Subregions of the inferior parietal lobule are affected in the progression to Alzheimer's disease

Retinal Microvasculature Causally Affects the Brain Cortical Structure: A Mendelian Randomization Study

Purpose

To reveal the causality between retinal vascular density (VD), fractal dimension (FD), and brain cortex structure using Mendelian randomization (MR).

Design

Cross-sectional study.

Participants

Genome-wide association studies of VD and FD involving 54 813 participants from the United Kingdom Biobank were used. The brain cortical features, including the cortical thickness (TH) and surface area (SA), were extracted from 51 665 patients across 60 cohorts. Surface area and TH were measured globally and in 34 functional regions using magnetic resonance imaging.

Methods

Bidirectional univariable MR (UVMR) was used to detect the causality between FD, VD, and brain cortex structure. Multivariable MR (MVMR) was used to adjust for confounding factors, including body mass index and blood pressure.

Main Outcome Measures

The global and regional measurements of brain cortical SA and TH.

Results

At the global level, higher VD is related to decreased TH (β = -0.0140 mm, 95% confidence interval: -0.0269 mm to -0.0011 mm, P = 0.0339). At the functional level, retinal FD is related to the TH of banks of the superior temporal sulcus and transverse temporal region without global weighted, as well as the SA of the posterior cingulate after adjustment. Vascular density is correlated with the SA of subregions of the frontal lobe and temporal lobe, in addition to the TH of the inferior temporal, entorhinal, and pars opercularis regions in both UVMR and MVMR. Bidirectional MR studies showed a causation between the SA of the parahippocampal and cauda middle frontal gyrus and retinal VD. No pleiotropy was detected.

Conclusions

Fractal dimension and VD causally influence the cortical structure and vice versa, indicating that the retinal microvasculature may serve as a biomarker for cortex structural changes. Our study provides insights into utilizing noninvasive fundus images to predict cortical structural deteriorations and neuropsychiatric disorders.

Financial Disclosures

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Gray matter and cognitive alteration related to chronic obstructive pulmonary disease patients: combining ALE meta-analysis and MACM analysis

Chronic obstructive pulmonary disease (COPD) is frequently comorbid with cognitive impairment, but it has not been paid enough attention, and its neuroanatomical characteristics have not been fully identified. Voxel-based morphometric (VBM) studies comparing gray matter (GM) abnormalities in COPD patients with healthy controls (HCs) were searched using 8 electronic databases from the inception to March 2023. Stereotactic data were extracted and tested for convergence and differences using the activation likelihood estimation (ALE) method. Moreover, based on the ALE results, a structural meta-analytic connectivity modeling (MACM) was conducted to explore the co-atrophy pattern in patients with COPD. Last, behavioral analysis was performed to assess the functional roles of the regions affected by COPD. In total, 11 studies on COPD with 949 participants were included. Voxel-based meta-analysis revealed significant GM abnormalities in the right postcentral gyrus (including inferior parietal lobule), left precentral gyrus, and left cingulate gyrus (including paracentral lobule) in patients with COPD compared with HCs. Further MACM analysis revealed a deeper co-atrophy pattern between the brain regions with abnormal GM structure and the insula in COPD patients. Behavioral analysis showed that the abnormal GM structure in the left cingulate gyrus (including paracentral lobule) was strongly associated with cognitive function, especially executive function. COPD comorbid with cognitive impairment has a specific neurostructural basis of GM structural abnormalities, which may also involve a deeper co-atrophy pattern between the insula. These findings enhance our understanding of the underlying neuropathogenesis and suggest potential imaging markers for cognitive impairment in COPD patients. PROSPERO registration number: CRD42022298722.

Neurometabolic topography and associations with cognition in Alzheimer's disease: A whole-brain high-resolution 3D MRSI study

INTRODUCTION

Altered neurometabolism, detectable via proton magnetic resonance spectroscopic imaging (1H-MRSI), is spatially heterogeneous and underpins cognitive impairments in Alzheimer's disease (AD). However, the spatial relationships between neurometabolic topography and cognitive impairment in AD remain unexplored due to technical limitations.

METHODS

We used a novel whole-brain high-resolution 1H-MRSI technique, with simultaneously acquired 18F-florbetapir positron emission tomography (PET) imaging, to investigate the relationship between neurometabolic topography and cognitive functions in 117 participants, including 22 prodromal AD, 51 AD dementia, and 44 controls.

RESULTS

Prodromal AD and AD dementia patients exhibited spatially distinct reductions in N-acetylaspartate, and increases in myo-inositol. Reduced N-acetylaspartate and increased myo-inositol were associated with worse global cognitive performance, and N-acetylaspartate correlated with five specific cognitive scores. Neurometabolic topography provides biological insights into diverse cognitive dysfunctions.

DISCUSSION

Whole-brain high-resolution 1H-MRSI revealed spatially distinct neurometabolic topographies associated with cognitive decline in AD, suggesting potential for noninvasive brain metabolic imaging to track AD progression.

HIGHLIGHTS

Whole-brain high-resolution 1H-MRSI unveils neurometabolic topography in AD. Spatially distinct reductions in NAA, and increases in mI, are demonstrated. NAA and mI topography correlates with global cognitive performance. NAA topography correlates with specific cognitive performance.

Tongue coating-dependent superior temporal sulcus remodeling in amnestic mild cognitive impairment

Tongue coating affects cognition, and cognitive decline at early stage also showed relations to functional and structural remodeling of superior temporal sulcus (STS) in amnestic mild cognitive impairment (aMCI). The potential correlation between disparate cognitive manifestations in aMCI patients with different tongue coatings, and corresponding mechanisms of STS remodeling remains uncharted. In this case-control study, aMCI patients were divided into thin coating (n = 18) and thick coating (n = 21) groups. All participants underwent neuropsychological evaluations and multimodal magnetic resonance imaging. Group comparisons were conducted in clinical assessments and neuroimaging measures of banks of the STS (bankssts). Generalized linear models were constructed to explore relationships between neuroimaging measures and cognition. aMCI patients in the thick coating group exhibited significantly poorer immediate and delayed recall and slower information processing speed (IPS) (P < 0.05), and decreased functional connectivity (FC) of bilateral bankssts with frontoparietal cortices (P < 0.05, AlphaSim corrected) compared to the thin coating group. It was found notable correlations between cognition encompassing recall and IPS, and FC of bilateral bankssts with frontoparietal cortices (P < 0.05, Bonferroni's correction), as well as interaction effects of group × regional homogeneity (ReHo) of right bankssts on the first immediate recall (P < 0.05, Bonferroni's correction). aMCI patients with thick coating exhibited poor cognitive performance, which might be attributed to decreased FC seeding from bankssts. Our findings strengthen the understanding of brain reorganization of STS via which tongue coating status impacts cognition in patients with aMCI.

Regional-based static and dynamic alterations in Alzheimer disease: a longitudinal study

BACKGROUND

 Alzheimer disease (AD) leads to cognitive decline and alters functional connectivity (FC) in key brain regions. Resting-state functional magnetic resonance imaging (rs-fMRI) assesses these changes using static-FC for overall correlation and dynamic-FC for temporal variability.

OBJECTIVE

 In AD, there is altered FC compared to normal conditions. The present study investigates possible region-specific functional abnormalities occurring longitudinally over 1 year. Our aim is to evaluate the potential usefulness of the static and dynamic approaches in identifying biomarkers of AD progression.

METHODS

 The study involved 15 AD and 20 healthy participants from the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) database, tracked over 2 visits within 1 year. Using constrained-independent component analysis, we assessed FC changes across 80-regions of interest in AD over the year, examining both static and dynamic conditions.

RESULTS

 The average regional FC decreased in AD compared to healthy subjects at baseline and after 1 year. The dynamic condition identifies similarities with a few additional changes in the FC compared to the static condition. In both analyses, the baseline assessment revealed reduced connectivity between the following regions: right-middle-occipital and left-superior-occipital, left-hippocampus and right-postcentral, left-lingual and left-fusiform, and precuneus and left-thalamus. Additionally, increased connectivity was found between the left-superior-occipital and precuneus regions. In the 1-year AD assessment, increased connectivity was noted between the right-superior-temporal-pole and right-insular, right-hippocampus and left-caudate, right-middle-occipital and right-superior-temporal-pole, and posterior-cingulate-cortex and middle-temporal-pole regions.

CONCLUSION

 Significant changes were observed at baseline in the frontal, occipital, and core basal-ganglia regions, progressing towards the temporal lobe and subcortical regions in the following year. After 1 year, we observed the aforementioned region-specific neurological differences in AD, significantly aiding diagnosis and disease tracking.

Inferring Alzheimer's disease pathologic traits from clinical measures in living adults

Background

Alzheimer's disease neuropathologic changes (AD-NC) are important for identify people with high risk for AD dementia (ADD) and subtyping ADD.

Objective

Develop imputation models based on clinical measures to infer AD-NC.

Methods

We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-β, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD.

Results

Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2=(0.188,0.316,0.262) respectively for amyloid-β, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC=(0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values<1.4 × 10-10.

Conclusion

Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.

Inferring Alzheimer's Disease Pathologic Traits from Clinical Measures in Living Adults

Background

Alzheimer's disease neuropathologic changes (AD-NC) are important to identify people with high risk for AD dementia (ADD) and subtyping ADD.

Objective

Develop imputation models based on clinical measures to infer AD-NC.

Methods

We used penalized generalized linear regression to train imputation models for four AD-NC traits (amyloid-β, tangles, global AD pathology, and pathologic AD) in Rush Memory and Aging Project decedents, using clinical measures at the last visit prior to death as predictors. We validated these models by inferring AD-NC traits with clinical measures at the last visit prior to death for independent Religious Orders Study (ROS) decedents. We inferred baseline AD-NC traits for all ROS participants at study entry, and then tested if inferred AD-NC traits at study entry predicted incident ADD and postmortem pathologic AD.

Results

Inferred AD-NC traits at the last visit prior to death were related to postmortem measures with R2 = (0.188,0.316,0.262) respectively for amyloid-β, tangles, and global AD pathology, and prediction Area Under the receiver operating characteristic Curve (AUC) 0.765 for pathologic AD. Inferred baseline levels of all four AD-NC traits predicted ADD. The strongest prediction was obtained by the inferred baseline probabilities of pathologic AD with AUC = (0.919,0.896) for predicting the development of ADD in 3 and 5 years from baseline. The inferred baseline levels of all four AD-NC traits significantly discriminated pathologic AD profiled eight years later with p-values < 1.4×10-10.

Conclusions

Inferred AD-NC traits based on clinical measures may provide effective AD biomarkers that can estimate the burden of AD-NC traits in aging adults.

Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis

Graph learning methods have achieved noteworthy performance in disease diagnosis due to their ability to represent unstructured information such as inter-subject relationships. While it has been shown that imaging, genetic and clinical data are crucial for degenerative disease diagnosis, existing methods rarely consider how best to use their relationships. How best to utilize information from imaging, genetic and clinical data remains a challenging problem. This study proposes a novel graph-based fusion (GBF) approach to meet this challenge. To extract effective imaging-genetic features, we propose an imaging-genetic fusion module which uses an attention mechanism to obtain modality-specific and joint representations within and between imaging and genetic data. Then, considering the effectiveness of clinical information for diagnosing degenerative diseases, we propose a multi-graph fusion module to further fuse imaging-genetic and clinical features, which adopts a learnable graph construction strategy and a graph ensemble method. Experimental results on two benchmarks for degenerative disease diagnosis (Alzheimers Disease Neuroimaging Initiative and Parkinson's Progression Markers Initiative) demonstrate its effectiveness compared to state-of-the-art graph-based methods. Our findings should help guide further development of graph-based models for dealing with imaging, genetic and clinical data.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

Gamma neuromodulation improves episodic memory and its associated network in amnestic mild cognitive impairment: a pilot study

Amnestic mild cognitive impairment (aMCI) is a predementia stage of Alzheimer's disease associated with dysfunctional episodic memory and limited treatment options. We aimed to characterize feasibility, clinical, and biomarker effects of noninvasive neurostimulation for aMCI. 13 individuals with aMCI received eight 60-minute sessions of 40-Hz (gamma) transcranial alternating current stimulation (tACS) targeting regions related to episodic memory processing. Feasibility, episodic memory, and plasma Alzheimer's disease biomarkers were assessed. Neuroplastic changes were characterized by resting-state functional connectivity (RSFC) and neuronal excitatory/inhibitory balance. Gamma tACS was feasible and aMCI participants demonstrated improvement in multiple metrics of episodic memory, but no changes in biomarkers. Improvements in episodic memory were most pronounced in participants who had the highest modeled tACS-induced electric fields and exhibited the greatest changes in RSFC. Increased RSFC was also associated with greater hippocampal excitability and higher baseline white matter integrity. This study highlights initial feasibility and the potential of gamma tACS to rescue episodic memory in an aMCI population by modulating connectivity and excitability within an episodic memory network.

Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease

Background: Alzheimer's disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from resting-state functional magnetic resonance imaging. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods: Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states, and then estimated a hidden Markov model and the occupancy rate for each subject. Then, we investigated the link between CCN dFC features and AD progression. Also, we investigated the link between sFC and AD progression and compared its results with dFC results. Results: Progression of AD symptoms was associated with increases in connectivity within the middle frontal gyrus. Also, the very mild AD (vmAD) showed less connectivity within the inferior parietal lobule (in both sFC and dFC) and between this region and the rest of CCN (in dFC analysis). Also, we found that within-middle frontal gyrus connectivity increases with AD progression in both sFC and dFC results. Finally, comparing with vmAD, we found that the normal brain spends significantly more time in a state with lower within-middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion: Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD.

Functional MRI-specific alterations in frontoparietal network in mild cognitive impairment: an ALE meta-analysis

Background

Mild cognitive impairment (MCI) depicts a transitory phase between healthy elderly and the onset of Alzheimer's disease (AD) with worsening cognitive impairment. Some functional MRI (fMRI) research indicated that the frontoparietal network (FPN) could be an essential part of the pathophysiological mechanism of MCI. However, damaged FPN regions were not consistently reported, especially their interactions with other brain networks. We assessed the fMRI-specific anomalies of the FPN in MCI by analyzing brain regions with functional alterations.

Methods

PubMed, Embase, and Web of Science were searched to screen neuroimaging studies exploring brain function alterations in the FPN in MCI using fMRI-related indexes, including the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity. We integrated distinctive coordinates by activating likelihood estimation, visualizing abnormal functional regions, and concluding functional alterations of the FPN.

Results

We selected 29 studies and found specific changes in some brain regions of the FPN. These included the bilateral dorsolateral prefrontal cortex, insula, precuneus cortex, anterior cingulate cortex, inferior parietal lobule, middle temporal gyrus, superior frontal gyrus, and parahippocampal gyrus. Any abnormal alterations in these regions depicted interactions between the FPN and other networks.

Conclusion

The study demonstrates specific fMRI neuroimaging alterations in brain regions of the FPN in MCI patients. This could provide a new perspective on identifying early-stage patients with targeted treatment programs.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432042, identifier: CRD42023432042.

Functional and structural alterations of dorsal attention network in preclinical and early-stage Alzheimer's disease

OBJECTIVES

Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) are known as the preclinical and early stage of Alzheimer's disease (AD). The dorsal attention network (DAN) is mainly responsible for the "top-down" attention process. However, previous studies mainly focused on single functional modality and limited structure. This study aimed to investigate the multimodal alterations of DAN in SCD and aMCI to assess their diagnostic value in preclinical and early-stage AD.

METHODS

Resting-state functional magnetic resonance imaging (MRI) was carried out to measure the fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and functional connectivity (FC). Structural MRI was used to calculate the gray matter volume (GMV) and cortical thickness. Moreover, receiver-operating characteristic (ROC) analysis was used to distinguish these alterations in SCD and aMCI.

RESULTS

The SCD and aMCI groups showed both decreased ReHo in the right middle temporal gyrus (MTG) and decreased GMV compared to healthy controls (HCs). Especially in the SCD group, there were increased fALFF and increased ReHo in the left inferior occipital gyrus (IOG), decreased fALFF and increased FC in the left inferior parietal lobule (IPL), and reduced cortical thickness in the right inferior temporal gyrus (ITG). Furthermore, functional and structural alterations in the SCD and aMCI groups were closely related to episodic memory (EM), executive function (EF), and information processing speed (IPS). The combination of multiple indicators of DAN had a high accuracy in differentiating clinical stages.

CONCLUSIONS

Our current study demonstrated functional and structural alterations of DAN in SCD and aMCI, especially in the MTG, IPL, and SPL. Furthermore, cognitive performance was closely related to these significant alterations. Our study further suggested that the combined multiple indicators of DAN could be acted as the latent neuroimaging markers of preclinical and early-stage AD for their high diagnostic value.

Neuroimaging studies of acupuncture on Alzheimer's disease: a systematic review

BACKGROUND

Acupuncture effectively improves cognitive function in Alzheimer's disease (AD). Many neuroimaging studies have found significant brain alterations after acupuncture treatment of AD, but the underlying central modulation mechanism is unclear.

OBJECTIVE

This review aims to provide neuroimaging evidence to understand the central mechanisms of acupuncture in patients with AD.

METHODS

Relevant neuroimaging studies about acupuncture for AD were retrieved from eight English and Chinese medicine databases (PubMed, Embase, Cochrane Library, Web of Science, SinoMed, CNKI, WF, VIP) and other resources from inception of databases until June 1, 2022, and their methodological quality was assessed using RoB 2.0 and ROBINS - I. Brain neuroimaging information was extracted to investigate the potential neural mechanism of acupuncture for AD. Descriptive statistics were used for data analysis.

RESULTS

Thirteen neuroimaging studies involving 275 participants were included in this review, and the overall methodological quality of included studies was moderate. The approaches applied included task-state functional magnetic resonance imaging (ts-fMRI; n = 9 studies) and rest-state functional magnetic resonance imaging (rs-fMRI; n = 4 studies). All studies focused on the instant effect of acupuncture on the brains of AD participants, including the cingulate gyrus, middle frontal gyrus, and cerebellum, indicating that acupuncture may regulate the default mode, central executive, and frontoparietal networks.

CONCLUSION

This study provides evidence of the neural mechanisms underlying the effect of acupuncture on AD involving cognitive- and motor-associated networks. However, this evidence is still in the preliminary investigation stage. Large-scale, well-designed, multimodal neuroimaging trials are still required to provide comprehensive insight into the central mechanism underlying the effect of acupuncture on AD. (Systematic review registration at PROSPERO, No. CRD42022331527).

Genetic Risk for Alzheimer Disease and Plasma Tau Are Associated With Accelerated Parietal Cortex Thickness Change in Middle-Aged Adults

Background and Objectives

Neuroimaging and biomarker studies in Alzheimer disease (AD) have shown well-characterized patterns of cortical thinning and altered biomarker concentrations of tau and β-amyloid (Aβ). However, earlier identification of AD has great potential to advance clinical care and determine candidates for drug trials. The extent to which AD risk markers relate to cortical thinning patterns in midlife is unknown. The first objective of this study was to examine cortical thickness change associated with genetic risk for AD among middle-aged military veterans. The second objective was to determine the relationship between plasma tau and Aβ and change in brain cortical thickness among veterans stratified by genetic risk for AD.

Methods

Participants consisted of post-9/11 veterans (N = 155) who were consecutively enrolled in the Translational Research Center for TBI and Stress Disorders prospective longitudinal cohort and were assessed for mild traumatic brain injury (TBI) and posttraumatic disorder (PTSD). Genome-wide polygenic risk scores (PRSs) for AD were calculated using summary results from the International Genomics of Alzheimer's Disease Project. T-tau and Aβ40 and Aβ42 plasma assays were run using Simoa technology. Whole-brain MRI cortical thickness change estimates were obtained using the longitudinal stream of FreeSurfer. Follow-up moderation analyses examined the AD PRS × plasma interaction on change in cortical thickness in AD-vulnerable regions.

Results

Higher AD PRS, signifying greater genetic risk for AD, was associated with accelerated cortical thickness change in a right hemisphere inferior parietal cortex cluster that included the supramarginal gyrus, angular gyrus, and intraparietal sulcus. Higher tau, but not Aβ42/40 ratio, was associated with greater cortical thickness change among those with higher AD PRS. Mild TBI and PTSD were not associated with cortical thickness change.

Discussion

Plasma tau, particularly when combined with genetic stratification for AD risk, can be a useful indicator of brain change in midlife. Accelerated inferior parietal cortex changes in midlife may be an important factor to consider as a marker of AD-related brain alterations.

Combined metabolic activators improve cognitive functions in Alzheimer's disease patients: a randomised, double-blinded, placebo-controlled phase-II trial

BACKGROUND

Alzheimer's disease (AD) is associated with metabolic abnormalities linked to critical elements of neurodegeneration. We recently administered combined metabolic activators (CMA) to the AD rat model and observed that CMA improves the AD-associated histological parameters in the animals. CMA promotes mitochondrial fatty acid uptake from the cytosol, facilitates fatty acid oxidation in the mitochondria, and alleviates oxidative stress.

METHODS

Here, we designed a randomised, double-blinded, placebo-controlled phase-II clinical trial and studied the effect of CMA administration on the global metabolism of AD patients. One-dose CMA included 12.35 g L-serine (61.75%), 1 g nicotinamide riboside (5%), 2.55 g N-acetyl-L-cysteine (12.75%), and 3.73 g L-carnitine tartrate (18.65%). AD patients received one dose of CMA or placebo daily during the first 28 days and twice daily between day 28 and day 84. The primary endpoint was the difference in the cognitive function and daily living activity scores between the placebo and the treatment arms. The secondary aim of this study was to evaluate the safety and tolerability of CMA. A comprehensive plasma metabolome and proteome analysis was also performed to evaluate the efficacy of the CMA in AD patients.

RESULTS

We showed a significant decrease of AD Assessment Scale-cognitive subscale (ADAS-Cog) score on day 84 vs day 0 (P = 0.00001, 29% improvement) in the CMA group. Moreover, there was a significant decline (P = 0.0073) in ADAS-Cog scores (improvement of cognitive functions) in the CMA compared to the placebo group in patients with higher ADAS-Cog scores. Improved cognitive functions in AD patients were supported by the relevant alterations in the hippocampal volumes and cortical thickness based on imaging analysis. Moreover, the plasma levels of proteins and metabolites associated with NAD + and glutathione metabolism were significantly improved after CMA treatment.

CONCLUSION

Our results indicate that treatment of AD patients with CMA can lead to enhanced cognitive functions and improved clinical parameters associated with phenomics, metabolomics, proteomics and imaging analysis. Trial registration ClinicalTrials.gov NCT04044131 Registered 17 July 2019, https://clinicaltrials.gov/ct2/show/NCT04044131.

Alzheimer's disease classification using cluster-based labelling for graph neural network on heterogeneous data

Biomarkers for Alzheimer's disease (AD) diagnosis do not always correlate reliably with cognitive symptoms, making clinical diagnosis inconsistent. In this study, the performance of a graphical neural network (GNN) classifier based on data-driven diagnostic classes from unsupervised clustering on heterogeneous data is compared to the performance of a classifier using clinician diagnosis as an outcome. Unsupervised clustering on tau-positron emission tomography (PET) and cognitive and functional assessment data was performed. Five clusters embedded in a non-linear uniform manifold approximation and project (UMAP) space were identified. The individual clusters revealed specific feature characteristics with respect to clinical diagnosis of AD, gender, family history, age, and underlying neurological risk factors (NRFs). In particular, one cluster comprised mainly diagnosed AD cases. All cases within this cluster were re-labelled AD cases. The re-labelled cases are characterized by high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, even though Aβ data was not used for clustering. A GNN model was trained using the re-labelled data with a multiclass area-under-the-curve (AUC) of 95.2%, higher than the AUC of a GNN trained on clinician diagnosis (91.7%; p = 0.02). Overall, our work suggests that more objective cluster-based diagnostic labels combined with GNN classification may have value in clinical risk stratification and diagnosis of AD.

Deep Spatio-Temporal Attention-based Recurrent Network from Dynamic Adaptive Functional Connectivity for MCI Identification

Most existing methods of constructing dynamic functional connectivity (dFC) network obtain the connectivity strength via the sliding window correlation (SWC) method, which estimates the connectivity strength at each time segment, rather than at each time point, and thus is difficult to produce accurate dFC network due to the influence of the window type and window width. Furthermore, the deep learning methods may not capture the discriminative spatio-temporal information that is closely related to disease, thus impacting the performance of (mild cognitive impairment) MCI identification. In this paper, a novel spatio-temporal attention-based bidirectional gated recurrent unit (STA-BiGRU) network is proposed to extract inherent spatio-temporal information from a dynamic adaptive functional connectivity (dAFC) network for MCI diagnosis. Specifically, we adopt a group lasso-based Kalman filter algorithm to obtain the dAFC network with more accurate connectivity strength at each time step. Then a spatial attention module with self-attention and a temporal attention module with multiple temporal attention vectors are incorporated into the BiGRU network to extract more discriminative disease-related spatio-temporal information. Finally, the spatio-temporal regularizations are employed to better guide the attention learning of STA-BiGRU network to enhance the robustness of the deep network. Experimental results show that the proposed framework achieves mean accuracies of 90.2%, 90.0%, and 81.5%, respectively, for three MCI classification tasks. This study provides a more effective deep spatio-temporal attention-based recurrent network and obtains good performance and interpretability of deep learning for psychiatry diagnosis research.

Harmonization of Multicenter Cortical Thickness Data by Linear Mixed Effect Model

Objective

Analyzing neuroimages being useful method in the field of neuroscience and neurology and solving the incompatibilities across protocols and vendors have become a major problem. We referred to this incompatibility as "center effects," and in this study, we attempted to correct such center effects of cortical feature obtained from multicenter magnetic resonance images (MRIs).

Methods

For MRI of a total of 4,321 multicenter subjects, the harmonized w-score was calculated by correcting biological covariates such as age, sex, years of education, and intercranial volume (ICV) as fixed effects and center information as a random effect. Afterward, we performed classification tasks using principal component analysis (PCA) and linear discriminant analysis (LDA) to check whether the center effect was successfully corrected from the harmonized w-score.

Results

First, an experiment was conducted to predict the dataset origin of a random subject sampled from two different datasets, and it was confirmed that the prediction accuracy of linear mixed effect (LME) model-based w-score was significantly closer to the baseline than that of raw cortical thickness. As a second experiment, we classified the data of the normal and patient groups of each dataset, and LME model-based w-score, which is biological-feature-corrected values, showed higher classification accuracy than the raw cortical thickness data. Afterward, to verify the compatibility of the dataset used for LME model training and the dataset that is not, intraobject comparison and w-score RMSE calculation process were performed.

Conclusion

Through comparison between the LME model-based w-score and existing methods and several classification tasks, we showed that the LME model-based w-score sufficiently corrects the center effects while preserving the disease effects from the dataset. We also showed that the preserved disease effects have a match with well-known disease atrophy patterns such as Alzheimer's disease or Parkinson's disease. Finally, through intrasubject comparison, we found that the difference between centers decreases in the LME model-based w-score compared with the raw cortical thickness and thus showed that our model well-harmonizes the data that are not used for the model training.

Investigation of Underlying Association Between Whole Brain Regions and Alzheimer’s Disease: A Research Based on an Artificial Intelligence Model

Alzheimer’s disease (AD) is the most common form of dementia, causing progressive cognitive decline. Radiomic features obtained from structural magnetic resonance imaging (sMRI) have shown a great potential in predicting this disease. However, radiomic features based on the whole brain segmented regions have not been explored yet. In our study, we collected sMRI data that include 80 patients with AD and 80 healthy controls (HCs). For each patient, the T1 weighted image (T1WI) images were segmented into 106 subregions, and radiomic features were extracted from each subregion. Then, we analyzed the radiomic features of specific brain subregions that were most related to AD. Based on the selective radiomic features from specific brain subregions, we built an integrated model using the best machine learning algorithms, and the diagnostic accuracy was evaluated. The subregions most relevant to AD included the hippocampus, the inferior parietal lobe, the precuneus, and the lateral occipital gyrus. These subregions exhibited several important radiomic features that include shape, gray level size zone matrix (GLSZM), and gray level dependence matrix (GLDM), among others. Based on the comparison among different algorithms, we constructed the best model using the Logistic regression (LR) algorithm, which reached an accuracy of 0.962. Conclusively, we constructed an excellent model based on radiomic features from several specific AD-related subregions, which could give a potential biomarker for predicting AD.

Central Auditory Functions of Alzheimer's Disease and Its Preclinical Stages: A Systematic Review and Meta-Analysis

In 2020, 55 million people worldwide were living with dementia, and this number is projected to reach 139 million in 2050. However, approximately 75% of people living with dementia have not received a formal diagnosis. Hence, they do not have access to treatment and care. Without effective treatment in the foreseeable future, it is essential to focus on modifiable risk factors and early intervention. Central auditory processing is impaired in people diagnosed with Alzheimer's disease (AD) and its preclinical stages and may manifest many years before clinical diagnosis. This study systematically reviewed central auditory processing function in AD and its preclinical stages using behavioural central auditory processing tests. Eleven studies met the full inclusion criteria, and seven were included in the meta-analyses. The results revealed that those with mild cognitive impairment perform significantly worse than healthy controls within channel adaptive tests of temporal response (ATTR), time-compressed speech test (TCS), Dichotic Digits Test (DDT), Dichotic Sentence Identification (DSI), Speech in Noise (SPIN), and Synthetic Sentence Identification-Ipsilateral Competing Message (SSI-ICM) central auditory processing tests. In addition, this analysis indicates that participants with AD performed significantly worse than healthy controls in DDT, DSI, and SSI-ICM tasks. Clinical implications are discussed in detail.

Integrative Factor Regression and Its Inference for Multimodal Data Analysis

Multimodal data, where different types of data are collected from the same subjects, are fast emerging in a large variety of scientific applications. Factor analysis is commonly used in integrative analysis of multimodal data, and is particularly useful to overcome the curse of high dimensionality and high correlations. However, there is little work on statistical inference for factor analysis based supervised modeling of multimodal data. In this article, we consider an integrative linear regression model that is built upon the latent factors extracted from multimodal data. We address three important questions: how to infer the significance of one data modality given the other modalities in the model; how to infer the significance of a combination of variables from one modality or across different modalities; and how to quantify the contribution, measured by the goodness-of-fit, of one data modality given the others. When answering each question, we explicitly characterize both the benefit and the extra cost of factor analysis. Those questions, to our knowledge, have not yet been addressed despite wide use of factor analysis in integrative multimodal analysis, and our proposal bridges an important gap. We study the empirical performance of our methods through simulations, and further illustrate with a multimodal neuroimaging analysis.

Cortical thickness is differently associated with ALDH2 rs671 polymorphism according to level of amyloid deposition

Accumulating evidence indicates that amyloid-beta (Aβ) deposition and biogenic aldehyde accumulation contribute to the pathogenesis of neurodegenerative diseases. Human aldehyde dehydrogenase 2 (ALDH2) metabolizes biogenic aldehydes produced in the brain to prevent damage. However, r671G>A, a single nucleotide polymorphism of ALDH2, causes aldehyde accumulation and decreased ALDH2 activity. We aimed to investigate whether Aβ deposition and rs671 polymorphism have an interaction effect on cortical thickness (CTh). We grouped 179 participants in the Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study as follows: amyloid (–) [A(–)] and amyloid (+) [A(+)] groups based on the Aβ deposition degree; A-carrier (AC) and GG (GG) groups based on the presence/absence of the rs671 A allele; and their combinations, i.e., A(–)AC, A(–)GG, A(+)AC, and A(+)GG groups. A multiple regression analysis identified nine regions of interest. Compared with the A(–)GG group, the A(–)AC group showed thinner CTh in all regions. There were no significant differences between the A(+)AC and A(+)GG groups. We observed an interaction effect of amyloid deposition and rs671 polymorphism on CTh. The CTh in the A(–) group appeared to be strongly influenced by rs671 polymorphism, which could have contributed to cortical thinning and biogenic aldehyde accumulation in the AC group. Additionally, CTh in the A(+) group appeared to be strongly influenced by amyloid deposition.

Altered Functional Connectivity Patterns of Parietal Subregions Contribute to Cognitive Dysfunction in Patients with White Matter Hyperintensities

BACKGROUND

The white matter hyperintensities (WMHs) are considered as one of the core neuroimaging findings of cerebral small vessel disease and independently associated with cognitive deficit. The parietal lobe is a heterogeneous area containing many subregions and play an important role in the processes of neurocognition.

OBJECTIVE

To explore the relationship between parietal subregions alterations and cognitive impairments in WHMs.

METHODS

Resting-state functional connectivity (rs-FC) analyses of parietal subregions were performed in 104 right-handed WMHs patients divided into mild (n = 39), moderate (n = 37), and severe WMHs (n = 28) groups according to the Fazekas scale and 36 healthy controls. Parietal subregions were defined using tractographic Human Brainnetome Atlas and included five subregions for superior parietal lobe, six subregions for inferior parietal lobe (IPL), and three subregions for precuneus. All participants underwent a neuropsychological test battery to evaluate emotional and general cognitive functions.

RESULTS

Differences existed between the rs-FC strength of IPL_R_6_2 with the left anterior cingulate gyrus, IPL_R_6_3 with the right dorsolateral superior frontal gyrus, and the IPL_R_6_5 with the left anterior cingulate gyrus. The connectivity strength between IPL_R_6_3 and the left anterior cingulate gyrus were correlated with AVLT-immediate and AVLT-recognition test in WMHs.

CONCLUSION

We explored the roles of parietal subregions in WMHs using rs-FC. The functional connectivity of parietal subregions with the cortex regions showed significant differences between the patients with WMHs and healthy controls which may be associated with cognitive deficits in WMHs.

Effect of different parietal hypoperfusion on neuropsychological characteristics in mild cognitive impairment

BACKGROUND

In early-stage amnestic mild cognitive impairment (aMCI), differences in the neuropsychological characteristics of each individual are subtle. We investigated differences in neuropsychological performance between aMCI patients with and without hypoperfusion in the medial parietal regions (MP). We further compared patients with hypoperfusion in the left and right lateral parietal regions.

METHODS

We examined 165 aMCI patients (mean age: 76.8 ± 5.5 years; 87 women) who had undergone neuropsychological measurement and single-photon emission computed tomography. We classified participants into two subgroups with and without hypoperfusion: MP hypoperfusion (+) and MP hypoperfusion (-); classification was based on Z-scores (calculated by three-dimensional stereotactic surface projection technique) of three regions of interest in the parietal lobes (i.e. MP regions including posterior cingulate cortex and precuneus and left and right inferior parietal lobules (lateral parietal regions)). The MP hypoperfusion (-) group was classified into left lateral parietal hypoperfusion (+) and right lateral parietal hypoperfusion (+) subgroups. We performed either univariate or multivariate ancova to compare neuropsychological scores for continuous variables between groups and examined dichotomous variables using χ² tests.

RESULTS

In the overall aMCI sample, scores on logical memory delayed recall in the MP hypoperfusion (+) group were significantly lower than those in the MP hypoperfusion (-) group. Total scores on Rey-Osterrieth Complex Figure Test delayed recall were also marginally lower in the MP hypoperfusion (+) group than in the MP hypoperfusion (-) group. Comparisons of neuropsychological test scores between the left and right lateral parietal hypoperfusion (+) groups revealed no significant differences.

CONCLUSIONS

The present findings suggest that MP hypoperfusion (+) is associated with more robust memory deficits than MP hypoperfusion (-). Combining neuropsychological tests and single-photon emission computed tomography findings may be useful for early detection of cognitive decline in aMCI.

Precision Repetitive Transcranial Magnetic Stimulation Over the Left Parietal Cortex Improves Memory in Alzheimer's Disease: A Randomized, Double-Blind, Sham-Controlled Study

Objective

We aim to study the effect of precision repetitive transcranial magnetic stimulation (rTMS) over the left parietal cortex on the memory and cognitive function in Alzheimer’s disease (AD).

Methods

Based on the resting-state functional magnetic resonance imaging, the left parietal cortex site with the highest functional connectivity to the hippocampus was selected as the target of rTMS treatment. Sixty-nine AD patients were randomized to either rTMS or sham treatment (five sessions/week for a total of 10 sessions). The Mini-Mental State Examination (MMSE), 12-Word Philadelphia Verbal Learning Test (PVLT), and Clinical Dementia Rating (CDR) were assessed at baseline and after the last session.

Results

After a 2-week treatment, compared to patients in the sham group, those in the rTMS group scored significantly higher on PVLT total score and its immediate recall subscale score. Moreover, in the rTMS group, there were significant improvements after the 2-week treatment, which were manifested in MMSE total score and its time orientation and recall subscale scores, as well as PVLT total score and its immediate recall and short delay recall subscale scores. In the sham group, the PVLT total score was significantly improved.

Conclusion

The target site of the left parietal cortex can improve AD patients’ cognitive function, especially memory, providing a potential therapy.

AD Course Map charts Alzheimer's disease progression

Alzheimer's disease (AD) is characterized by the progressive alterations seen in brain images which give rise to the onset of various sets of symptoms. The variability in the dynamics of changes in both brain images and cognitive impairments remains poorly understood. This paper introduces AD Course Map a spatiotemporal atlas of Alzheimer's disease progression. It summarizes the variability in the progression of a series of neuropsychological assessments, the propagation of hypometabolism and cortical thinning across brain regions and the deformation of the shape of the hippocampus. The analysis of these variations highlights strong genetic determinants for the progression, like possible compensatory mechanisms at play during disease progression. AD Course Map also predicts the patient's cognitive decline with a better accuracy than the 56 methods benchmarked in the open challenge TADPOLE. Finally, AD Course Map is used to simulate cohorts of virtual patients developing Alzheimer's disease. AD Course Map offers therefore new tools for exploring the progression of AD and personalizing patients care.

Left lateral parietal rTMS improves cognition and modulates resting brain connectivity in patients with Alzheimer's disease: Possible role of BDNF and oxidative stress

Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive neuromodulation technique which is increasingly used for cognitive impairment in Alzheimer's Disease (AD). Although rTMS has been shown to modify Brain-Derived Neurotrophic Factor (BDNF) and oxidative stress levels in many neurological and psychiatric diseases, there is still no study evaluating the relationship between memory performance, BDNF, oxidative stress, and resting brain connectivity following rTMS in Alzheimer's patients. Furthermore, there are increasing clinical data showing that the stimulation of strategic brain regions may lead to more robust improvements in memory functions compared to conventional rTMS. In this study, we aimed to evaluate the possible disease-modifying effects of rTMS on the lateral parietal cortex in AD patients who have the highest connectivity with the hippocampus. To fill the mentioned research gaps, we have evaluated the relationships between resting-state Functional Magnetic Resonance Imaging (fMRI), cognitive scores, blood BDNF levels, and total oxidative/antioxidant status to explain the therapeutic and potential disease-modifying effects of rTMS which has been applied at 20 Hz frequencies for two weeks. Our results showed significantly increased visual recognition memory functions and clock drawing test scores which were associated with elevated peripheral BDNF levels, and decreased oxidant status after two weeks of left lateral parietal TMS stimulation. Clinically our findings suggest that the left parietal region targeted rTMS application leads to significant improvement in familiarity-based cognition associated with the network connections between the left parietal region and the hippocampus.

Inclusion of African American/Black adults in a pilot brain proteomics study of Alzheimer's disease

Alzheimer's disease (AD) disproportionately affects certain racial and ethnic subgroups, such as African American/Black and Hispanic adults. Genetic, comorbid, and socioeconomic risk factors contribute to this disparity; however, the molecular contributions have been largely unexplored. Herein, we conducted a pilot proteomics study of postmortem brains from African American/Black and non-Hispanic White adults neuropathologically diagnosed with AD compared to closely-matched cognitively normal individuals. Examination of hippocampus, inferior parietal lobule, and globus pallidus regions using quantitative proteomics resulted in 568 differentially-expressed proteins in AD. These proteins were consistent with the literature and included glial fibrillary acidic protein, peroxiredoxin-1, and annexin A5. In addition, 351 novel proteins in AD were identified, which could partially be due to cohort diversity. From linear regression analyses, we identified 185 proteins with significant race x diagnosis interactions across various brain regions. These differences generally were reflective of differential expression of proteins in AD that occurred in only a single racial/ethnic group. Overall, this pilot study suggests that disease understanding can be furthered by including diversity in racial/ethnic groups; however, this must be done on a larger scale.

Associating Multi-Modal Brain Imaging Phenotypes and Genetic Risk Factors via a Dirty Multi-Task Learning Method

Brain imaging genetics becomes more and more important in brain science, which integrates genetic variations and brain structures or functions to study the genetic basis of brain disorders. The multi-modal imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary information. Unfortunately, we do not know the extent to which the phenotypic variance is shared among multiple imaging modalities, which further might trace back to the complex genetic mechanism. In this paper, we propose a novel dirty multi-task sparse canonical correlation analysis (SCCA) to study imaging genetic problems with multi-modal brain imaging quantitative traits (QTs) involved. The proposed method takes advantages of the multi-task learning and parameter decomposition. It can not only identify the shared imaging QTs and genetic loci across multiple modalities, but also identify the modality-specific imaging QTs and genetic loci, exhibiting a flexible capability of identifying complex multi-SNP-multi-QT associations. Using the state-of-the-art multi-view SCCA and multi-task SCCA, the proposed method shows better or comparable canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. In addition, the identified modality-consistent biomarkers, as well as the modality-specific biomarkers, provide meaningful and interesting information, demonstrating the dirty multi-task SCCA could be a powerful alternative method in multi-modal brain imaging genetics.

Digital biomarker-based individualized prognosis for people at risk of dementia

Background

Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models.

Method

We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments.

Results

Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance.

Conclusion

Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.

Medical Conditions Linked to Atherosclerosis Are Associated With Magnified Cortical Thinning in Individuals With Alcohol Use Disorders

AIMS

Magnetic resonance imaging (MRI) studies report widespread cortical thinning in individuals with alcohol use disorder (AUD), but did not consider potential effects of pro-atherogenic conditions such as hypertension, type 2 diabetes mellitus, hepatitis C seropositivity and hyperlipidemia on cortical thickness. The conditions are associated with regional cortical thinning in those without AUD. We predicted that individuals with concurrent AUD and pro-atherogenic conditions demonstrate the greatest regional cortical thinning in areas most vulnerable to decreased perfusion.

METHODS

Treatment-seeking individuals with AUD (n = 126) and healthy controls (CON; n = 49) completed a 1.5 T MRI study. Regional cortical thickness was quantitated via FreeSurfer. Individuals with AUD and pro-atherogenic conditions (Atherogenic+), AUD without pro-atherogenic conditions (Atherogenic-) and CON were compared on regional cortical thickness.

RESULTS

Individuals with AUD showed significant bilateral cortical thinning compared to CON, but Atherogenic+ demonstrated the most widespread and greatest magnitude of regional thinning, while Atherogenic- had reduced thickness primarily in anterior frontal and posterior parietal lobes. Atherogenic+ also showed a thinner cortex than Atherogenic- in lateral orbitofrontal and dorso/dorsolateral frontal cortex, mesial and lateral temporal and inferior parietal regions.

CONCLUSIONS

Our results demonstrate significant bilateral cortical thinning in individuals with AUD relative to CON, but the distribution and magnitude were influenced by comorbid pro-atherogenic conditions. The magnitude of cortical thinning in Atherogenic+ strongly corresponded to cortical watershed areas susceptible to decreased perfusion, which may result in morphometric abnormalities. The findings indicate that pro-atherogenic conditions may contribute to cortical thinning in those seeking treatment for AUD.

A Novel Connectome-Based Electrophysiological Study of Subjective Cognitive Decline Related to Alzheimer's Disease by Using Resting-State High-Density EEG EGI GES 300

Aim: To investigate for the first time the brain network in the Alzheimer’s disease (AD) spectrum by implementing a high-density electroencephalography (HD-EEG - EGI GES 300) study with 256 channels in order to seek if the brain connectome can be effectively used to distinguish cognitive impairment in preclinical stages. Methods: Twenty participants with AD, 30 with mild cognitive impairment (MCI), 20 with subjective cognitive decline (SCD) and 22 healthy controls (HC) were examined with a detailed neuropsychological battery and 10 min resting state HD-EEG. We extracted correlation matrices by using Pearson correlation coefficients for each subject and constructed weighted undirected networks for calculating clustering coefficient (CC), strength (S) and betweenness centrality (BC) at global (256 electrodes) and local levels (29 parietal electrodes). Results: One-way ANOVA presented a statistically significant difference among the four groups at local level in CC [F (3, 88) = 4.76, p = 0.004] and S [F (3, 88) = 4.69, p = 0.004]. However, no statistically significant difference was found at a global level. According to the independent sample t-test, local CC was higher for HC [M (SD) = 0.79 (0.07)] compared with SCD [M (SD) = 0.72 (0.09)]; t (40) = 2.39, p = 0.02, MCI [M (SD) = 0.71 (0.09)]; t (50) = 0.41, p = 0.004 and AD [M (SD) = 0.68 (0.11)]; t (40) = 3.62, p = 0.001 as well, while BC showed an increase at a local level but a decrease at a global level as the disease progresses. These findings provide evidence that disruptions in brain networks in parietal organization may potentially represent a key factor in the ability to distinguish people at early stages of the AD continuum. Conclusions: The above findings reveal a dynamically disrupted network organization of preclinical stages, showing that SCD exhibits network disorganization with intermediate values between MCI and HC. Additionally, these pieces of evidence provide information on the usefulness of the 256 HD-EEG in network construction.

Group Guided Fused Laplacian Sparse Group Lasso for Modeling Alzheimer's Disease Progression

As the largest cause of dementia, Alzheimer's disease (AD) has brought serious burdens to patients and their families, mostly in the financial, psychological, and emotional aspects. In order to assess the progression of AD and develop new treatment methods for the disease, it is essential to infer the trajectories of patients' cognitive performance over time to identify biomarkers that connect the patterns of brain atrophy and AD progression. In this article, a structured regularized regression approach termed group guided fused Laplacian sparse group Lasso (GFL-SGL) is proposed to infer disease progression by considering multiple prediction of the same cognitive scores at different time points (longitudinal analysis). The proposed GFL-SGL simultaneously exploits the interrelated structures within the MRI features and among the tasks with sparse group Lasso (SGL) norm and presents a novel group guided fused Laplacian (GFL) regularization. This combination effectively incorporates both the relatedness among multiple longitudinal time points with a general weighted (undirected) dependency graphs and useful inherent group structure in features. Furthermore, an alternating direction method of multipliers- (ADMM-) based algorithm is also derived to optimize the nonsmooth objective function of the proposed approach. Experiments on the dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI) show that the proposed GFL-SGL outperformed some other state-of-the-art algorithms and effectively fused the multimodality data. The compact sets of cognition-relevant imaging biomarkers identified by our approach are consistent with the results of clinical studies.

Altered Cingulate Cortex Functional Connectivity in Normal Aging and Mild Cognitive Impairment

Purpose

Resting-state functional Magnetic Resonance Imaging studies revealed that the brain is organized into specialized networks constituted by regions that show a coherent fluctuation of spontaneous activity. Among these networks, the cingulate cortex appears to play a crucial role, particularly in the default mode network, the dorsal attention network and the salience network. In the present study, we mapped the functional connectivity (FC) pattern of different regions of the cingulate cortex: the anterior cingulate cortex, midcingulate cortex and posterior cingulate cortex/retro splenial cortex, which have been in turn divided into a total of 9 subregions. We compared FC patterns of the cingulate subregions in a sample of mild cognitive impairment patients and healthy elderly subjects.

Methods

We enrolled 19 healthy elders (age range: 61-72 y.o.) and 16 Mild cognitive impairment patients (age range 64-87 y.o.). All participants had comparable levels of education (8-10 years) and were neurologically examined to exclude visual and motor impairments, major medical conditions, psychiatric or neurological disorders and consumption of psychotropic drugs. The diagnosis of mild cognitive impairment was performed according to Petersen criteria. Subjects were evaluated with Mini-Mental State Examination, Frontal Assessment Battery, and prose memory (Babcock story) tests. In addition, with functional Magnetic Resonance Imaging, we investigated resting-state network activities.

Results

Healthy elderly, compared to mild cognitive impairment, showed significant increased level of FC for the ventral part of the anterior cingulate cortex in correspondence to the bilateral caudate and ventromedial prefrontal cortex. Moreover, for the midcingulate cortex the healthy elderly group showed increased levels of FC in the somatomotor region, prefrontal cortex, and superior parietal lobule. Meanwhile, the mild cognitive impairment group showed an increased level of FC for the superior frontal gyrus, frontal eye field and orbitofrontal cortex compared to the healthy elderly group.

Conclusion

Our findings indicate that cognitive decline observed in mild cognitive impairment patients damages the global FC of the cingulate cortex, supporting the idea that abnormalities in resting-state activities of the cingulate cortex could be a useful additional tool in order to better understand the brain mechanisms of MCI.

Differentiated Regional Homogeneity in Progressive Mild Cognitive Impairment: A Study With Post Hoc Label

PURPOSE

Mild cognitive impairment (MCI) is considered to be the clinical transition stage between patients with cognitively intact geriatrics and Alzheimer's disease (AD). When observed longitudinally, however, a certain proportion of patients with MCI are expected to revert to a cognitively intact state (MCI_R) while others either remain in the MCI state (MCI_S) or deteriorate into AD (MCI_P). It is worthwhile to investigate the divergence in the brain activities of these MCI groups with different post hoc labels.

METHODS

In this study, we employed the regional homogeneity (ReHo) measure to explore the characteristics of local brain activity in these MCI groups.

RESULTS

Compared to age-matched normal controls, our results exhibited that (1) in MCI_R group, ReHo index showed an increase in the left insula and a decrease in the left superior temporal gyrus; (2) in MCI_S group, ReHo index increased in the left orbital part of the inferior frontal gyrus (IFG_orb) and decreased in the left inferior parietal lobe; and (3) in MCI_P group, ReHo index elevated in the left IFG_orb and decreased in the left putamen.

CONCLUSIONS

The distinct ReHo changes in the individual MCI groups indicated a potential evidence for differentially active interventions for a specific patient with MCI.

Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease

OBJECTIVE

Alzheimers disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from Magnetic Resonance Imaging (MRI) measures. Recently, the multi-task feature learning (MTFL) methods have been widely studied to predict cognitive outcomes and select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, the existing MTFL assumes the correlation among all the tasks is uniform, and the task relatedness is modeled by encouraging a common subset of features with neglecting the inherent structure of tasks and MRI features.

METHODS

In this paper, we proposed a generalized fused group lasso (GFGL) regularization to model the underlying structures, involving (1) a graph structure within tasks and (2) a group structure among the image features. Then, we present a multi-task learning framework (called GFGL-MTFL), combining the ℓ_{2, 1}-norm with the GFGL regularization, to model the flexible structures.

RESULTS

Through empirical evaluation and comparison with different baseline methods and the state-of-the-art MTL methods on data from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we illustrate that the proposed GFGL-MTFL method outperforms other methods in terms of both Mean Squared Error (nMSE) and weighted correlation coefficient (wR). Improvements are statistically significant for most scores (tasks).

CONCLUSIONS

The experimental results with real and synthetic data demonstrate that incorporating the two prior structures by the generalized fused group lasso norm into the multi task feature learning can improve the prediction performance over several state-of-the-art competing methods, and the estimated correlation of the cognitive functions and the identification of cognition relevant imaging markers are clinically and biologically meaningful.

Fully Bayesian spectral methods for imaging data

Medical imaging data with thousands of spatially correlated data points are common in many fields. Methods that account for spatial correlation often require cumbersome matrix evaluations which are prohibitive for data of this size, and thus current work has either used low-rank approximations or analyzed data in blocks. We propose a method that accounts for nonstationarity, functional connectivity of distant regions of interest, and local signals, and can be applied to large multi-subject datasets using spectral methods combined with Markov Chain Monte Carlo sampling. We illustrate using simulated data that properly accounting for spatial dependence improves precision of estimates and yields valid statistical inference. We apply the new approach to study associations between cortical thickness and Alzheimer's disease, and find several regions of the cortex where patients with Alzheimer's disease are thinner on average than healthy controls.

A Potential VEP Biomarker for Mild Cognitive Impairment: Evidence from Selective Visual Deficit of Higher-Level Dorsal Pathway

Visual dysfunctions are common in Alzheimer's disease (AD). Our aim was to establish a neurophysiological biomarker for amnestic mild cognitive impairment (aMCI). Visual evoked potentials (VEPs) were recorded in aMCI patients who later developed AD (n = 15) and in healthy older (n = 15) and younger controls (n = 15). Visual stimuli were optimized to separately activate lower and higher levels of the ventral and dorsal streams. We compared VEP parameters across the three groups of participants and conducted a linear correlation analysis between VEPs and data from neuropsychological tests. We then used a receiver operating characteristic (ROC) analysis to discriminate those with aMCI from those who were healthy older adults. The latency and phase of VEPs to lower-level stimuli (chromatic and achromatic gratings) were significantly affected by age but not by cognitive decline. Conversely, VEP latencies for higher-ventral (faces and kanji-words) and dorsal (kana-words and optic flow motion) stimuli were not affected by age, but they were significantly prolonged in aMCI patients. Interestingly, VEPs for higher-dorsal stimuli were related to outcomes of neuropsychological tests. Furthermore, the ROC analysis showed that the highest areas under the curve were obtained for VEP latencies in response to higher-dorsal stimuli. These results suggest aMCI-related functional impairment specific to higher-level visual processing. Further, dysfunction in the higher-level of the dorsal stream could be an early indicator of cognitive decline. Therefore, we conclude that VEPs associated with higher-level dorsal stream activity can be a sensitive biomarker for early detection of aMCI.

Modeling Alzheimer's disease cognitive scores using multi-task sparse group lasso

Alzheimer's disease (AD) is a severe neurodegenerative disorder characterized by loss of memory and reduction in cognitive functions due to progressive degeneration of neurons and their connections, eventually leading to death. In this paper, we consider the problem of simultaneously predicting several different cognitive scores associated with categorizing subjects as normal, mild cognitive impairment (MCI), or Alzheimer's disease (AD) in a multi-task learning framework using features extracted from brain images obtained from ADNI (Alzheimer's Disease Neuroimaging Initiative). To solve the problem, we present a multi-task sparse group lasso (MT-SGL) framework, which estimates sparse features coupled across tasks, and can work with loss functions associated with any Generalized Linear Models. Through comparisons with a variety of baseline models using multiple evaluation metrics, we illustrate the promising predictive performance of MT-SGL on ADNI along with its ability to identify brain regions more likely to help the characterization Alzheimer's disease progression.

Cortical signature of clock drawing performance in Alzheimer's disease and mild cognitive impairment

It is unclear whether clock drawing test (CDT) performance relies on a widely distributed cortical network, or whether this test predominantly taps into parietal cortex function. So far, associations between cortical integrity and CDT impairment in Alzheimer's disease (AD) and mild cognitive impairment (MCI) largely stem from cortical volume analyses. Given that volume is a product of thickness and surface area, investigation of the relationship between CDT and these two cortical measures might contribute to better understanding of this cognitive screening tool for AD. 38 patients with AD, 38 individuals with MCI and 31 healthy controls (HC) underwent CDT assessment and MRI at 3 Tesla. The surface-based analysis via Freesurfer enabled calculation of cortical thickness and surface area. CDT was scored according to the method proposed by Shulman and related to the two distinct cortical measurements. Higher CDT scores across the entire sample were associated with cortical thickness in bilateral temporal gyrus, the right supramarginal gyrus, and the bilateral parietal gyrus, respectively (p < 0.001 CWP corr.). Significant associations between CDT and cortical thickness reduction in the parietal lobe remained significant when analyses were restricted to AD individuals. There was no statistically significant association between CDT scores and surface area (p < 0.001 CWP corr.). In conclusion, CDT performance may be driven by cortical thickness alterations in regions previously identified as "AD vulnerable", i.e. regions predominantly including temporal and parietal lobes. Our results suggest that cortical features of distinct evolutionary and genetic origin differently contribute to CDT performance.

Association of Neuropathological Markers in the Parietal Cortex With Antemortem Cognitive Function in Persons With Mild Cognitive Impairment and Alzheimer Disease

The associations between cognitive function and neuropathological markers in patients with mild cognitive impairment (MCI) and Alzheimer disease (AD) remain only partly defined. We investigated relationships between antemortem global cognitive scores and β-amyloid (Aβ), tau, TDP-43, synaptic proteins and other key AD neuropathological markers assessed by biochemical approaches in postmortem anterior parietal cortex samples from 36 subjects (12 MCI, 12 AD and 12 not cognitively impaired) from the Religious Orders Study. Overall, the strongest negative correlation coefficients associated with global cognitive scores were obtained for insoluble phosphorylated tau (r2 = -0.484), insoluble Aβ42 (r2 = -0.389) and neurofibrillary tangle counts (r2 = -0.494) (all p < 0.001). Robust inverse associations with cognition scores were also established for TDP-43-positive cytoplasmic inclusions (r2 = -0.476), total insoluble tau (r2 = -0.385) and Aβ plaque counts (r2 = -0.426). Sarkosyl (SK)- or formic acid (FA)-extracted tau showed similar interrelations. On the other hand, synaptophysin (r2 = +0.335), pS403/404 TDP-43 (r2 = +0.265) and septin-3 (r2 = +0.257) proteins positively correlated with cognitive scores. This study suggests that tau and Aβ42 in their insoluble aggregated forms, synaptic proteins and TDP-43 are the markers in the parietal cortex that are most strongly associated with cognitive function. This further substantiates the relevance of investigating these markers to understand the pathogenesis of AD and develop therapeutic tools.

Long-Term Effects of Concussion on Electrophysiological Indices of Attention in Varsity College Athletes: An Event-Related Potential and Standardized Low-Resolution Brain Electromagnetic Tomography Approach

This study investigated the effects of a past concussion on electrophysiological indices of attention in college athletes. Forty-four varsity football athletes (22 with at least one past concussion) participated in three neuropsychological tests and a two-tone auditory oddball task while undergoing high-density event-related potential (ERP) recording. Athletes previously diagnosed with a concussion experienced their most recent injury approximately 4 years before testing. Previously concussed and control athletes performed equivalently on three neuropsychological tests. Behavioral accuracy and reaction times on the oddball task were also equivalent across groups. However, athletes with a concussion history exhibited significantly larger N2 and P3b amplitudes and longer P3b latencies. Source localization using standardized low-resolution brain electromagnetic tomography indicated that athletes with a history of concussion generated larger electrical current density in the left inferior parietal gyrus compared to control athletes. These findings support the hypothesis that individuals with a past concussion recruit compensatory neural resources in order to meet executive functioning demands. High-density ERP measures combined with source localization provide an important method to detect long-term neural consequences of concussion in the absence of impaired neuropsychological performance.

2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception

The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.