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Cerebrospinal fluid amyloid-β and cerebral microbleed are associated with distinct neuropsychiatric sub-syndromes in cognitively impaired patients

Alzheimer's Research & Therapy volume 16, Article number: 69 (2024) Cite this article

Abstract

Background

Neuropsychiatric symptoms (NPS) are prevalent in cognitively impaired individuals including Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI). Whereas several studies have reported the associations between NPS with AD pathologic biomarkers and cerebral small vessel disease (SVD), but it remains unknown whether AD pathology and SVD contribute to different sub-syndromes independently or aggravate same symptoms synergistically.

Method

We included 445 cognitively impaired individuals (including 316 MCI and 129 AD) with neuropsychiatric, cerebrospinal fluid (CSF) biomarkers (Aβ42, p-tau, and t-tau) and multi-model MRI data. Psychiatric symptoms were accessed by using the Neuropsychiatric Inventory (NPI). Visual assessment of SVD (white matter hyperintensity, microbleed, perivascular space, lacune) on MRI images was performed by experienced radiologist. Linear regression analyses were conducted to test the association between neuropsychiatric symptoms with AD pathology and CSVD burden after adjustment for age, sex, education, apolipoprotein E (APOE) ε4 carrier status, and clinical diagnosis.

Results

The NPI total scores were related to microbleed (estimate 2.424; 95% CI [0.749, 4.099]; P =0.005). Considering the sub-syndromes, the hyperactivity was associated with microbleed (estimate 0.925; 95% CI [0.115, 1.735]; P =0.025), whereas the affective symptoms were correlated to CSF level of Aβ42 (estimate -0.006; 95% CI [-0.011, -0.002]; P =0.005). Furthermore, we found the apathy sub-syndrome was associated with CSF t-tau/Aβ42 (estimate 0.636; 95% CI [0.078, 1.194]; P =0.041) and microbleed (estimate 0.693; 95% CI [0.046, 1.340]; P =0.036). In addition, we found a significant interactive effect between CSF t-tau/Aβ42 and microbleed (estimate 0.993; 95% CI [0.360, 1.626]; P =0.019) on severity of apathy sub-syndrome.

Conclusion

Our study showed that CSF Aβ42 was associated with affective symptoms, but microbleed was correlated with hyperactivity and apathy, suggesting the effect of AD pathology and SVD on different neuropsychiatric sub-syndromes.

Introduction

Neuropsychiatric symptoms (NPS) are prevalent in cognitively impaired individuals including Alzheimer’s disease (AD) dementia and mild cognitive impairment (MCI). It has been demonstrated that the presence of NPS is associated with faster cognitive decline [1], worse prognosis [2], and lower quality of life [3]. According to the consistent associations of specific symptoms, some NPS could be consolidated into a kind of potential sub-syndromes [4, 5]. Based on a large sample of AD patients, the European Alzheimer’s Disease Consortium proposed four kinds of neuropsychiatric sub-syndromes: hyperactivity, psychosis, affective symptoms, and apathy [6]. However, the neurobiological basis of NPS is still unclear.

A growing body of research examined the associations between NPS and AD-related biomarkers [7,8,9,10]. Specifically, several studies have found that amyloid deposition was associated with higher depressive symptoms [10,11,12]. In addition, investigators using longitudinal cohort have observed that Aβ pathology may drive the development of apathy and anxiety [9, 11, 13]. Also, a recent review has concluded that NPS are associated with SVD [14]. For example, the presence of multiple microbleed or the severity of white matter hyperintensities (WMH) are related to high NPS burden [15,16,17]. Thus, a potential mechanism is that the NPS could be the consequence of AD pathology or vascular dysfunction. But it remains unknown about specific contribution of AD pathology and SVD on NPS in cognitively impaired individuals respectively.

In this study, we aimed to explore the effect of AD pathology and SVD on psychiatric symptoms in cognitively impaired individuals. Based on previous findings, we hypothesized that AD pathology and SVD could contribute to different sub-syndromes independently and aggravate same symptoms synergistically.

Methods and materials

Study population

All data used in the current study were from the ADNI database (http://adni.loni.usc.edu/). This ongoing project was launched in 2003 to develop clinical, neuropsychological, and neuroimaging biomarkers for early disease detection and progression monitoring of AD.

The inclusion criteria of participants as follows: 1) subjects diagnosed with MCI or AD; 2) each subject had undergone neuropsychological and neuropsychiatric assessment as well as cerebrospinal fluid (CSF) biomarkers examination and multi-model MRI scan. According to the inclusion criteria, we included totally 445 participants (including 316 MCI and 129 AD) from ADNIGO/2 dataset.

According to the ADNI Procedures Manual (https://adni.loni.usc.edu/wp-content/uploads/2008/07/adni2-procedures-manual.pdf), the ADNI criteria for MCI were: 1) subjective memory complaints; 2) objective memory loss defined as scoring below an education-adjusted cut-off score on delayed recall of the Wechsler Memory Scale-Revised (WMS-R) logical memory test II subscale: ≤ 11 for 16 or more years of education, ≤ 9 for 8-15 years of education c. ≤ 6 for 0-7 years of education (the maximum score is 25); 3) a global Clinical Dementia Rating (CDR) score of 0.5, memory box score must be at least 0.5; 4) a Mini-Mental State Examination (MMSE) score of equal to, or higher than 24 (the maximum score is 30); and 5) general cognitive and functional performance sufficiently preserved such that a diagnosis of dementia could not be made by the site physician at the time of screening [18].

The ADNI criteria for AD were: 1) subjective memory concern; 2) abnormal memory function documented by scoring within the education-adjusted ranges on the WMS-R logical memory II: ≤ 8 for 16 or more years of education, ≤ 4 for 8-15 years of education c. ≤ 2 for 0-7 years of education (the maximum score is 25); 3) MMSE score between 20 and 26 (inclusive); 4) CDR ≥ 0.5; 5) met the NINCDS/ADRDA criteria for probable AD [18].

Neuropsychological assessment

Each participant has undergone neuropsychological examination including the MMSE, Montreal Cognitive Assessment (MoCA), Auditory Verbal Learning Test (AVLT), Logical Memory Test, Clock Drawing Test (CDT), Trial-Making Test-A (TMT-A), Trial-Making Test-B (TMT-B), Category Fluency Test, and Boston Naming Test (BNT) [18]. More details of the ADNI procedures of the cognitive assessment are publicly available on the website (www.loni.ucla.edu/ADNI).

Neuropsychiatric assessment

In this study, psychiatric symptoms were accessed by using the Neuropsychiatric Inventory (NPI), which is a structural interview for estimating behavioral and psychological symptoms including delusion, hallucinations, agitation, depression, anxiety, elation, apathy, disinhibition, irritability, aberrant motor behavior, night-time behavior, appetite [19,20,21]. Each domain contains four items: presence, severity, frequency, and care-giver distress. The score of each domain (0-12 points) was calculated by symptom frequency (0-4 points) multiplied by severity (0-3 points). Total scores range from 0 to 144 [19].

According to suggestions of the European Alzheimer’s Disease Consortium, we divided all psychiatric symptoms into four subsyndromes: (1) hyperactivity (agitation, elation, disinhibition, irritability, and aberrant motor behavior); (2) psychosis (delusion, hallucinations, and night-time behavior); (3) affective symptoms (anxiety and depression); (4) apathy (apathy and appetite) [6, 22].

Cerebrospinal fluid samples and quantification

CSF data were obtained by file “UPENNBIOMK_MASTER.csv” downloaded from the ADNI database. Levels of Aβ42, total tau (t-tau) and tau phosphorylated at threonine 181 (p-tau181) were measured using the multiplex 8 Luminex platform as previously described [23].

Imaging acquisition

MRI data were acquired with the uniform ADNIGO/2 scan acquisition protocol as described by Jack et al [24], including 3D-T1, axial T2-FLAIR, and axial T2* sequence. For the detailed parameters of MRI acquisition please see https://adni.loni.usc.edu/methods/documents/mri-protocols/.

Assessment of small vessel disease

SVD imaging markers included WMH, enlarged perivascular spaces (EPVS), microbleed, and lacunes. Visual assessment of SVD was performed by an experienced radiologist from our study team (Xiao Luo), blinding to the individual clinical diagnosis and neuropsychological data. WMH in periventricular regions and deep white matter were visual rated on T2-FLAIR images according to the 4-point Fazekas standard [25]. Severity of EPVS in basal ganglia (BG) and centrum semiovale (CSO) were assessed on T1-weighted images and rated as follow: 0 = none, 1 = 1-10, 2 = 11-20, 3 = 21-40 and 4 ≥ 40 EPVS 0 in any single slice, according to the previously proposed rating scale (https://www.ed.ac.uk/files/imports/fileManager/epvs-rating-scale-user-guide.pdf). Cerebral microbleeds were manifested as small, homogeneous round foci of hypointense on T2* images. Lacunes were referred to rounded or oval lesions, in the centrum semiovale, basal ganglia, internal capsule, or brainstem, with diameters between 3 mm and 20 mm, of CSF signal intensity on FLAIR images and high signal intensity on the rim. The microbleed and lacunes were defined as present (≥1 microbleeds or lacunes) or absent (no microbleeds or lacunes).

In order to further explore the potential effect of WMH volume, we downloaded the data of WMH volume segmented by Bayesian approach from ADNI dataset (ADNI_UCD_WMH_05_02_22_16Jan2024.csv) [26, 27].

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics Version 24 (IBM SPSS Statistics for Windows). Results with p <0.05 were considered statistically significant.

Regarding demographic and clinical data, between-group differences were estimated by using Mann-Whitney U-test and Chi-square test. Continuous variables were expressed as mean ± standard deviation, and categorical variables were expressed as median (Q1, Q3) or percentage respectively

Linear regression analyses were conducted to test the association between neuropsychiatric symptoms with AD pathology and CSVD burden. We assigned the CSF-derived biomarkers (Aβ42, p-tau181, t-tau, p-tau181/Aβ42, t-tau/Aβ42) and CSVD visual rating scores (PV-WMH, DM-WMH, BG-PVS, CSO-PVS, microbleed, and lacunes) as independent variables, and considered NPI total score as dependent variable. In order to explore effect on specific subsyndromes, we further repeated the linear regression analyses with NPI score of four subsyndromes as outcome variables respectively. All models have adjusted for age, sex (female=0, male=1), education, apolipoprotein E (APOE) ε4 carrier status (non-carrier=0, carrier=1) and clinical diagnosis (MCI=0, AD=1). In addition, when several independent variables were associated with dependent variable, we further estimated the interactive effect between theses variables. In order to the accuracy of this study, we also repeated above analyses by using the WMH volume to replace visual assessment (Fazekas score). Furthermore, we performed same linear regression models in each domain of NPS.

Results

Demographic and clinical characteristics

Table 1 summarized the demographic and clinical characteristics of study population. This study included 445 cognitively impaired participants (316 MCI and 129 AD patients). For CSF biomarkers, AD patients had lower CSF Aβ42 but higher CSF p-tau181, t-tau, p-tau181/Aβ42, and t-tau/Aβ42 than participants with MCI. Moreover, scores of NPI-assessed agitation, disinhibition, motor behavior, delusion, hallucinations, anxiety, apathy, and appetite were higher in AD group versus MCI patients.

Table 1 Demographic and clinical data of study population
Full size table

Associations of CSF biomarkers and CSVD markers with NPI total score

In the whole sample, the NPI total score was associated on univariate analyses with male (estimate 2.462; 95% CI [1.095, 3.830]; P <0.001), education (estimate -0.343; 95% CI [-0.602, -0.084]; P =0.010), clinical diagnosis (estimate 3.239; 95% CI [1.753, 4.725]; P <0.001), microbleed (estimate 2.977; 95% CI [1.189, 4.765]; P =0.001), and lacune (estimate 1.793; 95% CI [0.051, 3.535]; P =0.044) (Table 2). Only the association between NPI total scores and microbleed remains significant in univariate (model 2) and multivariable (model 3) analyses after adjusting for age, sex, education, APOE ε4 carrier status, and clinical diagnosis (Table 2).

Table 2 Association between NPI total scores with AD biomarkers and CSVD markers in in whole sample
Full size table

Associations of CSF biomarkers and CSVD markers with hyperactivity sub-syndromes

In the whole sample, the hyperactivity sub-syndromes were associated on univariate analyses with male (estimate 1.395; 95% CI [0.752, 2.037]; P <0.001), education (estimate -0.154; 95% CI [-0.276, -0.031]; P =0.014), clinical diagnosis (estimate 1.269; 95% CI [0.562, 1.976]; P <0.001), and microbleed (estimate 1.127; 95% CI [0.278, 1.976]; P =0.009) (Table 3). The association between hyperactivity syndrome and microbleed remained significant univariate (model 2) and multivariable (model 3) analyses after adjusting for age, sex, education, APOE ε4 carrier status, and clinical diagnosis (Table 3).

Table 3 Association between hyperactivity sub-syndromes with AD biomarkers and CSVD markers in whole sample
Full size table

Associations of CSF biomarkers and CSVD markers with psychosis sub-syndromes

In the whole sample, the psychosis sub-syndromes did not show significant correlation with AD biomarkers and CSVD markers either in univariate or multivariable analyses (Table 4).

Table 4 Association between psychosis sub-syndromes with AD biomarkers and CSVD markers in whole sample
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Associations of CSF biomarkers and CSVD markers with affective sub-syndromes

The affective symptoms were correlated to CSF Aβ42 levels (estimate -0.006; 95% CI [-0.009, -0.002]; P =0.003) (Table 5). This association remains unchanged in univariate (model 2) and multivariable (model 3) analyses after adjusting for age, sex, education, APOE ε4 carrier status, and clinical diagnosis (Table 5).

Table 5 Association between affective sub-syndromes with AD biomarkers and CSVD markers in whole sample
Full size table

Associations of CSF biomarkers and CSVD markers with apathy sub-syndromes

The apathy sub-syndromes were on univariate analyses associated with male (estimate 0.826; 95% CI [0.286, 1.366]; P =0.003), education (estimate -0.115; 95% CI [-0.217, -0.013]; P =0.027), clinical diagnosis (estimate 1.648; 95% CI [1.072, 2.225]; P <0.001), and microbleed (estimate 0.976; 95% CI [0.270, 1.682]; P =0.007) (Table 6). In univariate analysis after adjusting for age, sex, education, APOE ε4 carrier status, and clinical diagnosis, the apathy sub-syndromes showed correlation with CSF t-tau/ Aβ42 (estimate 0.372; 95% CI [0.278, 0.618]; P =0.044) and microbleed (estimate 0.695; 95% CI [0.041, 1.349]; P =0.037) (Table 6). In multivariable regression model 3, the apathy sub-syndromes were related to t-tau/Aβ42 (estimate 0.636; 95% CI [0.078, 1.194]; P =0.041) and microbleed (estimate 0.693; 95% CI [0.046, 1.340]; P =0.036) (Table 6). In addition, we found a significant interactive effect between t-tau/Aβ42 and microbleed on apathy sub-syndromes (estimate 0.993; 95% CI [0.360, 1.626]; P =0.019) (Supplementary Table 1).

Table 6 Association between apathy sub-syndromes with AD biomarkers and CSVD markers in whole sample
Full size table

Additional analyses

When including the total WMH volume instead of Fazekas score, the results remain unchanged (Supplementary Table 2-6). In addition, the results of linear regression models in each NPS domain were showed in Supplementary Table 7-18.

Discussion

In this study, we assessed the relationships of neuropsychiatric sub-syndromes, AD-related pathology, and SVD markers in cognitively impaired patients. Our findings are consistent with our initial hypothesis that AD pathology and vascular damage not only contribute to various psychiatric symptoms but also attack on same sub-syndrome synergistically. We found that: 1) CSF Aβ42 was related to affective sub-syndrome; 2) microbleed was associated with hyperactivity and NPI total scores; 3) both microbleed and CSF t-tau/Aβ42 were associated with apathetic symptom.

We found a negative association between CSF Aβ42 and affective symptoms including anxiety and depression. As previously demonstrated, lower CSF Aβ42 was related to more amyloid deposition, which has been found to be associated with more severe anxiety and depressive symptoms [9, 28, 29]. Two studies from the Netherlands have reported that the presence of anxiety was associated with abnormal CSF Aβ42, p-tau, and t-tau in individuals across the AD spectrum [29, 30]. Similarly, Krell-Roesch et al. observed that lower levels of CSF Aβ42 correlated with severer depressive and anxiety symptoms in cognitively unimpaired individuals [10]. As a core pathologic change of AD, amyloid deposition may drive the psychiatric symptoms [10]. In animal models, researchers have observed that amyloid plaque could cause the deficits of neurotransmitter systems, which is related to behavioral abnormalities [31]. Also, it has been reported that higher Aβ burden was associated with increasing anxious and depressive symptoms in both cognitively normal and cognitive impaired individuals [11, 32]. Therefore, affective subsyndrome may be mainly affected by amyloid deposition.

Furthermore, we also found the association between microbleed with NPI total scores and hyperactive sub-syndrome. Microbleed is a kind of imaging markers of cerebral SVD, which has been found that it could aggravate some psychiatric symptoms [33, 34]. In a community-based Asian cohort, Xu et al. observed that the presence of microbleed was associated with apathy, disinhibition and depression [33]. Also, previous study has found the relationship between SVD burden and multiple NPS, suggesting the driven role of vascular pathology to psychiatric symptoms[15]. The potential mechanisms may be that SVD could result in the degeneration of neurons and neural disconnections [35, 36], which further lead to disruption of functional network associated with NPS [37, 38].

Notably, we observed that apathy was associated with both microbleed and CSF t-tau/Aβ42. Apathy is the most common psychiatric symptoms in cognitively impaired patients[39]. Previously, researchers have found that apathic symptom was associated with amyloidosis and tauopathy based on CSF biomarkers or PET technique in cognitively impaired individuals [11, 40]. It has been observed that apathy correlated with prefrontal Aβ deposition in AD and MCI patients [41, 42]. In AD animal models, researchers have found that Aβ oligomers could reduce the glutamatergic synaptic transmission strength and plasticity, which further influence the neuronal activity [43]. Tau pathology may also result in focal neurotoxicity and the network disruption, leading to the appearance of apathy [40]. Moreover, previous studies have reported that increased severity of microbleed is associated with more serious apathy symptom [15, 44]. Thus, we considered that AD pathology and microbleed have synergistical effects on disruption of several cerebral networks which are related to the emergence of apathy. Methylphenidate may treat apathy symptoms by enhancing norepinephrine and dopamine actions in related neural circuits disrupted by AD and SVD pathology [45].

In addition, in our study we only observed the associations between hyperactive and apathic syndromes with microbleed but not WMH. One potential explanation is that our study population consisted of cognitive impaired patients rather than non-demented individuals [15]. Also, the mechanisms of WMH and microbleed could be various. Previous study has described the relationship between microbleed and amyloid [46]. Therefore, the microbleeds might play more central role in NPS than other SVD markers.

There are still several limitations in this study. First, the causal direction of the association among AD pathology, SVD, and NPS in this study cannot be established due to the cross-sectional study design. However, growing evidence implicates the psychiatric symptoms as consequences of AD pathology and vascular change [10, 14]. Thus, we considered the NPS as the outcome in statistical model to estimate the effect of AD pathology and SVD on NPS. Second, in the present study, we assessed the severity of NPS only by the NPI rather than more specific test such as Beck Depression Inventory II (BDI-II) and Beck Anxiety Inventory (BAI). Further studies should apply more accurate assessment for disentangling the relationship between the pathological burden and individual psychiatric symptom scores. Third, the sample size is relatively small, and the number of MCI patients in the study population was more than AD patients so that the NPS burdens were relatively lower. Studies with large sample size is needed to further confirmed the association of NPS, SVD and AD pathology especially in different clinical stage.

The strength of our study is to disentangling the distinct effects of AD biomarkers and SVD markers on neuropsychiatric subsyndromes in cognitively impaired individuals. Consist with previous studies, we found that CSF Aβ42 and microbleed were associated with affective and hyperactive sub-syndrome respectively. A novel finding is that we detected significant interaction between microbleed and CSF t-tau/Aβ42 on apathy subsyndrome, indicating the synergistic effect of AD pathology and vascular damage to some extent. Thus, we considered that AD pathology and vascular damage could influence the common and different NPS-related neural pathway, which further lead to the appearance of specific symptoms.

Conclusion

Our study among cognitively impaired patients showed that CSF Aβ42 was associated with affective symptoms, and microbleed was correlated with hyperactivity and apathy, suggesting the effect of AD pathology and SVD on different neuropsychiatric sub-syndromes. This should be taken into account in further research focusing on therapy of NPS.

Availability of data and materials

The data used in the preparation of this article were obtained from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database: http://adni.loni.usc.edu/.

Abbreviations

NPS:

Neuropsychiatric symptoms

AD:

Alzheimer’s disease

MCI:

Mild cognitive impairment

CSF:

Cerebrospinal fluid

SVD:

Small vessel disease

WMS-R:

Wechsler Memory Scale-Revised

CDR:

Clinical Dementia Rating

MMSE:

Mini-Mental State Examination

NPI:

Neuropsychiatric Inventory

MoCA:

Montreal Cognitive Assessment

AVLT:

Auditory Verbal Learning Test

CDT:

Clock Drawing Test

TMT-A:

Trial-Making Test-A

TMT-B:

Trial-Making Test-B

BNT:

Boston Naming Test

Aβ:

Amyloid-beta

p-tau181 :

Tau phosphorylated at threonine 181

t-tau:

Total tau

WMH:

White matter hyperintensities

EPVS:

Enlarged perivascular spaces

BG:

Basal ganglia

CSO:

Centrum semiovale

References

  1. Palmer K, Berger AK, Monastero R, Winblad B, Backman L, Fratiglioni L. Predictors of progression from mild cognitive impairment to Alzheimer disease. Neurology. 2007;68(19):1596–602.

    Article  CAS  PubMed  Google Scholar 

  2. Peters ME, Schwartz S, Han D, Rabins PV, Steinberg M, Tschanz JT, Lyketsos CG. Neuropsychiatric symptoms as predictors of progression to severe Alzheimer’s dementia and death: the Cache County Dementia Progression Study. Am J Psychiatry. 2015;172(5):460–5.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Cerejeira J, Lagarto L, Mukaetova-Ladinska EB. Behavioral and psychological symptoms of dementia. Front Neurol. 2012;3:73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Canevelli M, Adali N, Voisin T, Soto ME, Bruno G, Cesari M, Vellas B. Behavioral and psychological subsyndromes in Alzheimer’s disease using the Neuropsychiatric Inventory. Int J Geriatr Psychiatry. 2013;28(8):795–803.

    Article  PubMed  Google Scholar 

  5. Nowrangi MA, Lyketsos CG, Rosenberg PB. Principles and management of neuropsychiatric symptoms in Alzheimer’s dementia. Alzheimers Res Ther. 2015;7(1):12.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Aalten P, Verhey FR, Boziki M, Bullock R, Byrne EJ, Camus V, Caputo M, Collins D, De Deyn PP, Elina K, et al. Neuropsychiatric syndromes in dementia. Results from the European Alzheimer Disease Consortium: part I. Dement Geriatr Cogn Disord. 2007;24(6):457–63.

    Article  PubMed  Google Scholar 

  7. Miao R, Chen HY, Gill S, Naude J, Smith EE, Ismail Z. Alzheimer’s Disease Neuroimaging I: Plasma beta-Amyloid in Mild Behavioural Impairment - Neuropsychiatric Symptoms on the Alzheimer’s Continuum. J Geriatr Psychiatry Neurol. 2022;35(3):434–41.

    Article  PubMed  Google Scholar 

  8. Showraki A, Murari G, Ismail Z, Barfett JJ, Fornazzari L, Munoz DG, Schweizer TA, Fischer CE. Cerebrospinal Fluid Correlates of Neuropsychiatric Symptoms in Patients with Alzheimer’s Disease/Mild Cognitive Impairment: A Systematic Review. J Alzheimers Dis. 2019;71(2):477–501.

    Article  CAS  PubMed  Google Scholar 

  9. Johansson M, Stomrud E, Johansson PM, Svenningsson A, Palmqvist S, Janelidze S, van Westen D, Mattsson-Carlgren N, Hansson O. Development of apathy, anxiety, and depression in cognitively unimpaired older adults: effects of Alzheimer’s disease pathology and cognitive decline. Biol Psychiatry. 2022;92(1):34–43.

    Article  PubMed  Google Scholar 

  10. Krell-Roesch J, Rakusa M, Syrjanen JA, van Harten AC, Lowe VJ, Jack CR Jr, Kremers WK, Knopman DS, Stokin GB, Petersen RC, et al. Association between CSF biomarkers of Alzheimer’s disease and neuropsychiatric symptoms: Mayo Clinic Study of Aging. Alzheimers Dement. 2023;19(10):4498–506.

    Article  CAS  PubMed  Google Scholar 

  11. Johansson M, Stomrud E, Lindberg O, Westman E, Johansson PM, van Westen D, Mattsson N, Hansson O. Apathy and anxiety are early markers of Alzheimer’s disease. Neurobiol Aging. 2020;85:74–82.

    Article  CAS  PubMed  Google Scholar 

  12. Babulal GM, Ghoshal N, Head D, Vernon EK, Holtzman DM, Benzinger TL, Fagan AM, Morris JC, Roe CM. Mood changes in cognitively normal older adults are linked to alzheimer disease biomarker levels. Am J Geriatr Psychiatry. 2016;24(11):1095–104.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Sun L, Li W, Li G, Xiao S. Prefrontal Abeta pathology influencing the pathway from apathy to cognitive decline in non-dementia elderly. Transl Psychiatry. 2021;11:534.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Clancy U, Gilmartin D, Jochems ACC, Knox L, Doubal FN, Wardlaw JM. Neuropsychiatric symptoms associated with cerebral small vessel disease: a systematic review and meta-analysis. Lancet Psychiatry. 2021;8(3):225–36.

    Article  PubMed  Google Scholar 

  15. Cao QL, Sun Y, Hu H, Wang ZT, Tan L, Yu JT. Alzheimer’s disease neuroimaging I: association of cerebral small vessel disease burden with neuropsychiatric symptoms in non-demented elderly: a longitudinal study. J Alzheimers Dis. 2022;89(2):583–92.

    Article  PubMed  Google Scholar 

  16. Xu X, Chan QL, Hilal S, Goh WK, Venketasubramanian N. Cerebral microbleeds and neuropsychiatric symptoms in an elderly Asian cohort. J Neurol Neurosurg Psychiatry. 2016;88(1):jnnp-2016-313271.

    Google Scholar 

  17. Ozzoude M, Varriano B, Beaton D, Ramirez J, Adamo S, Holmes MF, Scott CJM, Gao F, Sunderland KM, McLaughlin P, et al. White matter hyperintensities and smaller cortical thickness are associated with neuropsychiatric symptoms in neurodegenerative and cerebrovascular diseases. Alzheimers Res Ther. 2023;15(1):114.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, Jack CR Jr, Jagust WJ, Shaw LM, Toga AW, et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI): clinical characterization. Neurology. 2010;74(3):201–9.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Cummings JL. The Neuropsychiatric Inventory: assessing psychopathology in dementia patients. Neurology. 1997;48(5 Suppl 6):S10-16.

    CAS  PubMed  Google Scholar 

  20. Mega MS, Cummings JL, Fiorello T, Gornbein J. The spectrum of behavioral changes in Alzheimer’s disease. Neurology. 1996;46(1):130–5.

    Article  CAS  PubMed  Google Scholar 

  21. Jeon YH, Sansoni J, Low LF, Chenoweth L, Zapart S, Sansoni E, Marosszeky N. Recommended measures for the assessment of behavioral disturbances associated with dementia. Am J Geriatr Psychiatry. 2011;19(5):403–15.

    Article  PubMed  Google Scholar 

  22. Aalten P, Verhey FR, Boziki M, Brugnolo A, Bullock R, Byrne EJ, Camus V, Caputo M, Collins D, De Deyn PP, et al. Consistency of neuropsychiatric syndromes across dementias: results from the European Alzheimer Disease Consortium Part II. Dement Geriatr Cogn Disord. 2008;25(1):1–8.

    Article  PubMed  Google Scholar 

  23. Shaw LM, Vanderstichele H, Knapik-Czajka M, Figurski M, Coart E, Blennow K, Soares H, Simon AJ, Lewczuk P, Dean RA, et al. Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI. Acta Neuropathol. 2011;121(5):597–609.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Jack CR Jr, Bernstein MA, Borowski BJ, Gunter JL, Fox NC, Thompson PM, Schuff N, Krueger G, Killiany RJ, Decarli CS, et al. Update on the magnetic resonance imaging core of the Alzheimer’s disease neuroimaging initiative. Alzheimers Dement. 2010;6(3):212–20.

    Article  PubMed  Google Scholar 

  25. Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging. AJR Am J Roentgenol. 1987;149(2):351–6.

    Article  CAS  PubMed  Google Scholar 

  26. DeCarli C, Miller BL, Swan GE, Reed T, Wolf PA, Garner J, Jack L, Carmelli D. Predictors of brain morphology for the men of the NHLBI twin study. Stroke. 1999;30(3):529–36.

    Article  CAS  PubMed  Google Scholar 

  27. DeCarli C, Fletcher E, Ramey V, Harvey D, Jagust WJ. Anatomical mapping of white matter hyperintensities (WMH): exploring the relationships between periventricular WMH, deep WMH, and total WMH burden. Stroke. 2005;36(1):50–5.

    Article  PubMed  Google Scholar 

  28. Zapater-Fajari M, Diaz-Galvan P, Cedres N, Rydberg Sterner T, Ryden L, Sacuiu S, et al. Biomarkers of Alzheimer's Disease and Cerebrovascular Disease in Relation to Depressive Symptomatology in Individuals With Subjective Cognitive Decline. J Gerontol A Biol Sci Med Sci. 2024;79(2).

  29. Ramakers IH, Verhey FR, Scheltens P, Hampel H, Soininen H, Aalten P, Rikkert MO, Verbeek MM, Spiru L, Blennow K, et al. Anxiety is related to Alzheimer cerebrospinal fluid markers in subjects with mild cognitive impairment. Psychol Med. 2013;43(5):911–20.

    Article  CAS  PubMed  Google Scholar 

  30. Banning LCP, Ramakers I, Kohler S, Bron EE, Verhey FRJ, de Deyn PP, Claassen J, Koek HL, Middelkoop HAM, van der Flier WM, et al. The Association Between Biomarkers and Neuropsychiatric Symptoms Across the Alzheimer’s Disease Spectrum. Am J Geriatr Psychiatry. 2020;28(7):735–44.

    Article  PubMed  Google Scholar 

  31. Chapman PF, White GL, Jones MW, Cooper-Blacketer D, Marshall VJ, Irizarry M, Younkin L, Good MA, Bliss TV, Hyman BT, et al. Impaired synaptic plasticity and learning in aged amyloid precursor protein transgenic mice. Nat Neurosci. 1999;2(3):271–6.

    Article  CAS  PubMed  Google Scholar 

  32. Pichet Binette A, Vachon-Presseau E, Morris J, Bateman R, Benzinger T, Collins DL, Poirier J, Breitner JCS, Villeneuve S, Dominantly Inherited Alzheimer N et al. Amyloid and Tau Pathology Associations With Personality Traits, Neuropsychiatric Symptoms, and Cognitive Lifestyle in the Preclinical Phases of Sporadic and Autosomal Dominant Alzheimer's Disease. Biol Psychiatry. 2021; 89(8):776-785.

  33. Xu X, Chan QL, Hilal S, Goh WK, Ikram MK, Wong TY, Cheng CY, Chen CL, Venketasubramanian N. Cerebral microbleeds and neuropsychiatric symptoms in an elderly Asian cohort. J Neurol Neurosurg Psychiatry. 2017;88(1):7–11.

    Article  PubMed  Google Scholar 

  34. Misquitta K, Dadar M, Louis Collins D, Tartaglia MC. Alzheimer’s Disease Neuroimaging I: White matter hyperintensities and neuropsychiatric symptoms in mild cognitive impairment and Alzheimer’s disease. Neuroimage Clin. 2020;28:102367.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Biesbroek JM, Weaver NA, Biessels GJ. Lesion location and cognitive impact of cerebral small vessel disease. Clin Sci (Lond). 2017;131(8):715–28.

    Article  PubMed  Google Scholar 

  36. Charidimou A, Martinez-Ramirez S, Reijmer YD, Oliveira-Filho J, Lauer A, Roongpiboonsopit D, Frosch M, Vashkevich A, Ayres A, Rosand J, et al. Total Magnetic Resonance Imaging Burden of Small Vessel Disease in Cerebral Amyloid Angiopathy: An Imaging-Pathologic Study of Concept Validation. JAMA Neurol. 2016;73(8):994–1001.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Sole-Padulles C, Castro-Fornieles J, de la Serna E, Sanchez-Gistau V, Romero S, Puig O, Calvo A, Bargallo N, Baeza I, Sugranyes G. Intrinsic functional connectivity of fronto-temporal networks in adolescents with early psychosis. Eur Child Adolesc Psychiatry. 2017;26(6):669–79.

    Article  PubMed  Google Scholar 

  38. Baker JT, Holmes AJ, Masters GA, Yeo BT, Krienen F, Buckner RL, Ongur D. Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder. JAMA Psychiatry. 2014;71(2):109–18.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Zhao QF, Tan L, Wang HF, Jiang T, Tan MS, Tan L, Xu W, Li JQ, Wang J, Lai TJ, et al. The prevalence of neuropsychiatric symptoms in Alzheimer’s disease: Systematic review and meta-analysis. J Affect Disord. 2016;190:264–71.

    Article  PubMed  Google Scholar 

  40. Kitamura S, Shimada H, Niwa F, Endo H, Shinotoh H, Takahata K, Kubota M, Takado Y, Hirano S, Kimura Y, et al. Tau-induced focal neurotoxicity and network disruption related to apathy in Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2018;89(11):1208–14.

    Article  PubMed  Google Scholar 

  41. Mori T, Shimada H, Shinotoh H, Hirano S, Eguchi Y, Yamada M, Fukuhara R, Tanimukai S, Zhang MR, Kuwabara S, et al. Apathy correlates with prefrontal amyloid beta deposition in Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2014;85(4):449–55.

    Article  PubMed  Google Scholar 

  42. Sun L, Li W, Li G, Xiao S. Alzheimer’s Disease Neuroimaging I: Prefrontal Abeta pathology influencing the pathway from apathy to cognitive decline in non-dementia elderly. Transl Psychiatry. 2021;11(1):534.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Palop JJ, Chin J, Roberson ED, Wang J, Thwin MT, Bien-Ly N, Yoo J, Ho KO, Yu GQ, Kreitzer A, et al. Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease. Neuron. 2007;55(5):697–711.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Cai X, Zhao H, Li Z, Ding Y, Huang Y. Detecting apathy in patients with cerebral small vessel disease. Front Aging Neurosci. 2022;14:933958.

    Article  PubMed  PubMed Central  Google Scholar 

  45. van Dyck CH, Arnsten AFT, Padala PR, Brawman-Mintzer O, Lerner AJ, Porsteinsson AP, Scherer RW, Levey AI, Herrmann N, Jamil N, et al. Neurobiologic Rationale for Treatment of Apathy in Alzheimer’s Disease With Methylphenidate. Am J Geriatr Psychiatry. 2021;29(1):51–62.

    Article  PubMed  Google Scholar 

  46. Haller S, Vernooij MW, Kuijer JPA, Larsson EM, Barkhof F. Cerebral microbleeds: imaging and clinical significance. Radiology. 2018;287(1):11–28.

    Article  PubMed  Google Scholar 

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Acknowledgements

The data collection and sharing for this project were funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense Award No. W81XWH-12-2-0012). ADNI was funded by the NIA, the NIBIB, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; Bio Clinica, Inc.; Biogen; Bristol-Myers Squibb Company; Cere Spir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; Euro Immun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; MesoScale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the AD Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuroimaging at the University of Southern California.

Data used in the preparation of this article were obtained from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database (http://www.adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni.loni.usc.edu/wp-content/up loads/how to apply/ADNI Acknowledgement List.pdf.

Full list of consortium members and affiliations for ADNI

The full list of members and affiliations could be found in https://adni.loni.usc.edu/wp-content/themes/freshnews-dev-v2/documents/policy/ADNI%20Groups%20Acknowledgements%20Journal%20Format.pdf

Funding

This work was funded by the National Natural Science Foundation of China (Grant No.81971577, Grant No.82271936, Grant No.82371190, Grant No. 82202090, Grant No.82302138, and Grant No.82001766), the China Postdoctoral Science Foundation (Grant No.2022M722751), Zhejiang Medical and Health Science and Technology Project (Grant No.2023567844), and Zhejiang Medical and Health Technology Plan Project of the Health Commission (Grant No.2021RC060).

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Authors and Affiliations

  1. Department of Radiology, Zhejiang University School of Medicine Second Affiliated Hospital, Shangcheng District, No.88 Jiefang Road, Hangzhou, 310009, China

    Qingze Zeng, Shuyue Wang, Xiao Luo, Kaicheng Li, Xiaopei Xu, Xiaocao Liu, Luwei Hong, Jixuan Li, Peiyu Huang & Minming Zhang

  2. Department of Neurology, Zhejiang University School of Medicine Second Affiliated Hospital, Shangcheng District, No.88 Jiefang Road, Hangzhou, 310009, China

    Yanbo Wang, Zheyu Li, Xinyi Zhang, Siyan Zhong, Zhirong Liu & Yanxing Chen

  3. Department of Neurology, Xinhua Hospital of Zhejiang Chinese Medical University, Hangzhou, China

    Yanbo Wang

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  1. Qingze Zeng
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Consortia

for behalf of Alzheimer’s Disease Neuroimaging Initiative

Contributions

Author contributions included conception and study design (QZ, YW, SW, XLuo, and KL), data collection or acquisition (QZ, XX), statistical analysis (QZ, XLiu, and LH), interpretation of results (JL, ZLi, XZ, and SZ), drafting the manuscript work or revising it critically for important intellectual content (QZ, ZLiu, PH, YC, and MZ) and approval of final version to be published and agreement to be accountable for the integrity and accuracy of all aspects of the work (All authors).

Corresponding authors

Correspondence to Yanxing Chen or Minming Zhang.

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Zeng, Q., Wang, Y., Wang, S. et al. Cerebrospinal fluid amyloid-β and cerebral microbleed are associated with distinct neuropsychiatric sub-syndromes in cognitively impaired patients. Alz Res Therapy 16, 69 (2024). https://doi.org/10.1186/s13195-024-01434-7

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Keywords

Alzheimer's Research & Therapy

ISSN: 1758-9193