A brain imaging repository of normal structural MRI across the life course: Brain Images of Normal Subjects (BRAINS).

The Brain Images of Normal Subjects (BRAINS) Imagebank (http://www.brainsimagebank.ac.uk) is an integrated repository project hosted by the University of Edinburgh and sponsored by the Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) collaborators. BRAINS provide sharing and archiving of detailed normal human brain imaging and relevant phenotypic data already collected in studies of healthy volunteers across the life-course. It particularly focusses on the extremes of age (currently older age, and in future perinatal) where variability is largest, and which are under-represented in existing databanks. BRAINS is a living imagebank where new data will be added when available. Currently BRAINS contains data from 808 healthy volunteers, from 15 to 81years of age, from 7 projects in 3 centres. Additional completed and ongoing studies of normal individuals from 1st to 10th decades are in preparation and will be included as they become available. BRAINS holds several MRI structural sequences, including T1, T2, T2* and fluid attenuated inversion recovery (FLAIR), available in DICOM (http://dicom.nema.org/); in future Diffusion Tensor Imaging (DTI) will be added where available. Images are linked to a wide range of 'textual data', such as age, medical history, physiological measures (e.g. blood pressure), medication use, cognitive ability, and perinatal information for pre/post-natal subjects. The imagebank can be searched to include or exclude ranges of these variables to create better estimates of 'what is normal' at different ages.

Improving data availability for brain image biobanking in healthy subjects: Practice-based suggestions from an international multidisciplinary working group.

Brain imaging is now ubiquitous in clinical practice and research. The case for bringing together large amounts of image data from well-characterised healthy subjects and those with a range of common brain diseases across the life course is now compelling. This report follows a meeting of international experts from multiple disciplines, all interested in brain image biobanking. The meeting included neuroimaging experts (clinical and non-clinical), computer scientists, epidemiologists, clinicians, ethicists, and lawyers involved in creating brain image banks. The meeting followed a structured format to discuss current and emerging brain image banks; applications such as atlases; conceptual and statistical problems (e.g. defining 'normality'); legal, ethical and technological issues (e.g. consents, potential for data linkage, data security, harmonisation, data storage and enabling of research data sharing). We summarise the lessons learned from the experiences of a wide range of individual image banks, and provide practical recommendations to enhance creation, use and reuse of neuroimaging data. Our aim is to maximise the benefit of the image data, provided voluntarily by research participants and funded by many organisations, for human health. Our ultimate vision is of a federated network of brain image biobanks accessible for large studies of brain structure and function.

Do brain image databanks support understanding of normal ageing brain structure? A systematic review.

OBJECTIVE: To document accessible magnetic resonance (MR) brain images, metadata and statistical results from normal older subjects that may be used to improve diagnoses of dementia. METHODS: We systematically reviewed published brain image databanks (print literature and Internet) concerned with normal ageing brain structure. RESULTS: From nine eligible databanks, there appeared to be 944 normal subjects aged ≥60 years. However, many subjects were in more than one databank and not all were fully representative of normal ageing clinical characteristics. Therefore, there were approximately 343 subjects aged ≥60 years with metadata representative of normal ageing, but only 98 subjects were openly accessible. No databank had the range of MR image sequences, e.g. T2*, fluid-attenuated inversion recovery (FLAIR), required to effectively characterise the features of brain ageing. No databank supported random subject retrieval; therefore, manual selection bias and errors may occur in studies that use these subjects as controls. Finally, no databank stored results from statistical analyses of its brain image and metadata that may be validated with analyses of further data. CONCLUSION: Brain image databanks require open access, more subjects, metadata, MR image sequences, searchability and statistical results to improve understanding of normal ageing brain structure and diagnoses of dementia. KEY POINTS: • We reviewed databanks with structural MR brain images of normal older people. • Among these nine databanks, 98 normal subjects ≥60 years were openly accessible. • None had all the required sequences, random subject retrieval or statistical results. • More access, subjects, sequences, metadata, searchability and results are needed. • These may improve understanding of normal brain ageing and diagnoses of dementia.

Impact of cannabis use on thalamic volume in people at familial high risk of schizophrenia.

BACKGROUND: No longitudinal study has yet examined the association between substance use and brain volume changes in a population at high risk of schizophrenia. AIMS: To examine the effects of cannabis on longitudinal thalamus and amygdala-hippocampal complex volumes within a population at high risk of schizophrenia. METHOD: Magnetic resonance imaging scans were obtained from individuals at high genetic risk of schizophrenia at the point of entry to the Edinburgh High-Risk Study (EHRS) and approximately 2 years later. Differential thalamic and amygdala-hippocampal complex volume change in high-risk individuals exposed (n = 25) and not exposed (n = 32) to cannabis in the intervening period was investigated using repeated-measures analysis of variance. RESULTS: Cannabis exposure was associated with bilateral thalamic volume loss. This effect was significant on the left (F = 4.47, P = 0.04) and highly significant on the right (F= 7.66, P= 0.008). These results remained significant when individuals using other illicit drugs were removed from the analysis. CONCLUSIONS: These are the first longitudinal data to demonstrate an association between thalamic volume loss and exposure to cannabis in currently unaffected people at familial high risk of developing schizophrenia. This observation may be important in understanding the link between cannabis exposure and the subsequent development of schizophrenia.

Functional Magnetic Resonance Imaging (fMRI) reproducibility and variance components across visits and scanning sites with a finger tapping task.

Multicentre MRI studies offer great potential to increase study power and flexibility, but it is not yet clear how reproducible the results from multiple centres may be. Here we present results from the multicentre study 'CaliBrain', examining the reproducibility of fMRI data within and between three sites. Fourteen subjects were scanned twice on three 1.5 T GE scanners using an identical scanning protocol. We present data from a motor task with three conditions, sequential and random finger tapping and rest. Similar activation maps were obtained for each site and visit; brain areas consistently activated during the task included the premotor, primary motor and supplementary motor areas, the striatum and cerebellum. Reproducibility was evaluated within and between sites by comparing the extent and spatial agreement of activation maps at both the subject and group levels. The results were within the range previously reported for similar tasks on single scanners and both measures were found to be comparable within and between sites, with between site reproducibility similar to the within site measures. A variance components analysis was used to examine the effects of site, subject and visit. The contributions of site and visit were small and reproducibility was similar between and within sites, whereas the variance between subjects, and unexplained variance was large. These findings suggest that we can have confidence in combined results from multicentre fMRI studies, at least when a consistent protocol is followed on similar machines in all participating scanning sites and care is taken to select homogeneous subject groups.

Functional magnetic resonance imaging of BDNF val66met polymorphism in unmedicated subjects at high genetic risk of schizophrenia performing a verbal memory task.

Multiple strands of evidence suggest a role for Brain Derived Neurotrophic Factor (BDNF) in the pathophysiology of schizophrenia. It is not yet clear, however, how BDNF may contribute to altered brain function seen in the disorder, or in those at high genetic risk. The current study examines functional imaging correlates of the BDNF val66met polymorphism in a population at high genetic risk of schizophrenia. Subjects at high genetic risk for the disorder (n=58) provided both BDNF genotyping and fMRI data while performing a verbal memory task. During encoding, participants were presented with a word and asked to make a 'living'/'non-living' classification. During retrieval, individuals were requested to make an 'old'/'new' word classification. For encoding, we report decreased activation of the inferior occipital cortex and a trend in the cingulate cortex in Val homozygote individuals relative to Met carriers. For retrieval, we report decreases in activation in the prefrontal, cingulate cortex and bilateral posterior parietal regions in Val homozygote individuals versus Met carriers. These findings add to previous evidence suggesting that genetic variation in the BDNF gene modulates prefrontal and limbic functioning and suggests that it may contribute to differences in brain function seen in those at high risk of the disorder.

A neuregulin 1 variant associated with abnormal cortical function and psychotic symptoms.

NRG1, encoding neuregulin 1, is a susceptibility gene for schizophrenia, but no functional mutation causally related to the disorder has yet been identified. Here we investigate the effects of a variant in the human NRG1 promoter region in subjects at high risk of schizophrenia. We show that this variant is associated with (i) decreased activation of frontal and temporal lobe regions, (ii) increased development of psychotic symptoms and (iii) decreased premorbid IQ.

Effects of the BDNF val66met polymorphism on prefrontal brain function in a population at high genetic risk of schizophrenia.

A single nucleotide polymorphism (val66met) in the brain derived neurotrophic factor (BDNF) gene has been shown to be a risk factor for a number of psychiatric disorders, including schizophrenia. This polymorphism has also been shown to have effects on prefrontal brain morphology and function. This study aims to clarify the effects of the val66met polymorphism on prefrontal brain function in a population at high genetic risk for schizophrenia. The Edinburgh High Risk Study has followed young individuals who had one first- or second-degree relative with schizophrenia and a minimum of one further genetic relative with the illness. A sample of 62 individuals provided both genetic and functional imaging data using the Hayling sentence completion task. Individuals with the BDNF ValVal (presumed risk) genotype (n = 41) showed relatively increased activation of the anterior cingulate cortex in relation to Met carrier individuals (n = 21) during sentence completion conditions versus baseline, against a background of similar levels of task performance. It appeared from further investigation that this relatively increased activation was attributable to a failure to disengage or suppress activation in the high risk ValVal group during the task condition, suggesting that BDNF may contribute to the abnormal default network reported in schizophrenia. These results suggest that this gene affects prefrontal brain function in those at high genetic risk for the disorder, unconfounded by medication effects. BDNF may therefore be one of the heritable factors involved in the development of abnormal prefrontal function in schizophrenia. © 2010 Wiley-Liss, Inc.

Structural abnormalities of ventrolateral and orbitofrontal cortex in patients with familial bipolar disorder.

OBJECTIVES: Abnormalities of ventral prefrontal function have been widely reported in bipolar disorder, but reports of structural abnormalities in the same region are less consistent. We examined the presence and location of ventral prefrontal abnormalities in a large sample of individuals with bipolar disorder and their relationship to gender, psychotic symptoms, and age. METHODS: Structural magnetic resonance imaging brain scans were carried out on 66 individuals with bipolar disorder, type I, and 66 controls. Voxel-based morphometry was used to examine differences in grey and white matter density between the groups and their relationship with a lifetime occurrence of psychotic symptoms and age. RESULTS: Reductions in grey matter density were seen in the left and right lateral orbital gyri and the right inferior frontal gyrus, while white matter density reductions were seen in the corona radiata and the left temporal stem. In contrast, hallucinations and positive symptoms were associated with grey matter reduction in the left middle temporal gyrus. Age was more strongly associated with the right inferior frontal gyrus grey matter reductions in the bipolar group than in the controls, but not with any other finding. CONCLUSION: Abnormalities of the ventral prefrontal cortex are likely to be involved in the aetiopathology of bipolar disorder, while hallucinations appear to be more closely associated with temporal lobe abnormality, extending earlier work in schizophrenia. Further prospective studies are required to comprehensively address the trajectory of these findings.

Prospective multi-centre Voxel Based Morphometry study employing scanner specific segmentations: procedure development using CaliBrain structural MRI data.

BACKGROUND: Structural Magnetic Resonance Imaging (sMRI) of the brain is employed in the assessment of a wide range of neuropsychiatric disorders. In order to improve statistical power in such studies it is desirable to pool scanning resources from multiple centres. The CaliBrain project was designed to provide for an assessment of scanner differences at three centres in Scotland, and to assess the practicality of pooling scans from multiple-centres. METHODS: We scanned healthy subjects twice on each of the 3 scanners in the CaliBrain project with T1-weighted sequences. The tissue classifier supplied within the Statistical Parametric Mapping (SPM5) application was used to map the grey and white tissue for each scan. We were thus able to assess within scanner variability and between scanner differences. We have sought to correct for between scanner differences by adjusting the probability mappings of tissue occupancy (tissue priors) used in SPM5 for tissue classification. The adjustment procedure resulted in separate sets of tissue priors being developed for each scanner and we refer to these as scanner specific priors. RESULTS: Voxel Based Morphometry (VBM) analyses and metric tests indicated that the use of scanner specific priors reduced tissue classification differences between scanners. However, the metric results also demonstrated that the between scanner differences were not reduced to the level of within scanner variability, the ideal for scanner harmonisation. CONCLUSION: Our results indicate the development of scanner specific priors for SPM can assist in pooling of scan resources from different research centres. This can facilitate improvements in the statistical power of quantitative brain imaging studies.

Correlations between fMRI activation and individual psychotic symptoms in un-medicated subjects at high genetic risk of schizophrenia.

BACKGROUND: It has been proposed that different types of psychopathology in schizophrenia may reflect distinguishable pathological processes. In the current study we aimed to address such associations in the absence of confounders such as medication and disease chronicity by examining specific relationships between fMRI activation and individual symptom severity scores in un-medicated subjects at high genetic risk of schizophrenia. METHODS: Associations were examined across two functional imaging paradigms: the Hayling sentence completion task, and an encoding/retrieval task, comprising encoding (at word classification) and retrieval (old word/new word judgement). Symptom severity was assessed using the positive and negative syndrome scale (PANSS). Items examined were hallucinations, delusions, and suspiciousness/persecution. RESULTS: Associations were seen in the anterior middle temporal gyrus in relation to hallucination scores during the sentence completion task, and in the medial temporal lobe in association with suspiciousness/persecution scores in the encoding/retrieval task. Cerebellar activation was associated with delusions and suspiciousness/persecution scores across both tasks with differing patterns of laterality. CONCLUSION: These results support a role for the lateral temporal cortex in hallucinations and medial temporal lobe in positive psychotic symptoms. They also highlight the potential role of the cerebellum in the formation of delusions. That the current results are seen in un-medicated high risk subjects indicates these associations are not specific to the established illness and are not related to medication effects.

Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging.

Brain MRI atlases may be used to characterize brain structural changes across the life course. Atlases have important applications in research, e.g., as registration and segmentation targets to underpin image analysis in population imaging studies, and potentially in future in clinical practice, e.g., as templates for identifying brain structural changes out with normal limits, and increasingly for use in surgical planning. However, there are several caveats and limitations which must be considered before successfully applying brain MRI atlases to research and clinical problems. For example, the influential Talairach and Tournoux atlas was derived from a single fixed cadaveric brain from an elderly female with limited clinical information, yet is the basis of many modern atlases and is often used to report locations of functional activation. We systematically review currently available whole brain structural MRI atlases with particular reference to the implications for population imaging through to emerging clinical practice. We found 66 whole brain structural MRI atlases world-wide. The vast majority were based on T1, T2, and/or proton density (PD) structural sequences, had been derived using parametric statistics (inappropriate for brain volume distributions), had limited supporting clinical or cognitive data, and included few younger (>5 and <18 years) or older (>60 years) subjects. To successfully characterize brain structural features and their changes across different stages of life, we conclude that whole brain structural MRI atlases should include: more subjects at the upper and lower extremes of age; additional structural sequences, including fluid attenuation inversion recovery (FLAIR) and T2* sequences; a range of appropriate statistics, e.g., rank-based or non-parametric; and detailed cognitive and clinical profiles of the included subjects in order to increase the relevance and utility of these atlases.

Grey matter changes can improve the prediction of schizophrenia in subjects at high risk.

BACKGROUND: We hypothesised that subjects at familial high risk of developing schizophrenia would have a reduction over time in grey matter, particularly in the temporal lobes, and that this reduction may predict schizophrenia better than clinical measurements. METHODS: We analysed magnetic resonance images of 65 high-risk subjects from the Edinburgh High Risk Study sample who had two scans a mean of 1.52 years apart. Eight of these 65 subjects went on to develop schizophrenia an average of 2.3 years after their first scan. RESULTS: Changes over time in the inferior temporal gyrus gave a 60% positive predictive value (likelihood ratio >10) of developing schizophrenia compared to the overall 13% risk in the cohort as a whole. CONCLUSION: Changes in grey matter could be used as part of a predictive test for schizophrenia in people at enhanced risk for familial reasons, particularly for positive predictive power, in combination with other clinical and cognitive predictive measures, several of which are strong negative predictors. However, because of the limited number of subjects, this test requires independent replication to confirm its validity.

White matter density in patients with schizophrenia, bipolar disorder and their unaffected relatives.

BACKGROUND: This study sought to assess white matter density in patients and relatives with histories of bipolar disorder and/or schizophrenia. METHODS: Subjects included those with schizophrenia from families affected by schizophrenia alone, those with bipolar disorder from families affected by bipolar disorder alone and those with bipolar disorder from families affected by both bipolar disorder and schizophrenia. Unaffected relatives of the three patient groups were also recruited. Subjects underwent an MRI brain scan which was analyzed using a white-matter optimized technique. RESULTS: Subjects with schizophrenia and bipolar disorder showed reduced white matter density in the anterior limb of the internal capsule which was not found in unaffected relatives. Reductions were found in frontal subgyral white matter density in affected subjects with a family history of schizophrenia only. CONCLUSIONS: Abnormal anterior internal capsule white matter may provide a structural substrate for both disorders.

Use of brain MRI atlases to determine boundaries of age-related pathology: the importance of statistical method.

INTRODUCTION: Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient's brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ± standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer's disease (AD) patients. METHODS: Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55-90 years), we created: a mean ± SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients. RESULTS: The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25-45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes. DISCUSSION: To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease.

Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI.

Permutation testing has been widely implemented in voxel-based morphometry (VBM) tools. However, this type of non-parametric inference has yet to be thoroughly compared with traditional parametric inference in VBM studies of brain structure. Here we compare both types of inference and investigate what influence the number of permutations in permutation testing has on results in an exemplar study of how gray matter proportion changes with age in a group of working age adults. High resolution T1-weighted volume scans were acquired from 80 healthy adults aged 25-64years. Using a validated VBM procedure and voxel-based permutation testing for Pearson product-moment coefficient, the effect sizes of changes in gray matter proportion with age were assessed using traditional parametric and permutation testing inference with 100, 500, 1000, 5000, 10000 and 20000 permutations. The statistical significance was set at P<0.05 and false discovery rate (FDR) was used to correct for multiple comparisons. Clusters of voxels with statistically significant (PFDR<0.05) declines in gray matter proportion with age identified with permutation testing inference (N≈6000) were approximately twice the size of those identified with parametric inference (N=3221voxels). Permutation testing with 10000 (N=6251voxels) and 20000 (N=6233voxels) permutations produced clusters that were generally consistent with each other. However, with 1000 permutations there were approximately 20% more statistically significant voxels (N=7117voxels) than with ≥10000 permutations. Permutation testing inference may provide a more sensitive method than traditional parametric inference for identifying age-related differences in gray matter proportion. Based on the results reported here, at least 10000 permutations should be used in future univariate VBM studies investigating age related changes in gray matter to avoid potential false findings. Additional studies using permutation testing in large imaging databanks are required to address the impact of model complexity, multivariate analysis, number of observations, sampling bias and data quality on the accuracy with which subtle differences in brain structure associated with normal aging can be identified.

Voxel-based morphometry of patients with schizophrenia or bipolar disorder and their unaffected relatives.

BACKGROUND: Structural brain abnormalities in schizophrenia are well replicated; many emerge before the onset of illness and are present in relatives who remain well. Structural changes in bipolar disorder are less clearly established. The possibility that structural abnormalities might provide a means by which the disorders might be separated is one that has attracted limited research effort. This study sought to examine these issues and clarify the associations of phenotypic expression and genetic liability. METHODS: Forty-nine control subjects, 71 patients, and 72 unaffected relatives were recruited for the study. Patients included those with schizophrenia from families affected by schizophrenia alone, those with bipolar disorder from families affected by bipolar disorder alone, and those with bipolar disorder from families affected by both bipolar disorder and schizophrenia. Unaffected relatives were recruited from the families of the three patient groups. Subjects underwent a magnetic resonance imaging scan of the brain, which was analyzed with a grey-matter-optimized, voxel-based morphometry technique. RESULTS: Compared with control subjects, all patient and relative groups showed evidence of reduced anterior thalamic gray matter. Reductions in middle prefrontal gyrus and dorsomedial thalamus were specific to participants with schizophrenia. CONCLUSIONS: Whereas prefrontal and dorsomedial thalamic gray matter reductions seem to be specific to schizophrenia, anterior thalamic reductions seem to be a marker of liability to psychosis in general. These results are discussed in the context of their functional role and in terms of their connections with other cortical and subcortical structures.

Voxel-based morphometry of grey matter densities in subjects at high risk of schizophrenia.

The grey matter (GM) segments from T1 structural magnetic resonance (MR) images of the brain in subjects at high risk of schizophrenia (n=146) were compared with normal control subjects (n=36) and first episode schizophrenic subjects (n=34) using automated voxel-based morphometry (VBM). The subjects were recruited for the Edinburgh High Risk Study (EHRS) and regional brain volumes had previously been measured using a semi-automated volumetric region of interest (ROI) method of analysis. For the current report, the images were processed using a study specific template and statistically analysed using the SPM99 program. The small volume correction tool in SPM was also used to restrict the analyses to specific voxels. Reductions in the probability of grey matter (GM) density were seen bilaterally in the anterior cingulate, and as a trend in the left parahippocampal gyrus for the high-risk vs. control subjects. In contrast, first episode schizophrenia subjects had less GM than high-risk subjects in several frontal and temporal regions. These results are compatible with the findings of our previous volumetric ROI analysis.

Structural and functional abnormalities of the amygdala in schizophrenia.

Schizophrenia is characterized by delusions and hallucinations, which tend to respond to treatment with dopamine receptor blockers, and a loss of motivation and affect, which do not. Structural magnetic resonance imaging (sMRI) has convincingly demonstrated reduced volumes of the amygdala-hippocampal complex (AHC) and other limbic and paralimbic structures, on both manual tracing and automated analyses. The Edinburgh High-Risk Study (EHRS) of initially healthy adolescents with at least two affected relatives has found that AHC volumes are reduced pre-morbidly but not to schizophrenic levels, suggesting that further volume reductions may be associated with the onset of schizophrenia. AHC volumes appear to be genetically mediated in families with a dominant pattern of transmission, whereas prefrontal lobe and basal ganglia volumes are related to genetic liability to schizophrenia in the generality of high-risk subjects. Temporal lobe volumes may fall as psychotic symptoms develop, in the context of drug abuse and stress. Neuropsychological testing has also demonstrated pre-morbid impairments and symptom-related deterioration. More detailed analyses of the temporal lobe changes on sMRI and fronto-temporal dysconnectivity on fMRI are in progress. These findings are discussed with reference to other indications of pre-morbid developmental disturbance in our high-risk subjects, animal models of schizophrenia, and reliable findings from neuropathological, neuropsychological, and functional imaging studies of patients with schizophrenia.

Variance in brain volume with advancing age: implications for defining the limits of normality.

BACKGROUND: Statistical models of normal ageing brain tissue volumes may support earlier diagnosis of increasingly common, yet still fatal, neurodegenerative diseases. For example, the statistically defined distribution of normal ageing brain tissue volumes may be used as a reference to assess patient volumes. To date, such models were often derived from mean values which were assumed to represent the distributions and boundaries, i.e. percentile ranks, of brain tissue volume. Since it was previously unknown, the objective of the present study was to determine if this assumption was robust, i.e. whether regression models derived from mean values accurately represented the distributions and boundaries of brain tissue volume at older ages. MATERIALS AND METHODS: We acquired T1-w magnetic resonance (MR) brain images of 227 normal and 219 Alzheimer's disease (AD) subjects (aged 55-89 years) from publicly available databanks. Using nonlinear regression within both samples, we compared mean and percentile rank estimates of whole brain tissue volume by age. RESULTS: In both the normal and AD sample, mean regression estimates of brain tissue volume often did not accurately represent percentile rank estimates (errors=-74% to 75%). In the normal sample, mean estimates generally underestimated differences in brain volume at percentile ranks below the mean. Conversely, in the AD sample, mean estimates generally underestimated differences in brain volume at percentile ranks above the mean. Differences between ages at the 5(th) percentile rank of normal subjects were ~39% greater than mean differences in the AD subjects. CONCLUSIONS: While more data are required to make true population inferences, our results indicate that mean regression estimates may not accurately represent the distributions of ageing brain tissue volumes. This suggests that percentile rank estimates will be required to robustly define the limits of brain tissue volume in normal ageing and neurodegenerative disease.