of these clusters showed a significant task-by-grammatical class interaction, which in derivational tasks was usually caused by noun‑specific activation and in picture naming and fluency tasks by verb‑specific activation. These results are discussed in the light of the cognitive processes underlying the individual experimental tasks. REFERENCES [1] Perani, D., Cappa, S.F., Schnur, T., Tettamanti, M., Collina, S., Rosa, M.M., Fazio, F. (1999). The neural correlates of verb and noun processing. A PET study. Brain, 122, 2337‑2344. [2] Aggujaro, S., Crepaldi, D., Pistarini, C., Taricco, M., Luzzatti, C. (2006). Neuro-anatomical correlates of impaired retrieval of verbs and nouns: Interaction of grammatical class, imageability and actionality. Journal of Neurolinguistics, 19, 175-194. [3] Tyler, L.K., Russell, R., Fadili, J., Moss, H.E. (2001). The neural representation of nouns and verbs: PET studies. Brain, 124, 1619-1634. [4] Ward, J.H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 158, 236-244. [5] Jobard, G., Crivello, F., and Tzourio-Mazoyer, N. (2003). Evaluation of the dual route theory of reading: a metanalysis of 35 neuroimaging studies. NeuroImage, 20, 693-712.
- Published
- 2008
4. Reasoning about Lava effusion: from Geographical Information Systems to Answer Set Programming
- Author
-
Cattinelli, I., Damiani, M., and Nucita, Andrea
- Published
- 2004
5. Reading the reading brain: A new meta-analysis of functional imaging data on reading
- Author
-
Cattinelli, I, Borghese, N, Gallucci, M, Paulesu, E, CATTINELLI, ISABELLA, GALLUCCI, MARCELLO, PAULESU, ERALDO, Cattinelli, I, Borghese, N, Gallucci, M, Paulesu, E, CATTINELLI, ISABELLA, GALLUCCI, MARCELLO, and PAULESU, ERALDO
- Abstract
Over the last 20 years, reading has been the focus of much research using functional imaging. A formal assessment of the implications of this work for a more general understanding of reading processes is still lacking. We performed a new meta-analysis based on an optimized hierarchical clustering algorithm which automatically groups activation peaks into clusters; the functional role of the clusters was assessed on the basis of statistical criteria. We considered the literature from 1992 to 2008, focussing exclusively on experiments based on single words or pseudowords from the following four classes of tasks: reading, lexical decision, phonological decision and semantic tasks. Our analysis was restricted to alphabetic orthographies and was based on 35 studies. We identified three networks: (1) a difficulty modulated network including Broca's area and attention-related brain regions; (2) a word-related network, primarily involving regions of the left temporal lobe and of the anterior fusiform region, known to participate to semantic processes; (3) a pseudoword-related network in the basal occipito-temporal regions and in the left inferior parietal cortex. These subnetworks constitute the basis upon which a plausible functional model of reading is proposed, where orthographic, phonological, and semantic processes are recruited to compute the phonology of a written stimulus based on cooperative and competitive mechanisms. The results of this meta-analysis held face validity when compared with the results of literature published until mid 2010, the time of completion of data collection.
- Published
- 2013
6. Clustering the lexicon in the brain: a meta-analysis of the neurofunctional evidence on noun and verb processing
- Author
-
Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, N, Luzzatti, C, Paulesu, E, CREPALDI, DAVIDE, BERLINGERI, MANUELA, CATTINELLI, ISABELLA, LUZZATTI, CLAUDIO GIUSEPPE, PAULESU, ERALDO, Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, N, Luzzatti, C, Paulesu, E, CREPALDI, DAVIDE, BERLINGERI, MANUELA, CATTINELLI, ISABELLA, LUZZATTI, CLAUDIO GIUSEPPE, and PAULESU, ERALDO
- Abstract
Although it is widely accepted that nouns and verbs are functionally independent linguistic entities, it is less clear whether their processing recruits different brain areas. This issue is particularly relevant for those theories of lexical semantics (and, more in general, of cognition) that suggest the embodiment of abstract concepts, i.e., based strongly on perceptual and motoric representations. This paper presents a formal meta analysis of the neuroimaging evidence on noun and verb processing in order to address this dichotomy more effectively at the anatomical level. We used a hierarchical clustering algorithm that grouped fMRI/PET activation peaks solely on the basis of spatial proximity. Cluster specificity for grammatical class was then tested on the basis of the noun verb distribution of the activation peaks included in each cluster. 32 clusters were identified: three were associated with nouns across different tasks (in the right inferior temporal gyrus, the left angular gyrus, and the left inferior parietal gyrus); one with verbs across different tasks (in the posterior part of the right middle temporal gyrus); and three showed verb specificity in some tasks and noun specificity in others (in the left and right inferior frontal gyrus and the left insula). These results do not support the popular tenets that verb processing is predominantly based in the left frontal cortex and noun processing relies specifically on temporal regions; nor do they support the idea that verb lexical semantic representations are heavily based on embodied motoric information. Our findings suggest instead that the cerebral circuits deputed to noun and verb processing lie in close spatial proximity in a wide network including frontal, parietal, and temporal regions. The data also indicate a predominant – but not exclusive – left lateralization of the network.
- Published
- 2013
7. Nouns and verbs in the brain: A meta-analysis of 27 fMRI and PET studies
- Author
-
Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, Luzzatti, C, CREPALDI, DAVIDE, BERLINGERI, MANUELA, CATTINELLI, ISABELLA, PAULESU, ERALDO, LUZZATTI, CLAUDIO GIUSEPPE, Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, Luzzatti, C, CREPALDI, DAVIDE, BERLINGERI, MANUELA, CATTINELLI, ISABELLA, PAULESU, ERALDO, and LUZZATTI, CLAUDIO GIUSEPPE
- Abstract
Although the functional independence of noun and verb lexical retrieval has been reliably established, it is not clear whether the processing of these grammatical classes recruits separate neural circuits [1]. Contrasting results might have emerged because different studies used different experimental and baseline tasks. The meta‐analysis described in this study aims at identifying (i) brain regions that are consistently associated with a given grammatical class across different tasks, and (ii) brain areas whose grammatical‐class specificity is modulated by the task used. We considered 620 activation peaks reported in 27 neuroimaging studies on nouns and verbs. A hierarchical cluster algorithm was used in order to automatically segregate groups of coordinates into separate clusters. This procedure produced a set of 52 clusters, which were assessed with a binomial test for specificity for grammatical class and/or task, and with a Fisher test for task‐by‐grammatical class interaction. One cluster was associated with nouns across different tasks in the right superior temporal gyrus; verb‐specific clusters were instead observed in the middle temporal gyrus bilaterally, in the right precuneus and in the left lingual gyrus. Other four clusters – located in the left inferior frontal gyrus, in the left insula, and in the left middle occipital gyrus – showed a task‐by‐grammatical class interaction. The impact of these results on the current neuroanatomical theories of noun and verb processing [2] will be discussed. [1] Berlingeri, M. et al. (2008), Cognitive Neuropsychology, 25, 528‐558. [2] Cappa, S. & Perani, D. (2003), Journal of Neurolinguistics, 16, 183‐189.
- Published
- 2010
8. Do Noun and Verb Processing Really Recruit Spatially-Segregated Neural Circuits?
- Author
-
Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, Luzzatti, C, CREPALDI, DAVIDE, BERLINGERI, MANUELA, CATTINELLI, ISABELLA, PAULESU, ERALDO, LUZZATTI, CLAUDIO GIUSEPPE, Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, Luzzatti, C, CREPALDI, DAVIDE, BERLINGERI, MANUELA, CATTINELLI, ISABELLA, PAULESU, ERALDO, and LUZZATTI, CLAUDIO GIUSEPPE
- Abstract
The functional independence of noun and verb lexical retrieval has been repeatedly demonstrated over the last decade (e.g., Rapp and Caramazza, 2002); however, the evidence produced so far on whether these processes recruit separate neural circuits is far less clear (e.g., Bedny & Thompson‐Schill, 2006; Berlingeri et al., 2008). This may be due to the lack of homogeneity in the tasks used in the various experiments. The meta‐analysis described in this study aims at disentangling the brain regions that are systematically associated with a given grammatical class from those whose grammatical‐class specificity is modulated by the task used. We collected 620 activation coordinates associated with either nouns or verbs in simple effect analyses from 22 neuroimaging studies published from 1996 to 2008. A hierarchical cluster algorithm was employed adopting the Ward (1963) criterion to automatically segregate groups of coordinates into separate clusters (mean standard deviation < 7.5 mm in the x, y, and z directions for each cluster; Jobard et al., 2003). This procedure produced a set of 49 clusters, which were assessed with a binomial test for specificity for grammatical class and/or task, and with a Fisher test for task‐by‐grammatical class interaction. Two clusters turned out to be associated with nouns across different tasks, while five were associated with verbs (Figure 1a); the locations of these clusters do not support spatially segregated neural circuits for nouns and verbs, as suggested in some previous work (e.g., Cappa & Perani, 2003). Five clusters showed instead a task‐by‐grammatical class interaction (Figure 1b), which was mainly driven by noun‐specific activation in derivational tasks and by verb‐specific activation in picture naming and fluency tasks. These results will be discussed in the light of the cognitive processes entailed by the individual experimental tasks.
- Published
- 2009
9. Nouns and verb in the brain: a meta-analysis of 27 fMRI and PET studies
- Author
-
Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, Luzzatti, C, CREPALDI, DAVIDE, BERLINGERI, MANUELA, PAULESU, ERALDO, LUZZATTI, CLAUDIO GIUSEPPE, Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, Luzzatti, C, CREPALDI, DAVIDE, BERLINGERI, MANUELA, PAULESU, ERALDO, and LUZZATTI, CLAUDIO GIUSEPPE
- Abstract
To date the evidence which has emerged from both anatomo‐correlative and neuroimaging studies investigating the brain areas responsible for noun and verb processing [e.g. 1, 2, 3] is inconsistent; this may be due to the lack of homogeneity in the tasks used in the various experiments. The metanalysis described in this study aims at disentangling brain regions that are systematically associated with a given grammatical class from those whose grammatical‐class specificity is modulated by the task used. We collected 441 activation peak coordinates associated with either nouns or verbs in simple effect analyses from 27 neuroimaging studies published from 1996 to 2008, and employed a hierarchical cluster algorithm adopting the Ward criterion [4] to automatically segregate groups of activation coordinates into separate clusters with mean standard deviations of less than 7.5 mm [5] in the three directions (x, y, z). This procedure produced a set of 37 clusters, which were tested with a chi‐square analysis for specificity for grammatical class and/or task. Nine of these clusters showed a significant task-by-grammatical class interaction, which in derivational tasks was usually caused by noun‐specific activation and in picture naming and fluency tasks by verb‐specific activation. These results are discussed in the light of the cognitive processes underlying the individual experimental tasks. REFERENCES [1] Perani, D., Cappa, S.F., Schnur, T., Tettamanti, M., Collina, S., Rosa, M.M., Fazio, F. (1999). The neural correlates of verb and noun processing. A PET study. Brain, 122, 2337‐2344. [2] Aggujaro, S., Crepaldi, D., Pistarini, C., Taricco, M., Luzzatti, C. (2006). Neuro-anatomical correlates of impaired retrieval of verbs and nouns: Interaction of grammatical class, imageability and actionality. Journal of Neurolinguistics, 19, 175-194. [3] Tyler, L.K., Russell, R., Fadili, J., Moss, H.E. (2001). The neural representation of nouns and verbs: PET studies. Brain, 124, 1619-1
- Published
- 2008
10. Extracorporeal techniques and adequacy
- Author
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Chapdelaine, I., primary, Mostovaya, I. M., additional, Blankestijn, P. J., additional, Bots, M. L., additional, van den Dorpel, M. A., additional, Nube, M. J., additional, ter Wee, P. W., additional, Grooteman, M. P. C., additional, Wang, B., additional, Wang, K., additional, Gayrard, N., additional, Ficheux, A., additional, Duranton, F., additional, Guzman, C., additional, Szwarc, I., additional, Bismuth-Mondolfo, J., additional, Brunet, P., additional, Servel, M. F., additional, Argiles, A., additional, Pedrini, L., additional, Mari, F., additional, Barbieri, C., additional, Cattinelli, I., additional, Bellocchio, F., additional, Amato, C., additional, Leypoldt, J. K., additional, Agar, B. U., additional, Culleton, B. F., additional, Eloot, S., additional, and Vanholder, R., additional
- Published
- 2013
- Full Text
- View/download PDF
11. Reading the reading brain: A new meta-analysis of functional imaging data on reading
- Author
-
Isabella Cattinelli, Marcello Gallucci, N. Alberto Borghese, Eraldo Paulesu, Cattinelli, I, Borghese, N, Gallucci, M, and Paulesu, E
- Subjects
Linguistics and Language ,Cognitive Neuroscience ,Word ,Experimental and Cognitive Psychology ,Stimulus (physiology) ,computer.software_genre ,M-PSI/02 - PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA ,Pseudowords ,Clustering ,Arts and Humanities (miscellaneous) ,Lexical decision task ,Meta-analysi ,Reading neural circuit ,Cluster analysis ,Face validity ,Data collection ,business.industry ,fMRI ,Phonology ,Hierarchical clustering ,Functional imaging ,PET ,Artificial intelligence ,business ,Psychology ,computer ,Natural language processing - Abstract
Over the last 20 years, reading has been the focus of much research using functional imaging. A formal assessment of the implications of this work for a more general understanding of reading processes is still lacking. We performed a new meta-analysis based on an optimized hierarchical clustering algorithm which automatically groups activation peaks into clusters; the functional role of the clusters was assessed on the basis of statistical criteria. We considered the literature from 1992 to 2008, focussing exclusively on experiments based on single words or pseudowords from the following four classes of tasks: reading, lexical decision, phonological decision and semantic tasks. Our analysis was restricted to alphabetic orthographies and was based on 35 studies. We identified three networks: (1) a difficulty modulated network including Broca's area and attention-related brain regions; (2) a word-related network, primarily involving regions of the left temporal lobe and of the anterior fusiform region, known to participate to semantic processes; (3) a pseudoword-related network in the basal occipito-temporal regions and in the left inferior parietal cortex. These subnetworks constitute the basis upon which a plausible functional model of reading is proposed, where orthographic, phonological, and semantic processes are recruited to compute the phonology of a written stimulus based on cooperative and competitive mechanisms. The results of this meta-analysis held face validity when compared with the results of literature published until mid 2010, the time of completion of data collection.
- Published
- 2013
12. Clustering the lexicon in the brain: a meta-analysis of the neurofunctional evidence on noun and verb processing
- Author
-
Nunzio Alberto Borghese, Isabella Cattinelli, Manuela Berlingeri, Davide Crepaldi, Eraldo Paulesu, Claudio Luzzatti, Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, N, Luzzatti, C, and Paulesu, E
- Subjects
nouns ,Lexical semantics ,neuroimaging ,noun-verb dissociation ,meta analysis ,clustering algorithm ,task demand ,left inferior frontal gyrus ,Inferior frontal gyrus ,Verb ,Neuroimaging ,M-PSI/02 - PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA ,computer.software_genre ,Lexicon ,Lateralization of brain function ,grammatical class ,lcsh:RC321-571 ,Behavioral Neuroscience ,Noun ,verbs ,Original Research Article ,clustering algorithms ,lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry ,Biological Psychiatry ,meta analysi ,business.industry ,Cognition ,Psychiatry and Mental health ,Meta-analysis ,Neuropsychology and Physiological Psychology ,Neurology ,Embodied cognition ,Artificial intelligence ,Psychology ,business ,computer ,Natural language processing ,Cognitive psychology ,Neuroscience - Abstract
Although it is widely accepted that nouns and verbs are functionally independent linguistic entities, it is less clear whether their processing recruits different brain areas. This issue is particularly relevant for those theories of lexical semantics (and, more in general, of cognition) that suggest the embodiment of abstract concepts, i.e., based strongly on perceptual and motoric representations. This paper presents a formal meta-analysis of the neuroimaging evidence on noun and verb processing in order to address this dichotomy more effectively at the anatomical level. We used a hierarchical clustering algorithm that grouped fMRI/PET activation peaks solely on the basis of spatial proximity. Cluster specificity for grammatical class was then tested on the basis of the noun-verb distribution of the activation peaks included in each cluster. Thirty-two clusters were identified: three were associated with nouns across different tasks (in the right inferior temporal gyrus, the left angular gyrus, and the left inferior parietal gyrus); one with verbs across different tasks (in the posterior part of the right middle temporal gyrus); and three showed verb specificity in some tasks and noun specificity in others (in the left and right inferior frontal gyrus and the left insula). These results do not support the popular tenets that verb processing is predominantly based in the left frontal cortex and noun processing relies specifically on temporal regions; nor do they support the idea that verb lexical-semantic representations are heavily based on embodied motoric information. Our findings suggest instead that the cerebral circuits deputed to noun and verb processing lie in close spatial proximity in a wide network including frontal, parietal, and temporal regions. The data also indicate a predominant—but not exclusive—left lateralization of the network.
- Published
- 2013
13. Nouns and verbs in the brain: A meta-analysis of 27 fMRI and PET studies
- Author
-
CREPALDI, DAVIDE, BERLINGERI, MANUELA, CATTINELLI, ISABELLA, PAULESU, ERALDO, LUZZATTI, CLAUDIO GIUSEPPE, Borghese, A, Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, and Luzzatti, C
- Subjects
None ,M-PSI/02 - PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA - Abstract
Although the functional independence of noun and verb lexical retrieval has been reliably established, it is not clear whether the processing of these grammatical classes recruits separate neural circuits [1]. Contrasting results might have emerged because different studies used different experimental and baseline tasks. The meta‑analysis described in this study aims at identifying (i) brain regions that are consistently associated with a given grammatical class across different tasks, and (ii) brain areas whose grammatical‑class specificity is modulated by the task used. We considered 620 activation peaks reported in 27 neuroimaging studies on nouns and verbs. A hierarchical cluster algorithm was used in order to automatically segregate groups of coordinates into separate clusters. This procedure produced a set of 52 clusters, which were assessed with a binomial test for specificity for grammatical class and/or task, and with a Fisher test for task‑by‑grammatical class interaction. One cluster was associated with nouns across different tasks in the right superior temporal gyrus; verb‑specific clusters were instead observed in the middle temporal gyrus bilaterally, in the right precuneus and in the left lingual gyrus. Other four clusters – located in the left inferior frontal gyrus, in the left insula, and in the left middle occipital gyrus – showed a task‑by‑grammatical class interaction. The impact of these results on the current neuroanatomical theories of noun and verb processing [2] will be discussed. [1] Berlingeri, M. et al. (2008), Cognitive Neuropsychology, 25, 528‑558. [2] Cappa, S. & Perani, D. (2003), Journal of Neurolinguistics, 16, 183‑189.
- Published
- 2010
14. Do Noun and Verb Processing Really Recruit Spatially-Segregated Neural Circuits?
- Author
-
CREPALDI, DAVIDE, BERLINGERI, MANUELA, CATTINELLI, ISABELLA, PAULESU, ERALDO, LUZZATTI, CLAUDIO GIUSEPPE, Borghese, A, Crepaldi, D, Berlingeri, M, Cattinelli, I, Borghese, A, Paulesu, E, and Luzzatti, C
- Subjects
verb ,PET ,noun ,fMRI ,anatomical independence ,anatomo‑functional correlation ,Neuroimaging ,grammatical cla ,functional independence ,neural circuit ,M-PSI/02 - PSICOBIOLOGIA E PSICOLOGIA FISIOLOGICA ,meta-analysi - Abstract
The functional independence of noun and verb lexical retrieval has been repeatedly demonstrated over the last decade (e.g., Rapp and Caramazza, 2002); however, the evidence produced so far on whether these processes recruit separate neural circuits is far less clear (e.g., Bedny & Thompson‑Schill, 2006; Berlingeri et al., 2008). This may be due to the lack of homogeneity in the tasks used in the various experiments. The meta‑analysis described in this study aims at disentangling the brain regions that are systematically associated with a given grammatical class from those whose grammatical‑class specificity is modulated by the task used. We collected 620 activation coordinates associated with either nouns or verbs in simple effect analyses from 22 neuroimaging studies published from 1996 to 2008. A hierarchical cluster algorithm was employed adopting the Ward (1963) criterion to automatically segregate groups of coordinates into separate clusters (mean standard deviation < 7.5 mm in the x, y, and z directions for each cluster; Jobard et al., 2003). This procedure produced a set of 49 clusters, which were assessed with a binomial test for specificity for grammatical class and/or task, and with a Fisher test for task‑by‑grammatical class interaction. Two clusters turned out to be associated with nouns across different tasks, while five were associated with verbs (Figure 1a); the locations of these clusters do not support spatially segregated neural circuits for nouns and verbs, as suggested in some previous work (e.g., Cappa & Perani, 2003). Five clusters showed instead a task‑by‑grammatical class interaction (Figure 1b), which was mainly driven by noun‑specific activation in derivational tasks and by verb‑specific activation in picture naming and fluency tasks. These results will be discussed in the light of the cognitive processes entailed by the individual experimental tasks.
- Published
- 2009
15. How to assess the risks associated with the usage of a medical device based on predictive modeling: the case of an anemia control model certified as medical device.
- Author
-
Barbieri C, Neri L, Chermisi M, Bolzoni E, Cattinelli I, Decker W, Stuard S, Martín-Guerrero JD, and Mari F
- Subjects
- Adult, Cohort Studies, Humans, Machine Learning, Renal Dialysis, Anemia, Hematinics
- Abstract
Background: The successful application of Machine Learning (ML) to many clinical problems can lead to its implementation as a medical device (MD), which is important to assess the associated risks., Methods: An anemia control model (ACM), certified as MD, may face adverse events as a result of wrong predictions that are translated into suggestions of doses of erythropoietic stimulating agents to dialysis patients. Risks are assessed as the combination of severity and probability of a given hazard. While severities are typically assessed by clinicians, probabilities are tightly related to the performance of the predictive model., Results: A postmarketing data set formed by all adult patients registered in French, Portuguese, and Spanish clinics, belonging to an international network, was considered; 3876 patients and 11,508 suggestions were eventually included. The achieved results show that there are no statistical differences between the probabilities of adverse events that are estimated in the ACM test set (using only Spanish clinics) and those actually observed in the postmarketing cohort., Conclusions: The risks of an ACM-MD can be accurately and robustly estimated, thus enhancing patients' safety. The proposed methodology is applicable to other clinical decisions based on predictive models since our proposal does not depend on the particular predictive model.
- Published
- 2021
- Full Text
- View/download PDF
16. Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks.
- Author
-
Pellicer-Valero OJ, Cattinelli I, Neri L, Mari F, Martín-Guerrero JD, and Barbieri C
- Subjects
- Hemoglobins analysis, Humans, Neural Networks, Computer, Prospective Studies, Renal Dialysis, Hematinics therapeutic use, Kidney Failure, Chronic diagnosis, Kidney Failure, Chronic therapy
- Abstract
Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to provide suggestions when some strict requirements are not met (such as having a three month history prior to the prediction). Here, recurrent neural networks (RNNs) are used to model whole patient histories, providing predictions at every time step since the very first day. Furthermore, an unprecedented amount of data (∼110,000 patients from many different medical centers in twelve countries, without exclusion criteria) was used to train it, thus allowing it to generalize for every single patient. The resulting model outperforms state-of-the-art Hemoglobin prediction, providing excellent results even when tested on a prospective dataset. Simultaneously, it allows to bring the benefits of algorithmic anemia control to a very large group of patients., (Copyright © 2020 Elsevier B.V. All rights reserved.)
- Published
- 2020
- Full Text
- View/download PDF
17. Development of an Artificial Intelligence Model to Guide the Management of Blood Pressure, Fluid Volume, and Dialysis Dose in End-Stage Kidney Disease Patients: Proof of Concept and First Clinical Assessment.
- Author
-
Barbieri C, Cattinelli I, Neri L, Mari F, Ramos R, Brancaccio D, Canaud B, and Stuard S
- Abstract
Background: Fluid volume and blood pressure (BP) management are crucial endpoints for end-stage kidney disease patients. BP control in clinical practice mainly relies on reducing extracellular fluid volume overload by diminishing targeted postdialysis weight. This approach exposes dialysis patients to intradialytic hypotensive episodes., Summary: Both chronic hypertension and intradialytic hypotension lead to adverse long-term outcomes. Achieving the optimal trade-off between adequate fluid removal and the risk of intradialytic adverse events is a complex task in clinical practice given the multiple patient-related and dialysis-related factors affecting the hemodynamic response to treatment. State-of-the-art artificial intelligence has been adopted in other complex decision-making tasks for dialysis patients and may help personalize the multiple dialysis-related prescriptions affecting patients' intradialytic hemodynamics. As a proof of concept, we developed a multiple-endpoint model predicting session-specific Kt/V, fluid volume removal, heart rate, and BP based on patient characteristics, historic hemodynamic responses, and dialysis-related prescriptions., Key Messages: The accuracy and precision of this preliminary model is extremely encouraging. Such analytic tools may be used to anticipate patients' reactions through simulation so that the best strategy can be chosen based on clinical judgment or formal utility functions.
- Published
- 2019
- Full Text
- View/download PDF
18. An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients.
- Author
-
Barbieri C, Molina M, Ponce P, Tothova M, Cattinelli I, Ion Titapiccolo J, Mari F, Amato C, Leipold F, Wehmeyer W, Stuard S, Stopper A, and Canaud B
- Subjects
- Aged, Darbepoetin alfa administration & dosage, Female, Hematinics administration & dosage, Humans, Kidney Failure, Chronic therapy, Male, Middle Aged, Renal Dialysis, Retrospective Studies, Anemia drug therapy, Artificial Intelligence, Clinical Decision-Making methods, Darbepoetin alfa therapeutic use, Decision Support Systems, Clinical, Hematinics therapeutic use, Hemoglobins analysis, Kidney Failure, Chronic complications
- Abstract
Managing anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses. Our retrospective study consisted of a 12-month control phase (standard anemia care), followed by a 12-month observation phase (ACM-guided care) encompassing 752 patients undergoing hemodialysis therapy in 3 NephroCare clinics located in separate countries. The percentage of hemoglobin values on target, the median darbepoetin dose, and individual hemoglobin fluctuation (estimated from the intrapatient hemoglobin standard deviation) were deemed primary outcomes. In the observation phase, median darbepoetin consumption significantly decreased from 0.63 to 0.46 μg/kg/month, whereas on-target hemoglobin values significantly increased from 70.6% to 76.6%, reaching 83.2% when the ACM suggestions were implemented. Moreover, ACM introduction led to a significant decrease in hemoglobin fluctuation (intrapatient standard deviation decreased from 0.95 g/dl to 0.83 g/dl). Thus, ACM support helped improve anemia outcomes of hemodialysis patients, minimizing erythropoietic-stimulating agent use with the potential to reduce the cost of treatment., (Copyright © 2016 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.)
- Published
- 2016
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19. Performance of a Predictive Model for Long-Term Hemoglobin Response to Darbepoetin and Iron Administration in a Large Cohort of Hemodialysis Patients.
- Author
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Barbieri C, Bolzoni E, Mari F, Cattinelli I, Bellocchio F, Martin JD, Amato C, Stopper A, Gatti E, Macdougall IC, Stuard S, and Canaud B
- Subjects
- Aged, Anemia blood, Anemia complications, Anemia pathology, Darbepoetin alfa blood, Disease Management, Erythropoiesis drug effects, Female, Ferric Compounds blood, Ferric Oxide, Saccharated, Glucaric Acid blood, Hematinics blood, Humans, Injections, Intravenous, Kidney Failure, Chronic blood, Kidney Failure, Chronic complications, Kidney Failure, Chronic pathology, Male, Middle Aged, Neural Networks, Computer, Renal Dialysis, Retrospective Studies, Anemia therapy, Darbepoetin alfa therapeutic use, Ferric Compounds therapeutic use, Glucaric Acid therapeutic use, Hematinics therapeutic use, Hemoglobins biosynthesis, Kidney Failure, Chronic therapy, Models, Statistical
- Abstract
Anemia management, based on erythropoiesis stimulating agents (ESA) and iron supplementation, has become an increasingly challenging problem in hemodialysis patients. Maintaining hemodialysis patients within narrow hemoglobin targets, preventing cycling outside target, and reducing ESA dosing to prevent adverse outcomes requires considerable attention from caregivers. Anticipation of the long-term response (i.e. at 3 months) to the ESA/iron therapy would be of fundamental importance for planning a successful treatment strategy. To this end, we developed a predictive model designed to support decision-making regarding anemia management in hemodialysis (HD) patients treated in center. An Artificial Neural Network (ANN) algorithm for predicting hemoglobin concentrations three months into the future was developed and evaluated in a retrospective study on a sample population of 1558 HD patients treated with intravenous (IV) darbepoetin alfa, and IV iron (sucrose or gluconate). Model inputs were the last 90 days of patients' medical history and the subsequent 90 days of darbepoetin/iron prescription. Our model was able to predict individual variation of hemoglobin concentration 3 months in the future with a Mean Absolute Error (MAE) of 0.75 g/dL. Error analysis showed a narrow Gaussian distribution centered in 0 g/dL; a root cause analysis identified intercurrent and/or unpredictable events associated with hospitalization, blood transfusion, and laboratory error or misreported hemoglobin values as the main reasons for large discrepancy between predicted versus observed hemoglobin values. Our ANN predictive model offers a simple and reliable tool applicable in daily clinical practice for predicting the long-term response to ESA/iron therapy of HD patients.
- Published
- 2016
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20. A novel approach to the problem of non-uniqueness of the solution in hierarchical clustering.
- Author
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Cattinelli I, Valentini G, Paulesu E, and Borghese NA
- Abstract
The existence of multiple solutions in clustering, and in hierarchical clustering in particular, is often ignored in practical applications. However, this is a non-trivial problem, as different data orderings can result in different cluster sets that, in turns, may lead to different interpretations of the same data. The method presented here offers a solution to this issue. It is based on the definition of an equivalence relation over dendrograms that allows developing all and only the significantly different dendrograms for the same dataset, thus reducing the computational complexity to polynomial from the exponential obtained when all possible dendrograms are considered. Experimental results in the neuroimaging and bioinformatics domains show the effectiveness of the proposed method.
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- 2013
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21. Computational intelligence for the Balanced Scorecard: studying performance trends of hemodialysis clinics.
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Cattinelli I, Bolzoni E, Chermisi M, Bellocchio F, Barbieri C, Mari F, Amato C, Menzer M, Stopper A, and Gatti E
- Subjects
- Algorithms, Cluster Analysis, Europe, Humans, Linear Models, Markov Chains, Neural Networks, Computer, Quality Improvement trends, Task Performance and Analysis, Time Factors, Treatment Outcome, Ambulatory Care Facilities trends, Artificial Intelligence trends, Benchmarking trends, Data Mining trends, Outcome and Process Assessment, Health Care trends, Quality Indicators, Health Care trends, Renal Dialysis trends
- Abstract
Objectives: The Balanced Scorecard (BSC) is a general, widely employed instrument for enterprise performance monitoring based on the periodic assessment of strategic Key Performance Indicators that are scored against preset targets. The BSC is currently employed as an effective management support tool within Fresenius Medical Care (FME) and is routinely analyzed via standard statistical methods. More recently, the application of computational intelligence techniques (namely, self-organizing maps) to BSC data has been proposed as a way to enhance the quantity and quality of information that can be extracted from it. In this work, additional methods are presented to analyze the evolution of clinic performance over time., Methods: Performance evolution is studied at the single-clinic level by computing two complementary indexes that measure the proportion of time spent within performance clusters and improving/worsening trends. Self-organizing maps are used in conjunction with these indexes to identify the specific drivers of the observed performance. The performance evolution for groups of clinics is modeled under a probabilistic framework by resorting to Markov chain properties. These allow a study of the probability of transitioning between performance clusters as time progresses for the identification of the performance level that is expected to become dominant over time., Results: We show the potential of the proposed methods through illustrative results derived from the analysis of BSC data of 109 FME clinics in three countries. We were able to identify the performance drivers for specific groups of clinics and to distinguish between countries whose performances are likely to improve from those where a decline in performance might be expected. According to the stationary distribution of the Markov chain, the expected trend is best in Turkey (where the highest performance cluster has the highest probability, P=0.46), followed by Portugal (where the second best performance cluster dominates, with P=0.50), and finally Italy (where the second best performance cluster has P=0.34)., Conclusion: These results highlight the ability of the proposed methods to extract insights about performance trends that cannot be easily extrapolated using standard analyses and that are valuable in directing management strategies within a continuous quality improvement policy., (Copyright © 2013 Elsevier B.V. All rights reserved.)
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- 2013
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22. Clustering the lexicon in the brain: a meta-analysis of the neurofunctional evidence on noun and verb processing.
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Crepaldi D, Berlingeri M, Cattinelli I, Borghese NA, Luzzatti C, and Paulesu E
- Abstract
Although it is widely accepted that nouns and verbs are functionally independent linguistic entities, it is less clear whether their processing recruits different brain areas. This issue is particularly relevant for those theories of lexical semantics (and, more in general, of cognition) that suggest the embodiment of abstract concepts, i.e., based strongly on perceptual and motoric representations. This paper presents a formal meta-analysis of the neuroimaging evidence on noun and verb processing in order to address this dichotomy more effectively at the anatomical level. We used a hierarchical clustering algorithm that grouped fMRI/PET activation peaks solely on the basis of spatial proximity. Cluster specificity for grammatical class was then tested on the basis of the noun-verb distribution of the activation peaks included in each cluster. Thirty-two clusters were identified: three were associated with nouns across different tasks (in the right inferior temporal gyrus, the left angular gyrus, and the left inferior parietal gyrus); one with verbs across different tasks (in the posterior part of the right middle temporal gyrus); and three showed verb specificity in some tasks and noun specificity in others (in the left and right inferior frontal gyrus and the left insula). These results do not support the popular tenets that verb processing is predominantly based in the left frontal cortex and noun processing relies specifically on temporal regions; nor do they support the idea that verb lexical-semantic representations are heavily based on embodied motoric information. Our findings suggest instead that the cerebral circuits deputed to noun and verb processing lie in close spatial proximity in a wide network including frontal, parietal, and temporal regions. The data also indicate a predominant-but not exclusive-left lateralization of the network.
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- 2013
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23. Use of Self-Organizing Maps for Balanced Scorecard analysis to monitor the performance of dialysis clinic chains.
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Cattinelli I, Bolzoni E, Barbieri C, Mari F, Martin-Guerrero JD, Soria-Olivas E, Martinez-Martinez JM, Gomez-Sanchis J, Amato C, Stopper A, and Gatti E
- Subjects
- Humans, Quality Indicators, Health Care organization & administration, Ambulatory Care Facilities organization & administration, Quality of Health Care organization & administration, Renal Dialysis
- Abstract
The Balanced Scorecard (BSC) is a validated tool to monitor enterprise performances against specific objectives. Through the choice and the evaluation of strategic Key Performance Indicators (KPIs), it provides a measure of the past company's outcome and allows planning future managerial strategies. The Fresenius Medical Care (FME) BSC makes use of 30 KPIs for a continuous quality improvement strategy within its dialysis clinics. Each KPI is monthly associated to a score that summarizes the clinic efficiency for that month. Standard statistical methods are currently used to analyze the BSC data and to give a comprehensive view of the corporate improvements to the top management. We herein propose the Self-Organizing Maps (SOMs) as an innovative approach to extrapolate information from the FME BSC data and to present it in an easy-readable informative form. A SOM is a computational technique that allows projecting high-dimensional datasets to a two-dimensional space (map), thus providing a compressed representation. The SOM unsupervised (self-organizing) training procedure results in a map that preserves similarity relations existing in the original dataset; in this way, the information contained in the high-dimensional space can be more easily visualized and understood. The present work demonstrates the effectiveness of the SOM approach in extracting useful information from the 30-dimensional BSC dataset: indeed, SOMs enabled both to highlight expected relationships between the KPIs and to uncover results not predictable with traditional analyses. Hence we suggest SOMs as a reliable complementary approach to the standard methods for BSC interpretation.
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- 2012
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24. Interacting with an artificial partner: modeling the role of emotional aspects.
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Cattinelli I, Goldwurm M, and Borghese NA
- Subjects
- Behavioral Sciences methods, Humans, Markov Chains, Reinforcement, Psychology, Social Behavior, Artificial Intelligence, Emotions, Neural Networks, Computer, Robotics methods, User-Computer Interface
- Abstract
In this paper we introduce a simple model based on probabilistic finite state automata to describe an emotional interaction between a robot and a human user, or between simulated agents. Based on the agent's personality, attitude, and nature, and on the emotional inputs it receives, the model will determine the next emotional state displayed by the agent itself. The probabilistic and time-varying nature of the model yields rich and dynamic interactions, and an autonomous adaptation to the interlocutor. In addition, a reinforcement learning technique is applied to have one agent drive its partner's behavior toward desired states. The model may also be used as a tool for behavior analysis, by extracting high probability patterns of interaction and by resorting to the ergodic properties of Markov chains.
- Published
- 2008
- Full Text
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