Abstract: Lateral Geniculate Nucleus (LGN) is the relay center
in the visual pathway as it receives most of the input information
from retinal ganglion cells (RGC) and sends to visual cortex. Low
threshold calcium currents (IT) at the membrane are the unique
indicator to characterize this firing functionality of the LGN neurons
gained by the RGC input. According to the LGN functional
requirements such as functional mapping of RGC to LGN, the
morphologies of the LGN neurons were developed. During the
neurological disorders like glaucoma, the mapping between RGC and
LGN is disconnected and hence stimulating LGN electrically using
deep brain electrodes can restore the functionalities of LGN. A
computational model was developed for simulating the LGN neurons
with three predominant morphologies each representing different
functional mapping of RGC to LGN. The firings of action potentials
at LGN neuron due to IT were characterized by varying the
stimulation parameters, morphological parameters and orientation. A
wide range of stimulation parameters (stimulus amplitude, duration
and frequency) represents the various strengths of the electrical
stimulation with different morphological parameters (soma size,
dendrites size and structure). The orientation (0-1800) of LGN
neuron with respect to the stimulating electrode represents the angle
at which the extracellular deep brain stimulation towards LGN
neuron is performed. A reduced dendrite structure was used in the
model using Bush–Sejnowski algorithm to decrease the
computational time while conserving its input resistance and total
surface area. The major finding is that an input potential of 0.4 V is
required to produce the action potential in the LGN neuron which is
placed at 100 μm distance from the electrode. From this study, it can
be concluded that the neuroprostheses under design would need to
consider the capability of inducing at least 0.4V to produce action
potentials in LGN.
Abstract: The main cause of Alzheimer disease (AD) was
believed to be mainly due to the accumulation of free radicals owing
to oxidative stress (OS) in brain tissue. The mechanism of the
neurotoxicity of Aluminum chloride (AlCl3) induced AD in
hippocampus Albino wister rat brain tissue, the curative & the
protective effects of Lipidium sativum group (LS) water extract were
assessed after 8 weeks by attenuated total reflection spectroscopy
ATR-IR and histologically by light microscope. ATR-IR results
revealed that the membrane phospholipid undergo free radical
attacks, mediated by AlCl3, primary affects the polyunsaturated fatty
acids indicated by the increased of the olefinic -C=CH sub-band area
around 3012 cm-1 from the curve fitting analysis. The narrowing in
the half band width (HBW) of the sνCH2 sub-band around 2852 cm-1
due to Al intoxication indicates the presence of trans form fatty acids
rather than gauch rotomer. The degradation of hydrocarbon chain to
shorter chain length, increasing in membrane fluidity, disorder, and
decreasing in lipid polarity in AlCl3 group indicated by the detected
changes in certain calculated area ratios compared to the control.
Administration of LS was greatly improved these parameters
compared to the AlCl3 group. Al influences the Aβ aggregation and
plaque formation, which in turn interferes to and disrupts the
membrane structure. The results also showed a marked increase in
the β-parallel and antiparallel structure, that characterize the Aβ
formation in Al-induced AD hippocampal brain tissue, indicated by
the detected increase in both amide I sub-bands around 1674, 1692
cm-1. This drastic increase in Aβ formation was greatly reduced in the
curative and protective groups compared to the AlCl3 group and
approached nearly the control values. These results supported too by
the light microscope. AlCl3 group showed significant marked
degenerative changes in hippocampal neurons. Most cells appeared
small, shrieked and deformed. Interestingly, the administration of LS
in curative and protective groups markedly decreases the amount of
degenerated cells compared to the non-treated group. In addition, the
intensity of congo red stained cells was decreased. Hippocampal
neurons looked more/or less similar to those of control. This study showed a promising therapeutic effect of Lipidium
sativum group (LS) on AD rat model that seriously overcome the
signs of oxidative stress on membrane lipid and restore the protein
misfolding.
Abstract: Brain-Computer Interfaces (BCIs) measure brain
signals activity, intentionally and unintentionally induced by users,
and provides a communication channel without depending on the
brain’s normal peripheral nerves and muscles output pathway.
Feature Selection (FS) is a global optimization machine learning
problem that reduces features, removes irrelevant and noisy data
resulting in acceptable recognition accuracy. It is a vital step
affecting pattern recognition system performance. This study presents
a new Binary Particle Swarm Optimization (BPSO) based feature
selection algorithm. Multi-layer Perceptron Neural Network
(MLPNN) classifier with backpropagation training algorithm and
Levenberg-Marquardt training algorithm classify selected features.
Abstract: Background: To compare the thinning patterns of the
ganglion cell-inner plexiform layer (GCIPL) and peripapillary retinal
nerve fiber layer (pRNFL) as measured using Cirrus high-definition
optical coherence tomography (HD-OCT) in patients with visual field
(VF) defects that respect the vertical meridian. Methods: Twenty eyes of eleven patients with VF defects that
respect the vertical meridian were enrolled retrospectively. The
thicknesses of the macular GCIPL and pRNFL were measured using
Cirrus HD-OCT. The 5% and 1% thinning area index (TAI) was
calculated as the proportion of abnormally thin sectors at the 5% and
1% probability level within the area corresponding to the affected VF.
The 5% and 1% TAI were compared between the GCIPL and pRNFL
measurements. Results: The color-coded GCIPL deviation map showed a
characteristic vertical thinning pattern of the GCIPL, which is also
seen in the VF of patients with brain lesions. The 5% and 1% TAI
were significantly higher in the GCIPL measurements than in the
pRNFL measurements (all P < 0.01). Conclusions: Macular GCIPL analysis clearly visualized a
characteristic topographic pattern of retinal ganglion cell (RGC) loss
in patients with VF defects that respect the vertical meridian, unlike
pRNFL measurements. Macular GCIPL measurements provide more
valuable information than pRNFL measurements for detecting the
loss of RGCs in patients with retrograde degeneration of the optic
nerve fibers.
Abstract: In this paper, we present an application of Riemannian
geometry for processing non-Euclidean image data. We consider the
image as residing in a Riemannian manifold, for developing a new
method to brain edge detection and brain extraction. Automating this
process is a challenge due to the high diversity in appearance brain
tissue, among different patients and sequences. The main contribution, in this paper, is the use of an edge-based
anisotropic diffusion tensor for the segmentation task by integrating
both image edge geometry and Riemannian manifold (geodesic,
metric tensor) to regularize the convergence contour and extract
complex anatomical structures. We check the accuracy of the
segmentation results on simulated brain MRI scans of single
T1-weighted, T2-weighted and Proton Density sequences. We
validate our approach using two different databases: BrainWeb
database, and MRI Multiple sclerosis Database (MRI MS DB). We
have compared, qualitatively and quantitatively, our approach with
the well-known brain extraction algorithms. We show that using
a Riemannian manifolds to medical image analysis improves the
efficient results to brain extraction, in real time, outperforming the
results of the standard techniques.
Abstract: The aim of the study is to compare behavioral and
EEG reactions in Turkic-speaking inhabitants of Siberia (Tuvinians
and Yakuts) and Russians during the recognition of syntax errors in
native and foreign languages. Sixty-three healthy aboriginals of the
Tyva Republic, 29 inhabitants of the Sakha (Yakutia) Republic, and
55 Russians from Novosibirsk participated in the study. EEG were
recorded during execution of error-recognition task in Russian and
English language (in all participants) and in native languages
(Tuvinian or Yakut Turkic-speaking inhabitants). Reaction time (RT)
and quality of task execution were chosen as behavioral measures.
Amplitude and cortical distribution of P300 and P600 peaks of ERP
were used as a measure of speech-related brain activity. In Tuvinians,
there were no differences in the P300 and P600 amplitudes as well as
in cortical topology for Russian and Tuvinian languages, but there
was a difference for English. In Yakuts, the P300 and P600
amplitudes and topology of ERP for Russian language were the same
as Russians had for native language. In Yakuts, brain reactions during
Yakut and English language comprehension had no difference, while
the Russian language comprehension was differed from both Yakut
and English. We found out that the Tuvinians recognized both Russian and
Tuvinian as native languages, and English as a foreign language. The
Yakuts recognized both English and Yakut as foreign languages, but
Russian as a native language. According to the inquirer, both
Tuvinians and Yakuts use the national language as a spoken
language, whereas they do not use it for writing. It can well be a
reason that Yakuts perceive the Yakut writing language as a foreign
language while writing Russian as their native.
Abstract: Current study established for EEG signal analysis in
patients with language disorder. Language disorder can be defined as
meaningful delay in the use or understanding of spoken or written
language. The disorder can include the content or meaning of
language, its form, or its use. Here we applied Z-score, power
spectrum, and coherence methods to discriminate the language
disorder data from healthy ones. Power spectrum of each channel in
alpha, beta, gamma, delta, and theta frequency bands was measured.
In addition, intra hemispheric Z-score obtained by scoring algorithm.
Obtained results showed high Z-score and power spectrum in
posterior regions. Therefore, we can conclude that peoples with
language disorder have high brain activity in frontal region of brain
in comparison with healthy peoples. Results showed that high coherence correlates with irregularities
in the ERP and is often found during complex task, whereas low
coherence is often found in pathological conditions. The results of the
Z-score analysis of the brain dynamics showed higher Z-score peak
frequency in delta, theta and beta sub bands of Language Disorder
patients. In this analysis there were activity signs in both hemispheres
and the left-dominant hemisphere was more active than the right.
Abstract: It has been known that a characteristic
Burst-Suppression (BS) pattern appears in EEG during the early
recovery period following Cardiac Arrest (CA). Here, to explore the
relationship between cortical and subcortical neural activities
underlying BS, extracellular activity in the parietal cortex and the
centromedian nucleus of the thalamus and extradural EEG were
recorded in a rodent CA model. During the BS, the cortical firing rate
is extraordinarily high, and that bursts in EEG correlate to dense spikes
in cortical neurons. Newly observed phenomena are that 1) thalamic
activity reemerges earlier than cortical activity following CA, and 2)
the correlation coefficient of cortical and thalamic activities rises
during BS period. These results would help elucidate the underlying
mechanism of brain recovery after CA injury.
Abstract: In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.
Abstract: Milk is considered as an essential and complete food.
The present study was conducted at Milk Plant Mohali especially in
reference to the procurement section where the cash inflow was
maximum, with the objective to achieve higher productivity and
reduce wastage of milk. In milk plant it was observed that during the
month of Jan-2014 to March-2014 the average procurement of milk
was Rs. 4, 19, 361 liter per month and cost of procurement of milk is
Rs 35/- per liter. The total cost of procurement thereby equal to Rs.
1crore 46 lakh per month, but there was mismatch in procurementproduction
of milk, which leads to an average loss of Rs. 12, 94, 405
per month. To solve the procurement-production problem Quality
Control Tools like brainstorming, Flow Chart, Cause effect diagram
and Pareto analysis are applied wherever applicable. With the
successful implementation of Quality Control tools an average saving
of Rs. 4, 59, 445 per month is done.
Abstract: Psychopathic disorders are taking an important part in
judge sentencing, especially in Canada. First, we will see how this
phenomenon can be illustrated by the high proportion of psychopath
offenders incarcerated in North American prisons. Many decisions in
Canadians courtrooms seem to point out that psychopathy is often
used as a strong argument by the judges to preserve public safety.
The fact that psychopathy is often associated with violence,
recklessness and recidivism, could explain why many judges consider
psychopathic disorders as an aggravating factor. Generally, the judge
reasoning is based on Article 753 of Canadian Criminal Code related
to dangerous offenders, which is used for individuals who show a
pattern of repetitive and persistent aggressive behaviour. Then we
will show how, with cognitive neurosciences, the psychopath’s
situation in courtrooms would probably change. Cerebral imaging
and news data provided by the neurosciences show that emotional
and volitional functions in psychopath’s brains are impaired.
Understanding these new issues could enable some judges to
recognize psychopathic disorders as a mitigating factor. Finally, two
important questions ought to be raised in this article: can exploring
psychopaths ‘brains really change the judge sentencing in Canadian
courtrooms? If yes, can judges consider psychopathy more as a
mitigating factor than an aggravating factor?
Abstract: EEG correlates of mathematical and trait anxiety level
were studied in 52 healthy Russian-speakers during execution of
error-recognition tasks with lexical, arithmetic and algebraic
conditions. Event-related spectral perturbations were used as a
measure of brain activity. The ERSP plots revealed alpha/beta
desynchronizations within a 500-3000 ms interval after task onset
and slow-wave synchronization within an interval of 150-350 ms.
Amplitudes of these intervals reflected the accuracy of error
recognition, and were differently associated with the three conditions.
The correlates of anxiety were found in theta (4-8 Hz) and beta2 (16-
20 Hz) frequency bands. In theta band the effects of mathematical
anxiety were stronger expressed in lexical, than in arithmetic and
algebraic condition. The mathematical anxiety effects in theta band
were associated with differences between anterior and posterior
cortical areas, whereas the effects of trait anxiety were associated
with inter-hemispherical differences. In beta1 and beta2 bands effects
of trait and mathematical anxiety were directed oppositely. The trait
anxiety was associated with increase of amplitude of
desynchronization, whereas the mathematical anxiety was associated
with decrease of this amplitude. The effect of mathematical anxiety
in beta2 band was insignificant for lexical condition but was the
strongest in algebraic condition. EEG correlates of anxiety in theta
band could be interpreted as indexes of task emotionality, whereas
the reaction in beta2 band is related to tension of intellectual
resources.
Abstract: In this study, the signal of brain electrical activities of
the sixteen students selected from the Department of Electrical and
Energy at Usak University have been recorded during a lecturer
performed happiness emotions for the first group and anger emotions
for the second group in different time while the groups were in the
classroom separately. The attention and meditation data extracted
from the recorded signals have been analyzed and evaluated toward
the teacher’s specific emotion states simultaneously. Attention levels
of students who are under influence of happiness emotions of the
lecturer have a positive trend and attention levels of students who are
under influence of anger emotions of the lecturer have a negative
trend. The meditation or mental relaxation levels of students who are
under influence of happiness emotions of the lecturer are 34.3%
higher comparing with the mental relaxation levels of students who
are under influence of anger emotions of the lecturer.
Abstract: Neural activity in the human brain starts from the
early stages of prenatal development. This activity or signals
generated by the brain are electrical in nature and represent not only
the brain function but also the status of the whole body. At the
present moment, three methods can record functional and
physiological changes within the brain with high temporal resolution
of neuronal interactions at the network level: the
electroencephalogram (EEG), the magnet oencephalogram (MEG),
and functional magnetic resonance imaging (fMRI); each of these has
advantages and shortcomings. EEG recording with a large number of
electrodes is now feasible in clinical practice. Multichannel EEG
recorded from the scalp surface provides very valuable but indirect
information about the source distribution. However, deep electrode
measurements yield more reliable information about the source
locations intracranial recordings and scalp EEG are used with the
source imaging techniques to determine the locations and strengths of
the epileptic activity. As a source localization method, Low
Resolution Electro-Magnetic Tomography (LORETA) is solved for
the realistic geometry based on both forward methods, the Boundary
Element Method (BEM) and the Finite Difference Method (FDM). In
this paper, we review the findings EEG- LORETA about epilepsy.
Abstract: Aim of this research study is to investigate and
establish the characteristics of brain dominances (BD) and multiple
intelligences (MI). This experimentation has been conducted for the
sample size of 552 undergraduate computer-engineering students. In
addition, mathematical formulation has been established to exhibit
the relation between thinking and intelligence, and its correlation has
been analyzed. Correlation analysis has been statistically measured
using Pearson’s coefficient. Analysis of the results proves that there
is a strong relational existence between thinking and intelligence.
This research is carried to improve the didactic methods in
engineering learning and also to improve e-learning strategies.
Abstract: To explore how the brain may recognise objects in its
general,accurate and energy-efficient manner, this paper proposes the
use of a neuromorphic hardware system formed from a Dynamic
Video Sensor (DVS) silicon retina in concert with the SpiNNaker
real-time Spiking Neural Network (SNN) simulator. As a first step
in the exploration on this platform a recognition system for dynamic
hand postures is developed, enabling the study of the methods used
in the visual pathways of the brain. Inspired by the behaviours of
the primary visual cortex, Convolutional Neural Networks (CNNs)
are modelled using both linear perceptrons and spiking Leaky
Integrate-and-Fire (LIF) neurons.
In this study’s largest configuration using these approaches, a
network of 74,210 neurons and 15,216,512 synapses is created and
operated in real-time using 290 SpiNNaker processor cores in parallel
and with 93.0% accuracy. A smaller network using only 1/10th of the
resources is also created, again operating in real-time, and it is able
to recognise the postures with an accuracy of around 86.4% - only
6.6% lower than the much larger system. The recognition rate of the
smaller network developed on this neuromorphic system is sufficient
for a successful hand posture recognition system, and demonstrates
a much improved cost to performance trade-off in its approach.
Abstract: Cerebellar ataxia is a steadily progressive
neurodegenerative disease associated with loss of motor control,
leaving patients unable to walk, talk, or perform activities of daily
living. Direct motor instruction in cerebella ataxia patients has limited
effectiveness, presumably because an inappropriate closed-loop
cerebellar response to the inevitable observed error confounds motor
learning mechanisms. Could the use of EEG based BCI provide
advanced biofeedback to improve motor imagery and provide a
“backdoor” to improving motor performance in ataxia patients? In
order to determine the feasibility of using EEG-based BCI control in
this population, we compare the ability to modulate mu-band power
(8-12 Hz) by performing a cued motor imagery task in an ataxia
patient and healthy control.
Abstract: The potential neuroprotective effect of Phyllantus
nuriri against Fe2+ and sodium nitroprusside (SNP) induced oxidative
stress in mitochondria of rats brain was evaluated. Cellular viability
was assessed by MTT reduction, reactive oxygen species (ROS)
generation was measured using the probe 2,7-dichlorofluoresce
indiacetate (DCFH-DA). Glutathione content was measured using
dithionitrobenzoic acid (DTNB). Fe2+ (10μM) and SNP (5μM)
significantly decreased mitochondrial activity, assessed by MTT
reduction assay, in a dose-dependent manner, this occurred in parallel
with increased glutathione oxidation, ROS production and lipid
peroxidation end-products (thiobarbituric acid reactive substances,
TBARS). The co-incubation with methanolic extract of Phyllantus
nuriri (10-200 μg/ml) reduced the disruption of mitochondrial
activity, gluthathione oxidation, ROS production as well as the
increase in TBARS levels caused by both Fe2+ and SNP in a dose
dependent manner. HPLC analysis of the extract revealed the
presence of gallic acid (20.540.01), caffeic acid (7.930.02), rutin
(25.310.05), quercetin (31.280.03) and kaemferol (14.360.01).
This result suggests that these phytochemicals account for the
protective actions of P. niruri against Fe2+ and SNP -induced
oxidative stress. Our results show that P. nuriri consist important
bioactive molecules in the search for an improved therapy against the
deleterious effects of Fe2+, an intrinsic producer of reactive oxygen
species (ROS), that leads to neuronal oxidative stress and
neurodegeneration.
Abstract: Theory of Mind (ToM) refers to the ability to infer
another’s mental state. With appropriate ToM, one can behave well in
social interactions. A growing body of evidence has demonstrated that
patients with temporal lobe epilepsy (TLE) may damage ToM by
affecting on regions of the underlying neural network of ToM.
However, the question of whether there is cerebral laterality for ToM
functions remains open. This study aimed to examine whether there is
cerebral lateralization for ToM abilities in TLE patients. Sixty-seven
adult TLE patients and 30 matched healthy controls (HC) were
recruited. Patients were classified into right (RTLE), left (LTLE), and
bilateral (BTLE) TLE groups on the basis of a consensus panel review
of their seizure semiology, EEG findings, and brain imaging results.
All participants completed an intellectual test and four tasks measuring
basic and advanced ToM. The results showed that, on all ToM tasks,
(1) each patient group performed worse than HC; (2) there were no
significant differences between LTLE and RTLE groups; and (3) the
BTLE group performed the worst. It appears that the neural network
responsible for ToM is distributed evenly between the cerebral
hemispheres.
Abstract: This work proposes a data-driven multiscale based
quantitative measures to reveal the underlying complexity of
electroencephalogram (EEG), applying to a rodent model of
hypoxic-ischemic brain injury and recovery. Motivated by that real
EEG recording is nonlinear and non-stationary over different
frequencies or scales, there is a need of more suitable approach over
the conventional single scale based tools for analyzing the EEG data.
Here, we present a new framework of complexity measures
considering changing dynamics over multiple oscillatory scales. The
proposed multiscale complexity is obtained by calculating entropies of
the probability distributions of the intrinsic mode functions extracted
by the empirical mode decomposition (EMD) of EEG. To quantify
EEG recording of a rat model of hypoxic-ischemic brain injury
following cardiac arrest, the multiscale version of Tsallis entropy is
examined. To validate the proposed complexity measure, actual EEG
recordings from rats (n=9) experiencing 7 min cardiac arrest followed
by resuscitation were analyzed. Experimental results demonstrate that
the use of the multiscale Tsallis entropy leads to better discrimination
of the injury levels and improved correlations with the neurological
deficit evaluation after 72 hours after cardiac arrest, thus suggesting an
effective metric as a prognostic tool.