PET network abnormalities and cognitive decline in patients with mild cognitive impairment

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.

Altered functional connectivity of nucleus accumbens subregions associates with non-motor symptoms in Parkinson's disease

AIMS

This study aimed to identify the functional connectivity (FC) changes of nucleus accumbens (NAc) subregions and characterize the association of network changes and non-motor symptoms (NMS) in Parkinson's disease (PD).

METHODS

We enrolled 129 PD patients and 106 healthy controls from our center and the PPMI (Parkinson's Progression Markers Initiative) database. The FC of the bilateral core and shell of the NAc were measured and compared between the two groups. We further used partial least squares correlation to reveal the relationships between altered FC of NAc subregions and manifestations of NMS of PD.

RESULTS

The subregions of left core, left shell, and right core had reduced FC with extensive brain regions in PD patients compared with healthy controls. These three subregions were commonly associated with depression, anxiety, apathy, and cognitive impairment. Moreover, the left core and left shell were associated with excessive daytime sleepiness, whereas the right core was associated with olfactory impairment and rapid eye movement sleep behavior disorder.

CONCLUSION

This study for the first time identified the neural network changes of NAc subregions in PD and the associations between network changes and phenotypes of NMS. Our findings provide new insights into the pathogenesis of NMS in PD.

Autonomic function predicts cognitive decline in mild cognitive impairment: Evidence from power spectral analysis of heart rate variability in a longitudinal study

Background

Despite the emerging clinical relevance of heart rate variability (HRV) as a potential biomarker of cognitive decline and as a candidate target for intervention, there is a dearth of research on the prospective relationship between HRV and cognitive change. In particular, no study has addressed this issue in subjects with a diagnosis of cognitive status including cognitive impairment.

Objective

To investigate HRV as a predictor of cognitive decline in subjects with normal cognition (NC) or Mild Cognitive Impairment (MCI). Specifically, we tested the literature-based hypothesis that the HRV response to different physical challenges would predict decline in different cognitive domains.

Methods

This longitudinal study represents the approximately 3-year follow-up of a previous cross-sectional study enrolling 80 older outpatients (aged ≥ 65). At baseline, power spectral analysis of HRV was performed on five-minute electrocardiographic recordings at rest and during a sympathetic (active standing) and a parasympathetic (paced breathing) challenge. We focused on normalized HRV measures [normalized low frequency power (LFn) and the low frequency to high frequency power ratio (LF/HF)] and on their dynamic response from rest to challenge (Δ HRV). Extensive neuropsychological testing was used to diagnose cognitive status at baseline and to evaluate cognitive change over the follow-up via annualized changes in cognitive Z-scores. The association between Δ HRV and cognitive change was explored by means of linear regression, unadjusted and adjusted for potential confounders.

Results

In subjects diagnosed with MCI at baseline a greater response to a sympathetic challenge predicted a greater decline in episodic memory [adjusted model: Δ LFn, standardized regression coefficient (β) = −0.528, p = 0.019; Δ LF/HF, β = −0.643, p = 0.001] whereas a greater response to a parasympathetic challenge predicted a lesser decline in executive functioning (adjusted model: Δ LFn, β = −0.716, p < 0.001; Δ LF/HF, β = −0.935, p < 0.001).

Conclusion

Our findings provide novel insight into the link between HRV and cognition in MCI. They contribute to a better understanding of the heart-brain connection, but will require replication in larger cohorts.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of neurodegenerative diseases. Many mental disorders involve a sharp decline in cognitive ability as a major symptom, which can be caused by abnormal connectivity patterns among several brain regions. However, conventional functional connectivity networks are usually constructed based on pairwise correlations among different brain regions. This approach ignores higher-order relationships, and cannot effectively characterize the high-order interactions of many brain regions working together. Recent neuroscience research suggests that higher-order relationships between brain regions are important for brain network analysis. Hyper-networks have been proposed that can effectively represent the interactions among brain regions. However, this method extracts the local properties of brain regions as features, but ignores the global topology information, which affects the evaluation of network topology and reduces the performance of the classifier. This problem can be compensated by a subgraph feature-based method, but it is not sensitive to change in a single brain region. Considering that both of these feature extraction methods result in the loss of information, we propose a novel machine learning classification method that combines multiple features of a hyper-network based on functional magnetic resonance imaging in Alzheimer's disease. The method combines the brain region features and subgraph features, and then uses a multi-kernel SVM for classification. This retains not only the global topological information, but also the sensitivity to change in a single brain region. To certify the proposed method, 28 normal control subjects and 38 Alzheimer's disease patients were selected to participate in an experiment. The proposed method achieved satisfactory classification accuracy, with an average of 91.60%. The abnormal brain regions included the bilateral precuneus, right parahippocampal gyrus\hippocampus, right posterior cingulate gyrus, and other regions that are known to be important in Alzheimer's disease. Machine learning classification combining multiple features of a hyper-network of functional magnetic resonance imaging data in Alzheimer's disease obtains better classification performance.

Involvement of mirror neuron system in prodromal Alzheimer's disease

BACKGROUND

Mirror neurons have been localized in several locations, including the inferior parietal lobule (IPL). Increase of EEG alpha3/alpha2 frequency power ratio has been detected in mild cognitive impairment (MCI) subjects who will convert in Alzheimer's disease (AD). We investigated the association of alpha3/alpha2 frequency power ratio with cortical thickness in IPL in MCI subjects.

METHODS

74 adult subjects with MCI underwent EEG recording and high resolution MRI. Alpha3/alpha2 frequency power ratio as well as cortical thickness were computed for each subject. Three MCI groups were obtained according to increasing tertile values of alpha3/alpha2 ratio. Difference of cortical thickness among the groups was estimated.

RESULTS

Higher alpha3/alpha2 frequency power ratio group had wider cortical thinning than other groups, mapped on the IPL, supramarginal gyrus and precuneus bilaterally.

CONCLUSIONS

High EEG alpha3/alpha2 frequency power ratio was associated with atrophy of IPL areas in MCI subjects.

GENERAL SIGNIFICANCE

The scientific hypothesis is divided into the following main points: 1) the theoretical background considering two recent theories, an evolutionary perspective theory and the theory of mind (ToM), which both track a possible relationship between prodromal AD and mirror system; 2) the relationship has been focused on the prodromal stage of Alzheimer's disease, that is a peculiar and very debated phase of the disease itself; and 3) not a generical relationship, but a focused anatomo-functional association has been proposed.

Multimodal Discrimination of Alzheimer's Disease Based on Regional Cortical Atrophy and Hypometabolism

Structural MR image (MRI) and ¹⁸F-Fluorodeoxyglucose-positron emission tomography (FDG-PET) have been widely employed in diagnosis of both Alzheimer’s disease (AD) and mild cognitive impairment (MCI) pathology, which has led to the development of methods to distinguish AD and MCI from normal controls (NC). Synaptic dysfunction leads to a reduction in the rate of metabolism of glucose in the brain and is thought to represent AD progression. FDG-PET has the unique ability to estimate glucose metabolism, providing information on the distribution of hypometabolism. In addition, patients with AD exhibit significant neuronal loss in cerebral regions, and previous AD research has shown that structural MRI can be used to sensitively measure cortical atrophy. In this paper, we introduced a new method to discriminate AD from NC based on complementary information obtained by FDG and MRI. For accurate classification, surface-based features were employed and 12 predefined regions were selected from previous studies based on both MRI and FDG-PET. Partial least square linear discriminant analysis was employed for making diagnoses. We obtained 93.6% classification accuracy, 90.1% sensitivity, and 96.5% specificity in discriminating AD from NC. The classification scheme had an accuracy of 76.5% and sensitivity and specificity of 46.5% and 89.6%, respectively, for discriminating MCI from AD. Our method exhibited a superior classification performance compared with single modal approaches and yielded parallel accuracy to previous multimodal classification studies using MRI and FDG-PET.

Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology

The relationships between clinical phenotype, β-amyloid (Aβ) deposition and neurodegeneration in Alzheimer's disease (AD) are incompletely understood yet have important ramifications for future therapy. The goal of this study was to utilize multimodality positron emission tomography (PET) data from a clinically heterogeneous population of patients with probable AD in order to: (1) identify spatial patterns of Aβ deposition measured by ((11)C)-labeled Pittsburgh Compound B (PiB-PET) and glucose metabolism measured by FDG-PET that correlate with specific clinical presentation and (2) explore associations between spatial patterns of Aβ deposition and glucose metabolism across the AD population. We included all patients meeting the criteria for probable AD (NIA-AA) who had undergone MRI, PiB and FDG-PET at our center (N = 46, mean age 63.0 ± 7.7, Mini-Mental State Examination 22.0 ± 4.8). Patients were subclassified based on their cognitive profiles into an amnestic/dysexecutive group (AD-memory; n = 27), a language-predominant group (AD-language; n = 10) and a visuospatial-predominant group (AD-visuospatial; n = 9). All patients were required to have evidence of amyloid deposition on PiB-PET. To capture the spatial distribution of Aβ deposition and glucose metabolism, we employed parallel independent component analysis (pICA), a method that enables joint analyses of multimodal imaging data. The relationships between PET components and clinical group were examined using a Receiver Operator Characteristic approach, including age, gender, education and apolipoprotein E ε4 allele carrier status as covariates. Results of the first set of analyses independently examining the relationship between components from each modality and clinical group showed three significant components for FDG: a left inferior frontal and temporoparietal component associated with AD-language (area under the curve [AUC] 0.82, p = 0.011), and two components associated with AD-visuospatial (bilateral occipito-parieto-temporal [AUC 0.85, p = 0.009] and right posterior cingulate cortex [PCC]/precuneus and right lateral parietal [AUC 0.69, p = 0.045]). The AD-memory associated component included predominantly bilateral inferior frontal, cuneus and inferior temporal, and right inferior parietal hypometabolism but did not reach significance (AUC 0.65, p = 0.062). None of the PiB components correlated with clinical group. Joint analysis of PiB and FDG with pICA revealed a correlated component pair, in which increased frontal and decreased PCC/precuneus PiB correlated with decreased FDG in the frontal, occipital and temporal regions (partial r = 0.75, p < 0.0001). Using multivariate data analysis, this study reinforced the notion that clinical phenotype in AD is tightly linked to patterns of glucose hypometabolism but not amyloid deposition. These findings are strikingly similar to those of univariate paradigms and provide additional support in favor of specific involvement of the language network, higher-order visual network, and default mode network in clinical variants of AD. The inverse relationship between Aβ deposition and glucose metabolism in partially overlapping brain regions suggests that Aβ may exert both local and remote effects on brain metabolism. Applying multivariate approaches such as pICA to multimodal imaging data is a promising approach for unraveling the complex relationships between different elements of AD pathophysiology.

Subregions of the inferior parietal lobule are affected in the progression to Alzheimer's disease

Changes in several regions within the brain have been associated with progression from healthy aging to Alzheimer's disease (AD), including the hippocampus, entorhinal cortex, and the inferior parietal lobule (IPL). In this study, the IPL was divided into three subregions: the gyrus, the banks of the sulcus, and the fundus to determine if these regions are independent of medial temporal regions in the progression of AD. Participants of the Alzheimer's disease Neuroimaging Initiative (Alzheimer's disease Neuroimaging initiative (ADNI); n = 54) underwent a structural magnetic resonance imaging (MRI) scan and neuropsychological examination, and were categorized as normal controls, mild cognitively impaired (MCI), or AD. FreeSurfer was initially used to identify the boundaries of the IPL. Each subregion was then manually traced based on FreeSurfer curvature intensities. Multivariate analyses of variance were used to compare groups. Results suggest that changes in thickness of the banks of the inferior parietal lobule are occurring early in the progression from normal to MCI, followed by changes in the gyrus and fundus, and these measures are related to neuropsychological performance.

Structure-Function-Based Quantitative Brain Image Analysis

Many neurodegenerative dementias produce significant alterations in the brain that are often not detectable by neurologic tests or with structural imaging. PET is ideally suited for monitoring cell/molecular events early in the course of a disease as well as during pharmacologic therapy. During the past 2 decades, molecular neuroimaging using PET and magnetic resonance (MR) has advanced elegantly and steadily gained importance in the clinical and research arenas. Software- and hardware-based multimodality brain imaging allowing the correlation between anatomic and molecular information has revolutionized clinical diagnosis and now offers unique capabilities for the clinical neuroimaging community and neuroscience researchers at large.

Failing compensatory mechanisms during working memory in older apolipoprotein E-epsilon4 healthy adults

How and when the known genetic risk allele, apolipoprotein E-epsilon4 (APOEepsilon4), confers risk to Alzheimer's disease has yet to be determined. We studied older adults and found that APOEepsilon4 carriers had greater neural activation in the medial frontal and parahippocampal gyrus during a memory task (cluster-corrected p < .01). When compared to a group of younger adults, interactive effects of age and APOEepsilon4 were found in the inferior frontal-anterior temporal region, one of the first areas to develop amyloid plaques in patients with Alzheimer's disease, and, in the posterior cingulate, one of the earliest areas to show decreased cerebral metabolism in Alzheimer's disease. Thus, abnormally high activation in fronto-temporal areas are present in both younger and older APOEepsilon4 carriers confronted with a working memory task when compared to non-APOEepsilon4 carriers. This effect, however, appears to diminish with age.

Covariance 99mTc-exametazime SPECT patterns in Alzheimer's disease and dementia with Lewy bodies: utility in differential diagnosis

(99m)Tc-exametazime single photon emission computed tomography (SPECT) scans of 36 patients with Alzheimer's disease (AD) and 30 with dementia with Lewy bodies (DLB) underwent region of interest (ROI) and principal component analysis (PCA). Principal component analysis was performed on the entire ROI data set. Principal components (PCs) were obtained, representing common intercorrelated regions in AD and DLB. Topographic expression that signified the extent to which a participant expressed the topographic covariance pattern was derived and used as a discriminatory variable. Principal components were identified, accounting for 77% of total data variance. Significant (PC x group) interaction was observed (P < .001). Topographic expression was significantly higher in DLB than AD (F(1,64) = 21.6, P < .001), and differentiated DLB from AD with sensitivity 73% specificity 72%. Calculating the topographic expression in an independent data set of 48 patients with AD and 23 with DLB gave sensitivity = 70%, specificity = 67%. Principal component analysis captures additional sources of variance and if perfusion SPECT is the only scan available, this procedure may offer extra information.

Combining MR imaging, positron-emission tomography, and CSF biomarkers in the diagnosis and prognosis of Alzheimer disease

BACKGROUND AND PURPOSE

Different biomarkers for AD may potentially be complementary in diagnosis and prognosis of AD. Our aim was to combine MR imaging, FDG-PET, and CSF biomarkers in the diagnostic classification and 2-year prognosis of MCI and AD, by examining the following: 1) which measures are most sensitive to diagnostic status, 2) to what extent the methods provide unique information in diagnostic classification, and 3) which measures are most predictive of clinical decline.

MATERIALS AND METHODS

ADNI baseline MR imaging, FDG-PET, and CSF data from 42 controls, 73 patients with MCI, and 38 patients with AD; and 2-year clinical follow-up data for 36 controls, 51 patients with MCI, and 25 patients with AD were analyzed. The hippocampus and entorhinal, parahippocampal, retrosplenial, precuneus, inferior parietal, supramarginal, middle temporal, lateral, and medial orbitofrontal cortices were used as regions of interest. CSF variables included Abeta42, t-tau, p-tau, and ratios of t-tau/Abeta42 and p-tau/Abeta42. Regression analyses were performed to determine the sensitivity of measures to diagnostic status as well as 2-year change in CDR-SB, MMSE, and delayed logical memory in MCI.

RESULTS

Hippocampal volume, retrosplenial thickness, and t-tau/Abeta42 uniquely predicted diagnostic group. Change in CDR-SB was best predicted by retrosplenial thickness; MMSE, by retrosplenial metabolism and thickness; and delayed logical memory, by hippocampal volume.

CONCLUSIONS

All biomarkers were sensitive to the diagnostic group. Combining MR imaging morphometry and CSF biomarkers improved diagnostic classification (controls versus AD). MR imaging morphometry and PET were largely overlapping in value for discrimination. Baseline MR imaging and PET measures were more predictive of clinical change in MCI than were CSF measures.

Principal component analysis in mild and moderate Alzheimer's disease--a novel approach to clinical diagnosis

Principal component analysis (PCA) provides a method to explore functional brain connectivity. The aim of this study was to identify regional cerebral blood flow (rCBF) distribution differences between Alzheimer's disease (AD) patients and controls (CTR) by means of volume of interest (VOI) analysis and PCA. Thirty-seven CTR, 30 mild AD (mildAD) and 27 moderate AD (modAD) subjects were investigated using single photon emission computed tomography with (99m)Tc-hexamethylpropylene amine oxime. Analysis of covariance (ANCOVA), PCA, and discriminant analysis (DA) were performed on 54 VOIs. VOI analysis identified in both mildAD and modAD subjects a decreased rCBF in six regions. PCA in mildAD subjects identified four principal components (PCs) in which the correlated VOIs showed a decreased level of rCBF, including regions that are typically affected early in the disease. In five PCs, including parietal-temporal-limbic cortex, and hippocampus, a significantly lower rCBF in correlated VOIs was found in modAD subjects. DA significantly discriminated the groups. The percentage of subjects correctly classified was 95, 70, and 81 for CTR, mildAD and modAD groups, respectively. PCA highlighted, in mildAD and modAD, relationships not evident when brain regions are considered as independent of each other, and it was effective in discriminating groups. These findings may allow neurophysiological inferences to be drawn regarding brain functional connectivity in AD that might not be possible with univariate analysis.

Problem solving, working memory, and motor correlates of association and commissural fiber bundles in normal aging: a quantitative fiber tracking study

Normal aging is accompanied by decline in selective cognitive and motor functions. A concurrent decline in regional white matter integrity, detectable with diffusion tensor imaging (DTI), potentially contributes to waning function. DTI analysis of white matter loci indicates an anterior-to-posterior gradient distribution of declining fractional anisotropy (FA) and increasing diffusivity with age. Quantitative fiber tracking can be used to determine regional patterns of normal aging of fiber systems and test the functional ramifications of the DTI metrics. Here, we used quantitative fiber tracking to examine age effects on commissural (genu and splenium), bilateral association (cingulate, inferior longitudinal fasciculus and uncinate), and fornix fibers in 12 young and 12 elderly healthy men and women and tested functional correlates with concurrent assessment of a wide range of neuropsychological abilities. Principal component analysis of cognitive and motor tests on which the elderly achieved significantly lower scores than the young group was used for data reduction and yielded three factors: Problem Solving, Working Memory, and Motor. Age effects--lower FA or higher diffusivity--in the elderly were prominent in anterior tracts, specifically, genu, fornix, and uncinate fibers. Differential correlations between FA or diffusivity in fiber tracts and scores on Problem Solving, Working Memory, or Motor factors provide convergent validity to the biological meaningfulness of the integrity of the fibers tracked. The observed pattern of relations supports the possibility that regional degradation of white matter fiber integrity is a biological source of age-related functional compromise and may have the potential to limit accessibility to alternative neural systems to compensate for compromised function.

A forward application of age associated gray and white matter networks

To capture patterns of normal age-associated atrophy, we previously used a multivariate statistical approach applied to voxel based morphometry that identified age-associated gray and white matter covariance networks (Brickman et al. [2007]: Neurobiol Aging 28:284-295). The current study sought to examine the stability of these patterns by forward applying the identified networks to an independent sample of neurologically healthy younger and older adults. Forty-two younger and 35 older adults were imaged with standard high-resolution structural magnetic resonance imaging. Individual images were spatially normalized and segmented into gray and white matter. Covariance patterns that were previously identified with scaled subprofile model analyses were prospectively applied to the current sample to identify to what degree the age-associated patterns were manifested. Older individuals were also assessed with a modified version of the Mini Mental State Examination (mMMSE). Gray matter covariance pattern expression discriminated between younger and older participants with high optimal sensitivity (100%) and specificity (90.5%). While the two groups differed in the degree of white matter pattern expression (t (75) = 5.26, P < 0.001), classification based on white matter expression was relatively low (sensitivity = 80% and specificity = 61.9%). Among older adults, chronological age was significantly associated with increased gray matter pattern expression (r (32) = 0.591, P < 0.001) but not with performance on the mMMSE (r (31) = -0.314, P = 0.085). However, gray matter pattern expression was significantly associated with performance on the mMMSE (r (31) = -0.405, P = 0.024). The findings suggest that the previously derived age-associated covariance pattern for gray matter is reliable and may provide information that is more functionally meaningful than chronological age.

Language performance in Alzheimer's disease and mild cognitive impairment: a comparative review

Mild cognitive impairment (MCI) manifests as memory impairment in the absence of dementia and progresses to Alzheimer's disease (AD) at a rate of around 15% per annum, versus 1-2% in the general population. It thus constitutes a primary target for investigation of early markers of AD. Language deficits occur early in AD, and performance on verbal tasks is an important diagnostic criterion for both AD and MCI. We review language performance in MCI, compare these findings to those seen in AD, and identify the primary issues in understanding language performance in MCI and selecting tasks with diagnostic and prognostic value.

Appearance modeling of 11C PiB PET images: characterizing amyloid deposition in Alzheimer's disease, mild cognitive impairment and healthy aging

Beta-amyloid (Abeta) deposition is one of the neuropathological hallmarks of Alzheimer's disease (AD), Abeta burden can be quantified using (11)C PiB PET. Neuropathological studies have shown that the initial plaques are located in the temporal and orbitofrontal cortices, extending later to the cingulate, frontal and parietal cortices (Braak and Braak, 1997). Previous studies have shown an overlap in (11)C PiB PET retention between AD, mild cognitive impairment (MCI) patients and normal elderly control (NC) participants. It has also been shown that there is a relationship between Abeta deposition and memory impairment in MCI patients. In this paper we explored the variability seen in 15 AD, 15 MCI and 18 NC by modeling the voxel data from spatially and uptake normalized PiB images using principal component analysis. The first two principal components accounted for 80% of the variability seen in the data, providing a clear separation between AD and NC, and allowing subsequent classification. The MCI cases were distributed along an apparent axis between the AD and NC group, closely aligned with the first principal component axis. The NC cases that were PiB(+) formed a distinct cluster that was between, but separated from the AD and PiB(-) NC clusters. The PiB(+) MCI were found to cluster with the AD cases, and exhibited a similar deposition pattern. The primary principal component score was found to correlate with episodic memory scores and mini mental status examination and it was observed that by varying the first principal component, a change in amyloid deposition could be derived that is similar to the expected progression of amyloid deposition observed from post mortem studies.

Multivariate and univariate neuroimaging biomarkers of Alzheimer's disease

We performed univariate and multivariate discriminant analysis of FDG-PET scans to evaluate their ability to identify Alzheimer's disease (AD). FDG-PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic AD-markers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker's diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures.

Functional imaging of cognition in Alzheimer's disease using positron emission tomography

Positron emission tomography in Alzheimer's disease (AD) demonstrates a metabolic decrease, predominantly in associative posterior cortices (comprising the posterior cingulate cortex), and also involving medial temporal structures and frontal regions at a lesser degree. The level of activity in this wide network is roughly correlated with dementia severity, but several confounds (such as age, education or subcortical ischemic lesions) may influence the brain-behaviour relationship. Univariate analyses allow one to segregate brain regions that are particularly closely related to specific neuropsychological performances. For example, a relationship was established between the activity in lateral associative cortices and semantic performance in AD. The role of semantic capacities (subserved by temporal or parietal regions) in episodic memory tasks was also emphasized. The residual activity in medial temporal structures was related to episodic memory abilities, as measured by free recall performance, cued recall ability and recognition accuracy. More generally, AD patients' performance on episodic memory tasks was correlated with the metabolism in several structures of Papez's circuit (including the medial temporal and posterior cingulate regions). Multivariate analyses should provide complementary information on impaired metabolic covariance in functional networks of brain regions and the consequences for AD patients' cognitive performance. More longitudinal studies are being conducted that should tell us more about the prognostic value of initial metabolic impairment and the neural correlates of progressive deterioration of cognitive performance in AD.

Neural network approaches and their reproducibility in the study of verbal working memory and Alzheimer's disease

As clinical and cognitive neurosciences mature, the need for sophisticated neuroimaging analysis becomes more apparent. Multivariate analysis techniques have recently received increasing attention because they have attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques. Multivariate approaches evaluate correlation/covariance of activation across brain regions, rather than proceeding on a voxel-by-voxel basis. Thus, their results can be more easily interpreted as a signature of neural networks. Univariate approaches, in contrast, cannot directly address functional connectivity in the brain. Apart from this conceptual difference, the covariance approach can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent, and often overly conservative, corrections for voxel-wise multiple comparisons. Multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. We provide two examples that illustrate different uses of multivariate techniques in cognitive and clinical neuroscience. We hope this contribution helps facilitate wider dissemination of these techniques in the research community.

Cognitive profiles of aging and aging-related conditions

This review presents some of the current research and thinking regarding the neuropsychological features associated with aging and aging-related conditions. In the context of the current longevity revolution, many older adults are increasingly concerned about their cognitive performance and the risk for cognitive decline. This makes it critically important to understand the neuropsychological profiles of normal aging and the cognitive features of conditions associated with aging, such as age-associated memory impairment, mild cognitive impairment and dementia. There are also several factors that can modify the neuropsychological abilities and outcomes associated with aging, including gender, genetic status, lifestyle issues and education. The authors point to the importance for future research to embrace a fluid or multifactorial approach to neuropsychology, to focus on those factors contributing to healthy cognition and successful aging, and to correlate neuropsychological changes with the results of neuroimaging.