Synaptic loss in the inferior temporal gyrus in mild cognitive impairment and Alzheimer's disease

Identifying potential causal effects of Parkinson's disease: A polygenic risk score-based phenome-wide association and mendelian randomization study in UK Biobank

There is considerable uncertainty regarding the associations between various risk factors and Parkinson's Disease (PD). This study systematically screened and validated a wide range of potential PD risk factors from 502,364 participants in the UK Biobank. Baseline data for 1851 factors across 11 categories were analyzed through a phenome-wide association study (PheWAS). Polygenic risk scores (PRS) for PD were used to diagnose Parkinson's Disease and identify factors associated with PD diagnosis through PheWAS. Two-sample Mendelian randomization (MR) analysis was employed to assess causal relationships. PheWAS results revealed 267 risk factors significantly associated with PD-PRS among the 1851 factors, and of these, 27 factors showed causal evidence from MR analysis. Compelling evidence suggests that fluid intelligence score, age at first sexual intercourse, cereal intake, dried fruit intake, and average total household income before tax have emerged as newly identified risk factors for PD. Conversely, maternal smoking around birth, playing computer games, salt added to food, and time spent watching television have been identified as novel protective factors against PD. The integration of phenotypic and genomic data may help to identify risk factors and prevention targets for PD.

Local functional connectivity abnormalities in mild cognitive impairment and Alzheimer's disease: A meta-analytic investigation using minimum Bayes factor activation likelihood estimation

Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer's disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at "very strong evidence" (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area - BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.

Dendritic spine head diameter predicts episodic memory performance in older adults

Episodic memory in older adults is varied and perceived to rely on numbers of synapses or dendritic spines. We analyzed 2157 neurons among 128 older individuals from the Religious Orders Study and Rush Memory and Aging Project. Analysis of 55,521 individual dendritic spines by least absolute shrinkage and selection operator regression and nested model cross-validation revealed that the dendritic spine head diameter in the temporal cortex, but not the premotor cortex, improved the prediction of episodic memory performance in models containing β amyloid plaque scores, neurofibrillary tangle pathology, and sex. These findings support the emerging hypothesis that, in the temporal cortex, synapse strength is more critical than quantity for memory in old age.

Cognitive and morphometric features of mild cognitive impairment reversion in early patients with Parkinson's disease

INTRODUCTION

Mild cognitive impairment is a common feature of Parkinson's disease (PD-MCI), even at the earliest disease stages. Growing evidence supports the instability of PD-MCI over time, without a consistent linear evolution to dementia, and in some patients, the potential of reversion to normal cognition. However, which features are associated with reversion from PD-MCI to normal cognition in early PD-MCI are not fully known.

METHODS

In our longitudinal study of early drug naïve PD patients, 65 of 134 (48 %) patients had PD-MCI at baseline. Study participants underwent comprehensive assessments at baseline and 1-year follow-up. Sixteen (24.6 %) patients with PD-MCI reverted to normal cognition (Reverters), and 49 (75.4 %) had persistent PD-MCI (Non-Reverters) after 1-year follow-up. We performed single- and multiple-variable logistic regression analyses to identify baseline variables predicting reversion of PD-MCI to normal cognition after 1-year. We also compared brain morphometric measures (cortical thickness and volumes) at baseline between the PD-MCI Reverters and Non-Reverters.

RESULTS

Higher educational level and better performance on measures of attention and memory at baseline predicted the reversion to normal cognition at 1-year follow-up. Reverters had greater cortical thickness in the left inferior temporal gyrus than Non-Reverters.

CONCLUSIONS

Our results show PD-MCI with a higher chance of reverting to normal cognition over time have a higher educational level, better frontotemporal-related cognitive function, and increased thickness of the inferior temporal lobe gyrus. These findings may potentially help researchers to select the candidates for clinical trials focusing on the treatment of cognitive impairment in the early stages of PD.

Microglial modulation as a therapeutic strategy in Alzheimer's disease: Focus on microglial preconditioning approaches

Alzheimer's disease (AD) is a progressive disease that causes an impairment of learning and memory. Despite the highly complex pathogenesis of AD, amyloid beta (Aβ) deposition and neurofibrillary tangles (NFTs) formation are the main hallmarks of AD. Neuroinflammation also has a crucial role in the development of AD. As the central nervous system's innate immune cells, microglial cells are activated in AD and induce inflammation by producing pro-inflammatory mediators. However, microglial activation is not always deleterious. M2-activated microglial cells are considered anti-inflammatory cells, which develop neuroprotection. Various approaches are proposed for managing AD, yet no effective therapy is available for this disorder. Considering the potential protective role of M2 microglia in neurodegenerative disorders and the improvement of these disorders by preconditioning approaches, it can be suggested that preconditioning of microglial cells may be beneficial for managing AD progression. Therefore, this study review microglial preconditioning approaches for preventing and improving AD.

Polygenic Score for Conscientiousness Is a Protective Factor for Reversion from Mild Cognitive Impairment to Normal Cognition

Spontaneous reversion from mild cognitive impairment (MCI) to normal cognition (NC) is little known. Based on the data of the Genetics of Personality Consortium and MCI participants from Alzheimer's Disease Neuroimaging Initiative, the authors investigate the effect of polygenic scores (PGS) for personality traits on the reversion of MCI to NC and its underlying neurobiology. PGS analysis reveals that PGS for conscientiousness (PGS-C) is a protective factor that supports the reversion from MCI to NC. Gene ontology enrichment analysis and tissue-specific enrichment analysis indicate that the protective effect of PGS-C may be attributed to affecting the glutamatergic synapses of subcortical structures, such as hippocampus, amygdala, nucleus accumbens, and caudate nucleus. The structural covariance network (SCN) analysis suggests that the left whole hippocampus and its subfields, and the left whole amygdala and its subnuclei show significantly stronger covariance with several high-cognition relevant brain regions in the MCI reverters compared to the stable MCI participants, which may help illustrate the underlying neural mechanism of the protective effect of PGS-C.

Synaptic protein CSF levels relate to memory scores in individuals without dementia

INTRODUCTION

We investigated how cerebrospinal fluid levels of synaptic proteins associate with memory function in normal cognition (CN) and mild cognitive impairment (MCI), and investigated the effect of amyloid positivity on these associations.

METHODS

We included 242 CN (105(43%) abnormal amyloid), and 278 MCI individuals (183(66%) abnormal amyloid) from EMIF-AD MBD and ADNI. For 181 (EMIF-AD MBD) and 36 (ADNI) proteins with a synaptic annotation in SynGO, associations with word learning recall were analysed with linear models.

RESULTS

Subsets of synaptic proteins showed lower levels with worse recall in preclinical AD (EMIF-AD MBD: 7, ADNI: 5 proteins, none overlapping), prodromal AD (EMIF-AD MBD only, 27 proteins) and non-AD MCI (EMIF-AD MBD: 1, ADNI: 7 proteins). The majority of these associations were specific to these groups.

DISCUSSION

Synaptic disturbance-related memory impairment occurred very early in AD, indicating it may be relevant to develop therapies targeting the synapse early in the disease.

ZCCHC17 knockdown phenocopies Alzheimer's disease-related loss of synaptic proteins and hyperexcitability

ZCCHC17 is a master regulator of synaptic gene expression and has recently been shown to play a role in splicing of neuronal mRNA. We previously showed that ZCCHC17 protein declines in Alzheimer's disease (AD) brain tissue before there is significant gliosis and neuronal loss, that ZCCHC17 loss partially replicates observed splicing abnormalities in AD brain tissue, and that maintenance of ZCCHC17 levels is predicted to support cognitive resilience in AD. Here, we assessed the functional consequences of reduced ZCCHC17 expression in primary cortical neuronal cultures using siRNA knockdown. Consistent with its previously identified role in synaptic gene expression, loss of ZCCHC17 led to loss of synaptic protein expression. Patch recording of neurons shows that ZCCHC17 loss significantly disrupted the excitation/inhibition balance of neurotransmission, and favored excitatory-dominant synaptic activity as measured by an increase in spontaneous excitatory post synaptic currents and action potential firing rate, and a decrease in spontaneous inhibitory post synaptic currents. These findings are consistent with the hyperexcitable phenotype seen in AD animal models and in patients. We are the first to assess the functional consequences of ZCCHC17 knockdown in neurons and conclude that ZCCHC17 loss partially phenocopies AD-related loss of synaptic proteins and hyperexcitability.

Altered Hierarchical Gradients of Intrinsic Neural Timescales in Mild Cognitive Impairment and Alzheimer's Disease

Alzheimer's disease (AD) is a devastating neurodegenerative disease that affects millions of seniors in the United States. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to study neurophysiology in AD and its prodromal condition, mild cognitive impairment (MCI). The intrinsic neural timescale (INT), which can be estimated through the magnitude of the autocorrelation of neural signals from rs-fMRI, is thought to quantify the duration that neural information is stored in a local circuit. Such heterogeneity of the timescales forms a basis of the brain functional hierarchy and captures an aspect of circuit dynamics relevant to excitation/inhibition balance, which is broadly relevant for cognitive functions. Given that, we applied rs-fMRI to test whether distinct changes of INT at different hierarchies are present in people with MCI, those progressing to AD (called Converter), and AD patients of both sexes. Linear mixed-effect model was implemented to detect altered hierarchical gradients across populations followed by pairwise comparisons to identify regional differences. High similarities between AD and Converter were observed. Specifically, the inferior temporal, caudate, and pallidum areas exhibit significant alterations in both AD and Converter. Distinct INT-related pathological changes in MCI and AD were found. For AD/Converter, neural information is stored for a longer time in lower hierarchical areas, while higher levels of hierarchy seem to be preferentially impaired in MCI leading to a less pronounced hierarchical gradient. These results inform that the INT holds great potential as an additional measure for AD prediction, even a stable biomarker for clinical diagnosis.

Process approach as a cognitive biomarker related to gray matter volume in mild cognitive impairment and Alzheimer's disease

BACKGROUND

Process approach is valuable for memory assessment in Alzheimer's disease (AD) and mild cognitive impairment (MCI), yet its underlying mechanisms remain elusive. This study aims to synergize the process approach with brain structure analysis to explore both the discriminative capacity and potential mechanisms underlying the process approach.

METHODS

37 subjects of MCI, 35 subjects of AD and 38 subjects of healthy control (HC) were included. The process approach in Auditory Verbal Learning Test (AVLT), including discriminability (A'), response bias (B"D), semantic clustering (LBCsem) and serial clustering (LBCser) was performed. The gray matter volume (GMV) was analyzed by voxel-based morphometry. Receiver operating characteristic (ROC) analysis and partial correlations were conducted to explore the value of the process approach and investigate the relationship between the process approach, traditional indices of AVLT and GMV.

RESULTS

ROC analysis showed the value of A', B"D and LBCser in differentiating MCI and AD. Combining AVLT-Immediately Recall (AVLT-IR) and LBCser showed a higher value in diagnosing MCI. Partial correlations revealed that in the MCI group, A' and B"D were mainly positively associated with GMV of the hippocampus and temporal lobe.

CONCLUSION

This study indicated that the process approach is a promising cognitive biomarker to detect MCI and AD.

MAD-Former: A Traceable Interpretability Model for Alzheimer's Disease Recognition Based on Multi-Patch Attention

The integration of structural magnetic resonance imaging (sMRI) and deep learning techniques is one of the important research directions for the automatic diagnosis of Alzheimer's disease (AD). Despite the satisfactory performance achieved by existing voxel-based models based on convolutional neural networks (CNNs), such models only handle AD-related brain atrophy at a single spatial scale and lack spatial localization of abnormal brain regions based on model interpretability. To address the above limitations, we propose a traceable interpretability model for AD recognition based on multi-patch attention (MAD-Former). MAD-Former consists of two parts: recognition and interpretability. In the recognition part, we design a 3D brain feature extraction network to extract local features, followed by constructing a dual-branch attention structure with different patch sizes to achieve global feature extraction, forming a multi-scale spatial feature extraction framework. Meanwhile, we propose an important attention similarity position loss function to assist in model decision-making. The interpretability part proposes a traceable method that can obtain a 3D ROI space through attention-based selection and receptive field tracing. This space encompasses key brain tissues that influence model decisions. Experimental results reveal the significant role of brain tissues such as the Fusiform Gyrus (FuG) in AD recognition. MAD-Former achieves outstanding performance in different tasks on ADNI and OASIS datasets, demonstrating reliable model interpretability.

Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease

BACKGROUND AND PURPOSE

Early- and late-onset Alzheimer's disease (EOAD and LOAD, respectively) share the same neuropathological hallmarks of amyloid and neurofibrillary tangles but have distinct cognitive features. We compared structural brain connectivity between the EOAD and LOAD groups using structural network efficiency and evaluated the association of structural network efficiency with the cognitive profile and pathological markers of Alzheimer's disease (AD).

METHODS

The structural brain connectivity networks of 80 AD patients (47 with EOAD and 33 with LOAD) and 57 healthy controls were reconstructed using diffusion-tensor imaging. Graph-theoretic indices were calculated and intergroup differences were evaluated. Correlations between network parameters and neuropsychological test results were analyzed. The correlations of the amyloid and tau burdens with network parameters were evaluated for the patients and controls.

RESULTS

Compared with the age-matched control group, the EOAD patients had increased global path length and decreased global efficiency, averaged local efficiency, and averaged clustering coefficient. In contrast, no significant differences were found in the LOAD patients. Locally, the EOAD patients showed decreases in local efficiency and the clustering coefficient over a wide area compared with the control group, whereas LOAD patients showed such decreases only within a limited area. Changes in network parameters were significantly correlated with multiple cognitive domains in EOAD patients, but only with Clinical Dementia Rating Sum-of-Boxes scores in LOAD patients. Finally, the tau burden was correlated with changes in network parameters in AD signature areas in both patient groups, while there was no correlation with the amyloid burden.

CONCLUSIONS

The impairment of structural network efficiency and its effects on cognition may differ between EOAD and LOAD.

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

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSION

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

A right convergence area of the prefrontal lobe is involved in the improvement of semantic fluency in patients with post-stroke aphasia

OBJECTIVES

This study aimed to longitudinally observe the improvement mechanism of semantic fluency in subacute post-stroke aphasia (PSA) patients using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Twelve PSA patients, about one month after onset, were enrolled in this study and received speech-language therapy (SLT) for one month. Auditory comprehension and semantic fluency were evaluated using the Western Aphasia Battery (WAB) and the Animal Fluency Test. Before and after treatment, rs-fMRI data were collected, and the dice similarity coefficient was used to measure the spatial similarity between each patient's lesion and a reference lesion. The left posterior inferior temporal gyrus (pITG) was used as a seed to calculate the normalized functional connectivity in whole-brain voxel analysis using DPABI software for statistical analysis.

RESULTS

The dice similarity coefficient between each patient's lesion and the reference lesion showed moderate to high intensity (0.57 ± 0.14) in the Montreal Neurological Institute space. After treatment, we found a significant increase in functional connectivity between the left pITG and the right prefrontal lobe convergence area (peak t = 8.219, Gaussian random field multiple comparison correction, voxel p < 0.001, cluster p < 0.05). The increase in functional connectivity was negatively correlated with the improvement in auditory comprehension (r =-0.707, p = 0.033) and positively correlated with the improvement in semantic fluency (r = 0.79, p = 0.02).

CONCLUSION

The improvement of semantic fluency in subacute PSA patients may require the participation of the right convergence area of the prefrontal lobe.

Synaptic density patterns in early Alzheimer's disease assessed by independent component analysis

Synaptic loss is a primary pathology in Alzheimer's disease and correlates best with cognitive impairment as found in post-mortem studies. Previously, we observed in vivo reductions of synaptic density with [11C]UCB-J PET (radiotracer for synaptic vesicle protein 2A) throughout the neocortex and medial temporal brain regions in early Alzheimer's disease. In this study, we applied independent component analysis to synaptic vesicle protein 2A-PET data to identify brain networks associated with cognitive deficits in Alzheimer's disease in a blinded data-driven manner. [11C]UCB-J binding to synaptic vesicle protein 2A was measured in 38 Alzheimer's disease (24 mild Alzheimer's disease dementia and 14 mild cognitive impairment) and 19 cognitively normal participants. [11C]UCB-J distribution volume ratio values were calculated with a whole cerebellum reference region. Principal components analysis was first used to extract 18 independent components to which independent component analysis was then applied. Subject loading weights per pattern were compared between groups using Kruskal-Wallis tests. Spearman's rank correlations were used to assess relationships between loading weights and measures of cognitive and functional performance: Logical Memory II, Rey Auditory Verbal Learning Test-long delay, Clinical Dementia Rating sum of boxes and Mini-Mental State Examination. We observed significant differences in loading weights among cognitively normal, mild cognitive impairment and mild Alzheimer's disease dementia groups in 5 of the 18 independent components, as determined by Kruskal-Wallis tests. Only Patterns 1 and 2 demonstrated significant differences in group loading weights after correction for multiple comparisons. Excluding the cognitively normal group, we observed significant correlations between the loading weights for Pattern 1 (left temporal cortex and the cingulate gyrus) and Clinical Dementia Rating sum of boxes (r = -0.54, P = 0.0019), Mini-Mental State Examination (r = 0.48, P = 0.0055) and Logical Memory II score (r = 0.44, P = 0.013). For Pattern 2 (temporal cortices), significant associations were demonstrated between its loading weights and Logical Memory II score (r = 0.34, P = 0.0384). Following false discovery rate correction, only the relationship between the Pattern 1 loading weights with Clinical Dementia Rating sum of boxes (r = -0.54, P = 0.0019) and Mini-Mental State Examination (r = 0.48, P = 0.0055) remained statistically significant. We demonstrated that independent component analysis could define coherent spatial patterns of synaptic density. Furthermore, commonly used measures of cognitive performance correlated significantly with loading weights for two patterns within only the mild cognitive impairment/mild Alzheimer's disease dementia group. This study leverages data-centric approaches to augment the conventional region-of-interest-based methods, revealing distinct patterns that differentiate between mild cognitive impairment and mild Alzheimer's disease dementia, marking a significant advancement in the field.

Exploring Spoken Discourse and Its Neural Correlates in Women With Alzheimer's Disease With Low Levels of Education and Socioeconomic Status

PURPOSE

Early impairments in spoken discourse abilities have been identified in Alzheimer's disease (AD). However, the impact of AD on spoken discourse and the associated neuroanatomical correlates have mainly been studied in populations with higher levels of education, although preliminary evidence seems to indicate that socioeconomic status (SES) and level of education have an impact on spoken discourse. The purpose of this study was to analyze microstructural variables in spoken discourse in people with AD with low-to-middle SES and low level of education and to study their association with gray matter (GM) density.

METHOD

Nine women with AD and 10 matched (age, SES, and education) women without brain injury (WWBI) underwent a neuropsychological assessment, which included two spoken discourse tasks, and structural magnetic resonance imaging. Microstructural variables were extracted from the discourse samples using NILC-Metrix software. Brain density, measured by voxel-based morphometry, was compared between groups and then correlated with the differentiating microstructural variables.

RESULTS

The AD group produced a lower diversity of verbal time moods and fewer words and sentences than WWBI but a greater diversity of pronouns, prepositions, and lexical richness. At the neural level, the AD group presented a lower GM density bilaterally in the hippocampus, the inferior temporal gyrus, and the anterior cingulate gyrus. Number of words and sentences produced were associated with GM density in the left parahippocampal gyrus, whereas the diversity of verbal moods was associated with the basal ganglia and the anterior cingulate gyrus bilaterally.

CONCLUSIONS

The present findings are mainly consistent with previous studies conducted in groups with higher levels of SES and education, but they suggest that atrophy in the left inferior temporal gyrus could be critical in AD in populations with lower levels of SES and education. This research provides evidence on the importance of pursuing further studies including people with various SES and education levels.

WHAT IS ALREADY KNOWN ON THIS SUBJECT

Spoken discourse has been shown to be affected in Alzheimer disease, but most studies have been conducted on individuals with middle-to-high SES and high educational levels.

WHAT THIS STUDY ADDS

The study reports on microstructural measures of spoken discourse in groups of women in the early stage of AD and healthy women, with low-to-middle SES and lower levels of education.

CLINICAL IMPLICATIONS OF THIS STUDY

This study highlights the importance of taking into consideration the SES and education level in spoken discourse analysis and in investigating the neural correlates of AD.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.24905046.

Shared and differing functional connectivity abnormalities of the default mode network in mild cognitive impairment and Alzheimer's disease

Alzheimer's disease (AD) and mild cognitive impairment (MCI) both show abnormal resting-state functional connectivity (rsFC) of default mode network (DMN), but it is unclear to what extent these abnormalities are shared. Therefore, we performed a comprehensive meta-analysis, including 31 MCI studies and 20 AD studies. MCI patients, compared to controls, showed decreased within-DMN rsFC in bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), precuneus/posterior cingulate cortex (PCC), right temporal lobes, and left angular gyrus and increased rsFC between DMN and left inferior temporal gyrus. AD patients, compared to controls, showed decreased rsFC within DMN in bilateral mPFC/ACC and precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC between DMN and right dorsolateral prefrontal cortex. Conjunction analysis showed shared decreased rsFC in mPFC/ACC and precuneus/PCC. Compared to MCI, AD had decreased rsFC in left precuneus/PCC and between DMN and left inferior occipital gyrus and increased rsFC in right temporal lobes. MCI and AD share a decreased within-DMN rsFC likely underpinning episodic memory deficits and neuropsychiatric symptoms, but differ in DMN rsFC alterations likely related to impairments in other cognitive domains such as language, vision, and execution. This may throw light on neuropathological mechanisms in these two stages of dementia.

Systematic comparison of 3D Deep learning and classical machine learning explanations for Alzheimer's Disease detection

Black-box deep learning (DL) models trained for the early detection of Alzheimer's Disease (AD) often lack systematic model interpretation. This work computes the activated brain regions during DL and compares those with classical Machine Learning (ML) explanations. The architectures used for DL were 3D DenseNets, EfficientNets, and Squeeze-and-Excitation (SE) networks. The classical models include Random Forests (RFs), Support Vector Machines (SVMs), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting (LightGBM), Decision Trees (DTs), and Logistic Regression (LR). For explanations, SHapley Additive exPlanations (SHAP) values, Local Interpretable Model-agnostic Explanations (LIME), Gradient-weighted Class Activation Mapping (GradCAM), GradCAM++ and permutation-based feature importance were implemented. During interpretation, correlated features were consolidated into aspects. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The validation includes internal and external validation on the Australian Imaging and Lifestyle flagship study of Ageing (AIBL) and the Open Access Series of Imaging Studies (OASIS). DL and ML models reached similar classification performances. Regarding the brain regions, both types focus on different regions. The ML models focus on the inferior and middle temporal gyri, and the hippocampus, and amygdala regions previously associated with AD. The DL models focus on a wider range of regions including the optical chiasm, the entorhinal cortices, the left and right vessels, and the 4th ventricle which were partially associated with AD. One explanation for the differences is the input features (textures vs. volumes). Both types show reasonable similarity to a ground truth Voxel-Based Morphometry (VBM) analysis. Slightly higher similarities were measured for ML models.

Free-water imaging reveals unique brain microstructural deficits in hispanic individuals with Dementia

Prior evidence suggests that Hispanic and non-Hispanic individuals differ in potential risk factors for the development of dementia. Here we determine whether specific brain regions are associated with cognitive performance for either ethnicity along various stages of Alzheimer's disease. For this cross-sectional study, we examined 108 participants (61 Hispanic vs. 47 Non-Hispanic individuals) from the 1Florida Alzheimer's Disease Research Center (1Florida ADRC), who were evaluated at baseline with diffusion-weighted and T1-weighted imaging, and positron emission tomography (PET) amyloid imaging. We used FreeSurfer to segment 34 cortical regions of interest. Baseline Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used as measures of cognitive performance. Group analyses assessed free-water measures (FW) and volume. Statistically significant FW regions based on ethnicity x group interactions were used in a stepwise regression function to predict total MMSE and MoCA scores. Random forest models were used to identify the most predictive brain-based measures of a dementia diagnosis separately for Hispanic and non-Hispanic groups. Results indicated elevated FW values for the left inferior temporal gyrus, left middle temporal gyrus, left banks of the superior temporal sulcus, left supramarginal gyrus, right amygdala, and right entorhinal cortex in Hispanic AD subjects compared to non-Hispanic AD subjects. These alterations occurred in the absence of different volumes of these regions in the two AD groups. FW may be useful in detecting individual differences potentially reflective of varying etiology that can influence cognitive decline and identify MRI predictors of cognitive performance, particularly among Hispanics.

Gray and White Matter Voxel-Based Morphometry of Alzheimer's Disease With and Without Significant Cerebrovascular Pathologies

Alzheimer's disease (AD) is the most common type of dementia, and AD individuals often present significant cerebrovascular disease (CVD) symptomology. AD with significant levels of CVD is frequently labeled mixed dementia (or sometimes AD-CVD), and the differentiation of these two neuropathologies (AD, AD-CVD) from each other is challenging, especially at early stages. In this study, we compared the gray matter (GM) and white matter (WM) volumes in AD (n = 83) and AD-CVD (n = 37) individuals compared with those of cognitively healthy controls (n = 85) using voxel-based morphometry (VBM) of their MRI scans. The control individuals, matched for age and sex with our two dementia groups, were taken from the ADNI. The VBM analysis showed widespread patterns of significantly lower GM and WM volume in both dementia groups compared to the control group (P < .05, family-wise error corrected). While comparing with AD-CVD, the AD group mainly demonstrated a trend of lower volumes in the GM of the left putamen and right hippocampus and WM of the right thalamus (uncorrected P < .005 with cluster threshold, K = 10). The AD-CVD group relative to AD tended to present lower GM and WM volumes, mainly in the cerebellar lobules and right brainstem regions, respectively (uncorrected P < .005 with cluster threshold, K = 10). Although finding a discriminatory feature in structural MRI data between AD and AD-CVD neuropathologies is challenging, these results provide preliminary evidence that demands further investigation in a larger sample size.

Accelerated Brain Atrophy, Microstructural Decline and Connectopathy in Age-Related Macular Degeneration

Age-related macular degeneration (AMD) has recently been linked to cognitive impairment. We hypothesized that AMD modifies the brain aging trajectory, and we conducted a longitudinal diffusion MRI study on 40 participants (20 with AMD and 20 controls) to reveal the location, extent, and dynamics of AMD-related brain changes. Voxel-based analyses at the first visit identified reduced volume in AMD participants in the cuneate gyrus, associated with vision, and the temporal and bilateral cingulate gyrus, linked to higher cognition and memory. The second visit occurred 2 years after the first and revealed that AMD participants had reduced cingulate and superior frontal gyrus volumes, as well as lower fractional anisotropy (FA) for the bilateral occipital lobe, including the visual and the superior frontal cortex. We detected faster rates of volume and FA reduction in AMD participants in the left temporal cortex. We identified inter-lingual and lingual-cerebellar connections as important differentiators in AMD participants. Bundle analyses revealed that the lingual gyrus had a lower streamline length in the AMD participants at the first visit, indicating a connection between retinal and brain health. FA differences in select inter-lingual and lingual cerebellar bundles at the second visit showed downstream effects of vision loss. Our analyses revealed widespread changes in AMD participants, beyond brain networks directly involved in vision processing.

Hypergraph convolutional network for longitudinal data analysis in Alzheimer's disease

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for tracking AD pathogenesis and diagnosis. However, existing methods tend to treat each time point equally without considering the temporal characteristics of longitudinal data. In this paper, we propose a weighted hypergraph convolution network (WHGCN) to use the internal correlations among different time points and leverage high-order relationships between subjects for AD detection. Specifically, we construct hypergraphs for sMRI data at each time point using the K-nearest neighbor (KNN) method to represent relationships between subjects, and then fuse the hypergraphs according to the importance of the data at each time point to obtain the final hypergraph. Subsequently, we use hypergraph convolution to learn high-order information between subjects while performing feature dimensionality reduction. Finally, we conduct experiments on 518 subjects selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and the results show that the WHGCN can get higher AD detection performance and has the potential to improve our understanding of the pathogenesis of AD.

Causal functional connectivity in Alzheimer's disease computed from time series fMRI data

Functional connectivity between brain regions is known to be altered in Alzheimer's disease and promises to be a biomarker for early diagnosis. Several approaches for functional connectivity obtain an un-directed network representing stochastic associations (correlations) between brain regions. However, association does not necessarily imply causation. In contrast, Causal Functional Connectivity (CFC) is more informative, providing a directed network representing causal relationships between brain regions. In this paper, we obtained the causal functional connectome for the whole brain from resting-state functional magnetic resonance imaging (rs-fMRI) recordings of subjects from three clinical groups: cognitively normal, mild cognitive impairment, and Alzheimer's disease. We applied the recently developed Time-aware PC (TPC) algorithm to infer the causal functional connectome for the whole brain. TPC supports model-free estimation of whole brain CFC based on directed graphical modeling in a time series setting. We compared the CFC outcome of TPC with that of other related approaches in the literature. Then, we used the CFC outcomes of TPC and performed an exploratory analysis of the difference in strengths of CFC edges between Alzheimer's and cognitively normal groups, based on edge-wise p-values obtained by Welch's t-test. The brain regions thus identified are found to be in agreement with literature on brain regions impacted by Alzheimer's disease, published by researchers from clinical/medical institutions.

Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

The environmental enrichment ameliorates chronic cerebral hypoperfusion-induced cognitive impairment by activating autophagy signaling pathway and improving synaptic function in hippocampus

BACKGROUND

Chronic cerebral hypoperfusion (CCH) is a frequently observed underlying pathology of both Alzheimer's disease (AD) and vascular dementia (VD), which is a common consequence of cerebral blood flow (CBF) dysregulation. Synaptic damage has been proven as a crucial causative factor for CCH-related cognitive impairment. This study aimed to investigate the neuroprotective impact of environmental enrichment (EE) intervention on CCH-induced synaptic destruction and the consequent cognitive impairment. Furthermore, the underlying mechanism of this neuroprotective effect was explored to provide new insights into therapeutic interventions for individuals suffering from AD or VD.

METHODS

In this experiment, all rats were initially acclimatized to a standard environment (SE) for a period of one week. On the seventh day, rats underwent either bilateral common carotid artery occlusion (2VO) surgery or sham surgery (Sham) before being subjected to a four-week procedure of exposure to an EE, except for the control group. During the EE or SE procedure, intraperitoneal injection of chloroquine (CQ) into rats was performed once daily for four weeks. Following this, cognitive function was assessed using the Morris water maze (MWM) test. The synapse ultrastructure was subsequently observed using transmission electron microscopy. Expression levels of autophagy-related proteins (LC3, LAMP1, and P62) and synapse-related proteins (Synapsin I and PSD-95) were detected through Western blotting. Finally, immunofluorescence was used to examine the expression levels of Synapsin I and PSD-95 and the colocalization of LAMP-1 and LC3 in the hippocampus.

RESULTS

After undergoing 2VO, rats exposed to SE exhibited cognitive impairment, autophagic dysfunction, and synapse damage. The synapse damage was evidenced by ultrastructural damage and degradation of synapse-related proteins. However, these effects were significantly mitigated by exposure to an EE intervention. Moreover, the intervention led to an improvement in autophagic dysfunction.

CONCLUSION

The study found that EE had a positive impact on CCH-induced synaptic damage. Specifically, EE was found to increase synaptic plasticity-associated proteins and postsynaptic density thickness, while decreasing synaptic space. This multifaceted effect resulted in an amelioration of CCH-induced cognitive impairment. It was shown that this beneficial outcome was mediated via the activation of the autophagy-lysosomal pathway. Overall, the findings suggest that EE may have a therapeutic potential for cognitive impairments associated with CCH through autophagy-mediated synaptic improvement.

Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Utilizing Multi-Modalities Data

BACKGROUND

Predicting cognition decline in patients with mild cognitive impairment (MCI) is crucial for identifying high-risk individuals and implementing effective management. To improve predicting MCI-to-AD conversion, it is necessary to consider various factors using explainable machine learning (XAI) models which provide interpretability while maintaining predictive accuracy. This study used the Explainable Boosting Machine (EBM) model with multimodal features to predict the conversion of MCI to AD during different follow-up periods while providing interpretability.

METHODS

This retrospective case-control study is conducted with data obtained from the ADNI database, with records of 1042 MCI patients from 2006 to 2022 included. The exposures included in this study were MRI biomarkers, cognitive scores, demographics, and clinical features. The main outcome was AD conversion from aMCI during follow-up. The EBM model was utilized to predict aMCI converting to AD based on three feature combinations, obtaining interpretability while ensuring accuracy. Meanwhile, the interaction effect was considered in the model. The three feature combinations were compared in different follow-up periods with accuracy, sensitivity, specificity, and AUC-ROC. The global and local explanations are displayed by importance ranking and feature interpretability plots.

RESULTS

The five-years prediction accuracy reached 85% (AUC = 0.92) using both cognitive scores and MRI markers. Apart from accuracies, we obtained features' importance in different follow-up periods. In early stage of AD, the MRI markers play a major role, while for middle-term, the cognitive scores are more important. Feature risk scoring plots demonstrated insightful nonlinear interactive associations between selected factors and outcome. In one-year prediction, lower right inferior temporal volume (<9000) is significantly associated with AD conversion. For two-year prediction, low left inferior temporal thickness (<2) is most critical. For three-year prediction, higher FAQ scores (>4) is the most important. During four-year prediction, APOE4 is the most critical. For five-year prediction, lower right entorhinal volume (<1000) is the most critical feature.

CONCLUSIONS

The established glass-box model EBMs with multimodal features demonstrated a superior ability with detailed interpretability in predicting AD conversion from MCI. Multi features with significant importance were identified. Further study may be of significance to determine whether the established prediction tool would improve clinical management for AD patients.

Altered local and remote functional connectivity in mild Alzheimer's disease patients with sleep disturbances

Objectives

This study aimed to investigate local and remote functional connectivity in mild Alzheimer's disease patients with sleep disturbances (ADSD) and those without sleep disturbances (ADNSD).

Methods

Thirty eight mild AD patients with sleep disturbances and 21 mild AD patients without sleep disturbances participated in this study. All subjects underwent neuropsychological assessments and 3.0 Tesla magnetic resonance scanning. Static and dynamic regional homogeneity (ReHo) were used to represent the local functional connectivity. Seed-based whole-brain functional connectivity was used to represent the remote functional connectivity. The seed was chosen based on the results of ReHo.

Results

Compared to ADNSD, ADSD showed decreased static ReHo in the left posterior central gyrus and the right cuneus and increased dynamic ReHo in the left posterior central gyrus. As for the remote functional connectivity, comparing ADSD to ADNSD, it was found that there was a decreased functional connection between the left posterior central gyrus and the left cuneus as well as the left calcarine.

Conclusion

The current study demonstrated that, compared with ADNSD, ADSD is impaired in both local and remote functional connectivity, manifested as reduced functional connectivity involving the primary sensory network and the primary visual network. The abnormality of the above functional connectivity is one of the reasons why sleep disorders promote cognitive impairment in AD. Moreover, sleep disorders change the temporal sequence of AD pathological damage to brain functional networks, but more evidence is needed to support this conclusion.

Application of the Semantic Fluency Test in the Screening of Mandarin-Chinese-Speaking Older Adults with Mild Dementia of the Alzheimer Type

Semantic fluency tests have been widely used as a screening test for dementia of Alzheimer type. However, few studies have explored the application of semantic fluency tests in Mandarin Chinese speakers. This study aimed to explore the feasibility of using different semantic fluency test categories to distinguish between older adults without cognitive impairments and those with dementia of Alzheimer type in Taiwan. A total of 58 healthy older adults and 54 individuals with dementia of Alzheimer type were recruited. Semantic categories of "animals", "fruits", "vegetables", "birds", "means of transportations" and "musical instruments" were administered to participants. The scores from two groups of participants for each category were analyzed. Significant differences in the test scores of each category between two groups of participants were found. The results also revealed that the variables related to whether the participant had dementia, gender, age, and years of schooling significantly influenced the semantic fluency scores for each category. Among all the demographic characteristic of participants, the diagnosis of dementia was the most determining factor. Furthermore, this study proposed optimal cutoff points and calculated the AUC for various test durations (i.e., 30 s, 45 s and 60 s) and semantic categories in the semantic fluency test, which may serve as a reference that would help clinical personnel distinguish between older adults without cognitive impairments and those with dementia of Alzheimer type in Taiwan.

Compilation of reported protein changes in the brain in Alzheimer's disease

Proteomic studies of human Alzheimer's disease brain tissue have potential to identify protein changes that drive disease, and to identify new drug targets. Here, we analyse 38 published Alzheimer's disease proteomic studies, generating a map of protein changes in human brain tissue across thirteen brain regions, three disease stages (preclinical Alzheimer's disease, mild cognitive impairment, advanced Alzheimer's disease), and proteins enriched in amyloid plaques, neurofibrillary tangles, and cerebral amyloid angiopathy. Our dataset is compiled into a searchable database (NeuroPro). We found 848 proteins were consistently altered in 5 or more studies. Comparison of protein changes in early-stage and advanced Alzheimer's disease revealed proteins associated with synapse, vesicle, and lysosomal pathways show change early in disease, but widespread changes in mitochondrial associated protein expression change are only seen in advanced Alzheimer's disease. Protein changes were similar for brain regions considered vulnerable and regions considered resistant. This resource provides insight into Alzheimer's disease brain protein changes and highlights proteins of interest for further study.

Altered structural covariance of locus coeruleus in individuals with significant memory concern and patients with mild cognitive impairment

The locus coeruleus (LC) is the site where tau accumulation is preferentially observed pathologically in Alzheimer's disease (AD) patients, but the changes in gray matter co-alteration patterns between the LC and the whole brain in the predementia phase of AD remain unclear. In this study, we estimated and compared the gray matter volume of the LC and its structural covariance (SC) with the whole brain among 161 normal healthy controls (HCs), 99 individuals with significant memory concern (SMC) and 131 patients with mild cognitive impairment (MCI). We found that SC decreased in MCI groups, which mainly involved the salience network and default mode network. These results imply that seeding from LC, the gray matter network disruption and disconnection appears early in the MCI group. The altered SC network seeding from the LC can serve as an imaging biomarker for discriminating the patients in the potential predementia phase of AD from the normal subjects.

Shared alterations in hippocampal structural covariance in subjective cognitive decline and migraine

Introduction

Subjective cognitive decline (SCD) and migraine are often comorbid. Hippocampal structural abnormalities have been observed in individuals with both SCD and migraine. Given the known structural and functional heterogeneity along the long axis (anterior to posterior) of the hippocampus, we aimed to identify altered patterns of structural covariance within hippocampal subdivisions associated with SCD and migraine comorbidities.

Methods

A seed-based structural covariance network analysis was applied to examine large-scale anatomical network changes of the anterior and posterior hippocampus in individuals with SCD, migraine and healthy controls. Conjunction analyses were used to identify shared network-level alterations in the hippocampal subdivisions in individuals with both SCD and migraine.

Results

Altered structural covariance integrity of the anterior and posterior hippocampus was observed in the temporal, frontal, occipital, cingulate, precentral, and postcentral areas in individuals with SCD and migraine compared with healthy controls. Conjunction analysis revealed that, in both SCD and migraine, altered structural covariance integrity was shared between the anterior hippocampus and inferior temporal gyri and between the posterior hippocampus and precentral gyrus. Additionally, the structural covariance integrity of the posterior hippocampus-cerebellum axis was associated with the duration of SCD.

Conclusion

This study highlighted the specific role of hippocampal subdivisions and specific structural covariance alterations within these subdivisions in the pathophysiology of SCD and migraine. These network-level changes in structural covariance may serve as potential imaging signatures for individuals who have both SCD and migraine.

White Matter Degeneration Pathways Associated With Tau Deposition in Alzheimer Disease

BACKGROUND AND OBJECTIVES

The dynamics of white matter (WM) changes are understudied in Alzheimer disease (AD). Our goal was to study the association between flortaucipir PET and WM health using neurite orientation dispersion and density imaging (NODDI) and evaluate its association with cognitive performance. Specifically, we focused on NODDI's Neurite Density Index (NDI), which aids in capturing axonal degeneration in WM and has greater specificity than single-shell diffusion MRI methods.

METHOD

We estimated regional flortaucipir PET standard uptake value ratios (SUVRs) from 3 regions corresponding to Braak stage I, III/IV, and V/VI to capture the spatial distribution pattern of the 3R/4R tau in AD. Then, we evaluated the associations between these measurements and NDIs in 29 candidate WM tracts using Pearson correlation and multiple regression models.

RESULTS

Based on 223 participants who were amyloid positive (mean age of 78 years and 57.0% male, 119 cognitively unimpaired, 56 mild cognitive impairment, and 48 dementia), the results showed that WM tracts NDI decreased with increasing regional Braak tau SUVRs. Of all the significant WM tracts, the uncinate fasciculus (r = -0.274 for Braak I, -0.311 for Braak III/IV, and -0.292 for Braak V/VI, p < 0.05) and cingulum adjoining hippocampus (r = -0.274, -0.288, -0.233, p < 0.05), both tracts anatomically connected to areas of early tau deposition, were consistently found to be within the top 5 distinguishing WM tracts associated with flortaucipir SUVRs. The increase in tau deposition measurable outside the medial temporal lobes in Braak III-VI was associated with a decrease in NDI in the middle and inferior temporal WM tracts. For cognitive performance, WM NDI had similar coefficients of determination (r 2 = 31%) as regional Braak flortaucipir SUVRs (29%), and together WM NDI and regional Braak flortaucipir SUVRs explained 46% of the variance in cognitive performance.

DISCUSSION

We found spatially dependent WM degeneration associated with regional flortaucipir SUVRs in Braak stages, suggesting a spatial pattern in WM damage. NDI, a specific marker of axonal density, provides complementary information about disease staging and progression in addition to tau deposition. Measurements of WM changes are important for the mechanistic understanding of multifactorial pathways through which AD causes cognitive dysfunction.

CLARITY increases sensitivity and specificity of fluorescence immunostaining in long-term archived human brain tissue

BACKGROUND

Post mortem human brain tissue is an essential resource to study cell types, connectivity as well as subcellular structures down to the molecular setup of the central nervous system especially with respect to the plethora of brain diseases. A key method is immunostaining with fluorescent dyes, which allows high-resolution imaging in three dimensions of multiple structures simultaneously. Although there are large collections of formalin-fixed brains, research is often limited because several conditions arise that complicate the use of human brain tissue for high-resolution fluorescence microscopy.

RESULTS

In this study, we developed a clearing approach for immunofluorescence-based analysis of perfusion- and immersion-fixed post mortem human brain tissue, termed human Clear Lipid-exchanged Acrylamide-hybridized Rigid Imaging / Immunostaining / In situ hybridization-compatible Tissue-hYdrogel (hCLARITY). hCLARITY is optimized for specificity by reducing off-target labeling and yields very sensitive stainings in human brain sections allowing for super-resolution microscopy with unprecedented imaging of pre- and postsynaptic compartments. Moreover, hallmarks of Alzheimer's disease were preserved with hCLARITY, and importantly classical 3,3'-diaminobenzidine (DAB) or Nissl stainings are compatible with this protocol. hCLARITY is very versatile as demonstrated by the use of more than 30 well performing antibodies and allows for de- and subsequent re-staining of the same tissue section, which is important for multi-labeling approaches, e.g., in super-resolution microscopy.

CONCLUSIONS

Taken together, hCLARITY enables research of the human brain with high sensitivity and down to sub-diffraction resolution. It therefore has enormous potential for the investigation of local morphological changes, e.g., in neurodegenerative diseases.

Spatial characterization of tangle-bearing neurons and ghost tangles in the human inferior temporal gyrus with three-dimensional imaging

Studies of post-mortem human tissue provide insight into pathological processes, but are inherently limited by practical considerations that limit the scale at which tissue can be examined, and the obvious issue that the tissue reflects only one time point in a continuous disease process. We approached this problem by adapting new tissue clearance techniques to an entire cortical area of human brain, which allows surveillance of hundreds of thousands of neurons throughout the depth of the entire cortical thickness. This approach allows detection of 'rare' events that may be difficult to detect in standard 5 micrometre-thick paraffin sections. For example, it is well established that neurofibrillary tangles begin within a neuron, and ultimately, in at least some instances, persist in the brain even after the neuron has died. These are referred to as 'ghost tangles', a term that appropriately implies their 'difficult to see' ephemeral qualities. We set out to find ghost tangles as one example of the power of the tissue clearance/image analysis techniques to detect rare events, and to learn what happens at the end-point of a tangle's life history. We were able to identify 8103 tau tangles, 132 465 neurons and 299 640 nuclei in tissue samples from three subjects with severe Alzheimer's disease (Braak V-VI) and 4 tau tangles, 200 447 neurons and 462 715 nuclei in tissue samples from three subjects with no significant tau pathology (Braak 0-I). Among these data, we located 57 ghost tangles, which makes them only 0.7% of the total tau tangles observed. We found that ghost tangles are more likely to be found in cortical layers 3 and 5 (49/57), with a select few scattered across other layers 1, 2, 4 and 6. This ability to find rare events, such as ghost tangles, in large enough quantities to statistically test their distribution exemplifies how tissue clearing can be used as a powerful tool for studying selective vulnerability or resilience to pathology across brain regions.

Neural Circuit Changes in Neurological Disorders: Evidence from in vivo Two-photon Imaging

Neural circuits, such as synaptic plasticity and neural activity, are critical components of healthy brain function. The consequent dynamic remodeling of neural circuits is an ongoing procedure affecting neuronal activities. Disruption of this essential process results in diseases. Advanced microscopic applications such as two-photon laser scanning microscopy have recently been applied to understand neural circuit changes during disease since it can visualize fine structural and functional cellular activation in living animals. In this review, we have summarized the latest work assessing the dynamic rewiring of postsynaptic dendritic spines and modulation of calcium transients in neurons of the intact living brain, focusing on their potential roles in neurological disorders (e.g. Alzheimer's disease, stroke, and epilepsy). Understanding the fine changes that occurred in the brain during disease is crucial for future clinical intervention developments.

Impairments of glutamatergic synaptic transmission in Alzheimer's disease

One of the hallmarks of Alzheimer's disease (AD) is structural cell damage and neuronal death in the brains of affected individuals. As these changes are irreversible, it is important to understand their origins and precursors in order to develop treatment strategies against AD. Here, we review evidence for AD-specific impairments of glutamatergic synaptic transmission by relating evidence from human AD subjects to functional studies in animal models of AD. The emerging picture is that early in the disease, the accumulation of toxic β-amyloid aggregates, particularly dimers and low molecular weight oligomers, disrupts glutamate reuptake, which leads to its extracellular accumulation causing neuronal depolarization. This drives the hyperactivation of neurons and might facilitate neuronal damage and degeneration through glutamate neurotoxicity.

Synapse pathology in Alzheimer's disease

Synapse loss and damage are central features of Alzheimer's disease (AD) and contribute to the onset and progression of its behavioural and physiological features. Here we review the literature describing synapse pathology in AD, from what we have learned from microscopy in terms of its impacts on synapse architecture, to the mechanistic role of Aβ, tau and glial cells, mitochondrial dysfunction, and the link with AD risk genes. We consider the emerging view that synapse pathology may operate at a further level, that of synapse diversity, and discuss the prospects for leveraging new synaptome mapping methods to comprehensively understand the molecular properties of vulnerable and resilient synapses. Uncovering AD impacts on brain synapse diversity should inform therapeutic approaches targeted at preserving or replenishing lost and damaged synapses and aid the interpretation of clinical imaging approaches that aim to measure synapse damage.

Application of orthogonal sparse joint non-negative matrix factorization based on connectivity in Alzheimer's disease research

Based on the mining of micro- and macro-relationships of genetic variation and brain imaging data, imaging genetics has been widely applied in the early diagnosis of Alzheimer's disease (AD). However, effective integration of prior knowledge remains a barrier to determining the biological mechanism of AD. This paper proposes a new connectivity-based orthogonal sparse joint non-negative matrix factorization (OSJNMF-C) method based on integrating the structural magnetic resonance image, single nucleotide polymorphism and gene expression data of AD patients; the correlation information, sparseness, orthogonal constraint and brain connectivity information between the brain image data and genetic data are designed as constraints in the proposed algorithm, which efficiently improved the accuracy and convergence through multiple iterative experiments. Compared with the competitive algorithm, OSJNMF-C has significantly smaller related errors and objective function values than the competitive algorithm, showing its good anti-noise performance. From the biological point of view, we have identified some biomarkers and statistically significant relationship pairs of AD/mild cognitive impairment (MCI), such as rs75277622 and BCL7A, which may affect the function and structure of multiple brain regions. These findings will promote the prediction of AD/MCI.

Altered Postcentral Connectivity after Sleep Deprivation Correlates to Impaired Risk Perception: A Resting-State Functional Magnetic Resonance Imaging Study

BACKGROUND

Previous studies revealed that sleep deprivation (SD) impairs risk perception and leads to poor decision-making efficiency. However, how risk perception is related to brain regions' communication after SD has not been elucidated. In this study, we investigated the neuropsychological mechanisms of SD-impaired risk perception.

METHODS

Nineteen healthy male adults were recruited and underwent resting-state functional magnetic resonance imaging during a state of rested wakefulness and after nearly 36 h of total SD. They then completed the balloon analog risk task, which was used to measure the risk perception ability of risky decision-making. Regional homogeneity (ReHo) and voxel-wise functional connectivity were used to investigate neurobiological changes caused by SD. Correlation analysis was used to investigate the relationship between changes in ReHo, function, and risk perception.

RESULTS

At the behavioral level, risk perception decreased after 36 h of SD. At the neural level, SD induced a significant increase in ReHo in the right postcentral gyrus and was positively correlated with risk perception changes. The functional connectivity between the right postcentral gyrus, left medial temporal gyrus, and right inferior temporal gyrus was enhanced. Critically, increased right postcentral gyrus and right inferior temporal gyrus connectivity positively correlated with changes in risk perception.

CONCLUSIONS

SD impairs the risk perception associated with altered postcentral connectivity. The brain requires more energy to process and integrate sensory and perceptual information after SD, which may be one possible reason for decreased risk perception ability after SD.

Amyloid Deposition and Dendritic Complexity of Corticocortical Projection Cells in Five Familial Alzheimer's Disease Mouse

In Alzheimer's disease and related dementias, amyloid beta (Aβ) and amyloid plaques can disrupt long-term synaptic plasticity, learning and memory and cognitive function. Plaque accumulation can disrupt corticocortical circuitry leading to abnormalities in sensory, motor, and cognitive processing. In this study, using 5xFAD (five Familial Alzheimer's Disease - FAD - mutations) mice, we evaluated amyloid plaque formation in different cortical areas, and whether differential amyloid accumulation across cortical fields correlates with changes in dendritic complexity of layer 3 corticocortical projection neurons and functional responses in the primary somatosensory cortex following whisker stimulation. We focused on three cortical areas: the primary somatosensory cortex (S1), the primary motor cortex (M1), and the prefrontal cortex (PFC including the anterior cingulate, prelimbic, and infralimbic subdivisions). We found that Aβ and amyloid plaque accumulation is not uniform across 5xFAD cortical areas, while there is no expression in littermate controls. We also found that there are differential layer 3 pyramidal cell dendritic complexity changes across the three areas in 5xFAD mice, compared to same age controls, with no apparent relation to differential amyloid accumulation. We used voltage-sensitive dye imaging (VSDi) to visualize neural activity in S1, M1 and PFC following whisker activation. Control mice show normal physiological responses in all three cortical areas, whereas 5xFAD mice only display physiological responses in S1. Taken together our results show that 5xFAD mutation affects the overall dendritic morphology of layer 3 pyramidal cells across sensory-motor and association cortex irrespective of the density and distribution of the Aβ amyloid proteins. Corticocortical circuitry between the sensory and motor/association areas is most likely disrupted in 5xFAD mice as cortical responses to whisker stimulation are altered.

Applying Retinal Vascular Structures Characteristics Coupling with Cortical Visual System in Alzheimer's Disease Spectrum Patients

Cortical visual system dysfunction is closely related to the progression of Alzheimer's Disease (AD), while retinal vascular structures play an important role in the integrity of the function of the visual network and are a potential biomarker of AD. This study explored the association between the cortical visual system and retinal vascular structures in AD-spectrum patients, and it established a screening tool to detect preclinical AD based on these parameters identified in a retinal examination. A total of 42 subjects were enrolled and were distributed into two groups: 22 patients with cognitive impairment and 20 healthy controls. All participants underwent neuropsychological tests, optical coherence tomography angiography and resting-state fMRI imaging. Seed-based functional connectivity analysis was used to construct the cortical visual network. The association of functional connectivity of the cortical visual system and retinal vascular structures was further explored in these subjects. This study found that the cognitive impairment group displayed prominently decreased functional connectivity of the cortical visual system mainly involving the right inferior temporal gyrus, left supramarginal gyrus and right postcentral gyrus. Meanwhile, we observed that retinal vascular structure characteristics deteriorated with the decline in functional connectivity in the cortical visual system. Our study provided novel insights into the aberrant cortical visual system in patients with cognitive impairment that strongly emphasized the critical role of retinal vascular structure characteristics, which could be used as potential biomarkers for diagnosing and monitoring the progression of AD.

Neuroanatomical and neurocognitive changes associated with subjective cognitive decline

Introduction

Subjective Cognitive Decline (SCD) can progress to mild cognitive impairment (MCI) and Alzheimer's disease (AD) dementia and thus may represent a preclinical stage of the AD continuum. However, evidence about structural changes observed in the brain during SCD remains inconsistent.

Materials and methods

This cross-sectional study aimed to evaluate, in subjects recruited from the CompAS project, neurocognitive and neurostructural differences between a group of forty-nine control subjects and forty-nine individuals who met the diagnostic criteria for SCD and exhibited high levels of subjective cognitive complaints (SCCs). Structural magnetic resonance imaging was used to compare neuroanatomical differences in brain volume and cortical thickness between both groups.

Results

Relative to the control group, the SCD group displayed structural changes involving frontal, parietal, and medial temporal lobe regions of critical importance in AD etiology and functionally related to several cognitive domains, including executive control, attention, memory, and language.

Conclusion

Despite the absence of clinical deficits, SCD may constitute a preclinical entity with a similar (although subtle) pattern of neuroanatomical changes to that observed in individuals with amnestic MCI or AD dementia.

Orthogonal latent space learning with feature weighting and graph learning for multimodal Alzheimer's disease diagnosis

Recent studies have shown that multimodal neuroimaging data provide complementary information of the brain and latent space-based methods have achieved promising results in fusing multimodal data for Alzheimer's disease (AD) diagnosis. However, most existing methods treat all features equally and adopt nonorthogonal projections to learn the latent space, which cannot retain enough discriminative information in the latent space. Besides, they usually preserve the relationships among subjects in the latent space based on the similarity graph constructed on original features for performance boosting. However, the noises and redundant features significantly corrupt the graph. To address these limitations, we propose an Orthogonal Latent space learning with Feature weighting and Graph learning (OLFG) model for multimodal AD diagnosis. Specifically, we map multiple modalities into a common latent space by orthogonal constrained projection to capture the discriminative information for AD diagnosis. Then, a feature weighting matrix is utilized to sort the importance of features in AD diagnosis adaptively. Besides, we devise a regularization term with learned graph to preserve the local structure of the data in the latent space and integrate the graph construction into the learning processing for accurately encoding the relationships among samples. Instead of constructing a similarity graph for each modality, we learn a joint graph for multiple modalities to capture the correlations among modalities. Finally, the representations in the latent space are projected into the target space to perform AD diagnosis. An alternating optimization algorithm with proved convergence is developed to solve the optimization objective. Extensive experimental results show the effectiveness of the proposed method.

Increased Hippocampal-Inferior Temporal Gyrus White Matter Connectivity following Donepezil Treatment in Patients with Early Alzheimer's Disease: A Diffusion Tensor Probabilistic Tractography Study

The incidence of Alzheimer's disease (AD) has been increasing each year, and a defective hippocampus has been primarily associated with an early stage of AD. However, the effect of donepezil treatment on hippocampus-related networks is unknown. Thus, in the current study, we evaluated the hippocampal white matter (WM) connectivity in patients with early-stage AD before and after donepezil treatment using probabilistic tractography, and we further determined the WM integrity and changes in brain volume. Ten patients with early-stage AD (mean age = 72.4 ± 7.9 years; seven females and three males) and nine healthy controls (HC; mean age = 70.7 ± 3.5 years; six females and three males) underwent a magnetic resonance (MR) examination. After performing the first MR examination, the patients received donepezil treatment for 6 months. The brain volumes and diffusion tensor imaging scalars of 11 regions of interest (the superior/middle/inferior frontal gyrus, the superior/middle/inferior temporal gyrus, the amygdala, the caudate nucleus, the hippocampus, the putamen, and the thalamus) were measured using MR imaging and DTI, respectively. Seed-based structural connectivity analyses were focused on the hippocampus. The patients with early AD had a lower hippocampal volume and WM connectivity with the superior frontal gyrus and higher mean diffusivity (MD) and radial diffusivity (RD) in the amygdala than HC (p < 0.05, Bonferroni-corrected). However, brain areas with a higher (or lower) brain volume and WM connectivity were not observed in the HC compared with the patients with early AD. After six months of donepezil treatment, the patients with early AD showed increased hippocampal-inferior temporal gyrus (ITG) WM connectivity (p < 0.05, Bonferroni-corrected).

Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images

BACKGROUND

Synapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the peripheral nervous system (PNS), where electrical or chemical signals rapidly travel through one cell to another with high spatial precision. Synaptic analysis, based on synapse numbers and fine morphology, is the basis for understanding neurological functions and diseases. Manual analysis of synaptic structures in electron microscopy (EM) images is often limited by low efficiency and subjective bias.

NEW METHOD

We developed a multifunctional synaptic analysis system based on several advanced deep learning (DL) models. The system achieved synapse counting in low-magnification EM images and synaptic ultrastructure analysis in high-magnification EM images.

RESULTS

The synapse counting system based on ResNet18 and a Faster R-CNN model had a mean average precision (mAP) of 92.55%. For synaptic ultrastructure analysis, the Faster R-CNN model based on ResNet50 achieved a mAP of 91.60%, the DeepLab v3 + model based on ResNet50 enabled high performance in presynaptic and postsynaptic membrane segmentation with a global accuracy of 0.9811, and the Faster R-CNN model based on ResNet18 achieved a mAP of 91.41% for synaptic vesicle detection.

CONCLUSIONS

The proposed multifunctional synaptic analysis system may help to overcome the experimental bias inherent in manual analysis, thereby facilitating EM image-based synaptic function studies.

Serotonin Degeneration and Amyloid-β Deposition in Mild Cognitive Impairment: Relationship to Cognitive Deficits

BACKGROUND

Neuropathological and neuroimaging studies have demonstrated degeneration of the serotonin system in Alzheimer's disease (AD). Neuroimaging studies have extended these observations to the preclinical stages of AD, mild cognitive impairment (MCI). Serotonin degeneration has been observed also in transgenic amyloid mouse models, prior to widespread cortical distribution of amyloid-β (Aβ).

OBJECTIVE

The present study evaluated the regional distribution of the serotonin transporter (5-HTT) and of Aβ in individuals with MCI and healthy older controls, as well as the contribution of 5-HTT and Aβ to cognitive deficits.

METHODS

Forty-nine MCI participants and 45 healthy older controls underwent positron emission tomography (PET) imaging of 5-HTT and Aβ, structural magnetic resonance imaging and neuropsychological assessments.

RESULTS

Lower cortical, striatal, and limbic 5-HTT and higher cortical Aβ was observed in MCIs relative to healthy controls. Lower 5-HTT, mainly in limbic regions, was correlated with greater deficits in auditory-verbal and visual-spatial memory and semantic, not phonemic fluency. Higher cortical A β was associated with greater deficits in auditory-verbal and visual-spatial memory and in semantic, not phonemic fluency. When modeling the association between cognition, gray matter volumes and Aβ, inclusion of 5-HTT in limbic and in select cortical regions significantly improved model fit for auditory-verbal and visual-spatial memory and semantic, but not phonemic fluency.

CONCLUSIONS

These results support the role of serotonin degeneration in the memory and semantic fluency deficits observed in MCI.

Vesicular Glutamate Transporter Changes in the Cortical Default Mode Network During the Clinical and Pathological Progression of Alzheimer's Disease

BACKGROUND

Altered glutamatergic neurotransmission may contribute to impaired default mode network (DMN) function in Alzheimer's disease (AD). Among the DMN hub regions, frontal cortex (FC) was suggested to undergo a glutamatergic plasticity response in prodromal AD, while the status of glutamatergic synapses in the precuneus (PreC) during clinical-neuropathological AD progression is not known.

OBJECTIVE

To quantify vesicular glutamate transporter VGluT1- and VGluT2-containing synaptic terminals in PreC and FC across clinical stages of AD.

METHODS

Unbiased sampling and quantitative confocal immunofluorescence of cortical VGluT1- and VGluT2-immunoreactive profiles and spinophilin-labeled dendritic spines were performed in cases with no cognitive impairment (NCI), mild cognitive impairment (MCI), mild-moderate AD (mAD), or moderate-severe AD (sAD).

RESULTS

In both regions, loss of VGluT1-positive profile density was seen in sAD compared to NCI, MCI, and mAD. VGluT1-positive profile intensity in PreC did not differ across groups, while in FC it was greater in MCI, mAD, and sAD compared to NCI. VGluT2 measures were stable in PreC while FC had greater VGluT2-positive profile density in MCI compared to sAD, but not NCI or mAD. Spinophilin measures in PreC were lower in mAD and sAD compared to NCI, while in FC they were stable across groups. Lower VGluT1 and spinophilin measures in PreC, but not FC, correlated with greater neuropathology.

CONCLUSION

Frank loss of VGluT1 in advanced AD relative to NCI occurs in both DMN regions. In FC, an upregulation of VGluT1 protein content in remaining glutamatergic terminals may contribute to this region's plasticity response in AD.

Uncovering the System Vulnerability and Criticality of Human Brain Under Dynamical Neuropathological Events in Alzheimer's Disease

BACKGROUND

Despite the striking efforts in investigating neurobiological factors behind the acquisition of amyloid-β (A), protein tau (T), and neurodegeneration ([N]) biomarkers, the mechanistic pathways of how AT[N] biomarkers spreading throughout the brain remain elusive.

OBJECTIVE

To disentangle the massive heterogeneities in Alzheimer's disease (AD) progressions and identify vulnerable/critical brain regions to AD pathology.

METHODS

In this work, we characterized the interaction of AT[N] biomarkers and their propagation across brain networks using a novel bistable reaction-diffusion model, which allows us to establish a new systems biology underpinning of AD progression. We applied our model to large-scale longitudinal neuroimages from the ADNI database and studied the systematic vulnerability and criticality of brains.

RESULTS

Our model yields long term prediction that is statistically significant linear correlated with temporal imaging data, produces clinically consistent risk prediction, and captures the Braak-like spreading pattern of AT[N] biomarkers in AD development.

CONCLUSIONS

Our major findings include (i) tau is a stronger indicator of regional risk compared to amyloid, (ii) temporal lobe exhibits higher vulnerability to AD-related pathologies, (iii) proposed critical brain regions outperform hub nodes in transmitting disease factors across the brain, and (iv) comparing the spread of neuropathological burdens caused by amyloid-β and tau diffusions, disruption of metabolic balance is the most determinant factor contributing to the initiation and progression of AD.

Characteristics and Potential Neural Substrates of Encoding and Retrieval During Memory Binding in Amnestic Mild Cognitive Impairment

BACKGROUND

Whether encoding or retrieval failure contributes to memory binding deficit in amnestic mild cognitive impairment (aMCI) has not been elucidated. Also, the potential brain structural substrates of memory binding remained undiscovered.

OBJECTIVE

To investigate the characteristics and brain atrophy pattern of encoding and retrieval performance during memory binding in aMCI.

METHODS

Forty-three individuals with aMCI and 37 cognitively normal controls were recruited. The Memory Binding Test (MBT) was used to measure memory binding performance. The immediate and delayed memory binding indices were computed by using the free and cued paired recall scores. Partial correlation analysis was performed to map the relationship between regional gray matter volume and memory binding performance.

RESULTS

The memory binding performance in the learning and retrieval phases was worse in the aMCI group than in the control group (F = 22.33 to 52.16, all p < 0.001). The immediate and delayed memory binding index in the aMCI group was lower than that in the control group (p < 0.05). The gray matter volume of the left inferior temporal gyrus was positively correlated with memory binding test scores (r = 0.49 to 0.61, p < 0.05) as well as the immediate (r = 0.39, p < 0.05) and delayed memory binding index (r = 0.42, p < 0.05) in the aMCI group.

CONCLUSION

aMCI may be primarily characterized by a deficit in encoding phase during the controlled learning process. Volumetric losses in the left inferior temporal gyrus may contribute to encoding failure.