Covariance PET patterns in early Alzheimer's disease and subjects with cognitive impairment but no dementia: utility in group discrimination and correlations with functional performance

Dementia risk and thalamic nuclei volumetry in healthy midlife adults: the PREVENT Dementia study

A reduction in the volume of the thalamus and its nuclei has been reported in Alzheimer's disease, mild cognitive impairment and asymptomatic individuals with risk factors for early-onset Alzheimer's disease. Some studies have reported thalamic atrophy to occur prior to hippocampal atrophy, suggesting thalamic pathology may be an early sign of cognitive decline. We aimed to investigate volumetric differences in thalamic nuclei in middle-aged, cognitively unimpaired people with respect to dementia family history and apolipoprotein ε4 allele carriership and the relationship with cognition. Seven hundred participants aged 40-59 years were recruited into the PREVENT Dementia study. Individuals were stratified according to dementia risk (approximately half with and without parental dementia history). The subnuclei of the thalamus of 645 participants were segmented on T1-weighted 3 T MRI scans using FreeSurfer 7.1.0. Thalamic nuclei were grouped into six regions: (i) anterior, (ii) lateral, (iii) ventral, (iv) intralaminar, (v) medial and (vi) posterior. Cognitive performance was evaluated using the computerized assessment of the information-processing battery. Robust linear regression was used to analyse differences in thalamic nuclei volumes and their association with cognitive performance, with age, sex, total intracranial volume and years of education as covariates and false discovery rate correction for multiple comparisons. We did not find significant volumetric differences in the thalamus or its subregions, which survived false discovery rate correction, with respect to first-degree family history of dementia or apolipoprotein ε4 allele status. Greater age was associated with smaller volumes of thalamic subregions, except for the medial thalamus, but only in those without a dementia family history. A larger volume of the mediodorsal medial nucleus (Pfalse discovery rate = 0.019) was associated with a faster processing speed in those without a dementia family history. Larger volumes of the thalamus (P = 0.016) and posterior thalamus (Pfalse discovery rate = 0.022) were associated with significantly worse performance in the immediate recall test in apolipoprotein ε4 allele carriers. We did not find significant volumetric differences in thalamic subregions in relation to dementia risk but did identify an interaction between dementia family history and age. Larger medial thalamic nuclei may exert a protective effect on cognitive performance in individuals without a dementia family history but have little effect on those with a dementia family history. Larger volumes of posterior thalamic nuclei were associated with worse recall in apolipoprotein ε4 carriers. Our results could represent initial dysregulation in the disease process; further study is needed with functional imaging and longitudinal analysis.

Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern

BACKGROUND

Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified.

METHODS

240 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions.

RESULTS

ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images.

CONCLUSIONS

While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.

The identification and cognitive correlation of perfusion patterns measured with arterial spin labeling MRI in Alzheimer's disease

BACKGROUND

Vascular dysfunction, including cerebral hypoperfusion, plays an important role in the pathogenesis and progression of Alzheimer's disease (AD), independent of amyloid and tau pathology. We established an AD-related perfusion pattern (ADRP) measured with arterial spin labeling (ASL) MRI using multivariate spatial covariance analysis.

METHODS

We obtained multimodal MRI including pseudo-continuous ASL and neurocognitive testing in a total of 55 patients with a diagnosis of mild to moderate AD supported by amyloid PET and 46 normal controls (NCs). An ADRP was established from an identification cohort of 32 patients with AD and 32 NCs using a multivariate analysis method based on scaled subprofile model/principal component analysis, and pattern expression in individual subjects was quantified for both the identification cohort and a validation cohort (23 patients with AD and 14 NCs). Subject expression score of the ADRP was then used to assess diagnostic accuracy and cognitive correlations in AD patients and compared with global and regional cerebral blood flow (CBF) in specific areas identified from voxel-based univariate analysis.

RESULTS

The ADRP featured negative loading in the bilateral middle and posterior cingulate and precuneus, inferior parietal lobule, and frontal areas, and positive loading in the right cerebellum and bilateral basal areas. Subject expression score of the ADRP was significantly elevated in AD patients compared with NCs (P < 0.001) and showed good diagnostic accuracy for AD with area under receiver-operator curve of 0.87 [95% CI (0.78-0.96)] in the identification cohort and 0.85 in the validation cohort. Moreover, there were negative correlations between subject expression score and global cognitive function and performance in various cognitive domains in patients with AD. The characteristics of the ADRP topography and subject expression scores were supported by analogous findings obtained with regional CBF.

CONCLUSIONS

We have reported a characteristic perfusion pattern associated with AD using ASL MRI. Subject expression score of this spatial covariance pattern is a promising MRI biomarker for the identification and monitoring of AD.

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.

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.

Spatial covariance analysis of FDG-PET and HMPAO-SPECT for the differential diagnosis of dementia with Lewy bodies and Alzheimer's disease

We investigated diagnostic characteristics of spatial covariance analysis (SCA) of FDG-PET and HMPAO-SPECT scans in the differential diagnosis of dementia with Lewy bodies (DLB) and Alzheimer's disease (AD), in comparison with visual ratings and region of interest (ROI) analysis. Sixty-seven patients (DLB 29, AD 38) had both HMPAO-SPECT and FDG-PET scans. Spatial covariance patterns were used to separate AD and DLB in an initial derivation group (DLB n=15, AD n=19), before being forward applied to an independent group (DLB n=14, AD n=19). Visual ratings were by consensus, with ROI analysis utilising medial occipital/medial temporal uptake ratios. SCA of HMPAO-SPECT performed poorly (AUC 0.59±0.10), whilst SCA of FDG-PET (AUC 0.83±0.07) was significantly better. For FDG-PET, SCA showed similar diagnostic performance to ROI analysis (AUC 0.84±0.08) and visual rating (AUC 0.82±0.08). In contrast to ROI analysis, there was little concordance between SCA and visual ratings of FDG-PET scans. We conclude that SCA of FDG-PET outperforms that of HMPAO-SPECT. SCA of FDG-PET also performed similarly to the other analytical approaches, despite the limitations of a relatively small SCA derivation group. Compared to visual rating, SCA of FDG-PET relies on different sources of group variance to separate DLB from AD.

Spatial normalization and quantification approaches of PET imaging for neurological disorders

Quantification approaches of positron emission tomography (PET) imaging provide user-independent evaluation of pathophysiological processes in living brains, which have been strongly recommended in clinical diagnosis of neurological disorders. Most PET quantification approaches depend on spatial normalization of PET images to brain template; however, the spatial normalization and quantification approaches have not been comprehensively reviewed. In this review, we introduced and compared PET template-based and magnetic resonance imaging (MRI)-aided spatial normalization approaches. Tracer-specific and age-specific PET brain templates were surveyed between 1999 and 2021 for ¹⁸F-FDG, ¹¹C-PIB, ¹⁸F-Florbetapir, ¹⁸F-THK5317, and etc., as well as adaptive PET template methods. Spatial normalization-based PET quantification approaches were reviewed, including region-of-interest (ROI)-based and voxel-wise quantitative methods. Spatial normalization-based ROI segmentation approaches were introduced, including manual delineation on template, atlas-based segmentation, and multi-atlas approach. Voxel-wise quantification approaches were reviewed, including voxel-wise statistics and principal component analysis. Certain concerns and representative examples of clinical applications were provided for both ROI-based and voxel-wise quantification approaches. At last, a recipe for PET spatial normalization and quantification approaches was concluded to improve diagnosis accuracy of neurological disorders in clinical practice.

Brain Reserve in a Case of Cognitive Resilience to Severe Leukoaraiosis

OBJECTIVE

Leukoaraiosis, or white matter rarefaction, is a common imaging finding in aging and is presumed to reflect vascular disease. When severe in presentation, potential congenital or acquired etiologies are investigated, prompting referral for neuropsychological evaluation in addition to neuroimaging. T2-weighted imaging is the most common magnetic resonance imaging (MRI) approach to identifying white matter disease. However, more advanced diffusion MRI techniques may provide additional insight into mechanisms that influence the abnormal T2 signal, especially when clinical presentations are discrepant with imaging findings.

METHOD

We present a case of a 74-year-old woman with severe leukoaraoisis. She was examined by a neurologist, neuropsychologist, and rheumatologist, and completed conventional (T1, T2-FLAIR) MRI, diffusion tensor imaging (DTI), and advanced single-shell, high b-value diffusion MRI (i.e., fiber ball imaging [FBI]).

RESULTS

The patient was found to have few neurological signs, no significant cognitive impairment, a negative workup for leukoencephalopathy, and a positive antibody for Sjogren's disease for which her degree of leukoaraiosis would be highly atypical. Tractography results indicate intact axonal architecture that was better resolved using FBI rather than DTI.

CONCLUSIONS

This case illustrates exceptional cognitive resilience in the face of severe leukoaraiosis and the potential for advanced diffusion MRI to identify brain reserve.

Cognitive reserve-mediated neural modulation of emotional control and regulation in people with late-life depression

Late-life depression (LLD) is an affective disorder that is highly prevalent among older people. Cognitive reserve (CR) refers to an active process that facilitates the flexibility and efficiency of the neural networks to compensate for impairments that emerge in consequence of brain pathology. The current functional magnetic resonance imaging study investigated whether and how CR affects emotional regulation, level of depression severity and neural activity associated with affective control during emotional Stroop (eStroop) task. Altogether, 90 older people participated in this study, 50 of whom suffered from LLD. We used years of education and verbal fluency capacity as proxies for CR. Clinical participants with relatively higher CR presented with milder degrees of depression, better eStroop performance and stronger neural activity in the middle frontal gyrus (MFG) involved with exercising affective control. Results of the mediation analysis indicated that both education and verbal fluency significantly mediated the association between the depression severity and MEG activity. These results suggest a negative association between CR and age-related clinical symptoms of emotional dysregulation. Our neurobehavioral findings provide supportive evidence that CR implies efficiency of top-down emotional regulation and operates as a protective factor against emotional and cognitive vulnerability in the aging brain.

Drawing pictures at encoding enhances memory in healthy older adults and in individuals with probable dementia

We explored the efficacy of drawing pictures as an encoding strategy to enhance memory performance in healthy older adults and individuals with probable dementia. In an incidental encoding phase, participants were asked to either draw a picture or write out each word from a set of 30 common nouns for 40 seconds each. Episodic memory for the target words was compared in a group of healthy older adults to individuals with probable dementia (MMSE/MOCA range 4 to 25). In two experiments we showed that recall and recognition performance was higher for words that were drawn than written out during encoding, for both participant groups. We suggest that incorporating visuo-perceptual information into memory enhanced performance by increasing reliance on visual-sensory brain regions, which are relatively intact in these populations. Our findings demonstrate that drawing is a valuable technique leading to measurable gains in memory performance for individuals with probable dementia.

Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [18F]FDG-PET (PETMETPAT)

Introduction

The implementation of spatial-covariance [¹⁸F]fluorodeoxyglucose positron emission tomography–based disease-related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND-PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease–related pattern (PDRP) expression scores.

Methods

Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined.

Results

Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences.

Discussion

Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.

Covariance statistics and network analysis of brain PET imaging studies

The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [18F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer's disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.

Cerebral Perfusion Insufficiency and Relationships with Cognitive Deficits in Alzheimer's Disease: A Multiparametric Neuroimaging Study

Micro- or macro-circulatory insufficiency has a negative impact in patients with Alzheimer’s disease (AD). This study used arterial spin-labeled magnetic resonance imaging (ASL-MRI) and ethylcysteinate dimer single-photon emission computed tomography (ECD-SPECT) in 50 patients with AD and 30 age-matched controls to investigate how hypoperfusion patterns were associated with gray matter atrophy and clinical data. All participants completed 3DT1-MRI, ECD-SPECT and ASL-MRI examinations. Medial temporal cortex (MTC) volumes were correlated with regional signals showing significantly lower relative cerebral blood flow (rCBF) in ASL-MRI or perfusion index (PI) in ECD-SPECT. Neurobehavioral scores served as the outcome measures. Regions with lower PI showed spatial similarities with atrophy in the medial, anterior and superior temporal lobes, posterior cingulate cortex and angular gyrus, while regions showing lower rCBF were localized to the distal branches of posterior cerebral artery territories (posterior parietal and inferior temporal lobe) and watershed areas (angular gyrus, precuneus, posterior cingulate gyrus and middle frontal cortex). rCBF values in watershed areas correlated with MTC volumes and language composite scores. Precuneus and angular gyrus hypoperfusion were associated with the corresponding cortical atrophy. Macro- or micro-vasculature perfusion integrities and cortical atrophy determined the overall perfusion imaging topography and contributed differently to the clinical outcomes.

Spinal Alterations of Reil Insula in Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disease that involves numerous cellular and biochemical mechanisms resulting in synaptic alterations and extensive neuronal loss. It is primarily characterized by impairment of memory, associated frequently with mood disorders. Continuous studies have shown that insula may be an important target of AD, but neuropathological alterations have not been described extensively. In the present study, we attempted to describe the morphometric and morphological changes of the spines of Reil insula in AD in comparison with normal aging using a silver impregnation technique. We classified spines into 3 types: (1) long neck, (2) short stubby, and (3) other types; and we measured and correlated the length of them in normal controls and in individuals with AD using ImageJ application. Statistical analysis was based on the Student t test on the basis of 360 cells in SPSS v.17.0, and significance was taken as P < .05.

Cerebral metabolic changes related to clinical parameters in idiopathic anosmic patients during olfactory stimulation: a pilot investigation

Idiopathic olfactory loss neural consequences have been studied especially by means of magnetic resonance imaging. Since other functional neuroimaging technique findings are lacking in the literature, present study used a validated ¹⁸F-2-fluoro-2-deoxy-D-glucose (FDG) functional positron emission tomography procedure under olfactory stimulation (OS) to assess brain changes in idiopathic anosmic patients (IAPs) in comparison with healthy subjects (HS). A voxel-based analysis between these groups was used to evaluate FDG uptake in the brain and perform a correlation analysis between metabolic responses and the Sniffin' stick test as well as intensity visuo-analogue scores and disease duration (DD). A significant relative decrease of glucose metabolism in the right and left frontal lobes, left insula, right parietal lobe, and left occipital, temporal and parietal lobes was found in IAPs during OS. The same condition resulted in a relative higher glucose metabolism in the right cerebellum in IAPs. Moreover, a negative correlation between DD and FDG uptake in the left temporo-parietal joint was found in IAPs. Such a correlation suggested a possible involvement of this area metabolic decrease in self-consciousness impairment, which is known to affect IAPs. Present preliminary functional results could be of interest to further deepen such neural impairments possibly useful for future perspective in pharmaceutical and rehabilitative protocols.

Regional Cerebral Blood Flow in Mild Cognitive Impairment and Alzheimer's Disease Measured with Arterial Spin Labeling Magnetic Resonance Imaging

Alzheimer's disease (AD) depicts dynamic changes in regional brain function from early stages of the disease. Arterial spin labeling- (ASL-) based MRI methods have been applied for detecting regional cerebral blood flow (rCBF) perfusion changes in patients with AD and mild cognitive impairment (MCI). Nevertheless, the results obtained from ASL studies in AD and MCI are still controversial, since rCBF maps may show both hypoperfusion or hyperperfusion areas in brain structures involved in different cognitive functions. The goal of this review is to provide the current state of the art regarding the role of ASL for detecting distinctive perfusion patterns in subjects with MCI and/or AD. The ability to obtain this information using a noninvasive and widely available modality such as ASL should greatly enhance the knowledge into the broad range of hemodynamically related changes taking place during the cognitive decline process in AD.

Distinct brain networks underlie cognitive dysfunction in Parkinson and Alzheimer diseases

OBJECTIVE

To determine whether cognitive impairment in Parkinson disease (PD) and Alzheimer disease (AD) derives from the same network pathology.

METHODS

We analyzed 18F-fluorodeoxyglucose PET scans from 40 patients with AD and 40 age-matched healthy controls from the Alzheimer's Disease Neuroimaging Initiative and scanned an additional 10 patients with AD and 10 healthy controls at The Feinstein Institute for Medical Research to derive an AD-related metabolic pattern (ADRP) analogous to our previously established PD cognition-related pattern (PDCP) and PD motor-related pattern (PDRP). We computed individual subject expression values for ADRP and PDCP in 89 patients with PD and correlated summary scores for cognitive functioning with network expression. We also evaluated changes in ADRP and PDCP expression in a separate group of 15 patients with PD scanned serially over a 4-year period.

RESULTS

Analysis revealed a significant AD-related metabolic topography characterized by covarying metabolic reductions in the hippocampus, parahippocampal gyrus, and parietal and temporal association regions. Expression of ADRP, but not PDCP, was elevated in both AD groups and correlated with worse cognitive summary scores. Patients with PD showed slight ADRP expression, due to topographic overlap with the network underlying PD motor-related pattern degeneration, but only their PDCP expression values increased as cognitive function and executive performance declined. Longitudinal data in PD disclosed an analogous dissociation of network expression.

CONCLUSIONS

Cognitive dysfunction in PD is associated with a specific brain network that is largely spatially and functionally distinct from that seen in relation to AD.

A spatial covariance 123I-5IA-85380 SPECT study of α4β2 nicotinic receptors in Alzheimer's disease

Alzheimer's disease (AD) is characterized by widespread degeneration of cholinergic neurons, particularly in the basal forebrain. However, the pattern of these deficits and relationship with known brain networks is unknown. In this study, we sought to clarify this and used ¹²³I-5-iodo-3-[2(S)-2-azetidinylmethoxy] pyridine (¹²³5IA-85380) single photon emission computed tomography to investigate spatial covariance of α4β2 nicotinic acetylcholine receptors in AD and healthy controls. Thirteen AD and 16 controls underwent ¹²³5IA-85380 and regional cerebral blood flow (^99mTc-exametazime) single photon emission computed tomography scanning. We applied voxel principal component (PC) analysis, generating series of principal component images representing common intercorrelated voxels across subjects. Linear regression generated specific α4β2 and regional cerebral blood flow covariance patterns that differentiated AD from controls. The α4β2 pattern showed relative decreased uptake in numerous brain regions implicating several networks including default mode, salience, and Papez hubs. Thus, as well as basal forebrain and brainstem cholinergic system dysfunction, cholinergic deficits mediated through nicotinic acetylcholine receptors could be evident within key networks in AD. These findings may be important for the pathophysiology of AD and its associated cognitive and behavioral phenotypes.

Regional covariance of muscarinic acetylcholine receptors in Alzheimer's disease using (R, R) [(123)I]-QNB SPECT

Alzheimer's disease (AD) is characterised by deficits in cholinergic neurotransmission and subsequent receptor changes. We investigated (123)I-iodo-quinuclidinyl-benzilate (QNB) SPECT images using spatial covariance analysis (SCA), to detect an M1/M4 receptor spatial covariance pattern (SCP) that distinguished AD from controls. Furthermore, a corresponding regional cerebral blood flow (rCBF) SCP was also derived. Thirty-nine subjects (15 AD and 24 healthy elderly controls) underwent (123)I-QNB and (99m)Tc-exametazime SPECT. Voxel SCA was simultaneously applied to the set of smoothed/registered scans, generating a series of eigenimages representing common intercorrelated voxels across subjects. Linear regression identified individual M1/M4 and rCBF SCPs that discriminated AD from controls. The M1/M4 SCP showed concomitant decreased uptake in medial temporal, inferior frontal, basal forebrain and cingulate relative to concomitant increased uptake in frontal poles, occipital, pre-post central and precuneus/superior parietal regions (F1,37 = 85.7, p < 0.001). A largely different perfusion SCP was obtained showing concomitant decreased rCBF in medial and superior temporal, precuneus, inferior parietal and cingulate relative to concomitant increased rCBF in cerebellum, pre-post central, putamen, fusiform and brain stem/midbrain regions (F1,37 = 77.5, p < 0.001). The M1/M4 SCP expression correlated with the duration of cognitive symptoms (r = 0.90, p < 0.001), whereas the rCBF SCP expression negatively correlated with MMSE, CAMCOG and CAMCOGmemory (r ≥ |0.63|, p ≤ 0.006). (123)I-QNB SPECT revealed an M1/M4 basocortical covariance pattern, distinct from rCBF, reflecting the duration of disease rather than current clinical symptoms. This approach could be more sensitive than univariate methods in characterising the cholinergic/rCBF changes that underpin the clinical phenotype of AD.

Hierarchical multivariate covariance analysis of metabolic connectivity

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

Diagnostic classification of arterial spin labeling and structural MRI in presenile early stage dementia

Because hypoperfusion of brain tissue precedes atrophy in dementia, the detection of dementia may be advanced by the use of perfusion information. Such information can be obtained noninvasively with arterial spin labeling (ASL), a relatively new MR technique quantifying cerebral blood flow (CBF). Using ASL and structural MRI, we evaluated diagnostic classification in 32 prospectively included presenile early stage dementia patients and 32 healthy controls. Patients were suspected of Alzheimer's disease (AD) or frontotemporal dementia. Classification was based on CBF as perfusion marker, gray matter (GM) volume as atrophy marker, and their combination. These markers were each examined using six feature extraction methods: a voxel-wise method and a region of interest (ROI)-wise approach using five ROI-sets in the GM. These ROI-sets ranged in number from 72 brain regions to a single ROI for the entire supratentorial brain. Classification was performed with a linear support vector machine classifier. For validation of the classification method on the basis of GM features, a reference dataset from the AD Neuroimaging Initiative database was used consisting of AD patients and healthy controls. In our early stage dementia population, the voxelwise feature-extraction approach achieved more accurate results (area under the curve (AUC) range = 86 - 91%) than all other approaches (AUC = 57 - 84%). Used in isolation, CBF quantified with ASL was a good diagnostic marker for dementia. However, our findings indicated only little added diagnostic value when combining ASL with the structural MRI data (AUC = 91%), which did not significantly improve over accuracy of structural MRI atrophy marker by itself.

Relating education, brain structure, and cognition: the role of cardiovascular disease risk factors

The protective effect of education on cognitive and brain health is well established. While the direct effects of individual cardiovascular disease (CVD) risk factors (i.e., hypertension, smoking, diabetes, and obesity) on cerebral structure have been investigated, little is understood about the possible interaction between the protective effect of education and the deleterious effects of CVD risk factors in predicting brain ageing and cognition. Using data from the PATH Through Life study (N = 266), we investigated the protective effect of education on cerebral structure and function and tested a possible mediating role of CVD risk factors. Higher education was associated with larger regional grey/white matter volumes in the prefrontal cortex in men only. The association between education and cognition was mediated by brain volumes but only for grey matter and only in relation to information processing speed. CVD risk factors did not mediate the association between regional volumes and cognition. This study provides additional evidence in support for a protective effect of education on cerebral structures and cognition. However, it does not provide support for a mediating role of CVD risk factors in these associations.

Characterization of disease-related covariance topographies with SSMPCA toolbox: effects of spatial normalization and PET scanners

To generate imaging biomarkers from disease-specific brain networks, we have implemented a general toolbox to rapidly perform scaled subprofile modeling (SSM) based on principal component analysis (PCA) on brain images of patients and normals. This SSMPCA toolbox can define spatial covariance patterns whose expression in individual subjects can discriminate patients from controls or predict behavioral measures. The technique may depend on differences in spatial normalization algorithms and brain imaging systems. We have evaluated the reproducibility of characteristic metabolic patterns generated by SSMPCA in patients with Parkinson's disease (PD). We used [(18) F]fluorodeoxyglucose PET scans from patients with PD and normal controls. Motor-related (PDRP) and cognition-related (PDCP) metabolic patterns were derived from images spatially normalized using four versions of SPM software (spm99, spm2, spm5, and spm8). Differences between these patterns and subject scores were compared across multiple independent groups of patients and control subjects. These patterns and subject scores were highly reproducible with different normalization programs in terms of disease discrimination and cognitive correlation. Subject scores were also comparable in patients with PD imaged across multiple PET scanners. Our findings confirm a very high degree of consistency among brain networks and their clinical correlates in PD using images normalized in four different SPM platforms. SSMPCA toolbox can be used reliably for generating disease-specific imaging biomarkers despite the continued evolution of image preprocessing software in the neuroimaging community. Network expressions can be quantified in individual patients independent of different physical characteristics of PET cameras.

Multivariate spatial covariance analysis of 99mTc-exametazime SPECT images in dementia with Lewy bodies and Alzheimer's disease: utility in differential diagnosis

We examined (99m)Tc-exametazime brain blood flow single-photon emission computed tomography (SPECT) images using a spatial covariance analysis (SCA) approach to assess its diagnostic value in distinguishing dementia with Lewy bodies (DLB) from Alzheimer's disease (AD). Voxel SCA was simultaneously applied to a set of preprocessed images (AD, n=40; DLB, n=26), generating a series of eigenimages representing common intercorrelated voxels in AD and DLB. Linear regression derived a spatial covariance pattern (SCP) that discriminated DLB from AD. To investigate the diagnostic value of the model SCP, the SCP was validated by applying it to a second, independent, AD and DLB cohort (AD, n=34; DLB, n=29). Mean SCP expressions differed between AD and DLB (F(1,64)=36.2, P<0.001) with good diagnostic accuracy (receiver operating characteristic (ROC) curve area 0.87, sensitivity 81%, specificity 88%). Forward application of the model SCP to the independent cohort revealed similar differences between groups (F(1,61)=38.4, P<0.001), also with good diagnostic accuracy (ROC 0.86, sensitivity 80%, specificity 80%). Multivariate analysis of blood flow SPECT data appears to be robust and shows good diagnostic accuracy in two independent cohorts for distinguishing DLB from AD.

Aspects of Attention Predict Real-World Task Performance in Alzheimer's Disease

More research is needed to examine the relationship between specific neuropsychological functions and observation-based daily activity tests in patients with Alzheimer's disease (AD). Fifty-six patients with AD were administered tests of attention and processing speed and an observation-based activities-of-daily-living (ADL) task. Complex short-term attention capacity best predicted real-world task performance, accounting for several domains of ADL functioning. These results suggest that complex attention requiring working-memory systems, but not simple attention or processing speed, account for moderate portions of variability in daily task performance. These results may aid in understanding the attentional processes required for performing daily activities and can be useful to health care professionals in treatment planning.

Bilateral symmetry aspects in computer-aided Alzheimer's disease diagnosis by single-photon emission-computed tomography imaging

OBJECTIVE

This paper explores the importance of the latent symmetry of the brain in computer-aided systems for diagnosing Alzheimer's disease (AD). Symmetry and asymmetry are studied from two points of view: (i) the development of an effective classifier within the scope of machine learning techniques, and (ii) the assessment of its relevance to the AD diagnosis in the early stages of the disease.

METHODS

The proposed methodology is based on eigenimage decomposition of single-photon emission-computed tomography images, using an eigenspace extension to accommodate odd and even eigenvectors separately. This feature extraction technique allows for support-vector-machine classification and image analysis.

RESULTS

Identification of AD patterns is improved when the latent symmetry of the brain is considered, with an estimated 92.78% accuracy (92.86% sensitivity, 92.68% specificity) using a linear kernel and a leave-one-out cross validation strategy. Also, asymmetries may be used to define a test for AD that is very specific (90.24% specificity) but not especially sensitive.

CONCLUSIONS

Two main conclusions are derived from the analysis of the eigenimage spectrum. Firstly, the recognition of AD patterns is improved when considering only the symmetric part of the spectrum. Secondly, asymmetries in the hypo-metabolic patterns, when present, are more pronounced in subjects with AD.

Partial least squares for discrimination in fMRI data

Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using principal component analysis (PCA), partial least squares (PLS) or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contain more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using functional magnetic resonance imaging as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk.

New computer-aided diagnosis of dementia using positron emission tomography: brain regional sensitivity-mapping method

PURPOSE

We devised a new computer-aided diagnosis method to segregate dementia using one estimated index (Total Z score) derived from the Brodmann area (BA) sensitivity map on the stereotaxic brain atlas. The purpose of this study is to investigate its accuracy to differentiate patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) from normal adults (NL).

METHODS

We studied 101 adults (NL: 40, AD: 37, MCI: 24) who underwent (18)FDG positron emission tomography (PET) measurement. We divided NL and AD groups into two categories: a training group with (Category A) and a test group without (Category B) clinical information. In Category A, we estimated sensitivity by comparing the standard uptake value per BA (SUVR) between NL and AD groups. Then, we calculated a summated index (Total Z score) by utilizing the sensitivity-distribution maps and each BA z-score to segregate AD patterns. To confirm the validity of this method, we examined the accuracy in Category B. Finally, we applied this method to MCI patients.

RESULTS

In Category A, we found that the sensitivity and specificity of differentiation between NL and AD were all 100%. In Category B, those were 100% and 95%, respectively. Furthermore, we found this method attained 88% to differentiate AD-converters from non-converters in MCI group.

CONCLUSIONS

The present automated computer-aided evaluation method based on a single estimated index provided good accuracy for differential diagnosis of AD and MCI. This good differentiation power suggests its usefulness not only for dementia diagnosis but also in a longitudinal study.

Arterial spin labelling reveals an abnormal cerebral perfusion pattern in Parkinson's disease

There is a need for objective imaging markers of Parkinson's disease status and progression. Positron emission tomography and single photon emission computed tomography studies have suggested patterns of abnormal cerebral perfusion in Parkinson's disease as potential functional biomarkers. This study aimed to identify an arterial spin labelling magnetic resonance-derived perfusion network as an accessible, non-invasive alternative. We used pseudo-continuous arterial spin labelling to measure cerebral grey matter perfusion in 61 subjects with Parkinson's disease with a range of motor and cognitive impairment, including patients with dementia and 29 age- and sex-matched controls. Principal component analysis was used to derive a Parkinson's disease-related perfusion network via logistic regression. Region of interest analysis of absolute perfusion values revealed that the Parkinson's disease pattern was characterized by decreased perfusion in posterior parieto-occipital cortex, precuneus and cuneus, and middle frontal gyri compared with healthy controls. Perfusion was preserved in globus pallidus, putamen, anterior cingulate and post- and pre-central gyri. Both motor and cognitive statuses were significant factors related to network score. A network approach, supported by arterial spin labelling-derived absolute perfusion values may provide a readily accessible neuroimaging method to characterize and track progression of both motor and cognitive status in Parkinson's disease.

Volumetric correlates of spatiotemporal working and recognition memory impairment in aged rhesus monkeys

Spatiotemporal and recognition memory are affected by aging in humans and macaque monkeys. To investigate whether these deficits are coupled with atrophy of memory-related brain regions, T(1)-weighted magnetic resonance images were acquired and volumes of the cerebrum, ventricles, prefrontal cortex (PFC), calcarine cortex, hippocampus, and striatum were quantified in young and aged rhesus monkeys. Subjects were tested on a spatiotemporal memory procedure (delayed response [DR]) that requires the integrity of the PFC and a medial temporal lobe-dependent recognition memory task (delayed nonmatching to sample [DNMS]). Region of interest analyses revealed that age inversely correlated with striatal, dorsolateral prefrontal cortex (dlPFC), and anterior cingulate cortex volumes. Hippocampal volume predicted acquisition of the DR task. Striatal volume correlated with DNMS acquisition, whereas total prefrontal gray matter, prefrontal white matter, and dlPFC volumes each predicted DNMS accuracy. A regional covariance analysis revealed that age-related volumetric changes could be captured in a distributed network that was coupled with declining performance across delays on the DNMS task. This volumetric analysis adds to growing evidence that cognitive aging in primates arises from region-specific morphometric alterations distributed across multiple memory-related brain systems, including subdivisions of the PFC.

Basics of multivariate analysis in neuroimaging data

Multivariate analysis techniques for neuroimaging data have recently received increasing attention as they have many attractive features that cannot be easily realized by the more commonly used univariate, voxel-wise, techniques(1,4,5,6,7). 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, on the other hand, cannot directly address interregional correlation in the brain. Multivariate approaches can also result in greater statistical power when compared with univariate techniques, which are forced to employ very stringent corrections for voxel-wise multiple comparisons. Further, multivariate techniques also lend themselves much better to prospective application of results from the analysis of one dataset to entirely new datasets. Multivariate techniques are thus well placed to provide information about mean differences and correlations with behavior, similarly to univariate approaches, with potentially greater statistical power and better reproducibility checks. In contrast to these advantages is the high barrier of entry to the use of multivariate approaches, preventing more widespread application in the community. To the neuroscientist becoming familiar with multivariate analysis techniques, an initial survey of the field might present a bewildering variety of approaches that, although algorithmically similar, are presented with different emphases, typically by people with mathematics backgrounds. We believe that multivariate analysis techniques have sufficient potential to warrant better dissemination. Researchers should be able to employ them in an informed and accessible manner. The current article is an attempt at a didactic introduction of multivariate techniques for the novice. A conceptual introduction is followed with a very simple application to a diagnostic data set from the Alzheimer s Disease Neuroimaging Initiative (ADNI), clearly demonstrating the superior performance of the multivariate approach.

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.

Cortical hypometabolism and hypoperfusion in Parkinson's disease is extensive: probably even at early disease stages

Recent cerebral blood flow (CBF) and glucose consumption (CMRglc) studies of Parkinson's disease (PD) revealed conflicting results. Using simulated data, we previously demonstrated that the often-reported subcortical hypermetabolism in PD could be explained as an artifact of biased global mean (GM) normalization, and that low-magnitude, extensive cortical hypometabolism is best detected by alternative data-driven normalization methods. Thus, we hypothesized that PD is characterized by extensive cortical hypometabolism but no concurrent widespread subcortical hypermetabolism and tested it on three independent samples of PD patients. We compared SPECT CBF images of 32 early-stage and 33 late-stage PD patients with that of 60 matched controls. We also compared PET FDG images from 23 late-stage PD patients with that of 13 controls. Three different normalization methods were compared: (1) GM normalization, (2) cerebellum normalization, (3) reference cluster normalization (Yakushev et al.). We employed standard voxel-based statistics (fMRIstat) and principal component analysis (SSM). Additionally, we performed a meta-analysis of all quantitative CBF and CMRglc studies in the literature to investigate whether the global mean (GM) values in PD are decreased. Voxel-based analysis with GM normalization and the SSM method performed similarly, i.e., both detected decreases in small cortical clusters and concomitant increases in extensive subcortical regions. Cerebellum normalization revealed more widespread cortical decreases but no subcortical increase. In all comparisons, the Yakushev method detected nearly identical patterns of very extensive cortical hypometabolism. Lastly, the meta-analyses demonstrated that global CBF and CMRglc values are decreased in PD. Based on the results, we conclude that PD most likely has widespread cortical hypometabolism, even at early disease stages. In contrast, extensive subcortical hypermetabolism is probably not a feature of PD.

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.

Experience and perspectives of caregivers of spouse with mild cognitive impairment

The purpose of this paper is to describe commonalities of the lived experience of being a spouse caregiver of a person with mild cognitive impairment (MCI). The Colaizzi method of empirical phenomenology was used for inter-viewing and analyzing data obtained from 10 spouse caregivers of persons with MCI. Four major themes were found and labeled: (a) Putting the Puzzle Pieces Together-There Really is Something Wrong; (b) A Downward Spiral into a World of Silence; (c) Consequences to Caregivers of Living in a World of Silence; (d) Taking Charge of Care. The findings of this study provided rich data to guide interventions to help caregivers to improve their awareness of MCI, gain new information and skills to deal more effectively with and adjust to the caregiving of their spouse with MCI over the long-term.

Long term caregiving: helping families of persons with mild cognitive impairment cope

The purpose of the paper is to describe common psychological and caregiving issues that can cause stress in family members of persons with mild cognitive impairment (PwMCI) in order to assist family members in providing care and support to the PwMCI while also caring for themselves over long periods of time. Because PwMCI and their family members have time to prepare for the future should the PwMCI no longer be able to participate in their own care, it is important that clinicians offer support, education, and referrals for services and interventions when needed. The results of a review and synthesis of the caregiving literature found that much information exists from educational and intervention programs designed to help caregivers of Alzheimer disease however little empirical information is available for clinicians to assist PwMCI and their family members. This paper provides valuable and practical information for clinicians and other care providers to assist family members of PwMCI to cope with the uncertainty of the diagnosis, prepare for the future, and manage their stress over the long-term.

Examining the multifactorial nature of cognitive aging with covariance analysis of positron emission tomography data

Research has indicated that there may be age-related and Alzheimer's disease (AD) -related reductions in regional cerebral blood flow (rCBF) in the brain. This study explored differences in age- and AD-related rCBF patterns in the context of cognitive aging using a multivariate approach to the analysis of H215O PET data. First, an rCBF covariance pattern that distinguishes between a group of younger and older adults was identified. Individual subject's expression of the identified age-related pattern was significantly correlated with their performance on tests of memory, even after controlling for the effect of age. This finding suggests that subject expression of the covariance pattern explained additional variation in performance on the memory tasks. The age-related covariance pattern was then compared to an AD-related covariance pattern. There was little evidence that the two covariance patterns were similar, and the age-related pattern did a poor job of differentiating between cognitively-healthy older adults and those with probable AD. The findings from this study are consistent with the multifactorial nature of cognitive aging.

Metabolic brain networks in neurodegenerative disorders: a functional imaging approach

Network analysis of functional brain imaging data is an innovative approach to study circuit abnormalities in neurodegenerative diseases. In Parkinson's disease, spatial covariance analysis of resting-state metabolic images has identified specific regional patterns associated with motor and cognitive symptoms. With functional imaging, these metabolic networks have recently been used to measure system-related progression and to evaluate novel treatment strategies. Network analysis is also being used to characterize specific functional biomarkers for Huntington's disease and Alzheimer's disease. These networks have been particularly helpful in uncovering compensatory mechanisms in genetically at-risk individuals. Ongoing developments in network applications are likely to enhance the role of functional imaging in the investigation of neurodegenerative disorders.

SVM-based CAD system for early detection of the Alzheimer's disease using kernel PCA and LDA

Single-photon emission tomography (SPECT) imaging has been widely used to guide clinicians in the early Alzheimer's disease (AD) diagnosis challenge. However, AD detection still relies on subjective steps carried out by clinicians, which entail in some way subjectivity to the final diagnosis. In this work, kernel principal component analysis (PCA) and linear discriminant analysis (LDA) are applied on functional images as dimension reduction and feature extraction techniques, which are subsequently used to train a supervised support vector machine (SVM) classifier. The complete methodology provides a kernel-based computer-aided diagnosis (CAD) system capable to distinguish AD from normal subjects with 92.31% accuracy rate for a SPECT database consisting of 91 patients. The proposed methodology outperforms voxels-as-features (VAF) that was considered as baseline approach, which yields 80.22% for the same SPECT database.

Preserved frontal memorial processing for pictures in patients with mild cognitive impairment

Amnestic mild cognitive impairment (aMCI) has been conceptualized as a transitional stage between healthy aging and Alzheimer's disease (AD). Therefore, understanding which aspects of memory are impaired and which remain relatively intact in these patients can be useful in determining who will ultimately go on to develop AD, and subsequently designing interventions to help patients live more engaged and independent lives. The dual-process model posits that recognition memory decisions can rely on either familiarity or recollection. Whereas research is fairly consistent in showing impaired recollection in patients with aMCI, the results have been mixed regarding familiarity. A noted difference between these studies investigating familiarity has been stimulus type. The goal of the current investigation was to use high-density event-related potentials (ERPs) to help elucidate the neural correlates of recognition decisions in patients with aMCI for words and pictures. We also hoped to help answer the question of whether patients can rely on familiarity to support successful recognition. Patients and controls participated in separate recognition memory tests of words and pictures while ERPs were recorded during retrieval. Results showed that ERP components typically associated with familiarity and retrieval monitoring were similar between groups for pictures. However, these components were diminished in the patient group for words. Based on recent work, the authors discuss the possibility that implicit conceptual priming could have contributed to the enhanced ERP correlate of familiarity. Further, the authors address the possibility that enhanced retrieval monitoring may be needed to modulate increased familiarity engendered by pictures.

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.

Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer's disease

For finite and noisy samples extraction of robust features or patterns which are representative of the population is a formidable task in which over-interpretation is not uncommon. In this work, resampling techniques have been applied to a sample of 42 FDG PET brain images of 19 healthy volunteers (HVs) and 23 Alzheimer's disease (AD) patients to assess the robustness of image features extracted through principal component analysis (PCA) and Fisher discriminant analysis (FDA). The objective of this work is to: 1) determine the relative variance described by the PCA to the population variance; 2) assess the robustness of the PCA to the population sample using the largest principal angle between PCA subspaces; 3) assess the robustness and accuracy of the FDA. Since the sample does not have histopathological data the impact of possible clinical misdiagnosis on the discrimination analysis is investigated. The PCA can describe up to 40% of the total population variability. Not more than the first three or four PCs can be regarded as robust on which a robust FDA can be build. Standard error images showed that regions close to the falx and around ventricles are less stable. Using the first three PCs, sensitivity and specificity were 90.5% and 96.9% respectively. The use of resampling techniques in the evaluation of the robustness of many multivariate image analysis methods enables researchers to avoid over-analysis when using these methods applied to many different neuroimaging studies often with small sample sizes.

Subcortical elevation of metabolism in Parkinson's disease--a critical reappraisal in the context of global mean normalization

In a recent issue of NeuroImage, we presented evidence that biased global mean (GM) normalization of brain PET data can generate the appearance of subcortical foci with relative hypermetabolism in patients with Parkinson's disease (PD), and other degenerative disorders. In a commentary to our article, Ma and colleagues presented a study seeking to establish that a pattern of widespread hypermetabolism, known as the Parkinson's disease related pattern (PDRP) is a genuine metabolic feature of PD. In the present paper, we respond to the arguments presented by Ma et al., and we provide a critical reappraisal of the evidence for the existence of the PDRP. To this end, we present new analyses of PET data sets, which demonstrate that very similar patterns of relative subcortical increases are seen in PD, Alzheimer's disease, hepatic encephalopathy, healthy aging, and simulation data. Furthermore, longitudinal studies of PD previously reported relative hypermetabolism in very small anatomical structures such as the subthalamic nucleus. We now demonstrate how focal hypermetabolism attributed to small nuclei can similarly arise as a consequence of GM normalization. Finally, we give a comprehensive summary of the entire deoxyglucose autoradiography literature on acquired parkinsonism in experimental animals. Based on this evidence, we conclude that (1) there is no quantitative evidence for widespread subcortical hypermetabolism in PD, (2) very similar patterns of subcortical hyperactivity are evident in various other brain disorders whenever GM normalization is utilized, and (3) the PDRP is not evident in animal models of PD. In the absence of quantitative evidence for the PDRP, our alternative interpretation of normalization bias seems the more parsimonious explanation for the reports of relative hypermetabolism in PD.

Differential diagnosis of parkinsonian syndromes using PCA-based functional imaging features

In the current paper, we describe methodologies for single subject differential diagnosis of degenerative brain disorders using multivariate principal component analysis (PCA) of functional imaging scans. An automated routine utilizing these methods is applied to positron emission tomography (PET) brain data to distinguish several discrete parkinsonian movement disorders with similar clinical manifestations. Disease specific expressions of voxel-based spatial covariance patterns are predetermined using the Scaled Subprofile Model (SSM/PCA) and a scalar measure of the manifestation of each pattern in prospective subject images is subsequently derived. Scores are automatically compared to reference values generated for each pathological condition in a corresponding set of patient and control scans. Diagnostic outcome is optimized using strategies such as the derivation of patterns in a voxel subspace that reflects contrasting image characteristics between conditions, or by using an independent patient population as controls. The prediction models for two, three and four way classification problems using direct scalar comparison as well as classical discriminant analysis are assessed in a composite training population comprised of three different patient classes and normal controls, and validated in a similar independent test population. Results illustrate that highly accurate diagnosis can often be achieved by simple comparison of scores utilizing optimized patterns.