Subtask Of Daily Activities To Evaluate Dementia

Subtask Of Daily Activities To Evaluate Dementia
When observing individuals performing everyday activities, clinicians can derive information about how well the individual is able to perform the
activities. If important steps are skipped or performed incorrectly then the benefit of the activity is not realized and the mistake may be indicative of a
health condition. Such activity mistakes may include forgetting to turn off the burner, keeping the refrigerator door open, or taking an unusually long
time to complete a relatively simple activity. When activities can be assessed in an individual's own environment, they can help in characterizing daily
behavior and pinpointing cognitive or physical difficulties [7]. The CASAS project monitors a subtask of daily activities to evaluate dementia
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Brief Description Of A Study Design
3.1 Brief description of study design The specimens were cut in slices every 4 mm and are directly marked by a pathologist. MRI images were
manually independently delineated by 2 radiologists and CHOL–PET images were both manually delineated by 2 nuclear physicians and by automatic
contours using SUV of 40%, 50%, 60%, 70%, 80%. All delineated images are stacked using landmarks from SPACE MRI as a reference. One with
the best match with pathological slices will be the most accurate. There are no other specific requirements or information to be collected from patients.
No unethical or unrealistic demands are noted. All patients might receive extra radiation due to this research. No extra benefits is yet determined from
these CHOL–PET imaging in the study, however considering their age it would not affect their quality of life as much as younger generations. Data
obtained could be used for further research and benefit future patients. 3.2 Design Bias: Apart from sample bias, Article addressed possible influences
of contour correlation apart from imaging modalities. All possible factors were compared and assessed independently. Positive correlations with the
result DSC were found in prostatectomy Gleason score and relative pathological tumor volume. Tumor configuration type IV and transition zone type
IV were found to have significantly less DSC than any other 3 types, which all had relatively similar DSC values. Use of linear models showed the
most influential factors are

Anxiety In Adolescent Rhesus Monkeys
Oler, et al. (2010) investigates the potential heritability of the anxious temperament (AT) phenotype and its related brain circuity. Perhaps some of the
most prevalent mental illnesses are anxiety disorders, where symptoms become noticeable in the prepubescent period. Although many experience
feelings of anxiety during adolescence, anxiety disorders are characterized by a continual anxious temperament beginning in youth and extending into
adulthood. This anxious temperament is marked by a high degree of both freezing behaviour and a lack of communication in response to strangers or
unfamiliar stimuli. Additionally, those with AT will often see an increase in autonomic activity. It is believed that AT has some genetic predisposition.
However, ... Show more content on Helpwriting.net ...
Of these regions, the left and right anterior temporal lobes (ATL) were the most relevant. The ATL houses the amygdala as well as the anterior
hippocampus (AH), which work together to regulate emotions and memory. Following attempts to locate distinct regions within the ATL related to AT,
they found that the right dorsal amygdala, which includes the central nucleus of amygdala (CeA), the amygdalostriatal transition zone (ASTZ), and the
left hippocampus showed the highest predictability of AT from FDG–PET voxels. A voxelwise map which assessed serotonin transporter density
(which is higher in CeA than the AH) was used in order to assess the spatial division between the two regions and their predictiveness of AT. The
results did show a significant division between the two predictive areas, thereby showing spatial dissociation. Pedigree and phenotype analysis found
that AT phenotype was a heritable trait. Furthermore, metabolic activity of glucose was significantly more heritable within AH than in the dorsal
amygdala, as shown from the voxel FDG–PET. The authors noted that heritability of amygdala metabolic activity could have potentially been achieved
with a larger

The Neonatal Brain Mri Image Database
As shown in the above figure 1, the input neonatal image from the database is preprocessed using techniques such as noise removal, inhomogeneity and
partial volume correction. Then from the preprocessed image the texture feature is extracted through EWT (Empirical Wavelet transform) and the most
relevant features like Entropy, Correlation, Contrast, Homogeneity and Dissimilarity are chosen by GLCM (Gray Level Co–Occurrence Matrix).
Based on the selected features the image is segmented by means of SOM–DCNN and then from the segmented image the tissues are classified through
the help of sparse auto encoder.
Consider is the neonatal brain MRI image database, where ' ' is the number of images and the vector function of each image in the neonatal brain MRI
image database is given as .
3.1 Preprocessing
The regions of an image containing different intensity values can be modelled as a finite Gaussian distribution. All the pixels in the same image
belongs to same class and energy function is given by (1)
Where is a set of Gaussian parameters; is the Gaussian mean and is the Gaussian variance for the object .
Also reduction of noise present in the image is important and it is done when the contextual constraint is taken into account as the weight of the pixel.
For the above equation 4 with zero mean only the variance is varied by additive Gaussian noise. By this way the Gaussian model suppress the
Gaussian noise and variation can be

Markov Random Fields ( 3D ) Microstructural Map Of Materials
I.Abstract: The objective of this proposal is to develop an open–source code to generate validated three–dimensional (3D) microstructural map of
materials by coupling Markov Random Fields (MRFs) with targeted experimental sampling. MRF is a mathematical model, in which state of a voxel
can be modeled by knowing the state of its neighbors. In this work, microstructures will be generated synthetically based on an adaptively measured set
of experimental micrographs. These microstructures will be used to fill in gaps in information at the component–scale level. Currently available
methods for microstructure synthesis in advanced aerospace alloys, such as Aluminum–Lithium (Al–Li) and Titanium (Ti), run into various difficulties
when modeling... Show more content on Helpwriting.net ...
Yet, new computational algorithms are needed to achieve microstructural reconstruction at the scale of entire engineering components. In this work,
MRF models will be integrated with advanced data acquisition techniques to investigate large scale adaptive computational microstructure
reconstruction.
IV.Proposed Work: The work will utilize an autonomous optical microstructure measurement platform, called Robo
–Met 3DВ® (shown in Fig 1), for
autonomous sampling and validation of MRF computational modeling. This system, currently available at the University of Michigan, sequentially
polishes away layers of material with high accuracy and enables metallographic etching and imaging of the microstructure of materials. Post–processing
reassembles these two–dimensional (2D) images, into 3D models [4]. In this work, robotic microscopy and the MRF algorithm will be used together to
computationally reconstruct microstructures at adaptive locations. The MRF algorithm utilizes an iterated convergence criterion that minimizes the
differences between the neighbors of a 3D voxel and the corresponding 2D experimental images. In this project, the iterative process will be carried
out in a multiscale fashion, starting with a coarse voxel to a finer mesh, once the coarser 3D image has converged to a local minimum. The next step in
this project will be to generate a montage of the 3D microstructures

Symptoms And Treatment Of Fibromyalgia
Fibromyalgia (FM) is characterized by chronic widespread pain with accompanying symptoms, such as fatigue, morning stiffness, insomnia, cognitive
dullness, depression and anxiety 1,2. Not only does FM impair life quality3, but it also increases disability and absence from work4. With the evolving
diagnostic criteria of FM5, the prevalence rate is estimated about 2~4% in the general population with female predominance 6,7. Despite genetic,
environmental and biochemical factors proposed as the underlying pathophysiology8,9, the exact mechanism pertaining to FM is still under debate.
Early research on peripheral neural or muscular damage in patients of FM failed to detect consistent evidence10. Therefore, central sensitization has
been... Show more content on Helpwriting.net ...
Multiple paradigms were applied to study the differential activation between FM patients and normal controls. With the stimuli of pressure16–18,
repetitive heat pulses19, nociceptive injection20, or even incision of forearm21, compared to normal, FM patients showed increased activation in
assorted pain–related brain areas, such as thalamus, primary (S1) as well as secondary (S2) somatosensory cortices, insula, and cingulate cortex16–21.
On the other hand, impaired descending inhibitory pain system was evinced from the results of decreased activation in rACC(rostral anterior cingulate
cortex) and thalamus18 and reduced resting–state functional connectivity between ACC with PAG (periaqueductal gray)22 in FM patients.
Although tasked–related fMRI helps us extract specific cognitive function in FM patients, the variety of different paradigms impede us from compiling
or comparing all these studies directly. Besides functional changes, research on structural changes in chronic pain patients has concurrently been in the
limelight. Voxel–based morphometry (VBM)23 is the most widely employed technique and has been implicated in several groups, including chronic
low back pain24,25, tension headache26, irritable bowel syndrome27, and chronic pelvic pain28. In Fibromyalgia, several articles using

Medicine has Flourished Since the Introduction of the...
Since the introduction of the magnetic resonance imaging unit, diagnostic medicine has flourished. The ability to see different pathologies in a
detailed soft tissue image has helped guide doctors in providing patients with better treatment and therefore a better prognosis. In many cases,
magnetic resonance imaging is the golden standard in diagnostic studies. This is especially true when studying blood vessels. The majority of
doctors turn to a magnetic resonance angiography(MRA) method called bright blood imaging when they are concerned with any blood flowing
vessels. A few main areas of concern include vessels in the brain, neck, thoracic, abdominal aorta, renals, and vessels in the legs called a "run off".
When doctors order an MRA using bright blood imaging they want to rule out serious pathologies such as aneurysms, aorta dissection, or stenosis.
Bright blood imaging uses a strong magnetic field and specific pulse sequences. Below we will discuss three different techniques and their own unique
advantages and disadvantages. Time–of–flight(TOF) also called inflow angiography, is a non–invasive procedure that produces flow contrast. The more
RF excitation pulses used in an image plane the more saturated the spins become so we want to limit the amount of RF pulses to the flowing spins
by setting up thin slabs perpendicular to the vessels or blood flow. New blood is always flowing in and out of the slice, and because the spins only
receive a few RF pulses, signal is

CBCT Lab Analysis
The CBCT is somewhat recent imaging modality, as the first commercial scanner being in use in 1998 for dental imaging aims (87). This is a subtype
of computed tomography which utilizes an x–ray beam in the form of cone instead of the conventional fan one (88). For standard full field of view for
CBCT, polychromatic X–rays are generated from a source tube generated by 100–120 kV tube potential using 1.5–10 mA current (89). The generated
X–ray photon energy is absorbed by different materials found beВ¬tween the X–ray source and detector. The remaining energy after materials
absorption is recorded on a film detector as expressed by intensity (I) that means energy per area and time. For example, the flat–panel detector consists
of a pixel array... Show more content on Helpwriting.net ...
Scatter artifacts resulted from scattered X–ray photons from the original path that is added to the primary intensity causing an underestiВ¬mation of
attenuation value in Eq. 1. Scattering has increased chance to occur with the use of larger detectors resulting in streak artefacts during reconstruction of
the CBCT image (90). Additionally, beam hardening happens as the energy levels of polychromatic X–ray beams used with the CBCT are not uniform,
so the lower energy photons can be absorbed easily at the edges of the scanned area causing hardening of the X–ray beam, which resulting in lower
gray values toward the subject center causing what is called cupВ¬ping artifact even if the density all over the subject is identical (91) Ring artifacts
caused by defects or un– calibrated components in the detector forming concentric rings in the CBCT imВ¬age. Dense metallic materials scanning can
cause severe streaking artifacts when their gray values are more than the maxiВ¬mum level of operation that the software can deal with (91). Patient
motion during CBCT scanning can cause shading or streaking artefacts that appear when the gray values are incorrectly registered on the resultant
image (90, 91). This artefact comВ¬monly showed as double contours in the CBCT image. Currently, CBCT based BMD measurement has been used
mainly to evaluate bone properties around dental implantaВ¬tion (92). It was shown that, BMD measured

Level Set Segmentation Paper
T.F.Chen [9] segmentation is the process of portioning the images, where we need to find the particular portion, there are several methods
segmentation such as active contour, etc. segmentation can be done both manually and automatically. Here the new technique of segmentation known
as level sets segmentation are described, the level set segmentation reduces the problems of finding the curves which is enclose with respect to the
region of interest. The implementation of this involves the normal speed and vector field, entropy condition etc. The implementation results produced
was two different curves, which can be splitted. M.M Dersouky and T.E Taha [10] this paper a computer aided diagnosis system was proposed to
provide a comprehensive... Show more content on Helpwriting.net ...
[14] uses the T2 weighted images for extracting the ROI for the diagnosis purpose where the ROI here is concentrated on the temporal region and
intracranial region of the brain as ROIs and in [16] the proposed methods are used for extracting the brain regions by considering the T1 Weighted
images so used to find , the seed point which are located on the brain tissues and then to perform the region growing.[17] here the feature extraction
are done by applying the new method for the feature extraction for the identification of the affected regions and then the diagnosing the cognitive
disorders foe the T1 weighted images. The method used in [14] are balloon model which gives the contour triangle approximated to a shape of the
temporal lobe regions by using only three points, AAM method are used for the statistical shape and texture model which can search for an object.
The third step was the temporal lobe region, which was included with in the intracranial region. The proposed method in [16] are as follows first is
analyzing distributions of brain tissues, which includes brain tissues, CSF, scalp and marrow. The second method was applying the threshold method
for removing non–brain tissues, where to find the upper and the lower bounds and the last method was to find seed point and performing region
growing. The methods of [17] are as follows, sparse logistic regression, feature dimension reduction for efficient classification etc. The temporal region
extraction and intracranial region extraction [14] was found to be 80.4 and 98 percent. The experimental results [17] was of 87.5% classification rate
and other parameters are nearly equal to the accuracy

Leveraging Time Series Data For Mri And Bold ( Blood...
Leveraging time series data to optimize fMRI analysis in small structures
Subcortical structures in the brain are important because of their role in memory, mood, and other vital functions. However, these structures are
typically smaller than cortical structures, which can inhibit MRI spatial resolution and image quality. In particular, it remains an open question as to
how best to work with fMRI data. Maximizing signal and BOLD (blood oxygen level dependent) sensitivity from these very small regions of interests
(ROIs) is a difficult task while maintaining spacial specificity (knowledge of whether a voxel lies within a region). By using time series data from the
fMRI scans and combining it with anatomical data about the region in various correlative algorithms, we were able to construct ROI masks of two
structures that are central components of the mesolimbic pathway (also known as the reward pathway): the ventral tegmental area (VTA) and nucleus
accumbens (NAc). These region masks had a higher low (< < 0.03) and had a lower standard deviation in mean signaling across 12 scans compared to
typical anatomically derived functional masks. Our findings promote a basic framework for mapping other central nervous system structures in the
brain with optimized ROIs.
Introduction:
Neuroimaging is a tool employed by neuroscientists who look to analyze specific regions of activity. Often, delineating these regions of activity is
difficult: brain anatomies vary from person to

Dysthymic Disorder Case Study
Dysthymic Disorder (DD) is a chronic, debilitating depressive illness characterized by depressed mood for 2 or more years1. Pathophysiology of DD,
however, remains poorly understood as few studies have assessed brain abnormalities in DD. Patients with DD have symptoms that fall along a
continuous spectrum for unipolar depression and respond similarly to medication2 as those with Major Depressive Disorder (MDD); therefore,
abnormalities in brain structure, function, and metabolism in DD previously have been inferred from those in MDD. Brain metabolism and how it is
altered in illness can be quantitatively assessed in vivo using Magnetic Resonance Spectroscopy (MRS), a noninvasive technique that quantifies several
endogenous neurochemicals, ... Show more content on Helpwriting.net ...
Although single voxel MRS generates spectra with high signal–to–noise ratio, a single MRS voxel cannot be placed in a homologous brain region
because of differing brain size and gray matter gyration across individuals. Furthermore, an MRS voxel across participants will contain differing
amounts of gray and white matter, with each tissue type containing differing amounts of metabolites will contribute differentially to MRS signal.
Therefore, variability in placement and amounts of differing tissue types increases variability in metabolite levels ascertained from single voxel MRS,
decreasing statistical power to detect abnormalities in metabolite levels. Second, extant studies often used a case–control design where MR data were
acquired only once in each participant. These studies therefore can assess only associations, but not causal relations, between metabolite levels and
mechanism of illness. Third, although treatment response and symptoms across unipolar depression fall along a continuum, abnormalities in brain
structure, function, and metabolism in DD may differ from those in MDD. Brain abnormalities in DD therefore cannot be inferred from studies of
MDD. For example, we showed23 that individuals at high risk for MDD compared with those at low risk had thinner cortex in the right hemisphere of
the brain, whereas patients with DD had thicker cortex across large portions of the brain24. Similarly, metabolic abnormalities in

Episodic Memory Paper
fMRI studies usually include a episodic memory (EM) task, because EM dysfunction is the important phenomena among the cognitive changes within a
3–year time, following a diagnosis in AD. Some of the research has illustrated that EM performance downtrend with healthy aging and is uptrend in
MCI and AD. Additionally, semantic memory (SM) task have successfully foretell the cognitive downstream and have an edge over EM tasks. It has
been realized that performance decline on SM tasks are less severe than EM declines in normal aging and MCI (Hodges & Patterson, 1995). The article
compares these two techniques and explains which technique can help us achieve accurate results. The study involved specifically chosen 78 healthy
older adults. Participants

Cbcorr Case Study
RESULTS The administration of ACZ was well tolerated in all individuals. There were no significant differences in heart rate (69.3 В± 9.6 vs. 67.2 В±
8.2 bpm, respectively, p = 0.14, paired t–test) and mean arterial pressure (104. 8 В± 10.9 vs. 106.2 В± 10.3 mm Hg, respectively, p = 0.2) between the
basal and the ACZ condition in the whole sample of subjects. Changes in Network Metrics The CBFcorr network corresponding to the control group
(control–CBFcorr network), Figures 2.A–2D, showed nine nodes with concurrent significant increases of Cnodal and SAnodal, five of them located in
ventral medial prefrontal cortices and precunei (Table II). The right precuneus presented the greatest Cnodal increase. On the other hand, four nodes
showed Cnodal... Show more content on Helpwriting.net ...
The voxel with the lowest p–value was found at the right superior frontal gyrus (medial part) (MNI: x, y, z= 9, 59, 28; PFWE = 0.002, T = 6.18). The
MCI group also showed a similar pattern of changes (Figure 4, Supplementary Table S2). Here, the right superior frontal gyrus (dorsolateral part) was
the voxel with the lowest p–value (MNI: x, y, z= 18, 53, 37; PFWE = 0.002, T = 5.83). Neither group showed a significant regional decrease. At global
level, there was a significant increase in gCBFACZ as compared with gCBFBasal in both groups (Control: 42.0 В± 5.5 vs. 54.9 В± 6.3 mL/min/100g;
p < 10–6; MCI: 38.9 В± 6.7 vs. 51.1 В± 7.3 mL/min/100g; p < 10–6.).The percent of increase in the control group (+31.4 В± 9.2) was comparable to
that found in the MCI group (+32.7 В± 12.9). Supplementary analysis Regional GMV was decreased significantly in the lateral and medial temporal
regions (including hippocampi) in the MCI group compared with the control group (Supplementary Figure S1 and Supplementary Table S3). In contrast
to GMV, no significant differences were observed between groups in

Attenuation Types Of Photon Energy
The CBCT is relatively recent technology, with the first commercial scanner being introduced in 1998 for dental imaging (87) This is a type of CT
that uses a cone X–ray beam instead of the conventional fan beam (88). For standard full field of view for CBCT, polychromatic X–rays are generated
from a source tube excited by 100–120 kV tube potential with 1.5–10 mA current (89). The emitted X–ray photon energy is absorbed by materials
located beВ¬tween the X–ray source and detector. The remaining energy after absorption is recorded on a detector as expressed by intensity (I) that
represents energy per area and time. For example, the flat–panel detector consists of a pixel array of hydrogenated amorphous silicon thin–film
transistors (88). A... Show more content on Helpwriting.net ...
1. Larger detectors have a greater chance of encountering the scattered X–ray phoВ¬tons leading to streak artefacts during reconstruction proВ¬cess for
the CBCT image (90). Beam hardening occur as the energy levels of polychromatic X–ray beams used in the CBCT are not identical, the lower energy
photons can be easily absorbed at the edges of the scanned subject resulting in hardening of the X–ray beam, which produces lower gray values toward
the center of the subject (cupВ¬ping artifact) even if the density of the subject is homogeВ¬nous (91) Defects or un calibrated components in the
detector may cause ring artefacts with concentric rings in the CBCT imВ¬age. Scanning dense metallic materials can cause severe streaking artifacts
when their gray values exceed the maxi–mum level of operation that the software can handle (91). Shading or streaking artefacts may be observed
when the gray values are incorrectly registered due to patient motion during CBCT scanning (90,91) This artefact comВ¬monly appears as double
contours in the CBCT image. To date, CBCT based BMD measurement has been used mainly to estimate bone properties for dental implantaВ¬tion
(92). The BMD measured from CBCT images showed a better predicting power to estimate cortical bone fracВ¬ture than assessed by the conventional
DXA images (93). More applications are performed for the diagnosis of oral bone disease, improvement of orthodontic treatment,

Model-Based Prediction Theory
2.1 How does model–based prediction work The way of the model–based prediction works can be described in two stages: 2.1.1 Preparations and
model training According to the certain case, different preparations are needed. Like in the study of Mitchell et al. (2008), the meaning of a word
as a vector of intermediate semantic features needed to be encoded from the occurrences of stimulus of this word within a large text corpus, and the
fMRI responses of each voxel also needed to be recorded. As in the case of Kay et al. (2008), they also need to record the fMRI data and in their case,
especially from visual areas V1, V2, and V3. After the preparations, the important step of estimating the model begins. They will use the tuning from...
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So this method is often used in a group of voxels that most related with the classification (e.g. VT cortex in the visual object classification), and if
needed to do it in the whole brain, other methods such as the searchlight method will be used to reach the whole brain level. But it is a different case
of the model–based prediction method. Because this method actually modeled the individual voxel, it is possible for them do model through all the
voxels in the whole brain (do not have a hypothesis of whether the encoding will localize in particular cortical regions) (Mitchell et al., 2008), or they
can also select the cortex that makes the most senses (Kay et al., 2008). As a summary, MVP classification analysis and the model–based prediction
differ in the level of modeling (multi–voxel vs. individual voxel), the job of the algorithm (pattern classification vs. multiple regression), the output
and the indicator (experimental condition vs. brain activity), and the range of voxels analyzed (a group of voxels in general vs. whole

Critique Of The Lode Runner
From the numerous challenging levels to the addictive gameplay and simple to use editor, there are many reasons why the original Lode Runner
is considered a classic by many people. Since its release in 1983 there has been a steady stream of sequels and ports for just about every system
imaginable, but the original still holds fond memories for most fans. Before the bombs, teleporters and other gimmicks added by later games, you
simply ran around avoiding enemies while recovering stolen gold. Your character was incapable of jumping and your only means of defending
yourself was a gun that could be used to create holes in the ground to trap enemies. Lode Runner Legacy continues in this tradition, but wraps up the
classic gameplay in a glossy new... Show more content on Helpwriting.net ...
The "Adventure" mode is the best place to start as it eases you into the game and makes sure that you understand all the basics before ramping up
the challenge. Spread across 50 different levels, this mode introduces new enemies along the way and levels are addictive enough that you'll
constantly want to have just one more go. Lode Runner has always been equal parts puzzle and action, so it is no surprise to see a "Puzzle" mode
also making an appearance. Also containing 50 different levels, this mode drops the enemies and makes it all about figuring out how to get to the
gold. Some of the puzzle levels definitely requires a bit of planning and lateral thinking to pull off, but they are just as addictive as the Adventure
mode. One of the biggest treats in Lode Runner Legacy is the "Classic" mode, which offers exactly what it sounds like; all 150 levels of the
original game. The levels in Classic mode also uses voxels instead of the original art style, but for the rest they are just as challenging and
entertaining as you remember them. Some versions of the original game, like the NES release, didn't include all of the original levels, so this is a
great opportunity to see what you have missed if you only had access to a cut–down version back in the

Difference Between Conventional Ct And Cbct Imaging
Introduction
Cone–beam computed tomography (CBCT) is a valuable tool used in dentistry. It can offer various advantages as mentioned by Delamare (2016): its
smaller size and lower cost compared to the conventional computed tomography (CT), fast acquisition, submillimeter resolution, a patient radiation
dose that is relatively low (approximately 0.08 mSv average) and user friendly interactive analysis features. Clinicians can use CBCT to determine the
location of impacted teeth or the inferior alveolar nerve, treatment planning for implant placement, endodontic evaluations or TMJ assessments and
lastly aid in locating the full extent of odontogenic pathology.
Main Differences Between Conventional CT and CBCT Imaging
There are several differences that distinguish conventional CT from CBCT. A traditional CT scanner is composed of anx–ray tube that transmits a
fan–shaped x–ray beam. These x–rays beams are directed through the patient and are captured by a detector ring. The detector ring will process the
amount of photons exiting the individual in order to yield a single plane image per scan (White and Pharoah, 2009). Each single–plane image is
composed of pixels. The product of the collimation of the scanner will produce a voxel, which is the volumetric element of a pixel. A rectangular
shaped "real" voxel is obtained from conventional CT scanners. Computer software will reformat the rectangular shaped voxels into a cubic format that
can be reconstructed into three

Determining Brain Observation
If we measure the brain activity, it can be possible for us to determine the way we think, memory, emotions. To measure brain's activity you have to
know the pattern of activity that is connected with each object, memory, or way of thinking. In the study mentioned in the first reading it shows the
power of fMRI that measured, the response to each picture, that was presented to participants. I think it is really interesting how each brain activity was
measured voxel to voxel. The importance of voxel was to determine which of them activated a specific picture, how strong activation was and what
were the brain areas activated. So the main point was to determine what neural pattern was followed for each class of objects presented. The average
accuracy

Brain Perfusion Visual Analysis
1.3.3 Image analysis The analysis of brain perfusion SPECT images is primarily visual. The visual analysis is based on a flexible and comprehensive
qualitative evaluation of the image, and even more importantly, it includes the reader's medical judgment and the particular diagnostic question in each
patient [Refs]. The visual analysis also helps to prevent misinterpretation when there are technical artifacts present in the image. However, the visual
analysis is susceptible to inter– reader variability, especially in cases where CBF alterations may be subtle. This is one of the reasons for the need for
quantitative methods of image analysis. Furthermore, quantitative methods are particularly useful in research, and make it easier, for example, to study
associations with clinical variables of interest in a particular disease. One of the quantitative methods used for years in brain perfusion SPECT has
been the method based on regions of interest (ROI). The method has evolved... Show more content on Helpwriting.net ...
Therefore, the relationship between brain regions (i.e. connectivity) is not considered in the analysis, thus ignoring important features of the
complex network that is the brain [Refs]. In recent years methods have been developed to study brain connectivity using neuroimaging [Refs], one
of which is based on graph theory that can be applied to brain perfusion SPECT data (it is described below). The quantitative methods explained so
far quantify the CBF (in one way or another) but in relative units, which implies that the regional CBF values are relative to the number of counts
corresponding to a reference value. The reference value may be the mean value of a specific brain region (e.g., the cerebellum) or the whole brain.
The reference value should not be affected by the pathology under study, so that the regional relative CBF values are due to the effect of the disease in
the patient and not to the reference

Magnetic Resonance Imaging Studies
When attempting to understand neuroscience literature, one may find themselves getting lost in the statistical data that comprises it. This type of
entrapment, can cause casual readers, and professionals alike to draw conclusions that are illusory. A bracket of Functional magnetic resonance
imaging(fMRI) studies that dealt with emotion, personality, and social cognition, are a prime example of how cluttered information can lead to illusory
beliefs. This begs the question; How does one determine what information can be trusted, and what are red flags while dissecting this type of literature?
In the studies that were conducted using fMRI, high correlations were observed between subject's brain activity, and their personality measure. The way
... Show more content on Helpwriting.net ...
As was stated, the highest correlation that a perfect experiment was expected to get was a .7. This estimation was not compliant with the data that was
actually taken from the experiments in question. Selecting voxels that had a high average correlation, or simply selecting a peak voxel, lead to a high
percentage of these non–independence error experiments to be well above the estimated maximum threshold, along with their frequencies topping the
charts. This outlying measures are results of voxel selection that contained noise that favored correlations that researches wanted to see. Negative
results where also reported by these non–independence tests. A negative result is one that does not reach the threshold that the researches set up, which
means that it does have correlation to what they are looking for. This happens in a manor of one of the correlations that researchers are looking for
being conducted independently, and another being conducted non–independently. These type of results can mislead readers into think that since there
are negative results that the entirety of the experiment must have been done in an honest way. This type of misconception furthers the need for close
evaluation and reconstruction of these types of assessments, to ensure reliability and

Prediction Of Selective Serotonin Reuptake Inhibitor...
What is the topic number of your assigned article and the title of the paper?
#5, Title: "Prediction of selective serotonin reuptake inhibitor response using diffusionВweighted
MRI"
Outline of the Main Points to be included in your Poster Presentation of your article
WHY: The purpose of this article was to present findings of a study that analyzed the difference inserotonin uptake in the brain between patients who
remitted to SSRI antidepressants and those who did not.
WHAT: I hope to learn more about serotonin uptake in the brain (specifically in the amygdala and hippocampus) and how this relates to depression
and its treatment. I also hope to learn how the researchers analyzed their data and how they decided to analyze it in a certain way based on the types
of data they obtained.
HOW:
1. Skim and outline:
a. What is the overall objective of this investigation? Why was the study undertaken?
The study was performed to reaffirm a previous observation that there is a difference in ability for serotonin to bind between depressed people who
respond to SSRI's and those who don't. The investigation focuses on the left and right amygdala and hippocampus. FA (fractional anisotropy) values of
SSRI remitters and nonВremitters in the hippocampus and amygdala were found by performing probabilistic tractography using the DWВMRI scan.
Probabilistic tractography was performed using an algorithm that computes probabilistic streamlines through each voxel. The weighted average FA is

Challenges Of Diffusion Flow Segination
Diffusion tensor imaging (DTI) provides at each voxel a symmetric and positive–definite 3times3 tensor, which captures the local diffusion anisotropy
and preferred diffusion orientation of water molecules in tissues [#Pierpaoli1996]. Thus, DTI provides additional orientation information in white
matter regions of the brain, which are normally captured as homogeneous areas in traditional MR images. It is expected that such orientation
information can improve the registration of white matter regions in the brain.
However, the use of DT images poses additional challenges that are not present in traditional scalar registration techniques. In addition to the fact that
DT images have higher dimensional data representation (six independent ... Show more content on Helpwriting.net ...
The diffusion coefficient is then modeled as a symmetric positive–definite (SPD) tensor boldsymbol{D}, known as the diffusion tensor [#Bihan2001].
It encodes the mobility of the molecules in each direction and the correlation between these directions [#Sundgren2004]. It can be mathematically
represented by a 3times3 matrix with six independent components, such that: boldsymbol{D}=left[begin{array}{ccc}
D_{xx} & D_{xy} & D_{xz}
D_{xy} & D_{yy} & D_{yz}
D_{xz} & D_{yz} & D_{zz}
end{array}right]
Using Singular Value Decomposition (SVD) a tensor boldsymbol{D} can be decomposed into a diagonal matrix with three non–negative elements
lambda_{1}, lambda_{2} and lambda_{3}, known as the eigenvalues, and a matrix composed of three orthogonal eigenvectors boldsymbol{e}_{1},
boldsymbol{e}_{2}, and boldsymbol{e}_{3}, known as the eigenvectors. Tensor boldsymbol{D} is usually represented by an ellipsoid such that
the lengths of the major axes of the ellipsoid are proportional to the square roots of the eigenvalues, and the directions of the three major axes of the
ellipsoid correspond to the three eigenvectors.
Some rotation invariant scalar indices can be derived based on the eigen–decomposition to quantify the diffusion diffusivity or anisotropy at a voxel.
Based on the rank of tensor boldsymbol{D}, Westin et al. [#Westin2002] categorized the diffusion into three cases. The linear (prolate) case: the
diffusion is primarily in one

What Are The Five Public Datasets?
The proposed approach was evaluated using 5 public datasets based on two types of experiments. It was compared to two related registration methods
and three state–of–the–art methods in terms of accuracy, solution regularity and computational cost. Subsection{Data} 5 public available brain MR
image datasets (BrainWeb, CUMC, IBSR, LPBA, OASIS) were selected in our experiments. These datasets have been used in image registration
projects [#Klein2009, #Ou2014, #Hellier2001, #Rohlfing2012] for performance evaluation. In these datasets, the whole head was captured by a
variety of imaging scanners and protocols. Accordingly, images had different size, voxel spacing and contrast level. All images were provided with
corresponding label... Show more content on Helpwriting.net ...
These labels were combined to produce an initial non–background binary mask. We then used the c3d tool provided by the ITK–SNAP package
[#Yushkevich2006] to sequentially perform a three–step operation on the initial mask: dilate by one voxel, fill holes with face connectivity and erode
by one voxel in each dimension. Such an operation resulted a solid mask for the brain only region. Structures outside the mask were stripped to
generate the brain images. An additional bias field inhomogeneity correction algorithm was performed based on the N3 algorithm [#Sled1998] using
the c3d tool. CUMC: 12 images were acquired on a 1.5 T Siemens scanner by the Columbia University Medical Center. These images had relatively
large variations in contrast. 128 class labels were provided covering detailed brain structures. This dataset was used for evaluation in [#Klein2009] and
a cached copy http://www.synapse.org/#!Synapse:syn3217817 was maintained by the author of [#Klein2009]. We used this copy in our experiment
because the original data source was inaccessible. IBSR: the Center of Morphometric Analysis at the Massachusetts General Hospital provided the
Internet Brain Segmentation Repository (IBSR) dataset. It contained 18 images with three types of voxel spacings. A skull stripped version
[#Rohlfing2012] including label modification was provided as an update to the original data at http://www.nitrc.org/projects/ibsr. 43

Convolutional Networks
Convolutional neural networks (CNN) are a recent method used in image segmentation. CNNs are semantic processes that determine the meanings
of images by viewing their wholes and their parts. For example, during the training phase, a CNN might be given several images of cars, but it
might also be given images of tires and headlights. One method for implementing CNNs is pooling. Figure 3 in an article by Long, Shelhamer and
Darrel (2014), illustrates images being gradually divided into larger blocks, and where each phase is a pool. The value of each block is the maximum
value of all the pixels inside that block. Long et al. conclude that "extending these classification net to segmentation, and improving the architecture of
with... Show more content on Helpwriting.net ...
Lesion segmentation methods can be applied to different regions of the body. LipkovГЎ et al. (2017), used an unsupervised segmentation algorithm to
find liver lesions in computed tomography (CT) scans. Researchers in this study agree that manual and semi–automated methods are impractical "with
an intra– and interobserver variability up to 11 % in volume difference on liver CT scans" (p. 1). Furthermore, they oppose supervised methods because
"a significant variation in lesions shape and structure compromise efficiency of supervised methods" (p. 2). In this experiment they use a phase
separation mechanism called the Cahn–Hilliard separation (CHS) method. The Cahn–Hilliard equation removes noise from the image and separates
healthy liver tissue from damaged liver tissue, and it can operate three–dimensional imaging data. They found that this method works effectively for
small lesions, although it cannot effectively separate lesions from other areas in the image that have similar visual characteristics. For this reason, CHS
only worked better than artificial neural networks (i.e. an assisted method) when the images lacked artifacts. Finally, CT scans generally produce less
detailed images, and therefore MRI scans might change their results. A study conducted by Egger et al. (2017) examined three different algorithms
from the Lesion Segmentation Toolbox (LST). The study used two unsupervised algorithms (LGA SPM8 and

Clinical Characteristics Of MRI Data
All MRI data were processed blind to the order of scan acquisition (baseline or week 10), treatment assignment, and participant clinical characteristics.
Anatomical MRI We applied automated tools to correct for large–scale variations in image intensities30 and to remove extracerebral tissue31. We then
removed connecting dura manually on each slice in the sagittal, axial, and coronal views. We first coregistered all participant brains to an initial
template using a similarity transform that maximized mutual information32 and then applied a high–dimensional, nonrigid fluid–flow algorithm33 to
warp each brain to the exact size and shape of the template brain. Brain tissue was segmented as gray and white matter with a semi–automated method
... Show more content on Helpwriting.net ...
We applied spectral fitting to the frequency–domain signal to identify peaks for N–acetyl–aspartate (NAA), creatine+phosphocreatine (Cr),
choline–containing compounds (Ch), and glutamate+glutamine (Glx). We modeled the spectrum for those peaks with Gaussian–Lorentzian curves,
which were fitted to the data using a least–squares curve–fitting routine. The areas under the fitted curves estimated concentrations of the metabolites in
each voxel of the brain. We performed quality control of the data by visually assessing the spectrum in each MRS voxel and rejected data from voxels
with strong lipid contamination, insufficient suppression of residual water, unresolved Cr and Ch peaks, or full width at half maximum > 12 Hz for the
peaks. We then computed background noise for the MR spectrum as the standard deviation of the real part of the complex spectrum free from the
metabolite signal, and then computed the signal–to–noise ratio (SNR) for each metabolite level. The average SNR for NAA was greater than 280, an
excellent SNR attributable to use of the 8–channel head coil. We then generated a spectroscopic image (SI) for each metabolite as the ratio of peak
area to background noise for each MRS voxel in the brain. Taking the ratio of the peak area to the background noise accounted for variations in
receiver and transmitter gain across scans. We subsequently processed the spectroscopic images to correct for both partial volume effects within each
MRS voxel and the dispersion

Physics : Magnetic Resonance Angiography ( Mra )
Magnetic Resonance Angiography (MRA) is a technique in which magnetic pulses are used to image pictures of blood vessels inside the body. A
MRA can be achieved using two techniques phase contrast and time of flight. A phase contrast can be done using complex subtraction and phase
subtraction. A time of flight (TOF) can be done using darkblood and bright blood. A dark blood imaging is visualized using inversion recovery,
saturation and dephasing. A bright blood imaging is analyzed using rephased /dephased, in–flow, use of contrast agents. Once a required MRA is
processed, it is envisioned using a maximum intensity projection (MIP). A phase contrast works on idea of introducing two bipolar gradient pulses for
flow compensation. Phase can ... Show more content on Helpwriting.net ...
A phase unwrapping can be done using manual segmentation or automatic segmentation. A simpleimage processing region growing algorithm can be
used to achieve that. A manual boundary drawing can be used to unwrap phase. In such a manual segmentation a phase or a magnitude image is
preferred to be worked with. Flow uncertainty can be pictured in flow quantification and sources of error needs to be figured out and those should be
resolved. The main sources are partial volume effect, poor segmentation and noise. Parameters like vessel size (actual vessel size, resolution),
signal–to–noise ratio (SNR) (Scan parameters, blood velocity, velocity threshold), vessel segmentation accuracy (Segmentation method, SNR )
would rule the impact of these errors. Certain pixels that comprise of both still and moving spins would affect the phase value which does not
reflect the correct/true velocity. Consider the flow is laminar and the assuming the magnitude signal behavior, this error can be demonstrated as a
function of a vessel size. This would be the partial volume effect. A poor segmentation would be to miss some of the pixel that contains blood flow.
Considering partial volume pixels were missed, this error can also be defined as function of the vessel. Noise can be denoted by considering the pug
flow and ignoring the phase wrapping as ПѓF/Fv в€
ќ (ve/vp)(1/SNR)(1/О») where ve and vp are venc and peak velocity respectively, while ПѓF/Fv is
the standard deviation of flow relative

A Short Period Of Critical Development
roplasticity?
Abstract
Introduction Traditionally neuroscientists believed that when you were born, after a short period of critical development just after birth, brain
plasticity was fixed. Over 100 years ago Santiago Ramon y Cajal, the father of modern neuroscience, was the first to famously suggest that the brain
could actually modify its structural and functional organisation post childhood in response to environmental stimuli saying "Every man can, if he so
desires, become the sculptor of his own brain". However lacking in sufficient evidence, until recently the majority of neuroscientists have believed in
the staticity of cognitive function. It was in the 1970's and 80's that controversial animal tests on monkeys infamously known as the 'Silver Spring
Monkeys', confirmed the notion that the brain, or more specifically the central nervous system, was able to change its structure and function, creating
new, more advantageous neural pathways for processing without the help of medication or surgery but as a response to environmental stimuli. The
effect has been explained by the Hebbian theory, in Donald Hebbs 'The Organisation of behaviour ', published in 1949, a theory in neuroscience that
proposes an explanation for the adaptation of neurone in the brain during the learning process. The theory attempts to explain associative or Hebbian
learning, in which simultaneous activation of cells leads to pronounced increases in synaptic strength between those cells, and

Analyzing Brain Function Using Voxel -based Morphometric...
Analyzing brain Function Using Voxel–Based Morphometric Scanning Method
A recent study shows, two groups of students, one group is comprised of 21 art students, and the other group consisted of 23 non artists. The process
used to analyze brain function is known as voxel –based morphometric scanning method. The first group observed, was the artists group. They two
variables of interest that were observed, was their drawing ability. This encompasses their overall ability to construct 3 dimensional objects, and draw
them with 3D perspective, to include a viewpoint, edges, and corners. (See figure 2) The other interest of area studied, was whether the participant was
an art student, or belonged to the group that belonged to the art group. The art students, and non– art students were shown to have large differences.
The research concluded that the group of students that were artists, had a substantially more mass or gray matter in the parietal lobe's peroneus area.
According to Chamberlain, this part of the brain I most likely inked to creativity and visual imagery. This part of the brain helps the artist to
manipulate objects in their brain, and help them to take it apart in order to understand its basic foundations. When comparing these two groups,
according to ( Makuuchi et al. (2003), Miall et al. (2009) and Schlegal et al(2012) , research shows that the regions of the brain that are associated with
visual–spatial and motor processing, are shown to be not only

The Selection Criteria For These Patients
The selection criteria for these patients included no history of hallucinations, no other diagnosed mental impairments or history of such, and no
major medical conditions or states (e.g. pregnancy). All participants were socially and ethnically homogenous from a hospital in Milan, Italy. The
following demographic and clinical information was collected: age, education, illness duration, age of onset of disorder, number of manic episodes,
number of psychotic episodes, sex, Hamilton Depression Rating Scale score, and "medication load". Interviews with at least one family member
corroborated the information given by the patient. Because medication and dosage level varied by patient, Radaelli et al. quantitatively standardized
medication load by comparing antipsychotics and their dosage to an equivalent dose of chlorpromazine hydrochloride on a scale of below, equal to,
or above (0, 1, or 2 respectively) the recommended dose, and then used the same scale for benzodiazepines; antidepressants and mood stabilizers were
coded as either low dose or high dose and quantified pursuant to schedule developed by Sackeim (Sackeim, 2001). The net medication load then
becomes the mathematical sum. The Pearson's chi–squared test was performed on collected clinical and demographic data. T1–weighted structural
magnetic resonance imaging was done on all patients; these images were then analyzed by specialized mapping software, underwent voxel–based
morphometry (a neuroimaging analysis process to

Functional MRI Essay
Recently, neuroscientists have improved the quality of research through the development of new tools that allow a better understanding of the brain.
Methods like Magnetic Resonance Imaging (MRI) offer to the scientists the capability of exploring the brain from a deep perspective combining both a
good spatial and time resolution (Nelson, 2008). Furthermore, the MRI images not only allow the study of the anatomy of the brain (i.e. structural
images), but also the activation of different areas when a task is being performed (i.e. functional MRI). For example MRI images can be utilized to do
comparisons in multiple brains in order to identify structural changes (i.e. gray matter loss) or to explore different patterns of activation when a
"reading comprehension" task is carried out while scanning the subjects; the latter is known as functional MRI (fMRI). Subsequently, it is possible to
make comparisons between healthy and unhealthy brains to understand the underlying neural mechanism of diseases (Gazzaniga, 2013).
The neuroimaging studies represent a challenge in terms of statistical analysis. For instance, there are many factors that can affect this sort of research:
the sample size, the ... Show more content on Helpwriting.net ...
In simpler words smoothing consist of "blurring" the images by using a small kernel. For example, when pre–processing structural MRI images
statistical parametric maps are created, meaning that the resulting images are made of voxels to which a probabilistic value has been assigned
depending on its intensity. Therefore, a voxel in the cortex of the brain is more likely to be gray matter. At the end of the preprocessing a spatial filter
(i.e. 8 mm Gaussian kernel) is applied, because thousands of voxels are being compared under the assumption of statistical independence of each

Describe The Main Hypothesis Tested In The Study
1.Describe the main hypothesis tested in the study.
The main hypothesis being tested within the study was whether or not the fusiform gyrus was a Region of Interest (ROI) that specialised in the
perception of the human face. This main hypothesis was confirmed in Part 1 of testing and was thus expanded in Part II and III, in which they
eliminated alternative hypothesis.
2.Explain why this new ROI technique is important for fMRI research.
The Functional magnetic resonance imaging (fMRI) is a functional neuroimaging procedure that used MRI technology to measure our brains activity
through blood flow changes and our voxels.
In past experiments the researcher would have been required to scan images of the entire brain for each comparison, ... Show more content on
Helpwriting.net ...
Explain one of the alternative accounts of the function of the FFA and how the current study is able to refute (prove wrong) this account.
Imaging studies in the past have reported that the region of the fusiform gyrus is more active during face recognition than object viewing, during face
matching than location matching and during viewing normal faces other than scrambled faces or textures. This research was a great beginning, but it
does not establish that this FFA is specifically involved in face recognition, as there is a range of alternative interpretations of what can activate our
brains. These alternative interpretations include low–level feature extraction, visual attention that is greater in faces and recognition of basic items and
recognition of human objects.
To test the first hypothesis the researcher ran an initial test to determine that the Region of Interest was the fusiform gyrus. Following this he then
measured the activity in this specific region to subjects using four comparison tasks. Each of these tasks tested the alternative accounts that were
previously listed.
Part III tested one of these alternative accounts for faces compared with another human object e.g hands. In these tests the data that was collected
displayed higher intensity reactions in the ROI when parts of the face were compared to viewing human

Verbal Fluency Test
CTT–1 Percentile: a percentile score in the first part of CTT; CTT–2 Percentile: a percentile score in the second part of CTT; CTT–1 NMNS:
near–misses in number sequence in the first part of CTT; CTT–2 NMNS: near–misses in number sequence in the second part of CTT; Our attention
was drawn to the perseveration rates in the Verbal Fluency Test. These rates are calculated both for the semantic and phonetic aspect of VFT.
During the phonetic task (t(12,1) = 1.68; p < 0.001), perseverations were observed in 6 HTN patients (33%; M = 0.15; SD = 0.37) and only in 3
individuals from the control group (15%; M = 0.75; SD = 0.97). During the semantic task (t(5,1) = 1.85; p < 0.05), perseverations were observed in 13
HTN patients... Show more content on Helpwriting.net ...
They have difficulties performing simple tasks that do not require mobilizing much of their cognitive resources. More complex tasks, however, such as
the second part of the Color Trail Test, pose no problems to them. fMRI results– evaluating functional changes to the brain In this article, the
evaluation of asymmetrical cerebral activation was limited to the dorsolateral region of the prefrontal cortex. In the literature on the subject these
regions, particularly the middle frontal gyrus, are considered neuroanatomical correlates of executive and control functions (Jodzio, 2008). To evaluate
the asymmetrical brain responses we used task contrast (Congruent vs Incongruent), which emphasized the importance of inhibitory control.
Calculations were performed with the LI–toolbox (Wilke, Lidzba, 2007). Then, using Student's t–test, we calculated group responses to contrasts of
interests for every examined

Fmri Technology Research Papers
Bashkansky 1 fMRI Studies So you've heard of fMRI studies. The thing that makes the brain light up with neat colors. But what is fMRI technology
really? There are many misconceptions and misunderstandings in regards to fMRI technology and studies. The following are some things to keep in
mind when scrutinizing those pesky fMRI studies.
Functional magnetic resonance imaging, or fMRI, is a technology that measures brain activity by detecting changes in blood flow to different parts of
the brain. When neurons are activated, or "fire", they use up the oxygen they have and for a couple seconds afterward receive more oxygen through
increased blood flow. fMRI technology measures this change in blood flow, allowing us to indirectly measure brain activity. A scanner is used to
take the resting state image, which is the image when the subject tries to relax as much as possible. Another scan is taken when the participant is
performing a certain task assigned to him, such as looking at a human face. Then researchers find the difference in blood flow between the two scans.
The final image is a conglomeration of the scans of in the neighborhood of a dozen people, not just a single person. The colors on the final image
represent the probability that the increased blood flow in a brain region was not due to random chance, ... Show more content on Helpwriting.net ...
If a brain region lights up then there's increased blood flow. However, that doesn't show the nature of the brain activity. It could be excitatory brain
activity, where the neurons are receiving and using new electrical information. Or it could be inhibitory activity, meaning the neurons fire in order to
stop the flow of electrical information. Inhibitory activity exists in order to regulate our brain operations. An inhibitory neuron suppresses whatever
electrical information it touches. Without inhibitory activity there would be chaos because an electrical signal would keep

Case Study Of MRI
Subsequent images produced by MRI are important in monitoring the progression of MS. Lesions in white brain matter regions would indicate
damage to axons, but they do not necessarily indicate someone has MS and can be the result of an accident. Neurologists find it useful to track the
progression of the disease by evaluating how lesions change in size and shape, and "accurate temporal measurements of neural changes may lead to a
better understanding of the disease" (O'Donnell, p. 16, 2001). After MRI scans are performed on a patient, the images can be segmented and an
analysis can be done to determine how the disease has changed since the patient's last brain scan. Neurologisits have several different classifications to
determine how MS ... Show more content on Helpwriting.net ...
This approach is more consistent than manual delineation, but still time consuming and not reproducible" (Jain et al., p. 372, 2015). Therefore it is
important for neurologists to agree on a single efficient method that will allow for more accurate and consistent results. When segmentation is
performed on an image, the goal is to divide the image into smaller non–overlapping sections that encapsulate a type of component. This can be
accomplished in many ways. Thresholding, for example, simply separates objects based on their color value. Edge–based segmentation classify objects
by first using an edge filter. This approach "is usually less than perfect. Often, a scientist will have to make changes to the results of automatic
segmentation" (Glasbey & Horgan, p. 21, 1995). Region–based segmentation group items based on similar properties and proximity. Sometimes
textures can disrupt these regions, which is problematic because "for most natural textures, simple statistical measures are of little use" (Delmas). It
might be possible to improve results by combining all of these methods, but none of them consider the larger meaning of the images they process.
There are two perspective approaches to image segmentation. Top down segmentation is associated with supervised methods, which means that
"features belong

Essay On Global Informatics
The new challenges for Informatics arising from the analysis of extremely large data sets The capacity and ease to store data on servers, whether
cloud or physical has increased drastically over last couple of years. Three of the market leaders in storage drives reported a combined shipment of
605 exabytes of data in 2016[1]. In biomedical engineering, there have been tens of thousands of terabytes of fMRI images with each image
containing more than thousands of voxel values and twitter generates 8 Terabytes of tweets every day. Data is getting generated and consumed at a
pace never before [2]. The main challenges for informatics arising from analysis of big data, namely, Systematic Biases, Overfitting and High
dimensionality, are briefed... Show more content on Helpwriting.net ...
Overfitting occurs when the model does not fit future states. This may be caused by assumptions that are made about the data or simply be caused by
the small size of the training database/ Overfitting can arise under other circumstances as well, even though the data are not changing. In low
dimensions, standard techniques such as the expectation–maximization algorithm for finite mixture models can be applied. In high dimensions,
however, we need to carefully regularize the estimating procedure to avoid overfitting or noise accumulation and to devise good computation
algorithms [5, 6]. One solution is to resort to sparsity assumptions, under which new statistical tools have been developed [7,8]. A conventional
database schema may be composed of many different attributes and not all attributes may be needed to solve a given data mining problem. In fact, the
use of some attributes may interfere with the correct completion of a data mining task. The use of other attributes sometimes simply increases the
overall complexity and decrease the efficiency of an algorithm. This problem is sometimes referred to as the dimensionality curse [9], meaning that
there are many attributes (dimensions) involved and it is difficult to determine which ones should be used. One solution to this high dimensionality
problem is to reduce the number of attributes, which is known as dimensionality reduction. PCA and random projection, there are many other dimension

Use Of A Traumatic Brain Injury
TBI termed as Traumatic Brain Injury is an intracranial injury that often occurs when an external force is applied on the brain. A 1.4 million people,
every year in the United States experience TBI, as reported by the Centers for Disease Control and Prevention (CDC) [2]. Approximately 5.3 million
Americans live with long–term disability as a result of TBI [4]. Advancements in the field of Magnetic Resonance Imaging (MRI) has helped image
TBI better. Techniques like Susceptibility Weighted Imaging (SWI), Diffusion Tensor Imaging (DTI), and Magnetic Resonance Spectroscopy (MRS)
helps in furnishing sensitive details like micro hemorrhages, white matter injury, and abnormal metabolic activities, respectively, in brain injury. In
order to... Show more content on Helpwriting.net ...
A Computed Tomography (CT) can be used to inspect large hemorrhages, that could be surgically excised, but in case of small hemorrhages, it is
insensitive. Small hemorrhages could be early contusions or diffuse axon injury (DAI). SWI comes handy in determining such small hemorrhages and
intracranial microhemmorhages. It is a high–resolution, fully velocity compensated, 3–dimensional gradient echo imaging sequence that is extremely
sensitive to blood products in hemorrhage and deoxyhemoglobin in venous blood. Normally, CT and MRI would spot traumatic hemorrhagic lesions
in cortical gray matter (GM), sub cortical white matter (WM), major white matter tracts, including corpus callosum and internal capsule, brainstem and
in the ventricles. SWI helps in localizing microhemmorhages lesions at GM/WM junction. The research paper explores about a comparison of SWI
and a conventional GRE performed by Tong et al. The outcome of it was a difference in lesion counts, which was highest in the brainstem/cerebellum
and corpus callosum, while, lowest in the frontal & parietal–temporal–occipital GM/WM. The research article also reviews a study by Wu et al about
patients with subarachnoid hemorrhage (SAH) using CT and SWI. The

Examining The Functional Connectivity Of A Whole Brain Atlas
Background: The brain is often considered the most complex organ in the human body. It is also the most difficult to understand, given the vast array
of different functions spread throughout its mass and the interconnected functionality of its regions that makes subdividing it based on form or function
a difficult task. Craddock, et al. (2012), in the article examined, attempt to produce a method for further examining the functional connectivity (FC) of
the regions of the brain and subdivide them based on spatial positioning and function. Understanding FC in the brain can significantly improve
understanding of many brain functions and the effect of outside stimulus of various parts of the brain. This examination will only be able to take
place by creating an accurate whole brain atlas. This creation of a whole brain atlas, or a map of the brain separating it into regions of interest (ROI)
based positioning and/or function, has typically been done via fMRI analysis of the brain combined with an anatomically based mapping procedure.
This study seeks to use fMRI technology combined with an algorithm to create voxel clusters or ROI's that optimize both spatial coherence and
homogeneity in regards to the FC of the individual ROIs that can closely match of the results of a voxel–wise analysis, while improving on
computation power required and interpretability.
Clinical Significance: The usefulness of this technology and methodology can be found in both neurology research

The Effects Of Marking Steating
Additionally, beam hardening happens as the energy levels of polychromatic X–ray beams used with the CBCT are not uniform, so the lower energy
photons can be absorbed easily at the edges of the scanned area causing hardening of the X–ray beam, which resulting in lower gray values toward the
subject center causing what is called cupВ¬ping artifact even if the density all over the subject is identical (91) Ring artifacts caused by defects or un–
calibrated components in the detector forming concentric rings in the CBCT imВ¬age. Dense metallic materials scanning can cause severe streaking
artifacts when their gray values are more than the maxiВ¬mum level of operation that the software can deal with (91). Patient motion during CBCT...
Show more content on Helpwriting.net ...
However, this simple method does not offer sufВ¬ficient quality of images (96). So, more studies are still needed to develop an optimal segmentation
technology specific to individual CBCT images. For over a century, conventional dental radiographs have been the dominant source of diagnostic
information on the maxillofacial complex (97). Cone–beam computed tomography (CBCT) is a more recent development, and its clinical application in
the field of dento–maxilla facial radiology is gaining importance and becoming widespread (98). Although the clinical relevance of dental diagnosis and
pre–surgical assessment of jaw bone density has been clearly demonstrated, the available research on CBCT–based bone quality is scant (99). A new
tool, the computed tomography index, was proposed to assess the BMD on CBCT images ;( 100) with it, CBCT images can be used to evaluate
women with osteoporosis, thus increasing the possibilities for screening and early detection. A recent study evaluated the mandibular index on CBCT
images and found that the use of this index in CBCT images was valid;(101) however, the index obtained from CBCT images did not compare well
with that derived from panoramic images, the imaging modality for which the classification was originally devised. Moreover, the CBCT analysis was
conducted in templates, i.e., static slices. These aspects could have influenced the results (101). It is considered

Essay On Brain Tissue
Brain tissue can be classified into three tissue types; grey matter (GM), white matter (WM) and cerebro–spinal fluid
(CSF). Initially, all MR images are segmented into three images, each represents one tissue type. The segmented images are then registered iteratively
with their average to estimate the deformations that best align the images together. These deformations are used to generate spatially normalized and
smoothed gray matter images. The image values after seg– mentation represent the probability of belonging to the tissue
(GM or WM) of every voxel. These features generate one part of the feature vector
In a parallel step, image texture features are extracted deriving the other part of the feature vector. Five texture features
are ... Show more content on Helpwriting.net ...
producing a measure of the local balance between the count of gray matter and non–gray matter voxels). Second, the smoothing helps ameliorate the
effects of misalignment of structures when the registration is imperfect. Third, it can increase sensitivity if the extent of smoothing matches the size of
an effect of interest. Fourth, smoothing renders the data more Gaussian distributed, improving the validity of the commonly used Gaussian random
field (GRF) theory thresholding approach. Typically between 4 and 16 mm full– width half maximum (FWHM) smoothing (with a Gaussian linear
filter) is applied. Smoothing is done to correct noise and small variations. Finally, spatially normalized images are generated. every voxel of the image.
These features generate one part of the feature vector
2) Texture features: Texture features reflect the regular changes of gray values in images. These changes in the values are correlated statistically and
spatially. Textural feature vector is constructed from the gray levelco–occurrence matrix.
The co–occurrence matrix and texture features were initially used for for image classification by Haralick [24]. GLCM estimate image properties
related to second–order statistics.
It accounts for the spatial inter–dependency of two pixels at specific neighboring positions. GLCM is created from the high resolution grayscale NIFTI
images. It is then normalized and used to calculate five different textural features; Contrast(1),
Correlation(2),

Subtask Of Daily Activities To Evaluate Dementia

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