Hippocampal and entorhinal atrophy in mild cognitive impairment: prediction of Alzheimer disease

Handgrip strength and gait speed relationships with cognitive impairment in Iranian older adults: Birjand longitudinal aging study

BACKGROUND

Cognitive impairment prevalence is rising with population aging. This study examined the relationship between handgrip strength and gait speed with cognitive impairment and performance in older adults.

METHODS

This cross-sectional study used baseline data from Birjand Longitudinal Aging Study (BLAS), including 1347 older adults. Cognitive function was evaluated using the six-item Cognitive Impairment Test (6-CIT). Gait speed was measured over six meters, and grip strength was assessed with hand dynamometers.

RESULTS

After adjusting for potential confounders, the association between low handgrip strength and cognitive impairment lost its significance (relative risk ratio (RRR) = 1.07, 95% CI: 0.70, 1.63). However, low gait speed remained significantly associated with dementia (RRR = 1.92, 95% CI: 1.23, 3.01) after adjusting for age, sex, education, body mass index (BMI), hypertension, and depressive symptoms.

CONCLUSION

Low gait speed is significantly associated with dementia, highlighting the importance of incorporating physical function measures into dementia risk assessments.

Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures

BACKGROUND AND PURPOSE

Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their data sets and/or the number of structures they can identify. This study evaluates the performance of 6 advanced deep learning models in segmenting 122 brain structures from T1-weighted MRI scans, aiming to identify the most effective model for clinical and research applications.

MATERIALS AND METHODS

A total of 1510 T1-weighted MRIs were used to compare 6 deep learning models for the segmentation of 122 distinct gray matter structures: nnU-Net, SegResNet, SwinUNETR, UNETR, U-Mamba_BOT, and U-Mamba_ Enc. Each model was rigorously tested for accuracy by using the dice similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD95). Additionally, the volume of each structure was calculated and compared between normal controls (NCs) and patients with Alzheimer disease (AD).

RESULTS

U-Mamba_Bot achieved the highest performance with a median DSC of 0.9112 (interquartile range [IQR]: 0.8957, 0.9250). nnU-Net achieved a median DSC of 0.9027 [IQR: 0.8847, 0.9205], and had the highest HD95 of 1.392 [IQR: 1.174, 2.029]. The value of each HD95 (<3 mm) indicates its superior capability in capturing detailed brain structures accurately. Following segmentation, volume calculations were performed, and the resultant volumes of NCs and patients with AD were compared. The volume changes observed in 13 brain substructures were all consistent with those reported in existing literature, reinforcing the reliability of the segmentation outputs.

CONCLUSIONS

This study underscores the efficacy of U-Mamba_Bot as a robust tool for detailed brain structure segmentation in T1-weighted MRI scans. The congruence of our volumetric analysis with the literature further validates the potential of advanced deep learning models to enhance the understanding of neurodegenerative diseases such as AD. Future research should consider larger data sets to validate these findings further and explore the applicability of these models in other neurologic conditions.

Sex-specific and cell-type-specific changes in chaperone-mediated autophagy across tissues during aging

Aging leads to progressive decline in organ and tissue integrity and function, partly due to loss of proteostasis and autophagy malfunctioning. A decrease with age in chaperone-mediated autophagy (CMA), a selective type of lysosomal degradation, has been reported in various organs and cells from rodents and humans. Disruption of CMA recapitulates features of aging, whereas activating CMA in mice protects against age-related diseases such as Alzheimer's, retinal degeneration and/or atherosclerosis. However, sex-specific and cell-type-specific differences in CMA with aging remain unexplored. Here, using CMA reporter mice and single-cell transcriptomic data, we report that most organs and cell types show CMA decline with age, with males exhibiting a greater decline with aging. Reduced CMA is often associated with fewer lysosomes competent for CMA. Transcriptional downregulation of CMA genes may further contribute to CMA decline, especially in males. These findings suggest that CMA differences may influence organ vulnerability to age-related degeneration.

Effects of exercise versus usual care on older adults with amnestic mild cognitive impairment: EXERT versus ADNI

INTRODUCTION

EXERT was a multisite randomized controlled trial (RCT) examining the effects of moderate-high intensity aerobic training (AX) versus lower-intensity stretching/balance/range of motion (SBR) on cognitive trajectories in older adults with amnestic mild cognitive impairment (aMCI).

METHODS

Preplanned post-hoc analyses were conducted to compare each arm to a propensity-matched usual care (no intervention) group from Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) selected for similarity across key characteristics. Differences in 12-month trajectories in the primary endpoint (ADAS-Cog-Exec) and magnetic resonance imaging (MRI) volumes in prespecified brain regions were compared.

RESULTS

AX and SBR showed significantly less 12-month cognitive decline than ADNI-1 (AX:n = 109, β = 0.169, 95% confidence interval [CI] 0.011-0.328; SBR:n = 105, β = 0.181, 95% CI 0.007-0.354). There were trends of less prefrontal cortex volume loss for both EXERT groups and less AD signature region volume loss for SBR relative to ADNI-1 over 12 months.

DISCUSSION

Moderate-high intensity aerobic or low-intensity flexibility exercise for 12 months in participants with aMCI may provide protection against decline relative to usual care.

CLINICAL TRIAL REGISTRATION

The EXERT clinical trial is registered at clinicaltrials.gov (NCT02814526).

HIGHLIGHTS

EXERT was a randomized controlled trial in sedentary older adults with aMCI. EXERT arms were propensity-matched to a usual care (no intervention) group (Alzheimer's Disease Neuroimaging Initiative 1 [ADNI-1]). High and low-intensity exercise arms had less 12-mo cognitive decline than ADNI-1. There were trends of less prefrontal cortex volume loss for each arm versus ADNI-1.

Cerebellar-hippocampal volume associations with behavioral outcomes following tDCS modulation

Here, we explore the relationship between transcranial direct current stimulation (tDCS) and brain-behavior interactions. We propose that tDCS perturbation allows for the investigation of relationships between brain volume and behavior. We focused on the hippocampus (HPC) and cerebellum (CB) regions that are implicated in our understanding of memory and motor skill acquisition. Seventy-four young adults (mean age: 22 ± 0.42 years, mean education: 14.7 ± 0.25 years) were randomly assigned to receive either anodal, cathodal, or sham stimulation. Following stimulation, participants completed computerized tasks assessing working memory and sequence learning in a magnetic resonance imaging (MRI) environment. We investigated the statistical interaction between CB and HPC volumes. Our findings showed that individuals with larger cerebellar volumes had shorter reaction times (RT) on a high-load working memory task in the sham stimulation group. In contrast, the anodal stimulation group exhibited faster RTs during the low-load working memory condition. These RT differences were associated with the cortical volumetric interaction between CB-HPC. Literature suggests that anodal stimulation down-regulates the CB and here, those with larger volumes perform more quickly, suggesting the potential need for additional cognitive resources to compensate for cerebellar downregulation or perturbation. This new insight suggests that tDCS can aid in revealing structure-function relationships, due to greater performance variability, especially in young adults. It may also reveal new targets of interest in the study of aging or in diseases where there is also greater behavioral variability.

Development and validation of the Chinese Naming Test (CNT): Diagnostic efficacy and correlation with Alzheimer's disease biomarkers

BackgroundNeuropsychological assessments are essential tools for the screening and diagnosis of patients with cognitive impairments. Cultural background differences significantly affect cognitive test performance. For China, which is rapidly aging, a culturally adaptive picture naming test is urgently needed.ObjectiveThis study aims to develop a Chinese naming test (CNT) adapted to the cultural background of Chinese people and to explore its correlation with Alzheimer's disease (AD) biomarkers.MethodsA total of 1459 participants were recruited, including 744 with normal cognition (NC), 492 with mild cognitive impairment (MCI), and 223 with dementia. All participants underwent a comprehensive neuropsychological assessment. The diagnostic capability of CNT was determined using Receiver Operating Characteristic curves. Part of participants underwent amyloid-β (Aβ) PET scans, tau-PET scans, and MRI scans. The relationships between CNT scores and Aβ and tau deposition, as well as brain structural changes, were analyzed.ResultsThe diagnostic capability of CNT for MCI showed a sensitivity of 68.7%, specificity of 75.6%, and AUC of 0.81; for dementia, the sensitivity was 72.7%, specificity was 89.5%, and AUC was 0.89. The correlation coefficient between CNT scores and brain Aβ burden was -0.11 (p = 0.024). CNT scores correlated with tau burden in different Braak stages (p < 0.05). The correlation coefficient between CNT scores and hippocampus atrophy was -0.15 (p = 0.003).ConclusionsThe CNT has good diagnostic performance in detecting MCI and dementia in Chinese population. There is a correlation between CNT scores and AD imaging markers, indicating that the CNT might has potential value in predicting cognitive changes and disease progression.

A central role for acetylcholine in entorhinal cortex function and dysfunction with age in humans and mice

Structural and functional changes in the entorhinal cortex (EC) are among the earliest signs of cognitive aging. Here, we ask whether a compromised cholinergic system influences early EC impairments and plays a primary role in EC cognition. We evaluated the relationship between loss of integrity of cholinergic inputs to the EC and cognitive deficits in otherwise healthy humans and mice. Using in vivo imaging (PET/MRI) in older humans and high-resolution imaging in wild-type mice and mice with genetic susceptibility to Alzheimer's disease pathology, we establish that loss of cholinergic input to the EC is, in fact, an early feature in cognitive aging. Through mechanistic studies in mice, we find a central role for EC-projecting cholinergic neurons in the expression of EC-related behaviors. Our data demonstrate that alterations to the cholinergic EC are an early, conserved feature of cognitive aging across species and may serve as an early predictor of cognitive status.

Enhanced Multimodal Low-Rank Embedding-Based Feature Selection Model for Multimodal Alzheimer's Disease Diagnosis

Identification of Alzheimer's disease (AD) with multimodal neuroimaging data has been receiving increasing attention. However, the presence of numerous redundant features and corrupted neuroimages within multimodal datasets poses significant challenges for existing methods. In this paper, we propose a feature selection method named Enhanced Multimodal Low-rank Embedding (EMLE) for multimodal AD diagnosis. Unlike previous methods utilizing convex relaxations of the -norm, EMLE exploits an -norm regularized projection matrix to obtain an embedding representation and select informative features jointly for each modality. The -norm, employing an upper-bounded nonconvex Minimax Concave Penalty (MCP) function to characterize sparsity, offers a superior approximation for the -norm compared to other convex relaxations. Next, a similarity graph is learned based on the self-expressiveness property to increase the robustness to corrupted data. As the approximation coefficient vectors of samples from the same class should be highly correlated, an MCP function introduced norm, i.e., matrix -norm, is applied to constrain the rank of the graph. Furthermore, recognizing that diverse modalities should share an underlying structure related to AD, we establish a consensus graph for all modalities to unveil intrinsic structures across multiple modalities. Finally, we fuse the embedding representations of all modalities into the label space to incorporate supervisory information. The results of extensive experiments on the Alzheimer's Disease Neuroimaging Initiative datasets verify the discriminability of the features selected by EMLE.

Shift work schedules alter immune cell regulation and accelerate cognitive impairment during aging

BACKGROUND

Disturbances of the sleep-wake cycle and other circadian rhythms typically precede the age-related deficits in learning and memory, suggesting that these alterations in circadian timekeeping may contribute to the progressive cognitive decline during aging. The present study examined the role of immune cell activation and inflammation in the link between circadian rhythm dysregulation and cognitive impairment in aging.

METHODS

C57Bl/6J mice were exposed to shifted light-dark (LD) cycles (12 h advance/5d) during early adulthood (from ≈ 4-6mo) or continuously to a "fixed" LD12:12 schedule. At middle age (13-14mo), the long-term effects of circadian rhythm dysregulation on cognitive performance, immune cell regulation and hippocampal microglia were analyzed using behavioral, flow cytometry and immunohistochemical assays.

RESULTS

Entrainment of the activity rhythm was stable in all mice on a fixed LD 12:12 cycle but was fully compromised during exposure to shifted LD cycles. Even during "post-treatment" exposure to standard LD 12:12 conditions, re-entrainment in shifted LD mice was marked by altered patterns of entrainment and increased day-to-day variability in activity onset times that persisted into middle-age. These alterations in light-dark entrainment were closely associated with dramatic impairment in the Barnes maze test for the entire group of shifted LD mice at middle age, well before cognitive decline was first observed in aged (18-22mo) animals maintained on fixed LD cycles. In conjunction with the effects of circadian dysregulation on cognition, shifted LD mice at middle age were distinguished by significant expansion of splenic B cells and B cell subtypes expressing the activation marker CD69 or inflammatory marker MHC Class II Invariant peptide (CLIP), differential increases in CLIP+, 41BB-Ligand+, and CD74 + B cells in the meningeal lymphatics, alterations in splenic T cell subtypes, and increased number and altered functional state of microglia in the dentate gyrus. In shifted LD mice, the expansion in splenic B cells was negatively correlated with cognitive performance; when B cell numbers were higher, performance was worse in the Barnes maze. These results indicate that disordered circadian timekeeping associated with early exposure to shift work-like schedules alone accelerates cognitive decline during aging in conjunction with altered regulation of immune cells and microglia in the brain.

An experimental framework for conjoint measures of olfaction, navigation, and motion as pre-clinical biomarkers of Alzheimer's disease

Elucidating Alzheimer's disease (AD) prodromal symptoms can resolve the outstanding challenge of early diagnosis. Based on intrinsically related substrates of olfaction and spatial navigation, we propose a novel experimental framework for their conjoint study. Artificial intelligence-driven multimodal study combining self-collected olfactory and motion data with available big clinical datasets can potentially promote high-precision early clinical screenings to facilitate timely interventions targeting neurodegenerative progression.

Exercise intervention on the brain structure and function of patients with mild cognitive impairment: systematic review based on magnetic resonance imaging studies

Objective

This systematic review evaluates the impact of exercise intervention in MCI patients and discusses the potential neural mechanisms.

Methods

A systematic search and screening of relevant literature was conducted in English and Chinese databases. Based on predefined keywords and criteria, 24 articles were assessed and analyzed.

Results

Structurally, a significant increase was observed in the hippocampal and gray matter volumes of MCI patients following exercise intervention, with a trend of improvement in cortical thickness and white matter integrity. Functionally, after the exercise intervention, there were significant changes in the local spontaneous brain activity levels, cerebral blood flow, and functional connectivity during rest and memory encoding and retrieval tasks in MCI patients.

Conclusion

Exercise may contribute to delaying neurodegenerative changes in brain structure and function in patients with MCI. However, the underlying neural mechanisms require further research.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023482419.

Verbal training can improve neurocognitive and reading performance by increasing white matter integrity and grey matter volume

INTRODUCTION

Dementia and mild cognitive impairment (MCI) present both health and economic challenges on a global scale, thus affecting millions of people, and is projected to increase significantly by the year 2050. Early language processing deficits are evident in those diagnosed with Alzheimer's disease and MCI. Recent advances in pharmacologic and non-pharmacologic interventions, including cognitive rehabilitation and training, show promising effects on cognitive functions. Articulation training, particularly, is highlighted for its potential in addressing the communication difficulties which those experiencing dementia and MCI face, based on the transmission deficit hypothesis. This study aimed to perform a voxel-wise comparison of morphological changes in grey matter volume as well as white matter integrity to represent the plastic changes in response to articulation training among older, healthy Japanese adults.

METHODS

Forty older, healthy Japanese adults were randomized into either a cognitive training group or a control group. The study involved comprehensive behavioral assessments, neuroimaging (including 3D anatomy, fMRI and Diffusion Tensor Imaging), and a structured verbal articulation training regimen. The training included reading tasks which focused on enhancing both articulation and phonological skills. Neuroimaging data were acquired using a 3 Tesla Siemens MR scanner, with the FSL tool being used for white matter analysis and the CAT12 toolbox for grey matter analysis.

RESULTS

Significant increases in grey matter volume were observed in certain regions, including the left Supplementary motor area, Postcentral gyrus, and bilateral Superior frontal gyrus among those in the training group. Correlations were noted between reading abilities and cortical volume in areas such as the left Middle temporal gyrus, pulvinar, and Middle occipital gyrus. White matter integrity also improved, as evidenced by increased fractional anisotropy in tracts such as the superior longitudinal fasciculus, corona radiata, and internal capsule. These findings suggest a link between articulation training and enhanced neuroplasticity in both grey and white matter regions related to language processing.

CONCLUSION

The study demonstrates that a 4-week verbal articulation training intervention period can lead to significant improvements in grey matter volume in the left Supplementary motor area, the left Postcentral gyrus, and the bilateral Superior frontal gyrus, as well as the integrity of white matter tracts associated with language processing among older adults. These results suggest that such training could be a valuable tool in enhancing cognitive functions related to language in older populations.

Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe

BACKGROUND

Environmental exposure to metal mixtures is common and may be associated with increased risk for neurodegenerative disorders including Alzheimer's disease. This study examined associations of mixed metal exposures with medial temporal lobe (MTL) MRI structural metrics and neuropsychological performance.

METHODS

Metal exposure history, whole blood metal, MRI R1 (1/T1) and R2* (1/T2*) metrics (estimates of brain Mn and Fe, respectively), and neuropsychological tests were obtained from subjects with/without a history of mixed metal exposure from welding fumes (42 exposed subjects; 31 controls). MTL structures (hippocampus, entorhinal and parahippocampal cortices) were assessed by morphologic (volume or cortical thickness) and diffusion tensor imaging [mean (MD), axial (AxD), radial diffusivity (RD), and fractional anisotropy (FA)] metrics. In exposed subjects, effects of mixed metal exposure on MTL structural and neuropsychological metrics were examined.

RESULTS

Compared to controls, exposed subjects displayed higher MD, AxD, and RD throughout all MTL ROIs (p's<0.001) with no morphological differences. They also had poorer performance in processing/psychomotor speed, executive, and visuospatial domains (p's<0.046). Long-term mixed metal exposure history indirectly predicted lower processing speed performance via lower parahippocampal FA (p's<0.023). Higher entorhinal R1 and whole blood Mn and Cu levels predicted higher entorhinal diffusivity (p's<0.043) and lower Delayed Story Recall performance (p=0.007).

DISCUSSION

Mixed metal exposure predicted certain MTL structural and neuropsychological features that are similar to those detected in Alzheimer's disease at-risk populations. These data warrant follow-up as they may illuminate a potential path for environmental exposure to brain changes associated with Alzheimer's disease-related health outcomes.

Pathogenesis, diagnostics, and therapeutics for Alzheimer's disease: Breaking the memory barrier

Alzheimer's disease (AD) is the most common cause of dementia and accounts for 60-70 % of all cases. It affects millions of people worldwide. AD poses a substantial economic burden on societies and healthcare systems. AD is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. As the prevalence of AD continues to increase, understanding its pathogenesis, improving diagnostic methods, and developing effective therapeutics have become paramount. This comprehensive review delves into the intricate mechanisms underlying AD, explores the current state of diagnostic techniques, and examines emerging therapeutic strategies. By revealing the complexities of AD, this review aims to contribute to the growing body of knowledge surrounding this devastating disease.

Cardiovascular dysautonomia and cognitive impairment in Parkinson's disease (Review)

Cognitive impairment is a prevalent non-motor symptom of Parkinson's disease (PD), which can result in significant disability and distress for patients and caregivers. There is a marked variation in the timing, characteristics and rate at which cognitive decline occurs in patients with PD. This decline can vary from normal cognition to mild cognitive impairment and dementia. Cognitive impairment is associated with several pathophysiological mechanisms, including the accumulation of β-amyloid and tau in the brain, oxidative stress and neuroinflammation. Cardiovascular autonomic dysfunctions are commonly observed in patients with PD. These dysfunctions play a role in the progression of cognitive impairment, the incidents of falls and even in mortality. The majority of symptoms of dysautonomia arise from changes in the peripheral autonomic nervous system, including both the sympathetic and parasympathetic nervous systems. Cardiovascular changes, including orthostatic hypotension, supine hypertension and abnormal nocturnal blood pressure (BP), can occur in both the early and advanced stages of PD. These changes tend to increase as the disease advances. The present review aimed to describe the cognitive changes in the setting of cardiovascular dysautonomia and to discuss strategies through which these changes can be modified and managed. It is a multifactorial process usually involving decreased blood flow to the brain, resulting in the development of cerebral ischemic lesions, an increased presence of abnormal white matter signals in the brain, and a potential influence on the process of neurodegeneration in PD. Another possible explanation is this association being independent observations of PD progression. Patients with clinical symptoms of dysautonomia should undergo 24-h ambulatory BP monitoring, as they are frequently subtle and underdiagnosed.

Lateral thinking: Neurodegeneration of the cortical cholinergic system in Alzheimer's disease

INTRODUCTION

Atrophy of the nucleus basalis of Meynert (NBM) is an early indicator of Alzheimer's disease (AD). However, reduced integrity of the NBM white matter tracts may be more relevant for cognitive impairment and progression to dementia than NBM volume. Research is needed to compare differences in NBM volume and integrity of the lateral and medial NBM tracts across early and later stages of AD progression.

METHODS

187 participants were included in this study who were either healthy controls (HC; n = 50) or had early mild cognitive impairment (EMCI; n = 50), late MCI (LMCI; n = 37), or AD (n = 50). NBM volume was calculated using voxel-based morphometry and mean diffusivity (MD) of the lateral and medial NBM tracts were extracted using probabilistic tractography. Between group differences in NBM volume and tract MD were compared using linear mixed models controlling for age, sex, and either total intracranial volume or MD of a control mask, respectively. Associations between NBM volume and tract MD with executive function, memory, language, and visuospatial function were also analysed.

RESULTS

NBM volume was smallest in AD followed by LMCI (p < 0.0001), with no difference between EMCI and HC. AD had highest MD for both tracts compared to all other groups (p < 0.01). Both MCI groups had higher lateral tract MD compared to HC (p < 0.05). Medial tract MD was higher in LMCI (p = 0.008), but not EMCI (p = 0.09) compared to HC. Higher lateral tract MD was associated with executive function (p = 0.001) and language (p = 0.02).

DISCUSSION

Integrity of the lateral NBM tract is most sensitive to the earliest stages of AD and should be considered an important therapeutic target for early detection and intervention.

Multiple Early Biomarkers to Predict Cognitive Decline in Dementia-Free Older Adults

INTRODUCTION

Baseline olfactory impairment, poor performance on cognitive test, and medial temporal lobe atrophy are considered biomarkers for predicting future cognitive decline in dementia-free older adults. However, the combined effect of these predictors has not been fully investigated.

METHODS

A group of 110 participants without dementia were continuously recruited into this study, and underwent olfactory, cognitive tests and MRI scanning at baseline and 5-year follow-up. Olfactory function was assessed using the University of Pennsylvania Smell Identification Test (UPSIT). Participants were divided into the cognitive decliners and non-decliners.

RESULTS

Among 87 participants who completed the 5-year follow-up, cognitive decline was present in 32 cases and 55 remained stable. Compared with non-decliners, cognitive decliners presented lower scores on both the UPSIT and the Montreal Cognitive Assessment (MoCA), and smaller hippocampal volume at baseline (all P < .001). The logistic regression analysis revealed that lower scores on UPSIT and MoCA, and smaller hippocampal volume were strongly associated with subsequent cognitive decline, respectively (all P < .001). For the prediction of cognitive decline, lower score on UPSIT performed the sensitivity of 63.6% and specificity of 81.2%, lower score on MoCA with the sensitivity of 74.5% and specificity of 65.6%, smaller hippocampal volume with the sensitivity of 70.9% and specificity of 78.1%, respectively. Combining three predictors resulted in the sensitivity of 83.6% and specificity of 93.7%.

CONCLUSIONS

The combination of olfactory test, cognitive test with structural MRI may enhance the predictive ability for future cognitive decline for dementia-free older adults.

3D Multimodal Fusion Network With Disease-Induced Joint Learning for Early Alzheimer's Disease Diagnosis

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

CAPS: a simple clinical tool for β-amyloid positivity prediction in clinical Alzheimer syndrome

Introduction

With the advent of anti-β-amyloid therapies, clinical distinction between Aβ + and Aβ- in cognitive impairment is becoming increasingly important for stratifying referral and better utilization of biomarker assays.

Methods

Cognitive profile, rate of decline, neuropsychiatric inventory questionnaire (NPI-Q), and imaging characteristics were collected from 52 subjects with possible/probable AD.

Results

Participants with Aβ+ status had lower baseline MMSE scores (24.50 vs. 26.85, p = 0.009) and higher total NPI-Q scores (2.73 vs. 1.18, p < 0.001). NPI-Q score was found to be the only independent predictor for β-amyloid positivity (p = 0.008). A simple scoring system, namely Clinical β-Amyloid Positivity Prediction Score (CAPS), was developed by using the following parameters: NPI-Q, rapidity of cognitive decline, and white matter microangiopathy. Data from 48 participants were included in the analysis of accuracy of CAPS. CAP Score of 3 or 4 successfully classified Aβ + individuals in 86.7% cases.

Discussion

Clinical β-Amyloid Positivity Prediction Score is a simple clinical tool for use in primary care and memory clinic settings to predict β-amyloid positivity in individuals with clinical Alzheimer Syndrome can potentially facilitate referral for Anti Aβ therapies.

Management of MCI in the Outpatient Setting

PURPOSE OF REVIEW

We review current literature related to the clinical assessment of Mild Cognitive Impairment (MCI). We compile recommendations related to the evaluation of MCI and examine literature regarding the use of clinical biomarkers in this assessment, the role of non-pharmacologic therapy in the prevention of cognitive decline, and recent approval of anti-amyloid therapy in the treatment of MCI.

RECENT FINDINGS

The role of imaging and plasma biomarkers in the clinical assessment of MCI has expanded. There is data that non-pharmacologic therapy may have a role in the prevention of neurocognitive decline. Anti-amyloid therapies have recently been approved for clinical use. Clinical assessment of MCI remains multifactorial and includes screening and treating for underlying psychiatric and medical co-morbidities. The use of biomarkers in clinical settings is expanding with the rise of anti-amyloid therapies. These new diagnostics and therapeutics require nuanced discussion of risks and benefits. Psychiatrist's skillset is uniquely suited for these complex evaluations.

The role of olfactory dysfunction in mild cognitive impairment and Alzheimer's disease: A meta-analysis

PURPOSE

This comprehensive meta-analysis investigates the association between olfactory deficits in mild cognitive impairment (MCI) and Alzheimer's disease (AD).

METHODS

A thorough search across databases identified articles analyzing olfactory status in MCI or AD patients. Methodological quality assessment followed PRISMA guidelines. Hedges' g effect size statistic computed standard mean differences and 95% confidence intervals. Moderator analysis was conducted.

RESULTS

Among the included studies (65 for MCI and 61 for AD), odor identification exhibited larger effect sizes compared to odor threshold and discrimination, in both MCI and AD samples. Moderate effect size is found in OI scores in MCI (k = 65, SE = 0.078, CI 95% = [-1.151, -0.844]). Furthermore, compared to MCI, AD had moderate to large heterogeneous effects in olfactory identification (k = 61, g = -2.062, SE = 0.125, CI 95% = [-2.308, -1.816]). Global cognitive status is positively related to olfactory identification impairment in both MCI (k = 57, Z = 2.74, p = 0.006) and AD (k = 53, Z = 5.03, p < 0.0001) samples.

CONCLUSION

Olfactory impairments exhibit a notable and substantial presence in MCI. Among these impairments, odor identification experiences the greatest decline in MCI, mirroring the primary sensory deficit observed in AD. Consequently, the incorporation of a straightforward odor identification test is advisable in the evaluation of individuals vulnerable to the onset of AD, offering a practical screening tool for early detection.

Selective vulnerability of hippocampal sub-regions in patients with subcortical vascular mild cognitive impairment

Early diagnosis of subcortical vascular mild cognitive impairment (svMCI) is clinically essential because it is the most reversible subtype of all cognitive impairments. Since structural alterations of hippocampal sub-regions have been well studied in neurodegenerative diseases with pathophysiological cognitive impairments, we were eager to determine whether there is a selective vulnerability of hippocampal sub-fields in patients with svMCI. Our study included 34 svMCI patients and 34 normal controls (NCs), with analysis of T1 images and Montreal Cognitive Assessment (MoCA) scores. Gray matter volume (GMV) of hippocampal sub-regions was quantified and compared between the groups, adjusting for age, sex, and education. Additionally, we explored correlations between altered GMV in hippocampal sub-fields and MoCA scores in svMCI patients. Patients with svMCI exhibited selectively reduced GMV in several left hippocampal sub-regions, such as the hippocampal tail, hippocampal fissure, CA1 head, ML-HP head, CA4 head, and CA3 head, as well as decreased GMV in the right hippocampal tail. Specifically, GMV in the left CA3 head was inversely correlated with MoCA scores in svMCI patients. Our findings indicate that the atrophy pattern of patients with svMCI was predominantly located in the left hippocampal sub-regions. The left CA3 might be a crucial area underlying the distinct pathophysiological mechanisms of cognitive impairments with subcortical vascular origins.

C, Howard. TM, Wilkins. KB, Seo. G, Brontë-Stewart. HM. Lateral Thinking: Pathway Specific Neurodegeneration of the Cortical Cholinergic System in Alzheimer's Disease

INTRODUCTION

Atrophy of the nucleus basalis of Meynert (NBM) is an early indicator of Alzheimer's disease (AD). However, reduced integrity of the NBM white matter tracts may be more relevant for cognitive impairment and progression to dementia than NBM volume. Research is needed to compare differences in NBM volume and integrity of the lateral and medial NBM tracts across early and later stages of AD progression.

METHODS

187 participants were included in this study who were either healthy controls (HC; n=50) or had early mild cognitive impairment (EMCI; n=50), late MCI (LMCI; n=37), or AD (n=50). NBM volume was calculated using voxel-based morphometry and mean diffusivity (MD) of the lateral and medial NBM tracts were extracted using probabilistic tractography. Between group differences in NBM volume and tract MD were compared using linear mixed models controlling for age, sex, and either total intracranial volume or MD of a control mask, respectively. Associations between NBM volume and tract MD with executive function, memory, language, and visuospatial function were also analysed.

RESULTS

NBM volume was smallest in AD followed by LMCI (p<0.0001), with no difference between EMCI and HC. AD had highest MD for both tracts compared to all other groups (p<0.001). Both MCI groups had higher lateral tract MD compared to HC (p<0.05). Medial tract MD was higher in LMCI (p=0.008), but not EMCI (p=0.09) compared to HC. Higher lateral tract MD was associated with executive function (p=0.001) and language (p=0.02).

DISCUSSION

Integrity of the lateral NBM tract is most sensitive to the earliest stages of AD and should be considered an important therapeutic target for early detection and intervention.

Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer's type

Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer's type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer's Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions-episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.

Hypoperfusion in Alzheimer's Disease-Prone Regions and Dementia Conversion in Parkinson's Disease

PURPOSE OF THE REPORT

Although early detection of individuals at risk of dementia conversion is important in patients with Parkinson's disease (PD), there is still no consensus on neuroimaging biomarkers for predicting future cognitive decline. We aimed to investigate whether cerebral perfusion patterns on early-phase 18 F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane ( 18 F-FP-CIT) PET have the potential to serve as a neuroimaging predictor for early dementia conversion in patients with PD.

MATERIALS AND METHODS

In this retrospective analysis, we enrolled 187 patients with newly diagnosed PD who underwent dual-phase 18 F-FP-CIT PET at initial assessment and serial cognitive assessments during the follow-up period (>5 years). Patients with PD were classified into 2 groups: the PD with dementia (PDD)-high-risk (PDD-H; n = 47) and the PDD-low-risk (PDD-L; n = 140) groups according to dementia conversion within 5 years of PD diagnosis. We explored between-group differences in the regional uptake in the early-phase 18 F-FP-CIT PET images. We additionally performed a linear discriminant analysis to develop a prediction model for early PDD conversion.

RESULTS

The PDD-H group exhibited hypoperfusion in Alzheimer's disease (AD)-prone regions (inferomedial temporal and posterior cingulate cortices, and insula) compared with the PDD-L group. A prediction model using regional uptake in the right entorhinal cortex, left amygdala, and left isthmus cingulate cortex could optimally distinguish the PDD-H group from the PDD-L group.

CONCLUSIONS

Regional hypoperfusion in the AD-prone regions on early-phase 18 F-FP-CIT PET can be a useful biomarker for predicting early dementia conversion in patients with PD.

Impairment of entorhinal cortex network activity in Alzheimer's disease

The entorhinal cortex (EC) stands out as a critical brain region affected in the early phases of Alzheimer's disease (AD), with some of the disease's pathological processes originating from this area, making it one of the most crucial brain regions in AD. Recent research highlights disruptions in the brain's network activity, characterized by heightened excitability and irregular oscillations, may contribute to cognitive impairment. These disruptions are proposed not only as potential therapeutic targets but also as early biomarkers for AD. In this paper, we will begin with a review of the anatomy and function of EC, highlighting its selective vulnerability in AD. Subsequently, we will discuss the disruption of EC network activity, exploring changes in excitability and neuronal oscillations in this region during AD and hypothesize that, considering the advancements in neuromodulation techniques, addressing the disturbances in the network activity of the EC could offer fresh insights for both the diagnosis and treatment of AD.

The interactive effect of intra-beat and inter-beat blood pressure variability on neurodegeneration in older adults

Blood pressure variability (BPV) and arterial stiffness are age-related hemodynamic risk factors for neurodegenerative disease, but it remains unclear whether they exert independent or interactive effects on brain health. When combined with high inter-beat BPV, increased intra-beat BPV indicative of arterial stiffness could convey greater pressure wave fluctuations deeper into the cerebrovasculature, exacerbating neurodegeneration. This interactive effect was studied in older adults using multiple markers of neurodegeneration, including medial temporal lobe (MTL) volume, plasma neurofilament light (NfL) and glial fibrillary acidic protein (GFAP). Older adults (N=105) without major neurological or systemic disease were recruited and underwent brain MRI and continuous BP monitoring to quantify inter-beat BPV through systolic average real variability (ARV) and intra-beat variability through arterial stiffness index (ASI). Plasma NfL and GFAP were assessed. The interactive effect of ARV and ASI on MTL atrophy, plasma NfL, and GFAP was studied using hierarchical linear regression. Voxel-based morphometry (VBM) was used to confirm region-of-interest analysis findings. The interaction between higher ARV and higher ASI was significantly associated with left-sided MTL atrophy in both the region-of-interest and false discovery rate-corrected VBM analysis. The interactive effect was also significantly associated with increased plasma NfL, but not GFAP. The interaction between higher ARV and higher ASI is independently associated with increased neurodegenerative markers, including MTL atrophy and plasma NfL, in independently living older adults. Findings could suggest the increased risk for neurodegeneration associated with higher inter-beat BPV may be compounded by increased intra-beat variability due to arterial stiffness.

Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples' transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with l2,1 -norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

Non-invasive neuromodulation of cerebello-hippocampal volume-behavior relationships

The study here explores the link between transcranial direct current stimulation (tDCS) and brain-behavior relationships. We propose that tDCS may indirectly influence the complex relationships between brain volume and behavior. We focused on the dynamics between the hippocampus (HPC) and cerebellum (CB) in cognitive processes, a relationship with significant implications for understanding memory and motor skills. Seventy-four young adults (mean age: 22±0.42 years, mean education: 14.7±0.25 years) were randomly assigned to receive either anodal, cathodal, or sham stimulation. Following stimulation, participants completed computerized tasks assessing working memory and sequence learning in a magnetic resonance imaging (MRI) environment. We investigated the statistical interaction between CB and HPC volumes. Our findings showed that individuals with larger cerebellar volumes had shorter reaction times (RT) on a high-load working memory task in the sham stimulation group. In contrast, the anodal stimulation group exhibited faster RTs during the low-load working memory condition. These RT differences were associated with the cortical volumetric interaction between CB-HPC. Literature suggests that anodal stimulation down-regulates the CB and here, those with larger volumes perform more quickly, suggesting the potential need for additional cognitive resources to compensate for cerebellar downregulation. This new insight suggests that tDCS can aid in revealing structure-function relationships, due to greater performance variability, especially in young adults. It may also reveal new targets of interest in the study of aging or in diseases where there is also greater behavioral variability.

Linking activity dyshomeostasis and sleep disturbances in Alzheimer disease

The presymptomatic phase of Alzheimer disease (AD) starts with the deposition of amyloid-β in the cortex and begins a decade or more before the emergence of cognitive decline. The trajectory towards dementia and neurodegeneration is shaped by the pathological load and the resilience of neural circuits to the effects of this pathology. In this Perspective, I focus on recent advances that have uncovered the vulnerability of neural circuits at early stages of AD to hyperexcitability, particularly when the brain is in a low-arousal states (such as sleep and anaesthesia). Notably, this hyperexcitability manifests before overt symptoms such as sleep and memory deficits. Using the principles of control theory, I analyse the bidirectional relationship between homeostasis of neuronal activity and sleep and propose that impaired activity homeostasis during sleep leads to hyperexcitability and subsequent sleep disturbances, whereas sleep disturbances mitigate hyperexcitability via negative feedback. Understanding the interplay among activity homeostasis, neuronal excitability and sleep is crucial for elucidating the mechanisms of vulnerability to and resilience against AD pathology and for identifying new therapeutic avenues.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

The use of individual-based FDG-PET volume of interest in predicting conversion from mild cognitive impairment to dementia

BACKGROUND

Based on a longitudinal cohort design, the aim of this study was to investigate whether individual-based 18F fluorodeoxyglucose positron emission tomography (18F-FDG-PET) regional signals can predict dementia conversion in patients with mild cognitive impairment (MCI).

METHODS

We included 44 MCI converters (MCI-C), 38 non-converters (MCI-NC), 42 patients with Alzheimer's disease with dementia, and 40 cognitively normal controls. Data from annual cognitive measurements, 3D T1 magnetic resonance imaging (MRI) scans, and 18F-FDG-PET scans were used for outcome analysis. An individual-based FDG-PET approach was applied using seven volumes of interest (VOIs), Z transformed using a normal FDG-PET template. Hypometabolism was defined as a Z score < -2 of regional standard uptake value ratio. For the longitudinal cognitive test scores, generalized estimating equations were used. A linear mixed-effects model was used to compare the temporal impact of cortical hypometabolism and cortical thickness degeneration.

RESULTS

The clinical follow-up period was 6.6 ± 3.8 years (range 3.1 to 16.0 years). The trend of cognitive decline could differentiate MCI-C from MCI-NC after 3 years of follow-up. In the baseline 18F-FDG-PET scan of the patients with MCI, medial temporal lobe (MTL; 94.7% sensitivity, 80.5% specificity) and posterior cingulate cortex (PCC; 89.5% sensitivity, 73.1% specificity) hypometabolism predicted conversion with high accuracy. 18F-FDG-PET hypometabolism preceded dementia conversion at an interval of 3.70 ± 1.68 years and was earlier than volumetric changes, with the exception of the MTL.

CONCLUSIONS

Our finding supports the use of individual-based 18F-FDG-PET analysis to predict MCI conversion to dementia. Reduced FDG-PET metabolism in the MTL and PCC were strongly associated with future cognitive decline in the MCI-C group. Changes in 18F-FDG-PET occurred 1 to 8 years prior to conversion to dementia. Progressive hypometabolism in the PCC, precuneus and lateral temporal lobe, but not MTL, preceded MRI findings at the MCI stage.

Loss of cholinergic input to the entorhinal cortex is an early indicator of cognitive impairment in natural aging of humans and mice

In a series of translational experiments using fully quantitative positron emission tomography (PET) imaging with a new tracer specific for the vesicular acetylcholine transporter ([18F]VAT) in vivo in humans, and genetically targeted cholinergic markers in mice, we evaluated whether changes to the cholinergic system were an early feature of age-related cognitive decline. We found that deficits in cholinergic innervation of the entorhinal cortex (EC) and decline in performance on behavioral tasks engaging the EC are, strikingly, early features of the aging process. In human studies, we recruited older adult volunteers that were physically healthy and without prior clinical diagnosis of cognitive impairment. Using [18F]VAT PET imaging, we demonstrate that there is measurable loss of cholinergic inputs to the EC that can serve as an early signature of decline in EC cognitive performance. These deficits are specific to the cholinergic circuit between the medial septum and vertical limb of the diagonal band (MS/vDB; CH1/2) to the EC. Using diffusion imaging, we further demonstrate impaired structural connectivity in the tracts between the MS/vDB and EC in older adults with mild cognitive impairment. Experiments in mouse, designed to parallel and extend upon the human studies, used high resolution imaging to evaluate cholinergic terminal density and immediate early gene (IEG) activity of EC neurons in healthy aging mice and in mice with genetic susceptibility to accelerated accumulation amyloid beta plaques and hyperphosphorylated mouse tau. Across species and aging conditions, we find that the integrity of cholinergic projections to the EC directly correlates with the extent of EC activation and with performance on EC-related object recognition memory tasks. Silencing EC-projecting cholinergic neurons in young, healthy mice during the object-location memory task impairs object recognition performance, mimicking aging. Taken together we identify a role for acetylcholine in normal EC function and establish loss of cholinergic input to the EC as an early, conserved feature of age-related cognitive decline in both humans and rodents.

Alzheimer's disease detection using data fusion with a deep supervised encoder

Alzheimer's disease (AD) is affecting a growing number of individuals. As a result, there is a pressing need for accurate and early diagnosis methods. This study aims to achieve this goal by developing an optimal data analysis strategy to enhance computational diagnosis. Although various modalities of AD diagnostic data are collected, past research on computational methods of AD diagnosis has mainly focused on using single-modal inputs. We hypothesize that integrating, or "fusing," various data modalities as inputs to prediction models could enhance diagnostic accuracy by offering a more comprehensive view of an individual's health profile. However, a potential challenge arises as this fusion of multiple modalities may result in significantly higher dimensional data. We hypothesize that employing suitable dimensionality reduction methods across heterogeneous modalities would not only help diagnosis models extract latent information but also enhance accuracy. Therefore, it is imperative to identify optimal strategies for both data fusion and dimensionality reduction. In this paper, we have conducted a comprehensive comparison of over 80 statistical machine learning methods, considering various classifiers, dimensionality reduction techniques, and data fusion strategies to assess our hypotheses. Specifically, we have explored three primary strategies: (1) Simple data fusion, which involves straightforward concatenation (fusion) of datasets before inputting them into a classifier; (2) Early data fusion, in which datasets are concatenated first, and then a dimensionality reduction technique is applied before feeding the resulting data into a classifier; and (3) Intermediate data fusion, in which dimensionality reduction methods are applied individually to each dataset before concatenating them to construct a classifier. For dimensionality reduction, we have explored several commonly-used techniques such as principal component analysis (PCA), autoencoder (AE), and LASSO. Additionally, we have implemented a new dimensionality-reduction method called the supervised encoder (SE), which involves slight modifications to standard deep neural networks. Our results show that SE substantially improves prediction accuracy compared to PCA, AE, and LASSO, especially in combination with intermediate fusion for multiclass diagnosis prediction.

Facial emotion recognition in individuals with mild cognitive impairment: An exploratory study

Understanding facial emotions is fundamental to interact in social environments and modify behavior accordingly. Neurodegenerative processes can progressively transform affective responses and affect social competence. This exploratory study examined the neurocognitive correlates of face recognition, in individuals with two mild cognitive impairment (MCI) etiologies (prodromal to dementia - MCI, or consequent to Parkinson's disease - PD-MCI). Performance on the identification and memorization of neutral and emotional facial expressions was assessed in 31 individuals with MCI, 26 with PD-MCI, and 30 healthy controls (HC). Individuals with MCI exhibited selective impairment in recognizing faces expressing fear, along with difficulties in remembering both neutral and emotional faces. Conversely, individuals with PD-MCI showed no differences compared with the HC in either emotion recognition or memory. In MCI, no significant association emerged between the memory for facial expressions and cognitive difficulties. In PD-MCI, regression analyses showed significant associations with higher-level cognitive functions in the emotional memory task, suggesting the presence of compensatory mechanisms. In a subset of participants, voxel-based morphometry revealed that the performance on emotional tasks correlated with regional changes in gray matter volume. The performance in the matching of negative expressions was predicted by volumetric changes in brain areas engaged in face and emotional processing, in particular increased volume in thalamic nuclei and atrophy in the right parietal cortex. Future studies should leverage on neuroimaging data to determine whether differences in emotional recognition are mediated by pathology-specific atrophic patterns.

Hippocampal Perfusion Affects Motor and Cognitive Functions in Parkinson Disease: An Early Phase 18 F-FP-CIT Positron Emission Tomography Study

OBJECTIVE

We investigated whether hippocampal perfusion changes are associated with cognitive decline, motor deficits, and the risk of dementia conversion in patients with Parkinson disease (PD).

METHODS

We recruited patients with newly diagnosed and nonmedicated PD and healthy participants who underwent dual phase 18 F-N-(3-fluoropropyl)-2β-carboxymethoxy-3β-(4-iodophenyl) nortropane positron emission tomography scans. Patients were classified into 3 groups according to hippocampal perfusion measured by standard uptake value ratios (SUVRs): (1) PD hippocampal hypoperfusion group (1 standard deviation [SD] below the mean hippocampal SUVR of healthy controls; PD-hippo-hypo), (2) PD hippocampal hyperperfusion group (1 SD above the mean; PD-hippo-hyper), and (3) the remaining patients (PD-hippo-normal). We compared the baseline cognitive performance, severity of motor deficits, hippocampal volume, striatal dopamine transporter (DAT) availability, and risk of dementia conversion among the groups.

RESULTS

We included 235 patients (PD-hippo-hypo, n = 21; PD-hippo-normal, n = 157; PD-hippo-hyper, n = 57) and 48 healthy participants. Patients in the PD-hippo-hypo group were older and had smaller hippocampal volumes than those in the other PD groups. The PD-hippo-hypo group showed less severely decreased DAT availability in the putamen than the other groups despite similar severities of motor deficit. The PD-hippo-hypo group had a higher risk of dementia conversion compared to the PD-hippo-normal (hazard ratio = 2.59, p = 0.013) and PD-hippo-hyper (hazard ratio = 3.73, p = 0.006) groups, despite similar cognitive performance at initial assessment between groups.

INTERPRETATION

Hippocampal hypoperfusion may indicate a reduced capacity to cope with neurodegenerative processes in terms of the development of motor deficits and cognitive decline in patients with PD. ANN NEUROL 2024;95:388-399.

Effects of mixed metal exposures on MRI diffusion features in the medial temporal lobe

Background

Environmental exposure to metal mixtures is common and may be associated with increased risk for neurodegenerative disorders including Alzheimer's disease.

Objective

This study examined associations of mixed metal exposures with medial temporal lobe (MTL) MRI structural metrics and neuropsychological performance.

Methods

Metal exposure history, whole blood metal, and neuropsychological tests were obtained from subjects with/without a history of mixed metal exposure from welding fumes (42 exposed subjects; 31 controls). MTL structures (hippocampus, entorhinal and parahippocampal cortices) were assessed by morphologic (volume, cortical thickness) and diffusion tensor imaging [mean (MD), axial (AD), radial diffusivity (RD), and fractional anisotropy (FA)] metrics. In exposed subjects, correlation, multiple linear, Bayesian kernel machine regression, and mediation analyses were employed to examine effects of single- or mixed-metal predictor(s) and their interactions on MTL structural and neuropsychological metrics; and on the path from metal exposure to neuropsychological consequences.

Results

Compared to controls, exposed subjects had higher blood Cu, Fe, K, Mn, Pb, Se, and Zn levels (p's<0.026) and poorer performance in processing/psychomotor speed, executive, and visuospatial domains (p's<0.046). Exposed subjects displayed higher MD, AD, and RD in all MTL ROIs (p's<0.040) and lower FA in entorhinal and parahippocampal cortices (p's<0.033), but not morphological differences. Long-term mixed-metal exposure history indirectly predicted lower processing speed performance via lower parahippocampal FA (p=0.023). Higher whole blood Mn and Cu predicted higher entorhinal diffusivity (p's<0.043) and lower Delayed Story Recall performance (p=0.007) without overall metal mixture or interaction effects.

Discussion

Mixed metal exposure predicted MTL structural and neuropsychological features that are similar to Alzheimer's disease at-risk populations. These data warrant follow-up as they may illuminate the path for environmental exposure to Alzheimer's disease-related health outcomes.

More Similar than Different: Memory, Executive Functions, Cortical Thickness, and Glucose Metabolism in Biomarker-Positive Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia

Background

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are typically associated with very different clinical and neuroanatomical presentations; however, there is increasing recognition of similarities.

Objective

To examine memory and executive functions, as well as cortical thickness, and glucose metabolism in AD and bvFTD signature brain regions.

Methods

We compared differences in a group of biomarker-defined participants with Alzheimer's disease and a group of clinically diagnosed participants with bvFTD. These groups were also contrasted with healthy controls (HC).

Results

As expected, memory functions were generally more impaired in AD, followed by bvFTD, and both clinical groups performed more poorly than the HC group. Executive function measures were similar in AD compared to bvFTD for motor sequencing and go/no-go, but bvFTD had more difficulty with a set shifting task. Participants with AD showed thinner cortex and lower glucose metabolism in the angular gyrus compared to bvFTD. Participants with bvFTD had thinner cortex in the insula and temporal pole relative to AD and healthy controls, but otherwise the two clinical groups were similar for other frontal and temporal signature regions.

Conclusions

Overall, the results of this study highlight more similarities than differences between AD and bvFTD in terms of cognitive functions, cortical thickness, and glucose metabolism. Further research is needed to better understand the mechanisms mediating this overlap and how these relationships evolve longitudinally.

Diagnosis of Alzheimer's Disease in Clinical Practice: Time to Incorporate Biomarkers?

Hippocampal dysfunction is associated with early clinical signs of Alzheimer's disease (AD). Due to the limited availability or invasiveness of current biomarkers, the AD diagnosis is usually based on cognitive assessment and structural brain imaging. The recent study by Lalive and colleagues examined the specificity of brain morphometry for the AD diagnosis in a memory clinic cohort with hippocampal-type amnestic syndrome. The results indicate that memory deficits and hippocampal atrophy are similar in AD and non-AD patients, highlighting their low diagnostic specificity. These findings challenge the traditional AD diagnosis and underscore the need for biomarkers to differentiate specific neuropathological entities.

Music Therapy as a Complementary Treatment in Patients with Dementia Associated to Alzheimer's Disease: A Systematic Review

Background

Alzheimer's disease (AD) is a complex condition that affects various aspects of a patient's life. Music therapy may be considered a beneficial supplementary tool to traditional therapies, that not fully address the range of AD manifestations.

Objective

The purpose of this systematic review is to investigate whether music therapy can have a positive impact on AD patients and on which symptoms.

Methods

The main research databases employed have been PubMed and Cochrane, using the keywords "dementia", "music therapy", "Alzheimer", "fMRI", "music", and "EEG".

Results

After removing duplicates and irrelevant studies, 23 were screened using set criteria, resulting in the final inclusion of 15 studies. The total number of participants included in these studies has been of 1,196 patients. For the fMRI analysis the search resulted in 28 studies on PubMed, two of which were included in the research; the total number of participants was of 124 individuals. The studies conducted with EEG were found using PubMed. The initial search resulted in 15 studies, but after a more accurate evaluation only 2 have been included in the analysis.

Conclusions

Even though the data currently available is not sufficient to draw conclusions supported by robust statistical power, the impact of music therapy on AD neuropsychiatric symptoms deserves great interest. Further research should be ushered, possibly multicentric studies, led with neuroimaging and other recent techniques, which can eventually open views on the music role in improving the cognitive status in AD.

Reduced tactile sensitivity is associated with mild cognitive impairment

BACKGROUND

Sensory impairment has been related to age-associated cognitive decline. While these associations were investigated primarily in the auditory and visual domain, other senses such as touch have rarely been studied. Thus, it remains open whether these results are specific for particular sensory domains, or rather point to a fundamental role of sensory deficits in cognitive decline.

METHODS

Data from 31 participants with mild cognitive impairment (MCI), 46 participants with frailty, and 23 non-clinical control participants (NCCs) were included. We assessed sensory function using visual acuity and contrast sensitivity, hearing threshold, and mechanical detection threshold. Cognitive function in participants with MCI was assessed using associative memory performance. Group differences on sensory thresholds were tested using analyses of covariance with age, sex, and years of education as covariates. Associations between measures within participants with MCI were evaluated using Spearman correlations.

FINDINGS

We found a significant difference in mechanical detection threshold between the groups (p < 0.001, η2 = 0.18). Participants with MCI showed significantly reduced tactile sensitivity compared to participants with frailty and NCCs. In participants with MCI, lower associative memory performance was significantly related to reduced tactile sensitivity (rs = 0.39, p = 0.031) and auditory acuity (rs = 0.41, p = 0.022).

INTERPRETATION

Our results indicate that reduced tactile sensitivity is related to cognitive decline. Prospective studies should investigate the age-related alterations of multimodal sensory processes and their contribution to dementia-related processes.

FUNDING

Deutsche Forschungsgemeinschaft (FL 156/41-1) and a grant of the Hector-Stiftung II, Weinheim, Germany.

Relationship between hippocampal subfield volumes and cognitive decline in healthy subjects

We examined the relationship between hippocampal subfield volumes and cognitive decline over a 4-year period in a healthy older adult population with the goal of identifying subjects at risk of progressive cognitive impairment which could potentially guide therapeutic interventions and monitoring. 482 subjects (68.1 years +/- 7.4; 52.9% female) from the Irish Longitudinal Study on Ageing underwent magnetic resonance brain imaging and a series of cognitive tests. Using K-means longitudinal clustering, subjects were first grouped into three separate global and domain-specific cognitive function trajectories; High-Stable, Mid-Stable and Low-Declining. Linear mixed effects models were then used to establish associations between hippocampal subfield volumes and cognitive groups. Decline in multiple hippocampal subfields was associated with global cognitive decline, specifically the presubiculum (estimate -0.20; 95% confidence interval (CI) -0.78 - -0.02; p = 0.03), subiculum (-0.44; -0.82 - -0.06; p = 0.02), CA1 (-0.34; -0.78 - -0.02; p = 0.04), CA4 (-0.55; -0.93 - -0.17; p = 0.005), molecular layer (-0.49; -0.87 - -0.11; p = 0.01), dentate gyrus (-0.57; -0.94 - -0.19; p = 0.003), hippocampal tail (-0.53; -0.91 - -0.15; p = 0.006) and HATA (-0.41; -0.79 - -0.03; p = 0.04), with smaller volumes for the Low-Declining cognition group compared to the High-Stable cognition group. In contrast to global cognitive decline, when specifically assessing the memory domain, cornu ammonis 1 subfield was not found to be associated with low declining cognition (-0.14; -0.37 - 0.10; p = 0.26). Previously published data shows that atrophy of specific hippocampal subfields is associated with cognitive decline but our study confirms the same effect in subjects asymptomatic at time of enrolment. This strengthens the predictive value of hippocampal subfield atrophy in risk of cognitive decline and may provide a biomarker for monitoring treatment efficacy.

Prediction of Progression from Mild Cognitive Impairment to Alzheimer's disease with Longitudinal and Multimodal Data

Introduction

Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) within a certain time frame is crucial for appropriate therapeutic interventions. However, it is challenging to capture the dynamic changes in cognitive and functional abilities over time, resulting in limited predictive performance. Our study aimed to investigate whether incorporating longitudinal multimodal data with advanced analytical methods could improve the capability to predict the risk of progressing to AD.

Methods

This study included participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI), a large-scale multi-center longitudinal study. Three data modalities, including demographic variables, neuropsychological tests, and neuroimaging measures were considered. A Long Short-Term Memory (LSTM) model using data collected at five-time points (baseline, 6-month, 12-month, 18-month, and 24-month) was developed to predict the risk of progression from MCI to AD within two years from the index exam (the exam at 24-month). In contrast, a random forest model was developed to predict the risk of progression just based on the data collected at the index exam.

Results

The study included 347 participants with MCI at 24-month (age: mean 75, SD 7 years; 39.8% women) from ADNI, of whom 77 converted to AD over a 2-year follow-up period. The longitudinal LSTM model showed superior prediction performance of MCI-to-AD progression (AUC 0.93±0.06) compared to the random forest model (AUC 0.90±0.09). A similar pattern was also observed across different age groups.

Discussion

Our study suggests that the incorporation of longitudinal data can provide better predictive performance for 2-year MCI-to-AD progression risk than relying solely on cross-sectional data. Therefore, repeated or multiple times routine health surveillance of MCI patients are essential in the early detection and intervention of AD.

Neuropsychological Performance in Alzheimer's Disease versus Late-Life Depression: A Systematic Review and Meta-Analysis

OBJECTIVE

Despite decades of research, neuropsychological tests (NPTs) that clearly differentiate between Alzheimer's disease (AD) and late-life depression (LLD) have yet to be agreed upon. Given this gap in knowledge and the rapid deployment of disease-modifying drugs for the two disorders, accurate clinical diagnosis using evidence-based assessment is essential. This study aims to systematically examine the literature to identify NPTs that would be able to differentiate AD and LLD.

METHOD

Databases and bibliographies were searched to identify articles for analysis. Two major inclusion criteria were that the studies compared neuropsychological functioning of AD versus LLD using normed NPTs and provided data for effect size calculation. Risk of bias was minimized by having independent coders for all steps in the review.

RESULTS

Forty-one studies met inclusion criteria (N = 2,797) and provided effect sizes for tests that were classified as belonging to 15 domains of functioning. The two groups were well differentiated by tasks of delayed contextual verbal memory as compared to immediate or non-contextual memory, recognition cueing, confrontation naming, visuospatial construction, and conceptualization. Specific NPTs that appear to be useful for differential diagnosis include the Rey Auditory Verbal Learning Test-Delayed Recognition; Boston Naming Test; the Dementia Rating Scale's memory, conceptualization, and construction subscales; and the CERAD Constructional Praxis.

CONCLUSIONS

The NPTs highlighted in this systematic review could be used as a relatively simple and cost-effective method to differentiate between patients with cognitive dysfunction due to AD versus LLD.

Brain-wide genome-wide colocalization study for integrating genetics, transcriptomics and brain morphometry in Alzheimer's disease

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. However, the AD mechanism has not yet been fully elucidated to date, hindering the development of effective therapies. In our work, we perform a brain imaging genomics study to link genetics, single-cell gene expression data, tissue-specific gene expression data, brain imaging-derived volumetric endophenotypes, and disease diagnosis to discover potential underlying neurobiological pathways for AD. To do so, we perform brain-wide genome-wide colocalization analyses to integrate multidimensional imaging genomic biobank data. Specifically, we use (1) the individual-level imputed genotyping data and magnetic resonance imaging (MRI) data from the UK Biobank, (2) the summary statistics of the genome-wide association study (GWAS) from multiple European ancestry cohorts, and (3) the tissue-specific cis-expression quantitative trait loci (cis-eQTL) summary statistics from the GTEx project. We apply a Bayes factor colocalization framework and mediation analysis to these multi-modal imaging genomic data. As a result, we derive the brain regional level GWAS summary statistics for 145 brain regions with 482,831 single nucleotide polymorphisms (SNPs) followed by posthoc functional annotations. Our analysis yields the discovery of a potential AD causal pathway from a systems biology perspective: the SNP chr10:124165615:G>A (rs6585827) mutation upregulates the expression of BTBD16 gene in oligodendrocytes, a specialized glial cells, in the brain cortex, leading to a reduced risk of volumetric loss in the entorhinal cortex, resulting in the protective effect on AD. We substantiate our findings with multiple evidence from existing imaging, genetic and genomic studies in AD literature. Our study connects genetics, molecular and cellular signatures, regional brain morphologic endophenotypes, and AD diagnosis, providing new insights into the mechanistic understanding of the disease. Our findings can provide valuable guidance for subsequent therapeutic target identification and drug discovery in AD.

Sleep Differentially and Profoundly Impairs Recall Memory in a Patient with Fornix Damage

In March 2020, C.T., a kind, bright, and friendly young woman underwent surgery for a midline tumor involving her septum pellucidum and extending down into her fornices bilaterally. Following tumor diagnosis and surgery, C.T. experienced significant memory deficits: C.T.'s family reported that she could remember things throughout the day, but when she woke up in the morning or following a nap, she would expect to be in the hospital, forgetting all the information that she had learned before sleep. The current study aimed to empirically validate C.T.'s pattern of memory loss and explore its neurological underpinnings. On two successive days, C.T. and age-matched controls watched an episode of a TV show and took a nap or stayed awake before completing a memory test. Although C.T. performed numerically worse than controls in both conditions, sleep profoundly exacerbated her memory impairment, such that she could not recall any details following a nap. This effect was replicated in a second testing session. High-resolution MRI scans showed evidence of the trans-callosal surgical approach's impact on the mid-anterior corpus callosum, indicated that C.T. had perturbed white matter particularly in the right fornix column, and demonstrated that C.T.'s hippocampal volumes did not differ from controls. These findings suggest that the fornix is important for processing episodic memories during sleep. As a key output pathway of the hippocampus, the fornix may ensure that specific memories are replayed during sleep, maintain the balance of sleep stages, or allow for the retrieval of memories following sleep.

Frontal and temporal lobe correlates of verbal learning and memory in aMCI and suspected Alzheimer's disease dementia

Alzheimer's disease is primarily known for deficits in learning and retaining new information. This has long been associated with pathological changes in the mesial temporal lobes. The role of the frontal lobes in memory in Alzheimer's disease is less well understood. In this study, we examined the role of the frontal lobes in learning, recognition, and retention of new verbal information, as well as the presence of specific errors (i.e., intrusions and false-positive errors). Participants included one hundred sixty-seven patients clinically diagnosed with amnestic mild cognitive impairment or suspected Alzheimer's disease dementia who were administered the California Verbal Learning Test and completed high-resolution MRI. We confirmed the role of the mesial temporal lobes in learning and retention, including the volumes of the hippocampus, entorhinal cortex, and parahippocampal gyrus. In addition, false-positive errors were associated with all volumes of the mesial temporal lobes and widespread areas within the frontal lobes. Errors of intrusion were related to the supplementary motor cortex and hippocampus. Most importantly, the mesial temporal lobes interacted with the frontal lobes for learning, recognition, and memory errors. Lower volumes in both regions explained more performance variance than any single structure. This study supports the interaction of the frontal lobes with the temporal lobes in many aspects of memory in Alzheimer's disease.

An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease

The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.

Predictive factors for Alzheimer's disease progression: a comprehensive retrospective analysis of 3,553 cases with 211 months follow-up

Background

There is conflicting data regarding the predictors of Alzheimer's Disease (AD), the most common form of dementia. The main objective of the study is to evaluate potential predictors of AD progression using a comprehensive follow-up dataset that includes functional/cognitive assessments, clinical and neuropsychiatric evaluations, and neuroimaging biomarkers such as hippocampal atrophy or white matter intensities (WMIs).

Method

A total of 161 AD cases were recruited from a dementia database consisting of individuals who consulted the Dementia Outpatient Clinic of the Neurology Department at Mersin University Medical Faculty between 2000 and 2022, under the supervision of the same senior author have at least 3 full evaluation follow-up visit including functional, clinical, biochemical, neuropsychological, and radiological screening. Data were exported and analyzed by experts accordingly.

Results

Mean follow-up duration of study sample was 71.66 ± 41.98, min 15 to max 211 months. The results showed a fast and slow progressive subgroup of our AD cases with a high sensitivity (Entropy = 0.836), with a close relationship with several cofactors and the level of disability upon admittance. Hippocampal atrophy and WMIs grading via Fazekas were found to be underestimated predictors of AD progression, and functional capacity upon admittance was also among the main stakeholders.

Conclusion

The study highlights the importance of evaluating multiple potential predictors for AD progression, including functional capacity upon admittance, hippocampal atrophy, and WMIs grading via Fazekas. Our findings provide insight into the complexity of AD progression and may contribute to the development of effective strategies for managing and treating AD.

Impact of social isolation on grey matter structure and cognitive functions: A population-based longitudinal neuroimaging study

Background

Social isolation has been suggested to increase the risk to develop cognitive decline. However, our knowledge on causality and neurobiological underpinnings is still limited.

Methods

In this preregistered analysis, we tested the impact of social isolation on central features of brain and cognitive ageing using a longitudinal population-based magnetic resonance imaging (MRI) study. We assayed 1992 cognitively healthy participants (50-82years old, 921women) at baseline and 1409 participants after~6y follow-up.

Results

We found baseline social isolation and change in social isolation to be associated with smaller volumes of the hippocampus and clusters of reduced cortical thickness. Furthermore, poorer cognitive functions (memory, processing speed, executive functions) were linked to greater social isolation, too.

Conclusions

Combining advanced neuroimaging outcomes with prevalent lifestyle characteristics from a well-characterized population of middle- to older aged adults, we provide evidence that social isolation contributes to human brain atrophy and cognitive decline. Within-subject effects of social isolation were similar to between-subject effects, indicating an opportunity to reduce dementia risk by promoting social networks.

Funding

European Union, European Regional Development Fund, Free State of Saxony, LIFE-Leipzig Research Center for Civilization Diseases, University of Leipzig, German Research Foundation.