Systematic Review of Different Neuroimaging Correlates in Mild Cognitive Impairment and Alzheimer's Disease

Role of Dietary Patterns in Older Adults with Cognitive Disorders: An Umbrella Review Utilizing Neuroimaging Biomarkers

Various dietary patterns (DPs) may benefit or harm cognitive status through their components. Publications assessing the impact of DPs on cognitive scores using neuropsychological tests have often led to less promising results. Recently, numerous meta-analyses and systematic reviews have utilized neuroimaging to identify more subtle brain-associated alterations related to cognition. Combining neuroimaging methods with neuropsychological assessments could clarify these findings. This umbrella review was conducted to systematically explore evidence on the impact of DPs on neuroimaging biomarkers in older adults with cognitive disorders. Scientific databases, including Scopus, PubMed, and Web of Science, were comprehensively searched from the earliest available data until May 11, 2024. Out of 89 papers, 15 meta-analyses and systematic reviews were included in our umbrella review. These selected papers addressed 27 DPs and their impact on neuroimaging biomarkers. Most selected papers were of moderate quality. Studies revealed that greater adherence to the Mediterranean diet (MedDiet) correlated with increased cortical thickness, improved glucose metabolism in the brain, and reduced amyloid-beta and tau deposition, as evidenced by magnetic resonance imaging and other neuroimaging techniques. Higher adherence to healthy DPs, such as the MedDiet, reduced the risk of Alzheimer's disease and mild cognitive impairment. In contrast, Western and high glycemic diets were associated with increased cognitive decline.

Brain white matter microstructural alterations in Behcet's syndrome correlate with cognitive impairment and disease severity: A diffusion tensor imaging study

OBJECTIVES

To evaluate the microstructural integrity of brain white matter tracts in patients with Neuro-Behcet's syndrome (NBS) and Behcet's syndrome (BS) without neurological manifestations using diffusion tensor imaging (DTI) and to investigate potential utility of DTI as a surrogate biomarker of neurocognitive functioning and disease severity.

METHODS

This cross-sectional study comprised 34 NBS patients and 32 BS patients without neurological involvement, identified based on the International Study Group of the Behcet's disease (ISGBD) and the International Consensus Recommendation (ICR) criteria, as well as 33 healthy controls. Cognitive functions, including attention, memory, language, abstraction, executive control, visuospatial skills, and sensorimotor performance were assessed using standardized questionnaires. DTI data were analyzed using tract-based spatial statistics (TBSS) and automated probabilistic tractography to investigate inter-group differences. Subsequently, correlations between tensor-derived parameters of white matter tracts, neurocognitive test scores, and disease severity measures were examined.

RESULTS

DTI revealed decreased fractional anisotropy and increased radial diffusivity, mean diffusivity, and axial diffusivity in both supratentorial and infratentorial white matter in NBS patients, indicating widespread loss of microstructural integrity. Moreover, this loss of integrity was also observed in BS patients without neurological manifestations, albeit to a lesser extent. In NBS patients, certain white matter tracts, including cingulum bundle, were associated with poor cognitive performance across multiple domains and disease severity.

DISCUSSION

DTI findings might potentially serve as a neuroimaging marker to predict the extent of neurocognitive impairment and disease severity associated with central nervous system involvement in BS.

Shared and Distinct White Matter Alterations in Major Depression and Bipolar Disorder: A Systematic Review and Meta-Analysis

BACKGROUND

Identifying white matter (WM) microstructural similarities and differences between major depressive disorder (MDD) and bipolar disorder (BD) is an important way to understand the potential neuropathological mechanism in emotional disorders. Numerous diffusion tensor imaging (DTI) studies over recent decades have confirmed the presence of WM anomalies in these two affective disorders, but the results were inconsistent. This study aimed to determine the statistical consistency of DTI findings for BD and MDD by using the coordinate-based meta-analysis (CBMA) approach.

METHODS

We performed a systematic search of tract-based spatial statistics (TBSS) studies comparing MDD or BD with healthy controls (HC) as of June 30, 2024. The seed-based d-mapping (SDM) was applied to investigate fractional anisotropy (FA) changes. Meta-regression was then used to analyze the potential correlations between demographics and neuroimaging alterations.

RESULTS

Regional FA reductions in the body of the corpus callosum (CC) were identified in both of these two diseases. Besides, MDD patients also exhibited decreased FA in the genu and splenium of the CC, as well as the left anterior thalamic projections (ATP), while BD patients showed FA reduction in the left median network, and cingulum in addition to the CC.

CONCLUSIONS

The results highlighted that altered integrity in the body of CC served as the shared basis of MDD and BD, and distinct microstructural WM abnormalities also existed, which might induce the various clinical manifestations of these two affective disorders. The study was registered on PROSPERO (http://www.crd.york.ac.uk/PROSPERO), registration number: CRD42022301929.

A diffusion tensor imaging white matter atlas of the domestic canine brain

There is increasing reliance on magnetic resonance imaging (MRI) techniques in both research and clinical settings. However, few standardized methods exist to permit comparative studies of brain pathology and function. To help facilitate these studies, we have created a detailed, MRI-based white matter atlas of the canine brain using diffusion tensor imaging. This technique, which relies on the movement properties of water, permits the creation of a three-dimensional diffusivity map of white matter brain regions that can be used to predict major axonal tracts. To generate an atlas of white matter tracts, thirty neurologically and clinically normal dogs underwent MRI imaging under anesthesia. High-resolution, three-dimensional T1-weighted sequences were collected and averaged to create a population average template. Diffusion-weighted imaging sequences were collected and used to generate diffusivity maps, which were then registered to the T1-weighted template. Using these diffusivity maps, individual white matter tracts-including association, projection, commissural, brainstem, olfactory, and cerebellar tracts-were identified with reference to previous canine brain atlas sources. To enable the use of this atlas, we created downloadable overlay files for each white matter tract identified using manual segmentation software. In addition, using diffusion tensor imaging tractography, we created tract files to delineate major projection pathways. This comprehensive white matter atlas serves as a standard reference to aid in the interpretation of quantitative changes in brain structure and function in clinical and research settings.

Microstructural Changes in the Corpus Callosum in Neurodegenerative Diseases

The corpus callosum, the largest white matter structure in the brain, plays a crucial role in interhemispheric communication and cognitive function. This review examines the microstructural changes observed in the corpus callosum across various neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis (ALS). New neuroimaging studies, mainly those that use diffusion tensor imaging (DTI) and advanced tractography methods, were put together to show how changes have happened in the organization of white matter and the connections between them. Some of the most common ways the corpus callosum breaks down are discussed, including less fractional anisotropy, higher mean diffusivity, and atrophy in certain regions. The relationship between these microstructural changes and cognitive decline, motor dysfunction, and disease progression is explored. Additionally, we consider the potential of corpus callosum imaging as a biomarker for early disease detection and monitoring. Studies show that people with these disorders have lower fractional anisotropy and higher mean diffusivity in the corpus callosum, often in ways that are specific to the disease. These changes often happen before gray matter atrophy and are linked to symptoms, which suggests that the corpus callosum could be used as an early sign of neurodegeneration. The review also highlights the implications of these findings for understanding disease mechanisms and developing therapeutic strategies. Future directions, including the application of advanced imaging techniques and longitudinal studies, are discussed to elucidate the role of corpus callosum degeneration in neurodegenerative processes. This review underscores the importance of the corpus callosum in understanding the pathophysiology of neurodegenerative diseases and its potential as a target for therapeutic interventions.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis

BACKGROUND

 The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights.

OBJECTIVE

 A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models.

METHODS

 A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI.

RESULTS

 A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone.

CONCLUSION

 Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics

BACKGROUND

This study aimed to establish radiomics models based on positron emission tomography (PET) images to longitudinally predict transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD).

METHODS

In our study, 278 MCI patients from the ADNI database were analyzed, where 60 transitioned to AD (pMCI) and 218 remained stable (sMCI) over 48 months. Patients were divided into a training set (n = 222) and a validation set (n = 56). We first employed voxel-based analysis of 18F-FDG PET images to identify brain regions that present significant SUV difference between pMCI and sMCI groups. Radiomic features were extracted from these regions, key features were selected, and predictive models were developed for individual and combined brain regions. The models' effectiveness was evaluated using metrics like AUC to determine the most accurate predictive model for MCI progression.

RESULTS

Voxel-based analysis revealed four brain regions implicated in the progression from MCI to AD. These include ROI1 within the Temporal lobe, ROI2 and ROI3 in the Thalamus, and ROI4 in the Limbic system. Among the predictive models developed for these individual regions, the model utilizing ROI4 demonstrated superior predictive accuracy. In the training set, the AUC for the ROI4 model was 0.803 (95% CI 0.736, 0.865), and in the validation set, it achieved an AUC of 0.733 (95% CI 0.559, 0.893). Conversely, the model based on ROI3 showed the lowest performance, with an AUC of 0.75 (95% CI 0.685, 0.809). Notably, the comprehensive model encompassing all identified regions (ROI total) outperformed the single-region models, achieving an AUC of 0.884 (95% CI 0.845, 0.921) in the training set and 0.816 (95% CI 0.705, 0.909) in the validation set, indicating significantly enhanced predictive capability for MCI progression to AD.

CONCLUSION

Our findings underscore the Limbic system as the brain region most closely associated with the progression from MCI to AD. Importantly, our study demonstrates that a PET brain radiomics model encompassing multiple brain regions (ROI total) significantly outperforms models based on single brain regions. This comprehensive approach more accurately identifies MCI patients at high risk of progressing to AD, offering valuable insights for non-invasive diagnostics and facilitating early and timely interventions in clinical settings.

The hippocampus as a structural and functional network epicentre for distant cortical thinning in neurocognitive aging

Alterations in grey matter (GM) and white matter (WM) are associated with memory impairment across the neurocognitive aging spectrum and theorised to spread throughout brain networks. Functional and structural connectivity (FC,SC) may explain widespread atrophy. We tested the effect of SC and FC to the hippocampus on cortical thickness (CT) of connected areas. In 419 (223 F) participants (agemean=73 ± 8) from the Alzheimer's Disease Neuroimaging Initiative, cortical regions associated with memory (Rey Auditory Verbal Learning Test) were identified using Lasso regression. Two structural equation models (SEM), for SC and resting-state FC, were fitted including CT areas, and SC and FC to the left and right hippocampus (LHIP,RHIP). LHIP (β=-0.150,p=<.001) and RHIP (β=-0.139,p=<.001) SC predicted left temporopolar/rhinal CT; RHIP SC predicted right temporopolar/rhinal CT (β=-0.191,p=<.001). LHIP SC predicted right fusiform/parahippocampal (β=-0.104,p=.011) and intraparietal sulcus/superior parietal CT (β=0.101,p=.028). Increased RHIP FC predicted higher left inferior parietal CT (β=0.132,p=.042) while increased LHIP FC predicted lower right fusiform/parahippocampal CT (β=-0.97; p=.023). The hippocampi may be epicentres for cortical thinning through disrupted connectivity.

Attenuated neural activity in processing decision-making feedback in uncertain conditions in patients with mild cognitive impairment

The present study aimed to explore the potential neural correlates during feedback evaluation during decision-making under risk and ambiguity in MCI. Nineteen individuals with MCI and twenty age-matched HCs were enrolled. Decision-making performance under risk and ambiguity was examined with the modified game of dice task (GDT) and an Iowa gambling task (IGT). Using task-related EEG data, reward positivity (RewP) and feedback P3 (fb-P3) were used to characterize participants' motivation and allocation of cognitive resources. Also, response time and event-related oscillation (ERO) were used to evaluate information processing speed, and the potent of post-feedback information integration and behavioral modulation. MCI patients had lower RewP (p = 0.022) and fb-P3 (p = 0.045) amplitudes in the GDT than HCs. Moreover, the amount and valence of feedback modulated the RewP (p = 0.008; p = 0.017) and fb-P3 (p < 0.001; p < 0.001). In the IGT, in addition to the significantly reduced fb-P3 observed in MCI patients (p = 0.010), the amount and valence of feedback modulated the RewP (p = 0.002; p = 0.020). Furthermore, MCI patients took longer to make decisions (t = 2.15, p = 0.041). The ERO analysis revealed that delta power was reduced in MCI (GDT: p = 0.045; p = 0.011). The findings suggest that, during feedback evaluation when making risky and ambiguous decisions, motivation, allocation of cognitive resources, information processing and neuronal excitability were attenuated in MCI. It implies that neural activity related to decision making was compromised in MCI.

Studying the Alzheimer's disease continuum using EEG and fMRI in single-modality and multi-modality settings

Alzheimer's disease (AD) is a biological, clinical continuum that covers the preclinical, prodromal, and clinical phases of the disease. Early diagnosis and identification of the stages of Alzheimer's disease (AD) are crucial in clinical practice. Ideally, biomarkers should reflect the underlying process (pathological or otherwise), be reproducible and non-invasive, and allow repeated measurements over time. However, the currently known biomarkers for AD are not suitable for differentiating the stages and predicting the trajectory of disease progression. Some objective parameters extracted using electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are widely applied to diagnose the stages of the AD continuum. While electroencephalography (EEG) has a high temporal resolution, fMRI has a high spatial resolution. Combined EEG and fMRI (EEG-fMRI) can overcome single-modality drawbacks and obtain multi-dimensional information simultaneously, and it can help explore the hemodynamic changes associated with the neural oscillations that occur during information processing. This technique has been used in the cognitive field in recent years. This review focuses on the different techniques available for studying the AD continuum, including EEG and fMRI in single-modality and multi-modality settings, and the possible future directions of AD diagnosis using EEG-fMRI.

Linking sarcopenia, brain structure and cognitive performance: a large-scale UK Biobank study

Sarcopenia refers to age-related loss of muscle mass and function and is related to impaired somatic and brain health, including cognitive decline and Alzheimer's disease. However, the relationships between sarcopenia, brain structure and cognition are poorly understood. Here, we investigate the associations between sarcopenic traits, brain structure and cognitive performance. We included 33 709 UK Biobank participants (54.2% female; age range 44-82 years) with structural and diffusion magnetic resonance imaging, thigh muscle fat infiltration (n = 30 561) from whole-body magnetic resonance imaging (muscle quality indicator) and general cognitive performance as indicated by the first principal component of a principal component analysis across multiple cognitive tests (n = 22 530). Of these, 1703 participants qualified for probable sarcopenia based on low handgrip strength, and we assigned the remaining 32 006 participants to the non-sarcopenia group. We used multiple linear regression to test how sarcopenic traits (probable sarcopenia versus non-sarcopenia and percentage of thigh muscle fat infiltration) relate to cognitive performance and brain structure (cortical thickness and area, white matter fractional anisotropy and deep and lower brain volumes). Next, we used structural equation modelling to test whether brain structure mediated the association between sarcopenic and cognitive traits. We adjusted all statistical analyses for confounders. We show that sarcopenic traits (probable sarcopenia versus non-sarcopenia and muscle fat infiltration) are significantly associated with lower cognitive performance and various brain magnetic resonance imaging measures. In probable sarcopenia, for the included brain regions, we observed widespread significant lower white matter fractional anisotropy (77.1% of tracts), predominantly lower regional brain volumes (61.3% of volumes) and thinner cortical thickness (37.9% of parcellations), with |r| effect sizes in (0.02, 0.06) and P-values in (0.0002, 4.2e-29). In contrast, we observed significant associations between higher muscle fat infiltration and widespread thinner cortical thickness (76.5% of parcellations), lower white matter fractional anisotropy (62.5% of tracts) and predominantly lower brain volumes (35.5% of volumes), with |r| effect sizes in (0.02, 0.07) and P-values in (0.0002, 1.9e-31). The regions showing the most significant effect sizes across the cortex, white matter and volumes were of the sensorimotor system. Structural equation modelling analysis revealed that sensorimotor brain regions mediate the link between sarcopenic and cognitive traits [probable sarcopenia: P-values in (0.0001, 1.0e-11); muscle fat infiltration: P-values in (7.7e-05, 1.7e-12)]. Our findings show significant associations between sarcopenic traits, brain structure and cognitive performance in a middle-aged and older adult population. Mediation analyses suggest that regional brain structure mediates the association between sarcopenic and cognitive traits, with potential implications for dementia development and prevention.

Alterations in hippocampus-centered morphological features and function of the progression from normal cognition to mild cognitive impairment

Mild cognitive impairment (MCI) is a significant precursor to dementia, highlighting the critical need for early identification of individuals at high risk of MCI to prevent cognitive decline. The study aimed to investigate the changes in brain structure and function before the onset of MCI. This study enrolled 19 older adults with progressive normal cognition (pNC) to MCI and 19 older adults with stable normal cognition (sNC). The gray matter (GM) volume and functional connectivity (FC) were estimated via magnetic resonance imaging during their normal cognition state 3 years prior. Additionally, spatial associations between FC maps and neurochemical profiles were examined using JuSpace. Compared to the sNC group, the pNC group showed decreased volume in the left hippocampus and left amygdala. The significantly positive correlation was observed between the GM volume of the left hippocampus and the MMSE scores after 3 years in pNC group. Besides, it showed that the pNC group had increased FC between the left hippocampus and the anterior-posterior cingulate gyrus, which was significantly correlated with the spatial distribution of dopamine D2 and noradrenaline transporter. Taken together, the study identified the abnormal brain characteristics before the onset of MCI, which might provide insight into clinical research.

Associations of Polycystic Ovary Syndrome With Indicators of Brain Health at Midlife in the CARDIA Cohort

BACKGROUND AND OBJECTIVES

Polycystic ovary syndrome (PCOS) is a common reproductive disorder associated with an adverse cardiometabolic profile early in life. Increasing evidence links cardiovascular risk factors, such as diabetes and hypertension, to accelerated cognitive aging. However, less is known about PCOS and its relationship to brain health, particularly at midlife. Our goal was to investigate possible associations between PCOS and midlife cognitive function and brain MRI findings in an ongoing prospective study.

METHODS

We used data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, a geographically diverse prospective cohort study of individuals who were 18-30 years at baseline (1985-1986) and followed for 30 years. We identified women with PCOS from an ancillary study (CARDIA Women's study (CWS); n = 1,163) as those with elevated androgen levels and/or hirsutism in conjunction with symptoms of oligomenorrhea. At year 30, participants completed cognitive testing, including the Montreal Cognitive Assessment, Rey Auditory Verbal Learning Test (RAVLT) (verbal learning and memory), Digit Symbol Substitution Test (processing speed and executive function), Stroop test (attention and cognitive control), and category and letter fluency tests (semantics and attention). A subset completed brain MRI to assess brain structure and white matter integrity. Multivariable linear regression models estimated the association between PCOS and outcomes, adjusting for age, race, education, and study center.

RESULTS

Of the 1163 women in CWS, 907 completed cognitive testing, and of these, 66 (7.1%) met criteria for PCOS (age 54.7 years). Women with and without PCOS were similar for age, BMI, smoking/drinking status, and income. At year 30, participants with PCOS performed lower (mean z score; 95% CI) on Stroop (-0.323 (-0.69 to -7.37); p = 0.008), RAVLT (-0.254 (-0.473 to -0.034); p = 0.002), and category fluency (-0.267 (-0.480 to -0.040); p = 0.02) tests. Of the 291 participants with MRI, 25 (8.5%) met PCOS criteria and demonstrated lower total white matter fractional anisotropy, a measure of white matter integrity (coefficient (95% CI) -0.013 (-0.021 to -0.005); p = 0.002), though not abnormal white matter.

DISCUSSION

Our results suggest that women with PCOS have lower cognitive performance and lower white matter integrity at midlife. Additional research is needed to confirm these findings and to determine potential mechanistic pathways including potential modifiable factors.

Functional magnetic resonance imaging study during resting state and visual oddball task in mild cognitive impairment

BACKGROUND

Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and dementia, and identifying early biomarkers is crucial for disease detection and intervention. Functional magnetic resonance imaging (fMRI) has the potential to identify changes in neural activity in MCI.

METHODS

We investigated neural activity changes in the visual network of the aMCI patients (n:20) and healthy persons (n:17) using resting-state fMRI and visual oddball task fMRI. We used independent component analysis to identify regions of interest and compared the activity between groups using a false discovery rate correction.

RESULTS

Resting-state fMRI revealed increased activity in the areas that have functional connectivity with the visual network, including the right superior and inferior lateral occipital cortex, the right angular gyrus and the temporo-occipital part of the right middle temporal gyrus (p-FDR = 0.008) and decreased activity in the bilateral thalamus and caudate nuclei, which are part of the frontoparietal network in the aMCI group (p-FDR = 0.002). In the visual oddball task fMRI, decreased activity was found in the right frontal pole, the right frontal orbital cortex, the left superior parietal lobule, the right postcentral gyrus, the right posterior part of the supramarginal gyrus, the right superior part of the lateral occipital cortex, and the right angular gyrus in the aMCI group.

CONCLUSION

Our results suggest the alterations in the visual network are present in aMCI patients, both during resting-state and task-based fMRI. These changes may represent early biomarkers of aMCI and highlight the importance of assessing visual processing in cognitive impairment. However, future studies with larger sample sizes and longitudinal designs are needed to confirm these findings.

Comparison of the Influence of Dual-Task Activities on Prefrontal Activation and Gait Variables in Older Adults with Mild Cognitive Impairment during Straight and Curved Walking

Background and Objectives: Mild cognitive impairment (MCI) is an early stage of dementia in which everyday tasks can be maintained; however, notable challenges may occur in memory, focus, and problem-solving skills. Therefore, motor-cognitive dual-task training is warranted to prevent cognitive decline and improve cognition in aging populations. This study aimed to determine the influence of such dual-task activities during straight and curved walking on the activities of the prefrontal cortex and associated gait variables in older adults with MCI. Materials and Methods: Twenty-seven older adults aged ≥65 years and identified as having MCI based on their scores (18-23) on the Korean Mini-Mental State Examination were enrolled. The participants performed four task scenarios in random order: walking straight, walking straight with a cognitive task, walking curved, and walking curved with a cognitive task. The activation of the prefrontal cortex, which is manifested by a change in the level of oxyhemoglobin, was measured using functional near-infrared spectroscopy. The gait speed and step count were recorded during the task performance. Results: Significant differences were observed in prefrontal cortex activation and gait variables (p < 0.05). Specifically, a substantial increase was observed in prefrontal cortex activation during a dual task compared with that during a resting-state (p < 0.013). Additionally, significant variations were noted in the gait speed and step count (p < 0.05). Conclusions: This study directly demonstrates the impact of motor-cognitive dual-task training on prefrontal cortex activation in older adults with MCI, suggesting the importance of including such interventions in enhancing cognitive function.

Memory encoding and retrieval by retrosplenial parvalbumin interneurons are impaired in Alzheimer's disease model mice

Memory deficits in Alzheimer's disease (AD) show a strong link with GABAergic interneuron dysfunctions.1,2,3,4,5,6,7 The ensemble dynamics of GABAergic interneurons represent memory encoding and retrieval,8,9,10,11,12 but how GABAergic interneuron dysfunction affects inhibitory ensemble dynamics in AD is unknown. As the retrosplenial cortex (RSC) is critical for episodic memory13,14,15,16 and is affected by β-amyloid accumulation in early AD,17,18,19,20,21 we address this question by performing Ca2+ imaging in RSC parvalbumin (PV)-expressing interneurons during a contextual fear memory task in healthy control mice and the 5XFAD mouse model of AD. We found that populations of PV interneurons responsive to aversive electric foot shocks during contextual fear conditioning (shock-responsive) significantly decreased in the 5XFAD mice, indicating dysfunctions in the recruitment of memory-encoding PV interneurons. In the control mice, ensemble activities of shock-responsive PV interneurons were selectively upregulated during the freezing epoch of the contextual fear memory retrieval, manifested by synaptic potentiation of PV interneuron-mediated inhibition. However, such changes in ensemble dynamics during memory retrieval and synaptic plasticity were both absent in the 5XFAD mice. Optogenetic silencing of PV interneurons during contextual fear conditioning in the control mice mimicked the memory deficits in the 5XFAD mice, while optogenetic activation of PV interneurons in the 5XFAD mice restored memory retrieval. These results demonstrate the critical roles of contextual fear memory-encoding PV interneurons for memory retrieval. Furthermore, synaptic dysfunction of PV interneurons may disrupt the recruitment of PV interneurons and their ensemble dynamics underlying contextual fear memory retrieval, subsequently leading to memory deficits in AD.

Ocular biomarkers of cognitive decline based on deep-learning retinal vessel segmentation

BACKGROUND

The current literature shows a strong relationship between retinal neuronal and vascular alterations in dementia. The purpose of the study was to use NFN+ deep learning models to analyze retinal vessel characteristics for cognitive impairment (CI) recognition.

METHODS

We included 908 participants from a community-based cohort followed for over 15 years (the prospective KaiLuan Study) who underwent brain magnetic resonance imaging (MRI) and fundus photography between 2021 and 2022. The cohort consisted of both cognitively healthy individuals (N = 417) and those with cognitive impairment (N = 491). We employed the NFN+ deep learning framework for retinal vessel segmentation and measurement. Associations between Retinal microvascular parameters (RMPs: central retinal arteriolar / venular equivalents, arteriole to venular ratio, fractal dimension) and CI were assessed by Pearson correlation. P < 0.05 was considered statistically significant. The correlation between the CI and RMPs were explored, then the correlation coefficients between CI and RMPs were analyzed. Random Forest nonlinear classification model was used to predict whether one having cognitive decline or not. The assessment criterion was the AUC value derived from the working characteristic curve.

RESULTS

The fractal dimension (FD) and global vein width were significantly correlated with the CI (P < 0.05). Age (0.193), BMI (0.154), global vein width (0.106), retinal vessel FD (0.099), and CRAE (0.098) were the variables in this model that were ranked in order of feature importance. The AUC values of the model were 0.799.

CONCLUSIONS

Establishment of a predictive model based on the extraction of vascular features from fundus images has a high recognizability and predictive power for cognitive function and can be used as a screening method for CI.

Thinner inner retinal layers are associated with lower cognitive performance, lower brain volume, and altered white matter network structure-The Maastricht Study

INTRODUCTION

The retina may provide non-invasive, scalable biomarkers for monitoring cerebral neurodegeneration.

METHODS

We used cross-sectional data from The Maastricht study (n = 3436; mean age 59.3 years; 48% men; and 21% with type 2 diabetes [the latter oversampled by design]). We evaluated associations of retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses with cognitive performance and magnetic resonance imaging indices (global grey and white matter volume, hippocampal volume, whole brain node degree, global efficiency, clustering coefficient, and local efficiency).

RESULTS

After adjustment, lower thicknesses of most inner retinal layers were significantly associated with worse cognitive performance, lower grey and white matter volume, lower hippocampal volume, and worse brain white matter network structure assessed from lower whole brain node degree, lower global efficiency, higher clustering coefficient, and higher local efficiency.

DISCUSSION

The retina may provide biomarkers that are informative of cerebral neurodegenerative changes in the pathobiology of dementia.

Effects of D-CAG chemotherapy regimen on cognitive function in patients with acute myeloid leukaemia: A resting-state functional magnetic resonance imaging study

Post-chemotherapy cognitive impairment, also known as 'chemobrain', is a common neurotoxic complication induced by chemotherapy, which has been reported in many cancer survivors who have undergone chemotherapy. In this study, we aimed to explore the effects of D-neneneba dicitabine, C-nenenebb cytarabine, A-aclamycin, G-granulocyte colony-stimulating factor (D-CAG) chemotherapy on cognitive function in patients with acute myeloid leukaemia (AML) and its possible central mechanisms. Twenty patients with AML and 25 matched healthy controls (HC) were enrolled in this study. The cognitive function of patients before and after D-CAG chemotherapy was evaluated by the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog). The resting-state functional magnetic resonance imaging data were collected from all patients before and after chemotherapy intervention, as well as HC. Then, resting-state functional magnetic resonance imaging data were preprocessed using DPABI software package and regional homogeneity (ReHo) values of brain regions were calculated. Finally, ReHo values between groups were compared by Resting-State fMRI Data Analysis software package with t-tests and Alphasim method was performed for multiple comparison correction. Moreover, associations between ReHo values of altered brain regions and the scores of FACT-Cog were analysed by Pearson correlation. The total FACT-Cog scores and four factor scores of AML patients increased significantly after treatment. ReHo values showed no significant changes in patients before treatment when compared with HC. Compared with HC, ReHo values of the right middle frontal gyrus, inferior frontal gyrus (opercular part), middle occipital gyrus, and left praecuneus decreased significantly, while ReHo values of the left inferior temporal gyrus, right middle temporal gyrus, and hippocampus increased significantly in patients after treatment. Compared with patients before treatment, ReHo values decreased significantly in the right middle frontal gyrus, inferior frontal gyrus (opercular part), and middle and inferior occipital gyri of patients after treatment. In addition, ReHo values of the right inferior frontal gyrus (opercular part) were negatively correlated with the total scores of FACT-Cog and factor scores of perceived cognitive impairment in patients after treatment. There were also negative correlations between ReHo values of the right middle frontal gyrus and perceived cognitive impairment scores. The present study confirmed that D-CAG chemotherapy might cause impaired subjective self-reported cognitive functioning in AML patients, which might be related to the decreased function of certain regions in the right prefrontal lobe. These findings provided further understanding of the mechanisms involved in post-chemotherapy cognitive impairment and would help develop new therapeutic strategies for 'chemobrain' in AML patients.

T1 and T2 weighted lesions and cognition in multiple Sclerosis: A systematic review and meta-analysis

BACKGROUND

Considering the different results regarding the correlation between Magnetic Resonance Imaging (MRI) structural measures and cognitive dysfunction in patients with MS, we aimed to perform a systematic review and meta-analysis study to investigate the correlation between T1 and T2 weighted lesions and cognitive scores to find the most robust MRI markers for cognitive function in MS population.

METHODS

The literature of this paper was identified through a comprehensive search of electronic datasets including PubMed, Scopus, Web of Science, and Embase in February 2022. Studies that reported the correlation between cognitive status and T1 and T2 weighted lesions in MS patients were selected.

RESULTS

21 studies with a total of 3771 MS patients with mean ages ranging from 30 to 57 years were entered into our study. Our analysis revealed that the volume of T1 lesions was significantly correlated with Symbol Digit Modality test (SDMT) (r: -0.30, 95 %CI: -0.59, -0.01) and Paced Auditory Serial-Addition Task (PASAT) scores (r: -0.23, 95 %CI: -0.36, -0.10). We investigated the correlation between T2 lesions and cognitive scores. The pooled estimates of z scores were significant for SDMT (r: -0.27, 95 %CI: -0.51, -0.03) and PASAT (r: -0.27, 95 %CI: -0.41, -0.13).

CONCLUSION

In conclusion, our systematic review and meta-analysis study provides strong evidence of the correlation between T1 and T2 lesions and cognitive function in MS patients. Further research is needed to explore the potential mechanisms underlying this relationship and to develop targeted interventions to improve cognitive outcomes in MS patients.

White matter alterations in mild cognitive impairment revealed by meta-analysis of diffusion tensor imaging using tract-based spatial statistics

The neuropathological mechanism of mild cognitive impairment (MCI) remains unclarified. Diffusion tensor imaging (DTI) studies revealed white matter (WM) microarchitecture alterations in MCI, but consistent findings and conclusions have not yet been drawn. The present coordinate-based meta-analysis (CBMA) of tract-based spatial statistics (TBSS) studies aimed to identify the most prominent and robust WM abnormalities in patients with MCI. A systematic search of relevant studies was conducted through January 2022 to identify TBSS studies comparing fractional anisotropy (FA) between MCI patients and healthy controls (HC). We used the seed-based d mapping (SDM) software to achieve the CBMA and analyze regional FA alterations in MCI. Meta-regression analysis was subsequently applied to explore the potential associations between clinical variables and FA changes. MCI patients demonstrated significantly decreased FA in widely distributed areas in the corpus callosum (CC), including the genu, body, and splenium of the CC, as well as one cluster in the left striatum. FA in the body of the CC and in three clusters in the splenium of the CC was negatively associated with the mean age. Additionally, FA in the genu of the CC and in three clusters in the splenium of the CC had negative correlations with the MMSE scores. Disrupted integrities of the CC and left striatum might play vital roles in the process of cognitive decline. These findings enhanced our understanding of the neural mechanism underlying WM neurodegeneration in MCI and provided perspectives for the early detection and intervention of dementia.Registration number: CRD42022235716.

Association between cerebrospinal fluid clusterin and biomarkers of Alzheimer's disease pathology in mild cognitive impairment: a longitudinal cohort study

Background

Clusterin, a glycoprotein implicated in Alzheimer's disease (AD), remains unclear. The objective of this study was to analyze the effect of cerebrospinal fluid (CSF) clusterin in relation to AD biomarkers using a longitudinal cohort of non-demented individuals.

Methods

We gathered a sample comprising 86 individuals under cognition normal (CN) and 134 patients diagnosed with MCI via the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. To investigate the correlation of CSF clusterin with cognitive function and markers of key physiological changes, we employed multiple linear regression and mixed-effect models. We undertook a causal mediation analysis to inspect the mediating influence of CSF clusterin on cognitive abilities.

Results

Pathological characteristics associated with baseline Aβ42, Tau, brain volume, exhibited a correlation with initial CSF clusterin in the general population, Specifically, these correlations were especially prominent in the MCI population; CSF Aβ42 (PCN = 0.001; PMCI = 0.007), T-tau (PCN < 0.001; PMCI < 0.001), and Mid temporal (PCN = 0.033; PMCI = 0.005). Baseline CSF clusterin level was predictive of measurable cognitive shifts in the MCI population, as indicated by MMSE (β = 0.202, p = 0.029), MEM (β = 0.186, p = 0.036), RAVLT immediate recall (β = 0.182, p = 0.038), and EF scores (β = 0.221, p = 0.013). In MCI population, the alterations in brain regions (17.87% of the total effect) mediated the effect of clusterin on cognition. It was found that variables such as age, gender, and presence of APOE ε4 carrier status, influenced some of these connections.

Conclusion

Our investigation underscored a correlation between CSF clusterin concentrations and pivotal AD indicators, while also highlighting clusterin's potential role as a protective factor for cognitive abilities in MCI patients.

Altered functional activity of the precuneus and superior temporal gyrus in patients with residual dizziness caused by benign paroxysmal positional vertigo

Objective

Benign paroxysmal positional vertigo (BPPV) is a common clinical vertigo disease, and the most effective treatment for this disease is canal repositioning procedures (CRP). Most patients return to normal after a single treatment. However, some patients still experience residual dizziness (RD) after treatment, and this disease's pathogenesis is currently unclear. The purpose of this study is to explore whether there are abnormal brain functional activities in patients with RD by using resting-state functional magnetic resonance imaging (rs-fMRI) and to provide imaging evidence for the study of the pathogenesis of RD.

Materials and methods

The BPPV patients in the Second Affiliated Hospital of Xuzhou Medical University had been included from December 2021 to November 2022. All patients had been received the collection of demographic and clinical characteristics (age, gender, involved semicircular canal, affected side, CRP times, BPPV course, duration of RD symptoms, and whether they had hypertension, diabetes, coronary heart disease.), scale assessment, including Dizziness Handicap Inventory (DHI), Hamilton Anxiety Inventory (HAMA), Hamilton Depression Inventory (HAMD), rs-fMRI data collection, CRP treatment, and then a one-month follow-up. According to the follow-up results, 18 patients with RD were included. At the same time, we selected 19 healthy individuals from our hospital's physical examination center who matched their age, gender as health controls (HC). First, the amplitude of low-frequency fluctuations (ALFF) analysis method was used to compare the local functional activities of the two groups of subjects. Then, the brain regions with different ALFF results were extracted as seed points. Functional connectivity (FC) analysis method based on seed points was used to explore the whole brain FC of patients with RD. Finally, a correlation analysis between clinical features and rs-fMRI data was performed.

Results

Compared to the HC, patients with RD showed lower ALFF value in the right precuneus and higher ALFF value in the right superior temporal gyrus (STG). When using the right STG as a seed point, it was found that the FC between the right STG, the right supramarginal gyrus (SMG), and the left precuneus was decreased in RD patients. However, no significant abnormalities in the FC were observed when using the right precuneus as a seed point.

Conclusion

In patients with RD, the local functional activity of the right precuneus is weakened, and the local functional activity of the right STG is enhanced. Furthermore, the FC between the right STG, the right SMG, and the left precuneus is weakened. These changes may explain the symptoms of dizziness, floating sensation, walking instability, neck tightness, and other symptoms in patients with RD to a certain extent.

The transition from Mild Cognitive Impairment of the Amnestic Type to early dementia: A phenomenological and neuropsychological case analysis

The scientific literature on neuropsychological correlates of Mild Cognitive Impairment of the Amnestic Type (MCI-A) often reports large group findings and employs multivariate statistics to describe domains of cognitive impairment associated with the transition of MCI-A to early dementia, typically of the Alzheimer's Type (AD). Individual patients may vary, however, in terms of specific changes in their neuropsychological test performance as they transition from MCI-A to probable AD. The subjective experiences of individuals during this time of transition can also vary but rarely are reported. Tracking both the patient's subjective experiences and their performance on neuropsychological measures provides a more complete picture of the patient's clinical situation. These combined sets of information help the clinical neuropsychologist provide a more individualized and personally relevant service. We present a phenomenological and neuropsychological case analysis of a 67-year-old woman who transitioned from MCI-A to probable early AD in an attempt to illustrate how such a combined analysis is helpful in their psychological care.

Functional activity of the caudate mediates the relation between early childhood microstructural variations and elevated metabolic syndrome scores

BACKGROUND

Metabolic syndrome score in children assesses the risk of developing cardiovascular disease in future. We aim to probe the role of the caudate in relation to the metabolic syndrome score. Furthermore, using both functional and structural neuroimaging, we aim to examine the interplay between functional and structural measures.

METHODS

A longitudinal birth cohort study with functional and structural neuroimaging data obtained at 4.5, 6.0 and 7.5 years and metabolic syndrome scores at 8.0 years was used. Pearson correlation and linear regression was used to test for correlation fractional anisotropy (FA) and fractional amplitude of low frequency fluctuations (fALFF) of the caudate with metabolic syndrome scores. Mediation analysis was used to test if later brain measures mediated the relation between earlier brain measures and metabolic syndrome scores. Inhibitory control was also tested as a mediator of the relation between caudate brain measures and metabolic syndrome scores.

RESULTS

FA at 4.5 years and fALFF at 7.5 years of the left caudate was significantly correlated with metabolic syndrome scores. Post-hoc mediation analysis showed that fALFF at 7.5 years fully mediated the relation between FA at 4.5 years and metabolic syndrome scores. Inhibitory control was significantly correlated with fALFF at 7.5 years, but did not mediate the relation between fALFF at 7.5 years and metabolic syndrome scores.

CONCLUSIONS

We found that variations in caudate microstructure at 4.5 years predict later variation in functional activity at 7.5 years. This later variation in functional activity fully mediates the relation between microstructural changes in early childhood and metabolic syndrome scores at 8.0 years.

An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease

The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.

Alzheimer's Disease: Insights from Large-Scale Brain Dynamics Models

Alzheimer's disease (AD) is a degenerative brain disease, and the condition is difficult to assess. In the past, numerous brain dynamics models have made remarkable contributions to neuroscience and the brain from the microcosmic to the macroscopic scale. Recently, large-scale brain dynamics models have been developed based on dual-driven multimodal neuroimaging data and neurodynamics theory. These models bridge the gap between anatomical structure and functional dynamics and have played an important role in assisting the understanding of the brain mechanism. Large-scale brain dynamics have been widely used to explain how macroscale neuroimaging biomarkers emerge from potential neuronal population level disturbances associated with AD. In this review, we describe this emerging approach to studying AD that utilizes a biophysically large-scale brain dynamics model. In particular, we focus on the application of the model to AD and discuss important directions for the future development and analysis of AD models. This will facilitate the development of virtual brain models in the field of AD diagnosis and treatment and add new opportunities for advancing clinical neuroscience.

The association between post-traumatic stress disorder (PTSD) and cognitive impairment: A systematic review of neuroimaging findings

BACKGROUND

Accumulating evidence suggests that post-traumatic stress disorder (PTSD) may increase the risk of various types of dementia. Despite the large number of studies linking these critical conditions, the underlying mechanisms remain unclear. The past decade has witnessed an exponential increase in interest on brain imaging research to assess the neuroanatomical underpinnings of PTSD. This systematic review provides a critical assessment of available evidence of neuroimaging correlates linking PTSD to a higher risk of dementia.

METHODS

The EMBASE, PubMed/MEDLINE, and SCOPUS electronic databases were systematically searched from 1980 to May 22, 2021 for original references on neuroimaging correlates of PTSD and risk of dementia. Literature search, screening of references, methodological quality appraisal of included articles as well as data extractions were independently conducted by at least two investigators. Eligibility criteria included: 1) a clear PTSD definition; 2) a subset of included participants must have developed dementia or cognitive impairment at any time point after the diagnosis of PTSD through any diagnostic criteria; and 3) brain imaging protocols [structural, molecular or functional], including whole-brain morphologic and functional MRI, and PET imaging studies linking PTSD to a higher risk of cognitive impairment/dementia.

RESULTS

Overall, seven articles met eligibility criteria, comprising findings from 366 participants with PTSD. Spatially convergent structural abnormalities in individuals with PTSD and co-occurring cognitive dysfunction involved primarily the bilateral frontal (e.g., prefrontal, orbitofrontal, cingulate cortices), temporal (particularly in those with damage to the hippocampi), and parietal (e.g., superior and precuneus) regions.

LIMITATIONS

A meta-analysis could not be performed due to heterogeneity and paucity of measurable data in the eligible studies.

CONCLUSIONS

Our systematic review provides putative neuroimaging correlates associated with PTSD and co-occurring dementia/cognitive impairment particularly involving the hippocampi. Further research examining neuroimaging features linking PTSD to dementia are clearly an unmet need of the field. Future imaging studies should provide a better control for relevant confounders, such as the selection of more homogeneous samples (e.g., age, race, education), a proper control for co-occurring disorders (e.g., co-occurring major depressive and anxiety disorders) as well as the putative effects of psychotropic medication use. Furthermore, prospective studies examining imaging biomarkers associated with a higher rate of conversion from PTSD to dementia could aid in the stratification of people with PTSD at higher risk for developing dementia for whom putative preventative interventions could be especially beneficial.

APOE ɛ4, but not polygenic Alzheimer's disease risk, is related to longitudinal decrease in hippocampal brain activity in non-demented individuals

The hippocampus is affected early in Alzheimer's disease (AD) and altered hippocampal functioning influences normal cognitive aging. Here, we used task-based functional MRI to assess if the APOE ɛ4 allele or a polygenic risk score (PRS) for AD was linked to longitudinal changes in memory-related hippocampal activation in normal aging (baseline age 50-95, n = 292; n = 182 at 4 years follow-up, subsequently non-demented for at least 2 years). Mixed-models were used to predict level and change in hippocampal activation by APOE ɛ4 status and PRS based on gene variants previously linked to AD at p ≤ 1, p < 0.05, or p < 5e-8 (excluding APOE). APOE ɛ4 and PRS_p<5e-8 significantly predicted AD risk in a larger sample from the same study population (n = 1542), while PRS_p≤1 predicted memory decline. APOE ɛ4 was linked to decreased hippocampal activation over time, with the most prominent effect in the posterior hippocampi, while PRS was unrelated to hippocampal activation at all p-thresholds. These results suggests a link for APOE ɛ4, but not for AD genetics in general, on functional changes of the hippocampi in normal aging.

Excellent Interrater Reliability for Manual Segmentation of the Medial Perirhinal Cortex

Objective: Evaluation of interrater reliability for manual segmentation of brain structures that are affected first by neurofibrillary tau pathology in Alzheimer's disease. Method: Medial perirhinal cortex, lateral perirhinal cortex, and entorhinal cortex were manually segmented by two raters on structural magnetic resonance images of 44 adults (20 men; mean age = 69.2 ± 10.4 years). Intraclass correlation coefficients (ICC) of cortical thickness and volumes were calculated. Results: Very high ICC values of manual segmentation for the cortical thickness of all regions (0.953-0.986) and consistently lower ICC values for volume estimates of the medial and lateral perirhinal cortex (0.705-0.874). Conclusions: The applied manual segmentation protocol allows different raters to achieve remarkably similar cortical thickness estimates for regions of the parahippocampal gyrus. In addition, the results suggest a preference for cortical thickness over volume as a reliable measure of atrophy, especially for regions affected by collateral sulcus variability (i.e., medial and lateral perirhinal cortex). The results provide a basis for future automated segmentation and collection of normative data.

Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms

Introduction

Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections).

Methods

Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used.

Results

Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants.

Discussion

In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.

Preclinical Brain Network Abnormalities in Patients with Subjective Cognitive Decline

BACKGROUND

Alzheimer's disease (AD) is the most common form of dementia worldwide. Currently there are no disease modifying treatments available. Detecting subjects with increased risk to develop dementia is essential for future clinical trials. Subjective cognitive decline (SCD) is a condition defining individuals who perceive a decrease in their own cognitive functioning in the absence of any detectable deficit on neuropsychological testing. SCD individuals show AD-related biomarkers abnormalities in cerebrospinal fluid.

OBJECTIVE

The aim of the present study was to assess brain functional connectivity (FC) changes in SCD individuals.

METHODS

23 SCD and 33 healthy subjects (HS) underwent an extensive neuropsychological assessment and 3T-MRI scanning including a T1-w volume and resting-state fMRI (RS-fMRI) to assess brain atrophy and brain FC.

RESULTS

No between-group differences in grey matter volumes were detected. SCD subjects compared to HS showed both increased and decreased FC in the executive and parietal networks. Associations between cognitive measures, mainly assessing working memory, and FC within brain networks were found both in SCD and HS separately.

CONCLUSIONS

SCD individuals showed FC abnormalities in networks involving fronto-parietal areas that may account for their lower visuo-spatial working memory performances. Dysfunctions in executive-frontal networks may be responsible for the cognitive decline subjectively experienced by SCD individuals despite the normal scores observed by formal neuropsychological assessment. The present study contributes to consider SCD individuals in an early AD stage with an increased risk of developing the disease in the long term.

Identification of inflammasome signaling proteins in neurons and microglia in early and intermediate stages of Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease that destroys memory and cognitive function. Inflammasome activation has been suggested to play a critical role in the neuroinflammatory response in AD progression, but the cell-type expression of inflammasome proteins in the brain has not been fully characterized. In this study, we used samples from the hippocampus formation, the subiculum, and the entorhinal cortex brain from 17 donors with low-level AD pathology and 17 intermediate AD donors to assess the expression of inflammasome proteins. We performed analysis of hippocampal thickness, β-amyloid plaques, and hyperphosphorylated tau to ascertain the cellular pathological changes that occur between low and intermediate AD pathology. Next, we determined changes in the cells that express the inflammasome sensor proteins NOD-like receptor proteins (NLRP) 1 and 3, and caspase-1. In addition, we stained section with IC100, a humanized monoclonal antibody directed against the inflammasome adaptor protein apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), and a commercially available anti-ASC antibody. Our results indicate that hippocampal cortical thickness did not significantly change between low and intermediate AD pathology, but there was an increase in pTau and β-amyloid clusters in intermediate AD cases. NLRP3 was identified mainly in microglial populations, whereas NLRP1 was seen in neuronal cytoplasmic regions. There was a significant increase of ASC in neurons labeled by IC100, whereas microglia in the hippocampus and subiculum were labeled with the commercial anti-ASC antibody. Caspase-1 was present in the parenchyma in the CA regions where amyloid and pTau were identified. Together, our results indicate increased inflammasome protein expression in the early pathological stages of AD, that IC100 identifies neurons in early stages of AD and that ASC expression correlates with Aβ and pTau in postmortem AD brains.

Early dementia diagnosis, MCI-to-dementia risk prediction, and the role of machine learning methods for feature extraction from integrated biomarkers, in particular for EEG signal analysis

INTRODUCTION

Dementia in its various forms represents one of the most frightening emergencies for the aging population. Cognitive decline-including Alzheimer's disease (AD) dementia-does not develop in few days; disease mechanisms act progressively for several years before clinical evidence.

METHODS

A preclinical stage, characterized by measurable cognitive impairment, but not overt dementia, is represented by mild cognitive impairment (MCI), which progresses to-or, more accurately, is already in a prodromal form of-AD in about half cases; people with MCI are therefore considered the population at risk for AD deserving special attention for validating screening methods.

RESULTS

Graph analysis tools, combined with machine learning methods, represent an interesting probe to identify the distinctive features of physiological/pathological brain aging focusing on functional connectivity networks evaluated on electroencephalographic data and neuropsychological/imaging/genetic/metabolic/cerebrospinal fluid/blood biomarkers.

DISCUSSION

On clinical data, this innovative approach for early diagnosis might provide more insight into pathophysiological processes underlying degenerative changes, as well as toward a personalized risk evaluation for pharmacological, nonpharmacological, and rehabilitation treatments.

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.

EMCI: A Novel EEG-Based Mental Workload Assessment Index of Mild Cognitive Impairment

As aging deepens, early detection of mild cognitive impairment (MCI) is increasingly important to prevent Alzheimer Dementia (AD) and improve the quality of life of older adults. In recent years, a large number of studies focus on the abnormal brain cognitive function of MCI, while ignoring the quantitative evaluation of MCI's mental workload. In this study, we propose a workload index for MCI screening, named EMCI, which is a linear discriminant cumulative estimate of subjects' electroencephalography (EEG) power spectra in α and β rhythms. Then, we design a matched prototype system to verify the effectiveness of EMCI. The results show that the EMCI is sensitive to changes of subjects' mental workload, and is significantly lower in MCI than in HC (Health control), which may be precisely caused by cognitive dysfunction. The proposed EMCI index can be used for online assessment of mental workload in older adults, which can help achieve quick screening of MCI and provide a critical window for clinical treatment interventions.

Hippocampal morphological atrophy and distinct patterns of structural covariance network in Alzheimer's disease and mild cognitive impairment

Elucidating distinct morphological atrophy patterns of Alzheimer's disease (AD) and its prodromal stage, namely, mild cognitive impairment (MCI) helps to improve early diagnosis and medical intervention of AD. On that account, we aimed to obtain distinct patterns of voxel-wise morphological atrophy and its further perturbation on structural covariance network in AD and MCI compared with healthy controls (HCs). T1-weighted anatomical images of matched AD, MCI, and HCs were included in this study. Gray matter volume was obtained using voxel-based morphometry and compared among three groups. In addition, structural covariance network of identified brain regions exhibiting morphological difference was constructed and compared between pairs of three groups. Thus, patients with AD have a reduced hippocampal volume and an increased rate of atrophy compared with MCI and HCs. MCI exhibited a decreased trend in bilateral hippocampal volume compared with HCs and the accelerated right hippocampal atrophy rate than HCs. In AD, the hippocampus further exhibited increased structural covariance connected to reward related brain regions, including the anterior cingulate cortex, the putamen, the caudate, and the insula compared with HCs. In addition, the patients with AD exhibited increased structural covariance of left hippocampus with the bilateral insula, the inferior frontal gyrus, the superior temporal gyrus, and the cerebellum than MCI. These results reveal distinct patterns of morphological atrophy in AD and MCI, providing new insights into pathology of AD.

Alteration of Visuospatial System as an Early Marker of Cognitive Decline: A Double-Center Neuroimaging Study

Amnestic-type mild cognitive impairment (a-MCI) represents the prodromal phase of Alzheimer's disease associated with a high conversion rate to dementia and serves as a potential golden period for interventions. In our study, we analyzed the role of visuospatial (VS) functions and networks in the recognition of a-MCI. We examined 78 participants (32 patients and 46 controls) in a double-center arrangement using neuropsychology, structural, and resting-state functional MRI. We found that imaging of the lateral temporal areas showed strong discriminating power since in patients only the temporal pole (F = 5.26, p = 0.034) and superior temporal gyrus (F = 8.04, p < 0.001) showed reduced cortical thickness. We demonstrated significant differences between controls and patients in various neuropsychological results; however, analysis of cognitive subdomains revealed that the largest difference was presented in VS skills (F = 8.32, p < 0.001). Functional connectivity analysis of VS network showed that patients had weaker connectivity between the left and right frontotemporal areas, while stronger local connectivity was presented between the left frontotemporal structures (FWE corrected p < 0.05). Our results highlight the remarkable potential of examining the VS system in the early detection of cognitive decline. Since resting-state setting of functional MRI simplifies the possible automatization of data analysis, detection of VS system alterations might provide a non-invasive biomarker of a-MCI.

Volume, density, and thickness brain abnormalities in mild cognitive impairment: an ALE meta-analysis controlling for age and education

Prior meta-analyses have provided important information regarding which brain areas are structurally compromised in individuals with mild cognitive impairment (MCI). These studies have not however separated volume, density, and thickness, controlled for important demographic influences, considered null findings, or recognized studies indicating increased brain volumes in MCI individuals. Furthermore, there is a question as to whether deficits extend into cortical regions, and also into the thalamus. This study aims to address these issues using activation likelihood estimation (ALE) analyses with a sample size more than twice that of prior meta-analyses. A total of 71 studies were identified and entered into the ALE analysis which consisted of 2262 with MCI and 1902 healthy controls. Three major clusters were identified showing decreased gray matter volume in the MCI group compared to controls, with the most salient decreases being in the hippocampus, parahippocampal gyrus, and the amygdala. Reduced thalamic volume was also observed, but to a lesser extent. Density was reduced in the left hippocampus, while thickness was reduced in the uncus. No significant cluster emerged from an ALE meta-analysis of studies finding volume increases in MCI individuals. While the MCI group was significantly older and less educated than controls, controlling for these factors still resulted in significant, albeit attenuated findings. These results support hippocampal and parahippocampal deficits in MCI, and further highlight the amygdala, thalamus, and uncus as other areas to be considered in future MCI studies.

Decision-Making Profiles and Their Associations with Cognitive Performance in Mild Cognitive Impairment

Background: It is crucial for older adults, especially those with mild cognitive impairment (MCI), to make profitable decisions routinely. However, the results regarding decision-making (DM) remain inconsistent. Objective: The present study assessed DM profiles under uncertainty conditions in individuals with MCI and their associations with multi-domain cognitive performance. Method: Fifty-three patients with MCI and forty-two age-, gender-, and education level-matched healthy controls (HCs) were administered a comprehensive neuropsychological battery test. The Iowa Gambling Task (IGT) and Game of Dice Task (GDT) were used to assess DM competence in conditions involving ambiguity and risk, respectively. In addition, Spearman’s correlations were used to examine relationships between GDT and multi-domain cognitive performance. Result: The final capital (FC) and frequency of utilization of negative feedback (FUNF) and positive feedback (FUPF) in the GDT were lower in MCI patients than in HCs. In addition, the number of shifts between safe and risky alternatives was significantly different across groups. However, IGT performance was comparable across groups. In the MCI patients, risky DM performance was associated with language, whereas in HCs was correlated with memory and executive functions. Besides, in MCI, performance on IGT was significantly correlated with social cognition. Conclusion: Individuals with mild cognitive impairment have difficulty utilizing feedback to make optimal decisions under risky situations. The association between decision-making performance and cognitive function is divergent regarding situational uncertainty and individuals’ cognitive status. In mild cognitive impairment and normal aging, decision-making under ambiguity needs further investigation.

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.

Distinct profiles of functional connectivity density aberrance in Alzheimer's disease and mild cognitive impairment

INTRODUCTION

Investigating the neuroimaging changes from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of great significance. However, the details about the distinct functional characteristics of AD and MCI remain unknown.

METHODS

In this study, we investigated distinct profiles of functional connectivity density (FCD) differences between AD and MCI compared with the normal population, aiming to depict the progressive brain changes from MCI to AD. As a data-driven method, FCD measures the profiles of FC for the given voxel at different scales. Resting-state functional magnetic resonance imaging (fMRI) images were obtained from patients with AD and MCI and matched healthy controls (HCs). One-way ANCOVA was used to investigate (global, long-range, and local) FCD differences among the three groups followed by post-hoc analysis controlling age, sex, and head motion.

RESULTS

The three groups exhibited significant global FCD differences in the superior frontal gyrus. The post-hoc results further showed that patients with AD had a significant increase in global FCD values than those with MCI and HCs. Patients with MCI exhibited an increased trend compared with HCs. We further identified brain regions contributing to the observed global FCD differences by conducting seed-based FC analysis. We also identified that the observed global FCD differences were the additive effects of altered FC between the superior frontal gyrus and the posterior default model network.

DISCUSSION

These results depicted the global information communication capability impairment in AD and MCI providing a new insight into the progressive brain changes from MCI to AD.

Saturation Transfer MRI for Detection of Metabolic and Microstructural Impairments Underlying Neurodegeneration in Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common causes of dementia and difficult to study as the pool of subjects is highly heterogeneous. Saturation transfer (ST) magnetic resonance imaging (MRI) methods are quantitative modalities with potential for non-invasive identification and tracking of various aspects of AD pathology. In this review we cover ST-MRI studies in both humans and animal models of AD over the past 20 years. A number of magnetization transfer (MT) studies have shown promising results in human brain. Increased computing power enables more quantitative MT studies, while access to higher magnetic fields improves the specificity of chemical exchange saturation transfer (CEST) techniques. While much work remains to be done, results so far are very encouraging. MT is sensitive to patterns of AD-related pathological changes, improving differential diagnosis, and CEST is sensitive to particular pathological processes which could greatly assist in the development and monitoring of therapeutic treatments of this currently incurable disease.