Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases
Neurodegenerative diseases (NDs), such as Alzheimer’s, Parkinson’s, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1089 |
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| author | Leonardo Ariel Cano Ana Lía Albarracín Alvaro Gabriel Pizá Cecilia Elisabet García-Cena Eduardo Fernández-Jover Fernando Daniel Farfán |
| author_facet | Leonardo Ariel Cano Ana Lía Albarracín Alvaro Gabriel Pizá Cecilia Elisabet García-Cena Eduardo Fernández-Jover Fernando Daniel Farfán |
| author_sort | Leonardo Ariel Cano |
| collection | DOAJ |
| description | Neurodegenerative diseases (NDs), such as Alzheimer’s, Parkinson’s, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject’s available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>17</mn></mrow></semantics></math></inline-formula> healthy volunteers (9 males, 8 females, age <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>28.66</mn><mo>±</mo><mn>8.8</mn></mrow></semantics></math></inline-formula> years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease. |
| first_indexed | 2024-03-07T22:14:55Z |
| format | Article |
| id | doaj.art-63bf4e533ddb4ec793a234b143063423 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:14:55Z |
| publishDate | 2024-02-01 |
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| record_format | Article |
| series | Sensors |
| spelling | doaj.art-63bf4e533ddb4ec793a234b1430634232024-02-23T15:33:32ZengMDPI AGSensors1424-82202024-02-01244108910.3390/s24041089Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative DiseasesLeonardo Ariel Cano0Ana Lía Albarracín1Alvaro Gabriel Pizá2Cecilia Elisabet García-Cena3Eduardo Fernández-Jover4Fernando Daniel Farfán5Neuroscience and Applied Technologies Laboratory (LINTEC), Bioengineering Department, Faculty of Exact Sciences and Technology (FACET), National University of Tucuman, Superior Institute of Biological Research (INSIBIO), National Scientific and Technical Research Council (CONICET), Av. Independencia 1800, San Miguel de Tucuman 4000, ArgentinaNeuroscience and Applied Technologies Laboratory (LINTEC), Bioengineering Department, Faculty of Exact Sciences and Technology (FACET), National University of Tucuman, Superior Institute of Biological Research (INSIBIO), National Scientific and Technical Research Council (CONICET), Av. Independencia 1800, San Miguel de Tucuman 4000, ArgentinaNeuroscience and Applied Technologies Laboratory (LINTEC), Bioengineering Department, Faculty of Exact Sciences and Technology (FACET), National University of Tucuman, Superior Institute of Biological Research (INSIBIO), National Scientific and Technical Research Council (CONICET), Av. Independencia 1800, San Miguel de Tucuman 4000, ArgentinaETSIDI-Center for Automation and Robotics, Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, SpainInstitute of Bioengineering, Universidad Miguel Hernández of Elche, 03202 Elche, SpainNeuroscience and Applied Technologies Laboratory (LINTEC), Bioengineering Department, Faculty of Exact Sciences and Technology (FACET), National University of Tucuman, Superior Institute of Biological Research (INSIBIO), National Scientific and Technical Research Council (CONICET), Av. Independencia 1800, San Miguel de Tucuman 4000, ArgentinaNeurodegenerative diseases (NDs), such as Alzheimer’s, Parkinson’s, amyotrophic lateral sclerosis, and frontotemporal dementia, among others, are increasingly prevalent in the global population. The clinical diagnosis of these NDs is based on the detection and characterization of motor and non-motor symptoms. However, when these diagnoses are made, the subjects are often in advanced stages where neuromuscular alterations are frequently irreversible. In this context, we propose a methodology to evaluate the cognitive workload (CWL) of motor tasks involving decision-making processes. CWL is a concept widely used to address the balance between task demand and the subject’s available resources to complete that task. In this study, multiple models for motor planning during a motor decision-making task were developed by recording EEG and EMG signals in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>17</mn></mrow></semantics></math></inline-formula> healthy volunteers (9 males, 8 females, age <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>28.66</mn><mo>±</mo><mn>8.8</mn></mrow></semantics></math></inline-formula> years). In the proposed test, volunteers have to make decisions about which hand should be moved based on the onset of a visual stimulus. We computed functional connectivity between the cortex and muscles, as well as among muscles using both corticomuscular and intermuscular coherence. Despite three models being generated, just one of them had strong performance. The results showed two types of motor decision-making processes depending on the hand to move. Moreover, the central processing of decision-making for the left hand movement can be accurately estimated using behavioral measures such as planning time combined with peripheral recordings like EMG signals. The models provided in this study could be considered as a methodological foundation to detect neuromuscular alterations in asymptomatic patients, as well as to monitor the process of a degenerative disease.https://www.mdpi.com/1424-8220/24/4/1089neurodegenerative diseasescognitive workloadstatistical modelingmotor planningdecision-makingfunctional connectivity |
| spellingShingle | Leonardo Ariel Cano Ana Lía Albarracín Alvaro Gabriel Pizá Cecilia Elisabet García-Cena Eduardo Fernández-Jover Fernando Daniel Farfán Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases Sensors neurodegenerative diseases cognitive workload statistical modeling motor planning decision-making functional connectivity |
| title | Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases |
| title_full | Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases |
| title_fullStr | Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases |
| title_full_unstemmed | Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases |
| title_short | Assessing Cognitive Workload in Motor Decision-Making through Functional Connectivity Analysis: Towards Early Detection and Monitoring of Neurodegenerative Diseases |
| title_sort | assessing cognitive workload in motor decision making through functional connectivity analysis towards early detection and monitoring of neurodegenerative diseases |
| topic | neurodegenerative diseases cognitive workload statistical modeling motor planning decision-making functional connectivity |
| url | https://www.mdpi.com/1424-8220/24/4/1089 |
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