Age-related gray matter volume changes in the brain during non-elderly adulthood

Alterations in brain structure in adults with anorexia nervosa and the impact of illness duration

BACKGROUND

Brain structure alterations have been reported in anorexia nervosa, but findings have been inconsistent. This may be due to inadequate sample size, sample heterogeneity or differences in methodology.

METHOD

High resolution magnetic resonance images were acquired of 33 adult participants with anorexia nervosa and 33 healthy participants, the largest study sample to date, in order to assess whole-brain volume, ventricular cerebrospinal fluid, white matter and grey matter volume. Voxel-based morphometry was conducted to assess regional grey matter volume. Levels of depression, anxiety, obsessionality and eating disorder-related symptoms were measured and used to explore correlations with brain structure.

RESULTS

Participants with anorexia nervosa had smaller brain volumes as well as a global decrease in grey matter volume with ventricular enlargement. Voxel-based morphometry revealed a decrease in grey matter volume spanning across the cerebellum, temporal, frontal and occipital lobes. A correlation was found between grey matter volume loss and duration of illness in the cerebellum and mesencephalon. No correlations were found with clinical measures.

CONCLUSIONS

Findings are in accordance with several previous studies on brain structure and match functional studies that have assessed the symptomatology of anorexia nervosa, such as body image distortion and cognitive bias to food. The correlation with duration of illness supports the implication of cerebellar atrophy in the maintenance of low weight and disrupted eating behaviour and illustrates its role in the chronic phase of anorexia nervosa. The lack of other correlations suggests that these findings are not related to the presence of co-morbid disorders.

Three-year longitudinal population-based volumetric MRI study in first-episode schizophrenia spectrum patients

BACKGROUND

Schizophrenia is a chronic brain disorder associated with structural brain abnormalities already present at the onset of the illness. Whether these brain abnormalities might progress over time is still under debate.

METHOD

The aim of this study was to investigate likely progressive brain volume changes in schizophrenia during the first 3 years after initiating antipsychotic treatment. The study included 109 patients with a schizophrenia spectrum disorder and a control group of 76 healthy subjects. Subjects received detailed clinical and cognitive assessment and structural magnetic resonance imaging (MRI) at regular time points during a 3-year follow-up period. The effects of brain changes on cognitive and clinical variables were examined along with the impact of potential confounding factors.

RESULTS

Overall, patients and healthy controls exhibited a similar pattern of brain volume changes. However, patients showed a significant lower progressive decrease in the volume of the caudate nucleus than control subjects (F 1,307.2 = 2.12, p = 0.035), with healthy subjects showing a greater reduction than patients during the follow-up period. Clinical and cognitive outcomes were not associated with progressive brain volume changes during the early years of the illness.

CONCLUSIONS

Brain volume abnormalities that have been consistently observed at the onset of non-affective psychosis may not inevitably progress, at least over the first years of the illness. Taking together with clinical and cognitive longitudinal data, our findings, showing a lack of brain deterioration in a substantial number of individuals, suggest a less pessimistic and more reassuring perception of the illness.

Gender-specific impact of personal health parameters on individual brain aging in cognitively unimpaired elderly subjects

Aging alters brain structure and function. Personal health markers and modifiable lifestyle factors are related to individual brain aging as well as to the risk of developing Alzheimer's disease (AD). This study used a novel magnetic resonance imaging (MRI)-based biomarker to assess the effects of 17 health markers on individual brain aging in cognitively unimpaired elderly subjects. By employing kernel regression methods, the expression of normal brain-aging patterns forms the basis to estimate the brain age of a given new subject. If the estimated age is higher than the chronological age, a positive brain age gap estimation (BrainAGE) score indicates accelerated atrophy and is considered a risk factor for developing AD. Within this cross-sectional, multi-center study 228 cognitively unimpaired elderly subjects (118 males) completed an MRI at 1.5Tesla, physiological and blood parameter assessments. The multivariate regression model combining all measured parameters was capable of explaining 39% of BrainAGE variance in males (p < 0.001) and 32% in females (p < 0.01). Furthermore, markers of the metabolic syndrome as well as markers of liver and kidney functions were profoundly related to BrainAGE scores in males (p < 0.05). In females, markers of liver and kidney functions as well as supply of vitamin B₁₂ were significantly related to BrainAGE (p < 0.05). In conclusion, in cognitively unimpaired elderly subjects several clinical markers of poor health were associated with subtle structural changes in the brain that reflect accelerated aging, whereas protective effects on brain aging were observed for markers of good health. Additionally, the relations between individual brain aging and miscellaneous health markers show gender-specific patterns. The BrainAGE approach may thus serve as a clinically relevant biomarker for the detection of subtly abnormal patterns of brain aging probably preceding cognitive decline and development of AD.

Moving forward: age effects on the cerebellum underlie cognitive and motor declines

Though the cortical contributions to age-related declines in motor and cognitive performance are well-known, the potential contributions of the cerebellum are less clear. The diverse functions of the cerebellum make it an important structure to investigate in aging. Here, we review the extant literature on this topic. To date, there is evidence to indicate that there are morphological age differences in the cerebellum that are linked to motor and cognitive behavior. Cerebellar morphology is often as good as - or even better - at predicting performance than the prefrontal cortex. We also touch on the few studies using functional neuroimaging and connectivity analyses that further implicate the cerebellum in age-related performance declines. Importantly, we provide a conceptual framework for the cerebellum influencing age differences in performance, centered on the notion of degraded internal models. The evidence indicating that cerebellar age differences associate with performance highlights the need for additional work in this domain to further elucidate the role of the cerebellum in age differences in movement control and cognitive function.

Age estimation using cortical surface pattern combining thickness with curvatures

Brain development and healthy aging have been proved to follow a specific pattern, which, in turn, can be applied to help doctors diagnose mental diseases. In this paper, we design a cortical surface pattern (CSP) combining the cortical thickness with curvatures, which constructs an accurate human age estimation model with relevance vector regression. We test our model with two public databases. One is the IXI database (360 healthy subjects aging from 20 to 82 years old were selected), and the other is the INDI database (303 subjects aging from 7 to 22 years old were selected). The results show that our model can achieve as small as 4.57 years deviation in the IXI database and 1.38 years deviation in the INDI database. Furthermore, we employ this surface pattern to age groups classification and get a remarkably high accuracy (97.77 %) and a significantly high sensitivity/specificity (97.30/98.10 %). These results suggest that our designed CSP combining thickness with curvatures is stable and sensitive to brain development, and it is much more powerful than voxel-based morphometry used in previous methods for age estimation.

Correspondence of executive function related functional and anatomical alterations in aging brain

Neurocognitive aging studies have focused on age-related changes in neural activity or neural structure but few studies have focused on relationships between the two. The present study quantitatively reviewed 24 studies of age-related changes in fMRI activation across a broad spectrum of executive function tasks using activation likelihood estimation (ALE) and 22 separate studies of age-related changes in gray matter using voxel-based morphometry (VBM). Conjunction analyses between functional and structural alteration maps were constructed. Overlaps were only observed in the conjunction of dorsolateral prefrontal cortex (DLPFC) gray matter reduction and functional hyperactivation but not hypoactivation. It was not evident that the conjunctions between gray matter and activation were related to task performance. Theoretical implications of these results are discussed.

Gray matter alterations in early aging: a diffusion magnetic resonance imaging study

Many studies have observed altered neurofunctional and structural organization in the aging brain. These observations from functional neuroimaging studies show a shift in brain activity from the posterior to the anterior regions with aging (PASA model), as well as a decrease in cortical thickness, which is more pronounced in the frontal lobe followed by the parietal, occipital, and temporal lobes (retrogenesis model). However, very little work has been done using diffusion MRI (dMRI) with respect to examining the structural tissue alterations underlying these neurofunctional changes in the gray matter. Thus, for the first time, we propose to examine gray matter changes using diffusion MRI in the context of aging. In this work, we propose a novel dMRI based measure of gray matter "heterogeneity" that elucidates these functional and structural models (PASA and retrogenesis) of aging from the viewpoint of diffusion MRI. In a cohort of 85 subjects (all males, ages 15-55 years), we show very high correlation between age and "heterogeneity" (a measure of structural layout of tissue in a region-of-interest) in specific brain regions. We examine gray matter alterations by grouping brain regions into anatomical lobes as well as functional zones. Our findings from dMRI data connects the functional and structural domains and confirms the "retrogenesis" hypothesis of gray matter alterations while lending support to the neurofunctional PASA model of aging in addition to showing the preservation of paralimbic areas during healthy aging.

Evolution of hippocampal shapes across the human lifespan

Aberrant hippocampal morphology plays an important role in the pathophysiology of aging. Volumetric analysis of the hippocampus has been performed in aging studies; however, the shape morphometry--which is potentially more informative in terms of related cognition--has yet to be examined. In this paper, we employed an advanced brain mapping technique, large deformation diffeomorphic metric mapping (LDDMM), and a dimensionality reduction approach, locally linear diffeomorphic metric embedding (LLDME), to explore age-related changes in hippocampal shape as delineated from magnetic resonance (MR) images of 302 healthy adults aged from 18 to 94 years. Compared with the hippocampal volumes, the hippocampal shapes clearly showed the nonlinear trajectory of biological aging across the human lifespan, where the variation of hippocampal shapes by age was characterized by a cubic polynomial. By integrating of LDDMM and LLDME, we were also able to illustrate the average hippocampal shapes in each individual decade. In addition, LDDMM and LLDME facilitated the identification of 63 years as a threshold beyond which hippocampal morphological changes were accelerated. Adults over 63 years of age showed the inward-deformation bilaterally in the head of the hippocampi and the left subiculum regardless of hippocampal volume reduction when compared to adults younger than 63. Hence, we demonstrated that the shape of anatomical structures added another dimension of structural morphological quantification beyond the volume in understanding aging.

Comparison of two nonlinear registration techniques to investigate brain atrophy patterns in normal aging

BACKGROUND AND PURPOSE

Automated anatomical labeling (AAL) provides an automatic brain region segmentation method to allow objective measurement of regional brain volume. Nonlinear registration plays a critical role in such automated region-based volumetry. The aim of this study was to compare age-related brain regional volume changes using two nonlinear registration methods in statistical parametric mapping (SPM).

MATERIAL AND METHODS

The study included 176 right-handed healthy participants (age range: 18-94years). A total of 90 brain regions for each subject were automatically extracted, based on the AAL atlas, and two nonlinear registration methods (Normalization and DARTEL Toolbox in SPM5) were applied. Three-way ANOVA was performed to estimate the effects of brain region, each registration method and each hemisphere on regional volumes. Age-related brain-volume changes were also investigated by linear regression analysis for each nonlinear registration method.

RESULTS

Significant differences were found in volume among different brain regions (P<0.001) with the two nonlinear registration methods (P=0.011). Volumes of the corresponding brain region were significantly different (P=0.037) between two hemispheres, and age-related volume reductions were unevenly distributed across regions. The most dramatic decreases in volume were found in the bilateral insula, middle frontal regions and cingulum. Rankings of the decreased brain regional volumes differed between the two registration techniques and adjustment methods.

CONCLUSION

The inferred age-related volume atrophy patterns based on the AAL atlas were largely dependent on the choice of registration methodology.

Age-related changes in brain structural covariance networks

Previous neuroimaging studies have suggested that cerebral changes over normal aging are not simply characterized by regional alterations, but rather by the reorganization of cortical connectivity patterns. The investigation of structural covariance networks (SCNs) using voxel-based morphometry is an advanced approach to examining the pattern of covariance in gray matter (GM) volumes among different regions of the human cortex. To date, how the organization of critical SCNs change during normal aging remains largely unknown. In this study, we used an SCN mapping approach to investigate eight large-scale networks in 240 healthy participants aged 18-89 years. These participants were subdivided into young (18-23 years), middle aged (30-58 years), and older (61-89 years) subjects. Eight seed regions were chosen from widely reported functional intrinsic connectivity networks. The voxels showing significant positive associations with these seed regions were used to describe the topological organization of an SCN. All of these networks exhibited non-linear patterns in their spatial extent that were associated with normal aging. These networks, except the primary motor network, had a distributed topology in young participants, a sharply localized topology in middle aged participants, and were relatively stable in older participants. The structural covariance derived using the primary motor cortex was limited to the ipsilateral motor regions in the young and older participants, but included contralateral homologous regions in the middle aged participants. In addition, there were significant between-group differences in the structural networks associated with language-related speech and semantics processing, executive control, and the default-mode network (DMN). Taken together, the results of this study demonstrate age-related changes in the topological organization of SCNs, and provide insights into normal aging of the human brain.

Confirming the diversity of the brain after normalization: an approach based on identity authentication

During the development of neuroimaging, numerous analyses were performed to identify population differences, such as studies on age, gender, and diseases. Researchers first normalized the brain image and then identified features that represent key differences between groups. In these studies, the question of whether normalization (a pre-processing step widely used in neuroimaging studies) reduces the diversity of brains was largely ignored. There are a few studies that identify the differences between individuals after normalization. In the current study, we analyzed brain diversity on an individual level, both qualitatively and quantitatively. The main idea was to utilize brain images for identity authentication. First, the brain images were normalized and registered. Then, a pixel-level matching method was developed to compute the identity difference between different images for matching. Finally, by analyzing the performance of the proposed brain recognition strategy, the individual differences in brain images were evaluated. Experimental results on a 150-subject database showed that the proposed approach could achieve a 100% identification ratio, which indicated distinct differences between individuals after normalization. Thus, the results proved that after the normalization stage, brain images retain their main distinguishing information and features. Based on this result, we suggest that diversity (individual differences) should be considered when conducting group analysis, and that this approach may facilitate group pattern classification.

Voxel-based Morphometry of Brain MRI in Normal Aging and Alzheimer's Disease

Voxel-based morphometry (VBM) using structural brain MRI has been widely used for assessment of normal aging and Alzheimer's disease (AD). VBM of MRI data comprises segmentation into gray matter, white matter, and cerebrospinal fluid partitions, anatomical standardization of all the images to the same stereotactic space using linear affine transformation and further non-linear warping, smoothing, and finally performing a statistical analysis. Two techniques for VBM are commonly used, optimized VBM using statistical parametric mapping (SPM) 2 or SPM5 with non-linear warping based on discrete cosine transforms and SPM8 plus non-linear warping based on diffeomorphic anatomical registration using exponentiated Lie algebra (DARTEL). In normal aging, most cortical regions prominently in frontal and insular areas have been reported to show age-related gray matter atrophy. In contrast, specific structures such as amygdala, hippocampus, and thalamus have been reported to be preserved in normal aging. On the other hand, VBM studies have demonstrated progression of atrophy mapping upstream to Braak's stages of neurofibrillary tangle deposition in AD. The earliest atrophy takes place in medial temporal structures. Stand-alone VBM software using SPM8 plus DARTEL running on Windows has been newly developed as an adjunct to the clinical assessment of AD. This software provides a Z-score map as a consequence of comparison of a patient's MRI with a normal database.

The changes of cerebral morphology related to aging in Taiwanese population

A cross-sectional study with the 3-dimensional (3D) MRI reconstruction technique was conducted to investigate cerebral complexity changes related to age differences in native Taiwanese population. In our sample of 85 participants aged between 25 and 81, age was associated with gradual ventricular expansion. A nonlinear quadratic relationship between white matter volume and age was found overall in the brain. Widespread age-related reduction in white matter was detected from late adulthood onwards. However, no significant age-related changes in the cortex and whole brain volume were determined throughout adulthood. These findings provided information in describing brain structural complexity, which might in the future serve as an objective diagnostic index or as a predictive parameter for neurological diseases. Our method then may be used for cross-cultural longitudinal studies to evaluate the effect of disease, environment and aging on the brain.

Resting-state functional connectivity in normal brain aging

The world is aging and, as the elderly population increases, age-related cognitive decline emerges as a major concern. Neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), allow the investigation of the neural bases of age-related cognitive changes in vivo. Typically, fMRI studies map brain activity while subjects perform cognitive tasks, but such paradigms are often difficult to implement on a wider basis. Resting-state fMRI (rs-fMRI) has emerged as an important alternative modality of fMRI data acquisition, during which no specific task is required. Due to such simplicity and the reliability of rs-fMRI data, this modality presents increased feasibility and potential for clinical application in the future. With rs-fMRI, fluctuations in regional brain activity can be detected across separate brain regions and the patterns of intercorrelation between the functioning of these regions are measured, affording quantitative indices of resting-state functional connectivity (RSFC). This review article summarizes the results of recent rs-fMRI studies that have documented a variety of aging-related RSFC changes in the human brain, discusses the neurophysiological hypotheses proposed to interpret such findings, and provides an overview of the future, highly promising perspectives in this field.

Accelerating cortical thinning: unique to dementia or universal in aging?

Does accelerated cortical atrophy in aging, especially in areas vulnerable to early Alzheimer's disease (AD), unequivocally signify neurodegenerative disease or can it be part of normal aging? We addressed this in 3 ways. First, age trajectories of cortical thickness were delineated cross-sectionally (n = 1100) and longitudinally (n = 207). Second, effects of undetected AD on the age trajectories were simulated by mixing the sample with a sample of patients with very mild to moderate AD. Third, atrophy in AD-vulnerable regions was examined in older adults with very low probability of incipient AD based on 2-year neuropsychological stability, CSF Aβ(1-42) levels, and apolipoprotein ε4 negativity. Steady decline was seen in most regions, but accelerated cortical thinning in entorhinal cortex was observed across groups. Very low-risk older adults had longitudinal entorhinal atrophy rates similar to other healthy older adults, and this atrophy was predictive of memory change. While steady decline in cortical thickness is the norm in aging, acceleration in AD-prone regions does not uniquely signify neurodegenerative illness but can be part of healthy aging. The relationship between the entorhinal changes and changes in memory performance suggests that non-AD mechanisms in AD-prone areas may still be causative for cognitive reductions.

Yoga meditation practitioners exhibit greater gray matter volume and fewer reported cognitive failures: results of a preliminary voxel-based morphometric analysis

Hatha yoga techniques, including physical postures (asanas), breathing exercises (pranayama), and meditation, involve the practice of mindfulness. In turn, yoga meditation practices may induce the state of mindfulness, which, when evoked recurrently through repeated practice, may accrue into trait or dispositional mindfulness. Putatively, these changes may be mediated by experience-dependent neuroplastic changes. Though prior studies have identified differences in gray matter volume (GMV) between long-term mindfulness practitioners and controls, no studies to date have reported on whether yoga meditation is associated with GMV differences. The present study investigated GMV differences between yoga meditation practitioners (YMP) and a matched control group (CG). The YMP group exhibited greater GM volume in frontal, limbic, temporal, occipital, and cerebellar regions; whereas the CG had no greater regional greater GMV. In addition, the YMP group reported significantly fewer cognitive failures on the Cognitive Failures Questionnaire (CFQ), the magnitude of which was positively correlated with GMV in numerous regions identified in the primary analysis. Lastly, GMV was positively correlated with the duration of yoga practice. Results from this preliminary study suggest that hatha yoga practice may be associated with the promotion of neuroplastic changes in executive brain systems, which may confer therapeutic benefits that accrue with repeated practice.

Voxelwise evaluation of white matter volumes in first-episode psychosis

The occurrence of white matter (WM) abnormalities in psychotic disorders has been suggested by several studies investigating brain pathology and diffusion tensor measures, but evidence assessing regional WM morphometry is still scarce and conflicting. In the present study, 122 individuals with first-episode psychosis (FEP) (62 fulfilling criteria for schizophrenia/schizophreniform disorder, 26 psychotic bipolar I disorder, and 20 psychotic major depressive disorder) underwent magnetic resonance imaging, as well as 94 epidemiologically recruited controls. Images were processed with the Statistical Parametric Mapping (SPM2) package, and voxel-based morphometry was used to compare groups (t-test) and subgroups (ANOVA). Initially, no regional WM abnormalities were observed when both groups (overall FEP group versus controls) and subgroups (i.e., schizophrenia/schizophreniform, psychotic bipolar I disorder, psychotic depression, and controls) were compared. However, when the voxelwise analyses were repeated excluding subjects with comorbid substance abuse or dependence, the resulting statistical maps revealed a focal volumetric reduction in right frontal WM, corresponding to the right middle frontal gyral WM/third subcomponent of the superior longitudinal fasciculus, in subjects with schizophrenia/schizophreniform disorder (n=40) relative to controls (n=89). Our results suggest that schizophrenia/schizophreniform disorder is associated with right frontal WM volume decrease at an early course of the illness.

Sparse representation of brain aging: extracting covariance patterns from structural MRI

An enhanced understanding of how normal aging alters brain structure is urgently needed for the early diagnosis and treatment of age-related mental diseases. Structural magnetic resonance imaging (MRI) is a reliable technique used to detect age-related changes in the human brain. Currently, multivariate pattern analysis (MVPA) enables the exploration of subtle and distributed changes of data obtained from structural MRI images. In this study, a new MVPA approach based on sparse representation has been employed to investigate the anatomical covariance patterns of normal aging. Two groups of participants (group 1:290 participants; group 2:56 participants) were evaluated in this study. These two groups were scanned with two 1.5 T MRI machines. In the first group, we obtained the discriminative patterns using a t-test filter and sparse representation step. We were able to distinguish the young from old cohort with a very high accuracy using only a few voxels of the discriminative patterns (group 1:98.4%; group 2:96.4%). The experimental results showed that the selected voxels may be categorized into two components according to the two steps in the proposed method. The first component focuses on the precentral and postcentral gyri, and the caudate nucleus, which play an important role in sensorimotor tasks. The strongest volume reduction with age was observed in these clusters. The second component is mainly distributed over the cerebellum, thalamus, and right inferior frontal gyrus. These regions are not only critical nodes of the sensorimotor circuitry but also the cognitive circuitry although their volume shows a relative resilience against aging. Considering the voxels selection procedure, we suggest that the aging of the sensorimotor and cognitive brain regions identified in this study has a covarying relationship with each other.

Longitudinal brain volumetric changes during one year in non-elderly healthy adults: a voxel-based morphometry study

Previous cross-sectional magnetic resonance imaging (MRI) studies of healthy aging in young adults have indicated the presence of significant inverse correlations between age and gray matter volumes, although not homogeneously across all brain regions. However, such cross-sectional studies have important limitations and there is a scarcity of detailed longitudinal MRI studies with repeated measures obtained in the same individuals in order to investigate regional gray matter changes during short periods of time in non-elderly healthy adults. In the present study, 52 healthy young adults aged 18 to 50 years (27 males and 25 females) were followed with repeated MRI acquisitions over approximately 15 months. Gray matter volumes were compared between the two times using voxel-based morphometry, with the prediction that volume changes would be detectable in the frontal lobe, temporal neocortex and hippocampus. Voxel-wise analyses showed significant (P < 0.05, family-wise error corrected) relative volume reductions of gray matter in two small foci located in the right orbitofrontal cortex and left hippocampus. Separate comparisons for males and females showed bilateral gray matter relative reductions in the orbitofrontal cortex over time only in males. We conclude that, in non-elderly healthy adults, subtle gray matter volume alterations are detectable after short periods of time. This underscores the dynamic nature of gray matter changes in the brain during adult life, with regional volume reductions being detectable in brain regions that are relevant to cognitive and emotional processes.

Neuroanatomical substrates of age-related cognitive decline

There are many reports of relations between age and cognitive variables and of relations between age and variables representing different aspects of brain structure and a few reports of relations between brain structure variables and cognitive variables. These findings have sometimes led to inferences that the age-related brain changes cause the age-related cognitive changes. Although this conclusion may well be true, it is widely recognized that simple correlations are not sufficient to warrant causal conclusions, and other types of correlational information, such as mediation and correlations between longitudinal brain changes and longitudinal cognitive changes, also have limitations with respect to causal inferences. These issues are discussed, and the existing results on relations of regional volume, white matter hyperintensities, and diffusion tensor imaging measures of white matter integrity to age and to measures of cognitive functioning are reviewed. It is concluded that at the current time the evidence that these aspects of brain structure are neuroanatomical substrates of age-related cognitive decline is weak. The final section contains several suggestions concerning measurement and methodology that may lead to stronger conclusions in the future.

Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

We recently proposed a novel method that aggregates the multidimensional aging pattern across the brain to a single value. This method proved to provide stable and reliable estimates of brain aging – even across different scanners. While investigating longitudinal changes in BrainAGE in about 400 elderly subjects, we discovered that patients with Alzheimer’s disease and subjects who had converted to AD within 3 years showed accelerated brain atrophy by +6 years at baseline. An additional increase in BrainAGE accumulated to a score of about +9 years during follow-up. Accelerated brain aging was related to prospective cognitive decline and disease severity. In conclusion, the BrainAGE framework indicates discrepancies in brain aging and could thus serve as an indicator for cognitive functioning in the future.

Are individual differences in rates of aging greater at older ages?

Although differences among people are frequently assumed to increase with age, cross-sectional comparisons of measures of brain structure and measures of cognitive functioning often reveal similar magnitudes of between-person variability across most of adulthood. The phenomenon of nearly constant variability despite systematically lower means with increased age suggests that individual differences in rates of aging may be relatively small, particularly compared with the individual differences apparent at any age. The current study examined between-person variability in cross-sectional means and in short-term longitudinal changes in 5 cognitive abilities at different ages in adulthood. The variability in both level and change in cognitive performance was found to be similar among healthy adults from 25 to 75 years of age in all 5 cognitive abilities. Furthermore, the correlations between scores at the first and second occasions were very high, and nearly the same magnitude at all ages. The results indicate that between-person differences in short-term cognitive changes are not inevitably greater among healthy older adults than among young adults.

Hippocampal and striatal gray matter volume are associated with a smoking cessation treatment outcome: results of an exploratory voxel-based morphometric analysis

RATIONALE

Compared to nonsmokers, smokers exhibit a number of potentially important differences in regional brain structure including reduced gray matter (GM) volume and/or density in areas including frontal and cingulate cortices, thalamus, and insula. However, associations between brain structure and smoking cessation treatment outcomes have not been reported.

OBJECTIVES

In the present analysis we sought to identify associations between regional GM volume--as measured by voxel-based morphometry (VBM)--and a smoking cessation treatment outcome (point prevalence abstinence at 4 weeks).

METHODS

Adult smokers underwent high-resolution anatomical MRI scanning prior to an open label smoking cessation treatment trial. VBM was conducted in SPM5 using the DARTEL algorithm and relapser vs. quitter groups were compared using independent sample t tests (p < 0.001, uncorrected). Analyses controlled for potentially confounding factors including years smoked, cigarettes per day, total intracranial volume (TIV), and sex.

RESULTS

Of 18 smokers, 8 achieved a 4-week point prevalence abstinence, confirmed by CO level (<or=8 ppm). After controlling for all covariates, compared to relapsers, quitters had significantly higher GM volume in the left putamen and right occipital lobe, while also significantly lower GM volume in bilateral hippocampus and right cuneus.

CONCLUSIONS

These preliminary results suggest that maintaining smoking abstinence is associated with higher pre-quit brain volume in regions that subserve habit learning and visual processing, and lower brain volume in regions that subserve long-term memory processes and visual information processing. Future, large-scale studies can determine whether brain structure variables can serve as clinically useful predictors of smoking cessation treatment outcome.

Brain structural variability due to aging and gender in cognitively healthy Elders: results from the Sao Paulo Ageing and Health study

BACKGROUND AND PURPOSE

Several morphometric MR imaging studies have investigated age- and sex-related cerebral volume changes in healthy human brains, most often by using samples spanning several decades of life and linear correlation methods. This study aimed to map the normal pattern of regional age-related volumetric reductions specifically in the elderly population.

MATERIALS AND METHODS

One hundred thirty-two eligible individuals (67-75 years of age) were selected from a community-based sample recruited for the São Paulo Ageing and Health (SPAH) study, and a cross-sectional MR imaging investigation was performed concurrently with the second SPAH wave. We used voxel-based morphometry (VBM) to conduct a voxelwise search for significant linear correlations between gray matter (GM) volumes and age. In addition, region-of-interest masks were used to investigate whether the relationship between regional GM (rGM) volumes and age would be best predicted by a nonlinear model.

RESULTS

VBM and region-of-interest analyses revealed selective foci of accelerated rGM loss exclusively in men, involving the temporal neocortex, prefrontal cortex, and medial temporal region. The only structure in which GM volumetric changes were best predicted by a nonlinear model was the left parahippocampal gyrus.

CONCLUSIONS

The variable patterns of age-related GM loss across separate neocortical and temporolimbic regions highlight the complexity of degenerative processes that affect the healthy human brain across the life span. The detection of age-related limbic GM decrease in men supports the view that atrophy in such regions should be seen as compatible with normal aging.

Voxel-based morphometry and automated lobar volumetry: the trade-off between spatial scale and statistical correction

Voxel-based morphometry (VBM) and automated lobar region of interest (ROI) volumetry are comprehensive and fast methods to detect differences in overall brain anatomy on magnetic resonance images. However, VBM and automated lobar ROI volumetry have detected dissimilar gray matter differences within identical image sets in our own experience and in previous reports. To gain more insight into how diverging results arise and to attempt to establish whether one method is superior to the other, we investigated how differences in spatial scale and in the need to statistically correct for multiple spatial comparisons influence the relative sensitivity of either technique to group differences in gray matter volumes. We assessed the performance of both techniques on a small dataset containing simulated gray matter deficits and additionally on a dataset of 22q11-deletion syndrome patients with schizophrenia (22q11DS-SZ) vs. matched controls. VBM was more sensitive to simulated focal deficits compared to automated ROI volumetry, and could detect global cortical deficits equally well. Moreover, theoretical calculations of VBM and ROI detection sensitivities to focal deficits showed that at increasing ROI size, ROI volumetry suffers more from loss in sensitivity than VBM. Furthermore, VBM and automated ROI found corresponding GM deficits in 22q11DS-SZ patients, except in the parietal lobe. Here, automated lobar ROI volumetry found a significant deficit only after a smaller sub-region of interest was employed. Thus, sensitivity to focal differences is impaired relatively more by averaging over larger volumes in automated ROI methods than by the correction for multiple comparisons in VBM. These findings indicate that VBM is to be preferred over automated lobar-scale ROI volumetry for assessing gray matter volume differences between groups.