In this study, the authors performed deep brain stimulation (DBS) of the subgenual anterior cingulate cortex (SACC) in a patient with a history of bipolar disorder. After a right thalamic stroke, intractable depression without mood elevation or a mixed state developed in this patient. He underwent bilateral SACC DBS and died 16 months afterwards. Anatomical connections were studied in this patient preoperatively and postmortem using diffusion tractography (DT). A comparison of in vivo and high resolution ex vivo connectivity patterns was performed as a measure of the utility of in vivo DT in presurgical planning for DBS. Diagnostic measures included neuropsychological testing, preoperative and ex vivo DT, and macroscopic neuropathological assessment. Post-DBS depression rating scores did not improve. In vivo and ex vivo DT revealed markedly reduced limbic projections from the thalamus and SACC to the amygdala in the right (stroke-affected) hemisphere. A highly selective right mediothalamic lesion was associated with the onset of refractory depression. Reduced amygdalar-thalamic and amygdalar-SACC connections could be a contraindication to DBS for depression. Correspondence between preoperative and higher resolution ex vivo DT supports the validity of DT as a presurgical planning tool for DBS.
Jennifer A. McNab, Natalie L. Voets, Ned Jenkinson, Waney Squier, Karla L. Miller, Guy M. Goodwin and Tipu Z. Aziz
Kalai A. Muthusamy, Bhooma R. Aravamuthan, Morten L. Kringelbach, Ned Jenkinson, Natalie L. Voets, Heidi Johansen-Berg, John F. Stein and Tipu Z. Aziz
The pedunculopontine nucleus (PPN) region of the brainstem has become a new stimulation target for the treatment of gait freezing, akinesia, and postural instability in advanced Parkinson disease (PD). Because PD locomotor symptoms are probably caused by excessive γ-aminobutyric acidergic inhibition of the PPN, low-frequency stimulation of the PPN may overcome this inhibition and improve the symptoms. However, the anatomical connections of this region in humans are not known in any detail.
Diffusion weighted magnetic resonance (MR) images were acquired at 1.5 teslas, and probabilistic tractography was used to trace the connections of the PPN region in eight healthy volunteers. A single seed voxel (2 × 2 × 2 mm) was chosen in the PPN just lateral to the decussation of the superior cerebellar peduncle, and the Diffusion Toolbox of the Oxford Centre for Functional Magnetic Resonance Imaging of the Brain was used to process the acquired MR images. The connections of each volunteer's PPN region were analyzed using a human brain MR imaging atlas.
The PPN region was connected with the cerebellum and spinal cord below and to the thalamus, pallidum, subthalamic nucleus, and motor cortex above. The regions of the primary motor cortex that control the trunk and upper and lower extremities had the highest connectivity compared with other parts of motor cortex.
These findings suggest that connections of the PPN region with the primary motor cortex, basal ganglia, thalamus, cerebellum, and spinal cord may play important roles in the regulation of movement by the PPN region. Diffusion tensor imaging tractography of the PPN region may be used preoperatively to optimize placement of stimulation electrodes and postoperatively it may also be useful to reassess electrode positions.
Laurent J. Livermore, Martin Isabelle, Ian M. Bell, Oliver Edgar, Natalie L. Voets, Richard Stacey, Olaf Ansorge, Claire Vallance and Puneet Plaha
Raman spectroscopy is a biophotonic tool that can be used to differentiate between different tissue types. It is nondestructive and no sample preparation is required. The aim of this study was to evaluate the ability of Raman spectroscopy to differentiate between glioma and normal brain when using fresh biopsy samples and, in the case of glioblastomas, to compare the performance of Raman spectroscopy to predict the presence or absence of tumor with that of 5-aminolevulinic acid (5-ALA)–induced fluorescence.
A principal component analysis (PCA)–fed linear discriminant analysis (LDA) machine learning predictive model was built using Raman spectra, acquired ex vivo, from fresh tissue samples of 62 patients with glioma and 11 glioma-free brain samples from individuals undergoing temporal lobectomy for epilepsy. This model was then used to classify Raman spectra from fresh biopsies from resection cavities after functional guided, supramaximal glioma resection. In cases of glioblastoma, 5-ALA–induced fluorescence at the resection cavity biopsy site was recorded, and this was compared with the Raman spectral model prediction for the presence of tumor.
The PCA-LDA predictive model demonstrated 0.96 sensitivity, 0.99 specificity, and 0.99 accuracy for differentiating tumor from normal brain. Twenty-three resection cavity biopsies were taken from 8 patients after supramaximal resection (6 glioblastomas, 2 oligodendrogliomas). Raman spectroscopy showed 1.00 sensitivity, 1.00 specificity, and 1.00 accuracy for predicting tumor versus normal brain in these samples. In the glioblastoma cases, where 5-ALA–induced fluorescence was used, the performance of Raman spectroscopy was significantly better than the predictive value of 5-ALA–induced fluorescence, which showed 0.07 sensitivity, 1.00 specificity, and 0.24 accuracy (p = 0.0009).
Raman spectroscopy can accurately classify fresh tissue samples into tumor versus normal brain and is superior to 5-ALA–induced fluorescence. Raman spectroscopy could become an important intraoperative tool used in conjunction with 5-ALA–induced fluorescence to guide extent of resection in glioma surgery.