MRI hippocampal and entorhinal cortex mapping in predicting conversion to Alzheimer's disease

Unraveling the complexity of human brain: Structure, function in healthy and disease states

The human brain stands as an intricate organ, embodying a nexus of structure, function, development, and diversity. This review delves into the multifaceted landscape of the brain, spanning its anatomical intricacies, diverse functional capacities, dynamic developmental trajectories, and inherent variability across individuals. The dynamic process of brain development, from early embryonic stages to adulthood, highlights the nuanced changes that occur throughout the lifespan. The brain, a remarkably complex organ, is composed of various anatomical regions, each contributing uniquely to its overall functionality. Through an exploration of neuroanatomy, neurophysiology, and electrophysiology, this review elucidates how different brain structures interact to support a wide array of cognitive processes, sensory perception, motor control, and emotional regulation. Moreover, it addresses the impact of age, sex, and ethnic background on brain structure and function, and gender differences profoundly influence the onset, progression, and manifestation of brain disorders shaped by genetic, hormonal, environmental, and social factors. Delving into the complexities of the human brain, it investigates how variations in anatomical configuration correspond to diverse functional capacities across individuals. Furthermore, it examines the impact of neurodegenerative diseases on the structural and functional integrity of the brain. Specifically, our article explores the pathological processes underlying neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Huntington's diseases, shedding light on the structural alterations and functional impairments that accompany these conditions. We will also explore the current research trends in neurodegenerative diseases and identify the existing gaps in the literature. Overall, this article deepens our understanding of the fundamental principles governing brain structure and function and paves the way for a deeper understanding of individual differences and tailored approaches in neuroscience and clinical practice-additionally, a comprehensive understanding of structural and functional changes that manifest in neurodegenerative diseases.

Molecular mechanisms linking type 2 diabetes mellitus and late-onset Alzheimer's disease: A systematic review and qualitative meta-analysis

Research evidence indicating common metabolic mechanisms through which type 2 diabetes mellitus (T2DM) increases risk of late-onset Alzheimer's dementia (LOAD) has accumulated over recent decades. The aim of this systematic review is to provide a comprehensive review of common mechanisms, which have hitherto been discussed in separate perspectives, and to assemble and evaluate candidate loci and epigenetic modifications contributing to polygenic risk linkages between T2DM and LOAD. For the systematic review on pathophysiological mechanisms, both human and animal studies up to December 2023 are included. For the qualitative meta-analysis of genomic bases, human association studies were examined; for epigenetic mechanisms, data from human studies and animal models were accepted. Papers describing pathophysiological studies were identified in databases, and further literature gathered from cited work. For genomic and epigenomic studies, literature mining was conducted by formalised search codes using Boolean operators in search engines, and augmented by GeneRif citations in Entrez Gene, and other sources (WikiGenes, etc.). For the systematic review of pathophysiological mechanisms, 923 publications were evaluated, and 138 gene loci extracted for testing candidate risk linkages. 3 57 publications were evaluated for genomic association and descriptions of epigenomic modifications. Overall accumulated results highlight insulin signalling, inflammation and inflammasome pathways, proteolysis, gluconeogenesis and glycolysis, glycosylation, lipoprotein metabolism and oxidation, cell cycle regulation or survival, autophagic-lysosomal pathways, and energy. Documented findings suggest interplay between brain insulin resistance, neuroinflammation, insult compensatory mechanisms, and peripheral metabolic dysregulation in T2DM and LOAD linkage. The results allow for more streamlined longitudinal studies of T2DM-LOAD risk linkages.

Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods

Over the past decades, morphometric analysis of brain MRI has contributed substantially to the understanding of healthy brain structure, development and aging as well as to improved characterisation of disease related pathologies. Certified commercial tools based on normative modeling of these metrics are meanwhile available for diagnostic purposes, but they are cost intensive and their clinical evaluation is still in its infancy. Here we have compared the performance of "ScanOMetrics", an open-source research-level tool for detection of statistical anomalies in individual MRI scans, depending on whether it is operated on the output of FreeSurfer or of the deep learning based brain morphometry tool DL + DiReCT. When applied to the public OASIS3 dataset, containing patients with Alzheimer's disease (AD) and healthy controls (HC), cortical thickness anomalies in patient scans were mainly detected in regions that are known as predilection areas of cortical atrophy in AD, regardless of the software used for extraction of the metrics. By contrast, anomaly detections in HCs were up to twenty-fold reduced and spatially unspecific using both DL + DiReCT and FreeSurfer. Progression of the atrophy pattern with clinical dementia rating (CDR) was clearly observable with both methods. DL + DiReCT provided results in less than 25 min, more than 15 times faster than FreeSurfer. This difference in computation time might be relevant when considering application of this or similar methodology as diagnostic decision support for neuroradiologists.

Inflammation subsequent to mild iron excess differentially alters regional brain iron metabolism, oxidation and neuroinflammation status in mice

Iron dyshomeostasis and neuroinflammation, characteristic features of the aged brain, and exacerbated in neurodegenerative disease, may induce oxidative stress-mediated neurodegeneration. In this study, the effects of potential priming with mild systemic iron injections on subsequent lipopolysaccharide (LPS)-induced inflammation in adult C57Bl/6J mice were examined. After cognitive testing, regional brain tissues were dissected for iron (metal) measurements by total reflection X-ray fluorescence and synchrotron radiation X-Ray fluorescence-based elemental mapping; and iron regulatory, ferroptosis-related, and glia-specific protein analysis, and lipid peroxidation by western blotting. Microglial morphology and astrogliosis were assessed by immunohistochemistry. Iron only treatment enhanced cognitive performance on the novel object location task compared with iron priming and subsequent LPS-induced inflammation. LPS-induced inflammation, with or without iron treatment, attenuated hippocampal heme oxygenase-1 and augmented 4-hydroxynonenal levels. Conversely, in the cortex, elevated ferritin light chain and xCT (light chain of System Xc-) were observed in response to LPS-induced inflammation, without and with iron-priming. Increased microglial branch/process lengths and astrocyte immunoreactivity were also increased by combined iron and LPS in both the hippocampus and cortex. Here, we demonstrate iron priming and subsequent LPS-induced inflammation led to iron dyshomeostasis, compromised antioxidant function, increased lipid peroxidation and altered neuroinflammatory state in a brain region-dependent manner.

Atrophy of hippocampal subfields and amygdala nuclei in subjects with mild cognitive impairment progressing to Alzheimer's disease

The hippocampus and amygdala are the first brain regions to show early signs of Alzheimer's Disease (AD) pathology. AD is preceded by a prodromal stage known as Mild Cognitive Impairment (MCI), a crucial crossroad in the clinical progression of the disease. The topographical development of AD has been the subject of extended investigation. However, it is still largely unknown how the transition from MCI to AD affects specific hippocampal and amygdala subregions. The present study is set to answer that question. We analyzed data from 223 subjects: 75 healthy controls, 52 individuals with MCI, and 96 AD patients obtained from the ADNI. The MCI group was further divided into two subgroups depending on whether individuals in the 48 months following the diagnosis either remained stable (N = 21) or progressed to AD (N = 31). A MANCOVA test evaluated group differences in the volume of distinct amygdala and hippocampal subregions obtained from magnetic resonance images. Subsequently, a stepwise linear discriminant analysis (LDA) determined which combination of magnetic resonance imaging parameters was most effective in predicting the conversion from MCI to AD. The predictive performance was assessed through a Receiver Operating Characteristic analysis. AD patients displayed widespread subregional atrophy. MCI individuals who progressed to AD showed selective atrophy of the hippocampal subiculum and tail compared to stable MCI individuals, who were undistinguishable from healthy controls. Converter MCI showed atrophy of the amygdala's accessory basal, central, and cortical nuclei. The LDA identified the hippocampal subiculum and the amygdala's lateral and accessory basal nuclei as significant predictors of MCI conversion to AD. The analysis returned a sensitivity value of 0.78 and a specificity value of 0.62. These findings highlight the importance of targeted assessments of distinct amygdala and hippocampus subregions to help dissect the clinical and pathophysiological development of the MCI to AD transition.

A comparison of story-recall metrics to predict hippocampal volume in older adults with and without cognitive impairment

Objective: Process-based scores of episodic memory tests, such as the recency ratio (Rr), have been found to compare favourably to, or to be better than, most conventional or "traditional" scores employed to estimate memory ability in older individuals (Bock et al., 2021; Bruno et al., 2019). We explored the relationship between process-based scores and hippocampal volume in older adults, while comparing process-based to traditional story recall-derived scores, to examine potential differences in their predictive abilities. Methods: We analysed data from 355 participants extracted from the WRAP and WADRC databases, who were classified as cognitively unimpaired, or exhibited mild cognitive impairment (MCI) or dementia. Story Recall was measured with the Logical Memory Test (LMT) from the Weschler Memory Scale Revised, collected within twelve months of the magnetic resonance imaging scan. Linear regression analyses were conducted with left or right hippocampal volume (HV) as outcomes separately, and with Rr, Total ratio, Immediate LMT, or Delayed LMT scores as predictors, along with covariates. Results: Higher Rr and Tr scores significantly predicted lower left and right HV, while Tr showed the best model fit of all, as indicated by AIC. Traditional scores, Immediate LMT and Delayed LMT, were significantly associated with left and right HV, but were outperformed by both process-based scores for left HV, and by Tr for right HV. Conclusions: Current findings show the direct relationship between hippocampal volume and all the LMT scores examined here, and that process-based scores outperform traditional scores as markers of hippocampal volume.

An imaging review of the hippocampus and its common pathologies

The hippocampus is a complex structure located in the mesial temporal lobe that plays a critical role in cognitive and memory-related processes. The hippocampal formation consists of the dentate gyrus, hippocampus proper, and subiculum, and its importance in the neural circuitry makes it a key anatomic structure to evaluate in neuroimaging studies. Advancements in imaging techniques now allow detailed assessment of hippocampus internal architecture and signal features that has improved identification and characterization of hippocampal abnormalities. This review aims to summarize the neuroimaging features of the hippocampus and its common pathologies. It provides an overview of the hippocampal anatomy on magnetic resonance imaging and discusses how various imaging techniques can be used to assess the hippocampus. The review explores neuroimaging findings related to hippocampal variants (incomplete hippocampal inversion, sulcal remnant and choroidal fissure cysts), and pathologies of neoplastic (astrocytoma and glioma, ganglioglioma, dysembryoplastic neuroepithelial tumor, multinodular and vacuolating neuronal tumor, and metastasis), epileptic (mesial temporal sclerosis and focal cortical dysplasia), neurodegenerative (Alzheimer's disease, progressive primary aphasia, and frontotemporal dementia), infectious (Herpes simplex virus and limbic encephalitis), vascular (ischemic stroke, arteriovenous malformation, and cerebral cavernous malformations), and toxic-metabolic (transient global amnesia and opioid-associated amnestic syndrome) etiologies.

Olfactory Dysfunction Associated with Cognitive Decline in an Elderly Population

BACKGROUND

In many neurological disorders, including Alzheimer disease, early olfactory dysfunction is observed.

OBJECTIVE

In order to determine if deficits in olfactory memory are present in the elderly and if olfactory dysfunction correlates with cognitive impairment in the aging population, olfactory testing has been done on seniors from the NuAge cohort accepting to participate in the Olfactory Response Cognition and Aging (ORCA) secondary sub-study. The t-Mini Mental Statement Examination and the Telephone Interview for Cognitive Status tests were done to assess cognition levels.

RESULTS

Overall, 94% of the ORCA cohort displayed olfactory dysfunction. Deficits in olfactory memory were also present. A correlation was observed between olfactory function and cognitive test scores. Moreover, in women who smoked, there was an association between olfactory memory and cognitive scores.

CONCLUSION

Our results suggest that olfactory dysfunction may predict impending cognitive decline and highlights the need for olfactory training in seniors to improve olfaction and overall well-being.

Topological changes of fast large-scale brain dynamics in mild cognitive impairment predict early memory impairment: a resting-state, source reconstructed, magnetoencephalography study

Functional connectivity has been used as a framework to investigate widespread brain interactions underlying cognitive deficits in mild cognitive impairment (MCI). However, many functional connectivity metrics focus on the average of the periodic activities, disregarding the aperiodic bursts of activity (i.e., the neuronal avalanches) characterizing the large-scale dynamic activities of the brain. Here, we apply the recently described avalanche transition matrix framework to source-reconstructed magnetoencephalography signals in a cohort of 32 MCI patients and 32 healthy controls to describe the spatio-temporal features of neuronal avalanches and explore their topological properties. Our results showed that MCI patients showed a more centralized network (as assessed by higher values of the degree divergence and leaf fraction) as compared to healthy controls. Furthermore, we found that the degree divergence (in the theta band) was predictive of hippocampal memory impairment. These findings highlight the role of the changes of aperiodic bursts in clinical conditions and may contribute to a more thorough phenotypical assessment of patients.

X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study

Introduction

Alzheimer's disease (AD) is a complex neurodegenerative disease with high heritability. Compared to autosomes, a higher proportion of disorder-associated genes on X chromosome are expressed in the brain. However, only a few studies focused on the identification of the susceptibility loci for AD on X chromosome.

Methods

Using the data from the Alzheimer's Disease Neuroimaging Initiative Study, we conducted an X chromosome-wide association study between 16 AD quantitative biomarkers and 19,692 single nucleotide polymorphisms (SNPs) based on both the cross-sectional and longitudinal studies.

Results

We identified 15 SNPs statistically significantly associated with different quantitative biomarkers of the AD. For the cross-sectional study, six SNPs (rs5927116, rs4596772, rs5929538, rs2213488, rs5920524, and rs5945306) are located in or near to six genes DMD, TBX22, LOC101928437, TENM1, SPANXN1, and ZFP92, which have been reported to be associated with schizophrenia or neuropsychiatric diseases in literature. For the longitudinal study, four SNPs (rs4829868, rs5931111, rs6540385, and rs763320) are included in or near to two genes RAC1P4 and AFF2, which have been demonstrated to be associated with brain development or intellectual disability in literature, while the functional annotations of other five novel SNPs (rs12157031, rs428303, rs5953487, rs10284107, and rs5955016) have not been found.

Discussion

15 SNPs were found statistically significantly associated with the quantitative biomarkers of the AD. Follow-up study in molecular genetics is needed to verify whether they are indeed related to AD. The findings in this article expand our understanding of the role of the X chromosome in exploring disease susceptibility, introduce new insights into the molecular genetics behind the AD, and may provide a mechanistic clue to further AD-related studies.

Procedural learning and retention relative to explicit learning and retention in mild cognitive impairment and Alzheimer's disease using a modification of the trail making test

Amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) dementia are characterized by pathological changes to the medial temporal lobes, resulting in explicit learning and retention reductions. Studies demonstrate that implicit/procedural memory processes are relatively intact in these populations, supporting different anatomical substrates for differing memory systems. This study examined differences between explicit and procedural learning and retention in individuals with aMCI and AD dementia relative to matched healthy controls. We also examined anatomical substrates using volumetric MRI. Results revealed expected difficulties with explicit learning and retention in individuals with aMCI and AD with relatively preserved procedural memory. Explicit verbal retention was associated with medial temporal cortex volumes. However, procedural retention was not related to medial temporal or basal ganglia volumes. Overall, this study confirms the dissociation between explicit relative to procedural learning and retention in aMCI and AD dementia and supports differing anatomical substrates.

Specific olfactory deficit patterns observed in seniors and associated with cognitive decline

Olfactory dysfunction is a common symptom in neurodegenerative disorders and is regarded as a potential early predictor of impending cognitive decline. This study was undertaken in order to determine if olfactory dysfunction observed in the elderly is due to a general loss of smell or the inability to detect specific odours, and if misidentification of odours correlates with cognitive scores. Seniors for the Olfactory Response and Cognition in Aging (ORCA) sub-study were recruited from the Quebec Nutrition and Successful Aging (NuAge) cohort. The University of Pennsylvania smell identification test (UPSIT) was performed to measure olfactory function and the telephone Mini Mental State Examination (t-MMSE) and the French version of the Telephone Interview for Cognitive Status Modified (F-TICS-m) for cognitive status. The results demonstrate that seniors exhibit specific olfactory loss and had severe difficulty in particular in identifying lemon, pizza, fruit punch, cheddar cheese and lime. Furthermore, there was a significant difference in the ability to detect certain odours between the sexes. Results also showed that misidentification of certain scents was associated with cognitive scores, and when the sexes were assessed separately sex-specific misidentification of cognitive-associated odours was observed. The relationship between the cognitive scores and scent misidentification suggests that impending cognitive decline may be highlighted by the inability to smell specific odours. Our study provides additional support for the testing of olfactory function in the elderly and suggests that loss of smell for particular scents may become a useful diagnostic tool.

Comorbidity-driven multi-modal subtype analysis in mild cognitive impairment of Alzheimer's disease

BACKGROUND

Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi-modality data-driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well-known risk factors for Alzheimer's disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD-related comorbidities along with other AD-relevant features and biomarkers.

METHODS

A total of 325 MCI subjects with 32 AD-relevant comorbidities and features were considered. Mixed-data clustering is applied to discover and compare MCI subtypes with and without including AD-related comorbidities. Finally, the relevance of each comorbidity-driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates.

RESULTS

We identified four (five) MCI subtypes: poor-, average-, good-, and best-AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity-driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity-driven MCI subtypes. Among the four comorbidity-driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test-related and plasma features, and by the proportion of comorbidities.

CONCLUSIONS

Our study indicates that AD comorbidities should be considered along with other diverse AD-relevant characteristics to better understand MCI heterogeneity.

Effect of APOE4 Allele and Gender on the Rate of Atrophy in the Hippocampus, Entorhinal Cortex, and Fusiform Gyrus in Alzheimer's Disease

BACKGROUND

The hippocampus, entorhinal cortex, and fusiform gyrus are brain areas that deteriorate during early-stage Alzheimer's disease (AD). The ApoE4 allele has been identified as a risk factor for AD development, is linked to an increase in the aggregation of amyloid ß (Aß) plaques in the brain, and is responsible for atrophy of the hippocampal area. However, to our knowledge, the rate of deterioration over time in individuals with AD, with or without the ApoE4 allele, has not been investigated.

METHOD

In this study, we, for the first time, analyze atrophy in these brain structures in AD patients with and without the ApoE4 using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

RESULTS

It was found that the rate of decrease in the volume of these brain areas over 12 months was related to the presence of ApoE4. Further, we found that neural atrophy was not different for female and male patients, unlike prior studies, suggesting that the presence of ApoE4 is not linked to the gender difference in AD.

CONCLUSION

Our results confirm and extend previous findings, showing that the ApoE4 allele gradually impacts brain regions impacted by AD.

Associations between cortical β-amyloid burden, fornix microstructure and cognitive processing of faces, places, bodies and other visual objects in early Alzheimer's disease

Using two imaging modalities, that is, Pittsburgh compound B (PiB) positron emission tomography (PET) and diffusion tensor imaging (DTI) the present study tested associations between cortical amyloid-beta (Aβ) burden and fornix microstructural changes with cognitive deficits in early Alzheimer's disease (AD), namely deficits in working memory (1-back) processing of visual object categories (faces, places, objects, bodies and verbal material). Second, we examined cortical Aβ associations with fornix microstructure. Seventeen early AD patients and 17 healthy-matched controls were included. Constrained spherical deconvolution-based tractography was used to segment the fornix and a control tract the central branch of the superior longitudinal fasciculus (CB-SLF) previously implicated in working memory processes. Standard uptake value ratios (SUVR) of Aβ were extracted from 45 cortical/subcortical regions from the AAL atlas and subject to principal component analysis for data reduction. Patients exhibited (i) impairments in cognitive performance (ii) reductions in fornix fractional anisotropy (FA) and (iii) increases in a component that loaded highly on cortical Aβ. There were no group differences in CB-SLF FA and in a component loading highly on subcortical Aβ. Partial correlation analysis in the patient group showed (i) positive associations between fornix FA and performance for all the visual object categories and (ii) a negative association between the cortical Aβ component and performance for the object categories but not for the remaining classes of visual stimuli. A subsequent analysis showed a positive association between overall cognition (performance across distinct 1-back task conditions) with fornix FA but no association with cortical Aβ burden, in keeping with influential accounts on early onset AD. This indicates that the fornix degenerates early in AD and contributes to deficits in working memory processing of visual object categories; though it is also important to acknowledge the importance of prospective longitudinal studies with larger samples. Overall, the effect sizes of fornical degeneration on visual working memory appeared stronger than the ones related to amyloid burden.

Sex-Specific Heterogeneity of Mild Cognitive Impairment Identified Based on Multi-Modal Data Analysis

BACKGROUND

Mild cognitive impairment (MCI), a prodromal phase of Alzheimer's disease (AD), is heterogeneous with different rates and risks of progression to AD. There are significant gender disparities in the susceptibility, prognosis, and outcomes in patients with MCI, with female being disproportionately negatively impacted.

OBJECTIVE

The aim of this study was to identify sex-specific heterogeneity of MCI using multi-modality data and examine the differences in the respective MCI subtypes with different prognostic outcomes or different risks for MCI to AD conversion.

METHODS

A total of 325 MCI subjects (146 women, 179 men) and 30 relevant features were considered. Mixed-data clustering was applied to women and men separately to discover gender-specific MCI subtypes. Gender differences were compared in the respective subtypes of MCI by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates.

RESULTS

We identified three MCI subtypes: poor-, good-, and best-prognosis for women and for men, separately. The subtype-wise comparison (for example, poor-prognosis subtype in women versus poor-prognosis subtype in men) showed significantly different means for brain volumetric, cognitive test-related, also for the proportion of comorbidities. Also, there were substantial gender differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD.

CONCLUSION

Analyzing sex-specific heterogeneity of MCI offers the opportunity to advance the understanding of the pathophysiology of both MCI and AD, allows stratification of risk in clinical trials of interventions, and suggests gender-based early intervention with targeted treatment for patients at risk of developing AD.

Medial Temporal Lobe Atrophy in Predementia Alzheimer's Disease: A Longitudinal Multi-Site Study Comparing Staging and A/T/N in a Clinical Research Cohort

BACKGROUND

Atrophy of the medial temporal lobe (MTL) is a biological characteristic of Alzheimer's disease (AD) and can be measured by segmentation of magnetic resonance images (MRI).

OBJECTIVE

To assess the clinical utility of automated volumetry in a cognitively well-defined and biomarker-classified multi-center longitudinal predementia cohort.

METHODS

We used Automatic Segmentation of Hippocampal Subfields (ASHS) to determine MTL morphometry from MRI. We harmonized scanner effects using the recently developed longitudinal ComBat. Subjects were classified according to the A/T/N system, and as normal controls (NC), subjective cognitive decline (SCD), or mild cognitive impairment (MCI). Positive or negative values of A, T, and N were determined by cerebrospinal fluid measurements of the Aβ42/40 ratio, phosphorylated and total tau. From 406 included subjects, longitudinal data was available for 206 subjects by stage, and 212 subjects by A/T/N.

RESULTS

Compared to A-/T-/N- at baseline, the entorhinal cortex, anterior and posterior hippocampus were smaller in A+/T+orN+. Compared to NC A- at baseline, these subregions were also smaller in MCI A+. Longitudinally, SCD A+ and MCI A+, and A+/T-/N- and A+/T+orN+, had significantly greater atrophy compared to controls in both anterior and posterior hippocampus. In the entorhinal and parahippocampal cortices, longitudinal atrophy was observed only in MCI A+ compared to NC A-, and in A+/T-/N- and A+/T+orN+ compared to A-/T-/N-.

CONCLUSION

We found MTL neurodegeneration largely consistent with existing models, suggesting that harmonized MRI volumetry may be used under conditions that are common in clinical multi-center cohorts.

Advanced Diagnostic Technique for Alzheimer's Disease using MRI Top-Ranked Volume and Surface-based Features

Background

Alzheimer's disease (AD) is the most dominant type of dementia that has not been treated completely yet. Few Alzheimer's patients are correctly diagnosed on time. Therefore, diagnostic tools are needed for better and more efficient diagnoses.

Objective

This study aimed to develop an efficient automated method to differentiate Alzheimer's patients from normal elderly and present the essential features with accurate Alzheimer's diagnosis.

Material and Methods

In this analytical study, 154 Magnetic Resonance Imaging (MRI) scans were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, preprocessed, and normalized by the head size for extracting features (volume, cortical thickness, Sulci depth, and Gyrification Index Features (GIF). Relief-F algorithm, t-test, and one way-ANOVA were used for feature ranking to obtain the most effective features representing the AD for the classification process. Finally, in the classification step, four classifiers were used with 10 folds cross-validation as follows: Gaussian Support Vector Machine (GSVM), Linear Support Vector Machine (LSVM), Weighted K-Nearest Neighbors (W-KNN), and Decision Tree algorithm.

Results

The LSVM classifier and W-KNN produce a testing accuracy of 100% with only seven features. Additionally, GSVM and decision tree produce a testing accuracy of 97.83% and 93.48%, respectively.

Conclusion

The proposed system represents an automatic and highly accurate AD detection with a few reliable and effective features and minimum time.

Can hippocampal subfield measures supply information that could be used to improve the diagnosis of Alzheimer's disease?

The diagnosis of Alzheimer's disease (AD) needs to be improved. We investigated if hippocampal subfield volume measured by structural imaging, could supply information, so that the diagnosis of AD could be improved. In this study, subjects were classified based on clinical, neuropsychological, and amyloid positivity or negativity using PET scans. Data from 478 elderly Korean subjects grouped as cognitively unimpaired β-amyloid-negative (NC), cognitively unimpaired β-amyloid-positive (aAD), mild cognitively impaired β-amyloid-positive (pAD), mild cognitively impaired-specific variations not due to dementia β-amyloid-negative (CIND), severe cognitive impairment β-amyloid-positive (ADD+) and severe cognitive impairment β-amyloid-negative (ADD-) were used. NC and aAD groups did not show significant volume differences in any subfields. The CIND did not show significant volume differences when compared with either the NC or the aAD (except L-HATA). However, pAD showed significant volume differences in Sub, PrS, ML, Tail, GCMLDG, CA1, CA4, HATA, and CA3 when compared with the NC and aAD. The pAD group also showed significant differences in the hippocampal tail, CA1, CA4, molecular layer, granule cells/molecular layer/dentate gyrus, and CA3 when compared with the CIND group. The ADD- group had significantly larger volumes than the ADD+ group in the bilateral tail, SUB, PrS, and left ML. The results suggest that early amyloid depositions in cognitive normal stages are not accompanied by significant bilateral subfield volume atrophy. There might be intense and accelerated subfield volume atrophy in the later stages associated with the cognitive impairment in the pAD stage, which subsequently could drive the progression to AD dementia. Early subfield volume atrophy associated with the β-amyloid burden may be characterized by more symmetrical atrophy in CA regions than in other subfields. We conclude that the hippocampal subfield volumetric differences from structural imaging show promise for improving the diagnosis of Alzheimer's disease.

Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes

Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.

Prediction and Modeling of Neuropsychological Scores in Alzheimer's Disease Using Multimodal Neuroimaging Data and Artificial Neural Networks

Background: In recent years, predicting and modeling the progression of Alzheimer's disease (AD) based on neuropsychological tests has become increasingly appealing in AD research. Objective: In this study, we aimed to predict the neuropsychological scores and investigate the non-linear progression trend of the cognitive declines based on multimodal neuroimaging data. Methods: We utilized unimodal/bimodal neuroimaging measures and a non-linear regression method (based on artificial neural networks) to predict the neuropsychological scores in a large number of subjects (n = 1143), including healthy controls (HC) and patients with mild cognitive impairment non-converter (MCI-NC), mild cognitive impairment converter (MCI-C), and AD. We predicted two neuropsychological scores, i.e., the clinical dementia rating sum of boxes (CDRSB) and Alzheimer's disease assessment scale cognitive 13 (ADAS13), based on structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) biomarkers. Results: Our results revealed that volumes of the entorhinal cortex and hippocampus and the average fluorodeoxyglucose (FDG)-PET of the angular gyrus, temporal gyrus, and posterior cingulate outperform other neuroimaging features in predicting ADAS13 and CDRSB scores. Compared to a unimodal approach, our results showed that a bimodal approach of integrating the top two neuroimaging features (i.e., the entorhinal volume and the average FDG of the angular gyrus, temporal gyrus, and posterior cingulate) increased the prediction performance of ADAS13 and CDRSB scores in the converting and stable stages of MCI and AD. Finally, a non-linear AD progression trend was modeled to describe the cognitive decline based on neuroimaging biomarkers in different stages of AD. Conclusion: Findings in this study show an association between neuropsychological scores and sMRI and FDG-PET biomarkers from normal aging to severe AD.

Integrated Positron Emission Tomography/Magnetic Resonance Imaging in clinical diagnosis of Alzheimer's disease

Alzheimer's disease (AD), a progressive neurodegenerative disease which seriously endangers the health of the aged, is the most common etiology of senile dementia. With the increasing progress of neuroimaging technology, more and more imaging methods have been applied to study Alzheimer's disease. The emergence of integrated PET/MRI (Positron Emission Tomography/Magnetic Resonance Imaging) is a major advance in multimodal molecular imaging with many advantages on the structure of resolution and contrast of image over computed tomography (CT), PET and MRI. PET/MRI is now used stepwise in neurodegenerative diseases, and also has broad prospect of application in the early diagnosis of AD. In this review, we emphatically introduce the imaging advances of AD including functional imaging and molecular imaging, the advantages of PET/MRI over other imaging methods and prospects of PET/MRI in AD clinical diagnosis, especially in early diagnosis, clinical assessment and prediction on AD.

Functional Integrity of Executive Control Network Contributed to Retained Executive Abilities in Mild Cognitive Impairment

Background: Mild cognitive impairment (MCI) is considered to be a transitional state between normal aging and Alzheimer's dementia (AD). Recent studies have indicated that executive function (EF) declines during MCI. However, only a limited number of studies have investigated the neural basis of EF deficits in MCI. Herein, we investigate the changes of regional brain spontaneous activity and functional connectivity (FC) of the executive control network (ECN) between high EF and low EF groups. Methods: According to EF composite score (ADNI-EF) from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we divided MCI into two groups, including the MCI-highEF group and MCI-lowEF group. Resting-state functional MRI was utilized to investigate the fractional amplitude of low-frequency fluctuation (fALFF) and ECN functional connectivity across 23 healthy controls (HC), 11 MCI-highEF, and 14 MCI-lowEF participants. Moreover, a partial correlation analysis was carried out to examine the relationship between altered fALFF or connectivity of the ECN and the ADNI-EF. Results: Compared to HC, the MCI-highEF participants demonstrated increased fALFF in the left superior temporal gyrus (STG), as well as decreased fALFF in the right precentral gyrus, right postcentral gyrus, and left middle frontal gyrus (MFG). The MCI-lowEF participants demonstrated increased fALFF in the cerebellar vermis and decreased fALFF in the left MFG. Additionally, compared to HC, the MCI-highEF participants indicated no significant difference in connectivity of the ECN. Furthermore, the MCI-lowEF participants showed increased ECN FC in the left cuneus and left MFG, as well as decreased ECN functional connectivity in the right parahippocampal gyrus (PHG). Notably, the altered fALFF in the left MFG was positively correlated to ADNI-EF, while the altered fALFF in cerebellar vermis is negatively correlated with ADNI-EF across the two MCI groups and the HC group. Altered ECN functional connectivity in the right PHG is negatively correlated to ADNI-EF, while altered ECN functional connectivity in the left cuneus is negatively correlated to ADNI-EF across the three groups. Conclusions: Our current study demonstrates the presence of different patterns of regional brain spontaneous activity and ECN FC in the MCI-highEF group and MCI-lowEF group. Furthermore, the ECN FC of the MCI-highEF group was not disrupted, which may contribute to retained EF in MCI.

A potential biomarker of preclinical Alzheimer's disease: The olfactory dysfunction and its pathogenesis-based neural circuitry impairments

The olfactory dysfunction can signal and act as a potential biomarker of preclinical AD. However, the precise regulatory mechanism of olfactory function on the neural pathogenesis of AD is still unclear. The impairment of neural networks in olfaction system has been shown to be tightly associated with AD. As key brain regions of the olfactory system, the olfactory bulb (OB) and the piriform cortex (PCx) have a profound influence on the olfactory function. Therefore, this review will explore the mechanism of olfactory dysfunction in preclinical AD in the perspective of abnormal neural networks in the OB and PCx and their associated brain regions, especially from two aspects of aberrant oscillations and synaptic plasticity damages, which help better understand the underlying mechanism of olfactory neural network damages related to AD.

Diet enriched in omega-3 fatty acids alleviates olfactory system deficits in APOE4 transgenic mice

Olfactory dysfunction is observed in several neurological disorders including Mild Cognitive Impairment (MCI) and Alzheimer disease (AD). These deficits occur early and correlate with global cognitive performance, depression and degeneration of olfactory regions in the brain. Despite extensive human studies, there has been little characterization of the olfactory system in models of AD. In order to determine if olfactory structural and/or molecular phenotypes are observed in a model expressing a genetic risk factor for AD, we assessed the olfactory bulb (OB) in APOE4 transgenic mice. A significant decrease in OB weight was observed at 12 months of age in APOE4 mice concurrent with inflammation and decreased NeuN expression. In order to determine if a diet rich in omega-3s may alleviate the olfactory system phenotypes observed, we assessed WT and APOE4 mice on a docosahexaenoic acid (DHA) diet. APOE4 mice on a DHA diet did not present with atrophy of the OB, and the alterations in NeuN and IBA-1 expression were alleviated. Furthermore, alterations in caspase mRNA and protein expression in the APOE4 OB were not observed with a DHA diet. Similar to the human AD condition, OB atrophy is an early phenotype in the APOE4 mice and concurrent with inflammation. These data support a link between the structural olfactory brain region atrophy and the olfactory dysfunction observed in AD and suggest that inflammation and cell death pathways may contribute to the olfactory deficits observed. Furthermore, the results suggest that diets enriched in DHA may provide benefit to APOE4 allele carriers.

Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling

BACKGROUND

Alzheimer's Disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer's disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans.

OBJECTIVE

This study aimed to determine whether the unsupervised discovering of latent classes of subjects with Mild Cognitive Impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk.

METHODS

Total 18 features relevant to the MCI to AD conversion process led to the identification of 681 subjects with early MCI. Subjects were divided into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering, and Gaussian Mixture Models (GMM) were used to describe the latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and Odds Ratios (OR) were computed for each discovered class.

RESULTS

Through consensus clustering, we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present in only two clusters.

CONCLUSION

We successfully discovered three different latent classes among MCI subjects with varied risks of MCI-to-AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of Alzheimer´s disease.

Clustering of Brain Tumor Based on Analysis of MRI Images Using Robust Principal Component Analysis (ROBPCA) Algorithm

Automated detection of brain tumor location is essential for both medical and analytical uses. In this paper, we clustered brain MRI images to detect tumor location. To obtain perfect results, we presented an unsupervised robust PCA algorithm to clustered images. The proposed method clusters brain MR image pixels to four leverages. The algorithm is implemented for five brain diseases such as glioma, Huntington, meningioma, Pick, and Alzheimer's. We used ten images of each disease to validate the optimal identification rate. According to the results obtained, 2% of the data in the bad leverage part of the image were determined, which acceptably discerned the tumor. Results show that this method has the potential to detect tumor location for brain disease with high sensitivity. Moreover, results show that the method for the Glioma images has approximately better results than others. However, according to the ROC curve for all selected diseases, the present method can find lesion location.

Magnetic Resonance Imaging Measurement of Entorhinal Cortex in the Diagnosis and Differential Diagnosis of Mild Cognitive Impairment and Alzheimer's Disease

Several magnetic resonance imaging studies have shown that the entorhinal cortex (ERC) is the first brain area related to pathologic changes in Alzheimer’s disease (AD), even before atrophy of the hippocampus (HP). However, change in ERC morphology (thickness, surface area and volume) in the progression from aMCI to AD, especially in the subtypes of aMCI (single-domain and multiple-domain: aMCI-s and aMCI-m), however, is still unclear. ERC thickness, surface area and volume were measured in 29 people with aMCI-s, 22 people with aMCI-m, 18 patients with AD and 26 age-/sex-matched healthy controls. Group comparisons of the ERC geometry measurements (including thickness, volume and surface area) were performed using analyses of covariance (ANCOVA). Furthermore, receiver operator characteristic (ROC) analyses and the area under the curve (AUC) were employed to investigate classification ability (HC, aMCI-s, aMCI-m and AD from each other). There was a significant decreasing tendency in ERC thickness from HC to aMCI-s to aMCI-m to finally AD in both the left and the right hemispheres (left hemisphere: HC > aMCI-s > AD; right hemisphere: aMCI-s > aMCI-m > AD). For ERC volume, both the AD group and the aMCI-m group showed significantly decreased volume on both sides compared with the HC group. In addition, the AD group also had significantly decreased volume on both sides compared with the aMCI-s group. As for the ERC surface area, no significant difference was identified among the four groups. Furthermore, the AUC results demonstrate that combined ERC parameters (thickness and volume) can better discriminate the four groups from each other than ERC thickness alone. Finally, and most importantly, relative to HP volume, the capacity of combined ERC parameters was better at discriminating between HC and aMCI-s, as well as aMCI-m and AD. ERC atrophy, particularly the combination of ERC thickness and volume, might be regarded as a promising candidate biomarker in the diagnosis and differential diagnosis of aMCI and AD.

Multi-Resemblance Multi-Target Low-Rank Coding for Prediction of Cognitive Decline With Longitudinal Brain Images

An effective presymptomatic diagnosis and treatment of Alzheimer's disease (AD) would have enormous public health benefits. Sparse coding (SC) has shown strong potential for longitudinal brain image analysis in preclinical AD research. However, the traditional SC computation is time-consuming and does not explore the feature correlations that are consistent over the time. In addition, longitudinal brain image cohorts usually contain incomplete image data and clinical labels. To address these challenges, we propose a novel two-stage Multi-Resemblance Multi-Target Low-Rank Coding (MMLC) method, which encourages that sparse codes of neighboring longitudinal time points are resemblant to each other, favors sparse code low-rankness to reduce the computational cost and is resilient to both source and target data incompleteness. In stage one, we propose an online multi-resemblant low-rank SC method to utilize the common and task-specific dictionaries in different time points to immune to incomplete source data and capture the longitudinal correlation. In stage two, supported by a rigorous theoretical analysis, we develop a multi-target learning method to address the missing clinical label issue. To solve such a multi-task low-rank sparse optimization problem, we propose multi-task stochastic coordinate coding with a sequence of closed-form update steps which reduces the computational costs guaranteed by a theoretical convergence proof. We apply MMLC on a publicly available neuroimaging cohort to predict two clinical measures and compare it with six other methods. Our experimental results show our proposed method achieves superior results on both computational efficiency and predictive accuracy and has great potential to assist the AD prevention.

Characteristics of TCM Constitution and Related Biomarkers for Mild Cognitive Impairment

INTRODUCTION

The incidence of Alzheimer's disease is on the rise, early detection of cognitive impairment of the elderly is very important. In traditional Chinese medicine, constitution is related to the susceptibility of the human body to diseases. Based on the theory of constitution of traditional Chinese medicine (TCM), the human population can be classified into 9 constitutions. However, little is known about the characteristics of medical constitution and related biomarkers in subjects with mild cognitive impairment (MCI).

METHODS

We measured the TCM Constitution of 214 subjects by using the Constitution in Chinese Medicine Questionnaire (CCMQ). MMSE and MoCA were used to assess cognitive function. The subjects were divided into mild cognitive impairment group (MCI, n = 152) and normal control group (NC, n = 62). The levels of serum Hcy and serum/urine 8-iso-PGF 2α were determined.

RESULTS

1) It was found that there was a significant difference in constitution types between MCI and NC. There were significant differences in MMSE and MoCA score, serum Hcy and serum/urine 8-iso-PGF 2a levels between the two groups. 2) In logistic regression analysis, the variables with statistical significance were TCM Constitution of Yang-Deficient, Phlegm-Dampness, Blood-Stasis and abnormal increase of Hcy (OR>1). 3) The MoCA scores had a positive correlation with the MMSE. A statistically significant inverse association was found between serum Hcy, blood and urine 8-iso-PGF 2a and scores of cognitive assessment in MCI.

CONCLUSION

Constitution types (Yang-Deficient, Phlegm-Dampness and Blood-Stasis) and abnormal serum Hcy elevation can be used as risk factors for MCI. MoCA scores can serve to detect MCI at early stage. Serum/urine 8-iso-PGF 2α has a certain relationship with MCI. Higher levels of serum/urine 8-iso-PGF 2α are more likely to be associated with MCI risk.

Volumetric MRI demonstrates atrophy of the olfactory cortex in AD

OBJECTIVE

Alzheimer disease (AD) is a chronic neurodegenerative disorder that affects millions of individuals worldwide. Symptoms include memory dysfunction and deficits in attention, planning, language, and overall cognitive function. Olfactory dysfunction is a common symptom of AD and evidence supports that it is an early marker. Furthermore, olfactory bulb and entorhinal cortex atrophy are well described in AD. However, in AD, no studies have assessed the olfactory cortex as a whole and if sex effects are observed.

METHODS

Magnetic Resonance Imaging was used to scan 39 participants with an average age of 72 years and included men and women. AAL Single-Subject Atlas (implemented in PNEURO tool - PMOD 3.8) was used to determine the volume of the olfactory cortex and the hippocampus. Olfactory cortex volume was lower in both men and women AD cases compared with controls. This decrease was more apparent in the left olfactory cortex and was influenced by age. As expected, hippocampal volume was also significantly reduced in AD. However, this was only observed in the male cohort. A significant correlation was observed between levels of education and hippocampal volume in controls that were not detected in the AD participants. Asymmetry was observed in the olfactory cortex volume when comparing left and right volumes in both the control and AD participants, which was not observed in the hippocampus.

RESULTS

These data highlight the importance of the role of olfactory cortical atrophy in the pathogenesis of AD and the interplay between the olfactory deficits and degeneration of olfactory regions in the brain.

Neurophysiological biomarkers using transcranial magnetic stimulation in Alzheimer's disease and mild cognitive impairment: A systematic review and meta-analysis

Transcranial magnetic stimulation (TMS) is a non-invasive neurophysiological tool that enables the investigation of cortical excitability in the human brain. Paired-pulse TMS paradigms include short- and long-interval intracortical inhibition (SICI/LICI), intracortical facilitation (ICF), and short-latency afferent inhibition (SAI), which can assess neurophysiological functions of GABAergic, glutamatergic, and cholinergic neural circuits, respectively. We conducted the first systematic review and meta-analysis to compare these TMS indices among patients with AD, mild cognitive impairment (MCI), and healthy controls (HC). Our meta-analyses indicated that RMT, SAI, SICI, and LICI were significantly lower in patients with AD, while ICF did not show a difference in patients with AD compared with HC. In patients with MCI, RMT and SAI were significantly lower than in HC. In conclusion, motor cortical excitability was increased, while cholinergic function was decreased in AD and MCI in comparison with HC and patients with AD had decreased GABAergic and glutamatergic functions compared with HC. Our results warrant further studies to differentiate AD, MCI, and HC, employing multimodal TMS neurophysiology.

The Peripheral Hearing and Central Auditory Processing Skills of Individuals With Subjective Memory Complaints

Purpose

This study examined the central auditory processing (CAP) assessment results of adults between 45 and 85 years of age with probable pre-clinical Alzheimer’s disease – i.e., individuals with subjective memory complaints (SMCs) as compared to those who were not reporting significant levels of memory complaints (non-SMCs). It was hypothesized that the SMC group would perform significantly poorer on tests of central auditory skills compared to participants with non-SMCs (control group).

Methods

A total of 95 participants were recruited from the larger Western Australia Memory Study and were classified as SMCs (N = 61; 20 males and 41 females, mean age 71.47 ±7.18 years) and non-SMCs (N = 34; 10 males, 24 females, mean age 68.85 ±7.69 years). All participants completed a peripheral hearing assessment, a CAP assessment battery including Dichotic Digits, Duration Pattern Test, Dichotic Sentence Identification, Synthetic Sentence Identification with Ipsilateral Competing Message (SSI-ICM) and the Quick-Speech-in-Noise, and a cognitive screening assessment.

Results

The SMCs group performed significantly poorer than the control group on SSI-ICM −10 and −20 dB signal-to-noise conditions. No significant differences were found between the two groups on the peripheral hearing threshold measurements and other CAP assessments.

Conclusions

The results suggest that individuals with SMCs perform poorly on specific CAP assessments in comparison to the controls. The poor CAP in SMC individuals may result in a higher cost to their finite pool of cognitive resources. The CAP results provide yet another biomarker that supports the hypothesis that SMCs may be a primary indication of neuropathological changes in the brain. Longitudinal follow up of individuals with SMCs, and decreased CAP abilities should inform whether this group is at higher risk of developing dementia as compared to non-SMCs and those SMC individuals without CAP difficulties.

A Robust Algorithm for Classification and Diagnosis of Brain Disease Using Local Linear Approximation and Generalized Autoregressive Conditional Heteroscedasticity Model

Regions detection has an influence on the better treatment of brain tumors. Existing algorithms in the early detection of tumors are difficult to diagnose reliably. In this paper, we introduced a new robust algorithm using three methods for the classification of brain disease. The first method is Wavelet-Generalized Autoregressive Conditional Heteroscedasticity-K-Nearest Neighbor (W-GARCH-KNN). The Two-Dimensional Discrete Wavelet (2D-DWT) is utilized as the input images. The sub-banded wavelet coefficients are modeled using the GARCH model. The features of the GARCH model are considered as the main property vector. The second method is the Developed Wavelet-GARCH-KNN (D-WGK), which solves the incompatibility of the WGK method for the use of a low pass sub-band. The third method is the Wavelet Local Linear Approximation (LLA)-KNN, which we used for modeling the wavelet sub-bands. The extracted features were applied separately to determine the normal image or brain tumor based on classification methods. The classification was performed for the diagnosis of tumor types. The empirical results showed that the proposed algorithm obtained a high rate of classification and better practices than recently introduced algorithms while requiring a smaller number of classification features. According to the results, the Low-Low sub-bands are not adopted with the GARCH model; therefore, with the use of homomorphic filtering, this limitation is overcome. The results showed that the presented Local Linear (LL) method was better than the GARCH model for modeling wavelet sub-bands.

Genome-wide association study of rate of cognitive decline in Alzheimer's disease patients identifies novel genes and pathways

INTRODUCTION

Variability exists in the disease trajectories of Alzheimer's disease (AD) patients. We performed a genome-wide association study to examine rate of cognitive decline (ROD) in patients with AD.

METHODS

We tested for interactions between genetic variants and time since diagnosis to predict the ROD of a composite cognitive score in 3946 AD cases and performed pathway analysis on the top genes.

RESULTS

Suggestive associations (P < 1.0 × 10-6 ) were observed on chromosome 15 in DNA polymerase-γ (rs3176205, P = 1.11 × 10-7 ), chromosome 7 (rs60465337,P = 4.06 × 10-7 ) in contactin-associated protein-2, in RP11-384F7.1 on chromosome 3 (rs28853947, P = 5.93 × 10-7 ), family with sequence similarity 214 member-A on chromosome 15 (rs2899492, P = 5.94 × 10-7 ), and intergenic regions on chromosomes 16 (rs4949142, P = 4.02 × 10-7 ) and 4 (rs1304013, P = 7.73 × 10-7 ). Significant pathways involving neuronal development and function, apoptosis, memory, and inflammation were identified.

DISCUSSION

Pathways related to AD, intelligence, and neurological function determine AD progression, while previously identified AD risk variants, including the apolipoprotein (APOE) ε4 and ε2 variants, do not have a major impact.

Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields

Background

Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI.

Methods

Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups.

Results

The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control.

Conclusions

The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.

Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment

BACKGROUND

Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year.

OBJECTIVES

To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI.

SEARCH METHODS

On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches.

SELECTION CRITERIA

We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter.

DATA COLLECTION AND ANALYSIS

Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available.

MAIN RESULTS

We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate.

AUTHORS' CONCLUSIONS

The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.

Deep learning-based classification of multi-categorical Alzheimer's disease data

It is urgent to find the appropriate technology for the early detection of Alzheimer's disease (AD) due to the unknown AD etiopathologies that bring about serious social problems. Early detection of mild cognitive impairment (MCI) has pivotal importance in delaying or preventing the AD onset. Herein, we utilize deep learning (DL) techniques for the purpose of multiclass classification between normal control, MCI, and AD subjects. We used multi-categorical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including brain imaging measurements, cognitive test results, cerebrospinal fluid measures, ApoE4 status, and age. We achieved an overall accuracy of 87.197% for our artificial neural network classifier and a similar overall accuracy of 88.275% for our 1D convolutional neural network classifier. We conclude that DL-based techniques are powerful tools in analyzing ADNI data although further method refinements are needed.

Statistical Model of Dynamic Markers of the Alzheimer's Pathological Cascade

Objectives

Alzheimer's disease (AD) is a progressive disease reflected in markers across assessment modalities, including neuroimaging, cognitive testing, and evaluation of adaptive function. Identifying a single continuum of decline across assessment modalities in a single sample is statistically challenging because of the multivariate nature of the data. To address this challenge, we implemented advanced statistical analyses designed specifically to model complex data across a single continuum.

Method

We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1,056), focusing on indicators from the assessments of magnetic resonance imaging (MRI) volume, fluorodeoxyglucose positron emission tomography (FDG-PET) metabolic activity, cognitive performance, and adaptive function. Item response theory was used to identify the continuum of decline. Then, through a process of statistical scaling, indicators across all modalities were linked to that continuum and analyzed.

Results

Findings revealed that measures of MRI volume, FDG-PET metabolic activity, and adaptive function added measurement precision beyond that provided by cognitive measures, particularly in the relatively mild range of disease severity. More specifically, MRI volume, and FDG-PET metabolic activity become compromised in the very mild range of severity, followed by cognitive performance and finally adaptive function.

Conclusion

Our statistically derived models of the AD pathological cascade are consistent with existing theoretical models.

Identification of Retinal Biomarkers in Alzheimer's Disease Using Optical Coherence Tomography: Recent Insights, Challenges, and Opportunities

This review will highlight recent insights into measuring retinal structure in Alzheimer’s disease (AD). A growing body of evidence indicates that disturbances in retinal blood flow and structure are related to cognitive function, which can severely impair vision. Optical coherence tomography (OCT) is an optical imaging technology that may allow researchers and physicians to gain deeper insights into retinal morphology and clarify the impact of AD on retinal health and function. Direct and noninvasive measurement of retinal morphology using OCT has provided useful diagnostic and therapeutic indications in several central nervous system (CNS) diseases, including AD, multiple sclerosis, and Parkinson disease. Despite several limitations, morphology assessment in the retinal layers is a significant advancement in the understanding of ocular diseases. Nevertheless, additional studies are required to validate the use of OCT in AD and its complications in the eye.

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data

INTRODUCTION

It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.

METHODS

A deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.

RESULTS

The deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning-based progression risk was combined with baseline clinical measures.

DISCUSSION

Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.

Automated Volumetry of Medial Temporal Lobe Subregions in Mild Cognitive Impairment and Alzheimer Disease

PURPOSE

Hippocampal subfield volumetry should be more useful than whole hippocampal (WH) volumetry for diagnosing Alzheimer disease (AD). This study sought to confirm this.

METHODS

We investigated cognitively normal (CN) participants and patients with mild cognitive impairment (MCI) or AD using high-resolution T2-weighted and 3-dimensional T1-weighted magnetic resonance imaging. Using medial temporal subregion volumetry, we investigated discriminative power for MCI and AD versus CN.

PATIENTS

We recruited 30 CN participants, 30 amnestic MCI patients, and 49 AD patients between April 2015 and October 2016.

RESULTS

For AD, discriminative power of the combined volumes of the subiculum, entorhinal cortex, and cornu ammonis 1 was highest [area under the curve (AUC)=0.915; 85.7% sensitivity, 86.7% specificity, 86.1% accuracy], and was significantly higher than that of the WH volume (AUC=0.887; 90.0% sensitivity, 75.5% specificity, 84.5% accuracy) (P=0.019). For MCI, discriminative power of the subiculum volume was highest (AUC=0.747; 80.0% sensitivity, 73.3% specificity, 76.7% accuracy), but was only slightly higher than that of the WH volume (AUC=0.730; 56.7% sensitivity, 90.0% specificity, 73.3% accuracy).

CONCLUSIONS

Using the combined volumes of the subiculum, entorhinal cortex, and cornu ammonis 1 may enable greater diagnostic accuracy compared with the WH volume or any single subfield in AD patients.

Prediction of Alzheimer's disease dementia with MRI beyond the short-term: Implications for the design of predictive models

Magnetic resonance imaging (MRI) volumetric measures have become a standard tool for the detection of incipient Alzheimer's Disease (AD) dementia in mild cognitive impairment (MCI). Focused on providing an earlier and more accurate diagnosis, sophisticated MRI machine learning algorithms have been developed over the recent years, most of them learning their non-disease patterns from MCI that remained stable over 2–3 years. In this work, we analyzed whether these stable MCI over short-term periods are actually appropriate training examples of non-disease patterns. To this aim, we compared the diagnosis of MCI patients at 2 and 5 years of follow-up and investigated its impact on the predictive performance of baseline volumetric MRI measures primarily involved in AD, i.e., hippocampal and entorhinal cortex volumes. Predictive power was evaluated in terms of the area under the ROC curve (AUC), sensitivity, and specificity in a trial sample of 248 MCI patients followed-up over 5 years. We further compared the sensitivity in those MCI that converted before 2 years and those that converted after 2 years. Our results indicate that 23% of the stable MCI at 2 years progressed in the next three years and that MRI volumetric measures are good predictors of conversion to AD dementia even at the mid-term, showing a better specificity and AUC as follow-up time increases. The combination of hippocampus and entorhinal cortex yielded an AUC that was significantly higher for the 5-year follow-up (AUC = 73% at 2 years vs. AUC = 84% at 5 years), as well as for specificity (56% vs. 71%). Sensitivity showed a non-significant slight decrease (81% vs. 78%). Remarkably, the performance of this model was comparable to machine learning models at the same follow-up times. MRI correctly identified most of the patients that converted after 2 years (with sensitivity >60%), and these patients showed a similar degree of abnormalities to those that converted before 2 years. This implies that most of the MCI patients that remained stable over short periods and subsequently progressed to AD dementia had evident atrophies at baseline. Therefore, machine learning models that use these patients to learn non-disease patterns are including an important fraction of patients with evident pathological changes related to the disease, something that might result in reduced performance and lack of biological interpretability.

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MRI predicted AD dementia significantly better when extending follow-up from 2 to 5 years.

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MRI was similarly able to detect AD in short and mid-term converters.

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Predictive models should not learn non-disease patterns from stable MCI over short periods.

Protective Effects of 5-HT1A Receptor Inhibition and 5-HT2A Receptor Stimulation Against Streptozotocin-Induced Apoptosis in the Hippocampus

INTRODUCTION

Intracerebroventricular administration of streptozotocin (icv-STZ) induced apoptosis changes in neurons similar to Alzheimer's disease. The serotonergic system via its receptor involved in survival of neurons. The present study examined the ability of selective 5-HT1A receptor antagonist (NAD-299) and 5-HT2A receptor agonist (TCB-2) to attenuate the apoptosis caused by the icv-STZ in the rat.

METHODS

The icv-STZ (3 mg/kg, 10 μL, twice) induced neuronal loss in the hippocampus of adult male rats. Animals were divided into naive control, sham-operated, STZ+saline (1 μL, icv), STZ+NAD-299 (5 μg/μL, icv), STZ+TCB-2 (5 μg/μL, icv), and STZ+NAD-299+TCB-2 (5 μg/μL of any agent, icv) groups. Following the 35 days' treatment period, neuronal apoptosis was detected using the Tunnel. Cells with morphological features of apoptotic cell were contended by microscopy.

RESULTS

TCB-2 and NAD-299 administration decreased number of apoptotic neurons in the treatment group compared with the STZ group. Combined treatment of STZ rat with NAD+TCB more decreased number of apoptotic cells in compare to TCB-2 or NAD-299 treated STZ groups.

CONCLUSION

Treatment with 5-HT1A receptor antagonist or 5-HT2A receptor agonist diminished apoptosis. The beneficial effect of 5HT1A receptor inhibition was potentiated with activation of 5-HT2A receptor in prevention of apoptosis in hippocampus.

Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer's Disease

Predicting progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and ¹⁸F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell’s C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Prediction of Conversion From Amnestic Mild Cognitive Impairment to Alzheimer's Disease Based on the Brain Structural Connectome

Background: Early prediction of disease progression in patients with amnestic mild cognitive impairment (aMCI) is important for early diagnosis and intervention of Alzheimer's disease (AD). Previous brain network studies have suggested topological disruptions of the brain connectome in aMCI patients. However, whether brain connectome markers at baseline can predict longitudinal conversion from aMCI to AD remains largely unknown.

Methods: In this study, 52 patients with aMCI and 26 demographically matched healthy controls from a longitudinal cohort were evaluated. During 2 years of follow-up, 26 patients with aMCI were retrospectively classified as aMCI converters and 26 patients remained stable as aMCI non-converters based on whether they were subsequently diagnosed with AD. For each participant, diffusion tensor imaging at baseline and deterministic tractography were used to map the whole-brain white matter structural connectome. Graph theoretical analysis was applied to investigate the convergent and divergent connectivity patterns of structural connectome between aMCI converters and non-converters.

Results: Disrupted topological organization of the brain structural connectome were identified in both aMCI converters and non-converters. More severe disruptions of structural connectivity in aMCI converters compared with non-converters were found, especially in the default-mode network regions and connections. Finally, a support vector machine-based classification demonstrated the good discriminative ability of structural connectivity in differentiating aMCI patients from controls with an accuracy of 98%, and in discriminating converters from non-converters with an accuracy of 81%.

Conclusion: Our study provides potential structural connectome/connectivity-based biomarkers for predicting disease progression in aMCI, which is important for the early diagnosis of AD.

Logistic regression error-in-covariate models for longitudinal high-dimensional covariates

We consider a logistic regression model for a binary response where part of its covariates are subject-specific random intercepts and slopes from a large number of longitudinal covariates. These random effect covariates must be estimated from the observed data, and therefore, the model essentially involves errors in covariates. Because of high dimension and high correlation of the random effects, we employ longitudinal principal component analysis to reduce the total number of random effects to some manageable number of random effects. To deal with errors in covariates, we extend the conditional-score equation approach to this moderate dimensional logistic regression model with random effect covariates. To reliably solve the conditional-score equations in moderate/high dimension, we apply a majorization on the first derivative of the conditional-score functions and a penalized estimation by the smoothly clipped absolute deviation. The method was evaluated through a set of simulation studies and applied to a data set with longitudinal cortical thickness of 68 regions of interest to identify biomarkers that are related to dementia transition.

Neuroimaging in aging and neurologic diseases

Neuroimaging biomarkers for neurologic diseases are important tools, both for understanding pathology associated with cognitive and clinical symptoms and for differential diagnosis. This chapter explores neuroimaging measures, including structural and functional measures from magnetic resonance imaging (MRI) and molecular measures primarily from positron emission tomography (PET), in healthy aging adults and in a number of neurologic diseases. The spectrum covers neuroimaging measures from normal aging to a variety of dementias: late-onset Alzheimer's disease [AD; including mild cognitive impairment (MCI)], familial and nonfamilial early-onset AD, atypical AD syndromes, posterior cortical atrophy (PCA), logopenic aphasia (lvPPA), cerebral amyloid angiopathy (CAA), vascular dementia (VaD), sporadic and familial behavioral-variant frontotemporal dementia (bvFTD), semantic dementia (SD), progressive nonfluent aphasia (PNFA), frontotemporal dementia with motor neuron disease (FTD-MND), frontotemporal dementia with amyotrophic lateral sclerosis (FTD-ALS), corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB), Parkinson's disease (PD) with and without dementia, and multiple systems atrophy (MSA). We also include a discussion of the appropriate use criteria (AUC) for amyloid imaging and conclude with a discussion of differential diagnosis of neurologic dementia disorders in the context of neuroimaging.