Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment

Structural Magnetic Resonance Imaging Markers of Alzheimer's Disease and Its Retranslation to Rodent Models

The importance of imaging biomarkers has been acknowledged in the diagnosis and in the follow-up of Alzheimer's disease (AD), one of the major causes of dementia. Next to the molecular biomarkers and PET imaging investigations, structural MRI approaches provide important information about the disease progression and about the pathomechanism. Furthermore,a growing body of literature retranslates these imaging biomarkers to various rodent models of the disease. The goal of this review is to provide an overview of the macro- and microstructural imaging biomarkers of AD, concentrating on atrophy measures and diffusion MRI alterations. A survey is also given of the imaging approaches used in rodent models of dementias that can promote drug development.

Evolving Evidence for the Value of Neuroimaging Methods and Biological Markers in Subjects Categorized with Subjective Cognitive Decline

There is evolving evidence that individuals categorized with subjective cognitive decline (SCD) are potentially at higher risk for developing objective and progressive cognitive impairment compared to cognitively healthy individuals without apparent subjective complaints. Interestingly, SCD, during advancing preclinical Alzheimer's disease (AD), may denote very early, subtle cognitive decline that cannot be identified using established standardized tests of cognitive performance. The substantial heterogeneity of existing SCD-related research data has led the Subjective Cognitive Decline Initiative (SCD-I) to accomplish an international consensus on the definition of a conceptual research framework on SCD in preclinical AD. In the area of biological markers, the cerebrospinal fluid signature of AD has been reported to be more prevalent in subjects with SCD compared to healthy controls; moreover, there is a pronounced atrophy, as demonstrated by magnetic resonance imaging, and an increased hypometabolism, as revealed by positron emission tomography, in characteristic brain regions affected by AD. In addition, SCD individuals carrying an apolipoprotein ɛ4 allele are more likely to display AD-phenotypic alterations. The urgent requirement to detect and diagnose AD as early as possible has led to the critical examination of the diagnostic power of biological markers, neurophysiology, and neuroimaging methods for AD-related risk and clinical progression in individuals defined with SCD. Observational studies on the predictive value of SCD for developing AD may potentially be of practical value, and an evidence-based, validated, qualified, and fully operationalized concept may inform clinical diagnostic practice and guide earlier designs in future therapy trials.

An evidence-based algorithm for the utility of FDG-PET for diagnosing Alzheimer's disease according to presence of medial temporal lobe atrophy

BACKGROUND

Imaging biomarkers for Alzheimer's disease include medial temporal lobe atrophy (MTLA) depicted on computed tomography (CT) or magnetic resonance imaging (MRI) and patterns of reduced metabolism on fluorodeoxyglucose positron emission tomography (FDG-PET).

AIMS

To investigate whether MTLA on head CT predicts the diagnostic usefulness of an additional FDG-PET scan.

METHOD

Participants had a clinical diagnosis of Alzheimer's disease (n = 37) or dementia with Lewy bodies (DLB; n = 30) or were similarly aged controls (n = 30). We visually rated MTLA on coronally reconstructed CT scans and, separately and blind to CT ratings, abnormal appearances on FDG-PET scans.

RESULTS

Using a pre-defined cut-off of MTLA ⩾5 on the Scheltens (0-8) scale, 0/30 controls, 6/30 DLB and 23/30 Alzheimer's disease had marked MTLA. FDG-PET performed well for diagnosing Alzheimer's disease v DLB in the low-MTLA group (sensitivity/specificity of 71%/79%), but in the high-MTLA group diagnostic performance of FDG-PET was not better than chance.

CONCLUSIONS

In the presence of a high degree of MTLA, the most likely diagnosis is Alzheimer's disease, and an FDG-PET scan will probably not provide significant diagnostic information. However, in cases without MTLA, if the diagnosis is unclear, an FDG-PET scan may provide additional clinically useful diagnostic information.

Neuroimaging of hippocampal atrophy in early recognition of Alzheimer's disease--a critical appraisal after two decades of research

As a characteristic feature of Alzheimer's disease (AD) hippocampal atrophy (HA) can be demonstrated in the majority of patients by using neuroimaging techniques in particular magnetic resonance imaging (MRI). Hippocampal atrophy is associated with declarative memory deficits and can also be associated with changes of adjacent medial temporal substructures such as the parahippocampal gyrus or the the entorhinal cortex. Similar findings are present in patients with mild cognitive impairment (MCI) albeit to a lesser extent. While these finding facilitate the diagnostic process in patients with clinical suspicious AD, the metric properties of hippocampal atrophy for delineating healthy aging from MCI and mild AD still appear to be rather limited; as such it is not sufficient to establish the diagnosis of AD (and even more so of MCI). This limitation partly refers to methodological issues and partly to the fact that hippocampal tissue integrity is subject to various pathogenetic influences other than AD. Moreover,the effects of hippocampal atrophy on the behavioral level (e.g. cognitive deficits) are modulated by the individual's cognitive reserve. From a clinical standpoint these observations are in line with the hypothesis that the onset and course of AD is influenced by a number of peristatic factors which are partly conceptualized in the concepts of brain and/or cognitive reserve. These complex interactions have to be considered when using the presence of hippocampal atrophy in the routine diagnostic procedure of AD.

Amide proton transfer magnetic resonance imaging of Alzheimer's disease at 3.0 Tesla: a preliminary study

Background:

Amide proton transfer (APT) imaging has recently emerged as an important contrast mechanism for magnetic resonance imaging (MRI) in the field of molecular and cellular imaging. The aim of this study was to evaluate the feasibility of APT imaging to detect cerebral abnormality in patients with Alzheimer's disease (AD) at 3.0 Tesla.

Methods:

Twenty AD patients (9 men and 11 women; age range, 67–83 years) and 20 age-matched normal controls (11 men and 9 women; age range, 63–82 years) underwent APT and traditional MRI examination on a 3.0 Tesla MRI system. The magnetic resonance ratio asymmetry (MTR_asym) values at 3.5 ppm of bilateral hippocampi (Hc), temporal white matter regions, occipital white matter regions, and cerebral peduncles were measured on oblique axial APT images. MTR_asym (3.5 ppm) values of the cerebral structures between AD patients and control subjects were compared with independent samples t-test. Controlling for age, partial correlation analysis was used to investigate the associations between mini-mental state examination (MMSE) and the various MRI measures among AD patients.

Results:

Compared with normal controls, MTR_asym (3.5 ppm) values of bilateral Hc were significantly increased in AD patients (right 1.24% ± 0.21% vs. 0.83% ± 0.19%, left 1.18% ± 0.18% vs. 0.80%± 0.17%, t = 3.039, 3.328, P = 0.004, 0.002, respectively). MTR_asym (3.5 ppm) values of bilateral Hc were significantly negatively correlated with MMSE (right r = −0.559, P = 0.013; left r = −0.461, P = 0.047).

Conclusions:

Increased MTR_asym (3.5 ppm) values of bilateral Hc in AD patients and its strong correlations with MMSE suggest that APT imaging could potentially provide imaging biomarkers for the noninvasive molecular diagnosis of AD.

Multimodal imaging in Alzheimer's disease: validity and usefulness for early detection

Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.

Multi-atlas segmentation of biomedical images: A survey

Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, et al. (2004), Klein, et al. (2005), and Heckemann, et al. (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of "atlases" (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003-2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

Consciousness, cognition and brain networks: New perspectives

A detailed analysis of the literature on consciousness and cognition mechanisms based on the neural networks theory is presented. The immune and inflammatory response to the anesthetic-surgical procedure induces modulation of neuronal plasticity by influencing higher cognitive functions. Anesthetic drugs can cause unconsciousness, producing a functional disruption of cortical and thalamic cortical integration complex. The external and internal perceptions are processed through an intricate network of neural connections, involving the higher nervous activity centers, especially the cerebral cortex. This requires an integrated model, formed by neural networks and their interactions with highly specialized regions, through large-scale networks, which are distributed throughout the brain collecting information flow of these perceptions. Functional and effective connectivity between large-scale networks, are essential for consciousness, unconsciousness and cognition. It is what is called the "human connectome" or map neural networks.

Quantitative Measurements in the Human Hippocampus and Related Areas: Correspondence between Ex-Vivo MRI and Histological Preparations

The decrease of volume estimates in different structures of the medial temporal lobe related to memory correlate with the decline of cognitive functions in neurodegenerative diseases. This study presents data on the association between MRI quantitative parameters of medial temporal lobe structures and their quantitative estimate in microscopic examination. Twelve control cases had ex-vivo MRI, and thereafter, the temporal lobe of both hemispheres was sectioned from the pole as far as the level of the splenium of the corpus callosum. Nissl stain was used to establish anatomical boundaries between structures in the medial temporal lobe. The study included morphometrical and stereological estimates of the amygdaloid complex, hippocampus, and temporal horn of the lateral ventricle, as well as different regions of grey and white matter in the temporal lobe. Data showed a close association between morphometric MRI images values and those based on the histological determination of boundaries. Only values in perimeter and circularity of the piamater were different. This correspondence is also revealed by the stereological study, although irregular compartments resulted in a lesser agreement. Neither age (< 65 yr and > 65 yr) nor hemisphere had any effect. Our results indicate that ex-vivo MRI is highly associated with quantitative information gathered by histological examination, and these data could be used as structural MRI biomarker in neurodegenerative diseases.

Volumetric changes in amygdala and entorhinal cortex and their relation to memory impairment in patients with medial temporal lobe epilepsy with visually normal MR imaging findings

OBJECTIVE

To explore the relation between parahippocampal structures, such as the amygdala and the entorhinal cortex (EC), with verbal and nonverbal memory in patients with medial temporal lobe epilepsy (MTLE) with visually normal MR imaging findings by volumetric measurements using magnetic resonance imaging (MRI).

METHODS

Thirty-six consecutive patients with MTLE presenting a non-sclerotic hippocampus though visual inspection were assessed by MRI to measure the volumes of the hippocampus, amygdale and EC, and by using the clinical memory scale (CMS), a test battery for verbal and nonverbal memory, where summation of all CMS subscale scores equals the memory quotient (MQ). The correlations between MRI volumetric data (Z scores or asymmetry indexes (AI; (L-R)/(L+R)), "L" and "R" refer to the left and right volumes of each structure, respectively), clinical variables and memory scale scores were analyzed using a principal component regression model.

RESULTS

Volumetric MRI revealed significant differences between the volumes of the hippocampus, EC, and right amygdala, but no differences in the volume of the left amygdala between the controls and the patients group. The patients group performed significantly worse in MQ (p < 0.01), the associate memory test (p < 0.01), directed memory test (p < 0.05), and the nonsense graphical recognition test (p < 0.05) compared to the control group. The asymmetry of the amygdala negatively correlated to verbal paired associates' recall and nonsense graphical recognition. The direct memory was positively related to the volume of the EC.

CONCLUSION

The volumetric asymmetry of the amygdala contributes to either verbal or nonverbal memory impairment in MTLE patients. Verbal memory may correlate with the volume of the EC.

T1rho MRI and CSF biomarkers in diagnosis of Alzheimer's disease

In the current study, we have evaluated the performance of magnetic resonance (MR) T1rho (T_1ρ) imaging and CSF biomarkers (T-tau, P-tau and Aβ-42) in characterization of Alzheimer's disease (AD) patients from mild cognitive impairment (MCI) and control subjects. With informed consent, AD (n = 27), MCI (n = 17) and control (n = 17) subjects underwent a standardized clinical assessment and brain MRI on a 1.5-T clinical-scanner. T_1ρ images were obtained at four different spin-lock pulse duration (10, 20, 30 and 40 ms). T_1ρ maps were generated by pixel-wise fitting of signal intensity as a function of the spin-lock pulse duration. T_1ρ values from gray matter (GM) and white matter (WM) of medial temporal lobe were calculated. The binary logistic regression using T_1ρ and CSF biomarkers as variables was performed to classify each group. T_1ρ was able to predict 77.3% controls and 40.0% MCI while CSF biomarkers predicted 81.8% controls and 46.7% MCI. T_1ρ and CSF biomarkers in combination predicted 86.4% controls and 66.7% MCI. When comparing controls with AD, T_1ρ predicted 68.2% controls and 73.9% AD, while CSF biomarkers predicted 77.3% controls and 78.3% for AD. Combination of T_1ρ and CSF biomarkers improved the prediction rate to 81.8% for controls and 82.6% for AD. Similarly, on comparing MCI with AD, T_1ρ predicted 35.3% MCI and 81.9% AD, whereas CSF biomarkers predicted 53.3% MCI and 83.0% AD. Collectively CSF biomarkers and T_1ρ were able to predict 59.3% MCI and 84.6% AD. On receiver operating characteristic analysis T_1ρ showed higher sensitivity while CSF biomarkers showed greater specificity in delineating MCI and AD from controls. No significant correlation between T_1ρ and CSF biomarkers, between T_1ρ and age, and between CSF biomarkers and age was observed. The combined use of T_1ρ and CSF biomarkers have promise to improve the early and specific diagnosis of AD. Furthermore, disease progression form MCI to AD might be easily tracked using these two parameters in combination.

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Increased T1rho was observed in MCI and AD compared to controls.

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Increased T-tau and P-tau and decreased Aβ1-42 were observed in MCI and AD.

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Combined biomarkers have promise to improve early and specific diagnosis of AD.

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MCI to AD progression might be tracked using these two biomarkers in combination.

The relationship between hippocampal volumes and nonverbal memory in patients with medial temporal lobe epilepsy

OBJECTIVE

To explore the involvement of medial temporal lobe structures such as the hippocampus, amygdala, and entorhinal cortex (EC) in memory consolidation by volumetric magnetic resonance imaging (MRI).

METHODS

Sixty-two consecutive patients with medial temporal lobe epilepsy (MMTLE) were assessed using the Clinical Memory Scale (CMS) and MRI to measure the volumes of the hippocampus, amygdala, and EC. Participants were grouped according to MRI findings into 3 groups: left MRI-positive (abnormal hippocampal formation on the left side; n=17), right MRI-positive (abnormal hippocampal formation on the left side; n=9), and MRI-negative (normal hippocampal formation; n=36). One-way analysis of variance (ANOVA) was used to assess group differences for all volumetric data (Z scores or asymmetry indexes (AI)), memory scale scores, and clinical parameters. Post hoc analyses were done with Fisher's least significant difference (LSD) tests. AI=100×(L-R)/(L+R). "L" and "R" refer to the left and right volumes of each structure, respectively.

RESULTS

The nonsense graphical recognition tests and the facial memory tests were significantly different between the three groups. Post hoc analyses showed that the right MRI-positive group performed significantly worse than the MRI-negative group on nonsense graphical recognition tests (P=0.008) and the left MRI-positive group had significantly lower scores than the MRI-negative group on facial memory tests (P=0.023).

CONCLUSIONS

Nonverbal memory was correlated with the status of the right hippocampus.

Structural imaging biomarkers of Alzheimer's disease: predicting disease progression

Optimized magnetic resonance imaging (MRI)-based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined prediction of the disease. In addition, they could serve as valuable tools when designing therapeutic studies of individuals at risk of AD. In this study, we combine (1) a novel method for grading medial temporal lobe structures with (2) robust cortical thickness measurements to predict AD among subjects with mild cognitive impairment (MCI) from a single T1-weighted MRI scan. Using AD and cognitively normal individuals, we generate a set of features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years. Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters. These features are the left and right hippocampi gradings and cortical thicknesses of the left precuneus, left superior temporal sulcus, and right anterior part of the parahippocampal gyrus. We show that these features are highly stable in cross-validation and enable a prediction accuracy of 72% using a simple linear discriminant classifier, the highest prediction accuracy obtained on the baseline Alzheimer's Disease Neuroimaging Initiative first phase cohort to date. The proposed structural features are consistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice.

Mild Cognitive Impairment: A Brief Review and Suggested Clinical Algorithm

Mild cognitive impairment (MCI) is a dynamic state between normal cognition and dementia, where interventions can be taken to stop or delay the progression to dementia. It is broadly of 2 types-amnestic, where memory loss is the chief concern and nonamnestic, where it is not. One variant of nonamnestic, dysexecutive, being more prevalent is sometimes known as a separate subtype by itself. Diagnosis of MCI is mostly clinical and is aided by various scales and neuropsychological testing. Functional imaging studies help in early detection and is superior to biomarkers or structural magnetic resonance imaging. Although there is no evidence supporting any pharmacological intervention, cognitive rehabilitation, memory training, and caregiver support play a strong role in limiting and sometimes reversing the ongoing cognitive decline. As the spectrum of MCI is heterogeneous, making the right diagnosis can be a challenging; hence, we need a systematic yet cost-effective algorithm for the timely management of MCI.

Decreased γ-aminobutyric acid levels in the parietal region of patients with Alzheimer's disease

PURPOSE

To determine whether there are in vivo differences of γ-aminobutyric acid (GABA) levels in frontal and parietal regions of Alzheimer's disease (AD) patients, compared with healthy controls using magnetic resonance spectroscopy ((1) H-MRS).

MATERIALS AND METHODS

Fifteen AD patients and fifteen age- and gender-matched healthy controls underwent (1) H-MRS of the frontal and parietal lobes using the "MEGA-Point Resolved Spectroscopy Sequence" (MEGA-PRESS) technique, and cognitive levels of subjects were evaluated using Mini-Mental State Examination (MMSE) tests. MRS data were processed using the Gannet program. Because the signal detected by MEGA-PRESS includes contributions from GABA, macromolecules and homocarnosine, it is labeled as "GABA+" rather than GABA. Differences of GABA+/Cr ratios between AD patients and controls were tested using covariance analysis, adjusting for gray matter fraction. The relationship between GABA+/Cr and MMSE scores was also analyzed.

RESULTS

Significant lower GABA+/Cr ratios were found in the parietal region of AD patients compared with controls (P = 0.041). In AD patients, no significant correlations between GABA+/Cr and MMSE scores were found in either the frontal (r = -0.164; P = 0.558) or parietal regions (r = 0.025; P = 0.929).

CONCLUSION

Decreased GABA+/Cr levels were present in the parietal region of patients with AD in vivo, suggesting that abnormalities of the GABAergic system may be present in the pathogenesis of AD.

Automated algorithm to measure changes in medial temporal lobe volume in Alzheimer disease

BACKGROUND

The change in volume of anatomic structures is as a sensitive indicator of Alzheimer disease (AD) progression. Although several methods are available to measure brain volumes, improvements in speed and automation are required. Our objective was to develop a fully automated, fast, and reliable approach to measure change in medial temporal lobe (MTL) volume, including primarily hippocampus.

METHODS

The MTL volume defined in an atlas image was propagated onto each baseline image and a level set algorithm was applied to refine the shape and smooth the boundary. The MTL of the baseline image was then mapped onto the corresponding follow-up image to measure volume change (ΔMTL). Baseline and 24 months 3D T1-weighted images from the Alzheimer Disease Neuroimaging Initiative (ADNI) were randomly selected for 50 normal elderly controls (NECs), 50 subjects with mild cognitive impairment (MCI) and 50 subjects with AD to test the algorithm. The method was compared to the FreeSurfer segmentation tools.

RESULTS

The average ΔMTL (mean±SEM) was 68±35mm(3) in NEC, 187±38mm(3) in MCI and 300±34mm(3) in the AD group and was significantly different (p<0.0001) between all three groups. The ΔMTL was correlated with cognitive decline.

COMPARISON WITH EXISTING METHOD(S)

Results for the FreeSurfer software were similar but did not detect significant differences between the MCI and AD groups.

CONCLUSION

This novel segmentation approach is fully automated and provides a robust marker of brain atrophy that shows different rates of atrophy over 2 years between NEC, MCI, and AD groups.