Study of brain morphology change in Alzheimer's disease and amnestic mild cognitive impairment compared with normal controls

Uncovering cerebral blood flow patterns corresponding to Amyloid-beta accumulations in patients across the Alzheimer's disease continuum using the arterial spin labeling

BACKGROUND

Alzheimer's disease (AD) is a progressive neurodegenerative disorder ranging from mild cognitive impairment (MCI) to AD dementia. Abnormal cerebral perfusion alterations, influenced by amyloid-beta (Aβ) accumulations, have been implicated in cognitive decline along this spectrum.

OBJECTIVE

This study investigates the relationship between cerebrospinal fluid (CSF) Aβ1-42 levels and regional cerebral blood flow (CBF) changes across the AD continuum using the Arterial Spin Labeling (ASL) technique.

METHODS

We analyzed data from 229 participants extracted from the ADNI cohort, comprising of 50 cognitively normal (CN), 13 subjective memory complaints (SMC), 83 early MCI (EMCI), 52 late MCI (LMCI), and 31 AD participants with complete ASL and CSF data. Correlations between Aβ1-42 levels and regional mean CBF values were assessed. Multiple linear regression models accounted for confounders, including age, gender, and education level.

RESULTS

Preliminary unadjusted analyses revealed strong positive correlations between Aβ1-42 levels and CBF in multiple regions, predominantly in the AD group. After adjusting for confounders, significant correlations in AD participants emerged in the left pars triangularis and left caudal middle frontal cortex. In the LMCI group, significant associations were identified in the right lateral occipital cortex, right inferior parietal cortex, and left amygdala.

CONCLUSION

These findings highlight the critical role of Aβ-driven CBF alterations in regions associated with higher cognitive functions and suggest that these patterns may serve as potential biomarkers for diagnosing and monitoring disease progression.

Statin is associated with higher cortical thickness in early Alzheimer's disease

BACKGROUND

The brain is the most cholesterol-rich organ, essential for myelination and neuronal function. Statins, widely used to lower cholesterol, cross the blood-brain barrier and may impact brain cholesterol synthesis. Despite their widespread use, the effects of statins on cortical regions relevant to Alzheimer's disease (AD) are not well understood. This study aimed to compare cortical thickness between statin-exposed and statin-unexposed older adults and evaluate the potential neuroprotective effects of statins.

METHODS

Data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The sample included 193 healthy controls (HC), 485 individuals with mild cognitive impairment (MCI), and 169 individuals with Alzheimer's disease (AD). Participants were categorized as statin users if they had used statins for at least two years. MRI data were processed using FreeSurfer software to estimate cortical thickness in 64 regions of interest. ANCOVA models assessed the association between statin use and cortical thickness at baseline, and linear mixed models evaluated longitudinal changes.

RESULTS

Statin use was associated with increased cortical thickness in multiple brain regions across HC, MCI, and AD participants. In HC, statin users had greater thickness in the right lateral occipital, left middle temporal, and left parahippocampal regions. MCI participants exhibited additional increases in the right cuneus, right posterior cingulate, and left superior temporal cortex. In AD, statin users had higher thickness in the right cuneus and right superior parietal lobule. Longitudinal analysis revealed no statin-related differences in cortical thickness changes among HC and AD groups, but in MCI, statins slowed cortical thinning in the left medial orbitofrontal cortex.

CONCLUSION

Statin use is associated with greater cortical thickness in older adults, particularly in those with MCI. These findings suggest that statins may have neuroprotective effects, potentially mitigating neurodegenerative changes in early cognitive decline. Further research with larger cohorts and longer follow-up periods is needed to confirm these findings and understand the mechanisms involved.

Systematic bias in surface area asymmetry measurements from automatic cortical parcellations

Anatomical asymmetry is a hallmark of the human brain and may reflect hemispheric differences in its functional organization. Widely used software like FreeSurfer can automate neuroanatomical measurements and facilitate studies of hemispheric asymmetry. However, patterns of surface area lateralization measured using FreeSurfer are curiously consistent across diverse samples. Here, we demonstrate systematic biases in these measurements obtained from the default processing pipeline. We compared surface area asymmetry measured from reconstructions of original brains vs. the same scans after flipping their left-right orientation. The default pipeline returned implausible asymmetry patterns between the original and flipped brains: Many structures were always left- or right-lateralized. Notably, these biases occur prominently in key speech and language regions. In contrast, manual labeling and curvature-based parcellations of key structures both yielded the expected reversals of left/right lateralization in flipped brains. We determined that these biases result from discrepancies in how regional labels are defined in the left vs. right hemisphere in the default cortical parcellation atlases. These biases are carried into individual parcellations because the FreeSurfer parcellation algorithm prioritizes vertex correspondence to the template atlas relative to individual neuroanatomical variation. We further demonstrate several straightforward, bias-free approaches to measuring surface area asymmetry, including using symmetric registration templates and parcellation atlases, vertex-wise analyses, and within-subject curvature-based parcellations. These results highlight theoretical concerns about using only the default processing stream to make inferences about population-level brain asymmetry and underscore the need for validating bias-free neuroanatomical measurements, particularly when studying regions where structural lateralization may underlie functional lateralization.

Volume Changes in Brain Subfields of Patients with Alzheimer's Disease After Transcranial Ultrasound Stimulation

Background/Objectives: Alzheimer's disease (AD) is characterized by progressive brain atrophy marked by cognitive decline and memory loss, which significantly affect patients' quality of life. Transcranial ultrasound stimulation (TUS) is a potential physical treatment for AD patients. However, the specific brain regions stimulated by TUS and its therapeutic effects remain unclear. Methods: In this study, magnetic resonance imaging (MRI) and FreeSurfer segmentation were employed to assess alterations in the brain volume of AD patients after TUS. Results: Our findings revealed significant volume increases in the corpus callosum (CC) and lateral orbitofrontal cortex (lOFC) in the TUS group. Moreover, the volumetric changes in the CC were strongly correlated with improvements in the Mini-Mental State Examination score, which is a widely used measure of cognitive function of AD patients. Conclusions: TUS has the potential to alleviate disease progression and offers a non-invasive therapeutic approach to the improvement of cognitive function in AD patients.

The Misdiagnosis and Underdiagnosis of Hepatic Encephalopathy

Patients with cirrhosis are at risk of developing hepatic encephalopathy (HE), which can present with a wide range of symptoms, including confusion, lethargy, inappropriate behavior, and altered sleep patterns. In addition to HE, patients with cirrhosis are at risk of developing mild cognitive impairment, dementia, and delirium, which have features closely resembling HE. Given the similar presentation of these conditions, misdiagnosis can and does occur. Mild cognitive impairment is common in individuals aged 50 years and older and can progress to dementia in those affected. Dementia and HE are both characterized by sleep disturbance and cognitive dysfunction, thus differentiating these conditions can be difficult. Furthermore, delirium can disrupt sleep patterns, and liver disease is recognized as a risk factor for its development. As HE is a cirrhosis-related complication, determining if a patient has undiagnosed cirrhosis is critical, particularly given the large number of patients with asymptomatic, compensated cirrhosis. Separately, underdiagnosis of minimal HE can occur even in patients with diagnosed liver disease, related, in part, to lack of testing. Given the availability of effective therapies for managing symptoms and preventing future episodes, accurate diagnosis of HE is essential.

Evaluating conversion from mild cognitive impairment to Alzheimer's disease with structural MRI: a machine learning study

Alzheimer's disease is a disabling neurodegenerative disorder for which no effective treatment currently exists. To predict the diagnosis of Alzheimer's disease could be crucial for patients' outcome, but current Alzheimer's disease biomarkers are invasive, time consuming or expensive. Thus, developing MRI-based computational methods for Alzheimer's disease early diagnosis would be essential to narrow down the phenotypic measures predictive of cognitive decline. Amnestic mild cognitive impairment (aMCI) is associated with higher risk for Alzheimer's disease, and here, we aimed to identify MRI-based quantitative rules to predict aMCI to possible Alzheimer's disease conversion, applying different machine learning algorithms sequentially. At baseline, T1-weighted brain images were collected for 104 aMCI patients and processed to obtain 146 volumetric measures of cerebral grey matter regions [regions of interest (ROIs)]. One year later, patients were classified as converters (aMCI-c = 32) or non-converters, i.e. clinically and neuropsychologically stable (aMCI-s = 72) based on cognitive performance. Feature selection was performed by random forest (RF), and the identified seven ROIs volumetric data were used to implement support vector machine (SVM) and decision tree (DT) classification algorithms. Both SVM and DT reached an average accuracy of 86% in identifying aMCI-c and aMCI-s. DT found a critical threshold volume of the right entorhinal cortex (EC-r) as the first feature for differentiating aMCI-c/aMCI-s. Almost all aMCI-c had an EC-r volume <1286 mm3, while more than half of the aMCI-s patients had a volume above the identified threshold for this structure. Other key regions for the classification between aMCI-c/aMCI-s were the left lateral occipital (LOC-l), the middle temporal gyrus and the temporal pole cortices. Our study reinforces previous evidence suggesting that the morphometry of the EC-r and LOC-l best predicts aMCI to Alzheimer's disease conversion. Further investigations are needed prior to deeming our findings as a broadly applicable predictive framework. However, here, a first indication was derived for volumetric thresholds that, being easy to obtain, may assist in early identification of Alzheimer's disease in clinical practice, thus contributing to establishing MRI as a useful non-invasive prognostic instrument for dementia onset.

Multiscale Analysis of Alzheimer's Disease Using Feature Fusion in Cognitive and Sensory Brain Regions

Introduction

This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.

Methods

Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.

Results

The research highlights the critical role of brain texture features, particularly in memory regions, for AD detection. Significant sex-specific differences are observed, with males showing significance in texture features in memory regions, volume in vision regions, and SUVR in speech regions, while females exhibit significance in texture features in memory and speech regions, and SUVR in vision regions. Additionally, the study analyzes how obesity affects features used in AD prediction models, clarifying its effects on speech and vision regions, particularly brain volume.

Conclusion

The findings contribute valuable insights into the effectiveness of feature fusion, sex-specific differences, and the impact of obesity on AD-related biomarkers, paving the way for future research in early AD detection strategies and cognitive impairment classification.

Effectiveness of neuro-feedback on Alzheimer's rehabilitation: a bibliometric analysis

BACKGROUND

Alzheimer's Disease (AD) is a neurodegenerative disorder with limited treatment options. Neurofeedback, a technique that trains brainwaves, has shown promise in addressing cognitive impairments.

OBJECTIVES

To conduct a bibliometric analysis to explore the current research on neurofeedback as a treatment for AD.

METHODS

A systematic literature review was performed based on PRISMA guidelines on 142 papers. Different bibliometric parameters like the author's country, author names, keywords, journal names, and country of citations were analyzed, and a network visualization chart was generated to understand the correlation of Alzheimer-related search terms to neurofeedback.

RESULTS

Research is concentrated in Europe and North America, with a significant gap in Asian countries. A growing body of evidence supports the potential benefits of neurofeedback for AD. A strong correlation has been found between neurofeedback and AD-related terms. Clinical trials suggest positive outcomes for neurofeedback in improving cognitive impairments and working memory.

CONCLUSION

Neurofeedback shows promise as a potential treatment for AD. Further research and clinical studies are needed to explore the full potential of neurofeedback for enhancing the quality of life for individuals with AD.

Association of glial fibrillary acid protein, Alzheimer's disease pathology and cognitive decline

Increasing evidence shows that neuroinflammation is a possible modulator of tau spread effects on cognitive impairment in Alzheimer's disease. In this context, plasma levels of the glial fibrillary acidic protein (GFAP) have been suggested to have a robust association with Alzheimer's disease pathophysiology. This study aims to assess the correlation between plasma GFAP and Alzheimer's disease pathology, and their synergistic effect on cognitive performance and decline. A cohort of 122 memory clinic subjects with amyloid and tau PET, MRI scans, plasma GFAP and Mini-Mental State Examination (MMSE) was included in the study. A subsample of 94 subjects had a follow-up MMSE score at ≥1 year after baseline. Regional and voxel-based correlations between Alzheimer's disease biomarkers and plasma GFAP were assessed. Mediation analyses were performed to evaluate the effects of plasma GFAP on the association between amyloid and tau PET and between tau PET and cognitive impairment and decline. GFAP was associated with increased tau PET ligand uptake in the lateral temporal and inferior temporal lobes in a strong left-sided pattern independently of age, sex, education, amyloid and APOE status (β = 0.001, P < 0.01). The annual rate of MMSE change was significantly and independently correlated with both GFAP (β = 0.006, P < 0.01) and global tau standardized uptake value ratio (β = 4.33, P < 0.01), but not with amyloid burden. Partial mediation effects of GFAP were found on the association between amyloid and tau pathology (13.7%) and between tau pathology and cognitive decline (17.4%), but not on global cognition at baseline. Neuroinflammation measured by circulating GFAP is independently associated with tau Alzheimer's disease pathology and with cognitive decline, suggesting neuroinflammation as a potential target for future disease-modifying trials targeting tau pathology.

Hypoperfusion of the Left Insula, Operculum, and Putamen on Technetium-99m Ethyl Cysteinate Dimer (99mTc-ECD) Single-Photon Emission Computed Tomography in Patients With Mild Cognitive Impairment and Early Alzheimer's Disease

OBJECTIVE

The easy Z-score imaging system (eZIS) objectively interprets brain perfusion. Using eZIS-processed images, we observed decreased regional cerebral blood flow (rCBF) in the left putamen of several patients with forgetfulness. This study aimed to examine this decrease using statistical image analysis.

METHODS

Cerebral perfusion single-photon emission computed tomography (SPECT) was performed on patients with mild cognitive impairment (MCI) and early Alzheimer's disease (AD). Normalized and corrected SPECT images were compared between patients and controls using Statistical Parametric Mapping software (SPM12, The Wellcome Center for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK). Eigenvariate values of clusters with significantly low and high rCBF were obtained. Principal component analysis (PCA) was then used to examine the relationships among these clusters.

RESULTS

We observed decreased rCBF in the left insula, operculum, and putamen, indicating that the reduction extended laterally from the initial eZIS-based findings. We obtained eight decreased and seven increased eigenvariate values for abnormal rCBF clusters. PCA revealed that the left insula, operculum, and putamen were the most influential principal components, along with the posterior cingulate and precuneus cortices. The eigenvariate values in these regions did not correlate with sex, diagnosis (AD or MCI), or cognitive scores.

CONCLUSIONS

We reported the decreased rCBF in the left insula, operculum, and putamen. However, its clinical significance beyond the associations with forgetfulness and its natural radiological course remains unclear.

Bayesian Tensor Modeling for Image-based Classification of Alzheimer's Disease

Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing literature on using Bayesian scalar-on-tensor regression techniques that use tensor-based representations for high-dimensional and spatially distributed covariates to predict continuous outcomes. However surprisingly, there is limited development on corresponding Bayesian classification methods relying on tensor-valued covariates. Standard approaches that vectorize the image are not desirable due to the loss of spatial structure, and alternate methods that use extracted features from the image in the predictive model may suffer from information loss. We propose a novel data augmentation-based Bayesian classification approach relying on tensor-valued covariates, with a focus on imaging predictors. We propose two data augmentation schemes, one resulting in a support vector machine (SVM) type of classifier, and another yielding a logistic regression classifier. While both types of classifiers have been proposed independently in literature, our contribution is to extend such existing methodology to accommodate high-dimensional tensor valued predictors that involve low rank decompositions of the coefficient matrix while preserving the spatial information in the image. An efficient Markov chain Monte Carlo (MCMC) algorithm is developed for implementing these methods. Simulation studies show significant improvements in classification accuracy and parameter estimation compared to routinely used classification methods. We further illustrate our method in a neuroimaging application using cortical thickness MRI data from Alzheimer's Disease Neuroimaging Initiative, with results displaying better classification accuracy throughout several classification tasks, including classification on pairs of the three diagnostic groups: normal control, AD patients, and MCI patients; gender classification (males vs females); and cognitive performance based on high and low levels of MMSE scores.

Iron accumulation/overload and Alzheimer's disease risk factors in the precuneus region: A comprehensive narrative review

Alzheimer's disease (AD) is a neurodegenerative disease that is characterized by amyloid plaques, neurofibrillary tangles, and neuronal loss. Early cerebral and body iron dysregulation and accumulation interact with AD pathology, particularly in the precuneus, a crucial functional hub in cognitive functions. Quantitative susceptibility mapping (QSM), a novel post-processing approach, provides insights into tissue iron levels and cerebral oxygen metabolism and reveals abnormal iron accumulation early in AD. Increased iron deposition in the precuneus can lead to oxidative stress, neuroinflammation, and accelerated neurodegeneration. Metabolic disorders (diabetes, non-alcoholic fatty liver disease (NAFLD), and obesity), genetic factors, and small vessel pathology contribute to abnormal iron accumulation in the precuneus. Therefore, in line with the growing body of literature in the precuneus region of patients with AD, QSM as a neuroimaging method could serve as a non-invasive biomarker to track disease progression, complement other imaging modalities, and aid in early AD diagnosis and monitoring.

Causal effects of sepsis on structural changes in cerebral cortex: A Mendelian randomization investigation

Previous research has shown a strong correlation between sepsis and brain structure. However, whether this relationship represents a causality remains elusive. In this study, we employed Mendelian randomization (MR) to probe the associations of genetically predicted sepsis and sepsis-related death with structural changes in specific brain regions. Genome-wide association study (GWAS) data for sepsis phenotypes (sepsis and sepsis-related death) were obtained from the IEU OpenGWAS. Correspondingly, GWAS data for brain structural traits (volume of the subcortical structure, cortical thickness, and surface area) were derived from the ENIGMA consortium. Inverse variance weighted was mainly utilized to assess the causal effects, while weighted median and MR-Egger regression served as complementary methods. Sensitivity analyses were implemented with Cochran Q test, MR-Egger regression, and MR-PRESSO. In addition, a reverse MR analysis was carried out to assess the possibility of reverse causation. We identified that genetic liability to sepsis was normally significantly associated with a reduced surface area of the postcentral gyrus (β = -35.5280, SE = 13.7465, P = .0096). The genetic liability to sepsis-related death showed a suggestive positive correlation with the surface area of fusiform gyrus (β = 11.0920, SE = 3.6412, P = .0023) and posterior cingulate gyrus (β = 3.6530, SE = 1.6684, P = .0286), While it presented a suggestive negative correlation with surface area of the caudal middle frontal gyrus (β = -11.4586, SE = 5.1501, P = .0261) and frontal pole (β = -1.0024, SE = 0.4329, P = .0206). We also indicated a possible bidirectional causal association between genetic liability to sepsis-related death and the thickness of the transverse temporal gyrus. Sensitivity analyses verified the robustness of the above associations. These findings suggested that genetically determined liability to sepsis might influence the specific brain structure in a causal way, offering new perspectives to investigate the mechanism of sepsis-related neuropsychiatric disorders.

Comparative study on structural and functional brain differences in mild cognitive impairment patients with tinnitus

Objective

Tinnitus may be associated with various brain changes. However, the degenerative changes in patients with tinnitus have not been extensively investigated. We aimed to evaluate degenerative, structural, and functional brain changes in patients with mild cognitive impairment (MCI) who also suffer from tinnitus.

Materials and methods

This study included participants aged 60 to 80 years with MCI and a hearing level better than 40 dB. The participants were classified into two groups: MCI with tinnitus (MCI-T) and MCI without tinnitus (MCI-NT). All patients underwent Tinnitus Handicap Inventory (THI), 3 T brain MRI, F18-florapronol PET, and F18-FDG PET.

Results

The MCI-T group exhibited higher β-amyloid deposition in the superior temporal gyrus, temporal pole, and middle temporal gyrus compared to the MCI-NT group (p < 0.05 for all). Additionally, the MCI-T group showed increased metabolism in the inferior frontal gyrus, insula, and anterior cingulate cortex (ACC) (p < 0.005 for all). The THI score was strongly correlated with increased volume in the insula, ACC, superior frontal gyrus, supplementary motor area, white matter near the hippocampus, and precentral gyrus (p < 0.05 for all). Moreover, the MCI-T group demonstrated higher metabolic activity in the default mode network (DMN) and lower activity in the executive control network (ECN) (p < 0.05 for all). In the MCI-T group, the posterior DMN was positively correlated with the visual network and negatively with the ECN, whereas in the MCI-NT group, it correlated positively with the ECN.

Conclusion

The MCI-T group exhibited greater β-amyloid accumulation in the auditory cortex and more extensive changes across various brain networks compared with the MCI-NT group, potentially leading to diverse clinical symptoms such as dementia with semantic deficits or depression. Tinnitus in MCI patients may serve as a biomarker for degenerative changes in the temporal lobe and alterations in brain network dynamics.

Multiple Transcriptomic Analyses Explore Potential Synaptic Biomarker Rabphilin-3A for Alzheimer's Disease

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder afflicting the elderly population worldwide. The identification of potential gene candidates for AD holds promises for diagnostic biomarkers and therapeutic targets. Employing a comprehensive strategy, this study integrated transcriptomic data from diverse data sources, including microarray and single-cell datasets from blood and tissue samples, enabling a detailed exploration of gene expression dynamics. Through this thorough investigation, 19 notable candidate genes were found with consistent expression changes across both blood and tissue datasets, suggesting their potential as biomarkers for AD. In addition, single cell sequencing analysis further highlighted their specific expression in excitatory and inhibitory neurons, the primary functional units in the brain, underscoring their relevance to AD pathology. Moreover, the functional enrichment analysis revealed that three of the candidate genes were downregulated in synaptic signaling pathway. Further validation experiments significantly showed reduced levels of rabphilin-3A (RPH3A) in 3xTg-AD model mice, implying its role in disease pathogenesis. Given its role in neurotransmitter exocytosis and synaptic function, further investigation into RPH3A and its interactions with neurotrophic proteins may provide valuable insights into the complex molecular mechanisms underlying synaptic dysfunction in AD.

The association between genetic variations and morphology-based brain networks changes in Alzheimer's disease

Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short- and long-range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology-based brain networks, leading to functional deficits; the second hypothesis is that protein-protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative-1 database. A co-sparse reduced-rank regression model was fit to study the relationship between non-synonymous mutations and morphology-based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co-immunoprecipitation (Co-IP) experiments. Eight genes affecting morphology-based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co-IP. Hence, gene variations influence morphology-based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co-IP, suggests that the effect is robust.

Effect of dorsolateral prefrontal cortex structural measures on neuroplasticity and response to paired-associative stimulation in Alzheimer's dementia

Long-term potentiation (LTP)-like activity can be induced by stimulation protocols such as paired associative stimulation (PAS). We aimed to determine whether PAS-induced LTP-like activity (PAS-LTP) of the dorsolateral prefrontal cortex (DLPFC) is associated with cortical thickness and other structural measures impaired in Alzheimer's dementia (AD). We also explored longitudinal relationships between these brain structures and PAS-LTP response after a repetitive PAS (rPAS) intervention. Mediation and regression analyses were conducted using data from randomized controlled trials with AD and healthy control participants. PAS-electroencephalography assessed DLPFC PAS-LTP. DLPFC thickness and surface area were acquired from T1-weighted magnetic resonance imaging. Fractional anisotropy and mean diffusivity (MD) of the superior longitudinal fasciculus (SLF)-a tract important to induce PAS-LTP-were measured with diffusion-weighted imaging. AD participants exhibited reduced DLPFC thickness and increased SLF MD. There was also some evidence that reduction in DLPFC thickness mediates DLPFC PAS-LTP impairment. Longitudinal analyses showed preliminary evidence that SLF MD, and to a lesser extent DLPFC thickness, is associated with DLPFC PAS-LTP response to active rPAS. This study expands our understanding of the relationships between brain structural changes and neuroplasticity. It provides promising evidence for a structural predictor to improving neuroplasticity in AD with neurostimulation. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Mediterranean diet protects against a neuroinflammatory cortical transcriptome: Associations with brain volumetrics, peripheral inflammation, social isolation, and anxiety in nonhuman primates (Macaca fascicularis)

Mediterranean diets may be neuroprotective and prevent cognitive decline relative to Western diets; however, the underlying biology is poorly understood. We assessed the effects of Western versus Mediterranean-like diets on RNAseq-generated transcriptional profiles in lateral temporal cortex and their relationships with longitudinal changes in neuroanatomy, circulating monocyte gene expression, and observations of social isolation and anxiety in 38 socially-housed, middle-aged female cynomolgus macaques (Macaca fascicularis). Diet resulted in differential expression of seven transcripts (FDR < 0.05). Cyclin dependent kinase 14 (CDK14), a proinflammatory regulator, was lower in the Mediterranean group. The remaining six transcripts [i.e., "lunatic fringe" (LFNG), mannose receptor C type 2 (MRC2), solute carrier family 3 member 2 (SLCA32), butyrophilin subfamily 2 member A1 (BTN2A1), katanin regulatory subunit B1 (KATNB1), and transmembrane protein 268 (TMEM268)] were higher in cortex of the Mediterranean group and generally associated with anti-inflammatory/neuroprotective pathways. KATNB1 encodes a subcomponent of katanin, important in maintaining microtubule homeostasis. BTN2A1 is involved in immunomodulation of γδ T-cells which have anti-neuroinflammatory and neuroprotective effects. CDK14, LFNG, MRC2, and SLCA32 are associated with inflammatory pathways. The latter four differentially expressed cortex transcripts were associated with peripheral monocyte transcript levels, neuroanatomical changes determined by MRI, and with social isolation and anxiety. These results provide important insights into the potential mechanistic processes linking diet, peripheral and central inflammation, and behavior. Collectively, our results provide evidence that, relative to Western diets, Mediterranean diets confer protection against peripheral and central inflammation which is reflected in preserved brain structure and socioemotional behavior. Ultimately, such protective effects may confer resilience to the development of neuropathology and associated disease.

Identifying discriminative features of brain network for prediction of Alzheimer's disease using graph theory and machine learning

Alzheimer's disease (AD) is a challenging neurodegenerative condition, necessitating early diagnosis and intervention. This research leverages machine learning (ML) and graph theory metrics, derived from resting-state functional magnetic resonance imaging (rs-fMRI) data to predict AD. Using Southwest University Adult Lifespan Dataset (SALD, age 21-76 years) and the Open Access Series of Imaging Studies (OASIS, age 64-95 years) dataset, containing 112 participants, various ML models were developed for the purpose of AD prediction. The study identifies key features for a comprehensive understanding of brain network topology and functional connectivity in AD. Through a 5-fold cross-validation, all models demonstrate substantial predictive capabilities (accuracy in 82-92% range), with the support vector machine model standing out as the best having an accuracy of 92%. Present study suggests that top 13 regions, identified based on most important discriminating features, have lost significant connections with thalamus. The functional connection strengths were consistently declined for substantia nigra, pars reticulata, substantia nigra, pars compacta, and nucleus accumbens among AD subjects as compared to healthy adults and aging individuals. The present finding corroborate with the earlier studies, employing various neuroimagining techniques. This research signifies the translational potential of a comprehensive approach integrating ML, graph theory and rs-fMRI analysis in AD prediction, offering potential biomarker for more accurate diagnostics and early prediction of AD.

Intertwined depressive and cognitive trajectories and the risk of dementia and death in older adults: a competing risk analysis

Background

Depressive symptoms and cognitive impairment often interact, rendering their associations controversial. To date, their joint trajectories and associations with dementia and death remain underexplored.

Aims

To explore the interactions between depressive symptoms and cognitive function, their developmental trajectories and the associations with all-cause dementia, Alzheimer's disease (AD) and all-cause death in older adults.

Methods

Data were from the Health and Retirement Study. Depressive symptoms and cognitive function were measured using the 8-item Centre for Epidemiologic Studies Depression Scale and the Telephone Interview of Cognitive Status, respectively. All-cause dementia and AD were defined by self-reported or proxy-reported physician diagnoses. All-cause death was determined by interviews. The restricted cubic spline, group-based trajectory modelling and subdistribution hazard regression were used.

Results

Significant interactions between depressive symptoms and cognitive function in 2010 in their association with new-onset all-cause dementia and AD from 2010 to 2020 were found, especially in women (p for interaction <0.05). Independent trajectory analysis showed that emerging or high (vs no) depressive trajectories and poor or rapidly decreased cognitive trajectories (vs very good) from 1996 to 2010 were at significantly higher risk of subsequent all-cause dementia, AD and all-cause death. 15 joint trajectories of depressive symptoms and cognitive function from 1996 to 2010 were determined, where rapidly decreased cognitive function was more common in those with no depressive symptoms. Compared with older adults with the trajectory of no depressive symptoms and very good cognitive function, those with the trajectory of no depressive symptoms but rapidly decreased cognitive function were much more likely to develop new-onset all-cause dementia and death, with subdistribution hazard ratios (95% confidence intervals) of 4.47 (2.99 to 6.67) and 1.84 (1.43 to 2.36), especially in women.

Conclusions

To effectively mitigate the risk of dementia and death, it is crucial to acknowledge the importance of preventing cognitive decline in older adults without depressive symptoms, particularly in women.

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

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

Single-Cell Transcriptional Profiling and Gene Regulatory Network Modeling in Tg2576 Mice Reveal Gender-Dependent Molecular Features Preceding Alzheimer-Like Pathologies

Alzheimer's disease (AD) onset and progression is influenced by a complex interplay of several environmental and genetic factors, one of them gender. Pronounced gender differences have been observed both in the relative risk of developing AD and in clinical disease manifestations. A molecular level understanding of these gender disparities is still missing, but could provide important clues on cellular mechanisms modulating the disease and reveal new targets for gender-oriented disease-modifying precision therapies. We therefore present here a comprehensive single-cell analysis of disease-associated molecular gender differences in transcriptomics data from the neocortex, one of the brain regions most susceptible to AD, in one of the most widely used AD mouse models, the Tg2576 model. Cortical areas are also most commonly used in studies of post-mortem AD brains. To identify disease-linked molecular processes that occur before the onset of detectable neuropathology, we focused our analyses on an age with no detectable plaques and microgliosis. Cell-type specific alterations were investigated at the level of individual genes, pathways, and gene regulatory networks. The number of differentially expressed genes (DEGs) was not large enough to build context-specific gene regulatory networks for each individual cell type, and thus, we focused on the study of cell types with dominant changes and included analyses of changes across the combination of cell types. We observed significant disease-associated gender differences in cellular processes related to synapse organization and reactive oxygen species metabolism, and identified a limited set of transcription factors, including Egr1 and Klf6, as key regulators of many of the disease-associated and gender-dependent gene expression changes in the model. Overall, our analyses revealed significant cell-type specific gene expression changes in individual genes, pathways and sub-networks, including gender-specific and gender-dimorphic changes in both upstream transcription factors and their downstream targets, in the Tg2576 AD model before the onset of overt disease. This opens a window into molecular events that could determine gender-susceptibility to AD, and uncovers tractable target candidates for potential gender-specific precision medicine for AD.

Age-related changes in the primary auditory cortex of newborn, adults and aging bottlenose dolphins (Tursiops truncatus) are located in the upper cortical layers

Introduction

The auditory system of dolphins and whales allows them to dive in dark waters, hunt for prey well below the limit of solar light absorption, and to communicate with their conspecific. These complex behaviors require specific and sufficient functional circuitry in the neocortex, and vicarious learning capacities. Dolphins are also precocious animals that can hold their breath and swim within minutes after birth. However, diving and hunting behaviors are likely not innate and need to be learned. Our hypothesis is that the organization of the auditory cortex of dolphins grows and mature not only in the early phases of life, but also in adults and aging individuals. These changes may be subtle and involve sub-populations of cells specificall linked to some circuits.

Methods

In the primary auditory cortex of 11 bottlenose dolphins belonging to three age groups (calves, adults, and old animals), neuronal cell shapes were analyzed separately and by cortical layer using custom computer vision and multivariate statistical analysis, to determine potential minute morphological differences across these age groups.

Results

The results show definite changes in interneurons, characterized by round and ellipsoid shapes predominantly located in upper cortical layers. Notably, neonates interneurons exhibited a pattern of being closer together and smaller, developing into a more dispersed and diverse set of shapes in adulthood.

Discussion

This trend persisted in older animals, suggesting a continuous development of connections throughout the life of these marine animals. Our findings further support the proposition that thalamic input reach upper layers in cetaceans, at least within a cortical area critical for their survival. Moreover, our results indicate the likelihood of changes in cell populations occurring in adult animals, prompting the need for characterization.

Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization

Background

Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer's disease (AD) through the assessment of brain atrophy.

Objective

Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker.

Methods

We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2.

Results

The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aβ42 values, demonstrated a tendency toward a more rapid increase of atrophy.

Conclusions

The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.

Alterations in Gray Matter Structural Networks in Amnestic Mild Cognitive Impairment: A Source-Based Morphometry Study

Background

Amnestic mild cognitive impairment (aMCI), considered as the prodromal stage of Alzheimer's disease, is characterized by isolated memory impairment and cerebral gray matter volume (GMV) alterations. Previous structural MRI studies in aMCI have been mainly based on univariate statistics using voxel-based morphometry.

Objective

We investigated structural network differences between aMCI patients and cognitively normal older adults by using source-based morphometry, a multivariate approach that considers the relationship between voxels of various parts of the brain.

Methods

Ninety-one aMCI patients and 80 cognitively normal controls underwent structural MRI and neuropsychological assessment. Spatially independent components (ICs) that covaried between participants were estimated and a multivariate analysis of covariance was performed with ICs as dependent variables, diagnosis as independent variable, and age, sex, education level, and site as covariates.

Results

aMCI patients exhibited reduced GMV in the precentral, temporo-cerebellar, frontal, and temporal network, and increased GMV in the left superior parietal network compared to controls (pFWER < 0.05, Holm-Bonferroni correction). Moreover, we found that diagnosis, more specifically aMCI, moderated the positive relationship between occipital network and Mini-Mental State Examination scores (pFWER < 0.05, Holm-Bonferroni correction).

Conclusions

Our results showed GMV alterations in temporo-fronto-parieto-cerebellar networks in aMCI, extending previous results obtained with univariate approaches.

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

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

The key role of depression and supramarginal gyrus in frailty: a cross-sectional study

Background

The age-related decrease in reserve and resistance to stressors is recognized as frailty, one of the most significant challenges identified in recent years. Despite a well-acknowledged association of frailty with cognitive impairment, depression, and gray matter morphology, no clear data are available regarding the nature of this relationship. This cross-sectional study aims to disentangle the role of the behavioral, neuropsychological, and neural components as predictors or moderators of frailty.

Methods

Ninety-six older adults (mean age = 75.49 ± 6.62) were consecutively enrolled and underwent a clinical and MRI (3 T) evaluation to assess frailty, physical activity, global cognitive level, depression, wellbeing, autonomy in daily living, cortical thickness, and subcortical volumes.

Results

Results showed a full mediation of depression on the link between cortical thickness and frailty, while the cognitive level showed no significant mediating role. In particular, left supramarginal thickness had a predicting role on depression, that in turn impacted frailty occurrence. Finally, handgrip weakness was an early key indicator of frailty in this study's cohort.

Conclusion

These data substantiate the role of depression in mediating the link between neural integrity of the supramarginal gyrus and frailty. In the complexity of frailty, handgrip weakness seems to be an early key indicator. These results are relevant for the design of rehabilitation interventions aimed at reversing the frail condition.

Bayesian workflow for the investigation of hierarchical classification models from tau-PET and structural MRI data across the Alzheimer's disease spectrum

Background

Alzheimer's disease (AD) diagnosis in its early stages remains difficult with current diagnostic approaches. Though tau neurofibrillary tangles (NFTs) generally follow the stereotypical pattern described by the Braak staging scheme, the network degeneration hypothesis (NDH) has suggested that NFTs spread selectively along functional networks of the brain. To evaluate this, we implemented a Bayesian workflow to develop hierarchical multinomial logistic regression models with increasing levels of complexity of the brain from tau-PET and structural MRI data to investigate whether it is beneficial to incorporate network-level information into an ROI-based predictive model for the presence/absence of AD.

Methods

This study included data from the Translational Biomarkers in Aging and Dementia (TRIAD) longitudinal cohort from McGill University's Research Centre for Studies in Aging (MCSA). Baseline and 1 year follow-up structural MRI and [18F]MK-6240 tau-PET scans were acquired for 72 cognitive normal (CN), 23 mild cognitive impairment (MCI), and 18 Alzheimer's disease dementia subjects. We constructed the four following hierarchical Bayesian models in order of increasing complexity: (Model 1) a complete-pooling model with observations, (Model 2) a partial-pooling model with observations clustered within ROIs, (Model 3) a partial-pooling model with observations clustered within functional networks, and (Model 4) a partial-pooling model with observations clustered within ROIs that are also clustered within functional brain networks. We then investigated which of the models had better predictive performance given tau-PET or structural MRI data as an input, in the form of a relative annualized rate of change.

Results

The Bayesian leave-one-out cross-validation (LOO-CV) estimate of the expected log pointwise predictive density (ELPD) results indicated that models 3 and 4 were substantially better than other models for both tau-PET and structural MRI inputs. For tau-PET data, model 3 was slightly better than 4 with an absolute difference in ELPD of 3.10 ± 1.30. For structural MRI data, model 4 was considerably better than other models with an absolute difference in ELPD of 29.83 ± 7.55 relative to model 3, the second-best model.

Conclusion

Our results suggest that representing the data generating process in terms of a hierarchical model that encompasses both ROI-level and network-level heterogeneity leads to better predictive ability for both tau-PET and structural MRI inputs over all other model iterations.

Functional gradients reveal altered functional segregation in patients with amnestic mild cognitive impairment and Alzheimer's disease

Alzheimer's disease and amnestic mild cognitive impairment are associated with disrupted functional organization in brain networks, involved with alteration of functional segregation. Connectome gradients are a new tool representing brain functional topological organization to smoothly capture the human macroscale hierarchy. Here, we examined altered topological organization in amnestic mild cognitive impairment and Alzheimer's disease by connectome gradient mapping. We further quantified functional segregation by gradient dispersion. Then, we systematically compared the alterations observed in amnestic mild cognitive impairment and Alzheimer's disease patients with those in normal controls in a two-dimensional functional gradient space from both the whole-brain level and module level. Compared with normal controls, the first gradient, which described the neocortical hierarchy from unimodal to transmodal regions, showed a more distributed and significant suppression in Alzheimer's disease than amnestic mild cognitive impairment patients. Furthermore, gradient dispersion showed significant decreases in Alzheimer's disease at both the global level and module level, whereas this alteration was limited only to limbic areas in amnestic mild cognitive impairment. Notably, we demonstrated that suppressed gradient dispersion in amnestic mild cognitive impairment and Alzheimer's disease was associated with cognitive scores. These findings provide new evidence for altered brain hierarchy in amnestic mild cognitive impairment and Alzheimer's disease, which strengthens our understanding of the progressive mechanism of cognitive decline.

Causality between inflammatory bowel disease and the cerebral cortex: insights from Mendelian randomization and integrated bioinformatics analysis

Background

Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn's disease (CD), is a chronic, progressive, and recurrent intestinal condition that poses a significant global health burden. The high prevalence of neuropsychiatric comorbidities in IBD necessitates the development of targeted management strategies.

Methods

Leveraging genetic data from genome-wide association studies and Immunochip genotype analyses of nearly 150,000 individuals, we conducted a two-sample Mendelian randomization study to elucidate the driving force of IBD, UC, and CD on cortical reshaping. Genetic variants mediating the causality were collected to disclose the biological pathways linking intestinal inflammation to brain dysfunction.

Results

Here, 115, 69, and 98 instrumental variables genetically predicted IBD, UC, and CD. We found that CD significantly decreased the surface area of the temporal pole gyrus (β = -0.946 mm2, P = 0.005, false discovery rate-P = 0.085). Additionally, we identified suggestive variations in cortical surface area and thickness induced by exposure across eight functional gyri. The top 10 variant-matched genes were STAT3, FOS, NFKB1, JAK2, STAT4, TYK2, SMAD3, IL12B, MYC, and CCL2, which are interconnected in the interaction network and play a role in inflammatory and immune processes.

Conclusion

We explore the causality between intestinal inflammation and altered cortical morphology. It is likely that neuroinflammation-induced damage, impaired neurological function, and persistent nociceptive input lead to morphological changes in the cerebral cortex, which may trigger neuropsychiatric disorders.

Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression

The biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region's contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R¯p2 for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (MAE ) and mean squared error (MSE) of BA estimates. On training data, r=0.92, MSE=70.94 years, MAE=6.57 years, and R¯2=0.81; on test data, r=0.90, MSE=81.96 years, MAE=7.00 years, and R¯2=0.79. The regions whose volumes contribute most to BA are the nucleus accumbens (R¯p2=7.27%), inferior temporal gyrus (R¯p2=4.03%), thalamus (R¯p2=3.61%), brainstem (R¯p2=3.29%), posterior lateral sulcus (R¯p2=3.22%), caudate nucleus (R¯p2=3.05%), orbital gyrus (R¯p2=2.96%), and precentral gyrus (R¯p2=2.80%). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation.

Complexity of Sex Differences and Their Impact on Alzheimer's Disease

Sex differences are present in brain morphology, sex hormones, aging processes and immune responses. These differences need to be considered for proper modelling of neurological diseases with clear sex differences. This is the case for Alzheimer's disease (AD), a fatal neurodegenerative disorder with two-thirds of cases diagnosed in women. It is becoming clear that there is a complex interplay between the immune system, sex hormones and AD. Microglia are major players in the neuroinflammatory process occurring in AD and have been shown to be directly affected by sex hormones. However, many unanswered questions remain as the importance of including both sexes in research studies has only recently started receiving attention. In this review, we provide a summary of sex differences and their implications in AD, with a focus on microglia action. Furthermore, we discuss current available study models, including emerging complex microfluidic and 3D cellular models and their usefulness for studying hormonal effects in this disease.

Estimating explainable Alzheimer's disease likelihood map via clinically-guided prototype learning

Identifying Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Using clinically-guided prototype learning, we propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs. Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold. Considering this pseudo map as an enriched reference, we employ an estimating network to approximate the AD likelihood map over a 3D sMRI scan. Additionally, we promote the explainability of such a likelihood map by revealing a comprehensible overview from clinical and morphological perspectives. During the inference, this estimated likelihood map served as a substitute for unseen sMRI scans for effectively conducting the downstream task while providing thorough explainable states.

Revealing the correlations between brain cortical characteristics and susceptibility genes for Alzheimer disease: a cross-sectional study

BACKGROUND

Alzheimer disease (AD) is a progressive neurodegenerative disease closely related to genes and characterized by the atrophy of the cerebral cortex. Correlations between imaging phenotypes and the susceptibility genes for AD, as demonstrated in the findings of genome-wide association studies (GWASs), still need to be addressed due to the complicated structure of the human cortex.

METHODS

In our study, an improved GWAS method, whole cortex characteristics GWAS (WCC-GWAS), was proposed. The WCC-GWAS uses multiple cortex characteristics of gray-matter volume (GMV), cortical thickness (CT), cortical surface area (CSA), and local gyrification index (LGI). A cohort of 496 participants was enrolled and divided into 4 groups: normal control (NC; n=122), early mild cognitive impairment (EMCI; n=196), late mild cognitive impairment (LMCI; n=62), and AD (n=116). Based on the Desikan-Killiany atlas, the brain was parcellated into 68 brain regions, and the WCC of each brain region was individually calculated. Four cortex characteristics of GMV, CT, CSA, and LGI across the 4 groups optimized with multiple comparisons and the ReliefF algorithm were taken as magnetic resonance imaging (MRI) brain phenotypes. Under the model of multiple linear additive genetic regression, the correlations between the MRI brain phenotypes and single-nucleotide polymorphisms (SNPs) were deduced.

RESULTS

The findings identified 2 prominent correlations. First, rs7309929 of neuron navigator 3 (NAV3) located on chromosome 12 correlated with the decreased GMV for the left middle temporal gyrus (P=0.0074). Second, rs11250992 of long intergenic non-protein-coding RNA 700 (LINC00700) located on chromosome 10 correlated with the decreased CT for the left supramarginal gyrus (P=0.0019).

CONCLUSIONS

The findings suggested that the correlations between phenotypes and genotypes could be effectively evaluated. The strategy of extracting MRI phenotypes as endophenotypes provided valuable indications in AD GWAS.

Comparative survey of multigraph integration methods for holistic brain connectivity mapping

One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.

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

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

Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression

Clinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we approach the problem of estimating a reference normative model across a massive population using a massive multi-center neuroimaging dataset. To this end, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) to complete the life-cycle of normative modeling. The proposed model provides the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed method will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.

Altered cortical thickness, degree centrality, and functional connectivity in middle-age type 2 diabetes mellitus

PURPOSE

This study aimed to investigate the changes in brain structure and function in middle-aged patients with type 2 diabetes mellitus (T2DM) using morphometry and blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI).

METHODS

A total of 44 middle-aged patients with T2DM and 45 matched healthy controls (HCs) were recruited. Surface-based morphometry (SBM) was used to evaluate the changes in brain morphology. Degree centrality (DC) and functional connectivity (FC) were used to evaluate the changes in brain function.

RESULTS

Compared with HCs, middle-aged patients with T2DM exhibited cortical thickness reductions in the left pars opercularis, left transverse temporal, and right superior temporal gyri. Decreased DC values were observed in the cuneus and precuneus in T2DM. Hub-based FC analysis of these regions revealed lower connectivity in the bilateral hippocampus and parahippocampal gyrus, left precuneus, as well as left frontal sup.

CONCLUSION

Cortical thickness, degree centrality, as well as functional connectivity were found to have significant changes in middle-aged patients with T2DM. Our observations provide potential evidence from neuroimaging for analysis to examine diabetes-related brain damage.

Neuroimaging Markers for Determining Former American Football Players at Risk for Alzheimer's Disease

NFL players, by virtue of their exposure to traumatic brain injury (TBI), are at higher risk of developing dementia and Alzheimer's disease (AD) than the general population. Early recognition and intervention before the onset of clinical symptoms could potentially avert/delay the long-term consequences of these diseases. Given that AD is thought to have a long pre-clinical incubation period, the aim of the current research was to determine whether former NFL players show evidence of incipient dementia in their structural imaging before diagnosis of AD. To identify neuroimaging markers of AD, against which former NFL players would be compared, we conducted a whole-brain volumetric analysis using a cohort of AD patients (ADNI clinical database) to produce a set of brain regions demonstrating sensitivity to early AD pathology (i.e., the “AD fingerprint”). A group of 46 former NFL players' brain magnetic resonance images were then interrogated using the AD fingerprint, that is, the former NFL subjects were compared volumetrically to AD patients using a T1-weighted magnetization-prepared rapid gradient echo sequence. The FreeSurfer image analysis suite (version 6.0) was used to obtain volumetric and cortical thickness data. The Automated Neuropsychological Assessment Metric-Version 4 was used to assess current cognitive functioning. A total of 55 brain regions demonstrated significant atrophy or ex vacuo dilatation bilaterally in AD patients versus controls. Of the 46 former NFL players, 41% demonstrated a greater than expected number of atrophied/dilated AD regions compared with age-matched controls, presumably reflecting AD pathology.

Relationship of sex differences in cortical thickness and memory among cognitively healthy subjects and individuals with mild cognitive impairment and Alzheimer disease

BACKGROUND

An aging society has increased rates of late onset Alzheimer disease dementia (ADD), the most common form of age-related dementia. This neurodegenerative disease disproportionately affects women.

METHODS

We use data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to examine sex differences in cortical thickness (CT) and memory performance. Analyses of covariance (ANCOVA) models were used to examine effects of sex and diagnosis (DX) on CT and verbal memory. For regions demonstrating significant interaction effects of sex and DX, we tested whether sex moderated cognition-thickness relationships. We used machine learning as a complementary method to explore multivariate CT differences between women and men.

RESULTS

Women demonstrated greater CT in many brain regions. More specifically, men showed relatively consistent CT declines in all stages, from normal control (NC) to ADD in the bilateral cingulate cortex, bilateral temporal regions, and left precuneus; women had more stable CT in these regions between NC and mild cognitive impairment (MCI) stages, but sharper declines from MCI to ADD. Similarly, for the Rey Auditory Verbal Learning Test (RAVLT), ANCOVA analyses showed that women had significantly better immediate and delayed recall scores than men, at NC and MCI stages, but greater differences, cross-sectionally, from MCI to ADD than men. We found significant sex moderation effects between RAVLT-immediate scores and CT of right isthmus-cingulate for all subjects across DX. Partial correlation analyses revealed that increased CT of right isthmus-cingulate was associated with better verbal learning in women, driven by positron emission tomography defined amyloid positive (Aβ+) subjects. Significant sex-moderation effects in cognition-thickness relationships were further found in the right middle-temporal, left precuneus, and left superior temporal regions in Aβ+ subjects. Using a machine learning approach, we investigated multivariate CT differences between women and men, showing an accuracy in classification of 75% for Aβ+ cognitively NC participants.

CONCLUSIONS

Sex differences in memory and CT can play a key role in the different vulnerability and progression of ADD in women compared to men. Machine learning indicates sex differences in CT are most relevant early in the ADD neurodegeneration.

Dementia Risk Factors Modify Hubs but Leave Other Connectivity Measures Unchanged in Asymptomatic Individuals: A Graph Theoretical Analysis

Background: Alzheimer's disease (AD) is the most common form of dementia with genetic and environmental risk contributing to its development. Graph theoretical analyses of brain networks constructed from structural and functional magnetic resonance imaging (MRI) measurements have identified connectivity changes in AD and individuals with mild cognitive impairment. However, brain connectivity in asymptomatic individuals at risk of AD remains poorly understood. Methods: We analyzed diffusion-weighted MRI data from 161 asymptomatic individuals (38-71 years) from the Cardiff Ageing and Risk of Dementia Study (CARDS). We calculated white matter tracts and constructed whole-brain, default mode network (DMN) and visual structural brain networks that incorporate multiple structural metrics as edge weights. We then calculated the relationship of three AD risk factors, namely Apolipoprotein-E ɛ4 (APOE4) genotype, family history of dementia (FH), and central obesity (Waist-Hip-Ratio [WHR]), on graph theoretical measures and hubs. Results: We observed no risk-related differences in clustering coefficients, characteristic path lengths, eccentricity, diameter, and radius across the whole-brain, DMN or visual system. However, a hub in the right paracentral lobule was present in all the high-risk groups (FH, APOE4, obese), but absent in low-risk groups (no FH, APOE4-ve, healthy WHR). Discussion: We identified no risk-related effects on graph theoretical metrics in the structural brain networks of cognitively healthy individuals. However, high risk was associated with a hub in the right paracentral lobule, a medial fronto-parietal cortical area with motor and sensory functions. This finding is consistent with accumulating evidence for right parietal cortex contributions in AD. If this phenotype is shown to predict symptom development in longitudinal studies, it could be used as an early biomarker of AD. Impact statement Alzheimer's disease (AD) is a common form of dementia that to date has no cure. Identifying early biomarkers will aid the discovery and development of treatments that may slow AD progression in the future. In this article, we report that asymptomatic individuals at heightened risk of dementia due to their family history, Apolipoprotein-E ɛ4 genotype, and central adiposity have a hub in the right paracentral lobule, which is absent in low-risk groups. If this phenotype were to predict the development of symptoms in a longitudinal study of the same cohort, it could provide an early biomarker of disease progression.

Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease

Background: Late-onset Alzheimer’s disease (LOAD) and early-onset Alzheimer’s disease (EOAD) are different subtypes of AD. This study aimed to build and validate radiomics models of the hippocampus for EOAD and young controls (YCs), LOAD and old controls (OCs), as well as EOAD and LOAD.

Methods: Thirty-six EOAD patients, 36 LOAD patients, 36 YCs, and 36 OCs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were enrolled and allocated to training and test sets of the EOAD-YC groups, LOAD-OC groups, and EOAD-LOAD groups. Independent external validation sets including 15 EOAD patients, 15 LOAD patients, 15 YCs, and 15 OCs from Shanghai Mental Health Center were constructed, respectively. Bilateral hippocampal segmentation and feature extraction were performed for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. Support vector machine (SVM) models were constructed based on the identified features to distinguish EOAD from YC subjects, LOAD from OC subjects, and EOAD from LOAD subjects. The areas under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the models.

Results: Three, three, and four features were selected for EOAD and YC subjects, LOAD and OC subjects, and EOAD and LOAD subjects, respectively. The AUC and accuracy of the SVM model were 0.90 and 0.77 in the test set and 0.91 and 0.87 in the validation set for EOAD and YC subjects, respectively; for LOAD and OC subjects, the AUC and accuracy were 0.94 and 0.86 in the test set and 0.92 and 0.78 in the validation set, respectively. For the SVM model of EOAD and LOAD subjects, the AUC was 0.87 and the accuracy was 0.79 in the test set; additionally, the AUC was 0.86 and the accuracy was 0.77 in the validation set.

Conclusion: The findings of this study provide insights into the potential of hippocampal radiomic features as biomarkers to diagnose EOAD and LOAD. This study is the first to show that SVM classification analysis based on hippocampal radiomic features is a valuable method for clinical applications in EOAD.

Therapeutic Benefits of Short-Arm Human Centrifugation in Multiple Sclerosis-A New Approach

Short-arm human centrifugation (SAHC) is proposed as a robust countermeasure to treat deconditioning and prevent progressive disability in a case of secondary progressive multiple sclerosis. Based on long-term physiological knowledge derived from space medicine and missions, artificial gravity training seems to be a promising physical rehabilitation approach toward the prevention of musculoskeletal decrement due to confinement and inactivity. So, the present study proposes a novel infrastructure based on SAHC to investigate the hypothesis that artificial gravity ameliorates the degree of disability. The patient was submitted to a 4-week training programme including three weekly sessions of 30 min of intermittent centrifugation at 1.5–2 g. During sessions, cardiovascular, muscle oxygen saturation (SmO₂) and electroencephalographic (EEG) responses were monitored, whereas neurological and physical performance tests were carried out before and after the intervention. Cardiovascular parameters improved in a way reminiscent of adaptations to aerobic exercise. SmO₂ decreased during sessions concomitant with increased g load, and, as training progressed, SmO₂ of the suffering limb dropped, both effects suggesting increased oxygen use, similar to that seen during hard exercise. EEG showed increased slow and decreased fast brain waves, with brain reorganization/plasticity evidenced through functional connectivity alterations. Multiple-sclerosis-related disability and balance capacity also improved. Overall, this study provides novel evidence supporting SAHC as a promising therapeutic strategy in multiple sclerosis, based on mechanical loading, thereby setting the basis for future randomized controlled trials.

INCloud: integrated neuroimaging cloud for data collection, management, analysis and clinical translations

Background

Neuroimaging techniques provide rich and accurate measures of brain structure and function, and have become one of the most popular methods in mental health and neuroscience research. Rapidly growing neuroimaging research generates massive amounts of data, bringing new challenges in data collection, large-scale data management, efficient computing requirements and data mining and analyses.

Aims

To tackle the challenges and promote the application of neuroimaging technology in clinical practice, we developed an integrated neuroimaging cloud (INCloud). INCloud provides a full-stack solution for the entire process of large-scale neuroimaging data collection, management, analysis and clinical applications.

Methods

INCloud consists of data acquisition systems, a data warehouse, automatic multimodal image quality check and processing systems, a brain feature library, a high-performance computing cluster and computer-aided diagnosis systems (CADS) for mental disorders. A unique design of INCloud is the brain feature library that converts the unit of data management from image to image features such as hippocampal volume. Connecting the CADS to the scientific database, INCloud allows the accumulation of scientific data to continuously improve the accuracy of objective diagnosis of mental disorders.

Results

Users can manage and analyze neuroimaging data on INCloud, without the need to download them to the local device. INCloud users can query, manage, analyze and share image features based on customized criteria. Several examples of 'mega-analyses' based on the brain feature library are shown.

Conclusions

Compared with traditional neuroimaging acquisition and analysis workflow, INCloud features safe and convenient data management and sharing, reduced technical requirements for researchers, high-efficiency computing and data mining, and straightforward translations to clinical service. The design and implementation of the system are also applicable to imaging research platforms in other fields.

A Comparative Analysis of MRI Automated Segmentation of Subcortical Brain Volumes in a Large Dataset of Elderly Subjects

In this study, we perform a comparative analysis of automated image segmentation of subcortical structures in the elderly brain. Manual segmentation is very time-consuming and automated methods are gaining importance as a clinical tool for diagnosis. The two most commonly used software libraries for brain segmentation -FreeSurfer and FSL- are put to work in a large dataset of 4,028 magnetic resonance imaging (MRI) scans collected for this study. We find a lack of linear correlation between the segmentation volume estimates obtained from FreeSurfer and FSL. On the other hand, FreeSurfer volume estimates tend to be larger thanFSL estimates of the areas putamen, thalamus, amygdala, caudate, pallidum, hippocampus, and accumbens. The characterization of the performance of brain segmentation algorithms in large datasets as the one presented here is a necessary step towards partially or fully automated end-to-end neuroimaging workflow both in clinical and research settings.

Neuroimaging Biomarkers Predicting the Efficacy of Multimodal Rehabilitative Intervention in the Alzheimer's Dementia Continuum Pathology

In this work we aimed to identify neural predictors of the efficacy of multimodal rehabilitative interventions in AD-continuum patients in the attempt to identify ideal candidates to improve the treatment outcome. Subjects in the AD continuum who participated in a multimodal rehabilitative treatment were included in the analysis [n = 82, 38 Males, mean age = 76 ± 5.30, mean education years = 9.09 ± 3.81, Mini Mental State Examination (MMSE) mean score = 23.31 ± 3.81]. All subjects underwent an MRI acquisition (1.5T) at baseline (T0) and a neuropsychological evaluation before (T0) and after intervention (T1). All subjects underwent an intensive multimodal cognitive rehabilitation (8–10 weeks). The MMSE and Neuropsychiatric Inventory (NPI) scores were considered as the main cognitive and behavioral outcome measures, and Delta change scores (T1–T0) were categorized in Improved (ΔMMSE > 0; ΔNPI < 0) and Not Improved (ΔMMSE ≤ 0; ΔNPI ≥ 0). Logistic Regression (LR) and Random Forest classification models were performed including neural markers (Medial Temporal Brain; Posterior Brain (PB); Frontal Brain (FB), Subcortical Brain indexes), neuropsychological (MMSE, NPI, verbal fluencies), and demographical variables (sex, age, education) at baseline. More than 50% of patients showed a positive effect of the treatment (ΔMMSE > 0: 51%, ΔNPI < 0: 52%). LR model on ΔMMSE (Improved vs. Not Improved) indicate a predictive role for MMSE score (p = 0.003) and PB index (p = 0.005), especially the right PB (p = 0.002) at baseline. The Random Forest analysis correctly classified 77% of cognitively improved and not improved AD patients. Concerning the NPI, LR model on ΔNPI (Improved vs. Not Improved) showed a predictive role of sex (p = 0.002), NPI (p = 0.005), PB index (p = 0.006), and FB index (p = 0.039) at baseline. The Random Forest reported a classification accuracy of 86%. Our data indicate that cognitive and behavioral status alone are not sufficient to identify best responders to a multidomain rehabilitation treatment. Increased neural reserve, especially in the parietal areas, is also relevant for the compensatory mechanisms activated by rehabilitative treatment. These data are relevant to support clinical decision by identifying target patients with high probability of success after rehabilitative programs on cognitive and behavioral functioning.

Effects of Meditation on Structural Changes of the Brain in Patients With Mild Cognitive Impairment or Alzheimer's Disease Dementia

Previous cross-sectional studies reported positive effects of meditation on the brain areas related to attention and executive function in the healthy elderly population. Effects of long-term regular meditation in persons with mild cognitive impairment (MCI) and Alzheimer's disease dementia (AD) have rarely been studied. In this study, we explored changes in cortical thickness and gray matter volume in meditation-naïve persons with MCI or mild AD after long-term meditation intervention. MCI or mild AD patients underwent detailed clinical and neuropsychological assessment and were assigned into meditation or non-meditation groups. High resolution T1-weighted magnetic resonance images (MRI) were acquired at baseline and after 6 months. Longitudinal symmetrized percentage changes (SPC) in cortical thickness and gray matter volume were estimated. Left caudal middle frontal, left rostral middle frontal, left superior parietal, right lateral orbitofrontal, and right superior frontal cortices showed changes in both cortical thickness and gray matter volume; the left paracentral cortex showed changes in cortical thickness; the left lateral occipital, left superior frontal, left banks of the superior temporal sulcus (bankssts), and left medial orbitofrontal cortices showed changes in gray matter volume. All these areas exhibited significantly higher SPC values in meditators as compared to non-meditators. Conversely, the left lateral occipital, and right posterior cingulate cortices showed significantly lower SPC values for cortical thickness in the meditators. In hippocampal subfields analysis, we observed significantly higher SPC in gray matter volume of the left CA1, molecular layer HP, and CA3 with a trend for increased gray matter volume in most other areas. No significant changes were found for the hippocampal subfields in the right hemisphere. Analysis of the subcortical structures revealed significantly increased volume in the right thalamus in the meditation group. The results of the study point out that long-term meditation practice in persons with MCI or mild AD leads to salutary changes in cortical thickness and gray matter volumes. Most of these changes were observed in the brain areas related to executive control and memory that are prominently at risk in neurodegenerative diseases.

Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data

Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://github.com/JaesikKim/temporal-GNN.