Medial temporal lobe connectivity and its associations with cognition in early Alzheimer's disease

BOLD amplitude correlates of preclinical Alzheimer's disease

Alzheimer's disease (AD) is characterized by a long preclinical stage during which molecular markers of amyloid beta and tau pathology rise, but there is minimal neurodegeneration or cognitive decline. Previous literature suggests that measures of brain function might be more sensitive to neuropathologic burden during the preclinical stage of AD than conventional measures of macrostructure, such as cortical thickness. Among studies that used resting-state functional Magnetic Resonance Imaging (fMRI) acquisitions with Blood Oxygenation Level Dependent (BOLD) contrast, most employed connectivity-based analytic approaches. Consequently, little is known about the effects of amyloid and tau pathology on amplitude of intrinsic BOLD signal fluctuations. To address this knowledge gap, we characterized the effects of preclinical and prodromal AD on the amplitude of low-frequency fluctuations (ALFF) of the BOLD signal both at the whole-brain level and at a more granular level focused on subregions of the medial temporal lobe. We observed reduced ALFF in both preclinical and prodromal AD. In preclinical AD, amyloid positivity was associated with a spatially diffuse ALFF reduction in the frontal, medial parietal, and lateral temporal association cortices. In contrast, tau pathology was negatively associated with ALFF in the entorhinal cortex. These ALFF effects were observed in the absence of observable macrostructural changes in preclinical AD and remained after adjusting for structural atrophy in prodromal AD, indicating that ALFF offers additional sensitivity to early disease processes beyond what is provided by traditional structural imaging biomarkers of neurodegeneration. We conclude that ALFF may be a promising imaging-based biomarker in preclinical AD.

Exploring the diagnostic potential of EEG theta power and interhemispheric correlation of temporal lobe activities in Alzheimer's Disease through random forest analysis

BACKGROUND

Considering the prevalence of Alzheimer's Disease (AD) among the aging population and the limited means of treatment, early detection emerges as a crucial focus area whereas electroencephalography (EEG) provides a promising diagnostic tool. To date, several studies indicated EEG dataset-based models sporting high diagnostic power in distinguishing patients with AD from healthy controls (HC). However, exploration into which features play a crucial role in the diagnosis remains limited.

METHODS

This study investigates the diagnostic capabilities of EEG for distinguishing patients with AD from HCs through random forest classification on EEG features. Band power and cross-correlation from the resting state EEG dataset of 22 HCs and 160 patients with AD were calculated using Welch's periodogram and Pearson's correlation, respectively. Welch's t-test was applied to identify features demonstrating significant differences between patients with AD and HCs. Band power and cross-correlation were analyzed using a random forest classifier (RFC) and feature-importance analysis. The importance of feature categories, defined as subsets of features grouped by frequency bands (for band power features) or brain regions (for cross-correlation features), was quantified by calculating their average occurrence across all hyperparameter configurations.

RESULT

Distinct patterns between the eyes-closed and eyes-open conditions in alpha power were not observed for patients with AD (vs. HC), whereas theta power (4-8 Hz) in all regions was higher in patients with AD (vs. HC)(p<0.05). Interhemispheric cross-correlation in the temporal lobes exhibited the most distinguishable distribution for the cross-correlation dataset. An RFC, exploring 512 models with varied hyperparameters followed by feature-importance analysis based on the mean decrease in impurity, highlighted "theta relative power" and "interhemispheric cross-correlation of channel pairs including temporal channels" as the most important features for distinguishing patients with AD from HCs. RFC on theta-band filtered cross-correlation dataset informed by important features demonstrated the robustness of important features across models with different hyperparameter settings.

DISCUSSION

The models achieved over 97% accuracy and 100% recall in test sets, although the interpretation of this extraordinarily high accuracy warrants caution due to the small dataset size with high data imbalance and the absence of external validation. This methodology demonstrates the efficacy of EEG-based metrics and machine learning in improving our understanding of EEG characteristics in patients with AD, emphasizing the potential of integrating machine learning techniques into clinical practices.

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In addition, interactions between SC and FC within distributed association regions are related to alterations in cognition or neurological diseases, considering the inherent linkage between neural function and structure. However, there is a scarcity of existing learning-based methods that leverage both modality-specific characteristics and high-order interactions between the two modalities for regression or classification. Hence, this study proposes an interpretable modality-specific and interactive graph convolutional network (MS-Inter-GCN) that incorporates modality-specific information, reflecting the unique neural mechanism for each modality, and structure-function interactions, capturing the underlying foundation provided by white-matter fiber tracts for high-level brain function. In MS-Inter-GCN, we generate modality-specific task-relevant embeddings separately from both FC and SC using a graph convolutional encoder-decoder module. Subsequently, we learn the interactive weights between corresponding regions of FC and SC, reflecting the coupling strength, by employing an interactive module on the embeddings of both modalities. A novel graph structure is constructed, which uses modality-specific task-relevant embeddings and inserts the interactive weights as edges connecting corresponding regions of two modalities, and then is used for the regression or classification task. Finally, a post-hoc explainable technology - GNNExplainer- is used to identify salient regions and connections of each modality as well as salient interactions between FC and SC associated with tasks. We apply the proposed framework to fluid cognition prediction, Parkinson's disease (PD), Alzheimer's disease (AD), and schizophrenia (SZ) classification. Experimental results demonstrate that our method outperforms the other ten state-of-the-art methods on multi-modal brain features on all tasks. The GNNExplainer identifies salient structural and functional regions and connections for fluid cognition, PD, AD, and SZ. It confirms that strong structure-function coupling within the executive and control networks, combined with weak coupling within the motor network, is associated with fluid cognition. Moreover, structure-function decoupling in specific brain regions serves as a marker for different diseases: decoupling of the prefrontal, superior parietal, and superior occipital cortices is a marker of PD; decoupling of the middle frontal and lateral parietal cortices, temporal pole, and subcortical regions is indicative of AD; and decoupling of the prefrontal, parietal, and temporal cortices, as well as the cerebellum, contributes to SZ.

Brain imaging-to-graph generation using adversarial hierarchical diffusion models for MCI causality analysis

Effective connectivity can describe the causal patterns among brain regions. These patterns have the potential to reveal the pathological mechanism and promote early diagnosis and effective drug development for cognitive disease. However, the current methods utilize software toolkits to extract empirical features from brain imaging to estimate effective connectivity. These methods heavily rely on manual parameter settings and may result in large errors during effective connectivity estimation. In this paper, a novel brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment (MCI) analysis. The proposed BIGG framework is based on the diffusion denoising probabilistic models (DDPM), where each denoising step is modeled as a generative adversarial network (GAN) to progressively translate the noise and conditional fMRI to effective connectivity. By introducing the diffusive factor, the denoising inference with a large sampling step size is more efficient and can maintain high-quality results. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model. The proposed model not only achieves superior prediction performance compared with other competing methods but also predicts MCI-related causal connections that are consistent with clinical studies.

Differential effects of aging, Alzheimer's pathology, and APOE4 on longitudinal functional connectivity and episodic memory in older adults

BACKGROUND

Both aging and Alzheimer's disease (AD) affect brain networks, with early disruptions occurring in regions involved in episodic memory. Few studies have, however, focused on distinguishing region-specific effects of AD-biomarker negative "normal" aging and early amyloid- and tau pathology on functional connectivity. Further, longitudinal studies combining imaging, biomarkers, and cognition are rare.

METHODS

We assessed resting-state functional connectivity (rsFC) strength and graph measures in the episodic memory network including the medial temporal lobe (MTL), posteromedial cortex (PMC), and medial prefrontal cortex alongside cognition over two years. For this preregistered study, we included 100 older adults who were amyloid- and tau-negative using CSF and PET measurements to investigate "normal" aging, and 70 older adults who had longitudinal CSF data available to investigate functional changes related to early AD pathology. All participants were cognitively unimpaired older adults from the PREVENT-AD cohort. We used region of interest (ROI)-to-ROI bivariate correlations, graph analysis, and multiple regression models.

RESULTS

In the amyloid- and tau-negative sample, rsFC strength within PMC, between parahippocampal cortex and inferomedial precuneus, and between posterior hippocampus and inferomedial precuneus decreased over time. Additionally, we observed a longitudinal decrease in global efficiency. Further, there was a steeper longitudinal decrease in rsFC and global efficiency with higher baseline age particularly of parahippocampal-gyrus regions. Further, lower rsFC strength within PMC was associated with poorer longitudinal episodic memory performance. In the sample with available CSF data, a steeper increase in rsFC between anterior hippocampus and superior precuneus was related to higher baseline AD pathology. Higher MTL-PMC rsFC strength was differentially associated with episodic memory trajectories depending on APOE4 genotype.

CONCLUSIONS

Our findings suggest differential effects of aging and AD pathology. Hypoconnectivity within PMC was related to aging and cognitive decline. MTL-PMC hyperconnectivity was related to early AD pathology and cognitive decline in APOE4 carriers. Future studies should investigate more diverse samples, nonetheless, our approach allowed us to identify longitudinal functional changes related to aging and early AD pathology, enhancing cross-sectional research. Hyperconnectivity has been proposed as a mechanism related to early AD pathology before, we now contribute specific functional connections to focus on in future research.

Parvalbumin interneurons in the hippocampal formation of individuals with Alzheimer's disease: a neuropathological study of abnormal phosphorylated tau in neurons

Alzheimer's disease (AD) is the most common neurodegenerative disorder in the elderly. Recent efforts have centered on understanding early events that trigger AD, aiming to facilitate early diagnosis and intervention for improved patient outcomes. The traditional histopathological features observed in AD encompass the extracellular accumulation of amyloid-beta protein and the intracellular abnormal phosphorylation of Tau protein (pTau). However, elucidating how these pathological hallmarks ultimately contribute to cognitive deficits remains a complex challenge. While AD is commonly conceptualized as a disorder characterized by synaptic failure, substantial knowledge gaps persist regarding the mechanisms underlying the onset and progression of the disease, underscoring the need for novel and more effective therapeutic approaches. In this context, the impairment of GABAergic paravalbumin (PV+) neurons has been proposed as a crucial factor contributing to neuronal network dysfunction and cognitive decline in AD. The presence of pTau in pyramidal neurons is directly linked to their impairment in AD; however, the effect of pTau in PV+ neurons remains unclear. In this present study, we analyzed the existence of PV+ neurons containing pTau using immunocytochemistry in the hippocampal formation and entorhinal cortex of human samples from diagnosed AD cases and individuals without neurological or psychiatric disorders. Two pTau isoforms, pTauAT8 and pTaupS396, corresponding to early and late stages of AD respectively, were examined. Our findings indicate that most PV+ neurons across the hippocampal formation and entorhinal cortex did not contain pTau in either group cases. Interestingly, while AD cases diagnosed with dementia exhibited a higher number of pTau+ neurons, the majority of PV+/pTau+ neurons were found in individuals with no neurological alterations. This suggests that the presence of pTau in PV+ neurons does not directly correlate with the overall abundance of pTau+ neurons. Given that PV+ neuron impairment is a key pathogenic mechanism in AD and is associated with cognitive decline, understanding the changes in PV+ neurons during AD progression could provide critical insights into the alterations of neuronal circuits underlying the disease.

Fibre density and cross-section associate with hallmark pathology in early Alzheimer's disease

BACKGROUND

Tau pathology in Alzheimer's disease (AD) propagates trans-synaptically along structurally connected brain networks and in synergy with amyloid pathology it induces synaptic damage. However, the in vivo relationship of amyloid, tau and synaptic density with white matter (WM) structural changes has been studied rather limitedly. Recent advances in diffusion MRI processing allow quantification of apparent fibre density and fibre cross-section on the fixel level, i.e., individual fibre populations within one voxel. The aim of this study was to investigate the hypothesis of axonal loss due to tau propagation and amyloid pathology and its association with synaptic density in early disease stages.

METHODS

Twenty-four patients with amnestic mild cognitive impairment (aMCI) and 23 healthy controls (HC) underwent baseline amyloid (11C-PiB/18F-NAV4694), tau (18F-MK-6240) and synaptic density (11C-UCB-J binding to SV2A) PET/MR in combination with diffusion MRI and cognitive assessments. A subset of 14 aMCI patients underwent follow-up visits after 2 years. First, a whole-brain fixel-based analysis was performed to identify differences in fibre density and fibre cross-section between HC and aMCI and longitudinally in the aMCI group. Next, a tract-of-interest analysis was performed, focusing on the temporal-cingulum bundle where most alterations have been shown in early AD. Tau and SV2A PET were quantified in the connected regions, i.e., hippocampus and posterior cingulate/precuneus (PCC-P). Amyloid PET centiloids were measured in the commonly used cortical composite volume-of-interest.

RESULTS

At baseline, multiple WM tracts showed lower fibre density and lower fibre cross-section in aMCI compared to HC, and these parameters further decreased longitudinally in the aMCI group. In the temporal cingulum bundle, reduced fibre density was significantly associated with reduced hippocampal synaptic density while increased hippocampal and PCC-P tau specifically correlated with reduced fibre cross-section. Increased global amyloid burden was associated with reduced fibre density and fibre cross-section in the temporal cingulum bundle.

CONCLUSIONS

Our results suggest that WM degeneration already occurs in the aMCI stage of AD and alterations in apparent fibre density and fibre cross-section of the temporal cingulum bundle are associated with AD hallmark pathology.

Age-associated proteins explain the role of medial temporal lobe networks in Alzheimer's disease

The structural connectivity (SC) of the medial temporal lobe and its associated cortical anterior temporal and posterior medial networks (MTL-AT-PM) is linked to pathologies and memory decline in Alzheimer's disease (AD). However, neuroimaging analyses cannot tell us how SC changes occur in AD at the molecular level and do not provide a means of intervening to slow/prevent pathology-related changes in MTL-AT-PM SC. The current study aimed to understand how and where AD-related changes occur within MTL-AT-PM using proteomics. We used a 4-step approach in 101 older adults from a local sample, aiming to understand how proteins and SC in combination at the multivariate level predict AD pathology, and to identify specific proteins related to SC and AD pathology. Separately, we validated the discovered proteins in relation to SC and AD pathology using ADNI sample. We identified 12 latent factors linking proteins and SC; five showed significant relationships with AD pathology and/or episodic memory. Insulin-like growth factor binding proteins and tumor necrosis factor receptors, and hippocampal/parahippocampal edges contributed most to AD-related latent factors. Fast causal inference found protein-protein, protein-SC, and protein-pathology pathways, with seven proteins showing directional links to SC and AD-related neurodegeneration. We validated these results by identifying significant relationships between six available proteins with SC and amyloid-beta and phosphorylated tau in ADNI. We identified multivariate relationships between proteins and MTL-AT-PM networks that add to our understanding of AD pathology and suggest specific non-pathological proteins that warrant further study in relation to brain networks and AD pathology as possible therapeutic targets.

Compensation versus deterioration across functional networks in amnestic mild cognitive impairment subtypes

Functional connectivity studies to detect neurophysiological correlates of amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease, have generated contradictory results in terms of compensation and deterioration, as most of the studies did not distinguish between the different aMCI subtypes: single-domain aMCI (sd-aMCI) and multiple-domain aMCI (md-aMCI). The present study aimed to characterize the neurophysiological correlates of aMCI subtypes by using resting-state functional magnetic resonance imaging. The study included sd-aMCI (n = 29), md-aMCI (n = 26), and control (n = 30) participants. The data were subjected to independent component analysis (ICA) to explore the default mode network (DMN) and the fronto-parietal control network (FPCN). Additionally, seed-based and moderation analyses were conducted to investigate the connectivity of the medial temporal lobe and functional networks. aMCI subtypes presented differences in functional connectivity relative to the control group: sd-aMCI participants displayed increased FPCN connectivity and reduced connectivity between the posterior parahippocampal gyrus (PHG) and medial structures; md-aMCI participants exhibited lower FPCN connectivity, higher anterior PHG connectivity with frontal structures and lower posterior PHG connectivity with central-parietal and temporo-occipital areas. Additionally, md-aMCI participants showed higher posterior PHG connectivity with structures of the DMN than both control and sd-aMCI participants, potentially indicating more severe cognitive deficits. The results showed gradual and qualitative neurofunctional differences between the aMCI subgroups, suggesting the existence of compensatory (sd-aMCI) and deterioration (md-aMCI) mechanisms in functional networks, mainly originated in the DMN. The findings support consideration of the subgroups as different stages of MCI within the Alzheimer disease continuum.

Task-radMBNet: An Improved Task-Driven Dynamic Graph Sparsity Pattern Radiomics-Based Morphological Brain Network for Alzheimer's Disease Characterization

Background: The study of task-driven dynamic adaptive graph sparsity patterns in Alzheimer's disease (AD) analysis is of great importance, as it allows for better focus on regions and connections of interest and enhances task sensitivity. Methods: In this study, we introduced a task-driven dynamic adaptive graph sparsity model (called task-driven radiomics-based morphological brain network [Task-radMBNet]) for AD diagnosis based on radiomics-based morphological brain network (radMBN). Specifically, the Task-radMBNet was established by devising a connectivity feature-based graph convolutional network (GCN) channel (called a connectivity-GCN channel) and a radiomics feature-based GCN channel (called a radiomics-GCN channel), where the two GCN channels shared a same dynamic sparse brain network in graph convolution but worked for different aims separately. The connectivity-GCN channel dynamically learned the graph's sparse topology that best suits the target task, while the radiomics-GCN channel combined radiomics node features and dynamic topology to improve AD diagnostic accuracy. Results: The Task-radMBNet achieved superior classification accuracy of 87.8% and 86.0% in early AD diagnosis across a total of 1273 subjects within the AD Neuroimaging Initiative and European Diffusion Tensor Imaging (DTI) Study on Dementia databases. We also visualized the topology heat map and important connectivity under different network sparse settings. Conclusions: The results demonstrated significant promise in the diagnosis of neurological disorders by integrating Task-radMBNet with radMBN.

The abnormal accumulation of pathological proteins and compensatory functional connectivity enhancement of insula subdivisions in mild cognitive impairment

Background

The insula is a critical node of the salience network responsible for initiating network switching, and its dysfunctional connections are linked to the mechanisms of mild cognitive impairment (MCI). This study aimed to explore the changes in functional connectivity (FC) of insular subregions in MCI patients with varying levels of cerebrospinal fluid (CSF) pathological proteins, and to investigate the impact of these proteins on the brain network alterations in MCI.

Methods

Based on CSF Amyloid-beta (Aβ, A) and phosphorylated tau protein (p-tau, T), MCI patients were classified into 54 A-T-, 28 A+T-, and 52 A+T+ groups. Seed-based FC analysis was employed to compare the FC differences of insular subregions across the three groups. Correlation analysis was further conducted to explore the relationship between altered FC and cognitive function. Finally, ROC curve analysis was used to assess the diagnostic value of altered FC of insular subregion in distinguishing between the groups.

Results

In the left ventral anterior insula, left dorsal anterior insula, and bilateral posterior insular subnetworks, both the A+T- and A+T+ groups showed increased FC compared to the A-T- group, with the A+T+ group showing further increased FC compared to the A+T- group. Additionally, FC of the left cerebellar posterior lobe was negatively correlated with RAVLT-learning, and FC of the left middle frontal gyrus was negatively correlated with p-tau levels. Finally, logistic regression analysis demonstrated that multivariable analysis had high sensitivity and specificity in distinguishing between the groups.

Conclusion

This study showed that MCI patients with abnormal CSF pathological protein levels exhibit compensatory increases in FC of insular subregions, which in turn affect cognitive function. Our findings contributed to a better understanding of the pathophysiology and underlying neural mechanisms of MCI.

Brain mechanical properties predict longitudinal cognitive change in aging and Alzheimer's disease

Age-related cognitive decline is a complex phenomenon that is influenced by various neurobiological processes at the molecular, cellular, and tissue levels. The extent of this decline varies between individuals and the underlying determinants of these differences are not fully understood. Two of the most prominent signs of cognitive decline in aging are the deterioration of episodic memory, which is a hallmark of Alzheimer's disease (AD), and the nearly always accompanying atrophy of the medial temporal lobe. Both cross-sectional and longitudinal studies have consistently demonstrated the strong relationship between these two, however, recent advanced imaging techniques have shown promise for predicting cognitive decline earlier than atrophy measures. In this study, we investigate the value of brain biomechanical properties, specifically in the medial temporal lobe, for predicting global cognitive decline along the normal aging and AD spectrum. Our results indicate that the medial temporal stiffness significantly predicts future cognitive decline beyond that achieved by measures of atrophy and amyloidosis. Measures of brain biomechanical properties may provide valuable prognostic information to enable more efficient study design and evaluation of potential interventions.

Odor induced functional connectivity alteration of POC-anterior frontal cortex-medial temporal cortex in patients with mild cognitive impairment

Background

Mild cognitive impairment (MCI) is associated with an increased risk of dementia in older adults. Olfactory impairment may indicate prodromal dementia, yet its underlying mechanisms are not fully understood. This study aimed to investigate the alterations in functional connectivity (FC) of odor-induced olfactory neural circuits in MCI patients.

Methods

The study included 39 MCI patients and 42 normal controls (NCs). All subjects underwent cognitive assessments, olfactory behavior tests, and odor-based functional magnetic resonance imaging (fMRI). Differences in FC within olfactory circuits were analyzed using the generalized psychophysiological interaction (gPPI) method.

Results

Mild cognitive impairment patients showed significant cognitive deficits, including lower scores on the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), alongside impairments in episodic memory, visuospatial memory, executive function, language, attention, olfactory threshold, and total olfactory function. Compared to NCs, MCI patients exhibited reduced activation in the bilateral primary olfactory cortex (bPOC) during olfactory stimulation. Odor-induced bPOC activation correlated with olfactory thresholds across the cohort. During odor stimulation, MCI patients showed increased FC from the bPOC to the right anterior frontal lobe, particularly the middle frontal gyrus (MFG) and superior frontal gyrus (SFG). Conversely, FC from the right anterior frontal lobe to the medial temporal cortex, including the fusiform and parahippocampal gyri, was reduced in MCI patients. Increased FC from the bPOC to the right SFG/MFG negatively correlated with episodic memory, while decreased FC to the right fusiform/parahippocampal gyri positively correlated with attention, language ability, and olfactory identification.

Conclusion

This study indicates that impaired FC within the primary olfactory cortex (POC)-anterior frontal cortex-medial temporal cortex circuit is a sensitive neuroimaging marker for early MCI identification. The primary dysfunction appears in the POC, suggesting that FC alterations from this region may provide novel diagnostic and therapeutic avenues for early intervention.

An objective and sensitive electrophysiological marker of word semantic categorization impairment in Alzheimer's disease

OBJECTIVE

Combining electroencephalographic (EEG) recording and fast periodic visual stimulation (FPVS) to provide an implicit, objective and sensitive electrophysiological measure of semantic word categorization impairment in Alzheimer's Disease (AD).

METHODS

Twenty-five AD patients and 25 matched elderly healthy controls were tested with a validated FPVS-EEG paradigm in which different written words of the same semantic category (cities) appear at a fixed frequency of 4 words per second (4 Hz) for 70 seconds. Words from a different semantic category (animal) appear every 4 stimuli (i.e., 1 Hz).

RESULTS

Frequency domain EEG analysis showed a robust response objectively identified at specific 1 Hz harmonics over the left occipito-temporal cortex for healthy controls, indexing automatic semantic categorization. However, only a negligible response, less than 25 % of healthy controls', was found in AD patients, this response being inversely correlated with the amount of Tau protein in the cerebrospinal fluid. The significant group difference was maximal when including an additional left central region, with only 2.5 min of testing providing a significant group difference.

CONCLUSION

A reduced semantic word categorisation EEG amplitude rapidly differentiates AD patients from healthy controls.

SIGNIFICANCE

FPVS-EEG provides a valuable electrophysiological index of semantic categorization impairment in AD.

Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis

Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations between genotypic and phenotypic data, as well as potential higher-order relationships among subjects when identifying bi-multivariate associations. In this paper, a novel method called deep hyper-Laplacian regularized self-representation learning based structured association analysis (DHRSAA) is proposed which can learn genotype-phenotype associations and obtain relevant biomarkers. Specifically, a deep neural network is used first to explore the nonlinear relationships among samples. Secondly, self-representation learning based on hyper-Laplacian regularization is utilized to reconstruct the original data. In particular, the introduction of hyper-Laplacian regularization ensures the local structure of the high-dimensional spatial embedding and explores the higher-order relationships among the samples. Moreover, the structural regularization term in the association analysis uncovers chain relationships among SNPs and graphical relationships among imaging QTs, thus making the obtained markers more interpretable and enhancing the biological significance of the method. The performance of the proposed method is validated on real neuroimaging genetics data. Experimental results show that DHRSAA displays better canonical correlation coefficients and recognizes clearer canonical weight patterns compared to several state-of-the-art methods, which suggests that the proposed DHRSAA achieves better performance and identifies disease-related biomarkers.

Knowledge-driven multi-graph convolutional network for brain network analysis and potential biomarker discovery

In brain network analysis, individual-level data can provide biological features of individuals, while population-level data can provide demographic information of populations. However, existing methods mostly utilize either individual- or population-level features separately, inevitably neglecting the multi-level characteristics of brain disorders. To address this issue, we propose an end-to-end multi-graph neural network model called KMGCN. This model simultaneously leverages individual- and population-level features for brain network analysis. At the individual level, we construct multi-graph using both knowledge-driven and data-driven approaches. Knowledge-driven refers to constructing a knowledge graph based on prior knowledge, while data-driven involves learning a data graph from the data itself. At the population level, we construct multi-graph using both imaging and phenotypic data. Additionally, we devise a pooling method tailored for brain networks, capable of selecting brain regions that impact brain disorders. We evaluate the performance of our model on two large datasets, ADNI and ABIDE, and experimental results demonstrate that it achieves state-of-the-art performance, with 86.87% classification accuracy for ADNI and 86.40% for ABIDE, accompanied by around 10% improvements in all evaluation metrics compared to the state-of-the-art models. Additionally, the biomarkers identified by our model align well with recent neuroscience research, indicating the effectiveness of our model in brain network analysis and potential biomarker discovery. The code is available at https://github.com/GN-gjh/KMGCN.

Associations of Coffee and Tea Consumption on Neural Network Connectivity: Unveiling the Role of Genetic Factors in Alzheimer's Disease Risk

BACKGROUND

Coffee and tea are widely consumed beverages, but their long-term effects on cognitive function and aging remain largely unexplored. Lifestyle interventions, particularly dietary habits, offer promising strategies for enhancing cognitive performance and preventing cognitive decline.

METHODS

This study utilized data from the UK Biobank cohort (n = 12,025) to examine the associations between filtered coffee, green tea, and standard tea consumption and neural network functional connectivity across seven resting-state networks. We focused on networks spanning prefrontal and occipital areas that are linked to complex cognitive and behavioral functions. Linear mixed models were used to assess the main effects of coffee and tea consumption, as well as their interactions with Apolipoprotein E (APOE) genetic risk-the strongest genetic risk factor for Alzheimer's disease (AD).

RESULTS

Higher filtered coffee consumption was associated with increased functional connectivity in several networks, including Motor Execution, Sensorimotor, Fronto-Cingular, and a Prefrontal + 'What' Pathway Network. Similarly, greater green tea intake was associated with enhanced connectivity in the Extrastriate Visual and Primary Visual Networks. In contrast, higher standard tea consumption was linked to reduced connectivity in networks such as Memory Consolidation, Motor Execution, Fronto-Cingular, and the "What" Pathway + Prefrontal Network. The APOE4 genotype and family history of AD influenced the relationship between coffee intake and connectivity in the Memory Consolidation Network. Additionally, the APOE4 genotype modified the association between standard tea consumption and connectivity in the Sensorimotor Network.

CONCLUSIONS

The distinct patterns of association between coffee, green tea, and standard tea consumption and resting-state brain activity may provide insights into AD-related brain changes. The APOE4 genotype, in particular, appears to play a significant role in modulating these relationships. These findings enhance our knowledge of how commonly consumed beverages may influence cognitive function and potentially AD risk among older adults.

The emerging field of non-invasive brain stimulation in Alzheimer's disease

Treating cognitive impairment is a holy grail of modern clinical neuroscience. In the past few years, non-invasive brain stimulation is increasingly emerging as a therapeutic approach to ameliorate performance in patients with cognitive impairment and as an augmentation approach in persons whose cognitive performance is within normal limits. In patients with Alzheimer's disease, better understanding of brain connectivity and function has allowed for the development of different non-invasive brain stimulation protocols. Recent studies have shown that transcranial stimulation methods enhancing brain plasticity with several modalities have beneficial effects on cognitive functions. Amelioration has been shown in preclinical studies on behaviour of transgenic mouse models for Alzheimer's pathology and in clinical studies with variable severity of cognitive impairment. While the field is still grappling with issues related to the standardization of target population, frequency, intensity, treatment duration and stimulated region, positive outcomes have been reported on cognitive functions and on markers of brain pathology. Here we review the most encouraging protocols based on repetitive transcranial magnetic stimulation, transcranial direct current stimulation, transcranial alternating current stimulation, visual-auditory stimulation, photobiomodulation and transcranial focused ultrasound, which have demonstrated efficacy to enhance cognitive functions or slow cognitive decline in patients with Alzheimer's disease. Beneficial non-invasive brain stimulation effects on cognitive functions are associated with the modulation of specific brain networks. The most promising results have been obtained targeting key hubs of higher-level cognitive networks, such as the frontal-parietal network and the default mode network. The personalization of stimulation parameters according to individual brain features sheds new light on optimizing non-invasive brain stimulation protocols for future applications.

Asymmetrical patterns of β-amyloid deposition and cognitive changes in Alzheimer's disease: the SILCODE study

The asymmetric pattern of β-amyloid plaque distribution across Alzheimer's disease clinical progression stages remains unclear. In this study, 66 participants with normal cognition, 59 with subjective cognitive decline, 12 with mild cognitive impairment, and 11 with Alzheimer's disease dementia were included in the Sino Longitudinal Study on Cognitive Decline (SILCODE) cohort. A regional asymmetry index, denoting the left-right asymmetry of β-amyloid plaques, was derived for each region based on the Anatomical Automatic Labeling atlas. The level of β-amyloid plaques in each region was compared among different clinical stages of Alzheimer's disease using the analysis of variance. An additional correlation analysis examined the relationship between each region of interest's cognitive performance scores and asymmetry index values. We found that β-amyloid appears to be lateralized in different stages of Alzheimer's disease. In addition, there is a significant correlation between β-amyloid asymmetry in various brain regions and cognition. The observed Aβ lateralization could potentially be utilized as a neuroimaging biomarker throughout AD progression.

Elevated plasma p-tau231 is associated with reduced generalization and medial temporal lobe dynamic network flexibility among healthy older African Americans

BACKGROUND

Phosphorylated tau (p-tau) and amyloid beta (Aβ) in human plasma may provide an affordable and minimally invasive method to evaluate Alzheimer's disease (AD) pathophysiology. The medial temporal lobe (MTL) is susceptible to changes in structural integrity that are indicative of the disease progression. Among healthy adults, higher dynamic network flexibility within the MTL was shown to mediate better generalization of prior learning, a measure which has been demonstrated to predict cognitive decline and neural changes in preclinical AD longitudinally. Recent developments in cognitive, neural, and blood-based biomarkers of AD risk that may correspond with MTL changes. However, there is no comprehensive study on how these generalization biomarkers, long-term memory, MTL dynamic network flexibility, and plasma biomarkers are interrelated. This study investigated (1) the relationship between long-term memory, generalization performance, and MTL dynamic network flexibility and (2) how plasma p-tau231, p-tau181, and Aβ42/Aβ40 influence generalization, long-term memory, and MTL dynamics in cognitively unimpaired older African Americans.

METHODS

148 participants (Meanage: 70.88,SDage: 6.05) were drawn from the ongoing longitudinal study, Pathways to Healthy Aging in African Americans conducted at Rutgers University-Newark. Cognition was evaluated with the Rutgers Acquired Equivalence Task (generalization task) and Rey Auditory Learning Test (RAVLT) delayed recall. MTL dynamic network connectivity was measured from functional Magnetic Resonance Imaging data. Plasma p-tau231, p-tau181, and Aβ42/Aβ40 were measured from blood samples.

RESULTS

There was a significant positive correlation between generalization performance and MTL Dynamic Network Flexibility (t = 3.372, β = 0.280, p < 0.001). There were significant negative correlations between generalization performance and plasma p-tau231 (t = -3.324, β = -0.265, p = 0.001) and p-tau181 (t = -2.408, β = -0.192, p = 0.017). A significant negative correlation was found between plasma p-tau231 and MTL Dynamic Network Flexibility (t = -2.825, β = -0.232, p = 0.005).

CONCLUSIONS

Increased levels of p-tau231 are associated with impaired generalization abilities and reduced dynamic network flexibility within the MTL. Plasma p-tau231 may serve as a potential biomarker for assessing cognitive decline and neural changes in cognitively unimpaired older African Americans.

CD36-mediated ROS/PI3K/AKT signaling pathway exacerbates cognitive impairment in APP/PS1 mice after noise exposure

There is an association between noise exposure and cognitive impairment, and noise may have a more severe impact on patients with Alzheimer's disease (AD) and mild cognitive impairment; however, the mechanisms need further investigation. This study used the classic AD animal model APP/PS1 mice to simulate the AD population, and C57BL/6J mice to simulate the normal population. We compared their cognitive abilities after noise exposure, analyzed changes in Cluster of Differentiation (CD) between the two types of mice using transcriptomics, identified the differential CD molecule: CD36 in APP/PS1 after noise exposure, and used its pharmacological inhibitor to intervene to explore the mechanism by which CD36 affects APP/PS1 cognitive abilities. Our study shows that noise exposure has a more severe impact on the cognitive abilities of APP/PS1 mice, and that the expression trends of differentiation cluster molecules differ significantly between C57BL/6J and APP/PS1 mice. Transcriptomic analysis showed that the expression of CD36 in the hippocampus of APP/PS1 mice increased by 2.45-fold after noise exposure (p < 0.001). Meanwhile, Western Blot results from the hippocampus and entorhinal cortex indicated that CD36 protein levels increased by approximately 1.5-fold (p < 0.001) and 1.3-fold (p < 0.05) respectively, after noise exposure in APP/PS1 mice. The changes in CD36 expression elevated oxidative stress levels in the hippocampus and entorhinal cortex, leading to a decrease in PI3K/AKT phosphorylation, which in turn increased M1-type microglia and A1-type astrocytes while reducing the numbers of M2-type microglia and A2-type astrocytes. This increased neuroinflammation in the hippocampus and entorhinal cortex, causing synaptic and neuronal damage in APP/PS1 mice, ultimately exacerbating cognitive impairment. These findings may provide new insights into the relationship between noise exposure and cognitive impairment, especially given the different expression trends of CD molecules in the two types of mice, which warrants further research.

BOLD Amplitude Correlates of Preclinical Alzheimer's Disease

Alzheimer's disease (AD) is characterized by a long preclinical stage during which molecular markers of amyloid beta and tau pathology rise, but there is minimal neurodegeneration or cognitive decline. Previous literature suggests that measures of brain function might be more sensitive to neuropathologic burden during the preclinical stage of AD than conventional measures of macrostructure, such as cortical thickness. However, among studies that used resting-state functional Magnetic Resonance Imaging (fMRI) acquisitions with Blood Oxygenation Level Dependent (BOLD) contrast, most employed connectivity-based analytic approaches, which discard information about the amplitude of spontaneous brain activity. Consequently, little is known about the effects of amyloid and tau pathology on BOLD amplitude. To address this knowledge gap, we characterized the effects of preclinical and prodromal AD on the amplitude of low-frequency fluctuations (ALFF) of the BOLD signal both at the whole-brain level and, at a more granular level, focused on subregions of the medial temporal lobe. We observed reduced ALFF in both preclinical and prodromal AD. In preclinical AD, amyloid positivity was associated with a spatially diffuse ALFF reduction in the frontal, medial parietal, and lateral temporal association cortices, while tau pathology was negatively associated with ALFF in the entorhinal cortex. These ALFF effects were observed in the absence of observable macrostructural changes in preclinical AD and remained after adjusting for structural atrophy in prodromal AD, indicating that ALFF offers additional sensitivity to early disease processes beyond what is provided by traditional structural imaging biomarkers of neurodegeneration. We conclude that ALFF may be a promising imaging-based biomarker for assessing the effects of amyloid-clearing immunotherapies in preclinical AD.

The Neuropsychological Assessment Battery Driving Scenes Test in a Dementia Clinic

OBJECTIVE

In dementia research, the Driving Scenes test from the Neuropsychological Assessment Battery has been shown to relate to memory, dementia diagnosis, and functional impairment. The aim of the current study was to examine Driving Scenes and its component scores, and their relationships with cognition and daily functioning, in a mixed dementia clinic sample.

METHOD

One hundred U.S. military veterans between the ages of 55 and 88 were administered a full neuropsychological protocol that included Driving Scenes.

RESULTS

The Driving Scenes score and its subscores were strongly related to memory skills, and there were additional subscore associations with language and visuospatial functions. Driving Scenes uniquely predicted reported bill payment difficulties and tendency to get lost while driving, which were not predicted by other performances across cognitive domains.

CONCLUSION

Driving Scenes is a clinically and functionally relevant measure of memory. Although the Driving Scenes total score remains useful in dementia evaluations, component scores and error scores contribute additional practical information.

A Map of Transcriptomic Signatures of Different Brain Areas in Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder that progressively involves brain regions with an often-predictable pattern. Damage to the brain appears to spread and worsen with time, but the molecular mechanisms underlying the region-specific distribution of AD pathology at different stages of the disease are still under-investigated. In this study, a whole-transcriptome analysis was carried out on brain samples from the hippocampus (HI), temporal and parietal cortices (TC and PC, respectively), cingulate cortex (CG), and substantia nigra (SN) of six subjects with a definite AD diagnosis and three healthy age-matched controls in duplicate. The transcriptomic results showed a greater number of differentially expressed genes (DEGs) in the TC (1571) and CG (1210) and a smaller number of DEGs in the HI (206), PC (109), and SN (60). Furthermore, the GSEA showed a difference between the group of brain areas affected early (HI and TC) and the group of areas that were subsequently involved (PC, CG, and SN). Notably, in the HI and TC, there was a significant downregulation of shared DEGs primarily involved in synaptic transmission, while in the PC, CG, and SN, there was a significant downregulation of genes primarily involved in protein folding and trafficking. The course of AD could follow a definite time- and severity-related pattern that arises from protein misfolding, as observed in the PC, CG, and SN, and leads to synaptic impairment, as observed in the HI and TC. Therefore, a map of the molecular and biological processes involved in AD pathogenesis may be traced. This could aid in the discovery of novel biological targets in order to develop effective and well-timed therapeutic approaches.

Spatial and temporal patterns of cortical mean diffusivity in Alzheimer's disease and suspected non-Alzheimer's disease pathophysiology

INTRODUCTION

The spatial and temporal patterns of cortical mean diffusivity (cMD), as well as its association with Alzheimer's disease (AD) and suspected non-Alzheimer's pathophysiology (SNAP), are not yet fully understood.

METHODS

We compared baseline (n = 617) and longitudinal changes (n = 421) of cMD, cortical thickness, and gray matter volume and their relations to vascular risk factors, amyloid beta (Aβ), and tau positron emission tomography (PET), and longitudinal cognitive decline in Aβ PET negative and positive older adults.

RESULTS

cMD increases were more sensitive to detecting brain structural alterations than cortical thinning and gray matter atrophy. Tau-related cMD increases partially mediated Aβ-related cognitive decline in AD, whereas vascular disease-related increased cMD levels substantially mediated age-related cognitive decline in SNAP.

DISCUSSION

These findings revealed the dynamic changes of microstructural and macrostructural indicators and their associations with AD and SNAP, providing novel insights into understanding upstream and downstream events of cMD in neurodegenerative disease.

HIGHLIGHTS

Cortical mean diffusivity (cMD) was more sensitive to detecting structural changes than macrostructural factors. Tau-related cMD increases partially mediated amyloid beta-related cognitive decline in Alzheimer's disease (AD). White matter hyperintensity-related higher cMD mainly explained the age-related cognitive decline in suspected non-Alzheimer's pathophysiology (SNAP). cMD may assist in tracking earlier neurodegenerative signs in AD and SNAP.

Differences in Grey Matter Concentrations and Functional Connectivity between Young Carriers and Non-Carriers of the APOE ε4 Genotype

Background: The pathophysiology of Alzheimer's disease (AD) may begin developing years or even decades prior to the manifestation of its first symptoms. The APOE ε4 genotype is a prominent genetic risk for AD that has been found to be associated with brain changes across the lifespan since early adulthood. Thus, studying brain changes that may occur in young adults with an APOE ε4 status is highly relevant. Objective: Examine potential differences in grey matter (GM) and functional connectivity (FC) in brains of cognitively healthy young APOE ε4 carriers and non-carriers, denoted here as ε4(+) and ε4(-), respectively. Methods: Three Tesla magnetic resonance imaging (MRI) brain scans were acquired from cognitively healthy young participants aged approximately 20 years (n = 151). Voxel-based morphometry (VBM) analysis was employed to identify potential structural differences in GM between ε4(+) and ε4(-). In a subsequent seed-based connectivity (SBC) analysis, brain regions that structurally differed in the VBM analysis were considered as seeds and correlated with all the remaining voxels across the brains to then measure the differences in FC between groups. Results: The VBM analysis suggested that ε4(+) (n = 28) had greater GM densities relative to ε4(-) (n = 123) in the left hippocampus and the left posterior insula (puncorr < 0.001). However, the effect did not survive the correction for multiple comparisons, suggesting minimal structural differences in this age range. In contrast, the SBC analysis indicated that ε4(+) exhibited significantly decreased FC between the left hippocampus and areas of the left middle temporal gyrus (n = 27) compared to ε4(-) (n = 102). These results remained significant after multiple comparisons (pFDR < 0.05). Lastly, no statistically significant differences in FC between groups were observed for the left insular seed (pFDR > 0.05). Discussion: These results suggest early structural and functional brain changes associated with the APOE ε4 genotype on young adults. Yet, they must be cautiously interpreted and contrasted with both older adults with genetic risk for AD and patients diagnosed with AD.

The neurophysiological mechanisms of medial prefrontal-perirhinal cortex circuit mediating temporal order memory decline in early stage of AD rats

The temporal component of episodic memory has been recognized as a sensitive behavioral marker in early stage of Alzheimer's disease (AD) patients. However, parallel studies in AD animals are currently lacking, and the underlying neural circuit mechanisms remain poorly understood. Using a novel AppNL-G-F knock-in (APP-KI) rat model, the developmental changes of temporal order memory (TOM) and the relationship with medial prefrontal cortex and perirhinal cortex (mPFC-PRH) circuit were determined through in vivo electrophysiology and microimaging technique. We observed a deficit in TOM performance during the object temporal order memory task (OTOMT) in APP-KI rats at 6 month old, which was not evident at 3 or 4 months of age. Alongside behavioral changes, we identified a gradually extensive and aggravated regional activation and functional alterations in the mPFC and PRH during the performance of OTOMT, which occurred prior to the onset of TOM deficits. Moreover, coherence analysis showed that the functional connectivity between the mPFC and PRH could predict the extent of future behavioral performance. Further analysis revealed that the aberrant mPFC-PRH interaction mainly attributed to the progressive deterioration of synaptic transmission, information flow and network coordination from mPFC to PRH, suggesting the mPFC dysfunction maybe the key area of origin underlying the early changes of TOM. These findings identify a pivotal role of the mPFC-PRH circuit in mediating the TOM deficits in the early stage of AD, which holds promising clinical translational value and offers potential early biological markers for predicting AD memory progression.

Neuroimaging analysis reveals distinct cerebral perfusion responses to fasting-postprandial metabolic switching in Alzheimer's disease patients

AIMS

Extended fasting-postprandial switch intermitting time has been shown to affect Alzheimer's disease (AD). Few studies have investigated the cerebral perfusion response to fasting-postprandial metabolic switching (FMS) in AD patients. We aimed to evaluate the cerebral perfusion response to FMS in AD patients.

METHODS

In total, 30 AD patients, 32 mild cognitive impairment (MCI) patients, and 30 healthy control individuals (HCs) were included in the quantification of cerebral perfusion via cerebral blood flow (CBF). The cerebral perfusion response to FMS was defined as the difference (ΔCBF) between fasting and postprandial CBF.

RESULTS

Patients with AD had a regional negative ΔCBF in the anterior temporal lobe, part of the occipital lobe and the parietal lobe under FMS stimulation, whereas HCs had no significant ΔCBF. The AD patients had lower ΔCBF values in the right anterior temporal lobe than the MCI patients and HCs. ΔCBF in the anterior temporal lobe was negatively correlated with cognitive severity and cognitive reserve factors in AD patients.

CONCLUSIONS

AD patients exhibited a poor ability to maintain cerebral perfusion homeostasis under FMS stimulation. The anterior temporal lobe is a distinct area that responds to FMS in AD patients and negatively correlates with cognitive function.

Mining Alzheimer's disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes

Introduction

Because Alzheimer's disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD.

Methods

This study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging.

Results

We leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging.

Discussion

The representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis.

Transcranial alternating current stimulation for neuropsychiatric disorders: a systematic review of treatment parameters and outcomes

Background

Transcranial alternating current stimulation (tACS) alters cortical excitability with low-intensity alternating current and thereby modulates aberrant brain oscillations. Despite the recent increase in studies investigating the feasibility and efficacy of tACS in treating neuropsychiatric disorders, its mechanisms, as well as optimal stimulation parameters, are not fully understood.

Objectives

This systematic review aimed to compile human research on tACS for neuropsychiatric disorders to delineate typical treatment parameters for these conditions and evaluate its outcomes.

Methods

A search for published studies and unpublished registered clinical trials was conducted through OVID (MEDLINE, PsycINFO, and Embase), ClinicalTrials.gov, and the International Clinical Trials Registry Platform. Studies utilizing tACS to treat neuropsychiatric disorders in a clinical trial setting were included.

Results

In total, 783 published studies and 373 clinical trials were screened; 53 published studies and 70 clinical trials were included. Published studies demonstrated a low risk of bias, as assessed by the Joanna Briggs Institute Critical Appraisal Tools. Neurocognitive, psychotic, and depressive disorders were the most common disorders treated with tACS. Both published studies (58.5%) and registered clinical trials (52%) most commonly utilized gamma frequency bands and tACS was typically administered at an intensity of 2 mA peak-to-peak, once daily for 20 or fewer sessions. Although the targeted brain locations and tACS montages varied across studies based on the outcome measures and specific pathophysiology of the disorders, the dorsolateral prefrontal cortex (DLPFC) was the most common target in both published studies (30.2%) and registered clinical trials (25.6%). Across studies that published results on tACS outcome measures, tACS resulted in enhanced symptoms and/or improvements in overall psychopathology for neurocognitive (all 11 studies), psychotic (11 out of 14 studies), and depressive (7 out of 8 studies) disorders. Additionally, 17 studies reported alterations in the power spectrum of the electroencephalogram around the entrained frequency band at the targeted locations following tACS.

Conclusion

Behavioral and cognitive symptoms have been positively impacted by tACS. The most consistent changes were reported in cognitive symptoms following gamma-tACS over the DLPFC. However, the paucity of neuroimaging studies for each neuropsychiatric condition highlights the necessity for replication studies employing biomarker- and mechanism-centric approaches.

Anterior-temporal hippocampal network mechanisms of left angular gyrus-navigated rTMS for memory improvement in aMCI: A sham-controlled study

INTRODUCTION

Neuro-navigated repetitive transcranial magnetic stimulation (rTMS) of the left angular gyrus has been broadly investigated for the treatment of amnestic mild cognitive impairment (aMCI). Although abnormalities in two hippocampal networks, the anterior-temporal (AT) and posterior-medial (PM) networks, are consistent with aMCI and are potential therapeutic targets for rTMS, the underlying mechanisms of the therapeutic effects of rTMS on hippocampal network connections remain unknown. Here, we assessed the impact of left angular gyrus rTMS on activity in these networks and explored whether the treatment response was due to the distance between the clinically applied target (the group average optimal site) and the personalized target in patients with aMCI.

METHODS

Sixty subjects clinically diagnosed with aMCI participated in this study after 20 sessions of sham-controlled rTMS targeting the left angular gyrus. Resting-state functional magnetic resonance imaging and neuropsychological assessments were performed before and after rTMS. Functional connectivity alterations in the PM and AT networks were assessed using seed-based functional connectivity analysis and two-factor repeated measures analysis of variance (ANOVA). We then computed the correlations between the functional connectivity changes and clinical rating scales. Finally, we examined whether the Euclidean distance between the clinically applied and personalized targets predicted the subsequent treatment response.

RESULTS

Compared with the sham group, the active rTMS group showed rTMS-induced deactivation of functional connectivity within the medial temporal lobe-AT network, with a negative correlation with episodic memory score changes. Moreover, the active rTMS lowers the interdependency of changes in the PM and AT networks. Finally, the Euclidean distance between the clinically applied and personalized target distances could predict subsequent network lever responses in the active rTMS group.

CONCLUSIONS

Neuro-navigated rTMS selectively modulates widespread functional connectivity abnormalities in the PM and AT hippocampal networks in aMCI patients, and the modulation of hippocampal-AT network connectivity can efficiently reverse memory deficits. The results also highlight the necessity of personalized targets for fMRI.

Phosphodiesterase 8 (PDE8): Distribution and Cellular Expression and Association with Alzheimer's Disease

Phosphodiesterase 8 (PDE8), as a member of PDE superfamily, specifically promotes the hydrolysis and degradation of intracellular cyclic adenosine monophosphate (cAMP), which may be associated with pathogenesis of Alzheimer's disease (AD). However, little is currently known about potential role in the central nervous system (CNS). Here we investigated the distribution and expression of PDE8 in brain of mouse, which we believe can provide evidence for studying the role of PDE8 in CNS and the relationship between PDE8 and AD. Here, C57BL/6J mice were used to observe the distribution patterns of two subtypes of PDE8, PDE8A and PDE8B, in different sexes in vivo by western blot (WB). Meanwhile, C57BL/6J mice were also used to demonstrate the distribution pattern of PDE8 in selected brain regions and localization in neural cells by WB and multiplex immunofluorescence staining. Furthermore, the triple transgenic (3×Tg-AD) mice and wild type (WT) mice of different ages were used to investigate the changes of PDE8 expression in the hippocampus and cerebral cortex during the progression of AD. PDE8 was found to be widely expressed in multiple tissues and organs including heart, kidney, stomach, brain, and liver, spleen, intestines, and uterus, with differences in expression levels between the two subtypes of PDE8A and PDE8B, as well as two sexes. Meanwhile, PDE8 was widely distributed in the brain, especially in areas closely related to cognitive function such as cerebellum, striatum, amygdala, cerebral cortex, and hippocampus, without differences between sexes. Furthermore, PDE8A was found to be expressed in neuronal cells, microglia and astrocytes, while PDE8B is only expressed in neuronal cells and microglia. PDE8A expression in the hippocampus of both female and male 3×Tg-AD mice was gradually increased with ages and PDE8B expression was upregulated only in cerebral cortex of female 3×Tg-AD mice with ages. However, the expression of PDE8A and PDE8B was apparently increased in both cerebral cortex and hippocampus in both female and male 10-month-old 3×Tg-AD mice compared WT mice. These results suggest that PDE8 may be associated with the progression of AD and is a potential target for its prevention and treatment in the future.

Divergent functional connectivity changes associated with white matter hyperintensities

Age-related white matter hyperintensities are a common feature and are known to be negatively associated with structural integrity, functional connectivity, and cognitive performance. However, this has yet to be fully understood mechanistically. We analyzed multiple MRI modalities acquired in 465 non-demented individuals from the Swedish BioFINDER study including 334 cognitively normal and 131 participants with mild cognitive impairment. White matter hyperintensities were automatically quantified using fluid-attenuated inversion recovery MRI and parameters from diffusion tensor imaging were estimated in major white matter fibre tracts. We calculated fMRI resting state-derived functional connectivity within and between predefined cortical regions structurally linked by the white matter tracts. How change in functional connectivity is affected by white matter lesions and related to cognition (in the form of executive function and processing speed) was explored. We examined the functional changes using a measure of sample entropy. As expected hyperintensities were associated with disrupted structural white matter integrity and were linked to reduced functional interregional lobar connectivity, which was related to decreased processing speed and executive function. Simultaneously, hyperintensities were also associated with increased intraregional functional connectivity, but only within the frontal lobe. This phenomenon was also associated with reduced cognitive performance. The increased connectivity was linked to increased entropy (reduced predictability and increased complexity) of the involved voxels' blood oxygenation level-dependent signal. Our findings expand our previous understanding of the impact of white matter hyperintensities on cognition by indicating novel mechanisms that may be important beyond this particular type of brain lesions.

Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia

AIM

Recent imaging studies have found significant abnormalities in the brain's functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients.

METHODS

We conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups.

RESULTS

In the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01).

CONCLUSION

HM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.

A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods

Accurate quantification of amyloid positron emission tomography (PET) is essential for early detection of and intervention in Alzheimer's disease (AD) but there is still a lack of studies comparing the performance of various automated methods. This study compared the PET-only method and PET-and-MRI-based method with a pre-trained deep learning segmentation model. A large sample of 1180 participants in the Catholic Aging Brain Imaging (CABI) database was analyzed to calculate the regional standardized uptake value ratio (SUVR) using both methods. The logistic regression models were employed to assess the discriminability of amyloid-positive and negative groups through 10-fold cross-validation and area under the receiver operating characteristics (AUROC) metrics. The two methods showed a high correlation in calculating SUVRs but the PET-MRI method, incorporating MRI data for anatomical accuracy, demonstrated superior performance in predicting amyloid-positivity. The parietal, frontal, and cingulate importantly contributed to the prediction. The PET-MRI method with a pre-trained deep learning model approach provides an efficient and precise method for earlier diagnosis and intervention in the AD continuum.

The characteristic pattern of functional connectivity density influenced by postmenopausal females in the preclinical stage of dementia

Subjective cognitive decline (SCD) is considered an early indicator of Alzheimer's disease. Previous evidence suggests that postmenopausal females are at heightened risk for developing dementia. However, the potential effects of gender (i.e. postmenopausal female) on functional connectivity density (FCD) in individuals with SCD are not well understood. A total of 56 healthy controls and 57 subjects with SCD were included. The short-range and long-range FCD (srFCD and lrFCD) mapping of each participant was calculated. The interactive effect of gender × diagnosis on the FCD was explored by two-way analysis of variance. The interaction effect of gender × diagnosis on lrFCD was primarily in the right middle frontal gyrus (MFG). The older males with SCD exhibited significantly enhanced lrFCD in the right MFG relative to other subgroups. The lrFCD of the right MFG was positively associated with cognitive performance in older females with SCD. Cognition-related functional terms were significantly related to the right MFG. Decreased lrFCD of the right MFG in cognitively normal older women may explain why postmenopausal females have a higher risk for progression to dementia than men. Furthermore, this altered pattern could be applied to identify individuals with a high risk for dementia.

Behaviour Hallmarks in Alzheimer's Disease 5xFAD Mouse Model

The 5xFAD transgenic mouse model widely used in Alzheimer's disease (AD) research recapitulates many AD-related phenotypes with a relatively early onset and aggressive age-dependent progression. Besides developing amyloid peptide deposits alongside neuroinflammation by the age of 2 months, as well as exhibiting neuronal decline by the age of 4 months that intensifies by the age of 9 months, these mice manifest a broad spectrum of behavioural impairments. In this review, we present the extensive repertoire of behavioural dysfunctions in 5xFAD mice, organised into four categories: motor skills, sensory function, learning and memory abilities, and neuropsychiatric-like symptoms. The motor problems, associated with agility and reflex movements, as well as balance and coordination, and skeletal muscle function, typically arise by the time mice reach 9 months of age. The sensory function (such as taste, smell, hearing, and vision) starts to deteriorate when amyloid peptide buildups and neuroinflammation spread into related anatomical structures. The cognitive functions, encompassing learning and memory abilities, such as visual recognition, associative, spatial working, reference learning, and memory show signs of decline from 4 to 6 months of age. Concerning neuropsychiatric-like symptoms, comprising apathy, anxiety and depression, and the willingness for exploratory behaviour, it is believed that motivational changes emerge by approximately 6 months of age. Unfortunately, numerous studies from different laboratories are often contradictory on the conclusions drawn and the identification of onset age, making preclinical studies in rodent models not easily translatable to humans. This variability is likely due to a range of factors associated with animals themselves, housing and husbandry conditions, and experimental settings. In the forthcoming studies, greater clarity in experimental details when conducting behavioural testing in 5xFAD transgenic mice could minimise the inconsistencies and could ensure the reliability and the reproducibility of the results.

Evaluating frontoparietal network topography for diagnostic markers of Alzheimer's disease

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer's disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

Prior-Guided Adversarial Learning With Hypergraph for Predicting Abnormal Connections in Alzheimer's Disease

Alzheimer's disease (AD) is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished the classification task, but few of them can accurately evaluate the changing characteristics of brain connectivity. In this work, a prior-guided adversarial learning with hypergraph (PALH) model is proposed to predict abnormal brain connections using triple-modality medical images. Concretely, a prior distribution from anatomical knowledge is estimated to guide multimodal representation learning using an adversarial strategy. Also, the pairwise collaborative discriminator structure is further utilized to narrow the difference in representation distribution. Moreover, the hypergraph perceptual network is developed to effectively fuse the learned representations while establishing high-order relations within and between multimodal images. Experimental results demonstrate that the proposed model outperforms other related methods in analyzing and predicting AD progression. More importantly, the identified abnormal connections are partly consistent with previous neuroscience discoveries. The proposed model can evaluate the characteristics of abnormal brain connections at different stages of AD, which is helpful for cognitive disease study and early treatment.

Effects of childhood maltreatment and major depressive disorder on functional connectivity in hippocampal subregions

Major Depressive Disorder (MDD) with childhood maltreatment is a prevalent clinical phenotype. Prior studies have observed abnormal hippocampal activity in MDD patients, considering the hippocampus as a single nucleus. However, there is limited research investigating the static and dynamic changes in hippocampal subregion functional connectivity (FC) in MDD patients with childhood maltreatment. Therefore, we employed static and dynamic FC analyses using hippocampal subregions, including the anterior hippocampus and posterior hippocampus, as seed regions to investigate the neurobiological alterations associated with MDD resulting from childhood maltreatment. This study involved four groups: MDD with (n = 48) and without childhood maltreatment (n = 30), as well as healthy controls with (n = 57) and without (n = 46) childhood maltreatment. Compared to MDD patients without childhood maltreatment, those with childhood maltreatment exhibit altered FC between the hippocampal subregion and multiple brain regions, including the anterior cingulate gyrus, superior frontal gyrus, putamen, calcarine gyrus, superior temporal gyrus, angular gyrus, and supplementary motor area. Additionally, dynamic FC between the right medial-2 hippocampal head and the right calcarine gyrus shows a positive correlation with childhood maltreatment across all its subtypes. Moreover, dFC between the right hippocampal tail and the left angular gyrus moderates the relationship between childhood maltreatment and the depression severity. Our findings of distinct FC patterns within hippocampal subregions provide new clues for understanding the neurobiological basis of MDD with childhood maltreatment.

Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models

Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.

Emergence of the brain-border immune niches and their contribution to the development of neurodegenerative diseases

Historically, the central nervous system (CNS) was regarded as 'immune-privileged', possessing its own distinct immune cell population. This immune privilege was thought to be established by a tight blood-brain barrier (BBB) and blood-cerebrospinal-fluid barrier (BCSFB), which prevented the crossing of peripheral immune cells and their secreted factors into the CNS parenchyma. However, recent studies have revealed the presence of peripheral immune cells in proximity to various brain-border niches such as the choroid plexus, cranial bone marrow (CBM), meninges, and perivascular spaces. Furthermore, emerging evidence suggests that peripheral immune cells may be able to infiltrate the brain through these sites and play significant roles in driving neuronal cell death and pathology progression in neurodegenerative disease. Thus, in this review, we explore how the brain-border immune niches may contribute to the pathogenesis of neurodegenerative disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). We then discuss several emerging options for harnessing the neuroimmune potential of these niches to improve the prognosis and treatment of these debilitative disorders using novel insights from recent studies.

Oxytocin attenuates hypothalamic injury-induced cognitive dysfunction by inhibiting hippocampal ERK signaling and Aβ deposition

In clinical settings, tumor compression, trauma, surgical injury, and other types of injury can cause hypothalamic damage, resulting in various types of hypothalamic dysfunction. Impaired release of oxytocin can lead to cognitive impairment and affect prognosis and long-term quality of life after hypothalamic injury. Hypothalamic injury-induced cognitive dysfunction was detected in male animals. Behavioral parameters were measured to assess the characteristics of cognitive dysfunction induced by hypothalamic-pituitary stalk lesions. Brains were collected for high-throughput RNA sequencing and immunostaining to identify pathophysiological changes in hippocampal regions highly associated with cognitive function after injury to corresponding hypothalamic areas. Through transcriptomic analysis, we confirmed the loss of oxytocin neurons after hypothalamic injury and the reversal of hypothalamic-induced cognitive dysfunction after oxytocin supplementation. Furthermore, overactivation of the ERK signaling pathway and β-amyloid deposition in the hippocampal region after hypothalamic injury were observed, and cognitive function was restored after inhibition of ERK signaling pathway overactivation. Our findings suggest that cognitive dysfunction after hypothalamic injury may be caused by ERK hyperphosphorylation in the hippocampal region resulting from a decrease in the number of oxytocin neurons, which in turn causes β-amyloid deposition.

Age-Related Brain Atrophy and the Positive Effects of Behavioral Enrichment in Middle-Aged Beagles

Aging dogs serve as a valuable preclinical model for Alzheimer's disease (AD) due to their natural age-related development of β-amyloid (Aβ) plaques, human-like metabolism, and large brains that are ideal for studying structural brain aging trajectories from serial neuroimaging. Here we examined the effects of chronic treatment with the calcineurin inhibitor (CNI) tacrolimus or the nuclear factor of activated T cells (NFAT)-inhibiting compound Q134R on age-related canine brain atrophy from a longitudinal study in middle-aged beagles (36 females, 7 males) undergoing behavioral enrichment. Annual MRI was analyzed using modern, automated techniques for region-of-interest-based and voxel-based volumetric assessments. We found that the frontal lobe showed accelerated atrophy with age, while the caudate nucleus remained relatively stable. Remarkably, the hippocampus increased in volume in all dogs. None of these changes were influenced by tacrolimus or Q134R treatment. Our results suggest that behavioral enrichment can prevent atrophy and increase the volume of the hippocampus but does not prevent aging-associated prefrontal cortex atrophy.

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.

Brain functional connectivity in hyperthyroid patients: systematic review

INTRODUCTION

Functional connectivity (FC) is the correlation between brain regions' activities, studied through neuroimaging techniques like fMRI. It helps researchers understand brain function, organization, and dysfunction. Hyperthyroidism, characterized by high serum levels of free thyroxin and suppressed thyroid stimulating hormone, can lead to mood disturbance, cognitive impairment, and psychiatric symptoms. Excessive thyroid hormone exposure can enhance neuronal death and decrease brain volume, affecting memory, attention, emotion, vision, and motor planning.

METHODS

We conducted thorough searches across Google Scholar, PubMed, Hinari, and Science Direct to locate pertinent articles containing original data investigating FC measures in individuals diagnosed with hyperthyroidism.

RESULTS

The systematic review identified 762 articles, excluding duplicates and non-matching titles and abstracts. Four full-text articles were included in this review. In conclusion, a strong bilateral hippocampal connection in hyperthyroid individuals suggests a possible neurobiological influence on brain networks that may affect cognitive and emotional processing.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO, CRD42024516216.

Multi-modal Neuroimaging Phenotyping of Mnemonic Anosognosia in the Aging Brain

BACKGROUND

Unawareness is a behavioral condition characterized by a lack of self-awareness of objective memory decline. In the context of Alzheimer's Disease (AD), unawareness may develop in predementia stages and contributes to disease severity and progression. Here, we use in-vivo multi-modal neuroimaging to profile the brain phenotype of individuals presenting altered self-awareness of memory during aging.

METHODS

Amyloid- and tau-PET (N = 335) and resting-state functional MRI (N = 713) imaging data of individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4)/Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study were used in this research. We applied whole-brain voxel-wise and region-of-interest analyses to characterize the cortical intersections of tau, amyloid, and functional connectivity networks underlying unawareness in the aging brain compared to aware, complainer and control groups.

RESULTS

Individuals with unawareness present elevated amyloid and tau burden in midline core regions of the default mode network compared to aware, complainer or control individuals. Unawareness is characterized by an altered network connectivity pattern featuring hyperconnectivity in the medial anterior prefrontal cortex and posterior occipito-parietal regions co-locating with amyloid and tau deposition.

CONCLUSIONS

Unawareness is an early behavioral biomarker of AD pathology. Failure of the self-referential system in unawareness of memory decline can be linked to amyloid and tau burden, along with functional network connectivity disruptions, in several medial frontal and parieto-occipital areas of the human brain.

Brain-based sex differences in schizophrenia: A systematic review of fMRI studies

Schizophrenia is a chronic psychiatric disorder with characteristic symptoms of delusions, hallucinations, lack of motivation, and paucity of thought. Recent evidence suggests that the symptoms of schizophrenia, negative symptoms in particular, vary widely between the sexes and that symptom onset is earlier in males. A better understanding of sex-based differences in functional magnetic resonance imaging (fMRI) studies of schizophrenia may provide a key to understanding sex-based symptom differences. This study aimed to summarize sex-based functional magnetic resonance imaging (fMRI) differences in brain activity of patients with schizophrenia. We searched PubMed and Scopus to find fMRI studies that assessed sex-based differences in the brain activity of patients with schizophrenia. We excluded studies that did not evaluate brain activity using fMRI, did not evaluate sex differences, and were nonhuman or in vitro studies. We found 12 studies that met the inclusion criteria for the current systematic review. Compared to females with schizophrenia, males with schizophrenia showed more blood oxygen level-dependent (BOLD) activation in the cerebellum, the temporal gyrus, and the right precuneus cortex. Male patients also had greater occurrence of low-frequency fluctuations in cerebral blood flow in frontal and parietal lobes and the insular cortex, while female patients had greater occurrence of low-frequency fluctuations in the hippocampus, parahippocampus, and lentiform nucleus. The current study summarizes fMRI studies that evaluated sex-based fMRI brain differences in schizophrenia that may help to shed light on the underlying pathophysiology and further understanding of sex-based differences in the clinical presentation and course of the disorder.

Efficacy and safety of repetitive Transcranial Magnetic Stimulation and transcranial Direct Current Stimulation in memory deficits in patients with Alzheimer's disease: Meta-analysis and systematic review

Repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are two of the most used non-pharmacological interventions for Alzheimer's Disease (AD). However, most of the clinical trials have focused on evaluating the effects on global cognition and not on specific cognitive functions. Therefore, considering that memory loss is one of the hallmark symptoms of AD, we aim to assess the efficacy and safety of tDCS and rTMS in memory deficits. For that, multilevel random effect models were performed considering the standardized mean difference (SMD) between active and sham stimulation. A total of 19 studies with 411 participants demonstrated positive effects in memory after tDCS (SMD=0.20, p = 0.04) and rTMS (SMD=0.44, p = 0.001). Subgroup analysis revealed that tDCS had greater efficacy when administered in temporal regions (SMD=0.32, p = 0.04), whereas rTMS was superior when applied in frontal regions (SMD=0.61, p < 0.001). Therefore, depending on the brain region of stimulation, both interventions produced a positive effect on memory symptoms in AD patients. Finally, the safety of both techniques was observed in the AD population after the reporting of almost no serious events.

Altered functional connectivity between the default mode network in primary angle-closure glaucoma patients

Previous studies have recognized glaucoma as a neurodegenerative disease that causes extensive brain damage and is closely associated with cognitive function. In this study, we employed functional MRI to examine the intrinsic functional connectivity patterns of the default mode network (DMN) in patients diagnosed with primary angle-closure glaucoma (PACG), exploring its association with cognitive dysfunction. A total of 34 patients diagnosed with PACG and 34 healthy controls (HC), who were matched in terms of sex, age, and education, were included in the control group. The posterior cingulate cortex (PCC) was selected as the region of interest to examine functional connectivity alterations. Compared with the HC group, functional connectivity was attenuated in left anterior cingulum cortex and left paracentral lobule between with PCC in the PACG group, the results are statistically significant. Our study revealed that patients with PACG exhibit weakened functional connectivity within the DMN. This finding suggests the presence of a neurological mechanism that is associated with both visual dysfunction and cognitive impairments in PACG patients. Furthermore, our study provides neuroimaging evidence that can aid in the exploration of spontaneous neurological alterations and facilitate a deeper investigation of alterations in the visual conduction pathways of PACG patients.