Multivariate neuroanatomical correlates of behavioral and psychological symptoms in dementia and the moderating role of education

Multisensory stimulation reduces neuropsychiatric symptoms and enhances cognitive function in older adults with dementia: A meta-analysis of randomized controlled trials

OBJECTIVE

Multisensory stimulation defined as engaging multiple senses (visual, olfactory, auditory, gustatory, and tactile), has been demonstrated to improve older adults' general health. However, its effectiveness in mitigating neuropsychiatric symptoms (NPSs) and cognitive deficits in older adults with dementia remains unclear. This meta-analysis evaluated the efficacy of multisensory stimulation in ameliorating NPSs and improving overall cognitive function in older adults with dementia.

METHODS

We searched eight databases to September 2024 without restriction. Older adults with all stages of dementia aged 65 years and above were included. To estimate the pooled effect size, Hedge's g (g) values were calculated using a random-effects model. Heterogeneity was assessed using the Q, I², and τ² statistics. Subgroup and meta-regression analyses were performed to identify moderators. Publication bias was assessed using Begg and Mazumdar's rank correlation and Egger's linear regression tests.

RESULTS

This review included 16 studies (974 patients). Multisensory stimulation significantly reduced agitation (g= -0.96; 95 %CI= -1.44, -0.48), apathy (g= -1.27; 95 %CI= -2.08, -0.46), and depression (g= -0.28; 95 %CI= -0.48, -0.07). Moreover, the intervention significantly improved overall cognitive function (g= 0.30; 95 %CI= 0.09, 0.52). However, multisensory stimulation had no significant effect on anxiety (g= -0.81; 95 %CI= -1.79, 0.17). Significant heterogeneity was observed in agitation, apathy, and anxiety. Moreover, meta-regression analyses by educational level (junior high school and above) revealed significant moderators in agitation.

CONCLUSIONS

Multisensory stimulation shows promise as a non-pharmacological intervention for older adults with dementia. It may effectively mitigate NPSs and improve cognitive function into clinical practice as an alternative therapeutic.

Multiomics Reveals Biological Mechanisms Linking Macroscale Structural Covariance Network Dysfunction With Neuropsychiatric Symptoms Across the Alzheimer's Disease Continuum

BACKGROUND

The high heterogeneity of neuropsychiatric symptoms (NPSs) hinders further exploration of their role in neurobiological mechanisms and Alzheimer's disease (AD). We aimed to delineate NPS patterns based on brain macroscale connectomics to understand the biological mechanisms of NPSs on the AD continuum.

METHODS

We constructed regional radiomics similarity networks for 550 participants (AD with NPSs [n = 376], AD without NPSs [n = 111], and normal control participants [n = 63]) from the CIBL (Chinese Imaging, Biomarkers, and Lifestyle) study. We identified regional radiomics similarity network connections associated with NPSs and then clustered distinct subtypes of AD with NPSs. An independent dataset (n = 189) and internal validation were performed to assess the robustness of the NPS subtypes. Subsequent multiomics analysis was performed to assess the distinct clinical phenotype and biological mechanisms in each NPS subtype.

RESULTS

AD patients with NPSs were clustered into severe (n = 187), moderate (n = 87), and mild (n = 102) NPS subtypes, each exhibiting distinct brain network dysfunction patterns. A high level of consistency in clustering NPSs was internally and externally validated. Severe and moderate NPS subtypes were associated with significant cognitive impairment, increased plasma p-tau181 (tau phosphorylated at threonine 181) levels, extensive decreased brain volume and cortical thickness, and accelerated cognitive decline. Gene set enrichment analysis revealed enrichment of differentially expressed genes in ion transport and synaptic transmission with variations for each NPS subtype. Genome-wide association study analysis defined the specific gene loci for each subtype of AD with NPSs (e.g., logical memory), consistent with clinical manifestations and progression patterns.

CONCLUSIONS

This study identified and validated 3 distinct NPS subtypes, underscoring the role of NPSs in neurobiological mechanisms and progression of the AD continuum.

Dementia Subtypes Defined Through Neuropsychiatric Symptom-Associated Brain Connectivity Patterns

Importance

Understanding the heterogeneity of neuropsychiatric symptoms (NPSs) and associated brain abnormalities is essential for effective management and treatment of dementia.

Objective

To identify dementia subtypes with distinct functional connectivity associated with neuropsychiatric subsyndromes.

Design, Setting, and Participants

Using data from the Open Access Series of Imaging Studies-3 (OASIS-3; recruitment began in 2005) and Alzheimer Disease Neuroimaging Initiative (ADNI; recruitment began in 2004) databases, this cross-sectional study analyzed resting-state functional magnetic resonance imaging (fMRI) scans, clinical assessments, and neuropsychological measures of participants aged 42 to 95 years. The fMRI data were processed from July 2022 to February 2024, with secondary analysis conducted from August 2022 to March 2024. Participants without medical conditions or medical contraindications for MRI were recruited.

Main Outcomes and Measures

A multivariate sparse canonical correlation analysis was conducted to identify functional connectivity-informed NPS subsyndromes, including behavioral and anxiety subsyndromes. Subsequently, a clustering analysis was performed on obtained latent connectivity profiles to reveal neurophysiological subtypes, and differences in abnormal connectivity and phenotypic profiles between subtypes were examined.

Results

Among 1098 participants in OASIS-3, 177 individuals who had fMRI and at least 1 NPS at baseline were included (78 female [44.1%]; median [IQR] age, 72 [67-78] years) as a discovery dataset. There were 2 neuropsychiatric subsyndromes identified: behavioral (r = 0.22; P = .002; P for permutation = .007) and anxiety (r = 0.19; P = .01; P for permutation = .006) subsyndromes from connectivity NPS-associated latent features. The behavioral subsyndrome was characterized by connections predominantly involving the default mode (within-network contribution by summed correlation coefficients = 54) and somatomotor (within-network contribution = 58) networks and NPSs involving nighttime behavior disturbance (R = -0.29; P < .001), agitation (R = -0.28; P = .001), and apathy (R = -0.23; P = .007). The anxiety subsyndrome mainly consisted of connections involving the visual network (within-network contribution = 53) and anxiety-related NPSs (R = 0.36; P < .001). By clustering individuals along these 2 subsyndrome-associated connectivity latent features, 3 subtypes were found (subtype 1: 45 participants; subtype 2: 43 participants; subtype 3: 66 participants). Patients with dementia of subtype 3 exhibited similar brain connectivity and cognitive behavior patterns to those of healthy individuals. However, patients with dementia of subtypes 1 and 2 had different dysfunctional connectivity profiles involving the frontoparietal control network (FPC) and somatomotor network (the difference by summed z values was 230 within the SMN and 173 between the SMN and FPC for subtype 1 and 473 between the SMN and visual network for subtype 2) compared with those of healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity (eg, the median [IQR] of the total score of NPSs was 2 [2-7] for subtype 3 vs 6 [3-8] for subtype 1; P = .04 and 5.5 [3-11] for subtype 2; P = .03) and longitudinal progression of cognitive impairment and behavioral dysfunction (eg, the overall interaction association between time and subtypes to orientation was F = 4.88; P = .008; using the time × subtype 3 interaction item as the reference level: β = 0.05; t = 2.6 for time × subtype 2; P = .01). These findings were further validated using a replication dataset of 193 participants (127 female [65.8%]; median [IQR] age, 74 [69-77] years) consisting of 154 newly released participants from OASIS-3 and 39 participants from ADNI.

Conclusions and Relevance

These findings may provide a novel framework to disentangle the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the timely development of targeted interventions for patients with dementia.

Malnutrition and neuropsychiatric symptoms in dementia: the Cache County Dementia Progression Study

OBJECTIVES

Among people with dementia, poor nutritional status has been associated with worse cognitive and functional decline, but few studies have examined its association with neuropsychiatric symptoms (NPS). We examined this topic in a population-based sample of persons with dementia.

DESIGN

Longitudinal, observational cohort study.

SETTING

Community.

PARTICIPANTS

Two hundred ninety-two persons with dementia (71.9% Alzheimer's disease, 56.2% women) were followed up to 6 years.

MEASUREMENTS

We used a modified Mini-Nutritional Assessment (mMNA) and the Neuropsychiatric Inventory (NPI) to evaluate nutritional status and NPS, respectively. Individual linear mixed effects models examined the associations between time-varying mMNA total score or clinical categories (malnourishment, risk for malnourishment, or well-nourished) and NPI total score (excluding appetite domain) or NPI individual domain or cluster (e.g. psychosis) scores. Covariates tested were dementia onset age, type, and duration, medical comorbidities, sex, apolipoprotein E (APOE) genotype, and education.

RESULTS

Compared to the well-nourished, those at risk for malnourishment and those malnourished had higher total NPI scores [b (95% CI) = 1.76 (0.04, 3.48) or 3.20 (0.62, 5.78), respectively], controlling for significant covariates. Higher mMNA total score (better nutritional status) was associated with lower total NPI [b (95% CI) = -0.58 (-0.86, -0.29)] and lower domain scores for psychosis [b (95% CI) = -0.08 (-0.16, .004)], depression [b (95% CI = -0.11 (-0.16, -0.05], and apathy [b (95% CI = -0.19 (-0.28, -0.11)].

CONCLUSIONS

Worse nutritional status is associated with more severe NPS. Dietary or behavioral interventions to prevent malnutrition may be beneficial for persons with dementia.

Defining Dementia Subtypes Through Neuropsychiatric Symptom-Linked Brain Connectivity Patterns

BACKGROUND

Dementia is highly heterogeneous, with pronounced individual differences in neuropsychiatric symptoms (NPS) and neuroimaging findings. Understanding the heterogeneity of NPS and associated brain abnormalities is essential for effective management and treatment of dementia.

METHODS

Using large-scale neuroimaging data from the Open Access Series of Imaging Studies (OASIS-3), we conducted a multivariate sparse canonical correlation analysis to identify functional connectivity-informed symptom dimensions. Subsequently, we performed a clustering analysis on the obtained latent connectivity profiles to reveal neurophysiological subtypes and examined differences in abnormal connectivity and phenotypic profiles between subtypes.

RESULTS

We identified two reliable neuropsychiatric subsyndromes - behavioral and anxiety in the connectivity-NPS linked latent space. The behavioral subsyndrome was characterized by the connections predominantly involving the default mode and somatomotor networks and neuropsychiatric symptoms involving nighttime behavior disturbance, agitation, and apathy. The anxiety subsyndrome was mainly contributed by connections involving the visual network and the anxiety neuropsychiatric symptom. By clustering individuals along these two subsyndromes-linked connectivity latent features, we uncovered three subtypes encompassing both dementia patients and healthy controls. Dementia in one subtype exhibited similar brain connectivity and cognitive-behavior patterns to healthy individuals. However, dementia in the other two subtypes showed different dysfunctional connectivity profiles involving the default mode, frontoparietal control, somatomotor, and ventral attention networks, compared to healthy individuals. These dysfunctional connectivity patterns were associated with differences in baseline dementia severity and longitudinal progression of cognitive impairment and behavioral dysfunction.

CONCLUSIONS

Our findings shed valuable insights into disentangling the neuropsychiatric and brain functional heterogeneity of dementia, offering a promising avenue to improve clinical management and facilitate the development of timely and targeted interventions for dementia patients.

The presence and severity of neuropsychiatric symptoms and their association with quality of life among patients with dementia

BACKGROUND

Neuropsychiatric symptoms are common manifestations of dementia. The presence and severity of these symptoms differ depending on different personal and contextual factors.

PURPOSE

This study aimed to investigate the presence and predictors of neuropsychiatric symptoms and the association between the severity of these symptoms and the quality of life in a sample of patients with dementia in Jordan.

METHODS

In this cross-sectional study, 112 patients with dementia residing in Jordanian nursing homes were recruited using the consecutive sampling method.

RESULTS

The mean severity of neuropsychiatric symptoms was 9.58. The most prevalent neuropsychiatric symptoms among patients were depression (61.6%), followed by irritability (55.4%), and a feeling of euphoria (54.5%). The regression analysis results indicated that gender, marital status, and dementia severity significantly predicted the neuropsychiatric symptoms severity score and explained 17.70% of the variance. A significant negative correlation between the severity of neuropsychiatric symptoms and quality of life was found.

CONCLUSION

The study's results indicate that our sample reported mild neuropsychiatric symptoms. These symptoms' high prevalence and persistence negatively impact patients' quality of life. The study's results can help mental health nurses determine the factors impacting effective treatment strategies to combat these symptoms. Future longitudinal studies are warranted to help explain the importance of early diagnosis and management of these symptoms in preventing dementia progression.

Effective Connectivity Between the Orbitofrontal Cortex and the Precuneus Differentiates Major Psychiatric Disorders: Results from a Transdiagnostic Spectral DCM Study

Translational psychiatry has been a hot topic in neurosciences research. The authors present a commentary on the relevant findings from a transdiagnostic study applicable to clinic practice. Additional discussion on conceptual and clinical insight into this current broad line of research is explored in the integration of multi-level paradigm in Psychiatry research.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

The roles of executive functioning, simple attention, and medial temporal lobes in early learning, late learning, and delayed recall

Fractionating performance of a verbal list-learning test can provide a nuanced interpretation of the relationship between brain networks and learning and memory abilities. Within older adult samples, including those with mild cognitive impairment and Alzheimer’s disease, cortical volumes for attention and executive functioning networks correlate more strongly with neuropsychological performance measures of early learning trials relative to late learning and delayed recall. In contrast, medial temporal lobe (MTL) structures, such as the hippocampus, are more strongly correlated to performance on late learning and delayed recall measures relative to early learning. We sought to extend these findings by evaluating the contributions of simple attention, executive function (EF), and MTL structures to learning and recall in a cognitively heterogeneous sample of older adults that included healthy controls (n = 54), adults with MCI (n = 63), and those with dementia (n = 13). We used canonical correlation analyses to test the hypotheses that the contributions of EF, simple attention, and the MTL to verbal memory would differ across phases of learning and recall. Results showed that relationships between the MTL and memory were the only ones to demonstrate a graded pattern of association, ranging from r = .46 to .57 across early learning, late learning, and delayed recall. Simple attention and EF were both significantly and moderately related to learning and recall, but those relationships did not vary across phases as hypothesized. We explore alternative interpretations for our discrepant findings, including the influence of sample characteristics and methodology, advocating for multivariate approaches.

The Assessment of Cognitive Reserve: A Systematic Review of the Most Used Quantitative Measurement Methods of Cognitive Reserve for Aging

The cognitive reserve (CR) is widely accepted as the active ability to cope with brain damage, using preexisting cognitive and compensatory processes. The common CR proxies used are the number of formal years of education, intelligence quotient (IQ) or premorbid functioning, occupation attainment, and participation in leisure activities. More recently, it has employed the level of literacy and engagement in high-level cognitive demand of professional activities. This study aims to identify and summarize published methodologies to assess the CR quantitatively. We searched for published studies on PubMed, ScienceDirect, and Web of Science between September 2018 and September 2021. We only included those studies that characterized the CR assessment methodology. The search strategy identified 1,285 publications, of which 25 were included. Most of the instruments targeted proxies individually. The lack of a gold standard tool that incorporates all proxies and cognitive tests highlights the need to develop a more holistic battery for the quantitative assessment of CR. Further studies should focus on a quantitative methodology that includes all these proxies supported by normative data to improve the use of CR as a valid measure in clinical contexts.

The Role of Individual Characteristics in Predicting Short- and Long-Term Cognitive and Psychological Benefits of Cognitive Stimulation Therapy for Mild-to-Moderate Dementia

Introduction: This study examined the role of individual characteristics in predicting short- and long-term benefits of the Italian version of Cognitive Stimulation Therapy (CST-IT), an evidence-based intervention for people with mild-to-moderate dementia.

Materials and Methods: Data were drawn from a sample (N = 123) of people with dementia (PwD) who took part in a multicenter controlled clinical trial of CST-IT. Assessments at pre-test, immediately after completing the treatment, and 3 months later investigated the following outcomes: general cognitive functioning and language, mood and behavior, everyday functioning, and quality of life. Age, education and baseline (pre-test) cognitive functioning, mood (depression) and behavioral and neuropsychiatric symptoms were considered as predictors of any short- and long-term benefits.

Results: Linear mixed-effects models showed that different individual characteristics -particularly education and age- influenced the benefits of CST-IT, depending on the outcome measures considered. Higher education predicted larger gains in general cognitive functioning and, along with less severe depressive symptoms, in language (magnification effects). Older age was associated with positive changes in mood (compensation effects). Albeit very modestly, older age was also associated with larger gains in everyday functioning (compensation effects). Gains in quality of life were predicted by older age and lower education (compensation effects). Baseline cognitive functioning, mood and/or behavioral symptoms broadly influenced performance too, but their role again depended on the outcomes considered.

Discussion: These findings underscore the importance of considering and further exploring how psychosocial interventions like CST are affected by individual characteristics in order to maximize their efficacy for PwD.

Multivariate patterns of brain-behavior associations across the adult lifespan

The nature of brain-behavior covariations with increasing age is poorly understood. In the current study, we used a multivariate approach to investigate the covariation between behavioral-health variables and brain features across adulthood. We recruited healthy adults aged 20–73 years-old (29 younger, mean age = 25.6 years; 30 older, mean age = 62.5 years), and collected structural and functional MRI (s/fMRI) during a resting-state and three tasks. From the sMRI, we extracted cortical thickness and subcortical volumes; from the fMRI, we extracted activation peaks and functional network connectivity (FNC) for each task. We conducted canonical correlation analyses between behavioral-health variables and the sMRI, or the fMRI variables, across all participants. We found significant covariations for both types of neuroimaging phenotypes (ps = 0.0004) across all individuals, with cognitive capacity and age being the largest opposite contributors. We further identified different variables contributing to the models across phenotypes and age groups. Particularly, we found behavior was associated with different neuroimaging patterns between the younger and older groups. Higher cognitive capacity was supported by activation and FNC within the executive networks in the younger adults, while it was supported by the visual networks’ FNC in the older adults. This study highlights how the brain-behavior covariations vary across adulthood and provides further support that cognitive performance relies on regional recruitment that differs between older and younger individuals.

Peripheral Leukocytosis Predicts Cognitive Decline but Not Behavioral Disturbances: A Nationwide Study of Alzheimer's and Parkinson's Disease Patients

INTRODUCTION

Peripheral and central nervous system inflammation have been linked to the classic symptoms of Parkinson's disease (PD) and Alzheimer's disease (AD). However, it remains unclear whether the analysis of routine systemic inflammatory markers could represent a useful prediction tool to identify clinical subtypes in patients with Parkinson's and Alzheimer's at higher risk of dementia-associated symptoms, such as behavioral and psychological symptoms of dementia (BPSD).

METHODS

We performed a multivariate logistic regression using the 2016 and 2017 National Inpatient Sample with International Classification of Diseases 10th edition codes to assess if pro-inflammatory white blood cells (WBCs) anomalies correlate with dementia and BPSD in patients with these disorders.

RESULTS

We found that leukocytosis was the most common WBC inflammatory marker identified in 3.9% of Alzheimer's and 3.3% Parkinson's patients. Leukocytosis was also found to be an independent risk factor for Parkinson's dementia. Multivariate analysis of both cohorts showed that leukocytosis is significantly decreased in patients with BPSD compared to patients without BPSD.

CONCLUSIONS

These results suggest a link between leukocytosis and the pathophysiology of cognitive dysfunction in both PD and AD. A better understanding of the role of systemic neuroinflammation on these devastating neurodegenerative disorders may facilitate the development of cost-effective blood biomarkers for patient's early diagnosis and more accurate prognosis.