scholarly journals Limitations of the use of the MP-RAGE to identify neural changes in the brain: recent cigarette smoking alters gray matter indices in the striatum

2015 ◽  
Vol 8 ◽  
Author(s):  
Teresa R. Franklin ◽  
Reagan R. Wetherill ◽  
Kanchana Jagannathan ◽  
Nathan Hager ◽  
Charles P. O'Brien ◽  
...  
Keyword(s):  
Cigarette Smoking ◽  
Gray Matter ◽  
The Brain

scholarly journals Voxel-based correlation of 18F-THK5351 accumulation and gray matter volume in the brain of cognitively normal older adults

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Yoko Shigemoto ◽  
Daichi Sone ◽  
Miho Ota ◽  
Norihide Maikusa ◽  
Masayo Ogawa ◽  
...  
Keyword(s):  
Older Adults ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Cognitively Normal ◽  
Matter Volume ◽  
The Brain

scholarly journals Gray-matter volume in methamphetamine dependence: Cigarette smoking and changes with abstinence from methamphetamine

2012 ◽  
Vol 125 (3) ◽  
pp. 230-238 ◽  
Author(s):  
Angelica M. Morales ◽  
Buyean Lee ◽  
Gerhard Hellemann ◽  
Joseph O’Neill ◽  
Edythe D. London
Keyword(s):  
Cigarette Smoking ◽  
Gray Matter ◽  
Gray Matter Volume ◽  
Matter Volume ◽  
Methamphetamine Dependence

Intrinsic gray-matter connectivity of the brain in major depressive disorder

2019 ◽  
Vol 251 ◽  
pp. 78-85 ◽  
Author(s):  
Huifeng Zhang ◽  
Meihui Qiu ◽  
Lei Ding ◽  
David Mellor ◽  
Gang Li ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Gray Matter ◽  
Major Depressive ◽  
The Brain

scholarly journals Associations of cigarette smoking with gray and white matter in the UK Biobank

2019 ◽  
Author(s):  
Joshua Gray ◽  
Matthew Thompson ◽  
Chelsie Benca-Bachman ◽  
Max Michael Owens ◽  
Mikela Murphy ◽  
...  
Keyword(s):  
White Matter ◽  
Cigarette Smoking ◽  
Gray Matter ◽  
Brain Structure ◽  
Mean Diffusivity ◽  
Medial Lemniscus ◽  
Uk Biobank ◽  
Matter Volume ◽  
Increased Risk ◽  
The Uk

Chronic cigarette smoking is associated with increased risk for myriad health consequences including cognitive decline and dementia, but research on the link between smoking and brain structure is nascent. We assessed the relationship of cigarette smoking (ever smoked, cigarettes per day, and duration) with gray and white matter using the UK Biobank cohort (gray matter N = 19,615; white matter N = 17,760), adjusting for numerous demographic and health confounders. Ever smoked and duration were associated with smaller total gray matter volume. Ever smoked was associated with reduced volume of the right VIIIa cerebellum, as well as elevated white matter hyperintensity volumes. Smoking duration was associated with reduced total white matter volume. With regard to specific tracts, ever smoked was associated with reduced fractional anisotropy in the left cingulate gyrus part of the cingulum, left posterior thalamic radiation, and bilateral superior thalamic radiation and increased mean diffusivity in the middle cerebellar peduncle, right medial lemniscus, bilateral posterior thalamic radiation, and bilateral superior thalamic radiation. Overall, we found significant associations of cigarette exposure with global measures of gray and white matter. Furthermore, we found select associations of ever smoked, but not cigarettes per day or duration, with specific gray and white matter regions. These findings inform our understanding of the connections between smoking and variation in brain structure and clarify potential mechanisms of risk for common neurological sequelae.

MR Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using TLBO Algorithm

2021 ◽  
pp. 2150034
Author(s):  
Sandhya Gudise ◽  
Giri Babu Kande ◽  
T. Satya Savithri
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Population Based ◽  
Spinal Fluid ◽  
Brain Image ◽  
Tlbo Algorithm ◽  
Wide Range ◽  
Skull Stripping ◽  
The Brain ◽  
Mr Brain

This paper proposes an advanced and precise technique for the segmentation of Magnetic Resonance Image (MRI) of the brain. Brain MRI segmentation is to be familiar with the anatomical structure, to recognize the deformities, and to distinguish different tissues which help in treatment planning and diagnosis. Nature’s inspired population-based evolutionary algorithms are extremely popular for a wide range of applications due to their best solutions. Teaching Learning Based Optimization (TLBO) is an advanced population-based evolutionary algorithm designed based on Teaching and Learning process of a classroom. TLBO uses common controlling parameters and it won’t require algorithm-specific parameters. TLBO is more appropriate to optimize the real variables which are fuzzy valued, computationally efficient, and does not require parameter tuning. In this work, the pixels of the brain image are automatically grouped into three distinct homogeneous tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluid (CSF) using the TLBO algorithm. The methodology includes skull stripping and filtering in the pre-processing stage. The outcomes for 10 MR brain images acquired by utilizing the proposed strategy proved that the three brain tissues are segmented accurately. The segmentation outputs are compared with the ground truth images and high values are obtained for the measure’s sensitivity, specificity, and segmentation accuracy. Four different approaches, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Electromagnetic Optimization (EMO) are likewise implemented to compare with the results of the proposed methodology. From the results, it can be proved that the proposed method performed effectively than the other.

scholarly journals 60 Precision Medicine in Developmental Pediatrics: Image-based Classification of Children with Autism Spectrum Disorder using Deep Learning

2020 ◽  
Vol 25 (Supplement_2) ◽  
pp. e25-e25
Author(s):  
Sarah MacEachern ◽  
Deepthi Rajashekar ◽  
Pauline Mouches ◽  
Nathan Rowe ◽  
Emily Mckenna ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Gray Matter ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Classification Model ◽  
Children With Asd ◽  
Adc Values ◽  
Deep Gray Matter ◽  
The Brain

Abstract Introduction/Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder resulting in challenges with social communication, sensory differences, and repetitive and restricted patterns of behavior. ASD affects approximately 1 in 66 children in North America, with boys being affected four times more frequently than girls. Currently, diagnosis is made primarily based on clinical features and no robust biomarker for ASD diagnosis has been identified. Potential image-based biomarkers to aid ASD diagnosis may include structural properties of deep gray matter regions in the brain. Objectives The primary objective of this work was to investigate if children with ASD show micro- and macrostructural alterations in deep gray matter structures compared to neurotypical children, and if these biomarkers can be used for an automatic ASD classification using deep learning. Design/Methods Quantitative apparent diffusion coefficient (ADC) magnetic resonance imaging data was obtained from 23 boys with ASD ages 0.8 – 19.6 years (mean 7.6 years) and 39 neurotypical boys ages 0.3 – 17.75 years (mean 7.6 years). An atlas-based method was used for volumetric analysis and extraction of median ADC values for each subject within the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. The extracted quantitative regional volumetric and median ADC values were then used for the development and evaluation of an automatic classification method using an artificial neural network. Results The classification model was evaluated using 10-fold cross validation resulting in an overall accuracy of 76%, which is considerably better than chance level (62%). Specifically, 33 neurotypical boys were correctly classified, whereas 6 neurotypical boys were incorrectly classified. For the ASD group, 14 boys were correctly classified, while 9 boys were incorrectly classified. This translates to a precision of 70% for the children with ASD and 79% for neurotypical boys. Conclusion To the best of our knowledge, this is the first method to classify children with ASD using micro- and macrostructural properties of deep gray matter structures in the brain. The first results of the proposed deep learning method to identify children with ASD using image-based biomarkers are promising and could serve as the platform to create a more accurate and robust deep learning model for clinical application.

Computer-aided volumetric evaluation method for gray matter in the brain and the spinal cord by T1-weighted inversion recovery MRI

2001 ◽  
Vol 1230 ◽  
pp. 1154-1155
Author(s):  
Nobuhiro Tsukamoto ◽  
Hideo Kumagai ◽  
Kiichiro Saitoh ◽  
Masahiko Monma ◽  
Yutaka Ando ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Gray Matter ◽  
Evaluation Method ◽  
Inversion Recovery ◽  
Computer Aided ◽  
The Brain

scholarly journals Effects of Temperature on Evoked Electrical Activity and Anoxic Injury in CNS White Matter

1992 ◽  
Vol 12 (6) ◽  
pp. 977-986 ◽  
Author(s):  
Peter K. Stys ◽  
Stephen G. Waxman ◽  
Bruce R. Ransom
Keyword(s):  
White Matter ◽  
Optic Nerve ◽  
Functional Outcome ◽  
Gray Matter ◽  
Functional Recovery ◽  
Temperature Sensitive ◽  
Anoxic Injury ◽  
Intracellular Ca ◽  
Effects Of Temperature ◽  
The Brain

Temperature is known to influence the extent of anoxic/ischemic injury in gray matter of the brain. We tested the hypothesis that small changes in temperature during anoxic exposure could affect the degree of functional injury seen in white matter, using the isolated rat optic nerve, a typical CNS white matter tract (Foster et al., 1982). Functional recovery after anoxia was monitored by quantitative assessment of the compound action potential (CAP) area. Small changes in ambient temperature, within a range of 32 to 42°C, mildly affected the CAP of the optic nerve under normoxic conditions. Reducing the temperature to <37°C caused a reversible increase in the CAP area and in the latencies of all three CAP peaks; increasing the temperature to >37°C had opposite effects. Functional recovery of white matter following 60 min of anoxia was strongly influenced by temperature during the period of anoxia. The average recovery of the CAP, relative to control, after 60 min of anoxia administered at 37°C was 35.4 ± 7%; when the temperature was lowered by 2.5°C (i.e., to 34.5°C) for the period of anoxic exposure, the extent of functional recovery improved to 64.6 ± 15% ( p < 0.00001). Lowering the temperature to 32°C during anoxic exposure for 60 min resulted in even greater functional recovery (100.5 ± 14% of the control CAP area). Conversely, if temperature was increased to >37°C during anoxia, the functional outcome worsened, e.g., CAP recovery at 42°C was 8.5 ± 7% ( p < 0.00001). Hypothermia (i.e., 32°C) for 30 min immediately following anoxia at 37°C did not improve the functional outcome. Many processes within the brain are temperature sensitive, including O2 consumption, and it is not clear which of these is most relevant to the observed effects of temperature on recovery of white matter from anoxic injury. Unlike the situation in gray matter, the temperature dependency of anoxic injury cannot be related to reduced release of excitotoxins like glutamate, because neurotransmitters play no role in the pathophysiology of anoxic damage in white matter (Ransom et al., 1990 a). It is more likely that temperature affects the rate of ion transport by the Na+–Ca2+ exchanger, the transporter responsible for intracellular Ca2+ loading during anoxia in white matter, and/or the rate of some destructive intracellular enzymatic mechanism(s) activated by pathological increases in intracellular Ca2+.

scholarly journals Lissencephaly-pachygyria and cerebellar hypoplasia in a calf

2016 ◽  
Vol 46 (9) ◽  
pp. 1622-1628
Author(s):  
Bianca Lemos dos Santos ◽  
Maria Cecília Florisbal Damé ◽  
Ana Carolina Barreto Coelho ◽  
Plínio Aguiar Oliveira ◽  
Clairton Marcolongo-Pereira ◽  
...  
Keyword(s):  
Gray Matter ◽  
Molecular Layer ◽  
Clinical Signs ◽  
Sporadic Case ◽  
Cerebellar Hypoplasia ◽  
Congenital Diseases ◽  
Central Nervous Systems ◽  
Thoracic And Abdominal ◽  
Molecular Layers ◽  
The Brain

ABSTRACT: A case of lissencephaly-pachygyria and cerebellar hypoplasia diagnosed in a Charolais x Tabapuã calf is described. The calf presented since birth, clinical signs characterized by apathy, prolonged recumbency, tremors of the head and neck, ataxia, hypermetria, difficulty walking, blindness and swelling of the joints of the four limbs. Due to the unfavorable prognosis, the animal was euthanized and necropsied at 34 days of age. At necropsy, a rudimentary development of the brain folds (gyri) and grooves (sulci) was observed, and the cerebellum was hypoplastic. The cut surface of the brain exhibited thickening of the gray matter (pachygyria) in the frontal, parietal, temporal and occipital cortices and narrowing of the white matter. In the organs of the thoracic and abdominal cavities, no significant lesions were observed. Histologically, cerebral cortex was thick and exhibited neuronal disorganization of the gray matter. The cerebellum had a thin molecular layer, and neuronal disorganization with ectopia of the Purkinje neurons in the region of the granular and molecular layers. There were no bacterial growths in cultures of joint swabs. This was the only case on the property, which suggests that this malformation, which has not previously been described in cattle, was a sporadic case, and it was not possible to determine its cause. Neurological lesions and clinical sings presented here should be considered in the differential diagnosis of congenital diseases of the central nervous systems of cattle.

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