Cerebral Reorganization after Hemispherectomy: A DTI Study.

BACKGROUND AND PURPOSE: Hemispherectomy is a neurosurgical procedure to treat children with intractable seizures. Postsurgical improvement of cognitive and behavioral functions is observed in children after hemispherectomy suggesting plastic reorganization of the brain. Our aim was to characterize changes in DTI scalars in WM tracts of the remaining hemisphere in children after hemispherectomy, assess the associations between WM DTI scalars and age at the operation and time since the operation, and evaluate the changes in GM fractional anisotropy values in patients compared with controls. MATERIALS AND METHODS: Patients with congenital or acquired neurologic diseases who required hemispherectomy and had high-quality postsurgical DTI data available were included in this study. Atlas- and voxel-based analyses of DTI raw data of the remaining hemisphere were performed. Fractional anisotropy and mean, axial, and radial diffusivity values were calculated for WM and GM regions. A linear regression model was used for correlation between DTI scalars and age at and time since the operation. RESULTS: Nineteen patients after hemispherectomy and 21 controls were included. In patients, a decrease in fractional anisotropy and axial diffusivity values and an increase in mean diffusivity and radial diffusivity values of WM regions were observed compared with controls (P < .05, corrected for multiple comparisons). In patients with acquired pathologies, time since the operation had a significant positive correlation with white matter fractional anisotropy values. In all patients, an increase in cortical GM fractional anisotropy values was found compared with controls (P < .05). CONCLUSIONS: Changes in DTI metrics likely reflect Wallerian and/or transneuronal degeneration of the WM tracts within the remaining hemisphere. In patients with acquired pathologies, postsurgical fractional anisotropy values correlated positively with elapsed time since the operation, suggesting a higher ability to recover compared with patients with congenital pathologies leading to hemispherectomy.

An area essential for linking word meanings to word forms: evidence from primary progressive aphasia.

We investigated the relationship between deficits in naming and areas of focal atrophy in primary progressive aphasia (a neurodegenerative disease that specifically affects language processing). We tested patients, across multiple input modalities, on traditional naming tasks (picture naming) and more complex tasks (sentence completion with a name, naming in response to a question) and obtained high resolution MRI. Across most tasks, error rates were correlated with atrophy in the left middle and posterior inferior temporal gyrus. Overall, this result converges with prior literature suggesting that this region plays a major role in modality independent lexical processing.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors.

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75 percent for high-grade neuroglial tumors, 55 percent for meningiomas and 56 percent for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98 percent, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.

Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis

High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis.

High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation: γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.

Neuroimaging in pineal tumors.

BACKGROUND AND PURPOSE: The authors report radiological findings in 11 tumors in the pineal region, which were histologically diagnosed as germinomas, pineocytomas pineoblastomas, ependymomas, teratomas, and astrocytomas. METHODS: Computed tomography (CT) was performed in seven patients and magnetic resonance imaging (MRI) was performed in all patients. RESULTS: CT showed a solid or solid/cystic mass with variable contrast enhancement. MRI showed a heterogeneous mass, with hypointense signal on T1 and iso/hyperintense signal on T2-weighted images (WI) and gadolinium enhancement. Extension to adjacent structures occurred in five patients and spread through the cerebral spinal fluid (CSF) in two. CONCLUSIONS: Pineal region tumors have no pathognomonic imaging pattern. MRI and CT are complementary in diagnosis and are important to determine localization, extension, and meningeal spread.

Lipidized giant-cell glioblastoma of cerebellum.

Glioblastoma multiforme is recognized rarely in the cerebellum. We describe a peculiar case with lipid accumulation in giant tumor cells, possibly the second example so far reported in this unusual location. A 46-year-old man with a 5-month history of headache, vomiting, dizziness and instability of gait, was found to have on magnetic resonance imaging an expanding mass situated deep in the left cerebellar hemisphere. The lesion was hypointense in T 1- and hyperintense in T2-weighted images, had poorly defined borders, peripheral edema and annular foci of contrast enhancement. Eight months after subtotal removal and radiotherapy, control MRI showed tumor recurrence with aggressive features. The patient was alive 15 months after operation but follow-up was eventually lost. Histologically, the tumor showed marked pleomorphism, with many giant cells characterized by finely vacuolated cytoplasm strongly suggestive of lipid accumulation. There were few, sometimes atypical mitotic figures and foci of endothelial proliferation. The tumor cells were strongly positive for GFAP, vimentin and S100 protein, all of which stressed the foamy appearance of the giant cells. About 15% of nuclei were positive for Ki-67. We considered the case to be a so-called lipidized glioblastoma, first recognized as a subtype by Kepes and Rubinstein [1981]. Differential diagnosis with anaplastic pleomorphic xanthoastrocytoma is discussed.

Focal status epilepticus as the first manifestation of paracoccidioidomycosis.

We report a 56-year old man with prolonged focal motor status epilepticus as the first clinical manifestation of paracoccidioidomycosis (PCM) and discuss this unusual presentation. We emphasize the need for a comprehensive work-up and increased awareness for central nervous system involvement in PCM, particularly in endemic areas.