Spatial patterns of brain amyloid-beta burden and atrophy rate associations in mild cognitive impairment

Age, sex, and APOE gene-specific associations between dynapenic obesity and dementia in a large cohort

OBJECTIVE

To investigate the associations between dynapenic obesity and the risk of dementia, and the modifying effects of age, sex, and the APOE gene, using a large population-based cohort.

METHODS

279,884 participants aged 55 and above from the UK Biobank were included. The participants were classified into four categories based on body mass index and hand grip strength: healthy, obesity, dynapenia, and dynapenic obesity. The incident dementia was identified based on linked hospital records and death register data. Cox proportional hazards regression models were used to estimate the associations, followed by age-, sex-, and apolipoprotein E (APOE) gene-stratified analyses.

RESULTS

During the median follow-up of 12.4 years, 5,170 (1.8%) participants developed dementia. Compared with the healthy group, participants with dynapenic obesity had 67% higher dementia risk (hazard ratio [HR]: 1.67, 95% confidence interval [CI]: 1.44-1.94). Compared with the healthy group, higher risks of dementia in participants with dynapenic obesity were respectively observed in male (HR: 2.03, 95% CI: 1.65-2.50), younger (<65 years, HR: 1.97, 95% CI: 1.55-2.50), and non-ε4-carrier (HR: 1.97, 95% CI: 1.60-2.44) (all P for interaction <0.05). In participants under 65 years and non-ε4-carrier, those with dynapenic obesity had the highest risk of dementia (HR: 2.63, 95% CI: 1.91-3.62), compared with the healthy group (P for second order interaction = 0.026).

CONCLUSIONS

Dynapenic obesity is associated with increased risks of dementia, especially in participants under 65 years and non-ε4-carrier, suggesting the importance of managing dynapenic obesity in the prevention of cognition-related disorders.

Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition

BACKGROUND

Mild cognitive impairment (MCI)/Alzheimer's disease (AD) is associated with cognitive decline beyond normal aging and linked to the alterations of brain volume quantified by magnetic resonance imaging (MRI) and amyloid-beta (Aβ) quantified by positron emission tomography (PET). Yet, the complex relationships between these regional imaging measures and cognition in MCI/AD remain unclear. Explainable artificial intelligence (AI) may uncover such relationships.

METHOD

We integrate the AI-based deep learning neural network and Shapley additive explanations (SHAP) approaches and introduce the Deep-SHAP method to investigate the multivariate relationships between regional imaging measures and cognition. After validating this approach on simulated data, we apply it to real experimental data from MCI/AD patients.

RESULTS

Deep-SHAP significantly predicted cognition using simulated regional features and identified the ground-truth simulated regions as the most significant multivariate predictors. When applied to experimental MRI data, Deep-SHAP revealed that the insula, lateral occipital, medial frontal, temporal pole, and occipital fusiform gyrus are the primary contributors to global cognitive decline in MCI/AD. Furthermore, when applied to experimental amyloid Pittsburgh compound B (PiB)-PET data, Deep-SHAP identified the key brain regions for global cognitive decline in MCI/AD as the inferior temporal, parahippocampal, inferior frontal, supratemporal, and lateral frontal gray matter.

CONCLUSION

Deep-SHAP method uncovered the multivariate relationships between regional brain features and cognition, offering insights into the most critical modality-specific brain regions involved in MCI/AD mechanisms.

Image Translation for Estimating Two-Dimensional Axial Amyloid-Beta PET From Structural MRI

BACKGROUND

Amyloid-beta and brain atrophy are hallmarks for Alzheimer's Disease that can be targeted with positron emission tomography (PET) and MRI, respectively. MRI is cheaper, less-invasive, and more available than PET. There is a known relationship between amyloid-beta and brain atrophy, meaning PET images could be inferred from MRI.

PURPOSE

To build an image translation model using a Conditional Generative Adversarial Network able to synthesize Amyloid-beta PET images from structural MRI.

STUDY TYPE

Retrospective.

POPULATION

Eight hundred eighty-two adults (348 males/534 females) with different stages of cognitive decline (control, mild cognitive impairment, moderate cognitive impairment, and severe cognitive impairment). Five hundred fifty-two subjects for model training and 331 for testing (80%:20%).

FIELD STRENGTH/SEQUENCE

3 T, T1-weighted structural (T1w).

ASSESSMENT

The testing cohort was used to evaluate the performance of the model using the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR), comparing the likeness of the overall synthetic PET images created from structural MRI with the overall true PET images. SSIM was computed in the overall image to include the luminance, contrast, and structural similarity components. Experienced observers reviewed the images for quality, performance and tried to determine if they could tell the difference between real and synthetic images.

STATISTICAL TESTS

Pixel wise Pearson correlation was significant, and had an R2 greater than 0.96 in example images. From blinded readings, a Pearson Chi-squared test showed that there was no significant difference between the real and synthetic images by the observers (P = 0.68).

RESULTS

A high degree of likeness across the evaluation set, which had a mean SSIM = 0.905 and PSNR = 2.685. The two observers were not able to determine the difference between the real and synthetic images, with accuracies of 54% and 46%, respectively.

CONCLUSION

Amyloid-beta PET images can be synthesized from structural MRI with a high degree of similarity to the real PET images.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 1.

A Longitudinal Study on Memory Enhancement in Subjective Cognitive Decline Patients: Clinical and Neuroimaging Perspectives

BACKGROUND

Subjective cognitive decline (SCD) refers to the self-reported persistent cognitive decline despite normal objective testing, increasing the risk of dementia compared to cognitively normal individuals.

OBJECTIVE

This study aims to investigate the attributes of SCD patients who demonstrated memory function improvement.

METHODS

In this prospective study of SCD, a total of 120 subjects were enrolled as part of a multicenter cohort study aimed at identifying predictors for the clinical progression to mild cognitive impairment or dementia (CoSCo study). All subjects underwent 18F-florbetaben PET and brain MRI scans at baseline and annual neuropsychological tests. At the 24-month follow-up, we classified SCD patients based on changes in memory function, the z-score of the Seoul verbal learning test delayed recall.

RESULTS

Of the 120 enrolled patients, 107 successfully completed the 24-month follow-up assessment. Among these, 80 patients (74.8%) with SCD exhibited memory function improvements. SCD patients with improved memory function had a lower prevalence of coronary artery disease at baseline and performed better in the trail-making test part B compared to those without improvement. Anatomical and biomarker analysis showed a lower frequency of amyloid PET positivity and larger volumes in the left and right superior parietal lobes in subjects with improved memory function.

CONCLUSIONS

Our prospective study indicates that SCD patients experiencing memory improvement over a 24-month period had a lower amyloid burden, fewer cardiovascular risk factors, and superior executive cognitive function. Identifying these key factors associated with cognitive improvement may assist clinicians in predicting future memory function improvements in SCD patients.

Relationship between sarcopenic obesity and cognitive function in patients with mild to moderate Alzheimer's disease

BACKGROUND

Previous research has linked sarcopenic obesity (SO) to cognitive function; however, the relationship between cognitive performance and SO Alzheimer's disease (AD) patients remains unclear. This study aimed to investigate their relationship in AD patients.

METHODS

One hundred and twenty mild to moderate AD patients and 56 normal controls were recruited. According to sarcopenia or obesity status, AD patients were classified into subgroups: normal, obesity, sarcopenia, and SO. Body composition, demographics, and sarcopenia parameters were assessed. Cognitive performance was evaluated using neuropsychological scales.

RESULTS

Among the 176 participants, the prevalence of SO in the moderate AD group was higher than in the normal control group. The moderate AD group had the lowest appendicular skeletal muscle mass index (ASMI) and the highest percentage of body fat (PBF). Hypertension and diabetes were more prevalent in the SO group than in the normal group among the subgroups. The sarcopenia and SO groups exhibited worse global cognitive function compared to the normal and obesity groups. Partial correlation analysis revealed that ASMI, PBF, and visceral fat area were associated with multiple cognitive domains scores. In logistic regression analysis, after adjusting for confounders, obesity was not found to be associated with AD. However, sarcopenia (odds ratio (OR) = 5.35, 95% CI: 1.27-22.46) and SO (OR = 5.84, 95% CI: 1.26-27.11) were identified as independent risk factors for AD.

CONCLUSIONS

SO was associated with cognitive dysfunction in AD patients. Moreover, the impact of SO on cognitive decline was greater than that of sarcopenia. Early identification and intervention for SO may have a positive effect on the occurrence and progression of AD.

Longitudinal associations between β-amyloid and cortical thickness in mild cognitive impairment

How beta-amyloid accumulation influences brain atrophy in Alzheimer's disease remains contentious with conflicting findings. We aimed to elucidate the correlations of regional longitudinal atrophy with cross-sectional regional and global amyloid in individuals with mild cognitive impairment and no cognitive impairment. We hypothesized that greater cortical thinning over time correlated with greater amyloid deposition, particularly within Alzheimer's disease characteristic regions in mild cognitive impairment, and weaker or no correlations in those with no cognitive impairment. 45 patients with mild cognitive impairment and 12 controls underwent a cross-sectional [¹¹C]-Pittsburgh Compound B PET and two retrospective longitudinal structural imaging (follow-up: 23.65 ± 2.04 months) to assess global/regional amyloid and regional cortical thickness, respectively. Separate linear mixed models were constructed to evaluate relationships of either global or regional amyloid with regional cortical thinning longitudinally. In patients with mild cognitive impairment, regional amyloid in the right banks of the superior temporal sulcus was associated with longitudinal cortical thinning in the right medial orbitofrontal cortex (P = 0.04 after False Discovery Rate correction). In the mild cognitive impairment group, greater right banks amyloid burden and less cortical thickness in the right medial orbitofrontal cortex showed greater visual and verbal memory decline over time, which was not observed in controls. Global amyloid was not associated with longitudinal cortical thinning in any locations in either group. Our findings indicate an increasing influence of amyloid on neurodegeneration and memory along the preclinical to prodromal spectrum. Future multimodal studies that include additional biomarkers will be well-suited to delineate the interplay between various pathological processes and amyloid and memory decline, as well as clarify their additive or independent effects along the disease deterioration.

Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning

Purpose

To investigate the MRI markers for the prediction of amyloid β (Aβ)-positivity in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to evaluate the differences in MRI markers between Aβ-positive (Aβ [+]) and -negative groups using the machine learning (ML) method.

Materials and Methods

This study included 139 patients with MCI and AD who underwent amyloid PET-CT and brain MRI. Patients were divided into Aβ (+) (n = 84) and Aβ-negative (n = 55) groups. Visual analysis was performed with the Fazekas scale of white matter hyperintensity (WMH) and cerebral microbleeds (CMB) scores. The WMH volume and regional brain volume were quantitatively measured. The multivariable logistic regression and ML using support vector machine, and logistic regression were used to identify the best MRI predictors of Aβ-positivity.

Results

The Fazekas scale of WMH (p = 0.02) and CMB scores (p = 0.04) were higher in Aβ (+). The volumes of hippocampus, entorhinal cortex, and precuneus were smaller in Aβ (+) (p < 0.05). The third ventricle volume was larger in Aβ (+) (p = 0.002). The logistic regression of ML showed a good accuracy (81.1%) with mini-mental state examination (MMSE) and regional brain volumes.

Conclusion

The application of ML using the MMSE, third ventricle, and hippocampal volume is helpful in predicting Aβ-positivity with a good accuracy.

Poorer sleep impairs brain health at midlife

Sleep is an emerging risk factor for dementia but its association with brain health remains unclear. This study included UK Biobank (n = 29,545; mean age = 54.65) participants at imaging visit with sleep measures and brain scans, and a subset (n = 14,206) with cognitive measures. Multiple linear regression analyses were conducted to study the associations between sleep and brain health. Every additional hour of sleep above 7 h/day was associated with 0.10-0.25% lower brain volumes. In contrast, a negative non-linear association was observed between sleep duration, grey matter, and hippocampal volume. Both longer (> 9 h/day) and shorter sleep (< 6 h/day) durations were associated with lower brain volumes and cognitive measures (memory, reaction time, fluid intelligence). Additionally, daytime dozing was associated with lower brain volumes (grey matter and left hippocampus volume) and lower cognitive measures (reaction time and fluid intelligence). Poor sleep (< 6 h/day, > 9 h/day, daytime dozing) at midlife was associated with lower brain health. Sleep may be an important target to improve brain health into old age and delay the onset of dementia.

Gait Indicators Contribute to Screening Cognitive Impairment: A Single- and Dual-Task Gait Study

Background: Screening cognitive impairment is complex and not an appliance for early screening. Gait performance is strongly associated with cognitive impairment. Objectives: We aimed to explore gait indicators that could potentially screen cognitive dysfunction. Methods: A total of 235 subjects were recruited from June 2021 to June 2022. Four gait tasks, including the walking test, the timed “Up & Go” test (TUG), foot pressure balance (FPB), and one-legged standing with eyes closed test (OLS-EC), were performed. Moreover, in the walking test, participants were instructed to walk at their usual pace for the single-gait test. For the dual-task tests, participants walked at their usual pace while counting backward from 100 by 1s. The data were analyzed by the independent sample t-test, univariate and multivariate logistic regression, a linear trend, stratified and interaction analysis, the receiver operating characteristic (ROC) curve, and Pearson’s correlations. Results: Among the 235 participants, 81 (34.5%) were men and 154 (65.5%) were women. The mean age of participants was 72 ± 7.836 years. The control, MCI, mild AD, and severe AD groups had means of 71, 63, 71, and 30, respectively. After adjusting for age, sex, education, and body mass index (BMI), the dual-task toe-off-ground angle (TOA) (odds ratio (OR) = 0.911, 95% confidence interval (CI): 0.847, 0.979), single-task TOA (OR = 0.904, 95% CI: 0.841−0.971), and the timed “Up & Go” time (TUGT) (OR = 1.515, 95% CI: 1.243−1.846) were significantly associated with an increased risk of cognitive impairment. In addition, the trend test and stratified analysis results had no significant differences (all p > 0.05). The area under the roc curve (AUC) values of TOA in the dual-task and TUGT were 0.812 and 0.847, respectively. Additionally, TOA < 36.75° in the dual-task, TOA < 38.90° in the single-task, and TUGT > 9.83 seconds (s) are likely to indicate cognitive impairment. The cognitive assessment scale scores were significantly correlated with TOA (all r > 0.3, p < 0.001) and TUGT (all r > 0.2), respectively. Conclusion: TOA and TUGT scores are, in some circumstances, associated with cognitive impairment; therefore, they can be used as simple initial screenings to identify patients at risk.

Impact of Amyloid Pathology in Mild Cognitive Impairment Subjects: The Longitudinal Cognition and Surface Morphometry Data

The amyloid framework forms the central medical theory related to Alzheimer disease (AD), and the in vivo demonstration of amyloid positivity is essential for diagnosing AD. On the basis of a longitudinal cohort design, the study investigated clinical progressive patterns by obtaining cognitive and structural measurements from a group of patients with amnestic mild cognitive impairment (MCI); the measurements were classified by the positivity (Aβ+) or absence (Aβ-) of the amyloid biomarker. We enrolled 185 patients (64 controls, 121 patients with MCI). The patients with MCI were classified into two groups on the basis of their [¹⁸F]flubetaben or [¹⁸F]florbetapir amyloid positron-emission tomography scan (Aβ+ vs. Aβ-, 67 vs. 54 patients) results. Data from annual cognitive measurements and three-dimensional T1 magnetic resonance imaging scans were used for between-group comparisons. To obtain longitudinal cognitive test scores, generalized estimating equations were applied. A linear mixed effects model was used to compare the time effect of cortical thickness degeneration. The cognitive decline trajectory of the Aβ+ group was obvious, whereas the Aβ- and control groups did not exhibit a noticeable decline over time. The group effects of cortical thickness indicated decreased entorhinal cortex in the Aβ+ group and supramarginal gyrus in the Aβ- group. The topology of neurodegeneration in the Aβ- group was emphasized in posterior cortical regions. A comparison of the changes in the Aβ+ and Aβ- groups over time revealed a higher rate of cortical thickness decline in the Aβ+ group than in the Aβ- group in the default mode network. The Aβ+ and Aβ- groups experienced different APOE ε4 effects. For cortical-cognitive correlations, the regions associated with cognitive decline in the Aβ+ group were mainly localized in the perisylvian and anterior cingulate regions. By contrast, the degenerative topography of Aβ- MCI was scattered. The memory learning curves, cognitive decline patterns, and cortical degeneration topographies of the two MCI groups were revealed to be different, suggesting a difference in pathophysiology. Longitudinal analysis may help to differentiate between these two MCI groups if biomarker access is unavailable in clinical settings.

Can a serious game-based cognitive training attenuate cognitive decline related to Alzheimer's disease? Protocol for a randomized controlled trial

BACKGROUND

Alzheimer's disease (AD) is a major public health issue. Cognitive interventions such as computerized cognitive trainings (CCT) are effective in attenuating cognitive decline in AD. However, in those at risk of dementia related to AD, results are heterogeneous. Efficacy and feasibility of CCT needs to be explored in depth. Moreover, underlying mechanisms of CCT effects on the three cognitive domains typically affected by AD (episodic memory, semantic memory and spatial abilities) remain poorly understood.

METHODS

In this bi-centric, randomized controlled trial (RCT) with parallel groups, participants (planned N = 162, aged 60-85 years) at risk for AD and with at least subjective cognitive decline will be randomized to one of three groups. We will compare serious game-based CCT against a passive wait list control condition and an active control condition (watching documentaries). Training will consist of daily at-home sessions for 10 weeks (50 sessions) and weekly on-site group meetings. Subsequently, the CCT group will continue at-home training for an additional twenty-weeks including monthly on-site booster sessions. Investigators conducting the cognitive assessments will be blinded. Group leaders will be aware of participants' group allocations. Primarily, we will evaluate change using a compound value derived from the comprehensive cognitive assessment for each of three cognitive domains. Secondary, longitudinal functional and structural magnetic resonance imaging (MRI) and evaluation of blood-based biomarkers will serve to investigate neuronal underpinnings of expected training benefits.

DISCUSSION

The present study will address several shortcomings of previous CCT studies. This entails a comparison of serious game-based CCT with both a passive and an active control condition while including social elements crucial for training success and adherence, the combination of at-home and on-site training, inclusion of booster sessions and assessment of physiological markers. Study outcomes will provide information on feasibility and efficacy of serious game-based CCT in older adults at risk for AD and will potentially generalize to treatment guidelines. Moreover, we set out to investigate physiological underpinnings of CCT induced neuronal changes to form the grounds for future individually tailored interventions and neuro-biologically informed trainings.

TRIAL REGISTRATION

This RCT was registered 1st of July 2020 at clinicaltrials.gov (Identifier NCT04452864).

Polygenic score for Alzheimer’s disease identifies differential atrophy in hippocampal subfield volumes

Hippocampal subfield atrophy is a prime structural change in the brain, associated with cognitive aging and neurodegenerative diseases such as Alzheimer’s disease. Recent developments in genome-wide association studies (GWAS) have identified genetic loci that characterize the risk of hippocampal volume loss based on the processes of normal and abnormal aging. Polygenic risk scores are the genetic proxies mimicking the genetic role of the pre-existing vulnerabilities of the underlying mechanisms influencing these changes. Discriminating the genetic predispositions of hippocampal subfield atrophy between cognitive aging and neurodegenerative diseases will be helpful in understanding the disease etiology. In this study, we evaluated the polygenic risk of Alzheimer’s disease (AD PGRS) for hippocampal subfield atrophy in 1,086 individuals (319 cognitively normal (CN), 591 mild cognitively impaired (MCI), and 176 Alzheimer’s disease dementia (ADD)). Our results showed a stronger association of AD PGRS effect on the left hemisphere than on the right hemisphere for all the hippocampal subfield volumes in a mixed clinical population (CN+MCI+ADD). The subfields CA1, CA4, hippocampal tail, subiculum, presubiculum, molecular layer, GC-ML-DG, and HATA showed stronger AD PGRS associations with the MCI+ADD group than with the CN group. The subfields CA3, parasubiculum, and fimbria showed moderately higher AD PGRS associations with the MCI+ADD group than with the CN group. Our findings suggest that the eight subfield regions, which were strongly associated with AD PGRS are likely involved in the early stage ADD and a specific focus on the left hemisphere could enhance the early prediction of ADD.

Network-wise concordance of multimodal neuroimaging features across the Alzheimer's disease continuum

Background

Concordance between cortical atrophy and cortical glucose hypometabolism within distributed brain networks was evaluated among cerebrospinal fluid (CSF) biomarker-defined amyloid/tau/neurodegeneration (A/T/N) groups.

Method

We computed correlations between cortical thickness and fluorodeoxyglucose metabolism within 12 functional brain networks. Differences among A/T/N groups (biomarker normal [BN], Alzheimer's disease [AD] continuum, suspected non-AD pathologic change [SNAP]) in network concordance and relationships to longitudinal change in cognition were assessed.

Results

Network-wise markers of concordance distinguish SNAP subjects from BN subjects within the posterior multimodal and language networks. AD-continuum subjects showed increased concordance in 9/12 networks assessed compared to BN subjects, as well as widespread atrophy and hypometabolism. Baseline network concordance was associated with longitudinal change in a composite memory variable in both SNAP and AD-continuum subjects.

Conclusions

Our novel study investigates the interrelationships between atrophy and hypometabolism across brain networks in A/T/N groups, helping disentangle the structure-function relationships that contribute to both clinical outcomes and diagnostic uncertainty in AD.

Divergent Connectivity Changes in Gray Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer's Disease

Alzheimer’s disease (AD) has a long preclinical stage that can last for decades prior to progressing toward amnestic mild cognitive impairment (aMCI) and/or dementia. Subjective cognitive decline (SCD) is characterized by self-experienced memory decline without any evidence of objective cognitive decline and is regarded as the later stage of preclinical AD. It has been reported that the changes in structural covariance patterns are affected by AD pathology in the patients with AD and aMCI within the specific large-scale brain networks. However, the changes in structural covariance patterns including normal control (NC), SCD, aMCI, and AD are still poorly understood. In this study, we recruited 42 NCs, 35 individuals with SCD, 43 patients with aMCI, and 41 patients with AD. Gray matter (GM) volumes were extracted from 10 readily identifiable regions of interest involved in high-order cognitive function and AD-related dysfunctional structures. The volume values were used to predict the regional densities in the whole brain by using voxel-based statistical and multiple linear regression models. Decreased structural covariance and weakened connectivity strength were observed in individuals with SCD compared with NCs. Structural covariance networks (SCNs) seeding from the default mode network (DMN), salience network, subfields of the hippocampus, and cholinergic basal forebrain showed increased structural covariance at the early stage of AD (referring to aMCI) and decreased structural covariance at the dementia stage (referring to AD). Moreover, the SCN seeding from the executive control network (ECN) showed a linearly increased extent of the structural covariance during the early and dementia stages. The results suggest that changes in structural covariance patterns as the order of NC-SCD-aMCI-AD are divergent and dynamic, and support the structural disconnection hypothesis in individuals with SCD.

Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis

Background: Graph theory and connectomics are new techniques for uncovering disease-induced changes in the brain's structural network. Most prior studied have focused on network statistics as biomarkers of disease. However, an emerging body of work involves exploring how the network serves as a conduit for the propagation of disease factors in the brain and has successfully mapped the functional and pathological consequences of disease propagation. In Alzheimer's disease (AD), progressive deposition of misfolded proteins amyloid and tau is well-known to follow fiber projections, under a "prion-like" trans-neuronal transmission mechanism, through which misfolded proteins cascade along neuronal pathways, giving rise to network spread. Methods: In this review, we survey the state of the art in mathematical modeling of connectome-mediated pathology spread in AD. Then we address several open questions that are amenable to mathematically precise parsimonious modeling of pathophysiological processes, extrapolated to the whole brain. We specifically identify current formal models of how misfolded proteins are produced, aggregate, and disseminate in brain circuits, and attempt to understand how this process leads to stereotyped progression in Alzheimer's and other related diseases. Conclusion: This review serves to unify current efforts in modeling of AD progression that together have the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico. Impact statement Graph theory is a powerful new approach that is transforming the study of brain processes. There do not exist many focused reviews of the subfield of graph modeling of how Alzheimer's and other dementias propagate within the brain network, and how these processes can be mapped mathematically. By providing timely and topical review of this subfield, we fill a critical gap in the community and present a unified view that can serve as an in silico test-bed for future hypothesis generation and testing. We also point to several open and unaddressed questions and controversies that future practitioners can tackle.

Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers

In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer's disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer's disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer's Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80-0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69-0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06-0.14 units of area under curve for cognitively unimpaired, and by 0.21-0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04-0.11 units of area under curve for cognitively unimpaired and 0.05-0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80-0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.

Association of Aβ deposition and regional synaptic density in early Alzheimer's disease: a PET imaging study with [11C]UCB-J

BACKGROUND

Attempts to associate amyloid-β (Aβ) pathogenesis with synaptic loss in Alzheimer's disease (AD) have thus far been limited to small numbers of postmortem studies. Aβ plaque burden is not well-correlated with indices of clinical severity or neurodegeneration-at least in the dementia stage-as deposition of Aβ reaches a ceiling. In this study, we examined in vivo the association between fibrillar Aβ deposition and synaptic density in early AD using positron emission tomography (PET). We hypothesized that global Aβ deposition would be more strongly inversely associated with hippocampal synaptic density in participants with amnestic mild cognitive impairment (aMCI; a stage of continued Aβ accumulation) compared to those with dementia (a stage of relative Aβ plateau).

METHODS

We measured SV2A binding ([11C]UCB-J) and Aβ deposition ([11C]PiB) in 14 participants with aMCI due to AD and 24 participants with mild AD dementia. Distribution volume ratios (DVR) with a cerebellar reference region were calculated for both tracers to investigate the association between global Aβ deposition and SV2A binding in hippocampus. Exploratory analyses examined correlations between both global and regional Aβ deposition and SV2A binding across a broad range of brain regions using both ROI- and surface-based approaches.

RESULTS

We observed a significant inverse association between global Aβ deposition and hippocampal SV2A binding in participants with aMCI (r = - 0.55, P = 0.04), but not mild dementia (r = 0.05, P = 0.82; difference statistically significant by Fisher z = - 1.80, P = 0.04). Exploratory analyses across other ROIs and whole brain analyses demonstrated no broad or consistent associations between global Aβ deposition and regional SV2A binding in either diagnostic group. ROI-based analyses of the association between regional Aβ deposition and SV2A binding also revealed no consistent pattern but suggested a "paradoxical" positive association between local Aβ deposition and SV2A binding in the hippocampus.

CONCLUSIONS

Our findings lend support to a model in which fibrillar Aβ is still accumulating in the early stages of clinical disease but approaching a relative plateau, a point at which Aβ may uncouple from neurodegenerative processes including synaptic loss. Future research should investigate the relationship between Aβ deposition and synaptic loss in larger cohorts beginning preclinically and followed longitudinally in conjunction with other biomarkers.

Prediction of Amyloid Positivity in Mild Cognitive Impairment Using Fully Automated Brain Segmentation Software

OBJECTIVE

To assess the predictive ability of regional volume information provided by fully automated brain segmentation software for cerebral amyloid positivity in mild cognitive impairment (MCI).

METHODS

This study included 130 subjects with amnestic MCI who participated in the Korean brain aging study of early diagnosis and prediction of Alzheimer's disease, an ongoing prospective cohort. All participants underwent comprehensive clinical assessment as well as ¹¹C-labeled Pittsburgh compound PET/MRI scans. The predictive ability of volumetric results provided by automated brain segmentation software was evaluated using binary logistic regression and receiver operating characteristic curve analysis.

RESULTS

Subjects were divided into two groups: one with Aβ deposition (58 subjects) and one without Aβ deposition (72 subjects). Among the varied volumetric information provided, the hippocampal volume percentage of intracranial volume (%HC/ICV), normative percentiles of hippocampal volume (HC_norm), and gray matter volume were associated with amyloid-β (Aβ) positivity (all P < 0.01). Multivariate analyses revealed that both %HC/ICV and HC_norm were independent significant predictors of Aβ positivity (all P < 0.001). In addition, prediction scores derived from %HC/ICV with age and HC_norm showed moderate accuracy in predicting Aβ positivity in MCI subjects (the areas under the curve: 0.739 and 0.723, respectively).

CONCLUSION

Relative hippocampal volume measures provided by automated brain segmentation software can be useful for screening cerebral Aβ positivity in clinical practice for patients with amnestic MCI. The information may also help clinicians interpret structural MRI to predict outcomes and determine early intervention for delaying the progression to Alzheimer's disease dementia.

Deterioration from healthy to mild cognitive impairment and Alzheimer's disease mirrored in corresponding loss of centrality in directed brain networks

OBJECTIVE

It is important to identify brain-based biomarkers that progressively deteriorate from healthy to mild cognitive impairment (MCI) to Alzheimer's disease (AD). Cortical thickness, amyloid-ß deposition, and graph measures derived from functional connectivity (FC) networks obtained using functional MRI (fMRI) have been previously identified as potential biomarkers. Specifically, in the latter case, betweenness centrality (BC), a nodal graph measure quantifying information flow, is reduced in both AD and MCI. However, all such reports have utilized BC calculated from undirected networks that characterize synchronization rather than information flow, which is better characterized using directed networks.

METHODS

Therefore, we estimated BC from directed networks using Granger causality (GC) on resting-state fMRI data (N = 132) to compare the following populations (p < 0.05, FDR corrected for multiple comparisons): normal control (NC), early MCI (EMCI), late MCI (LMCI) and AD. We used an additional metric called middleman power (MP), which not only characterizes nodal information flow as in BC, but also measures nodal power critical for information flow in the entire network.

RESULTS

MP detected more brain regions than BC that progressively deteriorated from NC to EMCI to LMCI to AD, as well as exhibited significant associations with behavioral measures. Additionally, graph measures obtained from conventional FC networks could not identify a single node, underscoring the relevance of GC.

CONCLUSION

Our findings demonstrate the superiority of MP over BC as well as GC over FC in our case. MP obtained from GC networks could serve as a potential biomarker for progressive deterioration of MCI and AD.

Imaging the evolution and pathophysiology of Alzheimer disease

Technologies for imaging the pathophysiology of Alzheimer disease (AD) now permit studies of the relationships between the two major proteins deposited in this disease - amyloid-β (Aβ) and tau - and their effects on measures of neurodegeneration and cognition in humans. Deposition of Aβ in the medial parietal cortex appears to be the first stage in the development of AD, although tau aggregates in the medial temporal lobe (MTL) precede Aβ deposition in cognitively healthy older people. Whether aggregation of tau in the MTL is the first stage in AD or a fairly benign phenomenon that may be transformed and spread in the presence of Aβ is a major unresolved question. Despite a strong link between Aβ and tau, the relationship between Aβ and neurodegeneration is weak; rather, it is tau that is associated with brain atrophy and hypometabolism, which, in turn, are related to cognition. Although there is support for an interaction between Aβ and tau resulting in neurodegeneration that leads to dementia, the unknown nature of this interaction, the strikingly different patterns of brain Aβ and tau deposition and the appearance of neurodegeneration in the absence of Aβ and tau are challenges to this model that ultimately must be explained.

Efficacy of motor imagery additional to motor-based therapy in the recovery of motor function of the upper limb in post-stroke individuals: a systematic review

Background. Motor imagery (MI) consists of the mental simulation of repetitive movements with the intention of promoting the learning of a motor skill. It seems to be an additional useful tool for motor-based therapy to potentiate the rehabilitation of the upper limb function of post-stroke individuals. Objective. To investigate whether MI combined with motor-based therapy is effective in recovering motor deficits of upper limbs from post-stroke individuals. Method. A systematic review of the literature was performed in the PEDro, LILACS, Cochrane, SCOPUS, Medline/PubMed and SciELO databases. Randomized controlled trials (RCTs) investigating the efficacy of MI associated with motor-based therapy compared with isolated motor-based therapy were included. The included outcomes were gross motor function and functional activities of the upper limb of post-stroke individuals. The physiotherapy evidence database scale was applied for evaluation of methodological quality. Results. Four RCTs were included, with a total of 104 participants, with methodological quality varying from moderate to high. There was a statistically significant improvement in upper limb motor function in all studies. Gross motor function was higher in MI associated with motor-based therapy compared to controls, but only in one study there was superiority in the results of functional activities of the upper limb. Conclusion. There is evidence showing that MI associated with motor-based therapy is an effective tool in improving the motor function of upper limbs of post-stroke individuals. However, more studies are needed to establish criteria for frequency and duration of intervention, and what better type of MI should be used.

Brain network alterations in individuals with and without mild cognitive impairment: parallel independent component analysis of AV1451 and AV45 positron emission tomography

BACKGROUND

Amyloid β (Aβ) and tau proteins are considered as critical factors that affect Alzheimer's disease (AD) and mild cognitive impairment (MCI). Although many studies have conducted on these two proteins, little study has investigated the relationship between their spatial distributions. This study aims to explore the associations of spatial patterns between Aβ deposition and tau deposition in patients with MCI and normal control (NC).

METHODS

We used multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with MCI and NC. All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database containing information of 65 patients with MCI and 75 NC who both had undergone AV45 (Aβ) and AV1451 (tau) PET. To assess the spatial distribution of Aβ and tau deposition, we employed parallel independent component analysis (pICA), which enabled the joint analysis of multimodal imaging data. pICA was conducted to identify the significant difference and correlation relationship of brain networks between Aβ PET and tau PET in MCI and NC groups.

RESULTS

Our results revealed the strongly correlated network between Aβ PET and tau PET were colocalized with the default-mode network (DMN). Simultaneously, in comparison of the spatial distribution between Aβ PET and tau PET, it was found that the significant differences between MCI and NC were mainly distributed in DMN, cognitive control network and visual networks. The altered brain networks obtained from pICA analysis are consistent with the abnormalities of brain network in MCI patients.

CONCLUSIONS

Findings suggested the abnormal spatial distribution regions of tau PET were correlated with the abnormal spatial distribution regions of Aβ PET, and both of which were located in DMN network. This study revealed that combining pICA with multimodal imaging data is an effective approach for distinguishing MCI patients from NC group.

Head trauma in sports - clinical characteristics, epidemiology and biomarkers

Traumatic brain injury (TBI) is clinically divided into a spectrum of severities, with mild TBI being the least severe form and a frequent occurrence in contact sports, such as ice hockey, American football, rugby, horse riding and boxing. Mild TBI is caused by blunt nonpenetrating head trauma that causes movement of the brain and stretching and tearing of axons, with diffuse axonal injury being a central pathogenic mechanism. Mild TBI is in principle synonymous with concussion; both have similar criteria in which the most important elements are acute alteration or loss of consciousness and/or post-traumatic amnesia following head trauma and no apparent brain changes on standard neuroimaging. Symptoms in mild TBI are highly variable and there are no validated imaging or fluid biomarkers to determine whether or not a patient with a normal computerized tomography scan of the brain has neuronal damage. Mild TBI typically resolves within a few weeks but 10-15% of concussion patients develop postconcussive syndrome. Repetitive mild TBI, which is frequent in contact sports, is a risk factor for a complicated recovery process. This overview paper discusses the relationships between repetitive head impacts in contact sports, mild TBI and chronic neurological symptoms. What are these conditions, how common are they, how are they linked and can they be objectified using imaging or fluid-based biomarkers? It gives an update on the current state of research on these questions with a specific focus on clinical characteristics, epidemiology and biomarkers.

Sex differences in the association between amyloid and longitudinal brain volume change in cognitively normal older adults

Objective

Amyloid positivity is a biomarker of AD pathology, yet the associations between amyloid positivity and brain volumetric changes, especially in the hippocampus, are inconsistent. We hypothesize that sex differences in associations may contribute to inconsistent findings among cognitively normal older adults.

Methods

Using linear mixed effects models, we examined the association of amyloid positivity with prospective volumetric changes (mean = 3.3 visits) of parahippocampal gyrus (phg), hippocampus, entorhinal cortex (erc), precuneus, and fusiform gyrus among 171 Baltimore Longitudinal Study of Aging participants aged ≥55 years. Amyloid positivity was defined by a mean ¹¹C-Pittsburgh Compound B (PiB) distribution volume ratio (DVR) cut-off of 1.062. All analyses included age, race, sex, education, APOE e4 carrier status, and two-way interactions of these covariates with time. Two-way interaction between sex and PiB+/− status and three-way interaction of sex and PiB+/− status with time were added to assess whether sex modified associations.

Results

PiB+ status was associated with greater volumetric declines in the phg (β = −0.036, SE = 0.011, p = 0.001) and erc (β = −0.019, SE = 0.009, p = 0.045). Sex modified the association of PiB+ status and rates of volumetric declines in fusiform (β = −0.117, SE = 0.049, p = 0.019). PiB+ males had steeper rates of volumetric declines in phg (β = −0.051, SE = 0.013, p < 0.001) and erc (β = −0.029, SE = 0.012, p = 0.014) than PiB- males, while there was no difference in rates of volumetric change between PiB+ and PiB- females.

Conclusions

Amyloidosis is a marker of entorhinal and parahippocampal volume loss. Amyloid positivity is a predictor of volume loss in brain regions affected by early AD pathology in men, but not women.

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Amyloid positivity is related to volume loss in regions of early AD pathology.

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Sex modified the association of amyloid positivity and brain volumetric changes.

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Amyloid-positive males were vulnerable to volume loss in regions of early AD.

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Females with and without amyloid positivity had similar volume changes.

MRS and DTI evidence of progressive posterior cingulate cortex and corpus callosum injury in the hyper-acute phase after Traumatic Brain Injury

The posterior cingulate cortex (PCC) and corpus callosum (CC) are susceptible to trauma, but injury often evades detection. PCC Metabolic disruption may predict CC white matter tract injury and the secondary cascade responsible for progression. While the time frame for the secondary cascade remains unclear in humans, the first 24 h (hyper-acute phase) are crucial for life-saving interventions.

Objectives: To test whether Magnetic Resonance Imaging (MRI) markers are detectable in the hyper-acute phase and progress after traumatic brain injury (TBI) and whether alterations in these parameters reflect injury severity.

Methods: Spectroscopic and diffusion-weighted MRI data were collected in 18 patients with TBI (within 24 h and repeated 7–15 days following injury) and 18 healthy controls (scanned once).

Results: Within 24 h of TBI N-acetylaspartate was reduced (F = 11.43, p = 0.002) and choline increased (F = 10.67, p = 0.003), the latter driven by moderate-severe injury (F = 5.54, p = 0.03). Alterations in fractional anisotropy (FA) and axial diffusivity (AD) progressed between the two time-points in the splenium of the CC (p = 0.029 and p = 0.013). Gradual reductions in FA correlated with progressive increases in choline (p = 0.029).

Conclusions: Metabolic disruption and structural injury can be detected within hours of trauma. Metabolic and diffusion parameters allow identification of severity and provide evidence of injury progression.

Brain β-Amyloid and Atrophy in Individuals at Increased Risk of Cognitive Decline

BACKGROUND AND PURPOSE

The relationship between brain β-amyloid and regional atrophy is still incompletely understood in elderly individuals at risk of dementia. Here, we studied the associations between brain β-amyloid load and regional GM and WM volumes in older adults who were clinically evaluated as being at increased risk of cognitive decline based on cardiovascular risk factors.

MATERIALS AND METHODS

Forty subjects (63-81 years of age) were recruited as part of a larger study, the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability. Neuroimaging consisted of PET using ¹¹C Pittsburgh compound-B and T1-weighted 3D MR imaging for the measurement of brain β-amyloid and GM and WM volumes, respectively. All subjects underwent clinical, genetic, and neuropsychological evaluations for the assessment of cognitive function and the identification of cardiovascular risk factors.

RESULTS

Sixteen subjects were visually evaluated as showing cortical β-amyloid (positive for β-amyloid). In the voxel-by-voxel analyses, no significant differences were found in GM and WM volumes between the samples positive and negative for β-amyloid. However, in the sample positive for β-amyloid, increases in ¹¹C Pittsburgh compound-B uptake were associated with reductions in GM volume in the left prefrontal (P = .02) and right temporal lobes (P = .04).

CONCLUSIONS

Our results show a significant association between increases in brain β-amyloid and reductions in regional GM volume in individuals at increased risk of cognitive decline. This evidence is consistent with a model in which increases in β-amyloid incite neurodegeneration in memory systems before cognitive impairment manifests.

Integrative linear discriminant analysis with guaranteed error rate improvement

Multiple types of data measured on a common set of subjects arise in many areas. Numerous empirical studies have found that integrative analysis of such data can result in better statistical performance in terms of prediction and feature selection. However, the advantages of integrative analysis have mostly been demonstrated empirically. In the context of two-class classification, we propose an integrative linear discriminant analysis method and establish a theoretical guarantee that it achieves a smaller classification error than running linear discriminant analysis on each data type individually. We address the issues of outliers and missing values, frequently encountered in integrative analysis, and illustrate our method through simulations and a neuroimaging study of Alzheimer's disease.

Progression of regional atrophy in the left hemisphere contributes to clinical and cognitive deterioration in multiple sclerosis: A 5-year study

In this longitudinal study, we investigated the regional patterns of focal lesions accumulation, and gray (GM) and white matter (WM) atrophy progression over a five-year follow-up (FU) in multiple sclerosis (MS) patients and their association with clinical and cognitive deterioration. Neurological, neuropsychological and brain MRI (dual-echo and 3D T1-weighted sequences) assessments were prospectively performed at baseline (T0) and after a median FU of 4.9 years from 66 MS patients (including relapse-onset and primary progressive MS) and 16 matched controls. Lesion probability maps were obtained. Longitudinal changes of GM and WM volumes and their association with clinical and cognitive deterioration were assessed using tensor-based morphometry and SPM12. At FU, 36/66 (54.5%) MS patients showed a significant disability worsening, 14/66 (21.2%) evolved to a worse clinical phenotype, and 18/63 (28.6%) developed cognitive deterioration. At T0, compared to controls, MS patients showed a widespread pattern of GM atrophy, involving cortex, deep GM and cerebellum, and atrophy of the majority of WM tracts, which further progressed at FU (P < 0.001, uncorrected). Compared to stable patients, those with clinical and cognitive worsening showed a left-lateralized pattern of GM and WM atrophy, involving deep GM, fronto-temporo-parieto-occipital regions, cerebellum, and several WM tracts (P < 0.001, uncorrected).GM and WM atrophy of relevant brain regions occur in MS after 5 years. A different vulnerability of the two brain hemispheres to irreversible structural damage may be among the factors contributing to clinical and cognitive worsening in these patients. Hum Brain Mapp 38:5648-5665, 2017. © 2017 Wiley Periodicals, Inc.

Hierarchical Organization of Tau and Amyloid Deposits in the Cerebral Cortex

Importance

Abnormal accumulation of tau and amyloid-β (Aβ) proteins in the human brain are 2 pathologic hallmarks of Alzheimer disease (AD). Because pathologic processes begin decades before the onset of the clinical manifestations, the study of the cortical distribution of early-stage pathologic alterations is critical in understanding the underpinnings of the disease.

Objectives

To identify the in vivo brain spatial distributions of tau and Aβ deposits in a sample of cognitively normal participants in the Harvard Aging Brain Study, determine spatial patterns of pathologic alterations, and provide means for improved individual in vivo staging.

Design, Setting, and Participants

Eighty-eight individuals from the general community underwent flortaucipir 18 T807 (18F-T807) and carbon 11-labeled Pittsburgh Compound B (11C-PiB) positron emission tomographic (PET) imaging. A voxel-level hierarchical clustering approach was used to obtain the main clustering partitions corresponding to the cortical distribution maps of 18F-T807 and 11C-PiB. Hierarchical relationships between areas of distinctive pathologic deposits were then studied. Using cerebellar gray reference, 18F-T807 data were expressed as standardized uptake value ratio, and 11C-PiB were given as distribution volume ratio.

Main Outcomes and Measures

Main in vivo and hierarchically organized tau and Aβ deposits in the elderly brain.

Results

Of the 88 study participants, 39 (44%) were men, with a mean (SD) age of 76.2 (6.2) years. The tau and Aβ maps both displayed optimal cortical partitions at 4 clusters. The tau deposits were grouped in the temporal lobe, distributed in heteromodal areas, medial and visual regions, and primary somatomotor cortex; the Aβ deposits were clustered in the heteromodal areas and rather patchy in distributed regions involving the primary cortices, medial structures, and temporal areas. Moreover, tau deposits in the temporal lobe and distributed heteromodal areas were tightly nested.

Conclusions and Relevance

Tau and Aβ deposits in the elderly brain generally display well-defined hierarchical cortical relationships as well as overlaps between the principal clusters of both pathologic alterations in the heteromodal association regions. These findings represent systematic, large-scale mechanisms of early AD pathology.

Multistage Grading of Amnestic Mild Cognitive Impairment: The Associated Brain Gray Matter Volume and Cognitive Behavior Characterization

Background and Purpose: It is well known that there is a wide range of different pathological stages related to Alzheimer's disease (AD) among patients with amnestic mild cognitive impairment (aMCI). Further refinement of the stages based on neuropsychological and neuroimaging methods is important for earlier disease detection, as well as for the development and evaluation of disease-modifying interventions.

Materials and Methods: In this cross-sectional study, 125 aMCI patients were classified into declined progressively three stages of mild, moderate and severe, utilizing the extreme groups approach (EGA) based on their memory function. Fifty-two patients, in addition to 24 cognitively normal subjects, were included in further structural MRI analyses. Characteristics of cognitive functions and brain structures across these newly defined stages were explored through general linear models.

Results: Almost all the non-memory cognitive functions showed progressive decline as memory function deteriorated. In addition, medial structures including the right hippocampus, right lingual and left fusiform gyrus, presented with greater gray matter (GM) atrophy during the later stages of aMCI (corrected p < 0.05). Correlations were found between GM volume of the lingual gyrus and processing speed (r = 0.419, p = 0.003) and between the fusiform gyrus and general cognitive function (r = 0.281, p = 0.046). Moreover, both cognitive function and GM volume presented non-linear trajectories over stages of aMCI.

Conclusion: Our study characterized the cognitive profiles along with the degree of episodic memory impairment, and these three stages of aMCI showed non-linear progressive decline in cognitive functions and GM volumes.

Cortical Amyloid β Deposition and Current Depressive Symptoms in Alzheimer Disease and Mild Cognitive Impairment

Depressive symptoms are frequently seen in patients with dementia and mild cognitive impairment (MCI). Evidence suggests that there may be a link between current depressive symptoms and Alzheimer disease (AD)-associated pathological changes, such as an increase in cortical amyloid-β (Aβ). However, limited in vivo studies have explored the relationship between current depressive symptoms and cortical Aβ in patients with MCI and AD. Our study, using a large sample of 455 patients with MCI and 153 patients with AD from the Alzheimer's disease Neuroimaging Initiatives, investigated whether current depressive symptoms are related to cortical Aβ deposition. Depressive symptoms were assessed using the Geriatric Depression Scale and Neuropsychiatric Inventory-depression/dysphoria. Cortical Aβ was quantified using positron emission tomography with the Aβ probe(18)F-florbetapir (AV-45).(18)F-florbetapir standardized uptake value ratio (AV-45 SUVR) from the frontal, cingulate, parietal, and temporal regions was estimated. A global AV-45 SUVR, defined as the average of frontal, cingulate, precuneus, and parietal cortex, was also used. We observed that current depressive symptoms were not related to cortical Aβ, after controlling for potential confounds, including history of major depression. We also observed that there was no difference in cortical Aβ between matched participants with high and low depressive symptoms, as well as no difference between matched participants with the presence and absence of depressive symptoms. The association between depression and cortical Aβ deposition does not exist, but the relationship is highly influenced by stressful events in the past, such as previous depressive episodes, and complex interactions of different pathways underlying both depression and dementia.

Amyloid status imputed from a multimodal classifier including structural MRI distinguishes progressors from nonprogressors in a mild Alzheimer's disease clinical trial cohort

INTRODUCTION

Mild-Alzheimer's disease (AD) subjects without significant Aβ pathology represent a confounding finding for clinical trials because they may not progress clinically on the expected trajectory, adding variance into analyses where slowing of progression is being measured.

METHODS

A prediction model based on structural magnetic resonance imaging (MRI) in combination with baseline demographics and clinical measurements was used to impute Aβ status of a placebo-treated mild-AD sub-cohort (N = 385) of patients participating in global phase 3 trials. The clinical trajectories of this cohort were evaluated over 18 months duration of the trial, stratified by imputed Aβ status within a mixed-model repeated measures statistical framework.

RESULTS

In the imputed Aβ-positive cohort, both cognitive (ADAS-Cog14 and MMSE) and functional (ADCS-iADL) measures declined more rapidly than in the undifferentiated population.

DISCUSSION

Our results demonstrate imputing Aβ status from MRI scans in mild-AD subjects may be a useful screening tool in global clinical trials if amyloid measurement is not available.

Multimodal Image Analysis in Alzheimer's Disease via Statistical Modelling of Non-local Intensity Correlations

The joint analysis of brain atrophy measured with magnetic resonance imaging (MRI) and hypometabolism measured with positron emission tomography with fluorodeoxyglucose (FDG-PET) is of primary importance in developing models of pathological changes in Alzheimer's disease (AD). Most of the current multimodal analyses in AD assume a local (spatially overlapping) relationship between MR and FDG-PET intensities. However, it is well known that atrophy and hypometabolism are prominent in different anatomical areas. The aim of this work is to describe the relationship between atrophy and hypometabolism by means of a data-driven statistical model of non-overlapping intensity correlations. For this purpose, FDG-PET and MRI signals are jointly analyzed through a computationally tractable formulation of partial least squares regression (PLSR). The PLSR model is estimated and validated on a large clinical cohort of 1049 individuals from the ADNI dataset. Results show that the proposed non-local analysis outperforms classical local approaches in terms of predictive accuracy while providing a plausible description of disease dynamics: early AD is characterised by non-overlapping temporal atrophy and temporo-parietal hypometabolism, while the later disease stages show overlapping brain atrophy and hypometabolism spread in temporal, parietal and cortical areas.

Magnetic resonance imaging in Alzheimer's Disease Neuroimaging Initiative 2

INTRODUCTION

Alzheimer's Disease Neuroimaging Initiative (ADNI) is now in its 10th year. The primary objective of the magnetic resonance imaging (MRI) core of ADNI has been to improve methods for clinical trials in Alzheimer's disease (AD) and related disorders.

METHODS

We review the contributions of the MRI core from present and past cycles of ADNI (ADNI-1, -Grand Opportunity and -2). We also review plans for the future-ADNI-3.

RESULTS

Contributions of the MRI core include creating standardized acquisition protocols and quality control methods; examining the effect of technical features of image acquisition and analysis on outcome metrics; deriving sample size estimates for future trials based on those outcomes; and piloting the potential utility of MR perfusion, diffusion, and functional connectivity measures in multicenter clinical trials.

DISCUSSION

Over the past decade the MRI core of ADNI has fulfilled its mandate of improving methods for clinical trials in AD and will continue to do so in the future.

Role of β-Amyloidosis and Neurodegeneration in Subsequent Imaging Changes in Mild Cognitive Impairment

IMPORTANCE

To understand how a model of Alzheimer disease pathophysiology based on β-amyloidosis and neurodegeneration predicts the regional anatomic expansion of hypometabolism and atrophy in persons with mild cognitive impairment (MCI).

OBJECTIVE

To define the role of β-amyloidosis and neurodegeneration in the subsequent progression of topographic cortical structural and metabolic changes in MCI.

DESIGN, SETTING, AND PARTICIPANTS

Longitudinal, observational study with serial brain imaging conducted from March 28, 2006, to January 6, 2015, using a population-based cohort. A total of 96 participants with MCI (all aged >70 years) with serial imaging biomarkers from the Mayo Clinic Study of Aging or Mayo Alzheimer's Disease Research Center were included. Participants were characterized initially as having elevated or not elevated brain β-amyloidosis (A+ or A-) based on 11C-Pittsburgh compound B positron emission tomography. They were further characterized initially by the presence or absence of neurodegeneration (N+ or N-), where the presence of neurodegeneration was defined by abnormally low hippocampal volume or hypometabolism in an Alzheimer disease-like pattern on 18fluorodeoxyglucose (FDG)-positron emission tomography.

MAIN OUTCOMES AND MEASURES

Regional FDG standardized uptake value ratio (SUVR) and gray matter volumes in medial temporal, lateral temporal, lateral parietal, and medial parietal regions.

RESULTS

In the primary regions of interest (ROI), the A+N+ group (n = 45) had lower FDG SUVR at baseline compared with the A+N- group (n = 17) (all 4 ROIs; P < .001). The A+N+ group also had lower FDG SUVR at baseline (all 4 ROIs; P < .01) compared with the A-N- group (n = 12). The A+N+ group had lower medial temporal gray matter volume at baseline (P < .001) compared with either the A+N- group or A-N- group. The A+N+ group showed large longitudinal declines in FDG SUVR (P < .05 for medial temporal, lateral temporal, and medial parietal regions) and gray matter volumes (P < .05 for medial temporal and lateral temporal regions) compared with the A-N+ group (n = 22). The A+N+ group also showed large longitudinal declines compared with the A-N- group on FDG SUVR (P < .05 for medial temporal and lateral parietal regions) and gray matter volumes (all 4 ROIs; P < .05) compared with the A+N- group. The A-N+ group did not show declines in FDG SUVR or gray matter volume compared with the A+N- or A-N- groups.

CONCLUSIONS AND RELEVANCE

Persons with MCI who were A+N+ demonstrated volumetric and metabolic worsening in temporal and parietal association areas, consistent with the expectation that the MCI stage in the Alzheimer pathway heralds incipient isocortical involvement. The A-N+ group, those with suspected non-Alzheimer pathophysiology, lacked a distinctive longitudinal volumetric or metabolic profile.

Amyloid pathology and axonal injury after brain trauma

OBJECTIVE

To image β-amyloid (Aβ) plaque burden in long-term survivors of traumatic brain injury (TBI), test whether traumatic axonal injury and Aβ are correlated, and compare the spatial distribution of Aβ to Alzheimer disease (AD).

METHODS

Patients 11 months to 17 years after moderate-severe TBI underwent (11)C-Pittsburgh compound B ((11)C-PiB)-PET, structural and diffusion MRI, and neuropsychological examination. Healthy aged controls and patients with AD underwent PET and structural MRI. Binding potential (BPND) images of (11)C-PiB, which index Aβ plaque density, were computed using an automatic reference region extraction procedure. Voxelwise and regional differences in BPND were assessed. In TBI, a measure of white matter integrity, fractional anisotropy, was estimated and correlated with (11)C-PiB BPND.

RESULTS

Twenty-eight participants (9 with TBI, 9 controls, 10 with AD) were assessed. Increased (11)C-PiB BPND was found in TBI vs controls in the posterior cingulate cortex and cerebellum. Binding in the posterior cingulate cortex increased with decreasing fractional anisotropy of associated white matter tracts and increased with time since injury. Compared to AD, binding after TBI was lower in neocortical regions but increased in the cerebellum.

CONCLUSIONS

Increased Aβ burden was observed in TBI. The distribution overlaps with, but is distinct from, that of AD. This suggests a mechanistic link between TBI and the development of neuropathologic features of dementia, which may relate to axonal damage produced by the injury.

Advanced Neuroimaging Methods Towards Characterization of Early Stages of Alzheimer's Disease

In the past 5 years, imaging network properties in the brain of patients with Alzheimer's disease (AD) has revolutionized our understanding of this disorder. Postmortem data had already suggested that the damage spreads along functional neural networks, but postmortem studies do not provide information on the temporal evolution of the damage in the same patient, essential to determine spreading. These data can be provided by functional and structural neuroimaging, which allow for the visualization over time of the progressive damage inflicted by AD. Functional networks can be mapped by determining the synchrony across brain regions of the blood oxygenation level dependence (BOLD) signal on functional magnetic resonance imaging (MRI) during quiet wakefulness. Other less extensively used techniques are also useful. For instance, amyloid deposition can be imaged and its progression mapped to determine whether it follows brain networks, and, if so, which are affected earliest. Network patterns of neurobiological changes, including tau deposition, may prove critical to our understanding of the neurobiology of AD and therefore open the way for therapeutic interventions.

Clinical Dementia Rating Scale Detects White Matter Changes in Older Adults at Risk for Alzheimer's Disease

This study investigated the putative changes in regional gray matter and cingulum bundle segments in mild cognitive impairment (MCI) by using two diagnostic criteria. Participants comprised 50 older adults with MCI and 22 healthy older controls (HC). The older adults with MCI were further divided into two groups defined by a global Clinical Dementia Rating (CDR) score of 0.5 and with (the CDR/NPT MCI group) or without (the CDR MCI group) objective cognitive impairments determined using neuropsychological tests (NPTs). Comparable regional gray matter integrity was observed among the three groups. However, the integrity of the right inferior segment of the cingulum bundle in the two MCI groups was more reduced than that in the HC group, and the CDR/NPT MCI group exhibited additional disruption in the left inferior cingulum bundle. The results also demonstrated that neuropsychological measures have greater predictive value for changes in white matter beyond the contribution of an informant-based instrument alone. Overall, the findings confirm the utility of informant-based assessment in detecting microstructural brain changes in high-risk older adults, even before objective cognitive impairment is evident. The findings also suggest that combining the neuropsychological measures with the informant-based assessment provided the greatest predictive value in assessing white matter disruption. The essential role of the white matter measurement as a biomarker for detecting individuals at a high risk of developing dementia was highlighted.

Aβ-related hyperactivation in frontoparietal control regions in cognitively normal elderly

The accumulation of amyloid-beta (Aβ) peptides, a pathologic hallmark of Alzheimer's disease, has been associated with functional alterations in cognitively normal elderly, most often in the context of episodic memory with a particular emphasis on the medial temporal lobes. The topography of Aβ deposition, however, highly overlaps with frontoparietal control (FPC) regions implicated in cognitive control/working memory. To examine Aβ-related functional alternations in the FPC regions during a working memory task, we imaged 42 young and 57 cognitively normal elderly using functional magnetic resonance imaging during a letter Sternberg task with varying load. Based on (18)F-florbetaben-positron emission tomography scan, we determined older subjects' amyloid positivity (Aβ+) status. Within brain regions commonly recruited by all subject groups during the delay period, age and Aβ deposition were independently associated with load-dependent frontoparietal hyperactivation, whereas additional compensatory Aβ-related hyperactivity was found beyond the FPC regions. The present results suggest that Aβ-related hyperactivation is not specific to the episodic memory system but occurs in the PFC regions as well.

Spatial patterns of atrophy, hypometabolism, and amyloid deposition in Alzheimer's disease correspond to dissociable functional brain networks

Recent neuroimaging studies of Alzheimer's disease (AD) have emphasized topographical similarities between AD-related brain changes and a prominent cortical association network called the default-mode network (DMN). However, the specificity of distinct imaging abnormalities for the DMN compared to other intrinsic connectivity networks (ICNs) of the limbic and heteromodal association cortex has not yet been examined systematically. We assessed regional amyloid load using AV45-PET, neuronal metabolism using FDG-PET, and gray matter volume using structural MRI in 473 participants from the Alzheimer's Disease Neuroimaging Initiative, including preclinical, predementia, and clinically manifest AD stages. Complementary region-of-interest and voxel-based analyses were used to assess disease stage- and modality-specific changes within seven principle ICNs of the human brain as defined by a standardized functional connectivity atlas. Amyloid deposition in AD dementia showed a preference for the DMN, but high effect sizes were also observed for other neocortical ICNs, most notably the frontoparietal-control network. Atrophic changes were most specific for an anterior limbic network, followed by the DMN, whereas other neocortical networks were relatively spared. Hypometabolism appeared to be a mixture of both amyloid- and atrophy-related profiles. Similar patterns of modality-dependent network specificity were also observed in the predementia and, for amyloid deposition, in the preclinical stage. These quantitative data confirm a high vulnerability of the DMN for multimodal imaging abnormalities in AD. However, rather than being selective for the DMN, imaging abnormalities more generally affect higher order cognitive networks and, importantly, the vulnerability profiles of these networks markedly differ for distinct aspects of AD pathology.

18F-FDG and 18F-florbetapir PET in clinical practice: regional analysis in mild cognitive impairment and Alzheimer disease

OBJECTIVE

The aim of this study is to compare regional cerebral metabolic rate of glucose metabolism and amyloid-β density in patients with Alzheimer disease (AD), mild cognitive impairment (MCI), and healthy elderly subjects.

METHODS

Eighteen patients (including 6 AD, 5 amnestic MCI, and 7 controls) were enrolled at the University Hospital of Tours, France, and submitted to clinical, neuropsychological, and MRI examinations. PET images using F-florbetapir (266 MBq) and F-FDG (185 MBq) were acquired. SUV ratios in specific regions were defined using PMOD3.2 software.

RESULTS

The mean values of F-FDG SUV ratio were significantly lower in frontal, anterior cingulate, and temporal regions in MCI patients than in normal elderly (-15%, -22%, and -11%, respectively). Alzheimer disease patients showed global cerebral metabolic rate of glucose metabolism decrease, especially in parietal and precuneus regions (-15% and -13% compared with healthy control subjects). Only precuneus cortex showed an increased F-florbetapir uptake in AD. There was no other significant regional difference in the amyloid-β density.

CONCLUSIONS

In this study, we observed regional brain metabolic changes between MCI, AD, and controls, whereas only precuneus showed an increased amyloid-β density in AD. F-florbetapir PET analysis needs to be visual and global, whereas F-FDG analysis can be regional.

Morphometric and histologic substrates of cingulate integrity in elders with exceptional memory capacity

This human study is based on an established cohort of "SuperAgers," 80+-year-old individuals with episodic memory function at a level equal to, or better than, individuals 20-30 years younger. A preliminary investigation using structural brain imaging revealed a region of anterior cingulate cortex that was thicker in SuperAgers compared with healthy 50- to 65-year-olds. Here, we investigated the in vivo structural features of cingulate cortex in a larger sample of SuperAgers and conducted a histologic analysis of this region in postmortem specimens. A region-of-interest MRI structural analysis found cingulate cortex to be thinner in cognitively average 80+ year olds (n = 21) than in the healthy middle-aged group (n = 18). A region of the anterior cingulate cortex in the right hemisphere displayed greater thickness in SuperAgers (n = 31) compared with cognitively average 80+ year olds and also to the much younger healthy 50-60 year olds (p < 0.01). Postmortem investigations were conducted in the cingulate cortex in five SuperAgers, five cognitively average elderly individuals, and five individuals with amnestic mild cognitive impairment. Compared with other subject groups, SuperAgers showed a lower frequency of Alzheimer-type neurofibrillary tangles (p < 0.05). There were no differences in total neuronal size or count between subject groups. Interestingly, relative to total neuronal packing density, there was a higher density of von Economo neurons (p < 0.05), particularly in anterior cingulate regions of SuperAgers. These findings suggest that reduced vulnerability to the age-related emergence of Alzheimer pathology and higher von Economo neuron density in anterior cingulate cortex may represent biological correlates of high memory capacity in advanced old age.

Prefrontal hypometabolism in Alzheimer disease is related to longitudinal amyloid accumulation in remote brain regions

In PET studies of patients with Alzheimer disease (AD), prominent hypometabolism can occur in brain regions without major amyloid load. These hypometabolism-only (HO) areas may not be explained easily as a consequence of local amyloid toxicity. The aim of this longitudinal multimodal imaging study was the investigation of locoregional and remote relationships between metabolism in HO areas and longitudinal amyloid increase in functionally connected brain areas, with a particular focus on intrinsic functional connectivity as a relevant linking mechanism between pathology and dysfunction.

METHODS

Fifteen AD patients underwent longitudinal examinations with (11)C-Pittsburgh compound B ((11)C-PiB) and (18)F-FDG PET (mean follow-up period, 2 y). The peak HO region was identified by the subtraction of equally thresholded statistical T maps (hypometabolism minus amyloid burden), resulting from voxel-based statistical parametric mapping group comparisons between the AD patients and 15 healthy controls. Then functionally connected and nonconnected brain networks were identified by means of seed-based intrinsic functional connectivity analysis of the resting-state functional MRI data of healthy controls. Finally, network-based, region-of-interest-based, and voxel-based correlations were calculated between longitudinal changes of normalized (11)C-PiB binding and (18)F-FDG metabolism.

RESULTS

Positive voxel-based and region-of-interest-based correlations were demonstrated between longitudinal (11)C-PiB increases in the HO-connected network, encompassing bilateral temporoparietal and frontal brain regions, and metabolic changes in the peak HO region as well as locoregionally within several AD-typical brain regions.

CONCLUSION

Our results indicate that in AD amyloid accumulation in remote but functionally connected brain regions may significantly contribute to longitudinally evolving hypometabolism in brain regions not strongly affected by local amyloid pathology, supporting the amyloid- and network-degeneration hypothesis.

Network Diffusion Model of Progression Predicts Longitudinal Patterns of Atrophy and Metabolism in Alzheimer's Disease

Alzheimer's disease pathology (AD) originates in the hippocampus and subsequently spreads to temporal, parietal, and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to transneuronal transmission of misfolded proteins along the projection pathways of affected neurons. A network diffusion model was recently proposed to mathematically predict disease topography resulting from transneuronal transmission on the brain's connectivity network. Here, we use this model to predict future patterns of regional atrophy and metabolism from baseline regional patterns of 418 subjects. The model accurately predicts end-of-study regional atrophy and metabolism starting from baseline data, with significantly higher correlation strength than given by the baseline statistics directly. The model's rate parameter encapsulates overall atrophy progression rate; group analysis revealed this rate to depend on diagnosis as well as baseline cerebrospinal fluid (CSF) biomarker levels. This work helps validate the model as a prognostic tool for Alzheimer's disease assessment.

Thickness network features for prognostic applications in dementia

Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer's disease but not its interregional covariation of thickness. We present novel features based on the inter-regional covariation of cortical thickness. Initially, the cortical labels of each subject are partitioned into small patches (graph nodes) by spatial k-means clustering. A graph is then constructed by establishing a link between 2 nodes if the difference in thickness between the nodes is below a certain threshold. From this binary graph, a thickness network is computed using nodal degree, betweenness, and clustering coefficient measures. Fusing them with multiple kernel learning, it is observed that thickness network features discriminate mild cognitive impairment (MCI) converters from controls (CN) with an area under curve (AUC) of 0.83, 74% sensitivity and 76% specificity on a large subset obtained from the Alzheimer's Disease Neuroimaging Initiative data set. A comparison of predictive utility in Alzheimer's disease and/or CN classification (AUC of 0.92, 80% sensitivity [SENS] and 90% specificity [SPEC]), in discriminating CN from MCI (converters and nonconverters combined; AUC of 0.75, SENS and SPEC of 64% and 73%, respectively) and in discriminating between MCI nonconverters and MCI converters (AUC of 0.68, SENS and SPEC of 65% and 64%) is also presented. ThickNet features as defined here are novel, can be derived from a single magnetic resonance imaging scan, and demonstrate the potential for the computer-aided prognostic applications.