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Alzheimer’s disease (AD) is a disease that gradually develops and causes degeneration of the cells of the brain. The leading cause of AD is dementia that results in a person’s inability to work independently. In the early stages of AD, a person forgets recent conversations or the occurrence of an event. In the later stages, there could be severe loss of memory such that the person is not able to even perform everyday tasks. The medicines currently available for AD may improve its symptoms on a temporary basis in the early stage of the disease. Since no treatment is available for curing AD, its detection becomes extremely important. As the clinical treatments are very expensive, the need for automated diagnosis of AD is of critical importance. In this paper, a deep learning model based on a convolutional neural network has been used and applied to four classes of images of AD that is very mild demented, mild demented, average demented, and non-demented. It was found that the moderate demented class had the highest accuracy of 98.9%, a classification error rate of 0.01, and a specificity of 0.992. Also, the lowest false positive rate of 0.007 was obtained.

Alzheimer’s Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.

Aim of this study was to explore the EEG functional connectivity in amnesic mild cognitive impairments (MCI) subjects with multidomain impairment in order to characterize the Default Mode Network (DMN) in converted MCI (cMCI), which converted to Alzheimer’s disease (AD), compared to stable MCI (sMCI) subjects. A total of 59 MCI subjects were recruited and divided -after appropriate follow-up- into cMCI or sMCI. They were further divided in MCI with linguistic domain (LD) impairment and in MCI with executive domain (ED) impairment. Small World (SW) index was measured as index of balance between integration and segregation brain processes. SW, computed restricting to nodes of DMN regions for all frequency bands, evaluated how they differ between MCI subgroups assessed through clinical and neuropsychological four-years follow-up. In addition, SW evaluated how this pattern differs between MCI with LD and MCI with ED. Results showed that SW index significantly decreased in gamma band in cMCI compared to sMCI. In cMCI with LD impairment, the SW index significantly decreased in delta band, while in cMCI with ED impairment the SW index decreased in delta and gamma bands and increased in alpha1 band. We propose that the DMN functional alterations in cognitive impairment could reflect an abnormal flow of brain information processing during resting state possibly associated to a status of pre-dementia.

Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer’s dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal decomposition-based approaches such as empirical mode decomposition (EMD), ensemble EMD (EEMD), and discrete wavelet transform (DWT) are presented to classify EEG segments of control subjects (CSs) and AD patients. Intrinsic mode functions (IMFs) are obtained from the signals using the EMD and EEMD methods, and the IMFs showing the most significant differences between the two groups are selected by applying previously suggested selection procedures. Five-time-domain and 5-spectral-domain features are calculated using selected IMFs, and five detail and approximation coefficients of DWT. Signal decomposition processes are conducted for both 1 min and 5 s EEG segment durations. For the 1 min segment duration, all the proposed approaches yield prominent classification performances. While the highest classification accuracies are obtained using EMD (91.8%) and EEMD (94.1%) approaches from the temporal/right brain cluster, the highest classification accuracy for the DWT (95.2%) approach is obtained from the temporal/left brain cluster for 1 min segment duration.

Instrumented gait analysis allows for the identification of walking parameters to predict cognitive decline and the worsening of dementia. The aim of this study was to perform a meta-analysis to better clarify which gait parameters are affected or modified with the progression of the dementia in a larger sample, as well as which gait assessment conditions (single-task or dual-task conditions) would be more sensitive to reflect the influence of dementia. Literature searches were conducted with the keywords “quantitative gait” OR “gait analysis” AND “dementia” AND “single-task” AND “dual-task,” and for “quantitative gait” OR “gait analysis” AND “dementia” AND “fall risk” on PubMed, EMBASE, the Cochrane Library, Scopus, and Web of Science. The results were used to perform a systematic review focussing on instrumental quantitative assessment of the walking of patients with dementia, during both single and dual tasks. The search was performed independently by two authors (C. R. and C. M.) from January 2018 to April 2020 using the PICOS criteria. Nine publications met the inclusion criteria and were included in the systematic review. Our meta-analysis showed that during a single task, most of the spatiotemporal parameters of gait discriminated best between patients with dementia and healthy controls, including speed, cadence, stride length, stride time, stride time variability, and stance time. In dual tasks, only speed, stride length, and stride time variability discriminated between the two groups. In addition, compared with spatial parameters (e.g. stride length), some temporal gait parameters were more correlated to the risk of falls during the comfortable walking in a single task, such as cadence, stride time, stride time variability, and stance time. During a dual task, only the variability of stride time was associated with the risk of falls.

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt–Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer’s Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

Computer-aided diagnosis of health problems and pathological conditions has become a substantial part of medical, biomedical, and computer science research. This paper focuses on the diagnosis of early and progressive dementia, building on the potential of deep learning (DL) models. The proposed computational framework exploits a magnetic resonance imaging (MRI) brain asymmetry biomarker, which has been associated with early dementia, and employs DL architectures for MRI image classification. Identification of early dementia is accomplished by an eight-layered convolutional neural network (CNN) as well as transfer learning of pretrained CNNs from ImageNet. Different instantiations of the proposed CNN architecture are tested. These are equipped with Softmax, support vector machine (SVM), linear discriminant (LD), or $k$ -nearest neighbor (KNN) classification layers, assembled as a separate classification module, which are attached to the core CNN architecture. The initial imaging data were obtained from the MRI directory of the Alzheimer’s disease neuroimaging initiative 3 (ADNI3) database. The independent testing dataset was created using image preprocessing and segmentation algorithms applied to unseen patients’ imaging data. The proposed approach demonstrates a 90.12% accuracy in distinguishing patients who are cognitively normal subjects from those who have Alzheimer’s disease (AD), and an 86.40% accuracy in detecting early mild cognitive impairment (EMCI).

The analysis of biological fluctuations provides an excellent route to probe the underlying mechanisms in maintaining internal homeostasis of the body, especially under the challenges of the ever-changing environment or disease processes. However, the features of nonlinearity and nonstationarity in physiological time series limit the reliability of the conventional analysis. Hilbert–Huang transform (HHT), based on nonlinear theory, is an innovative approach to extract the dynamic information at different time scales, in particular, from nonstationary signals. In this paper, HHT is introduced to analyze the alpha waves of human's electroencephalography (EEG), which seemly oscillate regularly between 8 and 12 Hz in healthy subject but getting irregular or disappeared in different demented status. Furthermore, conventional time–frequency analyses are adopted to collate the results from those methods and HHT. Finally, the potential usages of HHT are demonstrated in characterizing the biological signals qualitatively and quantitatively, including stationarity analysis, instantaneous frequency and amplitude modulation or correlation analysis. Such applications on EEG have successively disclosed the differences of alpha rhythms between normal and demented brains and the nonlinear characteristics of the underlying mechanisms. Hopefully, in addition to empower the studies of EEG varied in diseased, aging, and physiological processes, these methods might find other applications in EEG analysis.

Background: There is a growing evidence on the benefits of exercise for older people living with dementia in developed countries. However, cultural, health-care systems and environmental differences may impact on the uptake of exercise and outcomes in different regions of the world.

Objective: This study synthesised the available evidence examining the effectiveness of exercise interventions on improving physical function and reducing behavioural symptoms in community-dwelling older people living with dementia in Asia, and the impact on their informal carers.

Methods: Six databases were searched to November 2021. Randomised controlled trials (RCTs) or quasi-experimental studies evaluating exercise interventions for community-dwelling older people with dementia living in Asia were included. The Cochrane risk-of-bias tool for randomised trials and Downs and Black checklist had been used to assess methodological quality of the studies. Meta-analyses using a fixed effects model assessed the effects of exercise interventions where sufficient data were available. Mean difference (MD) with 95% confidence interval (CI) was used to pool results.

Results: Nine studies (five RCTs) were included (Hong Kong-4, China-1, South Korea-2, Taiwan-1, Indonesia-1). Exercise improved dynamic balance [Functional Reach (2 studies, $n=111$ people with dementia), $\mathrm{\text{MD}}=2.61$, 95% CI (1.55, 3.67)], but not for the Berg Balance Scale ($\mathrm{\text{MD}}=1.10$, 95% CI [$-$2.88, 5.07]), Timed Up and Go ($\mathrm{\text{MD}}=-3.47$, 95% CI [$-$7.27, 0.33]) and 5 times sit to stand tests ($\mathrm{\text{MD}}=-1.86$, 95% CI [$-$5.27, 1.54]). Single studies where data could not be pooled showed no effect of exercise on behavioural symptoms or impact on informal carers.

Conclusion: Exercise appeared to have a beneficial effect on improving balance performance among older people with dementia living in Asia, however, this evidence is limited and inconsistent, and should be interpreted with caution. Further high-quality large RCTs are necessary for advancing the evidence base of exercise interventions for this population.

The pathogenesis of Alzheimer’s disease (AD), a degenerative disease of the central nervous system, remains unclear. The main manifestations of AD include cognitive and behavioral disorders, neuropsychiatric symptoms, neuroinflammation, amyloid plaques, and neurofibrillary tangles. However, current drugs for AD once the dementia stage has been reached only treat symptoms and do not delay progression, and the research and development of targeted drugs for AD have reached a bottleneck. Thus, other treatment options are needed. Bioactive ingredients derived from plants are promising therapeutic agents. Specifically, Ginkgo biloba (Gb) extracts exert anti-oxidant, anticancer, neuroplastic, neurotransmitter-modulating, blood fluidity, and anti-inflammatory effects, offering alternative options in the treatment of cardiovascular, metabolic, and neurodegenerative diseases. The main chemical components of Gb include flavonoids, terpene lactones, proanthocyanidins, organic acids, polysaccharides, and amino acids. Gb and its extracts have shown remarkable therapeutic effects on various neurodegenerative diseases, including AD, with few adverse reactions. Thus, high-quality Gb extracts are a well-established treatment option for AD. In this review, we summarize the insights derived from traditional Chinese medicine, experimental models, and emerging clinical trials on the role of Gb and its chemical components in the treatment of the main clinical manifestations of AD.

This paper reviews missing person incidents that occurred in older persons with dementia reported in local newspapers from 1 January 1999 to 1 May 2002. Ten relevant incidents, two of which happened to the same person, were found in WiseNews, an electronic database of 21 local newspapers. There were four (44%) males and five (56%) females with a mean age of 77 (standard deviation=5). In six (60%) cases, the missing persons eloped from home and four (40%) outside of home. Nine (90%) of the cases required less than a day to three days to locate the missing person. Six (60%) cases resulted in injuries or death due to falls or traffic accidents. It is not uncommon for dementia patients to get lost. Public awareness and understanding of the phenomenon is very important because the survival of missing persons depends on the implementation of timely and effective search and rescue. This analysis confirms the need for a prospective study to further examine the characteristics of missing older adults and missing incidents, as well as the search strategies adopted by caregivers.

这篇文章旨在回顾由一九九九年一月一日至二零零二年五月一日以来在本地报章上报导过的有关老年痴呆症患者走失的个案。WiseNews是一个包含二十一份本地报章的电子资料库。我们通过WiseNews搜索到十宗有关的报导，其中两宗个案发生在同一个患者身上。走失患者当中有四名男性，五名女性，年龄由七十到八十六岁［平均数：七十七］。在六宗［六成］走失个案当中，患者是从居所走失的，而另外四宗［四成］则发生在居所以外的地方。九宗［九成］个案需要少於一天至三天的时间去寻回走失患者。六宗［六成］个案涉及受伤或死亡。走失现象在老年痴呆症患者当中颇为普遍，走失患者的安危取决於及时和有效率的搜索及拯救策略。因此，大众需要对这现象有更多的了解。本文的分析显示本地需要对此问题作前瞻性的调查，使本地社会及健康服务从业员能掌握走失痴呆症患者的特徵，一般走失的情况及照顾者面对这问题的策略，从而提供合宜的协助。