## Spatial Preprocessing John Ashburner john@fil.ion.ucl.ac.uk Overview of SPM Analysis Contents Smoothing Smoothing

Bailey, Katherine, Contributing Editor has reference to this Academic Journal, PHwiki organized this Journal Spatial Preprocessing John Ashburner john@fil.ion.ucl.ac.uk Smoothing Rigid registration Spatial normalisation With slides by Chloe Hutton in addition to Jesper Andersson Overview of SPM Analysis Motion Correction Smoothing Spatial Normalisation General Linear Model Statistical Parametric Map fMRI time-series Parameter Estimates Design matrix Anatomical Reference Contents Smoothing Rigid registration Spatial normalisation

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Smoothing Be as long as e convolution Convolved with a circle Convolved with a Gaussian Each voxel after smoothing effectively becomes the result of applying a weighted region of interest (ROI). Smoothing Why smooth Potentially increase sensitivity Inter-subject averaging Increase validity of SPM Smoothing is a convolution with a Gaussian kernel Gaussian convolution is separable Contents Smoothing Rigid registration Rigid-body trans as long as ms Optimisation & objective functions Interpolation Spatial normalisation

Within-subject Registration Assumes there is no shape change, in addition to motion is rigid-body Used by [realign] in addition to [coregister] functions The steps are: Registration – i.e. Optimising the parameters that describe a rigid body trans as long as mation between the source in addition to reference images Trans as long as mation – i.e. Re-sampling according to the determined trans as long as mation Affine Trans as long as ms Rigid-body trans as long as mations are a subset Parallel lines remain parallel Operations can be represented by: x1 = m11x0 + m12y0 + m13z0 + m14 y1 = m21x0 + m22y0 + m23z0 + m24 z1 = m31x0 + m32y0 + m33z0 + m34 Or as matrices: Y=Mx 2D Affine Trans as long as ms Translations by tx in addition to ty x1 = x0 + tx y1 = y0 + ty Rotation around the origin by radians x1 = cos() x0 + sin() y0 y1 = -sin() x0 + cos() y0 Zooms by sx in addition to sy x1 = sx x0 y1 = sy y0 Shear x1 = x0 + h y0 y1 = y0

2D Affine Trans as long as ms Translations by tx in addition to ty x1 = 1 x0 + 0 y0 + tx y1 = 0 x0 + 1 y0 + ty Rotation around the origin by radians x1 = cos() x0 + sin() y0 + 0 y1 = -sin() x0 + cos() y0 + 0 Zooms by sx in addition to sy: x1 = sx x0 + 0 y0 + 0 y1 = 0 x0 + sy y0 + 0 Shear x1 = 1 x0 + h y0 + 0 y1 = 0 x0 + 1 y0 + 0 3D Rigid-body Trans as long as mations A 3D rigid body trans as long as m is defined by: 3 translations – in X, Y & Z directions 3 rotations – about X, Y & Z axes The order of the operations matters Translations Pitch about x axis Roll about y axis Yaw about z axis Voxel-to-world Trans as long as ms Affine trans as long as m associated with each image Maps from voxels (x=1 nx, y=1 ny, z=1 nz) to some world co-ordinate system. e.g., Scanner co-ordinates – images from DICOM toolbox T&T/MNI coordinates – spatially normalised Registering image B (source) to image A (target) will update Bs vox-to-world mapping Mapping from voxels in A to voxels in B is by A-to-world using MA, then world-to-B using MB-1 MB-1 MA

Left- in addition to Right-h in addition to ed Coordinate Systems Analyze files are stored in a left-h in addition to ed system Talairach & Tournoux uses a right-h in addition to ed system Mapping between them requires a flip Affine trans as long as m with a negative determinant Optimisation Optimisation involves finding some best parameters according to an objective function, which is either minimised or maximised The objective function is often related to a probability based on some model Value of parameter Objective function Most probable solution (global optimum) Local optimum Local optimum Objective Functions as long as Image Registration Intra-modal Mean squared difference (minimise) Normalised cross correlation (maximise) Entropy of difference (minimise) Inter-modal (or intra-modal) Mutual in as long as mation (maximise) Normalised mutual in as long as mation (maximise) Entropy correlation coefficient (maximise) AIR cost function (minimise)

Mean-squared Difference Minimising mean-squared difference works as long as intra-modal registration (realignment) Simple relationship between intensities in one image, versus those in the other Assumes normally distributed differences Gauss-newton Optimisation Works best as long as least-squares Minimum is estimated by fitting a quadratic at each iteration Match images from same subject but different modalities: anatomical localisation of single subject activations achieve more precise spatial normalisation of functional image using anatomical image. Inter-modal registration

Mutual In as long as mation Used as long as between-modality registration Derived from joint histograms MI= ab P(a,b) log2 [P(a,b)/( P(a) P(b) )] Related to entropy: MI = -H(a,b) + H(a) + H(b) Where H(a) = -a P(a) log2P(a) in addition to H(a,b) = -a P(a,b) log2P(a,b) Image Trans as long as mations Images are re-sampled. An example in 2D: as long as y0=1 ny0 % loop over rows as long as x0=1 nx0 % loop over pixels in row x1 = tx(x0,y0,q) % trans as long as m according to q y1 = ty(x0,y0,q) if 1×1 nx1 & 1y1ny1 then % voxel in range f1(x0,y0) = f0(x1,y1) % assign re-sampled value end % voxel in range end % loop over pixels in row end % loop over rows What happens if x1 in addition to y1 are not integers Simple Interpolation Nearest neighbour Take the value of the closest voxel Tri-linear Just a weighted average of the neighbouring voxels f5 = f1 x2 + f2 x1 f6 = f3 x2 + f4 x1 f7 = f5 y2 + f6 y1

B-spline Interpolation B-splines are piecewise polynomials A continuous function is represented by a linear combination of basis functions 2D B-spline basis functions of degrees 0, 1, 2 in addition to 3 Nearest neighbour in addition to trilinear interpolation are the same as B-spline interpolation with degrees 0 in addition to 1. Re-sampling can introduce interpolation errors especially tri-linear interpolation Gaps between slices can cause aliasing artefacts Slices are not acquired simultaneously rapid movements not accounted as long as by rigid body model Image artefacts may not move according to a rigid body model image distortion image dropout Nyquist ghost Functions of the estimated motion parameters can be modelled as confounds in subsequent analyses Residual Errors from aligned fMRI Movement by Distortion Interaction of fMRI Subject disrupts B0 field, rendering it inhomogeneous => distortions in phase-encode direction Subject moves during EPI time series Distortions vary with subject orientation => shape varies

Movement by distortion interaction Correcting as long as distortion changes using Unwarp Estimate movement parameters. Estimate new distortion fields as long as each image: estimate rate of change of field with respect to the current estimate of movement parameters in pitch in addition to roll. Estimate reference from mean of all scans. Unwarp time series. + Andersson et al, 2001 Contents Smoothing Rigid registration Spatial normalisation Affine registration Nonlinear registration Regularisation

Spatial Normalisation – Reasons Inter-subject averaging Increase sensitivity with more subjects Fixed-effects analysis Extrapolate findings to the population as a whole Mixed-effects analysis St in addition to ard coordinate system e.g., Talairach & Tournoux space Spatial Normalisation – Objective Warp the images such that functionally homologous regions from different subjects are as close together as possible Problems: No exact match between structure in addition to function Different brains are organised differently Computational problems (local minima, not enough in as long as mation in the images, computationally expensive) Compromise by correcting gross differences followed by smoothing of normalised images Very hard to define a one-to-one mapping of cortical folding Use only approximate registration.

Spatial Normalisation – Non-linear Algorithm simultaneously minimises Mean squared difference between template in addition to source image Squared distance between parameters in addition to their known expectation De as long as mations consist of a linear combination of smooth basis functions These are the lowest frequencies of a 3D discrete cosine trans as long as m (DCT) Spatial Normalisation – Overfitting Template image Affine registration. (2 = 472.1) Non-linear registration without regularisation. (2 = 287.3) Non-linear registration using regularisation. (2 = 302.7) Without regularisation, the non-linear spatial normalisation can introduce unnecessary warps. References Friston et al (1995): Spatial registration in addition to normalisation of images. Human Brain Mapping 3:165-189 Collignon et al (1995): Automated multi-modality image registration based on in as long as mation theory. IPMI95 pp 263-274 Andersson et al (2001): Modeling geometric de as long as mations in EPI time series. Neuroimage 13:903-919 Thévenaz et al (2000): Interpolation revisited. IEEE Trans. Med. Imaging 19:739-758. Ashburner et al (1997): Incorporating prior knowledge into image registration. NeuroImage 6:344-352 Ashburner et al (1999): Nonlinear spatial normalisation using basis functions. Human Brain Mapping 7:254-266

## Bailey, Katherine Contributing Editor

Bailey, Katherine is from United States and they belong to Glam and they are from  Brisbane, United States got related to this Particular Journal. and Bailey, Katherine deal with the subjects like Home Decorating; Home Furnishings/Housewares; Home Improvements and Remodeling

## Journal Ratings by Eastern Oregon University

This Particular Journal got reviewed and rated by Eastern Oregon University and short form of this particular Institution is OR and gave this Journal an Excellent Rating.

## Thomas Midgley Jr. invented Chlorofluorocarbons (CFCs) Figure 15.29a: Ozone hole over Antarctica, October 1979 Figure 15.29b: Ozone hole over Antarctica, October 1986 Figure 15.29c: Ozone hole over Antarctica, October 2005 Evolution of fuel use

Wills, Brad, Meteorologist has reference to this Academic Journal, PHwiki organized this Journal By the end of the chapter, you will recognize that Chemistry is the study of matter, the physical substance of all materials. There is a difference between physical in addition to biological sciences that arises from natural science. DNA in addition to the purpose of the Human Genome Project – to explore, explore, explore The development of refrigerants is rooted as some interests in chemistry. A brief intro about the ozone layer. Distinctions of scientific discovery among basic science, applied science, in addition to technology. The use of fossil fuels used in everyday lifestyle in addition to global warming. The benefit in addition to risk analysis factors come into play.

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Chemistry is the study of matter in addition to the changes it can undergo. Chemistry is a member of the physical sciences Living in a World of Chemistry What does Chemistry Mean to You Maybe chemistry means to you. The amazing effect of nitric acid (b) on copper (a). A young man, Ira Remsen (1864-1927) dropped a copper penny into nitric acid to see what would happen. This is chemistry at work! What type of chemistry do you know that is daily in our lives Living in a World of Chemistry Natural Sciences have been associated with observation of nature. Our physical in addition to biological environment. The Natural Science Requires Mathematics Physical Sciences Study of matter in addition to energy Biological Sciences Study of living organisms Biochemistry Chemistry

Physical Sciences Study of matter in addition to energy Geology Astronomy Physics Chemistry Chemical Physics Cosmo Chemistry Geo Chemistry Analytical Physical Organic Inorganic Determination of structure & composition Basic physical processes of chemistry Carbon compounds Non-Carbon compounds Biological Sciences Study of living organisms Botany Zoology Microbiology Cell Biology Genetics Ecology Physiology Microscopic organism Cell structure in addition to Function Heredity Environment Function Biochemistry Chemistry Biological Sciences Toxicology Immunology Pharmacology Medicinal Chemistry Pathology Physical vs. Biological Sciences cont.

Living in a World of Chemistry DNA in addition to The Human Genome Project. DNA is an abbreviation as long as Deoxyribonucleic acid in addition to is present throughout our bodies. DNA provides the fingerprint of an individual. The study of DNA is one aspect of biochemistry An abstract picture of how DNA unravels in addition to is reproduced. In this way, a parent’s DNA is passed to the offspring. More to be discussed in Ch15. Living in a World of Chemistry DNA in addition to The Human Genome Project cont. In 2000, a revolution in our knowledge of DNA begins. A non-profit consortium called The Human Genome Project begins. The map of human DNA is completed in April 2003 where the location of every segment of human DNA is identified.

The original publication by the leaders of The Human Genome project make the following prophetic statement: Purpose of The Human Genome Project It has not escaped our notice that the more we learn about the human genome, the more there is to explore. Development of products that improve the health of humans, other animals, in addition to plants Underst in addition to ing of hereditary diseases Development of drugs to cure hereditary diseases Alternation of an individuals genetic makeup to prevent or cure a disease The study of why a drug can be effective in some individuals but not in others Exact matching of drugs to an individuals genetics makeup Development of improved agricultural crops in addition to animals Creation of genetically modified bacteria that will mass-produce desirable chemical products Use of genetic in as long as mation as long as a better underst in addition to ing of evolution Among these ongoing explorations are the following: Living in a World of Chemistry Air-Conditioning/Refrigerant. Refrigerants has been in use since the late 1800s. This process requires a fluid that absorbs heat as it evaporates, releases heat when it condenses, in addition to can be continuously cycled through evaporation in addition to condensation without breaking down. The development of chlorofluorocarbons, often referred to as CFCs, as much less hazardous refrigerants after WWII. Thomas Midgley Jr. invented Chlorofluorocarbons (CFCs) Credit: Kettering University

Living in a World of Chemistry Air-Conditioning/Refrigerant cont. The Problem of using CFCs did not emerge until 1970 that would cause damage to the environment. By the 1980, it became clear that CFCs reacted with the stratosphere, where they interact with the ozone, in addition to started to destroying it. The Ozone Layer (Depletion) Ozone in the stratosphere absorbs ultraviolet radiation, is like the Earths sunscreen. Increased ultraviolet exposure becomes harmful to the environment in addition to us. Figure 15.29a: Ozone hole over Antarctica, October 1979 Figure 15.29b: Ozone hole over Antarctica, October 1986

Figure 15.29c: Ozone hole over Antarctica, October 2005 Will learn more about ozone depletion later in the semester. Living in a World of Chemistry Distinguishing between basic science, applied science, in addition to technology. Basic science, or basic research, is the pursuit of knowledge about the universe with no short-term practical objectives as long as application in mind. Ex. Biochemists who struggled as long as years to underst in addition to exactly how DNA functions within cells were doing basic science. Applied science has the well-defined, short-term goal of solving a specific problem. Ex. The search as long as a better refrigerant by Midgley in addition to his colleagues. Living in a World of Chemistry Distinguishing between basic science, applied science, in addition to technology cont. Technology, also an application of scientific knowledge, is a bit more difficult to define. In essence, it is the sum of the way we apply science in the context of our society, our economic system, in addition to our industry. Ex. The first refrigerators in addition to automobiles air conditions designed to use CFCs were the products of a new technology or ways to manipulate DNA to make new medicines or other marketable products is referred to as biotechnology.

Living in a World of Chemistry Fossil Fuels in addition to Brief intro to Global Warming. Fossil fuels are coal, oil, in addition to natural gas to support consumer-driven societies in addition to their associated lifestyles. The fuels are burned in a variety of vehicles to propel down the road, through the air, or across the oceans. Other uses of fossil fuel include powering an engine, provide heat in addition to cool our homes, grow, harvest, provide health care, medicine, etc Fossil fuels are nonrenewable resources, where eventually supply will run out! Evolution of fuel use

Coal mining Living in a World of Chemistry Fossil Fuels in addition to Brief intro to Global Warming cont. Depletion of traditional sources of energy such as petroleum has led to the development of alternative sources of energy. Renewable energy sources can be replenished on a short time scale; e.g., solar power, wind, in addition to biomass. Water vapor in addition to carbon dioxide (CO2) are gases that are known to absorb in addition to trap heat radiation (Ex. in nuclear reactors).

Figure 15.15: Municipal solid waste recycling rates, 1960-2003 Source: EPA RECYCLE!!! Living in a World of Chemistry Fossil Fuels in addition to Brief intro to Global Warming cont. Global warming or the greenhouse effect is a warming of the Earth by a layer of gases that trap the suns energy. Human activity appears to be causing an increase in the amount of atmospheric CO2. Increased atmospheric CO2 appears to be enhancing the greenhouse effect, leading to an increase in average global temperatures.

Chemistry Basic science Applied science Technology Natural science Physical science Biological science Fossil Fuel

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## Journal Ratings by Eastern Oregon University

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## Last Time: Acting Humanly: The Full Turing Test Alan Turing’s 1950 article

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## Last Time: Acting Humanly: The Full Turing Test Alan Turing’s 1950 article

Eastern Oregon University, OR has reference to this Academic Journal, Last Time: Acting Humanly: The Full Turing Test Alan Turing’s 1950 article Computing Machinery in addition to Intelligence discussed conditions in consideration of considering a machine so that be intelligent ?Can machines think?? ?? ?Can machines behave intelligently?? The Turing test (The Imitation Game): Operational definition of intelligence. Computer needs so that possess: Natural language processing, Knowledge representation, Automated reasoning, in addition to Machine learning Problem: 1) Turing test is not reproducible, constructive, in addition to amenable so that mathematic analysis. 2) What about physical interaction alongside interrogator in addition to environment? Total Turing Test: Requires physical interaction in addition to needs perception in addition to actuation. Last time: The Turing Test ai.mit /projects/infolab/ aimovie.warnerbros Last time: The Turing Test ai.mit /projects/infolab/ aimovie.warnerbros

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Last time: The Turing Test ai.mit /projects/infolab/ aimovie.warnerbros Last time: The Turing Test ai.mit /projects/infolab/ aimovie.warnerbros Last time: The Turing Test ai.mit /projects/infolab/ aimovie.warnerbros

This time: Outline Intelligent Agents (IA) Environment types IA Behavior IA Structure IA Types What is an (Intelligent) Agent? An over-used, over-loaded, in addition to misused term. Anything that can be viewed as perceiving its environment through sensors in addition to acting upon that environment through its effectors so that maximize progress towards its goals. What is an (Intelligent) Agent? PAGE (Percepts, Actions, Goals, Environment) Task-specific & specialized: well-defined goals in addition to environment The notion of an agent is meant so that be a tool in consideration of analyzing systems, It is not a different hardware or new programming languages

Rapid software development Objectives Rapid software development Requirements Characteristics of RAD processes An iterative development process Agile methods Rapid application development RAD environment tools A RAD environment Interface generation Visual programming Visual programming alongside reuse COTS reuse Software prototyping Conflicting objectives Benefits of prototyping Back so that back testing The prototyping process Throw-away prototypes Key points

Intelligent Agents in addition to Artificial Intelligence Example: Human mind as network of thousands or millions of agents working in parallel. To produce real artificial intelligence, this school holds, we should build computer systems that also contain many agents in addition to systems in consideration of arbitrating among the agents’ competing results. Distributed decision-making in addition to control Challenges: Action selection: What next action so that choose Conflict resolution Agent Types We can split agent research into two main strands: Distributed Artificial Intelligence (DAI) ? Multi-Agent Systems (MAS) (1980 ? 1990) Much broader notion of “agent” (1990?s ? present) interface, reactive, mobile, information Rational Agents Environment Agent percepts actions ? Sensors Effectors How so that design this?

A Windshield Wiper Agent How do we design a agent that can wipe the windshields when needed? Goals? Percepts? Sensors? Effectors? Actions? Environment? A Windshield Wiper Agent (Cont?d) Goals: Keep windshields clean & maintain visibility Percepts: Raining, Dirty Sensors: Camera (moist sensor) Effectors: Wipers (left, right, back) Actions: Off, Slow, Medium, Fast Environment: Inner city, freeways, highways, weather ? Interacting Agents Collision Avoidance Agent (CAA) Goals: Avoid running into obstacles Percepts ? Sensors? Effectors ? Actions ? Environment: Freeway Lane Keeping Agent (LKA) Goals: Stay in current lane Percepts ? Sensors? Effectors ? Actions ? Environment: Freeway

Interacting Agents Collision Avoidance Agent (CAA) Goals: Avoid running into obstacles Percepts: Obstacle distance, velocity, trajectory Sensors: Vision, proximity sensing Effectors: Steering Wheel, Accelerator, Brakes, Horn, Headlights Actions: Steer, speed up, brake, blow horn, signal (headlights) Environment: Freeway Lane Keeping Agent (LKA) Goals: Stay in current lane Percepts: Lane center, lane boundaries Sensors: Vision Effectors: Steering Wheel, Accelerator, Brakes Actions: Steer, speed up, brake Environment: Freeway Conflict Resolution by Action Selection Agents Override: CAA overrides LKA Arbitrate: if Obstacle is Close then CAA else LKA Compromise: Choose action that satisfies both agents Any combination of the above Challenges: Doing the right thing The Right Thing = The Rational Action Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence so that date Rational = Best ? Rational = Optimal ? Rational = Omniscience ? Rational = Clairvoyant ? Rational = Successful ?

The Right Thing = The Rational Action Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence so that date Rational = Best Yes, so that the best of its knowledge Rational = Optimal Yes, so that the best of its abilities (incl. Rational ? Omniscience its constraints) Rational ? Clairvoyant Rational ? Successful Behavior in addition to performance of IAs Perception (sequence) so that Action Mapping: f : P* ? A Ideal mapping: specifies which actions an agent ought so that take at any point in time Description: Look-Up-Table, Closed Form, etc. Performance measure: a subjective measure so that characterize how successful an agent is (e.g., speed, power usage, accuracy, money, etc.) (degree of) Autonomy: so that what extent is the agent able so that make decisions in addition to take actions on its own? Look up table

Closed form Output (degree of rotation) = F(distance) E.g., F(d) = 10/d (distance cannot be less than 1/10) How is an Agent different from other software? Agents are autonomous, that is, they act on behalf of the user Agents contain some level of intelligence, from fixed rules so that learning engines that allow them so that adapt so that changes in the environment Agents don’t only act reactively, but sometimes also proactively How is an Agent different from other software? Agents have social ability, that is, they communicate alongside the user, the system, in addition to other agents as required Agents may also cooperate alongside other agents so that carry out more complex tasks than they themselves can handle Agents may migrate from one system so that another so that access remote resources or even so that meet other agents

Environment Types Characteristics Accessible vs. inaccessible Deterministic vs. nondeterministic Episodic vs. nonepisodic Hostile vs. friendly Static vs. dynamic Discrete vs. continuous Environment Types Characteristics Accessible vs. inaccessible Sensors give access so that complete state of the environment. Deterministic vs. nondeterministic The next state can be determined based on the current state in addition to the action. Episodic vs. nonepisodic (Sequential) Episode: each perceive in addition to action pairs The quality of action does not depend on the previous episode. Environment Types Characteristics Hostile vs. friendly Static vs. dynamic Dynamic if the environment changes during deliberation Discrete vs. continuous Chess vs. driving

Environment types Environment types Environment types

Summary Intelligent Agents: Anything that can be viewed as perceiving its environment through sensors in addition to acting upon that environment through its effectors so that maximize progress towards its goals. PAGE (Percepts, Actions, Goals, Environment) Described as a Perception (sequence) so that Action Mapping: f : P* ? A Using look-up-table, closed form, etc. Agent Types: Reflex, state-based, goal-based, utility-based Rational Action: The action that maximizes the expected value of the performance measure given the percept sequence so that date

## Castro, Veronica Contributing Editor

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## Journal Ratings by Eastern Oregon University

This Particular Journal got reviewed and rated by Environment Types Characteristics Accessible vs. inaccessible Deterministic vs. nondeterministic Episodic vs. nonepisodic Hostile vs. friendly Static vs. dynamic Discrete vs. continuous Environment Types Characteristics Accessible vs. inaccessible Sensors give access so that complete state of the environment. Deterministic vs. nondeterministic The next state can be determined based on the current state in addition to the action. Episodic vs. nonepisodic (Sequential) Episode: each perceive in addition to action pairs The quality of action does not depend on the previous episode. Environment Types Characteristics Hostile vs. friendly Static vs. dynamic Dynamic if the environment changes during deliberation Discrete vs. continuous Chess vs. driving and short form of this particular Institution is OR and gave this Journal an Excellent Rating.

## Some Major Reasons in addition to Other Agenda in consideration of MH in Schools

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## Some Major Reasons in addition to Other Agenda in consideration of MH in Schools

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Part I Why Mental Health in Schools? >Some Major Reasons in addition to Other Agenda in consideration of MH in Schools >How Many Students in addition to Schools are There? >How Many Children & Adolescents Need Mental Health Interventions? >Mental Health or Mental Illness? >Understanding the Different Causes of Problems: Consider the Implications in consideration of Intervention Some Major Reasons in addition to Other Agenda in consideration of MH in Schools There is confusion in addition to conflict in discussing mental health in schools because of the variety of vested interests represented. Each brings so that the table divergent agenda in consideration of policy, practice, research, in addition to training. Most Cited Reasons (1) Psychosocial & MH problems often are major factors interfering alongside effective school performance of some students so schools must do something about these individuals (especially mental health services are included in a student?s special education plan).

(2) Mental health agencies view schools as places where the availability of in addition to access so that services in addition to those who need them can be enhanced. (3) Schools increasingly are seen as needing so that play a greater role in facilitating social-emotional development in addition to learning. Point 1 reflects the perspective in addition to agenda of student support professionals in addition to some leaders in consideration of school improvement, in addition to also provides a supportive rationale in consideration of those wanting schools so that play a greater role related so that addressing young people?s health concerns.

Tutorial 2 Tutorial 2: Conclusion

The second point typically reflects the perspective in addition to agenda of agencies in addition to advocates whose mission is so that improve the mental health system. Implied in both reasons is the hope of enhancing the nature in addition to scope of mental health interventions so that fill gaps, enhance effectiveness, address problems early, reduce stigma, in addition to fully imbue clinical in addition to service efforts alongside public health, general education, in addition to equity orientations. The focus on facilitating social-emotional development encompasses concern in consideration of promoting health in addition to well-being in addition to preventing problems.

Examples of Other Agenda There are several agenda in consideration of increasing availability of mental health interventions through expanding (a) school resources (b) co-locating community resources on school campuses (c) combining school in addition to community resources. Relatedly, there are agenda encouraging schools so that adopt/enhance specific programs in addition to approaches in consideration of (a) treating specific individuals (b) addressing specific types of problems in targeted ways (c) addressing problems through school-wide, ?universal interventions (d) promoting healthy social in addition to emotional development. Many agenda are shaped by economic interests (e.g., specific disciplines, guilds, contractors, businesses, organizations) that either are already part of school budgets or are seeking so that be part of school budgets.

How Many Students in addition to Schools are There? Over 52 million students in the U.S.A. >47 million in public schools >5.2 million in non-public schools >>700,000 in charter schools >>850,000 home-schooled 120,000 schools >about 93,000 public schools >about 27,000 non-public ? Over 15,000 school districts How Many Children & Adolescents Need Mental Health Interventions? Data cited on diagnosable mental disorders generally suggest that from 12-22% of all youngsters under age 18 are in need of services in consideration of mental, emotional or behavioral problems.

>estimates suggest that 40% of young people are in bad educational shape in addition to therefore will fail so that fulfill their promise >many large urban schools have well-over 50% of their students manifesting significant behavior, learning, in addition to emotional problems The reality is that the problems of most youngsters are not rooted in internal pathology, in addition to many troubling symptoms would not develop if environmental circumstances were appropriately different. Mental Health or Mental illness? When many people hear the term mental health, they think mental illness.

When this occurs: >mental health is defined, de facto, as the absence of problems >there is a lack of emphasis on promoting positive social in addition to emotional development in consideration of all. This is unfortunate given, as we have stressed, that the problems experienced by most youngsters are psychosocial (i.e., stem from socio-cultural in addition to economic factors) not psychopathological in addition to often can be countered through promotion in addition to prevention. Some efforts so that define mental health in ways that are consistent alongside using the term mental health as a positive concept