Recognition Of Mild Cognitive Impairment Research Articles

Identification of Mild cognitive impairment based on quadruple GCN model constructed with multiple features from higher-order brain connectivity

Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease, which is associated with abnormal brain proteins, the recognition of MCI being a challenging task. Recent studies have shown that the performance of MCI identification can be improved by combining protein features captured in Positron Emission Computed Tomography(PET). Nevertheless, there are still great challenges in extracting effective features from the vast amount of information. Most brain networks only considered the unilateral features of nodes or edges, ignored the interactions between them. In response to this problem, our study proposed to combine the quadruple Siamese network and GCN with self-attention pooling(QS-SAGCN) for MCI identification. In detail, we constructed the multiple protein features network(MPN) and higher-order MPN(MPHN) by PET images to promote the MCI identification. Furthermore, a pooling operation with self-attention mechanism was incorporated into GCN(SAGCN), which considered the node characteristics and topology in the graph network to facilitate the acquisition of robust biomarkers, simultaneously. Additionally we combined quadruple Siamese network with SAGCN as classification framework to improve the identification accuracy. Our proposed MCI identification method was evaluated on 230 subjects (including 117 MCI subjects, 113 normal control subjects) with both 18F-AV-1451 PET and 18F-AV-45 PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results showed that 1) QS-SAGCN enhanced the ability of feature identification, laying the foundation for obtaining more effective biomarkers for MCI patients; 2) The MCI identification accuracy (93.5%) was obtained by combining QS-SAGCN and higher-order network, indicating that the framework had advantages in mental disorders recognition. Finally, through comparison, the accuracy of our proposed MCI recognition method was superior to some of the existing state-of-the-art methods. Overall, the MCI identification method in this study was effective and promising to assist in the diagnosis of MCI in future clinical practice.

Online CME-certified Survey Identifies Sources for Clinical Practice Gaps Among Neurologists and PCPs Regarding the Early Recognition of Alzheimer’s Disease (P14-6.005)

<h3>Objective:</h3> The goal of this study was to gain insight into the gaps in confidence, knowledge, and competence regarding the recognition of early symptomatic AD among both PCPs and neurologists. <h3>Background:</h3> Alzheimer’s disease (AD) is the most commonly diagnosed form of dementia in the US. It is now recognized that the pathogenesis of AD starts years before the manifestation of clinical symptoms. As a result, there is a desire to recognize patients with AD as early as possible in order to improve outcomes. <h3>Design/Methods:</h3> A CME-certified clinical practice assessment consisting of 25 multiple-choice questions on knowledge, competence, and confidence was made available to a global audience of neurologists and PCPs. Learners were queried on the following topics: pathophysiology of early AD and clinical relevant tools for the identification of early AD. Data were collected from February 16, 2023 through May 2, 2022. <h3>Results:</h3> A total of 382 neurologists and 387 PCPs completed the survey. Approximately a third of neurologists and half of PCPs reported low confidence in relating to the recognition of mild cognitive impairment (MCI). When presented with a case-based question on diagnosis of MCI, 59% of neurologists and 48% of PCPs selected the correct answer. 64% of neurologists and 68% of PCPs correctly answered questions on the diagnosis of AD. Questions about AD-related biomarkers were correctly answered by 57% of neurologists and 49% of PCPs. Both physician groups performed the worst on questions relating to components of cognitive assessment scale and the pathophysiology of AD. Geographic location of the learners did not substantially impact the results. <h3>Conclusions:</h3> This study demonstrated that a high proportion of neurologists and PCPs can benefit from additional education on concepts related to the recognition of MCI and mild dementia due to AD. Neurologists and PCPs are currently unprepared to accurately identify early AD in clinical practice. <b>Disclosure:</b> Piyali Chatterjee has nothing to disclose. Lisette Arnaud-Hevi has received personal compensation for serving as an employee of Medscape.Dr. Finnegan has nothing to disclose. The institution of Dr. Cohen has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Alnylam. The institution of Dr. Cohen has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Biogen. The institution of Dr. Cohen has received personal compensation in the range of $0-$499 for serving as a Consultant for Diamond Therapeutics Inc.. The institution of Dr. Cohen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Roche. The institution of Dr. Cohen has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for ProMIS Neurosciences Inc.. The institution of Dr. Cohen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for RetiSpec Inc.. The institution of Dr. Cohen has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for College of Physicians and Surgeons of Ontario. The institution of Dr. Cohen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for CME Outfitters. The institution of Dr. Cohen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Integritas. The institution of Dr. Cohen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for DBK Med.

Decreased Brain Structural Network Connectivity in Patients with Mild Cognitive Impairment: A Novel Fractal Dimension Analysis.

Mild cognitive impairment (MCI) is widely regarded to be the intermediate stage to Alzheimer's disease. Cerebral morphological alteration in cortical subregions can provide an accurate predictor for early recognition of MCI. Thirty patients with MCI and thirty healthy control subjects participated in this study. The Desikan-Killiany cortical atlas was applied to segment participants' cerebral cortex into 68 subregions. A complexity measure termed fractal dimension (FD) was applied to assess morphological changes in cortical subregions of participants. The MCI group revealed significantly decreased FD values in the bilateral temporal lobes, right parietal lobe including the medial temporal, fusiform, para hippocampal, and also the orbitofrontal lobes. We further proposed a novel FD-based brain structural network to compare network parameters, including intra- and inter-lobular connectivity between groups. The control group had five modules, and the MCI group had six modules in their brain networks. The MCI group demonstrated shrinkage of modular sizes with fewer components integrated, and significantly decreased global modularity in the brain network. The MCI group had lower intra- and inter-lobular connectivity in all lobes. Between cerebral lobes, the MCI patients may maintain nodal connections between both hemispheres to reduce connectivity loss in the lateral hemispheres. The method and results presented in this study could be a suitable tool for early detection of MCI.

Differentiation of patients with mild cognitive impairment and healthy controls based on computer assisted hand movement analysis: a proof-of-concept study

Mild cognitive impairment (MCI) is the prodromal phase of dementia, and it is highly underdiagnosed in the community. We aimed to develop an automated, rapid (< 5 min), electronic screening tool for the recognition of MCI based on hand movement analysis. Sixty-eight individuals participated in our study, 46 healthy controls and 22 patients with clinically defined MCI. All participants underwent a detailed medical assessment including neuropsychology and brain MRI. Significant differences were found between controls and MCI groups in mouse movement characteristics. Patients showed higher level of entropy for both the left (F = 5.24; p = 0.001) and the right hand (F = 8.46; p < 0.001). Longer time was required in MCI to perform the fine motor task (p < 0.005). Furthermore, we also found significant correlations between mouse movement parameters and neuropsychological test scores. Correlation was the strongest between motor parameters and Clinical Dementia Rating scale (CDR) score (average r: − 0.36, all p’s < 0.001). Importantly, motor parameters were not influenced by age, gender, or anxiety effect (all p’s > 0.05). Our study draws attention to the utility of hand movement analysis, especially to the estimation of entropy in the early recognition of MCI. It also suggests that our system might provide a promising tool for the cognitive screening of large populations.

A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification.

Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%.

Alzheimer's Disease Blood Biomarkers Associated With Neuroinflammation as Therapeutic Targets for Early Personalized Intervention

The definitive diagnosis of Alzheimer's Disease (AD) without the need for neuropathological confirmation remains a challenge in AD research today, despite efforts to uncover the molecular and biological underpinnings of the disease process. Furthermore, the potential for therapeutic intervention is limited upon the onset of symptoms, providing motivation for studying and treating the AD precursor mild cognitive impairment (MCI), the prodromal stage of AD instead. Applying machine learning classification to transcriptomic data of MCI, AD, and cognitively normal (CN) control patients, we identified differentially expressed genes that serve as biomarkers for the characterization and classification of subjects into MCI or AD groups. Predictive models employing these biomarker genes exhibited good classification performances for CN, MCI, and AD, significantly above random chance. The PI3K-Akt, IL-17, JAK-STAT, TNF, and Ras signaling pathways were also enriched in these biomarker genes, indicating their diagnostic potential and pathophysiological roles in MCI and AD. These findings could aid in the recognition of MCI and AD risk in clinical settings, allow for the tracking of disease progression over time in individuals as part of a therapeutic approach, and provide possible personalized drug targets for early intervention of MCI and AD.

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer's Disease Recognition.

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes. It has become an effective aid in the early diagnosis of AD by exploiting abnormal brain activity. Because the original EEG has the characteristics of weak amplitude, strong background noise and randomness, the research on intelligent AD recognition based on machine learning is still in the exploratory stage. This paper proposes the discriminant subspace low-rank representation (DSLRR) algorithm for EEG-based AD and mild cognitive impairment (MCI) recognition. The subspace learning and low-rank representation are flexibly integrated into a feature representation model. On the one hand, based on the low-rank representation, the graph discriminant embedding is introduced to constrain the representation coefficients, so that the robust representation coefficients can preserve the local manifold structure of the EEG data. On the other hand, the least squares regression, principle component analysis, and global graph embedding are introduced into the subspace learning, to make the model more discriminative. The objective function of DSLRR is solved by the inexact augmented Lagrange multiplier method. The experimental results show that the DSLRR algorithm has good classification performance, which is helpful for in-depth research on AD and MCI recognition.

Temporal Speech Parameters Detect Mild Cognitive Impairment in Different Languages: Validation and Comparison of the Speech-GAP Test® in English and Hungarian.

The development of automatic speech recognition (ASR) technology allows the analysis of temporal (time-based) speech parameters characteristic of mild cognitive impairment (MCI). However, no information has been available on whether the analysis of spontaneous speech can be used with the same efficiency in different language environments. The main goal of this international pilot study is to address the question of whether the Speech-Gap Test® (S-GAP Test®), previously tested in the Hungarian language, is appropriate for and applicable to the recognition of MCI in other languages such as English. After an initial screening of 88 individuals, English-speaking (n = 33) and Hungarianspeaking (n = 33) participants were classified as having MCI or as healthy controls (HC) based on Petersen's criteria. The speech of each participant was recorded via a spontaneous speech task. Fifteen temporal parameters were determined and calculated through ASR. Seven temporal parameters in the English-speaking sample and 5 in the Hungarian-speaking sample showed significant differences between the MCI and the HC groups. Receiver operating characteristics (ROC) analysis clearly distinguished the English-speaking MCI cases from the HC group based on speech tempo and articulation tempo with 100% sensitivity, and on three more temporal parameters with high sensitivity (85.7%). In the Hungarian-speaking sample, the ROC analysis showed similar sensitivity rates (92.3%). The results of this study in different native-speaking populations suggest that changes in acoustic parameters detected by the S-GAP Test® might be present across different languages.

611 Mild cognitive impairment is associated with subclinical left ventricular dysfunction as assessed by global longitudinal strain in hypertensive patients

Abstract Aims Left ventricular global longitudinal strain (GLS) detects subtle systolic abnormalities in various cardiovascular conditions, which represent significant risk factors for cognitive impairment and stroke. Specifically, GLS has emerged as a more precise myocardial function measure than left ventricular ejection fraction (LVEF). This study investigated the relationship of GLS with mild cognitive impairment (MCI) in hypertensive patients. Methods and results From February 2020 to October 2021 were enrolled hypertensive patients without atrial fibrillation and/or cerebrovascular and/or neurodegenerative diseases. Complete demographic, clinical characteristics, laboratory analyses, conventional echocardiographic parameters were collected. Finally, MCI was defined by accurate the Quick Mild Cognitive Impairment (QMCI) Screen corrected for age and education. This score explores spatial and temporal orientation, registration, delayed recall, clock design, logical memory, and verbal fluency in a brief time (5 min—score 0–100); a compliance questionnaire (Morisky medication adherence scale); a questionnaire on nutritional status (MNA). 81 hypertensive patients [66 ± 7.27 years; 9 (11%) female] were included in the study. Concerning echocardiographic evaluation, LVEF was 50.47 ± 9.95% and mean GLS was −16.00 ± 3.66. Mean QMCI corrected for age and education was 56.45 ± 9.37, and MCI was detected in 21 patients (26%). When comparing the patients with MCI (QMCItot &amp;lt;49.4) and without MCI (QMCItot &amp;gt;49.4), a statistically significant difference of GLS values was detected (no MCI: −16.52 ± 3.66 vs. MCI: −14.18 ± 3.23; P = 0.032); on the other hand, the two groups did not differ in LVEF (no MCI: 50.58 ± 9.70 vs. MCI: 48.86 ± 11.93; P = 0.864). Furthermore, excluding patients with FE ≥ 45% from the analysis, a statistically significant linear regression was observed between QMCI (corrected for age and education) and the GLS (P = 0.014) (Figure 1). Conclusions Compromised GLS, but not LV EF, is related to MCI in hypertensive patients who are free of clinical dementia, stroke, and neurodegenerative disease. Moreover, our study demonstrates for the first time the existence of a significant association between the QMCI and GLS; consequently, GLS could be an additional parameter in clinical practice for early recognition of MCI. However, studies on a larger population will be needed to confirm this association.

Using Optical Coherence Tomography to Screen for Cognitive Impairment and Dementia.

Screening for Alzheimer's disease and related disorders (ADRD) and mild cognitive impairment (MCI) could increase case identification, enhance clinical trial enrollment, and enable early intervention. MCI and ADRD screening would be most beneficial if detection measures reflect neurodegenerative changes. Optical coherence tomography (OCT) could be a marker of neurodegeneration (part of the amyloid-tau-neurodegeneration (ATN) framework). To determine whether OCT measurements can be used as a screening measure to detect individuals with MCI and ADRD. A retrospective cross-sectional study was performed on 136 participants with comprehensive clinical, cognitive, functional, and behavioral evaluations including OCT with a subset (n = 76) completing volumetric MRI. Pearson correlation coefficients tested strength of association between OCT and outcome measures. Receiver operator characteristic curves assessed the ability of OCT, patient-reported outcomes, and cognitive performance measures to discriminate between individuals with and without cognitive impairment. After controlling for age, of the 6 OCT measurements collected, granular cell layer-inner plexiform layer (GCL + IPL) thickness best correlated with memory, global cognitive performance, Clinical Dementia Rating, and hippocampal atrophy. GCL + IPL thickness provided good discrimination in cognitive status with a cut-off score of 75μm. Combining GCL + IPL thickness as a proxy marker for hippocampal atrophy with a brief patient-reported outcome and performance measure correctly classified 87%of MCI and ADRD participants. Multimodal approaches may improve recognition of MCI and ADRD. OCT has the potential to be a practical, non-invasive biomarker for ADRD providing a screening platform to quickly identify at-risk individuals for further clinical evaluation or research enrollment.

Automatic Diagnosis of Alzheimer's Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End-to-End Training.

Alzheimer's disease (AD) is an irreversible neurodegenerative disease, and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer's disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET : PET) is an effective biomarker of the symptom of AD and has been used as medical imaging data for diagnosing AD. Mild cognitive impairment (MCI) is regarded as an early symptom of AD, and it has been shown that MCI also has a certain biomedical correlation with PET. In this paper, we explore how to use 3D PET images to realize the effective recognition of MCI and thus achieve the early prediction of AD. This problem is then taken as the classification of three categories of PET images, including MCI, AD, and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three-category classification problem is divided into three binary classifications, and each binary classification is being realized with a CNN + SVM network. Then, the outputs of the three CNN + SVM networks are fused into a final three-category classification result. An end-to-end learning algorithm is developed to train the CNN + SVM networks, and a decision fusion algorithm is exploited to realize the fusion of the outputs of three CNN + SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.

The use of the Hungarian Test Your Memory (TYM-HUN), MMSE, and ADAS-Cog tests for patients with mild cognitive impairment (MCI) in a Hungarian population: a cross-sectional study

BackgroundMild cognitive impairment (MCI) often presages the development of Alzheimer’s disease (AD). Accurate and early identification of cognitive impairment will substantially reduce the burden on the family and alleviate the costs for the whole society. There is a need for testing methods that are easy to perform even in a general practitioner’s office, inexpensive and non-invasive, which could help the early recognition of mental decline. We have selected the Test Your Memory (TYM), which has proven to be reliable for detecting AD and MCI in several countries. Our study was designed to test the usability of the Hungarian version of the TYM (TYM-HUN) comparing with the Mini-Mental State Examination (MMSE) and the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) in MCI recognition in the Hungarian population.MethodsTYM test was translated and validated into Hungarian (TYM-HUN) in a previous study. The TYM-HUN test was used in conjunction with and compared with the MMSE and the ADAS-Cog. For our study, 50 subjects were selected: 25 MCI patients and 25 healthy controls (HC). Spearman’s rank correlation was used to analyse the correlation between the scores of MMSE and ADAS-Cog with TYM-HUN and the receiver operating characteristic (ROC) curve was established.ResultsMCI can be distinguished from normal aging using TYM-HUN. We established a ‘cut-off’ point of TYM-HUN (44/45points) where optimal sensitivity (80%) and specificity (96%) values were obtained to screen MCI. The total TYM-HUN scores significantly correlated with the MMSE scores (ρ = 0.626; p < 0.001) and ADAS-Cog scores (ρ = − 0.723; p < 0.001).ConclusionsOur results showed that the TYM-HUN is a reliable, fast, self-administered questionnaire with the right low threshold regarding MCI and can be used for the early diagnosis of cognitive impairment.

Prevalence of Mild Cognitive Impairment in the Elderly Population in Greece: Results From the HELIAD Study.

Timely recognition of mild cognitive impairment (MCI) is essential in optimizing prevention and treatment for Alzheimer disease. Because of the paucity of data on MCI epidemiology in Greece and the variability of worldwide published results, we investigated the prevalence and determinants of MCI in the elderly population in Greece. As part of the Hellenic Epidemiological Longitudinal Investigation of Aging and Diet (HELIAD), we randomly selected 1960 individuals 65 years and older to undergo full neurological and neuropsychological assessment by a multidisciplinary team. MCI was diagnosed according to the Petersen criteria. The age-standardized and gender-standardized prevalence of MCI in people aged 65 years and older in Greece is 13.11%. The amnestic and multidomain MCI subtypes are more common than their nonamnestic and single-domain counterparts, respectively. Almost two thirds of cases are because of suspected Alzheimer disease. Every additional year of age increases the odds of prevalent MCI by 7.4%, every additional year of education decreases the odds of MCI by 6.3%, and apolipoprotein E (APOE-ε4) carriage increases the odds of MCI by 57.9%. MCI prevalence in the elderly population in Greece is on par with previously reported rates. Prospective studies with robust methodology will enhance our understanding of the dementia continuum.

A robust swarm intelligence-based feature selection model for neuro-fuzzy recognition of mild cognitive impairment from resting-state fMRI

Individuals diagnosed with mild cognitive impairment (MCI) are at a high risk of transition to Alzheimer's disease (AD), but a diagnosis of MCI is challenging. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising tool for identifying patients with MCI, but an accurate and robust analysis method is needed to extract discriminative rs-fMRI features for classification between MCI patients and healthy people. In this paper, a new rs-fMRI data analysis approach based on Chaotic Binary Grey Wolf Optimization (CBGWO) and Adaptive Neuro-Fuzzy Inference System (ANFIS), namely (CBGWO-ANFIS), is presented to distinguish MCI patients based on rs-fMRI. CBGWO is a new feature selection model that attempts to reduce the number of features without loss of significant information for classification, and it uses the naïve Bayes criterion as a part of the objective function. Based on the chaos theory, the important parameters of GWO are estimated and tuned by using ten different chaos sequence maps. Subsequently, ANFIS is used to classify MCI patients and healthy people based on the subset of features retained by CBGWO. Experiments were carried out on 62 MCI patients and 65 normal controls (NC). Fractional amplitude of low frequency fluctuation (f-ALFF) was extracted from rs-fMRI as features. The results indicate that the proposed CBGWO-ANFIS approach with the Chebyshev chaos map shows a higher accuracy (around 86%), higher convergence speed, and shorter execution time than other chaos maps. Further, the proposed approach outperforms the conventional machine learning techniques and the recent meta-heuristic optimization algorithms. This study indicates that the proposed CBGWO-ANFIS approach on rs-fMRI could be a potential tool for early diagnosis of MCI.

Predictors of Quality of Life among Older People with Mild Cognitive Impairment Attending Urban Primary Care Clinics

ABSTRACT Objectives To study the prevalence of Mild Cognitive Impairment (MCI) among older people attending primary care clinics and its predictors of QOL. Methods A cross-sectional study was conducted at two primary care clinics in Kuala Lumpur, Malaysia, recruiting 271 participants by utilizing the universal sampling method. Every patient who attended both the clinics during the study period and met the inclusion and exclusion criteria were approached and briefed about the study. Patients who gave consent were recruited as study participants. Information on sociodemographic, medical condition, and lifestyle behaviors were obtained. The Montreal Cognitive Assessment (MoCA) was used to screen for MCI at a score < 23. The World Health Organization Quality of Life-BREF (WHOQOL-BREF) questionnaire was used to evaluate QOL. Results Prevalence of MCI was 27.3%. Lower QOL scores were found in the physical (67.3 ± 1.4), psychological (67.3 ± 1.4), social (66.9 ± 1.6) and environmental (71.3 ± 1.3) domains among participants with MCI. Among them, predictors of QOL were depression in the physical domain, age and stroke in the psychological domain, presence of other types of disorders in the social domain and diabetes and stroke in the environmental domain. Conclusions MCI was prevalent among study participants and were associated with poorer QOL in all domains of QOL. A better understanding of predictors of QOL in older people with MCI is deemed important. Clinical implication Routine cognitive screening at primary care clinics will facilitate early recognition of MCI and facilitates referral to memory clinics for further assessment and treatment.

A Comparative Atlas-Based Recognition of Mild Cognitive Impairment With Voxel-Based Morphometry.

An accurate and reliable brain partition atlas is vital to quantitatively investigate the structural and functional abnormalities in mild cognitive impairment (MCI), generally considered to be a prodromal phase of Alzheimer’s disease. In this paper, we proposed an automated structural classification method to identify MCI from healthy controls (HC) and investigated whether the classification performance was dependent on the brain parcellation schemes, including Automated Anatomical Labeling (AAL-90) atlas, Brainnetome (BN-246) atlas, and AAL-1024 atlas. In detail, structural magnetic resonance imaging (sMRI) data of 69 MCI patients and 63 HC matched well on gender, age, and education level were collected and analyzed with voxel-based morphometry method first, then the volume features of every region of interest (ROI) belonging to the above-mentioned three atlases were calculated and compared between MCI and HC groups, respectively. At last, the abnormal volume features were selected as the classification features for a proposed support vector machine based identification method. After the leave-one-out cross-validation to estimate the classification performance, our results reported accuracies of 83, 92, and 89% with AAL-90, BN-246, and AAL-1024 atlas, respectively, suggesting that future studies should pay more attention to the selection of brain partition schemes in the atlas-based studies. Furthermore, the consistent atrophic brain regions among three atlases were predominately located at bilateral hippocampus, bilateral parahippocampal, bilateral amygdala, bilateral cingulate gyrus, left angular gyrus, right superior frontal gyrus, right middle frontal gyrus, left inferior frontal gyrus, and left precentral gyrus.

A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech.

Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer’s disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive de-cline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI.Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech sig-nals, first manually (using the Praat software), and then automatically, with an automatic speech recogni-tion (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features.Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%.Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, auto-matic detection-based tool for screening MCI for the community.

Clinical Practice Guidelines for Management of Dementia.

Indian Psychiatric Society (IPS) published Clinical Practice Guidelines (CPGs) for management of dementia, in the year 2007. (http://www.indianjpsychiatry.org/cpg2007.asp). The current version of the CPG is an update of the earlier version of CPGs for management of dementia There were three separate CPGs for management of dementia, one each for Reversible Dementias, Alzheimer's Disease and Vascular Dementia, Please note that the present CPG on dementia deals all types of dementia together. The current version of the CPGs for dementia in elderly must be read in conjunction with the previous version of CPGs for dementia. The focus of the present CPG is to provide suggestions and clinical tips to differentiate dementia syndrome from other clinical conditions, identify the subtypes of dementia and then offer suggestions for management. These guidelines only provide a broad framework for assessment, management and follow-up of older people with dementia. While most of the recommendations are evidence based, these guidelines should not be considered as a substitute of professional knowledge and clinical judgment. The recommendations made as part of these guidelines should be tailored to address the clinical needs of the individual patient and the treatment setting.

Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference

The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.

Mild cognitive impairment: a concept useful for early detection and intervention of dementia.

As life expectancy increases around the globe, dementia has become an increasingly important public health issue that has created new challenges for communities' social service networks[1]. The early detection and prevention of dementia, particularly Alzheimer's disease (AD), necessitate the development of effective public health education programs about the early symptoms and preclinical changes of dementia. To promote this effort the concept of mild cognitive impairment (MCI) – which some experts consider an early form of AD – has been created. Despite tremendous progress over the last 20 years in understanding the neuropsychological, neuroimaging, and neurobiological characteristics of MCI, there remains controversy about the value of this construct. [2] Despite the problems of proving that MCI is a distinct disease entity, we contend that this clinical construct plays a crucial role in addressing the public health challenges of dementia that exist in contemporary societies. First, MCI is strongly related to dementia, especially AD. Studies in China and other countries have consistently found that elderly people with MCI are much more likely to develop dementia than those without MCI.[3] The cognitive regression and the patterns of cerebral atrophy in elderly persons with MCI are similar to those seen in individuals with AD, and the similarity is most pronounced in individuals with MCI who subsequently develop AD.[4] Brain imaging studies indicate that the structural and functional changes found in the brains of elderly persons with MCI are intermediate between those in normal elderly people and in elderly individuals with AD.[5] Second, interventions for persons with MCI can improve brain functioning. Cognitive or memory training can improve memory functioning in elderly people with MCI.[6],[7] Neuroimaging studies have shown that cognitive training can enhance brain activity in the frontal, temporal, and parietal areas.[8] And several randomized controlled trials have found that specific Chinese traditional medicines can reduce the transition from MCI to AD.[9],[10]These findings suggest that brain plasticity is preserved in elderly people with MCI and, thus, suggest that targeted interventions should be able to improve the cognitive functioning of individuals with MCI. Lastly, the recognition of MCI can help in the early detection of dementia. Recent diagnostic criteria of AD and MCI[11],[12] stress the value of pathological biomarkers including the neurotoxic β-amyloid protein (Aβ) and tau protein which either directly damage neurons or are the indicators of damaged neurons. Subsequent research focused on these biomarkers hold the promise of substantially improving the prevention and management of AD.[13] Research on the sensitivity and specificity of these biomarkers may find that their occurrence in individuals with MCI is predictive of progression to AD and, thus, merits more intensive follow-up and treatment. And if drugs targeting Aβ or tau protein prove effective, screening for these biomarkers in persons with MCI will identify those who are responsive to treatment and, thus, can prevent the progression to AD in these individuals. In clinical practice the use of cognitive tests to screen for MCI may be more feasible than the routine assessment of biomarkers. Several instruments from high-income counties (e.g the Montreal Cognitive Assessment [MoCA][14]) have been translated and adapted for use in China. But not all such screening tools are appropriate for use in mainland China where the cultural and educational background of the elderly, particularly those from rural areas, is quite different from that of the elderly in high-income countries. Thus, we designated a patent screening tool, the Screening Scale for Mild Cognitive Impairment (ZL201010508406.2)[15] which might overcome the problem and is more appropriate to use with Chinese elderly. Cultural and lifestyle factors also play a role in the adaptation of the cognitive training programs for elderly persons with MCI that have been primarily developed in high-income countries. It will also be important to develop China-specific programs for promoting life styles in the elderly that are conducive to brain health and the prevention of dementia. In summary, active research about the identification of MCI and about different interventions for persons with MCI is one of the most promising avenues that communities around the world have for facing the tremendous public health challenge of AD.