Multimodal Brain Imaging Research Articles

Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease.

Parkinson disease (PD) is a progressive disorder characterized by dopaminergic neurodegeneration in the brain. The development of parkinsonism is preceded by a long prodromal phase, and >50% of dopaminergic neurons can be lost from the substantia nigra by the time of the initial diagnosis. Therefore, validation of in vivo imaging biomarkers for early diagnosis and monitoring of disease progression is essential for future therapeutic developments. PET and single-photon emission CT targeting the presynaptic terminals of dopaminergic neurons can be used for early diagnosis by detecting axonal degeneration in the striatum. However, these techniques poorly differentiate atypical parkinsonian syndromes from PD, and their availability is limited in clinical settings. Advanced MRI in which pathological changes in the substantia nigra are visualized with diffusion, iron-sensitive susceptibility and neuromelanin-sensitive sequences potentially represents a more accessible imaging tool. Although these techniques can visualize the classic degenerative changes in PD, they might be insufficient for phenotyping or prognostication of heterogeneous aspects of PD resulting from extranigral pathologies. The retina is an emerging imaging target owing to its pathological involvement early in PD, which correlates with brain pathology. Retinal optical coherence tomography (OCT) is a non-invasive technique to visualize structural changes in the retina. Progressive parafoveal thinning and fovea avascular zone remodelling, as revealed by OCT, provide potential biomarkers for early diagnosis and prognostication in PD. As we discuss in this Review, multimodal imaging of the substantia nigra and retina is a promising tool to aid diagnosis and management of PD.

The pandemic brain: Neuroinflammation in non-infected individuals during the COVID-19 pandemic

While COVID-19 research has seen an explosion in the literature, the impact of pandemic-related societal and lifestyle disruptions on brain health among the uninfected remains underexplored. However, a global increase in the prevalence of fatigue, brain fog, depression and other “sickness behavior”-like symptoms implicates a possible dysregulation in neuroimmune mechanisms even among those never infected by the virus.We compared fifty-seven ‘Pre-Pandemic’ and fifteen ‘Pandemic’ datasets from individuals originally enrolled as control subjects for various completed, or ongoing, research studies available in our records, with a confirmed negative test for SARS-CoV-2 antibodies. We used a combination of multimodal molecular brain imaging (simultaneous positron emission tomography / magnetic resonance spectroscopy), behavioral measurements, imaging transcriptomics and serum testing to uncover links between pandemic-related stressors and neuroinflammation.Healthy individuals examined after the enforcement of 2020 lockdown/stay-at-home measures demonstrated elevated brain levels of two independent neuroinflammatory markers (the 18 kDa translocator protein, TSPO, and myoinositol) compared to pre-lockdown subjects. The serum levels of two inflammatory markers (interleukin-16 and monocyte chemoattractant protein-1) were also elevated, although these effects did not reach statistical significance after correcting for multiple comparisons. Subjects endorsing higher symptom burden showed higher TSPO signal in the hippocampus (mood alteration, mental fatigue), intraparietal sulcus and precuneus (physical fatigue), compared to those reporting little/no symptoms. Post-lockdown TSPO signal changes were spatially aligned with the constitutive expression of several genes involved in immune/neuroimmune functions.This work implicates neuroimmune activation as a possible mechanism underlying the non-virally-mediated symptoms experienced by many during the COVID-19 pandemic. Future studies will be needed to corroborate and further interpret these preliminary findings.

Multimodal Brain Imaging Fusion for the White-Matter Fiber Architecture in the Human Brain.

Multimodal Brain Imaging Fusion for the White-Matter Fiber Architecture in the Human Brain.

MMI-Fuse: Multimodal Brain Image Fusion With Multiattention Module

Medical imaging plays a pivotal role in the clinical diagnosis of brain disease. There are many imaging methods to detect the state of tissues in the brain. While these imaging methods have advantages, they also have shortcomings. For example, magnetic resonance imaging (MRI) contains structural information but no functional characteristics of tissue, while positron emission tomography (PET) possesses functional characteristics but no structural information. The attention mechanism has been widely used in image fusion tasks, such as fusion of infrared and visible images and medical images. However, those attention models lack a balance mechanism for multimodal image features, affecting the final fusion performance. This paper proposes an end-to-end multimodal brain image fusion framework, MMI-fuse. Specifically, we first apply an autoencoder to extract the features of source images. Then, an information preservation weighted channel spatial attention model (ICS) is proposed to fuse the image features. We set an adaptive weight according to the information preservation degree of features. Finally, we use a decoder model to restructure the fused medical image. The proposed method increased the quality of fused images and decreased the fusion time effectively by the help of the improved attention model and encoder-decoder structure. To validate the performance of the proposed method, we collected 1590 pairs of multimodal brain images from the Harvard dataset and performed extensive experiments. Seven methods and five metrics were selected for the comparison experiments. The results demonstrate that the proposed method achieved notable performance on both the visual quality and objective metric score among these seven approaches. Moreover, the proposed method takes the least time among all compared methods.

Task-Induced Pyramid and Attention GAN for Multimodal Brain Image Imputation and Classification in Alzheimer's Disease

With the advance of medical imaging technologies, multimodal images such as magnetic resonance images (MRI) and positron emission tomography (PET) can capture subtle structural and functional changes of brain, facilitating the diagnosis of brain diseases such as Alzheimer's disease (AD). In practice, multimodal images may be incomplete since PET is often missing due to high financial costs or availability. Most of the existing methods simply excluded subjects with missing data, which unfortunately reduced the sample size. In addition, how to extract and combine multimodal features is still challenging. To address these problems, we propose a deep learning framework to integrate a task-induced pyramid and attention generative adversarial network (TPA-GAN) with a pathwise transfer dense convolution network (PT-DCN) for imputation and classification of multimodal brain images. First, we propose a TPA-GAN to integrate pyramid convolution and attention module as well as disease classification task into GAN for generating the missing PET data with their MRI. Then, with the imputed multimodal images, we build a dense convolution network with pathwise transfer blocks to gradually learn and combine multimodal features for final disease classification. Experiments are performed on ADNI-1/2 datasets to evaluate our method, achieving superior performance in image imputation and brain disease diagnosis compared to state-of-the-art methods.

Increase the accuracy of invasive epilepsy treatment by using multimodality brain imaging

Increase the accuracy of invasive epilepsy treatment by using multimodality brain imaging

Serum Adiponectin and In Vivo Brain Amyloid Deposition in Cognitively Normal Older Adults: A Cohort Study.

High blood adiponectin has been associated with Alzheimer's disease (AD) dementia and related cognitive decline. We aimed to investigate the association between serum adiponectin level and in vivo AD pathologies. Cross-sectional and longitudinal study designs for the data of the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease, an ongoing prospective cohort study that began in 2014. A total of 283 cognitively normal older adults between 55 and 90 years of age were included in community and memory clinic setting. Participants underwent comprehensive clinical assessments, measurement of serum adiponectin level, and multimodal brain imaging, including Pittsburgh compound-B positron emission tomography (PET), AV-1451 PET, fluorodeoxyglucose (FDG)-PET, and MRI at baseline and 2-year follow-up. Serum adiponectin level was positively associated with global beta-amyloid protein (Aβ) retention and change therein over 2 years, but not with other AD neuroimaging markers including tau deposition, AD-related neurodegeneration, and white matter hyperintensities. Blood adiponectin level is associated with increased brain amyloid deposition, which suggests that adiponectin may be a potential target for therapeutic and preventive strategies against AD.

Research progress of quantitative multimodal brain imaging technology

Research progress of quantitative multimodal brain imaging technology

Integrating multiple brain imaging modalities does not boost prediction of subclinical atherosclerosis in midlife adults

BackgroundHuman neuroimaging evidence suggests that cardiovascular disease (CVD) risk may relate to functional and structural features of the brain. The present study tested whether combining functional and structural (multimodal) brain measures, derived from magnetic resonance imaging (MRI), would yield a multivariate brain biomarker that reliably predicts a subclinical marker of CVD risk, carotid-artery intima-media thickness (CA-IMT). MethodsNeuroimaging, cardiovascular, and demographic data were assessed in 324 midlife and otherwise healthy adults who were free of (a) clinical CVD and (b) use of medications for chronic illnesses (aged 30–51 years, 49% female). We implemented a prediction stacking algorithm that combined multimodal brain imaging measures and Framingham Risk Scores (FRS) to predict CA-IMT. We included imaging measures that could be easily obtained in clinical settings: resting state functional connectivity and structural morphology measures from T1-weighted images. ResultsOur models reliably predicted CA-IMT using FRS, as well as for several individual MRI measures; however, none of the individual MRI measures outperformed FRS. Moreover, stacking functional and structural brain measures with FRS did not boost prediction accuracy above that of FRS alone. ConclusionsCombining multimodal functional and structural brain measures through a stacking algorithm does not appear to yield a reliable brain biomarker of subclinical CVD, as reflected by CA-IMT.

The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network

The technology <span>of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% </span>accuracy.

Recent Advances and Future Directions in Brain MR Imaging Studies in Schizophrenia: Toward Elucidating Brain Pathology and Developing Clinical Tools.

Schizophrenia is a common severe psychiatric disorder that affects approximately 1% of general population through the life course. Historically, in Kraepelin's time, schizophrenia was a disease unit conceptualized as dementia praecox; however, since then, the disease concept has changed. Recent MRI studies had shown that the neuropathology of the brain in this disorder was characterized by mild progression before and after the onset of the disease, and that the brain alterations were relatively smaller than assumed. Although genetic factors contribute to the brain alterations in schizophrenia, which are thought to be trait differences, other changes include factors that are common in psychiatric diseases. Furthermore, it has been shown that the brain differences specific to schizophrenia were relatively small compared to other changes, such as those caused by brain development, aging, and gender. In addition, compared to the disease and participant factors, machine and imaging protocol differences could affect MRI signals, which should be addressed in multi-site studies. Recent advances in MRI modalities, such as multi-shell diffusion-weighted imaging, magnetic resonance spectroscopy, and multimodal brain imaging analysis, may be candidates to sharpen the characterization of schizophrenia-specific factors and provide new insights. The Brain/MINDS Beyond Human Brain MRI (BMB-HBM) project has been launched considering the differences and noises irrespective of the disease pathologies and includes the future perspectives of MRI studies for various psychiatric and neurological disorders. The sites use restricted MRI machines and harmonized multi-modal protocols, standardized image preprocessing, and traveling subject harmonization. Data sharing to the public will be planned in FY 2024. In the future, we believe that combining a high-quality human MRI dataset with genetic data, randomized controlled trials, and MRI for non-human primates and animal models will enable us to understand schizophrenia, elucidate its neural bases and therapeutic targets, and provide tools for clinical application at bedside.

Multimodal brain imaging demonstrates that both amyloid burden and glucose metabolism predict the severity of cognitive impairment in patients with Alzheimer’s disease

AbstractBackgroundThe purpose of this study is to assess brain glucose metabolism and Amyloid‐β burden in patients with Alzheimer’s disease with 3 Tesla PET/MR imaging and to seek possible relation of imaging findings with the extent of cognitive impairment.Method45 consecutive subjects with memory complaints were evaluated with a detailed neuropsychological test battery and F18‐Fluorodeoxyglucose(FDG)‐PET/MRI and F18‐Flutemetamol‐PET/MRI scans. The imaging modalities were performed with a hybrid 3T PET / MR camera at a maximum of 7 days intervals. Images were visually evaluated by two nuclear medicine experts. The centiloid scores were calculated through amyloid standardized uptake value ratios (SUVR) for all participants and regional glucose metabolism characteristics were analyzed.ResultThe study population consisted of 30 males and 15 females with a mean age of 65.9±8.2 years. Mean MMSE was 21.7±7.2 for the overall group. Five patients had imaging features suggestive of non‐AD type of dementias and were excluded from further analyses. Visual examination of F18‐Flutemetamol PET/MRI was consistent with the Alzheimer’s disease continuum in 29 cases, 9 of them demonstrated normal glucose metabolism, whereas 20 remaining cases showed F18‐FDG‐PET/MRI patterns characteristic to Alzheimer’s disease (hypometabolism in parieto‐temporal cortices and/or posterior cingulate cortex). In bivariate analysis, MMSE was positively correlated with the glucose metabolism in temporal lobe and posterior cingulate gyrus (r= 0.382, p=0.020; r=0.330, p=0.046; respectively), additionally, a significant negative correlation was observed between MMSE and amyloid burden designated by the centiloid score (r=‐0.461, p=0.004). Memory performance tested by Enhanced Cued Recall (ECR) was also positively associated with the glucose metabolism in posterior cingulate gyrus (r= 0.446, p=0.006), and negatively associated with the centiloid value (r= ‐0.458, p=0.005). The relationship between the ECR test and the centiloid value persisted when adjusted for age, gender, and year of education (β=‐0.121, p=0.021) in multivariate models.ConclusionGeneral cognitive ability and memory were predicted by imaging markers of regional glucose metabolism and amyloid deposition in this study population with early‐stage Alzheimer’s disease. Further research should investigate the predictive value of those imaging markers for the rate of cognitive decline. Acknowledgment: We acknowledge the support of GE Health Care for the [F18]flutemetamol doses.

Cognitive behavioral therapy for irritable bowel syndrome induces bidirectional alterations in the brain-gut-microbiome axis associated with gastrointestinal symptom improvement

BackgroundThere is growing recognition that bidirectional signaling between the digestive tract and the brain contributes to irritable bowel syndrome (IBS). We recently showed in a large randomized controlled trial that cognitive behavioral therapy (CBT) reduces IBS symptom severity. This study investigated whether baseline brain and gut microbiome parameters predict CBT response and whether response is associated with changes in the brain-gut-microbiome (BGM) axis.MethodsEighty-four Rome III-diagnosed IBS patients receiving CBT were drawn from the Irritable Bowel Syndrome Outcome Study (IBSOS; ClinicalTrials.gov NCT00738920) for multimodal brain imaging and psychological assessments at baseline and after study completion. Fecal samples were collected at baseline and post-treatment from 34 CBT recipients for 16S rRNA gene sequencing, untargeted metabolomics, and measurement of short-chain fatty acids. Clinical measures, brain functional connectivity and microstructure, and microbiome features associated with CBT response were identified by multivariate linear and negative binomial models.ResultsAt baseline, CBT responders had increased fecal serotonin levels, and increased Clostridiales and decreased Bacteroides compared to non-responders. A random forests classifier containing 11 microbial genera predicted CBT response with high accuracy (AUROC 0.96). Following treatment, CBT responders demonstrated reduced functional connectivity in regions of the sensorimotor, brainstem, salience, and default mode networks and changes in white matter in the basal ganglia and other structures. Brain changes correlated with microbiome shifts including Bacteroides expansion in responders.ConclusionsPre-treatment intestinal microbiota and serotonin levels were associated with CBT response, suggesting that peripheral signals from the microbiota can modulate central processes affected by CBT that generate abdominal symptoms in IBS. CBT response is characterized by co-correlated shifts in brain networks and gut microbiome that may reflect top-down effects of the brain on the microbiome during CBT.8UBWhk6mapSqW3WwW-uhWTVideo abstract

Multimodal brain imaging effect of \u201cAdjust Zang-fu and Arouse Spirit\u201d electroacupuncture on diabetic cognitive impairment: study protocol of a randomized, sham-controlled pilot trial

BackgroundDiabetic cognitive impairment (DCI) is a serious chronic complication caused by diabetes. The pathogenesis of DCI is complex, but brain nerve injury and brain nerve cell apoptosis are important pathological changes. Multimodal brain imaging is one of the most important techniques to study the neural mechanism of the brain. For the clinical treatment of DCI, there is no effective targeted Western medicine and a lack of clear drug intervention methods. Therefore, there is an urgent need to find effective complementary and alternative methods and clarify their mechanism. This research seeks to explore the multimodal brain imaging effect of “Adjust Zang-fu and Arouse Spirit” electroacupuncture for DCI.MethodsThis clinical research will be a randomized, sham-controlled pilot trial. Eligible participants will be randomly assigned to the intervention group (n = 60) and the control group (n = 30). The intervention group will be divided into the “Adjust Zang-fu and Arouse Spirit” electroacupuncture group (n = 30) and sham electroacupuncture group (n = 30). All participants will continue to receive routine hypoglycemic therapy. The treatment period is the same in both groups. The primary outcomes include functional magnetic resonance imaging (fMRI), magnetic resonance spectroscopy (MRS), Montreal Cognitive Assessment Scale (MoCA), and Clinical Dementia Rating (CDR). The secondary outcomes include blood glucose and blood lipid tests, Instrumental Activities of Daily Living Scale (IADL), Hachinski Ischemic Scale (HIS), Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS). Outcomes will be assessed at baseline and before and after treatment, and adverse events will be examined. Inter- and intragroup analyses will be performed.DiscussionThis randomized controlled study, combined with multimodal brain imaging techniques and a clinical evaluation scale, was designed to explore the mechanism of “Adjust Zang-fu and Arouse Spirit” electroacupuncture in improving the central nervous system in DCI.Trial registrationChinese Clinical Trial Registration ChiCTR2000040268. Registered on 26 November 2020

Serum zinc levels and in vivo beta-amyloid deposition in the human brain

BackgroundDespite the known associations between zinc levels and Alzheimer’s disease (AD) dementia and related cognitive impairment, the underlying neuropathological links remain poorly understood. We tested the hypothesis that serum zinc level is associated with cerebral beta-amyloid protein (Aβ) deposition. Additionally, we explored associations between serum zinc levels and other AD pathologies [i.e., tau deposition and AD-signature cerebral glucose metabolism (AD-CM)] and white matter hyperintensities (WMHs), which are measures of cerebrovascular injury.MethodsA total of 241 cognitively normal older adults between 55 and 90 years of age were enrolled. All the participants underwent comprehensive clinical assessments, serum zinc level measurement, and multimodal brain imaging, including Pittsburgh compound B-positron emission tomography (PET), AV-1451 PET, fluorodeoxyglucose (FDG)-PET, and magnetic resonance imaging. Zinc levels were stratified into three categories: < 80 μg/dL (low), 80 to 90 μg/dL (medium), and > 90 μg/dL (high).ResultsA low serum zinc level was significantly associated with increased Aβ retention. In addition, apolipoprotein E ε4 allele (APOE4) status moderated the association: the relationship between low zinc level and Aβ retention was significant only in APOE4 carriers. Although a low zinc level appeared to reduce AD-CM, the relationship became insignificant on sensitivity analysis including only individuals with no nutritional deficiency. The serum zinc level was associated with neither tau deposition nor the WMH volume.ConclusionsOur findings suggest that decreased serum zinc levels are associated with elevation of brain amyloid deposition. In terms of AD prevention, more attention needs to be paid to the role of zinc.

A multi-modal MRI analysis of brain structure and function in relation to OXT methylation in maltreated children and adolescents

Child maltreatment dysregulates the brain’s oxytocinergic system, resulting in dysfunctional attachment patterns. However, how the oxytocinergic system in children who are maltreated (CM) is epigenetically affected remains unknown. We assessed differences in salivary DNA methylation of the gene encoding oxytocin (OXT) between CM (n = 24) and non-CM (n = 31), alongside its impact on brain structures and functions using multi-modal brain imaging (voxel-based morphometry, diffusion tensor imaging, and task and resting-state functional magnetic resonance imaging). We found that CM showed higher promoter methylation than non-CM, and nine CpG sites were observed to be correlated with each other and grouped into one index (OXTmi). OXTmi was significantly negatively correlated with gray matter volume (GMV) in the left superior parietal lobule (SPL), and with right putamen activation during a rewarding task, but not with white matter structures. Using a random forest regression model, we investigated the sensitive period and type of maltreatment that contributed the most to OXTmi in CM, revealing that they were 5–8 years of age and physical abuse (PA), respectively. However, the presence of PA (PA+) was meant to reflect more severe cases, such as prolonged exposure to multiple types of abuse, than the absence of PA. PA+ was associated with significantly greater functional connectivity between the right putamen set as the seed and the left SPL and the left cerebellum exterior. The results suggest that OXT promoter hypermethylation may lead to the atypical development of reward and visual association structures and functions, thereby potentially worsening clinical aspects raised by traumatic experiences.

Genome-wide analysis identifies impaired axonogenesis in chronic overlapping pain conditions.

Chronic pain is often present at more than one anatomical location, leading to chronic overlapping pain conditions. Whether chronic overlapping pain conditions represent a distinct pathophysiology from the occurrence of pain at only one site is unknown. Using genome-wide approaches, we compared genetic determinants of chronic single-site versus multisite pain in the UK Biobank. We found that different genetic signals underlie chronic single-site and multisite pain with much stronger genetic contributions for the latter. Among 23 loci associated with multisite pain, nine loci replicated in the HUNT cohort, with the DCC netrin 1 receptor (DCC) as the top gene. Functional genomics identified axonogenesis in brain tissues as the major contributing pathway to chronic multisite pain. Finally, multimodal structural brain imaging analysis showed that DCC is most strongly expressed in subcortical limbic regions and is associated with alterations in the uncinate fasciculus microstructure, suggesting that DCC-dependent axonogenesis may contribute to chronic overlapping pain conditions via corticolimbic circuits.

Integrated structural and functional atlases of Asian children from infancy to childhood

The developing brain grows exponentially in the first few years of life. There is a need to have age-appropriate brain atlases that coherently characterize the geometry of the cerebral cortex, white matter tracts, and functional organization. This study employed multi-modal brain images of an Asian cohort and constructed brain structural and functional atlases for 6-month-old infants, 4.5-, 6-, and 7.5-year-old children. We exploited large deformation diffeomorphic metric mapping and probabilistic atlas generation approaches to integrate structural MRI and diffusion weighted images (DWIs) and to create the atlas where white matter tracts well fit into the cortical folding pattern. Based on this structural atlas, we then employed spectral clustering to parcellate the brain into functional networks from resting-state fMRI (rs-fMRI). Our results provided the atlas that characterizes the cortical folding geometry, subcortical regions, deep white matter tracts, as well as functional networks in a stereotaxic coordinate space for the four different age groups. The functional networks consisting of the primary cortex were well established in infancy and remained stable to childhood, while specific higher-order functional networks showed specific patterns of hemispherical, subcortical-cerebellar, and cortical-cortical integration and segregation from infancy to childhood. Our multi-modal fusion analysis demonstrated the use of the integrated structural and functional atlas for understanding coherent patterns of brain anatomical and functional development during childhood. Hence, our atlases can be potentially used to study coherent patterns of brain anatomical and functional development.

Multimodal Brain Age Prediction with Feature Selection and Comparison.

Brain age, an estimated biological age from anatomical and/or functional brain imaging data, and its deviation from the chronological age (brain age gap) have shown the potential to serve as biomarkers for characterizing typical brain development, the abnormal aging process, and early indicators of clinical neuropsychiatric problems. In this study, we leverage multimodal brain imaging data for brain age prediction. We studied and compared the performance of individual data modalities (gray matter density in components and regions of interest, cortical and subcortical anatomical features, resting-state functional connectivity) and different combinations of multiple data modalities using data collected from 1417 participants with age between 8 and 22 years. The result indicates that feature selection and multimodal imaging data can improve brain age prediction with linear support vector and partial least squares regression models. We have achieved a mean absolute error of 1.22 years on the test data with 188 features selected equally from all data sources, better than any individual source. After bias correction, the brain age gap was significantly associated with attention accuracy/speed and motor speed in addition to age. Our results conclude that traditional machine learning with proper feature selection can achieve similar if not better performance compared to complex deep learning neural network methods for the used sample size.

Cognitive reserve proxies, Alzheimer pathologies, and cognition

This study aimed to explore the moderating effects of the frequently used cognitive reserve (CR) proxies [i.e., education, premorbid intelligence quotient (pIQ), occupational complexity (OC), and lifetime cognitive activity (LCA)] on the relationships between various in vivo Alzheimer's disease (AD) pathologies and cognition. In total, 351 [268 cognitively unimpaired (CU), 83 cognitive impaired (CI)] older adults underwent multi-modal brain imaging to measure AD pathologies and cognitive assessments, and information on CR proxies was obtained. For overall participants, only education moderated the relationship between Aβ deposition and cognition. Education, pIQ, and LCA, but not OC, showed moderating effect on the relationship between AD-signature cerebral hypometabolism and cognition. In contrast, only OC had a moderating effect on the relationship between cortical atrophy of the AD-signature regions and cognition. Such moderation effects of the CR proxies were similarly observed in CI individuals, but most of them were not in CU individuals. The findings suggest that the proposed CR proxies have different moderating effects on the relationships between specific AD pathologies and cognition.