Integrity of cerebral white matter in type 1 diabetes. Reply to Wessels AM [letter]
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Magnetic resonance imaging was used to evaluate the influence of sex and brain size on compartmental brain volumes (grey matter, white matter, CSF) in a large and well-matched sample of neurologically normal women (n = 50) and men (n = 50). As expected, we found a significant sex difference for the absolute volumes of total brain, grey matter, white matter and CSF, with greater volumes for men. Relating these compartmental volume measures to brain volume resulting in proportional volume measures revealed a higher proportion of grey matter in women. No significant sex differences were found for white matter and CSF proportions. However, when the influence of sex was partialized out by regression analyses, brain volume explained 40-81% of the variance of the absolute grey matter, white matter and CSF volumes. Performing these regression analyses for the proportional volume measures revealed that brain volume explained approximately 16% of the variance in grey matter proportion. Sex or the interaction between sex and brain volume revealed no additional predicitve values. Interestingly, the correlation between brain volume and grey matter proportion was negative, with larger brains exhibiting relatively smaller proportions of grey matter. Thus, sex is not the main variable explaining the variability in grey matter volume. Rather, we suggest that brain size is the main variable determining the proportion of grey matter.
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Magnetic resonance imaging was used to evaluate the influence of sex and brain size on compartmental brain volumes (grey matter, white matter, CSF) in a large and well-matched sample of neurologically normal women (n = 50) and men (n = 50). As expected, we found a significant sex difference for the absolute volumes of total brain, grey matter, white matter and CSF, with greater volumes for men. Relating these compartmental volume measures to brain volume resulting in proportional volume measures revealed a higher proportion of grey matter in women. No significant sex differences were found for white matter and CSF proportions. However, when the influence of sex was partialized out by regression analyses, brain volume explained 40–81% of the variance of the absolute grey matter, white matter and CSF volumes. Performing these regression analyses for the proportional volume measures revealed that brain volume explained ∼16% of the variance in grey matter proportion. Sex or the interaction between sex and brain volume revealed no additional predicitve values. Interestingly, the correlation between brain volume and grey matter proportion was negative, with larger brains exhibiting relatively smaller proportions of grey matter. Thus, sex is not the main variable explaining the variability in grey matter volume. Rather, we suggest that brain size is the main variable determining the proportion of grey matter.
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The pathological effects of multiple sclerosis are not confined to lesions; tissues that appear normal on conventional magnetic resonance imaging scans are also affected, albeit subtly. One imaging technique that has proven sensitive to such effects is T1-relaxation time measurement, with previous work demonstrating abnormalities in normal-appearing white matter and grey matter. In this work we investigated the evolution of T1-relaxation time changes in normal-appearing white matter and grey matter in relapsing—remitting multiple sclerosis. Three- and five-year follow-up data from 35 people with clinically early (a mean of 1.6 years from first clinical event) relapsing—remitting multiple sclerosis and 15 healthy controls were analysed. T1-relaxation time histograms were extracted from normal-appearing white matter and grey matter, and mean, peak height and peak location values were estimated. T1-relaxation time peak height declined in the multiple sclerosis normal-appearing white matter and grey matter, but not the control group (rate difference p = 0.024 in normal-appearing white matter, in normal-appearing grey matter p = 0.038); other T1-relaxation time changes were not significantly different between groups. Changes in T1-relaxation time measures did not correlate with increases in brain T2-weighted lesion loads or Expanded Disability Status Scale scores. These results suggest that the processes underlying changes in normal-appearing white matter and grey matter T1-relaxation times are not immediately linked to white matter lesion formation, and may represent more diffuse but progressive sub-clinical pathology in relapsing—remitting multiple sclerosis.
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Occipital lobe
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Demyelination of the central nervous system is a prominent pathological hallmark of multiple sclerosis and affects both white and grey matter. However, demyelinated white and grey matter exhibit clear pathological differences, most notably the presence or absence of inflammation and activated glial cells in white and grey matter, respectively. In order to gain more insight into the differential pathology of demyelinated white and grey matter areas, we micro-dissected neighbouring white and grey matter demyelinated areas as well as normal-appearing matter from leucocortical lesions of human post-mortem material and used these samples for RNA sequencing. Our data show that even neighbouring demyelinated white and grey matter of the same leucocortical have a distinct gene expression profile and cellular composition. We propose that, based on their distinct expression profile, pathological processes in neighbouring white and grey matter are likely different which could have implications for the efficacy of treating grey matter lesions with current anti-inflammatory-based multiple sclerosis drugs.
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Abstract Objectives Accumulating evidence shows that formation of cortical grey matter lesions, characterized by accumulation of activated microglia, axonal transection, synaptic loss and neuronal apoptosis, is common in multiple sclerosis ( MS ) beginning at the early stage. Grey matter lesions are closely associated with disease progression and permanent disability in MS . At present, the precise molecular signature characteristic of grey matter damage in MS brains remains to be intensively characterized. Methods To elucidate this, we identified grey matter‐specific genes ( GMSG ) and white matter‐specific genes ( WMSG ) abundantly expressed in the normal human brain by analyzing a RNA ‐Seq dataset numbered SRP 033291, composed of the comprehensive transcriptome of separated grey matter and white matter samples. Then, we studied expression profiles of GMSG and WMSG in MS lesions by analyzing microarray datasets derived from representative cases of grey matter lesions and white matter lesions. Results We identified 714 RNA ‐Seq‐based GMSG closely related to neuronal functions and 378 WMSG with relevance to glial functions. Numerous WMSG , such as KLK 6, GJB 1 and MYRF , were downregulated in both grey matter and white matter lesions, whereas the expression of various GMSG , such as PVALB , NEUROD 6 and LINGO 1, was reduced exclusively in grey matter lesions. Conclusions Grey matter lesions of MS are characterized by underexpression of grey matter components, and the panel of RNA ‐Seq‐based GMSG and WMSG serves as molecular markers for discrimination between grey matter and white matter lesions of MS .
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Our aim was to explore a novel quantitative method [based upon an MRI-based image segmentation that allows actual calculation of grey matter, white matter and cerebrospinal fluid (CSF) volumes] for overcoming the difficulties associated with conventional techniques for measuring actual metabolic activity of the grey matter.We included four patients with normal brain MRI and fluorine-18 fluorodeoxyglucose (F-FDG)-PET scans (two women and two men; mean age 46±14 years) in this analysis. The time interval between the two scans was 0-180 days. We calculated the volumes of grey matter, white matter and CSF by using a novel segmentation technique applied to the MRI images. We measured the mean standardized uptake value (SUV) representing the whole metabolic activity of the brain from the F-FDG-PET images. We also calculated the white matter SUV from the upper transaxial slices (centrum semiovale) of the F-FDG-PET images. The whole brain volume was calculated by summing up the volumes of the white matter, grey matter and CSF. The global cerebral metabolic activity was calculated by multiplying the mean SUV with total brain volume. The whole brain white matter metabolic activity was calculated by multiplying the mean SUV for the white matter by the white matter volume. The global cerebral metabolic activity only reflects those of the grey matter and the white matter, whereas that of the CSF is zero. We subtracted the global white matter metabolic activity from that of the whole brain, resulting in the global grey matter metabolism alone. We then divided the grey matter global metabolic activity by grey matter volume to accurately calculate the SUV for the grey matter alone.The brain volumes ranged between 1546 and 1924 ml. The mean SUV for total brain was 4.8-7. Total metabolic burden of the brain ranged from 5565 to 9617. The mean SUV for white matter was 2.8-4.1. On the basis of these measurements we generated the grey matter SUV, which ranged from 8.1 to 11.3.The accurate metabolic activity of the grey matter can be calculated using the novel segmentation technique that we applied to MRI. By combining these quantitative data with those generated from F-FDG-PET images we were able to calculate the accurate metabolic activity of the grey matter. These types of measurements will be of great value in accurate analysis of the data from patients with neuropsychiatric disorders.
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