18FDG PET in vascular dementia: differentiation from Alzheimer's disease using voxel-based multivariate analysis

Autonomic responses to pain in aging and dementia

Increasing age and dementia are accompanied by an increased risk for undertreatment of pain owing to difficulty in assessing pain. Registration of autonomic responses to pain may contribute to a more reliable pain assessment. The aim of this review was to gain more insight into autonomic responses to pain in older persons with and without dementia. Literature searches were performed in the online databases MEDLINE and Web of Science. Seven studies on autonomic responses to pain in older people with or without dementia were included in the review. Autonomic responses to pain are present in older people with and without dementia, although they may be attenuated. Because no distinction could be made between different dementia subtypes based on these studies, predictions of changes in autonomic responses to pain have been made based on neuropathological changes. It can be concluded that autonomic responses to pain are attenuated in older people with and without dementia. Studies to specify the changes in the different autonomic responses for the different dementia subtypes are needed.

Cerebral blood flow by arterial spin labeling in poststroke dementia

OBJECTIVE

To investigate the relationship between cerebral blood flow and dementia in older stroke survivors and subjects with Alzheimer disease (AD).

METHODS

This cohort study used arterial spin labeling MRI at 3 T to examine cerebral blood flow (CBF). We scanned 39 patients 6 years after stroke. They were older than 75 years at the time of stroke and free of dementia 3 months poststroke, with 8 subsequently developing dementia. We also scanned 17 subjects with AD and 29 healthy control subjects. We determined the perfusion in regions of interest (ROIs). Hippocampal volume was also measured using a previously validated automated procedure.

RESULTS

The gray matter/white matter CBF ratio was reduced globally in the poststroke dementia (PSD) group (1.55 SD = 0.12) relative to control subjects (1.78 SD = 0.18; p = 0.03). The CBF ratio in a parietal ROI was reduced in the AD (1.34 SD = 0.31; p = 0.003), PSD (1.32 SD = 0.22; p = 0.041), and poststroke no-dementia (PSND) (1.44 SD = 0.34; p = 0.014) groups relative to that of control subjects (1.70 SD = 0.32). In subjects without stroke, the best predictor of dementia was hippocampus volume, whereas in the stroke group, it was the global CBF gray matter/white matter ratio. Hippocampus volume was not significantly different between the AD and PSD groups, and both had reduced hippocampi relative to those of control subjects and the PSND group.

CONCLUSIONS

We found evidence for both vascular and AD pathology in PSD, suggesting that both the direct impact of the stroke and subsequent development of AD-type changes play a role in the etiology of PSD.

A robust volumetric feature extraction approach for 3D neuroimaging retrieval

The increased volume of 3D neuroimaging data has created a need for efficient data management and retrieval. We suggest that image retrieval via robust volumetric features could benefit managing these large image datasets. In this paper, we introduce a new feature extraction method, based on disorder-oriented masks, that uses the volumetric spatial distribution patterns in 3D physiological parametric neurological images. Our preliminary results indicate that the proposed volumetric feature extraction approach could support reliable 3D neuroimaging data retrieval and management.

Brain Glucose Metabolism in Vascular White Matter Disease With Dementia

Background and Purpose— The boundary between vascular dementia and Alzheimer disease (AD) continues to be unclear. Some posit that gradually progressive vascular dementia, as with small vessel disease, is simply vascular disease plus AD. Because AD presents a characteristic pattern on fluorodeoxyglucose positron emission tomography, we sought to determine whether the fluorodeoxyglucose pattern of vascular dementia resembled more AD or the pattern in nondemented patients with severe microvascular brain disease.

Methods— Vascular disease patients were selected on the basis of confluent white matter lesions on both hemispheres. Among them, with a similar degree of vascular disease on MRI, neuropsychological testing separated groups with dementia and without dementia. Patients with AD and healthy controls were also studied. The 4 groups, with 12 subjects each, were matched by age, gender, and educational level. Fluorodeoxyglucose distribution was analyzed using both voxel-based and volume of interest methods.

Results— The AD group had the characteristic pattern of bilaterally decreased metabolism in parieto-temporal association cortex and precuneus. By contrast, patients with vascular disease and dementia had a similar anatomic pattern to that of the vascular patients without dementia, but with greater metabolic abnormalities, particularly in the frontal lobes and deep nuclei.

Conclusions— The anatomy of metabolic abnormalities in vascular disease with dementia suggests that, at least in some cases, dementia with vascular disease may be independent of AD. The metabolic abnormality involves the thalamus, caudate, and frontal lobe, a pattern concordant with the neuropsychological findings of impaired executive function characteristic of vascular dementia.

Understanding higher level gait disturbances in mild dementia in order to improve rehabilitation: 'last in-first out'

Predicting and anticipating disturbances in higher level gait is particularly relevant for patients with dementia as higher level gait appears to be closely related to higher level cognitive functioning. A phenomenon that could contribute to the understanding and prediction of disturbances in higher level gait and gait-related motor activity in the various subtypes of dementia is paraphrased as 'last in-first out'. 'Last in-first out' refers to the principle that neural circuits that mature late in development are the most vulnerable to neurodegeneration. The strength of relating symptoms to the 'last in-first out' principle is that a future symptom can be predicted and anticipated in a therapeutic way, even if the disease process has not already started. Therefore, the aim of this review is to provide new strategies for rehabilitation of higher level gait disturbances in dementia based upon the 'last in-first out' principle. These new strategies emerge from five neural networks: the superior longitudinal fasciculus, the uncinate fasciculus, the fronto-cerebellar and fronto-striatal connections, and the cingulum.

Comparing fludeoxyglucose F18-PET assessment of regional cerebral glucose metabolism and [11C]dihydrotetrabenazine-PET in evaluation of early dementia and mild cognitive impairment

OBJECTIVE

To compare assessment of regional cerebral metabolic changes with [(11)C]dihydrotetrabenazine (DTBZ)-positron emission tomography (PET) measurement of regional cerebral blood flow (K(1)) and fludeoxyglucose F18 (FDG)-PET measurement of regional cerebral glucose uptake (CMR(glc)) in a clinically representative sample of subjects with mild dementia and mild cognitive impairment (MCI).

DESIGN

[(11)C]Dihydrotetrabenazine-PET K(1) and FDG-PET CMR(glc) measurements were performed.

SETTING

University-based cognitive disorders clinic.

PARTICIPANTS

Fifty subjects with either mild dementia (Mini-Mental State Examination score > or = 18) or MCI. Their results were compared with those of 80 normal control subjects.

MAIN OUTCOME MEASURES

The DTBZ-PET regional K(1) and FDG-PET CMR(glc) measurements were compared with standard correlation analysis. The overall patterns of DTBZ-PET K(1) and FDG-PET CMR(glc) deficits were assessed with stereotaxic surface projections (SSPs) of parametric images.

RESULTS

The DTBZ-PET regional K(1) and FDG-PET CMR(glc) measurements were highly correlated, both within and between subjects. The SSP maps of deficits in DTBZ-PET regional K(1) and FDG-PET CMR(glc) measurements were markedly similar. The DTBZ-PET K(1) SSP maps exhibited a mild decrease in sensitivity relative to FDG-PET CMR(glc) maps.

CONCLUSIONS

Both DTBZ-PET K(1) and FDG-PET CMR(glc) measurements provide comparable information in assessment of regional cerebral metabolic deficits in mild dementia and MCI. Blood flow measures can assess regional cerebral metabolism deficits accurately in mild dementia and MCI. Blood flow assessments of regional cerebral metabolic deficits can be combined with tracer binding results to improve utility of PET imaging in mild dementia and MCI.

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.

Fluorodeoxyglucose-positron emission tomography in the differential diagnosis of early-onset dementia: a prospective, community-based study

Background

The aim of this study was to evaluate the diagnostic accuracy of positron emission tomography (PET) using F¹⁸ fluorodeoxyglucose (FDG) in the differential diagnosis of early-onset Alzheimer's disease (AD) and other dementias in a community-dwelling population.

Methods

A prospective sample of 102 individuals presenting consecutively to a primary care centre for examination of suspected early-onset dementing diseases. The mean age of symptom onset of dementia in our patients was 60.06 ± 4.28 years (mean ± 1SD, 95% lower confidence intervals (CI) 54.75, upper 63.37). Patients were evaluated using standard clinical criteria for the diagnosis of dementia. Functional neuroimaging data was obtained and nuclear medicine physicians blind to the clinical diagnosis generated FDG-PET diagnoses. Final clinical diagnoses based on all available data were then established and compared against PET diagnoses.

Results

Forty-nine patients received a final clinical diagnosis of early-stage AD (MMSE score 20.97 ± 5.10). There were 29 non-AD demented patients, 11 depressed patients and a miscellaneous group of 13 patients. Among patients with AD, the sensitivity and specificity of FDG-PET was 78% (95% CI: 66–90%) and 81% (95% CI: 68–86%), respectively. The positive likelihood ratio (PLR) for a FDG-PET scan positive for the diagnosis of AD was 4.11 (95% CI: 2.29–7.32) and negative likelihood ratio (NLR) for a negative FDG-PET scan in the absence of AD was 0.27 (95% CI: 0.16–0.46). The pre-test probability was 48% and post-test probability was 79.02%. The specificity of FDG-PET in the differential diagnosis of other dementias, including frontotemporal dementia, was greater than 95%.

Recruitment methods in this study provide a sample that may be more representative of patients in the general population and indicate that FDG-PET imaging can contribute to the diagnosis of AD in younger adults with major increases in the positive likelihood rates and post-test probability.

Conclusion

The high specificity of FDG-PET suggests this technique might help in the diagnosis of frontotemporal dementia and other forms of early-onset dementia.

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.

Cortical thinning in vascular mild cognitive impairment and vascular dementia of subcortical type

BACKGROUND AND PURPOSE

Amnestic mild cognitive impairment (MCI) is known to be a preclinical stage of Alzheimer's disease (AD). Similarly, MCI associated with small-vessel disease (svMCI), might be a forme froste of subcortical vascular dementia (SVaD). Patterns of cortical thinning in addition to the ischemia rating on MRI may further elucidate the clinical characteristics and pathogenesis of SVaD and svMCI. We tried to determine if svMCI differs from SVaD in the distribution of cortical atrophy, which may help understand the hierarchy between svMCI and SVaD and possibly also how svMCI evolves into SVaD.

METHODS

Twenty patients with SVaD, 34 patients with svMCI, 115 patients with AD, and 96 individuals with normal-cognition (NC) were imaged with magnetic resonance imaging (MRI) including 3-dimensional volumetric images for cortical thickness analysis across the entire brain.

RESULTS

Compared to NC, svMCI patients showed cortical thinning in inferior frontal and orbitofrontal gyri, anterior cingulate, insula, superior temporal gyrus, and lingual gyrus, while cortical thinning in SVaD patients involved all these areas plus dorsolateral prefrontal and temporal cortices.

CONCLUSION

Our findings suggest the presence of hierarchy between svMCI and SVaD, and that the cognitive decline from svMCI to SVaD is associated with lesions in dorsolateral prefrontal and temporal cortices.

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.

Parcellation of fMRI datasets with ICA and PLS--a data driven approach

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.

Choice of reference area in studies of Alzheimer's disease using positron emission tomography with fluorodeoxyglucose-F18

At present, there is still no consensus on the choice of the reference area in positron emission tomography (PET) studies of Alzheimer's disease (AD). In this study, PET scans with fluorodeoxyglucose-F18 were carried out in the following groups of subjects: 47 patients with probable AD, 8 patients with mild cognitive impairment, and 15 age-similar healthy subjects. Scans normalized to the cerebral global mean (CGM), cerebellum (CBL), and the primary sensorimotor cortex (SMC). We evaluated the effect of the different count normalization procedures on the accuracy of (18)F-FDG PET to detect AD-specific metabolic abnormalities (voxel-based group comparison) and to differentiate between patients and healthy subjects (ROI-based discriminant analysis) with regard to the degree of clinical deterioration. Metabolic reductions in groups of very mildly, mildly and moderate-to-severely affected patients appeared, respectively, 2.2, 2.6, and 2.7 times greater in spatial extent when tracer uptake was normalized to SMC rather than to CGM. The overall accuracy of discrimination was 94%, 91%, and 80% after normalization to SMC, CBL, and CGM, respectively. In general, normalization to SMC was somewhat superior to cerebellar normalization, allowing the detection of more pronounced metabolic deficits and the more accurate discrimination of patients from non-patients. Normalization to CGM should be used with great caution not only in advanced stages of dementia, but also in very mild AD cases.

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.

A life course of adiposity and dementia

Adiposity, commonly measured as body mass index (BMI), may influence or be influenced by brain structures and functions involved in dementia processes. Adipose tissue changes in degree and intensity over the lifespan, and has been shown to influence brain development in relationship to early and late measures of cognitive function, intelligence, and disorders of cognition such as dementia. A lower BMI is associated with prevalent dementia, potentially due to underlying brain pathologies and correspondingly greater rates of BMI or weight decline observed during the years immediately preceding clinical dementia onset. However, high BMI during mid-life or at least approximately 5-10 years preceding clinical dementia onset may increase risk. The interplay of adiposity and the brain occurring over the course of the lifespan will be discussed in relationship to developmental origins, mid-life sequelae, disruptions in brain structure and function, and late-life changes in cognition and dementia. Characterizing the life course of adiposity among those who do and do not become demented enhances understanding of biological underpinnings relevant for understanding the etiologies of both dementia and obesity and their co-existence.

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.

Functional imaging of cerebral blood flow and glucose metabolism in Parkinson's disease and Huntington's disease

Brain imaging of cerebral blood flow and glucose metabolism has been playing key roles in describing pathophysiology of Parkinson's disease (PD) and Huntington's disease (HD), respectively. Many biomarkers have been developed in recent years to investigate the abnormality in molecular substrate, track the time course of disease progression, and evaluate the efficacy of novel experimental therapeutics. A growing body of literature has emerged on neurobiology of these two movement disorders in resting states and in response to brain activation tasks. In this paper, we review the latest applications of these approaches in patients and normal volunteers at rest conditions. The discussions focus on brain mapping studies with univariate and multivariate statistical analyses on a voxel basis. In particular, we present data to validate the reproducibility and reliability of unique spatial covariance patterns related with PD and HD.

Neuroimaging in dementia

Neuroimaging has become increasingly important in the clinical assessment and diagnosis of dementia. Structural imaging with MRI and functional imaging techniques, such as positron emission tomography and single photon emission CT, increasingly are used to aid in the differential diagnosis and early detection of dementia. Imaging techniques also can track disease progression over time and may be useful to monitor treatment effects. The most important development in the field over the past decade is the ability to image amyloid in the brain. This technique will revolutionize patient management and care.

On the multivariate nature of brain metabolic impairment in Alzheimer's disease

We used principal component analysis to decompose functional images of patients with AD in orthogonal ensembles of brain regions with maximal metabolic covariance. Three principal components explained 38% of the total variance in a large sample of FDG-PET images obtained in 225 AD patients. One functional ensemble (PC2) included limbic structures from Papez's circuit (medial temporal regions, posterior and anterior cingulate cortex, thalamus); its disruption in AD patients was related to episodic memory impairment. Another principal component (PC1) illustrated major metabolic variance in posterior cerebral cortices, and patients' scores were correlated to instrumental functions (language and visuospatial abilities). PC3 comprised frontal, parietal, temporal and posteromedial (posterior cingulate and precuneus) cortices, and patients' scores were related to executive dysfunction and global cognitive impairment. The three main metabolic covariance networks converged in the posterior cingulate area that showed complex relationships with medial temporal structures within each PC. Individual AD scores were distributed as a continuum along PC axes: an individual combination of scores would determine specific clinical symptoms in each patient.

Pathological alterations of the climbing fibres of the cerebellum in vascular dementia: a Golgi and electron microscope study

The climbing fibres originating from the inferior olivary nucleus act as a powerful excitation on the Purkinje cells of the cerebellar cortex that may play a substantial role in the motor performances and the learning of new motor skills. In vascular dementia the existent vascular alterations may induce many hypoxic or ischemic phenomena, among the others, in the olivocerebellar system affecting the climbing fibres in their way to the molecular layer of the cerebellar cortex. In autopsy cases of vascular dementia, the application of silver impregnation technique and electron microscopy revealed a substantial decrease of the number of the climbing fibres in the cortex of the vermis, the flocculus and the cerebellar hemispheres. The presynaptic varicosities and the synaptic terminals of the climbing fibres on the Purkinje cell dendrites were decreased in number and moreover were characterized by a marked poverty of synaptic vesicles. The synaptic cleft was mostly abnormal and wider than 20 nm. Mitochondrial abnormalities, such as elongated mitochondria with disruption of the cristae were seen in the terminal branches of the climbing fibre arborization as well as in the presynaptic components. The blood capillaries demonstrated a considerable thickness of the basal membrane and perivascular astrocytic proliferation, whereas the tight junctions between the endothelial cells were ultrastructurally intact. We would hypothesize that the morphological and morphometric alterations of the climbing fibres of the cerebellar cortex in cases of vascular dementia might be associated with the frequently noticed difficulty in the performance of fine and skilful movements by the patients.

Identifying patients with obsessive-compulsive disorder using whole-brain anatomy

Structural neuroimaging studies have reported a variety of brain alterations between groups of obsessive-compulsive disorder (OCD) patients and healthy controls. However, the large heterogeneity in discrete anatomical measures that exists among patients prevents a clear discrimination of single patients from healthy subjects. This reduces the potential clinical applicability of structural neuroimaging studies. In the present study we assessed the feasibility of identifying OCD patients on the basis of whole-brain anatomical alterations. Whole-brain magnetic resonance images were collected from two consecutive samples of OCD outpatients (n=72 and n=30), and control subjects (n=72 and n=30). We computed the whole-brain (voxel-wise) pattern of structural difference between OCD patients and control subjects at the group level. A single expression value of this difference pattern was calculated for each subject, expressing their degree of 'OCD-like' anatomical alteration. Accuracy of patient classification based on these expression values was assessed using two validation approaches. Firstly, using a cross-validation method, we obtained a high classification accuracy (average of the sensitivity and specificity indices) of 93.1%. In a second assessment, which classified new groups of OCD patients and control subjects, overall accuracy was lower at 76.6%. Individual expression values for OCD patients were significantly correlated with overall symptom severity as measured by the Y-BOCS scale. Our results suggest that OCD patients can be identified on the basis of whole-brain structural alterations, although the accuracy of our approach may be limited by the inherent variability of psychiatric populations. Nevertheless, the anatomical characterization of individual patients may ultimately provide the psychiatrist with relevant biological information.