Subjective Cognitive Decline Subjects Research Articles

Olfaction and thyroid hormones in patients with subjective cognitive decline, non-amnestic and amnestic mild cognitive impairment.

Thyroid hormones may affect olfaction in different stages of cognitive impairment: subjective cognitive decline (SCD), non-amnestic (naMCI) and amnestic mild cognitive impairment (aMCI). Additionally, biometric parameters, depression, and neuropsychological performance are considered as possible influencing factors. Aretrospective single-center data analysis was conducted during the observation period 2001-2023, with n = 495 (52.3% female) SCD, naMCI and aMCI subjects, aged ≥50years, at the General Hospital of Vienna. The criterion olfactory function was objectively measured by Sniffin' Sticks© odor identification and subjectively through the Assessment of Self-Reported Olfactory Functioning test. Serum thyroid hormone levels, mainly thyroid-stimulating hormone, as well as T3, T4, fT3, and fT4, were used to assess thyroid function. Statistical analyses using IBM SPSS® 29.0.0 covered adjusted multiple linear regression models with hierarchical blocks to predict olfactory performance considering β‑weights. Of the study participants, 4.2% had hypothyroidism and 2.4% had hyperthyroidism. The majority exhibited normal thyroid function. One third (33.5%; 95% confidence interval, CI 29.4-37.0%) were hyposmic. The results indicate no substantial association between thyroid and olfactory functions. Increasing age (β = 0.20), lower performance in the Neuropsychological Test Battery Vienna (NTBV) dimensions verbal memory (β = -0.33) and attention (β = -0.12) appear to be risk factors for lower olfaction. Adiscrepancy between subjective and objective olfaction was found. Thyroid and olfactory functions had no substantial relationship. Higher fT4 correlated weakly with lower odor identification. Increasing age and decreased performance in two out of six NTBV dimensions are relevant prognostic factors for olfactory dysfunction.

Altered functional connectivity of primary olfactory cortex-hippocampus-frontal cortex in subjective cognitive decline during odor stimulation.

Subjective cognitive decline (SCD) is a high-risk population in the preclinical stage of Alzheimer's disease (AD), and olfactory dysfunction is a risk factor for dementia progression. The present study aimed to explore the patterns of functional connectivity (FC) changes in the olfactory neural circuits during olfactory stimulation in SCD subjects. A total of 56 SCD subjects and 56 normal controls (NCs) were included. All subjects were assessed with a cognitive scale, an olfactory behavior test, and olfactory task-based functional magnetic resonance imaging scanning. The FC differences in olfactory neural circuits between the two groups were analyzed by the generalized psychophysiological interaction. Additionally, we calculated and compared the activation of brain regions within the olfactory neural circuits during odor stimulation, the volumetric differences in brain regions showing FC differences between groups, and the correlations between neuroimaging indicators and olfactory behavioral and cognitive scale scores. During odor stimulation, the FC between the bilateral primary olfactory cortex (bPOC) and the right hippocampus in the SCD group was significantly reduced; while the FC between the right hippocampus and the right frontal cortex was significantly increased in the SCD group. The bPOC of all subjects showed significant activation, but no significant difference in activation between groups was found. No significant differences were observed in the volume of the brain regions within the olfactory neural circuits or in olfactory behavior between groups. The volume of the bPOC and right frontal cortex was significantly positively correlated with olfactory identification, and the volume of the right frontal cortex and right hippocampus was significantly correlated with cognitive functions. Furthermore, a significant correlation between the activation of bPOC and the olfactory threshold was found in the whole cohort. These results suggested that while the structure of the olfactory neural circuits and olfactory behavior in SCD subjects remained stable, there were significant changes observed in the FC of the olfactory neural circuits (specifically, the POC-hippocampus-frontal cortex neural circuits) during odor stimulation. These findings highlight the potential of FC alterations as sensitive imaging markers for identifying high-risk individuals in the early stage of AD.

Effects of 6-Month Combined Physical Exercise and Cognitive Training on Neuropsychological and Neurophysiological Function in Older Adults with Subjective Cognitive Decline: A Randomized Controlled Trial.

Multidomain intervention may delay or ameliorate cognitive decline in older adults at risk of Alzheimer's disease, particularly in the memory and inhibitory functions. However, no study systematically investigates the changes of brain function in cognitively-normal elderly with subjective cognitive decline (SCD) when they receive multidomain intervention. We aimed to examine whether a multidomain intervention could improve neuropsychological function and neurophysiological activities related to memory and inhibitory function in SCD subjects. Eight clusters with a total of 50 community-dwelling SCD older adults were single-blind, randomized into intervention group, which received physical and cognitive training, or control group, which received treatment as usual. For the neuropsychological function, a composite Z score from six cognitive tests was calculated and compared between two groups. For the neurophysiological activities, event-related potentials (ERPs) of memory function, including mismatch negativity (MMN) and memory-P3, as well as ERPs of inhibitory function, including sensory gating (SG) and inhibition-P3, were measured. Assessments were performed at baseline (T1), end of the intervention (T2), and 6 months after T2 (T3). For the neuropsychological function, the effect was not observed after the intervention. For the neurophysiological activities, improved MMN responses of ΔT2-T1 were observed in the intervention group versus the control group. The multidomain intervention produced a sustained effect on memory-P3 latencies of ΔT3-T1. However, there were no significant differences in changes of SG and inhibition-P3 between intervention and control groups. While not impactful on neuropsychological function, multidomain intervention enhances specific neurophysiological activities associated with memory function.

Diagnostic of Novel Plasma Biomarkers in Controls, SCD and MCI Subjects

AbstractBackgroundAlzheimer’s disease (AD) is the most common cause of dementia, and a challenging disease for the development of plasma biomarkers. Blood samples are the most easily collectable samples for medical diagnosis. This study evaluates the potential of plasma biomarkers in identifying early stages of AD.Method189 participants were recruited at the NYU Alzheimer Disease Research Center. This cohort includes n = 75 with normal cognition (NL), n = 77 with subjective cognitive decline (SCD), n = 37 with mild cognitive impairment (MCI). Comprehensive neuropsychological and magnetic resonance imaging evaluations were conducted for all patients. Plasma biomarkers assays (total tau [t‐tau], neurofilament light [NfL], glial fibrillary acid protein [GFAP], ubiquitin carboxyl‐terminal hydrolase L1 [UCH‐L1], Aβ1‐42, Aβ1‐40 and pTau181) were measured using the SIMOA SR‐X, a novel technology that employs highly sensitive immunoassays with a limit of detection (LOD) under 100 fg/ml.ResultThe levels of NfL, t‐Tau, GFAP and UCH‐L1 were measured using the Neurology 4‐plex A. NfL levels showed a significant difference between NL and SCD (one tailed t‐ test p = 0.0459). GFAP showed statistically significant differences between NL and MCI, (one tailed t‐test p = 0.0369). Aβ1‐42 showed a significant difference between SCD and MCI (one tailed t‐test p = 0.0493). pTau181 levels showed a significant difference between NL, SCD and NL, MCI (p = 0.0447 and p = 0.0439, respectively). The ratio of pTau181/Aβ1‐42 shows a significant difference between NL and MCI (one tailed t‐test p = 0.0187). Correlation of these biomarkers with brain imaging and cognitive measures is underway.ConclusionBiomarker levels of NfL, GFAP, Aβ1‐42, pTau181 and Ratio pTau181/Aβ1‐42 in plasma samples showed several significant differences between the three groups of subjects. Plasma biomarkers can be useful for the diagnosis of AD related pathology at early stages.

MRI or 18F-FDG PET for Brain Age Gap Estimation: Links to Cognition, Pathology, and Alzheimer Disease Progression.

Deviations of brain age from chronologic age, known as the brain age gap (BAG), have been linked to neurodegenerative diseases such as Alzheimer disease (AD). Here, we compare the associations of MRI-derived (atrophy) or 18F-FDG PET-derived (brain metabolism) BAG with cognitive performance, neuropathologic burden, and disease progression in cognitively normal individuals (CNs) and individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI). Methods: Machine learning pipelines were trained to estimate brain age from 185 matched T1-weighted MRI or 18F-FDG PET scans of CN from the Alzheimer's Disease Neuroimaging Initiative and validated in external test sets from the Open Access of Imaging and German Center for Neurodegenerative Diseases-Longitudinal Cognitive Impairment and Dementia studies. BAG was correlated with measures of cognitive performance and AD neuropathology in CNs, SCD subjects, and MCI subjects. Finally, BAG was compared between cognitively stable and declining individuals and subsequently used to predict disease progression. Results: MRI (mean absolute error, 2.49 y) and 18F-FDG PET (mean absolute error, 2.60 y) both estimated chronologic age well. At the SCD stage, MRI-based BAG correlated significantly with beta-amyloid1-42 (Aβ1-42) in cerebrospinal fluid, whereas 18F-FDG PET BAG correlated with memory performance. At the MCI stage, both BAGs were associated with memory and executive function performance and cerebrospinal fluid Aβ1-42, but only MRI-derived BAG correlated with phosphorylated-tau181/Aβ1-42 Lastly, MRI-estimated BAG predicted MCI-to-AD progression better than 18F-FDG PET-estimated BAG (areas under the curve, 0.73 and 0.60, respectively). Conclusion: Age was reliably estimated from MRI or 18F-FDG PET. MRI BAG reflected cognitive and pathologic markers of AD in SCD and MCI, whereas 18F-FDG PET BAG was sensitive mainly to early cognitive impairment, possibly constituting an independent biomarker of brain age-related changes.

Preclinical Brain Network Abnormalities in Patients with Subjective Cognitive Decline.

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

Brain Connectome Imaging Markers Research of Glucose Metabolism in the Early Stage of Alzheimer's Disease.

In recent years, increasing evidence had suggested that subjective cognitive decline (SCD) in unimpaired individuals may be the first symptom of Alzheimer's disease (AD). This study investigated the differences in the glucose metabolism network and the influence of the Apolipoprotein E (ApoE) gene between the SCD and normal control (NC) group by using graph theory. In this study, we included 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scans from Xuanwu Hospital in Beijing, China. 85 SCD subjects and 74 NC subjects were included. First, we calculated and compared network parameters between the two groups. We then identified the bilateral insula and bilateral parahippocampal gyrus as seed sites and studied the connections to the whole brain. The results showed that both the SCD and the NC showed small-world nature, but the metabolic network of SCD tended to be more regular. The clustering coefficient and local efficiency of SCD were significantly higher than those of NC (P<0.05). In addition, we found that carrying APOE resulted in enhanced metabolic connectivity, but with weaker aggregation and local information exchangeability. Our results suggested that there are differences in the glucose metabolic brain network between SCD and NC, suggesting that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.Clinical relevance- This study suggests that the graph-theoretic analysis method may provide evidence for the early pathological mechanism of AD.

18F-AV45 PET and MRI Reveal the Influencing Factors of Alzheimer's Disease Biomarkers in Subjective Cognitive Decline Population.

Subjective cognitive decline (SCD) is a self-perceived decline in cognitive ability, which exhibits no objective impairment but increased risk of conversion to mild cognitive impairment and Alzheimer's disease (AD). To investigate how influencing factors (risk gene, age, sex, and education) affect amyloid-β (Aβ) deposition and gray matter (GM) atrophy in SCD population. 281 SCD subjects were included in this study, who underwent clinical evaluation, cognitive ability assessment, apolipoprotein E (APOE) genotyping, 18F-Florbetapir positron emission computed tomography, and magnetic resonance imaging screening. Two-sample t tests and analysis of variance were performed based on voxel-wise outcome. In 281 SCD subjects with an average age of 63.86, 194 subjects (69.04%) were females, and 56 subjects carried APOE ɛ4 genes. Statistical results revealed APOE ɛ4 gene, age, and sex influenced Aβ deposition in different brain regions; moreover, only the interaction exhibited between age and APOE ɛ4 genes. The GM atrophy of hippocampal, amygdala, precentral, and occipital lobes occurred in the group age over 60. The GM volume of the hippocampal, frontal, and occipital lobe in females was less than males. Education had an effect only on cognitive function. In SCD, APOE ɛ4 gene, age, and sex significantly influenced Aβ deposition and APOE ɛ4 gene can interact with age in impacting Aβ deposition. Both age and sex can affect GM atrophy. The results suggested that female SCD with APOE ɛ4 genes and aged more than 60 years old might exhibit advanced AD biomarkers.

Predicting Conversion from Subjective Cognitive Decline to Mild Cognitive Impairment and Alzheimer's Disease Dementia Using Ensemble Machine Learning.

Subjective cognitive decline (SCD) may represent a preclinical stage of Alzheimer's disease (AD). Predicting progression of SCD patients is of great importance in AD-related research but remains a challenge. To develop and implement an ensemble machine learning (ML) algorithm to identify SCD subjects at risk of conversion to mild cognitive impairment (MCI) or AD. Ninety-nine SCD patients were included. Thirty-two progressed to MCI/AD, while 67 remained stable. To minimize the effect of class imbalance, both classes were balanced, and sensitivity was taken as evaluation metric. Bagging and boosting ML models were developed by using socio-demographic and clinical information, Mini-Mental State Examination and Geriatric Depression Scale (GDS) scores (feature-set 1a); socio-demographic characteristics and neuropsychological tests scores (feature-set 1b) and regional magnetic resonance imaging grey matter volumes (feature-set 2). The most relevant variables were combined to find the best model. Good prediction performances were obtained with feature-sets 1a and 2. The most relevant variables (variable importance exceeding 20%) were: Age, GDS, and grey matter volumes measured in four cortical regions of interests. Their combination provided the optimal classification performance (highest sensitivity and specificity) ensemble ML model, Extreme Gradient Boosting with over-sampling of the minority class, with performance metrics: sensitivity = 1.00, specificity = 0.92 and area-under-the-curve = 0.96. The median values based on fifty random train/test splits were sensitivity = 0.83 (interquartile range (IQR) = 0.17), specificity = 0.77 (IQR = 0.23) and area-under-the-curve = 0.75 (IQR = 0.11). A high-performance algorithm that could be translatable into practice was able to predict SCD conversion to MCI/AD by using only six predictive variables.

Association between Neuropsychological Performance and CSF Profile in Subjective Cognitive Decline: Towards the Diagnosis of Preclinical AD

BackgroundIn the perspective of novel treatments with disease-modifying drugs, a timely diagnosis of Alzheimer’s’ disease (AD) at preclinical phase represents a major issue. To this purpose, in clinical setting, there is the need to detect the earliest cognitive symptoms not yet fulfilling Mild Cognitive Impairment criteria, in order to proceed to biomarker assessment for diagnostic definition. In terms of cognitive performance, Subjective Cognitive Decline (SCD) is still a controversial entity, due to the difficulty of reliably measuring subtle deficits.ObjectiveTo evaluate the possibility to predict the presence of AD-like CSF pattern in SCD individuals, according to their neuropsychological performance assessed by means of both traditional and computerized measures.DesignRetrospective study.SettingClinical setting (Centre for Memory Disturbances, Section of Neurology, University Hospital of Perugia, Italy).Participants74 consecutive SCD subjects who underwent an in-depth (paper-pencil and computerized) neuropsychological assessment and CSF analysis for AD biomarkers (Aβ42/Aβ40 ratio, phospho-tau, total tau).MeasurementsNeuropsychological assessment was composed of traditional tests assessing five cognitive domains (verbal memory, attention, executive functions, language, visuo-spatial abilities) and computerized tasks from CAmbridge Neuropsychological Test Automated Battery (CANTAB) (Pattern Recognition Memory, Paired Associates Learning and Spatial Working Memory). According to their performance at traditional tests, SCD individuals were categorized into cognitively normal (CN) and subtle impaired (SI); with respect to CANTAB, they were defined as CANTAB− in presence of normal performance, and CANTAB+ in presence of at least one pathological score. The subgroup with completely normal performance was defined as CN/CANTAB−, and the subgroup with impairment in both measures as SI/CANTAB+. Differences in prevalence of A/T/N profile according to cognitive profiles were assessed by Fisher’s exact text for count data.ResultsNone of CN/CANTAB− subjects showed A+/T+ status. SI/CANTAB+ subjects showed a significantly high prevalence of A+/T+ profile (14/35, 40%, p=0.03 vs CN/CANTAB−).ConclusionThe neuropsychological profile may be of help in identifying SCD subjects requiring biomarker assessment. If confirmed in larger cohorts, the combination of traditional and computerized tests (namely, CANTAB) might represent a feasible strategy in clinical setting for carrying out biomarker assessment in individuals before the MCI stage. Detection of AD in these subjects would give them the highest chances to halt disease progression by means of disease modifying treatments.

Development of a home-based cognitive test for cognitive monitoring in subjective cognitive decline with high risk of Alzheimer's disease.

Subjective cognitive decline (SCD) indicates a self-perceived persistent cognitive worsening despite of normal performance in standard neuropsychological tests. Owing to its heterogeneity and potential risk of Alzheimer's disease, baseline biomarkers to predict cognitive decline are important. In the present study, we developed a home-based cognitive test (HCT) to monitor cognitive changes regularly without visiting hospitals. This study aims to compare cognitive and biomarker trajectories during a 48-month period between amyloid positive SCD and amyloid negative SCD subjects. Data will be collected from a prospective observational cohort study conducted in South Korea. Eighty participants with SCD aged ≥ 60 years are eligible for the study. All participants undergo annual neuropsychological tests and neurological examinations, bi-annual brain MRI scans and plasma amyloid markers, and baseline florbetaben Positron Emission Tomography scans. The amyloid burden and regional volumes will be measured. Cognitive and biomarker changes will be compared between the amyloid-positive SCD and amyloid negative SCD groups. Validation would be performed to assess reliability and feasibility of HCT. This study would suggest a perspective on SCD in terms of cognitive and biomarker trajectories. Baseline characteristics and biomarker status might affect faster cognitive decline and future biomarker trajectories. In addition, HCT could be an alternative option of in-person neuropsychological tests to track cognitive changes without visiting hospitals.

Altered functional connectivity of the default mode and dorsal attention network in subjective cognitive decline

Subjective cognitive decline (SCD) is a clinical condition in which performance on standardized cognitive tests does not indicate impairment like mild cognitive impairment (MCI), but self-experienced cognitive capacity persistently declines. We aimed to explore the functional connectivity (FC) characteristics of SCD subjects compared to healthy controls and MCI patients. Resting-state functional MRI was performed on 152 elderly subjects: 65 normal controls, 62 SCD subjects, and 25 MCI patients. A seed-based FC analysis was performed to compare groups. The major brain regions of the default mode network (DMN) and dorsal attention network (DAN), large-scale brain networks disrupted in patients with dementia, were selected as seed regions. As a result, the SCD group showed stronger FC than the MCI group between DMN seeds and the supramarginal gyrus. Both the SCD and MCI groups showed stronger FC between the left lateral parietal cortex and right dorsolateral prefrontal cortex. In the FC analysis centred on DAN seeds, both the SCD and MCI groups showed weaker FC of the right posterior intraparietal sulcus in the left anterior cingulate cortex and the left insula, compared to those in the control group. Within the SCD group, hyperconnectivity between the right lateral parietal cortex and left supramarginal gyrus was significantly correlated with better performance on the Controlled Oral Word Association Test. In conclusion, the SCD group showed several DMN- and DAN-related FC alterations, similar to the MCI group, but with distinct hyperconnectivity between DMN seeds and the supramarginal gyrus. In particular, SCD has DMN-related FC patterns distinct from those of MCI that are associated with verbal fluency retention.

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer’s Disease continuum?

Alzheimer’s disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states.

Altered pattern analysis and identification of subjective cognitive decline based on morphological brain network.

Subjective cognitive decline (SCD) is considered the first stage of Alzheimer’s disease (AD). Accurate diagnosis and the exploration of the pathological mechanism of SCD are extremely valuable for targeted AD prevention. However, there is little knowledge of the specific altered morphological network patterns in SCD individuals. In this present study, 36 SCD cases and 34 paired-matched normal controls (NCs) were recruited. The Jensen-Shannon distance-based similarity (JSS) method was implemented to construct and derive the attributes of multiple brain connectomes (i.e., morphological brain connections and global and nodal graph metrics) of individual morphological brain networks. A t-test was used to discriminate between the selected nodal graph metrics, while the leave-one-out cross-validation (LOOCV) was used to obtain consensus connections. Comparisons were performed to explore the altered patterns of connectome features. Further, the multiple kernel support vector machine (MK-SVM) was used for combining brain connectomes and differentiating SCD from NCs. We showed that the consensus connections and nodal graph metrics with the most discriminative ability were mostly found in the frontal, limbic, and parietal lobes, corresponding to the default mode network (DMN) and frontoparietal task control (FTC) network. Altered pattern analysis demonstrated that SCD cases had a tendency for modularity and local efficiency enhancement. Additionally, using the MK-SVM to combine the features of multiple brain connectomes was associated with optimal classification performance [area under the curve (AUC): 0.9510, sensitivity: 97.22%, specificity: 85.29%, and accuracy: 91.43%]. Therefore, our study highlighted the combination of multiple connectome attributes based on morphological brain networks and offered a valuable method for distinguishing SCD individuals from NCs. Moreover, the altered patterns of multidimensional connectome attributes provided a promising insight into the neuroimaging mechanism and early intervention in SCD subjects.

Grey matter changes on brain MRI in subjective cognitive decline: a systematic review

IntroductionPeople with subjective cognitive decline (SCD) report cognitive deterioration. However, their performance in neuropsychological evaluation falls within the normal range. The present study aims to analyse whether structural magnetic resonance imaging (MRI) reveals grey matter changes in the SCD population compared with healthy normal controls (HC).MethodsParallel systematic searches in PubMed and Web of Science databases were conducted, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Quality assessment was completed using the Newcastle-Ottawa Scale (NOS).ResultsFifty-one MRI studies were included. Thirty-five studies used a region of interest (ROI) analysis, 15 used a voxel-based morphometry (VBM) analysis and 10 studies used a cortical thickness (CTh) analysis. Ten studies combined both, VBM or CTh analysis with ROI analysis.ConclusionsMedial temporal structures, like the hippocampus or the entorhinal cortex (EC), seemed to present grey matter reduction in SCD compared with HC, but the samples and results are heterogeneous. Larger sample sizes could help to better determine if these grey matter changes are consistent in SCD subjects.

Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease?

Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer’s disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5–93.3%]) slightly outperformed PE (76.9% [60.3–93.4%]) and relative theta power–based models (76.9% [60.4–93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.

Reduced Inter-Voxel White Matter Integrity in Subjective Cognitive Decline: Diffusion Tensor Imaging With Tract-Based Spatial Statistics Analysis.

Subjective cognitive decline (SCD), a self-reported worsening in cognition concurrent with normal performance on standardized neuropsychological tests, has gained much attention due to its high risks in the development of mild cognitive impairments or Alzheimer’s disease. The existing cross-sectional diffusion tensor imaging (DTI) studies in SCD have shown extremely controversial findings. Furthermore, all of these studies investigated diffusion properties within the voxel, such as fractional anisotropy, mean diffusivity, or axial diffusivity (DA). However, it remains unclear whether individuals with SCD demonstrate alterations of diffusion profile between voxels and their neighbors, as indexed by local diffusion homogeneity (LDH). We selected 30 healthy controls (HCs) and 23 SCD subjects to acquire their whole-brain DTI. Diffusion images were compared using the tract-based spatial statistics method. Diffusion indices with significant between-group tract clusters were extracted from each individual for further region-of-interest (ROI)-based comparisons. Our results showed that subjects with SCD demonstrated reduced LDH in the left superior frontal gyrus (SFG) and DA in the right anterior cingulate cortex compared with the HC group. In contrast, the SCD group showed higher LDH values in the left lingual gyrus (LG) compared with the HC group. Notably, LDH in the left SFG was significantly and negatively correlated with LDH in the left LG. In conclusion, white matter (WM) integrity in the left SFG, right ACC, and left LG is altered in SCD, suggesting that individuals with SCD exhibit detectable changes in WM tracts before they demonstrate objective cognitive deficits.

Assessing clinical progression from subjective cognitive decline to mild cognitive impairment with incomplete multi-modal neuroimages.

Accurately assessing clinical progression from subjective cognitive decline (SCD) to mild cognitive impairment (MCI) is crucial for early intervention of pathological cognitive decline. Multi-modal neuroimaging data such as T1-weighted magnetic resonance imaging (MRI) and positron emission tomography (PET), help provide objective and supplementary disease biomarkers for computer-aided diagnosis of MCI. However, there are few studies dedicated to SCD progression prediction since subjects usually lack one or more imaging modalities. Besides, one usually has a limited number (e.g., tens) of SCD subjects, negatively affecting model robustness. To this end, we propose a Joint neuroimage Synthesis and Representation Learning (JSRL) framework for SCD conversion prediction using incomplete multi-modal neuroimages. The JSRL contains two components: 1) a generative adversarial network to synthesize missing images and generate multi-modal features, and 2) a classification network to fuse multi-modal features for SCD conversion prediction. The two components are incorporated into a joint learning framework by sharing the same features, encouraging effective fusion of multi-modal features for accurate prediction. A transfer learning strategy is employed in the proposed framework by leveraging model trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) with MRI and fluorodeoxyglucose PET from 863 subjects to both the Chinese Longitudinal Aging Study (CLAS) with only MRI from 76 SCD subjects and the Australian Imaging, Biomarkers and Lifestyle (AIBL) with MRI from 235 subjects. Experimental results suggest that the proposed JSRL yields superior performance in SCD and MCI conversion prediction and cross-database neuroimage synthesis, compared with several state-of-the-art methods.

Blink Rate Study in Patients with Alzheimer's Disease, Mild Cognitive Impairment and Subjective Cognitive Decline.

Blink rate (BR) is considered a marker of dopaminergic activity in humans. BR is increased in patients with Mild Cognitive Impairment (MCI), but no study has yet investigated whether BR changes with the progression of cognitive decline from MCI to Alzheimer's disease (AD) and whether BR abnormalities are present in subjects with Subjective Cognitive Decline (SCD). The aim of our study was to assess BR in patients with AD, MCI, and SCD and to correlate BR with demographic and clinical features of cognitive decline. We enrolled 22 subjects with SCD, 23 with MCI, and 18 with AD and a group of 20 age-matched healthy controls (HCs). Cognitive function was assessed by testing global cognitive status and frontal, attentional, memory, verbal, and visuospatial functions. BR was measured by counting the number of blinks per minute. MCI subjects had an increased BR (p <0.001), whereas AD subjects had a lower BR than HCs (p <0.05). Conversely, SCD subjects had a BR similar to HCs. No significant correlations emerged between neuropsychological scores and BR in SCD, MCI, and AD subjects. Increased BR in MCI likely reflects early compensatory mechanisms occurring before AD, whereas decreased BR in AD suggests dopaminergic system involvement in this condition.

Auto-weighted centralised multi-task learning via integrating functional and structural connectivity for subjective cognitive decline diagnosis.

Early diagnosis and intervention of mild cognitive impairment (MCI) and its early stage (i.e., subjective cognitive decline (SCD)) is able to delay or reverse the disease progression. However, discrimination between SCD, MCI and healthy subjects accurately remains challenging. This paper proposes an auto-weighted centralised multi-task (AWCMT) learning framework for differential diagnosis of SCD and MCI. AWCMT is based on structural and functional connectivity information inferred from magnetic resonance imaging (MRI). To be specific, we devise a novel multi-task learning algorithm to combine neuroimaging functional and structural connective information. We construct a functional brain network through a sparse and low-rank machine learning method, and also a structural brain network via fibre bundle tracking. Those two networks are constructed separately and independently. Multi-task learning is then used to identify features integration of functional and structural connectivity. Hence, we can learn each task's significance automatically in a balanced way. By combining the functional and structural information, the most informative features of SCD and MCI are obtained for diagnosis. The extensive experiments on the public and self-collected datasets demonstrate that the proposed algorithm obtains better performance in classifying SCD, MCI and healthy people than traditional algorithms. The newly proposed method has good interpretability as it is able to discover the most disease-related brain regions and their connectivity. The results agree well with current clinical findings and provide new insights into early AD detection based on the multi-modal neuroimaging technique.