Functional MRI Analysis of Cortical Regions to Distinguish Lewy Body Dementia From Alzheimer's Disease.

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

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 < 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 < 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.

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.

Cerebellar functional connectivity changes have been reported in Alzheimer's disease (AD), but a comprehensive framework integrating these findings is lacking. This retrospective study investigates the cerebello-thalamo-cortical (CTC) circuit in AD, using functional gradient analysis to elucidate deficits and potential biomarkers. We analyzed data from 246 participants enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-3; NCT02854033), including 58 with AD, 103 with mild cognitive impairment (MCI), and 85 cognitively normal (CN) controls, matched for age and sex. All individuals underwent comprehensive neuropsychological assessments (MMSE, MoCA, ADAS-Cog) and MRI scans. We extracted mean time series for 270 brain regions (an extended Power atlas) and computed pairwise functional connectivity, focusing on CTC circuitry. Thalamic and cerebellar connectivity gradients were derived using voxel-wise correlation matrices and the BrainSpace toolbox, defining thalamic and cerebellar masks from the Melbourne subcortical atlas and AAL atlas, respectively. ANCOVA with post hoc analyses, controlling for age and sex, was conducted to assess abnormal CTC connectivity across AD, MCI, and CN groups. LASSO regression identified edges within the CTC circuitry that significantly differed between AD and CN, MCI and CN, AD and MCI, as well as was used to construct Logistic classification model. Pearson correlations were performed to examine relationships between mean CTC connectivity, individual edges, and cognitive scores (MMSE, MoCA, ADAS-Cog). To explore the hierarchical organization of the thalamus and cerebellum, global gradient distributions were compared across groups using two-sample Kolmogorov-Smirnov tests. Additionally, ANCOVA was applied to compare subfield- and functional-level gradients of the thalamus and cerebellum among AD, MCI, and CN. False discovery rate (FDR) corrections were used, setting the statistical significance threshold was set at P < 0.05. AD and MCI individuals exhibited increased CTC connectivity compared to CN (all P < 0.05). Average CTC connectivity did not correlate with cognitive scores (P > 0.05), but specific CTC edges were correlated. LASSO regression identified 20 discriminative edges, achieving high accuracy in AD-CN classification (AUC = 0.92 training, AUC = 0.80 test). Thalamic and cerebellar gradient distributions differed significantly across groups (all P < 0.05), with specific regions showing distinct gradient scores. Five cerebellar functional networks exhibited decreased gradient scores. Significant CTC hyperconnectivity in AD and MCI compared with CN suggests early thalamic and cerebellar dysregulation. Classification analyses effectively distinguished AD vs. CN but were moderate for MCI vs. CN and limited for MCI vs. AD. Gradient analyses revealed global- and subfield-level disruptions in AD, emphasizing the role of thalamic and cerebellar interactions in cognitive decline and offering potential diagnostic markers and therapeutic targets.

Background:Alzheimer’s disease (AD) and dementia with Lewy bodies (DLB) are often misdiagnosed with each other because of similar symptoms including progressive memory loss. The metabolic network topology that describes inter-regional metabolic connections can be generated using fluorodeoxyglucose positron emission tomography (FDG-PET) data with the graph-theoretical method. We hypothesized that different metabolic connectivity underlies the symptoms of AD patients, DLB patients, and cognitively normal (CN) individuals.Objective:This study aimed to generate metabolic connectivity using FDG-PET data and assess the network topology to differentiate AD patients, DLB patients, and CN individuals.Methods:This study included 45 AD patients, 18 DLB patients, and 142 CN controls. We analyzed FDG-PET data using the graph-theoretical method and generated the network topology in AD patients, DLB patients, and CN individuals. We statistically assessed the topology with global and nodal parameters.Results:The whole metabolic network was preserved in CN; however, diffusely decreased connection was found in AD and partially but more deeply decreased connection was observed in DLB. The metabolic topology revealed that the right posterior cingulate and the left transverse temporal gyrus were significantly different between AD and DLB.Conclusion:The present findings indicate that metabolic connectivity decreased in both AD and DLB, compared with CN. DLB was characterized restricted but deeper stereotyped network disruption compared with AD. The right posterior cingulate and the left transverse temporal gyrus are significant regions in the metabolic connectivity for differentiating AD from DLB.

INTRODUCTIONHearing loss is identified as one of the largest modifiable risk factors for cognitive impairment and dementia. Studies evaluating this relationship have yielded mixed results.METHODSWe investigated the longitudinal relationship between self‐reported hearing loss and cognitive/functional performance in 695 cognitively normal (CN) and 941 participants with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative.RESULTSWithin CN participants with hearing loss, there was a significantly greater rate of cognitive decline per modified preclinical Alzheimer's cognitive composite. Within both CN and MCI participants with hearing loss, there was a significantly greater rate of functional decline per the functional activities questionnaire (FAQ). In CN and MCI participants, hearing loss did not significantly contribute to the risk of progression to a more impaired diagnosis.DISCUSSIONThese results confirm previous studies demonstrating a significant longitudinal association between self‐reported hearing loss and cognition/function but do not demonstrate an increased risk of conversion to a more impaired diagnosis.CLINICAL TRIAL REGISTRATION INFORMATIONNCT00106899 (ADNI: Alzheimer's Disease Neuroimaging Initiative, clinicaltrials.gov), NCT01078636 (ADNI‐GO: Alzheimer's Disease Neuroimaging Initiative Grand Opportunity, clinicaltrials.gov), NCT01231971 (ADNI2: Alzheimer's Disease Neuroimaging Initiative 2, clinicaltrials.gov), NCT02854033 (ADNI3: Alzheimer's Disease Neuroimaging Initiative 3, clinicaltrials.gov).HighlightsHearing loss is a potential modifiable risk factor for dementia.We assessed the effect of self‐reported hearing loss on cognition and function in the ADNI cohort.Hearing loss contributes to significantly faster cognitive and functional decline.Hearing loss was not associated with conversion to a more impaired diagnosis.

To determine the temporal sequence of objectively defined subtle cognitive difficulties (Obj-SCD) in relation to amyloidosis and neurodegeneration, the current study examined the trajectories of amyloid PET and medial temporal neurodegeneration in participants with Obj-SCD relative to cognitively normal (CN) and mild cognitive impairment (MCI) groups. A total of 747 Alzheimer's Disease Neuroimaging Initiative participants (305 CN, 153 Obj-SCD, 289 MCI) underwent neuropsychological testing and serial amyloid PET and structural MRI examinations. Linear mixed effects models examined 4-year rate of change in cortical 18F-florbetapir PET, entorhinal cortex thickness, and hippocampal volume in those classified as Obj-SCD and MCI relative to CN. Amyloid accumulation was faster in the Obj-SCD group than in the CN group; the MCI and CN groups did not significantly differ from each other. The Obj-SCD and MCI groups both demonstrated faster entorhinal cortical thinning relative to the CN group; only the MCI group exhibited faster hippocampal atrophy than CN participants. Relative to CN participants, Obj-SCD was associated with faster amyloid accumulation and selective vulnerability of entorhinal cortical thinning, whereas MCI was associated with faster entorhinal and hippocampal atrophy. Findings suggest that Obj-SCD, operationally defined using sensitive neuropsychological measures, can be identified prior to or during the preclinical stage of amyloid deposition. Further, consistent with the Braak neurofibrillary staging scheme, Obj-SCD status may track with early entorhinal pathologic changes, whereas MCI may track with more widespread medial temporal change. Thus, Obj-SCD may be a sensitive and noninvasive predictor of encroaching amyloidosis and neurodegeneration, prior to frank cognitive impairment associated with MCI.

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.

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.

Early differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) is important, but it remains challenging. Different profiles of speech and language impairments between AD and DLB have been suggested, but direct comparisons have not been investigated. We collected speech responses from 121 older adults comprising AD, DLB, and cognitively normal (CN) groups and investigated their acoustic, prosodic, and linguistic features. The AD group showed larger differences from the CN group than the DLB group in linguistic features, while the DLB group showed larger differences in prosodic and acoustic features. Machine-learning classifiers using these speech features achieved 87.0% accuracy for AD versus CN, 93.2% for DLB versus CN, and 87.4% for AD versus DLB. Our findings indicate the discriminative differences in speech features in AD and DLB and the feasibility of using these features in combination as a screening tool for identifying/differentiating AD and DLB.

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.

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, Aβ‐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 Aβ‐ 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.

(Reprinted with permission from American Psychiatric Association, http://psychiatryonline.org/guidelines).

Clinicopathologic phenotypes of dementia with Lewy bodies (DLB) and Alzheimer disease (AD) often overlap, making discrimination difficult. We performed resting state blood oxygen level-dependent (BOLD) functional connectivity MRI (fcMRI) to determine whether there were differences between AD and DLB. Participants (n = 88) enrolled in a longitudinal study of memory and aging underwent 3-T fcMRI. Clinical diagnoses of probable DLB (n = 15) were made according to published criteria. Cognitively normal control participants (n = 38) were selected for the absence of cerebral amyloid burden as imaged with Pittsburgh compound B (PiB). Probable AD cases (n = 35) met published criteria and had appreciable amyloid deposits with PiB imaging. Functional images were collected using a gradient spin-echo sequence sensitive to BOLD contrast (T2* weighting). Correlation maps selected a seed region in the combined bilateral precuneus. Participants with DLB had a functional connectivity pattern for the precuneus seed region that was distinct from AD; both the DLB and AD groups had functional connectivity patterns that differed from the cognitively normal group. In the DLB group, we found increased connectivity between the precuneus and regions in the dorsal attention network and the putamen. In contrast, we found decreased connectivity between the precuneus and other task-negative default regions and visual cortices. There was also a reversal of connectivity in the right hippocampus. Changes in functional connectivity in DLB indicate patterns of activation that are distinct from those seen in AD and may improve discrimination of DLB from AD and cognitively normal individuals. Since patterns of connectivity differ between AD and DLB groups, measurements of BOLD functional connectivity can shed further light on neuroanatomic connections that distinguish DLB from AD.

BackgroundEarly prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features.MethodsA total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction.ResultsDuring the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer’s continuum model was developed which could predict the Alzheimer’s continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91).ConclusionsThe risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.