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Bian Z, Wang B, Wu X, Wang K, Jiang Y. Development and Validation of Paradigms Based on the Global-First Topological Approach for Alzheimer's Disease Severity Staging. Neuropsychiatr Dis Treat 2024; 20:1225-1234. [PMID: 38883415 PMCID: PMC11178089 DOI: 10.2147/ndt.s460421] [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: 01/19/2024] [Accepted: 05/23/2024] [Indexed: 06/18/2024] Open
Abstract
Introduction Conventional methods like patient history, neuropsychological testing, cerebrospinal fluid examination, and magnetic resonance imaging are widely used to diagnose cases in the current clinical setting but are limited in classifying Alzheimer's disease (AD) stages. Patients with AD exhibit visual perception deficits, which may be a potential target to assess the severity of the disease according to visual paradigms. However, owing to the inconsistent forms of perceived objects, the defects of current visual processing paradigms often lead to inconsistent results and a lack of sensitivity and specificity. Methods We develop two paradigms based on global-first topological approach of visual perception, which avoids inconsistent results and lack of sensitivity and specificity owing to the inconsistent forms of perceived objects in traditional paradigms, delineate a unique detection strategy from perception organization (Experiment 1) and visual working memory (VWM) (Experiment 2). Results Except for the significant differences of the reaction times (RTs) between groups, significant differences were found when AD subjects recognize small figures due to the consistency of global and local figures in similarity test. The difference of RTs between recognizing global and local figures can be recognized in AD and mild cognitive impairment (MCI) group compared to healthy elderly (HE) in similarity test (Experiment 1). The memory capacity of AD patients was significantly lower than MCI group. Topological interference effect was observed in MCI and HE group, whereas MCI patients may have a greater difference trend in non-topological and topological changes than HE (Experiment 2). Conclusion Our paradigms provide a new strategy, which can assist clinical severity staging and linking topological approach of visual perception with pathophysiological processes in AD.
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Affiliation(s)
- Zhida Bian
- Anhui Medical University School of Basic Medicine, Hefei, 230032, People's Republic of China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, People's Republic of China
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, People's Republic of China
| | - Bo Wang
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, People's Republic of China
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, People's Republic of China
| | - Xingqi Wu
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, People's Republic of China
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230022, People's Republic of China
| | - Kai Wang
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, 230022, People's Republic of China
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, 230022, People's Republic of China
- Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Hefei, 230022, People's Republic of China
- The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, 230032, People's Republic of China
| | - Yi Jiang
- Anhui Medical University School of Basic Medicine, Hefei, 230032, People's Republic of China
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Psychology, Chinese Academy of Sciences, Beijing, People's Republic of China
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Butters E, Srinivasan S, O'Brien JT, Su L, Bale G. A promising tool to explore functional impairment in neurodegeneration: A systematic review of near-infrared spectroscopy in dementia. Ageing Res Rev 2023; 90:101992. [PMID: 37356550 DOI: 10.1016/j.arr.2023.101992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/15/2023] [Accepted: 06/22/2023] [Indexed: 06/27/2023]
Abstract
This systematic review aimed to evaluate previous studies which used near-infrared spectroscopy (NIRS) in dementia given its suitability as a diagnostic and investigative tool in this population. From 800 identified records which used NIRS in dementia and prodromal stages, 88 studies were evaluated which employed a range of tasks testing memory (29), word retrieval (24), motor (8) and visuo-spatial function (4), and which explored the resting state (32). Across these domains, dementia exhibited blunted haemodynamic responses, often localised to frontal regions of interest, and a lack of task-appropriate frontal lateralisation. Prodromal stages, such as mild cognitive impairment, revealed mixed results. Reduced cognitive performance accompanied by either diminished functional responses or hyperactivity was identified, the latter suggesting a compensatory response not present at the dementia stage. Despite clear evidence of alterations in brain oxygenation in dementia and prodromal stages, a consensus as to the nature of these changes is difficult to reach. This is likely partially due to the lack of standardisation in optical techniques and processing methods for the application of NIRS to dementia. Further studies are required exploring more naturalistic settings and a wider range of dementia subtypes.
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Affiliation(s)
- Emilia Butters
- Department of Electrical Engineering, University of Cambridge, 9 JJ Thomson Avenue, Cambridge CB3 0FA, UK; Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
| | - Sruthi Srinivasan
- Department of Electrical Engineering, University of Cambridge, 9 JJ Thomson Avenue, Cambridge CB3 0FA, UK
| | - John T O'Brien
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Li Su
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Department of Neuroscience, University of Sheffield, 385a Glossop Rd, Broomhall, Sheffield S10 2HQ, UK
| | - Gemma Bale
- Department of Physics, University of Cambridge, 19 JJ Thomson Avenue, Cambridge CB3 0FA, UK
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Neuropsychology of posteromedial parietal cortex and conversion factors from Mild Cognitive Impairment to Alzheimer's disease: systematic search and state-of-the-art review. Aging Clin Exp Res 2022; 34:289-307. [PMID: 34232485 PMCID: PMC8847304 DOI: 10.1007/s40520-021-01930-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 06/28/2021] [Indexed: 02/06/2023]
Abstract
In the present review, we discuss the rationale and the clinical implications of assessing visuospatial working memory (VSWM), awareness of memory deficits, and visuomotor control in patients with mild cognitive impairment (MCI). These three domains are related to neural activity in the posteromedial parietal cortex (PMC) whose hypoactivation seems to be a significant predictor of conversion from MCI to Alzheimer’s disease (AD) as indicated by recent neuroimaging evidence. A systematic literature search was performed up to May 2021. Forty-eight studies were included: 42 studies provided analytical cross-sectional data and 6 studies longitudinal data on conversion rates. Overall, these studies showed that patients with MCI performed worse than healthy controls in tasks assessing VSWM, awareness of memory deficits, and visuomotor control; in some cases, MCI patients’ performance was comparable to that of patients with overt dementia. Deficits in VSWM and metamemory appear to be significant predictors of conversion. No study explored the relationship between visuomotor control and conversion. Nevertheless, it has been speculated that the assessment of visuomotor abilities in subjects at high AD risk might be useful to discriminate patients who are likely to convert from those who are not. Being able to indirectly estimate PMC functioning through quick and easy neuropsychological tasks in outpatient settings may improve diagnostic and prognostic accuracy, and therefore, the quality of the MCI patient’s management.
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From 2D to VR Film: A Research on the Load of Different Cutting Rates Based on EEG Data Processing. INFORMATION 2021. [DOI: 10.3390/info12030130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Focusing on virtual reality (VR) and film cutting, this study compared and evaluated the effect of visual mode (2D, VR) and cutting rate (fast, medium, slow) on a load, to make an attempt for VR research to enter the cognitive field. This study uses a 2 × 3 experimental research design. Forty participants were divided into one of two groups randomly and watched films with three cutting rates. The subjective and objective data were collected during the experiment. The objective results confirm that VR films bring more powerful alpha, beta, theta wave activities, and bring a greater load. The subjective results confirm that the fast cutting rate brings a greater load. These results provide a theoretical support for further exploring the evaluation methods and standards of VR films and improving the viewing experience in the future.
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Lim LG, Ung WC, Chan YL, Lu CK, Sutoko S, Funane T, Kiguchi M, Tang TB. A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex. IEEE Trans Neural Syst Rehabil Eng 2020; 28:2367-2376. [PMID: 32986555 DOI: 10.1109/tnsre.2020.3026991] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.
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