Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls

CCADD: An online webserver for Alzheimer's disease detection from brain MRI

Alzheimer's disease (AD) imposes a growing burden on public health due to its impact on memory, cognition, behavior, and social skills. Early detection using non-invasive brain magnetic resonance images (MRI) is vital for disease management. We introduce CCADD (Corpus Callosum-based Alzheimer's Disease Detection), a user-friendly webserver that automatically identifies and segments the corpus callosum (CC) region from brain MRI slices. Extracted shape and size-based features of CC are fed into Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbor (KNN), and Artificial Neural Network (ANN) classifiers to predict AD or Mild Cognitive Impairment (MCI). Exhaustive benchmarking on ADNI data reveals high prediction accuracies for different AD severity levels. CCADD empowers clinicians and researchers for AD detection. This server is available at: http://www.hpppi.iicb.res.in/add.

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

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

Alzheimer's disease phenotype based upon the carrier status of the apolipoprotein E ɛ4 allele

The apolipoprotein E ɛ4 allele (APOE4) is universally acknowledged as the most potent genetic risk factor for Alzheimer's disease (AD). APOE4 promotes the initiation and progression of AD. Although the underlying mechanisms are unclearly understood, differences in lipid-bound affinity among the three APOE isoforms may constitute the basis. The protein APOE4 isoform has a high affinity with triglycerides and cholesterol. A distinction in lipid metabolism extensively impacts neurons, microglia, and astrocytes. APOE4 carriers exhibit phenotypic differences from non-carriers in clinical examinations and respond differently to multiple treatments. Therefore, we hypothesized that phenotypic classification of AD patients according to the status of APOE4 carrier will help specify research and promote its use in diagnosing and treating AD. Recent reviews have mainly evaluated the differences between APOE4 allele carriers and non-carriers from gene to protein structures, clinical features, neuroimaging, pathology, the neural network, and the response to various treatments, and have provided the feasibility of phenotypic group classification based on APOE4 carrier status. This review will facilitate the application of APOE phenomics concept in clinical practice and promote further medical research on AD.

Alzheimer's disease cortical morphological phenotypes are associated with TOMM40'523-APOE haplotypes

Both the APOE ε4 and TOMM40 rs10524523 ("523") genes have been associated with risk for Alzheimer's disease (AD) and neuroimaging biomarkers of AD. No studies have investigated the relationship of TOMM40'523-APOE ε4 on the structural complexity of the brain in AD individuals. We quantified brain morphology and multiple cortical attributes in individuals with mild cognitive impairment (MCI) and AD, then tested whether APOE ε4 or TOMM40 poly-T genotypes were related to AD morphological biomarkers in cognitively unimpaired (CU) and MCI/AD individuals. We identified several AD-specific phenotypes in brain morphology and found that TOMM40 poly-T short alleles are associated with early, AD-specific brain morphological differences in healthy aging. We observed decreased cortical thickness, sulcal depth, and fractal dimension in CU individuals with the poly-T short alleles. Moreover, in MCI/AD participants, the APOE ε4 (TOMM40 L) individuals had a higher rate of gene-related morphological markers indicative of AD. Our data suggest that TOMM40'523 is associated with early brain structure variations in the precuneus, temporal, and limbic cortices.

The association between retina thinning and hippocampal atrophy in Alzheimer's disease and mild cognitive impairment: a meta-analysis and systematic review

Introduction

The retina is the "window" of the central nervous system. Previous studies discovered that retinal thickness degenerates through the pathological process of the Alzheimer's disease (AD) continuum. Hippocampal atrophy is one of the typical clinical features and diagnostic criteria of AD. Former studies have described retinal thinning in normal aging subjects and AD patients, yet the association between retinal thickness and hippocampal atrophy in AD is unclear. The optical coherence tomography (OCT) technique has access the non-invasive to retinal images and magnetic resonance imaging can outline the volume of the hippocampus. Thus, we aim to quantify the correlation between these two parameters to identify whether the retina can be a new biomarker for early AD detection.

Methods

We systematically searched the PubMed, Embase, and Web of Science databases from inception to May 2023 for studies investigating the correlation between retinal thickness and hippocampal volume. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the study quality. Pooled correlation coefficient r values were combined after Fisher's Z transformation. Moderator effects were detected through subgroup analysis and the meta-regression method.

Results

Of the 1,596 citations initially identified, we excluded 1,062 studies after screening the titles and abstract (animal models, n = 99; irrelevant literature, n = 963). Twelve studies met the inclusion criteria, among which three studies were excluded due to unextractable data. Nine studies were eligible for this meta-analysis. A positive moderate correlation between the retinal thickness was discovered in all participants of with AD, mild cognitive impairment (MCI), and normal controls (NC) (r = 0.3469, 95% CI: 0.2490-0.4377, I2 = 5.0%), which was significantly higher than that of the AD group (r = 0.1209, 95% CI:0.0905-0.1510, I2 = 0.0%) (p < 0.05). Among different layers, the peripapillary retinal nerve fiber layer (pRNFL) indicated a moderate positive correlation with hippocampal volume (r = 0.1209, 95% CI:0.0905-0.1510, I2 = 0.0%). The retinal pigmented epithelium (RPE) was also positively correlated [r = 0.1421, 95% CI:(-0.0447-0.3192), I2 = 84.1%]. The retinal layers and participants were the main overall heterogeneity sources. Correlation in the bilateral hemisphere did not show a significant difference.

Conclusion

The correlation between RNFL thickness and hippocampal volume is more predominant in both NC and AD groups than other layers. Whole retinal thickness is positively correlated to hippocampal volume not only in AD continuum, especially in MCI, but also in NC.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, CRD42022328088.

The Relationship Between Coronary Artery Calcification and Carotid Intima Media Thickness and Hippocampal Volume: An Analysis From the Dallas Heart Study

BACKGROUND

Higher rates of dementia are reported in people with a history of coronary artery disease. Smaller hippocampal volume (HV) is a risk factor for the development of dementia.

OBJECTIVE

This study assessed whether coronary artery calcification (CAC) and carotid intima media thickness (CIMT) are associated with HV in participants from the Dallas Heart Study, a community-based study of Dallas County, Texas, residents.

METHODS

Data from a total of n = 1821 participants in the Dallas Heart Study with brain magnetic resonance imaging, CAC, and CIMT information were included in the present study, after excluding those with a history of myocardial infarction or stroke. To evaluate the effect of CAC and CIMT on total HV, 4 linear regression analyses were conducted in which the primary predictor was (1) CAC as a continuous metric; (2) CAC as a binary metric (CAC = 0 vs. CAC ≥ 1); (3) CAC as a continuous metric but only for those with CAC >0; and (4) CIMT as a continuous metric. Demographic and cardiovascular disease risk factors, as well as intracranial volume, were entered into the model as covariates.

RESULTS

Participants were largely women (58.2%) with a mean age of 49.7 ± 10.3 years. Forty-six percent of the sample reported being Black, and approximately 14% reported being Hispanic. All 3 variations of the CAC effect were nonsignificant predictors of total HV (β = -0.013, P = 0.602; β = -0.011, P = 0.650; β = 0.036, P = 0.354, respectively), as was the effect of CIMT (β = 0.009, P = 0.686).

CONCLUSIONS

Current findings suggest nonsignificant relationships between both CAC and CIMT and between CAC and total HV, while controlling for other related factors in a large, diverse, community-based sample of people without a history of myocardial infarction or stroke. In the context of existing evidence that both coronary artery disease and smaller HV are associated with the development of dementia, the present findings suggest that neither marker of the cardiovascular disease examined here is associated with a reduction in HV in the population studied. Longitudinal studies are needed to assess relationships between CAC and CIMT and between CAC and HV over time.

Neuropsychiatric Symptoms of Alzheimer's Disease: An Anatomic-Genetic Framework for Treatment Development

BACKGROUND

Despite the burden on patients and caregivers, there are no approved therapies for the neuropsychiatric symptoms of Alzheimer's disease (NPS-AD). This is likely due to an incomplete understanding of the underlying mechanisms.

OBJECTIVE

To review the neurobiological mechanisms of NPS-AD, including depression, psychosis, and agitation.

METHODS

Understanding that genetic encoding gives rise to the function of neural circuits specific to behavior, we review the genetics and neuroimaging literature to better understand the biological underpinnings of depression, psychosis, and agitation.

RESULTS

We found that mechanisms involving monoaminergic biosynthesis and function are likely key elements of NPS-AD and while current treatment approaches are in line with this, the lack of effectiveness may be due to contributions from additional mechanisms including neurodegenerative, vascular, inflammatory, and immunologic pathways.

CONCLUSION

Within an anatomic-genetic framework, development of novel effective biological targets may engage targets within these pathways but will require a better understanding of the heterogeneity in NPS-AD.

Hippocampal glutathione depletion with enhanced iron level in patients with mild cognitive impairment and Alzheimer’s disease compared with healthy elderly participants

Oxidative stress has been implicated in Alzheimer’s disease, and it is potentially driven by the depletion of primary antioxidant, glutathione, as well as elevation of the pro-oxidant, iron. Present study evaluates glutathione level by magnetic resonance spectroscopy, iron deposition by quantitative susceptibility mapping in left hippocampus, as well as the neuropsychological scores of healthy old participants (N = 25), mild cognitive impairment (N = 16) and Alzheimer’s disease patients (N = 31). Glutathione was found to be significantly depleted in mild cognitive impaired (P &lt; 0.05) and Alzheimer’s disease patients (P &lt; 0.001) as compared with healthy old participants. A significant higher level of iron was observed in left hippocampus region for Alzheimer’s disease patients as compared with healthy old (P &lt; 0.05) and mild cognitive impairment (P &lt; 0.05). Multivariate receiver-operating curve analysis for combined glutathione and iron in left hippocampus region provided diagnostic accuracy of 82.1%, with 81.8% sensitivity and 82.4% specificity for diagnosing Alzheimer’s disease patients from healthy old participants. We conclude that tandem glutathione and iron provides novel avenue to investigate further research in Alzheimer’s disease.

Galectin-3 Involvement in Cognitive Processes for New Therapeutic Considerations

Cognitive impairment may be a consequence of the normal aging process, but it may also be the hallmark of various neurodegenerative and psychiatric diseases. Early identification of individuals at particular risk for cognitive decline is critical, as it is imperative to maintain a cognitive reserve in these neuropsychiatric entities. In recent years, galectin-3 (Gal-3), a member of the galectin family, has received considerable attention with respect to aspects of neuroinflammation and neurodegeneration. The mechanisms behind the putative relationship between Gal-3 and cognitive impairment are not yet clear. Intrigued by this versatile molecule and its unique modular architecture, the latest data on this relationship are presented here. This mini-review summarizes recent findings on the mechanisms by which Gal-3 affects cognitive functioning in both animal and human models. Particular emphasis is placed on the role of Gal-3 in modulating the inflammatory response as a fine-tuner of microglia morphology and phenotype. A review of recent literature on the utility of Gal-3 as a biomarker is provided, and approaches to strategically exploit Gal-3 activities with therapeutic intentions in neuropsychiatric diseases are outlined.

Emerging therapeutic role of chondroitinase (ChABC) in neurological disorders and cancer

Abstract:

Proteoglycans are essential biomacromolecules that participate in matrix structure and organization, cell proliferation and migration, and cell surface signal transduction. However, their roles in physiology, particularly in CNS remain incompletely deciphered. Numerous studies highlight the elevated levels of chondroitin sulphate proteoglycans (CSPGs) in various diseases like cancers and neurological disorders like spinal cord injury (SCI), traumatic brain damage, neurodegenerative diseases, and are mainly implicated to hinder tissue repair. In such a context, chondroitinase ABC (ChABC), a therapeutic enzyme has shown immense hope to treat these diseases in several preclinical studies, primarily attributed to the digestion of the side chains of the proteoglycan chondroitin sulphate (CS) molecule. Despite extensive research, the progress in evolving the concept of therapeutic targeting of proteoglycans is still in its infancy. This review thus provides fresh insights into the emerging therapeutic applications of ChABC in various diseases apart from SCI and the underlying mechanisms.

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques

Background:

The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease.

Objective:

To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques.

Methods:

We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques.

Results:

Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients.

Conclusion:

Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

Honorific Speech Impairment: A Characteristic Sign of Frontotemporal Dementia

BACKGROUND

Individuals with the behavioral variant of frontotemporal dementia (bvFTD) exhibit various levels of abulia, disinhibition, impaired judgment, and decline in executive function. Empirical evidence has shown that individuals with bvFTD also often exhibit difficulty using honorific speech, which expresses respect to another party or addressee.

OBJECTIVE

To analyze differences in the ability to use honorific speech among individuals with bvFTD, individuals with dementia of the Alzheimer type (AD dementia), and individuals with normal cognition (NC).

METHOD

A total of 53 native Korean speakers (13 bvFTD, 20 AD dementia, and 20 NC) completed an experimental honorific speech task (HST) that involved both expressive and receptive tasks. We analyzed the number of correct responses and error patterns separately for an expressive task and for a receptive task.

RESULTS

The bvFTD group had significantly fewer correct responses on the HST compared with the AD dementia and NC groups. The bvFTD group exhibited more misjudgment errors in identifying nonhonorific speech as honorific speech in the expressive task, and significantly longer response times in the receptive task, than the AD dementia and NC groups. Significant associations were identified between HST scores and cortical atrophy in the temporal and frontotemporal lobes.

CONCLUSION

A decline in the ability to use honorific speech may be a diagnosable behavioral and psychiatric symptom for bvFTD in Korean-speaking individuals. This decline in individuals with bvFTD could be attributed to multiple factors, including social manners (politeness) and impaired social language use ability (pragmatics).

Fronto-temporal functional disconnection precedes hippocampal atrophy in clinically confirmed multi-domain amnestic Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) is fraught with high false positive diagnostic errors. The high rate of false positive diagnosis hampers attempts to identify reliable and valid biomarkers for MCI. Recent research suggests that aberrant functional neurocircuitries emerge prior to significant cognitive deficits. The aim of the present study was to examine this in clinically confirmed multi-domain amnestic-MCI (mdaMCI) using an established, multi-time point, methodology for minimizing false positive diagnosis. Structural and resting-state functional MRI data were acquired in healthy controls (HC, n=24), clinically-confirmed multi-domain amnestic-MCI (mdaMCI, n=14) and mild Alzheimer's Dementia (mAD, n=6). Group differences in cortical thickness, hippocampal volume and functional connectivity were investigated. Hippocampal subvolumes differentiated mAD from HC and mdaMCI. Functional decoupling of fronto-temporal networks implicated in memory and executive function differentiated HC and mdaMCI. Decreased functional connectivity in these networks was associated with poorer cognitive performance scores. Preliminary findings suggest the large-scale decoupling of fronto-temporal networks associated with cognitive decline precedes measurable structural neurodegeneration in clinically confirmed MCI and may represent a potential biomarker for disease progression.

Grey-matter brain healthcare quotient and cognitive function: A large cohort study of an MRI brain screening system in Japan

There is sometimes a divergence between brain atrophy and impairments in cognitive function. The present study aimed to assess the relationship between cognitive function and the grey-matter brain healthcare quotient (GM-BHQ), which represents brain volume as a deviation value. In addition, we aimed to investigate lifestyle factors that can help maintain cognitive function despite brain atrophy. A total of 1,757 adults included in a Japanese MRI brain screening cohort underwent MRI. We classified the participants into two age groups: under 65 years old (young adult/middle age group) and over 64 years old (elder group). The GM-BHQ was more strongly correlated with cognitive function in the young adult/middle age group than in the elder group (p < .01). Regression analysis revealed that years of education was associated with the maintenance of cognitive function despite brain atrophy (p < .05). In conclusion, our findings suggest that the relationship between brain volume and cognitive function becomes more obscure with age.

Hippocampal Glutathione Depletion and pH Increment in Alzheimer's Disease: An in vivo MRS Study

BACKGROUND

Oxidative stress plays a major role in Alzheimer's disease (AD) pathogenesis, and thus, antioxidant glutathione (GSH) has been actively investigated in mitigating the oxidative load. Significant hippocampal GSH depletion has been correlated with cognitive impairment in AD. Furthermore, postmortem studies indicated alterations in cellular-energy metabolism and hippocampal pH change toward alkalinity in AD.

OBJECTIVE

Concurrent analysis of hippocampal GSH and pH interplay in vivo on the same individual is quite unclear and hence requires investigation to understand the pathological events in AD.

METHODS

Total 39 healthy old (HO), 22 mild cognitive impairment (MCI), and 37 AD patients were recruited for hippocampal GSH using 1H-MRS MEGA-PRESS and pH using 2D 31P-MRSI with dual tuned (1H/31P) transmit/receive volume head coil on 3T-Philips scanner. All MRS data processing using KALPANA package and statistical analysis were performed MedCalc, respectively and NINS-STAT package.

RESULTS

Significant GSH depletion in the left and right hippocampus (LH and RH) among MCI and AD study groups as compared to HO was observed, whereas pH increased significantly in the LH region between HO and AD. Hippocampal GSH level negatively correlated with pH in both patient groups. The ROC analysis on the combined effect of GSH and pH in both hippocampal regions give accuracy for MCI (LH: 78.27%; RH: 86.96%) and AD (LH: 88%; RH: 78.26%) groups differentiating from HO.

CONCLUSION

Outcomes from this study provide further insights to metabolic alterations in terms of concurrent assessment of hippocampal GSH and pH levels in AD pathogenesis, aiding in early diagnosis of MCI and AD.

Predicting Brain Amyloid Using Multivariate Morphometry Statistics, Sparse Coding, and Correntropy: Validation in 1,101 Individuals From the ADNI and OASIS Databases

Biomarker assisted preclinical/early detection and intervention in Alzheimer’s disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (Aβ) plaques in the human brain. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that magnetic resonance imaging (MRI)-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain Aβ burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse-coding and Max-Pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain Aβ positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate Aβ positivity in people with mild cognitive impairment (MCI) [Accuracy (ACC) = 0.89 (ADNI)] and in cognitively unimpaired (CU) individuals [ACC = 0.79 (ADNI) and ACC = 0.81 (OASIS)]. These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

Variability in sub-threshold signaling linked to Alzheimer's disease emerges with age and amyloid plaque deposition in mouse ventral CA1 pyramidal neurons

The hippocampus is vulnerable to deterioration in Alzheimer's disease (AD). It is, however, a heterogeneous structure, which may contribute to the differential volumetric changes along its septotemporal axis during AD progression. Here, we investigated amyloid plaque deposition along the dorsoventral axis in two strains of transgenic AD (ADTg) mouse models. We also used patch-clamp physiology in these mice to probe for functional consequences of AD pathogenesis in ventral hippocampus, which we found bears significantly higher plaque burden in the aged ADTg group compared to corresponding dorsal regions. Despite dorsoventral differences in amyloid load, ventral CA1 pyramidal neurons of aged ADTg mice exhibited subthreshold physiological changes similar to those previously reported in dorsal neurons, indicative of an HCN channelopathy, but lacked exacerbated suprathreshold accommodation. Additionally, HCN channel function could be rescued by pharmacological manipulation of the endoplasmic reticulum. These observations suggest that an AD-linked HCN channelopathy emerges in both dorsal and ventral CA1 pyramidal neurons, but that the former encounter an additional integrative obstacle in the form of reduced intrinsic excitability.

Brain mechanisms underlying neuropsychiatric symptoms in Alzheimer's disease: a systematic review of symptom-general and -specific lesion patterns

Neuropsychiatric symptoms (NPSs) are common in patients with Alzheimer's disease (AD) and are associated with accelerated cognitive impairment and earlier deaths. This review aims to explore the neural pathogenesis of NPSs in AD and its association with the progression of AD. We first provide a literature overview on the onset times of NPSs. Different NPSs occur in different disease stages of AD, but most symptoms appear in the preclinical AD or mild cognitive impairment stage and develop progressively. Next, we describe symptom-general and -specific patterns of brain lesions. Generally, the anterior cingulate cortex is a commonly damaged region across all symptoms, and the prefrontal cortex, especially the orbitofrontal cortex, is also a critical region associated with most NPSs. In contrast, the anterior cingulate-subcortical circuit is specifically related to apathy in AD, the frontal-limbic circuit is related to depression, and the amygdala circuit is related to anxiety. Finally, we elucidate the associations between the NPSs and AD by combining the onset time with the neural basis of NPSs.

ANALYSES OF BRAIN CORTICAL CHANGES OF ALZHEIMER’S DISEASE

Alzheimer’s disease (AD) produces complicated cortical changes in gray matter (GM) of the human brain. However, alterations in the brain cortex have not been clearly addressed. In our study, a cohort of 236 cases MR data enrolled from the ADNI database was categorized into three groups of normal controls (NCs), mild cognitive impairment (MCI) and AD. The GM morphological differences were investigated among the three groups using the magnetic resonance (MR) GM characteristics of gray matter volume (GMV), cortical thickness (CT), cortical surface area (CSA) and local gyrification index (LGI) at the three levels of whole brain, bilateral hemispheres and critical brain regions. Totally, there were six critical brain regions for GMV, 11 for CT, 2 for CSA and 59 for LGI among the three groups for the no-division groups. Also, there were 11 critical brain regions for GMV, 15 for CT, 8 for CSA, 3 for LGI for female sub-groups and 4 critical brain regions for GMV, 11 for CT, 1 for CSA, 3 for LGI for male sub-groups. The four measured cortical characteristics showed reliable capability in the morphological description of GM changes of AD. In conclusion, the cortical characteristics of GMV, CT, CSA and LGI of critical brain regions showed valuable indications for GM changes of AD, and those characteristics could be used as imaging markers for AD prediction.

Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

BACKGROUND

Alzheimer's Disease (AD) is a neurodegenerative disease characterized by progressive loss of memory and general decline in cognitive functions. Multi-modal imaging such as structural MRI and DTI provide useful information for the classification of patients on the basis of brain biomarkers. Recently, CNN methods have emerged as powerful tools to improve classification using images.

NEW METHOD

In this paper, we propose a transfer learning scheme using Convolutional Neural Networks (CNNs) to automatically classify brain scans focusing only on a small ROI: e.g. a few slices of the hippocampal region. The network's architecture is similar to a LeNet-like CNN upon which models are built and fused for AD stage classification diagnosis. We evaluated various types of transfer learning through the following mechanisms: (i) cross-modal (sMRI and DTI) and (ii) cross-domain transfer learning (using MNIST) (iii) a hybrid transfer learning of both types.

RESULTS

Our method shows good performances even on small datasets and with a limited number of slices of small brain region. It increases accuracy with more than 5 points for the most difficult classification tasks, i.e., AD/MCI and MCI/NC.

COMPARISON WITH EXISTING METHODS

Our methodology provides good accuracy scores for classification over a shallow convolutional network. Besides, we focused only on a small region; i.e., the hippocampal region, where few slices are selected to feed the network. Also, we used cross-modal transfer learning.

CONCLUSIONS

Our proposed method is suitable for working with a shallow CNN network for low-resolution MRI and DTI scans. It yields to significant results even if the model is trained on small datasets, which is often the case in medical image analysis.

Relationship between inflammatory biomarker galectin-3 and hippocampal volume in a community study

Galectin-3 (Gal3) is expressed by microglia and performs functions including adhesion; activation of macrophages and fibroblasts, and mediates inflammatory responses in the hippocampus. The present study examined whether serum Gal3 levels predict hippocampal volume in a multi-ethnic, community-based sample. Results of a multiple linear regression (controlling for depression, serum creatinine level, age, BMI, total brain volume, MoCA score, sex, ethnicity, smoking status, history of diabetes) showed that Gal3 levels significantly predicted left (p = .027) but not right hippocampal volume. The relationship was stronger in men than women. Findings suggest this novel inflammatory biomarker is associated with human hippocampal volume.

One-year Follow-up Study of Hippocampal Subfield Atrophy in Alzheimer's Disease and Normal Aging

BACKGROUND

In this study, we investigated the effect of hippocampal subfield atrophy on the development of Alzheimer's disease (AD) by analyzing baseline magnetic resonance images (MRI) and images collected over a one-year follow-up period. Previous studies have suggested that morphological changes to the hippocampus are involved in both normal ageing and the development of AD. The volume of the hippocampus is an authentic imaging biomarker for AD. However, the diverse relationship of anatomical and complex functional connectivity between different subfields implies that neurodegenerative disease could lead to differences between the atrophy rates of subfields. Therefore, morphometric measurements at subfield-level could provide stronger biomarkers.

METHODS

Hippocampal subfield atrophies are measured using MRI scans, taken at multiple time points, and shape-based normalization to a Montreal neurological institute (MNI) ICBM 152 nonlinear atlas. Ninety subjects were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI), and divided equally into Healthy Controls (HC), AD, and mild cognitive impairment (MCI) groups. These subjects underwent serial MRI studies at three time-points: baseline, 6 months and 12 months.

RESULTS

We analyzed the subfield-level hippocampal morphometric effects of normal ageing and AD based on radial distance mapping and volume measurements. We identified a general trend and observed the largest hippocampal subfield atrophies in the AD group. Atrophy of the bilateral CA1, CA2- CA4 and subiculum subfields was higher in the case of AD than in MCI and HC. We observed the highest rate of reduction in the total volume of the hippocampus, especially in the CA1 and subiculum regions, in the case of MCI.

CONCLUSION

Our findings show that hippocampal subfield atrophy varies among the three study groups.

Applying surface-based morphometry to study ventricular abnormalities of cognitively unimpaired subjects prior to clinically significant memory decline

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A completely automated surface-based ventricular morphometry system.

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Generate a whole connected 3D ventricular shape model.

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Test-retest the system in two independent CU subject cohorts.

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Subregional ventricular abnormalities prior to clinically memory decline.

Ventricular volume (VV) is a widely used structural magnetic resonance imaging (MRI) biomarker in Alzheimer’s disease (AD) research. Abnormal enlargements of VV can be detected before clinically significant memory decline. However, VV does not pinpoint the details of subregional ventricular expansions. Here we introduce a ventricular morphometry analysis system (VMAS) that generates a whole connected 3D ventricular shape model and encodes a great deal of ventricular surface deformation information that is inaccessible by VV. VMAS contains an automated segmentation approach and surface-based multivariate morphometry statistics. We applied VMAS to two independent datasets of cognitively unimpaired (CU) groups. To our knowledge, it is the first work to detect ventricular abnormalities that distinguish normal aging subjects from those who imminently progress to clinically significant memory decline. Significant bilateral ventricular morphometric differences were first shown in 38 members of the Arizona APOE cohort, which included 18 CU participants subsequently progressing to the clinically significant memory decline within 2 years after baseline visits (progressors), and 20 matched CU participants with at least 4 years of post-baseline cognitive stability (non-progressors). VMAS also detected significant differences in bilateral ventricular morphometry in 44 Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects (18 CU progressors vs. 26 CU non-progressors) with the same inclusion criterion. Experimental results demonstrated that the ventricular anterior horn regions were affected bilaterally in CU progressors, and more so on the left. VMAS may track disease progression at subregional levels and measure the effects of pharmacological intervention at a preclinical stage.

Pre-trained MRI-based Alzheimer's disease classification models to classify memory clinic patients

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Multimodal MRI AD classification models were pre-trained on AD patients and controls.

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Generalisation of these models was tested on a multi-centre memory clinic data set.

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AD scores were assigned to AD patients, MCI patients and memory complainers.

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Anatomical MRI performed better than diffusion MRI and resting state fMRI.

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Combining imaging modalities did not improve the results over anatomical MRI only.

Anatomical magnetic resonance imaging (MRI), diffusion MRI and resting state functional MRI (rs-fMRI) have been used for Alzheimer’s disease (AD) classification. These scans are typically used to build models for discriminating AD patients from control subjects, but it is not clear if these models can also discriminate AD in diverse clinical populations as found in memory clinics.

To study this, we trained MRI-based AD classification models on a single centre data set consisting of AD patients (N = 76) and controls (N = 173), and used these models to assign AD scores to subjective memory complainers (N = 67), mild cognitive impairment (MCI) patients (N = 61), and AD patients (N = 61) from a multi-centre memory clinic data set. The anatomical MRI scans were used to calculate grey matter density, subcortical volumes and cortical thickness, the diffusion MRI scans were used to calculate fractional anisotropy, mean, axial and radial diffusivity, and the rs-fMRI scans were used to calculate functional connectivity between resting state networks and amplitude of low frequency fluctuations. Within the multi-centre memory clinic data set we removed scan site differences prior to applying the models.

For all models, on average, the AD patients were assigned the highest AD scores, followed by MCI patients, and later followed by SMC subjects. The anatomical MRI models performed best, and the best performing anatomical MRI measure was grey matter density, separating SMC subjects from MCI patients with an AUC of 0.69, MCI patients from AD patients with an AUC of 0.70, and SMC patients from AD patients with an AUC of 0.86. The diffusion MRI models did not generalise well to the memory clinic data, possibly because of large scan site differences. The functional connectivity model separated SMC subjects and MCI patients relatively good (AUC = 0.66). The multimodal MRI model did not improve upon the anatomical MRI model.

In conclusion, we showed that the grey matter density model generalises best to memory clinic subjects. When also considering the fact that grey matter density generally performs well in AD classification studies, this feature is probably the best MRI-based feature for AD diagnosis in clinical practice.

Acute Pannexin 1 Blockade Mitigates Early Synaptic Plasticity Defects in a Mouse Model of Alzheimer's Disease

Synaptic loss induced by soluble oligomeric forms of the amyloid β peptide (sAβos) is one of the earliest events in Alzheimer’s disease (AD) and is thought to be the major cause of the cognitive deficits. These abnormalities rely on defects in synaptic plasticity, a series of events manifested as activity-dependent modifications in synaptic structure and function. It has been reported that pannexin 1 (Panx1), a nonselective channel implicated in cell communication and intracellular signaling, modulates the induction of excitatory synaptic plasticity under physiological contexts and contributes to neuronal death under inflammatory conditions. Here, we decided to study the involvement of Panx1 in functional and structural defects observed in excitatory synapses of the amyloid precursor protein (APP)/presenilin 1 (PS1) transgenic (Tg) mice, an animal model of AD. We found an age-dependent increase in the Panx1 expression that correlates with increased Aβ levels in hippocampal tissue from Tg mice. Congruently, we also observed an exacerbated Panx1 activity upon basal conditions and in response to glutamate receptor activation. The acute inhibition of Panx1 activity with the drug probenecid (PBN) did not change neurodegenerative parameters such as amyloid deposition or astrogliosis, but it significantly reduced excitatory synaptic defects in the AD model by normalizing long-term potentiation (LTP) and depression and improving dendritic arborization and spine density in hippocampal neurons of the Tg mice. These results suggest a major contribution of Panx1 in the early mechanisms leading to the synaptopathy in AD. Indeed, PBN induced a reduction in the activation of p38 mitogen-activated protein kinase (MAPK), a kinase widely implicated in the early neurotoxic signaling in AD. Our data strongly suggest that an enhanced expression and activation of Panx1 channels contribute to the Aβ-induced cascades leading to synaptic dysfunction in AD.

Hippocampal plasticity underpins long-term cognitive gains from resistance exercise in MCI

Dementia affects 47 million individuals worldwide, and assuming the status quo is projected to rise to 150 million by 2050. Prevention of age-related cognitive impairment in older persons with lifestyle interventions continues to garner evidence but whether this can combat underlying neurodegeneration is unknown. The Study of Mental Activity and Resistance Training (SMART) trial has previously reported within-training findings; the aim of this study was to investigate the long-term neurostructural and cognitive impact of resistance exercise in Mild Cognitive Impairment (MCI). For the first time we show that hippocampal subareas particularly susceptible to volume loss in Alzheimer's disease (AD) are protected by resistance exercise for up to one year after training. One hundred MCI participants were randomised to one of four training groups: (1) Combined high intensity progressive resistance and computerised cognitive training (PRT+CCT), (2) PRT+Sham CCT, (3) CCT+Sham PRT, (4) Sham physical+sham cognitive training (SHAM+SHAM). Physical, neuropsychological and MRI assessments were carried out at baseline, 6 months (directly after training) and 18 months from baseline (12 months after intervention cessation). Here we report neuro-structural and functional changes over the 18-month trial period and the association with global cognitive and executive function measures. PRT but not CCT or PRT+CCT led to global long-term cognitive improvements above SHAM intervention at 18-month follow-up. Furthermore, hippocampal subfields susceptible to atrophy in AD were protected by PRT revealing an elimination of long-term atrophy in the left subiculum, and attenuation of atrophy in left CA1 and dentate gyrus when compared to SHAM+SHAM (p = 0.023, p = 0.020 and p = 0.027). These neuroprotective effects mediated a significant portion of long-term cognitive benefits. By contrast, within-training posterior cingulate plasticity decayed after training cessation and was unrelated to long term cognitive benefits. Neither general physical activity levels nor fitness change over the 18-month period mediated hippocampal trajectory, demonstrating that enduring hippocampal subfield plasticity is not a simple reflection of post-training changes in fitness or physical activity participation. Notably, resting-state fMRI analysis revealed that both the hippocampus and posterior cingulate participate in a functional network that continued to be upregulated following intervention cessation. Multiple structural mechanisms may contribute to the long-term global cognitive benefit of resistance exercise, developing along different time courses but functionally linked. For the first time we show that 6 months of high intensity resistance exercise is capable of not only promoting better cognition in those with MCI, but also protecting AD-vulnerable hippocampal subfields from degeneration for at least 12 months post-intervention. These findings emphasise the therapeutic potential of resistance exercise; however, future work will need to establish just how long-lived these outcomes are and whether they are sufficient to delay dementia.

Memory Performance Correlates of Hippocampal Subfield Volume in Mild Cognitive Impairment Subtype

The increased understanding that neuropathology begins decades before symptom onset, has led to the conceptualization and widespread utilization of Mild Cognitive Impairment (MCI) as an important transitional state between healthy aging and dementia. Further subcategorization to MCI subtype has led to more distinct prognoses and it is widely considered that amnestic and non-amnestic MCI (aMCI, naMCI) likely have distinct pathophysiologies. Yet, accurately classification remains contentious. Here, we differentiate hippocampal subfield volume between subtypes, diagnosed according to stringent clinical consensus criteria, where aMCI is characterized based on deficits in delayed recall (rather than encoding). We then identify memory performance correlates to subfield volume and associations with long-term cognitive performance and outcome. 3D T1-weighted structural MRI was acquired in 142 participants recruited from the Healthy Brain Aging (HBA) Clinic and diagnosed with aMCI (n = 38), naMCI (n = 84) or subjective memory complaints (SMC; n = 20). T1-weighted datasets were processed with the cortical and hippocampal subfield processing streams in FreeSurfer (v6.0). Subfield volumes, and associations with baseline and longitudinal objective memory scores were then examined. Subfield volumes were found to differentiate clinical profiles: subiculum, CA1, CA4 and dentate gyrus volumes were significantly reduced in aMCI compared to both naMCI and SMC. CA1 subfield volume was shown to predict concurrent memory performance in aMCI, while dentate gyrus volume significantly predicted longitudinal verbal learning and memory decline in the entire cohort. Our findings demonstrate that using a more stringent diagnostic approach to characterizing aMCI is well justified, as delayed recall deficits are strongly linked to underlying volumetric subfield reductions in CA1, CA4 and the dentate gyrus, subfields known to be associated with mnemonic processes. Further research is now warranted to replicate these findings in other MCI samples.

Spatially Regularized Shape Analysis of the Hippocampus Using P-Spline Based Shape Regression

Shape analysis is increasingly becoming important to study changes in brain structures in relation to clinical neurological outcomes. This is a challenging task due to the high dimensionality of shape representations and the often limited number of available shapes. Current techniques counter the poor ratio between dimensions and sample size by using regularization in shape space, but do not take into account the spatial relations within the shapes. This can lead to models that are biologically implausible and difficult to interpret. We propose to use P-spline based regression, which combines a generalized linear model (GLM) with the coefficients described as B-splines and a penalty term that constrains the regression coefficients to be spatially smooth. Owing to the GLM, this method can naturally predict both continuous and discrete outcomes and can include non-spatial covariates without penalization. We evaluated our method on hippocampus shapes extracted from magnetic resonance (MR) images of 510 non-demented, elderly people. We related the hippocampal shape to age, memory score, and sex. The proposed method retained the good performance of current techniques, such as ridge regression, but produced smoother coefficient fields that are easier to interpret.

A Retrospective Belgian Multi-Center MRI Biomarker Study in Alzheimer's Disease (REMEMBER)

Background:

Magnetic resonance imaging (MRI) acquisition/processing techniques assess brain volumes to explore neurodegeneration in Alzheimer’s disease (AD).

Objective:

We examined the clinical utility of MSmetrix and investigated if automated MRI volumes could discriminate between groups covering the AD continuum and could be used as a predictor for clinical progression.

Methods:

The Belgian Dementia Council initiated a retrospective, multi-center study and analyzed whole brain (WB), grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), cortical GM (CGM) volumes, and WM hyperintensities (WMH) using MSmetrix in the AD continuum. Baseline (n = 887) and follow-up (FU, n = 95) T1-weighted brain MRIs and time-linked neuropsychological data were available.

Results:

The cohort consisted of cognitively healthy controls (HC, n = 93), subjective cognitive decline (n = 102), mild cognitive impairment (MCI, n = 379), and AD dementia (n = 313). Baseline WB and GM volumes could accurately discriminate between clinical diagnostic groups and were significantly decreased with increasing cognitive impairment. MCI patients had a significantly larger change in WB, GM, and CGM volumes based on two MRIs (n = 95) compared to HC (FU>24months, p = 0.020). Linear regression models showed that baseline atrophy of WB, GM, CGM, and increased CSF volumes predicted cognitive impairment.

Conclusion:

WB and GM volumes extracted by MSmetrix could be used to define the clinical spectrum of AD accurately and along with CGM, they are able to predict cognitive impairment based on (decline in) MMSE scores. Therefore, MSmetrix can support clinicians in their diagnostic decisions, is able to detect clinical disease progression, and is of help to stratify populations for clinical trials.

Effects of repeated exposure to different concentrations of sevoflurane on the neonatal mouse hippocampus

Background and objectives

Developing brain is more vulnerable to environmental risk than is the developed brain. We evaluated the effects of repeated exposure to different concentrations of sevoflurane on the neonatal mouse hippocampus using stereological methods.

Methods

Eighteen neonatal male mice were randomly divided into three groups. Group A, inhaled sevoflurane at a concentration of 1.5%; Group B, inhaled sevoflurane at a concentration of 3%; and Group C (control group), inhaled only 100% oxygen. Treatments were applied for 30 min a day for 7 consecutive days. The hippocampal volume, dendrite length, number of neurons, and number of glial cells were evaluated in each group using stereological estimations.

Results

We identified a ∼2% reduction in the volume of the hippocampus in Group A compared to Group C. Mean hippocampal volume was ∼11% smaller in Group B than it was in Group C. However, these differences in hippocampal volume between the groups were not statistically significant (p > 0.05 for all). As for the number of neurons, we found significantly fewer neurons in Group A (∼29% less) and Group B (∼43% less) than we did in Group C (p < 0.05 for both). The dendrite length was ∼8% shorter in Group A and ∼11% shorter in Group B than it was in Group C.

Conclusions

Repeated exposure to sevoflurane, regardless of the concentration, reduced the volume of the neonatal mouse hippocampus, as well as the number of neurons and dendrite length.

[Effects of repeated exposure to different concentrations of sevoflurane on the neonatal mouse hippocampus]

BACKGROUND AND OBJECTIVES

Developing brain is more vulnerable to environmental risk than is the developed brain. We evaluated the effects of repeated exposure to different concentrations of sevoflurane on the neonatal mouse hippocampus using stereological methods.

METHODS

Eighteen neonatal male mice were randomly divided into three groups. Group A, inhaled sevoflurane at a concentration of 1.5%; Group B, inhaled sevoflurane at a concentration of 3%; and Group C (control group), inhaled only 100% oxygen. Treatments were applied for 30min a day for 7 consecutive days. The hippocampal volume, dendrite length, number of neurons, and number of glial cells were evaluated in each group using stereological estimations.

RESULTS

We identified a ∼2% reduction in the volume of the hippocampus in Group A compared to Group C. Mean hippocampal volume was ∼11% smaller in Group B than it was in Group C. However, these differences in hippocampal volume between the groups were not statistically significant (p>0.05 for all). As for the number of neurons, we found significantly fewer neurons in Group A (∼29% less) and Group B (∼43% less) than we did in Group C (p<0.05 for both). The dendrite length was ∼8% shorter in Group A and ∼11% shorter in Group B than it was in Group C.

CONCLUSIONS

Repeated exposure to sevoflurane, regardless of the concentration, reduced the volume of the neonatal mouse hippocampus, as well as the number of neurons and dendrite length.

Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability

BACKGROUND

Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD.

METHODS

We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01.

RESULTS

Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72-0.84); left smICC = 0.79 (95%CI 0.72-0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7-0.84); left smICC = 0.78 (95%CI 0.71-0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96-0.98); left smICC = 0.97 (95%CI 0.96-0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166).

CONCLUSIONS

The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets.

Regional hippocampal vulnerability in early multiple sclerosis: Dynamic pathological spreading from dentate gyrus to CA1

BACKGROUND

Whether hippocampal subfields are differentially vulnerable at the earliest stages of multiple sclerosis (MS) and how this impacts memory performance is a current topic of debate.

METHOD

We prospectively included 56 persons with clinically isolated syndrome (CIS) suggestive of MS in a 1-year longitudinal study, together with 55 matched healthy controls at baseline. Participants were tested for memory performance and scanned with 3 T MRI to assess the volume of 5 distinct hippocampal subfields using automatic segmentation techniques.

RESULTS

At baseline, CA4/dentate gyrus was the only hippocampal subfield with a volume significantly smaller than controls (p < .01). After one year, CA4/dentate gyrus atrophy worsened (-6.4%, p < .0001) and significant CA1 atrophy appeared (both in the stratum-pyramidale and the stratum radiatum-lacunosum-moleculare, -5.6%, p < .001 and -6.2%, p < .01, respectively). CA4/dentate gyrus volume at baseline predicted CA1 volume one year after CIS (R²  = 0.44 to 0.47, p < .001, with age, T2 lesion-load, and global brain atrophy as covariates). The volume of CA4/dentate gyrus at baseline was associated with MS diagnosis during follow-up, independently of T2-lesion load and demographic variables (p < .05). Whereas CA4/dentate gyrus volume was not correlated with memory scores at baseline, CA1 atrophy was an independent correlate of episodic verbal memory performance one year after CIS (ß = 0.87, p < .05).

CONCLUSION

The hippocampal degenerative process spread from dentate gyrus to CA1 at the earliest stage of MS. This dynamic vulnerability is associated with MS diagnosis after CIS and will ultimately impact hippocampal-dependent memory performance.

The bumps under the hippocampus

Shown in every neuroanatomy textbook, a key morphological feature is the bumpy ridges, which we refer to as hippocampal dentation, on the inferior aspect of the hippocampus. Like the folding of the cerebral cortex, hippocampal dentation allows for greater surface area in a confined space. However, examining numerous approaches to hippocampal segmentation and morphology analysis, virtually all published 3D renderings of the hippocampus show the inferior surface to be quite smooth or mildly irregular; we have rarely seen the characteristic bumpy structure on reconstructed 3D surfaces. The only exception is a 9.4T postmortem study (Yushkevich et al. [2009]: NeuroImage 44:385-398). An apparent question is, does this indicate that this specific morphological signature can only be captured using ultra high-resolution techniques? Or, is such information buried in the data we commonly acquire, awaiting a computation technique that can extract and render it clearly? In this study, we propose an automatic and robust super-resolution technique that captures the fine scale morphometric features of the hippocampus based on common 3T MR images. The method is validated on 9.4T ultra-high field images and then applied on 3T data sets. This method opens possibilities of future research on the hippocampus and other sub-cortical structural morphometry correlating the degree of dentation with a range of diseases including epilepsy, Alzheimer's disease, and schizophrenia. Hum Brain Mapp 39:472-490, 2018. © 2017 Wiley Periodicals, Inc.

Cerebrovascular resistance: effects on cognitive decline, cortical atrophy, and progression to dementia

See Markus (doi:10.1093/awx161) for a scientific commentary on this article.Evidence for vascular contributions to Alzheimer's disease has been increasingly identified, with increased blood pressure and decreased cerebral blood flow both linked to in vivo biomarkers and clinical progression of Alzheimer's disease. We therefore hypothesized that an elevated ratio of blood pressure to cerebral blood flow, indicative of cerebrovascular resistance, would exhibit earlier and more widespread associations with Alzheimer's disease than cerebral blood flow alone. Further, we predicted that increased cerebrovascular resistance and amyloid retention would synergistically influence cognitive performance trajectories, independent of neuronal metabolism. Lastly, we anticipated associations between cerebrovascular resistance and later brain atrophy, prior to amyloid accumulation. To evaluate these hypotheses, we investigated associations between cerebrovascular resistance and amyloid retention, cognitive decline, and brain atrophy, controlling for neuronal metabolism. North American older adults (n = 232) underwent arterial spin labelling magnetic resonance imaging to measure regional cerebral blood flow in brain regions susceptible to ageing and Alzheimer's disease. An estimated cerebrovascular resistance index was then calculated as the ratio of mean arterial pressure to regional cerebral blood flow. Positron emission tomography with 18F-florbetapir and fludeoxyglucose was used to quantify amyloid retention and neuronal metabolism, respectively. Cognitive performance was evaluated via annual assessments of global cognition, memory, and executive function. Results indicated diminished inferior parietal and temporal cerebral blood flow for patients with Alzheimer's disease (n = 33) relative to both non-demented groups, but no cerebral blood flow differences between non-demented amyloid-positive (n = 87) and amyloid-negative (n = 112) cases. In contrast, the cerebrovascular resistance index was significantly elevated in amyloid-positive versus amyloid-negative cases, with additional elevation in patients with Alzheimer's disease. Furthermore, cerebrovascular resistance index group differences were of greater statistical effect size and encompassed a greater number of brain regions than those for cerebral blood flow alone. Cognitive decline over 2-year follow-up was accelerated by elevated baseline cerebrovascular resistance index, particularly for amyloid-positive individuals. Increased baseline cerebrovascular resistance index also predicted greater progression to dementia, beyond that attributable to amyloid-positivity. Finally, increased cerebrovascular resistance index predicted greater regional atrophy among non-demented older adults who were amyloid-negative. Findings suggest that increased cerebrovascular resistance may represent a previously unrecognized contributor to Alzheimer's disease that is independent of neuronal hypometabolism, predates changes in brain perfusion, exacerbates and works synergistically with amyloidosis to produce cognitive decline, and drives amyloid-independent brain atrophy during the earliest stage of disease.

Monitoring disease progression in mild cognitive impairment: Associations between atrophy patterns, cognition, APOE and amyloid

BACKGROUND

A disease severity index (SI) for Alzheimer's disease (AD) has been proposed that summarizes MRI-derived structural measures into a single score using multivariate data analysis.

OBJECTIVES

To longitudinally evaluate the use of the SI to monitor disease progression and predict future progression to AD in mild cognitive impairment (MCI). Further, to investigate the association between longitudinal change in the SI and cognitive impairment, Apolipoprotein E (APOE) genotype as well as the levels of cerebrospinal fluid amyloid-beta 1-42 (Aβ) peptide.

METHODS

The dataset included 195 AD, 145 MCI and 228 control subjects with annual follow-up for three years, where 70 MCI subjects progressed to AD (MCI-p). For each subject the SI was generated at baseline and follow-ups using 55 regional cortical thickness and subcortical volumes measures that extracted by the FreeSurfer longitudinal stream.

RESULTS

MCI-p subjects had a faster increase of the SI over time (p < 0.001). A higher SI at baseline in MCI-p was related to progression to AD at earlier follow-ups (p < 0.001) and worse cognitive impairment (p < 0.001). AD-like MCI patients with the APOE ε4 allele and abnormal Aβ levels had a faster increase of the SI, independently (p = 0.003 and p = 0.004).

CONCLUSIONS

Longitudinal changes in the SI reflect structural brain changes and can identify MCI patients at risk of progression to AD. Disease-related brain structural changes are influenced independently by APOE genotype and amyloid pathology. The SI has the potential to be used as a sensitive tool to predict future dementia, monitor disease progression as well as an outcome measure for clinical trials.

Slowing gait and risk for cognitive impairment: The hippocampus as a shared neural substrate

OBJECTIVE

To identify the shared neuroimaging signature of gait slowing and cognitive impairment.

METHODS

We assessed a cohort of older adults (n = 175, mean age 73 years, 57% female, 65% white) with repeated measures of gait speed over 14 years, MRI for gray matter volume (GMV) at year 10 or 11, and adjudicated cognitive status at year 14. Gait slowing was calculated by bayesian slopes corrected for intercepts, with higher values indicating faster decline. GMV was normalized to intracranial volume, with lower values indicating greater atrophy for 10 regions of interest (hippocampus, anterior and posterior cingulate, primary and supplementary motor cortices, posterior parietal lobe, middle frontal lobe, caudate, putamen, pallidum). Nonparametric correlations adjusted for demographics, comorbidities, muscle strength, and knee pain assessed associations of time to walk with GMV. Logistic regression models calculated odds ratios (ORs) of gait slowing with dementia or mild cognitive impairment with and without adjustment for GMV.

RESULTS

Gait slowing was associated with cognitive impairment at year 14 (OR per 0.1 s/y slowing 1.47; 95% confidence interval 1.04-2.07). The right hippocampus was the only region that was related to both gait slowing (ρ = -0.16, p = 0.03) and cognitive impairment (OR 0.17, p = 0.009). Adjustment for right hippocampal volume attenuated the association of gait slowing with cognitive impairment by 23%.

CONCLUSIONS

The association between gait slowing and cognitive impairment is supported by a shared neural substrate that includes a smaller right hippocampus. This finding underscores the value of long-term gait slowing as an early indicator of dementia risk.

Relationships between cardiorespiratory fitness, hippocampal volume, and episodic memory in a population at risk for Alzheimer's disease

INTRODUCTION

Cardiorespiratory fitness (CRF) has been shown to be related to brain health in older adults. In individuals at risk for developing Alzheimer's disease (AD), CRF may be a modifiable risk factor that could attenuate anticipated declines in brain volume and episodic memory. The objective of this study was to determine the association between CRF and both hippocampal volume and episodic memory in a cohort of cognitively healthy older adults with familial and/or genetic risk for Alzheimer's disease (AD).

METHODS

Eighty-six enrollees from the Wisconsin Registry for Alzheimer's Prevention participated in this study. Participants performed a graded maximal exercise test, underwent a T-1 anatomical magnetic resonance imaging scan, and completed the Rey Auditory Verbal Learning Test (RAVLT).

RESULTS

There were no significant relationships between CRF and HV or RAVLT memory scores for the entire sample. When the sample was explored on the basis of gender, CRF was significantly associated with hippocampal volume for women. For men, significant positive associations were observed between CRF and RAVLT memory scores.

SUMMARY

These results suggest that CRF may be protective against both hippocampal volume and episodic memory decline in older adults at risk for AD, but that the relationships may be gender specific.

Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling

PURPOSE

Segmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largely on the availability of good training ground truth. However, the commonly used 3T MR images are of insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF. Therefore, they are not ideal for providing good ground truth label data for training learning-based methods. Recent advances in ultrahigh field 7T imaging make it possible to acquire images with excellent intensity contrast and signal-to-noise ratio.

METHODS

In this paper, the authors propose an algorithm based on random forest for segmenting 3T MR images by training a series of classifiers based on reliable labels obtained semiautomatically from 7T MR images. The proposed algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers for improved tissue segmentation.

RESULTS

The proposed method was validated on two datasets, i.e., 10 subjects collected at their institution and 797 3T MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 94.52% ± 0.9%, 89.49% ± 1.83%, and 79.97% ± 4.32% for WM, GM, and CSF, respectively, which are significantly better than the state-of-the-art methods (p-values < 0.021). For the ADNI dataset, the group difference comparisons indicate that the proposed algorithm outperforms state-of-the-art segmentation methods.

CONCLUSIONS

The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.

3D tract-specific local and global analysis of white matter integrity in Alzheimer's disease

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive decline in memory and other aspects of cognitive function. Diffusion‐weighted imaging (DWI) offers a non‐invasive approach to delineate the effects of AD on white matter (WM) integrity. Previous studies calculated either some summary statistics over regions of interest (ROI analysis) or some statistics along mean skeleton lines (Tract Based Spatial Statistic [TBSS]), so they cannot quantify subtle local WM alterations along major tracts. Here, a comprehensive WM analysis framework to map disease effects on 3D tracts both locally and globally, based on a study of 200 subjects: 49 healthy elderly normal controls, 110 with mild cognitive impairment, and 41 AD patients has been presented. 18 major WM tracts were extracted with our automated clustering algorithm—autoMATE (automated Multi‐Atlas Tract Extraction); we then extracted multiple DWI‐derived parameters of WM integrity along the WM tracts across all subjects. A novel statistical functional analysis method—FADTTS (Functional Analysis for Diffusion Tensor Tract Statistics) was applied to quantify degenerative patterns along WM tracts across different stages of AD. Gradually increasing WM alterations were found in all tracts in successive stages of AD. Among all 18 WM tracts, the fornix was most adversely affected. Among all the parameters, mean diffusivity (MD) was the most sensitive to WM alterations in AD. This study provides a systematic workflow to examine WM integrity across automatically computed 3D tracts in AD and may be useful in studying other neurological and psychiatric disorders. Hum Brain Mapp 38:1191–1207, 2017. © 2016 Wiley Periodicals, Inc.

T2-Imaging Changes in the Nigrosome-1 Relate to Clinical Measures of Parkinson's Disease

Background

The nigrosome-1 region of the substantia nigra (SN) undergoes the greatest and earliest dopaminergic neuron loss in Parkinson’s disease (PD). As T2-weighted magnetic resonance imaging (MRI) scans are often collected with routine clinical MRI protocols, this investigation aims to determine whether T2-imaging changes in the nigrosome-1 are related to clinical measures of PD and to assess their potential as a more clinically accessible biomarker for PD.

Methods

Voxel intensity ratios were calculated for T2-weighted MRI scans from 47 subjects from the Parkinson’s Progression Markers Initiative database. Three approaches were used to delineate the SN and nigrosome-1: (1) manual segmentation, (2) automated segmentation, and (3) area voxel-based morphometry. Voxel intensity ratios were calculated from voxel intensity values taken from the nigrosome-1 and two areas of the remaining SN. Linear regression analyses were conducted relating voxel intensity ratios with the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) sub-scores for each subject.

Results

For manual segmentation, linear regression tests consistently identified the voxel intensity ratio derived from the dorsolateral SN and nigrosome-1 (IR2) as predictive of nBehav (p = 0.0377) and nExp (p = 0.03856). For automated segmentation, linear regression tests identified IR2 as predictive of Subscore IA (nBehav) (p = 0.01134), Subscore IB (nExp) (p = 0.00336), Score II (mExp) (p = 0.02125), and Score III (mSign) (p = 0.008139). For the voxel-based morphometric approach, univariate simple linear regression analysis identified IR2 as yielding significant results for nBehav (p = 0.003102), mExp (p = 0.0172), and mSign (p = 0.00393).

Conclusion

Neuroimaging biomarkers may be used as a proxy of changes in the nigrosome-1, measured by MDS-UPDRS scores as an indicator of the severity of PD. The voxel intensity ratio derived from the dorsolateral SN and nigrosome-1 was consistently predictive of non-motor complex behaviors in all three analyses and predictive of non-motor experiences of daily living, motor experiences of daily living, and motor signs of PD in two of the three analyses. These results suggest that T2 changes in the nigrosome-1 may relate to certain clinical measures of PD. T2 changes in the nigrosome-1 may be considered when developing a more accessible clinical diagnostic tool for patients with suspected PD.

Longitudinal clinical score prediction in Alzheimer's disease with soft-split sparse regression based random forest

Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies, machine learning techniques have shown promising results on the prediction of AD clinical scores. However, there are multiple limitations in the current models such as linearity assumption and missing data exclusion. Here, we present a nonlinear supervised sparse regression-based random forest (RF) framework to predict a variety of longitudinal AD clinical scores. Furthermore, we propose a soft-split technique to assign probabilistic paths to a test sample in RF for more accurate predictions. In order to benefit from the longitudinal scores in the study, unlike the previous studies that often removed the subjects with missing scores, we first estimate those missing scores with our proposed soft-split sparse regression-based RF and then utilize those estimated longitudinal scores at all the previous time points to predict the scores at the next time point. The experiment results demonstrate that our proposed method is superior to the traditional RF and outperforms other state-of-art regression models. Our method can also be extended to be a general regression framework to predict other disease scores.

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.

Influence of APOE Genotype on Hippocampal Atrophy over Time - An N=1925 Surface-Based ADNI Study

The apolipoprotein E (APOE) e4 genotype is a powerful risk factor for late-onset Alzheimer’s disease (AD). In the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, we previously reported significant baseline structural differences in APOE e4 carriers relative to non-carriers, involving the left hippocampus more than the right—a difference more pronounced in e4 homozygotes than heterozygotes. We now examine the longitudinal effects of APOE genotype on hippocampal morphometry at 6-, 12- and 24-months, in the ADNI cohort. We employed a new automated surface registration system based on conformal geometry and tensor-based morphometry. Among different hippocampal surfaces, we computed high-order correspondences, using a novel inverse-consistent surface-based fluid registration method and multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance. At each time point, using Hotelling’s T² test, we found significant morphological deformation in APOE e4 carriers relative to non-carriers in the full cohort as well as in the non-demented (pooled MCI and control) subjects at each follow-up interval. In the complete ADNI cohort, we found greater atrophy of the left hippocampus than the right, and this asymmetry was more pronounced in e4 homozygotes than heterozygotes. These findings, combined with our earlier investigations, demonstrate an e4 dose effect on accelerated hippocampal atrophy, and support the enrichment of prevention trial cohorts with e4 carriers.

Regulation of the Postsynaptic Compartment of Excitatory Synapses by the Actin Cytoskeleton in Health and Its Disruption in Disease

Disruption of synaptic function at excitatory synapses is one of the earliest pathological changes seen in wide range of neurological diseases. The proper control of the segregation of neurotransmitter receptors at these synapses is directly correlated with the intact regulation of the postsynaptic cytoskeleton. In this review, we are discussing key factors that regulate the structure and dynamics of the actin cytoskeleton, the major cytoskeletal building block that supports the postsynaptic compartment. Special attention is given to the complex interplay of actin-associated proteins that are found in the synaptic specialization. We then discuss our current understanding of how disruption of these cytoskeletal elements may contribute to the pathological events observed in the nervous system under disease conditions with a particular focus on Alzheimer's disease pathology.

Combining multiple anatomical MRI measures improves Alzheimer's disease classification

Several anatomical MRI markers for Alzheimer's disease (AD) have been identified. Hippocampal volume, cortical thickness, and grey matter density have been used successfully to discriminate AD patients from controls. These anatomical MRI measures have so far mainly been used separately. The full potential of anatomical MRI scans for AD diagnosis might thus not yet have been used optimally. In this study, we therefore combined multiple anatomical MRI measures to improve diagnostic classification of AD. For 21 clinically diagnosed AD patients and 21 cognitively normal controls, we calculated (i) cortical thickness, (ii) cortical area, (iii) cortical curvature, (iv) grey matter density, (v) subcortical volumes, and (vi) hippocampal shape. These six measures were used separately and combined as predictors in an elastic net logistic regression. We made receiver operating curve plots and calculated the area under the curve (AUC) to determine classification performance. AUC values for the single measures ranged from 0.67 (cortical thickness) to 0.94 (grey matter density). The combination of all six measures resulted in an AUC of 0.98. Our results demonstrate that the different anatomical MRI measures contain complementary information. A combination of these measures may therefore improve accuracy of AD diagnosis in clinical practice. Hum Brain Mapp 37:1920-1929, 2016. © 2016 Wiley Periodicals, Inc.

Suspected non-Alzheimer disease pathophysiology--concept and controversy

Suspected non-Alzheimer disease pathophysiology (SNAP) is a biomarker-based concept that applies to individuals with normal levels of amyloid-β biomarkers in the brain, but in whom biomarkers of neurodegeneration are abnormal. The term SNAP has been applied to clinically normal individuals (who do not meet criteria for either mild cognitive impairment or dementia) and to individuals with mild cognitive impairment, but is applicable to any amyloid-negative, neurodegeneration-positive individual regardless of clinical status, except when the pathology underlying neurodegeneration can be reliably inferred from the clinical presentation. SNAP is present in ∼23% of clinically normal individuals aged >65 years and in ∼25% of mildly cognitively impaired individuals. APOE*ε4 is underrepresented in individuals with SNAP compared with amyloid-positive individuals. Clinically normal and mildly impaired individuals with SNAP have worse clinical and/or cognitive outcomes than individuals with normal levels of neurodegeneration and amyloid-β biomarkers. In this Perspectives article, we describe the available data on SNAP and address topical controversies in the field.