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Palmirotta C, Aresta S, Battista P, Tagliente S, Lagravinese G, Mongelli D, Gelao C, Fiore P, Castiglioni I, Minafra B, Salvatore C. Unveiling the Diagnostic Potential of Linguistic Markers in Identifying Individuals with Parkinson's Disease through Artificial Intelligence: A Systematic Review. Brain Sci 2024; 14:137. [PMID: 38391712 PMCID: PMC10886733 DOI: 10.3390/brainsci14020137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/22/2024] [Accepted: 01/25/2024] [Indexed: 02/24/2024] Open
Abstract
While extensive research has documented the cognitive changes associated with Parkinson's disease (PD), a relatively small portion of the empirical literature investigated the language abilities of individuals with PD. Recently, artificial intelligence applied to linguistic data has shown promising results in predicting the clinical diagnosis of neurodegenerative disorders, but a deeper investigation of the current literature available on PD is lacking. This systematic review investigates the nature of language disorders in PD by assessing the contribution of machine learning (ML) to the classification of patients with PD. A total of 10 studies published between 2016 and 2023 were included in this review. Tasks used to elicit language were mainly structured or unstructured narrative discourse. Transcriptions were mostly analyzed using Natural Language Processing (NLP) techniques. The classification accuracy (%) ranged from 43 to 94, sensitivity (%) ranged from 8 to 95, specificity (%) ranged from 3 to 100, AUC (%) ranged from 32 to 97. The most frequent optimal linguistic measures were lexico-semantic (40%), followed by NLP-extracted features (26%) and morphological consistency features (20%). Artificial intelligence applied to linguistic markers provides valuable insights into PD. However, analyzing measures derived from narrative discourse can be time-consuming, and utilizing ML requires specialized expertise. Moving forward, it is important to focus on facilitating the integration of both narrative discourse analysis and artificial intelligence into clinical practice.
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Affiliation(s)
- Cinzia Palmirotta
- Istituti Clinici Scientifici Maugeri IRCCS, Laboratory of Neuropsychology, Bari Institute, 70124 Bari, Italy
| | - Simona Aresta
- Istituti Clinici Scientifici Maugeri IRCCS, Laboratory of Neuropsychology, Bari Institute, 70124 Bari, Italy
| | - Petronilla Battista
- Istituti Clinici Scientifici Maugeri IRCCS, Laboratory of Neuropsychology, Bari Institute, 70124 Bari, Italy
| | - Serena Tagliente
- Istituti Clinici Scientifici Maugeri IRCCS, Laboratory of Neuropsychology, Bari Institute, 70124 Bari, Italy
| | - Gianvito Lagravinese
- Istituti Clinici Scientifici Maugeri IRCCS, Laboratory of Neuropsychology, Bari Institute, 70124 Bari, Italy
| | - Davide Mongelli
- Istituti Clinici Scientifici Maugeri IRCCS, Laboratory of Neuropsychology, Bari Institute, 70124 Bari, Italy
| | - Christian Gelao
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Unit of Bari Institute, 70124 Bari, Italy
| | - Pietro Fiore
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Unit of Bari Institute, 70124 Bari, Italy
- Department of Physical and Rehabilitation Medicine, University of Foggia, 71122 Foggia, Italy
| | - Isabella Castiglioni
- Department of Physics G. Occhialini, University of Milan-Bicocca, 20133 Milan, Italy
| | - Brigida Minafra
- Istituti Clinici Scientifici Maugeri IRCCS, Neurorehabilitation Unit of Bari Institute, 70124 Bari, Italy
| | - Christian Salvatore
- Department of Science, Technology and Society, University School for Advanced Studies IUSS Pavia, 27100 Pavia, Italy
- DeepTrace Technologies S.R.L., 20122 Milan, Italy
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Toro-Hernández FD, Migeot J, Marchant N, Olivares D, Ferrante F, González-Gómez R, González Campo C, Fittipaldi S, Rojas-Costa GM, Moguilner S, Slachevsky A, Chaná Cuevas P, Ibáñez A, Chaigneau S, García AM. Neurocognitive correlates of semantic memory navigation in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:15. [PMID: 38195756 PMCID: PMC10776628 DOI: 10.1038/s41531-024-00630-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 12/29/2023] [Indexed: 01/11/2024] Open
Abstract
Cognitive studies on Parkinson's disease (PD) reveal abnormal semantic processing. Most research, however, fails to indicate which conceptual properties are most affected and capture patients' neurocognitive profiles. Here, we asked persons with PD, healthy controls, and individuals with behavioral variant frontotemporal dementia (bvFTD, as a disease control group) to read concepts (e.g., 'sun') and list their features (e.g., hot). Responses were analyzed in terms of ten word properties (including concreteness, imageability, and semantic variability), used for group-level comparisons, subject-level classification, and brain-behavior correlations. PD (but not bvFTD) patients produced more concrete and imageable words than controls, both patterns being associated with overall cognitive status. PD and bvFTD patients showed reduced semantic variability, an anomaly which predicted semantic inhibition outcomes. Word-property patterns robustly classified PD (but not bvFTD) patients and correlated with disease-specific hypoconnectivity along the sensorimotor and salience networks. Fine-grained semantic assessments, then, can reveal distinct neurocognitive signatures of PD.
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Affiliation(s)
- Felipe Diego Toro-Hernández
- Graduate Program in Neuroscience and Cognition, Federal University of ABC, São Paulo, Brazil
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Joaquín Migeot
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Nicolás Marchant
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Daniela Olivares
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
- Laboratorio de Neuropsicología y Neurociencias Clínicas, Universidad de Chile, Santiago, Chile
| | - Franco Ferrante
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Raúl González-Gómez
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Cecilia González Campo
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- Global Brain Health Institute, University of California, San Francisco, California, USA; & Trinity College, Dublin, Ireland
| | - Gonzalo M Rojas-Costa
- Department of Radiology, Clínica las Condes, Santiago, Chile
- Advanced Epilepsy Center, Clínica las Condes, Santiago, Chile
- Join Unit FISABIO-CIPF, Valencia, Spain
- School of Medicine, Finis Terrae University, Santiago, Chile
- Health Innovation Center, Clínica Las Condes, Santiago, Chile
| | - Sebastian Moguilner
- Global Brain Health Institute, University of California, San Francisco, California, USA; & Trinity College, Dublin, Ireland
| | - Andrea Slachevsky
- Memory and Neuropsychiatric Center (CMYN), Neurology Department, Hospital del Salvador & Faculty of Medicine, University of Chile, Santiago, Chile
- Geroscience Center for Brain Health and Metabolism (GERO), Santiago, Chile
- Neuropsychology and Clinical Neuroscience Laboratory (LANNEC), Physiopatology Program - Institute of Biomedical Sciences (ICBM), Neuroscience and East Neuroscience Departments, Faculty of Medicine, University of Chile, Santiago, Chile
- Neurology and Psychiatry Department, Clínica Alemana-Universidad Desarrollo, Santiago, Chile
| | - Pedro Chaná Cuevas
- Facultad de Ciencias Médicas, Universidad de Santiago de Chile, Santiago, Chile
| | - Agustín Ibáñez
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
- Global Brain Health Institute, University of California, San Francisco, California, USA; & Trinity College, Dublin, Ireland
| | - Sergio Chaigneau
- Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
- Center for Cognition Research, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Adolfo M García
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile.
- Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina.
- Global Brain Health Institute, University of California, San Francisco, California, USA; & Trinity College, Dublin, Ireland.
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile.
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Marmolejo-Ramos F, Ospina R, García-Ceja E, Correa JC. Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning. JOURNAL OF STATISTICAL THEORY AND APPLICATIONS 2022; 21:175-185. [PMID: 36160758 PMCID: PMC9483296 DOI: 10.1007/s44199-022-00048-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/02/2022] [Indexed: 11/25/2022] Open
Abstract
AbstractIn The hitchhiker’s guide to responsible machine learning, Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent for both the data scientist and the end user. This review summarises BKZ’s book and elaborates on three elements key to ML analyses: inductive inference, causality, and interpretability.
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Affiliation(s)
- Fernando Marmolejo-Ramos
- Centre for Change and Complexity in Learning, University of South Australia, Adelaide, SA 5001 Australia
| | - Raydonal Ospina
- CASTLab, Department of Statistics, Universidade Federal de Pernambuco, Recife, Pernambuco 51280-000 Brazil
| | - Enrique García-Ceja
- Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, 64849 Monterrey, Nuevo León Mexico
| | - Juan C. Correa
- CESA Business School, Bogotá, Bogotá, DC, 110231 Colombia
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