Altered Functional Connectivity of Basal Ganglia in Mild Cognitive Impairment and Alzheimer's Disease

Anticipatory reaching motor behavior characterizes patients within the Alzheimer's disease continuum in a virtual reality environment

BACKGROUND

Alzheimer's Disease (AD) is characterized by progressive declines in cognitive and motor functions, impairing daily activities. Traditionally, AD diagnosis relies on cognitive assessments, but emerging evidence highlights motor function deficits as early indicators of AD and Mild Cognitive Impairment (MCI). These motor declines, which often precede cognitive symptoms, include slower and less accurate reaching movements. This study explored reaching actions in a Virtual Reality (VR) environment in AD and MCI patients to identify motor deficits and their link to cognitive decline.

METHODS

The study involved 61 right-handed participants (19 AD, 21 MCI, and 21 healthy age-matched controls), screened for cognitive health using a Mini-Mental State Examination (MMSE). Participants performed upper-limb motor tasks (sequentially reaching targets) in a Virtual Reality (VR). Kinematic data was recorded and analyzed focusing on task success rate, frequency of anticipatory responses, and direction of anticipatory responses. Statistical analysis was performed using Generalized Linear Mixed Models to differentiate the three groups of participants based on performance metrics, anticipation behavior, and the correlation between anticipation rate and MMSE score.

RESULTS

Both AD and MCI patients showed more anticipatory responses than healthy controls (HC), inversely related to success rates and cognitive function. AD patients exhibited lower success rates and a higher frequency of anticipatory responses, often biased toward previous trial targets, suggesting impaired motor planning or difficulty adapting to new cues. MCI patients showed an intermediate pattern, with more anticipatory responses than HC but comparable success rates. These results highlight the crucial role of anticipatory behavior in motor task performance, with AD patients displaying the most pronounced deficits.

CONCLUSIONS

This study highlights significant impairments of reaching movements in AD patients, particularly in terms of anticipatory behavior and success rates. The observed deficits suggest that kinematic metrics could serve as early biomarkers for diagnosis and intervention. These findings emphasize the importance of combining cognitive and sensorimotor assessments for the early detection of AD-related motor dysfunctions. Additionally, they highlight the potential of VR-based motor rehabilitation as a promising approach to address sensorimotor deficits in the AD continuum, improving both motor and cognitive outcomes.

Research progress on brain network imaging biomarkers of subjective cognitive decline

Purpose

Subjective cognitive decline (SCD) is an early manifestation of the Alzheimer's disease (AD) continuum, and accurately diagnosing SCD to differentiate it from neurotypical aging in older adults is a common challenge for researchers.

Methods

This review examines and summarizes relevant studies regarding the neuroimaging of the AD continuum, and comprehensively summarizes and outlines the SCD clinical features characterizing along with the corresponding neuroimaging changes involving structural, functional, and metabolic networks.

Results

The clinical characteristics of SCD include a subjective decline in self-perceived cognitive function, and there are significant imaging changes, such as reductions in gray matter volume in certain brain regions, abnormalities in the integrity of white matter tracts and diffusion metrics, alterations in functional connectivity between different sub-networks or within networks, as well as abnormalities in brain metabolic networks and cerebral blood flow perfusion.

Conclusion

The 147 referenced studies in this paper indicate that exploring the structural, functional, and metabolic network changes in the brain related to SCD through neuroimaging aims to enhance the goals and mission of brain science development programs: "Understanding the Brain," "Protecting the Brain," and "Creating the Brain," thereby strengthening researchers' investigation into the mechanisms of brain function. Early diagnosis of SCD, along with prompt intervention, can reduce the incidence of AD spectrum while improving patients' quality of life, even integrating numerous scientific research achievements into unified and established standards and applying them in clinical practice by doctors, thus all encouraging researchers to further investigate SCD issues in older adults.

Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network

Background

Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity.

Purpose

To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN).

Methods

Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores.

Results

The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1-30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores.

Conclusion

The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI.

Supplementary Information

The online version of this article (10.1007/s13755-023-00269-0) contains supplementary material, which is available to authorized users.

Combination of rs-fMRI, QSM, and ASL Reveals the Cerebral Neurovascular Coupling Dysfunction Is Associated With Cognitive Decline in Patients With Chronic Kidney Disease

BACKGROUND

Neurovascular coupling (NVC) reflects the close connection between neural activity and cerebral blood flow (CBF) responses, providing new insights to explore the neuropathological mechanisms of various diseases. Non-dialysis patients with chronic kidney disease (CKD) exhibit cognitive decline, but the underlying pathological mechanisms are unclear.

METHODS

The prospective study involved 53 patients with stage 1-3a CKD (CKD1-3a), 78 patients with stage 3b-5 CKD (CKD3b-5), and 52 healthy controls (HC). Our investigation involved voxel-based assessments of both global and regional BOLD signal characteristics. Additionally, we explored the correlations between neuroimaging indices, Montreal Cognitive Assessment (MoCA) scores, and clinical laboratory findings.

RESULTS

Compared to HC, the CKD3b-5 and CKD1-3a groups exhibited lower ALLF and ReHo in the default mode network (DMN), higher CBF in bilateral hippocampus (HIP), higher susceptibility values in bilateral caudate nucleus (CAU) and putamen (PUT), and lower susceptibility values in bilateral HIP. At the global level, the coupling coefficients were lower in CKD1-3a and CKD3b-5 groups than in HC. At the ROI level, the CBF-ALFF and CBF-ReHo coupling in HIP and basal ganglia regions were lower in CKD3b-5 groups than in the CKD1-3a group. Most importantly, susceptibility-ALFF in ANG.R may mediate the effects of phosphorus on cognitive decompensation in patients with CKD1-3a.

CONCLUSIONS

Non-dialysis patients with CKD exhibit abnormal NCV, which is associated with the cognitive decline. Specifically, the susceptibility-ALFF may serve as a valuable biomarker for early assessment of cognitive decline in CKD, offering insights into the pathogenesis of cognitive decline in CKD.

Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research

Over the past decades, numerous large-scale neuroimaging projects that involved the collection and release of multimodal data have been conducted globally. Distinguished initiatives such as the Human Connectome Project, UK Biobank, and Alzheimer's Disease Neuroimaging Initiative, among others, stand as remarkable international collaborations that have significantly advanced our understanding of the brain. With the advancement of big data technology, changes in healthcare models, and continuous development in biomedical research, various types of large-scale projects are being established and promoted worldwide. For project leaders, there is a need to refer to common principles in project construction and management. Users must also adhere strictly to rules and guidelines, ensuring data safety and privacy protection. Organizations must maintain data integrity, protect individual privacy, and foster stakeholders' trust. Regular updates to legislation and policies are necessary to keep pace with evolving technologies and emerging data-related challenges. CRITICAL RELEVANCE STATEMENT: By reviewing global large-scale neuroimaging projects, we have summarized the standards and norms for establishing and utilizing their data, and provided suggestions and opinions on some ethical issues, aiming to promote higher-quality neuroimaging data development. KEY POINTS: Global neuroimaging projects are increasingly advancing but still face challenges. Constructing and utilizing neuroimaging projects should follow set rules and guidelines. Effective data management and governance should be developed to support neuroimaging projects.

Evaluation of the Glymphatic System in Schizophrenia Spectrum Disorder Using Proton Magnetic Resonance Spectroscopy Measurement of Brain Macromolecule and Diffusion Tensor Image Analysis Along the Perivascular Space Index

BACKGROUND AND HYPOTHESIS

The glymphatic system (GS), a brain waste clearance pathway, is disrupted in various neurodegenerative and vascular diseases. As schizophrenia shares clinical characteristics with these conditions, we hypothesized GS disruptions in patients with schizophrenia spectrum disorder (SCZ-SD), reflected in increased brain macromolecule (MM) and decreased diffusion-tensor-image-analysis along the perivascular space (DTI-ALPS) index.

STUDY DESIGN

Forty-seven healthy controls (HCs) and 103 patients with SCZ-SD were studied. Data included 135 proton magnetic resonance spectroscopy (1H-MRS) sets, 96 DTI sets, with 79 participants contributing both. MM levels were quantified in the dorsal-anterior cingulate cortex (dACC), dorsolateral prefrontal cortex, and dorsal caudate (point resolved spectroscopy, echo-time = 35ms). Diffusivities in the projection and association fibers near the lateral ventricle were measured to calculate DTI-ALPS indices. General linear models were performed, adjusting for age, sex, and smoking. Correlation analyses examined relationships with age, illness duration, and symptoms severity.

STUDY RESULTS

MM levels were not different between patients and HCs. However, left, right, and bilateral DTI-ALPS indices were lower in patients compared with HCs (P < .001). In HCs, age was positively correlated with dACC MM and negatively correlated with left, right, and bilateral DTI-ALPS indices (P < .001). In patients, illness duration was positively correlated with dACC MM and negatively correlated with the right DTI-ALPS index (P < .05). In the entire population, dACC MM and DTI-ALPS indices showed an inverse correlation (P < .01).

CONCLUSIONS

Our results suggest potential disruptions in the GS of patients with SCZ-SD. Improving brain's waste clearance may offer a potential therapeutic approach for patients with SCZ-SD.

The White Matter Integrity and Functional Connection Differences of Fornix (Cres)/Stria Terminalis in Individuals with Mild Cognitive Impairment Induced by Occupational Aluminum Exposure

Long-term aluminum (Al) exposure increases the risk of mild cognitive impairment (MCI). The aim of the present study was to investigate the neural mechanisms of Al-induced MCI. In our study, a total of 52 individuals with occupational Al exposure >10 years were enrolled and divided into two groups: MCI (Al-MCI) and healthy controls (Al-HC). Plasma Al concentrations and Montreal Cognitive Assessment (MoCA) score were collected for all participants. And diffusion tensor imaging and resting-state functional magnetic resonance imaging were used to examine changes of white matter (WM) and functional connectivity (FC). There was a negative correlation between MoCA score and plasma Al concentration. Compared with the Al-HC, fractional anisotropy value for the right fornix (cres)/stria terminalis (FX/ST) was higher in the Al-MCI. Furthermore, there was a difference in FC between participants with and without MCI under Al exposure. We defined the regions with differing FC as a "pathway," specifically the connectivity from the right temporal pole to the right FX/ST, then to the right sagittal stratum, and further to the right anterior cingulate and paracingulate gyri and right inferior frontal gyrus, orbital part. In summary, we believe that the observed differences in WM integrity and FC in the right FX/ST between participants with and without MCI under long-term Al exposure may represent the neural mechanisms underlying MCI induced by Al exposure.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification

INTRODUCTION

Spatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images.

METHODS

We propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR).

RESULTS

In total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05).

CONCLUSION

The automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.

Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.

Topographic metabolism-function relationships in Alzheimer's disease: A simultaneous PET/MRI study

Disruptions of neural metabolism and function occur in parallel during Alzheimer's disease (AD). While many studies have shown diverse metabolic-functional relationships in specific brain regions, much less is known about how large-scale network-level functional activity is associated with the topology of metabolism in AD. In this study, we took the advantages of simultaneous PET/MRI and multivariate analyses to investigate the associations between AD-related stereotypical spatial patterns (topographies) of glucose metabolism, measured by fluorodeoxyglucose PET, and functional connectivity, measured by resting-state functional MRI. A total of 101 participants, including 37 patients with AD, 25 patients with mild cognitive impairment (MCI), and 39 cognitively normal controls, underwent PET/MRI scans and cognitive assessments. Three pairs of distinct but optimally correlated metabolic and functional topographies were identified, encompassing large-scale networks including the default-mode, executive and control, salience, attention, and subcortical networks. Importantly, the metabolic-functional associations were not only limited to one-to-one-corresponding regions, but also occur in remote and non-overlapping regions. Furthermore, both glucose metabolism and functional connectivity, as well as their linkages, exhibited various degrees of disruptions in patients with MCI and AD, and were correlated with cognitive decline. In conclusion, our results support distributed and heterogeneous topographic associations between metabolism and function, which are jeopardized by AD. Findings of this study may deepen our understanding of the pathological mechanism of AD through the perspectives of both local energy efficiency and long-term interactions between synaptic disruption and functional disconnection contributing to the clinical symptomatology in AD.

Cerebral abnormalities in HIV-infected individuals with neurocognitive impairment revealed by fMRI

Although the combination antiretroviral treatment (cART) has considerably lowered the risk of HIV associated dementia (HAD), the incidence of neurocognitive impairments (NCI) has not decreased likely due to the insidious and slow progressive nature of HIV infection. Recent studies showed that the resting-state functional magnetic resonance imaging (rs-fMRI) is a prominent technique in helping the non-invasive analysis of neucognitive impairment. Our study is to explore the neuroimaging characteristics among people living with HIV (PLWH) with or without NCI in terms of cerebral regional and neural network by rs-fMRI, based on the hypothesis that HIV patients with and without NCI have independent brain imaging characteristics. 33 PLWH with NCI and 33 PLWH without NCI, recruited from the Cohort of HIV-infected associated Chronic Diseases and Health Outcomes, Shanghai, China (CHCDO) which was established in 2018, were categorized into the HIV-NCI and HIV-control groups, respectively, based on Mini-Mental State Examination (MMSE) results. The two groups were matched in terms of sex, education and age. Resting-state fMRI data were collected from all participants to analyze the fraction amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) to assess regional and neural network alterations in the brain. Correlations between fALFF/FC values in specific brain regions and clinical characteristics were also examined. The results showed increased fALFF values in the bilateral calcarine gyrus, bilateral superior occipital gyrus, left middle occipital gyrus, and left cuneus in the HIV-NCI group compared to the HIV-control group. Additionally, increased FC values were observed between the right superior occipital gyrus and right olfactory cortex, bilateral gyrus rectus, and right orbital part of the middle frontal gyrus in the HIV-NCI group. Conversely, decreased FC values were found between the left hippocampus and bilateral medial prefrontal gyrus, as well as bilateral superior frontal gyrus. The study concluded that abnormal spontaneous activity in PLWH with NCI primarily occurred in the occipital cortex, while defects in brain networks were mostly associated with the prefrontal cortex. The observed changes in fALFF and FC in specific brain regions provide visual evidence to enhance our understanding of the central mechanisms underlying the development of cognitive impairment in HIV patients.

Structural Proteomic Profiling of Cerebrospinal Fluids to Reveal Novel Conformational Biomarkers for Alzheimer's Disease

Alzheimer's disease (AD) is the most common representation of dementia, with brain pathological hallmarks of protein abnormal aggregation, such as with amyloid beta and tau protein. It is well established that posttranslational modifications on tau protein, particularly phosphorylation, increase the likelihood of its aggregation and subsequent formation of neurofibrillary tangles, another hallmark of AD. As additional misfolded proteins presumably exist distinctly in AD disease states, which would serve as potential source of AD biomarkers, we used limited proteolysis-coupled with mass spectrometry (LiP-MS) to probe protein structural changes. After optimizing the LiP-MS conditions, we further applied this method to human cerebrospinal fluid specimens collected from healthy control, mild cognitive impairment (MCI), and AD subject groups to characterize proteome-wide misfolding tendencies as a result of disease progression. The fully tryptic peptides embedding LiP sites were compared with the half-tryptic peptides generated from internal cleavage of the same region to determine any structural unfolding or misfolding. We discovered hundreds of significantly up- and down-regulated peptides associated with MCI and AD indicating their potential structural changes in AD progression. Moreover, we detected 53 structurally changed regions in 12 proteins with high confidence between the healthy control and disease groups, illustrating the functional relevance of these proteins with AD progression. These newly discovered conformational biomarker candidates establish valuable future directions for exploring the molecular mechanism of designing therapeutic targets for AD.