Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review

Alterations in dynamic regional homogeneity within default mode network in patients with thyroid-associated ophthalmopathy

Thyroid-associated ophthalmopathy (TAO) is a significant autoimmune eye disease known for causing exophthalmos and substantial optic nerve damage. Prior investigations have solely focused on static functional MRI (fMRI) scans of the brain in TAO patients, neglecting the assessment of temporal variations in local brain activity. This study aimed to characterize alterations in dynamic regional homogeneity (dReHo) in TAO patients and differentiate between TAO patients and healthy controls using support vector machine (SVM) classification. Thirty-two patients with TAO and 32 healthy controls underwent resting-state fMRI scans. We calculated dReHo using sliding-window methods to evaluate changes in regional brain activity and compared these findings between the two groups. Subsequently, we employed SVM, a machine learning algorithm, to investigate the potential use of dReHo maps as diagnostic markers for TAO. Compared to healthy controls, individuals with active TAO demonstrated significantly higher dReHo values in the right angular gyrus, left precuneus, right inferior parietal as well as the left superior parietal gyrus. The SVM model demonstrated an accuracy ranging from 65.62 to 68.75% in distinguishing between TAO patients and healthy controls based on dReHo variability in these identified brain regions, with an area under the curve of 0.70 to 0.76. TAO patients showed increased dReHo in default mode network-related brain regions. The accuracy of classifying TAO patients and healthy controls based on dReHo was notably high. These results offer new insights for investigating the pathogenesis and clinical diagnostic classification of individuals with TAO.

Nodal degree centrality in the default mode-like network of the TgF344-AD Alzheimer's disease rat model as a measure of early network alterations

This study investigates brain network alterations in the default mode-like network (DMLN) at early stages of disease progression in a rat model of Alzheimer's disease (AD) with application in the development of early diagnostic biomarkers of AD in translational studies. Thirteen male TgF344-AD (TG) rats, and eleven male wild-types (WT) littermates underwent longitudinal resting-state fMRI at the age of 4 and 6 months (pre and early-plaque stages of AD). Alterations in connectivity within DMLN were characterized by calculating the nodal degree (ND), a graph theoretical measure of centrality. The ND values of the left CA2 subregion of the hippocampus was found to be significantly lower in the 4-month-old TG cohort compared to the age-matched WT littermates. Moreover, a lower ND value (hypo-connectivity) was observed in the right prelimbic cortex (prL) and basal forebrain in the 6-month-old TG cohort, compared to the same age WT cohort. Indeed, the ND pattern in the DMLN in both TG and WT cohorts showed significant differences across the two time points that represent pre-plaque and early plaque stages of disease progression. Our findings indicate that lower nodal degree (hypo-connectivity) in the left CA2 in the pre-plaque stage of AD and hypo-connectivity between the basal forebrain and the DMLN regions in the early-plaque stage demonstrated differences in comparison to healthy controls. These results suggest that a graph-theoretical measure such as the nodal degree, can characterize brain networks and improve our insights into the mechanisms underlying Alzheimer's disease.

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

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

Systematically altered connectome gradient in benign childhood epilepsy with centrotemporal spikes: Potential effect on cognitive function

OBJECTIVE

Benign childhood epilepsy with centrotemporal spikes (BECTS) affects brain network hierarchy and cognitive function; however, itremainsunclearhowhierarchical changeaffectscognition in patients with BECTS. A major aim of this study was to examine changes in the macro-network function hierarchy in BECTS and its potential contribution to cognitive function.

METHODS

Overall, the study included 50 children with BECTS and 69 healthy controls. Connectome gradient analysis was used to determine the brain network hierarchy of each group. By comparing gradient scores at each voxel level and network between groups, we assessed changes in whole-brain voxel-level and network hierarchy. Functional connectivity was used to detect the functional reorganization of epilepsy caused by these abnormal brain regions based on these aberrant gradients. Lastly, we explored the relationships between the change gradient and functional connectivity values and clinical variables and further predicted the cognitive function associated with BECTS gradient changes.

RESULTS

In children with BECTS, the gradient was extended at different network and voxel levels. The gradient scores frontoparietal network was increased in the principal gradient of patients with BECTS. The left precentral gyrus (PCG) and right angular gyrus gradient scores were significantly increased in the principal gradient of children with BECTS. Moreover, in regions of the brain with abnormal principal gradients, functional connectivity was disrupted. The left PCG gradient score of children with BECTS was correlated with the verbal intelligence quotient (VIQ), and the disruption of functional connectivity in brain regions with abnormal principal gradients was closely related to cognitive function. VIQ was significantly predicted by the principal gradient map of patients.

SIGNIFICANCE

The results indicate connectome gradient disruption in children with BECTS and its relationship to cognitive function, thereby increasing our understanding of the functional connectome hierarchy and providing potential biomarkers for cognitive function of children with BECTS.

Detecting Alzheimer's Disease Stages and Frontotemporal Dementia in Time Courses of Resting-State fMRI Data Using a Machine Learning Approach

Early, accurate diagnosis of neurodegenerative dementia subtypes such as Alzheimer's disease (AD) and frontotemporal dementia (FTD) is crucial for the effectiveness of their treatments. However, distinguishing these conditions becomes challenging when symptoms overlap or the conditions present atypically. Resting-state fMRI (rs-fMRI) studies have demonstrated condition-specific alterations in AD, FTD, and mild cognitive impairment (MCI) compared to healthy controls (HC). Here, we used machine learning to build a diagnostic classification model based on these alterations. We curated all rs-fMRIs and their corresponding clinical information from the ADNI and FTLDNI databases. Imaging data underwent preprocessing, time course extraction, and feature extraction in preparation for the analyses. The imaging features data and clinical variables were fed into gradient-boosted decision trees with fivefold nested cross-validation to build models that classified four groups: AD, FTD, HC, and MCI. The mean and 95% confidence intervals for model performance metrics were calculated using the unseen test sets in the cross-validation rounds. The model built using only imaging features achieved 74.4% mean balanced accuracy, 0.94 mean macro-averaged AUC, and 0.73 mean macro-averaged F1 score. It accurately classified FTD (F1 = 0.99), HC (F1 = 0.99), and MCI (F1 = 0.86) fMRIs but mostly misclassified AD scans as MCI (F1 = 0.08). Adding clinical variables to model inputs raised balanced accuracy to 91.1%, macro-averaged AUC to 0.99, macro-averaged F1 score to 0.92, and improved AD classification accuracy (F1 = 0.74). In conclusion, a multimodal model based on rs-fMRI and clinical data accurately differentiates AD-MCI vs. FTD vs. HC.

Decoupling of gray and white matter functional networks in cognitive impairment induced by occupational aluminum exposure

Aluminum (Al) is a low-toxic, accumulative substance with neurotoxicity properties that adversely affect human cognitive function. This study aimed to investigate the neurobiological mechanisms underlying cognitive impairment resulting from occupational Al exposure. Resting-state functional magnetic resonance imaging was conducted on 54 individuals with over 10 years of Al exposure. Al levels were measured, and cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). Subsequently, the K-means clustering algorithm was employed to identify functional gray matter (GM) and white matter (WM) networks. Two-sample t-tests were conducted between the cognition impairment group and the control group. Al exhibited a negative correlation with MoCA scores. Participants with cognitive impairment demonstrated reduced functional connectivity (FC) between the middle cingulum network (WM1) and anterior cingulum network (WM2), as well as between the executive control network (WM6) and limbic network (WM10). Notably, decreased FCs were observed between the executive control network (GM5) and WM1, WM4, WM6, and WM10. Additionally, the FC of GM5-GM4 and WM1-WM2 negatively correlated with Trail Making Test Part A (TMT-A) scores. Prolonged Al accumulation detrimentally affects cognition, primarily attributable to executive control and limbic network disruptions.

U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

Introduction

The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data.

Methods

In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics.

Results

We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement.

Conclusion

Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.

The enhanced connectivity between the frontoparietal, somatomotor network and thalamus as the most significant network changes of chronic low back pain

The prolonged duration of chronic low back pain (cLBP) inevitably leads to changes in the cognitive, attentional, sensory and emotional processing brain regions. Currently, it remains unclear how these alterations are manifested in the interplay between brain functional and structural networks. This study aimed to predict the Oswestry Disability Index (ODI) in cLBP patients using multimodal brain magnetic resonance imaging (MRI) data and identified the most significant features within the multimodal networks to aid in distinguishing patients from healthy controls (HCs). We constructed dynamic functional connectivity (dFC) and structural connectivity (SC) networks for all participants (n = 112) and employed the Connectome-based Predictive Modeling (CPM) approach to predict ODI scores, utilizing various feature selection thresholds to identify the most significant network change features in dFC and SC outcomes. Subsequently, we utilized these significant features for optimal classifier selection and the integration of multimodal features. The results revealed enhanced connectivity among the frontoparietal network (FPN), somatomotor network (SMN) and thalamus in cLBP patients compared to HCs. The thalamus transmits pain-related sensations and emotions to the cortical areas through the dorsolateral prefrontal cortex (dlPFC) and primary somatosensory cortex (SI), leading to alterations in whole-brain network functionality and structure. Regarding the model selection for the classifier, we found that Support Vector Machine (SVM) best fit these significant network features. The combined model based on dFC and SC features significantly improved classification performance between cLBP patients and HCs (AUC=0.9772). Finally, the results from an external validation set support our hypotheses and provide insights into the potential applicability of the model in real-world scenarios. Our discovery of enhanced connectivity between the thalamus and both the dlPFC (FPN) and SI (SMN) provides a valuable supplement to prior research on cLBP.

Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis

Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.

Longitudinal MRI analysis using a hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction

Alzheimer's disease is a progressive neurological disorder characterized by brain atrophy and cell death, leading to cognitive decline and impaired functioning. Previous research has primarily focused on using cross-sectional data for Alzheimer's disease identification, but analyzing longitudinal sequential MR images is crucial for improved diagnostic accuracy and understanding disease progression. However, existing deep learning models face challenges in learning spatial and temporal features from such data. To address these challenges, this study presents a novel hybrid DenseNet-BiLSTM method for Alzheimer's disease prediction using longitudinal MRI analysis. The proposed framework combines Convolutional DenseNet for spatial information extraction and joined BiLSTM layers for capturing temporal characteristics and relationships between longitudinal images at different time points. This approach overcomes issues like overfitting, vanishing gradients, and incomplete patient data. We evaluated the model on 684 longitudinal MRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including normal controls, individuals with mild cognitive impairment, and Alzheimer's disease patients. The results demonstrate high classification accuracy, with 95.28% for AD/CN, 88.19% for NC/MCI, 83.51% for sMCI/pMCI, and 92.14% for MCI/AD. These findings highlight the substantial improvement in Alzheimer's disease diagnosis achieved through the utilization of longitudinal MRI images. The contributions of this study lie in both the deep learning and medical domains. In the deep learning domain, our hybrid framework effectively learns spatial and temporal features from longitudinal data, addressing the challenges associated with multi-dimensional and sequential time series data. In the medical domain, our study emphasizes the importance of analyzing baseline and longitudinal MR images for accurate diagnosis and understanding disease progression.

Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disease resulting in memory loss and cognitive decline. Synaptic dysfunction is an early hallmark of the disease whose effects on whole-brain functional architecture can be identified using resting-state functional MRI (rsfMRI). Insights into mechanisms of early, whole-brain network alterations can help our understanding of the functional impact of AD's pathophysiology.

Methods

Here, we obtained rsfMRI data in the TgF344-AD rat model at the pre- and early-plaque stages. This model recapitulates the major pathological and behavioral hallmarks of AD. We used co-activation pattern (CAP) analysis to investigate if and how the dynamic organization of intrinsic brain functional networks states, undetectable by earlier methods, is altered at these early stages.

Results

We identified and characterized six intrinsic brain states as CAPs, their spatial and temporal features, and the transitions between the different states. At the pre-plaque stage, the TgF344-AD rats showed reduced co-activation of hub regions in the CAPs corresponding to the default mode-like and lateral cortical network. Default mode-like network activity segregated into two distinct brain states, with one state characterized by high co-activation of the basal forebrain. This basal forebrain co-activation was reduced in TgF344-AD animals mainly at the pre-plaque stage. Brain state transition probabilities were altered at the pre-plaque stage between states involving the default mode-like network, lateral cortical network, and basal forebrain regions. Additionally, while the directionality preference in the network-state transitions observed in the wild-type animals at the pre-plaque stage had diminished at the early-plaque stage, TgF344-AD animals continued to show directionality preference at both stages.

Discussion

Our study enhances the understanding of intrinsic brain state dynamics and how they are impacted at the early stages of AD, providing a nuanced characterization of the early, functional impact of the disease's neurodegenerative process.

Age Prediction Using Resting-State Functional MRI

The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain's health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.

Hub overload and failure as a final common pathway in neurological brain network disorders

Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.

Whole-Brain Dynamics Disruptions in the Progression of Alzheimer's Disease: Understanding the Influence of Amyloid-Beta and Tau

INTRODUCTION

Alzheimer's disease (AD) affects brain structure and function along its evolution, but brain network dynamic changes remain largely unknown.

METHODS

To understand how AD shapes brain activity, we investigated the spatiotemporal dynamics and resting state functional networks using the intrinsic ignition framework, which characterizes how an area transmits neuronal activity to others, resulting in different degrees of integration. Healthy participants, MCI, and AD patients were scanned using resting state fMRI. Mixed effects models were used to assess the impact of ABeta and tau, at the regional and whole-brain levels.

RESULTS

Dynamic complexity is progressively reduced, with Healthy participants showing higher metastability (i.e., a more complex dynamical regime over time) than observed in the other stages, while AD subjects showed the lowest.

DISCUSSION

Our study provides further insight into how AD modulates brain network dynamics along its evolution, progressively disrupting the whole-brain and resting state network dynamics.

Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine

BACKGROUND

Quantitative determination of the correlation between cognitive ability and functional biomarkers in the older brain is essential. To identify biomarkers associated with cognitive performance in the older, this study combined an index model specific for resting-state functional connectivity (FC) with a supervised machine learning method.

METHODS

Performance scores on conventional cognitive test scores and resting-state functional MRI data were obtained for 98 healthy older individuals and 90 healthy youth from two public databases. Based on the test scores, the older cohort was categorized into two groups: excellent and poor. A resting-state FC scores model (rs-FCSM) was constructed for each older individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using this model. To suggest the effectiveness of constructed model, the scores of these brain areas were used as feature matrix inputs for training an extreme learning machine. classification accuracy (CA) was then tested in separate groups and validated by N-fold cross-validation.

RESULTS

This learning study could effectively classify the cognitive status of healthy older individuals according to the model scores of frontal lobe, temporal lobe, and parietal lobe with a mean accuracy of 86.67%, which is higher than that achieved using conventional correlation analysis.

CONCLUSION

This classification study of the rs-FCSM may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms.

European intersocietal recommendations for the biomarker-based diagnosis of neurocognitive disorders

The recent commercialisation of the first disease-modifying drugs for Alzheimer's disease emphasises the need for consensus recommendations on the rational use of biomarkers to diagnose people with suspected neurocognitive disorders in memory clinics. Most available recommendations and guidelines are either disease-centred or biomarker-centred. A European multidisciplinary taskforce consisting of 22 experts from 11 European scientific societies set out to define the first patient-centred diagnostic workflow that aims to prioritise testing for available biomarkers in individuals attending memory clinics. After an extensive literature review, we used a Delphi consensus procedure to identify 11 clinical syndromes, based on clinical history and examination, neuropsychology, blood tests, structural imaging, and, in some cases, EEG. We recommend first-line and, if needed, second-line testing for biomarkers according to the patient's clinical profile and the results of previous biomarker findings. This diagnostic workflow will promote consistency in the diagnosis of neurocognitive disorders across European countries.

Normative Temporal Dynamics of Resting EEG Microstates

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

Functional connectivity-based classification of rapid eye movement sleep behavior disorder

BACKGROUND

Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose.

METHODS

We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs.

RESULTS

Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients.

CONCLUSIONS

Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD.

Positive affect disrupts neurodegeneration effects on cognitive training plasticity in older adults

Cognitive training for older adults varies in efficacy, but it is unclear why some older adults benefit more than others. Positive affective experience (PAE), referring to high positive valence and/or stable arousal states across everyday scenarios, and associated functional networks can protect plasticity mechanisms against Alzheimer's disease neurodegeneration, which may contribute to training outcome variability. The objective of this study is to investigate whether PAE explains variability in cognitive training outcomes by disrupting the adverse effect of neurodegeneration on plasticity. The study's design is a secondary analysis of a randomized control trial of cognitive training with concurrent real or sham brain stimulation (39 older adults with mild cognitive impairment; mean age, 71). Moderation analyses, with change in episodic memory or executive function as the outcome, PAE or baseline resting-state connectivity as the moderator and baseline neurodegeneration as the predictor are the methods used in the study. The result of the study is that PAE stability and baseline default mode network (DMN) connectivity disrupted the effect of neurodegeneration on plasticity in executive function but not episodic memory. The study concludes that PAE stability and degree of DMN integrity both explained cognitive training outcome variability, by reducing the adverse effect of neurodegeneration on cognitive plasticity. We highlight the need to account for PAE, brain aging factors and their interactions with plasticity in cognitive training.

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.

Dynamic reconfiguration of brain coactivation states that underlying working memory correlates with cognitive decline in clinically unimpaired older adults

Impairments in working memory (WM) are evident in both clinically diagnosed patients with mild cognitive decline and older adults at risk, as indicated by lower scores on neuropsychological tests. Examining the WM-related neural signatures in at-risk older adults becomes essential for timely intervention. WM functioning relies on dynamic brain activities, particularly within the frontoparietal system. However, it remains unclear whether the cognitive decline would be reflected in the decreased dynamic reconfiguration of brain coactivation states during WM tasks. We enrolled 47 older adults and assessed their cognitive function using the Montreal Cognitive Assessment. The temporal dynamics of brain coactivations during a WM task were investigated through graph-based time-frame modularity analysis. Four primary recurring states emerged: two task-positive states with positive activity in the frontoparietal system (dorsal attention and central executive); two task-negative states with positive activity in the default mode network accompanied by negative activity in the frontoparietal networks. Heightened WM load was associated with increased flexibility of the frontoparietal networks, but the cognitive decline was correlated with reduced capacity for neuroplastic changes in response to increased task demands. These findings advance our understanding of aberrant brain reconfiguration linked to cognitive decline, potentially aiding early identification of at-risk individuals.

Analysis of the relationship between mild cognitive impairment and serum klotho protein and insulin-like growth factor-1 in the elderly

BACKGROUND

Mild cognitive impairment (MCI) is a mild memory or cognitive impairment.

OBJECTIVE

To explore the relationship between serum klotho (K1) protein and insulin-like growth factor-1 and mild cognitive impairment in the elderly in order to provide accurate and appropriate indicators for clinical diagnosis and treatment of MCI.

METHODS

This randomized stratified study adopted a multistage cluster sampling method. 161 elderly patients with mild cognitive impairment were included as the MCI group, and 161 healthy people matched with the MCI group in gender, age and education were selected as the control group.

RESULTS

The levels of serum K1 protein and insulin-like growth factor-1 in the MCI group were lower than those in the control group (P< 0.05). Both IGF-1 and K1 had predictive value for MCI (P< 0.05). The area under the curve (AUC) of IGF-1 for predicting MCI was 0.859 (95% CI: 0.790∼0.929), and the AUC of K1 for predicting MCI was 0.793 (95% CI: 0.694∼0.892). The value of joint prediction of the two indicators was the highest, with an AUC of 0.939 (95% CI: 0.896-0.993).

CONCLUSION

High serum K1 and insulin-like growth factor-1 are the protective factors of cognitive impairment in MCI patients. Both IGF-1 and serum K1 proteins have predictive value for MCI, and the combination of the two indicators has the highest predictive value.

Functional connectome hierarchy dysfunction in Alzheimer's disease and its relationship with cognition and gene expression profiling

Numerous researches have shown that the human brain organizes as a continuum axis crossing from sensory motor to transmodal cortex. Functional network alterations were commonly found in Alzheimer's disease (AD). Whether the hierarchy of AD brain networks has changed and how these changes related to gene expression profiling and cognition is unclear. Using resting-state functional magnetic resonance imaging data from 233 subjects (185 AD patients and 48 healthy controls), we studied the changes in the functional network gradients in AD. Moreover, we investigated the relationships between gradient alterations and cognition, and gene expression profiling, respectively. We found that the second gradient organizes as a continuum axis crossing from the sensory motor to the transmodal cortex. Compared to the healthy controls, the secondary gradient scores of the visual and somatomotor network (SOM) increased significantly in AD, and the secondary gradient scores of default mode and frontoparietal network decreased significantly in AD. The secondary gradient scores of SOM and salience network (SAL) significantly positively correlated with memory function in AD. The secondary gradient in SAL also significantly positively correlated with language function. The AD-related second gradient alterations were spatially associated with the gene expression and the relevant genes enriched in neurobiology-related pathways, specially expressed in various tissues, cell types, and developmental stages. These findings suggested the changes in the functional network gradients in AD and deepened our understanding of the correlation between macroscopic gradient structure and microscopic gene expression profiling in AD.

Transcranial direct current stimulation of the right temporoparietal junction facilitates hippocampal spatial learning in Alzheimer's disease and mild cognitive impairment

OBJECTIVE

Spatial memory deficits are an early symptom in Alzheimer's disease (AD), reflecting the neurodegenerative processes in the neuronal navigation network such as in hippocampal and parietal cortical areas. As no effective treatment options are available, neuromodulatory interventions are increasingly evaluated. Against this backdrop, we investigated the neuromodulatory effect of anodal transcranial direct current stimulation (tDCS) on hippocampal place learning in patients with AD or mild cognitive impairment (MCI).

METHODS

In this randomized, double-blind, sham-controlled study with a cross-over design anodal tDCS of the right temporoparietal junction (2 mA for 20 min) was applied to 20 patients diagnosed with AD or MCI and in 22 healthy controls while they performed a virtual navigation paradigm testing hippocampal place learning.

RESULTS

We show an improved recall performance of hippocampal place learning after anodal tDCS in the patient group compared to sham stimulation but not in the control group.

CONCLUSIONS

These results suggest that tDCS can facilitate spatial memory consolidation via stimulating the parietal-hippocampal navigation network in AD and MCI patients.

SIGNIFICANCE

Our findings suggest that tDCS of the temporoparietal junction may restore spatial navigation and memory deficits in patients with AD and MCI.

Systematic reviews of machine learning in healthcare: a literature review

INTRODUCTION

The increasing availability of data and computing power has made machine learning (ML) a viable approach to faster, more efficient healthcare delivery.

METHODS

A systematic literature review (SLR) of published SLRs evaluating ML applications in healthcare settings published between1 January 2010 and 27 March 2023 was conducted.

RESULTS

In total 220 SLRs covering 10,462 ML algorithms were reviewed. The main application of AI in medicine related to the clinical prediction and disease prognosis in oncology and neurology with the use of imaging data. Accuracy, specificity, and sensitivity were provided in 56%, 28%, and 25% SLRs respectively. Internal and external validation was reported in 53% and less than 1% of the cases respectively. The most common modeling approach was neural networks (2,454 ML algorithms), followed by support vector machine and random forest/decision trees (1,578 and 1,522 ML algorithms, respectively).

EXPERT OPINION

The review indicated considerable reporting gaps in terms of the ML's performance, both internal and external validation. Greater accessibility to healthcare data for developers can ensure the faster adoption of ML algorithms into clinical practice.

Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden

Alzheimer's disease (AD) is the most common type of dementia, characterized by early memory impairments and gradual worsening of daily functions. AD-related pathology, such as amyloid-beta (Aβ) plaques, begins to accumulate many years before the onset of clinical symptoms. Predicting risk for AD via related pathology is critical as the preclinical stage could serve as a therapeutic time window, allowing for early management of the disease and reducing health and economic costs. Current methods for detecting AD pathology, however, are often expensive and invasive, limiting wide and easy access to a clinical setting. A non-invasive, cost-efficient platform, such as computerized cognitive tests, could be potentially useful to identify at-risk individuals as early as possible. In this study, we examined the diagnostic value of an episodic memory task, the mnemonic discrimination task (MDT), for predicting risk of cognitive impairment or Aβ burden. We constructed a random forest classification algorithm, utilizing MDT performance metrics and various neuropsychological test scores as input features, and assessed model performance using area under the curve (AUC). Models based on MDT performance metrics achieved classification results with an AUC of 0.83 for cognitive status and an AUC of 0.64 for Aβ status. Our findings suggest that mnemonic discrimination function may be a useful predictor of progression to prodromal AD or increased risk of Aβ load, which could be a cost-efficient, noninvasive cognitive testing solution for potentially wide-scale assessment of AD pathological and cognitive risk.

Neurophysiological markers in community-dwelling older adults with mild cognitive impairment: an EEG study

BACKGROUND

Neurodegeneration and structural changes in the brain due to amyloid deposition have been observed even in individuals with mild cognitive impairment (MCI). EEG measurement is considered an effective tool because it is noninvasive, has few restrictions on the measurement environment, and is simple and easy to use. In this study, we investigated the neurophysiological characteristics of community-dwelling older adults with MCI using EEG.

METHODS

Demographic characteristics, cognitive function, physical function, resting-state MRI and electroencephalogram (rs-EEG), event-related potentials (ERPs) during Simon tasks, and task proportion of correct responses and reaction times (RTs) were obtained from 402 healthy controls (HC) and 47 MCI participants. We introduced exact low-resolution brain electromagnetic tomography-independent component analysis (eLORETA-ICA) to assess the rs-EEG network in community-dwelling older adults with MCI.

RESULTS

A lower proportion of correct responses to the Simon task and slower RTs were observed in the MCI group (p < 0.01). Despite no difference in brain volume between the HC and MCI groups, significant decreases in dorsal attention network (DAN) activity (p < 0.05) and N2 amplitude of ERP (p < 0.001) were observed in the MCI group. Moreover, DAN activity demonstrated a correlation with education (Rs = 0.32, p = 0.027), global cognitive function (Rs = 0.32, p = 0.030), and processing speed (Rs = 0.37, p = 0.010) in the MCI group. The discrimination accuracy for MCI with the addition of the eLORETA-ICA network ranged from 0.7817 to 0.7929, and the area under the curve ranged from 0.8492 to 0.8495.

CONCLUSIONS

The eLORETA-ICA approach of rs-EEG using noninvasive and relatively inexpensive EEG demonstrates specific changes in elders with MCI. It may provide a simple and valid assessment method with few restrictions on the measurement environment and may be useful for early detection of MCI in community-dwelling older adults.

Comparison of the diagnostic accuracy of resting-state fMRI driven machine learning algorithms in the detection of mild cognitive impairment

Mild cognitive impairment (MCI) is a potential therapeutic window in the prevention of dementia; however, automated detection of early cognitive deterioration is an unresolved issue. The aim of our study was to compare various classification approaches to differentiate MCI patients from healthy controls, based on rs-fMRI data, using machine learning (ML) algorithms. Own dataset (from two centers) and ADNI database were used during the analysis. Three fMRI parameters were applied in five feature selection algorithms: local correlation, intrinsic connectivity, and fractional amplitude of low frequency fluctuations. Support vector machine (SVM) and random forest (RF) methods were applied for classification. We achieved a relatively wide range of 78-87% accuracy for the various feature selection methods with SVM combining the three rs-fMRI parameters. In the ADNI datasets case we can also see even 90% accuracy scores. RF provided a more harmonized result among the feature selection algorithms in both datasets with 80-84% accuracy for our local and 74-82% for the ADNI database. Despite some lower performance metrics of some algorithms, most of the results were positive and could be seen in two unrelated datasets which increase the validity of our methods. Our results highlight the potential of ML-based fMRI applications for automated diagnostic techniques to recognize MCI patients.

Subject classification and cross-time prediction based on functional connectivity and white matter microstructure features in a rat model of Alzheimer's using machine learning

BACKGROUND

The pathological process of Alzheimer's disease (AD) typically takes decades from onset to clinical symptoms. Early brain changes in AD include MRI-measurable features such as altered functional connectivity (FC) and white matter degeneration. The ability of these features to discriminate between subjects without a diagnosis, or their prognostic value, is however not established.

METHODS

The main trigger mechanism of AD is still debated, although impaired brain glucose metabolism is taking an increasingly central role. Here, we used a rat model of sporadic AD, based on impaired brain glucose metabolism induced by an intracerebroventricular injection of streptozotocin (STZ). We characterized alterations in FC and white matter microstructure longitudinally using functional and diffusion MRI. Those MRI-derived measures were used to classify STZ from control rats using machine learning, and the importance of each individual measure was quantified using explainable artificial intelligence methods.

RESULTS

Overall, combining all the FC and white matter metrics in an ensemble way was the best strategy to discriminate STZ rats, with a consistent accuracy over 0.85. However, the best accuracy early on was achieved using white matter microstructure features, and later on using FC. This suggests that consistent damage in white matter in the STZ group might precede FC. For cross-timepoint prediction, microstructure features also had the highest performance while, in contrast, that of FC was reduced by its dynamic pattern which shifted from early hyperconnectivity to late hypoconnectivity.

CONCLUSIONS

Our study highlights the MRI-derived measures that best discriminate STZ vs control rats early in the course of the disease, with potential translation to humans.

Sex-Related Disparities in the Resting State Functional Connectivity of the Locus Coeruelus and Salience Network in Preclinical Alzheimer's Disease

Recent studies have demonstrated the pivotal role of locus coeruleus (LC) and salience network (SN) resting state functional connectivity (rsFC) changes in the early stage of Alzheimer's disease (AD). Moreover, sex has been a crucial point of discussion in understanding AD pathology. We aimed to demonstrate the sex-related disparities in the functional connectivity (FC) of the SN and LC in preclinical AD. A total of 89 cognitively normal patients with evidence of amyloid beta (Aβ) accumulation ([18F] flutemetamol +) were recruited in the study. A seed-to-voxel analysis was conducted to measure the LC and SN rsFC differences between sexes. In addition, sex by Aβ interactive effects on FC values were analyzed with a general linear model. There were statistically significant sex by regional standardized uptake value ratio (SUVR) interactions in the LC FC with the parietal, frontal, and occipital cortices. Moreover, there was a significant sex by global SUVR interaction in the SN FC with the temporal cortex. The findings suggest that there are differential patterns of LC FC and SN FC in males and females with preclinical AD, which interact with regional Aβ deposition.

High-Frequency rTMS Could Improve Impaired Memory in Mild Cognitive Impairment Patients in China: A Randomized Controlled Study

OBJECTIVE

The purpose of this study was to investigate the effects of repetitive transcranial magnetic stimulation (rTMS) on improving memory deficits in mild cognitive impairment (MCI), as well as to provide visualized evidence for neuronal specificity by using resting-state functional magnetic resonance imaging.

MATERIALS AND METHODS

Forty MCI patients were enrolled to receive 10-session and sham-controlled 10Hz-rTMS over the left dorsolateral prefrontal cortex. The resting-state functional magnetic resonance imaging combined with memory scales assessment were performed before and after the intervention. To elucidate the therapeutic mechanism of rTMS, amplitude of low-frequency fluctuations (ALFF) and functional connectivity were calculated. The Pearson correlation was used to measure the relationship between ALFF and memory performance.

RESULTS

Compared with the sham group, ALFF significantly increased in the right insula, right inferior frontal gyrus-opercular part, and decreased in the left middle occipital gyrus, left angular gyrus, and left lingual gyrus after rTMS. The change in Auditory Verbal Learning Test scores were negatively correlated with ALFF decreases in the left lingual gyrus. Functional connectivity significantly increased between the posterior cingulate cortex and right supramarginal gyrus, and decreased between the right frontoinsular cortex and right supramarginal gyrus after intervention.

CONCLUSION

High-frequency rTMS over the left dorsolateral prefrontal cortex could facilitate improvement on impaired memory in patients with MCI via modulating the neuronal activity and brain network.

Network-based diagnostic probability estimation from resting-state functional magnetic resonance imaging

Brain functional connectivity is a useful biomarker for diagnosing brain disorders. Connectivity is measured using resting-state functional magnetic resonance imaging (rs-fMRI). Previous studies have used a sequential application of the graphical model for network estimation and machine learning to construct predictive formulas for determining outcomes (e.g., disease or health) from the estimated network. However, the resulting network had limited utility for diagnosis because it was estimated independent of the outcome. In this study, we proposed a regression method with scores from rs-fMRI based on supervised sparse hierarchical components analysis (SSHCA). SSHCA has a hierarchical structure that consists of a network model (block scores at the individual level) and a scoring model (super scores at the population level). A regression model, such as the multiple logistic regression model with super scores as the predictor, was used to estimate diagnostic probabilities. An advantage of the proposed method was that the outcome-related (supervised) network connections and multiple scores corresponding to the sub-network estimation were helpful for interpreting the results. Our results in the simulation study and application to real data show that it is possible to predict diseases with high accuracy using the constructed model.

Noninvasive automatic detection of Alzheimer's disease from spontaneous speech: a review

Alzheimer's disease (AD) is considered as one of the leading causes of death among people over the age of 70 that is characterized by memory degradation and language impairment. Due to language dysfunction observed in individuals with AD patients, the speech-based methods offer non-invasive, convenient, and cost-effective solutions for the automatic detection of AD. This paper systematically reviews the technologies to detect the onset of AD from spontaneous speech, including data collection, feature extraction and classification. First the paper formulates the task of automatic detection of AD and describes the process of data collection. Then, feature extractors from speech data and transcripts are reviewed, which mainly contains acoustic features from speech and linguistic features from text. Especially, general handcrafted features and deep embedding features are organized from different modalities. Additionally, this paper summarizes optimization strategies for AD detection systems. Finally, the paper addresses challenges related to data size, model explainability, reliability and multimodality fusion, and discusses potential research directions based on these challenges.

A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network

Background

The convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.

Objective

The aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.

Methods

First, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.

Results

Finally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.

Conclusion

These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.

Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment

Alzheimer's Disease (AZD) is a neurodegenerative disease for which there is now no known effective treatment. Mild cognitive impairment (MCI) is considered a precursor to AZD and affects cognitive abilities. Patients with MCI have the potential to recover cognitive health, can remain mildly cognitively impaired indefinitely or eventually progress to AZD. Identifying imaging-based predictive biomarkers for disease progression in patients presenting with evidence of very mild/questionable MCI (qMCI) can play an important role in triggering early dementia intervention. Dynamic functional network connectivity (dFNC) estimated from resting-state functional magnetic resonance imaging (rs-fMRI) has been increasingly studied in brain disorder diseases. In this work, employing a recent developed a time-attention long short-term memory (TA-LSTM) network to classify multivariate time series data. A gradient-based interpretation framework, transiently-realized event classifier activation map (TEAM) is introduced to localize the group-defining "activated" time intervals over the full time series and generate the class difference map. To test the trustworthiness of TEAM, we did a simulation study to validate the model interpretative power of TEAM. We then applied this simulation-validated framework to a well-trained TA-LSTM model which predicts the progression or recovery from questionable/mild cognitive impairment (qMCI) subjects after three years from windowless wavelet-based dFNC (WWdFNC). The FNC class difference map points to potentially important predictive dynamic biomarkers. Moreover, the more highly time-solved dFNC (WWdFNC) achieves better performance in both TA-LSTM and a multivariate CNN model than dFNC based on windowed correlations between timeseries, suggesting that better temporally resolved measures can enhance the model's performance.

Brain age prediction using the graph neural network based on resting-state functional MRI in Alzheimer's disease

Introduction

Alzheimer's disease (AD) is a neurodegenerative disease that significantly impacts the quality of life of patients and their families. Neuroimaging-driven brain age prediction has been proposed as a potential biomarker to detect mental disorders, such as AD, aiding in studying its effects on functional brain networks. Previous studies have shown that individuals with AD display impaired resting-state functional connections. However, most studies on brain age prediction have used structural magnetic resonance imaging (MRI), with limited studies based on resting-state functional MRI (rs-fMRI).

Methods

In this study, we applied a graph neural network (GNN) model on controls to predict brain ages using rs-fMRI in patients with AD. We compared the performance of the GNN model with traditional machine learning models. Finally, the post hoc model was also used to identify the critical brain regions in AD.

Results

The experimental results demonstrate that our GNN model can predict brain ages of normal controls using rs-fMRI data from the ADNI database. Moreover the differences between brain ages and chronological ages were more significant in AD patients than in normal controls. Our results also suggest that AD is associated with accelerated brain aging and that the GNN model based on resting-state functional connectivity is an effective tool for predicting brain age.

Discussion

Our study provides evidence that rs-fMRI is a promising modality for brain age prediction in AD research, and the GNN model proves to be effective in predicting brain age. Furthermore, the effects of the hippocampus, parahippocampal gyrus, and amygdala on brain age prediction are verified.

Three potential neurovascular pathways driving the benefits of mindfulness meditation for older adults

Mindfulness meditation has been shown to be beneficial for a range of different health conditions, impacts brain function and structure relatively quickly, and has shown promise with aging samples. Functional magnetic resonance imaging metrics provide insight into neurovascular health which plays a key role in both normal and pathological aging processes. Experimental mindfulness meditation studies that included functional magnetic resonance metrics as an outcome measure may point to potential neurovascular mechanisms of action relevant for aging adults that have not yet been previously examined. We first review the resting-state magnetic resonance studies conducted in exclusively older adult age samples. Findings from older adult-only samples are then used to frame the findings of task magnetic resonance imaging studies conducted in both clinical and healthy adult samples. Based on the resting-state studies in older adults and the task magnetic resonance studies in adult samples, we propose three potential mechanisms by which mindfulness meditation may offer a neurovascular therapeutic benefit for older adults: (1) a direct neurovascular mechanism via increased resting-state cerebral blood flow; (2) an indirect anti-neuroinflammatory mechanism via increased functional connectivity within the default mode network, and (3) a top-down control mechanism that likely reflects both a direct and an indirect neurovascular pathway.

Functional magnetic resonance imaging study on anxiety and depression disorders induced by chronic restraint stress in rats

Persistent and negative stress stimulation is one of the most important factors leading to anxiety and depression in individuals, and it can negatively affect the normal function and structure of brain-related regions. However, the maladaptive changes of brain neural networks in anxiety and depression induced by chronic stress have not been explored in detail. In this study, we analyzed the changes in global information transfer efficiency, stress related blood oxygen level dependent (BOLD)- and diffusion tensor imaging (DTI)- signals and functional connectivity (FC) in rat models based on resting-state functional magnetic resonance imaging (rs-fMRI). The results showed that compared to control group, rats treated with chronic restraint stress (CRS) for 5 weeks had reconstructed the small-world network properties. In addition, CRS group had increased coherence and activity in bilateral Striatum (ST_R & L), but decreased coherence and activity in unilateral (left) Frontal Association Cortex (FrA_L) and unilateral (left) Medial Entorhinal Cortex (MEC_L). DTI analysis and correlation analysis confirmed the disrupted integrity of MEC_L and ST_R & L and their correlation to anxiety- and depressive-liked behaviors. Functional connectivity further showed these regions of interest (ROI) had decreased positive correlations with several brain areas, respectively. Our study comprehensively revealed the adaptive changes of brain neural networks induced by chronic stress and emphasized the abnormal activity and functional connectivity of ST_R & L and MEC_L in the pathological condition.

Large-Scale Network Connectivity and Cognitive Function Changes After Exercise Training in Older Adults with Intact Cognition and Mild Cognitive Impairment

Background

Despite growing evidence regarding the association between exercise training (ET) and functional brain network connectivity, little is known about the effects of ET on large-scale within- and between-network functional connectivity (FC) of core brain networks.

Objective

We investigated the effects of ET on within- and between-network functional connectivity of the default mode network (DMN), frontoparietal network (FPN), and salience network (SAL) in older adults with intact cognition (CN) and older adults diagnosed with mild cognitive impairment (MCI). The association between ET-induced changes in FC and cognitive performance was examined.

Methods

33 older adults (78.0±7.0 years; 16 MCI and 17 CN) participated in this study. Before and after a 12-week walking ET intervention, participants underwent a graded exercise test, Controlled Oral Word Association Test (COWAT), Rey Auditory Verbal Learning Test (RAVLT), a narrative memory test (logical memory; LM), and a resting-state fMRI scan. We examined the within (_W) and between (_B) network connectivity of the DMN, FPN, and SAL. We used linear regression to examine associations between ET-related changes in network connectivity and cognitive function.

Results

There were significant improvements in cardiorespiratory fitness, COWAT, RAVLT, and LM after ET across participants. Significant increases in DMN_W and SAL_W, and DMN-FPN_B, DMN-SAL_B, and FPN-SAL_B were observed after ET. Greater SAL_W and FPN-SAL_B were associated with enhanced LM immediate recall performance after ET in both groups.

Conclusion

Increased within- and between-network connectivity following ET may subserve improvements in memory performance in older individuals with intact cognition and with MCI due to Alzheimer's disease.

Independent Component and Graph Theory Analyses Reveal Normalized Brain Networks on Resting-State Functional MRI After Working Memory Training in People With HIV

BACKGROUND

Cognitive training may partially reverse cognitive deficits in people with HIV (PWH). Previous functional MRI (fMRI) studies demonstrate that working memory training (WMT) alters brain activity during working memory tasks, but its effects on resting brain network organization remain unknown.

PURPOSE

To test whether WMT affects PWH brain functional connectivity in resting-state fMRI (rsfMRI).

STUDY TYPE

Prospective.

POPULATION

A total of 53 PWH (ages 50.7 ± 1.5 years, two women) and 53 HIV-seronegative controls (SN, ages 49.5 ± 1.6 years, six women).

FIELD STRENGTH/SEQUENCE

Axial single-shot gradient-echo echo-planar imaging at 3.0 T was performed at baseline (TL1), at 1-month (TL2), and at 6-months (TL3), after WMT.

ASSESSMENT

All participants had rsfMRI and clinical assessments (including neuropsychological tests) at TL1 before randomization to Cogmed WMT (adaptive training, n = 58: 28 PWH, 30 SN; nonadaptive training, n = 48: 25 PWH, 23 SN), 25 sessions over 5-8 weeks. All assessments were repeated at TL2 and at TL3. The functional connectivity estimated by independent component analysis (ICA) or graph theory (GT) metrics (eigenvector centrality, etc.) for different link densities (LDs) were compared between PWH and SN groups at TL1 and TL2.

STATISTICAL TESTS

Two-way analyses of variance (ANOVA) on GT metrics and two-sample t-tests on FC or GT metrics were performed. Cognitive (eg memory) measures were correlated with eigenvector centrality (eCent) using Pearson's correlations. The significance level was set at P < 0.05 after false discovery rate correction.

RESULTS

The ventral default mode network (vDMN) eCent differed between PWH and SN groups at TL1 but not at TL2 (P = 0.28). In PWH, vDMN eCent changes significantly correlated with changes in the memory ability in PWH (r = -0.62 at LD = 50%) and vDMN eCent before training significantly correlated with memory performance changes (r = 0.53 at LD = 50%).

DATA CONCLUSION

ICA and GT analyses showed that adaptive WMT normalized graph properties of the vDMN in PWH.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: 1.

Illuminating neurodegeneration: a future perspective on near-infrared spectroscopy in dementia research

SIGNIFICANCE

Dementia presents a global healthcare crisis, and neuroimaging is the main method for developing effective diagnoses and treatments. Yet currently, there is a lack of sensitive, portable, and low-cost neuroimaging tools. As dementia is associated with vascular and metabolic dysfunction, near-infrared spectroscopy (NIRS) has the potential to fill this gap.

AIM

This future perspective aims to briefly review the use of NIRS in dementia to date and identify the challenges involved in realizing the full impact of NIRS for dementia research, including device development, study design, and data analysis approaches.

APPROACH

We briefly appraised the current literature to assess the challenges, giving a critical analysis of the methods used. To assess the sensitivity of different NIRS device configurations to the brain with atrophy (as is common in most forms of dementia), we performed an optical modeling analysis to compare their cortical sensitivity.

RESULTS

The first NIRS dementia study was published in 1996, and the number of studies has increased over time. In general, these studies identified diminished hemodynamic responses in the frontal lobe and altered functional connectivity in dementia. Our analysis showed that traditional (low-density) NIRS arrays are sensitive to the brain with atrophy (although we see a mean decrease of 22% in the relative brain sensitivity with respect to the healthy brain), but there is a significant improvement (a factor of 50 sensitivity increase) with high-density arrays.

CONCLUSIONS

NIRS has a bright future in dementia research. Advances in technology - high-density devices and intelligent data analysis-will allow new, naturalistic task designs that may have more clinical relevance and increased reproducibility for longitudinal studies. The portable and low-cost nature of NIRS provides the potential for use in clinical and screening tests.

Effects of Remote Expressive Arts Program in Older Adults with Mild Cognitive Impairment: A Randomized Controlled Trial

BACKGROUND

Mild cognitive impairment (MCI) is a stage of cognitive ability loss with intact activities of daily living and an increased risk for the development of dementia.

OBJECTIVE

This study evaluated the intervention effect of remote expressive arts program (rEAP) on cognitive function in older adults with MCI and investigated the underlying neurobiological mechanisms.

METHODS

We assigned 73 older MCI patients to receive rEAP or health education (HE), who underwent neuropsychological evaluation and resting-state functional magnetic resonance imaging before and after treatment. Neuropsychological scores were analyzed using SPSS software, and regional homogeneity (ReHo) values and seed-based functional connectivity (FC) were analyzed using Matlab software.

RESULTS

The rEAP group showed more significant improvements in cognitive function than the HE group. rEAP affected spontaneous brain activity and brain networks. The ReHo values in the right anterior cingulate/paracingulate cortex and the left dorsolateral superior frontal gyrus significantly increased and decreased, respectively, in the rEAP and HE groups. Further, ReHo value changes were significantly associated with the corresponding neuropsychological test score changes in the rEAP group. Moreover, the rEAP group showed decreased FC between the posterior cingulate cortex and the right middle temporal gyrus and increased FC between the ventromedial prefrontal cortex and left angular gyrus.

CONCLUSION

The 12-week rEAP improved cognitive function in MCI patients. Additionally, the alterations of spontaneous brain network connections and activity helped improve and maintain cognitive function in MCI patients.

Altered nonlinear Granger causality interactions in the large-scale brain networks of patients with schizophrenia

Objective.It has been demonstrated that schizophrenia (SZ) is characterized by functional dysconnectivity involving extensive brain networks. However, the majority of previous studies utilizing resting-state functional magnetic resonance imaging (fMRI) to infer abnormal functional connectivity (FC) in patients with SZ have focused on the linear correlation that one brain region may influence another, ignoring the inherently nonlinear properties of fMRI signals.Approach. In this paper, we present a neural Granger causality (NGC) technique for examining the changes in SZ's nonlinear causal couplings. We develop static and dynamic NGC-based analyses of large-scale brain networks at several network levels, estimating complicated temporal and causal relationships in SZ patients.Main results. We find that the NGC-based FC matrices can detect large and significant differences between the SZ and healthy control groups at both the regional and subnetwork scales. These differences are persistent and significantly overlapped at various network sparsities regardless of whether the brain networks were built using static or dynamic techniques. In addition, compared to controls, patients with SZ exhibited extensive NGC confusion patterns throughout the entire brain.Significance. These findings imply that the NGC-based FCs may be a useful method for quantifying the abnormalities in the causal influences of patients with SZ, hence shedding fresh light on the pathophysiology of this disorder.

Estimating effective connectivity in Alzheimer's disease progression: A dynamic causal modeling study

Introduction

Alzheimer's disease (AD) affects the whole brain from the cellular level to the entire brain network structure. The causal relationship among brain regions concerning the different AD stages is not yet investigated. This study used Dynamic Causal Modeling (DCM) method to assess effective connectivity (EC) and investigate the changes that accompany AD progression.

Methods

We included the resting-state fMRI data of 34 AD patients, 31 late mild cognitive impairment (LMCI) patients, 34 early MCI (EMCI) patients, and 31 cognitive normal (CN) subjects selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The parametric Empirical Bayes (PEB) method was used to infer the effective connectivities and the corresponding probabilities. A linear regression analysis was carried out to test if the connection strengths could predict subjects' cognitive scores.

Results

The results showed that the connections reduced from full connection in the CN group to no connection in the AD group. Statistical analysis showed the connectivity strengths were lower for later-stage patients. Linear regression analysis showed that the connection strengths were partially predictive of the cognitive scores.

Discussion

Our results demonstrated the dwindling connectivity accompanying AD progression on causal relationships among brain regions and indicated the potential of EC as a loyal biomarker in AD progression.

Alterations of amplitude of low-frequency fluctuations and fractional amplitude of low-frequency fluctuations in end-stage renal disease on maintenance dialysis: An activation likelihood estimation meta-analysis

BACKGROUND

Cognitive impairment (CI) is a common complication of end-stage renal disease (ESRD). Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have identified abnormal spontaneous low-frequency brain activity in ESRD dialysis patients. However, these studies have reported inconsistent results. So far, no meta-analyses on this topic have been published. This meta-analysis aimed to identify the more consistently vulnerable brain regions in ESRD patients at rest and to reveal its possible neuropathophysiological mechanisms.

METHODS

We systematically searched PubMed, Cochrane Library, Web of Science, Medline, and EMBASE databases up to July 20, 2022 based on the amplitude of low-frequency fluctuation (ALFF) or fractional amplitude of low-frequency fluctuation (fALFF). Brain regions with abnormal spontaneous neural activity in ESRD compared to healthy controls (HCs) from previous studies were integrated and analyzed using an activation likelihood estimation (ALE) method. Jackknife sensitivity analysis was carried out to assess the reproducibility of the results.

RESULTS

In total, 11 studies (380 patients and 351 HCs) were included in the final analysis. According to the results of the meta-analysis, compared with HCs, ESRD patients had decreased ALFF/fALFF in the right precuneus, right cuneus, and left superior temporal gyrus (STG), while no brain regions with increased brain activity were identified. Jackknife sensitivity analysis showed that our results were highly reliable.

CONCLUSION

Compared to HCs, ESRD dialysis patients exhibit significant abnormalities in spontaneous neural activity associated with CI, occurring primarily in the default mode network, visual recognition network (VRN), and executive control network (ECN). This contributes to the understanding of its pathophysiological mechanisms.

SYSTEMATIC REVIEW REGISTRATION

[https://www.crd.york.ac.uk/prospero/], identifier [CRD42022348694].

Associations between disrupted functional brain network topology and cognitive impairment in patients with rectal cancer during chemotherapy

Introduction

Cognitive impairment has been identified in patients with non-central nervous system cancer received chemotherapy. Chemotherapy-induced changes in the brain are considered as the possible causes of the cognitive deficits of patients. This study aimed to explore chemotherapy-related functional brain changes and cognitive impairment in rectal cancer (RC) patients who had just finished chemotherapy treatment.

Methods

In this study, RC patients after chemotherapy (on the day patients received the last dose of chemotherapy) (n=30) and matched healthy controls (HCs) (n=30) underwent cognitive assessments, structural magnetic resonance imaging (MRI) and resting-state functional MRI. The functional brain networks were constructed by thresholding the partial correlation matrices of 90 brain regions in the Anatomical Automatic Labeling template and the topologic properties were evaluated by graph theory analysis. Moreover, correlations between altered topological measures and scores of cognitive scales were explored in the patient group.

Results

Compared with HCs, RC patients had lower scores of cognitive scales. The functional brain network had preserved small-world topological features but with a tendency towards higher path length in the whole network. In addition, patients had decreased nodal global efficiency (E_glo(i)) in the left superior frontal gyrus (dorsolateral), superior frontal gyrus (orbital part), inferior frontal gyrus (opercular part), inferior frontal gyrus (triangular part) and right inferior frontal gyrus (triangular part). Moreover, values of E_glo(i) in the superior and inferior frontal gyrus were positively associated with cognitive function in the patient group.

Conclusion

These results suggested that cognitive impairment was associated with disruptions of the topological organization in functional brain networks of RC patients who had just finished chemotherapy, which provided new insights into the pathophysiology underlying acute effects of chemotherapy on cognitive function.

Unravelling the relationship between amyloid accumulation and brain network function in normal aging and very mild cognitive decline: a longitudinal analysis

Pathological changes in the brain begin accumulating decades before the appearance of cognitive symptoms in Alzheimer's disease. The deposition of amyloid beta proteins and other neurotoxic changes occur, leading to disruption in functional connections between brain networks. Discrete characterization of the changes that take place in preclinical Alzheimer's disease has the potential to help treatment development by targeting the neuropathological mechanisms to prevent cognitive decline and dementia from occurring entirely. Previous research has focused on the cross-sectional differences in the brains of patients with mild cognitive impairment or Alzheimer's disease and healthy controls or has concentrated on the stages immediately preceding cognitive symptoms. The present study emphasizes the early preclinical phases of neurodegeneration. We use a longitudinal approach to examine the brain changes that take place during the early stages of cognitive decline in the Open Access Series of Imaging Studies-3 data set. Among 1098 participants, 274 passed the inclusion criteria (i.e. had at least two cognitive assessments and two amyloid scans). Over 90% of participants were healthy at baseline. Over 8-10 years, some participants progressed to very mild cognitive impairment (n = 48), while others stayed healthy (n = 226). Participants with cognitive decline show faster amyloid accumulation in the lateral temporal, motor and parts of the lateral prefrontal cortex. These changes in amyloid levels were linked to longitudinal increases in the functional connectivity of select networks, including default mode, frontoparietal and motor components. Our findings advance the understanding of amyloid staging and the corresponding changes in functional organization of large-scale brain networks during the progression of early preclinical Alzheimer's disease.

Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease

Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance.

Inherent regional brain activity changes in male obstructive sleep apnea with mild cognitive impairment: A resting-state magnetic resonance study

Obstructive sleep apnea (OSA) is the most common sleep disorder worldwide. Previous studies have shown that OSA patients are often accompanied by cognitive function loss, and the underlying neurophysiological mechanism is still unclear. This study aimed to determine whether there are differences in regional homogeneity (Reho) and functional connectivity (FC) across the brain between OSA patients with MCI (OSA-MCI) and those without MCI (OSA-nMCI) and whether such differences can be used to distinguish the two groups. Resting state magnetic resonance data were collected from 48 OSA-MCI patients and 47 OSA-nMCI patients. The brain regions with significant differences in Reho and FC between the two groups were identified, and the Reho and FC features were combined with machine learning methods for classification. Compared with OSA-nMCI patients, OSA-MCI patients showed significantly lower Reho in bilateral lingual gyrus and left superior temporal gyrus. OSA-MCI patients also showed significantly lower FC between the bilateral lingual gyrus and bilateral cuneus, left superior temporal gyrus and left middle temporal gyrus, middle frontal gyrus, and bilateral posterior cingulate/calcarine/cerebellar anterior lobe. Based on Reho and FC features, logistic regression classification accuracy was 0.87; sensitivity, 0.70; specificity, 0.89; and area under the curve, 0.85. Correlation analysis showed that MoCA scale score in OSA patients was significant positive correlation sleep efficiency and negatively correlation with neck circumference. In conclusion, our results showed that the OSA-MCI group showed decreased Reho and FC in specific brain regions compared with the OSA-nMCI group, which may help to understand the underlying neuroimaging mechanism of OSA leading to cognitive dysfunction and may serve as a potential biomarker to distinguish whether OSA is accompanied by cognitive impairment.

Quantitative Assessment of Resting-State Functional Connectivity MRI to Differentiate Amnestic Mild Cognitive Impairment, Late-Onset Alzheimer's Disease From Normal Subjects

BACKGROUND

Alzheimer disease (AD) is a neurological disorder with brain network dysfunction. Investigation of the brain network functional connectivity (FC) alterations using resting-state functional MRI (rs-fMRI) can provide valuable information about the brain network pattern in early AD diagnosis.

PURPOSE

To quantitatively assess FC patterns of resting-state brain networks and graph theory metrics (GTMs) to identify potential features for differentiation of amnestic mild cognitive impairment (aMCI) and late-onset AD from normal.

STUDY TYPE

Prospective.

SUBJECTS

A total of 14 normal, 16 aMCI, and 13 late-onset AD.

FIELD STRENGTH/SEQUENCE

A 3.0 T; rs-fMRI: single-shot 2D-EPI and T1-weighted structure: MPRAGE.

ASSESSMENT

By applying bivariate correlation coefficient and Fisher transformation on the time series of predefined ROIs' pairs, correlation coefficient matrixes and ROI-to-ROI connectivity (RRC) were extracted. By thresholding the RRC matrix (with a threshold of 0.15), a graph adjacency matrix was created to compute GTMs.

STATISTICAL TESTS

Region of interest (ROI)-based analysis: parametric multivariable statistical analysis (PMSA) with a false discovery rate using (FDR)-corrected P < 0.05 cluster-level threshold together with posthoc uncorrected P < 0.05 connection-level threshold. Graph-theory analysis (GTA): P-FDR-corrected < 0.05. One-way ANOVA and Chi-square tests were used to compare clinical characteristics.

RESULTS

PMSA differentiated AD from normal, with a significant decrease in FC of default mode, salience, dorsal attention, frontoparietal, language, visual, and cerebellar networks. Furthermore, significant increase in overall FC of visual and language networks was observed in aMCI compared to normal. GTA revealed a significant decrease in global-efficiency (28.05 < 45), local-efficiency (22.98 < 24.05), and betweenness-centrality (14.60 < 17.39) for AD against normal. Moreover, a significant increase in local-efficiency (33.46 > 24.05) and clustering-coefficient (25 > 20.18) were found in aMCI compared to normal.

DATA CONCLUSION

This study demonstrated resting-state FC potential as an indicator to differentiate AD, aMCI, and normal. GTA revealed brain integration and breakdown by providing concise and comprehensible statistics.

EVIDENCE LEVEL

1 TECHNICAL EFFICACY: Stage 2.