Early diagnosis of Alzheimer's disease: contribution of structural neuroimaging

Gray matter volume and corresponding covariance connectivity are biomarkers for major depressive disorder

The major depressive disorder (MDD) is a common and severe mental disorder. To identify a reliable biomarker for MDD is important for early diagnosis and prevention. Given easy access and high reproducibility, the structural magnetic resonance imaging (sMRI) is an ideal method to identify the biomarker for depression. In this study, sMRI data of first episode, treatment-naïve 66 MDD patients and 54 sex-, age-, and education-matched healthy controls (HC) were used to identify the differences in gray matter volume (GMV), group-level, individual-level covariance connections. Finally, the abnormal GMV and individual covariance connections were applied to classify MDD from HC. MDD patients showed higher GMV in middle occipital gyrus (MOG) and precuneus (PCun), and higher structural covariance connections between MOG and PCun. In addition, the Allen Human Brain Atlas (AHBA) was applied and revealed the genetic basis for the changes of gray matter volume. Importantly, we reported that GMV in MOG, PCun and structural covariance connectivity between MOG and PCun are able to discriminate MDD from HC. Our results revealed structural underpinnings for MDD, which may contribute towards early discriminating for depression.

Baseline Frontoparietal Gray Matter Volume Predicts Executive Function Performance in Aging and Mild Cognitive Impairment at 24-Month Follow-Up

Background

Executive dysfunction in mild cognitive impairment (MCI) has been associated with gray matter atrophy. Prior studies have yielded limited insight into associations between gray matter volume and executive function in early and late amnestic MCI (aMCI).

Objective

To examine the relative importance of predictors of executive function at 24 months and relationships between baseline regional gray matter volume and executive function performance at 24-month follow-up in non-demented older adults.

Methods

147 participants from the Alzheimer's Disease Neuroimaging Initiative (mean age = 70.6 years) completed brain magnetic resonance imaging and neuropsychological testing and were classified as cognitively normal (n = 49), early aMCI (n = 60), or late aMCI (n = 38). Analyses explored the importance of demographic, APOEɛ4, biomarker (p-tau/Aβ42, t-tau/Aβ42), and gray matter regions-of-interest (ROI) variables to 24-month executive function, whether ROIs predicted executive function, and whether relationships varied by baseline diagnostic status.

Results

Across all participants, baseline anterior cingulate cortex and superior parietal lobule volumes were the strongest predictors of 24-month executive function performance. In early aMCI, anterior cingulate cortex volume was the strongest predictor and demonstrated a significant interaction such that lower volume related to worse 24-month executive function in early aMCI. Educational attainment and inferior frontal gyrus volume were the strongest predictors of 24-month executive function performance for cognitively normal and late aMCI groups, respectively.

Conclusions

Baseline frontoparietal gray matter regions were significant predictors of executive function performance in the context of aMCI and may identify those at risk of Alzheimer's disease. Anterior cingulate cortex volume may predict executive function performance in early aMCI.

Alterations in cortical thickness of frontoparietal regions in patients with social anxiety disorder

Although functional changes of the frontal and (para)limbic area for emotional hyper-reactivity and emotional dysregulation are well documented in social anxiety disorder (SAD), prior studies on structural changes have shown mixed results. This study aimed to identify differences in cortical thickness between SAD and healthy controls (CON). Thirty-five patients with SAD and forty-two matched CON underwent structural magnetic resonance imaging. A vertex-based whole brain and regional analyses were conducted for between-group comparison. The whole-brain analysis revealed increased cortical thickness in the left insula, left superior parietal lobule, left superior temporal gyrus, and left frontopolar cortex in patients with SAD compared to CON, as well as decreased thickness in the left superior/middle frontal gyrus and left fusiform gyrus in patients (after multiple-correction). The results from the ROI analysis did not align with these findings at the statistically significant level after multiple corrections. Changes in cortical thickness were not correlated with social anxiety symptoms. While consistent results were not obtained from different analysis methods, the results from the whole-brain analysis suggest that patients with SAD exhibit distinct neural deficits in areas involved in salience, attention, and socioemotional processing.

Amyloid Pathology Modulates the Associations of Neuropsychiatric Symptoms with Cognitive Impairments and Neurodegeneration in Non-Demented Elderly

BACKGROUND

The associations between neuropsychiatric symptoms (NPSs) and Alzheimer's disease (AD) have been well-studied, yet gaps remain.

OBJECTIVE

We aimed to examine the associations of four subsyndromes (hyperactivity, psychosis, affective symptoms, and apathy) of NPSs with cognition, neurodegeneration, and AD pathologies.

METHODS

Totally 1,040 non-demented elderly (48.07% males) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were included. We assessed the relationships between NPSs and AD neuropathologies, cognition, neurodegeneration, and clinical correlates in cross-sectional and longitudinal via multiple linear regression, linear mixed effects, and Cox proportional hazard models. Causal mediation analyses were conducted to explore the mediation effects of AD pathologies on cognition and neurodegeneration.

RESULTS

We found that individuals with hyperactivity, psychosis, affective symptoms, or apathy displayed a poorer cognitive status, a lower CSF amyloid-β (Aβ) level and a higher risk of clinical conversion (p < 0.05). Hyperactivity and affective symptoms were associated with increasing cerebral Aβ deposition (p < 0.05). Except psychosis, the other three subsyndromes accompanied with faster atrophy of hippocampal volume (p < 0.05). Specific NPSs were predominantly associated with different cognitive domains decline through an 8-year follow-up (p < 0.05). Moreover, the relationships between NPSs and cognitive decline, neurodegeneration might be associated with Aβ, the mediation percentage varied from 6.05% to 17.51% (p < 0.05).

CONCLUSIONS

NPSs could be strongly associated with AD. The influences of NPSs on cognitive impairments, neurodegeneration might be partially associated with Aβ.

Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics

BACKGROUND

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus.

AIMS

We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC).

METHODS

We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification.

RESULTS

By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances.

CONCLUSIONS

The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.

Perspectives on pain in Down syndrome

Down syndrome (DS) or trisomy 21 is a genetic condition often accompanied by chronic pain caused by congenital abnormalities and/or conditions, such as osteoarthritis, recurrent infections, and leukemia. Although DS patients are more susceptible to chronic pain as compared to the general population, the pain experience in these individuals may vary, attributed to the heterogenous structural and functional differences in the central nervous system, which might result in abnormal pain sensory information transduction, transmission, modulation, and perception. We tried to elaborate on some key questions and possible explanations in this review. Further clarification of the mechanisms underlying such abnormal conditions induced by the structural and functional differences is needed to help pain management in DS patients.

Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer's Disease

Background

The AT[N] research framework focuses on three major biomarkers in Alzheimer's disease (AD): amyloid-β deposition (A), pathologic tau (T), and neurodegeneration [N].

Objective

We hypothesize that the diverse mechanisms such as A⟶T and A⟶[N] pathways from one brain region to others, may underlie the wide variation in clinical symptoms. We aim to uncover the causal-like effect of regional AT[N] biomarkers on cognitive decline as well as the interaction with non-modifiable risk factors such as age and APOE4.

Methods

We apply multi-variate statistical inference to uncover all possible mechanistic spreading pathways through which the aggregation of an upstream biomarker (e.g., increased amyloid level) in a particular brain region indirectly impacts cognitive decline, via the cascade build-up of a downstream biomarker (e.g., reduced metabolism level) in another brain region. Furthermore, we investigate the survival time for each identified region-to-region pathological pathway toward the AD onset.

Results

We have identified a collection of critical brain regions on which the amyloid burdens exert an indirect effect on the decline in memory and executive function (EF) domain, being mediated by the reduction of metabolism level at other brain regions. APOE4 status has been found not only involved in many A⟶N mechanistic pathways but also significantly contributes to the risk of developing AD.

Conclusion

Our major findings include 1) the region-to-region A⟶N⟶MEM and A⟶N⟶MEM pathways exhibit distinct spatial patterns; 2) APOE4 is significantly associated with both direct and indirect effects on the cognitive decline while sex difference has not been identified in the mediation analysis.

Automatic Analysis of MRI Images for Early Prediction of Alzheimer's Disease Stages Based on Hybrid Features of CNN and Handcrafted Features

Alzheimer's disease (AD) is considered one of the challenges facing health care in the modern century; until now, there has been no effective treatment to cure it, but there are drugs to slow its progression. Therefore, early detection of Alzheimer's is vital to take needful measures before it develops into brain damage which cannot be treated. Magnetic resonance imaging (MRI) techniques have contributed to the diagnosis and prediction of its progression. MRI images require highly experienced doctors and radiologists, and the analysis of MRI images takes time to analyze each slice. Thus, deep learning techniques play a vital role in analyzing a huge amount of MRI images with high accuracy to detect Alzheimer's and predict its progression. Because of the similarities in the characteristics of the early stages of Alzheimer's, this study aimed to extract the features in several methods and integrate the features extracted from more than one method into the same features matrix. This study contributed to the development of three methodologies, each with two systems, with all systems aimed at achieving satisfactory accuracy for the detection of AD and predicting the stages of its progression. The first methodology is by Feed Forward Neural Network (FFNN) with the features of GoogLeNet and DenseNet-121 models separately. The second methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models before and after high-dimensionality reduction of features using the Principal Component Analysis (PCA) algorithm. The third methodology is by FFNN network with combined features between GoogLeNet and Dense-121 models separately and features extracted by Discrete Wavelet Transform (DWT), Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) methods called handcrafted features. All systems yielded super results in detecting AD and predicting the stages of its progression. With the combined features of the DenseNet-121 and handcrafted, the FFNN achieved an accuracy of 99.7%, sensitivity of 99.64%, AUC of 99.56%, precision of 99.63%, and a specificity of 99.67%.

Convolutional Neural Networks to Classify Alzheimer’s Disease Severity Based on SPECT Images: A Comparative Study

Image recognition and neuroimaging are increasingly being used to understand the progression of Alzheimer’s disease (AD). However, image data from single-photon emission computed tomography (SPECT) are limited. Medical image analysis requires large, labeled training datasets. Therefore, studies have focused on overcoming this problem. In this study, the detection performance of five convolutional neural network (CNN) models (MobileNet V2 and NASNetMobile (lightweight models); VGG16, Inception V3, and ResNet (heavier weight models)) on medical images was compared to establish a classification model for epidemiological research. Brain scan image data were collected from 99 subjects, and 4711 images were used. Demographic data were compared using the chi-squared test and one-way analysis of variance with Bonferroni’s post hoc test. Accuracy and loss functions were used to evaluate the performance of CNN models. The cognitive abilities screening instrument and mini mental state exam scores of subjects with a clinical dementia rating (CDR) of 2 were considerably lower than those of subjects with a CDR of 1 or 0.5. This study analyzed the classification performance of various CNN models for medical images and proved the effectiveness of transfer learning in identifying the mild cognitive impairment, mild AD, and moderate AD scoring based on SPECT images.

Diagnostic performance of magnetic resonance imaging-based machine learning in Alzheimer's disease detection: a meta-analysis

PURPOSE

Advanced machine learning (ML) algorithms can assist rapid medical image recognition and realize automatic, efficient, noninvasive, and convenient diagnosis. We aim to further evaluate the diagnostic performance of ML to distinguish patients with probable Alzheimer's disease (AD) from normal older adults based on structural magnetic resonance imaging (MRI).

METHODS

The Medline, Embase, and Cochrane Library databases were searched for relevant literature published up until July 2021. We used the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to evaluate all included studies' quality and potential bias. Random-effects models were used to calculate pooled sensitivity and specificity, and the Deeks' test was used to assess publication bias.

RESULTS

We included 24 models based on different brain features extracted by ML algorithms in 19 papers. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver operating characteristic curve for ML in detecting AD were 0.85 (95%CI 0.81-0.89), 0.88 (95%CI 0.84-0.91), 7.15 (95%CI 5.40-9.47), 0.17 (95%CI 0.12-0.22), 43.34 (95%CI 26.89-69.84), and 0.93 (95%CI 0.91-0.95).

CONCLUSION

ML using structural MRI data performed well in diagnosing probable AD patients and normal elderly. However, more high-quality, large-scale prospective studies are needed to further enhance the reliability and generalizability of ML for clinical applications before it can be introduced into clinical practice.

Honey and Alzheimer's Disease-Current Understanding and Future Prospects

Alzheimer's disease (AD), a leading cause of dementia, has been a global concern. AD is associated with the involvement of the central nervous system that causes the characteristic impaired memory, cognitive deficits, and behavioral abnormalities. These abnormalities caused by AD is known to be attributed by extracellular aggregates of amyloid beta plaques and intracellular neurofibrillary tangles. Additionally, genetic factors such as abnormality in the expression of APOE, APP, BACE1, PSEN-1, and PSEN-2 play a role in the disease. As the current treatment aims to treat the symptoms and to slow the disease progression, there has been a continuous search for new nutraceutical agent or medicine to help prevent and cure AD pathology. In this quest, honey has emerged as a powerful nootropic agent. Numerous studies have demonstrated that the high flavonoids and phenolic acids content in honey exerts its antioxidant, anti-inflammatory, and neuroprotective properties. This review summarizes the effect of main flavonoid compounds found in honey on the physiological functioning of the central nervous system, and the effect of honey intake on memory and cognition in various animal model. This review provides a new insight on the potential of honey to prevent AD pathology, as well as to ameliorate the damage in the developed AD.

Using CNN Saliency Maps and EEG Modulation Spectra for Improved and More Interpretable Machine Learning-Based Alzheimer's Disease Diagnosis

Biomarkers based on resting-state electroencephalography (EEG) signals have emerged as a promising tool in the study of Alzheimer's disease (AD). Recently, a state-of-the-art biomarker was found based on visual inspection of power modulation spectrograms where three "patches" or regions from the modulation spectrogram were proposed and used for AD diagnostics. Here, we propose the use of deep neural networks, in particular convolutional neural networks (CNNs) combined with saliency maps, trained on power modulation spectrogram inputs to find optimal patches in a data-driven manner. Experiments are conducted on EEG data collected from fifty-four participants, including 20 healthy controls, 19 patients with mild AD, and 15 moderate-to-severe AD patients. Five classification tasks are explored, including the three-class problem, early-stage detection (control vs. mild-AD), and severity level detection (mild vs. moderate-to-severe). Experimental results show the proposed biomarkers outperform the state-of-the-art benchmark across all five tasks, as well as finding complementary modulation spectrogram regions not previously seen via visual inspection. Lastly, experiments are conducted on the proposed biomarkers to test their sensitivity to age, as this is a known confound in AD characterization. Across all five tasks, none of the proposed biomarkers showed a significant relationship with age, thus further highlighting their usefulness for automated AD diagnostics.

Disrupted Balance of Gray Matter Volume and Directed Functional Connectivity in Mild Cognitive Impairment and Alzheimer's Disease

BACKGROUND

Alterations in functional connectivity have been demonstrated in Alzheimer's disease (AD), an age-progressive neurodegenerative disorder that affects cognitive function; however, directional information flow has never been analyzed.

OBJECTIVE

This study aimed to determine changes in resting-state directional functional connectivity measured using a novel approach, granger causality density (GCD), in patients with AD, and mild cognitive impairment (MCI) and explore novel neuroimaging biomarkers for cognitive decline detection.

METHODS

In this study, structural MRI, resting-state functional magnetic resonance imaging, and neuropsychological data of 48 Alzheimer's Disease Neuroimaging Initiative participants were analyzed, comprising 16 patients with AD, 16 with MCI, and 16 normal controls. Volume-based morphometry (VBM) and GCD were used to calculate the voxel-based gray matter (GM) volumes and directed functional connectivity of the brain. We made full use of voxel-based between-group comparisons of VBM and GCD values to identify specific regions with significant alterations. In addition, Pearson's correlation analysis was conducted between directed functional connectivity and several clinical variables. Furthermore, receiver operating characteristic (ROC) analysis related to classification was performed in combination with VBM and GCD.

RESULTS

In patients with cognitive decline, abnormal VBM and GCD (involving inflow and outflow of GCD) were noted in default mode network (DMN)-related areas and the cerebellum. GCD in the DMN midline core system, hippocampus, and cerebellum was closely correlated with the Mini- Mental State Examination and Functional Activities Questionnaire scores. In the ROC analysis combining VBM with GCD, the neuroimaging biomarker in the cerebellum was optimal for the early detection of MCI, whereas the precuneus was the best in predicting cognitive decline progression and AD diagnosis.

CONCLUSION

Changes in GM volume and directed functional connectivity may reflect the mechanism of cognitive decline. This discovery could improve our understanding of the pathology of AD and MCI and provide available neuroimaging markers for the early detection, progression, and diagnosis of AD and MCI.

Impaired Facial Emotion Recognition and Gaze Direction Detection in Mild Alzheimer's Disease: Results from the PACO Study

BACKGROUND

Facial emotion recognition (FER) and gaze direction (GD) identification are core components of social cognition, possibly impaired in many psychiatric or neurological conditions. Regarding Alzheimer's disease (AD), current knowledge is controversial.

OBJECTIVE

The aim of this study was to explore FER and GD identification in mild AD compared to healthy controls.

METHODS

180 participants with mild AD drawn from the PACO study and 74 healthy elderly controls were enrolled. Participants were asked to complete three socio-cognitive tasks: face sex identification, recognition of facial emotions (fear, happiness, anger, disgust) expressed at different intensities, and GD discrimination. Multivariate analyses were conducted to compare AD participants and healthy controls.

RESULTS

Sex recognition was preserved. GD determination for subtle deviations was impaired in AD. Recognition of prototypically expressed facial emotions was preserved while recognition of degraded facial emotions was impacted in AD participants compared to controls. Use of multivariate analysis suggested significant alteration of low-expressed fear and disgust recognition in the AD group.

CONCLUSION

Our results showed emotion recognition and GD identification in patients with early-stage AD compared to elderly controls. These impairments could be the object of specific therapeutic interventions such as social cognition remediation or raising awareness of primary caregivers to improve the quality of life of patients with early AD.

Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease

Background

Functional connectivity and complexity analysis has been discretely studied to understand intricate brain dynamics. The current study investigates the interplay between functional connectivity and complexity using the Kuramoto mean-field model.

Method

Functional connectivity matrices are estimated using the weighted phase lag index and complexity measures through popularly used complexity estimators such as Lempel-Ziv complexity (LZC), Higuchi's fractal dimension (HFD), and fluctuation-based dispersion entropy (FDispEn). Complexity measures are estimated on real and simulated electroencephalogram (EEG) signals of patients with mild cognitive-impaired Alzheimer's disease (MCI-AD) and controls. Complexity measures are further applied to simulated signals generated from lesion-induced connectivity matrix and studied its impact. It is a novel attempt to study the relation between functional connectivity and complexity using a neurocomputational model.

Results

Real EEG signals from patients with MCI-AD exhibited reduced functional connectivity and complexity in anterior and central regions. A simulation study has also displayed significantly reduced regional complexity in the patient group with respect to control. A similar reduction in complexity was further evident in simulation studies with lesion-induced control groups compared with non-lesion-induced control groups.

Conclusion

Taken together, simulation studies demonstrate a positive influence of reduced connectivity in the model imparting a reduced complexity in the EEG signal. The study revealed the presence of a direct relation between functional connectivity and complexity with reduced connectivity, yielding a decreased EEG complexity.

Analysis of Hippocampus Evolution Patterns and Prediction of Conversion in Mild Cognitive Impairment Using Multivariate Morphometry Statistics

Background: Mild cognitive impairment (MCI), which is generally regarded as the prodromal stage of Alzheimer’s disease (AD), is associated with morphological changes in brain structures, particularly the hippocampus. However, the indicators for characterizing the deformation of hippocampus in conventional methods are not precise enough and ignore the evolution information with the course of disease. Objective: The purpose of this study was to investigate the temporal evolution pattern of MCI and predict the conversion of MCI to AD by using the multivariate morphometry statistics (MMS) as fine features. Methods: First, we extracted MMS features from MRI scans of 64 MCI converters (MCIc), 81 MCI patients who remained stable (MCIs), and 90 healthy controls (HC). To make full use of the time information, the dynamic MMS (DMMS) features were defined. Then, the areas with significant differences between pairs of the three groups were analyzed using statistical methods and the atrophy/expansion were identified by comparing the metrics. In parallel, patch selection, sparse coding, dictionary learning and maximum pooling were used for the dimensionality reduction and the ensemble classifier GentleBoost was used to classify MCIc and MCIs. Results: The longitudinal analysis revealed that the atrophy of both MCIc and MCIs mainly distributed in dorsal CA1, then spread to subiculum and other regions gradually, while the atrophy area of MCIc was larger and more significant. And the introduction of longitudinal information promoted the accuracy to 91.76% for conversion prediction. Conclusion: The dynamic information of hippocampus holds a huge potential for understanding the pathology of MCI.

A Mixed-Effects Model of Associations between Interleukin-6 and Hippocampal Volume

Previous studies report hippocampal volume loss can help predict conversion from normative aging to mild cognitive impairment (MCI) to dementia. Additionally, a growing literature indicates that stress-related allostatic load may increase disease vulnerability. The current study examined the relationship between stress related cytokines (i.e., interleukin-6 - IL-6), cognition as measured by Mini Mental Status scores (MMSE), and hippocampal volume. Mixed-models were employed to examine both within (across time) and between subjects effects of IL-6 and hippocampal volume on MMSE score among 566 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The within subjects analysis found left hippocampal volume significantly (p= .009) predicted MMSE score. Between subjects analysis found the effect of IL-6 on MMSE was moderated by right hippocampal volume (p = .001). These results replicate previous findings and also extend prior work demonstrating stress-related cytokines may play a role in Alzheimer's disease (AD) progression.

Multimodal Prediction of Alzheimer's Disease Severity Level Based on Resting-State EEG and Structural MRI

While several biomarkers have been developed for the detection of Alzheimer's disease (AD), not many are available for the prediction of disease severity, particularly for patients in the mild stages of AD. In this paper, we explore the multimodal prediction of Mini-Mental State Examination (MMSE) scores using resting-state electroencephalography (EEG) and structural magnetic resonance imaging (MRI) scans. Analyses were carried out on a dataset comprised of EEG and MRI data collected from 89 patients diagnosed with minimal-mild AD. Three feature selection algorithms were assessed alongside four machine learning algorithms. Results showed that while MRI features alone outperformed EEG features, when both modalities were combined, improved results were achieved. The top-selected EEG features conveyed information about amplitude modulation rate-of-change, whereas top-MRI features comprised information about cortical area and white matter volume. Overall, a root mean square error between predicted MMSE values and true MMSE scores of 1.682 was achieved with a multimodal system and a random forest regression model.

Magnetic Resonance Imaging Findings Among Individuals With Olfactory and Cognitive Impairment

OBJECTIVES

The underlying mechanism of the association between olfactory impairment and dementia may be explained by neurodegenerative changes detected on magnetic resonance imaging (MRI). The purpose of this systematic review is to describe neurodegenerative changes on MRI in patients with olfactory impairment and mild cognitive impairment (MCI) or dementia.

STUDY DESIGN

Systematic review.

METHODS

A literature search encompassing PubMed, Embase, Cochrane Library, Web of Science, Scopus, and Google Scholar for studies with MRI and olfactory testing among participants diagnosed with MCI or dementia was performed. Sample size, study design, cognitive impairment type, olfactory testing, and MRI findings were abstracted. Two investigators independently reviewed all articles.

RESULTS

The search yielded 556 nonduplicate abstracts, from which 86 articles were reviewed and 24 were included. Seventeen (71%) of 24 studies reported hippocampal volume findings, with 14 studies reporting a relationship between hippocampal volume and olfactory performance. Two (50%) of four prospective studies reported the potential utility of baseline hippocampal volume as a marker of dementia conversion from MCI. Five (21%) of 24 studies reporting olfactory functional MRI (fMRI) findings highlighted the utility of olfactory fMRI to identify individuals in the early stages of cognitive decline.

CONCLUSION

Current evidence suggests hippocampal volume correlates with olfactory performance in individuals with cognitive impairment, and that olfactory fMRI may improve early detection of AD. However, the predictive utility of these imaging markers is limited in prospective studies. MRI may be a useful modality for selecting patients at high risk of future cognitive decline for enrollment in early treatment trials. Laryngoscope, 2021.

Cerebral Blood Flow Is Associated with Diagnostic Class and Cognitive Decline in Alzheimer's Disease

BACKGROUND

Reliable cerebral blood flow (CBF) biomarkers using a noninvasive imaging technique are sought to facilitate early diagnosis and intervention in early Alzheimer's disease (AD).

OBJECTIVE

We aim to identify brain regions in which CBF values are affected and related to cognitive decline in early AD using a large cohort.

METHODS

Perfusion MRIs using continuous arterial spin labeling were acquired at 1.5 T in 58 normal controls (NC), 50 mild cognitive impairments (MCI), and 40 AD subjects from the Cardiovascular Health Study Cognition Study. Regional absolute CBF and normalized CBF (nCBF) values, without and with correction of partial volume effects, were compared across three groups. Association between regional CBF values and Modified Mini-Mental State Examination (3MSE) were investigated by multiple linear regression analyses adjusted for cardiovascular risk factors.

RESULTS

After correcting for partial volume effects and cardiovascular risk factors, ADs exhibited decreased nCBF with the strongest reduction in the bilateral posterior cingulate & precuneus region (p < 0.001) compared to NCs, and the strongest reduction in the bilateral superior medial frontal region (p < 0.001) compared to MCIs. MCIs exhibited the strongest nCBF decrease in the left hippocampus and nCBF increase in the right inferior frontal and insular region. The 3MSE scores within the symptomatic subjects were significantly associated with nCBF in the bilateral posterior and middle cingulate and parietal (p < 0.001), bilateral superior medial frontal (p < 0.001), bilateral temporoparietal (p < 0.02), and right hippocampus (p = 0.02) regions.

CONCLUSION

Noninvasive perfusion MRI can detect functional changes across diagnostic class and serve as a staging biomarker of cognitive status.

Predict Alzheimer's disease using hippocampus MRI data: a lightweight 3D deep convolutional network model with visual and global shape representations

BACKGROUND

Alzheimer's disease (AD) is a progressive and irreversible brain disorder. Hippocampus is one of the involved regions and its atrophy is a widely used biomarker for AD diagnosis. We have recently developed DenseCNN, a lightweight 3D deep convolutional network model, for AD classification based on hippocampus magnetic resonance imaging (MRI) segments. In addition to the visual features of the hippocampus segments, the global shape representations of the hippocampus are also important for AD diagnosis. In this study, we propose DenseCNN2, a deep convolutional network model for AD classification by incorporating global shape representations along with hippocampus segmentations.

METHODS

The data was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and was T1-weighted structural MRI from initial screening or baseline, including ADNI 1,2/GO and 3. DenseCNN2 was trained and evaluated with 326 AD subjects and 607 CN hippocampus MRI using 5-fold cross-validation strategy. DenseCNN2 was compared with other state-of-the-art machine learning approaches for the task of AD classification.

RESULTS

We showed that DenseCNN2 with combined visual and global shape features performed better than deep learning models with visual or global shape features alone. DenseCNN2 achieved an average accuracy of 0.925, sensitivity of 0.882, specificity of 0.949, and area under curve (AUC) of 0.978, which are better than or comparable to the state-of-the-art methods in AD classification. Data visualization analysis through 2D embedding of UMAP confirmed that global shape features improved class discrimination between AD and normal.

CONCLUSION

DenseCNN2, a lightweight 3D deep convolutional network model based on combined hippocampus segmentations and global shape features, achieved high performance and has potential as an efficient diagnostic tool for AD classification.

Cerebral Blood Flow Predicts Conversion of Mild Cognitive Impairment into Alzheimer's Disease and Cognitive Decline: An Arterial Spin Labeling Follow-up Study

BACKGROUND

This is the first longitudinal study to assess regional cerebral blood flow (rCBF) changes during the progression from normal control (NC) through mild cognitive impairment (MCI) and Alzheimer's disease (AD).

OBJECTIVE

We aim to determine if perfusion MRI biomarkers, derived from our prior cross-sectional study, can predict the onset and cognitive decline of AD.

METHODS

Perfusion MRIs using arterial spin labeling (ASL) were acquired in 15 stable-NC, 14 NC-to-MCI, 16 stable-MCI, and 18 MCI/AD-to-AD participants from the Cardiovascular Health Study (CHS) cognition study. Group comparisons, predictions of AD conversion and time to conversion, and Modified Mini-Mental State Examination (3MSE) from rCBF were performed.

RESULTS

Compared to the stable-NC group: 1) the stable-MCI group exhibited rCBF decreases in the right temporoparietal (p = 0.00010) and right inferior frontal and insula (p = 0.0094) regions; and 2) the MCI/AD-to-AD group exhibited rCBF decreases in the bilateral temporoparietal regions (p = 0.00062 and 0.0035). Compared to the NC-to-MCI group, the stable-MCI group exhibited a rCBF decrease in the right hippocampus region (p = 0.0053). The baseline rCBF values in the posterior cingulate cortex (PCC) (p = 0.0043), bilateral superior medial frontal regions (BSMF) (p = 0.012), and left inferior frontal (p = 0.010) regions predicted the 3MSE scores for all the participants at follow-up. The baseline rCBF in the PCC and BSMF regions predicted the conversion and time to conversion from MCI to AD (p < 0.05; not significant after multiple corrections).

CONCLUSION

We demonstrated the feasibility of ASL in detecting rCBF changes in the typical AD-affected regions and the predictive value of baseline rCBF on AD conversion and cognitive decline.

Brainstem atrophy in the early stage of Alzheimer's disease: a voxel-based morphometry study

Postmortem studies on patients with Alzheimer's disease (AD) have confirmed that the dorsal raphe nucleus (DRN) in the brainstem is the first brain structure affected in the earliest stage of AD. The present study examined the brainstem in the early stage of AD using magnetic resonance (MR) imaging. T1-weighted MR images of the brains of 81 subjects were obtained from the publicly available Open Access Series of Imaging Studies (OASIS) database, including 27 normal control (NC) subjects, 27 patients with very mild AD (AD-VM) and 27 patients with mild AD (AD-M). The brainstem was interactively segmented from the MR images using ITK-SNAP. The present voxel-based morphometry (VBM) study was designed to investigate the brainstem differences between the AD-VM/AD-M groups and the NC group. The results showed bilateral loss in the pons and the left part of the midbrain in the AD-M group compared to the NC group. The AD-M group showed greater loss in the left midbrain than the AD-VM group (P_FWEcorrected < 0.05). The results revealed that brainstem atrophy occurs in the early stages of AD (Clinical Dementia Rating = 0.5 and 1.0). Most of these findings were also investigated in a multicenter dataset. This is the first VBM study that provides evidence of brainstem alterations in the early stage of AD.

Long range early diagnosis of Alzheimer's disease using longitudinal MR imaging data

The enormous social and economic cost of Alzheimer's disease (AD) has driven a number of neuroimaging investigations for early detection and diagnosis. Towards this end, various computational approaches have been applied to longitudinal imaging data in subjects with Mild Cognitive Impairment (MCI), as serial brain imaging could increase sensitivity for detecting changes from baseline, and potentially serve as a diagnostic biomarker for AD. However, current state-of-the-art brain imaging diagnostic methods have limited utility in clinical practice due to the lack of robust predictive power. To address this limitation, we propose a flexible spatial-temporal solution to predict the risk of MCI conversion to AD prior to the onset of clinical symptoms by sequentially recognizing abnormal structural changes from longitudinal magnetic resonance (MR) image sequences. Firstly, our model is trained to sequentially recognize different length partial MR image sequences from different stages of AD. Secondly, our method is leveraged by the inexorably progressive nature of AD. To that end, a Temporally Structured Support Vector Machine (TS-SVM) model is proposed to constrain the partial MR image sequence's detection score to increase monotonically with AD progression. Furthermore, in order to select the best morphological features for enabling classifiers, we propose a joint feature selection and classification framework. We demonstrate that our early diagnosis method using only two follow-up MR scans is able to predict conversion to AD 12 months ahead of an AD clinical diagnosis with 81.75% accuracy.

Working Memory Decline in Alzheimer's Disease Is Detected by Complexity Analysis of Multimodal EEG-fNIRS

Alzheimer's disease (AD) is characterized by working memory (WM) failures that can be assessed at early stages through administering clinical tests. Ecological neuroimaging, such as Electroencephalography (EEG) and functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests to support AD early diagnosis within clinical settings. Multimodal EEG-fNIRS could measure brain activity along with neurovascular coupling (NC) and detect their modifications associated with AD. Data analysis procedures based on signal complexity are suitable to estimate electrical and hemodynamic brain activity or their mutual information (NC) during non-structured experimental paradigms. In this study, sample entropy of whole-head EEG and frontal/prefrontal cortex fNIRS was evaluated to assess brain activity in early AD and healthy controls (HC) during WM tasks (i.e., Rey-Osterrieth complex figure and Raven's progressive matrices). Moreover, conditional entropy between EEG and fNIRS was evaluated as indicative of NC. The findings demonstrated the capability of complexity analysis of multimodal EEG-fNIRS to detect WM decline in AD. Furthermore, a multivariate data-driven analysis, performed on these entropy metrics and based on the General Linear Model, allowed classifying AD and HC with an AUC up to 0.88. EEG-fNIRS may represent a powerful tool for the clinical evaluation of WM decline in early AD.

Six Visual Rating Scales as A Biomarker for Monitoring Atrophied Brain Volume in Parkinson's Disease

The focus of our investigation was to determine the feasibility of using six visual rating scales as whole-brain imaging markers for monitoring atrophied brain volume in Parkinson’s disease (PD). This was a prospective cross-sectional single-center observational study. A total of 98 PD patients were enrolled and underwent an MRI scan and a battery of neuropsychological evaluations. The brain volume was calculated using the online resource MRICloud. Brain atrophy was rated based on six visual rating scales. Correlation analysis was performed between visual rating scores and brain volume and clinical features. We found a significant negative correlation between the total scores of visual rating scores and quantitative brain volume, indicating that six visual rating scales reliably reflect whole brain atrophy in PD. Multiple linear regression-based analyses indicated severer non-motor symptoms were significantly associated with higher scores on the visual rating scales. Furthermore, we performed sample size calculations to evaluate the superiority of visual rating scales; the result show that using total scores of visual rating scales as an outcome measure, sample sizes for differentiating cognition injury require significantly fewer subjects (n = 177) compared with using total brain volume (n = 2524). Our data support the use of the total visual rating scores rather than quantitative brain volume as a biomarker for monitoring cerebral atrophy.

Prediction of clinical and biomarker conformed Alzheimer's disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample

Structural neuroimaging has been applied to the identification of individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, these methods are greatly impacted by age limiting their utility for detection of preclinical pathology. We built linear models for age based on multiple combined structural features using a large independent lifespan sample of 272 healthy adults across a wide age range from the Human Connectome Project Aging study. These models were then used to create a new support vector machine (SVM) training model in 136 AD and 268 control participants based on residues of fit from the expected age-effects relationship. Subsequent validation assessed the accuracy of the SVM model in new datasets. Finally, we applied the classifier to 276 individuals with MCI to evaluate prediction for early impairment and longitudinal cognitive change. The optimal 10-fold cross-validation accuracy was 93.07%, compared to 91.83% without age detrending. In the validation dataset, the classifier for AD obtained an accuracy of 84.85% (56/66), sensitivity of 85.36% (35/41) and specificity of 84% (21/25). Classification accuracy was improved when using the lifespan sample as opposed to the classification sample. Importantly, we observed cross-sectional greater AD specific biomarkers, as well as faster cognitive decline in MCI who were classified as more 'AD-like' (MCI-AD), and these effects were pronounced in individuals who were late MCI. The top five contributive features were volumes of left hippocampus, right hippocampus, left amygdala, the thickness of left and right middle temporal & parahippocampus gyrus. Linear detrending for age in SVM for combined structural features resulted in good performance for recognition of AD and AD-specific biomarkers, as well as prediction of MCI progression. Such procedures may be used in future work to enhance prediction in samples with atypical age distributions.

Predicting the progression of mild cognitive impairment to Alzheimer's disease by longitudinal magnetic resonance imaging-based dictionary learning

OBJECTIVE

Efficient prediction of the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is important for the early intervention and management of AD. The aim of our study was to develop a longitudinal structural magnetic resonance imaging-based prediction system for MCI progression.

METHODS

A total of 164 MCI patients with longitudinal data were collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After preprocessing, a discriminative dictionary learning framework was applied to differentiate MCI patches, avoiding the segmentation of regions of interest. Then, the proportion of patches classified as more severe atrophy patches in a patient was calculated as his or her feature to be input into a simple support vector machine. Finally, a new subject was predicted with fourfold cross-validation (CV), and the area under the receiver operating characteristic curve (AUC) was determined.

RESULTS

The average accuracy and AUC values after fourfold CV were 0.973 and 0.984, respectively. The effects of the data from one or two time points were also investigated.

CONCLUSION

The proposed prediction system achieves desirable and reliable performance in predicting progression for MCI patients. Additionally, the prediction of MCI progression with longitudinal data was more effective and accurate.

SIGNIFICANCE

The developed scheme is expected to advance the clinical research and treatment of MCI patients.

Proteomics as a reliable approach for discovery of blood-based Alzheimer's disease biomarkers: A systematic review and meta-analysis

In order to gauge the impact of proteomics in discovery of Alzheimer's disease (AD) blood-based biomarkers, this study had systematically reviewed articles published between 1984-2019. Articles that fulfilled the inclusion criteria were assessed for risk of bias. A meta-analysis was performed for replicable candidate biomarkers (CB). Of the 1651 articles that were identified, 17 case-control and two cohort studies, as well as three combined case-control and longitudinal designs were shortlisted. A total of 207 AD and mild cognitive impairment (MCI) CB were discovered, with 48 reported in >2 studies. This review highlights six CB, namely alpha-2-macroglobulin (α2M)_ps, pancreatic polypeptide (PP)_ps, apolipoprotein A-1 (ApoA-1)_ps, afamin_p, insulin growth factor binding protein-2 (IGFBP-2)_ps and fibrinogen-γ-chain_p, all of which exhibited consistent pattern of regulation in >three independent cohorts. They are involved in AD pathogenesis via amyloid-beta (Aβ), neurofibrillary tangles, diabetes and cardiovascular diseases (CVD). Meta-analysis indicated that ApoA-1_ps was significantly downregulated in AD (SMD = -1.52, 95% CI: -1.89, -1.16, p < 0.00001), with low inter-study heterogeneity (I² = 0%, p = 0.59). α2M_ps was significantly upregulated in AD (SMD = 0.83, 95% CI: 0.05, 1.62, p = 0.04), with moderate inter-study heterogeneity (I² = 41%, p = 0.19). Both CB are involved in Aβ formation. These findings provide important insights into blood-based AD biomarkers discovery via proteomics.

Application of Generalized Split Linearized Bregman Iteration algorithm for Alzheimer's disease prediction

In this paper, we applied a novel method for the detection of Alzheimer's disease (AD) based on a structural magnetic resonance imaging (sMRI) dataset. Specifically, the method involved a new classification algorithm of machine learning, named Generalized Split Linearized Bregman Iteration (GSplit LBI). It combines logistic regression and structural sparsity regularizations. In the study, 57 AD patients and 47 normal controls (NCs) were enrolled. We first extracted the entire brain gray matter volume values of all subjects and then used GSplit LBI to build a predictive classification model with a 10-fold full cross-validation method. The model accuracy achieved 90.44%. To further verify which voxels in the dataset have greater impact on the prediction results, we ranked the weight parameters and obtained the top 6% of the model parameters. To verify the generalization of model prediction and the stability of feature selection, we performed a cross-test on the Alzheimer's Disease Neuroimaging Initiative (ADNI) and a Chinese dataset and achieved good performances on different cohorts. Conclusively, based on the sMRI dataset, our algorithm not only had good performance in a local cohort with high accuracy but also had good generalization of model prediction and stability of feature selection in different cohorts.

Cerebrospinal Fluid and MRI Biomarkers in Neurodegenerative Diseases: A Retrospective Memory Clinic-Based Study

BACKGROUND

Cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) biomarkers of neurodegenerative diseases are relatively sensitive and specific in highly curated research cohorts, but proper validation for clinical use is mostly missing.

OBJECTIVE

We studied these biomarkers in a novel memory clinic cohort with a variety of different neurodegenerative diseases.

METHODS

This study consisted of 191 patients with subjective or objective cognitive impairment who underwent neurological, CSF biomarker (Aβ42, p-tau, and tau) and T1-weighted MRI examinations at Kuopio University Hospital. We assessed CSF and imaging biomarkers, including structural MRI focused on volumetric and cortical thickness analyses, across groups stratified based on different clinical diagnoses, including Alzheimer's disease (AD), frontotemporal dementia, dementia with Lewy bodies, Parkinson's disease, vascular dementia, and mild cognitive impairment (MCI), and subjects with no evidence of neurodegenerative disease underlying the cognitive symptoms. Imaging biomarkers were also studied by profiling subjects according to the novel amyloid, tau, and, neurodegeneration (AT(N)) classification.

RESULTS

Numerous imaging variables differed by clinical diagnosis, including hippocampal, amygdalar and inferior lateral ventricular volumes and entorhinal, lingual, inferior parietal and isthmus cingulate cortical thicknesses, at a false discovery rate (FDR)-corrected threshold for significance (analysis of covariance; p < 0.005). In volumetric comparisons by AT(N) profile, hippocampal volume significantly differed (p < 0.001) between patients with normal AD biomarkers and patients with amyloid pathology.

CONCLUSION

Our analysis suggests that CSF and MRI biomarkers function well also in clinical practice across multiple clinical diagnostic groups in addition to AD, MCI, and cognitively normal groups.

P300, Gray Matter Volume and Individual Characteristics Correlates in Healthy Elderly

We investigated whether P300-ERP and cognitive test performance differ for age, sex, and education in two groups of healthy elderly, and verified whether any correlations exist between P300 amplitude and latency and gray matter volume using whole brain voxel-by-voxel-based mapping, controlling for age, education, sex and Total Intracranial Volume (TIV). We used 32 channel electroencephalograms (EEG) to record the P300 responses and 3T Magnetic Resonance Imaging (MRI) to determine gray matter volume. We recruited 36 native-Dutch speaking healthy older subjects, equally divided in two sub-groups of 52-64 and 65-76 years old, administered a battery of cognitive tests and recorded their demographics, EEGs and task performance; additionally, 16 adults from the second sub-group underwent an MRI scan. We found significant differences between age groups in their cognitive tests performance, P300 amplitudes for the frontal and parietal electrodes for the most difficult task, and P300 latencies for frontal, central and parietal electrodes for all three tasks difficulty levels. Interesting, sex and education affected cognitive and P300 results. Higher education was related to higher accuracy, and P300 amplitudes and shorter latencies. Moreover, females exhibited higher P300 amplitudes and shorter latencies, and better cognitive tasks performance compared to males. Additionally, for the 16 adults underwent to MRI scan, we found positive correlations between P300 characteristics in frontal, central and parietal areas and gray matter volume, controlling for demographic variables and TIV, but also showing that age, sex, and education correlate with gray matter volume. These findings provide support that age, sex, and education affect an individual's cognitive, neurophysiological and structural characteristics, and therefore motivate the need to further investigate these in relation to P300 responses and gray matter volume in healthy elderly.

Harnessing the Power of Machine Learning in Dementia Informatics Research: Issues, Opportunities, and Challenges

Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever-continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records, etc. Making the best use of these diverse and strategic resources will lead to high-quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less effort has been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate future potential and research directions in applying advanced machine learning, such as deep learning, to dementia informatics.

Identifying brain changes related to cognitive aging using VBM and visual rating scales

Aging is often associated with changes in brain structures as well as in cognitive functions. Structural changes can be visualized with Magnetic Resonance Imaging (MRI) using voxel-based grey matter morphometry (VBM) and visual rating scales to assess atrophy level. Several MRI studies have shown that possible neural correlates of cognitive changes can be seen in normal aging. It is still not fully understood how cognitive function as measured by tests and demographic factors are related to brain changes in the MRI. We recruited 55 healthy elderly subjects aged 50–79 years. A battery of cognitive tests was administered to all subjects prior to MRI scanning. Our aim was to assess correlations between age, sex, education, cognitive test performance, and the said two MRI-based measures. Our results show significant differences in VBM grey matter volume for education level (≤ 12 vs. > 12 years), with a smaller amount of grey matter volume in subjects with lower educational levels, and for age in interaction with education, indicating larger grey matter volume for young, higher educated adults. Also, grey matter volume was found to be correlated with working memory function (Digit Span Backward). Furthermore, significant positive correlations were found between visual ratings and both age and education, showing larger atrophy levels with increasing age and decreasing level of education. These findings provide supportive evidence that MRI-VBM detects structural differences for education level, and correlates with educational level and age, and working memory task performance.

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VBM grey matter volume differences were significant for the interaction of age and education level.

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Grey matter volume correlated with education level and working memory function (Digit Span Backward).

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Significant correlations were found between visual rating scales and both age and education.

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VBM is able to detect structural differences for age and education, and correlates with education and working memory.

Complexity of Frontal Cortex fNIRS Can Support Alzheimer Disease Diagnosis in Memory and Visuo-Spatial Tests

Decline in visuo-spatial skills and memory failures are considered symptoms of Alzheimer's Disease (AD) and they can be assessed at early stages employing clinical tests. However, performance in a single test is generally not indicative of AD. Functional neuroimaging, such as functional Near Infrared Spectroscopy (fNIRS), may be employed during these tests in an ecological setting to support diagnosis. Indeed, neuroimaging should not alter clinical practice allowing free doctor-patient interaction. However, block-designed paradigms, necessary for standard functional neuroimaging analysis, require tests adaptation. Novel signal analysis procedures (e.g., signal complexity evaluation) may be useful to establish brain signals differences without altering experimental conditions. In this study, we estimated fNIRS complexity (through Sample Entropy metric) in frontal cortex of early AD and controls during three tests that assess visuo-spatial and short-term-memory abilities (Clock Drawing Test, Digit Span Test, Corsi Block Tapping Test). A channel-based analysis of fNIRS complexity during the tests revealed AD-induced changes. Importantly, a multivariate analysis of fNIRS complexity provided good specificity and sensitivity to AD. This outcome was compared to cognitive tests performances that were predictive of AD in only one test. Our results demonstrated the capabilities of fNIRS and complexity metric to support early AD diagnosis.

Disrupted Functional Connectivity of Cornu Ammonis Subregions in Amnestic Mild Cognitive Impairment: A Longitudinal Resting-State fMRI Study

Background: The cornu ammonis (CA), as part of the hippocampal formation, represents a primary target region of neural degeneration in amnestic mild cognitive impairment (aMCI). Previous studies have revealed subtle structural deficits of the CA subregions (CA1-CA3, bilateral) in aMCI; however, it is not clear how the network function is impacted by aMCI. The present study examined longitudinal changes in resting state functional connectivity (FC) of each CA subregion and how these changes relate to neuropsychological profiles in aMCI.

Methods: Twenty aMCI and 20 healthy control (HC) participants underwent longitudinal cognitive assessment and resting state functional MRI scans at baseline and 15 months afterward. Imaging data were processed with published routines in SPM8 and CONN software. Two-way analysis of covariance was performed with covariates of age, gender, education level, follow up interval, gray matter volume, mean FD, as well as global correlation (GCOR). Pearson’s correlation was conducted to evaluate the relationship between the longitudinal changes in CA subregional FC and neuropsychological performance in aMCI subjects.

Results: Resting state FC between the right CA1 and right middle temporal gyrus (MTG) as well as between the left CA2 and bilateral cuneal cortex (CC) were decreased in aMCI subjects as compared to HC. Longitudinal decrease in FC between the right CA1 and right MTG was correlated with reduced capacity of episodic memory in aMCI subjects.

Conclusion: The current findings suggest functional alterations in the CA subregions. CA1 connectivity with the middle temporal cortex may represent an important neural marker of memory dysfunction in aMCI.

Exploring diagnosis and imaging biomarkers of Parkinson's disease via iterative canonical correlation analysis based feature selection

Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately. In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis. Specifically, we proposed a novel iterative canonical correlation analysis (ICCA) feature selection method, aiming at exploiting MR images in a more comprehensive manner and fusing features of different types into a common space. To state succinctly, we first extract the feature vectors from the gray matter and the white matter tissues separately, represented as insights of two different anatomical feature spaces for the subject's brain. The ICCA feature selection method aims at iteratively finding the optimal feature subset from two sets of features that have inherent high correlation with each other. In experiments we have conducted thorough investigations on the optimal feature set extracted by our ICCA method. We also demonstrate that using the proposed feature selection method, the PD diagnosis performance is further improved, and also outperforms many state-of-the-art methods.

Altered Insulin/Insulin-Like Growth Factor Signaling in a Comorbid Rat model of Ischemia and β-Amyloid Toxicity

Ischemic stroke and diabetes are vascular risk factors for the development of impaired memory such as dementia and/or Alzheimer's disease. Clinical studies have demonstrated that minor striatal ischemic lesions in combination with β-amyloid (Aβ) load are critical in generating cognitive deficits. These cognitive deficits are likely to be associated with impaired insulin signaling. In this study, we examined the histological presence of insulin-like growth factor-I (IGF-1) and insulin receptor substrate (IRS-1) in anatomically distinct brain circuits compared with morphological brain damage in a co-morbid rat model of striatal ischemia (ET1) and Aβ toxicity. The results demonstrated a rapid increase in the presence of IGF-1 and IRS-1 immunoreactive cells in Aβ + ET1 rats, mainly in the ipsilateral striatum and distant regions with synaptic links to the striatal lesion. These regions included subcortical white matter, motor cortex, thalamus, dentate gyrus, septohippocampal nucleus, periventricular region and horizontal diagonal band of Broca in the basal forebrain. The alteration in IGF-1 and IRS-1 presence induced by ET1 or Aβ rats alone was not severe enough to affect the entire brain circuit. Understanding the causal or etiologic interaction between insulin and IGF signaling and co-morbidity after ischemia and Aβ toxicity will help design more effective therapeutics.

Gender-Specific Degeneration of Dementia-Related Subcortical Structures Throughout the Lifespan

Age-related changes in brain structure are a question of interest to a broad field of research. Structural decline has been consistently, but not unambiguously, linked to functional consequences, including cognitive impairment and dementia. One of the areas considered of crucial importance throughout this process is the medial temporal lobe, and primarily the hippocampal region. Gender also has a considerable effect on volume deterioration of subcortical grey matter (GM) structures, such as the hippocampus. The influence of age×gender interaction on disproportionate GM volume changes might be mediated by hormonal effects on the brain. Hippocampal volume loss appears to become accelerated in the postmenopausal period. This decline might have significant influences on neuroplasticity in the CA1 region of the hippocampus highly vulnerable to pathological influences. Additionally, menopause has been associated with critical pathobiochemical changes involved in neurodegeneration. The micro- and macrostructural alterations and consequent functional deterioration of critical hippocampal regions might result in clinical cognitive impairment-especially if there already is a decline in the cognitive reserve capacity. Several lines of potential vulnerability factors appear to interact in the menopausal period eventually leading to cognitive decline, mild cognitive impairment, or Alzheimer's disease. This focused review aims to delineate the influence of unmodifiable risk factors of neurodegenerative processes, i.e., age and gender, on critical subcortical GM structures in the light of brain derived estrogen effects. The menopausal period appears to be of key importance for the risk of cognitive decline representing a time of special vulnerability for molecular, structural, and functional influences and offering only a narrow window for potential protective effects.

Quantitative MRI Brain Studies in Mild Cognitive Impairment and Alzheimer's Disease: A Methodological Review

Classifying and predicting Alzheimer's disease (AD) in individuals with memory disorders through clinical and psychometric assessment is challenging, especially in mild cognitive impairment (MCI) subjects. Quantitative structural magnetic resonance imaging acquisition methods in combination with computer-aided diagnosis are currently being used for the assessment of AD. These acquisitions methods include voxel-based morphometry, volumetric measurements in specific regions of interest (ROIs), cortical thickness measurements, shape analysis, and texture analysis. This review evaluates the aforementioned methods in the classification of cases into one of the following three groups: normal controls, MCI, and AD subjects. Furthermore, the performance of the methods is assessed on the prediction of conversion from MCI to AD. In parallel, it is also assessed which ROIs are preferred in both classification and prognosis through the different states of the disease. Structural changes in the early stages of the disease are more pronounced in the medial temporal lobe, especially in the entorhinal cortex, whereas with disease progression, both entorhinal cortex and hippocampus offer similar discriminative power. However, for the conversion from MCI subjects to AD, entorhinal cortex provides better predictive accuracies rather than other structures, such as the hippocampus.

Early Emotional Attention is Impacted in Alzheimer's Disease: An Eye-Tracking Study

Emotional deficits have been repetitively reported in Alzheimer's disease (AD) without clearly identifying how emotional processing is impaired in this pathology. This paper describes an investigation of early emotional processing, as measured by the effects of emotional visual stimuli on a saccadic task involving both pro (PS) and anti (AS) saccades. Sixteen patients with AD and 25 age-matched healthy controls were eye-tracked while they had to quickly move their gaze toward a positive, negative, or neutral image presented on a computer screen (in the PS condition) or away from the image (in the AS condition). The age-matched controls made more AS mistakes for negative stimuli than for other stimuli, and triggered PSs toward negative stimuli more quickly than toward other stimuli. In contrast, patients with AD showed no difference with regard to the emotional category in any of the tasks. The present study is the first to highlight a lack of early emotional attention in patients with AD. These results should be taken into account in the care provided to patients with AD, since this early impairment might seriously degrade their overall emotional functioning.

Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease

There is great value to use of structural neuroimaging in the assessment of Alzheimer's disease (AD). However, to date, predictive value of structural imaging tend to range between 80% and 90% in accuracy and it is unclear why this is the case given that structural imaging should parallel the pathologic processes of AD. There is a possibility that clinical misdiagnosis relative to the gold standard pathologic diagnosis and/or additional brain pathologies are confounding factors contributing to reduced structural imaging classification accuracy. We examined potential factors contributing to misclassification of individuals with clinically diagnosed AD purely from cortical thickness measures. Correctly classified and incorrectly classified groups were compared across a range of demographic, biological, and neuropsychological data including cerebrospinal fluid biomarkers, amyloid imaging, white matter hyperintensity (WMH) volume, cognitive, and genetic factors. Individual subject analyses suggested that at least a portion of the control individuals misclassified as AD from structural imaging additionally harbor substantial AD biomarker pathology and risk, yet are relatively resistant to cognitive symptoms, likely due to "cognitive reserve," and therefore clinically unimpaired. In contrast, certain clinical control individuals misclassified as AD from cortical thickness had increased WMH volume relative to other controls in the sample, suggesting that vascular conditions may contribute to classification accuracy from cortical thickness measures. These results provide examples of factors that contribute to the accuracy of structural imaging in predicting a clinical diagnosis of AD, and provide important information about considerations for future work aimed at optimizing structural based diagnostic classifiers for AD.

Midlife systemic inflammatory markers are associated with late-life brain volume: The ARIC study

OBJECTIVE

To clarify the temporal relationship between systemic inflammation and neurodegeneration, we examined whether a higher level of circulating inflammatory markers during midlife was associated with smaller brain volumes in late life using a large biracial prospective cohort study.

METHODS

Plasma levels of systemic inflammatory markers (fibrinogen, albumin, white blood cell count, von Willebrand factor, and Factor VIII) were assessed at baseline in 1,633 participants (mean age 53 [5] years, 60% female, 27% African American) enrolled in the Atherosclerosis Risk in Communities Study. Using all 5 inflammatory markers, an inflammation composite score was created for each participant. We assessed episodic memory and regional brain volumes, using 3T MRI, 24 years later.

RESULTS

Each SD increase in midlife inflammation composite score was associated with 1,788 mm3 greater ventricular (p = 0.013), 110 mm3 smaller hippocampal (p = 0.013), 519 mm3 smaller occipital (p = 0.009), and 532 mm3 smaller Alzheimer disease signature region (p = 0.008) volumes, and reduced episodic memory (p = 0.046) 24 years later. Compared to participants with no elevated (4th quartile) midlife inflammatory markers, participants with elevations in 3 or more markers had, on average, 5% smaller hippocampal and Alzheimer disease signature region volumes. The association between midlife inflammation and late-life brain volume was modified by age and race, whereby younger participants and white participants with higher levels of systemic inflammation during midlife were more likely to show reduced brain volumes subsequently.

CONCLUSIONS

Our prospective findings provide evidence for what may be an early contributory role of systemic inflammation in neurodegeneration and cognitive aging.