Meta-analytic Comparison Between PIB-PET and FDG-PET Results in Alzheimer’s Disease and MCI

[Clinical guidelines «Cognitive disorders in the elderly and senile persons»]

Clinical guidelines «Cognitive disorders in the elderly and senile persons».

Utilizing fMRI to Guide TMS Targets: the Reliability and Sensitivity of fMRI Metrics at 3 T and 1.5 T

US Food and Drug Administration (FDA) cleared a Transcranial Magnetic Stimulation (TMS) system with functional Magnetic Resonance Imaging-guided (fMRI) individualized treatment protocol for major depressive disorder, which employs resting state-fMRI (RS-fMRI) functional connectivity (FC) to pinpoint the target individually to increase the accuracy and effeteness of the stimulation. Furthermore, task activation-guided TMS, as well as the use of RS-fMRI local metrics for targeted the specific abnormal brain regions, are considered a precise scheme for TMS targeting. Since 1.5 T MRI is more available in hospitals, systematic evaluation of the test-retest reliability and sensitivity of fMRI metrics on 1.5 T and 3 T MRI may provide reference for the application of fMRI-guided individualized-precise TMS stimulation. Twenty participants underwent three RS-fMRI scans and one scan of finger-tapping task fMRI with self-initiated (SI) and visual-guided (VG) conditions at both 3 T and 1.5 T. Then the location reliability derived by FC (with three seed regions) and peak activation were assessed by intra-individual distance. The test-retest reliability and sensitivity of five RS-fMRI local metrics were evaluated using intra-class correlation and effect size, separately. The intra-individual distance of peak activation location between 1.5 T and 3 T was 15.8 mm and 19 mm for two conditions, respectively. The intra-individual distance for the FC derived targets at 1.5 T was 9.6-31.2 mm, compared to that of 3 T (7.6-31.1 mm). The test-retest reliability and sensitivity of RS-fMRI local metrics showed similar trends on 1.5 T and 3 T. These findings hasten the application of fMRI-guided individualized TMS treatment in clinical practice.

Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition

BACKGROUND

Mild cognitive impairment (MCI)/Alzheimer's disease (AD) is associated with cognitive decline beyond normal aging and linked to the alterations of brain volume quantified by magnetic resonance imaging (MRI) and amyloid-beta (Aβ) quantified by positron emission tomography (PET). Yet, the complex relationships between these regional imaging measures and cognition in MCI/AD remain unclear. Explainable artificial intelligence (AI) may uncover such relationships.

METHOD

We integrate the AI-based deep learning neural network and Shapley additive explanations (SHAP) approaches and introduce the Deep-SHAP method to investigate the multivariate relationships between regional imaging measures and cognition. After validating this approach on simulated data, we apply it to real experimental data from MCI/AD patients.

RESULTS

Deep-SHAP significantly predicted cognition using simulated regional features and identified the ground-truth simulated regions as the most significant multivariate predictors. When applied to experimental MRI data, Deep-SHAP revealed that the insula, lateral occipital, medial frontal, temporal pole, and occipital fusiform gyrus are the primary contributors to global cognitive decline in MCI/AD. Furthermore, when applied to experimental amyloid Pittsburgh compound B (PiB)-PET data, Deep-SHAP identified the key brain regions for global cognitive decline in MCI/AD as the inferior temporal, parahippocampal, inferior frontal, supratemporal, and lateral frontal gray matter.

CONCLUSION

Deep-SHAP method uncovered the multivariate relationships between regional brain features and cognition, offering insights into the most critical modality-specific brain regions involved in MCI/AD mechanisms.

Automated detection of Alzheimer's disease: a multi-modal approach with 3D MRI and amyloid PET

Recent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer's disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease's causes.

A Multimodal Meta-Analytical Evidence of Functional and Structural Brain Abnormalities Across Alzheimer's Disease Spectrum

BACKGROUND

Numerous neuroimaging studies have reported that Alzheimer's disease (AD) spectrum have been linked to alterations in intrinsic functional activity and cortical thickness (CT) of some brain areas. However, the findings have been inconsistent and the correlation with the transcriptional profile and neurotransmitter systems remain largely unknown.

METHODS

We conducted a meta-analysis to identify multimodal differences in the amplitude of low-frequency fluctuation (ALFF)/fractional ALFF (fALFF) and CT in patients with AD and preclinical AD compared to healthy controls (HCs), using the Seed-based d Mapping with Permutation of Subject Images software. Transcriptional data were retrieved from the Allen Human Brain Atlas. The atlas-based nuclear imaging-derived neurotransmitter maps were investigated by JuSpace toolbox.

RESULTS

We included 26 ALFF/fALFF studies comprising 884 patients with AD and 1,020 controls, along with 52 studies comprising 2,046 patients with preclinical AD and 2,336 controls. For CT, we included 11 studies comprising 353 patients with AD and 330 controls. Overall, compared to HCs, patients with AD showed decreased ALFF/fALFF in the bilateral posterior cingulate gyrus (PCC)/precuneus and right angular gyrus, as well as increased ALFF/fALFF in the bilateral parahippocampal gyrus (PHG). Patients with peclinical AD showed decreased ALFF/fALFF in the left precuneus. Additionally, patients with AD displayed decreased CT in the bilateral PHG, left PCC, bilateral orbitofrontal cortex, sensorimotor areas and temporal lobe. Furthermore, gene sets related to brain structural and functional changes in AD and preclincal AD were enriched for G protein-coupled receptor signaling pathway, ion gated channel activity, and components of biological membrane. Functional and structural alterations in AD and preclinical AD were spatially associated with dopaminergic, serotonergic, and GABAergic neurotransmitter systems.

CONCLUSIONS

The multimodal meta-analysis demonstrated that patients with AD exhibited convergent functional and structural alterations in the PCC/precuneus and PHG, as well as cortical thinning in the primary sensory and motor areas. Furthermore, patients with preclinical AD showed reduced functional activity in the precuneus. AD and preclinical AD showed genetic modulations/neurotransmitter deficits of brain functional and structural impairments. These findings may provide new insights into the pathophysiology of the AD spectrum.

Convergent Multimodal Imaging Abnormalities in the Dorsal Precuneus in Subjective Cognitive Decline

Background

A range of imaging modalities have reported Alzheimer's disease-related abnormalities in individuals experiencing subjective cognitive decline (SCD). However, there has been no consistent local abnormality identified across multiple neuroimaging modalities for SCD.

Objective

We aimed to investigate the convergent local alterations in amyloid-β (Aβ) deposition, glucose metabolism, and resting-state functional MRI (RS-fMRI) metrics in SCD.

Methods

Fifty SCD patients (66.4±5.7 years old, 19 men [38%]) and 15 normal controls (NC) (66.3±4.4 years old, 5 men [33.3%]) were scanned with both [18F]-florbetapir PET and [18F]-fluorodeoxyglucose PET, as well as simultaneous RS-fMRI from February 2018 to November 2018. Voxel-wise metrics were retrospectively analyzed, including Aβ deposition, glucose metabolism, amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality(DC).

Results

The SCD group showed increased Aβ deposition and glucose metabolism (p < 0.05, corrected), as well as decreased ALFF, ReHo, and DC (p < 0.05, uncorrected) in the left dorsal precuneus (dPCu). Furthermore, the dPCu illustrated negative resting-state functional connectivity with the default mode network. Regarding global Aβ deposition positivity, the Aβ deposition in the left dPCu showed a gradient change, i.e., Aβ positive SCD > Aβ negative SCD > Aβ negative NC. Additionally, both Aβ positive SCD and Aβ negative SCD showed increased glucose metabolism and decreased RS-fMRI metrics in the dPCu.

Conclusions

The dorsal precuneus, an area implicated in early AD, shows convergent neuroimaging alterations in SCD, and might be more related to other cognitive functions (e.g., unfocused attention) than episodic memory.

Advances in diagnosing mild cognitive impairment and Alzheimer's disease using 11C-PIB- PET/CT and common neuropsychological tests

Alzheimer's disease (AD) is a critical health issue worldwide that has a negative impact on patients' quality of life, as well as on caregivers, society, and the environment. Positron emission tomography (PET)/computed tomography (CT) and neuropsychological scales can be used to identify AD and mild cognitive impairment (MCI) early, provide a differential diagnosis, and offer early therapies to impede the course of the illness. However, there are few reports of large-scale ¹¹C-PIB-PET/CT investigations that focus on the pathology of AD and MCI. Therefore, further research is needed to determine how neuropsychological test scales and PET/CT measurements of disease progression interact.

Oxidative Stress in Brain in Amnestic Mild Cognitive Impairment

Amnestic mild cognitive impairment (MCI), arguably the earliest clinical stage of Alzheimer disease (AD), is characterized by normal activities of daily living but with memory issues but no dementia. Oxidative stress, with consequent damaged key proteins and lipids, are prominent even in this early state of AD. This review article outlines oxidative stress in MCI and how this can account for neuronal loss and potential therapeutic strategies to slow progression to AD.

Neuroimaging in Dementia

OBJECTIVE

Neurodegenerative diseases are significant health concerns with regard to morbidity and social and economic hardship around the world. This review describes the state of the field of neuroimaging measures as biomarkers for detection and diagnosis of both slowly progressing and rapidly progressing neurodegenerative diseases, specifically Alzheimer disease, vascular cognitive impairment, dementia with Lewy bodies or Parkinson disease dementia, frontotemporal lobar degeneration spectrum disorders, and prion-related diseases. It briefly discusses findings in these diseases in studies using MRI and metabolic and molecular-based imaging (eg, positron emission tomography [PET] and single-photon emission computerized tomography [SPECT]).

LATEST DEVELOPMENTS

Neuroimaging studies with MRI and PET have demonstrated differential patterns of brain atrophy and hypometabolism in different neurodegenerative disorders, which can be useful in differential diagnoses. Advanced MRI sequences, such as diffusion-based imaging, and functional MRI (fMRI) provide important information about underlying biological changes in dementia and new directions for development of novel measures for future clinical use. Finally, advancements in molecular imaging allow clinicians and researchers to visualize dementia-related proteinopathies and neurotransmitter levels.

ESSENTIAL POINTS

Diagnosis of neurodegenerative diseases is primarily based on symptomatology, although the development of in vivo neuroimaging and fluid biomarkers is changing the scope of clinical diagnosis, as well as the research into these devastating diseases. This article will help inform the reader about the current state of neuroimaging in neurodegenerative diseases, as well as how these tools might be used for differential diagnoses.

Decoupling of Regional Cerebral Blood Flow and Brain Function Along the Alzheimer's Disease Continuum

BACKGROUND

Alzheimer's disease (AD) is accompanied with impaired neurovascular coupling. However, its early alteration remains elusive along the AD continuum.

OBJECTIVE

This study aimed to investigate the early disruption of neurovascular coupling in cognitively normal (CN) and mild cognitive impairment (MCI) elderly and its association with cognition and AD pathologies.

METHODS

We included 43 amyloid-β-negative CN participants and 38 amyloid-β-positive individuals (18 CN and 20 MCI) from the Alzheimer's Disease Neuroimaging Initiative dataset. Regional homogeneity (ReHo) map was used to represent neuronal activity and cerebral blood flow (CBF) map was used to represent cerebral blood perfusion. Neurovascular coupling was assessed by CBF/ReHo ratio at the voxel level. Analyses of covariance to detect the between-group differences and to further investigate the relations between CBF/ReHo ratio and AD biomarkers or cognition. In addition, the correlation of cerebral small vessel disease (SVD) burden and neurovascular coupling was assessed as well.

RESULTS

Related to amyloid-β-negative CN group, amyloid-β-positive groups showed decreased CBF/ReHo ratio mainly in the left medial and inferior temporal gyrus. Furthermore, lower CBF/ReHo ratio was associated with a lower Mini-Mental State Examination score as well as higher AD pathological burden. No association between CBF/ReHo ratio and SVD burden was observed.

CONCLUSION

AD pathology is a major correlate of the disturbed neurovascular coupling along the AD continuum, independent of SVD pathology. The CBF/ReHo ratio may be an index for detecting neurovascular coupling abnormalities, which could be used for early diagnosis in the future.

Lower brain glucose metabolism in normal ageing is predominantly frontal and temporal: A systematic review and pooled effect size and activation likelihood estimates meta-analyses

This review provides a qualitative and quantitative analysis of cerebral glucose metabolism in ageing. We undertook a systematic literature review followed by pooled effect size and activation likelihood estimates (ALE) meta-analyses. Studies were retrieved from PubMed following the PRISMA guidelines. After reviewing 635 records, 21 studies with 22 independent samples (n = 911 participants) were included in the pooled effect size analyses. Eight studies with eleven separate samples (n = 713 participants) were included in the ALE analyses. Pooled effect sizes showed significantly lower cerebral metabolic rates of glucose for older versus younger adults for the whole brain, as well as for the frontal, temporal, parietal, and occipital lobes. Among the sub-cortical structures, the caudate showed a lower metabolic rate among older adults. In sub-group analyses controlling for changes in brain volume or partial volume effects, the lower glucose metabolism among older adults in the frontal lobe remained significant, whereas confidence intervals crossed zero for the other lobes and structures. The ALE identified nine clusters of lower glucose metabolism among older adults, ranging from 200 to 2640 mm³ . The two largest clusters were in the left and right inferior frontal and superior temporal gyri and the insula. Clusters were also found in the inferior temporal junction, the anterior cingulate and caudate. Taken together, the results are consistent with research showing less efficient glucose metabolism in the ageing brain. The findings are discussed in the context of theories of cognitive ageing and are compared to those found in neurodegenerative disease.

Aberrant pattern of regional cerebral blood flow in mild cognitive impairment: A meta-analysis of arterial spin labeling magnetic resonance imaging

In mild cognitive impairment (MCI), cognitive decline is associated with abnormal changes of cerebral blood flow (CBF). Arterial spin labeling magnetic resonance imaging (ASL-MRI) is an effective method for assessing regional cerebral blood flow (rCBF). However, the CBF estimated via ASL-MRI in MCI often differs between studies, and the consistency of CBF changes in MCI is unclear. In this study, 13 ASL-MRI studies with 495 MCI patients and 441 health controls were screened out from PubMed, Embase, Cochrane, Web of Science, Wanfang, and CNKI. An activation likelihood estimation (ALE) meta-analysis was performed to explore the brain regions with abnormal CBF in MCI. It showed that the decreased CBF in MCI was identified in the precuneus, inferior parietal lobule (IPL), superior occipital gyrus (SOG), middle temporal gyrus (MTG), and middle occipital gyrus (MOG), while the increased CBF in MCI was identified in the lentiform nucleus (LN) compared with healthy controls. The study characterized the abnormal pattern of regional CBF in MCI, which would promote our knowledge of MCI and might be used as a biomarker in clinic.

The fractional amplitude of low-frequency fluctuations signals related to amyloid uptake in high-risk populations—A pilot fMRI study

Background

Patients with type 2 diabetes mellitus (T2DM) and subjective cognitive decline (SCD) have a higher risk to develop Alzheimer's Disease (AD). Resting-state-functional magnetic resonance imaging (rs-fMRI) was used to document neurological involvement in the two groups from the aspect of brain dysfunction. Accumulation of amyloid-β (Aβ) starts decades ago before the onset of clinical symptoms and may already have been associated with brain function in high-risk populations. However, this study aims to compare the patterns of fractional amplitude of low-frequency fluctuations (fALFF) maps between cognitively normal high-risk groups (SCD and T2DM) and healthy elderly and evaluate the association between regional amyloid deposition and local fALFF signals in certain cortical regions.

Materials and methods

A total of 18 T2DM, 11 SCD, and 18 healthy elderlies were included in this study. The differences in the fALFF maps were compared between HC and high-risk groups. Regional amyloid deposition and local fALFF signals were obtained and further correlated in two high-risk groups.

Results

Compared to HC, the altered fALFF signals of regions were shown in SCD such as the left posterior cerebellum, left putamen, and cingulate gyrus. The T2DM group illustrated altered neural activity in the superior temporal gyrus, supplementary motor area, and precentral gyrus. The correlation between fALFF signals and amyloid deposition was negative in the left anterior cingulate cortex for both groups. In the T2DM group, a positive correlation was shown in the right occipital lobe and left mesial temporal lobe.

Conclusion

The altered fALFF signals were demonstrated in high-risk groups compared to HC. Very early amyloid deposition in SCD and T2DM groups was observed to affect the neural activity mainly involved in the default mode network (DMN).

Reliability and Validity of Power Spectrum Slope (PSS): A Metric for Measuring Resting-State Functional Magnetic Resonance Imaging Activity of Single Voxels

Methods that capture the features of single voxels of resting-state fMRI (RS-fMRI) could precisely localize the abnormal spontaneous activity and hence guide precise brain stimulation. As one of these metrics, the amplitude of low-frequency fluctuation (ALFF) has been used in numerous studies, however, it is frequency-dependent and the division of frequency bands is still controversial. Based on the well-accepted power law of time series, this study proposed an approach, namely, power spectrum slope (PSS), to characterize the RS-fMRI time series of single voxels. Two metrics, i.e., linear coefficient b and power-law slope b' were used and compared with ALFF. The reliability and validity of the PSS approach were evaluated on public RS-fMRI datasets (n = 145 in total) of eyes closed (EC) and eyes open (EO) conditions after image preprocessing, with 21 subjects scanned two times for test-retest reliability analyses. Specifically, we used the paired t-test between EC and EO conditions to assess the validity and intra-class correlation (ICC) to assess the reliability. The results included the following: (1) PSS detected similar spatial patterns of validity (i.e., EC-EO differences) and less test-retest reliability with those of ALFF; (2) PSS linear coefficient b showed better validity and reliability than power-law slope b'; (3) While the PPS showed less validity in most regions, PSS linear coefficient b showed exclusive EC-EO difference in the medial temporal lobe which did not show in ALFF. The power spectrum plot in the parahippocampus showed a "cross-over" of power magnitudes between EC and EO conditions in the higher frequency bands (>0.1 Hz). These results demonstrated that PSS (linear coefficient b) is complementary to ALFF for detecting the local spontaneous activity.

Salience network anatomical and molecular markers are linked with cognitive dysfunction in mild cognitive impairment

BACKGROUND AND PURPOSE

Recent studies indicate disrupted functional mechanisms of salience network (SN) regions-right anterior insula, left anterior insula, and anterior cingulate cortex-in mild cognitive impairment (MCI). However, the underlying anatomical and molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multimodal-anatomical and molecular-markers could predict cognitive impairment better in MCI.

METHODS

Herein we quantified anatomical volumetric markers via structural MRI and molecular amyloid markers via PET with Pittsburgh compound B in SN regions of MCI (n = 33) and healthy controls (n = 27). From these markers, we built support vector machine learning models aiming to estimate cognitive dysfunction in MCI.

RESULTS

We found that anatomical markers are significantly reduced and molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < .05). These altered markers in MCI patients were associated with their worse cognitive performance (p < .05). Our machine learning-based modeling further suggested that the integration of multimodal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers.

CONCLUSIONS

These findings shed light on the underlying anatomical volumetric and molecular amyloid alterations in SN regions and show the significance of multimodal markers integration approach in better predicting cognitive impairment in MCI.

Simultaneous PET/fMRI Detects Distinctive Alterations in Functional Connectivity and Glucose Metabolism of Precuneus Subregions in Alzheimer's Disease

As a central hub in the interconnected brain network, the precuneus has been reported showing disrupted functional connectivity and hypometabolism in Alzheimer's disease (AD). However, as a highly heterogeneous cortical structure, little is known whether individual subregion of the precuneus is uniformly or differentially involved in the progression of AD. To this end, using a hybrid PET/fMRI technique, we compared resting-state functional connectivity strength (FCS) and glucose metabolism in dorsal anterior (DA_pcu), dorsal posterior (DP_pcu) and ventral (V_pcu) subregions of the precuneus among 20 AD patients, 23 mild cognitive impairment (MCI) patients, and 27 matched cognitively normal (CN) subjects. The sub-parcellation of precuneus was performed using a K-means clustering algorithm based on its intra-regional functional connectivity. For the whole precuneus, decreased FCS (p = 0.047) and glucose hypometabolism (p = 0.006) were observed in AD patients compared to CN subjects. For the subregions of the precuneus, decreased FCS was found in DP_pcu of AD patients compared to MCI patients (p = 0.011) and in V_pcu for both MCI (p = 0.006) and AD (p = 0.008) patients compared to CN subjects. Reduced glucose metabolism was found in DP_pcu of AD patients compared to CN subjects (p = 0.038) and in V_pcu of AD patients compared to both MCI patients (p = 0.045) and CN subjects (p < 0.001). For both FCS and glucose metabolism, DA_pcu remained relatively unaffected by AD. Moreover, only in V_pcu, disruptions in FCS (r = 0.498, p = 0.042) and hypometabolism (r = 0.566, p = 0.018) were significantly correlated with the cognitive decline of AD patients. Our results demonstrated a distinctively disrupted functional and metabolic pattern from ventral to dorsal precuneus affected by AD, with V_pcu and DA_pcu being the most vulnerable and conservative subregion, respectively. Findings of this study extend our knowledge on the differential roles of precuneus subregions in AD.

Multiparametric magnetic resonance imaging and positron emission tomography findings in neurodegenerative diseases: Current status and future directions

Neurodegenerative diseases (NDDs) are characterized by progressive neuronal loss, leading to dementia and movement disorders. NDDs broadly include Alzheimer's disease, frontotemporal lobar degeneration, parkinsonian syndromes, and prion diseases. There is an ever-increasing prevalence of mild cognitive impairment and dementia, with an accompanying immense economic impact, prompting efforts aimed at early identification and effective interventions. Neuroimaging is an essential tool for the early diagnosis of NDDs in both clinical and research settings. Structural, functional, and metabolic imaging modalities, including magnetic resonance imaging (MRI) and positron emission tomography (PET), are widely available. They show encouraging results for diagnosis, monitoring, and treatment response evaluation. The current review focuses on the complementary role of various imaging modalities in relation to NDDs, the qualitative and quantitative utility of newer MRI techniques, novel radiopharmaceuticals, and integrated PET/MRI in the setting of NDDs.

Detecting genetic associations with brain imaging phenotypes in Alzheimer's disease via a novel structured KCCA approach

Neuroimaging genetics has become an important research topic since it can reveal complex associations between genetic variants (i.e. single nucleotide polymorphisms (SNPs) and the structures or functions of the human brain. However, existing kernel mapping is difficult to directly use the sparse representation method in the kernel feature space, which makes it difficult for most existing sparse canonical correlation analysis (SCCA) methods to be directly promoted in the kernel feature space. To bridge this gap, we adopt a novel alternating projected gradient approach, gradient KCCA (gradKCCA) model to develop a powerful model for exploring the intrinsic associations among genetic markers, imaging quantitative traits (QTs) of interest. Specifically, this model solves kernel canonical correlation (KCCA) with an additional constraint that projection directions have pre-images in the original data space, a sparsity-inducing variant of the model is achieved through controlling the [Formula: see text]-norm of the preimages of the projection directions. We evaluate this model using Alzheimer's disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from Alzheimer's disease (AD) risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging (MRI) scans. Our results show that the algorithm not only outperforms the traditional KCCA method in terms of Root Mean Square Error (RMSE) and Correlation Coefficient (CC) but also identify the meaningful and relevant biomarkers of SNPs (e.g. rs157594 and rs405697), which are positively related to right Postcentral and right SupraMarginal brain regions in this study. Empirical results indicate its promising capability in revealing biologically meaningful neuroimaging genetics associations and improving the disease-related mechanistic understanding of AD.

Altered multimodal magnetic resonance parameters of basal nucleus of Meynert in Alzheimer's disease

OBJECTIVES

We aimed to examine how gray matter volume (GMV), regional blood flow (rCBF), and resting-state functional connectivity (FC) of the basal nucleus of Meynert (BNM) are altered in 40 patients with AD, relative to 30 healthy controls (HCs).

METHODS

We defined the BNM on the basis of a mask histochemically reconstructed from postmortem human brains. We examined GMV with voxel-based morphometry of high-resolution structural images, rCBF with arterial spin labeling imaging, and whole-brain FC with published routines. We performed partial correlations to explore how the imaging metrics related to cognitive and living status in patients with AD. Further, we employed receiver operating characteristic analysis to compute the "diagnostic" accuracy of these imaging markers.

RESULTS

AD relative to HC showed lower GMV and higher rCBF of the BNM as well as lower BNM connectivity with the right insula and cerebellum. In addition, the GMVs of BNM were correlated with cognitive and daily living status in AD. Finally, these imaging markers predicted AD (vs. HC) with an accuracy (area under the curve) of 0.70 to 0.86. Combination of BNM metrics provided the best prediction accuracy.

CONCLUSIONS

By combining multimode MR imaging, we demonstrated volumetric atrophy, hyperperfusion, and disconnection of the BNM in AD. These findings support cholinergic dysfunction as an etiological marker of AD and related dementia.

Disrupted Time-Dependent and Functional Connectivity Brain Network in Alzheimer's Disease: A Resting-State fMRI Study Based on Visibility Graph

Background:

Alzheimer's Disease (AD) is a progressive neurodegenerative disease with insidious onset, which is difficult to be reversed and cured. Therefore, discovering more precise biological information from neuroimaging biomarkers is crucial for accurate and automatic detection of AD.

Methods:

We innovatively used a Visibility Graph (VG) to construct the time-dependent brain networks as well as functional connectivity network to investigate the underlying dynamics of AD brain based on functional magnetic resonance imaging. There were 32 AD patients and 29 Normal Controls (NCs) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. First, the VG method mapped the time series of single brain region into networks. By extracting topological properties of the networks, the most significant features were selected as discriminant features into a supporting vector machine for classification. Furthermore, in order to detect abnormalities of these brain regions in the whole AD brain, functional connectivity among different brain regions was calculated based on the correlation of regional degree sequences.

Results:

According to the topology abnormalities exploration of local complex networks, we found several abnormal brain regions, including left insular, right posterior cingulate gyrus and other cortical regions. The accuracy of characteristics of the brain regions extracted from local complex networks was 88.52%. Association analysis demonstrated that the left inferior opercular part of frontal gyrus, right middle occipital gyrus, right superior parietal gyrus and right precuneus played a tremendous role in AD.

Conclusion:

These results would be helpful in revealing the underlying pathological mechanism of the disease.

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.

Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer's dementia: a meta-analysis

Brain ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG-PET) has been utilized to monitor disease conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer’s dementia (AD). However, the conversion patterns of FDG-PET metabolism across studies are not conclusive. We conducted a voxel-wise meta-analysis using Seed-based d Mapping that included 10 baseline voxel-wise FDG-PET comparisons between 93 aMCI converters and 129 aMCI non-converters from nine longitudinal studies. The most robust and reliable metabolic alterations that predicted conversion from aMCI to AD were localized in the left posterior cingulate cortex (PCC)/precuneus. Furthermore, meta-regression analyses indicated that baseline mean age and severity of cognitive impairment, and follow-up duration were significant moderators for metabolic alterations in aMCI converters. Our study revealed hypometabolism in the left PCC/precuneus as an early feature in the development of AD. This finding has important implications in understanding the neural substrates for AD conversion and could serve as a potential imaging biomarker for early detection of AD as well as for tracking disease progression at the predementia stage.

Altered Functional Connectivity of Insular Subregions in Alzheimer's Disease

Recent researches have demonstrated that the insula is the crucial hub of the human brain networks and most vulnerable region of Alzheimer’s disease (AD). However, little is known about the changes of functional connectivity of insular subregions in the AD patients. In this study, we collected resting-state functional magnetic resonance imaging (fMRI) data including 32 AD patients and 38 healthy controls (HCs). By defining three subregions of insula, we mapped whole-brain resting-state functional connectivity (RSFC) and identified several distinct RSFC patterns of the insular subregions: For positive connectivity, three cognitive-related RSFC patterns were identified within insula that suggest anterior-to-posterior functional subdivisions: (1) an dorsal anterior zone of the insula that exhibits RSFC with executive control network (ECN); (2) a ventral anterior zone of insula, exhibits functional connectivity with the salience network (SN); (3) a posterior zone along the insula exhibits functional connectivity with the sensorimotor network (SMN). In addition, we found significant negative connectivities between the each insular subregion and several special default mode network (DMN) regions. Compared with controls, the AD patients demonstrated distinct disruption of positive RSFCs in the different network (ECN and SMN), suggesting the impairment of the functional integrity. There were no differences of the positive RSFCs in the SN between the two groups. On the other hand, several DMN regions showed increased negative RSFCs to the sub-region of insula in the AD patients, indicating compensatory plasticity. Furthermore, these abnormal insular subregions RSFCs are closely correlated with cognitive performances in the AD patients. Our findings suggested that different insular subregions presented distinct RSFC patterns with various functional networks, which are differently affected in the AD patients.

Altered Functional Connectivity of the Basal Nucleus of Meynert in Mild Cognitive Impairment: A Resting-State fMRI Study

Background: Cholinergic dysfunction plays an important role in mild cognitive impairment (MCI). The basal nucleus of Meynert (BNM) provides the main source of cortical cholinergic innervation. Previous studies have characterized structural changes of the cholinergic basal forebrain in individuals at risk of developing Alzheimer’s disease (AD). However, whether and how functional connectivity of the BNM (BNM-FC) is altered in MCI remains unknown.

Objective: The aim of this study was to identify alterations in BNM-FC in individuals with MCI as compared to healthy controls (HCs), and to examine the relationship between these alterations with neuropsychological measures in individuals with MCI.

Method: One-hundred-and-one MCI patients and 103 HCs underwent resting-state functional magnetic resonance imaging (rs-fMRI). Imaging data were processed with SPM8 and CONN software. BNM-FC was examined via correlation in low frequency fMRI signal fluctuations between the BNM and all other brain voxels. Group differences were examined with a covariance analysis with age, gender, education level, mean framewise displacement (FD) and global correlation (GCOR) as nuisance covariates. Pearson’s correlation was conducted to evaluate the relationship between the BNM-FC and clinical assessments.

Result: Compared with HCs, individuals with MCI showed significantly decreased BNM-FC in the left insula extending into claustrum (insula/claustrum). Furthermore, greater decrease in BNM-FC with insula/claustrum was associated with more severe impairment in immediate recall during Auditory Verbal Learning Test (AVLT) in MCI patients.

Conclusion: MCI is associated with changes in BNM-FC to the insula/claustrum in relation to cognitive impairments. These new findings may advance research of the cholinergic bases of cognitive dysfunction during healthy aging and in individuals at risk of developing AD.

Utility of Molecular and Structural Brain Imaging to Predict Progression from Mild Cognitive Impairment to Dementia

This project compares three neuroimaging biomarkers to predict progression to dementia in subjects with mild cognitive impairment (MCI). Eighty-eight subjects with MCI and 40 healthy controls (HCs) were recruited. Subjects had a 3T magnetic resonance imaging (MRI) scan, and two positron emission tomography (PET) scans, one with Pittsburgh compound B ([11C]PIB) and one with fluorodeoxyglucose ([18F]FDG). MCI subjects were followed for up to 4 y and progression to dementia was assessed on an annual basis. MCI subjects had higher [11C]PIB binding potential (BPND) than HCs in multiple brain regions, and lower hippocampus volumes. [11C]PIB BPND, [18F]FDG standard uptake value ratio (SUVR), and hippocampus volume were associated with time to progression to dementia using a Cox proportional hazards model. [18F]FDG SUVR demonstrated the most statistically significant association with progression, followed by [11C]PIB BPND and then hippocampus volume. [11C]PIB BPND and [18F]FDG SUVR were independently predictive, suggesting that combining these measures is useful to increase accuracy in the prediction of progression to dementia. Hippocampus volume also had independent predictive properties to [11C]PIB BPND, but did not add predictive power when combined with the [18F]FDG SUVR data. This work suggests that PET imaging with both [11C]PIB and [18F]FDG may help to determine which MCI subjects are likely to progress to AD, possibly directing future treatment options.

Prediction of Progressive Mild Cognitive Impairment by Multi-Modal Neuroimaging Biomarkers

For patients with mild cognitive impairment (MCI), the likelihood of progression to probable Alzheimer's disease (AD) is important not only for individual patient care, but also for the identification of participants in clinical trial, so as to provide early interventions. Biomarkers based on various neuroimaging modalities could offer complementary information regarding different aspects of disease progression. The current study adopted a weighted multi-modality sparse representation-based classification method to combine data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, from three imaging modalities: Volumetric magnetic resonance imaging (MRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir PET. We included 117 normal controls (NC) and 110 MCI patients, 27 of whom progressed to AD within 36 months (pMCI), while the remaining 83 remained stable (sMCI) over the same time period. Modality-specific biomarkers were identified to distinguish MCI from NC and to predict pMCI among MCI. These included the hippocampus, amygdala, middle temporal and inferior temporal regions for MRI, the posterior cingulum, precentral, and postcentral regions for FDG-PET, and the hippocampus, amygdala, and putamen for florbetapir PET. Results indicated that FDG-PET may be a more effective modality in discriminating MCI from NC and in predicting pMCI than florbetapir PET and MRI. Combining modality-specific sensitive biomarkers from the three modalities boosted the discrimination accuracy of MCI from NC (76.7%) and the prediction accuracy of pMCI (82.5%) when compared with the best single-modality results (73.6% for MCI and 75.6% for pMCI with FDG-PET).

Three-Dimensional Eigenbrain for the Detection of Subjects and Brain Regions Related with Alzheimer's Disease

BACKGROUND

Considering that Alzheimer's disease (AD) is untreatable, early diagnosis of AD from the healthy elderly controls (HC) is pivotal. However, computer-aided diagnosis (CAD) systems were not widely used due to its poor performance.

OBJECTIVE

Inspired from the eigenface approach for face recognition problems, we proposed an eigenbrain to detect AD brains. Eigenface is only for 2D image processing and is not suitable for volumetric image processing since faces are usually obtained as 2D images.

METHODS

We extended the eigenbrain to 3D. This 3D eigenbrain (3D-EB) inherits the fundamental strategies in either eigenface or 2D eigenbrain (2D-EB). All the 3D brains were transferred to a feature space, which encoded the variation among known 3D brain images. The feature space was named as the 3D-EB, and defined as eigenvectors on the set of 3D brains. We compared four different classifiers: feed-forward neural network, support vector machine (SVM) with linear kernel, polynomial (Pol) kernel, and radial basis function kernel.

RESULTS

The 50x10-fold stratified cross validation experiments showed that the proposed 3D-EB is better than the 2D-EB. SVM with Pol kernel performed the best among all classifiers. Our "3D-EB + Pol-SVM" achieved an accuracy of 92.81% ± 1.99% , a sensitivity of 92.07% ± 2.48% , a specificity of 93.02% ± 2.22% , and a precision of 79.03% ± 2.37% . Based on the most important 3D-EB U1, we detected 34 brain regions related with AD. The results corresponded to recent literature.

CONCLUSIONS

We validated the effectiveness of the proposed 3D-EB by detecting subjects and brain regions related to AD.

Abnormal Changes of Brain Cortical Anatomy and the Association with Plasma MicroRNA107 Level in Amnestic Mild Cognitive Impairment

MicroRNA107 (Mir107) has been thought to relate to the brain structure phenotype of Alzheimer's disease. In this study, we evaluated the cortical anatomy in amnestic mild cognitive impairment (aMCI) and the relation between cortical anatomy and plasma levels of Mir107 and beta-site amyloid precursor protein (APP) cleaving enzyme 1 (BACE1). Twenty aMCI (20 aMCI) and 24 cognitively normal control (NC) subjects were recruited, and T1-weighted MR images were acquired. Cortical anatomical measurements, including cortical thickness (CT), surface area (SA), and local gyrification index (LGI), were assessed. Quantitative RT-PCR was used to examine plasma expression of Mir107, BACE1 mRNA. Thinner cortex was found in aMCI in areas associated with episodic memory and language, but with thicker cortex in other areas. SA decreased in aMCI in the areas associated with working memory and emotion. LGI showed a significant reduction in aMCI in the areas involved in language function. Changes in Mir107 and BACE1 messenger RNA plasma expression were correlated with changes in CT and SA. We found alterations in key left brain regions associated with memory, language, and emotion in aMCI that were significantly correlated with plasma expression of Mir107 and BACE1 mRNA. This combination study of brain anatomical alterations and gene information may shed lights on our understanding of the pathology of AD.

CLINICAL TRIAL REGISTRATION

http://www.ClinicalTrials.gov, identifier NCT01819545.

Detection of subjects and brain regions related to Alzheimer's disease using 3D MRI scans based on eigenbrain and machine learning

PURPOSE

Early diagnosis or detection of Alzheimer's disease (AD) from the normal elder control (NC) is very important. However, the computer-aided diagnosis (CAD) was not widely used, and the classification performance did not reach the standard of practical use. We proposed a novel CAD system for MR brain images based on eigenbrains and machine learning with two goals: accurate detection of both AD subjects and AD-related brain regions.

METHOD

First, we used maximum inter-class variance (ICV) to select key slices from 3D volumetric data. Second, we generated an eigenbrain set for each subject. Third, the most important eigenbrain (MIE) was obtained by Welch's t-test (WTT). Finally, kernel support-vector-machines with different kernels that were trained by particle swarm optimization, were used to make an accurate prediction of AD subjects. Coefficients of MIE with values higher than 0.98 quantile were highlighted to obtain the discriminant regions that distinguish AD from NC.

RESULTS

The experiments showed that the proposed method can predict AD subjects with a competitive performance with existing methods, especially the accuracy of the polynomial kernel (92.36 ± 0.94) was better than the linear kernel of 91.47 ± 1.02 and the radial basis function (RBF) kernel of 86.71 ± 1.93. The proposed eigenbrain-based CAD system detected 30 AD-related brain regions (Anterior Cingulate, Caudate Nucleus, Cerebellum, Cingulate Gyrus, Claustrum, Inferior Frontal Gyrus, Inferior Parietal Lobule, Insula, Lateral Ventricle, Lentiform Nucleus, Lingual Gyrus, Medial Frontal Gyrus, Middle Frontal Gyrus, Middle Occipital Gyrus, Middle Temporal Gyrus, Paracentral Lobule, Parahippocampal Gyrus, Postcentral Gyrus, Posterial Cingulate, Precentral Gyrus, Precuneus, Subcallosal Gyrus, Sub-Gyral, Superior Frontal Gyrus, Superior Parietal Lobule, Superior Temporal Gyrus, Supramarginal Gyrus, Thalamus, Transverse Temporal Gyrus, and Uncus). The results were coherent with existing literatures.

CONCLUSION

The eigenbrain method was effective in AD subject prediction and discriminant brain-region detection in MRI scanning.