The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog): Modifications and Responsiveness in Pre-Dementia Populations. A Narrative Review

The Parkinson's Disease Composite of Executive Functioning: A Measure for Detecting Cognitive Decline in Clinical Trials

BACKGROUND AND OBJECTIVES

Executive functioning is one of the first domains to be impaired in Parkinson disease (PD), and the majority of patients with PD eventually develop dementia. Thus, developing a cognitive endpoint measure specifically assessing executive functioning is critical for PD clinical trials. The objective of this study was to develop a cognitive composite measure that is sensitive to decline in executive functioning for use in PD clinical trials.

METHODS

We used cross-sectional and longitudinal follow-up data from PD participants enrolled in the PD Cognitive Genetics Consortium, a multicenter setting focused on PD. All PD participants with Trail Making Test, Digit Symbol, Letter-Number Sequencing, Semantic Fluency, and Phonemic Fluency neuropsychological data collected from March 2010 to February 2020 were included. Baseline executive functioning data were used to create the Parkinson's Disease Composite of Executive Functioning (PaCEF) through confirmatory factor analysis. We examined the changes in the PaCEF over time, how well baseline PaCEF predicts time to cognitive progression, and the required sample size estimates for PD clinical trials. PaCEF results were compared with the Montreal Cognitive Assessment (MoCA), individual tests forming the PaCEF, and tests of visuospatial, language, and memory functioning.

RESULTS

A total of 841 participants (251 no cognitive impairment [NCI], 480 mild cognitive impairment [MCI], and 110 dementia) with baseline data were included, of which the mean (SD) age was 67.1 (8.9) years and 270 were women (32%). Five hundred forty five PD participants had longitudinal neuropsychological data spanning 9 years (mean [SD] 4.5 [2.2] years) and were included in analyses examining cognitive decline. A 1-factor model of executive functioning with excellent fit (comparative fit index = 0.993, Tucker-Lewis index = 0.989, and root mean square error of approximation = 0.044) was used to calculate the PaCEF. The average annual change in PaCEF ranged from 0.246 points per year for PD-NCI participants who remained cognitively unimpaired to -0.821 points per year for PD-MCI participants who progressed to dementia. For PD-MCI, baseline PaCEF, but not baseline MoCA, significantly predicted time to dementia. Sample size estimates were 69%-73% smaller for PD-NCI trials and 16%-19% smaller for PD-MCI trials when using the PaCEF rather than MoCA as the endpoint.

DISCUSSION

The PaCEF is a sensitive measure of executive functioning decline in PD and will be especially beneficial for PD clinical trials.

Raman Spectroscopy Reveals Photobiomodulation-Induced α-Helix to β-Sheet Transition in Tubulins: Potential Implications for Alzheimer's and Other Neurodegenerative Diseases

In small clinical studies, the application of transcranial photobiomodulation (PBM), which typically delivers low-intensity near-infrared (NIR) to treat the brain, has led to some remarkable results in the treatment of dementia and several neurodegenerative diseases. However, despite the extensive literature detailing the mechanisms of action underlying PBM outcomes, the specific mechanisms affecting neurodegenerative diseases are not entirely clear. While large clinical trials are warranted to validate these findings, evidence of the mechanisms can explain and thus provide credible support for PBM as a potential treatment for these diseases. Tubulin and its polymerized state of microtubules have been known to play important roles in the pathology of Alzheimer's and other neurodegenerative diseases. Thus, we investigated the effects of PBM on these cellular structures in the quest for insights into the underlying therapeutic mechanisms. In this study, we employed a Raman spectroscopic analysis of the amide I band of polymerized samples of tubulin exposed to pulsed low-intensity NIR radiation (810 nm, 10 Hz, 22.5 J/cm2 dose). Peaks in the Raman fingerprint region (300-1900 cm-1)-in particular, in the amide I band (1600-1700 cm-1)-were used to quantify the percentage of protein secondary structures. Under this band, hidden signals of C=O stretching, belonging to different structures, are superimposed, producing a complex signal as a result. An accurate decomposition of the amide I band is therefore required for the reliable analysis of the conformation of proteins, which we achieved through a straightforward method employing a Voigt profile. This approach was validated through secondary structure analyses of unexposed control samples, for which comparisons with other values available in the literature could be conducted. Subsequently, using this validated method, we present novel findings of statistically significant alterations in the secondary structures of polymerized NIR-exposed tubulin, characterized by a notable decrease in α-helix content and a concurrent increase in β-sheets compared to the control samples. This PBM-induced α-helix to β-sheet transition connects to reduced microtubule stability and the introduction of dynamism to allow for the remodeling and, consequently, refreshing of microtubule structures. This newly discovered mechanism could have implications for reducing the risks associated with brain aging, including neurodegenerative diseases like Alzheimer's disease, through the introduction of an intervention following this transition.

The impact of six-week dihydrogen-pyrroloquinoline quinone supplementation on mitochondrial biomarkers, brain metabolism, and cognition in elderly individuals with mild cognitive impairment: a randomized controlled trial

OBJECTIVES

To assess the impact of medium-term supplementation with dihydrogen and pyrroloquinoline quinone (PQQ) on mitochondrial biomarkers, brain metabolism, and cognition in elderly individuals diagnosed with mild cognitive impairment.

DESIGN

A parallel-group, randomized, placebo-controlled, double-blind experimental design, maintaining a 1:1 allocation ratio between the experimental group (receiving the dihydrogen-producing minerals and PQQ) and the control group (receiving the placebo) throughout the trial.

SETTING AND PARTICIPANTS

Thirty-four elderly individuals with mild cognitive impairment (mean age 71.9 ± 3.8 years; 28 females) voluntarily provided written consent to participate in this trial. Participants were assigned in a double-blind parallel-group design to receive either a dihydrogen-PQQ mixture (Alpha Hope®, CalerieLife, Irvine, CA) or placebo twice daily for a 6-week intervention period.

METHODS

The primary endpoint was the change in serum brain-derived neurotrophic factor (BDNF) from baseline to the 6-week follow-up; secondary outcomes included cognitive function indices, specific metabolites in brain tissue, brain oxygenation, and the prevalence and severity of side effects. Interaction effects (time vs. intervention) were evaluated using two-way ANOVA with repeated measures and Friedman's 2-way ANOVA by ranks, for normally distributed data with homogeneous variances and non-homogeneous variances, respectively.

RESULTS

Dihydrogen-PQQ resulted in a significant elevation in serum BDNF levels at the six-week follow-up (P = 0.01); conversely, no changes in BDNF levels were observed in the placebo group throughout the study duration (P = 0.27). A non-significant trend in the impact of interventions on BDNF levels was observed (treatment vs. time interaction, P = 0.14), suggesting a tendency for dihydrogen-PQQ to upregulate BDNF levels compared to the placebo. A significant interaction effect was observed for the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) scores in the orientation domain (P = 0.03), indicating the superiority of dihydrogen-PQQ over placebo in enhancing this cognitive aspect. Cerebral oxygenation saturation exhibited a significant increase following the administration of the dihydrogen-PQQ mixture, from 48.4 ± 7.2% at baseline to 52.8 ± 6.6% at 6-week post-administration (P = 0.005). In addition, brain N-acetyl aspartate levels significantly increased at seven out of thirteen locations post-intervention in participants receiving the mixture (P ≤ 0.05).

CONCLUSIONS

Despite the limited number of participants included in the study for interpreting clinical parameters, the dihydrogen-PQQ mixture blend shows promise as a potential dietary intervention for enhancing mental orientation and brain metabolism in individuals with age-related mild cognitive decline.

The Involvement of Antioxidants in Cognitive Decline and Neurodegeneration: Mens Sana in Corpore Sano

With neurodegenerative disorders being on the rise, a great deal of research from multiple fields is being conducted in order to further knowledge and propose novel therapeutic interventions. Among these investigations, research on the role of antioxidants in contrasting cognitive decline is putting forward interesting and promising results. In this review, we aim to collect evidence that focused on the role of a variety of antioxidants and antioxidant-rich foods in improving or stabilizing cognitive functions, memory, and Alzheimer's disease, the most common neurodegenerative disorder. Specifically, we considered evidence collected on humans, either through longitudinal studies or randomized, placebo-controlled ones, which evaluated cognitive performance, memory abilities, or the progression level of neurodegeneration. Overall, despite a great deal of variety between study protocols, cohorts of participants involved, neuropsychological tests used, and investigated antioxidants, there is a solid trend that suggests that the properties of antioxidants may be helpful in hampering cognitive decline in older people. Thus, the help of future research that will further elucidate the role of antioxidants in neuroprotection will lead to the development of novel interventions that will take into account such findings to provide a more global approach to treating neurodegenerative disorders.

Study of electronic biofeedback combined with nursing intervention in the treatment of vascular cognitive impairment-no dementia

OBJECTIVE

To investigate the effects of electronic biofeedback combined with nursing intervention and conventional drug treatment on cognitive function in patients with vascular cognitive impairment-no dementia (VCIND).

METHODS

A total of 102 patients with VCIND treated in the Department of Neurology from January 2021 to May 2022 were enrolled and divided into the routine treatment group and biofeedback group according to different treatment methods. The routine treatment group was given conventional drug therapy and nursing intervention; for the biofeedback group, electronic biofeedback therapy was added, based on the routine treatment group. The Montreal Cognitive Assessment, (MoCA), Alzheimer's Disease Assessment Scale-Cognitive Subscale, (ADAS-cog), and Hamilton Depression Scale (HAMD) were checked before treatment, 2 weeks after treatment, and 3 months after treatment.

RESULTS

At 3 months of treatment, the scores of the MoCA and ADAS-cog scales in the biofeedback group were better than those in the routine treatment group, while no difference was detected in the HAMD scores before and after treatment and between the two groups.

CONCLUSION

Electronic biofeedback therapy for VCIND can significantly improve the MoCA score, reduce the ADAS-cog score and improve the cognitive level of patients and can be used as a complementary treatment for VCIND.

Nature's Toolbox for Alzheimer's Disease: A Review on the Potential of Natural Products as Alzheimer's Disease Drugs

Numerous clinical trials involving natural products have been conducted to observe cognitive performances and biomarkers in Alzheimer's Disease (AD) patients. However, to date, no natural-based drugs have been approved by the FDA as treatments for AD. In this review, natural product-based compounds that were tested in clinical trials from 2011 to 2023, registered at www.clinicaltrials.gov were reviewed. Thirteen compounds, encompassing 7 different mechanisms of action were covered. Several observations were deduced, which are: i) several compounds showed cognitive improvement, but these improvements may not extend to AD, ii) compounds that are endogenous to the human body showed better outcomes, and iii) Docosahexaenoic acid (DHA) and cerebrolysin had the most potential as AD drugs among the 13 compounds. Based on the current findings, natural products may be more suitable as a supplement than AD drugs in most cases. However, the studies covered here were conducted in a relatively short amount of time, where compounds acting on AD pathways may take time to show any effect. Given the diverse pathways that these natural products are involved in, they may potentially produce synergistic effects that would be beneficial in treating AD. Additionally, natural products benefit from both physicochemical properties being in more favorable ranges and active transport playing a more significant role than it does for synthetic compounds.

Predicting Alzheimer's progression in MCI: a DTI-based white matter network model

OBJECTIVE

This study aimed to identify features of white matter network attributes based on diffusion tensor imaging (DTI) that might lead to progression from mild cognitive impairment (MCI) and construct a comprehensive model based on these features for predicting the population at high risk of progression to Alzheimer's disease (AD) in MCI patients.

METHODS

This study enrolled 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Among them, 36 progressed to AD after four years of follow-up. A brain network was constructed for each patient based on white matter fiber tracts, and network attribute features were extracted. White matter network features were downscaled, and white matter markers were constructed using an integrated downscaling approach, followed by forming an integrated model with clinical features and performance evaluation.

RESULTS

APOE4 and ADAS scores were used as independent predictors and combined with white matter network markers to construct a comprehensive model. The diagnostic efficacy of the comprehensive model was 0.924 and 0.919, sensitivity was 0.864 and 0.900, and specificity was 0.871 and 0.815 in the training and test groups, respectively. The Delong test showed significant differences (P < 0.05) in the diagnostic efficacy of the combined model and APOE4 and ADAS scores, while there was no significant difference (P > 0.05) between the combined model and white matter network biomarkers.

CONCLUSIONS

A comprehensive model constructed based on white matter network markers can identify MCI patients at high risk of progression to AD and provide an adjunct biomarker helpful in early AD detection.

Minimal clinically important difference in Alzheimer's disease: Rapid review

INTRODUCTION

We conducted a rapid systematic review of minimal clinically important differences (MCIDs) for Alzheimer's disease (AD) trial endpoints.

METHODS

Two reviewers searched EMBASE, MEDLINE, and PubMed from inception to June 4, 2023.

RESULTS

Ten articles were retrieved. For mild cognitive impairment (MCI), a change of +2 to +3 points on the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), +1 points on the Clinical Dementia Rating scale sum of boxes (CDR-SB), -5 points on the integrated Alzheimer's Disease Rating Scale (iADRS), or -1 to -2 points on the Mini-Mental State Examination (MMSE) was considered meaningful. For patients with mild AD, a change of +3 on the ADAS-Cog, +2 points on CDR-SB, -9 points on the iADRS, or -2 points on the MMSE was considered meaningful. For patients with moderate to severe AD, a change of +2 points on the CDR-SB or a change of -1.4 to -3 points on the MMSE was considered meaningful.

CONCLUSION

This review identified previously published MCIDs for AD trial endpoints. Input from patients and caregivers will be needed to derive more meaningful endpoints and thresholds.

HIGHLIGHTS

This systematic rapid review identified thresholds for minimal clinically important differences (MCIDs) for recently used Alzheimer's disease (AD) trial endpoints: Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Clinical Dementia Rating scale sum of boxes (CDR-SB), integrated Alzheimer's Disease Rating Scale (iADRS), Mini-Mental State Examination (MMSE). MCIDs were higher for more severe stages of AD. Average treatment effects in recent trials of anti-amyloid disease modifying monoclonal antibodies are lower than previously published MCIDs. In future trials of disease modifying treatments for AD, the proportion of participants in each treatment group that experienced a clinically meaningful decline could be reported. More work is needed to incorporate the values and preferences of patients and care partners in deriving MCIDs.

Genetic-based patient stratification in Alzheimer's disease

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

Safety and feasibility of optimized transcranial direct current stimulation in patients with mild cognitive impairment due to Alzheimer's disease: a multicenter study protocol for a randomized controlled trial

Introduction

Transcranial direct current stimulation (tDCS) may effectively preserve and improve cognitive function in patients with mild cognitive impairment (MCI). Research has shown that Individual brain characteristics can influence the effects of tDCS. Computer three-dimensional brain modeling based on magnetic resonance imaging (MRI) has been suggested as an alternative for determining the most accurate tDCS electrode position based on the patients' individual brain characteristics to enhance tDCS effects. Therefore, this study aims to determine the feasibility and safety of applying tDCS treatment using optimized and personalized tDCS electrode positions in patients with Alzheimer's disease (AD)-induced MCI using computer modeling and compare the results with those of a sham group to improve cognitive function.

Method

A prospective active-sham group feasibility study was set to recruit 40 participants, who will be randomized into Optimized-tDCS and Sham-tDCS groups. The parameters for tDCS will be 2 mA (disk electrodes R = 1.5 cm) for 30 min during two sets of 15 sessions (2 weeks of resting period in between), using two electrodes in pairs. Using computer modeling, the tDCS electrode positions of each participant will be personalized. Outcome measurements are going to be obtained at three points: baseline, first post-test, and second post-test. The AD assessment scale-cognitive subscale (ADAS-Cog) and the Korean version of Mini-Mental State Examination (K-MMSE), together with other secondary outcomes and safety tests will be used.

Discussion

For the present study, we hypothesize that compared to a sham group, the optimized personalized tDCS application would be effective in improving the cognitive function of patients with AD-induced MCI and the participants would tolerate the tDCS intervention without any significant adverse effects.Clinical trial registration: https://cris.nih.go.kr, identifier [KCT0008918].

Association of multiple metabolic and cardiovascular markers with the risk of cognitive decline and mortality in adults with Alzheimer's disease and AD-related dementia or cognitive decline: a prospective cohort study

Background and objectives

There is a scarcity of data stemming from large-scale epidemiological longitudinal studies focusing on potentially preventable and controllable risk factors for Alzheimer's disease (AD) and AD-related dementia (ADRD). This study aimed to examine the effect of multiple metabolic factors and cardiovascular disorders on the risk of cognitive decline and AD/ADRD.

Methods

We analyzed a cohort of 6,440 participants aged 45-84 years at baseline. Multiple metabolic and cardiovascular disorder factors included the five components of the metabolic syndrome [waist circumference, high blood pressure (HBP), elevated glucose and triglyceride (TG) concentrations, and reduced high-density lipoprotein cholesterol (HDL-C) concentrations], C-reactive protein (CRP), fibrinogen, interleukin-6 (IL-6), factor VIII, D-dimer, and homocysteine concentrations, carotid intimal-medial thickness (CIMT), and urine albumin-to-creatinine ratio (ACR). Cognitive decline was defined using the Cognitive Abilities Screening Instrument (CASI) score, and AD/ADRD cases were classified using clinical diagnoses.

Results

Over an average follow-up period of 13 years, HBP and elevated glucose, CRP, homocysteine, IL-6, and ACR concentrations were significantly associated with the risk of mortality in the individuals with incident AD/ADRD or cognitive decline. Elevated D-dimer and homocysteine concentrations, as well as elevated ACR were significantly associated with incident AD/ADRD. Elevated homocysteine and ACR were significantly associated with cognitive decline. A dose-response association was observed, indicating that an increased number of exposures to multiple risk factors corresponded to a higher risk of mortality in individuals with cognitive decline or with AD/ADRD.

Conclusion

Findings from our study reaffirm the significance of preventable and controllable factors, including HBP, hyperglycemia, elevated CRP, D-dimer, and homocysteine concentrations, as well as, ACR, as potential risk factors for cognitive decline and AD/ADRD.

Cognitive Function Remains Associated With Functional Impairment in Profound Dementia: Alzheimer Disease and Dementia With Lewy Bodies

Background and Objectives

The Baylor Profound Mental Status Examination (BPMSE) was developed to assess cognitive function in the profound stage of dementia. The Clinical Dementia Rating (CDR) scale has been widely used in measuring functional performance in dementia. We aimed to determine whether cognitive function is related to overall functional impairment in profound dementia.

Methods

We selected 864 patients with probable Alzheimer disease (AD) and 25 patients with possible dementia with Lewy Bodies (DLB) cases with profound dementia by Mini-Mental Status Examination or/and clinical global impression. We used BPMSE to measure cognitive function and the CDR sum-of-boxes (CDR-SB) score to determine overall functional status. We used Spearman rank order correlation to examine the univariate association between CDR-SB and BPMSE in the 2 diagnostic groups separately and multivariable regression analysis to investigate whether BPMSE remained associated with functional status after adjustment for age, sex, education, and APOE ε4 genotype. We expected to see an inverse correlation between BPMSE and CDR-SB scores based on the directionality of the rating scale scoring.

Results

In both AD and DLB, total BPMSE scores had a significant inverse correlation with CDR-SB scores (AD: r = -0.453, p < 0.001; DLB: r = -0.489, p = 0.013). It is of interest that in DLB, the "attention" domain of BPMSE had the strongest association with CDR-SB (r = -0.700, p < 0.001) compared with other domains. The multivariable regression models showed that higher BPMSE scores (i.e., better cognitive function) remained significantly correlated with lower CDR-SB scores (i.e., better global function) in AD (CDR-SB: β = -0.340, p < 0.001), but the regression coefficient for BPMSE did not reach significance in the DLB model (CDR-SB: β = -0.298, p = 0.174).

Discussion

In patients with AD and DLB who enter the profound dementia stage, cognitive function is associated with the severity of functional impairment. The lack of significance for DLB in multivariable regression could be due to small sample size because the correlation magnitude is similar to that in AD.

Deep Brain Stimulation as an Emerging Therapy for Cognitive Decline in Alzheimer Disease: Systematic Review of Evidence and Current Targets

OBJECTIVE

With no cure for Alzheimer disease (AD), current efforts involve therapeutics that prevent further cognitive impairment. Deep brain stimulation (DBS) has been studied for its potential to mitigate AD symptoms. This systematic review investigates the efficacy of current and previous targets for their ability to slow cognitive decline in treating AD.

METHODS

A systematic review of the literature was performed through a search of the PubMed, Scopus, and Web of Science databases. Human studies between 1994 and 2023 were included. Sample size, cognitive outcomes, and complications were recorded for each study.

RESULTS

Fourteen human studies were included: 7 studies with 6 distinct cohorts (n = 56) targeted the fornix, 6 studies with 3 distinct cohorts (n = 17) targeted the nucleus basalis of Meynert (NBM), and 1 study (n = 3) investigated DBS of the ventral striatum (VS). The Alzheimer's Disease Assessment Scale-Cognitive Subscale, Mini-Mental State Examination, and Clinical Dementia Rating Scale Sum of Boxes were used as the primary outcomes. In 5 of 6 cohorts where DBS targeted the fornix, cognitive decline was slowed based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale or Mini-Mental State Examination scores. In 2 of 3 NBM cohorts, a similar reduction was reported. When DBS targeted the VS, the patients' Clinical Dementia Rating Scale Sum of Boxes scores indicated a slowed decline.

CONCLUSIONS

This review summarizes current evidence and addresses variability in study designs regarding the therapeutic benefit of DBS of the fornix, NBM, and VS. Because of varying study parameters, varying outcome measures, varying study durations, and limited cohort sizes, definitive conclusions regarding the utility of DBS for AD cannot be made. Further investigation is needed to determine the safety and efficacy of DBS for AD.

Safety and efficacy of acupuncture for mild cognitive impairment: a study protocol for clinical study

Background

Mild cognitive impairment (MCI) is an intermediary condition between typical cognitive decline that occurs owing to aging and dementia. It is necessary to implement an intervention to slow the progression from MCI to Alzheimer's disease. This manuscript reports the protocol for a clinical trial on the effect of acupuncture in patients with MCI.

Methods

The trial will be a randomized, prospective, parallel-arm, active-controlled trial. Sixty-four patients with MCI will be randomized to the Rehacom or acupuncture group (n = 32 each). The participants in the acupuncture group will receive electroacupuncture at GV24 (Shenting) and GV20 (Baihui) and acupuncture at EX-HN1 (Sishencong) once (30 min) a day, twice per week for 12 weeks. The patients in the Rehacom group will receive computerized cognitive rehabilitation using RehaCom software once (30 min) daily, twice weekly for 12 weeks. The primary outcome measure is the change in the Montreal Cognitive Assessment Scale score. The secondary outcome measures are the Geriatric Depression Scale, Alzheimer's Disease Assessment Scale-Korean version-cognitive subscale-3 scores, and European Quality of Life Five Dimensions Five Level Scale. The safety outcomes will include the incidence of adverse events, blood pressure, blood chemistry parameters, and pulse rate. The efficacy outcome will be assessed at baseline and at six weeks, 13 weeks, and 24 weeks after baseline.

Discussion

The findings of this protocol will provide information regarding the effects of acupuncture on MCI.

Clinical trial registration

https://cris.nih.go.kr/cris/search/detailSearch.do?search_lang=E&focus=reset_12&search_page=M&pageSize=10&page=undefined&seq=25579&status=5&seq_group=25579, KCT0008861.

A proof of concept phase II study with the PDE-4 inhibitor roflumilast in patients with mild cognitive impairment or mild Alzheimer's disease dementia (ROMEMA): study protocol of a double-blind, randomized, placebo-controlled, between-subjects trial

BACKGROUND

Research into the neurobiological underpinnings of learning and memory has demonstrated the cognitive-enhancing effects associated with diverse classes of phosphodiesterase (PDE) inhibitors. Specific PDE inhibitors have been identified to improve neuronal communication through selective inhibition of PDE activity. Roflumilast, a PDE4 inhibitor, has demonstrated efficacy in enhancing episodic memory in healthy adults and elderly participants with pronounced memory impairment, indicative of amnestic mild cognitive impairment (aMCI). In alignment with these findings, the present protocol aims to provide a proof of concept phase II of the potential of roflumilast to aid patients diagnosed with (a)MCI or mild Alzheimer's disease (AD) dementia.

METHODS

The study will be conducted according to a double-blind, randomized placebo-controlled, between-subjects design. Participants with (a)MCI and mild AD dementia will be recruited through the Memory Clinic at the Maastricht University Medical Centre + (MUMC +) in Maastricht, the Netherlands, alongside outreach through regional hospitals, and social media. The study will have three arms: placebo, 50 μg roflumilast, and 100 μg roflumilast, with a treatment duration of 24 weeks. The primary outcome measure will focus on the assessment of episodic memory, as evaluated through participants' performance on the 15-word Verbal Learning Task (VLT). Our secondary objectives are multifaceted, including an exploration of various cognitive domains. In addition, insights into the well-being and daily functioning of participants will be investigated through interviews with both the participants and their (informal) caregivers, we are interested in the well-being and daily functioning of the participants.

DISCUSSION

The outcomes of the present study aim to elucidate the significance of the PDE4 inhibition mechanism as a prospective therapeutic target for enhancing cognitive function in individuals with (a)MCI and mild AD dementia. Identifying positive effects within these patient cohorts could extend the relevance of this treatment to encompass a broader spectrum of neurological disorders.

TRIAL REGISTRATION

The Medical Ethics Committee of MUMC + granted ethics approval for the 4th version of the protocol on September 10th, 2020. The trial was registered at the European Drug Regulatory Affairs Clinical Trials (EudraCT) registered on the 19th of December 2019 ( https://www.clinicaltrialsregister.eu/ctr-search/trial/2019-004959-36/NL ) and ClinicalTrial.gov (NCT04658654, https://clinicaltrials.gov/study/NCT04658654?intr=roflumilast&cond=mci&rank=1 ) on the 8th of December 2020. The Central Committee on Research Involving Human Subjects (CCMO) granted approval on the 30th of September 2020.

Shortening the Alzheimer's disease assessment scale cognitive subscale

BACKGROUND

A short yet reliable cognitive measure is needed that separates treatment and placebo for treatment trials for Alzheimer's disease. Hence, we aimed to shorten the Alzheimer's Disease Assessment Scale Cognitive Subscale (ADAS-Cog) and test its use as an efficacy measure.

METHODS

Secondary data analysis of participant-level data from five pivotal clinical trials of donepezil compared with placebo for Alzheimer's disease (N = 2,198). Across all five trials, cognition was appraised using the original 11-item ADAS-Cog. Statistical analysis consisted of sample characterization, item response theory (IRT) to identify an ADAS-Cog short version, and mixed models for repeated-measures analysis to examine the effect sizes of ADAS-Cog change on the original and short versions in the placebo versus donepezil groups.

RESULTS

Based on IRT, a short ADAS-Cog was developed with seven items and two response options. The original and short ADAS-Cog correlated at baseline and at weeks 12 and 24 at 0.7. Effect sizes based on mixed modeling showed that the short and original ADAS-Cog separated placebo and donepezil comparably (ADAS-Cog original ES = 0.33, 95% CI = 0.29, 0.40, ADAS-Cog short ES = 0.25, 95% CI =0.23, 0.34).

CONCLUSIONS

IRT identified a short ADAS-cog version that separated donepezil and placebo, suggesting its clinical potential for assessment and treatment monitoring.

Transcranial pulse stimulation in Alzheimer's disease

BACKGROUND

Transcranial pulse stimulation (TPS) is a novel noninvasive ultrasonic brain stimulation that can increase cortical and corticospinal excitability, induce neuroplasticity, and increase functional connectivity within the brain. Several trials have confirmed its potential in treating Alzheimer's disease (AD).

OBJECTIVE

To investigate the effect and safety of TPS on AD.

DESIGN

A systematic review.

METHODS

PubMed, Embase via Ovid, Web of Science, Cochrane Library, CNKI (China National Knowledge Infrastructure), VIP (China Science and Technology Journal Database), and WanFang were searched from inception to April 1, 2023. Study selection, data extraction, and quality evaluation of the studies were conducted by two reviewers independently, with any controversy resolved by consensus. The Methodological Index for Nonrandomized Studies was used to assess the risk of bias.

RESULTS

Five studies were included in this review, with a total of 99 patients with AD. For cognitive performance, TPS significantly improved the scores of the CERAD (Consortium to Establish a Registry for Alzheimer's Disease) test battery, Alzheimer's Disease Assessment Scale (cognitive), Montreal Cognitive Assessment, and Mini-Mental Status Examination. For depressive symptoms, TPS significantly reduced the scores of the Alzheimer's Disease Assessment Scale (affective), Geriatric Depression Score, and Beck Depression Inventory. By functional magnetic resonance imaging, studies have shown that TPS improved cognitive performance in AD patients by increasing functional connectivity in the hippocampus, parahippocampal cortex, precuneus, and parietal cortex, and activating cortical activity in the bilateral hippocampus. TPS alleviated depressive symptoms in AD patients by decreasing functional connectivity between the ventromedial network (left frontal orbital cortex) and the salience network (right anterior insula). Adverse events in this review, including headache, worsening mood, jaw pain, nausea, and drowsiness, were reversible and lasted no longer than 1 day. No serious adverse events or complications were observed.

CONCLUSIONS

TPS is promising in improving cognitive performance and reducing depressive symptoms in patients with AD. TPS may be a safe adjunct therapy in the treatment of AD. However, these findings lacked a sham control and were limited by the small sample size of the included studies. Further research may be needed to better explore the potential of TPS.

PATIENT AND PUBLIC INVOLVEMENT

Patients and the public were not involved in this study.

Harnessing Speech-Derived Digital Biomarkers to Detect and Quantify Cognitive Decline Severity in Older Adults

INTRODUCTION

Current cognitive assessments suffer from floor/ceiling and practice effects, poor psychometric performance in mild cases, and repeated assessment effects. This study explores the use of digital speech analysis as an alternative tool for determining cognitive impairment. The study specifically focuses on identifying the digital speech biomarkers associated with cognitive impairment and its severity.

METHODS

We recruited older adults with varying cognitive health. Their speech data, recorded via a wearable microphone during the reading aloud of a standard passage, were processed to derive digital biomarkers such as timing, pitch, and loudness. Cohen's d effect size highlighted group differences, and correlations were drawn to the Montreal Cognitive Assessment (MoCA). A stepwise approach using a Random Forest model was implemented to distinguish cognitive states using speech data and predict MoCA scores based on highly correlated features.

RESULTS

The study comprised 59 participants, with 36 demonstrating cognitive impairment and 23 serving as cognitively intact controls. Among all assessed parameters, similarity, as determined by Dynamic Time Warping (DTW), exhibited the most substantial positive correlation (rho = 0.529, p < 0.001), while timing parameters, specifically the ratio of extra words, revealed the strongest negative correlation (rho = -0.441, p < 0.001) with MoCA scores. Optimal discriminative performance was achieved with a combination of four speech parameters: total pause time, speech-to-pause ratio, similarity via DTW, and intelligibility via DTW. Precision and balanced accuracy scores were found to be 88.1 ± 1.2% and 76.3 ± 1.3%, respectively.

DISCUSSION

Our research proposes that reading-derived speech data facilitates the differentiation between cognitively impaired individuals and cognitively intact, age-matched older adults. Specifically, parameters based on timing and similarity within speech data provide an effective gauge of cognitive impairment severity. These results suggest speech analysis as a viable digital biomarker for early detection and monitoring of cognitive impairment, offering novel approaches in dementia care.

Brain 11β-Hydroxysteroid Dehydrogenase Type 1 Occupancy by Xanamem™ Assessed by PET in Alzheimer's Disease and Cognitively Normal Individuals

BACKGROUND

11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) regulates intracellular cortisol and its inhibition by the small molecule inhibitor, Xanamem™, may provide a disease-modifying strategy for Alzheimer's disease (AD). Animal models suggest a range of 30-60% enzyme inhibition may suffice to provide neuroprotection.

OBJECTIVE

To determine the regional brain occupancy of 11β-HSD1 by Xanamem™ in cognitively normal participants (CN) and mild cognitive impairment (MCI)/mild AD patients to investigate potential dosing ranges for future efficacy studies.

METHODS

Seventeen MCI/AD and 23 CN were included. Regional brain time-activity curves (TAC), standardized uptake values (SUV40-60) and volume of distribution (VT) from Logan plot with image derived input function from 11C-TARACT positron emission tomography (PET) were used to assess the degree of 11β-HSD1 occupancy by increasing doses of Xanamem™ (5 mg, 10 mg, 20 mg or 30 mg daily for 7 days).

RESULTS

All measures showed high 11β-HSD1 occupancy with Xanamem to similar degree in CN and MCI/AD. The dose-response relationship was relatively flat above 5 mg. Respective median (interquartile range [Q1-Q3]) 11β-HSD1 occupancy in the MCI/AD and CN groups after treatment with 10 mg Xanamem were 80% [79-81%] and 75% [71-76%] in the neocortex, 69% [64-70%] and 61% [52-63%] in the medial temporal lobe, 80% [79-80%] and 73% [68-73%] in the basal ganglia, and 71% [67-75%] and 66% [62-68%] in the cerebellum.

CONCLUSIONS

TAC, SUV40-60, and VT measures indicate Xanamem achieves high target occupancy levels with near saturation at 10 mg daily. These data support exploration of doses of≤10 mg daily in future clinical studies.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach

Background

The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points.

Objective

To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach.

Methods

We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways.

Conclusions

Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

ATN Classification and Clinical Progression of the Amyloid-Negative Group in Alzheimer's Disease Neuroimaging Initiative Participants

Alzheimer's disease has recently been classified using three biological markers (amyloid [A], tau [T], and neurodegeneration [N]) to help elucidate its progression. We aimed to investigate whether there were differences between cognitive function and the clinical dementia symptoms over time relative to the ATN classification in the amyloid-negative group. In the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, 310 participants who underwent all the tests required for ATN classification were enrolled. The cognitive function score differences (Alzheimer's Disease Assessment Scale-Cognitive Subscale 13 [ADAS-Cog 13], Clinical Dementia Rating Sum of Boxes [CDR-SOB], and Mini-Mental State Examination [MMSE]) between the groups were analyzed using the analysis of covariance and score changes over time with a linear mixed-effects model. In the cross-sectional analysis, ADAS-Cog 13 scores were higher for A-T-N+ and A-T+N+ than for A-T-N- (p<0.001) and A-T+N- (p<0.001). In the longitudinal analysis, CDR-SOB scores for A-T+N+ deteriorated faster than A-T-N- (p<0.001), A-T+N- (p<0.001) and A-T-N+ (p<0.001). Hippocampal atrophy progressed faster in A-T-N+ (p<0.001) and A-T+N+ (p=0.02) than in A-T-N-. Through this study, we discovered that even in individuals classified as amyloid negative, neurodegeneration with tau deposition exacerbates cognitive decline and worsens clinical symptoms, underscoring the need for continuous monitoring and observation.

Efficacy and Concurrent Validity of Computerized Brain Training Based on Everyday Living (BTEL) Based on Instrumental Activities of Living for Cognitively Healthy Old Individuals: A Preliminary Study

Background

Interventions to prevent or attenuate cognitive decline and dementia in older adults are becoming increasingly important. Recently, cognitive training exercise can be via computer or mobile technology for independent or home use. Recent meta-analysis has reported that Computerized Cognitive Training (CCT) is effective at enhancing cognitive function in healthy older and Alzheimer's disease adults, although little is known about individual characteristics of each computerized program.

Objective

We developed a new CCT named Brain Training Based on Everyday Living (BTEL) to enhance cognitive capacity for Instrumental Activities of Daily Living (IADL). We aim to evaluate the efficacy of the BTEL among cognitively healthy old individuals and to explore its concurrent validity and construct concept.

Methods

We conducted a double-blind study where 106 individuals aged 65 years and older (intervened = 53, control = 53) worked on the active and placebo tasks three times a week over three months (clinical trial: UMIN000048730). The main results were examined using ANCOVA and calculating correlation coefficients.

Results

We found no effect on total score of the three tests; however, there was significant effect for the BTEL on: recognition in MMSE, and immediate recall in HDSR. The tasks are associated with prefrontal cortex. In addition, correlations indicated that each BTEL domain had some validity as a cognitive assessment tool. Different from previous CCT, we determined the neuropsychological characteristics of specific cognitive tasks of the BTEL to a certain degree.

Conclusions

We found modest efficacy of the BTEL in cognitively healthy old individuals and confirmed its concurrent validity and the conceptual construct.

The effect of black mulberry (Morus nigra) consumption on cognition in patients with mild-to-moderate Alzheimer's dementia: A pilot feasibility study

The purpose of this study was to test the feasibility and the possible effects of black mulberry on cognitive functions in Alzheimer's disease (AD). A total of 39 participants aged 65+ with mild-to-moderate AD were involved and 20 subjects were administered 20g of black mulberry concentrate daily for 12 weeks (intervention group), while 19 received no intervention (control group). Cognitive assessment utilized with Mini-Mental State Examination (MMSE) and AD Assessment Scale-Cognitive Subscale (ADAS-Cog) tests; Geriatric Depression Scale (GDS-15) used for depression screening. At the end of the study, although MMSE scores decreased (p = 0.003) and GDS-15 scores increased (p = 0.034) in control group, there was no change in intervention group. On the contrary, ADAS-Cog scores decreased in intervention group (p = 0.002) while the control group showed no improvement. This study showed that treatment of black mulberry (Morus nigra) for 12 weeks may slightly improve cognitive function in patients with AD.

Disease2Vec: Encoding Alzheimer's progression via disease embedding tree

For decades, a variety of predictive approaches have been proposed and evaluated in terms of their prediction capability for Alzheimer's Disease (AD) and its precursor - mild cognitive impairment (MCI). Most of them focused on prediction or identification of statistical differences among different clinical groups or phases, especially in the context of binary or multi-class classification. The continuous nature of AD development and transition states between successive AD related stages have been typically overlooked. Though a few progression models of AD have been studied recently, they were mainly designed to determine and compare the order of specific biomarkers. How to effectively predict the individual patient's status within a wide spectrum of continuous AD progression has been largely understudied. In this work, we developed a novel learning-based embedding framework to encode the intrinsic relations among AD related clinical stages by a set of meaningful embedding vectors in the latent space (Disease2Vec). We named this process as disease embedding. By Disease2Vec, our framework generates a disease embedding tree (DETree) which effectively represents different clinical stages as a tree trajectory reflecting AD progression and thus can be used to predict clinical status by projecting individuals onto this continuous trajectory. Through this model, DETree can not only perform efficient and accurate prediction for patients at any stages of AD development (across five fine-grained clinical groups instead of typical two groups), but also provide richer status information by examining the projecting locations within a wide and continuous AD progression process. (Code will be available: https://github.com/qidianzl/Disease2Vec.).

Effect of hyperbaric oxygen on symptoms of dementia in patients with delayed encephalopathy after acute carbon monoxide poisoning

OBJECTIVES

Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is the most severe complication of carbon monoxide poisoning, which seriously endangers patients' quality of life. This study aims to investigate the efficacy of hyperbaric oxygen (HBO2) on improving dementia symptoms in patients with DEACMP.

METHODS

A retrospective analysis was performed on DEACMP patients, who visited Xiangya Hospital, Central South University from June 2014 to June 2020. Among them, patients who received conventional drug treatment combined with HBO2 treatment were included in an HBO2 group, while those who only received conventional drug treatment were included in a control group. HBO2 was administered once daily. Patients in the HBO2 group received 6 courses of treatment, with each course consisting of 10 sessions. The Hasegawa Dementia Scale (HDS) was used to diagnose dementia, and the Clinical Dementia Rating (CDR) was used to grade the severity of dementia for DEACMP. The Alzheimer's Disease Assessment Scale-Cognitive Section (ADAS-Cog), the Functional Activities Questionnaire (FAQ), the Neuropsychiatric Inventory (NPI), and the Clinician's Interview-Based Impression of Change-Plus Caregiver Input (CIBIC-Plus) were performed to assess cognitive function, ability to perform activities of daily living (ADL), behavioral and psychological symptoms, and overall function. The study further analyzed the results of objective examinations related to patients' dementia symptoms, including magnetic resonance imaging detection of white matter lesions and abnormal electroencephalogram (EEG). The changes of the above indicators before and after treatment, as well as the differences between the 2 groups after treatment were compared.

RESULTS

There was no significant difference in the HDS score and CDR grading between the 2 groups before treatment (both P>0.05). After treatment, the score of ADAS-Cog, FAQ, NPI, and CIBIC Plus grading of the 2 groups were significantly improved, and the improvement of the above indicators in the HBO2 group was greater than that in the control group (all P<0.05). The effective rate of the HBO2 group in treating DEACMP was significantly higher than that of the control group (89.47% vs 65.87%, P<0.05). The objective examination results (white matter lesions and abnormal EEG) showed that the recovery of patients in the HBO2 group was better than that in the control group.

CONCLUSIONS

Hyperbaric oxygen can significantly relieve the symptoms of dementia in patients with DEACMP.

Next-generation cognitive assessment: Combining functional brain imaging, system perturbations and novel equipment interfaces

Conventional cognitive assessment is widely used in clinical and research settings, in educational institutions, and in the corporate world for personnel selection. Such approaches involve having a client, a patient, or a research participant complete a series of standardized cognitive tasks in order to challenge specific and global cognitive abilities, and then quantify performance for the desired end purpose. The latter may include a diagnostic confirmation of a disease, description of a state or ability, or matching cognitive characteristics to a particular occupational role requirement. Metrics derived from cognitive assessments are putatively informative about important features of the brain and its function. For this reason, the research sector also makes use of cognitive assessments, most frequently as a stimulus for cognitive activity from which to extract functional neuroimaging data. Such "task-related activations" form the core of the most widely used neuroimaging technologies, such as fMRI. Much of what we know about the brain has been drawn from the interleaving of cognitive assessments of various types with functional brain imaging technologies. Despite innovation in neuroimaging (i.e., quantifying the neural response), relatively little innovation has occurred on task presentation and volitional response measurement; yet these together comprise the core of cognitive performance. Moreover, even when cognitive assessment is interleaved with functional neuroimaging, this is most often undertaken in the research domain, rather than the primary applications of cognitive assessment in diagnosis and monitoring, education and personnel selection. There are new ways in which brain imaging-and even more importantly, brain modulation-technologies can be combined with automation and artificial intelligence to deliver next-generation cognitive assessment methods. In this review paper, we describe some prototypes for how this can be done and identify important areas for progress (technological and otherwise) to enable it to happen. We will argue that the future of cognitive assessment will include semi- and fully-automated assessments involving neuroimaging, standardized perturbations via neuromodulation technologies, and artificial intelligence. Furthermore, the fact that cognitive assessments take place in a social/interpersonal context-normally between the patient and clinician-makes the human-machine interface consequential, and this will also be discussed.

Would You Screen This Patient for Cognitive Impairment? : Grand Rounds Discussion From Beth Israel Deaconess Medical Center

Dementia, according to the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, is defined by a significant decline in 1 or more cognitive domains that interferes with a person's independence in daily activities. Mild cognitive impairment (MCI) differs from dementia in that the impairment is not sufficient to interfere with independence. For the purposes of this discussion, cognitive impairment (CI) includes both dementia and MCI. Various screening tests are available for CI. These tests ask patients to perform a series of tasks that assess 1 or more domains of cognitive function or ask a caregiver to report on the patient's abilities. A positive result on a screening test does not equate to a diagnosis of CI; rather, it should lead to additional testing to confirm the diagnosis. On review of the evidence, the U.S. Preventive Services Task Force (USPSTF) concluded in 2020 that the evidence was insufficient to assess the balance of benefits and harms of screening for CI in older adults ("I statement"). The USPSTF did clarify that although there is insufficient evidence, there may be important reasons to identify CI. In this article, 2 experts review the available evidence to answer the following questions: What screening tools are available, and how effective are they in identifying patients with CI? What interventions are available for patients found to have CI, to what extent do they improve patient outcomes, and what, if any, negative effects occur? And, would they recommend screening for CI, and why or why not?

Theory of mind deficits in Parkinson's disease are not modulated by dopaminergic medication

Introduction

Patients with Parkinson's disease (PD) exhibit deficits in social cognition, particularly with respect to Theory of Mind (ToM) capacities. It is unclear whether they are associated with PD-related dopamine deficiency and modulated by levodopa replacement therapy.

Methods

A total of 15 persons with PD and 13 healthy controls (HC) participated in the study. They performed different neuropsychological tasks, including the Faux Pas Recognition Test (FPRT), assessing different dimensions of cognitive ToM (e.g., detection, inappropriateness, intentions), and the Reading the Mind in the Eyes Test (RMET) as an index of affective ToM. Persons with PD were tested twice, once under their regular treatment and another time after at least 18 h of levodopa withdrawal (MED-ON and MED-OFF, respectively). On either occasion, serum drug levels and motor symptom severity [Unified Parkinson's Disease Rating Scale (UPDRS)] were measured.

Results

MED-ON and MED-OFF conditions in patients with PD were confirmed by higher serum drug levels in the former than in the latter state and a corresponding amelioration of the motor deficit. In so doing, no performance difference in any ToM-related task was identified as a function of the levodopa therapy. Generally, patients performed worse than controls in both affective and cognitive ToM tests.

Conclusion

Patients with PD have deficits in cognitive and affective ToM. Dopamine replacement, effective for improving the motor condition, does not appear to counteract these dysfunctions.

Retrospective observational study of a novel smartphone app on the management of patients with mild cognitive impairment or mild dementia

Introduction

In this study, we aimed to evaluate the feasibility, utility, and potential effects of LQ-M/D App, a smartphone application developed by Life Quest Inc., Tokyo, Japan, for patients with mild cognitive impairment (MCI) and mild dementia. The app incorporates cognitive and physical exercise training, lifestyle habit acquisition features, and a continuity improvement feature added in the post-update version to enhance user engagement. The continuity improvement feature includes the optimization of training content, and disease education, and enables family monitoring via a family app.

Methods

A retrospective analysis was conducted on app usage, cognitive and exercise training implementation and interruptions, questionnaire response rates, and cognitive assessments in a single institution. A total of 20 patients used the app, with 10 patients using the pre-update version without the continuity improvement feature, and the other 10 patients using the post-update version with the continuity improvement feature.

Results and Conclusion

The results demonstrated that the LQ-M/D App could be effectively used by the study population, and the continuity improvement feature positively influenced app usage in several aspects. Although a potential association between app usage and cognitive ability was suggested, the scatter in the data points warrants cautious interpretation. Limitations of the study included a small sample size, a single institution setting, and the retrospective nature of the study. In the future, a randomized controlled trial design using a larger sample size and multiple institutions to further evaluate the effectiveness of LQ-M/D App in managing MCI and mild dementia should be performed.

Disentangling accelerated cognitive decline from the normal aging process and unraveling its genetic components: A neuroimaging-based deep learning approach

Background

The progressive cognitive decline that is an integral component of AD unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and Alzheimer's disease between different chronological points.

Methods

We developed a deep-learning framework based on dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline.

Results

We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G>T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neuron and plays a role in controlling cell growth and differentiation. In addition, MUC7 and PROL1/OPRPNon chromosome 4 were significant at the gene level. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Furthermore, we found that the cognitive decline slope GWAS was positively correlated with previous AD GWAS.

Conclusion

Our deep learning model was demonstrated effective on extracting relevant neuroimaging features and predicting individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene. Our approach has the potential to disentangle accelerated cognitive decline from the normal aging process and to determine its related genetic factors, leveraging opportunities for early intervention.

Sensitivity of memory subtests and learning slopes from the ADAS-Cog to distinguish along the continuum of the NIA-AA Research Framework for Alzheimer's Disease

Despite extensive use of the Alzheimer's Disease (AD) Assessment Scale - Cognitive Subscale (ADAS-Cog) in AD research, exploration of memory subtests or process scores from the measure has been limited. The current study sought to establish validity for the ADAS-Cog Word Recall Immediate and Delayed Memory subtests and learning slope scores by showing that they are sensitive to AD biomarker status. Word Recall subtest and learning slope scores were calculated for 441 participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 90). All participants were categorized using the NIA-AA Research Framework - based on PET-imaging of β-amyloid (A) and tau (T) deposition - as Normal AD Biomarkers (A-T-), Alzheimer's Pathologic Change (A + T-), or Alzheimer's disease (A + T+). Memory subtest and learning slope performances were compared between biomarker status groups, and with regard to how well they discriminated samples with (A + T+) and without (A-T-) biomarkers. Lower Word Recall memory subtest scores - and scores for a particular learning slope calculation, the Learning Ratio - were observed for the AD (A + T+) group than the other biomarker groups. Memory subtest and Learning Ratio scores further displayed fair to good receiver operator characteristics when differentiating those with and without AD biomarkers. When comparing across learning slopes, the Learning Ratio metric consistently outperformed others. ADAS-Cog memory subtests and the Learning Ratio score are sensitive to AD biomarker status along the continuum of the NIA-AA Research Framework, and the results offer criterion validity for use of these subtests and process scores as unique markers of memory capacity.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease

BACKGROUND

Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD.

METHODS

We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset.

RESULTS

Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset.

CONCLUSIONS

The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

Effects of Exercise Alone or Combined With Cognitive Training and Vitamin D Supplementation to Improve Cognition in Adults With Mild Cognitive Impairment: A Randomized Clinical Trial

Importance

Exercise, cognitive training, and vitamin D may enhance cognition in older adults with mild cognitive impairment (MCI).

Objective

To determine whether aerobic-resistance exercises would improve cognition relative to an active control and if a multidomain intervention including exercises, computerized cognitive training, and vitamin D supplementation would show greater improvements than exercise alone.

Design, Setting, and Participants

This randomized clinical trial (the SYNERGIC Study) was a multisite, double-masked, fractional factorial trial that evaluated the effects of aerobic-resistance exercise, computerized cognitive training, and vitamin D on cognition. Eligible participants were between ages 65 and 84 years with MCI enrolled from September 19, 2016, to April 7, 2020. Data were analyzed from February 2021 to December 2022.

Interventions

Participants were randomized to 5 study arms and treated for 20 weeks: arm 1 (multidomain intervention with exercise, cognitive training, and vitamin D), arm 2 (exercise, cognitive training, and placebo vitamin D), arm 3 (exercise, sham cognitive training, and vitamin D), arm 4 (exercise, sham cognitive training, and placebo vitamin D), and arm 5 (control group with balance-toning exercise, sham cognitive training, and placebo vitamin D). The vitamin D regimen was a 10 000 IU dose 3 times weekly.

Main Outcomes and Measures

Primary outcomes were changes in ADAS-Cog-13 and Plus variant at 6 months.

Results

Among 175 randomized participants (mean [SD] age, 73.1 [6.6] years; 86 [49.1%] women), 144 (82%) completed the intervention and 133 (76%) completed the follow-up (month 12). At 6 months, all active arms (ie, arms 1 through 4) with aerobic-resistance exercise regardless of the addition of cognitive training or vitamin D, improved ADAS-Cog-13 when compared with control (mean difference, -1.79 points; 95% CI, -3.27 to -0.31 points; P = .02; d = 0.64). Compared with exercise alone (arms 3 and 4), exercise and cognitive training (arms 1 and 2) improved the ADAS-Cog-13 (mean difference, -1.45 points; 95% CI, -2.70 to -0.21 points; P = .02; d = 0.39). No significant improvement was found with vitamin D. Finally, the multidomain intervention (arm 1) improved the ADAS-Cog-13 score significantly compared with control (mean difference, -2.64 points; 95% CI, -4.42 to -0.80 points; P = .005; d = 0.71). Changes in ADAS-Cog-Plus were not significant.

Conclusions and Relevance

In this clinical trial, older adults with MCI receiving aerobic-resistance exercises with sequential computerized cognitive training significantly improved cognition, although some results were inconsistent. Vitamin D supplementation had no effect. Our findings suggest that this multidomain intervention may improve cognition and potentially delay dementia onset in MCI.

Trial Registration

ClinicalTrials.gov Identifier: NCT02808676.

Turning the Spotlight to Cholinergic Pharmacotherapy of the Human Language System

Even though language is essential in human communication, research on pharmacological therapies for language deficits in highly prevalent neurodegenerative and vascular brain diseases has received little attention. Emerging scientific evidence suggests that disruption of the cholinergic system may play an essential role in language deficits associated with Alzheimer's disease and vascular cognitive impairment, including post-stroke aphasia. Therefore, current models of cognitive processing are beginning to appraise the implications of the brain modulator acetylcholine in human language functions. Future work should be directed further to analyze the interplay between the cholinergic system and language, focusing on identifying brain regions receiving cholinergic innervation susceptible to modulation with pharmacotherapy to improve affected language domains. The evaluation of language deficits in pharmacological cholinergic trials for Alzheimer's disease and vascular cognitive impairment has thus far been limited to coarse-grained methods. More precise, fine-grained language testing is needed to refine patient selection for pharmacotherapy to detect subtle deficits in the initial phases of cognitive decline. Additionally, noninvasive biomarkers can help identify cholinergic depletion. However, despite the investigation of cholinergic treatment for language deficits in Alzheimer's disease and vascular cognitive impairment, data on its effectiveness are insufficient and controversial. In the case of post-stroke aphasia, cholinergic agents are showing promise, particularly when combined with speech-language therapy to promote trained-dependent neural plasticity. Future research should explore the potential benefits of cholinergic pharmacotherapy in language deficits and investigate optimal strategies for combining these agents with other therapeutic approaches.

Modeling Alzheimer's disease progression utilizing clinical trial and ADNI data to predict longitudinal trajectory of CDR-SB

There is strong interest in developing predictive models to better understand individual heterogeneity and disease progression in Alzheimer's disease (AD). We have built upon previous longitudinal AD progression models, using a nonlinear, mixed-effect modeling approach to predict Clinical Dementia Rating Scale - Sum of Boxes (CDR-SB) progression. Data from the Alzheimer's Disease Neuroimaging Initiative (observational study) and placebo arms from four interventional trials (N = 1093) were used for model building. The placebo arms from two additional interventional trials (N = 805) were used for external model validation. In this modeling framework, CDR-SB progression over the disease trajectory timescale was obtained for each participant by estimating disease onset time (DOT). Disease progression following DOT was described by both global progression rate (RATE) and individual progression rate (α). Baseline Mini-Mental State Examination and CDR-SB scores described the interindividual variabilities in DOT and α well. This model successfully predicted outcomes in the external validation datasets, supporting its suitability for prospective prediction and use in design of future trials. By predicting individual participants' disease progression trajectories using baseline characteristics and comparing these against the observed responses to new agents, the model can help assess treatment effects and support decision making for future trials.

Amyloid beta-correlated plasma metabolite dysregulation in Alzheimer's disease: an untargeted metabolism exploration using high-resolution mass spectrometry toward future clinical diagnosis

Introduction

Alzheimer's disease (AD) is a leading cause of dementia, and it has rapidly become an increasingly burdensome and fatal disease in society. Despite medical research advances, accurate recognition of AD remains challenging. Epidemiological evidence suggests that metabolic abnormalities are tied to higher AD risk.

Methods

This study utilized case-control analyses with plasma samples and identified a panel of 27 metabolites using high-resolution mass spectrometry in both the Alzheimer's disease (AD) and cognitively normal (CN) groups. All identified variables were confirmed using MS/MS with detected fragmented ions and public metabolite databases. To understand the expression of amyloid beta proteins in plasma, ELISA assays were performed for both amyloid beta 42 (Aβ42) and amyloid beta 40 (Aβ40).

Results

The levels of plasma metabolites PAGln and L-arginine were found to significantly fluctuate in the peripheral blood of AD patients. In addition, ELISA results showed a significant increase in amyloid beta 42 (Aβ42) in AD patients compared to those who were cognitively normal (CN), while amyloid beta 40 (Aβ40) did not show any significant changes between the groups. Furthermore, positive correlations were observed between Aβ42/Aβ40 and PAGln or L-arginine, suggesting that both metabolites could play a role in the pathology of amyloid beta proteins. Binary regression analysis with these two metabolites resulted in an optimal model of the ROC (AUC = 0.95, p < 0.001) to effectively discriminate between AD and CN.

Discussion

This study highlights the potential of advanced high-resolution mass spectrometry (HRMS) technology for novel plasma metabolite discovery with high stability and sensitivity, thus paving the way for future clinical studies. The results of this study suggest that the combination of PAGln and L-arginine holds significant potential for improving the diagnosis of Alzheimer's disease (AD) in clinical settings. Overall, these findings have important implications for advancing our understanding of AD and developing effective approaches for its future clinical diagnosis.

Differential relationships of NMDAR hypofunction and oxidative stress with cognitive decline

NMDAR hypofunction and oxidative stress are implicated in the pathogenesis of Alzheimer's disease. D-amino acid oxidase (DAO) regulates NMDAR function. Glutathione, superoxide dismutase, and catalase are three first-line endogenous antioxidants. This study explored the associations of these potential biomarkers with mild cognitive impairment. Cognitive function and blood levels of DAO, glutathione, superoxide dismutase, and catalase were measured in 63 mild cognitive impairment patients and 24 healthy individuals every 6 months for 2 years. Among the patients, DAO and glutathione levels at baseline contributed to the cognitive decline 2 years later. Among the healthy individuals, only glutathione levels were associated with cognitive change. The four biomarkers differed in change directions (upward vs. downward) in the patients and in the healthy individuals. Among patients, glutathione levels were negatively correlated with superoxide dismutase and positively correlated with catalase, and DAO levels were negatively correlated with superoxide dismutase. To our knowledge, this is the first study to demonstrate the differential associations of NMDAR hypofunction and oxidative stress with cognitive change between the mild cognitive impairment patients and healthy people. Glutathione may be regarded as an aging marker for both mild cognitive impairment and normal aging; and DAO, a biomarker exclusively for mild cognitive impairment.

Key questions for the evaluation of anti-amyloid immunotherapies for Alzheimer's disease

The clinical benefit associated with anti-amyloid immunotherapies, a new class of drugs for the treatment of Alzheimer's disease, is predicated on their ability to modify disease course by lowering brain amyloid levels. At the time of writing, two amyloid-lowering antibodies, aducanumab and lecanemab, have obtained United States Food and Drug Administration accelerated approval, with further agents of this class in the Alzheimer's disease treatment pipeline. Based on limited published clinical trial data to date, regulators, payors and physicians will need to assess their efficacy, clinical effectiveness and safety, as well as cost and accessibility. We propose that attention to three important questions related to treatment efficacy, clinical effectiveness and safety should guide evidence-based consideration of this important class of drugs. These are: (1) Were trial statistical analyses appropriate and did they convincingly support claims of efficacy? (2) Do reported treatment effects outweigh safety concerns and are they generalizable to a representative clinical population of people with Alzheimer's disease? and (3) Do the data convincingly demonstrate disease course modification, suggesting that increasing clinical benefits beyond the duration of the trials are likely? We suggest specific approaches to interpreting trial results for these drugs and highlight important areas of uncertainty where additional data and a cautious interpretation of existing results is warranted. Safe, effective and accessible treatments for Alzheimer's disease are eagerly awaited by millions of patients and their caregivers worldwide. While amyloid-targeting immunotherapies may be promising disease-modifying Alzheimer's disease treatments, rigorous and unbiased assessment of clinical trial data is critical to regulatory decision-making and subsequently determining their provision and utility in routine clinical practice. Our recommendations provide a framework for evidence-based appraisal of these drugs by regulators, payors, physicians and patients.

Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks

Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.

Frameworks for estimating causal effects in observational settings: comparing confounder adjustment and instrumental variables

To estimate causal effects, analysts performing observational studies in health settings utilize several strategies to mitigate bias due to confounding by indication. There are two broad classes of approaches for these purposes: use of confounders and instrumental variables (IVs). Because such approaches are largely characterized by untestable assumptions, analysts must operate under an indefinite paradigm that these methods will work imperfectly. In this tutorial, we formalize a set of general principles and heuristics for estimating causal effects in the two approaches when the assumptions are potentially violated. This crucially requires reframing the process of observational studies as hypothesizing potential scenarios where the estimates from one approach are less inconsistent than the other. While most of our discussion of methodology centers around the linear setting, we touch upon complexities in non-linear settings and flexible procedures such as target minimum loss-based estimation and double machine learning. To demonstrate the application of our principles, we investigate the use of donepezil off-label for mild cognitive impairment. We compare and contrast results from confounder and IV methods, traditional and flexible, within our analysis and to a similar observational study and clinical trial.

Translating clinical notes into quantitative measures-a real-world observation on the response to cholinesterase inhibitors or selective serotonin reuptake inhibitors prescribed to outpatients with dementia using electronic medical records

Objective: Cholinesterase inhibitors (CEIs) are prescribed for dementia to maintain or improve memory. Selective serotonin reuptake inhibitors (SSRIs) are also prescribed to manage psychiatric symptoms seen in dementia. What proportion of outpatients actually responds to these drugs is still unclear. Our objective was to investigate the responder rates of these medications in an outpatient setting using the electronic medical record (EMR). Methods: We used the Johns Hopkins EMR system to identify patients with dementia who were prescribed a CEI or SSRI for the first time between 2010 and 2021. Treatment effects were assessed through routinely documented clinical notes and free-text entries in which healthcare providers record clinical findings and impressions of patients. Responses were scored using a three-point Likert scale named the NOte-based evaluation method for Treatment Efficacy (NOTE) in addition to the Clinician's Interview-Based Impression of Change Plus caregiver input (CIBIC-plus), a seven-point Likert scale used in clinical trials. To validate NOTE, the relationships between NOTE and CIBIC-plus and between NOTE and change in MMSE (Mini-Mental State Examination) before and after medication were examined. Inter-rater reliability was evaluated using Krippendorff's alpha. The responder rates were calculated. Results: NOTE showed excellent inter-rater reliability and correlated well with CIBIC-plus and changes in MMSEs. Out of 115 CEI cases, 27.0% reported improvement and 34.8% reported stable symptoms in cognition; out of 225 SSRI cases, 69.3% reported an improvement in neuropsychiatric symptoms. Conclusion: NOTE showed high validity in measuring the pharmacotherapy effects based on unstructured clinical entries. Although our real-world observation included various types of dementia, the results were remarkably similar to what was reported in controlled clinical trials of Alzheimer's disease and its related neuropsychiatric symptoms.

Naoxin'an capsules protect brain function and structure in patients with vascular cognitive impairment

Introduction: Vascular cognitive impairment (VCI) is one of the most common types of dementia. Naoxin'an capsule (NXA), a traditional Chinese medicine compound, has been used to treat VCI for a long time in the clinic. Previous studies proved that the NXA capsules could ameliorate the cerebral mitochondrion deficits of VCI animals. This study aimed to investigate the protectiveness of NXA on human brain structure and function in patients with VCI. Methods: In total, 100 VCI patients were enrolled in this 24-week trial and randomly divided into the NXA capsules group (n = 50) and the ginkgo biloba capsules control group (n = 50). Before and after the treatment, cognitive behavior tests and multimodal brain magnetic resonance imaging were analyzed to comprehensively evaluate the effectiveness of NXA treatment on VCI patients after 24 weeks. Results: We found that the NXA group significantly improved overall cognitive ability (Alzheimer's Disease Assessment Scale-Cognitive section, p = 0.001; Mini-Mental Status Examination, p = 0.003), memory (Rey-Osterrieth Complex Figure test, p < 0.001) and executive function (Trail Making Test-A, p = 0.024) performance after treatment compared with the control group. For brain function, the degree of centrality in the left middle frontal gyrus, right postcentral gyrus, and left supplementary motor area increased in the NXA group and decreased in the ginkgo biloba group after treatment. The fractional amplitude of low-frequency fluctuation (fALFF) of the left precentral and right superior parietal gyrus increased, and the fALFF of the right parahippocampal and left inferior temporal gyrus decreased in the NXA group after treatment. For brain structure, the gray matter density of the left postcentral gyrus increased in the NXA group after treatment, and the total volume of white matter hyperintensity showed a decreasing trend but was not statistically significant. Furthermore, the improvement effect of NXA on executive function was associated with changes in brain function. Conclusion: These findings suggest that the NXA capsules improved cognitive performance and multiregional brain function, as well as gray matter structure in the postcentral gyrus.

Test-retest reliabilities and minimal detectable changes of 5 versions of the Alzheimer's Disease Assessment Scale-Cognitive Subscale in people with dementia

PURPOSE

To compare the test-retest reliability and minimal detectable change (MDC) of the commonly used versions of the Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) (the ADAS-Cog-11 (11 items), ADAS-Cog-3 (three items), ADAS-Cog-5-Subset (five items), ADAS-Cog-6-Subset (six items), and ADAS-Rasch (11 items)) in people with dementia.

MATERIALS AND METHODS

A repeated-assessments design (2 weeks apart) was used to examine the ADAS-Cog-11, ADAS-Cog-3, ADAS-Cog-5-Subset, ADAS-Cog-6-Subset, and ADAS-Rasch. Participants with dementia were recruited from one hospital, one elder care center, and two day-care centers using convenience sampling.

RESULTS

Fifty-two participants finished the assessments twice in two weeks. All versions showed high intraclass correlation coefficients (ICCs) (0.82-0.96), minimal standardized response means (-0.07 to 0.08) and low to acceptable MDC% (9.2-28.6%). The ADAS-Rasch had the highest ICC (0.96) and the lowest MDC%. The ADAS-Cog-3 had an ICC lower than 0.90 (0.82) and the highest MDC% (28.6%).

CONCLUSIONS

The ADAS-Rasch seems to be the most reliable version of the ADAS-Cog for group- and individual-level comparisons. The ADAS-Cog-3 may be a better choice for researchers for group-level comparisons because it requires fewer items to achieve acceptable reliability. The ADAS-Cog-11, ADAS-Cog-5-Subset, ADAS-Cog-6-Subset, and ADAS-Rasch could be considered for clinical usage for individual-level comparisons.Implications for rehabilitationThe ADAS-Rasch is the most reliable version of the ADAS-Cog for group- and individual-level comparisons due to its excellent test-retest reliability, lowest random measurement error and absence of a practice effect.The ADAS-Cog-5-Subset and ADAS-Cog-6-Subset might be good substitutes for the ADAS-Rasch in clinical settings because of their comparable reliability features and superior administration efficiency.

Class activation attention transfer neural networks for MCI conversion prediction

Accurate prediction of the trajectory of Alzheimer's disease (AD) from an early stage is of substantial value for treatment and planning to delay the onset of AD. We propose a novel attention transfer method to train a 3D convolutional neural network to predict which patients with mild cognitive impairment (MCI) will progress to AD within 3 years. A model is first trained on a separate but related source task (task we are transferring information from) to automatically learn regions of interest (ROI) from a given image. Next we train a model to simultaneously classify progressive MCI (pMCI) and stable MCI (sMCI) (the target task we want to solve) and the ROIs learned from the source task. The predicted ROIs are then used to focus the model's attention on certain areas of the brain when classifying pMCI versus sMCI. Thus, in contrast to traditional transfer learning, we transfer attention maps instead of transferring model weights from a source task to the target classification task. Our Method outperformed all methods tested including traditional transfer learning and methods that used expert knowledge to define ROI. Furthermore, the attention map transferred from the source task highlights known Alzheimer's pathology.

Uncovering heterogeneous cognitive trajectories in mild cognitive impairment: a data-driven approach

BACKGROUND

Given the complex and progressive nature of mild cognitive impairment (MCI), the ability to delineate and understand the heterogeneous cognitive trajectories is crucial for developing personalized medicine and informing trial design. The primary goals of this study were to examine whether different cognitive trajectories can be identified within subjects with MCI and, if present, to characterize each trajectory in relation to changes in all major Alzheimer's disease (AD) biomarkers over time.

METHODS

Individuals with a diagnosis of MCI at the first visit and ≥ 1 follow-up cognitive assessment were selected from the Alzheimer's Disease Neuroimaging Initiative database (n = 936; age 73 ± 8; 40% female; 16 ± 3 years of education; 50% APOE4 carriers). Based on the Alzheimer's Disease Assessment Scale-Cognitive Subscale-13 (ADAS-Cog-13) total scores from baseline up to 5 years follow-up, a non-parametric k-means longitudinal clustering method was performed to obtain clusters of individuals with similar patterns of cognitive decline. We further conducted a series of linear mixed-effects models to study the associations of cluster membership with longitudinal changes in other cognitive measures, neurodegeneration, and in vivo AD pathologies.

RESULTS

Four distinct cognitive trajectories emerged. Cluster 1 consisted of 255 individuals (27%) with a nearly non-existent rate of change in the ADAS-Cog-13 over 5 years of follow-up and a healthy-looking biomarker profile. Individuals in the cluster 2 (n = 336, 35%) and 3 (n = 240, 26%) groups showed relatively mild and moderate cognitive decline trajectories, respectively. Cluster 4, comprising about 11% of our study sample (n = 105), exhibited an aggressive cognitive decline trajectory and was characterized by a pronouncedly abnormal biomarker profile.

CONCLUSIONS

Individuals with MCI show substantial heterogeneity in cognitive decline. Our findings may potentially contribute to improved trial design and patient stratification.

EEG resting-state networks in Alzheimer's disease associated with clinical symptoms

Alzheimer's disease (AD) is a progressive neuropsychiatric disease affecting many elderly people and is characterized by progressive cognitive impairment of memory, visuospatial, and executive functions. As the elderly population is growing, the number of AD patients is increasing considerably. There is currently growing interest in determining AD's cognitive dysfunction markers. We used exact low-resolution-brain-electromagnetic-tomography independent-component-analysis (eLORETA-ICA) to assess activities of five electroencephalography resting-state-networks (EEG-RSNs) in 90 drug-free AD patients and 11 drug-free patients with mild-cognitive-impairment due to AD (ADMCI). Compared to 147 healthy subjects, the AD/ADMCI patients showed significantly decreased activities in the memory network and occipital alpha activity, where the age difference between the AD/ADMCI and healthy groups was corrected by linear regression analysis. Furthermore, the age-corrected EEG-RSN activities showed correlations with cognitive function test scores in AD/ADMCI. In particular, decreased memory network activity showed correlations with worse total cognitive scores for both Mini-Mental-State-Examination (MMSE) and Alzheimer's Disease-Assessment-Scale-cognitive-component-Japanese version (ADAS-J cog) including worse sub-scores for orientation, registration, repetition, word recognition and ideational praxis. Our results indicate that AD affects specific EEG-RSNs and deteriorated network activity causes symptoms. Overall, eLORETA-ICA is a useful, non-invasive tool for assessing EEG-functional-network activities and provides better understanding of the neurophysiological mechanisms underlying the disease.

Two Routes to Alzheimer's Disease Based on Differential Structural Changes in Key Brain Regions

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disorder with homogenous disease patterns. Neuropathological changes precede symptoms by up to two decades making neuroimaging biomarkers a prime candidate for early diagnosis, prognosis, and patient stratification.

OBJECTIVE

The goal of the study was to discern intermediate AD stages and their precursors based on neuroanatomical features for stratifying patients on their progression through different stages.

METHODS

Data include grey matter features from 14 brain regions extracted from longitudinal structural MRI and cognitive data obtained from 1,017 healthy controls and AD patients of ADNI. AD progression was modeled with a Hidden Markov Model, whose hidden states signify disease stages derived from the neuroanatomical data. To tie the progression in brain atrophy to a behavioral marker, we analyzed the ADAS-cog sub-scores in the stages.

RESULTS

The optimal model consists of eight states with differentiable neuroanatomical features, forming two routes crossing once at a very early point and merging at the final state. The cortical route is characterized by early and sustained atrophy in cortical regions. The limbic route is characterized by early decrease in limbic regions. Cognitive differences between the two routes are most noticeable in the memory domain with subjects from the limbic route experiencing stronger memory impairments.

CONCLUSION

Our findings corroborate that more than one pattern of grey matter deterioration with several discernable stages can be identified in the progression of AD. These neuroanatomical subtypes are behaviorally meaningful and provide a door into early diagnosis of AD and prognosis of the disease's progression.

Neuroimaging studies of acupuncture on Alzheimer's disease: a systematic review

BACKGROUND

Acupuncture effectively improves cognitive function in Alzheimer's disease (AD). Many neuroimaging studies have found significant brain alterations after acupuncture treatment of AD, but the underlying central modulation mechanism is unclear.

OBJECTIVE

This review aims to provide neuroimaging evidence to understand the central mechanisms of acupuncture in patients with AD.

METHODS

Relevant neuroimaging studies about acupuncture for AD were retrieved from eight English and Chinese medicine databases (PubMed, Embase, Cochrane Library, Web of Science, SinoMed, CNKI, WF, VIP) and other resources from inception of databases until June 1, 2022, and their methodological quality was assessed using RoB 2.0 and ROBINS - I. Brain neuroimaging information was extracted to investigate the potential neural mechanism of acupuncture for AD. Descriptive statistics were used for data analysis.

RESULTS

Thirteen neuroimaging studies involving 275 participants were included in this review, and the overall methodological quality of included studies was moderate. The approaches applied included task-state functional magnetic resonance imaging (ts-fMRI; n = 9 studies) and rest-state functional magnetic resonance imaging (rs-fMRI; n = 4 studies). All studies focused on the instant effect of acupuncture on the brains of AD participants, including the cingulate gyrus, middle frontal gyrus, and cerebellum, indicating that acupuncture may regulate the default mode, central executive, and frontoparietal networks.

CONCLUSION

This study provides evidence of the neural mechanisms underlying the effect of acupuncture on AD involving cognitive- and motor-associated networks. However, this evidence is still in the preliminary investigation stage. Large-scale, well-designed, multimodal neuroimaging trials are still required to provide comprehensive insight into the central mechanism underlying the effect of acupuncture on AD. (Systematic review registration at PROSPERO, No. CRD42022331527).

Simulating the multicausality of Alzheimer's disease with system dynamics

INTRODUCTION

In Alzheimer's disease (AD), cognitive decline is driven by various interlinking causal factors. Systems thinking could help elucidate this multicausality and identify opportune intervention targets.

METHODS

We developed a system dynamics model (SDM) of sporadic AD with 33 factors and 148 causal links calibrated with empirical data from two studies. We tested the SDM's validity by ranking intervention outcomes on 15 modifiable risk factors to two sets of 44 and 9 validation statements based on meta-analyses of observational data and randomized controlled trials, respectively.

RESULTS

The SDM answered 77% and 78% of the validation statements correctly. Sleep quality and depressive symptoms yielded the largest effects on cognitive decline with which they were connected through strong reinforcing feedback loops, including via phosphorylated tau burden.

DISCUSSION

SDMs can be constructed and validated to simulate interventions and gain insight into the relative contribution of mechanistic pathways.

Dual Semi-Supervised Learning for Classification of Alzheimer's Disease and Mild Cognitive Impairment Based on Neuropsychological Data

Deep learning has shown impressive diagnostic abilities in Alzheimer's disease (AD) research in recent years. However, although neuropsychological tests play a crucial role in screening AD and mild cognitive impairment (MCI), there is still a lack of deep learning algorithms only using such basic diagnostic methods. This paper proposes a novel semi-supervised method using neuropsychological test scores and scarce labeled data, which introduces difference regularization and consistency regularization with pseudo-labeling. A total of 188 AD, 402 MCI, and 229 normal controls (NC) were enrolled in the study from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We first chose the 15 features most associated with the diagnostic outcome by feature selection among the seven neuropsychological tests. Next, we proposed a dual semi-supervised learning (DSSL) framework that uses two encoders to learn two different feature vectors. The diagnosed 60 and 120 subjects were randomly selected as training labels for the model. The experimental results show that DSSL achieves the best accuracy and stability in classifying AD, MCI, and NC (85.47% accuracy for 60 labels and 88.40% accuracy for 120 labels) compared to other semi-supervised methods. DSSL is an excellent semi-supervised method to provide clinical insight for physicians to diagnose AD and MCI.