Gray Matters (2006)
Change in gray matter concentration (GMC) within the clusters in the posterior cingulate cortex (3A), the temporo-parietal junction (3B), the lateral cerebellum (3C) and the cerebellar vermis/brainstem (3D) in the MBSR and control group. Error bars show 95% confidence interval.
Gray Matters (2006)
Modern neuroscience has elucidated general mechanisms underlying the functional plasticity of the adult mammalian brain after limb deafferentation. However, little is known about possible structural alterations following amputation and chronic loss of afferent input in humans. Using voxel-based morphometry (VBM), based on high-resolution magnetic resonance images, we investigated the brain structure of 28 volunteers with unilateral limb amputation and compared them to healthy controls. Subjects with limb amputation exhibited a decrease in gray matter of the posterolateral thalamus contralateral to the side of the amputation. The thalamic gray matter differences were positively correlated with the time span after the amputation but not with the frequency or magnitude of coexisting phantom pain. Phantom limb pain was unrelated to thalamic structural variations, but was positively correlated to a decrease in brain areas related to the processing of pain. No gray matter increase was detected. The unilateral thalamic differences may reflect a structural correlate of the loss of afferent input as a secondary change following deafferentation.
Recently, activation-dependant structural brain plasticity in humans has been demonstrated in adults after three months of training a visio-motor skill. Learning three-ball cascade juggling was associated with a transient and highly selective increase in brain gray matter in the occipito-temporal cortex comprising the motion sensitive area hMT/V5 bilaterally. However, the exact time-scale of usage-dependant structural changes occur is still unknown. A better understanding of the temporal parameters may help to elucidate to what extent this type of cortical plasticity contributes to fast adapting cortical processes that may be relevant to learning.
Using a 3 Tesla scanner and monitoring whole brain structure we repeated and extended our original study in 20 healthy adult volunteers, focussing on the temporal aspects of the structural changes and investigated whether these changes are performance or exercise dependant. The data confirmed our earlier observation using a mean effects analysis and in addition showed that learning to juggle can alter gray matter in the occipito-temporal cortex as early as after 7 days of training. Neither performance nor exercise alone could explain these changes.
Data pre-processing and analysis were performed with SPM2 (Welcome Department of Cognitive Neurology, London, UK) running under Matlab (Mathworks, Sherborn, MA, USA) and described in detail elsewhere ; . In short, pre-processing involved coregistration, spatial normalization, gray matter segmentation and 10 mm spatial smoothing with a Gaussian kernel. For the pre-processing steps, we registered all scans of each subject to the first scan to remove positional differences between the scans of each individual. The parameters for the following spatial normalization to the template were estimated using the first scan of each individual and were applied to the scans from all time-points. To facilitate an optimal segmentation, we estimated normalization parameters while removing non-brain voxels (skull, sinus) using a previously described optimized protocol  and a scanner- and study-specific gray matter template. The optimized parameters, estimated while normalizing extracted GM images to the customized GM template, were reapplied to the original whole brain images. The images aligned with the stereotactic space defined by the Montreal Neurological Institute (MNI) , were corrected for non-uniformities in signal intensity and partitioned into gray (GM) and white matter (WM) and cerebrospinal fluid and background (CSF) using a modified mixture model cluster analysis. Subsequently, all segmented unmodulated images were smoothed by convolving them with an isotropic Gaussian kernel of 10 mm full-width at half maximum (FWHM).
Figure 2 top: Statistical parametric maps demonstrating the transient structural changes after 7 days compared to time point one. A gray matter expansion between the first and the second scan was found in the midtemporal area (hMT/V5) on the right side, demonstrating that learning to juggle can change the gray matter in hMT/V5 as early as after 7 days of training. Note, that this change is a trend only (p
Using the same paradigm, we are able to confirm and extend our previous finding of transient training-induced gray matter changes in the adult human brain. Our results show that dynamic alterations in gray matter structure can occur very rapidly within a time range of a single week (Figue 2).
As a general pattern, the increase in gray matter in all regions (Figure 1) is only detectable during constant training of the visual-motor skill and recedes when exercise is stopped, although the participants were still able to juggle. We suggest that the qualitative change (i.e. learning of a new task) is more critical for the brain to change its structure than simple training of this task once learned; however, when we detect such a change in brain structure, it may well be a combination of both. In the process of learning, it is a normative characteristic of the nervous system to change to be able to encode and appropriately implement new knowledge . Further studies need to address the question whether the skill as such or whether exercising this skill is more important for functional and structural adaptations of the brain.
It is an intriguing question why our brains do not expand over time, if we assume that that there is an increase in gray matter that is sustained with learning and/or practicing a skill. The most intuitive answer is, that the alterations are not sustained but that, once the learning process is over and the functional networks sufficient for the new task, the gray-matter changes reverse to their original size. However, given that such changes may last for at least 3 months without further exercising , we suggest that these regionally restricted changes are rather sublte and will not change the net-size or weight of the brain. It has also to be pointed out, that an increase in gray matter volume (i.e. a change of the classification of individual voxels from white to gray matter) will prompt an inverse effect (i.e. regionally loss in white matter volume) in adjacent white matter. The major future challenge is to understand the behavioural consequences and cellular mechanisms underlying training-induced neuroanatomic plasticity.
Nicotine modulates prefrontal processing when tested with functional imaging. Previous studies on changes in regional brain volumes in small samples, reporting different life-time exposure to nicotine, identified reduced volume in smokers in prefrontal areas but reported controversial results for other areas. We investigated the association of cigarette smoking and regional gray and white matter volume by using voxel-based morphometry (VBM) for T1-weighted high-resolution magnetic resonance imaging in 315 current-smokers and 659 never-smokers from the representative Study of Health in Pomerania (SHIP). Our study showed that in current-smokers smoking is significantly associated with gray matter volume loss in the prefrontal cortex, the anterior cingulate cortex, the insula, and the olfactory gyrus. White matter volumes were not relevantly reduced in current-smokers. In current-smokers, we found associations of gray matter loss and smoking exposure (pack-years) in the prefrontal cortex, the anterior and middle cingulate cortex, and the superior temporal and angular gyrus, which however did not stand corrections for multiple testing. We confirmed associations between smoking and gray matter differences in the prefrontal cortex, the anterior cingulate cortex and the insula in the general population of Pomerania (Germany). For the first time, we identified differences in brain volumes in the olfactory gyrus. Other cerebral regions did not show significant differences when correcting for multiple comparisons within the whole brain. The regions of structural deficits might be involved in addictive behavior and withdrawal symptoms, whereas further investigations have to show if the observed atrophies were caused by smoking itself or are preexisting differences between smoking and non-smoking individuals.
Not only nicotine but also other toxins are inhaled when smoking. If smoking is performed regularly over years, all these altered vascular and neural processes might result in relevant changes of the brain. Especially the neurotoxic capability of nicotine and vascular changes after smoking might be capable to alter gray (GM) and white matter (WM) of the brain after chronic consumption. Studies applying voxel-based morphometry (VBM; Ashburner and Friston, 2000) in small samples with
Statistical parametric maps superimposed on averaged T1-weighted images indicating significant smaller gray matter in the prefrontal and cingulate cortex, the insula, and the olfactory gyrus controlled for age, gender, and alcohol consumption in current-smokers compared with never-smokers (FWE-corrected for multiple comparisons, p
Our findings with respect to the PFC and the ACC are consistent with several results from other VBM investigations (PFC: Brody et al, 2004; Gallinat et al, 2006; Liao et al, 2010; Zhang et al, 2011; ACC: Gallinat et al, 2006; Liao et al, 2010). Findings of alterations in the insular cortex were contradictorily reported before. Whereas Gallinat et al (2006) found decreased insular GM density (unmodulated VBM), Zhang et al (2011) found an increase of insular GM density in smokers.
In the present study, we found no significant alterations in regional GM or WM volume in the thalamus or the cerebellum. Observations on cerebellar volume differences were almost exclusively shown in investigations of GM density. Only Kühn et al (2012), who focused their study on the cerebellum and used an approach specialized for this purpose, showed smaller cerebellar GM volume in smokers compared with non-smokers, whereas Gallinat et al (2006) showed only deficits in cerebellar GM density but not in cerebellar GM volume of smokers. 041b061a72