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Andreatta Maduro P, Guimarães MP, de Sousa Rodrigues M, Pereira Rolim Coimbra Pinto AP, da Mota Junior AA, Lima Rocha AS, Matoso JMD, Bavaresco Gambassi B, Schwingel PA. Comparing the Efficacy of Two Cognitive Screening Tools in Identifying Gray and White Matter Brain Damage among Older Adults. J Aging Res 2024; 2024:5527225. [PMID: 38690079 PMCID: PMC11060871 DOI: 10.1155/2024/5527225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Revised: 03/19/2024] [Accepted: 04/12/2024] [Indexed: 05/02/2024] Open
Abstract
Background Ageing is associated with structural changes in brain regions and functional decline in cognitive domains. Noninvasive tools for identifying structural damage in the brains of older adults are relevant for early treatment. Aims This study aims to evaluate and compare the accuracy of the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA©) in identifying gray and white matter brain damage in older individuals with varying degrees of cognitive impairment. Methods Ninety older adults (62 women) with an average age of 69 ± 7 years were enrolled and categorized as having no cognitive impairment (NCI), mild cognitive impairment (MCI), or moderate cognitive impairment (MoCI). Magnetic resonance imaging (MRI) was utilized to assess the number, volume, and distribution of brain damage. The Fazekas and Scheltens scales were applied to the brain MRIs, and inferential statistics were employed to compare variables among the groups. Results Cognitive impairment was observed in 56.7% of the participants (95% confidence interval (CI): 46.4-66.4%), with thirty-six older adults (40%) classified as MCI and 15 (17%) as MoCI. Cognitive impairment and medial temporal lobe (MTL) atrophy were found to be associated (p=0.001), exhibiting higher mean volume scales of the MTL atrophied area in the MoCI group (p < 0.001). The MMSE accurately revealed MTL atrophy based on the Scheltens (p < 0.05) and Fazekas (p < 0.05) scales. At the same time, the MoCA accurately identified periventricular white matter (PWM) abnormalities according to the Fazekas scale (p < 0.05). Conclusions The MMSE and MoCA screening tools effectively identified gray and white matter brain damage in older adults with varying degrees of cognitive impairment. Lower MMSE scores are associated with MTL atrophy and lesions, and lower MoCA scores are related to PWM lesions. The concurrent use of MMSE and MoCA is recommended for assessing structural changes in distinct brain regions.
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Affiliation(s)
- Paula Andreatta Maduro
- Post-Graduation Program in Health Sciences (PPGCS), University of Pernambuco (UPE), Recife, PE 50100-130, Brazil
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- University Hospital of the Federal University of Vale do São Francisco (HU-UNIVASF), Brazilian Hospital Services Company (EBSERH), Petrolina, PE 56304-205, Brazil
| | | | - Mateus de Sousa Rodrigues
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- University Hospital of the Federal University of Vale do São Francisco (HU-UNIVASF), Brazilian Hospital Services Company (EBSERH), Petrolina, PE 56304-205, Brazil
| | - Ana Paula Pereira Rolim Coimbra Pinto
- University Hospital of the Federal University of Vale do São Francisco (HU-UNIVASF), Brazilian Hospital Services Company (EBSERH), Petrolina, PE 56304-205, Brazil
| | - Américo Alves da Mota Junior
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- University Hospital of the Federal University of Vale do São Francisco (HU-UNIVASF), Brazilian Hospital Services Company (EBSERH), Petrolina, PE 56304-205, Brazil
| | - Alaine Souza Lima Rocha
- Post-Graduation Program in Health Sciences (PPGCS), University of Pernambuco (UPE), Recife, PE 50100-130, Brazil
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- Department of Physical Therapy, Federal University of Ceará (UFC), Fortaleza, CE 60430-450, Brazil
| | - Juliana Magalhães Duarte Matoso
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- Department of Clinical Medicine, Pedro Ernesto University Hospital, State University of Rio de Janeiro (UERJ), Rio de Janeiro, RJ 20551-030, Brazil
| | - Bruno Bavaresco Gambassi
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
- Post-Graduation Program in Management of Health Programs and Services (PPGGPSS), CEUMA University (UNICEUMA), São Luís, MA 65075-120, Brazil
| | - Paulo Adriano Schwingel
- Post-Graduation Program in Health Sciences (PPGCS), University of Pernambuco (UPE), Recife, PE 50100-130, Brazil
- Human Performance Research Laboratory (LAPEDH), UPE, Petrolina, PE 56328-900, Brazil
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Formica C, Bonanno L, Giambò FM, Maresca G, Latella D, Marra A, Cucinotta F, Bonanno C, Lombardo M, Tomarchio O, Quartarone A, Marino S, Calabrò RS, Lo Buono V. Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders. J Pers Med 2023; 13:1386. [PMID: 37763152 PMCID: PMC10533011 DOI: 10.3390/jpm13091386] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Alzheimer's disease (AD) is the most common form of neurodegenerative disorder. The prodromal phase of AD is mild cognitive impairment (MCI). The capacity to predict the transitional phase from MCI to AD represents a challenge for the scientific community. The adoption of artificial intelligence (AI) is useful for diagnostic, predictive analysis starting from the clinical epidemiology of neurodegenerative disorders. We propose a Machine Learning Model (MLM) where the algorithms were trained on a set of neuropsychological, neurophysiological, and clinical data to predict the diagnosis of cognitive decline in both MCI and AD patients. METHODS We built a dataset with clinical and neuropsychological data of 4848 patients, of which 2156 had a diagnosis of AD, and 2684 of MCI, for the Machine Learning Model, and 60 patients were enrolled for the test dataset. We trained an ML algorithm using RoboMate software based on the training dataset, and then calculated its accuracy using the test dataset. RESULTS The Receiver Operating Characteristic (ROC) analysis revealed that diagnostic accuracy was 86%, with an appropriate cutoff value of 1.5; sensitivity was 72%; and specificity reached a value of 91% for clinical data prediction with MMSE. CONCLUSION This method may support clinicians to provide a second opinion concerning high prognostic power in the progression of cognitive impairment. The MLM used in this study is based on big data that were confirmed in enrolled patients and given a credibility about the presence of determinant risk factors also supported by a cognitive test score.
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Affiliation(s)
- Caterina Formica
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Lilla Bonanno
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Fabio Mauro Giambò
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Giuseppa Maresca
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Desiree Latella
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Angela Marra
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Fabio Cucinotta
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Carmen Bonanno
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | | | - Orazio Tomarchio
- Department of Electrical Engineering, Electronics and Computer Science, University of Catania, 95131 Catania, Italy;
| | - Angelo Quartarone
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Silvia Marino
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Rocco Salvatore Calabrò
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
| | - Viviana Lo Buono
- IRCCS Centro Neurolesi “Bonino Pulejo”, 98124 Messina, Italy; (C.F.); (L.B.); (F.M.G.); (G.M.); (A.M.); (F.C.); (C.B.); (A.Q.); (S.M.); (R.S.C.); (V.L.B.)
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Incontri-Abraham D, Esparza-Salazar FJ, Ibarra A. Copolymer-1 as a potential therapy for mild cognitive impairment. Brain Cogn 2022; 162:105892. [PMID: 35841771 DOI: 10.1016/j.bandc.2022.105892] [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: 04/22/2022] [Accepted: 06/28/2022] [Indexed: 11/25/2022]
Abstract
Mild cognitive impairment (MCI) is a prodromal stage of memory impairment that may precede dementia. MCI is classified by the presence or absence of memory impairment into amnestic or non-amnestic MCI, respectively. More than 90% of patients with amnestic MCI who progress towards dementia meet criteria for Alzheimer's disease (AD). A combination of mechanisms promotes MCI, including intracellular neurofibrillary tangle formation, extracellular amyloid deposition, oxidative stress, neuronal loss, synaptodegeneration, cholinergic dysfunction, cerebrovascular disease, and neuroinflammation. However, emerging evidence indicates that neuroinflammation plays an important role in the pathogenesis of cognitive impairment. Unfortunately, there are currently no Food and Drug Administration (FDA)-approved drugs for MCI. Copolymer-1 (Cop-1), also known as glatiramer acetate, is a synthetic polypeptide of four amino acids approved by the FDA for the treatment of relapsing-remitting multiple sclerosis. Cop-1 therapeutic effect is attributed to immunomodulation, promoting a switch from proinflammatory to anti-inflammatory phenotype. In addition to its anti-inflammatory properties, it stimulates brain-derived neurotrophic factor (BDNF) secretion, a neurotrophin involved in neurogenesis and the generation of hippocampal long-term potentials. Moreover, BDNF levels are significantly decreased in patients with cognitive impairment. Therefore, Cop-1 immunization might promote synaptic plasticity and memory consolidation by increasing BDNF production in patients with MCI.
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Affiliation(s)
- Diego Incontri-Abraham
- Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México Campus Norte, Av. Universidad Anáhuac No. 46, Col. Lomas Anáhuac, Huixquilucan, CP 52786, Edo. de México, Mexico
| | - Felipe J Esparza-Salazar
- Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México Campus Norte, Av. Universidad Anáhuac No. 46, Col. Lomas Anáhuac, Huixquilucan, CP 52786, Edo. de México, Mexico
| | - Antonio Ibarra
- Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anáhuac México Campus Norte, Av. Universidad Anáhuac No. 46, Col. Lomas Anáhuac, Huixquilucan, CP 52786, Edo. de México, Mexico.
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Hartle L, Martorelli M, Balboni G, Souza R, Charchat-Fichman H. Diagnostic accuracy of CompCog: reaction time as a screening measure for mild cognitive impairment. ARQUIVOS DE NEURO-PSIQUIATRIA 2022; 80:570-579. [PMID: 35946705 PMCID: PMC9387195 DOI: 10.1590/0004-282x-anp-2021-0099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 08/23/2021] [Accepted: 08/31/2021] [Indexed: 06/15/2023]
Abstract
BACKGROUND Reaction time is affected under different neurological conditions but has not been much investigated considering all types of mild cognitive impairment (MCI). OBJECTIVE This study investigated the diagnostic accuracy of CompCog, a computerized cognitive screening battery focusing on reaction time measurements. METHODS A sample of 52 older adults underwent neuropsychological assessments, including CompCog, and medical appointments, to be classified as a control group or be diagnosed with MCI. The accuracy of CompCog for distinguishing between the two groups was calculated. RESULTS The results from diagnostic accuracy analyses showed that the AUCs of ROC curves were as high as 0.915 (CI 0.837-0.993). The subtest with the highest sensitivity and specificity (choice reaction time subtest) had 91.7% sensitivity and 89.3% specificity. The logistic regression final model correctly classified 92.3% of individuals, with 92.9% specificity and 91.7% sensitivity, and included only four variables from different subtests. CONCLUSIONS In summary, the study showed that reaction time assessed through CompCog is a good screening measure to differentiate between normal aging and MCI. Reaction time measurements in milliseconds were more accurate than correct answers. This test can form part of routine clinical tests to achieve the objectives of screening for MCI, indicating further procedures for investigation and diagnosis and planning interventions.
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Affiliation(s)
- Larissa Hartle
- Pontifícia Universidade Católica do Rio de Janeiro, Departamento de Psicologia, Rio de Janeiro RJ, Brazil
- Università degli Studi di Perugia, Dipartimento di Filosofia, scienze sociali, umane e della formazione, Perugia, Italia
| | - Marina Martorelli
- Pontifícia Universidade Católica do Rio de Janeiro, Departamento de Psicologia, Rio de Janeiro RJ, Brazil
| | - Giulia Balboni
- Università degli Studi di Perugia, Dipartimento di Filosofia, scienze sociali, umane e della formazione, Perugia, Italia
| | - Raquel Souza
- Pontifícia Universidade Católica do Rio de Janeiro, Departamento de Psicologia, Rio de Janeiro RJ, Brazil
| | - Helenice Charchat-Fichman
- Pontifícia Universidade Católica do Rio de Janeiro, Departamento de Psicologia, Rio de Janeiro RJ, Brazil
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Hartle L, Mograbi DC, Fichman HC, Faria CA, Sanchez MA, Ribeiro PCC, Lourenço RA. Predictors of functional impairment and awareness in people with dementia, mild cognitive impairment and healthy older adults from a middle-income country. Front Psychiatry 2022; 13:941808. [PMID: 35966468 PMCID: PMC9365969 DOI: 10.3389/fpsyt.2022.941808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE To investigate the demographic, clinical and cognitive correlates of functional capacity and its awareness in people with dementia (PwD; n = 104), mild cognitive impairment (PwMCI; n = 45) and controls (healthy older adults; n = 94) in a sample from a middle-income country. METHODS Dementia and MCI were diagnosed, respectively, with DSM-IV and Petersen criteria. Performance in activities of daily living (ADL) at three different levels [basic (The Katz Index of Independence), instrumental (Lawton instrumental ADL scale) and advanced (Reuben's advanced ADL scale)], measured through self- and informant-report, as well as awareness (discrepancy between self- and informant-report), were compared between groups. Stepwise regression models explored predictors of ADL and their awareness. RESULTS PwD showed impairment in all ADL levels, particularly when measured through informant-report. No differences were seen between controls and PwMCI regardless of measurement type. PwD differed in awareness of instrumental and basic, but not of advanced ADL, compared to controls. Age, gender, education and fluency were the most consistent predictors for ADL. Diagnosis was a significant predictor only for instrumental ADL. Awareness of basic ADL was predicted by memory, and awareness of instrumental ADL was predicted by general cognitive status, educational level, and diagnosis. CONCLUSION Results reinforce the presence of lack of awareness of ADL in PwD. Use of informant-reports and cognitive testing for fluency are suggested for the clinical assessment of ADL performance. Finally, assessment of instrumental ADL may be crucial for diagnostic purposes.
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Affiliation(s)
- Larissa Hartle
- Department of Psychology, Pontifícia Universidade Católica, Rio de Janeiro, Brazil
| | - Daniel C Mograbi
- Department of Psychology, Pontifícia Universidade Católica, Rio de Janeiro, Brazil.,Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Camila Assis Faria
- Department of Psychology, Pontifícia Universidade Católica, Rio de Janeiro, Brazil
| | - Maria Angélica Sanchez
- Department of Internal Medicine, Faculty of Medical Sciences, Rio de Janeiro State University, Rio de Janeiro, Brazil
| | - Pricila C C Ribeiro
- Department of Psychology, Faculty of Philosophy and Humanities, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Roberto Alves Lourenço
- Department of Internal Medicine, Faculty of Medical Sciences, Rio de Janeiro State University, Rio de Janeiro, Brazil.,Department of Medicine, Pontifícia Universidade Católica, Rio de Janeiro, Brazil
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