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almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
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CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
Different gray matter patterns in chronic schizophrenia and chronic bipolar d...Benjamin Cortes
Abstract: Gray matter (GM) volume deficits have been described in patients with schizophrenia (Sz) and bipolar disorder (BD), but to date, few studies have directly compared GM volumes between these syndromes with methods allowing for whole-brain comparisons. We have used structural magnetic resonance imaging (MRI) and voxel-based morphometry (VBM) to compare GM volumes between 38 Sz and 19 BD chronic patients. We also included 24 healthy controls. The results revealed a widespread cortical (dorsolateral and medial prefrontal and precentral) and cerebellar deficit as well as GM deficits in putamen and thalamus in Sz when compared to BD patients. Besides, a subcortical GM deficit was shown by Sz and BD groups when compared to the healthy controls, although a putaminal reduction was only evident in the Sz patients. In this comparison, the BD patients showed a limited cortical and subcortical GM deficit. These results support a partly different pattern of GM deficits associated to chronic Sz and chronic BD, with some degree of overlapping.
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Brain mri shows patterns in patients with depression
1. Brain MRI Shows Patterns in Patients with Depression, Anxiety
, Brain MRI
By Diagnostic Imaging Staff
Nov 20, 2017
Magnetic resonance images have shown a common pattern of structural abnormalities in the brains of people with major depression disorder
(MDD) and social anxiety disorder (SAD), according to a study to be presented at RSNA 2017.
Researchers from China used MR imaging to directly compare the brain structure between MDD and SAD, by assessing cortical thickness
alterations between non-comorbid medication-naïve MDD patients and SAD patients.
Thirty-seven patients with non-comorbid MDD, 24 with non-comorbid SAD, and 41 healthy controls underwent high resolution T1 weighted
images of their brain. The researchers performed vertex-based analysis of cortical thickness and group differences were compared by ANOVA
analysis followed by post-hoc analysis.
The results showed both MDD and SAD patients, relative to HC, showed cortical thickening in the bilateral medial prefrontal cortex, posterior
dorsolateral prefrontal cortex, insular cortex, left temporal pole, and right superior parietal cortex. There was cortical thinning in the left lateral
orbitofrontal cortex (OFC) and bilateral rostral middle frontal cortex.
In addition, patients with MDD showed specifically greater thickness in left fusiform, right lateral occipital cortex; thinner thickness in bilateral
lingual, and left cuneus. The patients with SAD showed specifically thinner cortical thickness in the right precentral cortex.
2. The researchers also found significant negative correlations between HAMD score and cortical thickness in the left superior frontal cortex, right
caudal medial frontal cortex and right insula among those in the MDD group.
The researchers concluded that MDD and SAD share a common pattern of gray matter abnormalities in sacilence network and dorsal attention
network.
“Our findings provide preliminary evidence of common and specific gray matter changes in MDD and SAD patients," study author Youjin Zhao,
MD, PhD, from Sichuan University in Chengdu, China, said in a release. "Future studies with larger sample sizes combined with machine
learning analysis may further aid the diagnostic and prognostic value of structural MRI."
Significant cortical thickness differences among the three groups. All regions survived clusterwise-correction (p
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