Fast Functional Magnetic Resonance Imaging (fast fMRI): uses MRI to measure nerve or brain activity directly
Uses MRI to detect the electromagnetic field that is generated by ionic currents (action potential)
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
Magnetic Resonance Angiography and VenographyAnjan Dangal
Introduction to MR Angiography and Venography Procedure of Brain . Includes Indication, MRI protocol, planning and anatomy as well as brief intoduction to physics behind MRA and MRV principle.
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
Magnetic Resonance Angiography and VenographyAnjan Dangal
Introduction to MR Angiography and Venography Procedure of Brain . Includes Indication, MRI protocol, planning and anatomy as well as brief intoduction to physics behind MRA and MRV principle.
Event Related Potentials, Cognitive Evoked Potentials. These are stimulus unrelated potentials, which depend on the patient's ability to differentiate between a rare stimulus and a common stimulus.
Event Related Potentials, Cognitive Evoked Potentials. These are stimulus unrelated potentials, which depend on the patient's ability to differentiate between a rare stimulus and a common stimulus.
A simple introduction to fMRI study design for social science and other researchers outside the field who might want to design a study using fMRI brain scanning technology
Article Review on Simultanoeus Optical Stimulation and Electrical Recording f...Md Kafiul Islam
This is a comprehensive review of article titled "Integrated device for combined optical neuromodulation and electrical recording for chronic in vivo applications" by Wang et al. 2012, appeared in JNE. The presentation was made during my Comprehensive Qualifying Examination (CQE) back in Jan, 2013
NIRS-BASED CORTICAL ACTIVATION ANALYSIS BY TEMPORAL CROSS CORRELATIONsipij
In this study we present a method of signal processing to determine dominant channels in near infrared spectroscopy (NIRS). To compare measuring channels and identify delays between them, cross correlation is computed. Furthermore, to find out possible dominant channels, a visual inspection was performed. The
outcomes demonstrated that the visual inspection exhibited evoked-related activations in the primary somatosensory cortex (S1) after stimulation which is consistent with comparable studies and the cross correlation study discovered dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and adjacent channels. For that reason, our results present a new
method to identify dominant regions in the cerebral cortex using near-infrared spectroscopy. These findings have also implications in the decrease of channels by eliminating irrelevant channels for the experiment.
EEG based Brain Computer Interface (BCI) establishes a new channel between human brain and the
surrounding environment in order to disseminate instructions to the outside world. It is based on the
recording of temporary EEG changes during different types of motor imagery such as imagination of
different hand movements. The spatial pattern of activated cortical areas during motor imagery is similar
to that of real time executed movement. Time domain features and frequency domain features are extracted
with emphasis on recognizing discriminative features representing EEG trials recorded during imagination
of different hand movements. Then, classification into different hand movements is carried out.
Mobile Phone Handset Radiation Effect on Brainwave Signal using EEG: A Review IJEEE
This paper talks about the growing concern in the people about the effect of mobile phone RF radiation on human brainwave signal using various techniques to capture those effects. Linear, Non- linear and statistical methods (t-tests) employed by various researchers to analyze variation in brainwaves.This paper gives comparison between brainwaves like Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz), Beta (13-30 Hz) and Gamma (30 Hz-70Hz) in terms of Frequency Progressive map and amplitude progressive tri-maps. Electroencephalography (EEG) gives a noninvasive way of measuring brainwave activity from sensors placed on the scalp of the human head. So, the aim of this paper is to study the effect of RF exposure from mobile phone on Human brainwave as brainwaves Brain wave can provide information of mental state of the individual.
An Artificial Neural Network Model for Classification of Epileptic Seizures U...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper the EEG signals are decomposed into a finite set of band
limited signals termed as Intrinsic mode functions. The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd and 4th IMF's are used to extract features of epileptic
signal. A neural network using back propagation algorithm is implemented for classification of epilepsy.
An overall accuracy of 99.8% is achieved in classification..
An artificial neural network model for classification of epileptic seizures u...ijsc
Epilepsy is one of the most common neurological disorders characterized by transient and unexpected
electrical disturbance in the brain. In This paper
the EEG signals are decomposed into a finite set of
bandlimited signals termed as Intrinsic mode functions.
The Hilbert transom is applied on these IMF’s to
calculate instantaneous frequencies. The 2nd,3rd an
d 4th IMF's are used to extract features of epilepticsignal. A neural network using back propagation alg
orithm is implemented for classification of epilepsy.An overall accuracy of 99.8% is achieved in classification..
In this paper we present the use of a signal processing technique to find dominant channels in
near infrared spectroscopy (NIRS). Cross correlation is computed to compare measuring
channels and identify delays among the channels. In addition, visual inspection was used to
detect potential dominant channels. The results showed that the visual analysis exposed painrelated
activations in the primary somatosensory cortex (S1) after stimulation which is
consistent with similar studies and the cross correlation analysis found dominant channels on
both cerebral hemispheres. The analysis also showed a relationship between dominant channels
and neighbouring channels. Therefore, our results present a new method to detect dominant
regions in the cerebral cortex using near-infrared spectroscopy. These results have also
implications in the reduction of number of channels by eliminating irrelevant channels for the
experiment.
CROSS CORRELATION ANALYSIS OF MULTI-CHANNEL NEAR INFRARED SPECTROSCOPYcscpconf
In this paper we present the use of a signal processing technique to find dominant channels in near infrared spectroscopy (NIRS). Cross correlation is computed to compare measuring channels and identify delays among the channels. In addition, visual inspection was used to detect potential dominant channels. The results showed that the visual analysis exposed painrelated activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies and the cross correlation analysis found dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels
and neighbouring channels. Therefore, our results present a new method to detect dominant regions in the cerebral cortex using near-infrared spectroscopy. These results have also implications in the reduction of number of channels by eliminating irrelevant channels for the experiment.
CROSS CORRELATION ANALYSIS OF MULTI-CHANNEL NEAR INFRARED SPECTROSCOPYcscpconf
In this paper we present the use of a signal processing technique to find dominant channels in near infrared spectroscopy (NIRS). Cross correlation is computed to compare measuring channels and identify delays among the channels. In addition, visual inspection was used to detect potential dominant channels. The results showed that the visual analysis exposed painrelated activations in the primary somatosensory cortex (S1) after stimulation which is consistent with similar studies and the cross correlation analysis found dominant channels on both cerebral hemispheres. The analysis also showed a relationship between dominant channels and neighbouring channels. Therefore, our results present a new method to detect dominant regions in the cerebral cortex using near-infrared spectroscopy. These results have also implications in the reduction of number of channels by eliminating irrelevant channels for the experiment
INHIBITION AND SET-SHIFTING TASKS IN CENTRAL EXECUTIVE FUNCTION OF WORKING ME...sipij
Understanding of neuro-dynamics of a complex higher cognitive process, Working Memory (WM) is
challenging. In WM, information processing occurs through four subsystems: phonological loop, visual
sketch pad, memory buffer and central executive function (CEF). CEF plays a principal role in WM. In this
study, our objective was to understand the neurospatial correlates of CEF during inhibition and set-shifting
processes. Thirty healthy educated subjects were selected. Event-Related Potential (ERP) related to visual
inhibition and set-shifting task was collected using 32 channel EEG system. Activation of those ERPs
components was analyzed using amplitudes of positive and negative peaks. Experiment was controlled
using certain parametric constraints to judge behavior, based on average responses in order to establish
relationship between ERP and local area of brain activation and represented using standardized low
resolution brain electromagnetic tomography. The average score of correct responses was higher for
inhibition task (87.5%) as compared to set-shifting task (59.5%). The peak amplitude of neuronal activity
for inhibition task was lower compared to set-shifting task in fronto-parieto-central regions. Hence this
proposed paradigm and technique can be used to measure inhibition and set-shifting neuronal processes in
understanding pathological central executive functioning in patients with neuro-psychiatric disorders.
Wavelet-based EEG processing for computer-aided seizure detection and epileps...IJERA Editor
Many Neurological disorders are very difficult to detect. One such Neurological disorder which we are going to discuss in this paper is Epilepsy. Epilepsy means sudden change in the behavior of a human being for a short period of time. This is caused due to seizures in the brain. Many researches are going onto detect epilepsy detection through analyzing EEG. One such method of epilepsy detection is proposed in this paper. This technique employs Discrete Wave Transform (DWT) method for pre-processing, Approximate Entropy (ApEn) to extract features and Artificial Neural Network (ANN) for classification. This paper presented a detailed survey of various methods that are being used for epilepsy detection and also proposes a wavelet based epilepsy detection method
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
The Gram stain is a fundamental technique in microbiology used to classify bacteria based on their cell wall structure. It provides a quick and simple method to distinguish between Gram-positive and Gram-negative bacteria, which have different susceptibilities to antibiotics
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
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Basavarajeeyam is a Sreshta Sangraha grantha (Compiled book ), written by Neelkanta kotturu Basavaraja Virachita. It contains 25 Prakaranas, First 24 Chapters related to Rogas& 25th to Rasadravyas.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
11. 21/08/2015
Volunteer studies
Reference Control Experiments.
• Each subject was tested with a control scan without stimulation prior to
the stimulation experiments.
• The control experiments showed spectral peaks due to heart beat and
respiration only.
• No specific frequency responses were recorded at the stimulation
frequency from the median nerve, somatosensory cortex or visual cortex
in the control experiments.
Figures for fast fMRI Fourier transform of MR time series of the median nerve, the motosensory and the
visual cortex at rest state (control experiments)
12. 21/08/2015
• ROI’s were selected in the median nerve
and in muscle tissue, Figs 2 and 3.
• Fig 4. shows a typical response at 2.8
Hz recorded from the ROI in the median nerve
(blue) but not from muscle tissue (red).
1- Responses in the Median Nerve
Median nerve
1 MR image shows an axial slice position in the
sagittal plane scout image
2 Anatomy MR Image 3 GE-EPI magnitude image in axial plane
during the ROI stimulated at 2.8Hz, with Z=2.5
A.U
f (Hz)
4 FT of MR time series 2.8Hz
Stimulation
frequency
13. 21/08/2015
3- Possible fast fMRI responses in the visual cortex
1 MR image shows an axial slice position in the
sagittal plane scout image
2 Functional overlay on MR Image ,
with Z=2.5
3 GE-EPI magnitude image in axial plane
showing the ROI stimulated at 2.8Hz
4 FTof MR time series 2.8Hz
•Fig. 2 shows a typical response on the overlay image from an
acquisition calculated with a Z score = 2.5.
•A typical ROI selected in the visual cortex is shown in Figure
3.
•Fig. 4 shows a Fourier transform of the MR time series
illustrating the frequency spectrum from the ROI in the visual
cortex during visual stimulation at 2.8Hz which correlates with
the task stimulation frequency.
14. 21/08/2015
2- mapping of motor sensory cortex function
1 MR image shows an axial slice position in the
sagittal plane scout image
2 Function overlay MR Image, with Z=2.5 3 GE-EPI magnitude image in axial plane
during the ROI stimulated at 2.7Hz.
A.U
f (Hz)
4 FT of MR time series 2.7Hz
•Figure 2 shows a Z map (Z>2.5) overlaid on the EPI
image showing response in the motor-sensory area.
•This location was selected in the motor cortex as shown
in the ROI in figure 3 using the axial plane for
acquisition.
•Figure 4 shows the spectral response from the motor-
sensory area at 2.7 Hz with SNR>3:1. and the heartbeat
at approximately 1.1 Hz.
Stimulation
frequency
15. 21/08/2015
Conclusions
• Evidence of fast fMRI responses in the median nerve, the
somatosensory and visual cortices during ROI area stimulation by TENS
and strobe light were observed at high stimulation frequencies > 2.5 Hz.
• Similar responses were observed at the applied stimulation frequencies
with SNR>3:1 in volunteers for the median nerve, the somatosensory
and visual cortices respectively.
• Fast fMRI did appear to detect weak response to the stimulated
frequencies and seeks to improve the spatial and temporal accuracy in
detecting neuronal function compared to conventional BOLD fMRI.
16. 21/08/2015
Acknowledgements
I gratefully acknowledge the sponsorship of the
Human Capacity Development Program in
Kurdistan Regional Government.
I take this opportunity to express my gratitude and regards to
Professor Martyn Paley/ Academic Radiology/ Department of
Cardiovascular Science for his exemplary guidance, monitoring
and constant encouragement throughout the course of this
study
Prof. Martyn Paley
Thank you very much to the
volunteers who participated in the
experiments we have carried out
under ethical permission from the
University.