114 research outputs found

    Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

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    The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

    Recognition and treatment of autoimmune encephalitis:Focus on the elderly patient

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    Recognition and treatment of autoimmune encephalitis:Focus on the elderly patient

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    Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data

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    This work was supported by the FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto (B-TIC-586-UGR20); the MCIN/AEI/10.13039/501100011033/ and FEDER \Una manerade hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion,Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18 and P20-00525 projects. Grant by F.J.M.M. RYC2021-030875-I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. Work by D.C.B. is supported by the MCIN/AEI/FJC2021-048082-I Juan de la Cierva Formacion'. Work by J.E.A. is supported by Next Generation EU Fund through a Margarita Salas Grant, and work by C.J.M. is supported by Ministerio de Universidades under the FPU18/04902 grant.Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-586-UGR20MCIN/AEI P20-00525FEDER \Una manerade hacer Europa RYC2021-030875-IJunta de AndaluciaEuropean Union (EU) Spanish Government RTI2018-098913-B100, CV20-45250, A-TIC-080-UGR18European Union (EU)Juan de la Cierva FormacionNext Generation EU Fund through a Margarita Salas GrantMinisterio de Universidades FPU18/0490

    An neuroscience approach to investigate creativity in engineers with the effects of indoor environment quality (IEQ)

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    Investigations of creativity have been an intriguing topic for a long time, but assessing creativity is extremely complex. Creativity is a cornerstone of engineering disciplines, so understanding creativity and how to enhance creative abilities through engineering education has received substantial attention. Fields outside of engineering are no stranger to neuro-investigations of creativity and although some neuro-response studies have been conducted to understand creativity in engineering, these studies need to map the engineering design and concept generation processes better. Using neuroimaging techniques alongside engineering design and concept generation processes is necessary for understanding how to improve creativity studies in engineering. Recently, a growing number of studies have revealed that some types of indoor environmental stimuli can enhance human creativity. Further, for generating creative ideas temporal dynamics of cognitive processes are critical. However, how the temporal dynamics of creativity are influenced by the indoor environment remains unclear. This research found that each stage of the temporal dynamics of creativity may be differently correlated with neural function. Further, indoor environmental factors may have various, and sometimes contrasting, effects on the temporal dynamics of creativity. Despite recent progress, there are significant gaps in understanding the effects of indoor environmental quality (IEQ), especially air quality and factors related to visual, thermal and acoustic comfort that are closely tied to performance on cognitive tasks. This is due to the lack of understanding of the effects of IEQ on human physiological and neural responses. Nonetheless, this is the first study to clarify the influence of indoor environmental settings on the temporal dynamics of creativity from the perspective of neuroscience

    Assessment of autonomic symptoms may assist with early identification of mild cognitive impairment with Lewy bodies

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    Funder: GE Healthcare; Id: http://dx.doi.org/10.13039/100006775Funder: Alzheimer's Research UK; Id: http://dx.doi.org/10.13039/501100002283Funder: NIHR Newcastle Biomedical Research Centre; Id: http://dx.doi.org/10.13039/501100012295Abstract: Objectives: Autonomic symptoms are a common feature of the synucleinopathies, and may be a distinguishing feature of prodromal Lewy body disease. We aimed to assess whether the cognitive prodrome of dementia with Lewy bodies, mild cognitive impairment (MCI) with Lewy bodies (MCI‐LB), would have more severe reported autonomic symptoms than cognitively healthy older adults, with MCI due to Alzheimer's disease (MCI‐AD) also included for comparison. We also aimed to assess the utility of an autonomic symptom scale in differentiating MCI‐LB from MCI‐AD. Methods: Ninety‐three individuals with MCI and 33 healthy controls were assessed with the Composite Autonomic Symptom Score 31‐item scale (COMPASS). Mild cognitive impairment patients also underwent detailed clinical assessment and differential classification of MCI‐AD or MCI‐LB according to current consensus criteria. Differences in overall COMPASS score and individual symptom sub‐scales were assessed, controlling for age. Results: Age‐adjusted severity of overall autonomic symptomatology was greater in MCI‐LB (Ratio = 2.01, 95% CI: 1.37–2.96), with higher orthostatic intolerance and urinary symptom severity than controls, and greater risk of gastrointestinal and secretomotor symptoms. MCI‐AD did not have significantly higher autonomic symptom severity than controls overall. A cut‐off of 4/5 on the COMPASS was sensitive to MCI‐LB (92%) but not specific to this (42% specificity vs. MCI‐AD and 52% vs. healthy controls). Conclusions: Mild cognitive impairment with Lewy bodies had greater autonomic symptom severity than normal ageing and MCI‐AD, but such autonomic symptoms are not a specific finding. The COMPASS‐31 may therefore have value as a sensitive screening test for early‐stage Lewy body disease

    Cerebral Small Vessel Disease and Cerebral Amyloid Angiopathy

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    Sporadic cerebral small vessel disease (SVD) is considered to be among the most commonly known neuropathological processes in the brain, hosting a crucial role in stroke, cognitive impairment, and functional loss in elderly subjects. We investigated clinical (neuroimaging and cognitive) biomarkers in the SVD, through a series of analyses from our five studies. Sporadic cerebral SVD is a complex ‘micro-world’ to be globally considered. All the relevant lesion types and SVD neuroimaging burden should be taken into account. The cumulative effects of microangiopathy burden in the brain of patients affected by SVD are crucial. Cognitive rehabilitation could represent a promising approach to prevent vascular dementia or to improve cognitive performances in patients with cerebral SVD. Longitudinal studies may provide more robust information about the progression and prognostic significance of our findings
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