Cognitively Normal Subjects Research Articles

Correlations between Cerebrospinal Fluid Biomarkers and Gray Matter Atrophy in Alzheimer's and Behavioural Variant Frontotemporal Dementia.

Distinguishing between frontotemporal dementia (FTD) and Alzheimer's disease (AD) in their early stages remains a significant clinical challenge. Cerebrospinal fluid (CSF) biomarkers (total Tau, phosphorylated Tau, and beta-amyloid) are promising candidates for identifying early differences between these conditions. This study investigates the relationship between grey matter density and CSF markers in the behavioural variant of frontotemporal dementia (bvFTD) and Alzheimer's disease (AD). CSF and 3D T1-weighted magnetic resonance (MR) images were acquired from 14 bvFTD patients, 15 AD patients, and 13 cognitively normal (CN) matched subjects. The CSF markers and their relative ratios (total Tau/beta-amyloid, phosphorylated Tau/beta-amyloid) were compared across the three groups. Voxel-based morphometry (VBM) was performed to characterize the anatomical changes in bvFTD and AD patients compared to CN subjects. Grey matter density maps were obtained by automatic segmentation of 3.0 Tesla 3D T1-Weighted MR Images, and their correlation with CSF markers and relative ratios was investigated. Results demonstrated that, as compared to CN subjects, AD patients are characterised by higher CSF total Tau levels and lower beta-amyloid levels; however, beta-amyloid and relative ratios discriminated AD from bvFTD. In addition, AD and bvFTD patients showed different patterns of atrophy, with AD exhibiting more central (temporal areas) and bvFTD more anterior (frontal areas) atrophy. A correlation was found between grey matter density maps and CSF marker concentrations in the AD group, with total Tau and phosphorylated Tau levels showing a high association with low grey matter density in the left superior temporal gyrus. The study concludes that while bvFTD lacks a CSF marker profile, CSF beta-amyloid levels are useful for differentiating AD from bvFTD. Furthermore, MR structural imaging can contribute significantly to distinguishing between the two pathologies.

Magnitude and kinetics of a set of neuroanatomic volume and thickness together with white matter hyperintensity is definitive of cognitive status and brain age

Even among the subjects classified as cognitively normal, there exists a subset of individuals at a given chronological age (CA) who harbor white matter hyperintensity (WMH) while another subset presents with low or undetectable WMH. Here, we conducted a comprehensive MRI segmentation of neuroanatomic structures along with WMH quantification in groups of cognitively normal (CN), cognitively impaired (CI) individuals, and individuals with an etiological diagnosis of cognitive impairment owing to Alzheimer’s Disease (CI-AD) across the early (50–64 years), intermediate (65–79 years), and late (≥80 years) age groups from the NACC cohort. Neuroanatomic volumetry quantification revealed that thinning of the parahippocampal gyrus in the early (p = 0.016) and intermediate age groups (p = 0.0001) along with an increase in CSF (p = 0.0009) delineates between CI and CI-AD subjects. Although, a significant loss of ~5–10% in volume of gray matter (p(CN vs CI) < 0.0001, p(CN vs CI-AD) < 0.0001), white matter (p(CN vs CI) = 0.002, p(CN vs CI-AD) = 0.0003) and hippocampus (p(CN vs CI) = 0.007, p(CN vs CI-AD) < 0.0001) was evident at the early age groups in the CI and CI-AD compared to CN but it was not distinct between CI and CI-AD. Using the neuroanatomic and WMH volume, and the supervised decision tree-based ML modeling, we have established that a minimum set of Three brain quantities; Total brain (GM + WM), CSF, and WMH volume, provide the Optimal quantitative features discriminative of cognitive status as CN, CI, and CI-AD. Furthermore, using the volume/thickness of 178 neuroanatomic structures, periventricular and deep WMH volume quantification for the 819 CN subjects, we have developed a quantitative index as ‘Brain Age’ (BA) depictive of neuroanatomic health at a given CA. Subjects with elevated WMH load (5–10 ml) had increased BA ( + 0.6 to +4 years) than the CA. Increased BA in the subjects with elevated WMH is suggestive of WMH-induced vascular insult leading to accelerated and early structural loss than expected for a given CA. Henceforth, this study establishes that quantification of WMH together with an optimal number of neuroanatomic features is mandatory to delve into the biological underpinning of aging and aging-associated cognitive disorders.

Retinal ganglion cell vulnerability to pathogenic tau in Alzheimer's disease.

Accumulation of pathological tau isoforms, especially hyperphosphorylated tau at serine 396 (pS396-tau) and tau oligomers, has been demonstrated in the retinas of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Previous studies have noted a decrease in retinal ganglion cells (RGCs) in AD patients, but the presence and impact of pathological tau isoforms in RGCs and RGC integrity, particularly in early AD stages, have not been explored. To investigate this, we examined retinal superior temporal cross-sections from 25 patients with MCI (due to AD) or AD dementia and 16 cognitively normal (CN) controls, matched for age and gender. We utilized the RGC marker ribonucleic acid binding protein with multiple splicing (RBPMS) and Nissl staining to assess neuronal density in the ganglion cell layer (GCL). Our study found that hypertrophic RGCs containing pS396-tau and T22-positive tau oligomers were more frequently observed in MCI and AD patients compared to CN subjects. Quantitative analyses indicated a decline in RGC integrity, with 46-55% and 55-56% reductions of RBPMS+ RGCs (P<0.01) and Nissl+ GCL neurons (P<0.01-0.001), respectively, in MCI and AD patients. This decrease in RGC count was accompanied by increases in necroptotic-like morphology and the cleaved caspase-3 apoptotic marker in RGCs of AD patients. Furthermore, there was a 2.1 to 3.1-fold increase (P<0.05-0.0001) in pS396-tau-laden RGCs in MCI and AD patients, with a greater abundance observed in individuals with higher Braak stages (V-VI), more severe clinical dementia ratings (CDR=3), and lower mini-mental state examination (MMSE) scores. Strong correlations were noted between the decline in RGCs and the total amount of retinal pS396-tau and pS396-tau+ RGCs, with pS396-tau+ RGC counts correlating significantly with brain neurofibrillary tangle scores (r= 0.71, P= 0.0001), Braak stage (r= 0.65, P= 0.0009), and MMSE scores (r= -0.76, P= 0.0004). These findings suggest that retinal tauopathy, characterized by pS396-tau and oligomeric tau in hypertrophic RGCs, is associated with and may contribute to RGC degeneration in AD. Future research should validate these findings in larger cohorts and explore noninvasive retinal imaging techniques that target tau pathology in RGCs to improve AD detection and monitor disease progression.

Functional connectivity differences of the olfactory network in Parkinson’s Disease, mild cognitive impairment and cognitively normal individuals: A resting-state fMRI study

Olfactory dysfunction is an early sign of such neurodegenerative diseases as Parkinson’s (PD) and Alzheimer’s (AD), and is often present in Mild Cognitive Impairment (MCI), a precursor of AD. Understanding neuro-temporal relationships, i.e., functional connectivity, between olfactory eloquent structures in such disorders, could shed light on their basic pathophysiology. To this end, we employed region-based analyses using resting-state functional magnetic resonance imaging (rs-fMRI) obtained from cognitively normal (CN), MCI, and PD patients with cognitive impairment (PD-CogImp). Using machine learning (linear and ensemble learning), we determined whether the identified functional patterns could classify abnormal function from normal function. Olfaction, as measured by objective testing, was found to be most strongly associated with diagnostic status, emphasizing the fundamental association of this primary sensory system with these conditions. Consistently lower functional connectivity was observed in the PD-CogImp cohort compared to the CN cohort among all identified brain regions. Differences were also found between PD-CogImp and MCI at the level of the orbitofrontal and cingulate cortices. MCI and CN subjects had different functional connectivity between the posterior orbitofrontal cortex and thalamus. Regardless of study group, males showed significantly higher connectivity than females in connections involving the orbitofrontal cortex. The logistic regression model trained using the top discriminatory features revealed that caudate was the most involved olfaction-related brain structure (accuracy = 0.88, Area under the Receiver operator characteristic curve of 0.90). In aggregate, our study demonstrates that resting functional connectivity among olfactory eloquent structures has potential value in better understanding the pathophysiology of several neurodegenerative diseases.

Tau pathology mediated the plasma biomarkers and cognitive function in patients with mild cognitive impairment

Glial fibrillary acidic protein (GFAP) and neurofilament light (NfL) are putative non-amyloid biomarkers indicative of ongoing inflammatory and neurodegenerative disease processes. Hence, this study aimed to demonstrate the relationship between plasma biomarkers (GFAP and NfL) and 18F-AV-1451 tau PET images, and to explore their effects on cognitive function. Ninety-one participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 20 participants from the Shanghai Action of Prevention Dementia for the Elderly (SHAPE) cohort underwent plasma biomarker testing, 18F-AV-1451 tau PET scans and cognitive function assessments. Within the ADNI, there were 42 cognitively normal (CN) individuals and 49 with mild cognitive impairment (MCI). Similarly, in the SHAPE, we had 10 CN and 10 MCI participants. We calculated the standardized uptake value ratios (SUVRs) for the regions of interest (ROIs) in the 18F-AV-1451 PET scans. Using plasma biomarkers and regional SUVRs, we trained machine learning models to differentiate between MCI and CN subjects with ADNI database and validated in SHAPE.Results showed that eight selected variables (including left amygdala SUVR, right amygdala SUVR, left entorhinal cortex SUVR, age, education, plasma NfL, plasma GFAP, plasma GFAP/ NfL) identified by LASSO could differentiate between the MCI and CN individuals, with AUC ranging from 0.783 to 0.926. Additionally, cognitive function was negatively associated with the plasma biomarkers and tau deposition in amygdala and left entorhinal cortex. Increased tau deposition in amygdala and left entorhinal cortex were related to increased plasma biomarkers. Moreover, tau pathology mediated the effect of plasma biomarkers level on the cognitive decline. The present study provides valuable insights into the association among plasma markers (GFAP and NfL), regional tau deposition and cognitive function. This study reports the mediation effect of brain regions tau deposition on the plasma biomarkers level and cognitive function, indicating the significance of tau pathology in the MCI patients.

Exploring white matter microstructural alterations in mild cognitive impairment: a multimodal diffusion MRI investigation utilizing diffusion kurtosis and free-water imaging.

Mild Cognitive Impairment (MCI) is a transitional stage from normal aging to dementia, characterized by noticeable changes in cognitive function that do not significantly impact daily life. Diffusion MRI (dMRI) plays a crucial role in understanding MCI by assessing white matter integrity and revealing early signs of axonal degeneration and myelin breakdown before cognitive symptoms appear. This study utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to compare white matter microstructure in individuals with MCI to cognitively normal (CN) individuals, employing advanced dMRI techniques such as diffusion kurtosis imaging (DKI), mean signal diffusion kurtosis imaging (MSDKI), and free water imaging (FWI). Analyzing data from 55 CN subjects and 46 individuals with MCI, this study found significant differences in white matter integrity, particularly in free water levels and kurtosis values, suggesting neuroinflammatory responses and microstructural integrity disruption in MCI. Moreover, negative correlations between Mini-Mental State Examination (MMSE) scores and free water levels in the brain within the MCI group point to the potential of these measures as early biomarkers for cognitive impairment. In conclusion, this study demonstrates how a multimodal advanced diffusion imaging approach can uncover early microstructural changes in MCI, offering insights into the neurobiological mechanisms behind cognitive decline.

18F]Flotaza for Aβ Plaque Diagnostic Imaging: Evaluation in Postmortem Human Alzheimer's Disease Brain Hippocampus and PET/CT Imaging in 5xFAD Transgenic Mice.

The diagnostic value of imaging Aβ plaques in Alzheimer's disease (AD) has accelerated the development of fluorine-18 labeled radiotracers with a longer half-life for easier translation to clinical use. We have developed [18F]flotaza, which shows high binding to Aβ plaques in postmortem human AD brain slices with low white matter binding. We report the binding of [18F]flotaza in postmortem AD hippocampus compared to cognitively normal (CN) brains and the evaluation of [18F]flotaza in transgenic 5xFAD mice expressing Aβ plaques. [18F]Flotaza binding was assessed in well-characterized human postmortem brain tissue sections consisting of HP CA1-subiculum (HP CA1-SUB) regions in AD (n = 28; 13 male and 15 female) and CN subjects (n = 32; 16 male and 16 female). Adjacent slices were immunostained with anti-Aβ and analyzed using QuPath. In vitro and in vivo [18F]flotaza PET/CT studies were carried out in 5xFAD mice. Post-mortem human brain slices from all AD subjects were positively IHC stained with anti-Aβ. High [18F]flotaza binding was measured in the HP CA1-SUB grey matter (GM) regions compared to white matter (WM) of AD subjects with GM/WM > 100 in some subjects. The majority of CN subjects had no decipherable binding. Male AD exhibited greater WM than AD females (AD WM♂/WM♀ > 5; p < 0.001) but no difference amongst CN WM. In vitro studies in 5xFAD mice brain slices exhibited high binding [18F]flotaza ratios (>50 versus cerebellum) in the cortex, HP, and thalamus. In vivo, PET [18F]flotaza exhibited binding to Aβ plaques in 5xFAD mice with SUVR~1.4. [18F]Flotaza is a new Aβ plaque PET imaging agent that exhibited high binding to Aβ plaques in postmortem human AD. Along with the promising results in 5xFAD mice, the translation of [18F]flotaza to human PET studies may be worthwhile.

Growth-Associated Protein43 and Tensor-Based Morphometry Indices in Mild Cognitive Impairment.

Growth-associated protein 43 (GAP-43) is found in the axonal terminal of neurons in the limbic system, which is affected in people with Alzheimer's disease (AD). We assumed GAP-43 may contribute to AD progression and serve as a biomarker. So, in a two-year follow-up study, we assessed GAP-43 changes and whether they are correlated with tensor-based morphometry (TBM) findings in patients with mild cognitive impairment (MCI). We included MCI and cognitively normal (CN) people with available baseline and follow-up cerebrospinal fluid (CSF) GAP-43 and TBM findings from the ADNI database. We assessed the difference between the two groups and correlations in each group at each time point. CSF GAP-43 and TBM measures were similar in the two study groups in all time points, except for the accelerated anatomical region of interest (ROI) of CN subjects that were significantly greater than those of MCI. The only significant correlations with GAP-43 observed were those inverse correlations with accelerated and non-accelerated anatomical ROI in MCI subjects at baseline. Plus, all TBM metrics decreased significantly in all study groups during the follow-up in contrast to CSF GAP-43 levels. Our study revealed significant associations between CSF GAP-43 levels and TBM indices among people of the AD spectrum.

Classification of Mild Cognitive Impairment and Alzheimer’s Disease Using Manual Motor Measures

Introduction: Manual motor problems have been reported in mild cognitive impairment (MCI) and Alzheimer’s disease (AD), but the specific aspects that are affected, their neuropathology, and potential value for classification modeling is unknown. The current study examined if multiple measures of motor strength, dexterity, and speed are affected in MCI and AD, related to AD biomarkers, and are able to classify MCI or AD. Methods: Fifty-three cognitively normal (CN), 33 amnestic MCI, and 28 AD subjects completed five manual motor measures: grip force, Trail Making Test A, spiral tracing, finger tapping, and a simulated feeding task. Analyses included (1) group differences in manual performance; (2) associations between manual function and AD biomarkers (PET amyloid β, hippocampal volume, and APOE ε4 alleles); and (3) group classification accuracy of manual motor function using machine learning. Results: Amnestic MCI and AD subjects exhibited slower psychomotor speed and AD subjects had weaker dominant hand grip strength than CN subjects. Performance on these measures was related to amyloid β deposition (both) and hippocampal volume (psychomotor speed only). Support vector classification well-discriminated control and AD subjects (area under the curve of 0.73 and 0.77, respectively) but poorly discriminated MCI from controls or AD. Conclusion: Grip strength and spiral tracing appear preserved, while psychomotor speed is affected in amnestic MCI and AD. The association of motor performance with amyloid β deposition and atrophy could indicate that this is due to amyloid deposition in and atrophy of motor brain regions, which generally occurs later in the disease process. The promising discriminatory abilities of manual motor measures for AD emphasize their value alongside other cognitive and motor assessment outcomes in classification and prediction models, as well as potential enrichment of outcome variables in AD clinical trials.

Differential atrophy along the longitudinal hippocampal axis in Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that primarily affects the hippocampus. Since hippocampal studies have highlighted a differential subregional regulation along its longitudinal axis, a more detailed analysis addressing subregional changes along the longitudinal hippocampal axis has the potential to provide new relevant biomarkers. This study included structural brain MRI data of 583 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Cognitively normal (CN) subjects, mild cognitively impaired (MCI) subjects and AD patients were conveniently selected considering the age and sex match between clinical groups. Structural MRI acquisitions were pre-processed and analysed with a new longitudinal axis segmentation method, dividing the hippocampus in three subdivisions (anterior, intermediate, and posterior). When normalizing the volume of hippocampal sub-divisions to total hippocampus, the posterior hippocampus negatively correlates with age only in CN subjects (r= -.31). The longitudinal ratio of hippocampal atrophy (anterior sub-division divided by the posterior one) shows a significant increase with age only in CN (r= .25). Overall, in AD, the posterior hippocampus is predominantly atrophied early on. Consequently, the anterior/posterior hippocampal ratio is an AD differentiating metric at early disease stages with potential for diagnostic and prognostic applications.

A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49–0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.

Associations of cerebrospinal fluid complement proteins with Alzheimer’s pathology, cognition, and brain structure in non-dementia elderly

BackgroundCerebrospinal fluid (CSF) complement activation is a key part of neuroinflammation that occurs in the early stages of Alzheimer’s disease (AD). However, the associations of CSF complement proteins with AD pathology, cognition, and structural neuroimaging biomarkers for AD have been rarely investigated.MethodsA total of 210 participants (125 mild cognitive impairment [MCI] patients and 85 normal controls) were included from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database who measured AD pathology, cognition, and neuroimaging at baseline and every 12 months. The mixed-effect linear models were utilized to investigate longitudinal associations of CSF complement proteins with AD pathology, cognition, and neuroimaging in cognitively normal (CN) and mild cognitive impairment (MCI) subjects. Causal mediation analyses were conducted to explore the potential mediators between CSF complement proteins and cognitive changes.ResultsWe found that the subjects with low CSF complement protein levels at baseline had worse outcomes in AD pathology, indicated by their lowest concentrations observed in A + and A + T + individuals. The reduced CSF complement proteins were associated with faster accumulation of tau among CN subjects and with cognitive decline and greater brain atrophy of specific regions among MCI subjects. Furthermore, mediation analyses showed that the effects of CSF complement proteins on cognitive performance were partially mediated by regional brain structures (mediation proportions range from 19.78 to 94.92%; p < 0.05).ConclusionsThis study demonstrated that CSF complement proteins were involved in the early progression of AD. Our results indicated that regional brain atrophy might be a plausible way to connect CSF complement protein levels and cognition.

Comparison of Commonly Measured Plasma and Cerebrospinal Fluid Proteins and Their Significance for the Characterization of Cognitive Impairment Status.

Although cerebrospinal fluid (CSF) amyloid-β42 peptide (Aβ42) and phosphorylated tau (p-tau) and blood p-tau are valuable for differential diagnosis of Alzheimer's disease (AD) from cognitively normal (CN) there is a lack of validated biomarkers for mild cognitive impairment (MCI). This study sought to determine how plasma and CSF protein markers compared in the characterization of MCI and AD status. This cohort study included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who had baseline levels of 75 proteins measured commonly in plasma and CSF (257 total, 46 CN, 143 MCI, and 68 AD). Logistic regression, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF) methods were used to identify the protein candidates for the disease classification. We observed that six plasma proteins panel (APOE, AMBP, C3, IL16, IGFBP2, APOD) outperformed the seven CSF proteins panel (VEGFA, HGF, PRL, FABP3, FGF4, CD40, RETN) as well as AD markers (CSF p-tau and Aβ42) to distinguish the MCI from AD [area under the curve (AUC) = 0.75 (plasma proteins), AUC = 0.60 (CSF proteins) and AUC = 0.56 (CSF p-tau and Aβ42)]. Also, these six plasma proteins performed better than the CSF proteins and were in line with CSF p-tau and Aβ42 in differentiating CN versus MCI subjects [AUC = 0.89 (plasma proteins), AUC = 0.85 (CSF proteins) and AUC = 0.89 (CSF p-tau and Aβ42)]. These results were adjusted for age, sex, education, and APOEϵ4 genotype. This study suggests that the combination of 6 plasma proteins can serve as an effective marker for differentiating MCI from AD and CN.

Distinct CSF biomarker‐associated DNA methylation in Alzheimer’s disease and cognitively normal subjects

AbstractBackgroundGrowing evidence has demonstrated that DNA methylation (DNAm) plays an important role in Alzheimer’s disease and that DNAm differences can be detected in the blood of AD subjects. Because access to human brain tissues is primarily limited to postmortem samples, most studies have correlated blood DNAm with clinical diagnosis of AD in living individuals. However, as the pathophysiological process of AD can begin many years before the onset of clinical symptoms, there is often disagreement between neuropathology in the brain and clinical phenotypes. Thus, the association of blood DNAm with changes in AD neuropathology, rather than with clinical data, would provide more relevant information on AD pathogenesis.MethodCSF biomarkers are well‐established AD endophenotypes and correlate significantly with neuropathology scores measured on postmortem brain samples. In this study, we performed a comprehensive analysis to identify blood DNA methylation associated with CSF pathological biomarkers for AD in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Our study included matched samples of whole blood DNA methylation, CSF Aβ42, phosphorylated tau181 (pTau181), and total tau (tTau) biomarkers data, measured on the same subjects and at the same clinical visits from a total of 202 subjects (123 cognitively normal, 79 AD cases).ResultWe identified a number of novel associations between blood DNAm and CSF Aβ42, pTau181, and total tau biomarkers, demonstrating that changes in various pathological processes in the CSF are also reflected in the blood epigenome. Overall, the CSF biomarker‐associated DNAm is relatively distinct in cognitively normal (CN) and AD subjects, highlighting the importance of analyzing omics data measured on cognitively normal subjects (which includes preclinical AD subjects) to identify diagnostic biomarkers, and considering disease stages in the development and testing of AD treatment strategies. Moreover, our pathway analysis of CSF biomarker associated‐DNAm from CN subjects revealed that biological processes associated with early brain impairment relevant to AD are marked by DNAm in the blood.ConclusionOur study provides a valuable resource for future mechanistic and biomarker studies of DNAm in AD.

Manual Motor Function as a Classifier for Alzheimer’s Disease

AbstractBackgroundIn contrast to numerous reports of gross motor problems in Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), fine motor function has been relatively understudied. We examined if performance on manual motor tasks can distinguish between cognitively normal (CN) subjects, and individuals with MCI or AD, and which fine motor features are most informative.Method47 CN, 25 MCI, and 16 AD participants completed computerized tests of unimanual (dominant and non‐dominant hand) tapping, synchronous bimanual tapping, and alternate bimanual tapping. Outcome measures included initial reaction time, tapping speed, and variance. We used imbalanced random forest learning for classification modeling. The model was trained on 70% of the data and tested on the remaining 30% using 5‐fold cross‐validation. To investigate the contribution of individual variables to the classification model, we conducted permutation feature importance analysis.ResultThe overall classification accuracy of the training data was 56%, while the overall classification accuracy of the test data was 70%. The group‐specific accuracy, defined as the proportion of subjects predicted to belong to a certain group who are truly a member of that group, was 76% for CN, 33% of amnestic MCI, and 86% for AD. In 50% of the cases amnestic MCI subjects were classified as control subjects, and in 17% of the cases as AD subjects. The group‐specific precision, defined as the proportion of subjects who received a certain group label who truly belonged to that group (i.e., ‘true positives’) was 76% for controls, 100% for amnestic MCI, and 45% for AD. The three most important features that were used for classification of the test hold‐out data were 1) variance in tapping speed during dominant hand finger tapping; 2) the number of dual tapping trials without gaps or onset delays; and 3) initial reaction time for non‐dominant hand finger tapping.ConclusionSupervised machine learning can discriminate CN and AD subjects based on finger tapping performance features, but is less effective for distinguishing MCI subjects from CN and AD subjects. Finger tapping performance could be a cost‐effective tool for augmenting existing AD biomarkers.

Increased neural activity relates to greater neuropsychiatric symptoms and beta‐amyloid plaque load in cognitively normal older adults

AbstractBackgroundEpidemiological studies have shown that individuals predisposed to experiencing psychological stress, and those affected with diseases associated with chronic stress such as depression and anxiety have an increased risk of developing Alzheimer’s disease (AD). These findings may imply that poor resilience to psychological stress is not simply a characteristic of dementia, but may reflect mechanisms involved in disease etiology. In this study, we examined the level of neural activity as a potential mechanism of linking individual susceptibility to psychological stress and AD‐related neuropathology across the spectrum of AD.MethodsA total of 166 participants (mean age = 74.21; 90 Females) spanning diagnostic spectrums along cognitively normal (CN) individuals and AD patients was selected for analysis from the ADNI2 database based on the availability of resting state fMRI and AV45 PET data. Neural activity was quantified by applying the amplitude of low frequency fluctuations (ALFF) method to time series of fMRI blood oxygenation‐level‐dependent signals (in the frequency range of 0.01‐0.08 Hz) obtained during resting‐state fMRI (rsfMRI) scan. Amyloid pathology was quantified by AV45‐SUVR measures that were normalized to the cerebellum. Mini‐Mental State Exam (MMSE) and the total Neuropsychiatric Inventory Questionnaire (NPI‐Q) scores were used as cognitive and stress susceptibility measures.ResultsBehavioral measures of stress were significantly associated with brain Aβ indices and neural activity across the entire cohort, spanning from CN older adults to AD patients (ps&lt;0.05). Relationships between the neural activity, stress responses, and Aβ deposition, however, differ by diagnostic groups. As stress indicators increased, CN subjects exhibited increased activity in several brain regions including the regions within the default mode network (DMN), while AD participants exhibited decreased activity across the brain. Moreover, as brain Aβ load increased, CN participants experienced DMN hyperactivity, while AD participants experienced hypoactivity.ConclusionThese results indicate a potential link between heightened stress responses and increased susceptibility to the development of AD pathologies through neuronal hyperactivity, especially in the preclinical stage of AD. Alleviating stress and regulating stress responses may be important modifiable factors and intervention strategies that could help prevent and intervene the development of AD pathology.

Identification and diagnostic potential of serum microRNAs as biomarkers for early detection of Alzheimer's disease.

This study aimed to investigate the differential expression of serum microRNAs in cognitive normal subjects (NC), patients with mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD), with the objective of identifying potential diagnostic biomarkers. A total of 320 clinical samples, including 32 MCI patients, 288 AD patients, and 288 healthy controls, were collected following international standards. The expression of microRNAs in serum was analyzed using the Agilent human microRNA oligonucleotide microarray, and bioinformatics methods were employed to predict target genes and their involvement in AD-related pathways. Among the 122 microRNAs screened, five microRNAs (hsa-miR-208a-5p, hsa-miR-125b-1-3p, hsa-miR-3194-3p, hsa-miR-4652-5p, and hsa-miR-4419a) exhibited differential expression and met quality control standards. Bioinformatics analysis revealed that the target genes of these microRNAs were involved in multiple AD-related pathways, which changed with disease progression. These findings demonstrate significant differences in serum microRNA expression between NC, MCI, and AD patients. Three microRNAs were identified as potential candidates for the development of diagnostic models for MCI and AD. The results highlight the crucial role of microRNAs in the pathogenesis of AD and provide a foundation for the development of novel therapeutic strategies and personalized treatment approaches for AD. This study contributes to the understanding of AD at the molecular level and offers potential avenues for early diagnosis and intervention in AD patients.

Structural biomarker‐based Alzheimer's disease detection via ensemble learning techniques

AbstractAlzheimer's disease (AD) is a degenerative neurological disorder with incurable characteristics. To identify the substantial solution, we used a structural biomarker (structural magnetic resonance imaging) to see the neurostructural changes in the different regions of the brain of AD, mild cognitive impairment, and cognitive normal subjects. In this study, we detected the AD and their subtypes by using the traditional machine learning and ensemble learning models. It is also identified the relative impact score of various cortical and subcortical regions of AD and their subtypes. Experimental study contains two levels of classification: binary and multiclass. The Ensemble_LR_SVM model in binary classification has 99% accuracy in detection. Random forest model in the multiclass has 82% of accuracy. In the cortical‐subcortical analysis, the right hemisphere's parahippocampal and entorhinal regions were discovered to be the most influential. Similarly, the inferior temporal and isthmus cingulate regions had a significant influence in the left hemisphere.

Development and validation of a deep learning-based automatic segmentation model for assessing intracranial volume: comparison with NeuroQuant, FreeSurfer, and SynthSeg.

To develop and validate a deep learning-based automatic segmentation model for assessing intracranial volume (ICV) and to compare the accuracy determined by NeuroQuant (NQ), FreeSurfer (FS), and SynthSeg. This retrospective study included 60 subjects [30 Alzheimer's disease (AD), 21 mild cognitive impairment (MCI), 9 cognitively normal (CN)] from a single tertiary hospital for the training and validation group (50:10). The test group included 40 subjects (20 AD, 10 MCI, 10 CN) from the ADNI dataset. We propose a robust ICV segmentation model based on the foundational 2D UNet architecture trained with four types of input images (both single and multimodality using scaled or unscaled T1-weighted and T2-FLAIR MR images). To compare with our model, NQ, FS, and SynthSeg were also utilized in the test group. We evaluated the model performance by measuring the Dice similarity coefficient (DSC) and average volume difference. The single-modality model trained with scaled T1-weighted images showed excellent performance with a DSC of 0.989 ± 0.002 and an average volume difference of 0.46% ± 0.38%. Our multimodality model trained with both unscaled T1-weighted and T2-FLAIR images showed similar performance with a DSC of 0.988 ± 0.002 and an average volume difference of 0.47% ± 0.35%. The overall average volume difference with our model showed relatively higher accuracy than NQ (2.15% ± 1.72%), FS (3.69% ± 2.93%), and SynthSeg (1.88% ± 1.18%). Furthermore, our model outperformed the three others in each subgroup of patients with AD, MCI, and CN subjects. Our deep learning-based automatic ICV segmentation model showed excellent performance for the automatic evaluation of ICV.

Plasma MMP-9/TIMP-1 ratio serves as a novel potential biomarker in Alzheimer's disease.

This study aimed to explore the diagnostic potential of plasma MMP-9, TIMP-1 and MMP-9/TIMP-1 ratio for Alzheimer's disease (AD). This retrospective study was performed in a cohort consisting of patients with AD (AD group) and cognitive normal subjects (HC group). Cerebrospinal fluid (CSF) classic biomarkers including Aβ42, Aβ40, total tau (t-tau), and phosphorylated tau (p-tau) levels, and plasma MMP-9 and TIMP-1 levels were measured by commercially available ELISA kits, respectively. The differential diagnostic potential of plasma MMP-9, TIMP-1 and MMP-9/TIMP-1 ratio was evaluated using the receiver operating characteristic curve analysis. It was observed that significantly elevated levels of plasma MMP-9 and MMP-9/TIMP-1 ratio in patients with AD than HC. Both MMP-9 and MMP-9/TIMP-1 ratios were negatively correlated with CSF Aβ42/Aβ40 ratio and positively correlated with CSF p-tau in AD group. ROC curve analysis showed better diagnostic accuracy of MMP-9/TIMP-1 ratio than MMP-9 for AD at a cutoff value of 1.35 with an area under the curve of 0.906 and sensitivity and specificity of 95.8% and 75%, respectively. Our findings encourage the use of plasma MMP-9/TIMP-1 ratio as a biomarker in the diagnosis of AD.