Spatial correlation maps of the hippocampus with cerebrospinal fluid biomarkers and cognition in Alzheimer's disease: A longitudinal study

This study is an observational study that takes the existing longitudinal data from Alzheimer''s disease Neuroimaging Initiative to examine the spatial correlation map of hippocampal subfield atrophy with CSF biomarkers and cognitive decline in the course of AD. This study included 421 healthy controls (HC), 557 patients of stable mild cognitive impairment (s‐MCI), 304 Alzheimer''s Disease (AD) patients, and 241 subjects who converted to be AD from MCI (c‐MCI), and 6,525 MRI scans in a period from 2004 to 2019. Our findings revealed that all the hippocampal subfields showed their accelerated atrophy rate from cognitively normal aging to stable MCI and AD. The presubiculum, dentate gyrus, and fimbria showed greater atrophy beyond the whole hippocampus in the HC, s‐MCI, and AD groups and corresponded to a greater decline of memory and attention in the s‐MCI group. Moreover, the higher atrophy rates of the subiculum and CA2/3, CA4 were also associated with a greater decline in attention in the s‐MCI group. Interestingly, patients with c‐MCI showed that the presubiculum atrophy was associated with CSF tau levels and corresponded to the onset age of AD and a decline in attention in patients with c‐MCI. These spatial correlation findings of the hippocampus suggested that the hippocampal subfields may not be equally impacted by normal aging, MCI, and AD, and their atrophy was selectively associated with declines in specific cognitive domains. The presubiculum atrophy was highlighted as a surrogate marker for the AD prognosis along with tau pathology and attention decline.     

Brain signatures based on structural MRI: Classification for MCI,PMCI, and AD

Structural MRI (sMRI) provides valuable information for understanding neurodegenerative illnesses such as Alzheimer''s Disease (AD) since it detects the brain''s cerebral atrophy. The development of brain networks utilizing single imaging data—sMRI is an understudied area that has the potential to provide a network neuroscientific viewpoint on the brain. In this paper, we proposed a framework for constructing a brain network utilizing sMRI data, followed by the extraction of signature networks and important regions of interest (ROIs). To construct a brain network using sMRI, nodes are defined as regions described by the brain atlas, and edge weights are determined using a distance measure called the Sorensen distance between probability distributions of gray matter tissue probability maps. The brain signatures identified are based on the changes in the networks of disease and control subjects. To validate the proposed methodology, we first identified the brain signatures and critical ROIs associated with mild cognitive impairment (MCI), progressive MCI (PMCI), and Alzheimer''s disease (AD) with 60 reference subjects (15 each of control, MCI, PMCI, and AD). Then, 200 examination subjects (50 each of control, MCI, PMCI, and AD) were selected to evaluate the identified signature patterns. Results demonstrate that the proposed framework is capable of extracting brain signatures and has a number of potential applications in the disciplines of brain mapping, brain communication, and brain network‐based applications.     

Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer's disease and mild cognitive impairment

BackgroundBrain imaging studies of early Alzheimer's disease (AD) have shown decreased metabolism predominantly in the posterior cingulate cortex (PCC), medial temporal lobe, and inferior parietal lobe. This study investigated functional connectivity between these regions, as well as connectivity between these regions and the whole brain.MethodsFunctional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) studies were performed in subjects with early AD, mild cognitive impairment (MCI), and normal controls.ResultsThe data indicate both decreased fiber connections and disrupted connectivity between the hippocampus and PCC in early AD. The MCI group showed reduced fiber numbers derived from PCC and hippocampus to the whole brain.ConclusionsThe fMRI and DTI results confirmed decreased connectivity from both the PCC and hippocampus to the whole brain in MCI and AD and reduction in connectivity between these two regions, which plausibly represents an early imaging biomarker for AD.     

Shape abnormalities of subcortical and ventricular structures in mild cognitive impairment and Alzheimer's disease: Detecting,quantifying, and predicting

This article assesses the feasibility of using shape information to detect and quantify the subcortical and ventricular structural changes in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients. We first demonstrate structural shape abnormalities in MCI and AD as compared with healthy controls (HC). Exploring the development to AD, we then divide the MCI participants into two subgroups based on longitudinal clinical information: (1) MCI patients who remained stable; (2) MCI patients who converted to AD over time. We focus on seven structures (amygdala, hippocampus, thalamus, caudate, putamen, globus pallidus, and lateral ventricles) in 754 MR scans (210 HC, 369 MCI of which 151 converted to AD over time, and 175 AD). The hippocampus and amygdala were further subsegmented based on high field 0.8 mm isotropic 7.0T scans for finer exploration. For MCI and AD, prominent ventricular expansions were detected and we found that these patients had strongest hippocampal atrophy occurring at CA1 and strongest amygdala atrophy at the basolateral complex. Mild atrophy in basal ganglia structures was also detected in MCI and AD. Stronger atrophy in the amygdala and hippocampus, and greater expansion in ventricles was observed in MCI converters, relative to those MCI who remained stable. Furthermore, we performed principal component analysis on a linear shape space of each structure. A subsequent linear discriminant analysis on the principal component values of hippocampus, amygdala, and ventricle leads to correct classification of 88% HC subjects and 86% AD subjects. Hum Brain Mapp 35:3701–3725, 2014. © 2014 Wiley Periodicals, Inc.     

Brain network hierarchy reorganization in Alzheimer's disease: A resting‐state functional magnetic resonance imaging study

Hierarchy is a fundamental organizational principle of the human brain network. Whether and how the network hierarchy changes in Alzheimer''s disease (AD) remains unclear. To explore brain network hierarchy alterations in AD and their clinical relevance. Forty‐nine healthy controls (HCs), 49 patients with mild cognitive impairment (MCI), and 49 patients with AD were included. The brain network hierarchy of each group was depicted by connectome gradient analyses. We assessed the network hierarchy changes by comparing the gradient values in each network across the AD, MCI, and HC groups. Whole‐brain voxel‐level gradient values were compared across the AD, MCI, and HC groups to identify abnormal brain regions. Finally, we examined the relationships between altered gradient values and clinical features. In the secondary gradient, the posterior default mode network (DMN) gradient values decreased significantly in patients with AD compared with HCs. Regionally, compared with HCs, both MCI and AD groups showed that most of the brain regions with increased gradient values were located in anterior DMN, while most of the brain regions with decreased gradient values were located in posterior DMN. The decrease of gradients in the left middle occipital gyrus was associated with better logical memory performance. The increase of gradients in the right middle frontal gyrus was associated with lower rates of dementia. The network hierarchy changed characteristically in patients with AD and was closely related to memory function and disease severity. These results provide a novel view for further understanding the underlying neuro‐mechanisms of AD.     

Biological heterogeneity in ADNI amnestic mild cognitive impairment

BackgroundPrevious work examining normal controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) identified substantial biological heterogeneity. We hypothesized that ADNI mild cognitive impairment (MCI) subjects would also exhibit heterogeneity with possible clinical implications.MethodsADNI subjects diagnosed with amnestic MCI (n = 138) were clustered based on baseline magnetic resonance imaging, cerebrospinal fluid, and serum biomarkers. The clusters were compared with respect to longitudinal atrophy, cognitive trajectory, and time to conversion.ResultsFour clusters emerged with distinct biomarker patterns: The first cluster was biologically similar to normal controls and rarely converted to Alzheimer's disease (AD) during follow-up. The second cluster had characteristics of early Alzheimer's pathology. The third cluster showed the most severe atrophy but barely abnormal tau levels and a substantial proportion converted to clinical AD. The fourth cluster appeared to be pre-AD and nearly all converted to AD.ConclusionsSubjects with MCI who were clinically similar showed substantial heterogeneity in biomarkers.     

Comparing 3 T and 1.5 T MRI for tracking Alzheimer's disease progression with tensor‐based morphometry

A key question in designing MRI‐based clinical trials is how the main magnetic field strength of the scanner affects the power to detect disease effects. In 110 subjects scanned longitudinally at both 3.0 and 1.5 T, including 24 patients with Alzheimer's Disease (AD) [74.8 ± 9.2 years, MMSE: 22.6 ± 2.0 at baseline], 51 individuals with mild cognitive impairment (MCI) [74.1 ± 8.0 years, MMSE: 26.6 ± 2.0], and 35 controls [75.9 ± 4.6 years, MMSE: 29.3 ± 0.8], we assessed whether higher‐field MR imaging offers higher or lower power to detect longitudinal changes in the brain, using tensor‐based morphometry (TBM) to reveal the location of progressive atrophy. As expected, at both field strengths, progressive atrophy was widespread in AD and more spatially restricted in MCI. Power analysis revealed that, to detect a 25% slowing of atrophy (with 80% power), 37 AD and 108 MCI subjects would be needed at 1.5 T versus 49 AD and 166 MCI subjects at 3 T; however, the increased power at 1.5 T was not statistically significant (α = 0.05) either for TBM, or for SIENA, a related method for computing volume loss rates. Analysis of cumulative distribution functions and false discovery rates showed that, at both field strengths, temporal lobe atrophy rates were correlated with interval decline in Alzheimer's Disease Assessment Scale‐cognitive subscale (ADAS‐cog), mini‐mental status exam (MMSE), and Clinical Dementia Rating sum‐of‐boxes (CDR‐SB) scores. Overall, 1.5 and 3 T scans did not significantly differ in their power to detect neurodegenerative changes over a year. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.     

Three Large-Scale Functional Brain Networks from Resting-State Functional MRI in Subjects with Different Levels of Cognitive Impairment

Normal aging and to a greater degree degenerative brain diseases such as Alzheimer''s disease (AD), cause changes in the brain''s structure and function. Degenerative changes in brain structure and decline in its function are associated with declines in cognitive ability. Early detection of AD is a key priority in dementia services and research. However, depending on the disease progression, neurodegenerative manifestations, such as cerebral atrophy, are detected late in course of AD. Functional changes in the brain may be an indirect indicator of trans-synaptic activity and they usually appear prior to structural changes in AD. Resting-state functional magnetic resonance imaging (RS-fMRI) has recently been highlighted as a new technique for interrogating intrinsic functional connectivity networks. Among the majority of RS-fMRI studies, the default mode network (DMN), salience network (SN), and central executive network (CEN) gained particular focus because alterations to their functional connectivity were observed in subjects who had AD, who had mild cognitive impairment (MCI), or who were at high risk for AD. Herein, we present a review of the current research on changes in functional connectivity, as measured by RS-fMRI. We focus on the DMN, SN, and CEN to describe RS-fMRI results from three groups: normal healthy aging, MCI and AD.     

Using machine learning to quantify structural MRI neurodegeneration patterns of Alzheimer's disease into dementia score: Independent validation on 8,834 images from ADNI,AIBL, OASIS,and MIRIAD databases

Biomarkers for dementia of Alzheimer's type (DAT) are sought to facilitate accurate prediction of the disease onset, ideally predating the onset of cognitive deterioration. T1‐weighted magnetic resonance imaging (MRI) is a commonly used neuroimaging modality for measuring brain structure in vivo, potentially providing information enabling the design of biomarkers for DAT. We propose a novel biomarker using structural MRI volume‐based features to compute a similarity score for the individual's structural patterns relative to those observed in the DAT group. We employed ensemble‐learning framework that combines structural features in most discriminative ROIs to create an aggregate measure of neurodegeneration in the brain. This classifier is trained on 423 stable normal control (NC) and 330 DAT subjects, where clinical diagnosis is likely to have the highest certainty. Independent validation on 8,834 unseen images from ADNI, AIBL, OASIS, and MIRIAD Alzheimer's disease (AD) databases showed promising potential to predict the development of DAT depending on the time‐to‐conversion (TTC). Classification performance on stable versus progressive mild cognitive impairment (MCI) groups achieved an AUC of 0.81 for TTC of 6 months and 0.73 for TTC of up to 7 years, achieving state‐of‐the‐art results. The output score, indicating similarity to patterns seen in DAT, provides an intuitive measure of how closely the individual's brain features resemble the DAT group. This score can be used for assessing the presence of AD structural atrophy patterns in normal aging and MCI stages, as well as monitoring the progression of the individual's brain along with the disease course.     

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data

IntroductionIt is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.MethodsA deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.ResultsThe deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning–based progression risk was combined with baseline clinical measures.DiscussionOur method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.     

Quantifying brain metabolism from FDG‐PET images into a probability of Alzheimer's dementia score

¹⁸F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) enables in‐vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG‐PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross‐validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0–3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus.     

Orientational changes of white matter fibers in Alzheimer's disease and amnestic mild cognitive impairment

White matter abnormalities represent early neuropathological events in neurodegenerative diseases such as Alzheimer''s disease (AD), investigating these white matter alterations would likely provide valuable insights into pathological changes over the course of AD. Using a novel mathematical framework called “Director Field Analysis” (DFA), we investigated the geometric microstructural properties (i.e., splay, bend, twist, and total distortion) in the orientation of white matter fibers in AD, amnestic mild cognitive impairment (aMCI), and cognitively normal (CN) individuals from the Alzheimer''s Disease Neuroimaging Initiative 2 database. Results revealed that AD patients had extensive orientational changes in the bilateral anterior thalamic radiation, corticospinal tract, inferior and superior longitudinal fasciculus, inferior fronto‐occipital fasciculus, and uncinate fasciculus in comparison with CN. We postulate that these orientational changes of white matter fibers may be partially caused by the expansion of lateral ventricle, white matter atrophy, and gray matter atrophy in AD. In contrast, aMCI individuals showed subtle orientational changes in the left inferior longitudinal fasciculus and right uncinate fasciculus, which showed a significant association with the cognitive performance, suggesting that these regions may be preferential vulnerable to breakdown by neurodegenerative brain disorders, thereby resulting in the patients'' cognitive impairment. To our knowledge, this article is the first to examine geometric microstructural changes in the orientation of white matter fibers in AD and aMCI. Our findings demonstrate that the orientational information of white matter fibers could provide novel insight into the underlying biological and pathological changes in AD and aMCI.     

Genetic influence of apolipoprotein E4 genotype on hippocampal morphometry: An N = 725 surface‐based Alzheimer's disease neuroimaging initiative study

The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database—the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High‐order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor‐based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T² test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD. Hum Brain Mapp 35:3903–3918, 2014. © 2014 Wiley Periodicals, Inc .     

The pilot European Alzheimer's Disease Neuroimaging Initiative of the European Alzheimer's Disease Consortium

BackgroundIn North America, the Alzheimer's Disease Neuroimaging Initiative (ADNI) has established a platform to track the brain changes of Alzheimer's disease. A pilot study has been carried out in Europe to test the feasibility of the adoption of the ADNI platform (pilot E-ADNI).MethodsSeven academic sites of the European Alzheimer's Disease Consortium (EADC) enrolled 19 patients with mild cognitive impairment (MCI), 22 with AD, and 18 older healthy persons by using the ADNI clinical and neuropsychological battery. ADNI compliant magnetic resonance imaging (MRI) scans, cerebrospinal fluid, and blood samples were shipped to central repositories. Medial temporal atrophy (MTA) and white matter hyperintensities (WMH) were assessed by a single rater by using visual rating scales.ResultsRecruitment rate was 3.5 subjects per month per site. The cognitive, behavioral, and neuropsychological features of the European subjects were very similar to their U.S. counterparts. Three-dimensional T1-weighted MRI sequences were successfully performed on all subjects, and cerebrospinal fluid samples were obtained from 77%, 68%, and 83% of AD patients, MCI patients, and controls, respectively. Mean MTA score showed a significant increase from controls (left, right: 0.4, 0.3) to MCI patients (0.9, 0.8) to AD patients (2.3, 2.0), whereas mean WMH score did not differ among the three diagnostic groups (between 0.7 and 0.9). The distribution of both MRI markers was comparable to matched US-ADNI subjects.ConclusionsAcademic EADC centers can adopt the ADNI platform to enroll MCI and AD patients and older controls with global cognitive and structural imaging features remarkably similar to those of the US-ADNI.     

Rich club analysis in the Alzheimer's disease connectome reveals a relatively undisturbed structural core network

Diffusion imaging can assess the white matter connections within the brain, revealing how neural pathways break down in Alzheimer's disease (AD). We analyzed 3‐Tesla whole‐brain diffusion‐weighted images from 202 participants scanned by the Alzheimer's Disease Neuroimaging Initiative–50 healthy controls, 110 with mild cognitive impairment (MCI) and 42 AD patients. From whole‐brain tractography, we reconstructed structural brain connectivity networks to map connections between cortical regions. We tested whether AD disrupts the “rich club” – a network property where high‐degree network nodes are more interconnected than expected by chance. We calculated the rich club properties at a range of degree thresholds, as well as other network topology measures including global degree, clustering coefficient, path length, and efficiency. Network disruptions predominated in the low‐degree regions of the connectome in patients, relative to controls. The other metrics also showed alterations, suggesting a distinctive pattern of disruption in AD, less pronounced in MCI, targeting global brain connectivity, and focusing on more remotely connected nodes rather than the central core of the network. AD involves severely reduced structural connectivity; our step‐wise rich club coefficients analyze points to disruptions predominantly in the peripheral network components; other modalities of data are needed to know if this indicates impaired communication among non rich club regions. The highly connected core was relatively preserved, offering new evidence on the neural basis of progressive risk for cognitive decline. Hum Brain Mapp 36:3087–3103, 2015. © 2015 Wiley Periodicals, Inc.     

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

We used a new method we developed for automated hippocampal segmentation, called the auto context model, to analyze brain MRI scans of 400 subjects from the Alzheimer's disease neuroimaging initiative. After training the classifier on 21 hand‐labeled expert segmentations, we created binary maps of the hippocampus for three age‐ and sex‐matched groups: 100 subjects with Alzheimer's disease (AD), 200 with mild cognitive impairment (MCI) and 100 elderly controls (mean age: 75.84; SD: 6.64). Hippocampal traces were converted to parametric surface meshes and a radial atrophy mapping technique was used to compute average surface models and local statistics of atrophy. Surface‐based statistical maps visualized links between regional atrophy and diagnosis (MCI versus controls: P = 0.008; MCI versus AD: P = 0.001), mini‐mental state exam (MMSE) scores, and global and sum‐of‐boxes clinical dementia rating scores (CDR; all P < 0.0001, corrected). Right but not left hippocampal atrophy was associated with geriatric depression scores (P = 0.004, corrected); hippocampal atrophy was not associated with subsequent decline in MMSE and CDR scores, educational level, ApoE genotype, systolic or diastolic blood pressure measures, or homocysteine. We gradually reduced sample sizes and used false discovery rate curves to examine the method's power to detect associations with diagnosis and cognition in smaller samples. Forty subjects were sufficient to discriminate AD from normal and correlate atrophy with CDR scores; 104, 200, and 304 subjects, respectively, were required to correlate MMSE with atrophy, to distinguish MCI from normal, and MCI from AD. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.     

Corpus callosum atrophy associated with the degree of cognitive decline in patients with Alzheimer's dementia or mild cognitive impairment: A meta-analysis of the region of interest structural imaging studies

Individual structural neuroimaging studies of the corpus callosum (CC) in Alzheimer's disease (AD) and mild cognitive impairment (MCI) with the region of interest (ROI) analysis have yielded inconsistent findings. The aim of this study was to conduct a meta-analysis of structural imaging studies using ROI technique to measure the CC midsagittal area changes in patients with AD or MCI. Databases of PubMed, the Cochrane Library, the ISI Web of Science, and Science Direct from inception to June 2014 were searched with key words “corpus callosum” or “callosal”, plus “Alzheimer's disease” or “mild cognitive impairment”. Twenty-three studies with 603 patients with AD, 146 with MCI, and 638 healthy controls were included in this meta-analysis. Effect size was used to measure the difference between patients with AD or MCI and healthy controls. Significant callosal atrophy was found in MCI patients with an effect size of −0.36 (95% CI, -0.57 to −0.14; P = 0.001). The degree of the CC atrophy in mild AD was less severe than that in moderate AD with a mean effect size −0.69 (95% CI, -0.89 to −0.49) versus −0.92 (95% CI, -1.16 to −0.69), respectively. Comparing with healthy controls, patients with MCI had atrophy in the anterior portion of the CC (i.e., rostrum and genu). In contrast, patients with AD had atrophy in both anterior and posterior portions (i.e., splenium). These results suggest that callosal atrophy may be related to the degree of cognitive decline in patients with MCI and AD, and it may be used as a biomarker for patients with cognitive deficit even before meeting the criteria for AD.     

Vitamin D deficiency is associated with reduced hippocampal volume and disrupted structural connectivity in patients with mild cognitive impairment

Vitamin D deficiency may exacerbate adverse neurocognitive outcomes in the progression of diseases such as Parkinson's, Alzheimer's, and other dementias. Mild cognitive impairment (MCI) is prodromal for these neurocognitive disorders and neuroimaging studies suggest that, in the elderly, this cognitive impairment is associated with a reduction in hippocampal volume and white matter structural integrity. To test whether vitamin D is associated with neuroanatomical correlates of MCI, we analyzed an existing structural and diffusion MRI dataset of elderly patients with MCI. Based on serum 25‐OHD levels, patients were categorized into serum 25‐OHD deficient (<12 ng/mL, n = 27) or not‐deficient (>12 ng/mL, n = 29). Freesurfer 6.0 was used to parcellate the whole brain into 164 structures and segment the hippocampal subfields. Whole‐brain structural connectomes were generated using probabilistic tractography with MRtrix. The network‐based statistic (NBS) was used to identify subnetworks of connections that significantly differed between the groups. We found a significant reduction in total hippocampal volume in the serum 25‐OHD deficient group especially in the CA1, molecular layer, dentate gyrus, and fimbria. We observed a connection deficit in 13 regions with the right hippocampus at the center of the disrupted network. Our results demonstrate that low vitamin D is associated with reduced volumes of hippocampal subfields and connection deficits in elderly people with MCI, which may exacerbate neurocognitive outcomes. Longitudinal studies are now required to determine if vitamin D can serve as a biomarker for Alzheimer's disease and if intervention can prevent the progression from MCI to major cognitive disorders.     

Correlations between Z‐scores of VSRAD and regional cerebral blood flow of SPECT in patients with Alzheimer's disease and mild cognitive impairment

Aims: The purpose of the present study was to investigate whether there were correlations between atrophy of the entorhinal cortex and individual regional cerebral blood flow (rCBF) in patients with Alzheimer's disease (AD) and amnestic mild cognitive impairment (MCI) to better clarify the relationships between morphological and functional changes in AD. Methods: Twenty‐six patients including sixteen AD and 10 amnestic MCI patients were enrolled. Z scores of voxel‐based specific regional analysis system for AD (VSRAD) were determined to assess the degree of atrophy of the entorhinal cortex. Single‐photon emission computed tomography (SPECT) and 3‐D stereotaxic region of interest template (3DSRT) were used to quantify absolute rCBF. Results: The Z scores of the entorhinal cortex were found to have significant negative correlations with the absolute rCBF in the bilateral hippocampus, thalamus and temporal regions. A negative correlation between Z scores and rCBF of the cerebellum region, especially on the right side, was also noted. Conclusions: Atrophy of the entorhinal cortex had an obvious functional relationship with rCBF changes in the hippocampus, thalamus, temporal lobe and cerebellum in AD and MCI patients, which was attributed to their close anatomical and physiological connections.     

Cholinergic plasticity in hippocampus of individuals with mild cognitive impairment: correlation with Alzheimer's neuropathology

Several recent studies indicate that activity of cholinergic enzymes in the cortex of people with mild cognitive impairment (MCI) and early Alzheimer's disease (AD) are preserved. We correlated levels of hippocampal choline acetyltransferase (ChAT) activity with the extent of AD lesions in subjects from the Religious Order Study, including cases with no cognitive impairment (NCI), MCI, and with mild to moderate AD. Hippocampal ChAT activity levels were also determined in a group of end-stage AD patients who were enrolled in the University of Pittsburgh Alzheimer's Disease Research Center. MCI subjects were characterized with increased hippocampal ChAT activity. This elevation was no longer present in mild AD cases, which were not different from NCI subjects. Severe AD cases showed markedly depleted hippocampal ChAT levels. In NCI, MCI, and mild-moderate AD, there was a positive correlation between hippocampal ChAT activity levels and progression of neuritic plaque pathology in entorhinal cortex and hippocampus. A significant elevation of hippocampal ChAT in the MCI group was found selectively in the limbic (i.e., entorhinal-hippocampal, III/IV) Braak stages. We hypothesize that cholinergic changes in the hippocampus of MCI subjects reflect a compensatory response to the progressive denervation of the hippocampus by lost entorhinal cortex input. Moreover, the present findings suggest that the short-term memory loss observed in MCI is not caused by cholinergic deficits; it more likely relates to disrupted entorhinal-hippocampal connectivity.