Acting Before; A Combined Strategy to Counteract the Onset and Progression of Dementia

Current Challenges in the Diagnosis of Progressive Neurocognitive Disorders: A Critical Review of the Literature and Recommendations for Primary and Secondary Care

Screening for early symptoms of cognitive impairment enables timely interventions for patients and their families. Despite the advances in dementia diagnosis, the current nosography of neurocognitive disorders (NCDs) seems to overlook some clinical manifestations and predictors that could contribute to understanding the conversion from an asymptomatic stage to a very mild one, eventually leading to obvious disease. The present review examines different diagnostic approaches in view of neurophysiological and neuropsychological evidence of NCD progression, which may be subdivided into: (1) preclinical stage; (2) transitional stage; (3) prodromal or mild stage; (4) major NCD. The absence of univocal criteria and the adoption of ambiguous or narrow labels might complicate the diagnostic process. In particular, it should be noted that: (1) only neuropathological hallmarks characterize preclinical NCD; (2) transitional NCD must be assessed through proactive neuropsychological protocols; (3) prodromal/mild NCDs are based on cognitive functional indicators; (4) major NCD requires well-established tools to evaluate its severity stage; (5) insight should be accounted for by both patient and informants. Therefore, the examination of evolving epidemiological and clinical features occurring at each NCD stage may orient primary and secondary care, allowing for more targeted prevention, diagnosis, and/or treatment of both cognitive and functional impairment.

Atrophy of specific amygdala subfields in subjects converting to mild cognitive impairment

Introduction

Accumulating evidence indicates that the amygdala exhibits early signs of Alzheimer's disease (AD) pathology. However, it is still unknown whether the atrophy of distinct subfields of the amygdala also participates in the transition from healthy cognition to mild cognitive impairment (MCI).

Methods

Our sample was derived from the AD Neuroimaging Initiative 3 and consisted of 97 cognitively healthy (HC) individuals, sorted into two groups based on their clinical follow-up: 75 who remained stable (s-HC) and 22 who converted to MCI within 48 months (c-HC). Anatomical magnetic resonance (MR) images were analyzed using a semi-automatic approach that combines probabilistic methods and a priori information from ex vivo MR images and histology to segment and obtain quantitative structural metrics for different amygdala subfields in each participant. Spearman's correlations were performed between MR measures and baseline and longitudinal neuropsychological measures. We also included anatomical measurements of the whole amygdala, the hippocampus, a key target of AD-related pathology, and the whole cortical thickness as a test of spatial specificity.

Results

Compared with s-HC individuals, c-HC subjects showed a reduced right amygdala volume, whereas no significant difference was observed for hippocampal volumes or changes in cortical thickness. In the amygdala subfields, we observed selected atrophy patterns in the basolateral nuclear complex, anterior amygdala area, and transitional area. Macro-structural alterations in these subfields correlated with variations of global indices of cognitive performance (measured at baseline and the 48-month follow-up), suggesting that amygdala changes shape the cognitive progression to MCI.

Discussion

Our results provide anatomical evidence for the early involvement of the amygdala in the preclinical stages of AD.

Highlights

Amygdala's atrophy marks elderly progression to mild cognitive impairment (MCI).Amygdala's was observed within the basolateral and amygdaloid complexes.Macro-structural alterations were associated with cognitive decline.No atrophy was found in the hippocampus and cortex.

Altered Medial Prefrontal Connectivity in Parkinson's Disease Patients with Somatic Symptoms

BACKGROUND

The high co-occurrence of somatic symptom disorder (SSD) in Parkinson's disease (PD) patients suggests overlapping pathophysiology. However, little is known about the neural correlates of SSD and their possible interactions with PD. Existing studies have shown that SSD is associated with reduced task-evoked activity in the medial prefrontal cortex (mPFC), a central node of the default-mode network (DMN). SSD is also associated with abnormal γ-aminobutyric acid (GABA) content, a marker of local inhibitory tone and regional hypoactivity, in the same area when SSD co-occurs with PD.

OBJECTIVES

To disentangle the individual and shared effects of SSD and PD on mPFC neurotransmission and connectivity patterns and help disclose the neural mechanisms of comorbidity in the PD population.

METHODS

The study cohort included 18 PD patients with SSD (PD + SSD), 18 PD patients, 13 SSD patients who did not exhibit neurologic disorders, and 17 healthy subjects (HC). Proton magnetic resonance (MR) spectroscopy evaluated GABA levels within a volume of interest centered on the mPFC. Resting-state functional MR imaging investigated the region's functional connectivity patterns.

RESULTS

Compared to HC or PD groups, the mPFC of SSD subjects exhibited higher GABA levels and connectivity. Higher mPFC connectivity involved DMN regions in SSD patients without PD and regions of the executive and attentional networks (EAN) in patients with PD comorbidity.

CONCLUSIONS

Aberrant reconfigurations of connectivity patterns between the mPFC and the EAN are distinct features of the PD + SSD comorbidity. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The Association Between Alzheimer's Disease-Related Markers and Physical Activity in Cognitively Normal Older Adults

Previous studies have indicated that physical activity may be beneficial in reducing the risk for Alzheimer's disease (AD), although the underlying mechanisms are not fully understood. The goal of this study was to evaluate the relationship between habitual physical activity levels and brain amyloid deposition and AD-related blood biomarkers (i.e., measured using a novel high-performance mass spectrometry-based assay), in apolipoprotein E (APOE) ε4 carriers and noncarriers. We evaluated 143 cognitively normal older adults, all of whom had brain amyloid deposition assessed using positron emission tomography and had their physical activity levels measured using the International Physical Activity Questionnaire (IPAQ). We observed an inverse correlation between brain amyloidosis and plasma beta-amyloid (Aβ)₁₋₄₂ but found no association between brain amyloid and plasma Aβ₁₋₄₀ and amyloid precursor protein (APP)_669−711. Additionally, higher levels of physical activity were associated with lower plasma Aβ₁₋₄₀, Aβ₁₋₄₂, and APP_669−711 levels in APOE ε4 noncarriers. The ratios of Aβ₁₋₄₀/Aβ₁₋₄₂ and APP_669−711/Aβ₁₋₄₂, which have been associated with higher brain amyloidosis in previous studies, differed between APOE ε4 carriers and non-carriers. Taken together, these data indicate a complex relationship between physical activity and brain amyloid deposition and potential blood-based AD biomarkers in cognitively normal older adults. In addition, the role of APOE ε4 is still unclear, and more studies are necessary to bring further clarification.

A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer's Disease Spectrum

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD.

OBJECTIVE

To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Alzheimer's Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion.

METHODS

We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables.

RESULTS

The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects.

CONCLUSION

Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.