Brain atrophy in Alzheimer's Disease and aging

Increases in regional brain volume across two native South American male populations

Industrialized environments, despite benefits such as higher levels of formal education and lower rates of infections, can also have pernicious impacts upon brain atrophy. Partly for this reason, comparing age-related brain volume trajectories between industrialized and non-industrialized populations can help to suggest lifestyle correlates of brain health. The Tsimane, indigenous to the Bolivian Amazon, derive their subsistence from foraging and horticulture and are physically active. The Moseten, a mixed-ethnicity farming population, are physically active but less than the Tsimane. Within both populations (N = 1024; age range = 46-83), we calculated regional brain volumes from computed tomography and compared their cross-sectional trends with age to those of UK Biobank (UKBB) participants (N = 19,973; same age range). Surprisingly among Tsimane and Moseten (T/M) males, some parietal and occipital structures mediating visuospatial abilities exhibit small but significant increases in regional volume with age. UKBB males exhibit a steeper negative trend of regional volume with age in frontal and temporal structures compared to T/M males. However, T/M females exhibit significantly steeper rates of brain volume decrease with age compared to UKBB females, particularly for some cerebro-cortical structures (e.g., left subparietal cortex). Across the three populations, observed trends exhibit no interhemispheric asymmetry. In conclusion, the age-related rate of regional brain volume change may differ by lifestyle and sex. The lack of brain volume reduction with age is not known to exist in other human population, highlighting the putative role of lifestyle in constraining regional brain atrophy and promoting elements of non-industrialized lifestyle like higher physical activity.

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.

Association and prediction of Life's Essential 8 score, genetic susceptibility with MCI, dementia, and MRI indices: A prospective cohort study

BACKGROUND

To examine the associations of Life's Essential 8 (LE8) and its predictive performance with mild cognitive impairment (MCI), dementia and brain MRI indices.

METHODS

We used cohort data from UK Biobank. LE8 was categorized into low (<50 score), moderate (50-79 score), and high (≥80 score) levels. Cox regression models considering death as a competing risk were used to estimate the hazard ratios (HRs) and 95%CI on the association between LE8 and MCI and dementia. Multivariable linear regression models were used to analyze LE8 every 10-score increase and brain MRI indices. Area under the curve (AUC) was used to measure the predictive performances of LE8.

RESULTS

We included 126,785 participants with a mean (SD) age of 56.0 (8.0) years and 53.5 % were female. The median follow-up was 13.0 years. Compared to individuals with a low LE8 score, those with a high LE8 score were associated with decreased risk of MCI (0.49, 95%CI: 0.40-0.62), all-cause dementia (0.60, 0.44-0.80), vascular dementia (VD, 0.44, 0.21-0.94), and non-Alzheimer non-vascular dementia (NAVD, 0.55, 0.35-0.84). High LE8 score was associated with increased total brain volume, hippocampus volume, grey matter volume, and grey matter in hippocampus volume (p all ≤0.001). LE8 combined age and sex had good performance for predicting all-cause dementia (AUC: 84.1 %), AD (85.4 %), VD (87.6 %), NAVD (81.4 %), and MCI (75.3 %).

LIMITATIONS

Our findings only reflect the characteristics of UKB participants.

CONCLUSIONS

High LE8 score was associated with reduced risk of MCI and dementia. It was also linked to brain MRI indices. LE8 score had good predicting performance for future risk of MCI and dementia.

Improving brain atrophy quantification with deep learning from automated labels using tissue similarity priors

Brain atrophy measurements derived from magnetic resonance imaging (MRI) are a promising marker for the diagnosis and prognosis of neurodegenerative pathologies such as Alzheimer's disease or multiple sclerosis. However, its use in individualized assessments is currently discouraged due to a series of technical and biological issues. In this work, we present a deep learning pipeline for segmentation-based brain atrophy quantification that improves upon the automated labels of the reference method from which it learns. This goal is achieved through tissue similarity regularization that exploits the a priori knowledge that scans from the same subject made within a short interval must have similar tissue volumes. To train the presented pipeline, we use unlabeled pairs of T1-weighted MRI scans having a tissue similarity prior, and generate the target brain tissue segmentations in a fully automated manner using the fsl_anat pipeline implemented in the FMRIB Software Library (FSL). Tissue similarity regularization is enforced during training through a weighted loss term that penalizes tissue volume differences between short-interval scan pairs from the same subject. In inference, the pipeline performs end-to-end skull stripping and brain tissue segmentation from a single T1-weighted MRI scan in its native space, i.e., without performing image interpolation. For longitudinal evaluation, each image is independently segmented first, and then measures of change are computed. We evaluate the presented pipeline in two different MRI datasets, MIRIAD and ADNI1, which have longitudinal and short-interval imaging from healthy controls (HC) and Alzheimer's disease (AD) subjects. In short-interval scan pairs, tissue similarity regularization reduces the quantification error and improves the consistency of measured tissue volumes. In the longitudinal case, the proposed pipeline shows reduced variability of atrophy measures and higher effect sizes of differences in annualized rates between HC and AD subjects. Our pipeline obtains a Cohen's d effect size of d=2.07 on the MIRIAD dataset, an increase from the reference pipeline used to train it (d=1.01), and higher than that of SIENA (d=1.73), a well-known state-of-the-art approach. In the ADNI1 dataset, the proposed pipeline improves its effect size (d=1.37) with respect to the reference pipeline (d=0.80) and surpasses SIENA (d=1.33). The proposed data-driven deep learning regularization reduces the biases and systematic errors learned from the reference segmentation method, which is used to generate the training targets. Improving the accuracy and reliability of atrophy quantification methods is essential to unlock brain atrophy as a diagnostic and prognostic marker in neurodegenerative pathologies.

BIN1 deficiency enhances ULK3-dependent autophagic flux and reduces dendritic size in mouse hippocampal neurons

Genome-wide association studies identified variants around the BIN1 (bridging integrator 1) gene locus as prominent risk factors for late-onset Alzheimer disease. In the present study, we decreased the expression of BIN1 in mouse hippocampal neurons to investigate its neuronal function. Bin1 knockdown via RNAi reduced the dendritic arbor size in primary cultured hippocampal neurons as well as in mature Cornu Ammonis 1 excitatory neurons. The AAV-mediated Bin1 RNAi knockdown also generated a significant regional volume loss around the injection sites at the organ level, as revealed by 7-Tesla structural magnetic resonance imaging, and an impaired spatial reference memory performance in the Barnes maze test. Unexpectedly, Bin1 knockdown led to concurrent activation of both macroautophagy/autophagy and MTOR (mechanistic target of rapamycin kinase) complex 1 (MTORC1). Autophagy inhibition with the lysosome inhibitor chloroquine effectively mitigated the Bin1 knockdown-induced dendritic regression. The subsequent molecular study demonstrated that increased expression of ULK3 (unc-51 like kinase 3), which is MTOR-insensitive, supported autophagosome formation in BIN1 deficiency. Reducing ULK3 activity with SU6668, a receptor tyrosine kinase inhibitor, or decreasing neuronal ULK3 expression through AAV-mediated RNAi, significantly attenuated Bin1 knockdown-induced hippocampal volume loss and spatial memory decline. In Alzheimer disease patients, the major neuronal isoform of BIN1 is specifically reduced. Our work suggests this reduction is probably an important molecular event that increases the autophagy level, which might subsequently promote brain atrophy and cognitive impairment through reducing dendritic structures, and ULK3 is a potential interventional target for relieving these detrimental effects.

Cortical lobar volume reductions associated with homocysteine-related subcortical brain atrophy and poorer cognition in healthy aging

Homocysteine (Hcy) is a cardiovascular risk factor implicated in cognitive impairment and cerebrovascular disease but has also been associated with Alzheimer's disease. In 160 healthy older adults (mean age = 69.66 ± 9.95 years), we sought to investigate the association of cortical brain volume with white matter hyperintensity (WMH) burden and a previously identified Hcy-related multivariate network pattern showing reductions in subcortical gray matter (SGM) volumes of hippocampus and nucleus accumbens with relative preservation of basal ganglia. We additionally evaluated the potential role of these brain imaging markers as a series of mediators in a vascular brain pathway leading to age-related cognitive dysfunction in healthy aging. We found reductions in parietal lobar gray matter associated with the Hcy-SGM pattern, which was further associated with WMH burden. Mediation analyses revealed that slowed processing speed related to aging, but not executive functioning or memory, was mediated sequentially through increased WMH lesion volume, greater Hcy-SGM pattern expression, and then smaller parietal lobe volume. Together, these findings suggest that volume reductions in parietal gray matter associated with a pattern of Hcy-related SGM volume differences may be indicative of slowed processing speed in cognitive aging, potentially linking cardiovascular risk to an important aspect of cognitive dysfunction in healthy aging.

Cognitive and Hippocampal Changes in Older Adults With Subjective Cognitive Decline After Acupuncture Intervention

OBJECTIVE

Converging evidence indicates that subjective cognitive decline (SCD) could be an early indicator of dementia. The hippocampus is the earliest affected region during the progression of cognitive impairment. However, little is known about whether and how acupuncture change the hippocampal structure and function of SCD individuals.

METHODS

Here, we used multi-modal MRI to reveal the mechanism of acupuncture in treating SCD. Seventy-two older participants were randomized into acupuncture or sham acupuncture group and treated for 12 weeks.

RESULTS

At the end of the intervention, compared to sham acupuncture, participants with acupuncture treatment showed improvement in composite Z score from multi-domain neuropsychological tests, as well as increased hippocampal volume and functional connectivity. Moreover, the greater white matter integrity of the fornix, which is the major output tract of the hippocampus, was shown in the acupuncture group.

CONCLUSION

These findings suggest that acupuncture may improve the cognitive function of SCD individuals, and increase hippocampal volume on the regional level and enhance the structural and functional connectivity of hippocampus on the connective level.

Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences

Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.

The associations of socioeconomic position with structural brain damage and connectivity and cognitive functioning: The Maastricht Study

BACKGROUND

Socioeconomic inequalities in cognitive impairment may partly act through structural brain damage and reduced connectivity. This study investigated the extent to which the association of early-life socioeconomic position (SEP) with later-life cognitive functioning is mediated by later-life SEP, and whether the associations of SEP with later-life cognitive functioning can be explained by structural brain damage and connectivity.

METHODS

We used cross-sectional data from the Dutch population-based Maastricht Study (n = 4,839; mean age 59.2 ± 8.7 years, 49.8% women). Early-life SEP was assessed by self-reported poverty during childhood and parental education. Later-life SEP included education, occupation, and current household income. Participants underwent cognitive testing and 3-T magnetic resonance imaging to measure volumes of white matter hyperintensities, grey matter, white matter, cerebrospinal fluid, and structural connectivity. Multiple linear regression analyses tested the associations between SEP, markers of structural brain damage and connectivity, and cognitive functioning. Mediation was tested using structural equation modeling.

RESULTS

Although there were direct associations between both indicators of SEP and later-life cognitive functioning, a large part of the association between early-life SEP and later-life cognitive functioning was explained by later-life SEP (72.2%). The extent to which structural brain damage or connectivity acted as mediators between SEP and cognitive functioning was small (up to 5.9%).

CONCLUSIONS

We observed substantial SEP differences in later-life cognitive functioning. Associations of structural brain damage and connectivity with cognitive functioning were relatively small, and only marginally explained the SEP gradients in cognitive functioning.

Prospective Longitudinal Perfusion in Probable Alzheimer's Disease Correlated with Atrophy in Temporal Lobe

Reduced cerebral blood flow (CBF) in the temporoparietal region and gray matter volumes (GMVs) in the temporal lobe were previously reported in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the temporal relationship between reductions in CBF and GMVs requires further investigation. This study sought to determine if reduced CBF is associated with reduced GMVs, or vice versa. Data came from 148 volunteers of the Cardiovascular Health Study Cognition Study (CHS-CS), including 58 normal controls (NC), 50 MCI, and 40 AD who had perfusion and structural MRIs during 2002-2003 (Time 2). Sixty-three of the 148 volunteers had follow-up perfusion and structural MRIs (Time 3). Forty out of the 63 volunteers received prior structural MRIs during 1997-1999 (Time 1). The relationships between GMVs and subsequent CBF changes, and between CBF and subsequent GMV changes were investigated. At Time 2, we observed smaller GMVs (p<0.05) in the temporal pole region in AD compared to NC and MCI. We also found associations between: (1) temporal pole GMVs at Time 2 and subsequent declines in CBF in this region (p=0.0014) and in the temporoparietal region (p=0.0032); (2) hippocampal GMVs at Time 2 and subsequent declines in CBF in the temporoparietal region (p=0.012); and (3) temporal pole CBF at Time 2 and subsequent changes in GMV in this region (p = 0.011). Therefore, hypoperfusion in the temporal pole may be an early event driving its atrophy. Perfusion declines in the temporoparietal and temporal pole follow atrophy in this temporal pole region.

A review of the application of exercise intervention on improving cognition in patients with Alzheimer's disease: mechanisms and clinical studies

Alzheimer's disease (AD) is the most common form of dementia, leading to sustained cognitive decline. An increasing number of studies suggest that exercise is an effective strategy to promote the improvement of cognition in AD. Mechanisms of the benefits of exercise intervention on cognitive function may include modulation of vascular factors by affecting cardiovascular risk factors, regulating cardiorespiratory health, and enhancing cerebral blood flow. Exercise also promotes neurogenesis by stimulating neurotrophic factors, affecting neuroplasticity in the brain. Additionally, regular exercise improves the neuropathological characteristics of AD by improving mitochondrial function, and the brain redox status. More and more attention has been paid to the effect of Aβ and tau pathology as well as sleep disorders on cognitive function in persons diagnosed with AD. Besides, there are various forms of exercise intervention in cognitive improvement in patients with AD, including aerobic exercise, resistance exercise, and multi-component exercise. Consequently, the purpose of this review is to summarize the findings of the mechanisms of exercise intervention on cognitive function in patients with AD, and also discuss the application of different exercise interventions in cognitive impairment in AD to provide a theoretical basis and reference for the selection of exercise intervention in cognitive rehabilitation in AD.

Interneuron diversity in the human dorsal striatum

Deciphering the striatal interneuron diversity is key to understanding the basal ganglia circuit and to untangling the complex neurological and psychiatric diseases affecting this brain structure. We performed snRNA-seq and spatial transcriptomics of postmortem human caudate nucleus and putamen samples to elucidate the diversity and abundance of interneuron populations and their inherent transcriptional structure in the human dorsal striatum. We propose a comprehensive taxonomy of striatal interneurons with eight main classes and fourteen subclasses, providing their full transcriptomic identity and spatial expression profile as well as additional quantitative FISH validation for specific populations. We have also delineated the correspondence of our taxonomy with previous standardized classifications and shown the main transcriptomic and class abundance differences between caudate nucleus and putamen. Notably, based on key functional genes such as ion channels and synaptic receptors, we found matching known mouse interneuron populations for the most abundant populations, the recently described PTHLH and TAC3 interneurons. Finally, we were able to integrate other published datasets with ours, supporting the generalizability of this harmonized taxonomy.

Olfactory Impairment and Frailty: A Systematic Review and Meta-Analysis

Importance

Olfactory impairment (OI) and frailty are prevalent conditions associated with aging, but studies investigating their association with each other have been discordant.

Objective

To summarize current evidence surrounding the association between OI and frailty.

Data Sources

PubMed, Embase, Cochrane Library, SCOPUS, and CINAHL from inception to November 28, 2023.

Study Selection

This study included observational studies investigating the association between objectively or subjectively assessed OI and objectively evaluated frailty among adults.

Data Extraction and Synthesis

Two independent authors extracted data into a structured template. Maximally adjusted estimates were pooled using a random-effects model, and statistical heterogeneity was evaluated using I2 values. Additional prespecified subgroup and sensitivity analyses were performed. This study used the Newcastle-Ottawa Scale for bias assessment and the Grading of Recommendations Assessment, Development and Evaluation framework for overall evidence quality evaluation.

Main Outcomes and Measures

The primary outcome was the cross-sectional association between OI and frailty, for which the odds of frailty were compared between participants with and without OI. The secondary outcome was the cross-sectional association between frailty and OI, for which the odds of OI were compared between participants with and without frailty.

Results

This study included 10 studies with 10 624 patients (52.9% female; mean [SD] age, 62.9 [9.6] years). The Newcastle-Ottawa Scale score of studies ranged from low to moderate. Grading of Recommendations Assessment, Development and Evaluation scores ranged from low to moderate. OI was associated with a 2.32-fold (odds ratio [OR], 2.32; 95% CI, 1.63-3.31; I2 = 0%) greater odds of frailty compared with individuals with healthy olfactory function. The odds of OI was progressively greater with categorical frailty status, with a 1.55-fold (OR, 1.55; 95% CI, 1.32-1.82; I2 = 0%), 2.28-fold (OR, 2.28; 95% CI, 1.96-2.65; I2 = 0%), and 4.67-fold (OR, 4.67; 95% CI, 2.77-7.86; I2 = 0%) increase in odds for individuals with prefrailty, frailty, and the most frailty, respectively, compared with robust individuals. The results demonstrated stability in subgroup analyses (geographical continent of study, objective vs subjective olfactory assessment) and sensitivity tests.

Conclusions and Relevance

The results of this systematic review and meta-analysis suggest that there is an association between OI and frailty, with an increase in the odds of OI with worsening categorical frailty status among individuals with prefrailty, frailty, and the most frailty. OI may be a potential biomarker for frailty. Future studies could delve into whether OI may be a modifiable risk factor for frailty.

Gray matter volume in women with the BRCA mutation with and without ovarian removal: evidence for increased risk of late-life Alzheimer's disease or dementia

OBJECTIVE

Ovarian removal prior to spontaneous/natural menopause (SM) is associated with increased risk of late life dementias including Alzheimer's disease. This increased risk may be related to the sudden and early loss of endogenous estradiol. Women with breast cancer gene mutations (BRCAm) are counseled to undergo oophorectomy prior to SM to significantly reduce their risk of developing breast, ovarian, and cervical cancers. There is limited evidence of the neurological effects of ovarian removal prior to the age of SM showing women without the BRCAm had cortical thinning in medial temporal lobe structures. A second study in women with BRCAm and bilateral salpingo-oophorectomy (BSO) noted changes in cognition.

METHODS

The present, cross-sectional study examined whole-brain differences in gray matter (GM) volume using high-resolution, quantitative magnetic resonance imaging in women with BRCAm and intact ovaries (BRCA-preBSO [study cohort with BRCA mutation prior to oophorectomy]; n = 9) and after surgery with (BSO + estradiol-based therapy [ERT]; n = 10) and without (BSO; n = 10) postsurgical estradiol hormone therapy compared with age-matched women (age-matched controls; n = 10) with their ovaries.

RESULTS

The BRCA-preBSO and BSO groups showed significantly lower GM volume in the left medial temporal and frontal lobe structures. BSO + ERT exhibited few areas of lower GM volume compared with age-matched controls. Novel to this study, we also observed that all three BRCAm groups exhibited significantly higher GM volume compared with age-matched controls, suggesting continued plasticity.

CONCLUSIONS

The present study provides evidence, through lower GM volume, to support both the possibility that the BRCAm, alone, and early life BSO may play a role in increasing the risk for late-life dementia. At least for BRCAm with BSO, postsurgical ERT seems to ameliorate GM losses.

Clinically feasible automated MRI volumetry of the brain as a prognostic marker in subjective and mild cognitive impairment

Background/aims

The number of patients suffering from cognitive decline and dementia increases, and new possible treatments are being developed. Thus, the need for time efficient and cost-effective methods to facilitate an early diagnosis and prediction of future cognitive decline in patients with early cognitive symptoms is becoming increasingly important. The aim of this study was to evaluate whether an MRI based software, NeuroQuant® (NQ), producing volumetry of the hippocampus and whole brain volume (WBV) could predict: (1) conversion from subjective cognitive decline (SCD) at baseline to mild cognitive impairment (MCI) or dementia at follow-up, and from MCI at baseline to dementia at follow-up and (2) progression of cognitive and functional decline defined as an annual increase in the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB) score.

Methods

MRI was performed in 156 patients with SCD or MCI from the memory clinic at Oslo University Hospital (OUH) that had been assessed with NQ and had a clinical follow-up examination. Logistic and linear regression analyses were performed with hippocampus volume and WBV as independent variables, and conversion or progression as dependent variables, adjusting for demographic and other relevant covariates including Mini-Mental State Examination-Norwegian Revised Version score (MMSE-NR) and Apolipoprotein E ɛ4 (APOE ɛ4) carrier status.

Results

Hippocampus volume, but not WBV, was associated with conversion to MCI or dementia, but neither were associated with conversion when adjusting for MMSE-NR. Both hippocampus volume and WBV were associated with progression as measured by the annual change in CDR-SB score in both unadjusted and adjusted analyses.

Conclusion

The results indicate that automated regional MRI volumetry of the hippocampus and WBV can be useful in predicting further cognitive decline in patients with early cognitive symptoms.

Proteomic analysis identifies HSP90AA1, PTK2B, and ANXA2 in the human entorhinal cortex in Alzheimer's disease: Potential role in synaptic homeostasis and Aβ pathology through microglial and astroglial cells

Alzheimer's disease (AD), the most prevalent neurodegenerative disorder worldwide, is clinically characterized by cognitive deficits. Neuropathologically, AD brains accumulate deposits of amyloid-β (Aβ) and tau proteins. Furthermore, these misfolded proteins can propagate from cell to cell in a prion-like manner and induce native proteins to become pathological. The entorhinal cortex (EC) is among the earliest areas affected by tau accumulation along with volume reduction and neurodegeneration. Neuron-glia interactions have recently come into focus; however, the role of microglia and astroglia in the pathogenesis of AD remains unclear. Proteomic approaches allow the determination of changes in the proteome to better understand the pathology underlying AD. Bioinformatic analysis of proteomic data was performed to compare ECs from AD and non-AD human brain tissue. To validate the proteomic results, western blot, immunofluorescence, and confocal studies were carried out. The findings revealed that the most disturbed signaling pathway was synaptogenesis. Because of their involvement in synapse function, relationship with Aβ and tau proteins and interactions in the pathway analysis, three proteins were selected for in-depth study: HSP90AA1, PTK2B, and ANXA2. All these proteins showed colocalization with neurons and/or astroglia and microglia and with pathological Aβ and tau proteins. In particular, ANXA2, which is overexpressed in AD, colocalized with amoeboid microglial cells and Aβ plaques surrounded by astrocytes. Taken together, the evidence suggests that unbalanced expression of HSP90AA1, PTK2B, and ANXA2 may play a significant role in synaptic homeostasis and Aβ pathology through microglial and astroglial cells in the human EC in AD.

Association of past and current sleep duration with structural brain differences: A large population-based study from the UK Biobank

OBJECTIVE

This study aimed to examine the association between past/current sleep duration and macro-/micro-structural brain outcomes and explore whether hypertension or social activity plays a role in such association.

METHODS

Within the UK Biobank, 40 436 dementia-free participants (age 40-70 years) underwent a baseline assessment followed by a brain magnetic resonance imaging (MRI) scan 9 years later. Past (baseline) and current (MRI scans) sleep duration (hours/day) were recorded and classified as short (≤5), intermediate (6-8), and long (≥9). Brain structural volumes and diffusion markers were assessed by MRI scans.

RESULTS

Compared with past intermediate sleep, past short sleep was related to smaller cortex volumes (standardized β [95 % CI]: -0.04 [-0.07, -0.02]) and lower regional fractional anisotropy (FA) (-0.08 [-0.13, -0.03]), while past long sleep was related to smaller regional subcortical volumes (standardized β: -0.04 to -0.07 for thalamus, accumbens, and hippocampus). Compared to current intermediate sleep, current short sleep was associated with smaller cortex volumes (-0.03 [-0.05, -0.01]), greater white matter hyperintensities (WMH) volumes (0.04 [0.01, 0.08]), and lower regional FA (-0.07 [-0.11, -0.02]). However, current long sleep was related to smaller total brain (-0.03 [-0.05, -0.02]), grey matter (-0.05 [-0.07, -0.03]), cortex (-0.05 [-0.07, -0.03]), regional subcortical volumes [standardized β: -0.05 to -0.09 for putamen, thalamus, hippocampus, and accumbens]), greater WMH volumes (0.06 [0.03, 0.09]), as well as lower regional FA (-0.05 [-0.09, -0.02]). The association between current long sleep duration and poor brain health was stronger among people with hypertension or low frequency of social activity (all Pinteraction <0.05).

CONCLUSIONS

Both past and current short/long sleep are associated with smaller brain volume and poorer white matter health in the brain, especially in individuals with hypertension and low frequency of social activity. Our findings highlight the need to maintain 6-8 h' sleep duration for healthy brain aging.

Deep learning-based super-resolution of structural brain MRI at 1.5 T: application to quantitative volume measurement

OBJECTIVE

This study investigated the feasibility of using deep learning-based super-resolution (DL-SR) technique on low-resolution (LR) images to generate high-resolution (HR) MR images with the aim of scan time reduction. The efficacy of DL-SR was also assessed through the application of brain volume measurement (BVM).

MATERIALS AND METHODS

In vivo brain images acquired with 3D-T1W from various MRI scanners were utilized. For model training, LR images were generated by downsampling the original 1 mm-2 mm isotropic resolution images. Pairs of LR and HR images were used for training 3D residual dense net (RDN). For model testing, actual scanned 2 mm isotropic resolution 3D-T1W images with one-minute scan time were used. Normalized root-mean-square error (NRMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used for model evaluation. The evaluation also included brain volume measurement, with assessments of subcortical brain regions.

RESULTS

The results showed that DL-SR model improved the quality of LR images compared with cubic interpolation, as indicated by NRMSE (24.22% vs 30.13%), PSNR (26.19 vs 24.65), and SSIM (0.96 vs 0.95). For volumetric assessments, there were no significant differences between DL-SR and actual HR images (p > 0.05, Pearson's correlation > 0.90) at seven subcortical regions.

DISCUSSION

The combination of LR MRI and DL-SR enables addressing prolonged scan time in 3D MRI scans while providing sufficient image quality without affecting brain volume measurement.

AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field

OBJECTIVES

The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management.

METHODS

We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline.

RESULTS

Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations.

CONCLUSION

AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.

Analysis of the alpha activity envelope in electroencephalography in relation to the ratio of excitatory to inhibitory neural activity

Alpha waves, one of the major components of resting and awake cortical activity in human electroencephalography (EEG), are known to show waxing and waning, but this phenomenon has rarely been analyzed. In the present study, we analyzed this phenomenon from the viewpoint of excitation and inhibition. The alpha wave envelope was subjected to secondary differentiation. This gave the positive (acceleration positive, Ap) and negative (acceleration negative, An) values of acceleration and their ratio (Ap-An ratio) at each sampling point of the envelope signals for 60 seconds. This analysis was performed on 36 participants with Alzheimer's disease (AD), 23 with frontotemporal dementia (FTD) and 29 age-matched healthy participants (NC) whose data were provided as open datasets. The mean values of the Ap-An ratio for 60 seconds at each EEG electrode were compared between the NC and AD/FTD groups. The AD (1.41 ±0.01 (SD)) and FTD (1.40 ±0.02) groups showed a larger Ap-An ratio than the NC group (1.38 ±0.02, p<0.05). A significant correlation between the envelope amplitude of alpha activity and the Ap-An ratio was observed at most electrodes in the NC group (Pearson's correlation coefficient, r = -0.92 ±0.15, mean for all electrodes), whereas the correlation was disrupted in AD (-0.09 ±0.21, p<0.05) and disrupted in the frontal region in the FTD group. The present method analyzed the envelope of alpha waves from a new perspective, that of excitation and inhibition, and it could detect properties of the EEG, Ap-An ratio, that have not been revealed by existing methods. The present study proposed a new method to analyze the alpha activity envelope in electroencephalography, which could be related to excitatory and inhibitory neural activity.

Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy

BACKGROUND

Migraine is a neurological disease with a significant genetic component and is characterized by recurrent and prolonged episodes of headache. Previous epidemiological studies have reported a higher risk of dementia in migraine patients. Neuroimaging studies have also shown structural brain atrophy in regions that are common to migraine and dementia. However, these studies are observational and cannot establish causality. The present study aims to explore the genetic causal relationship between migraine and dementia, as well as the mediation roles of brain structural changes in this association using Mendelian randomization (MR).

METHODS

We collected the genome-wide association study (GWAS) summary statistics of migraine and its two subtypes, as well as four common types of dementia, including Alzheimer's disease (AD), vascular dementia, frontotemporal dementia, and Lewy body dementia. In addition, we collected the GWAS summary statistics of seven longitudinal brain measures that characterize brain structural alterations with age. Using these GWAS, we performed Two-sample MR analyses to investigate the causal effects of migraine and its two subtypes on dementia and brain structural changes. To explore the possible mediation of brain structural changes between migraine and dementia, we conducted a two-step MR mediation analysis.

RESULTS

The MR analysis demonstrated a significant association between genetically predicted migraine and an increased risk of AD (OR = 1.097, 95% CI = [1.040, 1.158], p = 7.03 × 10- 4). Moreover, migraine significantly accelerated annual atrophy of the total cortical surface area (-65.588 cm2 per year, 95% CI = [-103.112, -28.064], p = 6.13 × 10- 4) and thalamic volume (-9.507 cm3 per year, 95% CI = [-15.512, -3.502], p = 1.91 × 10- 3). The migraine without aura (MO) subtype increased the risk of AD (OR = 1.091, 95% CI = [1.059, 1.123], p = 6.95 × 10- 9) and accelerated annual atrophy of the total cortical surface area (-31.401 cm2 per year, 95% CI = [-43.990, -18.811], p = 1.02 × 10- 6). The two-step MR mediation analysis revealed that thalamic atrophy partly mediated the causal effect of migraine on AD, accounting for 28.2% of the total effect.

DISCUSSION

This comprehensive MR study provided genetic evidence for the causal effect of migraine on AD and identified longitudinal thalamic atrophy as a potential mediator in this association. These findings may inform brain intervention targets to prevent AD risk in migraine patients.

Crosstalk between bone and brain in Alzheimer's disease: Mechanisms, applications, and perspectives

Alzheimer's disease (AD) is a neurodegenerative disease that involves multiple systems in the body. Numerous recent studies have revealed bidirectional crosstalk between the brain and bone, but the interaction between bone and brain in AD remains unclear. In this review, we summarize human studies of the association between bone and brain and provide an overview of their interactions and the underlying mechanisms in AD. We review the effects of AD on bone from the aspects of AD pathogenic proteins, AD risk genes, neurohormones, neuropeptides, neurotransmitters, brain-derived extracellular vesicles (EVs), and the autonomic nervous system. Correspondingly, we elucidate the underlying mechanisms of the involvement of bone in the pathogenesis of AD, including bone-derived hormones, bone marrow-derived cells, bone-derived EVs, and inflammation. On the basis of the crosstalk between bone and the brain, we propose potential strategies for the management of AD with the hope of offering novel perspectives on its prevention and treatment. HIGHLIGHTS: The pathogenesis of AD, along with its consequent changes in the brain, may involve disturbing bone homeostasis. Degenerative bone disorders may influence the progression of AD through a series of pathophysiological mechanisms. Therefore, relevant bone intervention strategies may be beneficial for the comprehensive management of AD.

Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data

BACKGROUND

For successful biomarker discovery, it is essential to develop computational frameworks that summarize high-dimensional neuroimaging data in terms of involved sub-systems of the brain, while also revealing underlying heterogeneous functional and structural changes covarying with specific cognitive and biological traits. However, unsupervised decompositions do not inculcate clinical assessment information, while supervised approaches extract only individual feature importance, thereby impeding qualitative interpretation at the level of subspaces.

NEW METHOD

We present a novel framework to extract robust multimodal brain subspaces associated with changes in a given cognitive or biological trait. Our approach involves active subspace learning on the gradients of a trained machine learning model followed by clustering to extract and summarize the most salient and consistent subspaces associated with the target variable.

RESULTS

Through a rigorous cross-validation procedure on an Alzheimer's disease (AD) dataset, our framework successfully extracts multimodal subspaces specific to a given clinical assessment (e.g., memory and other cognitive skills), and also retains predictive performance in standard machine learning algorithms. We also show that the salient active subspace directions occur consistently across randomly sub-sampled repetitions of the analysis.

COMPARISON WITH EXISTING METHOD(S)

Compared to existing unsupervised decompositions based on principle component analysis, the subspace components in our framework retain higher predictive information.

CONCLUSIONS

As an important step towards biomarker discovery, our framework not only uncovers AD-related brain regions in the associated brain subspaces, but also enables automated identification of multiple underlying structural and functional sub-systems of the brain that collectively characterize changes in memory and proficiency in cognitive skills related to brain disorders like AD.

Differential atrophy along the longitudinal hippocampal axis in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the hippocampus. Since hippocampal studies have highlighted a differential subregional regulation along its longitudinal axis, a more detailed analysis addressing subregional changes along the longitudinal hippocampal axis has the potential to provide new relevant biomarkers. This study included structural brain MRI data of 583 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitively normal (CN) subjects, mild cognitively impaired (MCI) subjects and AD patients were conveniently selected considering the age and sex match between clinical groups. Structural MRI acquisitions were pre-processed and analysed with a new longitudinal axis segmentation method, dividing the hippocampus in three subdivisions (anterior, intermediate, and posterior). When normalizing the volume of hippocampal sub-divisions to total hippocampus, the posterior hippocampus negatively correlates with age only in CN subjects (r = -.31). The longitudinal ratio of hippocampal atrophy (anterior sub-division divided by the posterior one) shows a significant increase with age only in CN (r = .25). Overall, in AD, the posterior hippocampus is predominantly atrophied early on. Consequently, the anterior/posterior hippocampal ratio is an AD differentiating metric at early disease stages with potential for diagnostic and prognostic applications.

Gaming expertise induces meso‑scale brain plasticity and efficiency mechanisms as revealed by whole-brain modeling

Video games are a valuable tool for studying the effects of training and neural plasticity on the brain. However, the underlying mechanisms related to plasticity-associated brain structural changes and their impact on brain dynamics are unknown. Here, we used a semi-empirical whole-brain model to study structural neural plasticity mechanisms linked to video game expertise. We hypothesized that video game expertise is associated with neural plasticity-mediated changes in structural connectivity that manifest at the meso‑scale level, resulting in a more segregated functional network topology. To test this hypothesis, we combined structural connectivity data of StarCraft II video game players (VGPs, n = 31) and non-players (NVGPs, n = 31), with generic fMRI data from the Human Connectome Project and computational models, to generate simulated fMRI recordings. Graph theory analysis on simulated data was performed during both resting-state conditions and external stimulation. VGPs' simulated functional connectivity was characterized by a meso‑scale integration, with increased local connectivity in frontal, parietal, and occipital brain regions. The same analyses at the level of structural connectivity showed no differences between VGPs and NVGPs. Regions that increased their connectivity strength in VGPs are known to be involved in cognitive processes crucial for task performance such as attention, reasoning, and inference. In-silico stimulation suggested that differences in FC between VGPs and NVGPs emerge in noisy contexts, specifically when the noisy level of stimulation is increased. This indicates that the connectomes of VGPs may facilitate the filtering of noise from stimuli. These structural alterations drive the meso‑scale functional changes observed in individuals with gaming expertise. Overall, our work sheds light on the mechanisms underlying structural neural plasticity triggered by video game experiences.

Spatial Dissection of the Distinct Cellular Responses to Normal Aging and Alzheimer's Disease in Human Prefrontal Cortex at Single-Nucleus Resolution

Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-β (Aβ), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aβ clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aβ plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD.

Age-related constraints on the spatial geometry of the brain

Age-related structural brain changes may be better captured by assessing complex spatial geometric differences rather than isolated changes to individual regions. We applied a novel analytic method to quantify age-related changes to the spatial anatomy of the brain by measuring expansion and compression of global brain shape and the distance between cross-hemisphere homologous regions. To test how global brain shape and regional distances are affected by aging, we analyzed 2,603 structural MRIs (range: 30-97 years). Increasing age was associated with global shape expansion across inferior-anterior gradients, global compression across superior-posterior gradients, and regional expansion between frontotemporal homologues. Specific patterns of global and regional expansion and compression were further associated with clinical impairment and distinctly related to deficits in various cognitive domains. These findings suggest that changes to the complex spatial anatomy and geometry of the aging brain may be associated with reduced efficiency and cognitive dysfunction in older adults.

No genetic causal associations between periodontitis and brain atrophy or cognitive impairment: evidence from a comprehensive bidirectional Mendelian randomization study

BACKGROUND

Observational studies have explored the relationships of periodontitis with brain atrophy and cognitive impairment, but these findings are limited by reverse causation, confounders and have reported conflicting results. Our study aimed to investigate the causal associations of periodontitis with brain atrophy and cognitive impairment through a comprehensive bidirectional Mendelian randomization (MR) research.

METHODS

We incorporated two distinct genome-wide association study (GWAS) summary datasets as an exploration cohort and a replication cohort for periodontitis. Four and eight metrics were selected for the insightful evaluation of brain atrophy and cognitive impairment, respectively. The former involved cortical thickness and surface area, left and right hippocampal volumes, with the latter covering assessments of cognitive performance, fluid intelligence scores, prospective memory, and reaction time for mild cognitive impairment to Alzheimer's disease (AD), Lewy body dementia, vascular dementia and frontotemporal dementia for severe situations. Furthermore, supplementary analyses were conducted to examine the associations between the longitudinal rates of change in brain atrophy and cognitive function metrics with periodontitis. The main analysis utilized the inverse variance weighting (IVW) method and evaluated the robustness of the results through a series of sensitivity analyses. For multiple tests, associations with p-values < 0.0021 were considered statistically significant, while p-values ≥ 0.0021 and < 0.05 were regarded as suggestive of significance.

RESULTS

In the exploration cohort, forward and reverse MR results revealed no causal associations between periodontitis and brain atrophy or cognitive impairment, and only a potential causal association was found between AD and periodontitis (IVW: OR = 0.917, 95% CI from 0.845 to 0.995, P = 0.038). Results from the replication cohort similarly corroborated the absence of a causal relationship. In the supplementary analyses, the longitudinal rates of change in brain atrophy and cognitive function were also not found to have causal relationships with periodontitis.

CONCLUSIONS

The MR analyses indicated a lack of substantial evidence for a causal connection between periodontitis and both brain atrophy and cognitive impairment.

Randomized controlled trials of non-pharmacological interventions for healthy seniors: Effects on cognitive decline, brain plasticity and activities of daily living-A 23-year scoping review

Little is known about the simultaneous effects of non-pharmacological interventions (NPI) on healthy older adults' behavior and brain plasticity, as measured by psychometric instruments and magnetic resonance imaging (MRI). The purpose of this scoping review was to compile an extensive list of randomized controlled trials published from January 1, 2000, to August 31, 2023, of NPI for mitigating and countervailing age-related physical and cognitive decline and associated cerebral degeneration in healthy elderly populations with a mean age of 55 and over. After inventorying the NPI that met our criteria, we divided them into six classes: single-domain cognitive, multi-domain cognitive, physical aerobic, physical non-aerobic, combined cognitive and physical aerobic, and combined cognitive and physical non-aerobic. The ultimate purpose of these NPI was to enhance individual autonomy and well-being by bolstering functional capacity that might transfer to activities of daily living. The insights from this study can be a starting point for new research and inform social, public health, and economic policies. The PRISMA extension for scoping reviews (PRISMA-ScR) checklist served as the framework for this scoping review, which includes 70 studies. Results indicate that medium- and long-term interventions combining non-aerobic physical exercise and multi-domain cognitive interventions best stimulate neuroplasticity and protect against age-related decline and that outcomes may transfer to activities of daily living.

The impact of APOE ε4 in Alzheimer's disease: a meta-analysis of voxel-based morphometry studies

Background

Alzheimer's disease (AD) is the most prevalent form of dementia, exerting substantial personal and societal impacts. The apolipoprotein E (APOE) ε4 allele is a known genetic factor that increases the risk of AD, contributing to more severe brain atrophy and exacerbated symptoms.

Purpose

We aim to provide a comprehensive review of the impacts of the APOE ε4 allele on brain atrophy in AD and mild cognitive impairment (MCI) as a transitional stage of AD.

Methods

We performed a coordinate-based meta-analysis of voxel-based morphometry (VBM) studies to identify the patterns of grey matter atrophy in APOE ε4 carriers vs. non-carriers. We obtained coordinate-based structural magnetic resonance imaging (MRI) data for 1135 individuals from 12 studies on PubMed and Google Scholar that met our inclusion criteria.

Results

We found significant atrophy in the hippocampus and parahippocampus of APOE ε4 carriers compared to non-carriers, especially within the AD and MCI groups, while healthy controls showed no significant atrophy in these regions.

Conclusion

Our meta-analysis sheds light on the significant link between the APOE ε4 allele and hippocampal atrophy in both AD and MCI, emphasizing the allele's critical influence on neurodegeneration, especially in the hippocampus. Our findings contribute to the understanding of the disease's pathology, potentially facilitating progress in early detection, targeted interventions, and personalized care strategies for individuals with the APOE ε4 allele who are at risk for Alzheimer's Disease.

Viscous dynamics associated with hypoexcitation and structural disintegration in neurodegeneration via generative whole-brain modeling

INTRODUCTION

Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) lack mechanistic biophysical modeling in diverse, underrepresented populations. Electroencephalography (EEG) is a high temporal resolution, cost-effective technique for studying dementia globally, but lacks mechanistic models and produces non-replicable results.

METHODS

We developed a generative whole-brain model that combines EEG source-level metaconnectivity, anatomical priors, and a perturbational approach. This model was applied to Global South participants (AD, bvFTD, and healthy controls).

RESULTS

Metaconnectivity outperformed pairwise connectivity and revealed more viscous dynamics in patients, with altered metaconnectivity patterns associated with multimodal disease presentation. The biophysical model showed that connectome disintegration and hypoexcitability triggered altered metaconnectivity dynamics and identified critical regions for brain stimulation. We replicated the main results in a second subset of participants for validation with unharmonized, heterogeneous recording settings.

DISCUSSION

The results provide a novel agenda for developing mechanistic model-inspired characterization and therapies in clinical, translational, and computational neuroscience settings.

Diurnal cortisol, neuroinflammation, and neuroimaging visual rating scales in memory clinic patients

BACKGROUND

Neuroinflammation is a hallmark of the Alzheimer's disease (AD) pathogenic process. Cortisol dysregulation may increase AD risk and is related to brain atrophy. This cross-sectional study aims to examine interactions of cortisol patterns and neuroinflammation markers in their association with neuroimaging correlates.

METHOD

134 participants were recruited from the Karolinska University Hospital memory clinic (Stockholm, Sweden). Four visual rating scales were applied to magnetic resonance imaging or computed tomography scans: medial temporal lobe atrophy (MTA), global cortical atrophy (GCA), white matter lesions (WML), and posterior atrophy. Participants provided saliva samples for assessment of diurnal cortisol patterns, and underwent lumbar punctures for cerebrospinal fluid (CSF) sampling. Three cortisol measures were used: the cortisol awakening response, total daily output, and the ratio of awakening to bedtime levels. Nineteen CSF neuroinflammation markers were categorized into five composite scores: proinflammatory cytokines, other cytokines, angiogenesis markers, vascular injury markers, and glial activation markers. Ordinal logistic regressions were conducted to assess associations between cortisol patterns, neuroinflammation scores, and visual rating scales, and interactions between cortisol patterns and neuroinflammation scores in relation to visual rating scales.

RESULT

Higher levels of angiogenesis markers were associated with more severe WML. Some evidence was found for interactions between dysregulated diurnal cortisol patterns and greater neuroinflammation-related biomarkers in relation to more severe GCA and WML. No associations were found between cortisol patterns and visual rating scales.

CONCLUSION

This study suggests an interplay between diurnal cortisol patterns and neuroinflammation in relation to brain structure. While this cross-sectional study does not provide information on causality or temporality, these findings suggest that neuroinflammation may be involved in the relationship between HPA-axis functioning and AD.

The interaction between insulin resistance and Alzheimer's disease: a review article

Insulin serves multiple functions as a growth-promoting hormone in peripheral tissues. It manages glucose metabolism by promoting glucose uptake into cells and curbing the production of glucose in the liver. Beyond this, insulin fosters cell growth, drives differentiation, aids protein synthesis, and deters degradative processes like glycolysis, lipolysis, and proteolysis. Receptors for insulin and insulin-like growth factor-1 are widely expressed in the central nervous system. Their widespread presence in the brain underscores the varied and critical functions of insulin signaling there. Insulin aids in bolstering cognition, promoting neuron extension, adjusting the release and absorption of catecholamines, and controlling the expression and positioning of gamma-aminobutyric acid (GABA). Importantly, insulin can effortlessly traverse the blood-brain barrier. Furthermore, insulin resistance (IR)-induced alterations in insulin signaling might hasten brain aging, impacting its plasticity and potentially leading to neurodegeneration. Two primary pathways are responsible for insulin signal transmission: the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) pathway, which oversees metabolic responses, and the mitogen-activated protein kinase (MAPK) pathway, which guides cell growth, survival, and gene transcription. This review aimed to explore the potential shared metabolic traits between Alzheimer's disease (AD) and IR disorders. It delves into the relationship between AD and IR disorders, their overlapping genetic markers, and shared metabolic indicators. Additionally, it addresses existing therapeutic interventions targeting these intersecting pathways.

Behavioral and dysexecutive variant of Alzheimer's disease: Insights from structural and molecular imaging studies

Frontal variant Alzheimer's disease (AD) manifests with either behavioral or dysexecutive syndromes. Recent efforts to gain a deeper understanding of this phenotype have led to a re-conceptualization of frontal AD. Behavioral (bAD) and dysexecutive (dAD) phenotypes could be considered subtypes, as suggested by both clinical and neuroimaging studies. In this review, we focused on imaging studies to highlight specific brain patterns in these two uncommon clinical AD phenotypes. Although studies did not compare directly these two variants, a common epicenter located in the frontal cortex could be inferred. On the contrary, 18F-FDG-PET findings suggested differing metabolic patterns, with bAD showing specific involvement of frontal regions and dAD exhibiting widespread alterations. Structural MRI findings confirmed this pattern, suggesting that degeneration might involve neural circuits associated with behavioral control in bAD and attentional networks in dAD. Furthermore, molecular imaging has identified different neocortical tau distribution in bAD and dAD patients compared to typical AD patients, although the distribution is remarkably heterogeneous. In contrast, Aβ deposition patterns are less differentiated between these atypical variants and typical AD. Although preliminary, these findings underscore the complexity of AD frontal phenotypes and suggest that they represent distinct entities. Further research is essential to refine our understanding of the pathophysiological mechanisms in frontal AD.

Causal association and mediating effect of blood biochemical metabolic traits and brain image-derived endophenotypes on Alzheimer's disease

Background

Recent genetic evidence supports that circulating biochemical and metabolic traits (BMTs) play a causal role in Alzheimer's disease (AD), which might be mediated by changes in brain structure. Here, we leveraged publicly available genome-wide association study data to investigate the intrinsic causal relationship between blood BMTs, brain image-derived phenotypes (IDPs) and AD.

Methods

Utilizing the genetic variants associated with 760 blood BMTs and 172 brain IDPs as the exposure and the latest AD summary statistics as the outcome, we analyzed the causal relationship between blood BMTs and brain IDPs and AD by using a two-sample Mendelian randomization (MR) method. Additionally, we used two-step/mediation MR to study the mediating effect of brain IDPs between blood BMTs and AD.

Results

Twenty-five traits for genetic evidence supporting a causal association with AD were identified, including 12 blood BMTs and 13 brain IDPs. For BMTs, glutamine consistently reduced the risk of AD in 3 datasets. For IDPs, specific alterations of cortical thickness (atrophy in frontal pole and insular lobe, and incrassation in superior parietal lobe) and subcortical volume (atrophy in hippocampus and its subgroups, left accumbens and left choroid plexus, and expansion in cerebral white matter) are vulnerable to AD. In the two-step/mediation MR analysis, superior parietal lobe, right hippocampal fissure and left accumbens were identified to play a potential mediating role among three blood BMTs and AD.

Conclusions

The results obtained in our study suggest that 12 circulating BMTs and 13 brain IDPs play a causal role in AD. Importantly, a subset of BMTs exhibit shared genetic architecture and potentially causal relationships with brain structure, which may contribute to the alteration of brain IDPs in AD.

Association of objective sleep duration with cognition and brain aging biomarkers in older adults

The neuropathological mechanisms underlying the association between sleep duration and mild cognitive impairment remain poorly understood. This population-based study included 2032 dementia-free people (age ≥ 60 years; 55.1% women) derived from participants in the Multimodal Interventions to Delay Dementia and Disability in Rural China; of these, data were available in 841 participants for Alzheimer's plasma biomarkers (e.g. amyloid-β, total tau and neurofilament light chain), 1044 for serum microvascular biomarkers (e.g. soluble adhesion molecules) and 834 for brain MRI biomarkers (e.g. whiter matter, grey matter, hippocampus, lacunes, enlarged perivascular spaces and white matter hyperintensity WMH). We used electrocardiogram-based cardiopulmonary coupling analysis to measure sleep duration, a neuropsychological test battery to assess cognitive function and the Petersen's criteria to define mild cognitive impairment. Data were analysed with multivariable logistic and general linear models. In the total sample (n = 2032), 510 participants were defined with mild cognitive impairment, including 438 with amnestic mild cognitive impairment and 72 with non-amnestic mild cognitive impairment. Long sleep duration (>8 versus 6-8 h) was significantly associated with increased likelihoods of mild cognitive impairment and non-amnestic mild cognitive impairment and lower scores in global cognition, verbal fluency, attention and executive function (Bonferroni-corrected P < 0.05). In the subsamples, long sleep duration was associated with higher plasma amyloid-β40 and total tau, a lower amyloid-β42/amyloid-β40 ratio and smaller grey matter volume (Bonferroni-corrected P < 0.05). Sleep duration was not significantly associated with serum-soluble adhesion molecules, white matter hyperintensity volume, global enlarged perivascular spaces and lacunes (P > 0.05). Alzheimer's and neurodegenerative pathologies may represent common pathways linking long sleep duration with mild cognitive impairment and low cognition in older adults.

Anorexigenic neuropeptides as anti-obesity and neuroprotective agents: exploring the neuroprotective effects of anorexigenic neuropeptides

Since 1975, the incidence of obesity has increased to epidemic proportions, and the number of patients with obesity has quadrupled. Obesity is a major risk factor for developing other serious diseases, such as type 2 diabetes mellitus, hypertension, and cardiovascular diseases. Recent epidemiologic studies have defined obesity as a risk factor for the development of neurodegenerative diseases, such as Alzheimer's disease (AD) and other types of dementia. Despite all these serious comorbidities associated with obesity, there is still a lack of effective antiobesity treatment. Promising candidates for the treatment of obesity are anorexigenic neuropeptides, which are peptides produced by neurons in brain areas implicated in food intake regulation, such as the hypothalamus or the brainstem. These peptides efficiently reduce food intake and body weight. Moreover, because of the proven interconnection between obesity and the risk of developing AD, the potential neuroprotective effects of these two agents in animal models of neurodegeneration have been examined. The objective of this review was to explore anorexigenic neuropeptides produced and acting within the brain, emphasizing their potential not only for the treatment of obesity but also for the treatment of neurodegenerative disorders.

Exploring morphological similarity and randomness in Alzheimer's disease using adjacent grey matter voxel-based structural analysis

BACKGROUND

Alzheimer's disease is characterized by large-scale structural changes in a specific pattern. Recent studies developed morphological similarity networks constructed by brain regions similar in structural features to represent brain structural organization. However, few studies have used local morphological properties to explore inter-regional structural similarity in Alzheimer's disease.

METHODS

Here, we sourced T1-weighted MRI images of 342 cognitively normal participants and 276 individuals with Alzheimer's disease from the Alzheimer's Disease Neuroimaging Initiative database. The relationships of grey matter intensity between adjacent voxels were defined and converted to the structural pattern indices. We conducted the information-based similarity method to evaluate the structural similarity of structural pattern organization between brain regions. Besides, we examined the structural randomness on brain regions. Finally, the relationship between the structural randomness and cognitive performance of individuals with Alzheimer's disease was assessed by stepwise regression.

RESULTS

Compared to cognitively normal participants, individuals with Alzheimer's disease showed significant structural pattern changes in the bilateral posterior cingulate gyrus, hippocampus, and olfactory cortex. Additionally, individuals with Alzheimer's disease showed that the bilateral insula had decreased inter-regional structural similarity with frontal regions, while the bilateral hippocampus had increased inter-regional structural similarity with temporal and subcortical regions. For the structural randomness, we found significant decreases in the temporal and subcortical areas and significant increases in the occipital and frontal regions. The regression analysis showed that the structural randomness of five brain regions was correlated with the Mini-Mental State Examination scores of individuals with Alzheimer's disease.

CONCLUSIONS

Our study suggested that individuals with Alzheimer's disease alter micro-structural patterns and morphological similarity with the insula and hippocampus. Structural randomness of individuals with Alzheimer's disease changed in temporal, frontal, and occipital brain regions. Morphological similarity and randomness provide valuable insight into brain structural organization in Alzheimer's disease.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

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.

Impact of CT-determined low kidney volume on renal function decline: a propensity score-matched analysis

OBJECTIVES

To investigate the relationship between low kidney volume and subsequent estimated glomerular filtration rate (eGFR) decline in eGFR category G2 (60-89 mL/min/1.73 m2) population.

METHODS

In this retrospective study, we evaluated 5531 individuals with eGFR category G2 who underwent medical checkups at our institution between November 2006 and October 2017. Exclusion criteria were absent for follow-up visit, missing data, prior renal surgery, current renal disease under treatment, large renal masses, and horseshoe kidney. We developed a 3D U-net-based automated system for renal volumetry on CT images. Participants were grouped by sex-specific kidney volume deviations set at mean minus one standard deviation. After 1:1 propensity score matching, we obtained 397 pairs of individuals in the low kidney volume (LKV) and control groups. The primary endpoint was progression of eGFR categories within 5 years, assessed using Cox regression analysis.

RESULTS

This study included 3220 individuals (mean age, 60.0 ± 9.7 years; men, n = 2209). The kidney volume was 404.6 ± 67.1 and 376.8 ± 68.0 cm3 in men and women, respectively. The low kidney volume (LKV) cutoff was 337.5 and 308.8 cm3 for men and women, respectively. LKV was a significant risk factor for the endpoint with an adjusted hazard ratio of 1.64 (95% confidence interval: 1.09-2.45; p = 0.02).

CONCLUSION

Low kidney volume may adversely affect subsequent eGFR maintenance; hence, the use of imaging metrics may help predict eGFR decline.

CRITICAL RELEVANCE STATEMENT

Low kidney volume is a significant predictor of reduced kidney function over time; thus, kidney volume measurements could aid in early identification of individuals at risk for declining kidney health.

KEY POINTS

• This study explores how kidney volume affects subsequent kidney function maintenance. • Low kidney volume was associated with estimated glomerular filtration rate decreases. • Low kidney volume is a prognostic indicator of estimated glomerular filtration rate decline.

Dysregulated AEBP1 and COLEC12 Genes in Late-Onset Alzheimer's Disease: Insights from Brain Cortex and Peripheral Blood Analysis

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by memory and cognitive impairment, often accompanied by alterations in mood, confusion, and, ultimately, a state of acute mental disturbance. The cerebral cortex is considered a promising area for investigating the underlying causes of AD by analyzing transcriptional patterns, which could be complemented by investigating blood samples obtained from patients. We analyzed the RNA expression profiles of three distinct areas of the brain cortex, including the frontal cortex (FC), temporal cortex (TC), and entorhinal cortex (EC) in patients with AD. Functional enrichment analysis was performed on the differentially expressed genes (DEGs) across the three regions. The two genes with the most significant expression changes in the EC region were selected for assessing mRNA expression levels in the peripheral blood of late-onset AD patients using quantitative PCR (qPCR). We identified eight shared DEGs in these regions, including AEBP1 and COLEC12, which exhibited prominent changes in expression. Functional enrichment analysis uncovered a significant association of these DEGs with the transforming growth factor-β (TGF-β) signaling pathway and processes related to angiogenesis. Importantly, we established a robust connection between the up-regulation of AEBP1 and COLEC12 in both the brain and peripheral blood. Furthermore, we have demonstrated the potential of AEBP1 and COLEC12 genes as effective diagnostic tools for distinguishing between late-onset AD patients and healthy controls. This study unveils the intricate interplay between AEBP1 and COLEC12 in AD and underscores their potential as markers for disease detection and monitoring.

Current Insights of Nanocarrier-Mediated Gene Therapeutics to Treat Potential Impairment of Amyloid Beta Protein and Tau Protein in Alzheimer's Disease

Alzheimer's disease (AD), is the major type of dementia and most progressive, irreversible widespread neurodegenerative disorder affecting the elderly worldwide. The prime hallmarks of Alzheimer's disease (AD) are beta-amyloid plaques (Aβ) and neurofibrillary tangles (NFT). In spite of recent advances and developments in targeting the hallmarks of AD, symptomatic medications that promise neuroprotective activity against AD are currently unable to treat degenerating brain clinically or therapeutically and show little efficacy. The extensive progress of AD therapies over time has resulted in the advent of disease-modifying medications with the potential to alleviate AD. However, due to the presence of a defensive connection between the vascular system and the neural tissues known as the blood-brain barrier (BBB), directing these medications to the site of action in the degenerating brain is the key problem. BBB acts as a highly selective semipermeable membrane that prevents any type of foreign substance from entering the microenvironment of neurons. To overcome this limitation, the revolutionary approach of nanoparticle(NP)/nanocarrier-mediated drug delivery system has marked the era with its unique property to cross, avoid, or disrupt the defensive BBB efficiently and release the modified drug at the target site of action. After comprehensive data mining, this review focuses on the detailed understanding of different types of nanoparticle(NP)/nanocarrier-mediated drug delivery system like liposomes, micelles, gold nanoparticles(NP), polymeric NPs, etc. which have promising potential in carrying the desired drug(cargo) to the location in the degenerated brain thus mitigating the Alzheimer's disease.

Somatostatin and the pathophysiology of Alzheimer's disease

Among the central features of Alzheimer's disease (AD) progression are altered levels of the neuropeptide somatostatin (SST), and the colocalisation of SST-positive interneurons (SST-INs) with amyloid-β plaques, leading to cell death. In this theoretical review, I propose a molecular model for the pathogenesis of AD based on SST-IN hypofunction and hyperactivity. Namely, hypofunctional and hyperactive SST-INs struggle to control hyperactivity in medial regions in early stages, leading to axonal Aβ production through excessive presynaptic GABAB inhibition, GABAB1a/APP complex downregulation and internalisation. Concomitantly, excessive SST-14 release accumulates near SST-INs in the form of amyloids, which bind to Aβ to form toxic mixed oligomers. This leads to differential SST-IN death through excitotoxicity, further disinhibition, SST deficits, and increased Aβ release, fibrillation and plaque formation. Aβ plaques, hyperactive networks and SST-IN distributions thereby tightly overlap in the brain. Conversely, chronic stimulation of postsynaptic SST2/4 on gulutamatergic neurons by hyperactive SST-INs promotes intense Mitogen-Activated Protein Kinase (MAPK) p38 activity, leading to somatodendritic p-tau staining and apoptosis/neurodegeneration - in agreement with a near complete overlap between p38 and neurofibrillary tangles. This model is suitable to explain some of the principal risk factors and markers of AD progression, including mitochondrial dysfunction, APOE4 genotype, sex-dependent vulnerability, overactive glial cells, dystrophic neurites, synaptic/spine losses, inter alia. Finally, the model can also shed light on qualitative aspects of AD neuropsychology, especially within the domains of spatial and declarative (episodic, semantic) memory, under an overlying pattern of contextual indiscrimination, ensemble instability, interference and generalisation.

Harmonizing T1-Weighted Images to Improve Consistency of Brain Morphology Among Different Scanner Manufacturers in Alzheimer's disease

BACKGROUND

Brain MRI scanner variability can introduce bias in measurements. Harmonizing scanner variability is crucial.

PURPOSE

To develop a harmonization method aimed at removing scanner variability, and to evaluate the consistency of results in multicenter studies.

STUDY TYPE

Retrospective.

POPULATION

Multicenter data from 170 healthy participants (males/females = 98/72; age = 73.8 ± 7.3) and 170 Alzheimer's disease patients (males/females = 98/72; age = 76.2 ± 8.5) were compared with reference data from another 340 participants.

FIELD STRENGTH/SEQUENCE

3-T, magnetization prepared rapid gradient echo and turbo field echo; 1.5-T, inversion recovery prepared fast spoiled gradient echo T1-weighted sequences.

ASSESSMENT

Gray matter (GM) brain images, obtained through segmentation of T1-weighted images, were utilized to evaluate the performance of the harmonization method using common orthogonal basis extraction (HCOBE) and four other methods (removal of artificial voxel effect by linear regression, RAVEL; Z_score; general linear model, GLM; ComBat). Linear discriminant analysis (LDA) was used to access the effectiveness of different methods in reducing scanner variability. The performance of harmonization methods in preserving GM volumes heterogeneity was evaluated by the similarity of the relationship between GM proportion and age in the reference and multicenter data. Furthermore, the consistency of the harmonized multicenter data with the reference data were evaluated based on classification results (train/test = 7/3) and brain atrophy.

STATISTICAL TESTS

Two-sample t-tests, area under the curve (AUC), and Dice coefficients were used to analyze the consistency of results from the reference and harmonized multicenter data. A P-value <0.01 was considered statistically significant.

RESULTS

HCOBE reduced the scanner variability from 0.09 before harmonization to 0.003 (ideal: 0, RAVEL/Z_score/GLM/ComBat = 0.087/0.003/0.006/0.013). GM volumes showed no significant difference (P = 0.52) between the reference and HCOBE-harmonized multicenter data. Consistency evaluation showed that AUC values of 0.95 for both reference and HCOBE-harmonized multicenter data (RAVEL/Z_score/GLM/ComBat = 0.86/0.86/0.84/0.89), and the Dice coefficient increased from 0.73 before harmonization to 0.82 (ideal: 1, RAVEL/Z_score/GLM/ComBat = 0.39/0.64/0.59/0.74).

DATA CONCLUSION

HCOBE may help to remove scanner variability and could improve the consistency of results in multicenter studies.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Delivering synaptic protein mRNAs via extracellular vesicles ameliorates cognitive impairment in a mouse model of Alzheimer's disease

BACKGROUND

Synaptic dysfunction with reduced synaptic protein levels is a core feature of Alzheimer's disease (AD). Synaptic proteins play a central role in memory processing, learning, and AD pathogenesis. Evidence suggests that synaptic proteins in plasma neuronal-derived extracellular vesicles (EVs) are reduced in patients with AD. However, it remains unclear whether levels of synaptic proteins in EVs are associated with hippocampal atrophy of AD and whether upregulating the expression of these synaptic proteins has a beneficial effect on AD.

METHODS

In this study, we included 57 patients with AD and 56 healthy controls. We evaluated their brain atrophy through magnetic resonance imaging using the medial temporal lobe atrophy score. We measured the levels of four synaptic proteins, including synaptosome-associated protein 25 (SNAP25), growth-associated protein 43 (GAP43), neurogranin, and synaptotagmin 1 in both plasma neuronal-derived EVs and cerebrospinal fluid (CSF). We further examined the association of synaptic protein levels with brain atrophy. We also evaluated the levels of these synaptic proteins in the brains of 5×FAD mice. Then, we loaded rabies virus glycoprotein-engineered EVs with messenger RNAs (mRNAs) encoding GAP43 and SNAP25 and administered these EVs to 5×FAD mice. After treatment, synaptic proteins, dendritic density, and cognitive function were evaluated.

RESULTS

The results showed that GAP43, SNAP25, neurogranin, and synaptotagmin 1 were decreased in neuronal-derived EVs but increased in CSF in patients with AD, and the changes corresponded to the severity of brain atrophy. GAP43 and SNAP25 were decreased in the brains of 5×FAD mice. The engineered EVs efficiently and stably delivered these synaptic proteins to the brain, where synaptic protein levels were markedly upregulated. Upregulation of synaptic protein expression could ameliorate cognitive impairment in AD by promoting dendritic density. This marks the first successful delivery of synaptic protein mRNAs via EVs in AD mice, yielding remarkable therapeutic effects.

CONCLUSIONS

Synaptic proteins are closely related to AD processes. Delivery of synaptic protein mRNAs via EVs stands as a promising effective precision treatment strategy for AD, which significantly advances the current understanding of therapeutic approaches for the disease.

Exploring sexual dimorphism in basal forebrain volume changes during aging and neurodegenerative diseases

Patients with neurodegenerative diseases exhibit diminished basal forebrain (BF) volume compared to healthy individuals. However, it's uncertain whether this difference is consistent between sexes. It has been reported that BF volume moderately atrophies during aging, but the effect of sex on BF volume changes during the normal aging process remains unclear. In the cross-sectional study, we observed a significant reduction in BF volume in patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared to Healthy Controls (HCs), especially in the Ch4 subregion. Notably, significant differences in BF volume between MCI and HCs were observed solely in the female group. Additionally, we identified asymmetrical atrophy in the left and right Ch4 subregions in female patients with AD. In the longitudinal analysis, we found that aging seemed to have a minimal impact on BF volume in males. Our study highlights the importance of considering sex as a research variable in brain science.

Predicting long-term progression of Alzheimer's disease using a multimodal deep learning model incorporating interaction effects

BACKGROUND

Identifying individuals with mild cognitive impairment (MCI) at risk of progressing to Alzheimer's disease (AD) provides a unique opportunity for early interventions. Therefore, accurate and long-term prediction of the conversion from MCI to AD is desired but, to date, remains challenging. Here, we developed an interpretable deep learning model featuring a novel design that incorporates interaction effects and multimodality to improve the prediction accuracy and horizon for MCI-to-AD progression.

METHODS

This multi-center, multi-cohort retrospective study collected structural magnetic resonance imaging (sMRI), clinical assessments, and genetic polymorphism data of 252 patients with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our deep learning model was cross-validated on the ADNI-1 and ADNI-2/GO cohorts and further generalized in the ongoing ADNI-3 cohort. We evaluated the model performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score.

RESULTS

On the cross-validation set, our model achieved superior results for predicting MCI conversion within 4 years (AUC, 0.962; accuracy, 92.92%; sensitivity, 88.89%; specificity, 95.33%) compared to all existing studies. In the independent test, our model exhibited consistent performance with an AUC of 0.939 and an accuracy of 92.86%. Integrating interaction effects and multimodal data into the model significantly increased prediction accuracy by 4.76% (P = 0.01) and 4.29% (P = 0.03), respectively. Furthermore, our model demonstrated robustness to inter-center and inter-scanner variability, while generating interpretable predictions by quantifying the contribution of multimodal biomarkers.

CONCLUSIONS

The proposed deep learning model presents a novel perspective by combining interaction effects and multimodality, leading to more accurate and longer-term predictions of AD progression, which promises to improve pre-dementia patient care.

Changes in microstructural similarity of hippocampal subfield circuits in pathological cognitive aging

The hippocampal networks support multiple cognitive functions and may have biological roles and functions in pathological cognitive aging (PCA) and its associated diseases, which have not been explored. In the current study, a total of 116 older adults with 39 normal controls (NC) (mean age: 52.3 ± 13.64 years; 16 females), 39 mild cognitive impairment (MCI) (mean age: 68.15 ± 9.28 years, 14 females), and 38 dementia (mean age: 73.82 ± 8.06 years, 8 females) were included. The within-hippocampal subfields and the cortico-hippocampal circuits were assessed via a micro-structural similarity network approach using T1w/T2w ratio and regional gray matter tissue probability maps, respectively. An analysis of covariance was conducted to identify between-group differences in structural similarities among hippocampal subfields. The partial correlation analyses were performed to associate changes in micro-structural similarities with cognitive performance in the three groups, controlling the effect of age, sex, education, and cerebral small-vessel disease. Compared with the NC, an altered T1w/T2w ratio similarity between left CA3 and left subiculum was observed in the mild cognitive impairment (MCI) and dementia. The left CA3 was the most impaired region correlated with deteriorated cognitive performance. Using these regions as seeds for GM similarity comparisons between hippocampal subfields and cortical regions, group differences were observed primarily between the left subiculum and several cortical regions. By utilizing T1w/T2w ratio as a proxy measure for myelin content, our data suggest that the imbalanced synaptic weights within hippocampal CA3 provide a substrate to explain the abnormal firing characteristics of hippocampal neurons in PCA. Furthermore, our work depicts specific brain structural characteristics of normal and pathological cognitive aging and suggests a potential mechanism for cognitive aging heterogeneity.

Mode-based morphometry: A multiscale approach to mapping human neuroanatomy

Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are two widely used neuroimaging techniques for investigating brain anatomy. These techniques rely on statistical inferences at individual points (voxels or vertices), clusters of points, or a priori regions-of-interest. They are powerful tools for describing brain anatomy, but offer little insights into the generative processes that shape a particular set of findings. Moreover, they are restricted to a single spatial resolution scale, precluding the opportunity to distinguish anatomical variations that are expressed across multiple scales. Drawing on concepts from classical physics, here we develop an approach, called mode-based morphometry (MBM), that can describe any empirical map of anatomical variations in terms of the fundamental, resonant modes-eigenmodes-of brain anatomy, each tied to a specific spatial scale. Hence, MBM naturally yields a multiscale characterization of the empirical map, affording new opportunities for investigating the spatial frequency content of neuroanatomical variability. Using simulated and empirical data, we show that the validity and reliability of MBM are either comparable or superior to classical vertex-based SBM for capturing differences in cortical thickness maps between two experimental groups. Our approach thus offers a robust, accurate, and informative method for characterizing empirical maps of neuroanatomical variability that can be directly linked to a generative physical process.