Decision letter: Stage-dependent differential influence of metabolic and structural networks on memory across Alzheimer’s disease continuum

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the two brain networks assessed using magnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) in patients with MCI. Methods: This study included 137 patients with MCI and 80 healthy controls (HCs). Sequential interictal scans were performed using FDG-PET and MRI. The MCI metabolic and structural brain networks were constructed according to the standardized uptake value ratio (SUVR) obtained using FDG-PET and gray matter volume obtained using MRI. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores. Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions by scanning the hubs and found that the betweenness centrality of the right calcarine fissure and its surrounding cortex (CAL.R), left lingual gyrus (LING.L), and left globus pallidus (PAL.L) differed significantly between HCs and patients with MCI in both structural and metabolic networks (all p<0.05). The volume of gray matter atrophy in the PAL.L was significantly positively correlated with comprehension of spoken language (p=0.024) and word-finding difficulty in spontaneous speech item scores (p=0.007) in the ADAS-cog. Glucose intake in the three key brain regions (CAL.R, LING.L, and PAL.L) was significantly negatively correlated with remembering test instructions items in ADAS-cog (p=0.020, p=0.014, and p=0.008, respectively). Conclusion: MRI brain networks showed more changes than FDG-PET brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

BackgroundNeuroinflammation is a key process in initiating and propagating Alzheimer’s disease (AD). Even though it is widely known that the deposit of amyloid plaques and CSF levels of amyloid distinguishes patients with AD or mild cognitive impairment (MCI) from cognitively normal (CN) individuals, little is known about the role of amyloid‐specific immune response in cognitive decline.MethodUsing a polyfunctionality assay typically used for detecting virus‐specific T cell responses, we tested participants from the Epidemiology of Mild Cognitive Impairment in Taiwan study (EMCIT) and the Taiwan Precision Medicine Initiative of Cognitive impairment and dementia (TPMIC) study to compare the amyloid‐specific T cell responses between CN and MCI individuals. The abilities of T cell response parameters and plasma p‐Tau181 to distinguish MCI from CN were tested.ResultResults from both cohorts showed an enhanced amyloid‐specific T‐cell response in individuals with MCI. In the EMCIT cohort, the individual’s amyloid‐specific CD4+ response frequency of total CD4+ cells was significantly larger in MCI (n = 69, 0.93%) than in CN (n = 69, 0.51%, p &lt; 0.001). CD4+ T cell response discriminated MCI versus CN (area under curve [AUC], 0.72 [0.64‐0.81]) with significantly higher accuracy than p‐Tau181 (AUC: 0.59 [0.5‐0.69], p &lt; 0.01). In the TPMIC cohort, both CD4+ and CD8+ response frequencies were higher in MCI individuals (n = 21, CD4: 1.2%, CD8: 2.02%) than in CN (n = 30, CD4: 0.14%, CD8:0.27%; both p &lt; 0.001). CD4+ T cell response frequency and CD8+ response frequency also outperform p‐Tau181 in their discriminative accuracy of MCI versus NC (CD4+ AUC, 0.97, [0.94‐1.01]; CD8+ AUC, 0.96, [0.92‐1.01]; p‐Tau181 AUC, 0.83, [0.69‐0.96]; both p &lt; 0.05).ConclusionOur study validates the amyloid hypothesis by showing that amyloid‐associated neuroinflammation is involved in the process of neurodegeneration and demonstrated the accuracy of using amyloid‐specific T cell response to discriminate MCI from CN individuals. The TPMIC cohort is an ongoing longitudinal study that includes amyloid PET results and thus we will investigate the prognostic value of amyloid‐T cell response in the future.

A challenge in developing informative neuroimaging biomarkers for early diagnosis of Alzheimer's disease is the need to identify biomarkers that are evident before the onset of clinical symptoms, and which have sufficient sensitivity and specificity on an individual patient basis. Recent literature suggests that spatial patterns of brain atrophy discriminate amongst Alzheimer's disease, mild cognitive impairment (MCI) and cognitively normal (CN) older adults with high accuracy on an individual basis, thereby offering promise that subtle brain changes can be detected during prodromal Alzheimer's disease stages. Here, we investigate whether these spatial patterns of brain atrophy can be detected in CN and MCI individuals and whether they are associated with cognitive decline. Images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used to construct a pattern classifier that recognizes spatial patterns of brain atrophy which best distinguish Alzheimer's disease patients from CN on an individual person basis. This classifier was subsequently applied to longitudinal magnetic resonance imaging scans of CN and MCI participants in the Baltimore Longitudinal Study of Aging (BLSA) neuroimaging study. The degree to which Alzheimer's disease-like patterns were present in CN and MCI subjects was evaluated longitudinally in relation to cognitive performance. The oldest BLSA CN individuals showed progressively increasing Alzheimer's disease-like patterns of atrophy, and individuals with these patterns had reduced cognitive performance. MCI was associated with steeper longitudinal increases of Alzheimer's disease-like patterns of atrophy, which separated them from CN (receiver operating characteristic area under the curve equal to 0.89). Our results suggest that imaging-based spatial patterns of brain atrophy of Alzheimer's disease, evaluated with sophisticated pattern analysis and recognition methods, may be useful in discriminating among CN individuals who are likely to be stable versus those who will show cognitive decline. Future prospective studies will elucidate the temporal dynamics of spatial atrophy patterns and the emergence of clinical symptoms.

A neuroinflammatory reaction in Alzheimer disease (AD) brains involves reactive astrocytes that overexpress monoamine oxidase-B (MAO-B). 18F-(S)-(2-methylpyrid-5-yl)-6-[(3-fluoro-2-hydroxy)propoxy]quinoline (18F-SMBT-1) is a novel 18F PET tracer highly selective for MAO-B. We characterized the clinical performance of 18F-SMBT-1 PET across the AD continuum as a potential surrogate marker of reactive astrogliosis. Methods: We assessed 18F-SMBT-1 PET regional binding in 77 volunteers (76 ± 5.5 y old; 41 women, 36 men) across the AD continuum: 57 who were cognitively normal (CN) (44 amyloid-β [Aβ]-negative [Aβ-] and 13 Aβ-positive [Aβ+]), 12 who had mild cognitive impairment (9 Aβ- and 3 Aβ+), and 8 who had AD dementia (6 Aβ+ and 2 Aβ-). All participants also underwent Aβ and tau PET imaging, 3-T MRI, and neuropsychologic evaluation. Tau imaging results were expressed in SUV ratios using the cerebellar cortex as a reference region, whereas Aβ burden was expressed in centiloids. 18F-SMBT-1 outcomes were expressed as SUV ratio using the subcortical white matter as a reference region. Results: 18F-SMBT-1 yielded high-contrast images at steady state (60-80 min after injection). When compared with the Aβ- CN group, there were no significant differences in 18F-SMBT-1 binding in the group with Aβ- mild cognitive impairment. Conversely, 18F-SMBT-1 binding was significantly higher in several cortical regions in the Aβ+ AD group but also was significantly lower in the mesial temporal lobe and basal ganglia. Most importantly, 18F-SMBT-1 binding was significantly higher in the same regions in the Aβ+ CN group as in the Aβ- CN group. When all clinical groups were considered together, 18F-SMBT-1 correlated strongly with Aβ burden and much less with tau burden. Although in most cortical regions 18F-SMBT-1 did not correlate with brain volumetrics, regions known for high MAO-B concentrations presented a direct association with hippocampal and gray matter volumes, whereas the occipital lobe was directly associated with white matter hyperintensity. 18F-SMBT-1 binding was inversely correlated with Mini Mental State Examination and the Australian Imaging Biomarkers and Lifestyle's Preclinical Alzheimer Cognitive Composite in some neocortical regions such as the frontal cortex, lateral temporal lobe, and supramarginal gyrus. Conclusion: Cross-sectional human PET studies with 18F-SMBT-1 showed that Aβ+ AD patients, but most importantly, Aβ+ CN individuals, had significantly higher regional 18F-SMBT-1 binding than Aβ- CN individuals. Moreover, in several regions in the brain, 18F-SMBT-1 retention was highly associated with Aβ load. These findings suggest that increased 18F-SMBT-1 binding is detectable at the preclinical stages of Aβ accumulation, providing strong support for its use as a surrogate marker of astrogliosis in the AD continuum.

BackgroundAmyloid deposition is considered the initial pathology in Alzheimer’s disease (AD), investigation of amyloid pathology and the risk factor for amyloid pathology and cognitive declines in the Chinese population is critical for personalized management. However, the prevalence of amyloid deposition and the risk factors for amyloid pathology are unknown in the Chinese population.MethodParticipants were recruited from urban communities and memory clinics in Shanghai and Zhengzhou, China. A total of 246 AD individuals, 274 Mild cognitive impairment (MCI) individuals, and 506 Normal cognition (NC) individuals were identified: 106 AD, 156 MCI and 506 NC from the Community‐based cohort (COMC) as well as 140 AD and 118 MCI from the Clinic‐based cohort (CLIC). Amyloid positivity and deposition were analyzed based on amyloid positron emission tomography (PET) scans. We also performed a partial least squares (PLS) analysis to evaluate the associations of cohort, age, sex, education level, and ApoE genotype with amyloid qualification, quantification results and global cognition in all 1026 participants.ResultAcross the two cohorts, the AD group displayed the significantly highest rate of amyloid positivity (211/246, 85.8%), followed by the MCI (122/274, 44.5%) and NC groups (136/506, 26.9%, all P &lt; 0.001). Participants from the CLIC displayed a higher rate of amyloid positivity than those from the COMC in the MCI (68/118, 57.6% vs. 54/156, 34.6%, P &lt; 0.001) and AD groups (128/140, 91.4% vs. 83/106, 78.3%, P = 0.006). MCI (SUVr: 1.37 ± 0.25 vs. 1.25 ± 0.18, P &lt; 0.001), Aß+ MCI (SUVr: 1.48 ± 0.27 vs. 1.37 ± 0.24, P = 0.03) and AD individuals (SUVr: 1.47 ± 0.23 vs. 1.39 ± 0.23, P = 0.01) in the CLIC displayed significantly greater amyloid deposition than those in the COMC. According to the PLS model, we found that the CLIC was significantly associated with positive amyloid deposition and severer amyloid deposition.ConclusionWe found that individuals from CLIC displayed a higher amyloid positive rate and severer amyloid deposition. Specifically, in a PLS model, The CLIC, was strongly associated with amyloid pathology.

BackgroundAmyloid deposition is considered the initial pathology in Alzheimer’s disease (AD), investigation of amyloid pathology and the risk factor for amyloid pathology and cognitive declines in the Chinese population is critical for personalized management. However, the prevalence of amyloid deposition and the risk factors for amyloid pathology are unknown in the Chinese population.MethodParticipants were recruited from urban communities and memory clinics in Shanghai and Zhengzhou, China. A total of 246 AD individuals, 274 Mild cognitive impairment (MCI) individuals, and 506 Normal cognition (NC) individuals were identified: 106 AD, 156 MCI and 506 NC from the Community‐based cohort (COMC) as well as 140 AD and 118 MCI from the Clinic‐based cohort (CLIC). Amyloid positivity and deposition were analyzed based on amyloid positron emission tomography (PET) scans. We also performed a partial least squares (PLS) analysis to evaluate the associations of cohort, age, sex, education level, and ApoE genotype with amyloid qualification, quantification results and global cognition in all 1026 participants.ResultAcross the two cohorts, the AD group displayed the significantly highest rate of amyloid positivity (211/246, 85.8%), followed by the MCI (122/274, 44.5%) and NC groups (136/506, 26.9%, all P &lt; 0.001). Participants from the CLIC displayed a higher rate of amyloid positivity than those from the COMC in the MCI (68/118, 57.6% vs. 54/156, 34.6%, P &lt; 0.001) and AD groups (128/140, 91.4% vs. 83/106, 78.3%, P = 0.006). MCI (SUVr: 1.37 ± 0.25 vs. 1.25 ± 0.18, P &lt; 0.001), Aβ+ MCI (SUVr: 1.48 ± 0.27 vs. 1.37 ± 0.24, P = 0.03) and AD individuals (SUVr: 1.47 ± 0.23 vs. 1.39 ± 0.23, P = 0.01) in the CLIC displayed significantly greater amyloid deposition than those in the COMC. According to the PLS model, we found that the CLIC was significantly associated with positive amyloid deposition and severer amyloid deposition.ConclusionWe found that individuals from CLIC displayed a higher amyloid positive rate and severer amyloid deposition. Specifically, in a PLS model, The CLIC, was strongly associated with amyloid pathology.

BackgroundPredicting brain age from neuroimaging data is an emerging field. The age gap (AG), the difference between chronological age (CA) and brain age (BA), is crucial for indicating individual neuroanatomical aging. Previous deep learning models faced challenges in generalizability and neuroanatomical interpretability. We incorporated patients with different dementia types, including dementia with Lewy bodies (DLB) and Alzheimer’s disease (AD), alongside mild cognitive impairment (MCI) and cognitive normal (CN) individuals. This inclusive strategy is essential for comprehensive mapping of neurocognitive trajectories and understanding distinct aging patterns across various cognitive conditions.MethodUtilizing T1‐weighted MRI images of n = 3,859 subjects (Table 1) from the CamCAN, NACC, and ADNI databases, this study aimed to predict brain age in four groups (CN, MCI, AD, and DLB). Structural MRI data were spatial normalized and skull‐striped. Then a 3D Convolutional Neural Network (CNN) based on the skull‐striped data was used for age prediction. The model’s architecture includes three convolutional layers with ReLU activation, max‐pooling, batch normalization, and dropout for regularization, ending with global average pooling and dense layers. The model was trained and validated on CN subjects. The trained model was used to predict age in MCI, DLB, and AD patients as well as the test set of CN subjects.ResultThe 3D CNN model accurately predicted brain age in the CN test set with an AG of 0.64 ± 2.74 years and an absolute AG of 1.86 ± 2.11 years (Figure 1 and Table 1). In DLB and AD patients, the average AG was 3.81 and 2.90 years, respectively, and significantly larger than 0 (P &lt; 10‐5), indicating accelerated aging patterns in these groups. The average AG of MCI was 0.09 years which was significantly smaller than that of both DLB and AD (P &lt; 10‐3), indicating the early stage of impairment in MCI patients.ConclusionOur 3D CNN model accurately predicted brain age in cognitively normal individuals and identified accelerated aging in DLB and AD patients. The model's precision highlights its potential for early detection and understanding of neurocognitive trajectories, contributing to advancements in neurological research and clinical diagnostics.

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

BackgroundThe allostatic interoceptive network (AIN) has been proposed as central to linking interoceptive processing and autonomic regulation and involved in emotional experience. Early pathologic changes in AD occur in hubs of the AIN, and changes in emotional reactivity have been identified at both the MCI and dementia stages of Alzheimer’s disease. Interoception has been proposed as a core feature in psychiatric disease, though relatively little attention has been paid to altered interoception in neuropsychiatric symptoms in dementia. We conducted a pilot analysis in cognitively normal (CN) participants and those with mild cognitive impairment (MCI) to identify alterations in functional connectivity of the AIN, default mode, and salience networks.MethodTwenty‐two participants from the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core cohort including CN participants (n = 11, mean age = 67 ± 7) and MCI (n = 11, mean age = 72 ± 7) received 3T brain MRI using T1 for gray/white matter segmentation and BOLD for resting‐state functional connectivity. BOLD time series data were pre‐processed and de‐noised using standard techniques. Connectivity analyses were performed using the CONN functional connectivity toolbox. Briefly, we performed a comparison of within‐network connectivity of the allostasis and interoception system (comprised of the DMN, SN, and AIN seed regions) between MCI and NC participants while adjusting for age (p &lt; 0.05 uncorrected).Result Table lists the baseline demographic and clinical characteristics for CN and MCI groups. Participants with MCI had greater connectivity between limbic regions of the AIN and salience network compared with the CN group, and decreased connectivity between the left lateral parietal cortex and multiple hubs of the salience network. Within the AIN seed regions, the amygdala and dorsal posterior and mid insula had increased connectivity (Figure; p &lt; 0.05 uncorrected).ConclusionWe find differences in functional connectivity between CN adults and persons with MCI for three brain networks. Our analyses provide preliminary support that changes in an integrated allostasis and interoceptive system, integrating components of the DMN and SN, may have implications for emotional dysregulation in ADRD.

AimsOur aim was to differentiate patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) from cognitively normal (CN) individuals and predict the progression from MCI to AD within a 3-year longitudinal follow-up. A newly developed Holo-Hilbert Spectral Analysis (HHSA) was applied to resting state EEG (rsEEG), and features were extracted and subjected to machine learning algorithms.MethodsA total of 205 participants were recruited from three hospitals, with CN (n = 51, MMSE > 26), MCI (n = 42, CDR = 0.5, MMSE ≥ 25), AD1 (n = 61, CDR = 1, MMSE < 25), AD2 (n = 35, CDR = 2, MMSE < 16), and AD3 (n = 16, CDR = 3, MMSE < 16). rsEEG was also acquired from all subjects. Seventy-two MCI patients (CDR = 0.5) were longitudinally followed up with two rsEEG recordings within 3 years and further subdivided into an MCI-stable group (MCI-S, n = 36) and an MCI-converted group (MCI-C, n = 36). The HHSA was then applied to the rsEEG data, and features were extracted and subjected to machine-learning algorithms.Results(a) At the group level analysis, the HHSA contrast of MCI and different stages of AD showed augmented amplitude modulation (AM) power of lower-frequency oscillations (LFO; delta and theta bands) with attenuated AM power of higher-frequency oscillations (HFO; beta and gamma bands) compared with cognitively normal elderly controls. The alpha frequency oscillation showed augmented AM power across MCI to AD1 with a reverse trend at AD2. (b) At the individual level of cross-sectional analysis, implementation of machine learning algorithms discriminated between groups with good sensitivity (Sen) and specificity (Spec) as follows: CN elderly vs. MCI: 0.82 (Sen)/0.80 (Spec), CN vs. AD1: 0.94 (Sen)/0.80 (Spec), CN vs. AD2: 0.93 (Sen)/0.90 (Spec), and CN vs. AD3: 0.75 (Sen)/1.00 (Spec). (c) In the longitudinal MCI follow-up, the initial contrasted HHSA between MCI-S and MCI-C groups showed significantly attenuated AM power of alpha and beta band oscillations. (d) At the individual level analysis of longitudinal MCI groups, deploying machine learning algorithms with the best seven features resulted in a sensitivity of 0.9 by the support vector machine (SVM) classifier, with a specificity of 0.8 yielded by the decision tree classifier.ConclusionIntegrating HHSA into EEG signals and machine learning algorithms can differentiate between CN and MCI as well as also predict AD progression at the MCI stage.

BackgroundThe relationship between biomarkers of metabolic syndrome and insulin resistance, plasma triglyceride/HDL cholesterol (TG/HDL-C) ratio, on the rate of cognitive decline in mild cognitive impairment (MCI) and dementia stages of Alzheimer’s disease (AD) is unknown. The role of peripheral and cerebrospinal fluid (CSF) levels of Apolipoprotein A1 (ApoA1), a key functional component of HDL, on cognitive decline also remains unclear among them. Here we evaluate baseline plasma TG/HDL-C ratio and CSF and plasma ApoA1 levels and their relation with cognitive decline in the MCI and Dementia stages of AD.Patients and methodsA retrospective longitudinal study (156 participants; 106 MCI, 50 AD dementia) from the Alzheimer’s Disease Neuroimaging Initiative, with an average of 4.0 (SD 2.8) years follow-up. Baseline plasma TG/HDL-C, plasma, and CSF ApoA1 and their relationship to inflammation and blood–brain barrier (BBB) biomarkers and longitudinal cognitive outcomes were evaluated. Multivariable linear mixed effect models were used to assess the effect of baseline analytes with longitudinal changes in Mini-Mental State Exam (MMSE), Clinical Dementia Rating–Sum of Boxes (CDR-SB), and Logical Memory delayed recall (LM) score after controlling for well-known covariates.ResultsA total of 156 participants included 98 women, 63%; mean age was 74.9 (SD 7.3) years. At baseline, MCI and dementia groups did not differ significantly in TG/HDL-C (Wilcoxon W statistic = 0.39, p = 0.39) and CSF ApoA1 levels (W = 3642, p = 0.29), but the dementia group had higher plasma ApoA1 than the MCI group (W = 4615, p = 0.01). Higher TG/HDL-C ratio was associated with faster decline in CDR-SB among MCI and dementia groups. Higher plasma ApoA1 was associated with faster decline in MMSE and LM among MCI, while in contrast higher CSF ApoA1 levels related to slower cognitive decline in MMSE among MCI. CSF and plasma ApoA1 also show opposite directional correlations with biomarkers of BBB integrity. CSF but not plasma levels of ApoA1 positively correlated to inflammation analytes in the AGE-RAGE signaling pathway in diabetic complications (KEGG ID:KO04933).ConclusionsBiomarkers of metabolic syndrome relate to rate of cognitive decline among MCI and dementia individuals. Elevated plasma TG/HDL-C ratio and plasma ApoA1 are associated with worse cognitive outcomes in MCI and dementia participants. CSF ApoA1 and plasma ApoA1 likely have different roles in AD progression in MCI stage.

USING EMERGING CEREBROSPINAL FLUID MARKERS TO CHARACTERIZE SUSPECTED NON-ALZHEIMER’S DISEASE PATHOPHYSIOLOGY (SNAP) IN INDIVIDUALS WITH MILD COGNITIVE IMPAIRMENT

ObjectiveTo investigate the associations between cerebral microhemorrhages (CMH) and cognitive decline across the Alzheimer’s dementia continuum.MethodsUsing the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we studied 619 participants, categorized into 221 cognitively normal (CN) participants, 281 patients with mild cognitive impairment (MCI), and 117 patients with Alzheimer’s disease (AD). CMH prevalence and distribution were determined using T2-weighted magnetic resonance imaging (MRI), focusing on the frontal, occipital, and parietal subcortical regions of interest (ROIs).Clinical dementia rating scale sum of boxes (CDR-SB) and mini-mental state examination (MMSE) were used for diagnosis and composite cognitive scores regarding visuospatial abilities, language, memory, and executive functions were used as outcome variables. Age, gender, and APOE ε4 positivity status were used as covariates.ResultsThe AD group displayed significantly elevated tau and P-tau levels compared to MCI and CN groups (p < 0.001). APOE ε4 positivity was 67.5% in the AD group, surpassing the 50.2% in MCI and 29% in CN individuals (p < 0.001). Cognitive assessments revealed that the AD group’s CDR-SB score and MMSE both significantly differed from these scores in the MCI and CN groups (p < 0.001). Overall, CMH prevalence was 27.7%, with a predominant distribution in the frontal subcortical ROIs. MCI subjects with CMH showed notably diminished ADNI Visuospatial Composite Scores compared to those without CMH. Age significantly predicted CMH in CN and MCI (p < 0.05). In AD participants, APOE ε4 heterozygotes (p = 0.02) and homozygotes (p = 0.01) hadincreased CMH likelihood.ConclusionCMHs are significantly associated with cognitive decline in patients with MCI. This association is more prominent in regard to the decline in visuospatial abilities.

BackgroundThe emergence of blood‐based biomarkers offers a cost‐effective and less invasive alternative to established neuroimaging and cerebrospinal fluid biomarkers. Newly developed fluid biomarkers, including N‐terminal tau fragment (NT1), have shown promise for identifying individuals at risk for Alzheimer’s disease (AD). Evidence has shown NT1 may be more abundant than full‐length tau across the AD continuum and has high sensitivity and specificity to separate cognitively normal (CN) individuals from those with mild cognitive impaired (MCI) and AD in discovery and replication cohorts. Here we quantify plasma NT1 in a large, well‐characterized cohort and examine the association between plasma NT1 and cross‐sectional clinical and biomarkers measures.MethodsSeven hundred and seventeen individuals enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI) who have plasma NT1, Aß‐PET, MRI, and clinical (Clinical Dementia Rating; CDR) measures were included in this study (Table 1). NT1 was assessed using Quanterix Simoa HD‐X platform. PET, MRI, clinical, and other plasma measures were derived using previously described procedures in ADNI. Linear regressions were performed to assess the cross‐sectional association of NT1 with clinical and biomarkers measures, after adjusting relevant covariates.ResultsNT1 levels were elevated in cognitively impaired (MCI/AD; CDR&gt;0) relative to CN (CDR=0) individuals (p=0.008, Figure 1A). Specifically, NT1 is elevated in the MCI group (CDR=0.5, MCI vs Aß‐ CN group: p=0.005), but not the AD group (CDR&gt;0.5, AD vs all other groups: p’s &gt;0.206, Figure 1B). NT1 was associated with plasma phosphorylated (p)Tau‐181 (p=1.27x10‐9, Figure 2A) and plasma neurofilament light chain (NfL; p=5.68x10‐6, Figure 2B) but not hippocampal volume (p=0.239).ConclusionPlasma NT1 differentiated CN from MCI/AD individuals and was elevated particularly in the early symptomatic phase of disease. Plasma NT1 was associated with plasma markers of tau and neurodegeneration. Together these results suggest that plasma NT1 may be a useful biomarker of AD‐related tau pathology and neurodegeneration.