Hippocampal volume and asymmetry in mild cognitive impairment and Alzheimer's disease: Meta-analyses of MRI studies

Identify the Atrophy of Alzheimer's Disease, Mild Cognitive Impairment and Normal Aging Using Morphometric MRI Analysis

Quantitatively assessing the medial temporal lobe (MTL) structures atrophy is vital for early diagnosis of Alzheimer's disease (AD) and accurately tracking of the disease progression. Morphometry characteristics such as gray matter volume (GMV) and cortical thickness have been proved to be valuable measurements of brain atrophy. In this study, we proposed a morphometric MRI analysis based method to explore the cross-sectional differences and longitudinal changes of GMV and cortical thickness in patients with AD, MCI (mild cognitive impairment) and the normal elderly. High resolution 3D MRI data was obtained from ADNI database. SPM8 plus DARTEL was carried out for data preprocessing. Two kinds of z-score map were calculated to, respectively, reflect the GMV and cortical thickness decline compared with age-matched normal control database. A volume of interest (VOI) covering MTL structures was defined by group comparison. Within this VOI, GMV, and cortical thickness decline indicators were, respectively, defined as the mean of the negative z-scores and the sum of the normalized negative z-scores of the corresponding z-score map. Kruskal-Wallis test was applied to statistically identify group wise differences of the indicators. Support vector machines (SVM) based prediction was performed with a leave-one-out cross-validation design to evaluate the predictive accuracies of the indicators. Linear least squares estimation was utilized to assess the changing rate of the indicators for the three groups. Cross-sectional comparison of the baseline decline indicators revealed that the GMV and cortical thickness decline were more serious from NC, MCI to AD, with statistic significance. Using a multi-region based SVM model with the two indicators, the discrimination accuracy between AD and NC, MCI and NC, AD and MCI was 92.7, 91.7, and 78.4%, respectively. For three-way prediction, the accuracy was 74.6%. Furthermore, the proposed two indicators could also identify the atrophy rate differences among the three groups in longitudinal analysis. The proposed method could serve as an automatic and time-sparing approach for early diagnosis and tracking the progression of AD.

Volumetric and shape analysis of the thalamus and striatum in amnestic mild cognitive impairment

Alterations in brain structures, including progressive neurodegeneration, are a hallmark in patients with Alzheimer's disease (AD). However, pathological mechanisms, such as the accumulation of amyloid and the proliferation of tau, are thought to begin years, even decades, before the initial clinical manifestations of AD. In this study, we compare the brain anatomy of amnestic mild cognitive impairment patients (aMCI, n = 16) to healthy subjects (CS, n = 22) using cortical thickness, subcortical volume, and shape analysis, which we believe to be complimentary to volumetric measures. We were able to replicate "classical" cortical thickness alterations in aMCI in the hippocampus, amygdala, putamen, insula, and inferior temporal regions. Additionally, aMCI showed significant thalamic and striatal shape differences. We observed higher global amyloid deposition in aMCI, a significant correlation between striatal displacement and global amyloid, and an inverse correlation between executive function and right-hemispheric thalamic displacement. In contrast, no volumetric differences were detected in thalamic, striatal, and hippocampal regions. Our results provide new evidence for early subcortical neuroanatomical changes in patients with aMCI, which are linked to cognitive abilities and amyloid deposition. Hence, shape analysis may aid in the identification of structural biomarkers for identifying individuals at highest risk of conversion to AD.

Brain atrophy in Alzheimer's Disease and aging

Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.

Differential association of left and right hippocampal volumes with verbal episodic and spatial memory in older adults

The hippocampus plays a critical role in verbal and spatial memory, thus any pathological damage to this formation may lead to cognitive impairment. It is suggested that right and left hippocampi are affected differentially in healthy or pathologic aging. The purpose of this study was to test the hypothesis that verbal episodic memory performance is associated with left hippocampal volume (HV) while spatial memory is associated with right HV. 115 non-demented adults over age 70 were drawn from the Einstein Aging Study. Verbal memory was measured using the free recall score from the Free and Cued Selective Reminding Test - immediate recall (FCSRT-IR), logical memory immediate and delayed subtests (LM-I and LM-II) from the Wechsler Memory Scale-Revised (WMS-R). Spatial Memory was measured using a computerized dot memory paradigm that has been validated for use in older adults. All participants underwent 3T MRI with subsequent automatized measurement of the volume of each hippocampus. The sample had a mean age of 78.7 years (SD=5.0); 57% were women, and 52% were white. Participants had a mean of 14.3 years (SD=3.5) of education. In regression models, two tests of verbal memory (FCSRT-IR free recall and LM-II) were positively associated with left HV, but not with right HV. Performance on the spatial memory task was associated with right HV, but not left HV. Our findings support the hypothesis that the left hippocampus plays a critical role in episodic verbal memory, while right hippocampus might be more important for spatial memory processing among non-demented older adults.

(1)H-MRS asymmetry changes in the anterior and posterior cingulate gyrus in patients with mild cognitive impairment and mild Alzheimer's disease

Alzheimer's disease (AD) is the most common cause of dementia worldwide. Amnestic mild cognitive impairment (aMCI) is often the prodromal stage to AD. Most patients with aMCI harbor the pathologic changes of AD and demonstrate transition to AD at a rate of 10%-15% per year. Patients with AD and aMCI experience progressive brain metabolite changes. Accumulating evidence indicates that the asymmetry changes of left and right brain happen in the early stage of AD. However, the features of asymmetry changes in both anterior cingulate gyrus (ACG) and posterior cingulate gyrus (PCG) are still unclear. Here, we examine the left-right asymmetry changes of metabolites in ACG and PCG. Fifteen cases of mild AD patients meeting criteria for probable AD of NINDS-ADRDA, thirteen cases of aMCI according to the Mayo Clinic Alzheimer's Disease Research Center criteria, and sixteen cases of age-matched normal controls (NC) received Proton magnetic resonance spectroscopy ((1)H-MRS) for measurement of NAA/mI, NAA/Cr, Cho/Cr, and mI/Cr ratios in the PCG and ACG bilaterally. We analyzed (1)H-MRS data by paired t-test to validate the left-right asymmetry of (1)H-MRS data in the PCG and ACG. In AD, there was a significant difference in mI/Cr between the left and right ACG (P<0.001) and the left and right PCG (P=0.007). In aMCI, there was a significant difference in mI/Cr between the left and right ACG (P<0.001). In NC, there were no differences in the ratio value of metabolites NAA/mI, NAA/Cr, Cho/Cr, and mI/Cr between the left and right ACG and PCG. Thus, the left-right asymmetry of mI/Cr in the ACG and PCG may be an important biological indicator of mild AD.

Alzheimer's Disease Cerebrospinal Fluid and Neuroimaging Biomarkers: Diagnostic Accuracy and Relationship to Drug Efficacy

Widely researched Alzheimer's disease (AD) biomarkers include in vivo brain imaging with PET and MRI, imaging of amyloid plaques, and biochemical assays of Aβ 1 - 42, total tau, and phosphorylated tau (p-tau-181) in cerebrospinal fluid (CSF). In this review, we critically evaluate these biomarkers and discuss their clinical utility for the differential diagnosis of AD. Current AD biomarker tests are either highly invasive (requiring CSF collection) or expensive and labor-intensive (neuroimaging), making them unsuitable for use in the primary care, clinical office-based setting, or to assess drug efficacy in clinical trials. In addition, CSF and neuroimaging biomarkers continue to face challenges in achieving required sensitivity and specificity and minimizing center-to-center variability (for CSF-Aβ 1 - 42 biomarkers CV = 26.5% ; http://www.alzforum.org/news/conference-coverage/paris-standardization-hurdle-spinal-fluid-imaging-markers). Although potentially useful for selecting patient populations for inclusion in AD clinical trials, the utility of CSF biomarkers and neuroimaging techniques as surrogate endpoints of drug efficacy needs to be validated. Recent trials of β- and γ-secretase inhibitors and Aβ immunization-based therapies in AD showed no significant cognitive improvements, despite changes in CSF and neuroimaging biomarkers. As we learn more about the dysfunctional cellular and molecular signaling processes that occur in AD, and how these processes are manifested in tissues outside of the brain, new peripheral biomarkers may also be validated as non-invasive tests to diagnose preclinical and clinical AD.

On the early diagnosis of Alzheimer's Disease from multimodal signals: A survey

INTRODUCTION

The number of Alzheimer's Disease (AD) patients is increasing with increased life expectancy and 115.4 million people are expected to be affected in 2050. Unfortunately, AD is commonly diagnosed too late, when irreversible damages have been caused in the patient.

OBJECTIVE

An automatic, continuous and unobtrusive early AD detection method would be required to improve patients' life quality and avoid big healthcare costs. Thus, the objective of this survey is to review the multimodal signals that could be used in the development of such a system, emphasizing on the accuracy that they have shown up to date for AD detection. Some useful tools and specific issues towards this goal will also have to be reviewed.

METHODS

An extensive literature review was performed following a specific search strategy, inclusion criteria, data extraction and quality assessment in the Inspec, Compendex and PubMed databases.

RESULTS

This work reviews the extensive list of psychological, physiological, behavioural and cognitive measurements that could be used for AD detection. The most promising measurements seem to be magnetic resonance imaging (MRI) for AD vs control (CTL) discrimination with an 98.95% accuracy, while electroencephalogram (EEG) shows the best results for mild cognitive impairment (MCI) vs CTL (97.88%) and MCI vs AD distinction (94.05%). Available physiological and behavioural AD datasets are listed, as well as medical imaging analysis steps and neuroimaging processing toolboxes. Some issues such as "label noise" and multi-site data are discussed.

CONCLUSIONS

The development of an unobtrusive and transparent AD detection system should be based on a multimodal system in order to take full advantage of all kinds of symptoms, detect even the smallest changes and combine them, so as to detect AD as early as possible. Such a multimodal system might probably be based on physiological monitoring of MRI or EEG, as well as behavioural measurements like the ones proposed along the article. The mentioned AD datasets and image processing toolboxes are available for their use towards this goal. Issues like "label noise" and multi-site neuroimaging incompatibilities may also have to be overcome, but methods for this purpose are already available.

Contribution of the Cholinergic System to Verbal Memory Performance in Mild Cognitive Impairment

Acetylcholine is critically involved in modulating learning and memory function, which both decline in neurodegeneration. It remains unclear to what extent structural and functional changes in the cholinergic system contribute to episodic memory dysfunction in mild cognitive impairment (MCI), in addition to hippocampal degeneration. A better understanding is critical, given that the cholinergic system is the main target of current symptomatic treatment in mild to moderate Alzheimer's disease. We simultaneously assessed the structural and functional integrity of the cholinergic system in 20 patients with MCI and 20 matched healthy controls and examined their effect on verbal episodic memory via multivariate regression analyses. Mediating effects of either cholinergic function or hippocampal volume on the relationship between cholinergic structure and episodic memory were computed. In MCI, a less intact structure and function of the cholinergic system was found. A smaller cholinergic structure was significantly correlated with a functionally more active cholinergic system in patients, but not in controls. This association was not modulated by age or disease severity, arguing against compensational processes. Further analyses indicated that neither functional nor structural changes in the cholinergic system influence verbal episodic memory at the MCI stage. In fact, those associations were fully mediated by hippocampal volume. Although the cholinergic system is structurally and functionally altered in MCI, episodic memory dysfunction results primarily from hippocampal neurodegeneration, which may explain the inefficiency of cholinergic treatment at this disease stage.

Multivariate statistical analysis of diffusion imaging parameters using partial least squares: Application to white matter variations in Alzheimer's disease

Diffusion magnetic resonance imaging (dMRI) is a unique technology that allows the noninvasive quantification of microstructural tissue properties of the human brain in healthy subjects as well as the probing of disease-induced variations. Population studies of dMRI data have been essential in identifying pathological structural changes in various conditions, such as Alzheimer's and Huntington's diseases (Salat et al., 2010; Rosas et al., 2006). The most common form of dMRI involves fitting a tensor to the underlying imaging data (known as diffusion tensor imaging, or DTI), then deriving parametric maps, each quantifying a different aspect of the underlying microstructure, e.g. fractional anisotropy and mean diffusivity. To date, the statistical methods utilized in most DTI population studies either analyzed only one such map or analyzed several of them, each in isolation. However, it is most likely that variations in the microstructure due to pathology or normal variability would affect several parameters simultaneously, with differing variations modulating the various parameters to differing degrees. Therefore, joint analysis of the available diffusion maps can be more powerful in characterizing histopathology and distinguishing between conditions than the widely used univariate analysis. In this article, we propose a multivariate approach for statistical analysis of diffusion parameters that uses partial least squares correlation (PLSC) analysis and permutation testing as building blocks in a voxel-wise fashion. Stemming from the common formulation, we present three different multivariate procedures for group analysis, regressing-out nuisance parameters and comparing effects of different conditions. We used the proposed procedures to study the effects of non-demented aging, Alzheimer's disease and mild cognitive impairment on the white matter. Here, we present results demonstrating that the proposed PLSC-based approach can differentiate between effects of different conditions in the same region as well as uncover spatial variations of effects across the white matter. The proposed procedures were able to answer questions on structural variations such as: "are there regions in the white matter where Alzheimer's disease has a different effect than aging or similar effect as aging?" and "are there regions in the white matter that are affected by both mild cognitive impairment and Alzheimer's disease but with differing multivariate effects?"

Hippocampal subfield volumetry in patients with subcortical vascular mild cognitive impairment

Memory impairment is a typical characteristic of patients with subcortical vascular mild cognitive impairment (svMCI) or with amnestic mild cognitive impairment (aMCI). The hippocampus, which plays an important role in the consolidation of information from short-term memory to long-term memory, is a heterogeneous structure that consists of several anatomically and functionally distinct subfields. However, whether distinct hippocampal subfields are differentially and selectively affected by svMCI pathology and whether these abnormal changes in hippocampal subfields are different between svMCI and aMCI patients are largely unknown. A total of 26 svMCI patients, 26 aMCI patients and 26 healthy controls matched according to age, gender and years of education were enrolled in this study. We utilized an automated hippocampal subfield segmentation method provided by FreeSurfer to estimate the volume of several hippocampal subfields, including the cornu ammonis (CA) areas, the dentate gyrus (DG), the subiculum and the presubiculum. Compared with controls, the left subiculum and presubiculum and the right CA4/DG displayed significant atrophy in patients with svMCI. Interestingly, we also found significant differences in the volume of the right CA1 between the svMCI and aMCI groups. Taken together, our results reveal region-specific vulnerability of hippocampal subfields to svMCI pathology and identify distinct hippocampal subfield atrophy patterns between svMCI and aMCI patients.

Quantitative sodium MRI of the human brain at 9.4 T provides assessment of tissue sodium concentration and cell volume fraction during normal aging

Sodium ion homeostasis is a fundamental property of viable tissue, allowing the tissue sodium concentration to be modeled as the tissue cell volume fraction. The modern neuropathology literature using ex vivo tissue from selected brain regions indicates that human brain cell density remains constant during normal aging and attributes the volume loss that occurs with advancing age to changes in neuronal size and dendritic arborization. Quantitative sodium MRI performed with the enhanced sensitivity of ultrahigh-field 9.4 T has been used to investigate tissue cell volume fraction during normal aging. This cross-sectional study (n = 49; 21-80 years) finds that the in vivo tissue cell volume fraction remains constant in all regions of the brain with advancing age in individuals who remain cognitively normal, extending the ex vivo literature reporting constant neuronal cell density across the normal adult age range. Cell volume fraction, as measured by quantitative sodium MRI, is decreased in diseases of cell loss, such as stroke, on a time scale of minutes to hours, and in response to treatment of brain tumors on a time scale of days to weeks. Neurodegenerative diseases often have prodromal periods of decades in which regional neuronal cell loss occurs prior to clinical presentation. If tissue cell volume fraction can detect such early pathology, this quantitative parameter may permit the objective measurement of preclinical disease progression. This current study in cognitively normal aging individuals provides the basis for the pursuance of investigations directed towards such neurodegenerative diseases.

Alzheimer's disease--subcortical vascular disease spectrum in a hospital-based setting: Overview of results from the Gothenburg MCI and dementia studies

The ability to discriminate between Alzheimer's disease (AD), subcortical vascular disease, and other cognitive disorders is crucial for diagnostic purposes and clinical trial outcomes. Patients with primarily subcortical vascular disease are unlikely to benefit from treatments targeting the AD pathogenic mechanisms and vice versa. The Gothenburg mild cognitive impairment (MCI) and dementia studies are prospective, observational, single-center cohort studies suitable for both cross-sectional and longitudinal analysis that outline the cognitive profiles and biomarker characteristics of patients with AD, subcortical vascular disease, and other cognitive disorders. The studies, the first of which started in 1987, comprise inpatients with manifest dementia and patients seeking care for cognitive disorders at an outpatient memory clinic. This article gives an overview of the major published papers (neuropsychological, imaging/physiology, and neurochemical) of the studies including the ongoing Gothenburg MCI study. The main findings suggest that subcortical vascular disease with or without dementia exhibit a characteristic neuropsychological pattern of mental slowness and executive dysfunction and neurochemical deviations typical of white matter changes and disturbed blood-brain barrier function. Our findings may contribute to better healthcare for this underrecognized group of patients. The Gothenburg MCI study has also published papers on multimodal prediction of dementia, and cognitive reserve.

Selective familiarity deficits in otherwise cognitively intact aging individuals with genetic risk for Alzheimer's disease

Introduction

Familiarity has been associated with integrity of the rhinal cortex. Thus, impairment in familiarity is expected in very early stages of Alzheimer's disease (AD). The apolipoprotein E (APOE) ε4 allele is a major risk factor for AD. Here, we investigated the effect of the APOE ε4 status on familiarity in cognitively normal aging individuals.

Methods

Eighty-one individuals aged between 55 and 80 years, 21 carriers and 60 noncarriers, were used in these analyses. A cognitive evaluation was performed on all participants to document the absence of objective cognitive deficits. The effect of APOE ε4 status on familiarity was tested using independent sample t test and an analysis of covariance controlling for age, gender, and education.

Results

The groups did not differ in term of age, education, and male/female ratio. APOE ε4 carriers showed a significant reduction in familiarity. No other cognitive deficit was observed in the group of ε4 carriers, relative to noncarriers.

Discussion

APOE ε4 is associated with a reduction in familiarity in the absence of other cognitive deficits. These results suggest that performance in familiarity could represent an early cognitive marker for individuals at risk of AD.

Hippocampal volume and hippocampal angle (a more practical marker) in mild cognitive impairment: A case-control magnetic resonance imaging study

Background:

Mild cognitive impairment (MCI) accompanies brain atrophy in neuroimaging investigations. The aim of this study was to compare MCI patients with the normal population for hippocampal volume (HV) and hippocampal angle (HA), and to assess the correlation between HV and HA.

Materials and Methods:

In a case-control study on 2014, in Kashani Hospital (Isfahan, Iran), 20 MCI patients were compared with 20 normal controls for HV and HA. Subjects were diagnosed with MCI or normal control, based on neuropsychiatry interview, which was confirmed by neuropsychiatry unit cognitive assessment tool (NUCOG). All magnetic resonance imaging scans were processed using the Free-Surfer software package for HV assessment. The HA was measured on the most rostral slice in which the uncal sulcus could be identified on a coronal plane. The data were analyzed using multiple analysis of co-variance and Pearson correlation.

Results:

The mean (standard deviation [SD]) score of NUCOG in control and case group were 91.05 (3.01) and 82.42 (3.57), respectively. Comparison of HV and HA scores in two groups, showed that mean (SD) HV and HA were not different between control and case groups, significantly, (P = 0.094 and P = 0.394, respectively). There was a negative correlation between the adjusted HV and the HA in case (r = −0.642, P = 0.004), and control groups (r = −0.654, P = 0.003).

Conclusion:

HV and HA were not different between MCI patients and normal controls; however, HA is correlated with HV negatively and may be used as an alternative factor because of more feasibility and availability in clinical settings in compared to HV.

Disrupted small world topology and modular organisation of functional networks in late-life depression with and without amnestic mild cognitive impairment

BACKGROUND

The topological architecture of the whole-brain functional networks in those with and without late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are unknown.

AIMS

To investigate the differences in the small-world measures and the modular community structure of the functional networks between patients with LLD and aMCI when occurring alone or in combination and cognitively healthy non-depressed controls.

METHODS

79 elderly participants (LLD (n=23), aMCI (n=18), comorbid LLD and aMCI (n=13), and controls (n=25)) completed neuropsychiatric assessments. Graph theoretical methods were employed on resting-state functional connectivity MRI data.

RESULTS

LLD and aMCI comorbidity was associated with the greatest disruptions in functional integration measures (decreased global efficiency and increased path length); both LLD groups showed abnormal functional segregation (reduced local efficiency). The modular network organisation was most variable in the comorbid group, followed by patients with LLD-only. Decreased mean global, local and nodal efficiency metrics were associated with greater depressive symptom severity but not memory performance.

CONCLUSIONS

Considering the whole brain as a complex network may provide unique insights on the neurobiological underpinnings of LLD with and without cognitive impairment.

Western diet is associated with a smaller hippocampus: a longitudinal investigation

BACKGROUND

Recent meta-analyses confirm a relationship between diet quality and both depression and cognitive health in adults. While the biological pathways that underpin these relationships are likely multitudinous, extensive evidence from animal studies points to the involvement of the hippocampus. The aim of this study was to examine the association between dietary patterns and hippocampal volume in humans, and to assess whether diet was associated with differential rates of hippocampal atrophy over time.

METHODS

Data were drawn from the Personality and Total Health Through Life Study and focused on a subsample of the cohort (n = 255) who were aged 60-64 years at baseline in 2001, completed a food frequency questionnaire, and underwent two magnetic resonance imaging scans approximately 4 years apart. Longitudinal generalized estimating equation linear regression models were used to assess the association between dietary factors and left and right hippocampal volumes over time.

RESULTS

Every one standard deviation increase in healthy "prudent" dietary pattern was associated with a 45.7 mm(3) (standard error 22.9 mm(3)) larger left hippocampal volume, while higher consumption of an unhealthy "Western" dietary pattern was (independently) associated with a 52.6 mm(3) (SE 26.6 mm(3)) smaller left hippocampal volume. These relationships were independent of covariates including age, gender, education, labour-force status, depressive symptoms and medication, physical activity, smoking, hypertension and diabetes. While hippocampal volume declined over time, there was no evidence that dietary patterns influenced this decline. No relationships were observed between dietary patterns and right hippocampal volume.

CONCLUSIONS

Lower intakes of nutrient-dense foods and higher intakes of unhealthy foods are each independently associated with smaller left hippocampal volume. To our knowledge, this is the first human study to demonstrate associations between diet and hippocampal volume concordant with data previously observed in animal models.

T2 signal intensity and volume abnormalities of hippocampal subregions in patients with amnestic mild cognitive impairment by magnetic resonance imaging

BACKGROUND

The volumetry of the hippocampal subregion may provide additional information in the early investigation of amnestic mild cognitive impairment (aMCI) and the T2 signal intensity (T2-SI) of the hippocampal subregion has not been well studied quantitatively by magnetic resonance imaging (MRI) in aMCI.

METHODS

Using combined MRI-based hippocampal volumetry and T2-SI at the levels of the whole hippocampus and hippocampal subregion, 18 patients with aMCI and 18 age-matched controls were investigated.

RESULTS

Significantly lower left whole hippocampal and hippocampal head volumes and higher T2-SI in the bilateral whole hippocampus and hippocampal head were shown, whereas atrophy of the right whole hippocampus and hippocampal subregion was not significant in aMCI. Additionally, correlations were found among the hippocampal volume, T2-SI and Mini-Mental State Examination (MMSE) scores for aMCI in the whole hippocampus and some hippocampal subregions and an almost perfect correlation was found between T2-SI of the left hippocampal head and MMSE scores regarding aMCI (r = -0.831, P = 0.000).

CONCLUSION

Abnormalities of the hippocampal volume and T2-SI were documented in aMCI, whereas T2-SI was implied to be more susceptible than the volume in the pathohistological progression in aMCI. Additionally, T2-SI in the left hippocampal head may be a potential biomarker to facilitate the early diagnosis of aMCI.

Investigating virtual reality navigation in amnestic mild cognitive impairment using fMRI

Spatial navigation requires a well-established network of brain regions, including the hippocampus, caudate nucleus, and retrosplenial cortex. Amnestic Mild Cognitive Impairment (aMCI) is a condition with predominantly memory impairment, conferring a high predictive risk factor for dementia. aMCI is associated with hippocampal atrophy and subtle deficits in spatial navigation. We present the first use of a functional Magnetic Resonance Imaging (fMRI) navigation task in aMCI, using a virtual reality analog of the Radial Arm Maze. Compared with controls, aMCI patients showed reduced activity in the hippocampus bilaterally, retrosplenial cortex, and left dorsolateral prefrontal cortex. Reduced activation in key areas for successful navigation, as well as additional regions, was found alongside relatively normal task performance. Results also revealed increased activity in the right dorsolateral prefrontal cortex in aMCI patients, which may reflect compensation for reduced activations elsewhere. These data support suggestions that fMRI spatial navigation tasks may be useful for staging of progression in MCI.

Impaired and preserved aspects of feedback learning in aMCI: contributions of structural connectivity

Distinct lines of research demonstrated that patients with amnestic mild cognitive impairment (aMCI), a potential precursor of Alzheimer disease (AD), are particularly impaired in remembering relations between items and that the use of emotional targets can facilitate memory in patients with AD. We link these findings by examining learning through positive and negative feedback in patients with aMCI, and explore its anatomic underpinnings with diffusion tensor imaging and tractography. Compared to healthy controls, patients with single-domain aMCI were impaired in learning from positive feedback, while learning from negative outcomes was preserved. Among pathways within the brain circuit involved in feedback learning, abnormal white matter microstructure was observed in tracts, which connect left-hemispheric amygdala with hippocampus and entorhinal cortex. In all participants, reduced white matter integrity in this left fiber tract was specifically associated with learning from positive outcomes. Microstructure of right-hemispheric tracts between amygdala and entorhinal cortex was related to learning from negative feedback, and was not compromised in aMCI patients. Our results provide new insight into how anatomical connections might contribute to impaired and preserved aspects of learning behaviors in the early AD process and indicate potential compensatory mechanisms.

White matter signal abnormality quality differentiates mild cognitive impairment that converts to Alzheimer's disease from nonconverters

The objective of this study was to assess how longitudinal change in the quantity and quality of white matter signal abnormalities (WMSAs) contributes to the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). The Mahalanobis distance of WMSA from normal-appearing white matter using T1-, T2-, and proton density-weighted MRI was defined as a quality measure for WMSA. Cross-sectional analysis of WMSA volume in 104 cognitively healthy older adults, 116 individuals with MCI who converted to AD within 3 years (mild cognitive impairment converter [MCI-C]), 115 individuals with MCI that did not convert in that time (mild cognitive impairment nonconverter [MCI-NC]), and 124 individuals with AD from the Alzheimer's Disease Neuroimaging Initiative revealed that WMSA volume was substantially greater in AD relative to the other groups but did not differ between MCI-NC and MCI-C. Longitudinally, MCI-C exhibited faster WMSA quality progression but not volume compared with matched MCI-NC beginning 18 months before MCI-C conversion to AD. The strongest difference in rate of change was seen in the time period starting 6 months before MCI-C conversion to AD and ending 6 months after conversion (p < 0.001). The relatively strong effect in this time period relative to AD conversion in the MCI-C was similar to the relative rate of change in hippocampal volume, a traditional imaging marker of AD pathology. These data demonstrate changes in white matter tissue properties that occur within WMSA in individuals with MCI that will subsequently obtain a clinical diagnosis of AD within 18 months. Individuals with AD have substantially greater WMSA volume than all MCI suggesting that there is a progressive accumulation of WMSA with progressive disease severity, and that quality change predates changes in this total volume. Given the timing of the changes in WMSA tissue quality relative to the clinical diagnosis of AD, these findings suggest that WMSAs are a critical component for this conversion and are a critical component of this clinical syndrome.

The association of brain structure with gait velocity in older adults: a quantitative volumetric analysis of brain MRI

INTRODUCTION

While cortical processes play an important role in controlling locomotion, the underlying structural brain changes associated with slowing of gait in aging are not yet fully established. Our study aimed to examine the relationship between cortical gray matter volume (GM), white matter volume (WM), ventricular volume (VV), hippocampal and hippocampal subfield volumes, and gait velocity in older adults free of dementia.

METHODS

Gait and cognitive performance was tested in 112 community-residing adults, age 70 years and over, participating in the Einstein Aging Study. Gait velocity (cm/s) was obtained using an instrumented walkway. Volumetric MRI measures were estimated using a FreeSurfer software. We examined the cross-sectional relationship of GM, WM, VV, and hippocampal total and subfield volumes and gait velocity using linear regression models. In complementary models, the effect of memory performance on the relationship between gait velocity and regional volumes was evaluated.

RESULTS

Slower gait velocity was associated with smaller cortical GM and total hippocampal volumes. There was no association between gait velocity and WM or VV. Among hippocampal subfields, only smaller presubiculum volume was significantly associated with decrease in gait velocity. Addition of the memory performance to the models attenuated the association between gait velocity and all volumetric measures.

CONCLUSIONS

Our findings indicate that total GM and hippocampal volumes as well as specific hippocampal subfield volumes are inversely associated with locomotor function. These associations are probably affected by cognitive status of study population.

Autophagy does not lead to the asymmetrical hippocampal injury in chronic stress

Chronic stress results in hippocampal injury, and impairs learning and memory ability of animals. However the cellular mechanisms underlying cell death within hippocampus remain elusive. The present employed the rat model of chronic unpredicted mild stress (CUMS) and examined the cellular mechanism responsible for learning and memory impairments. The results showed that in correlation to the decreased ability in novelty cognition and reverse learning, CUMS led to loss of CA3 neurons in hippocampus, especially in the right hippocampus. Interestingly, autophagy contributed to the cell loss but was asymmetrical on both sides. This suggested that CUMS resulted in asymmetrical hippocampal injuries, which is not fully determined by autophagy.

Dynamic cortical lateralization during olfactory discrimination learning

Bilateral cortical circuits are not necessarily symmetrical. Asymmetry, or cerebral lateralization, allows functional specialization of bilateral brain regions and has been described in humans for such diverse functions as perception, memory and emotion. There is also evidence for asymmetry in the human olfactory system, although evidence in non-human animal models is lacking. In the present study, we took advantage of the known changes in olfactory cortical local field potentials that occur over the course of odour discrimination training to test for functional asymmetry in piriform cortical activity during learning. Both right and left piriform cortex local field potential activities were recorded. The results obtained demonstrate a robust interhemispheric asymmetry in anterior piriform cortex activity that emerges during specific stages of odour discrimination learning, with a transient bias toward the left hemisphere. This asymmetry is not apparent during error trials. Furthermore, functional connectivity (coherence) between the bilateral anterior piriform cortices is learning- and context-dependent. Steady-state interhemispheric anterior piriform cortex coherence is reduced during the initial stages of learning and then recovers as animals acquire competent performance. The decrease in coherence is seen relative to bilateral coherence expressed in the home cage, which remains stable across conditioning days. Similarly, transient, trial-related interhemispheric coherence increases with task competence. Taken together, the results demonstrate transient asymmetry in piriform cortical function during odour discrimination learning until mastery, suggesting that each piriform cortex may contribute something unique to odour memory.

APOE genotype and neuroimaging markers of Alzheimer's disease: systematic review and meta-analysis

OBJECTIVE

We aimed to examine the association of apolipoprotein E (APOE) ɛ4 genotype with neuroimaging markers of Alzheimer's disease: hippocampal volume, brain amyloid deposition and cerebral metabolism.

METHODS

We performed a systematic review and meta-analysis of 14 cross-sectional studies identified in Pubmed from 1996 to 2014 (n=1628). The pooled standard mean difference (SMD) was used to estimate the association between APOE and hippocampal volume and amyloid deposition. Meta-analysis was performed using effect size signed differential mapping using coordinates extracted from clusters with statistically significant difference in cerebral metabolic rate for glucose between APOE ɛ4+ and ɛ4- groups.

RESULTS

APOE ɛ4 carrier status was associated with atrophic hippocampal volume (pooled SMD: -0.47; 95% CI -0.82 to -0.13; p=0.007) and increased cerebral amyloid positron emission tomography tracer (pooled SMD: 0.62, 95% CI 0.27 to 0.98, p=0.0006). APOE ɛ4 was also associated with decreased cerebral metabolism, especially in right middle frontal gyrus.

CONCLUSIONS

APOE ɛ4 was associated with atrophic hippocampal volume in MRI markers, increased cerebral amyloid deposition and cerebral hypometabolism. Theses associations may indicate the potential role of the APOE gene in the pathophysiology of Alzheimer's disease.

Can multi-modal neuroimaging evidence from hippocampus provide biomarkers for the progression of amnestic mild cognitive impairment?

Impaired structure and function of the hippocampus is a valuable predictor of progression from amnestic mild cognitive impairment (aMCI) to Alzheimer's disease (AD). As a part of the medial temporal lobe memory system, the hippocampus is one of the brain regions affected earliest by AD neuropathology, and shows progressive degeneration as aMCI progresses to AD. Currently, no validated biomarkers can precisely predict the conversion from aMCI to AD. Therefore, there is a great need of sensitive tools for the early detection of AD progression. In this review, we summarize the specific structural and functional changes in the hippocampus from recent aMCI studies using neurophysiological and neuroimaging data. We suggest that a combination of advanced multi-modal neuroimaging measures in discovering biomarkers will provide more precise and sensitive measures of hippocampal changes than using only one of them. These will potentially affect early diagnosis and disease-modifying treatments. We propose a new sequential and progressive framework in which the impairment spreads from the integrity of fibers to volume and then to function in hippocampal subregions. Meanwhile, this is likely to be accompanied by progressive impairment of behavioral and neuropsychological performance in the progression of aMCI to AD.

Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment

We evaluate a fully automatic technique for labeling hippocampal subfields and cortical subregions in the medial temporal lobe in in vivo 3 Tesla MRI. The method performs segmentation on a T2-weighted MRI scan with 0.4 × 0.4 × 2.0 mm(3) resolution, partial brain coverage, and oblique orientation. Hippocampal subfields, entorhinal cortex, and perirhinal cortex are labeled using a pipeline that combines multi-atlas label fusion and learning-based error correction. In contrast to earlier work on automatic subfield segmentation in T2-weighted MRI [Yushkevich et al., 2010], our approach requires no manual initialization, labels hippocampal subfields over a greater anterior-posterior extent, and labels the perirhinal cortex, which is further subdivided into Brodmann areas 35 and 36. The accuracy of the automatic segmentation relative to manual segmentation is measured using cross-validation in 29 subjects from a study of amnestic mild cognitive impairment (aMCI) and is highest for the dentate gyrus (Dice coefficient is 0.823), CA1 (0.803), perirhinal cortex (0.797), and entorhinal cortex (0.786) labels. A larger cohort of 83 subjects is used to examine the effects of aMCI in the hippocampal region using both subfield volume and regional subfield thickness maps. Most significant differences between aMCI and healthy aging are observed bilaterally in the CA1 subfield and in the left Brodmann area 35. Thickness analysis results are consistent with volumetry, but provide additional regional specificity and suggest nonuniformity in the effects of aMCI on hippocampal subfields and MTL cortical subregions.

Identification of Conversion from Normal Elderly Cognition to Alzheimer's Disease using Multimodal Support Vector Machine

Alzheimer's disease (AD) is one of the most serious progressive neurodegenerative diseases among the elderly, therefore the identification of conversion to AD at the earlier stage has become a crucial issue. In this study, we applied multimodal support vector machine to identify the conversion from normal elderly cognition to mild cognitive impairment (MCI) or AD based on magnetic resonance imaging and positron emission tomography data. The participants included two independent cohorts (Training set: 121 AD patients and 120 normal controls (NC); Testing set: 20 NC converters and 20 NC non-converters) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The multimodal results showed that the accuracy, sensitivity, and specificity of the classification between NC converters and NC non-converters were 67.5% , 73.33% , and 64% , respectively. Furthermore, the classification results with feature selection increased to 70% accuracy, 75% sensitivity, and 66.67% specificity. The classification results using multimodal data are markedly superior to that using a single modality when we identified the conversion from NC to MCI or AD. The model built in this study of identifying the risk of normal elderly converting to MCI or AD will be helpful in clinical diagnosis and pathological research.

Imaging endpoints for clinical trials in Alzheimer's disease

As the need to develop a successful disease-modifying treatment for Alzheimer's disease (AD) becomes more urgent, imaging is increasingly used in therapeutic trials. We provide an overview of how the different imaging modalities are used in AD studies and the current regulatory guidelines for their use in clinical trials as endpoints. We review the current literature for results of imaging endpoints of efficacy and safety in published clinical trials. We start with trials in mild to moderate AD, where imaging (largely magnetic resonance imaging (MRI)) has long played a role in inclusion and exclusion criteria; more recently, MRI has been used to identify adverse events and to measure rates of brain atrophy. The advent of amyloid imaging using positron emission tomography has led to trials incorporating amyloid measurements as endpoints and incidentally to the recognition of the high proportion of amyloid-negative individuals that may be recruited into these trials. Ongoing and planned trials now commonly include multimodality imaging: amyloid positron emission tomography, MRI and other modalities. At the same time, the failure of recent large profile trials in mild to moderate AD together with the realisation that there is a long prodromal period to AD has driven a push to move studies to earlier in the disease. Imaging has particularly important roles, alongside other biomarkers, in assessing efficacy because conventional clinical outcomes may have limited ability to detect treatment effects in these early stages.

Multimodality imaging of Alzheimer disease and other neurodegenerative dementias

Neurodegenerative diseases, such as Alzheimer disease, result in cognitive decline and dementia and are a leading cause of mortality in the growing elderly population. These progressive diseases typically have an insidious onset, with overlapping clinical features early in the disease course that make diagnosis challenging. The neurodegenerative diseases are associated with characteristic, although not completely understood, changes in the brain: abnormal protein deposition, synaptic dysfunction, neuronal injury, and neuronal death. Neuroimaging biomarkers-principally regional atrophy on structural MR imaging, patterns of hypometabolism on (18)F-FDG PET, and detection of cerebral amyloid plaque on amyloid PET--are able to evaluate the patterns of these abnormalities in the brain to improve early diagnosis and help predict the disease course. These techniques have unique strengths and synergies in multimodality evaluation of the patient with cognitive decline or dementia. This review discusses the key imaging biomarkers from MR imaging, (18)F-FDG PET, and amyloid PET; the imaging features of the most common neurodegenerative dementias; the role of various neuroimaging studies in differential diagnosis and prognosis; and some promising imaging techniques under development.

Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology

Blood proteins and their complexes have become the focus of a great deal of interest in the context of their potential as biomarkers of Alzheimer's disease (AD). We used a SOMAscan assay for quantifying 1001 proteins in blood samples from 331 AD, 211 controls, and 149 mild cognitive impaired (MCI) subjects. The strongest associations of protein levels with AD outcomes were prostate-specific antigen complexed to α1-antichymotrypsin (AD diagnosis), pancreatic prohormone (AD diagnosis, left entorhinal cortex atrophy, and left hippocampus atrophy), clusterin (rate of cognitive decline), and fetuin B (left entorhinal atrophy). Multivariate analysis found that a subset of 13 proteins predicted AD with an accuracy of area under the curve of 0.70. Our replication of previous findings provides further evidence that levels of these proteins in plasma are truly associated with AD. The newly identified proteins could be potential biomarkers and are worthy of further investigation.

A Predictive Study of Dirichlet Process Mixture Models for Curve Fitting

This paper examines the use of Dirichlet process (DP) mixtures for curve fitting. An important modelling aspect in this setting is the choice between constant or covariate-dependent weights. By examining the problem of curve fitting from a predictive perspective, we show the advantages of using covariate-dependent weights. These advantages are a result of the incorporation of covariate proximity in the latent partition. However, closer examination of the partition yields further complications, which arise from the vast number of total partitions. To overcome this, we propose to modify the probability law of the random partition to strictly enforce the notion of covariate proximity, while still maintaining certain properties of the DP. This allows the distribution of the partition to depend on the covariate in a simple manner and greatly reduces the total number of possible partitions, resulting in improved curve fitting and faster computations. Numerical illustrations are presented.

Hippocampal subfields differentially correlate with chronic pain in older adults

Although previous studies have demonstrated that the hippocampus plays a role in pain processing, the role of hippocampal subfields is uncertain. The goal of this study was to examine the relationship between hippocampal subfield volumes and chronic pain in nondemented older adults. The study sample included 86 community-residing adults age 70 or older who were free of dementia and recruited from the Einstein Aging Study. Chronic pain was defined as pain over the last 3 months, that was moderate or severe (minimum rating of 4 out of 10) most, or all of the time. Hippocampal subfield volumes were estimated using FreeSurfer software. We modeled the association between chronic pain and hippocampal and subfield volume using linear regression. The sample had a mean age of 80 and was 58% female. Chronic pain, present in 55% of the sample, was associated with smaller right and total hippocampal volumes, particularly in women, after adjusting for age, education, and intracranial volume (eTICV). In addition, in women, volume was significantly reduced in participants with chronic pain in right CA2-3 (β=-0.35, p=0.010), right CA4-DG (β=-0.35, p=0.011), left presubiculum (β=-0.29, p=0.030), and left fimbria (β=-0.30, p=0.023). In men, chronic pain was not associated with the volume of any of the hippocampal subfield volumes. Chronic pain in women is associated with a reduction in the volume of right hippocampus and also selected hippocampal subfields. Future studies should clarify the mechanisms underlying the association between regional hippocampal volumes and chronic pain, particularly in women.

Discerning mild cognitive impairment and Alzheimer Disease from normal aging: morphologic characterization based on univariate and multivariate models

RATIONALE AND OBJECTIVES

Differentiating mild cognitive impairment (MCI) and Alzheimer Disease (AD) from healthy aging remains challenging. This study aimed to explore the cerebral structural alterations of subjects with MCI or AD as compared to healthy elderly based on the individual and collective effects of cerebral morphologic indices using univariate and multivariate analyses.

MATERIALS AND METHODS

T1-weighted images (T1WIs) were retrieved from Alzheimer Disease Neuroimaging Initiative database for 116 subjects who were categorized into groups of healthy aging, MCI, and AD. Analysis of covariance (ANCOVA) and multivariate analysis of covariance (MANCOVA) were performed to explore the intergroup morphologic alterations indexed by surface area, curvature index, cortical thickness, and subjacent white matter volume with age and sex controlled as covariates, in 34 parcellated gyri regions of interest (ROIs) for both cerebral hemispheres based on the T1WI. Statistical parameters were mapped on the anatomic images to facilitate visual inspection.

RESULTS

Global rather than region-specific structural alterations were revealed in groups of MCI and AD relative to healthy elderly using MANCOVA. ANCOVA revealed that the cortical thickness decreased more prominently in entorhinal, temporal, and cingulate cortices and was positively correlated with patients' cognitive performance in AD group but not in MCI. The temporal lobe features marked atrophy of white matter during the disease dynamics. Significant intercorrelations were observed among the morphologic indices with univariate analysis for given ROIs.

CONCLUSIONS

Significant global structural alterations were identified in MCI and AD based on MANCOVA model with improved sensitivity. The intercorrelation among the morphologic indices may dampen the use of individual morphological parameter in featuring cerebral structural alterations. Decrease in cortical thickness is not reflective of the cognitive performance at the early stage of AD.

Grey-matter volume as a potential feature for the classification of Alzheimer's disease and mild cognitive impairment: an exploratory study

Specific patterns of brain atrophy may be helpful in the diagnosis of Alzheimer's disease (AD). In the present study, we set out to evaluate the utility of grey-matter volume in the classification of AD and amnestic mild cognitive impairment (aMCI) compared to normal control (NC) individuals. Voxel-based morphometric analyses were performed on structural MRIs from 35 AD patients, 27 aMCI patients, and 27 NC participants. A two-sample two-tailed t-test was computed between the NC and AD groups to create a map of abnormal grey matter in AD. The brain areas with significant differences were extracted as regions of interest (ROIs), and the grey-matter volumes in the ROIs of the aMCI patients were included to evaluate the patterns of change across different disease severities. Next, correlation analyses between the grey-matter volumes in the ROIs and all clinical variables were performed in aMCI and AD patients to determine whether they varied with disease progression. The results revealed significantly decreased grey matter in the bilateral hippocampus/parahippocampus, the bilateral superior/middle temporal gyri, and the right precuneus in AD patients. The grey-matter volumes were positively correlated with clinical variables. Finally, we performed exploratory linear discriminative analyses to assess the classifying capacity of grey-matter volumes in the bilateral hippocampus and parahippocampus among AD, aMCI, and NC. Leave-one-out cross-validation analyses demonstrated that grey-matter volumes in hippocampus and parahippocampus accurately distinguished AD from NC. These findings indicate that grey-matter volumes are useful in the classification of AD.

Why looking at the whole hippocampus is not enough-a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer's disease diagnosis

The hippocampus is one of the earliest affected brain regions in Alzheimer's disease (AD) and its dysfunction is believed to underlie the core feature of the disease-memory impairment. Given that hippocampal volume is one of the best AD biomarkers, our review focuses on distinct subfields within the hippocampus, pinpointing regions that might enhance the predictive value of current diagnostic methods. Our review presents how changes in hippocampal volume, shape, symmetry and activation are reflected by cognitive impairment and how they are linked with neurogenesis alterations. Moreover, we revisit the functional differentiation along the anteroposterior longitudinal axis of the hippocampus and discuss its relevance for AD diagnosis. Finally, we indicate that apart from hippocampal subfield volumetry, the characteristic pattern of hippocampal hyperactivation associated with seizures and neurogenesis changes is another promising candidate for an early AD biomarker that could become also a target for early interventions.

Deficits in episodic memory retrieval reveal impaired default mode network connectivity in amnestic mild cognitive impairment

Amnestic mild cognitive impairment (aMCI) is believed to represent a transitional stage between normal healthy ageing and the development of dementia. In particular, aMCI patients have been shown to have higher annual transition rates to Alzheimer's Disease (AD) than individuals without cognitive impairment. Despite intensifying interest investigating the neuroanatomical basis of this transition, there remain a number of questions regarding the pathophysiological process underlying aMCI itself. A number of recent studies in aMCI have shown specific impairments in connectivity within the default mode network (DMN), which is a group of regions strongly related to episodic memory capacities. However to date, no study has investigated the integrity of the DMN between patients with aMCI and those with a non-amnestic pattern of MCI (naMCI), who have cognitive impairment, but intact memory storage systems. In this study, we contrasted the DMN connectivity in 24 aMCI and 33 naMCI patients using seed-based resting state fMRI. The two groups showed no statistical difference in their DMN intra-connectivity. However when connectivity was analysed according to performance on measures of episodic memory retrieval, the two groups were separable, with aMCI patients demonstrating impaired functional connectivity between the hippocampal formation and the posterior cingulate cortex. We provide evidence that this lack of connectivity is driven by impaired communication from the posterior cingulate hub and does not simply represent hippocampal atrophy, suggesting that posterior cingulate degeneration is the driving force behind impaired DMN connectivity in aMCI.

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First trial to explore Default Mode Network (DMN) connectivity between MCI and naMCI

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Amnestic and nonamnestic MCI groups show similar overall DMN intra-connectivity.

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aMCI patients have connectivity deficits related to impaired memory retention.

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Impaired DMN connectivity is driven by deficits in posterior cingulate cortex.

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Alzheimer’s disease pathology likely evolves from PCC to hippocampus

Development of a brain MRI-based hidden Markov model for dementia recognition

BACKGROUND

Dementia is an age-related cognitive decline which is indicated by an early degeneration of cortical and sub-cortical structures. Characterizing those morphological changes can help to understand the disease development and contribute to disease early prediction and prevention. But modeling that can best capture brain structural variability and can be valid in both disease classification and interpretation is extremely challenging. The current study aimed to establish a computational approach for modeling the magnetic resonance imaging (MRI)-based structural complexity of the brain using the framework of hidden Markov models (HMMs) for dementia recognition.

METHODS

Regularity dimension and semi-variogram were used to extract structural features of the brains, and vector quantization method was applied to convert extracted feature vectors to prototype vectors. The output VQ indices were then utilized to estimate parameters for HMMs. To validate its accuracy and robustness, experiments were carried out on individuals who were characterized as non-demented and mild Alzheimer's diseased. Four HMMs were constructed based on the cohort of non-demented young, middle-aged, elder and demented elder subjects separately. Classification was carried out using a data set including both non-demented and demented individuals with a wide age range.

RESULTS

The proposed HMMs have succeeded in recognition of individual who has mild Alzheimer's disease and achieved a better classification accuracy compared to other related works using different classifiers. Results have shown the ability of the proposed modeling for recognition of early dementia.

CONCLUSION

The findings from this research will allow individual classification to support the early diagnosis and prediction of dementia. By using the brain MRI-based HMMs developed in our proposed research, it will be more efficient, robust and can be easily used by clinicians as a computer-aid tool for validating imaging bio-markers for early prediction of dementia.

Memory for the 2008 presidential election in healthy ageing and mild cognitive impairment

The present study examined memory accuracy and confidence for personal and public event details of the 2008 presidential election in healthy older adults and those with mild cognitive impairment (MCI). Participants completed phone interviews within a week after the election and after a 10-month delay. MCI patients and healthy older adults had comparable emotional reactions to learning the outcome of the election, with most people finding it to be a positive experience. After the delay period, details about the election were better remembered by all participants than a less emotionally arousing comparison event. However, MCI patients had more difficulty than healthy older adults correctly recalling details of public information about the election, although often the MCI patients could recognise the correct details. This is the first study to show that MCI patients' memory can benefit from emotionally arousing positive events, complementing the literature demonstrating similar effects for negative events.

Comparing manual and automatic segmentation of hippocampal volumes: reliability and validity issues in younger and older brains

We compared hippocampal volume measures obtained by manual tracing to automatic segmentation with FreeSurfer in 44 younger (20-30 years) and 47 older (60-70 years) adults, each measured with magnetic resonance imaging (MRI) over three successive time points, separated by four months. Retest correlations over time were very high for both manual and FreeSurfer segmentations. With FreeSurfer, correlations over time were significantly lower in the older than in the younger age group, which was not the case with manual segmentation. Pearson correlations between manual and FreeSurfer estimates were sufficiently high, numerically even higher in the younger group, whereas intra-class correlation coefficient (ICC) estimates were lower in the younger than in the older group. FreeSurfer yielded higher volume estimates than manual segmentation, particularly in the younger age group. Importantly, FreeSurfer consistently overestimated hippocampal volumes independently of manually assessed volume in the younger age group, but overestimated larger volumes in the older age group to a less extent, introducing a systematic age bias in the data. Age differences in hippocampal volumes were significant with FreeSurfer, but not with manual tracing. Manual tracing resulted in a significant difference between left and right hippocampus (right > left), whereas this asymmetry effect was considerably smaller with FreeSurfer estimates. We conclude that FreeSurfer constitutes a feasible method to assess differences in hippocampal volume in young adults. FreeSurfer estimates in older age groups should, however, be interpreted with care until the automatic segmentation pipeline has been further optimized to increase validity and reliability in this age group.