Corpus callosum anatomical changes in Alzheimer patients and the effect of acetylcholinesterase inhibitors on corpus callosum morphometry

Mining Alzheimer's disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes

Introduction

Because Alzheimer's disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD.

Methods

This study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging.

Results

We leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging.

Discussion

The representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis.

Understanding music and aging through the lens of Bayesian inference

Bayesian inference has recently gained momentum in explaining music perception and aging. A fundamental mechanism underlying Bayesian inference is the notion of prediction. This framework could explain how predictions pertaining to musical (melodic, rhythmic, harmonic) structures engender action, emotion, and learning, expanding related concepts of music research, such as musical expectancies, groove, pleasure, and tension. Moreover, a Bayesian perspective of music perception may shed new insights on the beneficial effects of music in aging. Aging could be framed as an optimization process of Bayesian inference. As predictive inferences refine over time, the reliance on consolidated priors increases, while the updating of prior models through Bayesian inference attenuates. This may affect the ability of older adults to estimate uncertainties in their environment, limiting their cognitive and behavioral repertoire. With Bayesian inference as an overarching framework, this review synthesizes the literature on predictive inferences in music and aging, and details how music could be a promising tool in preventive and rehabilitative interventions for older adults through the lens of Bayesian inference.

Predicting the efficacy of donepezil intervention in Alzheimer's disease patients using regional homogeneity in the inferior orbitofrontal cortex

BACKGROUND

Although donepezil is a commonly used drug for treating Alzheimer's disease (AD), the mechanisms by which it affects patients' functional brain activity, and thus modulates clinical symptoms, remain unclear.

METHODS

In the present study, we used resting-state functional magnetic resonance imaging (MRI) and regional homogeneity (ReHo) to investigate the effects of donepezil on local brain activity in AD patients. Resting-state functional MRI data were collected from 32 subjects: 16 healthy controls and 16 AD patients. All 16 AD patients underwent 6 months of donepezil treatment and received two MRI scans (pre- and post-intervention). Analysis of covariance and post hoc analyses were used to compare ReHo differences among the healthy controls, pre-intervention AD patients, and post-intervention AD patients. Pearson correlation analysis was used to examine relationships between ReHo values in differential brain regions and clinical symptoms.

RESULTS

Compared with healthy controls, post-intervention AD patients had reduced ReHo in the orbital part of the inferior frontal gyrus, and pre-intervention AD patients had reduced ReHo in the orbital part of the right inferior frontal gyrus. Pattern recognition models revealed that pre-intervention ReHo values in abnormal brain regions of AD patients were 76% accurate for predicting the efficacy of donepezil on cognitive function and 65% accurate for predicting its efficacy on depressive symptoms.

CONCLUSIONS

These findings deepen our understanding of the brain mechanisms underlying the clinical efficacy of donepezil in AD patients, and provide a novel way to predict its clinical efficacy in such patients.

Transcriptomic Profiling Reveals Neuroinflammation in the Corpus Callosum of a Transgenic Mouse Model of Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a widespread neurodegenerative disorder characterized by progressive cognitive decline, affecting a significant portion of the aging population. While the cerebral cortex and hippocampus have been the primary focus of AD research, accumulating evidence suggests that white matter lesions in the brain, particularly in the corpus callosum, play an important role in the pathogenesis of the disease.

OBJECTIVE

This study aims to investigate the gene expression changes in the corpus callosum of 5xFAD transgenic mice, a widely used AD mouse model.

METHODS

We conducted behavioral tests for spatial learning and memory in 5xFAD transgenic mice and performed RNA sequencing analyses on the corpus callosum to examine transcriptomic changes.

RESULTS

Our results show cognitive decline and demyelination in the corpus callosum of 5xFAD transgenic mice. Transcriptomic analysis reveals a predominance of upregulated genes in AD mice, particularly those associated with immune cells, including microglia. Conversely, downregulation of genes related to chaperone function and clock genes such as Per1, Per2, and Cry1 is also observed.

CONCLUSIONS

This study suggests that activation of neuroinflammation, disruption of chaperone function, and circadian dysfunction are involved in the pathogenesis of white matter lesions in AD. The findings provide insights into potential therapeutic targets and highlight the importance of addressing white matter pathology and circadian dysfunction in AD treatment strategies.

MRI-based morphometric analysis of corpus callosum dimensions of adults in Southeast Nigeria

The Corpus callosum (CC) is the largest commissural fibre tract, ensuring swift information transfer and integration in both cerebral hemispheres. Variations in morphometry exist. There is a paucity of data on CC dimensions in our subregion, and no standardized reference is available. The study aims to determine the CC dimensions among the adult population in southeast Nigeria. The result will provide reference ranges and form a benchmark for comparisons of CC-related pathologies. A retrospective study of CC morphometric dimensions in normal subjects who had cranial MRI over two years in Memfys Hospital, Enugu, Southeast Nigeria, using a 1.5T GE© 16 channel machine. The CC was segmentalized into seven subregions using the modified Witelson method with special computer software. All measurements were taken twice from the T1 mid-sagittal image, and the mean was used for computation. The results were analyzed using descriptive and inferential statistics. A total of 200 subjects were recruited for the study. The mean length and height of the CC were 75.58 ± 4.52 mm and 24.64 ± 3.40 mm, respectively. The width dimensions of the genu, body, rostrum and splenium were 10.88 ± 1.81 mm, 5.66 ± 1.32 mm, 3.65 ± 1.25 mm, and 10.02 ± 1.70 mm, respectively. No gender variations were noted among the different dimensions of CC (P = 0.90). The length and height of CC increase gradually with age and show a positive correlation. The width dimensions of the genu and splenium increase till middle age and subsequently decreases in line with brain atrophy (p = 0.0000& p = 0.004). Using Pearson's correlation test, no correlation was noted in the dimensions of the body and rostrum of the corpus callosum when related to age and sex. (P = 0.92 & p = 0.66). Reference ranges of CC dimensions in our subregion were presented, and variations exist in its different morphometric dimensions which are affected by brain atrophy. Gender does not influence the dimensions in our subpopulations.

Morphometric analysis of Corpus Callosum in autistic and typically developing Indian children

Corpus callosum (CC) is the largest commissural white matter bundle in the brain, responsible for the integration of information between hemispheres. Reduction in the size of the CC structure has been predominantly reported in children with autism spectrum disorder (ASD) compared to typically developing children (TD). However, most of these studies are based on high-functioning individuals with ASD but not on an inclusive sample of individuals with ASD with varying abilities. Our current study aimed to examine the CC morphometry between children with ASD and TD in the Indian population. We also compared CC morphometry in autistic children with autism severity, verbal IQ (VIQ) and full-scale IQ (FSIQ). T1-weighted structural images were acquired using a 3T MRI scanner to examine the CC measures in 62 ASD and 17 TD children. The length and height of the CC and the width of genu were decreased in children with ASD compared to TD. There was no significant difference in CC measures based on autism severity, VIQ or FSIQ among children with ASD. To our knowledge, this is the first neuroimaging study to include a significant number (n = 56) of low-functioning ASD children. Our findings suggest the atypical interhemispheric connectivity of CC in ASD.