Background: According to current knowledge, gamma frequency is closely related to the functioning of neural networks underlying the basic activity of the brain and mind. Disorders in mechanisms synchronizing brain activity observed in patients diagnosed with schizophrenia are at the roots of neurocognitive disorders and psychopathological symptoms of the disease. Synchronization mechanisms are also related to the structure and functional effectiveness of the white matter. So far, not many analysis has been conducted concerning changes in the image of high frequency in patients with comorbid schizophrenia and white matter damage. The aim of this research was to present specific features of gamma waves in subjects with different psychiatric diagnoses and condition of brain structure.
Methods: Quantitative analysis of an EEG record registered from a patient diagnosed with schizophrenia and comorbid white matter hyperintensities (SCH+WM), a patient with an identical diagnosis but without structural brain changes present in the MRI (SCH-WM) of a healthy control (HC). The range of gamma waves has been obtained by using analogue filters. In order to obtain precise analysis, gamma frequencies have been divided into three bands: 30-50Hz, 50-70Hz, 70-100Hz. Matching Pursuit algorithm has been used for signal analysis enabling assessing the changes in signal energy. Synchronization effectiveness of particular areas of the brain was measured with the aid of coherence value for selected pairs of electrodes.
Results: The electrophysiological signals recorded for the SCH+WM patient showed the highest signal energy level identified for all the analyzed bands compared to the results obtained for the same pairs of electrodes of the other subjects. Coherence results revealed hipercompensation for the SCH+WM patient and her level differed substantially compared to the results of the other subjects.
Conclusions: The coexistence of schizophrenia with white matter damage can significantly disturb parameters of neural activity with high frequencies. The paper discusses possible explanations for the obtained results.
The brain consist of about 75 percent water. The brain is composed of 40% gray and 60% whitematter. The whitematter is made of axons and dendrites that the neurons use to transmit signals. Function of whitematter is to give a pathway for connecting the different areas of the brain ( 1 ). Diffusion tensor imaging (DTI) is an advanced magnetic resonance (MR) technique imaging that has been developed for diagnostic and research in medicine. DTI measures the diffusion of water molecules that helps to investigate the fiber architecture of the brain whitematter ( 2
The fiber dissection technique involves peeling away white matter fiber tracts of the brain to display its three-dimensional anatomic arrangement. The intricate three-dimensional configuration and structure of the internal capsule (IC) is not well defined. By using the fiber dissection technique, our aim was to expose and study the IC to achieve a clearer conception of its configuration and relationships with neighboring white matter fibers and central nuclei. The lateral and medial aspects of the temporal lobes of twenty, previously frozen, formalin-fixed human brains were dissected under the operating microscope using the fiber dissection technique.
The details of the three-dimensional arrangement of the fibers within the IC were studied and a comprehensive understanding of their relations was achieved. The white matter fiber dissection provides an enhanced perspective of the intricate architecture of the internal structure of brain. This enhanced understanding of intrinsic brain anatomy, particularly of functional highly relevant fiber systems such as the internal capsule, is essential for performing modern neurosurgical procedures.
Objective: The ultimate anatomy of the Meyer’s loop continues to elude us. Diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) may be able to demonstrate, in vivo, the anatomy of the complex network of white matter fibers surrounding the Meyer’s loop and the optic radiations. This study aims at exploring the anatomy of the Meyer’s loop by using DTI and fiber tractography.
Methods: Ten healthy subjects underwent magnetic resonance imaging (MRI) with DTI at 3 T. Using a region-of-interest (ROI) based diffusion tensor imaging and fiber tracking software (Release 2.6, Achieva, Philips), sequential ROI were placed to reconstruct visual fibers and neighboring projection fibers involved in the formation of Meyer’s loop. The 3-dimensional (3D) reconstructed fibers were visualized by superimposition on 3-planar MRI brain images to enhance their precise anatomical localization and relationship with other anatomical structures.
Results: Several projection fiber including the optic radiation, occipitopontine/parietopontine fibers and posterior thalamic peduncle participated in the formation of Meyer’s loop. Two patterns of angulation of the Meyer’s loop were found. Conclusions: DTI with DTT provides a complimentary, in vivo, method to study the details of the anatomy of the Meyer’s loop.
study showing exon 9 skipping. Case report An 8-month-old boy was admitted to our pediatric neurology clinic with macrocephaly. He was born as the third child to consanguineous parents. His developmental milestones were normal; however, his parents noted that he had a large head. At admission, his head circumference was 52 cm (>97 percentile) and his neurological examination was unremarkable. His magnetic resonance imaging (MRI) scan revealed bilateral, diffuse, symmetric structural whitematter abnormalities, relatively sparing the cerebellum and bilateral
), social (age 5) and fine motor skills (age 5). Laboratory analysis showed a normal complete blood count, biocehmistry, blood gas analysis, renal and liver function tests and electrolyte levels. Thyroid function tests, vitamin B12 and folic acid levels, autoimmune markers markers including ANA, anti-ds DNA, C-ANCA, P-ANCA, were within reference ranges. Cranial magnetic resonance imaging (MRI) showed signal changes in the supratentorial region, subcortical whitematter, caudate and lentiform nuclei with sparing of the peri ventricular whitematter, without any sign of
. Posterior Reversible Encephalopathy Syndrome in a Patient with Severe Preeclampsia. Anesth Analg. 2007;105(1):184-186. 9. Pula JH, Eggenberger E. Posterior reversible encephalopathy syndrome. Curr Opin Ophtlamolol. 2008;19(6):479-84. 10. Pedraza R, Marik PE, Varon J. Posterior reversible encephalopathy syndrome: a review. Crit Care & Shock. 2009;12:135-143. 11. Shutter LA, Green JP, Newman NJ, et al. Cortical blindness and whitematter lesions in a patient receiving FK506 after liver transplantation. Neurology. 1993;43:2417-8. 12. Ehthiskam S, Hashmi HA. Posterior
Gliomas have an infiltrating growth pattern in the whitematter 9 , 10 , exemplified by their ability to grow in cranial nerves. 11 Tumor infiltration is commonly assessed by morphological T2-weighted images where the high tumor signal defines the outer borders of the tumor. 12 This concept of evaluating glioma growth through morphological MRI has been challenged by studies showing infiltrative growth in gliomas not perceived on T2-weighted images. 13 , 14 Studies have shown tumor growth up to several centimeters outside the morphological T2-boundary on MRI. 14 - 16
segmentation in patients with ano-rexia nervosa before and after weight normalization. Int J Eat Disord., 2003; 33(1): 33 – 44; DOI http://dx.doi.org/10.1002/eat.10111 . 4. Travis K., Golden N., Feldman H., Solomon M., Nguyen J., Mezer A., Yeatman J., Dougherty R. Abnormal whitematter properties in adolescent girls with anorexia nervosa. Neuroimage Clin., 2015; 9: 648-659; DOI http://dx.doi.org/10.1016/j.nicl.2015.10.008 . 5. Vogel K., Timmers I., Kumar V., Nickl-Jockschat T., Bastiani M., Roebroek A., Herpertz-Dahlmann B., Konrad K., Goebel R., Seitz J. Whitematter
References 1. Tajima-Pozo K, Ruiz-Manrique G, Yus M, Arrazola J, Montañes-Rada F. Correlation between amygdala volume and impulsivity in adults with attention-deficit hyperactivity disorder. Acta Neuropsychiatr 2015; 27(6): 362-7. 2. Carrey N, Bernier D, Emms M, Gunde E, Sparkes S, Macmaster FP, Rusak B. Smaller volumes of caudate nuclei in prepubertal children with ADHD: impact of age. J Psychiatr Res 2012; 46(8): 1066-72. 3. Nagel BJ, Bathula D, Herting M, Schmitt C, Kroenke CD, Fair D, Nigg JT. Altered whitematter microstructure in children with attention