Neuropsychiatric symptoms (NPSs) are common in patients with Alzheimer’s disease (AD) and are associated with accelerated cognitive impairment and earlier deaths. This review aims to explore the neural pathogenesis of NPSs in AD and its association with the progression of AD. We first provide a literature overview on the onset times of NPSs. Different NPSs occur in different disease stages of AD, but most symptoms appear in the preclinical AD or mild cognitive impairment stage and develop progressively. Next, we describe symptom-general and -specific patterns of brain lesions. Generally, the anterior cingulate cortex is a commonly damaged region across all symptoms, and the prefrontal cortex, especially the orbitofrontal cortex, is also a critical region associated with most NPSs. In contrast, the anterior cingulate-subcortical circuit is specifically related to apathy in AD, the frontal-limbic circuit is related to depression, and the amygdala circuit is related to anxiety. Finally, we elucidate the associations between the NPSs and AD by combining the onset time with the neural basis of NPSs.

Changes in brain structure are associated with aging, and accompanied by the gradual deterioration of cognitive functions, which manifests differently in males and females. Here, we quantify the age-related spatial aging patterns of brain gray and white matter structures, their volume reduction rate, their relationships with specific cognitive functions, as well as differences between males and females in a cross-sectional nondementia dataset. We found that both males and females showed extensive age-related decreases in the volumes of most gray matter and white matter regions. Females have larger regions where the volume decreases with age and a greater slope (females: 0.199%, males: 0.183%) of volume decrease in gray matter. For white matter, no significant sex differences were found in age-related regions, and the slope of volume decrease. More significant associations were identified between brain structures and cognition in males during aging than females. This study explored the age-related regional variations in gray matter and white matter, as well as the sex differences in a nondemented elderly population. This study helps to further understand the aging of the brain structure and sex differences in the aging of brain structures and provides new evidence for the aging of nondemented individuals.

The A/T/N research framework has been proposed for the diagnosis and prognosis of Alzheimer's disease (AD). However, the spatial distribution of ATN biomarkers and their relationship with cognitive impairment and neuropsychiatric symptoms (NPS) need further clarification in patients with AD. We scanned 83 AD patients and 38 cognitively normal controls who independently completed the mini‐mental state examination and Neuropsychiatric Inventory scales. Tau, Aβ, and hypometabolism spatial patterns were characterized using Statistical Parametric Mapping together with [18F]flortaucipir, [18F]florbetapir, and [18F]FDG positron emission tomography. Piecewise linear regression, two‐sample t ‐tests, and support vector machine algorithms were used to explore the relationship between tau, Aβ, and hypometabolism and cognition, NPS, and AD diagnosis. The results showed that regions with tau deposition are region‐specific and mainly occurred in inferior temporal lobes in AD, which extensively overlaps with the hypometabolic regions. While the deposition regions of Aβ were unique and the regions affected by hypometabolism were widely distributed. Unlike Aβ, tau and hypometabolism build up monotonically with increasing cognitive impairment in the late stages of AD. In addition, NPS in AD were associated with tau deposition closely, followed by hypometabolism, but not with Aβ. Finally, hypometabolism and tau had higher accuracy in differentiating the AD patients from controls (accuracy = 0.88, accuracy = 0.85) than Aβ (accuracy = 0.81), and the combined three were the highest (accuracy = 0.95). These findings suggest tau pathology is superior over Aβ and glucose metabolism to identify cognitive impairment and NPS. Its results support tau accumulation can be used as a biomarker of clinical impairment in AD.

Background Mild cognitive impairment (MCI) is the transitional zone between normal aging and Alzheimer’s dementia (AD), representing a group of subjects with higher risk of conversion to AD (Petersen, 2010). However, as accumulating evidence shows that there are a non‐negligible number of MCI subjects who revert to be cognitive normal (CN), additional measures are needed for better characterizations on the MCI groups (Petersen, 2014). Thus, the purpose of this study is to explore the structural covariance (SC) feature of subjects with different longitudinal clinical status changes. Method Participants were from the Beijing Aging Brain Rejuvenation Initiative (BABRI), with two clinical cognitive assessments and the baseline high‐resolution structural MRI data. And four groups were defined, the stable CN (sCN, n = 99), CN progressing to MCI (pCN, n = 23), stable MCI (sMCI, n = 29), and MCI reverting to CN (rMCI, n = 33), and the mean age and follow‐ups interval for CN and MCI subjects were 64.74 and 66.63, 2.46 and 1.45 years, respectively. And the seed‐based multivariate method (Alexander‐Bloch, 2013) was applied to identify the SC of default‐mode network (scDN), frontoparietal network (scFN) and hippocampal network (scHN), of which the covariance scores were calculated. To obtain the SC templates of three networks, 69 sCN were randomly selected, and the rest 30 sCN, with the other three groups, were included in establishing prediction model of clinical status changes based on network SC scores, by using the support vector machine method and leave‐one‐out cross‐validation. Moreover, baseline cognition was also added in the model. Result For classification between sCN and sMCI, the scHN score presented the highest accuracy (area under curve (AUC) = 0.89), and it was the scDN score showing the best performance in classifying sMCI and rMCI (AUC = 0.82), with the scFN score outperforming others in distinguishing pCN from sCN (AUC = 0.68). And when baseline cognition was also included, prediction accuracies of clinical changes were improved (AUC = 0.84 for sMCI vs. rMCI, AUC = 0.83 for sCN vs. pCN). Conclusion Taking together, our findings indicated the potential of network structural covariance in classifying MCI subjects with different risk of conversion.

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